def write_output_data(self, data):
try:
ju.write(self.args.output_json, data)
except Exception as e:
logging.error("could not write output json: %s",
self.args.output_json)
raise e
def mkjson(): #Only ascii as in dictionary contents
from allensdk.core.json_utilities import write
#can be serialised into dictionary contents.
for m in morphs:
write(str(m)+'.json',m.root)
return 0
def main():
input_data = ju.read(INPUT_JSON)
structures = input_data.get('structures')
manifest_file = input_data.get('manifest_file')
manifest_file = os.path.join(TOP_DIR, manifest_file)
log_level = input_data.get('log_level', logging.DEBUG)
logging.getLogger().setLevel(log_level)
# experiments to exclude
experiments_exclude = ju.read(EXPERIMENTS_EXCLUDE_JSON)
# get caching object
cache = VoxelModelCache(manifest_file=manifest_file)
output_file = os.path.join(OUTPUT_DIR, 'hyperparameters-%s.json' % OPTION)
results = dict()
for structure in structures:
logging.debug("Running cross validation for structure: %s", structure)
structure_id = get_structure_id(cache, structure)
results[structure] = fit_structure(cache,
structure_id,
experiments_exclude,
kernel=KERNEL,
model_option=OPTION)
# write results
ju.write(output_file, results)
def make_stimulus_ontology_from_lims(file_name):
if lq.able_to_connect_to_lims():
stims = lq.get_stimuli_description()
stim_ontology = make_stimulus_ontology(stims)
ju.write(file_name, stim_ontology)
logging.info("Updated stimulus ontology from LIMS")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input_json")
parser.add_argument("output_json")
parser.add_argument("--log_level", default=logging.DEBUG)
parser.add_argument("--input_dataset", default="FC")
parser.add_argument("--roi_field", default="roi_names")
parser.add_argument("--output_dataset", default="data")
args = parser.parse_args()
logging.getLogger().setLevel(args.log_level)
input_data = ju.read(args.input_json)
input_file, output_file = parse_input(input_data)
# read from "data"
input_h5 = h5py.File(input_file, "r")
traces = input_h5[args.input_dataset].value
roi_names = input_h5[args.roi_field][:]
input_h5.close()
dff = calculate_dff(traces)
# write to "data"
output_h5 = h5py.File(output_file, "w")
output_h5[args.output_dataset] = dff
output_h5[args.roi_field] = roi_names
output_h5.close()
output_data = {}
ju.write(args.output_json, output_data)
def main():
module = ags.ArgSchemaParser(schema_type=OptimizeParameters)
preprocess_results = ju.read(module.args["paths"]["preprocess_results"])
passive_results = ju.read(module.args["paths"]["passive_results"])
fit_style_data = ju.read(module.args["paths"]["fit_style"])
results = optimize(
hoc_files=module.args["paths"]["hoc_files"],
compiled_mod_library=module.args["paths"]["compiled_mod_library"],
morphology_path=module.args["paths"]["swc"],
features=preprocess_results["features"],
targets=preprocess_results["target_features"],
stim_params=StimParams(preprocess_results["stimulus"]),
passive_results=passive_results,
fit_type=module.args["fit_type"],
fit_style_data=fit_style_data,
seed=module.args["seed"],
ngen=module.args["ngen"],
mu=module.args["mu"],
storage_directory=module.args["paths"]["storage_directory"],
starting_population=module.args["paths"].get("starting_population",
None))
logging.info("Writing optimization output")
ju.write(module.args["output_json"], results)
def to_json(self, file_name=None):
"""JSON serialize object parameters to file or string.
Parameters
----------
file_name : string, optional (default None)
Path to .json file containing VoxelModelCache parameters. If None,
a string will be returned.
Returns
-------
string
If file_name == None, a string of the JSON serialization is returned.
"""
params = dict(resolution=self.resolution,
cache=self.cache,
manifest_file=self.manifest_file,
ccf_version=self.ccf_version,
base_uri=self.base_uri,
version=self.version)
if file_name is None:
return json_utilities.write_string(params)
json_utilities.write(file_name, params)
def from_file_name(cls, file_name, cache=True, **kwargs):
'''Alternative constructor using cache path file_name.
Parameters
----------
file_name : string
Path where storage_directories will be saved.
