def scat_data(self, d_star_sq=None):
if d_star_sq is None:
self.sigma_tot_sq=None
self.gamma_tot_sigma=None
self.gamma_tot=None
if d_star_sq is not None:
self.sigma_tot_sq = flex.double( d_star_sq.size() )
gaussians = {}
for chemical_type, n_atoms in self.asu_contents.items():
gaussians[chemical_type] = xray_scattering.wk1995(
chemical_type).fetch()
f0 = gaussians[chemical_type].at_d_star_sq(d_star_sq)
self.sigma_tot_sq += f0*f0*n_atoms
if(d_star_sq.size()>0):
## Protein part
gamma_prot = gamma_protein(d_star_sq)
self.gamma_prot = gamma_prot.gamma*self.fraction_protein
## Nucleotide part; needs to be completed
gamma_nuc = gamma_nucleic(d_star_sq)
self.gamma_nuc = gamma_nuc.gamma*self.fraction_nucleic ##
## Totals
self.gamma_tot = self.gamma_prot*self.fraction_protein +\
self.gamma_nuc*self.fraction_nucleic
self.gamma_tot_sigma = (gamma_prot.sigma_gamma*self.fraction_protein)*\
(gamma_prot.sigma_gamma*self.fraction_protein)+\
(gamma_nuc.sigma_gamma*self.fraction_nucleic)*\
(gamma_nuc.sigma_gamma*self.fraction_nucleic)
self.gamma_tot_sigma = flex.sqrt( self.gamma_tot_sigma )
def scat_data(self, d_star_sq=None):
if d_star_sq is None:
self.sigma_tot_sq = None
self.gamma_tot_sigma = None
self.gamma_tot = None
if d_star_sq is not None:
self.sigma_tot_sq = flex.double(d_star_sq.size())
gaussians = {}
for chemical_type, n_atoms in self.asu_contents.items():
gaussians[chemical_type] = xray_scattering.wk1995(
chemical_type).fetch()
f0 = gaussians[chemical_type].at_d_star_sq(d_star_sq)
self.sigma_tot_sq += f0 * f0 * n_atoms
if (d_star_sq.size() > 0):
## Protein part
gamma_prot = gamma_protein(d_star_sq)
self.gamma_prot = gamma_prot.gamma * self.fraction_protein
## Nucleotide part; needs to be completed
gamma_nuc = gamma_nucleic(d_star_sq)
self.gamma_nuc = gamma_nuc.gamma * self.fraction_nucleic ##
## Totals
self.gamma_tot = self.gamma_prot*self.fraction_protein +\
self.gamma_nuc*self.fraction_nucleic
self.gamma_tot_sigma = (gamma_prot.sigma_gamma*self.fraction_protein)*\
(gamma_prot.sigma_gamma*self.fraction_protein)+\
(gamma_nuc.sigma_gamma*self.fraction_nucleic)*\
(gamma_nuc.sigma_gamma*self.fraction_nucleic)
self.gamma_tot_sigma = flex.sqrt(self.gamma_tot_sigma)
def main():
parser = OptionParser(usage="usage: python %prog [options] file_name ...")
