From cde3eebfa829caa0eec6758ac9824d0882d816de Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Fri, 20 Jan 2023 17:28:12 +0100
Subject: [PATCH] Added debugging messages, using parallelization by default,
disabled slow and probably useless refined peak finding, added function to
find match between two train IDs.
---
pes_to_spec/model.py | 24 ++++++++++++++++++------
1 file changed, 18 insertions(+), 6 deletions(-)
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 96e8756..ff640e1 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -32,6 +32,11 @@ def matching_ids(a: np.ndarray, b: np.ndarray, c: np.ndarray) -> np.ndarray:
unique_ids = list(set(a).intersection(b).intersection(c))
return np.array(unique_ids)
+def matching_two_ids(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+ """Returns list of train IDs common to sets a and b."""
+ unique_ids = list(set(a).intersection(b))
+ return np.array(unique_ids)
+
class PromptNotFoundError(Exception):
"""
Exception representing the error condition generated by not finding the prompt peak.
@@ -66,6 +71,7 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator):
Returns: The object itself.
"""
+ print("Storing high resolution energy")
self.energy = np.copy(fit_params["energy"])
if len(self.energy.shape) == 2:
self.energy = self.energy[0,:]
@@ -80,6 +86,7 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator):
Returns: Smoothened out spectrum.
"""
+ print("Smoothing high-resolution spectrum")
if self.high_res_sigma <= 0:
return X
# use a default energy axis is none is given
@@ -191,6 +198,7 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
Returns: Concatenated and pre-processed low-resolution data of shape (train_id, features).
"""
+ print("Selecting area close to the peak")
if self.tof_start is None:
raise NotImplementedError("The low-resolution data cannot be transformed before the prompt has been identified. Call the fit function first.")
items = [X[k] for k in self.channels]
@@ -220,11 +228,11 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
best_guess = int(peak_idx[0])
# look around this estimate for the maximum
# this is probably not necessary
- min_search = max(best_guess - 10, 0)
- max_search = min(best_guess + 10, len(sum_low_res))
- restricted_arr = sum_low_res[min_search:max_search]
- improved_guess = min_search + int(np.argmax(restricted_arr))
- return improved_guess
+ #min_search = max(best_guess - 10, 0)
+ #max_search = min(best_guess + 10, len(sum_low_res))
+ #restricted_arr = sum_low_res[min_search:max_search]
+ #improved_guess = min_search + int(np.argmax(restricted_arr))
+ return best_guess
def fit(self, X: Dict[str, np.ndarray], y: Optional[Any]=None) -> TransformerMixin:
"""
@@ -236,6 +244,7 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
Returns: The object itself.
"""
+ print("Estimating peak position")
self.tof_start = self.estimate_prompt_peak(X)
return self
@@ -427,7 +436,7 @@ def _fit_estimator(estimator, X: np.ndarray, y: np.ndarray):
class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
- def __init__(self, estimator, *, n_jobs=None):
+ def __init__(self, estimator, *, n_jobs=8):
self.estimator = estimator
self.n_jobs = n_jobs
@@ -449,12 +458,14 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
"multi-output regression but has only one."
)
+ print(f"Fitting multiple regressors with n_jobs={self.n_jobs}")
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i]
)
for i in range(y.shape[1])
)
+ print("End of fit")
return self
@@ -469,6 +480,7 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
Multi-output targets predicted across multiple predictors.
Note: Separate models are generated for each predictor.
"""
+ print("Inferring ...")
y = Parallel(n_jobs=self.n_jobs)(
delayed(e.predict)(X, return_std) for e in self.estimators_
#delayed(e.predict)(X) for e in self.estimators_
--
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