default_initializers])
xforms_lib.register_plugins(get_setting('XFORMS_PLUGINS'))
keyword_lib = KeywordRegistry()
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(get_setting('KEYWORD_PLUGINS'))
# Generate the xfspec, which defines the sequence of events
# to run through (like a packaged-up script)
xfspec = []
# 0) set up initial context
xfspec.append(['nems.xforms.init_context', xforms_init_context])
# 1) Load the data
xfspec.extend(xhelp._parse_kw_string(load_keywords, xforms_lib))
# 2) generate a modelspec
xfspec.append(['nems.xforms.init_from_keywords', {'registry': keyword_lib}])
#xfspec.append(['nems.xforms.init_from_keywords', {}])
# 3) fit the data
xfspec.extend(xhelp._parse_kw_string(fit_keywords, xforms_lib))
# 4) generate a prediction (optional)
if autoPred:
if not _xform_exists(xfspec, 'nems.xforms.predict'):
xfspec.append(['nems.xforms.predict', {}])
# 5) add some performance statistics (optional)
if autoStats:
def fit_xforms_model(batch, cellid, modelname, save_analysis=False):
# parse modelname into loaders, modelspecs, and fit keys
load_keywords, model_keywords, fit_keywords = modelname.split("_")
# construct the meta data dict
meta = {
'batch': batch,
'cellid': cellid,
'modelname': modelname,
'loader': load_keywords,
'fitkey': fit_keywords,
'modelspecname': model_keywords,
'username': '******',
'labgroup': 'lbhb',
'public': 1,
'githash': os.environ.get('CODEHASH', ''),
'recording': load_keywords
}
xforms_kwargs = {}
xforms_init_context = {'cellid': cellid, 'batch': int(batch)}
recording_uri = None
kw_kwargs = {}
xforms_lib = KeywordRegistry(**xforms_kwargs)
xforms_lib.register_modules(
[default_loaders, default_fitters, default_initializers])
xforms_lib.register_plugins(get_setting('XFORMS_PLUGINS'))
keyword_lib = KeywordRegistry()
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(get_setting('KEYWORD_PLUGINS'))
# Generate the xfspec, which defines the sequence of events
# to run through (like a packaged-up script)
xfspec = []
# 0) set up initial context
if xforms_init_context is None:
xforms_init_context = {}
if kw_kwargs is not None:
xforms_init_context['kw_kwargs'] = kw_kwargs
xforms_init_context['keywordstring'] = model_keywords
xforms_init_context['meta'] = meta
xfspec.append(['nems.xforms.init_context', xforms_init_context])
# 1) Load the data
xfspec.extend(xhelp._parse_kw_string(load_keywords, xforms_lib))
# 2) generate a modelspec
xfspec.append(
['nems.xforms.init_from_keywords', {
'registry': keyword_lib
}])
# 3) fit the data
xfspec.extend(xhelp._parse_kw_string(fit_keywords, xforms_lib))
# Generate a prediction
xfspec.append(['nems.xforms.predict', {}])
# 4) add some performance statistics
xfspec.append(['nems.xforms.add_summary_statistics', {}])
# 5) plot
#xfspec.append(['nems_lbhb.lv_helpers.add_summary_statistics', {}])
# Create a log stream set to the debug level; add it as a root log handler
log_stream = io.StringIO()
ch = logging.StreamHandler(log_stream)
ch.setLevel(logging.DEBUG)
fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
formatter = logging.Formatter(fmt)
ch.setFormatter(formatter)
rootlogger = logging.getLogger()
rootlogger.addHandler(ch)
ctx = {}
for xfa in xfspec:
ctx = xforms.evaluate_step(xfa, ctx)
# Close the log, remove the handler, and add the 'log' string to context
log.info('Done (re-)evaluating xforms.')
ch.close()
rootlogger.removeFilter(ch)
log_xf = log_stream.getvalue()
modelspec = ctx['modelspec']
if save_analysis:
# save results
if get_setting('USE_NEMS_BAPHY_API'):
prefix = 'http://' + get_setting(
'NEMS_BAPHY_API_HOST') + ":" + str(
get_setting('NEMS_BAPHY_API_PORT')) + '/results/'
else:
prefix = get_setting('NEMS_RESULTS_DIR')
if type(cellid) is list:
cell_name = cellid[0].split("-")[0]
else:
cell_name = cellid
destination = os.path.join(prefix, str(batch), cell_name,
modelspec.get_longname())
modelspec.meta['modelpath'] = destination
modelspec.meta.update(meta)
log.info('Saving modelspec(s) to {0} ...'.format(destination))
xforms.save_analysis(destination,
recording=ctx['rec'],
modelspec=modelspec,
xfspec=xfspec,
figures=[],
log=log_xf)
# save performance and some other metadata in database Results table
nd.update_results_table(modelspec)
return xfspec, ctx