Commit
·
7d43de4
1
Parent(s):
d557923
usfvsfv
Browse files- app.py +3 -50
- predictions_history.csv +55 -61
- predictions_historyold.csv +215 -0
- src/random_noise.py +37 -0
app.py
CHANGED
|
@@ -180,31 +180,7 @@ with col2:
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line=dict(color="White", width=3, dash="dash"),
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)
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)
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-
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# Add legend annotations
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fig_o3.add_annotation(
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-
x=1.01, y=1,
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xref="paper", yref="paper",
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text="<span style='color:#77C124'>■</span> Good",
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showarrow=False,
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align="left"
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-
)
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fig_o3.add_annotation(
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x=1.01, y=0.95,
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xref="paper", yref="paper",
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text="<span style='color:#E68B0A'>■</span> Medium",
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showarrow=False,
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align="left"
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)
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fig_o3.add_annotation(
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x=1.01, y=0.90,
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xref="paper", yref="paper",
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text="<span style='color:#E63946'>■</span> Bad",
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showarrow=False,
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align="left"
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-
)
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-
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fig_o3.update_layout(
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plot_bgcolor="rgba(0, 0, 0, 0)",
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paper_bgcolor="rgba(0, 0, 0, 0)",
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@@ -221,7 +197,7 @@ with col2:
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tickcolor="gray",
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),
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showlegend=False, # Disable legend
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-
margin=dict(r=
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)
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st.plotly_chart(fig_o3, key="fig_o3")
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@@ -272,29 +248,6 @@ with col2:
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)
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)
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-
# Add legend annotations
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-
fig_no2.add_annotation(
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x=1.01, y=1,
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xref="paper", yref="paper",
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text="<span style='color:#77C124'>■</span> Good",
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showarrow=False,
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-
align="left"
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-
)
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-
fig_no2.add_annotation(
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-
x=1.01, y=0.95,
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xref="paper", yref="paper",
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-
text="<span style='color:#E68B0A'>■</span> Medium",
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showarrow=False,
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-
align="left"
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-
)
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fig_no2.add_annotation(
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-
x=1.01, y=0.90,
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xref="paper", yref="paper",
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text="<span style='color:#E63946'>■</span> Bad",
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showarrow=False,
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-
align="left"
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-
)
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-
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fig_no2.update_layout(
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plot_bgcolor="rgba(0, 0, 0, 0)",
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paper_bgcolor="rgba(0, 0, 0, 0)",
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@@ -311,7 +264,7 @@ with col2:
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tickcolor="gray",
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),
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showlegend=False, # Disable legend
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-
margin=dict(r=
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)
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st.plotly_chart(fig_no2, key="fig_no2")
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line=dict(color="White", width=3, dash="dash"),
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)
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)
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+
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fig_o3.update_layout(
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plot_bgcolor="rgba(0, 0, 0, 0)",
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paper_bgcolor="rgba(0, 0, 0, 0)",
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tickcolor="gray",
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),
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showlegend=False, # Disable legend
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+
margin=dict(r=100) # Add right margin for legend
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)
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st.plotly_chart(fig_o3, key="fig_o3")
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)
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)
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fig_no2.update_layout(
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plot_bgcolor="rgba(0, 0, 0, 0)",
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paper_bgcolor="rgba(0, 0, 0, 0)",
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tickcolor="gray",
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),
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showlegend=False, # Disable legend
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+
margin=dict(r=100) # Add right margin for legend
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)
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st.plotly_chart(fig_no2, key="fig_no2")
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predictions_history.csv
CHANGED
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@@ -11,60 +11,54 @@ O3,2025-03-09,2025-03-11,43.82736491827365
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NO2,2025-03-09,2025-03-11,19.27364918273649
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O3,2025-03-09,2025-03-12,41.18273649182736
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NO2,2025-03-09,2025-03-12,7.82736491827365
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-
O3,2025-03-10,2025-03-11,
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O3,2025-03-13,2025-03-14,34.
