def write_data_file(self):
nrows = len(self.cpartition.chain)
ncols = self.cpartition.num_tls_segments() + 1
tbl = table.StringTable(nrows, ncols, "?")
frag_id_iter = itertools.imap(lambda frag: frag.fragment_id, self.cpartition.chain.iter_fragments())
tbl.set_column(0, 0, frag_id_iter)
for itls, tls in enumerate(self.cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T
L = tls_group.itls_L
S = tls_group.itls_S
O = tls_group.origin
for frag in tls.iter_fragments():
atm = frag.get_atom("CA")
if atm is None:
continue
i = frag.ifrag
b_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
tbl[i, itls + 1] = atm.temp_factor - b_tls
open(self.txt_path, "w").write(str(tbl))
def calc_mean_biso_tls(chain, cpartition):
"""Calculated the mean B value per residue in the chain
as calculated in the chain optimization.
"""
num_res = chain.count_fragments()
biso = numpy.zeros(num_res, float)
for i, tls in enumerate(cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T
L = tls_group.itls_L
S = tls_group.itls_S
O = tls_group.origin
for frag in tls.iter_fragments():
n = 0
b_sum_tls = 0.0
for atm in frag.iter_all_atoms():
if atm.include is False:
continue
n += 1
b_sum_tls += Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
if n > 0:
biso[frag.ifrag] = b_sum_tls / n
return biso
def calc_mean_biso_tls(chain, cpartition):
"""Calculates the mean B value per residue in the chain (as calculated in
the chain optimization). It also performs a Skittles evaluation of the
junctions between the "C" and "N" atoms of neighbouring residues.
"""
chain_id = chain.chain_id
num_tls = cpartition.num_tls_segments()
num_res = chain.count_fragments()
biso = numpy.zeros(num_res, float)
for i, tls in enumerate(cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T # float(3)
L = tls_group.itls_L # array(3,3)
S = tls_group.itls_S # array(3): S[0], S[1], S[2]
O = tls_group.origin # array(3)
for frag in tls.iter_fragments():
n = 0
b_sum_tls = 0.0
for atm in frag.iter_all_atoms():
if atm.include is False:
continue
n += 1
b_sum_tls += Constants.U2B * TLS.calc_itls_uiso(
T, L, S, atm.position - O)
if n > 0:
biso[frag.ifrag] = b_sum_tls / n
return biso
def calc_mean_biso_tls(chain, cpartition):
"""Calculates the mean B value per residue in the chain (as calculated in
the chain optimization). It also performs a Skittles evaluation of the
junctions between the "C" and "N" atoms of neighbouring residues.
"""
chain_id = chain.chain_id
num_tls = cpartition.num_tls_segments()
num_res = chain.count_fragments()
biso = numpy.zeros(num_res, float)
for i, tls in enumerate(cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T # float(3)
L = tls_group.itls_L # array(3,3)
S = tls_group.itls_S # array(3): S[0], S[1], S[2]
O = tls_group.origin # array(3)
for frag in tls.iter_fragments():
n = 0
b_sum_tls = 0.0
for atm in frag.iter_all_atoms():
if atm.include is False:
continue
n += 1
b_sum_tls += Constants.U2B * TLS.calc_itls_uiso(
T, L, S, atm.position - O)
if n > 0:
biso[frag.ifrag] = b_sum_tls / n
return biso
def calc_residue_mean_rmsd(chain, cpartition):
"""Calculates the mean RMSD value per residue in a given chain.
"""
num_tls = cpartition.num_tls_segments()
num_res = chain.count_fragments()
#struct_id = cpartition.struct_id ## TODO: Find out how to get this
cmtx = numpy.zeros((num_tls, num_res), float)
i_ntls = 0
for i, tls in enumerate(cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T # float(3)
L = tls_group.itls_L # array(3,3)
S = tls_group.itls_S # array(3): S[0], S[1], S[2]
O = tls_group.origin # array(3)
for j, frag in enumerate(chain):
## NOTE: j = res_num, frag = Res(ALA,23,A)
## calculate a atom-normalized rmsd deviation for each residue
num_atoms = 0
msd_sum = 0.0
for atm in frag.iter_all_atoms():
if atm.include == False:
continue
num_atoms += 1
b_iso_tls = Constants.U2B * TLS.calc_itls_uiso(
T, L, S, atm.position - O)
delta = atm.temp_factor - b_iso_tls
msd_sum += delta**2
if num_atoms > 0:
msd = msd_sum / num_atoms
rmsd = math.sqrt(msd)
## set the cross prediction matrix
cmtx[i, j] = rmsd
return cmtx
def calc_residue_mean_rmsd(chain, cpartition):
"""Calculates the mean RMSD value per residue in a given chain.
