def filter_all(data_dir):
"""Removes STORM localizations from neighbouring cells and removes cell objects with too few for all conditions."""
gt_cells = load(
os.path.join(data_dir, 'cell_obj', 'cells_final_selected.hdf5'))
for ph in [10000, 1000, 500]:
print('Photons', ph)
with open(
os.path.join(data_dir, 'matched_names',
'm_cells_ph_{}_match.txt'.format(ph)), 'r') as f:
m_names = f.readlines()
m_names = list([n.rstrip() for n in m_names])
m_cells = load(
os.path.join(data_dir, 'cell_obj',
'cell_ph_{}_raw.hdf5'.format(ph)))
with open(
os.path.join(data_dir, 'matched_names',
'gt_cells_ph_{}_match.txt'.format(ph)), 'r') as f:
gt_names = f.readlines()
gt_names = list([n.rstrip() for n in gt_names])
m_final, gt_final, m_cells, gt_cells = filter_cells(
m_names, gt_names, m_cells, gt_cells)
with open(
os.path.join(data_dir, 'matched_names',
'gt_cells_ph_{}_match_filter.txt'.format(ph)),
'w') as f:
f.writelines(gt_final)
with open(
os.path.join(data_dir, 'matched_names',
'm_cells_ph_{}_match_filter.txt'.format(ph)),
'w') as f:
f.writelines(m_final)
for i, (m_, gt_) in tqdm(enumerate(zip(m_final, gt_final))):
m_i = m_cells.name.tolist().index(m_.rstrip())
g_i = gt_cells.name.tolist().index(gt_.rstrip())
try:
assert len(m_cells[m_i].data.data_dict['storm_inner']) == len(
gt_cells[g_i].data.data_dict['storm_inner'])
except AssertionError:
print('Assertion error:', i)
save(
os.path.join(data_dir, 'cell_obj',
'cell_ph_{}_filtered.hdf5'.format(ph)), m_cells)
def gen_cells(data_dir):
storm_i = np.load(os.path.join(data_dir, 'images', 'storm_inner.npy'))
storm_o = np.load(os.path.join(data_dir, 'images', 'storm_outer.npy'))
foci_i = np.load(os.path.join(data_dir, 'images', 'foci_inner.npy'))
foci_o = np.load(os.path.join(data_dir, 'images', 'foci_outer.npy'))
for ph in [10000, 1000, 500]:
print('Photons {}'.format(ph))
bin_predicted = tifffile.imread(
os.path.join(data_dir, 'images',
'binary_{}photons_predicted.tif'.format(ph)))
bf = np.load(
os.path.join(data_dir, 'images',
'bf_noise_{}_photons.npy'.format(ph)))
print('Filtering')
filtered_pred = filter_binaries(bin_predicted,
min_size=495,
max_size=2006.4,
min_minor=7.57,
max_minor=17.3,
min_major=15.41,
max_major=54.97)
data = Data()
data.add_data(filtered_pred, 'binary')
data.add_data(bf, 'brightfield')
data.add_data(storm_i, 'storm', 'storm_inner')
data.add_data(storm_o, 'storm', 'storm_outer')
data.add_data(foci_i, 'fluorescence', 'foci_inner')
data.add_data(foci_o, 'fluorescence', 'foci_outer')
print('Making cells')
m_cells = data_to_cells(data,
remove_multiple_cells=False,
remove_bordering=False)
print('Saving')
save(
os.path.join(data_dir, 'cell_obj',
'cell_ph_{}_raw.hdf5'.format(ph)), m_cells)
def optimize_all(data_dir):
"""Optimize the cell's coordinate systems for each condition based on different data elements"""
for ph in [10000, 1000, 500]:
print('Photons {}'.format(ph))
m_cells = load(
os.path.join(data_dir, 'cell_obj',
'cell_ph_{}_filtered.hdf5'.format(ph)))
print('Measured cells loaded')
print('binary')
optimize_cells = m_cells.copy()
res = optimize_cells.optimize_mp()
obj_vals = [r.objective_value for r in res]
np.savetxt(
os.path.join(data_dir, 'minimize_res',
