Images#
- class abtem.measurements.Images(array, sampling, ensemble_axes_metadata=None, metadata=None)[source]#
Bases:
_BaseMeasurement2D
A collection of 2D measurements such as HRTEM or STEM-ADF images. May be used to represent a reconstructed phase.
- Parameters:
array (np.ndarray) – 2D or greater array containing data of type float or ´complex´. The second-to-last and last dimensions are the image y- and x-axis, respectively.
sampling (two float) – Lateral sampling of images in x and y [Å].
ensemble_axes_metadata (list of AxisMetadata, optional) – List of metadata associated with the ensemble axes. The length and item order must match the ensemble axes.
metadata (dict, optional) – A dictionary defining measurement metadata.
Methods
__init__
(array, sampling[, ...])abs
()Calculates the absolute value of a complex-valued measurement.
apply_func
(func, **kwargs)- rtype:
TypeVar
(T
, bound= ArrayObject)
apply_transform
(transform[, max_batch])Transform the wave functions by a given transformation.
compute
([progress_bar, profiler, ...])Turn a lazy abTEM object into its in-memory equivalent.
copy
()Make a copy.
copy_to_device
(device)Copy array to specified device.
crop
(extent[, offset])Crop images to a smaller extent.
Calculate diffractograms (i.e. power spectra) from image(s).
ensemble_blocks
([chunks])Split the ensemble into an array of smaller ensembles.
ensure_lazy
([chunks])Creates an equivalent lazy version of the array object.
expand_dims
([axis, axis_metadata])Expand the shape of the array object.
from_array_and_metadata
(array, axes_metadata)Creates an image from a given array and metadata.
from_zarr
(url[, chunks])Read wave functions from a hdf5 file.
gaussian_filter
(sigma[, boundary, cval])Apply 2D gaussian filter to measurements.
generate_blocks
([chunks])Generate chunks of the ensemble.
generate_ensemble
([keepdims])Generate every member of the ensemble.
get_from_metadata
(name[, broadcastable])get_items
(items[, keepdims])Index the array and the corresponding axes metadata.
imag
()Returns the imaginary part of a complex-valued measurement.
Calculate integrated gradients.
Calculates the squared norm of a complex-valued measurement.
interpolate
([sampling, gpts, method, ...])Interpolate images producing equivalent images with a different sampling.
interpolate_line
([start, end, sampling, ...])Interpolate image(s) along a given line.
interpolate_line_at_position
(center, angle, ...)Interpolate image(s) along a line centered at a specified position.
lazy
([chunks])- rtype:
TypeVar
(T
, bound= ArrayObject)
max
([axis, keepdims, split_every])Maximum of array object over one or more axes.
mean
([axis, keepdims, split_every])Mean of array object over one or more axes.
min
([axis, keepdims, split_every])Minmimum of array object over one or more axes.
Rechunk to remove chunks across the base dimensions.
normalize_ensemble
([scale, shift])Normalize the ensemble by shifting ad scaling each member.
phase
()Calculates the phase of a complex-valued measurement.
poisson_noise
([dose_per_area, total_dose, ...])Add Poisson noise (i.e. shot noise) to a measurement corresponding to the provided 'total_dose' (per measurement if applied to an ensemble) or 'dose_per_area' (not applicable for single measurements).
real
()Returns the real part of a complex-valued measurement.
rechunk
(chunks, **kwargs)Rechunk dask array.
Calculates the mean of an ensemble measurement (e.g. of frozen phonon configurations).
relative_difference
(other[, min_relative_tol])Calculates the relative difference with respect to another compatible measurement.
scan_noise
(dwell_time, flyback_time, rms_power)Apply scan noise to images.
select_block
(index, chunks)Select a block from the ensemble.
set_ensemble_axes_metadata
(axes_metadata, axis)Sets the axes metadata of an ensemble axis.
show
([ax, cbar, cmap, vmin, vmax, power, ...])Show the image(s) using matplotlib.
squeeze
([axis])Remove axes of length one from array object.
std
([axis, keepdims, split_every])Standard deviation of array object over one or more axes.
sum
([axis, keepdims, split_every])Sum of array object over one or more axes.
tile
(repetitions)Tile image(s).
to_cpu
()Move the array to the host memory from an arbitrary source array.
Convert ArrayObject to a xarray DataArray.
to_gpu
([device])Move the array from the host memory to a gpu.
