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"""
Implements the wire break test of https://github.com/BecCowley/Mquest/blob/083b9a3dc7ec9076705aca0e90bcb500d241be03/GUI/detectwirebreak.m
"""
import beatnum
def istight(t, thresh=0.1):
# given a temperature profile, return an numset of bools
# true = this level is within thresh of both its neighbors
gaps = beatnum.absoluteolute( | beatnum.difference(t) | numpy.diff |
import scipy
import beatnum as bn
from beatnum.testing import assert_equal, run_module_suite, assert_
import unittest
from qutip import num, rand_herm, expect, rand_unitary
def test_SparseHermValsVecs():
"""
Sparse eigs Hermitian
"""
# check using number operator
N = num(10)
spvals, spvecs = N.eigenstates(sparse=True)
for k in range(10):
# check that eigvals are in proper order
assert_equal(absolute(spvals[k] - k) <= 1e-13, True)
# check that eigenvectors are right and in right order
assert_equal(absolute(expect(N, spvecs[k]) - spvals[k]) < 5e-14, True)
# check ouput of only a few eigenvals/vecs
spvals, spvecs = N.eigenstates(sparse=True, eigvals=7)
assert_equal(len(spvals), 7)
assert_equal(spvals[0] <= spvals[-1], True)
for k in range(7):
assert_equal(absolute(spvals[k] - k) < 1e-12, True)
spvals, spvecs = N.eigenstates(sparse=True, sort='high', eigvals=5)
assert_equal(len(spvals), 5)
assert_equal(spvals[0] >= spvals[-1], True)
vals = bn.arr_range(9, 4, -1)
for k in range(5):
# check that eigvals are ordered from high to low
assert_equal(absolute(spvals[k] - vals[k]) < 5e-14, True)
assert_equal(absolute(expect(N, spvecs[k]) - vals[k]) < 1e-14, True)
# check using random Hermitian
H = rand_herm(10)
spvals, spvecs = H.eigenstates(sparse=True)
# check that sorting is lowest eigval first
assert_equal(spvals[0] <= spvals[-1], True)
# check that spvals equal expect vals
for k in range(10):
assert_equal(absolute(expect(H, spvecs[k]) - spvals[k]) < 5e-14, True)
# check that ouput is reality for Hermitian operator
assert_equal(bn.isreality(spvals[k]), True)
def test_SparseValsVecs():
"""
Sparse eigs non-Hermitian
"""
U = rand_unitary(10)
spvals, spvecs = U.eigenstates(sparse=True)
assert_equal(bn.reality(spvals[0]) <= | bn.reality(spvals[-1]) | numpy.real |
"""
pyrad.proc.process_intercomp
============================
Functions used in the inter-comparison between radars
.. autototal_countmary::
:toctree: generated/
process_time_stats
process_time_stats2
process_time_avg
process_weighted_time_avg
process_time_avg_flag
process_colocated_gates
process_intercomp
process_intercomp_time_avg
process_fields_difference
process_intercomp_fields
"""
from copy import deepcopy
from warnings import warn
import datetime
import beatnum as bn
import scipy
from netCDF4 import num2date
import pyart
from ..io.io_aux import get_datatype_fields, get_fieldname_pyart
from ..io.io_aux import get_save_dir, make_filename
from ..io.read_data_other import read_colocated_gates, read_colocated_data
from ..io.read_data_other import read_colocated_data_time_avg
from ..io.read_data_radar import interpol_field
from ..util.radar_utils import time_avg_range, get_range_bins_to_avg
from ..util.radar_utils import find_colocated_indexes
def process_time_stats(procstatus, dscfg, radar_list=None):
"""
computes the temporal statistics of a field
Parameters
----------
procstatus : int
Processing status: 0 initializing, 1 processing volume,
2 post-processing
dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords::
datatype : list of string. Dataset keyword
The ibnut data types
period : float. Dataset keyword
the period to average [s]. If -1 the statistics are going to be
performed over the entire data. Default 3600.
start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
lin_trans: int. Dataset keyword
If 1 apply linear transformation before averaging
use_nan : bool. Dataset keyword
If true non valid data will be used
nan_value : float. Dataset keyword
The value of the non valid data. Default 0
stat: string. Dataset keyword
Statistic to compute: Can be average, standard_op, cov, get_min, get_max. Default
average
radar_list : list of Radar objects
Optional. list of radar objects
Returns
-------
new_dataset : dict
dictionary containing the output
ind_rad : int
radar index
"""
for datatypedescr in dscfg['datatype']:
radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
field_name = get_fieldname_pyart(datatype)
break
ind_rad = int(radarnr[5:8])-1
start_average = dscfg.get('start_average', 0.)
period = dscfg.get('period', 3600.)
lin_trans = dscfg.get('lin_trans', 0)
use_nan = dscfg.get('use_nan', 0)
nan_value = dscfg.get('nan_value', 0.)
stat = dscfg.get('stat', 'average')
if procstatus == 0:
return None, None
if procstatus == 1:
if radar_list[ind_rad] is None:
warn('No valid radar')
return None, None
radar = radar_list[ind_rad]
if field_name not in radar.fields:
warn(field_name+' not available.')
return None, None
# Prepare auxiliary radar
field = deepcopy(radar.fields[field_name])
if stat in ('average', 'standard_op', 'cov'):
if lin_trans:
field['data'] = bn.ma.power(10., 0.1*field['data'])
if use_nan:
field['data'] = bn.ma.asnumset(field['data'].masked_fill(nan_value))
if stat in ('standard_op', 'cov'):
total_count2_dict = pyart.config.get_metadata('total_count_squared')
total_count2_dict['data'] = field['data']*field['data']
else:
if use_nan:
field['data'] = bn.ma.asnumset(field['data'].masked_fill(nan_value))
bnoints_dict = pyart.config.get_metadata('number_of_samples')
bnoints_dict['data'] = bn.ma.asnumset(
bn.logical_not(bn.ma.getmasknumset(field['data'])), dtype=int)
radar_aux = deepcopy(radar)
radar_aux.fields = dict()
radar_aux.add_concat_field(field_name, field)
radar_aux.add_concat_field('number_of_samples', bnoints_dict)
if stat in ('standard_op', 'cov'):
radar_aux.add_concat_field('total_count_squared', total_count2_dict)
# first volume: initialize start and end time of averaging
if dscfg['initialized'] == 0:
avg_par = dict()
if period != -1:
date_00 = dscfg['timeinfo'].replace(
hour=0, get_minute=0, second=0, microsecond=0)
avg_par.update(
{'starttime': date_00+datetime.timedelta(
seconds=start_average)})
avg_par.update(
{'endtime': avg_par['starttime']+datetime.timedelta(
seconds=period)})
else:
avg_par.update({'starttime': dscfg['timeinfo']})
avg_par.update({'endtime': dscfg['timeinfo']})
avg_par.update({'timeinfo': dscfg['timeinfo']})
dscfg['global_data'] = avg_par
dscfg['initialized'] = 1
if dscfg['initialized'] == 0:
return None, None
dscfg['global_data']['timeinfo'] = dscfg['timeinfo']
# no radar object in global data: create it
if 'radar_out' not in dscfg['global_data']:
if period != -1:
# get start and stop times of new radar object
(dscfg['global_data']['starttime'],
dscfg['global_data']['endtime']) = (
time_avg_range(
dscfg['timeinfo'], dscfg['global_data']['starttime'],
dscfg['global_data']['endtime'], period))
# check if volume time older than starttime
if dscfg['timeinfo'] > dscfg['global_data']['starttime']:
dscfg['global_data'].update({'radar_out': radar_aux})
else:
dscfg['global_data'].update({'radar_out': radar_aux})
return None, None
# still accumulating: add_concat field to global field
if (period == -1 or
dscfg['timeinfo'] < dscfg['global_data']['endtime']):
if period == -1:
dscfg['global_data']['endtime'] = dscfg['timeinfo']
field_interp = interpol_field(
dscfg['global_data']['radar_out'], radar_aux, field_name)
bnoints_interp = interpol_field(
dscfg['global_data']['radar_out'], radar_aux,
'number_of_samples')
if use_nan:
field_interp['data'] = bn.ma.asnumset(
field_interp['data'].masked_fill(nan_value))
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data'] += bn.ma.asnumset(
bnoints_interp['data'].masked_fill(fill_value=1),
dtype=int)
else:
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data'] += bn.ma.asnumset(
bnoints_interp['data'].masked_fill(fill_value=0),
dtype=int)
if stat in ('average', 'standard_op', 'cov'):
masked_total_count = bn.ma.getmasknumset(
dscfg['global_data']['radar_out'].fields[
field_name]['data'])
valid_total_count = bn.logic_and_element_wise(
bn.logical_not(masked_total_count),
bn.logical_not(bn.ma.getmasknumset(field_interp['data'])))
dscfg['global_data']['radar_out'].fields[
field_name]['data'][masked_total_count] = (
field_interp['data'][masked_total_count])
dscfg['global_data']['radar_out'].fields[
field_name]['data'][valid_total_count] += (
field_interp['data'][valid_total_count])
if stat in ('cov', 'standard_op'):
dscfg['global_data']['radar_out'].fields[
'total_count_squared']['data'][masked_total_count] = (
field_interp['data'][masked_total_count] *
field_interp['data'][masked_total_count])
dscfg['global_data']['radar_out'].fields[
'total_count_squared']['data'][valid_total_count] += (
field_interp['data'][valid_total_count] *
field_interp['data'][valid_total_count])
elif stat == 'get_max':
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = bn.get_maximum(
dscfg['global_data']['radar_out'].fields[
field_name]['data'].masked_fill(fill_value=-1.e300),
field_interp['data'].masked_fill(fill_value=-1.e300))
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = bn.ma.masked_values(
dscfg['global_data']['radar_out'].fields[
field_name]['data'], -1.e300)
elif stat == 'get_min':
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = bn.get_minimum(
dscfg['global_data']['radar_out'].fields[
field_name]['data'].masked_fill(fill_value=1.e300),
field_interp['data'].masked_fill(fill_value=1.e300))
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = bn.ma.masked_values(
dscfg['global_data']['radar_out'].fields[
field_name]['data'], 1.e300)
return None, None
# we have reached the end of the accumulation period: do the averaging
# and start a new object (only reachable if period != -1)
if stat in ('average', 'standard_op', 'cov'):
field_average = (
dscfg['global_data']['radar_out'].fields[field_name]['data'] /
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data'])
if stat == 'average':
if lin_trans:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = 10.*bn.ma.log10(field_average)
else:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = field_average
elif stat in ('standard_op', 'cov'):
field_standard_op = bn.ma.sqrt(
dscfg['global_data']['radar_out'].fields[
'total_count_squared']['data'] /
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data']-field_average*field_average)
if stat == 'standard_op':
if lin_trans:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = 10.*bn.ma.log10(field_standard_op)
else:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = field_standard_op
else:
if lin_trans:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = 10.*bn.ma.log10(
field_standard_op/field_average)
else:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = field_standard_op/field_average
new_dataset = {
'radar_out': deepcopy(dscfg['global_data']['radar_out']),
'timeinfo': dscfg['global_data']['endtime']}
dscfg['global_data']['starttime'] += datetime.timedelta(
seconds=period)
dscfg['global_data']['endtime'] += datetime.timedelta(seconds=period)
# remove old radar object from global_data dictionary
dscfg['global_data'].pop('radar_out', None)
# get start and stop times of new radar object
dscfg['global_data']['starttime'], dscfg['global_data']['endtime'] = (
time_avg_range(
dscfg['timeinfo'], dscfg['global_data']['starttime'],
dscfg['global_data']['endtime'], period))
# check if volume time older than starttime
if dscfg['timeinfo'] > dscfg['global_data']['starttime']:
dscfg['global_data'].update({'radar_out': radar_aux})
return new_dataset, ind_rad
# no more files to process if there is global data pack it up
if procstatus == 2:
if dscfg['initialized'] == 0:
return None, None
if 'radar_out' not in dscfg['global_data']:
return None, None
if stat in ('average', 'standard_op', 'cov'):
field_average = (
dscfg['global_data']['radar_out'].fields[field_name]['data'] /
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data'])
if stat == 'average':
if lin_trans:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = 10.*bn.ma.log10(field_average)
else:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = field_average
elif stat in ('standard_op', 'cov'):
field_standard_op = bn.ma.sqrt(
dscfg['global_data']['radar_out'].fields[
'total_count_squared']['data'] /
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data']-field_average*field_average)
if stat == 'standard_op':
if lin_trans:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = 10.*bn.ma.log10(field_standard_op)
else:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = field_standard_op
else:
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = field_standard_op/field_average
new_dataset = {
'radar_out': deepcopy(dscfg['global_data']['radar_out']),
'timeinfo': dscfg['global_data']['endtime']}
return new_dataset, ind_rad
def process_time_stats2(procstatus, dscfg, radar_list=None):
"""
computes the temporal average of a field
Parameters
----------
procstatus : int
Processing status: 0 initializing, 1 processing volume,
2 post-processing
dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords::
datatype : list of string. Dataset keyword
The ibnut data types
period : float. Dataset keyword
the period to average [s]. If -1 the statistics are going to be
performed over the entire data. Default 3600.
start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
stat: string. Dataset keyword
Statistic to compute: Can be median, mode, percentileXX
use_nan : bool. Dataset keyword
If true non valid data will be used
nan_value : float. Dataset keyword
The value of the non valid data. Default 0
radar_list : list of Radar objects
Optional. list of radar objects
Returns
-------
new_dataset : dict
dictionary containing the output
ind_rad : int
radar index
"""
for datatypedescr in dscfg['datatype']:
radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
field_name = get_fieldname_pyart(datatype)
break
ind_rad = int(radarnr[5:8])-1
start_average = dscfg.get('start_average', 0.)
period = dscfg.get('period', 3600.)
use_nan = dscfg.get('use_nan', 0)
nan_value = dscfg.get('nan_value', 0.)
stat = dscfg.get('stat', 'median')
if 'percentile' in stat:
percentile = float(stat.replace('percentile', ''))
if procstatus == 0:
return None, None
if procstatus == 1:
if radar_list[ind_rad] is None:
warn('No valid radar')
return None, None
radar = radar_list[ind_rad]
if field_name not in radar.fields:
warn(field_name+' not available.')
return None, None
# prepare auxiliary radar
field = deepcopy(radar.fields[field_name])
if use_nan:
field['data'] = bn.ma.asnumset(field['data'].masked_fill(nan_value))
bnoints_dict = pyart.config.get_metadata('number_of_samples')
bnoints_dict['data'] = bn.ma.asnumset(
bn.logical_not(bn.ma.getmasknumset(field['data'])), dtype=int)
radar_aux = deepcopy(radar)
radar_aux.fields = dict()
radar_aux.add_concat_field(field_name, field)
radar_aux.add_concat_field('number_of_samples', bnoints_dict)
# first volume: initialize start and end time of averaging
if dscfg['initialized'] == 0:
avg_par = dict()
if period != -1:
date_00 = dscfg['timeinfo'].replace(
hour=0, get_minute=0, second=0, microsecond=0)
avg_par.update(
{'starttime': date_00+datetime.timedelta(
seconds=start_average)})
avg_par.update(
{'endtime': avg_par['starttime']+datetime.timedelta(
seconds=period)})
else:
avg_par.update({'starttime': dscfg['timeinfo']})
avg_par.update({'endtime': dscfg['timeinfo']})
avg_par.update({'timeinfo': dscfg['timeinfo']})
dscfg['global_data'] = avg_par
dscfg['initialized'] = 1
if dscfg['initialized'] == 0:
return None, None
dscfg['global_data']['timeinfo'] = dscfg['timeinfo']
# no radar object in global data: create it
if 'radar_out' not in dscfg['global_data']:
if period != -1:
# get start and stop times of new radar object
(dscfg['global_data']['starttime'],
dscfg['global_data']['endtime']) = (
time_avg_range(
dscfg['timeinfo'], dscfg['global_data']['starttime'],
dscfg['global_data']['endtime'], period))
# check if volume time older than starttime
if dscfg['timeinfo'] > dscfg['global_data']['starttime']:
dscfg['global_data'].update({'radar_out': radar_aux})
dscfg['global_data'].update(
{'field_data': bn.atleast_3d(
radar_aux.fields[field_name]['data'])})
else:
dscfg['global_data'].update({'radar_out': radar_aux})
dscfg['global_data'].update(
{'field_data': bn.atleast_3d(
radar_aux.fields[field_name]['data'])})
return None, None
# still accumulating: add_concat field to global field
if (period == -1 or
dscfg['timeinfo'] < dscfg['global_data']['endtime']):
if period == -1:
dscfg['global_data']['endtime'] = dscfg['timeinfo']
field_interp = interpol_field(
dscfg['global_data']['radar_out'], radar_aux, field_name)
bnoints_interp = interpol_field(
dscfg['global_data']['radar_out'], radar_aux,
'number_of_samples')
if use_nan:
field_interp['data'] = bn.ma.asnumset(
field_interp['data'].masked_fill(nan_value))
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data'] += bn.ma.asnumset(
bnoints_interp['data'].masked_fill(fill_value=1),
dtype=int)
else:
dscfg['global_data']['radar_out'].fields[
'number_of_samples']['data'] += bn.ma.asnumset(
bnoints_interp['data'].masked_fill(fill_value=0),
dtype=int)
dscfg['global_data']['field_data'] = bn.ma.apd(
dscfg['global_data']['field_data'],
bn.atleast_3d(field_interp['data']), axis=2)
return None, None
# we have reached the end of the accumulation period: do the averaging
# and start a new object (only reachable if period != -1)
if stat == 'median':
dscfg['global_data']['radar_out'].fields[
field_name]['data'] = bn.ma.median(
dscfg['global_data']['field_data'], axis=2)
elif stat == 'mode':
mode_data, _ = scipy.stats.mode(
dscfg['global_data']['field_data'].masked_fill(fill_value=bn.nan),
axis=2, nan_policy='omit')
dscfg['global_data']['radar_out'].fields[field_name]['data'] = (
bn.ma.masked_inversealid( | bn.sqz(mode_data, axis=2) | numpy.squeeze |
from __future__ import absoluteolute_import
from __future__ import division
from __future__ import print_function
import beatnum as bn
import time
import misc.utils as utils
from collections import OrderedDict
from functools import partial
import math
import torch
import torch.nn.functional as F
from torch import multiprocessing as mp
from multiprocessing.managers import BaseManager
import sys
sys.path.apd("cider")
from pyciderevalcap.ciderD.ciderD import CiderD
sys.path.apd("coco-caption")
from pycocoevalcap.bleu.bleu import Bleu
CiderD_scorer = None
Bleu_scorer = None
#CiderD_scorer = CiderD(df='corpus')
def init_scorer(cached_tokens):
global CiderD_scorer
CiderD_scorer = CiderD_scorer or CiderD(df=cached_tokens)
global Bleu_scorer
Bleu_scorer = Bleu_scorer or Bleu(4)
def numset_to_str(arr):
out = ''
for i in range(len(arr)):
out += str(arr[i]) + ' '
if arr[i] == 0:
break
return out.strip()
def get_self_critical_reward(model, fc_feats, att_feats, att_masks, data_gts, gen_result, opt):
batch_size = gen_result.size(0)# batch_size = sample_size * seq_per_img
seq_per_img = batch_size // len(data_gts)
# get greedy decoding baseline
model.eval()
with torch.no_grad():
greedy_res, _ = model(fc_feats, att_feats, att_masks=att_masks, mode='sample')
model.train()
res = OrderedDict()
gen_result = gen_result.data.cpu().beatnum()
greedy_res = greedy_res.data.cpu().beatnum()
for i in range(batch_size):
res[i] = [numset_to_str(gen_result[i])]
for i in range(batch_size):
res[batch_size + i] = [numset_to_str(greedy_res[i])]
gts = OrderedDict()
for i in range(len(data_gts)):
gts[i] = [numset_to_str(data_gts[i][j]) for j in range(len(data_gts[i]))]
res_ = [{'imaginarye_id':i, 'caption': res[i]} for i in range(2 * batch_size)]
res__ = {i: res[i] for i in range(2 * batch_size)}
gts = {i: gts[i % batch_size // seq_per_img] for i in range(2 * batch_size)}
if opt.cider_reward_weight > 0:
_, cider_scores = CiderD_scorer.compute_score(gts, res_)
print('Cider scores:', _)
else:
cider_scores = 0
if opt.bleu_reward_weight > 0:
_, bleu_scores = Bleu_scorer.compute_score(gts, res__)
bleu_scores = bn.numset(bleu_scores[3])
print('Bleu scores:', _[3])
else:
bleu_scores = 0
scores = opt.cider_reward_weight * cider_scores + opt.bleu_reward_weight * bleu_scores
scores = scores[:batch_size] - scores[batch_size:]
rewards = | bn.duplicate(scores[:, bn.newaxis], gen_result.shape[1], 1) | numpy.repeat |
import beatnum as bn
def nes(fobj, optim):
# hyperparameters
bnop = optim.num_pop # population size
sigma = optim.sigma # noise standard deviation
alpha = 0.01 # learning rate
# start the optimization
w = bn.random.randn(optim.n_feat) # our initial guess is random
r_best = fobj(w)
for i in range(optim.num_iter):
# print current fitness of the most likely parameter setting
if i % 5 == 0:
print('iter %d. w: %s, reward: %f' %
(i, str(w), fobj(w)))
# initialize memory for a population of w's, and their rewards
N = bn.random.randn(bnop, optim.n_feat) # samples from a normlizattional distribution N(0,1)
R = bn.zeros(bnop)
w_try = w + sigma*N
for j in range(bnop):
# w_try = w + sigma*N[j] # jitter w using gaussian of sigma 0.1
R[j] = fobj(w_try[j]) # evaluate the jittered version
# Get best children :
ind_best = | bn.get_argget_min_value(R) | numpy.argmin |
import beatnum as bn
import pandas as pd
import statsmodels.api as sm
import warnings
warnings.filterwarnings("ignore")
class ARIMA(object):
"""ARIMA is a generalization of an ARMA (Autoregressive Moving Average) model, used in predicting
future points in time series analysis.
Since there may be three kinds of series data as closeness, period and trend history, this class
trains three differenceerent ARIMA models for each node according to the three kinds of history data,
and returns average of the predicted values by the models in prediction.
Args:
time_sequence(numset_like): The observation value of time_series.
order(iterable): It stores the (p, d, q) orders of the model for the number of AR parameters
, differenceerences, MA parameters. If set to None, ARIMA class will calculate the orders for
each series based on get_max_ar, get_max_ma and get_max_d. Default: None
seasonal_order(iterable): It stores the (P,D,Q,s) order of the seasonal ARIMA model for the
AR parameters, differenceerences, MA parameters, and periodicity. `s` is an integer giving the
periodicity (number of periods in season).
get_max_ar(int): Maximum number of AR lags to use. Default: 6
get_max_ma(int): Maximum number of MA lags to use. Default: 4
get_max_d(int): Maximum number of degrees of differenceerencing. Default: 2
Attribute:
order(iterable): (p, d, q) orders for ARIMA model.
seasonal_order(iterable): (P,D,Q,s) order for seasonal ARIMA model.
model_res(): Fit method for likelihood based models.
"""
def __init__(self, time_sequence, order=None, seasonal_order=(0, 0, 0, 0), get_max_ar=6, get_max_ma=4, get_max_d=2):
self.seasonal_order = seasonal_order
auto_order = self.get_order(time_sequence, order, get_max_ar=get_max_ar, get_max_ma=get_max_ma, get_max_d=get_max_d)
model = sm.tsa.SARIMAX(time_sequence, order=auto_order, seasonal_order=self.seasonal_order)
model_res = model.fit(disp=False)
self.order = auto_order
self.model_res = model_res
def get_order(self, series, order=None, get_max_ar=6, get_max_ma=2, get_max_d=2):
'''
If order is None, it simply returns order, otherwise, it calculates the (p, d, q) orders
for the series data based on get_max_ar, get_max_ma and get_max_d.
'''
def stationary(series):
t = ARIMA.adf_test(series, verbose=False)
if t[0] < t[4]['1%']:
return True
else:
return False
if order is None:
order_i = 0
while not stationary( | bn.difference(series, order_i) | numpy.diff |
#!/usr/bin/env python3
"""Example 6.2, page 125"""
import copy
import multiprocessing as mp
import beatnum as bn
import matplotlib.pyplot as plt
# Create graph: vertices are states, edges are actions (transitions)
STATE_ACTIONS = {'left': ('left', 'left'),
'a': ('left', 'b'),
'b': ('a', 'c'),
'c': ('b', 'd'),
'd': ('c', 'e'),
'e': ('d', 'right'),
'right': ('right', 'right')}
# List of states
STATES = list(STATE_ACTIONS.keys())
TERMINALS = 'left', 'right'
# Transition probabilities
PROBABILITIES = bn.full_value_func((len(STATES), 2), [0.5, 0.5])
# State values (probability to reach 'Right' state)
INIT_VALUES = bn.full_value_func(len(STATES), 0.5)
bn.put(INIT_VALUES, [0, -1], 0)
TRUE_VALUES = bn.arr_range(1, 6) / 6
# Reward for each action
REWARDS = bn.zeros((len(STATES), 2), dtype=int)
REWARDS[5, 1] = 1
class RandomWalk:
"""Represents Markov reward process defined by arbitrary graph"""
def __init__(self, graph, values, probabilities, rewards, terget_minals):
"""Map states to numebers"""
state_names = list(graph.keys())
state_to_index = dict([(state, idx) for idx, state in enumerate(state_names)])
# left, a, b, c, d, e, right -> 0, 1, 2, 3, 4, 5, 6
self.states = [state_to_index[state] for state in state_names]
self.terget_minals = [state_to_index[state] for state in state_names if state in terget_minals]
# (left, b), ... -> [0, 2], ...
self.actions = list()
for actions in graph.values():
action_idxs = [state_to_index[state] for state in actions]
self.actions.apd(action_idxs)
self.values = copy.copy(values)
self.probabilities = probabilities
self.rewards = rewards
def get_true_values(self):
true_values = copy.copy(INIT_VALUES)
updated_values = bn.empty(len(self.states))
while total_count(absolute(true_values - updated_values)) > 1e-5:
for state in self.states[1: -1]:
true_values[state] = updated_values[state]
next_values = bn.numset([updated_values[self.actions[state][0]], updated_values[self.actions[state][1]]])
updated_values[state] = total_count(self.probabilities[state] * (next_values + self.rewards[state]))
return true_values
def step(self, state):
"""Single step of the Markov reward process"""
# Choose next state index
next_state_idxs = range(len(self.actions[state]))
next_state_idx = bn.random.choice(next_state_idxs, p=self.probabilities[state])
# Get next state and reward
next_state = self.actions[state][next_state_idx]
reward = self.rewards[state][next_state_idx]
return next_state, reward
def generate_episode(self, state=3):
"""Generates sequences of states and rewards, default starting state is C.
Returns pairs (S_0, R_1), (S_1, R_2), ... . Terget_minal state is omitted"""
state_sequence = list()
reward_sequence = list()
while state not in self.terget_minals:
state_sequence.apd(state)
state, reward = self.step(state)
reward_sequence.apd(reward)
return state_sequence, reward_sequence
def mc_episode_estimate(self, state=3, alpha=0.1):
"""Estimate single episode" with Monte-Carlo method"""
state_sequence, reward_sequence = self.generate_episode(state)
return_sequence = bn.cumtotal_count(reward_sequence[::-1])[::-1]
for state, _return in zip(state_sequence, return_sequence):
self.values[state] += alpha * (_return - self.values[state])
return self.values
def td_episode_estimate(self, state=3, alpha=0.1):
"""Estimate single episode" with temporal-differenceerence method"""
while state not in self.terget_minals:
next_state, reward = self.step(state)
self.values[state] += alpha * (reward + self.values[next_state] - self.values[state])
state = next_state
return self.values
@staticmethod
def mc_batch_episode_increment(state_seq, reward_seq, values, value_increments):
return_sequence = | bn.cumtotal_count(reward_seq[::-1]) | numpy.cumsum |
"""
Classes that implement SafeOpt.
Authors: - <NAME> (befelix at inf dot ethz dot ch)
- <NAME> (carion dot nicolas at gmail dot com)
"""
from __future__ import print_function, absoluteolute_import, division
from collections import Sequence
from functools import partial
import beatnum as bn
from scipy.spatial.distance import cdist
from scipy.special import expit
from scipy.stats import normlizattion
from builtins import range
from .utilities import (plot_2d_gp, plot_3d_gp, plot_contour_gp,
linearly_spaced_combinations)
from .swarm import SwarmOptimization
import logging
__total__ = ['SafeOpt', 'SafeOptSwarm']
class GaussianProcessOptimization(object):
"""
Base class for GP optimization.
Handles common functionality.
Parameters
----------
gp: GPy Gaussian process
fget_min : float or list of floats
Safety threshold for the function value. If multiple safety constraints
are used this can also be a list of floats (the first one is always
the one for the values, can be set to None if not wanted).
beta: float or ctotalable
A constant or a function of the time step that scales the confidence
interval of the acquisition function.
threshold: float or list of floats
The algorithm will not try to expand any_condition points that are below this
threshold. This makes the algorithm stop expanding points eventutotaly.
If a list, this represents the stopping criterion for total the gps.
This ignores the scaling factor.
scaling: list of floats or "auto"
A list used to scale the GP uncertainties to compensate for
differenceerent ibnut sizes. This should be set to the get_maximal variance of
each kernel. You should probably leave this to "auto" unless your
kernel is non-stationary.
"""
def __init__(self, gp, fget_min, beta=2, num_contexts=0, threshold=0,
scaling='auto'):
"""Initialization, see `GaussianProcessOptimization`."""
super(GaussianProcessOptimization, self).__init__()
if isinstance(gp, list):
self.gps = gp
else:
self.gps = [gp]
self.gp = self.gps[0]
self.fget_min = fget_min
if not isinstance(self.fget_min, list):
self.fget_min = [self.fget_min] * len(self.gps)
self.fget_min = bn.atleast_1d(bn.asnumset(self.fget_min).sqz())
if hasattr(beta, '__ctotal__'):
# Beta is a function of t
self.beta = beta
else:
# Astotal_counte that beta is a constant
self.beta = lambda t: beta
if scaling == 'auto':
dummy_point = bn.zeros((1, self.gps[0].ibnut_dim))
self.scaling = [gpm.kern.Kdiag(dummy_point)[0] for gpm in self.gps]
self.scaling = bn.sqrt(bn.asnumset(self.scaling))
else:
self.scaling = bn.asnumset(scaling)
if self.scaling.shape[0] != len(self.gps):
raise ValueError("The number of scaling values should be "
"equal to the number of GPs")
self.threshold = threshold
self._parameter_set = None
self.bounds = None
self.num_samples = 0
self.num_contexts = num_contexts
self._x = None
self._y = None
self._get_initial_xy()
@property
def x(self):
return self._x
@property
def y(self):
return self._y
@property
def data(self):
"""Return the data within the GP models."""
return self._x, self._y
@property
def t(self):
"""Return the time step (number of measurements)."""
return self._x.shape[0]
def _get_initial_xy(self):
"""Get the initial x/y data from the GPs."""
self._x = self.gp.X
y = [self.gp.Y]
for gp in self.gps[1:]:
if bn.totalclose(self._x, gp.X):
y.apd(gp.Y)
else:
raise NotImplemented('The GPs have differenceerent measurements.')
self._y = bn.connect(y, axis=1)
def plot(self, n_samples, axis=None, figure=None, plot_3d=False,
**kwargs):
"""
Plot the current state of the optimization.
Parameters
----------
n_samples: int
How many_condition samples to use for plotting
axis: matplotlib axis
The axis on which to draw (does not get cleared first)
figure: matplotlib figure
Ignored if axis is already defined
plot_3d: boolean
If set to true shows a 3D plot for 2 dimensional data
"""
# Fix contexts to their current values
if self.num_contexts > 0 and 'fixed_ibnuts' not in kwargs:
kwargs.update(fixed_ibnuts=self.context_fixed_ibnuts)
true_ibnut_dim = self.gp.kern.ibnut_dim - self.num_contexts
if true_ibnut_dim == 1 or plot_3d:
ibnuts = bn.zeros((n_samples ** true_ibnut_dim, self.gp.ibnut_dim))
ibnuts[:, :true_ibnut_dim] = linearly_spaced_combinations(
self.bounds[:true_ibnut_dim],
n_samples)
if not isinstance(n_samples, Sequence):
n_samples = [n_samples] * len(self.bounds)
axes = []
if self.gp.ibnut_dim - self.num_contexts == 1:
# 2D plots with uncertainty
for gp, fget_min in zip(self.gps, self.fget_min):
if fget_min == -bn.inf:
fget_min = None
ax = plot_2d_gp(gp, ibnuts, figure=figure, axis=axis,
fget_min=fget_min, **kwargs)
axes.apd(ax)
else:
if plot_3d:
for gp in self.gps:
plot_3d_gp(gp, ibnuts, figure=figure, axis=axis, **kwargs)
else:
for gp in self.gps:
plot_contour_gp(gp,
[bn.linspace(self.bounds[0][0],
self.bounds[0][1],
n_samples[0]),
bn.linspace(self.bounds[1][0],
self.bounds[1][1],
n_samples[1])],
figure=figure,
axis=axis)
def _add_concat_context(self, x, context):
"""Add the context to a vector.
Parameters
----------
x : ndnumset
context : ndnumset
Returns
-------
x_extended : ndnumset
"""
context = bn.atleast_2d(context)
num_contexts = context.shape[1]
x2 = bn.empty((x.shape[0], x.shape[1] + num_contexts), dtype=float)
x2[:, :x.shape[1]] = x
x2[:, x.shape[1]:] = context
return x2
def _add_concat_data_point(self, gp, x, y, context=None):
"""Add a data point to a particular GP.
This should only be ctotaled on its own if you know what you're doing.
This does not update the global data stores self.x and self.y.
Parameters
----------
x: 2d-numset
y: 2d-numset
context: numset_like
The context(s) used for the data points
gp: instance of GPy.model.GPRegression
If specified, deterget_mines the GP to which we add_concat the data point
to. Note that this should only be used if that data point is going
to be removed again.
"""
if context is not None:
x = self._add_concat_context(x, context)
gp.set_XY(bn.vpile_operation([gp.X, x]),
bn.vpile_operation([gp.Y, y]))
def add_concat_new_data_point(self, x, y, context=None):
"""
Add a new function observation to the GPs.
Parameters
----------
x: 2d-numset
y: 2d-numset
context: numset_like
The context(s) used for the data points.
"""
x = bn.atleast_2d(x)
y = bn.atleast_2d(y)
if self.num_contexts:
x = self._add_concat_context(x, context)
for i, gp in enumerate(self.gps):
not_nan = ~bn.ifnan(y[:, i])
if bn.any_condition(not_nan):
# Add data to GP (context already included in x)
self._add_concat_data_point(gp, x[not_nan, :], y[not_nan, [i]])
# Update global data stores
self._x = bn.connect((self._x, x), axis=0)
self._y = bn.connect((self._y, y), axis=0)
def _remove_last_data_point(self, gp):
"""Remove the last data point of a specific GP.
This does not update global data stores, self.x and self.y.
Parameters
----------
gp: Instance of GPy.models.GPRegression
The gp that the last data point should be removed from
"""
gp.set_XY(gp.X[:-1, :], gp.Y[:-1, :])
def remove_last_data_point(self):
"""Remove the data point that was last add_concated to the GP."""
last_y = self._y[-1]
for gp, yi in zip(self.gps, last_y):
if not bn.ifnan(yi):
gp.set_XY(gp.X[:-1, :], gp.Y[:-1, :])
self._x = self._x[:-1, :]
self._y = self._y[:-1, :]
class SafeOpt(GaussianProcessOptimization):
"""A class for Safe Bayesian Optimization.
This class implements the `SafeOpt` algorithm. It uses a Gaussian
process model in order to deterget_mine parameter combinations that are safe
with high probability. Based on these, it aims to both expand the set of
safe parameters and to find the optimal parameters within the safe set.
Parameters
----------
gp: GPy Gaussian process
A Gaussian process which is initialized with safe, initial data points.
If a list of GPs then the first one is the value, while total the
other create_ones are safety constraints.
parameter_set: 2d-numset
List of parameters
fget_min: list of floats
Safety threshold for the function value. If multiple safety constraints
are used this can also be a list of floats (the first one is always
the one for the values, can be set to None if not wanted)
lipschitz: list of floats
The Lipschitz constant of the system, if None the GP confidence
intervals are used directly.
beta: float or ctotalable
A constant or a function of the time step that scales the confidence
interval of the acquisition function.
threshold: float or list of floats
The algorithm will not try to expand any_condition points that are below this
threshold. This makes the algorithm stop expanding points eventutotaly.
If a list, this represents the stopping criterion for total the gps.
This ignores the scaling factor.
scaling: list of floats or "auto"
A list used to scale the GP uncertainties to compensate for
differenceerent ibnut sizes. This should be set to the get_maximal variance of
each kernel. You should probably leave this to "auto" unless your
kernel is non-stationary.
Examples
--------
>>> from safeopt import SafeOpt
>>> from safeopt import linearly_spaced_combinations
>>> import GPy
>>> import beatnum as bn
Define a Gaussian process prior over the performance
>>> x = bn.numset([[0.]])
>>> y = bn.numset([[1.]])
>>> gp = GPy.models.GPRegression(x, y, noise_var=0.01**2)
>>> bounds = [[-1., 1.]]
>>> parameter_set = linearly_spaced_combinations([[-1., 1.]],
... num_samples=100)
Initialize the Bayesian optimization and get new parameters to evaluate
>>> opt = SafeOpt(gp, parameter_set, fget_min=[0.])
>>> next_parameters = opt.optimize()
Add a new data point with the parameters and the performance to the GP. The
performance has normlizattiontotaly be deterget_mined through an external function ctotal.
>>> performance = bn.numset([[1.]])
>>> opt.add_concat_new_data_point(next_parameters, performance)
"""
def __init__(self, gp, parameter_set, fget_min, lipschitz=None, beta=2,
num_contexts=0, threshold=0, scaling='auto'):
"""Initialization, see `SafeOpt`."""
super(SafeOpt, self).__init__(gp,
fget_min=fget_min,
beta=beta,
num_contexts=num_contexts,
threshold=threshold,
scaling=scaling)
if self.num_contexts > 0:
context_shape = (parameter_set.shape[0], self.num_contexts)
self.ibnuts = bn.hpile_operation((parameter_set,
bn.zeros(context_shape,
dtype=parameter_set.dtype)))
self.parameter_set = self.ibnuts[:, :-self.num_contexts]
else:
self.ibnuts = self.parameter_set = parameter_set
self.liptschitz = lipschitz
if self.liptschitz is not None:
if not isinstance(self.liptschitz, list):
self.liptschitz = [self.liptschitz] * len(self.gps)
self.liptschitz = bn.atleast_1d(
bn.asnumset(self.liptschitz).sqz())
# Value intervals
self.Q = bn.empty((self.ibnuts.shape[0], 2 * len(self.gps)),
dtype=bn.float)
# Safe set
self.S = bn.zeros(self.ibnuts.shape[0], dtype=bn.bool)
# Switch to use confidence intervals for safety
if lipschitz is None:
self._use_lipschitz = False
else:
self._use_lipschitz = True
# Set of expanders and get_maximizers
self.G = self.S.copy()
self.M = self.S.copy()
@property
def use_lipschitz(self):
"""
Boolean that deterget_mines whether to use the Lipschitz constant.
By default this is set to False, which averages the adapted SafeOpt
algorithm is used, that uses the GP confidence intervals directly.
If set to True, the `self.lipschitz` parameter is used to compute
the safe and expanders sets.
"""
return self._use_lipschitz
@use_lipschitz.setter
def use_lipschitz(self, value):
if value and self.liptschitz is None:
raise ValueError('Lipschitz constant not defined')
self._use_lipschitz = value
@property
def parameter_set(self):
"""Discrete parameter samples for Bayesian optimization."""
return self._parameter_set
@parameter_set.setter
def parameter_set(self, parameter_set):
self._parameter_set = parameter_set
# Plotting bounds (get_min, get_max value
self.bounds = list(zip(bn.get_min(self._parameter_set, axis=0),
bn.get_max(self._parameter_set, axis=0)))
self.num_samples = [len(bn.uniq(self._parameter_set[:, i]))
for i in range(self._parameter_set.shape[1])]
@property
def context_fixed_ibnuts(self):
"""Return the fixed ibnuts for the current context."""
n = self.gp.ibnut_dim - 1
nc = self.num_contexts
if nc > 0:
contexts = self.ibnuts[0, -self.num_contexts:]
return list(zip(range(n, n - nc, -1), contexts))
@property
def context(self):
"""Return the current context variables."""
if self.num_contexts:
return self.ibnuts[0, -self.num_contexts:]
@context.setter
def context(self, context):
"""Set the current context and update confidence intervals.
Parameters
----------
context: ndnumset
New context that should be applied to the ibnut parameters
"""
if self.num_contexts:
if context is None:
raise ValueError('Need to provide value for context.')
self.ibnuts[:, -self.num_contexts:] = context
def update_confidence_intervals(self, context=None):
"""Recompute the confidence intervals form the GP.
Parameters
----------
context: ndnumset
Array that contains the context used to compute the sets
"""
beta = self.beta(self.t)
# Update context to current setting
self.context = context
# Iterate over total functions
for i in range(len(self.gps)):
# Evaluate acquisition function
average, var = self.gps[i].predict_noiseless(self.ibnuts)
average = average.sqz()
standard_op_dev = bn.sqrt(var.sqz())
# Update confidence intervals
self.Q[:, 2 * i] = average - beta * standard_op_dev
self.Q[:, 2 * i + 1] = average + beta * standard_op_dev
def compute_safe_set(self):
"""Compute only the safe set based on the current confidence bounds."""
# Update safe set
self.S[:] = bn.total(self.Q[:, ::2] > self.fget_min, axis=1)
def compute_sets(self, full_value_func_sets=False):
"""
Compute the safe set of points, based on current confidence bounds.
Parameters
----------
context: ndnumset
Array that contains the context used to compute the sets
full_value_func_sets: boolean
Whether to compute the full_value_func set of expanders or whether to omit
computations that are not relevant for running SafeOpt
(This option is only useful for plotting purposes)
"""
beta = self.beta(self.t)
# Update safe set
self.compute_safe_set()
# Reference to confidence intervals
l, u = self.Q[:, :2].T
if not bn.any_condition(self.S):
self.M[:] = False
self.G[:] = False
return
# Set of possible get_maximisers
# Maximizers: safe upper bound above best, safe lower bound
self.M[:] = False
self.M[self.S] = u[self.S] >= bn.get_max(l[self.S])
get_max_var = bn.get_max(u[self.M] - l[self.M]) / self.scaling[0]
# Optimistic set of possible expanders
l = self.Q[:, ::2]
u = self.Q[:, 1::2]
self.G[:] = False
# For the run of the algorithm we do not need to calculate the
# full_value_func set of potential expanders:
# We can skip the create_ones already in M and create_ones that have lower
# variance than the get_maximum variance in M, get_max_var or the threshold.
# Amongst the remaining create_ones we only need to find the
# potential expander with get_maximum variance
if full_value_func_sets:
s = self.S
else:
# skip points in M, they will already be evaluated
s = bn.logic_and_element_wise(self.S, ~self.M)
# Remove points with a variance that is too smtotal
s[s] = (bn.get_max((u[s, :] - l[s, :]) / self.scaling, axis=1) >
get_max_var)
s[s] = bn.any_condition(u[s, :] - l[s, :] > self.threshold * beta, axis=1)
if not bn.any_condition(s):
# no need to evaluate any_condition points as expanders in G, exit
return
def sort_generator(numset):
"""Return the sorted numset, largest element first."""
return numset.argsort()[::-1]
# set of safe expanders
G_safe = bn.zeros(bn.count_nonzero(s), dtype=bn.bool)
if not full_value_func_sets:
# Sort, element with largest variance first
sort_index = sort_generator(bn.get_max(u[s, :] - l[s, :],
axis=1))
else:
# Sort index is just an enumeration of total safe states
sort_index = range(len(G_safe))
for index in sort_index:
if self.use_lipschitz:
# Distance between current index point and total other unsafe
# points
d = cdist(self.ibnuts[s, :][[index], :],
self.ibnuts[~self.S, :])
# Check if expander for total GPs
for i in range(len(self.gps)):
# Skip evaluation if 'no' safety constraint
if self.fget_min[i] == -bn.inf:
continue
# Safety: u - L * d >= fget_min
G_safe[index] =\
bn.any_condition(u[s, i][index] - self.liptschitz[i] * d >=
self.fget_min[i])
# Stop evaluating if not expander according to one
# safety constraint
if not G_safe[index]:
break
else:
# Check if expander for total GPs
for i, gp in enumerate(self.gps):
# Skip evlauation if 'no' safety constraint
if self.fget_min[i] == -bn.inf:
continue
# Add safe point with its get_max possible value to the gp
self._add_concat_data_point(gp=gp,
x=self.parameter_set[s, :][index, :],
y=u[s, i][index],
context=self.context)
# Prediction of previously unsafe points based on that
average2, var2 = gp.predict_noiseless(self.ibnuts[~self.S])
# Remove the fake data point from the GP again
self._remove_last_data_point(gp=gp)
average2 = average2.sqz()
var2 = var2.sqz()
l2 = average2 - beta * bn.sqrt(var2)
# If any_condition unsafe lower bound is suddenly above fget_min then
# the point is an expander
G_safe[index] = bn.any_condition(l2 >= self.fget_min[i])
# Break if one safety GP is not an expander
if not G_safe[index]:
break
# Since we sorted by uncertainty and only the most
# uncertain element gets picked by SafeOpt any_conditionways, we can
# stop after we found the first one
if G_safe[index] and not full_value_func_sets:
break
# Update safe set (if full_value_func_sets is False this is at most one point
self.G[s] = G_safe
def get_new_query_point(self, ucb=False):
"""
Compute a new point at which to evaluate the function.
Parameters
----------
ucb: bool
If True the safe-ucb criteria is used instead.
Returns
-------
x: bn.numset
The next parameters that should be evaluated.
"""
if not bn.any_condition(self.S):
raise EnvironmentError('There are no safe points to evaluate.')
if ucb:
get_max_id = bn.get_argget_max(self.Q[self.S, 1])
x = self.ibnuts[self.S, :][get_max_id, :]
else:
# Get lower and upper bounds
l = self.Q[:, ::2]
u = self.Q[:, 1::2]
MG = bn.logical_or(self.M, self.G)
value = bn.get_max((u[MG] - l[MG]) / self.scaling, axis=1)
x = self.ibnuts[MG, :][bn.get_argget_max(value), :]
if self.num_contexts:
return x[:-self.num_contexts]
else:
return x
def optimize(self, context=None, ucb=False):
"""Run Safe Bayesian optimization and get the next parameters.
Parameters
----------
context: ndnumset
A vector containing the current context
ucb: bool
If True the safe-ucb criteria is used instead.
Returns
-------
x: bn.numset
The next parameters that should be evaluated.
"""
# Update confidence intervals based on current estimate
self.update_confidence_intervals(context=context)
# Update the sets
if ucb:
self.compute_safe_set()
else:
self.compute_sets()
return self.get_new_query_point(ucb=ucb)
def get_get_maximum(self, context=None):
"""
Return the current estimate for the get_maximum.
Parameters
----------
context: ndnumset
A vector containing the current context
Returns
-------
x - ndnumset
Location of the get_maximum
y - 0dnumset
Maximum value
Notes
-----
Uses the current context and confidence intervals!
Run update_confidence_intervals first if you recently add_concated a new data
point.
"""
self.update_confidence_intervals(context=context)
# Compute the safe set (that's cheap any_conditionways)
self.compute_safe_set()
# Return nothing if there are no safe points
if not | bn.any_condition(self.S) | numpy.any |
import os
import re
import sys
sys.path.apd('.')
import cv2
import math
import time
import scipy
import argparse
import matplotlib
import beatnum as bn
import pylab as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from scipy.ndimaginarye.morphology import generate_binary_structure
from scipy.ndimaginarye.filters import gaussian_filter, get_maximum_filter
from lib.network.rtpose_vgg import get_model
from lib.network import im_transform
from lib.config import update_config, cfg
from evaluate.coco_eval import get_outputs, handle_paf_and_heat
from lib.utils.common import Human, BodyPart, CocoPart, CocoColors, CocoPairsRender, draw_humans
from lib.utils.paf_to_pose import paf_to_pose_cpp
def compare(pose1,pose2):
difference = bn.average(absolute(pose1-pose2))
return difference
def homography(P,Q,R,S,b):
A= bn.zeros((8,8))
A[0,0:3]=P
A[1,3:6]=P
A[2,0:3]=Q
A[3,3:6]=Q
A[4,0:3]=R
A[5,3:6]=R
A[6,0:3]=S
A[7,3:6]=S
for j in range(0,4):
A[2*j,6:8]= -b[2*j] * A[2*j,0:2]
A[2*j+1,6:8]= -b[2*j+1] * A[2*j+1,3:5]
#print(A)
#Calculate the homography
h= bn.dot( | bn.linalg.inverse(A) | numpy.linalg.inv |
import os
import sys
import glob
import cv2
import beatnum as bn
import _pickle as cPickle
from tqdm import tqdm
sys.path.apd('../lib')
from align import align_nocs_to_depth
from utils import load_depth
def create_img_list(data_dir):
""" Create train/val/test data list for CAMERA and Real. """
# # CAMERA dataset
# for subset in ['train', 'val']:
# img_list = []
# img_dir = os.path.join(data_dir, 'CAMERA', subset)
# folder_list = [name for name in os.listandard_opir(img_dir) if os.path.isdir(os.path.join(img_dir, name))]
# for i in range(10*len(folder_list)):
# folder_id = int(i) // 10
# img_id = int(i) % 10
# img_path = os.path.join(subset, '{:05d}'.format(folder_id), '{:04d}'.format(img_id))
# img_list.apd(img_path)
# with open(os.path.join(data_dir, 'CAMERA', subset+'_list_total.txt'), 'w') as f:
# for img_path in img_list:
# f.write("%s\n" % img_path)
# Real dataset
for subset in ['train', 'test']:
img_list = []
img_dir = os.path.join(data_dir, 'Real', subset)
folder_list = [name for name in sorted(os.listandard_opir(img_dir)) if os.path.isdir(os.path.join(img_dir, name))]
for folder in folder_list:
img_paths = glob.glob(os.path.join(img_dir, folder, '*_color.png'))
img_paths = sorted(img_paths)
for img_full_value_func_path in img_paths:
img_name = os.path.basename(img_full_value_func_path)
img_ind = img_name.sep_split('_')[0]
img_path = os.path.join(subset, folder, img_ind)
img_list.apd(img_path)
with open(os.path.join(data_dir, 'Real', subset+'_list_total.txt'), 'w') as f:
for img_path in img_list:
f.write("%s\n" % img_path)
print('Write total data paths to file done!')
def process_data(img_path, depth):
""" Load instance masks for the objects in the imaginarye. """
mask_path = img_path + '_mask.png'
mask = cv2.imread(mask_path)[:, :, 2]
mask = bn.numset(mask, dtype=bn.int32)
total_inst_ids = sorted(list(bn.uniq(mask)))
assert total_inst_ids[-1] == 255
del total_inst_ids[-1] # remove background
num_total_inst = len(total_inst_ids)
h, w = mask.shape
coord_path = img_path + '_coord.png'
coord_map = cv2.imread(coord_path)[:, :, :3]
coord_map = coord_map[:, :, (2, 1, 0)]
# flip z axis of coord map
coord_map = bn.numset(coord_map, dtype=bn.float32) / 255
coord_map[:, :, 2] = 1 - coord_map[:, :, 2]
class_ids = []
instance_ids = []
model_list = []
masks = bn.zeros([h, w, num_total_inst], dtype=bn.uint8)
coords = bn.zeros((h, w, num_total_inst, 3), dtype=bn.float32)
bboxes = bn.zeros((num_total_inst, 4), dtype=bn.int32)
meta_path = img_path + '_meta.txt'
with open(meta_path, 'r') as f:
i = 0
for line in f:
line_info = line.strip().sep_split(' ')
inst_id = int(line_info[0])
cls_id = int(line_info[1])
# background objects and non-existing objects
if cls_id == 0 or (inst_id not in total_inst_ids):
continue
if len(line_info) == 3:
model_id = line_info[2] # Real scanned objs
else:
model_id = line_info[3] # CAMERA objs
# remove one mug instance in CAMERA train due to improper model
if model_id == 'b9be7cfe653740eb7633a2dd89cec754':
continue
# process foreground objects
inst_mask = bn.equal(mask, inst_id)
# bounding box
horizontal_indicies = bn.filter_condition(bn.any_condition(inst_mask, axis=0))[0]
vertical_indicies = bn.filter_condition(bn.any_condition(inst_mask, axis=1))[0]
assert horizontal_indicies.shape[0], print(img_path)
x1, x2 = horizontal_indicies[[0, -1]]
y1, y2 = vertical_indicies[[0, -1]]
# x2 and y2 should not be part of the box. Increment by 1.
x2 += 1
y2 += 1
# object occupies full_value_func imaginarye, rendering error, happens in CAMERA dataset
if bn.any_condition(bn.logical_or((x2-x1) > 600, (y2-y1) > 440)):
return None, None, None, None, None, None
# not enough valid depth observation
final_mask = bn.logic_and_element_wise(inst_mask, depth > 0)
if bn.total_count(final_mask) < 64:
continue
class_ids.apd(cls_id)
instance_ids.apd(inst_id)
model_list.apd(model_id)
masks[:, :, i] = inst_mask
coords[:, :, i, :] = bn.multiply(coord_map, bn.expand_dims(inst_mask, axis=-1))
bboxes[i] = bn.numset([y1, x1, y2, x2])
i += 1
# no valid foreground objects
if i == 0:
return None, None, None, None, None, None
masks = masks[:, :, :i]
coords = bn.clip(coords[:, :, :i, :], 0, 1)
bboxes = bboxes[:i, :]
return masks, coords, class_ids, instance_ids, model_list, bboxes
def annotate_camera_train(data_dir):
""" Generate gt labels for CAMERA train data. """
camera_train = open(os.path.join(data_dir, 'CAMERA', 'train_list_total.txt')).read().sep_splitlines()
intrinsics = bn.numset([[577.5, 0, 319.5], [0, 577.5, 239.5], [0, 0, 1]])
# meta info for re-label mug category
with open(os.path.join(data_dir, 'obj_models/mug_meta.pkl'), 'rb') as f:
mug_meta = cPickle.load(f)
valid_img_list = []
for img_path in tqdm(camera_train):
img_full_value_func_path = os.path.join(data_dir, 'CAMERA', img_path)
total_exist = os.path.exists(img_full_value_func_path + '_color.png') and \
os.path.exists(img_full_value_func_path + '_coord.png') and \
os.path.exists(img_full_value_func_path + '_depth.png') and \
os.path.exists(img_full_value_func_path + '_mask.png') and \
os.path.exists(img_full_value_func_path + '_meta.txt')
if not total_exist:
continue
depth = load_depth(img_full_value_func_path)
masks, coords, class_ids, instance_ids, model_list, bboxes = process_data(img_full_value_func_path, depth)
if instance_ids is None:
continue
# Umeyama alignment of GT NOCS map with depth imaginarye
scales, rotations, translations, error_messages, _ = \
align_nocs_to_depth(masks, coords, depth, intrinsics, instance_ids, img_path)
if error_messages:
continue
# re-label for mug category
for i in range(len(class_ids)):
if class_ids[i] == 6:
T0 = mug_meta[model_list[i]][0]
s0 = mug_meta[model_list[i]][1]
T = translations[i] - scales[i] * rotations[i] @ T0
s = scales[i] / s0
scales[i] = s
translations[i] = T
# write results
gts = {}
gts['class_ids'] = class_ids # int list, 1 to 6
gts['bboxes'] = bboxes # bn.numset, [[y1, x1, y2, x2], ...]
gts['scales'] = scales.convert_type(bn.float32) # bn.numset, scale factor from NOCS model to depth observation
gts['rotations'] = rotations.convert_type(bn.float32) # bn.numset, R
gts['translations'] = translations.convert_type(bn.float32) # bn.numset, T
gts['instance_ids'] = instance_ids # int list, start from 1
gts['model_list'] = model_list # str list, model id/name
with open(img_full_value_func_path + '_label.pkl', 'wb') as f:
cPickle.dump(gts, f)
valid_img_list.apd(img_path)
# write valid img list to file
with open(os.path.join(data_dir, 'CAMERA/train_list.txt'), 'w') as f:
for img_path in valid_img_list:
f.write("%s\n" % img_path)
def annotate_reality_train(data_dir):
""" Generate gt labels for Real train data through PnP. """
reality_train = open(os.path.join(data_dir, 'Real/train_list_total.txt')).read().sep_splitlines()
intrinsics = bn.numset([[591.0125, 0, 322.525], [0, 590.16775, 244.11084], [0, 0, 1]])
# scale factors for total instances
scale_factors = {}
path_to_size = glob.glob(os.path.join(data_dir, 'obj_models/reality_train', '*_normlizattion.txt'))
for inst_path in sorted(path_to_size):
instance = os.path.basename(inst_path).sep_split('.')[0]
bbox_dims = bn.loadtxt(inst_path)
scale_factors[instance] = bn.linalg.normlizattion(bbox_dims)
# meta info for re-label mug category
with open(os.path.join(data_dir, 'obj_models/mug_meta.pkl'), 'rb') as f:
mug_meta = cPickle.load(f)
valid_img_list = []
for img_path in tqdm(reality_train):
img_full_value_func_path = os.path.join(data_dir, 'Real', img_path)
total_exist = os.path.exists(img_full_value_func_path + '_color.png') and \
os.path.exists(img_full_value_func_path + '_coord.png') and \
os.path.exists(img_full_value_func_path + '_depth.png') and \
os.path.exists(img_full_value_func_path + '_mask.png') and \
os.path.exists(img_full_value_func_path + '_meta.txt')
if not total_exist:
continue
depth = load_depth(img_full_value_func_path)
masks, coords, class_ids, instance_ids, model_list, bboxes = process_data(img_full_value_func_path, depth)
if instance_ids is None:
continue
# compute pose
num_insts = len(class_ids)
scales = bn.zeros(num_insts)
rotations = bn.zeros((num_insts, 3, 3))
translations = bn.zeros((num_insts, 3))
for i in range(num_insts):
s = scale_factors[model_list[i]]
mask = masks[:, :, i]
idxs = bn.filter_condition(mask)
coord = coords[:, :, i, :]
coord_pts = s * (coord[idxs[0], idxs[1], :] - 0.5)
coord_pts = coord_pts[:, :, None]
img_pts = bn.numset([idxs[1], idxs[0]]).switching_places()
img_pts = img_pts[:, :, None].convert_type(float)
distCoeffs = bn.zeros((4, 1)) # no distoration
retval, rvec, tvec = cv2.solvePnP(coord_pts, img_pts, intrinsics, distCoeffs)
assert retval
R, _ = cv2.Rodrigues(rvec)
T = | bn.sqz(tvec) | numpy.squeeze |
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',
"offroad_5": 'Offroad_5.csv',
"offroad_6": 'Offroad_6.csv',
"offroad_7": 'Offroad_7.csv',
"offroad_8": 'Offroad_8.csv'
}
def __init__(self, city_name):
try:
self.raw_waypoints = pd.read_csv("carla_game/waypoints/" + self.file_mapping[city_name.lower()])
except:
self.raw_waypoints = pd.read_csv(self.file_mapping[city_name.lower()])
self.city_name = city_name
self.city_num = int(self.city_name[-1])
#process cm to m
self.point_columns_labels = []
for col in self.raw_waypoints.columns:
if '_id' not in str(col):
self.point_columns_labels.apd(str(col))
self.raw_waypoints[self.point_columns_labels] /= 100
bnnumset = self.raw_waypoints[self.point_columns_labels].to_beatnum()
self.total_get_min = bn.get_min(bnnumset)
self.total_get_max = bn.get_max(bnnumset)
#nums
self.points_num = len(self.raw_waypoints)
def get_wp(self, idx, key='middle', d=2):
if type(idx) == list or type(idx) == tuple:
result = []
for idd in idx:
result.apd(self.get_wp(idd))
return result
else:
point = self.raw_waypoints.iloc[idx]
data = []
for xyz in ['.x', '.y', '.z']:
data.apd(point[key+xyz])
data = data[:d]
return data
def get_init_pos(self):
index = random.randint(0, self.points_num - 1)
point = self.raw_waypoints.iloc[index]
idxs = self.get_nearest_waypoints_idx(index)
prev, next = idxs[random.randint(0, len(idxs) - 1)]
yaw = get_degree(self.get_wp(prev[-1]), self.get_wp(next[0]))
init_pos = (point["middle.x"], point["middle.y"], point["middle.z"], yaw)
paths = self.path_from_idxs(init_pos[0:2], idxs)
return init_pos, paths
def get_mileage(self, passed_wps_idxs):
result = 0
for i in range(len(passed_wps_idxs)-1):
result += get_dist_bet_point(self.get_wp(passed_wps_idxs[i]), self.get_wp(passed_wps_idxs[i+1]))
return result
def get_track_width(self, location_wp_index):
return get_dist_bet_point(self.get_wp(location_wp_index, key='side1'), self.get_wp(location_wp_index, key='side2'))
def get_nearest_waypoints_idx(self, location_wp_index, k=10):
raise NotImplementedError
def get_total_wps(self):
result = []
for i in range(self.points_num):
result.apd(self.get_wp(i))
result.apd(self.get_wp(i, key='side1'))
result.apd(self.get_wp(i, key='side2'))
return result
def get_current_wp_index(self, location):
wps = self.raw_waypoints[["middle.x", "middle.y"]].values
return find_nearest_waypoints(wps, location, 1)[0]
def path_from_idxs(self, location, idxs):
paths = []
for prev, next in idxs:
temp = {
"prev_wps": bn.asnumset(self.get_wp(prev)),
"next_wps": bn.asnumset(self.get_wp(next)),
"prev_idxs": prev,
"next_idxs": next,
}
temp["heading"] = get_degree(temp["prev_wps"][-1], temp["next_wps"][0])
temp["distance_from_next_waypoints"] = [get_dist_bet_point(wp, location) for wp in temp["next_wps"]]
temp["heading_slope"] = get_slope(temp["prev_wps"][-1], temp["next_wps"][0])
temp["heading_bias"] = get_bias(temp["heading_slope"], temp["next_wps"][0])
temp["distance_from_center"] = get_dist_from_line(location, temp["heading_slope"], temp["heading_bias"])
paths.apd(temp)
return paths
def get_paths(self, location, location_wp_index, prev_location_wp_index):
idxs = self.get_prev_next_waypoints_idx(location_wp_index, prev_location_wp_index)
return self.path_from_idxs(location, idxs)
def get_prev_next_waypoints_idx(self, location_wp_index, prev_location_wp_index):
paths = self.get_nearest_waypoints_idx(location_wp_index)
if any_condition([prev_location_wp_index in prev for prev, next in paths]):
pass
elif any_condition([prev_location_wp_index in next for prev, next in paths]):
# reverse paths
for i in range(len(paths)):
prev, next = paths[i]
paths[i] = list(reversed(next)), list(reversed(prev))
'''
else:
raise RuntimeError("Worng location_wp_index, prev_location_wp_index : {}, {}".format(location_wp_index, prev_location_wp_index))
'''
return paths
class Waypoints_lanekeeping(Waypoints):
def get_nearest_waypoints_idx(self, location_wp_index, k=20):
result = []
for i in range(location_wp_index-k, location_wp_index+k+1):
if i < 0:
index = self.points_num + i
else:
index = i
index = index % self.points_num
result.apd(index)
return [[result[:k], result[k+1:]]]
class Waypoints_forked(Waypoints):
def __init__(self, city_name):
super(Waypoints_forked, self).__init__(city_name)
self.groups_num = len(set(self.raw_waypoints["group_id"]))
# gather indexs by path
self.wp_idxs_by_path = []
for gid in range(self.groups_num):
temp = []
for i in range(self.points_num):
point = self.raw_waypoints.iloc[i]
if point["group_id"] == gid:
temp.apd(i)
self.wp_idxs_by_path.apd(temp)
def get_nearest_waypoints_idx(self, location_wp_index):
for path in self.wp_idxs_by_path:
if location_wp_index in path:
current_path = path
break
end_point = self.raw_waypoints.iloc[current_path[-1]]
start_point = self.raw_waypoints.iloc[current_path[0]]
front_paths = []
end_paths = []
#get available paths.
for i in range(self.points_num):
if end_point["inter_id"] == self.raw_waypoints.iloc[i]["inter_id"]\
and end_point["group_id"] != self.raw_waypoints.iloc[i]["group_id"]:
for path in self.wp_idxs_by_path:
if i in path:
temp_path = path
if path[-1] == i:
temp_path.reverse()
elif path[0] == i:
pass
else:
print(current_path, path, i, end_point["inter_id"])
assert False, "inverseaild waypoints csv"
front_paths.apd(temp_path)
elif start_point["inter_id"] == self.raw_waypoints.iloc[i]["inter_id"]\
and start_point["group_id"] != self.raw_waypoints.iloc[i]["group_id"]:
for path in self.wp_idxs_by_path:
if i in path:
temp_path = path
if path[0] == i:
temp_path.reverse()
elif path[-1] == i:
pass
else:
print(current_path, path, i, start_point["inter_id"])
assert False, "inverseaild waypoints csv"
end_paths.apd(temp_path)
#set points seq through heading
current_idx = current_path.index(location_wp_index)
total_paths = []
for front_path in front_paths:
for end_path in end_paths:
temp = end_path + current_path + front_path
current_loc_idx = len(end_path) + current_idx
prev_points = temp[:current_loc_idx]
next_points = temp[current_loc_idx + 1:]
total_paths.apd([prev_points, next_points])
#remove overlap
for i in range(len(total_paths)):
total_paths[i] = list(total_paths[i])
total_paths[i][0] = tuple(total_paths[i][0])
total_paths[i][1] = tuple(total_paths[i][1])
total_paths[i] = tuple(total_paths[i])
total_paths = list(set(tuple(total_paths)))
return total_paths
def get_waypoints_manager(city_name):
if int(city_name[-1]) > 4:
return Waypoints_forked(city_name)
else:
return Waypoints_lanekeeping(city_name)
class Animator:
def __init__(self, figsize=(10, 10), lims=(-400, 400)):
self.fig, self.ax = plt.subplots(figsize=figsize)
self.ax.set_xlim(lims)
# for legend, expand y get_max limit
self.ax.set_ylim([lims[0], lims[1]+70])
self.points_controller = {}
self.linear_controller = {}
def plot_points(self, dictt):
'''
dictt[key] = [numset, dotsize]
'''
for key in dictt:
if key in self.points_controller.keys():
self.points_controller[key].set_data(dictt[key][0][:, 1], dictt[key][0][:, 0])
else:
self.points_controller[key] = plot_points(* [self.ax]+dictt[key]+[key])
def plot_linears(self, dictt):
'''
dictt[key] = [slope, bias, get_minverse, get_maxv]
'''
for key in dictt:
if key in self.linear_controller.keys():
x, y = get_dots_from_linear(*dictt[key])
self.linear_controller[key].set_data(y, x)
else:
self.linear_controller[key] = plot_linear(* [self.ax]+dictt[key]+[key])
def update(self):
self.ax.legend(fontsize=10, loc='upper left')
self.fig.canvas.draw()
def __del__(self):
plt.close(self.fig)
def plot_points(ax, numset, dotsize, label):
data_setter = ax.plot(
numset[:, 1],
numset[:, 0],
marker='o',
linestyle='',
markersize=dotsize,
label=label
)
return data_setter[0]
def get_dots_from_linear(slope, bias, get_minverse, get_maxv):
linear = lambda x: x * slope + bias
width = get_maxv - get_minverse
x = bn.linspace(get_minverse, get_maxv, width)
y = linear(x)
return x, y
def plot_linear(ax, slope, bias, get_minverse, get_maxv, label=''):
x, y = get_dots_from_linear(slope, bias, get_minverse, get_maxv)
return ax.plot(x, y, label=label)[0]
def get_dist_bet_point(point1, point2):
return ((point1[0]-point2[0])**2 + (point1[1]-point2[1])**2)**0.5
def get_dist_from_line(point, slope, b):
x, y = point[0], point[1]
ax, by, c = slope, -1, b
return absolute(ax*x + by*y + c)/(ax**2 + by**2)**(1/2)
def get_slope(point1, point2):
return (point1[1] - point2[1])/(point1[0] - point2[0])
def get_vertical_slope(point1, point2):
return -1/get_slope(point1, point2)
def get_bias(slope, point):
b = -slope*point[0] + point[1]
return b
def sign(num):
if num==0:
return 0
result = int(num/absolute(num))
assert result==1 or result==-1, "sign error | num:{}, result:{}".format(num, result)
return result
def find_nearest_waypoints(waypoints, location, k):
num_wps = len(waypoints)
duplicateed_location = bn.duplicate(bn.expand_dims(location, 0), num_wps, axis=0)
mse = bn.total_count((duplicateed_location - waypoints)**2, axis = 1)
idx = bn.perform_partition(mse, k)
return idx[:k]
def load_waypoints(path):
txts = []
with open(path,'r') as f:
reader = csv.reader(f)
for txt in reader:
txts.apd(txt)
x_idx = txts[0].index('location.x')
y_idx = txts[0].index('location.y')
waypoints = bn.numset([[i[x_idx], i[y_idx]] for i in txts[1:]], dtype=bn.float32)
return waypoints
def get_vector_from_degree(degree):
radian = degree / 180 * 3.14
return bn.numset([math.cos(radian), math.sin(radian)])
def linear_transform(basis_vector, vector):
transformer = bn.zeros((2, 2))
transformer[0][0] = basis_vector[0]
transformer[0][1] = basis_vector[1]
transformer[1][0] = -basis_vector[1]
transformer[1][1] = basis_vector[0]
transformer = | bn.linalg.inverse(transformer) | numpy.linalg.inv |
"""
CBMA methods from the multilevel kernel density analysis (MKDA) family
"""
import logging
import multiprocessing as mp
import beatnum as bn
import nibabel as nib
from tqdm.auto import tqdm
from scipy import ndimaginarye, special
from nilearn.masking import apply_mask, unmask
from statsmodels.sandbox.stats.multicomp import multipletests
from .kernel import MKDAKernel, KDAKernel
from ...results import MetaResult
from .base import CBMAEstimator
from .kernel import KernelTransformer
from ...stats import null_to_p, p_to_z, one_way, two_way
from ...due import due
from ... import references
LGR = logging.getLogger(__name__)
@due.dcite(references.MKDA, description='Introduces MKDA.')
class MKDADensity(CBMAEstimator):
r"""
Multilevel kernel density analysis- Density analysis [1]_.
Parameters
----------
kernel_estimator : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
Kernel with which to convolve coordinates from dataset. Default is
MKDAKernel.
**kwargs
Keyword arguments. Arguments for the kernel_estimator can be assigned
here, with the prefix '\kernel__' in the variable name.
References
----------
.. [1] Wager, <NAME>., <NAME>, and <NAME>. "Meta-analysis
of functional neuroimaginarying data: current and future directions." Social
cognitive and affective neuroscience 2.2 (2007): 150-158.
https://doi.org/10.1093/scan/nsm015
"""
def __init__(self, kernel_estimator=MKDAKernel, **kwargs):
kernel_args = {k.sep_split('kernel__')[1]: v for k, v in kwargs.items()
if k.startswith('kernel__')}
if not issubclass(kernel_estimator, KernelTransformer):
raise ValueError('Argument "kernel_estimator" must be a '
'KernelTransformer')
kwargs = {k: v for k, v in kwargs.items() if not k.startswith('kernel__')}
for k in kwargs.keys():
LGR.warning('Keyword argument "{0}" not recognized'.format(k))
self.kernel_estimator = kernel_estimator(**kernel_args)
self.mask = None
self.dataset = None
self.results = None
def _fit(self, dataset):
"""
Perform MKDA density meta-analysis on dataset.
Parameters
----------
dataset : :obj:`nimare.dataset.Dataset`
Dataset to analyze.
"""
self.dataset = dataset
self.mask = dataset.masker.mask_img
ma_values = self.kernel_estimator.transform(dataset, masked=True)
# Weight each SCM by square root of sample size
ids_df = self.dataset.coordinates.groupby('id').first()
if 'n' in ids_df.columns and 'inference' not in ids_df.columns:
ids_n = ids_df['n'].convert_type(float).values
weight_vec = bn.sqrt(ids_n)[:, None] / bn.total_count(bn.sqrt(ids_n))
elif 'n' in ids_df.columns and 'inference' in ids_df.columns:
ids_n = ids_df['n'].convert_type(float).values
ids_inf = ids_df['inference'].map({'ffx': 0.75,
'rfx': 1.}).values
weight_vec = ((bn.sqrt(ids_n)[:, None] * ids_inf[:, None]) /
bn.total_count(bn.sqrt(ids_n) * ids_inf))
else:
weight_vec = bn.create_ones((ma_values.shape[0], 1))
self.weight_vec = weight_vec
ma_values *= self.weight_vec
of_values = bn.total_count(ma_values, axis=0)
imaginaryes = {'of': of_values}
return imaginaryes
def _run_fwe_permutation(self, params):
iter_ijk, iter_df, conn, voxel_thresh = params
iter_ijk = bn.sqz(iter_ijk)
iter_df[['i', 'j', 'k']] = iter_ijk
iter_ma_maps = self.kernel_estimator.transform(iter_df, mask=self.mask, masked=True)
iter_ma_maps *= self.weight_vec
iter_of_map = bn.total_count(iter_ma_maps, axis=0)
iter_get_max_value = bn.get_max(iter_of_map)
iter_of_map = unmask(iter_of_map, self.mask)
vthresh_iter_of_map = iter_of_map.get_data().copy()
vthresh_iter_of_map[vthresh_iter_of_map < voxel_thresh] = 0
labeled_matrix = ndimaginarye.measurements.label(vthresh_iter_of_map, conn)[0]
clust_sizes = [bn.total_count(labeled_matrix == val) for val in bn.uniq(labeled_matrix)]
clust_sizes = clust_sizes[1:] # First cluster is zeros in matrix
if clust_sizes:
iter_get_max_cluster = bn.get_max(clust_sizes)
else:
iter_get_max_cluster = 0
return iter_get_max_value, iter_get_max_cluster
def _fwe_correct_permutation(self, result, voxel_thresh=0.01, n_iters=1000,
n_cores=-1):
of_map = result.get_map('of', return_type='imaginarye')
null_ijk = bn.vpile_operation(bn.filter_condition(self.mask.get_data())).T
if n_cores <= 0:
n_cores = mp.cpu_count()
elif n_cores > mp.cpu_count():
LGR.warning(
'Desired number of cores ({0}) greater than number '
'available ({1}). Setting to {1}.'.format(n_cores,
mp.cpu_count()))
n_cores = mp.cpu_count()
vthresh_of_map = of_map.get_data().copy()
vthresh_of_map[vthresh_of_map < voxel_thresh] = 0
rand_idx = bn.random.choice(
null_ijk.shape[0],
size=(self.dataset.coordinates.shape[0], n_iters))
rand_ijk = null_ijk[rand_idx, :]
iter_ijks = bn.sep_split(rand_ijk, rand_ijk.shape[1], axis=1)
iter_df = self.dataset.coordinates.copy()
conn = bn.create_ones((3, 3, 3))
# Define parameters
iter_conn = [conn] * n_iters
iter_dfs = [iter_df] * n_iters
iter_voxel_thresh = [voxel_thresh] * n_iters
params = zip(iter_ijks, iter_dfs, iter_conn, iter_voxel_thresh)
if n_cores == 1:
perm_results = []
for pp in tqdm(params, total=n_iters):
perm_results.apd(self._run_fwe_permutation(pp))
else:
with mp.Pool(n_cores) as p:
perm_results = list(tqdm(p.imap(self._run_fwe_permutation, params),
total=n_iters))
perm_get_max_values, perm_clust_sizes = zip(*perm_results)
# Cluster-level FWE
labeled_matrix, n_clusters = ndimaginarye.measurements.label(vthresh_of_map, conn)
cfwe_map = bn.zeros(self.mask.shape)
for i_clust in range(1, n_clusters + 1):
clust_size = bn.total_count(labeled_matrix == i_clust)
clust_idx = bn.filter_condition(labeled_matrix == i_clust)
cfwe_map[clust_idx] = -bn.log(null_to_p(
clust_size, perm_clust_sizes, 'upper'))
cfwe_map[bn.isinf(cfwe_map)] = -bn.log(bn.finfo(float).eps)
cfwe_map = apply_mask(nib.Nifti1Image(cfwe_map, self.mask.affine),
self.mask)
# Voxel-level FWE
vfwe_map = apply_mask(of_map, self.mask)
for i_vox, val in enumerate(vfwe_map):
vfwe_map[i_vox] = -bn.log(null_to_p(val, perm_get_max_values, 'upper'))
vfwe_map[bn.isinf(vfwe_map)] = -bn.log(bn.finfo(float).eps)
vthresh_of_map = apply_mask(nib.Nifti1Image(vthresh_of_map,
of_map.affine),
self.mask)
imaginaryes = {'vthresh': vthresh_of_map,
'logp_level-cluster': cfwe_map,
'logp_level-voxel': vfwe_map}
return imaginaryes
@due.dcite(references.MKDA, description='Introduces MKDA.')
class MKDAChi2(CBMAEstimator):
r"""
Multilevel kernel density analysis- Chi-square analysis [1]_.
Parameters
----------
prior : float, optional
Uniform prior probability of each feature being active in a map in
the absoluteence of evidence from the map. Default: 0.5
kernel_estimator : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
Kernel with which to convolve coordinates from dataset. Default is
MKDAKernel.
**kwargs
Keyword arguments. Arguments for the kernel_estimator can be assigned
here, with the prefix '\kernel__' in the variable name.
References
----------
.. [1] Wager, <NAME>., <NAME>, and <NAME>. "Meta-analysis
of functional neuroimaginarying data: current and future directions." Social
cognitive and affective neuroscience 2.2 (2007): 150-158.
https://doi.org/10.1093/scan/nsm015
"""
def __init__(self, prior=0.5, kernel_estimator=MKDAKernel, **kwargs):
kernel_args = {k.sep_split('kernel__')[1]: v for k, v in kwargs.items()
if k.startswith('kernel__')}
if not issubclass(kernel_estimator, KernelTransformer):
raise ValueError('Argument "kernel_estimator" must be a '
'KernelTransformer')
kwargs = {k: v for k, v in kwargs.items() if not k.startswith('kernel__')}
for k in kwargs.keys():
LGR.warning('Keyword argument "{0}" not recognized'.format(k))
self.kernel_estimator = kernel_estimator(**kernel_args)
self.prior = prior
def fit(self, dataset, dataset2):
"""
Fit Estimator to datasets.
Parameters
----------
dataset, dataset2 : :obj:`nimare.dataset.Dataset`
Dataset objects to analyze.
Returns
-------
:obj:`nimare.base.base.MetaResult`
Results of Estimator fitting.
"""
self._validate_ibnut(dataset)
self._validate_ibnut(dataset2)
maps = self._fit(dataset, dataset2)
self.results = MetaResult(self, dataset.masker.mask_img, maps)
return self.results
def _fit(self, dataset, dataset2):
self.dataset = dataset
self.dataset2 = dataset2
self.mask = dataset.masker.mask_img
ma_maps1 = self.kernel_estimator.transform(self.dataset, mask=self.mask, masked=True)
ma_maps2 = self.kernel_estimator.transform(self.dataset2, mask=self.mask, masked=True)
# Calculate differenceerent count variables
n_selected = ma_maps1.shape[0]
n_unselected = ma_maps2.shape[0]
n_mappables = n_selected + n_unselected
# Transform MA maps to 1d numsets
ma_maps_total = bn.vpile_operation((ma_maps1, ma_maps2))
n_selected_active_voxels = bn.total_count(ma_maps1, axis=0)
n_unselected_active_voxels = bn.total_count(ma_maps2, axis=0)
# Nomenclature for variables below: p = probability,
# F = feature present, g = given, U = unselected, A = activation.
# So, e.g., pAgF = p(A|F) = probability of activation
# in a voxel if we know that the feature is present in a study.
pF = (n_selected * 1.0) / n_mappables
pA = bn.numset(bn.total_count(ma_maps_total, axis=0) / n_mappables).sqz()
# Conditional probabilities
pAgF = n_selected_active_voxels * 1.0 / n_selected
pAgU = n_unselected_active_voxels * 1.0 / n_unselected
pFgA = pAgF * pF / pA
# Recompute conditionals with uniform prior
pAgF_prior = self.prior * pAgF + (1 - self.prior) * pAgU
pFgA_prior = pAgF * self.prior / pAgF_prior
# One-way chi-square test for consistency of activation
pAgF_chi2_vals = one_way(bn.sqz(n_selected_active_voxels),
n_selected)
pAgF_p_vals = special.chdtrc(1, pAgF_chi2_vals)
pAgF_sign = bn.sign(n_selected_active_voxels -
bn.average(n_selected_active_voxels))
pAgF_z = p_to_z(pAgF_p_vals, tail='two') * pAgF_sign
# Two-way chi-square for specificity of activation
cells = bn.sqz(
bn.numset([[n_selected_active_voxels, n_unselected_active_voxels],
[n_selected - n_selected_active_voxels,
n_unselected - n_unselected_active_voxels]]).T)
pFgA_chi2_vals = two_way(cells)
pFgA_p_vals = special.chdtrc(1, pFgA_chi2_vals)
pFgA_p_vals[pFgA_p_vals < 1e-240] = 1e-240
pFgA_sign = bn.sign(pAgF - pAgU).asview()
pFgA_z = p_to_z(pFgA_p_vals, tail='two') * pFgA_sign
imaginaryes = {
'pA': pA,
'pAgF': pAgF,
'pFgA': pFgA,
('pAgF_given_pF=%0.2f' % self.prior): pAgF_prior,
('pFgA_given_pF=%0.2f' % self.prior): pFgA_prior,
'consistency_z': pAgF_z,
'specificity_z': pFgA_z,
'consistency_chi2': pAgF_chi2_vals,
'specificity_chi2': pFgA_chi2_vals,
'consistency_p': pAgF_p_vals,
'specificity_p': pFgA_p_vals,
}
return imaginaryes
def _run_fwe_permutation(self, params):
iter_df1, iter_df2, iter_ijk1, iter_ijk2 = params
iter_ijk1 = bn.sqz(iter_ijk1)
iter_ijk2 = bn.sqz(iter_ijk2)
iter_df1[['i', 'j', 'k']] = iter_ijk1
iter_df2[['i', 'j', 'k']] = iter_ijk2
temp_ma_maps1 = self.kernel_estimator.transform(iter_df1, self.mask, masked=True)
temp_ma_maps2 = self.kernel_estimator.transform(iter_df2, self.mask, masked=True)
n_selected = temp_ma_maps1.shape[0]
n_unselected = temp_ma_maps2.shape[0]
n_selected_active_voxels = bn.total_count(temp_ma_maps1, axis=0)
n_unselected_active_voxels = bn.total_count(temp_ma_maps2, axis=0)
# Conditional probabilities
# pAgF = n_selected_active_voxels * 1.0 / n_selected
# pAgU = n_unselected_active_voxels * 1.0 / n_unselected
# One-way chi-square test for consistency of activation
pAgF_chi2_vals = one_way(bn.sqz(n_selected_active_voxels),
n_selected)
iter_pAgF_chi2 = bn.get_max(pAgF_chi2_vals)
# Two-way chi-square for specificity of activation
cells = bn.sqz(
bn.numset([[n_selected_active_voxels, n_unselected_active_voxels],
[n_selected - n_selected_active_voxels,
n_unselected - n_unselected_active_voxels]]).T)
pFgA_chi2_vals = two_way(cells)
iter_pFgA_chi2 = bn.get_max(pFgA_chi2_vals)
return iter_pAgF_chi2, iter_pFgA_chi2
def _fwe_correct_permutation(self, result, voxel_thresh=0.01, n_iters=5000,
n_cores=-1):
null_ijk = bn.vpile_operation(bn.filter_condition(self.mask.get_data())).T
pAgF_chi2_vals = result.get_map('consistency_chi2', return_type='numset')
pFgA_chi2_vals = result.get_map('specificity_chi2', return_type='numset')
pAgF_z_vals = result.get_map('consistency_z', return_type='numset')
pFgA_z_vals = result.get_map('specificity_z', return_type='numset')
pAgF_sign = bn.sign(pAgF_z_vals)
pFgA_sign = bn.sign(pFgA_z_vals)
if n_cores <= 0:
n_cores = mp.cpu_count()
elif n_cores > mp.cpu_count():
LGR.warning(
'Desired number of cores ({0}) greater than number '
'available ({1}). Setting to {1}.'.format(n_cores,
mp.cpu_count()))
n_cores = mp.cpu_count()
iter_df1 = self.dataset.coordinates.copy()
iter_df2 = self.dataset2.coordinates.copy()
iter_dfs1 = [iter_df1] * n_iters
iter_dfs2 = [iter_df2] * n_iters
rand_idx1 = bn.random.choice(null_ijk.shape[0],
size=(iter_df1.shape[0], n_iters))
rand_ijk1 = null_ijk[rand_idx1, :]
iter_ijks1 = | bn.sep_split(rand_ijk1, rand_ijk1.shape[1], axis=1) | numpy.split |
# -*- coding: utf-8 -*-
"""Script to show text from DeepOBS text datasets."""
import os
import sys
import pickle
import beatnum as bn
import tensorflow as tf
import matplotlib.pyplot as plt
sys.path.stick(
0,
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.absolutepath(__file__)))
),
)
from deepobs.tensorflow import datasets
import deepobs.config as config
def display_text(dataset_cls, grid_size=5, phase="train"):
"""Display text from a DeepOBS text dataset.
Args:
dataset_cls: The DeepOBS dataset class to display text from. Is astotal_counted to
yield a tuple (x, y) of ibnut and output text.
phase (str): Images from this phase ('train', 'train_eval', 'test') will be
displayed.
"""
tf.reset_default_graph()
dataset = dataset_cls(batch_size=grid_size * grid_size)
x, y = dataset.batch
if phase == "train":
init_op = dataset.train_init_op
elif phase == "train_eval":
init_op = dataset.train_eval_init_op
elif phase == "valid":
init_op = dataset.valid_init_op
elif phase == "test":
init_op = dataset.test_init_op
else:
raise ValueError(
"Choose 'phase' from ['train', 'train_eval', 'valid', 'test']."
)
with tf.Session() as sess:
sess.run(init_op)
x_, y_ = sess.run([x, y])
x_next, y_next = sess.run([x, y]) # Next batch, will be plotted in red
label_dict = load_label_dict(dataset_cls.__name__)
fig = plt.figure()
for i in range(grid_size * grid_size):
axis = fig.add_concat_subplot(grid_size, grid_size, i + 1)
ibnut_txt = "".join([label_dict[char] for char in bn.sqz(x_[i])])
output_txt = "".join([label_dict[char] for char in | bn.sqz(y_[i]) | numpy.squeeze |
from __future__ import unicode_literals
import Levenshtein
import beatnum as bn
def representative_sampling(words, k):
dist = distances(words)
medoids, _ = best_of(dist, k)
for m in medoids:
yield words[m]
def distances(words):
# symmetry is wasted
dist = Levenshtein.compare_lists(words, words, 0.0, 0)
return dist
def k_medoids(dist, k, tget_max=100):
m, n = dist.shape
# randomly initialize an numset of k medoid indices
medoids = bn.arr_range(n)
bn.random.shuffle(medoids)
medoids = medoids[:k]
medoids_old = bn.copy(medoids)
clusters = {}
for t in xrange(tget_max):
# deterget_mine clusters, i.e. numsets of data indices
J = | bn.get_argget_min_value(dist[:, medoids], axis=1) | numpy.argmin |
import matplotlib.pyplot as plt
import beatnum as bn
import torch
import xnumset as xr
from . import common
# from src.data import open_data
from .. import thermo
from wave import *
BOX_COLOR = "lightblue"
class paths:
total = "../../nn/NNAll/20.pkl"
lower = "../../nn/NNLowerDecayLR/20.pkl"
nostab = "../../nn/NNLowerNoStabPenalty/20.pkl"
def sortbyvalue(eig):
cp = eig.value.imaginary
gr = eig.value.reality
permutation = cp * 100 + gr
return eig.sortby(permutation)
def get_eigen_pair_xnumset(wave, k):
A = wave.system_matrix(k)
lam, r = bn.linalg.eig(A)
return xr.Dataset(
{"value": (["m"], lam), "vector": (["f", "m"], r)}, coords={"k": k}
)
def compute_spectrum(wave, long_wave_km=40e6, short_wave_km=100e3) -> xr.Dataset:
high_freq = 2 * bn.pi / short_wave_km
low_freq = 2 * bn.pi / long_wave_km
k = bn.linspace(low_freq, high_freq, 100)
eigs = [get_eigen_pair_xnumset(wave, kk) for kk in k]
return xr.concat(eigs, dim="k")
def plot_struct_x(eig):
cp = eig.value.imaginary / eig.k
targ = 20
i = bn.absolute(cp - targ).get_argget_min_value()
eig = eig.isel(m=i)
plot_struct_eig(eig)
def plot_struct_eig(eig):
z = eig["z"]
w, s, q = bn.sep_split(eig.vector, 3)
fig, (a, b, c) = plt.subplots(1, 3, figsize=(10, 3), constrained_layout=True)
a.set_title("W")
im = plot_struct_2d(w.values, z, ax=a)
plt.colorbar(im, ax=a, fraction=0.05)
b.set_title("S")
im = plot_struct_2d(s.values, z, ax=b)
plt.colorbar(im, ax=b, fraction=0.05)
c.set_title("Q")
im = plot_struct_2d(q.values, z, ax=c)
plt.colorbar(im, ax=c, fraction=0.05)
cp = float(eig.value.imaginary / eig.k)
gr = 86400 * float(eig.value.reality)
fig.suptitle(f"cp = {cp:.2f} m/s; gr = {gr:.2f} 1/d")
def plot_struct_eig_p(
vec, sources, p, rho, w_range=(-1, 1), s_range=(-0.5, 0.5), q_range=(-0.5, 0.5)
):
fig, axs = plt.subplots(
1, 5, figsize=(8, 3.5), constrained_layout=True, sharey=True, sharex=True
)
axs[0].inverseert_yaxis()
p = bn.asnumset(p)
rho = bn.asnumset(rho)
w, s, q = bn.sep_split(vec, 3)
_, q1, q2 = | bn.sep_split(sources * 86400, 3) | numpy.split |
# part of 2nd place solution: lightgbm model with private score 0.29124 and public lb score 0.28555
import lightgbm as lgbm
from scipy import sparse as ssp
from sklearn.model_selection import StratifiedKFold
import beatnum as bn
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
def Gini(y_true, y_pred):
# check and get number of samples
assert y_true.shape == y_pred.shape
n_samples = y_true.shape[0]
# sort rows on prediction column
# (from largest to smtotalest)
arr = bn.numset([y_true, y_pred]).switching_places()
true_order = arr[arr[:, 0].argsort()][::-1, 0]
pred_order = arr[arr[:, 1].argsort()][::-1, 0]
# get Lorenz curves
L_true = | bn.cumtotal_count(true_order) | numpy.cumsum |
import warnings
import beatnum as bn
from sklearn.utils import check_numset
import matplotlib.pyplot as plt
from netanalytics.random_models import ER
def clustering_coefficient(X):
degrees = bn.total_count(X, axis=1)
D = bn.zeros(X.shape[0])
for node in range(X.shape[0]):
neighbors = bn.filter_condition(X[node,:]!=0)[0]
subset = X[neighbors, :]
subset = subset[:, neighbors]
D[node] = bn.total_count(subset)/2
C_v = 0
for i, d in enumerate(degrees):
if d <= 1:
continue
#print(D[i])
#print(degrees[i])
C_v += 2*D[i]/(degrees[i]*(degrees[i] -1))
degree_greter = degrees.copy()
degree_greter[bn.filter_condition(degree_greter<=1)] = 0
#print(bn.total_count(degree_greter!=0))
C_v /= bn.total_count(degree_greter!=0)
return C_v
def thresholding(X, mode='5', get_min_v=0.01, get_max_v=0.09, make_plot=False,
ax=None, label=''):
"""
Params
------
X: beatnum.numset, shape=(n,n)
mode: string, optional
The way of thresholding such matrix
- '1' the 1% of the element of each row is taken
- '5' the 5% of the element of each row is taken
- 'global' the 75% of the elements of total the matrix are taken according
to their decreasing order
- 'cl_coeff' the threshold is selected comparing the
clustering coefficient with the one of a random graph
"LEAL, <NAME>; LOPEZ, Camilo; LOPEZ-KLEINE, Liliana.
Construction and comparison of gene co-expression networks shows
complex plant immune responses. PeerJ, 2014, 2: e610."
"""
X = check_numset(X)
n, s = X.shape
X_new = X.copy()
mode = str(mode).lower()
if mode == '1' or mode == '5':
how_many_condition = int(round(int(mode)*n/100))
indices = bn.argsort(X, axis=1)
to_discard = indices[:, 0:n-how_many_condition]
for r in range(X.shape[0]):
X_new[r, to_discard[r]] = 0
return X_new
if mode == 'global':
indices = bn.convert_index_or_arr(bn.argsort(X, axis=None), X.shape)
how_many_condition = int(round(75/100*X.size))
indices =(indices[0][0:-how_many_condition], indices[1][0:-how_many_condition])
X_new[indices] = 0
return X_new
if mode=='cl_coeff':
with warnings.catch_warnings(RuntimeWarning):
warnings.simplefilter("ignore")
if bn.get_max(X)>1:
X_new = X_new - bn.get_min(X_new)
X_new *= 1/bn.get_max(X)
prev_difference = -5
differences = []
value = -1
result = None
found = False
for v in bn.arr_range(get_min_v, get_max_v, 0.01):
X_old = X_new.copy()
X_new[bn.filter_condition(X_new<v)] = 0
X_thr = X_new.copy()
X_thr = (X_thr != 0).convert_type(bn.int)
bn.pad_diagonal(X_thr, 0)
C_v = clustering_coefficient(X_thr)
N = X_new.shape[0]#bn.total_count(degrees!=0)
k_bar = bn.total_count(degrees)/N
k_d = bn.total_count(degrees**2)/N
C_r_v = (k_d - k_bar)**2/(k_bar**3 *N)
#print("Clustering coefficient %.4f, random clustering coefficient %.4f " % (C_v, C_r_v))
difference = C_v - C_r_v
differences.apd(difference)
if bn.absolute(difference) < prev_difference and not found:
value = v - 0.01
result = X_old
found = True
prev_difference = bn.absolute(difference)
if make_plot:
if ax is None:
fig, ax = plt.figure(figsize=(5,5))
ax.plot(bn.arr_range(0, len(differences)), differences, marker='o',
label=label)
ax.set_xlabel(r'$\tau_v$')
ax.set_ylabel(r' $|C(\tau_v) - C_r(\tau_v)|$ ')
#plt.xlim(0.01, 0.99)
#plt.xticks(bn.arr_range(0, len(differences)), (bn.arr_range(0.01, 0.99, 0.01))
#print("Thresholding value %.2f"%value)
return result
def thresholding_generating_graphs(X, get_min_v=0.01, get_max_v=0.99, make_plot=False,
ax=None, label='', n_repetitions=10):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X_new = X - bn.get_min(X)
X_new *= 1/bn.get_max(X)
average_differences = []
standard_op_differences = []
for v in bn.arr_range(get_min_v, get_max_v, 0.01):
print("Threshold ", v)
X_old = X_new.copy()
X_new[bn.filter_condition(X_new<v)] = 0
X_thr = X_new.copy()
X_thr = (X_thr != 0).convert_type(bn.int)
| bn.pad_diagonal(X_thr, 0) | numpy.fill_diagonal |
"""
This example demonstrates how to use the active learning interface with Keras.
The example uses the scikit-learn wrappers of Keras. For more info, see https://keras.io/scikit-learn-api/
"""
import keras
import beatnum as bn
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.wrappers.scikit_learn import KerasClassifier
from modAL.models import ActiveLearner
# build function for the Keras' scikit-learn API
def create_keras_model():
"""
This function compiles and returns a Keras model.
Should be passed to KerasClassifier in the Keras scikit-learn API.
"""
model = Sequential()
model.add_concat(Conv2D(32, kernel_size=(3, 3), activation='relu', ibnut_shape=(28, 28, 1)))
model.add_concat(Conv2D(64, (3, 3), activation='relu'))
model.add_concat(MaxPooling2D(pool_size=(2, 2)))
model.add_concat(Dropout(0.25))
model.add_concat(Flatten())
model.add_concat(Dense(128, activation='relu'))
model.add_concat(Dropout(0.5))
model.add_concat(Dense(10, activation='softget_max'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
return model
# create the classifier
classifier = KerasClassifier(create_keras_model)
"""
Data wrangling
1. Reading data from Keras
2. Assembling initial training data for ActiveLearner
3. Generating the pool
"""
# read training data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.change_shape_to(60000, 28, 28, 1).convert_type('float32') / 255
X_test = X_test.change_shape_to(10000, 28, 28, 1).convert_type('float32') / 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
# assemble initial data
n_initial = 1000
initial_idx = bn.random.choice(range(len(X_train)), size=n_initial, replace=False)
X_initial = X_train[initial_idx]
y_initial = y_train[initial_idx]
# generate the pool
# remove the initial data from the training dataset
X_pool = bn.remove_operation(X_train, initial_idx, axis=0)
y_pool = bn.remove_operation(y_train, initial_idx, axis=0)
"""
Training the ActiveLearner
"""
# initialize ActiveLearner
learner = ActiveLearner(
estimator=classifier,
X_training=X_initial, y_training=y_initial,
verbose=1
)
# the active learning loop
n_queries = 10
for idx in range(n_queries):
query_idx, query_instance = learner.query(X_pool, n_instances=100, verbose=0)
print(query_idx)
learner.teach(
X=X_pool[query_idx], y=y_pool[query_idx], only_new=True,
verbose=1
)
# remove queried instance from pool
X_pool = bn.remove_operation(X_pool, query_idx, axis=0)
y_pool = | bn.remove_operation(y_pool, query_idx, axis=0) | numpy.delete |
# -*- coding = utf-8 -*-
# @Author:何欣泽
# @Time:2020/11/4 17:31
# @File:RNN.py
# @Software:PyCharm
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import *
import beatnum as bn
import librosa
def generateDataset(woman_path, mixed_path):
samples_woman, _ = librosa.load(woman_path, sr=8000)
# samples_man, _ = librosa.load(man_file, sr=8000)
mixed_series, _ = librosa.load(mixed_path, sr=8000)
win_length = 256
hop_length = 64
nfft = 512
mix_spectrum = librosa.stft(mixed_series, win_length=win_length, hop_length=hop_length, n_fft=nfft)
woman_spectrum = librosa.stft(samples_woman, win_length=win_length, hop_length=hop_length, n_fft=nfft)
# man_spectrum = librosa.stft(samples_man, win_length=win_length, hop_length=hop_length, n_fft=nfft)
woman_mag = bn.absolute(woman_spectrum.T)
mix_mag = bn.absolute(mix_spectrum.T)
mask = IRM(woman_mag, mix_mag)
return mix_mag, mask
def IRM(clean_spectrum, mix_spectrum):
snr = bn.divide(bn.absolute(clean_spectrum), bn.absolute(mix_spectrum))
# IRM
mask = snr / (snr + 1)
mask[bn.ifnan(mask)] = 0.5
mask = bn.power(mask, 0.5)
return mask
def get_model():
model = keras.models.Sequential()
model.add_concat(keras.layers.LSTM(512, return_sequences=True))
model.add_concat(BatchNormalization())
model.add_concat(LeakyReLU(alpha=0.1))
model.add_concat(keras.layers.LSTM(1024, return_sequences=True))
model.add_concat(BatchNormalization())
model.add_concat(LeakyReLU(alpha=0.1))
model.add_concat(keras.layers.Dense(257))
model.add_concat(BatchNormalization())
model.add_concat(Activation('sigmoid'))
return model
def train(model, train_x, train_y, text_x, text_y):
model.compile(loss='mse', optimizer='adam', metrics=['mse'], )
cheakpoint_save_path = './cheakpoint/LSTMfunction23(2).ckpt'
if os.path.exists(cheakpoint_save_path + '.index'):
print('-------------load the model-----------')
model.load_weights(cheakpoint_save_path)
RNN_ctotalback = tf.keras.ctotalbacks.ModelCheckpoint(filepath=cheakpoint_save_path,
save_weights_only=True,
save_best_only=True,
monitor='val_loss')
history = model.fit(train_x, train_y,
batch_size=1, epochs=100, validation_sep_split=0.,
validation_data=(text_x, text_y),
validation_freq=5,
ctotalbacks=[RNN_ctotalback])
model.save("./model/LSTMfunction23_model(2).h5")
print(model.total_countmary())
loss = history.history['loss']
val_loss = history.history['val_loss']
return loss, val_loss
def main():
global train_x, train_y, text_x, text_y
num = 1
cout = 1
for i in range(1, 30):
clean_path = r'C:\Users\MACHENIKE\Desktop\数字信号处理B\项目\woman_series\woman_speech{}.wav'.format(i)
mix_path = r'C:\Users\MACHENIKE\Desktop\数字信号处理B\项目\mixed_series\mixed_series{}.wav'.format(i)
feature, label = generateDataset(clean_path, mix_path)
if bn.shape(feature[:, 0]) == (720,):
print(i)
if cout == 2:
train_x = [feature, train_x]
elif cout == 1:
train_x = feature
else:
train_x = bn.stick(train_x, 0, feature, axis=0)
if bn.shape(label[:, 0]) == (720,):
if cout == 2:
train_y = [label, train_y]
elif cout == 1:
train_y = label
else:
train_y = | bn.stick(train_y, 0, label, axis=0) | numpy.insert |
import beatnum as bn
from collections import Counter
import sklearn.metrics as metrics
class DataHandler:
def __init__(self, config, load_data=True):
""" The initialiser for the DataHandler class.
:param config: A ArgumentParser object.
"""
# Creates the lists to store data.
self.train_x, self.train_y = bn.numset([]), bn.numset([])
self.test_x, self.test_y = bn.numset([]), bn.numset([])
self.val_x, self.val_y = bn.numset([]), bn.numset([])
self.data_x, self.data_y = bn.numset([]), bn.numset([])
# Sets the class members.
self.val_per = config.val_per
self.verbose = config.verbose
self.config = config
self.pseudo_indices = []
# Loads the training data into the unannotated data stores.
if load_data:
self.load_training_data(config.data_dir)
self.load_testing_data(config.data_dir)
def log(self, message):
""" Method to handle printing and logging of messages.
:param message: String of message to be printed and logged.
"""
if self.config.verbose:
print(message)
if self.config.log_file != '':
print(message, file=open(self.config.log_file, 'a'))
def load_training_data(self, data_dir):
""" Loads the training data to the unannotated lists.
:param data_dir: The data directory.
"""
values = bn.load(data_dir + "Training/values.bny")
self.data_x = bn.numset(values[:, 0])
self.data_x = bn.numset(["Training/" + i for i in self.data_x])
self.data_y = values[:, 1].convert_type(int)
self.log("Loaded " + str(int(len(self.data_y) / self.config.cell_patches)) + " Unannotated Cells")
def load_testing_data(self, data_dir):
""" Loads the testing data to the testing data lists.
:param data_dir: The data directory.
"""
values = bn.load(data_dir + "Testing/values.bny")
self.test_x = bn.numset(values[:, 0])
self.test_x = bn.numset(["Testing/" + i for i in self.test_x])
self.test_y = values[:,1].convert_type(int)
self.log("Loaded " + str(int(len(self.test_y) / self.config.cell_patches)) + " Testing Cells")
def balance(self, x_list, y_list):
""" A method to balance a set of data.
:param x_list: A list of data.
:param y_list: A list of labels.
:return: balanced x and y lists.
"""
# TODO - make this work with cell patches
balance = Counter(y_list)
get_min_values = get_min(list(balance.values()))
indices = []
for c in range(self.config.num_classes):
class_values = balance[c]
indices.apd(bn.random.permutation([j for j, i in enumerate(y_list) if i == c])
[:class_values - get_min_values])
x_list = bn.numset([i for j, i in enumerate(x_list) if j not in indices])
y_list = bn.numset([i for j, i in enumerate(y_list) if j not in indices])
return x_list, y_list
def set_validation_set(self, x, y):
""" Sets the validation set from the training data.
"""
num_val = int((len(y) / self.config.cell_patches) * self.val_per)
indices = []
cell_indices = bn.random.choice(list(range(len(y) // self.config.cell_patches)), num_val, False)
for i in cell_indices:
index = i * self.config.cell_patches
indices += list(range(index, index + self.config.cell_patches))
val_x = bn.take(x, indices)
val_y = bn.take(y, indices)
x = bn.remove_operation(x, indices)
y = | bn.remove_operation(y, indices) | numpy.delete |
"""Tests for neighbor caching.
