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Merged
sschmidt23 merged 1 commit into from
Jun 25, 2026
Merged

switch to using structlog #41

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24 changes: 12 additions & 12 deletions src/rail/estimation/algos/tpz_lite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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}"
)

Expand Down Expand Up @@ -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
Expand All @@ -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])
Expand Down Expand Up @@ -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
Expand All @@ -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"
)

Expand All @@ -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:
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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}"]
Expand Down
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