class BiophysicalArchiver(object):
def __init__(self, archive_dir=None):
self.bp = BiophysicalApi('http://api.brain-map.org')
self.bp.cache_stimulus = True # change to False to not download the large stimulus NWB file
self.cta = CellTypesApi()
self.rma = RmaApi()
self.neuronal_model_download_endpoint = 'http://celltypes.brain-map.org/neuronal_model/download/'
self.template_names = {}
self.nwb_list = []
if archive_dir == None:
archive_dir = '.'
self.archive_dir = archive_dir
def get_template_names(self):
template_response = self.rma.model_query('NeuronalModelTemplate')
self.template_names = { t['id']: str(t['name']).replace(' ', '_') for t in template_response}
def get_cells(self):
return self.cta.list_cells(True, True)
def get_neuronal_models(self, specimen_ids):
return self.rma.model_query('NeuronalModel',
criteria='specimen[id$in%s]' % ','.join(str(i) for i in specimen_ids),
include='specimen',
num_rows='all')
def get_stimulus_file(self, neuronal_model_id):
result = self.rma.model_query('NeuronalModel',
criteria='[id$eq%d]' % (neuronal_model_id),
include="specimen(ephys_result(well_known_files(well_known_file_type[name$il'NWB*'])))",
tabular=['path'])
stimulus_filename = result[0]['path']
return stimulus_filename
stimulus_filename = os.path.basename(result[0]['path'])
return stimulus_filename
def archive_cell(self, ephys_result_id, specimen_id, template, neuronal_model_id):
url = self.neuronal_model_download_endpoint + "/%d" % (neuronal_model_id)
file_name = os.path.join(self.archive_dir, 'ephys_result_%d_specimen_%d_%s_neuronal_model_%d.zip' % (ephys_result_id,
specimen_id,
template,
neuronal_model_id))
self.rma.retrieve_file_over_http(url, file_name)
nwb_file = self.get_stimulus_file(neuronal_model_id)
shutil.copy(nwb_file, self.archive_dir)
self.nwb_list.append("%s\t%s" % (os.path.basename(nwb_file),
file_name))
def test_list_cells_mocked(mock_cells):
with patch.object(CellTypesApi, "model_query", return_value=mock_cells):
ctapi = CellTypesApi()
cells = ctapi.list_cells()
assert len(cells) == 3
flu_cells = [
cell for cell in cells
if cell['disease_categories'] == [('influenza')]
]
assert len(flu_cells) == 1
cells = ctapi.list_cells(require_reconstruction=True)
assert len(cells) == 1
cells = ctapi.list_cells(require_morphology=True)
assert len(cells) == 1
cells = ctapi.list_cells(reporter_status=['bob'])
assert len(cells) == 1
cells = ctapi.list_cells(species=['H**O SAPIENS'])
assert len(cells) == 1
cells = ctapi.list_cells(species=['mus musculus'])
assert len(cells) == 1
class CellTypesCache(Cache):
"""
Cache class for storing and accessing data from the Cell Types Database.
By default, this class will cache any downloaded metadata or files in
well known locations defined in a manifest file. This behavior can be
disabled.
Attributes
----------
api: CellTypesApi instance
The object used for making API queries related to the Cell Types Database
Parameters
----------
cache: boolean
Whether the class should save results of API queries to locations specified
in the manifest file. Queries for files (as opposed to metadata) must have a
file location. If caching is disabled, those locations must be specified
in the function call (e.g. get_ephys_data(file_name='file.nwb')).
manifest_file: string
File name of the manifest to be read. Default is "cell_types_manifest.json".
