NoiseModelling

https://noisemodelling.readthedocs.io/en/latest/
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NoiseModelling is a free and open-source Java library for producing environmental noise maps from...
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### Bash Script for NoiseModelling Tutorial

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Get_Started_Script.html

A Bash script to automate the 'Get Started - GUI' tutorial steps using NoiseModelling's ScriptRunner. It includes steps for uploading files, running noise calculations, and exporting results.

```bash
 1#! /bin/bash
 2
 3# Run the get started turorial
 4# https://noisemodelling.readthedocs.io/en/latest/Get_Started_Tutorial.html
 5
 6# Step 4: Upload files to database
 7# create (or load existing) database and load a shape file into the database
 8./bin/ScriptRunner -w ./ -s scripts/Import_and_Export/Import_File.groovy --pathFile resources/ground_type.shp
 9./bin/ScriptRunner -w ./ -s scripts/Import_and_Export/Import_File.groovy --pathFile resources/buildings.shp
10./bin/ScriptRunner -w ./ -s scripts/Import_and_Export/Import_File.groovy --pathFile resources/receivers.shp
11./bin/ScriptRunner -w ./ -s scripts/Import_and_Export/Import_File.groovy --pathFile resources/ROADS2.shp
12./bin/ScriptRunner -w ./ -s scripts/Import_and_Export/Import_File.groovy --pathFile resources/dem.geojson
13
14
15# Step 5: Run Calculation
16./bin/ScriptRunner -w ./ -s scripts/NoiseModelling/Noise_level_from_traffic.groovy --tableBuilding BUILDINGS --tableRoads ROADS2 --tableReceivers RECEIVERS --tableDEM DEM --tableGroundAbs GROUND_TYPE
17
18# Step 6: Export (& see) the results
19./bin/ScriptRunner -w ./ -s scripts/Import_and_Export/Export_Table.groovy --exportPath RECEIVERS_LEVEL.shp --tableToExport RECEIVERS_LEVEL

```

--------------------------------

### Run Docker Compose

Source: https://noisemodelling.readthedocs.io/en/latest/Architecture.html

Command to start NoiseModelling services using Docker Compose. Ensure Docker is installed and the docker-compose.yml file is in the current directory.

```bash
docker compose up -d
```

--------------------------------

### Run Podman Compose

Source: https://noisemodelling.readthedocs.io/en/latest/Architecture.html

Command to start NoiseModelling services using Podman Compose. Ensure Podman is installed and the docker-compose.yml file is in the current directory.

```bash
podman compose up -d
```

--------------------------------

### Check Java Version

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Requirements.html

Verify if Java is installed and check its version. The output should start with '11' or a higher version number.

```bash
java -version

```

--------------------------------

### Run NoiseModelling GUI (Windows)

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Get_Started_GUI.html

Execute this command in the terminal to start the NoiseModelling application and its local server on Windows.

```batch
NoiseModelling.exe
```

```batch
NoiseModelling_xxx\start_windows.bat
```

--------------------------------

### Run NoiseModelling GUI (Linux/Mac)

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Get_Started_GUI.html

Execute this command in the terminal to start the NoiseModelling application and its local server on Linux or macOS. Ensure the script has execute permissions.

```bash
NoiseModelling_xxx/start_linux_macos.sh
```

--------------------------------

### Verify JAVA_HOME on Linux/Mac

Source: https://noisemodelling.readthedocs.io/en/latest/_sources/Tutorial_Requirements.rst.txt

Confirm that the JAVA_HOME environment variable has been set correctly. This command should output the configured Java installation directory.

```bash
echo $JAVA_HOME
```

--------------------------------

### Set JAVA_HOME and PATH on Linux

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Requirements.html

Configure the JAVA_HOME environment variable and update the system's PATH to include the Java binary directory. Adapt the path to your specific Java installation.

```bash
export JAVA_HOME=/usr/lib/jvm/java-22-openjdk-amd64
export PATH="$JAVA_HOME/bin:$PATH"

