'''

Run the model for T items, with varying amount of sigma_output

Check how the Mixture model parameters vary

'''

print "Doing a piece of work for launcher_do_noise_output_effect"

all_parameters = utils.argparse_2_dict(args)

print all_parameters

if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:

print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']

if 'plots_during_simulation_callback' in all_parameters:

plots_during_simulation_callback = all_parameters['plots_during_simulation_callback']

del all_parameters['plots_during_simulation_callback']

else:

plots_during_simulation_callback = None

# Create DataIO

# (complete label with current variable state)

dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))

save_every = 1

run_counter = 0

# Parameters to vary

precision_sigmaoutput = 5

sigmaoutput_space = np.linspace(0.0, 0.5, precision_sigmaoutput)

# Result arrays

result_all_precisions = np.nan*np.ones((sigmaoutput_space.size, all_parameters['num_repetitions']))

result_em_fits = np.nan*np.ones((sigmaoutput_space.size, 6, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll, bic

# If desired, will automatically save all Model responses.

if all_parameters['collect_responses']:

result_responses = np.nan*np.ones((sigmaoutput_space.size, all_parameters['N'], all_parameters['num_repetitions']))

result_target = np.nan*np.ones((sigmaoutput_space.size, all_parameters['N'], all_parameters['num_repetitions']))

result_nontargets = np.nan*np.ones((sigmaoutput_space.size, all_parameters['N'], all_parameters['T']-1, all_parameters['num_repetitions']))

search_progress = progress.Progress(sigmaoutput_space.size*all_parameters['num_repetitions'])

for repet_i in xrange(all_parameters['num_repetitions']):

for sigmaoutput_i, sigma_output in enumerate(sigmaoutput_space):

print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())

print "Fit for sigma_output=%.3f, %d/%d" % (sigma_output, repet_i+1, all_parameters['num_repetitions'])

# Update parameter

all_parameters['sigma_output'] = sigma_output

### WORK WORK WORK work? ###

# Fix some parameters

# all_parameters['stimuli_generation'] = 'separated'

# all_parameters['slice_width'] = np.pi/64.

# Instantiate

(_, _, _, sampler) = launchers.init_everything(all_parameters)

# Sample

sampler.run_inference(all_parameters)

# Compute precision

print "get precision..."

result_all_precisions[sigmaoutput_i, repet_i] = sampler.get_precision()

# Fit mixture model

print "fit mixture model..."

curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)

result_em_fits[sigmaoutput_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL', 'bic')]

# If needed, store responses

if all_parameters['collect_responses']:

(responses, target, nontarget) = sampler.collect_responses()

result_responses[sigmaoutput_i, :, repet_i] = responses

result_target[sigmaoutput_i, :, repet_i] = target

result_nontargets[sigmaoutput_i, :, :(all_parameters['T']-1), repet_i] = nontarget

print "collected responses"

print result_all_precisions[sigmaoutput_i, repet_i], curr_params_fit

## Run callback function if exists

if plots_during_simulation_callback:

print "Doing plots..."

try:

# Best super safe, if this fails then the simulation must continue!

plots_during_simulation_callback['function'](locals(), plots_during_simulation_callback['parameters'])

print "plots done."

except:

print "error during plotting callback function", plots_during_simulation_callback['function'], plots_during_simulation_callback['parameters']

### /Work ###

search_progress.increment()

if run_counter % save_every == 0 or search_progress.done():

dataio.save_variables_default(locals())

run_counter += 1

# Finished

dataio.save_variables_default(locals())

print "All finished"

'''

Just like launcher_do_noise_output_effect but do plots as well

'''

all_parameters = utils.argparse_2_dict(args)

if all_parameters['code_type'] == 'hierarchical':

# Use ratio_conj for plotting/titles

if all_parameters['ratio_hierarchical'] is not None:

all_parameters['ratio_conj'] = all_parameters['ratio_hierarchical']

plotting_parameters = dict(axes=dict(ax_sigmaoutput_kappa=None, ax_sigmaoutput_mixtures=None))

### Now do the plots

def do_sigma_output_plot(variables_launcher_running, plotting_parameters):

dataio = variables_launcher_running['dataio']

sigmaoutput_space = variables_launcher_running['sigmaoutput_space']

result_em_fits_mean = utils.nanmean(variables_launcher_running['result_em_fits'], axis=-1)

result_em_fits_std = utils.nanstd(variables_launcher_running['result_em_fits'], axis=-1)

plt.ion()

# Memory curve kappa

def sigmaoutput_plot_kappa(sigmaoutput_space, result_em_fits_mean, result_em_fits_std=None, exp_name='', ax=None):

if ax is not None:

plt.figure(ax.get_figure().number)

ax.hold(False)

ax = utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 0], result_em_fits_std[..., 0], xlabel='sigma output', ylabel='Memory fidelity', linewidth=3, fmt='o-', markersize=8, label='Noise output effect', ax_handle=ax)

ax.hold(True)

ax.set_title("{{exp_name}} {T} {M} {ratio_conj:.2f} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']).format(exp_name=exp_name))

ax.legend()

# ax.set_xlim([0.9, T_space_exp.max()+0.1])

# ax.set_xticks(range(1, T_space_exp.max()+1))

# ax.set_xticklabels(range(1, T_space_exp.max()+1))

ax.get_figure().canvas.draw()

dataio.save_current_figure('noiseoutput_kappa_%s_T{T}_M{M}_ratio{ratio_conj}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']) % (exp_name))

return ax

# Plot EM Mixtures proportions

def sigmaoutput_plot_mixtures(sigmaoutput_space, result_em_fits_mean, result_em_fits_std, exp_name='', ax=None):

if ax is None:

