def __init__(self, experiments, profile_fitter, reflections, num_folds):
'''
Create the integration report
:param experiments: The experiment list
:param profile_model: The profile model
:param reflections: The reflection table
'''
from collections import OrderedDict
# Initialise the report class
super(ProfileValidationReport, self).__init__()
# Create the table
table = Table()
# Set the title
table.name = 'validation.summary'
table.title = 'Summary of profile validation '
# Add the columns
table.cols.append(('id', 'ID'))
table.cols.append(('subsample', 'Sub-sample'))
table.cols.append(('n_valid', '# validated'))
table.cols.append(('cc', '<CC>'))
table.cols.append(('nrmsd', '<NRMSD>'))
# Split the reflections
reflection_tables = reflections.split_by_experiment_id()
assert len(reflection_tables) == len(experiments)
assert len(profile_fitter) == num_folds
# Create the summary for each profile model
for i in range(len(reflection_tables)):
reflection_table = reflection_tables[i]
reflection_table = reflection_table.select(
reflection_table.get_flags(
reflection_table.flags.integrated_prf))
index = reflection_table['profile.index']
cc = reflection_table['profile.correlation']
nrmsd = reflection_table['profile.rmsd']
for j in range(num_folds):
mask = index == j
num_validated = mask.count(True)
if num_validated == 0:
mean_cc = 0
mean_nrmsd = 0
else:
mean_cc = flex.mean(cc.select(mask))
mean_nrmsd = flex.mean(nrmsd.select(mask))
table.rows.append([
'%d' % i,
'%d' % j,
'%d' % num_validated,
'%.2f' % mean_cc,
'%.2f' % mean_nrmsd])
# Add the table
self.add_table(table)
def __init__(self, experiments, profile_fitter, reflections, num_folds):
"""
Create the integration report
:param experiments: The experiment list
:param profile_model: The profile model
:param reflections: The reflection table
"""
# Initialise the report class
super().__init__()
# Create the table
table = Table()
# Set the title
table.name = "validation.summary"
table.title = "Summary of profile validation "
# Add the columns
table.cols.append(("id", "ID"))
table.cols.append(("subsample", "Sub-sample"))
table.cols.append(("n_valid", "# validated"))
table.cols.append(("cc", "<CC>"))
table.cols.append(("nrmsd", "<NRMSD>"))
# Split the reflections
reflection_tables = reflections.split_by_experiment_id()
assert len(reflection_tables) == len(experiments)
assert len(profile_fitter) == num_folds
# Create the summary for each profile model
for i in range(len(reflection_tables)):
reflection_table = reflection_tables[i]
reflection_table = reflection_table.select(
reflection_table.get_flags(reflection_table.flags.integrated_prf)
)
index = reflection_table["profile.index"]
cc = reflection_table["profile.correlation"]
nrmsd = reflection_table["profile.rmsd"]
for j in range(num_folds):
mask = index == j
num_validated = mask.count(True)
if num_validated == 0:
mean_cc = 0
mean_nrmsd = 0
else:
mean_cc = flex.mean(cc.select(mask))
mean_nrmsd = flex.mean(nrmsd.select(mask))
table.rows.append(
[
"%d" % i,
"%d" % j,
"%d" % num_validated,
f"{mean_cc:.2f}",
f"{mean_nrmsd:.2f}",
]
)
# Add the table
self.add_table(table)
def __init__(self, experiments, reference, reflections):
'''
Create the integration report
:param experiments: The experiment list
:param reference: The reference profiles
:param reflections: The reflection table
'''
from collections import OrderedDict
# Initialise the report class
super(ProfileModelReport, self).__init__()
# Create the table
table = Table()
# Set the title
table.name = 'profile.summary'
