66 lines
3.5 KiB
Python
66 lines
3.5 KiB
Python
import pandas as pd
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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# Load the data
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data = pd.read_csv('06-25_07-07.csv')
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# Convert 'Date/Time (--4:0:0)' column to datetime
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data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)'])
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# Calculate the time difference between consecutive points in minutes
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data['Time Diff (min)'] = data['Date/Time (--4:0:0)'].diff().dt.total_seconds() / 60
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# Set flow rate and specific heat capacity
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flow_rate_gpm = 10
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density_water = 62.4 # lbm/ft^3
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flow_rate_lbm_min = flow_rate_gpm * 0.133681 * density_water # Convert GPM to lbm/min
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# Calculate Q (BTU)
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cp_water = 1 # BTU/lbm°F
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data['Delta T (F)'] = data['Sewer Water Out'] - data['Sewer Water In']
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data['Q (BTU)'] = flow_rate_lbm_min * cp_water * data['Delta T (F)'] * data['Time Diff (min)']
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# Apply a smoothing function (moving average)
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window_size = 10
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data['Sewer Water In Smooth'] = data['Sewer Water In'].rolling(window=window_size, center=True).mean()
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data['Sewer Water Out Smooth'] = data['Sewer Water Out'].rolling(window=window_size, center=True).mean()
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data['Return Air Smooth'] = data['Return Air'].rolling(window=window_size, center=True).mean()
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data['Supply Air Smooth'] = data['Supply Air'].rolling(window=window_size, center=True).mean()
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data['Q Smooth'] = data['Q (BTU)'].rolling(window=window_size, center=True).mean()
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# Iterate through each calendar day and create separate plots
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for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique():
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# Filter data for the current day
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day_data = data[data['Date/Time (--4:0:0)'].dt.date == day]
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# Create subplot figure with secondary y-axis
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fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.1,
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subplot_titles=(f'Temperature at Inlets and Outlets of the Heat Exchanger and Transferred Heat - {day}',),
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specs=[[{"secondary_y": True}]])
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# Add traces for each line with spline smoothing and increased visibility
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fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
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line=dict(shape='spline', color='blue', width=2, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
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line=dict(shape='spline', color='red', width=2, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Return Air Smooth'], mode='lines', name='Return Air',
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line=dict(shape='spline', color='orange', width=2, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air',
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line=dict(shape='spline', color='green', width=2, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)',
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line=dict(shape='spline', color='purple', width=2, dash='solid')), secondary_y=True)
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# Update layout
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fig.update_layout(
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xaxis_title='Date/Time',
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yaxis_title='Temperature (F)',
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yaxis2_title='Transferred Heat (BTU/lb)',
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legend_title_text='Legend',
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font=dict(size=14),
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width=1400,
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height=800
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)
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# Save the plot (requires kaleido package)
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fig.write_image(f'temperature_variations_with_q_{day}.png') |