def cctbx_miller_fft_map_as_xplor_map(
self,
file_name,
title_lines=["cctbx.miller.fft_map"],
gridding_first=None,
gridding_last=None,
average=None,
standard_deviation=None):
if (gridding_first is None): gridding_first = (0,0,0)
if (gridding_last is None): gridding_last = self.n_real()
gridding_ = gridding(
n=self.n_real(),
first=gridding_first,
last=gridding_last)
data = self.real_map()
if (average is None or standard_deviation is None):
statistics = maptbx.statistics(data)
if (average is None): average = statistics.mean()
if (standard_deviation is None): standard_deviation = statistics.sigma()
writer(
file_name=file_name,
title_lines=title_lines,
unit_cell=self.unit_cell(),
gridding=gridding_,
data=data,
is_p1_cell=True,
average=average,
standard_deviation=standard_deviation)
def exercise(args,use_mrcfile=None):
exercise_with_tst_input_map(use_mrcfile=use_mrcfile)
exercise_with_tst_input_map_2(use_mrcfile=use_mrcfile)
for file_name in args:
print("\n",file_name,"use_mrcfile=",use_mrcfile)
if use_mrcfile:
m = iotbx.mrcfile.map_reader(file_name=file_name)
else:
m = iotbx.ccp4_map.map_reader(file_name=file_name)
print("header_min: ", m.header_min)
print("header_max: ", m.header_max)
print("header_mean:", m.header_mean)
print("header_rms: ", m.header_rms)
print("unit cell grid:", m.unit_cell_grid)
print("unit cell parameters:", m.unit_cell_parameters)
print("space group number: ", m.space_group_number)
print("map origin:", m.map_data().origin())
print("map grid: ", m.map_data().all())
map_stats = maptbx.statistics(m.data)
assert approx_equal(map_stats.min(), m.header_min)
assert approx_equal(map_stats.max(), m.header_max)
assert approx_equal(map_stats.mean(), m.header_mean)
if (m.header_rms != 0):
assert approx_equal(map_stats.sigma(), m.header_rms)
print()
def calculate_exp_i_two_phi_peaks(xray_structure, d_min,
min_peak_distance,
max_reduced_peaks):
f_h = xray_structure.structure_factors(
anomalous_flag=False,
d_min=d_min).f_calc()
two_i_phi_h = miller.array(
miller_set=f_h,
data=flex.polar(1, flex.arg(f_h.data())*2))
fft_map = two_i_phi_h.fft_map(
d_min=d_min,
symmetry_flags=maptbx.use_space_group_symmetry)
real_map = fft_map.real_map()
real_map = maptbx.copy(real_map, flex.grid(real_map.focus()))
stats = maptbx.statistics(real_map)
if (stats.max() != 0):
real_map /= abs(stats.max())
grid_tags = maptbx.grid_tags(real_map.focus())
grid_tags.build(fft_map.space_group_info().type(), fft_map.symmetry_flags())
grid_tags.verify(real_map)
peak_list = maptbx.peak_list(
data=real_map,
tags=grid_tags.tag_array(),
max_peaks=10*max_reduced_peaks,
interpolate=True)
reduced_peaks = peak_cluster_reduction(
crystal_symmetry=xray_structure,
peak_list=peak_list,
min_peak_distance=min_peak_distance,
max_reduced_peaks=max_reduced_peaks)
return reduced_peaks
def recycle():
for n, first, last in [[(5, 3, 4), (0, 0, 0), (3, 5, 6)],
[(4, 3, 5), (-1, -3, 4), (6, 4, 5)],
[(3, 4, 5), (-2, 3, 0), (-2, 3, 0)],
[(3, 4, 5), (-2, 3, 0), (-2, 3, 3)],
[(3, 4, 5), (-2, 3, 0), (-2, 8, 0)],
[(3, 4, 5), (-2, 3, 0), (-2, 9, 0)],
[(3, 4, 5), (-2, 3, 0), (3, 3, 0)],