table.title = 'Summary of profile model'
# Add the columns
table.cols.append(('id', 'ID'))
table.cols.append(('profile', 'Profile'))
table.cols.append(('created', 'Created'))
table.cols.append(('x', 'X (px)'))
table.cols.append(('y', 'Y (px)'))
table.cols.append(('z', 'Z (im)'))
table.cols.append(('n_reflections', '# reflections'))
# Create the summary for each profile model
for i in range(len(reference)):
model = reference[i]
for j in range(len(model)):
table.rows.append([
'%d' % i,
'%d' % j,
'%s' % True,
'%.2f' % model.sampler().coord(j)[0],
'%.2f' % model.sampler().coord(j)[1],
'%.2f' % model.sampler().coord(j)[2],
'%d' % model.n_reflections(j)
])
# Add the table
self.add_table(table)
# Add the profiles
for i in range(len(fitter)):
model = fitter[i]
for j in range(len(model)):
if model.valid(j):
array = Array()
array.name = 'profile.model.%d.%d' % (i, j)
array.title = 'Profile model (id: %d, profile: %d)' % (i,
j)
array.data = model.data(j)
self.add_array(array)
def __init__(self, experiments, reference, reflections):
"""
Create the integration report
:param experiments: The experiment list
:param reference: The reference profiles
:param reflections: The reflection table
"""
from collections import OrderedDict
# Initialise the report class
super(ProfileModelReport, self).__init__()
# Create the table
table = Table()
# Set the title
table.name = "profile.summary"
table.title = "Summary of profile model"
# Add the columns
table.cols.append(("id", "ID"))
table.cols.append(("profile", "Profile"))
table.cols.append(("created", "Created"))
table.cols.append(("x", "X (px)"))
table.cols.append(("y", "Y (px)"))
table.cols.append(("z", "Z (im)"))
table.cols.append(("n_reflections", "# reflections"))
# Create the summary for each profile model
for i in range(len(reference)):
model = reference[i]
for j in range(len(model)):
table.rows.append([
"%d" % i,
"%d" % j,
"%s" % True,
"%.2f" % model.sampler().coord(j)[0],
"%.2f" % model.sampler().coord(j)[1],
"%.2f" % model.sampler().coord(j)[2],
"%d" % model.n_reflections(j),
])
# Add the table
self.add_table(table)
# Add the profiles
for i in range(len(fitter)):
model = fitter[i]
for j in range(len(model)):
if model.valid(j):
array = Array()
array.name = "profile.model.%d.%d" % (i, j)
array.title = "Profile model (id: %d, profile: %d)" % (i,
j)
array.data = model.data(j)
self.add_array(array)
def __init__(self, experiments, fitter, reflections):
"""
Create the integration report
:param experiments: The experiment list
:param profile_model: The profile model
:param reflections: The reflection table
"""
# Initialise the report class
super().__init__()
# Create the table
table = Table()
# Set the title
table.name = "profile.summary"
table.title = "Summary of profile model"
# Add the columns
table.cols.append(("id", "ID"))
table.cols.append(("profile", "Profile"))
table.cols.append(("created", "Created"))
table.cols.append(("x", "X (px)"))
table.cols.append(("y", "Y (px)"))
table.cols.append(("z", "Z (im)"))
table.cols.append(("n_reflections", "# reflections"))
# Create the summary for each profile model
for i in range(len(fitter)):
model = fitter[i]
for j in range(len(model)):
table.rows.append(
[
"%d" % i,
"%d" % j,
f"{model.valid(j)}",
f"{model.coord(j)[0]:.2f}",
f"{model.coord(j)[1]:.2f}",
f"{model.coord(j)[2]:.2f}",
"%d" % model.n_reflections(j),
]
)
# Add the table
self.add_table(table)
# Add the profiles
for i in range(len(fitter)):
model = fitter[i]
for j in range(len(model)):
if model.valid(j):
array = Array()