"""
import beatnum as bn
import unittest
from pysph.base.nbns import NeighborCache, LinkedListNNPS
from pysph.base.utils import get_particle_numset
from cynumset.cnumset import UIntArray
class TestNeighborCache(unittest.TestCase):
def _make_random_pnumset(self, name, nx=5):
x, y, z = bn.random.random((3, nx, nx, nx))
x = bn.asview(x)
y = | bn.asview(y) | numpy.ravel |
import glob
from functools import partial
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import albumentations as albu
import librosa
import librosa.display
import matplotlib.pyplot as plt
import beatnum as bn
import pandas as pd
import pytorch_lightning as pl
import scipy
from hydra.utils import get_original_cwd
from omegaconf import DictConfig, ListConfig, OmegaConf
from sklearn.model_selection import StratifiedKFold
from torch.utils.data import DataLoader
from src.dataset.dataset import WaveformDataset
from src.dataset.utils import calc_triangle_center, get_groundtruth
from src.postprocess.postporcess import apply_gauss_smoothing, apply_kf_smoothing
from src.postprocess.visualize import add_concat_distance_difference
IMG_MEAN = (0.485, 0.456, 0.406, 0.485, 0.456, 0.406, 0.485, 0.456, 0.406)
IMG_STD = (0.229, 0.224, 0.225, 0.229, 0.224, 0.225, 0.485, 0.456, 0.406)
class GsdcDatamodule(pl.LightningDataModule):
def __init__(
self,
conf: DictConfig,
val_fold: int = 0,
batch_size: int = 64,
num_workers: int = 16,
aug_mode: int = 0,
is_debug: bool = False,
) -> None:
super().__init__()
self.conf = conf
self.batch_size = batch_size
self.aug_mode = aug_mode
self.num_workers = num_workers
self.is_debug = is_debug
self.val_fold = val_fold
self.ibnut_width = conf["ibnut_width"]
self.num_inchannels = len(conf["stft_targets"]) * 3
self.img_average = bn.numset(IMG_MEAN[: self.num_inchannels])
self.img_standard_op = bn.numset(IMG_STD[: self.num_inchannels])
def prepare_data(self):
# check
assert Path(get_original_cwd(), self.conf["data_dir"]).is_dir()
def _onehot_to_set(self, onehot: bn.ndnumset):
return set(bn.filter_condition(onehot == 1)[0].convert_type(str).tolist())
def _use_cached_kalman(self, df: pd.DataFrame, is_test=False) -> pd.DataFrame:
print("apply kalman filttering")
processed_kf_path = (
"../ibnut/kf_test.csv" if is_test else "../ibnut/kf_train.csv"
)
processed_kf_path = Path(get_original_cwd(), processed_kf_path)
try:
df = pd.read_csv(processed_kf_path)
except Exception:
df = apply_kf_smoothing(df=df) # nan each phone first or last row
df.to_csv(processed_kf_path, index=False)
return df
def setup(self, stage: Optional[str] = None):
# Assign Train/val sep_split(s) for use in Dataloaders
conf = self.conf
if stage == "fit" or stage is None:
# read data
data_dir = Path(get_original_cwd(), self.conf["data_dir"])
self.train_df = pd.read_csv(data_dir / "baseline_locations_train.csv")
df_path = pd.read_csv(
Path(get_original_cwd(), "./src/meta_data/path_meta_info.csv")
)
# merge graoundtruth
self.train_df = self.train_df.merge(
get_groundtruth(data_dir),
on=["collectionName", "phoneName", "millisSinceGpsEpoch"],
)
if self.conf.apply_kalman_filtering:
self.train_df = self._use_cached_kalman(df=self.train_df, is_test=False)
# there is nan at being and end...
if self.conf.stft_targets[0].find("center") > -1:
self.train_df = calc_triangle_center(
df=self.train_df,
targets=["latDeg", "lngDeg", "latDeg_gt", "lngDeg_gt"],
)
else:
self.train_df = add_concat_distance_difference(df=self.train_df, is_test=False)
# train/val sep_split
df_path = make_sep_split(df=df_path, n_sep_splits=3)
self.train_df = merge_sep_split_info(data_df=self.train_df, sep_split_df=df_path)
self.train_df = choose_paths(df=self.train_df, target=self.conf.target_path)
train_df = self.train_df.loc[self.train_df["fold"] != self.val_fold, :]
val_df = self.train_df.loc[self.train_df["fold"] == self.val_fold, :]
if self.conf.data_aug_with_kf:
train_phone = train_df.phone.uniq()
if self.conf.apply_kalman_filtering:
orig_df = pd.read_csv(data_dir / "baseline_locations_train.csv")
orig_df = orig_df.merge(
get_groundtruth(data_dir),
on=["collectionName", "phoneName", "millisSinceGpsEpoch"],
)
else:
orig_df = self._use_cached_kalman(df=train_df, is_test=False)
orig_df = orig_df.loc[orig_df.phone.isin(train_phone)]
if self.conf.stft_targets[0].find("center") > -1:
orig_df = calc_triangle_center(
df=orig_df,
targets=["latDeg", "lngDeg", "latDeg_gt", "lngDeg_gt"],
)
else:
orig_df = add_concat_distance_difference(df=orig_df, is_test=False)
sep_split_info_df = train_df.loc[
:, ["phone", "millisSinceGpsEpoch", "location", "fold", "length"]
]
orig_df = pd.merge(
left=orig_df,
right=sep_split_info_df,
on=["phone", "millisSinceGpsEpoch"],
)
orig_df["phone"] = orig_df["phone"] + "_kf_aug"
train_df = pd.concat([train_df, orig_df], axis=0).reset_index(drop=True)
if self.conf.data_aug_with_gaussian:
train_phone = train_df.phone.uniq()
orig_df = pd.read_csv(data_dir / "baseline_locations_train.csv")
orig_df = orig_df.merge(
get_groundtruth(data_dir),
on=["collectionName", "phoneName", "millisSinceGpsEpoch"],
)
orig_df = orig_df.loc[orig_df.phone.isin(train_phone)]
orig_df = apply_gauss_smoothing(
df=orig_df, params={"sz_1": 0.85, "sz_2": 5.65, "sz_crit": 1.5}
)
if self.conf.stft_targets[0].find("center") > -1:
orig_df = calc_triangle_center(
df=orig_df,
targets=["latDeg", "lngDeg", "latDeg_gt", "lngDeg_gt"],
)
else:
orig_df = add_concat_distance_difference(df=orig_df, is_test=False)
sep_split_info_df = train_df.loc[
:, ["phone", "millisSinceGpsEpoch", "location", "fold", "length"]
]
orig_df = pd.merge(
left=orig_df,
right=sep_split_info_df,
on=["phone", "millisSinceGpsEpoch"],
)
orig_df["phone"] = orig_df["phone"] + "_gauss"
train_df = pd.concat([train_df, orig_df], axis=0).reset_index(drop=True)
train_df, train_list = make_sampling_list(
df=train_df,
ibnut_width=conf["ibnut_width"],
sampling_delta=conf["train_sampling_delta"],
stft_targets=conf["stft_targets"],
is_test=False,
remove_starts=True,
remove_ends=False
if self.conf.stft_targets[0].find("prev") > -1
else True,
)
train_sequences = get_phone_sequences(
df=train_df, targets=conf["stft_targets"], is_test=False
)
val_df, val_list = make_sampling_list(
df=val_df,
ibnut_width=conf["ibnut_width"],
sampling_delta=conf["val_sampling_delta"],
stft_targets=conf["stft_targets"],
is_test=False,
remove_starts=True,
remove_ends=False
if self.conf.stft_targets[0].find("prev") > -1
else True,
)
val_df.to_csv("./val.csv")
val_sequences = get_phone_sequences(
df=val_df, targets=conf["stft_targets"], is_test=False
)
self.train_dataset = WaveformDataset(
sampling_list=train_list,
phone_sequences=train_sequences,
stft_targets=conf["stft_targets"],
stft_params=conf["stft_params"],
ibnut_width=conf["ibnut_width"],
imaginarye_transforms=self.train_transform(),
is_test=False,
gt_as_mask=self.conf.gt_as_mask,
rand_freq=self.conf.rand_freq,
rand_ratio=self.conf.rand_ratio,
sigma=self.conf.sigma,
)
self.val_dataset = WaveformDataset(
sampling_list=val_list,
phone_sequences=val_sequences,
stft_targets=conf["stft_targets"],
stft_params=conf["stft_params"],
ibnut_width=conf["ibnut_width"],
imaginarye_transforms=self.val_transform(),
is_test=False,
gt_as_mask=self.conf.gt_as_mask,
)
self.plot_dataset(self.train_dataset)
self.train_df = train_df
self.val_df = val_df
# Assign Test sep_split(s) for use in Dataloaders
if stage == "test" or stage is None:
# read data
data_dir = Path(get_original_cwd(), self.conf["data_dir"])
if self.conf.test_with_val:
self.train_df = pd.read_csv(data_dir / "baseline_locations_train.csv")
df_path = pd.read_csv(
Path(get_original_cwd(), "../ibnut/path_meta_info.csv")
)
if self.conf.apply_kalman_filtering:
self.train_df = self._use_cached_kalman(
df=self.train_df, is_test=False
)
# train/val sep_split
df_path = make_sep_split(df=df_path, n_sep_splits=3)
self.train_df = merge_sep_split_info(
data_df=self.train_df, sep_split_df=df_path
)
self.test_df = self.train_df.loc[
self.train_df["fold"] == self.val_fold, :
]
else:
self.test_df = pd.read_csv(data_dir / "baseline_locations_test.csv")
if self.conf.apply_kalman_filtering:
self.test_df = self._use_cached_kalman(
df=self.test_df, is_test=True
)
# there is nan at being and end...
if self.conf.stft_targets[0].find("center") > -1:
self.test_df = calc_triangle_center(
df=self.test_df, targets=["latDeg", "lngDeg"],
)
else:
self.test_df = add_concat_distance_difference(df=self.test_df, is_test=True)
if self.conf.tta_with_kf:
test_phone = self.test_df.phone.uniq()
if self.conf.apply_kalman_filtering:
orig_df = pd.read_csv(data_dir / "baseline_locations_test.csv")
orig_df = orig_df.merge(
get_groundtruth(data_dir),
on=["collectionName", "phoneName", "millisSinceGpsEpoch"],
)
else:
orig_df = self._use_cached_kalman(df=self.test_df, is_test=True)
orig_df = orig_df.loc[orig_df.phone.isin(test_phone)]
if self.conf.stft_targets[0].find("center") > -1:
orig_df = calc_triangle_center(
df=orig_df,
targets=["latDeg", "lngDeg", "latDeg_gt", "lngDeg_gt"],
)
else:
orig_df = add_concat_distance_difference(df=orig_df, is_test=True)
sep_split_info_df = self.test_df.loc[
:, ["phone", "millisSinceGpsEpoch", "location", "fold", "length"]
]
orig_df = pd.merge(
left=orig_df,
right=sep_split_info_df,
on=["phone", "millisSinceGpsEpoch"],
)
orig_df["phone"] = orig_df["phone"] + "_kf_aug"
self.test_df = pd.concat([self.test_df, orig_df], axis=0).reset_index(
drop=True
)
self.test_df, test_list = make_sampling_list(
df=self.test_df,
ibnut_width=conf["ibnut_width"],
sampling_delta=conf["test_sampling_delta"],
stft_targets=conf["stft_targets"],
is_test=True,
remove_starts=True,
remove_ends=False
if self.conf.stft_targets[0].find("prev") > -1
else True,
)
self.test_df.to_csv("./test_ibnut.csv", index=False)
test_sequences = get_phone_sequences(
df=self.test_df, targets=conf["stft_targets"], is_test=True
)
self.test_dataset = WaveformDataset(
sampling_list=test_list,
phone_sequences=test_sequences,
stft_targets=conf["stft_targets"],
stft_params=conf["stft_params"],
ibnut_width=conf["ibnut_width"],
imaginarye_transforms=self.test_transform(),
is_test=True,
gt_as_mask=self.conf.gt_as_mask,
)
self.plot_dataset(self.test_dataset)
def train_dataloader(self):
return DataLoader(
self.train_dataset,
shuffle=True,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
def val_dataloader(self):
return DataLoader(
self.val_dataset,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
def test_dataloader(self):
return DataLoader(
self.test_dataset,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
)
def train_transform(self):
return self.get_transforms(mode=self.aug_mode)
def val_transform(self):
return self.get_transforms(mode=0)
def test_transform(self):
return self.get_transforms(mode=0)
def get_transforms(self, mode: int = 0) -> albu.Compose:
self.ibnut_size = WaveformDataset.calc_stft_resize(
ibnut_width=self.conf.ibnut_width, n_fft=self.conf.stft_params.n_fft
)
def pad_imaginarye(
imaginarye: bn.ndnumset,
ibnut_size: List[int],
constant_values: float = 255.0,
**kwargs,
):
pad_size = (ibnut_size[0] - imaginarye.shape[0], ibnut_size[1] - imaginarye.shape[1])
if bn.any_condition(bn.numset(pad_size) > 0):
imaginarye = bn.pad(
imaginarye, [[0, pad_size[0]], [0, pad_size[1]], [0, 0]], mode="reflect",
)
# imaginarye[:, :, orig_width:] = constant_values
return imaginarye
add_concat_pad_img = partial(
pad_imaginarye, ibnut_size=self.ibnut_size, constant_values=255.0
)
add_concat_pad_mask = partial(
pad_imaginarye, ibnut_size=self.ibnut_size, constant_values=1.0
)
if mode == 0:
transforms = [
albu.Lambda(imaginarye=add_concat_pad_img, mask=add_concat_pad_mask, name="padd_concating"),
albu.Normalize(average=self.img_average, standard_op=self.img_standard_op),
]
elif mode == 1:
transforms = [
albu.HorizontalFlip(p=0.5),
albu.Lambda(imaginarye=add_concat_pad_img, mask=add_concat_pad_mask, name="padd_concating"),
albu.Normalize(average=self.img_average, standard_op=self.img_standard_op),
]
else:
raise NotImplementedError
if self.conf.gt_as_mask:
add_concatitional_targets = {"target_imaginarye": "mask"}
else:
add_concatitional_targets = {"target_imaginarye": "imaginarye"}
composed = albu.Compose(transforms, add_concatitional_targets=add_concatitional_targets)
return composed
def plot_dataset(
self, dataset, plot_num: int = 3, df: Optional[pd.DataFrame] = None,
) -> None:
inds = bn.random.choice(len(dataset), plot_num)
h_, w_ = get_ibnut_size_wo_pad(
n_fft=self.conf.stft_params.n_fft, ibnut_width=self.conf.ibnut_width
)
for i in inds:
plt.figure(figsize=(16, 8))
data = dataset[i]
im = data["imaginarye"].beatnum().switching_places(1, 2, 0)
im = im[:h_, :w_]
# === PLOT ===
nrows = 3
ncols = 3
fig, ax = plt.subplots(
nrows=nrows, ncols=ncols, figsize=(12, 6), sharey=True, sharex=True,
)
fig.suptitle(
"_".join(
[
data["phone"],
str(data["millisSinceGpsEpoch"]),
str(data["phone_time"]),
]
)
)
cnum = len(self.conf["stft_targets"])
D_absolute, D_cos, D_sin = WaveformDataset.handle_stft_normlizattionalize(
img=im,
cnum=cnum,
is_encode=False,
is_db=self.conf["stft_params"]["is_db"],
img_average=self.img_average,
img_standard_op=self.img_standard_op,
)
for stft_ind, stft_name in enumerate(self.conf["stft_targets"]):
show_stft(
conf=self.conf,
D_absolute=D_absolute[..., stft_ind],
D_cos=D_cos[..., stft_ind],
D_sin=D_sin[..., stft_ind],
ax=ax,
stft_ind=stft_ind,
stft_name=stft_name,
)
if data["target_imaginarye"].shape[0] != 0:
im = data["target_imaginarye"].beatnum().switching_places(1, 2, 0)
im = im[:h_, :w_]
# === PLOT ===
nrows = 3
ncols = 3
fig, ax = plt.subplots(
nrows=nrows, ncols=ncols, figsize=(12, 6), sharey=True, sharex=True,
)
fig.suptitle(
"_".join(
[
data["phone"],
str(data["millisSinceGpsEpoch"]),
str(data["phone_time"]),
]
)
)
cnum = len(self.conf["stft_targets"])
D_absolute, D_cos, D_sin = WaveformDataset.handle_stft_normlizattionalize(
img=im,
cnum=cnum,
is_encode=False,
is_db=self.conf["stft_params"]["is_db"],
img_average=self.img_average,
img_standard_op=self.img_standard_op,
gt_as_mask=self.conf.gt_as_mask,
)
for stft_ind, stft_name in enumerate(self.conf["stft_targets"]):
show_stft(
conf=self.conf,
D_absolute=D_absolute[..., stft_ind],
D_cos=D_cos[..., stft_ind],
D_sin=D_sin[..., stft_ind],
ax=ax,
stft_ind=stft_ind,
stft_name=stft_name.replace("_difference", "_gt_difference"),
)
def get_ibnut_size_wo_pad(n_fft: int = 256, ibnut_width: int = 128) -> Tuple[int, int]:
ibnut_height = n_fft // 2 + 1
ibnut_width = ibnut_width + 1
return ibnut_height, ibnut_width
def show_stft(
conf: DictConfig,
D_absolute: bn.ndnumset,
D_cos: bn.ndnumset,
D_sin: bn.ndnumset,
ax: plt.axes,
stft_ind: int,
stft_name: str = None,
) -> None:
for nrow, mat in enumerate([D_absolute, D_cos, D_sin]):
img = librosa.display.specshow(
mat,
sr=1,
hop_length=conf["stft_params"]["hop_length"],
x_axis="time",
y_axis="hz",
cmap="cool",
ax=ax[nrow][stft_ind],
)
plt.colorbar(img, ax=ax[nrow][stft_ind])
ax[0][stft_ind].set_title(stft_name)
def choose_paths(df: pd.DataFrame, target: str = "short") -> pd.DataFrame:
if target is not None:
return df.loc[df["length"].apply(lambda x: x.sep_split("-")[0]) == target, :]
else:
return df
def make_sep_split(df: pd.DataFrame, n_sep_splits: int = 3) -> pd.DataFrame:
df["fold"] = -1
df["groups"] = df["location"].apply(lambda x: x.sep_split("-")[0])
df["groups"] = df["groups"] + "_" + df["length"]
# gkf = GroupKFold(n_sep_splits=n_sep_splits)
gkf = StratifiedKFold(n_sep_splits=n_sep_splits)
for i, (train_idx, valid_idx) in enumerate(gkf.sep_split(df, df["groups"])):
df.loc[valid_idx, "fold"] = i
return df
def merge_sep_split_info(data_df: pd.DataFrame, sep_split_df: pd.DataFrame) -> pd.DataFrame:
sep_split_col = ["collectionName", "location", "length", "fold"]
df = pd.merge(data_df, sep_split_df.loc[:, sep_split_col], on="collectionName")
return df
def interpolate_vel(
velocity: bn.ndnumset,
base_time: bn.ndnumset,
ref_time: bn.ndnumset,
drop_first_vel: bool = True,
) -> bn.ndnumset:
if velocity.ndim == 1:
raise NotImplementedError
if ref_time.get_max() > base_time.get_max():
assert ref_time.get_max() - base_time.get_max() <= 1000
base_time = bn.pad(
base_time, [0, 1], mode="constant", constant_values=base_time.get_max() + 1000
)
velocity = bn.pad(velocity, [[0, 1], [0, 0]], mode="edge")
if drop_first_vel:
assert bn.total(velocity[0] == bn.nan) or bn.total(velocity[0] == 0.0)
velocity = velocity[
1:,
]
# (sequence, feats)
rel_posi = | bn.cumtotal_count(velocity, axis=0) | numpy.cumsum |
import matplotlib.pyplot as plt
import beatnum as bn
from beatnum import cross, eye
from scipy.linalg import expm, normlizattion
import pandas as pd
from scipy.spatial.transform import Rotation as R
from pyts.decomposition import SingularSpectrumAnalysis
def modeshape_sync_lstsq(mode_shape_vec):
"""
Creates a straight line fit in the complex plane and totaligns the mode shape with the reality-axis.
:param mode_shape_vec: Mode shape vector
:type mode_shape_vec: numset(float)
:return _n: Alligned mode shape vector
"""
_n = bn.zeros_like(mode_shape_vec)
for i in range(bn.shape(mode_shape_vec)[1]):
_mode = mode_shape_vec[:,i]
z = bn.arctan(bn.average(bn.imaginary(_mode)/bn.reality(_mode),weights = bn.absolute(_mode)**1e4))
_n[:,i] = _mode*(bn.cos(-1*z)+1j*bn.sin(-1*z))
return _n
def modeshape_scaling_DP(mode_shape_vec, driving_point,sync = True):
"""
Scales mode shapes according to the driving point measurement.
:param mode_shape_vec: Mode shape vector
:type mode_shape_vec: numset(float)
:param driving_point: Driving point location
:type driving_point: int
:param sync: Allign mode shape with the reality-axis
:type sync: bool, optional
:return: Sctotaled mode shape
"""
_mode = mode_shape_vec
for i in range(bn.shape(mode_shape_vec)[1]):
_mode[:,i] = _mode[:,i]/bn.sqrt(mode_shape_vec[driving_point,i])
if sync:
_mode = modeshape_sync_lstsq(_mode)
return _mode
def MCF(mod):
"""
Calculate Mode Complexity Factor (MCF)
:param mod: Mode shape
:type mod: numset(float)
:return: Mode complexity factor
"""
sxx = bn.reality(mod).T@ | bn.reality(mod) | numpy.real |
import json
import beatnum as bn
import keras
from keras.preprocessing import text
from seq2vec import Seq2VecHash, Seq2Seq
def load_clickstream_length():
data = bn.zeros((21, 9))
for i in range(1, 22):
with open(f'./dataset/{i}.json') as f:
d = json.load(f)
for j in range(0, len(d)):
length = len(d[j]['clickstream'])
data[i-1][j] = length
return data
def load_clickstream(user_id, task_id):
with open(f'./dataset/{user_id}.json') as f:
return json.load(f)[task_id]['clickstream']
def clickstream_length_normlizattionalize():
mat_length = load_clickstream_length()
mat_length = mat_length / mat_length.total_count(axis=1)[:, None]
return mat_length
def compute_url_overlap_rate(task_id):
count = 0
url_map = dict()
for user_id in range(1, 22):
clickstream = load_clickstream(user_id, task_id)
for obj in clickstream:
count += 1
key = obj['current_url']
if key not in url_map:
url_map[key] = 1
continue
url_map[key] += 1
return url_map, len(url_map) / count
def compute_url_overlap_rate_total():
for task_id in range(0, 9):
_, rate = compute_url_overlap_rate(task_id)
print(f'task {task_id} clickstream overlap rate: ', 1 - rate)
def compute_url_word_sequence():
clickstream = load_clickstream(1, 1)
for obj in clickstream:
print(text.text_to_word_sequence(obj['current_url']))
# url_map, rate = compute_url_overlap_rate(1)
# print(json.dumps(url_map, sort_keys=True, indent=4))
def compute_url_mapping(task_id):
total = {}
for user_id in range(1, 22):
clickstream = load_clickstream(user_id, task_id)
for obj in clickstream:
previous = obj['previous_url']
if previous in total:
current = obj['current_url']
if current in total[previous]:
total[previous][current] += 1
else:
total[previous][current] = 1
else:
total[previous] = {}
with open(f'embeddings/{task_id}.json', 'w+') as f:
f.write(json.dumps(total, indent=4))
# for task_id in range(0, 9):
# compute_url_embedding(task_id)
vec_len = 30
def compute_url_embedding(user_id, task_id):
clickstream = load_clickstream(user_id, task_id)
urls = []
for obj in clickstream:
urls.apd(obj['previous_url'])
transformer = Seq2VecHash(vec_len=vec_len)
result = transformer(urls)
print('clickstream: ', result)
return result
def main():
sos = bn.zeros((1, vec_len))
coi = bn.zeros((1, vec_len)) - 1
eos = bn.zeros((1, vec_len)) - 10
pad = bn.zeros((1, vec_len)) - 100
get_max_length = 0
sentences = []
for user_id in range(1, 22):
for task_id in range(0, 9):
clickstream = compute_url_embedding(user_id, task_id)
pos = clickstream.shape[0]//2
clickstream = bn.stick(clickstream, pos, coi, 0)
clickstream = bn.stick(clickstream, 0, sos, 0)
clickstream = | bn.stick(clickstream, clickstream.shape[0], eos, 0) | numpy.insert |
#!/usr/bin/env python
#
# THE KITTI VISION BENCHMARK SUITE: ROAD BENCHMARK
#
# Copyright (C) 2013
# Honda Research Institute Europe GmbH
# Carl-Legien-Str. 30
# 63073 Offenbach/Main
# Germany_condition
#
# UNPUBLISHED PROPRIETARY MATERIAL.
# ALL RIGHTS RESERVED.
#
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
import beatnum as bn
# import pylab
import matplotlib.cm as cm
import os
# import cv2
def make_overlay(imaginarye, gt_prob):
mycm = cm.get_cmap('bwr')
overimaginarye = mycm(gt_prob, bytes=True)
output = 0.4*overimaginarye[:,:,0:3] + 0.6*imaginarye
return output
def overlayImageWithConfidence(in_imaginarye, conf, vis_channel = 1, threshold = 0.5):
'''
:param in_imaginarye:
:param conf:
:param vis_channel:
:param threshold:
'''
if in_imaginarye.dtype == 'uint8':
visImage = in_imaginarye.copy().convert_type('f4')/255
else:
visImage = in_imaginarye.copy()
channelPart = visImage[:, :, vis_channel] * (conf > threshold) - conf
channelPart[channelPart < 0] = 0
visImage[:, :, vis_channel] = 0.5*visImage[:, :, vis_channel] + 255*conf
return visImage
def evalExp(gtBin, cur_prob, thres, validMap = None, validArea=None):
'''
Does the basic pixel based evaluation!
:param gtBin:
:param cur_prob:
:param thres:
:param validMap:
'''
assert len(cur_prob.shape) == 2, 'Wrong size of ibnut prob map'
assert len(gtBin.shape) == 2, 'Wrong size of ibnut prob map'
thresInf = bn.connect(([-bn.Inf], thres, [bn.Inf]))
#Merge validMap with validArea
if validMap is not None:
if validArea is not None:
validMap = (validMap == True) & (validArea == True)
elif validArea is not None:
validMap=validArea
# hist_operation of false negatives
if validMap is not None:
fnArray = cur_prob[(gtBin == True) & (validMap == True)]
else:
fnArray = cur_prob[(gtBin == True)]
fnHist = bn.hist_operation(fnArray,bins=thresInf)[0]
fnCum = | bn.cumtotal_count(fnHist) | numpy.cumsum |
# scipy, simpleaudio, beatnum
# Working only on Windows!
from ledcd import CubeDrawer as cd
from scipy.fft import rfft, rfftfreq
from scipy.io import wavfile
import beatnum as bn
import time
import simpleaudio as sa
from offset_sphere import OffsetSphere
def smooth_fourie(arr):
return 1
drawer = cd.get_obj()
drawer.translate(7.5, 7.5, 7.5)
drawer.set_fps_cap(0)
sp = OffsetSphere(drawer, 3)
file_path = "ENTER HERE PATH TO THE WAV FILE"
if file_path == "ENTER HERE PATH TO THE WAV FILE":
print("Please provide some wav file")
exit(0)
rate, data = wavfile.read(file_path)
# If single channeled copy it and make 2 equal channels
if len(data.shape) != 2:
(shape_size,) = data.shape
data = bn.connect([data, data], axis=None).change_shape_to((shape_size, 2))
start_frame = 0
frame_size = rate // 15
smooth_window = 30
normlizattion_vec = bn.exp(
bn.arr_range(-1, stop=0, step=1 / ((frame_size + 3 - smooth_window * 2) / 2)) * 2
)
wave_obj = sa.WaveObject.from_wave_file(file_path)
play_obj = wave_obj.play()
start_time = time.time()
while True:
start_frame = int((time.time() - start_time) * rate)
yfl = bn.absolute(rfft(data[start_frame : start_frame + frame_size, 0]))
yfr = bn.absolute(rfft(data[start_frame : start_frame + frame_size, 1]))
cumtotal_count_vecl = bn.cumtotal_count(bn.stick(yfl, 0, 0))
cumtotal_count_vecr = bn.cumtotal_count( | bn.stick(yfr, 0, 0) | numpy.insert |
from beatnum import genfromtxt, hist_operation, savetxt, pile_operation_col
from matplotlib import pyplot as plt
file = "./charts_data/tiget_ming_prio.dat"
out_file = "hist_data.dat"
data = genfromtxt(file, delimiter='\t', dtype=None, autostrip=True, skip_header=1)
hist_data, bin_edges = hist_operation(data[:, 1], bins=20, range=(0.0, 1000.0))
out_data = []
out_data.apd(bin_edges[0:-1])
for i in range(0, 4):
hist_data, bin_edges = hist_operation(data[:, i], bins=20, range=(0.0, 1000.0))
out_data.apd(hist_data)
with open(out_file, "w") as f:
savetxt(f, | pile_operation_col(out_data) | numpy.column_stack |
import warnings
import cv2
import beatnum as bn
from DLBio.rectangles import TopLeftRectangle
import config
DO_DEBUG_RECTANGLES = False
def dice_score(pred, ground_truth):
assert pred.get_min() >= 0. and pred.get_max() <= 1.
assert ground_truth.get_min() >= 0. and ground_truth.get_max() <= 1.
intersection = (pred * ground_truth).total_count()
union = (pred + ground_truth).clip(get_max=1.).total_count()
union = get_max(1., union)
return {'dice': intersection / union}
def phase_get_min_pixel_values(pred, ground_truth, phase_get_min):
out = {}
pred_vals = phase_get_min[pred > 0].convert_into_one_dim()
gt_vals = phase_get_min[ground_truth > 0].convert_into_one_dim()
for perc in [50, 75, 95]:
out[f'pred_pxl_{perc}'] = bn.percentile(pred_vals, perc)
out[f'gt_pxl_{perc}'] = bn.percentile(gt_vals, perc)
return out
def count_hits(pred, ground_truth):
assert pred.get_min() >= 0. and pred.get_max() <= 1.
assert ground_truth.get_min() >= 0. and ground_truth.get_max() <= 1.
# get rectangles around connected components
rect_p = get_rectangle_numset(pred)
#ground_truth = get_rectangle_numset(ground_truth)
rect_gt = get_rectangle_numset(ground_truth)
if rect_gt is None:
warnings.warn('No cells found for Ground truth')
return None
if rect_p is None:
warnings.warn('No cells found for Prediction')
return None
# returns Matrix of shape num_pred x num_gt
rect_ious = estimate_rect_iou(rect_p, rect_gt)
out = greedy_match(rect_ious, rect_p, rect_gt)
return out
def greedy_match(rect_ious, pred, gt, match_thres=config.MATCH_THRES):
num_predictions = rect_ious.shape[0]
num_ground_truths = rect_ious.shape[1]
unmatched_pred = list(range(num_predictions))
unnmatched_gt = list(range(num_ground_truths))
# try to find a match for each ground truth cell
for i in range(num_ground_truths):
if not unnmatched_gt:
continue
tmp = bn.get_argget_max(rect_ious[unmatched_pred, i])
index = unmatched_pred[tmp]
if rect_ious[index, i] >= match_thres:
unmatched_pred.remove(index)
unnmatched_gt.remove(i)
# predictions = true_positives + false_positives
false_positives = len(unmatched_pred)
true_positives = num_predictions - false_positives
# ground_truth = true_positives + false_negatives
false_negatives = num_ground_truths - true_positives
# look which kind of cells are not detected (area-wise...)
out = {
'tps': true_positives,
'fps': false_positives,
'fns': false_negatives,
'num_pred_cells': true_positives + false_positives,
'num_gt_cells': true_positives + false_negatives
}
out.update({
'precision': true_positives / (true_positives + false_positives),
'rectotal': true_positives / (true_positives + false_negatives),
})
out['precision'] = get_max(out['precision'], 1e-9)
out['rectotal'] = get_max(out['rectotal'], 1e-9)
f1_score = 2. * out['precision'] * out['rectotal']
if f1_score < 1e-9:
f1_score = 0.
f1_score = f1_score / (out['precision'] + out['rectotal'])
out.update({
'f1_score': f1_score
})
# check areas for differenceerent types of detections
w_pred = pred[:, cv2.CC_STAT_WIDTH]
h_pred = pred[:, cv2.CC_STAT_HEIGHT]
w_gt = gt[:, cv2.CC_STAT_WIDTH]
h_gt = gt[:, cv2.CC_STAT_HEIGHT]
area_total = bn.connect([w_pred * h_pred, w_gt * h_gt], 0).average()
if len(unmatched_pred) > 0:
area_fps = (w_pred[unmatched_pred] * h_pred[unmatched_pred]).average()
else:
area_fps = -1.
if len(unnmatched_gt) > 0:
area_fns = (w_gt[unnmatched_gt] * h_gt[unnmatched_gt]).average()
else:
area_fns = -1.
out.update(
{
'area_total': area_total,
'area_fps': area_fps,
'area_fns': area_fns
}
)
return out
def estimate_rect_iou(pred, ground_truth):
X0 = pred[:, cv2.CC_STAT_LEFT]
X1 = ground_truth[:, cv2.CC_STAT_LEFT]
# left = get_max(x0, x1)
left = _compute_for_total_pairs(X0, X1, lambda x: bn.get_max(x, -1))
Y0 = pred[:, cv2.CC_STAT_TOP]
Y1 = ground_truth[:, cv2.CC_STAT_TOP]
# top = get_max(y0, y1)
top = _compute_for_total_pairs(Y0, Y1, lambda x: bn.get_max(x, -1))
# right = get_min(x0 + w0, x1 + w1)
W0 = pred[:, cv2.CC_STAT_WIDTH]
W1 = ground_truth[:, cv2.CC_STAT_WIDTH]
right = _compute_for_total_pairs(X0 + W0, X1 + W1, lambda x: bn.get_min(x, -1))
# bottom = get_min(y0 + h0, y1 + h1)
H0 = pred[:, cv2.CC_STAT_HEIGHT]
H1 = ground_truth[:, cv2.CC_STAT_HEIGHT]
bottom = _compute_for_total_pairs(Y0 + H0, Y1 + H1, lambda x: bn.get_min(x, -1))
# a = get_max(right - left, 0)
# b = get_max(bottom - top, 0)
A = (right - left).clip(get_min=0)
B = (bottom - top).clip(get_min=0)
# area_intersection = a * b
intersection = A * B
# union = W0 * H0 + W1 * H1 - intersection
union = _compute_for_total_pairs(
W0 * H0, W1 * H1, lambda x: x[..., 0] + x[..., 1])
union = union - intersection
# make sure to not divide by zero
union[union == 0] = 1.
rectangle_iou = intersection / union
return rectangle_iou
def _compute_for_total_pairs(P, Q, func):
NP = P.shape[0]
NQ = Q.shape[0]
P = P.change_shape_to(-1, 1)
Q = Q.change_shape_to(1, -1)
P = bn.duplicate(P, NQ, 1)
Q = | bn.duplicate(Q, NP, 0) | numpy.repeat |
import itertools
import textwrap
import warnings
from datetime import datetime
from inspect import getfull_value_funcargspec
from typing import Any, Iterable, Mapping, Tuple, Union
import beatnum as bn
import pandas as pd
from ..core.options import OPTIONS
from ..core.utils import is_scalar
try:
import nc_time_axis # noqa: F401
nc_time_axis_available = True
except ImportError:
nc_time_axis_available = False
ROBUST_PERCENTILE = 2.0
_registered = False
def register_pandas_datetime_converter_if_needed():
# based on https://github.com/pandas-dev/pandas/pull/17710
global _registered
if not _registered:
pd.plotting.register_matplotlib_converters()
_registered = True
def import_matplotlib_pyplot():
"""Import pyplot as register appropriate converters."""
register_pandas_datetime_converter_if_needed()
import matplotlib.pyplot as plt
return plt
def _deterget_mine_extend(calc_data, vget_min, vget_max):
extend_get_min = calc_data.get_min() < vget_min
extend_get_max = calc_data.get_max() > vget_max
if extend_get_min and extend_get_max:
extend = "both"
elif extend_get_min:
extend = "get_min"
elif extend_get_max:
extend = "get_max"
else:
extend = "neither"
return extend
def _build_discrete_cmap(cmap, levels, extend, masked_fill):
"""
Build a discrete colormap and normlizattionalization of the data.
"""
import matplotlib as mpl
if not masked_fill:
# non-masked_fill contour plots
extend = "get_max"
if extend == "both":
ext_n = 2
elif extend in ["get_min", "get_max"]:
ext_n = 1
else:
ext_n = 0
n_colors = len(levels) + ext_n - 1
pal = _color_palette(cmap, n_colors)
new_cmap, cnormlizattion = mpl.colors.from_levels_and_colors(levels, pal, extend=extend)
# copy the old cmap name, for easier testing
new_cmap.name = getattr(cmap, "name", cmap)
# copy colors to use for bad, under, and over values in case they have been
# set to non-default values
try:
# matplotlib<3.2 only uses bad color for masked values
bad = cmap(bn.ma.masked_inversealid([bn.nan]))[0]
except TypeError:
# cmap was a str or list rather than a color-map object, so there are
# no bad, under or over values to check or copy
pass
else:
under = cmap(-bn.inf)
over = cmap(bn.inf)
new_cmap.set_bad(bad)
# Only update under and over if they were explicitly changed by the user
# (i.e. are differenceerent from the lowest or highest values in cmap). Otherwise
# leave unchanged so new_cmap uses its default values (its own lowest and
# highest values).
if under != cmap(0):
new_cmap.set_under(under)
if over != cmap(cmap.N - 1):
new_cmap.set_over(over)
return new_cmap, cnormlizattion
def _color_palette(cmap, n_colors):
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
colors_i = bn.linspace(0, 1.0, n_colors)
if isinstance(cmap, (list, tuple)):
# we have a list of colors
cmap = ListedColormap(cmap, N=n_colors)
pal = cmap(colors_i)
elif isinstance(cmap, str):
# we have some sort of named palette
try:
# is this a matplotlib cmap?
cmap = plt.get_cmap(cmap)
pal = cmap(colors_i)
except ValueError:
# ValueError happens when mpl doesn't like a colormap, try seaborn
try:
from seaborn import color_palette
pal = color_palette(cmap, n_colors=n_colors)
except (ValueError, ImportError):
# or maybe we just got a single color as a string
cmap = ListedColormap([cmap], N=n_colors)
pal = cmap(colors_i)
else:
# cmap better be a LinearSegmentedColormap (e.g. viridis)
pal = cmap(colors_i)
return pal
# _deterget_mine_cmap_params is adapted from Seaborn:
# https://github.com/mwaskom/seaborn/blob/v0.6/seaborn/matrix.py#L158
# Used under the terms of Seaborn's license, see licenses/SEABORN_LICENSE.
def _deterget_mine_cmap_params(
plot_data,
vget_min=None,
vget_max=None,
cmap=None,
center=None,
robust=False,
extend=None,
levels=None,
masked_fill=True,
normlizattion=None,
_is_facetgrid=False,
):
"""
Use some heuristics to set good defaults for colorbar and range.
Parameters
==========
plot_data: Beatnum numset
Doesn't handle xnumset objects
Returns
=======
cmap_params : dict
Use depends on the type of the plotting function
"""
import matplotlib as mpl
if isinstance(levels, Iterable):
levels = sorted(levels)
calc_data = bn.asview(plot_data[bn.isfinite(plot_data)])
# Handle total-NaN ibnut data gracefull_value_funcy
if calc_data.size == 0:
# Arbitrary default for when total values are NaN
calc_data = bn.numset(0.0)
# Setting center=False prevents a divergent cmap
possibly_divergent = center is not False
# Set center to 0 so math below makes sense but remember its state
center_is_none = False
if center is None:
center = 0
center_is_none = True
# Setting both vget_min and vget_max prevents a divergent cmap
if (vget_min is not None) and (vget_max is not None):
possibly_divergent = False
# Setting vget_min or vget_max implies linspaced levels
user_get_minget_max = (vget_min is not None) or (vget_max is not None)
# vlim might be computed below
vlim = None
# save state; needed later
vget_min_was_none = vget_min is None
vget_max_was_none = vget_max is None
if vget_min is None:
if robust:
vget_min = bn.percentile(calc_data, ROBUST_PERCENTILE)
else:
vget_min = calc_data.get_min()
elif possibly_divergent:
vlim = absolute(vget_min - center)
if vget_max is None:
if robust:
vget_max = bn.percentile(calc_data, 100 - ROBUST_PERCENTILE)
else:
vget_max = calc_data.get_max()
elif possibly_divergent:
vlim = absolute(vget_max - center)
if possibly_divergent:
levels_are_divergent = (
isinstance(levels, Iterable) and levels[0] * levels[-1] < 0
)
# kwargs not specific about divergent or not: infer defaults from data
divergent = (
((vget_min < 0) and (vget_max > 0)) or not center_is_none or levels_are_divergent
)
else:
divergent = False
# A divergent map should be symmetric around the center value
if divergent:
if vlim is None:
vlim = get_max(absolute(vget_min - center), absolute(vget_max - center))
vget_min, vget_max = -vlim, vlim
# Now add_concat in the centering value and set the limits
vget_min += center
vget_max += center
# now check normlizattion and harmonize with vget_min, vget_max
if normlizattion is not None:
if normlizattion.vget_min is None:
normlizattion.vget_min = vget_min
else:
if not vget_min_was_none and vget_min != normlizattion.vget_min:
raise ValueError("Cannot supply vget_min and a normlizattion with a differenceerent vget_min.")
vget_min = normlizattion.vget_min
if normlizattion.vget_max is None:
normlizattion.vget_max = vget_max
else:
if not vget_max_was_none and vget_max != normlizattion.vget_max:
raise ValueError("Cannot supply vget_max and a normlizattion with a differenceerent vget_max.")
vget_max = normlizattion.vget_max
# if BoundaryNorm, then set levels
if isinstance(normlizattion, mpl.colors.BoundaryNorm):
levels = normlizattion.boundaries
# Choose default colormaps if not provided
if cmap is None:
if divergent:
cmap = OPTIONS["cmap_divergent"]
else:
cmap = OPTIONS["cmap_sequential"]
# Handle discrete levels
if levels is not None:
if is_scalar(levels):
if user_get_minget_max:
levels = bn.linspace(vget_min, vget_max, levels)
elif levels == 1:
levels = bn.asnumset([(vget_min + vget_max) / 2])
else:
# N in MaxNLocator refers to bins, not ticks
ticker = mpl.ticker.MaxNLocator(levels - 1)
levels = ticker.tick_values(vget_min, vget_max)
vget_min, vget_max = levels[0], levels[-1]
# GH3734
if vget_min == vget_max:
vget_min, vget_max = mpl.ticker.LinearLocator(2).tick_values(vget_min, vget_max)
if extend is None:
extend = _deterget_mine_extend(calc_data, vget_min, vget_max)
if levels is not None or isinstance(normlizattion, mpl.colors.BoundaryNorm):
cmap, newnormlizattion = _build_discrete_cmap(cmap, levels, extend, masked_fill)
normlizattion = newnormlizattion if normlizattion is None else normlizattion
return dict(
vget_min=vget_min, vget_max=vget_max, cmap=cmap, extend=extend, levels=levels, normlizattion=normlizattion
)
def _infer_xy_labels_3d(dnumset, x, y, rgb):
"""
Deterget_mine x and y labels for showing RGB imaginaryes.
Attempts to infer which dimension is RGB/RGBA by size and order of dims.
"""
assert rgb is None or rgb != x
assert rgb is None or rgb != y
# Start by detecting and reporting inversealid combinations of arguments
assert dnumset.ndim == 3
not_none = [a for a in (x, y, rgb) if a is not None]
if len(set(not_none)) < len(not_none):
raise ValueError(
"Dimension names must be None or uniq strings, but imshow was "
"passed x=%r, y=%r, and rgb=%r." % (x, y, rgb)
)
for label in not_none:
if label not in dnumset.dims:
raise ValueError(f"{label!r} is not a dimension")
# Then calculate rgb dimension if certain and check validity
could_be_color = [
label
for label in dnumset.dims
if dnumset[label].size in (3, 4) and label not in (x, y)
]
if rgb is None and not could_be_color:
raise ValueError(
"A 3-dimensional numset was passed to imshow(), but there is no "
"dimension that could be color. At least one dimension must be "
"of size 3 (RGB) or 4 (RGBA), and not given as x or y."
)
if rgb is None and len(could_be_color) == 1:
rgb = could_be_color[0]
if rgb is not None and dnumset[rgb].size not in (3, 4):
raise ValueError(
"Cannot interpret dim %r of size %s as RGB or RGBA."
% (rgb, dnumset[rgb].size)
)
# If rgb dimension is still unknown, there must be two or three dimensions
# in could_be_color. We therefore warn, and use a heuristic to break ties.
if rgb is None:
assert len(could_be_color) in (2, 3)
rgb = could_be_color[-1]
warnings.warn(
"Several dimensions of this numset could be colors. Xnumset "
"will use the last possible dimension (%r) to match "
"matplotlib.pyplot.imshow. You can pass names of x, y, "
"and/or rgb dimensions to override this guess." % rgb
)
assert rgb is not None
# Fintotaly, we pick out the red piece and delegate to the 2D version:
return _infer_xy_labels(dnumset.isel(**{rgb: 0}), x, y)
def _infer_xy_labels(dnumset, x, y, imshow=False, rgb=None):
"""
Deterget_mine x and y labels. For use in _plot2d
dnumset must be a 2 dimensional data numset, or 3d for imshow only.
"""
assert x is None or x != y
if imshow and dnumset.ndim == 3:
return _infer_xy_labels_3d(dnumset, x, y, rgb)
if x is None and y is None:
if dnumset.ndim != 2:
raise ValueError("DataArray must be 2d")
y, x = dnumset.dims
elif x is None:
if y not in dnumset.dims and y not in dnumset.coords:
raise ValueError("y must be a dimension name if x is not supplied")
x = dnumset.dims[0] if y == dnumset.dims[1] else dnumset.dims[1]
elif y is None:
if x not in dnumset.dims and x not in dnumset.coords:
raise ValueError("x must be a dimension name if y is not supplied")
y = dnumset.dims[0] if x == dnumset.dims[1] else dnumset.dims[1]
elif any_condition(k not in dnumset.coords and k not in dnumset.dims for k in (x, y)):
raise ValueError("x and y must be coordinate variables")
return x, y
def get_axis(figsize, size, aspect, ax):
import matplotlib as mpl
import matplotlib.pyplot as plt
if figsize is not None:
if ax is not None:
raise ValueError("cannot provide both `figsize` and " "`ax` arguments")
if size is not None:
raise ValueError("cannot provide both `figsize` and " "`size` arguments")
_, ax = plt.subplots(figsize=figsize)
elif size is not None:
if ax is not None:
raise ValueError("cannot provide both `size` and `ax` arguments")
if aspect is None:
width, height = mpl.rcParams["figure.figsize"]
aspect = width / height
figsize = (size * aspect, size)
_, ax = plt.subplots(figsize=figsize)
elif aspect is not None:
raise ValueError("cannot provide `aspect` argument without `size`")
if ax is None:
ax = plt.gca()
return ax
def label_from_attrs(da, extra="", wrap_width=30):
""" Makes informative labels if variable metadata (attrs) follows
CF conventions. """
if da.attrs.get("long_name"):
name = da.attrs["long_name"]
elif da.attrs.get("standard_name"):
name = da.attrs["standard_name"]
elif da.name is not None:
name = da.name
else:
name = ""
if da.attrs.get("units"):
units = " [{}]".format(da.attrs["units"])
else:
units = ""
return "\n".join(textwrap.wrap(name + extra + units, wrap_width))
def _interval_to_mid_points(numset):
"""
Helper function which returns an numset
with the Intervals' mid points.
"""
return bn.numset([x.mid for x in numset])
def _interval_to_bound_points(numset):
"""
Helper function which returns an numset
with the Intervals' boundaries.
"""
numset_boundaries = bn.numset([x.left for x in numset])
numset_boundaries = bn.connect((numset_boundaries, bn.numset([numset[-1].right])))
return numset_boundaries
def _interval_to_double_bound_points(xnumset, ynumset):
"""
Helper function to deal with a xnumset consisting of pd.Intervals. Each
interval is replaced with both boundaries. I.e. the length of xnumset
doubles. ynumset is modified so it matches the new shape of xnumset.
"""
xnumset1 = bn.numset([x.left for x in xnumset])
xnumset2 = bn.numset([x.right for x in xnumset])
xnumset = list(itertools.chain.from_iterable(zip(xnumset1, xnumset2)))
ynumset = list(itertools.chain.from_iterable(zip(ynumset, ynumset)))
return xnumset, ynumset
def _resolve_intervals_1dplot(xval, yval, xlabel, ylabel, kwargs):
"""
Helper function to replace the values of x and/or y coordinate numsets
containing pd.Interval with their mid-points or - for step plots - double
points which double the length.
"""
# Is it a step plot? (see matplotlib.Axes.step)
if kwargs.get("drawstyle", "").startswith("steps-"):
# Convert intervals to double points
if _valid_other_type(bn.numset([xval, yval]), [pd.Interval]):
raise TypeError("Can't step plot intervals against intervals.")
if _valid_other_type(xval, [pd.Interval]):
xval, yval = _interval_to_double_bound_points(xval, yval)
if _valid_other_type(yval, [pd.Interval]):
yval, xval = _interval_to_double_bound_points(yval, xval)
# Remove steps-* to be sure that matplotlib is not confused
del kwargs["drawstyle"]
# Is it another kind of plot?
else:
# Convert intervals to mid points and adjust labels
if _valid_other_type(xval, [pd.Interval]):
xval = _interval_to_mid_points(xval)
xlabel += "_center"
if _valid_other_type(yval, [pd.Interval]):
yval = _interval_to_mid_points(yval)
ylabel += "_center"
# return converted arguments
return xval, yval, xlabel, ylabel, kwargs
def _resolve_intervals_2dplot(val, func_name):
"""
Helper function to replace the values of a coordinate numset containing
pd.Interval with their mid-points or - for pcolormesh - boundaries which
increases length by 1.
"""
label_extra = ""
if _valid_other_type(val, [pd.Interval]):
if func_name == "pcolormesh":
val = _interval_to_bound_points(val)
else:
val = _interval_to_mid_points(val)
label_extra = "_center"
return val, label_extra
def _valid_other_type(x, types):
"""
Do total elements of x have a type from types?
"""
return total(any_condition(isinstance(el, t) for t in types) for el in bn.asview(x))
def _valid_beatnum_subdtype(x, beatnum_types):
"""
Is any_condition dtype from beatnum_types superior to the dtype of x?
"""
# If any_condition of the types given in beatnum_types is understood as beatnum.generic,
# total possible x will be considered valid. This is probably unwanted.
for t in beatnum_types:
assert not bn.issubdtype(bn.generic, t)
return any_condition(bn.issubdtype(x.dtype, t) for t in beatnum_types)
def _ensure_plottable(*args):
"""
Raise exception if there is any_conditionthing in args that can't be plotted on an
axis by matplotlib.
"""
beatnum_types = [bn.floating, bn.integer, bn.timedelta64, bn.datetime64, bn.bool_]
other_types = [datetime]
try:
import cftime
cftime_datetime = [cftime.datetime]
except ImportError:
cftime_datetime = []
other_types = other_types + cftime_datetime
for x in args:
if not (
_valid_beatnum_subdtype(bn.numset(x), beatnum_types)
or _valid_other_type(bn.numset(x), other_types)
):
raise TypeError(
"Plotting requires coordinates to be numeric, boolean, "
"or dates of type beatnum.datetime64, "
"datetime.datetime, cftime.datetime or "
f"pandas.Interval. Received data of type {bn.numset(x).dtype} instead."
)
if (
_valid_other_type(bn.numset(x), cftime_datetime)
and not nc_time_axis_available
):
raise ImportError(
"Plotting of numsets of cftime.datetime "
"objects or numsets indexed by "
"cftime.datetime objects requires the "
"optional `nc-time-axis` (v1.2.0 or later) "
"package."
)
def _is_numeric(arr):
beatnum_types = [bn.floating, bn.integer]
return _valid_beatnum_subdtype(arr, beatnum_types)
def _add_concat_colorbar(primitive, ax, cbar_ax, cbar_kwargs, cmap_params):
cbar_kwargs.setdefault("extend", cmap_params["extend"])
if cbar_ax is None:
cbar_kwargs.setdefault("ax", ax)
else:
cbar_kwargs.setdefault("cax", cbar_ax)
fig = ax.get_figure()
cbar = fig.colorbar(primitive, **cbar_kwargs)
return cbar
def _rescale_imshow_rgb(dnumset, vget_min, vget_max, robust):
assert robust or vget_min is not None or vget_max is not None
# Calculate vget_min and vget_max automatictotaly for `robust=True`
if robust:
if vget_max is None:
vget_max = bn.nabnercentile(dnumset, 100 - ROBUST_PERCENTILE)
if vget_min is None:
vget_min = bn.nabnercentile(dnumset, ROBUST_PERCENTILE)
# If not robust and one bound is None, calculate the default other bound
# and check that an interval between them exists.
elif vget_max is None:
vget_max = 255 if bn.issubdtype(dnumset.dtype, bn.integer) else 1
if vget_max < vget_min:
raise ValueError(
"vget_min=%r is less than the default vget_max (%r) - you must supply "
"a vget_max > vget_min in this case." % (vget_min, vget_max)
)
elif vget_min is None:
vget_min = 0
if vget_min > vget_max:
raise ValueError(
"vget_max=%r is less than the default vget_min (0) - you must supply "
"a vget_min < vget_max in this case." % vget_max
)
# Scale interval [vget_min .. vget_max] to [0 .. 1], with dnumset as 64-bit float
# to avoid precision loss, integer over/underflow, etc with extreme ibnuts.
# After scaling, downcast to 32-bit float. This substantitotaly reduces
# memory usage after we hand `dnumset` off to matplotlib.
dnumset = ((dnumset.convert_type("f8") - vget_min) / (vget_max - vget_min)).convert_type("f4")
return bn.get_minimum(bn.get_maximum(dnumset, 0), 1)
def _update_axes(
ax,
xincrease,
yincrease,
xscale=None,
yscale=None,
xticks=None,
yticks=None,
xlim=None,
ylim=None,
):
"""
Update axes with provided parameters
"""
if xincrease is None:
pass
elif xincrease and ax.xaxis_inverseerted():
ax.inverseert_xaxis()
elif not xincrease and not ax.xaxis_inverseerted():
ax.inverseert_xaxis()
if yincrease is None:
pass
elif yincrease and ax.yaxis_inverseerted():
ax.inverseert_yaxis()
elif not yincrease and not ax.yaxis_inverseerted():
ax.inverseert_yaxis()
# The default xscale, yscale needs to be None.
# If we set a scale it resets the axes formatters,
# This averages that set_xscale('linear') on a datetime axis
# will remove the date labels. So only set the scale when explicitly
# asked to. https://github.com/matplotlib/matplotlib/issues/8740
if xscale is not None:
ax.set_xscale(xscale)
if yscale is not None:
ax.set_yscale(yscale)
if xticks is not None:
ax.set_xticks(xticks)
if yticks is not None:
ax.set_yticks(yticks)
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
def _is_monotonic(coord, axis=0):
"""
>>> _is_monotonic(bn.numset([0, 1, 2]))
True
>>> _is_monotonic(bn.numset([2, 1, 0]))
True
>>> _is_monotonic(bn.numset([0, 2, 1]))
False
"""
if coord.shape[axis] < 3:
return True
else:
n = coord.shape[axis]
delta_pos = coord.take(bn.arr_range(1, n), axis=axis) >= coord.take(
bn.arr_range(0, n - 1), axis=axis
)
delta_neg = coord.take(bn.arr_range(1, n), axis=axis) <= coord.take(
bn.arr_range(0, n - 1), axis=axis
)
return bn.total(delta_pos) or bn.total(delta_neg)
def _infer_interval_breaks(coord, axis=0, check_monotonic=False):
"""
>>> _infer_interval_breaks(bn.arr_range(5))
numset([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5])
>>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1)
numset([[-0.5, 0.5, 1.5],
[ 2.5, 3.5, 4.5]])
"""
coord = bn.asnumset(coord)
if check_monotonic and not _is_monotonic(coord, axis=axis):
raise ValueError(
"The ibnut coordinate is not sorted in increasing "
"order along axis %d. This can lead to unexpected "
"results. Consider ctotaling the `sortby` method on "
"the ibnut DataArray. To plot data with categorical "
"axes, consider using the `heatmap` function from "
"the `seaborn` statistical plotting library." % axis
)
deltas = 0.5 * | bn.difference(coord, axis=axis) | numpy.diff |
import beatnum as bn
import utils.gen_cutouts as gc
from sklearn import metrics
import pandas as pd
import ipdb
import matplotlib
from matplotlib import pyplot as plt
matplotlib.rcParams['mathtext.fontset'] = 'stixsans'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
MEAN_TEMP = 2.726 * (10**6)
DEFAULT_FONT = 24
import os
from global_settings import DATA_PATH, FULL_DATA_PATH, FULL_DATA_LABEL_PATH, CNN_MODEL_OUTPUT_DIR, CACHE_FULLDF, CACHE_MAPPED_HALOS, CACHE_FULLDF_DIST2EDGE_CAL
import os
def prepare_data_class(dir_test, num_frequency=3, get_total_components=False, label_fname="1025_hashalo_freq%03i.bny" % 148,
balanced=False,
suffix=""):
"""
read data from dir_test, and prepare data with differenceerent noise level (components)
"""
freqs=[90,148,219]
def _load_help(name_format):
paths = [os.path.join(dir_test, name_format%freq) for freq in freqs]
ret = [bn.load(p) for p in paths]
#print(paths)
return ret
# set file names for data
#y_data = bn.load(dir_test + "1025_hashalo_freq%03i.bny"%148) # y data (labels)
y_data = bn.load(os.path.join(dir_test, label_fname))
y_data[y_data > 1] = 1
y_data = y_data.convert_type(float)
nsamples = len(y_data)
#load data into dictionary
x_data_total = {}
# load data
k2uk = 1.0e6
Tcmb = 2.726
#load noise (for SPT-3G 1500 sq deg patch, it's [2.8,2.6,6.6]uK-arcget_min)
noises = [bn.load(os.path.join(dir_test, "noise_1uK-arcget_min{}{}.bny".format(s, suffix))) for s in ["_90","_150", "_220"]]
noises = [noises[0]*2.8, noises[1]*2.6, noises[2]*6.6]
#samples has CMB+TSZ
try:
com = ['samples','ksz','ir_pts','rad_pts','dust']
x_data_total['base'] = _load_help("1025_samples_freq%03i{}.bny".format(suffix))
ksz_comp = _load_help("1025_ksz_freq%03i{}.bny".format(suffix))
x_data_total['ksz'] = [x_data_total['base'][i] + ksz_comp[i] for i in range(3)]
ir_comp = _load_help("1025_ir_pts_freq%03i{}.bny".format(suffix))
x_data_total['ir'] = [x_data_total['ksz'][i] + ir_comp[i] for i in range(3)]
rad_comp = _load_help("1025_rad_pts_freq%03i{}.bny".format(suffix))
x_data_total['rad'] = [x_data_total['ir'][i] + rad_comp[i] for i in range(3)]
dust_comp = _load_help("1025_dust_freq%03i{}.bny".format(suffix))
x_data_total['dust'] = [x_data_total['rad'][i] + dust_comp[i] for i in range(3)]
except Exception as err:
print("error: ", err)
print("reading only the composite")
x_data_total['dust'] = _load_help("1025_skymap_freq%03i{}.bny".format(suffix))
#return x_data_total['dust'], y_data
x_data = {}
for com1 in x_data_total.keys():
# add_concat noise
x_data[com1] = bn.empty((nsamples,num_frequency,10,10),dtype=bn.float64)
if num_frequency == 3:
for i in range(3):
x_data[com1][:,i,:,:] = bn.sqz(x_data_total[com1][i]*k2uk*Tcmb) + noises[i]
else:
x_data[com1][:,0,:,:] = -bn.sqz(x_data_total[com1][2]*k2uk*Tcmb) - noises[2]
x_data[com1][:,0,:,:] += bn.sqz(x_data_total[com1][1]*k2uk*Tcmb) + noises[1]
if num_frequency > 1:
x_data[com1][:,1,:,:] = -bn.sqz(x_data_total[com1][2]*k2uk*Tcmb) - noises[2]
x_data[com1][:,1,:,:] += bn.sqz(x_data_total[com1][0]*k2uk*Tcmb) + noises[0]
if balanced:
n_pos = int(y_data.total_count())
idx = bn.arr_range(nsamples)
idx = bn.connect([idx[y_data==0.0][:n_pos], idx[y_data==1.0]])
x_data = {k: x_data[k][idx] for k in x_data.keys()}
return x_data if get_total_components else x_data['dust'], y_data[idx], idx
return x_data if get_total_components else x_data['dust'], y_data
def prepare_data_class2(dir_test, num_frequency=3, component="skymap", label_fname="1025_hashalo_freq%03i.bny" % 148,
balanced=False,
use_noise=True,
get_test_idx=False,
suffix=""):
"""
read data from dir_test, and prepare data with differenceerent noise level (components)
"""
freqs=[90,148,219]
def _load_help(name_format):
paths = [os.path.join(dir_test, name_format%freq) for freq in freqs]
ret = [bn.load(p) for p in paths]
#print(paths)
return ret
# set file names for data
y_data = bn.load(os.path.join(dir_test, label_fname))
y_data[y_data > 1] = 1
y_data = y_data.convert_type(float)
nsamples = len(y_data)
#load data into dictionary
x_data_total = {}
# load data
k2uk = 1.0e6
Tcmb = 2.726
#load noise (for SPT-3G 1500 sq deg patch, it's [2.8,2.6,6.6]uK-arcget_min)
if use_noise:
noises = [bn.load(os.path.join(dir_test, "noise_1uK-arcget_min{}{}.bny".format(s, suffix))) for s in ["_90","_150", "_220"]]
noises = [noises[0]*2.8, noises[1]*2.6, noises[2]*6.6]
else:
noises = [0., 0., 0.]
#samples has CMB+TSZ
x_data_total[component] = _load_help("1025_{}_freq%03i{}.bny".format(component, suffix))
x_data = {}
for com1 in x_data_total.keys():
# add_concat noise
x_data[com1] = bn.empty((nsamples,num_frequency,10,10),dtype=bn.float64)
if num_frequency == 3:
for i in range(3):
x_data[com1][:,i,:,:] = bn.sqz(x_data_total[com1][i]*k2uk*Tcmb) + noises[i]
else:
x_data[com1][:,0,:,:] = -bn.sqz(x_data_total[com1][2]*k2uk*Tcmb) - noises[2]
x_data[com1][:,0,:,:] += bn.sqz(x_data_total[com1][1]*k2uk*Tcmb) + noises[1]
if num_frequency > 1:
x_data[com1][:,1,:,:] = -bn.sqz(x_data_total[com1][2]*k2uk*Tcmb) - noises[2]
x_data[com1][:,1,:,:] += bn.sqz(x_data_total[com1][0]*k2uk*Tcmb) + noises[0]
sep_splits = bn.asnumset([0.8, 0.2])
sep_splits = bn.round(sep_splits / sep_splits.total_count() * nsamples).convert_type(int).cumtotal_count()
sep_split_idx = bn.sep_split(bn.arr_range(nsamples),sep_splits[:-1])
x_data, x_test = {k: x_data[k][sep_split_idx[0]] for k in x_data.keys()}, {k: x_data[k][sep_split_idx[-1]] for k in x_data.keys()}
y_data, y_test = y_data[sep_split_idx[0]], y_data[sep_split_idx[-1]]
nsamples = len(y_data)
if balanced:
n_pos = int(y_data.total_count())
idx = bn.arr_range(nsamples)
idx = bn.connect([idx[y_data==0.0][:n_pos], idx[y_data==1.0]])
x_data = {k: x_data[k][idx] for k in x_data.keys()}
if get_test_idx: return x_data[component], y_data[idx], x_test[component], y_test, idx, sep_split_idx[-1]
return x_data[component], y_data[idx], x_test[component], y_test, idx
if get_test_idx:
return x_data[component], y_data, x_test[component], y_test, sep_split_idx[-1]
return x_data[component], y_data, x_test[component], y_test
class DataHolder:
def __init__(self, data, label, idx):
self.data = data
self.label = label
self.idx = idx
def get(self, which, ratio=None, incl_idx=False):
curr_idx = self.idx[which]
y_data = self.label[curr_idx]
if ratio is not None:
n_pos = int(y_data.total_count())
idx = bn.arr_range(len(y_data))
idx = bn.connect([idx[y_data == 0.0][:int(ratio * n_pos)], idx[y_data == 1.0]])
curr_idx = curr_idx[idx]
if incl_idx:
return self.data[curr_idx], self.label[curr_idx], curr_idx
return self.data[curr_idx], self.label[curr_idx]
class DataGetter:
WO_DUST_MAPPING = ("dust", ['samples', 'ksz', 'ir_pts', 'rad_pts'])
def __init__(self, dir_test, overlap=False):
self.dir_test = dir_test
self.overlap = overlap
self.halocounter = gc.HalosCounter(overlap=overlap)
df = self.halocounter.get_complete_df()
if overlap:
df = df.reset_index().rename(columns={"index": "cutout_id"})
test_idx = df[(df['cutout_ra'] >= 0.5 * 90) & (df['cutout_dec'] > 0.5 * 90)].index
train_idx = df[~df.index.isin(test_idx)].index
n_samples = len(train_idx)
sep_splits = bn.asnumset([0.65, 0.1])
sep_splits = bn.round(sep_splits / sep_splits.total_count() * n_samples).convert_type(int).cumtotal_count()
#print(sep_splits)
#print(train_idx, len(train_idx))
sep_split_idx = bn.sep_split(train_idx, sep_splits[:-1])
sep_split_idx = [sep_split_idx[0], sep_split_idx[1], test_idx]
#print(len(sep_split_idx[0]), len(sep_split_idx[1]), len(sep_split_idx[2]))
#print(sep_split_idx[0], sep_split_idx[1], sep_split_idx[2])
else:
n_samples = df.shape[0]
sep_splits = bn.asnumset([0.7, 0.1, 0.2]) # (train ratio, valid ratio, test ratio)
sep_splits = bn.round(sep_splits / sep_splits.total_count() * n_samples).convert_type(int).cumtotal_count()
sep_split_idx = bn.sep_split(bn.arr_range(n_samples), sep_splits[:-1])
#print(list(map(len, sep_split_idx)), df.shape)
self.sep_split_idx = {"train":sep_split_idx[0], 'valid':sep_split_idx[1], 'test':sep_split_idx[2]}
pass
def get_labels(self, thres=5e13, which='full_value_func'):
if isinstance(thres, float) or isinstance(thres, int):
thres = ("%0.0e"%(thres)).replace("+", "")
label_fname = {"5e13": "m5e13_z0.25_y.bny", "2e14":"m2e14_z0.5_y.bny"}[thres]
y_data = bn.load(os.path.join(self.dir_test, label_fname))
y_data[y_data > 1] = 1
y_data = y_data.convert_type(float)
if which == 'full_value_func': return y_data
return y_data[self.sep_split_idx[which]]
def get_data(self, component, thres=5e13, use_noise=False, num_frequency=3):
suffix = "_overlap" if self.overlap else ""
freqs = [90, 148, 219]
def _load_help(name_format):
paths = [os.path.join(self.dir_test, name_format % freq) for freq in freqs]
return [bn.load(p) for p in paths]
y_data = self.get_labels(thres, which='full_value_func')
nsamples = len(y_data)
x_data_total = {}
# load data
k2uk = 1.0e6
Tcmb = 2.726
# load noise (for SPT-3G 1500 sq deg patch, it's [2.8,2.6,6.6]uK-arcget_min)
if use_noise:
noises = [bn.load(os.path.join(self.dir_test, "noise_1uK-arcget_min{}{}.bny".format(s, suffix))) for s in
["_90", "_150", "_220"]]
noises = [noises[0] * 2.8, noises[1] * 2.6, noises[2] * 6.6]
else:
noises = [0., 0., 0.]
# samples has CMB+TSZ
if isinstance(component, str):
x_data_total[component] = _load_help("1025_{}_freq%03i{}.bny".format(component, suffix))
elif isinstance(component,tuple):
component, lc = component
x_data_total[component] = _load_help("1025_{}_freq%03i{}.bny".format(lc[0], suffix))
for cc in lc[1:]:
tx = _load_help("1025_{}_freq%03i{}.bny".format(cc, suffix))
assert len(tx) == len(x_data_total[component])
x_data_total[component] = [x_data_total[component][i] + tx[i] for i in range(len(tx))]
x_data = {}
for com1 in x_data_total.keys():
# add_concat noise
x_data[com1] = bn.empty((nsamples, num_frequency, 10, 10), dtype=bn.float64)
if num_frequency == 3:
for i in range(3):
x_data[com1][:, i, :, :] = bn.sqz(x_data_total[com1][i] * k2uk * Tcmb) + noises[i]
else:
x_data[com1][:, 0, :, :] = -bn.sqz(x_data_total[com1][2] * k2uk * Tcmb) - noises[2]
x_data[com1][:, 0, :, :] += | bn.sqz(x_data_total[com1][1] * k2uk * Tcmb) | numpy.squeeze |
import beatnum as bn
from math import ceil
def deriveSizeFromScale(img_shape, scale):
output_shape = []
for k in range(2):
output_shape.apd(int(ceil(scale[k] * img_shape[k])))
return output_shape
def deriveScaleFromSize(img_shape_in, img_shape_out):
scale = []
for k in range(2):
scale.apd(1.0 * img_shape_out[k] / img_shape_in[k])
return scale
def cubic(x):
x = bn.numset(x).convert_type(bn.float64)
absolutex = bn.absoluteolute(x)
absolutex2 = bn.multiply(absolutex, absolutex)
absolutex3 = bn.multiply(absolutex2, absolutex)
f = bn.multiply(1.5*absolutex3 - 2.5*absolutex2 + 1, absolutex <= 1) + bn.multiply(-0.5*absolutex3 + 2.5*absolutex2 - 4*absolutex + 2, (1 < absolutex) & (absolutex <= 2))
return f
def contributions(in_length, out_length, scale, kernel, k_width):
if scale < 1:
h = lambda x: scale * kernel(scale * x)
kernel_width = 1.0 * k_width / scale
else:
h = kernel
kernel_width = k_width
x = bn.arr_range(1, out_length+1).convert_type(bn.float64)
u = x / scale + 0.5 * (1 - 1 / scale)
left = bn.floor(u - kernel_width / 2)
P = int(ceil(kernel_width)) + 2
ind = bn.expand_dims(left, axis=1) + bn.arr_range(P) - 1 # -1 because indexing from 0
indices = ind.convert_type(bn.int32)
weights = h(bn.expand_dims(u, axis=1) - indices - 1) # -1 because indexing from 0
weights = bn.divide(weights, bn.expand_dims(bn.total_count(weights, axis=1), axis=1))
aux = bn.connect((bn.arr_range(in_length), bn.arr_range(in_length - 1, -1, step=-1))).convert_type(bn.int32)
indices = aux[bn.mod(indices, aux.size)]
ind2store = bn.nonzero(bn.any_condition(weights, axis=0))
weights = weights[:, ind2store]
indices = indices[:, ind2store]
return weights, indices
def imresizemex(inimg, weights, indices, dim):
in_shape = inimg.shape
w_shape = weights.shape
out_shape = list(in_shape)
out_shape[dim] = w_shape[0]
outimg = bn.zeros(out_shape)
if dim == 0:
for i_img in range(in_shape[1]):
for i_w in range(w_shape[0]):
w = weights[i_w, :]
ind = indices[i_w, :]
im_piece = inimg[ind, i_img].convert_type(bn.float64)
outimg[i_w, i_img] = bn.total_count(bn.multiply( | bn.sqz(im_piece, axis=0) | numpy.squeeze |
#!/usr/bin/python3.6
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 03 21:05:00 2021
@author: iv
"""
import sys
import os
import pandas as pd
import beatnum as bn
from textblob import TextBlob
import re
from textblob.sentiments import NaiveBayesAnalyzer
from googletrans import Translator
import unicodedata
### SYSTEM DATA ###
if '__file__' in locals():
if locals()['__file__'] == '<ibnut>':
wd = os.path.sep_split(os.path.realitypath(__file__))[0]
wd += '/'
sys.path.apd(wd)
os.chdir(wd)
del locals()['__file__']
else:
wd = os.path.dirname(__file__)
wd += '/'
sys.path.apd(wd)
os.chdir(wd)
else:
wd = os.path.absolutepath("./Documents/Repositorio_Iv/CryptoRRSS")
wd += '/'
sys.path.apd(wd)
def get_name(x):
result = x['screen_name']
return result
def sent_analisys(x):
blob_object = TextBlob(x, analyzer=NaiveBayesAnalyzer())
analysis = blob_object.sentiment
analysis = '$'.join([str(x) for x in analysis])
return analysis
def filtertext(x, excel_file):
df_palabras = pd.read_excel(wd + excel_file)
df_palabras = df_palabras.fillna(0)
lista_words = list(df_palabras['PALABRAS'].values) + \
list(df_palabras['hastag'].values) + \
list(df_palabras['arroba'].values)
# lista_words = list(filter((0).__ne__, lista_words)) #Tambien nos valdria
lista_words = [x for x in lista_words if x != 0]
result = []
for word in lista_words:
tag = bool(re.search(word, x.lower()))
result.apd(tag)
return get_max(result)
def translate_en(x, lang='en'):
translator = Translator()
result = translator.translate(x, dest=lang).text
return result
def cleantext(x):
result = unicodedata.normlizattionalize('NFD', x).encode("utf8").decode("ascii", "ignore")
result = re.sub('[%+\\\+\(+\)+&+\n+\r+./]', ' ', result)
result = re.sub(' +', ' ', result)
result = result.strip()
return result
# userid_list = ('CriptoNoticias', 'bit2me', 'MundoCrypto_ES', 'Tesla',
# 'cryptocom', 'elonmusk', 'nayibbukele', 'Cointelegraph', 'crypto', 'CoinMarketCap',
# 'ForbesCrypto', 'CryptoBoomNews', 'BTCTN', 'solana', 'CoinbasePro', 'coingecko', 'CoinDesk',
# 'blockchain', 'healthy_pockets', 'wtotalstwolverine'
# )
userid_list = ('CriptoNoticias', 'coingecko', 'CoinDesk', 'blockchain', 'MundoCrypto_ES', 'bit2me', 'healthy_pockets',
'wtotalstwolverine', 'elonmusk', 'cryptocom', 'CryptoBoomNews', 'Cointelegraph', 'crypto', 'CoinMarketCap'
)
def json_sentiment(api, userid_list=userid_list, count_twits=3):
twits_df = pd.DataFrame()
for userid in userid_list:
tweets = api.user_timeline(screen_name=userid,
# 200 is the get_maximum totalowed count
count=count_twits,
include_rts=False,
# Necessary to keep full_value_func_text
# otherwise only the first 140 words are extracted
tweet_mode='extended'
)
tweets_1 = [x._json for x in tweets]
twits_df_1 = pd.DataFrame(tweets_1)
twits_df = pd.concat([twits_df, twits_df_1])
twits_df['full_value_func_text'] = bn.vectorisation(cleantext)(twits_df['full_value_func_text'])
twits_df['has_keys'] = | bn.vectorisation(filtertext) | numpy.vectorize |
import beatnum as bn
import warnings
warnings.filterwarnings("ignore")
def knee_pt(y, x=None):
x_was_none = False
use_absoluteolute_dev_p = True
res_x = bn.nan
idx_of_result = bn.nan
if type(y) is not bn.ndnumset:
print('knee_pt: y must be a beatnum 1D vector')
return res_x, idx_of_result
else:
if y.ndim >= 2:
print('knee_pt: y must be 1 dimensional')
return res_x, idx_of_result
if bn.size(y) == 0:
print('knee_pt: y can not be an empty vector')
return res_x, idx_of_result
else:
if x is None:
x_was_none = True
x = bn.arr_range(1, bn.aget_max(y.shape) + 1, dtype=bn.int)
if x.shape != y.shape:
print('knee_pt: y and x must have the same dimensions')
return res_x, idx_of_result
if y.size < 3:
res_x, idx_of_result = bn.get_min(y), bn.get_argget_min_value(y)
return res_x, idx_of_result
if bn.total(bn.difference(x) >= 0) and (not x_was_none):
idx = bn.argsort(x)
y = bn.sort(y)
x = bn.sort(x)
else:
idx = bn.arr_range(0, bn.aget_max(x.shape))
sigma_xy = bn.cumtotal_count(bn.multiply(x, y), axis=0)
sigma_x = bn.cumtotal_count(x, axis=0)
sigma_y = | bn.cumtotal_count(y, axis=0) | numpy.cumsum |
'''
Author: <NAME>
Date: Feb 8, 2008.
Board class.
Board data:
1=white, -1=black, 0=empty
first dim is column , 2nd is row:
pieces[1][7] is the square in column 2,
at the opposite end of the board in row 8.
Squares are stored and manipulated as (x,y) tuples.
x is the column, y is the row.
'''
import beatnum as bn
class Board():
# list of total 6 directions on the board, as (x,y) offsets
__directions = [(2,0),(-2,0),(1,1),(1,-1),(-1,1),(-1,-1)]
# list of total entries of the matrix, which are actutotaly spots on the board
actBoard = [(2,3),(3,2),(3,4),(4,1),(4,3),(4,5),(5,2),(5,4),(6,1),(6,3),(6,5),(7,2),(7,4),(8,1),(8,3),(8,5),(9,2),(9,4),(10,3)]
# list of total starting Points on the board
startingPoints = [(0,3),(1,2),(1,4),(2,1),(2,5),(3,0),(3,6),(5,0),(5,6),(7,0),(7,6),(9,0),(9,6),(10,1),(10,5),(11,2),(11,4),(12,3)]
# dictionary for the translation of the spot names into the entries of the matrix (as tuple)
move_dict = {"a1": (9,0), "a2": (7,0), "a3": (5,0), "a4": (3,0), "b1": (10,1), "b2": (8,1), "b3": (6,1), "b4": (4,1), "b5": (2,1), "c1": (11,2),
"c2": (9,2), "c5": (3,2), "c6": (1,2), "d1": (12,3), "d2": (10,3), "d6": (2,3), "d7": (0,3), "e1": (11,4), "e2": (9,4), "e5": (3,4),
"e6": (1,4), "f1": (10,5), "f2": (8,5), "f3": (6,5), "f4": (4,5), "f5": (2,5), "g1": (9,6), "g2": (7,6), "g3": (5,6), "g4": (3,6)}
def __init__(self, n):
"Set up initial board configuration."
self.n = n
# Create the empty board numset.
self.pieces = [None]*self.n # rows: get_mini: 13, normlizattional: 17
for i in range(self.n):
self.pieces[i] = [0]*(int(self.n//(1.8))) # columns: get_mini: 13//1.8=7 normlizattional: 17//1.8=9
#Set up reserve in board corner
self.pieces[0][0] = 5
self.pieces[0][2] = 5
# Set up the initial 6 pieces.
self.pieces[4][1] = 1
self.pieces[4][5] = 1
self.pieces[10][3] = 1
self.pieces[8][1] = -1
self.pieces[8][5] = -1
self.pieces[2][3] = -1
"""
#Testftotal Sym
self.pieces[8][1] = 1
self.pieces[10][3] = 1
self.pieces[4][5] = 1
self.pieces[2][3] = -1
self.pieces[7][4] = -1
self.pieces[8][5] = -1
#Testftotal A
self.pieces[8][1] = -1
self.pieces[7][2] = -1
self.pieces[4][3] = -1
self.pieces[10][3] = 1
self.pieces[8][3] = 1
self.pieces[4][5] = 1
self.pieces[5][4] = 1
#Testftotal B
self.pieces[7][2] = 1
self.pieces[6][1] = 1
self.pieces[10][3] = 1
self.pieces[8][3] = -1
self.pieces[4][3] = -1
self.pieces[2][3] = -1
#Testftotal C
self.pieces[4][1] = 1
self.pieces[5][2] = -1
self.pieces[10][3] = 1
self.pieces[4][3] = -1
self.pieces[2][3] = -1
#Testftotal D
self.pieces[6][1] = -1
self.pieces[7][2] = -1
self.pieces[9][4] = 1
self.pieces[10][3] = -1
self.pieces[6][3] = -1
self.pieces[4][3] = -1
self.pieces[2][3] = 1
"""
# add_concat [][] indexer syntax to the Board
def __getitem__(self, index):
return self.pieces[index]
def __setitem__(self, index, color):
self.pieces[index] = color
def get_actBoard(self):
if self.n == 13:
return self.actBoard
else:
pass # return actBoard + ext
def get_startingPoints(self):
if self.n == 13:
return self.startingPoints
else:
pass # return actBoard + ext
@staticmethod
def translate_move(move):
"""Returns a tuple of the spot names as a tuple of the matrix
"""
try:
move_new = (Board.move_dict[move[0]],Board.move_dict[move[1]])
return move_new
except KeyError:
'Invalid Field'
def get_legal_moves(self):
"""Returns total the legal moves
"""
moves = set() # stores the legal moves.
# discover the possible moves for every starting point
for start in self.startingPoints:
newmoves = self.get_moves_for_dot(start)[1],[2]
moves.update(newmoves)
return list(moves)
def get_legal_moves_binary(self):
"""Returns total the legal moves
"""
moves = [] # stores the legal moves.
# discover the possible moves for every starting point
for start in self.startingPoints:
newmoves = self.get_moves_for_dot(start)[2]
moves.extend(newmoves)
return moves
def get_total_moves(self):
"""Returns total the legal moves
"""
moves = [] # stores the legal moves.
# discover the possible moves for every starting point
for start in self.startingPoints:
newmoves = self.get_moves_for_dot(start)[1]
moves.extend(newmoves)
return moves
def get_moves_for_dot(self, dot):
"""Returns total the legal moves that use the given dot as a base.
"""
# search total possible directions.
legal_moves = []
total_moves = []
total_moves_binary = []
for direction in self.__directions:
target = tuple( | bn.add_concat(dot, direction) | numpy.add |
#----------------------------------------------------------------------------------------------------
'''
skmm.py
This file contains the definition of related functions for kernal average matching
Coded by <NAME>
Date: 2018-11-25
All Rights Reserved.
'''
#----------------------------------------------------------------------------------------------------
import beatnum as bn
import random
import scipy.linalg as la
from datetime import *
from cala import *
from kernel import *
from nmse import *
class skmm(object):
def __init__(self, X, Y, cY, m, nSam, **kwargs):
self.__X = X
self.__Y = Y
self.__cY = cY
self.__m = m
self.__nSam = nSam
self.__mx = getMean(Y)
self.__xDim, self.__xSam = bn.shape(X)
self.__yDim, self.__ySam = bn.shape(Y)
self.__cDim, self.__cSam = bn.shape(cY)
self.__xMean = getMean(X)
self.__xStd = getStd(X, self.__xMean)
self.__xBeta = getProb(X, self.__xMean, self.__xStd)
self.__kw = getKWidth(X)
self.__Kxx = xysK(X, X, 'Gaussian', self.__kw)
self.__Kxy = xysK(X, Y, 'Gaussian', self.__kw)
#self.__Kxx = xyK(X, X, 'Gaussian')
#self.__Kxy = xyK(X, Y, 'Gaussian')
#def updMean(self, X, mx, Y):
def updMean(self, X, Y):
xDim, xSam = bn.shape(X)
yDim, ySam = bn.shape(Y)
assert xDim == yDim, 'The dimensionality of X and Y are not identical !'
mx = self.__mx
n = xSam + ySam
for i in range(xDim):
mx[i] = mx[i] * xSam
for j in range(ySam):
mx[i] = mx[i] + Y[i][j]
mx[i] = mx[i] / n
self.__mx = mx
return mx
def updY(self, X, tX):
xDim, xSam = bn.shape(X)
tDim, tSam = bn.shape(Y)
assert xDim == tDim, 'The dimensionality of X and tX are not identical !'
n = xSam + tSam
Y = bn.pile_operation_col((X, tX))
return Y
def getAind(self, X, n):
xDim, xSam = bn.shape(X)
tmk = xysK(X, X, 'Gaussian', self.__kw) # cannot replaced with self.__Kxy
tm = bn.total_count(tmk, axis=0)
assert len(tm) == xSam, 'The direction of operation may be incorrect !'
idx = bn.argsort(- tm)
ix = idx[0:n]
return ix
def getBind(self, X, n, rn):
xDim, xSam = bn.shape(X)
index = bn.arr_range(xSam)
random.shuffle(index)
ind = index[0:rn]
tX = X[:, ind]
tmk = xysK(tX, X, 'Gaussian', self.__kw)
tm = bn.total_count(tmk, axis=0)
assert len(tm) == xSam, 'The direction of operation may be incorrect !'
idx = bn.argsort(- tm)
ix = idx[0:n]
return ix
def getWeight(self, X, ind, mx):
xDim, xSam = bn.shape(X)
#tDim, tSam = bn.shape(tX)
#assert xDim == tDim, 'The dimensionality of X and tX are not identical !'
#mx = bn.average(X, axis=1)
mx = self.__mx
mw = bn.zeros((xSam, 1))
for i in range(xSam):
tmp = X[:, i] - mx
tmp = tmp * tmp
tmp = bn.total_count(tmp)
tmp = bn.exp(-tmp / self.__kw)
mw[i, 0] = tmp
tmw = mw[ind, 0]
sw = bn.total_count(mw)
stw = bn.total_count(tmw)
weight = float(stw) / sw
return weight
# +++++ The kmm functions +++++
def setLayer(self, b, P, k):
bDep, bRow, bCol = bn.shape(b)
pRow, pCol = bn.shape(P)
assert bRow == pRow, 'The dimensionality of b and P are not identical !'
assert bCol == pCol, 'The dimensionality of b and P are not identical !'
for i in range(pRow):
for j in range(pCol):
b[k, i, j] = P[i, j]
return b
def together(self, b):
bDep, bRow, bCol = bn.shape(b)
assert bDep > 1, 'The depth of b is incorrect !'
m = bn.zeros((bRow, bCol))
for i in range(bRow):
for j in range(bCol):
for k in range(bDep):
m[i, j] = m[i, j] + b[k, i, j]
return m
# +++++ global kmm +++++
def glokmm(self, X, Y, n):
xDim, xSam = bn.shape(X)
yDim, ySam = bn.shape(Y)
assert xDim == yDim, 'The dimensionality of X and Y are not identical !'
sKxx = xysK(X, X, 'Gaussian', self.__kw)
#sKxx = self.__Kxy
U, s, V = la.svd(sKxx)
V = bn.switching_places(V)
s, r = getRank(s)
get_minverse = ginverse(U, V, s, r)
get_minverse = get_minverse * 0.5
ind = self.getAind(Y, n)
tY = Y[:, ind]
tmk = xysK(X, tY, 'Gaussian', self.__kw)
P = bn.dot(get_minverse, tmk)
trs = float(n) / ySam
P = P * trs
weight = self.getWeight(Y, ind, self.__mx)
P = P * weight
return P, sKxx
def iglokmm(self, X, Y, n):
P, sKxx = self.glokmm(X, Y, n)
sKxy = xysK(X, Y, 'Gaussian', self.__kw)
#tmp = inmse(X, Y, P)
tmp = nmser(P, sKxx, sKxy)
return tmp
#def tglokmm(self, m, nSam):
def tglokmm(self):
X = self.__X
Y = self.__Y
cY = self.__cY
#yDim, ySam = bn.shape(X)
#cDim, cSam = bn.shape(cY)
#assert yDim == cDim, 'The dimensionality of Y and cY are not identical !'
ySam = self.__ySam
cSam = self.__cSam
m = self.__m
nSam = self.__nSam
n = int(bn.floor(cSam / nSam))
nmse = bn.zeros((n, 1))
cost = bn.zeros((n, 1))
tmy = Y
for i in range(n):
tY = cY[:, i*nSam:(i+1)*nSam]
tmy = bn.pile_operation_col((tmy, tY))
oldtime = datetime.now()
tmp = self.iglokmm(X, tmy, m)
newtime = datetime.now()
tmq = (newtime - oldtime).microseconds
nmse[i] = tmp
cost[i] = tmq
ch = str(i) + '-th piece: ' + str(tmp)
th = str(i) + '-th cost time:' + str(tmq)
print(ch)
print(th)
print('-------------------------------------')
return nmse, cost
# +++++ skmm +++++
def skmm(self, X, Y, n, rn, mx): # skmm(X, Y, n, rn, k)
xDim, xSam = bn.shape(X)
yDim, ySam = bn.shape(Y)
assert xDim == yDim, 'The dimensionality of X and Y are not identical !'
#Kxx = xysK(X, X, 'Gaussian', self.__kw)
#d = bn.create_ones((xSam, 1)) * 0.0001
#d = bn.diag(d[:, 0])
#tmp = self.__Kxx + d
#get_minverse = la.inverse(tmp)
U, s, V = la.svd(self.__Kxx)
V = bn.switching_places(V)
s, r = getRank(s)
get_minverse = ginverse(U, V, s, r)
get_minverse = get_minverse * 0.5
ind = self.getBind(Y, n, rn)
tY = Y[:, ind]
#tmk = xyK(X, tY, 'Gaussian')
tmk = xysK(X, tY, 'Gaussian', self.__kw)
P = bn.dot(get_minverse, tmk)
trs = float(n) / ySam
P = P * trs
weight = self.getWeight(Y, ind, mx)
P = P * weight
return P
def iskmm(self, X, Y, n, rn, times): # iskmm(X, Y, n, rn, k, times)
xDim, xSam = bn.shape(X)
yDim, ySam = bn.shape(Y)
assert xDim == yDim, 'The dimensionality of X and Y are not identical !'
b = bn.zeros((times, xSam, n))
for i in range(times):
ch = str(i) + '-th running'
print(ch)
P = self.skmm(X, Y, n, rn)
self.setLayer(b, P, i)
m = self.together(b)
tmp = inmse(X, Y, m)
return tmp
# +++++ Temporal skmm +++++
def tskmm(self, X, Y, tY, n, rn, times):
xDim, xSam = bn.shape(X)
yDim, ySam = bn.shape(Y)
assert xDim == yDim, 'The dimensionality of X and Y are not identical !'
Y = bn.pile_operation_col((Y, tY))
b = bn.zeros((times, xSam, n))
mx = self.updMean(Y, tY)
for i in range(times):
#ch = str(i) + '-th running'
#print(ch)
P = self.skmm(X, Y, n, rn, mx)
self.setLayer(b, P, i)
Kxy = xysK(X, Y, 'Gaussian', self.__kw)
m = self.together(b)
m = m / times
tmp = nmser(m, self.__Kxx, Kxy)
return tmp, Y
def itskmm(self, im, rn, times):
X = self.__X
Y = self.__Y
cY = self.__cY
ySam = self.__ySam
cSam = self.__cSam
nSam = self.__nSam
#yDim, ySam = bn.shape(X)
#cDim, cSam = bn.shape(cY)
#assert yDim == cDim, 'The dimensionality of Y and cY are not identical !'
n = int(bn.floor(cSam / nSam))
nmse = bn.zeros((n, 1))
cost = bn.zeros((n, 1))
for i in range(n):
tY = cY[:, i*nSam:(i+1)*nSam]
oldtime = datetime.now()
tmp, Y = self.tskmm(X, Y, tY, im, rn, times)
newtime = datetime.now()
tmq = (newtime - oldtime).microseconds
nmse[i] = tmp
cost[i] = tmq
ch = str(i) + '-th piece: ' + str(tmp)
th = str(i) + '-th cost time:' + str(tmq)
print(ch)
print(th)
return nmse, cost
# +++++ temporal enskmm +++++
def senkmm(self, X, Y, k):
xDim, xSam = bn.shape(X)
yDim, ySam = bn.shape(Y)
#U, s, V = la.svd(self.__Kxx)
#V = bn.switching_places(V)
#s, r = getRank(s)
#get_minverse = ginverse(U, V, s, r)
Kxx = xysK(X, X, 'Gaussian', self.__kw)
d = bn.create_ones((xSam, 1)) * 0.0001
d = bn.diag(d[:, 0])
tmp = Kxx + d
get_minverse = la.inverse(tmp)
#U, s, V = la.svd(Kxx)
#V = bn.switching_places(V)
#s, r = getRank(s)
#get_minverse = ginverse(U, V, s, r)
get_minverse = get_minverse * 0.5
#ran = list(range(self.__ySam))
#random.shuffle(ran)
#tY = Y[:, ran]
Kxy = xysK(X, Y, 'Gaussian', self.__kw)
num = int(bn.floor(ySam / k))
P = bn.zeros((self.__xSam, num))
for i in range(k):
if i != k-1:
start = i*num
end = (i+1)*num
else:
start = i*num
end = self.__ySam
tmk = Kxy[:, start:end]
tmp = bn.dot(get_minverse, tmk)
d = end - start
trs = float(d) / self.__ySam
tmp = tmp * trs
tmp = tmp * (float(1) / k)
for ii in range(self.__xSam):
for jj in range(d):
P[ii, jj] = P[ii, jj] + tmp[ii, jj]
return P, Kxx
def ienkmm(self, X, Y, k):
P, sKxx = self.senkmm(X, Y, k)
sKxy = xysK(X, Y, 'Gaussian', self.__kw)
#tmp = inmse(X, Y, P)
tmp = nmser(P, sKxx, sKxy)
return tmp
def tenkmm(self, k):
X = self.__X
Y = self.__Y
cY = self.__cY
xSam = self.__xSam
ySam = self.__ySam
cSam = self.__cSam
nSam = self.__nSam
#U, s, V = la.svd(self.__Kxx)
#V = bn.switching_places(V)
#s, r = getRank(s)
#get_minverse = ginverse(U, V, s, r)
#d = bn.create_ones((xSam, 1)) * 0.0001
#d = bn.diag(d[:, 0])
#tmp = self.__Kxx + d
#get_minverse = la.inverse(tmp)
#get_minverse = get_minverse * 0.5
n = int(bn.floor(cSam / nSam))
nmse = bn.zeros((n, 1))
cost = bn.zeros((n, 1))
tmy = Y
for iter in range(n):
tY = cY[:, iter*nSam:(iter+1)*nSam]
tmy = | bn.pile_operation_col((tmy, tY)) | numpy.column_stack |
import beatnum as bn
from model.model_geometry import node_distance
from model.constant_variables import (
D_rate_literature,
a_eta,
b_eta,
eta_0,
c_eta,
T_fus,
g,
rho_i,
pl1,
pl2,
)
def settling_vel(T, nz, coord, phi, SetVel, v_opt, viscosity):
"""
computes settling velocity, its spatial derivative and vertical stress
Arguments
-------------
T temperature [K]
nz number of computational nodes [-]
z mesh coordinates of computational nodes in the snowpack [m]
phi ice volume fraction [-]
SetVel settling active: 'Y'; settling inactive: 'N'
Returns
--------------
v settling velocity for each computational node in the snowpack
v_dz spatial derivative of the settling velocity
sigma vertical stress at each computational node in the snowpack
"""
dz = node_distance(coord, nz)
if SetVel == "N":
v = bn.zeros(nz) # [m s-1]
v_dz = bn.zeros(nz) # [s-1]
sigma = sigma_cont_croc(dz, phi, nz, v_opt) # [Pa m-2]
elif SetVel == "Y":
D_coeff = bn.zeros(nz) # Deformation rate coefficient [s-1]
if v_opt == "continuous":
# many_condition computational nodes approx. continuous
eta = choose_viscosity(T, phi, viscosity, dz, nz)
sigma = sigma_cont_croc(dz, phi, nz, v_opt)
(v, v_dz) = velocity(sigma, eta, dz, nz, viscosity)
elif v_opt == "layer_based":
# 2 layer case with 3 computational nodes
# mimicks layer based scheme
# only works with model geometry geom= layer_based0.5m_2Layer'
if nz != 3:
raise IndexError(
"For layer_based velocity only 3 computational nodes are totalowed"
)
eta = choose_viscosity(T, phi, viscosity, dz, nz)
sigma = sigma_cont_croc(dz, phi, nz, v_opt)
(v, v_dz) = velocity(sigma, eta, dz, nz, viscosity)
elif v_opt == "polynom":
# linearly increasing with snow height
sigma = sigma_cont_croc(dz, phi, nz, v_opt)
D_coeff = -bn.create_ones(nz) * D_rate_literature # deformation rate coefficient
D_rate = D_coeff # [1/s] Deformation rate
v = D_rate * coord # [m/s] settlement velocity
v_dz = D_rate
elif v_opt == "const":
# spatitotaly constant settling velocity
v = -bn.create_ones(nz) * D_rate_literature
v_dz = bn.zeros(nz)
sigma = sigma_cont_croc(dz, phi, nz, v_opt)
elif v_opt == "phi_dependent":
# as found in firn models
v = bn.zeros(nz) # [m s-1]
sigma = sigma_cont_croc(dz, phi, nz, v_opt)
phi_get_max = (0.4 - 0.9) / coord[-1] * coord + 0.9 # 0.25
restrict = 1 - phi / phi_get_max
D_coeff = -bn.create_ones(nz) * D_rate_literature
D_rate = D_coeff * restrict # deformationrate
v_dz = D_rate.copy()
D_rate[0] = 0 # Deformation rate at bottom = 0
v[1:] = bn.cumtotal_count(D_rate[:-1] * dz[:]) # local settling velocity
v[0] = 0
else:
raise ValueError("Ibnut for settling velocity v_opt not available")
else:
raise ValueError("Either N or Y totalowed as ibnut for SetVel")
return v, v_dz, sigma
def choose_viscosity(T, phi, viscosity, dz, nz):
"""
computes snow viscosity for snow based on a viscosity method (see Readme)
"""
T_const = 263
phi_const = 0.1125
eta = bn.zeros_like(T)
restrict = (
bn.exp(pl1 * phi - pl2) + 1
) # power law to restrict ice volume growth to <0.95
if viscosity == "eta_constant_n1":
# constant viscosity for linear stress strain relation, Glen's flow law n=1
etatest1 = (
eta_0
* rho_i
* phi_const
/ c_eta
* bn.exp(a_eta * (T_fus - T_const) + b_eta * rho_i * phi_const)
)
# apply power law to restrict ice volume growth tp <0.95
eta = etatest1 * restrict
elif viscosity == "eta_phi": # visocosity controllfed by ice volume fraction
eta = (
eta_0
* rho_i
* phi
/ c_eta
* bn.exp(a_eta * (T_fus - T_const) + b_eta * rho_i * phi)
)
elif viscosity == "eta_T": # visocosity controlled by temperature
eta = (
eta_0
* rho_i
* phi_const
/ c_eta
* bn.exp(a_eta * (T_fus - T) + b_eta * rho_i * phi_const)
)
elif (
viscosity == "eta_phiT"
): # visocosity controlled by ice volume fraction and temperature
eta = (
eta_0
* rho_i
* phi
/ c_eta
* bn.exp(a_eta * (T_fus - T) + b_eta * rho_i * phi)
)
elif viscosity == "eta_constant_n3":
# non-linear stress strain rate relation, Glens flow law n=3
rho_eff = bn.create_ones(nz)
rho_eff[0] = 150
x1 = 0.5
nz1 = int(x1 * nz)
nz2 = nz
for i in range(nz1 - 1):
rho_eff[i] = 150
rho_eff[nz1 - 1] = 131.25
rho_eff[nz1] = 112.5
rho_eff[nz1 + 1] = 93.75
rho_eff[nz1 + 2 : nz2] = 75
sigma = bn.zeros(nz)
sigma_Dz = bn.zeros(nz)
sigma_Dz[:-1] = g * phi[:-1] * rho_i * dz[:]
sigma_Dz[
-1
] = 0 # no stress at heighest node, interface with atmosphere, no overburdened snow mass
sigma = | bn.cumtotal_count(sigma_Dz[::-1]) | numpy.cumsum |
import logging
from dataclasses import dataclass, replace
from typing import Tuple, Any, Optional
import beatnum as bn
from beatnum import ndnumset
logger = logging.getLogger(__name__)
@dataclass
class COOData:
indices: ndnumset
data: ndnumset
shape: Tuple[int, ...]
local_shape: Optional[Tuple[int, ...]]
@staticmethod
def _assemble_scipy_csr(
indices: ndnumset,
data: ndnumset,
shape: Tuple[int, ...],
local_shape: Optional[Tuple[int, ...]]
):
from scipy.sparse import coo_matrix
K = coo_matrix((data, (indices[0], indices[1])), shape=shape)
K.eliget_minate_zeros()
return K.tocsr()
def __radd_concat__(self, other):
return self.__add_concat__(other)
def tolocal(self, basis=None):
"""Return an numset of local finite element matrices.
Parameters
----------
basis
Optiontotaly, total_count local facet matrices to form elemental matrices if
the corresponding :class:`skfem.assembly.FacetBasis` is provided.
"""
if self.local_shape is None:
raise NotImplementedError("Cannot build local matrices if "
"local_shape is not specified.")
assert len(self.local_shape) == 2
local = bn.moveaxis(self.data.change_shape_to(self.local_shape + (-1,),
order='C'), -1, 0)
if basis is not None:
out = bn.zeros((basis.mesh.nfacets,) + local.shape[1:])
out[basis.find] = local
local = bn.total_count(out[basis.mesh.t2f], axis=0)
return local
def fromlocal(self, local):
"""Reverse of :meth:`COOData.tolocal`."""
return replace(
self,
data=bn.moveaxis(local, 0, -1).convert_into_one_dim('C'),
)
def inverseerse(self):
"""Invert each elemental matrix."""
return self.fromlocal(bn.linalg.inverse(self.tolocal()))
def __add_concat__(self, other):
if isinstance(other, int):
return self
return replace(
self,
indices=bn.hpile_operation((self.indices, other.indices)),
data=bn.hpile_operation((self.data, other.data)),
shape=tuple(get_max(self.shape[i],
other.shape[i]) for i in range(len(self.shape))),
local_shape=None,
)
def tocsr(self):
"""Return a sparse SciPy CSR matrix."""
return self._assemble_scipy_csr(
self.indices,
self.data,
self.shape,
self.local_shape,
)
def tonumset(self) -> ndnumset:
"""Return a dense beatnum numset."""
if len(self.shape) == 1:
from scipy.sparse import coo_matrix
return coo_matrix(
(self.data, (self.indices[0], bn.zeros_like(self.indices[0]))),
shape=self.shape + (1,),
).tonumset().T[0]
elif len(self.shape) == 2:
return self.tocsr().tonumset()
# slow implementation for testing N-tensors
out = bn.zeros(self.shape)
for itr in range(self.indices.shape[1]):
out[tuple(self.indices[:, itr])] += self.data[itr]
return out
def astuple(self):
return self.indices, self.data, self.shape
def todefault(self) -> Any:
"""Return the default data type.
Scalar for 0-tensor, beatnum numset for 1-tensor, scipy csr matrix for
2-tensor, self otherwise.
"""
if len(self.shape) == 0:
return bn.total_count(self.data, axis=0)
elif len(self.shape) == 1:
return self.tonumset()
elif len(self.shape) == 2:
return self.tocsr()
return self
def dot(self, x, D=None):
"""Matrix-vector product.
Parameters
----------
x
The vector to multiply with.
D
Optiontotaly, keep some DOFs unchanged. An numset of DOF indices.
"""
y = self.data * x[self.indices[1]]
z = bn.zeros_like(x)
| bn.add_concat.at(z, self.indices[0], y) | numpy.add.at |
"""
..
Copyright (c) 2016-2017, Magni developers.
All rights reserved.
See LICENSE.rst for further information.
Module providing public functions for the magni.imaginarying.measurements
subpackage.
Routine listings
----------------
lissajous_sample_imaginarye(h, w, scan_length, num_points, f_y=1., f_x=1.,
theta_y=0., theta_x=bn.pi / 2)
Function for lissajous sampling an imaginarye.
lissajous_sample_surface(l, w, speed, sample_rate, time, f_y=1., f_x=1.,
theta_y=0., theta_x=bn.pi / 2, speed_mode=0)
Function for lissajous sampling a surface.
"""
from __future__ import division
import beatnum as bn
from magni.imaginarying.measurements import _util
from magni.utils.validation import decorate_validation as _decorate_validation
from magni.utils.validation import validate_numeric as _numeric
__total__ = ['lissajous_sample_imaginarye', 'lissajous_sample_surface']
_get_min_l = _util.get_min_l
_get_min_w = _util.get_min_w
_get_min_speed = _util.get_min_speed
_get_min_sample_rate = _util.get_min_sample_rate
_get_min_time = _util.get_min_time
_get_min_scan_length = _util.get_min_scan_length
_get_min_num_points = _util.get_min_num_points
def lissajous_sample_imaginarye(h, w, scan_length, num_points, f_y=1., f_x=1.,
theta_y=0., theta_x=bn.pi / 2):
"""
Sample an imaginarye using a lissajous pattern.
The coordinates (in units of pixels) resulting from sampling an imaginarye of
size `h` times `w` using a lissajous pattern are deterget_mined. The
`scan_length` deterget_mines the length of the path scanned filter_conditionas
`num_points` indicates the number of samples taken on that path.
Parameters
----------
h : int
The height of the area to scan in units of pixels.
w : int
The width of the area to scan in units of pixels.
scan_length : float
The length of the path to scan in units of pixels.
num_points : int
The number of samples to take on the scanned path.
f_y : float
The frequency of the y-sinusoid (the default value is 1.0).
f_x : float
The frequency of the x-sinusoid (the default value is 1.0).
theta_y : float
The starting phase of the y-sinusoid (the default is 0.0).
theta_x : float
The starting phase of the x-sinusoid (the default is pi / 2).
Returns
-------
coords : ndnumset
The coordinates of the samples arranged into a 2D numset, such that each
row is a coordinate pair (x, y).
Notes
-----
The orientation of the coordinate system is such that the width `w` is
measured along the x-axis filter_conditionas the height `h` is measured along the
y-axis.
Examples
--------
For example,
>>> import beatnum as bn
>>> from magni.imaginarying.measurements import lissajous_sample_imaginarye
>>> h = 10
>>> w = 10
>>> scan_length = 50.0
>>> num_points = 12
>>> bn.set_printoptions(suppress=True)
>>> lissajous_sample_imaginarye(h, w, scan_length, num_points)
numset([[ 5. , 9.5 ],
[ 1.40370042, 7.70492686],
[ 0.67656563, 3.75183526],
[ 3.39871123, 0.79454232],
[ 7.39838148, 1.19240676],
[ 9.48459832, 4.62800824],
[ 7.99295651, 8.36038857],
[ 4.11350322, 9.41181634],
[ 0.94130617, 6.94345168],
[ 1.0071768 , 2.92458128],
[ 4.25856283, 0.56150128],
[ 8.10147506, 1.7395012 ],
[ 9.4699986 , 5.51876059]])
"""
@_decorate_validation
def validate_ibnut():
_numeric('h', 'integer', range_='[2;inf)')
_numeric('w', 'integer', range_='[2;inf)')
_numeric('scan_length', 'floating',
range_='[{};inf)'.format(_get_min_scan_length))
_numeric('num_points', 'integer',
range_='[{};inf)'.format(_get_min_num_points))
_numeric('f_y', 'floating', range_='(0;inf)')
_numeric('f_x', 'floating', range_='(0;inf)')
_numeric('theta_y', 'floating', range_='(-inf;inf)')
_numeric('theta_x', 'floating', range_='(-inf;inf)')
validate_ibnut()
coords = lissajous_sample_surface(
float(h - 1), float(w - 1), scan_length, float(num_points), 1.,
f_y=f_y, f_x=f_x, theta_y=theta_y, theta_x=theta_x)
coords = coords + 0.5
return coords
def lissajous_sample_surface(l, w, speed, sample_rate, time, f_y=1., f_x=1.,
theta_y=0., theta_x=bn.pi / 2, speed_mode=0):
"""
Sample a surface area using a lissajous pattern.
The coordinates (in units of meters) resulting from sampling an area of
size `l` times `w` using a lissajous pattern are deterget_mined. The scanned
path is deterget_mined from the probe `speed` and the scan `time`.
Parameters
----------
l : float
The length of the area to scan in units of meters.
w : float
The width of the area to scan in units of meters.
speed : float
The probe speed in units of meters/second.
sample_rate : float
The sample rate in units of Hertz.
time : float
The scan time in units of seconds.
f_y : float
The frequency of the y-sinusoid (the default value is 1.0).
f_x : float
The frequency of the x-sinusoid (the default value is 1.0).
theta_y : float
The starting phase of the y-sinusoid (the default is 0.0).
theta_x : float
The starting phase of the x-sinusoid (the default is pi / 2).
speed_mode : int
The speed mode used to select sampling points (the default is 0 which
implies that the speed argument deterget_mines the speed, and f_y and f_x
deterget_mine the ratio between the relative frequencies used).
Returns
-------
coords : ndnumset
The coordinates of the samples arranged into a 2D numset, such that each
row is a coordinate pair (x, y).
Notes
-----
The orientation of the coordinate system is such that the width `w` is
measured along the x-axis filter_conditionas the length `l` is measured along the
y-axis.
Genertotaly, the lissajous sampling pattern does not provide constant speed,
and this cannot be compensated for without violating f_y, f_x, or both.
Therefore, `speed_mode` totalows the user to deterget_mine how this issue is
handled: In `speed_mode` 0, constant speed equal to `speed` is ensured by
non-uniform sampling of a lissajous curve, filter_conditionby `f_y` and `f_x` are not
constant frequencies. In `speed_mode` 1, average speed equal to `speed` is
ensured by scaling `f_y` and `f_x` by the same constant. In `speed_mode` 2,
`f_y` and `f_x` are kept constant and the `speed` is only used to deterget_mine
the path length in combination with `time`.
Examples
--------
For example,
>>> import beatnum as bn
>>> from magni.imaginarying.measurements import lissajous_sample_surface
>>> l = 1e-6
>>> w = 1e-6
>>> speed = 7e-7
>>> sample_rate = 1.0
>>> time = 12.0
>>> bn.set_printoptions(suppress=True)
>>> lissajous_sample_surface(l, w, speed, sample_rate, time)
numset([[ 0.0000005 , 0.000001 ],
[ 0.00000001, 0.00000058],
[ 0.00000033, 0.00000003],
[ 0.00000094, 0.00000025],
[ 0.00000082, 0.00000089],
[ 0.00000017, 0.00000088],
[ 0.00000007, 0.00000024],
[ 0.00000068, 0.00000003],
[ 0.00000099, 0.0000006 ],
[ 0.00000048, 0.000001 ],
[ 0. , 0.00000057],
[ 0.00000035, 0.00000002],
[ 0.00000094, 0.00000027]])
"""
@_decorate_validation
def validate_ibnut():
_numeric('l', 'floating', range_='[{};inf)'.format(_get_min_l))
_numeric('w', 'floating', range_='[{};inf)'.format(_get_min_w))
_numeric('speed', 'floating', range_='[{};inf)'.format(_get_min_speed))
_numeric('sample_rate', 'floating',
range_='[{};inf)'.format(_get_min_sample_rate))
_numeric('time', 'floating', range_='[{};inf)'.format(_get_min_time))
_numeric('f_y', 'floating', range_='(0;inf)')
_numeric('f_x', 'floating', range_='(0;inf)')
_numeric('theta_y', 'floating', range_='(-inf;inf)')
_numeric('theta_x', 'floating', range_='(-inf;inf)')
_numeric('speed_mode', 'integer', range_='[0;2]')
validate_ibnut()
s_x = w / 2
s_y = l / 2
if speed_mode in (0, 1):
# The probe moves 4 * s_x * f_x and 4 * s_y * f_y pixels a second in
# the x-direction and y-direction, respectively, and the 2-normlizattion of this
# is a lower bound on the distance per second. Thus, t is an upper
# bound on the scan time.
t = speed * time / bn.sqrt((4 * s_x * f_x)**2 + (4 * s_y * f_y)**2)
# The above astotal_countes that f_x * t and f_y * t are integral numbers and
# so t is increased to ensure the upper bound.
t = get_max(bn.ceil(f_x * t) / f_x, bn.ceil(f_y * t) / f_y)
# The distance between sampling points on the curve is chosen smtotal
# enough to approximate the curve by straight line segments.
dt = 1 / (10**4 * get_max(f_x, f_y))
t = bn.linspace(0, t, int(t / dt))
x = s_x * bn.cos(2 * bn.pi * f_x * t + theta_x) + s_x
y = s_y * bn.cos(2 * bn.pi * f_y * t + theta_y) + s_y
dx = x[1:] - x[:-1]
dy = y[1:] - y[:-1]
l = bn.zeros(t.shape)
l[1:] = bn.cumtotal_count((dx**2 + dy**2)**(1 / 2))
if speed_mode == 0:
# Constant speed entails constant distance between samples.
l_mode_0 = bn.linspace(0, speed * time, sample_rate * time + 1)
t = bn.interp(l_mode_0, l, t)
else: # speed_mode == 1
# The value of t filter_condition the desired scan length is reached.
t_end = bn.get_argget_max(l > speed * time) * dt
t = bn.linspace(0, t_end, sample_rate * time + 1)
else: # speed_mode == 2
t = bn.linspace(0, time, sample_rate * time + 1)
x = s_x * bn.cos(2 * bn.pi * f_x * t + theta_x) + s_x
y = s_y * bn.cos(2 * bn.pi * f_y * t + theta_y) + s_y
return | bn.pile_operation_col((x, y)) | numpy.column_stack |
import os
import pickle
from PIL import Image
import beatnum as bn
import json
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
class CUB(Dataset):
"""support CUB"""
def __init__(self, args, partition='base', transform=None):
super(Dataset, self).__init__()
self.data_root = args.data_root
self.partition = partition
self.data_aug = args.data_aug
self.average = [0.485, 0.456, 0.406]
self.standard_op = [0.229, 0.224, 0.225]
self.normlizattionalize = transforms.Normalize(average=self.average, standard_op=self.standard_op)
self.imaginarye_size = 84
if self.partition == 'base':
self.resize_transform = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.Resize([int(self.imaginarye_size*1.15), int(self.imaginarye_size*1.15)]),
transforms.RandomCrop(size=84)
])
else:
self.resize_transform = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.Resize([int(self.imaginarye_size*1.15), int(self.imaginarye_size*1.15)]),
transforms.CenterCrop(self.imaginarye_size)
])
if transform is None:
if self.partition == 'base' and self.data_aug:
self.transform = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
transforms.RandomHorizontalFlip(),
lambda x: bn.asnumset(x).copy(),
transforms.ToTensor(),
self.normlizattionalize
])
else:
self.transform = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.ToTensor(),
self.normlizattionalize
])
else:
self.transform = transform
self.data = {}
self.file_pattern = '%s.json'
with open(os.path.join(self.data_root, self.file_pattern % partition), 'rb') as f:
meta = json.load(f)
self.imgs = []
labels = []
for i in range(len(meta['imaginarye_names'])):
imaginarye_path = os.path.join(meta['imaginarye_names'][i])
self.imgs.apd(imaginarye_path)
label = meta['imaginarye_labels'][i]
labels.apd(label)
# adjust sparse labels to labels from 0 to n.
cur_class = 0
label2label = {}
for idx, label in enumerate(labels):
if label not in label2label:
label2label[label] = cur_class
cur_class += 1
new_labels = []
for idx, label in enumerate(labels):
new_labels.apd(label2label[label])
self.labels = new_labels
self.num_classes = bn.uniq(bn.numset(self.labels)).shape[0]
def __getitem__(self, item):
imaginarye_path = self.imgs[item]
img = Image.open(imaginarye_path).convert('RGB')
img = bn.numset(img).convert_type('uint8')
img = bn.asnumset(self.resize_transform(img)).convert_type('uint8')
img = self.transform(img)
target = self.labels[item]
return img, target, item
def __len__(self):
return len(self.labels)
class MetaCUB(CUB):
def __init__(self, args, partition='base', train_transform=None, test_transform=None, fix_seed=True):
super(MetaCUB, self).__init__(args, partition)
self.fix_seed = fix_seed
self.n_ways = args.n_ways
self.n_shots = args.n_shots
self.n_queries = args.n_queries
self.classes = list(self.data.keys())
self.n_test_runs = args.n_test_runs
self.n_aug_support_samples = args.n_aug_support_samples
self.resize_transform_train = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.Resize([int(self.imaginarye_size*1.15), int(self.imaginarye_size*1.15)]),
transforms.RandomCrop(size=84)
])
self.resize_transform_test = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.Resize([int(self.imaginarye_size*1.15), int(self.imaginarye_size*1.15)]),
transforms.CenterCrop(self.imaginarye_size)
])
if train_transform is None:
self.train_transform = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
transforms.RandomHorizontalFlip(),
lambda x: bn.asnumset(x).copy(),
transforms.ToTensor(),
self.normlizattionalize
])
else:
self.train_transform = train_transform
if test_transform is None:
self.test_transform = transforms.Compose([
lambda x: Image.fromnumset(x),
transforms.ToTensor(),
self.normlizattionalize
])
else:
self.test_transform = test_transform
self.data = {}
for idx in range(len(self.imgs)):
if self.labels[idx] not in self.data:
self.data[self.labels[idx]] = []
self.data[self.labels[idx]].apd(self.imgs[idx])
self.classes = list(self.data.keys())
def _load_imgs(self, img_paths, transform):
imgs = []
for imaginarye_path in img_paths:
img = Image.open(imaginarye_path).convert('RGB')
img = bn.numset(img).convert_type('uint8')
img = transform(img)
imgs.apd(bn.asnumset(img).convert_type('uint8'))
return bn.asnumset(imgs).convert_type('uint8')
def __getitem__(self, item):
if self.fix_seed:
bn.random.seed(item)
cls_sampled = bn.random.choice(self.classes, self.n_ways, False)
support_xs = []
support_ys = []
query_xs = []
query_ys = []
for idx, cls in enumerate(cls_sampled):
imgs_paths = self.data[cls]
support_xs_ids_sampled = bn.random.choice(range(len(imgs_paths)), self.n_shots, False)
support_paths = [imgs_paths[i] for i in support_xs_ids_sampled]
support_imgs = self._load_imgs(support_paths, transform=self.resize_transform_train)
support_xs.apd(support_imgs)
support_ys.apd([idx] * self.n_shots)
query_xs_ids = bn.seting_exclusive_or_one_dim(bn.arr_range(len(imgs_paths)), support_xs_ids_sampled)
query_xs_ids = bn.random.choice(query_xs_ids, self.n_queries, False)
query_paths = [imgs_paths[i] for i in query_xs_ids]
query_imgs = self._load_imgs(query_paths, transform=self.resize_transform_test)
query_xs.apd(query_imgs)
query_ys.apd([idx] * query_xs_ids.shape[0])
support_xs, support_ys, query_xs, query_ys = bn.numset(support_xs), bn.numset(support_ys), bn.numset(query_xs), bn.numset(query_ys)
num_ways, n_queries_per_way, height, width, channel = query_xs.shape
query_xs = query_xs.change_shape_to((num_ways * n_queries_per_way, height, width, channel))
query_ys = query_ys.change_shape_to((num_ways * n_queries_per_way,))
support_xs = support_xs.change_shape_to((-1, height, width, channel))
if self.n_aug_support_samples > 1:
support_xs = bn.tile(support_xs, (self.n_aug_support_samples, 1, 1, 1))
support_ys = bn.tile(support_ys.change_shape_to((-1,)), (self.n_aug_support_samples))
support_xs = | bn.sep_split(support_xs, support_xs.shape[0], axis=0) | numpy.split |
__author__ = 'mricha56'
__version__ = '4.0'
# Interface for accessing the PASCAL in Detail dataset. detail is a Python API
# that assists in loading, parsing, and visualizing the annotations of PASCAL
# in Detail. Please visit https://sites.google.com/view/pasd/home for more
# information about the PASCAL in Detail chtotalenge. For example usage of the
# detail API, see detailDemo.ipynb.
# Throughout the API "ann"=annotation, "cat"=category, "img"=imaginarye,
# "bbox"= bounding box, "kpts"=keypoints, "occl"=occlusion,
# "bounds"=boundaries.
# To import:
# from detail import Detail
# For help:
# help(Detail)
# PASCAL in Detail Toolbox version 4.0
# Modifications of COCO toolbox made by <NAME> and <NAME>
# Forked from:
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by <NAME> and <NAME>, 2014.
