Comments (1)
The current implentation prenvents us from processing "large_format" data in many cases. One specific issue reported by ND via email ("Multi-D integrate of shifted will not fourier transform"). The problem is that the Multi-D processing functions create "large_format" data items. Core.function_fft
uses ndarray.copy()
to copy the incoming data, but this fails for h5py datasets because they don't have a copy()
method.
from niondata.
Related Issues (20)
- Add 1d and 2d cross correlation functions
- Use term 'rank' for dimension count to match NumPy
- Consider using 'navigation' and 'signal' to match HyperSpy nomenclature
- The resize method should work on RGB data
- function_concatenate should preserve data_descriptor of input data HOT 2
- Allow specifying a maximum shift for align and sequence_align functions HOT 1
- Allow choice of Fourier shift or regular shift in sequence_align functions
- Sequences of color images
- Sobel filter should be 2d for images HOT 5
- Change default "sequence shift" to using linear spline shift HOT 1
- Processing functions should not strip metadata from processed xdata. HOT 8
- Should register_template return offset relative to center pixel? HOT 3
- Consider mechanism to track additional data properties such as whether complex is Hermitian
- Add mechanism to indicate which parts of the data are valid
- Intermittent test failures in new multi dimensional functions
- FFT result should have sensible intensity units; subsequent FFT should result in original intensity units HOT 1
- Multiplying a calibrated image by an uncalibrated image should result in a calibrated image
- Processing should use the timestamp/timezone/timezone_offset from the source data
- Add function to perform cropping in either sequence/collection axes
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