From 5756d62664676f140742ec5235514cf9a8a2f04d Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Mon, 27 Feb 2023 13:46:12 +0100
Subject: [PATCH] Saving density.
---
pes_to_spec/model.py | 14 ++++++++++++--
1 file changed, 12 insertions(+), 2 deletions(-)
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 6fc1bd0..91bfa8a 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -708,6 +708,8 @@ class Model(TransformerMixin, BaseEstimator):
#self.fit_model = FitModel()
self.kde_xgm = None
+ self.mu_xgm = np.nan
+ self.std_xgm = np.nan
self.kde_intensity = None
self.mu_intensity = np.nan
self.std_intensity = np.nan
@@ -767,7 +769,9 @@ class Model(TransformerMixin, BaseEstimator):
Return: weights.
"""
self.kde_xgm = KernelDensity()
- self.kde_xgm.fit(intensity)
+ self.kde_xgm.fit(intensity.reshape(-1, 1))
+ self.mu_xgm = np.mean(intensity, axis=0)
+ self.sigma_xgm = np.mean(intensity, axis=0)
q = np.quantile(intensity, [0.10, 0.90])
l, h = q[0], q[1]
x = intensity*((intensity > l) & (intensity < h)) + l*(intensity <= l) + h*(intensity >= h)
@@ -805,7 +809,7 @@ class Model(TransformerMixin, BaseEstimator):
y_t = self.y_model.fit_transform(high_res_data, smoothen__energy=high_res_photon_energy)
intensity = np.sum(y_t, axis=1)
self.kde_intensity = KernelDensity()
- self.kde_intensity.fit(intensity)
+ self.kde_intensity.fit(intensity.reshape(-1, 1))
self.mu_intensity = np.mean(intensity, axis=0)
self.sigma_intensity = np.mean(intensity, axis=0)
#self.fit_model.set_params(fex__gamma=1.0/float(x_t.shape[0]))
@@ -931,6 +935,8 @@ class Model(TransformerMixin, BaseEstimator):
#self.channel_fit_model
DataHolder(self.mu_intensity),
DataHolder(self.sigma_intensity),
+ DataHolder(self.mu_xgm),
+ DataHolder(self.sigma_xgm),
self.ood,
self.kde_xgm,
self.kde_intensity,
@@ -952,6 +958,8 @@ class Model(TransformerMixin, BaseEstimator):
#channel_fit_model
mu_intensity,
sigma_intensity,
+ mu_xgm,
+ sigma_xgm,
ood,
kde_xgm,
kde_intensity,
@@ -966,6 +974,8 @@ class Model(TransformerMixin, BaseEstimator):
obj.ood = ood
obj.mu_intensity = mu_intensity.get_data()
obj.sigma_intensity = sigma_intensity.get_data()
+ obj.mu_xgm = mu_xgm.get_data()
+ obj.sigma_xgm = sigma_xgm.get_data()
obj.kde_xgm = kde_xgm
obj.kde_intensity = kde_intensity
return obj
--
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