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_completeness(self, limit = None, log = None): '''Compute a resolution limit where completeness < 0.5 (limit if set) or the full extent of the data. N.B. this completeness is with respect to the *maximum* completeness in a shell, to reflect triclinic cases.''' if limit is None: limit = Flags.get_completeness() bins, ranges = self.get_resolution_bins() s_s = [1.0 / (r[0] * r[0]) for r in reversed(ranges)] if limit == 0.0: return 1.0 / math.sqrt(max(s_s)) comp_s = [self.calculate_completeness(j) for j, bin in enumerate( reversed(bins))] if min(comp_s) > limit: return 1.0 / math.sqrt(max(s_s)) comp_f = fit(s_s, comp_s, 6) rlimit = limit * max(comp_s) if log: fout = open(log, 'w') for j, s in enumerate(s_s): d = 1.0 / math.sqrt(s) o = comp_s[j] m = comp_f[j] fout.write('%f %f %f %f\n' % (s, d, o, m)) fout.close() try: r_comp = 1.0 / math.sqrt( interpolate_value(s_s, comp_f, rlimit)) except: r_comp = 1.0 / math.sqrt(max(s_s)) return r_comp
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