Änderung: parquet_store.py

app/data/datasets.py

@@ -0,0 +1,3 @@
+from scriptloader import get_sql
+from db import get_conn
+

app/data/db.py

@@ -1,4 +1,7 @@
+import streamlit as st
 from sqlalchemy import create_engine, Text
+from data.scriptloader import get_sql
+import oracledb
 import pandas as pd
 from dotenv import load_dotenv
 from pathlib import Path
@@ -26,6 +29,25 @@ def get_conn(db):
             logging.info(f"Datenbank {db} konnte nicht gefunden werden")
     return engine
 
+def load_data(sql_file, db):
+    sql = get_sql(sql_file)
+    engine = get_conn(db)
+    with engine.connect():
+        df = pd.read_sql(sql, engine)
+    return df
+
+
+
+
+
+
+
+
+
+
+
+
+
 # def get_data(db):
 #     engine = get_conn(db)
 #     with engine.connect() as conn:

app/data/scriptloader.py

@@ -7,4 +7,4 @@ def get_sql(filename):
 
 
 if __name__ == "__main__":
-    print(get_sql("sales_umsatz"))
+    print(get_sql("ergebnis_kpi"))

app/data/sql/ergebnis_kpi.sql

@@ -0,0 +1,92 @@
+/*******************************************************************************************
+ * Basis-Daten aus der Kostenrechnung
+ *******************************************************************************************/
+with basis as(
+    Select
+        p.geschaeftsjahr as jahr,
+        p.geschaeftsperiode as monat,
+        p.periode_key,
+        b.zgrp1,
+        b.zgrp2,
+        b.zgrp3,
+        b.bezeichnung  as Bereich,
+        ko.kostenobjekt,
+        ko.bezeichnung as obj_bezeichnung,
+        ko.obj_text,
+        ko.objektgruppe,
+        ko.objekttyp,
+        ko.verantwortlicher,
+        ks.co_koa_grp,
+        ks.co_grp_bez,
+        ks.co_grp_text,
+        ks.kostenart, 
+        ks.koa_text,
+        coalesce(ws.uml_kz,'') as uml_kz,
+        coalesce(ws.zgrp_1_entlastung, '') as zgrp1_entlastung,
+        coalesce(ws.wert * -1, 0) as wert,
+        coalesce(ws.wert_plan * -1, 0) as wert_plan,
+        0 as wert_forecast
+    from
+        co_dw.bi.fact_wertsummen ws
+        left join co_dw.bi.dim_periode p
+            on ws.periode_key = p.periode_key 
+        left join co_dw.bi.dim_kostenobjekt ko
+            on ws.objekt_key = ko.objekt_key 
+        left join co_dw.bi.dim_koastruktur ks
+            on ws.kostenart = ks.kostenart 
+        left join co_dw.bi.dim_bereich b
+            on ko.bereich_key = b.bereich_key 
+    where
+        ks.co_koa_grp_01 = 'GMN001'
+        and ko.objektgruppe in ('HKST', 'VKST', 'KTRG', 'KtrBereich', 'KtrMNr')
+        and p.geschaeftsperiode < 14
+        and (p.geschaeftsjahr = :jahr or p.geschaeftsjahr = :jahr -1)
+),
+
+
+
+/*******************************************************************************************
+ * Gesamtergebnis
+ *******************************************************************************************/
+ergebnis_gesamt as (
+    select
+        zgrp1,
+        zgrp2,
+        zgrp3,
+        kostenobjekt,
+        obj_bezeichnung,
+        obj_text,
+        co_koa_grp,
+        co_grp_bez,
+        co_grp_text,
+        'Ergebnis' as main_kpi,
+        'Gesamtergebnis' as kpi,
+        round(sum(case when jahr = :jahr-1 then wert else 0 end),0) as vor_jahr,
+        round(sum(case when jahr = :jahr-1 and monat <= :monat then wert else 0 end),0) as vor_jahr_bis_monat,
+        round(sum(case when jahr = :jahr-1 and monat = :monat then wert else 0 end),0) as vor_jahr_monat,
+        round(sum(case when jahr = :jahr then wert else 0 end),0) as akt_jahr,
+        round(sum(case when jahr = :jahr and monat <= :monat then wert else 0 end),0) as akt_jahr_bis_monat,
+        round(sum(case when jahr = :jahr and monat = :monat then wert else 0 end),0) as akt_jahr_monat,
+        round(sum(case when jahr = :jahr then wert_plan else 0 end),0) as plan_akt_jahr,
+        round(sum(case when jahr = :jahr and monat <= :monat then wert_plan else 0 end),0) as plan_akt_jahr_bis_monat,
+        round(sum(case when jahr = :jahr and monat = :monat then wert_plan else 0 end),0) as plan_akt_jahr_monat,
+        round(sum(case when jahr = :jahr then wert_forecast else 0 end),0) as forecast_akt_jahr,
+        round(sum(case when jahr = :jahr and monat <= :monat then wert_forecast else 0 end),0) as forecast_akt_jahr_bis_monat,
+        round(sum(case when jahr = :jahr and monat = :monat then wert_forecast else 0 end),0) as forecast_akt_jahr_monat
+    from 
+        basis 
+    group by
+        zgrp1,
+        zgrp2,
+        zgrp3,
+        kostenobjekt,
+        obj_bezeichnung,
+        obj_text,
+        co_koa_grp,
+        co_grp_bez,
+        co_grp_text
+)
+
+
+select * from ergebnis_gesamt
+select * from co_dw.bi.Fact_Wertsummen

