def _get_figure_filepath(directory, modelspecs, format):
'''
Returns a filepath based on a directory, a list of modelspecs,
and a file format.
'''
# TODO: Probably need a smarter way to do figure names since figures
# can come from an arbitrary number of modelspecs. Could just
# concatenate names for each modelspec? But that would mean some
# really long filenames.
# For now just uses the long name of the first modelspec until
# a better solution is decided on.
mspec = modelspecs[0]
mname = ms.get_modelspec_longname(mspec)
fname = os.path.join(directory, mname) + "." + format
return fname
def fit_pop_model_xforms_baphy(cellid, batch, modelname, saveInDB=False):
"""
Fits a NEMS population model using baphy data
DEPRECATED ? Now should work for xhelp.fit_model_xform()
"""
raise NotImplementedError("Replaced by xhelper function?")
log.info("Preparing pop model: ({0},{1},{2})".format(
cellid, batch, modelname))
# Segment modelname for meta information
kws = modelname.split("_")
modelspecname = "-".join(kws[1:-1])
loadkey = kws[0]
fitkey = kws[-1]
if type(cellid) is list:
disp_cellid="_".join(cellid)
else:
disp_cellid=cellid
meta = {'batch': batch, 'cellid': disp_cellid, 'modelname': modelname,
'loader': loadkey, 'fitkey': fitkey,
'modelspecname': modelspecname,
'username': '******', 'labgroup': 'lbhb', 'public': 1,
'githash': os.environ.get('CODEHASH', ''),
'recording': loadkey}
uri_key = nems.utils.escaped_split(loadkey, '-')[0]
recording_uri = generate_recording_uri(cellid, batch, uri_key)
# pass cellid information to xforms so that loader knows which cells
# to load from recording_uri
xfspec = xhelp.generate_xforms_spec(recording_uri, modelname, meta,
xforms_kwargs={'cellid': cellid})
# actually do the fit
ctx, log_xf = xforms.evaluate(xfspec)
# save some extra metadata
modelspec = ctx['modelspec']
destination = '/auto/data/nems_db/results/{0}/{1}/{2}/'.format(
batch, disp_cellid, ms.get_modelspec_longname(modelspec))
modelspec.meta['modelpath'] = destination
modelspec.meta['figurefile'] = destination+'figure.0000.png'
modelspec.meta.update(meta)
# extra thing to save for pop model
modelspec.meta['cellids'] = ctx['val']['resp'].chans
# save results
log.info('Saving modelspec(s) to {0} ...'.format(destination))
save_data = xforms.save_analysis(destination,
recording=ctx['rec'],
modelspec=modelspec,
xfspec=xfspec,
figures=ctx['figures'],
log=log_xf)
savepath = save_data['savepath']
if saveInDB:
# save in database as well
nd.update_results_table(modelspec)
return savepath
def fit_model_xforms_baphy(cellid, batch, modelname,
autoPlot=True, saveInDB=False):
"""
DEPRECATED ? Now should work for xhelp.fit_model_xform()
Fit a single NEMS model using data from baphy/celldb
eg, 'ozgf100ch18_wc18x1_lvl1_fir15x1_dexp1_fit01'
generates modelspec with 'wc18x1_lvl1_fir15x1_dexp1'
based on this function in nems/scripts/fit_model.py
def fit_model(recording_uri, modelstring, destination):
xfspec = [
['nems.xforms.load_recordings', {'recording_uri_list': recordings}],
['nems.xforms.add_average_sig', {'signal_to_average': 'resp',
'new_signalname': 'resp',
'epoch_regex': '^STIM_'}],
['nems.xforms.split_by_occurrence_counts', {'epoch_regex': '^STIM_'}],
['nems.xforms.init_from_keywords', {'keywordstring': modelspecname}],
['nems.xforms.set_random_phi', {}],
['nems.xforms.fit_basic', {}],
# ['nems.xforms.add_summary_statistics', {}],
['nems.xforms.plot_summary', {}],
# ['nems.xforms.save_recordings', {'recordings': ['est', 'val']}],
['nems.xforms.fill_in_default_metadata', {}],
]
"""
raise NotImplementedError("Replaced by xhelper function?")
