Create pcolormesh figure for data_amount data

[veenstrajelmer]

• also load data_amount csv's in memory
• pcolormesh of data_amount dataframes
• with clim from 0% to 200%, relative to the median of each year so it is generic for ts/ext and varying ts frequencies
• these percentages then show the relative coverage per year, per station
• replace zeros with nans so they are white
• add figures to Missing/duplicated values in WATHTE timeseries and extremes Rijkswaterstaat/wm-ws-dl#39
• add max aantal waarnemingen (ddl limit) to boxplots in Missing/duplicated values in WATHTE timeseries and extremes Rijkswaterstaat/wm-ws-dl#39
• move all data_amount results to subfolder

Example code:

import pandas as pd
import matplotlib.pyplot as plt
plt.close("all")
import numpy as np

df = pd.read_csv(r"p:\11210325-005-kenmerkende-waarden\work\measurements_wl_18700101_20240101\data_amount_ts.csv")
df = df.set_index("Groeperingsperiode")
df[df==0] = np.nan
df_relative = df.div(df.median(axis=1), axis=0)
df_relative = df_relative.clip(upper=2)

# df = df_relative # this is useful for ts, because the frequency was changed from hourly to 10-minute

fig, ax = plt.subplots(figsize=(14,8))
pc = ax.pcolormesh(df.columns, df.index, df.values) # , cmap="turbo")
fig.colorbar(pc, ax=ax)
ax.tick_params(axis='x', labelrotation=90)
ax.grid(zorder=-5)
fig.tight_layout()