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Eric E Palmer edited this page Oct 23, 2026 · 1 revision

Translation Statistics

By TC


## Description This page is a users guide for [transStats.py](transStats.py) which is a python script that is meant for use in conjunction with [batchCorrelation](batchCorrelation) to yield some useful statistics about a region of interest in a shape model. This script expects an input in the form of [locations.txt](locations.txt), which is one of the outputs of the batch correlation process.
## Usage The syntax for this script is very simple, as it has one very specialized purpose. For general usage directions, running: 

python transStarts.py -h

Will print out the usage header.

A typical invocation of this script is as follows:


python transStats.py locations.txt

If you do not specify the input file, or any command line arguments, you will be prompted for a filename:


Input filename:

## Output Once a correct input file is given, the calculated statistics are printed to the screen. After every successful invocation, the version number and input file will be printed to the screen for traceability. 

transStats.py version: 1.1.1
Input file: locations.txt

Maximum: 42.3588285211
Minimum: 41.3077804618
Median: 41.748964396
Average: 41.7606542577 dx: -41.4010377 dy: 5.43761855
Standard Deviation: 0.280988725228
Marginal Bound from Average: +/- 5

Pass: 80.00%  Marginal: 0.00%  Fail: 20.00%

Correlation < 0.5: 4
	    0.5-0.6: 5
	    0.6-0.7: 7
	    0.7-0.8: 4
	    0.8-0.9: 0
	    > 0.9: 0


Pass:

	EE0115
	EE0116
	EE0117
	EE0118
	EE0119
	EE0127
	EE0128
	EE0129
	EE0130
	EE0131
	EE0139
	EE0140
	EE0141
	EE0142
	EE0143
	EE0162
	EE0163
	EE0164
	EE0165
	EE0166

Marginal:


Fail:

	EE0151
	EE0152
	EE0153
	EE0154
	EE0155

The first thing that is done is the removal of outliers. Because of the nature of cross-correlation, and shape models, it is often the case that maplets will correlate to the wrong location. This can happen for many reasons, but generally this means the topography in that area is weak and needs further processing. So as of version 1.1.1 of transStats.py outliers are classified as such if their translation is 100 pixels or more than the average translation of all the maplets. This is a somewhat arbitrary criteria, but we felt it was very safe to assume that if a maplet is correlating that far away from the average deviation, it is obviously a fail. So far this has been very robust and not exclusive of any good data.

The outliers are now placed into the "Fail" category, and a new average is calculated. All of the statistics before the pass/marginal/fail percentages are calculated AFTER the removal of the outliers, and give a variety of useful information. It is good to see that the maximum, average, and median translation magnitudes are very close together. This means there is good agreement of the data.

After the statistics are calculated the data is split, once again, into pass and marginal categories. As of version 1.1.1, the bound for classifying a maplet as marginal is if it translated 5 or more pixels from the new average that was calculated after removal of outliers. Again, this is largely arbitrary, but for our applications so far, 5 makes for a good bound as these maplets should probably still be looked at further. Future versions may support user specified fail and/or marginal criteria.

The last thing that is done is the calculation of a correlation histogram that shows how many maplets fall into each correlation score bin. For our purposes, anything 0.6 and above is very good as we have seen that two resamplings of the same truth area give a correlation score of 0.833.

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