"""
Trip Distribution
=================

On this example we calibrate a Synthetic Gravity Model that same model plus IPF (Fratar/Furness).
"""

## Imports
from uuid import uuid4
from tempfile import gettempdir
from os.path import join
from aequilibrae.utils.create_example import create_example
import pandas as pd
import numpy as np

# %%
# We create the example project inside our temp folder
fldr = join(gettempdir(), uuid4().hex)

project = create_example(fldr)

# %%

# We get the demand matrix directly from the project record
# so let's inspect what we have in the project
proj_matrices = project.matrices
proj_matrices.list()

# %%

# We get the demand matrix
demand = proj_matrices.get_matrix('demand_omx')
demand.computational_view(['matrix'])

# And the impedance
impedance = proj_matrices.get_matrix('skims')
impedance.computational_view(['time_final'])

# %%
# Let's have a function to plot the Trip Length Frequency Distribution
from math import log10, floor
import matplotlib.pyplot as plt


def plot_tlfd(demand, skim, name):
    plt.clf()
    b = floor(log10(skim.shape[0]) * 10)
    n, bins, patches = plt.hist(np.nan_to_num(skim.flatten(), 0), bins=b, weights=np.nan_to_num(demand.flatten()),
                                density=False, facecolor='g', alpha=0.75)

    plt.xlabel('Trip length')
    plt.ylabel('Probability')
    plt.title('Trip-length frequency distribution')
    plt.savefig(name, format="png")
    return plt


# %%\
from aequilibrae.distribution import GravityCalibration

# %%

for function in ['power', 'expo']:
    gc = GravityCalibration(matrix=demand, impedance=impedance, function=function, nan_as_zero=True)
    gc.calibrate()
    model = gc.model
    # we save the model
    model.save(join(fldr, f'{function}_model.mod'))

    # We can save an image for the resulting model
    _ = plot_tlfd(gc.result_matrix.matrix_view, impedance.matrix_view, join(fldr, f'{function}_tfld.png'))

    # We can save the result of applying the model as well
    # we can also save the calibration report
    with open(join(fldr, f'{function}_convergence.log'), 'w') as otp:
        for r in gc.report:
            otp.write(r + '\n')

# %%
# We save a trip length frequency distribution for the demand itself

plt = plot_tlfd(demand.matrix_view, impedance.matrix_view, join(fldr, 'demand_tfld.png'))
plt.show()
# %% md

## Forecast
#  * We create a set of * 'future' * vectors by applying some models
#  * We apply the model for both deterrence functions

# %%

from aequilibrae.distribution import Ipf, GravityApplication, SyntheticGravityModel
from aequilibrae.matrix import AequilibraeData
import numpy as np

# %%

zonal_data = pd.read_sql('Select zone_id, population, employment from zones order by zone_id', project.conn)
# We compute the vectors from our matrix


args = {'file_path': join(fldr, 'synthetic_future_vector.aed'),
        "entries": demand.zones,
        "field_names": ["origins", "destinations"],
        "data_types": [np.float64, np.float64],
        "memory_mode": True}

vectors = AequilibraeData()
vectors.create_empty(**args)

vectors.index[:] = zonal_data.zone_id[:]

# We apply a trivial regression-based model and balance the vectors
vectors.origins[:] = zonal_data.population[:] * 2.32
vectors.destinations[:] = zonal_data.employment[:] * 1.87
vectors.destinations *= vectors.origins.sum() / vectors.destinations.sum()

# %%

# We simply apply the models to the same impedance matrix now
for function in ['power', 'expo']:
    model = SyntheticGravityModel()
    model.load(join(fldr, f'{function}_model.mod'))

    outmatrix = join(proj_matrices.fldr, f'demand_{function}_model.aem')
    apply = GravityApplication()
    args = {"impedance": impedance,
            "rows": vectors,
            "row_field": "origins",
            "model": model,
            "columns": vectors,
            "column_field": "destinations",
            "nan_as_zero": True
            }

    gravity = GravityApplication(**args)
    gravity.apply()

    gravity.save_to_project(name=f'demand_{function}_model', file_name=f'demand_{function}_model.aem')

    # We get the output matrix and save it to OMX too,
    gravity.save_to_project(name=f'demand_{function}_model_omx', file_name=f'demand_{function}_model.omx')

# %%

# We update the matrices table/records and verify that the new matrices are indeed there
proj_matrices.update_database()
proj_matrices.list()

# %% md

### We now run IPF for the future vectors

# %%
args = {'matrix': demand,
        'rows': vectors,
        'columns': vectors,
        'column_field': "destinations",
        'row_field': "origins",
        'nan_as_zero': True}

ipf = Ipf(**args)
ipf.fit()

ipf.save_to_project(name='demand_ipf', file_name='demand_ipf.aem')
ipf.save_to_project(name='demand_ipf_omx', file_name='demand_ipf.omx')

# %%

proj_matrices.list()

# %%

project.close()