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import pandas as pd
import plotly.express as px
import streamlit as st
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")
doc_col, image_col = st.columns(2)
with doc_col:
st.write(
"""
The report analyzes LTE capacity based on:
- Dump file required
- BH Cell level KPI report in CSV format
- Availability and PRB usage thresholds
"""
)
with image_col:
st.image("./assets/lte_capacity.png", width=250)
file1, file2 = st.columns(2)
with file1:
uploaded_dump = st.file_uploader("Upload Dump file in xlsb format", type="xlsb")
with file2:
uploaded_bh_report = st.file_uploader(
"Upload LTE Busy Hour Report in CSV format", type="csv"
)
# Parameters
param_col1, param_col2 = st.columns(2)
param_col3, param_col4 = st.columns(2)
param_col5, param_col6 = st.columns(2)
with param_col1:
num_last_days = st.number_input(
"Number of last days for analysis", value=7, min_value=1
)
with param_col2:
num_threshold_days = st.number_input(
"Number of days for threshold", value=3, min_value=1
)
with param_col3:
availability_threshold = st.number_input(
"Availability threshold (%)", value=95.0, min_value=0.0, max_value=100.0
)
with param_col4:
prb_usage_threshold = st.number_input(
"PRB usage threshold (%)", value=80.0, min_value=0.0, max_value=100.0
)
with param_col5:
prb_diff_between_cells = st.number_input(
"Maximum PRB usage difference between cells (%)",
value=20.0,
min_value=0.0,
max_value=100.0,
)
with param_col6:
# DL PRB Util p TTI Lev_10
# E-UTRAN Avg PRB usage per TTI DL
main_prb_to_use = st.selectbox(
"Main PRB to use",
["DL PRB Util p TTI Lev_10", "E-UTRAN Avg PRB usage per TTI DL"],
index=1,
)
if uploaded_dump is not None and uploaded_bh_report is not None:
if st.button("Analyze Data", type="primary"):
with st.spinner("Processing data..."):
results = process_lte_bh_report(
dump_path=uploaded_dump,
bh_report_path=uploaded_bh_report,
num_last_days=num_last_days,
num_threshold_days=num_threshold_days,
availability_threshold=availability_threshold,
prb_usage_threshold=prb_usage_threshold,
prb_diff_between_cells_threshold=prb_diff_between_cells,
main_prb_to_use=main_prb_to_use,
)
if results is not None:
bh_report: pd.DataFrame = results[0]
lte_analysis_df: pd.DataFrame = results[1]
LteCapacity.final_results = convert_lte_analysis_dfs(
[lte_analysis_df, bh_report], ["LTE_Analysis", "LTE_BH_Report"]
)
st.download_button(
on_click="ignore",
type="primary",
label="Download the Analysis Report",
data=LteCapacity.final_results,
file_name="LTE_Analysis_Report.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
)
st.write(lte_analysis_df)
# Add dataframe and Pie chart with "final_comments" distribution
st.markdown("***")
st.markdown(":blue[**Final comment distribution**]")
final_comments_df = (
lte_analysis_df.groupby("final_comments")
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
final_comments_col1, final_comments_col2 = st.columns((1, 3))
with final_comments_col1:
st.write(final_comments_df)
with final_comments_col2:
fig = px.pie(
final_comments_df,
names="final_comments",
values="count",
hover_name="final_comments",
hover_data=["count"],
title="Final comment distribution",
)
fig.update_layout(height=600)
fig.update_traces(
texttemplate="%{label}: %{value}",
textfont_size=15,
textposition="outside",
)
st.plotly_chart(fig, use_container_width=True)
# Add dataframe and Pie chart with "final_comments" distribution where num_congested_cells > 0
st.markdown("***")
st.markdown(":blue[**Congested cells distribution**]")
congested_cells_df = (
lte_analysis_df[lte_analysis_df["num_congested_cells"] > 0]
.groupby("final_comments")
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
congested_cells_col1, congested_cells_col2 = st.columns((1, 3))
with congested_cells_col1:
st.write(congested_cells_df)
with congested_cells_col2:
fig = px.pie(
congested_cells_df,
names="final_comments",
values="count",
hover_name="final_comments",
hover_data=["count"],
title="Congested cells distribution",
)
fig.update_layout(height=600)
fig.update_traces(
texttemplate="%{label}: %{value}",
textfont_size=15,
textposition="outside",
)
st.plotly_chart(fig, use_container_width=True)
# Add dataframe and Bar chart with "final_comments" distribution where num_congested_cells > 0 per Region
st.markdown("***")
st.markdown(":blue[**Congested cells distribution per Region**]")
congested_cells_region_df = (
lte_analysis_df[lte_analysis_df["num_congested_cells"] > 0]
.groupby(["Region", "final_comments"])
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
congested_cells_region_col1, congested_cells_region_col2 = st.columns(
(1, 3)
)
with congested_cells_region_col1:
st.write(congested_cells_region_df)
with congested_cells_region_col2:
fig = px.bar(
congested_cells_region_df,
x="Region",
y="count",
color="final_comments",
title="Congested cells distribution per Region",
)
fig.update_layout(height=600)
fig.update_traces(
texttemplate="%{value}", textfont_size=15, textposition="outside"
)
st.plotly_chart(fig, use_container_width=True)
# Add dataframe and Bar chart with "final_comments" distribution where num_congested_cells > 0 per Region groupby region
st.markdown("***")
st.markdown(
":blue[**Congested cells distribution per Region groupby Region**]"
)
congested_cells_region_groupby_region_df = (
lte_analysis_df[lte_analysis_df["num_congested_cells"] > 0]
.groupby(["Region"])
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
(
congested_cells_region_groupby_region_col1,
congested_cells_region_groupby_region_col2,
) = st.columns((1, 3))
with congested_cells_region_groupby_region_col1:
st.write(congested_cells_region_groupby_region_df)
with congested_cells_region_groupby_region_col2:
fig = px.bar(
congested_cells_region_groupby_region_df,
x="Region",
y="count",
title="Congested cells distribution per Region groupby Region",
)
fig.update_layout(height=600)
fig.update_traces(
texttemplate="%{value}", textfont_size=15, textposition="outside"
)
st.plotly_chart(fig, use_container_width=True)
# create a map plot with scatter_map with code ,Longitude,Latitude,final_comments
st.markdown("***")
st.markdown(":blue[**Final comments distribution**]")
map_df = lte_analysis_df[
["code", "Longitude", "Latitude", "final_comments"]
].dropna(subset=["code", "Longitude", "Latitude", "final_comments"])
# add size column equalt to 20
map_df["size"] = 20
fig = px.scatter_map(
map_df,
lat="Latitude",
lon="Longitude",
color="final_comments",
size="size",
zoom=10,
height=600,
title="Final comments distribution",
hover_data={
"code": True,
"final_comments": True,
},
hover_name="code",
)
fig.update_layout(mapbox_style="open-street-map")
st.plotly_chart(fig, use_container_width=True)
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