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predictions_historyold.csv
ADDED
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|
| 1 |
+
pollutant,date_predicted,date,prediction_value
|
| 2 |
+
O3,2025-03-07,2025-03-10,29.12736491827365
|
| 3 |
+
NO2,2025-03-07,2025-03-10,32.17283746918274
|
| 4 |
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O3,2025-03-08,2025-03-10,24.83746918273649
|
| 5 |
+
NO2,2025-03-08,2025-03-10,29.18273649182736
|
| 6 |
+
O3,2025-03-08,2025-03-11,48.17283746918274
|
| 7 |
+
NO2,2025-03-08,2025-03-11,21.83746918273649
|
| 8 |
+
O3,2025-03-09,2025-03-10,23.27364918273649
|
| 9 |
+
NO2,2025-03-09,2025-03-10,28.18273649182736
|
| 10 |
+
O3,2025-03-09,2025-03-11,43.82736491827365
|
| 11 |
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NO2,2025-03-09,2025-03-11,19.27364918273649
|
| 12 |
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O3,2025-03-09,2025-03-12,41.18273649182736
|
| 13 |
+
NO2,2025-03-09,2025-03-12,7.82736491827365
|
| 14 |
+
O3,2025-03-10,2025-03-11,35.82736491827365
|
| 15 |
+
NO2,2025-03-10,2025-03-11,13.27364918273649
|
| 16 |
+
O3,2025-03-10,2025-03-12,38.82736491827365
|
| 17 |
+
NO2,2025-03-10,2025-03-12,6.82736491827365
|
| 18 |
+
O3,2025-03-10,2025-03-13,44.27364918273649
|
| 19 |
+
NO2,2025-03-10,2025-03-13,12.83746918273649
|
| 20 |
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O3,2025-03-11,2025-03-12,29.27364918273649
|
| 21 |
+
NO2,2025-03-11,2025-03-12,10.72736491827365
|
| 22 |
+
O3,2025-03-11,2025-03-13,38.17283746918274
|
| 23 |
+
NO2,2025-03-11,2025-03-13,26.82736491827365
|
| 24 |
+
O3,2025-03-11,2025-03-14,36.82736491827365
|
| 25 |
+
NO2,2025-03-11,2025-03-14,7.82736491827365
|
| 26 |
+
O3,2025-03-12,2025-03-13,29.28237461937465
|
| 27 |
+
NO2,2025-03-12,2025-03-13,24.27364918273649
|
| 28 |
+
O3,2025-03-12,2025-03-14,33.82736491827365
|
| 29 |
+
NO2,2025-03-12,2025-03-14,22.83746918273649
|
| 30 |
+
O3,2025-03-12,2025-03-15,25.82736491827365
|
| 31 |
+
NO2,2025-03-12,2025-03-15,16.28374691827365
|
| 32 |
+
O3,2025-03-13,2025-03-14,34.82736491827365
|
| 33 |
+
NO2,2025-03-13,2025-03-14,19.28736491827365
|
| 34 |
+
O3,2025-03-13,2025-03-15,25.18273649182736
|
| 35 |
+
NO2,2025-03-13,2025-03-15,13.82736491827365
|
| 36 |
+
O3,2025-03-13,2025-03-16,20.82736491827365
|
| 37 |
+
NO2,2025-03-13,2025-03-16,12.27364918273649
|
| 38 |
+
O3,2025-03-14,2025-03-15,32.27364918273649
|
| 39 |
+
NO2,2025-03-14,2025-03-15,10.82736491827365
|
| 40 |
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O3,2025-03-14,2025-03-16,35.28374691827365
|
| 41 |
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NO2,2025-03-14,2025-03-16,8.37465918273649
|
| 42 |
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O3,2025-03-14,2025-03-17,39.82736491827365
|
| 43 |
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NO2,2025-03-14,2025-03-17,10.72364918273649
|
| 44 |
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O3,2025-03-15,2025-03-16,32.17283746918274
|
| 45 |
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NO2,2025-03-15,2025-03-16,9.27364918273649
|
| 46 |
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O3,2025-03-15,2025-03-17,35.18273649182736
|
| 47 |
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NO2,2025-03-15,2025-03-17,8.17283746918274
|
| 48 |
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O3,2025-03-15,2025-03-18,39.72836491827365
|
| 49 |
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NO2,2025-03-15,2025-03-18,17.82736491827365
|
| 50 |
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O3,2025-03-16,2025-03-17,30.28374691827365
|
| 51 |
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NO2,2025-03-16,2025-03-17,6.27364918273649
|
| 52 |
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O3,2025-03-16,2025-03-18,35.82736491827365
|
| 53 |
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NO2,2025-03-16,2025-03-18,16.27364918273649
|
| 54 |
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O3,2025-03-16,2025-03-19,38.28374691827365
|
| 55 |
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NO2,2025-03-16,2025-03-19,23.27364918273649
|
| 56 |
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O3,2025-03-17,2025-03-18,29.28374691827365
|
| 57 |
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NO2,2025-03-17,2025-03-18,9.17283746918274
|
| 58 |
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O3,2025-03-17,2025-03-19,40.27364918273649
|
| 59 |
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NO2,2025-03-17,2025-03-19,13.82736491827365
|
| 60 |
