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utrecht-pollution-prediction

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NN implementation

by akseljoonas HF Staff - opened Oct 31, 2024

base: refs/heads/main

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from: refs/pr/1

Discussion Files changed

+504

-902

Files changed (15) hide show

.DS_Store +0 -0
app.py +24 -47
pages/admin.py +2 -2
past_pollution_data.csv +17 -35
past_weather_data.csv +1 -164
pollution_data.csv +14 -32
predictions_history.csv +71 -204
requirements.txt +0 -2
src/data_api_calls.py +10 -34
src/features_pipeline.py +6 -38
src/helper_functions.py +15 -59
src/past_data_api_calls.py +4 -20
src/predict.py +13 -103
test.ipynb +327 -0
weather_data.csv +0 -162

.DS_Store DELETED Viewed

Binary file (6.15 kB)

app.py CHANGED Viewed

@@ -2,6 +2,7 @@ import altair as alt
 import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
 from src.helper_functions import custom_metric_box, pollution_box
 from src.predict import get_data_and_predictions, update_data_and_predictions
@@ -44,7 +45,8 @@ col1, col2 = st.columns((1, 3))
 # Create a 3-column layout
 with col1:
     st.subheader("Current Weather")
     custom_metric_box(
         label="🥵 Temperature",
         value=f"{round(today['mean_temp'] * 0.1)} °C",
@@ -74,50 +76,38 @@ with col1:
 with col2:
     st.subheader("Current Pollution Levels")
     sub1, sub2 = st.columns((1, 1))
     # Ozone (O₃) Pollution Box
     with sub1:
         pollution_box(
             label="O<sub>3</sub>",
             value=f"{round(today['O3'])} µg/m³",
             delta=f"{round(int(today['O3']) - int(previous_day['O3']))} µg/m³",
-            threshold=120,
         )
         with st.expander("Learn more about O3", expanded=False):
             st.markdown(
                 """
-                *Ozone (O<sub>3</sub>)* is a gas that occurs both in the Earth's upper atmosphere and at ground level.
-                **Levels interpretation:**
-                * <span style='color:#77C124'>**Good**</span> (< 120 µg/m³): Safe for most people. Ideal conditions for outdoor activities.
-                * <span style='color:#E68B0A'>**Medium**</span> (120-240 µg/m³): May cause breathing discomfort for sensitive groups. People with respiratory conditions should limit prolonged outdoor exposure.
-                * <span style='color:#E63946'>**Bad**</span> (> 240 µg/m³): Health alert! Everyone may experience breathing difficulties. Avoid outdoor activities, especially during peak sunlight hours.
-                *Health Effects:* High levels can cause throat irritation, coughing, chest pain, and shortness of breath. Long-term exposure may lead to reduced lung function and chronic respiratory illness.
-                """,
                 unsafe_allow_html=True,
             )
     # Nitrogen Dioxide (NO₂) Pollution Box
     with sub2:
         pollution_box(
             label="NO<sub>2</sub>",
             value=f"{round(today['NO2'])} µg/m³",
             delta=f"{round(int(today['NO2']) - int(previous_day['NO2']))} µg/m³",
-            threshold=40,
         )
         with st.expander("Learn more about NO2", expanded=False):
             st.markdown(
                 """
-                *Nitrogen Dioxide (NO<sub>2</sub>)* is primarily produced by vehicles and industrial processes.
-                **Levels interpretation:**
-                * <span style='color:#77C124'>**Good**</span> (< 40 µg/m³): Healthy air quality. No restrictions on outdoor activities.
-                * <span style='color:#E68B0A'>**Medium**</span> (40-80 µg/m³): Moderate pollution. Sensitive individuals should consider reducing prolonged outdoor exertion.
-                * <span style='color:#E63946'>**Bad**</span> (> 80 µg/m³): Poor air quality. Everyone should reduce outdoor activities, particularly near high-traffic areas.
-                *Health Effects:* Can inflame airways, aggravate asthma, and increase susceptibility to respiratory infections. Long-term exposure may affect lung development in children and contribute to heart disease.
-                """,
                 unsafe_allow_html=True,
             )
@@ -128,12 +118,9 @@ with col2:
     def get_simple_color_scale(values, threshold):
         """Returns green for values below the threshold, orange for values between the threshold and 2x the threshold, and red for values above 2x the threshold."""
         return [
-            "#77C124"
-            if v < threshold
-            else "#E68B0A"
-            if v < 2 * threshold
-            else "#E63946"
-            for v in values
         ]
     # O3 Bar Plot (threshold: 40)
@@ -155,17 +142,13 @@ with col2:
     )
     # Add predicted values with reduced opacity
-    predicted_o3_colors = get_simple_color_scale(
-        o3_future_values, 40
-    )  # Color for future values
     fig_o3.add_trace(
         go.Bar(
             x=df["Date"][-3:],  # Dates for predicted values
             y=o3_future_values,
             name="O3 Predicted",
-            marker=dict(
-                color=predicted_o3_colors, opacity=0.5
-            ),  # Set opacity to 0.5 for predictions
             hovertemplate="%{x|%d-%b-%Y}<br>%{y} µg/m³<extra></extra>",
         )
     )
@@ -180,7 +163,7 @@ with col2:
             line=dict(color="White", width=3, dash="dash"),
         )
     )
     fig_o3.update_layout(
         plot_bgcolor="rgba(0, 0, 0, 0)",
         paper_bgcolor="rgba(0, 0, 0, 0)",
@@ -196,8 +179,7 @@ with col2:
             tickangle=-45,
             tickcolor="gray",
         ),
-        showlegend=False,  # Disable legend
-        margin=dict(r=100)  # Add right margin for legend
     )
     st.plotly_chart(fig_o3, key="fig_o3")
@@ -222,17 +204,13 @@ with col2:
     )
     # Add predicted values with reduced opacity
-    predicted_no2_colors = get_simple_color_scale(
-        no2_future_values, 120
-    )  # Color for future values
     fig_no2.add_trace(
         go.Bar(
             x=df["Date"][-3:],  # Dates for predicted values
             y=no2_future_values,
             name="NO2 Predicted",
-            marker=dict(
-                color=predicted_no2_colors, opacity=0.5
-            ),  # Set opacity to 0.5 for predictions
             hovertemplate="%{x|%d-%b-%Y}<br>%{y} µg/m³<extra></extra>",
         )
     )
@@ -263,8 +241,7 @@ with col2:
             tickangle=-45,
             tickcolor="gray",
         ),
-        showlegend=False,  # Disable legend
-        margin=dict(r=100)  # Add right margin for legend
     )
-    st.plotly_chart(fig_no2, key="fig_no2")

 import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
 from src.helper_functions import custom_metric_box, pollution_box
 from src.predict import get_data_and_predictions, update_data_and_predictions
 # Create a 3-column layout
 with col1:
     st.subheader("Current Weather")
     custom_metric_box(
         label="🥵 Temperature",
         value=f"{round(today['mean_temp'] * 0.1)} °C",
 with col2:
     st.subheader("Current Pollution Levels")
     sub1, sub2 = st.columns((1, 1))
     # Ozone (O₃) Pollution Box
     with sub1:
         pollution_box(
             label="O<sub>3</sub>",
             value=f"{round(today['O3'])} µg/m³",
             delta=f"{round(int(today['O3']) - int(previous_day['O3']))} µg/m³",
+            threshold=120
         )
         with st.expander("Learn more about O3", expanded=False):
             st.markdown(
                 """
+                *Ozone (O<sub>3</sub>)*: A harmful gas at ground level that can irritate the respiratory system and aggravate asthma.<br>
+                **Good/Bad**: "Good" means safe levels for most people, while "Bad" suggests harmful levels, especially for sensitive groups.
+                """,
                 unsafe_allow_html=True,
             )
     # Nitrogen Dioxide (NO₂) Pollution Box
     with sub2:
         pollution_box(
             label="NO<sub>2</sub>",
             value=f"{round(today['NO2'])} µg/m³",
             delta=f"{round(int(today['NO2']) - int(previous_day['NO2']))} µg/m³",
+            threshold=40
         )
         with st.expander("Learn more about NO2", expanded=False):
             st.markdown(
                 """
+                *Nitrogen Dioxide (NO<sub>2</sub>)*: A toxic gas that contributes to lung irritation and worsens asthma and other respiratory issues.<br>
+                **Good/Bad**: "Good" means safe air quality, while "Bad" indicates levels that could cause respiratory problems, especially for vulnerable individuals.
+                """,
                 unsafe_allow_html=True,
             )
     def get_simple_color_scale(values, threshold):
         """Returns green for values below the threshold, orange for values between the threshold and 2x the threshold, and red for values above 2x the threshold."""
         return [
+            "#77C124" if v < threshold else
+            "#E68B0A" if v < 2 * threshold else
+            "#E63946" for v in values
         ]
     # O3 Bar Plot (threshold: 40)
     )
     # Add predicted values with reduced opacity
+    predicted_o3_colors = get_simple_color_scale(o3_future_values, 40)  # Color for future values
     fig_o3.add_trace(
         go.Bar(
             x=df["Date"][-3:],  # Dates for predicted values
             y=o3_future_values,
             name="O3 Predicted",
+            marker=dict(color=predicted_o3_colors, opacity=0.5),  # Set opacity to 0.5 for predictions
             hovertemplate="%{x|%d-%b-%Y}<br>%{y} µg/m³<extra></extra>",
         )
     )
             line=dict(color="White", width=3, dash="dash"),
         )
     )
     fig_o3.update_layout(
         plot_bgcolor="rgba(0, 0, 0, 0)",
         paper_bgcolor="rgba(0, 0, 0, 0)",
             tickangle=-45,
             tickcolor="gray",
         ),
+        showlegend=False  # Disable legend
     )
     st.plotly_chart(fig_o3, key="fig_o3")
     )
     # Add predicted values with reduced opacity
+    predicted_no2_colors = get_simple_color_scale(no2_future_values, 120)  # Color for future values
     fig_no2.add_trace(
         go.Bar(
             x=df["Date"][-3:],  # Dates for predicted values
             y=no2_future_values,
             name="NO2 Predicted",
+            marker=dict(color=predicted_no2_colors, opacity=0.5),  # Set opacity to 0.5 for predictions
             hovertemplate="%{x|%d-%b-%Y}<br>%{y} µg/m³<extra></extra>",
         )
     )
             tickangle=-45,
             tickcolor="gray",
         ),
+        showlegend=False  # Disable legend
     )
+    st.plotly_chart(fig_no2, key="fig_no2")

