Moving Physical DB to github storage repository
Browse files- .gitignore +1 -1
- Changelog.md +8 -0
- app.py +1 -1
- apps/import_physical_db.py +7 -68
- utils/utils_vars.py +4 -1
.gitignore
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/__pycache__
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__pycache__
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/data2
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-
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# /physical_db
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/__pycache__
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__pycache__
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/data2
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/physical_db/physical_database.csv
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# /physical_db
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Changelog.md
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# CHANGELOGS
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## [0.2.1] - 2024-09-30
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- improve lac plot for 2G and 3G
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# CHANGELOGS
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## [0.2.3] - 2024-09-30
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- Moving physical database to github storage repository
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## [0.2.2] - 2024-09-30
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- Add invunit summary
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## [0.2.1] - 2024-09-30
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- improve lac plot for 2G and 3G
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app.py
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@@ -15,7 +15,7 @@ pages = {
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"Apps": [
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st.Page("apps/database_page.py", title="Generate Databases"),
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st.Page("apps/core_dump_page.py", title="Parse dump core"),
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st.Page("apps/import_physical_db.py", title="Physical Database
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st.Page("apps/gps_converter.py", title="GPS Converter"),
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st.Page("apps/distance.py", title="Distance Calculator"),
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],
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"Apps": [
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st.Page("apps/database_page.py", title="Generate Databases"),
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st.Page("apps/core_dump_page.py", title="Parse dump core"),
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st.Page("apps/import_physical_db.py", title="Physical Database Verification"),
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st.Page("apps/gps_converter.py", title="GPS Converter"),
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st.Page("apps/distance.py", title="Distance Calculator"),
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],
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apps/import_physical_db.py
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@@ -3,75 +3,14 @@ import os
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import pandas as pd
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import streamlit as st
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-
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physical_db_folder = "physical_db"
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physical_db_file = os.path.join(physical_db_folder, "physical_database.csv")
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# Ensure the folder exists
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if not os.path.exists(physical_db_folder):
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os.makedirs(physical_db_folder)
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# Streamlit app title
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st.title("Physical Database
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# Step 1: Upload a new CSV file
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uploaded_file = st.file_uploader(
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"Upload a CSV file of new physical database", type="csv"
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)
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if uploaded_file is not None:
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# Step 2: Check if the file contains the required columns
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df = pd.read_csv(uploaded_file)
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required_columns = [
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"CODE",
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"sector",
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"Code_Sector",
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"Azimut",
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"Longitude",
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"Latitude",
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"Hauteur",
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]
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if not all(col in df.columns for col in required_columns):
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st.error(
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f"Error: The file must contain the following columns: {', '.join(required_columns)}"
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)
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elif len(df) <= 500:
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st.error("Error: The file must contain more than 500 rows.")
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else:
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st.success(
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"File successfully validated. Click on the Save button to save the new physical database file."
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)
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# Display the DataFrame
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st.write(df)
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# Step 6: Add button to save the new file
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if st.button("Save the new physical database file", type="primary"):
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# Step 4: Remove everything in the folder physical_db/
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for file in os.listdir(physical_db_folder):
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os.remove(os.path.join(physical_db_folder, file))
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# Step 5: Rename and save the new file
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df.to_csv(physical_db_file, index=False)
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st.success(f"New physical database saved Successfully.")
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# # Step 7: Add button to download the existing physical_database.csv file
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# if os.path.exists(physical_db_file):
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# st.subheader("Download the existing physical_database.csv file")
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# with open(physical_db_file, "rb") as f:
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# st.download_button(
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# label="Download physical_database.csv",
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# data=f,
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# file_name="physical_database.csv",
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# )
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if st.button("Show actual physical database"):
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df_existing = pd.read_csv(physical_db_file)
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st.dataframe(df_existing)
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import pandas as pd
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import streamlit as st
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url = "https://raw.githubusercontent.com/DavMelchi/STORAGE/refs/heads/main/physical_db/physical_database.csv"
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# Streamlit app title
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st.title("Physical Database Verification")
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if st.button("Show actual physical database"):
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try:
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df_existing = pd.read_csv(url)
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st.dataframe(df_existing)
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except FileNotFoundError:
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st.warning("No physical_database.csv file with the url")
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utils/utils_vars.py
CHANGED
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import numpy as np
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import pandas as pd
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def get_physical_db():
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"""
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Returns:
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pd.DataFrame: A DataFrame containing the filtered columns.
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"""
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physical = pd.read_csv(r"./physical_db/physical_database.csv")
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physical = physical[["Code_Sector", "Azimut", "Longitude", "Latitude", "Hauteur"]]
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return physical
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import numpy as np
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import pandas as pd
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url = "https://raw.githubusercontent.com/DavMelchi/STORAGE/refs/heads/main/physical_db/physical_database.csv"
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def get_physical_db():
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"""
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Returns:
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pd.DataFrame: A DataFrame containing the filtered columns.
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"""
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# physical = pd.read_csv(r"./physical_db/physical_database.csv")
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physical = pd.read_csv(url)
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physical = physical[["Code_Sector", "Azimut", "Longitude", "Latitude", "Hauteur"]]
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return physical
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