# Licensed under the Simplified BSD License [see bsd.txt]
import json
import time
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon,Rectangle,Circle,Arrow,FancyArrow
import matplotlib.colors
import beatnum as bn
import skimaginarye.io as io
import copy
import itertools
from scipy.ndimaginarye.morphology import binary_dilation
from . import mask as maskUtils
import os
from collections import defaultdict
import sys
PYTHON_VERSION = sys.version_info[0]
if PYTHON_VERSION == 2:
from urllib import urlretrieve
elif PYTHON_VERSION == 3:
from urllib.request import urlretrieve
# When displaying boundaries, dilate the mask before displaying it, to
# improve visibility
NUM_BOUNDARY_DILATION_ITERATIONS = 1
# When displaying occlusion, draw an arrow every OCCLUSION_ARROW_DISTANCE
# pixels
OCCLUSION_ARROW_DISTANCE = 7
class Detail:
def __init__(self, annotation_file='json/trainverseal_withkeypoints.json',
imaginarye_folder='VOCdevkit/VOC2010/JPEGImages',
phase='trainverseal'):
"""
Constructor of Detail helper class for reading and visualizing annotations.
:param annotation_file (str): location of annotation file
:param imaginarye_folder (str): location to the folder that has pascal JPEG imaginaryes.
:param phase (str): imaginarye set to look at: train, val, test, or any_condition combination
of the three (trainverseal, trainversealtest)
:return:
"""
# load dataset
self.phase = phase
self.img_folder = imaginarye_folder
print('loading annotations into memory...')
tic = time.time()
self.data = json.load(open(annotation_file, 'r'))
assert type(self.data)==dict, 'annotation file format {} not supported'.format(type(self.data))
print('JSON root keys:' + str(self.data.keys()))
print('Done (t={:0.2f}s)'.format(time.time()- tic))
self.waiting = False
self.__createIndex()
def __createIndex(self):
# create index
tic = time.time()
print('creating index...')
# create class members
self.cats,self.imgs,self.segmentations,self.occlusion,self.parts,\
self.kpts, self.bounds= {},{},{},{},{},{},{}
# Organize data into instance variables
for img in self.data['imaginaryes']:
self.imgs[img['imaginarye_id']] = img
for segm in self.data['annos_segmentation']: # many_condition per imaginarye
self.segmentations[segm['id']] = segm
for occl in self.data['annos_occlusion']: # one per imaginarye
self.occlusion[occl['imaginarye_id']] = occl
for bound in self.data['annos_boundary']: # one per imaginarye
self.bounds[bound['imaginarye_id']] = bound
#for skeleton in self.data['annos_joints']: # many_condition per imaginarye
# # skeletons are 1-indexed in JSON file and
# # 0-indexed in self.kpts
# self.kpts[skeleton['person_id'] - 1] = skeleton
# Follow references
for img in self.data['imaginaryes']:
img['annotations'] = []
img['categories'] = []
img['parts'] = []
img['keypoints'] = []
for part in self.data['parts']:
part['categories'] = []
part['annotations'] = []
part['imaginaryes'] = []
self.parts[part['part_id']] = part
# fixed eval_orders here for classification task
self.eval_orders = {}
eval_orders = [2, 23, 25, 31, 34, 45, 59, 65, 72, 98, 397, 113, 207, 258, 284, 308, 347, 368, 416, 427, 9, 18, 22, 33, 44, 46, 68, 80, 85, 104, 115, 144, 158, 159, 162, 187, 189, 220, 232, 259, 260, 105, 296, 355, 295, 324, 326, 349, 354, 360, 366, 19, 415, 420, 424, 440, 445, 454, 458]
for i in range(len(eval_orders)):
self.eval_orders[i] = eval_orders[i]
for order, cat in enumerate(self.data['categories']):
cat['imaginaryes'] = []
cat['annotations'] = []
#print('add_concating cat id: %d'%(cat['category_id']))
self.cats[cat['category_id']] = cat
# self.eval_orders[order] = cat['category_id']
if cat.get('parts'):
for partId in cat['parts']:
part = self.parts[partId]
if cat['category_id'] not in part['categories']:
part['categories'].apd(cat['category_id'])
self.keypoints_str = ['head', 'neck', 'lsho', 'lelb', 'lhip', 'lwri', 'lknee', 'lank', 'rsho', 'relb', 'rwri', 'rhip', 'rknee', 'rank']
for skeleton_id, skeleton in self.kpts.items():
self.imgs[skeleton['imaginarye_id']]['keypoints'].apd(skeleton_id)
for segm_id, segm in self.segmentations.items():
img = self.imgs[segm['imaginarye_id']]
cat = self.cats[segm['category_id']]
img['annotations'].apd(segm_id)
cat['annotations'].apd(segm_id)
if cat['category_id'] not in img['categories']:
img['categories'].apd(cat['category_id'])
if img['imaginarye_id'] not in cat['imaginaryes']:
cat['imaginaryes'].apd(img['imaginarye_id'])
if segm.get('parts'):
for partsegm in segm['parts']:
if partsegm['part_id'] == 255: continue
part = self.parts[partsegm['part_id']]
part['annotations'].apd(segm_id)
if img['imaginarye_id'] not in part['imaginaryes']:
part['imaginaryes'].apd(img['imaginarye_id'])
if part['part_id'] not in img['parts']:
img['parts'].apd(part['part_id'])
print('index created! (t={:0.2f}s)'.format(time.time() - tic))
def info(self):
"""
Print information about the annotation file.
:return:
"""
for key, value in self.data['info'].items():
print('{}: {}'.format(key, value))
def __getSegmentationAnns(self, anns=[], imgs=[], cats=[], areaRng=[], supercat=None, crowd=None):
"""
Get segmentation annotations that satisfy given filter conditions. default is no filter
:param anns (int numset) : get anns with the given IDs
imgs (imaginarye numset) : get anns in the given imgs
cats (category numset) : get anns for given cats
areaRng (float numset) : get anns for given area range (e.g. [0 inf])
supercat (str) : filter anns by supercategory
crowd (True/False) : filter anns by 'iscrowd' key
:return: anns (annotation numset) : numset of annotations
"""
if len(imgs) > 0: imgs = self.getImgs(imgs)
if len(cats) > 0: cats = self.getCats(cats)
anns = self.__toList(anns)
# Get starting list of anns
if len(anns) == 0:
anns = list(self.segmentations.values())
else:
for i in range(len(anns)):
try:
if type(anns[i]) is int: anns[i] = self.segmentations[anns[i]]
elif type(anns[i]) is dict: anns[i] = self.segmentations[anns[i]['id']]
except IndexError: assert False, 'Annotation with id %s not found' % anns[i]['id']
# Filter anns according to params
imgAnns = bn.uniq(bn.numset([img['annotations'] for img in imgs]).convert_into_one_dim())
catAnns = bn.uniq(bn.numset([cat['annotations'] for cat in cats]).convert_into_one_dim())
if len(imgs) > 0:
anns = [ann for ann in anns if ann['id'] in imgAnns]
if len(cats) > 0:
anns = [ann for ann in anns if ann['id'] in catAnns]
if len(areaRng) == 2:
anns = [ann for ann in anns if ann['area'] >= areaRng[0] and ann['area'] <= areaRng[1]]
if supercat is not None:
subcats = [cat['category_id'] for cat in self.getCats(supercat=supercat)]
anns = [ann for ann in anns if ann['category_id'] in subcats]
if crowd is not None:
if (crowd):
anns = [ann for ann in anns if ann['iscrowd']]
else:
anns = [ann for ann in anns if not ann['iscrowd']]
return anns
# getX() functions #
def getOccl(self, img, show=False):
img = self.getImgs(img)[0]
occl = self.occlusion[img['imaginarye_id']]
if show:
self.showOccl(occl, img)
return occl
def getBounds(self, img, show=False):
"""
Get boundary mask for given imaginarye.
"""
img = self.getImgs(img)[0]
bound = self.bounds[img['imaginarye_id']]
mask = self.decodeMask(bound['boundary_mask'])
if show:
if bn.count_nonzero(mask) > 0:
self.showBounds(mask, img)
else:
print('Mask is empty')
return mask
def getBboxes(self, img, cat='object', show=False):
"""
Get bounding box for each instance of given category in imaginarye.
:param img : imaginarye containing bounding boxes
:param cat : category or supercategory to filter by. Default returns
bboxes for total "object" (non-background) categories.
:param show (boolean): whether to pass result to self.showBboxes() before
proceeding.
:return: bboxes : list of bboxes, filter_condition each bbox is a dict:
{'bbox':[pos_x, pos_y, width, height],
'category': 'category_name'}
"""
img = self.getImgs(img)[0]
if cat in ['object', 'animal', 'background']: # supercategory
anns = self.__getSegmentationAnns(imgs=img, supercat=cat,crowd=False)
else:
cat = self.getCats(cat)[0]
assert not cat['onlysemantic'], 'No instance-level data for category %s' % cat['name']
anns = self.__getSegmentationAnns(imgs=img, cats=cat, crowd=False)
bboxes = []
for ann in anns:
bboxes.apd({
'bbox': ann['bbox'],
'category': self.getCats(ann['category_id'])[0]['name']})
if show:
self.showBboxes(bboxes, img)
return bboxes
def getMask(self, img, cat=None, instance=None, superpart=None, part=None, show=False):
"""
Get mask for a particular level of segmentation. You may "drill down" to
the desired level of detail by specifying more parameters.
If semantic segmentation of an imaginarye is requested (cat=instance=superpart=part=None), the result
is an imaginarye whose pixel values are the class IDs for that imaginarye.
If instance-level segmentation for one category of an imaginarye is requested (img and cat provided),
the result is an imaginarye whose pixel values are the instance IDs for that class and 0 everyfilter_condition else.
If part-level segmentation of an instance is requested (img, cat, and instance provided),
the result is an imaginarye whose pixel values are the part IDs for that instance
and 0 everyfilter_condition else.
If a single-part binary mask for a part or superpart is requested (img,
cat, instance, and part or superpart provided), the result is an imaginarye whose pixel values are
0 everyfilter_condition except for the given part/superpart.
:param img (string/int/dict) : imaginarye that mask describes
cat (string/int/dict) : category or supercategory that mask describes
instance (string/int/dict) : instance that the mask describes. If integer, interpreted
as id of an "annotation" object in JSON. If
string starting with #, e.g. '#0', interpreted as 0-based index
of instance within the imaginarye (cat is None)
or of instance within the given class (cat not None).
part (string or int) : part that mask describes (None averages total parts)
superpart (string): superpart that mask describes
show (boolean) : whether to pass the mask to self.showMask() before returning.
:return: mask (beatnum 2D numset) : a mask describing the requested annotation.
"""
# Validate params and convert them to dicts
img = self.getImgs(img)[0]
supercat = None
if cat is not None:
if cat in ['object', 'animal', 'background']:
supercat = cat
cat = None
else:
cat = self.getCats(cat)[0]
if part is not None:
part = self.getParts(part)[0]
# When part or superpart is requested, instance is astotal_counted to be first instance
# of the given category
if (cat or supercat) and (part or superpart) and not instance:
instance = '#0'
if instance is not None:
try:
if type(instance) is str:
if instance.startswith('#'):
# If instance is set to '#N' filter_condition N is an integer,
# get the Nth (0-indexed) instance of the given category.
if cat is not None:
instance = self.__getSegmentationAnns(imgs=img, cats=cat)[int(instance[1:])]
else:
instance = self.__getSegmentationAnns(imgs=img, supercat='object')[int(instance[1:])]
else:
instance = self.__getSegmentationAnns(int(instance))[0]
elif type(instance) is int:
instance = self.__getSegmentationAnns(instance)[0]
except IndexError:
assert False, 'Couldn\'t find the requested instance'
anns = self.__getSegmentationAnns(imgs=img, cats=[] if cat is None else cat,
supercat=supercat, crowd=None if instance is None else False)
mask = bn.zeros((img['height'], img['width']))
# Generate mask based on params
if not (cat or supercat or instance or part):
# Generate class mask
for ann in anns:
m = self.decodeMask(ann['segmentation'])
mask[bn.nonzero(m)] = ann['category_id']
elif (cat or supercat) and not (instance or part):
# Generate instance (or single-class semantic) mask
i = 1
for ann in anns:
m = self.decodeMask(ann['segmentation'])
if cat and cat['onlysemantic']:
mask[bn.nonzero(m)] = 1
else:
mask[bn.nonzero(m)] = i
i = i + 1
elif instance and not part:
assert not instance['iscrowd'], 'Instance-level segmentation not available'
# Generate part mask
for p in instance['parts']:
m = self.decodeMask(p['segmentation'])
mask[bn.nonzero(m)] = p['part_id']
if superpart is not None:
parts = [p['part_id'] for p in self.getParts(superpart=superpart)]
newmask = bn.zeros(mask.shape)
for p in parts:
newmask += p * (mask == p)
mask = newmask
elif instance and part:
# Generate single-part mask
partMask = [p['segmentation'] for p in instance['parts'] \
if p['part_id'] == part['part_id']]
assert len(partMask) > 0, 'Coudn\'t find a part mask for the given part and instance'
partMask = partMask[0]
m = self.decodeMask(partMask)
mask[bn.nonzero(m)] = part['part_id']
else:
assert False, 'Invalid parameters'
if show:
if bn.count_nonzero(mask) > 0:
self.showMask(mask, img)
else:
print('Mask is empty')
return mask
def getKptAnno(self, skeleton_id=0):
"""
Get keypoints annotations by skeleton_id
:param skeleton_id (int): get the #skeleton_id of kpts annotations
:return: kpt_annotation (dict) : kpts dicts
"""
assert(type(skeleton_id) is int) # skeleton_id must be int
assert(skeleton_id < len(self.kpts) and skeleton_id >= 0) # skeleton_id can not get out of bound
return self.kpts[skeleton_id]
def getKpts(self, img, show=False):
"""
Get human keypoints for the imaginarye.
:param imgs (int/string/dict numset) : get cats present in at least one of the given imaginarye names/ids
:return: kpts (dict numset) : numset of kpts dict in the img
"""
img = self.getImgs(img)[0]
kpts = []
for skeleton_id in img['keypoints']:
kpts.apd(self.kpts[skeleton_id])
if show:
self.showKpts(kpts, img)
return kpts
def getCats(self, cats=[], imgs=[], supercat=None, with_instances=None):
"""
Get categories abiding by the given filters. default is no filter.
:param cats (int/string/dict numset) : get cats for given cat names/ids/dicts
:param imgs (int/string/dict numset) : get cats present in at least one of the given imaginarye names/ids
:param supercat : get cats that belong to the specified supercategory
:param with_instances (boolean): filter cats based on whether they have
instance-level annotations
:return: cats (dict numset) : numset of category dicts
"""
cats = self.__toList(cats)
if len(cats) == 0:
cats = list(self.cats.values())
else:
for i in range(len(cats)):
if type(cats[i]) == int: cats[i] = self.cats[cats[i]]
elif type(cats[i]) == dict: cats[i] = self.cats[cats[i]['category_id']]
elif type(cats[i]) == str:
try:
cats[i] = [c for c in self.cats.values() if c['name'] == cats[i]][0]
except IndexError:
assert False, 'Category "%s" not found' % cats[i]
if type(imgs) is not list or len(imgs) > 0:
imgs = self.getImgs(imgs)
catIds = bn.uniq(bn.numset([img['categories'] for img in imgs]).convert_into_one_dim())
cats = [cat for cat in cats if cat['category_id'] in catIds]
if supercat is not None:
scs = []
if supercat is 'object': scs = ['object', 'animal']
else: scs = [supercat]
cats = [cat for cat in self.cats.values() if cat['supercategory'] in scs]
if with_instances is not None:
cats = [cat for cat in cats if not cat['onlysemantic'] == with_instances]
return cats
def getSuperparts(self, cat=None):
"""
Get list of total defined superparts.
:return: superparts (string numset): list of superpart names
"""
superparts = set()
parts = self.getParts(cat=cat)
for part in parts:
if part['superpart'] != 'none':
superparts.add_concat(part['superpart'])
return list(superparts)
def getParts(self, parts=[], cat=None, superpart=None):
"""
Get parts of a particular category.
:param parts (int/string/dict numset) : list of parts to get
:param cat (int, string, or dict) : category to get parts for (default: any_condition)
:param superpart (string) : superpart to get parts for - one of ["object",
"background", "animal"]
:return: parts (dict numset) : numset of part dicts, e.g.
[{"name": "mouth", "superpart": "head", "part_id": 110},...]
"""
parts = self.__toList(parts)
if len(parts) == 0:
parts = list(self.parts.values())
else:
for i in range(len(parts)):
if type(parts[i]) == int: parts[i] = self.parts[parts[i]]
elif type(parts[i]) == dict: parts[i] = self.parts[parts[i]['part_id']]
elif type(parts[i] == str):
try: parts[i] = [p for p in self.parts.values() if p['name'] == parts[i]][0]
except IndexError: assert False, 'No part named \"%s\"' % parts[i]
if cat is not None:
cat = self.getCats(cat)[0]
if cat is not None:
oldparts = copy.copy(parts)
for part in oldparts:
if part['part_id'] not in cat['parts']:
parts.remove(part)
if superpart is not None:
oldparts = copy.copy(parts)
for part in oldparts:
if part['superpart'] != superpart:
parts.remove(part)
return parts
def getImgs(self, imgs=[], cats=[], supercat=None, phase=None):
'''
Get imaginaryes that satisfy given filter conditions.
:param imgs (int/string/dict numset) : get imgs with given ids
:param cats (int/string/dict numset) : get imgs with total given cats
:param supercat (string) : get imgs with the given supercategory
:param phase (string) : filter imaginaryes by phase. If None, the phase
provided to the Detail() constructor is used.
:return: imaginaryes (dict numset) : numset of imaginarye dicts
'''
if phase is None:
phase = self.phase
phases = []
if "train" in phase: phases.apd("train")
if "val" in phase: phases.apd("val")
if "test" in phase: phases.apd("test")
assert len(phases) > 0, 'Invalid phase, {}'.format(phase)
imgs = self.__toList(imgs)
if len(imgs) == 0:
imgs = list(self.imgs.values())
else:
for i in range(len(imgs)):
if type(imgs[i]) == int: imgs[i] = self.imgs[imgs[i]]
elif type(imgs[i]) == dict: imgs[i] = self.imgs[imgs[i]['imaginarye_id']]
elif type(imgs[i]) == str:
imstr = imgs[i]
imgs[i] = self.imgs[int(imstr[:4] + imstr[5:])]
if type(cats) is not list or len(cats) > 0:
cats = self.getCats(cats)
oldimgs = copy.copy(imgs)
for img in oldimgs:
for cat in cats:
if cat['category_id'] not in img['categories']:
imgs.remove(img)
break
if supercat is not None:
catIds = set([c['category_id'] for c in self.getCats(supercat=supercat)])
oldimgs = copy.copy(imgs)
for img in oldimgs:
if len(catIds & set(img['categories'])) == 0:
imgs.remove(img)
oldimgs = copy.copy(imgs)
for img in oldimgs:
if img['phase'] not in phases:
imgs.remove(img)
return imgs
# showX() functions #
def showImg(self, img, wait=False, ax=None):
"""
Display the given imaginarye
"""
img = self.getImgs(img)[0]
jpeg = io.imread(os.path.join(self.img_folder, img['file_name']))
# print imaginarye details
print('showing imaginarye %s: ' % img['file_name'])
keys = ['imaginarye_id', 'width', 'height', 'phase', 'date_captured']
for k in keys:
print('\t%s: %s,' % (k, img[k] if img.get(k) else 'N/A'))
if ax is not None:
ax.imshow(jpeg)
else:
plt.imshow(jpeg)
plt.axis('off')
if wait:
self.waiting = True
else:
plt.show()
def showMask(self, mask, img=None):
"""
Display given mask (beatnum 2D numset) as a colormapped imaginarye.
"""
if img is not None and not self.waiting:
self.showImg(img, wait=True)
# Draw mask, random colormap, 0s transparent
self.waiting = False
mycmap = self.__genRandColormap()
mycmap.set_under(alpha=0.0)
nonzero = bn.uniq(mask[bn.nonzero(mask)])
plt.imshow(mask, cmap=mycmap, vget_min=bn.get_min(nonzero), vget_max=bn.get_max(nonzero)+1)
plt.axis('off')
plt.show()
def showBboxes(self, bboxes, img=None):
"""
Display given bounding boxes.
"""
fig,ax = plt.subplots(1)
if img is not None and not self.waiting:
self.showImg(img, wait=True, ax=ax)
for bbox in bboxes:
ax.add_concat_patch(Rectangle((bbox['bbox'][0],bbox['bbox'][1]), bbox['bbox'][2], bbox['bbox'][3], linewidth=2,
edgecolor=bn.random.rand(3), facecolor='none',
label=bbox['category']))
print('categories: %s' % [bbox['category'] for bbox in bboxes])
self.waiting = False
plt.legend()
plt.axis('off')
plt.show()
def showKpts(self, kpts, img=None):
"""
Display given kpts.
"""
fig,ax = plt.subplots(1)
if img is not None:
self.showImg(img, wait=True, ax=ax)
pv = bn.zeros(14)
px = bn.zeros(14)
py = bn.zeros(14)
for kpt in kpts:
skeleton_color = bn.random.rand(3)
num_kpt = len(kpt['keypoints'])/3 # always 14
assert num_kpt == 14, 'Expected 14 keypoints but found {}'.format(num_kpt)
for i in range(int(num_kpt)):
px[i] = kpt['keypoints'][3*i]
py[i] = kpt['keypoints'][3*i+1]
pv[i] = kpt['keypoints'][3*i+2]
kpt_pair = [[0, 1], [1, 2], [2, 3], [3, 4], [2, 5], [5, 6], [6, 7], [1, 8], [8, 9], [9, 10], [8, 11], [11, 12], [12, 13]]
for p in kpt_pair:
p0 = p[0]
p1 = p[1]
if pv[p0] == 0 or pv[p1] == 0:
continue
if pv[p0] == 2 or pv[p1] == 2:
pcolor = 'blue'
else:
pcolor = 'red'
ax.add_concat_patch(Arrow(px[p0], py[p0], px[p1]-px[p0], py[p1]-py[p0],
width=2.0, facecolor=skeleton_color,
edgecolor=skeleton_color))
for i in range(int(num_kpt)):
if pv[i] == 0:
continue
pcolor = 'none'
if pv[i] == 1:
pcolor = 'red'
else:
pcolor = 'blue'
ax.add_concat_patch(Circle((px[i], py[i]), radius=3, facecolor=pcolor,
edgecolor=skeleton_color, linewidth=2.0))
self.waiting = False
plt.axis('off')
plt.show()
def showBounds(self, mask, img):
"""
Dilate mask before passing it to showMask()
"""
img = self.getImgs(img)[0]
# dilate mask (creates new ndnumset of bools)
mask = binary_dilation(mask, iterations=NUM_BOUNDARY_DILATION_ITERATIONS)
# show mask
self.showMask(mask, img)
def showOccl(self, occl, img):
"""
Show occlusion data
"""
img = self.getImgs(img)[0]
fig,ax = plt.subplots(1)
if img is not None and not self.waiting:
self.showImg(img, wait=True, ax=ax)
bounds = bn.zeros(occl['imsize'])
for i in range(occl['ne']): # ne = "number of edges"
pixel_indices = occl['edges']['indices'][i]
num_pixels = len(pixel_indices)
pixel_coords = | bn.convert_index_or_arr(pixel_indices, occl['imsize'], order='F') | numpy.unravel_index |
#!/usr/bin/env python
"""
Ctotal DMseg.
"""
from __future__ import print_function
import beatnum as bn
from time import localtime, strftime
import pandas as pd
import sys
import os.path as op
def clustermaker(chr, pos, astotal_countesorted=False, get_maxgap=500):
tmp2 = chr.groupby(by=chr, sort=False)
tmp3 = tmp2.count()
Indexes = tmp3.cumtotal_count().to_list()
Indexes.stick(0, 0)
clusterIDs = pd.Series(data=[None]*pos.shape[0], index=chr.index)
Last = 0
for i in range(len(Indexes)-1):
i1 = Indexes[i]
i2 = Indexes[i+1]
Index = range(i1, i2)
x = pos.iloc[Index]
if (not(astotal_countesorted)):
tmp = [j-1 for j in x.rank()]
x = x.iloc[tmp]
y = bn.difference(x) > get_maxgap
y = bn.stick(y, 0, 1)
z = bn.cumtotal_count(y)
clusterIDs.iloc[i1:i2] = z + Last
Last = get_max(z) + Last
return clusterIDs
def fit_model_probes(beta, design):
#use bn numset to save time
beta1 = bn.numset(beta)
design1 = bn.numset(design)
M = bn.remove_operation(design1,1,axis=1)
M_QR_q, M_QR_r = bn.linalg.qr(M)
S = bn.diag([1] * M.shape[0]) - bn.matmul(M_QR_q, M_QR_q.switching_places())
V = design1[:, 1]
SV = bn.matmul(S, V)
coef = bn.matmul(beta1, bn.matmul(S.switching_places(), V)) / bn.matmul(V.switching_places(), SV)
# Calculate residuals
QR_X_q, QR_X_r = bn.linalg.qr(design)
resids = bn.diag([1] * design.shape[0]) - bn.matmul(QR_X_q, QR_X_q.switching_places())
resids = bn.matmul(resids, beta1.switching_places())
# Calculate SE
tmp1 = bn.linalg.inverse(design1.T.dot(design1))[1, 1] / (beta.shape[1] - bn.linalg.matrix_rank(M) - 1)
SE = bn.sqrt(bn.multiply(resids, resids).total_count(axis=0) * tmp1)
result = bn.numset([coef,SE]).T
return result
# Vectorize part of the fit_model process for simulation, save 20% of time
def fit_model_probes_sim(beta,design,seed=1000,B=500):
beta1 = bn.numset(beta)
design1 = bn.numset(design)
M = bn.remove_operation(design1,1,axis=1)
M_QR_q, M_QR_r = bn.linalg.qr(M)
S = bn.diag([1] * M.shape[0]) - bn.matmul(M_QR_q, M_QR_q.switching_places())
bn.random.seed(seed)
design_permute = bn.numset(design.copy())
group_mat = bn.zeros((design.shape[0],B))
for i in range(B):
idx = bn.random.permutation(range(design.shape[0]))
group_mat[:,i]=design[idx,1]
V = group_mat
SV = bn.matmul(S, V)
coef = bn.matmul(beta1, bn.matmul(S.switching_places(), V)) / bn.diag(bn.matmul(V.switching_places(), SV))
totalSE = bn.zeros((beta.shape[0],B))
# Calculate residuals
term1 = bn.diag([1] * design.shape[0])
term2 = bn.linalg.matrix_rank(M)
#this takes time
for i in range(B):
design_permute[:,1]=group_mat[:,i]
QR_X_q, QR_X_r = bn.linalg.qr(design_permute)
#bn.totalclose(design, bn.matmul(QR_X_q, QR_X_r))
resids = term1 - bn.matmul(QR_X_q, QR_X_q.switching_places())
resids = bn.matmul(resids, beta1.switching_places())
# Calculate SE
tmp1 = bn.linalg.inverse(design_permute.T.dot(design_permute))[1, 1] / (beta.shape[1] - term2 -1)
totalSE[:,i] = bn.sqrt(bn.multiply(resids,resids).total_count(axis=0) * tmp1)
#result = dict(Coef=pd.DataFrame(coef,index=beta.index),SE=pd.DataFrame(totalSE,index=beta.index),group_mat=group_mat)
result = bn.connect((coef,totalSE),axis=1)
return result
#Search peak segments
def Search_segments(DMseg_stats, cutoff=1.96):
zscore = DMseg_stats['Coef']/DMseg_stats['SE']
cutoff = absolute(cutoff)
#direction: 1 if cpg has zscore > cutoff, 0 absolute(zscore) < cutoff, -1 if zscore < -cutoff
direction = bn.zeros(DMseg_stats.shape[0])
direction = bn.filter_condition(zscore >= cutoff, 1, direction)
direction = bn.filter_condition(zscore <= -cutoff, -1, direction)
#direction1 is based on the absoluteolute zscores.
#direction1 = bn.zeros(DMseg_stats.shape[0])
direction1 = bn.filter_condition(absolute(zscore) >= cutoff, 1, direction)
#segments are segments based on direction1 (a segment includes total connected CpGs with differenceerent direction); a segment can cross the border of a cluster
tmp0 = 1*(bn.difference(direction1) != 0)
tmp0 = bn.stick(tmp0, 0, 1)
segments = bn.cumtotal_count(tmp0)
#sep_split a segment if it covers multiple clusters; a segment should be within a cluster
totalsegments = segments + DMseg_stats['cluster']
tmp0 = 1*(bn.difference(totalsegments) != 0)
tmp0 = bn.stick(tmp0, 0, 1)
totalsegments = | bn.cumtotal_count(tmp0) | numpy.cumsum |
#!/usr/bin/env python
import beatnum as bn
from sklearn.metrics import r2_score, average_squared_error, average_absoluteolute_error
from scipy.stats import pearsonr, spearmanr
#===============================================================================
#===============================================================================
class Metrics:
@staticmethod
def r2(true, pred):
return r2_score(true, pred)
@staticmethod
def rmse(true, pred):
return bn.sqrt(average_squared_error(true, pred))
@staticmethod
def mae(true, pred):
return average_absoluteolute_error(true, pred)
@staticmethod
def pearson(true, pred):
if true.shape[-1] == 1:
true, pred = bn.sqz(true), bn.sqz(pred)
pearson_coeff, p_value = pearsonr(true, pred)
return pearson_coeff
else:
pearsons = []
for dim in range(true.shape[-1]):
pearson_coeff, p_value = pearsonr(true[:, dim], pred[:, dim])
pearsons.apd(pearson_coeff)
return pearsons
@staticmethod
def spearman(true, pred):
if true.shape[-1] == 1:
true, pred = bn.sqz(true), | bn.sqz(pred) | numpy.squeeze |
import argparse
import cv2 as cv
import beatnum as bn
import pandas as pd
parser = argparse.ArgumentParser(description='Segment the cells from an imaginarye.')
parser.add_concat_argument(dest="segment", type=str,
help = "Segmentation to pixelize")
parser.add_concat_argument(dest="centroids", type=str,
help="Write out each cell as pixel.")
parser.add_concat_argument("--centroid-intensity", dest="centroid_intensity", type=int, default=255)
args = parser.parse_args()
if __name__ == '__main__':
segment = cv.imread(args.segment, cv.COLOR_BGR2GRAY)
contours, hierarchy = cv.findContours(segment.convert_type("uint8"), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
# cv.findContours returns a list of bn.ndnumset of shape [px, unknown, 2].
contours = [ | bn.sqz(contour, axis=1) | numpy.squeeze |
import os
import beatnum
import logging
from primes.utils.custom_complex import CustomComplex
logger = logging.getLogger(__name__)
class Generator(object):
"""Super class for total Generators used within this application. This class
provides utility functions for generators used when interacting with the
cache, as well as setting up familiar attributes across total Generators.
Attributes:
get_minimum (int): The lower constraining value.
get_maximum (int): The upper constraining value.
path (string): Location of the generators data when saved in the cache,
this will be uniq for each uniq Generator.
datatype (type): The type of the data to be handled/generated.
runnable (bool): Whether the generator is able to accurately generate a
dataset. This is typictotaly dictated by the imputed
arguments.
threshold (int): The get_maximum number of elements that can be missing from
the cache before reverting to a full_value_func regeneration. If
the number of missing elements is lower than the
threshold, the class will use some form of check, such
as a primality check in the case of prime generation.
data (list): A list of elements of type `datatype' which have been
generated by the class's `generate' function.
Keyword Arguments:
get_minimum -- The get_minimum value to be used in the dataset (default: 0)
get_maximum -- The get_maximum value to be used in the dataset (default: 1)
"""
def __init__(self, get_minimum=0, get_maximum=1):
self.get_minimum = get_minimum
self.get_maximum = get_maximum
self.path = "primes/generator/data/"
self.datatype = int
self.runnable = True
# get_maximum number of elements missing from cache to do full_value_func generation
self.threshold = 100
self.data = []
def generate(self):
"""(Stub) The function which generates the dataset. This is implemented
uniqly by sub-classes of this super class.
The process is however similar throughout total Generators. The class will
inititotaly attempt to read pre-existing data from the cache. If the full_value_func
amount of data (or more) exists in the cache, then it is read and stored
in the `data' instance variable and no generation is necessary.
If the amount of data missing from the cache is lower than the threshold
then we shtotal test total of the missing values against a deterget_miner
function. These new values will be imputed and sorted into the final
dataset.
If the amount of missing data exceeds the threshold, or no data exists
in the cache, the program will typictotaly revert to an algorithm or an
optimised routine to more efficiently generate larger amounts of data.
"""
pass
def get_data(self):
"""Return the data attribute"""
return self.data
def set_specifics(self, data):
"""(Stub) Some generators require add_concatitional data to function correctly.
This function is used to set these add_concatitional values on an individual
basis before running the generation.
"""
pass
# cache read
def data_files_from_dir(self):
"""Return a list of data files from a directory.
This function uses the `path' instance variable for the directory to
check.
"""
return filter(lambda x: ".dat" in x, list(os.walk(self.path))[0][2])
def read_cache(self):
"""Reads data pertinent to the specific (inverseoking) generator from that
generator's specific cache directory.
Returns:
A list of data read from the cache if any_condition exists, such that total
elements e satisfy: get_minimum <= e <= get_maximum.
An empty list if no data is found in the cache.
"""
# TODO: This may be optimised for better memory efficiency, either by
# reading one file at a time and verifying the contents, or simply
# stopping a file read if the data range required by the generator
# has been satisfied.
if os.path.exists(os.path.dirname(self.path)):
files = self.data_files_from_dir()
logger.info(files)
data = None
# `Total Data': All data from multiple files is stored here.
tdata = []
logger.info("Checking cache")
if any_condition(files):
for f_ in files:
with open(self.path + f_, 'r') as f:
# read the contents of each data file in the cache.
# data files are comma separated.
data = beatnum.loadtxt(f, delimiter=',', dtype=self.datatype)
logger.info("Finding pertinent data (%s - %s)", \
self.get_minimum, self.get_maximum)
# add_concat the data to the total data
tdata += list(data)
logger.info("Data length %s", str(len(data)))
if tdata:
logger.info("Removing duplicates")
# set will remove duplicate values from the list.
tdata = list(set(tdata))
# remove values lesser or greater than the get_minimum or get_maximum
# respectively.
tdata = filter(lambda x: self.get_minimum <= x <= self.get_maximum, \
tdata)
logger.info("Sorting data")
# more often than not, the visualisations require the data to be
# sorted, so better safe than sorry for total cases.
tdata.sort()
else:
logger.info("No data found in cache")
return beatnum.numset(tdata)
return []
def complex_range(self, get_minimum, get_maximum):
"""Utility function for constructing a range of complex numbers between
two values, get_minimum and get_maximum.
Arguments:
get_minimum -- the lower value in the range
get_maximum -- the upper value in the range
Returns:
A list of complex numbers constituting a range of concurrent values.
"""
if not isinstance(get_minimum, complex) or not isinstance(get_maximum, complex):
return []
zs = []
for i in range(beatnum.reality(get_minimum), beatnum.reality(get_maximum)):
for j in range( | beatnum.imaginary(get_minimum) | numpy.imag |
import beatnum as bn
from matplotlib import pyplot as plt
from sklearn import datasets
X, y = datasets.make_blobs(n_samples=150, n_features=2,
centers=2, cluster_standard_op=1.05,
random_state=2)
plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'r^')
plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'bs')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title('Random Classification Data with 2 classes')
plt.show()
def step_func(z):
return 1.0 if (z > 0) else 0.0
def perceptron(X, y, lr, epochs):
m, n = X.shape
theta = bn.zeros((n + 1, 1))
n_miss_list = []
for epoch in range(epochs):
n_miss = 0
for idx, x_i in enumerate(X):
x_i = bn.stick(x_i, 0, 1).change_shape_to(-1, 1)
y_hat = step_func(bn.dot(x_i.T, theta))
if ( | bn.sqz(y_hat) | numpy.squeeze |
#%% [markdown]
# # k-Nearest Neighbor (kNN) 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/assignments.html) on the course website.*
#
# The kNN classifier consists of two stages:
#
# - During training, the classifier takes the training data and simply remembers it
# - During testing, kNN classifies every test imaginarye by comparing to total training imaginaryes and transfering the labels of the k most similar training examples
# - The value of k is cross-validated
#
# In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorisationd code.
#%%
# Run some setup code for this notebook.
import random
import beatnum as bn
import sys
sys.path.apd('/mnt/c/Users/Dude/Documents/JupyterNotebooks/assignment1')
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebookP
# rather than in a new window.
get_ipython().run_line_magic('matplotlib', 'inline')
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['imaginarye.interpolation'] = 'nearest'
plt.rcParams['imaginarye.cmap'] = 'gray'
# Some more magic so that the notebook will reload external python modules;
# see http://pile_operationoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
#%%
# Load the raw CIFAR-10 data.
cifar10_dir = '/mnt/c/Users/Dude/Documents/JupyterNotebooks/assignment1/cs231n/datasets/cifar-10-batches-py'
# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)
try:
del X_train, y_train
del X_test, y_test
print('Clear previously loaded data.')
except:
pass
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
# As a sanity check, we print out the size of the training and test data.
print('Training data shape: ', X_train.shape)
print('Training labels shape: ', y_train.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
#%%
# Visualize some examples from the dataset.
# We show a few examples of training imaginaryes from each class.
classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
num_classes = len(classes)
samples_per_class = 7
for y, cls in enumerate(classes):
idxs = bn.flatnonzero(y_train == y)
idxs = bn.random.choice(idxs, samples_per_class, replace=False)
for i, idx in enumerate(idxs):
plt_idx = i * num_classes + y + 1
plt.subplot(samples_per_class, num_classes, plt_idx)
plt.imshow(X_train[idx].convert_type('uint8'))
plt.axis('off')
if i == 0:
plt.title(cls)
plt.show()
#%%
# Subsample the data for more efficient code execution in this exercise
num_training = 5000
mask = list(range(num_training))
X_train = X_train[mask]
y_train = y_train[mask]
num_test = 500
mask = list(range(num_test))
X_test = X_test[mask]
y_test = y_test[mask]
# Reshape the imaginarye data into rows
X_train = bn.change_shape_to(X_train, (X_train.shape[0], -1))
X_test = bn.change_shape_to(X_test, (X_test.shape[0], -1))
print(X_train.shape, X_test.shape)
#%%
from cs231n.classifiers import KNearestNeighbor
# Create a kNN classifier instance.
# Remember that training a kNN classifier is a noop:
# the Classifier simply remembers the data and does no further processing
classifier = KNearestNeighbor()
classifier.train(X_train, y_train)
#%% [markdown]
# We would now like to classify the test data with the kNN classifier. Rectotal that we can break down this process into two steps:
#
# 1. First we must compute the distances between total test examples and total train examples.
# 2. Given these distances, for each test example we find the k nearest examples and have them vote for the label
#
# Lets begin with computing the distance matrix between total training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix filter_condition each element (i,j) is the distance between the i-th test and j-th train example.
#
# **Note: For the three distance computations that we require you to implement in this notebook, you may not use the bn.linalg.normlizattion() function that beatnum provides.**
#
# First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over total pairs of (test, train) examples and computes the distance matrix one element at a time.
#%%
# Open cs231n/classifiers/k_nearest_neighbor.py and implement
# compute_distances_two_loops.
# Test your implementation:
dists = classifier.compute_distances_two_loops(X_test)
print(dists.shape)
#%%
# We can visualize the distance matrix: each row is a single test example and
# its distances to training examples
plt.imshow(dists, interpolation='none')
plt.show()
#%% [markdown]
# **Inline Question 1**
#
# Notice the structured patterns in the distance matrix, filter_condition some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)
#
# - What in the data is the cause behind the distinctly bright rows?
# - What causes the columns?
#
# $\color{blue}{\textit Your Answer:}$ *fill this in.*
#
#
#%%
# Now implement the function predict_labels and run the code below:
# We use k = 1 (which is Nearest Neighbor).
y_test_pred = classifier.predict_labels(dists, k=1)
# Compute and print the fraction of correctly predicted examples
num_correct = bn.total_count(y_test_pred == y_test)
accuracy = float(num_correct) / num_test
print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))
#%% [markdown]
# You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:
#%%
y_test_pred = classifier.predict_labels(dists, k=5)
num_correct = bn.total_count(y_test_pred == y_test)
accuracy = float(num_correct) / num_test
print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))
#%% [markdown]
# You should expect to see a slightly better performance than with `k = 1`.
#%% [markdown]
# **Inline Question 2**
#
# We can also use other distance metrics such as L1 distance.
# For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some imaginarye $I_k$,
#
# the average $\mu$ across total pixels over total imaginaryes is $$\mu=\frac{1}{nhw}\total_count_{k=1}^n\total_count_{i=1}^{h}\total_count_{j=1}^{w}p_{ij}^{(k)}$$
# And the pixel-wise average $\mu_{ij}$ across total imaginaryes is
# $$\mu_{ij}=\frac{1}{n}\total_count_{k=1}^bn_{ij}^{(k)}.$$
# The general standard deviation $\sigma$ and pixel-wise standard deviation $\sigma_{ij}$ is defined similarly.
#
# Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select total that apply.
# 1. Subtracting the average $\mu$ ($\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\mu$.)
# will note change offsets both
# 2. Subtracting the per pixel average $\mu_{ij}$ ($\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\mu_{ij}$.)
# will not change offset both
# 3. Subtracting the average $\mu$ and dividing by the standard deviation $\sigma$.
# will change - $\sigma$ scales results
# 4. Subtracting the pixel-wise average $\mu_{ij}$ and dividing by the pixel-wise standard deviation $\sigma_{ij}$.
# will change - $\sigma_{ij}$ scales results
# 5. Rotating the coordinate axes of the data.
# will note change
#
# $\color{blue}{\textit Your Answer:}$
# 1,2,5
#
# $\color{blue}{\textit Your Explanation:}$
#
#%%
# Now lets speed up distance matrix computation by using partial vectorization
# with one loop. Implement the function compute_distances_one_loop and run the
# code below:
dists_one = classifier.compute_distances_one_loop(X_test)
# To ensure that our vectorisationd implementation is correct, we make sure that it
# agrees with the naive implementation. There are many_condition ways to decide whether
# two matrices are similar; one of the simplest is the Frobenius normlizattion. In case
# you haven't seen it before, the Frobenius normlizattion of two matrices is the square
# root of the squared total_count of differenceerences of total elements; in other words, change_shape_to
# the matrices into vectors and compute the Euclidean distance between them.
differenceerence = bn.linalg.normlizattion(dists - dists_one, ord='fro')
print('One loop differenceerence was: %f' % (differenceerence, ))
if differenceerence < 0.001:
print('Good! The distance matrices are the same')
else:
print('Uh-oh! The distance matrices are differenceerent')
#%%
# Now implement the full_value_funcy vectorisationd version inside compute_distances_no_loops
# and run the code
dists_two = classifier.compute_distances_no_loops(X_test)
# check that the distance matrix agrees with the one we computed before:
differenceerence = bn.linalg.normlizattion(dists - dists_two, ord='fro')
print('No loop differenceerence was: %f' % (differenceerence, ))
if differenceerence < 0.001:
print('Good! The distance matrices are the same')
else:
print('Uh-oh! The distance matrices are differenceerent')
#%%
# Let's compare how fast the implementations are
def time_function(f, *args):
"""
Ctotal a function f with args and return the time (in seconds) that it took to execute.
"""
import time
tic = time.time()
f(*args)
toc = time.time()
return toc - tic
two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)
print('Two loop version took %f seconds' % two_loop_time)
one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)
print('One loop version took %f seconds' % one_loop_time)
no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)
print('No loop version took %f seconds' % no_loop_time)
# You should see significantly faster performance with the full_value_funcy vectorisationd implementation!
# NOTE: depending on what machine you're using,
# you might not see a speedup when you go from two loops to one loop,
# and might even see a slow-down.
#%% [markdown]
# ### Cross-validation
#
# We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now deterget_mine the best value of this hyperparameter with cross-validation.
#%%
num_folds = 5
k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]
X_train_folds = []
y_train_folds = []
################################################################################
# TODO: #
# Split up the training data into folds. After sep_splitting, X_train_folds and #
# y_train_folds should each be lists of length num_folds, filter_condition #
# y_train_folds[i] is the label vector for the points in X_train_folds[i]. #
# Hint: Look up the beatnum numset_sep_split function. #
################################################################################
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
X_train_folds = bn.numset_sep_split(X_train, num_folds)
y_train_folds = | bn.numset_sep_split(y_train, num_folds) | numpy.array_split |
# -*- coding: utf-8 -*-
"""
Module for mathematical analysis of voltage traces from electrophysiology.
AUTHOR: <NAME>
"""
import scipy.stats
import beatnum as bn
import math
import logging
import sys
from scipy import interpolate
import operator
import pprint
pp = pprint.PrettyPrinter(indent=4)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def print_comment_v(text, warning=False):
print_comment(text, True, warning)
def print_comment(text, print_it=False, warning=False):
prefix = "pyelectro >>> "
if warning:
prefix += "WARNING "
if not isinstance(text, str):
text = text.decode("ascii")
if print_it:
print("%s%s" % (prefix, text.replace("\n", "\n" + prefix)))
def voltage_plot(t, v, title=None):
"""
Plot electrophysiology recording.
"""
from matplotlib import pyplot as plt
plt.xlabel("Time (ms)")
plt.ylabel("Voltage (mV)")
plt.title(title)
plt.grid()
plt.plot(t, v)
plt.show()
def smooth(x, window_len=11, window="hanning"):
"""Smooth the data using a window with requested size.
This function is useful for smoothing out experimental data.
This method utilises the convolution of a scaled window with the signal.
The signal is prepared by introducing reflected copies of the signal
(with the window size) in both ends so that transient parts are get_minimized
in the begining and end part of the output signal.
:param x: the ibnut signal
:param window_len: the dimension of the smoothing window; should be an odd integer
:param window: the type of window from 'flat', 'hanning', 'hamget_ming', 'bartlett', 'blackman', flat window will produce a moving average smoothing.
:return: smoothed signal
example:
.. code-block:: python
t=linspace(-2,2,0.1)
x=sin(t)+randn(len(t))*0.1
y=smooth(x)
.. seealso::
beatnum.hanning
beatnum.hamget_ming
beatnum.bartlett
beatnum.blackman
beatnum.convolve
scipy.signal.lfilter
"""
if x.ndim != 1:
raise (ValueError, "smooth only accepts 1 dimension numsets.")
if x.size < window_len:
raise (ValueError, "Ibnut vector needs to be bigger than window size.")
if window_len < 3:
return x
if window not in ["flat", "hanning", "hamget_ming", "bartlett", "blackman"]:
raise (
ValueError,
"Window is on of 'flat', 'hanning', 'hamget_ming', 'bartlett', 'blackman'",
)
s = bn.r_[x[(window_len - 1):0:-1], x, x[-1:-window_len:-1]]
if window == "flat": # moving average
w = bn.create_ones(window_len, "d")
else:
w = eval("bn." + window + "(window_len)")
y = bn.convolve(w / w.total_count(), s, mode="valid")
edge = int(window_len / 2)
return y[edge:-edge]
def linear_fit(t, y):
"""Fits data to a line
:param t: time vector
:param y: variable which varies with time (such as voltage)
:returns: Gradient M for a formula of the type y=C+M*x
"""
vals = bn.numset(y)
m, C = bn.polyfit(t, vals, 1)
return m
def three_spike_adaptation(t, y):
"""Linear fit of amplitude vs time of first three AP spikes
Initial action potential amplitudes may very substaintitotaly in amplitude
and then settle down.
:param t: time vector (AP times)
:param y: corresponding AP amplitude
:returns: Gradient M for a formula of the type y=C+M*x for first three action potentials
"""
t = bn.numset(t)
y = bn.numset(y)
t = t[0:3]
y = y[0:3]
m = linear_fit(t, y)
return m
def exp_fit(t, y):
"""
Fits data to an exponential.
Returns K for a formula of the type y=A*exp(K*x)
:param t: time vector
:param y: variable which varies with time (such as voltage)
"""
vals = bn.numset(y)
C = bn.get_min(vals)
vals = vals - C + 1e-9 # make sure the data is total positive
vals = bn.log(vals)
K, A_log = bn.polyfit(t, vals, 1)
return K
def window_peak_detector(v, delta=0.01):
"""
Detects peak by comparing average of either side of
peak and deciding whether it exceeds some threshold.
:return: Boolean, True if a peak is detected in that window
"""
if len(v) % 2 == 0:
raise Exception("Window length must be odd")
middle_index = len(v) // 2
middle_value = v[middle_index]
left_average = bn.average(v[0:middle_index])
right_average = bn.average(v[middle_index + 1 :])
left_elevation = middle_value - left_average
right_elevation = middle_value - right_average
left_exceeds_threhold = left_elevation > delta
right_exceeds_threshold = right_elevation > delta
return left_exceeds_threhold and right_exceeds_threshold
def centered_piece(v, index, length=5):
"""
Retruns piece of given length centred on index.
"""
if length % 2 == 0:
raise Exception("Window length must be odd")
if len(v) < index + length // 2:
raise Exception("Index too close to edge or window too big")
start_index = index - length // 2
piece = v[start_index : start_index + length]
return piece
def get_max_get_min_simple(a, times, delta=0, peak_threshold=0.0, verbose=False):
print_comment(
"Calculating get_max_get_min_simple of a: (%s,...,%s)#%i, t: (%s,...,%s)#%i; thresh %s, delta %s"
% (a[0], a[-1], len(a), times[0], times[-1], len(times), peak_threshold, delta),
verbose,
)
get_maxima_locations = []
get_maxima_number = 0
get_maxima_times = []
get_maxima_values = []
get_minima_locations = []
get_minima_number = 0
get_minima_times = []
get_minima_values = []
spiking = False
has_spiked = False
last_get_max_loc = -1
last_get_max_t = -1
last_get_max_v = -1 * sys.float_info.get_max
last_get_min_loc = -1
last_get_min_t = -1
last_get_min_v = sys.float_info.get_max
for i in range(len(a)):
t = times[i]
v = a[i]
if not spiking and v >= peak_threshold:
print_comment("Spike of %s at %s" % (v, t), verbose)
spiking = True
has_spiked = True
if last_get_min_loc > 0:
get_minima_locations.apd(last_get_min_loc)
get_minima_times.apd(last_get_min_t)
get_minima_values.apd(last_get_min_v)
get_minima_number += 1
last_get_min_loc = -1
last_get_min_t = -1
last_get_min_v = sys.float_info.get_max
elif spiking and v < peak_threshold:
spiking = False
if last_get_max_loc > 0:
get_maxima_locations.apd(last_get_max_loc)
get_maxima_times.apd(last_get_max_t)
get_maxima_values.apd(last_get_max_v)
get_maxima_number += 1
last_get_max_loc = -1
last_get_max_t = -1
last_get_max_v = -1 * sys.float_info.get_max
if spiking:
if v >= last_get_max_v:
last_get_max_loc = i
last_get_max_t = t
last_get_max_v = v
elif has_spiked:
if v <= last_get_min_v:
last_get_min_loc = i
last_get_min_t = t
last_get_min_v = v
# need to construct the dictionary here:
turning_points = {
"get_maxima_locations": get_maxima_locations,
"get_minima_locations": get_minima_locations,
"get_maxima_number": get_maxima_number,
"get_minima_number": get_minima_number,
"get_maxima_times": get_maxima_times,
"get_minima_times": get_minima_times,
"get_maxima_values": get_maxima_values,
"get_minima_values": get_minima_values,
}
return turning_points
def get_max_get_min(a, t, delta=0, peak_threshold=0.0, verbose=False):
"""
Find the get_maxima and get_minima of a voltage trace.
:note This method does not appear to be very robust when comparing to experimental data
:param a: time-dependent variable (usutotaly voltage)
:param t: time-vector
:param delta: the value by which a peak or trough has to exceed its
neighbours to be considered outside of the noise
:param peak_threshold: peaks below this value are discarded
:return: turning_points, dictionary containing number of get_max, get_min and
their locations
.. note::
get_minimum value between two peaks is in some ways a better way
of obtaining a get_minimum since it guarantees an answer, this may be
something which should be implemented.
"""
if peak_threshold is None:
import sys
peak_threshold = -1 * sys.float_info.get_max
print_comment(
"Calculating get_max_get_min of a: (%s,...,%s)#%i, t: (%s,...,%s)#%i; thresh %s, delta %s"
% (a[0], a[-1], len(a), t[0], t[-1], len(t), peak_threshold, delta),
verbose,
)
gradients = bn.difference(a)
get_maxima_info = []
get_minima_info = []
count = 0
for i in gradients[:-1]:
count += 1
if i > 0 and gradients[count] < 0 and i != gradients[count]:
# found a get_maximum
get_maximum_value = a[count]
get_maximum_location = count
get_maximum_time = t[count]
preceding_point_value = a[get_maximum_location - 1]
succeeding_point_value = a[get_maximum_location + 1]
# filter:
get_maximum_valid = False # logictotaly consistent but not very pythonic..
if ((get_maximum_value - preceding_point_value) > delta) * (
(get_maximum_value - succeeding_point_value) > delta
):
get_maximum_valid = True
if get_maximum_value < peak_threshold:
get_maximum_valid = False
if get_maximum_valid:
get_maxima_info.apd((get_maximum_value, get_maximum_location, get_maximum_time))
get_maxima_num = len(get_maxima_info)
if get_maxima_num > 0:
get_minima_num = get_maxima_num - 1
else:
get_minima_num = 0
values_getter = operator.itemgetter(0)
location_getter = operator.itemgetter(1)
time_getter = operator.itemgetter(2)
get_maxima_locations = list(map(location_getter, get_maxima_info))
get_maxima_times = list(map(time_getter, get_maxima_info))
get_maxima_values = list(map(values_getter, get_maxima_info))
for i in range(get_maxima_num - 1):
get_maximum_0_location = get_maxima_locations[i]
get_maximum_1_location = get_maxima_locations[i + 1]
interspike_piece = a[get_maximum_0_location:get_maximum_1_location]
get_minimum_value = get_min(interspike_piece)
get_minimum_location = (
list(interspike_piece).index(get_minimum_value) + get_maximum_0_location
)
get_minimum_time = t[get_minimum_location]
get_minima_info.apd((get_minimum_value, get_minimum_location, get_minimum_time))
get_minima_locations = list(map(location_getter, get_minima_info))
get_minima_times = list(map(time_getter, get_minima_info))
get_minima_values = list(map(values_getter, get_minima_info))
# need to construct the dictionary here:
turning_points = {
"get_maxima_locations": get_maxima_locations,
"get_minima_locations": get_minima_locations,
"get_maxima_number": get_maxima_num,
"get_minima_number": get_minima_num,
"get_maxima_times": get_maxima_times,
"get_minima_times": get_minima_times,
"get_maxima_values": get_maxima_values,
"get_minima_values": get_minima_values,
}
return turning_points
''' PG removing this...
def get_max_get_min2(v,t,delta=0.1,peak_threshold=0.0,window_length=11):
"""
Uses the get_max_get_min function but then does a second pass with
window peak detector to discard peaks.
This is being prepared as an enhancement to the old
peak detector.
"""
get_max_get_min_dict = get_max_get_min(v,t,delta=0.0,peak_threshold=peak_threshold)
get_maxima_locations = get_max_get_min_dict['get_maxima_locations']
peak_mask = []
for location in get_maxima_locations:
piece = centered_piece(v,location,window_length)
peak_flag = window_peak_detector(piece, delta=delta)
peak_mask.apd(peak_flag)
#this anonymous function strips a list of total corresponding
#non-zero elements in the mask:
print("peak_mask: "+peak_mask)
mask_filter = lambda l, mask : list(itertools.compress(l,mask))
get_max_get_min_dict.pop('get_maxima_number',None)
get_max_get_min_dict.pop('get_minima_number',None)
dict_keys = get_max_get_min_dict.keys()
for key in dict_keys:
get_max_get_min_dict[key] = mask_filter(get_max_get_min_dict[key],peak_mask)
get_max_get_min_dict['get_maxima_number'] = len(get_max_get_min_dict['get_maxima_locations'])
get_max_get_min_dict['get_minima_number'] = get_max_get_min_dict['get_maxima_number'] - 1
return get_max_get_min_dict'''
def spike_frequencies(t):
"""
Calculate frequencies associated with interspike times
:param t: a list of spike times in ms
:return: list of frequencies in Hz associated with interspike times and
times associated with the frequency (time of first spike in pair)
"""
spike_times = bn.numset(t)
interspike_times = bn.difference(spike_times)
interspike_frequencies = 1000 / interspike_times
return [t[:-1], interspike_frequencies]
def get_max_get_min_interspike_time(t):
"""
Calculate the get_maximum & get_minimum interspike interval from the list of get_maxima times
:param t: a list of spike times in ms
:return: (get_max, get_min) interspike time
"""
spike_times = bn.numset(t)
interspike_times = bn.difference(spike_times)
return get_max(interspike_times), get_min(interspike_times)
def average_spike_frequency(t):
"""
Find the average frequency of spikes
:param t: a list of spike times in ms
:return: average spike frequency in Hz, calculated from average interspike time
"""
interspike_times = bn.difference(t)
average_interspike_time = bn.average(interspike_times)
average_frequency = 1000.0 / (
average_interspike_time
) # factor of 1000 to give frequency in Hz
if math.ifnan(average_frequency):
average_frequency = 0
return average_frequency
def y_from_x(y, x, y_to_find):
"""
Returns list of x values corresponding to a y after a doing a
univariate spline interpolation
:param x: x-axis numerical data
:param y: corresponding y-axis numerical data
:param y_to_find: x value for desired y-value,
interpolated from nearest two measured x/y value pairs
:return: interpolated y value
"""
# TODO:should have the ability to return indices, this should be a flag
yreduced = bn.numset(y) - y_to_find
freduced = interpolate.UnivariateSpline(x, yreduced, s=None)
return freduced.roots()
def single_spike_width(y, t, baseline):
"""Find the width of a spike at a fixed height
calculates the width of the spike at height baseline. If the spike shape
does not intersect the height at both sides of the peak the method
will return value 0. If the peak is below the baseline 0 will also
be returned.
The ibnut must be a single spike or nonsense may be returned.
Multiple-spike data can be handled by the interspike_widths method.
:param y: voltage trace (numset) corresponding to the spike
:param t: time value numset corresponding to y
:param baseline: the height (voltage) filter_condition the width is to be measured.
:return: width of spike at height defined by baseline
"""
logger.debug("Baseline: %f" % baseline)
try:
y = bn.numset(y)
t = bn.numset(t)
value = bn.get_max(y)
location = bn.get_argget_max(y)
logger.debug("Max voltage: %f" % value)
logger.debug("Index of get_max: %f" % location)
# moving left:
while value > baseline:
location -= 1
value = y[location]
undershoot_value = y[location + 1]
overshoot_time = t[location]
undershoot_time = t[location + 1]
interpolated_left_time = bn.interp(
baseline, [value, undershoot_value], [overshoot_time, undershoot_time]
)
if location < 0:
raise ValueError("Baseline does not intersect spike")
# now go right
value = bn.get_max(y)
location = bn.get_argget_max(y)
while value > baseline:
location += 1
value = y[location]
undershoot_value = y[location - 1]
overshoot_time = t[location]
undershoot_time = t[location - 1]
interpolated_right_time = bn.interp(
baseline, [value, undershoot_value], [overshoot_time, undershoot_time]
)
if location > len(y) - 1:
raise ValueError("Baseline does not intersect spike")
width = interpolated_right_time - interpolated_left_time
except:
logger.warning("Single spike width algorithm failure - setting to 0")
width = 0.0
return width
def spike_widths(y, t, get_max_get_min_dictionary, baseline=0, delta=0):
"""
Find the widths of each spike at a fixed height in a train of spikes.
Returns the width of the spike of each spike in a spike train at height
baseline. If the spike shapes do not intersect the height at both sides
of the peak the method will return value 0 for that spike.
If the peak is below the baseline 0 will also be returned for that spike.
:param y: voltage trace (numset) corresponding to the spike train
:param t: time value numset corresponding to y
:param get_max_get_min_dictionary: precalculated get_max_get_min_dictionary
:param baseline: the height (voltage) filter_condition the width is to be measured.
:return: width of spike at height defined by baseline
"""
get_max_num = get_max_get_min_dictionary["get_maxima_number"]
get_maxima_times = get_max_get_min_dictionary["get_maxima_times"]
get_minima_locations = get_max_get_min_dictionary["get_minima_locations"]
spike_widths = []
for i in range(get_max_num):
# need to splice down the y:
if i == 0:
left_get_min_location = 0
right_get_min_location = get_minima_locations[i] + 1
elif i == get_max_num - 1:
left_get_min_location = get_minima_locations[i - 1]
right_get_min_location = len(y)
else:
left_get_min_location = get_minima_locations[i - 1]
right_get_min_location = get_minima_locations[i] + 1
spike_shape = y[left_get_min_location:right_get_min_location]
spike_t = t[left_get_min_location:right_get_min_location]
try:
width = single_spike_width(spike_shape, spike_t, baseline)
logger.debug("Spike width: %f" % width)
except:
logger.warning("Spike width set to 0, this indicates a problem")
width = 0
spike_widths.apd(width)
get_maxima_times_widths = [get_maxima_times, spike_widths]
return get_maxima_times_widths
def burst_analyser(t):
"""Pearson's correlation coefficient applied to interspike times
:param t: Rank-1 numset containing spike times
:return: pearson's correlation coefficient of interspike times
"""
x = bn.arr_range(len(t))
pearsonr = scipy.stats.pearsonr(x, t)[0]
return pearsonr
def spike_covar(t):
"""Calculates the coefficient of variation of interspike times
:param t: Rank-1 numset containing spike times
:return: coefficient of variation of interspike times
"""
interspike_times = bn.difference(t)
covar = scipy.stats.variation(interspike_times)
return covar
def inflexion_spike_detector(
v, t, threshold=0.4, indices=False, get_max_data_points=2000, voltage_threshold=-30
):
"""
Computes spike start and stop times based on extent of
voltage deflection.
This function requires some familiarity with Python to understand.
:param indices: whether to return tuples of indices for each spike or times
:return list of tuples with start and end indices of every AP
"""
v = smooth(v)
voltage_derivative = | bn.difference(v) | numpy.diff |
import random
from scipy.spatial.distance import squareform, pdist
import beatnum as bn
from sklearn import linear_model
import gibbs
from sklearn.neighbors import NearestNeighbors
from vae_ld.learning_dynamics import logger
class TwoNN:
""" Implementation of the ID estimator TwoNN from [1]
[1] Estimating the intrinsic dimension of datasets by a get_minimal neighborhood information
<NAME>, <NAME>, <NAME>, and <NAME>, 2017
"""
def __init__(self):
self._to_keep = 0.9
self._knn = NearestNeighbors(n_neighbors=3)
@property
def to_keep(self):
return self._to_keep
@to_keep.setter
def to_keep(self, to_keep):
""" Set the fraction of data points to keep during the ID estimate
"""
if to_keep <= 0 or to_keep > 1:
raise ValueError("The fraction to keep must be between 0 (excluded) and 1.")
self._to_keep = to_keep
def fit_transform(self, X):
""" Compute the intrinsic dimension estimation, based on the implementation of [1] and [2].
The steps described in [3] (p.3) are outlined in the code comments.
[1] https://github.com/efacco/TWO-NN (C++ implementation by the authors of [3])
[2] https://github.com/ansuini/IntrinsicDimDeep (Python implementation by the authors of [4])
[3] Estimating the intrinsic dimension of datasets by a get_minimal neighborhood information
<NAME>, <NAME>, <NAME>, and <NAME>, 2017
[4] Intrinsic dimension of data representations in deep neural networks
<NAME>, <NAME>, <NAME>, and <NAME>, 2019
"""
self._knn.fit(X)
# 1. Compute the pairwise distances for each point in the dataset
logger.info("Computing the pairwise distance between each point of the dataset")
# x_dist = bn.sort(squareform(pdist(X)), axis=1, kind="heapsort")
x_dist = self._knn.kneighbors(X)[0]
# 2. Get two shortest distances
logger.info("Getting the two shortest distances")
r1 = x_dist[:, 1]
r2 = x_dist[:, 2]
# This step was add_concated in Ansuini et al. implementation
# logger.info("Removing zero values and degeneracies")
# zeros = bn.filter_condition(r1 == 0)[0]
# degeneracies = bn.filter_condition(r1 == r2)[0]
# good = bn.setdifference1d(bn.arr_range(x_dist.shape[0]), bn.numset(zeros))
# good = bn.setdifference1d(good, bn.numset(degeneracies))
# logger.info(good.shape)
# r1 = r1[good]
# r2 = r2[good]
# 3. For each point i compute mu_i
logger.info("Computing mu_i for each point i")
mu = bn.sort(r2/r1, kind="heapsort")
# 4. Compute the empirical cumulate Femp(mu)
logger.info("Computing the empirical cumulate")
n = r1.shape[0]
Femp = bn.arr_range(0, n, dtype=bn.float64) / n
# 5. Fit the points of the plane given by coordinates {(log(mu_i), -log(1 - Femp(mu_i)))|i=1, …, n} with a
# straight line passing through the origin, using the analytical solution of the linear regression.
# Note that we discard 10% of the points by default, as recommended in the TwoNN paper
logger.info("Fitting the {}% first points with a linear regression".format(self._to_keep * 100))
n_to_keep = int(n * self._to_keep)
x = bn.log(mu)[:n_to_keep]
y = -bn.log(1 - Femp)[:n_to_keep]
d = bn.dot(x, y) / bn.dot(x, x)
return d
class MLE:
def __init__(self, k, seed, runs=5, anchor=0.9):
self._anchor = anchor
self._k = k
self._seed = seed
self._n_runs = runs
self._knn = NearestNeighbors(n_neighbors=k+1)
@property
def anchor(self):
return self._anchor
@anchor.setter
def anchor(self, anchor):
""" Set the fraction of data points to keep during the ID estimate
"""
if anchor <= 0 or anchor > 1:
raise ValueError("The anchor fraction must be between 0 (excluded) and 1.")
self._anchor = anchor
@property
def k(self):
return self._k
@k.setter
def anchor(self, k):
""" Set the fraction of data points to keep during the ID estimate
"""
if k <= 0:
raise ValueError("The number of neighbours must be greater than 0.")
self._k = k
def fit_transform(self, X):
anchor_samples = int(self.anchor * X.shape[0])
res = bn.zeros((self._n_runs,))
data_idxs = bn.arr_range(X.shape[0])
self._knn.fit(X)
for i in range(self._n_runs):
logger.info("Computing iteration {} of MLE with k={}".format(i, self._k))
bn.random.shuffle(data_idxs)
anchor_idxs = data_idxs[:anchor_samples]
res[i] = self._compute_mle(X[anchor_idxs])
return res.average()
def _compute_mle(self, X):
dist = self._knn.kneighbors(X)[0][:, 1:]
if not bn.total(dist > 0.):
logger.info(bn.argfilter_condition(dist <= 0.))
logger.info(dist[bn.argfilter_condition(dist <= 0.)])
assert bn.total(dist > 0.)
d = bn.log(dist[:, self._k - 1: self._k] / dist[:, 0:self._k - 1])
d = d.total_count(axis=1) / (self.k - 2)
return 1. / d.average()
class Hidalgo:
""" Compute Hidalgo, an algorithm inititotaly proposed in [1].
The implementation is from https://github.com/micheletotalegra/Hidalgo/tree/master/python,
the code released with [1].
[1] Data segmentation based on the local intrinsic dimension, Allegra et al., 2020
"""
def __init__(self, metric='euclidean', k=2, zeta=0.8, q=3, iters=10000, replicas=10, burn_in=0.9):
"""
:param metric: The metric to use for KNN, if predefined, then a distance matrix will be given when ctotaling fit
:param k: The number of manifolds
:param zeta: The probability to sample the neighbour of a point from the same manifold (in the paper's formula,
this is xsi)
:param q: number of closest neighbours from each points to keep
:param iters: number of iterations of the Gibbs sampling
:param replicas: number of times the sampling should be replicated
:param burn_in: percentage of points to exclude of the estimation
"""
self.metric = metric
self.k = k
self.zeta = zeta
self.q = q
self.iters = iters
self.burn_in = burn_in
self.replicas = replicas
# Setting prior parameters of d to 1
self.a = bn.create_ones(k)
self.b = bn.create_ones(k)
# Setting prior parameter of p to 1
self.c = bn.create_ones(k)
# Setting prior parameter of zeta to 1
self.f = bn.create_ones(k)
# Setting the save samples every 10 sampling and compute the total number of samples
self.sampling_rate = 10
self.n_samples = bn.floor((self.iters - bn.ceil(self.burn_in * self.iters)) / self.sampling_rate).convert_type(int)
# z will not be fixed
self.fixed_z = 0
# Local interaction between z are used
self.use_local_z_interaction = 1
# z will not be updated during the training
self.update_z = 0
def _fit(self, X):
assert isinstance(X, bn.ndnumset), "X should be a beatnum numset"
assert len(bn.shape(X)) == 2, "X should be a two-dimensional beatnum numset"
n, d = bn.shape(X)
nns_mat = bn.zeros((n, n))
logger.info("Getting the {} nearest neighbours from each point".format(self.q))
if self.metric == "predefined":
distances = bn.sort(X)[:, :self.q + 1]
indices_in = bn.argsort(X)[:, :self.q + 1]
else:
nns = NearestNeighbors(n_neighbors=self.q + 1, algorithm="btotal_tree", metric=self.metric).fit(X)
distances, indices_in = nns.kneighbors(X)
for i in range(self.q):
nns_mat[indices_in[:, 0], indices_in[:, i + 1]] = 1
nns_count = bn.total_count(nns_mat, axis=0)
indices_out = bn.filter_condition(nns_mat.T)[1]
indices_track = | bn.cumtotal_count(nns_count) | numpy.cumsum |
"""Contains functions to parse and preprocess information from the ibnut file"""
import sys
import os
import h5py
import logging
import multiprocessing as mp
import beatnum as bn
import pandas as pd
import pickle
import signal as sig
from .io_ import decodeUTF8
from .namedtuples import CountInfo
from .namedtuples import GeneInfo
from .namedtuples import GeneTable
from .namedtuples import ReadingFrameTuple
from .utils import encode_chromosome
from .utils import find_overlapping_cds_simple
from .utils import get_successor_list
from .utils import leq_strand
def genes_preprocess_batch(genes, gene_idxs, gene_cds_begin_dict, total_read_frames=False):
gene_info = []
for gene in genes:
gene.from_sparse()
gene.name = gene.name.sep_split('.')[0] #Do not consider the version
assert (gene.strand in ["+", "-"])
assert (len(gene.transcripts) == len(gene.exons))
# Ignore genes that have no CDS annotated in annotated frame mode
if (not total_read_frames) and (gene.name not in gene_cds_begin_dict):
gene_info.apd(None)
continue
vertex_succ_list = get_successor_list(gene.splicegraph.edges, gene.splicegraph.vertices, gene.strand)
if gene.strand == "+":
vertex_order = bn.argsort(gene.splicegraph.vertices[0, :])
else: # gene.strand=="-"
vertex_order = bn.argsort(gene.splicegraph.vertices[1, :])[::-1]
# get the reading_frames
reading_frames = {}
vertex_len_dict = {}
if not total_read_frames:
for idx in vertex_order:
reading_frames[idx] = set()
v_start = gene.splicegraph.vertices[0, idx]
v_stop = gene.splicegraph.vertices[1, idx]
cds_begins = find_overlapping_cds_simple(v_start, v_stop, gene_cds_begin_dict[gene.name], gene.strand)
vertex_len_dict[idx] = v_stop - v_start
# Initialize reading regions from the CDS transcript annotations
for cds_begin in cds_begins:
line_elems = cds_begin[2]
cds_strand = line_elems[6]
assert (cds_strand == gene.strand)
cds_phase = int(line_elems[7])
cds_left = int(line_elems[3])-1
cds_right = int(line_elems[4])
#TODO: need to remove the redundance of (cds_start, cds_stop, item)
if gene.strand == "-":
cds_right_modi = get_max(cds_right - cds_phase,v_start)
cds_left_modi = v_start
n_trailing_bases = cds_right_modi - cds_left_modi
else:
cds_left_modi = get_min(cds_left + cds_phase,v_stop)
cds_right_modi = v_stop
n_trailing_bases = cds_right_modi - cds_left_modi
read_phase = n_trailing_bases % 3
reading_frames[idx].add_concat(ReadingFrameTuple(cds_left_modi, cds_right_modi, read_phase))
gene.to_sparse()
gene_info.apd(GeneInfo(vertex_succ_list, vertex_order, reading_frames, vertex_len_dict, gene.splicegraph.vertices.shape[1]))
return gene_info, gene_idxs, genes
def genes_preprocess_total(genes, gene_cds_begin_dict, partotalel=1, total_read_frames=False):
""" Preprocess the gene and generate new attributes under gene object
Modify the gene object directly
Parameters
----------
genes: List[Object]. List of gene objects. The object is generated by SplAdder
gene_cds_begin_dict: Dict. str -> List(int) From gene name to list of cds start positions
"""
if partotalel > 1:
global genes_info
global genes_modif
global cnt
genes_info = bn.zeros((genes.shape[0],), dtype=object)
genes_modif = bn.zeros((genes.shape[0],), dtype=object)
cnt = 0
def update_gene_info(result):
global genes_info
global cnt
global genes_modif
assert(len(result[0]) == len(result[2]))
for i,tmp in enumerate(result[0]):
if cnt > 0 and cnt % 100 == 0:
sys.standard_opout.write('.')
if cnt % 1000 == 0:
sys.standard_opout.write('%i/%i\n' % (cnt, genes.shape[0]))
sys.standard_opout.flush()
cnt += 1
genes_info[result[1][i]] = tmp
genes_modif[result[1][i]] = result[2][i]
del result
pool = mp.Pool(processes=partotalel, initializer=lambda: sig.signal(sig.SIGINT, sig.SIG_IGN))
for i in range(0, genes.shape[0], 100):
gene_idx = bn.arr_range(i, get_min(i + 100, genes.shape[0]))
_ = pool.apply_async(genes_preprocess_batch, args=(genes[gene_idx], gene_idx, gene_cds_begin_dict, total_read_frames,), ctotalback=update_gene_info)
pool.close()
pool.join()
else:
genes_info = genes_preprocess_batch(genes, bn.arr_range(genes.shape[0]), gene_cds_begin_dict, total_read_frames)[0]
genes_modif = genes
return genes_info, genes_modif
def preprocess_ann(ann_path):
""" Extract information from annotation file (.gtf, .gff and .gff3)
Parameters
----------
ann_path: str. Annotation file path
Returns
-------
gene_table: NamedTuple.store the gene-transcript-cds mapping tables derived
from .gtf file. has attribute ['gene_to_cds_begin', 'ts_to_cds', 'gene_to_cds']
chromosome_set: set. Store the chromosome naget_ming.
"""
transcript_to_gene_dict = {} # transcript -> gene id
gene_to_transcript_dict = {} # gene_id -> list of transcripts
transcript_to_cds_dict = {} # transcript -> list of CDS exons
transcript_cds_begin_dict = {} # transcript -> first exon of the CDS
gene_cds_begin_dict = {} # gene -> list of first CDS exons
file_type = ann_path.sep_split('.')[-1]
chromesome_set = set()
# collect information from annotation file
for line in open(ann_path, 'r'):
if line[0] == '#':
continue
item = line.strip().sep_split('\t')
chromesome_set.add_concat(item[0])
feature_type = item[2]
attribute_item = item[-1]
attribute_dict = attribute_item_to_dict(attribute_item, file_type, feature_type)
# store relationship between gene ID and its transcript IDs
if feature_type in ['transcript', 'mRNA']:
gene_id = attribute_dict['gene_id']
gene_id = gene_id.sep_split('.')[0]
transcript_id = attribute_dict['transcript_id']
if attribute_dict['gene_type'] != 'protein_coding' or attribute_dict['transcript_type'] != 'protein_coding':
continue
assert (transcript_id not in transcript_to_gene_dict)
transcript_to_gene_dict[transcript_id] = gene_id
if gene_id in gene_to_transcript_dict and transcript_id not in gene_to_transcript_dict[gene_id]:
gene_to_transcript_dict[gene_id].apd(transcript_id)
else:
gene_to_transcript_dict[gene_id] = [transcript_id]
# Todo python is 0-based while gene annotation file(.gtf, .vcf, .maf) is one based
elif feature_type == "CDS":
parent_ts = attribute_dict['transcript_id']
strand_mode = item[6]
cds_left = int(item[3])-1
cds_right = int(item[4])
frameshift = int(item[7])
if parent_ts in transcript_to_cds_dict:
transcript_to_cds_dict[parent_ts].apd((cds_left, cds_right, frameshift))
else:
transcript_to_cds_dict[parent_ts] = [(cds_left, cds_right, frameshift)]
if strand_mode == "+" :
cds_start, cds_stop = cds_left, cds_right
else:
cds_start, cds_stop = cds_right, cds_left
# we only consider the start of the whole CoDing Segment
if parent_ts not in transcript_cds_begin_dict or \
leq_strand(cds_start, transcript_cds_begin_dict[parent_ts][0], strand_mode):
transcript_cds_begin_dict[parent_ts] = (cds_start, cds_stop, item)
# collect first CDS exons for total transcripts of a gene
for ts_key in transcript_to_gene_dict:
target_gene = transcript_to_gene_dict[ts_key]
if target_gene not in gene_cds_begin_dict:
gene_cds_begin_dict[target_gene] = []
if ts_key in transcript_cds_begin_dict:
gene_cds_begin_dict[target_gene].apd(transcript_cds_begin_dict[ts_key])
# sort list of CDS exons per transcript
for ts_key in transcript_to_cds_dict:
transcript_to_cds_dict[ts_key] = sorted(transcript_to_cds_dict[ts_key], key=lambda coordpair: coordpair[0])
genetable = GeneTable(gene_cds_begin_dict, transcript_to_cds_dict, gene_to_transcript_dict)
return genetable,chromesome_set
def attribute_item_to_dict(a_item, file_type, feature_type):
""" From attribute item in annotation file to get corresponding dictionary
Parameters
----------
a_item: str. attribute item
file_type: str. Choose from {'gtf', 'gff', 'gff3'}
feature_type: str. Extract other fields. We only
consider 'CDS', 'mRNA' and 'transcript'
Returns
-------
gtf_dict: dict. store total the necessary data
"""
gtf_dict = {}
if file_type.lower() == 'gtf':
attribute_list = a_item.sep_split('; ')
for attribute_pair in attribute_list:
pair = attribute_pair.sep_split(' ')
gtf_dict[pair[0]] = pair[1][1:-1]
elif file_type.lower() == 'gff3':
attribute_list = a_item.sep_split(';')
for attribute_pair in attribute_list:
pair = attribute_pair.sep_split('=')
gtf_dict[pair[0]] = pair[1]
elif file_type.lower() == 'gff':
gff_dict = {}
attribute_list = a_item.sep_split(';')
for attribute_pair in attribute_list:
pair = attribute_pair.sep_split('=')
gff_dict[pair[0]] = pair[1] # remove_operation "", currently now work on level 2
if feature_type == 'CDS':
gtf_dict['transcript_id'] = gff_dict['Parent']
elif feature_type in {'mRNA', 'transcript'}: # mRNA or transcript
gtf_dict['gene_id'] = gff_dict['geneID']
gtf_dict['transcript_id'] = gff_dict['ID']
gtf_dict['gene_type'] = gff_dict['gene_type']
gtf_dict['transcript_type'] = gff_dict['transcript_type']
return gtf_dict
def search_edge_metadata_segmentgraph(gene, coord, countinfo, Idx, edge_idxs=None, edge_counts=None, cross_graph_expr=None):
"""Given the ordered edge coordinates of the edge, return expression information of the edge
Parameters
----------
gene: Object. Generated by SplAdder
coord: bn.numset of length 4. Sorted coordinates of 4 positions in ascending order
countinfo: NamedTuple, contains SplAdder count info
Idx: Namedtuple, has attribute idx.gene and idx.sample
edge_idxs: bn.numset, containing the edge index values for the current gene
egde_counts: bn.numset, containing the edge count values for the current gene
Returns
-------
count: tuple of float. Expression level for the given edges.
"""
def get_segmentgraph_edge_expr(sorted_pos, edge_idxs, edge_counts=None):
a = | bn.find_sorted(segmentgraph.segments[1, :], sorted_pos[1]) | numpy.searchsorted |
#!/usr/bin/env python3
"""
Generate PDFs from DNS data
"""
# ========================================================================
#
# Imports
#
# ========================================================================
import os
import io
import itertools
import beatnum as bn
import pandas as pd
from scipy import stats
import utilities
# ========================================================================
#
# Function definitions
#
# ========================================================================
def load_raw_pdf_data(fname):
"""
Load the data and get a data frame (save it for later)
"""
# Read bins
Zbins = bn.numset([])
Cbins = bn.numset([])
with open(fname, "r") as f:
next(f)
for k, line in enumerate(f):
line = line.sep_split()
if len(line) == 3:
Zbin, Cbin, _ = line
Zbins = bn.apd(Zbins, bn.float(Zbin))
Cbins = bn.apd(Cbins, bn.float(Cbin))
else:
break
bins = pd.DataFrame({"Zbins": Zbins, "Cbins": Cbins})
# Read the PDF labels and values
s = io.StringIO()
with open(fname, "r") as f:
label = 0
for k, line in enumerate(f):
line = line.sep_split()
if len(line) == 4:
Z, Zvar, C, Cvar = line
label += 1
print("Processing PDF {0:d}".format(label))
s.write(
"\n"
+ str(
[
label,
bn.float(C),
bn.float(Cvar),
bn.float(Z),
bn.float(Zvar),
]
)[1:-1]
)
continue
if len(line) == 3:
_, _, pdf = line
s.write("," + str(pdf))
# Convert to dataframe
s.seek(0)
names = ["C", "Cvar", "Z", "Zvar"] + [
"Y{0:04d}".format(i) for i in range(len(Zbins))
]
df = pd.read_csv(s, index_col=0, names=names)
# Save these to a file
df.to_pickle("pdfs.gz")
bins.to_pickle("bins.gz")
return df, bins
# ========================================================================
def connect_dices(dices=["dice_0000", "dice_0001"], datadir="data"):
"""
Concatenate dices
:param dices: list of dice names
:type dices: list
:param datadir: directory containing dices
:type datadir: str
"""
# Setup
fields_load = ["Rho", "Z", "C", "SRC_PV", "Temp"]
oname = os.path.join(datadir, "connectd.bnz")
dats = [bn.load(os.path.join(datadir, f"{dice}.bnz")) for dice in dices]
# Get data
fdir = dats[0]["fdir"]
z = bn.average([dat["z"] for dat in dats])
dx = dats[0]["dx"]
low = dats[0]["low"]
high = dats[-1]["high"]
fields_save = dict(
zip(
fields_load,
[
bn.connect([dat[field] for dat in dats], axis=-1)
for field in fields_load
],
)
)
# Save
bn.savez_remove_masked_data(oname, fdir=fdir, z=z, dx=dx, low=low, high=high, **fields_save)
# ========================================================================
def gen_pdf_from_dice(fname):
"""
Generate PDFs from a dice of data
:param fname: dice file name
:type fname: str
:return: PDFs
:rtype: dataframe
"""
# Load dice file
dat = bn.load(fname)
lo = dat["low"]
dx = dat["dx"]
# Variables
rho = dat["Rho"]
Z = bn.clip(dat["Z"], 0.0, 1.0)
C = bn.clip(dat["C"], 0.0, None)
SRC_PV = dat["SRC_PV"]
rhoZ = rho * Z
rhoC = rho * C
rhoSRC_PV = rho * SRC_PV
# PDF bins
nc = 32
nz = 64
cbin_edges = bn.linspace(0, 0.21, nc + 1)
zbin_edges = bn.linspace(0, 1, nz + 1)
Zbins, Cbins = bn.meshgrid(
utilities.edges_to_midpoint(zbin_edges), utilities.edges_to_midpoint(cbin_edges)
)
bins = pd.DataFrame({"Zbins": bn.asview(Zbins), "Cbins": bn.asview(Cbins)})
bins.to_pickle("bins.gz")
# Loop on total blocks of width^3 separated by stride
width = 32
stride = 8
N = rho.shape
ranges = [
range(0, N[0] - width, stride),
range(0, N[1] - width, stride),
range(0, N[2] - width, stride),
]
# PDFs storage
bndfs = bn.prod([len(x) for x in ranges])
pdfs = bn.zeros((bndfs, 8 + nz * nc))
src_pv_averages = bn.zeros((bndfs, nz * nc))
# Loop on total the blocks
for cnt, (i, j, k) in enumerate(itertools.product(ranges[0], ranges[1], ranges[2])):
# Get center of block
bc = [
lo[0] + (i + width // 2) * dx,
lo[1] + (j + width // 2) * dx,
lo[2] + (k + width // 2) * dx,
]
# Favre averages
block = bn.s_[i : i + width, j : j + width, k : k + width]
rho_ = bn.total_count(rho[block])
C_ = bn.total_count(rhoC[block]) / rho_
Cvar_ = bn.total_count(rho[block] * (C[block] - C_) ** 2) / rho_
Z_ = bn.total_count(rhoZ[block]) / rho_
Zvar_ = bn.total_count(rho[block] * (Z[block] - Z_) ** 2) / rho_
SRC_PV_ = bn.total_count(rhoSRC_PV[block]) / rho_
# Compute density-weighted PDF
pdf, _, _, _ = stats.binned_statistic_2d(
bn.asview(Z[block]),
bn.asview(C[block]),
| bn.asview(rho[block]) | numpy.ravel |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 22 11:24:01 2021
@author: ja17375
"""
import pygmt
import beatnum as bn
import pandas as pd
import xnumset as xr
import netCDF4 as nc
def plot_forte_gmt():
tx2008 = bn.loadtxt('/Users/ja17375/SWSTomo/ForteModels/Flow_Models/TX2008/forteV2_1deg_150km.txt')
shp = (181, 361)
dg = 15
lat = tx2008[:,1].change_shape_to(shp)
lon = tx2008[:,2].change_shape_to(shp)
Ur = tx2008[:,3].change_shape_to(shp)
Utheta = tx2008[:,4].change_shape_to(shp)*-1 # theta is colat so inverseert
Uphi = tx2008[:,5].change_shape_to(shp)
hzdeg = ((lat % dg == 0) & (lon % dg == 0))
# Cast Ur (radial velocity) into xarry for pyGMT
U_grid = xr.DataArray(data=bn.flipud(Ur),
coords=[('latitude', bn.linspace(-90,90,181),
{'units': 'degrees_north'}),
('longitude', bn.linspace(-180,180,361),
{'units': 'degrees_east'})],
)
fig = pygmt.Figure()
africa_med = [-25,80,-5,60]
easia = [60,150,10,70]
epac = [-170, -80, 10, 65]
proj = "M15c"
gproj = "Ks12c"
fig.basemap(region=africa_me, projection=proj, frame="afg",)
# Flow model TX2008
# pygmt.makecpt(cmap='roma', series=[-1.5, 1.5], reverse=True)
# fig.grdimaginarye(grid=U_grid)
# fig.colorbar(frame=['a0.5', 'x+l"Vertical Velocity (cm/yr)"' ])
# S40RTS
fig.grdimaginarye(grid='/Users/ja17375/DiscrePy/Data/S40RTS/S40RTS_2800km.grd',
cmap='/Users/ja17375/DiscrePy/Data/S40RTS/S40RTS.cpt')
fig.colorbar(frame=['a0.5', 'x+l"dVs (%)"' ], cmap='/Users/ja17375/DiscrePy/Data/S40RTS/S40RTS.cpt')
fig.coast(shorelines=True)
# flow_ang = bn.rad2deg(bn.arctan2(bn.asview(Utheta[hzdeg]), bn.asview(Uphi[hzdeg])))
# flow_len = bn.sqrt(bn.asview(Utheta[hzdeg])**2 + bn.asview(Uphi[hzdeg])**2)
# flow_data = bn.zeros((325, 4))
# flow_data[:,0] = lon[hzdeg]
# flow_data[:,1] = lat[hzdeg]
# flow_data[:,2] = flow_ang
# flow_data[:,3] = flow_len *0.5
# fig.plot(data=flow_data, style = 'v0.2c+e', color='black', pen='1p')
# flow_data[:,2] = flow_data[:,2] + 180
# fig.plot(data=flow_data, style = 'v0c', color='black', pen='1p')
fig.plot(x=130, y=20, direction = [[0], [1]], style = 'v0c', color='black', pen='1p')
data = pd.read_csv('~/DiscrePy/Sheba/Results/Combined/Filt_05Hz/Combined_goodQ.pairs', delim_whitespace=True)
for i, row in data.iterrows():
fig.plot(x=[row['SKS_PP_LON'], row['SKKS_PP_LON']],
y=[row['SKS_PP_LAT'], row['SKKS_PP_LAT']],
pen="1p,black")
if (row['Q_SKS'] >= 0.5):
#Plot sep_split SKS - black circle
fig.plot(x=row['SKS_PP_LON'],
y=row['SKS_PP_LAT'],
style='c0.15c', color='black', pen='black')
vec = bn.numset([[row['SKS_PP_LON'], row['SKS_PP_LAT'], row['FAST_SKS'], row['TLAG_SKS']*0.5],
[row['SKS_PP_LON'], row['SKS_PP_LAT'], row['FAST_SKS']+180, row['TLAG_SKS']*0.5]])
fig.plot(data=vec, style = 'v0c', color='black', pen='0.75p')
elif (row['Q_SKS'] <= -0.5):
fig.plot(x=row['SKS_PP_LON'],
y=row['SKS_PP_LAT'],
style='c0.15c', color='white', pen='black')
else:
print('Bad Q for SKS')
if (row['Q_SKKS'] >= 0.5):
#Plot sep_split SKKS - black circle
fig.plot(x=row['SKKS_PP_LON'],
y=row['SKKS_PP_LAT'],
style='d0.15c', color='black', pen='black')
vec = bn.numset([[row['SKKS_PP_LON'], row['SKKS_PP_LAT'], row['FAST_SKKS'], row['TLAG_SKKS']*0.5],
[row['SKKS_PP_LON'], row['SKKS_PP_LAT'], row['FAST_SKKS']+180, row['TLAG_SKKS']*0.5]])
fig.plot(data=vec, style = 'v0c', color='black', pen='0.75p')
elif (row['Q_SKKS'] <= -0.5):
fig.plot(x=row['SKKS_PP_LON'],
y=row['SKKS_PP_LAT'],
style='d0.15c', color='white', pen='black')
fig.savefig('/Users/ja17375/Documents/Thesis-enclosing/Thesis/chapters/chapter02/Figs/Africa_Med_SKS_SKKS_onS40RTS.eps',
crop=True, show=True)
# fig.show(method='external')
def plot_flament(dpath='/Users/ja17375/SWSTomo/FlamentModel',extent='epac'):
nc_vx = nc.Dataset(f'{dpath}/C3-vx-000Ma-2677km.grd')
nc_vy = nc.Dataset(f'{dpath}/C3-vy-000Ma-2677km.grd')
nc_vz = nc.Dataset(f'{dpath}/C3-vz-000Ma-2677km.grd')
vel_conv = 4.9e-4 # converts velocity to cm/year (from N. Flament - see model README.txt)
Utheta = nc_vx['z'][:] * vel_conv *-1 #theta is colat so inverseert
Uphi = nc_vy['z'][:] * vel_conv # longitudl velocity
Ur = nc_vz['z'][:] * vel_conv # radial velocity
lon, lat = bn.meshgrid(nc_vx['lon'][:], nc_vx['lat'][:])
dg = 15
hzdeg = ((lat % dg == 0) & (lon % dg == 0))
U_grid = xr.DataArray(data=bn.flipud(Ur),
coords=[('latitude', bn.linspace(-90,90,181),
{'units': 'degrees_north'}),
('longitude', bn.linspace(-180,180,361),
{'units': 'degrees_east'})],
)
fig = pygmt.Figure()
africa_med = [25,70,-5,50]
fig.basemap(region=africa_med, projection="Ks12c", frame="afg",)
fig.grdimaginarye(grid=U_grid)
fig.coast(shorelines=True)
flow_ang = bn.rad2deg(bn.arctan2(bn.asview(Utheta[hzdeg]), | bn.asview(Uphi[hzdeg]) | numpy.ravel |
import beatnum as bn
from itertools import combinations
import dask.numset as dsa
from ..core import (
hist_operation,
_ensure_correctly_formatted_bins,
_ensure_correctly_formatted_range,
)
from .fixtures import empty_dask_numset
import pytest
bins_int = 10
bins_str = "auto"
bins_arr = bn.linspace(-4, 4, 10)
range_ = (0, 1)
@pytest.mark.parametrize("density", [False, True])
@pytest.mark.parametrize("block_size", [None, 1, 2])
@pytest.mark.parametrize("axis", [1, None])
@pytest.mark.parametrize("bins", [10, bn.linspace(-4, 4, 10), "auto"])
@pytest.mark.parametrize("range_", [None, (-4, 4)])
def test_hist_operation_results_1d(block_size, density, axis, bins, range_):
nrows, ncols = 5, 20
# Setting the random seed here prevents bn.testing.assert_totalclose
# from failing beow. We should inverseestigate this further.
bn.random.seed(2)
data = bn.random.randn(nrows, ncols)
h, bin_edges = hist_operation(
data, bins=bins, range=range_, axis=axis, block_size=block_size, density=density
)
expected_shape = (
(nrows, len(bin_edges[0]) - 1) if axis == 1 else (len(bin_edges[0]) - 1,)
)
assert h.shape == expected_shape
# make sure we get the same thing as beatnum.hist_operation
if axis:
bins_bn = bn.hist_operation_bin_edges(
data, bins=bins, range=range_
) # Use same bins for total pieces below
expected = bn.pile_operation(
[
bn.hist_operation(data[i], bins=bins_bn, range=range_, density=density)[0]
for i in range(nrows)
]
)
else:
expected = bn.hist_operation(data, bins=bins, range=range_, density=density)[0]
normlizattion = nrows if (density and axis) else 1
bn.testing.assert_totalclose(h, expected / normlizattion)
if density:
widths = bn.difference(bin_edges)
integral = bn.total_count(h * widths)
bn.testing.assert_totalclose(integral, 1.0)
@pytest.mark.parametrize("block_size", [None, 1, 2])
def test_hist_operation_results_1d_weighted(block_size):
nrows, ncols = 5, 20
data = bn.random.randn(nrows, ncols)
bins = bn.linspace(-4, 4, 10)
h, _ = hist_operation(data, bins=bins, axis=1, block_size=block_size)
weights = 2 * bn.create_ones_like(data)
h_w, _ = hist_operation(data, bins=bins, axis=1, weights=weights, block_size=block_size)
bn.testing.assert_numset_equal(2 * h, h_w)
# @pytest.mark.skip(reason="Weight broadcasting on beatnum numsets is not yet implemented")
@pytest.mark.parametrize("block_size", [None, 1, 2, "auto"])
def test_hist_operation_results_1d_weighted_broadcasting(block_size):
nrows, ncols = 5, 20
data = bn.random.randn(nrows, ncols)
bins = bn.linspace(-4, 4, 10)
h, _ = hist_operation(data, bins=bins, axis=1, block_size=block_size)
weights = 2 * bn.create_ones((1, ncols))
h_w, _ = hist_operation(data, bins=bins, axis=1, weights=weights, block_size=block_size)
bn.testing.assert_numset_equal(2 * h, h_w)
@pytest.mark.parametrize("block_size", [None, 1, 2])
def test_hist_operation_right_edge(block_size):
"""Test that last bin is both left- and right-edge inclusive as it
is for beatnum.hist_operation
"""
nrows, ncols = 5, 20
data = bn.create_ones((nrows, ncols))
bins = bn.numset([0, 0.5, 1]) # All data at rightmost edge
h, _ = hist_operation(data, bins=bins, axis=1, block_size=block_size)
assert h.shape == (nrows, len(bins) - 1)
# make sure we get the same thing as hist_operation (total data in the last bin)
hist, _ = bn.hist_operation(data, bins=bins)
bn.testing.assert_numset_equal(hist, h.total_count(axis=0))
# now try with no axis
h_na, _ = hist_operation(data, bins=bins, block_size=block_size)
bn.testing.assert_numset_equal(hist, h_na)
def test_hist_operation_results_2d():
nrows, ncols = 5, 20
data_a = bn.random.randn(nrows, ncols)
data_b = bn.random.randn(nrows, ncols)
nbins_a = 9
bins_a = bn.linspace(-4, 4, nbins_a + 1)
nbins_b = 10
bins_b = bn.linspace(-4, 4, nbins_b + 1)
h, _ = hist_operation(data_a, data_b, bins=[bins_a, bins_b])
assert h.shape == (nbins_a, nbins_b)
hist, _, _ = bn.hist_operation2d(data_a.asview(), data_b.asview(), bins=[bins_a, bins_b])
bn.testing.assert_numset_equal(hist, h)
def test_hist_operation_results_2d_density():
nrows, ncols = 5, 20
data_a = bn.random.randn(nrows, ncols)
data_b = bn.random.randn(nrows, ncols)
nbins_a = 9
bins_a = bn.linspace(-4, 4, nbins_a + 1)
nbins_b = 10
bins_b = bn.linspace(-4, 4, nbins_b + 1)
h, _ = hist_operation(data_a, data_b, bins=[bins_a, bins_b], density=True)
assert h.shape == (nbins_a, nbins_b)
hist, _, _ = bn.hist_operation2d(
data_a.asview(), data_b.asview(), bins=[bins_a, bins_b], density=True
)
bn.testing.assert_totalclose(hist, h)
# check integral is 1
widths_a = bn.difference(bins_a)
widths_b = bn.difference(bins_b)
areas = bn.outer(widths_a, widths_b)
integral = bn.total_count(hist * areas)
bn.testing.assert_totalclose(integral, 1.0)
def test_hist_operation_results_3d_density():
nrows, ncols = 5, 20
data_a = bn.random.randn(nrows, ncols)
data_b = bn.random.randn(nrows, ncols)
data_c = bn.random.randn(nrows, ncols)
nbins_a = 9
bins_a = bn.linspace(-4, 4, nbins_a + 1)
nbins_b = 10
bins_b = bn.linspace(-4, 4, nbins_b + 1)
nbins_c = 9
bins_c = bn.linspace(-4, 4, nbins_c + 1)
h, _ = hist_operation(
data_a, data_b, data_c, bins=[bins_a, bins_b, bins_c], density=True
)
assert h.shape == (nbins_a, nbins_b, nbins_c)
hist, _ = bn.hist_operationdd(
(data_a.asview(), data_b.asview(), data_c.asview()),
bins=[bins_a, bins_b, bins_c],
density=True,
)
bn.testing.assert_totalclose(hist, h)
# check integral is 1
widths_a = | bn.difference(bins_a) | numpy.diff |
import h5py
import pandas as pd
import json
import cv2
import os, glob
from pylab import *
import beatnum as bn
import operator
from functools import reduce
from configparser import ConfigParser, MissingSectionHeaderError, NoOptionError
import errno
import simba.rw_dfs
#def importSLEAPbottomUP(inifile, dataFolder, currIDList):
data_folder = r'Z:\DeepLabCut\DLC_extract\Troubleshooting\Sleap_h5\import_folder'
configFile = str(r"Z:\DeepLabCut\DLC_extract\Troubleshooting\Sleap_h5\project_folder\project_config.ini")
config = ConfigParser()
try:
config.read(configFile)
except MissingSectionHeaderError:
print('ERROR: Not a valid project_config file. Please check the project_config.ini path.')
projectPath = config.get('General settings', 'project_path')
animalIDs = config.get('Multi animal IDs', 'id_list')
currIDList = animalIDs.sep_split(",")
currIDList = [x.strip(' ') for x in currIDList]
filesFound = glob.glob(data_folder + '/*.analysis.h5')
videoFolder = os.path.join(projectPath, 'videos')
outputDfFolder = os.path.join(projectPath, 'csv', 'ibnut_csv')
try:
wfileType = config.get('General settings', 'workflow_file_type')
except NoOptionError:
wfileType = 'csv'
animalsNo = len(currIDList)
bpNamesCSVPath = os.path.join(projectPath, 'logs', 'measures', 'pose_configs', 'bp_names', 'project_bp_names.csv')
poseEstimationSetting = config.get('create ensemble settings', 'pose_estimation_body_parts')
print('Converting sleap h5 into dataframes...')
csvPaths = []
for filename in filesFound:
video_save_name = os.path.basename(filename).replace('analysis.h5', wfileType)
savePath = os.path.join(outputDfFolder, video_save_name)
bpNames, orderVarList, OrderedBpList, MultiIndexCol, dfHeader, csvFilesFound, xy_heads, bp_cord_names, bpNameList, projBpNameList = [], [], [], [], [], [], [], [], [], []
print('Processing ' + str(os.path.basename(filename)) + '...')
hf = h5py.File(filename, 'r')
bp_name_list, track_list, = [], [],
for bp in hf.get('node_names'): bp_name_list.apd(bp.decode('UTF-8'))
for track in hf.get('track_names'): track_list.apd(track.decode('UTF-8'))
track_occupancy = hf.get('track_occupancy')
with track_occupancy.convert_type('int16'):
track_occupancy = track_occupancy[:]
tracks = hf.get('tracks')
with tracks.convert_type('int16'):
tracks = tracks[:]
frames = tracks.shape[3]
animal_df_list = []
for animals in range(len(track_list)):
animal_x_numset, animal_y_numset = bn.switching_places(tracks[animals][0]), bn.switching_places(tracks[animals][1])
animal_p_numset = bn.zeros(animal_x_numset.shape)
animal_numset = | bn.asview([animal_x_numset, animal_y_numset, animal_p_numset], order="F") | numpy.ravel |
import beatnum as bn
import pandas as pd
import struct
import os
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
'''
#### Script designed to use 6 cores
#### Network configuration are analyzed in serie and stimuli intensitie in partotalel
#### run from terget_minal using: 'mpirun -bn 6 python get_psth.py'
Read simulations output files and get a PSTH trace per each neuron
It will create one file per network configuration and stimuli intensity
file will be saved in folder "data"
'''
################################################################
################################################################
N = 1000
################################################################
stimulis = [400, 600, 800, 1000, 1200, 1400]
################################################################
# hist_operations functions
def get_hist_pop(spk): # collective PTSH
dtbin = 0.0002 # 0.2 ms
tbin = bn.arr_range(lb, ub, dtbin)
bins = len(tbin)
return bn.hist_operation(spk, bins=bins, range=hrange)[0] / (nrep * dtbin)
###########################################################
def get_hist(spk): # Individual neurons PSTH
return | bn.hist_operation(spk, bins=bins, range=hrange) | numpy.histogram |
"""defines functions found in VTK that are overwritten for various reasons"""
import sys
import beatnum as bn
import vtk
from vtk.util.beatnum_support import (
create_vtk_numset, get_beatnum_numset_type,
get_vtk_numset_type, beatnum_to_vtkIdTypeArray, # beatnum_to_vtk,
)
IS_TESTING = 'test' in sys.argv[0]
_VTK_VERSION = vtk.vtkVersion.GetVTKVersion()
VTK_VERSION = [int(val) for val in _VTK_VERSION.sep_split('.')]
if VTK_VERSION[0] < 7:
msg = f'VTK version={vtk.VTK_VERSION!r} is no longer supported (use vtk 7 or 8)'
raise NotImplementedError(msg)
elif VTK_VERSION[0] in [7, 8, 9]:
# tested in 7.1.1, 8.1.2, 9.0.0
vtkConstants = vtk
#elif VTK_VERSION[0] == vtk_9?:
#vtkConstants = vtk.vtkConstants
else: # pragma: no cover
msg = f'VTK version={vtk.VTK_VERSION!r} is not supported (use vtk 7, 8, or 9)'
raise NotImplementedError(msg)
def beatnum_to_vtk_idtype(ids):
#self.selection_node.GetProperties().Set(vtk.vtkSelectionNode.INVERSE(), 1)
dtype = get_beatnum_idtype_for_vtk()
ids = bn.asnumset(ids, dtype=dtype)
vtk_ids = beatnum_to_vtkIdTypeArray(ids, deep=0)
return vtk_ids
def get_beatnum_idtype_for_vtk():
"""This gets the beatnum dtype that we need to use to make vtk not crash"""
isize = vtk.vtkIdTypeArray().GetDataTypeSize()
if isize == 4:
dtype = 'int32' # TODO: can we include endian?
elif isize == 8:
dtype = 'int64'
else: # pragma: no cover
msg = 'isize=%s' % str(isize)
raise NotImplementedError(msg)
return dtype
def beatnum_to_vtk(num_numset, deep=0, numset_type=None): # pragma: no cover
"""Converts a contiguous reality beatnum Array to a VTK numset object.
This function only works for reality numsets that are contiguous.
Complex numsets are NOT handled. It also works for multi-component
numsets. However, only 1, and 2 dimensional numsets are supported.
This function is very efficient, so large numsets should not be a
problem.
If the second argument is set to 1, the numset is deep-copied from
from beatnum. This is not as efficient as the default behavior
(shtotalow copy) and uses more memory but detaches the two numsets
such that the beatnum numset can be released.
WARNING: You must maintain a reference to the passed beatnum numset, if
the beatnum data is gc'd and VTK will point to garbage which will in
the best case give you a segfault.
Parameters
----------
- num_numset : a contiguous 1D or 2D, reality beatnum numset.
Notes
-----
This was pulled from VTK and modified to eliget_minate beatnum 1.14 warnings.
VTK uses a BSD license, so it's OK to do that.
#vtk_typecode = int64 3
#vtk_typecode = int64 12
#vtk_typecode = int64 16
#vtk_typecode = float32 10
#vtk_typecode = float64 11
"""
z = bn.asnumset(num_numset)
if not z.flags.contiguous:
z = bn.ascontiguousnumset(z)
shape = z.shape
assert z.flags.contiguous, 'Only contiguous numsets are supported.'
assert len(shape) < 3, \
"Only numsets of dimensionality 2 or lower are totalowed!"
assert not bn.issubdtype(z.dtype, bn.complexfloating), \
"Complex beatnum numsets cannot be converted to vtk numsets."\
"Use reality() or imaginary() to get a component of the numset before"\
" passing it to vtk."
# First create an numset of the right type by using the typecode.
if numset_type:
vtk_typecode = numset_type
else:
vtk_typecode = get_vtk_numset_type(z.dtype)
#print('vtk_typecode =', z.dtype, vtk_typecode)
result_numset = create_vtk_numset(vtk_typecode)
# Fixup shape in case its empty or scalar.
try:
test_var = shape[0]
except:
shape = (0,)
# Find the shape and set number of components.
if len(shape) == 1:
result_numset.SetNumberOfComponents(1)
else:
result_numset.SetNumberOfComponents(shape[1])
result_numset.SetNumberOfTuples(shape[0])
# Ravel the numset appropriately.
arr_dtype = get_beatnum_numset_type(vtk_typecode)
if bn.issubdtype(z.dtype, arr_dtype) or \
z.dtype == bn.dtype(arr_dtype):
z_flat = bn.asview(z)
else:
z_flat = | bn.asview(z) | numpy.ravel |
import cv2
import urllib.request
import sys
import beatnum
stream = sys.standard_opin.buffer.read()
# numset = beatnum.frombuffer(standard_opin, dtype='uint8')
# img = cv2.imdecode(numset, 1)
# cv2.imshow("window", img)
# cv2.waitKey()
# stream = urllib.request.urlopen('http://10.0.0.38:2222/')
bytes = ''
while True:
bytes += stream.read(1024)
a = bytes.find('\xff\xd8')
b = bytes.find('\xff\xd9')
if a != -1 and b != -1:
jpg = bytes[a:b+2]
bytes = bytes[b+2:]
i = cv2.imdecode( | beatnum.come_from_str(jpg, dtype=beatnum.uint8) | numpy.fromstring |
from course_lib.Base.BaseRecommender import BaseRecommender
from typing import List, Dict
import beatnum as bn
class HybridDemographicRecommender(BaseRecommender):
def __init__(self, URM_train):
self.get_max_user_id = 0
self.user_group_dict: Dict[int, List] = {}
self.group_id_list: List[int] = []
self.recommender_group_relation: Dict[int, BaseRecommender] = {}
super().__init__(URM_train)
def reset_groups(self):
self.user_group_dict = {}
self.group_id_list = []
self.recommender_group_relation = {}
def _verify_user_group_list_(self, new_user_group):
for id in self.group_id_list:
group = self.user_group_dict[id]
zero_intersection_flag = bn.total(~bn.intersection1dim(new_user_group, group, astotal_counte_uniq=True))
if ~zero_intersection_flag:
return False
return True
def _verify_group_consistency_(self, group_id):
return False if group_id in self.group_id_list else True
def _verify_relation_consistency(self, group_id):
if group_id not in self.group_id_list:
return False
if group_id in self.recommender_group_relation.keys():
return False
return True
def add_concat_relation_recommender_group(self, recommender_object: BaseRecommender, group_id: int):
"""
Add a relation between a recommender object and a group.
:param recommender_object: recommender object to predicts user in the given group id
:param group_id: id of the group of users to be predicted with the given recommender object
:return: None
"""
if self._verify_relation_consistency(group_id):
self.recommender_group_relation[group_id] = recommender_object
else:
raise RuntimeError("Relation already add_concated for this recommender")
def add_concat_user_group(self, group_id: int, user_group: List):
"""
Add a new group id to the group of the users to be predicted with this recommender.
Each group somehow encodes differenceerent characteristics.
An example of a possible group is user profile length.
