period_time_list = [[1880, 1914], [1950, 1973], [1987, 2010]]
label_list = ["1", "2", "3"]
fig_2 = EvT.plot_model_posterior(
indices=mods, #mods, #mods, #relevant_models,
plot_type="MAP",
y_axis_labels=[ #"AR(1)",
"M(5+)", "M(6)", "M(6+)", "M(7)", "M(7+)"
], #relevant_models],
log_format=False,
aspect='auto',
show_MAP_CPs=False,
start_plot=1880,
stop_plot=2010,
custom_colors=["green"], #custom_colors_models,
ax=ax_array[1],
xlab=None,
ylab=None,
period_time_list=period_time_list,
label_list=label_list,
number_offset=0.75,
number_fontsize=20,
period_line_thickness=4.0,
xlab_fontsize=14,
ylab_fontsize=14,
ylabel_coords=ylabel_coords,
xticks_fontsize=14,
yticks_fontsize=14,
window_len=12 * 8) #MAP variance 2 det: windowlen = 125
#MAP variance 1 trace: window_len = 12*8
if plot_3:
EvT.plot_model_posterior(
indices=mod, #mods, #mods, #relevant_models,
plot_type="trace", #"MAPVariance1_trace",
#y_axis_labels = [str(e) for e in all_models],#[#"AR(1)",
#"M(5+)", "M(6)",
# "M(6+)",
# "M(7)", "M(7+)"],#relevant_models],
time_range=time_range,
y_axis_labels=[],
log_format=False,
aspect='auto',
show_MAP_CPs=False,
#start_plot = 2002.75, stop_plot = 2003.75,
custom_colors=["blue", "orange"], # ["orange"], #custom_colors_models,
ax=ax_array[1], #ax_array[1], #None, #ax_array[1],
xlab=None, #ylab = None, #trace",period_time_list = None,
number_offset=1.0, #datetime.timedelta(days = 1),#0.75,
number_fontsize=20,
period_line_thickness=7.0,
xlab_fontsize=14,
ylab_fontsize=14,
xticks_fontsize=14,
yticks_fontsize=14,
ylabel_coords=ylabel_coords,
#ylab = None, #"Model posterior max",
#period_time_list = [
# [datetime.datetime(year = 2003, month = 2, day = 17, hour = 0),
# datetime.datetime(year = 2003, month = 2, day = 18, hour = 0)]],
#label_list = [["1"]],
#window_len = int(12*7*1),
period_time_list=
None, #[[datetime.datetime(year = 2003, month = 2, day = 17, hour = 0),
#datetime.datetime(year = 2003, month = 2, day = 18, hour = 0)]],
label_list=None, #[["1"]],
SGV=True,
log_det=True,
all_dates=all_dates,
true_CPs=true_CPs)
additional_legend_colors = ["blue"],
CP_exclude_indices = [7], #just obscures view
custom_colors = custom_colors_models,
custom_linestyles =custom_linestyles,
ax = ax_array[-1], figure = fig, space_to_colorbar = 0.275,
orientation = 'horizontal',
xlab=None, colorbar_location = "bottom",
ylabel_coords = ylabel_coords,
colorbar_ticks_num = 1)
if one_model_posterior_plot:
#custom_colors = ['purple', 'orange', 'red', 'green']
fig_3 = EvT.plot_model_posterior(indices= [1,2,5,6],#relevant_models.append(30),
plot_type = "trace",
y_axis_labels = ["mod1", "mod2", "mod3"],
log_format=False, aspect = 'auto',
show_MAP_CPs = False,
custom_colors = custom_colors_models,
ax = ax_array[2], xlab = None,
ylabel_coords = ylabel_coords)
# if one_model_posterior_plot:
##custom_colors = ['purple', 'orange', 'red', 'green']
# fig_3 = EvT.plot_model_posterior(indices=relevant_models,
# plot_type = "MAP",
# y_axis_labels = ["mod1", "mod2", "mod3"],
# log_format=False, aspect = 'auto',
# show_MAP_CPs = False,
# custom_colors = custom_colors_models,
# ax = None, xlab = None)
else:
for ind, mod in zip(range(0, len(relevant_models)), relevant_models):
format="pdf", dpi=800)
fig.savefig(os.path.join(baseline_working_directory, "Output", "ICML18_Figure5_Nile.png"),
format="png", dpi=800)
# Check that the figure has indeed been generated and report
if os.path.isfile(os.path.join(baseline_working_directory, "Output", "ICML18_Figure5_Nile.pdf")):
print("Figure 5 can be found in the Output directory")
"""STEP 4: Generate additional figures, if requested"""
if save_extra_figures:
# Plot the prediction error and variance
fig_pe, ax_pe = plt.subplots()
EvT.plot_prediction_error(river_height.reshape(T, 1, 1), indices=[0],
show_var=True, show_MAP_CPs=True,
up_to=250, ax=ax_pe)
fig_pe.savefig(os.path.join(baseline_working_directory, "Output", "ICML18_ExtraFigure_Nile_PredError.pdf"),
format="pdf", dpi=800)
if os.path.isfile(os.path.join(baseline_working_directory, "Output", "ICML18_ExtraFigure_Nile_PredError.pdf")):
print("Extra plot of the prediction error can be found in the Output directory")
# Plot the model posterior
fig_mp, ax_mp = plt.subplots()
EvT.plot_model_posterior(indices=[0, 1, 2], log_format=False, up_to=150,
plot_type="MAPVariance1_det", show_MAP_CPs=True, ax=ax_mp)
fig_mp.savefig(os.path.join(baseline_working_directory, "Output", "ICML18_ExtraFigure_Nile_ModelPosterior.pdf"),
format="pdf", dpi=800)
if os.path.isfile(os.path.join(baseline_working_directory, "Output", "ICML18_ExtraFigure_Nile_ModelPosterior.pdf")):
print("Extra plot of the model posterior can be found in the Output directory")