app/data/sql/ora_kostenobjekte.sql

@@ -0,0 +1,22 @@
+/* Orlacle (PENTA) Kostenobjekte */
+
+select 
+    GESCHAEFTSJAHR as jahr,
+    OBJEKTTYP as typ,
+    KOSTENOBJEKT as obj,
+    BEZEICHNUNG,
+    VERANTWORTLICHER,
+    FELD_2_X30 as vorgesetzter,
+    ZUORDNUNGSGRUPPE_1 as zgrp1,
+    ZUORDNUNGSGRUPPE_2 as zgrp2,
+    ZUORDNUNGSGRUPPE_3 as zgrp3,
+    ZUORDNUNGSGRUPPE_4 as zgrp4,
+    ZUORDNUNGSGRUPPE_5 as zgrp5,
+    ZUORDNUNGSGRUPPE_6 as zgrp6,
+    FELD_1_X30 as fertigung,
+    OBJEKTGRUPPE as objgrp,
+    sysdate
+from
+    pkos
+where
+    objekttyp in ('01', '02')

app/pages/costobjects.py

@@ -1,34 +1,186 @@
 import streamlit as st
 import pandas as pd
-from data.scriptloader import get_sql
+#from data.scriptloader import get_sql
-from data.db import get_conn
+from data.db import load_data
 from auth_runtime import require_login
-from ui.sidebar import build_sidebar, hide_sidebar_if_logged_out
+#from ui.sidebar import build_sidebar, hide_sidebar_if_logged_out
-from auth import get_fullname_for_user
+#from auth import get_fullname_for_user
+import numpy as np
+from tools.excel_export import df_to_excel_bytes
+from tools.help_text import get_help
 
-# hide_sidebar_if_logged_out()
 
-st.set_page_config(page_title="Co-App Home", page_icon="🏠")
+st.set_page_config(page_title="Co-App Home", page_icon="🏠", layout="wide")
 
 authenticator = require_login()
 st.session_state["authenticator"] = authenticator
 
+st.markdown("""
+<style>
+.block-container {
+    padding-top: 0.2rem;
+}
+</style>
+""", unsafe_allow_html=True)
 
+description = get_help("costobjects")
+with st.expander(label="ℹ️ Hilfe / Hinweise", expanded=False):    # ❓
+    st.markdown(description)
 
-def load_data():
+@st.cache_data
-    sql = get_sql("co_kostenobjekte")
+def cache_data() -> pd.DataFrame:
-    print(sql)
+    """
-    engine = get_conn("co_dw")
+    Load and cache the base dataset for this page.
-    with engine.connect() as conn:
+
-        df = pd.read_sql(sql, engine)
+    Streamlit reruns the script on every interaction; caching avoids
-        print(df)
+    repeated I/O and makes filtering feel instant.
+    """
+    try:
+        df = load_data("ora_kostenobjekte", "oracle")
         return df
+    except:
+        st.warning("Fehler beim Laden der Daten", icon="⚠️")
 
-st.dataframe(load_data())
 
