示例#1
0
    def plot_coverage_feature_space(self, axes=(0, 1), nb_stddev=1.0, specific_neurons=None, alpha_ellipses=0.5, facecolor='rand', ax=None, lim_factor=1.0):
        '''
            Show the features.
            Choose the 2 dimensions you want first.
        '''

        if specific_neurons is None:
            specific_neurons = self._ALL_NEURONS


        ells = [Ellipse(xy=self.neurons_preferred_stimulus[m, axes], width=nb_stddev*utils.kappa_to_stddev(self.neurons_sigma[m, axes[0]]), height=nb_stddev*utils.kappa_to_stddev(self.neurons_sigma[m, axes[1]]), angle=-np.degrees(self.neurons_angle[m])) for m in specific_neurons if not self.mask_neurons_unset[m]]

        if ax is None:
            fig = plt.figure()
            ax = fig.add_subplot(111, aspect='equal')

        for e in ells:
            ax.add_artist(e)
            e.set_clip_box(ax.bbox)
            e.set_alpha(alpha_ellipses)
            if facecolor is 'rand':
                e.set_facecolor(np.random.rand(3))
            elif facecolor is False or facecolor is None or facecolor == 'none' or facecolor == 'None':
                e.set_facecolor('none')
            else:
                e.set_facecolor(facecolor)
            e.set_transform(ax.transData)

        # ax.autoscale_view()
        # ax.set_xlim(-1.4*np.pi, 1.3*np.pi)
        # ax.set_ylim(-1.4*np.pi, 1.3*np.pi)
        ax.set_xlim(-lim_factor*np.pi, lim_factor*np.pi)
        ax.set_ylim(-lim_factor*np.pi, lim_factor*np.pi)

        ax.set_xticks((-np.pi, -np.pi / 2, 0, np.pi / 2., np.pi))
        ax.set_xticklabels((r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'), fontsize=17)
        ax.set_yticks((-np.pi, -np.pi / 2, 0, np.pi / 2., np.pi))
        ax.set_yticklabels((r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'), fontsize=17)

        ax.set_xlabel('Orientation', fontsize=14)
        ax.set_ylabel('Color', fontsize=14)

        ax.set_title('%d vs %d' % (axes[0]+1, axes[1]+1))
        fig.set_tight_layout(True)

        plt.draw()

        plt.show()

        return ax
  def plot_neuron_activity_1d(self,
                              neuron_index=0,
                              axis_to_vary=0,
                              fix_preferred_stim=True,
                              fixed_stim=None,
                              precision=100.,
                              normalise=True):
    '''
            Plot the activity of a neuron along one dimension. Either provide the stimulus to fix, or let it
            be at its preferred stimulus.
        '''

    feature_space = self.init_feature_space(precision=precision)
    activity = np.zeros(feature_space.shape)

    # Fix the rest of the stimulus
    if fix_preferred_stim:
      stimulus = self.neurons_preferred_stimulus[neuron_index].copy()
    elif fixed_stim is not None:
      stimulus = fixed_stim
    else:
      stimulus = np.zeros(self.R)

    # Compute the response.
    for i in xrange(feature_space.size):
      stimulus[axis_to_vary] = feature_space[i]
      activity[i] = self.get_neuron_response(neuron_index, stimulus)

    # Check the gaussian fit
    gaussian_approx = spst.norm.pdf(
        feature_space,
        self.neurons_preferred_stimulus[neuron_index, axis_to_vary],
        utils.kappa_to_stddev(self.neurons_sigma[neuron_index, axis_to_vary]))

    if normalise:
      activity /= np.max(activity)
      gaussian_approx /= np.max(gaussian_approx)

    # Plot everything
    plt.figure()
    plt.plot(feature_space, activity)
    plt.plot(feature_space, gaussian_approx)

    plt.legend(['Neuron activity', 'Gaussian approximation'])

    plt.show()
  def plot_neuron_activity(self, neuron_index=0, nb_stddev=1., precision=100):
    '''
            Plot the activity of one specific neuron over the whole input space.
        '''

    coverage_1D = self.init_feature_space(precision)
    activity = self.get_neuron_activity(neuron_index, precision=precision)

    # Plot it
    f = plt.figure()
    ax = f.add_subplot(111)
    im = ax.imshow(activity.T, origin='lower')
    # im.set_extent((-np.pi, np.pi, -np.pi, np.pi))
    im.set_extent((coverage_1D.min(), coverage_1D.max(), coverage_1D.min(),
                   coverage_1D.max()))
    ax.set_ylabel('Color')
    ax.set_xlabel('Orientation')
    # im.set_interpolation('nearest')
    f.colorbar(im)

    # Plot the ellipse showing one standard deviation
    e = Ellipse(
        xy=self.neurons_preferred_stimulus[neuron_index],
        width=nb_stddev * utils.kappa_to_stddev(
            self.neurons_sigma[neuron_index, 0]),
        height=nb_stddev * utils.kappa_to_stddev(
            self.neurons_sigma[neuron_index, 1]),
        angle=-np.degrees(self.neurons_angle[neuron_index]))

    ax.add_artist(e)
    e.set_clip_box(ax.bbox)
    e.set_alpha(0.5)
    e.set_facecolor('white')
    e.set_transform(ax.transData)

    ax.set_xticks((-np.pi, -np.pi / 2, 0, np.pi / 2., np.pi))
    ax.set_xticklabels(
        (r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
        fontsize=17)
    ax.set_yticks((-np.pi, -np.pi / 2, 0, np.pi / 2., np.pi))
    ax.set_yticklabels(
        (r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
        fontsize=17)

    # Change mouse over behaviour
    def report_pixel(x_mouse, y_mouse):
      # Extract loglik at that position
      try:
        x_display = x_mouse
        y_display = y_mouse

        x_i = np.argmin((coverage_1D - x_display)**2.)
        y_i = np.argmin((coverage_1D - y_display)**2.)

        return "x=%.2f y=%.2f value=%.2f" % (x_display, y_display,
                                             activity[x_i, y_i])
      except:
        return ""

    ax.format_coord = report_pixel

    plt.show()
samples_w = utils.wrap_angles(samples)

x = np.linspace(-np.pi, np.pi, 10000)

# KDE
samples_kde = stmokde.KDEUnivariate(samples)
samples_kde.fit()
samples_w_kde = stmokde.KDEUnivariate(samples_w)
samples_w_kde.fit()