pages/admin.py CHANGED Viewed

@@ -5,8 +5,8 @@ import streamlit as st
 from sklearn.metrics import mean_squared_error
 from src.data_api_calls import get_combined_data
-USERNAME = "dragonkiller"
-PASSWORD = "donkey"
 st.title("Admin Panel")

 from sklearn.metrics import mean_squared_error
 from src.data_api_calls import get_combined_data
+USERNAME = "admin"
+PASSWORD = "password"
 st.title("Admin Panel")

past_pollution_data.csv CHANGED Viewed

@@ -1,36 +1,18 @@
 date,NO2,O3
-2024-03-10,11.668918918918921,51.56918367346938
-2024-03-11,10.072266666666666,47.56326086956521
-2024-03-12,18.938266666666678,30.813999999999993
-2024-03-13,16.765890410958903,41.9352
-2024-03-14,16.93999999999999,40.3236
-2024-03-15,15.422297297297296,46.98500000000001
-2024-03-16,7.8115999999999985,72.5396
-2024-03-17,18.312837837837836,55.46920000000001
-2024-03-18,14.224266666666663,42.890800000000006
-2024-03-19,23.020684931506846,48.69565217391303
-2024-03-20,29.86027397260274,39.999387755102035
-2024-03-21,21.25573333333333,51.676599999999986
-2024-03-22,18.402266666666655,47.357
-2024-03-23,20.759999999999998,48.91148936170213
-2024-03-24,8.37305555555556,70.8304
-2024-03-25,14.175479452054791,61.0036
-2024-03-26,14.980533333333334,61.01260000000001
-2024-03-27,20.856666666666666,45.10062500000001
-2024-03-28,15.91,54.184200000000004
-2024-03-29,11.761267605633797,73.31940000000002
-2024-03-30,13.714054054054053,67.0428
-2024-03-31,15.803835616438358,57.12458333333333
-2024-04-01,13.02464788732394,48.08100000000001
-2024-04-02,14.910800000000005,58.49
-2024-04-03,15.47794520547946,59.6916
-2024-04-04,13.879864864864864,61.80291666666668
-2024-04-05,11.207777777777782,68.86779999999997
-2024-04-06,17.002112676056335,51.04679999999998
-2024-04-07,11.993066666666666,74.87919999999998
-2024-04-08,13.275540540540538,68.55125000000001
-2024-04-09,14.217246376811596,66.66978260869566
-2024-04-10,10.244285714285716,68.9824
-2024-04-11,12.350533333333333,70.38673469387756
-2024-04-12,14.588219178082195,40.88354166666665
-2024-04-13,11.41386666666667,44.19551020408163

 date,NO2,O3
+2023-10-18,10.8427027027027,39.81260000000001
+2023-10-19,17.97026666666666,31.779024390243908
+2023-10-20,17.233055555555563,18.7156
+2023-10-21,15.023599999999991,22.04
+2023-10-22,8.723378378378372,48.33439999999999
+2023-10-23,20.63426666666668,15.586000000000002
+2023-10-24,15.1156,24.62808510638297
+2023-10-25,22.88567567567568,27.117599999999992
+2023-10-26,21.53175675675676,13.3216
+2023-10-27,23.07226666666666,16.15416666666666
+2023-10-28,24.89121621621622,24.59040816326531
+2023-10-29,9.724428571428573,51.525200000000005
+2023-10-30,11.20205479452055,52.820600000000006
+2023-10-31,17.494666666666667,44.458541666666655
+2023-11-01,21.588095238095235,29.20631578947369
+2023-11-02,9.745714285714286,48.39760869565216
+2023-11-03,7.163243243243242,61.421599999999984

past_weather_data.csv CHANGED Viewed

@@ -15,167 +15,4 @@ date,temp,humidity,precip,windspeed,sealevelpressure,visibility,solarradiation
 2023-10-30,11.2,90.4,13.0,18.4,997.5,28.8,27.0
 2023-10-31,11.0,93.7,18.6,18.0,1000.7,17.9,29.8
 2023-11-01,12.4,88.5,4.9,25.9,997.8,32.6,31.5
-2023-11-02,11.0,80.0,8.7,46.4,976.4,33.6,21.5
-2023-11-03,9.6,83.3,7.9,32.4,981.6,31.0,40.1
-2023-11-04,9.3,88.7,10.7,25.9,977.6,35.3,20.4
-2023-11-05,10.5,88.4,5.6,25.2,977.1,29.0,25.6
-2023-11-06,9.7,87.4,1.8,31.7,998.2,37.3,45.2
-2023-11-07,9.7,85.1,0.4,22.3,1007.3,41.1,63.6
-2023-11-08,9.3,91.9,17.0,28.8,1008.2,21.2,37.0
-2023-11-09,9.7,89.2,8.5,22.3,999.0,24.2,37.3
-2023-11-10,7.5,90.1,4.4,22.3,995.3,34.9,20.8
-2023-11-11,7.5,87.1,5.7,14.8,1003.3,40.0,25.2
-2023-11-12,5.7,96.8,0.1,14.8,1006.4,8.8,26.2
-2023-11-13,9.5,90.3,5.2,31.7,1000.8,9.6,9.8
-2023-11-14,12.1,84.6,11.9,29.5,1001.4,14.8,16.0
-2023-11-15,11.1,84.6,5.3,18.4,1011.9,21.4,22.7
-2023-11-16,7.5,95.7,3.4,18.4,1013.5,19.9,11.8
-2023-11-17,5.1,93.8,0.0,11.2,1019.4,10.5,49.1
-2023-11-18,7.5,97.6,12.2,25.9,1016.5,7.3,7.5
-2023-11-19,12.6,87.1,5.7,25.9,1006.8,11.4,34.0
-2023-11-20,10.5,91.5,8.6,22.3,1007.9,16.6,39.4
-2023-11-21,7.4,97.0,0.2,18.4,1017.6,9.2,13.9
-2023-11-22,4.2,95.1,0.2,18.4,1028.2,6.3,26.3
-2023-11-23,11.2,88.4,1.0,25.9,1019.2,25.1,32.3
-2023-11-24,6.7,77.4,4.5,29.5,1011.8,31.3,36.5
-2023-11-25,5.4,83.6,1.9,25.9,1013.0,27.7,22.0
-2023-11-26,4.8,94.3,0.9,14.8,1014.3,27.0,13.3
-2023-11-27,4.4,96.7,17.0,18.4,998.8,11.5,4.1
-2023-11-28,2.3,82.1,3.0,25.9,1005.0,41.3,46.0
-2023-11-29,2.6,89.7,3.2,18.4,1001.9,39.2,26.9
-2023-11-30,-0.1,99.8,0.0,7.6,1002.3,2.2,17.7
-2023-12-01,-1.1,90.4,0.0,11.2,1007.7,9.0,31.2
-2023-12-02,-0.6,92.7,0.0,14.8,1013.8,6.4,26.2
-2023-12-03,1.0,89.9,1.8,22.3,1017.9,9.2,21.7
-2023-12-04,1.7,85.6,3.8,22.3,1006.0,22.8,14.2
-2023-12-05,1.8,96.7,2.8,18.4,1003.2,4.5,7.5
-2023-12-06,2.9,91.2,0.2,11.2,1014.6,18.2,17.6
-2023-12-07,0.0,96.3,0.0,22.3,1016.5,8.2,12.8
-2023-12-08,5.1,94.5,0.6,18.4,1007.1,7.0,12.0
-2023-12-09,7.9,91.5,7.7,25.9,1000.5,12.4,10.1
-2023-12-10,9.7,79.9,9.8,36.0,999.9,15.8,24.1
-2023-12-11,9.5,89.4,4.8,31.7,1000.7,15.2,13.1
-2023-12-12,8.0,96.2,7.9,14.8,1002.2,18.3,21.2
-2023-12-13,8.5,96.2,5.3,14.8,998.4,13.0,12.5
-2023-12-14,5.6,88.0,0.0,14.8,1018.2,28.9,21.7
-2023-12-15,8.3,84.5,0.0,14.8,1032.9,22.7,14.6
-2023-12-16,8.6,88.7,0.0,18.4,1037.7,20.1,8.9
-2023-12-17,8.9,86.0,0.0,22.3,1034.8,23.4,27.0
-2023-12-18,7.7,88.6,0.0,25.9,1029.2,13.0,19.7
-2023-12-19,8.0,97.4,11.2,33.5,1018.4,10.4,3.9
-2023-12-20,8.4,84.9,0.4,33.5,1012.7,19.2,14.6
-2023-12-21,10.2,82.3,7.0,48.2,999.6,24.9,7.5
-2023-12-22,9.0,81.8,7.9,37.1,1003.4,26.0,12.3
-2023-12-23,10.1,81.8,7.0,50.0,1007.5,35.8,14.4
-2023-12-24,11.5,91.9,19.2,38.9,1003.4,14.0,3.9
-2023-12-25,11.8,89.1,4.4,28.8,1005.6,27.4,15.1
-2023-12-26,8.5,82.4,14.1,36.0,1012.6,32.4,10.9
-2023-12-27,6.5,90.9,1.1,28.8,1013.5,23.5,6.4
-2023-12-28,10.9,75.8,0.0,40.7,1008.0,33.2,18.2
-2023-12-29,9.6,83.9,1.6,38.9,1005.8,32.7,16.1
-2023-12-30,8.1,83.3,0.0,21.6,1009.5,39.1,20.2
-2023-12-31,9.0,81.0,0.9,32.4,996.3,41.5,17.8
-2024-01-01,7.6,85.0,11.1,32.4,1000.9,26.4,15.4
-2024-01-02,10.3,92.0,20.5,43.2,988.9,15.6,6.5
-2024-01-03,9.7,87.5,15.6,39.6,988.4,19.8,18.5
-2024-01-04,7.7,94.9,10.1,14.4,1000.6,24.8,14.3
-2024-01-05,7.2,94.5,8.3,22.3,996.6,21.5,4.6
-2024-01-06,3.6,86.8,0.0,18.4,1010.7,34.4,5.8
-2024-01-07,0.1,80.9,0.0,28.8,1024.5,36.0,21.2
-2024-01-08,-1.6,70.2,0.0,29.5,1032.5,43.6,30.0
-2024-01-09,-3.0,58.0,0.0,22.3,1033.8,45.0,40.6
-2024-01-10,-3.2,59.9,0.0,14.8,1030.9,44.8,43.2
-2024-01-11,-2.4,87.5,0.0,11.2,1034.3,14.8,24.0
-2024-01-12,3.4,90.9,0.0,11.2,1033.0,22.8,13.1
-2024-01-13,3.6,94.3,0.3,18.4,1022.7,8.0,6.4
-2024-01-14,3.0,96.3,2.9,18.4,1009.6,10.5,11.2
-2024-01-15,1.8,87.8,4.2,18.4,1003.3,33.3,21.4
-2024-01-16,0.9,83.2,0.8,22.3,1007.1,38.4,38.0
-2024-01-17,-1.4,82.8,0.0,18.4,993.7,29.4,20.5
-2024-01-18,-1.8,91.5,0.0,14.8,1001.4,23.9,52.5
-2024-01-19,1.6,84.9,1.3,18.4,1018.8,37.7,54.9
-2024-01-20,-0.3,83.9,0.0,21.6,1026.5,18.3,43.7
-2024-01-21,3.0,74.7,0.1,31.7,1017.4,41.0,14.1
-2024-01-22,9.7,81.7,4.8,38.9,1006.8,21.1,37.8
-2024-01-23,8.1,82.3,5.0,32.4,1019.3,15.7,31.1
-2024-01-24,10.8,74.4,1.1,39.6,1018.9,18.0,31.1
-2024-01-25,7.0,91.8,1.3,14.8,1026.9,7.9,22.5
-2024-01-26,8.6,83.7,6.1,36.0,1024.5,19.4,55.3
-2024-01-27,4.2,85.5,0.0,18.4,1034.7,19.9,52.4
-2024-01-28,3.6,77.7,0.0,18.4,1028.0,33.0,56.0
-2024-01-29,6.3,82.9,0.0,11.2,1025.5,35.2,48.5
-2024-01-30,8.1,90.2,0.6,25.9,1027.1,24.3,21.9
-2024-01-31,6.5,82.0,0.0,29.5,1030.8,41.5,15.3
-2024-02-01,6.7,86.1,6.2,22.3,1029.3,14.6,70.9
-2024-02-02,7.4,91.1,0.0,25.9,1027.3,13.5,20.5
-2024-02-03,10.1,90.9,0.0,22.3,1024.7,27.4,21.4
-2024-02-04,10.6,90.2,1.1,29.5,1021.0,22.7,13.6
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-2024-02-10,10.4,91.5,0.1,11.2,986.1,38.0,39.4
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-2024-02-22,10.2,93.1,5.2,38.9,987.1,16.5,34.2
-2024-02-23,6.2,82.1,11.8,31.7,989.5,37.7,52.8
-2024-02-24,4.9,87.7,0.4,21.6,997.2,41.4,31.7
-2024-02-25,6.5,85.8,1.4,18.4,999.8,33.2,75.6
-2024-02-26,5.2,85.2,1.6,29.5,1005.8,36.5,27.3
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-2024-03-07,5.2,84.0,0.0,29.5,1023.5,13.4,114.2
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-2024-03-25,7.3,74.4,0.0,14.8,1004.9,28.5,142.8
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-2024-03-28,8.2,78.2,3.8,30.6,985.5,,98.0
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-2024-04-01,9.6,89.3,2.2,24.1,995.7,45.0,87.9
-2024-04-02,9.2,89.5,5.9,27.0,1004.2,41.3,69.2
-2024-04-03,11.0,90.8,7.3,25.2,1003.9,25.0,79.5
-2024-04-04,11.9,82.5,13.2,31.7,1004.6,23.7,106.8
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-2024-04-13,9.4,86.4,0.0,18.0,1023.4,27.9,59.5

