initial commit

.github/workflows/sync-hf.yml

@@ -0,0 +1,20 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://giswqs:[email protected]/spaces/giswqs/solara-geospatial main

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.vscode
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+.history/
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

Dockerfile

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+FROM jupyter/base-notebook:latest
+
+RUN mamba install -c conda-forge geopandas localtileserver -y && \
+    fix-permissions "${CONDA_DIR}" && \
+    fix-permissions "/home/${NB_USER}"
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+ENV PROJ_LIB='/opt/conda/share/proj'
+
+USER root
+RUN chown -R ${NB_UID} ${HOME}
+USER ${NB_USER}
+
+EXPOSE 8765
+
+CMD ["solara", "run", "main.py", "--host=0.0.0.0"]

LICENSE

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+MIT License
+
+Copyright (c) 2024 DavMelchi NPO Solutions
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: NPO Tools 
+emoji: 🌍
+colorFrom: green
+colorTo: red
+sdk: solara
+pinned: false
+license: mit
+app_port: 8765
+---
+
+## NPO Tools
+
+Apps 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.
+
+## How to use
+
+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.
+
+## How it works
+
+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.
+
+## Why
+
+The app is a simple way to generate the databases from the dump file. It saves time and effort.
+
+## 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)
+- [ ] Improve Dashboard
+- [ ] Add the ability to select columns
+- [ ] Error handling
+  

main.py

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+import solara
+
+from pages.core_dump import core_dump_process
+from pages.line_v import line_v
+from utils.utils import SharedSidebar
+
+
+@solara.component
+def line():
+    with solara.Sidebar():
+        SharedSidebar()
+    line_v()
+
+
+@solara.component
+def core_dump():
+    # with solara.Sidebar():
+    # SharedSidebar()
+    core_dump_process()
+
+
+routes = [
+    solara.Route(path="/", component=core_dump, label="Core Dump"),
+    solara.Route(path="line_v", component=line, label="Line_v"),
+]

pages/core_dump.py

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+import solara
+
+from processing.process_core_dump import dfs, parse_content
+from utils.custom_grid import AgGrid
+from utils.file_drop_utils import FileDropMultiple
+
+
+@solara.component
+def SharedSidebar():
+    with solara.Card("Dump Core parsing", style={"max-width": "500px"}):
+        solara.Markdown(
+            f"""
+            ### Used this tools to parse core dump.
+            *Drop the core dump files in .txt format to see the results*
+            """
+        )
+    with solara.Card(style={"max-width": "500px"}):
+        FileDropMultiple(parse_content=parse_content)
+
+
+@solara.component
+def core_dump_process():
+
+    with solara.Column() as main:
+        with solara.Sidebar():
+            SharedSidebar()
+        with solara.Card(title="Dump Core Parsed Results"):
+            if dfs.gsm_core_infos.value is not None:
+                with solara.Card(title="GSM Core Infos"):
+                    solara.CrossFilterSelect(dfs.gsm_core_infos.value, "LA cell name")
+                    solara.CrossFilterSlider(dfs.gsm_core_infos.value, "LAC_DECIMAL")
+                    solara.CrossFilterReport(dfs.gsm_core_infos.value)
+                    solara.CrossFilterDataFrame(dfs.gsm_core_infos.value)
+                    AgGrid(df=dfs.gsm_core_infos.value)
+            if dfs.wcdma_core_infos.value is not None:
+                with solara.Card(title="WCDMA Core Infos"):
+                    AgGrid(df=dfs.wcdma_core_infos.value)
+
+    return main