# Von Mises
samples_vonmises = utils.fit_vonmises_samples(samples, num_points=300, return_fitted_data=True, should_plot=False)
samples_w_vonmises = utils.fit_vonmises_samples(samples_w, num_points=300, return_fitted_data=True, should_plot=False)

plt.figure()
plt.hist(samples, bins=100, normed=True)
plt.plot(samples_vonmises['support'], samples_vonmises['fitted_data'], 'r', linewidth=3)
plt.plot(samples_kde.support, samples_kde.density, 'g', linewidth=3)


plt.figure()
plt.hist(samples_w, bins=100, normed=True)
plt.plot(samples_vonmises['support'], samples_vonmises['fitted_data'], 'r', linewidth=3)
plt.plot(samples_w_kde.support, samples_w_kde.density, 'g', linewidth=3)

print 'Target std: %.2f, fitted kappa: %.3f, corresponding std: %.3f' % (std_target, samples_w_vonmises['parameters'][1], utils.kappa_to_stddev(samples_w_vonmises['parameters'][1]))

plt.show()

def plots_specific_stimuli_hierarchical(data_pbs, generator_module=None):
    '''
        Reload and plot behaviour of mixed population code on specific Stimuli
        of 3 items.
    '''

    #### SETUP
    #
    savefigs = True
    savedata = True

    plot_per_min_dist_all = False
    specific_plots_paper = False
    plots_emfit_allitems = False
    plot_min_distance_effect = True

    should_fit_allitems_model = True
    # caching_emfit_filename = None
    caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfitallitems_uniquekappa.pickle')


    colormap = None  # or 'cubehelix'
    plt.rcParams['font.size'] = 16
    #
    #### /SETUP

    print "Order parameters: ", generator_module.dict_parameters_range.keys()

    result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
    result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
    result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
    result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results'])
    result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])

    all_args = data_pbs.loaded_data['args_list']

    nb_repetitions = np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']).shape[-1]
    print nb_repetitions
    nb_repetitions = result_responses_all.shape[-1]
    print nb_repetitions
    K = result_nontargets_all.shape[-2]
    N = result_responses_all.shape[-2]


    enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
    sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']

    MMlower_valid_space = data_pbs.loaded_data['datasets_list'][0]['MMlower_valid_space']
    ratio_space = MMlower_valid_space[:, 0]/float(np.sum(MMlower_valid_space[0]))

    print enforce_min_distance_space
    print sigmax_space
    print MMlower_valid_space
    print result_all_precisions_mean.shape, result_em_fits_mean.shape

    dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])

    # Relaod cached emfitallitems
    if caching_emfit_filename is not None:
        if os.path.exists(caching_emfit_filename):
            # Got file, open it and try to use its contents
            try:
                with open(caching_emfit_filename, 'r') as file_in:
                    # Load and assign values
                    cached_data = pickle.load(file_in)
                    result_emfitallitems = cached_data['result_emfitallitems']
                    should_fit_allitems_model = False

            except IOError:
                print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits"

    if plot_per_min_dist_all:
        # Do one plot per min distance.
        for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
            if savefigs:
                dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
            if savefigs:
                dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)


            # Plot estimated model precision (kappa)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot estimated Target, nontarget and random mixture components, in multiple subplots
            _, axes = plt.subplots(1, 3, figsize=(18, 6))
            plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)

            if savefigs:
                dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot Log-likelihood of Mixture model, sanity check
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

    if specific_plots_paper:
        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.09
        target_mindist_medium = 0.36
        target_mindist_high = 1.5

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        ## Do for each distance
        # for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
        for min_dist_i in xrange(enforce_min_distance_space.size):

            # Plot precision
            if False:
                utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])])

                if savefigs:
                    dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa fitted
            ax_handle = utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
            # Add distance between items in kappa units
            dist_items_kappa = utils.stddev_to_kappa(enforce_min_distance_space[min_dist_i])
            ax_handle.plot(ratio_space, dist_items_kappa*np.ones(ratio_space.size), 'k--', linewidth=3)
            plt.ylim([-0.1, np.max((np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]), 1.1*dist_items_kappa))])
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa-stddev fitted. Easier to visualize
            ax_handle = utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
            # Add distance between items in std dev units
            dist_items_std = (enforce_min_distance_space[min_dist_i])
            ax_handle.plot(ratio_space, dist_items_std*np.ones(ratio_space.size), 'k--', linewidth=3)
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            plt.ylim([0, 1.1*np.max((np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i]), dist_items_std))])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])


            if False:
                # Plot LLH
                utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                if savefigs:
                    dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

                # Plot mixture parameters, std
                utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
                plt.ylim([0.0, 1.1])
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                # plt.legend("Target", "Non-target", "Random")
                if savefigs:
                    dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

                # Mixture parameters, SEM
                utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
                plt.ylim([0.0, 1.1])
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                # plt.legend("Target", "Non-target", "Random")
                if savefigs:
                    dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

    if plots_emfit_allitems:
        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.15
        target_mindist_medium = 0.36
        target_mindist_high = 1.5

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i])

        if should_fit_allitems_model:

            # kappa, mixt_target, mixt_nontargets (K), mixt_random, LL, bic
            # result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan
            result_emfitallitems = np.empty((enforce_min_distance_space.size, ratio_space.size, K+5))*np.nan

            ## Do for each distance
            # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
            for min_dist_i in xrange(enforce_min_distance_space.size):
                # Fit the mixture model
                for ratio_i, ratio in enumerate(ratio_space):
                    print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i]
                    em_fit = em_circularmixture_allitems_uniquekappa.fit(
                        result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                        result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                        result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)))

                    result_emfitallitems[min_dist_i, ratio_i] = [em_fit['kappa'], em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]

            # Save everything to a file, for faster later plotting
            if caching_emfit_filename is not None:
                try:
                    with open(caching_emfit_filename, 'w') as filecache_out:
                        data_em = dict(result_emfitallitems=result_emfitallitems)
                        pickle.dump(data_em, filecache_out, protocol=2)
                except IOError:
                    print "Error writing out to caching file ", caching_emfit_filename


        ## Plots now, for each distance!
        # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
        for min_dist_i in xrange(enforce_min_distance_space.size):

