print 'use --help for help' print '-m/-m1 [map] for input map, -m1,-m2 for two input maps' print '-p/-p1 [pdb] for input pdb' print '-r [resolution]; -r1,r2 for the two map resolutions' print '-t [density threhold]; -t1,t2 for the two map thresholds' print '--nodust to disable dusting of difference maps' print '--softmask to apply softmask for masked map input' print '--refscale to consider second map (or model) amplitudes as reference for scaling the first map' print '-sw [shellwidth] to change the default shell width (1/Angstroms) for scaling (default 0.02)' print '-dp [probability] probability of finding dust (based on size) among the difference map densities (default (>)0.2)' print '--nofilt to disable lowpass filter before amplitude scaling (not recommended)' print '--noscale to disable amplitude scaling (not recommended)' print '\n\n###################################' tp = TempyParser() tp.generate_args() #COMMAND LINE OPTIONS m1 = tp.args.inp_map1 m2 = tp.args.inp_map2 m = tp.args.inp_map r1 = tp.args.res1 r2 = tp.args.res2 r = tp.args.res c1 = tp.args.thr1 c2 = tp.args.thr2 c = tp.args.thr p = tp.args.pdb p1 = tp.args.pdb1 p2 = tp.args.pdb2 apix = tp.args.apix
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