parser.add_option("-c", "--cutoff", type="float", default=6.05, metavar="FLOAT", help="maximum sin(theta)/lambda")
(options, args) = parser.parse_args()
if len(args) < 1:
parser.print_help()
return
cutoff = options.cutoff
for file_name in args:
tab = read_table(file_name)
if tab.element == "Es":
continue
wk = xray_scattering.wk1995(tab.element, True).fetch()
sel = tab.x <= cutoff
tab_x = tab.x.select(sel)
tab_y = tab.y.select(sel)
sigmas = flex.double(tab_x.size(), 0.0005)
wky = wk.at_x(tab_x)
errors_abs = flex.abs(wky - tab_y)
fit = scitbx.math.gaussian.fit(tab_x, tab_y, sigmas, wk)
errors_rel = fit.significant_relative_errors(1.0e-6)
print tab.element, tab.atomic_number,
print "max error < %.1fA-1 abs, rel: %7.4f %7.4f" % (cutoff, flex.max(errors_abs), flex.max(errors_rel))
for x, y, f, ea, er in zip(tab_x, tab_y, wky, errors_abs, errors_rel):
print "%7.4f %7.4f %7.4f %7.4f %7.4f" % (x, y, f, ea, er)
print
def show_literature_fits(label, n_terms, null_fit, n_points, e_other=None):
for lib in [xray_scattering.wk1995,
xray_scattering.it1992,
xray_scattering.two_gaussian_agarwal_isaacs,
xray_scattering.two_gaussian_agarwal_1978,
xray_scattering.one_gaussian_agarwal_1978]:
if (lib == xray_scattering.wk1995):
try:
lib_gaussian = xray_scattering.wk1995(label, True).fetch()
lib_source = "WK1995"
except Exception:
lib_gaussian = None
elif (lib == xray_scattering.it1992):
try:
lib_gaussian = xray_scattering.it1992(label, True).fetch()
lib_source = "IT1992"
except Exception:
lib_gaussian = None
elif (lib.table.has_key(label)):
lib_gaussian = lib.table[label]
lib_source = lib.source_short
else:
lib_gaussian = None
if (lib_gaussian is not None):
gaussian_fit = scitbx.math.gaussian.fit(
null_fit.table_x()[:n_points],
null_fit.table_y()[:n_points],
null_fit.table_sigmas()[:n_points],
lib_gaussian)
e = flex.max(gaussian_fit.significant_relative_errors())
show_fit_summary(lib_source, label, gaussian_fit, e,
e_other, lib_gaussian.n_terms())
def main():
parser = OptionParser(usage="usage: python %prog [options] file_name ...")
parser.add_option("-c",
"--cutoff",
type="float",
default=6.05,
metavar="FLOAT",
help="maximum sin(theta)/lambda")
(options, args) = parser.parse_args()
if (len(args) < 1):
parser.print_help()
return
cutoff = options.cutoff
for file_name in args:
tab = read_table(file_name)
if (tab.element == "Es"): continue
wk = xray_scattering.wk1995(tab.element, True).fetch()
sel = tab.x <= cutoff
tab_x = tab.x.select(sel)
tab_y = tab.y.select(sel)
sigmas = flex.double(tab_x.size(), 0.0005)
wky = wk.at_x(tab_x)
errors_abs = flex.abs(wky - tab_y)
fit = scitbx.math.gaussian.fit(tab_x, tab_y, sigmas, wk)
errors_rel = fit.significant_relative_errors(1.e-6)
print(tab.element, tab.atomic_number, end=' ')
print("max error < %.1fA-1 abs, rel: %7.4f %7.4f" %
(cutoff, flex.max(errors_abs), flex.max(errors_rel)))
for x, y, f, ea, er in zip(tab_x, tab_y, wky, errors_abs, errors_rel):
print("%7.4f %7.4f %7.4f %7.4f %7.4f" % (x, y, f, ea, er))
print()
def show_literature_fits(label, n_terms, null_fit, n_points, e_other=None):
for lib in [
xray_scattering.wk1995, xray_scattering.it1992,
xray_scattering.two_gaussian_agarwal_isaacs,
xray_scattering.two_gaussian_agarwal_1978,
xray_scattering.one_gaussian_agarwal_1978
]:
if (lib == xray_scattering.wk1995):
try:
lib_gaussian = xray_scattering.wk1995(label, True).fetch()
lib_source = "WK1995"
except Exception:
lib_gaussian = None
elif (lib == xray_scattering.it1992):
try:
lib_gaussian = xray_scattering.it1992(label, True).fetch()
lib_source = "IT1992"
except Exception:
lib_gaussian = None
elif (lib.table.has_key(label)):
lib_gaussian = lib.table[label]
lib_source = lib.source_short
else:
lib_gaussian = None
if (lib_gaussian is not None):
gaussian_fit = scitbx.math.gaussian.fit(
null_fit.table_x()[:n_points],
null_fit.table_y()[:n_points],
null_fit.table_sigmas()[:n_points], lib_gaussian)
e = flex.max(gaussian_fit.significant_relative_errors())
show_fit_summary(lib_source, label, gaussian_fit, e, e_other,
lib_gaussian.n_terms())
def ls_ff_weights(f_obs, atom, B):
d_star_sq_data = f_obs.d_star_sq().data()
table = wk1995(atom).fetch()
ff = table.at_d_star_sq(d_star_sq_data) * flex.exp(
-B / 4.0 * d_star_sq_data)
weights = 1.0 / flex.pow2(ff)
return weights
def run():
wk = xray_scattering.wk1995("O2-")
gaussian_fit = scitbx.math.gaussian.fit(table_2_stol, table_2_o2minus,
table_2_sigmas, wk.fetch())
print("max error:", flex.max(gaussian_fit.significant_relative_errors()))
cctbx.eltbx.gaussian_fit.write_plots(plots_dir="rez_plots",
label=wk.label(),
gaussian_fit=gaussian_fit)
def form_factor(ssi, absent_atom_type):
table = wk1995(absent_atom_type).fetch()
a_wk = table.array_of_a()
b_wk = table.array_of_b()
c_wk = table.c()
result_wk = c_wk
for i in xrange(5):
result_wk += a_wk[i] * math.exp(-b_wk[i] * ssi / 4.0)
return result_wk
def form_factor(ssi, absent_atom_type):
table=wk1995(absent_atom_type).fetch()
a_wk=table.array_of_a()
b_wk=table.array_of_b()
c_wk=table.c()
result_wk=c_wk
for i in xrange(5):
result_wk += a_wk[i]*math.exp(-b_wk[i]*ssi/4.0)
return result_wk
def f_ordered_solvent(f, n_water_atoms_absent, bf_atoms_absent,
absent_atom_type):
nsym = f.space_group().order_z()
n_lost_w = nsym * n_water_atoms_absent
data = f.data() * n_lost_w
d_star_sq_data = f.d_star_sq().data()
table = wk1995(absent_atom_type).fetch()
ff = table.at_d_star_sq(d_star_sq_data)
factor = ff * flex.exp(-bf_atoms_absent / 4.0 * d_star_sq_data)
f_by_m = miller.array(miller_set=f, data=data * factor)
return f_by_m
def form_factor(self, ss):
#
# W & K form-factor of atom C
#
table = wk1995(self.absent_atom_type).fetch()
a_wk = table.array_of_a()
b_wk = table.array_of_b()
c_wk = table.c()
result_wk = c_wk
for i in range(5):
result_wk += a_wk[i] * math.exp(-b_wk[i] * ss / 4.0)
return result_wk
def run():
wk = xray_scattering.wk1995("O2-")
gaussian_fit = scitbx.math.gaussian.fit(
table_2_stol,
table_2_o2minus,
table_2_sigmas,
wk.fetch())
print "max error:", flex.max(gaussian_fit.significant_relative_errors())
cctbx.eltbx.gaussian_fit.write_plots(
plots_dir="rez_plots",
label=wk.label(),
gaussian_fit=gaussian_fit)
def form_factor(absent_atom_type, ss):
#
# W & K form-factor for an atom, B=0.0
#
table = wk1995(absent_atom_type).fetch()
a_wk = table.array_of_a()
b_wk = table.array_of_b()
c_wk = table.c()
result_wk = c_wk
for i in xrange(5):
result_wk += a_wk[i] * math.exp(-b_wk[i] * ss / 4.0)
return result_wk
def form_factor(self,ss):
#
# W & K form-factor of atom C
#
table=wk1995(self.absent_atom_type).fetch()
a_wk=table.array_of_a()
b_wk=table.array_of_b()
c_wk=table.c()
result_wk=c_wk
for i in xrange(5):
