# coding=utf-8
"""
category: tutorial
title: Exploring model results
description: Explore the results returned by models
keywords: sapre, sapphire-total-fluence, get_result_names, get_model_result_by_name, get_variable_data
"""
from pprint import pprint

from nom_client.nom_client import NoMClient


"""
Initialise the NoM Client with a project name (optional) and the configuation data.
"""
nom_client = NoMClient(project_name="Exploring model results")


"""
Run the SAPPHIRE Total Fluence model
"""
trajectory_model = nom_client.get_model(model_name="trajectory")
trajectory_model.set_params(orbitSpecificationCode=205)
trajectory_result = nom_client.run_model(model=trajectory_model)

sapphire_model = nom_client.get_model(model_name="sapphire-total-fluence")
sapphire_model.set_external_input(external_input_name="trajectory", external_input=trajectory_result)
sapphire_result = nom_client.run_model(model=sapphire_model)


"""
The result object 'sapphire_result' is an instance of a 'ModelRun' class AND also a dictionary of 'NumericModelResult' classes.
The results contained within this ModelRun are documented on the model documentation pages
"""

# To list the available results from the model
print(sapphire_model.get_result_names())

# Or get the result names from the result object
print(sapphire_result.get_result_names())

# Or list the keys of the results dictionary
print(sapphire_result.keys())

# Loop over the model results
print("\n--- Model results ---")
for result_name, result in sapphire_result.items():
    print(result)

# Lets take a look at the 'full_ion_spectrum' result

print("\n--- full_ion_spectrum ---")

# Get the result using the 'get_model_result_by_name' method
# The 'full_ion_spectrum' is a NumericModelResult object. See online documentation for API description.
full_ion_spectrum = sapphire_result.get_model_result_by_name(result_name="full_ion_spectrum")
print(full_ion_spectrum)

# ... or access the result directly
full_ion_spectrum = sapphire_result["full_ion_spectrum"]
print(full_ion_spectrum)


# Lets list the variables within the 'full_ion_spectrum' result.
print("\n--- List the variables within the 'full_ion_spectrum' result' ---")
print(full_ion_spectrum.get_variable_names())


# Loop over the variables
print("\n--- Loop over the variables in the 'full_ion_spectrum' result' ---")
for variable_name, variable in full_ion_spectrum.items():
    print(variable)

# Grab the variables
energy_variable = full_ion_spectrum['energy']
species_variable = full_ion_spectrum['species']
integral_fluence_variable = full_ion_spectrum['integral_fluence']

"""
Looking at the details of the variables using 'get_metadata()'

This data contains 81 energy value, 92 species values, therfore the integral flux is a liast of dimension [92x81]

'energy' is row-varying with a dimensionality of {'cols': 1, 'rows': 81}
'species' is column varying (not row-varying) with a dimensionality of {'cols': 92, 'rows': 1}
'integral_fluence' is row-varying with a dimensionality of {'cols': 92, 'rows': 81}
"""

print("\n--- 'energy' variable ---\n")
pprint(energy_variable.get_metadata())
print("\n--- 'species' variable ---\n")
pprint(species_variable.get_metadata())
print("\n--- 'integral_fluence' variable ---\n")
pprint(integral_fluence_variable.get_metadata())



"""
To get all of the actual data for a particular result:
    format="list" (default) - All data resutned as a single multi-dimenstional list
    format="dict"           - All data retuned as a python dictionary with variable names as keys
    format="full_metadata"  - All data returned as a python dictionary with variable names as keys includinf all variable meta data
"""
all_data_values_list = full_ion_spectrum .get_all_data_values()
all_data_values_dict = full_ion_spectrum .get_all_data_values(format="dict")
all_data_values_full_meta = full_ion_spectrum .get_all_data_values(format="full_metadata")


"""
To get values for a particular variable:
"""
integral_fluence_data = full_ion_spectrum.get_variable_data(variable_name="integral_fluence")

"""
You can get more specific data by supplying additional parameters, in the case of 'integral fluence' 
we can specify any of its axes values, i.e. 'energy' or 'species'

We can print out the values for 'species' and then slice the fluence data further into a vector of values over energy
"""
species_data = full_ion_spectrum.get_variable_data(variable_name="species")
print("\n--- species data ---\n")
print(species_data)

# Get integral fluence data for species 'H'
species_data_H = full_ion_spectrum.get_variable_data(variable_name="integral_fluence", species="H")
print("\n--- species data (species: H)---\n")
print(species_data_H)

# Get all energy data
energy_data = full_ion_spectrum.get_variable_data(variable_name="energy")
print("\n--- energy data ---\n")
print(energy_data)

"""
Similarly we can specify species and an energy to reduce our data to a scalar value for that energy and species
"""
species_data_H_e = full_ion_spectrum.get_variable_data(variable_name="integral_fluence", species="H", energy=1.0e-01)
print("\n--- species data (energy: 1.0e-01, species: H)---\n")
print(species_data_H_e)