 
+def sidebar_filters(df) -> dict:
+    """
+    Render the sidebar UI and return the current filter selections.
+
+    This function should contain UI concerns only (widgets, layout),
+    and not data filtering logic, to keep the code maintainable.
+    """
+    
+    st.markdown("""
+        <style>
+        section[data-testid="stSidebar"] .block-container {
+            padding-top: 0.5rem;
+            padding-bottom: 0.5rem;
+        }
+        section[data-testid="stSidebar"] div[data-testid="stVerticalBlock"] > div {
+            gap: 0.2rem;
+        }
+        </style>
+    """, unsafe_allow_html=True)
+    
+    st.sidebar.header("Filter")
+    
+    if st.sidebar.button("Refresh (Global)"):
+        cache_data.clear()
+        st.rerun()
+    
+    filter_text = st.sidebar.text_input(label="Textsuche", placeholder="Suche Objekt, Text, Verantwortlicher")
+    col_s1, col_s2 = st.sidebar.columns(2)
+    with col_s1:
+        year = st.selectbox(label="Jahr", options=(2025, 2026), index=1)
+    with col_s2:
+        typ = st.selectbox(label="Typ", options=sorted(df["typ"].dropna().unique()), index=1)
+    obj = st.sidebar.multiselect("obj", sorted(df["obj"].dropna().unique()))
+    zgrp1 = st.sidebar.multiselect("ZGrp1", sorted(df["zgrp1"].dropna().unique()))
+    zgrp2 = st.sidebar.multiselect("ZGrp2", sorted(df["zgrp2"].dropna().unique()))
+    zgrp3 = st.sidebar.multiselect("ZGrp3", sorted(df["zgrp3"].dropna().unique()))
+    
+    return {"year": year, "filter_text": filter_text, "typ": typ, "obj": obj, "zgrp1": zgrp1, "zgrp2": zgrp2, "zgrp3": zgrp3}
+
+
+
+
+def build_mask(df: pd.DataFrame, sidebar_filter: dict) -> np.ndarray:
+    """
+    Build a boolean mask based on filter selections.
+
+    The mask approach keeps the logic readable and makes it easy
+    to add more conditions later.
+    """
+
+    mask = np.ones(len(df), dtype=bool)
+
+    filter_text = (sidebar_filter.get("filter_text") or "").strip()
+    if filter_text:
+        m1 = df["bezeichnung"].astype("string").str.contains(filter_text, case=False, na=False, regex=False)
+        m2 = df["verantwortlicher"].astype("string").str.contains(filter_text, case=False, na=False, regex=False)
+        m3 = df["vorgesetzter"].astype("string").str.contains(filter_text, case=False, na=False, regex=False)
+        mask &= (m1 | m2 | m3)
+
+    if sidebar_filter["year"]:
+        mask &= df["jahr"].eq(sidebar_filter["year"])
+    if sidebar_filter["typ"]:
+        mask &= df["typ"].eq(sidebar_filter["typ"])
+    if sidebar_filter["obj"]:
+        mask &= df["obj"].isin(sidebar_filter["obj"])
+    if sidebar_filter["zgrp1"]:
+        mask &= df["zgrp1"].isin(sidebar_filter["zgrp1"])
+    if sidebar_filter["zgrp2"]:
+        mask &= df["zgrp2"].isin(sidebar_filter["zgrp2"])
+    if sidebar_filter["zgrp3"]:
+        mask &= df["zgrp3"].isin(sidebar_filter["zgrp3"])
+    return mask
+
+
+
+
+def render_table(df):
+    """
+    Render the result table.
+
+    Keep this function presentation-only: it should not modify data.
+    """
+    st.markdown("### Übersicht Kostenobjekte")
+    st.dataframe(df, hide_index=True, width="stretch", height="stretch")
+
+
+def download_data(df):
+    df.to_excel(path)
+
+
+def page():
+    """
+    Page entry point: orchestrates data loading, UI, filtering, and rendering.
+    """
+
+    # -----------------------------
+    # Data loading (cached)
+    # ----------------------------- 
+    df = cache_data()
+
+
+    # -----------------------------
+    # UI (Sidebar)
+    # -----------------------------
+    sidebar_filter = sidebar_filters(df)
+
+
+    # -----------------------------
+    # Business logic (filtering)
+    # -----------------------------
+    mask = build_mask(df, sidebar_filter)
+
+
+    # -----------------------------
+    # Presentation
+    # -----------------------------
+    df_clean = df[df.columns[:-1]] # letzte Spalten entfernen (sysdate)
+    df_display = df_clean.loc[mask]
+    df_display = df_display.sort_values(by="obj", key=lambda s: pd.to_numeric(s, errors="coerce"))
+    render_table(df_display)
+    
+    col1, col2 = st.columns(2)
+    data_as_of = df["sysdate"].max()
+    row_count = len(df_display)
+    
+    with col1:
+        st.text(f"Datenstand: {data_as_of}")
+    with col2:
+        st.markdown(f"<div style='text-align: right;'>Anzahl Zeilen: {row_count}</div>", unsafe_allow_html=True)
+    
+    st.download_button(
+        "📊 Excel-Datei herunterladen",
+        data=df_to_excel_bytes(df_display),
+        file_name="kostenobjekte",
+        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+    )
+
 
