def plots_ratioMscaling(data_pbs, generator_module=None):
'''
Reload and plot precision/fits of a Mixed code.
'''
#### SETUP
#
savefigs = True
savedata = True
plots_pcolor_all = False
plots_effect_M_target_kappa = False
plots_kappa_fi_comparison = False
plots_multiple_fisherinfo = False
specific_plot_effect_R = True
specific_plot_effect_ratio_M = False
convert_M_realsizes = False
plots_pcolor_realsizes_Msubs = True
plots_pcolor_realsizes_Mtot = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
# interpolation_method = 'linear'
interpolation_method = 'nearest'
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = (utils.nanmean(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_all_precisions_std = (utils.nanstd(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_em_fits_mean = (utils.nanmean(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
result_em_fits_std = (utils.nanstd(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
result_fisherinfo_mean = (utils.nanmean(data_pbs.dict_arrays['result_fisher_info']['results'], axis=-1))
result_fisherinfo_std = (utils.nanstd(data_pbs.dict_arrays['result_fisher_info']['results'], axis=-1))
all_args = data_pbs.loaded_data['args_list']
result_em_fits_kappa = result_em_fits_mean[..., 0]
result_em_fits_target = result_em_fits_mean[..., 1]
result_em_fits_kappa_valid = np.ma.masked_where(result_em_fits_target < 0.8, result_em_fits_kappa)
# flat versions
result_parameters_flat = np.array(data_pbs.dict_arrays['result_all_precisions']['parameters_flat'])
result_all_precisions_mean_flat = np.mean(np.array(data_pbs.dict_arrays['result_all_precisions']['results_flat']), axis=-1)
result_em_fits_mean_flat = np.mean(np.array(data_pbs.dict_arrays['result_em_fits']['results_flat']), axis=-1)
result_fisherinfor_mean_flat = np.mean(np.array(data_pbs.dict_arrays['result_fisher_info']['results_flat']), axis=-1)
result_em_fits_kappa_flat = result_em_fits_mean_flat[..., 0]
result_em_fits_target_flat = result_em_fits_mean_flat[..., 1]
result_em_fits_kappa_valid_flat = np.ma.masked_where(result_em_fits_target_flat < 0.8, result_em_fits_kappa_flat)
M_space = data_pbs.loaded_data['parameters_uniques']['M'].astype(int)
ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']
R_space = data_pbs.loaded_data['parameters_uniques']['R'].astype(int)
num_repetitions = generator_module.num_repetitions
T = generator_module.T
print M_space
print ratio_space
print R_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape
dataio = DataIO.DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
MAX_DISTANCE = 100.
if convert_M_realsizes:
# alright, currently M*ratio_conj gives the conjunctive subpopulation,
# but only floor(M_conj**1/R) neurons are really used. So we should
# convert to M_conj_real and M_feat_real instead of M and ratio
result_parameters_flat_subM_converted = []
result_parameters_flat_Mtot_converted = []
for params in result_parameters_flat:
M = params[0]; ratio_conj = params[1]; R = int(params[2])
M_conj_prior = int(M*ratio_conj)
M_conj_true = int(np.floor(M_conj_prior**(1./R))**R)
M_feat_true = int(np.floor((M-M_conj_prior)/R)*R)
# result_parameters_flat_subM_converted contains (M_conj, M_feat, R)
result_parameters_flat_subM_converted.append(np.array([M_conj_true, M_feat_true, R]))
# result_parameters_flat_Mtot_converted contains (M_tot, ratio_conj, R)
result_parameters_flat_Mtot_converted.append(np.array([float(M_conj_true+M_feat_true), float(M_conj_true)/float(M_conj_true+M_feat_true), R]))