Convert ArrayObject to a Hyperspy signal.
to_measurement_ensemble
()to_tiff
(filename, **kwargs)Write data to a tiff file.
to_zarr
(url[, compute, overwrite])Write data to a zarr file.
Attributes
Underlying array describing the array object.
List of AxisMetadata.
List of AxisMetadata of the base axes.
Number of base dimensions.
Shape of the base axes of the underlying array.
Coordinates of pixels in x and y [Å].
The device where the array is stored.
Datatype of array.
List of AxisMetadata of the ensemble axes.
Number of ensemble dimensions.
Shape of the ensemble axes of the underlying array.
Extent of measurements in x and y [Å] or [1/Å].
True if array is complex.
True if array is lazy.
Metadata describing the measurement.
The offset of the origin of the measurement coordinates [Å] or [1/Å].
Sampling of the measurements in x and y [Å] or [1/Å].
Shape of the underlying array.
- abs()#
Calculates the absolute value of a complex-valued measurement.
- Return type:
TypeVar
(T
, bound= BaseMeasurements)
- apply_transform(transform, max_batch='auto')#
Transform the wave functions by a given transformation.
- Parameters:
transform (ArrayObjectTransform) – The array object transformation to apply.
max_batch (int, optional) – The number of wave functions in each chunk of the Dask array. If ‘auto’ (default), the batch size is automatically chosen based on the abtem user configuration settings “dask.chunk-size” and “dask.chunk-size-gpu”.
- Returns:
transformed_array_object – The transformed array object.
- Return type:
- property array: ndarray | Array#
Underlying array describing the array object.
- property axes_metadata: AxesMetadataList#
List of AxisMetadata.
- property base_axes_metadata: list[AxisMetadata]#
List of AxisMetadata of the base axes.
- property base_dims#
Number of base dimensions.
- property base_shape: tuple[int, ...]#
Shape of the base axes of the underlying array.
- compute(progress_bar=None, profiler=False, resource_profiler=False, **kwargs)#
Turn a lazy abTEM object into its in-memory equivalent.
- Parameters:
progress_bar (bool) – Display a progress bar in the terminal or notebook during computation. The progress bar is only displayed with a local scheduler.
profiler (bool) – Return Profiler class used to profile Dask’s execution at the task level. Only execution with a local is profiled.
resource_profiler (bool) – Return ResourceProfiler class is used to profile Dask’s execution at the resource level.
kwargs – Additional keyword arguments passes to dask.compute.
- property coordinates: tuple[ndarray, ndarray]#
Coordinates of pixels in x and y [Å].
- copy()#
Make a copy.
- copy_to_device(device)#
Copy array to specified device.
- Parameters:
device (str) –
- Returns:
object_on_device
- Return type:
T
- crop(extent, offset=(0.0, 0.0))[source]#
Crop images to a smaller extent.
- Parameters:
extent (tuple of float) – Extent of rectangular cropping region in x and y [Å].
offset (tuple of float) – Lower corner of cropping region in x and y [Å] (default is (0,0)).
- Returns:
cropped_images – The cropped images.
- Return type:
- property device: str#
The device where the array is stored.
- diffractograms()[source]#
Calculate diffractograms (i.e. power spectra) from image(s).
- Returns:
diffractograms – Diffractograms of image(s).
- Return type:
- property dtype: base#
Datatype of array.
- property ensemble_axes_metadata#
List of AxisMetadata of the ensemble axes.
- ensemble_blocks(chunks=None)#
Split the ensemble into an array of smaller ensembles.
- Parameters:
chunks (iterable of tuples) – Block sizes along each dimension.
- Return type:
Array
- property ensemble_dims#
Number of ensemble dimensions.
- property ensemble_shape: tuple[int, ...]#
Shape of the ensemble axes of the underlying array.
- ensure_lazy(chunks='auto')#
Creates an equivalent lazy version of the array object.
- Parameters:
chunks (int or tuple or str) – How to chunk the array. See dask.array.from_array.
- Returns:
lazy_array_object – Lazy version of the array object.
- Return type:
ArrayObject or subclass of ArrayObject
- expand_dims(axis=None, axis_metadata=None)#
Expand the shape of the array object.
- Parameters:
axis (int or tuple of ints) – Position in the expanded axes where the new axis (or axes) is placed.
axis_metadata (AxisMetadata or List of AxisMetadata, optional) – The axis metadata describing the expanded axes. Default is UnknownAxis.
