I used this function before, but this is the first time I see this warning. What would cause this?
Could anyone give me any suggestions to solve this? Thanks in advance!
What is a good way to save artificial lazy signals that are larger than memory? I notice that my ram consumption shoots up when I try saving a dask-created signal, even if I specify the chunks.
import hyperspy.api as hs
from hyperspy.axes import UniformDataAxis
import dask.array as da
from hyperspy.datasets.example_signals import EDS_SEM_Spectrum
from hyperspy._signals.eds_sem import LazyEDSSEMSpectrum
from hyperspy._signals.signal2d import LazySignal2D
s = EDS_SEM_Spectrum()
data = s.data
axis = UniformDataAxis(offset = -0.1, scale = 0.01, size = 1024, units="eV")
s2 = LazyEDSSEMSpectrum(data, axes = [axis])
s2.add_elements(s.metadata.Sample.elements)
s2.set_microscope_parameters(beam_energy=10.)
nav = LazySignal2D(da.random.random((2500, 1000)))
s = s2 * nav.T
print("Shape:", s.data.shape) # 2500, 1000, 1024 - ~20GB
s.save("lazy.hspy", compression=None, overwrite=True, chunks = (100, 1000, 1024))
Could this be a problem with dask 2021.04.0 and related to https://github.com/dask/dask/issues/7583#issue-863708913? We've pinned dask to below this version in kikuchipy because of sudden memory issues after 2021.04.0.
12 replies
Hmm, could someone please check if
conda create --name testdask hyperspy results in an error? I'm running mamba on my M1 Mac, and installing hyperspy is giving a really weird error today. Installing jupyter notebook works fine:
(base) ➜ ~ conda create --name testdask hyperspy
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: |
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError:
I've just gone over the linear fitting PR #2422 once again, and I'm considering squashing most commits so that it is one commit per "category" (Model, Components, Tests and Docs), and then force pushing to the PR branch.
1 reply
I'm wondering if that makes it easier to review it, or if that just complicates matters. I've taken all comments that were in #2422 into consideration, so I think squashing would be clearer and shouldn't introduce confusion.
I've tested it, so the branch will look like this: https://github.com/thomasaarholt/hyperspy/commits/linear_fit_squash_into_files
Hi, I noticed that the existing integration for DENSsolutions log files is outdated. I started work on a io_plugin to load log files from our Impulse software which is in csv format. However there is already another io_plugin that has the file_extension "csv". If I simply add the new one, the existing one stops working. Is there a proper way in hyperspy to deal with this issue? Thanks!
6 replies
Hello,
I'm interested by Hyperspy to treat my STEM-EELS data among other things.
I wanted to give a try with HypersyUI first but i cannot start the program.
I did the installation with conda and a python 3.7 version.
Here's attached the log:
https://pastebin.com/cG0t6trw
Apparently there is an issue with scipy.
Any idea what i could do to get it started ?
Many thanks
I'm interested by Hyperspy to treat my STEM-EELS data among other things.
I wanted to give a try with HypersyUI first but i cannot start the program.
I did the installation with conda and a python 3.7 version.
Here's attached the log:
https://pastebin.com/cG0t6trw
Apparently there is an issue with scipy.
Any idea what i could do to get it started ?
Many thanks
Follow-up.
Switching to python 3.8 fixed the issue.
Maybe an indication of which python version is needed will be helpful for other here:
https://hyperspy.org/hyperspyUI/installation.html
Switching to python 3.8 fixed the issue.
Maybe an indication of which python version is needed will be helpful for other here:
https://hyperspy.org/hyperspyUI/installation.html
Do you know which aspect of 3.8 affected Scipy?
hyperspyui does support python 3.7 and known to work. The error message you provided says that the error arises when import hyperspy and it seems that there was something wrong with your install, however there is nothing obvious in the error traceback to have of an idea of where the error could come from. If this is working fine now, I would not worry too much about it! :)
Hello,
My collaborators and me am developing a machine learning algorithm based on the scikit-learn framework. We would like our algorithm to be as easy to handle as possible by using hyperspy. If I understood correctly, the correct way to go is to use the
The problem we face is that our
I thought that since
Is there any solution ? Am I missing something ?
My collaborators and me am developing a machine learning algorithm based on the scikit-learn framework. We would like our algorithm to be as easy to handle as possible by using hyperspy. If I understood correctly, the correct way to go is to use the
decomposition function and use our estimator with the algorithm = key word argument .The problem we face is that our
fit method takes some arguments and they are not included in the parameters of the estimator object itself because it depends on the fitted data. (For example, but not limited to that, for graph regularization a 2D shape input is needed.)I thought that since
decomposition takes **kwargs they would be passed to the fitfunction of the algorithm object but it seems to me that I was wrong.Is there any solution ? Am I missing something ?