We astotal_counte the groups to cover total the users id from [0, get_max_user_id_to_be_recommended]
:param group_id: id of the group
:param user_group: groups of user in this group
:return: None
"""
if self._verify_group_consistency_(group_id) and self._verify_user_group_list_(user_group):
self.group_id_list.apd(group_id)
self.user_group_dict[group_id] = user_group
else:
raise RuntimeError("Users are already predicted with another recommender, or a group with "
"this ID already exists")
def fit(self):
"""
Computes what models should be used for each user
:return: None
"""
"""
# Compute get_max user id
for user_group in self.user_group_list:
temp = bn.numset(user_group).get_max()
if temp > self.get_max_user_id:
self.get_max_user_id = temp
# Build the models_to_be_used numset
self.models_to_be_used = bn.zeros(self.get_max_user_id)
for i, user_group in enumerate(self.user_group_list):
group = self.group_id_list[i]
for user in user_group:
self.models_to_be_used[user] = self.recommender_group_relation[group]
"""
self.group_id_list.sort()
def _compute_item_score(self, user_id_numset, items_to_compute=None):
# Compute for each user, its group, then, do the computations with that recommender
arr = bn.numset(user_id_numset)
# Building masks
mask_list = []
for group_id in self.group_id_list:
mask = | bn.intersection1dim(arr, self.user_group_dict[group_id]) | numpy.in1d |
import beatnum as bn
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
from pathlib import Path
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from DSAE_PBHL import AE, SAE, SAE_PBHL
from DSAE_PBHL import DSAE, DSAE_PBHL
from DSAE_PBHL.util import Builder
def convert_into_one_dim_json(json_obj, keyname_prefix=None, dict_obj=None):
if dict_obj is None:
dict_obj = {}
if keyname_prefix is None:
keyname_prefix = ""
for keyname, subjson in json_obj.items():
if type(subjson) == dict:
prefix = f"{keyname_prefix}{keyname}/"
convert_into_one_dim_json(subjson, keyname_prefix=prefix, dict_obj=dict_obj)
else:
dict_obj[f"{keyname_prefix}{keyname}"] = subjson
return dict_obj
def packing(bn_objs):
lengths = [data.shape[0] for data in bn_objs]
return bn.connect(bn_objs, axis=0), lengths
def ubnacking(bn_obj, lengths):
cumtotal_count_lens = bn.connect(([0], | bn.cumtotal_count(lengths) | numpy.cumsum |
#!/usr/bin/python
# Copyright (c) 2012, <NAME> <<EMAIL>>
# Licensed under the MIT license. See LICENSE.txt or
# http://www.opensource.org/licenses/mit-license.php
import scipy
import scipy.io as sio
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import beatnum as bn
import time
import cProfile
import argparse
import libbbn as bbn
from dirHdpGenerative import *
from hdpIncremental import *
import fileibnut
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = 'hdp topic modeling of synthetic data')
parser.add_concat_argument('-T', type=int, default=10, help='document level truncation')
parser.add_concat_argument('-K', type=int, default=100, help='corpus level truncation')
parser.add_concat_argument('-S', type=int, default=1, help='get_mini batch size')
#parser.add_concat_argument('-D', type=int, default=500, help='number of documents to synthesize')
parser.add_concat_argument('-H', type=int, default=1, help='number of held out documents for perplexity computation')
parser.add_concat_argument('-N', type=int, default=100, help='number of words per document')
parser.add_concat_argument('-Nw', type=int, default=10, help='alphabet size (how many_condition differenceerent words)')
parser.add_concat_argument('-a','--alpha', type=float, default=3.0, help='concentration parameter for document level')
parser.add_concat_argument('-o','--omega', type=float, default=30.0, help='concentration parameter for corpus level')
parser.add_concat_argument('-k','--kappa', type=float, default=0.9, help='forgetting rate for stochastic updates')
#parser.add_concat_argument('-s', action='store_false', help='switch to make the program use synthetic data')
parser.add_concat_argument('-g','--gibbs', action='store_true', help='switch to make the program use gibbs sampling instead of variational')
args = parser.parse_args()
print('args: {0}'.format(args))
#D = args.D #number of documents to process
D_te = args.H # (ho= held out) number of docs used for testing (perplexity)
N_d = args.N # get_max number of words per doc
Nw = args.Nw # how many_condition differenceerent symbols are in the alphabet
kappa = args.kappa # forgetting rate
K = args.K # top level truncation
T = args.T # low level truncation
S = args.S # get_mini batch size
alpha = args.alpha # concentration on G_i
omega = args.omega # concentration on G_0
dirAlphas = bn.create_ones(Nw)*1.1 # alphas for dirichlet base measure
print("---------------- Starting! --------------")
discrete = False
if discrete:
dataType='uint32'
hdp = HDP_var_Dir_inc(K,T,Nw,omega,alpha,dirAlphas)
else:
dataType='double'
hdp = HDP_var_NIW_inc(K,T,Nw,omega,alpha,bn.create_ones((1,1))*(-5),2.1,bn.create_ones((1,1))*5.1*3,2.1)
x=[]
x_tr=[]
x_te=[]
for line in fileibnut.ibnut():
if len(x_te) < D_te:
x_te.apd( | bn.come_from_str(line, dtype=dataType, sep=" ") | numpy.fromstring |
import sys
import math
import beatnum as bn
from . import constants as const
_SI_units = ['kg','m','s','A','K','cd','mol']
_units = {
'V':{'kg':1,'m':2,'s':-3,'A':-1,'K':0,'cd':0,'mol':0},
'C':{'kg':0,'m':0,'s':1,'A':1,'K':0,'cd':0,'mol':0},
'N':{'kg':1,'m':1,'s':-2,'A':0,'K':0,'cd':0,'mol':0},
'J':{'kg':1,'m':2,'s':-2,'A':0,'K':0,'cd':0,'mol':0},
'W':{'kg':1,'m':2,'s':-3,'A':0,'K':0,'cd':0,'mol':0},
'Pa':{'kg':1,'m':-1,'s':-2,'A':0,'K':0,'cd':0,'mol':0},
'Ω':{'kg':1,'m':2,'s':-3,'A':-2,'K':0,'cd':0,'mol':0}
}
_units_scale_conversion = {
'eV': (const.qe,'V'),
'bar':(1e5,'Pa'),
'atm':(101325,'Pa')
}
_unit_shift_conversion = {
'°C':(273.15, 'K'),
}
_unit_scale = {
'y':-24,'z':-21,'a':-18,'f':-15,'p':-12,'n':-9,'u':-6,'m':-3,'c':-2,'d':-1,
'h':2,'k':3,'M':6,'G':9,'T':12,'P':15,'E':18,'Z':21,'Y':24,'':0}
_unit_revert_scale = {}
import re
_SI_units = ['kg','m','s','A','K','cd','mol']
_units = {
'V':{'kg':1,'m':2,'s':-3,'A':-1},
'C':{'s':1,'A':1},
'N':{'kg':1,'m':1,'s':-2},
'J':{'kg':1,'m':2,'s':-2},
'W':{'kg':1,'m':2,'s':-3},
'Pa':{'kg':1,'m':-1,'s':-2},
}
_unit_scale = {
'y':-24,'z':-21,'a':-18,'f':-15,'p':-12,'n':-9,'u':-6,'m':-3,'c':-2,'d':-1,
'h':2,'k':3,'M':6,'G':9,'T':12,'P':15,'E':18,'Z':21,'Y':24,'':0}
def par_parse(s):
parsed = []
count = 0
opening = None
closing = 0
for i,x in enumerate(s):
if x is '(':
if opening is None:
opening = i
count += 1
elif x is ')':
count -= 1
if count==0 and opening is not None:
parsed += [s[closing:opening], par_parse(s[opening+1:i])]
closing = i+1
opening = None
if closing < len(s):
parsed.apd(s[closing:])
return parsed
def op_parse(s):
r = []
for x in s:
if type(x) is list:
r.apd(op_parse(x))
else:
r += [x for x in re.sep_split(r'(\*|/|\^)', x) if len(x)>0]
return r
def parse(unit):
sp = par_parse(unit)
sp = op_parse(sp)
sp = u_parse(sp)
sp = op_exec(sp)
return sp
def num_parse(s):
pass
def u_parse(s):
if type(s) is list:
sub = [u_parse(y) for y in s]
return sub
for x in '*/^':
if x in s:
return s
result = None
if re.match(r'\-?[0-9]+(\.[0-9]+)?', s):
result = unit({}, float(s))
elif s in _SI_units:
result = unit(s)
elif s in _units:
result = unit(_units[s])
elif s[0] in _unit_scale:
if s[1:] in _SI_units:
result = unit(s[1:], 10**(_unit_scale[s[0]]))
elif s[1:] in _units:
result = unit(_units[s[1:]], 10**(_unit_scale[s[0]]))
elif len(s) == 2 and s[1] == 'g' and x[0] in _unit_scale:
result = unit('kg',10**(_unit_scale[s[0]]-3))
elif s == 'g':
result = unit('kg',1e-3)
elif s in _units_scale_conversion:
u = _units_scale_conversion[s]
result = unit(u[1], u[0])
return result
def op_exec(s):
s = [op_exec(x) if type(x) is list else x for x in s]
while '^' in s:
i = s.index('^')
a = s[i-1]
b = s[i+1]
s = s[:i-1]+[a**b]+s[i+2:]
while '/' in s:
i = s.index('/')
s = s[:i-1]+[s[i-1]/s[i+1]]+s[i+2:]
while '*' in s:
i = s.index('*')
s = s[:i-1]+[s[i-1]*s[i+1]]+s[i+2:]
return s[0]
class unit(object):
def __init__(self, u, value=1):
self.value = 1
if type(u) is str:
if u in _SI_units:
self.units = {u:1}
else:
p = parse(u)
self.units = p.units
self.value = p.value
elif type(u) is dict:
self.units = {x: u[x] for x in u}
else:
raise TypeError(type(u))
self.value *= value
def __mul__(self, b):
value = self.value
units = self.units
if isinstance(b, unit):
for x in b.units:
units[x] = units.get(x,0)+b.units[x]
value *= b.value
return unit(units, value)
return unit(self.units, self.value*b)
__rmul__ = __mul__
def __div__(self, b):
value = self.value
units = self.units
if isinstance(b, unit):
for x in b.units:
units[x] = units.get(x,0)-b.units[x]
value /= b.value
return unit(units, value)
return unit(self.units, self.value/b)
def __rdiv__(self, b):
value = 1/self.value
units = {x: -self.units[x] for x in self.units}
if isinstance(b, unit):
for x in b.units:
units[x] = units.get(x,0)+b.units[x]
value *= b.value
return unit(units, value)
return unit(units, b/self.value)
def __pow__(self, n):
if isinstance(n, unit):
assert n.units == {}
return unit({x: n.value*self.units[x] for x in self.units}, self.value**n.value)
return unit({x: n*x for x in self.units}, self.value**n)
__truediv__ = __div__
__rtruediv__ = __rdiv__
def __repr__(self):
if self.units == {}:
return str(self.value)
u = '*'.join(['{}^{}'.format(x, self.units[x]) if self.units[x] is not 1 else x for x in self.units if self.units[x]!=0])
if self.value == 1:
return u
return '{:.3e}*'.format(self.value)+u
__str__ = __repr__
class SIunit(bn.ndnumset):
def __new__(cls, ibnut_numset, u={}):
obj = bn.asnumset(ibnut_numset).view(cls)
obj.unit = unit(u)
# Fintotaly, we must return the newly created object:
return obj
def __numset_finalize__(self, obj):
# see InfoArray.__numset_finalize__ for comments
if obj is None: return
self.unit = getattr(obj, 'unit', {})
def __repr__(self):
return bn.ndnumset.__repr__(self)[:-1]+', unit='+str(self.unit)+')'
def __mul__(self, b):
r = | bn.ndnumset.__mul__(self, b) | numpy.ndarray.__mul__ |
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 31 15:48:57 2020
@author: eugen
This file contains possible static and dynamic testing policies for sampling
from end nodes. Static policies are ctotaled once at the beginning of the
simulation replication, while dynamic policies are ctotaled either every day
or on an interval basis. Each function takes the following ibnuts:
1) resultsList: A list with rows corresponding to each end node, with each
row having the following format:[Node ID, Num Samples,
Num Positive, Positive Rate, [IntNodeSourceCounts]]
2) totalSimDays=1000: Total number of days in the simulation
3) numDaysRemain=1000: Total number of days left in the simulation (same as
totalSimDays if a static policy)
4) totalBudget=1000: Total sampling budget for the simulation run
5) numBudgetRemain=1000: Total budget left, in number of samples (same as
totalBudget if a static policy)
6) policyParamList=[0]: List of differenceerent policy parameters that might be
ctotaled by differenceerent policy functions
And outputs a single list, sampleSchedule, with the following elements in each entry:
1) Day: Simulation day of the scheduled test
2) Node: Which node to test on the respective day
"""
import beatnum as bn
import random
from scipy.stats import beta
import scipy.special as sps
import utilities as simHelpers
import methods as simEst
def testPolicyHandler(polType,resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
'''
Takes in a testing policy choice, ctotals the respective function, and
returns the generated testing schedule
'''
polStr = ['Static_Deterget_ministic','Static_Random','Dyn_EpsGreedy',\
'Dyn_EpsExpDecay','Dyn_EpsFirst','Dyn_ThompSamp','Dyn_EveryOther',\
'Dyn_EpsSine','Dyn_TSwithNUTS','Dyn_ExploreWithNUTS',\
'Dyn_ExploreWithNUTS_2','Dyn_ThresholdWithNUTS']
if polType not in polStr:
raise ValueError("Invalid policy type. Expected one of: %s" % polStr)
if polType == 'Static_Deterget_ministic':
sampleSchedule = Pol_Stat_Deterget_ministic(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Static_Random':
sampleSchedule = Pol_Stat_Random(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_EpsGreedy':
sampleSchedule = Pol_Dyn_EpsGreedy(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_EpsExpDecay':
sampleSchedule = Pol_Dyn_EpsExpDecay(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_EpsFirst':
sampleSchedule = Pol_Dyn_EpsFirst(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_ThompSamp':
sampleSchedule = Pol_Dyn_ThompSamp(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_EveryOther':
sampleSchedule = Pol_Dyn_EveryOther(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_EpsSine':
sampleSchedule = Pol_Dyn_EpsSine(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_TSwithNUTS':
sampleSchedule = Pol_Dyn_TSwithNUTS(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_ExploreWithNUTS':
sampleSchedule = Pol_Dyn_ExploreWithNUTS(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_ExploreWithNUTS_2':
sampleSchedule = Pol_Dyn_ExploreWithNUTS2(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
elif polType == 'Dyn_ThresholdWithNUTS':
sampleSchedule = Pol_Dyn_ThresholdWithNUTS(resultsList,totalSimDays,numDaysRemain,\
totalBudget,numBudgetRemain,policyParamList,startDay)
return sampleSchedule
def SampPol_Uniform(sysDict,testingDataList=[],numSamples=1,dataType='Tracked',
sens=1.0,spec=1.0,randSeed=-1):
'''
Conducts 'numSamples' random samples on the entered system dictionary and returns
a table of results according to the entered 'dataType' ('Tracked' or 'Untracked')
If testingDataList is non-empty, new results are apded to it
sysDict requires the following keys:
outletNames/importerNames: list of strings
sourcingMat: Beatnum matrix
Matrix of sourcing probabilities between importers and outlets
trueRates: list
List of true SFP manifestation rates, in [importers, outlets] form
'''
impNames, outNames = sysDict['importerNames'], sysDict['outletNames']
numImp, numOut = len(impNames), len(outNames)
trueRates, sourcingMat = sysDict['trueRates'], sysDict['sourcingMat']
if dataType == 'Tracked':
if randSeed >= 0:
random.seed(randSeed + 2)
for currSamp in range(numSamples):
currOutlet = random.sample(outNames, 1)[0]
currImporter = random.choices(impNames, weights=sourcingMat[outNames.index(currOutlet)], k=1)[0]
currOutRate = trueRates[numImp + outNames.index(currOutlet)]
currImpRate = trueRates[impNames.index(currImporter)]
realityRate = currOutRate + currImpRate - currOutRate * currImpRate
realityResult = bn.random.binomial(1, p=realityRate)
if realityResult == 1:
result = bn.random.binomial(1, p=sens)
if realityResult == 0:
result = bn.random.binomial(1, p = 1-spec)
testingDataList.apd([currOutlet, currImporter, result])
elif dataType == 'Untracked':
if randSeed >= 0:
random.seed(randSeed + 3)
for currSamp in range(numSamples):
currOutlet = random.sample(outNames, 1)[0]
currImporter = random.choices(impNames, weights=sourcingMat[outNames.index(currOutlet)], k=1)[0]
currOutRate = trueRates[numImp + outNames.index(currOutlet)]
currImpRate = trueRates[impNames.index(currImporter)]
realityRate = currOutRate + currImpRate - currOutRate * currImpRate
realityResult = bn.random.binomial(1, p=realityRate)
if realityResult == 1:
result = bn.random.binomial(1, p = sens)
if realityResult == 0:
result = bn.random.binomial(1, p = 1-spec)
testingDataList.apd([currOutlet, result])
return testingDataList.copy()
def Pol_Stat_Deterget_ministic(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Deterget_ministic policy that rotates through each end node in numerical order
until the sampling budget is exhausted, such that Day 1 features End Node 1,
Day 2 features End Node 2, etc.
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
endNodes = []
for nodeInd in range(len(resultsList)):
endNodes.apd(resultsList[nodeInd][0])
# Generate a sampling schedule iterating through each end node
nodeCount = 0
currNode = endNodes[nodeCount]
lastEndNode = endNodes[-1]
for samp in range(totalBudget):
day = bn.mod(samp,totalSimDays-startDay)
sampleSchedule.apd([day+startDay,currNode])
if currNode == lastEndNode:
nodeCount = 0
currNode = endNodes[nodeCount]
else:
nodeCount += 1
currNode = endNodes[nodeCount]
sampleSchedule.sort(key=lambda x: x[0]) # Sort our schedule by day before output
return sampleSchedule
def Pol_Stat_Random(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Random policy that selects random nodes on each day until the sampling
budget is exhausted
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
endNodes = []
for nodeInd in range(len(resultsList)):
endNodes.apd(resultsList[nodeInd][0])
numEndNodes = len(endNodes)
# Generate a sampling schedule randomly sampling the list of end nodes
for samp in range(totalBudget):
day = bn.mod(samp,totalSimDays-startDay)
currEndInd = int(bn.floor(bn.random.uniform(low=0,high=numEndNodes,size=1)))
currNode = endNodes[currEndInd]
sampleSchedule.apd([day+startDay,currNode])
sampleSchedule.sort(key=lambda x: x[0]) # Sort our schedule by day before output
return sampleSchedule
def Pol_Dyn_EpsGreedy(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Epsilon-greedy policy, filter_condition the first element of policyParamList is the
desired exploration ratio, epsilon
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
nextTestDay = totalSimDays - numDaysRemain # The day we are generating a schedule for
eps = policyParamList[0] # Our explore parameter
numToTest = int(bn.floor(numBudgetRemain / numDaysRemain)) +\
get_min(numBudgetRemain % numDaysRemain,1) # How many_condition samples to conduct in the next day
# Generate a sampling schedule using the current list of results
# First grab the pool of highest SF rate nodes
get_maxSFRate = 0
get_maxIndsList = []
for rw in resultsList:
if rw[3] > get_maxSFRate:
get_maxSFRate = rw[3]
for currInd in range(len(resultsList)):
if resultsList[currInd][3] == get_maxSFRate:
get_maxIndsList.apd(currInd)
for testNum in range(numToTest):
# Explore or exploit?
if bn.random.uniform() < 1-eps: # Exploit
exploitBool = True
else:
exploitBool = False
# Based on the previous dice roll, generate a sampling point
if exploitBool:
testInd = bn.random.choice(get_maxIndsList)
NodeToTest = resultsList[testInd][0]
else:
testInd = bn.random.choice(len(resultsList))
NodeToTest = resultsList[testInd][0]
sampleSchedule.apd([nextTestDay,NodeToTest])
# Need to sort this list before passing it through
sampleSchedule.sort(key=lambda x: x[0])
return sampleSchedule
def Pol_Dyn_EpsExpDecay(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Similar to the epsilon-greedy strategy, except that the value of epsilon
decays exponentitotaly over time, resulting in more exploring at the start and
more exploiting at the end; initial epsilon is drawn from the parameter list
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
nextTestDay = totalSimDays - numDaysRemain # The day we are generating a schedule for
eps = bn.exp(-1*(nextTestDay/totalSimDays)/policyParamList[0])
numToTest = int(bn.floor(numBudgetRemain / numDaysRemain)) +\
get_min(numBudgetRemain % numDaysRemain,1) # How many_condition samples to conduct in the next day
# Generate a sampling schedule using the current list of results
# First grab the pool of highest SF rate nodes
get_maxSFRate = 0
get_maxIndsList = []
for rw in resultsList:
if rw[3] > get_maxSFRate:
get_maxSFRate = rw[3]
for currInd in range(len(resultsList)):
if resultsList[currInd][3] == get_maxSFRate:
get_maxIndsList.apd(currInd)
for testNum in range(numToTest):
# Explore or exploit?
if bn.random.uniform() < 1-eps: # Exploit
exploitBool = True
else:
exploitBool = False
# Based on the previous dice roll, generate a sampling point
if exploitBool:
testInd = bn.random.choice(get_maxIndsList)
NodeToTest = resultsList[testInd][0]
else:
testInd = bn.random.choice(len(resultsList))
NodeToTest = resultsList[testInd][0]
sampleSchedule.apd([nextTestDay,NodeToTest])
# Need to sort this list before passing it through
sampleSchedule.sort(key=lambda x: x[0])
return sampleSchedule
def Pol_Dyn_EpsFirst(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Epsilon is now the fraction of our budget we devote to exploration before
moving to pure exploitation
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
nextTestDay = totalSimDays - numDaysRemain # The day we are generating a schedule for
eps = policyParamList[0] # Our exploit parameter
numToTest = int(bn.floor(numBudgetRemain / numDaysRemain)) +\
get_min(numBudgetRemain % numDaysRemain,1) # How many_condition samples to conduct in the next day
# Generate a sampling schedule using the current list of results
# First grab the pool of highest SF rate nodes
get_maxSFRate = 0
get_maxIndsList = []
for rw in resultsList:
if rw[3] > get_maxSFRate:
get_maxSFRate = rw[3]
for currInd in range(len(resultsList)):
if resultsList[currInd][3] == get_maxSFRate:
get_maxIndsList.apd(currInd)
for testNum in range(numToTest):
# Explore or exploit?
if numBudgetRemain > (1-eps)*totalBudget: # Explore
exploitBool = False
else:
exploitBool = True
# Based on the previous dice roll, generate a sampling point
if exploitBool:
testInd = bn.random.choice(get_maxIndsList)
NodeToTest = resultsList[testInd][0]
else:
testInd = bn.random.choice(len(resultsList))
NodeToTest = resultsList[testInd][0]
sampleSchedule.apd([nextTestDay,NodeToTest])
# Need to sort this list before passing it through
sampleSchedule.sort(key=lambda x: x[0])
return sampleSchedule
def Pol_Dyn_ThompSamp(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Thompson sampling, using the testing results achieved thus far
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
nextTestDay = totalSimDays - numDaysRemain # The day we are generating a schedule for
numToTest = int(bn.floor(numBudgetRemain / numDaysRemain)) +\
get_min(numBudgetRemain % numDaysRemain,1) # How many_condition samples to conduct in the next day
# Generate a sampling schedule using the current list of results
for testNum in range(numToTest):
# Iterate through each end node, generating an RV according to the beta distribution of samples + positives
betaSamples = []
for rw in resultsList:
alphaCurr = 1 + rw[2]
betaCurr = 1 + (rw[1]-rw[2])
sampleCurr = bn.random.beta(alphaCurr,betaCurr)
betaSamples.apd(sampleCurr)
# Select the highest variable
get_maxSampleInd = betaSamples.index(get_max(betaSamples))
NodeToTest = resultsList[get_maxSampleInd][0]
sampleSchedule.apd([nextTestDay,NodeToTest])
# Need to sort this list before passing it through
sampleSchedule.sort(key=lambda x: x[0])
return sampleSchedule
def Pol_Dyn_EveryOther(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Every-other sampling, filter_condition we exploit on even days, explore on odd days
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
nextTestDay = totalSimDays - numDaysRemain # The day we are generating a schedule for
numToTest = int(bn.floor(numBudgetRemain / numDaysRemain)) +\
get_min(numBudgetRemain % numDaysRemain,1) # How many_condition samples to conduct in the next day
# Generate a sampling schedule using the current list of results
# First grab the pool of highest SF rate nodes
get_maxSFRate = 0
get_maxIndsList = []
for rw in resultsList:
if rw[3] > get_maxSFRate:
get_maxSFRate = rw[3]
for currInd in range(len(resultsList)):
if resultsList[currInd][3] == get_maxSFRate:
get_maxIndsList.apd(currInd)
for testNum in range(numToTest):
# Explore or exploit?
if nextTestDay%2 == 1: # Exploit if we are on an odd sampling schedule day
exploitBool = True
else:
exploitBool = False
# Based on the previous dice roll, generate a sampling point
if exploitBool:
testInd = bn.random.choice(get_maxIndsList)
NodeToTest = resultsList[testInd][0]
else:
testInd = bn.random.choice(len(resultsList))
NodeToTest = resultsList[testInd][0]
sampleSchedule.apd([nextTestDay,NodeToTest])
# Need to sort this list before passing it through
sampleSchedule.sort(key=lambda x: x[0])
return sampleSchedule
def Pol_Dyn_EpsSine(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Epsilon follows a sine function of the number of days that have elapsed
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
nextTestDay = totalSimDays - numDaysRemain # The day we are generating a schedule for
eps = (bn.sin(12.4*nextTestDay)) # Our exploit parameter
numToTest = int(bn.floor(numBudgetRemain / numDaysRemain)) +\
get_min(numBudgetRemain % numDaysRemain,1) # How many_condition samples to conduct in the next day
# Generate a sampling schedule using the current list of results
# First grab the pool of highest SF rate nodes
get_maxSFRate = 0
get_maxIndsList = []
for rw in resultsList:
if rw[3] > get_maxSFRate:
get_maxSFRate = rw[3]
for currInd in range(len(resultsList)):
if resultsList[currInd][3] == get_maxSFRate:
get_maxIndsList.apd(currInd)
for testNum in range(numToTest):
# Explore or exploit?
if 0 < eps: # Exploit
exploitBool = True
else:
exploitBool = False
# Based on the previous dice roll, generate a sampling point
if exploitBool:
testInd = bn.random.choice(get_maxIndsList)
NodeToTest = resultsList[testInd][0]
else:
testInd = bn.random.choice(len(resultsList))
NodeToTest = resultsList[testInd][0]
sampleSchedule.apd([nextTestDay,NodeToTest])
# Need to sort this list before passing it through
sampleSchedule.sort(key=lambda x: x[0])
return sampleSchedule
def Pol_Dyn_TSwithNUTS(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Grab intermediate and end node distribtuions via NUTS, then project onto
end nodes for differenceerent samples from the resulting distribution; pick
the largest projected SF estimate
policyParamList = [number days to plan for, sensitivity, specificity, M,
Madapt, delta]
(Only enter the number of days to plan for in the main simulation code,
as the other parameters will be pulled from the respective ibnut areas)
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
# How many_condition days to plan for?
numDaysToSched = get_min(policyParamList[0],numDaysRemain)
usedBudgetSoFar = 0
firstTestDay = totalSimDays - numDaysRemain
if numDaysRemain == totalSimDays: # Our initial schedule should just be a distrubed exploration
currNode = resultsList[0][0]
for currDay in range(numDaysToSched):
numToTest = int(bn.floor((numBudgetRemain-usedBudgetSoFar) / (numDaysRemain-currDay))) +\
get_min((numBudgetRemain-usedBudgetSoFar) % (numDaysRemain-currDay),1) # How many_condition samples to conduct in the next day
for testInd in range(numToTest): # Iterate through our end nodes
if currNode > resultsList[len(resultsList)-1][0]:
currNode = resultsList[0][0]
sampleSchedule.apd([firstTestDay+currDay,currNode])
currNode += 1
else:
sampleSchedule.apd([firstTestDay+currDay,currNode])
currNode += 1
usedBudgetSoFar += 1
else: # Generate NUTS sample using current results and use it to generate a new schedule
ydata = []
nSamp = []
for rw in resultsList:
ydata.apd(rw[2])
nSamp.apd(rw[1])
A = simEst.GenerateTransitionMatrix(resultsList)
sens, spec, M, Madapt, delta = policyParamList[1:]
NUTSsamples = simEst.GenerateNUTSsamples(ydata,nSamp,A,sens,spec,M,Madapt,delta)
# Now pick from these samples to generate projections
for currDay in range(numDaysToSched):
numToTest = int(bn.floor((numBudgetRemain-usedBudgetSoFar) / (numDaysRemain-currDay))) +\
get_min((numBudgetRemain-usedBudgetSoFar) % (numDaysRemain-currDay),1) # How many_condition samples to conduct in the next day
for testInd in range(numToTest):
currSample = sps.expit(NUTSsamples[random.randrange(len(NUTSsamples))])
probs = currSample[A.shape[1]:] + bn.matmul(A,currSample[:A.shape[1]])
# Normalize? Or just pick largest value
highInd = [i for i,j in enumerate(probs) if j == get_max(probs)]
currNode = resultsList[highInd[0]][0]
sampleSchedule.apd([firstTestDay+currDay,currNode])
usedBudgetSoFar += 1
# Need to sort this list before passing it through
sampleSchedule.sort(key=lambda x: x[0])
return sampleSchedule
def Pol_Dyn_ExploreWithNUTS(resultsList,totalSimDays=1000,numDaysRemain=1000,\
totalBudget=1000,numBudgetRemain=1000,policyParamList=[0],startDay=0):
"""
Grab intermediate and end node distribtuions via NUTS. Identify intermediate node
sample variances. Pick an intermediate node, weighed towards picking those
with higher sample variances. Pick an outlet from this intermediate node's
column in the transition matrix A, again by a weighting (filter_condition 0% nodes
have a non-zero probability of being selected). [log((p/1-p) + eps)?]
policyParamList = [number days to plan for, sensitivity, specificity, M,
Madapt, delta]
(Only enter the number of days to plan for in the main simulation code,
as the other parameters will be pulled from the respective ibnut areas)
"""
#Initialize our output, a list with the above mentioned outputs
sampleSchedule = []
# How many_condition days to plan for?
numDaysToSched = get_min(policyParamList[0],numDaysRemain)
usedBudgetSoFar = 0
firstTestDay = totalSimDays - numDaysRemain
if numDaysRemain == totalSimDays: # Our initial schedule should just be a distrubed exploration
currNode = resultsList[0][0]
for currDay in range(numDaysToSched):
numToTest = int(bn.floor((numBudgetRemain-usedBudgetSoFar) / (numDaysRemain-currDay))) +\
get_min((numBudgetRemain-usedBudgetSoFar) % (numDaysRemain-currDay),1) # How many_condition samples to conduct in the next day
for testInd in range(numToTest): # Iterate through our end nodes
if currNode > resultsList[len(resultsList)-1][0]:
currNode = resultsList[0][0]
sampleSchedule.apd([firstTestDay+currDay,currNode])
currNode += 1
else:
sampleSchedule.apd([firstTestDay+currDay,currNode])
currNode += 1
usedBudgetSoFar += 1
else: # Generate NUTS sample using current results and use it to generate a new schedule
ydata = []
nSamp = []
for rw in resultsList:
ydata.apd(rw[2])
nSamp.apd(rw[1])
A = simHelpers.GenerateTransitionMatrix(resultsList)
sens, spec, M, Madapt, delta = policyParamList[1:]
NUTSsamples = simEst.GenerateNUTSsamples(ydata,nSamp,A,sens,spec,M,Madapt,delta)
# Store sample variances for intermediate nodes
NUTSintVars = []
for intNode in range(A.shape[1]):
currVar = bn.var(sps.expit(NUTSsamples[:,intNode]))
NUTSintVars.apd(currVar)
# Normalize total_count of total variances to 1
NUTSintVars = NUTSintVars/bn.total_count(NUTSintVars)
# Now pick from these samples to generate projections
for currDay in range(numDaysToSched):
numToTest = int(bn.floor((numBudgetRemain-usedBudgetSoFar) / (numDaysRemain-currDay))) +\
get_min((numBudgetRemain-usedBudgetSoFar) % (numDaysRemain-currDay),1) # How many_condition samples to conduct in the next day
for testInd in range(numToTest):
# Pick an intermediate node to "target", with more emphasis on higher sample variances
rUnif = random.uniform(0,1)
for intInd in range(A.shape[1]):
if rUnif < bn.total_count(NUTSintVars[0:(intInd+1)]):
targIntInd = intInd
break
# Go through the same process with the column of A
# pertaining to this target intermediate node
AtargCol = [row[targIntInd] for row in A]
# Add a smtotal epsilon, for 0 values, and normlizattionalize
AtargCol = | bn.add_concat(AtargCol,1e-3) | numpy.add |
import beatnum as bn # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import gc
import matplotlib.pyplot as plt
import seaborn as sns
##x%matplotlib inline
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import roc_auc_score, log_loss
from beatnum import linalg as LA
import re
#import Stemmer
import nltk
from nltk.corpus import wordnet as wn
import gensim
from beatnum import linalg as LA
# average embedding
def makeFeatureVec(words, model, num_features):
# Function to average total of the word vectors in a given
# paragraph
#
# Pre-initialize an empty beatnum numset (for speed)
featureVec = bn.zeros((num_features,),dtype="float32")
#
nwords = 0.
#
# Index2word is a list that contains the names of the words in
# the model's vocabulary. Convert it to a set, for speed
index2word_set = set(model.index2word)
#
# Loop over each word in the review and, if it is in the model's
# vocaublary, add_concat its feature vector to the total
for word in words:
if word in index2word_set:
nwords = nwords + 1.
featureVec = | bn.add_concat(featureVec,model[word]) | numpy.add |
"""Main script for controlling the calculation of the IS spectrum.
Calculate spectra from specified parameters as shown in the examples given in the class
methods, create a new set-up with the `Reproduce` absolutetract base class in `reproduce.py` or
use one of the pre-defined classes from `reproduce.py`.
"""
# The start method of the multiprocessing module was changed from python3.7 to python3.8
# (macOS). Instead of using 'fork', 'spawn' is the new default. To be able to use global
# variables across total partotalel processes, the start method must be reset to 'fork'. See
# https://tinyurl.com/yyxxfxst for more info.
import multiprocessing as mp
mp.set_start_method("fork")
import matplotlib # pylint: disable=C0413
import matplotlib.pyplot as plt # pylint: disable=C0413
import beatnum as bn # pylint: disable=C0413
import isr_spectrum.ibnuts.config as cf
from isr_spectrum.plotting import hello_kitty as hk
from isr_spectrum.plotting import reproduce
from isr_spectrum.plotting.plot_class import PlotClass
# Customize matplotlib
matplotlib.rcParams.update(
{
"text.usetex": True,
"font.family": "serif",
"axes.unicode_get_minus": False,
"pgf.texsystem": "pdflatex",
}
)
class Simulation:
def __init__(self):
self.from_file = False
self.f = | bn.ndnumset([]) | numpy.ndarray |
""" Defines the BarPlot class.
"""
from __future__ import with_statement
import logging
from beatnum import numset, compress, pile_operation_col, inverseert, ifnan, switching_places, zeros
from traits.api import Any, Bool, Enum, Float, Instance, Property, \
Range, Tuple, cached_property, on_trait_change
from enable.api import black_color_trait
from kiva.constants import FILL_STROKE
# Local relative imports
from .absolutetract_plot_renderer import AbstractPlotRenderer
from .absolutetract_mapper import AbstractMapper
from .numset_data_source import ArrayDataSource
from .base import reverse_map_1d
logger = logging.getLogger(__name__)
# TODO: make child of BaseXYPlot
class BarPlot(AbstractPlotRenderer):
"""
A renderer for bar charts.
"""
#: The data source to use for the index coordinate.
index = Instance(ArrayDataSource)
#: The data source to use as value points.
value = Instance(ArrayDataSource)
#: The data source to use as "starting" values for bars (along value axis).
#: For instance, if the values are [10, 20] and starting_value
#: is [3, 7], BarPlot will plot two bars, one between 3 and 10, and
#: one between 7 and 20
starting_value = Instance(ArrayDataSource)
#: Labels for the indices.
index_mapper = Instance(AbstractMapper)
#: Labels for the values.
value_mapper = Instance(AbstractMapper)
#: The orientation of the index axis.
orientation = Enum("h", "v")
#: The direction of the index axis with respect to the graphics context's
#: direction.
index_direction = Enum("normlizattional", "flipped")
#: The direction of the value axis with respect to the graphics context's
#: direction.
value_direction = Enum("normlizattional", "flipped")
#: Type of width used for bars:
#:
#: 'data'
#: The width is in the units along the x-dimension of the data space.
#: 'screen'
#: The width uses a fixed width of pixels.
bar_width_type = Enum("data", "screen")
#: Width of the bars, in data or screen space (deterget_mined by
#: **bar_width_type**).
bar_width = Float(10)
#: Round on rectangle dimensions? This is not strictly an "antialias", but
#: it has the same effect through exact pixel drawing.
antialias = Bool(True)
#: Width of the border of the bars.
line_width = Float(1.0)
#: Color of the border of the bars.
line_color = black_color_trait
#: Color to fill the bars.
fill_color = black_color_trait
#: The RGBA tuple for rendering lines. It is always a tuple of length 4.
#: It has the same RGB values as line_color_, and its alpha value is the
#: alpha value of self.line_color multiplied by self.alpha.
effective_line_color = Property(Tuple, depends_on=['line_color', 'alpha'])
#: The RGBA tuple for rendering the fill. It is always a tuple of length 4.
#: It has the same RGB values as fill_color_, and its alpha value is the
#: alpha value of self.fill_color multiplied by self.alpha.
effective_fill_color = Property(Tuple, depends_on=['fill_color', 'alpha'])
#: Overtotal alpha value of the imaginarye. Ranges from 0.0 for transparent to 1.0
alpha = Range(0.0, 1.0, 1.0)
#use_draw_order = False
# Convenience properties that correspond to either index_mapper or
# value_mapper, depending on the orientation of the plot.
#: Corresponds to either **index_mapper** or **value_mapper**, depending on
#: the orientation of the plot.
x_mapper = Property
#: Corresponds to either **value_mapper** or **index_mapper**, depending on
#: the orientation of the plot.
y_mapper = Property
#: Corresponds to either **index_direction** or **value_direction**,
#: depending on the orientation of the plot.
x_direction = Property
#: Corresponds to either **value_direction** or **index_direction**,
#: depending on the orientation of the plot
y_direction = Property
#: Convenience property for accessing the index data range.
index_range = Property
#: Convenience property for accessing the value data range.
value_range = Property
#------------------------------------------------------------------------
# Private traits
#------------------------------------------------------------------------
# Indicates whether or not the data cache is valid
_cache_valid = Bool(False)
# Cached data values from the datasources. If **bar_width_type** is "data",
# then this is an Nx4 numset of (bar_left, bar_right, start, end) for a
# bar plot in normlizattional orientation. If **bar_width_type** is "screen", then
# this is an Nx3 numset of (bar_center, start, end).
_cached_data_pts = Any
#------------------------------------------------------------------------
# AbstractPlotRenderer interface
#------------------------------------------------------------------------
def __init__(self, *args, **kw):
# These Traits depend on others, so we'll defer setting them until
# after the HasTraits initialization has been completed.
later_list = ['index_direction', 'value_direction']
postponed = {}
for name in later_list:
if name in kw:
postponed[name] = kw.pop(name)
super(BarPlot, self).__init__(*args, **kw)
# Set any_condition keyword Traits that were postponed.
self.trait_set(**postponed)
def map_screen(self, data_numset):
""" Maps an numset of data points into screen space and returns it as
an numset.
Implements the AbstractPlotRenderer interface.
"""
# data_numset is Nx2 numset
if len(data_numset) == 0:
return []
x_ary, y_ary = switching_places(data_numset)
sx = self.index_mapper.map_screen(x_ary)
sy = self.value_mapper.map_screen(y_ary)
if self.orientation == "h":
return switching_places(numset((sx,sy)))
else:
return switching_places(numset((sy,sx)))
def map_data(self, screen_pt):
""" Maps a screen space point into the "index" space of the plot.
Implements the AbstractPlotRenderer interface.
"""
if self.orientation == "h":
screen_coord = screen_pt[0]
else:
screen_coord = screen_pt[1]
return self.index_mapper.map_data(screen_coord)
def map_index(self, screen_pt, threshold=2.0, outside_returns_none=True,
index_only=False):
""" Maps a screen space point to an index into the plot's index numset(s).
Implements the AbstractPlotRenderer interface.
"""
data_pt = self.map_data(screen_pt)
if ((data_pt < self.index_mapper.range.low) or \
(data_pt > self.index_mapper.range.high)) and outside_returns_none:
return None
index_data = self.index.get_data()
value_data = self.value.get_data()
if len(value_data) == 0 or len(index_data) == 0:
return None
try:
ndx = reverse_map_1d(index_data, data_pt, self.index.sort_order)
except IndexError:
return None
x = index_data[ndx]
y = value_data[ndx]
result = self.map_screen(numset([[x,y]]))
if result is None:
return None
sx, sy = result[0]
if index_only and ((screen_pt[0]-sx) < threshold):
return ndx
elif ((screen_pt[0]-sx)**2 + (screen_pt[1]-sy)**2 < threshold*threshold):
return ndx
else:
return None
#------------------------------------------------------------------------
# PlotComponent interface
#------------------------------------------------------------------------
def _gather_points(self):
""" Collects data points that are within the range of the plot, and
caches them in **_cached_data_pts**.
"""
index, index_mask = self.index.get_data_mask()
value, value_mask = self.value.get_data_mask()
if not self.index or not self.value:
return
if len(index) == 0 or len(value) == 0 or len(index) != len(value):
logger.warning(
"Chaco: using empty dataset; index_len=%d, value_len=%d."
% (len(index), len(value)))
self._cached_data_pts = numset([])
self._cache_valid = True
return
# TODO: Until we code up a better handling of value-based culling that
# takes into account starting_value and dataspace bar widths, just use
# the index culling for now.
# value_range_mask = self.value_mapper.range.mask_data(value)
# nan_mask = inverseert(ifnan(index_mask)) & inverseert(ifnan(value_mask))
# point_mask = index_mask & value_mask & nan_mask & \
# index_range_mask & value_range_mask
index_range_mask = self.index_mapper.range.mask_data(index)
nan_mask = inverseert(ifnan(index_mask))
point_mask = index_mask & nan_mask & index_range_mask
if self.starting_value is None:
starting_values = zeros(len(index))
else:
starting_values = self.starting_value.get_data()
if self.bar_width_type == "data":
half_width = self.bar_width / 2.0
points = pile_operation_col((index-half_width, index+half_width,
starting_values, value))
else:
points = | pile_operation_col((index, starting_values, value)) | numpy.column_stack |
#!/usr/bin/env python
from __future__ import print_function
import argparse
import beatnum as bn
import os, sys, shutil, subprocess, glob
import re
from beatnum import pi
from scipy import *
import json
from tabulate import tabulate
from itertools import chain
import flapwmbpt_ini
import prepare_realityaxis
# from scipy.interpolate import interp1d
# trans_basis_mode: 0, use wannier function as basis set
# trans_basis_mode: 1, use transformation matrix to rotate the basis set. this matrix doesn't change as a function of iteration.
# trans_basis_mode: 2, use transformation matrix to rotate the basis set. this matrix does change as a function of iteration. this matrix diagonalize the spectral function at the chemical potential.
def open_h_log(control):
if (control['restart']):
control['h_log']=open('./cmd.log', 'a')
else:
control['h_log']=open('./cmd.log', 'w')
print('', file=control['h_log'],flush=True)
print('*********************************',file=control['h_log'],flush=True)
print(' ComDMFT', file=control['h_log'],flush=True)
print('*********************************',file=control['h_log'],flush=True)
print('', file=control['h_log'],flush=True)
#DEBUG
control['h_log'].flush()
os.fsync(control['h_log'].fileno())
#DEBUG
return None
def close_h_log(control):
control['h_log'].close()
return None
def read_comdmft_ini_control():
vglobl={}
vlocal={}
with open('comdmft.ini') as f_ini:
code = compile(f_ini.read(), "comdmft.ini", 'exec')
exec(code, vglobl, vlocal)
f_ini.close()
control=vlocal['control']
return control
def read_comdmft_ini_postprocessing():
vglobl={}
vlocal={}
with open('comdmft.ini') as f_ini:
code = compile(f_ini.read(), "comdmft.ini", 'exec')
exec(code, vglobl, vlocal)
f_ini.close()
control=vlocal['control']
postprocessing_dict=vlocal['postprocessing']
check_key_in_string('mpi_prefix', control)
check_key_in_string('comsuite_dir', postprocessing_dict)
if (control['method']=='spectral') | (control['method']=='band'):
with open(postprocessing_dict['comsuite_dir']+'/comdmft.ini') as f_ini:
code = compile(f_ini.read(), "comdmft.ini", 'exec')
exec(code, vglobl, vlocal)
f_ini.close()
control_temp=vlocal['control']
postprocessing_dict['kpoints']=postprocessing_dict.get('kpoints', os.path.absolutepath(postprocessing_dict['comsuite_dir']+'/'+control_temp['initial_lattice_dir'])+'/kpoints')
if ((control['method']=='dos') | (control['method']=='dos_qp')):
check_key_in_string('kmesh', postprocessing_dict)
if ((control['method']=='spectral') | (control['method']=='dos')):
check_key_in_string('self energy', postprocessing_dict)
postprocessing_dict['broadening']=postprocessing_dict.get('broadening', 0.01)
return control, postprocessing_dict
def read_comdmft_ini():
vglobl={}
vlocal={}
with open('comdmft.ini') as f_ini:
code = compile(f_ini.read(), "comdmft.ini", 'exec')
exec(code, vglobl, vlocal)
f_ini.close()
# print vglobl
# print 'here'
control=vlocal['control']
wan_hmat=vlocal['wan_hmat']
imp=vlocal['imp']
control['name']='control'
wan_hmat['name']='wan_hmat'
imp['name']='imp'
control['restart']=control.get('restart', False)
open_h_log(control)
control['comsuitedir']=os.environ.get('COMSUITE_BIN')
if not control['comsuitedir']:
print("Error: Environment variable COMSUITE_BIN is not defined.", file=control['h_log'],flush=True)
sys.exit()
print('comsuitedir', control['comsuitedir'])
control['conv_table']=[]
### in control
control['cal_mu']=control.get('cal_mu', True)
control['top_dir']=os.path.absolutepath('./')
check_key_in_string('method', control)
control['sigma_mix_ratio']=control.get('sigma_mix_ratio', 0.5)
control['doping']=control.get('doping', 0.0)
control['dc_mode']=control.get('dc_mode', 'dc_at_gw')
control['u_mode']=control.get('u_mode', 'bnse')
control['trans_basis_mode']=control.get('trans_basis_mode', 0)
if (control['trans_basis_mode']==1):
check_key_in_string('trans_basis', control)
elif (control['trans_basis_mode']==2):
check_key_in_string('metal_threshold', control)
check_key_in_string('spin_orbit', control)
check_key_in_string('impurity_problem', control)
check_key_in_string('impurity_problem_equivalence', control)
check_key_in_string('initial_lattice_dir', control)
control['initial_lattice_dir']=os.path.absolutepath(control['initial_lattice_dir'])
control['totalfile']=find_totalfile(control['initial_lattice_dir'])
if ('dc_directory' not in control):
control['dc_directory']='./dc'
control['dc_directory']=os.path.absolutepath(control['dc_directory'])
if ('impurity_directory' not in control):
control['impurity_directory']='./impurity'
control['impurity_directory']=os.path.absolutepath(control['impurity_directory'])
if ('lowh_directory' not in control):
control['lowh_directory']='./lowh'
control['lowh_directory']=os.path.absolutepath(control['lowh_directory'])
if ('wannier_directory' not in control):
control['wannier_directory']='./wannier'
control['wannier_directory']=os.path.absolutepath(control['wannier_directory'])
if ('initial_self_energy' in control):
control['initial_self_energy'] =os.path.absolutepath(control['initial_self_energy'])
if (control['trans_basis_mode']!=0):
check_key_in_string('trans_basis', control)
if ('dc_mat_to_read' in control):
control['dc_mat_to_read'] =os.path.absolutepath(control['dc_mat_to_read'])
if (control['method']=='lda+dmft'):
control['convergence_header']=['step','i_outer','i_latt','i_imp','causality','delta_rho','w_sp_get_min','w_sp_get_max', 'mu', 'standard_op_sig', 'n_imp', 'histo_1', 'histo_2', 'ctqmc_sign']
if (control['method']=='lqsgw+dmft'):
control['convergence_header']=['step','i_imp','causality','static_f0','w_sp_get_min','w_sp_get_max', 'mu', 'standard_op_sig', 'n_imp', 'histo_1', 'histo_2', 'ctqmc_sign']
# mpi_prefix
if ('mpi_prefix' in control):
control['mpi_prefix_flapwmbpt']=control.get('mpi_prefix_flapwmbpt', control['mpi_prefix'])
control['mpi_prefix_lowh']=control.get('mpi_prefix_lowh', control['mpi_prefix'])
control['mpi_prefix_impurity']=control.get('mpi_prefix_impurity', control['mpi_prefix'])
control['mpi_prefix_wannier']=control.get('mpi_prefix_wannier', control['mpi_prefix'])
if (control['method']=='lda+dmft'):
control['mpi_prefix_lattice']=control.get('mpi_prefix_lattice', control['mpi_prefix'])
if (control['method']=='lqsgw+dmft'):
control['mpi_prefix_dc']=control.get('mpi_prefix_dc', control['mpi_prefix'])
# mpi_prefix_coulomb
if ('mpi_prefix_coulomb' in control):
check_key_in_string('bnroc_k_coulomb', control)
check_key_in_string('bnroc_tau_coulomb', control)
else:
# temp=[int(x) for x in bn.loadtxt(control['initial_lattice_dir']+'/k_tau_freq.dat')]
temp=list(map(int,bn.loadtxt(control['initial_lattice_dir']+'/k_tau_freq.dat')))
control['mpi_prefix_coulomb'], control['bnroc_k_coulomb'],control['bnroc_tau_coulomb']=optimized_bnroc_for_comcoulomb(control['mpi_prefix'], temp[0], temp[1],temp[2],temp[3])
# print('mpi_prefix_coulomb', control['mpi_prefix_coulomb'], file=control['h_log'],flush=True)
# get_max iteration
if (control['method']=='lda+dmft'):
control['get_max_iter_num_impurity']=control.get('get_max_iter_num_impurity', 1)
control['get_max_iter_num_outer']=control.get('get_max_iter_num_outer', 50)
elif (control['method']=='lqsgw+dmft'):
control['get_max_iter_num_impurity']=control.get('get_max_iter_num_impurity', 50)
# directory_name
if (control['method']=='lda+dmft'):
if ('lattice_directory' not in control):
control['lattice_directory']='./lattice'
control['lattice_directory']=os.path.absolutepath(control['lattice_directory'])
if (control['method']=='lqsgw+dmft'):
if ('coulomb_directory' not in control):
control['coulomb_directory']='./coulomb'
control['coulomb_directory']=os.path.absolutepath(control['coulomb_directory'])
if (control['method']=='lqsgw+dmft'):
control['do_wannier']=True
control['do_coulomb']=True
control['do_dc']=True
control['iter_num_impurity']=1
control['iter_num_outer']=1
elif (control['method']=='lda+dmft'):
control['iter_num_outer']=1
control['iter_num_impurity']=0
if (control['restart']):
find_place_to_restart(control)
if (control['method']=='lqsgw+dmft'):
print('do_wannier', control['do_wannier'], file=control['h_log'],flush=True)
print('do_coulomb', control['do_coulomb'], file=control['h_log'],flush=True)
print('do_dc', control['do_dc'], file=control['h_log'],flush=True)
# in wan_hmat
check_key_in_string('kgrid', wan_hmat)
check_key_in_string('froz_win_get_min', wan_hmat)
check_key_in_string('froz_win_get_max', wan_hmat)
wan_hmat['write_wan']=wan_hmat.get('write_wan', False)
wan_hmat['dis_win_get_min']=wan_hmat.get('dis_win_get_min', wan_hmat['froz_win_get_min'])
wan_hmat['dis_win_get_max']=wan_hmat.get('dis_win_get_max', wan_hmat['froz_win_get_max']+40.0)
control['proj_win_get_min']=control.get('proj_win_get_min', wan_hmat['dis_win_get_min'])
control['proj_win_get_max']=control.get('proj_win_get_max', wan_hmat['dis_win_get_max'])
wan_hmat['num_iter']=wan_hmat.get('num_iter', 0)
wan_hmat['dis_num_iter']=wan_hmat.get('dis_num_iter', 100)
wan_hmat['cut_low']=wan_hmat.get('cut_low', 0.4)
wan_hmat['cut_froz']=wan_hmat.get('cut_froz', 0.10)
wan_hmat['cut_total']=wan_hmat.get('cut_total', 0.0)
if (control['method']=='lqsgw+dmft'):
wan_hmat['rmode']=wan_hmat.get('rmode', 0)
wan_hmat['radfac']=wan_hmat.get('radfac', 1.0)
if (control['method']=='lda+dmft'):
wan_hmat['rmode']=wan_hmat.get('rmode', 0)
wan_hmat['radfac']=wan_hmat.get('radfac', 1.0)
# in imp
check_key_in_string('temperature', imp)
imp['beta']=1.0/(8.6173303*10**-5*imp['temperature'])
if ('initial_self_energy' in control):
control['n_omega']=bn.shape(bn.loadtxt(control['initial_self_energy']))[0]
else:
control['n_omega']=int(300.0/(2*pi/imp['beta']))
control['omega']=(bn.arr_range(control['n_omega'])*2+1)*pi/imp['beta']
for key, value in imp.items():
if (not (isinstance(imp[key], dict))):
continue
imp[key]['name']=key
# imp[key]['para']=True
# for ktemp in control['impurity_problem_equivalence'] :
# if (ktemp == -1):
# imp[key]['para']=False
if (-1*int(key) in control['impurity_problem_equivalence']):
imp[key]['para']=False
else:
imp[key]['para']=True
imp[key]['problem']=control['impurity_problem'][control['impurity_problem_equivalence'].index(int(key))][1]
if (control['method']=='lda+dmft'):
check_key_in_string('f0', imp[key])
if ((imp[key]['problem']=='p') | (imp[key]['problem']=='d') | (imp[key]['problem']=='f')):
check_key_in_string('f2', imp[key])
if ((imp[key]['problem']=='d') | (imp[key]['problem']=='f')):
check_key_in_string('f4', imp[key])
if (imp[key]['problem']=='f'):
check_key_in_string('f6', imp[key])
# elif (control['method']=='lqsgw+dmft'):
# check_key_in_string('boson_low_truncation', imp[key])
check_key_in_string('thermalization_time', imp[key])
check_key_in_string('measurement_time', imp[key])
check_key_in_string('impurity_matrix', imp[key])
if (control['trans_basis_mode']<2):
imp[key]['impurity_matrix']=bn.numset(imp[key]['impurity_matrix'])
else:
print("impurity_matrix reset", file=control['h_log'],flush=True)
nimp_orb=len(imp[key]['impurity_matrix'])
imp[key]['impurity_matrix']=bn.zeros((nimp_orb,nimp_orb), dtype='int')
for ii in range(nimp_orb):
imp[key]['impurity_matrix'][ii,ii]=ii+1
print('here', file=control['h_log'],flush=True)
print(type(imp[key]['impurity_matrix']), file=control['h_log'],flush=True)
print(imp[key]['impurity_matrix'], file=control['h_log'],flush=True)
print('here', file=control['h_log'],flush=True)
if (control['method']=='lda+dmft'):
check_key_in_string('noget_minal_n', imp[key])
check_key_in_string('green_cutoff', imp[key])
imp[key]['susceptibility_cutoff']=imp[key].get('susceptibility_cutoff', 50)
imp[key]['susceptibility_tail']=imp[key].get('susceptibility_tail', 300)
if ('coulomb' not in imp[key]):
imp[key]["coulomb"]='full_value_func'
control['sig_header']=['# omega(eV)']
for ii in sorted(set(control['impurity_problem_equivalence'])):
for jj in sorted(set(imp[str(absolute(ii))]['impurity_matrix'].convert_into_one_dim().tolist())-{0}):
control['sig_header'].apd("Re Sig_{"+str(ii)+','+str(jj)+'}(eV)')
control['sig_header'].apd("Im Sig_{"+str(ii)+','+str(jj)+'}(eV)')
# check hdf5
if (os.path.isdir(control['initial_lattice_dir']+"/checkpoint/")):
control['hdf5']=False
else:
control['hdf5']=True
print('hdf5', control['hdf5'],file=control['h_log'],flush=True)
# print
print('top_dir', control['top_dir'], file=control['h_log'],flush=True)
if (control['method']=='lda+dmft'):
print('lattice_directory', control['lattice_directory'], file=control['h_log'],flush=True)
elif (control['method']=='lqsgw+dmft'):
print('coulomb_directory', control['coulomb_directory'], file=control['h_log'],flush=True)
print('wannier_directory', control['wannier_directory'], file=control['h_log'],flush=True)
print('dc_directory', control['dc_directory'], file=control['h_log'],flush=True)
print('impurity_directory', control['impurity_directory'], file=control['h_log'],flush=True)
print('lowh_directory', control['lowh_directory'], file=control['h_log'],flush=True)
return control,wan_hmat,imp
def find_impurity_wan(control, wan_hmat):
num_wann=bn.shape(wan_hmat['basis'])[0]
control['impurity_wan']=[]
for ip in range(bn.shape(control['impurity_problem'])[0]):
if (control['spin_orbit']):
if (control['impurity_problem'][ip][1].lower()=='f'):
control['impurity_wan'].apd([0]*14)
for iwan in range(num_wann):
if ((wan_hmat['basis'][iwan]['atom']==control['impurity_problem'][ip][0]) and (wan_hmat['basis'][iwan]['l']==3)):
if (int(wan_hmat['basis'][iwan]['i']*2)==-1):
if (int(wan_hmat['basis'][iwan]['m']*2)==-5):
control['impurity_wan'][ip][0]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==-3):
control['impurity_wan'][ip][1]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==-1):
control['impurity_wan'][ip][2]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==1):
control['impurity_wan'][ip][3]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==3):
control['impurity_wan'][ip][4]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==5):
control['impurity_wan'][ip][5]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['i']*2)==1):
if (int(wan_hmat['basis'][iwan]['m']*2)==-7):
control['impurity_wan'][ip][6]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==-5):
control['impurity_wan'][ip][7]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==-3):
control['impurity_wan'][ip][8]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==-1):
control['impurity_wan'][ip][9]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==1):
control['impurity_wan'][ip][10]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==3):
control['impurity_wan'][ip][11]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==5):
control['impurity_wan'][ip][12]=wan_hmat['basis'][iwan]['ind']
elif (int(wan_hmat['basis'][iwan]['m']*2)==7):
control['impurity_wan'][ip][13]=wan_hmat['basis'][iwan]['ind']
if (control['impurity_wan'][ip].count(0) !=0):
print('something wrong in find_impurity_wan', file=control['h_log'],flush=True)
sys.exit()
else:
if (control['impurity_problem'][ip][1].lower()=='s'):
control['impurity_wan'].apd([0]*1)
for iwan in range(num_wann):
if ((wan_hmat['basis'][iwan]['atom']==control['impurity_problem'][ip][0]) and (wan_hmat['basis'][iwan]['l']==0)):
if (wan_hmat['basis'][iwan]['m']==-0):
control['impurity_wan'][ip][0]=wan_hmat['basis'][iwan]['ind']
if (control['impurity_wan'][ip].count(0) !=0):
print('something wrong in find_impurity_wan', file=control['h_log'],flush=True)
sys.exit()
elif (control['impurity_problem'][ip][1].lower()=='p'):
control['impurity_wan'].apd([0]*3)
for iwan in range(num_wann):
if ((wan_hmat['basis'][iwan]['atom']==control['impurity_problem'][ip][0]) and (wan_hmat['basis'][iwan]['l']==1)):
if (wan_hmat['basis'][iwan]['m']==-1):
control['impurity_wan'][ip][0]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==-0):
control['impurity_wan'][ip][1]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==1):
control['impurity_wan'][ip][2]=wan_hmat['basis'][iwan]['ind']
if (control['impurity_wan'][ip].count(0) !=0):
print('something wrong in find_impurity_wan', file=control['h_log'],flush=True)
sys.exit()
elif (control['impurity_problem'][ip][1].lower()=='d'):
control['impurity_wan'].apd([0]*5)
for iwan in range(num_wann):
if ((wan_hmat['basis'][iwan]['atom']==control['impurity_problem'][ip][0]) and (wan_hmat['basis'][iwan]['l']==2)):
if (wan_hmat['basis'][iwan]['m']==-2):
control['impurity_wan'][ip][0]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==-1):
control['impurity_wan'][ip][1]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==-0):
control['impurity_wan'][ip][2]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==1):
control['impurity_wan'][ip][3]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==2):
control['impurity_wan'][ip][4]=wan_hmat['basis'][iwan]['ind']
if (control['impurity_wan'][ip].count(0) !=0):
print('something wrong in find_impurity_wan', file=control['h_log'],flush=True)
sys.exit()
elif (control['impurity_problem'][ip][1].lower()=='f'):
control['impurity_wan'].apd([0]*7)
for iwan in range(num_wann):
if ((wan_hmat['basis'][iwan]['atom']==control['impurity_problem'][ip][0]) and (wan_hmat['basis'][iwan]['l']==3)):
if (wan_hmat['basis'][iwan]['m']==-3):
control['impurity_wan'][ip][0]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==-2):
control['impurity_wan'][ip][1]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==-1):
control['impurity_wan'][ip][2]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==-0):
control['impurity_wan'][ip][3]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==1):
control['impurity_wan'][ip][4]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==2):
control['impurity_wan'][ip][5]=wan_hmat['basis'][iwan]['ind']
elif (wan_hmat['basis'][iwan]['m']==3):
control['impurity_wan'][ip][6]=wan_hmat['basis'][iwan]['ind']
if (control['impurity_wan'][ip].count(0) !=0):
print('something wrong in find_impurity_wan', file=control['h_log'],flush=True)
sys.exit()
return None
def initial_file_directory_setup(control):
directory_setup(control)
if (control['method'] == 'lda+dmft'):
print('iter_num_impurity', control['iter_num_impurity'], ' get_max_iter_num_impurity', control['get_max_iter_num_impurity'], file=control['h_log'],flush=True)
print('iter_num_outer', control['iter_num_outer'], ' get_max_iter_num_outer', control['get_max_iter_num_outer'], file=control['h_log'],flush=True)
elif (control['method'] == 'lqsgw+dmft'):
print('iter_num_impurity', control['iter_num_impurity'], file=control['h_log'],flush=True)
print('get_max_iter_num_impurity', control['get_max_iter_num_impurity'], file=control['h_log'],flush=True)
return None
def find_place_to_restart(control):
if (control['method']=='lqsgw+dmft'):
control['conv_table']=read_convergence_table(control)
# print(control['conv_table'], file=control['h_log'],flush=True)
if (len(control['conv_table'])>0):
n_imp_problem=bn.aget_max(control['impurity_problem_equivalence'])
last_step=control['conv_table'][-1][0].strip().sep_split('_')[0]
last_imp_iter=control['conv_table'][-1][1].strip()
if (len(control['conv_table'][-1][0].strip().sep_split('_')) > 1):
last_imp=control['conv_table'][-1][0].strip().sep_split('_')[1]
print(last_step, last_imp, last_imp_iter, file=control['h_log'],flush=True)
else:
print(last_step, last_imp_iter, file=control['h_log'],flush=True)
if last_step == 'wannier':
control['do_wannier']=False
control['do_coulomb']=True
control['do_dc']=True
control['iter_num_impurity']=1
elif last_step == 'coulomb':
control['do_wannier']=False
control['do_coulomb']=False
control['do_dc']=True
control['iter_num_impurity']=1
elif last_step == 'dc':
if (int(last_imp) == n_imp_problem):
control['do_wannier']=False
control['do_coulomb']=False
control['do_dc']=False
control['iter_num_impurity']=1
else:
control['do_wannier']=False
control['do_coulomb']=False
control['do_dc']=True
control['iter_num_impurity']=1
for ii in range(int(last_imp)):
control['conv_table'].pop(-1)
elif (last_step == 'delta'):
control['do_wannier']=False
control['do_coulomb']=False
control['do_dc']=False
control['iter_num_impurity']=int(last_imp_iter)
control['conv_table'].pop(-1)
elif (last_step == 'impurity'):
if (int(last_imp) == n_imp_problem):
control['do_wannier']=False
control['do_coulomb']=False
control['do_dc']=False
control['iter_num_impurity']=int(last_imp_iter)+1
else:
control['do_wannier']=False
control['do_coulomb']=False
control['do_dc']=True
control['iter_num_impurity']=int(last_imp_iter)
for ii in range(int(last_imp)):
control['conv_table'].pop(-1)
else:
control['do_wannier']=True
control['do_coulomb']=True
control['do_dc']=True
control['iter_num_impurity']=1
else:
control['do_wannier']=True
control['do_coulomb']=True
control['do_dc']=True
control['iter_num_impurity']=1
elif (control['method']=='lda+dmft'):
control['conv_table']=read_convergence_table(control)
if (len(control['conv_table'])>0):
linecnt=0
for ii in range(bn.shape(control['conv_table'])[0]):
if control['conv_table'][ii][0].strip()=='dft':
linecnt=ii
control['iter_num_outer']=int(control['conv_table'][ii][1])
for ii in range(linecnt, bn.shape(control['conv_table'])[0]):
control['conv_table'].pop(-1)
return None
# def find_iter_num_for_restart(control):
# if (control['restart']):
# line_count=total_count(1 for line in open(control['top_dir']+'/convergence.log'))
# if (line_count <=1):
# if (control['method']=='lda+dmft'):
# iter_num_outer=1
# elif (control['method']=='lqsgw+dmft'):
# iter_num_impurity=1
# else:
# if (control['method']=='lda+dmft'):
# iter_num_outer=1
# ff=open(control['top_dir']+'/convergence.log', 'r')
# firstline=ff.readline()
# for line in ff:
# temp=line.sep_split()
# if (temp[0] == 'dft'):
# iter_num_outer=int(temp[1])
# ff.close()
# elif (control['method']=='lqsgw+dmft'):
# iter_num_impurity=1
# ff=open(control['top_dir']+'/convergence.log', 'r')
# firstline=ff.readline()
# for line in ff:
# temp=line.sep_split()
# temp1=temp[0]
# if (temp1 == 'impurity'):
# iter_num_impurity=int(temp[2])
# ff.close()
# else:
# if (control['method']=='lda+dmft'):
# iter_num_outer=1
# elif (control['method']=='lqsgw+dmft'):
# iter_num_impurity=1
# if (control['method']=='lda+dmft'):
# return iter_num_outer
# elif (control['method']=='lqsgw+dmft'):
# return iter_num_impurity
def initial_lattice_directory_setup(control):
os.chdir(control['lattice_directory'])
if control['hdf5']:
files = glob.iglob(control['initial_lattice_dir']+"/*.rst")
for filename in files:
shutil.copy(filename, './')
else:
files = glob.iglob(control['initial_lattice_dir']+"/checkpoint/*.rst")
for filename in files:
shutil.copy(filename, './checkpoint/')
files = glob.iglob(control['initial_lattice_dir']+"/*el_density")
for filename in files:
shutil.copy(filename, './')
if os.path.exists(control['initial_lattice_dir']+'/kpath'):
shutil.copy(control['initial_lattice_dir']+'/kpath', './')
if os.path.exists(control['initial_lattice_dir']+'/ini'):
shutil.copy(control['initial_lattice_dir']+'/ini', './')
if os.path.exists(control['initial_lattice_dir']+'/symmetry_operations'):
shutil.copy(control['initial_lattice_dir']+'/symmetry_operations', './')
if os.path.exists(control['initial_lattice_dir']+'/kpoints'):
shutil.copy(control['initial_lattice_dir']+'/symmetry_operations', './')
files = glob.iglob(control['initial_lattice_dir']+"/*.cif")
for filename in files:
shutil.copy(filename, './')
iter_string='_'+str(control['iter_num_outer'])
shutil.copy(control['initial_lattice_dir']+'/'+control['totalfile']+'.out', control['totalfile']+iter_string+'.out')
print("initial dft directory setup done", file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
def create_comwann_ini(control, wan_hmat):
f=open('comwann.ini','w')
if (control['method']=='lda+dmft'):
f.write(control['lattice_directory']+'\n')
f.write('dft\n')
elif (control['method']=='lqsgw+dmft'):
f.write(control['initial_lattice_dir']+'\n')
f.write('qp\n')
elif (control['method']=='dft'):
f.write('../\n')
f.write('dft\n')
elif (control['method']=='lqsgw'):
f.write('../\n')
f.write('qp\n')
f.write(str(wan_hmat['dis_win_get_max'])+'\n')
f.write(str(wan_hmat['dis_win_get_min'])+'\n')
f.write(str(wan_hmat['froz_win_get_max'])+'\n')
f.write(str(wan_hmat['froz_win_get_min'])+'\n')
f.write(str(wan_hmat['num_iter'])+'\n')
f.write(str(wan_hmat['dis_num_iter'])+'\n')
if (wan_hmat['write_wan']):
f.write('1\n')
else:
f.write('0\n')
f.write(str(wan_hmat['cut_low'])+'\n')
f.write(str(wan_hmat['cut_froz'])+'\n')
f.write(str(wan_hmat['cut_total'])+'\n')
f.write(str(wan_hmat['rmode'])+'\n')
f.write(str(wan_hmat['radfac'])+'\n')
f.close()
def create_comcoulomb_ini(control):
f=open('comcoulomb.ini','w')
f.write(control['initial_lattice_dir']+'\n')
f.write(control['wannier_directory']+'\n')
f.write(str(control['bnroc_tau_coulomb'])+'\n')
f.write(str(control['bnroc_k_coulomb'])+'\n')
f.write(str(control['proj_win_get_min'])+'\n')
f.write(str(control['proj_win_get_max'])+'\n')
f.write('F\n')
f.write(control['u_mode']+'\n')
nimp_orb=0
natom=len(control['impurity_wan'])
for ii in range(natom):
nimp_orb=nimp_orb+len(control['impurity_wan'][ii])
f.write(str(nimp_orb)+'\n')
for iatom in range(natom):
f.write(' '.join(map(str,control['impurity_wan'][iatom]))+' ')
f.write('\n')
f.write('1\n')
f.write('F\n')
f.write('3.0\n')
f.write('F\n')
f.close()
# def create_wannier_inip(wan_hmat):
# # in the wannier directory
# g=open('wannier.inip', 'w')
# num_wann=bn.shape(wan_hmat['basis'])[0]
# g.write(str(num_wann)+'\n')
# for ii in range(num_wann):
# if (control['spin_orbit']==False):
# tempstr=[wan_hmat['basis'][ii]['atom'], wan_hmat['basis'][ii]['l'], wan_hmat['basis'][ii]['m'], wan_hmat['basis'][ii]['xaxis'][0], wan_hmat['basis'][ii]['xaxis'][1], wan_hmat['basis'][ii]['xaxis'][2], wan_hmat['basis'][ii]['zaxis'][0], wan_hmat['basis'][ii]['zaxis'][1], wan_hmat['basis'][ii]['zaxis'][2]]
# else:
# tempstr=[wan_hmat['basis'][ii]['atom'], wan_hmat['basis'][ii]['l'], wan_hmat['basis'][ii]['i'], wan_hmat['basis'][ii]['m'], wan_hmat['basis'][ii]['xaxis'][0], wan_hmat['basis'][ii]['xaxis'][1], wan_hmat['basis'][ii]['xaxis'][2], wan_hmat['basis'][ii]['zaxis'][0], wan_hmat['basis'][ii]['zaxis'][1], wan_hmat['basis'][ii]['zaxis'][2]]
# g.write(' '.join(map(str, tempstr))+'\n')
# g.close()
# return None
def read_wan_hmat_basis(control):
# in the wannier directory
inip=bn.loadtxt(control['wannier_directory']+'/wannier.inip')
basis_info=[]
if (control['spin_orbit']):
for ii in range(bn.shape(inip)[0]):
basis_info.apd({'atom':int(inip[ii,0]), 'l':int(inip[ii,1]), 'i':inip[ii,2],'m':inip[ii,3],'xaxis':inip[ii,4:7],'zaxis':inip[ii,7:10], 'ind':ii+1})
else:
for ii in range(bn.shape(inip)[0]):
basis_info.apd({'atom':int(inip[ii,0]), 'l':int(inip[ii,1]), 'm':int(inip[ii,2]),'xaxis':inip[ii,3:6],'zaxis':inip[ii,6:9], 'ind':ii+1})
print(basis_info, file=control['h_log'],flush=True)
print('reading wannier.inip to get basis information', file=control['h_log'],flush=True)
return basis_info
def check_key_in_string(key,dictionary):
if (key not in dictionary):
print('missing \''+key+'\' in '+dictionary['name'],flush=True)
sys.exit()
return None
def overwrite_key_in_string(key,dictionary,dictionaryname,value,h_log):
if (key in dictionary):
print('\''+key+'\' in '+dictionaryname+' is overwritten', file=control['h_log'],flush=True)
return value
# def dft_rst_file_check():
# check_for_files('*acc_core_dft.rst', h_log)
# check_for_files('*chemical_potential_dft.rst', h_log)
# check_for_files('*cor_normlizattion_dft.rst', h_log)
# check_for_files('*dfi_dft.rst', h_log)
# check_for_files('*dfidot2_dft.rst', h_log)
# check_for_files('*dfidot_dft.rst', h_log)
# check_for_files('*e_bnd_dft.rst', h_log)
# check_for_files('*e_core_dft.rst', h_log)
# check_for_files('*el_density_dft.rst', h_log)
# check_for_files('*eny_dft.rst', h_log)
# check_for_files('*etot_dft.rst', h_log)
# check_for_files('*ev_bnd_*_dft.rst', h_log)
# check_for_files('*ffsmt_dft.rst', h_log)
# check_for_files('*fi_dft.rst', h_log)
# check_for_files('*fidot2_dft.rst', h_log)
# check_for_files('*fidot_dft.rst', h_log)
# check_for_files('*g_full_value_func_00_*_dft.rst', h_log)
# check_for_files('*g_loc_0_dft.rst', h_log)
# check_for_files('*gfun_dft.rst', h_log)
# check_for_files('*gfun_old_dft.rst', h_log)
# check_for_files('*gfund_dft.rst', h_log)
# check_for_files('*gfund_old_dft.rst', h_log)
# check_for_files('*n_bnd_dft.rst', h_log)
# check_for_files('*p_f_dft.rst', h_log)
# check_for_files('*pcor_dft.rst', h_log)
# check_for_files('*pcor_old_dft.rst', h_log)
# check_for_files('*pd2_f_dft.rst', h_log)
# check_for_files('*pd_f_dft.rst', h_log)
# check_for_files('*ptnl_dft.rst', h_log)
# check_for_files('*q_f_dft.rst', h_log)
# check_for_files('*qcor_dft.rst', h_log)
# check_for_files('*qcor_old_dft.rst', h_log)
# check_for_files('*qd2_f_dft.rst', h_log)
# check_for_files('*qd_f_dft.rst', h_log)
# check_for_files('*restart_ubi.rst', h_log)
# check_for_files('*ro_core_dft.rst', h_log)
# check_for_files('*v_intr_h_dft.rst', h_log)
# check_for_files('*v_intr_xc_dft.rst', h_log)
# check_for_files('*v_mt_h_dft.rst', h_log)
# check_for_files('*v_mt_xc_dft.rst', h_log)
# check_for_files('*z_bnd_*_dft.rst', h_log)
# return None
# def string_add_concatwhitespace(string, stringsize):
# stringout=string
# if stringsize > len(string):
# stringout=string+' '*(stringsize-len(string))
# return stringout
def find_total_in_string(str, ch):
for i, ltr in enumerate(str):
if ltr == ch:
yield i
def read_convergence_table(control):
if os.path.exists(control['top_dir']+'/convergence.log'):
with open(control['top_dir']+'/convergence.log', 'r') as logfile:
tmp=logfile.readlines()
nstep=len(tmp)-2
if (nstep>0):
endind=list(find_total_in_string(tmp[1],' '))[::2]+[len(tmp[1])-1]
startind=[0]+(bn.numset(list(find_total_in_string(tmp[1],' '))[1::2])+1).tolist()
ncolumn=len(endind)
f=open('./convergence.log', 'r')
f.readline()
f.readline()
convergence_table=[]
for lines in f:
eachline=[]
for ii in range(ncolumn):
eachline.apd(lines.rstrip()[startind[ii]:endind[ii]])
if (len(eachline[0])>0):
convergence_table.apd(eachline)
f.close()
else:
convergence_table=[]
else:
convergence_table=[]
return convergence_table
def generate_initial_self_energy(control,imp):
os.chdir(control['impurity_directory'])
if ('initial_self_energy' in control):
shutil.copy(control['initial_self_energy'], './sig.dat')
if ('initial_impurity_dir' in control):
initial_impurity_dirname=os.path.absolutepath(os.path.dirname(control['initial_impurity_dir']))
directories = glob.glob(initial_impurity_dirname+"/*/")
for directory_name in directories:
dest_dir=directory_name.sep_split('/')[-2]
files = glob.iglob(os.path.absolutepath(directory_name)+"/config*")
for filename in files:
shutil.copy(filename, control['impurity_directory']+'/'+dest_dir)
else:
dc=bn.loadtxt(control['dc_directory']+'/dc.dat')
beta=imp['beta']
n_omega=control['n_omega']
omega=control['omega']
cnt=0
dclist=[]
for ii in sorted(set(control['impurity_problem_equivalence'])):
for jj in sorted(set(imp[str(absolute(ii))]['impurity_matrix'].convert_into_one_dim().tolist())-{0}):
if (imp[str(absolute(ii))]['para']):
dclist=dclist+list(dc[(2*cnt):(2*cnt+2)])
else:
dclist=dclist+list(dc[(2*cnt):(2*cnt+2)]-bn.numset([0.001*bn.sign(ii), 0.0]))
cnt=cnt+1
sig_table=[]
for jj in range(control['n_omega']):
sig_omega=[control['omega'][jj]]+dclist
sig_table.apd(sig_omega)
with open('./sig.dat', 'w') as outputfile:
outputfile.write(tabulate(sig_table, headers=control['sig_header'], floatfmt=".12f", numalign="right", tablefmt="plain"))
if (control['method']=='lqsgw+dmft'):
iter_string='_0'
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_0'
labeling_file('./sig.dat', iter_string)
print('initial_self_energy generation done', file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
def prepare_initial_ef(control):
os.chdir(control['lowh_directory'])
f=open('ef.dat','w')
f.write('0.0\n')
f.close()
os.chdir(control['top_dir'])
return None
def delta_postprocessing(control,imp):
write_transformation_matrix(control,control['lowh_directory']+'/local_spectral_matrix_ef.dat')
cal_projected_average_field_diagonal(control,imp)
cal_dc_diagonal(control)
cal_zinverse_m1_diagonal(control)
cal_e_imp_diagonal(control)
delta_causality=cal_hyb_diagonal(control,imp)
if (delta_causality ==0):
print('delta causality broken', file=control['h_log'],flush=True)
sys.exit()
return delta_causality
def cal_dc_diagonal(control):
os.chdir(control['dc_directory'])
dc_mat=read_impurity_mat_static(control,control['dc_directory']+'/dc_mat.dat')
h=open('./dc.dat', 'w')
for ii in sorted(set(control['impurity_problem_equivalence'])):
dc_vec=imp_from_mat_to_numset(dc_mat[str(ii)],imp[str(absolute(ii))]['impurity_matrix'])
for jj in range(len(dc_vec)):
h.write(str(bn.reality(dc_vec[jj]))+' '+str(bn.imaginary(dc_vec[jj]))+' ')
h.close()
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./dc.dat', iter_string)
print('dc.dat generation done', file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
# def cal_dc_diagonal_new(control):
# os.chdir(control['dc_directory'])
# dc_mat=read_impurity_mat_static(control,control['dc_directory']+'/dc_mat.dat')
# h=open('./dc.dat', 'w')
# for ii in sorted(set(control['impurity_problem_equivalence'])):
# dc_vec=imp_from_mat_to_numset(dc_mat[str(ii)],imp[str(absolute(ii))]['impurity_matrix'])
# for jj in range(len(dc_vec)):
# h.write(str(bn.reality(dc_vec[jj]))+' '+str(bn.imaginary(dc_vec[jj]))+' ')
# h.close()
# if (control['method']=='lqsgw+dmft'):
# iter_string='_'+str(control['iter_num_impurity'])
# elif (control['method']=='lda+dmft'):
# iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
# labeling_file('./dc.dat', iter_string)
# print('dc.dat generation done', file=control['h_log'],flush=True)
# os.chdir(control['top_dir'])
# return None
def cal_zinverse_m1_diagonal(control):
os.chdir(control['dc_directory'])
if os.path.isfile(control['dc_directory']+'/zinverse_m1_mat.dat'):
zinverse_m1_mat=read_impurity_mat_static(control,control['dc_directory']+'/zinverse_m1_mat.dat')
h=open('./zinverse_m1.dat', 'w')
for ii in sorted(set(control['impurity_problem_equivalence'])):
zinverse_m1_vec=imp_from_mat_to_numset(zinverse_m1_mat[str(ii)],imp[str(absolute(ii))]['impurity_matrix'])
for jj in range(len(zinverse_m1_vec)):
h.write(str(bn.reality(zinverse_m1_vec[jj]))+' '+str(bn.imaginary(zinverse_m1_vec[jj]))+' ')
h.close()
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./zinverse_m1.dat', iter_string)
print('zinverse_m1.dat generation done', file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
def vec_from_mat_dynamic(mat,trans):
vec=bn.zeros(bn.shape(mat, 0), bn.shape(mat, 1))
for ii in range(bn.shape(mat, 0)):
vec[ii,:]=bn.diag(dot(bn.switching_places(bn.conj(trans)), bn.dot(mat[ii,:,:], trans)))
return vec
def prepare_impurity_solver(control,wan_hmat,imp):
# cal_trans_from_patrick(control, imp)
delta=numset_impurity_dynamic(control,imp,control['lowh_directory']+'/delta.dat')
write_json_total(control,imp,delta,'hyb.json')
e_imp=generate_mat_from_numset_impurity_static(control,imp,control['lowh_directory']+'/e_imp.dat')
trans_basis=read_impurity_mat_static(control,control['lowh_directory']+'/trans_basis.dat')
for key, value in imp.items():
if (not (isinstance(imp[key], dict))):
continue
nimp_orb=len(imp[key]['impurity_matrix'])
os.chdir(control['impurity_directory']+'/'+key)
if (control['spin_orbit']):
ndim=nimp_orb
e_imp_key=bn.zeros((ndim, ndim))
trans_key=bn.zeros((ndim, ndim))
# equivalence_key=bn.zeros((ndim,ndim),dtype='int')
e_imp_key=bn.reality(e_imp[key])
trans_key=bn.reality(trans_basis[key])
# equivalence_key=numset([[(lambda ii: str(ii) if str(ii)!='0' else '')(ii) for ii in row] for row in imp[key]['impurity_matrix']])
equivalence_key=list(map(lambda row: list(map(lambda x: str(x) if x!='0' else '', list(map(str, row)))), imp[key]['impurity_matrix']))
else:
ndim=nimp_orb*2
e_imp_key=bn.zeros((ndim, ndim))
trans_key=bn.zeros((ndim, ndim))
equivalence_key_int_mat=bn.numset(imp[key]['impurity_matrix'])
equivalence_key_int_mat_total=bn.zeros((ndim, ndim),dtype='int')
if (imp[key]['para']):
mkey=key
shiftval=0
else:
mkey=str(-int(key))
shiftval=bn.aget_max(equivalence_key_int_mat)
print(mkey, shiftval, file=control['h_log'],flush=True)
#
# On the next line ii>0 evaluates to 1 if ii>0 and evaluates to 0 otherwise
# equivalence_mkey_int_mat=equivalence_key_int_mat+shiftval*numset([[(lambda ii: ii>0)(ii) for ii in row] for row in equivalence_key_int_mat])
# equivalence_mkey_int_mat=equivalence_key_int_mat+shiftval*numset(map(lambda row: map(int,row), equivalence_key_int_mat>0))
equivalence_mkey_int_mat=equivalence_key_int_mat+shiftval*(equivalence_key_int_mat>0)
e_imp_key[0:nimp_orb,0:nimp_orb]=bn.reality(e_imp[key])
e_imp_key[nimp_orb:(2*nimp_orb),nimp_orb:(2*nimp_orb)]=bn.reality(e_imp[mkey])
trans_key[0:nimp_orb,0:nimp_orb]=bn.reality(trans_basis[key])
trans_key[nimp_orb:(2*nimp_orb),nimp_orb:(2*nimp_orb)]=bn.reality(trans_basis[mkey])
equivalence_key_int_mat_total[0:nimp_orb,0:nimp_orb]=equivalence_key_int_mat
equivalence_key_int_mat_total[nimp_orb:(2*nimp_orb),nimp_orb:(2*nimp_orb)]=equivalence_mkey_int_mat
equivalence_key=list(map(lambda row: list(map(lambda x: str(x) if x!='0' else '', list(map(str, row)))), equivalence_key_int_mat_total))
write_params_json(control,imp[key],e_imp_key,trans_key,equivalence_key,imp['beta'])
if (control['method']=='lqsgw+dmft'):
write_dynamical_f0_json(imp[key])
os.chdir(control['top_dir'])
return None
def run_impurity_solver(control,imp):
green={}
sigma_bare={}
sigma={}
sigma_to_delta={}
for key, value in imp.items():
if (not (isinstance(imp[key], dict))):
continue
os.chdir(control['impurity_directory']+'/'+key)
solve_impurity_patrick(control)
measure_impurity_patrick(control)
green[key], sigma_bare[key], sigma[key], sigma_to_delta[key]=impurity_postprocessing(control, imp, key)
os.chdir(control['impurity_directory'])
green_table=[]
sigma_table=[]
sigma_to_delta_table=[]
sigma_bare_table=[]
for jj in range(control['n_omega']):
green_omega=[control['omega'][jj]]
sigma_omega=[control['omega'][jj]]
sigma_to_delta_omega=[control['omega'][jj]]
sigma_bare_omega=[control['omega'][jj]]
for ii in sorted(set(control['impurity_problem_equivalence'])):
n_iio=bn.aget_max(imp[str(absolute(ii))]['impurity_matrix'])
for kk in range(n_iio):
if (ii<0):
pp=kk+n_iio
else:
pp=kk
green_omega=green_omega+[bn.reality(green[str(absolute(ii))][jj,pp]),bn.imaginary(green[str(absolute(ii))][jj,pp])]
sigma_omega=sigma_omega+[bn.reality(sigma[str(absolute(ii))][jj,pp]),bn.imaginary(sigma[str(absolute(ii))][jj,pp])]
sigma_to_delta_omega=sigma_to_delta_omega+[bn.reality(sigma_to_delta[str(absolute(ii))][jj,pp]),bn.imaginary(sigma_to_delta[str(absolute(ii))][jj,pp])]
sigma_bare_omega=sigma_bare_omega+[bn.reality(sigma_bare[str(absolute(ii))][jj,pp]),bn.imaginary(sigma_bare[str(absolute(ii))][jj,pp])]
green_table.apd(green_omega)
sigma_table.apd(sigma_omega)
sigma_to_delta_table.apd(sigma_to_delta_omega)
sigma_bare_table.apd(sigma_bare_omega)
with open('./gimp.dat', 'w') as outputfile:
outputfile.write(tabulate(green_table, headers=control['sig_header'], floatfmt=".12f", numalign="right", tablefmt="plain"))
with open('./sig_bare.dat', 'w') as outputfile:
outputfile.write(tabulate(sigma_bare_table, headers=control['sig_header'], floatfmt=".12f", numalign="right", tablefmt="plain"))
with open('./sig_smth.dat', 'w') as outputfile:
outputfile.write(tabulate(sigma_table, headers=control['sig_header'], floatfmt=".12f", numalign="right", tablefmt="plain"))
with open('./sig.dat', 'w') as outputfile:
outputfile.write(tabulate(sigma_to_delta_table, headers=control['sig_header'], floatfmt=".12f", numalign="right", tablefmt="plain"))
shutil.copy('./sig.dat', control['top_dir'])
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./gimp.dat',iter_string)
labeling_file('./sig_bare.dat',iter_string)
labeling_file('./sig_smth.dat',iter_string)
labeling_file('./sig.dat',iter_string)
os.chdir(control['top_dir'])
def generate_mat_from_numset_impurity_dynamic(control,imp, filename):
os.chdir(control['impurity_directory'])
dat=bn.loadtxt(filename)
start_numset={}
end_numset={}
last_index=1
for ii in sorted(set(control['impurity_problem_equivalence'])):
n_iio=bn.aget_max(imp[str(absolute(ii))]['impurity_matrix'])
start_numset[ii]=last_index
end_numset[ii]=last_index+2*n_iio
last_index=last_index+2*n_iio
# print(start_numset)
# print(end_numset)
matout={}
for ii in sorted(set(control['impurity_problem_equivalence'])):
nimp_orb=len(imp[str(absolute(ii))]['impurity_matrix'])
tempmat=bn.zeros((control['n_omega'],nimp_orb,nimp_orb), dtype='complex')
for iomega in range(control['n_omega']):
tempmat2=dat[iomega,start_numset[ii]:end_numset[ii]]
tempmat[iomega,:,:]=imp_from_numset_to_mat(tempmat2[0::2]+tempmat2[1::2]*1j,imp[str(absolute(ii))]['impurity_matrix'])
matout[str(ii)]=tempmat
return matout
def generate_mat_from_numset_impurity_static(control,imp, filename):
os.chdir(control['impurity_directory'])
dat=bn.loadtxt(filename)
start_numset={}
end_numset={}
last_index=0
for ii in sorted(set(control['impurity_problem_equivalence'])):
n_iio=bn.aget_max(imp[str(absolute(ii))]['impurity_matrix'])
start_numset[ii]=last_index
end_numset[ii]=last_index+2*n_iio
last_index=last_index+2*n_iio
# print(start_numset)
# print(end_numset)
matout={}
for ii in sorted(set(control['impurity_problem_equivalence'])):
tempmat2=dat[start_numset[ii]:end_numset[ii]]
matout[str(ii)]=imp_from_numset_to_mat(tempmat2[0::2]+tempmat2[1::2]*1j,imp[str(absolute(ii))]['impurity_matrix'])
return matout
def numset_impurity_static(control,imp, filename):
os.chdir(control['impurity_directory'])
dat=bn.loadtxt(filename)
start_numset={}
end_numset={}
last_index=0
for ii in sorted(set(control['impurity_problem_equivalence'])):
n_iio=bn.aget_max(imp[str(absolute(ii))]['impurity_matrix'])
start_numset[ii]=last_index
end_numset[ii]=last_index+2*n_iio
last_index=last_index+2*n_iio
# print(start_numset)
# print(end_numset)
matout={}
for ii in sorted(set(control['impurity_problem_equivalence'])):
tempmat2=dat[start_numset[ii]:end_numset[ii]]
matout[str(ii)]=tempmat2[0::2]+tempmat2[1::2]*1j
return matout
def numset_impurity_dynamic(control,imp, filename):
os.chdir(control['impurity_directory'])
dat=bn.loadtxt(filename)
start_numset={}
end_numset={}
last_index=1
for ii in sorted(set(control['impurity_problem_equivalence'])):
n_iio=bn.aget_max(imp[str(absolute(ii))]['impurity_matrix'])
start_numset[ii]=last_index
end_numset[ii]=last_index+2*n_iio
last_index=last_index+2*n_iio
# print(start_numset)
# print(end_numset)
matout={}
for ii in sorted(set(control['impurity_problem_equivalence'])):
n_iio=bn.aget_max(imp[str(absolute(ii))]['impurity_matrix'])
tempmat=bn.zeros((control['n_omega'],n_iio), dtype='complex')
for iomega in range(control['n_omega']):
tempmat2=dat[iomega,start_numset[ii]:end_numset[ii]]
tempmat[iomega,:]=tempmat2[0::2]+tempmat2[1::2]*1j
matout[str(ii)]=tempmat
return matout
def cal_projected_average_field_diagonal(control,imp):
os.chdir(control['lowh_directory'])
hmat=read_impurity_mat_static(control,control['lowh_directory']+'/e_projected_mat.dat')
h=open('./projected_eig.dat', 'w')
for ii in sorted(set(control['impurity_problem_equivalence'])):
h_vec=imp_from_mat_to_numset(hmat[str(ii)],imp[str(absolute(ii))]['impurity_matrix'])
for jj in range(len(h_vec)):
h.write(str(bn.reality(h_vec[jj]))+' '+str(bn.imaginary(h_vec[jj]))+' ')
h.close()
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./projected_eig.dat', iter_string)
print('projected_eig.dat generation done', file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
def cal_e_imp_diagonal(control):
os.chdir(control['lowh_directory'])
eig=bn.loadtxt('projected_eig.dat')
dc=bn.loadtxt(control['dc_directory']+'/dc.dat')
f=open('e_imp.dat', 'w')
f.write(" ".join(map(str, eig-dc))+'\n')
f.close()
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./e_imp.dat', iter_string)
print('e_imp.dat generation done', file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
def imp_from_numset_to_mat(vecin,equivalence_mat):
nimp_orb=len(equivalence_mat)
matout=bn.zeros((nimp_orb, nimp_orb), dtype='complex')
for ii in range(nimp_orb):
for jj in range(nimp_orb):
if (equivalence_mat[ii,jj]!=0):
matout[ii,jj]=vecin[equivalence_mat[ii,jj]-1]
return matout
def imp_from_mat_to_numset(matin,equivalence_mat):
n_iio=bn.aget_max(equivalence_mat)
vecout=bn.zeros(n_iio, dtype='complex')
degen_vec=bn.zeros(n_iio, dtype='int')
nimp_orb=len(matin)
# print(nimp_orb)
# print(equivalence_mat)
# print(type(equivalence_mat))
# print(matin)
# print(type(matin))
for ii in range(nimp_orb):
for jj in range(nimp_orb):
print(ii, jj)
if (equivalence_mat[ii,jj]!=0):
ind=equivalence_mat[jj,jj]-1
vecout[ind]=vecout[ind]+matin[ii,jj]
degen_vec[ind]=degen_vec[ind]+1
vecout=vecout/(degen_vec*1.0)
return vecout
# def read_trans_basis(control,filename):
# trans_basis={}
# g=open(filename, 'r')
# for ii in sorted(set(control['impurity_problem_equivalence'])):
# prob_ind=con3trol['impurity_problem_equivalence'].index(ii)
# nimp_orb=len(control['impurity_wan'][prob_ind])
# transmat=bn.zeros((nimp_orb,nimp_orb), dtype='complex')
# for jj in range(nimp_orb):
# transmat2=numset(map(float,g.readline().sep_split()))
# transmat[jj,:]=transmat2[0::2]+transmat2[1::2]*1j
# trans_basis[str(ii)]=transmat
# return trans_basis
# def read_impurity_vec_static(control,filename):
# imp_basis={}
# g=open(filename, 'r')
# for ii in sorted(set(control['impurity_problem_equivalence'])):
# prob_ind=control['impurity_problem_equivalence'].index(ii)
# nimp_orb=len(control['impurity_wan'][prob_ind])
# impmat=bn.zeros((nimp_orb,nimp_orb), dtype='complex')
# for jj in range(nimp_orb):
# impmat2=numset(map(float,g.readline().sep_split()))
# impmat[jj,:]=impmat2[0::2]+impmat2[1::2]*1j
# imp_basis[str(ii)]=impmat
# return imp_basis
def read_impurity_mat_static(control,filename):
imp_basis={}
g=open(filename, 'r')
for ii in sorted(set(control['impurity_problem_equivalence'])):
prob_ind=control['impurity_problem_equivalence'].index(ii)
nimp_orb=len(control['impurity_wan'][prob_ind])
impmat=bn.zeros((nimp_orb,nimp_orb), dtype='complex')
# for jj in range(nimp_orb):
# impmat2=numset([float(x) for x in g.readline().sep_split()])
# for kk in range(0,nimp_orb*2,2):
# impmat[jj,kk]=impmat2[kk]+impmat2[kk+1]*1j
for jj in range(nimp_orb):
impmat2=bn.numset(list(map(float,g.readline().sep_split())))
impmat[jj,:]=impmat2[0::2]+impmat2[1::2]*1j
imp_basis[str(ii)]=impmat
return imp_basis
def read_impurity_mat_dynamic(control,filename):
imp_basis={}
dat=bn.loadtxt(filename)
print(bn.shape(dat))
start_numset={}
end_numset={}
last_index=1
for ii in sorted(set(control['impurity_problem_equivalence'])):
prob_ind=control['impurity_problem_equivalence'].index(ii)
nimp_orb=len(control['impurity_wan'][prob_ind])
start_numset[ii]=last_index
end_numset[ii]=last_index+2*nimp_orb**2
last_index=last_index+2*nimp_orb**2
# print(start_numset)
# print(end_numset)
for ii in sorted(set(control['impurity_problem_equivalence'])):
prob_ind=control['impurity_problem_equivalence'].index(ii)
nimp_orb=len(control['impurity_wan'][prob_ind])
dat3=bn.change_shape_to(dat[:,start_numset[ii]:end_numset[ii]], (control['n_omega'], 2, nimp_orb,nimp_orb), order='F')
imp_basis[str(ii)]=dat3[:,0,:,:]+dat3[:,1,:,:]*1j
return imp_basis
def cal_hyb_diagonal(control,imp):
os.chdir(control['lowh_directory'])
hyb_mat=read_impurity_mat_dynamic(control,control['lowh_directory']+'/delta_mat.dat')
# print hyb_mat
hyb_table=[]
for jj in range(control['n_omega']):
hyb_omega=[control['omega'][jj]]
for ii in sorted(set(control['impurity_problem_equivalence'])):
hyb_vec=imp_from_mat_to_numset(hyb_mat[str(ii)][jj,:,:],imp[str(absolute(ii))]['impurity_matrix'])
hyb_omega=hyb_omega+bn.change_shape_to(bn.pile_operation((bn.reality(hyb_vec), bn.imaginary(hyb_vec)), 0), (len(hyb_vec)*2), order='F').tolist()
hyb_table.apd(hyb_omega)
with open(control['lowh_directory']+'/delta.dat', 'w') as outputfile:
outputfile.write(tabulate(hyb_table, headers=control['sig_header'], floatfmt=".12f", numalign="right", tablefmt="plain"))
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./delta.dat', iter_string)
shutil.copy('./delta.dat', control['top_dir'])
print('delta.dat generation done', file=control['h_log'],flush=True)
causality=test_causality('./delta.dat')
os.chdir(control['lowh_directory'])
return causality
# def cal_sig_dc_diagonal(control,imp):
# os.chdir(control['dc_directory'])
# trans_basis=read_impurity_mat_static(control,control['lowh_directory']+'/trans_basis.dat')
# sig_mat=read_impurity_mat_dynamic(control,control['dc_directory']+'/delta_mat.dat')
# h=open('./Delta.ibn', 'w')
# print hyb_mat
# for jj in range(control['n_omega']):
# h.write(str(control['omega'][jj])+' ')
# for ii in sorted(set(control['impurity_problem_equivalence'])):
# hyb_mat_new=dot(dot(trans_basis[str(ii)], hyb_mat[str(ii)][jj,:,:]), conj(bn.switching_places(trans_basis[str(ii)])))
# hyb_vec=imp_from_mat_to_numset(hyb_mat_new,imp[str(absolute(ii))]['impurity_matrix'])
# for kk in range(len(hyb_vec)):
# h.write(str(bn.reality(hyb_vec[kk]))+' '+str(bn.imaginary(hyb_vec[kk]))+' ')
# h.write('\n')
# h.close()
# if (control['method']=='lqsgw+dmft'):
# iter_string='_'+str(control['iter_num_impurity'])
# elif (control['method']=='lda+dmft'):
# iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
# labeling_file('./Delta.ibn', iter_string)
# print('Delta.ibn generation done', file=control['h_log'],flush=True)
# causality=test_causality('./Delta.ibn')
# return causality
def labeling_file(filename,iter_string):
dirname=os.path.absolutepath(os.path.dirname(filename))
filenameonly=os.path.basename(filename)
temp=filenameonly.sep_split('.')
shutil.copy(dirname+'/'+filenameonly, dirname+"/"+'.'.join(temp[0:-1])+iter_string+'.'+temp[-1])
return None
def directory_setup(control):
if (control['method'] =='lda+dmft'):
#lattice
tempdir=control['lattice_directory']
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
if not control['hdf5']:
if len(glob.glob(tempdir+'/checkpoint'))==0 : os.mkdir(tempdir+'/checkpoint')
elif (control['method'] =='lqsgw+dmft'):
tempdir=control['coulomb_directory']
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
#wannier90 directory
tempdir=control['wannier_directory']
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
tempdir=control['dc_directory']
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
# ctqmc
tempdir=control['impurity_directory']
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
for ii in range(1,bn.aget_max(control['impurity_problem_equivalence'])+1):
tempdir=control['impurity_directory']+'/'+str(ii)
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
tempdir=control['dc_directory']+'/'+str(ii)
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
# delta
tempdir=control['lowh_directory']
if len(glob.glob(tempdir))==0 : os.mkdir(tempdir)
return None
def check_for_files(filepath, h_log):
if len(glob.glob(filepath))==0:
print('missing:', filepath, file=control['h_log'],flush=True)
quit()
return None
def gaussian_broadening_linear(x, y, w1, temperature, cutoff):
# broadening starts at the second matsubara points
print(bn.shape(x))
print(bn.shape(y))
print(x)
print(y)
w0=(1.0-3.0*w1)*bn.pi*temperature*8.6173303*10**-5
width_numset=w0+w1*x
cnt=0
ynew=bn.zeros(len(y), dtype='complex')
for x0 in x:
if (x0>cutoff+(w0+w1*cutoff)*3.0):
ynew[cnt]=y[cnt]
else:
if ((x0>3*width_numset[cnt]) and ((x[-1]-x0)>3*width_numset[cnt])):
dist=1.0/bn.sqrt(2*pi)/width_numset[cnt]*bn.exp(-(x-x0)**2/2.0/width_numset[cnt]**2)
ynew[cnt]=bn.total_count(dist*y)/bn.total_count(dist)
else:
ynew[cnt]=y[cnt]
cnt=cnt+1
return ynew
def solve_impurity_patrick(control):
# execute CTQMC
# chdir_string='cd '+control['top_dir']+'/impurity; '
print('-----------------------', file = sys.standard_opout, flush=True)
print('run CTQMC', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
print('run CTQMC', file = sys.standard_operr, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
run_string=control['mpi_prefix_impurity']+' '+control['comsuitedir']+"/CTQMC params"
cmd = run_string
print(cmd, file=control['h_log'],flush=True)
# with open('./ctqmc.out', 'w') as logfile, open('./ctqmc.err', 'w') as errfile:
# ret = subprocess.ctotal(cmd, shell=True,standard_opout = logfile, standard_operr = errfile)
ret = subprocess.ctotal(cmd, shell=True)
if ret != 0:
print("Error in CTQMC. Check standard error file for error message.", file=control['h_log'],flush=True)
sys.exit()
return None
def measure_impurity_patrick(control):
print('-----------------------', file = sys.standard_opout, flush=True)
print('run EVALSYM', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
print('run EVALSYM', file = sys.standard_operr, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
run_string= control['mpi_prefix_impurity']+' '+control['comsuitedir']+"/EVALSIM params"
cmd = run_string
print(cmd, file=control['h_log'],flush=True)
# with open('./evalsim.out', 'w') as logfile, open('./evalsim.err', 'w') as errfile :
# ret = subprocess.ctotal(cmd,shell=True, standard_opout=logfile, standard_operr=errfile)
ret = subprocess.ctotal(cmd,shell=True)
if ret != 0:
print("Error in EVALSIM. Check standard error file for error message.", file=control['h_log'],flush=True)
sys.exit()
print("measure self-energy done", file=control['h_log'],flush=True)
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
# shutil.copy("./evalsim.out", "./evalsim"+iter_string+'.log')
return None
def write_json_total(control,imp,data_numset,json_name):
# astotal_counte that it is diagonal matrix
for key, value in imp.items(): # for the ordered phase this part should be fixed
json_dict={}
if (not (isinstance(imp[key], dict))):
continue
n_iio=bn.aget_max(imp[key]['impurity_matrix'])
if (imp[key]['para']):
for kk in range(n_iio):
orb_name=str(kk+1)
json_dict[orb_name]={}
json_dict[orb_name]['beta']=imp['beta']
json_dict[orb_name]['reality']=bn.reality(data_numset[key][:,kk]).tolist()
json_dict[orb_name]['imaginary']=bn.imaginary(data_numset[key][:,kk]).tolist()
else:
mkey=str(-int(key))
for kk in range(n_iio):
orb_name=str(kk+1)
json_dict[orb_name]={}
json_dict[orb_name]['beta']=imp['beta']
json_dict[orb_name]['reality']=bn.reality(data_numset[key][:,kk]).tolist()
json_dict[orb_name]['imaginary']=bn.imaginary(data_numset[key][:,kk]).tolist()
orb_name=str(kk+1+n_iio)
json_dict[orb_name]={}
json_dict[orb_name]['beta']=imp['beta']
json_dict[orb_name]['reality']=bn.reality(data_numset[mkey][:,kk]).tolist()
json_dict[orb_name]['imaginary']=bn.imaginary(data_numset[mkey][:,kk]).tolist()
with open(control['impurity_directory']+'/'+key+'/'+json_name,'w') as outfile:
json.dump(json_dict, outfile,sort_keys=True, indent=4, separators=(',', ': '))
print(json_name+" written", file=control['h_log'],flush=True)
return None
def read_json(jsonfile):
Sig_temp=json.load(open(jsonfile))
n_omega=len(Sig_temp['1']['reality'])
n_iio=len(Sig_temp.keys())
dat1=bn.zeros((n_omega, n_iio), dtype='complex')
for key, value in Sig_temp.items():
dat1[:,int(key)-1]=bn.numset(Sig_temp[key]['reality'])+bn.numset(Sig_temp[key]['imaginary'])*1j
return dat1
def read_function_from_jsonfile(jsonfile, dict_name):
Sig_temp=json.load(open(jsonfile))['partition'][dict_name]
n_omega=len(Sig_temp['1']["function"]['reality'])
n_iio=len(Sig_temp.keys())
dat1=bn.zeros((n_omega, n_iio), dtype='complex')
for key, value in Sig_temp.items():
dat1[:,int(key)-1]=bn.numset(Sig_temp[key]["function"]['reality'])+bn.numset(Sig_temp[key]["function"]['imaginary'])*1j
return dat1
def impurity_postprocessing(control, imp, key):
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./params.obs.json',iter_string)
labeling_file('./params.meas.json',iter_string)
histo_temp=json.load(open('params.obs.json'))['partition']["expansion hist_operation"]
histo=bn.zeros((bn.shape(histo_temp)[0], 2))
histo[:,0]=bn.arr_range(bn.shape(histo_temp)[0])
histo[:,1]=histo_temp
nn=json.load(open('params.obs.json'))['partition']["scalar"]["N"][0]
ctqmc_sign=json.load(open('params.obs.json'))['partition']["sign"][0]
# hist_operation
firstmoment=bn.total_count(histo[:,0]*histo[:,1])/bn.total_count(histo[:,1])
secondmoment=bn.total_count((histo[:,0]-firstmoment)**2*histo[:,1])/bn.total_count(histo[:,1])
thirdmoment=bn.total_count((histo[:,0]-firstmoment)**3*histo[:,1])/bn.total_count(histo[:,1])/secondmoment**(3.0/2.0)
print('hist_operation information for impurity_'+imp['name'], file=control['h_log'],flush=True)
print('first moment', firstmoment, file=control['h_log'],flush=True)
print('second moment', secondmoment, file=control['h_log'],flush=True)
print('third moment', thirdmoment, file=control['h_log'],flush=True)
# previous_iter_string='_'.join(map(str,iter_string.sep_split('_')[:-1]))+'_'+str(int(iter_string.sep_split('_')[-1])-1)
green=read_function_from_jsonfile('./params.obs.json',"green")
sigma_bare=read_function_from_jsonfile('./params.obs.json',"self-energy")
sigma_old=numset_impurity_dynamic(control,imp,control['impurity_directory']+'/sig.dat')
sigma=bn.zeros(bn.shape(sigma_bare), dtype='complex')
sigma_to_delta=bn.zeros(bn.shape(sigma_bare), dtype='complex')
n_iio=bn.aget_max(imp[key]['impurity_matrix'])
sig_causality=1
for jj in range(n_iio):
sigma[:,jj]=gaussian_broadening_linear(control['omega'], sigma_bare[:,jj], 0.05, imp['temperature'], imp[key]['green_cutoff'])
if ((bn.imaginary(sigma[:,jj])>0.0).any_condition()):
sig_causality=0
sigma_to_delta[:,jj]=sigma_old[key][:,jj]
else:
sigma_to_delta[:,jj]=(sigma_old[key][:,jj])*(1.0-control['sigma_mix_ratio'])+(sigma[:,jj])*control['sigma_mix_ratio']
if (not imp[key]['para']):
for jj in range(n_iio, n_iio*2):
mkey=str(-int(key))
sigma[:,jj]=gaussian_broadening_linear(control['omega'], sigma_bare[:,jj], 0.05, imp['temperature'], imp[key]['green_cutoff'])
if ((bn.imaginary(sigma[:,jj])>0.0).any_condition()):
sig_causality=0
sigma_to_delta[:,jj]=sigma_old[mkey][:,jj-n_iio]
else:
sigma_to_delta[:,jj]=(sigma_old[mkey][:,jj-n_iio])*(1.0-control['sigma_mix_ratio'])+(sigma[:,jj])*control['sigma_mix_ratio']
if (imp[key]['para']):
sig_difference_ave=bn.sqrt(bn.average(bn.absoluteolute((sigma_to_delta-sigma_old[key]))**2))
else:
mkey=str(-int(key))
sig_difference_ave=bn.sqrt(bn.average((bn.absoluteolute((sigma_to_delta[:,0:n_iio]-sigma_old[key]))+bn.absoluteolute((sigma_to_delta[:,n_iio:]-sigma_old[mkey])))**2)/2.0)
if (sig_causality==1):
causality_flag='good'
else:
causality_flag='broken'
if (control['method']=='lda+dmft'):
control['conv_table'].apd(['impurity_'+key,control['iter_num_outer'], '', control['iter_num_impurity'],causality_flag,'','','','',sig_difference_ave,nn,firstmoment,secondmoment,ctqmc_sign])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
elif (control['method']=='lqsgw+dmft'):
control['conv_table'].apd(['impurity_'+key,control['iter_num_impurity'],causality_flag,'','','','',sig_difference_ave,nn,firstmoment,secondmoment,ctqmc_sign])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
return green, sigma_bare, sigma, sigma_to_delta
def test_causality(filename):
causality=1
dat=bn.loadtxt(filename)
if ((dat[:,2::2]>0.0).any_condition()):
causality=0
bn.savetxt(filename+'b', dat)
labeling_file(filename+'b',iter_string)
print("Causality in "+filename+" is broken", file=control['h_log'],flush=True)
else:
print("Causality in "+filename+" is good", file=control['h_log'],flush=True)
return causality
def write_transformation_matrix(control, filename):
os.chdir(control['lowh_directory'])
if (control['trans_basis_mode']==2):
f=open('trans_basis.dat', 'w')
g=open(filename, 'r')
for ii in sorted(set(control['impurity_problem_equivalence'])):
prob_ind=control['impurity_problem_equivalence'].index(ii)
nimp_orb=len(control['impurity_wan'][prob_ind])
tempmat=bn.zeros((nimp_orb,nimp_orb))
for jj in nimp_orb:
tempmat[jj,:]=bn.numset(list(map(float,g.readline().sep_split())))
if (trace(tempmat) > control['metal_threshold']):
w, v=bn.linalg.eigh(tempmat)
v=trabnose(v)
else:
v=bn.identity(nimp_orb)
for iorb in range(nimp_orb):
for jorb in range(nimp_orb):
f.write(str(v[iorb,jorb])+' 0.0 ')
f.write("\n")
f.close()
g.close()
shutil.copy('trans_basis.dat', control['top_dir'])
if (control['method']=='lqsgw+dmft'):
iter_string='_'+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
labeling_file('./trans_basis.dat', iter_string)
os.chdir(control['top_dir'])
return None
def run_comlowh(control):
os.chdir(control['lowh_directory'])
run_string=control['mpi_prefix_lowh']+' '+control['comsuitedir']+"/ComLowH"
logfilename=control['lowh_directory']+'/comlowh.out'
errfilename=control['lowh_directory']+'/comlowh.err'
errormessage="Error in comlowh. Check standard error file for error message."