"""
# manifest keys
CELLS_KEY = 'CELLS'
EPHYS_FEATURES_KEY = 'EPHYS_FEATURES'
MORPHOLOGY_FEATURES_KEY = 'MORPHOLOGY_FEATURES'
EPHYS_DATA_KEY = 'EPHYS_DATA'
EPHYS_SWEEPS_KEY = 'EPHYS_SWEEPS'
RECONSTRUCTION_KEY = 'RECONSTRUCTION'
MARKER_KEY = 'MARKER'
def __init__(self, cache=True, manifest_file='cell_types_manifest.json', base_uri=None):
super(CellTypesCache, self).__init__(
manifest=manifest_file, cache=cache)
self.api = CellTypesApi(base_uri=base_uri)
def get_cells(self, file_name=None, require_morphology=False, require_reconstruction=False, reporter_status=None):
"""
Download metadata for all cells in the database and optionally return a
subset filtered by whether or not they have a morphology or reconstruction.
Parameters
----------
file_name: string
File name to save/read the cell metadata as JSON. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
require_morphology: boolean
Filter out cells that have no morphological images.
require_reconstruction: boolean
Filter out cells that have no morphological reconstructions.
reporter_status: list
Filter for cells that have one or more cell reporter statuses.
"""
file_name = self.get_cache_path(file_name, self.CELLS_KEY)
if os.path.exists(file_name):
cells = json_utilities.read(file_name)
else:
cells = self.api.list_cells(False, False)
if self.cache:
json_utilities.write(file_name, cells)
if isinstance(reporter_status, string_types):
reporter_status = [reporter_status]
# filter the cells on the way out
return self.api.filter_cells(cells, require_morphology, require_reconstruction, reporter_status)
def get_ephys_sweeps(self, specimen_id, file_name=None):
"""
Download sweep metadata for a single cell specimen.
Parameters
----------
specimen_id: int
ID of a cell.
"""
file_name = self.get_cache_path(
file_name, self.EPHYS_SWEEPS_KEY, specimen_id)
if os.path.exists(file_name):
sweeps = json_utilities.read(file_name)
else:
sweeps = self.api.get_ephys_sweeps(specimen_id)
if self.cache:
json_utilities.write(file_name, sweeps)
return sweeps
def get_ephys_features(self, dataframe=False, file_name=None):
"""
Download electrophysiology features for all cells in the database.
Parameters
----------
file_name: string
File name to save/read the ephys features metadata as CSV.
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.
dataframe: boolean
Return the output as a Pandas DataFrame. If False, return
a list of dictionaries.
"""
file_name = self.get_cache_path(file_name, self.EPHYS_FEATURES_KEY)
if os.path.exists(file_name):
features_df = pd.DataFrame.from_csv(file_name)
else:
features_df = self.api.get_ephys_features(dataframe=True)
if self.cache:
features_df.to_csv(file_name)
if dataframe:
return features_df
else:
return features_df.to_dict('records')
def get_morphology_features(self, dataframe=False, file_name=None):
"""
Download morphology features for all cells with reconstructions in the database.
Parameters
----------
file_name: string
File name to save/read the ephys features metadata as CSV.
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.
dataframe: boolean
Return the output as a Pandas DataFrame. If False, return
a list of dictionaries.
"""
file_name = self.get_cache_path(
file_name, self.MORPHOLOGY_FEATURES_KEY)
if os.path.exists(file_name):
features_df = pd.DataFrame.from_csv(file_name)
else:
features_df = self.api.get_morphology_features(dataframe=True)
if self.cache:
features_df.to_csv(file_name)
if dataframe:
return features_df
else:
return features_df.to_dict('records')
def get_all_features(self, dataframe=False, require_reconstruction=True):
"""
Download morphology and electrophysiology features for all cells and merge them
into a single table.
Parameters
----------
dataframe: boolean
Return the output as a Pandas DataFrame. If False, return
a list of dictionaries.
require_reconstruction: boolean
Only return ephys and morphology features for cells that have
reconstructions. Default True.
"""
ephys_features = self.get_ephys_features(dataframe=True)
morphology_features = self.get_morphology_features(dataframe=True)
how = 'inner' if require_reconstruction else 'outer'
all_features = ephys_features.merge(morphology_features,
how=how,
on='specimen_id')
if dataframe:
return all_features
else:
return all_features.to_dict('records')
def get_ephys_data(self, specimen_id, file_name=None):
"""
Download electrophysiology traces for a single cell in the database.