LumiSpy v0.1.2 (https:/lumispy.org) is out on PyPi and conda-forge now (and on the AUR for arch users) for those who work with HyperSpy for e.g. CL and PL data. So far, we have a limited number of additional functionalities besides the provision of dedicated signal classes. Conversion to energy or wavenumber axes is already included, and the non-uniform axes support has matured so far as to be included in the release_next_minor branch of HyperSpy. We're happy for any user feedback, but also for the help of people who want to contribute routines to the project.
As a sidenote - as I was already at it - I also submitted pkgbuild files for pyxem and kikuchipy (and missing dependencies) to the AUR. In case there is any arch user among you who wants to test installing from there.
1 reply
Hello all, I have a question about the 'ranking' of NMF components in Hyperspy. If I perform NMF on a dataset with n as the desired number of output components, how does Hyperspy decide which component is #0, #1,..., #(n-1)? Is it also ranked by each component's proportion of the original data's variance a la PCA?
3 replies
Is there an argument that I need to include for
si.decomposition(alogrithm='ORPCA') to return the variance? I ran standard SVD the ORPC and am receiving the following:---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-99-659ba5d350c0> in <module>
----> 1 si.plot_explained_variance_ratio(threshold=4, xaxis_type='number')
c:\users\owner\documents\github\hyperspy\hyperspy\learn\mva.py in plot_explained_variance_ratio(self, n, log, threshold, hline, vline, xaxis_type, xaxis_labeling, signal_fmt, noise_fmt, fig, ax, **kwargs)
1427
1428 """
-> 1429 s = self.get_explained_variance_ratio()
1430
1431 n_max = len(self.learning_results.explained_variance_ratio)
c:\users\owner\documents\github\hyperspy\hyperspy\learn\mva.py in get_explained_variance_ratio(self)
1311 target = self.learning_results
1312 if target.explained_variance_ratio is None:
-> 1313 raise AttributeError(
1314 "The explained_variance_ratio attribute is "
1315 "`None`, did you forget to perform a PCA "
AttributeError: The explained_variance_ratio attribute is `None`, did you forget to perform a PCA decomposition?
@adriente
The Signal class HyperSpy uses the image order for indexing i.e. [x, y, z,…] (HyperSpy) vs […,z,y,x] (numpy)
The Signal class HyperSpy uses the image order for indexing i.e. [x, y, z,…] (HyperSpy) vs […,z,y,x] (numpy)
Hi All, I am having issues with loading a large EDX stack from hdf5. The following is what I am doing:
import hyperspy.api as hs
import h5py
import dask
import dask.array as da
f = h5py.File('data_path.hdf5', 'r')
edx_da = da.from_array(f['data_edx'], chunks=[1,128,128,4096])
f.close()
edx_hs = hs.signals.EDSTEMSpectrum(edx_da).as_lazy()
# <LazyEDSTEMSpectrum, title: , dimensions: (512, 512, 98|4096)>
test = edx_hs.inav[:,:,0]
test.compute()
The above is giving me the following error:
ValueError: Not a dataset (not a dataset)
Any ideas why / how to fix?
7 replies
Also need assistance with EDX. I'm getting 0s across the board from non-zero intensities when using CL.
In:
quant_elms = ['O_K', 'Si_K', 'Sc_K', 'Er_L', 'Nd_L', 'Yb_L', 'Lu_L']
quant_sigs = [result[i] for i in [10, -3, -5, 1, 8, -2, 4]]
quant_K = np.array([1.0, 0.885161, 0.98704, 1.45108, 1.61348, 1.6705, 1.69536])
print('Intensities')
print(pd.DataFrame(hs.stack(quant_sigs).data.T, columns=quant_elms))
quant = sroi_stack.quantification(quant_sigs, method='CL', factors=1/quant_K, composition_units='weight', max_iterations=100)
print('Wt%')
print(pd.DataFrame(hs.stack(quant).data.T, columns=quant_elms))
Out:
Intensities
[########################################] | 100% Completed | 0.1s
O_K Si_K Sc_K Er_L Nd_L Yb_L Lu_L
0 10.096292 10.968796 0.046850 0.051064 0.015106 0.048460 0.038284
1 6.665914 5.675549 2.449875 3.110012 1.423448 2.923658 2.596810
2 6.598061 5.350703 2.364560 3.020911 1.404513 2.716718 2.388128
3 7.454355 5.216240 0.247508 1.743997 7.806576 0.876016 0.638009
Wt%
[########################################] | 100% Completed | 0.1s
O_K Si_K Sc_K Er_L Nd_L Yb_L Lu_L
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
And even odder, if I iterate the nav dimension manually then I get a different result. The first and last positions are reasonable, but the rest are still 0s.
