Nanog timelapse example

Install and import the dependencies

%pip uninstall basicpy -yq
%pip install basicpy -q

Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.

from basicpy import BaSiC
from basicpy import data as bdata
from matplotlib import pyplot as plt

the value of the environment variable BASIC_DCT_BACKEND is not in ["JAX","SCIPY"]

Load the sample images

images = bdata.timelapse_nanog()
plt.imshow(images[10])

Downloading file 'timelapse_nanog.npz' from 'https://github.com/peng-lab/BaSiCPy/raw/dev/data/timelapse_nanog.npz' to '/home/docs/.cache/basicpy'.

<matplotlib.image.AxesImage at 0x7f8e3894faf0>

Fit the flatfield and darkfield (try 1)

basic = BaSiC(get_darkfield=True, lambda_flatfield_coef=10)
basic.fit(images)

WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
INFO:basicpy.basicpy:reweighting iteration 0
INFO:basicpy.basicpy:single-step optimization score: 7.678745532757603e-07.
INFO:basicpy.basicpy:mean of S: 1.0.
INFO:basicpy.basicpy:Iteration 0 finished.
INFO:basicpy.basicpy:reweighting iteration 1
INFO:basicpy.basicpy:single-step optimization score: 7.752265105409606e-07.
INFO:basicpy.basicpy:mean of S: 0.9999999403953552.
INFO:basicpy.basicpy:Iteration 1 finished.
INFO:basicpy.basicpy:reweighting score: 0.003535376861691475
INFO:basicpy.basicpy:elapsed time: 8.213936023000315 seconds
INFO:basicpy.basicpy:Reweighting converged.
INFO:basicpy.basicpy:=== BaSiC fit finished in 8.315074793999884 seconds ===

Plot the fit results (try 1)

fig, axes = plt.subplots(1, 3, figsize=(9, 3))
im = axes[0].imshow(basic.flatfield)
fig.colorbar(im, ax=axes[0])
axes[0].set_title("Flatfield")
im = axes[1].imshow(basic.darkfield)
fig.colorbar(im, ax=axes[1])
axes[1].set_title("Darkfield")
axes[2].plot(basic.baseline)
axes[2].set_xlabel("Frame")
axes[2].set_ylabel("Baseline")
fig.tight_layout()

Fit the flatfield and darkfield (try 2)

basic = BaSiC(
    get_darkfield=True, lambda_flatfield_coef=100
)  # increase lambda_flatfield_coef for smoother flatfield
basic.fit(images)

INFO:basicpy.basicpy:Initializing BaSiC 140248734624448 with parameters:
get_darkfield: True
lambda_flatfield_coef: 100

INFO:basicpy.basicpy:=== BaSiC fit started ===
INFO:basicpy.basicpy:reweighting iteration 0
INFO:basicpy.basicpy:single-step optimization score: 7.597092803734995e-07.
INFO:basicpy.basicpy:mean of S: 1.0000001192092896.
INFO:basicpy.basicpy:Iteration 0 finished.
INFO:basicpy.basicpy:reweighting iteration 1
INFO:basicpy.basicpy:single-step optimization score: 7.214820243461872e-07.
INFO:basicpy.basicpy:mean of S: 1.0.
INFO:basicpy.basicpy:Iteration 1 finished.
INFO:basicpy.basicpy:reweighting score: 0.00407834118232131
INFO:basicpy.basicpy:elapsed time: 6.1004404590003105 seconds
INFO:basicpy.basicpy:Reweighting converged.
INFO:basicpy.basicpy:=== BaSiC fit finished in 6.106918268999834 seconds ===

Plot the fit results (try 2)

fig, axes = plt.subplots(1, 3, figsize=(9, 3))
im = axes[0].imshow(basic.flatfield)
fig.colorbar(im, ax=axes[0])
axes[0].set_title("Flatfield")
im = axes[1].imshow(basic.darkfield)
fig.colorbar(im, ax=axes[1])
axes[1].set_title("Darkfield")
axes[2].plot(basic.baseline)
axes[2].set_xlabel("Frame")
axes[2].set_ylabel("Baseline")
fig.tight_layout()

Correct the original images

images_transformed = basic.transform(images, timelapse=True)

INFO:basicpy.basicpy:=== BaSiC transform started ===
INFO:basicpy.basicpy:unshading in 2 threads
INFO:basicpy.basicpy:=== BaSiC transform finished in 0.026028551000308653 seconds ===

Plot the corrected results

for i in range(0, 21, 5):
    fig, axes = plt.subplots(1, 2, figsize=(6, 3))
    im = axes[0].imshow(images[i])
    fig.colorbar(im, ax=axes[0])
    axes[0].set_title("Original")
    im = axes[1].imshow(images_transformed[i])
    fig.colorbar(im, ax=axes[1])
    axes[1].set_title("Corrected")
    fig.suptitle(f"frame {i}")
    fig.tight_layout()