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new file mode 100644
index 0000000..b0ba7a0
--- /dev/null
+++ b/.idea/inspectionProfiles/Project_Default.xml
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new file mode 100644
index 0000000..105ce2d
--- /dev/null
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\ No newline at end of file
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new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
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+</project>
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new file mode 100644
index 0000000..066038e
--- /dev/null
+++ b/.idea/workspace.xml
@@ -0,0 +1,217 @@
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\ No newline at end of file
diff --git a/06-25_07-07.csv b/06-25_07-07.csv
index 67d79f2..f1dcaaa 100644
--- a/06-25_07-07.csv
+++ b/06-25_07-07.csv
@@ -3434,3 +3434,306 @@ Date/Time (UTC),Date/Time (--4:0:0),Sewer Water In,Sewer Water Out,Return Air,Su
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+7/8/2024 7:29,7/8/2024 3:29,63.972118,64.447079,70.751102,70.621568
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+7/8/2024 7:44,7/8/2024 3:44,64.015296,64.360722,70.664746,70.751102
+7/8/2024 7:49,7/8/2024 3:49,63.842583,63.972118,70.664746,70.794281
+7/8/2024 7:54,7/8/2024 3:54,63.583514,64.360722,70.751102,70.664746
+7/8/2024 7:59,7/8/2024 3:59,64.101653,63.972118,70.621568,70.751102
+7/8/2024 8:04,7/8/2024 4:04,64.058474,64.274366,70.751102,70.707924
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+7/8/2024 8:34,7/8/2024 4:34,63.626692,64.360722,70.751102,70.664746
+7/8/2024 8:39,7/8/2024 4:39,63.66987,64.144831,70.664746,70.880637
+7/8/2024 8:44,7/8/2024 4:44,63.713048,63.92894,70.751102,70.794281
+7/8/2024 8:49,7/8/2024 4:49,63.583514,63.92894,70.751102,70.794281
+7/8/2024 8:54,7/8/2024 4:54,63.66987,64.360722,70.751102,70.751102
+7/8/2024 8:59,7/8/2024 4:59,63.583514,64.317544,70.664746,70.837459
+7/8/2024 9:04,7/8/2024 5:04,63.799405,64.015296,70.707924,70.707924
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+7/8/2024 9:19,7/8/2024 5:19,63.583514,63.92894,70.707924,70.535211
+7/8/2024 9:24,7/8/2024 5:24,63.713048,64.317544,70.707924,70.664746
+7/8/2024 9:29,7/8/2024 5:29,63.583514,64.188009,70.664746,70.751102
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+7/8/2024 9:39,7/8/2024 5:39,63.799405,64.058474,70.837459,70.794281
+7/8/2024 9:44,7/8/2024 5:44,63.540335,63.972118,70.794281,70.794281
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+7/8/2024 10:09,7/8/2024 6:09,63.92894,64.058474,70.664746,70.707924
+7/8/2024 10:14,7/8/2024 6:14,63.885761,64.188009,70.621568,70.535211
+7/8/2024 10:19,7/8/2024 6:19,63.92894,64.188009,70.621568,70.751102
+7/8/2024 10:24,7/8/2024 6:24,64.058474,64.835683,70.707924,70.621568
+7/8/2024 10:29,7/8/2024 6:29,63.92894,64.447079,70.707924,70.621568
+7/8/2024 10:34,7/8/2024 6:34,63.92894,64.4039,70.621568,70.707924
+7/8/2024 10:39,7/8/2024 6:39,63.885761,64.490257,70.57839,70.621568
+7/8/2024 10:44,7/8/2024 6:44,63.799405,63.799405,70.621568,70.707924
+7/8/2024 10:49,7/8/2024 6:49,63.799405,64.360722,70.751102,70.794281
+7/8/2024 10:54,7/8/2024 6:54,64.015296,64.274366,70.535211,70.621568
+7/8/2024 10:59,7/8/2024 6:59,64.058474,64.188009,70.621568,70.621568
+7/8/2024 11:04,7/8/2024 7:04,64.015296,64.274366,70.621568,70.621568
+7/8/2024 11:09,7/8/2024 7:09,63.92894,64.66297,70.57839,70.621568
+7/8/2024 11:14,7/8/2024 7:14,63.972118,64.144831,70.621568,70.621568
+7/8/2024 11:19,7/8/2024 7:19,63.885761,64.144831,70.57839,70.535211
+7/8/2024 11:24,7/8/2024 7:24,64.101653,64.144831,70.707924,70.751102
+7/8/2024 11:29,7/8/2024 7:29,63.972118,64.490257,70.664746,70.751102
+7/8/2024 11:34,7/8/2024 7:34,63.972118,64.66297,70.707924,70.664746
+7/8/2024 11:39,7/8/2024 7:39,63.972118,64.188009,70.57839,70.492033
+7/8/2024 11:44,7/8/2024 7:44,64.317544,64.66297,70.535211,70.535211
+7/8/2024 11:49,7/8/2024 7:49,64.188009,64.749326,70.57839,70.707924
+7/8/2024 11:54,7/8/2024 7:54,64.101653,64.533435,70.535211,70.621568
+7/8/2024 11:59,7/8/2024 7:59,64.188009,64.576613,70.57839,70.448855
+7/8/2024 12:04,7/8/2024 8:04,64.274366,64.792505,70.57839,70.57839
+7/8/2024 12:09,7/8/2024 8:09,64.231187,64.66297,70.492033,70.492033
+7/8/2024 12:14,7/8/2024 8:14,64.274366,64.706148,70.405677,70.57839
+7/8/2024 12:19,7/8/2024 8:19,64.360722,64.749326,70.535211,70.362498
+7/8/2024 12:24,7/8/2024 8:24,64.317544,64.835683,70.492033,70.621568
+7/8/2024 12:29,7/8/2024 8:29,64.490257,64.66297,70.492033,70.621568
+7/8/2024 12:34,7/8/2024 8:34,64.447079,64.533435,70.448855,70.621568
+7/8/2024 12:39,7/8/2024 8:39,64.447079,64.66297,70.57839,70.57839
+7/8/2024 12:44,7/8/2024 8:44,64.317544,64.66297,70.57839,70.57839
+7/8/2024 12:49,7/8/2024 8:49,64.533435,64.447079,70.448855,70.57839
+7/8/2024 12:54,7/8/2024 8:54,64.447079,64.66297,70.405677,70.448855
+7/8/2024 12:59,7/8/2024 8:59,64.447079,64.706148,70.535211,70.362498
+7/8/2024 13:04,7/8/2024 9:04,64.447079,64.749326,70.751102,70.751102
diff --git a/daily_plots.py b/daily_plots.py
index 365553c..c41d556 100644
--- a/daily_plots.py
+++ b/daily_plots.py
@@ -29,13 +29,13 @@ for day in pd.Index(data.index.date).unique():
     fig = go.Figure()
 
     fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
-                             line=dict(shape='spline', color='blue', width=4, dash='solid')))
+                             line=dict(shape='spline', color='blue', width=2, dash='solid')))
     fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
-                             line=dict(shape='spline', color='red', width=4, dash='solid')))
+                             line=dict(shape='spline', color='red', width=2, dash='solid')))
     fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Return Air Smooth'], mode='lines', name='Return Air',
-                             line=dict(shape='spline', color='orange', width=4, dash='solid')))
+                             line=dict(shape='spline', color='orange', width=2, dash='solid')))
     fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air',
-                             line=dict(shape='spline', color='green', width=4, dash='solid')))
+                             line=dict(shape='spline', color='green', width=2, dash='solid')))
 
     fig.update_layout(
         title=dict(
@@ -60,6 +60,6 @@ for day in pd.Index(data.index.date).unique():
     )
 
     # Save the plot (requires kaleido package)
-    fig.write_image(f'plotly_plot_{day}.png')
+    fig.write_image(f'temperature_variations_without_Q_{day}.png')
 
 
diff --git a/double_y.py b/double_y.py
index 5b34abb..764f421 100644
--- a/double_y.py
+++ b/double_y.py
@@ -1,44 +1,64 @@
 import pandas as pd
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
 
-# Load the data (assuming you've already loaded and processed it as shown)
+# Load the data
 data = pd.read_csv('06-25_07-07.csv')
 
-# Calculation of Q (BTU / lb)
-data['Q (BTU/lb)'] = data['Sewer Water Out'] - data['Sewer Water In']
-
 # 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/lb)'].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=(f'Temperature Variations and Transferred Heat from 25/06/2024-07/07/2024',),
-                        specs=[[{"secondary_y": True}]])
+                    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=4, dash='solid')), secondary_y=False)
+                         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=4, dash='solid')), secondary_y=False)
+                         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=4, dash='solid')), secondary_y=False)
+                         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=4, 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/lb)',
-                         line=dict(shape='spline', color='purple', width=4, dash='solid')), secondary_y=True)
-# Update layout
+                         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('plotly_plot_with_q.png')
\ No newline at end of file
+fig.write_image('temperature_variations_with_q.png')
+
+# Display the plot
+fig.show()
\ No newline at end of file
diff --git a/double_y_daily.py b/double_y_daily.py
index ccfb855..69394a3 100644
--- a/double_y_daily.py
+++ b/double_y_daily.py
@@ -2,22 +2,32 @@ import pandas as pd
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
-# Load the data (assuming you've already loaded and processed it as shown)
+# Load the data
 data = pd.read_csv('06-25_07-07.csv')
 
-# Calculation of Q (BTU / lb)
-data['Q (BTU/lb)'] = data['Sewer Water Out'] - data['Sewer Water In']
-
 # 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/lb)'].rolling(window=window_size, center=True).mean()
+data['Q Smooth'] = data['Q (BTU)'].rolling(window=window_size, center=True).mean()
 
 # Iterate through each calendar day and create separate plots
 for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique():
@@ -31,20 +41,21 @@ for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique():
 