**kwargs
Keyword arguments to be supplied to __init__
Returns
-------
cls : instance of GridDataApiPrerelease
'''
if os.path.exists(file_name):
storage_directories = json_utilities.read(file_name)
else:
storage_directories = _get_grid_storage_directories(
cls.GRID_DATA_DIRECTORY)
if cache:
Manifest.safe_make_parent_dirs(file_name)
json_utilities.write(file_name, storage_directories)
return cls(storage_directories, **kwargs)
def test_spike_times(self):
expected = [
2.937305, 3.16453 , 3.24271 , 4.1622 , 4.24182 ,
10.0898 , 10.132545, 10.176095, 10.2361 , 10.660655,
10.762125, 10.863465, 10.93833 , 11.140815, 11.19246 ,
11.24553 , 11.696305, 11.812655, 11.90469 , 12.056755,
12.15794 , 12.233905, 12.47577 , 12.741295, 12.82861 ,
12.923175, 18.05068 , 18.139875, 18.17693 , 18.221485,
18.24337 , 18.39981 , 18.470705, 18.759675, 18.82183 ,
18.877475, 18.91033 , 18.941195, 19.050515, 19.12557 ,
19.15963 , 19.188655, 19.226205, 19.29813 , 19.420665,
19.47627 , 19.763365, 19.824225, 19.897995, 19.93155 ,
20.04916 , 20.11832 , 20.148755, 20.18004 , 20.22173 ,
20.2433 , 20.40018 , 20.470915, 20.759715, 20.82156 ,
20.866465, 20.90807 , 20.939175]
bp = BiophysicalPerisomaticApi('http://api.brain-map.org')
bp.cache_stimulus = True # change to False to not download the large stimulus NWB file
neuronal_model_id = 472451419 # get this from the web site as above
bp.cache_data(neuronal_model_id, working_directory='neuronal_model')
cwd = os.path.realpath(os.curdir)
print(cwd)
os.chdir(os.path.join(cwd, 'neuronal_model'))
manifest = ju.read('manifest.json')
manifest['biophys'][0]['model_file'][0] = 'manifest_51.json'
manifest['runs'][0]['sweeps'] = [51]
ju.write('manifest_51.json', manifest)
subprocess.call(['nrnivmodl', './modfiles'])
run(Config().load('manifest_51.json'))
#os.chdir(cwd)
nwb_out = NwbDataSet('work/386049444.nwb')
spikes = nwb_out.get_spike_times(51)
numpy.testing.assert_array_almost_equal(spikes, expected)
def test_write_nan():
with patch(builtins.__name__ + ".open",
mock_open(),
create=True) as mo:
ju.write('/some/file/test.json', { "thing": float('nan')})
assert 'null' in str(mo().write.call_args_list[0])
def test_configure_model():
p = re.compile("(\d+)(.*?)_(.*)_mlin_model_config.json")
if not os.path.exists(OUT_DIR):
os.makedirs(OUT_DIR)
for mcf in MODEL_CONFIG_FILES:
fname = os.path.basename(mcf)
m = p.match(fname)
sid, cre, config = m.groups()
method_config_file = os.path.join(METHOD_CONFIG_DIR, "%s.json" % config)
prep_file = os.path.join(PREP_DIR, "%s_preprocessed_dict.json" % sid)
out_file = os.path.join(OUT_DIR, "%s_%s_model_config.json" % (sid, config))
if os.path.exists(prep_file):
print "testing", prep_file
out_config = configure_model(ju.read(method_config_file),
ju.read(prep_file))
ju.write(out_file, out_config)
test_config = ju.read(mcf)
cmpdict(test_config['neuron'], out_config['neuron'])
try:
cmpdict(test_config['optimizer'], out_config['optimizer'])
except ComparisonException, e:
if e.key != "param_fit_names":
raise e
else:
logging.error("preprocessor file %s does not exist" % prep_file)
def get_cells(self, file_name=None, require_morphology=False, require_reconstruction=False):
'''
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.