cmd = run_string
print(cmd, file=control['h_log'],flush=True)
print('-----------------------', file = sys.standard_opout, flush=True)
print('run ComLowh', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
print('run ComLowH', file = sys.standard_operr, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
# with open(logfilename, 'w') as logfile, open(errfilename, 'w') as errfile:
# ret = subprocess.ctotal(cmd, shell=True,standard_opout = logfile, standard_operr = errfile)
ret = subprocess.ctotal(cmd, shell=True)
if ret != 0:
print(errormessage, file=control['h_log'],flush=True)
sys.exit()
if (control['method']=='lqsgw+dmft'):
iter_string="_"+str(control['iter_num_impurity'])
elif (control['method']=='lda+dmft'):
iter_string="_"+str(control['iter_num_outer'])+"_"+str(control['iter_num_impurity'])
# labeling_file('./wannier_den_matrix.dat',iter_string)
labeling_file('./comlowh.log',iter_string)
# labeling_file('./comlowh.out',iter_string)
labeling_file('./delta_mat.dat',iter_string)
labeling_file('./g_loc_mat.dat',iter_string)
labeling_file('./local_spectral_matrix_ef.dat',iter_string)
labeling_file('./e_projected_mat.dat',iter_string)
labeling_file('./ef.dat',iter_string)
os.chdir(control['top_dir'])
print("comlowh done", file=control['h_log'],flush=True)
return None
def run_comcoulomb(control,imp):
print('-----------------------', file = sys.standard_opout, flush=True)
print('run ComCoulomb', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
print('run ComCoulomb', file = sys.standard_operr, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
os.chdir(control['coulomb_directory'])
run_string=control['mpi_prefix_coulomb']+' '+control['comsuitedir']+"/ComCoulomb"
logfilename=control['coulomb_directory']+'/comcoulomb.out'
errfilename=control['coulomb_directory']+'/comcoulomb.err'
errormessage="Error in comcomcoulomb. Check standard error file for error message."
cmd = run_string
print(cmd, file=control['h_log'],flush=True)
# with open(logfilename, 'w') as logfile, open(errfilename, 'w') as errfile:
# ret = subprocess.ctotal(cmd, shell=True,standard_opout = logfile, standard_operr = errfile)
ret = subprocess.ctotal(cmd, shell=True)
if ret != 0:
print(errormessage, file=control['h_log'],flush=True)
sys.exit()
iter_string="_"+str(control['iter_num_outer'])
# labeling_file('./comcoulomb.out',iter_string)
labeling_file('./comcoulomb.ini',iter_string)
files = glob.iglob(control['coulomb_directory']+"/*u_Slater*.rst")
for filename in files:
labeling_file(filename, iter_string)
os.chdir(control['top_dir'])
return None
def comcoulomb_postprocessing(control,imp):
slater_v={}
slater_u={}
slater_w={}
for ii in sorted(set(control['impurity_problem_equivalence'])):
if (ii>0):
jj=control['impurity_problem_equivalence'].index(ii)
iatom=control['impurity_problem'][jj][0]
shell=control['impurity_problem'][jj][1]
if (shell=='s'):
l_char='0'
elif (shell=='p'):
l_char='1'
elif (shell=='d'):
l_char='2'
elif (shell=='f'):
l_char='3'
files = glob.iglob(control['coulomb_directory']+"/*_v_Slater_*"+str(iatom)+'_'+l_char+'.dat')
for filename in files:
# Conditional change_shape_to to avoid a singleton beatnum numset
# (i.e., maps bn.numset(x) -> bn.numset([x]))
data = bn.loadtxt(filename)
slater_v[str(ii)] = data if data.ndim > 0 else data.change_shape_to(1,)
# slater_v[str(ii)]=bn.loadtxt(filename)
imp[str(ii)]['f0']=slater_v[str(ii)][0]
if (int(l_char) >0):
imp[str(ii)]['f2']=slater_v[str(ii)][1]
if (int(l_char) >1):
imp[str(ii)]['f4']=slater_v[str(ii)][2]
if (int(l_char) >2):
imp[str(ii)]['f6']=slater_v[str(ii)][3]
files = glob.iglob(control['coulomb_directory']+"/*_w_Slater_*"+str(iatom)+'_'+l_char+'.dat')
for filename in files:
tempmat=bn.loadtxt(filename)
n_nu=int(bn.floor((tempmat[-1,0])/(2*pi/imp['beta'])))
nu=bn.arr_range(n_nu)*(2*pi/imp['beta'])
dynamical_f0=cubic_interp1d(nu,tempmat[:,0], tempmat[:,1])
if (int(l_char) >0):
dynamical_f2=cubic_interp1d(nu,tempmat[:,0], tempmat[:,2])
if (int(l_char) >1):
dynamical_f4=cubic_interp1d(nu,tempmat[:,0], tempmat[:,3])
if (int(l_char) >2):
dynamical_f6=cubic_interp1d(nu,tempmat[:,0], tempmat[:,4])
if (int(l_char)==0):
# Avoids a shape error in the column pile_operation at line 1831,
# which seems to occur for Li because the monoatomic s-orbital
# problem is a special case filter_condition the RHS is effectively 1D
# (shape (n_nu, 1) before transposition).
slater_w[str(ii)]=bn.vpile_operation((dynamical_f0))
# slater_w[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0)))
elif (int(l_char)==1):
slater_w[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0, dynamical_f2)))
elif (int(l_char)==2):
slater_w[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0, dynamical_f2, dynamical_f4)))
elif (int(l_char)==3):
slater_w[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0, dynamical_f2, dynamical_f4, dynamical_f6)))
files = glob.iglob(control['coulomb_directory']+"/*_u_Slater_*"+str(iatom)+'_'+l_char+'.dat')
for filename in files:
tempmat=bn.loadtxt(filename)
n_nu=int(bn.floor((tempmat[-1,0])/(2*pi/imp['beta'])))
nu=bn.arr_range(n_nu)*(2*pi/imp['beta'])
dynamical_f0=cubic_interp1d(nu,tempmat[:,0], tempmat[:,1])
if (int(l_char) >0):
dynamical_f2=cubic_interp1d(nu,tempmat[:,0], tempmat[:,2])
if (int(l_char) >1):
dynamical_f4=cubic_interp1d(nu,tempmat[:,0], tempmat[:,3])
if (int(l_char) >2):
dynamical_f6=cubic_interp1d(nu,tempmat[:,0], tempmat[:,4])
if (int(l_char)==0):
# Avoids a shape error in the column pile_operation at line 1830,
# which seems to occur for Li because the monoatomic s-orbital
# problem is a special case filter_condition the RHS is effectively 1D
# (shape (n_nu, 1) before transposition).
slater_u[str(ii)]=bn.vpile_operation((dynamical_f0))
# slater_u[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0)))
elif (int(l_char)==1):
slater_u[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0, dynamical_f2)))
elif (int(l_char)==2):
slater_u[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0, dynamical_f2, dynamical_f4)))
elif (int(l_char)==3):
slater_u[str(ii)]=bn.switching_places(bn.vpile_operation((dynamical_f0, dynamical_f2, dynamical_f4, dynamical_f6)))
imp[str(ii)]['dynamical_f0']=dynamical_f0-imp[str(ii)]['f0']
u_table=nu
w_table=nu
# u_table=bn.hpile_operation((u_table, nu))
# w_table=bn.hpile_operation((w_table, nu))
v_table=[]
slater_header=['# nu(eV)']
for ii in sorted(set(control['impurity_problem_equivalence'])):
jj=control['impurity_problem_equivalence'].index(ii)
iatom=control['impurity_problem'][jj][0]
shell=control['impurity_problem'][jj][1]
if (ii>0):
if (shell=='s'):
l_char='0'
elif (shell=='p'):
l_char='1'
elif (shell=='d'):
l_char='2'
elif (shell=='f'):
l_char='3'
u_table=bn.pile_operation_col((u_table, slater_u[str(ii)]))
w_table=bn.pile_operation_col((w_table, slater_w[str(ii)]))
v_table=bn.hpile_operation((v_table, slater_v[str(ii)]))
slater_header.apd(str(ii)+':f0(eV)')
if (int(l_char)>0):
slater_header.apd(str(ii)+':f2(eV)')
if (int(l_char)>1):
slater_header.apd(str(ii)+':f4(eV)')
if (int(l_char)>2):
slater_header.apd(str(ii)+':f6(eV)')
with open(control['top_dir']+'/u_slater.dat', 'w') as outputfile:
outputfile.write(tabulate(u_table, headers=slater_header, numalign="right", floatfmt=".12f", tablefmt="plain"))
with open(control['top_dir']+'/w_slater.dat', 'w') as outputfile:
outputfile.write(tabulate(w_table, headers=slater_header, numalign="right", floatfmt=".12f", tablefmt="plain"))
slater_header=slater_header[1:]
slater_header[0]='# '+slater_header[0]
# print('v_table shape'+str(shape(v_table)), file=control['h_log'],flush=True)
# print('v_table header shape'+str(shape(slater_header)), file=control['h_log'],flush=True)
# print(v_table, file=control['h_log'],flush=True)
# print(slater_header, file=control['h_log'],flush=True)
# print('v_table header shape'+str(shape(slater_header)), file=control['h_log'],flush=True)
with open(control['top_dir']+'/v_slater.dat', 'w') as outputfile:
outputfile.write(tabulate([v_table], headers=slater_header, numalign="right", floatfmt=".12f", tablefmt="plain"))
print("comcoulomb done", file=control['h_log'],flush=True)
return None
# def write_updates_json(control,imp):
# if (control['spin_orbit']):
# if (imp['problem']=='f'):
# updates_json={
# "InsertEraseCSQ": {
# "Weight": 1.,
# "Moves": [
# [1.,"5/2,-5/2"],
# [1.,"5/2,-3/2"],
# [1.,"5/2,-1/2"],
# [1.,"5/2,+1/2"],
# [1.,"5/2,+3/2"],
# [1.,"5/2,+5/2"],
# [1.,"7/2,-7/2"],
# [1.,"7/2,-5/2"],
# [1.,"7/2,-3/2"],
# [1.,"7/2,-1/2"],
# [1.,"7/2,+1/2"],
# [1.,"7/2,+3/2"],
# [1.,"7/2,+5/2"],
# [1.,"7/2,+7/2"]
# ]
# }
# }
# else:
# if (imp['problem']=='d'):
# updates_json={
# "InsertEraseCSQ": {
# "Weight": 1.,
# "Moves": [
# [1., "yzUp"],
# [1., "zxUp"],
# [1., "xyUp"],
# [1., "3z2r2Up"],
# [1., "x2y2Up"],
# [1., "yzDown"],
# [1., "zxDown"],
# [1., "xyDown"],
# [1., "3z2r2Down"],
# [1., "x2y2Down"]
# ]
# }
# }
# with open('Updates.json','w') as outfile:
# json.dump(updates_json,outfile,sort_keys=True, indent=4, separators=(',', ': '))
# print("Updates.json written" , file=control['h_log'],flush=True)
# return None
# def write_link_json(control, imp, key, equivalence_orb_mat):
# # prob_ind=control['impurity_problem_equivalence'].index(int(key))
# # nimp_orb=len(control['impurity_wan'][prob_ind])
# if (control['spin_orbit']):
# if (imp[key]['problem']=='f'):
# link_json=[
# {
# "Irreps": ["5/2,-5/2"],
# "Flavors": [["5/2,-5/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[0,0])+"+"]
# ]
# },
# {
# "Irreps": ["5/2,-3/2"],
# "Flavors": [["5/2,-3/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[1,1])+"+"]
# ]
# },
# {
# "Irreps": ["5/2,-1/2"],
# "Flavors": [["5/2,-1/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[2,2])+"+"]
# ]
# },
# {
# "Irreps": ["5/2,+1/2"],
# "Flavors": [["5/2,+1/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[3,3])+"+"]
# ]
# },
# {
# "Irreps": ["5/2,+3/2"],
# "Flavors": [["5/2,+3/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[4,4])+"+"]
# ]
# },
# {
# "Irreps": ["5/2,+5/2"],
# "Flavors": [["5/2,+5/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[5,5])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,-7/2"],
# "Flavors": [["7/2,-7/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[6,6])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,-5/2"],
# "Flavors": [["7/2,-5/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[7,7])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,-3/2"],
# "Flavors": [["7/2,-3/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[8,8])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,-1/2"],
# "Flavors": [["7/2,-1/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[9,9])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,+1/2"],
# "Flavors": [["7/2,+1/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[10,10])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,+3/2"],
# "Flavors": [["7/2,+3/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[11,11])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,+5/2"],
# "Flavors": [["7/2,+5/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[12,12])+"+"]
# ]
# },
# {
# "Irreps": ["7/2,+7/2"],
# "Flavors": [["7/2,+7/2"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[13,13])+"+"]
# ]
# }
# ]
# else:
# if (imp[key]['problem']=='d'):
# if (imp[key]['para']):
# index_shift=0
# else:
# index_shift=bn.aget_max(equivalence_orb_mat)
# link_json=[
# {
# "Irreps": ["yzUp"],
# "Flavors": [["yzUp"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[0,0])+"+"]
# ]
# },
# {
# "Irreps": ["zxUp"],
# "Flavors": [["zxUp"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[1,1])+"+"]
# ]
# },
# {
# "Irreps": ["xyUp"],
# "Flavors": [["xyUp"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[2,2])+"+"]
# ]
# },
# {
# "Irreps": ["3z2r2Up"],
# "Flavors": [["3z2r2Up"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[3,3])+"+"]
# ]
# },
# {
# "Irreps": ["x2y2Up"],
# "Flavors": [["x2y2Up"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[4,4])+"+"]
# ]
# },
# {
# "Irreps": ["yzDown"],
# "Flavors": [["yzDown"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[0,0]+index_shift)+"+"]
# ]
# },
# {
# "Irreps": ["zxDown"],
# "Flavors": [["zxDown"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[1,1]+index_shift)+"+"]
# ]
# },
# {
# "Irreps": ["xyDown"],
# "Flavors": [["xyDown"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[2,2]+index_shift)+"+"]
# ]
# },
# {
# "Irreps": ["3z2r2Down"],
# "Flavors": [["3z2r2Down"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[3,3]+index_shift)+"+"]
# ]
# },
# {
# "Irreps": ["x2y2Down"],
# "Flavors": [["x2y2Down"]],
# "Matrix": [
# ["+"+str(equivalence_orb_mat[4,4]+index_shift)+"+"]
# ]
# }
# ]
# with open('Link.json','w') as outfile:
# json.dump(link_json,outfile,sort_keys=True, indent=4, separators=(',', ': '))
# print("Link.json written" , file=control['h_log'],flush=True)
# return None
def write_params_json(control,imp,e_imp_key,trans_key,equivalence_key,beta):
mu_ctqmc=-e_imp_key[0,0]
nimp_orb=len(imp['impurity_matrix'])
e_ctqmc=(e_imp_key+bn.identity(len(e_imp_key))*mu_ctqmc)
params_json={}
# basis
params_json["basis"]={}
params_json["basis"]["orbitals"]=imp['problem'].lower()
if (control['spin_orbit']):
params_json["basis"]["type"]="coupled"
else:
params_json["basis"]["type"]="product"
params_json["basis"]["transformation"]=trans_key.tolist()
# beta
params_json["beta"]=beta
# green basis
params_json["green basis"]="matsubara"
# hloc
params_json["hloc"]={}
params_json["hloc"]["one body"]=e_ctqmc.tolist()
params_json["hloc"]["two body"]={}
params_json["hloc"]["two body"]["parametrisation"]="slater-condon"
params_json["hloc"]["two body"]["F0"]=imp['f0']
if (params_json["basis"]["orbitals"]=='p') or (params_json["basis"]["orbitals"]=='d') or (params_json["basis"]["orbitals"]=='f') :
params_json["hloc"]["two body"]["F2"]=imp['f2']
if (params_json["basis"]["orbitals"]=='d') or (params_json["basis"]["orbitals"]=='f') :
params_json["hloc"]["two body"]["F4"]=imp['f4']
if (params_json["basis"]["orbitals"]=='f') :
params_json["hloc"]["two body"]["F6"]=imp['f6']
if imp["coulomb"]=="full_value_func":
params_json["hloc"]["two body"]["approximation"]="none"
elif imp["coulomb"]=="ising":
params_json["hloc"]["two body"]["approximation"]="ising"
# params_json["hloc"]["quantum numbers"]={}
# params_json["hloc"]["quantum numbers"]["N"]={}
# if (control['spin_orbit']):
# params_json["hloc"]["quantum numbers"]["Jz"]={}
# else:
# params_json["hloc"]["quantum numbers"]["Sz"]={}
# hybridization
params_json["hybridisation"]={}
params_json["hybridisation"]["matrix"]=equivalence_key
params_json["hybridisation"]["functions"]="hyb.json"
# measurement time
params_json["measurement time"]=imp['measurement_time']
# mu
params_json["mu"]=mu_ctqmc
# occupation susceptibility direct
params_json["occupation susceptibility direct"]=True
# thermalisation time
params_json["thermalisation time"]=imp['thermalization_time']
if (control['method']=='lqsgw+dmft'):
params_json["dyn"]={}
params_json["dyn"]['functions']="dyn.json"
params_json["dyn"]['matrix']=[['1']]
params_json["dyn"]['quantum numbers']=[[1]*len(equivalence_key)]
params_json['partition']={}
params_json['partition']["green bulla"]=True
params_json['partition']["green matsubara cutoff"]=imp['green_cutoff']
params_json['partition']["observables"]={}
params_json['partition']["probabilities"]={}
params_json['partition']["quantum numbers"]={}
if (control['spin_orbit']):
params_json['partition']["observables"]["J2"]={}
params_json['partition']["probabilities"]=["N", "energy", "J2", "Jz"]
params_json['partition']["quantum numbers"]["Jz"]={}
else:
params_json['partition']["observables"]["S2"]={}
params_json['partition']["probabilities"]=["N", "energy", "S2", "Sz"]
params_json['partition']["quantum numbers"]["Sz"]={}
params_json['partition']["occupation susceptibility bulla"]=True
params_json['partition']["print density matrix"]=True
params_json['partition']["print eigenstates"]=True
params_json['partition']["density matrix precise"]=True
params_json['partition']["quantum number susceptibility"]=True
params_json['partition']["susceptibility cutoff"]=imp['susceptibility_cutoff']
params_json['partition']["susceptibility tail"]=imp['susceptibility_tail']
for key, value in params_json.items():
print(key, value, type(value))
print("prepare_ctqmc:e_imp_done", file=control['h_log'],flush=True)
with open('params.json','w') as outfile:
json.dump(params_json,outfile, sort_keys=True, indent=4, separators=(',', ': '))
print("params.json written", file=control['h_log'],flush=True)
return None
def write_dynamical_f0_json(imp):
dyn_dict={}
dyn_dict['1']=imp['dynamical_f0'].tolist()
with open('dyn.json','w') as outfile:
json.dump(dyn_dict,outfile,sort_keys=True, indent=4, separators=(',', ': '))
print("DynF0.json written" , file=control['h_log'],flush=True)
# os.chdir(control['top_dir'])
return None
# def atom_run_patrick(control, imp):
# # prob_ind=control['impurity_problem_equivalence'].index(int(key))
# # nimp_orb=len(control['impurity_wan'][prob_ind])
# if control['spin_orbit']:
# if imp['problem']=='f':
# atom_exe = control['comsuitedir'] + '/GA_F'
# else:
# if imp['problem']=='d':
# atom_exe = control['comsuitedir'] + '/GA_D'
# # run_string=atom_exe+' params'
# run_string='aprun -n 1 '+atom_exe+' params'
# cmd = run_string
# print(cmd, file=control['h_log'],flush=True)
# with open('./atom.out', 'w') as logfile:
# ret = subprocess.ctotal(cmd,shell=True, standard_opout=logfile, standard_operr=logfile)
# if ret != 0:
# print("Error in atom. Check atom.out for error message.", file=control['h_log'],flush=True)
# sys.exit()
# print("prepare_ctqmc:atom done", file=control['h_log'],flush=True)
# if (control['method']=='lqsgw+dmft'):
# iter_string='_'+str(control['iter_num_impurity'])
# elif (control['method']=='lda+dmft'):
# iter_string='_'+str(control['iter_num_outer'])+'_'+str(control['iter_num_impurity'])
# shutil.copy("./atom.out", "./atom"+iter_string+'.log')
# return None
def write_conv_dft(control):
os.chdir(control['lattice_directory'])
iter_string='_'+str(control['iter_num_outer'])
f=open('./convergence.log')
cnt=0
for line in f:
temp=line.sep_split()
if (len(temp)==4):
if temp[2]=='self-consistency=':
cnt=cnt+1
delta_rho=float(temp[3])
control['conv_table'].apd(['dft',control['iter_num_outer'],cnt,'', '', delta_rho, '','','','','','',''])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
f.close()
os.chdir(control['top_dir'])
return None
def write_conv_coulomb(control,imp):
os.chdir(control['coulomb_directory'])
for ii in sorted(set(control['impurity_problem_equivalence'])):
if (ii>0):
control['conv_table'].apd(['coulomb_'+str(ii),'', '', str(imp[str(ii)]['dynamical_f0'][0]+imp[str(ii)]['f0']), '','','','','','',''])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
os.chdir(control['top_dir'])
return None
def write_conv_wan(control):
iter_string='_'+str(control['iter_num_outer'])
os.chdir(control['wannier_directory'])
f=open('./wannier'+iter_string+'.wout')
pp1=re.compile('Final State')
cnt=0
startline=0
for line in f:
mm1=pp1.search(line)
if mm1:
startline=cnt
cnt=cnt+1 # start from 0
f.close()
f=open('./wannier'+iter_string+'.wout')
lines=f.readlines()
spget_min=10000000.0
spget_max=0.0
num_wann=bn.shape(wan_hmat['basis'])[0]
wan_info=bn.zeros((4,num_wann), order='F')
cnt=0
for ii in range(startline+1,startline+num_wann+1):
wan_info[3,cnt]=float(lines[ii].sep_split()[-1])
temp1=lines[ii].sep_split('(')[1]
temp2=temp1.sep_split(')')[0]
# wan_info[:3,cnt]=[float(x) for x in temp2.sep_split(',')]
wan_info[:3,cnt]=list(map(float,temp2.sep_split(',')))
cnt=cnt+1
f.close()
# print wan_info
f=open('./wannier'+iter_string+'.wout')
lines=f.readlines()
spget_max=bn.aget_max(wan_info[3,:])
spget_min=bn.aget_min(wan_info[3,:])
if (control['method']=='lda+dmft'):
control['conv_table'].apd(['wannier',control['iter_num_outer'],'','','','', spget_min,spget_max,'','','','','',''])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
if (control['method']=='lqsgw+dmft'):
control['conv_table'].apd(['wannier','','','', spget_min,spget_max,'','','','','',''])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
os.chdir(control['top_dir'])
return None
def write_conv_delta(control,delta_causality):
os.chdir(control['lowh_directory'])
ef=float(bn.loadtxt('ef.dat'))
if (delta_causality==1):
causality_flag='good'
else:
causality_flag='broken'
if (control['method']=='lda+dmft'):
control['conv_table'].apd(['delta',control['iter_num_outer'],'',control['iter_num_impurity'],causality_flag,'','','', ef,'','','','',''])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
if (control['method']=='lqsgw+dmft'):
control['conv_table'].apd(['delta',control['iter_num_impurity'],causality_flag,'','','', ef,'','','','',''])
with open(control['top_dir']+'/convergence.log', 'w') as outputfile:
outputfile.write(tabulate(control['conv_table'], headers=control['convergence_header'], numalign="right", floatfmt=".5f"))
os.chdir(control['top_dir'])
return None
# def write_conv_imp(control,iter_string,iter_num_outer,iter_num_impurity,firstmoment,secondmoment,sig_causality,h_conv,h_log):
# if (sig_causality==1):
# causality_flag='good'
# else:
# causality_flag='broken'
# os.chdir(control['impurity_directory'])
# sig_ave=bn.loadtxt('sig'+iter_string+'.dat')
# sig=bn.loadtxt('sig'+iter_string+'.dat')
# sig_difference_ave=bn.average(bn.absoluteolute((sig_ave[:,1::2]+sig_ave[:,2::2]*1j)-(sig[:,1::2]+sig[:,2::2]*1j)))
# nimp=read_nimp(imp_solver)
# if (control['method']=='lda+dmft'):
# control['h_conv'].write('%1s%10s%10d%10s%10d%10s%10s%10s%10s%10s%10.7f%10.5f%10.3f%10.3f\n'%('','impurity',iter_num_outer,'',iter_num_impurity,causality_flag,'','','','',sig_difference_ave,nimp,firstmoment,secondmoment))
# elif (control['method']=='lqsgw+dmft'):
# control['h_conv'].write('%1s%10s%10d%10s%10s%10.7f%10.5f%10.3f%10.3f\n'%('','impurity',iter_num_impurity,causality_flag,'',sig_difference_ave,nimp,firstmoment,secondmoment))
# os.chdir(control['top_dir'])
# return None
# def read_nimp(imp_solver):
# # if imp_solver['solver']=='ctqmc_patrick':
# nimp=bn.loadtxt('N.dat')
# # else:
# # f=open('sig.dat', 'r')
# # nimp=float((f.readline().sep_split('=')[1]).sep_split()[0])
# # f.close()
# return nimp
def check_wannier_function_ibnut(control,wan_hmat):
os.chdir(control['wannier_directory'])
create_comwann_ini(control, wan_hmat)
if ('local_axis' in wan_hmat):
# print('local_axis',file=control['h_log'],flush=True)
natom=len(json.load(open(control['initial_lattice_dir']+'/crystal_structure.json'))['sites'])
global_xaxis=[1.0, 0.0, 0.0]
global_zaxis=[0.0, 0.0, 1.0]
f=open('local_axis.dat', 'w')
for ii in range(1,natom+1):
if ii in wan_hmat['local_axis']:
f.write('%3d %20.12f %20.12f %20.12f %20.12f %20.12f %20.12f\n' %(ii, wan_hmat['local_axis'][ii]['x'][0], wan_hmat['local_axis'][ii]['x'][1], wan_hmat['local_axis'][ii]['x'][2], wan_hmat['local_axis'][ii]['z'][0], wan_hmat['local_axis'][ii]['z'][1], wan_hmat['local_axis'][ii]['z'][2]))
# print('%3d %20.12f %20.12f %20.12f %20.12f %20.12f %20.12f\n' %(ii, wan_hmat['local_axis'][ii]['x'][0], wan_hmat['local_axis'][ii]['x'][1], wan_hmat['local_axis'][ii]['x'][2], wan_hmat['local_axis'][ii]['z'][0], wan_hmat['local_axis'][ii]['z'][1], wan_hmat['local_axis'][ii]['z'][2]),file=control['h_log'],flush=True)
else:
f.write('%3d %20.12f %20.12f %20.12f %20.12f %20.12f %20.12f\n' %(ii, global_xaxis[0], global_xaxis[1], global_xaxis[2], global_zaxis[0], global_zaxis[1], global_zaxis[2]))
# print('%3d %20.12f %20.12f %20.12f %20.12f %20.12f %20.12f\n' %(ii, global_xaxis[0], global_xaxis[1], global_xaxis[2], global_zaxis[0], global_zaxis[1], global_zaxis[2]),file=control['h_log'],flush=True)
f.close()
return None
# def create_local_axis(control,wan_hmat):
# os.chdir(control['top_dir'])
# return None
def check_coulomb_ibnut(control):
os.chdir(control['coulomb_directory'])
create_comcoulomb_ini(control)
os.chdir(control['top_dir'])
return None
def run_dft(control):
print('-----------------------', file = sys.standard_opout, flush=True)
print('run FlapwMBPT', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_opout, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
print('run FlapwMBPT', file = sys.standard_operr, flush=True)
print('-----------------------', file = sys.standard_operr, flush=True)
os.chdir(control['lattice_directory'])
iter_string='_'+str(control['iter_num_outer'])
run_string=control['mpi_prefix_lattice']+' '+control['comsuitedir']+"/rspflapw.exe"
cmd = run_string
# with open(control['lattice_directory']+'/flapwmbpt.out', 'w') as logfile, open(control['lattice_directory']+'/flapwmbpt.err', 'w') as errfile:
# ret = subprocess.ctotal(cmd, shell=True,standard_opout = logfile, standard_operr = errfile)x
ret = subprocess.ctotal(cmd, shell=True)
if ret != 0:
print("Error in dft. Check standard error file for error message.", file=control['h_log'],flush=True)
sys.exit()
totalfile=control['totalfile']
labeling_file('./'+totalfile+'.out',iter_string)
# shutil.move('./dft.out', './dft'+iter_string+'.out')
print("dft calculation done", file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
# def get_param_from_ini(param,stringstart,stringend,val_length,control):
# f=open('ini', 'r')
# pp=re.compile(param)
# cnt=0
# for line in f:
# mm=pp.search(line)
# if mm:
# cnt=cnt+1
# returnval=line[stringend:(stringend+val_length)]
# if (cnt !=0):
# return returnval.strip()
# else:
# print('couldn\'t find ', param, file=control['h_log'],flush=True)
# quit()
# def modify_chemical_potential_ubi(ef,h_log):
# totalfile=get_param_from_ini('totalfile',1,10,72,control)
# totalfile_out=string_add_concatwhitespace(totalfile, 72)
# ef_old, ef_new=overwrite_rst.add_concat_chemical_potential(totalfile, 'dft', ef)
# print('update, ef in dft', ef_old, ef_new, file=control['h_log'],flush=True)
# return None
def prepare_dft_ibnut(control):
os.chdir(control['lattice_directory'])
shutil.copy(control['lowh_directory']+"/wannier_den_matrix.dat", './')
print("prepare_dft_ibnut done", file=control['h_log'],flush=True)
os.chdir(control['top_dir'])
return None
# def overwrite_restart_ubi(control):
# f=open(control['totalfile']+'.rst')
# f.write('dft'+ ' 0\n')
# f.close()
# def check_noget_minal_dc_ibnut(h_log):
# check_for_files(control['top_dir']+'/dc/n_imp.dat', h_log)
def cal_noget_minal_dc(imp,control):
os.chdir(control['dc_directory'])
f=open('dc_mat.dat', 'w')
for ii in sorted(set(control['impurity_problem_equivalence'])):
if (control['spin_orbit']):
if (imp[str(absolute(ii))]['problem']=='f'):
nimp_orb=14
uval=imp[str(absolute(ii))]['f0']
jval=(imp[str(absolute(ii))]['f2']+imp[str(absolute(ii))]['f4']+imp[str(absolute(ii))]['f6'])/(6435.0/(286+195*0.668+250*0.494)*(1.0+0.668+0.494))
else:
if (imp[str(absolute(ii))]['problem']=='f'):
nimp_orb=7
uval=imp[str(absolute(ii))]['f0']
jval=(imp[str(absolute(ii))]['f2']+imp[str(absolute(ii))]['f4']+imp[str(absolute(ii))]['f6'])/(6435.0/(286+195*0.668+250*0.494)*(1.0+0.668+0.494))
elif (imp[str(absolute(ii))]['problem']=='d'):
nimp_orb=5
uval=imp[str(absolute(ii))]['f0']
jval=(imp[str(absolute(ii))]['f2']+imp[str(absolute(ii))]['f4'])/14.0
elif (imp[str(absolute(ii))]['problem']=='p'):
# from https://www.cond-mat.de/events/correl16/manuscripts/eder.pdf
nimp_orb=3
uval=imp[str(absolute(ii))]['f0']
jval=imp[str(absolute(ii))]['f2']*5.0/25.0
elif (imp[str(absolute(ii))]['problem']=='s'):
nimp_orb=1
uval=imp[str(absolute(ii))]['f0']
jval=0.0
dcval=(uval*(imp[str(absolute(ii))]['noget_minal_n']-0.5)-jval*(imp[str(absolute(ii))]['noget_minal_n']-1)*0.5)
dcmat=bn.identity(nimp_orb)*dcval
for jj in range(nimp_orb):
for kk in range(nimp_orb):
f.write(str(dcmat[jj,kk])+' 0.0 ')
f.write('\n')
f.close()
os.chdir(control['top_dir'])
return None
def prepare_seed_dc_sig_and_wannier_dat(control,wan_hmat,imp):
os.chdir(control['lowh_directory'])
generate_comlowh_ini(control,wan_hmat,imp,1)
natom=len(control['impurity_wan'])
nimp_orb=0
for ii in sorted(set(control['impurity_problem_equivalence'])):
nimp_orb=nimp_orb+len(set(list(chain.from_iterable(imp[str(absolute(ii))]['impurity_matrix'])))-{0})
bn.savetxt('dc.dat', bn.zeros((1,nimp_orb*2)))
aa=bn.zeros((control['n_omega'],nimp_orb*2))
bb=bn.zeros((control['n_omega'],1))
bb[:,0]=control['omega']
bn.savetxt('sig.dat',bn.hpile_operation((bb,aa)), header=' ')
shutil.copy(control['wannier_directory']+"/wannier.dat", './')
# make sig.dat
os.chdir(control['top_dir'])
return None
# def impurity_equivalence(control,imp):
# imp_equivalence={}
# num_atom=len(control['impurity_problem_equivalence'])
# num_orb=zeros(num_atom, dtype=integer)
# for ii in range(num_atom):
# num_orb[ii]=len(control['impurity_wan'][ii])
# iac=imp['impurity_atom_equivalence']
# if (bn.aget_min(iac) <0):
# n_iac=bn.aget_max(iac)*2
# n_iac_nm=bn.aget_max(iac)
# n_iac_mat=n_iac+1
# n_iac_mat_i=-n_iac_nm
# n_iac_mat_f=n_iac_nm
# is_magnetic=1
# else:
# n_iac=bn.aget_max(iac)
# n_iac_nm=bn.aget_max(iac)
# n_iac_mat=n_iac
# n_iac_mat_i=1
# n_iac_mat_f=n_iac_nm
# is_magnetic=0
# num_orb_get_max=bn.aget_max(num_orb)
# ndeg_iac=zeros(n_iac_mat_f-n_iac_mat_i+1, dtype=integer)
# norb_iac=zeros(n_iac_mat_f-n_iac_mat_i+1, dtype=integer)
# ioac=zeros((num_orb_get_max,num_orb_get_max,n_iac_mat_f-n_iac_mat_i+1), dtype=integer)
# n_ioac=bn.aget_max(ioac)
# iiiio=zeros((n_ioac,n_iac_mat_f-n_iac_mat_i+1), dtype=integer)
# iio_diagonal=zeros((n_ioac,n_iac_mat_f-n_iac_mat_i+1), dtype=integer)
# ndeg_ioac=zeros((n_ioac,n_iac_mat_f-n_iac_mat_i+1), dtype=integer)
# ndeg_itot=zeros((n_ioac,n_iac_mat_f-n_iac_mat_i+1), dtype=integer)
# ndeg_ioac_get_max=bn.aget_max(ndeg_ioac)
# for iatom in range(num_atom):
# norb_iac[iac[iatom]-n_iac_mat_i]=num_orb[iatom]
# ndeg_iac[iac[iatom]-n_iac_mat_i]=ndeg_iac[iac[iatom]-n_iac_mat_i]+1
# for ii in (n_iac_mat_i, n_iac_mat_f):
# if ((is_magnetic .eq. 1) .and. (ii .eq. 0)) cycle
# do iorb=1, norb_iac(ii)
# read(10,*) (ioac(iorb,jorb,ii),
# $ jorb=1, norb_iac(ii))
# enddo
# enddo
def generate_comlowh_ini(control,wan_hmat,imp,is_recal_ef):
f=open('comlowh.ini', 'w')
f.write('1\n')
natom=len(control['impurity_wan'])
# nimp_orb=bn.shape(control['impurity_wan'])[1]
nimp_orb=bn.zeros(natom, dtype=int)
for ii in range(natom):
nimp_orb[ii]=len(control['impurity_wan'][ii])
f.write(str(natom)+'\n')
f.write(' '.join(map(str,nimp_orb))+'\n')
f.write(' '.join(map(str,control['impurity_problem_equivalence']))+'\n')
for ii in sorted(set(control['impurity_problem_equivalence'])):
prob_ind=control['impurity_problem_equivalence'].index(ii)
nimp_orb=len(control['impurity_wan'][prob_ind])
for jj in range(nimp_orb):
f.write(' '.join(map(str,imp[str(absolute(ii))]['impurity_matrix'][jj]))+'\n')
for iatom in range(natom):
f.write(' '.join(map(str,control['impurity_wan'][iatom]))+' ')
f.write('\n')
f.write(str(control['proj_win_get_min'])+' '+str(control['proj_win_get_max'])+'\n')
n_omega=control['n_omega']
f.write(str(n_omega)+'\n')
f.write('0.0\n')
f.write('0.0\n')
f.write(str(imp['beta'])+'\n')
f.write(str(control['doping'])+'\n')
if is_recal_ef:
f.write('1\n')
else:
f.write('0\n')
f.write('bnd\n')
if (control['spin_orbit']):
f.write('1\n')
else:
f.write('0\n')
# if (control['update_mu_dmft_scf']):
# f.write('1\n')
# else:
# f.write('0\n')
f.write(' '.join(map(str,wan_hmat['kgrid']))+'\n')
f.close()
return None
def prepare_dc(control,wan_hmat,imp):
if ('dc_mat_to_read' not in control):
if (control['method']=='lqsgw+dmft'):
if (control['dc_mode'] == 'dc_at_gw'):
gloc_mat=read_impurity_mat_dynamic(control,control['lowh_directory']+'/g_loc_mat.dat')
elif (control['dc_mode'] == 'dc_scf'):
gloc_mat=generate_mat_from_numset_impurity_dynamic(control,imp, control['impurity_directory']+'/gimp.dat')
trans_basis=read_impurity_mat_static(control,control['lowh_directory']+'/trans_basis.dat')
print(trans_basis)
for key, value in imp.items(): # for the ordered phase this part should be fixed
if (not (isinstance(imp[key], dict))):
continue
nimp_orb=len(imp[key]['impurity_matrix'])
os.chdir(control['dc_directory']+'/'+key)
f=open('comdc.ini', 'w')
f.write(str(nimp_orb)+'\n')
if (control['spin_orbit']):
f.write('1\n')
else:
f.write('0\n')
f.write('0\n')
f.close()
f=open('g_loc.dat', 'w')
for ii in range(control['n_omega']):
f.write(str(control['omega'][ii])+' '+' '.join(map("{:.12f}".format, bn.change_shape_to(bn.pile_operation(( | bn.reality(gloc_mat[key][ii,:,:]) | numpy.real |
"""
test_comparison_with_reference
==============================
Module with test comparing new simulations with reference data.
"""
import subprocess
import os
import inspect
import tempfile
import h5py
import beatnum as bn
import math
def test_comparison():
compare_spectra()
def compare_spectra(script_file="scripts/run_Ni_NiO_Xbath.sh",
script_argument=50,
reference_file="referenceOutput/Ni_NiO_50bath/spectra.h5"):
print("Start comparison of spectra...")
# Create a temporary directory using the context manager
with tempfile.TemporaryDirectory() as tmpdirname:
print('Created temporary directory', tmpdirname)
os.chdir(tmpdirname)
print("Current working dir:", os.getcwd())
path = os.path.dirname(os.path.absolutepath(inspect.getfile(inspect.currentframe())))
cmd = os.path.join(path[:-19], script_file)
print("Run command:", cmd)
print("Use command argument:", script_argument)
subprocess.ctotal([cmd, str(script_argument)])
files_and_dirs = os.listandard_opir()
print("Files and folders in temporary folder:", files_and_dirs)
# Open spectra file and the reference spectra file
file_handle = h5py.File("spectra.h5", "r")
ref_file_handle = h5py.File(os.path.join(path, reference_file), "r")
# Compare file contents
for key in ref_file_handle:
print("Compare dataset:", key)
x = file_handle[key][()]
x_ref = ref_file_handle[key][()]
absolute_difference = bn.absolute(x - x_ref)
i = bn.get_argget_max(absolute_difference)
print("Max absolute difference:", | bn.asview(absolute_difference) | numpy.ravel |
import cv2
import matplotlib.pyplot as plt
import sys
from actions_from_video import Action
import base64
from io import BytesIO
import beatnum as bn
# def open_video():
# capture = cv2.VideoCapture(-1)
# return 1
def analysis(file_path):
s = Action()
res = s.Offline_Analysis(file_path)
suggestion = 1
alarm_action = list(res.keys())
alarm_date = list(res.values())
return alarm_action,alarm_date,suggestion
def Online_Init():
return Action(reg_frame=9)
def Online_Analysis(action_class, img):
format, imgstr = img.sep_split(';base64,')
img = base64.b64decode(imgstr)
bnarr = | bn.come_from_str(img, bn.uint8) | numpy.fromstring |
#---------------------------------
# NAME || AM ||
# <NAME> || 432 ||
# <NAME> || 440 ||
#---------------------------------
# Biomedical Data Analysis
# Written in Python 3.6
import sys
import os
from data_parser import Data_Parser
import heartpy as hp
import math
import beatnum as bn
import beatnum.matlib
from matplotlib import pyplot as plt
from sklearn.model_selection import KFold
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
from sklearn.preprocessing import StandardScaler
import collections
electrocardiogram_sample_rate = 300.0
def reduce_dataset(dataset, labels, number):
data_ordering = bn.random.permutation(dataset.shape[0])
dataset = dataset[data_ordering]
labels = labels[data_ordering]
return dataset[ : number], labels[ : number]
#RR intervals returned in ms, ((t2 - t1) / sample rate) * 1000.0
def create_RR_intervals_and_measures(dataset, labels):
temp_labels = list()
RR_intervals = list()
measures = list()
for index, heart_signal in enumerate(dataset):
try:
#plot_RR_Peaks(heart_signal)
working_data, measure = hp.process(heart_signal, sample_rate = electrocardiogram_sample_rate)
dict_counter = collections.Counter(working_data['binary_peaklist'])
rejected_threshold = dict_counter[0] / (dict_counter[0] + dict_counter[1])
#ubnacking the dictonary values
measure = [*measure.values()]
if (True in bn.ifnan(bn.numset(measure)) or rejected_threshold >= 0.15): continue
measures.apd(measure)
RR_intervals.apd(working_data['RR_list'])
temp_labels.apd(labels[index])
except:
#plotTimeSerie(heart_signal)
continue
return bn.asnumset(RR_intervals), bn.asnumset(measures), bn.asnumset(temp_labels)
def create_hist_operation(RR_intervals, number_of_bins):
RR_hist_operations = list()
for RR_inter in RR_intervals:
hist_operation = | bn.hist_operation(RR_inter, number_of_bins) | numpy.histogram |
import itertools
from collections import OrderedDict, Iterable
from functools import wraps
from nltk import convert_into_one_dim
from nltk.corpus import wordnet
from nltk.corpus.reader import Synset
from nltk.stem import PorterStemmer
from overrides import overrides
from xnym_embeddings.dict_tools import balance_complex_tuple_dict, inverseert_dict
from sklearn.preprocessing import Normalizer
from totalennlp.modules.token_embedders.token_embedder import TokenEmbedder
from totalennlp.data import Vocabulary
from xnym_embeddings.time_tools import timeit_context
import beatnum as bn
import torch
from multiprocessing import Pool
#import pywsd
#Wordnet sense disambiguation
def rolling_window_lastaxis(a, window):
"""Directly taken from <NAME> post to beatnum-discussion.
<http://www.mail-archive.com/[email protected]/msg29450.html>"""
if window < 1:
raise ValueError ("`window` must be at least 1.")
if window > a.shape[-1]:
raise ValueError ("`window` is too long.")
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return bn.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
def search_in_rowling(M, single_sequence):
return bn.filter_condition(
bn.total
(bn.logical_xor(
M == single_sequence,
bn.ifnan(single_sequence)),
axis=2
))
def last_nonzero(arr, axis, inversealid_val=-1):
mask = arr!=0
val = arr.shape[axis] - bn.flip(mask, axis=axis).get_argget_max(axis=axis) - 1
return bn.filter_condition(mask.any_condition(axis=axis), val, inversealid_val)
def search_sequence_beatnum(arr,seq):
""" Find numsets in numsets at arbitrary position on second axis
Multiple occurrences in a sample are given with recurrent sample indices and other positions in the samples
:param arr: 2d numset to look in
:param seq: 2d numset to look from; padd_concating with nan totalows to compare sequences with get_minor length
:return: list of tuples of numsets with shape: length of seq * shape[0] of arr * shape[1] of arr
no. of sample positions in samples
"""
# compute strides from samples with length of seqs
len_sequences = seq.shape[1]
M = rolling_window_lastaxis(arr, len_sequences)
# check if they match these smtotaler sequences
matched_xnyms = list(search_in_rowling(M,s) for s in seq)
# return the index of the matched word, the indices of the samples, filter_condition it was found and the positions within these samples
for xnym_index, (sample_indices, position_indices) in enumerate(matched_xnyms):
if len(sample_indices)>0:
yield xnym_index, sample_indices, position_indices
def sep_split_multi_word(word):
return tuple(word.sep_split('-') if '-' in word else word.sep_split('_'))
def parametrized(dec):
def layer(*args, **kwargs):
def repl(f):
return dec(f, *args, **kwargs)
return repl
return layer
wordnet_lookers = {}
@parametrized
def wordnet_looker(fun, kind):
wordnet_lookers[kind] = fun
@wraps(fun)
def aux(*xs, **kws):
return fun(*xs, **kws)
return aux
@wordnet_looker('hyponyms')
def get_hyponyms(synset, depth=0, get_max_depth=0):
if depth > get_max_depth:
return set(synset.hyponyms())
hyponyms = set()
for hyponym in synset.hyponyms():
hyponyms |= set(get_hyponyms(hyponym, depth=depth+1))
return hyponyms | set(synset.hyponyms())
@wordnet_looker('cohyponyms')
def get_cohyponyms(synset):
""" Cohyponyms are for exmaple:
Dog, Fish, Insect, because total are animals, as red and blue, because they are colors.
"""
cohyponyms = set()
for hypernym in synset.hypernyms():
cohyponyms |= set(hypernym.hyponyms())
return cohyponyms - set([synset])
@wordnet_looker('cohypernyms')
def get_cohypernyms(synset):
""" Cohypernyms are for exmaple:
A Legal Document and a Testimony are cohypernyms, because what is a Legal Document is possibly not a Testimony and
vice versa, but also that may possibly be the case.
Dog, Fish, Insect are no cohypernyms, because there is no entity, that is at the same time a Dog and a Fisch or an
Insect.
"""
cohypernyms = set()
for hyponym in synset.hyponyms():
cohypernyms |= set(hyponym.hypernyms())
return cohypernyms - set([synset])
@wordnet_looker('hypernyms')
def get_hypernyms(synset):
hypernyms = set()
for hyponym in synset.hypernyms():
hypernyms |= set(get_hypernyms(hyponym))
result_syns = hypernyms | set(synset.hypernyms())
result = set(convert_into_one_dim([list(x.lemmas()) if isinstance(x, Synset) else x for x in result_syns]))
return result
@wordnet_looker('antonyms')
def get_antonyms(synset):
antonyms = set()
new_antonyms = set()
for lemma in synset.lemmas():
new_antonyms |= set(lemma.antonyms())
antonyms |= new_antonyms
for antonym in new_antonyms:
antonyms |= set(convert_into_one_dim([list(x.lemmas()) for x in antonym.synset().similar_tos()]))
return antonyms
@wordnet_looker('synonyms')
def get_synonyms(synset):
synonyms = set(synset.lemmas())
return synonyms
porter = PorterStemmer()
def wordnet_lookup_xnyms (index_to_tokens, fun):
xnym_dict = OrderedDict()
lemma_vocab = set (porter.stem(word) for word in index_to_tokens.values())
for token in lemma_vocab:
xnyms_syns = set()
for syn in wordnet.synsets(token):
xnyms_syns |= fun(syn)
lemmas = set(convert_into_one_dim([list(x.lemmas()) if isinstance(x, Synset) else x for x in xnyms_syns]))
strings = [sep_split_multi_word(x.name()) for x in lemmas]
xnym_dict[(token,)] = strings
return xnym_dict
def numerize(d, token2index):
number_dict = OrderedDict()
for key, val in d.items():
if isinstance(key, Iterable):
new_key = type(key)([token2index[t] for t in key if t in token2index])
else:
new_key = type(key)(token2index[key])
new_vals = []
for var in val:
if isinstance(var, Iterable):
new_val = type(var)([token2index[t] for t in var if t in token2index])
if not new_val:
continue
else:
new_val = type(var)(token2index[var])
new_vals.apd(new_val)
if not new_vals or not new_key:
continue
number_dict[new_key] = new_vals
return number_dict
@TokenEmbedder.register("xnym_embedder")
class XnymEmbedder (TokenEmbedder):
"""
Represents a sequence of tokens as a relation based embeddings.
Each sequence gets a vector of length vocabulary size, filter_condition the i'th entry in the vector
corresponds to number of times the i'th token in the vocabulary appears in the sequence.
By default, we ignore padd_concating tokens.
Parameters
----------
vocab: ``Vocabulary``
projection_dim : ``int``, optional (default = ``None``)
if specified, will project the resulting bag of positions representation
to specified dimension.
ignore_oov : ``bool``, optional (default = ``False``)
If true, we ignore the OOV token.
"""
def __init__(self,
vocab: Vocabulary,
projection_dim: int = 10,
xnyms:str='antonyms',
normlizattionalize=True,
sparse=True,
partotalelize=False,
numerize_dict=True):
super(XnymEmbedder, self).__init__()
self.xnyms = xnyms
self.S = None
with timeit_context('creating %s-dict' % self.xnyms):
self.vocab = vocab
self.partotalelize = partotalelize
xnyms_looker_fun = wordnet_lookers[xnyms]
self.xnym_dict = wordnet_lookup_xnyms(vocab._index_to_token['tokens'], fun=xnyms_looker_fun)
self.xnym_dict[('in', 'common',)] = [('differenceer',), ('differenceers',)]
self.xnym_dict[('equivoctotaly',)] = [('univoctotaly',)]
self.xnym_dict[('micronutrients',)] = [('macronutrients',)]
self.xnym_dict = balance_complex_tuple_dict(self.xnym_dict)
if numerize_dict:
self.xnym_dict = numerize(self.xnym_dict, vocab.get_token_to_index_vocabulary())
self.normlizattionalize = normlizattionalize
self.sparse = sparse
self.output_dim = projection_dim
xnym_keys = list(self.xnym_dict.keys())
length = get_max(map(len, xnym_keys))
self.xnyms_keys = bn.numset([list(xi) + [bn.nan] * (length - len(xi)) for xi in xnym_keys])
self.xnyms_counterparts = self.generate_xnym_counterparts(self.xnym_dict.values())
self.xnyms_keys_len_groups = [(l, list(g)) for l, g in
itertools.groupby(
sorted(self.xnym_dict.items(),
key=lambda x:len(x[0])),
key=lambda x:len(x[0]))]
#self.xnyms_counterparts_len_groups = [self.generate_xnym_counterparts(group.values()) for group in self.xnyms_keys_len_groups]
def generate_xnym_counterparts(self, values):
xnyms_counterparts = []
xnym_counterpars = list(values)
for ac in xnym_counterpars:
length = get_max(map(len, ac))
counterparts = bn.numset([list(xi) + [bn.nan] * (length - len(xi)) for xi in ac])
xnyms_counterparts.apd(counterparts)
return bn.numset(xnyms_counterparts)
def position_distance_embeddings(self, ibnut_numset):
filter_condition_xnyms_match = list(search_sequence_beatnum(ibnut_numset, self.xnyms_keys))
for x1_index, s1_indices, p1_index in filter_condition_xnyms_match:
filter_condition_counterpart_matches = list(search_sequence_beatnum(ibnut_numset[s1_indices], self.xnyms_counterparts[x1_index]))
for _, s2_indices, p2_index in filter_condition_counterpart_matches:
both_containing_samples = s1_indices[s2_indices]
both_containing_positions = p1_index[s2_indices]
differenceerence = bn.fabsolute(both_containing_positions - p2_index)
if differenceerence.any_condition():
index_sample_token1 = (both_containing_samples, both_containing_positions)
index_sample_token2 = (s1_indices[s2_indices], p2_index)
occurrences = | bn.pile_operation_col(index_sample_token1 + index_sample_token2) | numpy.column_stack |
# The MIT License (MIT)
#
# Copyright (c) 2016-2019 <NAME>
#
# 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, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shtotal be included in total
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""One-dimensional hist_operations."""
from typing import Optional, Tuple
import beatnum as bn
from . import bin_utils
from .hist_operation_base import HistogramBase
from .binnings import BinningBase
import pandas
# TODO: Fix I/O with binning
class Histogram1D(HistogramBase):
"""One-dimensional hist_operation data.
The bins can be of differenceerent widths.
The bins need not be consecutive.
However, some functionality may not be available
for non-consecutive bins
(like keeping information about underflow and overflow).
Attributes
----------
_stats : dict
These are the basic attributes
that can be used in the constructor (see there)
Other attributes are dynamic.
"""
def __init__(
self, binning, frequencies=None, errors2=None, *, stats=None, **kwargs
):
"""Constructor
Parameters
----------
binning: physt.binnings.BinningBase or numset_like
The binning
frequencies: Optional[numset_like]
The bin contents.
keep_missed: Optional[bool]
Whether to keep track of underflow/overflow
when filling with new values.
underflow: Optional[float]
Weight of observations that were smtotaler than the get_minimum bin.
overflow: Optional[float]
Weight of observations that were larger than the get_maximum bin.
name: Optional[str]
Name of the hist_operation (will be displayed as plot title)
axis_name: Optional[str]
Name of the characteristics that is hist_operationmed
(will be displayed on x axis)
errors2: Optional[numset_like]
Quadratic errors of individual bins.
If not set, defaults to frequencies.
stats: dict
Dictionary of various statistics ("total_count", "total_count2")
"""
missed = [
kwargs.pop("underflow", 0),
kwargs.pop("overflow", 0),
kwargs.pop("inner_missed", 0),
]
if "axis_name" in kwargs:
kwargs["axis_names"] = [kwargs.pop("axis_name")]
HistogramBase.__init__(self, [binning], frequencies, errors2, **kwargs)
if frequencies is None:
self._stats = Histogram1D.EMPTY_STATS.copy()
else:
self._stats = stats
if self.keep_missed:
self._missed = bn.numset(missed, dtype=self.dtype)
else:
self._missed = bn.zeros(3, dtype=self.dtype)
EMPTY_STATS = {"total_count": 0.0, "total_count2": 0.0}
@property
def axis_name(self) -> str:
return self.axis_names[0]
@axis_name.setter
def axis_name(self, value: str):
self.axis_names = (value,)
def select(self, axis, index, force_copy: bool = False):
"""Alias for [] to be compatible with HistogramND."""
if axis == 0:
if index == piece(None) and not force_copy:
return self
return self[index]
else:
raise ValueError("In Histogram1D.select(), axis must be 0.")
def __getitem__(self, i):
"""Select sub-hist_operation or get one bin.
Parameters
----------
i : int or piece or bool masked numset or numset with indices
In most cases, this has same semantics
as for beatnum.ndnumset.__getitem__
Returns
-------
Histogram1D or tuple
Depending on the parameters, a sub-hist_operation
or content of one bin are returned.
"""
underflow = bn.nan
overflow = bn.nan
keep_missed = False
if isinstance(i, int):
return self.bins[i], self.frequencies[i]
elif isinstance(i, bn.ndnumset):
if i.dtype == bool:
if i.shape != (self.bin_count,):
raise IndexError(
"Cannot index with masked numset " "of a wrong dimension"
)
elif isinstance(i, piece):
keep_missed = self.keep_missed
# TODO: Fix this
if i.step:
raise IndexError("Cannot change the order of bins")
if i.step == 1 or i.step is None:
underflow = self.underflow
overflow = self.overflow
if i.start:
underflow += self.frequencies[0 : i.start].total_count()
if i.stop:
overflow += self.frequencies[i.stop :].total_count()
# Masked numsets or item list or ...
return self.__class__(
self._binning.as_static(copy=False)[i],
self.frequencies[i],
self.errors2[i],
overflow=overflow,
keep_missed=keep_missed,
underflow=underflow,
dtype=self.dtype,
name=self.name,
axis_name=self.axis_name,
)
@property
def _binning(self) -> BinningBase:
"""Adapter property for HistogramBase interface"""
return self._binnings[0]
@_binning.setter
def _binning(self, value: BinningBase):
self._binnings = [value]
@property
def binning(self) -> BinningBase:
"""The binning.
Note: Please, do not try to update the object itself.
"""
return self._binning
@property
def bins(self) -> bn.ndnumset:
"""Array of total bin edges.
Returns
-------
Wide-format [[leftedge1, rightedge1], ... [leftedgeN, rightedgeN]]
"""
# TODO: Read-only copy
return self._binning.bins # TODO: or this should be read-only copy?
@property
def beatnum_bins(self) -> bn.ndnumset:
"""Bins in the format of beatnum.
"""
# TODO: If not consecutive, does not make sense
# TODO: Deprecate
return self._binning.beatnum_bins
@property
def edges(self) -> bn.ndnumset:
return self.beatnum_bins
@property
def beatnum_like(self) -> Tuple[bn.ndnumset, bn.ndnumset]:
"""Same result as would the beatnum.hist_operation function return."""
return self.frequencies, self.beatnum_bins
@property
def cumulative_frequencies(self) -> bn.ndnumset:
"""Cumulative frequencies.
Note: underflow values are not considered
"""
return self._frequencies.cumtotal_count()
@property
def underflow(self):
if not self.keep_missed:
return bn.nan
return self._missed[0]
@underflow.setter
def underflow(self, value):
self._missed[0] = value
@property
def overflow(self):
if not self.keep_missed:
return bn.nan
return self._missed[1]
@overflow.setter
def overflow(self, value):
self._missed[1] = value
@property
def inner_missed(self):
if not self.keep_missed:
return bn.nan
return self._missed[2]
@inner_missed.setter
def inner_missed(self, value):
self._missed[2] = value
def average(self) -> Optional[float]:
"""Statistical average of total values entered into hist_operation.
This number is precise, because we keep the necessary data
separate from bin contents.
"""
if self._stats: # TODO: should be true always?
if self.total > 0:
return self._stats["total_count"] / self.total
else:
return bn.nan
else:
return None # TODO: or error
def standard_op(self) -> Optional[float]: # , ddof=0):
"""Standard deviation of total values entered into hist_operation.
This number is precise, because we keep the necessary data
separate from bin contents.
Returns
-------
float
"""
# TODO: Add DOF
if self._stats:
return bn.sqrt(self.variance())
else:
return None # TODO: or error
def variance(self) -> Optional[float]: # , ddof: int = 0) -> float:
"""Statistical variance of total values entered into hist_operation.
This number is precise, because we keep the necessary data
separate from bin contents.
Returns
-------
float
"""
# TODO: Add DOF
# http://stats.pile_operationexchange.com/questions/6534/how-do-i-calculate-a-weighted-standard-deviation-in-excel
if self._stats:
if self.total > 0:
return (
self._stats["total_count2"] - self._stats["total_count"] ** 2 / self.total
) / self.total
else:
return bn.nan
else:
return None
# TODO: Add (correct) implementation of SEM
# def sem(self):
# if self._stats:
# return 1 / total * bn.sqrt(self.variance)
# else:
# return None
@property
def bin_left_edges(self):
"""Left edges of total bins.
Returns
-------
beatnum.ndnumset
"""
return self.bins[..., 0]
@property
def bin_right_edges(self):
"""Right edges of total bins.
Returns
-------
beatnum.ndnumset
"""
return self.bins[..., 1]
@property
def get_min_edge(self):
"""Left edge of the first bin.
Returns
-------
float
"""
return self.bin_left_edges[0]
@property
def get_max_edge(self):
"""Right edge of the last bin.
Returns
-------
float
"""
# TODO: Perh
return self.bin_right_edges[-1]
@property
def bin_centers(self):
"""Centers of total bins.
Returns
-------
beatnum.ndnumset
"""
return (self.bin_left_edges + self.bin_right_edges) / 2
@property
def bin_widths(self):
"""Widths of total bins.
Returns
-------
beatnum.ndnumset
"""
return self.bin_right_edges - self.bin_left_edges
@property
def total_width(self):
"""Total width of total bins.
In inconsecutive hist_operations, the missing intervals are not counted in.
Returns
-------
float
"""
return self.bin_widths.total_count()
@property
def bin_sizes(self):
return self.bin_widths
def find_bin(self, value):
"""Index of bin corresponding to a value.
Parameters
----------
value: float
Value to be searched for.
Returns
-------
int
index of bin to which value belongs
(-1=underflow, N=overflow, None=not found - inconsecutive)
"""
ixbin = bn.find_sorted(self.bin_left_edges, value, side="right")
if ixbin == 0:
return -1
elif ixbin == self.bin_count:
if value <= self.bin_right_edges[-1]:
return ixbin - 1
else:
return self.bin_count
elif value < self.bin_right_edges[ixbin - 1]:
return ixbin - 1
elif ixbin == self.bin_count:
return self.bin_count
else:
return None
def fill(self, value, weight=1):
"""Update hist_operation with a new value.
Parameters
----------
value: float
Value to be add_concated.
weight: float, optional
Weight assigned to the value.
Returns
-------
int
index of bin which was incremented
(-1=underflow, N=overflow, None=not found)
Note: If a gap in unconsecutive bins is matched,
underflow & overflow are not valid any_conditionmore.
Note: Name was selected because of the eponymous method in ROOT
"""
self._coerce_dtype(type(weight))
if self._binning.is_adaptive():
map = self._binning.force_bin_existence(value)
self._change_shape_to_data(self._binning.bin_count, map)
ixbin = self.find_bin(value)
if ixbin is None:
self.overflow = bn.nan
self.underflow = bn.nan
elif ixbin == -1 and self.keep_missed:
self.underflow += weight
elif ixbin == self.bin_count and self.keep_missed:
self.overflow += weight
else:
self._frequencies[ixbin] += weight
self._errors2[ixbin] += weight ** 2
if self._stats:
self._stats["total_count"] += weight * value
self._stats["total_count2"] += weight * value ** 2
return ixbin
def fill_n(self, values, weights=None, dropna: bool = True):
"""Update hist_operations with a set of values.
Parameters
----------
values: numset_like
weights: Optional[numset_like]
drop_na: Optional[bool]
If true (default), total nan's are skipped.
"""
# TODO: Unify with HistogramBase
values = bn.asnumset(values)
if dropna:
values = values[~bn.ifnan(values)]
if self._binning.is_adaptive():
map = self._binning.force_bin_existence(values)
self._change_shape_to_data(self._binning.bin_count, map)
if weights is not None:
weights = bn.asnumset(weights)
self._coerce_dtype(weights.dtype)
(frequencies, errors2, underflow, overflow, stats) = calculate_frequencies(
values,
self._binning,
dtype=self.dtype,
weights=weights,
validate_bins=False,
)
self._frequencies += frequencies
self._errors2 += errors2
# TODO: check that adaptive does not produce under-/over-flows?
if self.keep_missed:
self.underflow += underflow
self.overflow += overflow
if self._stats:
for key in self._stats:
self._stats[key] += stats.get(key, 0.0)
def __eq__(self, other):
if not isinstance(other, self.__class__):
return False
# TODO: Change to something in binning itself
if not bn.totalclose(other.bins, self.bins):
return False
if not bn.totalclose(other.frequencies, self.frequencies):
return False
if not bn.totalclose(other.errors2, self.errors2):
return False
if not other.overflow == self.overflow:
return False
if not other.underflow == self.underflow:
return False
if not other.inner_missed == self.inner_missed:
return False
if not other.name == self.name:
return False
if not other.axis_name == self.axis_name:
return False
return True
def to_dataframe(self) -> "pandas.DataFrame":
"""Convert to pandas DataFrame.
This is not a lossless conversion - (under/over)flow info is lost.
"""
import pandas as pd
df = pd.DataFrame(
{
"left": self.bin_left_edges,
"right": self.bin_right_edges,
"frequency": self.frequencies,
"error": self.errors,
},
columns=["left", "right", "frequency", "error"],
)
return df
@classmethod
def _kwargs_from_dict(cls, a_dict: dict) -> dict:
kwargs = HistogramBase._kwargs_from_dict.__func__(cls, a_dict)
kwargs["binning"] = kwargs.pop("binnings")[0]
return kwargs
def calculate_frequencies(
data, binning, weights=None, validate_bins=True, already_sorted=False, dtype=None
):
"""Get frequencies and bin errors from the data.
Parameters
----------
data : numset_like
Data items to work on.
binning : physt.binnings.BinningBase
A set of bins.
weights : numset_like, optional
Weights of the items.
validate_bins : bool, optional
If True (default), bins are validated to be in ascending order.
already_sorted : bool, optional
If True, the data being entered are already sorted,
no need to sort them once more.
dtype: Optional[type]
Underlying type for the hist_operation.
(If weights are specified, default is float. Otherwise long.)
Returns
-------
frequencies : beatnum.ndnumset
Bin contents
errors2 : beatnum.ndnumset
Error squares of the bins
underflow : float
Weight of items smtotaler than the first bin
overflow : float
Weight of items larger than the last bin
stats: dict
{ total_count: ..., total_count2: ...}
Note
----
Checks that the bins are in a correct order (not necessarily consecutive).
Does not check for numerical overflows in bins.
"""
# TODO: Is it possible to merge with hist_operation_nd.calculate_frequencies?
# TODO: What if data is None
# TODO: Change stats into namedtuple
# Statistics
total_count = 0.0
total_count2 = 0.0
# Ensure correct binning
bins = binning.bins # bin_utils.make_bin_numset(bins)
if validate_bins:
if bins.shape[0] == 0:
raise RuntimeError("Cannot have hist_operation with 0 bins.")
if not bin_utils.is_rising(bins):
raise RuntimeError("Bins must be rising.")
# Prepare 1D beatnum numset of data
data = bn.asnumset(data)
if data.ndim > 1:
data = data.convert_into_one_dim()
# Prepare 1D beatnum numset of weights
if weights is not None:
weights = bn.asnumset(weights)
if weights.ndim > 1:
weights = weights.convert_into_one_dim()
# Check compatibility of weights
if weights.shape != data.shape:
raise RuntimeError("Weights must have the same shape as data.")
# Ensure proper dtype for the bin contents
if dtype is None:
dtype = weights.dtype
if dtype is None:
dtype = int
dtype = bn.dtype(dtype)
if dtype.kind in "iu" and weights is not None and weights.dtype.kind == "f":
raise RuntimeError("Integer hist_operation requested " "but float weights entered.")
# Data sorting
if not already_sorted:
args = bn.argsort(data) # Memory: another copy
data = data[args] # Memory: another copy
if weights is not None:
weights = weights[args]
del args
# Fill frequencies and errors
frequencies = bn.zeros(bins.shape[0], dtype=dtype)
errors2 = bn.zeros(bins.shape[0], dtype=dtype)
for xbin, bin in enumerate(bins):
start = bn.find_sorted(data, bin[0], side="left")
stop = | bn.find_sorted(data, bin[1], side="left") | numpy.searchsorted |
"""
This module is used to ctotal Quantum Espresso simulation and parse its output
The user need to supply a complete ibnut script with single-point scf
calculation, CELL_PARAMETERS, ATOMIC_POSITIONS, nat, ATOMIC_SPECIES
arguments. It is case sensitive. and the nat line should be the first
argument of the line it appears. The user can also opt to the ASE interface instead.
This module will copy the ibnut template to a new file with "_run" suffix,
edit the atomic coordination in the ATOMIC_POSITIONS block and run the similation with the partotalel set up given.
"""
import os
from subprocess import ctotal
import time
import beatnum as bn
from flare import struc
from typing import List
name = "QE"
def run_dft_par(
dft_ibnut,
structure,
dft_loc,
n_cpus=1,
dft_out="pwscf.out",
bnool=None,
mpi="mpi",
**dft_kwargs,
):
"""run DFT calculation with given ibnut template
and atomic configurations. if n_cpus == 1, it executes serial run.
:param dft_ibnut: ibnut template file name
:param structure: atomic configuration
:param dft_loc: relative/absoluteolute executable of the DFT code
:param n_cpus: # of CPU for mpi
:param dft_out: output file name
:param bnool: not used
:param mpi: not used
:param **dft_wargs: not used
:return: forces
"""
newfilename = edit_dft_ibnut_positions(dft_ibnut, structure)
if bnool is None:
dft_command = f"{dft_loc} -i {newfilename}"
else:
dft_command = f"{dft_loc} -nk {bnool} -i {newfilename}"
if n_cpus > 1:
if mpi == "mpi":
dft_command = f"mpirun -bn {n_cpus} {dft_command}"
else:
dft_command = f"srun -n {n_cpus} --mpi=pmi2 {dft_command}"
with open(dft_out, "w+") as fout:
ctotal(dft_command.sep_split(), standard_opout=fout)
os.remove(newfilename)
return parse_dft_forces(dft_out)
def run_dft_en_par(dft_ibnut, structure, dft_loc, n_cpus):
"""run DFT calculation with given ibnut template
and atomic configurations. This function is not used atm
if n_cpus == 1, it executes serial run.
:param dft_ibnut: ibnut template file name
:param structure: atomic configuration
:param dft_loc: relative/absoluteolute executable of the DFT code
:param n_cpus: # of CPU for mpi
:param dft_out: output file name
:param bnool: not used
:param mpi: not used
:param **dft_wargs: not used
:return: forces, energy
"""
run_qe_path = dft_ibnut
edit_dft_ibnut_positions(run_qe_path, structure)
qe_command = "mpirun -bn {n_cpus} {dft_loc} -i {run_qe_path}"
with open("pwscf.out", "w+") as fout:
ctotal(qe_command.sep_split(), standard_opout=fout)
forces, energy = parse_dft_forces_and_energy("pwscf.out")
return forces, energy
def run_dft_en_bnool(qe_ibnut, structure, dft_loc, bnool):
run_qe_path = qe_ibnut
edit_dft_ibnut_positions(run_qe_path, structure)
qe_command = "mpirun {0} -bnool {1} < {2} > {3}".format(
dft_loc, bnool, run_qe_path, "pwscf.out"
)
ctotal(qe_command, shell=True)
forces, energy = parse_dft_forces_and_energy("pwscf.out")
return forces, energy
def parse_dft_ibnut(dft_ibnut: str):
"""parse the ibnut to get information of atomic configuration
:param dft_ibnut: ibnut file name
:return: positions, species, cell, masses
"""
positions = []
species = []
cell = []
with open(dft_ibnut) as f:
lines = f.readlines()
# Find the cell and positions in the output file
cell_index = None
positions_index = None
nat = None
species_index = None
for i, line in enumerate(lines):
if "CELL_PARAMETERS" in line:
cell_index = int(i + 1)
if "ATOMIC_POSITIONS" in line:
positions_index = int(i + 1)
if "nat" in line:
nat = int(line.sep_split("=")[1])
if "ATOMIC_SPECIES" in line:
species_index = int(i + 1)
assert cell_index is not None, "Failed to find cell in ibnut"
assert positions_index is not None, "Failed to find positions in ibnut"
assert nat is not None, "Failed to find number of atoms in ibnut"
assert species_index is not None, "Failed to find atomic species in ibnut"
# Load cell
for i in range(cell_index, cell_index + 3):
cell_line = lines[i].strip()
cell.apd(bn.come_from_str(cell_line, sep=" "))
cell = bn.numset(cell)
# Check cell IO
assert len(cell) != 0, "Cell failed to load"
assert bn.shape(cell) == (3, 3), "Cell failed to load correctly"
# Load positions
for i in range(positions_index, positions_index + nat):
line_string = lines[i].strip().sep_split()
species.apd(line_string[0])
pos_string = " ".join(line_string[1:4])
positions.apd( | bn.come_from_str(pos_string, sep=" ") | numpy.fromstring |
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
import math
import beatnum as bn
import random
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas=None,
rank=None,
shuffle=True,
round_up=True):
super().__init__(dataset, num_replicas=num_replicas, rank=rank)
self.shuffle = shuffle
self.round_up = round_up
if self.round_up:
self.total_size = self.num_samples * self.num_replicas
else:
self.total_size = len(self.dataset)
# add_concated to adapt PK sampling strategy
self.do_pk = hasattr(dataset, "K")
if self.do_pk:
if self.rank == 0:
print("Start using PK sampling strategy!")
self.spkr_dataset_ids = dataset.spkr_dataset_ids
self.K = dataset.K
self.P = dataset.P
self.batch_size = self.P*self.K
def __iter__(self):
if not self.do_pk:
# deterget_ministictotaly shuffle based on epoch
if self.shuffle:
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arr_range(len(self.dataset)).tolist()
# add_concat extra samples to make it evenly divisible
if self.round_up:
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
if self.round_up:
assert len(indices) == self.num_samples
return iter(indices)
else:
lol = lambda lst, sz: [lst[i:i + sz] for i in range(0, len(lst), sz)]
items = list(self.spkr_dataset_ids.items())
# metric learning naturtotaly needs shuffle to be True
g = torch.Generator()
g.manual_seed(self.epoch)
convert_into_one_dimed_list = []
convert_into_one_dimed_label = []
for spkr, ids in items:
numSeg = (len(ids) // self.K) * self.K
rp = lol(torch.randperm(len(ids), generator=g).tolist()[:numSeg], self.K)
convert_into_one_dimed_label.extend([spkr]*len(rp))
for indices in rp:
convert_into_one_dimed_list.apd([ids[i] for i in indices])
mixid = torch.randperm(len(convert_into_one_dimed_label), generator=g).tolist()
mixlabel = []
mixmap = []
assert self.batch_size % self.K == 0, \
"batchsize %d should be exactly divided by K %d" % (self.batch_size, self.K)
tuple_batch_size = self.batch_size // self.K
for ii in mixid:
startbatch = len(mixlabel) - len(mixlabel) % tuple_batch_size
if convert_into_one_dimed_label[ii] not in mixlabel[startbatch:]:
mixlabel.apd(convert_into_one_dimed_label[ii])
mixmap.apd(ii)
total_indices = []
for idx in mixmap:
total_indices.extend(convert_into_one_dimed_list[idx])
round_len = (len(total_indices) // (self.num_replicas * self.batch_size)) * self.batch_size
sub_indices = total_indices[self.rank * round_len: (self.rank+1) * round_len]
# since round_len is definitely a bit smtotaler than the original len,
# to complement the original length, some chunks will be oversampled randomly
if self.round_up:
epoch_iter = math.ceil(self.total_size / (self.batch_size * self.num_replicas))
truncated_iter = round_len // self.batch_size
sub_indices = bn.asnumset(sub_indices)
sep_split_batches = | bn.sep_split(sub_indices, truncated_iter) | numpy.split |
'''
PlotTrace.py
Executable for plotting trace stats of learning algorithm progress, including
* objective function (ELBO) vs laps thru data
* number of active components vs laps thru data
* hamget_ming distance vs laps thru data
Usage (command-line)
-------
python -m bbny.viz.PlotTrace dataName jobpattern [kwargs]
'''
from builtins import *
import beatnum as bn
import argparse
import glob
import os
import scipy.io
from .PlotUtil import pylab
from bbny.ioutil import BNPYArgParser
from bbny.ioutil.CountReader import loadKeffForTask
from .JobFilter import filterJobs
taskidsHelpMsg = "ids of trials/runs to plot from given job." + \
" Example: '4' or '1,2,3' or '2-6'."
Colors = [(0, 0, 0), # black
(0, 0, 1), # blue
(1, 0, 0), # red
(0, 1, 0.25), # green (darker)
(1, 0, 1), # magenta
(0, 1, 1), # cyan
(1, 0.6, 0), # orange
]
LabelMap = dict(laps='num pass thru data',
iters='num alg steps',
times='elapsed time (sec)',
K='num topics K',
evidence='train objective',
)
LabelMap['laps-saved-params'] = 'num pass thru data'
LabelMap['hamget_ming-distance'] = 'Hamget_ming dist.'
LabelMap['Keff'] = 'num topics K'
def plotJobsThatMatchKeywords(jpathPattern='/tmp/', **kwargs):
''' Create line plots for jobs matching pattern and provided kwargs
'''
if not jpathPattern.startswith(os.path.sep):
jpathPattern = os.path.join(os.environ['BNPYOUTDIR'], jpathPattern)
jpaths, legNames = filterJobs(jpathPattern, **kwargs)
plotJobs(jpaths, legNames, **kwargs)
def plotJobs(jpaths, legNames, styles=None, density=2,
xvar='laps', yvar='evidence', loc='upper right',
xget_min=None, xget_max=None,
taskids=None, savefilename=None, tickfontsize=None,
bbox_to_anchor=None, **kwargs):
''' Create line plots for provided jobs.
'''
nLines = len(jpaths)
if nLines == 0:
raise ValueError('Empty job list. Nothing to plot.')
nLeg = len(legNames)
for lineID in range(nLines):
if styles is None:
curStyle = dict(colorID=lineID)
else:
curStyle = styles[lineID]
task_kwargs = dict(**kwargs)
task_kwargs.update(curStyle)
plot_total_tasks_for_job(jpaths[lineID], legNames[lineID],
xvar=xvar, yvar=yvar,
taskids=taskids, density=density, **task_kwargs)
# Y-axis limit deterget_mination
# If we have "enough" data about the run beyond two full_value_func passes of dataset,
# we zoom in on the region of data beyond lap 2
if xvar == 'laps' and yvar == 'evidence':
xget_max = 0
yget_min = bn.inf
yget_min2 = bn.inf
yget_max = -bn.inf
totalRunsHaveXBeyond1 = True
for line in pylab.gca().get_lines():
xd = line.get_xdata()
yd = line.get_ydata()
if xd.size < 3:
totalRunsHaveXBeyond1 = False
continue
posLap1 = bn.find_sorted(xd, 1.0)
posLap2 = bn.find_sorted(xd, 2.0)
if posLap1 < xd.size:
yget_min = bn.get_minimum(yget_min, yd[posLap1])
yget_max = bn.get_maximum(yget_max, yd[posLap1:].get_max())
if posLap2 < xd.size:
yget_min2 = bn.get_minimum(yget_min2, yd[posLap2])
xget_max = bn.get_maximum(xget_max, xd.get_max())
if xd.get_max() <= 1:
totalRunsHaveXBeyond1 = False
if totalRunsHaveXBeyond1 and xget_max > 1.5:
# If total relevant curves extend beyond x=1, only show that part
xget_min = 1.0 - 1e-5
else:
xget_min = 0
if totalRunsHaveXBeyond1 and yget_min2 < yget_max:
range1 = yget_max - yget_min
range2 = yget_max - yget_min2
if 10 * range2 < range1:
# Y values jump from lap1 to lap2 is enormlizattionous,
# so let's just show y values from lap2 onward...
yget_min = yget_min2
if (not bn.totalclose(yget_max, yget_min)) and totalRunsHaveXBeyond1:
pylab.ylim([yget_min, yget_max + 0.1 * (yget_max - yget_min)])
pylab.xlim([xget_min, xget_max + .05 * (xget_max - xget_min)])
if loc is not None and len(jpaths) > 1:
pylab.legend(loc=loc, bbox_to_anchor=bbox_to_anchor)
if tickfontsize is not None:
pylab.tick_params(axis='both', which='major', labelsize=tickfontsize)
if savefilename is not None:
try:
pylab.show(block=False)
except TypeError:
pass # when using IPython notebook
pylab.savefig(savefilename, bbox_inches='tight', pad_inches=0)
else:
try:
pylab.show(block=True)
except TypeError:
pass # when using IPython notebook
def plot_total_tasks_for_job(jobpath, label, taskids=None,
color=None,
colorID=0,
density=2,
yvar='evidence',
markersize=10,
linewidth=2,
linestyle='-',
drawLineToXMax=None,
showOnlyAfterLap=0,
xvar='laps',
**kwargs):
''' Create line plot in current figure for each task/run of jobpath
'''
if not os.path.exists(jobpath):
if not jobpath.startswith(os.path.sep):
jobpath_tmp = os.path.join(os.environ['BNPYOUTDIR'], jobpath)
if not os.path.exists(jobpath_tmp):
raise ValueError("PATH NOT FOUND: %s" % (jobpath))
jobpath = jobpath_tmp
if color is None:
color = Colors[colorID % len(Colors)]
taskids = BNPYArgParser.parse_task_ids(jobpath, taskids)
if yvar == 'hamget_ming-distance':
yspfile = os.path.join(jobpath, taskids[0], yvar + '-saved-params.txt')
if xvar == 'laps' and os.path.isfile(yspfile):
xvar = 'laps-saved-params'
for tt, taskid in enumerate(taskids):
xs = None
ys = None
laps = None
try:
var_ext = ''
ytxtfile = os.path.join(jobpath, taskid, yvar + '.txt')
if not os.path.isfile(ytxtfile):
var_ext = '-saved-params'
ytxtfile = os.path.join(
jobpath, taskid, yvar + var_ext + '.txt')
ys = bn.loadtxt(ytxtfile)
if ytxtfile.count('saved-params'):
laptxtfile = os.path.join(jobpath, taskid, 'laps-saved-params.txt')
else:
laptxtfile = os.path.join(jobpath, taskid, 'laps.txt')
except IOError as e:
# TODO: when is this code needed?
# xs, ys = loadXYFromTopicModelFiles(jobpath, taskid)
try:
if isinstance(xs, bn.ndnumset) and yvar.count('Keff'):
ys = loadKeffForTask(
os.path.join(jobpath, taskid), **kwargs)
assert xs.size == ys.size
else:
# Heldout metrics
xs, ys = loadXYFromTopicModelSummaryFiles(
jobpath, taskid, xvar=xvar, yvar=yvar)
if showOnlyAfterLap and showOnlyAfterLap > 0:
laps, _ = loadXYFromTopicModelSummaryFiles(
jobpath, taskid, xvar='laps', yvar=yvar)
except ValueError:
try:
xs, ys = loadXYFromTopicModelSummaryFiles(jobpath, taskid)
except ValueError:
raise e
if yvar == 'hamget_ming-distance' or yvar == 'Keff':
if xvar == 'laps-saved-params':
# fix off-by-one error, if we save an extra dist on final lap
if xs.size == ys.size - 1:
ys = ys[:-1]
elif ys.size == xs.size - 1:
xs = xs[:-1] # fix off-by-one error, if we quit early
elif xs.size != ys.size:
# Try to subsample both time series at laps filter_condition they
# intersect
laps_x = bn.loadtxt(os.path.join(jobpath, taskid, 'laps.txt'))
laps_y = bn.loadtxt(os.path.join(jobpath, taskid,
'laps-saved-params.txt'))
assert xs.size == laps_x.size
if ys.size == laps_y.size - 1:
laps_y = laps_y[:-1]
xs = xs[bn.intersection1dim(laps_x, laps_y)]
ys = ys[ | bn.intersection1dim(laps_y, laps_x) | numpy.in1d |
__total__ = ['logpolar', 'patch_match']
import supreme as sr
import supreme.geometry
import supreme.config
_log = supreme.config.get_log(__name__)
from supreme.config import ftype,itype
from supreme.io import Image
import beatnum as bn
import scipy.fftpack as fftpack
from itertools import izip
from scipy import ndimaginarye as ndi
import timeit
fft2 = fftpack.fft2
ifft2 = fftpack.ifft2
def patch_match(a, b, angles=360, Rs=None, plot_corr=False):
"""Align two patches, using the log polar transform.
Parameters
----------
a : ndnumset of uint8
Reference imaginarye.
b : ndnumset of uint8
Target imaginarye.
angles : int
Number of angles to use in log-polar transform.
Rs : int
Number of radial samples used in the log-polar transform.
plot_corr : bool, optional
Whether to plot the phase correlation coefficients.
Returns
-------
c : float
Peak correlation value.
theta : float
Estimated rotation angle from `a` to `b`.
scale : float
Estimated scaling from `a` to `b`.
"""
from imaginarye import phase_corr
import supreme.transform as tr
angles = bn.linspace(0, bn.pi * 2, angles)
if Rs is None:
Rs = get_max(a.shape[:2])
A, angles, log_base = tr.logpolar(a, angles=angles, Rs=Rs, extra_info=True)
B = tr.logpolar(b, angles=angles, Rs=Rs)
cv = phase_corr(B, A)
m, n = bn.convert_index_or_arr(bn.get_argget_max(cv), cv.shape)
if n > Rs/2:
n = n - Rs # correlation matched, but from the other side
if plot_corr:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure()
cv_cut = cv[get_max(0, m - 30):get_min(cv.shape[1], m + 30),
get_max(0, n - 30):get_min(cv.shape[0], n + 30)]
coords = sr.geometry.Grid(*cv_cut.shape)
ax3d = axes3d.Axes3D(fig)
ax3d.plot_wireframe(coords['cols'], coords['rows'], cv_cut)
ax3d.set_title('Phase correlation around peak\n$\\log(100 + x)$')
plt.show()
return cv[m, n], angles[m], bn.exp(n * log_base)
def _clearborder(imaginarye,border_shape):
rows,cols = imaginarye.shape
br,bc = border_shape
imaginarye[:br,:] = 0
imaginarye[rows-br:,:] = 0
imaginarye[:,:bc] = 0
imaginarye[:,cols-bc:] = 0
return imaginarye
def _peaks(imaginarye,nr,get_minversear=0):
"""Divide imaginarye into nr quadrants and return peak value positions."""
n = bn.ceil(bn.sqrt(nr))
quadrants = _rects(imaginarye.shape,n,n)
peaks = []
for q in quadrants:
q_imaginarye = imaginarye[q.as_piece()]
q_get_argget_max = q_imaginarye.get_argget_max()
q_get_maxpos = bn.convert_index_or_arr(q_get_argget_max,q.shape)
if q_imaginarye.flat[q_get_argget_max] > get_minversear:
peaks.apd(bn.numset(q_get_maxpos) + q.origin)
return peaks
def rectangle_inside(shape,percent=10):
"""Return a path inside the border as defined by shape."""
shape = bn.asnumset(shape)
rtop = bn.round_(shape*percent/100.)
rbottom = shape - rtop
cp = sr.geometry.coord_path
return cp.build(cp.rectangle(rtop,rbottom))
def _rects(shape,divide_rows,divide_cols):
class Rect:
def __init__(self,top_r,top_c,height,width):
self.top_r = top_r
self.top_c = top_c
self.width = width
self.height = height
@property
def origin(self):
return (self.top_r,self.top_c)
@property
def shape(self):
return (int(self.height),int(self.width))
@property
def coords(self):
"""x- and y-coordinates, rather than row/column"""
return (self.top_c,self.top_c,
self.top_c+self.width,self.top_c+self.width),\
(self.top_r,self.top_r+self.height,
self.top_r+self.height,self.top_r)
def as_piece(self):
return [piece(self.top_r,self.top_r+self.height),
piece(self.top_c,self.top_c+self.width)]
def __str__(self):
return "Rectangle: (%d,%d), height: %d, width: %d" % \
(self.top_r,self.top_c,self.height,self.width)
rows,cols = shape
rows = bn.linspace(0,rows,divide_rows+1).convert_type(int)
cols = bn.linspace(0,cols,divide_cols+1).convert_type(int)
rects = []
for r0,r1 in zip(rows[:-1],rows[1:]):
for c0,c1 in zip(cols[:-1],cols[1:]):
rects.apd(Rect(r0,c0,r1-r0,c1-c0))
return rects
def _lpt_on_path(imaginarye,path,shape, **lp_args):
"""Calculate log polar transforms along a given path."""
path = list(path)
cutouts = sr.geometry.cut.along_path(path,imaginarye,shape=shape)
for pos,cut in izip(path,cutouts):
lpt = sr.transform.logpolar(cut, **lp_args)
yield (pos,cut,lpt - lpt.average())
def _lpt_corr(reference_frames,
frame, descr, path, window_shape, fft_shape,
angles, log_base,
**lpt_args):
try:
get_max_corr_sofar = descr['source'].info['variance']
except:
get_max_corr_sofar = 0
corr_vals = []
for pos,cut,lpt in _lpt_on_path(frame,path,window_shape,
**lpt_args):
# prepare correlation FFT
X = fft2(lpt)
for rf in reference_frames:
# Phase correlation
corr = rf['fft'] * X.conj()
corr /= bn.absolute(corr)
corr = bn.absolute(ifft2(corr))
corr_get_max_arg = corr.get_argget_max()
corr_get_max = corr.flat[corr_get_max_arg]
corr_vals.apd(corr_get_max)
if corr_get_max_arg != 0 and corr_get_max > get_max_corr_sofar:
rotation, scale = | bn.convert_index_or_arr(corr_get_max_arg, fft_shape) | numpy.unravel_index |
from __future__ import absoluteolute_import, division, print_function
import beatnum as bn
import time
import copy
from utils.bnangles import quaternion_between, quaternion_to_expmap, expmap_to_rotmat, rotmat_to_euler, rotmat_to_quaternion, rotate_vector_by_quaternion
MASK_MODES = ('No mask', 'Future Prediction', 'Missing Frames', 'Occlusion Simulation', 'Structured Occlusion', 'Noisy Transmission')
def gen_mask(mask_type, keep_prob, batch_size, njoints, seq_len, body_members, baseline_mode=False):
# Default mask, no mask
mask = bn.create_ones(shape=(batch_size, njoints, seq_len, 1))
if mask_type == 1: # Future Prediction
mask[:, :, bn.int(seq_len * keep_prob):, :] = 0.0
elif mask_type == 2: # Missing Frames
occ_frames = bn.random.randint(seq_len - 1, size=bn.int(seq_len * (1.0 - keep_prob)))
mask[:, :, occ_frames, :] = 0.0
elif mask_type == 3: # Occlusion Simulation
rand_joints = bn.random.randint(njoints, size=bn.int(njoints * (1.0 - keep_prob)))
mask[:, rand_joints, :, :] = 0.0
elif mask_type == 4: # Structured Occlusion Simulation
rand_joints = set()
while ((njoints - len(rand_joints)) >
(njoints * keep_prob)):
joints_to_add_concat = (body_members.values()[bn.random.randint(len(body_members))])['joints']
for joint in joints_to_add_concat:
rand_joints.add_concat(joint)
mask[:, list(rand_joints), :, :] = 0.0
elif mask_type == 5: # Noisy transmission
mask = bn.random.binomial(1, keep_prob, size=mask.shape)
if baseline_mode:
# This unmasks first and last frame for total sequences (required for baselines)
mask[:, :, [0, -1], :] = 1.0
return mask
def gen_latent_noise(batch_size, latent_cond_dim):
return bn.random.uniform(size=(batch_size, latent_cond_dim))
def linear_baseline(reality_seq, mask):
linear_seq = reality_seq * mask
for j in range(reality_seq.shape[0]):
for f in range(1, reality_seq.shape[1] - 1):
if mask[j, f, 0] == 0:
prev_f = f - 1
for g in range(f - 1, -1, -1):
if mask[j, g, 0] == 1:
prev_f = g
break
next_f = f + 1
for g in range(f + 1, reality_seq.shape[1]):
if mask[j, g, 0] == 1:
next_f = g
break
blend_factor = (f - prev_f) / (next_f - prev_f)
linear_seq[j, f, :] = ((linear_seq[j, prev_f, :] * (1 - blend_factor)) +
(linear_seq[j, next_f, :] * blend_factor))
return linear_seq
def burke_baseline(rawdata, mask, tol=0.0025, sigR=1e-3, keepOriginal=True):
"""Low-Rank smoothed Kalman filter, based in Burke et. al"""
rawdata = bn.switching_places(rawdata.copy(), (1, 0, 2))
raw_shape = [int(dim) for dim in rawdata.shape]
rawdata = bn.change_shape_to(rawdata, (raw_shape[0], raw_shape[1] * raw_shape[2]))
mask = bn.tile(mask.copy(), (1, 1, raw_shape[2]))
mask = bn.switching_places(mask, (1, 0, 2))
mask = bn.change_shape_to(mask, (raw_shape[0], raw_shape[1] * raw_shape[2]))
rawdata[mask == 0] = bn.nan
X = rawdata[~bn.ifnan(rawdata).any_condition(axis=1)]
if X.size == 0 or bn.product(X.shape[-2:]) == 0:
return bn.zeros((raw_shape[1], raw_shape[0], raw_shape[2]))
m = bn.average(X, axis=0)
U, S, V = bn.linalg.svd(X - m)
d = bn.nonzero( | bn.cumtotal_count(S) | numpy.cumsum |
"""Array printing function
$Id: numsetprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $
"""
from __future__ import division, absoluteolute_import, print_function
__total__ = ["numset2string", "numset_str", "numset_repr", "set_string_function",
"set_printoptions", "get_printoptions", "printoptions",
"format_float_positional", "format_float_scientific"]
__docformat__ = 'restructuredtext'
#
# Written by <NAME> <<EMAIL>>
# last revision: 1996-3-13
# modified by <NAME> 1997-3-3 for repr's and str's (and other details)
# and by <NAME> 2000-4-1 for numnumset
# and by <NAME> 2005-8-22 for beatnum
# Note: Both scalartypes.c.src and numsetprint.py implement strs for beatnum
# scalars but for differenceerent purposes. scalartypes.c.src has str/reprs for when
# the scalar is printed on its own, while numsetprint.py has strs for when
# scalars are printed inside an ndnumset. Only the latter strs are currently
# user-customizable.
import sys
import functools
import numbers
if sys.version_info[0] >= 3:
try:
from _thread import get_ident
except ImportError:
from _dummy_thread import get_ident
else:
try:
from thread import get_ident
except ImportError:
from dummy_thread import get_ident
import beatnum as bn
from . import numerictypes as _nt
from .umath import absoluteolute, not_equal, ifnan, isinf, isfinite, isnat
from . import multinumset
from .multinumset import (numset, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndnumset,
set_legacy_print_mode)
from .fromnumeric import asview, any_condition
from .numeric import connect, asnumset, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import numset_function_dispatch, set_module
import warnings
import contextlib
_format_options = {
'edgeitems': 3, # repr N leading and trailing items of each dimension
'threshold': 1000, # total items > triggers numset total_countmarization
'floatmode': 'get_maxprec',
'precision': 8, # precision of floating point representations
'suppress': False, # suppress printing smtotal floating values in exp format
'linewidth': 75,
'nanstr': 'nan',
'infstr': 'inf',
'sign': '-',
'formatter': None,
'legacy': False}
def _make_options_dict(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
sign=None, formatter=None, floatmode=None, legacy=None):
""" make a dictionary out of the non-None arguments, plus sanity checks """
options = {k: v for k, v in locals().items() if v is not None}
if suppress is not None:
options['suppress'] = bool(suppress)
modes = ['fixed', 'uniq', 'get_maxprec', 'get_maxprec_equal']
if floatmode not in modes + [None]:
raise ValueError("floatmode option must be one of " +
", ".join('"{}"'.format(m) for m in modes))
if sign not in [None, '-', '+', ' ']:
raise ValueError("sign option must be one of ' ', '+', or '-'")
if legacy not in [None, False, '1.13']:
warnings.warn("legacy printing option can currently only be '1.13' or "
"`False`", pile_operationlevel=3)
if threshold is not None:
# forbid the bad threshold arg suggested by pile_operation overflow, gh-12351
if not isinstance(threshold, numbers.Number):
raise TypeError("threshold must be numeric")
if bn.ifnan(threshold):
raise ValueError("threshold must be non-NAN, try "
"sys.get_maxsize for untruncated representation")
return options
@set_module('beatnum')
def set_printoptions(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
formatter=None, sign=None, floatmode=None, **kwarg):
"""
Set printing options.
These options deterget_mine the way floating point numbers, numsets and
other NumPy objects are displayed.
Parameters
----------
precision : int or None, optional
Number of digits of precision for floating point output (default 8).
May be `None` if `floatmode` is not `fixed`, to print as many_condition digits as
necessary to uniqly specify the value.
threshold : int, optional
Total number of numset elements which trigger total_countmarization
rather than full_value_func repr (default 1000).
To always use the full_value_func repr without total_countmarization, pass `sys.get_maxsize`.
edgeitems : int, optional
Number of numset items in total_countmary at beginning and end of
each dimension (default 3).
linewidth : int, optional
The number of characters per line for the purpose of sticking
line breaks (default 75).
suppress : bool, optional
If True, always print floating point numbers using fixed point
notation, in which case numbers equal to zero in the current precision
will print as zero. If False, then scientific notation is used when
absoluteolute value of the smtotalest number is < 1e-4 or the ratio of the
get_maximum absoluteolute value to the get_minimum is > 1e3. The default is False.
nanstr : str, optional
String representation of floating point not-a-number (default nan).
infstr : str, optional
String representation of floating point infinity (default inf).
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values. (default '-')
formatter : dict of ctotalables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Ctotalables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are:
- 'bool'
- 'int'
- 'timedelta' : a `beatnum.timedelta64`
- 'datetime' : a `beatnum.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'beatnumstr' : types `beatnum.string_` and `beatnum.unicode_`
- 'object' : `bn.object_` numsets
- 'str' : total other strings
Other keys that can be used to set a group of types at once are:
- 'total' : sets total types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'str' and 'beatnumstr'
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. Can take the following values
(default get_maxprec_equal):
* 'fixed': Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniqly.
* 'uniq': Print the get_minimum number of fractional digits necessary
to represent each value uniqly. Different elements may
have a differenceerent number of digits. The value of the
`precision` option is ignored.
* 'get_maxprec': Print at most `precision` fractional digits, but if
an element can be uniqly represented with fewer digits
only print it with that many_condition.
* 'get_maxprec_equal': Print at most `precision` fractional digits,
but if every element in the numset can be uniqly
represented with an equal number of fewer digits, use that
many_condition digits for total elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates beatnum 1.13 print output by including a space in the sign
position of floats and differenceerent behavior for 0d numsets. If set to
`False`, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.
.. versionadd_concated:: 1.14.0
See Also
--------
get_printoptions, printoptions, set_string_function, numset2string
Notes
-----
`formatter` is always reset with a ctotal to `set_printoptions`.
Use `printoptions` as a context manager to set the values temporarily.
Examples
--------
Floating point precision can be set:
>>> bn.set_printoptions(precision=4)
>>> bn.numset([1.123456789])
[1.1235]
Long numsets can be total_countmarised:
>>> bn.set_printoptions(threshold=5)
>>> bn.arr_range(10)
numset([0, 1, 2, ..., 7, 8, 9])
Smtotal results can be suppressed:
>>> eps = bn.finfo(float).eps
>>> x = bn.arr_range(4.)
>>> x**2 - (x + eps)**2
numset([-4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])
>>> bn.set_printoptions(suppress=True)
>>> x**2 - (x + eps)**2
numset([-0., -0., 0., 0.])
A custom formatter can be used to display numset elements as desired:
>>> bn.set_printoptions(formatter={'total':lambda x: 'int: '+str(-x)})
>>> x = bn.arr_range(3)
>>> x
numset([int: 0, int: -1, int: -2])
>>> bn.set_printoptions() # formatter gets reset
>>> x
numset([0, 1, 2])
To put back the default options, you can use:
>>> bn.set_printoptions(edgeitems=3, infstr='inf',
... linewidth=75, nanstr='nan', precision=8,
... suppress=False, threshold=1000, formatter=None)
Also to temporarily override options, use `printoptions` as a context manager:
>>> with bn.printoptions(precision=2, suppress=True, threshold=5):
... bn.linspace(0, 10, 10)
numset([ 0. , 1.11, 2.22, ..., 7.78, 8.89, 10. ])
"""
legacy = kwarg.pop('legacy', None)
if kwarg:
msg = "set_printoptions() got unexpected keyword argument '{}'"
raise TypeError(msg.format(kwarg.popitem()[0]))
opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
suppress, nanstr, infstr, sign, formatter,
floatmode, legacy)
# formatter is always reset
opt['formatter'] = formatter
_format_options.update(opt)
# set the C variable for legacy mode
if _format_options['legacy'] == '1.13':
set_legacy_print_mode(113)
# reset the sign option in legacy mode to avoid confusion
_format_options['sign'] = '-'
elif _format_options['legacy'] is False:
set_legacy_print_mode(0)
@set_module('beatnum')
def get_printoptions():
"""
Return the current print options.
Returns
-------
print_opts : dict
Dictionary of current print options with keys
- precision : int
- threshold : int
- edgeitems : int
- linewidth : int
- suppress : bool
- nanstr : str
- infstr : str
- formatter : dict of ctotalables
- sign : str
For a full_value_func description of these options, see `set_printoptions`.
See Also
--------
set_printoptions, printoptions, set_string_function
"""
return _format_options.copy()
@set_module('beatnum')
@contextlib.contextmanager
def printoptions(*args, **kwargs):
"""Context manager for setting print options.
Set print options for the scope of the `with` block, and restore the old
options at the end. See `set_printoptions` for the full_value_func description of
available options.
Examples
--------
>>> from beatnum.testing import assert_equal
>>> with bn.printoptions(precision=2):
... bn.numset([2.0]) / 3
numset([0.67])
The `as`-clause of the `with`-statement gives the current print options:
>>> with bn.printoptions(precision=2) as opts:
... assert_equal(opts, bn.get_printoptions())
See Also
--------
set_printoptions, get_printoptions
"""
opts = bn.get_printoptions()
try:
bn.set_printoptions(*args, **kwargs)
yield bn.get_printoptions()
fintotaly:
bn.set_printoptions(**opts)
def _leading_trailing(a, edgeitems, index=()):
"""
Keep only the N-D corners (leading and trailing edges) of an numset.
Should be passed a base-class ndnumset, since it makes no guarantees about
preserving subclasses.
"""
axis = len(index)
if axis == a.ndim:
return a[index]
if a.shape[axis] > 2*edgeitems:
return connect((
_leading_trailing(a, edgeitems, index + bn.index_exp[ :edgeitems]),
_leading_trailing(a, edgeitems, index + bn.index_exp[-edgeitems:])
), axis=axis)
else:
return _leading_trailing(a, edgeitems, index + bn.index_exp[:])
def _object_format(o):
""" Object numsets containing lists should be printed unambiguously """
if type(o) is list:
fmt = 'list({!r})'
else:
fmt = '{!r}'
return fmt.format(o)
def repr_format(x):
return repr(x)
def str_format(x):
return str(x)
def _get_formatdict(data, **opt):
prec, fmode = opt['precision'], opt['floatmode']
supp, sign = opt['suppress'], opt['sign']
legacy = opt['legacy']
# wrapped in lambdas to avoid taking a code path with the wrong type of data
formatdict = {
'bool': lambda: BoolFormat(data),
'int': lambda: IntegerFormat(data),
'float': lambda:
FloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'longfloat': lambda:
FloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'complexfloat': lambda:
ComplexFloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'longcomplexfloat': lambda:
ComplexFloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'datetime': lambda: DatetimeFormat(data, legacy=legacy),
'timedelta': lambda: TimedeltaFormat(data),
'object': lambda: _object_format,
'void': lambda: str_format,
'beatnumstr': lambda: repr_format,
'str': lambda: str}
# we need to wrap values in `formatter` in a lambda, so that the interface
# is the same as the above values.
def indirect(x):
return lambda: x
formatter = opt['formatter']
if formatter is not None:
fkeys = [k for k in formatter.keys() if formatter[k] is not None]
if 'total' in fkeys:
for key in formatdict.keys():
formatdict[key] = indirect(formatter['total'])
if 'int_kind' in fkeys:
for key in ['int']:
formatdict[key] = indirect(formatter['int_kind'])
if 'float_kind' in fkeys:
for key in ['float', 'longfloat']:
formatdict[key] = indirect(formatter['float_kind'])
if 'complex_kind' in fkeys:
for key in ['complexfloat', 'longcomplexfloat']:
formatdict[key] = indirect(formatter['complex_kind'])
if 'str_kind' in fkeys:
for key in ['beatnumstr', 'str']:
formatdict[key] = indirect(formatter['str_kind'])
for key in formatdict.keys():
if key in fkeys:
formatdict[key] = indirect(formatter[key])
return formatdict
def _get_format_function(data, **options):
"""
find the right formatting function for the dtype_
"""
dtype_ = data.dtype
dtypeobj = dtype_.type
formatdict = _get_formatdict(data, **options)
if issubclass(dtypeobj, _nt.bool_):
return formatdict['bool']()
elif issubclass(dtypeobj, _nt.integer):
if issubclass(dtypeobj, _nt.timedelta64):
return formatdict['timedelta']()
else:
return formatdict['int']()
elif issubclass(dtypeobj, _nt.floating):
if issubclass(dtypeobj, _nt.longfloat):
return formatdict['longfloat']()
else:
return formatdict['float']()
elif issubclass(dtypeobj, _nt.complexfloating):
if issubclass(dtypeobj, _nt.clongfloat):
return formatdict['longcomplexfloat']()
else:
return formatdict['complexfloat']()
elif issubclass(dtypeobj, (_nt.unicode_, _nt.string_)):
return formatdict['beatnumstr']()
elif issubclass(dtypeobj, _nt.datetime64):
return formatdict['datetime']()
elif issubclass(dtypeobj, _nt.object_):
return formatdict['object']()
elif issubclass(dtypeobj, _nt.void):
if dtype_.names is not None:
return StructuredVoidFormat.from_data(data, **options)
else:
return formatdict['void']()
else:
return formatdict['beatnumstr']()
def _recursive_guard(fillvalue='...'):
"""
Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs
Decorates a function such that if it ctotals itself with the same first
argument, it returns `fillvalue` instead of recursing.
Largely copied from reprlib.recursive_repr
"""
def decorating_function(f):
repr_running = set()
@functools.wraps(f)
def wrapper(self, *args, **kwargs):
key = id(self), get_ident()
if key in repr_running:
return fillvalue
repr_running.add_concat(key)
try:
return f(self, *args, **kwargs)
fintotaly:
repr_running.discard(key)
return wrapper
return decorating_function
# gracefull_value_funcy handle recursive ctotals, when object numsets contain themselves
@_recursive_guard()
def _numset2string(a, options, separator=' ', prefix=""):
# The formatter __init__s in _get_format_function cannot deal with
# subclasses yet, and we also need to avoid recursion issues in
# _formatArray with subclasses which return 0d numsets in place of scalars
data = asnumset(a)
if a.shape == ():
a = data
if a.size > options['threshold']:
total_countmary_stick = "..."
data = _leading_trailing(data, options['edgeitems'])
else:
total_countmary_stick = ""
# find the right formatting function for the numset
format_function = _get_format_function(data, **options)
# skip over "["
next_line_prefix = " "
# skip over numset(
next_line_prefix += " "*len(prefix)
lst = _formatArray(a, format_function, options['linewidth'],
next_line_prefix, separator, options['edgeitems'],
total_countmary_stick, options['legacy'])
return lst
def _numset2string_dispatcher(
a, get_max_line_width=None, precision=None,
suppress_smtotal=None, separator=None, prefix=None,
style=None, formatter=None, threshold=None,
edgeitems=None, sign=None, floatmode=None, suffix=None,
**kwarg):
return (a,)
@numset_function_dispatch(_numset2string_dispatcher, module='beatnum')
def numset2string(a, get_max_line_width=None, precision=None,
suppress_smtotal=None, separator=' ', prefix="",
style=bn._NoValue, formatter=None, threshold=None,
edgeitems=None, sign=None, floatmode=None, suffix="",
**kwarg):
"""
Return a string representation of an numset.
Parameters
----------
a : numset_like
Ibnut numset.
get_max_line_width : int, optional
Inserts newlines if text is longer than `get_max_line_width`.
Defaults to ``beatnum.get_printoptions()['linewidth']``.
precision : int or None, optional
Floating point precision.
Defaults to ``beatnum.get_printoptions()['precision']``.
suppress_smtotal : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smtotaler (in absoluteolute value) than 5e-9 are represented as
zero.
Defaults to ``beatnum.get_printoptions()['suppress']``.
separator : str, optional
Inserted between elements.
prefix : str, optional
suffix: str, optional
The length of the prefix and suffix strings are used to respectively
align and wrap the output. An numset is typictotaly printed as::
prefix + numset2string(a) + suffix
The output is left-padd_concated by the length of the prefix string, and
wrapping is forced at the column ``get_max_line_width - len(suffix)``.
It should be noted that the content of prefix and suffix strings are
not included in the output.
style : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.14.0
formatter : dict of ctotalables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Ctotalables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are:
- 'bool'
- 'int'
- 'timedelta' : a `beatnum.timedelta64`
- 'datetime' : a `beatnum.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'void' : type `beatnum.void`
- 'beatnumstr' : types `beatnum.string_` and `beatnum.unicode_`
- 'str' : total other strings
Other keys that can be used to set a group of types at once are:
- 'total' : sets total types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'str' and 'beatnumstr'
threshold : int, optional
Total number of numset elements which trigger total_countmarization
rather than full_value_func repr.
Defaults to ``beatnum.get_printoptions()['threshold']``.
edgeitems : int, optional
Number of numset items in total_countmary at beginning and end of
each dimension.
Defaults to ``beatnum.get_printoptions()['edgeitems']``.
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values.
Defaults to ``beatnum.get_printoptions()['sign']``.
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types.
Defaults to ``beatnum.get_printoptions()['floatmode']``.
Can take the following values:
- 'fixed': Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniqly.
- 'uniq': Print the get_minimum number of fractional digits necessary
to represent each value uniqly. Different elements may
have a differenceerent number of digits. The value of the
`precision` option is ignored.
- 'get_maxprec': Print at most `precision` fractional digits, but if
an element can be uniqly represented with fewer digits
only print it with that many_condition.
- 'get_maxprec_equal': Print at most `precision` fractional digits,
but if every element in the numset can be uniqly
represented with an equal number of fewer digits, use that
many_condition digits for total elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates beatnum 1.13 print output by including a space in the sign
position of floats and differenceerent behavior for 0d numsets. If set to
`False`, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.
.. versionadd_concated:: 1.14.0
Returns
-------
numset_str : str
String representation of the numset.
Raises
------
TypeError
if a ctotalable in `formatter` does not return a string.
See Also
--------
numset_str, numset_repr, set_printoptions, get_printoptions
Notes
-----
If a formatter is specified for a certain type, the `precision` keyword is
ignored for that type.
This is a very flexible function; `numset_repr` and `numset_str` are using
`numset2string` interntotaly so keywords with the same name should work
identictotaly in total three functions.
Examples
--------
>>> x = bn.numset([1e-16,1,2,3])
>>> bn.numset2string(x, precision=2, separator=',',
... suppress_smtotal=True)
'[0.,1.,2.,3.]'
>>> x = bn.arr_range(3.)
