def save_unit_spike_trains(units, stimulus_list, c_add_unit_figures, c_add_retina_figure,
by='angle'):
print("Creating bar unit spike trains")
if by == 'angle':
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
partial(sorted, key=lambda e: e["stimulus"]["angle"]),
)
nplots = get_nplots(stimulus_list,by)
response = glia.apply_pipeline(get_solid,units)
result = glia.plot_units(plot_spike_trains_by_angle,response, nplots=nplots,
ncols=3,ax_xsize=10, ax_ysize=5,
figure_title="Unit spike train by BAR angle")
elif by == 'width':
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
partial(sorted, key=lambda e: e["stimulus"]["width"]),
)
nplots = get_nplots(stimulus_list,by)
response = glia.apply_pipeline(get_solid,units)
result = glia.plot_units(plot_spike_trains_by_trial,response, nplots=nplots,
ncols=3,ax_xsize=10, ax_ysize=5,
figure_title="Unit spike train by BAR angle")
# nplots = len(speed_widths)
c_add_unit_figures(result)
glia.close_figs([fig for the_id,fig in result])
def save_acuity_chart(units, stimulus_list, c_unit_fig, c_add_retina_figure,
prepend, append):
"Compare SOLID light wedge to BAR response in corresponding ascending width."
print("Creating acuity chart v3.")
get_solids = glia.compose(
glia.f_create_experiments(stimulus_list,
prepend_start_time=prepend,
append_lifespan=append),
glia.f_has_stimulus_type(["SOLID"]),
)
solids = glia.apply_pipeline(get_solids, units, progress=True)
# offset to avoid diamond pixel artifacts
get_bars_by_speed = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
partial(sorted, key=lambda x: x["stimulus"]["angle"]),
partial(sorted, key=lambda x: x["stimulus"]["width"]),
partial(glia.group_by, key=lambda x: x["stimulus"]["speed"]))
bars_by_speed = glia.apply_pipeline(get_bars_by_speed,
units,
progress=True)
speeds = list(glia.get_unit(bars_by_speed)[1].keys())
for speed in sorted(speeds):
print("Plotting acuity for speed {}".format(speed))
plot_function = partial(plot_acuity_v3,
prepend=prepend,
append=append,
speed=speed)
filename = "acuity-{}".format(speed)
result = glia.plot_units(
plot_function,
partial(c_unit_fig, filename),
solids,
bars_by_speed,
nplots=1,
ncols=1,
ax_xsize=5,
ax_ysize=15,
figure_title="Bars with speed {}".format(speed))
plot_function = partial(plot_dissimilarity,
prepend=prepend,
append=append,
speed=speed)
filename = "dissimilarity-{}".format(speed)
result = glia.plot_units(
plot_function,
partial(c_unit_fig, filename),
solids,
bars_by_speed,
nplots=1,
ncols=1,
ax_xsize=7,
ax_ysize=7,
figure_title="Dissimilarity matrix for bars with speed {}".format(
speed))
def save_acuity_direction(units, stimulus_list, c_unit_fig,
c_add_retina_figure):
"Make one direction plot per speed"
get_direction = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
partial(filter, lambda x: x["stimulus"]["barColor"] == "white"),
partial(sorted, key=lambda e: e["stimulus"]["angle"]),
partial(glia.group_by,
key=lambda x: x["stimulus"]["speed"],
value=lambda x: x))
response = glia.apply_pipeline(get_direction, units, progress=True)
speeds = list(glia.get_unit(response)[1].keys())
nspeeds = len(speeds)
for speed in sorted(speeds):
print("Plotting DS for speed {}".format(speed))
plot_function = partial(plot_unit_response_for_speed, speed=speed)
filename = "direction-{}".format(speed)
glia.plot_units(
plot_function,
partial(c_unit_fig, filename),
response,
subplot_kw={"projection": "polar"},
ax_xsize=7,
ax_ysize=7,
figure_title="Units spike train for speed {}".format(speed),
transpose=True)
def get_fr_dsi_osi(units, stimulus_list):
get_bar_firing_rate = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
glia.f_group_by_stimulus(),
glia.f_calculate_firing_rate_by_stimulus(),
)
bar_firing_rate = glia.apply_pipeline(get_bar_firing_rate,
units,
progress=True)
get_bar_dsi = glia.compose(glia.by_speed_width_then_angle,
glia.calculate_dsi_by_speed_width)
bar_dsi = glia.apply_pipeline(get_bar_dsi, bar_firing_rate, progress=True)
get_bar_osi = glia.compose(glia.by_speed_width_then_angle,
glia.calculate_osi_by_speed_width)
bar_osi = glia.apply_pipeline(get_bar_osi, bar_firing_rate, progress=True)
