feat: improve streamlit docs and clean navigation icons

README.md

@@ -1,5 +1,5 @@
----
-title: Db Query
+---
+title: OML DB Tools
 emoji: 🏢
 colorFrom: red
 colorTo: green
@@ -9,49 +9,68 @@ app_file: app.py
 pinned: false
 ---
 
-## Db Query
+## OML DB Tools (Streamlit)
+
+OML DB Tools is a multi-page Streamlit application for telecom engineering workflows:
+- database generation from OML dumps
+- CIQ generation and CIQ verification
+- dump core parsing
+- UE capability parsing
+- geospatial utilities (GPS conversion, distance, KML generation)
+- KPI and capacity analysis tools
+
+## Quick Start
+
+1. Create and activate a Python virtual environment.
+2. Install dependencies:
+   - `pip install -r requirements.txt`
+3. Run the Streamlit app:
+   - `.\run_app.ps1 -App streamlit`
+   - or `python -m streamlit run app.py`
+
+Default local URL: `http://localhost:8501`
 
-This is a Streamlit app for generating 2G, 3G, and LTE databases from an Excel dump file. The app reads the dump file and processes the data. The data is then saved to an Excel file. The Excel file is then downloaded.
+## Authentication
 
-## How to use
+The app uses Streamlit secrets for login:
+- `username`
+- `password`
 
-1. Run the app by executing `streamlit run app.py` in the terminal.
-2. Upload the dump file to the app.
-3. Click the buttons to generate the databases.
-4. Download the databases.
+Configure them in your local Streamlit secrets file before sharing the app.
 
-## How it works
+## Main Functional Areas
 
-The app reads the dump file and processes the data. The data is then saved to an excel file. The excel file is then downloaded.
+1. Apps
+   - Database processing, CIQ generators, CIQ verification, dump core parser, UE capability parser, GPS tools, KML generator, clustering, dump compare.
+2. Capacity Analysis
+   - GSM/WBTS/WCEL/LCG/LTE capacity analysis.
+3. Paging Analysis
+   - GSM LAC load analysis.
+4. KPI Analysis
+   - LTE traffic drop, KPI anomaly detection, traffic analysis.
+5. Documentations
+   - In-app user documentation pages (from the top navigation).
 
-## Why
+## Input Formats (most used)
 
-The app is a simple way to generate the databases from the dump file. It saves time and effort.
+- Dump files: `.xlsb`
+- CIQ files: `.xlsx` / `.xls`
+- KPI reports: `.csv` (depending on page)
+- UE capability exports: `.txt`
+- GPS / distance / KML helpers: `.xlsx`
+
+## In-App Documentation
+
+Use `Documentations > Documentation Home` in the app for:
+- page-by-page usage guides
+- required columns and file templates
+- troubleshooting notes
+- known limitations
 
 ## Hosted Version
 
-You can access the hosted version of the app at [https://davmelchi-db-query.hf.space/](https://davmelchi-db-query.hf.space/).
-
-## TODO
-
-- [x] check if required sheets exist in the dump file
-- [x] Add a download button for all databases
-- [x] Add option to download Neighbors database
-- [x] Add page to update physical db
-- [x] Add Core dump checking App
-- [x] Add site config band in database
-- [x] Add TRX database
-- [x] Add  MRBTS with code
-- [x] Check TCH from MAL sheet
-- [x] Add Analitic dashboards for each database (Count of NE)
-- [x] Add kml generation
-- [x] Parameters Distribution App
-- [x] Symetric neighbours checking
-- [x] Add the ability to select columns
-- [x] Add authentication
-- [x] Initial frequency distribution chart GSM
-- [x] Add paging analysis App
-- [x] Add capacity analysis App
-- [ ] Improve Dashboard
-- [ ] Error handling
-- [ ] Add KPI analysis App
+https://davmelchi-db-query.hf.space/
+
+## Release Notes
+
+See `Changelog.md` for version history.

app.py

@@ -1,5 +1,37 @@
 import streamlit as st
 
+st.set_page_config(
+    page_title="NPO DB Query",
+    page_icon=":computer:",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        "About": "**NPO DB Query v0.2.15**",
+    },
+)
+
+
+def apply_icon_color_css() -> None:
+    st.markdown(
+        """
+        <style>
+        section[data-testid="stSidebar"] {
+            display: none !important;
+        }
+        div[data-testid="stSidebarCollapsedControl"] {
+            display: none !important;
+        }
+        span[data-testid="stIconMaterial"] {
+            color: #2563eb !important;
+        }
+        div[data-baseweb="popover"] span[data-testid="stIconMaterial"] {
+            color: #1d4ed8 !important;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
 
 # Authentication function
 def check_password():
@@ -80,7 +112,7 @@ def check_password():
                 "password_correct" in st.session_state
                 and not st.session_state["password_correct"]
             ):
-                st.error("😕 User not known or password incorrect")
+                st.error("User not known or password incorrect")
 
             # Login form with improved input fields
             st.text_input("Username", key="username", placeholder="Enter your username")
@@ -102,115 +134,292 @@ def check_password():
 
 # Only show the app if authentication is successful
 if check_password():
-    st.set_page_config(
-        page_title="NPO DB Query",
-        page_icon="💻",
-        layout="wide",
-        initial_sidebar_state="expanded",
-        menu_items={
-            "About": "**📡 NPO DB Query v0.2.12**",
-        },
-    )
+    apply_icon_color_css()
 
     pages = {
         "Apps": [
-            st.Page("apps/database_page.py", title="🏡Generate Databases"),
             st.Page(
-                "apps/parameters_distribution.py", title="📊Parameters distribution"
+                "apps/database_page.py",
+                title="Generate Databases",
+                icon=":material/home:",
+            ),
+            st.Page(
+                "apps/parameters_distribution.py",
+                title="Parameters distribution",
+                icon=":material/tune:",
+            ),
+            st.Page(
+                "apps/ciq_2g_generator.py",
+                title="CIQ 2G Generator",
+                icon=":material/description:",
+            ),
+            st.Page(
+                "apps/ciq_3g_generator.py",
+                title="CIQ 3G Generator",
+                icon=":material/description:",
+            ),
+            st.Page(
+                "apps/ciq_4g_generator.py",
+                title="CIQ 4G Generator",
+                icon=":material/description:",
+            ),
+            st.Page(
+                "apps/ciq_verification.py",
+                title="CIQ Verification",
+                icon=":material/fact_check:",
+            ),
+            st.Page(
+                "apps/core_dump_page.py",
+                title="Parse dump core",
+                icon=":material/terminal:",
+            ),
+            st.Page(
+                "apps/ue_capability_parser.py",
+                title="UE Capability Parser",
+                icon=":material/memory:",
+            ),
+            st.Page(
+                "apps/gps_converter.py",
+                title="GPS Converter",
+                icon=":material/explore:",
+            ),
+            st.Page(
+                "apps/dbm_watt_calculator.py",
+                title="dBm <> Watt Calculator",
+                icon=":material/calculate:",
+            ),
+            st.Page(
+                "apps/distance.py",
+                title="Distance Calculator",
+                icon=":material/social_distance:",
             ),
-            st.Page("apps/ciq_2g_generator.py", title="🧾 CIQ 2G Generator"),
-            st.Page("apps/ciq_3g_generator.py", title="🧾 CIQ 3G Generator"),
-            st.Page("apps/ciq_4g_generator.py", title="🧾 CIQ 4G Generator"),
-            st.Page("apps/ciq_verification.py", title="🔍 CIQ Verification"),
-            st.Page("apps/core_dump_page.py", title="📠Parse dump core"),
-            st.Page("apps/gps_converter.py", title="🧭GPS Converter"),
-            st.Page("apps/dbm_watt_calculator.py", title="dBm <> Watt Calculator"),
-            st.Page("apps/distance.py", title="🛰Distance Calculator"),
             st.Page(
                 "apps/multi_points_distance_calculator.py",
-                title=" 🗺 Multi Points Distance Calculator",
+                title="Multi Points Distance Calculator",
+                icon=":material/route:",
             ),
             st.Page(
                 "apps/sector_kml_generator.py",
-                title="📡 Sector KML Generator",
+                title="Sector KML Generator",
+                icon=":material/map:",
             ),
             st.Page(
                 "apps/clustering.py",
-                title="📡 Automatic Site Clustering",
+                title="Automatic Site Clustering",
+                icon=":material/hub:",
+            ),
+            st.Page(
+                "apps/fnb_parser.py",
+                title="F4NB Extractor",
+                icon=":material/article:",
+            ),
+            st.Page(
+                "apps/dump_compare.py",
+                title="Dump Compare",
+                icon=":material/compare_arrows:",
             ),
-            st.Page("apps/fnb_parser.py", title="📄 F4NB Extractor"),
-            st.Page("apps/dump_compare.py", title="📊 Dump Compare"),
             st.Page(
-                "apps/import_physical_db.py", title="🌏Physical Database Verification"
+                "apps/import_physical_db.py",
+                title="Physical Database Verification",
+                icon=":material/public:",
             ),
         ],
         "Capacity Analysis": [
             st.Page(
                 "apps/kpi_analysis/gsm_capacity.py",
-                title=" 📊 GSM Capacity Analysis",
+                title="GSM Capacity Analysis",
+                icon=":material/bar_chart:",
             ),
             st.Page(
                 "apps/kpi_analysis/wbts_capacty.py",
-                title=" 📊 WBTS Capacity BB and CE Analysis",
+                title="WBTS Capacity BB and CE Analysis",
+                icon=":material/developer_board:",
             ),
             st.Page(
                 "apps/kpi_analysis/wcel_capacity.py",
-                title=" 📊 WCEL Capacity Analysis",
+                title="WCEL Capacity Analysis",
+                icon=":material/cell_tower:",
             ),
             st.Page(
                 "apps/kpi_analysis/lcg_analysis.py",
-                title=" 📊 LCG Capacity Analysis",
+                title="LCG Capacity Analysis",
+                icon=":material/stacked_line_chart:",
             ),
             st.Page(
                 "apps/kpi_analysis/lte_capacity.py",
-                title=" 📊 LTE Capacity Analysis",
+                title="LTE Capacity Analysis",
+                icon=":material/signal_cellular_alt:",
             ),
         ],
         "Paging Analysis": [
             st.Page(
                 "apps/kpi_analysis/gsm_lac_load.py",
-                title=" 📊 GSM LAC Load Analysis",
+                title="GSM LAC Load Analysis",
+                icon=":material/sms:",
             ),
         ],
         "KPI Analysis": [
             st.Page(
                 "apps/kpi_analysis/lte_drop_trafic.py",
-                title=" 📊 LTE Drop Traffic Analysis",
+                title="LTE Drop Traffic Analysis",
+                icon=":material/trending_down:",
             ),
             st.Page(
                 "apps/kpi_analysis/anomalie.py",
-                title=" 📊 KPIs Anomaly Detection",
+                title="KPIs Anomaly Detection",
+                icon=":material/warning:",
             ),
             st.Page(
                 "apps/kpi_analysis/trafic_analysis.py",
-                title=" 📊 Trafic Analysis",
+                title="Trafic Analysis",
+                icon=":material/analytics:",
             ),
         ],
         "Documentations": [
             st.Page(
-                "documentations/database_doc.py", title="📚Databases Documentation"
+                "documentations/index_doc.py",
+                title="Documentation Home",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/database_doc.py",
+                title="Databases Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/core_dump_doc.py",
+                title="Dump Core Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/ue_capability_doc.py",
+                title="UE Capability Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/ciq_verification_doc.py",
+                title="CIQ Verification Documentation",
+                icon=":material/menu_book:",
             ),
             st.Page(
-                "documentations/core_dump_doc.py", title="📗Dump core Documentation"
+                "documentations/gps_converter_doc.py",
+                title="GPS Converter Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/distance_doc.py",
+                title="Distance Calculator Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/sector_kml_doc.py",
+                title="Sector KML Generator Documentation",
+                icon=":material/menu_book:",
             ),
             st.Page(
                 "documentations/gsm_capacity_docs.py",
-                title="📘GSM Capacity Documentation",
+                title="GSM Capacity Documentation",
+                icon=":material/menu_book:",
             ),
             st.Page(
                 "documentations/lte_capacity_docs.py",
-                title="📘LTE Capacity Documentation",
+                title="LTE Capacity Documentation",
+                icon=":material/menu_book:",
             ),
             st.Page(
                 "documentations/trafic_analysis_doc.py",
-                title="📘Trafic Analysis Documentation",
+                title="Traffic Analysis Documentation",
+                icon=":material/menu_book:",
             ),
             st.Page(
                 "documentations/anomaly_detection_doc.py",
-                title="📘Anomaly Detection Documentation",
+                title="Anomaly Detection Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/dump_compare_doc.py",
+                title="Dump Compare Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/clustering_doc.py",
+                title="Clustering Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/import_physical_db_doc.py",
+                title="Physical Database Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/multi_points_distance_doc.py",
+                title="Multi Points Distance Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/dump_analysis_doc.py",
+                title="Dump Analytics Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/wbts_capacity_doc.py",
+                title="WBTS Capacity Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/wcel_capacity_doc.py",
+                title="WCEL Capacity Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/lcg_analysis_doc.py",
+                title="LCG Analysis Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/gsm_lac_load_doc.py",
+                title="GSM LAC Load Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/ciq_2g_doc.py",
+                title="CIQ 2G Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/ciq_3g_doc.py",
+                title="CIQ 3G Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/ciq_4g_doc.py",
+                title="CIQ 4G Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/dbm_watt_doc.py",
+                title="dBm Watt Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/fnb_parser_doc.py",
+                title="F4NB Extractor Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/lte_drop_traffic_doc.py",
+                title="LTE Drop Traffic Documentation",
+                icon=":material/menu_book:",
+            ),
+            st.Page(
+                "documentations/parameters_distribution_doc.py",
+                title="Parameters Distribution Documentation",
+                icon=":material/menu_book:",
             ),
         ],
     }
 
