Method and variable name modifications

README.md

@@ -35,10 +35,10 @@
 
 ## ✨ Key Features
 
-- Modify parameters via the `calibration.cal` file.
-- Run SWAT+ simulations.
-- Perform sensitivity analysis on model parameters using the [SALib](https://github.com/SALib/SALib) Python package, with support for parallel computation.
-- Compute performance metrics using widely adopted indicators and derive Sobol sensitivity indices.
+- Modify model parameters through the `calibration.cal` file.
+- Run SWAT+ simulations seamlessly.
+- Compute performance metrics using widely adopted indicators.
+- Perform sensitivity analysis on model parameters using the [SALib](https://github.com/SALib/SALib) Python package, with support for parallel computation; currently, only Sobol sampling and Sobol indices are supported.
 
 
 ## 📥 Install pySWATPlus

docs/changelog.md

@@ -2,28 +2,34 @@
 
 ## Version 1.2.0 (Month DD, YYYY, not released yet)
 
-- Introduced the `pySWATPlus.DataManager` class with the following methods to support data processing workflows:
-
-    - `read_sensitive_dfs`: Reads sensitivity simulation data generated by the `simulation_by_sobol_sample` method in the `pySWATPlus.SensitivityAnalyzer` class.  
-    - `simulated_timeseries_df`: Moved from the `pySWATPlus.SensitivityAnalyzer` class for improved modularity.
+- All SWAT+ simulations with modified parameters are now configured through the `calibration.cal` file, eliminating the need to read and modify individual input files.
 
 - Introduced the `pySWATPlus.PerformanceMetrics` class to compute performance metrics between simulated and observed values using the following indicators:
 
-    - Nash–Sutcliffe Efficiency  
-    - Kling–Gupta Efficiency  
-    - Mean Squared Error  
-    - Root Mean Squared Error  
-    - Percent Bias
-    - Mean Absolute Relative Error
+  - Nash–Sutcliffe Efficiency
+  - Kling–Gupta Efficiency
+  - Mean Squared Error
+  - Root Mean Squared Error 
+  - Percent Bias
+  - Mean Absolute Relative Error
+
+- Updated the `pySWATPlus.SensitivityAnalyzer` class:
 
-- Added the `sobol_indices` method to the `pySWATPlus.SensitivityAnalyzer`** class for computing Sobol indices using the available indicators in the `pySWATPlus.PerformanceMetrics` class.
+  - Renamed the method `simulation_by_sobol_sample` to `simulation_by_sample_parameters` to standardize naming and allow different sampling techniques in the future.
+  - Added `parameter_sensitivity_indices` for computing sensitivity indices using the available indicators in the `pySWATPlus.PerformanceMetrics` class.
+
+- Introduced the `pySWATPlus.DataManager` class with methods to support data processing workflows:
+
+  - `read_sensitive_dfs`: Reads sensitivity simulation data generated by the `simulation_by_sobol_sample` method in the `pySWATPlus.SensitivityAnalyzer` class.  
+  - `simulated_timeseries_df`: Moved from the `pySWATPlus.SensitivityAnalyzer` class to improve modularity.
 
-- Added new methods to the `pySWATPlus.TxtinoutReader` class:
+- Updated the `pySWATPlus.TxtinoutReader` class:
 
-    - `set_simulation_timestep`: Modifies the simulation timestep in the `time.sim` file.  
-    - `set_print_interval`: Modifies the print interval in the `print.prt` file.
+  - Added `set_simulation_timestep` to modify the simulation timestep in the `time.sim` file.  
+  - Added `set_print_interval` to modify the print interval in the `print.prt` file.
+  - Added `set_print_period` to modify the print period in the `print.prt` file for recording simulated results.  
+  - Renamed `set_begin_and_end_date` to `set_simulation_period` for better consistency.
 
-- All SWAT+ simulations with modified parameters are now configured through the `calibration.cal` file, eliminating the need to read and modify individual input files.
 
