'''

Main plotting function to show the evolution of Kappa.

'''

if ax is None:

f, ax = plt.subplots()

ax = utils.plot_mean_std_area(

T_space, mean, np.ma.masked_invalid(yerror).filled(0.0),

ax_handle=ax, linewidth=3, markersize=8, **args)

# ax.legend(prop={'size': 15}, loc='best')

if title:

ax.set_title('Kappa: %s' % title)

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

ax.set_ylim([0.0, max(np.max(mean)*1.1, ax.get_ylim()[1])])

ax.set_xticks(range(1, T_space.max()+1))

ax.set_xticklabels(range(1, T_space.max()+1))

ax.get_figure().canvas.draw()

**args):

'''

Main plotting function to show the evolution of an EM Mixture.

'''

if ax is None:

f, ax = plt.subplots()

utils.plot_mean_std_area(

T_space, mean, np.ma.masked_invalid(yerror).filled(0.0),

ax_handle=ax, linewidth=3, markersize=8, **args)

# ax.legend(prop={'size': 15}, loc='best')

if title:

ax.set_title('Mixture prop: %s' % title)

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

ax.set_ylim([0.0, 1.01])

ax.set_xticks(range(1, T_space.max()+1))

ax.set_xticklabels(range(1, T_space.max()+1))

ax.get_figure().canvas.draw()

"""

This class does plots akin to paper, but for the Sequential dataset.

"""

def __init__(self, fit_experiment_sequential,

do_histograms_errors_triangle=True,

do_mixtcurves_lasttrecall_fig6=True,

do_mixtcurves_collapsedpowerlaw_fig7=True,

):

self.fit_exp = fit_experiment_sequential

self.experiment_id = self.fit_exp.experiment_id

self.collapsed_em_fits = None

self.do_histograms_errors_triangle = do_histograms_errors_triangle

self.do_mixtcurves_lasttrecall_fig6 = do_mixtcurves_lasttrecall_fig6

self.do_mixtcurves_collapsedpowerlaw_fig7 = do_mixtcurves_collapsedpowerlaw_fig7

print "Doing Sequential plots for %s. \nHist %d, Fig6 %d, Fig7 %d" % (

self.experiment_id,

self.do_histograms_errors_triangle,

self.do_mixtcurves_lasttrecall_fig6,

self.do_mixtcurves_collapsedpowerlaw_fig7

)

def do_plots(self):

'''

Do all plots for that FitExperimentAllT.

These correspond to a particular experiment_id only, not multiple.

'''

if self.do_histograms_errors_triangle:

self.plots_histograms_errors_triangle()

if self.do_mixtcurves_lasttrecall_fig6:

self.plots_mixtcurves_lasttrecall_fig6()

if self.do_mixtcurves_collapsedpowerlaw_fig7:

self.plots_mixtcurves_collapsedpowerlaw_fig7()

def plots_histograms_errors_triangle(self, size=12):

'''

Histograms of errors, for all n_items/trecall conditions.

'''

# Do the plots

f, axes = plt.subplots(

ncols=self.fit_exp.T_space.size,

nrows=2*self.fit_exp.T_space.size,

figsize=(size, 2*size))

angle_space = np.linspace(-np.pi, np.pi, 51)

for n_items_i, n_items in enumerate(self.fit_exp.T_space):

for trecall_i, trecall in enumerate(self.fit_exp.T_space):

if trecall <= n_items:

print "\n=== N items: {}, trecall: {}".format(

n_items, trecall)

# Sample

self.fit_exp.setup_experimental_stimuli(n_items, trecall)

if 'samples' in self.fit_exp.get_names_stored_responses():

self.fit_exp.restore_responses('samples')

else:

self.fit_exp.sampler.force_sampling_round()

self.fit_exp.store_responses('samples')

responses, targets, nontargets = (

self.fit_exp.sampler.collect_responses())

