Warning
All the features described in this chapter are in beta state.
Although most of them work as described, their operation may not always be optimal, well-documented and/or consistent with their in-memory counterparts.
Therefore, although efforts will be taken to minimise major disruptions, the syntax and features described here may change in patch and minor HyperSpy releases. If you experience issues with HyperSpy's lazy features please report them to the developers.
.. versionadded:: 1.2
HyperSpy makes it possible to analyse data larger than the available memory by providing "lazy" versions of most of its signals and functions. In most cases the syntax remains the same. This chapter describes how to work with data larger than memory using the :py:class:`~._signals.lazy.LazySignal` class and its derivatives.
If the data is large and not loaded by HyperSpy (for example a hdf5.Dataset
or similar), first wrap it in dask.array.Array
as shown here and then pass it
as normal and call as_lazy()
:
>>> import h5py
>>> f = h5py.File("myfile.hdf5") # Load the file
>>> data = f['/data/path'] # Get the data
>>> import dask.array as da # Import dask to wrap
>>> chunks = (1000,100) # Chunk as appropriate
>>> x = da.from_array(data, chunks=chunks) # Wrap the data in dask
>>> s = hs.signals.Signal1D(x).as_lazy() # Create the lazy signal
To load the data lazily, pass the keyword lazy=True
. As an example,
loading a 34.9 GB .blo
file on a regular laptop might look like:
>>> s = hs.load("shish26.02-6.blo", lazy=True)
>>> s
<LazySignal2D, title: , dimensions: (400, 333|512, 512)>
>>> s.data
dask.array<array-e..., shape=(333, 400, 512, 512), dtype=uint8, chunksize=(20, 12, 512, 512)>
>>> print(s.data.dtype, s.data.nbytes / 1e9)
uint8 34.9175808
>>> s.change_dtype("float") # To be able to perform decomposition, etc.
>>> print(s.data.dtype, s.data.nbytes / 1e9)
float64 279.3406464
Loading the dataset in the original unsigned integer format would require around 35GB of memory. To store it in a floating-point format one would need almost 280GB of memory. However, with the lazy processing both of these steps are near-instantaneous and require very little computational resources.
.. versionadded:: 1.4 :py:meth:`~._signals.lazy.LazySignal.close_file`
Currently when loading an hdf5 file lazily the file remains open at
least while the signal exists. In order to close it explicitly, use the
:py:meth:`~._signals.lazy.LazySignal.close_file` method. Alternatively,
you could close it on calling :py:meth:`~._signals.lazy.LazySignal.compute`
by passing the keyword argument close_file=True
e.g.:
>>> s = hs.load("file.hspy", lazy=True)
>>> ssum = s.sum(axis=0)
>>> ssum.compute(close_file=True) # closes the file.hspy file
Occasionally the full dataset consists of many smaller files. To combine them
into a one large LazySignal
, we can :ref:`stack<signal.stack_split>` them
lazily (both when loading or afterwards):
>>> siglist = hs.load("*.hdf5")
>>> s = hs.stack(siglist, lazy=True)
>>> # Or load lazily and stack afterwards:
>>> siglist = hs.load("*.hdf5", lazy=True)
>>> s = hs.stack(siglist) # no need to pass 'lazy', as signals already lazy
>>> # Or do everything in one go:
>>> s = hs.load("*.hdf5", lazy=True, stack=True)
To convert a regular HyperSpy signal to a lazy one such that any future operations are only performed lazily, use the :py:meth:`~.signal.BaseSignal.as_lazy` method:
>>> s = hs.signals.Signal1D(np.arange(150.).reshape((3, 50)))
>>> s
<Signal1D, title: , dimensions: (3|50)>
>>> sl = s.as_lazy()
>>> sl
<LazySignal1D, title: , dimensions: (3|50)>
:ref:`mva.decomposition` algorithms for machine learning often perform large matrix manipulations, requiring significantly more memory than the data size. To perform decomposition operation lazily, HyperSpy provides access to several "online" algorithms as well as dask's lazy SVD algorithm. Online algorithms perform the decomposition by operating serially on chunks of data, enabling the lazy decomposition of large datasets. In line with the standard HyperSpy signals, lazy :py:meth:`~._signals.lazy.LazySignal.decomposition` offers the following online algorithms:
Available lazy decomposition algorithms in HyperSpyAlgorithm | Method |
---|---|
"SVD" (default) | :py:func:`dask.array.linalg.svd` |
"PCA" | :py:class:`sklearn.decomposition.IncrementalPCA` |
"ORPCA" | :py:class:`~.learn.rpca.ORPCA` |
"ORNMF" | :py:class:`~.learn.ornmf.ORNMF` |
.. seealso:: :py:meth:`~.learn.mva.MVA.decomposition` for more details on decomposition with non-lazy signals.