In:
weight_percent = [sroi_stack.inav[i].quantification([sig.inav[i] for sig in quant_sigs],
method='CL', factors=quant_K, composition_units='weight', max_iterations=100, convergence_criterion=0.001)
for i in range(0,4)]
print(pd.DataFrame(np.array([[j.data[0] for j in i] for i in weight_percent]), columns=quant_elms))
Out:
O_K Si_K Sc_K Er_L Nd_L Yb_L Lu_L
0 50.240269 48.313808 0.230111 0.368721 0.121285 0.402830 0.322976
1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3 24.858340 15.397206 0.814680 8.439159 42.003570 4.880007 3.607039
The quant function definitely takes somethings from the metadata especially when using absorption correction. Might this be the source of your error?
@k8macarthur Yes those are good checks.
I have checked the metadata and all edges are included:
Acquisition_instrument
TEM
Detector
EDS
azimuth_angle = 0.0
elevation_angle = 35.0
energy_resolution_MnKa = 131.45115116593425
number_of_frames = 58
Stage
tilt_alpha = 0.005
tilt_beta = -0.0
x = -0.000321
y = -8.1e-05
z = -2.5e-05
beam_energy = 200.0
camera_length = 73.0
magnification = 20000.0
microscope = Titan
General
date = 2021-08-18
original_filename = 1032 SI 23500 x HAADF-BF_original.emd
time = 10:32:08-04:00
time_zone = Eastern Daylight Time
title = Stack of EDS
Sample
elements = ['C', 'Er', 'Ga', 'Lu', 'Mo', 'N', 'Nd', 'O', 'Pt', 'Sc', 'Si', 'Yb']
xray_lines <list>
[0] = C_Ka
[1] = Er_La
[10] = Nd_Lb1
[11] = Nd_Lb2
[12] = Nd_Lb3
[13] = Nd_Lg1
[14] = Nd_Ma
[15] = Nd_Mg
[16] = Nd_Mz
[17] = O_Ka
[18] = Pt_Ma
[19] = Sc_Ka
[2] = Er_Mb
[20] = Sc_Kb
[21] = Si_Ka
[22] = Yb_La
[23] = Yb_Ma
[24] = Yb_Mb
[3] = Er_Mz
[4] = Ga_La
[5] = Lu_La
[6] = Lu_Ma
[7] = Mo_La
[8] = N_Ka
[9] = Nd_La
Signal
binned = True
signal_type = EDS_TEMI have not yet tried the zeta or cross_section quantifications, but I did try feeding directly into from hyperspy.misc.eds.utils import quantification_cliff_lorimer as suggested by @thomasaarholt. Using the util directly gave results for all navigation indices. There must be something going wrong in quant when the attributes are pulled from the signal.
7 replies
Hello all, I am trying to sum specific frames while opening JEOL.pts files, but the file opened is always the full integration of frames for the EDSSpectrum signal. If I use the sum_frames=False, I can open all the frames, but this is very memory-consuming. Follow an example of what I am doing: S=hs.load("data.pts", sum_frames=True, first_frame=0, last_frame=10). When I do it with emd files the code works but not with the pts files. I tried also with the .asw from Jeol and the problem is the same. Does someone know what can I do to solve this issue? I cannot process the data if I open all frames without summing because of memory.
6 replies
I am trying to register a new signal type. I have done as it is written here : (https://hyperspy.readthedocs.io/en/latest/dev_guide/writing_extensions.html) I have added an entry point in my setup.py and I have created a hyperspy_extension.yaml (in the same folder of setup.py) with all the required info. But when I use hs.print_known_signal_types(), my new signal does not appear.
I found a way to overcome this problem by puting
import hyperspy.extensions as e
e.ALL_EXTENSIONS["signals"]["MySignal"] = {'signal_type': 'MySignal',
'signal_dimension': 1,
'dtype': 'real',
'lazy': False,
'module': 'mymodule.datasets.signal'}in the signal.py file.
What am i doing wrong ?
7 replies
Hi all, I am trying to use Pipeline following the indications in the user guide. After executing '''pipe = Pipeline([("scaler", MinMaxScaler()), ("PCA", PCA())]) out = s.decomposition(algorithm=pipe, return_info=True) ''' I want to reconstruct a denoised model. I need to use the inverse_transform of the scaler, but I don't know how to call it. thanks
3 replies