Geoscience Machine Learning bits and bobs – data completeness

2016 Machine learning contest – Society of Exploration Geophysicists

In a previous post I showed how to use pandas.isnull to find out, for each well individually, if a column has any null values, and sum to get how many, for each column. Here is one of the examples (with more modern, pandaish syntax compared to the example in the previous post:

for well, data in training_data.groupby('Well Name'): 
print(well)
print (data.isnull().values.any())
print (data.isnull().sum(), '\n')

Simple and quick, the output showed met that  – for example – the well ALEXANDER D is missing 466 samples from the PE log:

ALEXANDER D
True
Facies         0
Formation      0
Well Name      0
Depth          0
GR             0
ILD_log10      0
DeltaPHI       0
PHIND          0
PE           466
NM_M           0
RELPOS         0
dtype: int64

A more appealing and versatile alternative, which I discovered after the contest, comes with the matrix function form the missingno library. With the code below I can turn each well into a Pandas DataFrame on the fly, then a missingno matrix plot.

for well, data in training_data.groupby('Well Name'): 

msno.matrix(data, color=(0., 0., 0.45)) 
fig = plt.gcf()
fig.set_size_inches(20, np.round(len(data)/100)) # heigth of the plot for each well reflects well length 
axes=fig.get_axes()
axes[0].set_title(well, color=(0., 0.8, 0.), fontsize=14, ha='center')

I find that looking at these two plots provides a very compelling and informative way to inspect data completeness, and I am wondering if they couldn’t be used to guide the strategy to deal with missing data, together with domain knowledge from petrophysics.

Interpreting the dendrogram in a top-down fashion, as suggested in the library documentation, my first thoughts are that this may suggest trying to predict missing values in a sequential fashion rather than for all logs at once. For example, looking at the largest cluster on the left, and starting from top right, I am thinking of testing use of GR to first predict missing values in RDEP, then both to predict missing values in RMED, then DTC. Then add CALI and use all logs completed so far to predict RHOB, and so on.

Naturally, this strategy will need to be tested against alternative strategies using lithology prediction accuracy. I would do that in the context of learning curves: I am imagining comparing the training and crossvalidation error first using only non NaN rows, then replace all NANs with mean, then compare separately this sequential log completing strategy with an all-in one strategy.

Busting bad colormaps with Python and Panel

I have not done much work with, or written here on the blog about colormaps and perception in quite some time.

Last spring, however, I decided to build a web-based app to show the effects of using a bad colormaps. This stemmed from two needs: first, to further my understanding of Panel, after working through the awesome tutorial by James Bednar, Panel: Dashboards (at PyData Austin 2019); and second, to enable people to explore interactively the effects of bad colormaps on their perception, and consequently the ability to on interpret faults on a 3D seismic horizon.

I introduced the app at the Transform 2020 virtual subsurface conference, organized by Software Underground last June. Please watch the recording of my lightning talk as it explains in detail the machinery behind it.

I am writing this post in part to discuss some changes to the app. Here’s how it looks right now:

The most notable change is the switch from one drop-down selector to two-drop-down selectors, in order to support both the Matplotlib collection and the Colorcet collection of colormaps. Additionally, the app has since been featured in the resource list on the Awesome Panel site, an achievement I am really proud of.

awesome_panel

You can try the app yourself by either running the notebook interactively with Binder, by clicking on the button below:
Binder

or, by copying and pasting this address into your browser:

https://mybinder.org/v2/gh/mycarta/Colormap-distorsions-Panel-app/master?urlpath=%2Fpanel%2FDemonstrate_colormap_distortions_interactive_Panel

Let’s look at a couple of examples of insights I gained from using the app. For those that jumped straight to this example, the top row shows:

  • the horizon, plotted using the benchmark grayscale colormap, on the left
  • the horizon intensity, derived using skimage.color.rgb2gray, in the middle
  • the Sobel edges detected on the intensity, on the right

and the bottom row,  shows:

  • the horizon, plotted using the Matplotlib gist_rainbow colormap, on the left
  • the intensity of the colormapped, in the middle. This is possible thanks to a function that makes a figure (but does not display it), plots the horizon with the specified colormap, then saves plot in the canvas to an RGB numpy array
  • the Sobel edges detected on the colormapped intensity, on the right

I think the effects of this colormaps are already apparent when comparing the bottom left plot to the top left plot. However, simulating perception can be quite revealing for those that have not considered these effects before. The intensity in the bottom middle plot is very washed out in the areas corresponding to green color in the bottom left, and as a result, many of the faults are not visible any more, or only with much difficulty, which is demonstrated by the Sobel edges in the bottom right.

And if you are not quite convinced yet, I have created these hill-shaded maps, using Matt Hall”s delightful function from this notebook (and check his blog post):

Below is another example, using the Colocrcet cet_rainbow which is is one of Peter Kovesi’s perceptually uniform colormaps.  I use many of Peter’s colormaps, but never used this one, because I use my own perceptual rainbow, which does not have  a fully saturated yellow, or a fully saturated red. I think the app demonstrate, that even though they are more subtle , this rainbow still is introducing some artifacts. The yellow colour creates narrow flat bands, visible in the intensity and Sobel plots, and indicated by yellow arrows; the red colour is also bad as usual, causing an artificial decrease in intensity(magenta arrows).

Do you know any cool apps?

I’d like to pick up my Apps page, which I sort of abandoned a while back.

If you have any great app to recommend, I’d love to hear about it so please add them in the comment section to this post. I am looking for Apps for Android and iPhone/iPad in the following categories – ideally free or very low-cost, possibly open-source:

Geology

Geophysics

Cartography and mapping

Planetary Science

Image Processing

Visualization