lumicks.pylake.correlated_stack.CorrelatedStack¶

class CorrelatedStack(image_name, align=True)¶

CorrelatedStack acquired with Bluelake. Bluelake can export stacks of images to various formats. These can be opened and correlated to timeline data using CorrelatedStack.

Parameters:	image_name : str Filename for the image stack. Typically a TIFF file recorded from a camera in Bluelake. align : bool If enabled, multi-channel images will be reconstructed from the image alignment metadata from Bluelake. The default value is `True`.

Examples

from lumicks import pylake

# Loading a stack.
stack = pylake.CorrelatedStack("example.tiff")

# Making a plot where force is correlated to images in the stack.
file = pylake.File("example.h5")
stack.plot_correlated(file.force1x)

# Determine the force trace averaged over frame 2...9.
file.force1x.downsampled_over(stack[2:10].timestamps)

__init__(image_name, align=True)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(image_name[, align])	Initialize self.
`export_tiff`(file_name[, roi])	Export a video of a particular scan plot
`from_data`(data[, name, start_idx, stop_idx])	Construct CorrelatedStack from image stack object
`plot`([frame, channel, show_title])	Plot image from image stack
`plot_correlated`(channel_slice[, frame, …])	Downsample channel on a frame by frame basis and plot the results.

Attributes

`num_frames`	Number of frames in the stack.
`raw`	Raw frame data.
`start`	Starting time stamp of the stack.
`stop`	Final time stamp of the stack.
`timestamps`	List of time stamps.

__getitem__(item)¶: All indexing is in frames

export_tiff(file_name, roi=None)¶

Export a video of a particular scan plot

Parameters:	file_name : str File name to export to. roi : list_like region of interest in pixel values [xmin, xmax, ymin, ymax]

classmethod from_data(data, name=None, start_idx=0, stop_idx=None)¶

Construct CorrelatedStack from image stack object

Parameters:	data : TiffStack TiffStack object. name : str Plot label of the correlated stack start_idx : int Index at the first frame. stop_idx: int Index beyond the last frame.

plot(frame=0, channel='rgb', show_title=True, **kwargs)¶

Plot image from image stack

Parameters:	frame : int, optional Index of the frame to plot. channel : ‘rgb’, ‘red’, ‘green’, ‘blue’, None; optional Channel to plot for RGB images (None defaults to ‘rgb’) Not used for grayscale images show_title : bool, optional Controls display of auto-generated plot title **kwargs Forwarded to `matplotlib.pyplot.imshow()`.

plot_correlated(channel_slice, frame=0, reduce=<function mean>, channel='rgb')¶

Downsample channel on a frame by frame basis and plot the results. The downsampling function (e.g. np.mean) is evaluated for the time between a start and end time of a frame. Note: In environments which support interactive figures (e.g. jupyter notebook with ipywidgets or interactive python) this plot will be interactive.

Parameters:

channel_slice : pylake.channel.Slice: Data slice that we with to downsample.
frame : int: Frame to show.
reduce : callable: The function which is going to reduce multiple samples into one. The default is numpy.mean(), but numpy.sum() could also be appropriate for some cases e.g. photon counts.
channel : ‘rgb’, ‘red’, ‘green’, ‘blue’, None; optional: Channel to plot for RGB images (None defaults to ‘rgb’) Not used for grayscale images

Examples

from lumicks import pylake

file = pylake.File("example.h5")
stack = pylake.CorrelatedStack("example.tiff")
stack.plot_correlated(file.force1x, frame=5)

num_frames¶: Number of frames in the stack.

raw¶: Raw frame data.

start¶: Starting time stamp of the stack.

stop¶: Final time stamp of the stack.

timestamps¶: List of time stamps.