def simu_subtomo(op,
packing_op,
output,
save_tomo=0,
save_target=1,
save_tomo_slice=0):
"""
:param
op: parameter of convert pdb/ density map to tomogram
packing_op: parameter of packing process
output: path of output file
save_tomo: 1 save, 0 not save, if the large subtomogram.mrc of the whole packing scene(including 5-10
macromolecules) will be saved
save_target: 1 save, 0 not save, if the subtomogram.mrc of the target will be saved
save_tomo_slice: 1 save, 0 not save, if the sliced image will be saved.
:return:
target_simu_tomo = {'tomo': subtomogram of target macromolecule, .mrc file
'density_map': density map, .mrc file
'info': {'loc': coordinate of target macromolecule
'rotate': the rotation angle of this macromolecule (ZYZ, Euler angle)
'name': the name of a macromolecule}}
the json file of packing result and target will be saved in '../IOfile/json'
"""
from map_tomo import pdb2map as PM
from map_tomo import iomap as IM
from map_tomo import mrc2singlepic as MS
from map_tomo import merge_map as MM
from map_tomo import map2tomogram as MT
from packing_single_sphere import simulate as SI
# convert pdb to map
# ms = PM.pdb2map(op['map'])
# for n in ms:
# v = ms[n]
# IM.map2mrc(v, op['map']['map_single_path']+ '/{}.mrc'.format(n))
# read density map from mrc
# rootdir = op['map']['map_single_path']
# v = IM.readMrcMapDir(rootdir)
# print('read density map done')
v = op['v']
# get packing info
target_name = packing_op['target']
print("Name", target_name)
packing_result = SI.packing_with_target(packing_op)
print("Print", packing_result.keys())
protein_name = packing_result['optimal_result']['pdb_id']
x = packing_result['optimal_result']['x'] / 10
y = packing_result['optimal_result']['y'] / 10
z = packing_result['optimal_result']['z'] / 10
# print('initialization',packing_result['optimal_result']['initialization'])
x0 = np.array(packing_result['optimal_result']['initialization'][0]) / 10
y0 = np.array(packing_result['optimal_result']['initialization'][1]) / 10
z0 = np.array(packing_result['optimal_result']['initialization'][2]) / 10
box_size = packing_result['general_info']['box_size'] / 10
print('get packing info done')
# merge map to hugemap, save random angle in packing_result
initmap, init_angle_list = MM.merge_map(v, protein_name, np.int64(x0),
np.int64(y0), np.int64(z0),
np.int64(box_size))
packmap, pack_angle_list = MM.merge_map(v, protein_name, np.int64(x),
np.int64(y), np.int64(z),
np.int64(box_size))
packing_result['optimal_result']['initmap_rotate_angle'] = init_angle_list
packing_result['optimal_result']['packmap_rotate_angle'] = pack_angle_list
print('merge huge map done ')
# save packing info
with open(output['json']['pack'], 'w') as f:
json.dump(packing_result, f, cls=MM.NumpyEncoder)
print('save packing info')
# convert packmap to tomogram
tomo = MT.map2tomo(packmap, op['tomo'])
print('convert to tomo')
if save_tomo != 0:
# save init & pack map to mrc
IM.map2mrc(initmap, output['initmap']['mrc'])
IM.map2mrc(packmap, output['packmap']['mrc'])
# save init & pack map to png
IM.map2png(initmap, output['initmap']['png'])
IM.map2png(packmap, output['packmap']['png'])
# save pack tomo to mrc and png
IM.map2mrc(tomo, output['tomo']['mrc'])
IM.map2png(tomo, output['tomo']['png'])
# trim hugemap
# trim_initmap = MM.trim_margin(initmap)
# trim_packmap = MM.trim_margin(packmap)
# trim_tomo = MM.trim_margin(tomo)
# print('initmap shape',initmap.shape)
# print('trimmed shape',trim_initmap.shape)
# print('packmap shape',packmap.shape)
# print('trimmed shape',trim_packmap.shape)
# print('tomo shape',tomo.shape)
# print('trimmed shape',trim_tomo.shape)
# IM.map2mrc(trim_initmap, output['initmap']['trim'])
# IM.map2mrc(trim_packmap, output['packmap']['trim'])
# IM.map2mrc(trim_tomo, output['tomo']['trim'])
# trim target
i = protein_name.index(target_name)
