About hrds

hrds is a python package for obtaining points from a set of rasters at different resolutions. You can request a point and hrds will return a value based on the highest resolution dataset (as defined by the user) available at that point, blending datasets in a buffer region to ensure consistency.

Current release:

Paper:

Prerequisites

• python 3+
• numpy
• scipy
• osgeo.gdal (pygdal) to read and write raster data

hrds is available on conda-forge, so you can install easily using:

bash conda config --add channels conda-forge conda install hrds It is possible to list all of the versions of hrds available on your platform with:

bash conda search hrds --channel conda-forge

On Debian-based Linux you can also install manually. To install pygdal, first install the libgdal-dev packages and binaries:

bash sudo apt-get install libgdal-dev gdal-bin

To install pygdal, first check which version of gdal is installed: bash gdal-config --version

pygdal can be installed using pip, specifying the version obtained from the command above. Note that you may need to increase the minor version number, e.g. from 2.1.3 to 2.1.3.3.

bash pip install pygdal==2.1.3.3 Replace 2.1.3.3 with the output from the gdal-config command.

You can then install hrds from source using the standard:

bash python setup.py install or from PyPi:

bash pip install hrds

Functionality

• Create buffer zones as a preprocessing step if needed

• Obtain value at a point based on user-defined priority of rasters

The software assumes all rasters are already in the same projection space and using the same datum.

This example loads in an XYZ file and obtains data at each point, replacing the Z value with that from hrds.

```python

from hrds import HRDS

points = [] with open("test_mesh.csv",'r') as f: for line in f: row = line.split(",") # grab X and Y points.append([float(row[0]), float(row[1])])

bathy = HRDS("gebcouk.tif", rasters=("emodutm.tif", "inspiredata.tif"), distances=(700, 200)) bathy.setbands()

print(len(points))

with open("output.xyz","w") as f: for p in points: f.write(str(p[0])+"\t"+str(p[1])+"\t"+str(bathy.get_val(p))+"\n") ```

This will turn this: bash $ head test_mesh.csv 805390.592314,5864132.9269,0 805658.162910036,5862180.30440542,0 805925.733505999,5860227.68191137,0 806193.304101986,5858275.05941714,0 806460.874698054,5856322.43692232,0 806728.445294035,5854369.81442814,0 806996.015889997,5852417.19193409,0 807263.586486046,5850464.56943942,0 807531.157082069,5848511.94694493,0 807798.727678031,5846559.32445088,0

into this:

bash $ head output.xyz 805390.592314 5864132.9269 -10.821567728305235 805658.16291 5862180.30441 2.721575532084955 805925.733506 5860227.68191 2.528217188012767 806193.304102 5858275.05942 3.1063558741547865 806460.874698 5856322.43692 5.470234157891056 806728.445294 5854369.81443 1.382685066254607 806996.01589 5852417.19193 1.8997482922322515 807263.586486 5850464.56944 4.0836843606647335 807531.157082 5848511.94694 -2.39508079759155 807798.727678 5846559.32445 -2.401006071401176

An example of use via thetis:

```python import firedrake import thetis from hrds import HRDS

mesh2d = firedrake.Mesh('test_mesh.msh') # mesh file

P12d = firedrake.FunctionSpace(mesh2d, 'CG', 1) bathymetry2d = firedrake.Function(P12d, name="bathymetry") bvector = bathymetry2d.dat.data bathy = HRDS("gebcouk.tif", rasters=("emodutm.tif", "inspiredata.tif"), distances=(700, 200)) bathy.setbands() for i, (xy) in enumerate(mesh2d.coordinates.dat.data): bvector[i] = bathy.get_val(xy) thetis.File('bathy.pvd').write(bathymetry2d)

rest of thetis code

```

These images show the original data in QGIS in the top right, with each data set using a different colour scheme (GEBCO - green-blue; EMOD - grey; UK Gov - plasma - highlighted by the black rectangle).The red line is the boundary of the mesh used (see figure below). Both the EMOD and UK Gov data has NODATA areas, which are shown as transparent here, hence the curved left edge of the EMOD data. The figure also shows the buffer regions created around the two higher resolution datasets (top left), with black showing that data isn't used to white where it is 100% used. The effect of NODATA is clear here. The bottom panel shows a close-up of the UK Gov data with the buffer overlayed as a transparancy from white (not used) to black (100% UK Gov). The coloured polygon is the area of the high resolution mesh (see below).

After running the code above, we produce this blended dataset. Note the coarse mesh used here - it's not realistic for a model simulation!

If we then zoom-in to the high resolution area we can see the high resolution UK Gov data being used and with no obvious lines between datasets.

It is also possible to use HRDS as a simple interpolation routine by specifying a single raster: ```python

from hrds import HRDS

points = [] with open("test_mesh.csv",'r') as f: for line in f: row = line.split(",") # grab X and Y points.append([float(row[0]), float(row[1])])

bathy = HRDS("gebcouk.tif") bathy.setbands()

print(len(points))

with open("output.xyz","w") as f: for p in points: f.write(str(p[0])+"\t"+str(p[1])+"\t"+str(bathy.get_val(p))+"\n") ```

Community

We welcome suggestions for future improvements, bug reports and other issues via the issue tracker. Anyone wishing to contribute code should contact Jon Hill (jon.hill@york.ac.uk) to discuss.