def map_contour(m, t=-1.):
mName = os.path.basename(m).split('.')[0]
#print 'reading map'
emmap = MapParser.readMRC(m)
c1 = None
if t != -1.0:
zeropeak, ave, sigma1 = emmap._peak_density()
if not zeropeak is None: c1 = zeropeak + (t * sigma1)
else:
c1 = 0.0
return mName, emmap, c1
def test_tempy_sccc(self):
''' Test the tempy sccc score based on the files
provided. Use this as a baseline for the second
chimeraX test. '''
# the sigma factor determines the width of the Gaussian distribution used to describe each atom
sim_sigma_coeff = 0.187
path_test = "./"
m = os.path.join(path_test, '1akeA_10A.mrc')
p = os.path.join(path_test, '1ake_mdl1.pdb')
r = 10.0
rb_file = os.path.join(path_test, '1ake_mdl1_rigid.txt')
scorer = ScoringFunctions()
# read map file
emmap = MapParser.readMRC(m)
# read PDB file
structure_instance = PDBParser.read_PDB_file('pdbfile',
p,
hetatm=False,
water=False)
SCCC_list_structure_instance = []
# read rigid body file and generate structure instances for each segment
listRB = RBParser.read_FlexEM_RIBFIND_files(rb_file,
structure_instance)
# score each rigid body segment
listsc_sccc = []
for RB in listRB:
# sccc score
score_SCCC = scorer.SCCC(emmap, r, sim_sigma_coeff,
structure_instance, RB)
listsc_sccc.append(score_SCCC)
self.assertTrue(len(listRB) == 6)
self.assertTrue(abs(round(listsc_sccc[0], 4) - 0.954) < 0.01)
self.assertTrue(abs(round(listsc_sccc[1], 4) - 0.427) < 0.01)
self.assertTrue(abs(round(listsc_sccc[2], 4) - 0.624) < 0.01)
self.assertTrue(abs(round(listsc_sccc[3], 4) - 0.838) < 0.01)
self.assertTrue(abs(round(listsc_sccc[4], 4) - 0.971) < 0.01)
self.assertTrue(abs(round(listsc_sccc[5], 4) - 0.928) < 0.01)
def _ccc(self, mapname, modelname, res):
path_test = "./"
m = os.path.join(path_test, mapname)
emmap1 = MapParser.readMRC(m)
p = os.path.join(path_test, modelname)
structure_instance = PDBParser.read_PDB_file('pdbfile',
p,
hetatm=False,
water=False)
blurrer = StructureBlurrer()
t = 1.5
c1 = None
c2 = None
#calculate map contour
zeropeak, ave, sigma1 = emmap1._peak_density()
if not zeropeak is None: c1 = zeropeak + (t * sigma1)
mt = 0.1
if res > 20.0: mt = 2.0
elif res > 10.0: mt = 1.0
elif res > 6.0: mt = 0.5
#emmap2 = blurrer.gaussian_blur(structure_instance, res, densMap=emmap1)
emmap2 = blurrer.gaussian_blur_real_space(structure_instance,
res,
sigma_coeff=0.187,
densMap=emmap1,
normalise=True)
# calculate model contour - emmap1 apparently?
c2 = mt * emmap2.std()
sc = ScoringFunctions()
_, ovr = sc.CCC_map(emmap1, emmap2, c1, c2, 3, cmode=False)
ccc, _ = sc.CCC_map(emmap1, emmap2, c1, c2, cmode=False)
print("Printing CCC", ccc, ovr, c1, c2)
return (ccc, ovr)
def generate_csv_file(workingDir):
emDirectory = workingDir+"/simulated/EM"
numFiles = numOfFilesSubdir(emDirectory)
currFileNum = 0
#generate CSV file of training set
print 'Generate CSV file of features...'
