Source code for allensdk.core.mouse_connectivity_cache

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from allensdk.config.manifest_builder import ManifestBuilder
from allensdk.api.cache import Cache
from allensdk.api.queries.mouse_connectivity_api import MouseConnectivityApi
from allensdk.deprecated import deprecated

from . import json_utilities
from .reference_space_cache import ReferenceSpaceCache

import nrrd
import os
import pandas as pd
import numpy as np
from allensdk.config.manifest import Manifest
import warnings
import operator as op
import functools


[docs]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 = 1.1 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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
[docs] 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
[docs] 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)
[docs] 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
[docs] 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
[docs] 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)
[docs] 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}
[docs] 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