result_parameters_flat_subM_converted = np.array(result_parameters_flat_subM_converted)
result_parameters_flat_Mtot_converted = np.array(result_parameters_flat_Mtot_converted)
if plots_pcolor_all:
if convert_M_realsizes:
def plot_interp(points, data, currR_indices, title='', points_label='', xlabel='', ylabel=''):
utils.contourf_interpolate_data_interactive_maxvalue(points[currR_indices][..., :2], data[currR_indices], xlabel=xlabel, ylabel=ylabel, title='%s, R=%d' % (title, R), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False)
if savefigs:
dataio.save_current_figure('pcolortrueM%s_%s_R%d_log_%s_{label}_{unique_id}.pdf' % (points_label, title, R, interpolation_method))
all_datas = [dict(name='precision', data=result_all_precisions_mean_flat), dict(name='kappa', data=result_em_fits_kappa_flat), dict(name='kappavalid', data=result_em_fits_kappa_valid_flat), dict(name='target', data=result_em_fits_target_flat), dict(name='fisherinfo', data=result_fisherinfor_mean_flat)]
all_points = []
if plots_pcolor_realsizes_Msubs:
all_points.append(dict(name='sub', data=result_parameters_flat_subM_converted, xlabel='M_conj', ylabel='M_feat'))
if plots_pcolor_realsizes_Mtot:
all_points.append(dict(name='tot', data=result_parameters_flat_Mtot_converted, xlabel='Mtot', ylabel='ratio_conj'))
for curr_points in all_points:
for curr_data in all_datas:
for R_i, R in enumerate(R_space):
currR_indices = curr_points['data'][:, 2] == R
plot_interp(curr_points['data'], curr_data['data'], currR_indices, title=curr_data['name'], points_label=curr_points['name'], xlabel=curr_points['xlabel'], ylabel=curr_points['ylabel'])
else:
# Do one pcolor for M and ratio per R
for R_i, R in enumerate(R_space):
# Check evolution of precision given M and ratio
utils.pcolor_2d_data(result_all_precisions_mean[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='precision, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_precision_R%d_log_{label}_{unique_id}.pdf' % R)
# Show kappa
try:
utils.pcolor_2d_data(result_em_fits_kappa_valid[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='kappa, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_kappa_R%d_log_{label}_{unique_id}.pdf' % R)
except ValueError:
pass
# Show probability on target
utils.pcolor_2d_data(result_em_fits_target[..., R_i], log_scale=False, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='target, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_target_R%d_{label}_{unique_id}.pdf' % R)
# # Show Fisher info
utils.pcolor_2d_data(result_fisherinfo_mean[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='fisher info, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_fisherinfo_R%d_log_{label}_{unique_id}.pdf' % R)
plt.close('all')
if plots_effect_M_target_kappa:
def plot_ratio_target_kappa(ratio_target_kappa_given_M, target_kappa, R):
f, ax = plt.subplots()
ax.plot(M_space, ratio_target_kappa_given_M)
ax.set_xlabel('M')
ax.set_ylabel('Optimal ratio')
ax.set_title('Optimal Ratio for kappa %d, R=%d' % (target_kappa, R))
if savefigs:
dataio.save_current_figure('optratio_M_targetkappa%d_R%d_{label}_{unique_id}.pdf' % (target_kappa, R))
target_kappas = np.array([100, 200, 300, 500, 1000, 3000])
for R_i, R in enumerate(R_space):
for target_kappa in target_kappas:
dist_to_target_kappa = (result_em_fits_kappa[..., R_i] - target_kappa)**2.