Parameters
----------
specimen_id: int
The ID of a cell specimen to download.
file_name: string
File name to save/read the ephys features metadata as CSV.
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.
Returns
-------
NwbDataSet
A class instance with helper methods for retrieving stimulus
and response traces out of an NWB file.
"""
file_name = self.get_cache_path(
file_name, self.EPHYS_DATA_KEY, specimen_id)
if not os.path.exists(file_name):
self.api.save_ephys_data(specimen_id, file_name)
return NwbDataSet(file_name)
def get_reconstruction(self, specimen_id, file_name=None):
"""
Download and open a reconstruction for a single cell in the database.
Parameters
----------
specimen_id: int
The ID of a cell specimen to download.
file_name: string
File name to save/read the reconstruction SWC.
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.
Returns
-------
Morphology
A class instance with methods for accessing morphology compartments.
"""
file_name = self.get_cache_path(
file_name, self.RECONSTRUCTION_KEY, specimen_id)
if file_name is None:
raise Exception(
"Please enable caching (CellTypes.cache = True) or specify a save_file_name.")
if not os.path.exists(file_name):
self.api.save_reconstruction(specimen_id, file_name)
return swc.read_swc(file_name)
def get_reconstruction_markers(self, specimen_id, file_name=None):
"""
Download and open a reconstruction marker file for a single cell in the database.
Parameters
----------
specimen_id: int
The ID of a cell specimen to download.
file_name: string
File name to save/read the reconstruction marker.
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.
Returns
-------
Morphology
A class instance with methods for accessing morphology compartments.
"""
file_name = self.get_cache_path(
file_name, self.MARKER_KEY, specimen_id)
if file_name is None:
raise Exception(
"Please enable caching (CellTypes.cache = True) or specify a save_file_name.")
if not os.path.exists(file_name):
try:
self.api.save_reconstruction_markers(specimen_id, file_name)
except LookupError as e:
logging.warning(e.args)
return []
return swc.read_marker_file(file_name)
def build_manifest(self, file_name):
"""
Construct a manifest for this Cache class and save it in a file.
Parameters
----------
file_name: string
File location to save the manifest.
"""
mb = ManifestBuilder()
mb.add_path('BASEDIR', '.')
mb.add_path(self.CELLS_KEY, 'cells.json',
typename='file', parent_key='BASEDIR')
mb.add_path(self.EPHYS_DATA_KEY, 'specimen_%d/ephys.nwb',
typename='file', parent_key='BASEDIR')
mb.add_path(self.EPHYS_FEATURES_KEY, 'ephys_features.csv',
typename='file', parent_key='BASEDIR')
mb.add_path(self.MORPHOLOGY_FEATURES_KEY, 'morphology_features.csv',
typename='file', parent_key='BASEDIR')
mb.add_path(self.RECONSTRUCTION_KEY, 'specimen_%d/reconstruction.swc',
typename='file', parent_key='BASEDIR')
mb.add_path(self.MARKER_KEY, 'specimen_%d/reconstruction.marker',
typename='file', parent_key='BASEDIR')
mb.add_path(self.EPHYS_SWEEPS_KEY, 'specimen_%d/ephys_sweeps.json',
typename='file', parent_key='BASEDIR')
mb.write_json_file(file_name)
class CellTypesCache(Cache):
"""
Cache class for storing and accessing data from the Cell Types Database.
By default, this class will cache any downloaded metadata or files in
well known locations defined in a manifest file. This behavior can be
disabled.
Attributes
----------
api: CellTypesApi instance
The object used for making API queries related to the Cell Types Database
Parameters
----------
cache: boolean
Whether the class should save results of API queries to locations specified
in the manifest file. Queries for files (as opposed to metadata) must have a
file location. If caching is disabled, those locations must be specified
in the function call (e.g. get_ephys_data(file_name='file.nwb')).
manifest_file: string
File name of the manifest to be read. Default is "cell_types_manifest.json".
