Plotting Press-Schechter using Python

[mahya]

How can I plot "Press-Schechter" function using Python for redshift 0, 0.5 and 1?

 

[jedishrfu]

What are you looking for?

1) a plotting package for python or
2) a program that does a Press-Schechter simulation and plots it

Is this a homework assignment or project for a course?

There are Python programs available that you can find via Google but you'd probably need to plot the results yourself.

 

[mahya]

I'm looking for a program that plots the Press-Schechter function for these 3 redshifts. It's actually part of a project for a course.

 

[jedishrfu]

There are some python packages that support it:

http://roban.github.io/CosmoloPy/

and

http://www.nullege.com/codes/show/src@yt-1.6.1@yt@extensions@HaloMassFcn.py

You might be able to adapt them to do plots

 

[LudusRex]

The Press-Schechter function is a commonly used tool in cosmology for predicting the abundance of dark matter halos in the universe. To plot the Press-Schechter function using Python, you will need to first import the necessary libraries and modules, such as numpy and matplotlib.

Next, you will need to define the Press-Schechter function itself. This can be done by writing a function that takes in the parameters of mass, redshift, and a cosmology model, and returns the predicted abundance of dark matter halos.

Once the function is defined, you can use a loop to calculate the Press-Schechter function for each desired redshift value (0, 0.5, and 1 in this case). The mass values can also be varied within the loop to create a range of predictions.

Finally, you can use matplotlib to plot the results on a graph, with the x-axis representing the mass values and the y-axis representing the predicted abundance of dark matter halos. This will allow you to visually compare the predictions at different redshifts.

It is important to note that the Press-Schechter function is just one of many models used to predict the abundance of dark matter halos, and it is always recommended to compare the results with other models and observational data.