# print('i',i)
target_packmap, loc_r = MM.trim_target(packmap,
np.array([x[i], y[i],
z[i]]), op['target_size'])
target_tomo, loc_r = MM.trim_target(tomo, np.array([x[i], y[i], z[i]]),
op['target_size'], loc_r)
if save_target != 0:
IM.map2mrc(target_packmap, output['packmap']['target']['mrc'])
IM.map2mrc(target_tomo, output['tomo']['target']['mrc'])
IM.map2png(target_packmap, output['packmap']['target']['png'])
IM.map2png(target_tomo, output['tomo']['target']['png'])
print('trim target done')
# save target info
target_info = dict()
target_info['loc'] = loc_r
target_info['rotate'] = pack_angle_list[i]
target_info['name'] = packing_op['target']
with open(output['json']['target'], 'w') as f:
json.dump(target_info, f, cls=MM.NumpyEncoder)
print('get target info done')
if save_tomo_slice != 0:
# convert packmap & tomo to separate pictures
MS.mrc2singlepic(output['packmap']['mrc'],
output['packmap']['png'] + 'packmap{}/'.format(num),
'packmap{}'.format(num))
MS.mrc2singlepic(output['tomo']['mrc'],
output['tomo']['png'] + 'tomo{}/'.format(num),
'tomo{}'.format(num))
target_simu_tomo = dict()
target_simu_tomo['tomo'] = target_tomo
target_simu_tomo['density_map'] = target_packmap
target_simu_tomo['info'] = target_info
print('all done')
return target_simu_tomo
def mknewdir(s1):
if not os.path.exists(s1):
os.makedirs(s1)
mknewdir('./IOfile/pdbfile/')
mknewdir('./IOfile/map_single')
mknewdir('./IOfile/json/')
mknewdir('./IOfile/packmap/target/mrc')
mknewdir('./IOfile/packmap/target/png')
mknewdir('./IOfile/tomo/target/mrc')
mknewdir('./IOfile/tomo/target/png')
# convert pdb file into single ball and get the center and radius of this ball.
boundary_shpere = P2B.pdb2ball_single(PDB_ori_path = 'IOfile/pdbfile/', show_log = 0)
packing_op = {#'target': '2byu',
'random_protein_number': 0,
'PDB_ori_path': 'IOfile/pdbfile/',
'iteration':5001,
'step':1,
'show_img': 0,
'show_log': 0,
'boundary_shpere': boundary_shpere
}
# convert pdb to map
import map_tomo.pdb2map as PM
import map_tomo.iomap as IM
ms = PM.pdb2map(op['map'])
for n in ms:
v = ms[n]
IM.map2mrc(v, op['map']['map_single_path']+ '/{}.mrc'.format(n))
print('convert pdb to map')
# read density map from mrc
rootdir = op['map']['map_single_path']
v = IM.readMrcMapDir(rootdir)
print('read density map done')
op['v'] = v
# generate subtomogram dataset
num = 0
for num in range(1000):
# '1w6t' '2bo9' '2gho' '3d2f' '1eqr' '1vrg' '3k7a' '1jgq' '1vpx'
# the above macromolecules may encounter some error
# set target macromolecule name
if num > 899:
packing_op['target'] = '3hhb'
elif num > 799:
packing_op['target'] = '2h12'
elif num > 699:
packing_op['target'] = '2ldb'
elif num > 599:
packing_op['target'] = '6t3e'
# else:
# pass
# # print(num, 'Pass')
elif num > 499:
packing_op['target'] = '4d4r'
elif num > 399:
packing_op['target'] = '3gl1'
elif num > 299:
packing_op['target'] = '2byu'
elif num > 199:
packing_op['target'] = '1yg6'
elif num > 99:
packing_op['target'] = '1f1b'
else:
packing_op['target'] = '1bxn'
if num >= 0:
print(num, packing_op['target'], 'start')
output = {
'initmap': {
'mrc': 'IOfile/initmap/mrc/initmap{}.mrc'.format(num),
'png': 'IOfile/initmap/png/initmap{}.png'.format(num),
'trim': 'Ofile/initmap/trim/initmap{}T.mrc'.format(num)},
'packmap': {
'mrc': 'IOfile/packmap/mrc/packmap{}.mrc'.format(num),
