ax = plt.subplot(3, 1, 2)
nplt.timeseries_from_epoch([val['resp']], epoch, ax=ax)
raster = rec['resp'].extract_epoch(epoch)
ax = plt.subplot(3, 1, 3)
plt.imshow(raster[:, 0, :])
plt.tight_layout()
# see what a "traditional" NEMS model looks like
nems_modelname = "ozgf.fs100.ch18-ld-sev_dlog-wc.18x2.g-do.2x15-lvl.1-dexp.1_init-basic"
xfspec, ctx = load_model_xform(cellid, batch=batch, modelname=nems_modelname)
nplt.quickplot(ctx)
ex = gui.browse_xform_fit(ctx, xfspec)
##
batch, cellid = 308, 'AMT018a-09-1'
modelname = 'ozgf.fs100.ch18-ld-sev_dlog-wc.18x4.g-fir.2x15x2-relu.2-wc.2x1-lvl.1-dexp.1_init.tf.rb5-tf.n'
xfspec, ctx = fit_model_xform(cellid, batch=batch, modelname=modelname)
nplt.quickplot(ctx)
ex = gui.browse_xform_fit(ctx, xfspec)
###Plot complexity of model versus how effective it was
batch = 308
metric = 'r_test'
metric2 = 'n_parms'
metric3 = 'se_test'
query = "SELECT {0}, {1}, {2}, {3} FROM NarfResults WHERE batch = 308".format(
metric, metric2, metric3, 'modelname')
def launch_model_browser(self, ctx, xfspec):
"""Launches the model browser and keeps a reference so it's not garbage collected."""
self.model_browser = editors.browse_xform_fit(ctx, xfspec)
from nems.gui.editors import browse_xform_fit
import nems.xform_helper as xhelp
from nems.xforms import normalize_sig
from nems_lbhb.gcmodel.figures.simulation import (build_toy_stp_cell,
build_toy_gc_cell,
build_toy_LN_cell)
_DEFAULT_CELL = 'TAR009d-42-1'
_DEFAULT_BATCH = 289
_DEFAULT_MODEL = 'ozgf.fs100.ch18-ld-contrast.ms30.cont.n.off0-sev_dlog.f-wc.18x3.g-fir.3x15-lvl.1-ctk3-dsig.d_gc2.PF'
if __name__ == 'main':
xfspec, ctx = xhelp.load_model_xform(_DEFAULT_CELL, _DEFAULT_BATCH,
_DEFAULT_MODEL)
val = ctx['val']
# val = normalize_sig(val, 'stim', 'minmax')['rec']
# val = normalize_sig(val, 'resp', 'minmax')['rec']
#gc_sim = build_toy_gc_cell(0, 0, 0, -0.5) #base, amp, shift, kappa
#gc_sim[-2]['fn_kwargs']['compute_contrast'] = True
stp_sim = build_toy_stp_cell([0, 0.1], [0.08, 0.08]) #u, tau
#LN_sim = build_toy_LN_cell()
ctx['val'] = stp_sim.evaluate(val)
ctx['modelspec'] = stp_sim
browse_xform_fit(ctx, xfspec)
batch = 289
cellid = 'TAR009d-42-1'
modelname = "ozgf.fs100.ch18-ld-sev_dlog.f-wc.18x3.g-stp.3-fir.3x15-lvl.1-dexp.1_init-basic"
batch, cellid = 308, 'AMT018a-09-1'
modelname = 'ozgf.fs100.ch18-ld-sev_dlog-wc.18x4.g-fir.2x15x2-relu.2-wc.2x1-lvl.1-dexp.1_tf.n.rb10'
modelname2 = 'ozgf.fs100.ch18-ld-sev_dlog-wc.18x4.g-fir.2x15x2-relu.2-wc.2x1-lvl.1-dexp.1_tf.n.rb5'
modelname2 = None
GUI = True
xfspec, ctx = xhelp.load_model_xform(cellid, batch, modelname)
if GUI:
# interactive model browser (matplotlib embedded Qt)
ex = gui.browse_xform_fit(ctx, xfspec)
if modelname2 is not None:
xfspec2, ctx2 = xhelp.load_model_xform(cellid, batch, modelname2)
ex2 = gui.browse_xform_fit(ctx2,
xfspec2,
control_widget=ex.editor.global_controls)
#aw = browse_context(ctx, rec='val', signals=['stim', 'pred', 'resp'])
#aw = browse_context(ctx, signals=['state', 'psth', 'pred', 'resp'])
else:
# static model summary plot (matplotlib native)
#ctx['modelspec'][1]['plot_fn_idx']=2
time_range = (0, 5)
# ----------------------------------------------------------------------------
# GENERATE PLOTS
# GOAL: Plot the predictions made by your results vs the real response.
# Compare performance of results with other metrics.
logging.info('Generating summary plot...')
# set modelspec to plot psth for big vs. small pupil
modelspec[0]['plot_fn_idx'] = 5
# Generate a summary plot
modelspec.quickplot(rec=val, epoch='REFERENCE', include_input=False)
#plot timeseries seperately overides quickplot errors
epoch_bounds = val['resp'].get_epoch_bounds('REFERENCE')
possible_occurrences = np.arange(epoch_bounds.shape[1])
occurrence = possible_occurrences[0]
time_range = epoch_bounds[occurrence]
nems.plots.timeseries.timeseries_from_signals(
signals=[val['resp'], val['pred']],
channels=0,
no_legend=False,
time_range=time_range,
rec=val,
sig_name=None)
if USE_GUI:
# interactive gui
ex = gui.browse_xform_fit({'modelspec': modelspec, 'val': val}, [])