 
 if __name__ == "__main__":
-    df = load_data()
+    df = page()
-    print(df)

app/pages/management_first_level.py

@@ -0,0 +1,255 @@
+import streamlit as st
+import pandas as pd
+from data.scriptloader import get_sql
+from data.db import get_conn
+from auth_runtime import require_login
+from ui.sidebar import build_sidebar, hide_sidebar_if_logged_out
+from auth import get_fullname_for_user
+from sqlalchemy import text
+import duckdb
+import altair as alt
+from tools.helpers import display_value, calc_variance_pct
+
+# hide_sidebar_if_logged_out()
+
+st.set_page_config(page_title="Co-App Home", page_icon="🏠", layout="wide")
+
+authenticator = require_login()
+st.session_state["authenticator"] = authenticator
+
+DISPLAY_UNIT = "Mio. €"
+
+def load_data():
+    sql = get_sql("ergebnis_kpi")
+    engine = get_conn("co_dw")
+    with engine.connect() as conn:
+        df = pd.read_sql(text(sql), con=conn, params={"jahr": 2025, "monat": 12})
+    return df
+
+
+# def calc_variance_pct(actual, plan):
+#     variance = actual - plan
+#     if plan == 0:
+#         return None
+#     if actual * plan < 0:
+#         return None
+#     return variance / abs(plan)
+
+
+
+
+
+
+
+def build_dashboard():
+    
+    df = load_data()
+    # st.dataframe(df)
+    
+    be_ist_kum = df["akt_jahr"].sum()
+    
+    #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    # Kalkulation KPI Betriebsergebnis
+    #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    operating_result_actual_ytd = df["akt_jahr_bis_monat"].sum()
+    operating_result_actual_ytd_str = f"{int(operating_result_actual_ytd)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
+    operating_result_plan_ytd = df["plan_akt_jahr_bis_monat"].sum()
+    operating_result_actual_ytd_py = df["vor_jahr_bis_monat"].sum()
+    operating_result_variance = operating_result_actual_ytd - operating_result_plan_ytd
+    operating_result_vaiance_pct = calc_variance_pct(operating_result_actual_ytd, operating_result_plan_ytd)
+
+
+    #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    # Dashboard - Ebene 1 Betriebsergebnis
+    #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++  
+
+    col_1, col_2 = st.columns(2, border=True,vertical_alignment="center")
+
+    with col_1:
+        st.metric(label="Betriebsergebnis", value=operating_result_actual_ytd)
+
+    operation_result_monthly = df["akt_jahr_monat"].sum()
+    
+    be_ist_vorjahr_kum = df["vor_jahr"].sum()
+    be_ist_monat = df["akt_jahr_monat"].sum()
+    be_ist_vorjahr_monat = df["vor_jahr_monat"].sum()
+
+
+    be_ist_kum_anz = f"{int(be_ist_kum)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
+    be_ist_monat_anz = f"{int(be_ist_monat)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
+    be_ist_vorjahr_kum_anz = f"{int(be_ist_vorjahr_kum)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
+    be_ist_monat_vorjahr_anz = f"{int(be_ist_vorjahr_monat)/ANZ_EINHEIT:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".") + " Mio. €"
+    
+    
+    #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    # Ebene 1 - Ergebnis-KPIs
+    #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    
+    # Beispiel-Daten (ersetze das durch deine echten Monatswerte)
+    months = pd.date_range(end=pd.Timestamp.today().normalize(), periods=12, freq="MS")
+    values = pd.Series([120, 80, -30, 50, 40, 10, -20, 60, 70, 30, 20, -10], index=months)
+    df = pd.DataFrame({"month": months, "BE": values}).set_index("month")
+
+    current = df["BE"].iloc[-1]
+    prev = df["BE"].iloc[-2]
+    delta = current - prev
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        st.metric(
+            "Betriebsergebnis (intern)",
+            f"{current:,.0f} €",
+            f"{delta:,.0f} €"
+        )
+
+        with st.expander("Verlauf letzte 12 Monate", expanded=False):
+            # st.bar_chart(df["BE"])
+            # fig, ax = plt.subplots()
+            # ax.plot(df.index, df["BE"], marker="o")
+            # ax.axhline(0, linewidth=1)  # Nulllinie für negative Werte
+
+            # ax.set_xlabel("")
+            # ax.set_ylabel("€")
+            # ax.tick_params(axis="x", rotation=45)
+
+            # st.pyplot(fig, clear_figure=True)
