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import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO
st.title("KPI Std. Deviation")
# KPI Dropdown with units
kpi_options = {
'CRF': '%',
'Feed Water Temp': '°C',
'S:F': '',
'SSC': 'kg/kg',
'SWC': 'kg/kg',
'Make up water': 'kL'
}
kpi_selected = st.selectbox("Select KPI", list(kpi_options.keys()))
unit = kpi_options[kpi_selected]
# Inputs for Min, Max, and Mean
col1, col2, col3 = st.columns(3)
with col1:
min_val = st.number_input(f"Enter Min Value ({unit})", value=60.0)
with col2:
mean_val = st.number_input(f"Enter Mean (Avg) Value ({unit})", value=100.0)
with col3:
max_val = st.number_input(f"Enter Max Value ({unit})", value=140.0)
# Inputs for Probability Densities
col4, col5, col6 = st.columns(3)
with col4:
min_pdf = st.number_input("Enter Min Value Probability Density", value=0.0)
with col5:
mean_pdf = st.number_input("Enter Mean Value Probability Density", value=1.0)
with col6:
max_pdf = st.number_input("Enter Max Value Probability Density", value=0.0)
# Validation
if max_val > mean_val > min_val:
# Generate X values (200 points evenly spaced)
x = np.linspace(min_val, max_val, 200)
# Custom quadratic bell shape through interpolation of given PDF values
A = np.array([
[min_val**2, min_val, 1],
[mean_val**2, mean_val, 1],
[max_val**2, max_val, 1]
])
B = np.array([min_pdf, mean_pdf, max_pdf])
coeffs = np.linalg.solve(A, B)
a, b, c = coeffs
# Compute Y values based on custom quadratic
y = a * x**2 + b * x + c
y = np.maximum(y, 0) # Ensure no negative values
df = pd.DataFrame({
f"{kpi_selected} Value ({unit})": x,
"Probability Density": y
})
# Plot
fig, ax = plt.subplots()
ax.plot(x, y, color='royalblue', linewidth=2, label='Deviation Curve')
# Markers for Min, Mean, Max
ax.plot(min_val, min_pdf, 'o', color='blue')
ax.plot(mean_val, mean_pdf, 'o', color='green')
ax.plot(max_val, max_pdf, 'o', color='red')
ax.annotate(f'{min_val} {unit}', (min_val, min_pdf), textcoords="offset points", xytext=(-10,10), ha='center', color='blue')
ax.annotate(f'{mean_val} {unit}', (mean_val, mean_pdf), textcoords="offset points", xytext=(0,10), ha='center', color='green')
ax.annotate(f'{max_val} {unit}', (max_val, max_pdf), textcoords="offset points", xytext=(10,10), ha='center', color='red')
ax.set_title(f"{kpi_selected} - Std. Deviation Curve")
ax.set_xlabel(f"{kpi_selected} ({unit})")
ax.set_ylabel("Probability Density")
ax.grid(True, which='both', linestyle='--', linewidth=0.5, alpha=0.7)
st.pyplot(fig)
# Download data as CSV
csv = df.to_csv(index=False).encode('utf-8')
st.download_button(
label="Download Data as CSV",
data=csv,
file_name=f"{kpi_selected}_custom_bell_curve_data.csv",
mime='text/csv'
)
# Download plot as PNG
buf = BytesIO()
fig.savefig(buf, format="png")
st.download_button(
label="Download Plot as PNG",
data=buf.getvalue(),
file_name=f"{kpi_selected}_custom_bell_curve_plot.png",
mime="image/png"
)
else:
st.warning("Please ensure that: Min < Mean < Max to generate a valid bell curve.")
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