csv_file_path = workingDir+"/simulated/training_set.csv"
with open(csv_file_path, "w") as csvFile:
csvFileWriter = csv.writer(csvFile, delimiter=',',quotechar='"', quoting=csv.QUOTE_MINIMAL)
for aaDirName in os.listdir(emDirectory):
if aaDirName in aAList:
emAaDir = "{0}/{1}".format(emDirectory, aaDirName)
for emfileName in os.listdir(emAaDir):
emFilePath = "{0}/{1}".format(emAaDir,emfileName)
emMap = MapParser.readMRC(emFilePath)
csvFileWriter.writerow([aaDirName] + features(emMap))
currFileNum += 1
sys.stdout.write('\r{:4}/{:4} ({:5.4}%), current file: {}'.format(currFileNum, numFiles, currFileNum*100./numFiles, emFilePath))# comma to suppress the newline
sys.stdout.flush()
print '\n',
def findMaxEMMapDimensions(workingDir):
"""
Find the maximal EM map dimensions
:param workingDir:
:return:
"""
print 'Finding the maximal EM map dimensions...'
emDirectory = workingDir+"/simulated/EM"
maxEmMapSize = [0,0,0]
for aaDirName in os.listdir(emDirectory):
if aaDirName in aAList:
emAaDir = "{0}/{1}".format(emDirectory, aaDirName)
for emfileName in os.listdir(emAaDir):
emFilePath = "{0}/{1}".format(emAaDir,emfileName)
emMap=MapParser.readMRC(emFilePath)
emMapSize = emMap.box_size()
if emMapSize[0]>maxEmMapSize[0]:
maxEmMapSize[0] = emMapSize[0]
if emMapSize[1]>maxEmMapSize[1]:
maxEmMapSize[1] = emMapSize[1]
if emMapSize[2]>maxEmMapSize[2]:
maxEmMapSize[2] = emMapSize[2]
return maxEmMapSize
#emmap = blurrer.gaussian_blur(structure_instance, res,densMap=emmap_1,normalise=True)
modelmap = blurrer.gaussian_blur_real_space(structure_instance, res,sigma_coeff=0.187,densMap=emmap,normalise=True)
return pName,modelmap, structure_instance
#GET INPUT DATA
output_synthetic_map = False
if flag_example:
m1 = os.path.join(path_example,'emd_1046.map')
m2 = os.path.join(path_example,'emd_1047_resampled_1046.mrc')
r1 = 23.5
r2 = 14.5
Name1 = os.path.basename(m1).split('.')[0]
Name2 = os.path.basename(m2).split('.')[0]
c1 = 0.0607
c2 = 0.0597
emmap1=MapParser.readMRC(m1)
emmap2=MapParser.readMRC(m2)
emmap1.fullMap = (emmap1.fullMap-emmap1.mean())#emmap1.mean())/emmap1.std()
flag_filt = False
sw = 0.001
elif all(x is None for x in [m,m1,m2]):
# for 2 models
if None in [p1,p2]:
sys.exit('Input two maps or a map and model, map resolution(s) (required) and contours (optional)')
r1 = r2 = r = 4.0
Name1,emmap1,c1,p1inst = model_contour(p1,res=4.0,emmap=False,t=0.1)
if c2 is None: Name2,emmap2,c2,p2inst = model_contour(p2,res=r,emmap=False,t=0.1)
else: p2Name,emmap2 = blur_model(p2,res=r,emmap=False)
flag_filt = False
flag_scale = False
elif None in [m1,m2]:
path_out='Test_Files'
if os.path.exists(path_out)==True:
print "%s exists" %path_out
else:
os.mkdir(path_out)
os.chdir(path_out)
structure_instance=PDBParser.read_PDB_file('1J6Z','1J6Z.pdb',hetatm=False,water=False)
print structure_instance
blurrer = StructureBlurrer()
EnsembleGeneration=EnsembleGeneration()
scorer = ScoringFunctions()
map_target=MapParser.readMRC('emd_5168_monomer.mrc') #read target map
map_probe = blurrer.gaussian_blur(structure_instance, 6.6,densMap=map_target)#create a simulated map from the structure instance
#Create a Random ensemble of 10 structures randomly within 5 A translation and 60 deg rotation.
