def print_from_file(self, modesfile):
env.logger.info("Printing modes from file", modesfile)
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
modes, atm_group = prody.parseNMD(modesfile)
title=modesfile.split(".")
modes.setTitle(title[0])
print_offset = 1
#print_offset = 738
plt.rcParams.update({'font.size': 24})
plt.rcParams['xtick.major.pad'] = 10
plt.rcParams['ytick.major.pad'] = 10
for count in range(6):
#array with lenghtes of vectors
a3d = (modes[count].getArrayNx3()**2).sum(axis=1)**0.5
plt.plot(a3d[:], label=("Mode "+str(count+1)))
plt.xlabel('Residue index')
plt.ylabel('Lenght of fluctuation vector')
locs,labels = plt.xticks()
new_labels = ['%d' % (a+print_offset) for a in locs]
plt.xticks(locs,new_labels)
plt.ylim([0,0.8])
plt.xlim([print_offset, modes.numAtoms()])
lgd = plt.legend(loc='center right', bbox_to_anchor=(1.6,0.5))
plt.savefig(str(modes.getTitle())+"_mode_1_to_6.pdf", bbox_extra_artists=(lgd, ), bbox_inches='tight')
env.logger.info("Printing modes to file finished")
def read_prody_normal_modes(self):
prody_molecule, chimera2prody = convert_chimera_molecule_to_prody(
self.molecule)
modes = prody.parseNMD(self.path)[0]
samples = prody.sampleModes(modes=modes[self.modes],
atoms=prody_molecule,
n_confs=self.n_samples,
rmsd=self.rmsd)
samples.addCoordset(prody_molecule)
samples_coords = [sample.getCoords() for sample in samples]
return modes, samples_coords, chimera2prody, prody_molecule
def NMA_plots(filename, outfile):
"""Creates a plot of the most important modes for a system.
Parameters
----------
filename : str
An NMD file.
outfile: str
Name of the output PNG.
Returns
-------
outfile : png
The rendered normal mode plot.
"""
NMA_data, Atom_Group = prd.parseNMD(filename)
eigens = NMA_data.getEigvals()
# If you have specific X-tick labels, set them up in plot_ticks
#labels_top, places_top = plot_ticks(NMA_data)
scales = []
temp = open(filename)
lines = temp.readlines()
temp.close()
for line in lines:
if 'mode' in line[:5]:
scales.append(float(line.split()[:3][-1]))
# Make an array of the number of atoms for plotting
x_vals = np.arange(0, NMA_data.numAtoms(), 1)
fig = plt.figure(figsize=(10, 8), dpi=300)
ax = fig.add_subplot(1, 1, 1)
for i in range(num_of_modes):
dataset = [
np.linalg.norm(NMA_data.getEigvecs()[:, i][n:n + 3]) * scales[i] *
eigens[i] for n in range(0, NMA_data.numEntries(), 3)
]
ax.bar(x_vals, dataset, width=1.0, label="Mode " + str(i + 1))
ax_top = ax.twiny()
ax_top.set_xticks(places_top)
ax_top.set_xticklabels(labels_top, fontdict=None, minor=False)
ax.legend()
ax.set_xlabel("Residue Number")
ax.set_ylabel("PCA Square Fluctuations")
# Remove white space at edge
ax.set_xlim([0, x_vals.size])
plt.tight_layout()
fig.savefig(outfile, dpi=300)
plt.close()
def prody_anm(self, variables, txtOutput):
'''
PRODY DRIVER is the function to read in variables from GUI input and
used to run a prody normal mode calculation using the anisotropic network model
(ANM) on a structure provide in a pdb file.
