import panel as pn
import pandas as pd
import os

from consts import INVERTER_ID_MAPPING, TEMPERATURE_COLUMNS_TO_USE, CURRENT, IRRADIANCE, VOLTAGE, POWER_DC, POWER_AC, T_AMBIENT, T_MODULE, T_HEATSINK, T_CPU, T_BOARD
from plotting import (
    create_heatmap,
    create_iv_plot_with_power_curves,
    create_iv_plot,
    create_iv_plot_with_power_and_temperature_curves,
    create_iv_plot_with_temperature_curves,
)

# Function to load chunked data based on month selection
def load_timeseries_data_for_month(month):
    start_date = pd.to_datetime(month).strftime('%Y-%m-01')
    end_date = (pd.to_datetime(month) + pd.offsets.MonthEnd()).strftime('%Y-%m-%d')
    file_name = f"enel_timeseries_data_{start_date}_{end_date}.csv"
    if not os.path.exists(file_name):
        return None, file_name
    return pd.read_csv(file_name, index_col=0, header=[0, 2], parse_dates=True), file_name

def load_other_features_data_for_month(month):
    start_date = pd.to_datetime(month).strftime('%Y-%m-01')
    end_date = (pd.to_datetime(month) + pd.offsets.MonthEnd()).strftime('%Y-%m-%d')
    file_name = f"enel_other_features_data_{start_date}_{end_date}.csv"
    if not os.path.exists(file_name):
        return None, file_name
    return pd.read_csv(file_name, index_col=0, header=0, parse_dates=True), file_name

# Define the available months
months = pd.date_range(start='2022-09-01', end='2023-08-31', freq='MS').strftime('%Y-%m').tolist()

kpi_data = pd.read_csv(
    r"kpi_data.csv", index_col=0, header=0, parse_dates=True
)

daily_timeseries_data = pd.read_csv(
    r"daily_aggregated_timeseries_data.csv",
    index_col=0,
    header=0,
    parse_dates=True,
)

# Initialize Panel extension
pn.extension("plotly")

# Widgets for selecting month and columns
month_selector = pn.widgets.Select(
    name='Choose a Month',
    options=months,
)

inverter_ids = pn.widgets.MultiSelect(
    name="Inverter IDs",
    options=list(INVERTER_ID_MAPPING.keys()),
    size=8,
    disabled=True,
)

# Line Plots
plot_power_curves = pn.widgets.Checkbox(name="Plot P-dc and P-ac", value=False, disabled=True)
plot_temperature_curves = pn.widgets.Checkbox(name="Plot Temperatures", value=False, disabled=True)

# Heatmaps
heatmap_pr = pn.widgets.Checkbox(name="Heat Map PR", value=False)
heatmap_sy = pn.widgets.Checkbox(name="Heat Map SY", value=False)
heatmap_current = pn.widgets.Checkbox(name="Heat Map - Current", value=False)
heatmap_voltage = pn.widgets.Checkbox(name="Heat Map - Voltage", value=False)
heatmap_power = pn.widgets.Checkbox(name="Heat Map - Power", value=False)
heatmap_irradiance = pn.widgets.Checkbox(name="Heat Map - Irradiance", value=False)
heatmap_temperature = pn.widgets.Checkbox(name="Heat Map - Temperature Heatsink", value=False)

# Create a loading spinner
loading_spinner = pn.indicators.LoadingSpinner(width=50, height=50, visible=False)

# Global variables to store loaded data
loaded_timeseries_data = None
loaded_other_features_data = None
loaded_month = None

# Function to load data based on selected month
@pn.depends(month_selector.param.value, watch=True)
def load_data(selected_month):
    global loaded_timeseries_data, loaded_other_features_data, loaded_month
    if not selected_month:
        return
    loading_spinner.visible = True
    timeseries_data, timeseries_file = load_timeseries_data_for_month(selected_month)
    other_features_data, other_features_file = load_other_features_data_for_month(selected_month)
    if timeseries_data is None or other_features_data is None:
        loading_spinner.visible = False
        return pn.pane.Markdown(f"Files not found: {timeseries_file if timeseries_data is None else ''} {other_features_file if other_features_data is None else ''}")
    loaded_timeseries_data = timeseries_data
    loaded_other_features_data = other_features_data
    loaded_month = selected_month
    loading_spinner.visible = False
    inverter_ids.disabled = False
    plot_power_curves.disabled = False
    plot_temperature_curves.disabled = False
    return pn.pane.Markdown(f"Data loaded for month: {selected_month}")

# Panel interactive functions
@pn.depends(
    inverter_ids.param.value,
    plot_power_curves.param.value,
    plot_temperature_curves.param.value
)
def update_iv_plot(inverter_ids, plot_power_curves, plot_temperature_curves):
    if loaded_timeseries_data is None or loaded_other_features_data is None:
        return pn.pane.Markdown("No data loaded.")
    