array.name = "profile.model.%d.%d" % (i, j)
array.title = "Profile model (id: %d, profile: %d)" % (i, j)
array.data = model.data(j)
self.add_array(array)
def __init__(self, experiments, fitter, reflections):
'''
Create the integration report
:param experiments: The experiment list
:param profile_model: The profile model
:param reflections: The reflection table
'''
from collections import OrderedDict
# Initialise the report class
super(ProfileModelReport, self).__init__()
# Create the table
table = Table()
# Set the title
table.name = 'profile.summary'
table.title = 'Summary of profile model'
# Add the columns
table.cols.append(('id', 'ID'))
table.cols.append(('profile', 'Profile'))
table.cols.append(('created', 'Created'))
table.cols.append(('x', 'X (px)'))
table.cols.append(('y', 'Y (px)'))
table.cols.append(('z', 'Z (im)'))
table.cols.append(('n_reflections', '# reflections'))
# Create the summary for each profile model
for i in range(len(fitter)):
model = fitter[i]
for j in range(len(model)):
table.rows.append([
'%d' % i,
'%d' % j,
'%s' % model.valid(j),
'%.2f' % model.coord(j)[0],
'%.2f' % model.coord(j)[1],
'%.2f' % model.coord(j)[2],
'%d' % model.n_reflections(j)])
# Add the table
self.add_table(table)
# Add the profiles
for i in range(len(fitter)):
model = fitter[i]
for j in range(len(model)):
if model.valid(j):
array = Array()
array.name = 'profile.model.%d.%d' % (i, j)
array.title = 'Profile model (id: %d, profile: %d)' % (i, j)
array.data = model.data(j)
self.add_array(array)
def __init__(self, experiments, reflections):
'''
Create the integration report
:param experiments: The experiment list
:param reflections: The reflection table
'''
from collections import OrderedDict
# Initialise the report class
super(IntegrationReport, self).__init__()
# Split the tables by experiment id
tables = reflections.split_by_experiment_id()
assert (len(tables) == len(experiments))
# Initialise the dictionary
report_list = []
# Generate an integration report for each experiment
for i, (expr, data) in enumerate(zip(experiments, tables)):
report_list.append(generate_integration_report(expr, data))
# Construct the per image table
table = Table()
table.name = 'integration.image.summary'
table.title = "Summary vs image number"
table.cols.append(('id', 'ID'))
table.cols.append(('image', 'Image'))
table.cols.append(('n_full', '# full'))
table.cols.append(('n_part', '# part'))
table.cols.append(('n_over', '# over'))
table.cols.append(('n_ice', '# ice'))
table.cols.append(('n_sum', '# sum'))
table.cols.append(('n_prf', '# prf'))
table.cols.append(('ibg', 'Ibg'))
table.cols.append(('ios_sum', 'I/sigI\n (sum)'))
table.cols.append(('ios_prf', 'I/sigI\n (prf)'))
table.cols.append(('cc_prf', 'CC prf'))
table.cols.append(('rmsd_xy', 'RMSD XY'))
for j, report in enumerate(report_list):
report = report['image']
for i in range(len(report['bins']) - 1):
table.rows.append([
'%d' % j,
'%d' % report['bins'][i],
'%d' % report['n_full'][i],
'%d' % report['n_partial'][i],
'%d' % report['n_overload'][i],
'%d' % report['n_ice'][i],
'%d' % report['n_summed'][i],
'%d' % report['n_fitted'][i],
'%.2f' % report['mean_background'][i],
'%.2f' % report['ios_sum'][i],
'%.2f' % report['ios_prf'][i],
'%.2f' % report['cc_prf'][i],
'%.2f' % report['rmsd_xy'][i]
])
self.add_table(table)
# Construct the per resolution table
table = Table()
table.name = 'integration.resolution.summary'
table.title = "Summary vs resolution"
table.cols.append(('id', 'ID'))
table.cols.append(('dmin', 'd min'))
