def draw_encoder_perfs(agg):
freqs,lags = get_freqs_and_lags()
electrodes = [ROSTRAL_CAUDAL_ELECTRODES_LEFT,
ROSTRAL_CAUDAL_ELECTRODES_RIGHT,
ROSTRAL_CAUDAL_ELECTRODES_LEFT]
sites = [('GreBlu9508M', 'Site4', 'Call1', 'L'),
('WhiWhi4522M', 'Site2', 'Call2', 'R'),
('YelBlu6903F', 'Site3', 'Call3', 'L')
]
weights = list()
for bird,block,segment,hemi in sites:
i = (agg.df.bird == bird) & (agg.df.block == block) & (agg.df.segment == segment) & (agg.df.hemi == hemi)
ii = i & (agg.df.decomp == 'self_locked')
assert ii.sum() == 1
wkey = agg.df[ii]['wkey'].values[0]
lfp_eperf = agg.encoder_perfs[wkey]
lfp_decoder_weights = agg.decoder_weights[wkey]
print 'lfp_decoder_weights.shape=',lfp_decoder_weights.shape
lfp_sal_weights = lfp_decoder_weights[:, :, REDUCED_ACOUSTIC_PROPS.index('sal')]
lfp_q2_weights = lfp_decoder_weights[:, :, REDUCED_ACOUSTIC_PROPS.index('q2')]
weights.append( (lfp_eperf, lfp_sal_weights, lfp_q2_weights) )
figsize = (24, 13)
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(top=0.95, bottom=0.02, right=0.97, left=0.03, hspace=0.25, wspace=0.25)
nrows = 3
ncols = 3
for k,(lfp_eperf,lfp_sal_weights,lfp_q2_weights) in enumerate(weights):
index2electrode = electrodes[k]
ax = plt.subplot(nrows, ncols, k*ncols + 1)
plt.imshow(lfp_eperf, interpolation='nearest', aspect='auto', vmin=0, cmap=magma)
plt.yticks(range(len(index2electrode)), ['%d' % e for e in index2electrode])
plt.xticks(range(len(freqs)), ['%d' % f for f in freqs], rotation=45)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Electrode')
plt.colorbar(label='Encoder R2')
ax = plt.subplot(nrows, ncols, k*ncols + 2)
absmax = np.abs(lfp_sal_weights).max()
plt.imshow(lfp_sal_weights, interpolation='nearest', aspect='auto', vmin=-absmax, vmax=absmax, cmap=plt.cm.seismic)
plt.yticks(range(len(index2electrode)), ['%d' % e for e in index2electrode])
plt.xticks(range(len(freqs)), ['%d' % f for f in freqs], rotation=45)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Electrode')
plt.colorbar(label='Decoder Weight')
ax = plt.subplot(nrows, ncols, k*ncols + 3)
absmax = np.abs(lfp_sal_weights).max()
plt.imshow(lfp_q2_weights, interpolation='nearest', aspect='auto', vmin=-absmax, vmax=absmax, cmap=plt.cm.seismic)
plt.yticks(range(len(index2electrode)), ['%d' % e for e in index2electrode])
plt.xticks(range(len(freqs)), ['%d' % f for f in freqs], rotation=45)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Electrode')
plt.colorbar(label='Decoder Weight')
fname = os.path.join(get_this_dir(), 'encoder_weights.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
def build_graph(bird='GreBlu9508M', block='Site4', segment='Call1', hemi='L', aprop='q2'):
# map electrodes to positions
df = pd.read_csv('/auto/tdrive/mschachter/data/aggregate/electrode_data+dist.csv')
i = (df.bird == bird) & (df.block == block) & (df.hemisphere == hemi)
g = df[i].groupby('electrode')
electrode_props = dict()
for e,gdf in g:
assert len(gdf) == 1
reg = str(gdf.region.values[0])
if reg.startswith('CM'):
reg = 'CM'
elif 'NCM' in reg:
reg = 'NCM'
elif reg in ['L', 'L2A', 'L2B', 'L2', 'L1', 'L3']:
reg = 'L'
electrode_props[e] = {'dist_midline':float(gdf.dist_midline.values[0]),
'dist_l2a':float(gdf.dist_l2a.values[0]),
'region':reg
}
#TODO get the lags in a less hacky way
data_dir = '/auto/tdrive/mschachter/data'
pfile = os.path.join(data_dir, 'GreBlu9508M', 'transforms', 'PairwiseCF_GreBlu9508M_Site4_Call1_L_raw.h5')
hf = h5py.File(pfile, 'r')
lags_ms = hf.attrs['lags']
hf.close()
lags_absmax = 6.
lags_i = np.abs(lags_ms) < lags_absmax
lags_short = lags_ms[lags_i]
# read the aggregator file
agg_file = os.path.join(data_dir, 'aggregate', 'pard.h5')
agg = AcousticEncoderDecoderAggregator.load(agg_file)
i = (agg.df.decomp == 'self+cross_locked_lim_-%d_%d' % (int(lags_absmax), int(lags_absmax))) & (agg.df.bird == bird) & (agg.df.block == block) & \
(agg.df.segment == segment) & (agg.df.hemi == hemi)
print 'i.sum()=%d' % i.sum()
assert i.sum() == 1, 'i.sum()=%d' % i.sum()
# get decoder weights
electrode_order = ROSTRAL_CAUDAL_ELECTRODES_RIGHT
if hemi == 'L':
electrode_order = ROSTRAL_CAUDAL_ELECTRODES_LEFT
electrode_order = [int(x) for x in electrode_order]
# get segment/decomp props
wkey = agg.df[i].wkey.values[0]
iindex = agg.df[i].iindex.values[0]
# get decoder weights
decoder_weights = agg.decoder_weights[wkey]
W = decoder_weights[:, :, :, REDUCED_ACOUSTIC_PROPS.index(aprop)]
# create a directed graph
g = nx.DiGraph()
# add a node for each electrode
for e in electrode_order:
g.add_node(e, dist_midline=electrode_props[e]['dist_midline'], dist_l2a=electrode_props[e]['dist_l2a'],
region=electrode_props[e]['region'], electrode=e)
# connect up the nodes
for k,e1 in enumerate(electrode_order):
for j in range(k):
e2 = electrode_order[j]
# get the coherency function and reduce it to a left and right side
cf = W[k, j, :]
assert len(cf) == len(lags_short)
cleft = np.sum(cf[lags_short <= 0]) # goes from e2 to e1
cright = np.sum(cf[lags_short >= 0]) # goes from e1 to e2
# add edges to the graph
g.add_edge(e2, e1, weight=float((cleft)))
g.add_edge(e1, e2, weight=float((cright)))
# plot the edge weights
ewts = np.array([g.edge[e1][e2]['weight'] for e1,e2 in g.edges()])
plt.figure()
plt.hist(ewts, bins=20, color='c')
plt.xlabel('Edge Weight')
plt.show()
# threshold out the edges with low weights
wthresh = 0.3
for e1,e2 in [(e1,e2) for e1,e2 in g.edges() if abs(g.edge[e1][e2]['weight']) < wthresh]:
g.remove_edge(e1, e2)
# write the graph to a file
nx.write_gexf(g, '/tmp/%s_graph.gexf' % aprop)
