From 7ef1e48c5a5cd77580d3edf29b05281ebd29a853 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Thu, 5 Oct 2023 19:09:39 +0200
Subject: [PATCH] Fixed axis for fft.
---
pes_to_spec/__init__.py | 2 +-
pes_to_spec/model.py | 8 ++++----
pes_to_spec/test/prepare_plots.py | 7 ++++---
3 files changed, 9 insertions(+), 8 deletions(-)
diff --git a/pes_to_spec/__init__.py b/pes_to_spec/__init__.py
index a564b2c..5f0058f 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 = "0.3.1"
+VERSION = "0.3.2"
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 3eb39f4..57cf076 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -53,14 +53,14 @@ def deconv(y: np.ndarray, yhat: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.
calculate the deconvolution between them.
"""
# subtract the mean spectra to remove the FEL bandwidth
- yhat_s = yhat - np.mean(yhat, keepdims=True, axis=(0, 1))
- y_s = y - np.mean(y, keepdims=True, axis=(0, 1))
+ yhat_s = yhat - np.mean(yhat, keepdims=True, axis=0)
+ y_s = y - np.mean(y, keepdims=True, axis=0)
# Fourier transforms
Yhat = np.fft.fft(yhat_s)
Y = np.fft.fft(y_s)
# spectral power of the assumed "true" signal (the grating spectrometer data)
- Syy = np.mean(np.absolute(Y)**2, axis=(0, 1))
- Syh = np.mean(Y*np.conj(Yhat), axis=(0, 1))
+ Syy = np.mean(np.absolute(Y)**2, axis=0)
+ Syh = np.mean(Y*np.conj(Yhat), axis=0)
# approximate transfer function as the ratio of power spectrum densities
H = Syh/Syy
return np.fft.fftshift(np.fft.ifft(H)), H, Syy
diff --git a/pes_to_spec/test/prepare_plots.py b/pes_to_spec/test/prepare_plots.py
index caa569f..31ada1d 100755
--- a/pes_to_spec/test/prepare_plots.py
+++ b/pes_to_spec/test/prepare_plots.py
@@ -469,9 +469,10 @@ def plot_pes(df: pd.DataFrame,
#cax[ch].xaxis.label.set_color(col[ch])
#cax[ch].title.set_color(col[ch])
if not tof:
- locator = matplotlib.ticker.MultipleLocator(2)
- locator.view_limits(1000, 1010)
- cax[ch].xaxis.set_major_locator(locator)
+ #locator = matplotlib.ticker.MultipleLocator(2)
+ #locator.view_limits(1000, 1010)
+ #cax[ch].xaxis.set_major_locator(locator)
+ cax[ch].set_xticks(np.arange(998, 1010, 2))
ax.legend(frameon=False, loc='center')
plt.tight_layout()
fig.savefig(filename)
--
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