Update/calculate hourly airqualitydata using bigqdata

[NicholasTurner23]

Description

Create default calibrated fields for non airqo device measurements.

Summary by CodeRabbit

• New Features

  • Enhanced sensor calibration now includes two additional measurement fields for particulate matter, providing improved tracking of refined air quality readings.

• Refactor

  • Streamlined the calibration process to ensure updated predicted values are accurately assigned.

[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request updates the data calibration process in the AirQoDataUtils class. Two new columns, pm2_5_calibrated_value and pm10_calibrated_value, are introduced in the calibrate_data method, initialized with np.nan. The method now includes these columns in its input variables and assigns predictions from random forest and lasso models to them based on city groupings. Additionally, a redundant comment has been removed to improve the clarity of the calibration logic.

Changes

File(s)	Change Summary
src/.../airqo_utils.py	Added new columns (pm2_5_calibrated_value, pm10_calibrated_value) initialized to np.nan; updated calibration logic to assign predictions from random forest and lasso models; removed a redundant comment.

Sequence Diagram(s)

sequenceDiagram
    participant Client as Client
    participant Utils as AirQoDataUtils
    participant Data as DataFrame
    participant RFModel as RandomForestModel
    participant LassoModel as LassoModel

    Client->>Utils: Call calibrate_data(data)
    Utils->>Data: Initialize pm2_5_calibrated_value & pm10_calibrated_value (np.nan)
    Utils->>Data: Group data by city
    Utils->>RFModel: Predict PM2.5 values for group
    Utils->>LassoModel: Predict PM10 values for group
    RFModel-->>Utils: Return PM2.5 predictions
    LassoModel-->>Utils: Return PM10 predictions
    Utils->>Data: Update DataFrame with calibrated values
    Utils->>Client: Return calibrated DataFrame

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Suggested reviewers

• Baalmart
• BenjaminSsempala

Poem

In lines of code, new fields arise,
PM2.5 and PM10, under digital skies,
Predictions flow where logic complies,
A fusion of models in a data ballet,
With clarity restored in each display,
Cheers to the code that lights our way! 🌟

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[coderabbitai]

Actionable comments posted: 1

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 6f0282b and 388a57b.

📒 Files selected for processing (1)

• src/workflows/airqo_etl_utils/airqo_utils.py (2 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (2)

• GitHub Check: Analyze (python)
• GitHub Check: Analyze (javascript)

🔇 Additional comments (1)

src/workflows/airqo_etl_utils/airqo_utils.py (1)

621-622: LGTM! Good initialization of calibrated value columns.

Using np.nan for initializing the calibrated value columns is a good practice as it properly represents missing values in pandas DataFrames.

[coderabbitai]

⚠️ Potential issue

Remove calibrated value columns from input variables.

Including pm2_5_calibrated_value and pm10_calibrated_value in the input variables list could cause issues:

1. These columns are initialized with np.nan, and line 641 drops rows with missing values in input variables.
2. These are target variables that we're trying to predict, not input features.

Apply this diff to fix the issue:

            "error_pm10",
            "pm2_5_pm10",
            "pm2_5_pm10_mod",
-            "pm2_5_calibrated_value",
-            "pm10_calibrated_value",
        ]

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	"pm2_5_calibrated_value",
	"pm10_calibrated_value",
	"error_pm10",
	"pm2_5_pm10",
	"pm2_5_pm10_mod",
	]