            # Plot now
            _, ax = plt.subplots()
            ax.plot(ratio_space, result_emfitallitems[min_dist_i, :, 1:5], linewidth=3)
            plt.ylim([0.0, 1.1])
            plt.legend(['Target', 'Nontarget 1', 'Nontarget 2', 'Random'], loc='upper left')

            if savefigs:
                dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

    if plot_min_distance_effect:
        conj_receptive_field_size = 2.*np.pi/((all_args[0]['M']*ratio_space)**0.5)

        target_vs_nontargets_mindist_ratio = result_emfitallitems[..., 1]/np.sum(result_emfitallitems[..., 1:4], axis=-1)
        nontargetsmean_vs_targnontarg_mindist_ratio = np.mean(result_emfitallitems[..., 2:4]/np.sum(result_emfitallitems[..., 1:4], axis=-1)[..., np.newaxis], axis=-1)

        for ratio_conj_i, ratio_conj in enumerate(ratio_space):
            # Do one plot per ratio, putting the receptive field size on each
            f, ax = plt.subplots()

            ax.plot(enforce_min_distance_space[1:], target_vs_nontargets_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='target mixture')
            ax.plot(enforce_min_distance_space[1:], nontargetsmean_vs_targnontarg_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='non-target mixture')
            # ax.plot(enforce_min_distance_space[1:], result_emfitallitems[1:, ratio_conj_i, 1:5], linewidth=3)

            ax.axvline(x=conj_receptive_field_size[ratio_conj_i]/2., color='k', linestyle='--', linewidth=2)
            ax.axvline(x=conj_receptive_field_size[ratio_conj_i]*2., color='r', linestyle='--', linewidth=2)

            plt.legend(loc='upper left')
            plt.grid()
            # ax.set_xlabel('Stimuli separation')
            # ax.set_ylabel('Ratio Target to Non-targets')
            plt.axis('tight')
            ax.set_ylim([0.0, 1.0])
            ax.set_xlim([enforce_min_distance_space[1:].min(), enforce_min_distance_space[1:].max()])

            if savefigs:
                dataio.save_current_figure('ratio%.2f_mindistpred_ratiotargetnontarget_{label}_{unique_id}.pdf' % ratio_conj)



    variables_to_save = ['nb_repetitions']

    if savedata:
        dataio.save_variables_default(locals(), variables_to_save)
        dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')

    plt.show()

    return locals()
def plots_specific_stimuli_mixed(data_pbs, generator_module=None):
    '''
        Reload and plot behaviour of mixed population code on specific Stimuli
        of 3 items.
    '''

    #### SETUP
    #
    savefigs = True
    savedata = True

    plot_per_min_dist_all = False
    specific_plots_paper = False
    specific_plots_emfits = True

    colormap = None  # or 'cubehelix'
    plt.rcParams['font.size'] = 16
    #
    #### /SETUP

    print "Order parameters: ", generator_module.dict_parameters_range.keys()

    result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
    result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
    result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
    result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results'])
    result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])

    nb_repetitions = result_responses_all.shape[-1]
    K = result_nontargets_all.shape[-2]
    N = result_responses_all.shape[-2]

    enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
    sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
    ratio_space = data_pbs.loaded_data['datasets_list'][0]['ratio_space']

    print enforce_min_distance_space
    print sigmax_space
    print ratio_space
    print result_all_precisions_mean.shape, result_em_fits_mean.shape
    print result_responses_all.shape

    dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])


    if plot_per_min_dist_all:
        # Do one plot per min distance.
        for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
            if savefigs:
                dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
            if savefigs:
                dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)


            # Plot estimated model precision
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot estimated Target, nontarget and random mixture components, in multiple subplots
            _, axes = plt.subplots(1, 3, figsize=(18, 6))
            plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)

            if savefigs:
                dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot Log-likelihood of Mixture model, sanity check
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

    if specific_plots_paper:
        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.15
        target_mindist_medium = 0.36
        target_mindist_high = 1.5

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        ## Do for each distance
        for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
        # for min_dist_i in xrange(enforce_min_distance_space.size):
            # Plot precision
            utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])])

            if savefigs:
                dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa fitted
            utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
            plt.ylim([-0.1, np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0])])
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa-stddev fitted. Easier to visualize
            utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            plt.ylim([0, 1.1*np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i])])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])


            # Plot LLH
            utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot mixture parameters, std
            utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
            plt.ylim([0.0, 1.1])
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            # plt.legend("Target", "Non-target", "Random")
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Mixture parameters, SEM
            utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
            plt.ylim([0.0, 1.1])
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            # plt.legend("Target", "Non-target", "Random")
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

    if specific_plots_emfits:
        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.15
        target_mindist_medium = 0.36
        target_mindist_high = 1.5

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i])

        # kappa (K+1), mixt_target, mixt_nontargets (K), mixt_random, LL, bic
        result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan

        ## Do for each distance
        for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
            # Fit the mixture model
            for ratio_i, ratio in enumerate(ratio_space):
                print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i]
                em_fit = em_circularmixture_allitems.fit(
                    result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                    result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                    result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)))

                result_emfitallitems[min_dist_plotting_i, ratio_i] = em_fit['kappa'].tolist() + [em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]

            # Plot now
            _, ax = plt.subplots()
            ax.plot(ratio_space, result_emfitallitems[min_dist_plotting_i, :, 3:7])

            if savefigs:
                dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])



    all_args = data_pbs.loaded_data['args_list']
    variables_to_save = ['nb_repetitions']

    if savedata:
        dataio.save_variables_default(locals(), variables_to_save)

        dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')


    plt.show()

    return locals()
def plots_specific_stimuli_hierarchical(data_pbs, generator_module=None):
    '''
        Reload and plot behaviour of mixed population code on specific Stimuli
        of 3 items.
    '''

    #### SETUP
    #
    savefigs = True
    savedata = True

    plot_per_min_dist_all = True
    specific_plots_paper = True

    colormap = None  # or 'cubehelix'
    plt.rcParams['font.size'] = 16
    #
    #### /SETUP

    print "Order parameters: ", generator_module.dict_parameters_range.keys()

    result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
    result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)

    nb_repetitions = np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']).shape[-1]

    enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
    sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']

    MMlower_valid_space = data_pbs.loaded_data['datasets_list'][0]['MMlower_valid_space']
    ratio_space = MMlower_valid_space[:, 0]/float(np.sum(MMlower_valid_space[0]))

    print enforce_min_distance_space
    print sigmax_space
    print MMlower_valid_space
    print result_all_precisions_mean.shape, result_em_fits_mean.shape

    dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])


    if plot_per_min_dist_all:
        # Do one plot per min distance.
        for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
            if savefigs:
                dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
            if savefigs:
                dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)


            # Plot estimated model precision
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot estimated Target, nontarget and random mixture components, in multiple subplots
            _, axes = plt.subplots(1, 3, figsize=(18, 6))
            plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)

            if savefigs:
                dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot Log-likelihood of Mixture model, sanity check
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

    if specific_plots_paper:
        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.09
        target_mindist_medium = 0.36
        target_mindist_high = 1.5

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        ## Do for each distance
        # for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
        for min_dist_i in xrange(enforce_min_distance_space.size):
            # Plot precision
            utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])

            if savefigs:
                dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa fitted
            utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa-stddev fitted. Easier to visualize
            utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])


            # Plot LLH
            utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot mixture parameters
            utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
            plt.ylim([0.0, 1.1])
            # plt.legend("Target", "Non-target", "Random")
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot mixture parameters, SEM
            utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
            plt.ylim([0.0, 1.1])
            # plt.legend("Target", "Non-target", "Random")
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])



    all_args = data_pbs.loaded_data['args_list']
    variables_to_save = ['result_all_precisions_mean', 'result_em_fits_mean', 'result_all_precisions_std', 'result_em_fits_std', 'result_em_kappastddev_mean', 'result_em_kappastddev_std', 'enforce_min_distance_space', 'sigmax_space', 'ratio_space', 'all_args']

    if savedata:
        dataio.save_variables(variables_to_save, locals())
        dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')

    plt.show()

    return locals()
def plots_specific_stimuli_mixed(data_pbs, generator_module=None):
    '''
        Reload and plot behaviour of mixed population code on specific Stimuli
        of 3 items.
    '''

    #### SETUP
    #
    savefigs = True
    savedata = True

    plot_per_min_dist_all = False
    specific_plots_paper = False
    plots_emfit_allitems = False
    plot_min_distance_effect = True

    compute_bootstraps = False

    should_fit_allitems_model = True
    # caching_emfit_filename = None
    mixturemodel_to_use = 'allitems_uniquekappa'
    # caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfitallitems_uniquekappa.pickle')
    # mixturemodel_to_use = 'allitems_fikappa'

    caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfit%s.pickle' % mixturemodel_to_use)

    compute_fisher_info_perratioconj = True
    caching_fisherinfo_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_fisherinfo.pickle')

    colormap = None  # or 'cubehelix'
    plt.rcParams['font.size'] = 16
    #
    #### /SETUP

    print "Order parameters: ", generator_module.dict_parameters_range.keys()

    all_args = data_pbs.loaded_data['args_list']
    result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
    result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
    result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
    result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
    result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
    result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results'])
    result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])

    nb_repetitions = result_responses_all.shape[-1]
    K = result_nontargets_all.shape[-2]
    N = result_responses_all.shape[-2]

    enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
    sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
    ratio_space = data_pbs.loaded_data['datasets_list'][0]['ratio_space']

    print enforce_min_distance_space
    print sigmax_space
    print ratio_space
    print result_all_precisions_mean.shape, result_em_fits_mean.shape
    print result_responses_all.shape

    dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])

    # Reload cached emfitallitems
    if caching_emfit_filename is not None:
        if os.path.exists(caching_emfit_filename):
            # Got file, open it and try to use its contents
            try:
                with open(caching_emfit_filename, 'r') as file_in:
                    # Load and assign values
                    print "Reloader EM fits from cache", caching_emfit_filename
                    cached_data = pickle.load(file_in)
                    result_emfitallitems = cached_data['result_emfitallitems']
                    mixturemodel_used = cached_data.get('mixturemodel_used', '')

                    if mixturemodel_used != mixturemodel_to_use:
                        print "warning, reloaded model used a different mixture model class"
                    should_fit_allitems_model = False

            except IOError:
                print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits"


    # Load the Fisher Info from cache if exists. If not, compute it.
    if caching_fisherinfo_filename is not None:
        if os.path.exists(caching_fisherinfo_filename):
            # Got file, open it and try to use its contents
            try:
                with open(caching_fisherinfo_filename, 'r') as file_in:
                    # Load and assign values
                    cached_data = pickle.load(file_in)
                    result_fisherinfo_mindist_sigmax_ratio = cached_data['result_fisherinfo_mindist_sigmax_ratio']
                    compute_fisher_info_perratioconj = False

            except IOError:
                print "Error while loading ", caching_fisherinfo_filename, "falling back to computing the Fisher Info"

    if compute_fisher_info_perratioconj:
        # We did not save the Fisher info, but need it if we want to fit the mixture model with fixed kappa. So recompute them using the args_dicts

        result_fisherinfo_mindist_sigmax_ratio = np.empty((enforce_min_distance_space.size, sigmax_space.size, ratio_space.size))

        # Invert the all_args_i -> min_dist, sigmax indexing
        parameters_indirections = data_pbs.loaded_data['parameters_dataset_index']

        # min_dist_i, sigmax_level_i, ratio_i
        for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
            for sigmax_i, sigmax in enumerate(sigmax_space):
                # Get index of first dataset with the current (min_dist, sigmax) (no need for the others, I think)
                arg_index = parameters_indirections[(min_dist, sigmax)][0]

                # Now using this dataset, reconstruct a RandomFactorialNetwork and compute the fisher info
                curr_args = all_args[arg_index]

                for ratio_conj_i, ratio_conj in enumerate(ratio_space):
                    # Update param
                    curr_args['ratio_conj'] = ratio_conj
                    # curr_args['stimuli_generation'] = 'specific_stimuli'

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

                    # Theo Fisher info
                    result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_i, ratio_conj_i] = sampler.estimate_fisher_info_theocov()

                    print "Min dist: %.2f, Sigmax: %.2f, Ratio: %.2f: %.3f" % (min_dist, sigmax, ratio_conj, result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_i, ratio_conj_i])


        # Save everything to a file, for faster later plotting
        if caching_fisherinfo_filename is not None:
            try:
                with open(caching_fisherinfo_filename, 'w') as filecache_out:
                    data_cache = dict(result_fisherinfo_mindist_sigmax_ratio=result_fisherinfo_mindist_sigmax_ratio)
                    pickle.dump(data_cache, filecache_out, protocol=2)
            except IOError:
                print "Error writing out to caching file ", caching_fisherinfo_filename


    if plot_per_min_dist_all:
        # Do one plot per min distance.
        for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
            if savefigs:
                dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Show log precision
            utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
            if savefigs:
                dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)


            # Plot estimated model precision
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot estimated Target, nontarget and random mixture components, in multiple subplots
            _, axes = plt.subplots(1, 3, figsize=(18, 6))
            plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)

            if savefigs:
                dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

            # Plot Log-likelihood of Mixture model, sanity check
            utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
            if savefigs:
                dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)

    if specific_plots_paper:
        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.15
        target_mindist_medium = 0.36
        target_mindist_high = 1.5

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        ## Do for each distance
        # for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
        for min_dist_i in xrange(enforce_min_distance_space.size):
            # Plot precision
            if False:
                utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])])

                if savefigs:
                    dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa fitted
            ax_handle = utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
            # Add distance between items in kappa units
            dist_items_kappa = utils.stddev_to_kappa(enforce_min_distance_space[min_dist_i])
            ax_handle.plot(ratio_space, dist_items_kappa*np.ones(ratio_space.size), 'k--', linewidth=3)
            plt.ylim([-0.1, np.max((np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]), 1.1*dist_items_kappa))])
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

