feat: first commit
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commit
6f4c89a756
3436
06-25_07-07.csv
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3436
06-25_07-07.csv
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65
daily_plots.py
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65
daily_plots.py
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import pandas as pd
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import plotly.graph_objects as go
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# Load the data (assuming you've already loaded and processed it as shown)
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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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# Set the datetime column as the index
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data.set_index('Date/Time (--4:0:0)', inplace=True)
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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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# Ensure index is a datetime index for ease of filtering
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data.index = pd.to_datetime(data.index)
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# Loop through each calendar day and create separate plots
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for day in pd.Index(data.index.date).unique():
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# Filter data for the current day
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day_data = data[data.index.date == day]
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# Create the plot for the current day
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fig = go.Figure()
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fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
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line=dict(shape='spline', color='blue', width=4, dash='solid')))
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fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
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line=dict(shape='spline', color='red', width=4, dash='solid')))
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fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Return Air Smooth'], mode='lines', name='Return Air',
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line=dict(shape='spline', color='orange', width=4, dash='solid')))
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fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air',
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line=dict(shape='spline', color='green', width=4, dash='solid')))
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fig.update_layout(
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title=dict(
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text=f'Temperature at Inlets and Outlets of the Heat Exchanger - {day}',
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font=dict(size=20, family='Arial', color='black', weight='bold')
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),
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xaxis_title=dict(
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text='Date/Time',
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font=dict(size=18, family='Arial', color='black', weight='bold')
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),
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yaxis_title=dict(
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text='Temperature (F)',
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font=dict(size=18, family='Arial', color='black', weight='bold'),
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),
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legend=dict(
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title=dict(font=dict(size=15, family='Arial', color='black', weight='bold')),
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font=dict(size=14, family='Arial', color='black', weight='bold')
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),
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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'plotly_plot_{day}.png')
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44
double_y.py
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44
double_y.py
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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 (assuming you've already loaded and processed it as shown)
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data = pd.read_csv('06-25_07-07.csv')
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# Calculation of Q (BTU / lb)
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data['Q (BTU/lb)'] = data['Sewer Water Out'] - data['Sewer Water In']
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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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# 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/lb)'].rolling(window=window_size, center=True).mean()
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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 Variations and Transferred Heat from 25/06/2024-07/07/2024',),
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specs=[[{"secondary_y": True}]])
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fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
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line=dict(shape='spline', color='blue', width=4, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
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line=dict(shape='spline', color='red', width=4, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Return Air Smooth'], mode='lines', name='Return Air',
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line=dict(shape='spline', color='orange', width=4, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Supply Air Smooth'], mode='lines', name='Supply Air',
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line=dict(shape='spline', color='green', width=4, dash='solid')), secondary_y=False)
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fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)',
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line=dict(shape='spline', color='purple', width=4, 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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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('plotly_plot_with_q.png')
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55
double_y_daily.py
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55
double_y_daily.py
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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 (assuming you've already loaded and processed it as shown)
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data = pd.read_csv('06-25_07-07.csv')
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# Calculation of Q (BTU / lb)
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data['Q (BTU/lb)'] = data['Sewer Water Out'] - data['Sewer Water In']
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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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# 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/lb)'].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=4, 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=4, 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=4, 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=4, 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=4, 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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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'plotly_plot_{day}.png')
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70
main.py
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70
main.py
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import pandas as pd
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import plotly.graph_objects as go
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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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# Set the datetime column as the index
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data.set_index('Date/Time (--4:0:0)', inplace=True)
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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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# Create the plot
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fig = go.Figure()
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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=data.index, y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
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line=dict(shape='spline', color='blue', width=4, dash='solid')))
