class MouseConnectivityCache(Cache):
"""
Cache class for storing and accessing data related to the adult mouse
Connectivity Atlas. 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
----------
resolution: int
Resolution of grid data to be downloaded when accessing projection volume,
the annotation volume, and the annotation volume. Must be one of (10, 25,
50, 100). Default is 25.
api: MouseConnectivityApi instance
Used internally to make API queries.
Parameters
----------
resolution: int
Resolution of grid data to be downloaded when accessing projection volume,
the annotation volume, and the annotation volume. Must be one of (10, 25,
50, 100). Default is 25.
ccf_version: string
Desired version of the Common Coordinate Framework. This affects the annotation
volume (get_annotation_volume) and structure masks (get_structure_mask).
Must be one of (MouseConnectivityApi.CCF_2015, MouseConnectivityApi.CCF_2016).
Default: MouseConnectivityApi.CCF_2016
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_projection_density(file_name='file.nrrd')).
manifest_file: string
File name of the manifest to be read. Default is "mouse_connectivity_manifest.json".
"""
CCF_VERSION_KEY = "CCF_VERSION"
ANNOTATION_KEY = "ANNOTATION"
TEMPLATE_KEY = "TEMPLATE"
PROJECTION_DENSITY_KEY = "PROJECTION_DENSITY"
INJECTION_DENSITY_KEY = "INJECTION_DENSITY"
INJECTION_FRACTION_KEY = "INJECTION_FRACTION"
DATA_MASK_KEY = "DATA_MASK"
STRUCTURE_UNIONIZES_KEY = "STRUCTURE_UNIONIZES"
EXPERIMENTS_KEY = "EXPERIMENTS"
STRUCTURES_KEY = "STRUCTURES"
STRUCTURE_MASK_KEY = "STRUCTURE_MASK"
def __init__(
self,
resolution=None,
cache=True,
manifest_file="mouse_connectivity_manifest.json",
ccf_version=None,
base_uri=None,
):
super(MouseConnectivityCache, self).__init__(manifest=manifest_file, cache=cache)
if resolution is None:
self.resolution = MouseConnectivityApi.VOXEL_RESOLUTION_25_MICRONS
else:
self.resolution = resolution
self.api = MouseConnectivityApi(base_uri=base_uri)
if ccf_version is None:
ccf_version = MouseConnectivityApi.CCF_VERSION_DEFAULT
self.ccf_version = ccf_version
def get_annotation_volume(self, file_name=None):
"""
Read the annotation volume. Download it first if it doesn't exist.
Parameters
----------
file_name: string
File name to store the annotation volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.ANNOTATION_KEY, self.ccf_version, self.resolution)
if file_name is None:
raise Exception("No save file name provided for annotation volume.")
if os.path.exists(file_name):
annotation, info = nrrd.read(file_name)
else:
Manifest.safe_make_parent_dirs(file_name)
annotation, info = self.api.download_annotation_volume(self.ccf_version, self.resolution, file_name)
return annotation, info
def get_template_volume(self, file_name=None):
"""
Read the template volume. Download it first if it doesn't exist.
Parameters
----------
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.TEMPLATE_KEY, self.resolution)
if file_name is None:
raise Exception("No save file provided for annotation volume.")
if os.path.exists(file_name):
annotation, info = nrrd.read(file_name)
else:
Manifest.safe_make_parent_dirs(file_name)
annotation, info = self.api.download_template_volume(self.resolution, file_name)
return annotation, info
def get_projection_density(self, experiment_id, file_name=None):
"""
Read a projection density volume for a single experiment. Download it
first if it doesn't exist. Projection density is the proportion of
of projecting pixels in a grid voxel in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.PROJECTION_DENSITY_KEY, experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_make_parent_dirs(file_name)
self.api.download_projection_density(file_name, experiment_id, self.resolution)
return nrrd.read(file_name)
def get_injection_density(self, experiment_id, file_name=None):
"""
Read an injection density volume for a single experiment. Download it
first if it doesn't exist. Injection density is the proportion of
projecting pixels in a grid voxel only including pixels that are
part of the injection site in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.INJECTION_DENSITY_KEY, experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_make_parent_dirs(file_name)
self.api.download_injection_density(file_name, experiment_id, self.resolution)
return nrrd.read(file_name)
def get_injection_fraction(self, experiment_id, file_name=None):
"""
Read an injection fraction volume for a single experiment. Download it
first if it doesn't exist. Injection fraction is the proportion of
pixels in the injection site in a grid voxel in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.INJECTION_FRACTION_KEY, experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_make_parent_dirs(file_name)
self.api.download_injection_fraction(file_name, experiment_id, self.resolution)
return nrrd.read(file_name)
def get_data_mask(self, experiment_id, file_name=None):
"""
Read a data mask volume for a single experiment. Download it
first if it doesn't exist. Data mask is a binary mask of
voxels that have valid data. Only use valid data in analysis!
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.DATA_MASK_KEY, experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_make_parent_dirs(file_name)
self.api.download_data_mask(file_name, experiment_id, self.resolution)
return nrrd.read(file_name)
def get_ontology(self, file_name=None):
"""
Read the list of adult mouse structures and return an Ontology 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.
"""
return Ontology(self.get_structures(file_name))
def get_structures(self, file_name=None):
"""
Read the list of adult mouse structures and return a Pandas DataFrame.
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.
"""
file_name = self.get_cache_path(file_name, self.STRUCTURES_KEY)
if os.path.exists(file_name):
structures = pd.DataFrame.from_csv(file_name)
else:
structures = OntologiesApi(base_uri=self.api.api_url).get_structures(1)
structures = pd.DataFrame(structures)
if self.cache:
Manifest.safe_make_parent_dirs(file_name)
structures.to_csv(file_name)
structures.set_index(["id"], inplace=True, drop=False)
return structures
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)
if os.path.exists(file_name):
experiments = json_utilities.read(file_name)
else:
experiments = self.api.experiment_source_search(injection_structures="root")
# removing these elements because they are specific to a particular
# resolution
for e in experiments:
del e["num-voxels"]
del e["injection-volume"]
del e["sum"]
del e["name"]
if self.cache:
Manifest.safe_make_parent_dirs(file_name)
json_utilities.write(file_name, experiments)
# 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 filter_experiments(self, experiments, cre=None, injection_structure_ids=None):
"""
Take a list of experiments and filter them by cre status and injection structure.
Parameters
----------
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.
"""
if cre is True:
experiments = [e for e in experiments if e["transgenic-line"]]
elif cre is False:
experiments = [e for e in experiments if not e["transgenic-line"]]
elif cre is not None:
experiments = [e for e in experiments if e["transgenic-line"] in cre]
if injection_structure_ids is not None:
descendant_ids = self.get_ontology().get_descendant_ids(injection_structure_ids)
experiments = [e for e in experiments if e["structure-id"] in descendant_ids]
return experiments
def get_experiment_structure_unionizes(
self,
experiment_id,
file_name=None,
is_injection=None,
structure_ids=None,
include_descendants=False,
hemisphere_ids=None,
):
"""
Retrieve the structure unionize data for a specific experiment. Filter by
structure, injection status, and hemisphere.
Parameters
----------
experiment_id: int
ID of the experiment of interest. Corresponds to section_data_set_id in the API.
file_name: string
File name to save/read the experiments list. 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.
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records for a specific set of structures.