 
 ## Version 1.1.0 (August 26, 2025)

docs/index.md

@@ -8,9 +8,10 @@
 
 ## ✨ Key Features
 
-- Modify parameters via the `calibration.cal` file.
-- Run SWAT+ simulations.
-- Perform sensitivity analysis on model parameters using the [SALib](https://github.com/SALib/SALib) Python package, with support for parallel computation.
+- Modify model parameters through the `calibration.cal` file.
+- Run SWAT+ simulations seamlessly.
+- Compute performance metrics using widely adopted indicators.
+- Perform sensitivity analysis on model parameters using the [SALib](https://github.com/SALib/SALib) Python package, with support for parallel computation; currently, only Sobol sampling and Sobol indices are supported.
 
 
 ## 📥 Install pySWATPlus

docs/userguide/data_analysis.md

@@ -32,18 +32,18 @@ print(output)
 
 ## Read Sensitivity Simulation Data
 
-The sensitivity analysis performed using the [`simulation_by_sobol_sample`](https://swat-model.github.io/pySWATPlus/api/sensitivity_analyzer/#pySWATPlus.SensitivityAnalyzer.simulation_by_sobol_sample) method generates a file named `sensitivity_simulation.json` within the simulation directory.  
-This JSON file contains all the information required for Sobol sensitivity analysis, including:
+The sensitivity analysis performed using the
+[`simulation_by_sample_parameters`](https://swat-model.github.io/pySWATPlus/api/sensitivity_analyzer/#pySWATPlus.SensitivityAnalyzer.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`: Sobol problem definition  
+- `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:
 
 ```python
 output = pySWATPlus.DataManager().read_sensitive_dfs(
-    sim_file=r"C:\Users\Username\simulation_folder\sensitivity_simulation.json",
+    sensim_file=r"C:\Users\Username\simulation_folder\sensitivity_simulation.json",
     df_name='channel_sd_mon_df',
     add_problem=True,
     add_sample=True
@@ -65,7 +65,7 @@ To compute performance metrics for the desired indicators:
 
 ```python
 output = pySWATPlus.SensitivityAnalyzer().scenario_indicators(
-    sim_file=r"C:\Users\Username\simulation_folder\sensitivity_simulation.json",
+    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",
@@ -76,22 +76,7 @@ output = pySWATPlus.SensitivityAnalyzer().scenario_indicators(
 )
 ```
 
-## Sobol Indices
 
-The available indicators can also be used to compute Sobol indices (first, second, and total orders) along with their confidence intervals.
-
-```python
-output = pySWATPlus.SensitivityAnalyzer().sobol_indices(
-    sim_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=['KGE', 'RMSE'],
-    json_file=r"C:\Users\Username\data_analysis\sobol_indices.json"
-)
-```
 
 
 

docs/userguide/sensitivity_interface.md

@@ -1,7 +1,6 @@
-# Sensitivity Analysis
+# Sensitivity Interface
 
-Sensitivity analysis helps quantify how variation in input parameters affects model outputs. This tutorial demonstrates how to perform sensitivity analysis on SWAT+ model parameters.  
-The parameter sampling is handled by the [SALib](https://github.com/SALib/SALib) Python package using [Sobol](https://doi.org/10.1016/S0378-4754(00)00270-6) sampling from a defined parameter space.
+Sensitivity interface helps quantify how variation in input parameters affects model outputs. This tutorial demonstrates how to perform sensitivity analysis on SWAT+ model parameters.  
 
 
 ## Configuration Settings
@@ -15,34 +14,34 @@ import pySWATPlus
 
 # Initialize the project's TxtInOut folder
 txtinout_reader = pySWATPlus.TxtinoutReader(
-    path=r"C:\Users\Username\project\Scenarios\Default\TxtInOut"
+    tio_dir=r"C:\Users\Username\project\Scenarios\Default\TxtInOut"
 )
 
-# Copy required files to an empty custom directory
-target_dir = r"C:\Users\Username\custom_folder" 
-target_dir = txtinout_reader.copy_required_files(
-    target_dir=target_dir
+# Copy required files to an empty simulation directory
+sim_dir = r"C:\Users\Username\custom_folder" 
+sim_dir = txtinout_reader.copy_required_files(
+    sim_dir=sim_dir
 )
 
-# Initialize TxtinoutReader with the custom directory
-target_reader = pySWATPlus.TxtinoutReader(
-    path=target_dir
+# Initialize TxtinoutReader with the simulation directory
+sim_reader = pySWATPlus.TxtinoutReader(
+    tio_dir=sim_dir
 )
 
 # Disable CSV file generation to save time
-target_reader.disable_csv_print()
+sim_reader.disable_csv_print()
 
 # Disable daily time series in print.prt (saves time and space)
-target_reader.enable_object_in_print_prt(
+sim_reader.enable_object_in_print_prt(
     obj=None,
     daily=False,
     monthly=True,
     yearly=True,
     avann=True
 )
 
-# Run a trial simulation to verify expected time series outputs
-target_reader.run_swat(
+# Set simulation period and run a trial simulation to verify expected time series outputs
+sim_reader.run_swat(
     begin_date='01-Jan-2010',
     end_date='31-Dec-2012',
     warmup=1,
@@ -51,14 +50,16 @@ target_reader.run_swat(
     }  # enable daily time series for 'channel_sd'
 ```
 