result_wk += a_wk[i]*math.exp(-b_wk[i]*ss/4.0)
return result_wk
def f_ordered_solvent(f,
n_water_atoms_absent,
bf_atoms_absent,
absent_atom_type):
nsym = f.space_group().order_z()
n_lost_w = nsym * n_water_atoms_absent
data = f.data() * n_lost_w
d_star_sq_data = f.d_star_sq().data()
table = wk1995(absent_atom_type).fetch()
ff = table.at_d_star_sq(d_star_sq_data)
factor = ff * flex.exp(-bf_atoms_absent/4.0*d_star_sq_data)
f_by_m = miller.array(miller_set = f, data = data*factor)
return f_by_m
def exercise_wk1995():
e = xray_scattering.wk1995("c1")
assert e.table() == "WK1995"
assert e.label() == "C"
g = e.fetch()
assert approx_equal(g.array_of_a(),
(2.657506, 1.078079, 1.490909, -4.241070, 0.713791))
assert approx_equal(g.array_of_b(),
(14.780758, 0.776775, 42.086842, -0.000294, 0.239535))
assert approx_equal(g.c(), 4.297983)
assert approx_equal(g.at_stol_sq(0), 5.99719834328)
assert approx_equal(g.at_stol_sq(1. / 9), 2.26895371584)
assert approx_equal(g.at_stol(1. / 9), 4.93735084739)
assert approx_equal(g.at_d_star_sq(1. / 9), 4.04679561237)
e = xray_scattering.wk1995("yb2+", True)
assert e.label() == "Yb2+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.443794)
assert approx_equal(g.array_of_b()[4], 0.001463)
assert approx_equal(g.c(), -23.214935)
e = xray_scattering.wk1995(" yB3+")
assert e.label() == "Yb3+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.191629)
n = 0
for e in xray_scattering.wk1995_iterator():
n += 1
if (n == 213):
assert e.label() == "Pu6+"
else:
assert e.label() != "Pu6+"
d = xray_scattering.wk1995(e.label(), True)
assert d.label() == e.label()
assert n == 213
i = xray_scattering.wk1995_iterator()
j = iter(i)
assert i is j
def exercise_wk1995():
e = xray_scattering.wk1995("c1")
assert e.table() == "WK1995"
assert e.label() == "C"
g = e.fetch()
assert approx_equal(g.array_of_a(),
(2.657506,1.078079,1.490909,-4.241070,0.713791))
assert approx_equal(g.array_of_b(),
(14.780758,0.776775,42.086842,-0.000294,0.239535))
assert approx_equal(g.c(), 4.297983)
assert approx_equal(g.at_stol_sq(0), 5.99719834328)
assert approx_equal(g.at_stol_sq(1./9), 2.26895371584)
assert approx_equal(g.at_stol(1./9), 4.93735084739)
assert approx_equal(g.at_d_star_sq(1./9), 4.04679561237)
e = xray_scattering.wk1995("yb2+", True)
assert e.label() == "Yb2+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.443794)
assert approx_equal(g.array_of_b()[4], 0.001463)
assert approx_equal(g.c(), -23.214935)
e = xray_scattering.wk1995(" yB3+")
assert e.label() == "Yb3+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.191629)
n = 0
for e in xray_scattering.wk1995_iterator():
n += 1
if (n == 213):
assert e.label() == "Pu6+"
else:
assert e.label() != "Pu6+"
d = xray_scattering.wk1995(e.label(), True)
assert d.label() == e.label()
assert n == 213
i = xray_scattering.wk1995_iterator()
j = iter(i)
assert i is j
def getXScatteringFactor(el, q):
"""Get the x-ray scattering factor for an element over the q range.
If cctbx is not available, f(q) = 1 is used.
"""
try:
import cctbx.eltbx.xray_scattering as xray
wk1995 = xray.wk1995(el)
g = wk1995.fetch()
# at_stol - at sin(theta)/lambda = Q/(4*pi)
f = numpy.asarray( map( g.at_stol, q/(4*numpy.pi) ) )
return f
except ImportError:
return 1
def getXScatteringFactor(el, q):
"""Get the x-ray scattering factor for an element over the q range.