If None, return all records. Default None.
include_descendants: boolean
Include all descendant records for specified structures. Default False.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
file_name = self.get_cache_path(file_name, self.STRUCTURE_UNIONIZES_KEY, experiment_id)
if os.path.exists(file_name):
unionizes = pd.DataFrame.from_csv(file_name)
else:
unionizes = self.api.get_structure_unionizes([experiment_id])
unionizes = pd.DataFrame(unionizes)
# rename section_data_set_id column to experiment_id
unionizes.columns = ["experiment_id" if c == "section_data_set_id" else c for c in unionizes.columns]
if self.cache:
Manifest.safe_make_parent_dirs(file_name)
unionizes.to_csv(file_name)
return self.filter_structure_unionizes(
unionizes, is_injection, structure_ids, include_descendants, hemisphere_ids
)
def filter_structure_unionizes(
self, unionizes, is_injection=None, structure_ids=None, include_descendants=False, hemisphere_ids=None
):
"""
Take a list of unionzes and return a subset of records filtered by injection status, structure, and
hemisphere.
Parameters
----------
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records for a set of structures.
If None, return all records. Default None.
include_descendants: boolean
Include all descendant records for specified structures. Default False.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
if is_injection is not None:
unionizes = unionizes[unionizes.is_injection == is_injection]
if structure_ids is not None:
if include_descendants:
structure_ids = self.get_ontology().get_descendant_ids(structure_ids)
else:
structure_ids = set(structure_ids)
unionizes = unionizes[unionizes["structure_id"].isin(structure_ids)]
if hemisphere_ids is not None:
unionizes = unionizes[unionizes["hemisphere_id"].isin(hemisphere_ids)]
return unionizes
def get_structure_unionizes(
self, experiment_ids, is_injection=None, structure_ids=None, include_descendants=False, hemisphere_ids=None
):
"""
Get structure unionizes for a set of experiment IDs. Filter the results by injection status,
structure, and hemisphere.
Parameters
----------
experiment_ids: list
List of experiment IDs. Corresponds to section_data_set_id in the API.
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records for a specific set of structures.
If None, return all records. Default None.
include_descendants: boolean
Include all descendant records for specified structures. Default False.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
unionizes = [
self.get_experiment_structure_unionizes(
eid,
is_injection=is_injection,
structure_ids=structure_ids,
include_descendants=include_descendants,
hemisphere_ids=hemisphere_ids,
)
for eid in experiment_ids
]
return pd.concat(unionizes, ignore_index=True)
def get_projection_matrix(
self,
experiment_ids,
projection_structure_ids,
hemisphere_ids=None,
parameter="projection_volume",
dataframe=False,
):
unionizes = self.get_structure_unionizes(
experiment_ids,
is_injection=False,
structure_ids=projection_structure_ids,
include_descendants=False,
hemisphere_ids=hemisphere_ids,
)
hemisphere_ids = set(unionizes["hemisphere_id"].values.tolist())
nrows = len(experiment_ids)
ncolumns = len(projection_structure_ids) * len(hemisphere_ids)
matrix = np.empty((nrows, ncolumns))
matrix[:] = np.NAN
row_lookup = {}
for idx, e in enumerate(experiment_ids):
row_lookup[e] = idx
column_lookup = {}
columns = []
cidx = 0
hlabel = {1: "-L", 2: "-R", 3: ""}
o = self.get_ontology()
for hid in hemisphere_ids:
for sid in projection_structure_ids:
column_lookup[(hid, sid)] = cidx
label = o[sid].iloc[0]["acronym"] + hlabel[hid]
columns.append({"hemisphere_id": hid, "structure_id": sid, "label": label})
cidx += 1
for _, row in unionizes.iterrows():
ridx = row_lookup[row["experiment_id"]]
k = (row["hemisphere_id"], row["structure_id"])
cidx = column_lookup[k]
matrix[ridx, cidx] = row[parameter]
if dataframe:
all_experiments = self.get_experiments(dataframe=True)
rows_df = all_experiments.loc[experiment_ids]
cols_df = pd.DataFrame(columns)
return {"matrix": matrix, "rows": rows_df, "columns": cols_df}
else:
return {"matrix": matrix, "rows": experiment_ids, "columns": columns}
def get_structure_mask(self, structure_id, file_name=None, annotation_file_name=None):
"""
Read a 3D numpy array shaped like the annotation volume that has non-zero values where
voxels belong to a particular structure. This will take care of identifying substructures.
Parameters
----------
structure_id: int
ID of a structure.
file_name: string
File name to store the structure mask. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
annotation_file_name: string
File name to store the annotation volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.STRUCTURE_MASK_KEY, structure_id)
if os.path.exists(file_name):
return nrrd.read(file_name)
else:
ont = self.get_ontology()
structure_ids = ont.get_descendant_ids([structure_id])
annotation, _ = self.get_annotation_volume(annotation_file_name)
mask = self.make_structure_mask(structure_ids, annotation)
if self.cache:
Manifest.safe_make_parent_dirs(file_name)
nrrd.write(file_name, mask)
return mask, None
def make_structure_mask(self, structure_ids, annotation):
"""
Look at an annotation volume and identify voxels that have values
in a list of structure ids.
Parameters
----------
structure_ids: list
List of IDs to look for in the annotation volume
annotation: np.ndarray
Numpy array filled with IDs.
"""
m = np.zeros(annotation.shape, dtype=np.uint8)
for _, sid in enumerate(structure_ids):
m[annotation == sid] = 1
return m
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.
"""
manifest_builder = ManifestBuilder()
manifest_builder.add_path("BASEDIR", ".")
manifest_builder.add_path(self.EXPERIMENTS_KEY, "experiments.json", parent_key="BASEDIR", typename="file")
manifest_builder.add_path(self.STRUCTURES_KEY, "structures.csv", parent_key="BASEDIR", typename="file")
manifest_builder.add_path(
self.STRUCTURE_UNIONIZES_KEY, "experiment_%d/structure_unionizes.csv", parent_key="BASEDIR", typename="file"
)
manifest_builder.add_path(self.CCF_VERSION_KEY, "%s", parent_key="BASEDIR", typename="dir")
manifest_builder.add_path(
self.ANNOTATION_KEY, "annotation_%d.nrrd", parent_key=self.CCF_VERSION_KEY, typename="file"
)
manifest_builder.add_path(self.TEMPLATE_KEY, "average_template_%d.nrrd", parent_key="BASEDIR", typename="file")
manifest_builder.add_path(
self.INJECTION_DENSITY_KEY, "experiment_%d/injection_density_%d.nrrd", parent_key="BASEDIR", typename="file"
)
manifest_builder.add_path(
self.INJECTION_FRACTION_KEY,
"experiment_%d/injection_fraction_%d.nrrd",
parent_key="BASEDIR",
typename="file",
)
manifest_builder.add_path(
self.DATA_MASK_KEY, "experiment_%d/data_mask_%d.nrrd", parent_key="BASEDIR", typename="file"
)
manifest_builder.add_path(
self.PROJECTION_DENSITY_KEY,
"experiment_%d/projection_density_%d.nrrd",
parent_key="BASEDIR",
typename="file",
)
manifest_builder.add_path(
self.STRUCTURE_MASK_KEY, "structure_masks/structure_%d.nrrd", parent_key="BASEDIR", typename="file"
)
manifest_builder.write_json_file(file_name)
class MouseConnectivityCache(Cache):
"""
Cache class for storing and accessing data related to the adult mouse
Connectivity Atlas. 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
----------
resolution: int
Resolution of grid data to be downloaded when accessing projection volume,
the annotation volume, and the annotation volume. Must be one of (10, 25,
50, 100). Default is 25.
api: MouseConnectivityApi instance
Used internally to make API queries.
