def resolution_merged_isigma(self, limit = None, log = None):
'''Compute a resolution limit where either Mn(I/sigma) = 1.0 (limit if
set) or the full extent of the data.'''
if limit is None:
limit = Flags.get_misigma()
bins, ranges = self.get_resolution_bins()
misigma_s = get_positive_values(
[self.calculate_merged_isigma(bin) for bin in bins])
s_s = [1.0 / (r[0] * r[0]) for r in ranges][:len(misigma_s)]
if min(misigma_s) > limit:
return 1.0 / math.sqrt(max(s_s))
misigma_f = log_fit(s_s, misigma_s, 6)
if log:
fout = open(log, 'w')
for j, s in enumerate(s_s):
d = 1.0 / math.sqrt(s)
o = misigma_s[j]
m = misigma_f[j]
fout.write('%f %f %f %f\n' % (s, d, o, m))
fout.close()
try:
r_misigma = 1.0 / math.sqrt(
interpolate_value(s_s, misigma_f, limit))
except:
r_misigma = 1.0 / math.sqrt(max(s_s))
return r_misigma
def new_resolution_unmerged_isigma(self, limit = None, log = None):
'''Compute a resolution limit where either I/sigma = 1.0 (limit if
set) or the full extent of the data.'''
if limit is None:
limit = Flags.get_isigma()
bins, ranges = self.get_resolution_bins()
isigma_s = get_positive_values(
[self.calculate_unmerged_isigma(bin) for bin in bins])
s_s = [1.0 / (r[0] * r[0]) for r in ranges][:len(isigma_s)]
if min(isigma_s) > limit:
return 1.0 / math.sqrt(max(s_s))
for _l, s in enumerate(isigma_s):
if s < limit:
break
if _l > 10 and _l < (len(isigma_s) - 10):
start = _l - 10
end = _l + 10
elif _l <= 10:
start = 0
end = 20
elif _l >= (len(isigma_s) - 10):
start = -20
end = -1
_s_s = s_s[start:end]
_isigma_s = isigma_s[start:end]
_isigma_f = log_fit(_s_s, _isigma_s, 3)
if log:
fout = open(log, 'w')
for j, s in enumerate(_s_s):
d = 1.0 / math.sqrt(s)
o = _isigma_s[j]
m = _isigma_f[j]
fout.write('%f %f %f %f\n' % (s, d, o, m))
fout.close()
try:
r_isigma = 1.0 / math.sqrt(interpolate_value(_s_s, _isigma_f,
limit))
except:
r_isigma = 1.0 / math.sqrt(max(_s_s))
return r_isigma
def resolution_rmerge(self, limit = None, log = None):
'''Compute a resolution limit where either rmerge = 1.0 (limit if
set) or the full extent of the data. N.B. this fit is only meaningful
for positive values.'''
if limit is None:
limit = Flags.get_rmerge()
bins, ranges = self.get_resolution_bins()
if limit == 0.0:
return ranges[-1][0]
rmerge_s = get_positive_values(
[self.calculate_rmerge(bin) for bin in bins])
s_s = [1.0 / (r[0] * r[0]) for r in ranges][:len(rmerge_s)]
if limit == 0.0:
return 1.0 / math.sqrt(max(s_s))
if limit > max(rmerge_s):
return 1.0 / math.sqrt(max(s_s))
rmerge_f = log_inv_fit(s_s, rmerge_s, 6)
if log:
fout = open(log, 'w')
for j, s in enumerate(s_s):
d = 1.0 / math.sqrt(s)
o = rmerge_s[j]
m = rmerge_f[j]
fout.write('%f %f %f %f\n' % (s, d, o, m))
fout.close()
try:
r_rmerge = 1.0 / math.sqrt(interpolate_value(s_s, rmerge_f, limit))
except:
r_rmerge = 1.0 / math.sqrt(max(s_s))
return r_rmerge