list_rotate_models=EnsembleGeneration.randomise_structs(structure_instance, 10, 5, 60, v_grain=30, rad=False,write=True)
#CCC score from starting fit
line='%s %s\n'%('1J6Z',scorer.CCC(map_probe,map_target))
count=0
#loop to score each of the alternative fits in the ensemble
for mod in list_rotate_models:
count+=1
mod_name=mod[0]
mod_structure_instance=mod[1]
map_probe = blurrer.gaussian_blur(mod_structure_instance, 6.6,densMap=map_target,sigma_coeff=0.187)
flag_example = True
#GET INPUT DATA
if flag_example:
m = os.path.join(path_example, '1akeA_10A.mrc')
p = os.path.join(path_example, '1ake_mdl1.pdb')
r = 10.0
rb_file = os.path.join(path_example, '1ake_mdl1_rigid.txt')
elif None in [m1, m2]:
# for one map and model
m = tp.args.inp_map
if m is None: m = m1
assert os.path.isfile(m)
print 'reading map'
Name1 = os.path.basename(m).split('.')[0]
emmap1 = MapParser.readMRC(m)
if r1 is None and r is None:
sys.exit('Input a map and model, map resolution (required)')
elif r1 is None:
r1 = r
if all(x is None for x in [p, p1, p2]):
sys.exit('Input a map and model, map resolution (required)')
elif None in [p1, p2]:
p = tp.args.pdb
else:
sys.exit('Input a map and model, map resolution (required)')
rb_file = tp.args.rigidfile
if rb_file is None: sys.exit('Rigid body file missing')
# make class instances for density simulation (blurring), scoring and plot scores
blurrer = StructureBlurrer()
# Read pdb
try:
prot = PDBParser.read_PDB_file(input_config["--outdir"],
input_config["--ipdb"],
hetatm=False,
water=False)
# Get number of components
comps = prot.split_into_chains()
ncomp = len(comps)
except:
print "Error in reading pdb coordinate file\n"
usage()
exit(0)
try:
emmap = MapParser.readMRC(input_config["--imap"])
emmap.normalise()
except:
print "Error in reading map density file\n"
usage()
exit(0)
try:
res = float(input_config["--res"])
if res == 0.0:
raise
except:
print "Error in reading map resolution value\n"
usage()
exit(0)
#Set the machine names for prallel processing
def transform_map(self, matR, transvec, m1, m2, c1, c2):
mat = matR.T
emmap1 = MapParser.readMRC(m1)
emmap2 = MapParser.readMRC(m2)
# geometric centre of map
vec_centre = emmap2.centre()
spacing = emmap2.apix
# to work on the box transformations, get the box centre irrespective of origin
vec_centre.x = vec_centre.x - emmap2.x_origin()
vec_centre.y = vec_centre.y - emmap2.y_origin()
vec_centre.z = vec_centre.z - emmap2.z_origin()
# calculate new box dimensions, after rotation
new_centre = emmap2._box_transform(matR)
output_shape = (int(new_centre.x / spacing),
int(new_centre.y / spacing),
int(new_centre.z / spacing))
new_centre.x = new_centre.x / 2
new_centre.y = new_centre.y / 2
new_centre.z = new_centre.z / 2
# offset for rotation
offset = emmap2._rotation_offset(mat, vec_centre, new_centre)
#APPLY ROTATION
emmap2 = emmap2._matrix_transform_offset(mat, output_shape, offset.x,
offset.y, offset.z)
offset_x = new_centre.x - vec_centre.x
offset_y = new_centre.y - vec_centre.y
offset_z = new_centre.z - vec_centre.z
emmap2 = emmap2.shift_origin(-offset_x, -offset_y, -offset_z)
# TRANSLATION COMPONENT
a14, a24, a34 = transvec[0], transvec[1], transvec[2]
emmap_2 = emmap2.shift_origin(