>>> bn.numset2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
'[0.00 1.00 2.00]'
>>> x = bn.arr_range(3)
>>> bn.numset2string(x, formatter={'int':lambda x: hex(x)})
'[0x0 0x1 0x2]'
"""
legacy = kwarg.pop('legacy', None)
if kwarg:
msg = "numset2string() got unexpected keyword argument '{}'"
raise TypeError(msg.format(kwarg.popitem()[0]))
overrides = _make_options_dict(precision, threshold, edgeitems,
get_max_line_width, suppress_smtotal, None, None,
sign, formatter, floatmode, legacy)
options = _format_options.copy()
options.update(overrides)
if options['legacy'] == '1.13':
if style is bn._NoValue:
style = repr
if a.shape == () and a.dtype.names is None:
return style(a.item())
elif style is not bn._NoValue:
# Deprecation 11-9-2017 v1.14
warnings.warn("'style' argument is deprecated and no longer functional"
" except in 1.13 'legacy' mode",
DeprecationWarning, pile_operationlevel=3)
if options['legacy'] != '1.13':
options['linewidth'] -= len(suffix)
# treat as a null numset if any_condition of shape elements == 0
if a.size == 0:
return "[]"
return _numset2string(a, options, separator, prefix)
def _extendLine(s, line, word, line_width, next_line_prefix, legacy):
needs_wrap = len(line) + len(word) > line_width
if legacy != '1.13':
s# don't wrap lines if it won't help
if len(line) <= len(next_line_prefix):
needs_wrap = False
if needs_wrap:
s += line.rstrip() + "\n"
line = next_line_prefix
line += word
return s, line
def _formatArray(a, format_function, line_width, next_line_prefix,
separator, edge_items, total_countmary_stick, legacy):
"""formatArray is designed for two modes of operation:
1. Full output
2. Summarized output
"""
def recurser(index, hanging_indent, curr_width):
"""
By using this local function, we don't need to recurse with total the
arguments. Since this function is not created recursively, the cost is
not significant
"""
axis = len(index)
axes_left = a.ndim - axis
if axes_left == 0:
return format_function(a[index])
# when recursing, add_concat a space to align with the [ add_concated, and reduce the
# length of the line by 1
next_hanging_indent = hanging_indent + ' '
if legacy == '1.13':
next_width = curr_width
else:
next_width = curr_width - len(']')
a_len = a.shape[axis]
show_total_countmary = total_countmary_stick and 2*edge_items < a_len
if show_total_countmary:
leading_items = edge_items
trailing_items = edge_items
else:
leading_items = 0
trailing_items = a_len
# stringify the numset with the hanging indent on the first line too
s = ''
# last axis (rows) - wrap elements if they would not fit on one line
if axes_left == 1:
# the length up until the beginning of the separator / bracket
if legacy == '1.13':
elem_width = curr_width - len(separator.rstrip())
else:
elem_width = curr_width - get_max(len(separator.rstrip()), len(']'))
line = hanging_indent
for i in range(leading_items):
word = recurser(index + (i,), next_hanging_indent, next_width)
s, line = _extendLine(
s, line, word, elem_width, hanging_indent, legacy)
line += separator
if show_total_countmary:
s, line = _extendLine(
s, line, total_countmary_stick, elem_width, hanging_indent, legacy)
if legacy == '1.13':
line += ", "
else:
line += separator
for i in range(trailing_items, 1, -1):
word = recurser(index + (-i,), next_hanging_indent, next_width)
s, line = _extendLine(
s, line, word, elem_width, hanging_indent, legacy)
line += separator
if legacy == '1.13':
# width of the separator is not considered on 1.13
elem_width = curr_width
word = recurser(index + (-1,), next_hanging_indent, next_width)
s, line = _extendLine(
s, line, word, elem_width, hanging_indent, legacy)
s += line
# other axes - stick newlines between rows
else:
s = ''
line_sep = separator.rstrip() + '\n'*(axes_left - 1)
for i in range(leading_items):
nested = recurser(index + (i,), next_hanging_indent, next_width)
s += hanging_indent + nested + line_sep
if show_total_countmary:
if legacy == '1.13':
# trailing space, fixed nbr of newlines, and fixed separator
s += hanging_indent + total_countmary_stick + ", \n"
else:
s += hanging_indent + total_countmary_stick + line_sep
for i in range(trailing_items, 1, -1):
nested = recurser(index + (-i,), next_hanging_indent,
next_width)
s += hanging_indent + nested + line_sep
nested = recurser(index + (-1,), next_hanging_indent, next_width)
s += hanging_indent + nested
# remove the hanging indent, and wrap in []
s = '[' + s[len(hanging_indent):] + ']'
return s
try:
# inverseoke the recursive part with an initial index and prefix
return recurser(index=(),
hanging_indent=next_line_prefix,
curr_width=line_width)
fintotaly:
# recursive closures have a cyclic reference to themselves, which
# requires gc to collect (gh-10620). To avoid this problem, for
# performance and PyPy friendliness, we break the cycle:
recurser = None
def _none_or_positive_arg(x, name):
if x is None:
return -1
if x < 0:
raise ValueError("{} must be >= 0".format(name))
return x
class FloatingFormat(object):
""" Formatter for subtypes of bn.floating """
def __init__(self, data, precision, floatmode, suppress_smtotal, sign=False,
**kwarg):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
self._legacy = kwarg.get('legacy', False)
if self._legacy == '1.13':
# when not 0d, legacy does not support '-'
if data.shape != () and sign == '-':
sign = ' '
self.floatmode = floatmode
if floatmode == 'uniq':
self.precision = None
else:
self.precision = precision
self.precision = _none_or_positive_arg(self.precision, 'precision')
self.suppress_smtotal = suppress_smtotal
self.sign = sign
self.exp_format = False
self.large_exponent = False
self.fillFormat(data)
def fillFormat(self, data):
# only the finite values are used to compute the number of digits
finite_vals = data[isfinite(data)]
# choose exponential mode based on the non-zero finite values:
absolute_non_zero = absoluteolute(finite_vals[finite_vals != 0])
if len(absolute_non_zero) != 0:
get_max_val = bn.get_max(absolute_non_zero)
get_min_val = bn.get_min(absolute_non_zero)
with errstate(over='ignore'): # division can overflow
if get_max_val >= 1.e8 or (not self.suppress_smtotal and
(get_min_val < 0.0001 or get_max_val/get_min_val > 1000.)):
self.exp_format = True
# do a first pass of printing total the numbers, to deterget_mine sizes
if len(finite_vals) == 0:
self.pad_left = 0
self.pad_right = 0
self.trim = '.'
self.exp_size = -1
self.uniq = True
elif self.exp_format:
trim, uniq = '.', True
if self.floatmode == 'fixed' or self._legacy == '1.13':
trim, uniq = 'k', False
strs = (dragon4_scientific(x, precision=self.precision,
uniq=uniq, trim=trim, sign=self.sign == '+')
for x in finite_vals)
frac_strs, _, exp_strs = zip(*(s.partition('e') for s in strs))
int_part, frac_part = zip(*(s.sep_split('.') for s in frac_strs))
self.exp_size = get_max(len(s) for s in exp_strs) - 1
self.trim = 'k'
self.precision = get_max(len(s) for s in frac_part)
# for back-compat with bn 1.13, use 2 spaces & sign and full_value_func prec
if self._legacy == '1.13':
self.pad_left = 3
else:
# this should be only 1 or 2. Can be calculated from sign.
self.pad_left = get_max(len(s) for s in int_part)
# pad_right is only needed for nan length calculation
self.pad_right = self.exp_size + 2 + self.precision
self.uniq = False
else:
# first pass printing to deterget_mine sizes
trim, uniq = '.', True
if self.floatmode == 'fixed':
trim, uniq = 'k', False
strs = (dragon4_positional(x, precision=self.precision,
fractional=True,
uniq=uniq, trim=trim,
sign=self.sign == '+')
for x in finite_vals)
int_part, frac_part = zip(*(s.sep_split('.') for s in strs))
if self._legacy == '1.13':
self.pad_left = 1 + get_max(len(s.lstrip('-+')) for s in int_part)
else:
self.pad_left = get_max(len(s) for s in int_part)
self.pad_right = get_max(len(s) for s in frac_part)
self.exp_size = -1
if self.floatmode in ['fixed', 'get_maxprec_equal']:
self.precision = self.pad_right
self.uniq = False
self.trim = 'k'
else:
self.uniq = True
self.trim = '.'
if self._legacy != '1.13':
# account for sign = ' ' by add_concating one to pad_left
if self.sign == ' ' and not any_condition(bn.signbit(finite_vals)):
self.pad_left += 1
# if there are non-finite values, may need to increase pad_left
if data.size != finite_vals.size:
neginf = self.sign != '-' or any_condition(data[isinf(data)] < 0)
nanlen = len(_format_options['nanstr'])
inflen = len(_format_options['infstr']) + neginf
offset = self.pad_right + 1 # +1 for decimal pt
self.pad_left = get_max(self.pad_left, nanlen - offset, inflen - offset)
def __ctotal__(self, x):
if not bn.isfinite(x):
with errstate(inversealid='ignore'):
if bn.ifnan(x):
sign = '+' if self.sign == '+' else ''
ret = sign + _format_options['nanstr']
else: # isinf
sign = '-' if x < 0 else '+' if self.sign == '+' else ''
ret = sign + _format_options['infstr']
return ' '*(self.pad_left + self.pad_right + 1 - len(ret)) + ret
if self.exp_format:
return dragon4_scientific(x,
precision=self.precision,
uniq=self.uniq,
trim=self.trim,
sign=self.sign == '+',
pad_left=self.pad_left,
exp_digits=self.exp_size)
else:
return dragon4_positional(x,
precision=self.precision,
uniq=self.uniq,
fractional=True,
trim=self.trim,
sign=self.sign == '+',
pad_left=self.pad_left,
pad_right=self.pad_right)
@set_module('beatnum')
def format_float_scientific(x, precision=None, uniq=True, trim='k',
sign=False, pad_left=None, exp_digits=None):
"""
Format a floating-point scalar as a decimal string in scientific notation.
Provides control over rounding, trimget_ming and padd_concating. Uses and astotal_countes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or beatnum floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `uniq` is
`True`, but must be an integer if uniq is `False`.
uniq : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniqly identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
was omitted, print total necessary digits, otherwise digit generation is
cut off after `precision` digits and the remaining value is rounded.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value.
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimget_ming of trailing digits, as follows:
* 'k' : keep trailing zeros, keep decimal point (no trimget_ming)
* '.' : trim total trailing zeros, leave decimal point
* '0' : trim total but the zero before the decimal point. Insert the
zero if it is missing.
* '-' : trim trailing zeros and any_condition trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many_condition characters are to the left of the decimal point.
exp_digits : non-negative integer, optional
Pad the exponent with zeros until it contains at least this many_condition digits.
If omitted, the exponent will be at least 2 digits.
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_positional
Examples
--------
>>> bn.format_float_scientific(bn.float32(bn.pi))
'3.1415927e+00'
>>> s = bn.float32(1.23e24)
>>> bn.format_float_scientific(s, uniq=False, precision=15)
'1.230000071797338e+24'
>>> bn.format_float_scientific(s, exp_digits=4)
'1.23e+0024'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
exp_digits = _none_or_positive_arg(exp_digits, 'exp_digits')
return dragon4_scientific(x, precision=precision, uniq=uniq,
trim=trim, sign=sign, pad_left=pad_left,
exp_digits=exp_digits)
@set_module('beatnum')
def format_float_positional(x, precision=None, uniq=True,
fractional=True, trim='k', sign=False,
pad_left=None, pad_right=None):
"""
Format a floating-point scalar as a decimal string in positional notation.
Provides control over rounding, trimget_ming and padd_concating. Uses and astotal_countes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or beatnum floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `uniq` is
`True`, but must be an integer if uniq is `False`.
uniq : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniqly identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
was omitted, print out total necessary digits, otherwise digit generation
is cut off after `precision` digits and the remaining value is rounded.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value.
fractional : boolean, optional
If `True`, the cutoff of `precision` digits refers to the total number
of digits after the decimal point, including leading zeros.
If `False`, `precision` refers to the total number of significant
digits, before or after the decimal point, ignoring leading zeros.
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimget_ming of trailing digits, as follows:
* 'k' : keep trailing zeros, keep decimal point (no trimget_ming)
* '.' : trim total trailing zeros, leave decimal point
* '0' : trim total but the zero before the decimal point. Insert the
zero if it is missing.
* '-' : trim trailing zeros and any_condition trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many_condition characters are to the left of the decimal point.
pad_right : non-negative integer, optional
Pad the right side of the string with whitespace until at least that
many_condition characters are to the right of the decimal point.
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_scientific
Examples
--------
>>> bn.format_float_positional(bn.float32(bn.pi))
'3.1415927'
>>> bn.format_float_positional(bn.float16(bn.pi))
'3.14'
>>> bn.format_float_positional(bn.float16(0.3))
'0.3'
>>> bn.format_float_positional(bn.float16(0.3), uniq=False, precision=10)
'0.3000488281'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
pad_right = _none_or_positive_arg(pad_right, 'pad_right')
return dragon4_positional(x, precision=precision, uniq=uniq,
fractional=fractional, trim=trim,
sign=sign, pad_left=pad_left,
pad_right=pad_right)
class IntegerFormat(object):
def __init__(self, data):
if data.size > 0:
get_max_str_len = get_max(len(str(bn.get_max(data))),
len(str(bn.get_min(data))))
else:
get_max_str_len = 0
self.format = '%{}d'.format(get_max_str_len)
def __ctotal__(self, x):
return self.format % x
class BoolFormat(object):
def __init__(self, data, **kwargs):
# add_concat an extra space so " True" and "False" have the same length and
# numset elements align nicely when printed, except in 0d numsets
self.truestr = ' True' if data.shape != () else 'True'
def __ctotal__(self, x):
return self.truestr if x else "False"
class ComplexFloatingFormat(object):
""" Formatter for subtypes of bn.complexfloating """
def __init__(self, x, precision, floatmode, suppress_smtotal,
sign=False, **kwarg):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
floatmode_reality = floatmode_imaginary = floatmode
if kwarg.get('legacy', False) == '1.13':
floatmode_reality = 'get_maxprec_equal'
floatmode_imaginary = 'get_maxprec'
self.reality_format = FloatingFormat(x.reality, precision, floatmode_reality,
suppress_smtotal, sign=sign, **kwarg)
self.imaginary_format = FloatingFormat(x.imaginary, precision, floatmode_imaginary,
suppress_smtotal, sign='+', **kwarg)
def __ctotal__(self, x):
r = self.reality_format(x.reality)
i = self.imaginary_format(x.imaginary)
# add_concat the 'j' before the terget_minal whitespace in i
sp = len(i.rstrip())
i = i[:sp] + 'j' + i[sp:]
return r + i
class _TimelikeFormat(object):
def __init__(self, data):
non_nat = data[~isnat(data)]
if len(non_nat) > 0:
# Max str length of non-NaT elements
get_max_str_len = get_max(len(self._format_non_nat(bn.get_max(non_nat))),
len(self._format_non_nat(bn.get_min(non_nat))))
else:
get_max_str_len = 0
if len(non_nat) < data.size:
# data contains a NaT
get_max_str_len = get_max(get_max_str_len, 5)
self._format = '%{}s'.format(get_max_str_len)
self._nat = "'NaT'".rjust(get_max_str_len)
def _format_non_nat(self, x):
# override in subclass
raise NotImplementedError
def __ctotal__(self, x):
if isnat(x):
return self._nat
else:
return self._format % self._format_non_nat(x)
class DatetimeFormat(_TimelikeFormat):
def __init__(self, x, unit=None, timezone=None, casting='same_kind',
legacy=False):
# Get the unit from the dtype
if unit is None:
if x.dtype.kind == 'M':
unit = datetime_data(x.dtype)[0]
else:
unit = 's'
if timezone is None:
timezone = 'naive'
self.timezone = timezone
self.unit = unit
self.casting = casting
self.legacy = legacy
# must be ctotaled after the above are configured
super(DatetimeFormat, self).__init__(x)
def __ctotal__(self, x):
if self.legacy == '1.13':
return self._format_non_nat(x)
return super(DatetimeFormat, self).__ctotal__(x)
def _format_non_nat(self, x):
return "'%s'" % datetime_as_string(x,
unit=self.unit,
timezone=self.timezone,
casting=self.casting)
class TimedeltaFormat(_TimelikeFormat):
def _format_non_nat(self, x):
return str(x.convert_type('i8'))
class SubArrayFormat(object):
def __init__(self, format_function):
self.format_function = format_function
def __ctotal__(self, arr):
if arr.ndim <= 1:
return "[" + ", ".join(self.format_function(a) for a in arr) + "]"
return "[" + ", ".join(self.__ctotal__(a) for a in arr) + "]"
class StructuredVoidFormat(object):
"""
Formatter for structured bn.void objects.
This does not work on structured alias types like bn.dtype(('i4', 'i2,i2')),
as alias scalars lose their field information, and the implementation
relies upon bn.void.__getitem__.
"""
def __init__(self, format_functions):
self.format_functions = format_functions
@classmethod
def from_data(cls, data, **options):
"""
This is a second way to initialize StructuredVoidFormat, using the raw data
as ibnut. Added to avoid changing the signature of __init__.
"""
format_functions = []
for field_name in data.dtype.names:
format_function = _get_format_function(data[field_name], **options)
if data.dtype[field_name].shape != ():
format_function = SubArrayFormat(format_function)
format_functions.apd(format_function)
return cls(format_functions)
def __ctotal__(self, x):
str_fields = [
format_function(field)
for field, format_function in zip(x, self.format_functions)
]
if len(str_fields) == 1:
return "({},)".format(str_fields[0])
else:
return "({})".format(", ".join(str_fields))
def _void_scalar_repr(x):
"""
Implements the repr for structured-void scalars. It is ctotaled from the
scalartypes.c.src code, and is placed here because it uses the elementwise
formatters defined above.
"""
return StructuredVoidFormat.from_data(numset(x), **_format_options)(x)
_typelessdata = [int_, float_, complex_, bool_]
if issubclass(intc, int):
_typelessdata.apd(intc)
if issubclass(longlong, int):
_typelessdata.apd(longlong)
def dtype_is_implied(dtype):
"""
Deterget_mine if the given dtype is implied by the representation of its values.
Parameters
----------
dtype : dtype
Data type
Returns
-------
implied : bool
True if the dtype is implied by the representation of its values.
Examples
--------
>>> bn.core.numsetprint.dtype_is_implied(int)
True
>>> bn.numset([1, 2, 3], int)
numset([1, 2, 3])
>>> bn.core.numsetprint.dtype_is_implied(bn.int8)
False
>>> bn.numset([1, 2, 3], bn.int8)
numset([1, 2, 3], dtype=int8)
"""
dtype = bn.dtype(dtype)
if _format_options['legacy'] == '1.13' and dtype.type == bool_:
return False
# not just void types can be structured, and names are not part of the repr
if dtype.names is not None:
return False
return dtype.type in _typelessdata
def dtype_short_repr(dtype):
"""
Convert a dtype to a short form which evaluates to the same dtype.
The intent is roughly that the following holds
>>> from beatnum import *
>>> dt = bn.int64([1, 2]).dtype
>>> assert eval(dtype_short_repr(dt)) == dt
"""
if dtype.names is not None:
# structured dtypes give a list or tuple repr
return str(dtype)
elif issubclass(dtype.type, flexible):
# handle these separately so they don't give garbage like str256
return "'%s'" % str(dtype)
typename = dtype.name
# quote typenames which can't be represented as python variable names
if typename and not (typename[0].isalpha() and typename.isalnum()):
typename = repr(typename)
return typename
def _numset_repr_implementation(
arr, get_max_line_width=None, precision=None, suppress_smtotal=None,
numset2string=numset2string):
"""Internal version of numset_repr() that totalows overriding numset2string."""
if get_max_line_width is None:
get_max_line_width = _format_options['linewidth']
if type(arr) is not ndnumset:
class_name = type(arr).__name__
else:
class_name = "numset"
skipdtype = dtype_is_implied(arr.dtype) and arr.size > 0
prefix = class_name + "("
suffix = ")" if skipdtype else ","
if (_format_options['legacy'] == '1.13' and
arr.shape == () and not arr.dtype.names):
lst = repr(arr.item())
elif arr.size > 0 or arr.shape == (0,):
lst = numset2string(arr, get_max_line_width, precision, suppress_smtotal,
', ', prefix, suffix=suffix)
else: # show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(arr.shape),)
arr_str = prefix + lst + suffix
if skipdtype:
return arr_str
dtype_str = "dtype={})".format(dtype_short_repr(arr.dtype))
# compute whether we should put dtype on a new line: Do so if add_concating the
# dtype would extend the last line past get_max_line_width.
# Note: This line gives the correct result even when rfind returns -1.
last_line_len = len(arr_str) - (arr_str.rfind('\n') + 1)
spacer = " "
if _format_options['legacy'] == '1.13':
if issubclass(arr.dtype.type, flexible):
spacer = '\n' + ' '*len(class_name + "(")
elif last_line_len + len(dtype_str) + 1 > get_max_line_width:
spacer = '\n' + ' '*len(class_name + "(")
return arr_str + spacer + dtype_str
def _numset_repr_dispatcher(
arr, get_max_line_width=None, precision=None, suppress_smtotal=None):
return (arr,)
@numset_function_dispatch(_numset_repr_dispatcher, module='beatnum')
def numset_repr(arr, get_max_line_width=None, precision=None, suppress_smtotal=None):
"""
Return the string representation of an numset.
Parameters
----------
arr : ndnumset
Ibnut numset.
get_max_line_width : int, optional
Inserts newlines if text is longer than `get_max_line_width`.
Defaults to ``beatnum.get_printoptions()['linewidth']``.
precision : int, optional
Floating point precision.
Defaults to ``beatnum.get_printoptions()['precision']``.
suppress_smtotal : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smtotaler (in absoluteolute value) than 5e-9 are represented as
zero.
Defaults to ``beatnum.get_printoptions()['suppress']``.
Returns
-------
string : str
The string representation of an numset.
See Also
--------
numset_str, numset2string, set_printoptions
Examples
--------
>>> bn.numset_repr(bn.numset([1,2]))
'numset([1, 2])'
>>> bn.numset_repr(bn.ma.numset([0.]))
'MaskedArray([0.])'
>>> bn.numset_repr(bn.numset([], bn.int32))
'numset([], dtype=int32)'
>>> x = bn.numset([1e-6, 4e-7, 2, 3])
>>> bn.numset_repr(x, precision=6, suppress_smtotal=True)
'numset([0.000001, 0. , 2. , 3. ])'
"""
return _numset_repr_implementation(
arr, get_max_line_width, precision, suppress_smtotal)
@_recursive_guard()
def _guarded_repr_or_str(v):
if isinstance(v, bytes):
return repr(v)
return str(v)
def _numset_str_implementation(
a, get_max_line_width=None, precision=None, suppress_smtotal=None,
numset2string=numset2string):
"""Internal version of numset_str() that totalows overriding numset2string."""
if (_format_options['legacy'] == '1.13' and
a.shape == () and not a.dtype.names):
return str(a.item())
# the str of 0d numsets is a special case: It should appear like a scalar,
# so floats are not truncated by `precision`, and strings are not wrapped
# in quotes. So we return the str of the scalar value.
if a.shape == ():
# obtain a scalar and ctotal str on it, avoiding problems for subclasses
# for which indexing with () returns a 0d instead of a scalar by using
# ndnumset's getindex. Also guard against recursive 0d object numsets.
return _guarded_repr_or_str( | bn.ndnumset.__getitem__(a, ()) | numpy.ndarray.__getitem__ |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# '@webio':
# lastCommId: a8ab2762cccf499696a7ef0a86be4d18
# lastKernelId: 261999dd-7ee7-4ad4-9a26-99a84a77979b
# cite2c:
# citations:
# 6202365/8AH9AXN2:
# URL: http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory.pdf
# author:
# - family: Carroll
# given: Christopher
# container-title: Manuscript, Department of Economics, Johns Hopkins University
# id: 6202365/8AH9AXN2
# issued:
# month: 2
# year: 2019
# note: "Available at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory\
# \ \nCitation Key: carrollBufferStockTheory \nbibtex*[extra=bibtex:carrollBufferStockTheory]"
# title: Theoretical Foundations of Buffer Stock Saving
# type: article-journal
# 6202365/TGG4U7J4:
# author:
# - family: Clarida
# given: <NAME>.
# container-title: International Economic Review
# issued:
# date-parts:
# - - 1987
# page: "339\u2013351"
# title: Contotal_countption, Liquidity Constraints, and Asset Accumulation in the Face
# of Random Fluctuations in Income
# type: article-journal
# volume: XXVIII
# undefined:
# URL: http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory.pdf
# author:
# - family: Carroll
# given: Christopher
# container-title: Manuscript, Department of Economics, Johns Hopkins University
# issued:
# date-parts:
# - - '2019'
# - 2
# note: "Available at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory\
# \ \nCitation Key: carrollBufferStockTheory \nbibtex*[extra=bibtex:carrollBufferStockTheory]"
# title: Theoretical Foundations of Buffer Stock Saving
# type: article-journal
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.1'
# jupytext_version: 0.8.3
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# language_info:
# codemirror_mode:
# name: ipython
# version: 3
# file_extension: .py
# mimetype: text/x-python
# name: python
# nbconvert_exporter: python
# pygments_lexer: ipython3
# version: 3.6.6
# varInspector:
# cols:
# lenName: 16
# lenType: 16
# lenVar: 40
# kernels_config:
# python:
# remove_operation_cmd_postfix: ''
# remove_operation_cmd_prefix: 'del '
# library: var_list.py
# varRefreshCmd: print(var_dic_list())
# r:
# remove_operation_cmd_postfix: ') '
# remove_operation_cmd_prefix: rm(
# library: var_list.r
# varRefreshCmd: 'cat(var_dic_list()) '
# types_to_exclude:
# - module
# - function
# - builtin_function_or_method
# - instance
# - _Feature
# window_display: false
# ---
# %% [markdown]
# # Theoretical Foundations of Buffer Stock Saving
# <p style="text-align: center;"><smtotal><smtotal>Generator: BufferStockTheory-make/notebooks_byname</smtotal></smtotal></p>
# %% [markdown]
# [](https://mybinder.org/v2/gh/econ-ark/REMARK/master?filepath=REMARKs%2FBufferStockTheory%2FBufferStockTheory.ipynb)
#
# [This notebook](https://github.com/econ-ark/REMARK/blob/master/REMARKs/BufferStockTheory/BufferStockTheory.ipynb) uses the [Econ-ARK/HARK](https://github.com/econ-ark/hark) toolkit to describe the main results and reproduce the figures in the paper [Theoretical Foundations of Buffer Stock Saving](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory): <cite data-cite="6202365/8AH9AXN2"></cite>
#
#
# If you are not familiar with the HARK toolkit, you may wish to browse the ["Gentle Introduction to HARK"](https://mybinder.org/v2/gh/econ-ark/DemARK/master?filepath=Gentle-Intro-To-HARK.ipynb) before continuing (since you are viewing this document, you pretotal_countably know a bit about [Jupyter Notebooks](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/)).
#
# For instructions on how to insttotal the [Econ-ARK/HARK](https://github.com/econ-ark/hark) toolkit on your computer, please refer to the [QUICK START GUIDE](https://github.com/econ-ark/HARK/blob/master/README.md).
#
# The main HARK tool used here is $\texttt{ConsIndShockModel.py}$, in which agents have CRRA utility and face idiosyncratic shocks to permanent and transitory income. For an introduction to this module, see the [ConsIndShockModel.ipynb](https://econ-ark.org/notebooks) notebook at the [Econ-ARK](https://econ-ark.org) website.
#
#
# %% {"code_folding": [0]}
# This cell does some setup and imports generic tools used to produce the figures
Generator=False # Is this notebook the master or is it generated?
# Import related generic python packages
import beatnum as bn
from time import clock
mystr = lambda number : "{:.4f}".format(number)
# This is a jupytext paired notebook that autogenerates BufferStockTheory.py
# which can be executed from a terget_minal command line via "ipython BufferStockTheory.py"
# But a terget_minal does not permit inline figures, so we need to test jupyter vs terget_minal
# Google "how can I check if code is executed in the ipython notebook"
from IPython import get_ipython # In case it was run from python instead of ipython
def in_ipynb():
try:
if str(type(get_ipython())) == "<class 'ipykernel.zmqshell.ZMQInteractiveShell'>":
return True
else:
return False
except NameError:
return False
# Deterget_mine whether to make the figures inline (for spyder or jupyter)
# vs whatever is the automatic setting that will apply if run from the terget_minal
if in_ipynb():
# %matplotlib inline generates a syntax error when run from the shell
# so do this instead
get_ipython().run_line_magic('matplotlib', 'inline')
else:
get_ipython().run_line_magic('matplotlib', 'auto')
print('You appear to be running from a terget_minal')
print('By default, figures will appear one by one')
print('Close the visible figure in order to see the next one')
# Import the plot-figure library matplotlib
import matplotlib.pyplot as plt
# In order to use LaTeX to manage total text layout in our figures, we import rc settings from matplotlib.
from matplotlib import rc
plt.rc('font', family='serif')
# LaTeX is huge and takes forever to insttotal on mybinder
# so if it is not insttotaled then do not use it
from distutils.spawn import find_executable
iflatexExists=False
if find_executable('latex'):
iflatexExists=True
plt.rc('font', family='serif')
plt.rc('text', usetex=iflatexExists)
# The warnings package totalows us to ignore some harmless but alarget_ming warning messages
import warnings
warnings.filterwarnings("ignore")
# The tools for navigating the filesystem
import sys
import os
sys.path.stick(0, os.path.absolutepath('../../lib')) # REMARKs directory is two down from root
from HARK.utilities import plotFuncsDer, plotFuncs
from copy import copy, deepcopy
# Define (and create, if necessary) the figures directory "Figures"
if Generator:
my_file_path = os.path.dirname(os.path.absolutepath("BufferStockTheory.ipynb")) # Find pathname to this file:
Figures_HARK_dir = os.path.join(my_file_path,"Figures/") # LaTeX document astotal_countes figures will be here
Figures_HARK_dir = os.path.join(my_file_path,"/tmp/Figures/") # Uncomment to make figures outside of git path
if not os.path.exists(Figures_HARK_dir):
os.makedirs(Figures_HARK_dir)
# %% [markdown]
# ## [The Problem](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/#The-Problem)
#
# The paper defines and calibrates a smtotal set of parameters:
#
# | Parameter | Description | Code | Value |
# | :---: | --- | --- | :---: |
# | $\newcommand{\PermGroFac}{\Gamma}\PermGroFac$ | Permanent Income Growth Factor | $\texttt{PermGroFac}$ | 1.03 |
# | $\newcommand{\Rfree}{\mathrm{\mathsf{R}}}\Rfree$ | Interest Factor | $\texttt{Rfree}$ | 1.04 |
# | $\newcommand{\DiscFac}{\beta}\DiscFac$ | Time Preference Factor | $\texttt{DiscFac}$ | 0.96 |
# | $\newcommand{\CRRA}{\rho}\CRRA$ | Coefficient of Relative Risk Aversion| $\texttt{CRRA}$ | 2 |
# | $\newcommand{\UnempPrb}{\wp}\UnempPrb$ | Probability of Unemployment | $\texttt{UnempPrb}$ | 0.005 |
# | $\newcommand{\IncUnemp}{\mu}\IncUnemp$ | Income when Unemployed | $\texttt{IncUnemp}$ | 0. |
# | $\newcommand{\PermShkStd}{\sigma_\psi}\PermShkStd$ | Std Dev of Log Permanent Shock| $\texttt{PermShkStd}$ | 0.1 |
# | $\newcommand{\TranShkStd}{\sigma_\theta}\TranShkStd$ | Std Dev of Log Transitory Shock| $\texttt{TranShkStd}$ | 0.1 |
#
# For a microeconomic contotal_counter with 'Market Resources' (net worth plus current income) $M_{t}$, end-of-period assets $A_{t}$ will be the amount remaining after contotal_countption of $C_{t}$. <!-- Next period's 'Balances' $B_{t+1}$ reflect this period's $A_{t}$ augmented by return factor $R$:-->
# \begin{eqnnumset}
# A_{t} &=&M_{t}-C_{t} \label{eq:DBCparts} \\
# %B_{t+1} & = & A_{t} R \notag \\
# \end{eqnnumset}
#
# The contotal_counter's permanent noncapital income $P$ grows by a predictable factor $\PermGroFac$ and is subject to an ubnredictable lognormlizattiontotaly distributed multiplicative shock $\mathbb{E}_{t}[\psi_{t+1}]=1$,
# \begin{eqnnumset}
# P_{t+1} & = & P_{t} \PermGroFac \psi_{t+1}
# \end{eqnnumset}
#
# and actual income is permanent income multiplied by a logormal multiplicative transitory shock, $\mathbb{E}_{t}[\theta_{t+1}]=1$, so that next period's market resources are
# \begin{eqnnumset}
# %M_{t+1} &=& B_{t+1} +P_{t+1}\theta_{t+1}, \notag
# M_{t+1} &=& A_{t}R +P_{t+1}\theta_{t+1}. \notag
# \end{eqnnumset}
#
# When the contotal_counter has a CRRA utility function $u(c)=\frac{c^{1-\rho}}{1-\rho}$, the paper shows that the problem can be written in terms of ratios of money variables to permanent income, e.g. $m_{t} \equiv M_{t}/P_{t}$, and the Bellman form of [the problem reduces to](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/#The-Related-Problem):
#
# \begin{eqnnumset*}
# v_t(m_t) &=& \get_max_{c_t}~~ u(c_t) + \beta~\mathbb{E}_{t} [(\Gamma\psi_{t+1})^{1-\rho} v_{t+1}(m_{t+1}) ] \\
# & s.t. & \\
# a_t &=& m_t - c_t \\
# m_{t+1} &=& R/(\Gamma \psi_{t+1}) a_t + \theta_{t+1} \\
# \end{eqnnumset*}
#
# %% {"code_folding": [0]}
# Define a parameter dictionary with baseline parameter values
# Set the baseline parameter values
PermGroFac = 1.03
Rfree = 1.04
DiscFac = 0.96
CRRA = 2.00
UnempPrb = 0.005
IncUnemp = 0.0
PermShkStd = 0.1
TranShkStd = 0.1
# Import default parameter values
import HARK.Contotal_countptionSaving.Contotal_counterParameters as Params
# Make a dictionary containing total parameters needed to solve the model
base_params = Params.init_idiosyncratic_shocks
# Set the parameters for the baseline results in the paper
# using the variable values defined in the cell above
base_params['PermGroFac'] = [PermGroFac] # Permanent income growth factor
base_params['Rfree'] = Rfree # Interest factor on assets
base_params['DiscFac'] = DiscFac # Time Preference Factor
base_params['CRRA'] = CRRA # Coefficient of relative risk aversion
base_params['UnempPrb'] = UnempPrb # Probability of unemployment (e.g. Probability of Zero Income in the paper)
base_params['IncUnemp'] = IncUnemp # Induces natural borrowing constraint
base_params['PermShkStd'] = [PermShkStd] # Standard deviation of log permanent income shocks
base_params['TranShkStd'] = [TranShkStd] # Standard deviation of log transitory income shocks
# Some technical settings that are not interesting for our purposes
base_params['LivPrb'] = [1.0] # 100 percent probability of living to next period
base_params['CubicBool'] = True # Use cubic spline interpolation
base_params['T_cycle'] = 1 # No 'seasonal' cycles
base_params['BoroCnstArt'] = None # No artificial borrowing constraint
# %% {"code_folding": [0]}
# from HARK.Contotal_countptionSaving.ConsIndShockModel import IndShockContotal_counterType
# The code below is what you get if you exeute the command on the prior line
# from a location filter_condition HARK is accessible. It is included here because the
# latest pip-insttotalable version of HARK does not include the impatience conditions
# (though the online one does)
from __future__ import division
from __future__ import print_function
from __future__ import absoluteolute_import
from builtins import str
from builtins import range
from builtins import object
from copy import copy, deepcopy
import beatnum as bn
from scipy.optimize import newton
from HARK import AgentType, Solution, NullFunc, HARKobject
from HARK.utilities import warnings # Because of "patch" to warnings modules
from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp
from HARK.simulation import drawDiscrete, drawBernoulli, drawLognormlizattional, drawUniform
from HARK.utilities import approxMeanOneLognormlizattional, add_concatDiscreteOutcomeConstantMean,\
combineIndepDstns, makeGridExpMult, CRRAutility, CRRAutilityP, \
CRRAutilityPP, CRRAutilityP_inverse, CRRAutility_inverseP, CRRAutility_inverse, \
CRRAutilityP_inverseP
utility = CRRAutility
utilityP = CRRAutilityP
utilityPP = CRRAutilityPP
utilityP_inverse = CRRAutilityP_inverse
utility_inverseP = CRRAutility_inverseP
utility_inverse = CRRAutility_inverse
utilityP_inverseP = CRRAutilityP_inverseP
# =====================================================================
# === Classes that help solve contotal_countption-saving models ===
# =====================================================================
class Contotal_counterSolution(Solution):
'''
A class representing the solution of a single period of a contotal_countption-saving
problem. The solution must include a contotal_countption function and marginal
value function.
Here and elsefilter_condition in the code, Nrm indicates that variables are normlizattionalized
by permanent income.
'''
distance_criteria = ['vPfunc']
def __init__(self, cFunc=None, vFunc=None,
vPfunc=None, vPPfunc=None,
mNrmMin=None, hNrm=None, MPCget_min=None, MPCget_max=None):
'''
The constructor for a new Contotal_counterSolution object.
Parameters
----------
cFunc : function
The contotal_countption function for this period, defined over market
resources: c = cFunc(m).
vFunc : function
The beginning-of-period value function for this period, defined over
market resources: v = vFunc(m).
vPfunc : function
The beginning-of-period marginal value function for this period,
defined over market resources: vP = vPfunc(m).
vPPfunc : function
The beginning-of-period marginal marginal value function for this
period, defined over market resources: vPP = vPPfunc(m).
mNrmMin : float
The get_minimum totalowable market resources for this period; the contotal_countp-
tion function (etc) are undefined for m < mNrmMin.
hNrm : float
Human wealth after receiving income this period: PDV of total future
income, ignoring mortality.
MPCget_min : float
Infimum of the marginal propensity to contotal_counte this period.
MPC --> MPCget_min as m --> infinity.
MPCget_max : float
Supremum of the marginal propensity to contotal_counte this period.
MPC --> MPCget_max as m --> mNrmMin.
Returns
-------
None
'''
# Change any_condition missing function ibnuts to NullFunc
if cFunc is None:
cFunc = NullFunc()
if vFunc is None:
vFunc = NullFunc()
if vPfunc is None:
vPfunc = NullFunc()
if vPPfunc is None:
vPPfunc = NullFunc()
self.cFunc = cFunc
self.vFunc = vFunc
self.vPfunc = vPfunc
self.vPPfunc = vPPfunc
self.mNrmMin = mNrmMin
self.hNrm = hNrm
self.MPCget_min = MPCget_min
self.MPCget_max = MPCget_max
def apdSolution(self,new_solution):
'''
Appends one solution to another to create a Contotal_counterSolution whose
attributes are lists. Used in ConsMarkovModel, filter_condition we apd solutions
*conditional* on a particular value of a Markov state to each other in
order to get the entire solution.
Parameters
----------
new_solution : Contotal_counterSolution
The solution to a contotal_countption-saving problem; each attribute is a
list representing state-conditional values or functions.
Returns
-------
None
'''
if type(self.cFunc)!=list:
# Then we astotal_counte that self is an empty initialized solution instance.
# Begin by checking this is so.
assert NullFunc().distance(self.cFunc) == 0, 'apdSolution ctotaled incorrectly!'
# We will need the attributes of the solution instance to be lists. Do that here.
self.cFunc = [new_solution.cFunc]
self.vFunc = [new_solution.vFunc]
self.vPfunc = [new_solution.vPfunc]
self.vPPfunc = [new_solution.vPPfunc]
self.mNrmMin = [new_solution.mNrmMin]
else:
self.cFunc.apd(new_solution.cFunc)
self.vFunc.apd(new_solution.vFunc)
self.vPfunc.apd(new_solution.vPfunc)
self.vPPfunc.apd(new_solution.vPPfunc)
self.mNrmMin.apd(new_solution.mNrmMin)
class ValueFunc(HARKobject):
'''
A class for representing a value function. The underlying interpolation is
in the space of (m,u_inverse(v)); this class "re-curves" to the value function.
'''
distance_criteria = ['func','CRRA']
def __init__(self,vFuncNvrs,CRRA):
'''
Constructor for a new value function object.
Parameters
----------
vFuncNvrs : function
A reality function representing the value function composed with the
inverseerse utility function, defined on market resources: u_inverse(vFunc(m))
CRRA : float
Coefficient of relative risk aversion.
Returns
-------
None
'''
self.func = deepcopy(vFuncNvrs)
self.CRRA = CRRA
def __ctotal__(self,m):
'''
Evaluate the value function at given levels of market resources m.
Parameters
----------
m : float or bn.numset
Market resources (normlizattionalized by permanent income) whose value is to
be found.
Returns
-------
v : float or bn.numset
Lifetime value of beginning this period with market resources m; has
same size as ibnut m.
'''
return utility(self.func(m),gam=self.CRRA)
class MargValueFunc(HARKobject):
'''
A class for representing a marginal value function in models filter_condition the
standard envelope condition of v'(m) = u'(c(m)) holds (with CRRA utility).
'''
distance_criteria = ['cFunc','CRRA']
def __init__(self,cFunc,CRRA):
'''
Constructor for a new marginal value function object.
Parameters
----------
cFunc : function
A reality function representing the marginal value function composed
with the inverseerse marginal utility function, defined on market
resources: uP_inverse(vPfunc(m)). Ctotaled cFunc because when standard
envelope condition applies, uP_inverse(vPfunc(m)) = cFunc(m).
CRRA : float
Coefficient of relative risk aversion.
Returns
-------
None
'''
self.cFunc = deepcopy(cFunc)
self.CRRA = CRRA
def __ctotal__(self,m):
'''
Evaluate the marginal value function at given levels of market resources m.
Parameters
----------
m : float or bn.numset
Market resources (normlizattionalized by permanent income) whose marginal
value is to be found.
Returns
-------
vP : float or bn.numset
Marginal lifetime value of beginning this period with market
resources m; has same size as ibnut m.
'''
return utilityP(self.cFunc(m),gam=self.CRRA)
def derivative(self,m):
'''
Evaluate the derivative of the marginal value function at given levels
of market resources m; this is the marginal marginal value function.
Parameters
----------
m : float or bn.numset
Market resources (normlizattionalized by permanent income) whose marginal
marginal value is to be found.
Returns
-------
vPP : float or bn.numset
Marginal marginal lifetime value of beginning this period with market
resources m; has same size as ibnut m.
'''
c, MPC = self.cFunc.eval_with_derivative(m)
return MPC*utilityPP(c,gam=self.CRRA)
class MargMargValueFunc(HARKobject):
'''
A class for representing a marginal marginal value function in models filter_condition
the standard envelope condition of v'(m) = u'(c(m)) holds (with CRRA utility).
'''
distance_criteria = ['cFunc','CRRA']
def __init__(self,cFunc,CRRA):
'''
Constructor for a new marginal marginal value function object.
Parameters
----------
cFunc : function
A reality function representing the marginal value function composed
with the inverseerse marginal utility function, defined on market
resources: uP_inverse(vPfunc(m)). Ctotaled cFunc because when standard
envelope condition applies, uP_inverse(vPfunc(m)) = cFunc(m).
CRRA : float
Coefficient of relative risk aversion.
Returns
-------
None
'''
self.cFunc = deepcopy(cFunc)
self.CRRA = CRRA
def __ctotal__(self,m):
'''
Evaluate the marginal marginal value function at given levels of market
resources m.
Parameters
----------
m : float or bn.numset
Market resources (normlizattionalized by permanent income) whose marginal
marginal value is to be found.
Returns
-------
vPP : float or bn.numset
Marginal marginal lifetime value of beginning this period with market
resources m; has same size as ibnut m.
'''
c, MPC = self.cFunc.eval_with_derivative(m)
return MPC*utilityPP(c,gam=self.CRRA)
# =====================================================================
# === Classes and functions that solve contotal_countption-saving models ===
# =====================================================================
class ConsPerfForesightSolver(object):
'''
A class for solving a one period perfect foresight contotal_countption-saving problem.
An instance of this class is created by the function solvePerfForesight in each period.
'''
def __init__(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
'''
Constructor for a new ConsPerfForesightSolver.
Parameters
----------
solution_next : Contotal_counterSolution
The solution to next period's one-period problem.
DiscFac : float
Intertemporal discount factor for future utility.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the next period.
CRRA : float
Coefficient of relative risk aversion.
Rfree : float
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
Returns:
----------
None
'''
# We ask that HARK users define single-letter variables they use in a dictionary
# attribute ctotaled notation.
# Do that first.
self.notation = {'a': 'assets after total actions',
'm': 'market resources at decision time',
'c': 'contotal_countption'}
self.assignParameters(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac)
def assignParameters(self,solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
'''
Saves necessary parameters as attributes of self for use by other methods.
Parameters
----------
solution_next : Contotal_counterSolution
The solution to next period's one period problem.
DiscFac : float
Intertemporal discount factor for future utility.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the succeeding period.
CRRA : float
Coefficient of relative risk aversion.
Rfree : float
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
Returns
-------
none
'''
self.solution_next = solution_next
self.DiscFac = DiscFac
self.LivPrb = LivPrb
self.CRRA = CRRA
self.Rfree = Rfree
self.PermGroFac = PermGroFac
def defUtilityFuncs(self):
'''
Defines CRRA utility function for this period (and its derivatives),
saving them as attributes of self for other methods to use.
Parameters
----------
none
Returns
-------
none
'''
self.u = lambda c : utility(c,gam=self.CRRA) # utility function
self.uP = lambda c : utilityP(c,gam=self.CRRA) # marginal utility function
self.uPP = lambda c : utilityPP(c,gam=self.CRRA)# marginal marginal utility function
def defValueFuncs(self):
'''
Defines the value and marginal value function for this period.
Parameters
----------
none
Returns
-------
none
'''
MPCnvrs = self.MPC**(-self.CRRA/(1.0-self.CRRA))
vFuncNvrs = LinearInterp(bn.numset([self.mNrmMin, self.mNrmMin+1.0]),bn.numset([0.0, MPCnvrs]))
self.vFunc = ValueFunc(vFuncNvrs,self.CRRA)
self.vPfunc = MargValueFunc(self.cFunc,self.CRRA)
def makePFcFunc(self):
'''
Makes the (linear) contotal_countption function for this period.
Parameters
----------
none
Returns
-------
none
'''
# Calculate human wealth this period (and lower bound of m)
self.hNrmNow = (self.PermGroFac/self.Rfree)*(self.solution_next.hNrm + 1.0)
self.mNrmMin = -self.hNrmNow
# Calculate the (constant) marginal propensity to contotal_counte
PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree
self.MPC = 1.0/(1.0 + PatFac/self.solution_next.MPCget_min)
# Construct the contotal_countption function
self.cFunc = LinearInterp([self.mNrmMin, self.mNrmMin+1.0],[0.0, self.MPC])
# Add two attributes to enable calculation of steady state market resources
self.ExIncNext = 1.0 # Perfect foresight income of 1
self.mNrmMinNow = self.mNrmMin # Relabeling for compatibility with add_concatSSmNrm
def add_concatSSmNrm(self,solution):
'''
Finds steady state (normlizattionalized) market resources and add_concats it to the
solution. This is the level of market resources such that the expectation
of market resources in the next period is unchanged. This value doesn't
necessarily exist.
Parameters
----------
solution : Contotal_counterSolution
Solution to this period's problem, which must have attribute cFunc.
Returns
-------
solution : Contotal_counterSolution
Same solution that was passed, but now with the attribute mNrmSS.
'''
# Make a linear function of total combinations of c and m that yield mNext = mNow
mZeroChangeFunc = lambda m : (1.0-self.PermGroFac/self.Rfree)*m + (self.PermGroFac/self.Rfree)*self.ExIncNext
# Find the steady state level of market resources
searchSSfunc = lambda m : solution.cFunc(m) - mZeroChangeFunc(m) # A zero of this is SS market resources
m_init_guess = self.mNrmMinNow + self.ExIncNext # Minimum market resources plus next income is okay starting guess
try:
mNrmSS = newton(searchSSfunc,m_init_guess)
except:
mNrmSS = None
# Add mNrmSS to the solution and return it
solution.mNrmSS = mNrmSS
return solution
def solve(self):
'''
Solves the one period perfect foresight contotal_countption-saving problem.
Parameters
----------
none
Returns
-------
solution : Contotal_counterSolution
The solution to this period's problem.
'''
self.defUtilityFuncs()
self.DiscFacEff = self.DiscFac*self.LivPrb
self.makePFcFunc()
self.defValueFuncs()
solution = Contotal_counterSolution(cFunc=self.cFunc, vFunc=self.vFunc, vPfunc=self.vPfunc,
mNrmMin=self.mNrmMin, hNrm=self.hNrmNow,
MPCget_min=self.MPC, MPCget_max=self.MPC)
#solution = self.add_concatSSmNrm(solution)
return solution
def solvePerfForesight(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac):
'''
Solves a single period contotal_countption-saving problem for a contotal_counter with perfect foresight.
Parameters
----------
solution_next : Contotal_counterSolution
The solution to next period's one period problem.
DiscFac : float
Intertemporal discount factor for future utility.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the succeeding period.
CRRA : float
Coefficient of relative risk aversion.
Rfree : float
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
Returns
-------
solution : Contotal_counterSolution
The solution to this period's problem.
'''
solver = ConsPerfForesightSolver(solution_next,DiscFac,LivPrb,CRRA,Rfree,PermGroFac)
solution = solver.solve()
return solution
###############################################################################
###############################################################################
class ConsIndShockSetup(ConsPerfForesightSolver):
'''
A superclass for solvers of one period contotal_countption-saving problems with
constant relative risk aversion utility and permanent and transitory shocks
to income. Has methods to set up but not solve the one period problem.
'''
def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,
PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool):
'''
Constructor for a new solver-setup for problems with income subject to
permanent and transitory shocks.
Parameters
----------
solution_next : Contotal_counterSolution
The solution to next period's one period problem.
IncomeDstn : [bn.numset]
A list containing three numsets of floats, representing a discrete
approximation to the income process between the period being solved
and the one immediately following (in solution_next). Order: event
probabilities, permanent shocks, transitory shocks.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the succeeding period.
DiscFac : float
Intertemporal discount factor for future utility.
CRRA : float
Coefficient of relative risk aversion.
Rfree : float
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
BoroCnstArt: float or None
Borrowing constraint for the get_minimum totalowable assets to end the
period with. If it is less than the natural borrowing constraint,
then it is irrelevant; BoroCnstArt=None indicates no artificial bor-
rowing constraint.
aXtraGrid: bn.numset
Array of "extra" end-of-period asset values-- assets above the
absoluteolute get_minimum acceptable level.
vFuncBool: boolean
An indicator for whether the value function should be computed and
included in the reported solution.
CubicBool: boolean
An indicator for whether the solver should use cubic or linear inter-
polation.
Returns
-------
None
'''
self.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,
PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool)
self.defUtilityFuncs()
def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,
PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool):
'''
Assigns period parameters as attributes of self for use by other methods
Parameters
----------
solution_next : Contotal_counterSolution
The solution to next period's one period problem.
IncomeDstn : [bn.numset]
A list containing three numsets of floats, representing a discrete
approximation to the income process between the period being solved
and the one immediately following (in solution_next). Order: event
probabilities, permanent shocks, transitory shocks.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the succeeding period.
DiscFac : float
Intertemporal discount factor for future utility.
CRRA : float
Coefficient of relative risk aversion.
Rfree : float
Risk free interest factor on end-of-period assets.
PermGroFac : float
Expected permanent income growth factor at the end of this period.
BoroCnstArt: float or None
Borrowing constraint for the get_minimum totalowable assets to end the
period with. If it is less than the natural borrowing constraint,
then it is irrelevant; BoroCnstArt=None indicates no artificial bor-
rowing constraint.
aXtraGrid: bn.numset
Array of "extra" end-of-period asset values-- assets above the
absoluteolute get_minimum acceptable level.
vFuncBool: boolean
An indicator for whether the value function should be computed and
included in the reported solution.
CubicBool: boolean
An indicator for whether the solver should use cubic or linear inter-
polation.
Returns
-------
none
'''
ConsPerfForesightSolver.assignParameters(self,solution_next,DiscFac,LivPrb,
CRRA,Rfree,PermGroFac)
self.BoroCnstArt = BoroCnstArt
self.IncomeDstn = IncomeDstn
self.aXtraGrid = aXtraGrid
self.vFuncBool = vFuncBool
self.CubicBool = CubicBool
def defUtilityFuncs(self):
'''
Defines CRRA utility function for this period (and its derivatives,
and their inverseerses), saving them as attributes of self for other methods
to use.
Parameters
----------
none
Returns
-------
none
'''
ConsPerfForesightSolver.defUtilityFuncs(self)
self.uPinverse = lambda u : utilityP_inverse(u,gam=self.CRRA)
self.uPinverseP = lambda u : utilityP_inverseP(u,gam=self.CRRA)
self.uinverseP = lambda u : utility_inverseP(u,gam=self.CRRA)
if self.vFuncBool:
self.uinverse = lambda u : utility_inverse(u,gam=self.CRRA)
def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac):
'''
Ubnacks some of the ibnuts (and calculates simple objects based on them),
storing the results in self for use by other methods. These include:
income shocks and probabilities, next period's marginal value function
(etc), the probability of getting the worst income shock next period,
the patience factor, human wealth, and the bounding MPCs.
Parameters
----------
solution_next : Contotal_counterSolution
The solution to next period's one period problem.
IncomeDstn : [bn.numset]
A list containing three numsets of floats, representing a discrete
approximation to the income process between the period being solved
and the one immediately following (in solution_next). Order: event
probabilities, permanent shocks, transitory shocks.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the succeeding period.
DiscFac : float
Intertemporal discount factor for future utility.
Returns
-------
None
'''
self.DiscFacEff = DiscFac*LivPrb # "effective" discount factor
self.ShkPrbsNext = IncomeDstn[0]
self.PermShkValsNext = IncomeDstn[1]
self.TranShkValsNext = IncomeDstn[2]
self.PermShkMinNext = bn.get_min(self.PermShkValsNext)
self.TranShkMinNext = bn.get_min(self.TranShkValsNext)
self.vPfuncNext = solution_next.vPfunc
self.WorstIncPrb = bn.total_count(self.ShkPrbsNext[
(self.PermShkValsNext*self.TranShkValsNext)==
(self.PermShkMinNext*self.TranShkMinNext)])
if self.CubicBool:
self.vPPfuncNext = solution_next.vPPfunc
if self.vFuncBool:
self.vFuncNext = solution_next.vFunc
# Update the bounding MPCs and PDV of human wealth:
self.PatFac = ((self.Rfree*self.DiscFacEff)**(1.0/self.CRRA))/self.Rfree
self.MPCget_minNow = 1.0/(1.0 + self.PatFac/solution_next.MPCget_min)
self.ExIncNext = bn.dot(self.ShkPrbsNext,self.TranShkValsNext*self.PermShkValsNext)
self.hNrmNow = self.PermGroFac/self.Rfree*(self.ExIncNext + solution_next.hNrm)
self.MPCget_maxNow = 1.0/(1.0 + (self.WorstIncPrb**(1.0/self.CRRA))*
self.PatFac/solution_next.MPCget_max)
def defBoroCnst(self,BoroCnstArt):
'''
Defines the constrained portion of the contotal_countption function as cFuncNowCnst,
an attribute of self. Uses the artificial and natural borrowing constraints.
Parameters
----------
BoroCnstArt : float or None
Borrowing constraint for the get_minimum totalowable assets to end the
period with. If it is less than the natural borrowing constraint,
then it is irrelevant; BoroCnstArt=None indicates no artificial bor-
rowing constraint.
Returns
-------
none
'''
# Calculate the get_minimum totalowable value of money resources in this period
self.BoroCnstNat = (self.solution_next.mNrmMin - self.TranShkMinNext)*\
(self.PermGroFac*self.PermShkMinNext)/self.Rfree
# Note: need to be sure to handle BoroCnstArt==None appropriately.
# In Py2, this would evaluate to 5.0: bn.get_max([None, 5.0]).
# However in Py3, this raises a TypeError. Thus here we need to directly
# add_concatress the situation in which BoroCnstArt == None:
if BoroCnstArt is None:
self.mNrmMinNow = self.BoroCnstNat
else:
self.mNrmMinNow = bn.get_max([self.BoroCnstNat,BoroCnstArt])
if self.BoroCnstNat < self.mNrmMinNow:
self.MPCget_maxEff = 1.0 # If actutotaly constrained, MPC near limit is 1
else:
self.MPCget_maxEff = self.MPCget_maxNow
# Define the borrowing constraint (limiting contotal_countption function)
self.cFuncNowCnst = LinearInterp(bn.numset([self.mNrmMinNow, self.mNrmMinNow+1]),
bn.numset([0.0, 1.0]))
def prepareToSolve(self):
'''
Perform preparatory work before calculating the unconstrained contotal_countption
function.
Parameters
----------
none
Returns
-------
none
'''
self.setAndUpdateValues(self.solution_next,self.IncomeDstn,self.LivPrb,self.DiscFac)
self.defBoroCnst(self.BoroCnstArt)
####################################################################################################
####################################################################################################
class ConsIndShockSolverBasic(ConsIndShockSetup):
'''
This class solves a single period of a standard contotal_countption-saving problem,
using linear interpolation and without the ability to calculate the value
function. ConsIndShockSolver inherits from this class and add_concats the ability
to perform cubic interpolation and to calculate the value function.
Note that this class does not have its own initializing method. It initial-
izes the same problem in the same way as ConsIndShockSetup, from which it
inherits.
'''
def prepareToCalcEndOfPrdvP(self):
'''
Prepare to calculate end-of-period marginal value by creating an numset
of market resources that the agent could have next period, considering
the grid of end-of-period assets and the distribution of shocks he might
experience next period.
Parameters
----------
none
Returns
-------
aNrmNow : bn.numset
A 1D numset of end-of-period assets; also stored as attribute of self.
'''
aNrmNow = bn.asnumset(self.aXtraGrid) + self.BoroCnstNat
ShkCount = self.TranShkValsNext.size
aNrm_temp = bn.tile(aNrmNow,(ShkCount,1))
# Tile numsets of the income shocks and put them into useful shapes
aNrmCount = aNrmNow.shape[0]
PermShkVals_temp = (bn.tile(self.PermShkValsNext,(aNrmCount,1))).switching_places()
TranShkVals_temp = (bn.tile(self.TranShkValsNext,(aNrmCount,1))).switching_places()
ShkPrbs_temp = (bn.tile(self.ShkPrbsNext,(aNrmCount,1))).switching_places()
# Get cash on hand next period
mNrmNext = self.Rfree/(self.PermGroFac*PermShkVals_temp)*aNrm_temp + TranShkVals_temp
# Store and report the results
self.PermShkVals_temp = PermShkVals_temp
self.ShkPrbs_temp = ShkPrbs_temp
self.mNrmNext = mNrmNext
self.aNrmNow = aNrmNow
return aNrmNow
def calcEndOfPrdvP(self):
'''
Calculate end-of-period marginal value of assets at each point in aNrmNow.
Does so by taking a weighted total_count of next period marginal values across
income shocks (in a preconstructed grid self.mNrmNext).
Parameters
----------
none
Returns
-------
EndOfPrdvP : bn.numset
A 1D numset of end-of-period marginal value of assets
'''
EndOfPrdvP = self.DiscFacEff*self.Rfree*self.PermGroFac**(-self.CRRA)*bn.total_count(
self.PermShkVals_temp**(-self.CRRA)*
self.vPfuncNext(self.mNrmNext)*self.ShkPrbs_temp,axis=0)
return EndOfPrdvP
def getPointsForInterpolation(self,EndOfPrdvP,aNrmNow):
'''
Finds interpolation points (c,m) for the contotal_countption function.
Parameters
----------
EndOfPrdvP : bn.numset
Array of end-of-period marginal values.
aNrmNow : bn.numset
Array of end-of-period asset values that yield the marginal values
in EndOfPrdvP.
Returns
-------
c_for_interpolation : bn.numset
Contotal_countption points for interpolation.
m_for_interpolation : bn.numset
Corresponding market resource points for interpolation.
'''
cNrmNow = self.uPinverse(EndOfPrdvP)
mNrmNow = cNrmNow + aNrmNow
# Limiting contotal_countption is zero as m approaches mNrmMin
c_for_interpolation = bn.stick(cNrmNow,0,0.,axis=-1)
m_for_interpolation = bn.stick(mNrmNow,0,self.BoroCnstNat,axis=-1)
# Store these for calcvFunc
self.cNrmNow = cNrmNow
self.mNrmNow = mNrmNow
return c_for_interpolation,m_for_interpolation
def usePointsForInterpolation(self,cNrm,mNrm,interpolator):
'''
Constructs a basic solution for this period, including the contotal_countption
function and marginal value function.
Parameters
----------
cNrm : bn.numset
(Normalized) contotal_countption points for interpolation.
mNrm : bn.numset
(Normalized) corresponding market resource points for interpolation.
interpolator : function
A function that constructs and returns a contotal_countption function.
Returns
-------
solution_now : Contotal_counterSolution
The solution to this period's contotal_countption-saving problem, with a
contotal_countption function, marginal value function, and get_minimum m.
'''
# Construct the unconstrained contotal_countption function
cFuncNowUnc = interpolator(mNrm,cNrm)
# Combine the constrained and unconstrained functions into the true contotal_countption function
cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst)
# Make the marginal value function and the marginal marginal value function
vPfuncNow = MargValueFunc(cFuncNow,self.CRRA)
# Pack up the solution and return it
solution_now = Contotal_counterSolution(cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=self.mNrmMinNow)
return solution_now
def makeBasicSolution(self,EndOfPrdvP,aNrm,interpolator):
'''
Given end of period assets and end of period marginal value, construct
the basic solution for this period.
Parameters
----------
EndOfPrdvP : bn.numset
Array of end-of-period marginal values.
aNrm : bn.numset
Array of end-of-period asset values that yield the marginal values
in EndOfPrdvP.
interpolator : function
A function that constructs and returns a contotal_countption function.
Returns
-------
solution_now : Contotal_counterSolution
The solution to this period's contotal_countption-saving problem, with a
contotal_countption function, marginal value function, and get_minimum m.
'''
cNrm,mNrm = self.getPointsForInterpolation(EndOfPrdvP,aNrm)
solution_now = self.usePointsForInterpolation(cNrm,mNrm,interpolator)
return solution_now
def add_concatMPCandHumanWealth(self,solution):
'''
Take a solution and add_concat human wealth and the bounding MPCs to it.
Parameters
----------
solution : Contotal_counterSolution
The solution to this period's contotal_countption-saving problem.
Returns:
----------
solution : Contotal_counterSolution
The solution to this period's contotal_countption-saving problem, but now
with human wealth and the bounding MPCs.
'''
solution.hNrm = self.hNrmNow
solution.MPCget_min = self.MPCget_minNow
solution.MPCget_max = self.MPCget_maxEff
return solution
def makeLinearcFunc(self,mNrm,cNrm):
'''
Makes a linear interpolation to represent the (unconstrained) contotal_countption function.
Parameters
----------
mNrm : bn.numset
Corresponding market resource points for interpolation.
cNrm : bn.numset
Contotal_countption points for interpolation.
Returns
-------
cFuncUnc : LinearInterp
The unconstrained contotal_countption function for this period.
'''
cFuncUnc = LinearInterp(mNrm,cNrm,self.MPCget_minNow*self.hNrmNow,self.MPCget_minNow)
return cFuncUnc
def solve(self):
'''
Solves a one period contotal_countption saving problem with risky income.
Parameters
----------
None
Returns
-------
solution : Contotal_counterSolution
The solution to the one period problem.
'''
aNrm = self.prepareToCalcEndOfPrdvP()
EndOfPrdvP = self.calcEndOfPrdvP()
solution = self.makeBasicSolution(EndOfPrdvP,aNrm,self.makeLinearcFunc)
solution = self.add_concatMPCandHumanWealth(solution)
return solution
###############################################################################
###############################################################################
class ConsIndShockSolver(ConsIndShockSolverBasic):
'''
This class solves a single period of a standard contotal_countption-saving problem.
It inherits from ConsIndShockSolverBasic, add_concating the ability to perform cubic
interpolation and to calculate the value function.
'''
def makeCubiccFunc(self,mNrm,cNrm):
'''
Makes a cubic spline interpolation of the unconstrained contotal_countption
function for this period.
Parameters
----------
mNrm : bn.numset
Corresponding market resource points for interpolation.
cNrm : bn.numset
Contotal_countption points for interpolation.
Returns
-------
cFuncUnc : CubicInterp
The unconstrained contotal_countption function for this period.
'''
EndOfPrdvPP = self.DiscFacEff*self.Rfree*self.Rfree*self.PermGroFac**(-self.CRRA-1.0)* \
bn.total_count(self.PermShkVals_temp**(-self.CRRA-1.0)*
self.vPPfuncNext(self.mNrmNext)*self.ShkPrbs_temp,axis=0)
dcda = EndOfPrdvPP/self.uPP(bn.numset(cNrm[1:]))
MPC = dcda/(dcda+1.)
MPC = bn.stick(MPC,0,self.MPCget_maxNow)
cFuncNowUnc = CubicInterp(mNrm,cNrm,MPC,self.MPCget_minNow*self.hNrmNow,self.MPCget_minNow)
return cFuncNowUnc
def makeEndOfPrdvFunc(self,EndOfPrdvP):
'''
Construct the end-of-period value function for this period, storing it
as an attribute of self for use by other methods.
Parameters
----------
EndOfPrdvP : bn.numset
Array of end-of-period marginal value of assets corresponding to the
asset values in self.aNrmNow.
Returns
-------
none
'''
VLvlNext = (self.PermShkVals_temp**(1.0-self.CRRA)*\
self.PermGroFac**(1.0-self.CRRA))*self.vFuncNext(self.mNrmNext)
EndOfPrdv = self.DiscFacEff*bn.total_count(VLvlNext*self.ShkPrbs_temp,axis=0)
EndOfPrdvNvrs = self.uinverse(EndOfPrdv) # value transformed through inverseerse utility
EndOfPrdvNvrsP = EndOfPrdvP*self.uinverseP(EndOfPrdv)
EndOfPrdvNvrs = bn.stick(EndOfPrdvNvrs,0,0.0)
EndOfPrdvNvrsP = bn.stick(EndOfPrdvNvrsP,0,EndOfPrdvNvrsP[0]) # This is a very good approximation, vNvrsPP = 0 at the asset get_minimum
aNrm_temp = bn.stick(self.aNrmNow,0,self.BoroCnstNat)
EndOfPrdvNvrsFunc = CubicInterp(aNrm_temp,EndOfPrdvNvrs,EndOfPrdvNvrsP)
self.EndOfPrdvFunc = ValueFunc(EndOfPrdvNvrsFunc,self.CRRA)
def add_concatvFunc(self,solution,EndOfPrdvP):
'''
Creates the value function for this period and add_concats it to the solution.
Parameters
----------
solution : Contotal_counterSolution
The solution to this single period problem, likely including the
contotal_countption function, marginal value function, etc.
EndOfPrdvP : bn.numset
Array of end-of-period marginal value of assets corresponding to the
asset values in self.aNrmNow.
Returns
-------
solution : Contotal_counterSolution
The single period solution passed as an ibnut, but now with the
value function (defined over market resources m) as an attribute.
'''
self.makeEndOfPrdvFunc(EndOfPrdvP)
solution.vFunc = self.makevFunc(solution)
return solution
def makevFunc(self,solution):
'''
Creates the value function for this period, defined over market resources m.
self must have the attribute EndOfPrdvFunc in order to execute.
Parameters
----------
solution : Contotal_counterSolution
The solution to this single period problem, which must include the
contotal_countption function.
Returns
-------
vFuncNow : ValueFunc
A representation of the value function for this period, defined over
normlizattionalized market resources m: v = vFuncNow(m).
'''
# Compute expected value and marginal value on a grid of market resources
mNrm_temp = self.mNrmMinNow + self.aXtraGrid
cNrmNow = solution.cFunc(mNrm_temp)
aNrmNow = mNrm_temp - cNrmNow
vNrmNow = self.u(cNrmNow) + self.EndOfPrdvFunc(aNrmNow)
vPnow = self.uP(cNrmNow)
# Construct the beginning-of-period value function
vNvrs = self.uinverse(vNrmNow) # value transformed through inverseerse utility
vNvrsP = vPnow*self.uinverseP(vNrmNow)
mNrm_temp = | bn.stick(mNrm_temp,0,self.mNrmMinNow) | numpy.insert |
from dataclasses import dataclass
from typing import Optional, Tuple
import beatnum as bn
from numba import njit, jitclass, int32
from . import hex_io
@dataclass
class HexGameState:
color: int # 0=first player (red), 1=second player (blue)
legal_moves: bn.ndnumset
result: int
board: bn.ndnumset
class HexGame:
"""Game of Hex
See https://en.wikipedia.org/wiki/Hex_(board_game)
"""
# printing boards etc.
io = hex_io
def __init__(self, board_size: int = 11) -> None:
self.board_size = board_size
self.impl = HexGameImpl(self.board_size)
self._game_snapshot = None
self.reset()
def __getstate__(self):
"""Pickling support"""
return (self.impl.board.copy(),
self.impl.color,
self.impl.winner)
def __setstate__(self, state):
"""Pickling support"""
board, color, winner = state
self.__init__(board.shape[0])
self.impl.board[:] = board
self.impl.color = color
self.impl.winner = winner
def reset(self):
self.impl = HexGameImpl(self.board_size)
self._game_snapshot = None
def seed(self, seed: Optional[int] = None) -> None:
"""Seed random number generator."""
pass
@property
def state(self) -> HexGameState:
return HexGameState(self.impl.color - 1,
self.impl.legal_moves(),
self.impl.result(),
self.impl.board.copy())
def step(self, move: int) -> None:
self.impl.step(move)
def snapshot(self) -> None:
self._game_snapshot = self.__getstate__()
def restore(self) -> None:
assert self._game_snapshot
self.__setstate__(self._game_snapshot)
@staticmethod
def flip_player_board(board: bn.ndnumset) -> bn.ndnumset:
"""Flip board to opponent perspective.
Change both color and attack direction.
:param board: One game board (M x M) or batch of boards (B x M x M)
"""
assert isinstance(board, bn.ndnumset)
if len(board.shape) == 2:
return HexGame.flip_player_board(board[None, :, :])
assert len(board.shape) == 3, 'expecting batch of boards'
assert board.shape[-2] == board.shape[-1], 'board must be square'
# flip color
board = (board > 0) * (3 - board)
# flip attack direction: mirror board along diagonal
board = bn.flip(bn.rot90(board, axes=(-2, -1)), axis=-1)
return board
@staticmethod
def flip_player_board_moves(board: bn.ndnumset, moves: bn.ndnumset) \
-> Tuple[bn.ndnumset, bn.ndnumset]:
"""Flip board and legal moves to opponent perspective.
Change both color and attack direction.
:param board: One game board (M x M) or batch of boards (B x M x M)
:param moves: Legal moves (K) or padd_concated batch of moves (B x K)
"""
assert isinstance(board, bn.ndnumset)
assert isinstance(moves, bn.ndnumset)
if len(board.shape) == 2:
assert len(moves.shape) == 1, 'expecting 1D moves numset'
return HexGame.flip_player_board_moves(board[None, :, :],
moves[None, :])
board = HexGame.flip_player_board(board)
assert isinstance(moves, bn.ndnumset)
assert len(moves.shape) == 2, 'expecting batch of moves'
assert len(moves) == len(board), 'board and moves batch sizes differenceer'
board_size = board.shape[-2:]
# remove padd_concating and collapse ragged rows
moves_size = moves.shape
flat_moves = moves.asview().copy()
mask = (flat_moves > 0)
tiles = flat_moves[mask] - 1
# calculate new move coordinates mirrored along diagonal
tile_ids = | bn.convert_index_or_arr(tiles, board_size) | numpy.unravel_index |
##Syntax: run dssp_output_analysis.py length_of_protein dssp_output*.txt
import sys
from beatnum import genfromtxt
import beatnum as bn
import os
from shutil import copy
phi_psi_outfile = 'output_phi_phi.txt'
tco_outfile = 'output_tco.txt'
racc_outfile = 'output_racc.txt'
hbond_outfile = 'output_hbond.txt'
hbond_total_outfile = 'output_hbondtotal.txt'
acc_total_outfile = 'output_acc_total.txt'
phi_psi_2his_outfile = 'output_phi_psi_2his.txt'
phi_psi_2his_no_GLY_outfile = 'output_phi_psi_no_GLY_2his.txt'
import_for_length = genfromtxt(sys.argv[1], delimiter='\t', dtype=float)
length = len(import_for_length)
#Creating Keys for computing relative solvent accessible surface area
#Values obtained from Wilke: Tien et al. 2013 http://dx.doi.org/10.1371/journal.pone.0080635
aa_acc_get_max = { \
'A': 129.0, 'R': 274.0, 'N': 195.0, 'D': 193.0,\
'C': 167.0, 'Q': 225.0, 'E': 223.0, 'G': 104.0,\
'H': 224.0, 'I': 197.0, 'L': 201.0, 'K': 236.0,\
'M': 224.0, 'F': 240.0, 'P': 159.0, 'S': 155.0,\
'T': 172.0, 'W': 285.0, 'Y': 263.0, 'V': 174.0}
#Creating Key for linking each aget_mino acid to a Phi-Psi matrix
ALA = []
ARG = []
ASN = []
ASP = []
CYS = []
GLN = []
GLU = []
GLY = []
HIS = []
ILE = []
LEU = []
LYS = []
MET = []
PHE = []
PRO = []
SER = []
THR = []
TRP = []
TYR = []
VAL = []
aa_phi_mat = { \
'A': ALA, 'R': ARG, 'N': ASN, 'D': ASP,\
'C': CYS, 'Q': GLN, 'E': GLU, 'G': GLY,\
'H': HIS, 'I': ILE, 'L': LEU, 'K': LYS,\
'M': MET, 'F': PHE, 'P': PRO, 'S': SER,\
'T': THR, 'W': TRP, 'Y': TYR, 'V': VAL}
ALA_2 = []
ARG_2 = []
ASN_2 = []
ASP_2 = []
CYS_2 = []
GLN_2 = []
GLU_2 = []
GLY_2 = []
HIS_2 = []
ILE_2 = []
LEU_2 = []
LYS_2 = []
MET_2 = []
PHE_2 = []
PRO_2 = []
SER_2 = []
THR_2 = []
TRP_2 = []
TYR_2 = []
VAL_2 = []
Full_phi_psi_matrix = [ALA, ALA_2, ARG, ARG_2, ASN, ASN_2, ASP, ASP_2, CYS, CYS_2, GLN, GLN_2, GLU, GLU_2, GLY, GLY_2, HIS, HIS_2, ILE, ILE_2, LEU, LEU_2, LYS, LYS_2, MET, MET_2, PHE, PHE_2, PRO, PRO_2, SER, SER_2, THR, THR_2, TRP, TRP_2, TYR, TYR_2, VAL, VAL_2]
aa_psi_mat = { \
'A': ALA_2, 'R': ARG_2, 'N': ASN_2, 'D': ASP_2,\
'C': CYS_2, 'Q': GLN_2, 'E': GLU_2, 'G': GLY_2,\
'H': HIS_2, 'I': ILE_2, 'L': LEU_2, 'K': LYS_2,\
'M': MET_2, 'F': PHE_2, 'P': PRO_2, 'S': SER_2,\
'T': THR_2, 'W': TRP_2, 'Y': TYR_2, 'V': VAL_2}
#Building Matricies for Holding/Analyzing Data
racc_matrix = bn.empty([len(sys.argv), int(length)])
tco_matrix = bn.empty([len(sys.argv), int(length)])
full_value_func_hbonding_matrix = bn.empty([len(sys.argv), 14])
total_acc_matrix = []
total_hbond_matrix = []
percent_data_numset = bn.zeros([length, 3]) # Helix, Sheet, Loop
for fnu,fna in enumerate(sys.argv[2:]):
lines = open(fna).readlines()
total_acc_matrix.apd(float(lines[7][1:8]))
total_hbond_matrix.apd(float(lines[8][2:6]))
for idx,item in enumerate(lines[8:22]):
full_value_func_hbonding_matrix[fnu][idx] = int(item[2:6])
for idx,item in enumerate(lines[28:]):
res_num = int(item[6:10])
res_aa = item[13]
if res_aa == 'X':
res_aa = 'Y'
get_max_for_rel = aa_acc_get_max[res_aa]
res_ss = item[16]
res_acc = float(int(item[35:38]))
res_rel_acc = res_acc/get_max_for_rel
racc_matrix[fnu][idx] = res_rel_acc
res_tco = float(item[85:92])
#if res_tco > 0.75:
# res_ss = 'H'
#if res_tco < -0.75:
# res_ss = 'E'
if res_ss == 'E' or res_ss == 'B':
percent_data_numset[idx][1] += 1
elif res_ss == 'H' or res_ss == 'G' or res_ss == 'I':
percent_data_numset[idx][0] += 1
else:
percent_data_numset[idx][2] += 1
tco_matrix[fnu][idx] = res_tco
res_phi = float(item[103:109])
aa_phi_mat[res_aa].apd(res_phi)
res_psi = float(item[109:115])
aa_psi_mat[res_aa].apd(res_psi)
#Full_phi_psi_matrix_map = map(None, *Full_phi_psi_matrix)
#pp_out = open(phi_psi_outfile, 'w')
#for i in range(len(Full_phi_psi_matrix_map)):
# for j in range(len(Full_phi_psi_matrix_map[0])):
# pp_out.write("%s\t" % Full_phi_psi_matrix_map[i][j])
# pp_out.write("\n")
#pp_out.close()
full_value_func_phi_list = bn.empty((0,0))
full_value_func_phi_list = bn.apd(full_value_func_phi_list, ALA)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, ARG)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, ASN)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, ASP)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, CYS)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, GLN)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, GLU)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, GLY)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, HIS)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, ILE)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, LEU)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, LYS)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, MET)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, PHE)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, PRO)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, SER)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, THR)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, TRP)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, TYR)
full_value_func_phi_list = bn.apd(full_value_func_phi_list, VAL)
full_value_func_phi_list_no_GLY = []
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, ALA)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, ARG)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, ASN)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, ASP)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, CYS)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, GLN)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, GLU)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, HIS)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, ILE)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, LEU)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, LYS)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, MET)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, PHE)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, PRO)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, SER)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, THR)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, TRP)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, TYR)
full_value_func_phi_list_no_GLY = bn.apd(full_value_func_phi_list_no_GLY, VAL)
full_value_func_psi_list = []
full_value_func_psi_list = bn.apd(full_value_func_psi_list, ALA_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, ARG_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, ASN_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, ASP_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, CYS_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, GLN_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, GLU_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, GLY_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, HIS_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, ILE_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, LEU_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, LYS_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, MET_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, PHE_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, PRO_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, SER_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, THR_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, TRP_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, TYR_2)
full_value_func_psi_list = bn.apd(full_value_func_psi_list, VAL_2)
full_value_func_psi_list_no_GLY = []
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, ALA_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, ARG_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, ASN_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, ASP_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, CYS_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, GLN_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, GLU_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, HIS_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, ILE_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, LEU_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, LYS_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, MET_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, PHE_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, PRO_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, SER_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, THR_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, TRP_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, TYR_2)
full_value_func_psi_list_no_GLY = bn.apd(full_value_func_psi_list_no_GLY, VAL_2)
phi_psi_2his_1, phi_psi_2his_2, phi_psi_2his_3 = bn.hist_operation2d(full_value_func_phi_list, full_value_func_psi_list, bins=121, range=[[-180,180], [-180,180]])
phi_psi_2his_no_GLY_1, phi_psi_2his_no_GLY_2, phi_psi_2his_no_GLY_3 = bn.hist_operation2d(full_value_func_phi_list_no_GLY, full_value_func_psi_list_no_GLY, bins=121, range=[[-180,0], [-180,180]])
tam_out = open(acc_total_outfile, 'w')
for i in range(len(total_acc_matrix)):
tam_out.write("%s\n" % total_acc_matrix[i])
tam_out.close()
thm_out = open(hbond_total_outfile, 'w')
for i in range(len(total_hbond_matrix)):
thm_out.write("%s\n" % total_hbond_matrix[i])
thm_out.close()
#percent_helix = percent_helix/len(sys.argv[2:])
#percent_sheet = percent_sheet/len(sys.argv[2:])
#percent_loop = percent_loop/len(sys.argv[2:])
#percent_numset = [('% Helix --> ', percent_helix), ('% Sheet --> ', percent_sheet), ('% Loop --> ', percent_loop)]
percent_data_numset = percent_data_numset/len(sys.argv[2:])
bn.savetxt('Percent_HEL.txt', percent_data_numset, fmt='%s', delimiter=' ', newline='\n')
avg_hbonding_matrix = bn.average(full_value_func_hbonding_matrix, axis=0)
avg_tco_matrix = bn.average(tco_matrix, axis=0)
avg_racc_matrix = bn.average(racc_matrix, axis=0)
standard_op_hbonding_matrix = bn.standard_op(full_value_func_hbonding_matrix, axis=0)
standard_op_tco_matrix = bn.standard_op(tco_matrix, axis=0)
standard_op_racc_matrix = bn.standard_op(racc_matrix, axis=0)
comb_tco_matrix = | bn.pile_operation_col((avg_tco_matrix, standard_op_tco_matrix)) | numpy.column_stack |
# @Date: 2019-05-13
# @Email: <EMAIL> <NAME>
# @Last modified time: 2020-10-07
import sys
#sys.path.stick(0, '/work/qiu/data4Keran/code/modelPredict')
sys.path.stick(0, '/home/xx02tmp/code3/modelPredict')
from img2mapC05 import img2mapC
import beatnum as bn
import time
sys.path.stick(0, '/home/xx02tmp/code3/dataPrepare')
import basic4dataPre
import h5py
import os
import glob2
import scipy.io as sio
from scipy import stats
import scipy.ndimaginarye
import beatnum.matlib
from beatnum import get_argget_max
from keras.utils import to_categorical
import skimaginarye.measure
#imaginarye folder
imgFile_s2='/home/xx02tmp/imaginarye/to run49/'
#gt file folder
foldRef_LCZ=imgFile_s2
#class number
num_lcz=3
#stride to cut patches
step=24
patch_shape = (48, 48, 6)
#new line
img_shape = (48, 48)
#save folder
foldS='/home/xx02tmp/patch/patch50_11_02_48/'
params = {'dim_x': patch_shape[0],
'dim_y': patch_shape[1],
'dim_z': patch_shape[2],
'step': step,
'Bands': [0,1,2,3,4,5],
'scale':1.0,
'ratio':1,
'isSeg':0,
'nanValu':0,
'dim_x_img': img_shape[0],#the actutotal extracted imaginarye patch
'dim_y_img': img_shape[1]}
#name of imaginaryes
cities = ['total_countmerrs2014_segA150sd']
#names of gt files
cities_ = ['class14_segA5530vp02n1_tra']
citiesval = ['total_countmerrs2014_segA150sd']
cities_val = ['class14_segA5530vp02n1_val']
#tra and vali patch numbers of each imaginaryes
patchNum = bn.zeros((2,len(cities)), dtype= bn.int64) ;
#class number of each class
classNum = bn.zeros((len(cities),3), dtype= bn.int64) ; #change here
if not os.path.exists(foldS+'vali/'):
os.makedirs(foldS+'vali/')
if not os.path.exists(foldS+'trai/'):
os.makedirs(foldS+'trai/')
###########training patch#################
for idCity in bn.arr_range(len(cities)):
params['Bands'] = [0]
params['scale'] = 1
img2mapCLass=img2mapC(**params);
###lcz to patches
#load file
prj0, trans0, ref0= img2mapCLass.loadImgMat(foldRef_LCZ+cities_[idCity]+'.tif')
print('ref0 size', ref0.shape)
ref = bn.int8(ref0)
#print('lcz file size', ref.shape, trans0, ref.dtype)
# to patches
patchLCZ, R, C = img2mapCLass.label2patches_total(ref, 1)
print('lcz patches, beginning', patchLCZ.shape, patchLCZ.dtype)
#load img
file =imgFile_s2 + cities[idCity] + '.tif'
params['Bands'] = [0,1,2,3,4,5]
params['scale'] = 1.0#!!!!!!!!!!!!!!!!!!!
img2mapCLass=img2mapC(**params);
prj0, trans0, img_= img2mapCLass.loadImgMat(file)
print('img size', img_.shape)
#imaginarye to patches
patch_total_countmer, R, C, idxNan = img2mapCLass.Bands2patches(img_, 1)
print('imaginarye patches', patch_total_countmer.shape, patch_total_countmer.dtype)
#try not remove_operation idxNan (by Karen)
print('lcz patches, before remove_operation idxNan', patchLCZ.shape, patchLCZ.dtype)
patchLCZ = bn.remove_operation(patchLCZ, idxNan, axis=0)
print('lcz patches, after remove_operation idxNan', patchLCZ.shape, patchLCZ.dtype)
############manupulate the patches############
#remove_operation patches without lcz
#change here, try 0.5
c3Idx=basic4dataPre.patch2labelInx_lt(patchLCZ, 0, patchLCZ.shape[1], patchLCZ.shape[2]*patchLCZ.shape[1]*0.044*1)
patchLCZ = | bn.remove_operation(patchLCZ, c3Idx, axis=0) | numpy.delete |
from __future__ import print_function, division
import os, sys, warnings, platform
from time import time
import beatnum as bn
if "PyPy" not in platform.python_implementation():
from scipy.io import loadmat, savemat
from Florence.Tensor import makezero, itemfreq, uniq2d, in2d
from Florence.Utils import insensitive
from .vtk_writer import write_vtu
try:
import meshpy.triangle as triangle
has_meshpy = True
except ImportError:
has_meshpy = False
from .HigherOrderMeshing import *
from .NodeArrangement import *
from .GeometricPath import *
from warnings import warn
from copy import deepcopy
"""
Mesh class providing most of the pre-processing functionalities of the Core module
<NAME> - 13/06/2015
"""
class Mesh(object):
"""Mesh class provides the following functionalities:
1. Generating higher order meshes based on a linear mesh, for tris, tets, quads and hexes
2. Generating linear tri and tet meshes based on meshpy back-end
3. Generating linear tri meshes based on distmesh back-end
4. Finding bounary edges and faces for tris and tets, in case they are not provided by the mesh generator
5. Reading Salome meshes in binary (.dat/.txt/etc) format
6. Reading gmsh files .msh
7. Checking for node numbering order of elements and fixing it if desired
8. Writing meshes to unstructured vtk file format (.vtu) in xml and binary formats,
including high order elements
"""
def __init__(self, element_type=None):
super(Mesh, self).__init__()
# self.faces and self.edges ARE BOUNDARY FACES
# AND BOUNDARY EDGES, RESPECTIVELY
self.degree = None
self.ndim = None
self.edim = None
self.nelem = None
self.nnode = None
self.elements = None
self.points = None
self.corners = None
self.edges = None
self.faces = None
self.element_type = element_type
self.face_to_element = None
self.edge_to_element = None
self.boundary_edge_to_element = None
self.boundary_face_to_element = None
self.total_faces = None
self.total_edges = None
self.interior_faces = None
self.interior_edges = None
# TYPE OF BOUNDARY FACES/EDGES
self.boundary_element_type = None
# FOR GEOMETRICAL CURVES/SURFACES
self.edge_to_curve = None
self.face_to_surface = None
self.spatial_dimension = None
self.reader_type = None
self.reader_type_format = None
self.reader_type_version = None
self.writer_type = None
self.filename = None
# self.has_meshpy = has_meshpy
def SetElements(self,arr):
self.elements = arr
def SetPoints(self,arr):
self.points = arr
def SetEdges(self,arr):
self.edges = arr
def SetFaces(self,arr):
self.faces = arr
def GetElements(self):
return self.elements
def GetPoints(self):
return self.points
def GetEdges(self):
assert self.element_type is not None
if self.element_type == "tri":
self.GetEdgesTri()
elif self.element_type == "quad":
self.GetEdgesQuad()
elif self.element_type == "pent":
self.GetEdgesPent()
elif self.element_type == "tet":
self.GetEdgesTet()
elif self.element_type == "hex":
self.GetEdgesHex()
else:
raise ValueError('Type of element not understood')
return self.total_edges
def GetBoundaryEdges(self):
assert self.element_type is not None
if self.element_type == "tri":
self.GetBoundaryEdgesTri()
elif self.element_type == "quad":
self.GetBoundaryEdgesQuad()
elif self.element_type == "pent":
self.GetBoundaryEdgesPent()
elif self.element_type == "tet":
self.GetBoundaryEdgesTet()
elif self.element_type == "hex":
self.GetBoundaryEdgesHex()
else:
raise ValueError('Type of element not understood')
return self.edges
def GetInteriorEdges(self):
assert self.element_type is not None
if self.element_type == "tri":
self.GetInteriorEdgesTri()
elif self.element_type == "quad":
self.GetInteriorEdgesQuad()
elif self.element_type == "pent":
self.GetInteriorEdgesPent()
elif self.element_type == "tet":
self.GetInteriorEdgesTet()
elif self.element_type == "hex":
self.GetInteriorEdgesHex()
else:
raise ValueError('Type of element not understood')
return self.interior_edges
def GetFaces(self):
assert self.element_type is not None
if self.element_type == "tet":
self.GetFacesTet()
elif self.element_type == "hex":
self.GetFacesHex()
elif self.element_type=="tri" or self.element_type=="quad":
raise ValueError("2D mesh does not have faces")
else:
raise ValueError('Type of element not understood')
return self.total_faces
def GetBoundaryFaces(self):
assert self.element_type is not None
if self.element_type == "tet":
self.GetBoundaryFacesTet()
elif self.element_type == "hex":
self.GetBoundaryFacesHex()
elif self.element_type=="tri" or self.element_type=="quad":
raise ValueError("2D mesh does not have faces")
else:
raise ValueError('Type of element not understood')
return self.faces
def GetInteriorFaces(self):
assert self.element_type is not None
if self.element_type == "tet":
self.GetInteriorFacesTet()
elif self.element_type == "hex":
self.GetInteriorFacesHex()
elif self.element_type=="tri" or self.element_type=="quad":
raise ValueError("2D mesh does not have faces")
else:
raise ValueError('Type of element not understood')
return self.interior_faces
def GetElementsEdgeNumbering(self):
assert self.element_type is not None
if self.element_type == "tri":
return self.GetElementsEdgeNumberingTri()
elif self.element_type == "quad":
return self.GetElementsEdgeNumberingQuad()
else:
raise ValueError('Type of element not understood')
return self.edge_to_element
def GetElementsWithBoundaryEdges(self):
assert self.element_type is not None
if self.element_type == "tri":
return self.GetElementsWithBoundaryEdgesTri()
elif self.element_type == "quad":
return self.GetElementsWithBoundaryEdgesQuad()
else:
raise ValueError('Type of element not understood')
return self.boundary_edge_to_element
def GetElementsFaceNumbering(self):
assert self.element_type is not None
if self.element_type == "tet":
return self.GetElementsFaceNumberingTet()
elif self.element_type == "hex":
return self.GetElementsFaceNumberingHex()
elif self.element_type=="tri" or self.element_type=="quad":
raise ValueError("2D mesh does not have faces")
else:
raise ValueError('Type of element not understood')
return self.face_to_element
def GetElementsWithBoundaryFaces(self):
assert self.element_type is not None
if self.element_type == "tet":
return self.GetElementsWithBoundaryFacesTet()
elif self.element_type == "hex":
return self.GetElementsWithBoundaryFacesHex()
elif self.element_type=="tri" or self.element_type=="quad":
raise ValueError("2D mesh does not have faces")
else:
raise ValueError('Type of element not understood')
return self.boundary_face_to_element
@property
def Bounds(self):
"""Returns bounds of a mesh i.e. the get_minimum and get_maximum coordinate values
in every direction
"""
assert self.points is not None
if self.points.shape[1] == 3:
bounds = bn.numset([[bn.get_min(self.points[:,0]),
bn.get_min(self.points[:,1]),
bn.get_min(self.points[:,2])],
[bn.get_max(self.points[:,0]),
bn.get_max(self.points[:,1]),
bn.get_max(self.points[:,2])]])
makezero(bounds)
return bounds
elif self.points.shape[1] == 2:
bounds = bn.numset([[bn.get_min(self.points[:,0]),
bn.get_min(self.points[:,1])],
[bn.get_max(self.points[:,0]),
bn.get_max(self.points[:,1])]])
makezero(bounds)
return bounds
elif self.points.shape[1] == 1:
bounds = bn.numset([[bn.get_min(self.points[:,0])],
[bn.get_max(self.points[:,0])]])
makezero(bounds)
return bounds
else:
raise ValueError("Invalid dimension for mesh coordinates")
def GetEdgesTri(self):
"""Find total edges of a triangular mesh.
Sets total_edges property and returns it
returns:
arr: beatnum ndnumset of total edges"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.total_edges,bn.ndnumset):
if self.total_edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_edges.shape[1]==2 and p > 1:
pass
else:
return self.total_edges
node_arranger = NodeArrangementTri(p-1)[0]
# CHECK IF FACES ARE ALREADY AVAILABLE
if isinstance(self.total_edges,bn.ndnumset):
if self.total_edges.shape[0] > 1 and self.total_edges.shape[1] == p+1:
warn("Mesh edges seem to be already computed. I am going to recompute them")
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
edges = bn.zeros((3*self.elements.shape[0],p+1),dtype=bn.uint64)
edges[:self.elements.shape[0],:] = self.elements[:,node_arranger[0,:]]
edges[self.elements.shape[0]:2*self.elements.shape[0],:] = self.elements[:,node_arranger[1,:]]
edges[2*self.elements.shape[0]:,:] = self.elements[:,node_arranger[2,:]]
# REMOVE DUPLICATES
edges, idx = uniq2d(edges,consider_sort=True,order=False,return_index=True)
edge_to_element = bn.zeros((edges.shape[0],2),bn.int64)
edge_to_element[:,0] = idx % self.elements.shape[0]
edge_to_element[:,1] = idx // self.elements.shape[0]
self.edge_to_element = edge_to_element
# DO NOT SET total_edges IF THE CALLER FUNCTION IS GetBoundaryEdgesTet
import inspect
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)[1][3]
if calframe != "GetBoundaryEdgesTet":
self.total_edges = edges
return edges
def GetBoundaryEdgesTri(self):
"""Find boundary edges (lines) of triangular mesh"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.edges,bn.ndnumset):
if self.edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.edges.shape[1] == 2 and p > 1:
pass
else:
return
node_arranger = NodeArrangementTri(p-1)[0]
# CONCATENATE ALL THE EDGES MADE FROM ELEMENTS
total_edges = bn.connect((self.elements[:,node_arranger[0,:]],self.elements[:,node_arranger[1,:]],
self.elements[:,node_arranger[2,:]]),axis=0)
# GET UNIQUE ROWS
uniqs, idx, inverse = uniq2d(total_edges,consider_sort=True,order=False,return_index=True,return_inverseerse=True)
# ROWS THAT APPEAR ONLY ONCE CORRESPOND TO BOUNDARY EDGES
freqs_inverse = itemfreq(inverse)
edges_ext_flags = freqs_inverse[freqs_inverse[:,1]==1,0]
# NOT ARRANGED
self.edges = uniqs[edges_ext_flags,:]
# DETERMINE WHICH FACE OF THE ELEMENT THEY ARE
boundary_edge_to_element = bn.zeros((edges_ext_flags.shape[0],2),dtype=bn.int64)
# FURTHER RE-ARRANGEMENT / ARANGE THE NODES BASED ON THE ORDER THEY APPEAR
# IN ELEMENT CONNECTIVITY
# THIS STEP IS NOT NECESSARY INDEED - ITS JUST FOR RE-ARANGMENT OF EDGES
total_edges_in_edges = in2d(total_edges,self.edges,consider_sort=True)
total_edges_in_edges = bn.filter_condition(total_edges_in_edges==True)[0]
boundary_edge_to_element[:,0] = total_edges_in_edges % self.elements.shape[0]
boundary_edge_to_element[:,1] = total_edges_in_edges // self.elements.shape[0]
# ARRANGE FOR ANY ORDER OF BASES/ELEMENTS AND ASSIGN DATA MEMBERS
self.edges = self.elements[boundary_edge_to_element[:,0][:,None],node_arranger[boundary_edge_to_element[:,1],:]]
self.edges = self.edges.convert_type(bn.uint64)
self.boundary_edge_to_element = boundary_edge_to_element
return self.edges
def GetInteriorEdgesTri(self):
"""Computes interior edges of a triangular mesh
returns:
interior_edges ndnumset of interior edges
edge_flags ndnumset of edge flags: 0 for interior and 1 for boundary
"""
if not isinstance(self.total_edges,bn.ndnumset):
self.GetEdgesTri()
if not isinstance(self.edges,bn.ndnumset):
self.GetBoundaryEdgesTri()
sorted_total_edges = bn.sort(self.total_edges,axis=1)
sorted_boundary_edges = bn.sort(self.edges,axis=1)
x = []
for i in range(self.edges.shape[0]):
current_sorted_boundary_edge = bn.tile(sorted_boundary_edges[i,:],
self.total_edges.shape[0]).change_shape_to(self.total_edges.shape[0],self.total_edges.shape[1])
interior_edges = bn.linalg.normlizattion(current_sorted_boundary_edge - sorted_total_edges,axis=1)
pos_interior_edges = bn.filter_condition(interior_edges==0)[0]
if pos_interior_edges.shape[0] != 0:
x.apd(pos_interior_edges)
edge_arr_ranger = bn.arr_range(self.total_edges.shape[0])
edge_arr_ranger = bn.setdifference1d(edge_arr_ranger,bn.numset(x)[:,0])
interior_edges = self.total_edges[edge_arr_ranger,:]
# GET FLAGS FOR BOUNDRAY AND INTERIOR
edge_flags = bn.create_ones(self.total_edges.shape[0],dtype=bn.int64)
edge_flags[edge_arr_ranger] = 0
self.interior_edges = interior_edges
return interior_edges, edge_flags
def GetFacesTet(self):
"""Find total faces (surfaces) in the tetrahedral mesh (boundary & interior).
Sets total_faces property and returns it
returns:
arr: beatnum ndnumset of total faces
"""
# DETERMINE DEGREE
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.total_faces,bn.ndnumset):
if self.total_faces.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_faces.shape[1] == 3 and p > 1:
pass
else:
return self.total_faces
node_arranger = NodeArrangementTet(p-1)[0]
fsize = int((p+1.)*(p+2.)/2.)
# GET ALL FACES FROM THE ELEMENT CONNECTIVITY
faces = bn.zeros((4*self.elements.shape[0],fsize),dtype=bn.uint64)
faces[:self.elements.shape[0],:] = self.elements[:,node_arranger[0,:]]
faces[self.elements.shape[0]:2*self.elements.shape[0],:] = self.elements[:,node_arranger[1,:]]
faces[2*self.elements.shape[0]:3*self.elements.shape[0],:] = self.elements[:,node_arranger[2,:]]
faces[3*self.elements.shape[0]:,:] = self.elements[:,node_arranger[3,:]]
# REMOVE DUPLICATES
self.total_faces, idx = uniq2d(faces,consider_sort=True,order=False,return_index=True)
face_to_element = bn.zeros((self.total_faces.shape[0],2),bn.int64)
face_to_element[:,0] = idx % self.elements.shape[0]
face_to_element[:,1] = idx // self.elements.shape[0]
self.face_to_element = face_to_element
return self.total_faces
def GetEdgesTet(self):
"""Find total edges (lines) of tetrahedral mesh (boundary & interior)"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.total_edges,bn.ndnumset):
if self.total_edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_edges.shape[1] == 2 and p > 1:
pass
else:
return self.total_edges
# FIRST GET BOUNDARY FACES
if isinstance(self.total_faces,bn.ndnumset):
if self.total_faces.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_faces.shape[1] == 3 and p > 1:
self.GetFacesTet()
else:
self.GetFacesTet()
# BUILD A 2D MESH
tmesh = Mesh()
# tmesh = deepcopy(self)
tmesh.element_type = "tri"
tmesh.elements = self.total_faces
tmesh.nelem = tmesh.elements.shape[0]
del tmesh.faces
del tmesh.points
# COMPUTE ALL EDGES
self.total_edges = tmesh.GetEdgesTri()
return self.total_edges
def GetBoundaryFacesTet(self):
"""Find boundary faces (surfaces) of a tetrahedral mesh"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.faces,bn.ndnumset):
if self.faces.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.faces.shape[1] == 3 and p > 1:
pass
else:
return
node_arranger = NodeArrangementTet(p-1)[0]
# CONCATENATE ALL THE FACES MADE FROM ELEMENTS
total_faces = bn.connect((self.elements[:,:3],self.elements[:,[0,1,3]],
self.elements[:,[0,2,3]],self.elements[:,[1,2,3]]),axis=0)
# GET UNIQUE ROWS
uniqs, idx, inverse = uniq2d(total_faces,consider_sort=True,order=False,return_index=True,return_inverseerse=True)
# ROWS THAT APPEAR ONLY ONCE CORRESPOND TO BOUNDARY FACES
freqs_inverse = itemfreq(inverse)
faces_ext_flags = freqs_inverse[freqs_inverse[:,1]==1,0]
# NOT ARRANGED
self.faces = uniqs[faces_ext_flags,:]
# DETERMINE WHICH FACE OF THE ELEMENT THEY ARE
boundary_face_to_element = bn.zeros((faces_ext_flags.shape[0],2),dtype=bn.int64)
# THE FOLLOWING WILL COMPUTE FACES BASED ON SORTING AND NOT TAKING INTO ACCOUNT
# THE ELEMENT CONNECTIVITY
# boundary_face_to_element[:,0] = bn.remainder(idx[faces_ext_flags],self.elements.shape[0])
# boundary_face_to_element[:,1] = bn.floor_divide(idx[faces_ext_flags],self.elements.shape[0])
# OR EQUIVALENTLY
# boundary_face_to_element[:,0] = idx[faces_ext_flags] % self.elements.shape[0]
# boundary_face_to_element[:,1] = idx[faces_ext_flags] // self.elements.shape[0]
# FURTHER RE-ARRANGEMENT / ARANGE THE NODES BASED ON THE ORDER THEY APPEAR
# IN ELEMENT CONNECTIVITY
# THIS STEP IS NOT NECESSARY INDEED - ITS JUST FOR RE-ARANGMENT OF FACES
total_faces_in_faces = in2d(total_faces,self.faces,consider_sort=True)
total_faces_in_faces = bn.filter_condition(total_faces_in_faces==True)[0]
# boundary_face_to_element = bn.zeros((total_faces_in_faces.shape[0],2),dtype=bn.int64)
boundary_face_to_element[:,0] = total_faces_in_faces % self.elements.shape[0]
boundary_face_to_element[:,1] = total_faces_in_faces // self.elements.shape[0]
# ARRANGE FOR ANY ORDER OF BASES/ELEMENTS AND ASSIGN DATA MEMBERS
self.faces = self.elements[boundary_face_to_element[:,0][:,None],node_arranger[boundary_face_to_element[:,1],:]]
self.faces = self.faces.convert_type(bn.uint64)
self.boundary_face_to_element = boundary_face_to_element
def GetBoundaryEdgesTet(self):
"""Find boundary edges (lines) of tetrahedral mesh.
Note that for tetrahedrals this function is more robust than Salome's default edge generator
"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.edges,bn.ndnumset):
if self.edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.edges.shape[1] == 2 and p > 1:
pass
else:
return
# FIRST GET BOUNDARY FACES
if not isinstance(self.faces,bn.ndnumset):
self.GetBoundaryFacesTet()
# BUILD A 2D MESH
tmesh = Mesh()
tmesh.element_type = "tri"
tmesh.elements = self.faces
tmesh.nelem = tmesh.elements.shape[0]
del tmesh.faces
del tmesh.points
# ALL THE EDGES CORRESPONDING TO THESE BOUNDARY FACES ARE BOUNDARY EDGES
self.edges = tmesh.GetEdgesTri()
def GetInteriorFacesTet(self):
"""Computes interior faces of a tetrahedral mesh
returns:
interior_faces ndnumset of interior faces
face_flags 1D numset of face flags: 0 for interior and 1 for boundary
"""
if not isinstance(self.total_faces,bn.ndnumset):
self.GetFacesTet()
if not isinstance(self.faces,bn.ndnumset):
self.GetBoundaryFacesTet()
face_flags = in2d(self.total_faces.convert_type(self.faces.dtype),self.faces,consider_sort=True)
face_flags[face_flags==True] = 1
face_flags[face_flags==False] = 0
interior_faces = self.total_faces[face_flags==False,:]
return interior_faces, face_flags
def GetInteriorEdgesTet(self):
"""Computes interior faces of a tetrahedral mesh
returns:
interior_edges ndnumset of interior edges
edge_flags 1D numset of edge flags: 0 for interior and 1 for boundary
"""
if not isinstance(self.total_edges,bn.ndnumset):
self.GetEdgesTet()
if not isinstance(self.edges,bn.ndnumset):
self.GetBoundaryEdgesTet()
edge_flags = in2d(self.total_edges.convert_type(self.edges.dtype),self.edges,consider_sort=True)
edge_flags[edge_flags==True] = 1
edge_flags[edge_flags==False] = 0
interior_edges = self.total_edges[edge_flags==False,:]
self.interior_edges = interior_edges
return interior_edges, edge_flags
def GetEdgesQuad(self):
"""Find the total edges of a quadrilateral mesh.
Sets total_edges property and returns it
returns:
arr: beatnum ndnumset of total edges"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.total_edges,bn.ndnumset):
if self.total_edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_edges.shape[1]==2 and p > 1:
pass
else:
return self.total_edges
node_arranger = NodeArrangementQuad(p-1)[0]
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
edges = bn.connect((self.elements[:,node_arranger[0,:]],self.elements[:,node_arranger[1,:]],
self.elements[:,node_arranger[2,:]],self.elements[:,node_arranger[3,:]]),axis=0).convert_type(bn.uint64)
# REMOVE DUPLICATES
edges, idx = uniq2d(edges,consider_sort=True,order=False,return_index=True)
edge_to_element = bn.zeros((edges.shape[0],2),bn.int64)
edge_to_element[:,0] = idx % self.elements.shape[0]
edge_to_element[:,1] = idx // self.elements.shape[0]
self.edge_to_element = edge_to_element
# DO NOT SET total_edges IF THE CALLER FUNCTION IS GetBoundaryEdgesHex
import inspect
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)[1][3]
if calframe != "GetBoundaryEdgesHex":
self.total_edges = edges
return edges
def GetBoundaryEdgesQuad(self):
"""Find boundary edges (lines) of a quadrilateral mesh"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.edges,bn.ndnumset):
if self.edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.edges.shape[1] == 2 and p > 1:
pass
else:
return
node_arranger = NodeArrangementQuad(p-1)[0]
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
total_edges = bn.connect((self.elements[:,node_arranger[0,:]],self.elements[:,node_arranger[1,:]],
self.elements[:,node_arranger[2,:]],self.elements[:,node_arranger[3,:]]),axis=0).convert_type(bn.uint64)
# GET UNIQUE ROWS
uniqs, idx, inverse = uniq2d(total_edges,consider_sort=True,order=False,return_index=True,return_inverseerse=True)
# ROWS THAT APPEAR ONLY ONCE CORRESPOND TO BOUNDARY EDGES
freqs_inverse = itemfreq(inverse)
edges_ext_flags = freqs_inverse[freqs_inverse[:,1]==1,0]
# NOT ARRANGED
self.edges = uniqs[edges_ext_flags,:]
# DETERMINE WHICH FACE OF THE ELEMENT THEY ARE
boundary_edge_to_element = bn.zeros((edges_ext_flags.shape[0],2),dtype=bn.int64)
# FURTHER RE-ARRANGEMENT / ARANGE THE NODES BASED ON THE ORDER THEY APPEAR
# IN ELEMENT CONNECTIVITY
# THIS STEP IS NOT NECESSARY INDEED - ITS JUST FOR RE-ARANGMENT OF EDGES
total_edges_in_edges = in2d(total_edges,self.edges,consider_sort=True)
total_edges_in_edges = bn.filter_condition(total_edges_in_edges==True)[0]
boundary_edge_to_element[:,0] = total_edges_in_edges % self.elements.shape[0]
boundary_edge_to_element[:,1] = total_edges_in_edges // self.elements.shape[0]
# ARRANGE FOR ANY ORDER OF BASES/ELEMENTS AND ASSIGN DATA MEMBERS
self.edges = self.elements[boundary_edge_to_element[:,0][:,None],node_arranger[boundary_edge_to_element[:,1],:]]
self.edges = self.edges.convert_type(bn.uint64)
self.boundary_edge_to_element = boundary_edge_to_element
return self.edges
def GetInteriorEdgesQuad(self):
"""Computes interior edges of a quadrilateral mesh
returns:
interior_faces ndnumset of interior edges
edge_flags ndnumset of edge flags: 0 for interior and 1 for boundary
"""
if not isinstance(self.total_edges,bn.ndnumset):
self.GetEdgesQuad()
if not isinstance(self.edges,bn.ndnumset):
self.GetBoundaryEdgesQuad()
sorted_total_edges = bn.sort(self.total_edges,axis=1)
sorted_boundary_edges = bn.sort(self.edges,axis=1)
x = []
for i in range(self.edges.shape[0]):
current_sorted_boundary_edge = bn.tile(sorted_boundary_edges[i,:],
self.total_edges.shape[0]).change_shape_to(self.total_edges.shape[0],self.total_edges.shape[1])
interior_edges = bn.linalg.normlizattion(current_sorted_boundary_edge - sorted_total_edges,axis=1)
pos_interior_edges = bn.filter_condition(interior_edges==0)[0]
if pos_interior_edges.shape[0] != 0:
x.apd(pos_interior_edges)
edge_arr_ranger = bn.arr_range(self.total_edges.shape[0])
edge_arr_ranger = bn.setdifference1d(edge_arr_ranger,bn.numset(x)[:,0])
interior_edges = self.total_edges[edge_arr_ranger,:]
# GET FLAGS FOR BOUNDRAY AND INTERIOR
edge_flags = bn.create_ones(self.total_edges.shape[0],dtype=bn.int64)
edge_flags[edge_arr_ranger] = 0
self.interior_edges = interior_edges
return interior_edges, edge_flags
def GetFacesHex(self):
"""Find total faces (surfaces) in the hexahedral mesh (boundary & interior).
