import numpy as np
import pandas as pd
import logging
import math

import pvlib

from Utilities.Processes import (
    calculate_no_of_panels,
)

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):
    # get number of panels required
    no_of_panels = calculate_no_of_panels(
        c["array"]["system_size"], c["panel"]["peak_power"]
    )

    # calculate pitch
    pitch = c["array"]["spacing"] + c["panel"]["dimensions"]["thickness"]
    # calculate minimum pitch if we don't want panel overlap at all
    min_pitch = c["panel"]["dimensions"]["length"] * math.cos(
        panel_tilt / 180 * math.pi
    )
    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"] + c["panel"]["dimensions"]["length"])
        )
        / 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 calculate_energy_production_vertical(c):
    panel_coordinates, no_of_panels = define_grid_layout(c, panel_tilt=90)
    solar_positions, clearsky_data = get_solar_data(c)

    # split the solar positions data into morning and afternoon, using solar azimuth of
    # 180 degrees as the threshold
    morning_solar_positions = solar_positions[solar_positions["azimuth"] <= 180]
    afternoon_solar_positions = solar_positions[solar_positions["azimuth"] > 180]

    # 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

    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_to_axis_offset = 0
    shaded_row_rotation = 90
    shading_row_rotation = 90
    axis_tilt = 0
    axis_azimuth = 90

    morning_shaded_fraction = pvlib.shading.shaded_fraction1d(
        solar_zenith=solar_positions["zenith"],
        solar_azimuth=solar_positions["azimuth"],
        axis_azimuth=axis_azimuth,
        shaded_row_rotation=shaded_row_rotation,
        shading_row_rotation=shading_row_rotation,
        collector_width=collector_width,
        pitch=pitch,
        surface_to_axis_offset=surface_to_axis_offset,
        axis_tilt=axis_tilt,
    )
    morning_shaded_fraction = morning_shaded_fraction * no_of_shaded_rows / no_of_rows

    afternoon_shaded_fraction = pvlib.shading.shaded_fraction1d(
        solar_zenith=solar_positions["zenith"],
        solar_azimuth=solar_positions["azimuth"],
        axis_azimuth=axis_azimuth + 180,
        shaded_row_rotation=shaded_row_rotation,
        shading_row_rotation=shading_row_rotation,
        collector_width=collector_width,
        pitch=pitch,
        surface_to_axis_offset=surface_to_axis_offset,
        axis_tilt=axis_tilt,
    )
    afternoon_shaded_fraction = (
        afternoon_shaded_fraction * no_of_shaded_rows / no_of_rows
    )
    logger.info(
        f"Shaded fraction calculated for morning and afternoon solar positions."
    )

    # calculate irradiance on plane of array
    poa_front = pvlib.irradiance.get_total_irradiance(
        surface_tilt=90,
        surface_azimuth=axis_azimuth,
        solar_zenith=solar_positions["zenith"],
        solar_azimuth=solar_positions["azimuth"],
        dni=clearsky_data["dni"],
        ghi=clearsky_data["ghi"],
        dhi=clearsky_data["dhi"],
        surface_type="urban",
    )
    # drop rows with poa_global NaN values
    poa_front = poa_front.dropna(subset=["poa_global"])

    poa_rear = pvlib.irradiance.get_total_irradiance(
        surface_tilt=180 - 90,
        surface_azimuth=axis_azimuth + 180,
        solar_zenith=solar_positions["zenith"],
        solar_azimuth=solar_positions["azimuth"],
        dni=clearsky_data["dni"],
        ghi=clearsky_data["ghi"],
        dhi=clearsky_data["dhi"],
        surface_type="urban",
    )
    # drop rows with poa_global NaN values
    poa_rear = poa_rear.dropna(subset=["poa_global"])

    effective_front = (
        poa_front["poa_global"]
        * (1 - morning_shaded_fraction)
        * c["panel"]["efficiency"]
    )
    effective_rear = (
        poa_rear["poa_global"]
        * (1 - afternoon_shaded_fraction)
        * c["panel"]["bifaciality"]
        * c["panel"]["efficiency"]
    )

    energy_front = effective_front * 15 / 60 / 1e3
    energy_rear = effective_rear * 15 / 60 / 1e3
    total_hourly_energy_m2 = energy_front + energy_rear
    energy_total = total_hourly_energy_m2.sum()
    logger.info(f"Energy yield calculated: {energy_total} kWh/m2")

    panel_area = c["panel"]["dimensions"]["length"] * c["panel"]["dimensions"]["width"]
    total_area = panel_area * no_of_panels
    total_hourly_energy = total_hourly_energy_m2 * total_area
    total_energy = total_hourly_energy.sum()
    logger.info(f"Total energy yield calculated: {total_energy} kWh")

    return total_hourly_energy


def calculate_energy_production_horizontal(c):
    panel_coordinates, no_of_panels = define_grid_layout(c, panel_tilt=0)
    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

    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_to_axis_offset = 0
    shaded_row_rotation = 0
    shading_row_rotation = 0
    axis_tilt = 0
    axis_azimuth = 180  # south facing

    shaded_fraction = pvlib.shading.shaded_fraction1d(
        solar_zenith=solar_positions["zenith"],
        solar_azimuth=solar_positions["azimuth"],
        axis_azimuth=axis_azimuth,
        shaded_row_rotation=shaded_row_rotation,
        shading_row_rotation=shading_row_rotation,
        collector_width=collector_width,
        pitch=pitch,
        surface_to_axis_offset=surface_to_axis_offset,
        axis_tilt=axis_tilt,
    )
    shaded_fraction = shaded_fraction * no_of_shaded_rows / no_of_rows
    logger.info(f"Shaded fraction calculated for solar positions.")

    poa = pvlib.irradiance.get_total_irradiance(
        surface_tilt=0,
        surface_azimuth=axis_azimuth,
        solar_zenith=solar_positions["zenith"],
        solar_azimuth=solar_positions["azimuth"],
        dni=clearsky_data["dni"],
        ghi=clearsky_data["ghi"],
        dhi=clearsky_data["dhi"],
        surface_type="urban",
    )
    poa = poa.dropna(subset=["poa_global"])

    effective_front = (
        poa["poa_global"] * (1 - shaded_fraction) * c["panel"]["efficiency"]
    )

    total_hourly_energy_m2 = effective_front * 15 / 60 / 1e3
    energy_total = total_hourly_energy_m2.sum()
    logger.info(f"Energy yield calculated: {energy_total} kWh/m2")

    panel_area = c["panel"]["dimensions"]["length"] * c["panel"]["dimensions"]["width"]
    total_area = panel_area * no_of_panels
    total_hourly_energy = total_hourly_energy_m2 * total_area
    total_energy = total_hourly_energy.sum()
    logger.info(f"Total energy yield calculated: {total_energy} kWh")

    return total_hourly_energy