Update documetation according to modifed codes

Author: debpal

docs/userguide/data_analysis.md

@@ -2,7 +2,12 @@
 
 This section explains how to analysis data generated by different interfaces.
 
-## Time Series Data
+## Simulation Data
+
+This section analyzes the simulation data generated using the
+[`run_swat`](https://swat-model.github.io/pySWATPlus/api/txtinout_reader/#pySWATPlus.TxtinoutReader.run_swat) method.
+
+### Extract 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.
@@ -22,13 +27,33 @@ output = pySWATPlus.DataManager().simulated_timeseries_df(
 )
 ```
 
-## Statistics from Daily Time Series
+### Model Performance
+
+Model performance can be evaluated by comparing simulated outputs with observed data, using selected indicators available in the property [`indicator_names`](https://swatmodel.github.io/pySWATPlus/api/performance_metrics/#pySWATPlus.PerformanceMetrics.indicator_names).
+
+
+```python
+output = pySWATPlus.PerformanceMetrics().indicator_from_file(
+    sim_file=r"C:\Users\Username\custom_folder\channel_sd_day.txt",
+    sim_col='flo_out',
+    extract_sim={
+        'has_units': True,
+        'apply_filter': {'name': ['cha561']}
+    },
+    obs_file=r"C:\Users\Username\observed_folder\discharge_daily.csv",
+    date_format='%Y-%m-%d',
+    obs_col='discharge',
+    indicators=['NSE', 'KGE', 'RMSE']
+)
+```
+
+### Statistics from Daily Time Series
 
 Monthly and yearly statistics such as maximum, minimum, mean, and standard deviation derived from daily time series data help summarize and interpret simulation variability over time.
 The following interface computes monthly and yearly statistical summaries for a Hydrological Response Unit (HRU) based on daily simulated flow discharge data. These metrics provide insight into seasonal patterns, flow extremes, and overall hydrological stability.
 
 ```python
-output = pySWATPlus.DataManager().simulated_timeseries_df(
+output = pySWATPlus.DataManager().hru_stats_from_daily_simulation(
     sim_file=r"C:\Users\Username\custom_folder\channel_sd_day.txt",
     has_units=True,
     gis_id=561,
@@ -37,7 +62,11 @@ output = pySWATPlus.DataManager().simulated_timeseries_df(
 )
 ```
 
-## Read Sensitivity Simulation Data
+## Sensitivity Simulation Scenarios
+
+This section analyzes the time series scenarios generated from the sensitivity analysis based on sampled parameters.
+
+### Read Time Series Scenarios
 
 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:
@@ -46,7 +75,7 @@ The sensitivity analysis performed using the
 - `sample`: List of generated samples  
 - `simulation`: Simulated `DataFrame` corresponding to each sample  
 
-To retrieve the selected `DataFrame` for all scenarios, use:
+To retrieve the selected time series `DataFrame` for all scenarios, use:
 
 ```python
 output = pySWATPlus.DataManager().read_sensitive_dfs(
@@ -57,31 +86,23 @@ output = pySWATPlus.DataManager().read_sensitive_dfs(
 )
 ```
 
-## Scenario Metrics
-
-For a selected `DataFrame`, scenario metrics across all simulations can be computed by comparing model outputs with observed data.
+### Scenario Performance
 
+For a selected `DataFrame`, the performance of all sensitivity scenarios can be assessed by comparing simulated outputs with observed data, using selected indicators available in the property [`indicator_names`](https://swatmodel.github.io/pySWATPlus/api/performance_metrics/#pySWATPlus.PerformanceMetrics.indicator_names).
 
-- To get the mapping between available indicators and their abbreviations:
 
-    ```python
-    indicators = pySWATPlus.PerformanceMetrics().indicator_names
-    ```
-
-- To compute metrics for all scenarios using the desired indicators:
-
-    ```python
-    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"
-    )
-    ```
+```python
+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"
+)
+```
 
 
 

docs/userguide/sensitivity_interface.md

@@ -61,6 +61,12 @@ Currently, it supports [Sobol](https://doi.org/10.1016/S0378-4754(00)00270-6) sa
 - Structured export of results for downstream analysis
 
 
+!!! warning
+    Before executing the following high-level interafce, comment out the code shown in
+    [`Configuration Settings`](https://swat-model.github.io/pySWATPlus/userguide/sensitivity_interface/#configuration-settings)
+    to avoid overwriting configuration files that may occur when rerunning the setup, which could affect the target results.
+
+
 ```python
 # Define parameter space
 parameters = [
@@ -129,7 +135,7 @@ output = pySWATPlus.SensitivityAnalyzer().parameter_sensitivity_indices(
 
 ## Integrated Simulation and Sensitivity
 
-To compute sensitivity indices directly for multiple outputs against observed data and skip saving the detailed simulation results, use the following interface:
+To compute sensitivity indices for multiple outputs against observed data without saving detailed simulation time series for each parameter sample, use the following interface.
 
 
 ```python

docs/userguide/swatplus_simulation.md

@@ -4,7 +4,7 @@
 
 To run a `SWAT+` simulation, the `TxtInOut` folder must include all required input files, which are created during simulation setup in the [SWAT+ Editor](https://github.com/swat-model/swatplus-editor) via `QSWAT+`.
 
-Additionally, the `TxtInOut` folder must contain the `SWAT+` executable. If you need help locating it, open the **`Run SWAT+`** tab in the `SWAT+ Editor` to find its path. Once located, copy the executable file into the `TxtInOut` folder. If the executable is missing, the simulation will fail.
+Additionally, the `TxtInOut` folder must contain the `SWAT+` executable. The executable file compatible with your operating system and `SWAT+` version can be obtained from the [official GitHub releases](https://github.com/swat-model/swatplus/releases).
 
 Once the `TxtInOut` folder is properly configured with the necessary input files and the `SWAT+` executable, you can initialize the `TxtinoutReader` class to interact with the `SWAT+` model: