def get_pipeline_version(self):
""" Returns the AI pipeline version number, stored in the
metadata field 'generated_by'. If that field is
missing, version 0.0 is returned.
Borrowed from the AllenSDK
Returns
-------
int tuple: (major, minor)
"""
try:
with h5py.File(self.nwb_file, 'r') as f:
if 'generated_by' in f["general"]:
info = f["general/generated_by"]
# generated_by stores array of keys and values
# keys are even numbered, corresponding values are in
# odd indices
for i in range(len(info)):
if to_str(info[i]) == 'version':
version = to_str(info[i+1])
break
toks = version.split('.')
if len(toks) >= 2:
major = int(toks[0])
minor = int(toks[1])
except: # noqa: E722
minor = 0
major = 0
return major, minor
def get_unit_name(stim_attrs):
if 'unit' in stim_attrs:
unit = to_str(stim_attrs["unit"])
elif 'units' in stim_attrs:
unit = to_str(stim_attrs["units"])
else:
unit = None
return unit
def get_value(self, name, sweep_num, default_val):
"""
looks for key in lab notebook and returns the value associated with
the specified sweep, or the default value if no value is found
(NaN and empty strings are considered to be non-values)
name_number has 3 dimensions -- the first has shape
(#fields * 9) and stores the key names. the second looks
to store units for those keys. The third is numeric text
but it's role isn't clear
val_number has 3 dimensions -- the first has a shape of
(#fields * 9). there are many hundreds of elements in this
dimension. they look to represent the full array of values
(for each field for each multipatch) for a given point in
time, and thus given sweep
Parameters
----------
name: str
sweep_num: int
default_val: dict
Returns
-------
values obtained from lab notebook
"""
numeric_fields = [to_str(c) for c in self.colname_number[0]]
text_fields = [to_str(c) for c in self.colname_text[0]]
if name in numeric_fields:
sweep_idx = numeric_fields.index("SweepNum")
enable_idx = None
if name in self.enabled:
enable_col = self.enabled[name]
enable_idx = numeric_fields.index(enable_col)
field_idx = numeric_fields.index(name)
return self.get_numeric_value(name, field_idx, sweep_idx, enable_idx, sweep_num, default_val)
elif name in text_fields:
if "Sweep #" in text_fields:
sweep_idx = text_fields.index("Sweep #")
else:
sweep_idx = text_fields.index("SweepNum")
enable_idx = None
if name in self.enabled:
enable_col = self.enabled[name]
enable_idx = text_fields.index(enable_col)
field_idx = text_fields.index(name)
return self.get_text_value(name, field_idx, sweep_idx, enable_idx, sweep_num, default_val)
else:
return default_val
def get_clamp_mode(self, sweep_num):
attrs = self.nwb_data.get_sweep_attrs(sweep_num)
try:
ancestry = attrs["ancestry"]
time_series_type = to_str(ancestry[-1])
if "CurrentClamp" in time_series_type:
clamp_mode = self.CURRENT_CLAMP
elif "VoltageClamp" in time_series_type:
clamp_mode = self.VOLTAGE_CLAMP
else:
raise Exception(
"Unable to determine clamp mode in {}".format(sweep_num))
return clamp_mode
except:
logging.debug("Error in reading stim from ancestry")
try:
if "CurrentClamp" in attrs['neurodata_type']:
clamp_mode = self.CURRENT_CLAMP
elif "VoltageClamp" in attrs['neurodata_type']:
clamp_mode = self.VOLTAGE_CLAMP
except:
logging.debug("Error in reading stim from neurodata")
return clamp_mode
def _select(cursor, query, parameters=None):
if parameters is None:
cursor.execute(query)
else:
pg8000.paramstyle = 'numeric'
cursor.execute(query, parameters)
columns = [to_str(d[0]) for d in cursor.description]
return [dict(zip(columns, c)) for c in cursor.fetchall()]
def get_stimulus_code(self, sweep_num):
stim_code = self._nwb_data.get_stim_code(sweep_num)
if not stim_code:
stim_code = self.notebook.get_value("Stim Wave Name", sweep_num, "")
logging.debug("Reading stim_code from Labnotebook")
if len(stim_code) == 0:
raise Exception(
