def as_csv(spotfinder_results, out=None):
if out is None:
import sys
out = sys.stdout
columns = as_columns(spotfinder_results)
from iotbx import csv_utils
csv_utils.writer(out, columns.values(), field_names=columns.keys())
def as_csv(spotfinder_results, out=None):
if out is None:
import sys
out = sys.stdout
columns = as_columns(spotfinder_results)
from iotbx import csv_utils
csv_utils.writer(out, columns.values(), field_names=columns.keys())
def exercise_reader():
from iotbx import csv_utils
x = (1,2,3,4,5)
y = (6,7,8,9,10)
f = open_tmp_file()
field_names = ('x','y')
csv_utils.writer(f, (x,y), field_names=field_names,delimiter=';')
f.close()
f = open(f.name, 'r')
a = csv_utils.reader(f, data_type=int, field_names=True,delimiter=';')
f.close()
assert tuple(a.data[0]) == x
assert tuple(a.data[1]) == y
x = (1,2,3,4,5)
y = (1.1,2.2,3.3,4.4,5.5)
f = open_tmp_file()
csv_utils.writer(f, (x,y))
f.close()
f = open(f.name, 'r')
data_type_list = (int, float)
a = csv_utils.reader(f, data_type_list=data_type_list)
f.close()
assert tuple(a.data[0]) == x
assert tuple(a.data[1]) == y
f = open(f.name, 'r')
data_type_list = (int, float)
try:
a = csv_utils.reader(f, data_type=int,
data_type_list=data_type_list)
# Can't pass data_type AND data_type_list
except AssertionError:
pass
else:
raise Exception_expected
f.close()
f = open(f.name, 'r')
a = csv_utils.reader(f)
f.close()
assert list(a.data[0]) == [str(i) for i in x]
assert list(a.data[1]) == [str(i) for i in y]
def exercise_reader():
from iotbx import csv_utils
x = (1, 2, 3, 4, 5)
y = (6, 7, 8, 9, 10)
f = open_tmp_file()
field_names = ('x', 'y')
csv_utils.writer(f, (x, y), field_names=field_names, delimiter=';')
f.close()
f = open(f.name, 'r')
a = csv_utils.reader(f, data_type=int, field_names=True, delimiter=';')
f.close()
assert tuple(a.data[0]) == x
assert tuple(a.data[1]) == y
x = (1, 2, 3, 4, 5)
y = (1.1, 2.2, 3.3, 4.4, 5.5)
f = open_tmp_file()
csv_utils.writer(f, (x, y))
f.close()
f = open(f.name, 'r')
data_type_list = (int, float)
a = csv_utils.reader(f, data_type_list=data_type_list)
f.close()
assert tuple(a.data[0]) == x
assert tuple(a.data[1]) == y
f = open(f.name, 'r')
data_type_list = (int, float)
try:
a = csv_utils.reader(f, data_type=int, data_type_list=data_type_list)
# Can't pass data_type AND data_type_list
except AssertionError:
pass
else:
raise Exception_expected
f.close()
f = open(f.name, 'r')
a = csv_utils.reader(f)
f.close()
assert list(a.data[0]) == [str(i) for i in x]
assert list(a.data[1]) == [str(i) for i in y]
def exercise_writer():
from iotbx import csv_utils
x = (1, 2, 3, 4, 5)
y = (6, 7, 8, 9, 10)
f = open_tmp_file()
field_names = ('x', 'y')
csv_utils.writer(f, (x, y), field_names=field_names)
f.close()
f = open(f.name, 'r')
content = [l.strip() for l in f.readlines()]
text = ['x,y']
text += ['%s,%s' % (row[0], row[1]) for row in zip(x, y)]
assert content == text
f.close()
x = (1, 2, 3, 4, 5)
y = (6, 7, 8, 9, 10)
f = open_tmp_file()
csv_utils.writer(f, (x, y), delimiter=';')
f.close()
f = open(f.name, 'r')
content = [l.strip() for l in f.readlines()]
text = ['%s;%s' % (row[0], row[1]) for row in zip(x, y)]
assert content == text
f.close()
x = flex.int(x)
y = flex.int(y)
f = open_tmp_file()
csv_utils.writer(f, (x, y), field_names=field_names)
f.close()
f = open(f.name, 'r')
content = [l.strip() for l in f.readlines()]
text = ['x,y']
text += ['%s,%s' % (row[0], row[1]) for row in zip(x, y)]
assert content == text
f.close()
y.append(11)
f = open_tmp_file()
try:
csv_utils.writer(f, (x, y), field_names=field_names)
except AssertionError:
pass
else:
raise Exception_expected
f.close()
def exercise_writer():
from iotbx import csv_utils
x = (1,2,3,4,5)
y = (6,7,8,9,10)
f = open_tmp_file()
field_names = ('x','y')
csv_utils.writer(f, (x,y), field_names=field_names)
f.close()
f = open(f.name, 'r')
content = f.readlines()
text = ['x,y\r\n']
text += ['%s,%s\r\n' %(row[0],row[1]) for row in zip(x,y)]
assert content == text
f.close()
x = (1,2,3,4,5)
y = (6,7,8,9,10)
f = open_tmp_file()
csv_utils.writer(f, (x,y), delimiter=';')
f.close()
f = open(f.name, 'r')
content = f.readlines()
