coniston/q_calculation.py

import pandas as pd
import plotly.graph_objects as go

# Load the data
data = pd.read_csv('06-25_07-07.csv')

# Convert 'Date/Time (--4:0:0)' column to datetime
data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)'])

# Calculate the time difference between consecutive points in minutes
data['Time Diff (min)'] = data['Date/Time (--4:0:0)'].diff().dt.total_seconds() / 60

# Set flow rate and specific heat capacity
flow_rate_gpm = 10
density_water = 62.4  # lbm/ft^3
flow_rate_lbm_min = flow_rate_gpm * 0.133681 * density_water  # Convert GPM to lbm/min

# Calculate Q (BTU)
cp_water = 1  # BTU/lbm°F
data['Delta T (F)'] = data['Sewer Water Out'] - data['Sewer Water In']
data['Q (BTU)'] = flow_rate_lbm_min * cp_water * data['Delta T (F)'] * data['Time Diff (min)']

# Set the datetime column as the index
data.set_index('Date/Time (--4:0:0)', inplace=True)

# Apply a smoothing function (moving average)
window_size = 10
data['Q Smooth'] = data['Q (BTU)'].rolling(window=window_size, center=True).mean()
# Ensure index is a datetime index for ease of filtering
data.index = pd.to_datetime(data.index)
# Create the plot
fig = go.Figure()
# Add traces for each line with spline smoothing and increased visibility
fig.add_trace(go.Scatter(x=data.index, y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)',
                         line=dict(shape='spline', color='blue', width=2, dash='solid')))

# Add shading to separate days
for i in range((data.index[-1] - data.index[0]).days + 1):
    start = data.index[0] + pd.Timedelta(days=i)
    end = start + pd.Timedelta(days=1)
    fig.add_vrect(
        x0=start, x1=end,
        fillcolor='grey' if i % 2 == 0 else 'white',
        opacity=0.1,
        line_width=0
    )

# Update layout for better styling and to set figure size
fig.update_layout(
    title=dict(
        text='Transferred Heat',
        font=dict(size=20, family='Arial', color='black', weight='bold')
    ),
    xaxis_title=dict(
        text='Date/Time',
        font=dict(size=18, family='Arial', color='black', weight='bold')
    ),
    yaxis_title=dict(
        text='Energy (BTU)',
        font=dict(size=18, family='Arial', color='black', weight='bold')
    ),
    legend=dict(
        title=dict(font=dict(size=15, family='Arial', color='black', weight='bold')),
        font=dict(size=14, family='Arial', color='black', weight='bold')
    ),
    font=dict(size=14),
    width=1400,  # Set the width of the figure
    height=800   # Set the height of the figure
)

# Save the plot (requires kaleido package)
fig.write_image('heat.png')

# Display the plot
fig.show()