Scientific Python 3

Understanding the total amount of biomass (the total mass of all individuals) in forests is important for understanding the global carbon budget and how the earth will respond to increases in carbon dioxide emissions. Measuring the mass of entire trees is difficult, and it’s pretty much impossible to weigh an entire forest (even if we were willing to clear cut a forest for science), but fortunately we can estimate the mass of a tree based on its diameter.

There are lots of equations for estimating the mass of a tree from its diameter, but one good option is the equation M = 0.124*D^(2.53), where M is measured in kg of dry (above-ground) biomass and D is in cm d.b.h. (Brown 1997). We’re going to estimate the total tree biomass for trees in a 96 hectare area of the Western Ghats in India.

1. Write a function that takes an array/Series of tree diameters as an argument and returns an array/Series of tree masses.
2. The raw data is available on Ecologyical Archives, but unfortunately due to poor database structure using all of the trees would be a hassle. You could try to solve this problem yourself, but it turns out that someone else has already solved it for you. Install the EcoData Retriever and use it to download and cleanup this data automatically (using the command line interface the command would be retriever install csv Ramesh2010 and the data will be stored in Ramesh2010_macroplots.csv) and import it into Python.
3. If you look at the file or the metadata carefully you’ll notice that the data is actually in girth (i.e., circumference, which is equal to pi * diameter) rather than diameter. Write a function to take an array/Series of circumferences as an argument and returns an array/Series of diameters. Use the math module to get an accurate value of pi.
4. Use the two functions you’ve written to estimate the total biomass (i.e., the sum of the masses) of trees in this dataset and print the result to the screen.