hparray: Reference

Extends functionality of np.ndarray for hyperspectral data

class hypers.core.array.hparray(input_array: Union[list, numpy.ndarray, hypers.core.array.hparray])

Extend functionality of a numpy array for hyperspectral data

The usual numpy.ndarray attributes and methods are available as well as some additional ones that extend functionality.

input_array: Union[list, np.ndarray]

The array to convert. This should either be a 2d/3d/4d numpy array (type np.ndarray) or list.

mean_image: np.ndarray

Provides the mean image by averaging across the spectral dimension. e.g. if the shape of the original array is (100, 100, 512), then the image dimension shape is (100, 100) and the spectral dimension shape is (512,). So the mean image will be an array of shape (100, 100).

mean_spectrum: np.ndarray

Provides the mean spectrum by averaging across the image dimensions. e.g. if the shape of the original array is (100, 100, 512), then the image dimension shape is (100, 100) and the spectral dimension shape is (512,). So the mean spectrum will be an array of shape (512,).


all([axis, out, keepdims, where])

Returns True if all elements evaluate to True.

any([axis, out, keepdims, where])

Returns True if any of the elements of a evaluate to True.

argmax([axis, out])

Return indices of the maximum values along the given axis.

argmin([axis, out])

Return indices of the minimum values along the given axis.

argpartition(kth[, axis, kind, order])

Returns the indices that would partition this array.

argsort([axis, kind, order])

Returns the indices that would sort this array.

astype(dtype[, order, casting, subok, copy])

Copy of the array, cast to a specified type.


Swap the bytes of the array elements

choose(choices[, out, mode])

Use an index array to construct a new array from a set of choices.

clip([min, max, out])

Return an array whose values are limited to [min, max].


Collapse the array into a 2d array

compress(condition[, axis, out])

Return selected slices of this array along given axis.


Complex-conjugate all elements.


Return the complex conjugate, element-wise.


Return a copy of the array.

cumprod([axis, dtype, out])

Return the cumulative product of the elements along the given axis.

cumsum([axis, dtype, out])

Return the cumulative sum of the elements along the given axis.

diagonal([offset, axis1, axis2])

Return specified diagonals.

dot(b[, out])

Dot product of two arrays.


Dump a pickle of the array to the specified file.


Returns the pickle of the array as a string.


Fill the array with a scalar value.


Return a copy of the array collapsed into one dimension.

getfield(dtype[, offset])

Returns a field of the given array as a certain type.


Copy an element of an array to a standard Python scalar and return it.


Insert scalar into an array (scalar is cast to array’s dtype, if possible)

max([axis, out, keepdims, initial, where])

Return the maximum along a given axis.

mean([axis, dtype, out, keepdims, where])

Returns the average of the array elements along given axis.

min([axis, out, keepdims, initial, where])

Return the minimum along a given axis.


Return the array with the same data viewed with a different byte order.


Return the indices of the elements that are non-zero.

partition(kth[, axis, kind, order])

Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array.


Interactive plotting to interact with hyperspectral data

prod([axis, dtype, out, keepdims, initial, …])

Return the product of the array elements over the given axis

ptp([axis, out, keepdims])

Peak to peak (maximum - minimum) value along a given axis.

put(indices, values[, mode])

Set a.flat[n] = values[n] for all n in indices.


Return a flattened array.

repeat(repeats[, axis])

Repeat elements of an array.

reshape(shape[, order])

Returns an array containing the same data with a new shape.

resize(new_shape[, refcheck])

Change shape and size of array in-place.

round([decimals, out])

Return a with each element rounded to the given number of decimals.

searchsorted(v[, side, sorter])

Find indices where elements of v should be inserted in a to maintain order.

setfield(val, dtype[, offset])

Put a value into a specified place in a field defined by a data-type.

setflags([write, align, uic])



Returns smoothened hp.hparray

sort([axis, kind, order])

Sort an array in-place.


Remove axes of length one from a.

std([axis, dtype, out, ddof, keepdims, where])

Returns the standard deviation of the array elements along given axis.

sum([axis, dtype, out, keepdims, initial, where])

Return the sum of the array elements over the given axis.

swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

take(indices[, axis, out, mode])

Return an array formed from the elements of a at the given indices.


Construct Python bytes containing the raw data bytes in the array.

tofile(fid[, sep, format])

Write array to a file as text or binary (default).


Return the array as an a.ndim-levels deep nested list of Python scalars.


A compatibility alias for tobytes, with exactly the same behavior.

trace([offset, axis1, axis2, dtype, out])

Return the sum along diagonals of the array.


Returns a view of the array with axes transposed.

var([axis, dtype, out, ddof, keepdims, where])

Returns the variance of the array elements, along given axis.

view([dtype][, type])

New view of array with the same data.


Collapse the array into a 2d array

Collapses the array into a 2d array, where the first dimension is the collapsed image dimensions and the second dimension is the spectral dimension.


The collapsed 2d numpy array.


>>> import numpy as np
>>> import hypers as hp
>>> data = np.random.rand(40, 30, 1000)
>>> x = hp.hparray(data)
>>> collapsed = x.collapse()
>>> collapsed.shape
(1200, 1000)
Return type


property nfeatures

Returns the number of features (size of the spectral dimension) in the dataset


Size of the spectral dimension

property nsamples

Returns the number of samples (total number of spatial pixels) in the dataset


Total number of samples

property nspatial

Returns the shape of the spatial dimensions


Tuple of the shape of the spatial dimensions


Interactive plotting to interact with hyperspectral data

Note that at the moment only the ‘pyqt’ backend has been implemented. This means that PyQt is required to be installed and when this method is called, a separate window generated by PyQt will pop up. It is still possible to use this in a Jupyter environment, however the cell that calls this method will remain frozen until the window is closed.

backend: str

Backend to use. Default is ‘pyqt’.

smoothen(method='savgol', **kwargs)

Returns smoothened hp.hparray

method: str

Method to use to smooth the array. Default is ‘savgol’. + ‘savgol’: Savitzky-Golay filter.


Keyword arguments for the relevant method used. + method=’savgol’

kwargs for the scipy.signal.savgol_filter implementation


The smoothened array with the same dimensions as the original array.

Return type