Proxy objects (`cdl.proxy`)#

The cdl.proxy module provides a way to access DataLab features from a proxy class.

The list of compute methods accessible from the proxy objects is available in the Calling processor methods using proxy objects section.

Remote proxy#

The remote proxy is used when DataLab is started from a different process than the proxy. In this case, the proxy connects to DataLab XML-RPC server.

class cdl.proxy.RemoteProxy(autoconnect: bool = True)[source]#

DataLab remote proxy class.

This class provides access to DataLab features from a proxy class. This is the remote version of proxy, which is used when DataLab is started from a different process than the proxy.

Parameters:

autoconnect (bool) – Automatically connect to DataLab XML-RPC server.

Raises:

ConnectionRefusedError – Unable to connect to DataLab
ValueError – Invalid timeout (must be >= 0.0)
ValueError – Invalid number of retries (must be >= 1)

Note

The proxy object also allows to access DataLab computing methods exposed by the processor classes (see Calling processor methods using proxy objects).

Examples

Here is a simple example of how to use RemoteProxy in a Python script or in a Jupyter notebook:

>>> from cdl.proxy import RemoteProxy
>>> proxy = RemoteProxy()
Connecting to DataLab XML-RPC server...OK (port: 28867)
>>> proxy.get_version()
'1.0.0'
>>> proxy.add_signal("toto", np.array([1., 2., 3.]), np.array([4., 5., -1.]))
True
>>> proxy.get_object_titles()
['toto']
>>> proxy["toto"]  # from title
<cdl.core.model.signal.SignalObj at 0x7f7f1c0b4a90>
>>> proxy[1]  # from number
<cdl.core.model.signal.SignalObj at 0x7f7f1c0b4a90>
>>> proxy[1].data
array([1., 2., 3.])
>>> proxy.set_current_panel("image")

add_annotations_from_items(items: list, refresh_plot: bool = True, panel: str | None = None) → None#

Add object annotations (annotation plot items).

Parameters:

items (list) – annotation plot items
refresh_plot (bool | None) – refresh plot. Defaults to True.
panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used.

Add image data to DataLab.

Parameters:

title (str) – Image title
data (numpy.ndarray) – Image data
xunit (str | None) – X unit. Defaults to None.
yunit (str | None) – Y unit. Defaults to None.
zunit (str | None) – Z unit. Defaults to None.
xlabel (str | None) – X label. Defaults to None.
ylabel (str | None) – Y label. Defaults to None.
zlabel (str | None) – Z label. Defaults to None.

Returns:

True if image was added successfully, False otherwise

Return type:

bool

Raises:

ValueError – Invalid data dtype

add_label_with_title(title: str | None = None, panel: str | None = None) → None#

Add a label with object title on the associated plot

Parameters:

title (str | None) – Label title. Defaults to None. If None, the title is the object title.
panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used.

add_object(obj: SignalObj | ImageObj) → None#

Add object to DataLab.

Parameters:: obj (SignalObj | ImageObj) – Signal or image object

add_signal(title: str, xdata: ndarray, ydata: ndarray, xunit: str | None = None, yunit: str | None = None, xlabel: str | None = None, ylabel: str | None = None) → bool#

Add signal data to DataLab.

Parameters:

title (str) – Signal title
xdata (numpy.ndarray) – X data
ydata (numpy.ndarray) – Y data
xunit (str | None) – X unit. Defaults to None.
yunit (str | None) – Y unit. Defaults to None.
xlabel (str | None) – X label. Defaults to None.
ylabel (str | None) – Y label. Defaults to None.

Returns:

True if signal was added successfully, False otherwise

Return type:

bool

Raises:

ValueError – Invalid xdata dtype
ValueError – Invalid ydata dtype

calc(name: str, param: DataSet | None = None) → None#

Call compute function name in current panel’s processor.

Parameters:

name – Compute function name
param – Compute function parameter. Defaults to None.

Raises:

ValueError – unknown function

close_application() → None#: Close DataLab application

connect(port: str | None = None, timeout: float | None = None, retries: int | None = None) → None#

Try to connect to DataLab XML-RPC server.

Parameters:

port (str | None) – XML-RPC port to connect to. If not specified, the port is automatically retrieved from DataLab configuration.
timeout (float | None) – Timeout in seconds. Defaults to 5.0.
retries (int | None) – Number of retries. Defaults to 10.