'm_cells_ph_{}_binary.txt'.format(ph)), obj_vals)
save(
os.path.join(data_dir, 'cell_obj',
'm_cells_ph_{}_filtered_binary.hdf5'.format(ph)),
optimize_cells)
print('brightfield')
optimize_cells = m_cells.copy()
res = optimize_cells.optimize_mp('brightfield')
obj_vals = [r.objective_value for r in res]
np.savetxt(
os.path.join(data_dir, 'minimize_res',
'm_cells_ph_{}_brightfield.txt'.format(ph)), obj_vals)
save(
os.path.join(data_dir, 'cell_obj',
'm_cells_ph_{}_filtered_brightfield.hdf5'.format(ph)),
optimize_cells)
print('storm inner')
optimize_cells = m_cells.copy()
res = optimize_cells.optimize_mp('storm_inner')
obj_vals = [r.objective_value for r in res]
np.savetxt(
os.path.join(data_dir, 'minimize_res',
'm_cells_ph_{}_storm.txt'.format(ph)), obj_vals)
save(
os.path.join(data_dir, 'cell_obj',
'm_cells_ph_{}_filtered_storm_inner.hdf5'.format(ph)),
optimize_cells)
if reload:
for ph in photons:
im = np.load(
os.path.join(data_dir, 'images',
'bf_noise_{}_photons.npy'.format(ph)))
np.save(
os.path.join(data_dir, 'plot_vars',
'bf_noise_{}_photons.npy'.format(ph)), im[0])
for condition in conditions:
cells = load(
os.path.join(
data_dir, 'cell_obj',
'm_cells_ph_{}_filtered_{}.hdf5'.format(ph, condition)))
save(
os.path.join(data_dir, 'plot_vars',
'cells_{}_{}_photons.hdf5'.format(condition, ph)),
cells[:50])
cell_dict = {}
for ph in photons:
cell_dict[ph] = {}
for condition in conditions:
cell_dict[ph][condition] = load(
os.path.join(data_dir, 'plot_vars',
'cells_{}_{}_photons.hdf5'.format(condition, ph)))
imgs = {
ph: np.load(
os.path.join(data_dir, 'plot_vars',
'bf_noise_{}_photons.npy'.format(ph)))
for ph in photons
def measure_r(file_path):
bf_rdist = np.loadtxt(file_path)
x, y = bf_rdist.T
mid_val = (np.min(y) + np.max(y)) / 2
imin = np.argmin(y)
imax = np.argmax(y)
y_select = y[imin:imax] if imax > imin else y[imax:imin][::-1]
x_select = x[imin:imax] if imax > imin else x[imax:imin][::-1]
try:
assert np.all(np.diff(y_select) > 0)
except AssertionError:
print('Radial distribution not monotonically increasing')
r = np.interp(mid_val, y_select, x_select)
return r
if __name__ == '__main__':
np.random.seed(42)
cell_list = gen_synthcells(25000)
# Remove cells with incorrect amount of data elements (missing STORM data)
b = np.array([len(cell.data.names) == 6 for cell in cell_list])
data_dir = r'.'
if not os.path.exists(os.path.join(data_dir, 'cell_obj')):
os.mkdir(os.path.join(data_dir, 'cell_obj'))
save(os.path.join(data_dir, 'cell_obj', 'cells_final_selected.hdf5'), cell_list[b])
import matplotlib.pyplot as plt
from colicoords import Cell, load, save, CellPlot
import os
cell = load(r'img191c002.hdf5')
data = cell.data.copy()
cell_raw = Cell(data[:, 1:-1])
reload = False
if reload:
cell_bin = cell_raw.copy()
cell_bin.optimize()
save('cell_bin.hdf5', cell_bin)
cell_bf = cell_raw.copy()
cell_bf.optimize('brightfield')
cell_bf.measure_r()
save('cell_bf.hdf5', cell_bf)
cell_flu = cell_raw.copy()
cell_flu.optimize('gain50')
cell_flu.measure_r()
save('cell_flu.hdf5', cell_flu)
else:
cell_bin = load('cell_bin.hdf5')
cell_bf = load('cell_bf.hdf5')
cell_flu = load('cell_flu.hdf5')
fig_width = 8.53534 / 2.54
fig, axes = plt.subplots(3, 3, figsize=(fig_width, fig_width))