From ff252a880199d8bcf16b54a0002f1d83264ca5f5 Mon Sep 17 00:00:00 2001
From: Andrey Aristov <aaristov@pasteur.fr>
Date: Sun, 29 May 2022 13:04:18 +0200
Subject: [PATCH] sequential align
---
align.py | 73 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 73 insertions(+)
create mode 100644 align.py
diff --git a/align.py b/align.py
new file mode 100644
index 0000000..4c02f46
--- /dev/null
+++ b/align.py
@@ -0,0 +1,73 @@
+from droplet_growth import mic, register, poisson
+import tifffile as tf
+import os
+import dask.array as da
+from functools import partial
+from zarr_tools import convert
+import yaml
+import fire
+
+def align_multichip(BF_TRITC_2D_path, out_path, concentrations_path, template_path, labels_path, fit_poisson, nmax=10):
+ with open(concentrations_path, 'r') as f:
+ concentrations_dct = (yaml.safe_load(f))
+ concentrations = concentrations_dct['concentrations']
+ unit = concentrations_dct['units']
+ tif_paths = [f'{os.path.dirname(BF_TRITC_2D_path)}/{c}{unit}_aligned.tif' for c in concentrations]
+ data = da.from_zarr(BF_TRITC_2D_path+'/0/')
+ template16 = tf.imread(template_path)
+ big_labels = tf.imread(labels_path)
+
+ fun = partial(
+ align2D,
+ template16=template16,
+ big_labels=big_labels,
+ unit=unit,
+ fit_poisson=fit_poisson,
+ nmax=nmax )
+ aligned = list(map(lambda x: fun(*x), zip(data, tif_paths,concentrations)) )
+ daligned = da.from_array(aligned)
+ convert.to_zarr(
+ daligned,
+ path=out_path,
+ steps=4,
+ name=['BF','TRITC','mask'],
+ colormap=['gray','green','blue'],
+ lut=((1000,30000),(440, 600),(0,501)),
+ )
+ return out_path
+
+
+def align2D(stack_dask, path_tif, ab, template16, big_labels, unit='μg_mL', fit_poisson=True, nmax=10):
+ print(ab)
+ try:
+ aligned = tf.imread(path_tif)
+ print(f'already alinged: {path_tif}')
+ return aligned
+ except FileNotFoundError:
+ pass
+
+ data = stack_dask.compute()
+ aligned, tvec = register.align_stack(
+ data,
+ path_to_save=path_tif,
+ template16=template16,
+ mask2=big_labels,
+ binnings=(2,16,2)
+ )
+ counts = mic.get_cell_numbers(aligned[1], aligned[2], threshold_abs=2, plot=False, bf=aligned[0])
+
+ counts.loc[:,'[AB]'] = ab
+
+ if fit_poisson:
+ l = poisson.fit(
+ counts.query(f'n_cells < {nmax}').n_cells,
+ title=f"automatic {ab}{unit.replace('_','/')}",
+ save_fig_path=path_tif.replace('.tif', '-counts-hist.png')
+ )
+ counts.loc[:, 'poisson fit'] = l
+
+ counts.to_csv((cp := path_tif.replace('.tif', '-counts.csv')), index=None)
+ return aligned
+
+if __name__ == "__main__":
+ fire.Fire(align_multichip)
--
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