def load_and_plot_results():
# %% Plot and save as PDF
# This set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
parameter_file = output_folder.joinpath("optimized_parameters.csv")
parameters = read_parameters_from_csv_file(str(parameter_file))
print(f"Optimized parameters loaded:\n {parameters}")
result1 = output_folder.joinpath("dataset1.nc")
fig1 = plot_overview(result1, linlog=True, show_data=True)
timestamp = datetime.today().strftime("%y%m%d_%H%M")
fig1.savefig(output_folder.joinpath(f"plot_overview_1of2_{timestamp}.pdf"),
bbox_inches="tight")
result2 = output_folder.joinpath("dataset2.nc")
fig2 = plot_overview(result2, linlog=True)
timestamp = datetime.today().strftime("%y%m%d_%H%M")
fig2.savefig(output_folder.joinpath(f"plot_overview_2of2_{timestamp}.pdf"),
bbox_inches="tight")
plt.show()
)
# optimize
result = optimize(scheme)
# %% Save results
result.save(str(output_folder))
# %% Plot results
# Set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
# TODO: enhance plot_overview to handle multiple datasets
result_datafile1 = output_folder.joinpath("dataset1.nc")
result_datafile2 = output_folder.joinpath("dataset2.nc")
result_datafile3 = output_folder.joinpath("dataset3.nc")
fig1 = plot_overview(result_datafile1, linlog=True, linthresh=1)
fig1.savefig(
output_folder.joinpath("plot_overview_sim3d_d1.pdf"),
bbox_inches="tight",
)
fig2 = plot_overview(result_datafile2, linlog=True, linthresh=1)
fig2.savefig(
output_folder.joinpath("plot_overview_sim3d_d2.pdf"),
bbox_inches="tight",
)
fig3 = plot_overview(result_datafile3, linlog=True, linthresh=1)
fig3.savefig(
output_folder.joinpath("plot_overview_sim3d_d3.pdf"),
bbox_inches="tight",
save_result(result_path=str(output_folder),
result=result,
format_name="yml",
allow_overwrite=True)
except FileExistsError as error:
print(f"catching error: {error}")
timestamp = datetime.today().strftime("%y%m%d_%H%M")
save_result(
result_path=str(output_folder.joinpath(timestamp)),
result=result,
format_name="yml",
allow_overwrite=True,
)
# %% Plot and save as PDF
# This set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
fig1 = plot_overview(result.data["dataset1"], linlog=True)
timestamp = datetime.today().strftime("%y%m%d_%H%M")
fig1.savefig(output_folder.joinpath(f"plot_overview_1of2_{timestamp}.pdf"),
bbox_inches="tight")
fig2 = plot_overview(result.data["dataset2"], linlog=True)
timestamp = datetime.today().strftime("%y%m%d_%H%M")
fig2.savefig(output_folder.joinpath(f"plot_overview_2of2_{timestamp}.pdf"),
bbox_inches="tight")
plt.show()
result.save(str(output_folder))
# evt plotten
# %% Set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
# %%
# TODO: enhance plot_overview to handle multiple datasets
result_datafile1 = output_folder.joinpath("dataset1.nc")
result_datafile2 = output_folder.joinpath("dataset2.nc")
result_datafile3 = output_folder.joinpath("dataset3.nc")
result_datafile4 = output_folder.joinpath("dataset4.nc")
result_datafile5 = output_folder.joinpath("dataset5.nc")
result_datafile6 = output_folder.joinpath("dataset6.nc")
fig1 = plot_overview(result_datafile1, linlog=True, linthresh=5)
fig1.savefig(
output_folder.joinpath("plot_overview_dummy1.pdf"),
bbox_inches="tight",
)
fig2 = plot_overview(result_datafile2, linlog=True, linthresh=5)
fig2.savefig(
output_folder.joinpath("plot_overview_dummy2.pdf"),
bbox_inches="tight",
)
fig3 = plot_overview(result_datafile3, linlog=True, linthresh=5)
fig3.savefig(
output_folder.joinpath("plot_overview_dummy3.pdf"),
bbox_inches="tight",
# %%
start = timer()
# Warning: this may take a while (several seconds per iteration)
result = optimize(scheme, verbose=True)
end = timer()
print(f"Total time: {end - start}")
result.save(str(output_folder))
end2 = timer()
print(f"Saving took: {end2 - end}")
# %%
print(result.markdown(True))
# %%
res = result.data["dataset1"]
# Tip: print the xarray object to explore its content
print(res)
# %% Set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
# %%
fig = plot_overview(result_datafile, linlog=False)
# note species concentration plot still needs work to match styles between the two locatable axis
# %%
fig.savefig(output_folder.joinpath(f"plot_overview_{result_name}.pdf"),
bbox_inches="tight")
parameters = read_parameters_from_yaml_file(
script_folder.joinpath(PARAMETERS_FILE_PATH))
# %% Validate model and parameters
print(model.validate(parameters=parameters))
# %% Construct the analysis scheme
scheme = Scheme(model,
parameters, {"dataset1": dataset},
maximum_number_function_evaluations=10)
# %% Optimize the analysis scheme (and estimate parameters)
result = optimize(scheme)
# %% Basic print of results
print(result.markdown(True))
# %% Save the results
result.save(str(output_folder))
# %% Plot and save as PDF
# This set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
fig = plot_overview(result, linlog=True)
timestamp = datetime.today().strftime("%y%m%d_%H%M")
fig.savefig(output_folder.joinpath(f"plot_overview_{timestamp}.pdf"),
bbox_inches="tight")