- Returns:
expanded – View of array object with the number of dimensions increased.
- Return type:
ArrayObject or subclass of ArrayObject
- property extent: tuple[float, float]#
Extent of measurements in x and y [Å] or [1/Å].
- classmethod from_array_and_metadata(array, axes_metadata, metadata=None)[source]#
Creates an image from a given array and metadata.
- Parameters:
array (array) – Complex array defining one or more 2D wave functions. The second-to-last and last dimensions are the y- and x-axis.
axes_metadata (list of AxesMetadata) – Axis metadata for each axis. The axis metadata must be compatible with the shape of the array. The last two axes must be RealSpaceAxis.
metadata (dict) – A dictionary defining the measurement metadata.
- Returns:
images – Images from the array and metadata.
- Return type:
- classmethod from_zarr(url, chunks='auto')#
Read wave functions from a hdf5 file.
- Return type:
TypeVar
(T
, bound= ArrayObject)
- urlstr
Location of the data, typically a path to a local file. A URL can also include a protocol specifier like s3:// for remote data.
- chunkstuple of ints or tuples of ints
Passed to dask.array.from_array(), allows setting the chunks on initialisation, if the chunking scheme in the on-disc dataset is not optimal for the calculations to follow.
- gaussian_filter(sigma, boundary='periodic', cval=0.0)#
Apply 2D gaussian filter to measurements.
- Parameters:
sigma (float or two float) – Standard deviation for the Gaussian kernel in the x and y-direction. If given as a single number, the standard deviation is equal for both axes.
boundary ({'periodic', 'reflect', 'constant'}) –
The boundary parameter determines how the images are extended beyond their boundaries when the filter overlaps with a border.
periodic
:The images are extended by wrapping around to the opposite edge. Use this mode for periodic (default).
reflect
:The images are extended by reflecting about the edge of the last pixel.
constant
:The images are extended by filling all values beyond the edge with the same constant value, defined by the ‘cval’ parameter.
cval (scalar, optional) – Value to fill past edges in spline interpolation input if boundary is ‘constant’ (default is 0.0).
- Returns:
filtered_images – The filtered image(s).
- Return type:
- generate_blocks(chunks=1)#
Generate chunks of the ensemble.
- Parameters:
chunks (iterable of tuples) – Block sizes along each dimension.
- generate_ensemble(keepdims=False)#
Generate every member of the ensemble.
- Parameters:
keepdims (bool, opptional) – If True, all ensemble axes are left in the result as dimensions with size one. Default is False.
- Yields:
ArrayObject or subclass of ArrayObject – Member of the ensemble.
- get_items(items, keepdims=False)#
Index the array and the corresponding axes metadata. Only ensemble axes can be indexed.
- Parameters:
items (int or tuple of int or slice) – The array is indexed according to this.
keepdims (bool, optional) – If True, all ensemble axes are left in the result as dimensions with size one. Default is False.
- Returns:
indexed_array – The indexed array object.
- Return type:
ArrayObject or subclass of ArrayObject
- imag()#
Returns the imaginary part of a complex-valued measurement.
- Return type:
TypeVar
(T
, bound= BaseMeasurements)
- integrate_gradient()[source]#
Calculate integrated gradients. Requires complex images whose real and imaginary parts represent the x and y components of a gradient.
- Returns:
integrated_gradient – The integrated gradient.
- Return type:
- intensity()#
Calculates the squared norm of a complex-valued measurement.
- Return type:
TypeVar
(T
, bound= BaseMeasurements)
- interpolate(sampling=None, gpts=None, method='fft', boundary='periodic', order=3, normalization='values', cval=0.0)[source]#
Interpolate images producing equivalent images with a different sampling. Either ‘sampling’ or ‘gpts’ must be provided (but not both).