float(a14) * spacing,
float(a24) * spacing,
float(a34) * spacing)
emmap_1 = emmap1.copy()
# CROP BOX TO REDUCE ARRAY SIZE
emmap_1._crop_box(c1, 2)
emmap_2._crop_box(c2, 2)
# DETERMINE A COMMON ALIGNMENT BOX
spacing = emmap_2.apix
if emmap_2.apix < emmap_1.apix: spacing = emmap_1.apix
grid_shape, new_ori = emmap_1._alignment_box(emmap_2, spacing)
# INTERPOLATE TO NEW GRID
emmap_1 = emmap_1._interpolate_to_grid(grid_shape, spacing, new_ori)
emmap_2 = emmap_2._interpolate_to_grid(grid_shape, spacing, new_ori)
sc = ScoringFunctions()
ccc = sc.CCF_mask_zero(emmap_1, emmap_2, c1, c2)
mi = sc.MI(emmap_1, emmap_2)
env = sc.map_envelope_score(emmap_1, emmap_2, c1, c2)
nv = sc.normal_vector_score(emmap_1, emmap_2,
float(c1) - (emmap1.std() * 0.05),
float(c1) + (emmap1.std() * 0.05))
nv = sc.normal_vector_score(emmap_1,
emmap_2,
float(c1) - (emmap1.std() * 0.05),
float(c1) + (emmap1.std() * 0.05),
Filter='Sobel')
return ccc, mi, env, nv, nv_s
from TEMPy.StructureParser import PDBParser
from TEMPy.MapParser import MapParser
import numpy as np
# define point for rotation
# tempy examples use COM from input structure
# rotating against 0 0 0 doesn't seem to work
import TEMPy.Vector as Vector
com = Vector.Vector(90, 90, 90)
# read in map
target_map = MapParser.readMRC('GLIC_pH5_half1_unfil.mrc') #read target map
# rotate along x, y, z
target_map = target_map.rotate_by_axis_angle(1, 0, 0,
np.rad2deg(-3.1396619777494124),
com)
target_map = target_map.rotate_by_axis_angle(0, 1, 0,
np.rad2deg(0.0005038746980934731),
com)
target_map = target_map.rotate_by_axis_angle(0, 0, 1,
np.rad2deg(2.125868534775962),
com)
# translate along x, y, z
target_map = target_map.translate(-42, -58, 5)
# save map
target_map.write_to_MRC_file('moved.mrc') # Writing out to MRC file
def test_tempy_smoc(self):
''' Test the tempy smoc score based on the files
provided. Use this as a baseline for the second
chimeraX test. It is taken straight from the
score_smoc.py example tutorial.'''
list_labels = []
tp = TempyParser()
tp.generate_args()
# the sigma factor determines the width of the Gaussian distribution used to describe each atom
sim_sigma_coeff = 0.187
#score window
win = 9
path_test = os.getcwd()
map_file = os.path.join(path_test, '1akeA_10A.mrc')
res_map = 10.0
DATADIR = path_test
list_to_check = ['1ake_mdl1.pdb']
if len(list_labels) == 0:
list_labels = [x.split('.')[0]
for x in list_to_check] #['initial','final']
list_styles = [
':', '-.', '--', '-', '-', ':', '-.', '--', '-', '-', ':', '-.',
'--', '-', '-', ':', '-.', '--', '-', '-', ':', '-.', '--', '-',
'-'
] #'--'
z_score_check = 2
def model_tree(list_coord1, distpot=3.5, list_coord2=None):
try:
from scipy.spatial import cKDTree
coordtree = cKDTree(list_coord2)
except ImportError:
from scipy.spatial import KDTree
coordtree = KDTree(list_coord12)
if list_coord2 != None:
neigh_points = coordtree.query_ball_point(list_coord1, distpot)
return neigh_points
start_pdb = list_to_check[0]
iter_num = len(list_to_check)
intermed_file = ""
slow = 0.50
shigh = 0.25 # fraction of structure fitted reasonably well initially
rigidbody_file = None
sc = ScoringFunctions()
emmap = MapParser.readMRC(map_file)
rfilepath = rigidbody_file