Raises:

ConnectionRefusedError – Unable to connect to DataLab
ValueError – Invalid timeout (must be >= 0.0)
ValueError – Invalid number of retries (must be >= 1)

context_no_refresh() → Generator[None, None, None]#

Return a context manager to temporarily disable auto refresh.

Returns:: Context manager

Example

>>> with proxy.context_no_refresh():
...     proxy.add_image("image1", data1)
...     proxy.compute_fft()
...     proxy.compute_wiener()
...     proxy.compute_ifft()
...     # Auto refresh is disabled during the above operations

delete_metadata(refresh_plot: bool = True, keep_roi: bool = False) → None#

Delete metadata of selected objects

Parameters:

refresh_plot – Refresh plot. Defaults to True.
keep_roi – Keep ROI. Defaults to False.

disconnect() → None#: Disconnect from DataLab XML-RPC server.

get_current_panel() → str#

Return current panel name.

Returns:: Panel name (valid values: “signal”, “image”, “macro”))
Return type:: str

get_group_titles_with_object_infos() → tuple[list[str], list[list[str]], list[list[str]]]#

Return groups titles and lists of inner objects uuids and titles.

Returns:: groups titles, lists of inner objects uuids and titles
Return type:: Tuple

get_method_list() → list[str]#: Return list of available methods.

get_object(nb_id_title: int | str | None = None, panel: str | None = None) → SignalObj | ImageObj#

Get object (signal/image) from index.

Parameters:

nb_id_title – Object number, or object id, or object title. Defaults to None (current object).
panel – Panel name. Defaults to None (current panel).

Returns:

Object

Raises:

KeyError – if object not found

get_object_shapes(nb_id_title: int | str | None = None, panel: str | None = None) → list#

Get plot item shapes associated to object (signal/image).

Parameters:

nb_id_title – Object number, or object id, or object title. Defaults to None (current object).
panel – Panel name. Defaults to None (current panel).

Returns:

List of plot item shapes

get_object_titles(panel: str | None = None) → list[str]#

Get object (signal/image) list for current panel. Objects are sorted by group number and object index in group.

Parameters:: panel – panel name (valid values: “signal”, “image”, “macro”). If None, current data panel is used (i.e. signal or image panel).
Returns:: List of object titles
Raises:: ValueError – if panel not found

get_object_uuids(panel: str | None = None) → list[str]#

Get object (signal/image) uuid list for current panel. Objects are sorted by group number and object index in group.

Parameters:: panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used.
Returns:: list of object uuids
Return type:: list[str]
Raises:: ValueError – if panel not found

classmethod get_public_methods() → list[str]#

Return all public methods of the class, except itself.

Returns:: List of public methods
Return type:: list[str]

get_sel_object_uuids(include_groups: bool = False) → list[str]#

Return selected objects uuids.

Parameters:: include_groups – If True, also return objects from selected groups.
Returns:: List of selected objects uuids.

get_version() → str#

Return DataLab public version.

Returns:: DataLab version
Return type:: str

import_h5_file(filename: str, reset_all: bool | None = None) → None#

Open DataLab HDF5 browser to Import HDF5 file.

Parameters:

filename (str) – HDF5 file name
reset_all (bool | None) – Reset all application data. Defaults to None.

import_macro_from_file(filename: str) → None#

Import macro from file

Parameters:: filename – Filename.

is_connected() → bool#: Return True if connected to DataLab XML-RPC server.

load_from_files(filenames: list[str]) → None#

Open objects from files in current panel (signals/images).

Parameters:: filenames – list of file names

open_h5_files(h5files: list[str] | None = None, import_all: bool | None = None, reset_all: bool | None = None) → None#

Open a DataLab HDF5 file or import from any other HDF5 file.

Parameters:

h5files (list[str] | None) – List of HDF5 files to open. Defaults to None.
import_all (bool | None) – Import all objects from HDF5 files. Defaults to None.
reset_all (bool | None) – Reset all application data. Defaults to None.

raise_window() → None#: Raise DataLab window

reset_all() → None#: Reset all application data

run_macro(number_or_title: int | str | None = None) → None#

Run macro.