INPUT: variable descriptions:
pdbfile: input pdb file (reference)
OUTPUT:
model_anm_extended_bb.nmd
model_traverse.dcd
model_samp.dcd
model_samp.pdb
model_anm_sqflucts.txt
model_anm_kirchhoff.txt
model_anm_hessian.txt
model_anm_cross-correlations.hm
model_anm_cross-correlations.txt
model_anm_covariance.txt
model_anm_beta.txt
model_anm_evalues.txt
model_anm_evectors.txt
model_anm_extended_all.nmd
model_anm.nmd
txtOutput: TK handler for output to GUI textbox
files stored in ~/runname/prody directory:
outfile: output filename
'''
log = self.log
pgui = self.run_utils.print_gui
# start gui output
pgui("\n%s \n" % ('=' * 60))
pgui("DATA FROM RUN: %s \n\n" % time.asctime( time.gmtime( time.time() ) ))
mvars = self.mvars
#path = os.path.join(os.getcwd(),mvars.runname, 'prody')
path = os.path.join(mvars.runname, 'prody')
direxist = os.path.exists(path)
if(direxist == 0):
try:
result = os.system('mkdir -p ' + path)
except:
message = 'can not create project directory: ' + path
message += '\nstopping here\n'
print_failure(message, txtOutput)
if(result != 0):
message = 'can not create project directory: ' + path
message += '\nstopping here\n'
print_failure(message, txtOutput)
if mvars.advanced_usage == 1:
run_cmd = prody_exe + mvars.advanced_usage_cmd
os.system(run_cmd)
run_cmd = 'mv *.nmd *.txt *.hm prody'
os.system(run_cmd)
exit()
# display progress
fraction_done = (0 + 1) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
prody.confProDy(verbosity='none') #option to set silent verbosity
model = mvars.pdbfile[0:len(mvars.pdbfile) - 4]
run_cmd = prody_exe + ' anm ' + \
mvars.pdbfile + ' -t all -n ' + str(mvars.number_modes) + ' -a'
log.info('staring prody_exe %s' % run_cmd)
prody_run = subprocess.Popen(run_cmd,shell=True,executable='/bin/bash')
prody_run.wait()
#prody.confProDy(verbosity='none') #option to set silent verbosity
file_anm = model + '_anm_extended_all.nmd'
# display progress
fraction_done = (1 + 1) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
# parse nmd file with resuts extended to all atoms
log.info('staring prody.parseNMD %s' % file_anm)
mod, ag = prody.parseNMD(file_anm, type=None)
allatoms = ag.copy()
# set up to randomly sample number_conformations_samp modes
log.info('staring prody.sampleModes')
ensemble = prody.sampleModes(mod[:mvars.number_modes],
ag,
n_confs=mvars.number_conformations_samp,
rmsd=mvars.rmsd_conformations_samp)
ensemble
log.info('staring prody ensemble and writing pdb/dcd files')
allatoms.addCoordset(ensemble)
prody.writePDB('model_samp.pdb', allatoms)
prody.writeDCD('model_samp.dcd', allatoms)
trajectory_names = []
# display progress
fraction_done = (1 + 2) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
log.info('starting prody traverse')
for i in xrange(0, mvars.number_modes):
#print i
# setup to tranverse slowest mode
traverse = prody.traverseMode(
mod[i],
allatoms,
n_steps=mvars.number_steps_traverse,
rmsd=mvars.rmsd_traverse)
traverse
prody.writeDCD('traverse.dcd', traverse)
this_dcd = str(os.path.join(path, 'traverse_' + str(i) + '.dcd'))
cmd = 'mv traverse.dcd ' + this_dcd
os.system(cmd)
trajectory_names.append(this_dcd)
# display progress
fraction_done = (1 + 7) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
m1 = sasmol.SasMol(0)
m2 = sasmol.SasMol(0)
m1.read_pdb(mvars.pdbfile)
m2.read_pdb(mvars.pdbfile,fastread=True)
mvars.dcdfile = mvars.runname + '.dcd'
log.info('opening new dcd file to store trajectory: %s' %
os.path.join(self.runpath, mvars.dcdfile))
outfile_name = str(os.path.join(path, mvars.dcdfile))
dcdoutfile = m2.open_dcd_write(outfile_name)
count = 0
coor = numpy.zeros((1,m2.natoms(),3),numpy.float32)
for this_trajectory_name in trajectory_names:
dcdfile = m1.open_dcd_read(this_trajectory_name)
number_of_frames = dcdfile[2]
for j in xrange(number_of_frames):
m1.read_dcd_step(dcdfile,j)
coor[0,:,:] = m1.coor()[0]
m2.setCoor(coor)
m2.write_dcd_step(dcdoutfile,0, count + 1)
count += 1
m2.close_dcd_write(dcdoutfile)
log.info('moving files to runname / prody')
file_anm = model + '_anm.nmd'
mod, ag = prody.parseNMD(file_anm, type=None)
mod1 = prody.parsePDB(mvars.pdbfile)
calphas = mod1.select('calpha')
bb_anm, bb_atoms = prody.extendModel(mod, calphas, mod1.select(
'backbone')) # extend model to backbone atoms
prody.writeNMD('model_anm_extended_bb.nmd', bb_anm, bb_atoms)
cmd = 'mv model_samp.pdb ' + path + os.sep + os.path.basename(model) + '_samp.pdb'
os.system(cmd)
cmd = 'mv model_samp.dcd ' + path + os.sep + os.path.basename(model) + '_samp.dcd'
os.system(cmd)
cmd = 'mv model_anm_extended_bb.nmd ' + \
model + '_anm_extended_bb.nmd'
os.system(cmd)
cmd = 'mv *.hm *.nmd *.txt ' + path + os.sep
os.system(cmd)
# display progress
fraction_done = (1 + 9) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
return