'png': 'IOfile/packmap/png/packmap{}.png'.format(num),
'trim': 'IOfile/packmap/trim/packmap{}T.mrc'.format(num),
'target': {
'mrc': 'IOfile/packmap/target/mrc/packtarget{}.mrc'.format(num),
'png': 'IOfile/packmap/target/png/packtarget{}.png'.format(num)}},
'tomo': {
'mrc': 'IOfile/tomo/mrc/tomo{}.mrc'.format(num),
'png': 'IOfile/tomo/png/tomo{}.png'.format(num),
'trim': 'IOfile/tomo/trim/tomo{}T.mrc'.format(num),
'target': {
'mrc': 'IOfile/tomo/target/mrc/tomotarget{}.mrc'.format(num),
'png': 'IOfile/tomo/target/png/tomotarget{}.png'.format(num)}},
'json': {
'pack': 'IOfile/json/packing{}.json'.format(num),
'target': 'IOfile/json/target{}.json'.format(num)}}
# do simulation
SS.simu_subtomo(op, packing_op, output, save_tomo=0, save_target=1, save_tomo_slice=0)
print(num, packing_op['target'], 'Done')
num = num + 1
print('all Done')
output = {
'packmap': {
'target': {
'mrc': 'IOfile/test/' + pdbid + '/map{}.mrc'.format(num),
'png': 'IOfile/test/' + pdbid + '/map{}.png'.format(num)
}
},
'tomo': {
'target': {
'mrc': 'IOfile/test/' + pdbid + '/tomo{}.mrc'.format(num),
'png': 'IOfile/test/' + pdbid + '/tomo{}.png'.format(num)
}
},
'json': {
'pack': 'IOfile/test/' + pdbid + '/packing{}.json'.format(num),
'target': 'IOfile/test/' + pdbid + '/target{}.json'.format(num)
}
}
import map_tomo.map2tomogram as MT
target_tomo = MT.map2tomo(target_packmap, op['tomo'])
print('convert to tomo')
import map_tomo.iomap as IM
IM.map2mrc(target_tomo, output['tomo']['target']['mrc'])
print('save tomo')
print('Done\n')
num = num + 1
print('all Done')
'random_protein_number': 4,
'PDB_ori_path': 'IOfile/pdbfile/',
'iteration': 5001,
'step': 1,
'show_img': 0,
'show_log': 0,
'boundary_shpere': boundary_shpere
}
# convert pdb to map
import map_tomo.pdb2map as PM
import map_tomo.iomap as IM
ms = PM.pdb2map(op['map'])
for n in ms:
v = ms[n]
IM.map2mrc(v, op['map']['map_single_path'] + '/{}.mrc'.format(n))
print('convert pdb to map')
# read density map from mrc
rootdir = op['map']['map_single_path']
v = IM.readMrcMapDir(rootdir)
print('read density map done')
op['v'] = v
# generate subtomogram dataset
num = 0
for num in range(1000):
# '1w6t' '2bo9' '2gho' '3d2f' '1eqr' '1vrg' '3k7a' '1jgq' '1vpx'
# the above macromolecules may encounter some error
# set target macromolecule name
'png': 'IOfile/tomo/target/png/tomotarget{}.png'.format(num)
}
},
'json': {
'pack': 'IOfile/json/packing{}.json'.format(num),
'target': 'IOfile/json/target{}.json'.format(num)
}
}
# convert pdb to map
import map_tomo.pdb2map as PM
import map_tomo.iomap as IM
ms = PM.pdb2map(op['map'])
for n in ms:
v = ms[n]
IM.map2mrc(v, 'IOfile/map_single/{}.mrc'.format(n))
# read density map from mrc
rootdir = '/ldap_shared/home/v_sinuo_liu/Simple-Packing/IOfile/map_single'
v = IM.readMrcMapDir(rootdir)
# get packing info
import packing_single_sphere.simulate as SI
target_name = '1bxn'
packing_result = SI.packing_with_target(target_protein=target_name,
random_protein_number=4,
PDB_ori_path=op['map']['pdb_dir'])
protein_name = packing_result['optimal_result']['pdb_id']
x = packing_result['optimal_result']['x'] / 10
y = packing_result['optimal_result']['y'] / 10
z = packing_result['optimal_result']['z'] / 10
def simu_subtomo(op,
packing_op,
output,
save_tomo=0,
save_target=1,
save_tomo_slice=0):
'''
:param op: parameter of convert pdb/ density map to tomogram
:param packing_op: parameter of packing process
:param output: path of output file
:param save_tomget: 1 save, 0 not save, if the subtomogram.mrc of the target will be saved
:param save_tomo_slice: 1 save, 0 not save, if the sliced image will be saved.
