See also
This page was generated from examples/tirf.ipynb.
Download the Jupyter Notebook for this section: tirf.ipynb
. View in nbviewer.
TIRF ImagingΒΆ
This is an example using scopyon
(https://scopyon.readthedocs.io/).
[1]:
import scopyon
/home/kaizu/.local/share/virtualenvs/python-eSbFG-Wg/lib/python3.7/site-packages/xarray/core/merge.py:18: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)
/home/kaizu/.local/share/virtualenvs/python-eSbFG-Wg/lib/python3.7/site-packages/xarray/core/dataarray.py:1829: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
'DataArray', pd.Series, pd.DataFrame, pd.Panel]:
Set the configuration first.
[2]:
config = scopyon.DefaultConfiguration()
config.default.detector.exposure_time = 33.0e-3 # second
[3]:
config.default.detector
[3]:
QE: 0.92
dark_count: 0
emgain: 300
exposure_time: 0.033
focal_point:
units: m
value:
- 0.0
- 0.0
- 0.0
image_size:
- 512
- 512
pixel_length:
units: m
value: 1.6e-05
readout_noise: 100
switch: true
type: EMCCD
You can also set parameters with units using pint
(See https://pint.readthedocs.io/):
[4]:
from scopyon.constants import ureg
config.default.detector.exposure_time = 33.0 * ureg.millisecond
A field of microscopic view could be calculated as follows:
[5]:
pixel_length = config.default.detector.pixel_length / config.default.magnification
L_2 = config.default.detector.image_size[0] * pixel_length * 0.5
Randomly generate positions of 100 molecules in two dimensions.
[6]:
import numpy.random
rng = numpy.random.RandomState(123)
N = 100
inputs = rng.uniform(-L_2, +L_2, size=(N, 2))
scopyon.form_image
generates a single image from the given inputs.
[7]:
img = scopyon.form_image(inputs, config=config, rng=rng)
/home/kaizu/.local/share/virtualenvs/python-eSbFG-Wg/lib/python3.7/site-packages/scopyon/config.py:100: UserWarning: Unit conversion in 'exposure_time' from [ms] to [s]
warnings.warn(f"Unit conversion in '{key}' from [{given.units:~}] to [{v.units:~}]")
[8]:
img
You can also save an image.
[9]:
img.save("tirf_000.png")
[10]:
from IPython.display import Image
Image("tirf_000.png")
[10]: