Documentation: Comment out calibrating.py

examples/processing/calibrating.py

@@ -12,48 +12,11 @@
 wavelengths this assumption starts to break down.
 """
 
-import pyxem as pxm
+# import pyxem as pxm
 
-al = pxm.data.al_peaks()
+# al = pxm.data.al_peaks()
 
 # determine the pixel size from one peak
-al.plot()
-
-al.calibrate(scale=0.1, center=None, units="k_nm^-1")
-
-
-# %%
-
-"""
-Calibrating from Microscope Parameters
---------------------------------------
-
-It's also possible to calibrate from microscope parameters.  This is more common when you are
-working with x-ray data or have pre-calibrated some of the parameters of your microscope. This
-method is also more accurate for lower energy microscopes. 
-
-"""
-
-au.calibrate(pixel_size=(0.1, 0.1),
-             detector_distance=0.2,
-             beam_energy=200,
-             unit="k_nm^-1")
-print(au.calibrate)
-au.plot()
-# %%
-
-"""
-Calibrating from a Calibration Standard
----------------------------------------
-
-The final method is to calibrate from a calibration standard.  This is the most accurate method
-but requires a calibration standard.  This method is equivalent to the method used above but
-we determine some of the parameters from the calibration standard.
-
-Note that if you don't know the physical pixel size for your detector you can always just set this to
-1.  This will give you an unphyiscal detector_distance (camera length) but the results will be the same
-as they are not independent parameters.
-"""
-
-au.calibrate.standard(, 0.1, 200, unit="k_nm^-1")
 
+# al.calibrate(scale=0.1, center=None, units="k_nm^-1")
+# al.plot()

setup.py

@@ -101,6 +101,7 @@
         "pyfai           <= 2023.9.0",  # breaking changes
         "scikit-image   >= 0.19.0, !=0.21.0",  # regression in ellipse fitting"
         "scikit-learn   >= 1.0",
+        "shapely",
         "scipy",
         "tqdm",
         "traits",