            # Plot kappa-stddev fitted. Easier to visualize
            ax_handle = utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
            # Add distance between items in std dev units
            dist_items_std = (enforce_min_distance_space[min_dist_i])
            ax_handle.plot(ratio_space, dist_items_std*np.ones(ratio_space.size), 'k--', linewidth=3)
            # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
            plt.ylim([0, 1.1*np.max((np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i]), dist_items_std))])
            if savefigs:
                dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])


            if False:
                # Plot LLH
                utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                if savefigs:
                    dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

                # Plot mixture parameters, std
                utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
                plt.ylim([0.0, 1.1])
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                # plt.legend("Target", "Non-target", "Random")
                if savefigs:
                    dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

                # Mixture parameters, SEM
                utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
                plt.ylim([0.0, 1.1])
                # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
                # plt.legend("Target", "Non-target", "Random")
                if savefigs:
                    dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

    if plots_emfit_allitems:
        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.15
        target_mindist_medium = 0.36
        target_mindist_high = 1.5

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i])

        if should_fit_allitems_model:

            # kappa, mixt_target, mixt_nontargets (K), mixt_random, LL, bic
            # result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan
            result_emfitallitems = np.empty((enforce_min_distance_space.size, ratio_space.size, K+5))*np.nan

            ## Do for each distance
            # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
            for min_dist_i in xrange(enforce_min_distance_space.size):
                # Fit the mixture model
                for ratio_i, ratio in enumerate(ratio_space):
                    print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i]

                    if mixturemodel_to_use == 'allitems_uniquekappa':
                        em_fit = em_circularmixture_allitems_uniquekappa.fit(
                            result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                            result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                            result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)))
                    elif mixturemodel_to_use == 'allitems_fikappa':
                        em_fit = em_circularmixture_allitems_kappafi.fit(result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                            result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
                            result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)),
                            kappa=result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_level_i, ratio_i])
                    else:
                        raise ValueError("Wrong mixturemodel_to_use, %s" % mixturemodel_to_use)

                    result_emfitallitems[min_dist_i, ratio_i] = [em_fit['kappa'], em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]

            # Save everything to a file, for faster later plotting
            if caching_emfit_filename is not None:
                try:
                    with open(caching_emfit_filename, 'w') as filecache_out:
                        data_em = dict(result_emfitallitems=result_emfitallitems, target_sigmax=target_sigmax)
                        pickle.dump(data_em, filecache_out, protocol=2)
                except IOError:
                    print "Error writing out to caching file ", caching_emfit_filename


        ## Plots now, for each distance!
        # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
        for min_dist_i in xrange(enforce_min_distance_space.size):

            # Plot now
            _, ax = plt.subplots()
            ax.plot(ratio_space, result_emfitallitems[min_dist_i, :, 1:5], linewidth=3)
            plt.ylim([0.0, 1.1])
            plt.legend(['Target', 'Nontarget 1', 'Nontarget 2', 'Random'], loc='upper left')

            if savefigs:
                dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])

    if plot_min_distance_effect:
        conj_receptive_field_size = 2.*np.pi/((all_args[0]['M']*ratio_space)**0.5)

        target_vs_nontargets_mindist_ratio = result_emfitallitems[..., 1]/np.sum(result_emfitallitems[..., 1:4], axis=-1)
        nontargetsmean_vs_targnontarg_mindist_ratio = np.mean(result_emfitallitems[..., 2:4]/np.sum(result_emfitallitems[..., 1:4], axis=-1)[..., np.newaxis], axis=-1)

        for ratio_conj_i, ratio_conj in enumerate(ratio_space):
            # Do one plot per ratio, putting the receptive field size on each
            f, ax = plt.subplots()

            ax.plot(enforce_min_distance_space[1:], target_vs_nontargets_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='target mixture')
            ax.plot(enforce_min_distance_space[1:], nontargetsmean_vs_targnontarg_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='non-target mixture')
            # ax.plot(enforce_min_distance_space[1:], result_emfitallitems[1:, ratio_conj_i, 1:5], linewidth=3)

            ax.axvline(x=conj_receptive_field_size[ratio_conj_i]/2., color='k', linestyle='--', linewidth=2)
            ax.axvline(x=conj_receptive_field_size[ratio_conj_i]*2., color='r', linestyle='--', linewidth=2)

            plt.legend(loc='upper left')
            plt.grid()
            # ax.set_xlabel('Stimuli separation')
            # ax.set_ylabel('Ratio Target to Non-targets')
            plt.axis('tight')
            ax.set_ylim([0.0, 1.0])
            ax.set_xlim([enforce_min_distance_space[1:].min(), enforce_min_distance_space[1:].max()])

            if savefigs:
                dataio.save_current_figure('ratio%.2f_mindistpred_ratiotargetnontarget_{label}_{unique_id}.pdf' % ratio_conj)


    if compute_bootstraps:
        ## Bootstrap evaluation

        # We need to choose 3 levels of min_distances
        target_sigmax = 0.25
        target_mindist_low = 0.15
        target_mindist_medium = 0.5
        target_mindist_high = 1.

        sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
        min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
        min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
        min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))

        # cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap.pickle')
        cache_bootstrap_fn = '/Users/loicmatthey/Dropbox/UCL/1-phd/Work/Visual_working_memory/code/git-bayesian-visual-working-memory/Experiments/specific_stimuli/specific_stimuli_corrected_mixed_sigmaxmindistance_autoset_repetitions5mult_collectall_281113_outputs/cache_bootstrap.pickle'
        try:
            with open(cache_bootstrap_fn, 'r') as file_in:
                # Load and assign values
                cached_data = pickle.load(file_in)
                bootstrap_ecdf_bays_sigmax_T = cached_data['bootstrap_ecdf_bays_sigmax_T']
                bootstrap_ecdf_allitems_sum_sigmax_T = cached_data['bootstrap_ecdf_allitems_sum_sigmax_T']
                bootstrap_ecdf_allitems_all_sigmax_T = cached_data['bootstrap_ecdf_allitems_all_sigmax_T']
                should_fit_bootstrap = False

        except IOError:
            print "Error while loading ", cache_bootstrap_fn

        ratio_i = 0

        # bootstrap_allitems_nontargets_allitems_uniquekappa = em_circularmixture_allitems_uniquekappa.bootstrap_nontarget_stat(
        # result_responses_all[min_dist_level_low_i, sigmax_level_i, ratio_i].flatten(),
        # result_target_all[min_dist_level_low_i, sigmax_level_i, ratio_i].flatten(),
        # result_nontargets_all[min_dist_level_low_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)),
        # sumnontargets_bootstrap_ecdf=bootstrap_ecdf_allitems_sum_sigmax_T[sigmax_level_i][K]['ecdf'],
        # allnontargets_bootstrap_ecdf=bootstrap_ecdf_allitems_all_sigmax_T[sigmax_level_i][K]['ecdf']

