import numpy as np
from plotseq import plot_seq
Z = np.loadtxt('Z.txt')
plot_seq(Z, (2, 2), colormap='jet', filename="img/clustering_au30_area_2k")
else:
model = None
model = MPI_COMM.bcast(model, root=0)
scoreinds = score_dir(extractor, model, dir_path, limit=None, batch_size=200)
if MPI_RANK == 0:
labeler = SeqLabeler(seq_files)
else:
labeler = None
labeler = MPI_COMM.bcast(labeler, root=0)
scoreinds = relabel(labeler, scoreinds)
if MPI_RANK == 0:
Z = np.empty([NY, NX])
Z[:] = np.nan
for score, idx in scoreinds:
if score is not None:
ix, iy = idx2XY(idx, NX)
if ix < NY:
Z[ix, iy] = score
logging.debug('Z matrix has %d nans' % sum(1 for row in Z for z in row if np.isnan(z)))
np.savetxt('Z_au31.txt', Z)
logging.info('Write Z matrix into Z_au31.txt in ' + os.path.dirname(os.path.abspath(__file__)))
from plotseq import plot_seq
# # Z = np.loadtxt('Z.txt')
plot_seq(Z, step, colormap='jet', filename=scratch + "img/clustering_" + case_name)
