Quick Start Guide: Event Rates

Getting started with Takahe is very straightforward: All you have to do is import takahe to get going. Make sure you also have a sample dataset. Takahe assumes that all datasets follow this prescription (a more intelligent loader is being brainstormed):

m1

m2

a0

e0

weight

evolution_age

rejuvenation_age

The simplest way to load in a block of data is to point Takahe towards a directory containing data files. To do so, ensure your directory contains files matching the following naming convention:

• Remnant-Birth-bin-imf135_300-z{Zi}_StandardJJ.dat

where {Zi} is a BPASS-formatted metallicity (e.g., 020 for solar). This directory must contain files corresponding to each of the 13 BPASS metallicities - Takahe does not fail silently and will complain if files are missing.

To load in your files, run takahe.load.from_directory(your_path) where your_path is a string pointing to your directory.

This returns a dictionary of Pandas dataframes corresponding to the files. It is indexed by a Takahe-formatted metallicity. Thus, to access the dataframe corresponding to the 0.7 solar metallicity file, one should use:

df_block = takahe.load.from_directory('Datasets/MyData')
Z = takahe.helpers.format_metallicity('z014')
df = df_block[Z]

Now, let’s compute the event rate of a given sample. Having run the above code, we can now use:

event_rate = takahe.event_rates.composite_event_rates(df_block)
event_rate.plot()

Calling event_rate.plot() is a proxy for calling a (modified) version of matplotlib’s plt.plot() method - so if you want to do anything fancy you may need to run your code before event_rate.plot().