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*`inverter_example.py`: Makes input data depending on the inverter type and compares it with the type with no data and runs the ML model along with a comparison plot using `plotly`. This is the file that you need to run in order to run the ML model. An example output with Enphase is demonstrated below:
* Once all the processing is done, make sure that your return type is of `pd.DataFrame` that has 2 colums, namely
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*`timestamp`: `timestamp=datetime.fromtimestamp(interval_end_time_in_unix_epochs, tz=timezone.utc).strftime('%Y-%m-%d %H:%M:%S')`, and then convert the timestamp column to `pd.to_datetime`
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*`power_kw`: Power in **KiloWatts.** An example is shown below with the formatted `pd.DataFrame`
*`test_make_pv_data.py`: Mocks the `make_pv_data()` function `data.py` file using various type of inverters and the `None` value too using `pytest`
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* Run this using `pytest tests/data/test_make_pv_data.py`
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*`test_process_pv_data.py`: Mocks the `process_pv_data()` function in the `data.py` file using various type of inverters and the `None` value too using `pytest`
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* Run this using `pytest tests/data/test_process_pv_data.py`
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*`inverters/`
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*`test_process_enphase_data.py`: Mocks the `process_enphase_data()` function in the `/inverters/enphase.py` file using `pytest`
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* Run this using `pytest tests/inverters/test_process_enphase_data.py`
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