Parameters
----------
resolution: int
Resolution of grid data to be downloaded when accessing projection volume,
the annotation volume, and the annotation volume. Must be one of (10, 25,
50, 100). Default is 25.
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_projection_density(file_name='file.nrrd')).
manifest_file: string
File name of the manifest to be read. Default is "mouse_connectivity_manifest.json".
"""
ANNOTATION_KEY = 'ANNOTATION'
TEMPLATE_KEY = 'TEMPLATE'
PROJECTION_DENSITY_KEY = 'PROJECTION_DENSITY'
INJECTION_DENSITY_KEY = 'INJECTION_DENSITY'
INJECTION_FRACTION_KEY = 'INJECTION_FRACTION'
DATA_MASK_KEY = 'DATA_MASK'
STRUCTURE_UNIONIZES_KEY = 'STRUCTURE_UNIONIZES'
EXPERIMENTS_KEY = 'EXPERIMENTS'
STRUCTURES_KEY = 'STRUCTURES'
STRUCTURE_MASK_KEY = 'STRUCTURE_MASK'
def __init__(self,
resolution=25,
cache=True,
manifest_file='mouse_connectivity_manifest.json',
base_uri=None):
super(MouseConnectivityCache, self).__init__(manifest=manifest_file,
cache=cache)
self.resolution = resolution
self.api = MouseConnectivityApi(base_uri=base_uri)
def get_annotation_volume(self, file_name=None):
"""
Read the annotation volume. Download it first if it doesn't exist.
Parameters
----------
file_name: string
File name to store the annotation volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.ANNOTATION_KEY,
self.resolution)
if file_name is None:
raise Exception(
"No save file name provided for annotation volume.")
if os.path.exists(file_name):
annotation, info = nrrd.read(file_name)
else:
Manifest.safe_mkdir(os.path.dirname(file_name))
annotation, info = self.api.download_annotation_volume(
self.resolution, file_name)
return annotation, info
def get_template_volume(self, file_name=None):
"""
Read the template volume. Download it first if it doesn't exist.
Parameters
----------
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.TEMPLATE_KEY,
self.resolution)
if file_name is None:
raise Exception("No save file provided for annotation volume.")
if os.path.exists(file_name):
annotation, info = nrrd.read(file_name)
else:
Manifest.safe_mkdir(os.path.dirname(file_name))
annotation, info = self.api.download_template_volume(
self.resolution, file_name)
return annotation, info
def get_projection_density(self, experiment_id, file_name=None):
"""
Read a projection density volume for a single experiment. Download it
first if it doesn't exist. Projection density is the proportion of
of projecting pixels in a grid voxel in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.PROJECTION_DENSITY_KEY,
experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_mkdir(os.path.dirname(file_name))
self.api.download_projection_density(file_name, experiment_id,
self.resolution)
return nrrd.read(file_name)
def get_injection_density(self, experiment_id, file_name=None):
"""
Read an injection density volume for a single experiment. Download it
first if it doesn't exist. Injection density is the proportion of
projecting pixels in a grid voxel only including pixels that are
part of the injection site in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.INJECTION_DENSITY_KEY,
experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_mkdir(os.path.dirname(file_name))
self.api.download_injection_density(file_name, experiment_id,
self.resolution)
return nrrd.read(file_name)
def get_injection_fraction(self, experiment_id, file_name=None):
"""
Read an injection fraction volume for a single experiment. Download it
first if it doesn't exist. Injection fraction is the proportion of
pixels in the injection site in a grid voxel in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.INJECTION_FRACTION_KEY,
experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_mkdir(os.path.dirname(file_name))
self.api.download_injection_fraction(file_name, experiment_id,
self.resolution)
return nrrd.read(file_name)
def get_data_mask(self, experiment_id, file_name=None):
"""
Read a data mask volume for a single experiment. Download it
first if it doesn't exist. Data mask is a binary mask of
voxels that have valid data. Only use valid data in analysis!
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.DATA_MASK_KEY,
experiment_id, self.resolution)
if file_name is None:
raise Exception("No file name to save volume.")
if not os.path.exists(file_name):
Manifest.safe_mkdir(os.path.dirname(file_name))
self.api.download_data_mask(file_name, experiment_id,
self.resolution)
return nrrd.read(file_name)
def get_ontology(self, file_name=None):
"""
Read the list of adult mouse structures and return an Ontology 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.
"""
return Ontology(self.get_structures(file_name))
def get_structures(self, file_name=None):
"""
Read the list of adult mouse structures and return a Pandas DataFrame.
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.
"""
file_name = self.get_cache_path(file_name, self.STRUCTURES_KEY)
if os.path.exists(file_name):
structures = pd.DataFrame.from_csv(file_name)
else:
structures = OntologiesApi().get_structures(1)
structures = pd.DataFrame(structures)
if self.cache:
Manifest.safe_mkdir(os.path.dirname(file_name))
structures.to_csv(file_name)
structures.set_index(['id'], inplace=True, drop=False)
return structures
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)
if os.path.exists(file_name):
experiments = json_utilities.read(file_name)
else:
experiments = self.api.experiment_source_search(
injection_structures='root')
# removing these elements because they are specific to a particular resolution
for e in experiments:
del e['num-voxels']
del e['injection-volume']
del e['sum']
del e['name']
if self.cache:
Manifest.safe_mkdir(os.path.dirname(file_name))
json_utilities.write(file_name, experiments)
# 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 filter_experiments(self,
experiments,
cre=None,
injection_structure_ids=None):
"""
Take a list of experiments and filter them by cre status and injection structure.
Parameters
----------
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.
"""
if cre == True:
experiments = [e for e in experiments if e['transgenic-line']]
elif cre == False:
experiments = [e for e in experiments if not e['transgenic-line']]
elif cre is not None:
experiments = [
e for e in experiments if e['transgenic-line'] in cre
]
if injection_structure_ids is not None:
descendant_ids = self.get_ontology().get_descendant_ids(
injection_structure_ids)
experiments = [
e for e in experiments if e['structure-id'] in descendant_ids
]
return experiments
def get_experiment_structure_unionizes(self,
experiment_id,
file_name=None,
is_injection=None,
structure_ids=None,
hemisphere_ids=None):
"""
Retrieve the structure unionize data for a specific experiment. Filter by
structure, injection status, and hemisphere.
Parameters
----------
experiment_id: int
ID of the experiment of interest. Corresponds to section_data_set_id in the API.
file_name: string
File name to save/read the experiments list. 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.
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records that are inside a specific set of structures.
If None, return all records. Default None.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
file_name = self.get_cache_path(file_name,
self.STRUCTURE_UNIONIZES_KEY,
experiment_id)
if os.path.exists(file_name):
unionizes = pd.DataFrame.from_csv(file_name)
else:
unionizes = self.api.get_structure_unionizes([experiment_id])
unionizes = pd.DataFrame(unionizes)
# rename section_data_set_id column to experiment_id
unionizes.columns = [
'experiment_id' if c == 'section_data_set_id' else c
for c in unionizes.columns
]
if self.cache:
Manifest.safe_mkdir(os.path.dirname(file_name))
unionizes.to_csv(file_name)
return self.filter_structure_unionizes(unionizes, is_injection,
structure_ids, hemisphere_ids)
def filter_structure_unionizes(self,
unionizes,
is_injection=None,
structure_ids=None,
hemisphere_ids=None):
"""
Take a list of unionzes and return a subset of records filtered by injection status, structure, and
hemisphere.