     pg = st.navigation(pages, position="top")
     pg.run()
+
+

apps/ciq_2g_generator.py

@@ -15,7 +15,7 @@ Génère les exports CIQ 2G à partir du dump OML + CIQ brut.
 """
 )
 
-st.subheader("📄 Sample files")
+st.subheader("Sample files")
 samples_dir = Path(__file__).resolve().parents[1] / "samples"
 sample_ciq_2g = samples_dir / "CIQ_2G.xlsx"
 sample_forbidden = samples_dir / "FORBIDEN_SCF.xlsx"
@@ -60,7 +60,7 @@ if dump_file is None:
     st.info("Upload dump xlsb + CIQ brut Excel to generate CIQ 2G.")
     st.stop()
 
-st.subheader("✅ Génération BCF libre")
+st.subheader("Generation BCF libre")
 if st.button("Generate BCF libre", type="secondary"):
     try:
         with st.spinner("Generating BCF libre..."):
@@ -85,7 +85,7 @@ if free_bcf_bytes:
         mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
     )
 
-st.subheader("✅ Génération CIQ 2G")
+st.subheader("Generation CIQ 2G")
 if ciq_file is None:
     st.info("Upload CIQ brut 2G Excel to generate CIQ 2G.")
 

apps/ciq_3g_generator.py

@@ -13,7 +13,7 @@ Génère les exports CIQ 3G (WBTS + WCEL) à partir du CIQ brut.
 """
 )
 
-st.subheader("📄 Sample files")
+st.subheader("Sample files")
 samples_dir = Path(__file__).resolve().parents[1] / "samples"
 sample_ciq_3g = samples_dir / "CIQ_3G.xlsx"
 

apps/ciq_4g_generator.py

@@ -13,7 +13,7 @@ Génère les exports CIQ 4G à partir du CIQ brut.
 """
 )
 
-st.subheader("📄 Sample files")
+st.subheader("Sample files")
 samples_dir = Path(__file__).resolve().parents[1] / "samples"
 sample_ciq_4g = samples_dir / "CIQ_LTE.xlsx"
 

apps/ciq_verification.py

@@ -54,15 +54,15 @@ with col3:
 
 # Validation
 if dump_file is None:
-    st.info("⬆️ Veuillez uploader le fichier dump (xlsb).")
+    st.info("Veuillez uploader le fichier dump (xlsb).")
     st.stop()
 
 if ciq_2g_file is None and ciq_3g_file is None and ciq_lte_file is None:
-    st.warning("⚠️ Au moins un fichier CIQ (2G, 3G ou LTE) est requis.")
+    st.warning("Au moins un fichier CIQ (2G, 3G ou LTE) est requis.")
     st.stop()
 
 # Verify button
-if st.button("🔎 Vérifier", type="primary"):
+if st.button("Verifier", type="primary"):
     try:
         results_2g = None
         results_3g = None
@@ -71,7 +71,7 @@ if st.button("🔎 Vérifier", type="primary"):
         with st.spinner("Traitement en cours..."):
             # Process 2G if provided
             if ciq_2g_file is not None:
-                st.text("📶 Traitement 2G...")
+                st.text("Traitement 2G...")
                 dump_gsm = process_dump_gsm(dump_file)
                 dump_file.seek(0)  # Reset file pointer
                 ciq_2g_df = read_ciq_file(ciq_2g_file)
@@ -79,7 +79,7 @@ if st.button("🔎 Vérifier", type="primary"):
 
             # Process 3G if provided
             if ciq_3g_file is not None:
-                st.text("📶 Traitement 3G...")
+                st.text("Traitement 3G...")
                 dump_wcdma = process_dump_wcdma(dump_file)
                 dump_file.seek(0)  # Reset file pointer
                 ciq_3g_df = read_ciq_file(ciq_3g_file)
@@ -87,7 +87,7 @@ if st.button("🔎 Vérifier", type="primary"):
 
             # Process LTE if provided
             if ciq_lte_file is not None:
-                st.text("📶 Traitement LTE...")
+                st.text("Traitement LTE...")
                 dump_lte = process_dump_lte(dump_file)
                 dump_file.seek(0)  # Reset file pointer
                 ciq_lte_df = read_ciq_file(ciq_lte_file)
@@ -106,10 +106,10 @@ if st.button("🔎 Vérifier", type="primary"):
             st.session_state["verify_sheets"] = sheets
             st.session_state["verify_excel_bytes"] = excel_bytes
 
-        st.success("✅ Vérification terminée!")
+        st.success("Verification terminee.")
 
     except Exception as e:
-        st.error(f"❌ Erreur: {e}")
+        st.error(f"Erreur: {e}")
         import traceback
         st.code(traceback.format_exc())
 
@@ -127,9 +127,9 @@ def display_stats(stats: dict, tech: str):
     with col1:
         st.metric("Total Cells", stats["total_cells"])
     with col2:
-        st.metric("✅ OK", stats["ok_count"])
+        st.metric("OK", stats["ok_count"])
     with col3:
-        st.metric("⚠️ Mismatch", stats["mismatch_count"])
+        st.metric("Mismatch", stats["mismatch_count"])
     with col4:
         st.metric("❓ Not Found", stats["not_found_count"])
 
@@ -170,7 +170,7 @@ def style_results(df: pd.DataFrame) -> pd.DataFrame:
 
 if sheets:
     st.divider()
-    st.subheader("📊 Résultats de la vérification")
+    st.subheader("Resultats de la verification")
 
     tabs = []
     tab_names = []
@@ -188,7 +188,7 @@ if sheets:
 
         if results_2g:
             with tabs[tab_idx]:
-                st.markdown("### 📶 Vérification 2G")
+                st.markdown("### Verification 2G")
                 display_stats(results_2g[1], "2G")
                 st.dataframe(
                     style_results(results_2g[0]),
@@ -199,7 +199,7 @@ if sheets:
 
         if results_3g:
             with tabs[tab_idx]:
-                st.markdown("### 📶 Vérification 3G")
+                st.markdown("### Verification 3G")
                 display_stats(results_3g[1], "3G")
                 st.dataframe(
                     style_results(results_3g[0]),
@@ -210,7 +210,7 @@ if sheets:
 
         if results_lte:
             with tabs[tab_idx]:
-                st.markdown("### 📶 Vérification LTE")
+                st.markdown("### Verification LTE")
                 display_stats(results_lte[1], "LTE")
                 st.dataframe(
                     style_results(results_lte[0]),
@@ -221,7 +221,7 @@ if sheets:
 if excel_bytes:
     st.divider()
     st.download_button(
-        label="📥 Télécharger le rapport de vérification (Excel)",
+        label="Telecharger le rapport de verification (Excel)",
         data=excel_bytes,
         file_name="CIQ_Verification_Report.xlsx",
         mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",

apps/dump_compare.py

@@ -40,7 +40,7 @@ def detect_dist_col(columns):
 
 # === Interface Streamlit ===
 
-st.title("📊 Dump Compare Tool")
+st.title("Dump Compare Tool")
 st.markdown(
     ":blue[**Upload the old and new dumps, then input the object class (comma-separated) to compare**]"
 )
@@ -153,9 +153,9 @@ if st.button("Run Comparison", type="primary", use_container_width=True):
                     logs.append(f"No changes in '{sheet}'")
 
             except Exception as e:
-                logs.append(f"❌ Error in '{sheet}': {e}")
+                logs.append(f"Error in '{sheet}': {e}")
 
-        st.success(f"✅ Comparison completed. Total changes: {total}")
+        st.success(f"Comparison completed. Total changes: {total}")
         for log in logs:
             st.write(log)
 

apps/fnb_parser.py

@@ -243,11 +243,7 @@ def process_files_to_dataframe(uploaded_files) -> pd.DataFrame:
 
 
 def main() -> None:
-    st.set_page_config(
-        page_title="F4NB Extractor to Excel", page_icon="📄", layout="wide"
-    )
-
-    st.title("📄 F4NB Extractor to Excel")
+    st.title("F4NB Extractor to Excel")
     st.markdown(
         "Convert F4NB Word documents into a tidy Excel / DataFrame containing site & sector information.\n"
         "Upload one or many F4NB `.docx` files and hit **Process**."
@@ -292,7 +288,7 @@ def main() -> None:
         with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
             df.to_excel(writer, index=False, sheet_name="Extract")
         st.download_button(
-            label="💾 Download Excel",
+            label="Download Excel",
             data=buffer.getvalue(),
             file_name="extracted_fnb.xlsx",
             mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
@@ -317,7 +313,7 @@ def main() -> None:
                 )
             )
             if not geo_df.empty:
-                st.subheader("🗺️ Site Locations")
+                st.subheader("Site Locations")
                 fig = px.scatter_map(
                     geo_df,
                     lat="Latitude",

apps/kpi_analysis/gsm_capacity.py

@@ -9,7 +9,7 @@ from utils.convert_to_excel import (  # Import convert_dfs from the appropriate
 )
 from utils.kpi_analysis_utils import GsmCapacity
 
-st.title(" 📊 GSM Capacity Analysis")
+st.title("GSM Capacity Analysis")
 doc_col, image_col = st.columns(2)
 
 with doc_col:

apps/kpi_analysis/gsm_lac_load.py

@@ -173,7 +173,7 @@ def analyze_lac_load(dump_path: str, hourly_report_path: str) -> List[pd.DataFra
 
 def display_ui() -> None:
     """Display the Streamlit user interface."""
-    st.title(" 📊 GSM LAC Load Analysis")
+    st.title("GSM LAC Load Analysis")
     doc_col, image_col = st.columns(2)
 
     with doc_col:

apps/kpi_analysis/lcg_analysis.py

@@ -11,7 +11,7 @@ class LcgCapacity:
 