raise DeprecationWarning("Replaced by xhelp.fit_model_xforms")
log.info('Initializing modelspec(s) for cell/batch %s/%d...',
cellid, int(batch))
# Segment modelname for meta information
kws = nems.utils.escaped_split(modelname, '_')
old = False
if (len(kws) > 3) or ((len(kws) == 3) and kws[1].startswith('stategain')
and not kws[1].startswith('stategain.')):
# Check if modelname uses old format.
log.info("Using old modelname format ... ")
old = True
modelspecname = nems.utils.escaped_join(kws[1:-1], '_')
else:
modelspecname = nems.utils.escaped_join(kws[1:-1], '-')
loadkey = kws[0]
fitkey = kws[-1]
meta = {'batch': batch, 'cellid': cellid, 'modelname': modelname,
'loader': loadkey, 'fitkey': fitkey, 'modelspecname': modelspecname,
'username': '******', 'labgroup': 'lbhb', 'public': 1,
'githash': os.environ.get('CODEHASH', ''),
'recording': loadkey}
if old:
recording_uri = ogru(cellid, batch, loadkey)
xfspec = oxfh.generate_loader_xfspec(loadkey, recording_uri)
xfspec.append(['nems_lbhb.old_xforms.xforms.init_from_keywords',
{'keywordstring': modelspecname, 'meta': meta}])
xfspec.extend(oxfh.generate_fitter_xfspec(fitkey))
xfspec.append(['nems.analysis.api.standard_correlation', {},
['est', 'val', 'modelspec', 'rec'], ['modelspec']])
if autoPlot:
log.info('Generating summary plot ...')
xfspec.append(['nems.xforms.plot_summary', {}])
else:
# uri_key = nems.utils.escaped_split(loadkey, '-')[0]
# recording_uri = generate_recording_uri(cellid, batch, uri_key)
log.info("DONE? Moved handling of registry_args to xforms_init_context")
recording_uri = None
# registry_args = {'cellid': cellid, 'batch': int(batch)}
registry_args = {}
xforms_init_context = {'cellid': cellid, 'batch': int(batch)}
xfspec = xhelp.generate_xforms_spec(recording_uri, modelname, meta,
xforms_kwargs=registry_args,
xforms_init_context=xforms_init_context)
log.info(xfspec)
# actually do the loading, preprocessing, fit
ctx, log_xf = xforms.evaluate(xfspec)
# save some extra metadata
modelspec = ctx['modelspec']
# this code may not be necessary any more.
destination = '/auto/data/nems_db/results/{0}/{1}/{2}/'.format(
batch, cellid, ms.get_modelspec_longname(modelspec))
modelspec.meta['modelpath'] = destination
modelspec.meta['figurefile'] = destination+'figure.0000.png'
modelspec.meta.update(meta)
# save results
log.info('Saving modelspec(s) to {0} ...'.format(destination))
save_data = xforms.save_analysis(destination,
recording=ctx['rec'],
modelspec=modelspec,
xfspec=xfspec,
figures=ctx['figures'],
log=log_xf)
savepath = save_data['savepath']
# save in database as well
if saveInDB:
# TODO : db results finalized?
nd.update_results_table(modelspec)
return savepath
ctx['modelspecs'][0][0]['meta']['r_test']
ctxF=ctx.copy()
# 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()
# save some extra metadata
modelspec = ctx['modelspec']
#
destination = '/auto/data/nems_db/results/{0}/{1}/{2}/'.format(
batch, cellid, ms.get_modelspec_longname(modelspec))
modelspec['meta']['modelpath'] = destination
modelspec['meta']['figurefile'] = destination+'figure.0000.png'
# save results
if saveToFile:
log.info('Saving modelspec(s) to {0} ...'.format(destination))
xforms.save_analysis(destination,
recording=ctx['rec'],
modelspec=modelspec,
xfspec=xfspec,
figures=ctx['figures'],
log=log_xf)
# save in database as well
if saveInDB:
def fit_model_xforms_baphy(cellid,
batch,
modelname,
autoPlot=True,
saveInDB=False):
"""
Fits a single NEMS model using data from baphy/celldb
eg, 'ozgf100ch18_wc18x1_lvl1_fir15x1_dexp1_fit01'
generates modelspec with 'wc18x1_lvl1_fir15x1_dexp1'
based on this function in nems/scripts/fit_model.py
def fit_model(recording_uri, modelstring, destination):
xfspec = [
['nems.xforms.load_recordings', {'recording_uri_list': recordings}],
['nems.xforms.add_average_sig', {'signal_to_average': 'resp',
'new_signalname': 'resp',
'epoch_regex': '^STIM_'}],
['nems.xforms.split_by_occurrence_counts', {'epoch_regex': '^STIM_'}],
['nems.xforms.init_from_keywords', {'keywordstring': modelspecname}],
['nems.xforms.set_random_phi', {}],
['nems.xforms.fit_basic', {}],
# ['nems.xforms.add_summary_statistics', {}],
['nems.xforms.plot_summary', {}],
# ['nems.xforms.save_recordings', {'recordings': ['est', 'val']}],
['nems.xforms.fill_in_default_metadata', {}],
]
"""
log.info('Initializing modelspec(s) for cell/batch {0}/{1}...'.format(
cellid, batch))
# parse modelname
kws = modelname.split("_")
loader = kws[0]
modelspecname = "_".join(kws[1:-1])
fitter = kws[-1]
# generate xfspec, which defines sequence of events to load data,
# generate modelspec, fit data, plot results and save
xfspec = generate_loader_xfspec(cellid, batch, loader)
xfspec.append(
['nems.xforms.init_from_keywords', {
'keywordstring': modelspecname
}])
# parse the fit spec: Use gradient descent on whole data set(Fast)
if fitter == "fit01":
# prefit strf
log.info("Prefitting STRF without other modules...")
