poetry run emacs an.org
import tifffile as tff
import numpy as np
import aicsimageio
import matplotlib.pyplot as plt
import seaborn as sb
import impy
from pathlib import Path
import skimage
import scipy
import imageio
import pandas as pd
cd "/home/dati/dt-evolv/data/2022-11-16/13401/TimePoint_1"
cd "/home/dati/dt-evolv/data/2022-11-16/13401/TimePoint_1"
mkdir -p thumbs
fd _thumb -0 | xargs -0 mv -t ./thumbs
pwd
cd "/home/dati/dt-evolv/data/2022-11-16/13401/TimePoint_1"
fp2 = "/home/dati/dt-evolv/data/2022-11-16/13401/TimePoint_1/6w-20Xph1-SpikeTest6-DA_A01_s1_w2B3DEDFEB-FB52-4D83-8176-CC090AD62AC4.tif"
fp1 = "/home/dati/dt-evolv/data/2022-11-16/13401/TimePoint_1/6w-20Xph1-SpikeTest6-DA_A01_s1_w19A3DA0A6-B9DD-475E-ABED-27415F9DE6EB.tif"
i1 = impy.imread(fp1)
i2 = impy.imread(fp2)
skimage.io.imshow(i2)
p = Path(".")
out1 = Path("./1024")
out2 = Path("./512")
out1.mkdir()
out2.mkdir()
import utils
p = Path(".")
s_in = ["A01", "B01", "A02", "A03"]
s_out = ["A01", "B01", "A02", "A03"]
for ins, outs in zip(s_in, s_out):
for i in range(1, 3):
print(ins, outs, i)
fp1 = sorted(p.glob(f"**/*{ins}*s{i}_w1*"))[0]
fp2 = sorted(p.glob(f"**/*{ins}*s{i}_w2*"))[0]
c1 = tff.imread(fp1)
c2 = tff.imread(fp2)
# clip1 = int(utils.bg(c1)[0])
clip2 = int(utils.bg(c2)[0])
# c1r = skimage.util.img_as_ubyte(skimage.exposure.rescale_intensity(c1.clip(clip1, c1.max() * 0.95)))
# c2r = skimage.util.img_as_ubyte(skimage.exposure.rescale_intensity(c2.clip(clip2, c2.max() * 0.95)))
c1r = skimage.util.img_as_ubyte((c1))
c2r = skimage.util.img_as_ubyte(skimage.exposure.rescale_intensity(c2.clip(clip2,65000)))
cc = np.dstack((c2r, c1r, np.zeros_like(c1r)))
cc = skimage.transform.downscale_local_mean(cc, (4, 4, 1))
imageio.imwrite(out2 / f"{outs}_s{i}.png", cc)
A01 A01 1 Lossy conversion from float64 to uint8. Range [0.0, 213.4375]. Convert image to uint8 prior to saving to suppress this warning. A01 A01 2 Lossy conversion from float64 to uint8. Range [0.0, 232.125]. Convert image to uint8 prior to saving to suppress this warning. B01 B01 1 Lossy conversion from float64 to uint8. Range [0.0, 203.8125]. Convert image to uint8 prior to saving to suppress this warning. B01 B01 2 Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. A02 A02 1 Lossy conversion from float64 to uint8. Range [0.0, 230.6875]. Convert image to uint8 prior to saving to suppress this warning. A02 A02 2 Lossy conversion from float64 to uint8. Range [0.0, 201.3125]. Convert image to uint8 prior to saving to suppress this warning. A03 A03 1 Lossy conversion from float64 to uint8. Range [0.0, 189.875]. Convert image to uint8 prior to saving to suppress this warning. A03 A03 2 Lossy conversion from float64 to uint8. Range [0.0, 165.5]. Convert image to uint8 prior to saving to suppress this warning.
cd "/home/dati/dt-evolv/data/2022-11-16/13401/TimePoint_1"
p = Path(".")
out2 = Path("./512n")
out2.mkdir()
import utils
p = Path(".")
s_in = ["A01", "B01", "A02", "B02", "A03", "B03"]
s_out = ["A01", "B01", "A02", "B02", "A03", "B03"]
for ins, outs in zip(s_in, s_out):
print(ins)
for i in range(1, 901):
fp1 = sorted(p.glob(f"**/*{ins}*s{i}_w1*"))[0]
fp2 = sorted(p.glob(f"**/*{ins}*s{i}_w2*"))[0]
c1 = tff.imread(fp1)
c2 = tff.imread(fp2)
c1 = skimage.transform.downscale_local_mean(c1, 4)
c2 = skimage.transform.downscale_local_mean(c2, 4)
clip1 = int(np.sum(utils.bg(c1)))
clip2 = int(np.sum(utils.bg(c2)))
c1r = skimage.util.img_as_ubyte(skimage.exposure.rescale_intensity(c1.clip(clip1, c1.max() * 0.95)))
c2r = skimage.util.img_as_ubyte(skimage.exposure.rescale_intensity(c2.clip(clip2, c2.max() * 0.95)))
cc = np.dstack((c2r, c1r, np.zeros_like(c1r)))
imageio.imwrite(out2 / f"{outs}_s{i}.png", cc)
A01 B01 A02 B02 A03 B03