 
 # Streamlit UI
-st.title(" 📊 LCG Analysis")
+st.title("LCG Analysis")
 doc_col, image_col = st.columns(2)
 
 with doc_col:

apps/kpi_analysis/lte_capacity.py

@@ -6,7 +6,7 @@ from process_kpi.process_lte_capacity import process_lte_bh_report
 from utils.convert_to_excel import convert_lte_analysis_dfs
 from utils.kpi_analysis_utils import LteCapacity
 
-st.title("📊 LTE Capacity Analysis")
+st.title("LTE Capacity Analysis")
 doc_col, image_col = st.columns(2)
 
 with doc_col:

apps/kpi_analysis/lte_drop_trafic.py

@@ -76,7 +76,7 @@ if uploaded_file:
 
     if not result.empty:
         st.download_button(
-            label="📥 Download affected cells",
+            label="Download affected cells",
             data=convert_df(result),
             file_name="traffic_drop_cells.xlsx",
             mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",

apps/kpi_analysis/trafic_analysis.py

@@ -860,7 +860,7 @@ def monthly_data_analysis(df: pd.DataFrame) -> pd.DataFrame:
 
 
 ############################## UI #########################
-st.title("📊 Global Trafic Analysis - 2G / 3G / LTE")
+st.title("Global Trafic Analysis - 2G / 3G / LTE")
 doc_col, image_col = st.columns(2)
 
 with doc_col:
@@ -908,7 +908,7 @@ with sla_lte_col:
     sla_lte = st.number_input("LTE Cell availability SLA (%)", value=98.0)
 
 if len(pre_range) != 2 or len(post_range) != 2:
-    st.warning("⚠️ Please select 2 dates for each period (pre and post).")
+    st.warning("Please select 2 dates for each period (pre and post).")
     st.stop()
 if not all([two_g_file, three_g_file, lte_file]):
     st.info("Please upload all 3 reports and select the comparison periods.")
@@ -916,7 +916,7 @@ if not all([two_g_file, three_g_file, lte_file]):
 
 # Warning if pre and post periode are the same
 if pre_range == post_range:
-    st.warning("⚠️ Pre and post periode are the same.")
+    st.warning("Pre and post periode are the same.")
     st.stop()
 
 # Warning if pre and post are overlapping
@@ -1655,7 +1655,7 @@ if TraficAnalysis.last_period_df is not None:
             "Top_Critical_Sites",
         ],
     )
-    # 📥 Bouton de téléchargement
+    # Bouton de telechargement
     st.download_button(
         on_click="ignore",
         type="primary",

apps/kpi_analysis/wbts_capacty.py

@@ -7,7 +7,7 @@ from utils.convert_to_excel import convert_dfs
 
 # Streamlit UI
 
-st.title(" 📊 WBTS Capacity Analysis")
+st.title("WBTS Capacity Analysis")
 doc_col, image_col = st.columns(2)
 
 with doc_col:

apps/kpi_analysis/wcel_capacity.py

@@ -10,7 +10,7 @@ from utils.convert_to_excel import convert_wcel_capacity_dfs
 
 # Streamlit UI
 
-st.title(" 📊 WCEL Capacity Analysis")
+st.title("WCEL Capacity Analysis")
 doc_col, image_col = st.columns(2)
 
 with doc_col:

apps/parameters_distribution.py

@@ -3,7 +3,7 @@ import plotly.express as px
 import streamlit as st
 from pyxlsb import open_workbook
 
-st.title("📊 Parameters distribution Analyzer")
+st.title("Parameters distribution Analyzer")
 
 uploaded_file = st.file_uploader("Upload an .xlsb Dump file", type="xlsb")
 
@@ -29,7 +29,7 @@ if uploaded_file:
 
             if parameters:
                 for param in parameters:
-                    st.markdown(f"---\n### 🔹 {param}")
+                    st.markdown(f"---\n### {param}")
                     col1, col2 = st.columns(2)
 
                     # Distribution table

apps/sector_kml_generator.py

@@ -5,7 +5,7 @@ import streamlit as st
 
 from utils.kml_creator import generate_kml_from_df
 
-st.title("📡 Telecom Sector KML Generator")
+st.title("Telecom Sector KML Generator")
 
 
 # display mandatory columns
@@ -86,10 +86,10 @@ if uploaded_file is not None:
 
         # Download button
         st.download_button(
-            label="📥 Download KML",
+            label="Download KML",
             data=kml_data,
             file_name=f"Sectors_kml_{datetime.now()}.kml",
             mime="application/vnd.google-earth.kml+xml",
         )
 
-        st.success("KML file generated successfully! 🎉")
+        st.success("KML file generated successfully.")

documentations/DOC_COVERAGE.md

@@ -0,0 +1,43 @@
+# Documentation Coverage Matrix
+
+Last update: 2026-02-23
+
+Status values:
+- `done`: dedicated documentation page exists in `documentations/` and is linked in navigation
+- `partial`: no dedicated page, but basic in-app guidance exists
+- `todo`: no dedicated page and limited guidance
+
+| App page | Dedicated doc | Status | Notes |
+| --- | --- | --- | --- |
+| `apps/database_page.py` | `documentations/database_doc.py` | done | Main DB generation flow documented |
+| `apps/core_dump_page.py` | `documentations/core_dump_doc.py` | done | Input/output and parsing behavior documented |
+| `apps/ue_capability_parser.py` | `documentations/ue_capability_doc.py` | done | Sheets, options, and benchmark mode documented |
+| `apps/ciq_verification.py` | `documentations/ciq_verification_doc.py` | done | Required files and report structure documented |
+| `apps/ciq_2g_generator.py` | `documentations/ciq_2g_doc.py` | done | 2G CIQ workflow and optional forbidden BCF documented |
+| `apps/ciq_3g_generator.py` | `documentations/ciq_3g_doc.py` | done | 3G CIQ parameters and generation flow documented |
+| `apps/ciq_4g_generator.py` | `documentations/ciq_4g_doc.py` | done | 4G CIQ parameters and generation flow documented |
+| `apps/gps_converter.py` | `documentations/gps_converter_doc.py` | done | Excel + manual conversion workflows documented |
+| `apps/distance.py` | `documentations/distance_doc.py` | done | Two-point distance workflow documented |
+| `apps/multi_points_distance_calculator.py` | `documentations/multi_points_distance_doc.py` | done | Dual-dataset nearest-distance workflow documented |
+| `apps/sector_kml_generator.py` | `documentations/sector_kml_doc.py` | done | Required columns and KML export documented |
+| `apps/dbm_watt_calculator.py` | `documentations/dbm_watt_doc.py` | done | Unit conversion modes and constraints documented |
+| `apps/fnb_parser.py` | `documentations/fnb_parser_doc.py` | done | DOCX extraction and export workflow documented |
+| `apps/parameters_distribution.py` | `documentations/parameters_distribution_doc.py` | done | Per-parameter distribution analysis documented |
+| `apps/dump_compare.py` | `documentations/dump_compare_doc.py` | done | Old/new dump comparison and ZIP export documented |
+| `apps/clustering.py` | `documentations/clustering_doc.py` | done | Clustering methods and map output documented |
+| `apps/import_physical_db.py` | `documentations/import_physical_db_doc.py` | done | Local physical DB inspection documented |
+| `apps/dump_analysis.py` | `documentations/dump_analysis_doc.py` | done | Analytics module behavior documented |
+| `apps/kpi_analysis/gsm_capacity.py` | `documentations/gsm_capacity_docs.py` | done | Dedicated technical/user doc exists |
+| `apps/kpi_analysis/lte_capacity.py` | `documentations/lte_capacity_docs.py` | done | Dedicated technical/user doc exists |
+| `apps/kpi_analysis/wbts_capacty.py` | `documentations/wbts_capacity_doc.py` | done | WBTS thresholds and outputs documented |
+| `apps/kpi_analysis/wcel_capacity.py` | `documentations/wcel_capacity_doc.py` | done | WCEL thresholds/charts/maps documented |
+| `apps/kpi_analysis/lcg_analysis.py` | `documentations/lcg_analysis_doc.py` | done | LCG analysis flow documented |
+| `apps/kpi_analysis/gsm_lac_load.py` | `documentations/gsm_lac_load_doc.py` | done | LAC load workflow and filters documented |
+| `apps/kpi_analysis/lte_drop_trafic.py` | `documentations/lte_drop_traffic_doc.py` | done | Drop detection logic and map output documented |
+| `apps/kpi_analysis/trafic_analysis.py` | `documentations/trafic_analysis_doc.py` | done | Input/output and usage flow documented |
+| `apps/kpi_analysis/anomalie.py` | `documentations/anomaly_detection_doc.py` | done | KPI anomaly workflow documented |
+
+## Next Priorities
+1. Add screenshots/examples for each major workflow in documentation pages.
+2. Add automated doc lint checks (required sections + link validity).
+3. Keep release-note driven doc updates for each new app feature.

documentations/README_DOCS.md

@@ -0,0 +1,42 @@
+# Documentation Standard (Streamlit)
+
+Last update: 2026-02-23
+
+## Goal
+This file defines the writing standard for all in-app documentation pages in `documentations/*.py`.
+
+## Required Sections (for every page)
+1. Objective
+2. When to use this tool
+3. Input files and accepted formats
+4. Required columns/fields (if applicable)
+5. Step-by-step usage
+6. Outputs generated
+7. Frequent errors and fixes
+8. Minimal reproducible example
+9. Known limitations
+10. Version and last update date
+
+## Writing Rules
+- Write for end users first.
+- Keep examples concrete and copy/paste friendly.
+- Keep naming aligned with UI labels and button names.
+- Avoid implementation details unless they help troubleshoot user issues.
+- Include exact file extensions (`.xlsb`, `.xlsx`, `.csv`, `.txt`).
+
+## Quality Checklist
+- [ ] All 10 required sections are present.
+- [ ] File formats are explicit.
+- [ ] Required columns are listed exactly as expected by the app.
+- [ ] At least 3 frequent user errors are documented.
+- [ ] One minimal example is provided.
+- [ ] A date is present in the page.
+
+## Naming Convention
+- File name: `<tool_name>_doc.py`
+- Page title in navigation: `<Tool Name> Documentation`
+
+## Review Process
+1. Verify the doc against the actual page in `apps/`.
+2. Open the Streamlit page and confirm terminology matches UI.
+3. Update `documentations/DOC_COVERAGE.md` status.