"""
# manifest keys
CELLS_KEY = 'CELLS'
EPHYS_FEATURES_KEY = 'EPHYS_FEATURES'
MORPHOLOGY_FEATURES_KEY = 'MORPHOLOGY_FEATURES'
EPHYS_DATA_KEY = 'EPHYS_DATA'
EPHYS_SWEEPS_KEY = 'EPHYS_SWEEPS'
RECONSTRUCTION_KEY = 'RECONSTRUCTION'
MARKER_KEY = 'MARKER'
def __init__(self, cache=True, manifest_file='cell_types_manifest.json', base_uri=None):
super(CellTypesCache, self).__init__(
manifest=manifest_file, cache=cache)
self.api = CellTypesApi(base_uri=base_uri)
def get_cells(self, file_name=None, require_morphology=False, require_reconstruction=False, reporter_status=None):
"""
Download metadata for all cells in the database and optionally return a
subset filtered by whether or not they have a morphology or reconstruction.
Parameters
----------
file_name: string
File name to save/read the cell metadata as JSON. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
require_morphology: boolean
Filter out cells that have no morphological images.
require_reconstruction: boolean
Filter out cells that have no morphological reconstructions.
reporter_status: list
Filter for cells that have one or more cell reporter statuses.
"""
file_name = self.get_cache_path(file_name, self.CELLS_KEY)
if os.path.exists(file_name):
cells = json_utilities.read(file_name)
else:
cells = self.api.list_cells(False, False)
if self.cache:
json_utilities.write(file_name, cells)
if isinstance(reporter_status, string_types):
reporter_status = [reporter_status]
# filter the cells on the way out
return self.api.filter_cells(cells, require_morphology, require_reconstruction, reporter_status)
def get_ephys_sweeps(self, specimen_id, file_name=None):
"""
Download sweep metadata for a single cell specimen.
Parameters
----------
specimen_id: int
ID of a cell.
"""
file_name = self.get_cache_path(
file_name, self.EPHYS_SWEEPS_KEY, specimen_id)
if os.path.exists(file_name):
sweeps = json_utilities.read(file_name)
else:
sweeps = self.api.get_ephys_sweeps(specimen_id)
if self.cache:
json_utilities.write(file_name, sweeps)
return sweeps
def get_ephys_features(self, dataframe=False, file_name=None):
"""
Download electrophysiology features for all cells in the database.
Parameters
----------
file_name: string
File name to save/read the ephys features metadata as CSV.
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.
dataframe: boolean
Return the output as a Pandas DataFrame. If False, return
a list of dictionaries.
"""
file_name = self.get_cache_path(file_name, self.EPHYS_FEATURES_KEY)
if os.path.exists(file_name):
features_df = pd.DataFrame.from_csv(file_name)
else:
features_df = self.api.get_ephys_features(dataframe=True)
if self.cache:
features_df.to_csv(file_name)
if dataframe:
return features_df
else:
return features_df.to_dict('records')
def get_morphology_features(self, dataframe=False, file_name=None):
"""
Download morphology features for all cells with reconstructions in the database.
Parameters
----------
file_name: string
File name to save/read the ephys features metadata as CSV.
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.
dataframe: boolean
Return the output as a Pandas DataFrame. If False, return
a list of dictionaries.
"""
file_name = self.get_cache_path(
file_name, self.MORPHOLOGY_FEATURES_KEY)
if os.path.exists(file_name):
features_df = pd.DataFrame.from_csv(file_name)
else:
features_df = self.api.get_morphology_features(dataframe=True)
if self.cache:
features_df.to_csv(file_name)
if dataframe:
return features_df
else:
return features_df.to_dict('records')
def get_all_features(self, dataframe=False, require_reconstruction=True):
"""
Download morphology and electrophysiology features for all cells and merge them
into a single table.
Parameters
----------
dataframe: boolean
Return the output as a Pandas DataFrame. If False, return
a list of dictionaries.
require_reconstruction: boolean
Only return ephys and morphology features for cells that have
reconstructions. Default True.
