<s> from __future__ import absolute_import , division , print_function <EOL> import logging <EOL> import numpy as np <EOL> from numpy . testing import assert_array_almost_equal <EOL> from nose . tools import assert_raises <EOL> import skbeam . core . utils as utils <EOL> from skbeam . core . correlation import ( multi_tau_auto_corr , <EOL> auto_corr_scat_factor , <EOL> lazy_one_time , <EOL> lazy_two_time , two_time_corr , <EOL> two_time_state_to_results , <EOL> one_time_from_two_time ) <EOL> from skbeam . core . mask import bad_to_nan_gen <EOL> logger = logging . getLogger ( __name__ ) <EOL> def setup ( ) : <EOL> global num_levels , num_bufs , xdim , ydim , stack_size , img_stack , rois <EOL> num_levels = <NUM_LIT:6> <EOL> num_bufs = <NUM_LIT:4> <EOL> xdim = <NUM_LIT> <EOL> ydim = <NUM_LIT> <EOL> stack_size = <NUM_LIT:100> <EOL> img_stack = np . random . randint ( <NUM_LIT:1> , <NUM_LIT:3> , ( stack_size , xdim , ydim ) ) <EOL> rois = np . zeros_like ( img_stack [ <NUM_LIT:0> ] ) <EOL> rois [ <NUM_LIT:0> : xdim // <NUM_LIT:10> , <NUM_LIT:0> : ydim // <NUM_LIT:10> ] = <NUM_LIT:5> <EOL> rois [ xdim // <NUM_LIT:10> : xdim // <NUM_LIT:5> , ydim // <NUM_LIT:10> : ydim // <NUM_LIT:5> ] = <NUM_LIT:3> <EOL> def test_lazy_vs_original ( ) : <EOL> setup ( ) <EOL> full_gen_one = lazy_one_time ( <EOL> img_stack , num_levels , num_bufs , rois ) <EOL> for gen_state_one in full_gen_one : <EOL> pass <EOL> g2 , lag_steps = multi_tau_auto_corr ( num_levels , num_bufs , <EOL> rois , img_stack ) <EOL> assert np . all ( g2 == gen_state_one . g2 ) <EOL> assert np . all ( lag_steps == gen_state_one . lag_steps ) <EOL> full_gen_two = lazy_two_time ( rois , img_stack , stack_size , <EOL> num_bufs , num_levels ) <EOL> for gen_state_two in full_gen_two : <EOL> pass <EOL> final_gen_result_two = two_time_state_to_results ( gen_state_two ) <EOL> two_time = two_time_corr ( rois , img_stack , stack_size , <EOL> num_bufs , num_levels ) <EOL> assert np . all ( two_time [ <NUM_LIT:0> ] == final_gen_result_two . g2 ) <EOL> assert np . all ( two_time [ <NUM_LIT:1> ] == final_gen_result_two . lag_steps ) <EOL> def test_lazy_two_time ( ) : <EOL> setup ( ) <EOL> full_gen = lazy_two_time ( rois , img_stack , stack_size , <EOL> stack_size , <NUM_LIT:1> ) <EOL> for full_state in full_gen : <EOL> pass <EOL> final_result = two_time_state_to_results ( full_state ) <EOL> assert np . average ( final_result . g2 - <NUM_LIT:1> ) < <NUM_LIT> <EOL> gen_first_half = lazy_two_time ( rois , img_stack [ : stack_size // <NUM_LIT:2> ] , stack_size , <EOL> num_bufs = stack_size , num_levels = <NUM_LIT:1> ) <EOL> for first_half_state in gen_first_half : <EOL> pass <EOL> gen_second_half = lazy_two_time ( rois , img_stack [ stack_size // <NUM_LIT:2> : ] , <EOL> stack_size , num_bufs = stack_size , <EOL> num_levels = <NUM_LIT:1> , <EOL> two_time_internal_state = first_half_state ) <EOL> for second_half_state in gen_second_half : <EOL> pass <EOL> result = two_time_state_to_results ( second_half_state ) <EOL> assert np . all ( full_state . g2 == result . g2 ) <EOL> def test_lazy_one_time ( ) : <EOL> setup ( ) <EOL> full_gen = lazy_one_time ( img_stack , num_levels , num_bufs , rois ) <EOL> for full_result in full_gen : <EOL> pass <EOL> assert np . average ( full_result . g2 - <NUM_LIT:1> ) < <NUM_LIT> <EOL> gen_first_half = lazy_one_time ( <EOL> img_stack [ : stack_size // <NUM_LIT:2> ] , num_levels , num_bufs , rois ) <EOL> for first_half_result in gen_first_half : <EOL> pass <EOL> gen_second_half = lazy_one_time ( <EOL> img_stack [ stack_size // <NUM_LIT:2> : ] , num_levels , num_bufs , rois , <EOL> internal_state = first_half_result . internal_state <EOL> ) <EOL> for second_half_result in gen_second_half : <EOL> pass <EOL> assert np . all ( full_result . g2 == <EOL> second_half_result . g2 ) <EOL> def test_two_time_corr ( ) : <EOL> setup ( ) <EOL> y = [ ] <EOL> for i in range ( <NUM_LIT:50> ) : <EOL> y . append ( img_stack [ <NUM_LIT:0> ] ) <EOL> two_time = two_time_corr ( rois , np . asarray ( y ) , <NUM_LIT:50> , <EOL> num_bufs = <NUM_LIT:50> , num_levels = <NUM_LIT:1> ) <EOL> assert np . all ( two_time [ <NUM_LIT:0> ] ) <EOL> assert_raises ( ValueError , two_time_corr , rois , np . asarray ( y ) , <NUM_LIT:50> , <EOL> num_bufs = <NUM_LIT> , num_levels = <NUM_LIT:1> ) <EOL> def test_auto_corr_scat_factor ( ) : <EOL> num_levels , num_bufs = <NUM_LIT:3> , <NUM_LIT:4> <EOL> tot_channels , lags , dict_lags = utils . multi_tau_lags ( num_levels , num_bufs ) <EOL> beta = <NUM_LIT:0.5> <EOL> relaxation_rate = <NUM_LIT> <EOL> baseline = <NUM_LIT:1.0> <EOL> g2 = auto_corr_scat_factor ( lags , beta , relaxation_rate , baseline ) <EOL> assert_array_almost_equal ( g2 , np . array ( [ <NUM_LIT> , <NUM_LIT:1.0> , <NUM_LIT:1.0> , <NUM_LIT:1.0> , <NUM_LIT:1.0> , <EOL> <NUM_LIT:1.0> , <NUM_LIT:1.0> , <NUM_LIT:1.0> ] ) , decimal = <NUM_LIT:8> ) <EOL> def test_bad_images ( ) : <EOL> setup ( ) <EOL> g2 , lag_steps = multi_tau_auto_corr ( <NUM_LIT:4> , num_bufs , <EOL> rois , img_stack ) <EOL> bad_img_list = [ <NUM_LIT:3> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT> ] <EOL> images = bad_to_nan_gen ( img_stack , bad_img_list ) <EOL> g2_n , lag_steps_n = multi_tau_auto_corr ( <NUM_LIT:4> , num_bufs , <EOL> rois , images ) <EOL> assert_array_almost_equal ( g2 [ : , <NUM_LIT:0> ] , g2_n [ : , <NUM_LIT:0> ] , decimal = <NUM_LIT:3> ) <EOL> assert_array_almost_equal ( g2 [ : , <NUM_LIT:1> ] , g2_n [ : , <NUM_LIT:1> ] , decimal = <NUM_LIT:3> ) <EOL> def test_one_time_from_two_time ( ) : <EOL> num_lev = <NUM_LIT:1> <EOL> num_buf = <NUM_LIT:10> <EOL> x_dim = <NUM_LIT:10> <EOL> y_dim = <NUM_LIT:10> <EOL> stack = <NUM_LIT:10> <EOL> imgs = np . random . randint ( <NUM_LIT:1> , <NUM_LIT:3> , ( stack , x_dim , y_dim ) ) <EOL> roi = np . zeros_like ( imgs [ <NUM_LIT:0> ] ) <EOL> roi [ <NUM_LIT:0> : x_dim // <NUM_LIT:10> , <NUM_LIT:0> : y_dim // <NUM_LIT:10> ] = <NUM_LIT:5> <EOL> roi [ x_dim // <NUM_LIT:10> : x_dim // <NUM_LIT:5> , y_dim // <NUM_LIT:10> : y_dim // <NUM_LIT:5> ] = <NUM_LIT:3> <EOL> g2 , lag_steps , _state = two_time_corr ( roi , imgs , stack , <EOL> num_buf , num_lev ) <EOL> one_time = one_time_from_two_time ( g2 ) <EOL> assert_array_almost_equal ( one_time [ <NUM_LIT:0> , : ] , np . array ( [ <NUM_LIT:1.0> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT> , <EOL> <NUM_LIT> , <NUM_LIT:0.5> , <NUM_LIT> , <NUM_LIT> , <EOL> <NUM_LIT> , <NUM_LIT:0.1> ] ) ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> import nose <EOL> nose . runmodule ( argv = [ '<STR_LIT>' , '<STR_LIT>' ] , exit = False ) </s>