# Targets

errors_targets = utils.wrap_angles(targets - responses)

utils.hist_angular_data(

errors_targets,

bins=angle_space,

# title='N=%d, trecall=%d' % (n_items, trecall),

norm='density',

ax_handle=axes[2*n_items_i, trecall_i],

pretty_xticks=False)

axes[2*n_items_i, trecall_i].set_ylim([0., 1.4])

axes[2*n_items_i, trecall_i].xaxis.set_major_locator(

plt.NullLocator())

axes[2*n_items_i, trecall_i].yaxis.set_major_locator(

plt.NullLocator())

# Nontargets

if n_items > 1:

errors_nontargets = utils.wrap_angles((

responses[:, np.newaxis] - nontargets).flatten())

utils.hist_angular_data(

errors_nontargets,

bins=angle_space,

# title='Nontarget %s N=%d' % (dataset['name'], n_items),

norm='density',

ax_handle=axes[2*n_items_i + 1, trecall_i],

pretty_xticks=False)

axes[2*n_items_i + 1, trecall_i].set_ylim([0., 0.3])

axes[2*n_items_i + 1, trecall_i].xaxis.set_major_locator(plt.NullLocator())

axes[2*n_items_i + 1, trecall_i].yaxis.set_major_locator(plt.NullLocator())

else:

axes[2*n_items_i, trecall_i].axis('off')

axes[2*n_items_i + 1, trecall_i].axis('off')

return axes

def plots_mixtcurves_lasttrecall_fig6(self,

num_repetitions=1,

use_cache=True,

use_sem=True,

size=6):

'''

Plots memory fidelity and mixture proportions for the last item

recall.

This reproduces Figure 6 in Gorgo 11.

'''

T_space = self.fit_exp.T_space

data_em_fits = self.fit_exp.get_data_em_fits()

model_em_fits = self.fit_exp.get_model_em_fits(

num_repetitions, use_cache)

if use_sem:

errorbars = 'sem'

else:

errorbars = 'std'

f, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))

# Memory fidelity last trecall

# Data

_plot_kappa_mean_error(

T_space,

data_em_fits['mean'][

'kappa'][:, 0],

data_em_fits[errorbars][

'kappa'][:, 0],

label="Data",

fmt="o-",

ax=axes[0])

# Model

_plot_kappa_mean_error(

T_space,

model_em_fits['mean'][

'kappa'][:, 0],

model_em_fits[errorbars][

'kappa'][:, 0],

label='Model',

fmt="o-",

ax=axes[0],

xlabel='items', ylabel='Memory fidelity $[rad^{-2}]$')

axes[0].legend(loc='upper right', bbox_to_anchor=(1., 1.))

# Mixture proportions last trecall

# Model

_plot_emmixture_mean_error(

T_space,

model_em_fits['mean'][

'mixt_target_tr'][:, 0],

model_em_fits[errorbars][

'mixt_target_tr'][:, 0],

label='Target',

fmt="o-",

ax=axes[1])

_plot_emmixture_mean_error(

T_space,

model_em_fits['mean'][

'mixt_nontargets_tr'][:, 0],

model_em_fits[errorbars][

'mixt_nontargets_tr'][:, 0],

label='Nontarget',

fmt="o-",

ax=axes[1])

_plot_emmixture_mean_error(

T_space,

model_em_fits['mean'][

'mixt_random_tr'][:, 0],

model_em_fits[errorbars][

'mixt_random_tr'][:, 0],

label='Random',

fmt="o-",

ax=axes[1],

xlabel='items', ylabel='Mixture proportions')

# Data

_plot_emmixture_mean_error(

T_space,

data_em_fits['mean'][

'mixt_target_tr'][:, 0],

data_em_fits[errorbars][

'mixt_target_tr'][:, 0],

label='Data target',

fmt="s--",

ax=axes[1])