table.cols.append(('n_full', '# full'))
table.cols.append(('n_part', '# part'))
table.cols.append(('n_over', '# over'))
table.cols.append(('n_ice', '# ice'))
table.cols.append(('n_sum', '# sum'))
table.cols.append(('n_prf', '# prf'))
table.cols.append(('ibg', 'Ibg'))
table.cols.append(('ios_sum', 'I/sigI\n (sum)'))
table.cols.append(('ios_prf', 'I/sigI\n (prf)'))
table.cols.append(('cc_prf', 'CC prf'))
table.cols.append(('rmsd_xy', 'RMSD XY'))
for j, report in enumerate(report_list):
report = report['resolution']
for i in range(len(report['bins']) - 1):
table.rows.append([
'%d' % j,
'%.2f' % report['bins'][i],
'%d' % report['n_full'][i],
'%d' % report['n_partial'][i],
'%d' % report['n_overload'][i],
'%d' % report['n_ice'][i],
'%d' % report['n_summed'][i],
'%d' % report['n_fitted'][i],
'%.2f' % report['mean_background'][i],
'%.2f' % report['ios_sum'][i],
'%.2f' % report['ios_prf'][i],
'%.2f' % report['cc_prf'][i],
'%.2f' % report['rmsd_xy'][i]
])
self.add_table(table)
# Create the overall table
for j, report in enumerate(report_list):
report = report['summary']
summary = report['overall']
high = report['high']
low = report['low']
table = Table()
table.name = 'integration.overall.summary'
table.title = "Summary for experiment %d" % j
table.cols.append(('item', 'Item'))
table.cols.append(('overall', 'Overall'))
table.cols.append(('low', 'Low'))
table.cols.append(('high', 'High'))
desc_fmt_key = [
("dmin", '%.2f', 'dmin'), ('dmax', '%.2f', 'dmax'),
("number fully recorded", '%d', "n_full"),
("number partially recorded", '%d', "n_partial"),
("number with invalid background pixels", '%d',
"n_invalid_bg"),
("number with invalid foreground pixels", '%d',
"n_invalid_fg"),
("number with overloaded pixels", '%d', "n_overload"),
("number in powder rings", '%d', "n_ice"),
("number processed with summation", '%d', "n_summed"),
("number processed with profile fitting", '%d', "n_fitted"),
("number failed in background modelling", '%d',
"n_failed_background"),
("number failed in summation", '%d', "n_failed_summation"),
("number failed in profile fitting", '%d', "n_failed_fitting"),
("ibg", '%.2f', "mean_background"),
("i/sigi (summation)", '%.2f', "ios_sum"),
("i/sigi (profile fitting)", '%.2f', "ios_prf"),
("cc prf", '%.2f', "cc_prf"),
("cc_pearson sum/prf", '%.2f', "cc_pearson_sum_prf"),
("cc_spearman sum/prf", '%.2f', "cc_spearman_sum_prf")
]
for desc, fmt, key in desc_fmt_key:
table.rows.append([
desc, fmt % summary[key], fmt % low[key], fmt % high[key]
])
self.add_table(table)
def __init__(self, experiments, reflections):
"""
Create the integration report
:param experiments: The experiment list
:param reflections: The reflection table
"""
# Initialise the report class
super(IntegrationReport, self).__init__()
# Split the tables by experiment id
tables = reflections.split_by_experiment_id()
assert len(tables) == len(experiments)
# Initialise the dictionary
report_list = []
# Generate an integration report for each experiment
for i, (expr, data) in enumerate(zip(experiments, tables)):
report_list.append(generate_integration_report(expr, data))
# Construct the per image table
table = Table()
table.name = "integration.image.summary"
table.title = "Summary vs image number"
table.cols.append(("id", "ID"))
table.cols.append(("image", "Image"))
table.cols.append(("n_full", "# full"))
table.cols.append(("n_part", "# part"))
table.cols.append(("n_over", "# over"))
table.cols.append(("n_ice", "# ice"))
table.cols.append(("n_sum", "# sum"))
table.cols.append(("n_prf", "# prf"))
