Cimetta

1600 masl

TARGET_SITE_ID = 1

from datetime import date, datetime, timedelta, timezone

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

from startleiter.utils import get_engine, get_flights, get_predictions

engine = get_engine()
df_flight = get_flights(engine, TARGET_SITE_ID)
df_pred = get_predictions(engine, TARGET_SITE_ID)

today_idx = df_pred.reftime==pd.to_datetime(date.today())
df_pred[today_idx].drop(["id", "reftime", "source_id", "leadtime_days"], axis=1)

Predictions for the next days. {#cell-predictions}

	validtime	flying_probability	max_altitude_masl	max_distance_km
24679	2024-11-17	0.801955	3600.0	150.0
24686	2024-11-18	0.734257	3100.0	50.0
24693	2024-11-19	0.659884	3600.0	100.0
24700	2024-11-20	0.743713	3100.0	50.0
24707	2024-11-21	0.930108	2600.0	100.0

sns.set()

ts = df_flight.set_index("datetime").sort_index()
keep_cols = ["date", "max_altitude_m", "length_km", "airtime_hours"]
ts = ts[keep_cols]
empty_row = pd.Series(name=pd.Timestamp.today(tz="UTC"), data=[None,] * len(keep_cols), index=keep_cols)
ts = pd.concat([ts, empty_row.to_frame().transpose()])

# compute reference statistics
n_years = 6
clim = ts.resample("1D").mean(numeric_only=True)
clim = clim.groupby(clim.index.isocalendar().week)
clim = clim.rolling(window=f"{n_years * 365}D", center=False).mean().reset_index(0, drop=True)
clim = clim.sort_index().rolling("20D", center=True).mean()

clim_counts = ts.date.resample("1D").count()
clim_counts = clim_counts.where(clim_counts > 0)
clim_counts = clim_counts.groupby(clim_counts.index.isocalendar().week)
clim_counts = clim_counts.rolling(window=f"{n_years * 365}D", center=False).mean().reset_index(0, drop=True)
clim_counts = clim_counts.sort_index().rolling("20D", center=True).mean()

end = datetime.now(timezone.utc)
start = end - timedelta(days=90)
clim = clim[clim.index > start]
clim_counts = clim_counts[clim_counts.index > start]
dfs = df_flight[df_flight.datetime > start]
dfg = dfs.groupby(df_flight.date).datetime.count()

fig, axs = plt.subplots(4, sharex=True, figsize=(7, 10))

# reference lines
axs[0].plot(clim_counts.index, clim_counts.values, color="tab:red")
axs[1].plot(clim.index, clim.max_altitude_m, color="tab:red")
axs[2].plot(clim.index, clim.length_km, color="tab:red")
axs[3].plot(clim.index, clim.airtime_hours, color="tab:red")
axs[3].tick_params(axis="x", labelrotation=45)

# predictions
pred = df_pred[df_pred.leadtime_days==0].set_index("validtime").sort_index()
axs[0].twinx().step(pred.index, pred.flying_probability, where="post", color="gray", alpha=0.5)
twinx = plt.gca()
twinx.grid(None)
twinx.set_ylim([0, 1])
twinx.set_ylabel("Flyability []")
axs[1].step(pred.index, pred.max_altitude_masl, where="post", color="gray", alpha=0.5)
axs[2].step(pred.index, pred.max_distance_km, where="post", color="gray", alpha=0.5)

# plot individual flights
ax0 = sns.scatterplot(x=dfg.index, y=dfg.values, marker="x", ax=axs[0])
ax1 = sns.scatterplot(x="datetime", y="max_altitude_m", marker="x", data=dfs, ax=axs[1])
ax2 = sns.scatterplot(x="datetime", y="length_km", marker="x", data=dfs, ax=axs[2])
ax3 = sns.scatterplot(x="datetime", y="airtime_hours", marker="x", data=dfs, ax=axs[3])

ax0.set(ylabel="No. flights per day")
ax1.set(ylabel="Max altitude [m]")
ax2.set(ylabel="Distance [km]")
ax3.set(ylabel="Airtime [h]")

ax0.set_xlim([start, end])
ax1.set_xlim([start, end])
ax2.set_xlim([start, end])
ax3.set_xlim([start, end])

plt.tight_layout()

Figure 1: Overview over the past 90 days