From 69e6b0e2135b77e236568ef7b9e9f0965b828589 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Wed, 1 Mar 2023 14:15:27 +0100
Subject: [PATCH] Switched back to fftconvolve.
---
pes_to_spec/__init__.py | 2 +-
pes_to_spec/model.py | 8 ++++----
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/pes_to_spec/__init__.py b/pes_to_spec/__init__.py
index cc1b4a3..1f74782 100644
--- a/pes_to_spec/__init__.py
+++ b/pes_to_spec/__init__.py
@@ -2,4 +2,4 @@
Estimate high-resolution photon spectrometer data from low-resolution non-invasive measurements.
"""
-VERSION = "0.1.6"
+VERSION = "0.1.7"
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index f17347a..0dcb032 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -3,7 +3,7 @@ from __future__ import annotations
import joblib
import numpy as np
-from scipy.signal import convolve
+from scipy.signal import fftconvolve
#from scipy.signal import find_peaks_cwt
from scipy.optimize import fmin_l_bfgs_b
from sklearn.decomposition import PCA
@@ -331,7 +331,7 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator):
gaussian = np.exp(-0.5*(energy - mu)**2/self.high_res_sigma**2)
gaussian /= np.sum(gaussian, axis=1, keepdims=True)
# apply it to the data
- high_res_gc = convolve(X, gaussian, mode="same", axes=1)
+ high_res_gc = fftconvolve(X, gaussian, mode="same", axes=1)
return high_res_gc
def inverse_transform(self, Xt: np.ndarray) -> np.ndarray:
@@ -519,7 +519,7 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
gaussian = np.exp(-0.5*(axis - sum_low_res.shape[0]//2)**2/20**2)
gaussian /= np.sum(gaussian, axis=0, keepdims=True)
# apply it to the data
- smoothened = convolve(sum_low_res, gaussian, mode="same", axes=0)
+ smoothened = fftconvolve(sum_low_res, gaussian, mode="same", axes=0)
peak_idx = [np.argmax(smoothened)]
if len(peak_idx) < 1:
raise PromptNotFoundError()
@@ -1011,7 +1011,7 @@ class Model(TransformerMixin, BaseEstimator):
M = self.wiener_filter.shape[0]
B = expected.shape[0]
assert expected.shape[1] == M
- deconvolved = convolve(expected,
+ deconvolved = fftconvolve(expected,
np.broadcast_to(self.wiener_filter.reshape(1, -1), (B, M)),
mode="same",
axes=1)
--
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