def load_histogram(self, infn_hist):
loader = Loader()
shape, ticks, hist_arr = loader.load_histogram(infn_hist)
self._hcel = hist_arr[:, :-1]
self._count = hist_arr[:, -1]
n_cel, n_dim = self._hcel.shape
if n_dim != self.mode:
raise ValueError(
"Input histogram dimension does match with the initial dimension."
)
self._total = n_cel
self.shape = shape
def describe_view(ins_hist, ins_desc, desc_voc, xlab, ylab, outs):
assert desc_voc in VALID_DESCVOCS
loader = Loader()
desc = DTMNormDescribe if desc_voc == 'dtmnorm' else GaussianDescribe
desc_parms = loader.load_describes_parms(ins_desc, desc, mode=2)
h_shape, ticks_vec, hist_arr = loader.load_histogram(ins_hist)
csr_mat = csr_matrix((hist_arr[:, -1], (hist_arr[:, 0], hist_arr[:, 1])),
shape=h_shape,
dtype=int)
# plot_heatmap(ticks_vec[1], ticks_vec[0], csr_mat.toarray(), xlabel=xlabel, ylabel=ylabel, outfn=outfn)
plot_heatmap_ellipse_covs(ticks_vec[1],
ticks_vec[0],
csr_mat.toarray(),
desc_parms,
base=10,
scales=(1.5, 3),
xlabel=xlab,
ylabel=ylab,
outfn=outs)
def load_hcel_weights(in_hist,
in_hcel2avgfeat,
mode=2,
wtcol_index=1,
sep=','):
loader = Loader()
_, _, hist_arr = loader.load_histogram(in_hist)
nhcubes = len(hist_arr)
hcube2index = dict(zip(map(tuple, hist_arr[:, :2]), range(nhcubes)))
hcube_weight = np.zeros(nhcubes) #np.empty((nhcubes, nfeat))
with open(in_hcel2avgfeat, 'r') as fp:
for line in fp.readlines():
if line.startswith('#'): continue
tok = line.strip().split(sep)
pos = tuple(map(int, tok[:mode]))
hcube_weight[hcube2index[pos]] = float(tok[wtcol_index + mode - 1])
fp.close()
return hcube_weight