:return:
target_simu_tomo = { 'tomo' : subtomogram of target macromolecule, .mrc file
'density_map': density map, .mrc file
'info': { 'loc' : coordinate of target macromolecule
'rotate' : the rotation angle of this macromolecule (ZYZ, Euler angle)
'name' : the name of a macromolecule
}
}
the json file of packing result and target will be saved in '../IOfile/json'
'''
import datetime
starttime = datetime.datetime.now()
# # convert pdb to map
# # If you already have the map to each single macromolecule,
# # then change the op['map']['map_single_path'] to the path of the single density map.
# # If you need to generate map yourself, please uncomment line 89-99 and comment line 100.
# import map_tomo.pdb2map as PM
# ms = PM.pdb2map(op['map'])
# for n in ms:
# v = ms[n]
# IM.map2mrc(v, op['map']['map_single_path']+ '/{}.mrc'.format(n))
#
# # read density map from mrc
# rootdir = op['map']['map_single_path']
# v = IM.readMrcMapDir(rootdir)
# print('read density map done')
v = op['v']
# get packing info
import packing_single_sphere.simulate as SI
target_name = packing_op['target']
packing_result = SI.packing_with_target(packing_op)
protein_name = packing_result['optimal_result']['pdb_id']
x = packing_result['optimal_result']['x'] / 10
y = packing_result['optimal_result']['y'] / 10
z = packing_result['optimal_result']['z'] / 10
# print('initialization',packing_result['optimal_result']['initialization'])
x0 = np.array(packing_result['optimal_result']['initialization'][0]) / 10
y0 = np.array(packing_result['optimal_result']['initialization'][1]) / 10
z0 = np.array(packing_result['optimal_result']['initialization'][2]) / 10
box_size = packing_result['general_info']['box_size'] / 10
print('get packing info done')
# merge map to hugemap, save random angle in packing_result
import map_tomo.merge_map as MM
initmap, init_angle_list = MM.merge_map(v, protein_name, x0, y0, z0,
box_size)
packmap, pack_angle_list = MM.merge_map(v, protein_name, x, y, z, box_size)
packing_result['optimal_result']['initmap_rotate_angle'] = init_angle_list
packing_result['optimal_result']['packmap_rotate_angle'] = pack_angle_list
print('merge huge map done ')
# save packing info
with open(output['json']['pack'], 'w') as f:
json.dump(packing_result, f, cls=MM.NumpyEncoder)
print('save packing info done')
# convert packmap to tomogram
if save_tomo != 0:
# save init & pack map to mrc
IM.map2mrc(initmap, output['initmap']['mrc'])
IM.map2mrc(packmap, output['packmap']['mrc'])
# save init & pack map to png
IM.map2png(initmap, output['initmap']['png'])
IM.map2png(packmap, output['packmap']['png'])
#save pack tomo to mrc and png
IM.map2mrc(tomo, output['tomo']['mrc'])
IM.map2png(tomo, output['tomo']['png'])
# # trim hugemap: cut the volume without macromolecule
# trim_initmap = MM.trim_margin(initmap)
# trim_packmap = MM.trim_margin(packmap)
# trim_tomo = MM.trim_margin(tomo)
# print('initmap shape',initmap.shape)
# print('trimmed shape',trim_initmap.shape)
# print('packmap shape',packmap.shape)
# print('trimmed shape',trim_packmap.shape)
# print('tomo shape',tomo.shape)
# print('trimmed shape',trim_tomo.shape)
# IM.map2mrc(trim_initmap, output['initmap']['trim'])
# IM.map2mrc(trim_packmap, output['packmap']['trim'])
# IM.map2mrc(trim_tomo, output['tomo']['trim'])
# trim target
i = protein_name.index(target_name)
target_packmap, loc_r = MM.trim_target(packmap,
np.array([x[i], y[i],
z[i]]), op['target_size'])
print('trim target done')