documentations/anomaly_detection_doc.py

@@ -1,125 +1,65 @@
-import streamlit as st
+import streamlit as st
 
 st.markdown(
     """
 # KPI Anomaly Detection Documentation
 
-## Overview
-The KPI Anomaly Detection application is designed to automatically identify and analyze anomalies in Key Performance Indicators (KPIs) using change point detection algorithms. It helps in identifying significant changes in KPI trends that may indicate network issues or other important events.
-
-## Features
-
-### 1. Data Processing
-- Supports both CSV and Excel file formats
-- Automatic date parsing and data cleaning
-- Handles missing values appropriately
-- Processes multiple KPIs in a single run
-
-### 2. Anomaly Detection
-- Utilizes the PELT (Pruned Exact Linear Time) algorithm for change point detection
-- Configurable penalty parameter to control sensitivity
-- Identifies both sudden and gradual changes in KPI trends
-- Filters out insignificant changes based on mean value differences
-
-### 3. Visualization
-- Interactive time series plots with Plotly
-- Visual indicators for detected change points
-- Displays initial and final mean values for comparison
-- Responsive design for different screen sizes
-
-### 4. Reporting
-- Export detected anomalies to Excel
-- Separate sheets for each KPI
-- Includes all relevant data points and change point indicators
-
-## Input Requirements
-
-### Required File Format
-- **CSV or Excel** file containing time series KPI data
-- First 5 columns should be (in order):
-  1. Date/Time
-  2. Controller ID
-  3. BTS ID
-  4. Cell ID
-  5. DN (Directory Number)
-- Remaining columns should contain KPI values
-
-### Data Requirements
-- At least 30 data points per cell for reliable detection
-- Consistent time intervals between measurements
-- Numeric values for KPI columns
-
-## Usage
-
-### 1. Upload Data
-- Click "Upload KPI file" and select your CSV or Excel file
-- The application will automatically detect the file format
-
-### 2. Configure Detection
-- Adjust the "Penalty" parameter to control sensitivity:
-  - Lower values = More sensitive (more change points detected)
-  - Higher values = Less sensitive (only major changes detected)
-- Default value of 2.5 works well for most cases
-
-### 3. Review Results
-- The application will display a list of KPIs with detected anomalies
-- Select a KPI and cell to view detailed analysis
-- The plot shows:
-  - KPI values over time (blue line)
-  - Detected change points (red markers)
-  - Initial mean (gray dotted line)
-  - Final mean (black dashed line)
-
-### 4. Export Results
-- Click "Generate Excel file with anomalies" to export all detected anomalies
-- Each KPI is saved in a separate sheet
-- The Excel file includes all data points with change point indicators
-
-## Technical Details
-
-### Algorithm
-- Uses the PELT (Pruned Exact Linear Time) algorithm from the `ruptures` library
-- Model: RBF (Radial Basis Function) kernel for detecting changes in mean
-- Automatic pruning of similar change points
-
-### Performance Considerations
-- Processing time depends on:
-  - Number of cells in the dataset
-  - Number of KPIs
-  - Length of the time series
-- Large datasets may take several minutes to process
-- Results are cached for better performance when adjusting parameters
-
-## Troubleshooting
-
-### Common Issues
-1. **No anomalies detected**
-   - Try reducing the penalty value
-   - Check if the data contains enough variation
-   - Ensure there are at least 30 data points per cell
-
-2. **Too many false positives**
-   - Increase the penalty value
-   - Check data for noise or outliers
-   - Consider pre-processing the data
-
-3. **File format errors**
-   - Ensure the file is not open in another program
-   - Check that the file is not corrupted
-   - Verify the column structure matches requirements
-
-## Best Practices
-1. Start with the default penalty value (2.5) and adjust as needed
-2. For large datasets, consider processing in smaller chunks
-3. Review detected anomalies in context with network events or changes
-4. Regularly update the application to get the latest improvements
-
-## Dependencies
-- Python 3.7+
-- pandas
-- numpy
-- plotly
-- ruptures
-- streamlit
+## 1. Objective
+Detect KPI trend breaks (change points) per cell and export anomaly-focused datasets for investigation.
+
+## 2. When to use this tool
+Use this page when you need to:
+- detect abrupt KPI behavior changes
+- isolate affected cells per KPI
+- visualize change points over time
+- export anomaly evidence to Excel
+
+## 3. Input files and accepted formats
+- Required: KPI file in `.csv`
+- Expected delimiter in current implementation: `;`
+
+## 4. Required columns
+The first 5 columns are interpreted as:
+1. date/time
+2. controller
+3. BTS
+4. cell
+5. DN
+All remaining columns are treated as KPI series.
+
+## 5. Step-by-step usage
+1. Open `KPI Analysis > KPIs Anomaly Detection`.
+2. Upload the KPI CSV file.
+3. Set `Penalty` (lower = more sensitive, higher = less sensitive).
+4. Review detected KPI list and affected cells.
+5. Inspect plot with detected change points.
+6. Generate and download Excel anomalies report.
+
+## 6. Outputs generated
+- interactive anomaly plot for selected KPI/cell
+- per-KPI anomaly datasets
+- downloadable Excel file (one sheet per KPI)
+
+## 7. Frequent errors and fixes
+- No anomalies detected.
+  - Fix: reduce `Penalty` or verify KPI variability.
+- Parsing/date issues.
+  - Fix: check first column date format and CSV delimiter (`;`).
+- Too many false positives.
+  - Fix: increase `Penalty` and inspect noisy KPIs.
+
+## 8. Minimal reproducible example
+- Input: CSV with at least 30 points per cell and one KPI showing a level shift.
+- Action: upload file, set penalty to `2.5`, inspect one KPI/cell.
+- Expected result: chart with change-point markers and exportable Excel output.
+
+## 9. Known limitations
+- Cells with fewer than 30 points are ignored.
+- Detection quality is sensitive to noise and seasonality.
+- Current uploader is configured for CSV input only.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
 """
 )

documentations/ciq_2g_doc.py

@@ -0,0 +1,63 @@
+import streamlit as st
+
+st.markdown(
+    """
+# CIQ 2G Generator Documentation
+
+## 1. Objective
+Generate CIQ 2G deliverables from OML dump and CIQ raw input, including optional free BCF extraction.
+
+## 2. When to use this tool
+Use this page when preparing or validating 2G CIQ before deployment.
+
+## 3. Input files and accepted formats
+- Required: dump file (`.xlsb`)
+- Required for CIQ generation: CIQ raw 2G file (`.xlsx` or `.xls`)
+- Optional: forbidden BCF list (`.xlsx`, `.xls`, `.xlsb`)
+
+## 4. Required columns/fields
+Forbidden BCF file should include one of these pairs:
+- `BSC` and `BCF`
+- or `BSCID` and `BCFID`
+
+Optional parameters:
+- `MCC` (default: 610)
+- `MNC` (default: 2)
+
+## 5. Step-by-step usage
+1. Open `Apps > CIQ 2G Generator`.
+2. Upload dump `.xlsb`.
+3. Optionally generate and download `BCF libre`.
+4. Upload CIQ brut 2G file.
+5. Set MCC/MNC if needed.
+6. Click `Generate` and review sheet tabs.
+7. Download final CIQ Excel.
+
+## 6. Outputs generated
+- optional `BCF_LIBRE.xlsx`
+- generated CIQ 2G workbook (download as `CIQ_2G.xlsx`)
+- in-app sheet preview tabs
+
+## 7. Frequent errors and fixes
+- Missing dump file.
+  - Fix: upload `.xlsb` first.
+- CIQ generation blocked.
+  - Fix: upload CIQ brut 2G file.
+- Forbidden BCF mismatch.
+  - Fix: verify expected column names.
+
+## 8. Minimal reproducible example
+- Input: valid dump `.xlsb` + `samples/CIQ_2G.xlsx`.
+- Action: click `Generate` with default MCC/MNC.
+- Expected result: sheet tabs and downloadable `CIQ_2G.xlsx`.
+
+## 9. Known limitations
+- Processing time depends on dump size.
+- Source CIQ template quality directly affects output.
+- Forbidden list handling depends on expected BSC/BCF column format.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/ciq_3g_doc.py

@@ -0,0 +1,56 @@
+import streamlit as st
+
+st.markdown(
+    """
+# CIQ 3G Generator Documentation
+
+## 1. Objective
+Generate 3G CIQ outputs (WBTS/WCEL) from a raw CIQ Excel file.
+
+## 2. When to use this tool
+Use this page when building 3G CIQ outputs with naming and band conventions.
+
+## 3. Input files and accepted formats
+- Required: CIQ brut 3G file (`.xlsx` or `.xls`)
+
+## 4. Required columns/fields
+Required structure must match expectations of the 3G processing pipeline.
+User-configurable inputs:
+- `Year suffix` (default: `25`)
+- `Bands string` (default: `G9G18U9U21L8L18L26`)
+
+## 5. Step-by-step usage
+1. Open `Apps > CIQ 3G Generator`.
+2. Upload CIQ brut 3G Excel file.
+3. Set `Year suffix` and `Bands string` if needed.
+4. Click `Generate`.
+5. Review generated sheets.
+6. Download final workbook.
+
+## 6. Outputs generated
+- in-app tabs for generated sheets
+- downloadable workbook `CIQ_3G.xlsx`
+
+## 7. Frequent errors and fixes
+- Missing input file.
+  - Fix: upload `.xlsx`/`.xls` CIQ 3G file.
+- Generation error.
+  - Fix: verify CIQ template structure and required columns.
+- Unexpected naming output.
+  - Fix: validate `Year suffix` and `Bands string` values.
+
+## 8. Minimal reproducible example
+- Input: `samples/CIQ_3G.xlsx`
+- Action: keep defaults and click `Generate`.
+- Expected result: populated sheets and downloadable `CIQ_3G.xlsx`.
+
+## 9. Known limitations
+- Output quality depends on CIQ raw file quality.
+- Custom naming conventions require correct user parameters.
+- Schema deviations can break generation.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/ciq_4g_doc.py

@@ -0,0 +1,58 @@
+import streamlit as st
+
+st.markdown(
+    """
+# CIQ 4G Generator Documentation
+
+## 1. Objective
+Generate 4G CIQ outputs from raw CIQ Excel input with configurable naming and MCC/MNC values.
+
+## 2. When to use this tool
+Use this page when preparing LTE CIQ outputs for deployment planning or verification.
+
+## 3. Input files and accepted formats
+- Required: CIQ brut 4G file (`.xlsx` or `.xls`)
+
+## 4. Required columns/fields
+Input must follow expected 4G CIQ raw schema.
+User-configurable inputs:
+- `Year suffix` (default: `25`)
+- `Bands string` (default: `G9G18U9U21L8L18L26`)
+- `MCC` (default: 610)
+- `MNC` (default: 2)
+
+## 5. Step-by-step usage
+1. Open `Apps > CIQ 4G Generator`.
+2. Upload CIQ brut 4G file.
+3. Set suffix/bands/MCC/MNC values.
+4. Click `Generate`.
+5. Review generated sheet tabs.
+6. Download `CIQ_4G.xlsx`.
+
+## 6. Outputs generated
+- generated sheets preview in tabs
+- downloadable workbook `CIQ_4G.xlsx`
+
+## 7. Frequent errors and fixes
+- Missing CIQ file.
+  - Fix: upload `.xlsx`/`.xls` input.
+- Processing error.
+  - Fix: validate source template and required columns.
+- Wrong MCC/MNC values in output.
+  - Fix: verify numeric MCC/MNC before generation.
+
+## 8. Minimal reproducible example
+- Input: `samples/CIQ_LTE.xlsx`
+- Action: use defaults and click `Generate`.
+- Expected result: generated sheet tabs and downloadable `CIQ_4G.xlsx`.
+
+## 9. Known limitations
+- Strict dependency on CIQ raw schema.
+- Incorrect parameter values can propagate to outputs.
+- No dump cross-check is performed in this page.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/ciq_verification_doc.py

@@ -0,0 +1,63 @@
+import streamlit as st
+
+st.markdown(
+    """
+# CIQ Verification Documentation
+
+## 1. Objective
+Verify CIQ parameter values against the OML dump database for 2G, 3G, and LTE.
+
+## 2. When to use this tool
+Use this page before integration/deployment to quickly detect:
+- parameter mismatches
+- missing references in dump
+- per-technology CIQ consistency issues
+
+## 3. Input files and accepted formats
+- Required: dump file in `.xlsb`
+- Optional CIQ files: `.xlsx` or `.xls`
+  - CIQ 2G
+  - CIQ 3G
+  - CIQ LTE
+- Rule: at least one CIQ file must be uploaded.
+
+## 4. Required fields and structure
+- Dump and CIQ files must contain expected network parameter columns used by verification functions.
+- Each technology is processed independently when its CIQ file is provided.
+
+## 5. Step-by-step usage
+1. Open `Apps > CIQ Verification`.
+2. Upload dump `.xlsb`.
+3. Upload one or more CIQ files (2G/3G/LTE).
+4. Click `Verifier`.
+5. Review per-technology metrics (`OK`, `Mismatch`, `Not Found`).
+6. Download the Excel verification report.
+
+## 6. Outputs generated
+- tabbed result tables per technology
+- summary metrics for each processed technology
+- downloadable Excel report: `CIQ_Verification_Report.xlsx`
+
+## 7. Frequent errors and fixes
+- Message: dump is required.
+  - Fix: upload `.xlsb` before running verification.
+- Message: at least one CIQ file required.
+  - Fix: upload at least one CIQ 2G/3G/LTE file.
+- Runtime verification error.
+  - Fix: validate CIQ headers and sheet integrity.
+
+## 8. Minimal reproducible example
+- Input: one dump `.xlsb` + one CIQ LTE `.xlsx`.
+- Action: click `Verifier`.
+- Expected result: LTE tab with status highlights + Excel report button.
+
+## 9. Known limitations
+- Verification quality depends on CIQ template consistency.
+- Mixed naming standards can produce `NOT_FOUND` results.
+- Processing large files may take noticeable time.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/clustering_doc.py

