Commit
·
09afa32
1
Parent(s):
7d43de4
usfvsfv
Browse files- predictions_history.csv +0 -10
- predictions_historyold.csv +0 -215
- src/random_noise.py +0 -37
predictions_history.csv
CHANGED
@@ -59,16 +59,6 @@ O3,2025-03-17,2025-03-19,33.902081716854376
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NO2,2025-03-17,2025-03-19,17.035250016088494
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O3,2025-03-17,2025-03-20,56.13338974244566
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NO2,2025-03-17,2025-03-20,34.74925137364262
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O3,2025-03-19,2025-03-20,14.19915504166628
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NO2,2025-03-19,2025-03-20,40.367920343944874
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O3,2025-03-19,2025-03-21,8.055352596285765
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NO2,2025-03-19,2025-03-21,14.036055689494942
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O3,2025-03-19,2025-03-22,21.683555991473177
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NO2,2025-03-19,2025-03-22,-0.0935552468181555
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O3,2025-03-20,2025-03-21,22.577240569315755
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NO2,2025-03-20,2025-03-21,22.432680154231203
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O3,2025-03-20,2025-03-22,20.23852948376169
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NO2,2025-03-20,2025-03-22,11.41259533531298
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O3,2025-03-18,2025-03-19,24.34677204803371
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NO2,2025-03-18,2025-03-19,24.214191960527593
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O3,2025-03-18,2025-03-20,7.97083270245124
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NO2,2025-03-17,2025-03-19,17.035250016088494
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O3,2025-03-17,2025-03-20,56.13338974244566
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NO2,2025-03-17,2025-03-20,34.74925137364262
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O3,2025-03-18,2025-03-19,24.34677204803371
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NO2,2025-03-18,2025-03-19,24.214191960527593
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O3,2025-03-18,2025-03-20,7.97083270245124
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predictions_historyold.csv
DELETED
@@ -1,215 +0,0 @@
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pollutant,date_predicted,date,prediction_value
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O3,2025-03-07,2025-03-10,29.12736491827365
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NO2,2025-03-07,2025-03-10,32.17283746918274
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O3,2025-03-08,2025-03-10,24.83746918273649
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NO2,2025-03-08,2025-03-10,29.18273649182736
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O3,2025-03-08,2025-03-11,48.17283746918274
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NO2,2025-03-08,2025-03-11,21.83746918273649
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O3,2025-03-09,2025-03-10,23.27364918273649
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NO2,2025-03-09,2025-03-10,28.18273649182736
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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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O3,2025-03-10,2025-03-11,35.82736491827365
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NO2,2025-03-10,2025-03-11,13.27364918273649
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O3,2025-03-10,2025-03-12,38.82736491827365
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NO2,2025-03-10,2025-03-12,6.82736491827365
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O3,2025-03-10,2025-03-13,44.27364918273649
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NO2,2025-03-10,2025-03-13,12.83746918273649
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O3,2025-03-11,2025-03-12,29.27364918273649
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NO2,2025-03-11,2025-03-12,10.72736491827365
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O3,2025-03-11,2025-03-13,38.17283746918274
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NO2,2025-03-11,2025-03-13,26.82736491827365
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O3,2025-03-11,2025-03-14,36.82736491827365
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NO2,2025-03-11,2025-03-14,7.82736491827365
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O3,2025-03-12,2025-03-13,29.28237461937465
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NO2,2025-03-12,2025-03-13,24.27364918273649
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O3,2025-03-12,2025-03-14,33.82736491827365
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NO2,2025-03-12,2025-03-14,22.83746918273649
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O3,2025-03-12,2025-03-15,25.82736491827365
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NO2,2025-03-12,2025-03-15,16.28374691827365
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O3,2025-03-13,2025-03-14,34.82736491827365
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NO2,2025-03-13,2025-03-14,19.28736491827365
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O3,2025-03-13,2025-03-15,25.18273649182736
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NO2,2025-03-13,2025-03-15,13.82736491827365
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O3,2025-03-13,2025-03-16,20.82736491827365
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NO2,2025-03-13,2025-03-16,12.27364918273649
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O3,2025-03-14,2025-03-15,32.27364918273649
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NO2,2025-03-14,2025-03-15,10.82736491827365
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O3,2025-03-14,2025-03-16,35.28374691827365
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NO2,2025-03-14,2025-03-16,8.37465918273649
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O3,2025-03-14,2025-03-17,39.82736491827365
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NO2,2025-03-14,2025-03-17,10.72364918273649
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O3,2025-03-15,2025-03-16,32.17283746918274
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NO2,2025-03-15,2025-03-16,9.27364918273649
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O3,2025-03-15,2025-03-17,35.18273649182736
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NO2,2025-03-15,2025-03-17,8.17283746918274
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O3,2025-03-15,2025-03-18,39.72836491827365
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NO2,2025-03-15,2025-03-18,17.82736491827365
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O3,2025-03-16,2025-03-17,30.28374691827365
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NO2,2025-03-16,2025-03-17,6.27364918273649
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O3,2025-03-16,2025-03-18,35.82736491827365
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NO2,2025-03-16,2025-03-18,16.27364918273649
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O3,2025-03-16,2025-03-19,38.28374691827365
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NO2,2025-03-16,2025-03-19,23.27364918273649
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O3,2025-03-17,2025-03-18,29.28374691827365
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NO2,2025-03-17,2025-03-18,9.17283746918274
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O3,2025-03-17,2025-03-19,40.27364918273649
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NO2,2025-03-17,2025-03-19,13.82736491827365
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O3,2025-03-17,2025-03-20,46.82736491827365
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NO2,2025-03-17,2025-03-20,31.82736491827365
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O3,2025-03-19,2025-03-20,14.19915504166628
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NO2,2025-03-19,2025-03-20,40.367920343944874
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O3,2025-03-19,2025-03-21,8.055352596285765
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NO2,2025-03-19,2025-03-21,14.036055689494942
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O3,2025-03-19,2025-03-22,21.683555991473177
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NO2,2025-03-19,2025-03-22,-0.0935552468181555
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O3,2025-03-20,2025-03-21,22.577240569315755
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NO2,2025-03-20,2025-03-21,22.432680154231203
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O3,2025-03-20,2025-03-22,20.23852948376169
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NO2,2025-03-20,2025-03-22,11.41259533531298
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O3,2025-03-18,2025-03-19,15.83040101295743
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NO2,2025-03-18,2025-03-19,22.232031452640506
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O3,2025-03-18,2025-03-20,8.43595332555887
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NO2,2025-03-18,2025-03-20,12.655780613516464
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O3,2025-03-18,2025-03-21,17.750529013666352
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NO2,2025-03-18,2025-03-21,3.373909662791565
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O3,2025-03-19,2025-03-20,14.19915504166628
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NO2,2025-03-19,2025-03-20,40.367920343944874
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O3,2025-03-19,2025-03-21,8.055352596285765
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NO2,2025-03-19,2025-03-21,14.036055689494942
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O3,2025-03-19,2025-03-22,21.683555991473177
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NO2,2025-03-19,2025-03-22,-0.0935552468181555
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O3,2025-03-20,2025-03-21,22.577240569315755
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O3,2025-03-25,2025-03-28,37.00433182727376
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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
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215 |
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NO2,2025-04-10,2025-04-13,12.477293479529985
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|
src/random_noise.py
DELETED
@@ -1,37 +0,0 @@
|
|
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 |
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|
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}.")
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