table.cols.append(("ibg", "Ibg"))
table.cols.append(("ios_sum", "I/sigI\n (sum)"))
table.cols.append(("ios_prf", "I/sigI\n (prf)"))
table.cols.append(("cc_prf", "CC prf"))
table.cols.append(("rmsd_xy", "RMSD XY"))
for j, report in enumerate(report_list):
report = report["image"]
for i in range(len(report["bins"]) - 1):
table.rows.append([
"%d" % j,
"%d" % (report["bins"][i] + 1),
"%d" % report["n_full"][i],
"%d" % report["n_partial"][i],
"%d" % report["n_overload"][i],
"%d" % report["n_ice"][i],
"%d" % report["n_summed"][i],
"%d" % report["n_fitted"][i],
"%.2f" % report["mean_background"][i],
"%.2f" % report["ios_sum"][i],
"%.2f" % report["ios_prf"][i],
"%.2f" % report["cc_prf"][i],
"%.2f" % report["rmsd_xy"][i],
])
self.add_table(table)
# Construct the per resolution table
table = Table()
table.name = "integration.resolution.summary"
table.title = "Summary vs resolution"
table.cols.append(("id", "ID"))
table.cols.append(("dmin", "d min"))
table.cols.append(("n_full", "# full"))
table.cols.append(("n_part", "# part"))
table.cols.append(("n_over", "# over"))
table.cols.append(("n_ice", "# ice"))
table.cols.append(("n_sum", "# sum"))
table.cols.append(("n_prf", "# prf"))
table.cols.append(("ibg", "Ibg"))
table.cols.append(("ios_sum", "I/sigI\n (sum)"))
table.cols.append(("ios_prf", "I/sigI\n (prf)"))
table.cols.append(("cc_prf", "CC prf"))
table.cols.append(("rmsd_xy", "RMSD XY"))
for j, report in enumerate(report_list):
report = report["resolution"]
for i in range(len(report["bins"]) - 1):
table.rows.append([
"%d" % j,
"%.2f" % report["bins"][i],
"%d" % report["n_full"][i],
"%d" % report["n_partial"][i],
"%d" % report["n_overload"][i],
"%d" % report["n_ice"][i],
"%d" % report["n_summed"][i],
"%d" % report["n_fitted"][i],
"%.2f" % report["mean_background"][i],
"%.2f" % report["ios_sum"][i],
"%.2f" % report["ios_prf"][i],
"%.2f" % report["cc_prf"][i],
"%.2f" % report["rmsd_xy"][i],
])
self.add_table(table)
# Create the overall table
for j, report in enumerate(report_list):
report = report["summary"]
summary = report["overall"]
high = report["high"]
low = report["low"]
table = Table()
table.name = "integration.overall.summary"
table.title = "Summary for experiment %d" % j
table.cols.append(("item", "Item"))
table.cols.append(("overall", "Overall"))
table.cols.append(("low", "Low"))
table.cols.append(("high", "High"))
desc_fmt_key = [
("dmin", "%.2f", "dmin"),
("dmax", "%.2f", "dmax"),
("number fully recorded", "%d", "n_full"),
("number partially recorded", "%d", "n_partial"),
("number with invalid background pixels", "%d",
"n_invalid_bg"),
("number with invalid foreground pixels", "%d",
"n_invalid_fg"),
("number with overloaded pixels", "%d", "n_overload"),
("number in powder rings", "%d", "n_ice"),
("number processed with summation", "%d", "n_summed"),
("number processed with profile fitting", "%d", "n_fitted"),
("number failed in background modelling", "%d",
"n_failed_background"),
("number failed in summation", "%d", "n_failed_summation"),
("number failed in profile fitting", "%d", "n_failed_fitting"),
("ibg", "%.2f", "mean_background"),
("i/sigi (summation)", "%.2f", "ios_sum"),
("i/sigi (profile fitting)", "%.2f", "ios_prf"),
("cc prf", "%.2f", "cc_prf"),
("cc_pearson sum/prf", "%.2f", "cc_pearson_sum_prf"),
("cc_spearman sum/prf", "%.2f", "cc_spearman_sum_prf"),
]
for desc, fmt, key in desc_fmt_key:
table.rows.append([
desc, fmt % summary[key], fmt % low[key], fmt % high[key]
])
self.add_table(table)
def run(self):
"""Run the script."""