@@ -0,0 +1,62 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Automatic Site Clustering Documentation
+
+## 1. Objective
+Cluster sites from geographic coordinates with a configurable max sites per cluster.
+
+## 2. When to use this tool
+Use this page to build operational clusters for planning, field operations, or optimization workloads.
+
+## 3. Input files and accepted formats
+- Required: one Excel file in `.xlsx`
+- Sample: `samples/Site_Clustering.xlsx`
+
+## 4. Required columns/fields
+You must select:
+- latitude column
+- longitude column
+- region column
+- site code column
+
+## 5. Step-by-step usage
+1. Open `Apps > Automatic Site Clustering`.
+2. Upload `.xlsx` dataset.
+3. Select columns and set `Max sites per cluster`.
+4. Choose clustering method:
+   - uniform cluster size (Hilbert curve)
+   - lower-than-max non-uniform clusters (KMeans)
+5. Optionally enable region mixing.
+6. Click `Run Clustering` and download output.
+
+## 6. Outputs generated
+- clustered dataset with a `Cluster` column
+- cluster size charts
+- map visualization by cluster
+- downloadable file: `clustered_sites.xlsx`
+
+## 7. Frequent errors and fixes
+- Invalid map or missing points.
+  - Fix: verify numeric latitude/longitude values.
+- Unexpected cluster composition.
+  - Fix: tune `Max sites per cluster` and method choice.
+- Empty output.
+  - Fix: ensure uploaded file is not empty and selected columns are correct.
+
+## 8. Minimal reproducible example
+- Input: `samples/Site_Clustering.xlsx`
+- Action: run with default `max_sites=25`, no region mixing.
+- Expected result: cluster assignment, charts, map, and downloadable Excel.
+
+## 9. Known limitations
+- KMeans outcome can vary with data distribution.
+- Hilbert strategy is coordinate-normalization based.
+- Extreme outliers can reduce cluster interpretability.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/core_dump_doc.py

@@ -1,34 +1,59 @@
-import streamlit as st
+import streamlit as st
 
 st.markdown(
     """
-## Core Dump Page Documentation
-
-### Overview
-This script is designed to process and display core dump files. It provides a user-friendly interface for uploading files, processing data, and displaying the results.
-
-### Getting Started
-Upload a core dump file: Use the file uploader to select a core dump file in .txt format.
-Process the data: Once the file is uploaded, the app will process the data and display the results.
-Use the "Download icon" in the table to download the processed data.
-
-
-### Data Processed and Display
-The processed data is displayed in the following format:
-
-- Global Cell ID: The global cell ID is displayed in a table format.
-- LA Cell Name: The LA cell name is displayed in a table format.
-- WCDMA Service Area Number: The WCDMA service area number is displayed in a table format.
-- WCDMA Service Area Name: The WCDMA service area name is displayed in a table format.
-
-
-### Troubleshooting
-1. Error messages: If an error occurs during processing, an error message will be displayed. Please check the file format and contents before trying again.
-2. File format: Only .txt files are supported. Please convert your file to the correct format before uploading.
-
-
-### Contact
-If you have any questions or issues with the app, please contact [Dav Melchi] at [davmelchi@gmail.com].
-            
-            """
+# Dump Core Documentation
+
+## 1. Objective
+Parse dump core text files and extract key 2G/3G identifiers with hexadecimal-to-decimal conversion.
+
+## 2. When to use this tool
+Use this page when you need quick checks of:
+- Global cell ID vs LA cell name (2G)
+- 3G service area number and name
+- LAC / Cell ID / SAC conversions to decimal
+
+## 3. Input files and accepted formats
+- Required: one or multiple `.txt` dump core files
+
+## 4. Required fields in file content
+The parser looks for lines containing:
+- `Global cell ID`
+- `LA cell name`
+- `3G service area number`
+- `3G service area name`
+
+## 5. Step-by-step usage
+1. Open `Apps > Parse dump core`.
+2. (Optional) Download sample file from the page.
+3. Upload one or multiple `.txt` files.
+4. Review generated 2G and 3G tables.
+
+## 6. Outputs generated
+Two result tables are displayed:
+- `2G CORE DUMP DATA` with `LAC_DECIMAL` and `Cell_ID_DECIMAL`
+- `3G CORE DUMP DATA` with `LAC_DECIMAL` and `SAC_ID_DECIMAL`
+
+## 7. Frequent errors and fixes
+- Empty output table.
+  - Fix: confirm the uploaded file contains expected key strings.
+- Decimal conversion errors.
+  - Fix: verify extracted IDs are valid hexadecimal values.
+- Wrong file format.
+  - Fix: upload `.txt` only.
+
+## 8. Minimal reproducible example
+- Input: `samples/dump_core.txt`
+- Action: upload file to the page.
+- Expected result: two tables with converted decimal columns.
+
+## 9. Known limitations
+- Parsing is line-pattern based (strict keywords).
+- No Excel export button is provided in this page.
+- Mixed/irregular dump formats may require cleanup before upload.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
 )

documentations/database_doc.py

@@ -1,71 +1,70 @@
-import streamlit as st
+import streamlit as st
 
 st.markdown(
     """
-## Database Processing App Documentation
-### Overview
-This app is designed to process and generate databases from uploaded dump files. It provides a user-friendly interface for uploading files, selecting database types, and downloading processed databases.
+# Databases Documentation
 
-### Getting Started
-1. Upload a dump file: Click on the "Upload updated dump file" button to select a file in .xlsb format.
-2. Select a database type: Choose a database type from the available options (2G, 3G, LTE, All, or NEI).
-3. Download the processed database: Once the processing is complete, click on the "Download" button to save the processed database in .xlsx format.
+## 1. Objective
+Generate telecom databases and KML outputs from a dump file (`.xlsb`) using the `Database processing` page.
 
-### Required Sheets
-The app requires the following sheets to be present in the uploaded file:
+## 2. When to use this tool
+Use this page when you need one or several outputs from the same dump:
+- 2G / 3G / LTE databases
+- NEI database
+- INVUNIT database
+- ADJL / ATL / Nice datasets (when full dump is available)
+- 2G / 3G / LTE KML files
 
-1. **2G :**
-    - BTS
-    - BCF
-    - TRX
-    - MAL
-2. **3G :**
-    - WCEL
-    - WBTS
-    - WNCEL
-3. **LTE :**
-    - LNBTS
-    - LNCEL
-    - LNCEL_FDD
-    - LNCEL_TDD
-4. **NEI :**
-    - ADCE
-    - ADJS
-    - ADJI
-    - ADJG
-    - ADJW
-    - BTS
-    - WCEL
-5. **MRBTS :**
-    - MRBTS
-6. **INVUNIT :**
-    - INVUNIT
+## 3. Input files and accepted formats
+- Required: one dump file in `.xlsb`
+- Optional behavior toggle: `Exclude decommissioned 2G BSCs` (enabled by default)
 
-Please ensure that these sheets are present in the uploaded file to avoid any errors.
+## 4. Required sheets
+Depending on selected outputs, the dump should contain:
+- 2G: `BTS`, `BCF`, `TRX`, `MAL`
+- 3G: `WCEL`, `WBTS`, `WNCEL`
+- LTE: `LNBTS`, `LNCEL`, `LNCEL_FDD`, `LNCEL_TDD`
+- Neighbors: `ADCE`, `ADJS`, `ADJI`, `ADJG`, `ADJW`, `BTS`, `WCEL`
+- INVUNIT: `INVUNIT`
+- Full dump extras: `MRBTS` and related full-dump dependencies
 
-### Database Types
-The app supports the following database types:
+## 5. Step-by-step usage
+1. Open `Apps > Generate Databases`.
+2. Upload your `.xlsb` dump.
+3. Review available buttons (enabled according to detected sheets).
+4. Click the needed generation button (`Generate 2G DB`, `Generate All DBs`, etc.).
+5. Wait for completion message and download the generated file.
 
-    - 2G: Process 2G database
-    - 3G: Process 3G database
-    - LTE: Process LTE database
-    - All: Process all databases (2G, 3G, and LTE)
-    - NEI: Process neighbors database
-    
-### Troubleshooting
+## 6. Outputs generated
+The page can generate downloadable files such as:
+- `2G database_<timestamp>.xlsx`
+- `3G database_<timestamp>.xlsx`
+- `LTE database_<timestamp>.xlsx`
+- `All databases_<timestamp>.xlsx`
+- `Neighbors databases_<timestamp>.xlsx`
+- `INVUNIT database_<timestamp>.xlsx`
+- KML files for 2G/3G/LTE
 
-- Error messages: If an error occurs during processing, an error message will be displayed. Please check the file format and required sheets before trying again.
-- File format: Only .xlsb files are supported. Please convert your file to the correct format before uploading.
+## 7. Frequent errors and fixes
+- Error: required sheets missing.
+  - Fix: verify the dump contains the required tabs listed above.
+- Error: unsupported file type.
+  - Fix: upload only `.xlsb`.
+- Long processing or timeout feeling.
+  - Fix: generate one dataset at a time for very large dumps.
 
+## 8. Minimal reproducible example
+- Input: one valid full dump `.xlsb` containing 2G/3G/LTE sheets.
+- Action: click `Generate All DBs`.
+- Expected result: download button appears for `All databases_<timestamp>.xlsx`.
 
+## 9. Known limitations
+- Processing time increases with dump size.
+- Behavior depends on source dump consistency.
+- Some outputs are only available when full dump requirements are met.
 
-### FAQ
-- What is the maximum file size allowed?: The maximum file size allowed is 200MB.
-- How long does the processing take?: The processing time depends on the file size and complexity. Please wait for the processing to complete.
-
-
-### Contact
-If you have any questions or issues with the app, please contact [Dav Melchi] at [davmelchi@gmail.com].
-
-    """
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
 )

documentations/dbm_watt_doc.py

@@ -0,0 +1,56 @@
+import streamlit as st
+
+st.markdown(
+    """
+# dBm Watt Calculator Documentation
+
+## 1. Objective
+Convert power values between dBm, Watt (W), and milliwatt (mW).
+
+## 2. When to use this tool
+Use this page for quick RF power conversions during planning, checks, and reporting.
+
+## 3. Input files and accepted formats
+No file upload is required.
+Manual numeric input only.
+
+## 4. Required fields
+Three conversion modes are available:
+- `dBm -> W + mW`
+- `W -> dBm + mW`
+- `mW -> dBm + W`
+
+## 5. Step-by-step usage
+1. Open `Apps > dBm <> Watt Calculator`.
+2. Select conversion mode.
+3. Enter input value.
+4. Click `Convert`.
+5. Read decimal and scientific notation outputs.
+
+## 6. Outputs generated
+- converted values in two units
+- decimal and scientific formatting
+
+## 7. Frequent errors and fixes
+- Error for non-positive W input.
+  - Fix: use value strictly greater than 0.
+- Error for non-positive mW input.
+  - Fix: use value strictly greater than 0.
+- Unexpected value.
+  - Fix: verify unit and conversion mode selection.
+
+## 8. Minimal reproducible example
+- Input: mode `dBm -> W + mW`, value `30`.
+- Action: click `Convert`.
+- Expected result: approximately `1 W` and `1000 mW`.
+
+## 9. Known limitations
+- Manual one-value conversion only.
+- No batch file conversion in current version.
+- No history of previous calculations.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/distance_doc.py

