Creating a Custom Virtual Mask

Virtual images are easily created from a series of (currently) 4 seperate masks. These masks are 8 bit images with values:

0: Negative Mask, pixel values subtracted 1: Default value, pixels not accounted for 2: Positive Mask, pixel values added

Additionally virtual images can be

from deapi import Client
import matplotlib.pyplot as plt
import time
from skimage.draw import disk

c = Client()
c.usingMmf = False
c.connect(port=13241)  # connect to the running DE Server

c.virtual_masks[0][:] = 1  # Set everything to 1
c.virtual_masks[0].plot()  # plot the current v0 mask

/home/runner/work/deapi/deapi/deapi/data_types.py:537: UserWarning: Virtual mask shape is not set to Arbitrary. Setting to Arbitrary.
  warnings.warn(

<Axes: >

Changing the virtual mask

Each of the virtual masks will act like a numpy array such that you can use numpy fancy indexing to slice or assign the array. For example we can set a portion of the mask to be 2 (positive mask)

c.virtual_masks[0][0:25] = 2  # first 25 pixels
c.virtual_masks[0].name = "Sum Virt Img"
c.virtual_masks[0].plot()

<Axes: >

Subtracting and adding

Different modes can be used with each virtual image. These are set using The calculation attribute. Lets try that with Virtual image 1

c.virtual_masks[1].calculation = "Difference"
c.virtual_masks[1].name = "Diff Virt Img"
c.virtual_masks[1][1::2] = 0  # every other pixel subtracts
c.virtual_masks[1][::2] = 2  # every other pixel subtracts
c.virtual_masks[1].plot()

<Axes: >

Creating a Virtual BrightField Image

c.virtual_masks[2].calculation = "Sum"
c.virtual_masks[2].name = "VBF"
shape = c.virtual_masks[2][:].shape
rr, cc = disk((shape[0] // 2, shape[1] // 2), 100)
c.virtual_masks[2][:] = 1
c.virtual_masks[2][rr, cc] = 2
c.virtual_masks[2].plot()

<Axes: >

# Plotting Multiple Virtual Images
# --------------------------------
# We can also make a tableau of virtual images using the matplotlib.pyplot
# package and passing an Axis to the plot function

fig, axs = plt.subplots(1, 2)
for a, v in zip(axs, c.virtual_masks):
    v.plot(ax=a)

Starting an Acquisition

Once we have set up an experiment we can start an acquisition using the start acquisition function

c["Frames Per Second"] = 5000  # 5000 frames per second
c.scan(enable="On", size_x=128, size_y=128)
c.start_acquisition()

while c.acquiring:  # wait for acquisition to finish and then plot the results
    time.sleep(1)

fig, axs = plt.subplots(1, 3)
for a, virt in zip(axs, ["virtual_image0", "virtual_image1", "virtual_image2"]):
    data, _, _, _ = c.get_result(virt)
    a.imshow(data)