_plot_emmixture_mean_error(

T_space,

data_em_fits['mean'][

'mixt_nontargets_tr'][:, 0],

data_em_fits[errorbars][

'mixt_nontargets_tr'][:, 0],

label='Data nontarget',

fmt="s--",

ax=axes[1])

_plot_emmixture_mean_error(

T_space,

data_em_fits['mean'][

'mixt_random_tr'][:, 0],

data_em_fits[errorbars][

'mixt_random_tr'][:, 0],

label='Data random',

fmt="s--",

ax=axes[1])

axes[1].legend(loc='upper left', bbox_to_anchor=(1., 1.))

f.suptitle('Fig 6: Last trecall')

f.canvas.draw()

return axes

def plots_mixtcurves_collapsedpowerlaw_fig7(self,

num_repetitions=1,

use_cache=True,

use_sem=True,

size=6):

'''

Plots memory fidelity and mixture proportions for all nitems, with

trecall on the x-axis.

This reproduces Figure 7 in Gorgo 11.

'''

T_space = self.fit_exp.T_space

data_em_fits = self.fit_exp.get_data_em_fits()

model_em_fits = self.fit_exp.get_model_em_fits(

num_repetitions, use_cache)

# Do the plot

if use_sem:

errorbars = 'sem'

else:

errorbars = 'std'

_, axes_data = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))

_, axes_model = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))

# Data

for nitems_i, nitems in enumerate(T_space):

# Memory fidelity

_plot_kappa_mean_error(

T_space[:nitems],

data_em_fits['mean'][

'kappa'][nitems_i, :nitems],

data_em_fits[errorbars][

'kappa'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_data[0, 0])

axes_data[0, 0].set_ylim((0, 11))

# Mixture proportions

_plot_emmixture_mean_error(

T_space[:nitems],

data_em_fits['mean'][

'mixt_target_tr'][nitems_i, :nitems],

data_em_fits[errorbars][

'mixt_target_tr'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_data[0, 1])

_plot_emmixture_mean_error(

T_space[:nitems],

data_em_fits['mean'][

'mixt_nontargets_tr'][nitems_i, :nitems],

data_em_fits[errorbars][

'mixt_nontargets_tr'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_data[1, 0])

_plot_emmixture_mean_error(

T_space[:nitems],

data_em_fits['mean'][

'mixt_random_tr'][nitems_i, :nitems],

data_em_fits[errorbars][

'mixt_random_tr'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_data[1, 1])

# Model

for nitems_i, nitems in enumerate(T_space):

# Memory fidelity

_plot_kappa_mean_error(

T_space[:nitems],

model_em_fits['mean'][

'kappa'][nitems_i, :nitems],

model_em_fits[errorbars][

'kappa'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_model[0, 0])

axes_model[0, 0].set_ylim((0, 11))

# Mixture proportions

_plot_emmixture_mean_error(

T_space[:nitems],

model_em_fits['mean'][

'mixt_target_tr'][nitems_i, :nitems],

model_em_fits[errorbars][

'mixt_target_tr'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_model[0, 1])

_plot_emmixture_mean_error(

T_space[:nitems],

model_em_fits['mean'][

'mixt_nontargets_tr'][nitems_i, :nitems],

model_em_fits[errorbars][

'mixt_nontargets_tr'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_model[1, 0])

_plot_emmixture_mean_error(

T_space[:nitems],

model_em_fits['mean'][

'mixt_random_tr'][nitems_i, :nitems],

model_em_fits[errorbars][

'mixt_random_tr'][nitems_i, :nitems],

label='%d items' % nitems,

xlabel='Serial order (reversed)',

fmt="o-",

zorder=7 - nitems,

ax=axes_model[1, 1])

axes_data[0, 1].legend(loc='upper left', bbox_to_anchor=(1., 1.))

axes_model[0, 1].legend(loc='upper left', bbox_to_anchor=(1., 1.))

axes_data[0, 0].figure.canvas.draw()

axes_model[0, 0].figure.canvas.draw()