-## Sobol-Based Interface
+## Sensitivity Simulation
 
-This high-level interface builds on the above configuration to run sensitivity simulations using Sobol sampling. It includes:
+This high-level interface builds on the above configuration to run sensitivity simulations using sampling, powered by the [SALib](https://github.com/SALib/SALib) Python package.  
+Currently, it supports [Sobol](https://doi.org/10.1016/S0378-4754(00)00270-6) sampling from a defined parameter space. The interface provides:
+
+- Automatic generation of samples for the parameter space  
+- Parallel computation to accelerate simulations  
+- Output extraction with filtering options  
+- Structured export of results for downstream analysis
 
-- Automatic generation of Sobol samples for the parameter space  
-- Parallel computation to speed up simulations  
-- Output extraction with filtering options
-- Structured export of results for downstream analysis  
 
 ```python
 # Sensitivity parameter space
@@ -78,7 +79,7 @@ parameters = [
 ]
 
 # Target data extraction from sensitivity simulation
-simulation_data = {
+extract_data = {
     'channel_sdmorph_yr.txt': {
         'has_units': True,
         'ref_day': 15,
@@ -97,13 +98,30 @@ simulation_data = {
 
 # Sensitivity simulation
 if __name__ == '__main__':
-    output = pySWATPlus.SensitivityAnalyzer().simulation_by_sobol_sample(
+    output = pySWATPlus.SensitivityAnalyzer().simulation_by_sample_parameters(
         parameters=parameters,
         sample_number=1,
-        simulation_folder=r"C:\Users\Username\simulation_folder",
-        txtinout_folder=target_dir,
-        simulation_data=simulation_data,
-        clean_setup=True
+        sensim_dir=r"C:\Users\Username\simulation_folder",
+        txtinout_folder=sim_dir,
+        extract_data=extract_data
     )
     print(output)
+```
+
+## Sensitivity Indices
+
+Sensitivity indices (first, second, and total orders) are computed using the indicators available in the `pySWATPlus.PerformanceMetrics` class, along with their confidence intervals.
+
+
+```python
+output = pySWATPlus.SensitivityAnalyzer().parameter_sensitivity_indices(
+    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=['KGE', 'RMSE'],
+    json_file=r"C:\Users\Username\sensitivity_indices.json"
+)
 ```

docs/userguide/swatplus_simulation.md

@@ -12,10 +12,10 @@ Once the `TxtInOut` folder is properly configured with the necessary input files
 import pySWATPlus
 