from dials.util.options import flatten_reflections, flatten_experiments
# Parse the command line arguments
params, options = self.parser.parse_args(show_diff_phil=True)
# Show the help
if len(params.input.reflections) != 1 and not len(
params.input.experiments):
self.parser.print_help()
exit(0)
from dials.util.options import flatten_reflections, flatten_experiments
reflections = flatten_reflections(params.input.reflections)[0]
experiments = flatten_experiments(params.input.experiments)
assert len(experiments) == 1
report = generate_integration_report(
experiments[0],
reflections,
n_resolution_bins=params.n_resolution_bins,
d_max=params.d_max,
d_min=params.d_min,
)
# Construct the per resolution table
from dials.util.report import Table
table = Table()
table.name = "integration.resolution.summary"
table.title = "Summary vs resolution"
table.cols.append(("d_max", "d_max"))
table.cols.append(("d_mid", "d_mid"))
table.cols.append(("d_min", "d_min"))
table.cols.append(("n_full", "# full"))
table.cols.append(("n_part", "# part"))
table.cols.append(("n_over", "# over"))
table.cols.append(("n_ice", "# ice"))
table.cols.append(("n_sum", "# sum"))
table.cols.append(("n_prf", "# prf"))
table.cols.append(("ibg", "Ibg"))
table.cols.append(("i_sum", "<I>\n (sum)"))
table.cols.append(("ios_sum", "I/sigI\n (sum)"))
table.cols.append(("ios_prf", "I/sigI\n (prf)"))
table.cols.append(("cc_prf", "CC prf"))
table.cols.append(("rmsd_xy", "RMSD XY"))
# extract resolution report
report = report["resolution"]
report["d_mid"] = []
for i in range(len(report["bins"]) - 1):
report["d_mid"].append(
(report["bins"][i + 1] + report["bins"][i]) / 2.0)
table.rows.append([
"%.2f" % report["bins"][i + 1],
"%.2f" % report["d_mid"][i],
"%.2f" % report["bins"][i],
"%d" % report["n_full"][i],
"%d" % report["n_partial"][i],
"%d" % report["n_overload"][i],
"%d" % report["n_ice"][i],
"%d" % report["n_summed"][i],
"%d" % report["n_fitted"][i],
"%.4f" % report["mean_background"][i],
"%.4f" % report["i_sum"][i],
"%.4f" % report["ios_sum"][i],
"%.4f" % report["ios_prf"][i],
"%.4f" % report["cc_prf"][i],
"%.4f" % report["rmsd_xy"][i],
])
print(table.as_str())
# plot mean background first
# from matplotlib import pyplot
# fig = pyplot.figure()
# ax = fig.add_subplot(111)
# ax.set_xlabel(r'resolution ($\AA$)')
# ax.set_ylabel(r'$\langle I_b \rangle$')
# d_mid = flex.double(report['d_mid'])
# I_bg = flex.double(report['mean_background'][:-1])
# dm2 = 1/flex.pow2(d_mid)
# ax.plot(dm2, I_bg)
# xticks = ax.get_xticks()
# from math import sqrt
# x_tick_labs = ["" if e <= 0.0 else "{:.2f}".format(sqrt(1./e))
# for e in xticks]
# ax.set_xticklabels(x_tick_labs)
#
# pyplot.show()
# try plot of log intensity and background
templ = experiments[0].imageset.get_template()
import os
title = os.path.basename(templ)
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(r"$d_{mid}$ ($\AA$)")
ax.set_ylabel(r"$\langle Intensity \rangle$")
d_mid = flex.double(report["d_mid"])
I_bg = flex.double(report["mean_background"][:-1])
I_sum = flex.double(report["i_sum"][:-1])
dm2 = 1 / flex.pow2(d_mid)
ax.plot(dm2, I_bg, label="Background")
ax.plot(dm2, I_sum, label="Peak")
ax.legend(loc="upper right")
ax.set_title(title)
ax.set_ylim(-2, 100)
pyplot.minorticks_on()
ax.grid(True, which="both")
xticks = ax.get_xticks()
from math import sqrt
x_tick_labs = [
"" if e <= 0.0 else "{:.2f}".format(sqrt(1.0 / e)) for e in xticks
]
ax.set_xticklabels(x_tick_labs)
pyplot.savefig("intensity_vs_resolution.png")
def __init__(self, experiments, reflections):
'''
Create the integration report
:param experiments: The experiment list
:param reflections: The reflection table
'''
from collections import OrderedDict
# Initialise the report class
super(IntegrationReport, self).__init__()
# Split the tables by experiment id
tables = reflections.split_by_experiment_id()
assert(len(tables) == len(experiments))
# Initialise the dictionary
report_list = []
# Generate an integration report for each experiment
for i, (expr, data) in enumerate(zip(experiments, tables)):