Parameters:: number_or_title – Macro number, or macro title. Defaults to None (current macro).
Raises:: ValueError – if macro not found

save_to_h5_file(filename: str) → None#

Save to a DataLab HDF5 file.

Parameters:: filename (str) – HDF5 file name

select_groups(selection: list[int | str] | None = None, panel: str | None = None) → None#

Select groups in current panel.

Parameters:

selection – List of group numbers (1 to N), or list of group uuids, or None to select all groups. Defaults to None.
panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used. Defaults to None.

select_objects(selection: list[int | str], panel: str | None = None) → None#

Select objects in current panel.

Parameters:

selection – List of object numbers (1 to N) or uuids to select
panel – panel name (valid values: “signal”, “image”). If None, current panel is used. Defaults to None.

set_current_panel(panel: str) → None#

Switch to panel.

Parameters:: panel (str) – Panel name (valid values: “signal”, “image”, “macro”))

stop_macro(number_or_title: int | str | None = None) → None#

Stop macro.

Parameters:: number_or_title – Macro number, or macro title. Defaults to None (current macro).
Raises:: ValueError – if macro not found

toggle_auto_refresh(state: bool) → None#

Toggle auto refresh state.

Parameters:: state (bool) – Auto refresh state

toggle_show_titles(state: bool) → None#

Toggle show titles state.

Parameters:: state (bool) – Show titles state

Local proxy#

The local proxy is used when DataLab is started from the same process as the proxy. In this case, the proxy is directly connected to DataLab main window instance. The typical use case is high-level scripting.

class cdl.proxy.LocalProxy(cdl: CDLMainWindow | ServerProxy | None = None)[source]#

DataLab local proxy class.

This class provides access to DataLab features from a proxy class. This is the local version of proxy, which is used when DataLab is started from the same process as the proxy.

Parameters:: cdl (CDLMainWindow) – CDLMainWindow instance.

Note

The proxy object also allows to access DataLab computing methods exposed by the processor classes (see Calling processor methods using proxy objects).

add_signal(title: str, xdata: ndarray, ydata: ndarray, xunit: str | None = None, yunit: str | None = None, xlabel: str | None = None, ylabel: str | None = None) → bool[source]#

Add signal data to DataLab.

Parameters:

title (str) – Signal title
xdata (numpy.ndarray) – X data
ydata (numpy.ndarray) – Y data
xunit (str | None) – X unit. Defaults to None.
yunit (str | None) – Y unit. Defaults to None.
xlabel (str | None) – X label. Defaults to None.
ylabel (str | None) – Y label. Defaults to None.

Returns:

True if signal was added successfully, False otherwise

Return type:

bool

Raises:

ValueError – Invalid xdata dtype
ValueError – Invalid ydata dtype

Add image data to DataLab.

Parameters:

title (str) – Image title
data (numpy.ndarray) – Image data
xunit (str | None) – X unit. Defaults to None.
yunit (str | None) – Y unit. Defaults to None.
zunit (str | None) – Z unit. Defaults to None.
xlabel (str | None) – X label. Defaults to None.
ylabel (str | None) – Y label. Defaults to None.
zlabel (str | None) – Z label. Defaults to None.

Returns:

True if image was added successfully, False otherwise

Return type:

bool

Raises:

ValueError – Invalid data dtype

calc(name: str, param: DataSet | None = None) → None[source]#

Call compute function name in current panel’s processor.

Parameters:

name – Compute function name
param – Compute function parameter. Defaults to None.

Raises:

ValueError – unknown function

get_object(nb_id_title: int | str | None = None, panel: str | None = None) → SignalObj | ImageObj[source]#

Get object (signal/image) from index.

Parameters:

nb_id_title – Object number, or object id, or object title. Defaults to None (current object).
panel – Panel name. Defaults to None (current panel).

Returns:

Object

Raises:

KeyError – if object not found

get_object_shapes(nb_id_title: int | str | None = None, panel: str | None = None) → list[source]#

Get plot item shapes associated to object (signal/image).

Parameters:

nb_id_title – Object number, or object id, or object title. Defaults to None (current object).
panel – Panel name. Defaults to None (current panel).

Returns:

List of plot item shapes

add_annotations_from_items(items: list, refresh_plot: bool = True, panel: str | None = None) → None[source]#

Add object annotations (annotation plot items).

