def resolution_cc_half(self, limit = None, log = None):
'''Compute a resolution limit where cc_half < 0.5 (limit if
set) or the full extent of the data.'''
if limit is None:
limit = self._params.cc_half
cc_s = flex.double(
[b.cc_one_half for b in self._merging_statistics.bins]).reversed()
s_s = flex.double(
[1/b.d_min**2 for b in self._merging_statistics.bins]).reversed()
p = self._params.cc_half_significance_level
if p is not None:
significance = flex.bool(
[b.cc_one_half_significance for b in self._merging_statistics.bins]).reversed()
cc_half_critical_value = flex.double(
[b.cc_one_half_critical_value for b in self._merging_statistics.bins]).reversed()
# index of last insignificant bin
i = flex.last_index(significance, False)
if i is None or i == len(significance) - 1:
i = 0
else:
i += 1
else:
i = 0
cc_f = fit(s_s[i:], cc_s[i:], 6)
stamp("rch: fits")
rlimit = limit * max(cc_s)
if log:
fout = open(log, 'w')
for j, s in enumerate(s_s):
d = 1.0 / math.sqrt(s)
o = cc_s[j]
m = cc_f[j]
fout.write('%f %f %f %f\n' % (s, d, o, m))
fout.close()
try:
r_cc = 1.0 / math.sqrt(
interpolate_value(s_s[i:], cc_f, rlimit))
except:
r_cc = 1.0 / math.sqrt(max(s_s[i:]))
stamp("rch: done : %s" % r_cc)
if self._params.plot:
plot = resolution_plot('CC1/2')
plot.plot(s_s[i:], cc_f, label='fit')
plot.plot(s_s, cc_s, label='CC1/2')
if p is not None:
plot.plot(
s_s, cc_half_critical_value, label='Confidence limit (p=%g)' %p)
plot.plot_resolution_limit(r_cc)
plot.savefig('cc_half.png')
return r_cc
def reduce_raw_data(raw_data,
qmax,
bandwidth,
level=0.05,
q_background=None,
outfile=''):
log2 = sys.stdout
with open(outfile, "a") as log:
print >> log, " ==== Data reduction ==== "
print >> log, " Preprocessing of data increases efficiency of shape retrieval procedure.\n"
print >> log, " - Interpolation stepsize : %4.3e" % bandwidth
print >> log, " - Uniform density criteria: level is set to : %4.3e" % level
print >> log, " maximum q to consider : %4.3e" % qmax
print >> log2, " ==== Data reduction ==== "
print >> log2, " Preprocessing of data increases efficiency of shape retrieval procedure.\n"
print >> log2, " - Interpolation stepsize : %4.3e" % bandwidth
print >> log2, " - Uniform density criteria: level is set to : %4.3e" % level
print >> log2, " maximum q to consider : %4.3e" % qmax
qmin_indx = flex.max_index(raw_data.i)
qmin = raw_data.q[qmin_indx]
if qmax > raw_data.q[-1]:
qmax = raw_data.q[-1]
with open(outfile, "a") as log:
print >> log, " Resulting q range to use in search: q start : %4.3e" % qmin
print >> log, " q stop : %4.3e" % qmax
print >> log2, " Resulting q range to use in search: q start : %4.3e" % qmin
print >> log2, " q stop : %4.3e" % qmax
raw_q = raw_data.q[qmin_indx:]
raw_i = raw_data.i[qmin_indx:]
raw_s = raw_data.s[qmin_indx:]
### Take care of the background (set zero at very high q) ###
if (q_background is not None):
cutoff = flex.bool(raw_q > q_background)
q_bk_indx = flex.last_index(cutoff, False)
if (q_bk_indx < raw_q.size()):
bkgrd = flex.mean(raw_i[q_bk_indx:])
with open(f, "a") as log:
print >> log, "Background correction: I=I-background, where background=", bkgrd
print >> log2, "Background correction: I=I-background, where background=", bkgrd
raw_i = flex.abs(raw_i - bkgrd)
q = flex.double(range(int(
(qmax - qmin) / bandwidth) + 1)) * bandwidth + qmin
raw_data.i = flex.linear_interpolation(raw_q, raw_i, q)
raw_data.s = flex.linear_interpolation(raw_q, raw_s, q)
raw_data.q = q
return raw_data
def had_phase_transition(self):
if len(self.differences) < 5: return False
i_max = flex.max_index(self.differences)
noise_before = (self.differences
< self.noise_level_before*self.differences[i_max])
before = flex.last_index(noise_before[:i_max], True)
if before is None: before = -1
before += 1
if i_max - before < 4: return False
negative_after = self.differences < 0
after = flex.first_index(negative_after[i_max:], True)
if after is None: return False
after += i_max
if after - before < 10: return False
if len(self.values) - after < 10: return False
tail_stats = scitbx.math.basic_statistics(self.differences[-5:])
if (tail_stats.max_absolute
> self.noise_level_after*self.differences[i_max]): return False
return True
def had_phase_transition(self):
if len(self.differences) < 5: return False
i_max = flex.max_index(self.differences)
noise_before = (self.differences <
self.noise_level_before * self.differences[i_max])
before = flex.last_index(noise_before[:i_max], True)
if before is None: before = -1
before += 1
if i_max - before < 4: return False
negative_after = self.differences < 0
after = flex.first_index(negative_after[i_max:], True)
if after is None: return False
after += i_max
if after - before < 10: return False
if len(self.values) - after < 10: return False
tail_stats = scitbx.math.basic_statistics(self.differences[-5:])
if (tail_stats.max_absolute >
self.noise_level_after * self.differences[i_max]):
return False
return True
def resolution_cc_half(self, limit=None, log=None):
'''Compute a resolution limit where cc_half < 0.5 (limit if
set) or the full extent of the data.'''