        # TODO FINISH HERE

    variables_to_save = ['nb_repetitions']

    if savedata:
        dataio.save_variables_default(locals(), variables_to_save)

        dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')


    plt.show()


    return locals()
def plots_misbinding_logposterior(data_pbs, generator_module=None):
    '''
        Reload 3D volume runs from PBS and plot them

    '''


    #### SETUP
    #
    savedata = False
    savefigs = True

    plot_logpost = False
    plot_error = False
    plot_mixtmodel = True
    plot_hist_responses_fisherinfo = True
    compute_plot_bootstrap = False
    compute_fisher_info_perratioconj = True

    # mixturemodel_to_use = 'original'
    mixturemodel_to_use = 'allitems'
    # mixturemodel_to_use = 'allitems_kappafi'

    caching_fisherinfo_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_fisherinfo.pickle')


    #
    #### /SETUP

    print "Order parameters: ", generator_module.dict_parameters_range.keys()

    result_all_log_posterior = np.squeeze(data_pbs.dict_arrays['result_all_log_posterior']['results'])
    result_all_thetas = np.squeeze(data_pbs.dict_arrays['result_all_thetas']['results'])

    ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']

    print ratio_space
    print result_all_log_posterior.shape

    N = result_all_thetas.shape[-1]

    result_prob_wrong = np.zeros((ratio_space.size, N))
    result_em_fits = np.empty((ratio_space.size, 6))*np.nan

    all_args = data_pbs.loaded_data['args_list']

    fixed_means = [-np.pi*0.6, np.pi*0.6]
    all_angles = np.linspace(-np.pi, np.pi, result_all_log_posterior.shape[-1])

    dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])


    plt.rcParams['font.size'] = 18


    if plot_hist_responses_fisherinfo:

        # From cache
        if caching_fisherinfo_filename is not None:
            if os.path.exists(caching_fisherinfo_filename):
                # Got file, open it and try to use its contents
                try:
                    with open(caching_fisherinfo_filename, 'r') as file_in:
                        # Load and assign values
                        cached_data = pickle.load(file_in)
                        result_fisherinfo_ratio = cached_data['result_fisherinfo_ratio']
                        compute_fisher_info_perratioconj = False

                except IOError:
                    print "Error while loading ", caching_fisherinfo_filename, "falling back to computing the Fisher Info"

        if compute_fisher_info_perratioconj:
            # We did not save the Fisher info, but need it if we want to fit the mixture model with fixed kappa. So recompute them using the args_dicts

            result_fisherinfo_ratio = np.empty(ratio_space.shape)

            # Invert the all_args_i -> ratio_conj direction
            parameters_indirections = data_pbs.loaded_data['parameters_dataset_index']

            for ratio_conj_i, ratio_conj in enumerate(ratio_space):
                # Get index of first dataset with the current ratio_conj (no need for the others, I think)
                arg_index = parameters_indirections[(ratio_conj,)][0]

                # Now using this dataset, reconstruct a RandomFactorialNetwork and compute the fisher info
                curr_args = all_args[arg_index]

                curr_args['stimuli_generation'] = lambda T: np.linspace(-np.pi*0.6, np.pi*0.6, T)

                (random_network, data_gen, stat_meas, sampler) = launchers.init_everything(curr_args)

                # Theo Fisher info
                result_fisherinfo_ratio[ratio_conj_i] = sampler.estimate_fisher_info_theocov()

                del curr_args['stimuli_generation']

            # Save everything to a file, for faster later plotting
            if caching_fisherinfo_filename is not None:
                try:
                    with open(caching_fisherinfo_filename, 'w') as filecache_out:
                        data_cache = dict(result_fisherinfo_ratio=result_fisherinfo_ratio)
                        pickle.dump(data_cache, filecache_out, protocol=2)
                except IOError:
                    print "Error writing out to caching file ", caching_fisherinfo_filename

        # Now plots. Do histograms of responses (around -pi/6 and pi/6), add Von Mises derived from Theo FI on top, and vertical lines for the correct target/nontarget angles.
        for ratio_conj_i, ratio_conj in enumerate(ratio_space):
            # Histogram
            ax = utils.hist_angular_data(result_all_thetas[ratio_conj_i], bins=100, title='ratio %.2f, fi %.0f' % (ratio_conj, result_fisherinfo_ratio[ratio_conj_i]))
            bar_heights, _, _ = utils.histogram_binspace(result_all_thetas[ratio_conj_i], bins=100, norm='density')

            # Add Fisher info prediction on top
            x = np.linspace(-np.pi, np.pi, 1000)
            if result_fisherinfo_ratio[ratio_conj_i] < 700:
                # Von Mises PDF
                utils.plot_vonmises_pdf(x, utils.stddev_to_kappa(1./result_fisherinfo_ratio[ratio_conj_i]**0.5), mu=fixed_means[-1], ax_handle=ax, linewidth=3, color='r', scale=np.max(bar_heights), fmt='-')
            else:
                # Switch to Gaussian instead
                utils.plot_normal_pdf(x, mu=fixed_means[-1], std=1./result_fisherinfo_ratio[ratio_conj_i]**0.5, ax_handle=ax, linewidth=3, color='r', scale=np.max(bar_heights), fmt='-')

            # ax.set_xticks([])
            # ax.set_yticks([])

            # Add vertical line to correct target/nontarget
            ax.axvline(x=fixed_means[0], color='g', linewidth=2)
            ax.axvline(x=fixed_means[1], color='r', linewidth=2)

            ax.get_figure().canvas.draw()

            if savefigs:
                # plt.tight_layout()
                dataio.save_current_figure('results_misbinding_histresponses_vonmisespdf_ratioconj%.2f{label}_{unique_id}.pdf' % (ratio_conj))



    if plot_logpost:
        for ratio_conj_i, ratio_conj in enumerate(ratio_space):
            # ax = utils.plot_mean_std_area(all_angles, nanmean(result_all_log_posterior[ratio_conj_i], axis=0), nanstd(result_all_log_posterior[ratio_conj_i], axis=0))

            # ax.set_xlim((-np.pi, np.pi))
            # ax.set_xticks((-np.pi, -np.pi / 2, 0, np.pi / 2., np.pi))
            # ax.set_xticklabels((r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'))
            # ax.set_yticks(())