"""
ephys_features = self.get_ephys_features(dataframe=True)
morphology_features = self.get_morphology_features(dataframe=True)
how = 'inner' if require_reconstruction else 'outer'
all_features = ephys_features.merge(morphology_features,
how=how,
on='specimen_id')
if dataframe:
return all_features
else:
return all_features.to_dict('records')
def get_ephys_data(self, specimen_id, file_name=None):
"""
Download electrophysiology traces for a single cell in the database.
Parameters
----------
specimen_id: int
The ID of a cell specimen to download.
file_name: string
File name to save/read the ephys features metadata as CSV.
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.
Returns
-------
NwbDataSet
A class instance with helper methods for retrieving stimulus
and response traces out of an NWB file.
"""
file_name = self.get_cache_path(
file_name, self.EPHYS_DATA_KEY, specimen_id)
if not os.path.exists(file_name):
self.api.save_ephys_data(specimen_id, file_name)
return NwbDataSet(file_name)
def get_reconstruction(self, specimen_id, file_name=None):
"""
Download and open a reconstruction for a single cell in the database.
Parameters
----------
specimen_id: int
The ID of a cell specimen to download.
file_name: string
File name to save/read the reconstruction SWC.
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.
Returns
-------
Morphology
A class instance with methods for accessing morphology compartments.
"""
file_name = self.get_cache_path(
file_name, self.RECONSTRUCTION_KEY, specimen_id)
if file_name is None:
raise Exception(
"Please enable caching (CellTypes.cache = True) or specify a save_file_name.")
if not os.path.exists(file_name):
self.api.save_reconstruction(specimen_id, file_name)
return swc.read_swc(file_name)
def get_reconstruction_markers(self, specimen_id, file_name=None):
"""
Download and open a reconstruction marker file for a single cell in the database.
Parameters
----------
specimen_id: int
The ID of a cell specimen to download.
file_name: string
File name to save/read the reconstruction marker.
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.
Returns
-------
Morphology
A class instance with methods for accessing morphology compartments.
"""
file_name = self.get_cache_path(
file_name, self.MARKER_KEY, specimen_id)
if file_name is None:
raise Exception(
"Please enable caching (CellTypes.cache = True) or specify a save_file_name.")
if not os.path.exists(file_name):
try:
self.api.save_reconstruction_markers(specimen_id, file_name)
except LookupError as e:
logging.warning(e.args)
return []
return swc.read_marker_file(file_name)
def build_manifest(self, file_name):
"""
Construct a manifest for this Cache class and save it in a file.
Parameters
----------
file_name: string
File location to save the manifest.
"""
mb = ManifestBuilder()
mb.add_path('BASEDIR', '.')
mb.add_path(self.CELLS_KEY, 'cells.json',
typename='file', parent_key='BASEDIR')
mb.add_path(self.EPHYS_DATA_KEY, 'specimen_%d/ephys.nwb',
typename='file', parent_key='BASEDIR')
mb.add_path(self.EPHYS_FEATURES_KEY, 'ephys_features.csv',
typename='file', parent_key='BASEDIR')
mb.add_path(self.MORPHOLOGY_FEATURES_KEY, 'morphology_features.csv',
typename='file', parent_key='BASEDIR')
mb.add_path(self.RECONSTRUCTION_KEY, 'specimen_%d/reconstruction.swc',
typename='file', parent_key='BASEDIR')
mb.add_path(self.MARKER_KEY, 'specimen_%d/reconstruction.marker',
typename='file', parent_key='BASEDIR')
mb.add_path(self.EPHYS_SWEEPS_KEY, 'specimen_%d/ephys_sweeps.json',
typename='file', parent_key='BASEDIR')
mb.write_json_file(file_name)
from allensdk.api.queries.biophysical_perisomatic_api import \
BiophysicalPerisomaticApi
from allensdk.api.queries.cell_types_api import CellTypesApi
import allensdk.core.swc as swc
import os
from allensdk.core.nwb_data_set import NwbDataSet
from IPython import __main__
bp = BiophysicalPerisomaticApi('http://api.brain-map.org')
ct = CellTypesApi()
cells = ct.list_cells()
#Below is one way, I can create URL, queries and get the result in a local python dictionary.