@@ -0,0 +1,62 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Distance Calculator Documentation
+
+## 1. Objective
+Compute geodesic distance (meters) between two coordinate points for each row in an Excel dataset.
+
+## 2. When to use this tool
+Use this page when you need row-level distance calculation between:
+- source and destination points
+- serving and neighbor points
+- any two coordinate pairs in the same row
+
+## 3. Input files and accepted formats
+- Required: `.xlsx` file containing coordinates for two points.
+
+## 4. Required columns/fields
+Select 4 columns from the uploaded file:
+- latitude of point 1
+- longitude of point 1
+- latitude of point 2
+- longitude of point 2
+
+Columns must be numeric or convertible to numeric coordinates.
+
+## 5. Step-by-step usage
+1. Open `Apps > Distance Calculator`.
+2. (Optional) Download sample file.
+3. Upload Excel file.
+4. Select the 4 coordinate columns.
+5. Click `CALCULATE DISTANCE`.
+6. Review the resulting table.
+
+## 6. Outputs generated
+- a new column: `distance_meters`
+- interactive table with original data plus distance result
+
+## 7. Frequent errors and fixes
+- Calculation error for one or more rows.
+  - Fix: check selected columns contain valid numeric latitude/longitude.
+- No output after click.
+  - Fix: confirm all 4 selectors are set correctly.
+- Wrong distances.
+  - Fix: verify coordinates are in decimal degrees (not DMS text).
+
+## 8. Minimal reproducible example
+- Input: `samples/distance.xlsx`
+- Action: map point 1/point 2 columns and click `CALCULATE DISTANCE`.
+- Expected result: `distance_meters` populated for each row.
+
+## 9. Known limitations
+- Only Excel `.xlsx` input is supported.
+- No dedicated export button on this page.
+- Invalid rows can fail row-level distance computation.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/dump_analysis_doc.py

@@ -0,0 +1,56 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Dump Analytics Documentation
+
+## 1. Objective
+Explain the analytics dashboard displayed after generating full databases with stats.
+
+## 2. When to use this tool
+Use this documentation when reviewing post-generation charts and metrics for GSM/WCDMA/LTE (FDD/TDD).
+
+## 3. Input files and accepted formats
+No direct upload in this module.
+It is fed by the `Generate All DBs and Show Stats` flow from `Apps > Generate Databases`.
+
+## 4. Required fields
+The analytics rely on precomputed structures in `utils.utils_vars` (site, GSM, WCDMA, LTE metrics).
+
+## 5. Step-by-step usage
+1. Open `Apps > Generate Databases`.
+2. Upload a full dump `.xlsb`.
+3. Click `Generate All DBs and Show Stats`.
+4. Open Data and Chart tabs.
+5. Use this page to interpret the displayed indicators.
+
+## 6. Outputs generated
+Displayed analytics include:
+- site counts and band distributions
+- GSM controller/LAC/TRX distributions
+- WCDMA RNC/LAC/admin-state distributions
+- LTE FDD/TDD distributions (bands, PCI, TAC, admin-state)
+
+## 7. Frequent errors and fixes
+- Empty analytics sections.
+  - Fix: ensure full dump criteria are met before stats generation.
+- Missing distributions.
+  - Fix: verify source dump has required technology sheets.
+- Inconsistent counts.
+  - Fix: regenerate all DBs and ensure no parsing errors occurred.
+
+## 8. Minimal reproducible example
+- Input: full dump `.xlsb` containing 2G/3G/LTE sheets.
+- Action: run `Generate All DBs and Show Stats`.
+- Expected result: populated Data and Chart tabs with multi-technology metrics.
+
+## 9. Known limitations
+- Not exposed as a standalone top-level app page.
+- Availability depends on successful preprocessing in database workflow.
+- Large dumps can impact rendering speed.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/dump_compare_doc.py

@@ -0,0 +1,54 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Dump Compare Documentation
+
+## 1. Objective
+Compare two dump files and detect row-level and parameter-level differences per object class.
+
+## 2. When to use this tool
+Use this page when you need to identify what changed between an old and a new dump before integration.
+
+## 3. Input files and accepted formats
+- Required: old dump `.xlsb`
+- Required: new dump `.xlsb`
+- Required: object classes (comma-separated, for example `BCF,BTS,WCEL`)
+
+## 4. Required columns/fields
+For each requested object class sheet, the tool expects a Dist Name-like column (detected by matching `dist` + `name`).
+
+## 5. Step-by-step usage
+1. Open `Apps > Dump Compare`.
+2. Upload old and new `.xlsb` files.
+3. Enter object classes separated by commas.
+4. Click `Run Comparison`.
+5. Review logs and download ZIP results.
+
+## 6. Outputs generated
+- in-app logs per object class
+- ZIP archive (`differences.zip`) containing one Excel per sheet with detected differences
+
+## 7. Frequent errors and fixes
+- Dist_Name column not found.
+  - Fix: verify sheet headers and naming consistency.
+- No common rows between dumps.
+  - Fix: verify the same object class and naming standard in both files.
+- Sheet read error.
+  - Fix: ensure object class exists in both dumps.
+
+## 8. Minimal reproducible example
+- Input: two dumps containing sheet `BTS` with same Dist_Name domain and at least one changed parameter.
+- Action: run comparison on `BTS`.
+- Expected result: one `BTS_differences.xlsx` inside `differences.zip`.
+
+## 9. Known limitations
+- Header detection is pattern-based and may fail on highly custom sheets.
+- Column comparison requires matching column names after cleaning.
+- Export is split by object class only.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/fnb_parser_doc.py

@@ -0,0 +1,59 @@
+import streamlit as st
+
+st.markdown(
+    """
+# F4NB Extractor Documentation
+
+## 1. Objective
+Extract site and sector information from F4NB Word documents (`.docx`) and export structured Excel output.
+
+## 2. When to use this tool
+Use this page when transforming design documents into tabular data for analysis and mapping.
+
+## 3. Input files and accepted formats
+- Required: one or multiple `.docx` files
+- Sample file available: `samples/FN4B.docx`
+
+## 4. Required fields in document content
+Parser looks for common labels in table cells such as:
+- code/site name
+- locality/address
+- coordinates (`X`, `Y`, `Z`)
+- sector parameters (`Azimuth`, `Height`, `Tilt mecanique`, `Tilt electrique`)
+
+## 5. Step-by-step usage
+1. Open `Apps > F4NB Extractor`.
+2. Upload one or more `.docx` files.
+3. Click `Process`.
+4. Review extracted dataframe.
+5. Download Excel output.
+6. Review map if coordinates are parsed.
+
+## 6. Outputs generated
+- extracted dataframe (site + sector rows)
+- downloadable file `extracted_fnb.xlsx`
+- optional map using converted decimal coordinates
+
+## 7. Frequent errors and fixes
+- No data extracted.
+  - Fix: verify input document follows expected table structure.
+- Coordinate conversion fails.
+  - Fix: clean coordinate format and direction markers.
+- Missing map points.
+  - Fix: ensure X/Y values are valid and parseable.
+
+## 8. Minimal reproducible example
+- Input: `samples/FN4B.docx`
+- Action: upload and click `Process`.
+- Expected result: sector rows displayed and Excel download available.
+
+## 9. Known limitations
+- Extraction is heuristic and template-dependent.
+- Highly custom DOCX layouts may require parser updates.
+- Map relies on successful coordinate conversion.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/gps_converter_doc.py

@@ -0,0 +1,65 @@
+import streamlit as st
+
+st.markdown(
+    """
+# GPS Converter Documentation
+
+## 1. Objective
+Convert coordinates between Decimal and DMS (Degree-Minute-Second) formats, from Excel files or manual input.
+
+## 2. When to use this tool
+Use this page when you need to:
+- normalize coordinate format before mapping/export
+- convert DMS logs into decimal values
+- convert decimal values into DMS for reporting
+
+## 3. Input files and accepted formats
+Two workflows are available:
+- Import Excel mode: `.xlsx` file
+- Manual mode: direct coordinate input in UI
+
+## 4. Required columns/fields
+For Excel mode, select two columns from uploaded file:
+- latitude column
+- longitude column
+
+For manual mode:
+- Decimal -> DMS: numeric lat/lon
+- DMS -> Decimal: valid DMS strings with direction (N/S/E/W)
+
+## 5. Step-by-step usage
+1. Open `Apps > GPS Converter`.
+2. Choose tab: `Import Excel` or `Manual Input`.
+3. In Excel mode, upload `.xlsx` and select latitude/longitude columns.
+4. Select conversion type (`DMS to Decimal` or `Decimal to DMS`).
+5. Click `CONVERT`.
+6. Review converted table and map visualization.
+
+## 6. Outputs generated
+- converted latitude/longitude columns
+- interactive grid display
+- map preview for valid coordinates
+
+## 7. Frequent errors and fixes
+- Invalid decimal ranges.
+  - Fix: latitude must be [-90, 90], longitude [-180, 180].
+- DMS parse error.
+  - Fix: use valid DMS format with direction letter.
+- Empty map.
+  - Fix: verify converted columns contain valid numeric coordinates.
+
+## 8. Minimal reproducible example
+- Input: `samples/Decimal_to_DMS.xlsx`
+- Action: choose latitude/longitude columns, set `Decimal to DMS`, click `CONVERT`.
+- Expected result: converted columns + map rendered from original decimal values.
+
+## 9. Known limitations
+- Conversion errors are strict by coordinate range.
+- Map only renders rows with valid numeric coordinates.
+- Mixed malformed rows may require source cleanup.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/gsm_lac_load_doc.py

@@ -0,0 +1,60 @@
+import streamlit as st
+
+st.markdown(
+    """
+# GSM LAC Load Documentation
+
+## 1. Objective
+Analyze GSM LAC paging load by combining dump data and hourly KPI report.
+
+## 2. When to use this tool
+Use this page to detect overloaded LACs and review paging behavior per BSC/LAC over time.
+
+## 3. Input files and accepted formats
+- Required: dump file in `.xlsb`
+- Required: hourly KPI report in `.csv`
+- Recommendation: at least 7 days of hourly KPI data
+
+## 4. Required columns/fields
+From dump processing:
+- `ID_BTS`, `BSC`, `code`, `locationAreaIdLAC`, plus TRX fields
+From KPI report (after cleaning):
+- `BSC_name`, `BTS_name`, `Paging_messages_on_air_interface`, `DELETE_PAGING_COMMAND_c003038`, `datetime`
+
+## 5. Step-by-step usage
+1. Open `Paging Analysis > GSM LAC Load Analysis`.
+2. Upload dump `.xlsb` and hourly report `.csv`.
+3. Click `Analyze Data`.
+4. Review max paging/utilization table.
+5. Filter BSC and BSC_Lac in interactive section.
+6. Review trend charts and filtered dataframe.
+
+## 6. Outputs generated
+- detailed aggregated `lac_load_df`
+- max paging summary per `BSC_Lac`
+- line charts for paging and delete paging command
+- interactive filtered data table
+
+## 7. Frequent errors and fixes
+- CSV parsing issue.
+  - Fix: ensure `;` delimiter and expected KPI columns.
+- Missing BSC/LAC context after merge.
+  - Fix: verify BTS naming and code extraction consistency.
+- No output after analysis.
+  - Fix: ensure both files are uploaded and contain valid data.
+
+## 8. Minimal reproducible example
+- Input: one valid dump `.xlsb` + one hourly KPI CSV with paging fields.
+- Action: run analysis and filter one BSC.
+- Expected result: summary table and non-empty trend charts.
+
+## 9. Known limitations
+- Depends on naming consistency for code extraction from BTS name.
+- Utilization formula uses fixed reference value in implementation.
+- Hourly report schema deviations can break analysis.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/import_physical_db_doc.py

