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import pandas as pd
import plotly.express as px
import streamlit as st
from process_kpi.process_wcel_capacity import (
WcelCapacity,
load_and_process_wcel_capacity_data,
)
from utils.convert_to_excel import convert_wcel_capacity_dfs
# Streamlit UI
st.title(" 📊 WCEL Capacity Analysis")
doc_col, image_col = st.columns(2)
with doc_col:
st.write(
"""This app allows you to analyze the capacity of WCELS in a network.
It provides insights into the utilization of BB and CE resources,
helping you identify potential capacity issues and plan for upgrades.
The report should be run with a minimum of 3 days of data.
- Daily Aggregated
- WCEL level
- Exported in CSV format.
"""
)
with image_col:
st.image("./assets/wcel_capacity.png", width=400)
uploaded_file = st.file_uploader(
"Upload WCEL capacity report in CSV format", type="csv"
)
param_col1, param_col2, param_col3 = st.columns(3)
param_col4, param_col5, param_col6 = st.columns(3)
if uploaded_file is not None:
WcelCapacity.final_results = None
with param_col1:
num_last_days = st.number_input(
"Number of days for analysis",
min_value=3,
max_value=30,
value=7,
)
with param_col2:
num_threshold_days = st.number_input(
"Number of days for threshold",
min_value=1,
max_value=30,
value=2,
)
with param_col3:
availability_threshold = st.number_input(
"Availability threshold (%)", value=99, min_value=0, max_value=100
)
with param_col4:
iub_frameloss_threshold = st.number_input(
"IUB frameloss threshold (%)",
value=100,
min_value=0,
max_value=10000000,
)
with param_col5:
hsdpa_congestion_rate_iub_threshold = st.number_input(
"HSDPA Congestion Rate IUB threshold (%)",
value=10,
min_value=0,
max_value=100,
)
with param_col6:
fails_treshold = st.number_input(
"Fails threshold (%)", value=10, min_value=0, max_value=10000000
)
if st.button("Analyze Data", type="primary"):
with st.spinner("Processing data..."):
results = load_and_process_wcel_capacity_data(
uploaded_file,
num_last_days,
num_threshold_days,
availability_threshold,
iub_frameloss_threshold,
hsdpa_congestion_rate_iub_threshold,
fails_treshold,
)
if results is not None:
wcel_analysis_df = results[0]
kpi_df = results[1]
WcelCapacity.final_results = convert_wcel_capacity_dfs(
[wcel_analysis_df, kpi_df], ["wcel_analysis", "kpi"]
)
st.download_button(
on_click="ignore",
type="primary",
label="Download the Analysis Report",
data=WcelCapacity.final_results,
file_name="WCEL_Capacity_Report.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
)
st.write(wcel_analysis_df)
# Add dataframe and Bar chart with "final_comments" distribution
st.markdown("***")
st.markdown(":blue[**Final comment distribution**]")
final_comments_df = (
wcel_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.bar(
final_comments_df,
x="final_comments",
y="count",
title="Final Comments Distribution",
text="count",
)
fig.update_traces(textposition="outside")
fig.update_layout(height=600)
st.plotly_chart(fig)
# Add dataframe and Pie chart with "operational_comments" distribution
st.markdown("***")
st.markdown(":blue[**Operational comment distribution**]")
operational_comments_df = (
wcel_analysis_df.groupby("operational_comments")
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
operational_comments_df["percent"] = (
operational_comments_df["count"] / operational_comments_df["count"].sum()
) * 100
operational_comments_col1, operational_comments_col2 = st.columns((1, 3))
with operational_comments_col1:
st.write(operational_comments_df)
with operational_comments_col2:
fig = px.pie(
operational_comments_df,
names="operational_comments",
values="count",
hover_name="operational_comments",
hover_data=["count", "percent"],
title="Operational Comments Distribution",
)
fig.update_layout(height=600)
fig.update_traces(
texttemplate="<b>%{label}</b><br> %{value} <b>(%{customdata[1]:.1f}%)</b>",
textfont_size=15,
textposition="outside",
)
st.plotly_chart(fig)
# Add dataframe and Bar chart with "operational_comments" distribution per Region
st.markdown("***")
st.markdown(":blue[**Operational comment distribution per Region**]")
operational_comments_df = (