'''
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)
# filter the cells on the way out
return self.api.filter_cells(cells, require_morphology, require_reconstruction)
def main():
module = ags.ArgSchemaParser(schema_type=ModelSelectionParameters)
swc_path = module.args["paths"]["swc"]
fit_style_paths = module.args["paths"]["fit_styles"]
best_fit_json_path = module.args["paths"]["best_fit_json_path"]
passive = ju.read(module.args["paths"]["passive_results"])
preprocess = ju.read(module.args["paths"]["preprocess_results"])
fits = module.args["paths"]["fits"]
fit_results = ms.fit_info(fits)
best_fit = ms.select_model(fit_results, module.args["paths"], passive,
preprocess["v_baseline"],
module.args["noise_1_sweeps"],
module.args["noise_2_sweeps"])
if best_fit is None:
raise Exception("Failed to find acceptable optimized model")
logging.info("building fit data")
fit_style_data = ju.read(
module.args["paths"]["fit_styles"][best_fit["fit_type"]])
fit_data = ms.build_fit_data(best_fit["params"], passive, preprocess,
fit_style_data)
logging.info("writing fit data: %s", best_fit_json_path)
ju.write(best_fit_json_path, fit_data)
output = {
"paths": {
"fit_json": best_fit_json_path,
}
}
logging.info("writing output json: %s", module.args["output_json"])
ju.write(module.args["output_json"], output)
def debug(container_id, local=False, plots=None):
SCRIPT = "/data/informatics/CAM/analysis/allensdk/allensdk/internal/pipeline_modules/run_observatory_container_thumbnails.py"
SDK_PATH = "/data/informatics/CAM/analysis/allensdk/"
OUTPUT_DIR = "/data/informatics/CAM/analysis/containers"
container_dir = os.path.join(OUTPUT_DIR, str(container_id))
input_data = []
for exp in get_container_info(container_id):
exp_data = robsth.get_input_data(exp['id'])
exp_input_json = os.path.join(exp_data["output_directory"],
"input.json")
input_data.append(
dict(input_json=exp_input_json,
output_json=os.path.join(exp_data["output_directory"],
"output.json")))
Manifest.safe_make_parent_dirs(exp_input_json)
ju.write(exp_input_json, exp_data)
run_module(SCRIPT,
input_data,
container_dir,
sdk_path=SDK_PATH,
pbs=dict(vmem=32,
job_name="cthumbs_%d" % container_id,
walltime="10:00:00"),
local=local,
optional_args=['--types=' + ','.join(plots)] if plots else None)
def test_1(glif_api, neuronal_model_id):
glif_api.get_neuronal_model(neuronal_model_id)
glif_api.cache_stimulus_file('stimulus.nwb')
neuron_config = glif_api.get_neuron_config()
json_utilities.write('neuron_config.json', neuron_config)
ephys_sweeps = glif_api.get_ephys_sweeps()
json_utilities.write('ephys_sweeps.json', ephys_sweeps)
def test_1(glif_api, neuronal_model_id):
glif_api.get_neuronal_model(neuronal_model_id)
glif_api.cache_stimulus_file('stimulus.nwb')
neuron_config = glif_api.get_neuron_config()
json_utilities.write('neuron_config.json', neuron_config)
ephys_sweeps = glif_api.get_ephys_sweeps()
json_utilities.write('ephys_sweeps.json', ephys_sweeps)
def get_cell_specimens(self,
file_name=None,
ids=None,
experiment_container_ids=None,
simple=True):
""" Return cell specimens that have certain properies.
Parameters
----------
file_name: string
File name to save/read the cell specimens. If file_name is None,
the file_name will be pulled out of the manifest. If caching
is disabled, no file will be saved. Default is None.
ids: list
List of cell specimen ids.
experiment_container_ids: list
List of experiment container ids.
simple: boolean
Whether or not to simplify the dictionary properties returned by this method
to a more concise subset.