```

--------------------------------

### Prepare Sensor Data using Prepare_Sensors

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Data_Assimilation.html

Prepares sensor data for assimilation by extracting data within a specified time range and splitting it into training and validation sets. Requires start and end timestamps, training ratio, working folder path, and target SRID.

```Groovy
new Prepare_Sensors().exec(connection,[
                "startDate":"2024-08-25 06:30:00",
                "endDate": "2024-08-25 07:30:00",
                "trainingRatio": 0.8,
                "workingFolder": "./resources/dataAssimilation/",
                "targetSRID": 2056
])
```

--------------------------------

### Verify JAVA_HOME on Linux/macOS

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Requirements.html

Confirm that the JAVA_HOME environment variable has been set correctly. This command should output the path to your Java installation directory.

```bash
echo $JAVA_HOME

```

--------------------------------

### Execute a WPS Process with OwsLib

Source: https://noisemodelling.readthedocs.io/en/latest/_sources/WPS_Builder.rst.txt

Execute a specific process on a WPS server using the OwsLib Python library. This example demonstrates how to submit a process and retrieve its status.

```python
from owslib.wps import WPSConnection, ProcessInputs, LiteralOutput

# Connect to the WPS service
wps = WPSConnection("http://localhost:8080/wps/WebProcessingService")

# Get the process to execute
process = wps.describeprocess("myProcessId")

# Define inputs for the process
inputs = ProcessInputs()
inputs.add("myInput", literal=True, data=["myInputValue"])

# Execute the process asynchronously
output = wps.execute("myProcessId", inputs, async_=True)

# Print the status and links to monitor the job
print(output.statusLocation)
print(output.getlinks())

# To execute synchronously, remove async_=True and handle the result directly
# output = wps.execute("myProcessId", inputs)
# print(output.status)
```

--------------------------------

### Import Shapefile to PostGIS using ScriptRunner

Source: https://noisemodelling.readthedocs.io/en/latest/NoiseModellingOnPostGIS.html

Use this command-line example to import a Shapefile into a PostGIS database via NoiseModelling's ScriptRunner. Ensure all necessary parameters like working directory, script path, database credentials, and file paths are correctly provided.

```bash
./bin/ScriptRunner \
    -w /path/to/working/dir \
    -s src/main/groovy/org/noise_planet/noisemodelling/scripts/Import_and_Export/Import_File.groovy \
    -d noisemodelling_db \
    -u noisemodelling \
    -p noisemodelling \
    --host localhost \
    --port 5432 \
    --pathFile /path/to/your/buildings.shp \
    --tableName BUILDINGS

```

--------------------------------

### Check Java Version on Linux/Mac

Source: https://noisemodelling.readthedocs.io/en/latest/_sources/Tutorial_Requirements.rst.txt

Verify if Java is installed and check its version. The command should output a version starting with '11' or higher.

```bash
java -version
```

--------------------------------

### Get Java Home Path on macOS

Source: https://noisemodelling.readthedocs.io/en/latest/_sources/Tutorial_Requirements.rst.txt

Retrieve the directory to be used as JAVA_HOME on macOS. This command helps locate the installed Java version.

```bash
/usr/libexec/java_home -v latest
```

--------------------------------

### Prepare Sensor Data with Groovy

Source: https://noisemodelling.readthedocs.io/en/latest/_sources/Tutorial_Data_Assimilation.rst.txt

Use this script to prepare sensor data by specifying time ranges, training ratios, working folders, and target SRID. Ensure all required parameters are correctly formatted.

```groovy
new Prepare_Sensors().exec(connection,[
                    "startDate":"2024-08-25 06:30:00",
                    "endDate": "2024-08-25 07:30:00",
                    "trainingRatio": 0.8,
                    "workingFolder": "./resources/dataAssimilation/",
                    "targetSRID": 2056
    ])
```

--------------------------------

### Get Java Home Directory on macOS

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Requirements.html

Retrieve the directory path for the latest installed Java version on macOS. This is used to set the JAVA_HOME environment variable.

```bash
/usr/libexec/java_home -v latest

```

--------------------------------

### Run NoiseModelling Script with Arguments

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Get_Started_Script.html

Execute a specific NoiseModelling WPS script using ScriptRunner from the terminal. Ensure the installation path and script paths are correct. This example imports a ground type shapefile.

```bash
1cd /home/user/NoiseModelling/
2
3./bin/ScriptRunner -w ./ -s scripts/Import_and_Export/Import_File.groovy --pathFile resources/ground_type.shp