The default signal navigator is the sum of the signal across all signal dimensions and all but 1 or 2 navigation dimensions. If the dataset is large, this can take a significant amount of time to perform with every plot. By default, a navigator is computed with minimally required approach to obtain a good signal-to-noise ratio image: the sum is taken on a single chunk of the signal space, in order to avoid to compute the navigator for the whole dataset. In the following example, the signal space is divided in 25 chunks (5 along on each axis), and therefore computing the navigation will only be perfomed over a small subset of the whole dataset by taking the sum on only 1 chunk out of 25:
>>> import dask.array as da
>>> import hyperspy.api as hs
>>> data = da.random.random((100, 100, 1000, 1000), chunks=('auto', 'auto', 200, 200))
>>> s = hs.signals.Signal2D(data).as_lazy()
>>> s.plot()
In the example above, the calculation of the navigation is fast but the actual visualisation of the dataset is slow, each for each navigation index change, 25 chunks of the dataset needs to be fetched from the harddrive. In the following example, the signal space contains a single chunk (instead of 25, in the previous example) and the calculating the navigator will then be slower (~20x) because the whole dataset will need to processed, however in this case, the visualisation will be faster, because only a single chunk will fetched from the harddrive when changing navigation indices:
>>> data = da.random.random((100, 100, 1000, 1000), chunks=('auto', 'auto', 1000, 1000))
>>> s = hs.signals.Signal2D(data).as_lazy()
>>> s.plot()
This approach depends heavily on the chunking of the data and may not be always suitable. The :py:meth:`~hyperspy._signals.lazy.LazySignal.compute_navigator` can be used to calculate the navigator efficient and store the navigator, so that it can be used when plotting and saved for the later loading of the dataset. The :py:meth:`~hyperspy._signals.lazy.LazySignal.compute_navigator` has optional argument to specify the index where the sum needs to be calculated and how to rechunk the dataset when calculating the navigator. This allows to efficiently calculate the navigator without changing the actual chunking of the dataset, since the rechunking only takes during the computation of the navigator:
>>> data = da.random.random((100, 100, 1000, 1000), chunks=('auto', 'auto', 100, 100))
>>> s = hs.signals.Signal2D(data).as_lazy()
>>> s.compute_navigator(chunks_number=5)
>>> s.plot()
>>> data = da.random.random((100, 100, 2000, 400), chunks=('auto', 'auto', 100, 100))
>>> s = hs.signals.Signal2D(data).as_lazy()
>>> s
<LazySignal2D, title: , dimensions: (100, 100|400, 2000)>
>>> s.compute_navigator(chunks_number=(2, 10))
>>> s.plot()
>>> s.navigator.original_metadata
└── sum_from = [slice(200, 400, None), slice(1000, 1200, None)]
The index can also be specified following the :ref:`HyperSpy indexing signal1D <signal.indexing>` syntax for float and interger.
>>> data = da.random.random((100, 100, 2000, 400), chunks=('auto', 'auto', 100, 100))
>>> s = hs.signals.Signal2D(data).as_lazy()
>>> s
<LazySignal2D, title: , dimensions: (100, 100|400, 2000)>
>>> s.compute_navigator(index=0, chunks_number=(2, 10))
>>> s.navigator.original_metadata
└── sum_from = [slice(0, 200, None), slice(0, 200, None)]
An alternative is to calculate the navigator separately and store it in the signal using the :py:attr:`~hyperspy._signals.lazy.LazySignal.navigator` setter.
