Blank squares in output plots

[siqiwang55]

Hi, I tried running the codes provided in the tutorial, but instead of getting the same volcano plots as demonstrated in the tutorial, there are some blank squares in my output plots (attached). Anyone understand what the problem might be?

I'm using: Python: 3.8 matplotlib: 3.3.4 numpy: 1.20.1 scipy: 1.6.0 mpmath: 1.2.1 ase: 3.21.1

pretty_production_rate.pdf all_rates.pdf

[755452800]

I had the same issue with much larger blank squares. Have you fixed the problem?

[siqiwang55]

I had the same issue with much larger blank squares. Have you fixed the problem?

Hi Steven,

I did find a way to temporarily solve this problem, but I don't know if it is the actual correct solution we should be using. Anyway, you can try modifying the following code in mkm_job.py:

from catmap import analyze vm = analyze.VectorMap(model) vm.plot_variable = 'rate' #tell the model which output to plot vm.log_scale = True #rates should be plotted on a log-scale vm.min = 1e-25 #minimum rate to plot vm.max = 1e2 #maximum rate to plot vm.plot(save='rate.pdf') #draw the plot and save it as "rate.pdf"

increase the vm.min value to 1e-20 or something larger, and the blank squares would disappear. Hope this works for you, and if you find another solution please let me know.

[755452800]

Hi Siqi, I just read the history commits and find the solution to the colormap issue, simply add:

  from catmap import analyze
  vm = analyze.VectorMap(model)
  vm.plot_variable = 'rate' #tell the model which output to plot
  vm.log_scale = True #rates should be plotted on a log-scale
  vm.min = 1e-25 #minimum rate to plot
  vm.max = 1e2 #maximum rate to plot
+ vm.colormap = "jet"
  vm.plot(save='rate.pdf') #draw the plot and save it as "rate.pdf"

and the colormap will use the older version "jet" pattern, which in my sense is more beautiful.

[siqiwang55]

Perfect, I'll try this out, thanks for letting me know! And here's my email address siqi.wang55@outlook.com I'd be happy to have any further discussion regarding CatMap with you.

[GumiJiong]

I've found that this issue is caused by a data-structure transferring error in the function ReactionModel.map_to_array(), and some of the values are lower than your set vm.min (e.g. 1e-25) would be wrongly transferred to -1e-25, so the following np.log10() function would return a NaN, which leads to the blank in the output plot.

This numerical error can be fixed by adding the following code in row 272 of https://github.com/SUNCAT-Center/catmap/blob/master/catmap/analyze/analysis_base.py and reinstalling the package:

if self.log_scale and dim == 2:
    for i in range(0,len(maparray)): 
        for j in range(0,len(maparray[0])):
            for k in range(0,len(maparray[0][0])):
                if maparray[i][j][k] < 0:
                    maparray[i][j][k]=-1*maparray[i][j][k]
    maparray = np.log10(maparray)

[angelsu21]

I've found that this issue is caused by a data-structure transferring error in the function ReactionModel.map_to_array(), and some of the values are lower than your set vm.min (e.g. 1e-25) would be wrongly transferred to -1e-25, so the following np.log10() function would return a NaN, which leads to the blank in the output plot.

This numerical error can be fixed by adding the following code in row 272 of https://github.com/SUNCAT-Center/catmap/blob/master/catmap/analyze/analysis_base.py and reinstalling the package:

if self.log_scale and dim == 2:
    for i in range(0,len(maparray)): 
        for j in range(0,len(maparray[0])):
            for k in range(0,len(maparray[0][0])):
                if maparray[i][j][k] < 0:
                    maparray[i][j][k]=-1*maparray[i][j][k]
    maparray = np.log10(maparray)

Inspired by your comment, I found a mistake of model.py in data interpolation.

The following code in row 1292 of https://github.com/SUNCAT-Center/catmap/blob/master/catmap/model.py need to be adjusted。

It should be zi = np.exp(z_num) instead of zi = np.exp(z_num)*z_sign

[mhangaard]

Hi Siqi, I just read the history commits and find the solution to the colormap issue, simply add:

  from catmap import analyze
  vm = analyze.VectorMap(model)
  vm.plot_variable = 'rate' #tell the model which output to plot
  vm.log_scale = True #rates should be plotted on a log-scale
  vm.min = 1e-25 #minimum rate to plot
  vm.max = 1e2 #maximum rate to plot
+ vm.colormap = "jet"
  vm.plot(save='rate.pdf') #draw the plot and save it as "rate.pdf"

and the colormap will use the older version "jet" pattern, which in my sense is more beautiful.

See more choices for colormaps here: https://matplotlib.org/stable/tutorials/colors/colormaps.html

Note that you should never use jet for anything. The reason is that brightness and contrast vary non-linearly, thus misrepresenting distance between values. It misrepresents values even worse if printed in black and white or viewed by a colorblind person.

I think the perceptually uniform sequential colormaps are best suited for volcano plots.

[755452800]

Hi Siqi, I just read the history commits and find the solution to the colormap issue, simply add:

  from catmap import analyze
  vm = analyze.VectorMap(model)
  vm.plot_variable = 'rate' #tell the model which output to plot
  vm.log_scale = True #rates should be plotted on a log-scale
  vm.min = 1e-25 #minimum rate to plot
  vm.max = 1e2 #maximum rate to plot
+ vm.colormap = "jet"
  vm.plot(save='rate.pdf') #draw the plot and save it as "rate.pdf"

and the colormap will use the older version "jet" pattern, which in my sense is more beautiful.

See more choices for colormaps here: https://matplotlib.org/stable/tutorials/colors/colormaps.html

Note that you should never use jet for anything. The reason is that brightness and contrast vary non-linearly, thus misrepresenting distance between values. It misrepresents values even worse if printed in black and white or viewed by a colorblind person.

I think the perceptually uniform sequential colormaps are best suited for volcano plots.

Thanks for your advice!