def overall_report(data):
# Start by adding some overall numbers
report = {
"n": len(reflections),
"n_full": data["full"].count(True),
"n_partial": data["full"].count(False),
"n_overload": data["over"].count(True),
"n_ice": data["ice"].count(True),
"n_summed": data["sum"].count(True),
"n_fitted": data["prf"].count(True),
"n_integated": data["int"].count(True),
"n_invalid_bg": data["ninvbg"].count(True),
"n_invalid_fg": data["ninvfg"].count(True),
"n_failed_background": data["fbgd"].count(True),
"n_failed_summation": data["fsum"].count(True),
"n_failed_fitting": data["fprf"].count(True),
}
# Compute mean background
try:
report["mean_background"] = flex.mean(
data["background.mean"].select(data["int"])
)
except Exception:
report["mean_background"] = 0.0
# Compute mean I/Sigma summation
try:
report["ios_sum"] = flex.mean(data["intensity.sum.ios"].select(data["sum"]))
except Exception:
report["ios_sum"] = 0.0
# Compute mean I/Sigma profile fitting
try:
report["ios_prf"] = flex.mean(data["intensity.prf.ios"].select(data["prf"]))
except Exception:
report["ios_prf"] = 0.0
# Compute the mean profile correlation
try:
report["cc_prf"] = flex.mean(
data["profile.correlation"].select(data["prf"])
)
except Exception:
report["cc_prf"] = 0.0
# Compute the correlations between summation and profile fitting
try:
mask = data["sum"] & data["prf"]
Isum = data["intensity.sum.value"].select(mask)
Iprf = data["intensity.prf.value"].select(mask)
report["cc_pearson_sum_prf"] = pearson_correlation_coefficient(Isum, Iprf)
report["cc_spearman_sum_prf"] = spearman_correlation_coefficient(Isum, Iprf)
except Exception:
report["cc_pearson_sum_prf"] = 0.0
report["cc_spearman_sum_prf"] = 0.0
# Return the overall report
return report
def overall_report(data):
# Start by adding some overall numbers
report = OrderedDict()
report['n'] = len(reflections)
report['n_full'] = data['full'].count(True)
report['n_partial'] = data['full'].count(False)
report['n_overload'] = data['over'].count(True)
report['n_ice'] = data['ice'].count(True)
report['n_summed'] = data['sum'].count(True)
report['n_fitted'] = data['prf'].count(True)
report['n_integated'] = data['int'].count(True)
report['n_invalid_bg'] = data['ninvbg'].count(True)
report['n_invalid_fg'] = data['ninvfg'].count(True)
report['n_failed_background'] = data['fbgd'].count(True)
report['n_failed_summation'] = data['fsum'].count(True)
report['n_failed_fitting'] = data['fprf'].count(True)
# Compute mean background
try:
report['mean_background'] = flex.mean(
data['background.mean'].select(data['int']))
except Exception:
report['mean_background'] = 0.0
# Compute mean I/Sigma summation
try:
report['ios_sum'] = flex.mean(
data['intensity.sum.ios'].select(data['sum']))
except Exception:
report['ios_sum'] = 0.0
# Compute mean I/Sigma profile fitting
try:
report['ios_prf'] = flex.mean(
data['intensity.prf.ios'].select(data['prf']))
except Exception:
report['ios_prf'] = 0.0
# Compute the mean profile correlation
try:
report['cc_prf'] = flex.mean(
data['profile.correlation'].select(data['prf']))
except Exception:
report['cc_prf'] = 0.0
# Compute the correlations between summation and profile fitting
try:
mask = data['sum'] & data['prf']
Isum = data['intensity.sum.value'].select(mask)
Iprf = data['intensity.prf.value'].select(mask)
report['cc_pearson_sum_prf'] = pearson_correlation_coefficient(Isum, Iprf)
report['cc_spearman_sum_prf'] = spearman_correlation_coefficient(Isum, Iprf)
except Exception:
report['cc_pearson_sum_prf'] = 0.0
report['cc_spearman_sum_prf'] = 0.0
# Return the overall report
return report