>>> data = da.random.random((100, 100, 1000, 1000), chunks=('auto', 'auto', 100, 100))
>>> s = hs.signals.Signal2D(data).as_lazy()
>>> s
<LazySignal2D, title: , dimensions: (100, 100|1000, 1000)>
>>> # for fastest results, just pick one signal space pixel
>>> nav = s.isig[500, 500]
>>> # Alternatively, sum as per default behaviour of non-lazy signal
>>> nav = s.sum(s.axes_manager.signal_axes)
>>> nav
<LazySignal2D, title: , dimensions: (|100, 100)>
>>> # Compute the result
>>> nav.compute()
[########################################] | 100% Completed | 13.1s
>>> s.navigator = nav
>>> s.plot()
Alternatively, it is possible to not have a navigator, and use sliders instead:
>>> s
<LazySignal2D, title: , dimensions: (200, 200|512, 512)>
>>> s.plot(navigator='slider')
.. versionadded:: 1.7
Lazy data processing on GPUs requires explicitly transferring the data to the GPU.
On linux, it is recommended to use the dask_cuda library (not supported on windows) to manage the dask scheduler. As for CPU lazy processing, if the dask scheduler is not specified, the default scheduler will be used.
>>> from dask_cuda import LocalCUDACluster
>>> from dask.distributed import Client
>>> cluster = LocalCUDACluster()
>>> client = Client(cluster)
>>> import hyperspy.api as hs
>>> import cupy as cp
>>> import dask.array as da
>>> # Create a dask array
>>> data = da.random.random(size=(20, 20, 100, 100))
>>> print(data)
... dask.array<random_sample, shape=(20, 20, 100, 100), dtype=float64,
... chunksize=(20, 20, 100, 100), chunktype=numpy.ndarray>
>>> # convert the dask chunks from numpy array to cupy array
>>> data = data.map_blocks(cp.asarray)
>>> print(data)
... dask.array<random_sample, shape=(20, 20, 100, 100), dtype=float64,
... chunksize=(20, 20, 100, 100), chunktype=cupy.ndarray>
>>> # Create the signal
>>> s = hs.signals.Signal2D(data).as_lazy()
Note
See the dask blog on Richardson Lucy (RL) deconvolution for an example of lazy processing on GPUs using dask and cupy
Most curve-fitting functionality will automatically work on models created from lazily loaded signals. HyperSpy extracts the relevant chunk from the signal and fits to that.
The linear 'lstsq'
optimizer supports fitting the entire dataset in a vectorised manner
using :py:func:`dask.array.linalg.lstsq`. This can give potentially enormous performance benefits over fitting
with a nonlinear optimizer, but comes with the restrictions explained in the :ref:`linear fitting<linear_fitting-label>` section.
Despite the limitations detailed below, most HyperSpy operations can be performed lazily. Important points are:
Data saved in the HDF5 format is typically divided into smaller chunks which can be loaded separately into memory,
allowing lazy loading. Chunk size can dramatically affect the speed of various HyperSpy algorithms, so chunk size is
worth careful consideration when saving a signal. HyperSpy's default chunking sizes are probably not optimal
for a given data analysis technique. For more comprehensible documentation on chunking,
see the dask array chunks and best practices docs. The chunks saved into HDF5 will
match the dask array chunks in s.data.chunks
when lazy loading.
Chunk shape should follow the axes order of the numpy shape (s.data.shape
), not the hyperspy shape.
The following example shows how to chunk one of the two navigation dimensions into smaller chunks:
>>> import dask.array as da
>>> data = da.random.random((10, 200, 300))
>>> data.chunksize
(10, 200, 300)
>>> s = hs.signals.Signal1D(data).as_lazy()
>>> s # Note the reversed order of navigation dimensions
<LazSignal1D, title: , dimensions: (200, 10|300)>
>>> s.save('chunked_signal.hspy', chunks=(10, 100, 300)) # Chunking first hyperspy dimension (second array dimension)
>>> s2 = hs.load('chunked_signal.hspy', lazy=True)
>>> s2.data.chunksize
(10, 100, 300)
To get the chunk size of given axes, the :py:meth:`~._signals.lazy.LazySignal.get_chunk_size` method can be used:
>>> import dask.array as da
>>> data = da.random.random((10, 200, 300))
>>> data.chunksize
(10, 200, 300)
>>> s = hs.signals.Signal1D(data).as_lazy()
>>> s.get_chunk_size() # All navigation axes
((10,), (200,))
>>> s.get_chunk_size(0) # The first navigation axis
((200,),)
.. versionadded:: 2.0.0
Starting in version 2.0.0 HyperSpy does not automatically rechunk datasets as
this can lead to reduced performance. The rechunk
or optimize
keyword argument
can be set to True
to let HyperSpy automatically change the chunking which
could potentially speed up operations.