 # Replace this with the path to your project's TxtInOut folder
-txtinout_folder = r"C:\Users\Username\project\Scenarios\Default\TxtInOut"
+txtinout_dir = r"C:\Users\Username\project\Scenarios\Default\TxtInOut"
 
 txtinout_reader = pySWATPlus.TxtinoutReader(
-    path=txtinout_folder
+    tio_dir=txtinout_dir
 )
 ```
 
@@ -42,26 +42,26 @@ To keep your original `TxtInOut` folder unchanged, it is recommended to run `SWA
 
     ```python
     # Replace this with your desired empty custom directory
-    target_dir = r"C:\Users\Username\custom_folder" 
+    sim_dir = r"C:\Users\Username\custom_folder" 
 
     # Ensure the required files are copied to the custom directory
     txtinout_reader.copy_required_files(
-        target_dir=target_dir
+        sim_dir=sim_dir
     )
     ```
 
 - Initialize `TxtinoutReader` class for the custom directory
 
     ```python
-    target_reader = pySWATPlus.TxtinoutReader(
-        path=target_dir
+    sim_reader = pySWATPlus.TxtinoutReader(
+        tio_dir=sim_dir
     )
     ```
 
 - Run simulation
 
     ```python
-    target_reader.run_swat()
+    sim_reader.run_swat()
     ```
 
 ## Step-wise Configurations and Simulations
@@ -74,9 +74,9 @@ The following steps demonstrate how to configure parameters in a custom director
 
     ```python
     # Update timeline in `time.sim` file
-    target_reader.set_simulation_period(
+    sim_reader.set_simulation_period(
         begin_date='01-Jan-2012',
-        end_date='31-Dec-2016',
+        end_date='31-Dec-2016'
     )
     ```
 
@@ -93,7 +93,7 @@ The following steps demonstrate how to configure parameters in a custom director
 
     ```python
     # Ensure simulation outputs for `channel_sd` object in `print.prt` file  
-    target_reader.enable_object_in_print_prt(
+    sim_reader.enable_object_in_print_prt(
         obj='channel_sd',
         daily=False,
         monthly=True,
@@ -102,12 +102,22 @@ The following steps demonstrate how to configure parameters in a custom director
     )
     ```
 
-- Set output print interval within the simulation period:
+- Set print interval within the simulation period:
 
     ```python
-    # Set ouput print every other day  
-    target_reader.set_print_interval(
-        interval=2
+    # Set print interval in `print.prt` file
+    sim_reader.set_print_interval(
+        interval=1
+    )
+    ```
+
+- Set print period within the simulation timeline to record result in output files:
+
+    ```python
+    # Set print period in `print.prt` file  
+    sim_reader.set_print_period(
+        begin_date='15-Jun-2012',
+        end_date='15-Jun-2016'
     )
     ```
 
@@ -121,7 +131,7 @@ The following steps demonstrate how to configure parameters in a custom director
             'value': 0.5
         }
     ]
-    target_reader.run_swat(
+    sim_reader.run_swat(
         parameters=parameters
     )
     ```
@@ -148,13 +158,15 @@ parameters = [
 
 # Run SWAT+ simulation from the original `TxtInOut` folder
 txtinout_reader.run_swat(
-    target_dir=r"C:\Users\Username\custom_folder",  # mandatory
+    sim_dir=r"C:\Users\Username\custom_folder",  # mandatory
     parameters=parameters,  # optional
-    begin_date='01-Jan-2012', # optional
-    end_date= '31-Dec-2016', # optional
+    begin_date='01-Jan-2012',  # optional
+    end_date= '31-Dec-2016',  # optional
+    simulation_timestep=0,  # optional
     warmup=1,  # optional
     print_prt_control={'channel_sd': {'daily': False}},  # optional
+    print_begin_date='15-Jun-2012',  # optional
+    print_end_date='15-Jun-2016',  # optional
     print_interval=1  # optional
 )
 ```
-