From 0cb38432e5bb02fb726a9ff5d41ffd3cc8da5587 Mon Sep 17 00:00:00 2001
From: Eric Charles <badass@stanford.edu>
Date: Wed, 24 Jun 2026 11:14:38 -0700
Subject: [PATCH] switch to using structlog

---
 src/rail/estimation/algos/tpz_lite.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/src/rail/estimation/algos/tpz_lite.py b/src/rail/estimation/algos/tpz_lite.py
index b268771..5a158e0 100644
--- a/src/rail/estimation/algos/tpz_lite.py
+++ b/src/rail/estimation/algos/tpz_lite.py
@@ -155,7 +155,7 @@ def run(self):
         if self._parallel == MPI_PARALLEL:
             self._comm.Barrier()
         if self._rank == 0:
-            print(
+            self.log.info(
                 f"self._parallel is {self._parallel}, number of processors we will use is {self._size}"
             )
 
@@ -196,9 +196,9 @@ def run(self):
                 f"value of {self.config.tree_strategy} not valid! Valid values for tree_strategy are 'native' or 'sklearn'"
             )
         if self.config.tree_strategy == "sklearn" and self._rank == 0:
-            print("using sklearn decision trees")
+            self.log.info("using sklearn decision trees")
         if self.config.tree_strategy == "native" and self._rank == 0:
-            print("using native TPZ decision trees")
+            self.log.info("using native TPZ decision trees")
 
         # TPZ expects a param called `keyatt` that is just the redshift column, copy redshift_col
         self.config.keyatt = self.config.redshift_col
@@ -213,8 +213,8 @@ def run(self):
         # npdata = np.array(list(training_data.values()))
         trainkeys = self.config.bands + self.config.err_bands
         trainkeys.append(self.config.redshift_col)
-        print(trainkeys)
-        print("STOP")
+        self.log.info(trainkeys)
+        self.log.info("STOP")
         ncols = len(trainkeys)
         nvals = len(training_data[self.config.redshift_col])
         npdata = np.zeros([ncols, nvals])
@@ -244,7 +244,7 @@ def run(self):
         # not how I would have done things, but we're keeping it to try to duplicate MLZ's code exactly.
         if self.config.n_random > 1:
             if self._rank == 0:
-                print(f"creating {self.config.n_random} random realizations...")
+                self.log.info(f"creating {self.config.n_random} random realizations...")
                 traindata.make_random(ntimes=int(self.config.n_random))
                 temprandos = traindata.BigRan
             else:  # pragma: no cover
@@ -264,7 +264,7 @@ def run(self):
 
         ntot = int(self.config.n_random * self.config.n_trees)
         if self._rank == 0:
-            print(
+            self.log.info(
                 f"making a total of {ntot} trees for {self.config.n_random} random realizations * {self.config.n_trees} bootstraps"
             )
 
@@ -278,16 +278,16 @@ def run(self):
             for i in range(Nproc):
                 Xs_0, Xs_1 = utils_mlz.get_limits(ntot, Nproc, i)
                 if Xs_0 == Xs_1:  # pragma: no cover
-                    print(f"idle...  -------------> to core  {i}")
+                    self.log.info(f"idle...  -------------> to core  {i}")
                 else:
-                    print(f"{Xs_0} - {Xs_1} -------------> to core {i}")
+                    self.log.info(f"{Xs_0} - {Xs_1} -------------> to core {i}")
 
         treedict = {}
         if self._parallel == MPI_PARALLEL:
             self._comm.Barrier()
         # copy some stuff from the runMLZ script:
         for kss in range(s0, s1):
-            print(f"making {kss + 1} of {ntot}...")
+            self.log.info(f"making {kss + 1} of {ntot}...")
             if self.config.n_random > 1:
                 ir = kss // int(self.config.n_trees)
                 if ir != 0:
@@ -320,7 +320,7 @@ def run(self):
         if self._parallel == MPI_PARALLEL:
             if self._rank == 0:
                 for i in range(1, self._size, 1):
-                    print(f"receiving data from rank {i}")
+                    self.log.info(f"receiving data from rank {i}")
                     xdata = self._comm.recv(source=i, tag=11)
                     for key in xdata:
                         treedict[key] = xdata[key]
@@ -442,7 +442,7 @@ def _process_chunk(self, start, end, inputdata, first):
 
         # Load trees
         alltreedict = self.model["treedict"]
-        print(f"loading {ntot} total trees from model")
+        self.log.info(f"loading {ntot} total trees from model")
         for k in range(ntot):
 
             S = alltreedict[f"tree_{k}"]