"""
.. _example_usage_distribution:

Trip Distribution
=================

In 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
print(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()
#%%
# Forecast
# --------
# We create a set of *'future'* vectors by applying some models
# and 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")
    args = {
        "impedance": impedance,
        "rows": vectors,
        "row_field": "origins",
        "model": model,
        "columns": vectors,
        "column_field": "destinations",
        "nan_as_zero": True,
    }

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

    # 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()
print(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")

# %%

print(proj_matrices.list())

# %%

project.close()