import numpy as np
import pandas as pd
import logging
import math
import matplotlib.pyplot as pl

import pvlib
from pvfactors.geometry import OrderedPVArray
from pvfactors.engine import PVEngine

from Utilities.Processes import calculate_no_of_panels, calculate_required_system_size

logger = logging.getLogger(__name__)


def get_location(c):
    location = pvlib.location.Location(
        latitude=c["environment"]["location"]["latitude"],
        longitude=c["environment"]["location"]["longitude"],
        tz=c["simulation_date_time"]["tz"],
    )

    return location


def define_grid_layout(c, panel_tilt):
    no_of_panels = calculate_no_of_panels(
        c["array"]["system_size"], c["panel"]["peak_power"]
    )

    # calculate pitch
    pitch = c["array"]["spacing"]
    # calculate minimum pitch if we don't want panel overlap at all
    min_pitch = np.round(
        c["panel"]["dimensions"]["length"] * math.cos(panel_tilt / 180 * math.pi), 1
    )
    if pitch < min_pitch:
        logger.warning(
            f"Spacing is less than minimum pitch. Setting spacing to {min_pitch}."
        )
        pitch = min_pitch
    logger.info(f"Pitch between panels: {pitch}m")

    # get maximum number of panels based on spacing and dimensions
    max__panels_per_row = np.floor(
        (
            c["environment"]["roof"]["dimensions"]["width"]
            - (2 * c["array"]["edge_setback"] + c["panel"]["dimensions"]["width"])
        )
        / c["panel"]["dimensions"]["width"]
    )
    max_number_of_rows = np.floor(
        (
            c["environment"]["roof"]["dimensions"]["length"]
            - (2 * c["array"]["edge_setback"])
        )
        / pitch
    )
    max_no_of_panels = max__panels_per_row * max_number_of_rows
    logger.info(
        f"Number of panels required: {no_of_panels}, Maximum panels possible: {max_no_of_panels}"
    )
    if no_of_panels > max_no_of_panels:
        no_of_panels = max_no_of_panels
        logger.warning(
            f"Number of panels required exceeds maximum possible. Setting number of panels to {no_of_panels}."
        )
    else:
        logger.info(
            f"Number of panels required is within the maximum possible. Setting number of panels to {no_of_panels}."
        )

    # coordinate of panel determined by bottom left corner
    # x - row wise position, y - column wise position, z - height
    # first panel in row 1 is at (0, 0, 0)
    # nth panel in row 1 is at ((n-1)*panel_width, 0, 0)
    # first panel in nth row is at (0, (n-1)*(panel_thickness + spacing), 0)

    # create matrices for x, y, z coordinates of panels
    x = []
    y = []
    z = []
    counter = 0
    for j in range(int(max_number_of_rows)):
        for i in range(int(max__panels_per_row)):
            if counter < no_of_panels:
                x.append(i * c["panel"]["dimensions"]["width"])
                y.append(j * pitch)
                z.append(0)
                counter += 1
            else:
                break

    coordinates = pd.DataFrame(
        {
            "x": x,
            "y": y,
            "z": z,
        }
    )
    return coordinates, no_of_panels


def get_solar_data(c):
    logger.info(
        f"Getting solar position data for {c['simulation_date_time']['start']} to {c['simulation_date_time']['end']}"
    )
    """
    Function to get solar position from PVLib
    """
    location = get_location(c)

    times = pd.date_range(
        c["simulation_date_time"]["start"],
        c["simulation_date_time"]["end"],
        freq="15min",
        tz=location.tz,
    )

    # Get solar position data using PVLib
    solar_positions = location.get_solarposition(times)
    # filter solar positions to only include times when the sun is above the horizon
    solar_positions = solar_positions[solar_positions["apparent_elevation"] > 0]
    # get datetime range from solar_positions
    day_times = solar_positions.index
    clearsky_data = location.get_clearsky(day_times)

    return solar_positions, clearsky_data


def sanity_check_minimum_pitch(c):
    solar_positions, _ = get_solar_data(c)
    zenith = solar_positions["zenith"].values
    solar_positions["shadow_length"] = (c["panel"]["dimensions"]["length"]) * np.tan(
        zenith / 180 * np.pi
    )
    return solar_positions


def calculate_energy_production(c, orientation):
    orientation = c["array"]["orientation"][orientation]
    c = calculate_required_system_size(c)
    panel_coordinates, no_of_panels = define_grid_layout(
        c, panel_tilt=orientation["panel_tilt"]
    )
    solar_positions, clearsky_data = get_solar_data(c)