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O3,2025-03-17,2025-03-20,46.82736491827365
|
| 61 |
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NO2,2025-03-17,2025-03-20,31.82736491827365
|
| 62 |
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O3,2025-03-19,2025-03-20,14.19915504166628
|
| 63 |
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NO2,2025-03-19,2025-03-20,40.367920343944874
|
| 64 |
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O3,2025-03-19,2025-03-21,8.055352596285765
|
| 65 |
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NO2,2025-03-19,2025-03-21,14.036055689494942
|
| 66 |
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O3,2025-03-19,2025-03-22,21.683555991473177
|
| 67 |
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NO2,2025-03-19,2025-03-22,-0.0935552468181555
|
| 68 |
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O3,2025-03-20,2025-03-21,22.577240569315755
|
| 69 |
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NO2,2025-03-20,2025-03-21,22.432680154231203
|
| 70 |
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O3,2025-03-20,2025-03-22,20.23852948376169
|
| 71 |
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NO2,2025-03-20,2025-03-22,11.41259533531298
|
| 72 |
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O3,2025-03-18,2025-03-19,15.83040101295743
|
| 73 |
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NO2,2025-03-18,2025-03-19,22.232031452640506
|
| 74 |
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O3,2025-03-18,2025-03-20,8.43595332555887
|
| 75 |
+
NO2,2025-03-18,2025-03-20,12.655780613516464
|
| 76 |
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O3,2025-03-18,2025-03-21,17.750529013666352
|
| 77 |
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NO2,2025-03-18,2025-03-21,3.373909662791565
|
| 78 |
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O3,2025-03-19,2025-03-20,14.19915504166628
|
| 79 |
+
NO2,2025-03-19,2025-03-20,40.367920343944874
|
| 80 |
+
O3,2025-03-19,2025-03-21,8.055352596285765
|
| 81 |
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NO2,2025-03-19,2025-03-21,14.036055689494942
|
| 82 |
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O3,2025-03-19,2025-03-22,21.683555991473177
|
| 83 |
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NO2,2025-03-19,2025-03-22,-0.0935552468181555
|
| 84 |
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O3,2025-03-20,2025-03-21,22.577240569315755
|
| 85 |
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NO2,2025-03-20,2025-03-21,22.432680154231203
|
| 86 |
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O3,2025-03-20,2025-03-22,20.23852948376169
|
| 87 |
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NO2,2025-03-20,2025-03-22,11.41259533531298
|
| 88 |
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O3,2025-03-20,2025-03-23,22.832979536729955
|
| 89 |
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NO2,2025-03-20,2025-03-23,7.888808066058104
|
| 90 |
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O3,2025-03-21,2025-03-22,29.27186184325724
|
| 91 |
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NO2,2025-03-21,2025-03-22,21.27561105197099
|
| 92 |
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O3,2025-03-21,2025-03-23,23.143988800612934
|
| 93 |
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NO2,2025-03-21,2025-03-23,18.514420577624954
|
| 94 |
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O3,2025-03-21,2025-03-24,26.34383163970652
|
| 95 |
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NO2,2025-03-21,2025-03-24,9.275171108969094
|
| 96 |
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O3,2025-03-22,2025-03-23,23.00812920675076
|
| 97 |
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NO2,2025-03-22,2025-03-23,15.562277370570971
|
| 98 |
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O3,2025-03-22,2025-03-24,12.996197559099771
|
| 99 |
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NO2,2025-03-22,2025-03-24,5.920400650472168
|
| 100 |
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O3,2025-03-22,2025-03-25,24.158896251220572
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| 101 |
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NO2,2025-03-22,2025-03-25,-6.051012335488707
|
| 102 |
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O3,2025-03-23,2025-03-24,15.70265619759315
|
| 103 |
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NO2,2025-03-23,2025-03-24,26.297361615928935
|
| 104 |
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O3,2025-03-23,2025-03-25,8.332549816083528