Parameters:

items (list) – annotation plot items
refresh_plot (bool | None) – refresh plot. Defaults to True.
panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used.

add_object(obj: SignalObj | ImageObj) → None[source]#

Add object to DataLab.

Parameters:: obj (SignalObj | ImageObj) – Signal or image object

add_label_with_title(title: str | None = None, panel: str | None = None) → None#

Add a label with object title on the associated plot

Parameters:

title (str | None) – Label title. Defaults to None. If None, the title is the object title.
panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used.

close_application() → None#: Close DataLab application

context_no_refresh() → Generator[None, None, None]#

Return a context manager to temporarily disable auto refresh.

Returns:: Context manager

Example

>>> with proxy.context_no_refresh():
...     proxy.add_image("image1", data1)
...     proxy.compute_fft()
...     proxy.compute_wiener()
...     proxy.compute_ifft()
...     # Auto refresh is disabled during the above operations

delete_metadata(refresh_plot: bool = True, keep_roi: bool = False) → None#

Delete metadata of selected objects

Parameters:

refresh_plot – Refresh plot. Defaults to True.
keep_roi – Keep ROI. Defaults to False.

get_current_panel() → str#

Return current panel name.

Returns:: Panel name (valid values: “signal”, “image”, “macro”))
Return type:: str

get_group_titles_with_object_infos() → tuple[list[str], list[list[str]], list[list[str]]]#

Return groups titles and lists of inner objects uuids and titles.

Returns:: groups titles, lists of inner objects uuids and titles
Return type:: Tuple

get_object_titles(panel: str | None = None) → list[str]#

Get object (signal/image) list for current panel. Objects are sorted by group number and object index in group.

Parameters:: panel – panel name (valid values: “signal”, “image”, “macro”). If None, current data panel is used (i.e. signal or image panel).
Returns:: List of object titles
Raises:: ValueError – if panel not found

get_object_uuids(panel: str | None = None) → list[str]#

Get object (signal/image) uuid list for current panel. Objects are sorted by group number and object index in group.

Parameters:: panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used.
Returns:: list of object uuids
Return type:: list[str]
Raises:: ValueError – if panel not found

classmethod get_public_methods() → list[str]#

Return all public methods of the class, except itself.

Returns:: List of public methods
Return type:: list[str]

get_sel_object_uuids(include_groups: bool = False) → list[str]#

Return selected objects uuids.

Parameters:: include_groups – If True, also return objects from selected groups.
Returns:: List of selected objects uuids.

get_version() → str#

Return DataLab public version.

Returns:: DataLab version
Return type:: str

import_h5_file(filename: str, reset_all: bool | None = None) → None#

Open DataLab HDF5 browser to Import HDF5 file.

Parameters:

filename (str) – HDF5 file name
reset_all (bool | None) – Reset all application data. Defaults to None.

import_macro_from_file(filename: str) → None#

Import macro from file

Parameters:: filename – Filename.

load_from_files(filenames: list[str]) → None#

Open objects from files in current panel (signals/images).

Parameters:: filenames – list of file names

open_h5_files(h5files: list[str] | None = None, import_all: bool | None = None, reset_all: bool | None = None) → None#

Open a DataLab HDF5 file or import from any other HDF5 file.

Parameters:

h5files (list[str] | None) – List of HDF5 files to open. Defaults to None.
import_all (bool | None) – Import all objects from HDF5 files. Defaults to None.
reset_all (bool | None) – Reset all application data. Defaults to None.

raise_window() → None#: Raise DataLab window

reset_all() → None#: Reset all application data

run_macro(number_or_title: int | str | None = None) → None#

Run macro.

Parameters:: number_or_title – Macro number, or macro title. Defaults to None (current macro).
Raises:: ValueError – if macro not found

save_to_h5_file(filename: str) → None#

Save to a DataLab HDF5 file.

Parameters:: filename (str) – HDF5 file name

select_groups(selection: list[int | str] | None = None, panel: str | None = None) → None#

Select groups in current panel.

Parameters:

selection – List of group numbers (1 to N), or list of group uuids, or None to select all groups. Defaults to None.
panel (str | None) – panel name (valid values: “signal”, “image”). If None, current panel is used. Defaults to None.

select_objects(selection: list[int | str], panel: str | None = None) → None#

Select objects in current panel.