    # the first row is always not shaded so exclude
    no_of_rows = np.unique(panel_coordinates["y"]).shape[0]
    no_of_shaded_rows = no_of_rows - 1

    no_of_panels_in_row = np.unique(panel_coordinates["x"]).shape[0]

    collector_width = c["panel"]["dimensions"]["length"]
    # calculate delta between unique y coordinates of panels to get pitch
    pitch = np.unique(panel_coordinates["y"])[1] - np.unique(panel_coordinates["y"])[0]
    surface_azimuth = orientation["surface_azimuth"]
    axis_azimuth = orientation["axis_azimuth"]

    gcr = np.divide(c["panel"]["dimensions"]["length"], pitch)
    gcr = min(1, gcr)
    logger.info(f"Ground coverage ratio: {gcr}")

    pvrow_height = (
        c["panel"]["dimensions"]["length"] * orientation["pvrow_height_ratio_to_length"]
    )

    pvarray_parameters = {
        "n_pvrows": 3,  # number of pv rows
        "pvrow_height": pvrow_height,  # height of pvrows (measured at center / torque tube)
        "pvrow_width": c["panel"]["dimensions"]["length"],  # width of pvrows
        "axis_azimuth": axis_azimuth,  # azimuth angle of rotation axis
        "gcr": gcr,  # ground coverage ratio
    }

    pvarray = OrderedPVArray.init_from_dict(pvarray_parameters)
    engine = PVEngine(pvarray)

    inputs = pd.DataFrame(
        {
            "dni": clearsky_data["dni"],
            "dhi": clearsky_data["dhi"],
            "solar_zenith": solar_positions["zenith"],
            "solar_azimuth": solar_positions["azimuth"],
            "surface_tilt": np.repeat(orientation["panel_tilt"], len(solar_positions)),
            "surface_azimuth": np.repeat(surface_azimuth, len(solar_positions)),
        }
    )
    inputs.index = clearsky_data.index
    inputs.index.name = "index"

    engine.fit(
        inputs.index,
        inputs.dni,
        inputs.dhi,
        inputs.solar_zenith,
        inputs.solar_azimuth,
        inputs.surface_tilt,
        inputs.surface_azimuth,
        albedo=0.2,
    )

    pvarray = engine.run_full_mode(fn_build_report=lambda pvarray: pvarray)

    gamma_pdc = c["panel"]["temperature_coefficient"]
    temp_cell = c["panel"]["nominal_operating_cell_temperature"]
    p_row = no_of_panels_in_row * c["panel"]["peak_power"]
    p_middle_rows = (no_of_panels - 2 * no_of_panels_in_row) * c["panel"]["peak_power"]
    pdc_first_row_front = pvlib.pvsystem.pvwatts_dc(
        pdc0=p_row,
        gamma_pdc=gamma_pdc,
        temp_cell=temp_cell,
        g_poa_effective=pvarray.ts_pvrows[0].front.get_param_weighted("qinc"),
    )
    pdc_last_row_front = pvlib.pvsystem.pvwatts_dc(
        pdc0=p_row,
        gamma_pdc=gamma_pdc,
        temp_cell=temp_cell,
        g_poa_effective=pvarray.ts_pvrows[2].front.get_param_weighted("qinc"),
    )
    pdc_middle_rows_front = pvlib.pvsystem.pvwatts_dc(
        pdc0=p_middle_rows,
        gamma_pdc=gamma_pdc,
        temp_cell=temp_cell,
        g_poa_effective=pvarray.ts_pvrows[1].front.get_param_weighted("qinc"),
    )
    pdc_front = pdc_first_row_front + pdc_last_row_front + pdc_middle_rows_front

    total_hourly_energy = pdc * 15 / 60 / 1e3  # convert to kWh

    total_energy = total_hourly_energy.sum()
    logger.info(f"Total energy yield calculated: {total_energy} kWh")

    return total_hourly_energy, no_of_panels