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 save_unit_response_by_angle(units, stimulus_list, c_unit_fig,
c_add_retina_figure):
print("Calculating DSI & OSI")
bar_firing_rate, bar_dsi, bar_osi = get_fr_dsi_osi(units, stimulus_list)
print("plotting unit response by angle")
analytics = glia.by_speed_width_then_angle(
glia.get_unit(bar_firing_rate)[1])
nplots = len(list(analytics.keys()))
del analytics
if nplots > 1:
ncols = 3
else:
ncols = 1
result = glia.plot_units(plot_unit_response_by_angle,
partial(c_unit_fig, "angle"),
bar_firing_rate,
bar_dsi,
bar_osi,
nplots=nplots,
subplot_kw={"projection": "polar"},
ax_xsize=4,
ax_ysize=5,
ncols=3)
# c_unit_fig(result)
# glia.close_figs([fig for the_id,fig in result])
print("plotting unit DSI/OSI table")
result = glia.plot_units(plot_unit_dsi_osi_table,
partial(c_unit_fig, "selectivity_table"),
bar_firing_rate,
bar_dsi,
bar_osi,
ax_xsize=16,
ax_ysize=3)
# c_unit_fig(result)
# glia.close_figs([fig for the_id,fig in result])
fig_population, ax = plt.subplots(2, 1)
print("plotting population by DSI & OSI")
plot_population_dsi_osi(fig_population, ax, (bar_dsi, bar_osi))
c_add_retina_figure("DSI_OSI", fig_population)
plt.close(fig_population)
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_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_response_by_angle(units, stimulus_list, c_add_unit_figures, c_add_retina_figure):
print("Calculating DSI & OSI")
bar_firing_rate, bar_dsi, bar_osi = get_fr_dsi_osi(units, stimulus_list)
print("plotting unit response by angle")
analytics = glia.by_speed_width_then_angle(glia.get_unit(bar_firing_rate)[1])
nplots = len(list(analytics.keys()))
del analytics
if nplots>1:
ncols=3
else:
ncols=1
result = glia.plot_units(plot_unit_response_by_angle,
bar_firing_rate,bar_dsi,bar_osi,
nplots=nplots, subplot_kw={"projection": "polar"},
ax_xsize=4, ax_ysize=5, ncols=3)
c_add_unit_figures(result)
glia.close_figs([fig for the_id,fig in result])
print("plotting unit DSI/OSI table")
result = glia.plot_units(plot_unit_dsi_osi_table,
bar_firing_rate,bar_dsi,bar_osi,
ax_xsize=6, ax_ysize=4)
c_add_unit_figures(result)
glia.close_figs([fig for the_id,fig in result])
fig_population,ax = plt.subplots(2,1)
print("plotting population by DSI & OSI")
plot_population_dsi_osi(ax, (bar_dsi, bar_osi))
c_add_retina_figure(fig_population)
plt.close(fig_population)
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])