best_dist_to_target_kappa = np.argmin(dist_to_target_kappa, axis=1)
ratio_target_kappa_given_M = np.ma.masked_where(dist_to_target_kappa[np.arange(dist_to_target_kappa.shape[0]), best_dist_to_target_kappa] > MAX_DISTANCE, ratio_space[best_dist_to_target_kappa])
# replot
plot_ratio_target_kappa(ratio_target_kappa_given_M, target_kappa, R)
plt.close('all')
if plots_kappa_fi_comparison:
# result_em_fits_kappa and fisher info
if True:
for R_i, R in enumerate(R_space):
for M_tot_selected_i, M_tot_selected in enumerate(M_space[::2]):
# M_conj_space = ((1.-ratio_space)*M_tot_selected).astype(int)
# M_feat_space = M_tot_selected - M_conj_space
f, axes = plt.subplots(2, 1)
axes[0].plot(ratio_space, result_em_fits_kappa[2*M_tot_selected_i, ..., R_i])
axes[0].set_xlabel('ratio')
axes[0].set_title('Fitted kappa')
axes[1].plot(ratio_space, utils.stddev_to_kappa(1./result_fisherinfo_mean[2*M_tot_selected_i, ..., R_i]**0.5))
axes[1].set_xlabel('ratio')
axes[1].set_title('kappa_FI')
f.suptitle('M_tot %d' % M_tot_selected, fontsize=15)
f.set_tight_layout(True)
if savefigs:
dataio.save_current_figure('comparison_kappa_fisher_R%d_M%d_{label}_{unique_id}.pdf' % (R, M_tot_selected))
plt.close(f)
if plots_multiple_fisherinfo:
target_fisherinfos = np.array([100, 200, 300, 500, 1000])
for R_i, R in enumerate(R_space):
for target_fisherinfo in target_fisherinfos:
dist_to_target_fisherinfo = (result_fisherinfo_mean[..., R_i] - target_fisherinfo)**2.
utils.pcolor_2d_data(dist_to_target_fisherinfo, log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='Fisher info, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_distfi%d_R%d_log_{label}_{unique_id}.pdf' % (target_fisherinfo, R))
plt.close('all')
if specific_plot_effect_R:
M_target = 356
if convert_M_realsizes:
M_tot_target = M_target
delta_around_target = 30
filter_points_totM_indices = np.abs(result_parameters_flat_Mtot_converted[:, 0] - M_tot_target) < delta_around_target
# first check landscape
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_totM_indices][..., 1:], result_em_fits_kappa_flat[filter_points_totM_indices], xlabel='ratio_conj', ylabel='R', title='kappa, M_target=%d +- %d' % (M_tot_target, delta_around_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_pcolortrueMtot_kappa_M%d_log_%s_{label}_{unique_id}.pdf' % (M_tot_target, interpolation_method))
# Then plot distance to specific kappa
# target_kappa = 1.2e3
target_kappa = 580
dist_target_kappa_flat = np.abs(result_em_fits_kappa_flat - target_kappa)
mask_greater_than = 5e3
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_totM_indices][..., 1:], dist_target_kappa_flat[filter_points_totM_indices], xlabel='ratio_conj', ylabel='R', title='dist kappa %d, M_target=%d +- %d' % (target_kappa, M_tot_target, delta_around_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_greater_than=mask_greater_than, mask_smaller_than=0)
if savefigs:
dataio.save_current_figure('specific_pcolortrueMtot_distkappa%d_M%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, M_tot_target, interpolation_method))
else:
# Choose a M, find which ratio gives best fit to a given kappa
M_target_i = np.argmin(np.abs(M_space - M_target))
utils.pcolor_2d_data(result_em_fits_kappa[M_target_i], log_scale=True, x=ratio_space, y=R_space, xlabel='ratio', ylabel='R', ylabel_format="%d", title='Kappa, M %d' % (M_target))
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_Reffect_pcolor_kappa_M%dT%d_log_{label}_{unique_id}.pdf' % (M_target, T))
# target_kappa = np.ma.mean(result_em_fits_kappa_valid[M_target_i])
# target_kappa = 5*1e3
# target_kappa = 1.2e3
target_kappa = 580
# dist_target_kappa = np.abs(result_em_fits_kappa_valid[M_target_i] - target_kappa)
dist_target_kappa = result_em_fits_kappa[M_target_i]/target_kappa
dist_target_kappa[dist_target_kappa > 2.0] = 2.0