Parameters:

selection – List of object numbers (1 to N) or uuids to select
panel – panel name (valid values: “signal”, “image”). If None, current panel is used. Defaults to None.

set_current_panel(panel: str) → None#

Switch to panel.

Parameters:: panel (str) – Panel name (valid values: “signal”, “image”, “macro”))

stop_macro(number_or_title: int | str | None = None) → None#

Stop macro.

Parameters:: number_or_title – Macro number, or macro title. Defaults to None (current macro).
Raises:: ValueError – if macro not found

toggle_auto_refresh(state: bool) → None#

Toggle auto refresh state.

Parameters:: state (bool) – Auto refresh state

toggle_show_titles(state: bool) → None#

Toggle show titles state.

Parameters:: state (bool) – Show titles state

Proxy context manager#

The proxy context manager is a convenient way to handle proxy creation and destruction. It is used as follows:

with proxy_context("local") as proxy:
    proxy.add_signal(...)

The proxy type can be “local” or “remote”. For remote proxy, the port can be specified as “remote:port”.

Note

The proxy context manager allows to use the proxy in various contexts (Python script, Jupyter notebook, etc.). It also allows to switch seamlessly between local and remote proxy, keeping the same code inside the context.

cdl.proxy.proxy_context(what: str) → Generator[LocalProxy | RemoteProxy, None, None][source]#

Context manager handling CDL proxy creation and destruction.

Parameters:

what (str) – proxy type (“local” or “remote”) For remote proxy, the port can be specified as “remote:port”

Yields:

Generator[LocalProxy | RemoteProxy, None, None] –

proxy: LocalProxy if what == “local” RemoteProxy if what == “remote” or “remote:port”

Example

with proxy_context(“local”) as proxy:: proxy.add_signal(…)

Calling processor methods using proxy objects#

All the proxy objects provide access to the DataLab computing methods exposed by the processor classes:

Number of compute methods#

Number of compute methods#
Signal	Image	Total
58	99	157

Signal processing#

The following table lists the signal processor methods - it is automatically generated from the source code:

Signal processor methods#
Compute method	Description
`compute_abs()`	Compute absolute value with `cdl.computation.signal.compute_abs()`
`compute_addition_constant()`	Compute sum with a constant
`compute_astype()`	Convert data type with `cdl.computation.signal.compute_astype()`
`compute_average()`	Compute average with `cdl.computation.signal.compute_addition()`
`compute_bandpass()`	Compute band-pass filter
`compute_bandstop()`	Compute band-stop filter
`compute_bandwidth_3db()`	Compute bandwidth at -3dB
`compute_calibration()`	Compute data linear calibration
`compute_clip()`	Compute maximum data clipping
`compute_contrast()`	Compute contrast with `cdl.computation.signal.compute_contrast()`
`compute_convolution()`	Compute convolution
`compute_derivative()`	Compute derivative
`compute_detrending()`	Compute detrending
`compute_difference()`	Compute difference between two signals
`compute_difference_constant()`	Compute difference with a constant
`compute_division()`	Compute division between two signals
`compute_division_constant()`	Compute division by a constant
`compute_dynamic_parameters()`	Compute Dynamic Parameters (ENOB, SINAD, THD, SFDR, SNR)
`compute_exp()`	Compute Log10 with `cdl.computation.signal.compute_exp()`
`compute_fft()`	Compute FFT with `cdl.computation.signal.compute_fft()`
`compute_fit()`	Compute fitting curve using an interactive dialog
`compute_fw1e2()`	Compute FW at 1/e² with `cdl.computation.signal.compute_fw1e2()`
`compute_fwhm()`	Compute FWHM with `cdl.computation.signal.compute_fwhm()`
`compute_gaussian_filter()`	Compute gaussian filter
`compute_highpass()`	Compute high-pass filter
`compute_histogram()`	Compute histogram
`compute_ifft()`	Compute iFFT with `cdl.computation.signal.compute_ifft()`
`compute_im()`	Compute imaginary part with `cdl.computation.signal.compute_im()`
`compute_integral()`	Compute integral with `cdl.computation.signal.compute_integral()`
`compute_interpolation()`	Compute interpolation
`compute_log10()`	Compute Log10 with `cdl.computation.signal.compute_log10()`
`compute_lowpass()`	Compute high-pass filter
`compute_magnitude_spectrum()`	Compute magnitude spectrum
`compute_moving_average()`	Compute moving average
`compute_moving_median()`	Compute moving median
`compute_multigaussianfit()`	Compute multi-Gaussian fitting curve using an interactive dialog
`compute_normalize()`	Normalize data with `cdl.computation.signal.compute_normalize()`
`compute_offset_correction()`	Compute offset correction
`compute_peak_detection()`	Detect peaks from data
`compute_phase_spectrum()`	Compute phase spectrum
`compute_polyfit()`	Compute polynomial fitting curve
`compute_power()`	Compute power with `cdl.computation.signal.compute_power()`
`compute_product()`	Compute product with `cdl.computation.signal.compute_product()`
`compute_product_constant()`	Compute product with a constant
`compute_psd()`	Compute power spectral density
`compute_quadratic_difference()`	Compute quadratic difference between two signals
`compute_re()`	Compute real part with `cdl.computation.signal.compute_re()`
`compute_resampling()`	Compute resampling
`compute_reverse_x()`	Reverse X axis with `cdl.computation.signal.compute_reverse_x()`
`compute_roi_extraction()`	Extract Region Of Interest (ROI) from data
`compute_sampling_rate_period()`	Compute sampling rate and period (mean and std)
`compute_sqrt()`	Compute square root with `cdl.computation.signal.compute_sqrt()`
`compute_stats()`	Compute data statistics
`compute_sum()`	Compute sum with `cdl.computation.signal.compute_addition()`
`compute_swap_axes()`	Swap data axes with `cdl.computation.signal.compute_swap_axes()`
`compute_wiener()`	Compute Wiener filter
`compute_windowing()`	Compute windowing
`compute_x_at_minmax()`	Compute x at min/max

