Avi Vajpeyi avivajpeyi

## auckland_workshop.ipynb

      
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              May 1, 2024 02:34
            
              
                emri_workshop.ipynb
              
          
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## nz_retirement_webscraper.ipynb

      
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              April 8, 2024 03:55
            
              
                nz_retirement_webscraper.ipynb
              
          
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## data.txt
x, y1, y2, y3
-1.000000000000000000e+01,-2.783594765403233851e+01,-1.204022393902481225e+00,3.275208397006052508e+01
-9.979979979979979987e+00,-2.913978273157271914e+01,-2.574527558844526443e+00,3.133560607143683185e+01
-9.959959959959959974e+00,-2.850114189577414336e+01,-2.001793782131751076e+00,3.185801415492995403e+01
-9.939939939939939961e+00,-2.717892662061836262e+01,-7.447210379480599940e-01,3.306039272821760022e+01
-9.919919919919919948e+00,-2.749220176960979245e+01,-1.122372886107044554e+00,3.262367590575326659e+01
-9.899899899899899935e+00,-3.027697757957611202e+01,-3.970758456187003738e+00,2.971186542865121183e+01
-9.879879879879879923e+00,-2.828955122211405282e+01,-2.046174009260742999e+00,3.156868906004671871e+01
-9.859859859859859910e+00,-2.933093678787727754e+01,-3.149632922417013869e+00,3.039317051089041044e+01
-9.839839839839839897e+00,-2.922273837131307772e+01,-3.102738773680232764e+00,3.036375621889418852e+01

## fi_lartillot_simulation_gpu_nsamp.ipynb

      
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              February 23, 2024 00:02
            
              
                fi_lartillot_simulation_gpu_nsamp.ipynb
              
          
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## compare_lnl_with_binned_and_non_binned_data.ipynb

      
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              December 14, 2023 05:06
            
              
                compare_lnl_with_binned_and_non_binned_data.ipynb
              
          
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## gist:f2526ee8d0ee6793858530246c76dda8
1.316612898901990074e-11
1.310852607588080058e-11
1.315380719573569983e-11
1.313443649770330023e-11
1.321001723945729947e-11
1.304954113729150040e-11
1.308680160905609982e-11
1.311480657825579943e-11
1.310126595848399982e-11
1.314370884666620009e-11

## hacking_with_kate.ipynb

      
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              September 7, 2023 06:21
            
              
                hacking_with_kate
              
          
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## lisa_wd_strain
1.33917e-21
1.32898e-21
1.31868e-21
1.30757e-21
1.2953e-21
1.28223e-21
1.26952e-21
1.25879e-21
1.25162e-21
1.24902e-21

## ml_timeseries_example.py
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt


def model(x, a2, a3):
    return a2 * x + a3 * x ** 2


def generate_training_data(x, a2_range, a3_range):

## overlap_hist.py
DATA = """
9.774773911876862842e-01
9.688606881965630135e-01
9.986587443750223336e-01
9.990195636006277136e-01
9.958378924502575558e-01
9.867260277991389827e-01
9.958952375862618611e-01
9.981319719615894259e-01
9.930206642722759769e-01
	x, y1, y2, y3
	-1.000000000000000000e+01,-2.783594765403233851e+01,-1.204022393902481225e+00,3.275208397006052508e+01
	-9.979979979979979987e+00,-2.913978273157271914e+01,-2.574527558844526443e+00,3.133560607143683185e+01
	-9.959959959959959974e+00,-2.850114189577414336e+01,-2.001793782131751076e+00,3.185801415492995403e+01
	-9.939939939939939961e+00,-2.717892662061836262e+01,-7.447210379480599940e-01,3.306039272821760022e+01
	-9.919919919919919948e+00,-2.749220176960979245e+01,-1.122372886107044554e+00,3.262367590575326659e+01
	-9.899899899899899935e+00,-3.027697757957611202e+01,-3.970758456187003738e+00,2.971186542865121183e+01
	-9.879879879879879923e+00,-2.828955122211405282e+01,-2.046174009260742999e+00,3.156868906004671871e+01
	-9.859859859859859910e+00,-2.933093678787727754e+01,-3.149632922417013869e+00,3.039317051089041044e+01
	-9.839839839839839897e+00,-2.922273837131307772e+01,-3.102738773680232764e+00,3.036375621889418852e+01
	1.316612898901990074e-11
	1.310852607588080058e-11
	1.315380719573569983e-11
	1.313443649770330023e-11
	1.321001723945729947e-11
	1.304954113729150040e-11
	1.308680160905609982e-11
	1.311480657825579943e-11
	1.310126595848399982e-11
	1.314370884666620009e-11
	1.33917e-21
	1.32898e-21
	1.31868e-21
	1.30757e-21
	1.2953e-21
	1.28223e-21
	1.26952e-21
	1.25879e-21
	1.25162e-21
	1.24902e-21
	import numpy as np
	import tensorflow as tf
	import matplotlib.pyplot as plt


	def model(x, a2, a3):
	return a2 * x + a3 * x ** 2


	def generate_training_data(x, a2_range, a3_range):
	DATA = """
	9.774773911876862842e-01
	9.688606881965630135e-01
	9.986587443750223336e-01
	9.990195636006277136e-01
	9.958378924502575558e-01
	9.867260277991389827e-01
	9.958952375862618611e-01
	9.981319719615894259e-01
	9.930206642722759769e-01