import allensdk
import mpi4py as MPI
import numpy as np
import csv
import pickle
import sys
#import glob
def __main__():
print 'main'
import os
from allensdk.api.queries.cell_types_api import CellTypesApi
import allensdk.core.swc as swc
import matplotlib.pyplot as plt
import numpy as np
import math
import pprint
import csv
#output_file = 'sholl_all.csv'
#output_file = 'sholl_apical_dendrite_only.csv'
output_file = 'sholl_basal_dendrite_only.csv'
ct = CellTypesApi()
cells = ct.list_cells(require_reconstruction=True)
print "Cells with reconstructions: ", len(cells)
download_file = False
allow_types = [3]
radii = np.array(range(0,1000,10))
radii_square = np.square( radii )
#
# Computation based on line-sphere intersection formula
# https://en.wikipedia.org/wiki/Line%E2%80%93sphere_intersection
#
with open( output_file, 'wb' ) as csvfile :
cwriter = csv.writer( csvfile, delimiter=',', quotechar='"' )
def download_glif_models(cell_ids,
base_dir,
incl_ephys=True,
force_overwrite=False):
"""Goes through the list of cell_ids and downloads cell config and ephys data in base_dir/cell_<ID>. Then looks up
all possible models and downloads model files int base_dir/cell_<ID>/<MODEL_TYPE>_<MODEL_ID>/
"""
# Determine the best url for connecting to cell-types db
try:
# see if we can connect to interal cell-types db
request = requests.get('http://icelltypes/')
if request.status_code == 200:
base_uri = 'http://icelltypes/'
else:
base_uri = None
except Exception:
base_uri = None # use the default url
base_dir = base_dir if base_dir.endswith('/') else base_dir + '/'
valid_cells = []
ct_api = CellTypesApi(base_uri)
for cell in ct_api.list_cells():
if cell['id'] in cell_ids:
# create directory for cell
cell_home = '{}cell_{}/'.format(base_dir, cell['id'])
if not os.path.exists(cell_home):
os.makedirs(cell_home)
# save metadata
cell_metadata_file = cell_home + 'cell_metadata.json'
if force_overwrite or not os.path.exists(cell_metadata_file):
print('Saving metadata for cell {} in {}'.format(
cell['id'], cell_metadata_file))
json_utilities.write(cell_metadata_file, cell)
else:
print('File {} already exists. Skipping'.format(
cell_metadata_file))
# save ephys data
if incl_ephys:
cell_ephys_file = cell_home + 'ephys_data.nwb'
if force_overwrite or not os.path.exists(cell_ephys_file):
print('Saving ephys data for cell {} in {}'.format(
cell['id'], cell_ephys_file))
ct_api.save_ephys_data(cell['id'], cell_ephys_file)
else:
print('File {} already exists. Skipping'.format(
cell_ephys_file))
# save sweeps file
sweeps_file = cell_home + 'ephys_sweeps.json'
if force_overwrite or not os.path.exists(sweeps_file):
print('- Saving sweeps file to {}'.format(sweeps_file))
ephys_sweeps = ct_api.get_ephys_sweeps(cell['id'])
json_utilities.write(sweeps_file, ephys_sweeps)
else:
print('- File {} already exits. Skipping.'.format(sweeps_file))
# keep track of valid ids
valid_cells.append(cell['id'])
cell_ids.remove(cell['id'])
for cid in cell_ids:
print('Warning: cell #{} was not found in cell-types database'.format(
cid))
# Iterate through each all available models and find ones correspoding to cell list
glif_models = {} # map model-id to their directory
glif_api = GlifApi(base_uri=base_uri)
for model in glif_api.list_neuronal_models():
if model['specimen_id'] in valid_cells:
# save model files <BASE_DIR>/cell_<CELL_ID>/<MODEL_TYPE>_<MODEL_ID>/
cell_id = model['specimen_id']
model_id = model['id']
model_type = model[
'neuronal_model_template_id'] #['id'] # type of model, GLIF-LIF, GLIF-ASC, etc
type_name = model_id2name.get(model_type, None)
if type_name is None:
print(
'Warning: Unknown model type {} ({}) for cell/model {}/{}'.
