NewTaskRequirements.md

Required files for creating a new PV Validation Hub Task

config.json

Example JSON:

{
    "category_name": "Time Shift Analysis",
    "function_name": "detect_time_shifts",
    "comparison_type": "time_series",
    "performance_metrics": [
        "runtime",
        "mean_absolute_error"
    ],
    "allowable_kwargs": [
        "latitude",
        "longitude",
        "data_sampling_frequency"
    ],
    "ground_truth_compare": [
        "time_series"
    ],
    "public_results_table": "time-shift-public-metrics.json",
    "private_results_columns": [
        "system_id",
        "file_name",
        "run_time",
        "data_requirements",
        "mean_absolute_error_time_series",
        "data_sampling_frequency",
        "issue"
    ],
    "plots": [
        {
            "type": "histogram",
            "x_val": "mean_absolute_error_time_series",
            "color_code": "issue",
            "title": "Time Series MAE Distribution by Issue",
            "save_file_path": "mean_absolute_error_time_series_dist.png"
        },
        {
            "type": "histogram",
            "x_val": "mean_absolute_error_time_series",
            "color_code": "data_sampling_frequency",
            "title": "Time Series MAE Distribution by Sampling Frequency",
            "save_file_path": "mean_absolute_error_time_series_dist.png"
        },
        {
            "type": "histogram",
            "x_val": "run_time",
            "title": "Run Time Distribution",
            "save_file_path": "run_time_dist.png"
        }
    ]
}

system_metadata.csv

Required columns:

system_id,name,azimuth,tilt,elevation,latitude,longitude,tracking,climate_type,dc_capacity

file_metadata.csv

Required columns:

file_id,system_id,file_name,timezone,data_sampling_frequency,issue

template.py (Marimo template with cli args input)

Marimo python file will need to input data from mo.cli_args() method

Example:

def create_df_from_cli_args():
        args = mo.cli_args().to_dict()
        data = args.get("results_df")
        rows = []
        for row in data:
            rows.append(json.loads(row))

        df = pd.DataFrame.from_records(rows)
        return df

csv data files

file names must match what is included in the file_name in the file_metadata.csv

ground truth csv data files

file names must match what is included in the data files folder

Markdown files for Task

description.md

The markdown file used for the description tab in an analysis.

dataset.md

The markdown file to describe the dataset in the data tab.

shortdesc.md

The markdown file that is used on the card.

SubmissionInstructions.md

The markdown file that is used on the Submission Instructions tab in the analysis.