Addressing PR comments.

txpipe/twopoint.py

@@ -289,8 +289,6 @@ def add_data_points(self, S, results):
 
         # Add the covariance.  There are several different jackknife approaches
         # available - see the treecorr docs
-        print(comb[0])
-        print(dir(comb[0]))
         if treecorr.__version__.startswith("4.2."):
             if self.rank == 0:
                 print("Using old TreeCorr - covariance may be slow. "
@@ -438,6 +436,7 @@ def call_treecorr(self, i, j, k):
         """
         import sacc
         import pickle
+        #TODO: fix up the caching code
         if self.name == "TXTwoPoint":
             pickle_filename = self.get_output("twopoint_data_real_raw") + f".checkpoint-{i}-{j}-{k}.pkl"
             #pickle_filename = f"treecorr-cache-{i}-{j}-{k}.pkl"

txpipe/twopoint_fourier.py

@@ -143,7 +143,6 @@ def run(self):
         if self.rank == 0:
             nband = ell_bins.get_n_bands()
             ell_effs = ell_bins.get_effective_ells()
-            # this ell_effs variable is not used anywhere else, should we just remove it?
             print(f"Chosen {nband} ell bin bands with effective ell values and ranges:")
             for i in range(nband):
                 leff = ell_effs[i]
@@ -587,7 +586,6 @@ def compute_power_spectra(
 
         if self.config["analytic_noise"]:
             # we are going to subtract the noise afterwards
-            #if k == POS_POS:
             c = nmt.compute_full_master(
                 field_i,
                 field_j,
@@ -643,7 +641,6 @@ def window_pixel(ell, nside):
         ls = ell_bins.get_effective_ells()
 
         c_beam = c / window_pixel(ls, pixel_scheme.nside) ** 2
-        #print("window_pixel(ls, pixel_scheme.nside) ** 2", window_pixel(ls, pixel_scheme.nside) ** 2)
         print("c_beam, k, i, j", c_beam, k, i, j)
 
         # this has shape n_cls, n_bpws, n_cls, lmax+1

txpipe/utils/nmt_utils.py

@@ -71,14 +71,14 @@ def apply_window(self, b, c_ell):
         return (c_ell[ell] * weight).sum() / weight.sum()
 
     @classmethod
-    def from_binning_info(self, ell_min, ell_max, n_ell, ell_spacing):
+    def from_binning_info(cls, ell_min, ell_max, n_ell, ell_spacing):
         # Creating the ell binning from the edges using this Namaster constructor.
         if ell_spacing == "log":
             edges = np.unique(np.geomspace(ell_min, ell_max, n_ell).astype(int))
         else:
             edges = np.unique(np.linspace(ell_min, ell_max, n_ell).astype(int))
 
-        ell_bins = self.from_edges(edges[:-1], edges[1:], is_Dell=False)
+        ell_bins = cls.from_edges(edges[:-1], edges[1:], is_Dell=False)
 
         return ell_bins