Server-scripts/vivohost/generateAlinaChart.py

import pandas as pd
import matplotlib.pyplot as plt
import os

def interpolate_daily(df):
    df = df.copy()
    df["Datum"] = pd.to_datetime(df["Datum"], errors="coerce")
    df = df.sort_values("Datum").set_index("Datum")

    daily_index = pd.date_range(df.index.min(), df.index.max(), freq="D")

    return (
        df.reindex(daily_index)
          .interpolate(method="time")
          .reset_index()
          .rename(columns={"index": "Datum"})
    )

def plot_percentiles(ax, x, data_dict, p_keys, colors=None, linewidths=None, labels=None,
    linestyle=None,  fill=True, fill_color="lightcoral", fill_alpha=0.25):
    """
    Plot multiple percentile curves from a dictionary like who_head_daily.

    Parameters
    ----------
    ax : matplotlib axis
    x : array-like (x values)
    data_dict : dict-like, e.g. who_head_daily
    p_keys : list of keys, e.g. ["P3", "P50", "P97"]
    colors : list of colors (optional)
    linewidths : list of linewidths (optional)
    fill : whether to fill between min and max percentile
    :param labels:
    """

    n = len(x)

    if colors is None:
        colors = [None] * len(p_keys)
    if linewidths is None:
        linewidths = [1.5] * len(p_keys)

    # Plot lines
    for i, key in enumerate(p_keys):
        ax.plot(
            x,
            data_dict[key][:n],
            color=colors[i],
            linewidth=linewidths[i],
            label=labels[i],
            linestyle=linestyle[i]
        )

    # Fill between min and max percentile
    if fill and len(p_keys) >= 2:
        lower = data_dict[p_keys[0]][:n]
        upper = data_dict[p_keys[-1]][:n]

        ax.fill_between(
            x,
            lower,
            upper,
            color=fill_color,
            alpha=fill_alpha
        )

    return ax

# Build output path from ods_path
ods_path = "/data/Nextcloud/Documents/Dokumente/Shared/Alina/Wachstum.ods"
output_dir = os.path.dirname(ods_path)
output_path = os.path.join(output_dir, "Wachstum.png")

# Load specific sheets into data structures (DataFrames)
alina_df = pd.read_excel(
    ods_path,
    sheet_name="Alina",
    engine="odf"
)

who_weight_df = pd.read_excel(
    ods_path,
    sheet_name="WHO Gewicht",
    engine="odf"
)
who_height_df = pd.read_excel(
    ods_path,
    sheet_name="WHO Größe",
    engine="odf"
)
who_head_df = pd.read_excel(
    ods_path,
    sheet_name="WHO Kopfumfang",
    engine="odf"
)
def calculateDateFromBrithday(df, birthday ):

    new_df = pd.DataFrame(columns=["Datum"])
    for idx, (val1, val2) in enumerate(zip(df["Month"], df["Week"])):
        if not pd.isna(val1):
            new_df.loc[idx, "Datum"] = (birthday + pd.DateOffset(months=val1))
        if not pd.isna(val2):
            new_df.loc[idx, "Datum"] = (birthday + pd.DateOffset(weeks=val2))

    return pd.to_datetime(new_df["Datum"], format="%d.%m.%Y", errors="coerce")

alina_df["Datum"] = pd.to_datetime(alina_df["Datum"],format="%d.%m.%Y",errors="coerce")

# Ensure Datum is datetime (safe to run even if already converted)
who_weight_df["Datum"] = calculateDateFromBrithday(who_weight_df,alina_df["Datum"][0] )
who_height_df["Datum"] = calculateDateFromBrithday(who_height_df,alina_df["Datum"][0] )
who_head_df["Datum"] = calculateDateFromBrithday(who_head_df,alina_df["Datum"][0] )

who_weight_daily = interpolate_daily(who_weight_df)
who_height_daily = interpolate_daily(who_height_df)
who_head_daily   = interpolate_daily(who_head_df)


# Plot
last_alina_date = alina_df["Datum"].max() + pd.DateOffset(months=2)
x = who_weight_daily["Datum"][who_weight_daily["Datum"] <= last_alina_date]
fig, axes = plt.subplots(
    nrows=3,
    ncols=1,
    figsize=(12, 10),
    sharex=True
)

percentile_list = ['P1', 'P3',  'P10',  'P25',
       'P50', 'P75', 'P90', 'P97', 'P99']
colors_list = ["lightcoral" if p != "P50" else "darkred" for p in percentile_list]
linewidths_list = [1.5 if p != "P50" else 2.5 for p in percentile_list]
label_list = [p if p == "P50" or p == "P3" or p == "P97" else "" for p in percentile_list]
linestyle_list = ["--" if p == "P3" or p == "P97" else "-" for p in percentile_list]
# ---------- 1. WEIGHT ----------
ax = axes[0]
plot_percentiles(
    ax,
    x,
    who_weight_daily,
    percentile_list,
    colors=colors_list,
    linewidths=linewidths_list,
    labels=label_list,
    linestyle=linestyle_list
)