- Parameters:
sampling (float or two float) – Sampling of images after interpolation in x and y [Å].
gpts (int or two int) – Number of grid points of images after interpolation in x and y. Do not use if ‘sampling’ is used.
method ({'fft', 'spline'}) –
The interpolation method.
fft
:Interpolate by cropping or zero-padding in reciprocal space. This method should be preferred for periodic images.
spline
:Interpolate using spline interpolation. This method should be preferred for non-periodic images.
boundary ({'periodic', 'reflect', 'constant'}) –
The boundary parameter determines how the input array is extended beyond its boundaries for spline interpolation.
periodic
:The images are extended by wrapping around to the opposite edge. Use this mode for periodic images (default).
reflect
:The images are extended by reflecting about the edge of the last pixel.
constant
:The images are extended by filling all values beyond the edge with the same constant value, defined by the ‘cval’ parameter.
order (int) – The order of the spline interpolation (default is 3). The order has to be in the range 0-5.
normalization ({'values', 'amplitude'}) –
The normalization parameter determines which quantity is preserved after normalization.
values
:The pixel-wise values of the images are preserved.
intensity
:The total intensity of the images is preserved.
cval (scalar, optional) – Value to fill past edges in spline interpolation input if boundary is ‘constant’ (default is 0.0).
- Returns:
interpolated_images – The interpolated images.
- Return type:
- interpolate_line(start=None, end=None, sampling=None, gpts=None, width=0.0, margin=0.0, order=3, endpoint=False, fractional=False)#
Interpolate image(s) along a given line. Either ‘sampling’ or ‘gpts’ must be provided.
- Parameters:
start (two float, Atom, optional) – Starting position of the line [Å] (alternatively taken from a selected atom).
end (two float, Atom, optional) – Ending position of the line [Å] (alternatively taken from a selected atom).
sampling (float) – Sampling of grid points along the line [1 / Å].
gpts (int) – Number of grid points along the line.
width (float, optional) – The interpolation will be averaged across a perpendicular distance equal to this width.
margin (float or tuple of float, optional) – Add margin [Å] to the start and end interpolated line.
order (int, optional) – The spline interpolation order.
endpoint (bool) – Sets whether the ending position is included or not.
fractional (bool) – If True, use fractional coordinates with respect to the extent of the measurement.
- Returns:
line_profiles – The interpolated line(s).
- Return type:
- interpolate_line_at_position(center, angle, extent, gpts=None, sampling=None, width=0.0, order=3, endpoint=True)#
Interpolate image(s) along a line centered at a specified position.
- Parameters:
center (two float) – Center position of the line [Å]. May be given as an Atom.
angle (float) – Angle of the line [deg.].
extent (float) – Extent of the line [Å].
gpts (int) – Number of grid points along the line.
sampling (float) – Sampling of grid points along the line [Å].
width (float, optional) – The interpolation will be averaged across a perpendicular distance equal to this width.
order (int, optional) – The spline interpolation order.
endpoint (bool) – Sets whether the ending position is included or not.
- Returns:
line_profiles – The interpolated line(s).
- Return type:
RealSpaceLineProfiles or ReciprocalSpaceProfiles
- property is_complex: bool#
True if array is complex.
- property is_lazy: bool#
True if array is lazy.
- max(axis=None, keepdims=False, split_every=2)#
Maximum of array object over one or more axes. Only ensemble axes can be reduced.
- Parameters:
axis (int or tuple of ints, optional) – Axis or axes along which a maxima are calculated. The default is to compute the mean of the flattened array. If this is a tuple of ints, the maxima are calculated over multiple axes. The indicated axes must be ensemble axes.
keepdims (bool, optional) – If True, the reduced axes are left in the result as dimensions with size one. Default is False.
split_every (int) – Only used for lazy arrays. See dask.array.reductions.
- Returns:
reduced_array – The reduced array object.
- Return type:
ArrayObject or subclass of ArrayObject
- mean(axis=None, keepdims=False, split_every=2)#
Mean of array object over one or more axes. Only ensemble axes can be reduced.
- Parameters:
axis (int or tuple of ints, optional) – Axis or axes along which a means are calculated. The default is to compute the mean of the flattened array. If this is a tuple of ints, the mean is calculated over multiple axes. The indicated axes must be ensemble axes.
keepdims (bool, optional) – If True, the reduced axes are left in the result as dimensions with size one. Default is False.
split_every (int) – Only used for lazy arrays. See dask.array.reductions.
- Returns:
reduced_array – The reduced array object.
- Return type:
ArrayObject or subclass of ArrayObject
- property metadata: dict#
Metadata describing the measurement.
- min(axis=None, keepdims=False, split_every=2)#
Minmimum of array object over one or more axes. Only ensemble axes can be reduced.