+            df_reset = df.reset_index()
+            df_reset["Monat"] = df_reset["month"].dt.strftime("%Y-%m")
+
+            chart = (
+                alt.Chart(df_reset)
+                .mark_bar(size=28)  # Balkendicke
+                .encode(
+                    x=alt.X("Monat:N", sort=None, title=""),
+                    y=alt.Y("BE:Q", title="€"),
+                    color=alt.condition(
+                        alt.datum.BE < 0,
+                        alt.value("#d62728"),  # rot
+                        alt.value("#2ca02c"),  # grün
+                    )
+                )
+            )
+
+            labels = (
+                alt.Chart(df_reset)
+                .mark_text(dy=-8)
+                .encode(
+                    x="Monat:N",
+                    y="BE:Q",
+                    text=alt.Text("BE:Q", format=",.0f")
+                )
+            )
+            spark = (
+                alt.Chart(df_reset)
+                .mark_line(point=True)
+                .encode(
+                    x=alt.X("Monat:N", axis=None),
+                    y=alt.Y("BE:Q", axis=None)
+                )
+                .properties(height=60)
+            )
+
+            st.altair_chart(spark, use_container_width=True)
+            # st.altair_chart(chart + labels, use_container_width=True)
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    # col_operating_result, col_contribution_margin = st.columns(2,border=True)
+    
+    # col_ue_n, col_ae_f, col_ab_f = st.columns(3,border=True,)
+
+    # with col_ue_n:
+    #     with st.expander(label="Umsatz",):
+            
+    #         st.metric(label="BE-Ist kumuliert (monat)",value=f"{be_ist_kum_anz} ({be_ist_monat_anz})", delta="-5", border=True)
+    #         st.text("Umsatz")
+    # with col_ae_f:
+    #     st.text("Auftragseingang fest")
+    # with col_ab_f:
+    #     st.text("Auftragsbestand fest")
+    
+    
+    
+    
+    # with col_operating_result:
+    #     internal_operating_result = f"BE = {be_ist_kum_anz} Mio. €"
+    #     st.metric(label="BE-Ist kumuliert (monat)",value=f"{internal_operating_result} ({be_ist_monat_anz})", delta="-5", border=True)
+        
+        
+        # with st.expander(label=st.markdown(f"# {internal_operating_result}")):
+        #     st.text("Verlauf...")
+        # # st.text("ERGTAB")
+
+        # st.metric(label="BE-Ist kumuliert (monat)",value=f"{be_ist_kum_anz} ({be_ist_monat_anz})", delta="-5", border=True)
+    
+   
+
+    
+
+
+
+    # erg = duckdb.sql(""" 
+    #             select
+    #                 'Umsatz' as Kostenart,
+    #                 sum(case when co_koa_grp = 'CO1000' then akt_jahr else 0 end) as Aktuell,
+    #                 sum(case when co_koa_grp = 'CO1000' then plan_akt_jahr else 0 end) as Plan,
+    #                 sum(case when co_koa_grp = 'CO1000' then vor_jahr else 0 end) as Vorjahr
+    #             from
+    #                 df
+    # """).fetchdf()
+    
+    # st.metric(label="BE-Ist kumuliert (monat)",value=f"{be_ist_kum_anz} ({be_ist_monat_anz})", delta="-5", border=True)
+    # st.dataframe(erg, hide_index=True)
+
+    # col_erg_ist = st.columns(1)
+
+    # with col_erg_ist:   
+        
+    #     st.metric(label="BE-Ist (monat)",value=be_ist_monat_anz, delta="-5", border=True)
+
+    #     col1, col2 = st.columns(2)
+
+    #     with col1:
+    #         # st.metric(label="BE-Ist (kumuliert)",value=be_ist_kum_anz, delta="-5", border=True)
+    #         with st.success("Ergebnis"):
+    #             st.button(f"{be_ist_kum_anz}")
+    #         # st.success("plus 10% zu Vorjahr")
+    #     # with col2:
+    #     #     st.error("-10% zu Plan")
+    
+    # # with col_erg_plan:
+    # #     st.info("minus 5%")
+    # #     st.warning("minus 10%")
+    # #     st.success("plus 10%")
+    # #     st.error("minus 20%")
+
+    # # with col_erg_vorjahr:
+    # #     st.metric(label="BE-Vorjahr (kumuliert)",value=be_ist_vorjahr_kum_anz, delta="-5", border=True)
+    # #     st.metric(label="BE-Vorjahr (monat)",value=be_ist_monat_anz, delta="-5", border=True)
+    
+    # # st.dataframe(load_data())
+
+
+
+
+if __name__ == "__main__":
+    df = build_dashboard()