Parameters
----------
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records that are inside a specific set of structures.
If None, return all records. Default None.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
if is_injection is not None:
unionizes = unionizes[unionizes.is_injection == is_injection]
if structure_ids is not None:
descendant_ids = self.get_ontology().get_descendant_ids(
structure_ids)
unionizes = unionizes[unionizes['structure_id'].isin(
descendant_ids)]
if hemisphere_ids is not None:
unionizes = unionizes[unionizes['hemisphere_id'].isin(
hemisphere_ids)]
return unionizes
def get_structure_unionizes(self,
experiment_ids,
is_injection=None,
structure_ids=None,
hemisphere_ids=None):
"""
Get structure unionizes for a set of experiment IDs. Filter the results by injection status,
structure, and hemisphere.
Parameters
----------
experiment_ids: list
List of experiment IDs. Corresponds to section_data_set_id in the API.
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records that are inside a specific set of structures.
If None, return all records. Default None.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
unionizes = [
self.get_experiment_structure_unionizes(
eid,
is_injection=is_injection,
structure_ids=structure_ids,
hemisphere_ids=hemisphere_ids) for eid in experiment_ids
]
return pd.concat(unionizes, ignore_index=True)
def get_projection_matrix(self,
experiment_ids,
projection_structure_ids,
hemisphere_ids=None,
parameter='projection_volume',
dataframe=False):
unionizes = self.get_structure_unionizes(experiment_ids,
is_injection=False,
hemisphere_ids=hemisphere_ids)
unionizes = unionizes[unionizes.structure_id.isin(
projection_structure_ids)]
projection_structure_ids = set(
unionizes['structure_id'].values.tolist())
hemisphere_ids = set(unionizes['hemisphere_id'].values.tolist())
nrows = len(experiment_ids)
ncolumns = len(projection_structure_ids) * len(hemisphere_ids)
matrix = np.empty((nrows, ncolumns))
matrix[:] = np.NAN
row_lookup = {}
for idx, e in enumerate(experiment_ids):
row_lookup[e] = idx
column_lookup = {}
columns = []
cidx = 0
hlabel = {1: '-L', 2: '-R', 3: ''}
o = self.get_ontology()
for hid in hemisphere_ids:
for sid in projection_structure_ids:
column_lookup[(hid, sid)] = cidx
label = o[sid].iloc[0]['acronym'] + hlabel[hid]
columns.append({
'hemisphere_id': hid,
'structure_id': sid,
'label': label
})
cidx += 1
for _, row in unionizes.iterrows():
ridx = row_lookup[row['experiment_id']]
k = (row['hemisphere_id'], row['structure_id'])
cidx = column_lookup[k]
matrix[ridx, cidx] = row[parameter]
if dataframe:
all_experiments = self.get_experiments(dataframe=True)
rows_df = all_experiments.loc[experiment_ids]
cols_df = pd.DataFrame(columns)
return {'matrix': matrix, 'rows': rows_df, 'columns': cols_df}
else:
return {
'matrix': matrix,
'rows': experiment_ids,
'columns': columns
}
def get_structure_mask(self,
structure_id,
file_name=None,
annotation_file_name=None):
"""
Read a 3D numpy array shaped like the annotation volume that has non-zero values where
voxels belong to a particular structure. This will take care of identifying substructures.
Parameters
----------
structure_id: int
ID of a structure.
file_name: string
File name to store the structure mask. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
annotation_file_name: string
File name to store the annotation volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.STRUCTURE_MASK_KEY,
structure_id)
if os.path.exists(file_name):
return nrrd.read(file_name)
else:
ont = self.get_ontology()
structure_ids = ont.get_descendant_ids([structure_id])
annotation, _ = self.get_annotation_volume(annotation_file_name)
mask = self.make_structure_mask(structure_ids, annotation)
if self.cache:
Manifest.safe_mkdir(os.path.dirname(file_name))
nrrd.write(file_name, mask)
return mask, None
def make_structure_mask(self, structure_ids, annotation):
"""
Look at an annotation volume and identify voxels that have values
in a list of structure ids.
Parameters
----------
structure_ids: list
List of IDs to look for in the annotation volume
annotation: np.ndarray
Numpy array filled with IDs.
"""
m = np.zeros(annotation.shape, dtype=np.uint8)
for _, sid in enumerate(structure_ids):
m[annotation == sid] = 1
return m
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.
"""
manifest_builder = ManifestBuilder()
manifest_builder.add_path('BASEDIR', '.')
manifest_builder.add_path(self.EXPERIMENTS_KEY,
'experiments.json',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.STRUCTURES_KEY,
'structures.csv',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.STRUCTURE_UNIONIZES_KEY,
'experiment_%d/structure_unionizes.csv',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.ANNOTATION_KEY,
'annotation_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.TEMPLATE_KEY,
'average_template_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.INJECTION_DENSITY_KEY,
'experiment_%d/injection_density_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.INJECTION_FRACTION_KEY,
'experiment_%d/injection_fraction_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.DATA_MASK_KEY,
'experiment_%d/data_mask_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.PROJECTION_DENSITY_KEY,
'experiment_%d/projection_density_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.STRUCTURE_MASK_KEY,
'structure_masks/structure_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.write_json_file(file_name)
from allensdk.api.queries.mouse_connectivity_api import MouseConnectivityApi
mca = MouseConnectivityApi()
# get metadata for all non-Cre experiments
experiments = mca.experiment_source_search(injection_structures='root', transgenic_lines=0)
# download the projection density volume for one of the experiments
pd = mca.download_projection_density('example.nrrd', experiments[0]['id'], resolution=25)
class ABA(Paths):
"""
This class handles interaction with the Allen Brain Atlas datasets and APIs to get structure trees,
experimental metadata and results, tractography data etc.
"""
# useful vars for analysis
excluded_regions = ["fiber tracts"]
resolution = 25
def __init__(self, projection_metric = "projection_energy", base_dir=None, **kwargs):
"""
Set up file paths and Allen SDKs
:param base_dir: path to directory to use for saving data (default value None)
:param path_fiprojection_metricle: - str, metric to quantify the strength of projections from the Allen Connectome. (default value 'projection_energy')
:param kwargs: can be used to pass path to individual data folders. See brainrender/Utils/paths_manager.py
"""
Paths.__init__(self, base_dir=base_dir, **kwargs)
self.projection_metric = projection_metric
# get mouse connectivity cache and structure tree
self.mcc = MouseConnectivityCache(manifest_file=os.path.join(self.mouse_connectivity_cache, "manifest.json"))
self.structure_tree = self.mcc.get_structure_tree()
# get ontologies API and brain structures sets
self.oapi = OntologiesApi()
self.get_structures_sets()
# get reference space
self.space = ReferenceSpaceApi()
self.spacecache = ReferenceSpaceCache(
manifest=os.path.join(self.annotated_volume, "manifest.json"), # downloaded files are stored relative to here
resolution=self.resolution,
reference_space_key="annotation/ccf_2017" # use the latest version of the CCF
)
self.annotated_volume, _ = self.spacecache.get_annotation_volume()
# mouse connectivity API [used for tractography]
self.mca = MouseConnectivityApi()
# Get tree search api
self.tree_search = TreeSearchApi()
# Get some metadata about experiments
self.all_experiments = self.mcc.get_experiments(dataframe=True)
self.strains = sorted([x for x in set(self.all_experiments.strain) if x is not None])
self.transgenic_lines = sorted(set([x for x in set(self.all_experiments.transgenic_line) if x is not None]))
####### GET EXPERIMENTS DATA
def get_structures_sets(self):
"""
Get the Allen's structure sets.