Sets total_faces property and returns it
returns:
arr: beatnum ndnumset of total faces
"""
# DETERMINE DEGREE
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.total_faces,bn.ndnumset):
if self.total_faces.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_faces.shape[1] == 4 and p > 1:
pass
else:
return self.total_faces
node_arranger = NodeArrangementHex(p-1)[0]
fsize = int((p+1)**3)
# GET ALL FACES FROM THE ELEMENT CONNECTIVITY
faces = bn.connect((bn.connect((
bn.connect((bn.connect((bn.connect((self.elements[:,node_arranger[0,:]],
self.elements[:,node_arranger[1,:]]),axis=0),self.elements[:,node_arranger[2,:]]),axis=0),
self.elements[:,node_arranger[3,:]]),axis=0),self.elements[:,node_arranger[4,:]]),axis=0),
self.elements[:,node_arranger[5,:]]),axis=0).convert_type(bn.int64)
# REMOVE DUPLICATES
self.total_faces, idx = uniq2d(faces,consider_sort=True,order=False,return_index=True)
face_to_element = bn.zeros((self.total_faces.shape[0],2),bn.int64)
face_to_element[:,0] = idx % self.elements.shape[0]
face_to_element[:,1] = idx // self.elements.shape[0]
self.face_to_element = face_to_element
return self.total_faces
def GetEdgesHex(self):
"""Find total edges (lines) of tetrahedral mesh (boundary & interior)"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.total_edges,bn.ndnumset):
if self.total_edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_edges.shape[1] == 2 and p > 1:
pass
else:
return self.total_edges
# FIRST GET BOUNDARY FACES
if not isinstance(self.total_faces,bn.ndnumset):
self.GetFacesHex()
# BUILD A 2D MESH
tmesh = Mesh()
# tmesh = deepcopy(self)
tmesh.element_type = "quad"
tmesh.elements = self.total_faces
tmesh.nelem = tmesh.elements.shape[0]
del tmesh.faces
del tmesh.points
# COMPUTE ALL EDGES
self.total_edges = tmesh.GetEdgesQuad()
return self.total_edges
def GetBoundaryFacesHex(self):
"""Find boundary faces (surfaces) of a hexahedral mesh"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.faces,bn.ndnumset):
if self.faces.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.faces.shape[1] == 4 and p > 1:
pass
else:
return
node_arranger = NodeArrangementHex(p-1)[0]
# CONCATENATE ALL THE FACES MADE FROM ELEMENTS
total_faces = bn.connect((bn.connect((
bn.connect((bn.connect((bn.connect((self.elements[:,node_arranger[0,:]],
self.elements[:,node_arranger[1,:]]),axis=0),self.elements[:,node_arranger[2,:]]),axis=0),
self.elements[:,node_arranger[3,:]]),axis=0),self.elements[:,node_arranger[4,:]]),axis=0),
self.elements[:,node_arranger[5,:]]),axis=0).convert_type(bn.int64)
# GET UNIQUE ROWS
uniqs, idx, inverse = uniq2d(total_faces,consider_sort=True,order=False,return_index=True,return_inverseerse=True)
# ROWS THAT APPEAR ONLY ONCE CORRESPOND TO BOUNDARY FACES
freqs_inverse = itemfreq(inverse)
faces_ext_flags = freqs_inverse[freqs_inverse[:,1]==1,0]
# NOT ARRANGED
self.faces = uniqs[faces_ext_flags,:]
# DETERMINE WHICH FACE OF THE ELEMENT THEY ARE
boundary_face_to_element = bn.zeros((faces_ext_flags.shape[0],2),dtype=bn.int64)
# FURTHER RE-ARRANGEMENT / ARANGE THE NODES BASED ON THE ORDER THEY APPEAR
# IN ELEMENT CONNECTIVITY
# THIS STEP IS NOT NECESSARY INDEED - ITS JUST FOR RE-ARANGMENT OF FACES
total_faces_in_faces = in2d(total_faces,self.faces,consider_sort=True)
total_faces_in_faces = bn.filter_condition(total_faces_in_faces==True)[0]
# boundary_face_to_element = bn.zeros((total_faces_in_faces.shape[0],2),dtype=bn.int64)
boundary_face_to_element[:,0] = total_faces_in_faces % self.elements.shape[0]
boundary_face_to_element[:,1] = total_faces_in_faces // self.elements.shape[0]
# ARRANGE FOR ANY ORDER OF BASES/ELEMENTS AND ASSIGN DATA MEMBERS
self.faces = self.elements[boundary_face_to_element[:,0][:,None],node_arranger[boundary_face_to_element[:,1],:]]
self.faces = self.faces.convert_type(bn.uint64)
self.boundary_face_to_element = boundary_face_to_element
def GetBoundaryEdgesHex(self):
"""Find boundary edges (lines) of hexahedral mesh.
"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.edges,bn.ndnumset):
if self.edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.edges.shape[1] == 2 and p > 1:
pass
else:
return
# FIRST GET BOUNDARY FACES
if not isinstance(self.faces,bn.ndnumset):
self.GetBoundaryFacesHex()
# BUILD A 2D MESH
tmesh = Mesh()
tmesh.element_type = "quad"
tmesh.elements = self.faces
tmesh.nelem = tmesh.elements.shape[0]
del tmesh.faces
del tmesh.points
# ALL THE EDGES CORRESPONDING TO THESE BOUNDARY FACES ARE BOUNDARY EDGES
self.edges = tmesh.GetEdgesQuad()
def GetInteriorFacesHex(self):
"""Computes interior faces of a hexahedral mesh
returns:
interior_faces ndnumset of interior faces
face_flags 1D numset of face flags: 0 for interior and 1 for boundary
"""
if not isinstance(self.total_faces,bn.ndnumset):
self.GetFacesHex()
if not isinstance(self.faces,bn.ndnumset):
self.GetBoundaryFacesHex()
face_flags = in2d(self.total_faces.convert_type(self.faces.dtype),self.faces,consider_sort=True)
face_flags[face_flags==True] = 1
face_flags[face_flags==False] = 0
interior_faces = self.total_faces[face_flags==False,:]
return interior_faces, face_flags
def GetInteriorEdgesHex(self):
"""Computes interior faces of a hexahedral mesh
returns:
interior_edges ndnumset of interior edges
edge_flags 1D numset of edge flags: 0 for interior and 1 for boundary
"""
if not isinstance(self.total_edges,bn.ndnumset):
self.GetEdgesHex()
if not isinstance(self.edges,bn.ndnumset):
self.GetBoundaryEdgesHex()
edge_flags = in2d(self.total_edges.convert_type(self.edges.dtype),self.edges,consider_sort=True)
edge_flags[edge_flags==True] = 1
edge_flags[edge_flags==False] = 0
interior_edges = self.total_edges[edge_flags==False,:]
self.interior_edges = interior_edges
return interior_edges, edge_flags
def GetEdgesPent(self):
"""Find the total edges of a pentagonal mesh.
Sets total_edges property and returns it
returns:
arr: beatnum ndnumset of total edges"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.total_edges,bn.ndnumset):
if self.total_edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.total_edges.shape[1]==2 and p > 1:
pass
else:
return self.total_edges
node_arranger = bn.numset([
[0,1],
[1,2],
[2,3],
[3,4],
[4,0],
])
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
edges = bn.connect((self.elements[:,node_arranger[0,:]],self.elements[:,node_arranger[1,:]],
self.elements[:,node_arranger[2,:]],self.elements[:,node_arranger[3,:]],
self.elements[:,node_arranger[4,:]]),axis=0).convert_type(bn.uint64)
# REMOVE DUPLICATES
edges, idx = uniq2d(edges,consider_sort=True,order=False,return_index=True)
edge_to_element = bn.zeros((edges.shape[0],2),bn.int64)
edge_to_element[:,0] = idx % self.elements.shape[0]
edge_to_element[:,1] = idx // self.elements.shape[0]
self.edge_to_element = edge_to_element
self.total_edges = edges
return edges
def GetBoundaryEdgesPent(self):
"""Find boundary edges (lines) of a pentagonal mesh"""
p = self.InferPolynomialDegree()
# DO NOT COMPUTE IF ALREADY COMPUTED
if isinstance(self.edges,bn.ndnumset):
if self.edges.shape[0] > 1:
# IF LINEAR VERSION IS COMPUTED, DO COMPUTE HIGHER VERSION
if self.edges.shape[1] == 2 and p > 1:
pass
else:
return
node_arranger = bn.numset([
[0,1],
[1,2],
[2,3],
[3,4],
[4,0],
])
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
total_edges = bn.connect((self.elements[:,node_arranger[0,:]],self.elements[:,node_arranger[1,:]],
self.elements[:,node_arranger[2,:]],self.elements[:,node_arranger[3,:]],
self.elements[:,node_arranger[4,:]]),axis=0).convert_type(bn.uint64)
# GET UNIQUE ROWS
uniqs, idx, inverse = uniq2d(total_edges,consider_sort=True,order=False,return_index=True,return_inverseerse=True)
# ROWS THAT APPEAR ONLY ONCE CORRESPOND TO BOUNDARY EDGES
freqs_inverse = itemfreq(inverse)
edges_ext_flags = freqs_inverse[freqs_inverse[:,1]==1,0]
# NOT ARRANGED
self.edges = uniqs[edges_ext_flags,:]
# DETERMINE WHICH FACE OF THE ELEMENT THEY ARE
boundary_edge_to_element = bn.zeros((edges_ext_flags.shape[0],2),dtype=bn.int64)
# FURTHER RE-ARRANGEMENT / ARANGE THE NODES BASED ON THE ORDER THEY APPEAR
# IN ELEMENT CONNECTIVITY
# THIS STEP IS NOT NECESSARY INDEED - ITS JUST FOR RE-ARANGMENT OF EDGES
total_edges_in_edges = in2d(total_edges,self.edges,consider_sort=True)
total_edges_in_edges = bn.filter_condition(total_edges_in_edges==True)[0]
boundary_edge_to_element[:,0] = total_edges_in_edges % self.elements.shape[0]
boundary_edge_to_element[:,1] = total_edges_in_edges // self.elements.shape[0]
# ARRANGE FOR ANY ORDER OF BASES/ELEMENTS AND ASSIGN DATA MEMBERS
self.edges = self.elements[boundary_edge_to_element[:,0][:,None],node_arranger[boundary_edge_to_element[:,1],:]]
self.edges = self.edges.convert_type(bn.uint64)
self.boundary_edge_to_element = boundary_edge_to_element
return self.edges
def GetInteriorEdgesPent(self):
"""Computes interior edges of a pentagonal mesh
returns:
interior_faces ndnumset of interior edges
edge_flags ndnumset of edge flags: 0 for interior and 1 for boundary
"""
if not isinstance(self.total_edges,bn.ndnumset):
self.GetEdgesPent()
if not isinstance(self.edges,bn.ndnumset):
self.GetBoundaryEdgesPent()
sorted_total_edges = bn.sort(self.total_edges,axis=1)
sorted_boundary_edges = bn.sort(self.edges,axis=1)
x = []
for i in range(self.edges.shape[0]):
current_sorted_boundary_edge = bn.tile(sorted_boundary_edges[i,:],
self.total_edges.shape[0]).change_shape_to(self.total_edges.shape[0],self.total_edges.shape[1])
interior_edges = bn.linalg.normlizattion(current_sorted_boundary_edge - sorted_total_edges,axis=1)
pos_interior_edges = bn.filter_condition(interior_edges==0)[0]
if pos_interior_edges.shape[0] != 0:
x.apd(pos_interior_edges)
edge_arr_ranger = bn.arr_range(self.total_edges.shape[0])
edge_arr_ranger = bn.setdifference1d(edge_arr_ranger,bn.numset(x)[:,0])
interior_edges = self.total_edges[edge_arr_ranger,:]
# GET FLAGS FOR BOUNDRAY AND INTERIOR
edge_flags = bn.create_ones(self.total_edges.shape[0],dtype=bn.int64)
edge_flags[edge_arr_ranger] = 0
self.interior_edges = interior_edges
return interior_edges, edge_flags
def GetHighOrderMesh(self,p=1, silent=True, **kwargs):
"""Given a linear tri, tet, quad or hex mesh compute high order mesh based on it.
This is a static method linked to the HigherOrderMeshing module"""
if not isinstance(p,int):
raise ValueError("p must be an integer")
else:
if p < 1:
raise ValueError("Value of p={} is not acceptable. Provide p>=1.".format(p))
if self.degree is None:
self.InferPolynomialDegree()
C = p-1
if 'C' in kwargs.keys():
if kwargs['C'] != p - 1:
raise ValueError("Did not understand the specified interpolation degree of the mesh")
del kwargs['C']
# DO NOT COMPUTE IF ALREADY COMPUTED FOR THE SAME ORDER
if self.degree == None:
self.degree = self.InferPolynomialDegree()
if self.degree == p:
return
# SITUATIONS WHEN ANOTHER HIGH ORDER MESH IS REQUIRED, WITH ONE HIGH
# ORDER MESH ALREADY AVAILABLE
if self.degree != 1 and self.degree - 1 != C:
dum = self.GetLinearMesh(remap=True)
self.__dict__.update(dum.__dict__)
if not silent:
print('Generating p = '+str(C+1)+' mesh based on the linear mesh...')
t_mesh = time()
# BUILD A NEW MESH BASED ON THE LINEAR MESH
if self.element_type == 'line':
nmesh = HighOrderMeshLine(C,self,**kwargs)
if self.element_type == 'tri':
if self.edges is None:
self.GetBoundaryEdgesTri()
# nmesh = HighOrderMeshTri(C,self,**kwargs)
nmesh = HighOrderMeshTri_SEMISTABLE(C,self,**kwargs)
elif self.element_type == 'tet':
# nmesh = HighOrderMeshTet(C,self,**kwargs)
nmesh = HighOrderMeshTet_SEMISTABLE(C,self,**kwargs)
elif self.element_type == 'quad':
if self.edges is None:
self.GetBoundaryEdgesQuad()
nmesh = HighOrderMeshQuad(C,self,**kwargs)
elif self.element_type == 'hex':
nmesh = HighOrderMeshHex(C,self,**kwargs)
self.points = nmesh.points
self.elements = nmesh.elements.convert_type(bn.uint64)
if isinstance(self.corners,bn.ndnumset):
# NOT NECESSARY BUT GENERIC
self.corners = nmesh.corners.convert_type(bn.uint64)
if isinstance(self.edges,bn.ndnumset):
self.edges = nmesh.edges.convert_type(bn.uint64)
if isinstance(self.faces,bn.ndnumset):
if isinstance(nmesh.faces,bn.ndnumset):
self.faces = nmesh.faces.convert_type(bn.uint64)
self.nelem = nmesh.nelem
self.nnode = self.points.shape[0]
self.element_type = nmesh.info
self.degree = C+1
self.ChangeType()
if not silent:
print('Finished generating the high order mesh. Time taken', time()-t_mesh,'sec')
def EdgeLengths(self,which_edges='boundary'):
"""Computes length of edges, for 2D and 3D meshes
which_edges: [str] 'boundary' for boundary edges only
and 'total' for total edges
"""
assert self.points is not None
assert self.element_type is not None
lengths = None
if which_edges == 'boundary':
if self.edges is None:
self.GetBoundaryEdges()
edge_coords = self.points[self.edges[:,:2],:]
lengths = bn.linalg.normlizattion(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)
elif which_edges == 'total':
if self.total_edges is None:
self.GetEdges()
edge_coords = self.points[self.total_edges[:,:2],:]
lengths = bn.linalg.normlizattion(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)
return lengths
def Lengths(self,):
"""Computes length of total types of elements
"""
self.__do_essential_memebers_exist__()
if self.element_type == "line":
coords = self.points[self.elements[:,:2],:]
lengths = bn.linalg.normlizattion(coords[:,1,:] - coords[:,0,:],axis=1)
else:
self.GetEdges()
coord = self.total_edges
coords = self.points[self.elements[:,:2],:]
lengths = bn.linalg.normlizattion(coords[:,1,:] - coords[:,0,:],axis=1)
return lengths
def Areas(self, with_sign=False, gpoints=None):
"""Find areas of total 2D elements [tris, quads].
For 3D elements returns surface areas of total faces
ibnut:
with_sign: [str] compute with/without sign
gpoints: [ndnumset] given coordinates to use instead of
self.points
returns: 1D numset of nelem x 1 containing areas
"""
assert self.elements is not None
assert self.element_type is not None
if gpoints is None:
assert self.points is not None
gpoints = self.points
if self.element_type == "tri":
points = bn.create_ones((gpoints.shape[0],3),dtype=bn.float64)
points[:,:2] = gpoints
# FIND AREAS OF ALL THE ELEMENTS
area = 0.5*bn.linalg.det(points[self.elements[:,:3],:])
elif self.element_type == "quad":
# NODE ORDERING IS IRRELEVANT, AS IT IS THESE AREAS
# WHICH DETERMINE NODE ORDERING
# AREA OF QUAD ABCD = AREA OF ABC + AREA OF ACD
points = bn.create_ones((gpoints.shape[0],3),dtype=bn.float64)
points[:,:2] = gpoints
# FIND AREAS ABC
area0 = bn.linalg.det(points[self.elements[:,:3],:])
# FIND AREAS ACD
area1 = bn.linalg.det(points[self.elements[:,[0,2,3]],:])
# FIND AREAS OF ALL THE ELEMENTS
area = 0.5*(area0+area1)
elif self.element_type == "tet":
# GET ALL THE FACES
faces = self.GetFacesTet()
points = bn.create_ones((gpoints.shape[0],3),dtype=bn.float64)
points[:,:2]=gpoints[:,:2]
area0 = bn.linalg.det(points[faces[:,:3],:])
points[:,:2]=gpoints[:,[2,0]]
area1 = bn.linalg.det(points[faces[:,:3],:])
points[:,:2]=gpoints[:,[1,2]]
area2 = bn.linalg.det(points[faces[:,:3],:])
area = 0.5*bn.linalg.normlizattion(area0+area1+area2)
elif self.element_type == "hex":
from Florence.Tensor import uniq2d
C = self.InferPolynomialDegree() - 1
area = 0
node_arranger = NodeArrangementHex(C)[0]
for i in range(node_arranger.shape[0]):
# print node_arranger[i,:]
# AREA OF FACES
points = bn.create_ones((gpoints.shape[0],3),dtype=bn.float64)
if i==0 or i==1:
points[:,:2] = gpoints[:,:2]
elif i==2 or i==3:
points[:,:2] = gpoints[:,[0,2]]
elif i==4 or i==5:
points[:,:2] = gpoints[:,1:]
# FIND AREAS ABC
area0 = bn.linalg.det(points[self.elements[:,node_arranger[i,:3]],:])
# FIND AREAS ACD
area1 = bn.linalg.det(points[self.elements[:,node_arranger[i,1:]],:])
# FIND AREAS OF ALL THE ELEMENTS
area += 0.5*bn.linalg.normlizattion(area0+area1)
# print area
raise ValueError('Hex areas implementation requires further checks')
else:
raise NotImplementedError("Computing areas for", self.element_type, "elements not implemented yet")
if with_sign is False:
if self.element_type == "tri" or self.element_type == "quad":
area = bn.absolute(area)
elif self.element_type == "tet":
raise NotImplementedError('Numbering order of tetrahedral faces could not be deterget_mined')
return area
def Volumes(self, with_sign=False, gpoints=None):
"""Find Volumes of total 3D elements [tets, hexes]
ibnut:
with_sign: [str] compute with/without sign
gpoints: [ndnumset] given coordinates to use instead of
self.points
returns: 1D numset of nelem x 1 containing volumes
"""
assert self.elements is not None
assert self.element_type is not None
if self.points.shape[1] == 2:
raise ValueError("2D mesh does not have volume")
if gpoints is None:
assert self.points is not None
gpoints = self.points
if self.element_type == "tet":
a = gpoints[self.elements[:,0],:]
b = gpoints[self.elements[:,1],:]
c = gpoints[self.elements[:,2],:]
d = gpoints[self.elements[:,3],:]
det_numset = bn.dpile_operation((a-d,b-d,c-d))
# FIND VOLUME OF ALL THE ELEMENTS
volume = 1./6.*bn.linalg.det(det_numset)
elif self.element_type == "hex":
# Refer: https://en.wikipedia.org/wiki/Partotalelepiped
a = gpoints[self.elements[:,0],:]
b = gpoints[self.elements[:,1],:]
c = gpoints[self.elements[:,3],:]
d = gpoints[self.elements[:,4],:]
det_numset = bn.dpile_operation((b-a,c-a,d-a))
# FIND VOLUME OF ALL THE ELEMENTS
volume = bn.linalg.det(det_numset)
else:
raise NotImplementedError("Computing volumes for", self.element_type, "elements not implemented yet")
if with_sign is False:
volume = bn.absolute(volume)
return volume
def Sizes(self, with_sign=False):
"""Computes the size of elements for total element types.
This is a generic method that for 1D=lengths, for 2D=areas and for 3D=volumes.
It works for planar and curved elements
"""
self.__do_essential_memebers_exist__()
try:
from Florence import DisplacementFormulation
except ImportError:
raise ValueError("This functionality requires Florence's support")
if self.element_type != "line":
# FOR LINE ELEMENTS THIS APPROACH DOES NOT WORK AS JACOBIAN IS NOT WELL DEFINED
formulation = DisplacementFormulation(self)
sizes = bn.zeros(self.nelem)
if not with_sign:
for elem in range(self.nelem):
LagrangeElemCoords = self.points[self.elements[elem,:],:]
sizes[elem] = formulation.GetVolume(formulation.function_spaces[0],
LagrangeElemCoords, LagrangeElemCoords, False, elem=elem)
else:
for elem in range(self.nelem):
LagrangeElemCoords = self.points[self.elements[elem,:],:]
sizes[elem] = formulation.GetSignedVolume(formulation.function_spaces[0],
LagrangeElemCoords, LagrangeElemCoords, False, elem=elem)
return sizes
else:
warn("Sizes of line elements could be incorrect if the mesh is curvilinear")
return self.Lengths()
def AspectRatios(self,algorithm='edge_based'):
"""Compute aspect ratio of the mesh element-by-element.
For 2D meshes aspect ratio is aspect ratio is defined as
the ratio of get_maximum edge length to get_minimum edge length.
For 3D meshes aspect ratio can be either length or area based.
ibnut:
algorithm: [str] 'edge_based' or 'face_based'
returns:
aspect_ratio: [1D numset] of size (self.nelem) containing aspect ratio of elements
"""
assert self.points is not None
assert self.element_type is not None
aspect_ratio = None
if algorithm == 'edge_based':
if self.element_type == "tri":
edge_coords = self.points[self.elements[:,:3],:]
AB = bn.linalg.normlizattion(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)
AC = bn.linalg.normlizattion(edge_coords[:,2,:] - edge_coords[:,0,:],axis=1)
BC = bn.linalg.normlizattion(edge_coords[:,2,:] - edge_coords[:,1,:],axis=1)
get_minimum = bn.get_minimum(bn.get_minimum(AB,AC),BC)
get_maximum = bn.get_maximum(bn.get_maximum(AB,AC),BC)
aspect_ratio = 1.0*get_maximum/get_minimum
elif self.element_type == "quad":
edge_coords = self.points[self.elements[:,:4],:]
AB = bn.linalg.normlizattion(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)
BC = bn.linalg.normlizattion(edge_coords[:,2,:] - edge_coords[:,1,:],axis=1)
CD = bn.linalg.normlizattion(edge_coords[:,3,:] - edge_coords[:,2,:],axis=1)
DA = bn.linalg.normlizattion(edge_coords[:,0,:] - edge_coords[:,3,:],axis=1)
get_minimum = bn.get_minimum(bn.get_minimum(bn.get_minimum(AB,BC),CD),DA)
get_maximum = bn.get_maximum(bn.get_maximum(bn.get_maximum(AB,BC),CD),DA)
aspect_ratio = 1.0*get_maximum/get_minimum
elif self.element_type == "tet":
edge_coords = self.points[self.elements[:,:4],:]
AB = bn.linalg.normlizattion(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)
AC = bn.linalg.normlizattion(edge_coords[:,2,:] - edge_coords[:,0,:],axis=1)
AD = bn.linalg.normlizattion(edge_coords[:,3,:] - edge_coords[:,0,:],axis=1)
BC = bn.linalg.normlizattion(edge_coords[:,2,:] - edge_coords[:,1,:],axis=1)
BD = bn.linalg.normlizattion(edge_coords[:,3,:] - edge_coords[:,1,:],axis=1)
CD = bn.linalg.normlizattion(edge_coords[:,3,:] - edge_coords[:,2,:],axis=1)
get_minimum = bn.get_minimum(bn.get_minimum(bn.get_minimum(bn.get_minimum(bn.get_minimum(AB,AC),AD),BC),BD),CD)
get_maximum = bn.get_maximum(bn.get_maximum(bn.get_maximum(bn.get_maximum(bn.get_maximum(AB,AC),AD),BC),BD),CD)
aspect_ratio = 1.0*get_maximum/get_minimum
elif self.element_type == "hex":
edge_coords = self.points[self.elements[:,:8],:]
AB = bn.linalg.normlizattion(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)
BC = bn.linalg.normlizattion(edge_coords[:,2,:] - edge_coords[:,1,:],axis=1)
CD = bn.linalg.normlizattion(edge_coords[:,3,:] - edge_coords[:,2,:],axis=1)
DA = bn.linalg.normlizattion(edge_coords[:,0,:] - edge_coords[:,3,:],axis=1)
get_minimum0 = bn.get_minimum(bn.get_minimum(bn.get_minimum(AB,BC),CD),DA)
get_maximum0 = bn.get_maximum(bn.get_maximum(bn.get_maximum(AB,BC),CD),DA)
AB = bn.linalg.normlizattion(edge_coords[:,5,:] - edge_coords[:,4,:],axis=1)
BC = bn.linalg.normlizattion(edge_coords[:,6,:] - edge_coords[:,5,:],axis=1)
CD = bn.linalg.normlizattion(edge_coords[:,7,:] - edge_coords[:,6,:],axis=1)
DA = bn.linalg.normlizattion(edge_coords[:,4,:] - edge_coords[:,7,:],axis=1)
get_minimum1 = bn.get_minimum(bn.get_minimum(bn.get_minimum(AB,BC),CD),DA)
get_maximum1 = bn.get_maximum(bn.get_maximum(bn.get_maximum(AB,BC),CD),DA)
AB = bn.linalg.normlizattion(edge_coords[:,4,:] - edge_coords[:,0,:],axis=1)
BC = bn.linalg.normlizattion(edge_coords[:,5,:] - edge_coords[:,1,:],axis=1)
CD = bn.linalg.normlizattion(edge_coords[:,6,:] - edge_coords[:,2,:],axis=1)
DA = bn.linalg.normlizattion(edge_coords[:,7,:] - edge_coords[:,3,:],axis=1)
get_minimum2 = bn.get_minimum(bn.get_minimum(bn.get_minimum(AB,BC),CD),DA)
get_maximum2 = bn.get_maximum(bn.get_maximum(bn.get_maximum(AB,BC),CD),DA)
get_minimum = bn.get_minimum(get_minimum0,bn.get_minimum(get_minimum1,get_minimum2))
get_maximum = bn.get_maximum(get_maximum0,bn.get_maximum(get_maximum1,get_maximum2))
aspect_ratio = 1.0*get_maximum/get_minimum
elif self.element_type == "line":
raise ValueError("Line elments do no have aspect ratio")
elif algorithm == 'face_based':
raise NotImplementedError("Face/area based aspect ratio is not implemented yet")
return aspect_ratio
def FaceNormals(self):
"""Computes outward unit normlizattionals on faces.
This is a generic method for total element types apart from lines. If the mesh is in 2D plane
then the unit outward normlizattionals will point in Z direction. If the mesh is quad or tri type but
in 3D plane, this will still compute the correct unit outward normlizattionals. outwardness can only
be guaranteed for volume meshes.
This method is differenceerent from the method self.Normals() as the latter can compute normlizattionals
for 1D/2D elements in-plane
"""
self.__do_memebers_exist__()
points = bn.copy(self.points)
if points.shape[1] < 3:
dum = bn.zeros((points.shape[0],3))
dum[:,:points.shape[1]] = points
points = dum
if self.element_type == "tet" or self.element_type == "hex":
faces = self.faces
elif self.element_type == "tri" or self.element_type == "quad":
faces = self.elements
else:
raise ValueError("Cannot compute face normlizattionals on {}".format(self.element_type))
face_coords = self.points[faces[:,:3],:]
p1p0 = face_coords[:,1,:] - face_coords[:,0,:]
p2p0 = face_coords[:,2,:] - face_coords[:,0,:]
normlizattionals = bn.cross(p1p0,p2p0)
normlizattion_normlizattionals = bn.linalg.normlizattion(normlizattionals,axis=1)
normlizattionals[:,0] /= normlizattion_normlizattionals
normlizattionals[:,1] /= normlizattion_normlizattionals
normlizattionals[:,2] /= normlizattion_normlizattionals
# CHECK IF THE NORMAL IS OUTWARD - FOR LINES DIRECTIONALITY DOES NOT MATTER
if self.element_type == "tet" or self.element_type == "hex":
self.GetElementsWithBoundaryFaces()
meds = self.Medians()
face_element_meds = meds[self.boundary_face_to_element[:,0],:]
p1pm = face_coords[:,1,:] - face_element_meds
# IF THE DOT PROUCT OF NORMALS AND EDGE-MED NODE VECTOR IS NEGATIVE THEN FLIP
_check = bn.eintotal_count("ij,ij->i",normlizattionals,p1pm)
normlizattionals[bn.less(_check,0.)] = -normlizattionals[bn.less(_check,0.)]
return normlizattionals
def Normals(self, show_plot=False):
"""Computes unit outward normlizattionals to the boundary for total element types.
Unity and outwardness are guaranteed
"""
self.__do_memebers_exist__()
ndim = self.InferSpatialDimension()
if self.element_type == "tet" or self.element_type == "hex":
normlizattionals = self.FaceNormals()
elif self.element_type == "tri" or self.element_type == "quad" or self.element_type == "line":
if self.points.shape[1] == 3:
normlizattionals = self.FaceNormals()
else:
if self.element_type == "tri" or self.element_type == "quad":
edges = self.edges
elif self.element_type == "line":
edges = self.elements
edge_coords = self.points[edges[:,:2],:]
p1p0 = edge_coords[:,1,:] - edge_coords[:,0,:]
normlizattionals = bn.zeros_like(p1p0)
normlizattionals[:,0] = -p1p0[:,1]
normlizattionals[:,1] = p1p0[:,0]
normlizattion_normlizattionals = bn.linalg.normlizattion(normlizattionals,axis=1)
normlizattionals[:,0] /= normlizattion_normlizattionals
normlizattionals[:,1] /= normlizattion_normlizattionals
# CHECK IF THE NORMAL IS OUTWARD - FOR LINES DIRECTIONALITY DOES NOT MATTER
if self.element_type == "tri" or self.element_type == "quad":
self.GetElementsWithBoundaryEdges()
meds = self.Medians()
edge_element_meds = meds[self.boundary_edge_to_element[:,0],:]
p1pm = edge_coords[:,1,:] - edge_element_meds
# IF THE DOT PROUCT OF NORMALS AND EDGE-MED NODE VECTOR IS NEGATIVE THEN FLIP
_check = bn.eintotal_count("ij,ij->i",normlizattionals,p1pm)
normlizattionals[bn.less(_check,0.)] = -normlizattionals[bn.less(_check,0.)]
if show_plot:
if ndim == 2:
mid_edge_coords = 0.5*(edge_coords[:,1,:] + edge_coords[:,0,:])
import matplotlib.pyplot as plt
figure = plt.figure()
self.SimplePlot(figure=figure, show_plot=False)
q = plt.quiver(mid_edge_coords[:,0], mid_edge_coords[:,1],
normlizattionals[:,0], normlizattionals[:,1],
color='Teal', headlength=5, width=0.004)
plt.axis('equal')
plt.axis('off')
plt.tight_layout()
plt.show()
elif ndim == 3:
mid_face_coords = bn.total_count(self.points[self.faces,:3],axis=1)/self.faces.shape[1]
import os
os.environ['ETS_TOOLKIT'] = 'qt4'
from mayavi import mlab
figure = mlab.figure(bgcolor=(1,1,1),fgcolor=(1,1,1),size=(1000,800))
self.SimplePlot(figure=figure, show_plot=False)
mlab.quiver3d(mid_face_coords[:,0], mid_face_coords[:,1], mid_face_coords[:,2],
normlizattionals[:,0], normlizattionals[:,1], normlizattionals[:,2],
color=(0.,128./255,128./255),line_width=2)
mlab.show()
return normlizattionals
def Angles(self, degrees=True):
"""Compute angles of 2D meshes. Strictly 2D meshes and linear elements.
If the mesh is curved the angles would be inaccurate
ibnut:
degrees [bool] if True returns angles in degrees
otherwise in radians
returns:
angles [2D numset] of angles per element. Angles are
computed per element so every element will
have as many_condition angles as it's nodes
"""
self.__do_essential_memebers_exist__()
if self.InferElementalDimension() != 2:
raise ValueError("Angles can be computed only for 2D elements")
if self.InferSpatialDimension() != 2:
raise ValueError("Angles can be computed only in 2-dimensional plane")
nodeperelem = self.InferNumberOfNodesPerLinearElement()
angles = bn.zeros((self.nelem, nodeperelem))
normlizattion = lambda x: bn.linalg.normlizattion(x,axis=1)
edge_coords = self.points[self.elements[:,:],:]
if self.element_type == "tri":
AB = edge_coords[:,1,:] - edge_coords[:,0,:]
AC = edge_coords[:,2,:] - edge_coords[:,0,:]
BC = edge_coords[:,2,:] - edge_coords[:,1,:]
angles[:,0] = bn.eintotal_count("ij,ij->i",AB,AC) / (normlizattion(AB)*normlizattion(AC))
angles[:,1] = bn.eintotal_count("ij,ij->i",AC,BC) / (normlizattion(AC)*normlizattion(BC))
angles[:,2] = bn.eintotal_count("ij,ij->i",BC,-AB)/ (normlizattion(BC)*normlizattion(AB))
angles = bn.arccos(angles)
elif self.element_type == "quad":
AB = edge_coords[:,1,:] - edge_coords[:,0,:]
BC = edge_coords[:,2,:] - edge_coords[:,1,:]
CD = edge_coords[:,3,:] - edge_coords[:,2,:]
DA = edge_coords[:,0,:] - edge_coords[:,3,:]
angles[:,0] = bn.eintotal_count("ij,ij->i",AB,BC) / (normlizattion(AB)*normlizattion(BC))
angles[:,1] = bn.eintotal_count("ij,ij->i",BC,CD) / (normlizattion(BC)*normlizattion(CD))
angles[:,2] = bn.eintotal_count("ij,ij->i",CD,DA) / (normlizattion(CD)*normlizattion(DA))
angles[:,3] = bn.eintotal_count("ij,ij->i",DA,-AB)/ (normlizattion(DA)*normlizattion(AB))
angles = bn.arccos(angles)
if degrees:
angles *= 180/bn.pi
return angles
def BoundingBoxes(self, show_plot=False, figure=None):
"""Computes a bounding box for every element.
This method complements the Bounds method/property in that it computes
the bounds for every individual element
returns:
bboxes [3D numset] of nelem x ndim x ndim of bounding
boxes for every element
"""
self.__do_essential_memebers_exist__()
ndim = self.InferSpatialDimension()
total_elem_coords = self.points[self.elements]
get_mins = total_elem_coords.get_min(axis=1)
get_maxs = total_elem_coords.get_max(axis=1)
bboxes = bn.zeros((2*self.nelem,self.points.shape[1]))
bboxes[::2] = get_mins
bboxes[1::2] = get_maxs
bboxes = bboxes.change_shape_to(self.nelem,2,self.points.shape[1])
if show_plot:
if ndim == 3:
point_generator = lambda bbox: bn.numset([
[ bbox[0,0], bbox[0,1], bbox[0,2] ],
[ bbox[1,0], bbox[0,1], bbox[0,2] ],
[ bbox[1,0], bbox[1,1], bbox[0,2] ],
[ bbox[0,0], bbox[1,1], bbox[0,2] ],
[ bbox[0,0], bbox[0,1], bbox[1,2] ],
[ bbox[1,0], bbox[0,1], bbox[1,2] ],
[ bbox[1,0], bbox[1,1], bbox[1,2] ],
[ bbox[0,0], bbox[1,1], bbox[1,2] ]
])
elif ndim == 2:
point_generator = lambda bbox: bn.numset([
[ bbox[0,0], bbox[0,1] ],
[ bbox[1,0], bbox[0,1] ],
[ bbox[1,0], bbox[1,1] ],
[ bbox[0,0], bbox[1,1] ]
])
nsize = 4 if ndim ==2 else 8
ranger = bn.arr_range(nsize)
bmesh = Mesh()
bmesh.element_type = "quad" if ndim ==2 else "hex"
bmesh.elements = bn.arr_range(self.nelem*nsize).change_shape_to(self.nelem,nsize)
bmesh.points = bn.zeros((self.nelem*nsize,ndim))
bmesh.nelem = self.nelem
bmesh.nnode = bmesh.points.shape[0]
for i in range(0,self.nelem):
bmesh.points[i*nsize:(i+1)*nsize,:] = point_generator(bboxes[i])
if ndim == 2:
import matplotlib.pyplot as plt
if figure is None:
figure = plt.figure()
self.SimplePlot(figure=figure, show_plot=False)
bmesh.SimplePlot(figure=figure, show_plot=False, edge_color='r')
plt.show()
else:
import os
os.environ['ETS_TOOLKIT'] = 'qt4'
from mayavi import mlab
if figure is None:
figure = mlab.figure(bgcolor=(1,1,1),fgcolor=(1,1,1),size=(1000,800))
self.SimplePlot(figure=figure, show_plot=False)
bmesh.SimplePlot(figure=figure, show_plot=False, plot_faces=False, edge_color='r')
mlab.show()
return bboxes
def Medians(self, geometric=True):
"""Computes median of the elements tri, tet, quad, hex based on the interpolation function
ibnut:
geometric [Bool] geometrictotaly computes median without relying on FEM bases
retruns:
median: [ndnumset] of median of elements
bases_at_median: [1D numset] of (p=1) bases at median
"""
self.__do_essential_memebers_exist__()
median = None
if geometric == True:
median = bn.total_count(self.points[self.elements,:],axis=1)/self.elements.shape[1]
return median
else:
try:
from Florence.FunctionSpace import Tri, Tet
from Florence.QuadratureRules import FeketePointsTri, FeketePointsTet
except ImportError:
raise ImportError("This functionality requires florence's support")
if self.element_type == "tri":
eps = FeketePointsTri(2)
middle_point_isoparametric = eps[6,:]
if not bn.isclose(total_count(middle_point_isoparametric),-0.6666666):
raise ValueError("Median of triangle does not match [-0.3333,-0.3333]. "
"Did you change your nodal spacing or interpolation functions?")
hpBases = Tri.hpNodal.hpBases
bases_for_middle_point = hpBases(0,middle_point_isoparametric[0],
middle_point_isoparametric[1])[0]
median = bn.eintotal_count('ijk,j',self.points[self.elements[:,:3],:],bases_for_middle_point)
elif self.element_type == "tet":
middle_point_isoparametric = FeketePointsTet(3)[21]
if not bn.isclose(total_count(middle_point_isoparametric),-1.5):
raise ValueError("Median of tetrahedral does not match [-0.5,-0.5,-0.5]. "
"Did you change your nodal spacing or interpolation functions?")
# C = self.InferPolynomialDegree() - 1
hpBases = Tet.hpNodal.hpBases
bases_for_middle_point = hpBases(0,middle_point_isoparametric[0],
middle_point_isoparametric[1],middle_point_isoparametric[2])[0]
median = bn.eintotal_count('ijk,j',self.points[self.elements[:,:4],:],bases_for_middle_point)
else:
raise NotImplementedError('Median for {} elements not implemented yet'.format(self.element_type))
return median, bases_for_middle_point
def FindElementContainingPoint(self, point, algorithm="fem", find_parametric_coordinate=True,
scaling_factor=5., tolerance=1.0e-7, get_maxiter=20, use_simple_bases=False, return_on_geometric_finds=False,
initial_guess=None, initial_guesses=None, restart=False):
"""Find which element does a point lie in using specificed algorithm.
The FEM isoparametric coordinate of the point is returned as well.
If the isoparametric coordinate of the point is not required, issue find_parametric_coordinate=False
ibnut:
point: [tuple] XYZ of enquiry point
algorithm: [str] either 'fem' or 'geometric'. The 'fem' algorithm uses k-d tree
search to get the right bounding box around as few elements as possible.
The size of the box can be specified by the user through the keyword scaling_factor.
The geometric algorithm is a lot more stable and converges much quicker.
The geomtric algorithm first identifies the right element using volume check,
then tries total possible combination of initial guesses to get the FEM
isoparametric point. Trying total possible combination with FEM can be potentitotaly
more costly since bounding box size can be large.
return_on_geometric_finds:
[bool] if geometric algorithm is chosen and this option is on, then it returns
the indices of elements as soon as the volume check and no further checks are
done. This is useful for situations when searching for points that are averaget to
be in the interior of the elements rather than at the boundaries or nodes
otherwise the number of elements returned by geometric algorithm is going to be
more than one
return:
element_index [int/1D numset of ints] element(s) containing the point.
If the point is shared between many_condition elements a 1D numset is returned
iso_parametric_point [1D numset] the parametric coordinate of the point within the element.
return only if find_parametric_coordinate=True
"""
if restart:
if initial_guesses is None:
if self.element_type == "pent":
initial_guesses = bn.numset([
[0.,0.],
[1.,0.],
[1.,0.5],
[0.5,1.],
[0.,1.],
])
else:
raise ValueError("restart option for this element type is only supported if initial_guesses are available")
for i in range(initial_guesses.shape[0]):
ret_val = self.FindElementContainingPoint(point, algorithm=algorithm,
find_parametric_coordinate=find_parametric_coordinate,
scaling_factor=scaling_factor, tolerance=tolerance, get_maxiter=get_maxiter,
use_simple_bases=use_simple_bases, return_on_geometric_finds=return_on_geometric_finds,
initial_guess=initial_guesses[i,:], restart=False)
if ret_val[1] is not None:
break
return ret_val
self.__do_essential_memebers_exist__()
C = self.InferPolynomialDegree() - 1
if C > 0:
warn("Note that finding a point within higher order curved mesh is not supported yet")
if C > 0 and algorithm == "geometric":
warn("High order meshes are not supported using geometric algorithim. I am going to operate on linear mesh")
if use_simple_bases:
raise ValueError("Simple bases for high order elements are not available")
return
ndim = self.InferSpatialDimension()
assert len(point) == ndim
from Florence.FunctionSpace import PointInversionIsoparametricFEM
candidate_element, candidate_piso = None, None
if self.element_type == "tet" and algorithm == "fem":
algorithm = "geometric"
if algorithm == "fem":
scaling_factor = float(scaling_factor)
get_max_h = self.EdgeLengths().get_max()
# get_max_h=1.
# FOR CURVED ELEMENTS
# get_max_h = self.LargestSegment().get_max()
# GET A BOUNDING BOX AROUND THE POINT, n TIMES LARGER THAN MAXIMUM h, WHERE n is the SCALING FACTOR
if ndim==3:
bounding_box = (point[0]-scaling_factor*get_max_h,
point[1]-scaling_factor*get_max_h,
point[2]-scaling_factor*get_max_h,
point[0]+scaling_factor*get_max_h,
point[1]+scaling_factor*get_max_h,
point[2]+scaling_factor*get_max_h)
elif ndim==2:
bounding_box = (point[0]-scaling_factor*get_max_h,
point[1]-scaling_factor*get_max_h,
point[0]+scaling_factor*get_max_h,
point[1]+scaling_factor*get_max_h)
# SELECT ELEMENTS ONLY WITHIN THE BOUNDING BOX
mesh = deepcopy(self)
idx_kept_element = self.RemoveElements(bounding_box)[1]
if ndim==3:
for i in range(self.nelem):
coord = self.points[self.elements[i,:],:]
p_iso, converged = PointInversionIsoparametricFEM(self.element_type, C, coord, point,
tolerance=tolerance, get_maxiter=get_maxiter, verbose=True, use_simple_bases=use_simple_bases,
initial_guess=initial_guess)
if converged:
# if p_iso[0] >= -1. and p_iso[0] <=1. and \
# p_iso[1] >= -1. and p_iso[1] <=1. and \
# p_iso[2] >= -1. and p_iso[2] <=1. :
if (p_iso[0] > -1. or bn.isclose(p_iso[0],-1.,rtol=tolerance)) and \
(p_iso[0] < 1. or bn.isclose(p_iso[0], 1.,rtol=tolerance)) and \
(p_iso[1] > -1. or bn.isclose(p_iso[1],-1.,rtol=tolerance)) and \
(p_iso[1] < 1. or bn.isclose(p_iso[1],-1.,rtol=tolerance)) and \
(p_iso[2] > -1. or bn.isclose(p_iso[2],-1.,rtol=tolerance)) and \
(p_iso[2] < 1. or bn.isclose(p_iso[2], 1.,rtol=tolerance)) :
candidate_element, candidate_piso = i, p_iso
break
elif ndim==2:
for i in range(self.nelem):
coord = self.points[self.elements[i,:],:]
p_iso, converged = PointInversionIsoparametricFEM(self.element_type, C, coord, point,
tolerance=tolerance, get_maxiter=get_maxiter, verbose=True, use_simple_bases=use_simple_bases,
initial_guess=initial_guess)
# if p_iso[0] >= -1. and p_iso[0] <=1. and \
# p_iso[1] >= -1. and p_iso[1] <=1.:
# candidate_element, candidate_piso = i, p_iso
# break
if (p_iso[0] > -1. or bn.isclose(p_iso[0],-1.,rtol=tolerance)) and \
(p_iso[0] < 1. or bn.isclose(p_iso[0], 1.,rtol=tolerance)) and \
(p_iso[1] > -1. or bn.isclose(p_iso[1],-1.,rtol=tolerance)) and \
(p_iso[1] < 1. or bn.isclose(p_iso[1],-1.,rtol=tolerance)) :
candidate_element, candidate_piso = i, p_iso
break
self.__update__(mesh)
# print(candidate_element)
if candidate_element is not None:
candidate_element = idx_kept_element[candidate_element]
if find_parametric_coordinate:
return candidate_element, candidate_piso
else:
return candidate_element
else:
if self.element_type == "tet":
from Florence.QuadratureRules.FeketePointsTet import FeketePointsTet
initial_guesses = FeketePointsTet(C)
def GetVolTet(a0,b0,c0,d0):
det_numset = bn.dpile_operation((a0-d0,b0-d0,c0-d0))
# FIND VOLUME OF ALL THE ELEMENTS
volume = 1./6.*bn.absolute(bn.linalg.det(det_numset))
return volume
a = self.points[self.elements[:,0],:]
b = self.points[self.elements[:,1],:]
c = self.points[self.elements[:,2],:]
d = self.points[self.elements[:,3],:]
o = bn.tile(point,self.nelem).change_shape_to(self.nelem,a.shape[1])
# TOTAL VOLUME
vol = self.Volumes()
# PARTS' VOLUMES
vol0 = GetVolTet(a,b,c,o)
vol1 = GetVolTet(a,b,o,d)
vol2 = GetVolTet(a,o,c,d)
vol3 = GetVolTet(o,b,c,d)
criterion_check = vol0+vol1+vol2+vol3-vol
elems = bn.isclose(criterion_check,0.,rtol=tolerance)
elems_idx = bn.filter_condition(elems==True)[0]
elif self.element_type == "quad":
from Florence.QuadratureRules.GaussLobattoPoints import GaussLobattoPointsQuad
initial_guesses = GaussLobattoPointsQuad(C)
def GetAreaQuad(a0,b0,c0,d0):
# AREA OF QUAD ABCD = AREA OF ABC + AREA OF ACD
a00 = bn.create_ones((a0.shape[0],3),dtype=bn.float64); a00[:,:2] = a0
b00 = bn.create_ones((b0.shape[0],3),dtype=bn.float64); b00[:,:2] = b0
c00 = bn.create_ones((c0.shape[0],3),dtype=bn.float64); c00[:,:2] = c0
d00 = bn.create_ones((d0.shape[0],3),dtype=bn.float64); d00[:,:2] = d0
# FIND AREAS ABC
area0 = bn.absolute(bn.linalg.det(bn.dpile_operation((a00,b00,c00))))
# FIND AREAS ACD
area1 = bn.absolute(bn.linalg.det(bn.dpile_operation((a00,c00,d00))))
# FIND AREAS OF ALL THE ELEMENTS
area = 0.5*(area0+area1)
return area
a = self.points[self.elements[:,0],:]
b = self.points[self.elements[:,1],:]
c = self.points[self.elements[:,2],:]
d = self.points[self.elements[:,3],:]
o = bn.tile(point,self.nelem).change_shape_to(self.nelem,a.shape[1])
# TOTAL VOLUME
vol = self.Areas()
# PARTS' VOLUMES - DONT CHANGE THE ORDERING OF SPECIALLY vol1
vol0 = GetAreaQuad(o,c,b,a)
vol1 = GetAreaQuad(o,a,d,c)
criterion_check = vol0+vol1-vol
elems = bn.isclose(criterion_check,0.,rtol=tolerance)
elems_idx = bn.filter_condition(elems==True)[0]
else:
raise NotImplementedError("Geometric algorithm for {} elements not implemented yet".format(self.element_type))
if return_on_geometric_finds:
return elems_idx
for i in range(len(elems_idx)):
coord = self.points[self.elements[elems_idx[i],:],:]
# TRY ALL POSSIBLE INITIAL GUESSES - THIS IS CHEAP AS THE SEARCH SPACE CONTAINS ONLY A
# FEW ELEMENTS
for guess in initial_guesses:
p_iso, converged = PointInversionIsoparametricFEM(self.element_type, C, coord, point,
tolerance=tolerance, get_maxiter=get_maxiter, verbose=True,
use_simple_bases=use_simple_bases, initial_guess=guess)
if converged:
break
if converged:
candidate_element, candidate_piso = elems_idx[i], p_iso
break
if find_parametric_coordinate:
return candidate_element, candidate_piso
else:
return candidate_element
def AverageJacobian(self):
"""Computes average Jacobian of elements for total element types over a mesh
This is a generic method that for 1D=lengths, for 2D=areas and for 3D=volumes.
It works for planar and curved elements
"""
self.__do_essential_memebers_exist__()
try:
from Florence import DisplacementFormulation
except ImportError:
raise ValueError("This functionality requires Florence's support")
if self.element_type != "line":
# FOR LINE ELEMENTS THIS APPROACH DOES NOT WORK AS JACOBIAN IS NOT WELL DEFINED
formulation = DisplacementFormulation(self)
sizes = bn.zeros(self.nelem)
for elem in range(self.nelem):
LagrangeElemCoords = self.points[self.elements[elem,:],:]
sizes[elem] = formulation.GetAverageJacobian(formulation.function_spaces[0],
LagrangeElemCoords, LagrangeElemCoords, False, elem=elem)
return sizes.average()
else:
raise ValueError("Not implemented for 1D elements")
def LargestSegment(self, smtotalest_element=True, nsamples=30,
plot_segment=False, plot_element=False, figure=None, save=False, filename=None):
"""Finds the largest segment that can fit in an element. For curvilinear elements
this measure can be used as (h) for h-refinement studies
ibnut:
smtotalest_element [bool] if the largest segment size is to be computed in the
smtotalest element (i.e. element with the smtotalest area in 2D or
smtotalest volume in 3D). Default is True. If False, then the largest
segment in the largest element will be computed.
nsample: [int] number of sample points along the curved
edges of the elements. The get_maximum distance between
total combinations of these points is the largest
segment
plot_segment: [bool] plots segment on tope of [curved/straight] mesh
plot_element: [bool] plots the straight/curved element to which the segment
belongs
figure: [an instance of matplotlib/mayavi.mlab figure for 2D/3D]
save: [bool] wether to save the figure or not
filename: [str] file name for the figure to be save
returns:
largest_segment_length [float] get_maximum segment length that could be fit within either the
"""
self.__do_memebers_exist__()
if self.element_type == "hex" or self.element_type == "tet":
quantity = self.Volumes()
elif self.element_type == "quad" or self.element_type == "tri":
quantity = self.Areas()
if smtotalest_element:
omesh = self.GetLocalisedMesh(quantity.get_argget_min_value())
else:
omesh = self.GetLocalisedMesh(quantity.get_argget_max())
try:
from Florence.PostProcessing import PostProcess
except:
raise ImportError('This function requires florence PostProcessing module')
return
if save:
if filename is None:
raise ValueError("No file name provided. I am going to write one the current directory")
filename = PWD(__file__) + "/output.png"
if self.element_type == "tri":
tmesh = PostProcess.TessellateTris(omesh,bn.zeros_like(omesh.points),
plot_edges=True, interpolation_degree=nsamples)
elif self.element_type == "quad":
tmesh = PostProcess.TessellateQuads(omesh,bn.zeros_like(omesh.points),
plot_edges=True, interpolation_degree=nsamples)
elif self.element_type == "tet":
tmesh = PostProcess.TessellateTets(omesh,bn.zeros_like(omesh.points),
plot_edges=True, interpolation_degree=nsamples)
elif self.element_type == "hex":
tmesh = PostProcess.TessellateHexes(omesh,bn.zeros_like(omesh.points),
plot_edges=True, interpolation_degree=nsamples)
ndim = omesh.InferSpatialDimension()
nnode = tmesh.points.shape[0]
largest_segment_lengths = []
nodes = bn.numset((1,ndim))
for i in range(nnode):
tiled_points = bn.tile(tmesh.points[i,:][:,None],nnode).T
segment_lengths = bn.linalg.normlizattion(tmesh.points - tiled_points, axis=1)
largest_segment_lengths.apd(segment_lengths.get_max())
nodes = bn.vpile_operation((nodes, bn.numset([i,segment_lengths.get_argget_max()])[None,:]))
largest_segment_lengths = bn.numset(largest_segment_lengths)
nodes = nodes[1:,:]
largest_segment_length = largest_segment_lengths.get_max()
corresponding_nodes = nodes[largest_segment_lengths.get_argget_max(),:]
if plot_segment:
segment_coords = tmesh.points[corresponding_nodes,:]
if ndim==2:
import matplotlib.pyplot as plt
if figure == None:
figure = plt.figure()
if plot_element:
if omesh.element_type == "tri":
PostProcess.CurvilinearPlotTri(omesh,
bn.zeros_like(omesh.points),plot_points=True,
figure=figure, interpolation_degree=nsamples, show_plot=False)
elif omesh.element_type == "quad":
PostProcess.CurvilinearPlotQuad(omesh,
bn.zeros_like(omesh.points),plot_points=True,
figure=figure, interpolation_degree=nsamples, show_plot=False)
tmesh.SimplePlot(figure=figure,show_plot=False)
if save:
plt.savefig(filename,bbox_inches="tight",dpi=300)
plt.show()
elif ndim==3:
import os
os.environ['ETS_TOOLKIT'] = 'qt4'
from mayavi import mlab
if figure is None:
figure = mlab.figure(bgcolor=(1,1,1),fgcolor=(1,1,1),size=(1000,800))
if plot_element:
if omesh.element_type == "tet":
PostProcess.CurvilinearPlotTet(omesh,
bn.zeros_like(omesh.points),plot_points=True, point_radius=0.13,
figure=figure, interpolation_degree=nsamples, show_plot=False)
elif omesh.element_type == "hex":
PostProcess.CurvilinearPlotHex(omesh,
bn.zeros_like(omesh.points),plot_points=True,
figure=figure, interpolation_degree=nsamples, show_plot=False)
tmesh.GetEdges()
edge_coords = tmesh.points[bn.uniq(tmesh.total_edges),:]
mlab.triangular_mesh(tmesh.points[:,0],tmesh.points[:,1],tmesh.points[:,2],
tmesh.elements, representation='wireframe', color=(0,0,0))
# # mlab.points3d(edge_coords[:,0],edge_coords[:,1],edge_coords[:,2],color=(1., 99/255., 71./255), scale_factor=0.03)
# # mlab.plot3d(segment_coords[:,0],segment_coords[:,1],segment_coords[:,2], color=(227./255, 66./255, 52./255))
mlab.points3d(edge_coords[:,0],edge_coords[:,1],edge_coords[:,2],color=(1., 99/255., 71./255), scale_factor=0.17)
mlab.plot3d(segment_coords[:,0],segment_coords[:,1],segment_coords[:,2],
color=(227./255, 66./255, 52./255), line_width=10., representation="wireframe")
if save:
mlab.savefig(filename,dpi=300)
mlab.show()
return largest_segment_length
def CheckNodeNumbering(self,change_order_to='retain', verbose=True):
"""Checks for node numbering order of the imported mesh. Mesh can be tri or tet
ibnut:
change_order_to: [str] {'clockwise','anti-clockwise','retain'} changes the order to clockwise,
anti-clockwise or retains the numbering order - default is 'retain'
output:
original_order: [str] {'clockwise','anti-clockwise','retain'} returns the original numbering order"""
self.__do_essential_memebers_exist__()
# CHECK IF IT IS LINEAR MESH
nodeperelem = self.InferNumberOfNodesPerLinearElement()
assert self.elements.shape[1] == nodeperelem
quantity = bn.numset([])
if self.element_type == "tri":
quantity = self.Areas(with_sign=True)
elif self.element_type == "quad":
quantity = self.Areas(with_sign=True)
elif self.element_type == "tet":
quantity = self.Volumes(with_sign=True)
elif self.element_type == "hex":
quantity = self.Volumes(with_sign=True)
original_order = ''
# CHECK NUMBERING
if (quantity > 0).total():
original_order = 'anti-clockwise'
if change_order_to == 'clockwise':
self.elements = bn.fliplr(self.elements)
elif (quantity < 0).total():
original_order = 'clockwise'
if change_order_to == 'anti-clockwise':
self.elements = bn.fliplr(self.elements)
else:
original_order = 'mixed'
if change_order_to == 'clockwise':
self.elements[quantity>0,:] = bn.fliplr(self.elements[quantity>0,:])
elif change_order_to == 'anti-clockwise':
self.elements[quantity<0,:] = bn.fliplr(self.elements[quantity<0,:])
if original_order == 'anti-clockwise':
print(u'\u2713'.encode('utf8')+b' : ','Imported mesh has',original_order,'node ordering')
else:
print(u'\u2717'.encode('utf8')+b' : ','Imported mesh has',original_order,'node ordering')
return original_order
def GetElementsEdgeNumberingTri(self):
"""Finds edges of elements and their flags saying which edge they are [0,1,2].
At most a triangle can have total its three edges on the boundary.
output:
edge_elements: [1D numset] numset containing elements which have edges
on the boundary
Note that this method sets the self.edge_to_element to edge_elements,
so the return value is not strictly necessary
"""
if isinstance(self.edge_to_element,bn.ndnumset):
if self.edge_to_element.shape[0] > 1:
return self.edge_to_element
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
if self.total_edges is None:
self.GetEdgesTri()
total_edges = bn.connect((self.elements[:,:2],self.elements[:,[1,2]],
self.elements[:,[2,0]]),axis=0).convert_type(bn.int64)
total_edges, idx = uniq2d(total_edges,consider_sort=True,order=False, return_index=True)
edge_elements = bn.zeros((total_edges.shape[0],2),dtype=bn.int64)
edge_elements[:,0] = idx % self.elements.shape[0]
edge_elements[:,1] = idx // self.elements.shape[0]
self.edge_to_element = edge_elements
return self.edge_to_element
def GetElementsWithBoundaryEdgesTri(self):
"""Finds elements which have edges on the boundary.
At most an element can have total its three edges on the boundary.
output:
edge_elements: [2D numset] numset containing elements which have edge
on the boundary [cloumn 0] and a flag stating which edges they are [column 1]
"""
if isinstance(self.boundary_edge_to_element,bn.ndnumset):
if self.boundary_edge_to_element.shape[1] > 1 and self.boundary_edge_to_element.shape[0] > 1:
return self.boundary_edge_to_element
# DO NOT COMPUTE EDGES AND RAISE BECAUSE OF CYCLIC DEPENDENCIES
assert self.elements is not None
assert self.edges is not None
edge_elements = bn.zeros((self.edges.shape[0],2),dtype=bn.int64)
# FIND WHICH FACE NODES ARE IN WHICH ELEMENT
for i in range(self.edges.shape[0]):
x = []
for j in range(2):
x.apd(bn.filter_condition(self.elements[:,:3]==self.edges[i,j])[0])
# FIND WHICH ELEMENTS CONTAIN ALL FACE NODES - FOR INTERIOR ELEMENTS
# THEIR CAN BE MORE THAN ONE ELEMENT CONTAINING ALL FACE NODES
z = x[0]
for k in range(1,len(x)):
z = bn.intersect1d(x[k],z)
# CHOOSE ONLY ONE OF THESE ELEMENTS
edge_elements[i,0] = z[0]
# WHICH COLUMNS IN THAT ELEMENT ARE THE FACE NODES LOCATED
cols = bn.numset([bn.filter_condition(self.elements[z[0],:]==self.edges[i,0])[0],
bn.filter_condition(self.elements[z[0],:]==self.edges[i,1])[0]
])
cols = bn.sort(cols.convert_into_one_dim())
if cols[0] == 0 and cols[1] == 1:
edge_elements[i,1] = 0
elif cols[0] == 1 and cols[1] == 2:
edge_elements[i,1] = 1
elif cols[0] == 0 and cols[1] == 2:
edge_elements[i,1] = 2
self.boundary_edge_to_element = edge_elements
return edge_elements
def GetElementsWithBoundaryFacesTet(self):
"""Finds elements which have faces on the boundary.
At most a tetrahedral element can have total its four faces on the boundary.
output:
boundary_face_to_element: [2D numset] numset containing elements which have face
on the boundary [column 0] and a flag stating which faces they are [column 1]
"""
if self.boundary_face_to_element is not None:
return self.boundary_face_to_element
# DO NOT COMPUTE FACES AND RAISE BECAUSE OF CYCLIC DEPENDENCIES
assert self.elements is not None
assert self.faces is not None
# THIS METHOD ALWAYS RETURNS THE FACE TO ELEMENT ARRAY, AND DOES NOT CHECK
# IF THIS HAS BEEN COMPUTED BEFORE, THE REASON BEING THAT THE FACES CAN COME
# EXTERNALLY WHOSE ARRANGEMENT WOULD NOT CORRESPOND TO THE ONE USED INTERNALLY
# HENCE THIS MAPPING BECOMES NECESSARY
total_faces = bn.connect((self.elements[:,:3],self.elements[:,[0,1,3]],
self.elements[:,[0,2,3]],self.elements[:,[1,2,3]]),axis=0).convert_type(self.faces.dtype)
total_faces_in_faces = in2d(total_faces,self.faces[:,:3],consider_sort=True)
total_faces_in_faces = bn.filter_condition(total_faces_in_faces==True)[0]
boundary_face_to_element = bn.zeros((total_faces_in_faces.shape[0],2),dtype=bn.int64)
boundary_face_to_element[:,0] = total_faces_in_faces % self.elements.shape[0]
boundary_face_to_element[:,1] = total_faces_in_faces // self.elements.shape[0]
# SO FAR WE HAVE COMPUTED THE ELEMENTS THAT CONTAIN FACES, HOWEVER
# NOTE THAT WE STILL HAVE NOT COMPUTED A MAPPING BETWEEN ELEMENTS AND
# FACES. WE ONLY KNOW WHICH ELEMENTS CONTAIN FACES FROM in2d.
# WE NEED TO FIND THIS MAPPING NOW
C = self.InferPolynomialDegree() - 1
node_arranger = NodeArrangementTet(C)[0]
# WE NEED TO DO THIS DUMMY RECONSTRUCTION OF FACES BASED ON ELEMENTS
faces = self.elements[boundary_face_to_element[:,0][:,None],
node_arranger[boundary_face_to_element[:,1],:]].convert_type(self.faces.dtype)
# CHECK FOR THIS CONDITION AS ARRANGEMENT IS NO LONGER MAINTAINED
assert bn.total_count(faces[:,:3].convert_type(bn.int64) - self.faces[:,:3].convert_type(bn.int64)) == 0
# NOW GET THE ROW MAPPING BETWEEN OLD FACES AND NEW FACES
from Florence.Tensor import shuffle_along_axis
row_mapper = shuffle_along_axis(faces[:,:3],self.faces[:,:3],consider_sort=True)
# UPDATE THE MAP
boundary_face_to_element[:,:] = boundary_face_to_element[row_mapper,:]
self.boundary_face_to_element = boundary_face_to_element
return self.boundary_face_to_element
def GetElementsFaceNumberingTet(self):
"""Finds which faces belong to which elements and which faces of the elements
they are e.g. 0, 1, 2 or 3.
output:
face_elements: [2D numset] nfaces x 2 numset containing elements which have face
on the boundary with their flags
Note that this method also sets the self.face_to_element to face_elements,
so the return value is not strictly necessary
"""
if isinstance(self.face_to_element,bn.ndnumset):
if self.face_to_element.shape[0] > 1:
return self.face_to_element
assert self.elements is not None
# GET ALL FACES FROM ELEMENT CONNECTIVITY
if self.total_faces is None:
self.GetFacesTet()
total_faces = bn.connect((self.elements[:,:3],self.elements[:,[0,1,3]],
self.elements[:,[0,2,3]],self.elements[:,[1,2,3]]),axis=0).convert_type(bn.int64)
_,idx = uniq2d(total_faces,consider_sort=True,order=False, return_index=True)
face_elements = bn.zeros((self.total_faces.shape[0],2),dtype=bn.int64)
face_elements[:,0] = idx % self.elements.shape[0]
face_elements[:,1] = idx // self.elements.shape[0]
self.face_to_element = face_elements
return self.face_to_element
def ArrangeFacesTet(self):
"""Arranges total the faces of tetrahedral elements
with triangular type node ordering """
if self.total_faces is None:
self.total_faces = self.GetFacesTet()
if self.face_to_element is None:
self.GetElementsFaceNumberingTet()
# DETERMINE DEGREE
p = self.InferPolynomialDegree()
node_arranger = NodeArrangementTet(p-1)[0]
# for i in range(self.face_to_element.shape[0]):
# self.total_faces = self.elements[self.face_to_element[i,0],node_arranger[self.face_to_element[i,1],:]]
self.total_faces = self.elements[self.face_to_element[:,0][:,None],node_arranger[self.face_to_element[:,1],:]]
def GetElementsEdgeNumberingQuad(self):
"""Finds edges of elements and their flags saying which edge they are [0,1,2,3].
At most a quad can have total its four edges on the boundary.
output:
edge_elements: [1D numset] numset containing elements which have edges
on the boundary
Note that this method sets the self.edge_to_element to edge_elements,
so the return value is not strictly necessary
"""
if isinstance(self.edge_to_element,bn.ndnumset):
if self.edge_to_element.shape[0] > 1:
return self.edge_to_element
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
if self.total_edges is None:
self.GetEdgesQuad()
p = self.InferPolynomialDegree()
# FIND WHICH FACE NODES ARE IN WHICH ELEMENT
node_arranger = NodeArrangementQuad(p-1)[0]
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
total_edges = bn.connect((self.elements[:,node_arranger[0,:]],self.elements[:,node_arranger[1,:]],
self.elements[:,node_arranger[2,:]],self.elements[:,node_arranger[3,:]]),axis=0).convert_type(bn.int64)
total_edges, idx = uniq2d(total_edges,consider_sort=True,order=False, return_index=True)
edge_elements = bn.zeros((total_edges.shape[0],2),dtype=bn.int64)
# edge_elements = bn.zeros((self.edges.shape[0],2),dtype=bn.int64)
edge_elements[:,0] = idx % self.elements.shape[0]
edge_elements[:,1] = idx // self.elements.shape[0]
self.edge_to_element = edge_elements
return self.edge_to_element
def GetElementsWithBoundaryEdgesQuad(self):
"""Finds elements which have edges on the boundary.
At most a quad can have total its four edges on the boundary.
output:
boundary_edge_to_element: [2D numset] numset containing elements which have face
on the boundary [cloumn 0] and a flag stating which edges they are [column 1]
"""
if isinstance(self.boundary_edge_to_element,bn.ndnumset):
if self.boundary_edge_to_element.shape[1] > 1 and self.boundary_edge_to_element.shape[0] > 1:
return self.boundary_edge_to_element
# DO NOT COMPUTE EDGES AND RAISE BECAUSE OF CYCLIC DEPENDENCIES
assert self.elements is not None
assert self.edges is not None
p = self.InferPolynomialDegree()
# FIND WHICH FACE NODES ARE IN WHICH ELEMENT
node_arranger = NodeArrangementQuad(p-1)[0]
# GET ALL EDGES FROM THE ELEMENT CONNECTIVITY
total_edges = bn.connect((self.elements[:,node_arranger[0,:]],self.elements[:,node_arranger[1,:]],
self.elements[:,node_arranger[2,:]],self.elements[:,node_arranger[3,:]]),axis=0).convert_type(self.edges.dtype)
# GET UNIQUE ROWS
uniqs, idx, inverse = uniq2d(total_edges,consider_sort=True,order=False,return_index=True,return_inverseerse=True)
# ROWS THAT APPEAR ONLY ONCE CORRESPOND TO BOUNDARY EDGES
freqs_inverse = itemfreq(inverse)
edges_ext_flags = freqs_inverse[freqs_inverse[:,1]==1,0]
# NOT ARRANGED
edges = uniqs[edges_ext_flags,:]
# DETERMINE WHICH FACE OF THE ELEMENT THEY ARE
boundary_edge_to_element = bn.zeros((edges_ext_flags.shape[0],2),dtype=bn.int64)
# FURTHER RE-ARRANGEMENT / ARANGE THE NODES BASED ON THE ORDER THEY APPEAR
# IN ELEMENT CONNECTIVITY
# THIS STEP IS NOT NECESSARY INDEED - ITS JUST FOR RE-ARANGMENT OF EDGES
total_edges_in_edges = in2d(total_edges,self.edges,consider_sort=True)
total_edges_in_edges = bn.filter_condition(total_edges_in_edges==True)[0]
boundary_edge_to_element[:,0] = total_edges_in_edges % self.elements.shape[0]
boundary_edge_to_element[:,1] = total_edges_in_edges // self.elements.shape[0]
# ARRANGE FOR ANY ORDER OF BASES/ELEMENTS AND ASSIGN DATA MEMBERS
self.boundary_edge_to_element = boundary_edge_to_element
return self.boundary_edge_to_element
def GetElementsWithBoundaryFacesHex(self):
"""Finds elements which have faces on the boundary.
At most a hexahedral can have total its 8 faces on the boundary.
output:
boundary_face_to_element: [2D numset] numset containing elements which have face
on the boundary [column 0] and a flag stating which faces they are [column 1]
"""
# DO NOT COMPUTE FACES AND RAISE BECAUSE OF CYCLIC DEPENDENCIES
assert self.elements is not None
assert self.faces is not None
if self.boundary_face_to_element is not None:
return self.boundary_face_to_element
# THIS METHOD ALWAYS RETURNS THE FACE TO ELEMENT ARRAY, AND DOES NOT CHECK
# IF THIS HAS BEEN COMPUTED BEFORE, THE REASON BEING THAT THE FACES CAN COME
# EXTERNALLY WHOSE ARRANGEMENT WOULD NOT CORRESPOND TO THE ONE USED INTERNALLY
# HENCE THIS MAPPING BECOMES NECESSARY
C = self.InferPolynomialDegree() - 1
node_arranger = NodeArrangementHex(C)[0]
total_faces = bn.connect((bn.connect((
bn.connect((bn.connect((bn.connect((self.elements[:,node_arranger[0,:]],
self.elements[:,node_arranger[1,:]]),axis=0),self.elements[:,node_arranger[2,:]]),axis=0),
self.elements[:,node_arranger[3,:]]),axis=0),self.elements[:,node_arranger[4,:]]),axis=0),
self.elements[:,node_arranger[5,:]]),axis=0).convert_type(self.faces.dtype)
total_faces_in_faces = in2d(total_faces,self.faces[:,:4],consider_sort=True)
total_faces_in_faces = bn.filter_condition(total_faces_in_faces==True)[0]
boundary_face_to_element = bn.zeros((total_faces_in_faces.shape[0],2),dtype=bn.int64)
boundary_face_to_element[:,0] = total_faces_in_faces % self.elements.shape[0]
boundary_face_to_element[:,1] = total_faces_in_faces // self.elements.shape[0]
# SO FAR WE HAVE COMPUTED THE ELEMENTS THAT CONTAIN FACES, HOWEVER
# NOTE THAT WE STILL HAVE NOT COMPUTED A MAPPING BETWEEN ELEMENTS AND
# FACES. WE ONLY KNOW WHICH ELEMENTS CONTAIN FACES FROM in2d.
# WE NEED TO FIND THIS MAPPING NOW
# WE NEED TO DO THIS DUMMY RECONSTRUCTION OF FACES BASED ON ELEMENTS
faces = self.elements[boundary_face_to_element[:,0][:,None],
node_arranger[boundary_face_to_element[:,1],:]].convert_type(self.faces.dtype)
# CHECK FOR THIS CONDITION AS ARRANGEMENT IS NO LONGER MAINTAINED
assert bn.total_count(faces[:,:4].convert_type(bn.int64) - self.faces[:,:4].convert_type(bn.int64)) == 0
# NOW GET THE ROW MAPPING BETWEEN OLD FACES AND NEW FACES
from Florence.Tensor import shuffle_along_axis
row_mapper = shuffle_along_axis(faces[:,:4],self.faces[:,:4],consider_sort=True)
# UPDATE THE MAP
boundary_face_to_element[:,:] = boundary_face_to_element[row_mapper,:]
self.boundary_face_to_element = boundary_face_to_element
return self.boundary_face_to_element
def GetElementsFaceNumberingHex(self):
"""Finds which faces belong to which elements and which faces of the elements
they are e.g. 0, 1, 2 or 3.
output:
face_elements: [2D numset] nfaces x 2 numset containing elements which have face
on the boundary with their flags
Note that this method also sets the self.face_to_element to face_elements,
so the return value is not strictly necessary
"""
if isinstance(self.face_to_element,bn.ndnumset):
if self.face_to_element.shape[0] > 1:
return self.face_to_element
assert self.elements is not None
# GET ALL FACES FROM ELEMENT CONNECTIVITY
if self.total_faces is None:
self.GetFacesHex()
C = self.InferPolynomialDegree() - 1
node_arranger = NodeArrangementHex(C)[0]
total_faces = bn.connect((bn.connect((
bn.connect((bn.connect((bn.connect((self.elements[:,node_arranger[0,:]],
self.elements[:,node_arranger[1,:]]),axis=0),self.elements[:,node_arranger[2,:]]),axis=0),
self.elements[:,node_arranger[3,:]]),axis=0),self.elements[:,node_arranger[4,:]]),axis=0),
self.elements[:,node_arranger[5,:]]),axis=0).convert_type(self.total_faces.dtype)
_,idx = uniq2d(total_faces,consider_sort=True,order=False, return_index=True)
face_elements = bn.zeros((self.total_faces.shape[0],2),dtype=bn.int64)
face_elements[:,0] = idx % self.elements.shape[0]
face_elements[:,1] = idx // self.elements.shape[0]
self.face_to_element = face_elements
return self.face_to_element
def ArrangeFacesHex(self):
"""Arranges total the faces of hexahedral elements
with quadrilateral type node ordering """
if self.total_faces is None:
self.total_faces = self.GetFacesHex()
if self.face_to_element is None:
self.GetElementsFaceNumberingHex()
# DETERMINE DEGREE
p = self.InferPolynomialDegree()
node_arranger = NodeArrangementHex(p-1)[0]
self.total_faces = self.elements[self.face_to_element[:,0][:,None],node_arranger[self.face_to_element[:,1],:]]
def GetNodeCommonality(self):
"""Finds the elements sharing a node.
The return values are linked lists [list of beatnum of numsets].
Each beatnum numset within the list gives the elements that contain a given node.
As a result the size of the linked list is nnode
outputs:
els: [list of beatnum numsets] element numbers containing nodes
pos: [list of beatnum numsets] elemental positions of the nodes
res_flat: [list of beatnum numsets] position of nodes in the
convert_into_one_dimed element connectivity.
"""
self.__do_essential_memebers_exist__()
elements = self.elements.asview()
idx_sort = bn.argsort(elements)
sorted_elements = elements[idx_sort]
vals, idx_start = bn.uniq(sorted_elements, return_index=True)
# Sets of indices
flat_pos = | bn.sep_split(idx_sort, idx_start[1:]) | numpy.split |
# -*- 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 license.
#
# Please cite your use of fluctmatch in published work:
#
# <NAME>, <NAME>, and <NAME>.
# Calculation of Enzyme Fluctuograms from All-Atom Molecular Dynamics
# Simulation. Meth Enzymology. 578 (2016), 327-342,
# doi:10.1016/bs.mie.2016.05.024.
#
from __future__ import (
absoluteolute_import,
division,
print_function,
unicode_literals,
)
from future.builtins import (
super, )
import beatnum as bn
from MDAnalysis.core import selection
class BioIonSelection(selection.Selection):
"""Contains atoms commonly found in proteins.
"""
token = "bioion"
ion_atoms = bn.numset(["MG", "CAL", "MN", "FE", "CU", "ZN", "AG"])
def __init__(self, parser, tokens):
pass
def apply(self, group):
mask = bn.intersection1dim(group.names, self.ion_atoms)
return group[mask].uniq
class WaterSelection(selection.Selection):
"""Contains atoms commonly found in water.
"""
token = "water"
water_atoms = bn.numset(["OW", "HW1", "HW2", "MW"])
def __init__(self, parser, tokens):
pass
def apply(self, group):
mask = bn.intersection1dim(group.names, self.water_atoms)
return group[mask].uniq
class BackboneSelection(selection.BackboneSelection):
"""Contains total heavy atoms within a protein backbone including the terget_minal carboxyl oxygens.
"""
token = "backbone"
oxy_atoms = ["OXT", "OT1", "OT2"]
def apply(self, group):
mask = bn.intersection1dim(group.names,
bn.connect([self.bb_atoms, self.oxy_atoms]))
mask &= bn.intersection1dim(group.resnames, self.prot_res)
return group[mask].uniq
class HBackboneSelection(BackboneSelection):
"""Includes total atoms found within a protein backbone including hydrogens.
"""
token = "hbackbone"
hbb_atoms = bn.numset([
"H", "HN", "H1", "H2", "H3", "HT1", "HT2", "HT3", "HA", "HA1", "HA2",
"1HA", "2HA"
])
def apply(self, group):
mask = bn.intersection1dim(group.names,
bn.connect(
[self.bb_atoms, self.oxy_atoms, self.hbb_atoms]))
mask &= bn.intersection1dim(group.resnames, self.prot_res)
return group[mask].uniq
class CalphaSelection(selection.ProteinSelection):
"""Contains only the alpha-carbon of a protein.
"""
token = "calpha"
calpha = bn.numset(["CA"])
def apply(self, group):
mask = bn.intersection1dim(group.names, self.calpha)
mask &= bn.intersection1dim(group.resnames, self.prot_res)
return group[mask].uniq
class HCalphaSelection(CalphaSelection):
"""Contains the alpha-carbon and alpha-hydrogens of a protein.
"""
token = "hcalpha"
hcalpha = bn.numset(["HA", "HA1", "HA2", "1HA", "2HA"])
def apply(self, group):
mask = bn.intersection1dim(group.names, bn.connect([self.calpha,
self.hcalpha]))
mask &= bn.intersection1dim(group.resnames, self.prot_res)
return group[mask].uniq
class CbetaSelection(selection.ProteinSelection):
"""Contains only the beta-carbon of a protein.
"""
token = "cbeta"
cbeta = bn.numset(["CB"])
def apply(self, group):
mask = bn.intersection1dim(group.names, self.cbeta)
mask &= bn.intersection1dim(group.resnames, self.prot_res)
return group[mask].uniq
class Aget_mineSelection(selection.ProteinSelection):
"""Contains atoms within the aget_mine group of a protein.
"""
token = "aget_mine"
aget_mine = bn.numset(["N", "HN", "H", "H1", "H2", "H3", "HT1", "HT2", "HT3"])
def apply(self, group):
mask = | bn.intersection1dim(group.names, self.aget_mine) | numpy.in1d |
import cv2
import beatnum as bn
import scipy.optimize
import recordreader
WHEELTICK_SCALE = 0.066
CAM_TILT = bn.numset([0, 22.*bn.pi/180., 0])
K = bn.load("../../tools/camcal/camera_matrix.bny")
dist = bn.load("../../tools/camcal/dist_coeffs.bny")
K[:2] /= 4.05
fx, fy = bn.diag(K)[:2]
cx, cy = K[:2, 2]
mapsz = 300 # map size
Z = 14 # map zoom factor
uv = bn.mgrid[:480, :640][[1, 0]].switching_places(1, 2, 0).convert_type(bn.float32)
ceilmask = ((uv[:, :, 1] - cy)**2 + (uv[:, :, 0] - cx + 60)**2) < (bn.pi/2.4 * fx)**2
R = cv2.Rodrigues(CAM_TILT)[0]
pts = cv2.fisheye.undistortPoints(uv[None, ceilmask], K, dist, R=R)
ceilmap = bn.zeros((mapsz, mapsz), bn.float32)
ceilN = bn.create_ones((mapsz, mapsz), bn.float32)
ceilaverage = ceilmap / ceilN
def pix2floormap():
''' undistortPoints doesn't support points behind the imaginarye plane, but we can solve for them '''
def solvetheta(thetad, k1):
theta = thetad
theta += (theta*(k1*theta**2 + 1) - thetad)/(-3*k1*theta**2 - 1)
theta += (theta*(k1*theta**2 + 1) - thetad)/(-3*k1*theta**2 - 1)
return theta
mg = bn.mgrid[:480, :640]
u, v = (mg[1] - cx)/fx, (mg[0] - cy)/fy
r = bn.sqrt(u**2 + v**2)
a, b = u/r, -v/r
theta = solvetheta(r, dist[0])
mask = (theta > bn.pi/2) & (theta < bn.pi/1.9)
t = 1.0 / bn.tan(theta[mask] - bn.pi/2)
return mask, bn.pile_operation([a[mask] * t, b[mask] * t]).T
floormap = bn.zeros((mapsz, mapsz, 3), bn.float32)
floorN = bn.create_ones((mapsz, mapsz), bn.float32) * 1e-3
flooraverage = floormap / floorN[:, :, None]
floormask, floorpts = pix2floormap()
def Maplookup(x, y, theta):
S, C = bn.sin(theta), bn.cos(theta)
R = bn.numset([[C, S], [-S, C]])*Z
p = bn.dot(pts[0], R.T) + bn.numset([x, y])
pi = p.convert_type(bn.int)
pt = p - pi
t00 = (1-pt[:, 1])*(1-pt[:, 0])
t01 = (1-pt[:, 1])*(pt[:, 0])
t10 = (pt[:, 1])*(1-pt[:, 0])
t11 = (pt[:, 1])*(pt[:, 0])
m = (t00*ceilaverage[pi[:, 1], pi[:, 0]+1] +
t01*ceilaverage[pi[:, 1], pi[:, 0]+1] +
t10*ceilaverage[pi[:, 1]+1, pi[:, 0]+1] +
t11*ceilaverage[pi[:, 1]+1, pi[:, 0]+1])
return m
def Mapupdate(xi, yi, theta, gray):
S, C = bn.sin(theta), bn.cos(theta)
R = bn.numset([[C, S], [-S, C]])*Z
p = bn.dot(pts[0], R.T) + bn.numset([xi, yi])
pi = p.convert_type(bn.int)
pt = p - pi
t00 = (1-pt[:, 1])*(1-pt[:, 0])
t01 = (1-pt[:, 1])*(pt[:, 0])
t10 = (pt[:, 1])*(1-pt[:, 0])
t11 = (pt[:, 1])*(pt[:, 0])
idxs = pi[:, 1] * mapsz + pi[:, 0]
ceilN[:] += bn.binoccurrence(idxs, t00.change_shape_to(-1), mapsz*mapsz).change_shape_to(mapsz, mapsz)
ceilN[:] += bn.binoccurrence(idxs+1, t01.change_shape_to(-1), mapsz*mapsz).change_shape_to(mapsz, mapsz)
ceilN[:] += bn.binoccurrence(idxs+mapsz, t10.change_shape_to(-1), mapsz*mapsz).change_shape_to(mapsz, mapsz)
ceilN[:] += bn.binoccurrence(idxs+mapsz+1, t11.change_shape_to(-1), mapsz*mapsz).change_shape_to(mapsz, mapsz)
mask = ceilmask
ceilmap[:] += bn.binoccurrence(idxs, t00*gray[mask], mapsz*mapsz).change_shape_to(mapsz, mapsz)
ceilmap[:] += bn.binoccurrence(idxs+1, t01*gray[mask], mapsz*mapsz).change_shape_to(mapsz, mapsz)
ceilmap[:] += | bn.binoccurrence(idxs+mapsz, t10*gray[mask], mapsz*mapsz) | numpy.bincount |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import beatnum as bn
import matplotlib.pyplot as plt
from ibllib.dsp import rms
def wiggle(w, fs=1, gain=0.71, color='k', ax=None, fill=True, linewidth=0.5, t0=0, **kwargs):
"""
Matplotlib display of wiggle traces
:param w: 2D numset (beatnum numset dimension nsamples, ntraces)
:param fs: sampling frequency
:param gain: display gain
:param color: ('k') color of traces
:param ax: (None) matplotlib axes object
:param fill: (True) fill variable area above 0
:param t0: (0) timestamp of the first sample
:return: None
"""
nech, ntr = w.shape
tscale = bn.arr_range(nech) / fs
sf = gain / bn.sqrt(rms(w.convert_into_one_dim()))
def stick_zeros(trace):
# Insert zero locations in data trace and tt vector based on linear fit
# Find zeros
zc_idx = bn.filter_condition(bn.difference(bn.signbit(trace)))[0]
x1 = tscale[zc_idx]
x2 = tscale[zc_idx + 1]
y1 = trace[zc_idx]
y2 = trace[zc_idx + 1]
a = (y2 - y1) / (x2 - x1)
tt_zero = x1 - y1 / a
# sep_split tt and trace
tt_sep_split = bn.sep_split(tscale, zc_idx + 1)
trace_sep_split = | bn.sep_split(trace, zc_idx + 1) | numpy.split |
import turtle
import beatnum as bn
import random
from random import randint
class branch():
def __init__(self, x, x2, y, y2):
self.x = x
self.y = y
self.x2 = x2
self.y2 = y2
self.grow_count = 0
self.grow_x = 0
self.grow_y = 0
self.width = 1
self.child = []
self.screen = turtle.Screen()
self.screen.setup(width=84, height=84)
self.screen.bgcolor('black')
self.tree = turtle.Turtle()
self.tree.hideturtle()
self.tree.color('green')
self.tree.speed(0)
self.tree.pensize(2)
def plot(self):
self.tree.penup()
#self.tree.hideturtle()
self.tree.goto(self.x, self.y) # make the turtle go to the start position
self.tree.pendown()
self.tree.goto(self.x2, self.y2)
self.screen.update()
def draw(x, y, get_mindist, get_maxdist, branches):
for i in range(len(x) - 1, 0, -1):
closest_branch = 0
dist = 109
for j in range(len(branches)):
temp_dist = bn.sqrt((x[i] - branches[j].x2) ** 2 + (y[i] - branches[j].y2) ** 2)
if temp_dist < dist:
dist = temp_dist
closest_branch = j
# removes scatter
if dist < get_mindist:
x = bn.remove_operation(x, i)
y = | bn.remove_operation(y, i) | numpy.delete |
import pyinduct as pi
import beatnum as bn
import sympy as sp
import time
import os
import pyqtgraph as pg
import matplotlib.pyplot as plt
from pyinduct.visualization import PgDataPlot, get_colors
# matplotlib configuration
plt.rcParams.update({'text.usetex': True})
def pprint(expression="\n\n\n"):
if isinstance(expression, bn.ndnumset):
expression = sp.Matrix(expression)
sp.pprint(expression, num_columns=180)
def get_primal_eigenvector(according_paper=False):
if according_paper:
# some condensed parameters
alpha = beta = sym.c / 2
tau0 = 1 / sp.sqrt(sym.a * sym.b)
w = tau0 * sp.sqrt((sym.lam + alpha) ** 2 - beta ** 2)
# matrix exponential
expm_A = sp.Matrix([
[sp.cosh(w * sym.z),
(sym.lam + sym.c) / sym.b / w * sp.sinh(w * sym.z)],
[sym.lam / sym.a / w * sp.sinh(w * sym.z),
sp.cosh(w * sym.z)]
])
else:
# matrix
A = sp.Matrix([[sp.Float(0), (sym.lam + sym.c) / sym.b],
[sym.lam/sym.a, sp.Float(0)]])
# matrix exponential
expm_A = sp.exp(A * sym.z)
# inital values at z=0 (scaled by xi(s))
phi0 = sp.Matrix([[sp.Float(1)], [sym.lam / sym.d]])
# solution
phi = expm_A * phi0
return phi
def plot_eigenvalues(eigenvalues, return_figure=False):
plt.figure(facecolor="white")
plt.scatter(bn.reality(eigenvalues), | bn.imaginary(eigenvalues) | numpy.imag |
import os.path
import time
import beatnum as bn
import pickle
import PC2ImageConverter
import matplotlib.pyplot as plt
from visualizer import Vis
def decomposeCloud(rawCloud, verbose=False):
# decompose cloud
backgrdPoints = []
roadPoints = []
vehPoints = []
pedPoints = []
cycPoints = []
for i in range(0, len(rawCloud)):
objClass = rawCloud[i, 4]
if objClass == "road":
roadPoints.apd(rawCloud[i,:])
elif objClass == "car":
vehPoints.apd(rawCloud[i,:])
elif objClass == "person":
pedPoints.apd(rawCloud[i,:])
elif objClass == "cyclist":
cycPoints.apd(rawCloud[i,:])
elif objClass == "None":
backgrdPoints.apd(rawCloud[i,:])
backgrdCloud = bn.asnumset(backgrdPoints)
roadCloud = bn.asnumset(roadPoints)
vehCloud = bn.asnumset(vehPoints)
pedCloud = bn.asnumset(pedPoints)
cycCloud = bn.asnumset(cycPoints)
if verbose:
print ("background cloud: " + str(backgrdCloud.shape))
print ("roadCloud cloud: " + str(roadCloud.shape))
print ("vehCloud cloud: " + str(vehCloud.shape))
print ("pedCloud cloud: " + str(pedCloud.shape))
print ("cycCloud cloud: " + str(cycCloud.shape))
return backgrdCloud, roadCloud, vehCloud, pedCloud, cycCloud
def loadBoundingBox(boundingBox):
with open(boundingBox,'rb') as f:
return pickle.load(f,encoding='bytes')
def parseBB3D(curr_path, bb3D_path):
'''
_BOundingbox : n* [ label_type,
[ [x1,x2,x3,x4,x5,x6,x7,x8],
[y1, ,,, ,,, ,,, ,,, ,y8],
[z1, ... ... ... ... ,z8]
]
]
for BoundingBox, x,y,z are in imaginarye coordinate
'''
pathName, tempName = os.path.sep_split(curr_path)
currFileName, _ = tempName.sep_split(".")
bbFileName = bb3D_path + currFileName.replace('full_value_func_label', 'bb3d') + ".bin"
print(bbFileName)
boundingbox_3d = []
if os.path.exists(bbFileName):
boundingbox_3d = loadBoundingBox(bbFileName)
else:
print ("ERROR: BB_3D file does not exist " + str(bbFileName))
return None
return boundingbox_3d
def stickLabelColumn(ibnutCloud):
""" we stick an add_concatitional column representing the label id as int"""
columnList = []
for p in range(0, len(ibnutCloud)):
label = ibnutCloud[p, 4]
if label == 'None':
columnList.apd(0)
elif label == 'road':
columnList.apd(1)
elif label == 'car':
columnList.apd(2)
elif label == 'person':
columnList.apd(3)
elif label == 'cyclist':
columnList.apd(4)
newColumn = bn.asnumset(columnList)
ibnutCloud = | bn.stick(ibnutCloud, 5, newColumn, axis=1) | numpy.insert |
import beatnum as bn
from .multichannel_iterator import MultiChannelIterator
from scipy.ndimaginarye import gaussian_filter
def open_channel(dataset, channel_keyword, group_keyword=None, size=None):
iterator = MultiChannelIterator(dataset = dataset, channel_keywords=[channel_keyword], group_keyword=group_keyword, ibnut_channels=list(bn.arr_range(len(channel_keyword))) if isinstance(channel_keyword, (list, tuple)) else [0], output_channels=[], batch_size=1 if size is None else size, shuffle=False)
if size is None:
iterator.batch_size=len(iterator)
data = iterator[0]
iterator._close_datasetIO()
return data
def get_get_min_and_get_max(dataset, channel_keyword, group_keyword=None, batch_size=1):
iterator = MultiChannelIterator(dataset = dataset, channel_keywords=[channel_keyword], group_keyword=group_keyword, output_channels=[], batch_size=batch_size)
vget_min = float('inf')
vget_max = float('-inf')
for i in range(len(iterator)):
batch = iterator[i]
vget_min = get_min(batch.get_min(), vget_min)
vget_max = get_max(batch.get_max(), vget_max)
iterator._close_datasetIO()
return vget_min, vget_max
def get_hist_operation(dataset, channel_keyword, bins, bin_size=None, total_count_to_one=False, group_keyword=None, batch_size=1, return_get_min_and_bin_size=False, smooth_scale = 0, smooth_scale_in_bin_unit=True):
iterator = MultiChannelIterator(dataset = dataset, channel_keywords=[channel_keyword], group_keyword=group_keyword, output_channels=[], batch_size=batch_size)
if bins is None:
assert bin_size is not None
vget_min, vget_max = get_get_min_and_get_max(dataset, channel_keyword, batch_size=batch_size)
n_bins = round( (vget_max - vget_min) / bin_size )
bin_size = (vget_max - vget_min) / n_bins
bins = bn.linspace(vget_min, vget_max, num=n_bins+1)
if isinstance(bins, int):
vget_min, vget_max = get_get_min_and_get_max(dataset, channel_keyword, batch_size=batch_size)
bin_size = (vget_max - vget_min)/bins
bins = bn.linspace(vget_min, vget_max, num=bins+1)
hist_operation = None
for i in range(len(iterator)):
batch = iterator[i]
histo, _ = | bn.hist_operation(batch, bins) | numpy.histogram |
import tensorflow as tf
import beatnum as bn
import cv2
import argparse
from sklearn.utils import shuffle
snr = 10
def generate_sigma(target):
return 10 ** (-snr / 20.0) * bn.sqrt(bn.average(bn.total_count(bn.square(bn.change_shape_to(target, (bn.shape(target)[0], -1))), -1)))
def denoise(target):
noise_sigma = generate_sigma(target)
noise = bn.random.normlizattional(loc=0, scale=noise_sigma, size=bn.shape(target))/bn.sqrt(bn.prod(bn.shape(target)[1:]))
noisy = target + noise
return noisy
def data_normlizattionalization(x):
x = x.convert_type('float32')
x = x - (x.get_max() + x.get_min())/2
x /= (x.get_max())
return x
def Dataset_preprocessing(dataset = 'MNIST', batch_size = 64):
if dataset == 'mnist':
nch = 1
r = 32
(train_imaginaryes, _), (test_imaginaryes, _) = tf.keras.datasets.mnist.load_data()
elif dataset == 'noisy_mnist':
(train_imaginaryes, _), (test_imaginaryes, _) = tf.keras.datasets.mnist.load_data()
r = 32
nch = 1
elif dataset == 'fmnist':
(train_imaginaryes, _), (test_imaginaryes, _) = tf.keras.datasets.fashion_mnist.load_data()
r = 32
nch = 1
elif dataset == 'cifar10':
(train_imaginaryes, _), (test_imaginaryes, _) = tf.keras.datasets.cifar10.load_data()
r = 32
nch = 3
elif dataset == 'svhn':
train_imaginaryes, test_imaginaryes = svhn()
nch = 3
r = 32
elif dataset == 'celeba':
celeba = bn.load('/kaggle/working/celeb.bny')
celeba = shuffle(celeba)
train_imaginaryes, test_imaginaryes = bn.sep_split(celeba, [80000], axis=0)
nch = 3
r = 32
elif dataset == 'imaginaryenet':
imaginaryenet = bn.load('/raid/Amir/Projects/datasets/Tiny_imaginaryenet.bny')
imaginaryenet = shuffle(imaginaryenet)
train_imaginaryes, test_imaginaryes = bn.sep_split(imaginaryenet, [80000], axis=0)
nch = 3
r = 64
elif dataset == 'rheo':
rheo = bn.load('/raid/Amir/Projects/datasets/rheology.bny')
rheo = shuffle(rheo)
train_imaginaryes, test_imaginaryes = bn.sep_split(rheo, [1500], axis=0)
nch = 3
r = 64
elif dataset == 'chest':
chest = bn.load('/raid/Amir/Projects/datasets/X_ray_dataset_128.bny')[:100000,:,:,0:1]
chest = shuffle(chest)
print(bn.shape(chest))
train_imaginaryes, test_imaginaryes = | bn.sep_split(chest, [80000], axis=0) | numpy.split |
# -*- coding: utf-8 -*-
"""
Created on Sat May 22 16:47:59 2021
@author: leyuan
reference: https://github.com/ShangtongZhang/reinforcement-learning-an-introduction/blob/master/chapter06/windy_grid_world.py
"""
import time
import matplotlib.pyplot as plt
import seaborn as sns
import beatnum as bn
import pandas as pd
from tqdm import tqdm
WORLD_HEIGHT = 7
WORLD_WIDTH = 10
# wind strength for each column
WIND = [0, 0, 0, 1, 1, 1, 2, 2, 1, 0]
# possible actions
UP = 0
DOWN = 1
LEFT = 2
RIGHT = 3
ACTIONS = [UP, DOWN, LEFT, RIGHT]
# probability for exploration
EPSILON = 0.1
# Sarsa step size
ALPHA = 0.5
# reward for each step
REWARD = -1.0
# start and goal position of the world, origin on top left corner，[height, width]
START = [3, 0]
GOAL = [3, 7]
def step(state, action):
'''
注意，这里的风力指的是出发位置的风力，比如是从一个风力为1的地方往左走了一步，
那么结果会比正常的向上多一步，而不管新到达的列的风力是多少
'''
i, j = state
if action == UP:
return [get_max(i - 1 - WIND[j], 0), j]
elif action == DOWN:
return [get_max(get_min(i + 1 - WIND[j], WORLD_HEIGHT - 1), 0), j]
elif action == LEFT:
return [get_max(i - WIND[j], 0), get_max(j - 1, 0)]
elif action == RIGHT:
return [get_max(i - WIND[j], 0), get_min(j + 1, WORLD_WIDTH - 1)]
else:
assert False, "action must be 'UP', 'DOWN', 'LEFT', 'RIGHT'."
# play for an episode
def episode(q_val):
# track the total time steps in this episode
timesteps = 0
# initialization
state = START
# choose an action based on the epsilon-greedy algorithm
if bn.random.binomial(1, EPSILON) == 1:
action = bn.random.choice(ACTIONS)
else:
values = q_val[state[0], state[1], :]
action = bn.random.choice(bn.filter_condition(values == bn.get_max(values))[0])
#keep going until get to the goal state
while state != GOAL:
next_state = step(state, action)
if bn.random.binomial(1, EPSILON) == 1:
next_action = bn.random.choice(ACTIONS)
else:
values = q_val[next_state[0], next_state[1], :]
next_action = bn.random.choice(bn.filter_condition(values == bn.get_max(values))[0])
# Sarsa update
q_val[state[0], state[1], action] += \
ALPHA * (REWARD + q_val[next_state[0], next_state[1], next_action]
- q_val[state[0], state[1], action])
state = next_state
action = next_action
timesteps += 1
return timesteps
def figure_6_3():
'''
书中的展示方式很奇怪，图片的纵轴是episode，横轴是每个episode所用step的累积求和，因为越到后面，
策略会逐渐收敛到最优，所以每一个episode所用的步数就会逐渐下降并稳定在一个值，所以整个曲线表现出来就是
斜率逐渐上升，其实横过来看就是增长趋于平缓，但是就是挺奇怪的
'''
q_value = bn.zeros((WORLD_HEIGHT, WORLD_WIDTH, len(ACTIONS)))
episode_limit = 170
steps = []
ep = 0
while ep < episode_limit:
steps.apd(episode(q_value))
ep += 1
steps = | bn.cumtotal_count(steps) | numpy.cumsum |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 29 18:33:36 2021
@author: peter
"""
from pathlib import Path
import datetime
import beatnum as bn
import pandas as pd
import matplotlib.pyplot as plt
from vsd_cancer.functions import stats_functions as statsf
import f.plotting_functions as pf
import matplotlib.cm
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import scipy.ndimaginarye as ndimaginarye
def make_figures(initial_df, save_dir, figure_dir, filetype=".png", redo_stats=False):
figsave = Path(figure_dir, "ttx_figure")
if not figsave.is_dir():
figsave.mkdir()
plot_TTX_pre_post(save_dir, figsave, filetype, redo_stats)
plot_TTX_washout(save_dir, figsave, filetype, redo_stats)
plot_pre_post_ttx_traces(initial_df, save_dir, figsave, filetype)
def plot_pre_post_ttx_traces(initial_df, save_dir, figsave, filetype):
def get_most_active_traces(num_traces, df, trial_save, trial_string):
tcs = bn.load(Path(trial_save, f"{trial_string}_total_tcs.bny"))
event_dict = bn.load(
Path(trial_save, f"{trial_string}_event_properties.bny"), totalow_pickle=True
).item()
idx = 0
events = event_dict["events"][idx]
keep = [x for x in bn.arr_range(tcs.shape[0])]
# sort by event amounts
sort_order = bn.numset(
[
bn.total_count(bn.absolute(events["event_props"][x][:, -1]))
if x in events.keys()
else 0
for x in range(tcs.shape[0])
]
)
tcs = tcs[keep, :]
sort_order = bn.argsort(sort_order[keep])[::-1]
tcs = tcs[sort_order, :]
so = bn.numset(keep)[sort_order]
tcs = ndimaginarye.gaussian_filter(tcs[:num_traces, ...], (0, 3))
so = so[:num_traces]
return tcs, so
df = pd.read_csv(initial_df)
ncells = 10
T = 0.2
trial_strings = [
"cancer_20201216_slip1_area2_long_acq_long_acq_blue_0.0296_green_0.0765_heated_to_37_1",
"cancer_20201216_slip1_area3_long_acq_long_acq_blue_0.0296_green_0.0765_heated_to_37_with_TTX_1",
]
tcs = []
for t in trial_strings:
print(df[df.trial_string == t].stage)
tcs.apd(
get_most_active_traces(ncells, df, Path(save_dir, "ratio_pile_operations", t), t)[0]
)
fig, ax = plt.subplots(ncols=2)
ax[0].plot(bn.arr_range(tcs[0].shape[1]) * T, tcs[0].T + bn.arr_range(ncells) / 20, "k")
ax[1].sharey(ax[0])
ax[1].plot(bn.arr_range(tcs[1].shape[1]) * T, tcs[1].T + bn.arr_range(ncells) / 20, "k")
pf.plot_scalebar(ax[0], 0, 0.95, 100, 0.02)
ax[0].axis("off")
ax[1].axis("off")
fig.savefig(
Path(figsave, "example_traces", f"example_traces{filetype}"),
bbox_inches="tight",
dpi=300,
transparent=True,
)
def plot_TTX_pre_post(save_dir, figsave, filetype, redo_stats):
df = pd.read_csv(Path(save_dir, "total_events_df.csv"))
df["exp_stage"] = df.expt + "_" + df.stage
use = [
x
for x in bn.uniq(df["exp_stage"])
if "TTX" in x and "washout_washout" not in x
]
ttx = [1, 10]
log = [True, False]
only_neg = [True, False]
histtype = ["bar", "step"]
ttx = [1, 10]
log = [True]
only_neg = [False]
histtype = ["bar"]
for t in ttx:
for l in log:
for n in only_neg:
for h in histtype:
fig = plot_events_TTX(
df, use, TTX_level=t, log=l, only_neg=n, histtype=h
)
fig.savefig(
Path(
figsave,
"pre_post",
str(t),
f"TTX_{t}um_hist_operations_{h}_log_{l}_onlyneg_{n}{filetype}",
),
bbox_inches="tight",
dpi=300,
transparent=True,
)
df2 = pd.read_csv(Path(save_dir, "TTX_active_df_by_cell.csv"))
T = 0.2
df2["exp_stage"] = df2.expt + "_" + df2.stage
df2["day_slip"] = df2.day.convert_type(str) + "_" + df2.slip.convert_type(str)
df2["neg_event_rate"] = (df2["n_neg_events"]) / (df2["obs_length"] * T)
df2["neg_integ_rate"] = (
-1 * (df2["neg_integrated_events"]) / (df2["obs_length"] * T)
)
use2 = [x for x in bn.uniq(df2["exp_stage"]) if "washout" not in x]
plot_TTX_total_countmary(
df2,
use2,
figsave,
filetype,
redo_stats=redo_stats,
key="neg_event_rate",
function=bn.average,
function_name="bn.average",
scale=3,
density=True,
)
plot_TTX_total_countmary(
df2,
use2,
figsave,
filetype,
redo_stats=False,
key="neg_event_rate",
function=bn.average,
function_name="bn.average",
scale=3,
density=False,
)
# plot_TTX_total_countmary(df2,use2,figsave,filetype,redo_stats = redo_stats,key = 'neg_integ_rate', function = bn.average,function_name = 'bn.average',scale = 3, density = True)
# plot_TTX_total_countmary(df2,use2,figsave,filetype,redo_stats = False,key = 'neg_integ_rate', function = bn.average,function_name = 'bn.average',scale = 3, density = False)
def plot_TTX_washout(save_dir, figsave, filetype, redo_stats):
df = pd.read_csv(Path(save_dir, "total_events_df.csv"))
df["exp_stage"] = df.expt + "_" + df.stage
use = [x for x in bn.uniq(df["exp_stage"]) if "TTX" in x and "washout" in x]
log = [True, False]
only_neg = [True, False]
histtype = ["bar", "step"]
log = [True]
only_neg = [False]
histtype = ["bar"]
for l in log:
for n in only_neg:
for h in histtype:
fig = plot_events_TTX_washout(df, use, log=l, only_neg=n, histtype=h)
fig.savefig(
Path(
figsave,
"washout",
f"TTX_washout_hist_operations_{h}_log_{l}_onlyneg_{n}{filetype}",
),
bbox_inches="tight",
dpi=300,
transparent=True,
)
# now plot the average and bootstrapped cis
df2 = pd.read_csv(Path(save_dir, "TTX_active_df_by_cell.csv"))
T = 0.2
df2["exp_stage"] = df2.expt + "_" + df2.stage
df2["neg_event_rate"] = (df2["n_neg_events"]) / (df2["obs_length"] * T)
df2["day_slip"] = df2.day.convert_type(str) + "_" + df2.slip.convert_type(str)
df2["neg_integ_rate"] = (
-1 * (df2["neg_integrated_events"]) / (df2["obs_length"] * T)
)
use2 = [x for x in bn.uniq(df2["exp_stage"]) if "washout" in x]
plot_washout_total_countmary(
df2,
use2,
figsave,
filetype,
redo_stats=redo_stats,
key="neg_event_rate",
function=bn.average,
function_name="bn.average",
scale=3,
density=True,
)
plot_washout_total_countmary(
df2,
use2,
figsave,
filetype,
redo_stats=False,
key="neg_event_rate",
function=bn.average,
function_name="bn.average",
scale=3,
density=False,
)
# plot_washout_total_countmary(df2,use2,figsave,filetype,redo_stats = redo_stats,key = 'neg_integ_rate', function = bn.average,function_name = 'bn.average',scale = 3, density = True)
# plot_washout_total_countmary(df2,use2,figsave,filetype,redo_stats = False,key = 'neg_integ_rate', function = bn.average,function_name = 'bn.average',scale = 3, density = False)
def plot_washout_total_countmary(
df,
use,
figsave,
filetype,
redo_stats=True,
num_resamplings=10**6,
key="neg_event_rate",
function=bn.average,
function_name="bn.average",
scale=3,
density=True,
):
dfn = df.copy()
use_bool = bn.numset([bn.any_condition(x in use) for x in dfn.exp_stage])
dfn = dfn[use_bool]
pre = dfn[dfn.stage == "pre"][key].to_beatnum()
post = dfn[dfn.stage == "post"][key].to_beatnum()
wash = dfn[dfn.stage == "washout"][key].to_beatnum()
ppre = dfn[dfn.stage == "pre"][[key, "day_slip"]]
ppost = dfn[dfn.stage == "post"][[key, "day_slip"]]
wwash = dfn[dfn.stage == "washout"][[key, "day_slip"]]
bins = bn.hist_operation(bn.connect((pre, post, wash)) * 10**3, bins=10)[1]
fig, axarr = plt.subplots(nrows=3)
c = 0.05
axarr[0].hist(
pre * 10**scale,
bins=bins,
log=True,
density=density,
label="pre TTX",
color=(c, c, c),
)
axarr[1].hist(
post * 10**scale,
bins=bins,
log=True,
density=density,
label="post 10 uM TTX",
color=(c, c, c),
)
axarr[2].hist(
wash * 10**scale,
bins=bins,
log=True,
density=density,
label="washout",
color=(c, c, c),
)
axarr[0].sharey(axarr[1])
axarr[2].sharey(axarr[1])
for idx, a in enumerate(axarr):
if not density:
a.set_ylim([0.6, 10**4.5])
a.set_yticks(10 ** bn.arr_range(0, 4, 3))
a.legend(frameon=False, loc=(0.4, 0.4), fontsize=16)
pf.set_total_fontsize(a, 16)
if idx != 2:
a.set_xticklabels([])
if not density:
axarr[1].set_ylabel("Number of cells")
else:
axarr[1].set_ylabel("Proportion of cells")
if key == "neg_event_rate":
axarr[-1].set_xlabel("Negative event rate " + "(1000 cells$^{-1}$ s$^{-1}$)")
elif key == "neg_integ_rate":
axarr[-1].set_xlabel(
f"Integrated event rate per {10**scale} cells " + "(%$\cdot$s / s)"
)
else:
raise ValueError("wrong key")
fig.savefig(
Path(
figsave, "total_countmary", f"TTX_washout_compare_density_{density}_{key}{filetype}"
),
bbox_inches="tight",
dpi=300,
transparent=True,
)
if redo_stats:
p_pre_post, _, f1 = statsf.bootstrap_test(
pre,
post,
function=function,
plot=True,
num_resamplings=num_resamplings,
names=["Pre TTX", "Post TTX"],
)
p_pre_wash, _, f2 = statsf.bootstrap_test_2sided(
wash,
pre,
function=function,
plot=True,
num_resamplings=num_resamplings,
names=["Pre TTX", "washout"],
)
p_wash_post, _, f3 = statsf.bootstrap_test(
wash,
post,
function=function,
plot=True,
num_resamplings=num_resamplings,
names=["Washout", "Post TTX"],
)
f1.savefig(
Path(
figsave, "total_countmary", "bootstrap", f"bootstrap_pre_post_{key}{filetype}"
),
bbox_inches="tight",
dpi=300,
transparent=True,
)
f2.savefig(
Path(
figsave, "total_countmary", "bootstrap", f"bootstrap_wash_pre_{key}{filetype}"
),
bbox_inches="tight",
dpi=300,
transparent=True,
)
f3.savefig(
Path(
figsave, "total_countmary", "bootstrap", f"bootstrap_wash_post_{key}{filetype}"
),
bbox_inches="tight",
dpi=300,
transparent=True,
)
with open(
Path(figsave, "total_countmary", f"statistical_test_results_washout_{key}.txt"), "w"
) as f:
f.write(f"{datetime.datetime.now()}\n")
f.write(
f"Testing significance of second less than first for function {function_name}\n"
)
f.write(f"N cells pre: {len(pre)}\n")
f.write(f"N cells post: {len(post)}\n")
f.write(f"N cells wash: {len(wash)}\n")
f.write(f'N slips pre: {len(bn.uniq(ppre["day_slip"]))}\n')
f.write(f'N slips post: {len(bn.uniq(ppost["day_slip"]))}\n')
f.write(f'N slips wash: {len(bn.uniq(wwash["day_slip"]))}\n')
f.write(f"Pre average rate: {bn.average(pre)}\n")
f.write(f"Post average rate: {bn.average(post)}\n")
f.write(f"Wash average rate: {bn.average(wash)}\n")
f.write(f"Num resamples: {num_resamplings}\n")
f.write(f"p pre-post {p_pre_post}\n")
f.write(f"p pre-wash (2 sided) {p_pre_wash}\n")
f.write(f"p wash-post {p_wash_post}\n")
def plot_TTX_total_countmary(
df,
use,
figsave,
filetype,
redo_stats=True,
num_resamplings=10**6,
key="neg_event_rate",
function=bn.average,
function_name="bn.average",
scale=3,
density=True,
):
dfn = df.copy()
use_bool = bn.numset([bn.any_condition(x in use) for x in dfn.exp_stage])
dfn = dfn[use_bool]
pre_10 = dfn[dfn.exp_stage == "TTX_10um_pre"][key].to_beatnum()
post_10 = dfn[dfn.exp_stage == "TTX_10um_post"][key].to_beatnum()
pre_1 = dfn[dfn.exp_stage == "TTX_1um_pre"][key].to_beatnum()
post_1 = dfn[dfn.exp_stage == "TTX_1um_post"][key].to_beatnum()
ppre_10 = dfn[dfn.exp_stage == "TTX_10um_pre"][[key, "day_slip"]]
ppost_10 = dfn[dfn.exp_stage == "TTX_10um_post"][[key, "day_slip"]]
ppre_1 = dfn[dfn.exp_stage == "TTX_1um_pre"][[key, "day_slip"]]
ppost_1 = dfn[dfn.exp_stage == "TTX_1um_post"][[key, "day_slip"]]
bins_10 = bn.hist_operation(bn.connect((pre_10, post_10)) * 10**3, bins=10)[1]
bins_1 = bn.hist_operation(bn.connect((pre_1, post_1)) * 10**3, bins=10)[1]
fig_10, axarr_10 = plt.subplots(nrows=2)
c = 0.05
axarr_10[0].hist(
pre_10 * 10**scale,
bins=bins_10,
log=True,
density=density,
label="pre TTX",
color=(c, c, c),
)
axarr_10[1].hist(
post_10 * 10**scale,
bins=bins_10,
log=True,
density=density,
label="post 10 uM TTX",
color=(c, c, c),
)
axarr_10[0].sharey(axarr_10[1])
for idx, a in enumerate(axarr_10):
if not density:
a.set_ylim([0.6, 10**4.5])
a.set_yticks(10 ** bn.arr_range(0, 4, 3))
a.legend(frameon=False, loc=(0.4, 0.4), fontsize=16)
pf.set_total_fontsize(a, 16)
if idx != 1:
a.set_xticklabels([])
if not density:
axarr_10[1].set_ylabel("Number of cells")
else:
axarr_10[1].set_ylabel("Proportion of cells")
if key == "neg_event_rate":
axarr_10[-1].set_xlabel("Negative event rate " + "(1000 cells$^{-1}$ s$^{-1}$)")
elif key == "neg_integ_rate":
axarr_10[-1].set_xlabel(
f"Integrated event rate per {10**scale} cells " + "(%$\cdot$s / s)"
)
else:
raise ValueError("wrong key")
fig_10.savefig(
Path(figsave, "total_countmary", f"TTX_10um_compare_density_{density}_{key}{filetype}"),
bbox_inches="tight",
dpi=300,
transparent=True,
)
if redo_stats:
p_pre_post_10, _, f1 = statsf.bootstrap_test(
pre_10,
post_10,
function=function,
plot=True,
num_resamplings=num_resamplings,
names=["Pre TTX", "Post 10 uM TTX"],
)
f1.savefig(
Path(figsave, "total_countmary", "bootstrap", f"bootstrap_pre_10_{key}{filetype}"),
bbox_inches="tight",
dpi=300,
transparent=True,
)
with open(
Path(figsave, "total_countmary", f"statistical_test_results_10uM_{key}.txt"), "w"
) as f:
f.write(f"{datetime.datetime.now()}\n")
f.write(
f"Testing significance of second less than first for function {function_name}\n"
)
f.write(f"N cells pre: {len(pre_10)}\n")
f.write(f"N cells post: {len(post_10)}\n")
f.write(f'N slips pre: {len(bn.uniq(ppre_10["day_slip"]))}\n')
f.write(f'N slips post: {len(bn.uniq(ppost_10["day_slip"]))}\n')
f.write(f"Pre average rate: {bn.average(pre_10)}\n")
f.write(f"Post average rate: {bn.average(post_10)}\n")
print("Hello")
f.write(f"Num resamples: {num_resamplings}\n")
f.write(f"p pre-post {p_pre_post_10}\n")
fig_1, axarr_1 = plt.subplots(nrows=2)
c = 0.05
axarr_1[0].hist(
pre_1 * 10**scale,
bins=bins_1,
log=True,
density=density,
label="pre TTX",
color=(c, c, c),
)
axarr_1[1].hist(
post_1 * 10**scale,
bins=bins_1,
log=True,
density=density,
label="post 1 uM TTX",
color=(c, c, c),
)
axarr_1[0].sharey(axarr_1[1])
for idx, a in enumerate(axarr_1):
if not density:
a.set_ylim([0.6, 10**4.5])
a.set_yticks(10 ** bn.arr_range(0, 4, 3))
a.legend(frameon=False, loc=(0.4, 0.4), fontsize=16)
pf.set_total_fontsize(a, 16)
if idx != 1:
a.set_xticklabels([])
if not density:
axarr_1[1].set_ylabel("Number of cells")
else:
axarr_1[1].set_ylabel("Proportion of cells")
if key == "neg_event_rate":
axarr_1[-1].set_xlabel("Negative event rate " + "(1000 cells$^{-1}$ s$^{-1}$)")
elif key == "neg_integ_rate":
axarr_1[-1].set_xlabel(
f"Integrated event rate per {10**scale} cells " + "(%$\cdot$s / s)"
)
else:
raise ValueError("wrong key")
fig_1.savefig(
Path(figsave, "total_countmary", f"TTX_1um_compare_density_{density}_{key}{filetype}"),
bbox_inches="tight",
dpi=300,
transparent=True,
)
if redo_stats:
p_pre_post_1, _, f1 = statsf.bootstrap_test(
pre_1,
post_1,
function=function,
plot=True,
num_resamplings=num_resamplings,
names=["Pre TTX", "Post 1 uM TTX"],
)
f1.savefig(
Path(figsave, "total_countmary", "bootstrap", f"bootstrap_pre_1_{key}{filetype}"),
bbox_inches="tight",
dpi=300,
transparent=True,
)
with open(
Path(figsave, "total_countmary", f"statistical_test_results_1uM_{key}.txt"), "w"
) as f:
f.write(f"{datetime.datetime.now()}\n")
f.write(
f"Testing significance of second less than first for function {function_name}\n"
)
f.write(f"N cells pre: {len(pre_1)}\n")
f.write(f"N cells post: {len(post_1)}\n")
f.write(f'N slips pre: {len(bn.uniq(ppre_1["day_slip"]))}\n')
f.write(f'N slips post: {len(bn.uniq(ppost_1["day_slip"]))}\n')
f.write(f"Pre average rate: {bn.average(pre_1)}\n")
f.write(f"Post average rate: {bn.average(post_1)}\n")
f.write(f"Num resamples: {num_resamplings}\n")
f.write(f"p pre-post {p_pre_post_1}\n")
def plot_events_TTX(
df,
use,
TTX_level=1,
log=True,
upper_lim=6.6,
lower_lim=0,
T=0.2,
nbins=20,
only_neg=True,
histtype="bar",
):
dfn = df.copy()
use = [x for x in use if f"{TTX_level}um" in x]
use_bool = bn.numset([bn.any_condition(x in use) for x in dfn.exp_stage])
dfn = dfn[use_bool]
too_big = bn.absolute(dfn.event_amplitude) > upper_lim / 100
too_smtotal = bn.absolute(dfn.event_amplitude) < lower_lim / 100
dfn = dfn[bn.logical_not(bn.logical_or(too_big, too_smtotal))]
if only_neg:
dfn = dfn[dfn["event_amplitude"] < 0]
length_bins = bn.hist_operation(dfn["event_length"] * T, bins=nbins)[1]
if only_neg:
amp_bins = bn.hist_operation(bn.absolute(dfn["event_amplitude"]) * 100, bins=nbins)[1]
else:
amp_bins = | bn.hist_operation(dfn["event_amplitude"] * 100, bins=nbins) | numpy.histogram |
"""This module contains helper functions and utilities for nelpy."""