Returns
-------
list of dictionaries
"""
file_name = self.get_cache_path(file_name, self.CELL_SPECIMENS_KEY)
if os.path.exists(file_name):
cell_specimens = ju.read(file_name)
else:
cell_specimens = self.api.get_cell_metrics()
if self.cache:
ju.write(file_name, cell_specimens)
cell_specimens = self.api.filter_cell_specimens(
cell_specimens,
ids=ids,
experiment_container_ids=experiment_container_ids)
# drop the thumbnail columns
if simple:
mappings = self._get_stimulus_mappings()
thumbnails = [
m['item'] for m in mappings
if m['item_type'] == 'T' and m['level'] == 'R'
]
for cs in cell_specimens:
for t in thumbnails:
del cs[t]
return cell_specimens
def write_output(output_dir, acronym, sid, data_dict, suffix=''):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
ouput_json = os.path.join(output_dir, "%s_%s.json" % (acronym, suffix))
output_data = dict(structure_acronym=acronym, structure_id=sid)
output_data.update(data_dict)
ju.write(ouput_json, output_data)
def mcc(fn_temp_dir):
storage_dirs = {"111" : os.path.join(fn_temp_dir, "111"),
"222" : os.path.join(fn_temp_dir, "222")}
file_name = os.path.join(fn_temp_dir, 'storage_directories.json')
json_utilities.write(file_name, storage_dirs)
manifest_path = os.path.join(fn_temp_dir, 'manifest.json')
return MouseConnectivityCachePrerelease(
manifest_file=manifest_path, storage_directories_file_name=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:
self.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 configured_glif_api(glif_api, neuronal_model_id, neuron_config_file,
ephys_sweeps_file):
glif_api.get_neuronal_model(neuronal_model_id)
neuron_config = glif_api.get_neuron_config()
json_utilities.write(neuron_config_file, neuron_config)
ephys_sweeps = glif_api.get_ephys_sweeps()
json_utilities.write(ephys_sweeps_file, ephys_sweeps)
return glif_api
def configured_glif_api(glif_api, neuronal_model_id, neuron_config_file,
ephys_sweeps_file):
glif_api.get_neuronal_model(neuronal_model_id)
neuron_config = glif_api.get_neuron_config()
json_utilities.write(neuron_config_file, neuron_config)
ephys_sweeps = glif_api.get_ephys_sweeps()
json_utilities.write(ephys_sweeps_file, ephys_sweeps)
return glif_api
def main():
module = ags.ArgSchemaParser(schema_type=MorphologySummaryParameters)
run_morphology_summary(module.args["pia_transform"],
module.args["relative_soma_depth"],
module.args["soma_depth"], module.args["swc_file"],
module.args["thumbnail_file"],
module.args["cortex_thumbnail_file"],
module.args["normal_depth_thumbnail_file"],
module.args["high_resolution_thumbnail_file"])
ju.write(module.args["output_json"], {})
def main():
"""
Usage:
python run_sweep_extraction.py
--input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=SweepExtractionParameters)
output = run_sweep_extraction(**module.args)
json_utilities.write(module.args["output_json"], output)
def main():
args = parse_arguments()
sweep_list = ju.read(args.sweep_list_file)
data = find_sweeps(sweep_list)
ju.write(args.output_file, data)
if len(errs > 0):
for err in errs:
logging.error(err)
sys.exit(1)
def main():
"""
Usage:
python run_sweep_extraction.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=SweepExtractionParameters)
output = run_sweep_extraction(
module.args["input_nwb_file"], module.args.get("input_h5_file", None),
module.args.get("stimulus_ontology_file", None))
ju.write(module.args["output_json"], output)
def main():
module = ags.ArgSchemaParser(schema_type=ConsolidateParameters)
preprocess_results = ju.read(module.args["paths"]["preprocess_results"])
is_spiny = preprocess_results["is_spiny"]
info = ju.read(module.args["paths"]["passive_info"])
if info["should_run"]:
fit_1_path = module.args["paths"]["passive_fit_1"]
fit_1 = ju.read(fit_1_path)
fit_2_path = module.args["paths"]["passive_fit_2"]
fit_2 = ju.read(fit_2_path)
fit_3_path = module.args["paths"]["passive_fit_elec"]
fit_3 = ju.read(fit_3_path)
ra, cm1, cm2 = cpf.compare_runs(preprocess_results, fit_1, fit_2,
fit_3)
else:
ra = 100.
cm1 = 1.
if is_spiny:
cm2 = 2.
else:
cm2 = 1.