"""
summary_structures = self.structure_tree.get_structures_by_set_id([167587189]) # main summary structures
summary_structures = [s for s in summary_structures if s["acronym"] not in self.excluded_regions]
self.structures = pd.DataFrame(summary_structures)
# Other structures sets
try:
all_sets = pd.DataFrame(self.oapi.get_structure_sets())
except:
print("Could not retrieve data, possibly because there is no internet connection.")
else:
sets = ["Summary structures of the pons", "Summary structures of the thalamus",
"Summary structures of the hypothalamus", "List of structures for ABA Fine Structure Search",
"Structures representing the major divisions of the mouse brain", "Summary structures of the midbrain", "Structures whose surfaces are represented by a precomputed mesh"]
self.other_sets = {}
for set_name in sets:
set_id = all_sets.loc[all_sets.description == set_name].id.values[0]
self.other_sets[set_name] = pd.DataFrame(self.structure_tree.get_structures_by_set_id([set_id]))
self.all_avaliable_meshes = sorted(self.other_sets["Structures whose surfaces are represented by a precomputed mesh"].acronym.values)
def print_structures_list_to_text(self):
"""
Saves the name of every brain structure for which a 3d mesh (.obj file) is available in a text file.
"""
s = self.other_sets["Structures whose surfaces are represented by a precomputed mesh"].sort_values('acronym')
with open('all_regions.txt', 'w') as o:
for acr, name in zip(s.acronym.values, s['name'].values):
o.write("({}) -- {}\n".format(acr, name))
def load_all_experiments(self, cre=False):
"""
This function downloads all the experimental data from the MouseConnectivityCache and saves the unionized results
as pickled pandas dataframes. The process is slow, but the ammount of disk space necessary to save the data is small,
so it's worth downloading all the experiments at once to speed up subsequent analysis.
:param cre: Bool - data from either wild time or cre mice lines (Default value = False)
"""
if not cre: raise NotImplementedError("Only works for wild type sorry")
# Downloads all experiments from allen brain atlas and saves the results as an easy to read pkl file
for acronym in self.structures.acronym.values:
print("Fetching experiments for : {}".format(acronym))
structure = self.structure_tree.get_structures_by_acronym([acronym])[0]
experiments = self.mcc.get_experiments(cre=cre, injection_structure_ids=[structure['id']])
print(" found {} experiments".format(len(experiments)))
try:
structure_unionizes = self.mcc.get_structure_unionizes([e['id'] for e in experiments],
is_injection=False,
structure_ids=self.structures.id.values,
include_descendants=False)
except: pass
structure_unionizes.to_pickle(os.path.join(self.output_data, "{}.pkl".format(acronym)))
def print_structures(self):
"""
Prints the name of every structure in the structure tree to the console.
"""
acronyms, names = self.structures.acronym.values, self.structures['name'].values
sort_idx = np.argsort(acronyms)
acronyms, names = acronyms[sort_idx], names[sort_idx]
[print("({}) - {}".format(a, n)) for a,n in zip(acronyms, names)]
def experiments_source_search(self, SOI, *args, source=True, **kwargs):
"""
Returns data about experiments whose injection was in the SOI, structure of interest
:param SOI: str, structure of interest. Acronym of structure to use as seed for teh search
:param *args:
:param source: (Default value = True)
:param **kwargs:
"""
"""
list of possible kwargs
injection_structures : list of integers or strings
Integer Structure.id or String Structure.acronym.
target_domain : list of integers or strings, optional
Integer Structure.id or String Structure.acronym.
injection_hemisphere : string, optional
'right' or 'left', Defaults to both hemispheres.
target_hemisphere : string, optional
'right' or 'left', Defaults to both hemispheres.
transgenic_lines : list of integers or strings, optional
Integer TransgenicLine.id or String TransgenicLine.name. Specify ID 0 to exclude all TransgenicLines.
injection_domain : list of integers or strings, optional
Integer Structure.id or String Structure.acronym.
primary_structure_only : boolean, optional
product_ids : list of integers, optional
Integer Product.id
start_row : integer, optional
For paging purposes. Defaults to 0.
num_rows : integer, optional
For paging purposes. Defaults to 2000.
"""
transgenic_id = kwargs.pop('transgenic_id', 0) # id = 0 means use only wild type
primary_structure_only = kwargs.pop('primary_structure_only', True)
if not isinstance(SOI, list): SOI = [SOI]
if source:
injection_structures=SOI
target_domain = None
else:
injection_structures = None
target_domain = SOI
return pd.DataFrame(self.mca.experiment_source_search(injection_structures=injection_structures,
target_domain = target_domain,
transgenic_lines=transgenic_id,
primary_structure_only=primary_structure_only))
def experiments_target_search(self, *args, **kwargs):
"""
:param *args:
:param **kwargs:
"""
return self.experiments_source_search(*args, source=False, **kwargs)
def fetch_experiments_data(self, experiments_id, *args, average_experiments=False, **kwargs):
"""
Get data and metadata for expeirments in the Allen Mouse Connectome project.
