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app3.py
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app3.py
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import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Function to convert ppm to percentage
def ppm_to_percent(CH4, C2H4, C2H2):
total = CH4 + C2H4 + C2H2
if total == 0:
return 0, 0, 0
return 100 * CH4 / total, 100 * C2H4 / total, 100 * C2H2 / total
# Define zone checking functions
def check_PD_zone(CH4, C2H4, C2H2):
return CH4 >= 98 and C2H4 <= 2 and C2H2 <= 2
def check_D1_zone(CH4, C2H4, C2H2):
return C2H2 >= 13 and C2H4 <= 23
def check_D2_zone(CH4, C2H4, C2H2):
return (C2H4 >= 23 and C2H2 >= 29) or (C2H4 >= 23 and C2H4 <= 40 and C2H2 >= 13 and C2H2 <= 29)
def check_DT_zone(CH4, C2H4, C2H2):
return (C2H2 >= 15 and C2H2 <= 29 and C2H4 >= 40) or (C2H2 >= 13 and C2H2 <= 15 and C2H4 >= 40 and C2H4 <= 50) or (C2H4 <= 50 and C2H2 >= 4 and C2H2 <= 13)
def check_T1_zone(CH4, C2H4, C2H2):
return CH4 >= 76 and CH4 <= 98 and C2H2 <= 4 and C2H4 <= 20
def check_T2_zone(CH4, C2H4, C2H2):
return CH4 >= 46 and CH4 <= 80 and C2H4 >= 20 and C2H4 <= 50 and C2H2 <= 4
def check_T3_zone(CH4, C2H4, C2H2):
return CH4 <= 50 and C2H2 <= 15 and C2H4 >= 50
@st.cache_data
def load_data(file_path):
return pd.read_excel(file_path)
def main():
st.title('Duval Triangle for DGA Interpretation')
# Mapping of user-friendly names to file paths
file_options = {
"BOSA_T401": r"C:\Users\peter\Documents\Python\Python-ideas\timeseries_transformers\matej\Xfmr-Fault-Type-Prediction-main\gas_data.xlsx",
"BOSA_T402": r"C:\Users\peter\Documents\Python\Python-ideas\timeseries_transformers\matej\Xfmr-Fault-Type-Prediction-main\gas_data2.xlsx",
"Additional Gas Data": 'gas_data2.xlsx'
}
# Dropdown to select the data file
selected_option = st.selectbox(
"Select transformer:",
options=list(file_options.keys()) # Display user-friendly names
)
# Load selected data using the file path corresponding to the chosen option
gas_data = load_data(file_options[selected_option])
# Convert ppm to percentage and apply zone checks
gas_data[['CH4%', 'C2H4%', 'C2H2%']] = gas_data.apply(
lambda row: ppm_to_percent(row['CH4_ppm'], row['C2H4_ppm'], row['C2H2_ppm']), axis=1, result_type="expand")
gas_data['Zone'] = gas_data.apply(lambda row: (
("PD " if check_PD_zone(row['CH4%'], row['C2H4%'], row['C2H2%']) else "") +
("D1 " if check_D1_zone(row['CH4%'], row['C2H4%'], row['C2H2%']) else "") +
("D2 " if check_D2_zone(row['CH4%'], row['C2H4%'], row['C2H2%']) else "") +
("DT " if check_DT_zone(row['CH4%'], row['C2H4%'], row['C2H2%']) else "") +
("T1 " if check_T1_zone(row['CH4%'], row['C2H4%'], row['C2H2%']) else "") +
("T2 " if check_T2_zone(row['CH4%'], row['C2H4%'], row['C2H2%']) else "") +
("T3 " if check_T3_zone(row['CH4%'], row['C2H4%'], row['C2H2%']) else "")
).strip(), axis=1)
# Prepare the figure
fig = make_subplots(
rows=2, cols=1,
row_heights=[0.7, 0.3],
specs=[[{"type": "ternary"}], [{"type": "table"}]],
vertical_spacing=0.1
)
# Add scatter and line traces for the ternary plot
traces = [
go.Scatterternary({
'a': [98, 1, 98], 'b': [0, 0, 2], 'c': [2, 0, 0],
'mode': 'lines', 'fill': 'toself', 'name': 'PD'
}),
go.Scatterternary({
'a': [0, 0, 64, 87], 'b': [1, 77, 13, 13], 'c': [0, 23, 23, 0],
'mode': 'lines', 'fill': 'toself', 'name': 'D1'
}),
go.Scatterternary({
'a': [0, 0, 31, 47, 64], 'b': [77, 29, 29, 13, 13], 'c': [23, 71, 40, 40, 23],
'mode': 'lines', 'fill': 'toself', 'name': 'D2'
}),
go.Scatterternary({
'a': [0, 0, 35, 46, 96, 87, 47, 31], 'b': [29, 15, 15, 4, 4, 13, 13, 29], 'c': [71, 85, 50, 50, 0, 0, 40, 40],
'mode': 'lines', 'fill': 'toself', 'name': 'DT'
}),
go.Scatterternary({
'a': [76, 80, 98, 98, 96], 'b': [4, 0, 0, 2, 4], 'c': [20, 20, 2, 0, 0],
'mode': 'lines', 'fill': 'toself', 'name': 'T1'
}),
go.Scatterternary({
'a': [46, 50, 80, 76], 'b': [4, 0, 0, 4], 'c': [50, 50, 20, 20],
'mode': 'lines', 'fill': 'toself', 'name': 'T2'
}),
go.Scatterternary({
'a': [0, 0, 50, 35], 'b': [15, 0, 0, 15], 'c': [85, 1, 50, 50],
'mode': 'lines', 'fill': 'toself', 'name': 'T3'
}),
# Add other traces as needed
]
# Add zone traces to the plot
for trace in traces:
fig.add_trace(trace, row=1, col=1)
# Example traces (predefined zones)
# Include your Scatterternary traces here
# Add scatter traces for individual data points dynamically
for index, row in gas_data.iterrows():
color = 'blue' # Default color, can use different colors based on zone
fig.add_trace(go.Scatterternary({
'a': [row['CH4%']], 'b': [row['C2H2%']], 'c': [row['C2H4%']],
'mode': 'markers', 'marker': {'symbol': 'circle', 'size': 10,'color': row['Color']},
'name': f"{row['Zone']} - {row['Fault location']}"
}), row=1, col=1)
# Table trace for the second row
fig.add_trace(
go.Table(
header=dict(
values=['Fault Location', 'CH4%', 'C2H4%', 'C2H2%', 'Zone'],
fill_color='paleturquoise',
align='left'
),
cells=dict(
values=[
gas_data['Fault location'],
gas_data['CH4%'].round(2),
gas_data['C2H4%'].round(2),
gas_data['C2H2%'].round(2),
gas_data['Zone']
],
fill_color='lavender',
align='left'
)
),
row=2, col=1
)
# Update layout for better presentation
fig.update_layout(
title='Duval Triangle Analysis',
height=1000,
ternary={
'sum': 100,
'aaxis': {'title': 'CH4%','linewidth': 2, 'ticks': 'outside'},
'baxis': {'title': 'C2H2%','linewidth': 2, 'ticks': 'outside'},
'caxis': {'title': 'C2H4%','linewidth': 2, 'ticks': 'outside'}
}
)
st.plotly_chart(fig, use_container_width=True)
if __name__ == "__main__":
main()