# dist_target_kappa[dist_target_kappa < 0.5] = 0.5
utils.pcolor_2d_data(dist_target_kappa, log_scale=False, x=ratio_space, y=R_space, xlabel='ratio', ylabel='R', ylabel_format="%d", title='Kappa dist %.2f, M %d' % (target_kappa, M_target), cmap='RdBu_r')
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_Reffect_pcolor_distkappa%d_M%dT%d_log_{label}_{unique_id}.pdf' % (target_kappa, M_target, T))
# Plot the probability of being on-target
utils.pcolor_2d_data(result_em_fits_target[M_target_i], log_scale=False, x=ratio_space, y=R_space, xlabel='ratio', ylabel='R', ylabel_format="%d", title='target mixture proportion, M %d' % (M_target), vmin=0.0, vmax=1.0) #cmap='RdBu_r'
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_Reffect_pcolor_target_M%dT%d_{label}_{unique_id}.pdf' % (M_target, T))
if specific_plot_effect_ratio_M:
# try to do the same plots as in ratio_scaling_M but with the current data
R_target = 2
interpolation_method = 'cubic'
mask_greater_than = 1e3
if convert_M_realsizes:
# filter_points_targetR_indices = np.abs(result_parameters_flat_Mtot_converted[:, -1] - R_target) == 0
filter_points_targetR_indices = np.abs(result_parameters_flat_subM_converted[:, -1] - R_target) == 0
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., :-1], result_em_fits_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False)
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_subM_converted[filter_points_targetR_indices][..., :-1], result_em_fits_kappa_flat[filter_points_targetR_indices], xlabel='M_conj', ylabel='M_feat', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsubM_kappa_R%d_log_%s_{label}_{unique_id}.pdf' % (R_target, interpolation_method))
# Then plot distance to specific kappa
target_kappa = 580
# target_kappa = 2000
dist_target_kappa_flat = np.abs(result_em_fits_kappa_flat - target_kappa)
dist_target_kappa_flat = result_em_fits_kappa_flat/target_kappa
dist_target_kappa_flat[dist_target_kappa_flat > 1.45] = 1.45
dist_target_kappa_flat[dist_target_kappa_flat < 0.5] = 0.5
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_smaller_than=0, show_slider=False, mask_greater_than =mask_greater_than)
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_smaller_than=0, show_slider=False, mask_greater_than =mask_greater_than)
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_subM_converted[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M_conj', ylabel='M_feat', title='Kappa dist %.2f, R=%d' % (target_kappa, R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_greater_than=mask_greater_than, mask_smaller_than=0, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsubM_distkappa%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, R_target, interpolation_method))
## Scatter plot, works better...
f, ax = plt.subplots()
ss = ax.scatter(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], 500, c=(dist_target_kappa_flat[filter_points_targetR_indices]),
norm=matplotlib.colors.LogNorm())
plt.colorbar(ss)
ax.set_xlabel('M')
ax.set_ylabel('ratio')
ax.set_xlim((result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0].min()*0.8, result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0].max()*1.03))
ax.set_ylim((-0.05, result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1].max()*1.05))
ax.set_title('Kappa dist %.2f, R=%d' % (target_kappa, R_target))
if savefigs:
dataio.save_current_figure('specific_ratioM_scattertotM_distkappa%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, R_target, interpolation_method))
## Spline interpolation
distkappa_spline_int_params = spint.bisplrep(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], np.log(dist_target_kappa_flat[filter_points_targetR_indices]), kx=3, ky=3, s=1)
if True:
M_interp_space = np.linspace(100, 740, 100)
ratio_interp_space = np.linspace(0.0, 1.0, 100)