[(3, 4, 5), (-2, 3, 0), (4, 3, 0)]]:
gridding = iotbx.xplor.map.gridding(n=n, first=first, last=last)
flex_grid = gridding.as_flex_grid()
data = 20000 * flex.random_double(size=flex_grid.size_1d()) - 10000
data.resize(flex_grid)
stats = maptbx.statistics(data)
iotbx.xplor.map.writer(file_name="tmp.map",
title_lines=["regression test"],
unit_cell=uctbx.unit_cell(
(10, 20, 30, 80, 90, 100)),
gridding=gridding,
data=data,
average=stats.mean(),
standard_deviation=stats.sigma())
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["regression test"]
assert read.gridding.n == n
assert read.gridding.first == first
assert read.gridding.last == last
assert read.unit_cell.is_similar_to(
uctbx.unit_cell((10, 20, 30, 80, 90, 100)))
assert eps_eq(read.average, stats.mean(), eps=1.e-4)
assert eps_eq(read.standard_deviation, stats.sigma(), eps=1.e-4)
assert read.data.origin() == first
assert read.data.last(False) == last
assert read.data.focus() == data.focus()
assert eps_eq(read.data, data, eps=1.e-4)
def run_fast_nv1995(f_obs, f_calc_fixed, f_calc_p1,
symmetry_flags, gridding, grid_tags, verbose):
if (f_calc_fixed is None):
f_part = flex.complex_double()
else:
f_part = f_calc_fixed.data()
assert f_obs.anomalous_flag() == f_calc_p1.anomalous_flag()
fast_nv1995 = translation_search.fast_nv1995(
gridding=gridding,
space_group=f_obs.space_group(),
anomalous_flag=f_obs.anomalous_flag(),
miller_indices_f_obs=f_obs.indices(),
f_obs=f_obs.data(),
f_part=f_part,
miller_indices_p1_f_calc=f_calc_p1.indices(),
p1_f_calc=f_calc_p1.data())
assert fast_nv1995.target_map().all() == gridding
map_stats = maptbx.statistics(fast_nv1995.target_map())
if (0 or verbose):
map_stats.show_summary()
grid_tags.build(f_obs.space_group_info().type(), symmetry_flags)
assert grid_tags.n_grid_misses() == 0
assert grid_tags.verify(fast_nv1995.target_map())
peak_list = maptbx.peak_list(
data=fast_nv1995.target_map(),
tags=grid_tags.tag_array(),
peak_search_level=1,
max_peaks=10,
interpolate=True)
if (0 or verbose):
print "gridding:", gridding
for i,site in enumerate(peak_list.sites()):
print "(%.4f,%.4f,%.4f)" % site, "%.6g" % peak_list.heights()[i]
assert approx_equal(map_stats.max(), flex.max(peak_list.grid_heights()))
return peak_list
def cctbx_miller_fft_map_as_xplor_map(
self,
file_name,
title_lines=["cctbx.miller.fft_map"],
gridding_first=None,
gridding_last=None,
average=None,
standard_deviation=None,
):
if gridding_first is None:
gridding_first = (0, 0, 0)
if gridding_last is None:
gridding_last = self.n_real()
gridding_ = gridding(n=self.n_real(), first=gridding_first, last=gridding_last)
data = self.real_map()
if average is None or standard_deviation is None:
statistics = maptbx.statistics(data)
if average is None:
average = statistics.mean()
if standard_deviation is None:
standard_deviation = statistics.sigma()
writer(
file_name=file_name,
title_lines=title_lines,
unit_cell=self.unit_cell(),
gridding=gridding_,
data=data,
is_p1_cell=True,
average=average,
standard_deviation=standard_deviation,
)
def read_xplor(file_name):
a = iotbx.xplor.map.reader(file_name=file_name)
