add explanation for powerlawfit strategy

Author: kmualim

src/compute_powerlaw_fit_from_hic.py

@@ -109,7 +109,7 @@ def do_powerlaw_fit(HiC):
     # Juicebox output is in sparse matrix format. This is an attempt to get a "mean" hic_contact for each distance bin since we don't have the total # of bins available.
     HiC_summary = HiC.groupby('dist_for_fit').agg({'hic_contact' : 'sum'})
     HiC_summary['hic_contact'] = HiC_summary.hic_contact / HiC_summary.hic_contact.sum() #technically this normalization should be over the entire genome (not just to maxWindow). Will only affect intercept though..
-    res = stats.linregress(np.log(HiC_summary['dist_for_fit']), np.log(HiC_summary['hic_contact']))
+    res = stats.linregress(np.log(HiC_summary.index),np.log(HiC_summary['hic_contact']))
 
     hic_mean_var = HiC.groupby('dist_for_fit').agg({'hic_contact' : ['mean','var']})
     hic_mean_var.columns = ['mean', 'var']