def get_structure_tree(self, file_name=None, structure_graph_id=1):
"""
Read the list of adult mouse structures and return an StructureTree
instance.
Parameters
----------
file_name: string
File name to save/read the structures table. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
structure_graph_id: int
Build a tree using structure only from the identified structure graph.
"""
file_name = self.get_cache_path(file_name, self.STRUCTURE_TREE_KEY)
return OntologiesApi(self.api.api_url).get_structures_with_sets(
strategy='lazy',
path=file_name,
pre=StructureTree.clean_structures,
post=lambda x: StructureTree(StructureTree.clean_structures(x)),
structure_graph_ids=structure_graph_id,
**Cache.cache_json())
def get_structure_tree(self, file_name=None, structure_graph_id=1):
"""
Read the list of adult mouse structures and return an StructureTree
instance.
Parameters
----------
file_name: string
File name to save/read the structures table. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
structure_graph_id: int
Build a tree using structure only from the identified structure graph.
"""
file_name = self.get_cache_path(file_name, self.STRUCTURE_TREE_KEY)
return OntologiesApi(self.api.api_url).get_structures_with_sets(
strategy='lazy',
path=file_name,
pre=StructureTree.clean_structures,
post=lambda x: StructureTree(StructureTree.clean_structures(x)),
structure_graph_ids=structure_graph_id,
**Cache.cache_json())
def _get_stimulus_mappings(self, file_name=None):
""" Returns a mapping of which metrics are related to which stimuli. Internal use only. """
file_name = self.get_cache_path(file_name, self.STIMULUS_MAPPINGS_KEY)
mappings = self.api.get_stimulus_mappings(path=file_name,
strategy='lazy',
**Cache.cache_json())
return mappings
def _get_stimulus_mappings(self, file_name=None):
""" Returns a mapping of which metrics are related to which stimuli. Internal use only. """
file_name = self.get_cache_path(file_name, self.STIMULUS_MAPPINGS_KEY)
mappings = self.api.get_stimulus_mappings(path=file_name,
strategy='lazy',
**Cache.cache_json())
return mappings
def get_affine_parameters(self,
section_data_set_id,
direction='trv',
file_name=None):
''' Extract the parameters of the 3D affine tranformation mapping this section data set's image-space stack to
CCF-space (or vice-versa).
Parameters
----------
section_data_set_id : int
download the parameters for this data set.
direction : str, optional
Valid options are:
trv : "transform from reference to volume". Maps CCF points to image space points. If you are
resampling data into CCF, this is the direction you want.
tvr : "transform from volume to reference". Maps image space points to CCF points.
file_name : str
If provided, store the downloaded file here.
Returns
-------
alignment : numpy.ndarray
4 X 3 matrix. In order to transform a point [X_1, X_2, X_3] run
np.dot([X_1, X_2, X_3, 1], alignment). In
to build a SimpleITK affine transform run:
transform = sitk.AffineTransform(3)
transform.SetParameters(alignment.flatten())
'''
if not direction in ('trv', 'tvr'):
raise ArgumentError(
'invalid direction: {}. direction must be one of tvr, trv'.
format(direction))
file_name = self.get_cache_path(file_name, self.ALIGNMENT3D_KEY)
raw_alignment = self.api.download_alignment3d(
strategy='lazy',
path=file_name,
section_data_set_id=section_data_set_id,
**Cache.cache_json())
alignment_re = re.compile('{}_(?P<index>\d+)'.format(direction))
alignment = np.zeros((4, 3), dtype=float)
for entry, value in raw_alignment.items():
match = alignment_re.match(entry)
if match is not None:
alignment.flat[int(match.group('index'))] = value
return alignment
def get_cells(self, file_name=None,
require_morphology=False,
require_reconstruction=False,
reporter_status=None,
species=None,
simple=True):
"""
Download metadata for all cells in the database and optionally return a
subset filtered by whether or not they have a morphology or reconstruction.