If cctbx is not available, f(q) = 1 is used.
"""
try:
import cctbx.eltbx.xray_scattering as xray
wk1995 = xray.wk1995(el)
g = wk1995.fetch()
# at_stol - at sin(theta)/lambda = Q/(4*pi)
f = numpy.asarray( map( g.at_stol, q/(4*numpy.pi) ) )
return f
except ImportError:
return 1
def run(args):
assert len(args) == 0
sds_it = xray_scattering.it1992("H").fetch()
sds_wk = xray_scattering.wk1995("H").fetch()
sds_ng = xray_scattering.n_gaussian_table_entry("H", 6).gaussian()
iso_it = xray_scattering.it1992("Hiso").fetch()
iso_wk = xray_scattering.wk1995("Hiso").fetch()
iso_ng = xray_scattering.n_gaussian_table_entry("Hiso", 6).gaussian()
print "@with g0"
print '@ s0 legend "SDS ITC Tab 6.1.1.2"'
for i,lbl in enumerate(["SDS IT", "SDS WK", "SDS NG",
"ISO IT", "ISO WK", "ISO NG"]):
print '@ s%d legend "%s"' % (i+1, lbl)
print "@ s0 symbol 1"
print "@ s0 line linestyle 0"
for x,y in itc_tab_6112:
print x, y
print "&"
n_samples = 1000
for g in [sds_it, sds_wk, sds_ng, iso_it, iso_wk, iso_ng]:
for i_stol in xrange(n_samples+1):
stol = 6 * i_stol / n_samples
print stol, g.at_stol(stol)
print "&"
def run(args):
assert len(args) == 0
sds_it = xray_scattering.it1992("H").fetch()
sds_wk = xray_scattering.wk1995("H").fetch()
sds_ng = xray_scattering.n_gaussian_table_entry("H", 6).gaussian()
iso_it = xray_scattering.it1992("Hiso").fetch()
iso_wk = xray_scattering.wk1995("Hiso").fetch()
iso_ng = xray_scattering.n_gaussian_table_entry("Hiso", 6).gaussian()
print("@with g0")
print('@ s0 legend "SDS ITC Tab 6.1.1.2"')
for i, lbl in enumerate(
["SDS IT", "SDS WK", "SDS NG", "ISO IT", "ISO WK", "ISO NG"]):
print('@ s%d legend "%s"' % (i + 1, lbl))
print("@ s0 symbol 1")
print("@ s0 line linestyle 0")
for x, y in itc_tab_6112:
print(x, y)
print("&")
n_samples = 1000
for g in [sds_it, sds_wk, sds_ng, iso_it, iso_wk, iso_ng]:
for i_stol in range(n_samples + 1):
stol = 6 * i_stol / n_samples
print(stol, g.at_stol(stol))
print("&")
def ensure_common_symbols():
lbl_it = []
for e in xray_scattering.it1992_iterator(): lbl_it.append(e.label())
lbl_it.sort()
lbl_wk = []
for e in xray_scattering.wk1995_iterator(): lbl_wk.append(e.label())
lbl_wk.sort()
assert lbl_wk == lbl_it
lbl_ng = []
for i_entry in xrange(xray_scattering.n_gaussian_table_size()):
lbl_ng.append(xray_scattering.n_gaussian_table_entry(i_entry, 6).label())
lbl_ng.sort()
assert lbl_ng == lbl_it
#
for label in xray_scattering.standard_labels_list():
it = xray_scattering.it1992(label, True).fetch()
wk = xray_scattering.wk1995(label, True).fetch()
ng = xray_scattering.n_gaussian_table_entry(label, 0, 0).gaussian()
assert approx_equal(wk.at_stol(0)/it.at_stol(0), 1, 5.e-3)
def alpha_beta(f_dist, n_atoms_included, n_nonwater_atoms_absent,
n_water_atoms_absent, bf_atoms_absent, final_error,
absent_atom_type):
nsym = f_dist.space_group().order_z()
ss = 1. / flex.pow2(f_dist.d_spacings().data())
n_part = nsym * n_atoms_included
