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:

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) β†’ 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: β€œsignal”, β€œimage”). If None, current panel is used.

add_image(title: str, data: ndarray, xunit: str | None = None, yunit: str | None = None, zunit: str | None = None, xlabel: str | None = None, ylabel: str | None = None, zlabel: str | None = None) β†’ bool#

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) β†’ 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: β€œsignal”, β€œimage”). If None, current panel is used.

add_object(obj: SignalObj | ImageObj) β†’ 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) β†’ 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:
calc(name: str, param: DataSet | None = None) β†’ 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() β†’ None#

Close DataLab application

connect(port: str | None = None, timeout: float | None = None, retries: int | None = None) β†’ 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:
context_no_refresh() β†’ 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) β†’ None#

Delete metadata of selected objects

Parameters:
  • refresh_plot – Refresh plot. Defaults to True.

  • keep_roi – Keep ROI. Defaults to False.

disconnect() β†’ None#

Disconnect from DataLab XML-RPC server.

get_current_panel() β†’ str#

Return current panel name.

Returns:

Panel name (valid values: β€œsignal”, β€œimage”, β€œmacro”))

Return type:

str

get_group_titles_with_object_infos() β†’ 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() β†’ list[str]#

Return list of available methods.

get_object(nb_id_title: int | str | None = None, panel: str | None = None) β†’ 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) β†’ 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) β†’ 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: β€œsignal”, β€œimage”, β€œmacro”). 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) β†’ 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: β€œsignal”, β€œimage”). 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() β†’ 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) β†’ 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() β†’ str#

Return DataLab public version.

Returns:

DataLab version

Return type:

str

import_h5_file(filename: str, reset_all: bool | None = None) β†’ 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) β†’ None#

Import macro from file

Parameters:

filename – Filename.

is_connected() β†’ bool#

Return True if connected to DataLab XML-RPC server.

load_from_files(filenames: list[str]) β†’ 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) β†’ 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() β†’ None#

Raise DataLab window

reset_all() β†’ None#

Reset all application data

run_macro(number_or_title: int | str | None = None) β†’ 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) β†’ 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) β†’ 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: β€œsignal”, β€œimage”). If None, current panel is used. Defaults to None.

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

Select objects in current panel.

Parameters:
  • selection – List of object numbers (1 to N) or uuids to select

  • panel – panel name (valid values: β€œsignal”, β€œimage”). If None, current panel is used. Defaults to None.

set_current_panel(panel: str) β†’ None#

Switch to panel.

Parameters:

panel (str) – Panel name (valid values: β€œsignal”, β€œimage”, β€œmacro”))

stop_macro(number_or_title: int | str | None = None) β†’ 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) β†’ None#

Toggle auto refresh state.

Parameters:

state (bool) – Auto refresh state

toggle_show_titles(state: bool) β†’ 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) β†’ 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:
add_image(title: str, data: ndarray, xunit: str | None = None, yunit: str | None = None, zunit: str | None = None, xlabel: str | None = None, ylabel: str | None = None, zlabel: str | None = None) β†’ bool[source]#

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) β†’ 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) β†’ 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) β†’ 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) β†’ 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: β€œsignal”, β€œimage”). If None, current panel is used.

add_object(obj: SignalObj | ImageObj) β†’ 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) β†’ 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: β€œsignal”, β€œimage”). If None, current panel is used.

close_application() β†’ None#

Close DataLab application

context_no_refresh() β†’ 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) β†’ None#

Delete metadata of selected objects

Parameters:
  • refresh_plot – Refresh plot. Defaults to True.

  • keep_roi – Keep ROI. Defaults to False.

get_current_panel() β†’ str#

Return current panel name.

Returns:

Panel name (valid values: β€œsignal”, β€œimage”, β€œmacro”))

Return type:

str

get_group_titles_with_object_infos() β†’ 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) β†’ 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: β€œsignal”, β€œimage”, β€œmacro”). 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) β†’ 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: β€œsignal”, β€œimage”). 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() β†’ 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) β†’ 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() β†’ str#

Return DataLab public version.

Returns:

DataLab version

Return type:

str

import_h5_file(filename: str, reset_all: bool | None = None) β†’ 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) β†’ None#

Import macro from file

Parameters:

filename – Filename.

load_from_files(filenames: list[str]) β†’ 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) β†’ 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() β†’ None#

Raise DataLab window

reset_all() β†’ None#

Reset all application data

run_macro(number_or_title: int | str | None = None) β†’ 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) β†’ 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) β†’ 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: β€œsignal”, β€œimage”). If None, current panel is used. Defaults to None.

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

Select objects in current panel.

Parameters:
  • selection – List of object numbers (1 to N) or uuids to select

  • panel – panel name (valid values: β€œsignal”, β€œimage”). If None, current panel is used. Defaults to None.

set_current_panel(panel: str) β†’ None#

Switch to panel.

Parameters:

panel (str) – Panel name (valid values: β€œsignal”, β€œimage”, β€œmacro”))

stop_macro(number_or_title: int | str | None = None) β†’ 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) β†’ None#

Toggle auto refresh state.

Parameters:

state (bool) – Auto refresh state

toggle_show_titles(state: bool) β†’ 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 β€œlocal” or β€œremote”. For remote proxy, the port can be specified as β€œremote: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) β†’ Generator[LocalProxy | RemoteProxy, None, None][source]#

Context manager handling CDL proxy creation and destruction.

Parameters:

what (str) – proxy type (β€œlocal” or β€œremote”) For remote proxy, the port can be specified as β€œremote:port”

Yields:

Generator[LocalProxy | RemoteProxy, None, None] –

proxy

LocalProxy if what == β€œlocal” RemoteProxy if what == β€œremote” or β€œremote:port”

Example

with proxy_context(β€œlocal”) 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:

See also

The list of processor methods is available in tables below.

There are two ways to call a processor method:

  1. Using the calc() method of the proxy object:

# Call a method without parameter
proxy.calc("compute_average")

# This is equivalent to:
proxy.calc("average")

# Call a method with parameters
p = cdl.param.MovingAverageParam.create(n=30)
proxy.calc("compute_moving_average", p)
  1. Directly calling the processor method from the proxy object:

# Call a method without parameter
proxy.compute_average()

# Call a method with parameters
p = cdl.param.MovingAverageParam.create(n=30)
proxy.compute_moving_average(p)

Warning

The compute_{name} methods are not statically defined in the proxy classes (and not even dynamically). They are nevertheless available through the proxy objects thanks to the magic method __getattr__() which forwards the call to the calc() method. However, this means that the methods are not listed in the proxy classes documentation, and they are not available in the auto-completion feature of your IDE.

Number of compute methods#

Number of compute methods#

Signal

Image

Total

59

100

159

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_arithmetic()

Compute arithmetic operation between two signals

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/eΒ² 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 with:

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 using

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_arithmetic()

Compute arithmetic operation between two images

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 using

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 using

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 using

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 with:

compute_rotate()

Rotate data arbitrarily using

compute_rotate270()

Rotate data 270Β° with cdl.computation.image.compute_rotate270()

compute_rotate90()

Rotate data 90Β° 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