# utils.pcolor_2d_data(spint.bisplev(M_interp_space, ratio_interp_space, distkappa_spline_int_params), y=ratio_interp_space, x=M_interp_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target), ticks_interpolate=11)
utils.pcolor_2d_data(np.exp(spint.bisplev(M_interp_space, ratio_interp_space, distkappa_spline_int_params)), y=ratio_interp_space, x=M_interp_space, ylabel='ratio conjunctivity', xlabel='M', xlabel_format="%d", title='Ratio kappa/%.2f, R %d' % (target_kappa, R_target), ticks_interpolate=11, log_scale=False, cmap='RdBu_r')
# plt.scatter(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], marker='o', c='b', s=5)
# plt.scatter(np.argmin(np.abs(M_interp_space[:, np.newaxis] - result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0]), axis=0), np.argmin(np.abs(ratio_interp_space[:, np.newaxis] - result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1]), axis=0), marker='o', c='b', s=5)
else:
M_interp_space = M_space
ratio_interp_space = ratio_space
utils.pcolor_2d_data(spint.bisplev(M_interp_space, ratio_interp_space, distkappa_spline_int_params), y=ratio_interp_space, x=M_interp_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target))
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsplinetotM_distkappa%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, R_target, interpolation_method))
### Distance to Fisher Info
target_fi = 2*target_kappa
dist_target_fi_flat = np.abs(result_fisherinfor_mean_flat - target_fi)
dist_target_fi_flat = result_fisherinfor_mean_flat/target_fi
dist_target_fi_flat[dist_target_fi_flat > 1.45] = 1.45
dist_target_fi_flat[dist_target_fi_flat < 0.5] = 0.5
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False)
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_subM_converted[filter_points_targetR_indices][..., :-1], dist_target_fi_flat[filter_points_targetR_indices], xlabel='M_conj', ylabel='M_feat', title='FI dist %.2f, R=%d' % (target_fi, R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_greater_than=mask_greater_than, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsubM_distfi%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_fi, R_target, interpolation_method))
distFI_spline_int_params = spint.bisplrep(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], np.log(dist_target_fi_flat[filter_points_targetR_indices]), kx=3, ky=3, s=1)
M_interp_space = np.linspace(100, 740, 100)
ratio_interp_space = np.linspace(0.0, 1.0, 100)
# utils.pcolor_2d_data(spint.bisplev(M_interp_space, ratio_interp_space, spline_int_params), y=ratio_interp_space, x=M_interp_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target), ticks_interpolate=11)
utils.pcolor_2d_data(np.exp(spint.bisplev(M_interp_space, ratio_interp_space, distFI_spline_int_params)), y=ratio_interp_space, x=M_interp_space, ylabel='ratio conjunctivity', xlabel='M', xlabel_format="%d", title='Ratio FI/%.2f, R %d' % (target_fi, R_target), ticks_interpolate=11, log_scale=False, cmap='RdBu_r')
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsplinetotM_distfi%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_fi, R_target, interpolation_method))
else:
R_target_i = np.argmin(np.abs(R_space - R_target))
utils.pcolor_2d_data(result_em_fits_kappa_valid[..., R_target_i], log_scale=True, y=ratio_space, x=M_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa, R %d' % (R_target))
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolor_kappa_R%d_log_{label}_{unique_id}.pdf' % (R_target))
# target_kappa = np.ma.mean(result_em_fits_kappa_valid[R_target_i])
# target_kappa = 5*1e3
target_kappa = 580
dist_target_kappa = np.ma.masked_greater(np.abs(result_em_fits_kappa_valid[..., R_target_i] - target_kappa), mask_greater_than*5)
utils.pcolor_2d_data(dist_target_kappa, log_scale=True, y=ratio_space, x=M_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target))