n_lost_p = nsym * n_nonwater_atoms_absent
n_lost_w = nsym * n_water_atoms_absent
f_dist_data = flex.abs(f_dist.data())
a_d = flex.exp(-0.25 * ss * final_error**2 * math.pi**3)
d_star_sq_data = f_dist.d_star_sq().data()
assert approx_equal(ss, d_star_sq_data)
table = wk1995(absent_atom_type).fetch()
ff = table.at_d_star_sq(d_star_sq_data)
factor = ff * flex.exp(-bf_atoms_absent / 4.0 * d_star_sq_data)
b_d = ((1.-a_d*a_d)*n_part+n_lost_p+n_lost_w*(1.-f_dist_data*f_dist_data))*\
factor*factor
alpha = f_dist.array(data=a_d)
beta = f_dist.array(data=b_d)
return alpha, beta
def ensure_common_symbols():
lbl_it = []
for e in xray_scattering.it1992_iterator():
lbl_it.append(e.label())
lbl_it.sort()
lbl_wk = []
for e in xray_scattering.wk1995_iterator():
lbl_wk.append(e.label())
lbl_wk.sort()
assert lbl_wk == lbl_it
lbl_ng = []
for i_entry in range(xray_scattering.n_gaussian_table_size()):
lbl_ng.append(
xray_scattering.n_gaussian_table_entry(i_entry, 6).label())
lbl_ng.sort()
assert lbl_ng == lbl_it
#
for label in xray_scattering.standard_labels_list():
it = xray_scattering.it1992(label, True).fetch()
wk = xray_scattering.wk1995(label, True).fetch()
ng = xray_scattering.n_gaussian_table_entry(label, 0, 0).gaussian()
assert approx_equal(wk.at_stol(0) / it.at_stol(0), 1, 5.e-3)
def alpha_beta(f_dist,
n_atoms_included,
n_nonwater_atoms_absent,
n_water_atoms_absent,
bf_atoms_absent,
final_error,
absent_atom_type):
nsym = f_dist.space_group().order_z()
ss = 1./flex.pow2(f_dist.d_spacings().data())
n_part = nsym * n_atoms_included
n_lost_p = nsym * n_nonwater_atoms_absent
n_lost_w = nsym * n_water_atoms_absent
f_dist_data = flex.abs(f_dist.data())
a_d = flex.exp( -0.25 * ss * final_error**2 * math.pi**3 )
d_star_sq_data = f_dist.d_star_sq().data()
assert approx_equal(ss,d_star_sq_data)
table = wk1995(absent_atom_type).fetch()
ff = table.at_d_star_sq(d_star_sq_data)
factor = ff * flex.exp(-bf_atoms_absent/4.0*d_star_sq_data)
b_d = ((1.-a_d*a_d)*n_part+n_lost_p+n_lost_w*(1.-f_dist_data*f_dist_data))*\
factor*factor
alpha = f_dist.array(data = a_d)
beta = f_dist.array(data = b_d)
return alpha, beta
def run(file_name, args, cutoff, params,
zig_zag=False, six_term=False, full_fits=None,
plots_dir="itvc_fits_plots", verbose=0):
tab = itvc_section61_io.read_table6111(file_name)
chunk_n = 1
chunk_i = 0
if (len(args) > 0 and len(args[0].split(",")) == 2):
chunk_n, chunk_i = [int(i) for i in args[0].split(",")]
args = args[1:]
if (not six_term and not zig_zag):
if (not os.path.isdir(plots_dir)):
print "No plots because target directory does not exist (mkdir %s)." % \
plots_dir
plots_dir = None
if (chunk_n > 1):
assert plots_dir is not None
stols_more = cctbx.eltbx.gaussian_fit.international_tables_stols
sel = stols_more <= cutoff + 1.e-6
stols = stols_more.select(sel)
i_chunk = 0
for element in tab.elements + ["O2-", "SDS"]:
if (len(args) > 0 and element not in args): continue