assert a.title_lines == [
' REMARKS FILENAME="cns.map"',
' REMARKS DATE:15-May-2004 02:15:56 created by user: rwgk',
' REMARKS VERSION:1.1'
]
assert a.gridding.n == (24, 24, 40)
assert a.gridding.first == (1, -4, -6)
assert a.gridding.last == (10, 0, -3)
assert a.unit_cell.is_similar_to(
uctbx.unit_cell(
(7.41407939496, 7.41407939496, 12.6039349714, 90, 90, 120)))
assert approx_equal(a.average, -0.5274E-10)
assert approx_equal(a.standard_deviation, 0.1792E+00)
assert a.data.origin() == (1, -4, -6)
assert a.data.last(False) == (10, 0, -3)
assert a.data.focus() == (11, 1, -2)
assert approx_equal(a.data[:10], [
-2.63210E-01, -4.36970E-01, -5.71930E-01, -6.09230E-01, -2.07220E-01,
-4.15100E-01, -6.11970E-01, -7.13380E-01, -2.05500E-01, -3.60990E-01
])
assert approx_equal(a.data[40:50], [
-4.08540E-01, -4.77320E-01, -5.16210E-01, -4.84100E-01, -2.93930E-01,
-3.58500E-01, -4.40170E-01, -4.92110E-01, -2.19660E-01, -2.40570E-01
])
assert approx_equal(a.data[-10:], [
-2.13550E-01, -4.87250E-01, -4.51260E-02, -2.13540E-01, -4.57070E-01,
-6.38040E-01, -3.51570E-01, -5.98030E-01, -7.60270E-01, -7.62940E-01
])
stats = maptbx.statistics(a.data)
assert approx_equal(stats.min(), -0.78098)
assert approx_equal(stats.max(), 0.27233)
assert approx_equal(stats.mean(), -0.363687)
assert approx_equal(stats.sigma(), 0.20679)
s = StringIO()
a.show_summary(out=s, prefix="$")
assert not show_diff(
s.getvalue(), """\
$Title lines: 3
$ REMARKS FILENAME="cns.map"
$ REMARKS DATE:15-May-2004 02:15:56 created by user: rwgk
$ REMARKS VERSION:1.1
$Gridding:
$ n: (24, 24, 40)
$ first: (1, -4, -6)
$ last: (10, 0, -3)
$Total number of data points: 200
$ min: -0.78098
$ max: 0.27233
$ mean: -0.363687
$ sigma: 0.20679
""")
return a
def read_xplor(file_name):
a = iotbx.xplor.map.reader(file_name=file_name)
assert a.title_lines == [
' REMARKS FILENAME="cns.map"',
' REMARKS DATE:15-May-2004 02:15:56 created by user: rwgk',
' REMARKS VERSION:1.1']
assert a.gridding.n == (24,24,40)
assert a.gridding.first == (1,-4,-6)
assert a.gridding.last == (10,0,-3)
assert a.unit_cell.is_similar_to(
uctbx.unit_cell((7.41407939496,7.41407939496,12.6039349714,90,90,120)))
assert approx_equal(a.average, -0.5274E-10)
assert approx_equal(a.standard_deviation, 0.1792E+00)
assert a.data.origin() == (1,-4,-6)
assert a.data.last(False) == (10,0,-3)
assert a.data.focus() == (11,1,-2)
assert approx_equal(a.data[:10],
[-2.63210E-01, -4.36970E-01, -5.71930E-01, -6.09230E-01, -2.07220E-01,
-4.15100E-01, -6.11970E-01, -7.13380E-01, -2.05500E-01, -3.60990E-01])
assert approx_equal(a.data[40:50],
[-4.08540E-01, -4.77320E-01, -5.16210E-01, -4.84100E-01, -2.93930E-01,
-3.58500E-01, -4.40170E-01, -4.92110E-01, -2.19660E-01, -2.40570E-01])
assert approx_equal(a.data[-10:],
[-2.13550E-01, -4.87250E-01, -4.51260E-02, -2.13540E-01, -4.57070E-01,
-6.38040E-01, -3.51570E-01, -5.98030E-01, -7.60270E-01, -7.62940E-01])
stats = maptbx.statistics(a.data)
assert approx_equal(stats.min(), -0.78098)
assert approx_equal(stats.max(), 0.27233)
assert approx_equal(stats.mean(), -0.363687)
assert approx_equal(stats.sigma(), 0.20679)
