jlaehne on RELEASE_next_major
Remove setting `original_metada… Privatise `twin_function` (depr… Privatise `twin_inverse_functio… and 6 more (compare)
Sorry if this is a too naive question to ask here.
I am trying to plot my EDS spectrum using hyperspy. When I add new elements using
add_xray_lines_markers['Cu_Ka'] I cannot find a way to increase the size of the element labels when I plot it.
Also as a personal request, it would be really helpful for the community if the more experienced colleagues can add their Jupyter notebooks to update the 'examples' folder in git with some published data.
Could someone on Mac try opening a new jupyter notebook instance, and typing
import hyperspy.components1d.A<TAB>? Where
<TAB> is pressing the tab button. It should autocomplete to
Arctan, but for some reason it isn't doing so on my Mac.
Please specify if you're on one of the new M1 mac's if you try this. I'm on an M1 mac, and it doesn't work here. See #2968 for details.
multifitmethod to fit a custom numerical model to some hyperspectral microscopy data (as a
Signal1D). I'm trying to think of how I could wrap this custom model as a method in a hyperspy
Componentsubclass, however, the model depends on the spatial position of the spectrum. Is there a way to refer to the current navigation coordinates of the spectrum being fitted by
multifitwhen defining the method inside the
Componentsubclass? Sorry for my complex wording in the question, but I can't think of a simpler way to express the issue!
RosettaSciIO. Any new file readers or improvements to existing ones will then have to be contributed to that repo. For the moment, anyone familiar with the code is welcome to review the PR preparing this split: hyperspy/hyperspy#2972
s.plot(autoscale=""), see here: https://hyperspy.org/hyperspy-doc/current/user_guide/visualisation.html?highlight=autoscale
import matplotlib.pyplot as plt s.plot(autoscale="") plt.ylim(low, high)
m = s.create_model()probably just with the default included background), rather than the shortcut
s.remove_background, you can use
highare in calibrated units. Use it in a for loop if you have several regions you want to exclude, or remove everything by passing really low and high values and then add in the regions you want with
s.add_signal_range(low, high). See here in the docs for more methods like a reset method.
s_background = m.as_signal()and then subtract it
s_signal = s - s_background.
s.data[s.data < 0] = 0
s.plot() plt.xlim(-0.1, 0.1) plt.ylim(50, None) plt.yscale('log')