.. versionadded:: 1.7.0
For more recent versions of dask (dask>2021.11) when using hyperspy in a jupyter notebook a helpful html representation is available.
>>> import numpy as np
>>> import hyperspy.api as hs
>>> data = np.zeros((20, 20, 10, 10, 10))
>>> s = hs.signals.Signal2D(data)
>>> s
This helps to visualize the chunk structure and identify axes where the chunk spans the entire axis (bolded axes).
Upon saving lazy signals, the result of computations is stored on disk.
In order to store the lazy signal in memory (i.e. make it a normal HyperSpy signal) it has a :py:meth:`~._signals.lazy.LazySignal.compute` method:
>>> s
<LazySignal2D, title: , dimensions: (|512, 512)>
>>> s.compute()
[########################################] | 100% Completed | 0.1s
>>> s
<Signal2D, title: , dimensions: (|512, 512)>
When using lazy signals the computation of the data is delayed until requested. However, the changes to the axes properties are performed when running a given function that modfies them i.e. they are not performed lazily. This can lead to hard to debug issues when the result of a given function that is computed lazily depends on the value of the axes parameters that may have changed before the computation is requested. Therefore, in order to avoid such issues, it is reccomended to explicitly compute the result of all functions that are affected by the axes parameters. This is the reason why e.g. the result of :py:meth:`~._signals.signal1d.Signal1D.shift1D` is not lazy.
Dask is a flexible library for parallel computing in Python. All of the lazy operations in hyperspy run through dask. Dask can be used to run computations on a single machine or scaled to a cluster. The following example shows how to use dask to run computations on a variety of different hardware:
The single threaded scheduler in dask is useful for debugging and testing. It is not recommended for general use.
>>> import dask
>>> import hyperspy.api as hs
>>> import numpy as np
>>> import dask.array as da
>>> # setting the scheduler to single-threaded globally
>>> dask.config.set(scheduler='single-threaded')
Alternatively, you can set the scheduler to single-threaded for a single function call by
setting the scheduler
keyword argument to 'single-threaded'
.
Or for something like plotting you can set the scheduler to single-threaded for the
duration of the plotting call by using the with dask.config.set
context manager.
>>> s.compute(scheduler="single-threaded") # uses single-threaded scheduler
>>> with dask.config.set(scheduler='single-threaded'):
>>> s.plot() # uses single-threaded scheduler to compute each chunk and then passes one chunk the memory
Dask has two schedulers available for single machines.
- Threaded Scheduler:
- Fastest to set up but only provides parallelism through threads so only non python functions will be parallelized. This is good if you have largely numpy code and not too many cores.
- Processes Scheduler:
- Each task (and all of the necessary dependencies) are shipped to different processes. As such it has a larger set up time. This preforms well for python dominated code.
>>> import dask
>>> dask.config.set(scheduler='processes') # overwrite default with multiprocessing scheduler
>>> # Any hyperspy code will now use the multiprocessing scheduler
>>> s.compute() # uses multiprocessing scheduler
>>> dask.config.set(scheduler='threads') # overwrite default with threading scheduler
>>> # Any hyperspy code will now use the threading scheduler
>>> s.compute() # uses threading scheduler
The recommended way to use dask is with the distributed scheduler. This allows you to scale your computations
to a cluster of machines. The distributed scheduler can be used on a single machine as well. dask-distributed
also gives you access to the dask dashboard which allows you to monitor your computations.
Some operations such as the matrix decomposition algorithms in hyperspy don't currently work with the distributed scheduler.