pages/line_v.py

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+import textwrap
+from typing import List, Optional, cast
+
+import pandas as pd
+import solara
+from solara.components.file_drop import FileInfo
+
+from utils.custom_grid import AgGrid
+from utils.file_drop_utils import FileDropMultiple
+
+data_global_cell_id = []
+data_la_cell_name = []
+data_wcdma_service_area_number = []
+data_wcdma_service_area_name = []
+
+
+class dfs:
+    gsm_core_infos = solara.reactive(cast(Optional[pd.DataFrame], None))
+    wcdma_core_infos = solara.reactive(cast(Optional[pd.DataFrame], None))
+
+
+def parse_content(all_files):
+
+    data_global_cell_id = []
+    data_la_cell_name = []
+    data_wcdma_service_area_number = []
+    data_wcdma_service_area_name = []
+
+    for file in all_files:
+        all_data_global_cell_id = []
+        all_data_la_cell_name = []
+        all_data_wcdma_service_area_number = []
+        all_data_wcdma_service_area_name = []
+        content = file.decode("utf-8").splitlines()
+
+        for line in content:
+            if "Global cell ID" in line:
+                data_global_cell_id.append([line.split("=")[1].strip()])
+            elif "LA cell name" in line:
+                data_la_cell_name.append([line.split("=")[1].strip()])
+            elif "3G service area number" in line:
+                data_wcdma_service_area_number.append([line.split("=")[1].strip()])
+            elif "3G service area name" in line:
+                data_wcdma_service_area_name.append([line.split("=")[1].strip()])
+
+        # Append the extracted data for the current file to the overall lists
+        all_data_global_cell_id.extend(data_global_cell_id)
+        all_data_la_cell_name.extend(data_la_cell_name)
+        all_data_wcdma_service_area_number.extend(data_wcdma_service_area_number)
+        all_data_wcdma_service_area_name.extend(data_wcdma_service_area_name)
+
+    # Create a DataFrame from the extracted data
+    df_global_cell_id = pd.DataFrame(
+        all_data_global_cell_id, columns=["Global cell ID"]
+    )
+    df_la_cell_name = pd.DataFrame(all_data_la_cell_name, columns=["LA cell name"])
+    df_wcdma_service_area_number = pd.DataFrame(
+        all_data_wcdma_service_area_number, columns=["3G service area number"]
+    )
+    df_wcdma_service_area_name = pd.DataFrame(
+        all_data_wcdma_service_area_name, columns=["3G service area name"]
+    )
+
+    # add index column df_global_cell_id and df_la_cell_name  and dfa_wcdma_service_area_numbera and df_wcdma_service_area_name
+    df_global_cell_id.insert(0, "index", range(0, len(df_global_cell_id)))
+    df_la_cell_name.insert(0, "index", range(0, len(df_la_cell_name)))
+    df_wcdma_service_area_number.insert(
+        0, "index", range(0, len(df_wcdma_service_area_number))
+    )
+    df_wcdma_service_area_name.insert(
+        0, "index", range(0, len(df_wcdma_service_area_name))
+    )
+
+    # Merge global_cell_id and la_cell_name on index
+    df_la_cell_name = df_la_cell_name.merge(df_global_cell_id, on="index")
+
+    # merge wcdma_service_area_number and wcdma_service_area_name on index
+    df_wcdma_service_area_name = df_wcdma_service_area_name.merge(
+        df_wcdma_service_area_number, on="index"
+    )
+
+    df_la_cell_name["LAC_ID"] = df_la_cell_name["Global cell ID"].str[5:]
+    df_wcdma_service_area_name["LAC_ID"] = df_wcdma_service_area_name[
+        "3G service area number"
+    ].str[5:]
+    df_la_cell_name["LAC"] = df_la_cell_name["LAC_ID"].str[:4]
+    df_la_cell_name["Cell ID"] = df_la_cell_name["LAC_ID"].str[4:]
+    df_wcdma_service_area_name["LAC"] = df_wcdma_service_area_name["LAC_ID"].str[:4]
+    df_wcdma_service_area_name["Cell ID"] = df_wcdma_service_area_name["LAC_ID"].str[4:]
+
+    # convert LAC to from HEXadecimal to DECimal
+    df_la_cell_name["LAC_DECIMAL"] = df_la_cell_name["LAC"].apply(lambda x: int(x, 16))
+    df_la_cell_name["Cell_ID_DECIMAL"] = df_la_cell_name["Cell ID"].apply(
+        lambda x: int(x, 16)
+    )
+    df_wcdma_service_area_name["LAC_DECIMAL"] = df_wcdma_service_area_name["LAC"].apply(
+        lambda x: int(x, 16)
+    )
+    df_wcdma_service_area_name["Cell_ID_DECIMAL"] = df_wcdma_service_area_name[
+        "Cell ID"
+    ].apply(lambda x: int(x, 16))
+
+    df_la_cell_name = df_la_cell_name.reset_index(drop=True)
+
+    dfs.gsm_core_infos.value = df_la_cell_name
+    dfs.wcdma_core_infos.value = df_wcdma_service_area_name
+
+    print(df_la_cell_name)
+    print(df_wcdma_service_area_name)
+
+
+@solara.component
+def line_v():
+    solara.Title("Dump Core Parsing")
+
+    # with solara.Sidebar():
+    # files = FileDropMultiple()
+    # parse_content(all_files=files)
+
+    with solara.Column() as main:
+
+        FileDropMultiple(parse_content=parse_content)
+
+        with solara.Card(title="Dump Core Parsed Results"):
+            if dfs.gsm_core_infos.value is not None:
+                with solara.Card(title="GSM Core Infos"):
+                    solara.CrossFilterSelect(dfs.gsm_core_infos.value, "LA cell name")
+                    solara.CrossFilterSlider(dfs.gsm_core_infos.value, "LAC_DECIMAL")
+                    solara.CrossFilterReport(dfs.gsm_core_infos.value)
+                    solara.CrossFilterDataFrame(dfs.gsm_core_infos.value)
+                    AgGrid(df=dfs.gsm_core_infos.value)
+            if dfs.wcdma_core_infos.value is not None:
+                with solara.Card(title="WCDMA Core Infos"):
+                    AgGrid(df=dfs.wcdma_core_infos.value)
+
+    return main
+
+    # with solara.AppBar():
+    #     solara.lab.ThemeToggle()
+
+
+# @solara.component
+# def Layout(children):
+#     route, routes = solara.use_route()
+#     dark_effective = solara.lab.use_dark_effective()
+#     return solara.AppLayout(children=children, toolbar_dark=dark_effective, color=None)

