def draw_sparse_matrix(
array_filename,
output_image,
vmax=DEFAULT_SATURATION_THRESHOLD,
max_size_matrix=DEFAULT_MAX_SIZE_MATRIX,
):
"""Draw a quick preview of a sparse matrix with automated
binning and normalization.
"""
matrix = np.loadtxt(array_filename, dtype=np.int32, skiprows=1)
try:
row, col, data = matrix.T
except ValueError:
row, col, data = matrix
size = max(np.amax(row), np.amax(col)) + 1
S = sparse.coo_matrix((data, (row, col)), shape=(size, size))
if max_size_matrix <= 0:
binning = 1
else:
binning = (size // max_size_matrix) + 1
binned_S = hcs.bin_sparse(S, subsampling_factor=binning)
dense_S = binned_S.todense()
dense_S = dense_S + dense_S.T - np.diag(np.diag(dense_S))
normed_S = hcs.normalize_dense(dense_S)
spaceless_pdf_plot_maker(normed_S, output_image, vmax=vmax)
def draw(subnetwork, filename):
try:
# Numpy array format
row = subnetwork[:, 0]
col = subnetwork[:, 1]
data = subnetwork[:, 2]
except TypeError:
# Scipy sparse format
row = subnetwork.row
col = subnetwork.col
data = subnetwork.data
row_indices = stats.rankdata(
np.concatenate((row, col)), method="dense"
)
col_indices = stats.rankdata(
np.concatenate((col, row)), method="dense"
)
data = np.concatenate((data, data))
# print("Row length: {}, col length: {}, data length: {}"
# "".format(len(row_indices), len(col_indices), len(data)))
unique_row = np.unique(row)
unique_col = np.unique(col)
# print("Network shape: {},{}".format(len(unique_row),
# len(unique_col)))
size = len(np.unique(np.concatenate((unique_row, unique_col)))) + 1
# print("Size of matrix to draw: {}".format(size))
try:
sparse_subnet = sparse.coo_matrix(
(data, (row_indices, col_indices)), shape=(size, size)
)
binning_factor = (size // max_size_matrix) + 1
binned_subnet = hcs.bin_sparse(
sparse_subnet, subsampling_factor=binning_factor
)
dense_subnet = binned_subnet.todense()
diagonal = np.diag(np.diag(dense_subnet))
normed_subnet = hcs.normalize_dense(dense_subnet - diagonal)
vmax = np.percentile(normed_subnet, saturation_threshold)
spaceless_pdf_plot_maker(normed_subnet, filename, vmax=vmax)
except MemoryError:
logger.warning(
"Warning, couldn't save matrix due to memory issues"
)