Periods

%%sql

CREATE OR REPLACE TABLE periods (
    name VARCHAR,
    begin TIMESTAMP,
    fin TIMESTAMP
);

INSERT INTO periods (name, begin, fin)
VALUES
    ('18 grad', '2023-10-24 22:49:00', '2023-10-27 22:49:00'),
    ('20 grad', '2023-10-27 22:49:00', '2023-10-30 22:49:00')
;

%%sql

periods << SELECT
  p.name,
  p.begin,
  p.fin,
  24.0 * 60.0 * AVG(d.cm) AS cd
FROM periods p
JOIN waermestrom_minute d
ON d.minute BETWEEN p.begin AND p.fin
GROUP BY p.name, p.begin, p.fin
;

import plotly.express as px
import plotly.graph_objects as go
fig = px.bar(strom_per_day, y='cd', x='date')
fig.update_xaxes(
    rangeslider_visible=True,
    rangeselector=dict(
        buttons=list([
            dict(count=15, label="15d", step="day", stepmode="backward"),
            dict(count=1, label="1m", step="month", stepmode="backward"),
            dict(count=6, label="6m", step="month", stepmode="backward"),
            dict(count=1, label="YTD", step="year", stepmode="todate"),
            dict(count=1, label="1y", step="year", stepmode="backward"),
            dict(step="all")
        ])
    )
)
fig.update_xaxes(rangeslider_thickness = 0.1)
fig.show()

for index, row in periods.iterrows():
    fig.add_shape(
        type='line',
        x0=row['begin'],
        y0=row['cd'],
        x1=row['fin'],
        y1=row['cd'],
        line=dict(color='Red'),
        xref='x',
        yref='y'
    )
fig.show()

periods

we have to brind the temp data and other climatic variables here