In [1]:

from zfish.multi_table.tables_io import scan_resources
from gt_utils import squeeze_plot
import polars as pl

r = (
    scan_resources()  # scan dataframes -> LazyResources
    .collect()  # materialize dataframes -> Resources
    .pipe_tables(
        lambda x: x.with_columns(pl.col("roi").cast(pl.String))
    ) # cast roi columns to string to save space panel (c)
) 

In [2]:

r.pipe(squeeze_plot)

dataset
table	n	pk	cols
wells	30	well	7
rois	212	roi	4
multiscale_levels	6	m	3
object_types	8	o	2
label_images	10,116	o:8, m:6, roi:212	2
label_objects	1,376,201	label, o:4, roi:212	9
channels	25	c	4
images	20,352	c:25, m:6, roi:212	1

In [3]:

pred = pl.col('roi').str.starts_with('B')
(
    r.filter(pred)
    .pipe(squeeze_plot)
)

dataset
table	n	pk	cols
wells	4	well	7
rois	31	roi	4
multiscale_levels	6	m	3
object_types	8	o	2
label_images	1,488	o:8, m:6, roi:31	2
label_objects	192,419	label, o:4, roi:31	9
channels	25	c	4
images	2,976	c:19, m:6, roi:31	1

In [4]:

(
    r.filter(pl.col("o") == "nucleiRaw3")
    .filter(pl.col("m") == 1)
    .pipe(squeeze_plot)
)

dataset
table	n	pk	cols
wells	30	well	7
rois	212	roi	4
multiscale_levels	1	m=1	3
object_types	1	o=nucleiRaw3	2
label_images	212	roi	2
label_objects	459,657	label, roi:212	9
channels	25	c	4
images	3,392	c:25, roi:212	1

In [5]:

(
    r.filter(pl.col("row") == "B")
    .filter(pl.col("col") == "05")
    .filter(pl.col("m") == 1)
    .filter(pl.col("o") == "nucleiRaw3")
    .filter(pl.col("wavelength") == 405)
    .pipe(squeeze_plot)
)

dataset
table	n	pk	cols
wells	1	well=B05	7
rois	9	roi	4
multiscale_levels	1	m=1	3
object_types	1	o=nucleiRaw3	2
label_images	9	roi	2
label_objects	21,270	label, roi:9	9
channels	4	c	4
images	36	c:4, roi:9	1