Read Output

This section explains how to read output data generated from SWAT+ simulations.
It covers accessing results from both standard simulations and Sobol-based sensitivity analyses.

Read 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 generates a file called sensitivity_simulation.json within the simulation folder. This JSON file stores all information necessary for Sobol simulation analysis, including:

• problem: the Sobol problem definition
• sample: the generated sample array
• simulation: simulation outputs stored as JSON strings

To retrieve and process this data for further analysis:

import json
import numpy
import pandas
import io

# Path to the JSON file generated by the Sobol interface
json_file = r"C:\Users\Username\simulation_folder\sensitivity_simulation_sobol.json"

# Load the JSON file
with open(json_file, 'r') as input_json:
    sensitivity_dict = json.load(input_json)

# Retrieve the Sobol problem definition
problem_dict = sensitivity_dict['problem']

# Retrieve the Sobol sample array
sample_array = numpy.array(sensitivity_dict['sample'])

# Retrieve targeted DataFrames from sensitivity simulations
simulation_dict = sensitivity_dict['simulation']
df_name = 'channel_sd_mon_df'  # Example output
df_dict = {}

for sim_key, sim_val in simulation_dict.items():
    # Convert JSON string to pandas DataFrame
    df = pandas.read_json(
        path_or_buf=io.StringIO(sim_val[df_name])
    )
    # Ensure 'date' column is in datetime.date format
    df['date'] = df['date'].dt.date
    df_dict[sim_key] = df