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import os
from . import json_utilities as ju
from allensdk.api.cache import Cache
from allensdk.api.queries.brain_observatory_api import BrainObservatoryApi
from allensdk.config.manifest_builder import ManifestBuilder
from .brain_observatory_nwb_data_set import BrainObservatoryNwbDataSet
import allensdk.brain_observatory.stimulus_info as stim_info
import six
from dateutil.parser import parse as parse_date
[docs]class BrainObservatoryCache(Cache):
"""
Cache class for storing and accessing data from the Brain Observatory.
By default, this class will cache any downloaded metadata or files in
well known locations defined in a manifest file. This behavior can be
disabled.
Attributes
----------
api: BrainObservatoryApi instance
The object used for making API queries related to the Brain
Observatory.
Parameters
----------
cache: boolean
Whether the class should save results of API queries to locations specified
in the manifest file. Queries for files (as opposed to metadata) must have a
file location. If caching is disabled, those locations must be specified
in the function call (e.g. get_ophys_experiment_data(file_name='file.nwb')).
manifest_file: string
File name of the manifest to be read. Default is "brain_observatory_manifest.json".
"""
EXPERIMENT_CONTAINERS_KEY = 'EXPERIMENT_CONTAINERS'
EXPERIMENTS_KEY = 'EXPERIMENTS'
CELL_SPECIMENS_KEY = 'CELL_SPECIMENS'
EXPERIMENT_DATA_KEY = 'EXPERIMENT_DATA'
STIMULUS_MAPPINGS_KEY = 'STIMULUS_MAPPINGS'
MANIFEST_VERSION=None
def __init__(self, cache=True, manifest_file='brain_observatory_manifest.json', base_uri=None, api=None):
super(BrainObservatoryCache, self).__init__(
manifest=manifest_file, cache=cache, version=self.MANIFEST_VERSION)
if api is None:
self.api = BrainObservatoryApi(base_uri=base_uri)
else:
self.api = api
[docs] def get_all_targeted_structures(self):
""" Return a list of all targeted structures in the data set. """
containers = self.get_experiment_containers(simple=False)
targeted_structures = set(
[c['targeted_structure']['acronym'] for c in containers])
return sorted(list(targeted_structures))
[docs] def get_all_cre_lines(self):
""" Return a list of all cre driver lines in the data set. """
containers = self.get_experiment_containers(simple=False)
cre_lines = set([_find_specimen_cre_line(c['specimen'])
for c in containers])
return sorted(list(cre_lines))
[docs] def get_all_reporter_lines(self):
""" Return a list of all reporter lines in the data set. """
containers = self.get_experiment_containers(simple=False)
reporter_lines = set([_find_specimen_reporter_line(c['specimen'])
for c in containers])
return sorted(list(reporter_lines))
[docs] def get_all_imaging_depths(self):
""" Return a list of all imaging depths in the data set. """
containers = self.get_experiment_containers(simple=False)
imaging_depths = set([c['imaging_depth'] for c in containers])
return sorted(list(imaging_depths))
[docs] def get_all_session_types(self):
""" Return a list of all stimulus sessions in the data set. """
exps = self.get_ophys_experiments(simple=False)
names = set([exp['stimulus_name'] for exp in exps])
return sorted(list(names))
[docs] def get_all_stimuli(self):
""" Return a list of all stimuli in the data set. """
return sorted(list(stim_info.all_stimuli()))
[docs] def get_experiment_containers(self, file_name=None,
ids=None,
targeted_structures=None,
imaging_depths=None,
cre_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().
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(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())
transgenic_lines = _merge_transgenic_lines(cre_lines, transgenic_lines)
containers = self.api.filter_experiment_containers(containers, ids=ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
transgenic_lines=transgenic_lines,
include_failed=include_failed)
if simple:
containers = [{
'id': c['id'],
'imaging_depth': c['imaging_depth'],
'targeted_structure': c['targeted_structure']['acronym'],
'cre_line': _find_specimen_cre_line(c['specimen']),
'reporter_line': _find_specimen_reporter_line(c['specimen']),
'donor_name': c['specimen']['donor']['external_donor_name'],
'specimen_name': c['specimen']['name'],
'tags': _find_container_tags(c),
'failed': c['failed']
} for c in containers]
return containers
[docs] def get_ophys_experiment_stimuli(self, experiment_id):
""" For a single experiment, return the list of stimuli present in that experiment. """
exps = self.get_ophys_experiments(ids=[experiment_id])
if len(exps) == 0:
return None
return stim_info.stimuli_in_session(exps[0]['session_type'])
[docs] def get_ophys_experiments(self, file_name=None,
ids=None,
experiment_container_ids=None,
targeted_structures=None,
imaging_depths=None,
cre_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().
