LROC-Elelvation-Stats

This project is about visualizing the differences in LROC mosiac control methods and returing some basic statics of the differences

You will need bundleout text files from mosaics to run this script. If you do, you can either run it between two or three files by using the appropriate script

Some words of Caution

In general the 2 file version of the script should be used over the 3 file version. It runs faster and in my opinion can produce some more helpful plots. But if needed to compare all three types at the same time it is useful.

Additionally the plots do not save so either you will need to sceenshot the plots or just run the script again especially if you want the 3D plots. There is no current plan to add in a save system.

Basic Backround

In general

no ground no DTM is the least accurate as it only matches the images in the X, Y plane

no DTM is moderatly accurate as this uses 3D points to try and match up the elevations between images

DTM is the highest accuracy as this uses Digital Terrain Models that have the most accurate points to try and match up the previous 3D points to DTM points

When creating a mosaic one of these three methods can be used to adjust the mosaic for accuracy

This script will then graph measured points (points that will have a latitude, longitude, and elevation) from each type out giving a visual of what the mosaic looks like. Then it will find various statistical differces between the images. Then it will plot a measure of how many meters each point is off from highest accuracy.

USAGE

NOTE: these scripts can be any sort of bundleout files and can be put in any order these are just the best conventions

2 files

usage: LROC_elevation_stats_2_file.py [-h] DTM noDTM

Process two bundleout txt files for statistics

positional arguments:

DTM: Bundleout txt file from being run with a dtm and ground points EX: ground_DTM_bundleout.txt

noDTM: Bundleout txt file from being run anyway chosen EX: No_ground_noDTM_bundleout.txt

optional arguments: -h, --help show this help message and exit

3 files

usage: LROC_elevation_stats_3_file.py [-h] DTM noDTM no_ground_noDTM

Process three bundleout txt files for statistics

positional arguments:

DTM: Bundleout txt file from being run with a dtm and ground points EX: ground_DTM_bundleout.txt

noDTM: Bundleout txt file from being run without a dtm but ground points EX: ground_noDTM_bundleout.txt

no_ground_noDTM: Bundleout txt file from being run without a dtm or ground points EX: No_ground_No_DTM_bundleout.txt

optional arguments: -h, --help show this help message and exit

Elevation Stats functions

This file contains all the functions used in the main scripts, a description of each function follows.

append_to_array_from_file_regex(file, regex)

Given a file and a regex this function will parse each matched line and take out relevent data (full_id, latitude, longitude, elevation, id_number). Then will return an array of every detailing every match

    an example of one of the lists in the array
    ['grdD_00002', 19.45977657, 31.03212227, 1736598.16432, 2]

absolute_difference(value1, value2)

This function simply returns the absolute difference between two numbers rounded to 5 decimal places

array_elements_in_common(array_1, array_2, element_to_get)

Given two arrays and a certain element, this function checks for lists in the arrays that have the same id number then returns an array of the element for matching points

    EX: X_DTM_noDTM = esf.array_elements_in_common(DTM_point_array, noDTM_point_array, 1) This returns the latitude for each point given that they match
    
    WARNING: because the latitude of two same points will never be exactly the same this will return the average between the two elements. 
    In practice this would rarely cause any noticable differences  
    
    Also as such should never be used on elevation data, ONLY latitude and longitude data

array_element_differences(array_1, array_2, element_to_get)

Given two arrays and a certain element, this function checks that two lists in the arrays have the same id number then returns an array of the difference in the elements for matching points

    EX: DTM_by_noDTM_elevation = esf.array_element_differences(DTM_point_array, noDTM_point_array, 3)

percent_difference(value1, value2)

Returns the percent difference between two values

    NOTE: this function is never actually used in the main script currently but remains for added functionality

file_writer(array1, array2, array_name1, array_name2, output_file)

Writes statistics given two arrays (computes point-to-point, mean, and standard deviation of differences arrays for latitude, longitude, and elevation

    Example of output file

DTM by no DTM

Lat

Point to point : 1e-05

Mean : 2.8147680656358475e-06

Standard Deviation: 4.4971948360100105e-06

Long

Point to point : 2e-05

Mean : 5.055222467655412e-06

Standard Deviation: 6.071399517494202e-06

Elevation

Point to point : 0.73585

Mean : 0.1754549005995582

Standard Deviation: 0.18246948862017853

    NOTE: is formated better in actual output file

class MatchRegexLine:

This class is used to extract data from each line that matches the regex and is how append_to_array_from_file_regex(file, regex) function is able to create it's arrays

Basic function of the main scripts

How LROC_elevation_stats_2_file and 3_file work

Initilization steps

First and foremost the program will take 2 or 3 command line arguments which should be the bundleout text files intented to be used. If any confusion occurs when adding in the comand line arguments use the -h argument or look at the usage section of this documentation.

Then the program will ask you what you want the arguments to be called in the graphs, you can input any name you would like.

Math steps

Array's will be created with all relevent data from points in the bundleout text file. First a plot is made of those points for all files for a general visulization. From those arrays the point to point, mean, and standard deviation for Latitude, Longitude, and elevation will be determined.

Graphing steps

The bulk of the program will be graphing out various histograms and scatter plots which look at the elevation differences.