Lehman Garrison lgarrison

## SAO_python_worksheet.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                lgarrison
                / SAO_python_worksheet.ipynb
            
            
              Created
              August 14, 2018 15:57
            
              
                Simple simulations with Python and NumPy
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## scale_free_sigma8.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                lgarrison
                / scale_free_sigma8.ipynb
            
            
              Last active
              October 25, 2019 19:18
            
              
                sigma8 in Scale-Free Cosmologies
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## high_performance_python.ipynb

      
              1 file
            
          
              2 forks
            
          
              0 comments
            
          
              4 stars
            
          
                lgarrison
                / high_performance_python.ipynb
            
            
              Last active
              July 23, 2020 14:10
            
              
                High-Performance Python, or When to Write For Loops in Python
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## repeat_histogram.py
# Note: this function uses the term 'radius', but the histogramming is completely general

import numpy as np

def RadialBin(radii, values, bin_edges, bin_indices=None):
    """
    Radial histogramming that supports fast re-evaluation
    for the same `radii` with new `values` by passing back `bin_indices`.

    Parameters

## RR_autocorrelations.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
                lgarrison
                / RR_autocorrelations.ipynb
            
            
              Last active
              July 24, 2020 12:52
            
              
                A note on the RR term in autocorrelations
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## swot vs corrfunc
swot: https://github.com/jcoupon/swot/
corrfunc: https://github.com/manodeep/Corrfunc

To test the speed and agreement of swot vs corrfuc, we generated random
datasets of increasing size and measured the w(theta) autocorrelation function.

Swot has an approximation parameter 'OA' (opening angle) that critically impacts its runtime.
We tried OA=0.03 and OA='no'.  However, even the conservative value of OA=0.03
caused >10% errors in wtheta for this randomized configuration.  Thus, it should
be used with caution.
	# Note: this function uses the term 'radius', but the histogramming is completely general

	import numpy as np

	def RadialBin(radii, values, bin_edges, bin_indices=None):
	"""
	Radial histogramming that supports fast re-evaluation
	for the same `radii` with new `values` by passing back `bin_indices`.

	Parameters
	swot: https://github.com/jcoupon/swot/
	corrfunc: https://github.com/manodeep/Corrfunc

	To test the speed and agreement of swot vs corrfuc, we generated random
	datasets of increasing size and measured the w(theta) autocorrelation function.

	Swot has an approximation parameter 'OA' (opening angle) that critically impacts its runtime.
	We tried OA=0.03 and OA='no'. However, even the conservative value of OA=0.03
	caused >10% errors in wtheta for this randomized configuration. Thus, it should
	be used with caution.