def test_boxplots_with_color():
data = {'group_1':np.random.randn(50), 'group_2':np.random.randn(50)*2, 'group_3':np.random.randn(50)-2}
plt.figure()
boxplot_with_colors(data, group_names=['group_1', 'group_2', 'group_3'], group_colors=['r', 'g', 'b'])
plt.show()
def plot_lfp_and_spike_perf_boxplot(agg, ax=None, lfp_decomp='full_psds'):
if ax is None:
ax = plt.gca()
df = get_lfp_and_spike_perfs_for_boxplot(agg, lfp_decomp=lfp_decomp)
freqs, lags = get_freqs_and_lags()
bp_data = dict()
i = (df.f == -1) & ~np.isnan(df.r2) & (df.r2 > 0)
bp_data['Spike\nRate'] = df.r2[i].values
for f in freqs:
i = (df.f == f) & (df.r2 > 0) & ~np.isnan(df.r2)
bp_data['%d' % f] = df.r2[i].values
group_names = ['Spike\nRate']
group_names.extend(['%d' % f for f in freqs])
group_colors = [COLOR_RED_SPIKE_RATE]
group_colors.extend([COLOR_BLUE_LFP]*len(freqs))
boxplot_with_colors(bp_data, group_names, ax=ax, group_colors=group_colors, box_alpha=0.95)
plt.ylim(0, 0.8)
plt.ylabel('Encoder R2')
plt.xlabel('Frequency (Hz)')
xticks = ['Spike\nRate', 'LFP\n(0-30Hz)', 'LFP\n(30-80Hz)', 'LFP\n(80-190Hz)']
plt.xticks(np.arange(len(xticks))+1, xticks)
def test_boxplots_with_color():
data = {
'group_1': np.random.randn(50),
'group_2': np.random.randn(50) * 2,
'group_3': np.random.randn(50) - 2
}
plt.figure()
boxplot_with_colors(data,
group_names=['group_1', 'group_2', 'group_3'],
group_colors=['r', 'g', 'b'])
plt.show()
def draw_perfs(agg):
# filter out some data points
i = (agg.df.cc > 0.05) & (agg.df.region != '?') & (agg.df.region != 'HP') \
& ~np.isnan(agg.df.dist_l2a) & ~np.isnan(agg.df.dist_midline) \
& (agg.df.dist_l2a > -1) & (agg.df.dist_l2a < 1)
df = agg.df[i]
print len(df)
print df.region.unique()
# make a scatter plot of performance by anatomical location
fig = plt.figure(figsize=(23, 10), facecolor='w')
fig.subplots_adjust(left=0.05, right=0.95, wspace=0.25, hspace=0.25)
gs = plt.GridSpec(2, 100)
ax = plt.subplot(gs[:, :55])
plt.scatter(df.dist_midline, df.dist_l2a, c=df.cc, s=64, cmap=plt.cm.afmhot_r, alpha=0.7)
plt.ylim(-1, 1)
plt.xlim(0, 2.5)
for x, y, r in zip(df.dist_midline, df.dist_l2a, df.region):
plt.text(x, y - 0.05, r, fontsize=9)
cbar = plt.colorbar(label='Linear Encoder Performance (cc)')
plt.xlabel('Dist to Midline (mm)')
plt.ylabel('Dist to L2A (mm)')
region_stats = list()
regions_to_use = ['L1', 'L2', 'L3', 'CM', 'NCM']
for reg in regions_to_use:
i = df.region == reg
region_stats.append({'cc': df[i].cc, 'cc_mean': df[i].cc.mean(), 'region': reg})
region_stats.sort(key=operator.itemgetter('cc_mean'), reverse=True)
regions = [x['region'] for x in region_stats]
region_ccs = {x['region']: x['cc'] for x in region_stats}
ax = plt.subplot(gs[:45, 65:])
boxplot_with_colors(region_ccs, group_names=regions, ax=ax, group_colors=['k'] * len(regions), box_alpha=0.7)
plt.xlabel('Region')
plt.ylabel('Linear Encoder Performance (cc)')
fname = os.path.join(get_this_dir(), 'encoder_perf.svg')
plt.savefig(fname, facecolor=fig.get_facecolor(), edgecolor='none')