Parameters
----------
file_name: string
File name to save/read the cell metadata as JSON. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
require_morphology: boolean
Filter out cells that have no morphological images.
require_reconstruction: boolean
Filter out cells that have no morphological reconstructions.
reporter_status: list
Filter for cells that have one or more cell reporter statuses.
species: list
Filter for cells that belong to one or more species. If None, return all.
Must be one of [ CellTypesApi.MOUSE, CellTypesApi.HUMAN ].
"""
file_name = self.get_cache_path(file_name, self.CELLS_KEY)
cells = self.api.list_cells_api(path=file_name,
strategy='lazy',
**Cache.cache_json())
if isinstance(reporter_status, string_types):
reporter_status = [reporter_status]
# filter the cells on the way out
cells = self.api.filter_cells_api(cells,
require_morphology,
require_reconstruction,
reporter_status,
species,
simple)
return cells
def get_cells(self,
file_name=None,
require_morphology=False,
require_reconstruction=False,
reporter_status=None,
species=None,
simple=True):
"""
Download metadata for all cells in the database and optionally return a
subset filtered by whether or not they have a morphology or reconstruction.
Parameters
----------
file_name: string
File name to save/read the cell metadata as JSON. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
require_morphology: boolean
Filter out cells that have no morphological images.
require_reconstruction: boolean
Filter out cells that have no morphological reconstructions.
reporter_status: list
Filter for cells that have one or more cell reporter statuses.
species: list
Filter for cells that belong to one or more species. If None, return all.
Must be one of [ CellTypesApi.MOUSE, CellTypesApi.HUMAN ].
"""
file_name = self.get_cache_path(file_name, self.CELLS_KEY)
cells = self.api.list_cells_api(path=file_name,
strategy='lazy',
**Cache.cache_json())
if isinstance(reporter_status, string_types):
reporter_status = [reporter_status]
# filter the cells on the way out
cells = self.api.filter_cells_api(cells, require_morphology,
require_reconstruction,
reporter_status, species, simple)
return cells
def get_affine_parameters(self, section_data_set_id, direction='trv', file_name=None):
''' Extract the parameters of the 3D affine tranformation mapping this section data set's image-space stack to
CCF-space (or vice-versa).
Parameters
----------
section_data_set_id : int
download the parameters for this data set.
direction : str, optional
Valid options are:
trv : "transform from reference to volume". Maps CCF points to image space points. If you are
resampling data into CCF, this is the direction you want.
tvr : "transform from volume to reference". Maps image space points to CCF points.
file_name : str
If provided, store the downloaded file here.
Returns
-------
alignment : numpy.ndarray
4 X 3 matrix. In order to transform a point [X_1, X_2, X_3] run
np.dot([X_1, X_2, X_3, 1], alignment). In
to build a SimpleITK affine transform run:
transform = sitk.AffineTransform(3)
transform.SetParameters(alignment.flatten())
'''
if not direction in ('trv', 'tvr'):
raise ArgumentError('invalid direction: {}. direction must be one of tvr, trv'.format(direction))
file_name = self.get_cache_path(file_name, self.ALIGNMENT3D_KEY)
raw_alignment = self.api.download_alignment3d(
strategy='lazy',
path=file_name,
section_data_set_id=section_data_set_id,
**Cache.cache_json())
alignment_re = re.compile('{}_(?P<index>\d+)'.format(direction))