     # Add traces for each line with spline smoothing and increased visibility
     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',
-                             line=dict(shape='spline', color='blue', width=4, dash='solid')), secondary_y=False)
+                             line=dict(shape='spline', color='blue', width=2, dash='solid')), secondary_y=False)
     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',
-                             line=dict(shape='spline', color='red', width=4, dash='solid')), secondary_y=False)
+                             line=dict(shape='spline', color='red', width=2, dash='solid')), secondary_y=False)
     fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Return Air Smooth'], mode='lines', name='Return Air',
-                             line=dict(shape='spline', color='orange', width=4, dash='solid')), secondary_y=False)
+                             line=dict(shape='spline', color='orange', width=2, dash='solid')), secondary_y=False)
     fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air',
-                             line=dict(shape='spline', color='green', width=4, dash='solid')), secondary_y=False)
+                             line=dict(shape='spline', color='green', width=2, dash='solid')), secondary_y=False)
     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)',
-                             line=dict(shape='spline', color='purple', width=4, dash='solid')), secondary_y=True)
+                             line=dict(shape='spline', color='purple', width=2, dash='solid')), secondary_y=True)
 
     # Update layout
     fig.update_layout(
         xaxis_title='Date/Time',
         yaxis_title='Temperature (F)',
+        yaxis2_title='Transferred Heat (BTU/lb)',
         legend_title_text='Legend',
         font=dict(size=14),
         width=1400,
@@ -52,4 +63,4 @@ for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique():
     )
 
     # Save the plot (requires kaleido package)
-    fig.write_image(f'plotly_plot_{day}.png')
\ No newline at end of file
+    fig.write_image(f'temperature_variations_with_q_{day}.png')
\ No newline at end of file
diff --git a/main.py b/main.py
index bad5b7c..a4e9e3e 100644
--- a/main.py
+++ b/main.py
@@ -21,13 +21,13 @@ fig = go.Figure()
 
 # Add traces for each line with spline smoothing and increased visibility
 fig.add_trace(go.Scatter(x=data.index, y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
-                         line=dict(shape='spline', color='blue', width=4, dash='solid')))
+                         line=dict(shape='spline', color='blue', width=2, dash='solid')))
 fig.add_trace(go.Scatter(x=data.index, y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
-                         line=dict(shape='spline', color='red', width=4, dash='solid')))
+                         line=dict(shape='spline', color='red', width=2, dash='solid')))
 fig.add_trace(go.Scatter(x=data.index, y=data['Return Air Smooth'], mode='lines', name='Return Air',
-                         line=dict(shape='spline', color='orange', width=4, dash='solid')))
+                         line=dict(shape='spline', color='orange', width=2, dash='solid')))
 fig.add_trace(go.Scatter(x=data.index, y=data['Supply Air Smooth'], mode='lines', name='Supply Air',
-                         line=dict(shape='spline', color='green', width=4, dash='solid')))
+                         line=dict(shape='spline', color='green', width=2, dash='solid')))
 
 # Add shading to separate days
 for i in range((data.index[-1] - data.index[0]).days + 1):
@@ -43,7 +43,7 @@ for i in range((data.index[-1] - data.index[0]).days + 1):
 # Update layout for better styling and to set figure size
 fig.update_layout(
     title=dict(
-        text='Temperature Variations from 25/06/2024-07/07/2024',
+        text='Temperature Variations from 25/06/2024-08/07/2024',
         font=dict(size=20, family='Arial', color='black', weight='bold')
     ),
     xaxis_title=dict(
@@ -64,7 +64,7 @@ fig.update_layout(
 )
 
 # Save the plot (requires kaleido package)
-fig.write_image('plotly_plot.png')
+fig.write_image('temperatures without Q.png')
 
 # Display the plot
 fig.show()
diff --git a/q_calculation.py b/q_calculation.py
index 6a2eb2a..83ff54d 100644
--- a/q_calculation.py
+++ b/q_calculation.py
@@ -1,29 +1,38 @@
 import pandas as pd
 import plotly.graph_objects as go
 
-# Load the data (assuming you've already loaded and processed it as shown)
+# Load the data
 data = pd.read_csv('06-25_07-07.csv')
-# Calculation of Q (BTU / lb)
-t_sewer_in = data['Sewer Water In'].to_list()
-t_sewer_out = data['Sewer Water Out'].to_list()  # corrected column name
-q = [t_sewer_out[i] - t_sewer_in[i] for i in range(len(t_sewer_out))]
-data['Q (BTU/lb)'] = q
+
 # 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/lb)'].rolling(window=window_size, center=True).mean()
+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=4, dash='solid')))
+                         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):
@@ -47,7 +56,7 @@ fig.update_layout(
         font=dict(size=18, family='Arial', color='black', weight='bold')
     ),
     yaxis_title=dict(
-        text='Energy (BTU/lb)',
+        text='Energy (BTU)',
         font=dict(size=18, family='Arial', color='black', weight='bold')
     ),
     legend=dict(