app/tools/excel_export.py

@@ -0,0 +1,16 @@
+import pandas as pd
+import io
+
+def df_to_excel_bytes(df):
+
+    """
+    Convert a DataFrame to an in-memory XLSX file.
+
+    Returns a BytesIO object suitable for st.download_button(data=...).
+    """
+
+    buffer = io.BytesIO()
+    with pd.ExcelWriter(buffer, engine="openpyxl") as writer:
+        df.to_excel(writer, index=False, sheet_name="Daten")
+    buffer.seek(0)
+    return buffer

app/tools/help_text.py

@@ -0,0 +1,5 @@
+from app_db.app_db import get_value
+
+def get_help(dashboard :str):
+    helptext = get_value(f"select dash_description from dashboards where dash_name = '{dashboard}.py'")
+    return helptext

app/tools/helpers.py

@@ -0,0 +1,79 @@
+
+def calc_variance_pct(actual, plan):
+    """
+    Calculates the percentage variance between actual and plan values
+    for reporting purposes.
+
+    The percentage variance is only returned if it is economically
+    meaningful and interpretable:
+    - The plan value must not be zero
+    - Actual and plan must have the same sign (no sign change)
+
+    In cases where a percentage variance would be misleading
+    (e.g. sign change from loss to profit), the function returns None
+    and absolute variance should be used instead.
+
+    Parameters
+    ----------
+    actual : float | int
+        Actual (realized) value.
+    plan : float | int
+        Planned or budgeted value.
+
+    Returns
+    -------
+    float | None
+        Percentage variance relative to the absolute plan value,
+        or None if the percentage variance is not meaningful.
+    """
+
+    variance = actual - plan
+    if plan == 0:
+        return None
+    if actual * plan < 0:
+        return None
+    return variance / abs(plan)
+
+
+
+
+def display_value(value, unit):
+    """
+    Formats a numeric KPI value for reporting output based on the configured
+    display unit (e.g. €, T€, Mio. €).
+
+    - Scales the input value according to DISPLAY_UNIT
+    - Applies European number formatting (thousands separator, decimal comma)
+    - Returns 'n/a' if the input value is None
+
+    Parameters
+    ----------
+    value : float | int | None
+        Raw KPI value in base currency (e.g. EUR).
+
+    Returns
+    -------
+    str
+        Formatted value including display unit, ready for dashboard display.
+    """    
+    
+    if value is None:
+        return "n/a"
+    
+    unit_factors = {
+        "Mio. €": 1_000_000,
+        "T€": 1_000,
+        "€": 1,
+    }
+
+    factor = unit_factors.get(unit, 1)
+    scaled = value / factor
+    formatted = f"{scaled:,.2f}"
+    formatted = (
+        formatted
+        .replace(",", "X")
+        .replace(".", ",")
+        .replace("X", ".")
+    )
+    
+    return f"{formatted} {unit}"

config/settings.py

@@ -0,0 +1,11 @@
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve().parents[1]
+
+STORAGE_DIR = BASE_DIR / "storage" 
+DB_DIR = STORAGE_DIR / "app_db" 
+DB_PATH = DB_DIR / "app.db"
+DWH_DIR = STORAGE_DIR / "dwh" 
+
+
+print(BASE_DIR, DB_DIR)

migrations/20260226_095800_add_col_dashname_dashboards.sql

@@ -0,0 +1,8 @@
+begin;
+
+alter table dashboards
+add column dash_name text not null default "";
+
+INSERT INTO schema_version (version) VALUES ('20260226_095800_add_col_dashname_dashboards');
+
+COMMIT;