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolor_distkappa%d_R%d_log_{label}_{unique_id}.pdf' % (target_kappa, R_target))
all_args = data_pbs.loaded_data['args_list']
variables_to_save = []
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='higher_dimensions_R')
plt.show()
return locals()
def plots_fitting_experiments_random(data_pbs, generator_module=None):
'''
Reload 2D volume runs from PBS and plot them
'''
#### SETUP
#
savefigs = True
savedata = True
savemovies = True
plots_per_T = True
scatter3d_all_T = True
# do_relaunch_bestparams_pbs = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
# parameters: ratio_conj, sigmax, T
# Extract data
result_fitexperiments_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['results_flat'])
result_fitexperiments_all_flat = np.array(data_pbs.dict_arrays['result_fitexperiments_all']['results_flat'])
result_parameters_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['parameters_flat'])
# Extract order of datasets
experiment_ids = data_pbs.loaded_data['datasets_list'][0]['fitexperiment_parameters']['experiment_ids']
parameter_names_sorted = data_pbs.dataset_infos['parameters']
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
# Compute some stuff
result_fitexperiments_bic_avg = utils.nanmean(result_fitexperiments_flat[:,0], axis=-1)
T_space = np.unique(result_parameters_flat[:, 2])
if plots_per_T:
for T in T_space:
currT_indices = result_parameters_flat[:, 2] == T
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat[currT_indices][..., :2], result_fitexperiments_bic_avg[currT_indices], xlabel='Ratio_conj', ylabel='sigma x', title='BIC, T %d' % T, interpolation_numpoints=200, interpolation_method='nearest', log_scale=False)
if savefigs:
dataio.save_current_figure('contourf_fittingexp_bic_random_ratiosigma_T%d_{label}_{unique_id}.pdf' % (T))
if scatter3d_all_T:
nb_best_points_per_T = 2
size_normal_points = 8
size_best_points = 50
# Get the best points for a specific T
def best_points_T(result_dist_to_use, T):
currT_indices = result_parameters_flat[:, 2] == T
best_points_indices_currT = np.argsort(result_dist_to_use[currT_indices])[:nb_best_points_per_T]
# Indirect it all
return np.nonzero(currT_indices)[0][best_points_indices_currT]
def best_points_allT(result_dist_to_use):
return np.array(utils.flatten_list([best_points_T(result_dist_to_use, T) for T in T_space]))
def plot_scatter(all_vars, result_dist_to_use_name, title=''):
fig = plt.figure()
ax = Axes3D(fig)
result_dist_to_use = all_vars[result_dist_to_use_name]
utils.scatter3d(result_parameters_flat[:, 0], result_parameters_flat[:, 1], result_parameters_flat[:, 2], s=size_normal_points, c=np.log(result_dist_to_use), xlabel=parameter_names_sorted[0], ylabel=parameter_names_sorted[1], zlabel=parameter_names_sorted[2], title=title, ax_handle=ax)
best_points_result_dist_to_use = best_points_allT(result_dist_to_use)
utils.scatter3d(result_parameters_flat[best_points_result_dist_to_use, 0], result_parameters_flat[best_points_result_dist_to_use, 1], result_parameters_flat[best_points_result_dist_to_use, 2], c='r', s=size_best_points, ax_handle=ax)
print "Best points, %s:" % title
print '\n'.join(['ratio %.2f, sigmax %.2f, T %d: %f' % (result_parameters_flat[i, 0], result_parameters_flat[i, 1], result_parameters_flat[i, 2], result_dist_to_use[i]) for i in best_points_result_dist_to_use])
if savefigs:
dataio.save_current_figure('scatter3d_%s_{label}_{unique_id}.pdf' % result_dist_to_use_name)
if savemovies:
try:
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_{label}_{unique_id}.mp4' % result_dist_to_use_name), bitrate=8000, min_duration=8)
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_{label}_{unique_id}.gif' % result_dist_to_use_name), nb_frames=30, min_duration=8)
except Exception:
# Most likely wrong aggregator...
print "failed when creating movies for ", result_dist_to_use_name
ax.view_init(azim=90, elev=10)
dataio.save_current_figure('scatter3d_view2_%s_{label}_{unique_id}.pdf' % result_dist_to_use_name)
return ax
plot_scatter(locals(), 'result_fitexperiments_bic_avg', 'BIC all T')
# if plot_per_ratio:
# # Plot the evolution of loglike as a function of sigmax, with std shown
# for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# ax = utils.plot_mean_std_area(sigmax_space, result_log_posterior_mean[ratio_conj_i], result_log_posterior_std[ratio_conj_i])
# ax.get_figure().canvas.draw()
# if savefigs:
# dataio.save_current_figure('results_fitexp_%s_loglike_ratioconj%.2f_{label}_global_{unique_id}.pdf' % (exp_dataset, ratio_conj))
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['experiment_ids', 'parameter_names_sorted']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='fitting_experiments')
plt.show()
return locals()
def plot_interp(points, data, currR_indices, title='', points_label='', xlabel='', ylabel=''):
utils.contourf_interpolate_data_interactive_maxvalue(points[currR_indices][..., :2], data[currR_indices], xlabel=xlabel, ylabel=ylabel, title='%s, R=%d' % (title, R), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False)
if savefigs:
dataio.save_current_figure('pcolortrueM%s_%s_R%d_log_%s_{label}_{unique_id}.pdf' % (points_label, title, R, interpolation_method))
def plots_fitting_experiments_random(data_pbs, generator_module=None):
'''
Reload 2D volume runs from PBS and plot them
'''
#### SETUP
#
savefigs = True
savedata = True
plots_per_T = False
plots_interpolate = False
# do_relaunch_bestparams_pbs = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
# parameters: ratio_conj, sigmax, T
# Extract data
result_fitexperiments_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['results_flat'])
result_fitexperiments_all_flat = np.array(data_pbs.dict_arrays['result_fitexperiments_all']['results_flat'])
result_parameters_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['parameters_flat'])
# Extract order of datasets
experiment_ids = data_pbs.loaded_data['datasets_list'][0]['fitexperiment_parameters']['experiment_ids']
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
# Compute some stuff
result_fitexperiments_bic_avg = utils.nanmean(result_fitexperiments_flat[:, 0, 0], axis=-1)
T_space = np.unique(result_parameters_flat[:, 2])
if plots_per_T:
for T in T_space:
currT_indices = result_parameters_flat[:, 2] == T
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat[currT_indices][..., :2], result_fitexperiments_bic_avg[currT_indices], xlabel='Ratio_conj', ylabel='sigma x', title='BIC, T %d' % T, interpolation_numpoints=200, interpolation_method='nearest', log_scale=False)
# Interpolate
if plots_interpolate:
rcscale_target = 5.0
rbf_interpolator = spint.Rbf(result_parameters_flat[:,0], result_parameters_flat[:,1], result_parameters_flat[:,2], result_parameters_flat[:,3], result_fitexperiments_bic_avg, smooth = 0.0)
param1_space = np.linspace(0.01, 1.0, 50)
param2_space = np.linspace(0.01, 1.0, 50)
param3_space = np.array([rcscale_target])
param4_space = np.array([rcscale_target])
params_crossspace = np.array(utils.cross(param1_space, param2_space, param3_space, param4_space))
interpolated_data = rbf_interpolator(params_crossspace[:, 0], params_crossspace[:, 1], params_crossspace[:, 2], params_crossspace[:, 3]).reshape((param1_space.size, param2_space.size))
# utils.pcolor_2d_data(interpolated_data, param1_space, param2_space, 'ratio', 'sigmax', 'interpolated, fixing rcscales= %.2f' % rcscale_target)
points_closeby = ((result_parameters_flat[:, 2] - rcscale_target)**2 + (result_parameters_flat[:, 3] - rcscale_target)**2) < 0.5
plt.figure()
plt.imshow(interpolated_data, extent=(param1_space.min(), param1_space.max(), param2_space.min(), param2_space.max()))
plt.scatter(result_parameters_flat[points_closeby, 0], result_parameters_flat[points_closeby, 1], s=100, c=result_fitexperiments_bic_avg[points_closeby], marker='o')
# if plot_per_ratio:
# # Plot the evolution of loglike as a function of sigmax, with std shown
# for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# ax = utils.plot_mean_std_area(sigmax_space, result_log_posterior_mean[ratio_conj_i], result_log_posterior_std[ratio_conj_i])
# ax.get_figure().canvas.draw()
# if savefigs:
# dataio.save_current_figure('results_fitexp_%s_loglike_ratioconj%.2f_{label}_global_{unique_id}.pdf' % (exp_dataset, ratio_conj))
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['experiment_ids']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='fitting_experiments')
plt.show()
return locals()