 2023-10-30,11.2,90.4,13.0,18.4,997.5,28.8,27.0
 2023-10-31,11.0,93.7,18.6,18.0,1000.7,17.9,29.8
 2023-11-01,12.4,88.5,4.9,25.9,997.8,32.6,31.5
+2023-11-02,11,80,8.7,46.4,976.4,33.6,21.5

pollution_data.csv CHANGED Viewed

@@ -1,33 +1,15 @@
 date,NO2,O3
-2025-03-10,24.919333333333334,20.79696
-2025-03-11,11.161066666666665,38.56911999999999
-2025-03-12,13.623918918918923,33.15556451612904
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-2025-03-17,4.88472222222222,32.64704918032787
-2025-03-18,10.625555555555557,31.403166666666667
-2025-03-19,17.86186666666666,33.13487804878048
-2025-03-20,40.07733333333333,38.95047999999999
-2025-03-21,22.156712328767117,40.23235772357723
-2025-03-22,13.245675675675677,36.57975409836066
-2025-03-23,13.348493150684929,34.08422764227641
-2025-03-24,14.885,29.136693548387104
-2025-03-25,15.633918918918914,36.095967741935475
-2025-03-26,11.872916666666667,30.601999999999993
-2025-03-27,19.970799999999993,26.6366935483871
-2025-03-28,36.30826666666667,34.62607999999999
-2025-03-29,17.113835616438358,29.705365853658535
-2025-03-30,9.202972972972969,29.544508196721306
-2025-03-31,4.272,32.74226890756302
-2025-04-01,6.463928571428573,30.599166666666672
-2025-04-02,8.217500000000001,30.378512396694212
-2025-04-03,6.883880597014924,34.092792792792785
-2025-04-04,16.400958904109583,36.781382113821145
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-2025-04-09,6.972162162162163,33.09658536585365
-2025-04-10,4.2907142857142855,35.82308333333334

 date,NO2,O3
+2024-10-17,22.804605103280675,22.769159859976643
+2024-10-18,23.26858769887009,23.30733245729302
+2024-10-19,23.91006441223834,23.1717142857143
+2024-10-20,22.57323754789273,23.53784452296821
+2024-10-21,21.1457004830918,24.02069565217393
+2024-10-22,21.77657980456027,23.33588571428572
+2024-10-23,21.974793814433,22.21468879668051
+2024-10-24,25.51256756756757,20.91370967741937
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+2024-10-26,24.46423484380123,18.70331123489324
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+2024-10-28,23.337567567567568,26.82861788617886
+2024-10-29,16.53533209586906,23.28254887605004
+2024-10-30,22.26162162162162,18.03443548387097