:param experiments_id: int, list, np.ndarray with ID of experiments whose data need to be fetched
:param *args:
:param average_experiments: (Default value = False)
:param **kwargs:
"""
if isinstance(experiments_id, np.ndarray):
experiments_id = [int(x) for x in experiments_id]
elif not isinstance(experiments_id, list):
experiments_id = [experiments_id]
if [x for x in experiments_id if not isinstance(x, int)]:
raise ValueError("Invalid experiments_id argument: {}".format(experiments_id))
default_structures_ids = self.structures.id.values
is_injection = kwargs.pop('is_injection', False) # Include only structures that are not injection
structure_ids = kwargs.pop('structure_ids', default_structures_ids) # Pass IDs of structures of interest
hemisphere_ids= kwargs.pop('hemisphere_ids', None) # 1 left, 2 right, 3 both
if not average_experiments:
return pd.DataFrame(self.mca.get_structure_unionizes(experiments_id,
is_injection = is_injection,
structure_ids = structure_ids,
hemisphere_ids = hemisphere_ids))
else:
raise NotImplementedError("Need to find a way to average across experiments")
unionized = pd.DataFrame(self.mca.get_structure_unionizes(experiments_id,
is_injection = is_injection,
structure_ids = structure_ids,
hemisphere_ids = hemisphere_ids))
for regionid in list(set(unionized.structure_id)):
region_avg = unionized.loc[unionized.structure_id == regionid].mean(axis=1)
####### ANALYSIS ON EXPERIMENTAL DATA
def analyze_efferents(self, ROI, projection_metric = None):
"""
Loads the experiments on ROI and looks at average statistics of efferent projections
:param ROI: str, acronym of brain region of interest
:param projection_metric: if None, the default projection metric is used, otherwise pass a string with metric to use (Default value = None)
"""
if projection_metric is None:
projection_metric = self.projection_metric
experiment_data = pd.read_pickle(os.path.join(self.output_data, "{}.pkl".format(ROI)))
experiment_data = experiment_data.loc[experiment_data.volume > self.volume_threshold]
# Loop over all structures and get the injection density
results = {"left":[], "right":[], "both":[], "id":[], "acronym":[], "name":[]}
for target in self.structures.id.values:
target_acronym = self.structures.loc[self.structures.id == target].acronym.values[0]
target_name = self.structures.loc[self.structures.id == target].name.values[0]
exp_target = experiment_data.loc[experiment_data.structure_id == target]
exp_target_hemi = self.hemispheres(exp_target.loc[exp_target.hemisphere_id == 1],
exp_target.loc[exp_target.hemisphere_id == 2],
exp_target.loc[exp_target.hemisphere_id == 3])
proj_energy = self.hemispheres(np.nanmean(exp_target_hemi.left[projection_metric].values),
np.nanmean(exp_target_hemi.right[projection_metric].values),
np.nanmean(exp_target_hemi.both[projection_metric].values)
)
for hemi in self.hemispheres_names:
results[hemi].append(proj_energy._asdict()[hemi])
results["id"].append(target)
results["acronym"].append(target_acronym)
results["name"].append(target_name)
results = pd.DataFrame.from_dict(results).sort_values("right", na_position = "first")
return results
def analyze_afferents(self, ROI, projection_metric = None):
"""[Loads the experiments on ROI and looks at average statistics of afferent projections]
:param ROI: str, acronym of region of itnerest
:param projection_metric: if None, the default projection metric is used, otherwise pass a string with metric to use (Default value = None)
"""
if projection_metric is None:
projection_metric = self.projection_metric
ROI_id = self.structure_tree.get_structures_by_acronym([ROI])[0]["id"]
# Loop over all strctures and get projection towards SOI
results = {"left":[], "right":[], "both":[], "id":[], "acronym":[], "name":[]}
for origin in self.structures.id.values:
origin_acronym = self.structures.loc[self.structures.id == origin].acronym.values[0]
origin_name = self.structures.loc[self.structures.id == origin].name.values[0]
experiment_data = pd.read_pickle(os.path.join(self.output_data, "{}.pkl".format(origin_acronym)))
experiment_data = experiment_data.loc[experiment_data.volume > self.volume_threshold]
exp_target = experiment_data.loc[experiment_data.structure_id == SOI_id]
exp_target_hemi = self.hemispheres(exp_target.loc[exp_target.hemisphere_id == 1], exp_target.loc[exp_target.hemisphere_id == 2], exp_target.loc[exp_target.hemisphere_id == 3])
proj_energy = self.hemispheres(np.nanmean(exp_target_hemi.left[projection_metric].values),
np.nanmean(exp_target_hemi.right[projection_metric].values),
np.nanmean(exp_target_hemi.both[projection_metric].values)
)
for hemi in self.hemispheres_names:
results[hemi].append(proj_energy._asdict()[hemi])
results["id"].append(origin)
results["acronym"].append(origin_acronym)
results["name"].append(origin_name)
results = pd.DataFrame.from_dict(results).sort_values("right", na_position = "first")
return results
####### GET TRACTOGRAPHY AND SPATIAL DATA
def get_projection_tracts_to_target(self, p0=None, **kwargs):
"""
Gets tractography data for all experiments whose projections reach the brain region or location of iterest.
:param p0: list of 3 floats with XYZ coordinates of point to be used as seed (Default value = None)
:param **kwargs:
"""
# check args
if p0 is None:
raise ValueError("Please pass coordinates")
elif isinstance(p0, np.ndarray):
p0 = list(p0)
elif not isinstance(p0, (list, tuple)):
raise ValueError("Invalid argument passed (p0): {}".format(p0))
tract = self.mca.experiment_spatial_search(seed_point=p0, **kwargs)
if isinstance(tract, str):
raise ValueError('Something went wrong with query, query error message:\n{}'.format(tract))
else:
return tract
### OPERATIONS ON STRUCTURE TREES
def get_structure_ancestors(self, regions, ancestors=True, descendants=False):
"""
Get's the ancestors of the region(s) passed as arguments
:param regions: str, list of str with acronums of regions of interest
:param ancestors: if True, returns the ancestors of the region (Default value = True)
:param descendants: if True, returns the descendants of the region (Default value = False)
"""
if not isinstance(regions, list):
struct_id = self.structure_tree.get_structures_by_acronym([regions])[0]['id']
return pd.DataFrame(self.tree_search.get_tree('Structure', struct_id, ancestors=ancestors, descendants=descendants))
else:
ancestors = []
for region in regions:
struct_id = self.structure_tree.get_structures_by_acronym([region])[0]['id']
ancestors.append(pd.DataFrame(self.tree_search.get_tree('Structure', struct_id, ancestors=ancestors, descendants=descendants)))
return ancestors
def get_structure_descendants(self, regions):
return self.get_structure_ancestors(regions, ancestors=False, descendants=True)
def get_structure_from_coordinates(self, p0):
"""
Given a point in the Allen Mouse Brain reference space, returns the brain region that the point is in.
:param p0: list of floats with XYZ coordinates.
"""
voxel = np.round(np.array(p0) / self.resolution).astype(int)
try:
structure_id = self.annotated_volume[voxel[0], voxel[1], voxel[2]]
except:
return None
# Each voxel in the annotation volume is annotated as specifically as possible
structure = self.structure_tree.get_structures_by_id([structure_id])[0]
return structure
mca = MouseConnectivityApi()
print mca
# get metadata for all non-Cre experiments
#experiments = mca.experiment_source_search(injection_structures='root', transgenic_lines=0)
# download the projection density volume for one of the experiments
#pd = mca.download_projection_density('example.nrrd', experiments[0]['id'], resolution=25)
#enter selected projection numbers here, e.g. 167794131, 297231636, 287495026
id1 = 167794131
id2 = 297231636
id3 = 272736450
# import allensdk python api
from allensdk.api.queries.mouse_connectivity_api import MouseConnectivityApi
mca = MouseConnectivityApi()
# get metadata for all experiments
experiments = mca.experiment_source_search(injection_structures=['VIS','PTLp','RSP'])
# find selected experiments and format filenames, **Use the %paste magic function if pasting into ipython
for i in range(len(experiments)):
if (experiments[i]['id'] == id1):
fn1 = str(experiments[i]['injection-structures'][0]['abbreviation']) + '_' + str(experiments[i]['id']) + '_' + str(experiments[i]['transgenic-line'])
print fn1
if (experiments[i]['id'] == id2):
fn2 = str(experiments[i]['injection-structures'][0]['abbreviation']) + '_' + str(experiments[i]['id']) + '_' + str(experiments[i]['transgenic-line'])
print fn2
if (experiments[i]['id'] == id3):
fn3 = str(experiments[i]['injection-structures'][0]['abbreviation']) + '_' + str(experiments[i]['id']) + '_' + str(experiments[i]['transgenic-line'])
print fn3
# download selected experiment projection density files at 25 um resolution
mca.download_projection_density(fn1 + '.nrrd', id1, resolution=25)
mca.download_projection_density(fn2 + '.nrrd', id2, resolution=25)
mca.download_projection_density(fn3 + '.nrrd', id3, resolution=25)
class MouseConnectivityCache(ReferenceSpaceCache):
"""
Cache class for storing and accessing data related to the adult mouse
Connectivity Atlas. 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
----------
resolution: int
Resolution of grid data to be downloaded when accessing projection volume,
the annotation volume, and the annotation volume. Must be one of (10, 25,
50, 100). Default is 25.
api: MouseConnectivityApi instance
Used internally to make API queries.