            # ax.get_figure().canvas.draw()

            # if savefigs:
            #     dataio.save_current_figure('results_misbinding_logpost_ratioconj%.2f_{label}_global_{unique_id}.pdf' % ratio_conj)


            # Compute the probability of answering wrongly (from fitting mixture distrib onto posterior)
            for n in xrange(result_all_log_posterior.shape[1]):
                result_prob_wrong[ratio_conj_i, n], _, _ = utils.fit_gaussian_mixture_fixedmeans(all_angles, np.exp(result_all_log_posterior[ratio_conj_i, n]), fixed_means=fixed_means, normalise=True, return_fitted_data=False, should_plot=False)

        # ax = utils.plot_mean_std_area(ratio_space, nanmean(result_prob_wrong, axis=-1), nanstd(result_prob_wrong, axis=-1))
        plt.figure()
        plt.plot(ratio_space, utils.nanmean(result_prob_wrong, axis=-1))

        # ax.get_figure().canvas.draw()
        if savefigs:
            dataio.save_current_figure('results_misbinding_probwrongpost_allratioconj_{label}_global_{unique_id}.pdf')

    if plot_error:

        ## Compute Standard deviation/precision from samples and plot it as a function of ratio_conj
        stats = utils.compute_mean_std_circular_data(utils.wrap_angles(result_all_thetas - fixed_means[1]).T)

        f = plt.figure()
        plt.plot(ratio_space, stats['std'])
        plt.ylabel('Standard deviation [rad]')

        if savefigs:
            dataio.save_current_figure('results_misbinding_stddev_allratioconj_{label}_global_{unique_id}.pdf')

        f = plt.figure()
        plt.plot(ratio_space, utils.compute_angle_precision_from_std(stats['std'], square_precision=False), linewidth=2)
        plt.ylabel('Precision [$1/rad$]')
        plt.xlabel('Proportion of conjunctive units')
        plt.grid()

        if savefigs:
            dataio.save_current_figure('results_misbinding_precision_allratioconj_{label}_global_{unique_id}.pdf')

        ## Compute the probability of misbinding
        # 1) Just count samples < 0 / samples tot
        # 2) Fit a mixture model, average over mixture probabilities
        prob_smaller0 = np.sum(result_all_thetas <= 1, axis=1)/float(result_all_thetas.shape[1])

        em_centers = np.zeros((ratio_space.size, 2))
        em_covs = np.zeros((ratio_space.size, 2))
        em_pk = np.zeros((ratio_space.size, 2))
        em_ll = np.zeros(ratio_space.size)
        for ratio_conj_i, ratio_conj in enumerate(ratio_space):
            cen_lst, cov_lst, em_pk[ratio_conj_i], em_ll[ratio_conj_i] = pygmm.em(result_all_thetas[ratio_conj_i, np.newaxis].T, K = 2, max_iter = 400, init_kw={'cluster_init':'fixed', 'fixed_means': fixed_means})

            em_centers[ratio_conj_i] = np.array(cen_lst).flatten()
            em_covs[ratio_conj_i] = np.array(cov_lst).flatten()

        # print em_centers
        # print em_covs
        # print em_pk

        f = plt.figure()
        plt.plot(ratio_space, prob_smaller0)
        plt.ylabel('Misbound proportion')
        if savefigs:
            dataio.save_current_figure('results_misbinding_countsmaller0_allratioconj_{label}_global_{unique_id}.pdf')

        f = plt.figure()
        plt.plot(ratio_space, np.max(em_pk, axis=-1), 'g', linewidth=2)
        plt.ylabel('Mixture proportion, correct')
        plt.xlabel('Proportion of conjunctive units')
        plt.grid()
        if savefigs:
            dataio.save_current_figure('results_misbinding_emmixture_allratioconj_{label}_global_{unique_id}.pdf')


        # Put everything on one figure
        f = plt.figure(figsize=(10, 6))
        norm_for_plot = lambda x: (x - np.min(x))/np.max((x - np.min(x)))
        plt.plot(ratio_space, norm_for_plot(stats['std']), ratio_space, norm_for_plot(utils.compute_angle_precision_from_std(stats['std'], square_precision=False)), ratio_space, norm_for_plot(prob_smaller0), ratio_space, norm_for_plot(em_pk[:, 1]), ratio_space, norm_for_plot(em_pk[:, 0]))
        plt.legend(('Std dev', 'Precision', 'Prob smaller 1', 'Mixture proportion correct', 'Mixture proportion misbinding'))
        # plt.plot(ratio_space, norm_for_plot(compute_angle_precision_from_std(stats['std'], square_precision=False)), ratio_space, norm_for_plot(em_pk[:, 1]), linewidth=2)
        # plt.legend(('Precision', 'Mixture proportion correct'), loc='best')
        plt.grid()
        if savefigs:
            dataio.save_current_figure('results_misbinding_allmetrics_allratioconj_{label}_global_{unique_id}.pdf')


    if plot_mixtmodel:
        # Fit Paul's model
        target_angle = np.ones(N)*fixed_means[1]
        nontarget_angles = np.ones((N, 1))*fixed_means[0]

        for ratio_conj_i, ratio_conj in enumerate(ratio_space):
            print "Ratio: ", ratio_conj

            responses = result_all_thetas[ratio_conj_i]

            if mixturemodel_to_use == 'allitems_kappafi':
                curr_params_fit = em_circularmixture_allitems_kappafi.fit(responses, target_angle, nontarget_angles, kappa=result_fisherinfo_ratio[ratio_conj_i])
            elif mixturemodel_to_use == 'allitems':
                curr_params_fit = em_circularmixture_allitems_uniquekappa.fit(responses, target_angle, nontarget_angles)
            else:
                curr_params_fit = em_circularmixture.fit(responses, target_angle, nontarget_angles)

            result_em_fits[ratio_conj_i] = [curr_params_fit['kappa'], curr_params_fit['mixt_target']] + utils.arrnum_to_list(curr_params_fit['mixt_nontargets']) + [curr_params_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]

            print curr_params_fit


        if False:
            f, ax = plt.subplots()
            ax2 = ax.twinx()

            # left axis, kappa
            ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 0], 0*result_em_fits[:, 0], xlabel='Proportion of conjunctive units', ylabel="Inverse variance $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa', color='k')

            # Right axis, mixture probabilities
            utils.plot_mean_std_area(ratio_space, result_em_fits[:, 1], 0*result_em_fits[:, 1], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Target')
            utils.plot_mean_std_area(ratio_space, result_em_fits[:, 2], 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Nontarget')
            utils.plot_mean_std_area(ratio_space, result_em_fits[:, 3], 0*result_em_fits[:, 3], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Random')

            lines, labels = ax.get_legend_handles_labels()
            lines2, labels2 = ax2.get_legend_handles_labels()
            ax.legend(lines + lines2, labels + labels2, fontsize=12, loc='right')