alignment = np.zeros((4, 3), dtype=float)
for entry, value in raw_alignment.items():
match = alignment_re.match(entry)
if match is not None:
alignment.flat[int(match.group('index'))] = value
return alignment
def test_excpt(mkdir, ju_read_url_get, ju_read, ju_write):
@cacheable()
def get_hemispheres_excpt():
return RmaApi().model_query(model='Hemisphere',
excpt=['symbol'])
df = get_hemispheres_excpt(path='/xyz/abc/example.json',
strategy='create',
**Cache.cache_json())
assert 'whatever' in df[0]
ju_read_url_get.assert_called_once_with(
'http://api.brain-map.org/api/v2/data/query.json?q=model::Hemisphere,rma::options%5Bexcept$eqsymbol%5D')
ju_write.assert_called_once_with('/xyz/abc/example.json', _msg)
ju_read.assert_called_once_with('/xyz/abc/example.json')
mkdir.assert_called_once_with('/xyz/abc')
def test_cacheable_json(from_csv, mkdir, ju_read_url_get, ju_read, ju_write):
@cacheable()
def get_hemispheres():
return RmaApi().model_query(model='Hemisphere')
df = get_hemispheres(path='/xyz/abc/example.json',
strategy='create',
**Cache.cache_json())
assert 'whatever' in df[0]
ju_read_url_get.assert_called_once_with(
'http://api.brain-map.org/api/v2/data/query.json?q=model::Hemisphere')
assert not from_csv.called, 'from_csv should not have been called'
ju_write.assert_called_once_with('/xyz/abc/example.json',
_msg)
ju_read.assert_called_once_with('/xyz/abc/example.json')
def test_cacheable_json(read_csv, mkdir, ju_read_url_get, ju_read, ju_write):
@cacheable()
def get_hemispheres():
return RmaApi().model_query(model='Hemisphere')
df = get_hemispheres(path='/xyz/abc/example.json',
strategy='create',
**Cache.cache_json())
assert 'whatever' in df[0]
ju_read_url_get.assert_called_once_with(
'http://api.brain-map.org/api/v2/data/query.json?q=model::Hemisphere')
assert not read_csv.called, 'read_csv should not have been called'
ju_write.assert_called_once_with('/xyz/abc/example.json',
_msg)
ju_read.assert_called_once_with('/xyz/abc/example.json')
def test_excpt(mkdir, ju_read_url_get, ju_read, ju_write):
@cacheable()
def get_hemispheres_excpt():
return RmaApi().model_query(model='Hemisphere',
excpt=['symbol'])
df = get_hemispheres_excpt(path='/xyz/abc/example.json',
strategy='create',
**Cache.cache_json())
assert 'whatever' in df[0]
ju_read_url_get.assert_called_once_with(
'http://api.brain-map.org/api/v2/data/query.json?q=model::Hemisphere,rma::options%5Bexcept$eqsymbol%5D')
ju_write.assert_called_once_with('/xyz/abc/example.json', _msg)
ju_read.assert_called_once_with('/xyz/abc/example.json')
mkdir.assert_called_once_with('/xyz/abc')
def get_ephys_sweeps(self, specimen_id, file_name=None):
"""
Download sweep metadata for a single cell specimen.
Parameters
----------
specimen_id: int
ID of a cell.
"""
file_name = self.get_cache_path(
file_name, self.EPHYS_SWEEPS_KEY, specimen_id)
sweeps = self.api.get_ephys_sweeps(specimen_id,
strategy='lazy',
path=file_name,
**Cache.cache_json())
return sweeps
def get_ephys_sweeps(self, specimen_id, file_name=None):
"""
Download sweep metadata for a single cell specimen.
Parameters
----------
specimen_id: int
ID of a cell.
"""
file_name = self.get_cache_path(file_name, self.EPHYS_SWEEPS_KEY,
specimen_id)
sweeps = self.api.get_ephys_sweeps(specimen_id,
strategy='lazy',
path=file_name,
**Cache.cache_json())
return sweeps
def get_experiments(self,
dataframe=False,
file_name=None,
cre=None,
injection_structure_ids=None):
"""
Read a list of experiments that match certain criteria. If caching is enabled,
this will save the whole (unfiltered) list of experiments to a file.
