Metropolis 2 heat maps for conflicts, intrusions, and density.

Note that we renamed the files by adding the concept to the front of the file and are using full words for the wind and rogue. And also separating the uncertaintiy level with an underscore. We removed anything related to the date of the simulation. Aso removed Flight_intention. All files were then placed in the same directory. The code runs with LOSLOG, REGLOG, and CONFLOG.

GeneralUsage:

python main.py --logtype REGLOG --concept decentralised --density very_low --mix 40 --uncertainty none --create gpkgs

This creates the geopackage files for REGLOG of the decentralised concept for very_low densities with a mix of 40 and no uncertainities.

Run the following for more information:

python main.py --help

Ensure that the following directories exist prior to running the code.

• /[ABSOLUTE PATH TO HEATMAP REPO]/tmp temporary files.
• /[ABSOLUTE PATH TO HEATMAP REPO]/geotif location of raw raster heat maps.
• /[ABSOLUTE PATH TO HEATMAP REPO]/gpkgs location of geopackages.
• /[ABSOLUTE PATH TO HEATMAP REPO]/images location of heat maps.
• /[ABSOLUTE PATH TO HEATMAP REPO]/results location of M2 logs.

RUN THIS IN SHELL FOR RENAMING

The *.log files were renamed inside each folder from the Metropolis 2 output.

The names should be {CONCEPT}_{LOGNAME}_{DENSITY}_{MIX}_{REPITITON}_{UNCERTAINTY_TYPE}_{UNCERTAINTY_LEVEL}.log, an example is hybrid_REGLOG_very_low_40_8_rogue_2.log or centralised_LOSLOG_low_50_8.log.

Below is an example of what to run inside each of the three folders. The example is specific to Output_Decentralised directory

> for f in *.log; do mv "$f" "decentralised_${f/_2022*./.}"; done
> for f in *.log; do mv "$f" "${f/Flight_intention/}"; done

> rename 's/R1/rogue_1/' *R1*
> rename 's/R2/rogue_2/' *R2*
> rename 's/R3/rogue_3/' *R3*

> rename 's/W1/wind_1/' *W1*
> rename 's/W3/wind_3/' *W3*
> rename 's/W5/wind_5/' *W5*

Once this is finished place the files in theresults/ directory.

The steps for hybrid and centralised concepts are the same. Just change the first line to:

> for f in *.log; do mv "$f" "centralised_${f/_2022*./.}"; done

or

> for f in *.log; do mv "$f" "hybrid_${f/_2022*./.}"; done

Step 1: Parsing the logs and create geopackages.

This step creates the vector data that is used for Step 2.

python main.py --logtype REGLOG --concept decentralised --density very_low --mix 40 --uncertainty none --create gpkgs

Ensure that the following directory is created,

# gpkgs directory
gpkg_dir = "/[ABSOLUTE PATH TO HEATMAP REPO]/gpkgs"

Step 2: Creating the raster data

To generate the raster files, *.geotif, a pyqgis environment is needed. See here for more information. After installing qgis, add the path to the executable in line 11 of geotifcreate.py.

Here is an example used to create the geotifs,

python main.py --logtype REGLOG --concept decentralised --density very_low --mix 40 --uncertainty none --create geotifs

Step 3: Generating the heatmaps

Although it is possible to create the heatmaps from pyqgis it is a bit easier to do it from the QGIS python console. In order to create heatmaps, run image_exporter.py from the QGIS python console. vienna.qgz is a qgis project that includes other data to export in the images. Ensure that these directories in image_exporter.py point to the correct location:

# image directory
image_dir = "/[ABSOLUTE PATH TO HEATMAP REPO]/images"

# get the style file
style_dir = "/[ABSOLUTE PATH TO HEATMAP REPO]/styles"

# geotiff directory
geotiff_dir = '"/[ABSOLUTE PATH TO HEATMAP REPO]/geotif'

Metropolis 2 noise maps.

The noise calculation is performed in the data analysis platform. However, a different grid was used with unifrom distribution (100m). If you are running it, replace interest_points.json with the one in this repository. Then to create the GPKGs for the noise maps, run GpkgCreator_noise_heatmaps.py.

In the D3.2 report, only the noise maps for the decentralised_high_40_4, centralised_high_40_4, and hybrid_high_40_4 are shown. To create this copy the relvant GPKGs (1 for centralised, 2 for hybrid, 3 for decentralised) into the noise_data directory and load them into the QGIS project. For each layer, create a field called noise_40 with the following code CASE WHEN 'noise' > 40 THEN 40 ELSE 0 END. This will ensure that only points with a noise level higher than 40 decibels are saved. Then run a kernel density estimation algorithm in QGIS with the following parameters.

{ 'DECAY' : 0, 
  'INPUT' : '3_high_40_4_noise.gpkg|layername=3_high_40_4_noise', 
  'KERNEL' : 3, 
  'OUTPUT' : 'decentralised_high_40_4.tif', 
  'OUTPUT_VALUE' : 0, 
  'PIXEL_SIZE' : 4, 
  'RADIUS' : 400, # meters
  'RADIUS_FIELD' : '', 
  'WEIGHT_FIELD' : 'noise_40' 
}

When this is is ran, then apply the noise_style.qml for better visualization.