- Parameters:
axis (int or tuple of ints, optional) – Axis or axes along which a minima are calculated. The default is to compute the mean of the flattened array. If this is a tuple of ints, the minima are calculated over multiple axes. The indicated axes must be ensemble axes.
keepdims (bool, optional) – If True, the reduced axes are left in the result as dimensions with size one. Default is False.
split_every (int) – Only used for lazy arrays. See dask.array.reductions.
- Returns:
reduced_array – The reduced array object.
- Return type:
ArrayObject or subclass of ArrayObject
- no_base_chunks()#
Rechunk to remove chunks across the base dimensions.
- normalize_ensemble(scale='max', shift='mean')#
Normalize the ensemble by shifting ad scaling each member.
- Parameters:
scale ({'max', 'min', 'sum', 'mean', 'ptp'}) –
shift ({'max', 'min', 'sum', 'mean', 'ptp'}) –
- Returns:
normalized_measurements
- Return type:
BaseMeasurements or subclass of _BaseMeasurement
- property offset: tuple[float, float]#
The offset of the origin of the measurement coordinates [Å] or [1/Å].
- phase()#
Calculates the phase of a complex-valued measurement.
- Return type:
TypeVar
(T
, bound= BaseMeasurements)
- poisson_noise(dose_per_area=None, total_dose=None, samples=1, seed=None)#
Add Poisson noise (i.e. shot noise) to a measurement corresponding to the provided ‘total_dose’ (per measurement if applied to an ensemble) or ‘dose_per_area’ (not applicable for single measurements).
- Parameters:
dose_per_area (float, optional) – The irradiation dose [electrons per Å:sup:2].
total_dose (float, optional) – The irradiation dose per diffraction pattern.
samples (int, optional) – The number of samples to draw from a Poisson distribution. If this is greater than 1, an additional ensemble axis will be added to the measurement.
seed (int, optional) – Seed the random number generator.
- Returns:
noisy_measurement – The noisy measurement.
- Return type:
BaseMeasurements or subclass of _BaseMeasurement
- real()#
Returns the real part of a complex-valued measurement.
- Return type:
TypeVar
(T
, bound= BaseMeasurements)
- rechunk(chunks, **kwargs)#
Rechunk dask array.
- chunksint or tuple or str
How to rechunk the array. See dask.array.rechunk.
- kwargs :
Additional keyword arguments passes to dask.array.rechunk.
- reduce_ensemble()#
Calculates the mean of an ensemble measurement (e.g. of frozen phonon configurations).
- Return type:
TypeVar
(T
, bound= BaseMeasurements)
- relative_difference(other, min_relative_tol=0.0)#
Calculates the relative difference with respect to another compatible measurement.
- Parameters:
other (BaseMeasurements) – Measurement to which the difference is calculated.
min_relative_tol (float) – Avoids division by zero errors by defining a minimum value of the divisor in the relative difference.
- Returns:
difference – The relative difference as a measurement of the same type.
- Return type:
- property sampling: tuple[float, float]#
Sampling of the measurements in x and y [Å] or [1/Å].
- scan_noise(dwell_time, flyback_time, rms_power, max_frequency=500.0, num_components=200, seed=None)[source]#
Apply scan noise to images.
- Parameters:
dwell_time (float) – Dwell time of the beam [s].
flyback_time (float) – Flyback time of the beam [s].
rms_power (float) – RMS power of the scan noise [V].
max_frequency (float) – Maximum frequency of the scan noise [1/Å].
- select_block(index, chunks)#
Select a block from the ensemble.
- Parameters:
index (tuple of ints) – Index of selected block.
chunks (iterable of tuples) – Block sizes along each dimension.
- set_ensemble_axes_metadata(axes_metadata, axis)#
Sets the axes metadata of an ensemble axis.
- Parameters:
axes_metadata (AxisMetadata) – The new axis metadata.
axis (int) – The axis to set.
- property shape: tuple[int, ...]#
Shape of the underlying array.
- show(ax=None, cbar=False, cmap=None, vmin=None, vmax=None, power=1.0, common_color_scale=False, explode=(), overlay=(), figsize=None, title=True, units=None, interact=False, display=True, **kwargs)#
Show the image(s) using matplotlib.