Image processing#

The following table lists the image processor methods - it is automatically generated from the source code:

Image processor methods#
Compute method	Description
`compute_abs()`	Compute absolute value with `cdl.computation.image.compute_abs()`
`compute_addition_constant()`	Compute sum with a constant
`compute_adjust_gamma()`	Compute gamma correction
`compute_adjust_log()`	Compute log correction
`compute_adjust_sigmoid()`	Compute sigmoid correction
`compute_all_denoise()`	Compute all denoising filters
`compute_all_edges()`	Compute all edges filters
`compute_all_morphology()`	Compute all morphology filters
`compute_all_threshold()`	Compute all threshold algorithms
`compute_astype()`	Convert data type with `cdl.computation.image.compute_astype()`
`compute_average()`	Compute average with `cdl.computation.image.compute_addition()`
`compute_average_profile()`	Compute average profile
`compute_binning()`	Binning image with `cdl.computation.image.compute_binning()`
`compute_black_tophat()`	Compute Black Top-Hat
`compute_blob_dog()`	Compute blob detection using Difference of Gaussian method
`compute_blob_doh()`	Compute blob detection using Determinant of Hessian method
`compute_blob_log()`	Compute blob detection using Laplacian of Gaussian method
`compute_blob_opencv()`	Compute blob detection using OpenCV
`compute_butterworth()`	Compute Butterworth filter
`compute_calibration()`	Compute data linear calibration
`compute_canny()`	Compute Canny filter
`compute_centroid()`	Compute image centroid
`compute_clip()`	Compute maximum data clipping
`compute_closing()`	Compute morphological closing
`compute_contour_shape()`	Compute contour shape fit
`compute_denoise_bilateral()`	Compute bilateral filter denoising
`compute_denoise_tophat()`	Denoise using White Top-Hat
`compute_denoise_tv()`	Compute Total Variation denoising
`compute_denoise_wavelet()`	Compute Wavelet denoising
`compute_difference()`	Compute difference between two images
`compute_difference_constant()`	Compute difference with a constant
`compute_dilation()`	Compute Dilation
`compute_division()`	Compute division between two images
`compute_division_constant()`	Compute division by a constant
`compute_enclosing_circle()`	Compute minimum enclosing circle
`compute_equalize_adapthist()`	Adaptive histogram equalization
`compute_equalize_hist()`	Histogram equalization
`compute_erosion()`	Compute Erosion
`compute_exp()`	Compute Log10 with `cdl.computation.image.compute_exp()`
`compute_farid()`	Compute Farid filter
`compute_farid_h()`	Compute Farid filter (horizontal)
`compute_farid_v()`	Compute Farid filter (vertical)
`compute_fft()`	Compute FFT with `cdl.computation.image.compute_fft()`
`compute_flatfield()`	Compute flat field correction
`compute_fliph()`	Flip data horizontally
`compute_flipv()`	Flip data vertically with `cdl.computation.image.compute_flipv()`
`compute_gaussian_filter()`	Compute gaussian filter
`compute_histogram()`	Compute histogram with `cdl.computation.image.compute_histogram()`
`compute_hough_circle_peaks()`	Compute peak detection based on a circle Hough transform
`compute_ifft()`	Compute iFFT with `cdl.computation.image.compute_ifft()`