_, ax = plt.subplots()

if ax is not None:

plt.figure(ax.get_figure().number)

ax.hold(False)

# mixture probabilities

print result_em_fits_mean[..., 1]

result_em_fits_mean[np.isnan(result_em_fits_mean)] = 0.0

result_em_fits_std[np.isnan(result_em_fits_std)] = 0.0

utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 1], result_em_fits_std[..., 1], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target')

ax.hold(True)

utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 2], result_em_fits_std[..., 2], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget')

utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 3], result_em_fits_std[..., 3], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Random')

ax.legend(prop={'size':15})

ax.set_title("{{exp_name}} {T} {M} {ratio_conj:.2f} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']).format(exp_name=exp_name))

ax.set_ylim([0.0, 1.1])

ax.get_figure().canvas.draw()

dataio.save_current_figure('memorycurves_emfits_%s_T{T}_M{M}_ratio{ratio_conj}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']) % (exp_name))

return ax

# Do plots

plotting_parameters['axes']['ax_sigmaoutput_kappa'] = sigmaoutput_plot_kappa(sigmaoutput_space, result_em_fits_mean, result_em_fits_std, exp_name='kappa', ax=plotting_parameters['axes']['ax_sigmaoutput_kappa'])

plotting_parameters['axes']['ax_sigmaoutput_mixtures'] = sigmaoutput_plot_mixtures(sigmaoutput_space, result_em_fits_mean, result_em_fits_std, exp_name='mixt probs', ax=plotting_parameters['axes']['ax_sigmaoutput_mixtures'])

if all_parameters.get('do_plots_during_simulation', False):

# Define the callback function.

all_parameters['plots_during_simulation_callback'] = dict(function=do_sigma_output_plot, parameters=plotting_parameters)

# Run the launcher_do_noise_output_effect, plots are done during the runs automatically

other_launcher_results = launcher_do_noise_output_effect(all_parameters)

else:

# Run the launcher_do_noise_output_effect, will do plots later

other_launcher_results = launcher_do_noise_output_effect(args)

# Do the plots

do_sigma_output_plot(other_launcher_results, plotting_parameters)

# Return the output of the other launcher.

'''

Just like launcher_do_noise_output_effect_withplots do plots as well, while the simulation is going on

'''

all_parameters = utils.argparse_2_dict(args)

all_parameters['do_plots_during_simulation'] = True

'''

Run the model for 1..T items, varying sigma_output

'''

print "Doing a piece of work for launcher_do_noise_output_effect_allT"

all_parameters = utils.argparse_2_dict(args)

print all_parameters

if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:

print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']

if 'plots_during_simulation_callback' in all_parameters:

plots_during_simulation_callback = all_parameters['plots_during_simulation_callback']

del all_parameters['plots_during_simulation_callback']

else:

plots_during_simulation_callback = None

# Create DataIO

# (complete label with current variable state)

dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))

save_every = 1

run_counter = 0

# Parameters to vary

T_max = all_parameters['T']

T_space = np.arange(1, T_max+1)

repetitions_axis = -1

# Parameters to vary

precision_sigmaoutput = 20

sigmaoutput_space = np.linspace(0.0, 0.5, precision_sigmaoutput)

# Result arrays

result_all_precisions = np.nan*np.ones((sigmaoutput_space.size, T_max, all_parameters['num_repetitions']))

result_em_fits = np.nan*np.ones((sigmaoutput_space.size, T_max, 6, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll, bic

search_progress = progress.Progress(sigmaoutput_space.size*T_max*all_parameters['num_repetitions'])

for repet_i in xrange(all_parameters['num_repetitions']):

for sigmaoutput_i, sigma_output in enumerate(sigmaoutput_space):

for T_i, T in enumerate(T_space):

print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())

print "Fit for sigma_output=%.3f, T %d, %d/%d" % (sigma_output, T, repet_i+1, all_parameters['num_repetitions'])

# Update parameter

all_parameters['sigma_output'] = sigma_output

all_parameters['T'] = T

### WORK WORK WORK work? ###

# Fix some parameters

# all_parameters['stimuli_generation'] = 'separated'

# all_parameters['slice_width'] = np.pi/64.

# Instantiate

(_, _, _, sampler) = launchers.init_everything(all_parameters)

# Sample

sampler.run_inference(all_parameters)

# Compute precision

print "get precision..."

result_all_precisions[sigmaoutput_i, T_i, repet_i] = sampler.get_precision()

# Fit mixture model

print "fit mixture model..."

curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)

result_em_fits[sigmaoutput_i, T_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL', 'bic')]

print result_all_precisions[sigmaoutput_i, T_i, repet_i], curr_params_fit

## Run callback function if exists

if plots_during_simulation_callback:

print "Doing plots..."

try:

# Best super safe, if this fails then the simulation must continue!

plots_during_simulation_callback['function'](locals(), plots_during_simulation_callback['parameters'])

print "plots done."

except:

print "error during plotting callback function", plots_during_simulation_callback['function'], plots_during_simulation_callback['parameters']

### /Work ###

search_progress.increment()

if run_counter % save_every == 0 or search_progress.done():

dataio.save_variables_default(locals())

run_counter += 1

# Finished

dataio.save_variables_default(locals())

print "All finished"