In part two of the exercises for the final two weeks of the course we will combine the thermochronometer age prediction code used last week with an age dataset from the Bhutan Himalaya to quantify the long-term rates of erosion recorded in the thermochronometer data. These results will be presented in detail in your final report for the course.
I suggest that you add horizontal lines to your plots of the thermochronometer data to show the predicted ages you calculate. If you have read in the data file with the values for latitude stored in a variable latitude, you can plot a predicted age predicted_age as a horizontal line as follows:
plt.plot([min(latitude), max(latitude)], [predicted_age, predicted_age], 'k-')
plt.show()This will create a horizontal line from the minimum latitude to the maximum latitude with a vertical value of predicted_age. The "trick" here is to put lists into the plt.plot() command instead of variables. Lists are values separated by commas within square brackets ([ ]), and here we just give 2 values in each list for the x and y points that define the ends of the line.
- Course lecture materials
- Past exercise materials
- Scientific journal articles
- Some final report references
- Google scholar
- Web of Science (Only works on campus or via VPN)
- Web pages
- Books