Parameters
----------
dataframe: boolean
Return the list of experiments as a Pandas DataFrame. If False,
return a list of dictionaries. Default False.
file_name: string
File name to save/read the structures table. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
cre: boolean or list
If True, return only cre-positive experiments. If False, return only
cre-negative experiments. If None, return all experients. If list, return
all experiments with cre line names in the supplied list. Default None.
injection_structure_ids: list
Only return experiments that were injected in the structures provided here.
If None, return all experiments. Default None.
"""
file_name = self.get_cache_path(file_name, self.EXPERIMENTS_KEY)
experiments = self.api.get_experiments_api(path=file_name,
strategy='lazy',
**Cache.cache_json())
for e in experiments:
# renaming id
e['id'] = e['data_set_id']
del e['data_set_id']
# simplify trangsenic line
tl = e.get('transgenic_line', None)
if tl:
e['transgenic_line'] = tl['name']
# parse the injection structures
injs = [int(i) for i in e['injection_structures'].split('/')]
e['injection_structures'] = injs
e['primary_injection_structure'] = injs[0]
# remove storage dir
del e['storage_directory']
# filter the read/downloaded list of experiments
experiments = self.filter_experiments(experiments, cre,
injection_structure_ids)
if dataframe:
experiments = pd.DataFrame(experiments)
experiments.set_index(['id'], inplace=True, drop=False)
return experiments
def get_cell_specimens(self,
file_name=None,
ids=None,
experiment_container_ids=None,
include_failed=False,
simple=True,
filters=None):
""" Return cell specimens that have certain properies.
Parameters
----------
file_name: string
File name to save/read the cell specimens. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of cell specimen ids.
experiment_container_ids: list
List of experiment container ids.
include_failed: bool
Whether to include cells from failed experiment containers
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
filters: list of dicts
List of filter dictionaries. The Allen Brain Observatory web site can
generate filters in this format to reproduce a filtered set of cells
found there. To see what these look like, visit
http://observatory.brain-map.org/visualcoding, perform a cell search
and apply some filters (e.g. find cells in a particular area), then
click the "view these cells in the AllenSDK" link on the bottom-left
of the search results page. This will take you to a page that contains
a code sample you can use to apply those same filters via this argument.
For more detail on the filter syntax, see BrainObservatoryApi.dataframe_query.
Returns
-------
list of dictionaries
"""
file_name = self.get_cache_path(file_name, self.CELL_SPECIMENS_KEY)
cell_specimens = self.api.get_cell_metrics(
path=file_name,
strategy='lazy',
pre=lambda x: [y for y in x],
**Cache.cache_json())
cell_specimens = self.api.filter_cell_specimens(
cell_specimens,
ids=ids,
experiment_container_ids=experiment_container_ids,
include_failed=include_failed,
filters=filters)
# drop the thumbnail columns
if simple:
mappings = self._get_stimulus_mappings()
thumbnails = [
m['item'] for m in mappings
if m['item_type'] == 'T' and m['level'] == 'R'
]
for cs in cell_specimens:
for t in thumbnails:
del cs[t]
return cell_specimens
def get_experiment_containers(self, file_name=None,
ids=None,
targeted_structures=None,
imaging_depths=None,
cre_lines=None,
reporter_lines=None,
transgenic_lines=None,
include_failed=False,
simple=True):
""" Get a list of experiment containers matching certain criteria.
Parameters
----------
file_name: string
File name to save/read the experiment containers. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of experiment container ids.
targeted_structures: list
List of structure acronyms. Must be in the list returned by
BrainObservatoryCache.get_all_targeted_structures().
imaging_depths: list
List of imaging depths. Must be in the list returned by
BrainObservatoryCache.get_all_imaging_depths().
cre_lines: list
List of cre lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines().
reporter_lines: list
List of reporter lines. Must be in the list returned by
BrainObservatoryCache.get_all_reporter_lines().
transgenic_lines: list
List of transgenic lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines() or.
