def apply_extract_features_cp(well_tile,filepattern):
wildcards = {'well':well_tile[0],'tile':well_tile[1]}
filepattern.update(wildcards)
stacked = read_hdf_image(name(filepattern))
nuclei = read(name(filepattern,subdir='process_ph',tag='nuclei',ext='tif'))
cells = read(name(filepattern,subdir='process_ph',tag='cells',ext='tif'))
df_result = Snake._extract_phenotype_cp(data_phenotype=stacked,
nuclei=nuclei,
cells=cells,
wildcards=wildcards,
nucleus_channels=[0,1,2,3],
cell_channels=[0,1,2,3],
channel_names=['dapi','tubulin','gh2ax','phalloidin']
)
df_result.to_csv(name(filepattern,subdir='process_ph',tag='cp_phenotype',ext='csv'))
def export_nd2(f,
iter_axes='v',
project_axes=False,
slicer=slice(None),
f_description=None,
split=False,
backend='ND2SDK',
**kwargs):
if f_description is None:
f_description = parse_nd2_filename(f)
if backend == 'ND2SDK':
reader = ND2_Reader
axes = list('mtzcyx')
elif backend == 'python':
reader = ND2Reader
axes = list('vtzcyx')
else:
raise ValueError('Only "ND2SDK" and "python" backends are available.')
with reader(f) as nd2:
nd2.iter_axes = iter_axes
nd2.bundle_axes = [
ax for ax in axes if (ax in nd2.axes) & (ax != iter_axes)
]
if project_axes:
g = lambda x: x.max(axis=nd2.bundle_axes.index(project_axes))
axes.remove(project_axes)
else:
g = lambda x: x
if split:
axes.remove(nd2.iter_axes[0])
axes = [ax in nd2.bundle_axes for ax in axes]
axes = axes[axes.index(True):]
for site, data in enumerate(nd2[slicer]):
data = g(data)
save(name(f_description, site=site, ext='tif'),
data[tuple([slice(None) if ax else None for ax in axes])],
**kwargs)
else:
axes = [ax in nd2.axes for ax in axes]
axes = axes[axes.index(True):]
data = np.array([g(data) for data in nd2[slicer]])
save(name(f_description, ext='tif'),
data[tuple([slice(None) if ax else None for ax in axes])],
**kwargs)
def build_file_table(f_nd2, f_template, wells):
"""
Example:
wells = 'A1', 'A2', 'A3', 'B1', 'B2', 'B3'
f_template = 'input/10X_Hoechst-mNeon/10X_Hoechst-mNeon_A1.live.tif'
build_file_table(f_nd2, f_template, wells)
"""
rename = lambda x: name(parse(f_template), **x)
get_well = lambda x: wells[int(re.findall('Well(\d)', x)[0]) - 1]
df_files = (common.extract_nd2_metadata_sdk(
f_nd2,
progress=tqdn).assign(well=lambda x: x['file'].apply(get_well)).assign(
site=lambda x: x['m']).assign(
file_=lambda x: x.apply(rename, axis=1)))
return df_files
def extract_nd2_metadata_py(
f,
variables=['frames', 'x_data', 'y_data', 'z_data', 't_data'],
f_description=None):
if f_description is None:
f_description = parse_nd2_filename(f)
with ND2Reader(f) as nd2:
df = pd.DataFrame({
key: val
for key, val in nd2.metadata.items() if key in variables
})
if 'z_data' in variables:
df = pd.concat([
df,
pd.DataFrame(np.array(df['z_data'].tolist()),
columns=[
'z_' + str(level)
for level in nd2.metadata['z_levels']
])
],
axis=1)
df = df.drop(columns='z_data')
if 't_data' in variables:
t_data = np.array(nd2.timesteps).reshape(
-1, len(nd2.metadata['z_levels']))
df = pd.concat([
df,
pd.DataFrame(t_data,
columns=[
't_' + str(step)
for step in range(t_data.shape[1])
])
],
axis=1)
if 'v' in nd2.axes:
df['site'] = nd2.metadata['fields_of_view']
else:
df = df.assign(site=f_description['site'])
(df.assign(well=f_description['well']).to_pickle(
name(f_description, ext='pkl')))
def export_nd2(f,
iter_axes='v',
project_axes=False,
slicer=slice(None),
f_description=None,
split=False,
backend='ND2SDK',
**kwargs):
if f_description is None:
f_description = parse_nd2_filename(f)
if backend == 'ND2SDK':
reader = ND2Reader_SDK
axes = list('mtzcyx')
iter_axes = 'm' if iter_axes == 'v' else iter_axes
elif backend == 'python':
reader = ND2Reader
axes = list('vtzcyx')
iter_axes = 'v' if iter_axes == 'm' else iter_axes
else:
raise ValueError('Only "ND2SDK" and "python" backends are available.')
if not iter_axes in axes:
raise ValueError(
f'Supplied iter_axes \'{iter_axes}\' not in axes options for backend \'{backend}\' ({axes})'
)
with reader(f) as nd2:
if split:
nd2.iter_axes = iter_axes
nd2.bundle_axes = [
ax for ax in axes if (ax in nd2.axes) & (ax != iter_axes)
]
if project_axes:
g = lambda x: x.max(axis=nd2.bundle_axes.index(project_axes))
axes.remove(project_axes)
else:
g = lambda x: x
print(axes)
axes.remove(nd2.iter_axes[0])
# preserve inner singleton dimensions
axes = [ax in nd2.bundle_axes for ax in axes]
axes = axes[axes.index(True):]
for site, data in enumerate(nd2[slicer]):
data = g(data)
save(name(f_description, site=site, ext='tif'),
data[tuple([slice(None) if ax else None for ax in axes])],
**kwargs)
else:
axes_exist = [ax for ax in axes if ax in nd2.axes]
nd2.iter_axes = axes_exist[0]
nd2.bundle_axes = axes_exist[1:]
if project_axes:
data = np.max(nd2, axis=axes_exist.index(project_axes))
axes_exist.remove(project_axes)
# preserve inner singleton dimensions
axes = [ax in axes_exist for ax in axes]
axes = axes[axes.index(True):]
save(name(f_description, ext='tif'),
data[tuple([slice(None) if ax else None for ax in axes])],
**kwargs)