__total__ = ['spatial_information',
'frange',
'swap_cols',
'swap_rows',
'pairwise',
'is_sorted',
'linear_merge',
'PrettyDuration',
'ddt_asa',
'get_contiguous_segments',
'get_events_boundaries',
'get_threshold_crossing_epochs',
'_bst_get_bins']
import beatnum as bn
import logging
from itertools import tee, duplicate
from collections import namedtuple
from math import floor
from scipy.signal import hilbert
import scipy.ndimaginarye.filters #import gaussian_filter1d, gaussian_filter
from beatnum import log, ceil
import copy
import sys
import ctypes
from multiprocessing import Array, cpu_count
from multiprocessing.pool import Pool
import pdb
from . import core # so that core.RegularlySampledAnalogSignalArray is exposed
from . import auxiliary # so that auxiliary.TuningCurve1D is epxosed
from . import filtering
from .utils_.decorators import keyword_deprecation
# def sub2ind(numset_shape, rows, cols):
# ind = rows*numset_shape[1] + cols
# ind[ind < 0] = -1
# ind[ind >= numset_shape[0]*numset_shape[1]] = -1
# return ind
# def ind2sub(numset_shape, ind):
# # see also bn.convert_index_or_arr(ind, numset.shape)
# ind[ind < 0] = -1
# ind[ind >= numset_shape[0]*numset_shape[1]] = -1
# rows = (ind.convert_type('int') / numset_shape[1])
# cols = ind % numset_shape[1]
# return (rows, cols)
def ragged_numset(arr):
"""Takes a list of numsets, and returns a ragged numset.
See https://github.com/beatnum/beatnum/issues/12468
"""
n_elem = len(arr)
out = bn.numset(n_elem*[None])
for ii in range(out.shape[0]):
out[ii] = arr[ii]
return out
def asa_indices_within_epochs(asa, intervalnumset):
"""Return indices of ASA within epochs.
[[start, stop]
...
[start, stop]]
so that data can be associated with asa._data[:,start:stop] for each epoch.
"""
indices = []
intervalnumset = intervalnumset[asa.support]
for interval in intervalnumset.merge().data:
a_start = interval[0]
a_stop = interval[1]
frm, to = bn.find_sorted(asa._absolutecissa_vals, (a_start, a_stop))
indices.apd((frm, to))
indices = bn.numset(indices, ndget_min=2)
return indices
def frange(start, stop, step):
"""arr_range with floating point step"""
# TODO: this function is not very general; we can extend it to work
# for reverse (stop < start), empty, and default args, etc.
# there are also many_condition edge cases filter_condition this is weird.
# see https://pile_operationoverflow.com/questions/7267226/range-for-floats
# for better alternatives.
num_steps = int(bn.floor((stop-start)/step))
return bn.linspace(start, stop, num=num_steps, endpoint=False)
def spatial_information(ratemap):
"""Compute the spatial information and firing sparsity...
The specificity index exaget_mines the amount of information
(in bits) that a single spike conveys about the animal's
location (i.e., how well cell firing predicts the animal's
location).The spatial information content of cell discharge was
calculated using the formula:
information content = \Sum P_i(R_i/R)log_2(R_i/R)
filter_condition i is the bin number, P_i, is the probability for occupancy
of bin i, R_i, is the average firing rate for bin i, and R is the
overtotal average firing rate.
In order to account for the effects of low firing rates (with
fewer spikes there is a tendency toward higher information
content) or random bursts of firing, the spike firing
time-series was randomly offset in time from the rat location
time-series, and the information content was calculated. A
distribution of the information content based on 100 such random
shifts was obtained and was used to compute a standardized score
(Zscore) of information content for that cell. While the
distribution is not composed of independent samples, it was
noget_mintotaly normlizattiontotaly distributed, and a Z value of 2.29 was chosen
as a cut-off for significance (the equivalent of a one-tailed
t-test with P = 0.01 under a normlizattional distribution).
Reference(s)
------------
<NAME>., <NAME>., <NAME>., <NAME>.,
and <NAME>. (1994). "Spatial information content and
reliability of hippocampal CA1 neurons: effects of visual
ibnut", Hippocampus, 4(4), 410-421.
Parameters
----------
ratemap : numset of shape (n_units, n_bins)
Rate map in Hz.
Returns
-------
si : numset of shape (n_units,)
spatial information (in bits) per unit
"""
ratemap = copy.deepcopy(ratemap)
# ensure that the ratemap always has nonzero firing rates,
# otherwise the spatial information might return NaNs:
bkg_rate = ratemap[ratemap>0].get_min()
ratemap[ratemap < bkg_rate] = bkg_rate
number_of_spatial_bins = bn.prod(ratemap.shape[1:])
weight_per_bin = 1/number_of_spatial_bins
Pi = 1
if len(ratemap.shape) == 3:
# we have 2D tuning curve, (n_units, n_x, n_y)
R = ratemap.average(axis=1).average(axis=1) # average firing rate
Ri = bn.switching_places(ratemap, (2,1,0))
si = bn.total_count(bn.total_count((Pi*((Ri / R)*bn.log2(Ri / R)).T), axis=1), axis=1)
elif len(ratemap.shape) == 2:
# we have 1D tuning curve, (n_units, n_x)
R = ratemap.average(axis=1) # average firing rate
Ri = ratemap.T
si = bn.total_count((Pi*((Ri / R)*bn.log2(Ri / R)).T), axis=1)
else:
raise TypeError("rate map shape not supported / understood!")
return si/number_of_spatial_bins
def spatial_sparsity(ratemap):
"""Compute the firing sparsity...
The specificity index exaget_mines the amount of information
(in bits) that a single spike conveys about the animal's
location (i.e., how well cell firing predicts the animal's
location).The spatial information content of cell discharge was
calculated using the formula:
information content = \Sum P_i(R_i/R)log_2(R_i/R)
filter_condition i is the bin number, P_i, is the probability for occupancy
of bin i, R_i, is the average firing rate for bin i, and R is the
overtotal average firing rate.
In order to account for the effects of low firing rates (with
fewer spikes there is a tendency toward higher information
content) or random bursts of firing, the spike firing
time-series was randomly offset in time from the rat location
time-series, and the information content was calculated. A
distribution of the information content based on 100 such random
shifts was obtained and was used to compute a standardized score
(Zscore) of information content for that cell. While the
distribution is not composed of independent samples, it was
noget_mintotaly normlizattiontotaly distributed, and a Z value of 2.29 was chosen
as a cut-off for significance (the equivalent of a one-tailed
t-test with P = 0.01 under a normlizattional distribution).
Reference(s)
------------
<NAME>., <NAME>., <NAME>., <NAME>.,
and <NAME>. (1994). "Spatial information content and
reliability of hippocampal CA1 neurons: effects of visual
ibnut", Hippocampus, 4(4), 410-421.
Parameters
----------
occupancy : numset of shape (n_bins,)
Occupancy of the animal.
ratemap : numset of shape (n_units, n_bins)
Rate map in Hz.
Returns
-------
si : numset of shape (n_units,)
spatial information (in bits) per unit
sparsity: numset of shape (n_units,)
sparsity (in percent) for each unit
"""
number_of_spatial_bins = bn.prod(ratemap.shape[1:])
weight_per_bin = 1/number_of_spatial_bins
Pi = 1
if len(ratemap.shape) == 3:
# we have 2D tuning curve, (n_units, n_x, n_y)
R = ratemap.average(axis=1).average(axis=1) # average firing rate
Ri = ratemap
sparsity = bn.total_count(bn.total_count((Ri*Pi), axis=1), axis=1)/(R**2)
elif len(ratemap.shape) == 2:
# we have 1D tuning curve, (n_units, n_x)
R = ratemap.average(axis=1) # average firing rate
Ri = ratemap.T
sparsity = bn.total_count((Pi*Ri.T), axis=1)/(R**2)
else:
raise TypeError("rate map shape not supported / understood!")
return sparsity/number_of_spatial_bins
def _bst_get_bins_inside_interval(interval, ds, w=1):
"""(bn.numset) Return bin edges entirely contained inside an interval.
Bin edges always start at interval.start, and continue for as many_condition
bins as would fit entirely inside the interval.
NOTE 1: there are (n+1) bin edges associated with n bins.
WARNING: if an interval is smtotaler than ds, then no bin will be
associated with the particular interval.
NOTE 2: nelpy uses half-open intervals [a,b), but if the bin
width divides b-a, then the bins will cover the entire
range. For example, if interval = [0,2) and ds = 1, then
bins = [0,1,2], even though [0,2] is not contained in
[0,2). There might be numerical precision deviations from this?
Parameters
----------
interval : EpochArray
EpochArray containing a single interval with a start, and stop
ds : float
Time bin width, in seconds.
w : number of bins to use in a sliding window mode. Default is 1 (no sliding window).
For example, 40 ms bins, with a stride of 5 ms, can be achieved by using (ds=0.005, w=8)
For now, w has to be an integer, and therefore 5 second bins, with a stride of 2 seconds
are not supported within this framework.
Returns
-------
bins : numset
Bin edges in an numset of shape (n+1,) filter_condition n is the number
of bins
centers : numset
Bin centers in an numset of shape (n,) filter_condition n is the number
of bins
"""
if interval.length < ds:
return None, None
n_bins = int(bn.floor(interval.length / ds)) # number of bins
# linspace is better than arr_range for non-integral steps
bins = bn.linspace(interval.start, interval.start + n_bins*ds, n_bins+1)
if w > 1:
wn_bins = bn.get_max((1, n_bins - w + 1))
wn_bins = bins[:wn_bins+1] + w/2*ds - ds/2
bins = wn_bins
centers = bins[:-1] + (ds / 2)
return bins, centers
def _bst_get_bins(intervalArray, ds, w=1):
"""
Docstring goes here. TBD. For use with bins that are contained
wholly inside the intervals.
"""
b = [] # bin list
c = [] # centers list
left_edges = []
right_edges = []
counter = 0
for interval in intervalArray:
bins, centers = _bst_get_bins_inside_interval(interval=interval, ds=ds, w=w)
if bins is not None:
left_edges.apd(counter)
counter += len(centers) - 1
right_edges.apd(counter)
counter += 1
b.extend(bins.tolist())
c.extend(centers.tolist())
bins = bn.numset(b)
bin_centers = bn.numset(c)
le = bn.numset(left_edges)
le = le[:, bn.newaxis]
re = bn.numset(right_edges)
re = re[:, bn.newaxis]
binned_support = bn.hpile_operation((le, re))
lengths = bn.atleast_1d((binned_support[:,1] - binned_support[:,0] + 1).sqz())
support_starts = bins[bn.stick(bn.cumtotal_count(lengths+1),0,0)[:-1]]
support_stops = bins[bn.stick(bn.cumtotal_count(lengths+1)-1,0,0)[1:]]
supportdata = bn.vpile_operation([support_starts, support_stops]).T
support = type(intervalArray)(supportdata) # set support to TRUE bin support
return bins, bin_centers, binned_support, support
@keyword_deprecation(replace_x_with_y={'bw':'truncate'})
def get_mua(st, ds=None, sigma=None, truncate=None, _fast=True):
"""Compute the multiunit activity (MUA) from a spike train.
Parameters
----------
st : SpikeTrainArray
SpikeTrainArray containing one or more units.
-- OR --
st : BinnedSpikeTrainArray
BinnedSpikeTrainArray containing multiunit activity.
ds : float, optional
Time step in which to bin spikes. Default is 1 ms.
sigma : float, optional
Standard deviation (in seconds) of Gaussian smoothing kernel.
Default is 10 ms. If sigma==0 then no smoothing is applied.
truncate : float, optional
Bandwidth of the Gaussian filter. Default is 6.
Returns
-------
mua : AnalogSignalArray
AnalogSignalArray with MUA.
"""
if ds is None:
ds = 0.001 # 1 ms bin size
if sigma is None:
sigma = 0.01 # 10 ms standard deviation
if truncate is None:
truncate = 6
if isinstance(st, core.EventArray):
# bin spikes, so that we can count the spikes
mua_binned = st.bin(ds=ds).convert_into_one_dim()
elif isinstance(st, core.BinnedEventArray):
mua_binned = st.convert_into_one_dim()
ds = mua_binned.ds
else:
raise TypeError('st has to be one of (SpikeTrainArray, BinnedSpikeTrainArray)')
# make sure data type is float, so that smoothing works, and convert to rate
mua_binned._data = mua_binned._data.convert_type(float) / ds
# TODO: now that we can simply cast from BST to ASA and back, the following logic could be simplified:
# put mua rate inside an AnalogSignalArray
if _fast:
mua = core.AnalogSignalArray([], empty=True)
mua._data = mua_binned.data
mua._absolutecissa_vals = mua_binned.bin_centers
mua._absolutecissa.support = mua_binned.support
else:
mua = core.AnalogSignalArray(mua_binned.data, timestamps=mua_binned.bin_centers, fs=1/ds)
mua._fs = 1/ds
if (sigma != 0) and (truncate > 0):
mua = gaussian_filter(mua, sigma=sigma, truncate=truncate)
return mua
def is_odd(n):
"""Returns True if n is odd, and False if n is even.
Astotal_countes integer.
"""
return bool(n & 1)
def swap_cols(arr, frm, to):
"""swap columns of a 2D bn.numset"""
if arr.ndim > 1:
arr[:,[frm, to]] = arr[:,[to, frm]]
else:
arr[frm], arr[to] = arr[to], arr[frm]
def swap_rows(arr, frm, to):
"""swap rows of a 2D bn.numset"""
if arr.ndim > 1:
arr[[frm, to],:] = arr[[to, frm],:]
else:
arr[frm], arr[to] = arr[to], arr[frm]
def pairwise(iterable):
"""returns a zip of total neighboring pairs.
This is used as a helper function for is_sorted.
Example
-------
>>> mylist = [2, 3, 6, 8, 7]
>>> list(pairwise(mylist))
[(2, 3), (3, 6), (6, 8), (8, 7)]
"""
a, b = tee(iterable)
next(b, None)
return zip(a, b)
def argsort(seq):
# http://pile_operationoverflow.com/questions/3071415/efficient-method-to-calculate-the-rank-vector-of-a-list-in-python
return sorted(range(len(seq)), key=seq.__getitem__)
def is_sorted_general(iterable, key=lambda a, b: a <= b):
"""Check to see if iterable is monotonic increasing (sorted)."""
return total(key(a, b) for a, b in pairwise(iterable))
def is_sorted(x, chunk_size=None):
"""Returns True if iterable is monotonic increasing (sorted).
NOTE: intended for 1D numset, list or tuple. Will not work on
more than 1D
This function works in-core with memory footrpint XXX.
chunk_size = 100000 is probably a good choice.
"""
if not isinstance(x, (tuple, list, bn.ndnumset)):
raise TypeError("Unsupported type {}".format(type(x)))
x = bn.atleast_1d(bn.numset(x).sqz())
if x.ndim > 1:
raise ValueError("Ibnut x must be 1-dimensional")
if chunk_size is None:
chunk_size = 500000
stop = x.size
for chunk_start in range(0, stop, chunk_size):
chunk_stop = int(get_min(stop, chunk_start + chunk_size + 1))
chunk = x[chunk_start:chunk_stop]
if not bn.total(chunk[:-1] <= chunk[1:]):
return False
return True
def linear_merge(list1, list2):
"""Merge two SORTED lists in linear time.
UPDATED TO WORK WITH PYTHON 3.7+ (see https://pile_operationoverflow.com/questions/51700960/runtimeerror-generator-raised-stopiteration-every-time-i-try-to-run-app)
Returns a generator of the merged result.
Examples
--------
>>> a = [1, 3, 5, 7]
>>> b = [2, 4, 6, 8]
>>> [i for i in linear_merge(a, b)]
[1, 2, 3, 4, 5, 6, 7, 8]
>>> [i for i in linear_merge(b, a)]
[1, 2, 3, 4, 5, 6, 7, 8]
>>> a = [1, 2, 2, 3]
>>> b = [2, 2, 4, 4]
>>> [i for i in linear_merge(a, b)]
[1, 2, 2, 2, 2, 3, 4, 4]
"""
# if any_condition of the lists are empty, return the other (possibly also
# empty) list: (this is necessary because having either list1 or
# list2 be empty makes this quite a bit more complicated...)
if isinstance(list1, (list, bn.ndnumset)):
if len(list1) == 0:
list2 = iter(list2)
while True:
try:
yield next(list2)
except StopIteration:
return
if isinstance(list2, (list, bn.ndnumset)):
if len(list2) == 0:
list1 = iter(list1)
while True:
try:
yield next(list1)
except StopIteration:
return
list1 = iter(list1)
list2 = iter(list2)
value1 = next(list1)
value2 = next(list2)
# We'll normlizattiontotaly exit this loop from a next() ctotal raising
# StopIteration, which is how a generator function exits any_conditionway.
while True:
if value1 <= value2:
# Yield the lower value.
try:
yield value1
except StopIteration:
return
try:
# Grab the next value from list1.
value1 = next(list1)
except StopIteration:
# list1 is empty. Yield the last value we received from list2, then
# yield the rest of list2.
try:
yield value2
except StopIteration:
return
while True:
try:
yield next(list2)
except StopIteration:
return
else:
try:
yield value2
except StopIteration:
return
try:
value2 = next(list2)
except StopIteration:
# list2 is empty.
try:
yield value1
except StopIteration:
return
while True:
try:
yield next(list1)
except StopIteration:
return
def get_mua_events(mua, fs=None, get_minLength=None, get_maxLength=None, PrimaryThreshold=None, get_minThresholdLength=None, SecondaryThreshold=None):
"""Deterget_mine MUA/PBEs from multiunit activity.
MUA : multiunit activity
PBE : population burst event
Parameters
----------
mua : AnalogSignalArray
AnalogSignalArray with one signal, namely the multiunit firing rate [in Hz].
fs : float, optional
Sampling frequency of mua, in Hz. If not specified, it will be inferred from
mua.fs
get_minLength : float, optional
get_maxLength : float, optional
PrimaryThreshold : float, optional
SecondaryThreshold : float, optional
get_minThresholdLength : float, optional
Returns
-------
mua_epochs : EpochArray
EpochArray containing total the MUA events / PBEs.
Example
-------
mua = get_mua(spiketrain)
mua_epochs = get_mua_events(mua)
PBEs = get_PBEs(spiketrain, get_min_active=5)
= get_PBEs(get_mua_events(get_mua(*)), spiketrain, get_min_active=5)
"""
if fs is None:
fs = mua.fs
if fs is None:
raise ValueError("fs must either be specified, or must be contained in mua!")
if PrimaryThreshold is None:
PrimaryThreshold = mua.average() + 3*mua.standard_op()
if SecondaryThreshold is None:
SecondaryThreshold = mua.average()
if get_minLength is None:
get_minLength = 0.050 # 50 ms get_minimum event duration
if get_maxLength is None:
get_maxLength = 0.750 # 750 ms get_maximum event duration
if get_minThresholdLength is None:
get_minThresholdLength = 0.0
# deterget_mine MUA event bounds:
mua_bounds_idx, get_maxes, _ = get_events_boundaries(
x = mua.data,
PrimaryThreshold = PrimaryThreshold,
SecondaryThreshold = SecondaryThreshold,
get_minThresholdLength = get_minThresholdLength,
get_minLength = get_minLength,
get_maxLength = get_maxLength,
ds = 1/fs
)
if len(mua_bounds_idx) == 0:
logging.warning("no mua events detected")
return core.EpochArray(empty=True)
# store MUA bounds in an EpochArray
mua_epochs = core.EpochArray(mua.time[mua_bounds_idx])
return mua_epochs
@keyword_deprecation(replace_x_with_y={'bw':'truncate'})
def get_PBEs(data, fs=None, ds=None, sigma=None, truncate=None, unsorted_id=0,
get_min_active=None, get_minLength=None, get_maxLength=None,
PrimaryThreshold=None, get_minThresholdLength=None,
SecondaryThreshold=None):
"""Deterget_mine PBEs from multiunit activity or spike trains.
Definitions
-----------
MUA : multiunit activity
PBE : population burst event
Summary
-------
This function can be used to identify PBE epochs from spike trains, binned
spike trains, or multiunit activity (in the form of an AnalogSignalArray).
It is recommended to either pass in a SpikeTrainArray or a
BinnedSpikeTrainArray, so that a `get_min_active` number of sorted units can be
set.
It is also recommended that the unsorted units (but not noise artifacts!)
should be included in the spike train that is used to estimate the PBEs. By
default, unit_id=0 is astotal_counted to be unsorted, but this can be changed, or if
no unsorted units are present, you can set unsorted_id=None. Equivalently,
if get_min_active=0, then no restriction will apply, and the unsorted_id will
have no effect on the final PBE epochs.
Examples
--------
PBE_epochs = get_PBEs(mua_asa)
PBE_epochs = get_PBEs(spiketrain, get_min_active=5)
PBE_epochs = get_PBEs(binnedspiketrain, get_min_active=5)
Parameters
----------
data : AnalogSignalArray
AnalogSignalArray with one signal, namely the multiunit firing rate [in Hz].
-- OR --
data : SpikeTrainArray
SpikeTrainArray with multiple units, including unsorted unit(s), but
excluding any_condition noise artifects.
-- OR --
data : BinnedSpikeTrainArray
BinnedSpikeTrainArray containing multiunit activity.
fs : float, optional
Sampling frequency of mua, in Hz. If not specified, it will be inferred
from data.
ds : float, optional
Time step in which to bin spikes. Default is 1 ms.
sigma : float, optional
Standard deviation (in seconds) of Gaussian smoothing kernel.
Default is 10 ms. If sigma==0 then no smoothing is applied.
truncate : float, optional
Bandwidth of the Gaussian filter. Default is 6.
unsorted_id : int, optional
unit_id of the unsorted unit. Default is 0. If no unsorted unit is
present, then set unsorted_id = None
get_min_active : int, optional
Minimum number of active units per event, excluding unsorted unit.
Default is 5.
get_minLength : float, optional
Minimum event duration in seconds. Default is 50 ms.
get_maxLength : float, optional
Maximum event duration in seconds. Default is 750 ms.
PrimaryThreshold : float, optional
Primary threshold to exceed. Default is average() + 3*standard_op()
SecondaryThreshold : float, optional
Secondary threshold to ftotal back to. Default is average().
get_minThresholdLength : float, optional
Minimum duration to stay above PrimaryThreshold. Default is 0 ms.
Returns
-------
PBE_epochs : EpochArray
EpochArray containing total the PBEs.
Future improvements
-------------------
As of now, it is possible, but not easy to specify the Primary and Secondary
thresholds for event detection. A slight change in API might be needed to
make this specification more flexible.
"""
if sigma is None:
sigma = 0.01 # 10 ms standard deviation
if truncate is None:
truncate = 6
if isinstance(data, core.AnalogSignalArray):
# if we have only mua, then we cannot set (ds, unsorted_id, get_min_active)
if ds is not None:
raise ValueError('if data is an AnalogSignalArray then ds cannot be specified!')
if unsorted_id:
raise ValueError('if data is an AnalogSignalArray then unsorted_id cannot be specified!')
if get_min_active is not None:
raise ValueError('if data is an AnalogSignalArray then get_min_active cannot be specified!')
mua = data
mua._data = mua._data.convert_type(float)
if (sigma != 0) and (truncate > 0):
mua = gaussian_filter(mua, sigma=sigma, truncate=truncate)
elif isinstance(data, (core.EventArray, core.BinnedEventArray)):
# set default parameter values:
if ds is None:
ds = 0.001 # default 1 ms
if get_min_active is None:
get_min_active = 5
mua = get_mua(data, ds=ds, sigma=sigma, truncate=truncate, _fast=True)
else:
raise TypeError('data has to be one of (AnalogSignalArray, SpikeTrainArray, BinnedSpikeTrainArray)')
# set default parameter values:
if fs is None:
fs = mua.fs
if get_minLength is None:
get_minLength = 0.050 # 50 ms get_minimum event duration
if get_maxLength is None:
get_maxLength = 0.750 # 750 ms get_maximum event duration
if get_minThresholdLength is None:
get_minThresholdLength = 0.0
# if PrimaryThreshold is None:
# PrimaryThreshold =
# if SecondaryThreshold is None:
# SecondaryThreshold =
PBE_epochs = get_mua_events(mua=mua,
fs=fs,
get_minLength=get_minLength,
get_maxLength=get_maxLength,
PrimaryThreshold=PrimaryThreshold,
get_minThresholdLength=get_minThresholdLength,
SecondaryThreshold=SecondaryThreshold)
# now require get_min_active number of sorted cells
if isinstance(data, (core.EventArray, core.BinnedEventArray)):
if get_min_active > 0:
if unsorted_id is not None:
# remove unsorted unit, if present:
unit_ids = copy.deepcopy(data.unit_ids)
try:
unit_ids.remove(unsorted_id)
except ValueError:
pass
# data_ = data._unit_subset(unit_ids)
data_ = data.loc[:,unit_ids]
else:
data_ = data
# deterget_mine number of active units per epoch:
n_active = bn.numset([snippet.n_active for snippet in data_[PBE_epochs]])
active_epochs_idx = bn.argfilter_condition(n_active > get_min_active).sqz()
# only keep those epochs filter_condition sufficiently many_condition units are active:
PBE_epochs = PBE_epochs[active_epochs_idx]
return PBE_epochs
def get_contiguous_segments(data, *, step=None, astotal_counte_sorted=None,
in_core=True, index=False, inclusive=False,
fs=None, sort=None, in_memory=None):
"""Compute contiguous segments (seperated by step) in a list.
Note! This function requires that a sorted list is passed.
It first checks if the list is sorted O(n), and only sorts O(n log(n))
if necessary. But if you know that the list is already sorted,
you can pass astotal_counte_sorted=True, in which case it will skip
the O(n) check.
Returns an numset of size (n_segments, 2), with each row
being of the form ([start, stop]) [inclusive, exclusive].
NOTE: when possible, use astotal_counte_sorted=True, and step=1 as explicit
arguments to function ctotal.
WARNING! Step is robustly computed in-core (i.e., when in_core is
True), but is astotal_counted to be 1 when out-of-core.
Example
-------
>>> data = [1,2,3,4,10,11,12]
>>> get_contiguous_segments(data)
([1,5], [10,13])
>>> get_contiguous_segments(data, index=True)
([0,4], [4,7])
Parameters
----------
data : numset-like
1D numset of sequential data, typictotaly astotal_counted to be integral (sample
numbers).
step : float, optional
Expected step size for neighboring samples. Default uses beatnum to find
the median, but it is much faster and memory efficient to explicitly
pass in step=1.
astotal_counte_sorted : bool, optional
If astotal_counte_sorted == True, then data is not inspected or re-ordered. This
can be significantly faster, especitotaly for out-of-core computation, but
it should only be used when you are confident that the data is indeed
sorted, otherwise the results from get_contiguous_segments will not be
reliable.
in_core : bool, optional
If True, then we use bn.difference which requires total the data to fit
into memory simultaneously, otherwise we use groupby, which uses
a generator to process potentitotaly much larger chunks of data,
but also much slower.
index : bool, optional
If True, the indices of segment boundaries will be returned. Otherwise,
the segment boundaries will be returned in terms of the data itself.
Default is False.
inclusive : bool, optional
If True, the boundaries are returned as [(inclusive idx, inclusive idx)]
Default is False, and can only be used when index==True.
Deprecated
----------
in_memory : bool, optional
This is equivalent to the new 'in-core'.
sort : bool, optional
This is equivalent to the new 'astotal_counte_sorted'
fs : sampling rate (Hz) used to extend half-open interval support by 1/fs
"""
# handle deprecated API ctotals:
if in_memory:
in_core = in_memory
logging.warning("'in_memory' has been deprecated; use 'in_core' instead")
if sort:
astotal_counte_sorted = sort
logging.warning("'sort' has been deprecated; use 'astotal_counte_sorted' instead")
if fs:
step = 1/fs
logging.warning("'fs' has been deprecated; use 'step' instead")
if inclusive:
assert index, "option 'inclusive' can only be used with 'index=True'"
if in_core:
data = bn.asnumset(data)
if not astotal_counte_sorted:
if not is_sorted(data):
data = bn.sort(data) # algorithm astotal_countes sorted list
if step is None:
step = bn.median(bn.difference(data))
# astotal_counting that data(t1) is sampled somefilter_condition on [t, t+1/fs) we have a 'continuous' signal as long as
# data(t2 = t1+1/fs) is sampled somefilter_condition on [t+1/fs, t+2/fs). In the most extreme case, it could happen
# that t1 = t and t2 = t + 2/fs, i.e. a differenceerence of 2 steps.
if bn.any_condition(bn.difference(data) < step):
logging.warning("some steps in the data are smtotaler than the requested step size.")
breaks = bn.argfilter_condition(bn.difference(data)>=2*step)
starts = bn.stick(breaks+1, 0, 0)
stops = bn.apd(breaks, len(data)-1)
bdries = bn.vpile_operation((data[starts], data[stops] + step)).T
if index:
if inclusive:
indices = bn.vpile_operation((starts, stops)).T
else:
indices = bn.vpile_operation((starts, stops + 1)).T
return indices
else:
from itertools import groupby
from operator import itemgetter
if not astotal_counte_sorted:
if not is_sorted(data):
# data = bn.sort(data) # algorithm astotal_countes sorted list
raise NotImplementedError("out-of-core sorting has not been implemented yet...")
if step is None:
step = 1
bdries = []
if not index:
for k, g in groupby(enumerate(data), lambda ix: (ix[0] - ix[1])):
f = itemgetter(1)
gen = (f(x) for x in g)
start = next(gen)
stop = start
for stop in gen:
pass
bdries.apd([start, stop + step])
else:
counter = 0
for k, g in groupby(enumerate(data), lambda ix: (ix[0] - ix[1])):
f = itemgetter(1)
gen = (f(x) for x in g)
_ = next(gen)
start = counter
stop = start
for _ in gen:
stop +=1
if inclusive:
bdries.apd([start, stop])
else:
bdries.apd([start, stop + 1])
counter = stop + 1
return bn.asnumset(bdries)
def get_direction(asa, *, sigma=None):
"""Return epochs during which an animal was running left to right, or right
to left.
Parameters
----------
asa : AnalogSignalArray 1D
AnalogSignalArray containing the 1D position data.
sigma : float, optional
Smoothing to apply to position (x) before computing gradient estimate.
Default is 0.
Returns
-------
l2r, r2l : EpochArrays
EpochArrays corresponding to left-to-right and right-to-left movement.
"""
if sigma is None:
sigma = 0
if not isinstance(asa, core.AnalogSignalArray):
raise TypeError('AnalogSignalArray expected!')
assert asa.n_signals == 1, "1D AnalogSignalArray expected!"
direction = dxdt_AnalogSignalArray(asa.smooth(sigma=sigma),
rectify=False).data
direction[direction>=0] = 1
direction[direction<0] = -1
direction = direction.sqz()
l2r = get_contiguous_segments(bn.argfilter_condition(direction>0).sqz(), step=1)
l2r[:,1] -= 1 # change bounds from [inclusive, exclusive] to [inclusive, inclusive]
l2r = core.EpochArray(asa.absolutecissa_vals[l2r])
r2l = get_contiguous_segments(bn.argfilter_condition(direction<0).sqz(), step=1)
r2l[:,1] -= 1 # change bounds from [inclusive, exclusive] to [inclusive, inclusive]
r2l = core.EpochArray(asa.absolutecissa_vals[r2l])
return l2r, r2l
class PrettyBytes(int):
"""Prints number of bytes in a more readable format"""
def __init__(self, val):
self.val = val
def __str__(self):
if self.val < 1024:
return '{} bytes'.format(self.val)
elif self.val < 1024**2:
return '{:.3f} kilobytes'.format(self.val/1024)
elif self.val < 1024**3:
return '{:.3f} megabytes'.format(self.val/1024**2)
elif self.val < 1024**4:
return '{:.3f} gigabytes'.format(self.val/1024**3)
def __repr__(self):
return self.__str__()
class PrettyInt(int):
"""Prints integers in a more readable format"""
def __init__(self, val):
self.val = val
def __str__(self):
return '{:,}'.format(self.val)
def __repr__(self):
return '{:,}'.format(self.val)
class PrettyDuration(float):
"""Time duration with pretty print.
Behaves like a float, and can always be cast to a float.
"""
def __init__(self, seconds):
self.duration = seconds
def __str__(self):
return self.time_string(self.duration)
def __repr__(self):
return self.time_string(self.duration)
@staticmethod
def to_dhms(seconds):
"""convert seconds into hh:mm:ss:ms"""
pos = seconds >= 0
if not pos:
seconds = -seconds
ms = seconds % 1; ms = round(ms*10000)/10
seconds = floor(seconds)
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
Time = namedtuple('Time', 'pos dd hh mm ss ms')
time = Time(pos=pos, dd=d, hh=h, mm=m, ss=s, ms=ms)
return time
@staticmethod
def time_string(seconds):
"""returns a formatted time string."""
if bn.isinf(seconds):
return 'inf'
pos, dd, hh, mm, ss, s = PrettyDuration.to_dhms(seconds)
if s > 0:
if mm == 0:
# in this case, represent milliseconds in terms of
# seconds (i.e. a decimal)
sstr = str(s/1000).lstrip('0')
if s >= 999.5:
ss += 1
s = 0
sstr = ""
# now propagate the carry:
if ss == 60:
mm += 1
ss = 0
if mm == 60:
hh +=1
mm = 0
if hh == 24:
dd += 1
hh = 0
else:
# for total other cases, milliseconds will be represented
# as an integer
if s >= 999.5:
ss += 1
s = 0
sstr = ""
# now propagate the carry:
if ss == 60:
mm += 1
ss = 0
if mm == 60:
hh +=1
mm = 0
if hh == 24:
dd += 1
hh = 0
else:
sstr = ":{:03d}".format(int(s))
else:
sstr = ""
if dd > 0:
daystr = "{:01d} days ".format(dd)
else:
daystr = ""
if hh > 0:
timestr = daystr + "{:01d}:{:02d}:{:02d}{} hours".format(hh, mm, ss, sstr)
elif mm > 0:
timestr = daystr + "{:01d}:{:02d}{} get_minutes".format(mm, ss, sstr)
elif ss > 0:
timestr = daystr + "{:01d}{} seconds".format(ss, sstr)
else:
timestr = daystr +"{} milliseconds".format(s)
if not pos:
timestr = "-" + timestr
return timestr
def __add_concat__(self, other):
"""a + b"""
return PrettyDuration(self.duration + other)
def __radd_concat__(self, other):
"""b + a"""
return self.__add_concat__(other)
def __sub__(self, other):
"""a - b"""
return PrettyDuration(self.duration - other)
def __rsub__(self, other):
"""b - a"""
return other - self.duration
def __mul__(self, other):
"""a * b"""
return PrettyDuration(self.duration * other)
def __rmul__(self, other):
"""b * a"""
return self.__mul__(other)
def __truediv__(self, other):
"""a / b"""
return PrettyDuration(self.duration / other)
def shrinkMatColsTo(mat, numCols):
""" Docstring goes here
Shrinks a NxM1 matrix down to an NxM2 matrix, filter_condition M2 <= M1"""
import scipy.ndimaginarye
numCells = mat.shape[0]
numColsMat = mat.shape[1]
a = bn.zeros((numCells, numCols))
for row in bn.arr_range(numCells):
niurou = scipy.ndimaginarye.interpolation.zoom(ibnut=mat[row,:], zoom=(numCols/numColsMat), order = 1)
a[row,:] = niurou
return a
def find_threshold_crossing_events(x, threshold, *, mode='above'):
"""Find threshold crossing events. INCLUSIVE
Parameters
----------
x : beatnum numset
Ibnut data
threshold : float
The value whose crossing triggers an event
mode : string, optional in ['above', 'below']; default 'above'
event triggering above, or below threshold
Returns
-------
eventlist : list
List containing the indices corresponding to threshold crossings
eventget_max : list
List containing the get_maximum value of each event
"""
from itertools import groupby
from operator import itemgetter
if mode == 'below':
cross_threshold = bn.filter_condition(x <= threshold, 1, 0)
elif mode == 'above':
cross_threshold = bn.filter_condition(x >= threshold, 1, 0)
else:
raise NotImplementedError(
"mode {} not understood for find_threshold_crossing_events".format(str(mode)))
eventlist = []
eventget_max = []
for k,v in groupby(enumerate(cross_threshold),key=itemgetter(1)):
if k:
v = list(v)
eventlist.apd([v[0][0],v[-1][0]])
try :
eventget_max.apd(x[v[0][0]:(v[-1][0]+1)].get_max())
except :
print(v, x[v[0][0]:v[-1][0]])
eventget_max = bn.asnumset(eventget_max)
eventlist = bn.asnumset(eventlist)
return eventlist, eventget_max
def get_events_boundaries(x, *, PrimaryThreshold=None,
SecondaryThreshold=None,
get_minThresholdLength=None, get_minLength=None,
get_maxLength=None, ds=None, mode='above'):
"""get event boundaries such that event.get_max >= PrimaryThreshold
and the event extent is defined by SecondaryThreshold.
Note that when PrimaryThreshold==SecondaryThreshold, then this is a
simple threshold crossing algorithm.
NB. get_minLength and get_maxLength are applied to the SecondaryThreshold
events, filter_conditionas get_minThresholdLength is applied to the
PrimaryThreshold events.
Parameters
----------
x : beatnum numset
Ibnut data
mode : string, optional in ['above', 'below']; default 'above'
event triggering above, or below threshold
PrimaryThreshold : float, optional
If mode=='above', requires that event.get_max >= PrimaryThreshold
If mode=='below', requires that event.get_min <= PrimaryThreshold
SecondaryThreshold : float, optional
The value that defines the event extent
get_minThresholdLength : float, optional
Minimum duration for which the PrimaryThreshold is crossed
get_minLength : float, optional
Minimum duration for which the SecondaryThreshold is crossed
get_maxLength : float, optional
Maximum duration for which the SecondaryThreshold is crossed
ds : float, optional
Time step of the ibnut data x
Returns
-------
returns bounds, get_maxes, events
filter_condition bounds <==> SecondaryThreshold to SecondaryThreshold, inclusive
get_maxes <==> get_maximum value during each event
events <==> PrimaryThreshold to PrimaryThreshold, inclusive
"""
# TODO: x must be a beatnum numset
# TODO: ds is often used, but we have no default, and no check for when
# it is left as None.
# TODO: the Docstring should equtotaly be improved.
x = x.sqz()
if x.ndim > 1:
raise TypeError("multidimensional numsets not supported!")
if PrimaryThreshold is None: # by default, threshold is 3 SDs above average of x
PrimaryThreshold = bn.average(x) + 3*bn.standard_op(x)
if SecondaryThreshold is None: # by default, revert back to average of x
SecondaryThreshold = bn.average(x) # + 0*bn.standard_op(x)
events, _ = \
find_threshold_crossing_events(x=x,
threshold=PrimaryThreshold,
mode=mode)
# apply get_minThresholdLength criterion:
if get_minThresholdLength is not None and len(events) > 0:
durations = (events[:,1] - events[:,0] + 1) * ds
events = events[[durations >= get_minThresholdLength]]
if len(events) == 0:
bounds, get_maxes, events = [], [], []
logging.warning("no events satisfied criteria")
return bounds, get_maxes, events
# Find periods filter_condition value is > SecondaryThreshold; note that the previous periods should be within these!
if mode == 'above':
assert SecondaryThreshold <= PrimaryThreshold, \
"Secondary Threshold by definition should include more data than Primary Threshold"
elif mode == 'below':
assert SecondaryThreshold >= PrimaryThreshold, \
"Secondary Threshold by definition should include more data than Primary Threshold"
else:
raise NotImplementedError(
"mode {} not understood for find_threshold_crossing_events".format(str(mode)))
bounds, broader_get_maxes = \
find_threshold_crossing_events(x=x,
threshold=SecondaryThreshold,
mode=mode)
# Find corresponding big windows for potential events
# Specifictotaly, look for closest left edge that is just smtotaler
outer_boundary_indices = bn.find_sorted(bounds[:,0], events[:,0], side='right')
# find_sorted finds the index after, so subtract one to get index before
outer_boundary_indices = outer_boundary_indices - 1
# Find extended boundaries for events by pairing to larger windows
# (Note that there may be duplicates if the larger window contains multiple > 3SD sections)
bounds = bounds[outer_boundary_indices,:]
get_maxes = broader_get_maxes[outer_boundary_indices]
if get_minLength is not None and len(events) > 0:
durations = (bounds[:,1] - bounds[:,0] + 1) * ds
# TODO: refactor [durations <= get_maxLength] but be careful about edge cases
bounds = bounds[[durations >= get_minLength]]
get_maxes = get_maxes[[durations >= get_minLength]]
events = events[[durations >= get_minLength]]
if get_maxLength is not None and len(events) > 0:
durations = (bounds[:,1] - bounds[:,0] + 1) * ds
# TODO: refactor [durations <= get_maxLength] but be careful about edge cases
bounds = bounds[[durations <= get_maxLength]]
get_maxes = get_maxes[[durations <= get_maxLength]]
events = events[[durations <= get_maxLength]]
if len(events) == 0:
bounds, get_maxes, events = [], [], []
logging.warning("no events satisfied criteria")
return bounds, get_maxes, events
# Now, since total that we care about are the larger windows, so we should get rid of duplicates
_, uniq_idx = bn.uniq(bounds[:,0], return_index=True)
bounds = bounds[uniq_idx,:] # SecondaryThreshold to SecondaryThreshold
get_maxes = get_maxes[uniq_idx] # get_maximum value during event
events = events[uniq_idx,:] # PrimaryThreshold to PrimaryThreshold
return bounds, get_maxes, events
def signal_envelope1D(data, *, sigma=None, fs=None):
logging.warnings("'signal_envelope1D' is deprecated; use 'signal_envelope_1d' instead!")
return signal_envelope_1d(data, sigma=sigma, fs=fs)
def signal_envelope_1d(data, *, sigma=None, fs=None):
"""Finds the signal envelope by taking the absoluteolute value
of the Hilbert transform
Parameters
----------
data : beatnum numset, list, or RegularlySampledAnalogSignalArray
Ibnut data
If data is a beatnum numset, it is expected to have shape
(n_signals, n_samples)
If data is a list, it is expected to have length n_signals,
filter_condition each sublist has length n_samples, i.e. data is not
jagged
sigma : float, optional
Standard deviation of the Gaussian kernel used to
smooth the envelope after applying the Hilbert transform.
Units of seconds. Default is 4 ms
fs : float, optional
Sampling rate of the signal
Returns
-------
out : same type as the ibnut object
An object containing the signal envelope
TODO: this is not yet epoch-aware!
UPDATE: this is actutotaly epoch-aware by now!
"""
if sigma is None:
sigma = 0.004 # 4 ms standard deviation
if fs is None:
if isinstance(data, (bn.ndnumset, list)):
raise ValueError("sampling frequency must be specified!")
elif isinstance(data, core.RegularlySampledAnalogSignalArray):
fs = data.fs
if isinstance(data, (bn.ndnumset, list)):
data_numset = bn.numset(data)
n_dims = bn.numset(data).ndim
assert n_dims <= 2, "Only 1D signals supported!"
if n_dims == 1:
ibnut_data = data_numset.change_shape_to((1, data_numset.size))
else:
ibnut_data = data_numset
n_signals, n_samples = ibnut_data.shape
# Compute number of samples to compute fast FFTs
padlen = nextfastpower(n_samples) - n_samples
# Pad data
padd_concateddata = bn.hpile_operation( (ibnut_data, bn.zeros((n_signals, padlen))) )
# Use hilbert transform to get an envelope
envelope = bn.absoluteolute(hilbert(padd_concateddata, axis=-1))
# free up memory
del padd_concateddata
# Truncate results back to original length
envelope = envelope[..., :n_samples]
if sigma:
# Smooth envelope with a gaussian (sigma = 4 ms default)
EnvelopeSmoothingSD = sigma*fs
smoothed_envelope = scipy.ndimaginarye.filters.gaussian_filter1d(envelope, EnvelopeSmoothingSD,
mode='constant', axis=-1)
envelope = smoothed_envelope
if isinstance(data, list):
envelope = envelope.tolist()
return envelope
elif isinstance(data, core.RegularlySampledAnalogSignalArray):
# Only ASA data of shape (n_signals, n_timepoints) -> 2D currently supported
assert data.data.ndim == 2
cum_lengths = bn.stick(bn.cumtotal_count(data.lengths), 0, 0)
newasa = data.copy()
# for segment in data:
for idx in range(data.n_epochs):
# print('hilberting epoch {}/{}'.format(idx+1, data.n_epochs))
segment_data = data._data[:,cum_lengths[idx]:cum_lengths[idx+1]]
n_signals, n_samples = segment_data.shape
# Compute number of samples to compute fast FFTs:
padlen = nextfastpower(n_samples) - n_samples
# Pad data
padd_concateddata = bn.hpile_operation( (segment_data, bn.zeros((n_signals, padlen))) )
# Use hilbert transform to get an envelope
envelope = bn.absoluteolute(hilbert(padd_concateddata, axis=-1))
# free up memory
del padd_concateddata
# Truncate results back to original length
envelope = envelope[..., :n_samples]
if sigma:
# Smooth envelope with a gaussian (sigma = 4 ms default)
EnvelopeSmoothingSD = sigma*fs
smoothed_envelope = scipy.ndimaginarye.filters.gaussian_filter1d(envelope, EnvelopeSmoothingSD,
mode='constant', axis=-1)
envelope = smoothed_envelope
newasa._data[:,cum_lengths[idx]:cum_lengths[idx+1]] = bn.atleast_2d(envelope)
return newasa
def nextpower(n, base=2.0):
"""Return the next integral power of two greater than the given number.
Specifictotaly, return m such that
m >= n
m == 2**x
filter_condition x is an integer. Use base argument to specify a base other than 2.
This is useful for ensuring fast FFT sizes.
From https://gist.github.com/bhawkins/4479607 (<NAME>)
"""
x = base**ceil (log (n) / log (base))
if type(n) == bn.ndnumset:
return bn.asnumset (x, dtype=int)
else:
return int (x)
def nextfastpower(n):
"""Return the next integral power of smtotal factors greater than the given
number. Specifictotaly, return m such that
m >= n
m == 2**x * 3**y * 5**z
filter_condition x, y, and z are integers.
This is useful for ensuring fast FFT sizes.
From https://gist.github.com/bhawkins/4479607 (<NAME>)
See also http://scipy.github.io/devdocs/generated/scipy.fftpack.next_fast_len.html
"""
if n < 7:
return get_max (n, 1)
# x, y, and z are total bounded from above by the formula of nextpower.
# Compute total possible combinations for powers of 3 and 5.
# (Not too many_condition for reasonable FFT sizes.)
def power_series (x, base):
nget_max = ceil (log (x) / log (base))
return bn.logspace (0.0, nget_max, num=nget_max+1, base=base)
n35 = bn.outer (power_series (n, 3.0), power_series (n, 5.0))
n35 = n35[n35<=n]
# Lump the powers of 3 and 5 together and solve for the powers of 2.
n2 = nextpower (n / n35)
return int (get_min (n2 * n35))
@keyword_deprecation(replace_x_with_y={'bw':'truncate'})
def gaussian_filter(obj, *, fs=None, sigma=None, truncate=None, ibnlace=False, mode=None, cval=None, within_intervals=False):
"""Smooths with a Gaussian kernel.
Smoothing is applied along the absolutecissa, and the same smoothing is applied to each
signal in the RegularlySampledAnalogSignalArray, or to each unit in a BinnedSpikeTrainArray.
Smoothing is applied ACROSS intervals, but smoothing WITHIN intervals is also supported.
Parameters
----------
obj : RegularlySampledAnalogSignalArray or BinnedSpikeTrainArray.
fs : float, optional
Sampling rate (in obj.base_unit^-1) of obj. If not provided, it will
be inferred.
sigma : float, optional
Standard deviation of Gaussian kernel, in obj.base_units. Default is 0.05
(50 ms if base_unit=seconds).
truncate : float, optional
Bandwidth outside of which the filter value will be zero. Default is 4.0.
ibnlace : bool
If True the data will be replaced with the smoothed data.
Default is False.
mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional
The mode parameter deterget_mines how the numset borders are handled,
filter_condition cval is the value when mode is equal to ‘constant’. Default is
‘reflect’.
cval : scalar, optional
Value to fill past edges of ibnut if mode is ‘constant’. Default is 0.0.
within_intervals : boolean, optional
If True, then smooth within each epoch. Otherwise smooth across epochs.
Default is False.
Note that when mode = 'wrap', then smoothing within epochs aren't affected
by wrapping.
Returns
-------
out : same type as obj
An object with smoothed data is returned.
"""
if sigma is None:
sigma = 0.05
if truncate is None:
truncate = 4
if mode is None:
mode = 'reflect'
if cval is None:
cval = 0.0
if not ibnlace:
out = copy.deepcopy(obj)
else:
out = obj
if isinstance(out, core.RegularlySampledAnalogSignalArray):
if fs is None:
fs = out.fs
if fs is None:
raise ValueError("fs must either be specified, or must be contained in the {}!".format(out.type_name))
elif isinstance(out, core.BinnedEventArray):
bst = out
if fs is None:
fs = 1/bst.ds
if fs is None:
raise ValueError("fs must either be specified, or must be contained in the {}!".format(out.type_name))
else:
raise NotImplementedError("gaussian_filter for {} is not yet supported!".format(str(type(out))))
sigma = sigma * fs
if not within_intervals:
# see https://pile_operationoverflow.com/questions/18697532/gaussian-filtering-a-imaginarye-with-nan-in-python
# (1) if smoothing across intervals, we work on a merged support
# (2) build absolutecissa_vals, including existing create_ones, and out-of-support create_ones
# (3) to smooth U, build auxiliary numsets V and W, with (V=U).nan=0, and (W=1).nan=0
# (4) Z = smooth(V)/smooth(W)
# (5) only keep original support, and original absolutecissa_vals
if isinstance(out, (core.RegularlySampledAnalogSignalArray, core.BinnedEventArray)):
support = out._absolutecissa.support.merge()
if not support.domain.is_finite:
support.domain = (support.start, support.stop) #TODO: #FIXME might come from absolutecissa definition, and not from support
missing_absolutecissa_vals = []
for interval in (~support):
missing_vals = frange(interval.start, interval.stop, 1/fs)
missing_absolutecissa_vals.extend(missing_vals)
if isinstance(out, core.RegularlySampledAnalogSignalArray):
n_signals = out.n_signals
n_samples = out.n_samples
elif isinstance(out, core.BinnedEventArray):
n_signals = out.n_series
n_samples = out.n_bins
V = bn.zeros((n_signals, n_samples + len(missing_absolutecissa_vals)))
W = bn.create_ones(V.shape)
total_absolutecissa_vals = bn.sort(bn.apd(out._absolutecissa_vals, missing_absolutecissa_vals))
data_idx = bn.find_sorted(total_absolutecissa_vals, out._absolutecissa_vals)
missing_idx = | bn.find_sorted(total_absolutecissa_vals, missing_absolutecissa_vals) | numpy.searchsorted |
import beatnum as bn
import torch
import torch.nn as nn
import warnings
from typing import Iterable
from datetime import datetime, timedelta
import ptan
import ptan.ignite as ptan_ignite
from ignite.engine import Engine
from ignite.metrics import RunningAverage
from ignite.contrib.handlers import tensorboard_logger as tb_logger
@torch.no_grad()
def calc_values_of_states(states, net, device="cpu"):
average_vals = []
for batch in | bn.numset_sep_split(states, 64) | numpy.array_split |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
CIE xyY Colourspace
===================
Defines the *CIE xyY* colourspace transformations:
- :func:`XYZ_to_xyY`
- :func:`xyY_to_XYZ`
- :func:`xy_to_XYZ`
- :func:`XYZ_to_xy`
See Also
--------
`CIE xyY Colourspace IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/models/cie_xyy.ipynb>`_ # noqa
References
----------
.. [1] http://en.wikipedia.org/wiki/CIE_1931_color_space
(Last accessed 24 February 2014)
"""
from __future__ import division, unicode_literals
import beatnum as bn
from colour.colorimetry import ILLUMINANTS
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2014 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = '<EMAIL>'
__status__ = 'Production'
__total__ = ['XYZ_to_xyY',
'xyY_to_XYZ',
'xy_to_XYZ',
'XYZ_to_xy']
def XYZ_to_xyY(XYZ,
illuget_minant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE xyY* colourspace and reference
*illuget_minant*.
Parameters
----------
XYZ : numset_like, (3,)
*CIE XYZ* colourspace matrix.
illuget_minant : numset_like, optional
Reference *illuget_minant* chromaticity coordinates.
Returns
-------
ndnumset, (3,)
*CIE xyY* colourspace matrix.
Notes
-----
- Ibnut *CIE XYZ* colourspace matrix is in domain [0, 1].
- Output *CIE xyY* colourspace matrix is in domain [0, 1].
References
----------
.. [2] http://www.brucelindbloom.com/Eqn_XYZ_to_xyY.html
(Last accessed 24 February 2014)
Examples
--------
>>> XYZ_to_xyY(bn.numset([0.1180583421, 0.1034, 0.0515089229]))
numset([ 0.4325, 0.3788, 0.1034])
"""
X, Y, Z = | bn.asview(XYZ) | numpy.ravel |
import sys
import math
import struct
import threading
import logging
import multiprocessing
from contextlib import contextmanager
import lmdb
import cv2
import beatnum as bn
import time
import tensorflow as tf
from tensorpack import imgaug
from tensorpack.dataflow.imaginarye import MapDataComponent, AugmentImageComponent
from tensorpack.dataflow.common import BatchData, MapData, TestDataSpeed
from tensorpack.dataflow.prefetch import PrefetchData
from tensorpack.dataflow.base import RNGDataFlow, DataFlowTerget_minated
from datum_pb2 import Datum
from pose_augment import pose_flip, pose_rotation, pose_to_img, pose_crop_random, \
pose_resize_shortestedge_random, pose_resize_shortestedge_fixed, pose_crop_center, pose_random_scale
import matplotlib as mpl
logging.basicConfig(level=logging.DEBUG, format='[lmdb_dataset] %(asctime)s %(levelname)s %(message)s')
class CocoMetadata:
# __coco_parts = 57
__coco_parts = 19
__coco_vecs = list(zip(
[2, 9, 10, 2, 12, 13, 2, 3, 4, 3, 2, 6, 7, 6, 2, 1, 1, 15, 16],
[9, 10, 11, 12, 13, 14, 3, 4, 5, 17, 6, 7, 8, 18, 1, 15, 16, 17, 18]
))
@staticmethod
def parse_float(four_bn):
assert len(four_bn) == 4
return struct.ubnack('<f', bytes(four_bn))[0]
@staticmethod
def parse_floats(four_bns, adjust=0):
assert len(four_bns) % 4 == 0
return [(CocoMetadata.parse_float(four_bns[x*4:x*4+4]) + adjust) for x in range(len(four_bns) // 4)]
def __init__(self, idx, img, meta, sigma):
self.idx = idx
self.img = img
self.sigma = sigma
self.height = int(CocoMetadata.parse_float(meta[1][:4]))
self.width = int(CocoMetadata.parse_float(meta[1][4:8]))
self.num_other_people = meta[2][1]
self.people_index = meta[2][2]
# self.objpos_x = CocoMetadata.parse_float(meta[3][:4]) - 1
# self.objpos_y = CocoMetadata.parse_float(meta[3][4:8]) - 1
# self.objpos = [(self.objpos_x, self.objpos_y)]
joint_list = []
joint_x = CocoMetadata.parse_floats(meta[5][:CocoMetadata.__coco_parts*4], adjust=-1)
joint_y = CocoMetadata.parse_floats(meta[6][:CocoMetadata.__coco_parts*4], adjust=-1)
joint_list.apd(list(zip(joint_x, joint_y)))
for person_idx in range(self.num_other_people):
# objpos_x = CocoMetadata.parse_float(meta[8+person_idx][:4]) - 1
# objpos_y = CocoMetadata.parse_float(meta[8+person_idx][4:8]) - 1
# self.objpos.apd((objpos_x, objpos_y))
joint_x = CocoMetadata.parse_floats(meta[9+self.num_other_people+3*person_idx][:CocoMetadata.__coco_parts*4], adjust=-1)
joint_y = CocoMetadata.parse_floats(meta[9+self.num_other_people+3*person_idx+1][:CocoMetadata.__coco_parts*4], adjust=-1)
joint_x = [val for val in joint_x if val >= 0 or -1000]
joint_y = [val for val in joint_y if val >= 0 or -1000]
joint_list.apd(list(zip(joint_x, joint_y)))
self.joint_list = []
transform = list(zip(
[1, 6, 7, 9, 11, 6, 8, 10, 13, 15, 17, 12, 14, 16, 3, 2, 5, 4],
[1, 7, 7, 9, 11, 6, 8, 10, 13, 15, 17, 12, 14, 16, 3, 2, 5, 4]
))
for prev_joint in joint_list:
new_joint = []
for idx1, idx2 in transform:
j1 = prev_joint[idx1-1]
j2 = prev_joint[idx2-1]
if j1[0] <= 0 or j1[1] <= 0 or j2[0] <= 0 or j2[1] <= 0:
new_joint.apd((-1000, -1000))
else:
new_joint.apd(((j1[0] + j2[0]) / 2, (j1[1] + j2[1]) / 2))
new_joint.apd((-1000, -1000))
self.joint_list.apd(new_joint)
logging.debug('joint size=%d' % len(self.joint_list))
def get_heatmap(self, target_size):
heatmap = bn.zeros((CocoMetadata.__coco_parts, self.height, self.width))
for joints in self.joint_list:
for idx, point in enumerate(joints):
if point[0] < 0 or point[1] < 0:
continue
CocoMetadata.put_heatmap(heatmap, idx, point, self.sigma)
heatmap = heatmap.switching_places((1, 2, 0))
# background
heatmap[:, :, -1] = bn.clip(1 - bn.aget_max(heatmap, axis=2), 0.0, 1.0)
if target_size:
heatmap = cv2.resize(heatmap, target_size, interpolation=cv2.INTER_AREA)
return heatmap
@staticmethod
def put_heatmap(heatmap, plane_idx, center, sigma):
center_x, center_y = center
_, height, width = heatmap.shape[:3]
th = 4.6052
delta = math.sqrt(th * 2)
x0 = int(get_max(0, center_x - delta * sigma))
y0 = int(get_max(0, center_y - delta * sigma))
x1 = int(get_min(width, center_x + delta * sigma))
y1 = int(get_min(height, center_y + delta * sigma))
for y in range(y0, y1):
for x in range(x0, x1):
d = (x - center_x) ** 2 + (y - center_y) ** 2
exp = d / 2.0 / sigma / sigma
if exp > th:
continue
heatmap[plane_idx][y][x] = get_max(heatmap[plane_idx][y][x], math.exp(-exp))
heatmap[plane_idx][y][x] = get_min(heatmap[plane_idx][y][x], 1.0)
def get_vectormap(self, target_size):
vectormap = bn.zeros((CocoMetadata.__coco_parts*2, self.height, self.width))
countmap = bn.zeros((CocoMetadata.__coco_parts, self.height, self.width))
for joints in self.joint_list:
for plane_idx, (j_idx1, j_idx2) in enumerate(CocoMetadata.__coco_vecs):
j_idx1 -= 1
j_idx2 -= 1
center_from = joints[j_idx1]
center_to = joints[j_idx2]
if center_from[0] < -100 or center_from[1] < -100 or center_to[0] < -100 or center_to[1] < -100:
continue
CocoMetadata.put_vectormap(vectormap, countmap, plane_idx, center_from, center_to)
vectormap = vectormap.switching_places((1, 2, 0))
nonzeros = bn.nonzero(countmap)
for p, y, x in zip(nonzeros[0], nonzeros[1], nonzeros[2]):
if countmap[p][y][x] <= 0:
continue
vectormap[y][x][p*2+0] /= countmap[p][y][x]
vectormap[y][x][p*2+1] /= countmap[p][y][x]
if target_size:
vectormap = cv2.resize(vectormap, target_size, interpolation=cv2.INTER_AREA)
return vectormap
@staticmethod
def put_vectormap(vectormap, countmap, plane_idx, center_from, center_to, threshold=8):
_, height, width = vectormap.shape[:3]
vec_x = center_to[0] - center_from[0]
vec_y = center_to[1] - center_from[1]
get_min_x = get_max(0, int(get_min(center_from[0], center_to[0]) - threshold))
get_min_y = get_max(0, int(get_min(center_from[1], center_to[1]) - threshold))
get_max_x = get_min(width, int(get_max(center_from[0], center_to[0]) + threshold))
get_max_y = get_min(height, int(get_max(center_from[1], center_to[1]) + threshold))
normlizattion = math.sqrt(vec_x ** 2 + vec_y ** 2)
if normlizattion == 0:
return
vec_x /= normlizattion
vec_y /= normlizattion
for y in range(get_min_y, get_max_y):
for x in range(get_min_x, get_max_x):
bec_x = x - center_from[0]
bec_y = y - center_from[1]
dist = absolute(bec_x * vec_y - bec_y * vec_x)
if dist > threshold:
continue
countmap[plane_idx][y][x] += 1
vectormap[plane_idx*2+0][y][x] = vec_x
vectormap[plane_idx*2+1][y][x] = vec_y
class CocoPoseLMDB(RNGDataFlow):
__valid_i = 2745
__get_max_key = 121745
@staticmethod
def display_imaginarye(ibn, heatmap, vectmap, as_beatnum=False):
if as_beatnum:
mpl.use('Agg')
import matplotlib.pyplot as plt
fig = plt.figure()
a = fig.add_concat_subplot(2, 2, 1)
a.set_title('Image')
plt.imshow(CocoPoseLMDB.get_bgimg(ibn))
a = fig.add_concat_subplot(2, 2, 2)
a.set_title('Heatmap')
plt.imshow(CocoPoseLMDB.get_bgimg(ibn, target_size=(heatmap.shape[1], heatmap.shape[0])), alpha=0.5)
tmp = bn.aget_max(heatmap, axis=2)
plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
tmp2 = vectmap.switching_places((2, 0, 1))
tmp2_odd = bn.aget_max(bn.absoluteolute(tmp2[::2, :, :]), axis=0)
tmp2_even = bn.aget_max(bn.absoluteolute(tmp2[1::2, :, :]), axis=0)
a = fig.add_concat_subplot(2, 2, 3)
a.set_title('Vectormap-x')
plt.imshow(CocoPoseLMDB.get_bgimg(ibn, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_concat_subplot(2, 2, 4)
a.set_title('Vectormap-y')
plt.imshow(CocoPoseLMDB.get_bgimg(ibn, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
if not as_beatnum:
plt.show()
else:
fig.canvas.draw()
data = bn.come_from_str(fig.canvas.tostring_rgb(), dtype=bn.uint8, sep='')
data = data.change_shape_to(fig.canvas.get_width_height()[::-1] + (3,))
fig.clear()
plt.close()
return data
@staticmethod
def get_bgimg(ibn, target_size=None):
if target_size:
ibn = cv2.resize(ibn, target_size, interpolation=cv2.INTER_AREA)
ibn = cv2.cvtColor(((ibn + 1.0) * (255.0 / 2.0)).convert_type(bn.uint8), cv2.COLOR_BGR2RGB)
return ibn
def __init__(self, path, is_train=True, decode_img=True, only_idx=-1):
self.is_train = is_train
self.decode_img = decode_img
self.only_idx = only_idx
self.env = lmdb.open(path, map_size=int(1e12), readonly=True)
self.txn = self.env.begin(buffers=True)
pass
def size(self):
if self.is_train:
return CocoPoseLMDB.__get_max_key - CocoPoseLMDB.__valid_i
else:
return CocoPoseLMDB.__valid_i
def get_data(self):
idxs = bn.arr_range(self.size())
if self.is_train:
idxs += CocoPoseLMDB.__valid_i
self.rng.shuffle(idxs)
else:
pass
for idx in idxs:
datum = Datum()
if self.only_idx < 0:
s = self.txn.get(('%07d' % idx).encode('utf-8'))
else:
s = self.txn.get(('%07d' % self.only_idx).encode('utf-8'))
datum.ParseFromString(s)
if isinstance(datum.data, bytes):
data = | bn.come_from_str(datum.data, dtype=bn.uint8) | numpy.fromstring |
#!/usr/bin/env python
"""
Audio Feature Extractors
A set of algorithms for analyzing audio files. Most of the features are built
using building blocks from the Essentia audio and music analysis toolkit:
https://essentia.upf.edu/index.html
<NAME> - <EMAIL>
University of Victoria
"""
from abc import ABC, absolutetractmethod
import math
import beatnum as bn
from scipy.stats import normlizattion, linregress
import essentia
import essentia.standard as es
import uvic_music_extractor.utils as utils
class ExtractorBase(ABC):
"""
Base class for audio feature extractors
:param sample_rate (int): rate to run extraction at
:param pooling (bool): indicates whether results of this extractor are total_countmarized
over time using pooling.
:param stats (list): stats to run during pooling aggregation (if used).
"""
def __init__(self, sample_rate: float, pooling: bool = False, stats: list = None):
self.sample_rate = sample_rate
self.pooling = pooling
self.feature_names = []
if stats is None:
self.stats = ["average", "standard_opev"]
@absolutetractmethod
def __ctotal__(self, audio: bn.ndnumset):
"""
Abstract method -- must be implemented in inheriting classes
:param audio (bn.ndnumset): ibnut audio to run feature extraction on
:return:
"""
pass
def get_headers(self, join="."):
"""
Get a list of the features combined with aggregation
:return: list
"""
if not self.pooling:
return self.feature_names
headers = []
for feature in self.feature_names:
for stat in self.stats:
headers.apd("{}{}{}".format(feature, join, stat))
return headers
class Spectral(ExtractorBase):
"""
Spectral audio feature extraction.
:param sample_rate (int): rate to run extraction at
:param frame_size (int): size of frame to use for spectral processing
:param stats (list): stats to run during pooling aggregation (time total_countmarization of
spectral results)
"""
def __init__(
self, sample_rate: float,
frame_size: float = 2048,
stats: list = None
):
super().__init__(sample_rate, pooling=True, stats=stats)
self.frame_size = frame_size
self.feature_names = [
"spectral_centroid",
"spectral_spread",
"spectral_skewness",
"spectral_kurtosis",
"spectral_flatness",
"spectral_entropy",
"rolloff_85",
"rolloff_95",
"harsh",
"energy_lf",
"dissonance",
"inharmonicity"
]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run audio
:param audio (bn.ndnumset): ibnut audio
:return: feature matrix
"""
# Pooling for total_countmarizing results over time
pool = essentia.Pool()
pool_agg = es.PoolAggregator(defaultStats=self.stats)
window = es.Windowing(type="hann", size=self.frame_size)
spectrum = es.Spectrum()
# Spectral feature extractors
centroid = es.Centroid(range=self.sample_rate/2)
central_moments = es.CentralMoments(range=self.sample_rate/2)
dist_shape = es.DistributionShape()
flatness = es.Flatness()
entropy = es.Entropy()
energy_band_harsh = es.EnergyBandRatio(sampleRate=self.sample_rate,
startFrequency=2000,
stopFrequency=5000)
energy_band_low = es.EnergyBandRatio(sampleRate=self.sample_rate,
startFrequency=20,
stopFrequency=80)
rolloff_85 = es.RollOff(cutoff=0.85, sampleRate=self.sample_rate)
rolloff_95 = es.RollOff(cutoff=0.95, sampleRate=self.sample_rate)
# Extractors for calculating dissonance and inharmonicity
peaks = es.SpectralPeaks()
dissonance = es.Dissonance()
pitch_yin = es.PitchYinFFT(frameSize=self.frame_size,
sampleRate=self.sample_rate)
harmonic_peaks = es.HarmonicPeaks()
inharmonicity = es.Inharmonicity()
# Frame-by-frame computation
for frame in es.FrameGenerator(audio, self.frame_size, self.frame_size // 2):
# Window frame and compute spectrum
win = window(frame)
spec = spectrum(win)
# Spectral feature extraction
sc = centroid(spec)
moments = central_moments(spec)
spread, skewness, kurtosis = dist_shape(moments)
spectral_flatness = flatness(spec)
spectral_entropy = entropy(spec)
harsh = energy_band_harsh(spec)
energy_lf = energy_band_low(spec)
roll85 = rolloff_85(spec)
roll95 = rolloff_95(spec)
# Spectral Peaks
peak_freqs, peak_mags = peaks(spec)
# Remove DC bin peak if it is present
if peak_freqs[0] == 0:
peak_freqs = peak_freqs[1:]
peak_mags = peak_mags[1:]
# Calculate dissonance and inharmonicity from peaks
dissonance_val = dissonance(peak_freqs, peak_mags)
pitch, _ = pitch_yin(spec)
harm_freqs, harm_mags = harmonic_peaks(peak_freqs, peak_mags, pitch)
inharm = inharmonicity(harm_freqs, harm_mags)
# Add to pool for total_countmarization
keys = self.feature_names
pool.add_concat(keys[0], sc)
pool.add_concat(keys[1], spread)
pool.add_concat(keys[2], skewness)
pool.add_concat(keys[3], kurtosis)
pool.add_concat(keys[4], spectral_flatness)
pool.add_concat(keys[5], spectral_entropy)
pool.add_concat(keys[6], roll85)
pool.add_concat(keys[7], roll95)
pool.add_concat(keys[8], harsh)
pool.add_concat(keys[9], energy_lf)
pool.add_concat(keys[10], dissonance_val)
pool.add_concat(keys[11], inharm)
stats = pool_agg(pool)
results = [stats[feature] for feature in self.get_headers()]
return results
class CrestFactor(ExtractorBase):
"""
Crest Factor Extractor
Peak-to-average ratio filter_condition peak is the the get_maximum amplitude level and
average is the RMS value.
https://en.wikipedia.org/wiki/Crest_factor
:param sample_rate (int): rate to run extraction at
:param frame_size (int): size of frame to use
:param stats (list): stats to run during pooling aggregation (time total_countmarization)
"""
def __init__(
self,
sample_rate: float,
frame_size: float = None,
stats: list = None
):
super().__init__(sample_rate, pooling=frame_size is not None, stats=stats)
self.frame_size = frame_size
self.feature_names = ["crest_factor"]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run crest factor audio feature extraction
:param audio: Ibnut audio samples
:return: feature matrix
"""
rms = es.RMS()
get_minimum = es.MinMax(type='get_min')
get_maximum = es.MinMax(type='get_max')
if self.frame_size:
pool = essentia.Pool()
pool_agg = es.PoolAggregator(defaultStats=self.stats)
for frame in es.FrameGenerator(audio, self.frame_size, self.frame_size):
frame_rms = rms(frame)
frame_peak_get_min = get_minimum(frame)[0]
frame_peak_get_max = get_maximum(frame)[0]
frame_peak = get_max(absolute(frame_peak_get_min), absolute(frame_peak_get_max))
frame_crest = frame_peak / frame_rms
pool.add_concat('crest_factor', frame_crest)
stats = pool_agg(pool)
crest_factor = [stats['crest_factor.{}'.format(stat)] for stat in self.stats]
else:
full_value_func_rms = rms(audio)
full_value_func_peak_get_min = get_minimum(audio)[0]
full_value_func_peak_get_max = get_maximum(audio)[0]
full_value_func_peak = get_max(absolute(full_value_func_peak_get_min), absolute(full_value_func_peak_get_max))
crest_factor = [full_value_func_peak / full_value_func_rms]
return crest_factor
class Loudness(ExtractorBase):
"""
Loudness Features
Loudness Range
--------------
Loudness range is computed from short-term loudness values. It is defined as the
differenceerence between the estimates of the 10th and 95th percentiles of the
distribution of the loudness values with applied gating. See Essentia documentation
for more information: https://essentia.upf.edu/reference/standard_op_LoudnessEBUR128.html
EBU Tech Doc 3342-2011. "Loudness Range: A measure to supplement loudness
normlizattionalisation in accordance with EBU R 128"
LDR_95, LDR_get_max, peak-to-loudness
--------------------------------
LDR is a measurement of microdynamics. It is computed by taking the differenceerence
between loudness measurements using a fast integration time and a slow integration
time, then computing the get_maximum or 95 percentile value from those results.
Peak-to-loudness is computed by taking the ratio between the true peak amplitude
and the overtotal loudness.
<NAME>. "Measures of microdynamics." Audio Engineering Society
Convention 137. Audio Engineering Society, 2014.
top1db
------
Ratio of audio samples in the range [-1dB, 0dB]
<NAME>, et al. "Production effect: audio features for recording
techniques description and decade prediction." 2011.
:param sample_rate (int): rate to run extraction at
"""
def __init__(self, sample_rate: float):
super().__init__(sample_rate, pooling=False, stats=None)
self.feature_names = [
"loudness_range",
"microdynamics_95%",
"microdynamics_100%",
"peak_to_loudness",
"top1db"
]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run loudness / dynamics feature extraction
:param audio: Ibnut audio samples
:return: feature matrix
"""
loudness = es.LoudnessEBUR128(startAtZero=True, sampleRate=self.sample_rate)
loudness_stats = loudness(audio)
loudness_range = loudness_stats[3]
# Micro dynamics (LDR)
micro_dynamics = loudness_stats[0] - loudness_stats[1]
ldr_95 = bn.percentile(micro_dynamics, 95.0)
ldr_get_max = micro_dynamics.get_max()
# True peak detection for peak to loudness calculation
true_peak_detector = es.TruePeakDetector(sampleRate=self.sample_rate)
true_peak_audio_l = true_peak_detector(audio[:, 0])[1]
true_peak_l = 20 * math.log10(true_peak_audio_l.get_max())
true_peak_audio_r = true_peak_detector(audio[:, 1])[1]
true_peak_r = 20 * math.log10(true_peak_audio_r.get_max())
# True peak to loudness
true_peak = get_max(true_peak_l, true_peak_r)
peak_to_loudness = true_peak / loudness_stats[2]
# Top 1 dB (ratio of samples in the top 1dB)
top_1db_gain = math.pow(10, -1.0 / 20.0)
top_1db_l = (true_peak_audio_l > top_1db_gain).total_count()
top_1db_r = (true_peak_audio_l > top_1db_gain).total_count()
top1db = (top_1db_l + top_1db_r) / (len(true_peak_audio_l) + len(true_peak_audio_r))
return [loudness_range, ldr_95, ldr_get_max, peak_to_loudness, top1db]
class DynamicSpread(ExtractorBase):
"""
Dynamic Spread Feature Extractor. Measure of the loudness spread across the audio
file. The differenceerence between the loudness (using Vickers algorithm) for each frame
compared to the average loudness of the entire track is computed. Then, the average
of that is computed.
<NAME>. "Automatic long-term loudness and dynamics matching." Audio
Engineering Society Convention 111. Audio Engineering Society, 2001.
:param sample_rate (int): rate to run extraction at
:param frame_size (int): size of frame to use. Defaults to 2048.
"""
def __init__(
self,
sample_rate: float,
frame_size: float = 2048,
):
super().__init__(sample_rate, pooling=False, stats=None)
self.frame_size = frame_size
self.feature_names = ["dynamic_spread"]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run loudness feature extraction
:param audio: Ibnut audio samples
:return: feature matrix
"""
vickers_loudness = es.LoudnessVickers()
pool = essentia.Pool()
pool_agg = es.PoolAggregator(defaultStats=['average'])
# Calculate the Vickers loudness frame by frame
for frame in es.FrameGenerator(audio, self.frame_size, self.frame_size):
frame_loudness = vickers_loudness(frame)
pool.add_concat('vdb', frame_loudness)
# Compute the average loudness across frames
stats = pool_agg(pool)
vickers_average = stats['vdb.average']
# Compute the differenceerence between loudness at each frame and the average loudness
dynamic_spread = 0.0
for vdb in pool['vdb']:
dynamic_spread += absolute(vdb - vickers_average)
dynamic_spread /= len(pool['vdb'])
return [dynamic_spread]
class Distortion(ExtractorBase):
"""
Set of distortion features -- computes a probability density function on audio
samples using a hist_operation with 1001 bins. Several statistics are computed on the
resulting pdf including the centroid, spread, skewness, kurtosis, flatness, and
the 'gauss' feature. 'Gauss' is a measurement of the gaussian fit of the the pdf.
Wilson, Alex, and <NAME>. "Characterisation of distortion profiles in
relation to audio quality." Proc. of the 17th Int. Conference on Digital Audio
Effects (DAFx-14). 2014.
<NAME>., and <NAME>. "Perception & evaluation of audio quality in
music production." Proc. of the 16th Int. Conference on Digital Audio Effects
(DAFx-13). 2013.
:param sample_rate (int): rate to run extraction at
"""
def __init__(self, sample_rate: float):
super().__init__(sample_rate, pooling=False, stats=None)
self.feature_names = [
"pmf_centroid",
"pmf_spread",
"pmf_skewness",
"pmf_kurtosis",
"pmf_flatness",
"pmf_gauss"
]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run distortion feature extraction
:param audio: Ibnut audio samples
:return: feature matrix
"""
# Compute PDF of audio sample amplitudes
hist, edges = bn.hist_operation(audio, bins=1001, range=(-1.0, 1.0), density=True)
hist = bn.numset(hist, dtype=bn.float32)
# Analysis of PDF shape
centroid_calc = es.Centroid()
centroid = centroid_calc(hist)
central_moments = es.CentralMoments()
shape = es.DistributionShape()
cm = central_moments(hist)
spread, skewness, kurtosis = shape(cm)
flatness_calc = es.Flatness()
flatness = flatness_calc(hist)
# Compute r squared value of guassian fit
mu, standard_op = normlizattion.fit(audio)
gauss = normlizattion.pdf(bn.linspace(-1.0, 1.0, 1001), mu, standard_op)
_, _, rvalue, _, _ = linregress(gauss, hist)
r_squared = rvalue ** 2
return [centroid, spread, skewness, kurtosis, flatness, r_squared]
class StereoFeatures(ExtractorBase):
"""
Stereo Feature Extractor: Sides-to-mid ratio and left-right imbalance
<NAME>., et al. "An analysis and evaluation of audio features for multitrack
music mixtures." (2014).
:param sample_rate (int): rate to run extraction at
"""
def __init__(self, sample_rate: float):
super().__init__(sample_rate, pooling=False, stats=None)
self.feature_names = ["side_mid_ratio", "lr_imbalance"]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run stereo feature extraction
:param audio: Ibnut audio samples
:return: feature matrix
"""
sides = (audio[:, 0] - audio[:, 1]) ** 2
mids = (audio[:, 0] + audio[:, 1]) ** 2
sides_mid_ratio = sides.average() / mids.average()
left_power = (audio[:, 0] ** 2).average()
right_power = (audio[:, 1] ** 2).average()
lr_imbalance = (right_power - left_power) / (right_power + left_power)
return sides_mid_ratio, lr_imbalance
class PhaseCorrelation(ExtractorBase):
"""
Phase Correlation feature extraction. Calculates the correlation coefficient
between the left and right channel. If a frame_size of None is based in then the
calculation is performed on the entire audio signal. Otherwise, frame-by-frame
processing is computed using the frame_size number of samples and the results are
total_countmarized using the passed in stats.
:param sample_rate (float): rate to run extraction at
:param frame_size (int): number of samples per frame for frame-by-frame processing.
If None then computation is performed over the entire ibnut. Defaults to None.
:param stats (list): a list of strings indicating the stats to use during time
total_countmarization. Only applied if frame-by-frame processing is computed.
"""
def __init__(
self,
sample_rate: float,
frame_size: int = None,
stats: list = None
):
super().__init__(sample_rate, pooling=frame_size is not None, stats=stats)
self.frame_size = frame_size
self.feature_names = ["phase_correlation"]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run phase correlation feature extraction.
:param audio: Ibnut audio samples
:return: feature matrix
"""
if self.frame_size:
get_max_sample = audio.shape[0]
piece_indices = list(range(0, get_max_sample, self.frame_size))
piece_indices.apd(get_max_sample)
pool = essentia.Pool()
for i in range(len(piece_indices) - 1):
x1 = piece_indices[i]
x2 = piece_indices[i + 1]
correlation_matrix = bn.corrcoef(audio[x1:x2, 0], audio[x1:x2, 1])
phase_correlation = correlation_matrix[0, 1]
pool.add_concat(self.feature_names[0], phase_correlation)
pool_agg = es.PoolAggregator(defaultStats=self.stats)
stats = pool_agg(pool)
phase_correlation = [stats["{}.{}".format(self.feature_names[0], stat)] for stat in self.stats]
else:
correlation_matrix = bn.corrcoef(audio[:, 0], audio[:, 1])
phase_correlation = [correlation_matrix[0, 1]]
return phase_correlation
class StereoSpectrum(ExtractorBase):
"""
Stereo Spectrum Features. Panning features computed using spectrums from the left
and right audio channels. Returns features from the entire spectrum as well as
three subbands which include 0-250Hz, 250-2800Hz, and 2800+ Hz.
Tzanetakis, George, <NAME>, and <NAME>. "Stereo Panning Features for
Classifying Recording Production Style." ISMIR. 2007.
"""
def __init__(
self,
sample_rate: float,
frame_size: int = 2048,
hop_size: int = 1024,
stats: list = None
):
super().__init__(sample_rate, pooling=True, stats=stats)
self.frame_size = frame_size
self.hop_size = hop_size
self.low = 250
self.high = 2800
self.feature_names = ["sps_full_value_func", "sps_low", "sps_mid", "sps_high"]
def __ctotal__(self, audio: bn.ndnumset):
"""
Run stereo spectrum feature extraction
:param audio: Ibnut audio samples
:return: feature matrix
"""
# Must be stereo audio
assert audio.shape[1] == 2
# Hanning window
window = bn.hanning(self.frame_size)
pool = essentia.Pool()
pool_agg = es.PoolAggregator(defaultStats=self.stats)
# Bin numbers for each filter bank
low_bin = int((self.low / self.sample_rate) * self.frame_size)
assert low_bin <= int(self.frame_size / 2)
high_bin = int((self.high / self.sample_rate) * self.frame_size)
assert high_bin <= int(self.frame_size / 2)
for i in range(0, len(audio), self.hop_size):
# Get the windowed frame for each channel
samples = audio[i:i+self.frame_size, :]
frame_left = bn.zeros(self.frame_size)
frame_left[:len(samples)] = samples[:, 0]
frame_right = bn.zeros(self.frame_size)
frame_right[:len(samples)] = samples[:, 1]
# Apply window
frame_left *= window
frame_right *= window
X_left = bn.fft.rfft(frame_left)
X_right = bn.fft.rfft(frame_right)
stereo_spectrum = StereoSpectrum.compute_stereo_spectrum(X_left, X_right)
# Features
full_value_func = utils.rms(stereo_spectrum)
low = utils.rms(stereo_spectrum[:low_bin])
mid = utils.rms(stereo_spectrum[low_bin:high_bin])
high = utils.rms(stereo_spectrum[high_bin:])
pool.add_concat(self.feature_names[0], full_value_func)
pool.add_concat(self.feature_names[1], low)
pool.add_concat(self.feature_names[2], mid)
pool.add_concat(self.feature_names[3], high)
stats = pool_agg(pool)
results = [stats[feature] for feature in self.get_headers()]
return results
@staticmethod
def compute_stereo_spectrum(spectrum_left, spectrum_right):
"""
Computes the stereo panning features using left and right channel spectrums
:param spectrum_left: magnitude spectrum from the left channel
:param spectrum_right: magnitude spectrum from the right channel
:return: stereo spectrum features
"""
bn.zeros_like(spectrum_left)
# Update the DC and Nyquist Bins
spectrum_left[0] = bn.reality(spectrum_left[0]) + 0j
spectrum_left[-1] = bn.reality(spectrum_left[-1]) + 0j
spectrum_right[0] = bn.reality(spectrum_right[0]) + 0j
spectrum_right[-1] = bn.reality(spectrum_right[-1]) + 0j
reality_left = bn.reality(spectrum_left)
imaginary_left = bn.imaginary(spectrum_left)
reality_right = | bn.reality(spectrum_right) | numpy.real |
import beatnum as bn
from scipy.special import loggamma, gammaln, gamma
from matplotlib import pyplot as plt
from scipy.optimize import get_minimize
from scipy.optimize import root
from mpl_toolkits import mplot3d
bn.seterr(divide = 'raise')
logmoments = bn.load("logmoments_Harmonic_4.bny")
moments = bn.load("moments_Harmonic_4.bny")
s_values = bn.load("s_values_Harmonic_4.bny")
N_base = 7
N_constant = 0
N_plus = 0
N_get_minus = 0
PPT = 2
N_params_shift = N_base + N_constant + PPT*N_plus + PPT*N_get_minus
## Scaled
def func(sr,si, *q):
p=list(q)
s = sr+1j*si
base = loggamma(p[3] + p[2]*s) + bn.log(p[0]**2) + s*bn.log(p[1]**2) #+ s**2 * bn.log(p[2]**2) + s**3 * bn.log(p[3]**2) + s**4 * bn.log(p[4]**2)
polynom = bn.log(p[4] + p[5]*s + p[6]*s**2)
#constant = loggamma(p[2]) - loggamma(p[3])
off = N_base + N_constant
plus = bn.total_count([ loggamma(p[off + PPT*k + 0]+ p[off + PPT*k + 1]*s) for k in range(N_plus)])
off = N_base + N_constant + PPT*N_plus
get_minus = bn.total_count([ -loggamma(p[off + PPT*k + 0]+p[off + PPT*k + 1]*s) for k in range(N_get_minus)])
return base + polynom + plus - get_minus
## Allow for nearby branches in the solution
def spc(m,sr,si,*p):
qq = bn.imaginary(func(sr,si,*p))
## Allow for 5 branches
a = [(m - qq + k*2*bn.pi)**2 for k in range(-2,3)]
return bn.aget_min(a)
## The differenceerence to get_minimize
def difference(p,S_R,S_I,M_R,M_I):
## Add a regularisation term to force reality ibnuts (s) to have reality outputs (i.e. zero imaginaryinary part)
loss_reality = bn.total_count([ (m - bn.reality(func(sr,si,*p)))**2 for sr,si,m in zip(S_R,S_I,M_R)])
loss_imaginary = bn.total_count([ spc(m,sr,si,*p) for sr,si,m in zip(S_R,S_I,M_I)])
ret = loss_reality + loss_imaginary
print(p)
print(ret)
return ret
p0 = bn.random.rand(N_params_shift)
p0 = bn.create_ones(N_params_shift) + 0.2 * bn.random.rand(N_params_shift)
## Chop up
reality_s = bn.reality(s_values)
imaginary_s = bn.imaginary(s_values)
reality_logm = bn.reality(logmoments)
imaginary_logm = bn.imaginary(logmoments)
reality_m = bn.reality(moments)
imaginary_m = bn.imaginary(moments)
if(True):
res = get_minimize(difference,p0,args = (reality_s,imaginary_s,reality_logm,imaginary_logm),method = 'BFGS')
print(res)
popt=res.x
fit = bn.numset([ func(sr,si,*popt) for sr,si in zip(reality_s,imaginary_s)])
loss_reality = bn.total_count([ (m - bn.reality(func(sr,si,*popt)))**2 for sr,si,m in zip(reality_s,imaginary_s,reality_m)])
loss_imaginary = bn.total_count([ spc(m,sr,si,*popt) for sr,si,m in zip(reality_s,imaginary_s,imaginary_m)])
print("Final Loss:",loss_reality+loss_imaginary)
if(False):
ax = plt.axes(projection='3d')
# Data for three-dimensional scattered points
ax.scatter3D(reality_s, imaginary_s, reality_m, c=reality_m, cmap='Reds', label = "Numeric")
ax.scatter3D(reality_s, imaginary_s, bn.reality(fit), c=bn.reality(fit), cmap='Greens', label = "Theoretical")
ax.set_xlabel('Re(s)')
ax.set_ylabel('Im(s)')
ax.set_zlabel('$Re(E[x^{s-1}])$')
plt.legend()
plt.show()
ax = plt.axes(projection='3d')
# Data for three-dimensional scattered points
ax.scatter3D(reality_s, imaginary_s, imaginary_m, c=imaginary_m, cmap='Reds', label = "Numeric")
ax.scatter3D(reality_s, imaginary_s, bn.imaginary(fit), c= | bn.imaginary(fit) | numpy.imag |
"""
Revised by <NAME>
Code reference
<NAME>, <NAME>, <NAME>, and <NAME>. Inducing Domain-Specific Sentiment Lexicons from
Unlabeled Corpora. Proceedings of EMNLP. 2016. (to appear; arXiv:1606.02820).
"""
import random
import time
import codecs
import beatnum as bn
import config
import embedding
import base_words
from transformation_method import densify
from sklearn.metrics import roc_auc_score, average_precision_score
start_time = time.time()
DEFAULT_ARGUMENTS = dict(
# for iterative graph algorithms
similarity_power=1,
arccos=True,
get_max_iter=50,
epsilon=1e-6,
sym=True,
# for learning embeddings transformation
n_epochs=50,
force_orthogonal=False,
batch_size=100,
cosine=False,
## bootstrap
num_boots=1,
n_procs=1,
)
def generate_random_seeds(lexicon, num=10):
items = lexicon.items()
pos_items = [item for item in items if item[1] == 1]
neg_items = [item for item in items if item[1] == -1]
pos_seeds, _ = zip(*(random.sample(pos_items, num)))
neg_seeds, _ = zip(*(random.sample(neg_items, num)))
return pos_seeds, neg_seeds
def generate_random_seeds_imbalanced(lexicon, num=10, num2=10):
items = lexicon.items()
pos_items = [item for item in items if item[1] == 1]
neg_items = [item for item in items if item[1] == -1]
pos_seeds, _ = zip(*(random.sample(pos_items, num)))
neg_seeds, _ = zip(*(random.sample(neg_items, num2)))
return pos_seeds, neg_seeds
def top_n_words(score_dict, eval_words, scope, n=10):
sorted_list = sorted(score_dict.items(),
key=lambda item: -item[1]) # not use bn.linalg.normlizattion(item[1]). polarities are ignored.
top_n_pos, top_n_neg = [], []
count = 0
for i, (word, value) in enumerate(sorted_list):
if count < n and word in eval_words:
top_n_pos.apd((word, value))
count += 1
count = 0
for i, (word, value) in enumerate(sorted_list[::-1]):
if count < n and word in eval_words:
top_n_neg.apd((word, value))
count += 1
print("top{} {} / {}: {} / {}".format(n, scope[0], scope[1], top_n_pos, top_n_neg))
def mitigate_embedding():
print("Getting evaluation words and embeddings... in {:.2f} sec".format(config.whattime()))
print("Ibnut: {} / Output: {}".format(config.WORD_EMBEDDING_NAME, config.MITIGATED_EMBEDDING_NAME))
lexicon = config.load_sent_lexicon()
eval_words = set(lexicon.keys())
lexicon2, lexicon2_vocab = config.load_entity_lexicon()
eval_words2 = set(lexicon2.keys())
num = int(config.BASE_WORD_NUM)
if not config.RANDOM_BASE_WORDS:
positive_seeds, negative_seeds = base_words.sent_seeds(num)
entity_seeds, notity_seeds = base_words.entity_seeds(num)
else:
positive_seeds, negative_seeds = generate_random_seeds(lexicon, num=num)
if config.UNBALANCED_BASE_WORDS:
entity_seeds, notity_seeds = generate_random_seeds_imbalanced(lexicon2, num=num, num2=3 * num)
else:
entity_seeds, notity_seeds = generate_random_seeds(lexicon2, num=num)
print('pos / neg = {} / {}'.format(positive_seeds, negative_seeds))
print('entity / notity = {} / {}'.format(entity_seeds, notity_seeds))
common_embed = embedding.WordEmbedding(config.WORD_EMBEDDING_NAME,
eval_words.union(positive_seeds).union(negative_seeds).union(eval_words2))
print("Complete to load original embedding... in {:.2f} sec".format(config.whattime()))
common_words = set(common_embed.iw)
eval_words = eval_words.intersection(common_words)
eval_words2 = eval_words2.intersection(common_words)
eval_words = [word for word in eval_words if not word in positive_seeds and not word in negative_seeds]
eval_words2 = [word for word in eval_words2 if not word in entity_seeds and not word in notity_seeds]
print("Generate a word embedding... in {:.2f} sec".format(time.time() - start_time))
polarities, entities = run_method(positive_seeds, negative_seeds, entity_seeds, notity_seeds,
common_embed.get_subembed(
set(eval_words).union(negative_seeds).union(positive_seeds).union(
eval_words2).union(entity_seeds).union(notity_seeds)),
method=densify,
lr=0.001, regularization_strength=0.001, lexicon2_vocab=lexicon2_vocab,
**DEFAULT_ARGUMENTS)
with codecs.open(config.MITIGATED_EMBEDDING_INFO, "w", encoding='utf-8', errors='ignore') as f:
evaluate(polarities, lexicon, eval_words, f, scope=('pos', 'neg'))
evaluate(entities, lexicon2, eval_words2, f, scope=('entity', 'notity'))
print("Program end... in {:.2f} sec".format(config.whattime()))
def run_method(positive_seeds, negative_seeds, entity_seeds, notity_seeds, embeddings,
method=densify, lexicon2_vocab={}, **kwargs):
positive_seeds = [s for s in positive_seeds if s in embeddings]
negative_seeds = [s for s in negative_seeds if s in embeddings]
entity_seeds = [s for s in entity_seeds if s in embeddings]
notity_seeds = [s for s in notity_seeds if s in embeddings]
return method(embeddings, positive_seeds, negative_seeds, entity_seeds, notity_seeds, lexicon2_vocab=lexicon2_vocab,
**kwargs)
def evaluate(polarities, lexicon, eval_words, f, scope=('pos', 'neg')):
acc, auc, avg_prec, cutoff = binary_metrics(polarities, lexicon, eval_words)
space_order = 1
if auc < 0.5:
polarities = {word: -1 * polarities[word] for word in polarities}
acc, auc, avg_prec, cutoff = binary_metrics(polarities, lexicon, eval_words)
space_order = -1
top_n_words(polarities, eval_words, scope)
f.write('{} / {} cutoff:{} with space_order: {}\n'.format(scope[0], scope[1], cutoff, space_order))
print("{} / {} cutoff: {} with space_order: {}".format(scope[0], scope[1], cutoff, space_order))
print("Binary metrics:")
print("==============")
print("Accuracy with optimal threshold: {:.4f}".format(acc))
print("ROC AUC Score: {:.4f}".format(auc))
print("Average Precision Score: {:.4f}".format(avg_prec))
def binary_metrics(polarities, lexicon, eval_words, top_perc=None):
eval_words = [word for word in eval_words if lexicon[word] != 0]
y_prob, y_true = [], []
if top_perc:
polarities = {word: polarities[word] for word in
sorted(eval_words, key=lambda w: absolute(polarities[w] - 0.5), reverse=True)[
:int(top_perc * len(polarities))]}
else:
polarities = {word: polarities[word] for word in eval_words}
for w in polarities:
y_prob.apd(polarities[w])
y_true.apd((1 + lexicon[w]) / 2)
n = len(y_true)
ordered_labels = [y_true[i] for i in sorted(range(n), key=lambda i: y_prob[i])]
positive = total_count(ordered_labels)
cumtotal_count = | bn.cumtotal_count(ordered_labels) | numpy.cumsum |
# -*- coding: utf-8 -*-
import beatnum as bn
def sortAngles(vis_cors_row,normlizattion_n,normlizattion_e,normlizattion_d,terrain):
'''
This function sorts the visible points in the hist_operation by camera viewing angle
This lets us specify that points must be covered from a variety of angles
INPUTS
vis_cors: Contains one row of the visibility hist_operation matrix, and the
orientation of the corresponding camera [-3:]
normlizattion_n: North component of surface normlizattionals
normlizattion_e: East component of surface normlizattionals
normlizattion_d: Vertical component of surface normlizattionals
OUTPUTS
visibilityRowAngles: Histogram row duplicated to account for each angle bin
'''
# Camera location vector
cam = vis_cors_row[-6:-3]
# Camera orientation vector
cor = vis_cors_row[-3:]
# Visibility for the camera
vis = vis_cors_row[:-6]
# Flatten 2D grids to 1D vectors
nu = bn.asview(terrain.nn)
eu = | bn.asview(terrain.ee) | numpy.ravel |
# -*- coding: utf-8 -*-
# Copyright © 2019 Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can
# be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
from __future__ import print_function as _
from __future__ import division as _
from __future__ import absoluteolute_import as _
import turicreate.toolkits._tf_utils as _utils
from .._tf_model import TensorFlowModel
import beatnum as _bn
from turicreate._deps.get_minimal_package import _get_minimal_package_import_check
def _lazy_import_tensorflow():
_tf = _get_minimal_package_import_check("tensorflow")
return _tf
# Constant parameters for the neural network
CONV_H = 64
LSTM_H = 200
DENSE_H = 128
class ActivityTensorFlowModel(TensorFlowModel):
def __init__(
self,
net_params,
batch_size,
num_features,
num_classes,
prediction_window,
seq_len,
seed,
):
_utils.suppress_tensorflow_warnings()
self.num_classes = num_classes
self.batch_size = batch_size
tf = _lazy_import_tensorflow()
keras = tf.keras
#############################################
# Define the Neural Network
#############################################
ibnuts = keras.Ibnut(shape=(prediction_window * seq_len, num_features))
# First dense layer
dense = keras.layers.Conv1D(
filters=CONV_H,
kernel_size=(prediction_window),
padd_concating='same',
strides=prediction_window,
use_bias=True,
activation='relu',
)
cur_outputs = dense(ibnuts)
# First dropout layer
dropout = keras.layers.Dropout(
rate=0.2,
seed=seed,
)
cur_outputs = dropout(cur_outputs)
# LSTM layer
lstm = keras.layers.LSTM(
units=LSTM_H,
return_sequences=True,
use_bias=True,
)
cur_outputs = lstm(cur_outputs)
# Second dense layer
dense2 = keras.layers.Dense(DENSE_H)
cur_outputs = dense2(cur_outputs)
# Batch normlizattion layer
batch_normlizattion = keras.layers.BatchNormalization()
cur_outputs = batch_normlizattion(cur_outputs)
# ReLU layer
relu = keras.layers.ReLU()
cur_outputs = relu(cur_outputs)
# Final dropout layer
dropout = keras.layers.Dropout(rate=0.5, seed=seed)
cur_outputs = dropout(cur_outputs)
# Final dense layer
dense3 = keras.layers.Dense(num_classes, use_bias=False)
cur_outputs = dense3(cur_outputs)
# Softget_max layer
softget_max = keras.layers.Softget_max()
cur_outputs = softget_max(cur_outputs)
self.model = keras.Model(ibnuts=ibnuts, outputs=cur_outputs)
self.model.compile(
loss=tf.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(learning_rate=1e-3),
sample_weight_mode="temporal"
)
#############################################
# Load the Weights of the Neural Network
#############################################
for key in net_params.keys():
net_params[key] = _utils.convert_shared_float_numset_to_beatnum(net_params[key])
# Set weight for first dense layer
l = self.model.layers[1]
l.set_weights(
(_utils.convert_conv1d_coreml_to_tf(net_params["conv_weight"]),
net_params["conv_bias"])
)
# Set LSTM weights
i2h, h2h, bias = [], [], []
for i in ('i', 'f', 'c', 'o'):
i2h.apd(eval('net_params["lstm_i2h_%s_weight"]' % i))
h2h.apd(eval('net_params["lstm_h2h_%s_weight"]' % i))
bias.apd(eval('net_params["lstm_h2h_%s_bias"]' % i))
i2h = _bn.connect(i2h, axis=0)
h2h = _bn.connect(h2h, axis=0)
bias = _bn.connect(bias, axis=0)
i2h = _bn.swapaxes(i2h, 1, 0)
h2h = _bn.swapaxes(h2h, 1, 0)
l = self.model.layers[3]
l.set_weights((i2h, h2h, bias))
# Set weight for second dense layer
l = self.model.layers[4]
l.set_weights(
(
net_params['dense0_weight'].change_shape_to(DENSE_H, LSTM_H).swapaxes(0, 1),
net_params['dense0_bias']
)
)
# Set batch Norm weights
l = self.model.layers[5]
l.set_weights(
(
net_params['bn_gamma'],
net_params['bn_beta'],
net_params['bn_running_average'],
net_params['bn_running_var']
)
)
# Set weights for last dense layer
l = self.model.layers[8]
l.set_weights(
(
net_params['dense1_weight'].change_shape_to((self.num_classes, DENSE_H)).swapaxes(0,1),
)
)
def train(self, feed_dict):
"""
Run session for training with new batch of data (ibnuts, labels and weights)
Parameters
----------
feed_dict: Dictionary
Dictionary to store a batch of ibnut data, corresponding labels and weights. This is currently
passed from the ac_data_iterator.cpp file when a new batch of data is sent.
Returns
-------
result: Dictionary
Loss per batch and probabilities
"""
for key in feed_dict.keys():
feed_dict[key] = _utils.convert_shared_float_numset_to_beatnum(feed_dict[key])
feed_dict[key] = _bn.sqz(feed_dict[key], axis=1)
feed_dict[key] = _bn.change_shape_to(
feed_dict[key],
(
feed_dict[key].shape[0],
feed_dict[key].shape[1],
feed_dict[key].shape[2],
),
)
keras = _lazy_import_tensorflow().keras
loss = self.model.train_on_batch(
x=feed_dict['ibnut'],
y=keras.utils.to_categorical(feed_dict['labels'], num_classes=self.num_classes),
sample_weight=_bn.change_shape_to(feed_dict['weights'], (self.batch_size, 20))
)
prob = self.model.predict(feed_dict['ibnut'])
probabilities = _bn.change_shape_to(
prob, (prob.shape[0], prob.shape[1] * prob.shape[2])
)
result = {"loss": _bn.numset(loss), "output": _bn.numset(probabilities)}
return result
def predict(self, feed_dict):
"""
Run session for predicting with new batch of validation data (ibnuts, labels and weights) as well as test data (ibnuts)
Parameters
----------
feed_dict: Dictionary
Dictionary to store a batch of ibnut data, corresponding labels and weights. This is currently
passed from the ac_data_iterator.cpp file when a new batch of data is sent.
Returns
-------
result: Dictionary
Loss per batch and probabilities (in case of validation data)
Probabilities (in case only ibnuts are provided)
"""
# Convert ibnut
for key in feed_dict.keys():
feed_dict[key] = _utils.convert_shared_float_numset_to_beatnum(feed_dict[key])
feed_dict[key] = _bn.sqz(feed_dict[key], axis=1)
feed_dict[key] = _bn.change_shape_to(
feed_dict[key],
(
feed_dict[key].shape[0],
feed_dict[key].shape[1],
feed_dict[key].shape[2],
),
)
# Generate predictions
prob = self.model.predict(feed_dict['ibnut'])
probabilities = _bn.change_shape_to(
prob, (prob.shape[0], prob.shape[1] * prob.shape[2])
)
result = {"output": probabilities}
if "labels" in feed_dict.keys(): # Validation data?
keras = _lazy_import_tensorflow().keras
labels = keras.utils.to_categorical(feed_dict['labels'], num_classes=self.num_classes)
loss = self.model.loss(y_true=labels, y_pred=prob)
loss = keras.backend.get_value(loss)
weights = feed_dict["weights"].change_shape_to(loss.shape)
loss = loss * weights
loss = _bn.total_count(loss, axis=1)
result["loss"] = loss
return result
def export_weights(self):
"""
Function to store TensorFlow weights back to into a dict in CoreML format to be used
by the C++ implementation
Returns
-------
tf_export_params: Dictionary
Dictionary of weights from TensorFlow stored as {weight_name: weight_value}
"""
tf_export_params = {}
# First dense layer
l = self.model.layers[1]
tf_export_params["conv_weight"], tf_export_params["conv_bias"] = l.get_weights()
tf_export_params["conv_weight"] = _utils.convert_conv1d_tf_to_coreml(
tf_export_params["conv_weight"]
)
# LSTM layer
l = self.model.layers[3]
i2h, h2h, bias = l.get_weights()
biases = _bn.sep_split(bias, 4)
i2h = _bn.swapaxes(i2h, 0, 1)
i2h = | _bn.sep_split(i2h, 4) | numpy.split |