dict_str_scores = {}
if rigidbody_file is not None:
rfilepath = os.path.join(DATADIR, rigidbody_file)
list_zscores = []
curdir = os.getcwd()
rerun_ct = 0
flag_rerun = 0
it = 0
dict_reslist = {}
# TODO - this whole bit needs a cleanup I think
while iter_num > 0:
dict_chains_scores = {}
out_iter_pdb = list_to_check[it]
lab = list_labels[it]
if os.path.isfile(os.path.join(DATADIR, out_iter_pdb)):
#read pdb
structure_instance = PDBParser.read_PDB_file('pdbfile',
os.path.join(
DATADIR,
out_iter_pdb),
hetatm=False,
water=False)
#get scores
dict_ch_scores, dict_chain_res = sc.SMOC(
emmap, res_map, structure_instance, win, rfilepath,
sim_sigma_coeff)
else:
print('PDB file not found:', out_iter_pdb)
for ch in dict_ch_scores:
flagch = 1
dict_res_scores = dict_ch_scores[ch]
#get res number list (for ref)
if it == 0:
dict_reslist[ch] = dict_chain_res[ch][:]
try:
if len(dict_reslist[ch]) == 0:
print('Chain missing:', out_iter_pdb, ch)
flagch = 0
continue
except KeyError:
print('Chain not common:', ch, out_iter_pdb)
flagch = 0
continue
try:
reslist = dict_reslist[ch]
except KeyError:
print('Chain not common:', ch, out_iter_pdb)
flagch = 0
continue
if not ch in dict_chains_scores: dict_chains_scores[ch] = {}
scorelist = []
for res in reslist:
try:
scorelist.append(dict_res_scores[res])
except KeyError:
if reslist.index(res) <= 0:
scorelist.append(
dict_res_scores[reslist[reslist.index(res) +
1]])
else:
try:
scorelist.append(
dict_res_scores[reslist[reslist.index(res)
- 1]])
except IndexError:
scorelist.append(0.0)
#save scores for each chain
curscore = "{0:.2f}".format(round(scorelist[-1], 2))
try:
dict_chains_scores[ch][res][it] = str(curscore)
except KeyError:
dict_chains_scores[ch][res] = [str(0.0)
] * len(list_to_check)
dict_chains_scores[ch][res][it] = str(curscore)
dict_str_scores[lab] = dict_chains_scores
#calc ratio between current and prev scores
if it > 0:
score_cur = scorelist[:]
score_inc = [(1 + x) / (1 + y)
for x, y in zip(score_cur, score_prev)][:]
score_diff = [(x - y)
for x, y in zip(score_cur, score_prev)][:]
#calculate z-scores
npscorelist = np.array(scorelist)
try:
list_zscores.append((npscorelist - np.mean(npscorelist)) /
np.std(npscorelist))
except:
list_zscores.append((npscorelist - np.mean(npscorelist)))
#calculate low and high score bounds
list_sccc = scorelist[:]
score_prev = scorelist[:]
list_sccc.sort()
#save avg of highest and lowest 20%
avglow = list_sccc[int(len(list_sccc) * slow)]
if avglow == 0.0: avglow = 0.00001
avghigh = list_sccc[int(len(list_sccc) * (1 - shigh))]
if it == 0:
avghigh1 = list_sccc[int(len(list_sccc) * (1 - shigh))]
curratio = avghigh / avglow
self.assertTrue(abs(avghigh - 0.967) < 0.01)
self.assertTrue(abs(avglow - 0.956) < 0.01)
self.assertTrue(
abs(sum(scorelist) / len(scorelist) - 0.899) < 0.01)
#include smoc scores as b-factor records
for x in structure_instance.atomList:
cur_chain = x.chain
cur_res = x.get_res_no()
if not cur_chain in dict_reslist.keys(): continue
if cur_chain in dict_chains_scores.keys():
try:
x.temp_fac = dict_chains_scores[cur_chain][cur_res][it]
except:
print('Residue missing: ', cur_res, ch, out_iter_pdb)
x.temp_fac = 0.0
else:
x.temp_fac = 0.0
it = it + 1
iter_num = iter_num - 1
