Data Analysis

This section explains how to analysis data generated by different interfaces.

Time Series Data

A standard SWAT+ simulation generates TXT files with time series columns: day, mon, and yr for day, month, and year, respectively. The following method creates a time series DataFrame that includes a new date column with datetime.date objects and save the resulting DataFrame to a JSON file.

import pySWATPlus

output = pySWATPlus.DataManager().simulated_timeseries_df(
    data_file=r"C:\Users\Username\custom_folder\channel_sd_mon.txt",
    has_units=True,
    begin_date='01-Jun-2011',
    end_date='01-Jun-2013',
    ref_day=15,
    apply_filter={'name': ['cha561']},
    usecols=['name', 'flo_out'],
    json_file=r"C:\Users\Username\output_folder\tmp.json"
)

print(output)

Read Sensitivity Simulation Data

The sensitivity analysis performed using the simulation_by_sample_parameters method generates a file named sensitivity_simulation.json. This JSON file contains all the information required for sensitivity analysis, including:

problem: Problem definition dictionary
sample: List of generated samples
simulation: Simulated DataFrame corresponding to each sample

To retrieve the selected DataFrame for all scenarios, use:

output = pySWATPlus.DataManager().read_sensitive_dfs(
    sensim_file=r"C:\Users\Username\simulation_folder\sensitivity_simulation.json",
    df_name='channel_sd_mon_df',
    add_problem=True,
    add_sample=True
)

Scenario Metrics

For a selected DataFrame, scenario metrics across all simulations can be computed by comparing model outputs with observed data.

NSE: Nash–Sutcliffe Efficiency KGE: Kling–Gupta Efficiency MSE: Mean Squared Error RMSE: Root Mean Squared Error PBIAS: Percent Bias MARE: Mean Absolute Relative Error

To get the mapping between available indicators and their abbreviations:
```
indicators = pySWATPlus.PerformanceMetrics().indicator_names
```

To compute metrics for all scenarios using the desired indicators:

output = pySWATPlus.SensitivityAnalyzer().scenario_indicators(
    sensim_file=r"C:\Users\Username\simulation_folder\sensitivity_simulation.json",
    df_name='channel_sd_mon_df',
    sim_col='flo_out',
    obs_file=r"C:\Users\Username\observed_folder\discharge_monthly.csv",
    date_format='%Y-%m-%d',
    obs_col='discharge',
    indicators=['NSE', 'MSE'],
    json_file=r"C:\Users\Username\data_analysis\performance_metrics.json"
)