predictions_history.csv CHANGED Viewed

@@ -1,205 +1,72 @@
 pollutant,date_predicted,date,prediction_value
-O3,2025-03-07,2025-03-10,29.12736491827365
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-O3,2025-03-09,2025-03-11,43.82736491827365
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-O3,2025-03-09,2025-03-12,41.18273649182736
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-O3,2025-03-10,2025-03-12,28.97324258455377
-NO2,2025-03-10,2025-03-12,2.675595876631019
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-O3,2025-03-11,2025-03-12,25.9523779513165
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-NO2,2025-03-13,2025-03-15,11.553101018528771
-O3,2025-03-13,2025-03-16,12.624739887205287
-NO2,2025-03-13,2025-03-16,7.642681078487097
-O3,2025-03-14,2025-03-15,22.365131907828072
-NO2,2025-03-14,2025-03-15,9.206815880750476
-O3,2025-03-14,2025-03-16,32.017319322990026
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-O3,2025-03-14,2025-03-17,42.47898074566649
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-O3,2025-03-15,2025-03-16,30.85376488004041
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-O3,2025-03-15,2025-03-17,29.114949145265868
-NO2,2025-03-15,2025-03-17,5.268687933737249
-O3,2025-03-15,2025-03-18,44.099121719367034
-NO2,2025-03-15,2025-03-18,14.849306779455823
-O3,2025-03-16,2025-03-17,36.25252428642855
-NO2,2025-03-16,2025-03-17,1.4750256174958913
-O3,2025-03-16,2025-03-18,38.67185924197556
-NO2,2025-03-16,2025-03-18,13.411510252962042
-O3,2025-03-16,2025-03-19,43.92144424141553
-NO2,2025-03-16,2025-03-19,22.924043705613457
-O3,2025-03-17,2025-03-18,20.593729497451083
-NO2,2025-03-17,2025-03-18,9.233383939306108
-O3,2025-03-17,2025-03-19,33.902081716854376
-NO2,2025-03-17,2025-03-19,17.035250016088494
-O3,2025-03-17,2025-03-20,56.13338974244566
-NO2,2025-03-17,2025-03-20,34.74925137364262
-O3,2025-03-18,2025-03-19,24.34677204803371
-NO2,2025-03-18,2025-03-19,24.214191960527593
-O3,2025-03-18,2025-03-20,7.97083270245124
-NO2,2025-03-18,2025-03-20,16.653190550698003
-O3,2025-03-18,2025-03-21,8.828820106189312
-NO2,2025-03-18,2025-03-21,3.2143497350615284
-O3,2025-03-19,2025-03-20,14.19915504166628
-NO2,2025-03-19,2025-03-20,40.367920343944874
-O3,2025-03-19,2025-03-21,8.055352596285765
-NO2,2025-03-19,2025-03-21,14.036055689494942
-O3,2025-03-19,2025-03-22,20.683555991473177
-NO2,2025-03-19,2025-03-22,-0.0935552468181555
-O3,2025-03-20,2025-03-21,22.577240569315755
-NO2,2025-03-20,2025-03-21,22.432680154231203
-O3,2025-03-20,2025-03-22,20.23852948376169
-NO2,2025-03-20,2025-03-22,11.41259533531298
-O3,2025-03-20,2025-03-23,22.832979536729955
-NO2,2025-03-20,2025-03-23,7.888808066058104
-O3,2025-03-21,2025-03-22,29.27186184325724
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-O3,2025-03-21,2025-03-23,23.143988800612934
-NO2,2025-03-21,2025-03-23,18.514420577624954
-O3,2025-03-21,2025-03-24,26.34383163970652
-NO2,2025-03-21,2025-03-24,9.275171108969094
-O3,2025-03-22,2025-03-23,23.00812920675076
-NO2,2025-03-22,2025-03-23,15.562277370570971
-O3,2025-03-22,2025-03-24,12.996197559099771
-NO2,2025-03-22,2025-03-24,5.920400650472168
-O3,2025-03-22,2025-03-25,24.158896251220572
-NO2,2025-03-22,2025-03-25,-6.051012335488707
-O3,2025-03-23,2025-03-24,15.70265619759315
-NO2,2025-03-23,2025-03-24,26.297361615928935
-O3,2025-03-23,2025-03-25,8.332549816083528
-NO2,2025-03-23,2025-03-25,13.670842154040486
-O3,2025-03-23,2025-03-26,16.640077837303092
-NO2,2025-03-23,2025-03-26,11.462778135172028
-O3,2025-03-24,2025-03-25,18.25717694098837
-NO2,2025-03-24,2025-03-25,27.234622346278005
-O3,2025-03-24,2025-03-26,17.54807704356461
-NO2,2025-03-24,2025-03-26,18.168652599687817
-O3,2025-03-24,2025-03-27,25.874950013940516
-NO2,2025-03-24,2025-03-27,3.541491205431615
-O3,2025-03-25,2025-03-26,26.688534177437624
-NO2,2025-03-25,2025-03-26,26.172909735803763
-O3,2025-03-25,2025-03-27,22.93922854687444
-NO2,2025-03-25,2025-03-27,17.04426781640162
-O3,2025-03-25,2025-03-28,37.00433182727376
-NO2,2025-03-25,2025-03-28,17.247288148167
-O3,2025-03-26,2025-03-27,23.496437048396636
-NO2,2025-03-26,2025-03-27,9.872558444066575
-O3,2025-03-26,2025-03-28,17.716182866254996
-NO2,2025-03-26,2025-03-28,7.405832864834853
-O3,2025-03-26,2025-03-29,33.928031024704914
-NO2,2025-03-26,2025-03-29,1.3356425602430129
-O3,2025-03-27,2025-03-28,18.0789893495609
-NO2,2025-03-27,2025-03-28,18.761355163500543
-O3,2025-03-27,2025-03-29,22.977101731471706
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-O3,2025-03-27,2025-03-30,36.92937655932295
-NO2,2025-03-27,2025-03-30,2.569373566235953
-O3,2025-03-28,2025-03-29,27.524148971900186
-NO2,2025-03-28,2025-03-29,19.1485078134881
-O3,2025-03-28,2025-03-30,33.07029361561123
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-O3,2025-03-28,2025-03-31,30.133600528481026
-NO2,2025-03-28,2025-03-31,16.63377069601797
-O3,2025-03-29,2025-03-30,26.462689686248368
-NO2,2025-03-29,2025-03-30,16.153029443852326
-O3,2025-03-29,2025-03-31,16.172149663837978
-NO2,2025-03-29,2025-03-31,17.84386761678244
-O3,2025-03-29,2025-04-01,28.038883473085395
-NO2,2025-03-29,2025-04-01,18.001427465080745
-O3,2025-03-30,2025-03-31,16.56578070101913
-NO2,2025-03-30,2025-03-31,12.841512127042206
-O3,2025-03-30,2025-04-01,22.04973178816912
-NO2,2025-03-30,2025-04-01,6.470749466590821
-O3,2025-03-30,2025-04-02,40.10754059007936
-NO2,2025-03-30,2025-04-02,5.260238354460856
-O3,2025-03-31,2025-04-01,24.99970385367518
-NO2,2025-03-31,2025-04-01,29.49563240424814
-O3,2025-03-31,2025-04-02,32.925366432171295
-NO2,2025-03-31,2025-04-02,21.08528292976339
-O3,2025-03-31,2025-04-03,34.65227806432316
-NO2,2025-03-31,2025-04-03,11.27957285246689
-O3,2025-04-01,2025-04-02,31.338953889921164
-NO2,2025-04-01,2025-04-02,9.294640606870104
-O3,2025-04-01,2025-04-03,26.34526907465127
-NO2,2025-04-01,2025-04-03,2.9112579889493126
-O3,2025-04-01,2025-04-04,34.32904595771874
-NO2,2025-04-01,2025-04-04,3.3062236947570725
-O3,2025-04-02,2025-04-03,20.37484627473415
-NO2,2025-04-02,2025-04-03,11.38854008691647
-O3,2025-04-02,2025-04-04,22.468836814322444
-NO2,2025-04-02,2025-04-04,17.648823824145026
-O3,2025-04-02,2025-04-05,38.60737590027399
-NO2,2025-04-02,2025-04-05,10.377586026633834
-O3,2025-04-03,2025-04-04,24.751156641679092
-NO2,2025-04-03,2025-04-04,8.1832337826329
-O3,2025-04-03,2025-04-05,30.653881777797142
-NO2,2025-04-03,2025-04-05,5.930226199118156
-O3,2025-04-03,2025-04-06,39.13759349011889
-NO2,2025-04-03,2025-04-06,3.124098380335983
-O3,2025-04-04,2025-04-05,31.26363040306683
-NO2,2025-04-04,2025-04-05,18.758274587589337
-O3,2025-04-04,2025-04-06,29.538996361827696
-NO2,2025-04-04,2025-04-06,16.415631516076708
-O3,2025-04-04,2025-04-07,33.42217106979134
-NO2,2025-04-04,2025-04-07,18.33858043667427
-O3,2025-04-05,2025-04-06,21.46606590783967
-NO2,2025-04-05,2025-04-06,11.069079275537549
-O3,2025-04-05,2025-04-07,16.974627609571066
-NO2,2025-04-05,2025-04-07,10.121135864507396
-O3,2025-04-05,2025-04-08,23.59865980201226
-NO2,2025-04-05,2025-04-08,8.865132211553512
-O3,2025-04-06,2025-04-07,18.51982788551233
-NO2,2025-04-06,2025-04-07,13.25807843915571
-O3,2025-04-06,2025-04-08,15.573212634149264
-NO2,2025-04-06,2025-04-08,15.474301950935176
-O3,2025-04-06,2025-04-09,27.320954486040108
-NO2,2025-04-06,2025-04-09,12.667124755491672
-O3,2025-04-07,2025-04-08,17.412329098434125
-NO2,2025-04-07,2025-04-08,16.86076406720009
-O3,2025-04-07,2025-04-09,17.66027075077956
-NO2,2025-04-07,2025-04-09,11.15509361078738
-O3,2025-04-07,2025-04-10,24.189150332489294
-NO2,2025-04-07,2025-04-10,2.3511603061021873
-O3,2025-04-08,2025-04-09,24.751293692949535
-NO2,2025-04-08,2025-04-09,14.9695000207066
-O3,2025-04-08,2025-04-10,15.39258599235248
-NO2,2025-04-08,2025-04-10,10.681758679689471
-O3,2025-04-08,2025-04-11,21.865457395477375
-NO2,2025-04-08,2025-04-11,5.189639157063457
-O3,2025-04-09,2025-04-10,21.415752542433893
-NO2,2025-04-09,2025-04-10,7.414993834257501
-O3,2025-04-09,2025-04-11,18.43157639079524
-NO2,2025-04-09,2025-04-11,13.55873029709923
-O3,2025-04-09,2025-04-12,34.53675335273856
-NO2,2025-04-09,2025-04-12,12.344843722837314
-O3,2025-04-10,2025-04-11,28.314367145255684
-NO2,2025-04-10,2025-04-11,18.384325902671144
-O3,2025-04-10,2025-04-12,29.281284097019913
-NO2,2025-04-10,2025-04-12,16.552657783375896
-O3,2025-04-10,2025-04-13,42.4379491717469
-NO2,2025-04-10,2025-04-13,12.477293479529983