flag = i_chunk % chunk_n == chunk_i
i_chunk += 1
if (not flag):
continue
results = {}
results["fit_parameters"] = params
if (element == "SDS"):
wrk_lbl = element
from cctbx.eltbx.development.hydrogen_plots import fit_input
fi = fit_input()
sel = fi.stols <= cutoff + 1.e-6
null_fit = scitbx.math.gaussian.fit(
fi.stols.select(sel),
fi.data.select(sel),
fi.sigmas.select(sel),
xray_scattering.gaussian(0, False))
null_fit_more = scitbx.math.gaussian.fit(
fi.stols,
fi.data,
fi.sigmas,
xray_scattering.gaussian(0, False))
else:
wrk_lbl = xray_scattering.wk1995(element, True)
if (element != "O2-"):
entry = tab.entries[element]
null_fit = scitbx.math.gaussian.fit(
stols,
entry.table_y[:stols.size()],
entry.table_sigmas[:stols.size()],
xray_scattering.gaussian(0, False))
null_fit_more = scitbx.math.gaussian.fit(
stols_more,
entry.table_y[:stols_more.size()],
entry.table_sigmas[:stols_more.size()],
xray_scattering.gaussian(0, False))
else:
rrg_stols_more = rez_rez_grant.table_2_stol
sel = rrg_stols_more <= cutoff + 1.e-6
rrg_stols = rrg_stols_more.select(sel)
null_fit = scitbx.math.gaussian.fit(
rrg_stols,
rez_rez_grant.table_2_o2minus[:rrg_stols.size()],
rez_rez_grant.table_2_sigmas[:rrg_stols.size()],
xray_scattering.gaussian(0, False))
null_fit_more = scitbx.math.gaussian.fit(
rrg_stols_more,
rez_rez_grant.table_2_o2minus[:rrg_stols_more.size()],
rez_rez_grant.table_2_sigmas[:rrg_stols_more.size()],
xray_scattering.gaussian(0, False))
if (zig_zag):
results[wrk_lbl] = cctbx.eltbx.gaussian_fit.zig_zag_fits(
label=wrk_lbl,
null_fit=null_fit,
null_fit_more=null_fit_more,
params=params)
elif (full_fits is not None):
assert len(full_fits.all[wrk_lbl]) == 1
results[wrk_lbl] = cctbx.eltbx.gaussian_fit.decremental_fits(
label=wrk_lbl,
null_fit=null_fit,
full_fit=full_fits.all[wrk_lbl][0],
params=params,
plots_dir=plots_dir,
verbose=verbose)
elif (not six_term):
results[wrk_lbl] = cctbx.eltbx.gaussian_fit.incremental_fits(
label=wrk_lbl,
null_fit=null_fit,
params=params,
plots_dir=plots_dir,
verbose=verbose)
else:
best_min = scitbx.math.gaussian_fit.fit_with_golay_starts(
label=wrk_lbl,
null_fit=null_fit,
null_fit_more=null_fit_more,
params=params)
g = best_min.final_gaussian_fit
results[wrk_lbl] = [xray_scattering.fitted_gaussian(
stol=g.table_x()[-1], gaussian_sum=g)]
sys.stdout.flush()
pickle_file_name = "%s_fits.pickle" % identifier(wrk_lbl)
easy_pickle.dump(pickle_file_name, results)
def ls_ff_weights(f_obs, atom, B): d_star_sq_data = f_obs.d_star_sq().data() table = wk1995(atom).fetch() ff = table.at_d_star_sq(d_star_sq_data) * flex.exp(-B/4.0*d_star_sq_data) weights = 1.0/flex.pow2(ff) return weights
def run(file_name,
args,
cutoff,
params,
zig_zag=False,
six_term=False,
full_fits=None,
plots_dir="itvc_fits_plots",
verbose=0):
tab = itvc_section61_io.read_table6111(file_name)
chunk_n = 1
chunk_i = 0
if (len(args) > 0 and len(args[0].split(",")) == 2):
chunk_n, chunk_i = [int(i) for i in args[0].split(",")]
