fix warning.

README.md

@@ -3,7 +3,7 @@
 Test of SARS-CoV-2 viral-load age-causal-dependence via a non-parametric, hierarchical, Bayesian, and causal model. The
 following is a Python implementation of the model described in
 
-* Guardiani M. et al., Non-parametric Bayesian Causal Modeling of the SARS-CoV-2 Viral Load Distribution vs. Patient's
+* Guardiani et al., Non-parametric Bayesian Causal Modeling of the SARS-CoV-2 Viral Load Distribution vs. Patient's
   Age, [preprint](https://arxiv.org/abs/2105.13483) [1].
 
 Please cite [1] when making use of this code.
@@ -28,9 +28,8 @@ running the script `data.py` which outputs (for the two datasets respectively)
 
 <img src="./plots/data_Cobas_Dataset.png" width="425"/> <img src="./plots/data_LC_480_Dataset.png" width="425"/>
 
-These datasets have been extracted from Drosten et
-al. [preprint](https://virologie-ccm.charite.de/fileadmin/user_upload/microsites/m_cc05/virologie-ccm/dateien_upload/Weitere_Dateien/Charite_SARS-CoV-2_viral_load_2020-06-02.pdf) (
-Fig. 6).
+These datasets have been extracted from Jones et
+al. [preprint](https://virologie-ccm.charite.de/fileadmin/user_upload/microsites/m_cc05/virologie-ccm/dateien_upload/Weitere_Dateien/Charite_SARS-CoV-2_viral_load_2020-06-02.pdf) (Fig. 6).
 
 ## Run the code
 

src/utilities.py

@@ -21,6 +21,7 @@ import string
 import nifty8 as ift
 import numpy as np
 
+
 class GeomMaskOperator(ift.LinearOperator):
     """
     Copyright @ Jakob Roth
@@ -56,6 +57,7 @@ class GeomMaskOperator(ift.LinearOperator):
         res[self._slices] = x
         return ift.Field(self.domain, res)
 
+
 class DomainBreak2D(ift.LinearOperator):
     def __init__(self, domain):
         self._domain = ift.DomainTuple.make(domain)
@@ -131,7 +133,7 @@ class y_averager(ift.LinearOperator):
         self._check_input(x, mode)
         val = x.val
 
-        if self._alpha is not 0:
+        if self._alpha != 0:
             weights = np.power(10, self._rv.val * self._alpha) * self._dV
             res = np.sum((val * weights), axis=1)
             res = np.power(res, 1. / self._alpha)