"""
Save/load a study
=================
The objective of this example is to demonstrate how to save the structures created within a script session to disk in order to be able to load them in a future session.

There are several possible ways to achieve this:

- with the standard pickle module
- with openturns's Study

"""

# %%
import openturns as ot
import pickle

ot.Log.Show(ot.Log.NONE)

# %%
# create objects to save
distribution = ot.Normal(4.0, 3.0)
function = ot.SymbolicFunction(["x1", "x2"], ["x1 + x2"])

# %%
# **With the pickle module**
#
# The objects are retrieved in the same order they are stored.

# %%
# save objects
with open("study.pkl", "wb") as f:
    pickle.dump(distribution, f)
    pickle.dump(function, f)

# %%
# load saved objects
with open("study.pkl", "rb") as f:
    loaded_distribution = pickle.load(f)
    loaded_function = pickle.load(f)
str(loaded_distribution), str(loaded_function)

# %%
# **With OpenTURNS' Study**
#
# In order to be able to manipulate the objects contained in a Study, it is necessary to:
#
# - create the same empty structure in the new study,
# - fill this new empty structure with the content of the loaded structure, identified with its name or its id.
#
# Each object is identified whether with:
#
# - its name: it is useful to give names to the objects we want to save. If no name has been given by the user, we can use the default name. The name of each saved object can be checked in the output XML file or with the python `print` command (applied to the `Study` object).
# - its id number: this id number is unique to each object. It distinguishes objects with identical type and name (like the default name "Unnamed"). This id number may be checked by printing the study **after** it has been loaded in the python interface (with the `print` command). It can differ from the id number indicated in the XML file the study was loaded from.
# - for HDF5 storage (see below): the id serves both as xml id and hdf5 dataset name. Id uniqueness forbids any misleading in reading/writing hdf5 datasets.

# %%
# Create a Study Object
study = ot.Study()

# %%
# Associate it to an XML file
fileName = "study.xml"
study.setStorageManager(ot.XMLStorageManager(fileName))

# %%
# Alternatively, large amounts of data can be stored in binary HDF5 file. An XML file (`study_h5.xml`) serves as header for binary data, which are stored in the automatically created `study_h5.h5` file.
study_h5 = ot.Study()
fileName_h5 = "study_h5.xml"
study_h5.setStorageManager(ot.XMLH5StorageManager(fileName_h5))

# %%
# Add an object to the study; at this point it is not written to disk yet
study.add("distribution", distribution)
study.add("function", function)

# %%
# Save the study; this writes into the file
study.save()

# %%
# Create a new study associated to the same file
study = ot.Study()
study.setStorageManager(ot.XMLStorageManager(fileName))

# %%
# Load the file and all its objects
study.load()

# %%
# Check the content of the myStudy
print("Study = ", study)

# %%
# List names of stored objects
study.printLabels()

# %%
# Check our 'distribution' labelled object was loaded
study.hasObject("distribution")

# %%
# Load the objects; we must create a void object of the desired type (or parent type)
distribution2 = ot.Normal()
function2 = ot.Function()
study.fillObject("distribution", distribution2)
study.fillObject("function", function2)
str(distribution2), str(function2)