return (bar_firing_rate, bar_dsi, bar_osi)
def simulated_test(units, stimulus_list):
# assert len(next(iter(units.values())).spike_train)==2200
test_pipeline = glia.compose(glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["GRATING"]),
glia.f_group_by_stimulus(),
glia.f_calculate_firing_rate_by_stimulus())
firing_rates = glia.apply_pipeline(test_pipeline, units, progress=True)
for stimulus, rates in next(iter(firing_rates.values())).items():
for rate in rates:
assert np.isclose(rate, 60, 1)
def save_unit_spike_trains(units, stimulus_list, c_add_unit_figures, c_add_retina_figure, prepend, append):
print("Creating solid unit spike trains")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list,prepend_start_time=prepend,append_lifespan=append),
glia.f_has_stimulus_type(["SOLID"]),
)
response = glia.apply_pipeline(get_solid,units)
plot_function = partial(plot_spike_trains,prepend_start_time=prepend,append_lifespan=append)
result = glia.plot_units(plot_function,response,ncols=1,ax_xsize=10, ax_ysize=5)
c_add_unit_figures(result)
glia.close_figs([fig for the_id,fig in result])
def save_integrity_chart(units, stimulus_list, c_unit_fig, c_add_retina_figure):
print("Creating integrity chart")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list,prepend_start_time=1,append_lifespan=2),
glia.f_has_stimulus_type(["SOLID"]),
filter_lifespan
)
response = glia.apply_pipeline(get_solid,units, progress=True)
plot_function = partial(plot_spike_trains,prepend_start_time=1,append_lifespan=2)
glia.plot_units(plot_function,c_unit_fig,response,ncols=1,ax_xsize=10, ax_ysize=5,
figure_title="Integrity Test (5 Minute Spacing)")
def save_unit_spike_trains(units, stimulus_list, c_unit_fig, c_add_retina_figure, prepend, append):
print("Creating solid unit spike trains")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list,prepend_start_time=prepend,append_lifespan=append),
glia.f_has_stimulus_type(["SOLID"]),
)
response = glia.apply_pipeline(get_solid,units, progress=True)
plot_function = partial(plot_spike_trains,prepend_start_time=prepend,append_lifespan=append)
result = glia.plot_units(plot_function,response,ncols=1,ax_xsize=10, ax_ysize=5)
c_unit_fig(result)
glia.close_figs([fig for the_id,fig in result])
def save_unit_spike_trains(units,
stimulus_list,
c_add_unit_figures,
c_add_retina_figure,
by='angle'):
print("Creating bar unit spike trains")
if by == 'angle':
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
partial(sorted, key=lambda e: e["stimulus"]["angle"]),
)
nplots = get_nplots(stimulus_list, by)
response = glia.apply_pipeline(get_solid, units, progress=True)
result = glia.plot_units(plot_spike_trains_by_angle,
response,
nplots=nplots,
ncols=3,
ax_xsize=10,
ax_ysize=5,
figure_title="Unit spike train by BAR angle")
elif by == 'width':
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
partial(sorted, key=lambda e: e["stimulus"]["width"]),
)
nplots = get_nplots(stimulus_list, by)
response = glia.apply_pipeline(get_solid, units, progress=True)
result = glia.plot_units(plot_spike_trains_by_trial,
response,
nplots=nplots,
ncols=3,
ax_xsize=10,
ax_ysize=5,
figure_title="Unit spike train by BAR angle")
# nplots = len(speed_widths)
c_add_unit_figures(result)
glia.close_figs([fig for the_id, fig in result])
def save_integrity_chart_v2(units, stimulus_list, c_unit_fig, c_add_retina_figure):
print("Creating integrity chart")
get_integrity= glia.compose(
glia.f_create_experiments(stimulus_list),
glia.filter_integrity,
partial(glia.group_by,
key=lambda x: x["stimulus"]["metadata"]["group"]),
glia.group_dict_to_list,
)
response = glia.apply_pipeline(get_integrity,units, progress=True)
chronological = glia.apply_pipeline(
partial(sorted,key=lambda x: x[0]["stimulus"]["stimulusIndex"]),
response)
plot_function = partial(glia.raster_group)
# c = partial(c_unit_fig,"kinetics-{}".format(i))
glia.plot_units(plot_function,c_unit_fig,chronological,ncols=1,ax_xsize=10, ax_ysize=5,
figure_title="Integrity Test (5 Minute Spacing)")
ntrial = len(glia.get_unit(response)[1])