@@ -0,0 +1,51 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Physical Database Verification Documentation
+
+## 1. Objective
+Display the current physical database reference used by the app.
+
+## 2. When to use this tool
+Use this page when you need to inspect the latest physical database content before validation tasks.
+
+## 3. Input files and accepted formats
+No user upload is required.
+The page reads local file:
+- `physical_db/physical_database.csv`
+
+## 4. Required columns/fields
+No fixed schema is enforced by this page. It displays the CSV as-is.
+
+## 5. Step-by-step usage
+1. Open `Apps > Physical Database Verification`.
+2. Click `Show actual physical database`.
+3. Review the displayed table.
+
+## 6. Outputs generated
+- data table rendered from `physical_database.csv`
+
+## 7. Frequent errors and fixes
+- File not found warning.
+  - Fix: ensure `physical_db/physical_database.csv` exists in repository.
+- Empty table.
+  - Fix: verify CSV has data rows and valid delimiter.
+- Parsing issue.
+  - Fix: confirm CSV encoding and structure.
+
+## 8. Minimal reproducible example
+- Input: existing `physical_db/physical_database.csv`
+- Action: click show button.
+- Expected result: full CSV displayed as Streamlit dataframe.
+
+## 9. Known limitations
+- Read-only page (no edit/upload flow).
+- No filtering/export controls in current implementation.
+- Depends entirely on local file availability.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/index_doc.py

@@ -0,0 +1,64 @@
+import streamlit as st
+
+st.title("Documentation Home")
+st.markdown(
+    """
+Welcome to the in-app documentation hub.
+
+Use this page as a starting point to:
+- pick the right tool for your workflow
+- check input formats and required columns
+- troubleshoot common issues quickly
+
+Recommended reading order for new users:
+1. Databases Documentation
+2. CIQ Verification Documentation
+3. UE Capability Documentation
+4. GPS / Distance / Sector KML documentation
+"""
+)
+
+st.subheader("Core Documentation")
+st.page_link("documentations/database_doc.py", label="Databases Documentation")
+st.page_link("documentations/core_dump_doc.py", label="Dump Core Documentation")
+st.page_link("documentations/ue_capability_doc.py", label="UE Capability Documentation")
+st.page_link("documentations/ciq_verification_doc.py", label="CIQ Verification Documentation")
+st.page_link("documentations/ciq_2g_doc.py", label="CIQ 2G Documentation")
+st.page_link("documentations/ciq_3g_doc.py", label="CIQ 3G Documentation")
+st.page_link("documentations/ciq_4g_doc.py", label="CIQ 4G Documentation")
+
+st.subheader("Geo Utilities")
+st.page_link("documentations/gps_converter_doc.py", label="GPS Converter Documentation")
+st.page_link("documentations/distance_doc.py", label="Distance Calculator Documentation")
+st.page_link("documentations/sector_kml_doc.py", label="Sector KML Generator Documentation")
+st.page_link("documentations/dbm_watt_doc.py", label="dBm Watt Documentation")
+
+st.subheader("KPI and Capacity")
+st.page_link("documentations/gsm_capacity_docs.py", label="GSM Capacity Documentation")
+st.page_link("documentations/lte_capacity_docs.py", label="LTE Capacity Documentation")
+st.page_link("documentations/wbts_capacity_doc.py", label="WBTS Capacity Documentation")
+st.page_link("documentations/wcel_capacity_doc.py", label="WCEL Capacity Documentation")
+st.page_link("documentations/lcg_analysis_doc.py", label="LCG Analysis Documentation")
+st.page_link("documentations/gsm_lac_load_doc.py", label="GSM LAC Load Documentation")
+st.page_link("documentations/trafic_analysis_doc.py", label="Traffic Analysis Documentation")
+st.page_link("documentations/anomaly_detection_doc.py", label="Anomaly Detection Documentation")
+
+st.subheader("Operational Tools")
+st.page_link("documentations/dump_compare_doc.py", label="Dump Compare Documentation")
+st.page_link("documentations/clustering_doc.py", label="Clustering Documentation")
+st.page_link("documentations/multi_points_distance_doc.py", label="Multi Points Distance Documentation")
+st.page_link("documentations/import_physical_db_doc.py", label="Physical Database Documentation")
+st.page_link("documentations/dump_analysis_doc.py", label="Dump Analytics Documentation")
+st.page_link("documentations/fnb_parser_doc.py", label="F4NB Extractor Documentation")
+st.page_link("documentations/parameters_distribution_doc.py", label="Parameters Distribution Documentation")
+st.page_link("documentations/lte_drop_traffic_doc.py", label="LTE Drop Traffic Documentation")
+
+st.subheader("Governance")
+st.markdown(
+    """
+- Coverage matrix: `documentations/DOC_COVERAGE.md`
+- Writing standard: `documentations/README_DOCS.md`
+
+Last update: 2026-02-23
+"""
+)

documentations/lcg_analysis_doc.py

@@ -0,0 +1,58 @@
+import streamlit as st
+
+st.markdown(
+    """
+# LCG Capacity Documentation
+
+## 1. Objective
+Analyze LCG utilization behavior and detect imbalance/capacity pressure.
+
+## 2. When to use this tool
+Use this page for LCG-level dimensioning checks and region-level optimization decisions.
+
+## 3. Input files and accepted formats
+- Required: LCG report in `.csv`
+- Data recommendation: at least 3 days, daily aggregated, LCG-level export
+
+## 4. Required columns/fields
+Input CSV must include columns expected by `load_and_process_lcg_data`.
+Outputs include `final_comments` and geographic fields for map plotting.
+
+## 5. Step-by-step usage
+1. Open `Capacity Analysis > LCG Capacity Analysis`.
+2. Upload LCG CSV report.
+3. Set analysis days, threshold days, utilization threshold, and LCG difference threshold.
+4. Click `Analyze Data`.
+5. Review tables, charts, and map.
+6. Download report.
+
+## 6. Outputs generated
+- LCG analysis dataframe
+- KPI dataframe
+- final comment distributions (global and per region)
+- map of final comments
+- downloadable file: `LCG_Capacity_Report.xlsx`
+
+## 7. Frequent errors and fixes
+- Validation warning on days settings.
+  - Fix: ensure threshold days <= analysis days and analysis days >= 3.
+- Processing error.
+  - Fix: verify CSV schema and numeric fields.
+- Empty map.
+  - Fix: confirm presence of `Longitude`/`Latitude` and non-empty comments.
+
+## 8. Minimal reproducible example
+- Input: valid LCG CSV with at least 7 days.
+- Action: run default settings.
+- Expected result: analysis table, distributions, map, and Excel download.
+
+## 9. Known limitations
+- Quality depends on source KPI completeness.
+- Thresholds may require tuning by market behavior.
+- Map view requires valid coordinates.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/lte_drop_traffic_doc.py

@@ -0,0 +1,60 @@
+import streamlit as st
+
+st.markdown(
+    """
+# LTE Drop Traffic Documentation
+
+## 1. Objective
+Detect LTE cells with significant traffic drop by comparing recent days against long-term average.
+
+## 2. When to use this tool
+Use this page for early detection of degraded LTE traffic behavior and location-based prioritization.
+
+## 3. Input files and accepted formats
+- Required: traffic report in `.csv`
+- Current parser expects `;` delimiter.
+
+## 4. Required columns/fields
+Input CSV should contain at least:
+- `PERIOD_START_TIME` (format `%m.%d.%Y`)
+- `LNCEL name`
+- `4G/LTE DL Traffic Volume (GBytes)`
+- `4G/LTE UL Traffic Volume (GBytes)`
+
+## 5. Step-by-step usage
+1. Open `KPI Analysis > LTE Drop Traffic Analysis`.
+2. Upload CSV report.
+3. Set number of last days and drop threshold.
+4. Review affected cells table.
+5. Explore trend plot by selected cell.
+6. Review map and download affected cells.
+
+## 6. Outputs generated
+- affected cells dataframe with `drop_%`
+- downloadable file `traffic_drop_cells.xlsx`
+- trend plot per selected cell
+- map of dropped cells
+
+## 7. Frequent errors and fixes
+- Date parsing error.
+  - Fix: ensure `PERIOD_START_TIME` matches `%m.%d.%Y` format.
+- Empty affected cells list.
+  - Fix: lower threshold or increase analysis window.
+- Missing map coordinates.
+  - Fix: ensure physical DB mapping by code is available.
+
+## 8. Minimal reproducible example
+- Input: LTE traffic CSV with several days and measurable drop on some cells.
+- Action: set threshold to `50%` and run analysis.
+- Expected result: non-empty affected cell list and downloadable Excel.
+
+## 9. Known limitations
+- Uses mean-based long-term vs recent-day comparison.
+- Relies on code matching against physical database for map.
+- Strict date format requirement in input parser.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/multi_points_distance_doc.py

@@ -0,0 +1,62 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Multi Points Distance Calculator Documentation
+
+## 1. Objective
+Calculate distances between two datasets of points and identify closest matches.
+
+## 2. When to use this tool
+Use this page to compare reference sites against candidate/neighbor sites and find nearest pairs.
+
+## 3. Input files and accepted formats
+- Required: Dataset 1 in `.xlsx`
+- Required: Dataset 2 in `.xlsx`
+- Samples: `samples/Dataset1.xlsx`, `samples/Dataset2.xlsx`
+
+## 4. Required columns/fields
+For each dataset, select:
+- code column
+- latitude column
+- longitude column
+
+## 5. Step-by-step usage
+1. Open `Apps > Multi Points Distance Calculator`.
+2. Upload both Excel datasets.
+3. Select code/latitude/longitude columns for each dataset.
+4. Set minimum distance threshold (km).
+5. Click `Calculate Distances`.
+6. Review closest matches and download CSV outputs.
+
+## 6. Outputs generated
+- closest matches table
+- closest matches below threshold table
+- downloadable files:
+  - `all_distances.csv`
+  - `closest_matches.csv`
+  - `closest_matches_<threshold>km.csv`
+
+## 7. Frequent errors and fixes
+- Processing error after upload.
+  - Fix: verify selected columns and coordinate formats.
+- No close matches below threshold.
+  - Fix: increase threshold value.
+- Unexpected distances.
+  - Fix: ensure coordinates are decimal degrees.
+
+## 8. Minimal reproducible example
+- Input: `Dataset1.xlsx` and `Dataset2.xlsx` samples.
+- Action: select columns and run with threshold `5 km`.
+- Expected result: closest matches plus downloadable CSV files.
+
+## 9. Known limitations
+- Only `.xlsx` inputs are supported.
+- Quality depends on coordinate accuracy.
+- Large datasets may take longer to compute.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/parameters_distribution_doc.py

@@ -0,0 +1,57 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Parameters Distribution Documentation
+
+## 1. Objective
+Explore value distributions of selected parameters in any object class sheet from an OML dump.
+
+## 2. When to use this tool
+Use this page for quick parameter profiling, outlier spotting, and baseline checks by object class.
+
+## 3. Input files and accepted formats
+- Required: dump file in `.xlsb`
+
+## 4. Required columns/fields
+No fixed parameter list is required.
+Workflow:
+- select object class (sheet)
+- select one or multiple parameters (columns)
+
+## 5. Step-by-step usage
+1. Open `Apps > Parameters distribution`.
+2. Upload dump `.xlsb`.
+3. Select object class sheet.
+4. Select one or more parameters.
+5. Review distribution table and bar chart per parameter.
+
+## 6. Outputs generated
+For each selected parameter:
+- value count table
+- percentage distribution
+- bar chart
+
+## 7. Frequent errors and fixes
+- Selected sheet appears empty.
+  - Fix: verify sheet contains readable data rows.
+- Parameter list missing expected fields.
+  - Fix: check selected object class is correct.
+- Large cardinality chart is hard to read.
+  - Fix: filter/clean source data before analysis.
+
+## 8. Minimal reproducible example
+- Input: valid `.xlsb` dump.
+- Action: select sheet `BTS`, then one categorical parameter.
+- Expected result: count/percentage table and bar chart.
+
+## 9. Known limitations
+- High-cardinality fields can produce very dense charts.
+- No export button in current implementation.
+- Requires readable xlsb sheet via current engine.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/sector_kml_doc.py

