Category Archives: Challenge

My first Py

Recently I went out on site and collected two reasonably large data sets which could not be easily handled by proprietary software.  This rather fundamental need to be able to actually use my survey data inspired me to have a go at the first ArchaeoPy challenge and to start to get to grips with the Python language.  Having completed and uploaded some script, I was encouraged by Popefinn and Chrys Harris to share it with you in this post.

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Geophysical Challenge 1 - Need some help?

It has been a few weeks since we have posted our first geophysical challenge. We thought to create a tutorial post for the challenge to help you along! First things first, you will need to download the data for this challenge. Save this data to the same folder you will be saving your python challenge 1 script to. Now open Spyder. We have provided a step-by-step tutorial with information about each step, to help you along.

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Geophysical Challenge 1

Following on from yesterday's Geolunch, here's the first Applied Geophysical ArchaeoPy challenge:

Load XYZ .CSV file and plot the data within python

You can do this in any way you want but some hints and tips are given below:

  1. Download the data from http://www.archaeopy.org/wp-content/uploads/2014/04/xyz.csv
  2. Numpy.loadtxt to load in a CSV file
  3. Matplotlib.mlab.griddata to grid the XYZ data
  4. Matplotlib.pyplot to plot the data
  5. If you get stuck, google is your friend!
Extra Credit
Its not very often that we use completely raw geophysical data. Usually we'll have to apply some processing steps to make the data more usable.
A simple initial processing step is a Zero Mean Traverse. We don't need any extra software packages to do this.
Numpy.mean can be used to calculate the mean of a group of values, or more usefully return the means of rows/columns in an array.
Numpy.subtract can be used to remove that value from rows/columns.
You might want to use loops and array indexing to do this but you shouldn't have to.
Upload your example to the ArchaeoPY Bitbucket (details here).
Prize for the most readable and well documented code.