 pollutant,date_predicted,date,prediction_value
+O3,2024-10-14,2024-10-17,31.25335185244893
+NO2,2024-10-14,2024-10-17,26.421736787446267
+O3,2024-10-15,2024-10-17,22.00005767760448
+NO2,2024-10-15,2024-10-17,28.59511317503212
+O3,2024-10-16,2024-10-17,9.657466070999735
+NO2,2024-10-16,2024-10-17,17.065168790519902
+O3,2024-10-15,2024-10-18,6.561248
+NO2,2024-10-15,2024-10-18,26.443672
+O3,2024-10-16,2024-10-18,19.782418
+NO2,2024-10-16,2024-10-18,36.453956
+O3,2024-10-17,2024-10-18,16.08841798553393
+NO2,2024-10-17,2024-10-18,32.0458143607889
+O3,2024-10-16,2024-10-19,24.031357603260783
+NO2,2024-10-16,2024-10-19,20.08389395558791
+O3,2024-10-17,2024-10-19,21.031357603260783
+NO2,2024-10-17,2024-10-19,27.08389395558791
+O3,2024-10-17,2024-10-20,20.48486247979324
+NO2,2024-10-17,2024-10-20,23.84300578029378
+O3,2024-10-18,2024-10-19,22.304547122637445
+NO2,2024-10-18,2024-10-19,20.80017116560889
+O3,2024-10-18,2024-10-20,31.25335185244893
+NO2,2024-10-18,2024-10-20,29.732316066240585
+O3,2024-10-18,2024-10-21,28.67755196805434
+NO2,2024-10-18,2024-10-21,35.04638743773354
+O3,2024-10-19,2024-10-20,26.421736787446267
+NO2,2024-10-19,2024-10-20,27.399885723190767
+O3,2024-10-19,2024-10-21,17.065168790519902
+NO2,2024-10-19,2024-10-21,18.992352714813563
+O3,2024-10-19,2024-10-22,17.39682962048955
+NO2,2024-10-19,2024-10-22,22.85061675885908
+O3,2024-10-20,2024-10-21,22.00005767760448
+NO2,2024-10-20,2024-10-21,18.27191592927812
+O3,2024-10-20,2024-10-22,29.00940466937953
+NO2,2024-10-20,2024-10-22,19.50739766963497
+O3,2024-10-20,2024-10-23,20.062134354543343
+NO2,2024-10-20,2024-10-23,23.65746607099973
+O3,2024-10-21,2024-10-22,17.497382318189132
+NO2,2024-10-21,2024-10-22,28.59511317503212
+O3,2024-10-21,2024-10-23,16.519952190354232
+NO2,2024-10-21,2024-10-23,30.192389708351826
+O3,2024-10-21,2024-10-24,28.19940385112904
+NO2,2024-10-21,2024-10-24,17.9525039623211
+O3,2024-10-22,2024-10-23,16.093074246425157
+NO2,2024-10-22,2024-10-23,25.217639978187005
+O3,2024-10-22,2024-10-24,23.605545201596552
+NO2,2024-10-22,2024-10-24,29.004701753536988
+O3,2024-10-23,2024-10-24,26.56486295059828
+NO2,2024-10-23,2024-10-24,20.15373733747257
+O3,2024-10-24,2024-10-25,10.33808859423279
+NO2,2024-10-24,2024-10-25,25.68519991558237
+O3,2024-10-24,2024-10-26,16.000984317626852
+NO2,2024-10-24,2024-10-26,25.760307451092384
+O3,2024-10-24,2024-10-27,19.64377495640328
+NO2,2024-10-24,2024-10-27,31.210576791105115
+O3,2024-10-25,2024-10-26,20.48055947200643
+NO2,2024-10-25,2024-10-26,23.95723903986424
+O3,2024-10-25,2024-10-27,11.088152958498888
+NO2,2024-10-25,2024-10-27,32.274494671100506
+O3,2024-10-25,2024-10-28,-0.7175631399505704
+NO2,2024-10-25,2024-10-28,40.86107800019054
+O3,2024-10-28,2024-10-29,22.13652238154496
+NO2,2024-10-28,2024-10-29,31.608886931951144
+O3,2024-10-28,2024-10-30,15.841669224
+NO2,2024-10-28,2024-10-30,34.564284711452984
+O3,2024-10-28,2024-10-31,22.35944571003375
+NO2,2024-10-28,2024-10-31,34.37482132111927
+O3,2024-10-30,2024-10-31,15.98046542733637
+NO2,2024-10-30,2024-10-31,29.77507241979599
+O3,2024-10-30,2024-11-01,21.135906183680472
+NO2,2024-10-30,2024-11-01,28.38872595850704
+O3,2024-10-30,2024-11-02,19.67426015042635

requirements.txt CHANGED Viewed

@@ -10,5 +10,3 @@ http.client
 datetime
 huggingface-hub
 python-dotenv
-torch
-safetensors

 datetime
 huggingface-hub
 python-dotenv

src/data_api_calls.py CHANGED Viewed

@@ -14,11 +14,7 @@ WEATHER_DATA_FILE = "weather_data.csv"
 POLLUTION_DATA_FILE = "pollution_data.csv"
-def update_weather_data() -> None:
-    """
-    Updates weather data by fetching data.
-    If the data file exists, it appends new data. If not, it creates a new file.
-    """
     today = date.today().isoformat()
     if os.path.exists(WEATHER_DATA_FILE):
@@ -54,11 +50,7 @@ def update_weather_data() -> None:
         sys.exit()
-def update_pollution_data() -> None:
-    """
-    Updates pollution data for NO2 and O3.
-    The new data is appended to the existing pollution data file.
-    """
     O3 = []
     NO2 = []
     particles = ["NO2", "O3"]
@@ -121,21 +113,14 @@ def update_pollution_data() -> None:
     updated_data.to_csv(POLLUTION_DATA_FILE, index=False)
-def get_combined_data() -> pd.DataFrame:
-    """
-    Combines weather and pollution data for the last 7 days.
-    Returns:
-        pd.DataFrame: A DataFrame containing the combined weather and pollution data.
-    """
     weather_df = pd.read_csv(WEATHER_DATA_FILE)
     today = pd.Timestamp.now().normalize()
     seven_days_ago = today - pd.Timedelta(days=7)
     weather_df["date"] = pd.to_datetime(weather_df["date"])
-    weather_df = weather_df[
-        (weather_df["date"] >= seven_days_ago) & (weather_df["date"] <= today)
-    ]
     weather_df.insert(1, "NO2", None)
     weather_df.insert(2, "O3", None)
@@ -147,8 +132,6 @@ def get_combined_data() -> pd.DataFrame:
     weather_df = weather_df[columns]
     columns.insert(9, columns.pop(6))
     weather_df = weather_df[columns]
-    print(weather_df)
     combined_df = weather_df
@@ -183,18 +166,11 @@ def get_combined_data() -> pd.DataFrame:
     combined_df["global_radiation"] = combined_df["global_radiation"].astype(int)
     pollution_df = pd.read_csv(POLLUTION_DATA_FILE)
     pollution_df["date"] = pd.to_datetime(pollution_df["date"])
-    pollution_df = pollution_df[
-        (pollution_df["date"] >= seven_days_ago) & (pollution_df["date"] <= today)
-    ]
-    print(pollution_df)
-    combined_df = pd.merge(combined_df, pollution_df[["date", "NO2", "O3"]], on="date", how="left")
-    combined_df = combined_df.drop(columns=["NO2_x", "O3_x"])
-    combined_df = combined_df.rename(columns={"NO2_y": "NO2", "O3_y": "O3"})
-    cols = ["date", "NO2", "O3", "wind_speed", "mean_temp", "global_radiation", "percipitation",
-        "pressure", "minimum_visibility", "humidity", "weekday"]
-    combined_df = combined_df[cols]
-    print(combined_df)
     return combined_df

 POLLUTION_DATA_FILE = "pollution_data.csv"
+def update_weather_data():
     today = date.today().isoformat()
     if os.path.exists(WEATHER_DATA_FILE):
         sys.exit()
+def update_pollution_data():
     O3 = []
     NO2 = []
     particles = ["NO2", "O3"]
     updated_data.to_csv(POLLUTION_DATA_FILE, index=False)
+def get_combined_data():
     weather_df = pd.read_csv(WEATHER_DATA_FILE)
     today = pd.Timestamp.now().normalize()
     seven_days_ago = today - pd.Timedelta(days=7)
     weather_df["date"] = pd.to_datetime(weather_df["date"])
+    weather_df = weather_df[(weather_df["date"] >= seven_days_ago) & (weather_df["date"] <= today)]
     weather_df.insert(1, "NO2", None)
     weather_df.insert(2, "O3", None)
     weather_df = weather_df[columns]
     columns.insert(9, columns.pop(6))
     weather_df = weather_df[columns]
     combined_df = weather_df
     combined_df["global_radiation"] = combined_df["global_radiation"].astype(int)
     pollution_df = pd.read_csv(POLLUTION_DATA_FILE)
     pollution_df["date"] = pd.to_datetime(pollution_df["date"])
+    pollution_df = pollution_df[(pollution_df["date"] >= seven_days_ago) & (pollution_df["date"] <= today)]
+    combined_df["NO2"] = pollution_df["NO2"]
+    combined_df["O3"] = pollution_df["O3"]
     return combined_df

src/features_pipeline.py CHANGED Viewed

@@ -6,6 +6,7 @@ import numpy as np
 import pandas as pd
 from dotenv import load_dotenv
 from huggingface_hub import hf_hub_download, login
 from src.past_data_api_calls import get_past_combined_data
 warnings.filterwarnings("ignore")
@@ -15,44 +16,11 @@ login(token=os.getenv("HUGGINGFACE_DOWNLOAD_TOKEN"))
 def create_features(
-    data: pd.DataFrame,
-    target_particle: str,  # Added this parameter
-    lag_days: int = 7,
-    sma_days: int = 7,
-) -> pd.DataFrame:
-    """
-    Create features for predicting air quality particles (NO2 or O3) based on historical weather data.
-    This function performs several feature engineering tasks, including:
-    - Creating lagged features for specified pollutants.
-    - Calculating rolling mean (SMA) features.
-    - Adding sine and cosine transformations of the weekday and month.
-    - Incorporating historical data for the same date in the previous year.
-    Parameters:
-    ----------
-    data : pd.DataFrame
-        A DataFrame containing historical weather and air quality data with a 'date' column.
-    target_particle : str
-        The target particle for prediction, must be either 'O3' or 'NO2'.
-    lag_days : int, optional
-        The number of days for which lagged features will be created. Default is 7.
-    sma_days : int, optional
-        The window size for calculating the simple moving average (SMA). Default is 7.
-    Returns:
-    -------
-    pd.DataFrame
-        A DataFrame containing the transformed features, ready for modeling.
-    Raises:
-    ------
-    ValueError
-        If target_particle is not 'O3' or 'NO2'.
-    """
     lag_features = [
         "NO2",
         "O3",