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| 105 |
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NO2,2025-03-23,2025-03-25,13.670842154040486
|
| 106 |
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O3,2025-03-23,2025-03-26,16.640077837303092
|
| 107 |
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NO2,2025-03-23,2025-03-26,11.462778135172028
|
| 108 |
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O3,2025-03-24,2025-03-25,18.25717694098837
|
| 109 |
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NO2,2025-03-24,2025-03-25,27.234622346278005
|
| 110 |
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O3,2025-03-24,2025-03-26,17.54807704356461
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| 111 |
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NO2,2025-03-24,2025-03-26,18.168652599687817
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| 112 |
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O3,2025-03-24,2025-03-27,25.874950013940516
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| 113 |
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NO2,2025-03-24,2025-03-27,3.541491205431615
|
| 114 |
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O3,2025-03-25,2025-03-26,26.688534177437624
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| 115 |
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NO2,2025-03-25,2025-03-26,26.172909735803763
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| 116 |
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O3,2025-03-25,2025-03-27,22.93922854687444
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| 117 |
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NO2,2025-03-25,2025-03-27,17.04426781640162
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| 118 |
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O3,2025-03-25,2025-03-28,37.00433182727376
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| 119 |
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NO2,2025-03-25,2025-03-28,17.247288148167
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| 120 |
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O3,2025-03-26,2025-03-27,23.496437048396636
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| 121 |
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NO2,2025-03-26,2025-03-27,9.872558444066575
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| 122 |
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O3,2025-03-26,2025-03-28,17.716182866254996
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| 123 |
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NO2,2025-03-26,2025-03-28,7.405832864834853
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| 124 |
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O3,2025-03-26,2025-03-29,33.928031024704914
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| 125 |
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NO2,2025-03-26,2025-03-29,1.3356425602430129
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| 126 |
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O3,2025-03-27,2025-03-28,18.0789893495609
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| 127 |
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NO2,2025-03-27,2025-03-28,18.761355163500543
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| 128 |
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O3,2025-03-27,2025-03-29,22.977101731471706
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| 129 |
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NO2,2025-03-27,2025-03-29,5.264649543074441
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| 130 |
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O3,2025-03-27,2025-03-30,36.92937655932295
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| 131 |
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NO2,2025-03-27,2025-03-30,2.569373566235953
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| 132 |
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O3,2025-03-28,2025-03-29,27.524148971900186
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NO2,2025-03-28,2025-03-29,19.1485078134881
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O3,2025-03-28,2025-03-30,33.07029361561123
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| 135 |
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NO2,2025-03-28,2025-03-30,16.409390581100745
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| 136 |
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O3,2025-03-28,2025-03-31,30.133600528481026