BrainObservatoryCache.get_all_reporter_lines().
include_failed: boolean
Whether or not to include failed experiment containers.
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
Returns
-------
list of dictionaries
"""
_assert_not_string(targeted_structures, "targeted_structures")
_assert_not_string(cre_lines, "cre_lines")
_assert_not_string(reporter_lines, "reporter_lines")
_assert_not_string(transgenic_lines, "transgenic_lines")
file_name = self.get_cache_path(
file_name, self.EXPERIMENT_CONTAINERS_KEY)
containers = self.api.get_experiment_containers(path=file_name,
strategy='lazy',
**Cache.cache_json())
containers = self.api.filter_experiment_containers(containers, ids=ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
cre_lines=cre_lines,
reporter_lines=reporter_lines,
transgenic_lines=transgenic_lines,
include_failed=include_failed,
simple=simple)
return containers
def get_ophys_experiments(self,
file_name=None,
ids=None,
experiment_container_ids=None,
targeted_structures=None,
imaging_depths=None,
cre_lines=None,
reporter_lines=None,
transgenic_lines=None,
stimuli=None,
session_types=None,
cell_specimen_ids=None,
include_failed=False,
require_eye_tracking=False,
simple=True):
""" Get a list of ophys experiments matching certain criteria.
Parameters
----------
file_name: string
File name to save/read the ophys experiments. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of ophys experiment ids.
experiment_container_ids: list
List of experiment container ids.
targeted_structures: list
List of structure acronyms. Must be in the list returned by
BrainObservatoryCache.get_all_targeted_structures().
imaging_depths: list
List of imaging depths. Must be in the list returned by
BrainObservatoryCache.get_all_imaging_depths().
cre_lines: list
List of cre lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines().
reporter_lines: list
List of reporter lines. Must be in the list returned by
BrainObservatoryCache.get_all_reporter_lines().
transgenic_lines: list
List of transgenic lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines() or.
BrainObservatoryCache.get_all_reporter_lines().
stimuli: list
List of stimulus names. Must be in the list returned by
BrainObservatoryCache.get_all_stimuli().
session_types: list
List of stimulus session type names. Must be in the list returned by
BrainObservatoryCache.get_all_session_types().
cell_specimen_ids: list
Only include experiments that contain cells with these ids.
include_failed: boolean
Whether or not to include experiments from failed experiment containers.
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
require_eye_tracking: boolean
If True, only return experiments that have eye tracking results. Default: False.
Returns
-------
list of dictionaries
"""
_assert_not_string(targeted_structures, "targeted_structures")
_assert_not_string(cre_lines, "cre_lines")
_assert_not_string(reporter_lines, "reporter_lines")
_assert_not_string(transgenic_lines, "transgenic_lines")
_assert_not_string(stimuli, "stimuli")
_assert_not_string(session_types, "session_types")
file_name = self.get_cache_path(file_name, self.EXPERIMENTS_KEY)