 import pandas as pd
 from dotenv import load_dotenv
 from huggingface_hub import hf_hub_download, login
 from src.past_data_api_calls import get_past_combined_data
 warnings.filterwarnings("ignore")
 def create_features(
+    data,
+    target_particle,  # Added this parameter
+    lag_days=7,
+    sma_days=7,
+):
     lag_features = [
         "NO2",
         "O3",

src/helper_functions.py CHANGED Viewed

@@ -1,26 +1,9 @@
 import streamlit as st
-def custom_metric_box(label: str, value: str) -> None:
-    """
-    Create a styled metric box with a compact layout.
-    This function generates a styled markdown box displaying a label and its corresponding value.
-    Parameters:
-    ----------
-    label : str
-        The text label to display in the metric box.
-    value : str
-        The value to be displayed in the metric box, typically representing a metric.
-    Returns:
-    -------
-    None
-    """
-    st.markdown(
-        f"""
         <div style="
             padding: 5px;
             margin-bottom: 5px;
@@ -36,42 +19,17 @@ def custom_metric_box(label: str, value: str) -> None:
                 <p style="font-size: 18px; font-weight: bold; margin: 0;">{value}</p>  <!-- Smaller metric -->
             </div>
         </div>
-    """,
-        unsafe_allow_html=True,
-    )
-def pollution_box(label: str, value: str, delta: str, threshold: float) -> None:
-    """
-    Create a pollution metric box with a side-by-side layout and fixed width.
-    Parameters:
-    ----------
-    label : str
-        The text label representing the type of pollution or metric.
-    value : str
-        The value of the pollution metric.
-    delta : str
-        The change in pollution level.
-    threshold : float
-        The threshold value to determine pollution level status.
-    """
-    current_value = float(value.split()[0])
-    # Determine status and color based on the same logic as get_simple_color_scale
-    if current_value < threshold:
-        status = "Good"
-        status_color = "#77C124"
-    elif current_value < 2 * threshold:
-        status = "Medium"
-        status_color = "#E68B0A"
-    else:
-        status = "Bad"
-        status_color = "#E63946"
     # Render the pollution box
-    st.markdown(
-        f"""
         <div style="
             background: rgba(255, 255, 255, 0.05);
             border-radius: 16px;
@@ -82,10 +40,8 @@ def pollution_box(label: str, value: str, delta: str, threshold: float) -> None:
             padding: 15px;
             margin-bottom: 10px;
         ">
-            <h4 style="font-size: 24px; font-weight: bold; margin: 0;">{label}</h4>
-            <p style="font-size: 36px; font-weight: bold; color: {status_color}; margin: 0;">{status}</p>
-            <p style="font-size: 18px; margin: 0;">{value}</p>
         </div>
-    """,
-        unsafe_allow_html=True,
-    )

 import streamlit as st
+# Custom function to create styled metric boxes with compact layout
+def custom_metric_box(label, value):
+    st.markdown(f"""
         <div style="
             padding: 5px;
             margin-bottom: 5px;
                 <p style="font-size: 18px; font-weight: bold; margin: 0;">{value}</p>  <!-- Smaller metric -->
             </div>
         </div>
+    """, unsafe_allow_html=True)
+# Custom function to create pollution metric boxes with side-by-side layout for label and value
+# Custom function to create pollution metric boxes with side-by-side layout and fixed width
+def pollution_box(label, value, delta, threshold):
+    # Determine if the pollution level is "Good" or "Bad"
+    status = "Good" if float(value.split()[0]) < threshold else "Bad"
+    status_color = "#77C124" if status == "Good" else "#E68B0A"
     # Render the pollution box
+    st.markdown(f"""
         <div style="
             background: rgba(255, 255, 255, 0.05);
             border-radius: 16px;
             padding: 15px;
             margin-bottom: 10px;
         ">
+            <h4 style="font-size: 24px; font-weight: bold; margin: 0;">{label}</h4>  <!-- Bigger label -->
+            <p style="font-size: 36px; font-weight: bold; color: {status_color}; margin: 0;">{status}</p>  <!-- Good/Bad with color -->
+            <p style="font-size: 18px; margin: 0;">{value}</p>  <!-- Smaller value where delta used to be -->
         </div>
+    """, unsafe_allow_html=True)

src/past_data_api_calls.py CHANGED Viewed

@@ -14,11 +14,7 @@ PAST_WEATHER_DATA_FILE = "past_weather_data.csv"
 PAST_POLLUTION_DATA_FILE = "past_pollution_data.csv"
-def update_past_weather_data() -> None:
-    """
-    Updates past weather data.
-    The data is saved to a CSV file. If the file already exists, new data is appended.
-    """
     last_year_date = date.today() - timedelta(days=365)
     if os.path.exists(PAST_WEATHER_DATA_FILE):
@@ -55,13 +51,7 @@ def update_past_weather_data() -> None:
         sys.exit()
-def update_past_pollution_data() -> tuple[list[float], list[float]]:
-    """
-    Updates past pollution data for NO2 and O3.
-    Returns:
-        tuple: A tuple containing two lists with NO2 and O3 average values.
-    """
     O3 = []
     NO2 = []
     particles = ["NO2", "O3"]
@@ -75,7 +65,7 @@ def update_past_pollution_data() -> tuple[list[float], list[float]]:
         last_date = pd.to_datetime(existing_data["date"]).max()
         if last_date >= pd.to_datetime(last_year_date):
             print("Data is already up to date.")
-            return [], []
         else:
             start_date = last_date.date()
             end_date = last_year_date + timedelta(days=3)
@@ -139,13 +129,7 @@ def update_past_pollution_data() -> tuple[list[float], list[float]]:
     return NO2, O3
-def get_past_combined_data() -> pd.DataFrame:
-    """
-    Retrieves and combines past weather and pollution data.
-    Returns:
-        pd.DataFrame: A DataFrame containing the combined past weather and pollution data.
-    """
     update_past_weather_data()
     update_past_pollution_data()

 PAST_POLLUTION_DATA_FILE = "past_pollution_data.csv"
+def update_past_weather_data():
     last_year_date = date.today() - timedelta(days=365)
     if os.path.exists(PAST_WEATHER_DATA_FILE):
         sys.exit()
+def update_past_pollution_data():
     O3 = []
     NO2 = []
     particles = ["NO2", "O3"]
         last_date = pd.to_datetime(existing_data["date"]).max()
         if last_date >= pd.to_datetime(last_year_date):
             print("Data is already up to date.")
+            return
         else:
             start_date = last_date.date()
             end_date = last_year_date + timedelta(days=3)
     return NO2, O3
+def get_past_combined_data():
     update_past_weather_data()
     update_past_pollution_data()

src/predict.py CHANGED Viewed

@@ -3,9 +3,9 @@ from datetime import date, datetime, timedelta
 import joblib
 import pandas as pd
-import torch
 from dotenv import load_dotenv
 from huggingface_hub import hf_hub_download, login
 from src.data_api_calls import (
     get_combined_data,
     update_pollution_data,
@@ -17,110 +17,33 @@ load_dotenv()
 login(token=os.getenv("HUGGINGFACE_DOWNLOAD_TOKEN"))
-def load_nn() -> torch.nn.Module:
-    """
-    Loads the neural network model for air pollution forecasting.
-    Returns:
-        torch.nn.Module: The loaded neural network model.
-    """
-    import torch.nn as nn
-    from huggingface_hub import PyTorchModelHubMixin
-    class AirPollutionNet(nn.Module, PyTorchModelHubMixin):
-        def __init__(self, input_size: int, layers: list[int], dropout_rate: float):
-            super(AirPollutionNet, self).__init__()
-            self.layers_list = nn.ModuleList()
-            in_features = input_size
-            for units in layers:
-                self.layers_list.append(nn.Linear(in_features, units))
-                self.layers_list.append(nn.ReLU())
-                self.layers_list.append(nn.Dropout(p=dropout_rate))
-                in_features = units
-            self.output = nn.Linear(in_features, 3)  # Output size is 3 for next 3 days
-        def forward(self, x: torch.Tensor) -> torch.Tensor:
-            """
-            Forward pass of the neural network.
-            Args:
-                x (torch.Tensor): Input tensor.
-            Returns:
-                torch.Tensor: Output tensor after passing through the network.
-            """
-            for layer in self.layers_list:
-                x = layer(x)
-            x = self.output(x)
-            return x
-    model = AirPollutionNet.from_pretrained(
-        "akseljoonas/Utrecht_pollution_forecasting_NO2"
-    )
-    return model
-def load_model(particle: str) -> object:
-    """
-    Loads the forecasting model based on the specified particle.
-    Args:
-        particle (str): The type of particle ("O3" or "NO2").
-    Returns:
-        object: The loaded model (either a neural network or a support vector regression model).
-    """
     repo_id = f"elisaklunder/Utrecht-{particle}-Forecasting-Model"
     if particle == "O3":
         file_name = "O3_svr_model.pkl"
-        model_path = hf_hub_download(repo_id=repo_id, filename=file_name)
-        model = joblib.load(model_path)
-    else:
-        model = load_nn()
     return model
-def run_model(particle: str, data: pd.DataFrame) -> list:
-    """
-    Runs the model for the specified particle and makes predictions based on the input data.
-    Args:
-        particle (str): The type of particle ("O3" or "NO2").
-        data (pd.DataFrame): The input data for making predictions.
-    Returns:
-        list: The predictions for the specified particle.
-    """
     input_data = create_features(data=data, target_particle=particle)
     model = load_model(particle)
-    if particle == "NO2":
-        with torch.no_grad():
-            prediction = model(torch.tensor(input_data.values, dtype=torch.float32))
-        repo_id = "akseljoonas/Utrecht_pollution_forecasting_NO2"
-        file_name = "target_scaler_NO2.joblib"
-        path = hf_hub_download(repo_id=repo_id, filename=file_name)
-    else:
-        prediction = model.predict(input_data)
-        repo_id = f"elisaklunder/Utrecht-{particle}-Forecasting-Model"
-        file_name = f"target_scaler_{particle}.joblib"
-        path = hf_hub_download(repo_id=repo_id, filename=file_name)
     target_scaler = joblib.load(path)
     prediction = target_scaler.inverse_transform(prediction)
     return prediction
-def update_data_and_predictions() -> None:
-    """
-    Updates the weather and pollution data, makes predictions for O3 and NO2,
-    and stores them in a CSV file.
-    """
     update_weather_data()
     update_pollution_data()
@@ -166,16 +89,7 @@ def update_data_and_predictions() -> None:
     combined_data.to_csv(PREDICTIONS_FILE, index=False)
-def get_data_and_predictions() -> tuple[pd.DataFrame, list, list]:
-    """
-    Retrieves combined data and today's predictions for O3 and NO2.
-    Returns:
-        tuple: A tuple containing:
-            - week_data (pd.DataFrame): The combined data for the week.
-            - list: Predictions for O3.
-            - list: Predictions for NO2.
-    """
     week_data = get_combined_data()
     PREDICTIONS_FILE = "predictions_history.csv"
@@ -193,7 +107,3 @@ def get_data_and_predictions() -> tuple[pd.DataFrame, list, list]:
     ].values
     return week_data, [o3_predictions], [no2_predictions]
-if __name__ == "__main__":
-    update_data_and_predictions()