args = args[1:]
if (not six_term and not zig_zag):
if (not os.path.isdir(plots_dir)):
print("No plots because target directory does not exist (mkdir %s)." % \
plots_dir)
plots_dir = None
if (chunk_n > 1):
assert plots_dir is not None
stols_more = cctbx.eltbx.gaussian_fit.international_tables_stols
sel = stols_more <= cutoff + 1.e-6
stols = stols_more.select(sel)
i_chunk = 0
for element in tab.elements + ["O2-", "SDS"]:
if (len(args) > 0 and element not in args): continue
flag = i_chunk % chunk_n == chunk_i
i_chunk += 1
if (not flag):
continue
results = {}
results["fit_parameters"] = params
if (element == "SDS"):
wrk_lbl = element
from cctbx.eltbx.development.hydrogen_plots import fit_input
fi = fit_input()
sel = fi.stols <= cutoff + 1.e-6
null_fit = scitbx.math.gaussian.fit(
fi.stols.select(sel), fi.data.select(sel),
fi.sigmas.select(sel), xray_scattering.gaussian(0, False))
null_fit_more = scitbx.math.gaussian.fit(
fi.stols, fi.data, fi.sigmas,
xray_scattering.gaussian(0, False))
else:
wrk_lbl = xray_scattering.wk1995(element, True)
if (element != "O2-"):
entry = tab.entries[element]
null_fit = scitbx.math.gaussian.fit(
stols, entry.table_y[:stols.size()],
entry.table_sigmas[:stols.size()],
xray_scattering.gaussian(0, False))
null_fit_more = scitbx.math.gaussian.fit(
stols_more, entry.table_y[:stols_more.size()],
entry.table_sigmas[:stols_more.size()],
xray_scattering.gaussian(0, False))
else:
rrg_stols_more = rez_rez_grant.table_2_stol
sel = rrg_stols_more <= cutoff + 1.e-6
rrg_stols = rrg_stols_more.select(sel)
null_fit = scitbx.math.gaussian.fit(
rrg_stols,
rez_rez_grant.table_2_o2minus[:rrg_stols.size()],
rez_rez_grant.table_2_sigmas[:rrg_stols.size()],
xray_scattering.gaussian(0, False))
null_fit_more = scitbx.math.gaussian.fit(
rrg_stols_more,
rez_rez_grant.table_2_o2minus[:rrg_stols_more.size()],
rez_rez_grant.table_2_sigmas[:rrg_stols_more.size()],
xray_scattering.gaussian(0, False))
if (zig_zag):
results[wrk_lbl] = cctbx.eltbx.gaussian_fit.zig_zag_fits(
label=wrk_lbl,
null_fit=null_fit,
null_fit_more=null_fit_more,
params=params)
elif (full_fits is not None):
assert len(full_fits.all[wrk_lbl]) == 1
results[wrk_lbl] = cctbx.eltbx.gaussian_fit.decremental_fits(
label=wrk_lbl,
null_fit=null_fit,
full_fit=full_fits.all[wrk_lbl][0],
params=params,
plots_dir=plots_dir,
verbose=verbose)
elif (not six_term):
results[wrk_lbl] = cctbx.eltbx.gaussian_fit.incremental_fits(
label=wrk_lbl,
null_fit=null_fit,
params=params,
plots_dir=plots_dir,
verbose=verbose)
else:
best_min = scitbx.math.gaussian_fit.fit_with_golay_starts(
label=wrk_lbl,
null_fit=null_fit,
null_fit_more=null_fit_more,
params=params)
g = best_min.final_gaussian_fit
results[wrk_lbl] = [
xray_scattering.fitted_gaussian(stol=g.table_x()[-1],
gaussian_sum=g)
]
sys.stdout.flush()
pickle_file_name = "%s_fits.pickle" % identifier(wrk_lbl)
easy_pickle.dump(pickle_file_name, results)