text = ['%s;%s\r\n' %(row[0],row[1]) for row in zip(x,y)]
assert content == text
x = flex.int(x)
y = flex.int(y)
f = open_tmp_file()
csv_utils.writer(f, (x,y), field_names=field_names)
f.close()
f = open(f.name, 'r')
content = f.readlines()
text = ['x,y\r\n']
text += ['%s,%s\r\n' %(row[0],row[1]) for row in zip(x,y)]
assert content == text
f.close()
y.append(11)
f = open_tmp_file()
try:
csv_utils.writer(f, (x,y), field_names=field_names)
except AssertionError:
pass
else:
raise Exception_expected
f.close()
def exercise(self, debug=False):
if debug:
distribution = distributions.normal_distribution()
observed_deviations = (
self.fo2.data() - self.scale_factor*self.fc.as_intensity_array().data())
observed_deviations = observed_deviations.select(
flex.sort_permutation(observed_deviations))
expected_deviations = distribution.quantiles(observed_deviations.size())
csv_utils.writer(
open('delta_F_npp.csv', 'wb'), (expected_deviations, observed_deviations))
# first with the correct absolute structure
gaussian = absolute_structure.hooft_analysis(self.fo2, self.fc)
analyses = [gaussian]
NPP = absolute_structure.bijvoet_differences_probability_plot(gaussian)
if self.use_students_t_errors:
nu_calc = absolute_structure.maximise_students_t_correlation_coefficient(
NPP.y, min_nu=1, max_nu=200)
t_analysis = absolute_structure.students_t_hooft_analysis(
self.fo2, self.fc, nu_calc, probability_plot_slope=NPP.fit.slope())
analyses.append(gaussian)
tPP = absolute_structure.bijvoet_differences_probability_plot(
t_analysis, use_students_t_distribution=True, students_t_nu=nu_calc)
if tPP.distribution.degrees_of_freedom() < 100:
assert tPP.correlation.coefficient() > NPP.correlation.coefficient()
else:
assert approx_equal(NPP.correlation.coefficient(), 1, 0.005)
for analysis in analyses:
assert approx_equal(analysis.hooft_y, 0, 1e-2)
assert approx_equal(analysis.p2_true, 1)
assert approx_equal(analysis.p2_false, 0)
assert approx_equal(analysis.p3_true, 1)
assert approx_equal(analysis.p3_false, 0)
assert approx_equal(analysis.p3_racemic_twin, 0)
if debug:
csv_utils.writer(open('npp.csv', 'wb'), (NPP.x,NPP.y))
if self.use_students_t_errors:
csv_utils.writer(open('tpp.csv', 'wb'), (tPP.x,tPP.y))
assert approx_equal(NPP.fit.y_intercept(), 0)
# and now with the wrong absolute structure
gaussian = absolute_structure.hooft_analysis(self.fo2, self.fc_i)
analyses = [gaussian]
NPP = absolute_structure.bijvoet_differences_probability_plot(gaussian)
if self.use_students_t_errors:
nu_calc = absolute_structure.maximise_students_t_correlation_coefficient(
NPP.y, min_nu=1, max_nu=200)
t_analysis = absolute_structure.students_t_hooft_analysis(
self.fo2, self.fc_i, nu_calc, probability_plot_slope=NPP.fit.slope())
analyses.append(gaussian)
tPP = absolute_structure.bijvoet_differences_probability_plot(
t_analysis, use_students_t_distribution=True)
if tPP.distribution.degrees_of_freedom() < 100:
assert tPP.correlation.coefficient() > NPP.correlation.coefficient()
else:
assert approx_equal(NPP.correlation.coefficient(), 1, 0.002)
assert approx_equal(NPP.fit.y_intercept(), 0)
for analysis in analyses:
assert approx_equal(analysis.hooft_y, 1, 1e-2)
assert approx_equal(analysis.p2_true, 0)
assert approx_equal(analysis.p2_false, 1)
assert approx_equal(analysis.p3_true, 0)
assert approx_equal(analysis.p3_false, 1)
assert approx_equal(analysis.p3_racemic_twin, 0)
# test for the case of a racemic twin
gaussian = absolute_structure.hooft_analysis(self.fo2_twin, self.fc)
analyses = [gaussian]
NPP = absolute_structure.bijvoet_differences_probability_plot(gaussian)
if self.use_students_t_errors:
nu_calc = absolute_structure.maximise_students_t_correlation_coefficient(
NPP.y, min_nu=1, max_nu=200)
t_analysis = absolute_structure.students_t_hooft_analysis(
self.fo2_twin, self.fc, nu_calc, probability_plot_slope=NPP.fit.slope())
tPP = absolute_structure.bijvoet_differences_probability_plot(
t_analysis, use_students_t_distribution=True)
if tPP.distribution.degrees_of_freedom() < 100:
assert tPP.correlation.coefficient() > NPP.correlation.coefficient()
else:
assert approx_equal(NPP.correlation.coefficient(), 1, 0.002)