            # ax.set_xlim([0.9, 5.1])
            # ax.set_xticks(range(1, 6))
            # ax.set_xticklabels(range(1, 6))
            plt.grid()

            f.canvas.draw()

        if True:
            # Mixture probabilities
            ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 1], 0*result_em_fits[:, 1], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", linewidth=3, fmt='-', markersize=8, label='Target')
            utils.plot_mean_std_area(ratio_space, result_em_fits[:, 2], 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='-', markersize=8, label='Nontarget')
            utils.plot_mean_std_area(ratio_space, result_em_fits[:, 3], 0*result_em_fits[:, 3], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='-', markersize=8, label='Random')

            ax.legend(loc='right')

            # ax.set_xlim([0.9, 5.1])
            # ax.set_xticks(range(1, 6))
            # ax.set_xticklabels(range(1, 6))
            plt.grid()

            if savefigs:
                dataio.save_current_figure('results_misbinding_emmixture_allratioconj_{label}_global_{unique_id}.pdf')

        if True:
            # Kappa
            # ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 0], 0*result_em_fits[:, 0], xlabel='Proportion of conjunctive units', ylabel="$\kappa [rad^{-2}]$", linewidth=3, fmt='-', markersize=8, label='Kappa')
            ax = utils.plot_mean_std_area(ratio_space, utils.kappa_to_stddev(result_em_fits[:, 0]), 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Standard deviation [rad]", linewidth=3, fmt='-', markersize=8, label='Mixture model $\kappa$')

            # Add Fisher Info theo
            ax = utils.plot_mean_std_area(ratio_space, utils.kappa_to_stddev(result_fisherinfo_ratio), 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Standard deviation [rad]", linewidth=3, fmt='-', markersize=8, label='Fisher Information', ax_handle=ax)

            ax.legend(loc='best')

            # ax.set_xlim([0.9, 5.1])
            # ax.set_xticks(range(1, 6))
            # ax.set_xticklabels(range(1, 6))
            plt.grid()

            if savefigs:
                dataio.save_current_figure('results_misbinding_kappa_allratioconj_{label}_global_{unique_id}.pdf')

    if compute_plot_bootstrap:
        ## Compute the bootstrap pvalue for each ratio
        #       use the bootstrap CDF from mixed runs, not the exact current ones, not sure if good idea.

        bootstrap_to_load = 1
        if bootstrap_to_load == 1:
            cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap_mixed_from_bootstrapnontargets.pickle')
            bootstrap_ecdf_sum_label = 'bootstrap_ecdf_allitems_sum_sigmax_T'
            bootstrap_ecdf_all_label = 'bootstrap_ecdf_allitems_all_sigmax_T'
        elif bootstrap_to_load == 2:
            cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap_misbinding_mixed.pickle')
            bootstrap_ecdf_sum_label = 'bootstrap_ecdf_allitems_sum_ratioconj'
            bootstrap_ecdf_all_label = 'bootstrap_ecdf_allitems_all_ratioconj'

        try:
            with open(cache_bootstrap_fn, 'r') as file_in:
                # Load and assign values
                cached_data = pickle.load(file_in)
                assert bootstrap_ecdf_sum_label in cached_data
                assert bootstrap_ecdf_all_label in cached_data
                should_fit_bootstrap = False

        except IOError:
            print "Error while loading ", cache_bootstrap_fn

        # Select the ECDF to use
        if bootstrap_to_load == 1:
            sigmax_i = 3    # corresponds to sigmax = 2, input here.
            T_i = 1         # two possible targets here.
            bootstrap_ecdf_sum_used = cached_data[bootstrap_ecdf_sum_label][sigmax_i][T_i]['ecdf']
            bootstrap_ecdf_all_used = cached_data[bootstrap_ecdf_all_label][sigmax_i][T_i]['ecdf']
        elif bootstrap_to_load == 2:
            ratio_conj_i = 4
            bootstrap_ecdf_sum_used = cached_data[bootstrap_ecdf_sum_label][ratio_conj_i]['ecdf']
            bootstrap_ecdf_all_used = cached_data[bootstrap_ecdf_all_label][ratio_conj_i]['ecdf']


        result_pvalue_bootstrap_sum = np.empty(ratio_space.size)*np.nan
        result_pvalue_bootstrap_all = np.empty((ratio_space.size, nontarget_angles.shape[-1]))*np.nan

        for ratio_conj_i, ratio_conj in enumerate(ratio_space):
            print "Ratio: ", ratio_conj

            responses = result_all_thetas[ratio_conj_i]

            bootstrap_allitems_nontargets_allitems_uniquekappa = em_circularmixture_allitems_uniquekappa.bootstrap_nontarget_stat(responses, target_angle, nontarget_angles,
                sumnontargets_bootstrap_ecdf=bootstrap_ecdf_sum_used,
                allnontargets_bootstrap_ecdf=bootstrap_ecdf_all_used)

            result_pvalue_bootstrap_sum[ratio_conj_i] = bootstrap_allitems_nontargets_allitems_uniquekappa['p_value']
            result_pvalue_bootstrap_all[ratio_conj_i] = bootstrap_allitems_nontargets_allitems_uniquekappa['allnontarget_p_value']

        ## Plots
        # f, ax = plt.subplots()
        # ax.plot(ratio_space, result_pvalue_bootstrap_all, linewidth=2)

        # if savefigs:
        #     dataio.save_current_figure("pvalue_bootstrap_all_ratioconj_{label}_{unique_id}.pdf")

        f, ax = plt.subplots()
        ax.plot(ratio_space, result_pvalue_bootstrap_sum, linewidth=2)
        plt.grid()

        if savefigs:
            dataio.save_current_figure("pvalue_bootstrap_sum_ratioconj_{label}_{unique_id}.pdf")


    # plt.figure()
    # plt.plot(ratio_MMlower, results_filtered_smoothed/np.max(results_filtered_smoothed, axis=0), linewidth=2)
    # plt.plot(ratio_MMlower[np.argmax(results_filtered_smoothed, axis=0)], np.ones(results_filtered_smoothed.shape[-1]), 'ro', markersize=10)
    # plt.grid()
    # plt.ylim((0., 1.1))
    # plt.subplots_adjust(right=0.8)
    # plt.legend(['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='center right', bbox_to_anchor=(1.3, 0.5))
    # plt.xticks(np.linspace(0, 1.0, 5))

    variables_to_save = ['target_angle', 'nontarget_angles']

    if savedata:
        dataio.save_variables_default(locals(), variables_to_save)
        dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='misbindings')


    plt.show()

    return locals()