"""
num_tls = cpartition.num_tls_segments()
num_res = chain.count_fragments()
#struct_id = cpartition.struct_id ## TODO: Find out how to get this
cmtx = numpy.zeros((num_tls, num_res), float)
i_ntls = 0
for i, tls in enumerate(cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T # float(3)
L = tls_group.itls_L # array(3,3)
S = tls_group.itls_S # array(3): S[0], S[1], S[2]
O = tls_group.origin # array(3)
for j, frag in enumerate(chain):
## NOTE: j = res_num, frag = Res(ALA,23,A)
## calculate a atom-normalized rmsd deviation for each residue
num_atoms = 0
msd_sum = 0.0
for atm in frag.iter_all_atoms():
if atm.include == False:
continue
num_atoms += 1
b_iso_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
delta = atm.temp_factor - b_iso_tls
msd_sum += delta**2
if num_atoms > 0:
msd = msd_sum / num_atoms
rmsd = math.sqrt(msd)
## set the cross prediction matrix
cmtx[i,j] = rmsd
return cmtx
def calc_residue_mean_rmsd(chain, cpartition):
num_tls = cpartition.num_tls_segments()
num_res = chain.count_fragments()
cmtx = numpy.zeros((num_tls, num_res), float)
for i, tls in enumerate(cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T
L = tls_group.itls_L
S = tls_group.itls_S
O = tls_group.origin
for j, frag in enumerate(chain):
## calculate a atom-normalized rmsd deviation for each residue
num_atoms = 0
msd_sum = 0.0
for atm in frag.iter_all_atoms():
if atm.include == False:
continue
num_atoms += 1
b_iso_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
delta = atm.temp_factor - b_iso_tls
msd_sum += delta**2
if num_atoms > 0:
msd = msd_sum / num_atoms
rmsd = math.sqrt(msd)
## set the cross prediction matrix
cmtx[i,j] = rmsd
return cmtx
def write_data_file(self):
nrows = len(self.cpartition.chain)
ncols = self.cpartition.num_tls_segments() + 1
tbl = table.StringTable(nrows, ncols, "?")
frag_id_iter = itertools.imap(lambda frag: frag.fragment_id, self.cpartition.chain.iter_fragments())
tbl.set_column(0, 0, frag_id_iter)
for itls, tls in enumerate(self.cpartition.iter_tls_segments()):
tls_group = tls.tls_group
T = tls_group.itls_T
L = tls_group.itls_L
S = tls_group.itls_S
O = tls_group.origin
for frag in tls.iter_fragments():
## FIXME: This should be able to handle either one
atm = frag.get_atom("CA") ## for amino acids
# atm = frag.get_atom("P") ## for nucleic acids
if atm is None:
continue
i = frag.ifrag
b_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
tbl[i, itls + 1] = atm.temp_factor - b_tls
open(self.txt_path, "w").write(str(tbl))
flatfile_write(
"CA_TLS_Differance_Plot: data",
"CTDP",
"DATA",
str(tbl),
self.chain.chain_id,
self.cpartition.num_tls_segments(),
)
def make_script(self):
console.debug_stdoutln(">gnuplots.py->UIso_vs_UtlsIso_Histogram()")
tls = self.tls
## generate data and png paths
basename = "%s_CHAIN%s_TLS%s_BoBc" % (self.chain.struct.structure_id, self.chain.chain_id, tls.filename_label())
self.set_basename(basename)
## write out the data file
tls_group = tls.tls_group
T = tls_group.itls_T
L = tls_group.itls_L
S = tls_group.itls_S
O = tls_group.origin
## create a histogram of (Uiso - Utls_iso)
bdiff_min = 0.0
bdiff_max = 0.0
for atm in tls_group:
b_iso_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
bdiff = atm.temp_factor - b_iso_tls
bdiff_min = min(bdiff_min, bdiff)
bdiff_max = max(bdiff_max, bdiff)
## compute the bin width and range to bin over
brange = (bdiff_max - bdiff_min) + 2.0
num_bins = int(brange)
bin_width = brange / float(num_bins)
bins = [0 for n in xrange(num_bins)]
## name the bins with their mean value
bin_names = []
for n in xrange(num_bins):
bin_mean = bdiff_min + (float(n) * bin_width) + (bin_width / 2.0)
bin_names.append(bin_mean)
## count the bins
for atm in tls_group:
b_iso_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
bdiff = atm.temp_factor - b_iso_tls
bin = int((bdiff - bdiff_min) / bin_width)
bins[bin] += 1
## write out the gnuplot input file
fil = open(self.txt_path, "w")
fil.write("## Histogram of atoms in the TLS group binned by\n")
fil.write("## the difference of their isotropic temperature factors\n")
fil.write("## from the isotropic values predicted from the TLS model.\n")
fil.write("##\n")
fil.write("## Structure ----------------: %s\n" % (self.chain.struct.structure_id))
fil.write("## Chain --------------------: %s\n" % (self.chain.chain_id))
fil.write("## TLS Group Residue Range --: %s\n" % (tls.display_label()))
job_dir = os.path.join(conf.TLSMD_WORK_DIR, conf.globalconf.job_id, "ANALYSIS")
if os.path.basename(os.getcwd()) != "ANALYSIS":
flatfile = open("../%s.dat" % conf.globalconf.job_id, "a+")
else:
flatfile = open("%s.dat" % conf.globalconf.job_id, "a+")
flatfile.write("\nCCCC UIso_vs_UtlsIso_Histogram\n")
flatfile.write("%s %s,%s.0 <DATA>\n" % ("UVUH", self.chain.chain_id, self.cpartition.num_tls_segments()))
for i in xrange(len(bins)):
fil.write("%f %d\n" % (bin_names[i], bins[i]))
flatfile.write("%f %d\n" % (bin_names[i], bins[i]))
flatfile.write("%s %s,%s.0 </DATA>\n" % ("UVUH", self.chain.chain_id, self.cpartition.num_tls_segments()))
flatfile.close()
fil.close()
## modify script template
script = _UISO_VS_UTLSISO_HISTOGRAM_TEMPLATE
script = script.replace("<txtfile>", self.txt_path)
title = "Histogram of Observed B_{iso} - B_{tls} for TLS Group %s" % (tls.display_label())
script = script.replace("<title>", title)
script = script.replace("<rgb>", tls.color.rgbs)
flat_script = script.replace("\n", ";")
flatfile_write(
"UIso_vs_UtlsIso_Histogram: script",
"UVUH",
"SCRIPT",
flat_script,
self.chain.chain_id,
self.cpartition.num_tls_segments(),
)
return script
def make_script(self):
tls = self.tls
## generate data and png paths
basename = "%s_CHAIN%s_TLS%s_BoBc" % (
self.chain.struct.structure_id,
self.chain.chain_id,
tls.filename_label())
self.set_basename(basename)
## write out the data file
tls_group = tls.tls_group
T = tls_group.itls_T
L = tls_group.itls_L
S = tls_group.itls_S
O = tls_group.origin
## create a histogram of (Uiso - Utls_iso)
bdiff_min = 0.0
bdiff_max = 0.0
for atm in tls_group:
b_iso_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
bdiff = atm.temp_factor - b_iso_tls
bdiff_min = min(bdiff_min, bdiff)
bdiff_max = max(bdiff_max, bdiff)
## compute the bin width and range to bin over
brange = (bdiff_max - bdiff_min) + 2.0
num_bins = int(brange)
bin_width = brange / float(num_bins)
bins = [0 for n in xrange(num_bins)]
## name the bins with their mean value
bin_names = []
for n in xrange(num_bins):
bin_mean = bdiff_min + (float(n) * bin_width) + (bin_width / 2.0)
bin_names.append(bin_mean)
## count the bins
for atm in tls_group:
b_iso_tls = Constants.U2B * TLS.calc_itls_uiso(T, L, S, atm.position - O)
bdiff = atm.temp_factor - b_iso_tls
bin = int((bdiff - bdiff_min)/ bin_width)
bins[bin] += 1
## write out the gnuplot input file
fil = open(self.txt_path, "w")
fil.write("## Histogram of atoms in the TLS group binned by\n")
fil.write("## the difference of their isotropic temperature factors\n")
fil.write("## from the isotropic values predicted from the TLS model.\n")
fil.write("##\n")
fil.write("## Structure ----------------: %s\n" % (self.chain.struct.structure_id))
fil.write("## Chain --------------------: %s\n" % (self.chain.chain_id))
fil.write("## TLS Group Residue Range --: %s\n" % (tls.display_label()))
for i in xrange(len(bins)):
fil.write("%f %d\n" % (bin_names[i], bins[i]))
fil.close()
## modify script template
script = _UISO_VS_UTLSISO_HISTOGRAM_TEMPLATE
script = script.replace("<txtfile>", self.txt_path)
title = "Histogram of Observed B_{iso} - B_{tls} for TLS Group %s" % (tls.display_label())
script = script.replace("<title>", title)
script = script.replace("<rgb>", tls.color.rgbs)
return script