```

--------------------------------

### Execute a WPS Process with OWSLib

Source: https://noisemodelling.readthedocs.io/en/latest/WPS_Builder.html

This script demonstrates how to execute a specific WPS process asynchronously using OWSLib. It includes setting up the connection, defining inputs, executing the process, and monitoring its progress. The script prints the results upon successful completion or an error message if it fails. Requires the OWSLib Python library.

```python
import logging
import sys
from owslib.wps import WebProcessingService, monitorExecution

def main():
    # Configure the root logger to output to stdout
    logging.basicConfig(stream=sys.stdout, level=logging.INFO)

    url = "http://localhost:8000/builder/ows"

    # use java web token for authentification
    jwt_token = ""
    wps = WebProcessingService(url, skip_caps=False, headers={"Authorization": jwt_token})

    target_id = "Database_Manager:Display_Database"

    inputs = [('showColumns', "true")]

    # Execute (sync or async)
    execution = wps.execute(target_id, inputs)

    # Monitor progress if it's a long task
    monitorExecution(execution, sleepSecs=2)

    if execution.isSucceded():
        for output in execution.processOutputs:
            print(f"Result {output.identifier}: {output.data}")
    else:
        print("Execution failed.")

if __name__ == "__main__":
    main()
```

--------------------------------

### Example Point Source GeoJSON

Source: https://noisemodelling.readthedocs.io/en/latest/_sources/Tutorial_Noise_Map_From_Point_Source.rst.txt

This is an example of a GeoJSON file representing a single point source with its properties and geometry.

```json
{
   "type": "FeatureCollection",
   "name": "Point_Source",
   "crs": {"type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::2154" } },
   "features": [{"type": "Feature", "properties": { "PK": 1, "HZD500": 90.0 },
                 "geometry": {"type": "Point", "coordinates": [223771.0727, 6757583.2983, 0.0]}
                }]
   }
```

--------------------------------

### Find Java Installation Path on Linux

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Requirements.html

Determine the installation path of the Java executable on Linux systems. This path is used to set the JAVA_HOME environment variable.

```bash
readlink -f "$(command -v java)"

```

--------------------------------

### Execute All Possible Configuration WPS Block (Groovy)

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Data_Assimilation.html

Execute the All_Possible_Configuration WPS script using a Groovy script. This method requires establishing a connection and passing traffic and temperature values as a map.

```groovy
new All_Possible_Configuration().exec(connection,[
               "trafficValues": "0.01,1.0,2.0",
               "temperatureValues": "10,15,20"
])
```

--------------------------------

### Execute All Possible Configuration WPS Block (CLI)

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Data_Assimilation.html

Use this command to execute the All_Possible_Configuration WPS script via the command line. It requires the script path and parameters for traffic and temperature values.

```bash
1./bin/wps_scripts -w ./ -s scripts/Data_Assimilation/All_Possible_Configuration.groovy -trafficValues "0.01,1,2" -temperatureValues "10,15,20"
```

--------------------------------

### Extract Best Configuration for Noise Simulation

Source: https://noisemodelling.readthedocs.io/en/latest/_sources/Tutorial_Data_Assimilation.rst.txt

Use the Extract_Best_Configuration WPS script to find the optimal simulation map by minimizing the difference between observed and simulated noise levels.

```groovy
new Extract_Best_Configuration().exec(connection,[
                    "observationTable": "SENSORS_MEASUREMENTS_TRAINING",
                    "noiseMapTable": "RECEIVERS_LEVEL",
                    "tempToleranceThreshold"  : 5
    ])
```

--------------------------------

### Export Table with Export_Table

Source: https://noisemodelling.readthedocs.io/en/latest/Tutorial_Data_Assimilation.html

Use this block to export a specified table, for example, as a Shapefile, for visualization in GIS applications. Provide the export path and the table name to be exported.

```Groovy
new Export_Table().exec(connection,
                ["exportPath": "results/ASSIMILATED_MAPS.shp",
                 "tableToExport": "ASSIMILATED_MAPS"
])

```
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