ntrain = int(np.ceil(ntrial/2))
ntest = int(np.floor(ntrial/2))
tvt = glia.TVT(ntrain,ntest,0)
classification_data = glia.apply_pipeline(
glia.f_split_list(tvt),
response)
units_accuracy = glia.pmap(unit_classification_accuracy,classification_data)
plot_directory = os.path.join(config.plot_directory,"00-all")
os.makedirs(plot_directory, exist_ok=True)
with open(plot_directory + "/best_units.txt", "w") as f:
sorted_units = sorted(units_accuracy.items(),
key=lambda z: max(z[1]["off"],z[1]["on"]),
reverse=True)
for u in sorted_units:
f.write(str(u)+"\n")
c_add_retina_figure("integrity_accuracy",plot_units_accuracy(units_accuracy))
def save_unit_psth(units, stimulus_list, c_add_unit_figures, c_add_retina_figure, prepend, append):
print("Creating solid unit PSTH")
get_psth = glia.compose(
glia.f_create_experiments(stimulus_list,prepend_start_time=prepend,append_lifespan=append),
glia.f_has_stimulus_type(["SOLID"]),
glia.f_group_by_stimulus(),
glia.concatenate_by_stimulus
)
psth = glia.apply_pipeline(get_psth,units)
plot_function = partial(plot_psth,prepend_start_time=prepend,append_lifespan=append)
result = glia.plot_units(partial(plot_function,bin_width=0.01),psth,ax_xsize=10, ax_ysize=5)
c_add_unit_figures(result)
glia.close_figs([fig for the_id,fig in result])
def save_unit_psth(units, stimulus_list, c_unit_fig, c_add_retina_figure, prepend, append):
print("Creating solid unit PSTH")
get_psth = glia.compose(
glia.f_create_experiments(stimulus_list,prepend_start_time=prepend,append_lifespan=append),
glia.f_has_stimulus_type(["SOLID"]),
glia.f_group_by_stimulus(),
glia.concatenate_by_stimulus
)
psth = glia.apply_pipeline(get_psth,units, progress=True)
plot_function = partial(plot_psth,prepend_start_time=prepend,append_lifespan=append)
result = glia.plot_units(partial(plot_function,bin_width=0.01),psth,ax_xsize=10, ax_ysize=5)
c_unit_fig(result)
glia.close_figs([fig for the_id,fig in result])
def simulated_test(units, stimulus_list): assert len(next(iter(units.values())).spike_train)==2200 test_pipeline = glia.compose( glia.f_create_experiments(stimulus_list), glia.f_has_stimulus_type(["GRATING"]), glia.f_group_by_stimulus(), glia.f_calculate_firing_rate_by_stimulus() ) firing_rates = glia.apply_pipeline(test_pipeline, units) for stimulus,rates in next(iter(firing_rates.values())).items(): for rate in rates: assert rate==1
def save_raster(units, stimulus_list, c_unit_fig, c_add_retina_figure,
sort_by=glia.group_lifespan):
print("Creating spike train raster plot")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
partial(glia.group_by,
key=lambda x: x["stimulus"]["metadata"]["group"]),
glia.group_dict_to_list,
partial(sorted,key=sort_by)
)
response = glia.apply_pipeline(get_solid,units, progress=True)
glia.plot_units(glia.raster_group,c_unit_fig,response,nplots=1,
ncols=1,ax_xsize=15, ax_ysize=10)
def get_fr_dsi_osi(units, stimulus_list):
get_bar_firing_rate = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["BAR"]),
glia.f_group_by_stimulus(),
glia.f_calculate_firing_rate_by_stimulus(),
)
bar_firing_rate = glia.apply_pipeline(get_bar_firing_rate,units)
get_bar_dsi = glia.compose(
glia.by_speed_width_then_angle,
glia.calculate_dsi_by_speed_width
)
bar_dsi = glia.apply_pipeline(get_bar_dsi,bar_firing_rate)
get_bar_osi = glia.compose(
glia.by_speed_width_then_angle,
glia.calculate_osi_by_speed_width
)
bar_osi = glia.apply_pipeline(get_bar_osi,bar_firing_rate)
return (bar_firing_rate, bar_dsi, bar_osi)
def save_unit_wedges_v2(units, stimulus_list, c_unit_fig, c_add_retina_figure):
print("Creating solid unit wedges")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["SOLID","WAIT"]),
partial(glia.group_by,
key=lambda x: x["stimulus"]["metadata"]["group"]),
glia.group_dict_to_list,
partial(sorted,key=lambda x: get_lifespan(x[1]))
)
response = glia.apply_pipeline(get_solid,units, progress=True)
glia.plot_units(plot_spike_train_triplet,c_unit_fig,response,nplots=1,
ncols=1,ax_xsize=10, ax_ysize=5)
def save_unit_kinetics(units, stimulus_list, c_unit_fig, c_add_retina_figure):
