Fixed axis for fft.

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"

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

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)