processing/process_core_dump.py

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+from typing import List, Optional, cast
+
+import pandas as pd
+import solara
+
+data_global_cell_id = []
+data_la_cell_name = []
+data_wcdma_service_area_number = []
+data_wcdma_service_area_name = []
+
+
+class dfs:
+    gsm_core_infos = solara.reactive(cast(Optional[pd.DataFrame], None))
+    wcdma_core_infos = solara.reactive(cast(Optional[pd.DataFrame], None))
+
+
+def parse_content(all_files):
+
+    data_global_cell_id = []
+    data_la_cell_name = []
+    data_wcdma_service_area_number = []
+    data_wcdma_service_area_name = []
+
+    for file in all_files:
+        all_data_global_cell_id = []
+        all_data_la_cell_name = []
+        all_data_wcdma_service_area_number = []
+        all_data_wcdma_service_area_name = []
+        content = file.decode("utf-8").splitlines()
+
+        for line in content:
+            if "Global cell ID" in line:
+                data_global_cell_id.append([line.split("=")[1].strip()])
+            elif "LA cell name" in line:
+                data_la_cell_name.append([line.split("=")[1].strip()])
+            elif "3G service area number" in line:
+                data_wcdma_service_area_number.append([line.split("=")[1].strip()])
+            elif "3G service area name" in line:
+                data_wcdma_service_area_name.append([line.split("=")[1].strip()])
+
+        # Append the extracted data for the current file to the overall lists
+        all_data_global_cell_id.extend(data_global_cell_id)
+        all_data_la_cell_name.extend(data_la_cell_name)
+        all_data_wcdma_service_area_number.extend(data_wcdma_service_area_number)
+        all_data_wcdma_service_area_name.extend(data_wcdma_service_area_name)
+
+    # Create a DataFrame from the extracted data
+    df_global_cell_id = pd.DataFrame(
+        all_data_global_cell_id, columns=["Global cell ID"]
+    )
+    df_la_cell_name = pd.DataFrame(all_data_la_cell_name, columns=["LA cell name"])
+    df_wcdma_service_area_number = pd.DataFrame(
+        all_data_wcdma_service_area_number, columns=["3G service area number"]
+    )
+    df_wcdma_service_area_name = pd.DataFrame(
+        all_data_wcdma_service_area_name, columns=["3G service area name"]
+    )
+
+    # add index column df_global_cell_id and df_la_cell_name  and dfa_wcdma_service_area_numbera and df_wcdma_service_area_name
+    df_global_cell_id.insert(0, "index", range(0, len(df_global_cell_id)))
+    df_la_cell_name.insert(0, "index", range(0, len(df_la_cell_name)))
+    df_wcdma_service_area_number.insert(
+        0, "index", range(0, len(df_wcdma_service_area_number))
+    )
+    df_wcdma_service_area_name.insert(
+        0, "index", range(0, len(df_wcdma_service_area_name))
+    )
+
+    # Merge global_cell_id and la_cell_name on index
+    df_la_cell_name = df_la_cell_name.merge(df_global_cell_id, on="index")
+
+    # merge wcdma_service_area_number and wcdma_service_area_name on index
+    df_wcdma_service_area_name = df_wcdma_service_area_name.merge(
+        df_wcdma_service_area_number, on="index"
+    )
+
+    df_la_cell_name["LAC_ID"] = df_la_cell_name["Global cell ID"].str[5:]
+    df_wcdma_service_area_name["LAC_ID"] = df_wcdma_service_area_name[
+        "3G service area number"
+    ].str[5:]
+    df_la_cell_name["LAC"] = df_la_cell_name["LAC_ID"].str[:4]
+    df_la_cell_name["Cell ID"] = df_la_cell_name["LAC_ID"].str[4:]
+    df_wcdma_service_area_name["LAC"] = df_wcdma_service_area_name["LAC_ID"].str[:4]
+    df_wcdma_service_area_name["Cell ID"] = df_wcdma_service_area_name["LAC_ID"].str[4:]
+
+    # convert LAC to from HEXadecimal to DECimal
+    df_la_cell_name["LAC_DECIMAL"] = df_la_cell_name["LAC"].apply(lambda x: int(x, 16))
+    df_la_cell_name["Cell_ID_DECIMAL"] = df_la_cell_name["Cell ID"].apply(
+        lambda x: int(x, 16)
+    )
+    df_wcdma_service_area_name["LAC_DECIMAL"] = df_wcdma_service_area_name["LAC"].apply(
+        lambda x: int(x, 16)
+    )
+    df_wcdma_service_area_name["Cell_ID_DECIMAL"] = df_wcdma_service_area_name[
+        "Cell ID"
+    ].apply(lambda x: int(x, 16))
+
+    df_la_cell_name = df_la_cell_name.reset_index(drop=True)
+
+    dfs.gsm_core_infos.value = df_la_cell_name
+    dfs.wcdma_core_infos.value = df_wcdma_service_area_name