Parameters
----------
resolution: int
Resolution of grid data to be downloaded when accessing projection volume,
the annotation volume, and the annotation volume. Must be one of (10, 25,
50, 100). Default is 25.
ccf_version: string
Desired version of the Common Coordinate Framework. This affects the annotation
volume (get_annotation_volume) and structure masks (get_structure_mask).
Must be one of (MouseConnectivityApi.CCF_2015, MouseConnectivityApi.CCF_2016).
Default: MouseConnectivityApi.CCF_2016
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_projection_density(file_name='file.nrrd')).
manifest_file: string
File name of the manifest to be read. Default is "mouse_connectivity_manifest.json".
"""
PROJECTION_DENSITY_KEY = 'PROJECTION_DENSITY'
INJECTION_DENSITY_KEY = 'INJECTION_DENSITY'
INJECTION_FRACTION_KEY = 'INJECTION_FRACTION'
DATA_MASK_KEY = 'DATA_MASK'
STRUCTURE_UNIONIZES_KEY = 'STRUCTURE_UNIONIZES'
EXPERIMENTS_KEY = 'EXPERIMENTS'
MANIFEST_VERSION = 1.2
SUMMARY_STRUCTURE_SET_ID = 167587189
DEFAULT_STRUCTURE_SET_IDS = tuple([SUMMARY_STRUCTURE_SET_ID])
@property
def default_structure_ids(self):
if not hasattr(self, '_default_structure_ids'):
tree = self.get_structure_tree()
default_structures = tree.get_structures_by_set_id(
MouseConnectivityCache.DEFAULT_STRUCTURE_SET_IDS)
self._default_structure_ids = [
st['id'] for st in default_structures
]
return self._default_structure_ids
def __init__(self,
resolution=None,
cache=True,
manifest_file='mouse_connectivity_manifest.json',
ccf_version=None,
base_uri=None,
version=None):
if version is None:
version = self.MANIFEST_VERSION
if resolution is None:
resolution = MouseConnectivityApi.VOXEL_RESOLUTION_25_MICRONS
if ccf_version is None:
ccf_version = MouseConnectivityApi.CCF_VERSION_DEFAULT
super(MouseConnectivityCache,
self).__init__(resolution,
reference_space_key=ccf_version,
cache=cache,
manifest=manifest_file,
version=version)
self.api = MouseConnectivityApi(base_uri=base_uri)
def get_projection_density(self, experiment_id, file_name=None):
"""
Read a projection density volume for a single experiment. Download it
first if it doesn't exist. Projection density is the proportion of
of projecting pixels in a grid voxel in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.PROJECTION_DENSITY_KEY,
experiment_id, self.resolution)
self.api.download_projection_density(file_name,
experiment_id,
self.resolution,
strategy='lazy')
return nrrd.read(file_name)
def get_injection_density(self, experiment_id, file_name=None):
"""
Read an injection density volume for a single experiment. Download it
first if it doesn't exist. Injection density is the proportion of
projecting pixels in a grid voxel only including pixels that are
part of the injection site in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.INJECTION_DENSITY_KEY,
experiment_id, self.resolution)
self.api.download_injection_density(file_name,
experiment_id,
self.resolution,
strategy='lazy')
return nrrd.read(file_name)
def get_injection_fraction(self, experiment_id, file_name=None):
"""
Read an injection fraction volume for a single experiment. Download it
first if it doesn't exist. Injection fraction is the proportion of
pixels in the injection site in a grid voxel in [0,1].
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.INJECTION_FRACTION_KEY,
experiment_id, self.resolution)
self.api.download_injection_fraction(file_name,
experiment_id,
self.resolution,
strategy='lazy')
return nrrd.read(file_name)
def get_data_mask(self, experiment_id, file_name=None):
"""
Read a data mask volume for a single experiment. Download it
first if it doesn't exist. Data mask is a binary mask of
voxels that have valid data. Only use valid data in analysis!
Parameters
----------
experiment_id: int
ID of the experiment to download/read. This corresponds to
section_data_set_id in the API.
file_name: string
File name to store the template volume. If it already exists,
it will be read from this file. If file_name is None, the
file_name will be pulled out of the manifest. Default is None.
"""
file_name = self.get_cache_path(file_name, self.DATA_MASK_KEY,
experiment_id, self.resolution)
self.api.download_data_mask(file_name,
experiment_id,
self.resolution,
strategy='lazy')
return nrrd.read(file_name)
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)
if os.path.exists(file_name):
experiments = json_utilities.read(file_name)
else:
experiments = self.api.experiment_source_search(
injection_structures='root')
# removing these elements because they are specific to a particular
# resolution
for e in experiments:
del e['num-voxels']
del e['injection-volume']
del e['sum']
del e['name']
if self.cache:
Manifest.safe_make_parent_dirs(file_name)
json_utilities.write(file_name, experiments)
# 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 filter_experiments(self,
experiments,
cre=None,
injection_structure_ids=None):
"""
Take a list of experiments and filter them by cre status and injection structure.
Parameters
----------
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.
"""
if cre is True:
experiments = [e for e in experiments if e['transgenic-line']]
elif cre is False:
experiments = [e for e in experiments if not e['transgenic-line']]
elif cre is not None:
cre = [c.lower() for c in cre]
experiments = [
e for e in experiments if e['transgenic-line'].lower() in cre
]
if injection_structure_ids is not None:
structure_ids = MouseConnectivityCache.validate_structure_ids(
injection_structure_ids)
descendant_ids = reduce(op.add, self.get_structure_tree()\
.descendant_ids(injection_structure_ids))
experiments = [
e for e in experiments if e['structure-id'] in descendant_ids
]
return experiments
def get_experiment_structure_unionizes(self,
experiment_id,
file_name=None,
is_injection=None,
structure_ids=None,
include_descendants=False,
hemisphere_ids=None):
"""
Retrieve the structure unionize data for a specific experiment. Filter by
structure, injection status, and hemisphere.
Parameters
----------
experiment_id: int
ID of the experiment of interest. Corresponds to section_data_set_id in the API.
file_name: string
File name to save/read the experiments list. 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.
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records for a specific set of structures.
If None, return all records. Default None.
include_descendants: boolean
Include all descendant records for specified structures. Default False.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
file_name = self.get_cache_path(file_name,
self.STRUCTURE_UNIONIZES_KEY,
experiment_id)
filter_fn = functools.partial(self.filter_structure_unionizes,
is_injection=is_injection,
structure_ids=structure_ids,
include_descendants=include_descendants,
hemisphere_ids=hemisphere_ids)
col_rn = lambda x: pd.DataFrame(x).rename(
columns={'section_data_set_id': 'experiment_id'})
return self.api.get_structure_unionizes(
[experiment_id],
path=file_name,
strategy='lazy',
pre=col_rn,
post=filter_fn,
writer=lambda p, x: pd.DataFrame(x).to_csv(p),
reader=pd.DataFrame.from_csv)
def rank_structures(self,
experiment_ids,
is_injection,
structure_ids=None,
hemisphere_ids=None,
rank_on='normalized_projection_volume',
n=5,
threshold=10**-2):
'''Produces one or more (per experiment) ranked lists of brain structures, using a specified data field.