assert approx_equal(NPP.fit.y_intercept(), 0)
for analysis in analyses:
assert approx_equal(analysis.hooft_y, 0.5, 1e-2)
assert approx_equal(analysis.p3_true, 0)
assert approx_equal(analysis.p3_false, 0)
assert approx_equal(analysis.p3_racemic_twin, 1)
def exercise(self, debug=False):
if debug:
distribution = distributions.normal_distribution()
observed_deviations = (
self.fo2.data() - self.scale_factor*self.fc.as_intensity_array().data())
observed_deviations = observed_deviations.select(
flex.sort_permutation(observed_deviations))
expected_deviations = distribution.quantiles(observed_deviations.size())
csv_utils.writer(
open('delta_F_npp.csv', 'wb'), (expected_deviations, observed_deviations))
# first with the correct absolute structure
gaussian = absolute_structure.hooft_analysis(self.fo2, self.fc)
analyses = [gaussian]
NPP = absolute_structure.bijvoet_differences_probability_plot(gaussian)
if self.use_students_t_errors:
nu_calc = absolute_structure.maximise_students_t_correlation_coefficient(
NPP.y, min_nu=1, max_nu=200)
t_analysis = absolute_structure.students_t_hooft_analysis(
self.fo2, self.fc, nu_calc, probability_plot_slope=NPP.fit.slope())
analyses.append(gaussian)
tPP = absolute_structure.bijvoet_differences_probability_plot(
t_analysis, use_students_t_distribution=True, students_t_nu=nu_calc)
if tPP.distribution.degrees_of_freedom() < 100:
assert tPP.correlation.coefficient() > NPP.correlation.coefficient()
else:
assert approx_equal(NPP.correlation.coefficient(), 1, 0.005)
for analysis in analyses:
assert approx_equal(analysis.hooft_y, 0, 1e-2)
assert approx_equal(analysis.p2_true, 1)
assert approx_equal(analysis.p2_false, 0)
assert approx_equal(analysis.p3_true, 1)
assert approx_equal(analysis.p3_false, 0)
assert approx_equal(analysis.p3_racemic_twin, 0)
if debug:
csv_utils.writer(open('npp.csv', 'wb'), (NPP.x,NPP.y))
if self.use_students_t_errors:
csv_utils.writer(open('tpp.csv', 'wb'), (tPP.x,tPP.y))
assert approx_equal(NPP.fit.y_intercept(), 0)
# and now with the wrong absolute structure
gaussian = absolute_structure.hooft_analysis(self.fo2, self.fc_i)
analyses = [gaussian]
NPP = absolute_structure.bijvoet_differences_probability_plot(gaussian)
if self.use_students_t_errors:
nu_calc = absolute_structure.maximise_students_t_correlation_coefficient(
NPP.y, min_nu=1, max_nu=200)
t_analysis = absolute_structure.students_t_hooft_analysis(
self.fo2, self.fc_i, nu_calc, probability_plot_slope=NPP.fit.slope())
analyses.append(gaussian)
tPP = absolute_structure.bijvoet_differences_probability_plot(
t_analysis, use_students_t_distribution=True)
if tPP.distribution.degrees_of_freedom() < 100:
assert tPP.correlation.coefficient() > NPP.correlation.coefficient()
else:
assert approx_equal(NPP.correlation.coefficient(), 1, 0.002)
assert approx_equal(NPP.fit.y_intercept(), 0)
for analysis in analyses:
assert approx_equal(analysis.hooft_y, 1, 1e-2)
assert approx_equal(analysis.p2_true, 0)
assert approx_equal(analysis.p2_false, 1)
assert approx_equal(analysis.p3_true, 0)
assert approx_equal(analysis.p3_false, 1)
assert approx_equal(analysis.p3_racemic_twin, 0)
# test for the case of a racemic twin
gaussian = absolute_structure.hooft_analysis(self.fo2_twin, self.fc)
analyses = [gaussian]
NPP = absolute_structure.bijvoet_differences_probability_plot(gaussian)
if self.use_students_t_errors:
nu_calc = absolute_structure.maximise_students_t_correlation_coefficient(
NPP.y, min_nu=1, max_nu=200)
t_analysis = absolute_structure.students_t_hooft_analysis(
self.fo2_twin, self.fc, nu_calc, probability_plot_slope=NPP.fit.slope())
tPP = absolute_structure.bijvoet_differences_probability_plot(
t_analysis, use_students_t_distribution=True)
if tPP.distribution.degrees_of_freedom() < 100:
assert tPP.correlation.coefficient() > NPP.correlation.coefficient()
else:
assert approx_equal(NPP.correlation.coefficient(), 1, 0.002)
assert approx_equal(NPP.fit.y_intercept(), 0)
for analysis in analyses:
assert approx_equal(analysis.hooft_y, 0.5, 1e-2)
assert approx_equal(analysis.p3_true, 0)
assert approx_equal(analysis.p3_false, 0)
assert approx_equal(analysis.p3_racemic_twin, 1)