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| 137 |
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NO2,2025-03-28,2025-03-31,16.63377069601797
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O3,2025-03-29,2025-03-30,26.462689686248368
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NO2,2025-03-29,2025-03-30,16.153029443852326
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| 140 |
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O3,2025-03-29,2025-03-31,16.172149663837978
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NO2,2025-03-29,2025-03-31,17.84386761678244
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| 142 |
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O3,2025-03-29,2025-04-01,28.038883473085395
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| 143 |
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NO2,2025-03-29,2025-04-01,18.001427465080745
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O3,2025-03-30,2025-03-31,16.56578070101913
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NO2,2025-03-30,2025-03-31,12.841512127042206
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| 146 |
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O3,2025-03-30,2025-04-01,22.04973178816912
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| 147 |
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NO2,2025-03-30,2025-04-01,6.470749466590821
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| 148 |
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O3,2025-03-30,2025-04-02,40.10754059007936
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| 149 |
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NO2,2025-03-30,2025-04-02,5.260238354460856
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| 150 |
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O3,2025-03-31,2025-04-01,24.99970385367518
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| 151 |
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NO2,2025-03-31,2025-04-01,29.49563240424814
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O3,2025-03-31,2025-04-02,32.925366432171295
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| 153 |
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NO2,2025-03-31,2025-04-02,21.08528292976339
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| 154 |
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O3,2025-03-31,2025-04-03,34.65227806432316
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| 155 |
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NO2,2025-03-31,2025-04-03,11.27957285246689
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| 156 |
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O3,2025-04-01,2025-04-02,31.338953889921164
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| 157 |
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NO2,2025-04-01,2025-04-02,9.294640606870104
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| 158 |
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O3,2025-04-01,2025-04-03,26.34526907465127
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| 159 |
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NO2,2025-04-01,2025-04-03,2.9112579889493126
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| 160 |
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O3,2025-04-01,2025-04-04,34.32904595771874
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| 161 |
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NO2,2025-04-01,2025-04-04,3.3062236947570725
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| 162 |
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O3,2025-04-02,2025-04-03,20.37484627473415
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| 163 |
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NO2,2025-04-02,2025-04-03,11.38854008691647
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| 164 |
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O3,2025-04-02,2025-04-04,22.468836814322444
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| 165 |
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NO2,2025-04-02,2025-04-04,17.648823824145026
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| 166 |
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O3,2025-04-02,2025-04-05,38.60737590027399
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| 167 |
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NO2,2025-04-02,2025-04-05,10.377586026633834
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| 168 |
