Cytation 5#

%load_ext autoreload
%autoreload 2

import matplotlib.pyplot as plt
from pylabrobot.plate_reading import ImageReader, Cytation5Backend, ImagingMode

# for imaging, we need an environment variable to point to the Spinnaker GenTL file
import os
os.environ["SPINNAKER_GENTL64_CTI"] = "/usr/local/lib/spinnaker-gentl/Spinnaker_GenTL.cti"

import logging
logger = logging.getLogger("pylabrobot.plate_reading.biotek")
logger.setLevel(logging.DEBUG)

pr = ImageReader(name="PR", size_x=0,size_y=0,size_z=0, backend=Cytation5Backend())
await pr.setup(use_cam=True)

await pr.backend.get_firmware_version()

'1320200  Version 2.07'

await pr.backend.get_current_temperature()

23.5

await pr.open()

await pr.close()

Plate reading#

data = await pr.read_absorbance(wavelength=434)
plt.imshow(data)

<matplotlib.image.AxesImage at 0x1353cc790>

../_images/5eee8870eb453c15f3efb3faee31af79a34649e97ca88d5a88516c0e21050766.png

data = await pr.read_fluorescence(
  excitation_wavelength=485, emission_wavelength=528, focal_height=7.5
)
plt.imshow(data)

<matplotlib.image.AxesImage at 0x16e144850>

../_images/c6bed6539984cc3e8c8ef03fb086557665c1b50f355ad89dfc39239bc993d2a1.png

data = await pr.read_luminescence(focal_height=4.5)
plt.imshow(data)

<matplotlib.image.AxesImage at 0x16e1a83d0>

../_images/e435cc3192c6734bc98ad01fc5e6fa6361820552e6d55feb97a96edcf5f3d4f7.png

Shaking#

await pr.backend.shake(shake_type=Cytation5Backend.ShakeType.LINEAR)

await pr.backend.stop_shaking()

Imaging#

Installation#

See Cytation 5 imager installation instructions.

Usage#

im = await pr.capture(
  well=(1, 1),
  mode=ImagingMode.BRIGHTFIELD,
  focal_height="auto",  # PLR supports auto-focus
  exposure_time=5,
  gain=10
)
plt.imshow(im, cmap="gray", vmin=0, vmax=255)

<matplotlib.image.AxesImage at 0x177fe1880>

../_images/dedb95170b70edf8e1a41a9e491f45e8d3fae42eb644f20742cbaac3c8393dab.png

im = await pr.capture(
  well=(1, 1),
  mode=ImagingMode.PHASE_CONTRAST,
  focal_height=3,
  exposure_time=12,
  gain=24
)
plt.imshow(im, cmap="gray", vmin=0, vmax=255)

<matplotlib.image.AxesImage at 0x345a47a60>

../_images/6504343e4a6e7737071f8914372a885403cb756deece64cb39defe4178c5a4aa.png

await pr.backend.set_gain(24)
im = await pr.capture(
  well=(1, 1),
  mode=ImagingMode.GFP,
  focal_height=3,
  exposure_time=1904
)
plt.imshow(im, cmap="viridis", vmin=0, vmax=255)

<matplotlib.image.AxesImage at 0x3459d5d90>

../_images/861eef77b74ce52502867969335e16dcaadc1a29377f042616c0b8c4e5e3c8a4.png

im = await pr.capture(
  well=(1, 1),
  mode=ImagingMode.TEXAS_RED,
  focal_height=3,
  exposure_time=1904
)
plt.imshow(im, cmap="gray", vmin=0, vmax=255)

<matplotlib.image.AxesImage at 0x14c7d7880>

../_images/cb8d2cebf05226526b38000f07dab0e8c4dff34a2f43ebfea7127ddaab00262d.png

import time
import numpy as np

exposure_time = 1904

# first time setting imaging mode is slower
_ = await pr.capture(well=(1, 1), mode=ImagingMode.BRIGHTFIELD, focal_height=3.3, exposure_time=exposure_time)

l = []
for i in range(10):
  t0 = time.monotonic_ns()
  _ = await pr.capture(well=(1, 1), mode=ImagingMode.BRIGHTFIELD, focal_height=3.3, exposure_time=exposure_time)
  t1 = time.monotonic_ns()
  l.append((t1 - t0) / 1e6)

print(f"{np.mean(l):.2f} ms ± {np.std(l):.2f} ms")
print(f"Overhead: {(np.mean(l) - exposure_time):.2f} ms")

2089.59 ms ± 15.72 ms
Overhead: 185.59 ms