exps = self.api.get_ophys_experiments(path=file_name,
strategy='lazy',
**Cache.cache_json())
# NOTE: Ugly hack to update the 'fail_eye_tracking' field
# which is using True/False values for the previous eye mapping
# implementation. This will also need to be fixed in warehouse.
# ----- Start of ugly hack -----
response = self.api.template_query('brain_observatory_queries',
'all_eye_mapping_files')
session_ids_with_eye_tracking: set = {
entry['attachable_id']
for entry in response if entry['attachable_type'] == "OphysSession"
}
for indx, exp in enumerate(exps):
try:
ophys_session_id = ophys_experiment_session_id_map[exp['id']]
if ophys_session_id in session_ids_with_eye_tracking:
exps[indx]['fail_eye_tracking'] = False
else:
exps[indx]['fail_eye_tracking'] = True
except KeyError:
exps[indx]['fail_eye_tracking'] = True
# ----- End of ugly hack -----
if cell_specimen_ids is not None:
cells = self.get_cell_specimens(ids=cell_specimen_ids)
cell_container_ids = set(
[cell['experiment_container_id'] for cell in cells])
if experiment_container_ids is not None:
experiment_container_ids = list(
set(experiment_container_ids) - cell_container_ids)
else:
experiment_container_ids = list(cell_container_ids)
exps = self.api.filter_ophys_experiments(
exps,
ids=ids,
experiment_container_ids=experiment_container_ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
cre_lines=cre_lines,
reporter_lines=reporter_lines,
transgenic_lines=transgenic_lines,
stimuli=stimuli,
session_types=session_types,
include_failed=include_failed,
require_eye_tracking=require_eye_tracking,
simple=simple)
return exps
def get_experiment_containers(self,
file_name=None,
ids=None,
targeted_structures=None,
imaging_depths=None,
cre_lines=None,
reporter_lines=None,
transgenic_lines=None,
include_failed=False,
simple=True):
""" Get a list of experiment containers matching certain criteria.
Parameters
----------
file_name: string
File name to save/read the experiment containers. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of experiment container ids.
targeted_structures: list
List of structure acronyms. Must be in the list returned by
BrainObservatoryCache.get_all_targeted_structures().
imaging_depths: list
List of imaging depths. Must be in the list returned by
BrainObservatoryCache.get_all_imaging_depths().
cre_lines: list
List of cre lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines().
reporter_lines: list
List of reporter lines. Must be in the list returned by
BrainObservatoryCache.get_all_reporter_lines().
transgenic_lines: list
List of transgenic lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines() or.
BrainObservatoryCache.get_all_reporter_lines().
include_failed: boolean
Whether or not to include failed experiment containers.
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
Returns
-------
list of dictionaries
"""
_assert_not_string(targeted_structures, "targeted_structures")
_assert_not_string(cre_lines, "cre_lines")
_assert_not_string(reporter_lines, "reporter_lines")
_assert_not_string(transgenic_lines, "transgenic_lines")
file_name = self.get_cache_path(file_name,
self.EXPERIMENT_CONTAINERS_KEY)
containers = self.api.get_experiment_containers(path=file_name,
strategy='lazy',
**Cache.cache_json())
containers = self.api.filter_experiment_containers(
containers,
ids=ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
cre_lines=cre_lines,
reporter_lines=reporter_lines,
transgenic_lines=transgenic_lines,
include_failed=include_failed,
simple=simple)
return containers
def get_ophys_experiments(self, file_name=None,
ids=None,
experiment_container_ids=None,
targeted_structures=None,
imaging_depths=None,
cre_lines=None,
reporter_lines=None,
transgenic_lines=None,
stimuli=None,
session_types=None,
cell_specimen_ids=None,
include_failed=False,
require_eye_tracking=False,
simple=True):
""" Get a list of ophys experiments matching certain criteria.
Parameters
----------
file_name: string
File name to save/read the ophys experiments. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of ophys experiment ids.
experiment_container_ids: list
List of experiment container ids.
targeted_structures: list
List of structure acronyms. Must be in the list returned by
BrainObservatoryCache.get_all_targeted_structures().
imaging_depths: list
List of imaging depths. Must be in the list returned by
BrainObservatoryCache.get_all_imaging_depths().
cre_lines: list
List of cre lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines().
reporter_lines: list
List of reporter lines. Must be in the list returned by
BrainObservatoryCache.get_all_reporter_lines().
transgenic_lines: list
List of transgenic lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines() or.
BrainObservatoryCache.get_all_reporter_lines().
stimuli: list
List of stimulus names. Must be in the list returned by
BrainObservatoryCache.get_all_stimuli().
session_types: list
List of stimulus session type names. Must be in the list returned by
BrainObservatoryCache.get_all_session_types().
cell_specimen_ids: list
Only include experiments that contain cells with these ids.
include_failed: boolean
Whether or not to include experiments from failed experiment containers.
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
require_eye_tracking: boolean
If True, only return experiments that have eye tracking results. Default: False.
Returns
-------
list of dictionaries
"""
_assert_not_string(targeted_structures, "targeted_structures")
_assert_not_string(cre_lines, "cre_lines")
_assert_not_string(reporter_lines, "reporter_lines")
_assert_not_string(transgenic_lines, "transgenic_lines")
_assert_not_string(stimuli, "stimuli")
_assert_not_string(session_types, "session_types")
file_name = self.get_cache_path(file_name, self.EXPERIMENTS_KEY)
exps = self.api.get_ophys_experiments(path=file_name,
strategy='lazy',
**Cache.cache_json())
if cell_specimen_ids is not None:
cells = self.get_cell_specimens(ids=cell_specimen_ids)
cell_container_ids = set([cell['experiment_container_id'] for cell in cells])
if experiment_container_ids is not None:
experiment_container_ids = list(set(experiment_container_ids) - cell_container_ids)
else:
experiment_container_ids = list(cell_container_ids)
exps = self.api.filter_ophys_experiments(exps,
ids=ids,
experiment_container_ids=experiment_container_ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
cre_lines=cre_lines,
reporter_lines=reporter_lines,
transgenic_lines=transgenic_lines,
stimuli=stimuli,
session_types=session_types,
include_failed=include_failed,
require_eye_tracking=require_eye_tracking,
simple=simple)
return exps
def get_cell_specimens(self,
file_name=None,
ids=None,
experiment_container_ids=None,
include_failed=False,
simple=True,
filters=None):
""" Return cell specimens that have certain properies.
Parameters
----------
file_name: string
File name to save/read the cell specimens. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of cell specimen ids.
experiment_container_ids: list
List of experiment container ids.
include_failed: bool
Whether to include cells from failed experiment containers
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
filters: list of dicts
List of filter dictionaries. The Allen Brain Observatory web site can
generate filters in this format to reproduce a filtered set of cells
found there. To see what these look like, visit
http://observatory.brain-map.org/visualcoding, perform a cell search
and apply some filters (e.g. find cells in a particular area), then
click the "view these cells in the AllenSDK" link on the bottom-left
of the search results page. This will take you to a page that contains
a code sample you can use to apply those same filters via this argument.
For more detail on the filter syntax, see BrainObservatoryApi.dataframe_query.
Returns
-------
list of dictionaries
"""
file_name = self.get_cache_path(file_name, self.CELL_SPECIMENS_KEY)
cell_specimens = self.api.get_cell_metrics(path=file_name,
strategy='lazy',
pre= lambda x: [y for y in x],
**Cache.cache_json())
cell_specimens = self.api.filter_cell_specimens(cell_specimens,
ids=ids,
experiment_container_ids=experiment_container_ids,
include_failed=include_failed,
filters=filters)
# drop the thumbnail columns
if simple:
mappings = self._get_stimulus_mappings()
thumbnails = [m['item'] for m in mappings if m[
'item_type'] == 'T' and m['level'] == 'R']
for cs in cell_specimens:
for t in thumbnails:
del cs[t]
return cell_specimens
def get_experiments(self, dataframe=False, file_name=None, cre=None, injection_structure_ids=None):
"""
Read a list of experiments that match certain criteria. If caching is enabled,
this will save the whole (unfiltered) list of experiments to a file.
Parameters
----------
dataframe: boolean
Return the list of experiments as a Pandas DataFrame. If False,
return a list of dictionaries. Default False.
file_name: string
File name to save/read the structures table. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
cre: boolean or list
If True, return only cre-positive experiments. If False, return only
cre-negative experiments. If None, return all experients. If list, return
all experiments with cre line names in the supplied list. Default None.
injection_structure_ids: list
Only return experiments that were injected in the structures provided here.
If None, return all experiments. Default None.
