pygmt.Figure.legend , pygmt.subplot and seaborn.heatmap


I think I am pushing the boundaries of gmt pslegend through a call to pygmt.Figure.legend via my attempt to build a heatmap and a table of values that went into making the heatmap. The rows on the heatmap or not the same as the rows on the table – i.e. the table data is comprised of forecasted weather parameters and the heatmap is a normalisation of various parameters that indicate the effect those combined parameters (from the table) impact on various activities based on thresholds. The heatmap is made from a Pandas Dataframe which itself is made from a culmination of xarray datasets.

In my current workflow, I save the heatmap as a PNG and the table as a CSV and then use a high-end program (like Apple Numbers) to format the CSV table (fill values of cells that meet certain thresholds, change font weight, etc.) and then man-draulically re-shape the heatmap PNG to align with the formatted table. The process can take several minutes and sometimes the urgency of this product makes having it all produce under one routine a much more beneficial outcome.

So my attempt here is remove the human piece of this workflow by making the table with pygmt.Figure.legend and the heatmap with seaborn. Of course, if I could do a heatmap with GMT/pygmt I would, and possibly this could be done by utilising various CPT files for normalised ‘heatmap rows’. However, my attempt to use a CPT file on the table have failed. Possibly there is an example of an input pslegend file out there of someone who is filling table cells with a CPT file.

Attached is the input pslegend file that I am writing-to in a loop over the Pandas.Dataframe object for constructing the weather table. I also attach here an example of seaborn.heatmap PNG file that I’m attempting to align beneath the pygmt.Figure.legend generated table.

Here are the few lines of my most recent attempt to align the table and heatmap .

 fig = pygmt.Figure()
    with fig.subplot(nrows=2, ncols=1, figsize=("15c", "15c") , frame="l"):
        fig.legend( region="0/1/0/1" , projection="x15c" , spec="./impact_table.txt" ,
                    position="+w25c", panel=[0,0] )
        fig.image( imagefile="./heatmap.png", region="0/1/0/1" , position="g2/1+w25c+jCM", box=False , panel=[1,0] )

Thanks in advance for any assistance you can offer.


impact_table.txt (5.9 KB)

Hi Dan,

to me this sounds that you could GMT-ify your workflow a bit more by making a simple grid (using xyz2grd) from your normalised table to generate the heatmap with appropriate grid cell sizes and numbers of x/y cells, assign colours like via a cpt and then plot the table/text (your csv) using the xy info (what you currently put into the legend as numbers of rows/cols) to plot the value via text (ie partly replace stuff you are currently doing with legend).

So you would have to turn the column/row assignment in your impact_table.txt into x and y coords that you feed this to text. Instead of this

N 3 1 1 1 1 1 1 1 1 1 1 1 1 1
L 10p,Courier-New r COMBINED WAVE (M)
L 10p,Courier-New c 1.6
L 10p,Courier-New c 1.7
L 10p,Courier-New c 1.8

would look like this

4 2 1.6 # 4th column, 2nd row, the first after the COMBINED WAVE(M) text
4 3 1.7 #5th col, 3d row value
4 4 1.8

where you then specify font, justification, size etc via the pygmt.text call. Afterwards you just have to figure out where to place your legend (COMBINED WAVE (M)) - either using text or legend.

You could even get more creative using plot to plot a vector in that box to indicate directions similar to what MagicSeaweed is doing for swell directions (see plot).


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