import pandas as pd

# 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)']

# Apply a smoothing function (moving average)
window_size = 10
data['Sewer Water In Smooth'] = data['Sewer Water In'].rolling(window=window_size, center=True).mean()
data['Sewer Water Out Smooth'] = data['Sewer Water Out'].rolling(window=window_size, center=True).mean()
data['Return Air Smooth'] = data['Return Air'].rolling(window=window_size, center=True).mean()
data['Supply Air Smooth'] = data['Supply Air'].rolling(window=window_size, center=True).mean()
data['Q Smooth'] = data['Q (BTU)'].rolling(window=window_size, center=True).mean()

# Plotting the data
import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.1,
                    subplot_titles=('Temperature Variations and Transferred Heat from 25/06/2024-08/07/2024',),
                    specs=[[{"secondary_y": True}]])

fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
                         line=dict(shape='spline', color='blue', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
                         line=dict(shape='spline', color='red', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Return Air Smooth'], mode='lines', name='Return Air',
                         line=dict(shape='spline', color='orange', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Supply Air Smooth'], mode='lines', name='Supply Air',
                         line=dict(shape='spline', color='green', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU)',
                         line=dict(shape='spline', color='purple', width=2, dash='solid')), secondary_y=True)

# Update layout with secondary y-axis title
fig.update_layout(
    xaxis_title='Date/Time',
    yaxis_title='Temperature (F)',
    yaxis2_title='Transferred Heat (BTU)',
    legend_title_text='Legend',
    font=dict(size=14),
    width=1400,
    height=800
)

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

# Display the plot
fig.show()