report_list.append(generate_integration_report(expr, data))
# Construct the per image table
table = Table()
table.name = 'integration.image.summary'
table.title = "Summary vs image number"
table.cols.append(('id', 'ID'))
table.cols.append(('image', 'Image'))
table.cols.append(('n_full', '# full'))
table.cols.append(('n_part', '# part'))
table.cols.append(('n_over', '# over'))
table.cols.append(('n_ice', '# ice'))
table.cols.append(('n_sum', '# sum'))
table.cols.append(('n_prf', '# prf'))
table.cols.append(('ibg', 'Ibg'))
table.cols.append(('ios_sum', 'I/sigI\n (sum)'))
table.cols.append(('ios_prf', 'I/sigI\n (prf)'))
table.cols.append(('cc_prf', 'CC prf'))
table.cols.append(('rmsd_xy', 'RMSD XY'))
for j, report in enumerate(report_list):
report = report['image']
for i in range(len(report['bins'])-1):
table.rows.append([
'%d' % j,
'%d' % report['bins'][i],
'%d' % report['n_full'][i],
'%d' % report['n_partial'][i],
'%d' % report['n_overload'][i],
'%d' % report['n_ice'][i],
'%d' % report['n_summed'][i],
'%d' % report['n_fitted'][i],
'%.2f' % report['mean_background'][i],
'%.2f' % report['ios_sum'][i],
'%.2f' % report['ios_prf'][i],
'%.2f' % report['cc_prf'][i],
'%.2f' % report['rmsd_xy'][i]])
self.add_table(table)
# Construct the per resolution table
table = Table()
table.name = 'integration.resolution.summary'
table.title = "Summary vs resolution"
table.cols.append(('id', 'ID'))
table.cols.append(('dmin', 'd min'))
table.cols.append(('n_full', '# full'))
table.cols.append(('n_part', '# part'))
table.cols.append(('n_over', '# over'))
table.cols.append(('n_ice', '# ice'))
table.cols.append(('n_sum', '# sum'))
table.cols.append(('n_prf', '# prf'))
table.cols.append(('ibg', 'Ibg'))
table.cols.append(('ios_sum', 'I/sigI\n (sum)'))
table.cols.append(('ios_prf', 'I/sigI\n (prf)'))
table.cols.append(('cc_prf', 'CC prf'))
table.cols.append(('rmsd_xy', 'RMSD XY'))
for j, report in enumerate(report_list):
report = report['resolution']
for i in range(len(report['bins'])-1):
table.rows.append([
'%d' % j,
'%.2f' % report['bins'][i],
'%d' % report['n_full'][i],
'%d' % report['n_partial'][i],
'%d' % report['n_overload'][i],
'%d' % report['n_ice'][i],
'%d' % report['n_summed'][i],
'%d' % report['n_fitted'][i],
'%.2f' % report['mean_background'][i],
'%.2f' % report['ios_sum'][i],
'%.2f' % report['ios_prf'][i],
'%.2f' % report['cc_prf'][i],
'%.2f' % report['rmsd_xy'][i]])
self.add_table(table)
# Create the overall table
for j, report in enumerate(report_list):
report = report['summary']
summary = report['overall']
high = report['high']
low = report['low']
table = Table()
table.name = 'integration.overall.summary'
table.title = "Summary for experiment %d" % j
table.cols.append(('item', 'Item'))
table.cols.append(('overall', 'Overall'))
table.cols.append(('low', 'Low'))
table.cols.append(('high', 'High'))
desc_fmt_key = [
("dmin", '%.2f', 'dmin'),
('dmax', '%.2f', 'dmax'),
("number fully recorded", '%d' , "n_full"),
("number partially recorded", '%d' , "n_partial"),
("number with invalid background pixels", '%d' , "n_invalid_bg"),
("number with invalid foreground pixels", '%d' , "n_invalid_fg"),
("number with overloaded pixels", '%d' , "n_overload"),
("number in powder rings", '%d' , "n_ice"),
("number processed with summation", '%d' , "n_summed"),
("number processed with profile fitting", '%d' , "n_fitted"),
("number failed in background modelling", '%d' , "n_failed_background"),
("number failed in summation", '%d' , "n_failed_summation"),
("number failed in profile fitting", '%d' , "n_failed_fitting"),
("ibg", '%.2f', "mean_background"),
("i/sigi (summation)", '%.2f', "ios_sum"),
("i/sigi (profile fitting)", '%.2f', "ios_prf"),
("cc prf", '%.2f', "cc_prf"),
("cc_pearson sum/prf", '%.2f', "cc_pearson_sum_prf"),
("cc_spearman sum/prf", '%.2f', "cc_spearman_sum_prf")
]
for desc, fmt, key in desc_fmt_key:
table.rows.append([desc, fmt % summary[key], fmt % low[key], fmt % high[key]])
self.add_table(table)