ax.plot(alina_df["Datum"], alina_df["Gewicht"],
        color="green", linewidth=1.5, marker="o", label="Alina")

last_idx = alina_df["Gewicht"].last_valid_index()
who_row = who_weight_daily.loc[who_weight_daily["Datum"] == alina_df["Datum"][last_idx]].iloc[0]
perzentil = (alina_df["Gewicht"][last_idx]-who_row["P50"])/((who_row["P97"]-who_row["P50"])/47)+50

ax.text(alina_df["Datum"].iloc[-1]+pd.DateOffset(days=1), alina_df["Gewicht"].iloc[-1]+0.5,f"{perzentil:.2f} %",
    fontsize=10,
    ha="left",
    va="bottom",
    color="green")

ax.set_ylabel("Gewicht [kg]")
ax.set_title("Wachstum")
# Enable minor ticks
ax.minorticks_on()

# Configure major grid (e.g., solid, darker)
ax.grid(which='major', color='black', linestyle='-', linewidth=0.8)

# Configure minor grid (e.g., dashed, lighter, thinner)
ax.grid(which='minor', color='gray', linestyle='--', linewidth=0.4, alpha=0.5)
ax.legend()


# ---------- 2. HEIGHT ----------
ax = axes[1]
plot_percentiles(
    ax,
    x,
    who_height_daily,
    percentile_list,
    colors=colors_list,
    linewidths=linewidths_list,
    labels=label_list,
    linestyle=linestyle_list
)
ax.plot(alina_df["Datum"], alina_df["Größe"],
        color="green", linewidth=1.5, marker="o")

last_idx = alina_df["Größe"].last_valid_index()
who_row = who_height_daily.loc[who_height_daily["Datum"] == alina_df["Datum"][last_idx]].iloc[0]
perzentil = (alina_df["Größe"][last_idx]-who_row["P50"])/((who_row["P97"]-who_row["P50"])/47)+50

ax.text(alina_df["Datum"].iloc[-1]+pd.DateOffset(days=1), alina_df["Größe"].iloc[-1]+0.5,f"{perzentil:.2f} %",
    fontsize=10,
    ha="left",
    va="bottom",
    color="green")


ax.set_ylabel("Größe [cm]")
# Enable minor ticks
ax.minorticks_on()

# Configure major grid (e.g., solid, darker)
ax.grid(which='major', color='black', linestyle='-', linewidth=0.8)
# Configure minor grid (e.g., dashed, lighter, thinner)
ax.grid(which='minor', color='gray', linestyle='--', linewidth=0.4, alpha=0.5)


# ---------- 3. HEAD CIRCUMFERENCE ----------
ax = axes[2]

plot_percentiles(
    ax,
    x,
    who_head_daily,
    percentile_list,
    colors=colors_list,
    linewidths=linewidths_list,
    labels=label_list,
    linestyle=linestyle_list
)

ax.plot(alina_df["Datum"], alina_df["Kopfumfang"],
        color="green", linewidth=1.5, marker="o")

last_idx = alina_df["Kopfumfang"].last_valid_index()
who_row = who_head_daily.loc[who_head_daily["Datum"] == alina_df["Datum"][last_idx]].iloc[0]
perzentil = (alina_df["Kopfumfang"][last_idx]-who_row["P50"])/((who_row["P97"]-who_row["P50"])/47)+50

ax.text(alina_df["Datum"].iloc[-1]+pd.DateOffset(days=1), alina_df["Kopfumfang"].iloc[-1]+0.5,f"{perzentil:.2f} %",
    fontsize=10,
    ha="left",
    va="bottom",
    color="green")

ax.set_ylabel("Kopfumfang [cm]")
# Enable minor ticks
ax.minorticks_on()

# Configure major grid (e.g., solid, darker)
ax.grid(which='major', color='black', linestyle='-', linewidth=0.8)

# Configure minor grid (e.g., dashed, lighter, thinner)
ax.grid(which='minor', color='gray', linestyle='--', linewidth=0.4, alpha=0.5)

# ---------- X‑AXIS ----------
axes[-1].set_xlabel("Datum")

N = 7  # label every Nth tick

axes[-1].set_xticks(x[::N])
axes[-1].set_xticklabels(
    x.iloc[::N].dt.strftime("%d.%m.%Y"),
    rotation=45,
    ha="right"
)

plt.tight_layout()
plt.savefig(output_path, dpi=150)
plt.close()

print("Fin")