>>> from dask.distributed import Client
>>> from dask.distributed import LocalCluster
>>> import dask.array as da
>>> import hyperspy.api as hs
>>> cluster = LocalCluster()
>>> client = Client(cluster)
>>> client
>>> # Any calculation will now use the distributed scheduler
>>> s # lazy signal
>>> s.plot() # uses distributed scheduler to compute each chunk and then passes one chunk the memory
>>> s.compute() # uses distributed scheduler
Running computation on remote cluster can be done easily using dask_jobqueue
>>> from dask_jobqueue import SLURMCluster # or what ever scheduler you use
>>> from dask.distributed import Client
>>> cluster = SLURMCluster(cores=48,
memory='120Gb',
walltime="01:00:00",
queue='research')
>>> cluster.scale(jobs=3) # get 3 nodes
>>> client = Client(cluster)
>>> client
Any calculation will now use the distributed scheduler
>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> repeated_data = da.repeat(da.array(s.data[np.newaxis, :]),10, axis=0)
>>> s = hs.signals.Signal1D(repeated_data).as_lazy()
>>> summed = s.map(np.sum, inplace=False)
>>> s.compute()
Most operations can be performed lazily. However, lazy operations come with a few limitations and constraints that we detail below.
An important limitation when using LazySignal
is the inability to modify
existing data (immutability). This is a logical consequence of the DAG (tree
structure, explained in :ref:`lazy_details`), where a complete history of the
processing has to be stored to traverse later.
In fact, lazy evaluation removes the need for such operation, since only additional tree branches are added, requiring very little resources. In practical terms the following fails with lazy signals:
>>> s = hs.signals.BaseSignal([0]).as_lazy()
>>> s += 1
Traceback (most recent call last):
File "<ipython-input-6-1bd1db4187be>", line 1, in <module>
s += 1
File "<string>", line 2, in __iadd__
File "/home/fjd29/Python/hyperspy3/hyperspy/signal.py", line 1591, in _binary_operator_ruler
getattr(self.data, op_name)(other)
AttributeError: 'Array' object has no attribute '__iadd__'
However, when operating lazily there is no clear benefit to using in-place operations. So, the operation above could be rewritten as follows:
>>> s = hs.signals.BaseSignal([0]).as_lazy()
>>> s = s + 1
Or even better:
>>> s = hs.signals.BaseSignal([0]).as_lazy()
>>> s1 = s + 1
- Histograms for a
LazySignal
do not supportknuth
andblocks
binning algorithms. - CircleROI sets the elements outside the ROI to
np.nan
instead of using a masked array, becausedask
does not support masking. As a convenience,nansum
,nanmean
and othernan*
signal methods were added to mimic the workflow as closely as possible.
The most efficient format supported by HyperSpy to write data is the :external+rsciio:py:ref:`ZSpy format <zspy-format>`, mainly because it supports writing concurrently from multiple threads or processes. This also allows for smooth interaction with dask-distributed for efficient scaling.
Standard HyperSpy signals load the data into memory for fast access and processing. While this behaviour gives good performance in terms of speed, it obviously requires at least as much computer memory as the dataset, and often twice that to store the results of subsequent computations. This can become a significant problem when processing very large datasets on consumer-oriented hardware.
HyperSpy offers a solution for this problem by including :py:class:`~._signals.lazy.LazySignal` and its derivatives. The main idea of these classes is to perform any operation (as the name suggests) lazily (delaying the execution until the result is requested (e.g. saved, plotted)) and in a blocked fashion. This is achieved by building a "history tree" (formally called a Directed Acyclic Graph (DAG)) of the computations, where the original data is at the root, and any further operations branch from it. Only when a certain branch result is requested, the way to the root is found and evaluated in the correct sequence on the correct blocks.
The "magic" is performed by (for the sake of simplicity) storing the data not
as numpy.ndarray
, but dask.array.Array
(see the
dask documentation). dask
offers a couple of advantages:
- Arbitrary-sized data processing is possible. By only loading a couple of chunks at a time, theoretically any signal can be processed, albeit slower. In practice, this may be limited: (i) some operations may require certain chunking pattern, which may still saturate memory; (ii) many chunks should fit into the computer memory comfortably at the same time.
- Loading only the required data. If a certain part (chunk) of the data is not required for the final result, it will not be loaded at all, saving time and resources.
- Able to extend to a distributed computing environment (clusters).
:py:
dask.distributed
(see the dask documentation) offers a straightforward way to expand the effective memory for computations to that of a cluster, which allows performing the operations significantly faster than on a single machine.