passive = {
"ra": ra,
"cm": {
"soma": cm1,
"axon": cm1,
"dend": cm2
},
"e_pas": preprocess_results["v_baseline"]
}
passive["e_pas"] = preprocess_results["v_baseline"]
if preprocess_results["has_apical"]:
passive["cm"]["apic"] = cm2
passive_results_path = module.args["paths"]["passive_results"]
ju.write(passive_results_path, passive)
output = {
"paths": {
"passive_results": passive_results_path,
}
}
ju.write(module.args["output_json"], output)
def test_from_json(fn_temp_dir):
# ------------------------------------------------------------------------
# tests alternative constructor
manifest_file = 'manifest.json'
resolution = 100
path = os.path.join(fn_temp_dir, 'input.json')
input_data = dict(manifest_file=manifest_file, resolution=resolution)
json_utilities.write(path, input_data)
cache = VoxelModelCache.from_json(path)
assert cache.manifest_file == manifest_file
assert cache.resolution == resolution
def _get_stimulus_mappings(self, file_name=None):
""" Returns a mapping of which metrics are related to which stimuli. Internal use only. """
file_name = self.get_cache_path(file_name, self.STIMULUS_MAPPINGS_KEY)
if os.path.exists(file_name):
mappings = ju.read(file_name)
else:
mappings = self.api.get_stimulus_mappings()
if self.cache:
ju.write(file_name, mappings)
return mappings
def main():
"""
Usage:
> python generate_se_input.py --specimen_id SPECIMEN_ID --cell_dir CELL_DIR
> python generate_se_input.py --input_nwb_file input_nwb_file --cell_dir CELL_DIR
"""
kwargs = parse_args()
se_input = generate_se_input(**kwargs)
input_json = os.path.join(kwargs["cell_dir"], 'se_input.json')
ju.write(input_json, se_input)
def main():
"""
Usage:
python run_qc.py --input_json INPUT_JSON --output_json OUTPUT_JSON
"""
module = ags.ArgSchemaParser(schema_type=QcParameters)
output = run_qc(module.args.get("stimulus_ontology_file",
None), module.args["cell_features"],
module.args["sweep_features"], module.args["qc_criteria"])
ju.write(module.args["output_json"], output)
def main():
"""
Usage:
> python generate_fx_input.py --specimen_id SPECIMEN_ID --cell_dir CELL_DIR
> python generate_fx_input.py --input_nwb_file INPUT_NWB_FILE --cell_dir CELL_DIR
"""
kwargs = parse_args()
se_input = generate_se_input(**kwargs)
cell_dir = kwargs["cell_dir"]
lu.configure_logger(cell_dir)
if not os.path.exists(cell_dir):
os.makedirs(cell_dir)
ju.write(os.path.join(cell_dir, 'se_input.json'), se_input)
se_output = run_sweep_extraction(
se_input["input_nwb_file"], se_input.get("input_h5_file", None),
se_input.get("stimulus_ontology_file", None))
ju.write(os.path.join(cell_dir, 'se_output.json'), se_output)
sp.drop_tagged_sweeps(se_output["sweep_features"])
qc_input = generate_qc_input(se_input, se_output)
ju.write(os.path.join(cell_dir, 'qc_input.json'), qc_input)
qc_output = run_qc(qc_input.get("stimulus_ontology_file",
None), qc_input["cell_features"],
qc_input["sweep_features"], qc_input["qc_criteria"])
ju.write(os.path.join(cell_dir, 'qc_output.json'), qc_output)
if kwargs["specimen_id"]:
manual_sweep_states = lq.get_sweep_states(kwargs["specimen_id"])
elif kwargs["input_nwb_file"]:
manual_sweep_states = []
sp.override_auto_sweep_states(manual_sweep_states,
qc_output["sweep_states"])
sp.assign_sweep_states(qc_output["sweep_states"],
se_output["sweep_features"])
fx_input = generate_fx_input(se_input,
se_output,
cell_dir,
plot_figures=True)
ju.write(os.path.join(cell_dir, 'fx_input.json'), fx_input)
def get_experiments(self,
dataframe=False,
file_name=None,
cre=None,
injection_structure_ids=None,
age=None,
gender=None,
workflow_state=None,
workflows=None,
project_code=None):
"""Read a list of experiments.
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.