wcel_analysis_df.groupby(["Region", "operational_comments"])
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
operational_comments_col1, operational_comments_col2 = st.columns((1, 3))
with operational_comments_col1:
st.write(operational_comments_df)
with operational_comments_col2:
fig = px.bar(
operational_comments_df,
x="Region",
y="count",
color="operational_comments",
title="Operational Comments Distribution per Region",
text="count",
)
fig.update_traces(textposition="outside")
fig.update_layout(height=600)
st.plotly_chart(fig)
# Add dataframe and Pie chart with "fails_comments" distribution
st.markdown("***")
st.markdown(":blue[**Fails comment distribution**]")
fails_comments_df = (
wcel_analysis_df.groupby("fails_comments")
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
# replace empty strings with "Cell OK"
fails_comments_df["fails_comments"] = fails_comments_df[
"fails_comments"
].replace("", "Cell OK")
fails_comments_df["percent"] = (
fails_comments_df["count"] / fails_comments_df["count"].sum()
) * 100
fails_comments_col1, fails_comments_col2 = st.columns((1, 3))
with fails_comments_col1:
st.write(fails_comments_df)
with fails_comments_col2:
fig = px.pie(
fails_comments_df,
names="fails_comments",
values="count",
hover_name="fails_comments",
hover_data=["count", "percent"],
title="Fails Comments Distribution",
)
fig.update_layout(height=600)
fig.update_traces(
texttemplate="<b>%{label}</b><br> %{value} <b>(%{customdata[1]:.1f}%)</b>",
textfont_size=15,
textposition="outside",
)
st.plotly_chart(fig)
# Add dataframe and Bar chart with "fails_comments" distribution per Region
st.markdown("***")
st.markdown(":blue[**Fails comment distribution per Region**]")
fails_comments_df = (
wcel_analysis_df.groupby(["Region", "fails_comments"])
.size()
.reset_index(name="count")
.sort_values(by="count", ascending=False)
)
# replace empty strings with "Cell OK"
fails_comments_df["fails_comments"] = fails_comments_df[
"fails_comments"
].replace("", "Cell OK")
fails_comments_col1, fails_comments_col2 = st.columns((1, 3))
with fails_comments_col1:
st.write(fails_comments_df)
with fails_comments_col2:
fig = px.bar(
fails_comments_df,
x="Region",
y="count",
color="fails_comments",
title="Fails Comments Distribution per Region",
text="count",
)
fig.update_traces(textposition="outside", textfont_size=15)
fig.update_layout(height=600)
st.plotly_chart(fig)
# create a map plot with scatter_map with code ,Longitude,Latitude,fails_comments
st.markdown("***")
st.markdown(":blue[**Fails comments distribution**]")
fails_comments_map_df = wcel_analysis_df[
["code", "Longitude", "Latitude", "fails_comments"]
].dropna(subset=["code", "Longitude", "Latitude", "fails_comments"])
# replace empty strings with "Cell OK"
fails_comments_map_df["fails_comments"] = fails_comments_map_df[
"fails_comments"
].replace("", "Cell OK")
# add size column equalt to 20
fails_comments_map_df["size"] = 20
fig = px.scatter_map(
fails_comments_map_df,
lat="Latitude",
lon="Longitude",
color="fails_comments",
size="size",
zoom=10,
height=600,
title="Fails comments distribution",
hover_data={
"code": True,
"fails_comments": True,
},
hover_name="code",
)
fig.update_layout(mapbox_style="open-street-map")
st.plotly_chart(fig, use_container_width=True)
# create a map plot with scatter_map with code ,Longitude,Latitude,operational_comments
operational_comments_map_df = wcel_analysis_df[
["code", "Longitude", "Latitude", "operational_comments"]
].dropna(subset=["code", "Longitude", "Latitude", "operational_comments"])
# replace empty strings with "Cell OK"
operational_comments_map_df["operational_comments"] = (
operational_comments_map_df["operational_comments"].replace("", "Cell OK")
)
# add size column equalt to 20
operational_comments_map_df["size"] = 20
fig = px.scatter_map(
operational_comments_map_df,
lat="Latitude",
lon="Longitude",
color="operational_comments",
size="size",
zoom=10,
height=600,
title="Operational comments distribution",
hover_data={
"code": True,
"operational_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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