def test_tempy_nmi(self):
''' Test the tempy nmi score based on the files
provided. Use this as a baseline for the second
chimeraX test. '''
path_test = "./"
m = os.path.join(path_test, 'emd_5168.map')
p = os.path.join(path_test, 'emd_5170.map')
sc = ScoringFunctions()
rez1 = 6.6
rez2 = 15.0
Name1, emmap1, c1 = map_contour(m, t=1.5)
Name2, emmap2, c2 = map_contour(p, t=1.5)
print(rez1, rez2, c1, c2, emmap1.apix, emmap2.apix)
if not sc.mapComparison(emmap1, emmap2):
emmap1._crop_box(c1, 0.5)
emmap2._crop_box(c2, 0.5)
if rez1 > 1.25 * rez2:
emmap_2 = lpfilter(emmap2, rez1)
emmap1, emmap2 = match_grid(emmap1, emmap_2, c1, c2)
elif rez2 > 1.25 * rez1:
emmap_1 = lpfilter(emmap1, rez2)
emmap1, emmap2 = match_grid(emmap_1, emmap2, c1, c2)
else:
emmap1, emmap2 = match_grid(emmap1, emmap2, c1, c2)
nmi = 0
try:
nmi = sc.MI(emmap1, emmap2, c1, c2, 1, None, None, True)
if nmi < 0.0: nmi = 0.0
except:
self.assertTrue(False)
print_exc()
nmi = 0.0
self.assertTrue(abs(round(nmi, 5) - 1.0492) < 0.001)
# Now test with a model and map
p = os.path.join(path_test, '1J6Z.pdb')
m = os.path.join(path_test, 'emd_5168_monomer.mrc')
res = 6.6
Name1 = os.path.basename(m).split('.')[0]
Name2 = os.path.basename(p).split('.')[0]
emmap1 = MapParser.readMRC(m)
structure_instance = PDBParser.read_PDB_file(Name2,
p,
hetatm=False,
water=False)
blurrer = StructureBlurrer()
emmap2 = blurrer.gaussian_blur(structure_instance, res, densMap=emmap1)
c1 = 9.7
c2 = 1.0
nmi = 0
try:
nmi = sc.MI(emmap1, emmap2, c1, c2, 1, None, None, True)
if nmi < 0.0: nmi = 0.0
except:
self.assertTrue(False)
print_exc()
nmi = 0.0
self.assertTrue(abs(round(nmi, 5) - 1.0575) < 0.001)
#import numpy as np
import os
from TEMPy.class_arg import TempyParser
tp = TempyParser()
tp.generate_args()
if not tp.args.inp_map is None:
m1 = tp.args.inp_map
elif not tp.args.inp_map1 is None:
m1 = tp.args.inp_map1
else:
sys.exit('Input map missing')
print 'reading map'
m1Name = os.path.basename(m1).split('.')[0]
emmap_1 = MapParser.readMRC(m1)
if not tp.args.thr is None: c1 = tp.args.thr
elif not tp.args.thr1 is None: c1 = tp.args.thr1
else:
print 'calculating contour'
zeropeak, ave, sigma1 = emmap_1._peak_density()
if not zeropeak is None: c1 = zeropeak + (1.5 * sigma1)
else:
sys.exit('Contour level required')
level = c1
sigma = emmap_1.fullMap.std()
sigma = abs(sigma)
try:
emmap_1.fullMap = emmap_1._label_patches(level - 0.02 * sigma)[0]
from TEMPy.StructureParser import PDBParser
from TEMPy.MapParser import MapParser
from TEMPy.StructureBlurrer import StructureBlurrer
import numpy as np
import sys
# define point for rotation
# tempy examples use COM from input structure
# rotating against 0 0 0 doesn't seem to work
import TEMPy.Vector as Vector
com = Vector.Vector(90, 90, 90)
# read in map
target_map = MapParser.readMRC(sys.argv[1]) #read target map
# read in structure
structure_instance = PDBParser.read_PDB_file('structure_id', sys.argv[2])
# translate along x, y, z
structure_instance.translate(42, 58, -5)
# rotate along x, y, z
structure_instance.rotate_by_axis_angle(0,
0,
1,
np.rad2deg(-2.125868534775962),
com=com)
structure_instance.rotate_by_axis_angle(0,
1,
0,
np.rad2deg(-0.0005038746980934731),