"""
file_name = self.get_cache_path(file_name,
self.EXPERIMENTS_PRERELEASE_KEY)
if os.path.exists(file_name):
experiments = json_utilities.read(file_name)
else:
experiments = self.api.get_experiments()
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, age,
gender, workflow_state,
workflows, project_code)
if dataframe:
experiments = pd.DataFrame(experiments)
experiments.set_index(['id'], inplace=True, drop=False)
return experiments
def QueryAPI(model,
criteriaString,
includeString=None,
optionsString=None,
writeOut=False):
# Send a query to the Allen API, and assemble results
# Initiate RMA API for Allen data retrieval
api = RmaApi()
# Settings for retrieval
rows = []
blockSize = 2000
done = False
startRow = 0
# for i in range(0, total_rows, blockSize):
while not done:
print("Row %d, attempting to retrieve %d rows..." %
(startRow, blockSize))
tot_rows = len(rows)
# apiQueryPartial = partial(api.model_query,model=model,criteria=criteriaString,
# startRow=startRow,num_rows=blockSize)
rows += api.model_query(model=model,
criteria=criteriaString,
include=includeString,
options=optionsString,
start_row=startRow,
num_rows=blockSize)
numRows = len(
rows) - tot_rows # additional rows retrieved on running the query
startRow += numRows
print("%d rows retrieved." % numRows)
# Check if we're at the end of the road
if numRows == 0 or numRows < blockSize:
done = True
# Write out the results as they come in, if requested
if writeOut:
json_utilities.write(json_file_name, rows)
print("Wrote to %s" % json_file_name)
return rows
def QueryAPI(model,
criteriaString,
includeString="",
optionsString="",
writeOut=[]):
# Initiate RMA API for Allen data retrieval
api = RmaApi()
# Settings for retrieval
rows = []
blockSize = 2000
done = False
startRow = 0
# for i in range(0, total_rows, blockSize):
while not done:
print "Row %d, attempting to retrieve %d rows..." % (startRow,
blockSize)
tot_rows = len(rows)
if len(includeString) == 0:
rows += api.model_query(model=model,
criteria=criteriaString,
options=optionsString,
start_row=startRow,
num_rows=blockSize)
else:
rows += api.model_query(model=model,
criteria=criteriaString,
include=includeString,
options=optionsString,
start_row=startRow,
num_rows=blockSize)
numRows = len(
rows) - tot_rows # additional rows retrieved on running the query
startRow += numRows
print "%d rows retrieved." % numRows
# Check if we're at the end of the road
if numRows == 0 or numRows < blockSize:
done = True
# Write out the results as they come in, if requested
if isinstance(writeOut, basestring):
json_utilities.write(json_file_name, rows)
print "Wrote to %s" % json_file_name
return rows
def main():
"""
Usage:
> python generate_pipeline_input.py --specimen_id SPECIMEN_ID --cell_dir CELL_DIR
> python generate_pipeline_input.py --input_nwb_file INPUT_NWB_FILE --cell_dir CELL_DIR
"""
kwargs = parse_args()
pipe_input = generate_pipeline_input(**kwargs)
cell_dir = kwargs["cell_dir"]
input_json = os.path.join(cell_dir, 'pipeline_input.json')
ju.write(input_json, pipe_input)
def main():
module = ags.ArgSchemaParser(schema_type=PopulationSelectionParameters)
print module.args
fits = module.args["paths"]["fits"]
populations = ps.population_info(fits)
starting_populations = ps.select_starting_population(populations)
output = {
"paths": {
"starting_populations": starting_populations,
}
}
ju.write(module.args["output_json"], output)
def test_download():
if os.path.exists(OUTPUT_DIR):
shutil.rmtree(OUTPUT_DIR)
os.makedirs(OUTPUT_DIR)
glif_api = GlifApi()
glif_api.get_neuronal_model(NEURONAL_MODEL_ID)
glif_api.cache_stimulus_file(os.path.join(OUTPUT_DIR, '%d.nwb' % NEURONAL_MODEL_ID))
neuron_config = glif_api.get_neuron_config()
json_utilities.write(os.path.join(OUTPUT_DIR, '%d_neuron_config.json' % NEURONAL_MODEL_ID), neuron_config)
ephys_sweeps = glif_api.get_ephys_sweeps()
json_utilities.write(os.path.join(OUTPUT_DIR, 'ephys_sweeps.json'), ephys_sweeps)
def get_ephys_sweeps(self, specimen_id, file_name=None):
"""
Download sweep metadata for a single cell specimen.
Parameters
----------
specimen_id: int
ID of a cell.