requirements.txt

@@ -0,0 +1,4 @@
+solara
+python-calamine
+XlsxWriter
+plotly.express

utils/custom_grid.py

@@ -0,0 +1,51 @@
+import pandas as pd
+import solara
+from ipyaggrid import Grid
+
+
+@solara.component
+def AgGrid(df: pd.DataFrame, **kwargs):
+
+    grid_options = {
+        "columnDefs": [{"headerName": col, "field": col} for col in df.columns],
+        # "enableSorting": True,
+        # "enableFilter": True,
+        # "editable": True,
+        # "resizable": True,
+        # "sortable": True,
+    }
+
+    def update_data():
+        widget = solara.get_widget(el)
+        widget.grid_options = grid_options
+        widget.update_grid_data(df.to_dict("records"))
+
+    el = Grid.element(
+        grid_data=df.to_dict("records"),
+        grid_options=grid_options,
+        quick_filter=True,
+        theme="ag-theme-balham",
+        columns_fit="auto",
+        index=False,
+        keep_multiindex=False,
+        **kwargs,
+    )
+
+    solara.use_effect(update_data, [df])
+
+
+@solara.component
+def Page():
+    df = solara.use_reactive(
+        pd.DataFrame(
+            {
+                "Nom": ["Alice", "Bob", "Charlie"],
+                "Âge": [25, 30, 35],
+                "Ville": ["Paris", "Lyon", "Marseille"],
+            }
+        )
+    )
+
+    AgGrid(
+        df=df.value,
+    )

utils/file_drop_utils.py

@@ -0,0 +1,31 @@
+from typing import List, cast
+
+import solara
+from solara.components.file_drop import FileInfo
+
+# parse_content
+
+
+@solara.component
+def FileDropMultiple(parse_content: callable):
+    content, set_content = solara.use_state(cast(List[bytes], []))
+    filename, set_filename = solara.use_state(cast(List[str], []))
+    size, set_size = solara.use_state(cast(List[int], []))
+
+    def on_file(files: List[FileInfo]):
+        set_filename([f["name"] for f in files])
+        set_size([f["size"] for f in files])
+        set_content([f["file_obj"].read() for f in files])
+
+    solara.FileDropMultiple(
+        label="Drag and drop files(s) here.",
+        on_file=on_file,
+        # lazy=True,  # We will only read the first 100 bytes
+        # on_total_progress=lambda *args: None,
+    )
+    if content:
+        solara.Info(f"Number of uploaded files: {len(filename)}")
+        for f, s, c in zip(filename, size, content):
+            solara.Info(f"File {f} has total length: {s}\n")
+        parse_content(all_files=content)
+        # return content

utils/utils.py

@@ -0,0 +1,20 @@
+import solara
+
+selected_template = solara.reactive("plotly")
+def change_theme(e):
+    print (selected_template.value)
+templates =["plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white", "none"]
+
+
+@solara.component
+def SharedSidebar():
+    with solara.Card("Solara + Plotly Graphs", style={"max-width": "500px"}):
+        solara.Markdown(
+            f"""
+            ###This project utilizes the Solara Framework to create interactive graphs using Plotly.
+            *The code and examples in this project are based on the Plotly tutorial by Derek Banas.* 
+            *Please refer to his repository for the original tutorial [here](https://github.com/derekbanas/plotly-tutorial/blob/master/Plotly%20Tut.ipynb)*
+            """
+        )
+    with solara.Card(style={"max-width": "500px"}):
+        solara.Select(label="Themes", value=selected_template, values=templates, on_value=change_theme)