 import joblib
 import pandas as pd
 from dotenv import load_dotenv
 from huggingface_hub import hf_hub_download, login
 from src.data_api_calls import (
     get_combined_data,
     update_pollution_data,
 login(token=os.getenv("HUGGINGFACE_DOWNLOAD_TOKEN"))
+def load_model(particle):
     repo_id = f"elisaklunder/Utrecht-{particle}-Forecasting-Model"
     if particle == "O3":
         file_name = "O3_svr_model.pkl"
+    elif particle == "NO2":
+        file_name = "NO2_svr_model.pkl"
+    model_path = hf_hub_download(repo_id=repo_id, filename=file_name)
+    model = joblib.load(model_path)
     return model
+def run_model(particle, data):
     input_data = create_features(data=data, target_particle=particle)
     model = load_model(particle)
+    prediction = model.predict(input_data)
+    repo_id = f"elisaklunder/Utrecht-{particle}-Forecasting-Model"
+    file_name = f"target_scaler_{particle}.joblib"
+    path = hf_hub_download(repo_id=repo_id, filename=file_name)
     target_scaler = joblib.load(path)
     prediction = target_scaler.inverse_transform(prediction)
     return prediction
+def update_data_and_predictions():
     update_weather_data()
     update_pollution_data()
     combined_data.to_csv(PREDICTIONS_FILE, index=False)
+def get_data_and_predictions():
     week_data = get_combined_data()
     PREDICTIONS_FILE = "predictions_history.csv"
     ].values
     return week_data, [o3_predictions], [no2_predictions]

test.ipynb ADDED Viewed

	@@ -0,0 +1,327 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\elikl\\Documents\\Uni\\yr3\\ML for industry\\utrecht-pollution-prediction\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from src.predict import get_data_and_predictions\n",
+    "from src.data_api_calls import get_combined_data\n",
+    "from src.past_data_api_calls import get_past_combined_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data is already up to date.\n",
+      "Data is already up to date.\n",
+      "Number of rows with missing values dropped: 7\n",
+      "Data is already up to date.\n",
+      "Number of rows with missing values dropped: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "week_data, predictions_O3, predictions_NO2 = get_data_and_predictions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>NO2</th>\n",
+       "      <th>O3</th>\n",
+       "      <th>wind_speed</th>\n",
+       "      <th>mean_temp</th>\n",
+       "      <th>global_radiation</th>\n",
+       "      <th>percipitation</th>\n",
+       "      <th>pressure</th>\n",
+       "      <th>minimum_visibility</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>weekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2024-10-17</td>\n",
+       "      <td>22.804605</td>\n",
+       "      <td>22.769160</td>\n",
+       "      <td>51</td>\n",
+       "      <td>169</td>\n",
+       "      <td>43</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10100</td>\n",
+       "      <td>371</td>\n",
+       "      <td>86</td>\n",
+       "      <td>Thursday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2024-10-18</td>\n",
+       "      <td>23.268500</td>\n",
+       "      <td>23.307332</td>\n",
+       "      <td>21</td>\n",
+       "      <td>155</td>\n",
+       "      <td>42</td>\n",
+       "      <td>39</td>\n",
+       "      <td>10140</td>\n",
+       "      <td>45</td>\n",
+       "      <td>97</td>\n",
+       "      <td>Friday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2024-10-19</td>\n",
+       "      <td>23.910064</td>\n",
+       "      <td>23.171714</td>\n",
+       "      <td>41</td>\n",
+       "      <td>147</td>\n",
+       "      <td>43</td>\n",
+       "      <td>16</td>\n",
+       "      <td>10141</td>\n",
+       "      <td>228</td>\n",
+       "      <td>89</td>\n",
+       "      <td>Saturday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2024-10-20</td>\n",
+       "      <td>22.573238</td>\n",
+       "      <td>23.537845</td>\n",
+       "      <td>81</td>\n",
+       "      <td>155</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10160</td>\n",
+       "      <td>415</td>\n",
+       "      <td>83</td>\n",
+       "      <td>Sunday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2024-10-21</td>\n",
+       "      <td>21.145700</td>\n",
+       "      <td>24.020696</td>\n",
+       "      <td>58</td>\n",
+       "      <td>144</td>\n",
+       "      <td>27</td>\n",
+       "      <td>43</td>\n",
+       "      <td>10206</td>\n",
+       "      <td>220</td>\n",
+       "      <td>92</td>\n",
+       "      <td>Monday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-10-22</td>\n",
+       "      <td>21.776580</td>\n",
+       "      <td>23.335886</td>\n",
+       "      <td>53</td>\n",
+       "      <td>114</td>\n",
+       "      <td>57</td>\n",
+       "      <td>49</td>\n",
+       "      <td>10269</td>\n",
+       "      <td>226</td>\n",
+       "      <td>92</td>\n",
+       "      <td>Tuesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2024-10-23</td>\n",
+       "      <td>21.974794</td>\n",
+       "      <td>22.214689</td>\n",
+       "      <td>36</td>\n",
+       "      <td>112</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10328</td>\n",
+       "      <td>65</td>\n",
+       "      <td>97</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2024-10-24</td>\n",
+       "      <td>25.512568</td>\n",
+       "      <td>20.913710</td>\n",
+       "      <td>56</td>\n",
+       "      <td>104</td>\n",
+       "      <td>62</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10247</td>\n",
+       "      <td>130</td>\n",
+       "      <td>94</td>\n",
+       "      <td>Thursday</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date        NO2         O3  wind_speed  mean_temp  global_radiation  \\\n",
+       "0 2024-10-17  22.804605  22.769160          51        169                43   \n",
+       "1 2024-10-18  23.268500  23.307332          21        155                42   \n",
+       "2 2024-10-19  23.910064  23.171714          41        147                43   \n",
+       "3 2024-10-20  22.573238  23.537845          81        155                 0   \n",
+       "4 2024-10-21  21.145700  24.020696          58        144                27   \n",
+       "5 2024-10-22  21.776580  23.335886          53        114                57   \n",
+       "6 2024-10-23  21.974794  22.214689          36        112                12   \n",
+       "7 2024-10-24  25.512568  20.913710          56        104                62   \n",
+       "\n",
+       "   percipitation  pressure  minimum_visibility  humidity    weekday  \n",
+       "0              6     10100                 371        86   Thursday  \n",
+       "1             39     10140                  45        97     Friday  \n",
+       "2             16     10141                 228        89   Saturday  \n",
+       "3              5     10160                 415        83     Sunday  \n",
+       "4             43     10206                 220        92     Monday  \n",
+       "5             49     10269                 226        92    Tuesday  \n",
+       "6              0     10328                  65        97  Wednesday  \n",
+       "7              0     10247                 130        94   Thursday  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "week_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10.33808859, 16.00098432, 19.64377496]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions_O3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[25.68519992, 25.76030745, 31.21057679]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions_NO2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.data_api_calls import get_combined_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'<' not supported between instances of 'Timestamp' and 'str'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mget_combined_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m2024-10-10\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\elikl\\Documents\\Uni\\yr3\\ML for industry\\utrecht-pollution-prediction\\src\\data_api_calls.py:136\u001b[0m, in \u001b[0;36mget_combined_data\u001b[1;34m(input_date)\u001b[0m\n\u001b[0;32m    133\u001b[0m     start_date \u001b[38;5;241m=\u001b[39m end_date \u001b[38;5;241m-\u001b[39m timedelta(\u001b[38;5;241m7\u001b[39m)\n\u001b[0;32m    135\u001b[0m update_weather_data(start_date, end_date)\n\u001b[1;32m--> 136\u001b[0m \u001b[43mupdate_pollution_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_date\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    138\u001b[0m weather_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(WEATHER_DATA_FILE)\n\u001b[0;32m    140\u001b[0m weather_df\u001b[38;5;241m.\u001b[39minsert(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNO2\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
+      "File \u001b[1;32mc:\\Users\\elikl\\Documents\\Uni\\yr3\\ML for industry\\utrecht-pollution-prediction\\src\\data_api_calls.py:123\u001b[0m, in \u001b[0;36mupdate_pollution_data\u001b[1;34m(start_date, end_date)\u001b[0m\n\u001b[0;32m    121\u001b[0m updated_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([existing_data, new_data], ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    122\u001b[0m updated_data\u001b[38;5;241m.\u001b[39mdrop_duplicates(subset\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m, keep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlast\u001b[39m\u001b[38;5;124m\"\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 123\u001b[0m updated_data \u001b[38;5;241m=\u001b[39m \u001b[43mupdated_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msort_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43mby\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    124\u001b[0m updated_data\u001b[38;5;241m.\u001b[39mto_csv(POLLUTION_DATA_FILE, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "File \u001b[1;32mc:\\Users\\elikl\\Documents\\Uni\\yr3\\ML for industry\\utrecht-pollution-prediction\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:7200\u001b[0m, in \u001b[0;36mDataFrame.sort_values\u001b[1;34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[0;32m   7197\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ascending, (\u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mlist\u001b[39m)):\n\u001b[0;32m   7198\u001b[0m         ascending \u001b[38;5;241m=\u001b[39m ascending[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m-> 7200\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[43mnargsort\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   7201\u001b[0m \u001b[43m        \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mascending\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mascending\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_position\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_position\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\n\u001b[0;32m   7202\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   7203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   7204\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inplace:\n",
+      "File \u001b[1;32mc:\\Users\\elikl\\Documents\\Uni\\yr3\\ML for industry\\utrecht-pollution-prediction\\.venv\\Lib\\site-packages\\pandas\\core\\sorting.py:439\u001b[0m, in \u001b[0;36mnargsort\u001b[1;34m(items, kind, ascending, na_position, key, mask)\u001b[0m\n\u001b[0;32m    437\u001b[0m     non_nans \u001b[38;5;241m=\u001b[39m non_nans[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m    438\u001b[0m     non_nan_idx \u001b[38;5;241m=\u001b[39m non_nan_idx[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m--> 439\u001b[0m indexer \u001b[38;5;241m=\u001b[39m non_nan_idx[\u001b[43mnon_nans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margsort\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[0;32m    440\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ascending:\n\u001b[0;32m    441\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
+      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'Timestamp' and 'str'"
+     ]
+    }
+   ],
+   "source": [
+    "get_combined_data(\"2024-10-10\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

