hyperspy.misc.array_tools module¶
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hyperspy.misc.array_tools.are_aligned(shape1, shape2)¶ Check if two numpy arrays are aligned.
- Parameters
shape2 (shape1,) –
- Returns
isaligned
- Return type
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hyperspy.misc.array_tools.calculate_bins_histogram(data)¶ Calculate number of bins according to the Freedman Diaconis or the Sturges rules, taking the maximum of these two. See the numpy.histogram documentation for more details.
- Parameters
data (numpy array) – Input data.
- Returns
bins – Number of bins.
- Return type
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hyperspy.misc.array_tools.dict2sarray(dictionary, sarray=None, dtype=None)¶ Populates a struct array from a dictionary
- Parameters
dictionary (dict) –
sarray (struct array or None) – Either sarray or dtype must be given. If sarray is given, it is populated from the dictionary.
dtype (None, numpy dtype or dtype list) – If sarray is None, dtype must be given. If so, a new struct array is created according to the dtype, which is then populated.
- Returns
- Return type
Structure array
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hyperspy.misc.array_tools.get_array_memory_size_in_GiB(shape, dtype)¶ Given the size and dtype returns the amount of memory that such an array needs to allocate
- Parameters
shape (tuple) –
dtype (data-type) – The desired data-type for the array.
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hyperspy.misc.array_tools.homogenize_ndim(*args)¶ Given any number of arrays returns the same arrays reshaped by adding facing dimensions of size 1.
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hyperspy.misc.array_tools.numba_histogram¶ param data: Input data. The histogram is computed over the flattened array. :type data: numpy array :param bins: Number of bins :type bins: int :param ranges: The lower and upper range of the bins. :type ranges: (float, float)
- Returns
hist – The values of the histogram.
- Return type
array
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hyperspy.misc.array_tools.rebin(a, new_shape=None, scale=None, crop=True)¶ Rebin array.
rebin ndarray data into a smaller or larger array based on a linear interpolation. Specify either a new_shape or a scale. Scale of 1== no binning. Scale less than one results in up-sampling.
- Parameters
a (numpy array) –
new_shape (a list of floats or integer, default None) – For each dimension specify the new_shape of the np.array. This will then be converted into a scale.
scale (a list of floats or integer, default None) – For each dimension specify the new:old pixel ratio, e.g. a ratio of 1 is no binning and a ratio of 2 means that each pixel in the new spectrum is twice the size of the pixels in the old spectrum. The length of the list should match the dimension of the numpy array. *Note : Only one of scale or new_shape should be specified otherwise the function will not run*
crop (bool, default True) –
When binning by a non-integer number of pixels it is likely that the final row in each dimension contains less than the full quota to fill one pixel.
e.g. 5*5 array binned by 2.1 will produce two rows containing 2.1 pixels and one row containing only 0.8 pixels worth. Selection of crop=’True’ or crop=’False’ determines whether or not this ‘black’ line is cropped from the final binned array or not.
Please note that if crop=False is used, the final row in each dimension may appear black, if a fractional number of pixels are left over. It can be removed but has been left to preserve total counts before and after binning.
- Returns
- Return type
numpy array
Examples
>>> a=rand(6,4); b=rebin(a,scale=(3,2)) >>> a=rand(6); b=rebin(a,scale=(2,))
Notes
Fast re_bin function Adapted from scipy cookbook If rebin function fails with error stating that the function is ‘not binned and therefore cannot be rebinned’, add binned to metadata with: >>> s.metadata.Signal.binned = True
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hyperspy.misc.array_tools.sarray2dict(sarray, dictionary=None)¶ Converts a struct array to an ordered dictionary