xfspec.append(['nems.xforms.fit_basic_init', {}])
xfspec.append(['nems.xforms.fit_basic', {}])
elif fitter == "fitjk01":
# prefit strf
log.info("Prefitting STRF without NL then JK...")
xfspec.append(['nems.xforms.fit_basic_init', {}])
xfspec.append(['nems.xforms.split_for_jackknife', {'njacks': 10}])
xfspec.append(['nems.xforms.fit_basic', {}])
elif fitter == "fit02":
# no pre-fit
log.info("Performing full fit...")
xfspec.append(['nems.xforms.fit_basic', {}])
else:
raise ValueError('unknown fitter string')
xfspec.append(['nems.xforms.add_summary_statistics', {}])
if autoPlot:
# GENERATE PLOTS
log.info('Generating summary plot...')
xfspec.append(['nems.xforms.plot_summary', {}])
# actually do the fit
ctx, log_xf = xforms.evaluate(xfspec)
# save some extra metadata
modelspecs = ctx['modelspecs']
if 'CODEHASH' in os.environ.keys():
githash = os.environ['CODEHASH']
else:
githash = ""
meta = {
'batch': batch,
'cellid': cellid,
'modelname': modelname,
'loader': loader,
'fitter': fitter,
'modelspecname': modelspecname,
'username': '******',
'labgroup': 'lbhb',
'public': 1,
'githash': githash,
'recording': loader
}
if not 'meta' in modelspecs[0][0].keys():
modelspecs[0][0]['meta'] = meta
else:
modelspecs[0][0]['meta'].update(meta)
destination = '/auto/data/tmp/modelspecs/{0}/{1}/{2}/'.format(
batch, cellid, ms.get_modelspec_longname(modelspecs[0]))
modelspecs[0][0]['meta']['modelpath'] = destination
modelspecs[0][0]['meta']['figurefile'] = destination + 'figure.0000.png'
# save results
xforms.save_analysis(destination,
recording=ctx['rec'],
modelspecs=modelspecs,
xfspec=xfspec,
figures=ctx['figures'],
log=log_xf)
log.info('Saved modelspec(s) to {0} ...'.format(destination))
# save in database as well
if saveInDB:
# TODO : db results
nd.update_results_table(modelspecs[0])
return ctx
# shorthand for:
# ctx = {}
# for xfa in xfspec:
# ctx = xforms.evaluate_step(xfa, ctx)
if browse_results:
import nems.gui.editors as gui
ex = gui.browse_xform_fit(ctx, xfspec)
if save_results:
# ----------------------------------------------------------------------------
# SAVE YOUR RESULTS
# save results to file
destination = os.path.join(results_dir, str(batch), xforms.get_meta(ctx)['cellid'],
ms.get_modelspec_longname(ctx['modelspec']))
log.info('Saving modelspec(s) to {0} ...'.format(destination))
xforms.save_analysis(destination,
recording=ctx['rec'],
modelspec=ctx['modelspec'],
xfspec=xfspec,
figures=ctx['figures'],
log=log_xf)
# save summary of results to a database
log.info('Saving metadata to db ...')
modelspec = ctx['modelspec']
modelspec.meta['modelpath'] = destination
modelspec.meta['figurefile'] = destination + 'figure.0000.png'
nd.update_results_table(modelspec)