weather_data.csv CHANGED Viewed

@@ -13,165 +13,3 @@ date,temp,humidity,precip,windspeed,sealevelpressure,visibility,solarradiation
 2024-10-28,12.4,91.8,1.1,31.7,1021.8,12.8,27.3
 2024-10-29,13.8,95.9,0.2,20.5,1023.1,8.1,16.0
 2024-10-30,12.7,92.9,0.6,9.4,1027.5,12.5,32.8
-2024-10-31,12.5,89.9,0.0,11.2,1027.1,17.1,70.6
-2024-11-01,9.9,96.9,0.0,14.4,1024.3,3.2,10.0
-2024-11-02,10.0,87.6,0.5,14.4,1030.9,17.9,36.1
-2024-11-03,5.6,91.4,0.0,7.2,1030.3,14.0,37.7
-2024-11-04,7.0,89.8,0.0,18.0,1027.6,13.9,38.4
-2024-11-05,5.9,92.8,0.0,10.8,1024.0,7.3,33.1
-2024-11-06,8.2,94.3,0.0,7.6,1030.2,3.5,27.1
-2024-11-07,5.7,95.4,0.0,18.0,1033.1,3.7,11.6
-2024-11-08,4.7,91.6,0.0,14.4,1028.5,5.0,10.8
-2024-11-09,6.4,93.7,0.5,7.6,1025.4,5.0,13.0
-2024-11-10,9.0,94.0,0.0,10.8,1027.4,5.4,28.1
-2024-11-11,10.3,86.1,4.8,21.6,1028.7,16.0,25.6
-2024-11-12,8.3,86.0,0.0,18.4,1032.5,24.1,24.8
-2024-11-13,8.0,92.1,0.0,10.8,1032.5,13.2,21.5
-2024-11-14,10.0,89.6,0.3,14.4,1028.6,19.7,23.5
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-2024-11-22,3.1,86.2,8.6,21.6,1001.4,35.5,23.2
-2024-11-23,4.2,84.6,9.4,28.8,1011.5,34.8,13.6
-2024-11-24,13.8,68.4,0.9,28.8,1003.5,43.6,34.7
-2024-11-25,12.6,78.6,4.6,32.4,1002.8,30.8,7.3
-2024-11-26,7.7,87.7,0.1,25.2,1015.6,24.6,30.4
-2024-11-27,7.4,91.6,11.1,28.8,1012.2,15.7,5.5
-2024-11-28,7.0,80.8,1.2,14.4,1024.5,42.3,41.5
-2024-11-29,3.7,88.6,0.0,14.4,1031.4,26.0,44.4
-2024-11-30,3.6,83.5,0.0,18.0,1027.3,32.8,39.5
-2024-12-01,3.5,82.6,0.2,22.7,1019.6,23.3,41.3
-2024-12-02,9.7,92.5,4.8,21.6,1011.9,19.3,18.9
-2024-12-03,5.9,84.6,2.0,14.4,1018.5,30.7,31.8
-2024-12-04,3.8,92.8,1.2,10.8,1024.9,25.3,11.2
-2024-12-05,6.5,93.3,13.2,25.2,1012.8,8.7,3.9
-2024-12-06,8.8,78.9,7.5,32.4,1007.6,16.9,16.6
-2024-12-07,7.7,87.7,7.4,28.8,995.1,21.5,24.0
-2024-12-08,6.7,91.8,1.0,21.6,1007.8,23.2,14.8
-2024-12-09,6.3,81.2,0.0,28.8,1026.8,40.2,8.0
-2024-12-10,5.2,83.7,0.0,21.6,1031.1,33.7,5.1
-2024-12-11,3.7,84.7,0.0,14.4,1032.2,17.3,5.0
-2024-12-12,3.4,95.6,0.5,10.8,1031.7,5.8,7.7
-2024-12-13,1.9,87.8,0.0,14.4,1027.6,7.9,5.3
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-2024-12-17,8.3,90.7,0.0,18.0,1025.9,20.9,11.9
-2024-12-18,10.0,86.0,0.6,37.8,1010.7,27.7,7.5
-2024-12-19,8.4,86.9,14.3,36.0,1000.7,23.8,5.6
-2024-12-20,5.9,80.5,0.3,21.6,1016.7,29.7,34.4
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-2024-12-22,6.1,76.5,7.5,25.2,999.3,27.3,17.9
-2024-12-23,6.2,80.4,5.5,28.8,1013.9,24.2,29.5
-2024-12-24,5.3,95.6,2.7,14.4,1025.1,10.3,7.3
-2024-12-25,8.8,98.7,0.3,10.8,1032.9,2.6,7.8
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-2025-01-06,9.2,82.9,3.8,46.8,984.1,35.3,9.9
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-2025-03-06,7.2,72.3,0.0,14.4,1017.7,21.5,128.1
-2025-03-07,9.5,68.1,0.0,14.4,1016.1,38.3,134.3
-2025-03-08,9.9,63.3,0.0,21.6,1012.9,32.3,143.2
-2025-03-09,9.5,64.5,0.0,14.4,1006.0,17.6,131.5
-2025-03-10,8.4,71.3,0.0,11.2,1002.4,10.2,140.8
-2025-03-11,5.9,78.7,0.0,18.0,1003.7,19.3,90.3
-2025-03-12,4.6,79.3,0.3,14.8,1000.7,34.6,118.4
-2025-03-13,3.0,82.5,0.0,14.4,1000.6,30.1,62.0
-2025-03-14,2.2,74.6,0.0,18.0,1009.5,33.8,95.8
-2025-03-15,3.7,67.3,0.0,21.6,1019.9,42.5,129.6
-2025-03-16,4.3,71.3,0.0,14.8,1023.3,35.9,168.3
-2025-03-17,6.3,60.0,0.0,25.9,1028.6,36.0,128.6
-2025-03-18,5.0,41.8,0.0,25.2,1028.3,43.5,175.3
-2025-03-19,6.8,56.2,0.0,14.4,1024.0,39.3,200.8
-2025-03-20,10.2,64.0,0.0,10.8,1021.4,24.6,141.9
-2025-03-21,14.3,54.9,0.0,32.4,1013.5,23.8,158.8
-2025-03-22,15.3,51.4,0.0,22.3,1004.1,34.3,118.0
-2025-03-23,13.4,70.9,0.3,14.4,1003.7,37.0,110.6
-2025-03-24,9.9,83.3,0.0,14.8,1014.5,7.5,155.5
-2025-03-25,8.3,79.7,0.0,14.8,1020.5,9.3,100.2
-2025-03-26,8.5,80.9,0.0,18.0,1024.2,27.3,60.8
-2025-03-27,7.0,77.7,0.0,11.2,1021.3,13.9,208.4
-2025-03-28,6.7,88.5,0.5,18.0,1012.7,13.3,73.3
-2025-03-29,7.9,77.5,0.0,14.4,1018.7,25.1,182.0
-2025-03-30,10.1,71.2,0.4,28.8,1016.6,21.1,112.8
-2025-03-31,9.0,68.7,0.0,21.6,1026.6,28.0,212.8
-2025-04-01,8.0,70.8,0.0,25.2,1028.2,27.4,223.3
-2025-04-02,11.7,53.8,0.0,32.4,1024.4,41.0,220.0
-2025-04-03,14.2,51.6,0.0,28.8,1022.9,38.4,221.0
-2025-04-04,14.1,54.8,0.0,18.4,1020.5,33.9,230.8
-2025-04-05,10.8,57.4,0.0,21.6,1019.7,37.5,236.0
-2025-04-06,8.2,41.9,0.0,20.5,1024.5,24.1,241.4
-2025-04-07,7.9,49.1,0.0,13.3,1026.2,24.1,239.5
-2025-04-08,9.9,53.7,0.0,14.8,1026.9,24.1,221.1
-2025-04-09,8.1,73.4,0.0,25.9,1026.9,11.2,229.1
-2025-04-10,9.5,73.4,0.0,22.3,1028.3,12.9,203.9

 2024-10-28,12.4,91.8,1.1,31.7,1021.8,12.8,27.3
 2024-10-29,13.8,95.9,0.2,20.5,1023.1,8.1,16.0
 2024-10-30,12.7,92.9,0.6,9.4,1027.5,12.5,32.8