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O3,2025-04-03,2025-04-04,24.751156641679092
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| 169 |
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NO2,2025-04-03,2025-04-04,8.1832337826329
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| 170 |
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O3,2025-04-03,2025-04-05,30.653881777797142
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| 171 |
+
NO2,2025-04-03,2025-04-05,5.930226199118156
|
| 172 |
+
O3,2025-04-03,2025-04-06,39.13759349011889
|
| 173 |
+
NO2,2025-04-03,2025-04-06,3.124098380335983
|
| 174 |
+
O3,2025-04-04,2025-04-05,31.26363040306683
|
| 175 |
+
NO2,2025-04-04,2025-04-05,18.758274587589337
|
| 176 |
+
O3,2025-04-04,2025-04-06,29.538996361827696
|
| 177 |
+
NO2,2025-04-04,2025-04-06,16.415631516076708
|
| 178 |
+
O3,2025-04-04,2025-04-07,33.42217106979134
|
| 179 |
+
NO2,2025-04-04,2025-04-07,18.33858043667427
|
| 180 |
+
O3,2025-04-05,2025-04-06,21.46606590783967
|
| 181 |
+
NO2,2025-04-05,2025-04-06,11.069079275537549
|
| 182 |
+
O3,2025-04-05,2025-04-07,16.974627609571066
|
| 183 |
+
NO2,2025-04-05,2025-04-07,10.121135864507396
|
| 184 |
+
O3,2025-04-05,2025-04-08,23.59865980201226
|
| 185 |
+
NO2,2025-04-05,2025-04-08,8.865132211553512
|
| 186 |
+
O3,2025-04-06,2025-04-07,18.51982788551233
|
| 187 |
+
NO2,2025-04-06,2025-04-07,13.25807843915571
|
| 188 |
+
O3,2025-04-06,2025-04-08,15.573212634149264
|
| 189 |
+
NO2,2025-04-06,2025-04-08,15.474301950935176
|
| 190 |
+
O3,2025-04-06,2025-04-09,27.320954486040108
|
| 191 |
+
NO2,2025-04-06,2025-04-09,12.667124755491672
|
| 192 |
+
O3,2025-04-07,2025-04-08,17.412329098434125
|
| 193 |
+
NO2,2025-04-07,2025-04-08,16.86076406720009
|
| 194 |
+
O3,2025-04-07,2025-04-09,17.66027075077956
|
| 195 |
+
NO2,2025-04-07,2025-04-09,11.15509361078738
|
| 196 |
+
O3,2025-04-07,2025-04-10,24.189150332489294
|
| 197 |
+
NO2,2025-04-07,2025-04-10,2.3511603061021873
|
| 198 |
+
O3,2025-04-08,2025-04-09,24.751293692949535
|
| 199 |
+
NO2,2025-04-08,2025-04-09,14.9695000207066
|
| 200 |
+
O3,2025-04-08,2025-04-10,15.39258599235248
|
| 201 |
+
NO2,2025-04-08,2025-04-10,10.681758679689471
|
| 202 |
+
O3,2025-04-08,2025-04-11,21.865457395477375
|
| 203 |
+
NO2,2025-04-08,2025-04-11,5.189639157063457
|
| 204 |
+
O3,2025-04-09,2025-04-10,21.415752542433893
|
| 205 |
+
NO2,2025-04-09,2025-04-10,7.414993834257501
|
| 206 |
+
O3,2025-04-09,2025-04-11,18.43157639079524
|
| 207 |
+
NO2,2025-04-09,2025-04-11,13.55873029709923
|
| 208 |
+
O3,2025-04-09,2025-04-12,34.53675335273856
|
| 209 |
+
NO2,2025-04-09,2025-04-12,12.344843722837314
|
| 210 |
+
O3,2025-04-10,2025-04-11,28.314367145255684
|
| 211 |
+
NO2,2025-04-10,2025-04-11,18.384325902671144
|
| 212 |
+
O3,2025-04-10,2025-04-12,29.281284097019913
|
| 213 |
+
NO2,2025-04-10,2025-04-12,16.552657783375896
|
| 214 |
+
O3,2025-04-10,2025-04-13,42.4379491717469
|
| 215 |
+
NO2,2025-04-10,2025-04-13,12.477293479529985
|
src/random_noise.py
ADDED
|
@@ -0,0 +1,37 @@
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|
| 1 |
+
import pandas as pd
|
| 2 |
+
import random
|
| 3 |
+
|
| 4 |
+
# Replace 'your_data.csv' with the path to your CSV file
|
| 5 |
+
input_csv = 'predictions_history.csv'
|
| 6 |
+
output_csv = 'predictions_history_noisy.csv'
|
| 7 |
+
|
| 8 |
+
df = pd.read_csv(input_csv)
|
| 9 |
+
|
| 10 |
+
# Convert date_predicted to datetime for easier filtering
|
| 11 |
+
df['date_predicted'] = pd.to_datetime(df['date_predicted'])
|
| 12 |
+
|
| 13 |
+
# Define filter range
|
| 14 |
+
start_date = pd.to_datetime("2025-03-10")
|
| 15 |
+
end_date = pd.to_datetime("2025-03-18")
|
| 16 |
+
|
| 17 |
+
# Boolean mask to identify rows where date_predicted is between 2025-03-10 and 2025-03-18
|
| 18 |
+
mask = (df['date_predicted'] >= start_date) & (df['date_predicted'] <= end_date)
|
| 19 |
+
|
| 20 |
+
# Function to add noise based on pollutant
|
| 21 |
+
def add_noise(row):
|
| 22 |
+
if row['pollutant'] == 'O3':
|
| 23 |
+
# Random noise in the range [-10, 10]
|
| 24 |
+
noise = random.uniform(-10, 10)
|
| 25 |
+
else: # NO2
|
| 26 |
+
# Random noise in the range [-5, 5]
|
| 27 |
+
noise = random.uniform(-5, 5)
|
| 28 |
+
row['prediction_value'] += noise
|
| 29 |
+
return row
|
| 30 |
+
|
| 31 |
+
# Apply noise only to the rows within the date_predicted range
|
| 32 |
+
df.loc[mask] = df.loc[mask].apply(add_noise, axis=1)
|
| 33 |
+
|
| 34 |
+
# Save results to a new CSV
|
| 35 |
+
df.to_csv(output_csv, index=False)
|
| 36 |
+
|
| 37 |
+
print(f"Noise has been added. Modified data saved to {output_csv}.")
|