def linear_save(volumes, dir_name, frames, niter, reg, rate, subregion='all', step = 1, model_id = None) :
v1 = volumes[0]
v2 = volumes[1]
m1 = v1.matrix(step = step, subregion = subregion)
m2 = v2.matrix(step = step, subregion = subregion)
from .utils import convolutional_barycenter, convolutional_barycenter_cpu, convolutional_barycenter_gpu
import mrcfile
import progressbar
rate_function = RateMap[rate.lower()]
all_weights = rate_function(frames)
all_weights = [(round(i,6),round(1-i,6)) for i in all_weights]
for i,weights in enumerate(all_weights):
if i%5 == 0 :
print('(%i of %i)'%(i,frames))
padding = len(str(frames))+1
str_i = str(i)
padded_i = str_i.zfill(padding)
result_file = dir_name + '/' + padded_i + 'linear_%s_%s_weights%s.mrc'%(v1.name,v2.name, str(weights))
import time
t0 = time.time()
m = weights[0]*m1+weights[1]*m2
print(time.time()-t0)
from chimerax.map.data import ArrayGridData
from chimerax.map.data.mrc import save
m_grid = ArrayGridData(m)
save(m_grid, result_file)
def ot_save(volumes,
dir_name,
frames,
niter,
reg,
rate,
subregion='all',
step=1,
model_id=None,
name1=None,
name2=None):
v1 = volumes[0]
v2 = volumes[1]
m1 = v1.matrix(step=step, subregion=subregion)
m2 = v2.matrix(step=step, subregion=subregion)
from .utils import convolutional_barycenter, convolutional_barycenter_cpu, convolutional_barycenter_gpu
import mrcfile
import progressbar
rate_function = RateMap[rate.lower()]
all_weights = rate_function(frames)
all_weights = [(round(i, 6), round(1 - i, 6)) for i in all_weights]
for i, weights in enumerate(all_weights):
if i % 5 == 0:
print('(%i of %i)' % (i, frames))
padding = len(str(frames)) + 1
str_i = str(i)
padded_i = str_i.zfill(padding)
if name1 == None:
name1 = v1.name
if name2 == None:
name2 = v2.name
#result_file = dir_name + '/' +padded_i +'ot_%s_%s_weights%s.mrc'%(v1.name,v2.name, str(weights))
result_file = dir_name + '/' + padded_i + 'ot_%s_%s_weights%s.mrc' % (
name1, name2, str(weights))
#result_file = dir_name + '/ot_%s_%s_weights%s.mrc'%(v1.name,v2.name, str(weights))
m = convolutional_barycenter([m1, m2],
reg,
weights,
niter=niter,
verbose=False,
cupy_var=cupy_var,
filter_func=filter_func)
from chimerax.map.data import ArrayGridData
from chimerax.map.data.mrc import save
m_grid = ArrayGridData(m)
save(m_grid, result_file)