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(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())
transgenic_lines = _merge_transgenic_lines(cre_lines, transgenic_lines)
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,
transgenic_lines=transgenic_lines,
stimuli=stimuli,
session_types=session_types,
include_failed=include_failed,
require_eye_tracking=require_eye_tracking)
if simple:
exps = [{
'id': e['id'],
'imaging_depth': e['imaging_depth'],
'targeted_structure': e['targeted_structure']['acronym'],
'cre_line': _find_specimen_cre_line(e['specimen']),
'reporter_line': _find_specimen_reporter_line(e['specimen']),
'acquisition_age_days': _find_experiment_acquisition_age(e),
'experiment_container_id': e['experiment_container_id'],
'session_type': e['stimulus_name'],
'donor_name': e['specimen']['donor']['external_donor_name'],
'specimen_name': e['specimen']['name'],
'fail_eye_tracking': e.get('fail_eye_tracking', None)
} for e in exps]
return exps
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
[docs] 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
[docs] def get_ophys_experiment_data(self, ophys_experiment_id, file_name=None):
""" Download the NWB file for an ophys_experiment (if it hasn't already been
downloaded) and return a data accessor object.
Parameters
----------
file_name: string
File name to save/read the data set. 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.
ophys_experiment_id: integer
id of the ophys_experiment to retrieve
Returns
-------
BrainObservatoryNwbDataSet
"""
file_name = self.get_cache_path(
file_name, self.EXPERIMENT_DATA_KEY, ophys_experiment_id)
self.api.save_ophys_experiment_data(ophys_experiment_id, file_name, strategy='lazy')
return BrainObservatoryNwbDataSet(file_name)
[docs] def build_manifest(self, file_name):
"""
Construct a manifest for this Cache class and save it in a file.
Parameters
----------
file_name: string
File location to save the manifest.
"""
mb = ManifestBuilder()
mb.set_version(self.MANIFEST_VERSION)
mb.add_path('BASEDIR', '.')
mb.add_path(self.EXPERIMENT_CONTAINERS_KEY,
'experiment_containers.json', typename='file', parent_key='BASEDIR')
mb.add_path(self.EXPERIMENTS_KEY, 'ophys_experiments.json',
typename='file', parent_key='BASEDIR')
mb.add_path(self.EXPERIMENT_DATA_KEY, 'ophys_experiment_data/%d.nwb',
typename='file', parent_key='BASEDIR')
mb.add_path(self.CELL_SPECIMENS_KEY, 'cell_specimens.json',
typename='file', parent_key='BASEDIR')
mb.add_path(self.STIMULUS_MAPPINGS_KEY, 'stimulus_mappings.json',
typename='file', parent_key='BASEDIR')
mb.write_json_file(file_name)
def _find_specimen_cre_line(specimen):
try:
return next(tl['name'] for tl in specimen['donor']['transgenic_lines']
if tl['transgenic_line_type_name'] == 'driver' and
'Cre' in tl['name'])
except StopIteration:
return None
def _find_specimen_reporter_line(specimen):
try:
return next(tl['name'] for tl in specimen['donor']['transgenic_lines']
if tl['transgenic_line_type_name'] == 'reporter')
except StopIteration:
return None
def _find_experiment_acquisition_age(exp):
try:
return (parse_date(exp['date_of_acquisition']) - parse_date(exp['specimen']['donor']['date_of_birth'])).days
except KeyError as e:
return None
def _merge_transgenic_lines(*lines_list):
transgenic_lines = set()
for lines in lines_list:
if lines is not None:
for line in lines:
transgenic_lines.add(line)
if len(transgenic_lines):
return list(transgenic_lines)
else:
return None
def _find_container_tags(container):
""" Custom logic for extracting tags from donor conditions. Filtering
out tissuecyte tags. """
conditions = container['specimen']['donor'].get('conditions', [])
return [c['name'] for c in conditions if not c['name'].startswith('tissuecyte')]
def _assert_not_string(arg, name):
if isinstance(arg, six.string_types):
raise TypeError(
"Argument '%s' with value '%s' is a string type, but should be a list." % (name, arg))