Visualisation of LSDTopoTools output

You need to update your path in your .bashrc file. To do that, include this line: export PATH="/exports/csce/datastore/geos/groups/LSDTopoData/miniconda/miniconda3/bin:$PATH"

Go onto one of our servers.

Type source activate LSDMT3

You are finished, but to use LSDMappingTools, you need to get them. Use git to clone the repository

$ git clone https://github.com/LSDtopotools/LSDMappingTools.git

To run the mapping scripts you need to go into the LSDMappingTools directory and run them while in the LSDMT3 environment.

Go into your directory that has the most space. Usually this is your datastore directory.

Make a directory for miniconda, then download miniconda into it:

$ mkdir miniconda
$ cd miniconda

Figure out where you are:

$ pwd

Copy the resulting path. You will use this in a second.

Now get miniconda:

$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
$ sh Miniconda3-latest-Linux-x86_64.sh

Now in the command prompt, it will ask for a location of miniconda 3. Use the location that pwd just gave you (a few steps earlier)

You need to update your path in your .bashrc file. Include this line export PATH="/your/path/to/miniconda/miniconda3/bin:$PATH"

IMPORTANT: you need to change the path so it reflects your directories!!! But you do need to include the /bin part of the above path.

Then you can create the environment from an environment file. The one you want is the conda_list_July2019.txt in this directory. https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file

You can do this with:

$ wget https://raw.githubusercontent.com/LSDtopotools/LSDMappingTools/master/conda_environments/LSDMT_version_0.2.2.txt
$ conda create --name LSDMT3 --file LSDMT_version_0.2.2.txt

This will take ages. When it is finished you can activate it with

conda activate LSDMT3

Type source activate LSDMT3

You are finished, but to use LSDMappingTools, you need to get them. Use git to clone the repository

$ git clone https://github.com/LSDtopotools/LSDMappingTools.git

To run the mapping scripts you need to go into the LSDMappingTools directory and run them while in the LSDMT3 environment.

First I needed to get Miniconda in LSDTopoData:

$ cd miniconda
$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
$ sh Miniconda3-latest-Linux-x86_64.sh

In the command prompt, it asked for a location of miniconda 3. Use the location in LSDTopoData: /exports/csce/datastore/geos/groups/LSDTopoData/miniconda/miniconda3

I created a new environment.

conda create -n LSDMT3 python=3.6.7

I then spent ages trying to get it to work because python GDAL is evil.

Finally, when it worked, I exported the conda environment: conda env export > environment.yml

If you are using our docker implementation (this is recommended), start the container and go to the correct folder:

$ docker run -it -v C:\LSDTopoTools\:/LSDTopoTools lsdtopotools/lsdtt_viz_docker
# Start_LSDTT.sh
# cd /LSDTopoTools/LSDMappingTools/Tests

If you installed the LSDMT environment on your host machine:

> activate LSDTT

Call the LSDMappingTools_test.py python script. This script is called with a single number: 1,2 or 3. These make different plots.

Start the docker container:

$ docker run -it -v C:\LSDTopoTools\:/LSDTopoTools lsdtopotools/lsdtt_viz_docker

Run a script to grab and unpack the data:

$ Get_LSDTT_example_data.sh

The data for these examples is in /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya

Find a place to put the data.

Download and unzip the data. In a terminal you would do this:

$ wget https://github.com/LSDtopotools/ExampleTopoDatasets/archive/master.zip
$ unzip master.zip

First, you need to either start the docker container or activate the LSDMT environment, depending on what system you are using.

In the docker container, you can always get the latest version of LSDMappingTools using the startup script:

# Start_LSDTT.sh

Now, go to the LSDMappingTools directory. In the docker container, this is always at /LSDTopoTools/LSDMappingTools

You also need to look up the location of your data.

In this example we are going to use more advanced example data. You will have needed to have followed these instructions first.

We have a lot of different example datasets so I am afraid the path to this data is a bit long winded. Sorry. It is /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya

So, lets start with a basic call to plot the elevation over a hillshade.

This prints a figure in the raster_plots directory. The resulting figure looks like:

Figure 4. A simple draped elevation plot

Why don’t we alter this figure a bit. We can change the colourbar location, or the colourbar label, for example:

$ python PlotBasicRasters.py -dir /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya -fname Mega_divide -drape_fname Mega_divide -PD True -drape_cbar_label "Elevation (m)" -drape_cbar_loc bottom -dpi 100

You may wish to play with some of the two other flags and see the results:

To call the basemap image, you can use:

$ python PlotBasicRasters.py -dir /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya -fname Mega_divide -dpi 100 -BM True -bmrem 20

The figure looks like this:

Figure 6. A simple basemap.

You can also try to modify the latitude and longitude line spacing by mydifying the flag -bmpsm 0.5. This is a multiplier of how many lat-long lines you want in relation to the width of the map. It uses the smaller of the width and height of the basemap. Try varying this between 0.2 and 3 and see what happens.

We can start with a very basic basin plot:

$ python PlotBasicRasters.py -dir /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya -fname Mega_divide -dpi 100 -PB True

The resulting figure looks like this:

Figure 8. A simple basin plot.

The basin numbering is determined algorithmically, but you might want to rename your basins. To do this, you use the -rename_dict flag. This takes the form of a python dictionary. You have keys and values. Each key is the index of the basin (the number in the previous figure) and the value is the new name. These are separated by colons (:). To do multiple basins you separate these key:value pairs with commas. Here is an example:

$ python PlotBasicRasters.py -dir /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya -fname Mega_divide -dpi 100 -PB True -rename_dict 0:supa,1:dupa

The resulting figure looks like this

Figure 9. A simple basin plot with renamed basins.

You can also select basins by using the -basin_keys flag. If you leave this blank, it assumes you want all basins. If you include a comma separated list, it only keeps the basins you have named. The basin indices used are the original, not renamed basin indices. That is, if you are using -rename_dict you need to keep track of the original indices of the basins.

An example is:

$ python PlotBasicRasters.py -dir /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya -fname Mega_divide -dpi 100 -PB True -rename_dict 0:supa,1:dupa -basin_keys 0,1,5,6

Again, you must ensure there are no spaces in the list because if there are, the python code will assume this is a new flag and will crash. Here is what the figure looks like:

Figure 10. A basin plot with selected basins with some renaming.

The basin selection algorithms work with channel plotting as well. You can do this with the -PCh flag, or alternatively you can use the -PBC flag to have some semitransparent basins under the channels.

$ python PlotBasicRasters.py -dir /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya -fname Mega_divide -dpi 100 -basin_keys 0,1,5,6 -PBC True

The figure looks like:

Figure 11. A basin plot with selected basins with some renaming channels.

You can also change the colouring of the channels using a flag -SimpleChFmt. You can choose elevation, drainage_area, source_key, or basin_key to colour your channels. This works with both the -PCh and -PBC flags:

$ python PlotBasicRasters.py -dir /LSDTopoTools/data/ExampleTopoDatasets/ChiAnalysisData/Meghalaya -fname Mega_divide -dpi 100 -basin_keys 0,1,5,6 -PBC True -SimpleChFmt source_key

The figure looks like this:

Figure 12. A basin plot with selected basins with some renaming channels.