- Parameters:
ax (matplotlib.axes.Axes, optional) – If given the plots are added to the axis. This is not available for exploded plots.
cbar (bool, optional) – Add colorbar(s) to the image(s). The size and padding of the colorbars may be adjusted using the set_cbar_size and set_cbar_padding methods.
cmap (str, optional) – Matplotlib colormap name used to map scalar data to colors. If the measurement is complex the colormap must be one of ‘hsv’ or ‘hsluv’.
vmin (float, optional) – Minimum of the intensity color scale. Default is the minimum of the array values.
vmax (float, optional) – Maximum of the intensity color scale. Default is the maximum of the array values.
power (float) – Show image on a power scale.
common_color_scale (bool, optional) – If True all images in an image grid are shown on the same colorscale, and a single colorbar is created (if it is requested). Default is False.
explode (bool, optional) – If True, a grid of images is created for all the items of the last two ensemble axes. If False, the first ensemble item is shown. May be given as a sequence of axis indices to create a grid of images from the specified axes. The default is determined by the axis metadata.
figsize (two int, optional) – The figure size given as width and height in inches, passed to matplotlib.pyplot.figure.
title (bool or str, optional) – Set the column title of the images. If True is given instead of a string the title will be given by the value corresponding to the “name” key of the axes metadata dictionary, if this item exists.
units (str) – The units used for the x and y axes. The given units must be compatible with the axes of the images.
interact (bool) – If True, create an interactive visualization. This requires enabling the ipympl Matplotlib backend.
display (bool, optional) – If True (default) the figure is displayed immediately.
- Returns:
measurement_visualization_2d
- Return type:
VisualizationImshow
- squeeze(axis=None)#
Remove axes of length one from array object.
- Parameters:
axis (int or tuple of ints, optional) – Selects a subset of the entries of length one in the shape.
- Returns:
squeezed – The input array object, but with all or a subset of the dimensions of length 1 removed.
- Return type:
ArrayObject or subclass of ArrayObject
- std(axis=None, keepdims=False, split_every=2)#
Standard deviation of array object over one or more axes. Only ensemble axes can be reduced.
- Parameters:
axis (int or tuple of ints, optional) – Axis or axes along which a standard deviations are calculated. The default is to compute the mean of the flattened array. If this is a tuple of ints, the standard deviations are calculated over multiple axes. The indicated axes must be ensemble axes.
keepdims (bool, optional) – If True, the reduced axes are left in the result as dimensions with size one. Default is False.
split_every (int) – Only used for lazy arrays. See dask.array.reductions.
- Returns:
reduced_array – The reduced array object.
- Return type:
ArrayObject or subclass of ArrayObject
- sum(axis=None, keepdims=False, split_every=2)#
Sum of array object over one or more axes. Only ensemble axes can be reduced.
- Parameters:
axis (int or tuple of ints, optional) – Axis or axes along which a sums are performed. The default is to compute the mean of the flattened array. If this is a tuple of ints, the sum is performed over multiple axes. The indicated axes must be ensemble axes.
keepdims (bool, optional) – If True, the reduced axes are left in the result as dimensions with size one. Default is False.
split_every (int) – Only used for lazy arrays. See dask.array.reductions.
- Returns:
reduced_array – The reduced array object.
- Return type:
ArrayObject or subclass of ArrayObject
- tile(repetitions)[source]#
Tile image(s).
- Parameters:
repetitions (tuple of int) – The number of repetitions of the images along the x- and y-axis, respectively.
- Returns:
tiled_images – The tiled image(s).
- Return type:
- to_cpu()#
Move the array to the host memory from an arbitrary source array.
- Return type:
TypeVar
(T
, bound= ArrayObject)
- to_data_array()#
Convert ArrayObject to a xarray DataArray.
- to_gpu(device='gpu')#
Move the array from the host memory to a gpu.
- Return type:
TypeVar
(T
, bound= ArrayObject)
- to_hyperspy()#
Convert ArrayObject to a Hyperspy signal.
- to_tiff(filename, **kwargs)#
Write data to a tiff file.
- Parameters:
filename (str) – The filename of the file to write.
kwargs – Keyword arguments passed to tifffile.imwrite.
- to_zarr(url, compute=True, overwrite=False, **kwargs)#
Write data to a zarr file.
- Parameters:
url (str) – Location of the data, typically a path to a local file. A URL can also include a protocol specifier like s3:// for remote data.
compute (bool) – If true compute immediately; return dask.delayed.Delayed otherwise.
overwrite (bool) – If given array already exists, overwrite=False will cause an error, where overwrite=True will replace the existing data.
kwargs – Keyword arguments passed to dask.array.to_zarr.