"""
file_name = self.get_cache_path(file_name, self.EPHYS_SWEEPS_KEY, specimen_id)
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 test_optimize_neuron():
p = re.compile("(\d+)_(.*)_model_config.json")
if not os.path.exists(OUT_DIR):
os.makedirs(OUT_DIR)
for model_config_file in MODEL_CONFIG_FILES:
logging.info("testing %s" % model_config_file)
fname = os.path.basename(model_config_file)
m = p.match(fname)
sid, config = m.groups()
data_config_file = DATA_CONFIG_PATTERN % int(sid)
data_config = ju.read(data_config_file)
nwb_file = data_config["filename"]
sweep_list = data_config["sweeps"].values()
model_config = ju.read(model_config_file)
#DBG
model_config['optimizer']['inner_iterations'] = 1
model_config['optimizer']['outer_iterations'] = 1
#DBG
sweep_index = { s['sweep_number']:s for s in sweep_list }
optimizer, best_param, begin_param = optimize_neuron(model_config, sweep_index)
out_file = os.path.join(OUT_DIR, "%s_%s_neuron_config.json" % (sid, config))
ju.write(out_file, optimizer.experiment.neuron.to_dict())
out_config_file = os.path.join(OUT_DIR, "%s_%s_optimized_model_config.json" % (sid, config))
ju.write(out_config_file, {
'optimizer': optimizer.to_dict(),
'neuron': optimizer.experiment.neuron.to_dict()
})
def test_preprocess_neuron():
logging.getLogger().setLevel(logging.DEBUG)
p = re.compile("(\d+)_data_config.json")
dt = 5e-05
bessel = { 'N': 4, 'Wn': .1 }
cut = 0
if not os.path.exists(OUT_DIR):
os.makedirs(OUT_DIR)
for data_config_file in DATA_CONFIG_FILES:
logging.info("testing %s" % data_config_file)
fname = os.path.basename(data_config_file)
m = p.match(fname)
sid = m.groups()
test_data_file = os.path.join(TEST_DIR, "%s_preprocessed_dict.json" % sid)
if not os.path.exists(test_data_file):
logging.warning("no test file %s" % test_data_file)
continue
out_file = os.path.join(OUT_DIR, "%s_preprocessed_dict.json" % sid)
data_config = ju.read(data_config_file)
nwb_file = data_config["filename"]
sweep_list = data_config["sweeps"].values()
d = preprocess_neuron(nwb_file, sweep_list, dt, cut, bessel)
dictionary = ju.read(test_data_file)
ju.write(out_file, d)
errs = []
assert_equal(d['El'], 0.0, 'El', errs)
assert_equal(d['El_reference'], dictionary['El']['El_noise']['measured']['mean'], 'El_reference', errs)
assert_equal(d['deltaV'], None, 'deltaV', errs)
assert_equal(d['dt'], dictionary['dt_used_for_preprocessor_calculations'], 'dt', errs)
assert_equal(d['R_input'], dictionary['resistance']['R_lssq_Wrest']['mean'], 'R_input', errs)
assert_equal(d['C'], dictionary['capacitance']['C_lssq_Wrest']['mean'], 'C', errs)
assert_equal(d['th_inf'], dictionary['th_inf']['via_Vmeasure']['from_zero'], 'th_inf', errs)
assert_equal(d['th_adapt'], dictionary['th_adapt']['from_95percentile_noise']['deltaV'], 'th_adapt', errs)
assert_equal(d['spike_cut_length'], dictionary['spike_cutting']['NOdeltaV']['cut_length'], 'spike_cut_length', errs)
assert_equal(d['spike_cutting_intercept'], dictionary['spike_cutting']['NOdeltaV']['intercept'], 'spike_cutting_intercept', errs)
assert_equal(d['spike_cutting_slope'], dictionary['spike_cutting']['NOdeltaV']['slope'], 'spike_cutting_slope', errs)
assert_equal(d['asc_amp_array'], dictionary['asc']['amp'], 'asc_amp_array', errs)
assert_equal(d['asc_tau_array'], 1./np.array(dictionary['asc']['k']), 'asc_tau_array', errs)
nlp = d['nonlinearity_parameters']
assert_equal(nlp['line_param_RV_all'], dictionary['nonlinearity_parameters']['line_param_RV_all'], 'line_param_RV_all', errs)
assert_equal(nlp['line_param_ElV_all'], dictionary['nonlinearity_parameters']['line_param_ElV_all'], 'line_param_ElV_all', errs)
ta = d['threshold_adaptation']
assert_equal(ta['a_spike_component_of_threshold'], dictionary['threshold_adaptation']['a_spike_component_of_threshold'], 'a_spike', errs)
assert_equal(ta['b_spike_component_of_threshold'], dictionary['threshold_adaptation']['b_spike_component_of_threshold'], 'b_spike', errs)
assert_equal(ta['a_voltage_component_of_threshold'], dictionary['threshold_adaptation']['a_voltage_component_of_threshold'], 'a_voltage', errs)
assert_equal(ta['b_voltage_component_of_threshold'], dictionary['threshold_adaptation']['b_voltage_component_of_threshold'], 'b_voltage', errs)
mlin = d['MLIN']
assert_equal(mlin['var_of_section'], dictionary['MLIN']['var_of_section'], 'var_of_section', errs)
assert_equal(mlin['sv_for_expsymm'], dictionary['MLIN']['sv_for_expsymm'], 'sv_for_expsymm', errs)
assert_equal(mlin['tau_from_AC'], dictionary['MLIN']['tau_from_AC'], 'tau_from_AC', errs)
if len(errs) > 0:
for err in errs:
logging.error(err)
raise Exception("Preprocessor outputs did not match.")
