def draw_perf_by_freq(agg, data_dir='/auto/tdrive/mschachter/data'):
etypes = ['rate', 'both']
df = dict()
for ein in etypes:
df[ein] = get_encoder_perf_data_for_psd(agg, ein)
flat_data = dict()
for ein in etypes:
flat_data[ein] = list()
bp_data = dict()
band_names = ['0-30Hz', '30-80Hz', '80-190Hz']
for k,f in enumerate([int(x) for x in agg.freqs]):
bp_data[band_names[k]] = list()
for ein in etypes:
i = df[ein].f == f
r2 = df[ein].r2[i].values
r2 = r2[r2 > 0]
bp_data[band_names[k]].append(r2)
flat_data[ein].append([r2.mean(), r2.std()])
grouped_boxplot(bp_data, group_names=band_names, subgroup_names=['Rate', 'Rate+Sync'],
subgroup_colors=[COLOR_RED_SPIKE_RATE, COLOR_CRIMSON_SPIKE_SYNC],
box_width=0.8, box_spacing=1.0, legend_loc='lower right')
clrs = {'rate':COLOR_RED_SPIKE_RATE, 'both':COLOR_CRIMSON_SPIKE_SYNC}
for k,ein in enumerate(etypes):
perf = np.array(flat_data[ein])
# plt.errorbar(agg.freqs, perf[:, 0], yerr=perf[:, 1], c=clrs[ein], linewidth=8.0, elinewidth=5.0,
# ecolor='k', alpha=0.6, capthick=0.)
ax = plt.gca()
xmin,xmax = ax.get_xlim()
# plt.plot(np.linspace(xmin, xmax, len(agg.freqs)), perf[:, 0], c=clrs[ein], linewidth=8.0, alpha=0.7)
plt.xlabel('LFP Frequency (Hz)')
plt.ylabel('Spike->LFP Encoder R2')
plt.axis('tight')
plt.ylim(0, 1.0)
plt.xlim(0, 9)
def draw_acoustic_perf_boxplots(agg, df_me):
aprops_to_display = ['sal', 'meanspect', 'entropyspect', 'q2', 'maxAmp', 'skewtime']
aprops_names = {'sal':'sal', 'meanspect':'freq\nmean', 'entropyspect':'freq\nentropy', 'q2':'freq\nmedian', 'maxAmp':'max\namp', 'skewtime':'time\nskew'}
decomps = ['self_spike_rate', 'self_locked', 'self+cross_locked']
sub_names = ['Spike Rate', 'LFP PSD', 'LFP Pairwise']
sub_clrs = [COLOR_RED_SPIKE_RATE, COLOR_BLUE_LFP, COLOR_PURPLE_LFP_CROSS]
# decomps = ['self_locked', 'self+cross_locked']
# sub_names = ['LFP PSD', 'LFP Pairwise']
# sub_clrs = [COLOR_BLUE_LFP, COLOR_PURPLE_LFP_CROSS]
bp_data = dict()
for aprop in aprops_to_display:
bd = list()
for decomp in decomps:
i = (df_me.decomp == decomp) & (df_me.aprop == aprop)
perfs = df_me.r2[i].values
bd.append(perfs)
bp_data[aprops_names[aprop]] = bd
figsize = (19, 3.5)
fig = plt.figure(figsize=figsize)
plt.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.99, hspace=0.40, wspace=0.20)
grouped_boxplot(bp_data, group_names=[aprops_names[aprop] for aprop in aprops_to_display], subgroup_names=sub_names,
subgroup_colors=sub_clrs, box_spacing=1.)
plt.xlabel('Acoustic Feature')
plt.ylabel('Decoder R2')
fname = os.path.join(get_this_dir(), 'perf_boxplots.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
def draw_category_perf_and_confusion(agg, df_me):
df0 = df_me[df_me.band == 0]
lfp_perfs = df0['perf_category_lfp'].values
spike_perfs = df0['perf_category_spike'].values
spike_rate_perfs = df0['perf_category_spike_rate'].values
bp_data = {'Vocalization Type':[lfp_perfs, spike_perfs, spike_rate_perfs]}
cmats = dict()
for decomp in ['locked', 'spike_psd', 'spike_rate']:
# compute average confusion matrix for spikes and LFP
i = (agg.df.e1 == -1) & (agg.df.e2 == -1) & (agg.df.decomp == decomp) & (agg.df.band == 0) & \
(agg.df.aprop == 'category') & (agg.df.exfreq == False) & (agg.df.exel == False)
print '%s, i.sum()=%d' % (decomp, i.sum())
df = agg.df[i]
ci = df.cmat_index.values
C = agg.confusion_matrices[ci, :, :]
Cmean = C.mean(axis=0)
cnames = agg.stim_class_names[ci][0]
# reorder confusion matrix
Cro = np.zeros_like(Cmean)
for k,cname1 in enumerate(cnames):
for j,cname2 in enumerate(cnames):
i1 = DECODER_CALL_TYPES.index(cname1)
i2 = DECODER_CALL_TYPES.index(cname2)
Cro[i1, i2] = Cmean[k, j]
cmats[decomp] = Cro
figsize = (16, 12)
fig = plt.figure(figsize=figsize)
plt.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.99, hspace=0.40, wspace=0.20)
# gs = plt.GridSpec(1, 100)
gs = plt.GridSpec(2, 2)
# make a boxplot
ax = plt.subplot(gs[0, 0])
grouped_boxplot(bp_data, subgroup_names=['LFP', 'Spike PSD', 'Spike Rate'],
subgroup_colors=[COLOR_BLUE_LFP, COLOR_YELLOW_SPIKE, COLOR_RED_SPIKE_RATE], box_spacing=1.5, ax=ax)
plt.xticks([])
plt.ylabel('PCC')
plt.title('Vocalization Type Decoder Performance')
# plot the mean LFP confusion matrix
ax = plt.subplot(gs[0, 1])
plt.imshow(cmats['locked'], origin='lower', interpolation='nearest', aspect='auto', vmin=0, vmax=1, cmap=plt.cm.afmhot)
xtks = [CALL_TYPE_SHORT_NAMES[ct] for ct in DECODER_CALL_TYPES]
plt.xticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.yticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.colorbar(label='PCC')
plt.title('Mean LFP Decoder Confusion Matrix')
# plot the mean spike confusion matrix
ax = plt.subplot(gs[1, 0])
plt.imshow(cmats['spike_psd'], origin='lower', interpolation='nearest', aspect='auto', vmin=0, vmax=1, cmap=plt.cm.afmhot)
xtks = [CALL_TYPE_SHORT_NAMES[ct] for ct in DECODER_CALL_TYPES]
plt.xticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.yticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.colorbar(label='PCC')
plt.title('Mean Spike PSD Decoder Confusion Matrix')
fname = os.path.join(get_this_dir(), 'perf_boxplots_category.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
# plot the mean spike rate confusion matrix
ax = plt.subplot(gs[1, 1])
plt.imshow(cmats['spike_rate'], origin='lower', interpolation='nearest', aspect='auto', vmin=0, vmax=1, cmap=plt.cm.afmhot)
xtks = [CALL_TYPE_SHORT_NAMES[ct] for ct in DECODER_CALL_TYPES]
plt.xticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.yticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.colorbar(label='PCC')
plt.title('Mean Spike Rate Decoder Confusion Matrix')
fname = os.path.join(get_this_dir(), 'perf_boxplots_category.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