    if not inverter_ids:
        return pn.pane.Markdown("No Inverters selected for Plotting.")
    else:
        # Plot IV + Power + Temperature Curves
        if plot_power_curves and plot_temperature_curves:
            return create_iv_plot_with_power_and_temperature_curves(
                loaded_timeseries_data, loaded_other_features_data, inverter_ids
            )
        # Plot IV + Temperature Curves
        elif (not plot_power_curves) and plot_temperature_curves:
            return create_iv_plot_with_temperature_curves(
                loaded_timeseries_data, loaded_other_features_data, inverter_ids
            )
        # Plot IV + Power Curves
        elif plot_power_curves and (not plot_temperature_curves):
            return create_iv_plot_with_power_curves(loaded_timeseries_data, inverter_ids)
        # Plot only IV Curves
        else:
            return create_iv_plot(loaded_timeseries_data, inverter_ids)


@pn.depends(heatmap_pr.param.value)
def update_heatmap_pr(heatmap_pr):
    if heatmap_pr:
        pr_df = kpi_data.filter(like="pr")
        pr_df.columns = [i.split("-")[1] for i in pr_df.columns]
        pr_heatmap = create_heatmap(pr_df, "PR Heatmap")
        return pn.Row(pr_heatmap)
    return pn.pane.Markdown("")


@pn.depends(heatmap_sy.param.value)
def update_heatmap_sy(heatmap_sy):
    if heatmap_sy:
        sy_df = kpi_data.filter(like="daily_specific_yield")
        sy_df.columns = [i.split("-")[1] for i in sy_df.columns]
        sy_heatmap = create_heatmap(sy_df, "SY Heatmap")
        return pn.Row(sy_heatmap)
    return pn.pane.Markdown("")


@pn.depends(heatmap_current.param.value)
def update_heatmap_current(heatmap_current):
    if heatmap_current:
        current_df = daily_timeseries_data.filter(like="I")
        current_df.columns = [i.split("-")[1] for i in current_df.columns]
        current_heatmap = create_heatmap(current_df, "Current Heatmap")
        return pn.Row(current_heatmap)
    return pn.pane.Markdown("")


@pn.depends(heatmap_voltage.param.value)
def update_heatmap_voltage(heatmap_voltage):
    if heatmap_voltage:
        voltage_df = daily_timeseries_data.filter(like="V")
        voltage_df.columns = [i.split("-")[1] for i in voltage_df.columns]
        voltage_heatmap = create_heatmap(voltage_df, "Voltage Heatmap")
        return pn.Row(voltage_heatmap)
    return pn.pane.Markdown("")


@pn.depends(heatmap_power.param.value)
def update_heatmap_power(heatmap_power):
    if heatmap_power:
        power_df = daily_timeseries_data.filter(like="P")
        power_df.columns = [i.split("-")[1] for i in power_df.columns]
        power_heatmap = create_heatmap(power_df, "Power Heatmap")
        return pn.Row(power_heatmap)
    return pn.pane.Markdown("")


@pn.depends(heatmap_irradiance.param.value)
def update_heatmap_irradiance(heatmap_irradiance):
    if heatmap_irradiance:
        irradiance_df = daily_timeseries_data.filter(like="G")
        irradiance_df.columns = [i.split("-")[1] for i in irradiance_df.columns]
        irradiance_heatmap = create_heatmap(irradiance_df, "Irradiance Heatmap")
        return pn.Row(irradiance_heatmap)
    return pn.pane.Markdown("")


@pn.depends(heatmap_temperature.param.value)
def update_heatmap_temperature(heatmap_temperature):
    if heatmap_temperature:
        temp_df = daily_timeseries_data.filter(like="THeatSink")
        temp_df.columns = [i.split("-")[1] for i in temp_df.columns]
        temp_heatmap = create_heatmap(temp_df, "T-Heatsink Heatmap")
        return pn.Row(temp_heatmap)
    return pn.pane.Markdown("")


# Create dashboard layout
dashboard = pn.Column(
    "# ENEL Dashboard",
    month_selector,
    loading_spinner,
    pn.panel(load_data),
    # IV Plots
    pn.Row(
        pn.Column(inverter_ids, plot_power_curves, plot_temperature_curves),
        pn.panel(update_iv_plot, sizing_mode="stretch_width"),
    ),
    # Heatmaps
    pn.Row(
        pn.Column(heatmap_pr), pn.panel(update_heatmap_pr, sizing_mode="stretch_width")
    ),
    pn.Row(
        pn.Column(heatmap_sy), pn.panel(update_heatmap_sy, sizing_mode="stretch_width")
    ),
    pn.Row(
        pn.Column(heatmap_current),
        pn.panel(update_heatmap_current, sizing_mode="stretch_width"),
    ),
    pn.Row(
        pn.Column(heatmap_voltage),
        pn.panel(update_heatmap_voltage, sizing_mode="stretch_width"),
    ),
    pn.Row(
        pn.Column(heatmap_power),
        pn.panel(update_heatmap_power, sizing_mode="stretch_width"),
    ),
    pn.Row(
        pn.Column(heatmap_irradiance),
        pn.panel(update_heatmap_irradiance, sizing_mode="stretch_width"),
    ),
    pn.Row(
        pn.Column(heatmap_temperature),
        pn.panel(update_heatmap_temperature, sizing_mode="stretch_width"),
    ),
)

# Serve the dashboard
dashboard.servable()