def make_labels_level_img(img_path, level, prefix=None, show=False):
"""Replace labels in an image with their parents at the given level.
Labels that do not fall within a parent at that level will remain in place.
Args:
img_path: Path to the base image from which the corresponding
registered image will be found.
level: Ontological level at which to group child labels.
prefix: Start of path for output image; defaults to None to
use ``img_path`` instead.
show: True to show the images after generating them; defaults to False.
"""
# load original labels image and setup ontology dictionary
labels_sitk = sitk_io.load_registered_img(img_path,
config.RegNames.IMG_LABELS.value,
get_sitk=True)
labels_np = sitk.GetArrayFromImage(labels_sitk)
ref = ontology.load_labels_ref(config.load_labels)
labels_ref_lookup = ontology.create_aba_reverse_lookup(ref)
ids = list(labels_ref_lookup.keys())
for key in ids:
keys = [key, -1 * key]
for region in keys:
if region == 0: continue
# get ontological label
label = labels_ref_lookup[abs(region)]
label_level = label[ontology.NODE][config.ABAKeys.LEVEL.value]
if label_level == level:
# get children (including parent first) at given level
# and replace them with parent
label_ids = ontology.get_children_from_id(
labels_ref_lookup, region)
labels_region = np.isin(labels_np, label_ids)
print("replacing labels within", region)
labels_np[labels_region] = region
labels_level_sitk = sitk_io.replace_sitk_with_numpy(labels_sitk, labels_np)
# generate an edge image at this level
labels_edge = vols.make_labels_edge(labels_np)
labels_edge_sikt = sitk_io.replace_sitk_with_numpy(labels_sitk,
labels_edge)
# write and optionally display labels level image
imgs_write = {
config.RegNames.IMG_LABELS_LEVEL.value.format(level):
labels_level_sitk,
config.RegNames.IMG_LABELS_EDGE_LEVEL.value.format(level):
labels_edge_sikt,
}
out_path = prefix if prefix else img_path
sitk_io.write_reg_images(imgs_write, out_path)
if show:
for img in imgs_write.values():
if img: sitk.Show(img)
def plot_region_development(metric, size=None, show=True):
"""Plot regions across development for the given metric.
Args:
metric (str): Column name of metric to track.
size (List[int]): Sequence of ``width, height`` to size the figure;
defaults to None.
show (bool): True to display the image; defaults to True.
"""
# set up access to data frame columns
id_cols = ["Age", "Condition"]
extra_cols = ["RegionName"]
cond_col = "Region"
# assume that vol stats file is given first, then region IDs;
# merge in region names and levels
df_regions = pd.read_csv(config.filenames[1])
df = pd.read_csv(config.filename).merge(
df_regions[["Region", "RegionName", "Level"]], on="Region", how="left")
# convert sample names to ages
ages = ontology.rel_to_abs_ages(df["Sample"].unique())
df["Age"] = df["Sample"].map(ages)
# get large super-structures for normalization to brain tissue, where
# "non-brain" are spinal cord and ventricles, which are variably labeled
df_base = df[df["Region"] == 15564]
ids_nonbr_large = (17651, 126651558)
dfs_nonbr_large = [df[df["Region"] == n] for n in ids_nonbr_large]
# get data frame with region IDs of all non-brain structures removed
labels_ref_lookup = ontology.LabelsRef(
config.load_labels).load().ref_lookup
ids_nonbr = []
for n in ids_nonbr_large:
ids_nonbr.extend(ontology.get_children_from_id(labels_ref_lookup, n))
label_id = config.atlas_labels[config.AtlasLabels.ID]
if label_id is not None:
# show only selected region and its children
ids = ontology.get_children_from_id(labels_ref_lookup, label_id)
df = df[np.isin(df["Region"], ids)]
df_brain = df.loc[~df["Region"].isin(ids_nonbr)]
levels = np.sort(df["Level"].unique())
conds = df["Condition"].unique()
# get aggregated whole brain tissue for normalization
cols_show = (*id_cols, cond_col, *extra_cols, metric)
if dfs_nonbr_large:
# add all large non-brain structures
df_nonbr = dfs_nonbr_large[0]
for df_out in dfs_nonbr_large[1:]:
df_nonbr = df_io.normalize_df(df_nonbr, id_cols, cond_col, None,
[metric], extra_cols, df_out,
df_io.df_add)
# subtract them from whole organism to get brain tissue alone,
# updating given metric in db_base
df_base = df_io.normalize_df(df_base, id_cols, cond_col, None,
[metric], extra_cols, df_nonbr,
df_io.df_subtract)
df_base.loc[:, "RegionName"] = "Brain tissue"
print("Brain {}:".format(metric))
df_io.print_data_frame(df_base.loc[:, cols_show], "\t")
df_base_piv, regions = df_io.pivot_with_conditions(df_base, id_cols,
"RegionName", metric)
# plot lines with separate styles for each condition and colors for
# each region name
linestyles = ("--", "-.", ":", "-")
num_conds = len(conds)
linestyles = linestyles * (num_conds // (len(linestyles) + 1) + 1)
if num_conds < len(linestyles):
# ensure that 1st and last styles are dashed and solid unless
linestyles = (*linestyles[:num_conds - 1], linestyles[-1])
lines_params = {
"labels": (metric, "Post-Conceptional Age"),
"linestyles": linestyles,
"size": size,
"show": show,
"ignore_invis": True,
"groups": conds,
"marker": ".",
}
line_params_norm = lines_params.copy()
line_params_norm["labels"] = ("Fraction", "Post-Conceptional Age")
plot_2d.plot_lines(config.filename,
"Age",
regions,
title="Whole Brain Development ({})".format(metric),
suffix="_dev_{}_brain".format(metric),
df=df_base_piv,
**lines_params)
for level in levels:
# plot raw metric at given level
df_level = df.loc[df["Level"] == level]
print("Raw {}:".format(metric))
df_io.print_data_frame(df_level.loc[:, cols_show], "\t")
df_level_piv, regions = df_io.pivot_with_conditions(
df_level, id_cols, "RegionName", metric)
plot_2d.plot_lines(config.filename,
"Age",
regions,
title="Structure Development ({}, Level {})".format(
metric, level),
suffix="_dev_{}_level{}".format(metric, level),
df=df_level_piv,
**lines_params)
# plot metric normalized to whole brain tissue; structures
# above removed regions will still contain them
df_brain_level = df_brain.loc[df_brain["Level"] == level]
df_norm = df_io.normalize_df(df_brain_level, id_cols, cond_col, None,
[metric], extra_cols, df_base)
print("{} normalized to whole brain:".format(metric))
df_io.print_data_frame(df_norm.loc[:, cols_show], "\t")
df_norm_piv, regions = df_io.pivot_with_conditions(
df_norm, id_cols, "RegionName", metric)
plot_2d.plot_lines(
config.filename,
"Age",
regions,
units=(None, config.plot_labels[config.PlotLabels.X_UNIT]),
title=("Structure Development Normalized to Whole "
"Brain ({}, Level {})".format(metric, level)),
suffix="_dev_{}_level{}_norm".format(metric, level),
df=df_norm_piv,
**line_params_norm)