print("Creating solid unit kinetics")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
partial(glia.group_by,
key=lambda x: x["stimulus"]["metadata"]["group"]),
glia.group_dict_to_list,
partial(sorted,key=lambda x: get_lifespan(x[2]))
)
response = glia.apply_pipeline(get_solid,units, progress=True)
# glia.plot_units(plot_group_spike_train,c_unit_fig,response,nplots=1,
# ncols=1,ax_xsize=10, ax_ysize=5)
glia.plot_units(glia.raster_group,c_unit_fig,response,nplots=1,
ncols=1,ax_xsize=10, ax_ysize=5)
def save_unit_wedges(units, stimulus_list, c_unit_fig, c_add_retina_figure, prepend, append):
print("Creating solid unit wedges")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list,prepend_start_time=prepend,append_lifespan=append),
glia.f_has_stimulus_type(["SOLID"]),
partial(sorted,key=lambda x: x["stimulus"]["lifespan"])
)
response = glia.apply_pipeline(get_solid,units, progress=True)
colors = set()
for solid in glia.get_unit(response)[1]:
colors.add(solid["stimulus"]["backgroundColor"])
ncolors = len(colors)
plot_function = partial(plot_spike_trains,prepend_start_time=prepend,
append_lifespan=append)
glia.plot_units(plot_function,c_unit_fig,response,nplots=ncolors,
ncols=min(ncolors,5),ax_xsize=10, ax_ysize=5)
def save_unit_kinetics_v1(units, stimulus_list, c_unit_fig, c_add_retina_figure):
print("Creating solid unit kinetics")
for i in range(5):
s = i*150
e = (i+1)*150
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
lambda x: x[s:e],
partial(glia.group_by,
key=lambda x: x["stimulus"]["metadata"]["group"]),
glia.group_dict_to_list,
partial(sorted,key=lambda x: get_lifespan(x[2]))
)
response = glia.apply_pipeline(get_solid,units, progress=True)
c = partial(c_unit_fig,"kinetics-{}".format(i))
glia.plot_units(glia.raster_group,c,response,nplots=1,
ncols=1,ax_xsize=10, ax_ysize=5)
def save_integrity_chart_vFail(units, stimulus_list, c_unit_fig, c_add_retina_figure):
print("Creating integrity chart")
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.filter_integrity,
partial(glia.group_by,
key=lambda x: x["stimulus"]["metadata"]["group"]),
glia.group_dict_to_list,
integrity_fix_hack,
partial(sorted,key=lambda x: x[0]["stimulus"]["stimulusIndex"])
)
response = glia.apply_pipeline(get_solid,units, progress=True)
plot_function = partial(glia.raster_group)
# c = partial(c_unit_fig,"kinetics-{}".format(i))
glia.plot_units(plot_function,c_unit_fig,response,ncols=1,ax_xsize=10, ax_ysize=5,
figure_title="Integrity Test (5 Minute Spacing)")
units_accuracy = glia.pmap(ideal_unit_classification_accuracy, response)
c_add_retina_figure("integrity_accuracy",plot_units_accuracy(units_accuracy))
def filter_units_by_accuracy(units, stimulus_list, threshold=0.8):
ntrial = len(list(filter(
lambda x: 'metadata' in x['stimulus'] and "label" in x['stimulus']['metadata'] and \
x['stimulus']['metadata']['label']=='integrity',
stimulus_list)))/3
ntrain = int(np.ceil(ntrial/2))
ntest = int(np.floor(ntrial/2))
tvt = glia.TVT(ntrain,ntest,0)
get_solid = glia.compose(
glia.f_create_experiments(stimulus_list),
glia.filter_integrity,
partial(glia.group_by,
key=lambda x: x["stimulus"]["metadata"]["group"]),
glia.group_dict_to_list,
glia.f_split_list(tvt)
)
classification_data = glia.apply_pipeline(get_solid,units, progress=True)
units_accuracy = glia.pmap(unit_classification_accuracy,classification_data)
filter_threshold = glia.f_filter(lambda k,x: x['on']>threshold or x['off']>threshold)
return set(filter_threshold(units_accuracy).keys())
def save_unit_spike_trains(units,
stimulus_list,
c_unit_fig,
c_add_retina_figure,
width=None,
height=None):
print("Creating grating unit spike trains")
get_solid = glia.compose(glia.f_create_experiments(stimulus_list),
glia.f_has_stimulus_type(["GRATING"]),
glia.f_split_by_wavelength())
response = glia.apply_pipeline(get_solid, units, progress=True)
nplots = len(glia.get_unit(response)[1])
result = glia.plot_units(
plot_spike_trains,
response,
nplots=nplots,
ncols=3,
ax_xsize=10,
ax_ysize=5,
figure_title="Unit spike train by GRATING waveperiod")
c_unit_fig(result)
glia.close_figs([fig for the_id, fig in result])