if limit is None:
limit = self._params.cc_half
if self._params.cc_half_method == 'sigma_tau':
cc_s = flex.double([
b.cc_one_half_sigma_tau for b in self._merging_statistics.bins
]).reversed()
else:
cc_s = flex.double([
b.cc_one_half for b in self._merging_statistics.bins
]).reversed()
s_s = flex.double([
1 / b.d_min**2 for b in self._merging_statistics.bins
]).reversed()
p = self._params.cc_half_significance_level
if p is not None:
if self._params.cc_half_method == 'sigma_tau':
significance = flex.bool([
b.cc_one_half_sigma_tau_significance
for b in self._merging_statistics.bins
]).reversed()
cc_half_critical_value = flex.double([
b.cc_one_half_sigma_tau_critical_value
for b in self._merging_statistics.bins
]).reversed()
else:
significance = flex.bool([
b.cc_one_half_significance
for b in self._merging_statistics.bins
]).reversed()
cc_half_critical_value = flex.double([
b.cc_one_half_critical_value
for b in self._merging_statistics.bins
]).reversed()
# index of last insignificant bin
i = flex.last_index(significance, False)
if i is None or i == len(significance) - 1:
i = 0
else:
i += 1
else:
i = 0
if self._params.cc_half_fit == 'tanh':
cc_f = tanh_fit(s_s[i:], cc_s[i:], iqr_multiplier=4)
else:
cc_f = fit(s_s[i:], cc_s[i:], 6)
stamp("rch: fits")
rlimit = limit * max(cc_s)
if log:
fout = open(log, 'w')
for j, s in enumerate(s_s):
d = 1.0 / math.sqrt(s)
o = cc_s[j]
m = cc_f[j]
fout.write('%f %f %f %f\n' % (s, d, o, m))
fout.close()
try:
r_cc = 1.0 / math.sqrt(interpolate_value(s_s[i:], cc_f, rlimit))
except Exception:
r_cc = 1.0 / math.sqrt(max(s_s[i:]))
stamp("rch: done : %s" % r_cc)
if self._params.plot:
plot = resolution_plot('CC1/2')
plot.plot(s_s[i:], cc_f, label='fit')
plot.plot(s_s, cc_s, label='CC1/2')
if p is not None:
plot.plot(s_s,
cc_half_critical_value,
label='Confidence limit (p=%g)' % p)
plot.plot_resolution_limit(r_cc)
plot.savefig('cc_half.png')
return r_cc
def resolution_cc_half(self, limit=None):
"""Compute a resolution limit where cc_half < 0.5 (limit if
set) or the full extent of the data."""
if limit is None:
limit = self._params.cc_half
if self._params.cc_half_method == "sigma_tau":
cc_s = flex.double(
[b.cc_one_half_sigma_tau for b in self._merging_statistics.bins]
).reversed()
else:
cc_s = flex.double(
[b.cc_one_half for b in self._merging_statistics.bins]
).reversed()
s_s = flex.double(
[1 / b.d_min ** 2 for b in self._merging_statistics.bins]
).reversed()
p = self._params.cc_half_significance_level
if p is not None:
if self._params.cc_half_method == "sigma_tau":
significance = flex.bool(
[
b.cc_one_half_sigma_tau_significance
for b in self._merging_statistics.bins
]
).reversed()
cc_half_critical_value = flex.double(
[
b.cc_one_half_sigma_tau_critical_value
for b in self._merging_statistics.bins
]
).reversed()
else:
significance = flex.bool(
[b.cc_one_half_significance for b in self._merging_statistics.bins]
).reversed()
cc_half_critical_value = flex.double(
[
b.cc_one_half_critical_value
for b in self._merging_statistics.bins
]
).reversed()
# index of last insignificant bin
i = flex.last_index(significance, False)
if i is None or i == len(significance) - 1:
i = 0
else:
i += 1
else:
i = 0
if self._params.cc_half_fit == "tanh":
cc_f = tanh_fit(s_s[i:], cc_s[i:], iqr_multiplier=4)
else:
cc_f = fit(s_s[i:], cc_s[i:], 6)
logger.debug("rch: fits")
rlimit = limit * max(cc_s)
for j, s in enumerate(s_s[i:]):
logger.debug("%f %f %f %f\n", s, 1.0 / math.sqrt(s), cc_s[i + j], cc_f[j])
try:
r_cc = 1.0 / math.sqrt(interpolate_value(s_s[i:], cc_f, rlimit))
except Exception:
r_cc = 1.0 / math.sqrt(max(s_s[i:]))
logger.debug("rch: done : %s", r_cc)
if self._params.plot:
plot = resolution_plot("CC1/2")
plot.plot(s_s[i:], cc_f, label="fit")
plot.plot(s_s, cc_s, label="CC1/2")
if p is not None:
plot.plot(
s_s, cc_half_critical_value, label="Confidence limit (p=%g)" % p
)
plot.plot_resolution_limit(r_cc)
plot.savefig("cc_half.png")
return r_cc