@@ -0,0 +1,63 @@
+import streamlit as st
+
+st.markdown(
+    """
+# Sector KML Generator Documentation
+
+## 1. Objective
+Generate a KML file from sector-level Excel data for geographic visualization.
+
+## 2. When to use this tool
+Use this page when you need to:
+- visualize telecom sectors in GIS/KML viewers
+- share sector orientation and metadata
+- prepare map overlays for field/optimization teams
+
+## 3. Input files and accepted formats
+- Required: one `.xlsx` file containing sector data.
+
+## 4. Required columns
+The uploaded file must contain all required columns:
+- `code`
+- `name`
+- `Azimut`
+- `Longitude`
+- `Latitude`
+- `size`
+- `color`
+
+Any additional column is exported in sector description metadata.
+
+## 5. Step-by-step usage
+1. Open `Apps > Sector KML Generator`.
+2. (Optional) Download sample file from the page.
+3. Upload your Excel file.
+4. Ensure required columns are present.
+5. Download generated KML.
+
+## 6. Outputs generated
+- downloadable KML file named like `Sectors_kml_<timestamp>.kml`
+
+## 7. Frequent errors and fixes
+- Missing required columns error.
+  - Fix: rename columns exactly as required.
+- Empty/invalid geometry in output.
+  - Fix: verify `Latitude`/`Longitude` and azimuth values.
+- Unexpected style/color rendering.
+  - Fix: validate color codes and supported color naming.
+
+## 8. Minimal reproducible example
+- Input: `samples/Sector_kml.xlsx`
+- Action: upload file and click download when generated.
+- Expected result: valid KML file ready for map tools.
+
+## 9. Known limitations
+- Input schema is case-sensitive for required column names.
+- Only `.xlsx` is supported.
+- Invalid coordinate values may produce unusable geometry.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/trafic_analysis_doc.py

@@ -1,118 +1,68 @@
-import streamlit as st
+import streamlit as st
 
 st.markdown(
     """
-
-# Traffic Analysis Application Documentation
-
-## Overview
-The Traffic Analysis application is a Streamlit-based tool designed to analyze and visualize 2G, 3G, and LTE network traffic data. It provides insights into voice and data traffic patterns, performs comparative analysis between different time periods, and generates visual reports.
-
-## Features
-
-### 1. Multi-Technology Support
-- **2G Traffic Analysis**
-  - Voice traffic analysis
-  - Data traffic analysis
-  - PS (Packet Switched) traffic metrics
-
-- **3G Traffic Analysis**
-  - CS (Circuit Switched) voice traffic
-  - PS data traffic
-  - Combined traffic metrics
-
-- **LTE Traffic Analysis**
-  - UL/DL data traffic
-  - Combined traffic volume
-
-### 2. Comparative Analysis
-- Pre/Post period comparison
-- Traffic trend analysis
-- Monthly traffic aggregation
-- Traffic growth/decline metrics
-
-### 3. Visualization
-- Interactive maps showing traffic distribution
-- Top N sites analysis
-- Traffic trend charts
-- Comparative bar charts
-
-## Input Requirements
-
-### Required Files
-1. **2G Traffic Report**
-   - Required columns: `BCF name`, `PERIOD_START_TIME`, `TRAFFIC_PS DL`, `PS_UL_Load`
-   - Format: CSV or ZIP containing CSV
-
-2. **3G Traffic Report**
-   - Required columns: `WBTS name`, `PERIOD_START_TIME`, `Total CS traffic - Erl`, `Total_Data_Traffic`
-   - Format: CSV or ZIP containing CSV
-
-3. **LTE Traffic Report**
-   - Required columns: `LNBTS name`, `PERIOD_START_TIME`, `4G/LTE DL Traffic Volume (GBytes)`, `4G/LTE UL Traffic Volume (GBytes)`
-   - Format: CSV or ZIP containing CSV
-
-## Usage
-
-### 1. Data Upload
-- Upload the three required reports using the file uploaders
-- Supported formats: CSV or ZIP (containing CSV)
-
-### 2. Date Range Selection
-- **Pre-period**: Select the baseline period for comparison
-- **Post-period**: Select the period to compare against the baseline
-- **Last period**: Select the most recent period for current analysis
-
-### 3. Analysis Configuration
-- Set the number of top traffic sites to display
-- The application will automatically process and analyze the data
-
-### 4. Results
-- **Summary Analysis**: Overview of traffic metrics
-- **Top Sites**: Lists and charts of highest traffic sites
-- **Maps**: Geographical visualization of traffic distribution
-- **Monthly Trends**: Traffic patterns over time
-
-## Technical Implementation
-
-### Key Functions
-
-1. **Data Processing**
-   - preprocess_2g(): Processes 2G traffic data
-   - preprocess_3g(): Processes 3G traffic data
-   - preprocess_lte(): Processes LTE traffic data
-
-2. **Analysis Functions**
-   - merge_and_compare(): Combines and compares traffic data across technologies
-   - monthly_data_analysis(): Aggregates data by month for trend analysis
-
-3. **Visualization**
-   - Interactive maps using Plotly
-   - Bar charts for top sites
-   - Data tables with sortable columns
-
-## Dependencies
-- Python 3.7+
-- pandas
-- plotly
-- streamlit
-- numpy
-
-## Output
-The application generates:
-1. Summary tables of traffic metrics
-2. Interactive visualizations
-3. Exportable reports in Excel format
-
-## Best Practices
-1. Ensure input files follow the required format
-2. Select appropriate date ranges for meaningful comparison
-3. Review top traffic sites for network optimization opportunities
-4. Use the monthly analysis to identify long-term trends
-
-## Troubleshooting
-- If data doesn't load, check file formats and required columns
-- Ensure date ranges are properly selected
-- Verify that uploaded files contain valid data
+# Traffic Analysis Documentation
+
+## 1. Objective
+Analyze and compare 2G/3G/LTE traffic behavior across pre/post periods and visualize trends, top sites, and maps.
+
+## 2. When to use this tool
+Use this page when you need:
+- pre/post traffic comparison
+- top traffic site ranking
+- trend visualization over time
+- map-based traffic review
+- exportable summary outputs
+
+## 3. Input files and accepted formats
+Typical inputs are KPI traffic exports in `.csv` (or zip containing CSV, depending on source export).
+You usually provide one report per RAT:
+- 2G traffic report
+- 3G traffic report
+- LTE traffic report
+
+## 4. Required fields (typical)
+Required columns depend on RAT and source report format. Common fields include:
+- site/cell identifiers (`BCF name`, `WBTS name`, `LNBTS name`)
+- timestamp/date (`PERIOD_START_TIME`)
+- traffic metrics (voice/data indicators by RAT)
+
+## 5. Step-by-step usage
+1. Open `KPI Analysis > Trafic Analysis`.
+2. Upload available traffic reports (2G/3G/LTE).
+3. Configure period filters (pre/post/last period).
+4. Choose analysis options (top N, thresholds, drill-down filters).
+5. Review tables, charts, and maps.
+6. Download generated report when available.
+
+## 6. Outputs generated
+- comparative summary tables
+- top sites by voice/data traffic
+- trend charts
+- map visualizations
+- downloadable report (Excel) when generated
+
+## 7. Frequent errors and fixes
+- Empty charts or missing KPIs.
+  - Fix: verify required traffic columns exist and are numeric.
+- Date filtering produces no data.
+  - Fix: confirm date format and selected ranges.
+- Unexpected aggregation result.
+  - Fix: check identifier consistency (site/cell naming between reports).
+
+## 8. Minimal reproducible example
+- Input: one valid 2G, one valid 3G, one valid LTE traffic CSV covering overlapping dates.
+- Action: select pre/post periods and run analysis.
+- Expected result: non-empty summary tables and top-sites charts.
+
+## 9. Known limitations
+- Results depend on source export quality and naming consistency.
+- Very large reports can increase rendering time.
+- Mixed naming standards across RATs can reduce merge quality.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
 """
 )

documentations/wbts_capacity_doc.py

@@ -0,0 +1,57 @@
+import streamlit as st
+
+st.markdown(
+    """
+# WBTS Capacity Documentation
+
+## 1. Objective
+Analyze WBTS resource usage and classify capacity risk based on BB/CE comments.
+
+## 2. When to use this tool
+Use this page when planning WBTS upgrades or investigating BB/CE load pressure.
+
+## 3. Input files and accepted formats
+- Required: WBTS capacity report in `.csv`
+- Data recommendation: at least 3 days, daily aggregated, WBTS-level export
+
+## 4. Required columns/fields
+Input CSV must contain the fields expected by `process_kpi.process_wbts_capacity.load_data`.
+Typical outputs include `bb_comments` and `ce_comments`.
+
+## 5. Step-by-step usage
+1. Open `Capacity Analysis > WBTS Capacity BB and CE Analysis`.
+2. Upload WBTS CSV report.
+3. Set analysis days, threshold days, and threshold value.
+4. Click `Analyze Data`.
+5. Review table and BB/CE distributions.
+6. Download Excel report.
+
+## 6. Outputs generated
+- analysis table with WBTS comments
+- BB comments distribution chart
+- CE comments distribution chart
+- downloadable file: `WBTS_Analysis_Report.xlsx`
+
+## 7. Frequent errors and fixes
+- Parsing/analysis error.
+  - Fix: verify CSV structure and required columns.
+- Empty comment distributions.
+  - Fix: ensure uploaded report contains valid WBTS-level KPI rows.
+- Unexpected threshold behavior.
+  - Fix: confirm threshold values align with KPI scale.
+
+## 8. Minimal reproducible example
+- Input: WBTS daily aggregated CSV with >= 3 days.
+- Action: run default parameters and analyze.
+- Expected result: populated WBTS table + Excel download button.
+
+## 9. Known limitations
+- Relies on expected input schema from processing module.
+- Very short time windows may produce weak conclusions.
+- No geographic map visualization on this page.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)

documentations/wcel_capacity_doc.py

@@ -0,0 +1,62 @@
+import streamlit as st
+
+st.markdown(
+    """
+# WCEL Capacity Documentation
+
+## 1. Objective
+Evaluate WCEL capacity and service quality using availability, congestion, and fail-related thresholds.
+
+## 2. When to use this tool
+Use this page for 3G capacity monitoring, issue prioritization, and region-level reporting.
+
+## 3. Input files and accepted formats
+- Required: WCEL capacity report in `.csv`
+- Data recommendation: at least 3 days, daily aggregated, WCEL-level report
+
+## 4. Required columns/fields
+Input CSV must provide fields required by `load_and_process_wcel_capacity_data`.
+Output analysis includes fields such as:
+- `final_comments`
+- `operational_comments`
+- `fails_comments`
+- coordinates (`Latitude`, `Longitude`) for map views
+
+## 5. Step-by-step usage
+1. Open `Capacity Analysis > WCEL Capacity Analysis`.
+2. Upload WCEL CSV report.
+3. Configure thresholds and analysis windows.
+4. Click `Analyze Data`.
+5. Review analysis table, distributions, and maps.
+6. Download report.
+
+## 6. Outputs generated
+- detailed WCEL analysis dataframe
+- KPI dataframe
+- comment distribution charts (global and by region)
+- map views for fails and operational comments
+- downloadable file: `WCEL_Capacity_Report.xlsx`
+
+## 7. Frequent errors and fixes
+- Processing error.
+  - Fix: validate CSV structure and numeric KPI fields.
+- Empty maps.
+  - Fix: ensure `Latitude` and `Longitude` are populated.
+- Over/under sensitivity.
+  - Fix: tune thresholds to match operational baseline.
+
+## 8. Minimal reproducible example
+- Input: WCEL CSV with valid KPI and coordinate fields.
+- Action: run with defaults and review output charts.
+- Expected result: analysis table + map visualizations + downloadable report.
+
+## 9. Known limitations
+- Depends on strict KPI schema expected by processing module.
+- Coordinate gaps reduce map coverage.
+- Large datasets can increase rendering time.
+
+## 10. Version and update date
+- Documentation version: 1.0
+- Last update: 2026-02-23
+"""
+)