"""
file_name = self.get_cache_path(file_name, self.EXPERIMENTS_KEY)
experiments = self.api.get_experiments_api(path=file_name,
strategy='lazy',
**Cache.cache_json())
for e in experiments:
# renaming id
e['id'] = e['data_set_id']
del e['data_set_id']
# simplify trangsenic line
tl = e.get('transgenic_line', None)
if tl:
e['transgenic_line'] = tl['name']
# parse the injection structures
injs = [ int(i) for i in e['injection_structures'].split('/') ]
e['injection_structures'] = injs
e['primary_injection_structure'] = injs[0]
# remove storage dir
del e['storage_directory']
# filter the read/downloaded list of experiments
experiments = self.filter_experiments(
experiments, cre, injection_structure_ids)
if dataframe:
experiments = pd.DataFrame(experiments)
experiments.set_index(['id'], inplace=True, drop=False)
return experiments
def get_ophys_experiments(self,
file_name=None,
ids=None,
experiment_container_ids=None,
targeted_structures=None,
imaging_depths=None,
cre_lines=None,
reporter_lines=None,
transgenic_lines=None,
stimuli=None,
session_types=None,
cell_specimen_ids=None,
include_failed=False,
require_eye_tracking=False,
simple=True):
""" Get a list of ophys experiments matching certain criteria.
Parameters
----------
file_name: string
File name to save/read the ophys experiments. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of ophys experiment ids.
experiment_container_ids: list
List of experiment container ids.
targeted_structures: list
List of structure acronyms. Must be in the list returned by
BrainObservatoryCache.get_all_targeted_structures().
imaging_depths: list
List of imaging depths. Must be in the list returned by
BrainObservatoryCache.get_all_imaging_depths().
cre_lines: list
List of cre lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines().
reporter_lines: list
List of reporter lines. Must be in the list returned by
BrainObservatoryCache.get_all_reporter_lines().
transgenic_lines: list
List of transgenic lines. Must be in the list returned by
BrainObservatoryCache.get_all_cre_lines() or.
BrainObservatoryCache.get_all_reporter_lines().
stimuli: list
List of stimulus names. Must be in the list returned by
BrainObservatoryCache.get_all_stimuli().
session_types: list
List of stimulus session type names. Must be in the list returned by
BrainObservatoryCache.get_all_session_types().
cell_specimen_ids: list
Only include experiments that contain cells with these ids.
include_failed: boolean
Whether or not to include experiments from failed experiment containers.
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
require_eye_tracking: boolean
If True, only return experiments that have eye tracking results. Default: False.
Returns
-------
list of dictionaries
"""
_assert_not_string(targeted_structures, "targeted_structures")
_assert_not_string(cre_lines, "cre_lines")
_assert_not_string(reporter_lines, "reporter_lines")
_assert_not_string(transgenic_lines, "transgenic_lines")
_assert_not_string(stimuli, "stimuli")
_assert_not_string(session_types, "session_types")
file_name = self.get_cache_path(file_name, self.EXPERIMENTS_KEY)
exps = self.api.get_ophys_experiments(path=file_name,
strategy='lazy',
**Cache.cache_json())
if cell_specimen_ids is not None:
cells = self.get_cell_specimens(ids=cell_specimen_ids)
cell_container_ids = set(
[cell['experiment_container_id'] for cell in cells])
if experiment_container_ids is not None:
experiment_container_ids = list(
set(experiment_container_ids) - cell_container_ids)
else:
experiment_container_ids = list(cell_container_ids)
exps = self.api.filter_ophys_experiments(
exps,
ids=ids,
experiment_container_ids=experiment_container_ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
cre_lines=cre_lines,
reporter_lines=reporter_lines,
transgenic_lines=transgenic_lines,
stimuli=stimuli,
session_types=session_types,
include_failed=include_failed,
require_eye_tracking=require_eye_tracking,
simple=simple)
return exps