Parameters
----------
experiment_ids : list of int
Obtain injection_structures for these experiments.
is_injection : boolean
Use data from only injection (or non-injection) unionizes.
structure_ids : list of int, optional
Consider only these structures. It is a good idea to make sure that these structures are not spatially
overlapping; otherwise your results will contain redundant information. Defaults to the summary
structures - a brain-wide list of nonoverlapping mid-level structures.
hemisphere_ids : list of int, optional
Consider only these hemispheres (1: left, 2: right, 3: both). Like with structures,
you might get redundant results if you select overlapping options. Defaults to [1, 2].
rank_on : str, optional
Rank unionize data using this field (descending). Defaults to normalized_projection_volume.
n : int, optional
Return only the top n structures.
threshold : float, optional
Consider only records whose data value - specified by the rank_on parameter - exceeds this value.
Returns
-------
list :
Each element (1 for each input experiment) is a list of dictionaries. The dictionaries describe the top
injection structures in descending order. They are specified by their structure and hemisphere id fields and
additionally report the value specified by the rank_on parameter.
'''
output_keys = [
'experiment_id', rank_on, 'hemisphere_id', 'structure_id'
]
filter_fields = lambda fieldname: fieldname in output_keys
if hemisphere_ids is None:
hemisphere_ids = [1, 2]
if structure_ids is None:
structure_ids = self.default_structure_ids
unionizes = self.get_structure_unionizes(experiment_ids,
is_injection=is_injection,
structure_ids=structure_ids,
hemisphere_ids=hemisphere_ids,
include_descendants=False)
unionizes = unionizes[unionizes[rank_on] > threshold]
results = []
for eid in experiment_ids:
this_experiment_unionizes = unionizes[unionizes['experiment_id'] ==
eid]
this_experiment_unionizes = this_experiment_unionizes.sort_values(
by=rank_on, ascending=False)
this_experiment_unionizes = this_experiment_unionizes.select(
filter_fields, axis=1)
records = this_experiment_unionizes.to_dict('record')
if len(records) > n:
records = records[:n]
results.append(records)
return results
def filter_structure_unionizes(self,
unionizes,
is_injection=None,
structure_ids=None,
include_descendants=False,
hemisphere_ids=None):
"""
Take a list of unionzes and return a subset of records filtered by injection status, structure, and
hemisphere.
Parameters
----------
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records for a set of structures.
If None, return all records. Default None.
include_descendants: boolean
Include all descendant records for specified structures. Default False.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
if is_injection is not None:
unionizes = unionizes[unionizes.is_injection == is_injection]
if structure_ids is not None:
structure_ids = MouseConnectivityCache.validate_structure_ids(
structure_ids)
if include_descendants:
structure_ids = reduce(
op.add,
self.get_structure_tree().descendant_ids(structure_ids))
else:
structure_ids = set(structure_ids)
unionizes = unionizes[unionizes['structure_id'].isin(
structure_ids)]
if hemisphere_ids is not None:
unionizes = unionizes[unionizes['hemisphere_id'].isin(
hemisphere_ids)]
return unionizes
def get_structure_unionizes(self,
experiment_ids,
is_injection=None,
structure_ids=None,
include_descendants=False,
hemisphere_ids=None):
"""
Get structure unionizes for a set of experiment IDs. Filter the results by injection status,
structure, and hemisphere.
Parameters
----------
experiment_ids: list
List of experiment IDs. Corresponds to section_data_set_id in the API.
is_injection: boolean
If True, only return unionize records that disregard non-injection pixels.
If False, only return unionize records that disregard injection pixels.
If None, return all records. Default None.
structure_ids: list
Only return unionize records for a specific set of structures.
If None, return all records. Default None.
include_descendants: boolean
Include all descendant records for specified structures. Default False.
hemisphere_ids: list
Only return unionize records that disregard pixels outside of a hemisphere.
or set of hemispheres. Left = 1, Right = 2, Both = 3. If None, include all
records [1, 2, 3]. Default None.
"""
unionizes = [
self.get_experiment_structure_unionizes(
eid,
is_injection=is_injection,
structure_ids=structure_ids,
include_descendants=include_descendants,
hemisphere_ids=hemisphere_ids) for eid in experiment_ids
]
return pd.concat(unionizes, ignore_index=True)
def get_projection_matrix(self,
experiment_ids,
projection_structure_ids=None,
hemisphere_ids=None,
parameter='projection_volume',
dataframe=False):
if projection_structure_ids is None:
projection_structure_ids = self.default_structure_ids
unionizes = self.get_structure_unionizes(
experiment_ids,
is_injection=False,
structure_ids=projection_structure_ids,
include_descendants=False,
hemisphere_ids=hemisphere_ids)
hemisphere_ids = set(unionizes['hemisphere_id'].values.tolist())
nrows = len(experiment_ids)
ncolumns = len(projection_structure_ids) * len(hemisphere_ids)
matrix = np.empty((nrows, ncolumns))
matrix[:] = np.NAN
row_lookup = {}
for idx, e in enumerate(experiment_ids):
row_lookup[e] = idx
column_lookup = {}
columns = []
cidx = 0
hlabel = {1: '-L', 2: '-R', 3: ''}
acronym_map = self.get_structure_tree().value_map(
lambda x: x['id'], lambda x: x['acronym'])
for hid in hemisphere_ids:
for sid in projection_structure_ids:
column_lookup[(hid, sid)] = cidx
label = acronym_map[sid] + hlabel[hid]
columns.append({
'hemisphere_id': hid,
'structure_id': sid,
'label': label
})
cidx += 1
for _, row in unionizes.iterrows():
ridx = row_lookup[row['experiment_id']]
k = (row['hemisphere_id'], row['structure_id'])
cidx = column_lookup[k]
matrix[ridx, cidx] = row[parameter]
if dataframe:
warnings.warn("dataframe argument is deprecated.")
all_experiments = self.get_experiments(dataframe=True)
rows_df = all_experiments.loc[experiment_ids]
cols_df = pd.DataFrame(columns)
return {'matrix': matrix, 'rows': rows_df, 'columns': cols_df}
else:
return {
'matrix': matrix,
'rows': experiment_ids,
'columns': columns
}
def add_manifest_paths(self, manifest_builder):
"""
Construct a manifest for this Cache class and save it in a file.
Parameters
----------
file_name: string
File location to save the manifest.
"""
manifest_builder = super(MouseConnectivityCache,
self).add_manifest_paths(manifest_builder)
manifest_builder.add_path(self.EXPERIMENTS_KEY,
'experiments.json',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.STRUCTURE_UNIONIZES_KEY,
'experiment_%d/structure_unionizes.csv',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.INJECTION_DENSITY_KEY,
'experiment_%d/injection_density_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.INJECTION_FRACTION_KEY,
'experiment_%d/injection_fraction_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.DATA_MASK_KEY,
'experiment_%d/data_mask_%d.nrrd',
parent_key='BASEDIR',
typename='file')
manifest_builder.add_path(self.PROJECTION_DENSITY_KEY,
'experiment_%d/projection_density_%d.nrrd',
parent_key='BASEDIR',
typename='file')
return manifest_builder
from allensdk.api.queries.mouse_connectivity_api import MouseConnectivityApi
mca = MouseConnectivityApi()
# get metadata for all non-Cre experiments
experiments = mca.experiment_source_search(injection_structures='root',
transgenic_lines=0)
# download the projection density volume for one of the experiments
pd = mca.download_projection_density('example.nrrd',
experiments[0]['id'],
resolution=25)
