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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
return pd.read_csv(file_name, index_col=0, header=[0, 1, 2], parse_dates=True)
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
return pd.read_csv(file_name, index_col=0, header=0, parse_dates=True)
# 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",
value=[list(INVERTER_ID_MAPPING.keys())[0]],
options=list(INVERTER_ID_MAPPING.keys()),
size=8,
)
# Line Plots
plot_power_curves = pn.widgets.Checkbox(name="Plot P-dc and P-ac", value=False)
plot_temperature_curves = pn.widgets.Checkbox(name="Plot Temperatures", value=False)
# 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)
# Global variables to store loaded data
loaded_timeseries_data = None
loaded_other_features_data = None
loaded_month = None
# Panel interactive functions
@pn.depends(
month_selector.param.value,
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, selected_month):
global loaded_timeseries_data, loaded_other_features_data, loaded_month
if not selected_month:
return pn.pane.Markdown("Please select a month to load data.")
if loaded_month != selected_month:
loaded_timeseries_data = load_timeseries_data_for_month(selected_month)
loaded_other_features_data = load_other_features_data_for_month(selected_month)
loaded_month = selected_month
if loaded_timeseries_data is None or loaded_other_features_data is None:
return pn.pane.Markdown("No data available for the selected month.")
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,
# 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()
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