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fig.add_trace(go.Scatter(x=data.index, y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
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line=dict(shape='spline', color='red', width=4, dash='solid')))
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fig.add_trace(go.Scatter(x=data.index, y=data['Return Air Smooth'], mode='lines', name='Return Air',
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line=dict(shape='spline', color='orange', width=4, dash='solid')))
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fig.add_trace(go.Scatter(x=data.index, y=data['Supply Air Smooth'], mode='lines', name='Supply Air',
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line=dict(shape='spline', color='green', width=4, dash='solid')))
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# Add shading to separate days
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for i in range((data.index[-1] - data.index[0]).days + 1):
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start = data.index[0] + pd.Timedelta(days=i)
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end = start + pd.Timedelta(days=1)
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fig.add_vrect(
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x0=start, x1=end,
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fillcolor='grey' if i % 2 == 0 else 'white',
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opacity=0.1,
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line_width=0
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)
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# Update layout for better styling and to set figure size
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fig.update_layout(
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title=dict(
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text='Temperature Variations from 25/06/2024-07/07/2024',
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font=dict(size=20, family='Arial', color='black', weight='bold')
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),
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xaxis_title=dict(
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text='Date/Time',
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font=dict(size=18, family='Arial', color='black', weight='bold')
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),
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yaxis_title=dict(
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text='Temperature (F)',
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font=dict(size=18, family='Arial', color='black', weight='bold')
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),
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legend=dict(
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title=dict(font=dict(size=15, family='Arial', color='black', weight='bold')),
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font=dict(size=14, family='Arial', color='black', weight='bold')
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),
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font=dict(size=14),
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width=1400, # Set the width of the figure
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height=800 # Set the height of the figure
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)
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# Save the plot (requires kaleido package)
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fig.write_image('plotly_plot.png')
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# Display the plot
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fig.show()
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66
q_calculation.py
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66
q_calculation.py
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import pandas as pd
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import plotly.graph_objects as go
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# Load the data (assuming you've already loaded and processed it as shown)
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data = pd.read_csv('06-25_07-07.csv')
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# Calculation of Q (BTU / lb)
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t_sewer_in = data['Sewer Water In'].to_list()
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t_sewer_out = data['Sewer Water Out'].to_list() # corrected column name
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q = [t_sewer_out[i] - t_sewer_in[i] for i in range(len(t_sewer_out))]
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data['Q (BTU/lb)'] = q
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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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# Set the datetime column as the index
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data.set_index('Date/Time (--4:0:0)', inplace=True)
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# Apply a smoothing function (moving average)
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window_size = 10
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data['Q Smooth'] = data['Q (BTU/lb)'].rolling(window=window_size, center=True).mean()
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# Ensure index is a datetime index for ease of filtering
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data.index = pd.to_datetime(data.index)
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# Create the plot
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fig = go.Figure()
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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=data.index, y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)',
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line=dict(shape='spline', color='blue', width=4, dash='solid')))
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# Add shading to separate days
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for i in range((data.index[-1] - data.index[0]).days + 1):
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start = data.index[0] + pd.Timedelta(days=i)
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end = start + pd.Timedelta(days=1)
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fig.add_vrect(
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x0=start, x1=end,
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fillcolor='grey' if i % 2 == 0 else 'white',
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opacity=0.1,
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line_width=0
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)
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# Update layout for better styling and to set figure size
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fig.update_layout(
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title=dict(
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text='Transferred Heat',
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font=dict(size=20, family='Arial', color='black', weight='bold')
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),
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xaxis_title=dict(
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text='Date/Time',
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font=dict(size=18, family='Arial', color='black', weight='bold')
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),
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yaxis_title=dict(
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text='Energy (BTU/lb)',
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font=dict(size=18, family='Arial', color='black', weight='bold')
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),
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legend=dict(
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title=dict(font=dict(size=15, family='Arial', color='black', weight='bold')),
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font=dict(size=14, family='Arial', color='black', weight='bold')
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),
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font=dict(size=14),
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width=1400, # Set the width of the figure
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height=800 # Set the height of the figure
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)
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# Save the plot (requires kaleido package)
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fig.write_image('heat.png')
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# Display the plot
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fig.show()
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