#===============================================================================
from allensdk.api.queries.glif_api import GlifApi
from allensdk.core.cell_types_cache import CellTypesCache
import allensdk.core.json_utilities as json_utilities
neuronal_model_id = 566302806
# download model metadata
glif_api = GlifApi()
nm = glif_api.get_neuronal_models_by_id([neuronal_model_id])[0]
# download the model configuration file
nc = glif_api.get_neuron_configs([neuronal_model_id])[neuronal_model_id]
neuron_config = glif_api.get_neuron_configs([neuronal_model_id])
json_utilities.write('neuron_config.json', neuron_config)
# download information about the cell
ctc = CellTypesCache()
ctc.get_ephys_data(nm['specimen_id'], file_name='stimulus.nwb')
ctc.get_ephys_sweeps(nm['specimen_id'], file_name='ephys_sweeps.json')
#===============================================================================
# example 2
#===============================================================================
import allensdk.core.json_utilities as json_utilities
from allensdk.model.glif.glif_neuron import GlifNeuron
# initialize the neuron
neuron_config = json_utilities.read('neuron_config.json')['566302806']
def wrap(self, fn, path, cache,
save_as_json=True,
return_dataframe=False,
index=None,
rename=None,
**kwargs):
'''make an rma query, save it and return the dataframe.
Parameters
----------
fn : function reference
makes the actual query using kwargs.
path : string
where to save the data
cache : boolean
True will make the query, False just loads from disk
save_as_json : boolean, optional
True (default) will save data as json, False as csv
return_dataframe : boolean, optional
True will cast the return value to a pandas dataframe, False (default) will not
index : string, optional
column to use as the pandas index
rename : list of string tuples, optional
(new, old) columns to rename
kwargs : objects
passed through to the query function
Returns
-------
dict or DataFrame
data type depends on return_dataframe option.
Notes
-----
Column renaming happens after the file is reloaded for json
'''
if cache == True:
json_data = fn(**kwargs)
if save_as_json == True:
ju.write(path, json_data)
else:
df = pd.DataFrame(json_data)
self.rename_columns(df, rename)
if index is not None:
df.set_index([index], inplace=True)
df.to_csv(path)
# read it back in
if save_as_json == True:
if return_dataframe == True:
data = pj.read_json(path, orient='records')
self.rename_columns(data, rename)
if index != None:
data.set_index([index], inplace=True)
else:
data = ju.read(path)
elif return_dataframe == True:
data = pd.DataFrame.from_csv(path)
else:
raise ValueError('save_as_json=False cannot be used with return_dataframe=False')
return data
from allensdk.api.queries.glif_api import GlifApi
import allensdk.core.json_utilities as json_utilities
neuronal_model_id = 472423251
glif_api = GlifApi()
glif_api.get_neuronal_model(neuronal_model_id)
glif_api.cache_stimulus_file('stimulus.nwb')
neuron_config = glif_api.get_neuron_config()
json_utilities.write('neuron_config.json', neuron_config)
ephys_sweeps = glif_api.get_ephys_sweeps()
json_utilities.write('ephys_sweeps.json', ephys_sweeps)