#

# Load in multiple noisy realizations of a given simulated wave, and

# characterize the performance of AWARE on detecting the wave

#

import os

import cPickle as pickle

import numpy as np

import matplotlib.pyplot as plt

import astropy.units as u

import copy

# Main AWARE processing and detection code

import aware

import util

# AWARE utilities

import aware_utils

# Wave simulation code

import test_wave2d

# Simulated wave parameters

import swave_params

# Mapcube handling tools

import mapcube_tools

# Simulated data

# TODO - run the same analysis on multiple noisy realizations of the simulated

# TODO - data. Generate average result plots over the multiple realizations

# TODO - either as relative error or absolute error as appropriate.

# TODO - First mode: use multiple noisy realizations of the same model.

# TODO - Second mode: choose a bunch of simulated data parameters, and then

# TODO - randomly select values for them (within reason).

# TODO - The recovered parameters should be reasonably close to the simulated

# TODO - parameters.

#

#

random_seed = 42

np.random.seed(random_seed)

# Select the wave

#example = 'wavenorm4_slow'

example = 'no_noise_no_solar_rotation_slow'

# What type of output do we want to analyze

mctype = 'finalmaps'

# Number of images

max_steps = 2

# Accumulation in the time direction

accum = 2

# Summing in the spatial directions

spatial_summing = [4, 4]*u.pix

# Radii of the morphological operations

radii = [[5, 5], [11, 11], [22, 22]]

# Position measuring choices

position_choice = 'average'

error_choice = 'width'

# Degree of polynomial to fit

n_degree = 1

# Unraveling factor

unraveling_factor = 1.0

# Output directory

output = '~/eitwave/'

# Output types

otypes = ['img', 'pkl']

# RANSAC

ransac_kwargs = {"random_state": random_seed}

# Special designation: an extra description added to the file and directory

# names in order to differentiate between experiments on the same example wave.

special_designation = ''

# Output directories and filename

odir = os.path.expanduser(output)

otypes_dir = {}

otypes_filename = {}

# Morphological radii

sradii = ''

for r in radii:

for v in r:

sradii = sradii + str(v) + '_'

sradii = sradii[0: -1]

# Load in the wave params

params = swave_params.waves(lon_start=-180 * u.degree + 0.0 * u.degree)[example]

# Unraveling params are different compared to the wave definition params

params_unravel = copy.deepcopy(params)

# Sum over many of the original bins used to create the wave in an attempt to

# beat down transform artifacts

params_unravel['lon_bin'] = unraveling_factor * params['lon_bin']

params_unravel['lat_bin'] = unraveling_factor * params['lat_bin']

# Move zero location of longitudinal reconstruction relative to the

# wavefront

# params_unravel['lon_min'] = params_unravel['lon_min']

# params_unravel['lon_max'] = params_unravel['lon_max']

# Storage for the results

results = []

# Go through all the test waves, and apply AWARE.

# Let the user which trial is happening

print('\nSimulating %s ' % example)

print(' - position choice = %s' % position_choice)

print(' - error choice = %s' % error_choice)

print(' - unraveling factor = %f' % unraveling_factor)

# Simulate the wave and return a dictionary

out = test_wave2d.simulate_wave2d(params=params, max_steps=max_steps,

verbose=True, output=['raw', 'transformed', 'noise', 'finalmaps'])

t0 = out['transformed'][0]

b0 = util.map_hpc_to_hg_rotate(t0, epi_lon=0, epi_lat=0.0, lon_bin=1.0, lat_bin=1.0)