format(model_type,
model['neuronal_model_template']['name'], cell_id,
model_id))
type_name = model_type
model_home_dir = '{}cell_{}/{}_{}/'.format(base_dir, cell_id,
type_name, model_id)
glif_models[model_id] = model_home_dir
# go through all the found models, download necessary files
n_models = len(glif_models)
for i, (gid, home_dir) in enumerate(glif_models.iteritems()):
print('Processing model {} ({} of {})'.format(gid, (i + 1), n_models))
model_metadata = glif_api.get_neuronal_model(gid)
if not os.path.exists(home_dir):
os.makedirs(home_dir)
# save model metadata
metadata_file = home_dir + 'metadata.json'
if force_overwrite or not os.path.exists(metadata_file):
print('- Saving metadata file to {}'.format(metadata_file))
#print type(metadata_file)
with open(metadata_file, 'wb') as fp:
json.dump(model_metadata, fp, indent=2)
else:
print('- File {} already exits. Skipping.'.format(metadata_file))
# get neuron configuration file
config_file = home_dir + 'config.json'
if force_overwrite or not os.path.exists(config_file):
print('- Saving configuration file to {}'.format(config_file))
neuron_config = glif_api.get_neuron_config()
json_utilities.write(config_file, neuron_config)
else:
print('- File {} already exits. Skipping.'.format(config_file))
from allensdk.api.queries.cell_types_api import CellTypesApi ct = CellTypesApi() # a list of dictionaries containing metadata for cells with reconstructions cells = ct.list_cells(require_reconstruction=True) # download the electrophysiology data for one cell ct.save_ephys_data(cells[0]['id'], 'example.nwb') # download the reconstruction for the same cell ct.save_reconstruction(cells[0]['id'], 'example.swc')
from neuroml import NeuroMLDocument
from neuroml import Network
from neuroml import Population
from neuroml import Location
from neuroml import Instance
from neuroml import IncludeType
import neuroml.writers as writers
pp = pprint.PrettyPrinter(indent=4)
ct = CellTypesApi()
# use the SDK API to get a list of single cells and corresponding metadata
cell_metadata_list = ct.list_cells(require_reconstruction=True)
# convert that list of cells' metadata to a python dictionary for easy access
cell_meta_dict = {}
dataset_ids = []
for i in range(len(cell_metadata_list)):
id = cell_metadata_list[i]['id']
dataset_ids.append(id)
cell_meta_dict[id] = cell_metadata_list[i]
cells = ['467703703', '323452245']
cells = ['467703703', '471141261', '320207387', '471141261', '324493977', '464326095']
cells = None # i.e. all...
# Why? Good question...
from neuroml import NeuroMLDocument
from neuroml import Network
from neuroml import Population
from neuroml import Location
from neuroml import Instance
from neuroml import IncludeType
import neuroml.writers as writers
pp = pprint.PrettyPrinter(indent=4)
ct = CellTypesApi()
# use the SDK API to get a list of single cells and corresponding metadata
cell_metadata_list = ct.list_cells(require_reconstruction=True)
# convert that list of cells' metadata to a python dictionary for easy access
cell_meta_dict = {}
dataset_ids = []
for i in range(len(cell_metadata_list)):
id = cell_metadata_list[i]['id']
dataset_ids.append(id)
cell_meta_dict[id] = cell_metadata_list[i]
cells = ['467703703', '323452245']
cells = [
'467703703', '471141261', '320207387', '471141261', '324493977',
'464326095'
]
