Hello,
I am using VSCode as an IDE. I use the jupyter plugin that enables jupyter notebooks to be used in the interface of VSCode. Everything was working well until recently. Now when I have a cell with :
import hyperspy.api as hsThe kernel dies. It is working well with import hyperspy though. Is that a known issue ? Is anybody else using VSCode and the hyperspy api ?
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I try to find a way to save my "file.hspy" using the save() function to a specific path, but I can't see a "path" argument in the function description. Ideally, I was hoping to get the path from the hs.load() function ("load.path/of/my/loadedfile") and use it to save to the same folder without having to manually enter a path ( I have many datasets from many different folders).
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Is there a way of adapting 'fit_component' function to work using multiple threads on a CPU? The only mention of parallel processes that I found in the docs was in SAMFire and in the map function. I have a relatively specific order of component fits (for EELS core-loss data) that I wouldn't want to change. Any advice on how to approach this would be appreciated!
(I just recently got my hands on a computer, where parallelising would save a significant amount of time)
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I should fork the github repo, but for me it is not very intuitive, what the best way to write extensions are.
I have read the documentation on this, from HyperSpys websites, but i need some elaboration.
Any advice is highly appriciated!
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Hello, i have a followup question for my question above.
I want to contribute to a package that already excist (The eds packages). How would i go about using my contribution? By forking the repo and including my methodes in the relevant classes or should i create my own class, and have that inherit from eds? In the latter case, i am struggeling to get the inheritance to work properly, i have written some code, but when i try to apply my method too a dataset it exclaim that EDSSpectrum does not have the attribute, should correct inheritance not fix this? Or is the only way to create my code in a forked repo of the relevant signal?
This might be a trivial question but i cant seem to find the answer. (Maybe i lack some understandig of classes)
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I used a "g1 = hs.model.components1D.Gaussian()" to do this fit and want to get the center of this peak.
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however when I run my version of that cell I end up with two seperate figures, instead of a single figure with two subfigures like what is seen above. While I know how to do this with matplotlib, I was wanting to leverage the built in hyperspy tools so the metadata/ axis manger information is automatically applied, as opposed to having to do the heavy lifting again. Any insights would be appricaiated.
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Hi All, I am trying to open .hspy files written with
RELEASE_next_minorbranch with the released 1.6.5 version and I get the following error:TypeError: __init__() got an unexpected keyword argument '_type'. Any ideas how to fix / work-around?
Never mind. Just reading with h5py and populating axes metadata works good enough. Thanks!
@thomasaarholt Hi,
I try to get quantified EDS from a sample with many elements (Al,C,Co,Cu,Cr,Mo,O,Pt,W,Zn) with a strong peak overlap between Cr-L (0.571 eV) and O-K (0.523 eV).
One layer is expected to be CrC but appears as CrO... the quantification output 30% of O in it, which is very unexpected for several reasons. Checking the EDS spectrum manually I can see the peak shift by maybe 1 channel when seeing Cr-L instead of O-K but the model fitting doesn't.
I am using the EDS model fitting and after several hours in the documentation I come here to ask: Is there a solution to this? is there a function somewhere that can for instance estimate Cr-L from the Cr-K intensity and subtract it from the O-K signal before saving the O-K intensity? I hope this question is clear enough!
Regards,
Olivier
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I am trying to run the following code :
import hyperspy.api as hs
input_filename = "file1.emd"
spim = hs.load(input_filename)[-1]
# the output of the load function is a list in which the last element is an EDSTEM object.
spim.change_dtype("float")
spim.crop(1,70,400)
spim.crop(2,0.3,19.0)
spim.decomposition(True)It ouputs : ValueError: All the data are masked, change the mask.
It seems to me that the crop functions are at the source of this issue since when I comment them everything is fine.
I am asking here to check if I am missing something. But I will post an issue on github if not.
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