`compute_im()`	Compute imaginary part with `cdl.computation.image.compute_im()`
`compute_laplace()`	Compute Laplace filter
`compute_line_profile()`	Compute profile along a vertical or horizontal line
`compute_log10()`	Compute Log10 with `cdl.computation.image.compute_log10()`
`compute_logp1()`	Compute base 10 logarithm
`compute_magnitude_spectrum()`	Compute magnitude spectrum
`compute_moving_average()`	Compute moving average
`compute_moving_median()`	Compute moving median
`compute_normalize()`	Normalize data with `cdl.computation.image.compute_normalize()`
`compute_offset_correction()`	Compute offset correction
`compute_opening()`	Compute morphological opening
`compute_peak_detection()`	Compute 2D peak detection
`compute_phase_spectrum()`	Compute phase spectrum
`compute_prewitt()`	Compute Prewitt filter
`compute_prewitt_h()`	Compute Prewitt filter (horizontal)
`compute_prewitt_v()`	Compute Prewitt filter (vertical)
`compute_product()`	Compute product with `cdl.computation.image.compute_product()`
`compute_product_constant()`	Compute product with a constant
`compute_psd()`	Compute Power Spectral Density (PSD)
`compute_quadratic_difference()`	Compute quadratic difference between two images
`compute_radial_profile()`	Compute radial profile
`compute_re()`	Compute real part with `cdl.computation.image.compute_re()`
`compute_rescale_intensity()`	Rescale image intensity levels
`compute_resize()`	Resize image with `cdl.computation.image.compute_resize()`
`compute_roberts()`	Compute Roberts filter
`compute_roi_extraction()`	Extract Region Of Interest (ROI) from data
`compute_rotate()`	Rotate data arbitrarily
`compute_rotate270()`	Rotate data 270° with `cdl.computation.image.compute_rotate270()`
`compute_rotate90()`	Rotate data 90° with `cdl.computation.image.compute_rotate90()`
`compute_scharr()`	Compute Scharr filter
`compute_scharr_h()`	Compute Scharr filter (horizontal)
`compute_scharr_v()`	Compute Scharr filter (vertical)
`compute_segment_profile()`	Compute profile along a segment
`compute_sobel()`	Compute Sobel filter
`compute_sobel_h()`	Compute Sobel filter (horizontal)
`compute_sobel_v()`	Compute Sobel filter (vertical)
`compute_stats()`	Compute data statistics
`compute_sum()`	Compute sum with `cdl.computation.image.compute_addition()`
`compute_swap_axes()`	Swap data axes with `cdl.computation.image.compute_swap_axes()`
`compute_threshold()`	Compute parametric threshold
`compute_threshold_isodata()`	Compute threshold using Isodata algorithm
`compute_threshold_li()`	Compute threshold using Li algorithm
`compute_threshold_mean()`	Compute threshold using Mean algorithm
`compute_threshold_minimum()`	Compute threshold using Minimum algorithm
`compute_threshold_otsu()`	Compute threshold using Otsu algorithm
`compute_threshold_triangle()`	Compute threshold using Triangle algorithm
`compute_threshold_yen()`	Compute threshold using Yen algorithm
`compute_white_tophat()`	Compute White Top-Hat
`compute_wiener()`	Compute Wiener filter

Proxy objects (cdl.proxy)#

Remote proxy#

Local proxy#

Proxy context manager#

Calling processor methods using proxy objects#

Number of compute methods#

Signal processing#

Image processing#

Proxy objects (`cdl.proxy`)#