# import map_tomo.map2tomogram as MT
# target_tomo = MT.map2tomo(target_packmap, op['tomo'])
# print('convert to tomo')
if save_target != 0:
IM.map2mrc(target_packmap, output['packmap']['target']['mrc'])
# IM.map2mrc(target_tomo, output['tomo']['target']['mrc'])
# IM.map2png(target_packmap, output['packmap']['target']['png'])
# IM.map2png(target_tomo, output['tomo']['target']['png'])
# save target info
target_info = {}
target_info['loc'] = loc_r
target_info['rotate'] = pack_angle_list[i]
target_info['name'] = packing_op['target']
target_info['SNR'] = op['tomo']['model']['SNR']
with open(output['json']['target'], 'w') as f:
json.dump(target_info, f, cls=MM.NumpyEncoder)
print('get target info done')
if save_tomo_slice != 0:
# convert packmap & tomo to separate pictures
import map_tomo.mrc2singlepic as MS
MS.mrc2singlepic(output['packmap']['mrc'],
output['packmap']['png'] + 'packmap{}/'.format(num),
'packmap{}'.format(num))
MS.mrc2singlepic(output['tomo']['mrc'],
output['tomo']['png'] + 'tomo{}/'.format(num),
'tomo{}'.format(num))
target_simu_tomo = {}
# target_simu_tomo['tomo'] = target_tomo
target_simu_tomo['density_map'] = target_packmap
target_simu_tomo['info'] = target_info
print('all done')
endtime = datetime.datetime.now()
print('simulation time:', (endtime - starttime).seconds, 'seconds')
return target_simu_tomo
def simu_subtomo(op,
packing_op,
output,
save_tomo=0,
save_target=1,
save_tomo_slice=0):
'''
:param op: parameter of convert pdb/ density map to tomogram
:param packing_op: parameter of packing process
:param output: path of output file
:param save_tomo: 1 save, 0 not save, if the large subtomogram.mrc of the whole packing scene(including 5-10 macromolecules) will be saved
:param save_target: 1 save, 0 not save, if the subtomogram.mrc of the target will be saved
:param save_tomo_slice: 1 save, 0 not save, if the sliced image will be saved.
:return:
target_simu_tomo = { 'tomo' : subtomogram of target macromolecule, .mrc file
'density_map': density map, .mrc file
'info': { 'loc' : coordinate of target macromolecule
'rotate' : the rotation angle of this macromolecule (ZYZ, Euler angle)
'name' : the name of a macromolecule
}
}
the json file of packing result and target will be saved in '../IOfile/json'
'''
# # convert pdb to map
import map_tomo.pdb2map as PM
import map_tomo.iomap as IM
# ms = PM.pdb2map(op['map'])
# for n in ms:
# v = ms[n]
# IM.map2mrc(v, op['map']['map_single_path']+ '/{}.mrc'.format(n))
# # read density map from mrc
# rootdir = op['map']['map_single_path']
# v = IM.readMrcMapDir(rootdir)
# print('read density map done')
v = op['v']
# get packing info
import packing_single_sphere.simulate as SI
target_name = packing_op['target']
packing_result = SI.packing_with_target(packing_op)
protein_name = packing_result['optimal_result']['pdb_id']
# x = packing_result['optimal_result']['x']/10
# y = packing_result['optimal_result']['y']/10
# z = packing_result['optimal_result']['z']/10
# print(x,y,z)
# print('initialization',packing_result['optimal_result']['initialization'])
# x0 = np.array(packing_result['optimal_result']['initialization'][0])/10
# y0 = np.array(packing_result['optimal_result']['initialization'][1])/10
# z0 = np.array(packing_result['optimal_result']['initialization'][2])/10
values = [-1, 1]
x0 = np.array(packing_result['optimal_result']['initialization'][0]) / 50
y0 = np.array(packing_result['optimal_result']['initialization'][1]) / 50