"Could not read stimulus wave name from lab notebook")
return to_str(stim_code)
def get_clamp_mode(self, sweep_num):
attrs = self._nwb_data.get_sweep_attrs(sweep_num)
ancestry = attrs["ancestry"]
time_series_type = to_str(ancestry[-1])
if "CurrentClamp" in time_series_type:
clamp_mode = self.CURRENT_CLAMP
elif "VoltageClamp" in time_series_type:
clamp_mode = self.VOLTAGE_CLAMP
else:
raise Exception("Unable to determine clamp mode in {}".format(sweep_num))
return clamp_mode
def get_value(self, name, sweep_num, default_val):
# name_number has 3 dimensions -- the first has shape
# (#fields * 9) and stores the key names. the second looks
# to store units for those keys. The third is numeric text
# but it's role isn't clear
numeric_fields = [to_str(c) for c in self.colname_number[0]]
text_fields = [to_str(c) for c in self.colname_text[0]]
# val_number has 3 dimensions -- the first has a shape of
# (#fields * 9). there are many hundreds of elements in this
# dimension. they look to represent the full array of values
# (for each field for each multipatch) for a given point in
# time, and thus given sweep
if name in numeric_fields:
sweep_idx = numeric_fields.index("SweepNum")
enable_idx = None
if name in self.enabled:
enable_col = self.enabled[name]
enable_idx = numeric_fields.index(enable_col)
field_idx = numeric_fields.index(name)
return self.get_numeric_value(name, field_idx, sweep_idx,
enable_idx, sweep_num, default_val)
elif name in text_fields:
# first check to see if file includes old version of column name
if "Sweep #" in text_fields:
sweep_idx = text_fields.index("Sweep #")
else:
sweep_idx = text_fields.index("SweepNum")
enable_idx = None
if name in self.enabled:
enable_col = self.enabled[name]
enable_idx = text_fields.index(enable_col)
field_idx = text_fields.index(name)
return self.get_text_value(name, field_idx, sweep_idx, enable_idx,
sweep_num, default_val)
else:
return default_val
def get_stimulus_units(self, sweep_num):
attrs = self.nwb_data.get_sweep_attrs(sweep_num)
ancestry = attrs["ancestry"]
# stim units are based on timeseries type
time_series_type = to_str(ancestry[-1])
if "CurrentClamp" in time_series_type:
units = 'pA'
elif "VoltageClamp" in time_series_type:
units = 'mV'
else:
raise Exception(
"Unable to determine clamp mode in {}".format(sweep_num))
return units
def get_nwb_version(nwb_file):
"""
Return a dict with `major` and `full` NWB version as read from the NWB file.
"""
with h5py.File(nwb_file, 'r') as f:
if "nwb_version" in f: # In version 0 and 1 this is a dataset
nwb_version = get_scalar_value(f["nwb_version"][()])
nwb_version_str = to_str(nwb_version)
if nwb_version is not None and re.match("^NWB-", nwb_version_str):
return {"major": int(nwb_version_str[4]), "full": nwb_version_str}
elif "nwb_version" in f.attrs: # but in version 2 this is an attribute
nwb_version = f.attrs["nwb_version"]
if nwb_version is not None and re.match("^2", nwb_version):
return {"major": 2, "full": nwb_version}
return {"major": None, "full": None}
def get_nwb_version(nwb_file: str) -> Dict[str, Any]:
"""
Find version of the nwb file
Parameters
----------
nwb_file
Returns
-------
dict in the format:
{
`major`: str
`full` str.
}
"""
with h5py.File(nwb_file, 'r') as f:
if "nwb_version" in f: # In version 0 and 1 this is a dataset
nwb_version = get_scalar_value(f["nwb_version"][()])
nwb_version_str = py2to3.to_str(nwb_version)
if nwb_version is not None and re.match("^NWB-", nwb_version_str):
return {
"major": int(nwb_version_str[4]),
"full": nwb_version_str
}
elif "nwb_version" in f.attrs: # in version 2 this is an attribute
nwb_version = f.attrs["nwb_version"]
if nwb_version is not None and (re.match("^2", nwb_version) or
re.match("^NWB-2", nwb_version)):
return {"major": 2, "full": nwb_version}
return {"major": None, "full": None}