z0 = np.array(packing_result['optimal_result']['initialization'][2]) / 50
x0 = 150 + random.choice(values) * x0
y0 = 150 + random.choice(values) * y0
z0 = 150 + random.choice(values) * z0
x = np.array(x0)
y = np.array(y0)
z = np.array(z0)
box_size = packing_result['general_info']['box_size'] / 10
packing_result['general_info']['box_size'] = 3000
box_size = 300
# print('[x0,y0,z0]:',x0,y0,z0)
print('box_size:', box_size)
print('get packing info done')
# merge map to hugemap, save random angle in packing_result
import map_tomo.merge_map as MM
initmap, init_angle_list = MM.merge_map(v, protein_name, x0, y0, z0,
box_size)
# packmap,pack_angle_list = MM.merge_map(v, protein_name, x, y, z, box_size)
packmap, pack_angle_list = initmap, init_angle_list
packing_result['optimal_result']['initmap_rotate_angle'] = init_angle_list
packing_result['optimal_result']['packmap_rotate_angle'] = pack_angle_list
print('merge huge map done ')
# save packing info
with open(output['json']['pack'], 'w') as f:
json.dump(packing_result, f, cls=MM.NumpyEncoder)
print('save packing info')
# convert packmap to tomogram
import map_tomo.map2tomogram as MT
tomo = MT.map2tomo(packmap, op['tomo'])
print('convert to tomo')
if save_tomo != 0:
# save init & pack map to mrc
IM.map2mrc(initmap, output['initmap']['mrc'])
IM.map2mrc(packmap, output['packmap']['mrc'])
# save init & pack map to png
IM.map2png(initmap, output['initmap']['png'])
IM.map2png(packmap, output['packmap']['png'])
#save pack tomo to mrc and png
IM.map2mrc(tomo, output['tomo']['mrc'])
IM.map2png(tomo, output['tomo']['png'])
# # trim hugemap
# trim_initmap = MM.trim_margin(initmap)
# trim_packmap = MM.trim_margin(packmap)
# trim_tomo = MM.trim_margin(tomo)
# print('initmap shape',initmap.shape)
# print('trimmed shape',trim_initmap.shape)
# print('packmap shape',packmap.shape)
# print('trimmed shape',trim_packmap.shape)
# print('tomo shape',tomo.shape)
# print('trimmed shape',trim_tomo.shape)
# IM.map2mrc(trim_initmap, output['initmap']['trim'])
# IM.map2mrc(trim_packmap, output['packmap']['trim'])
# IM.map2mrc(trim_tomo, output['tomo']['trim'])
# trim target
i = protein_name.index(target_name)
print('i:', i)
print('x:', x)
print('x[i]:', x[i])
target_packmap, loc_r = MM.trim_target(packmap,
np.array([x[i], y[i],
z[i]]), op['target_size'])
target_tomo, loc_r = MM.trim_target(tomo, np.array([x[i], y[i], z[i]]),
op['target_size'], loc_r)
if save_target != 0:
IM.map2mrc(target_packmap, output['packmap']['target']['mrc'])
IM.map2mrc(target_tomo, output['tomo']['target']['mrc'])
IM.map2png(target_packmap, output['packmap']['target']['png'])
IM.map2png(target_tomo, output['tomo']['target']['png'])
print('trim target done')
# save target info
target_info = {}
target_info['loc'] = loc_r
target_info['rotate'] = pack_angle_list[i]
target_info['name'] = packing_op['target']
with open(output['json']['target'], 'w') as f:
json.dump(target_info, f, cls=MM.NumpyEncoder)
print('get target info done')
if save_tomo_slice != 0:
# convert packmap & tomo to separate pictures
import map_tomo.mrc2singlepic as MS
MS.mrc2singlepic(output['packmap']['mrc'],
output['packmap']['png'] + 'packmap{}/'.format(num),
'packmap{}'.format(num))
MS.mrc2singlepic(output['tomo']['mrc'],
output['tomo']['png'] + 'tomo{}/'.format(num),
'tomo{}'.format(num))
target_simu_tomo = {}
target_simu_tomo['tomo'] = target_tomo
target_simu_tomo['density_map'] = target_packmap
target_simu_tomo['info'] = target_info
print('all done')
return target_simu_tomo