s = StringIO()
a.show_summary(out=s, prefix="$")
assert not show_diff(s.getvalue(), """\
$Title lines: 3
$ REMARKS FILENAME="cns.map"
$ REMARKS DATE:15-May-2004 02:15:56 created by user: rwgk
$ REMARKS VERSION:1.1
$Gridding:
$ n: (24, 24, 40)
$ first: (1, -4, -6)
$ last: (10, 0, -3)
$Total number of data points: 200
$ min: -0.78098
$ max: 0.27233
$ mean: -0.363687
$ sigma: 0.20679
""")
return a
def show_summary(self, out=None, prefix=""):
if (out is None): out = sys.stdout
print >> out, prefix+"Title lines:", len(self.title_lines)
for line in self.title_lines:
print >> out, prefix+" "+line.rstrip()
g = self.gridding
print >> out, prefix+"Gridding:"
print >> out, prefix+" n: ", g.n
print >> out, prefix+" first:", g.first
print >> out, prefix+" last: ", g.last
print >> out, prefix+"Total number of data points:", self.data.size()
stats = maptbx.statistics(self.data)
print >> out, prefix+" min: %.6g" % stats.min()
print >> out, prefix+" max: %.6g" % stats.max()
print >> out, prefix+" mean: %.6g" % stats.mean()
print >> out, prefix+" sigma: %.6g" % stats.sigma()
def show_summary(self, out=None, prefix=""):
if out is None:
out = sys.stdout
print >> out, prefix + "Title lines:", len(self.title_lines)
for line in self.title_lines:
print >> out, prefix + " " + line.rstrip()
g = self.gridding
print >> out, prefix + "Gridding:"
print >> out, prefix + " n: ", g.n
print >> out, prefix + " first:", g.first
print >> out, prefix + " last: ", g.last
print >> out, prefix + "Total number of data points:", self.data.size()
stats = maptbx.statistics(self.data)
print >> out, prefix + " min: %.6g" % stats.min()
print >> out, prefix + " max: %.6g" % stats.max()
print >> out, prefix + " mean: %.6g" % stats.mean()
print >> out, prefix + " sigma: %.6g" % stats.sigma()
def __init__(self,
fft_map=None,
unit_cell=None, raw_map=None,
periodic=False,
positive_iso_level=None,
iso_level_positive_range_fraction=None,
negative_iso_level=None,
iso_level_negative_range_fraction=None,
wires=True,
**kwds):
if fft_map is not None:
unit_cell = fft_map.unit_cell()
super(map_viewer, self).__init__(unit_cell=unit_cell,
light_position=(-1, 1, 1, 0),
**kwds)
assert (fft_map is not None
or (unit_cell is not None and raw_map is not None))
assert (positive_iso_level is not None
or iso_level_positive_range_fraction is not None)
assert (bool(negative_iso_level is not None)
^ bool(iso_level_negative_range_fraction is None))
if fft_map is not None:
self.rho = fft_map.real_map()
else:
self.rho = raw_map
density_stats = maptbx.statistics(self.rho)
self.min_density = density_stats.min()
self.max_density = density_stats.max()
if positive_iso_level is not None:
self.positive_iso_level = positive_iso_level
else:
self.positive_iso_level = (
iso_level_positive_range_fraction*self.max_density)
if (negative_iso_level is None
and iso_level_negative_range_fraction is not None):
negative_iso_level = (
iso_level_negative_range_fraction*self.min_density)
self.negative_iso_level = negative_iso_level
self.periodic = periodic
self.wires = wires
self.compute_triangulation()
def __init__(self,
fft_map=None,
unit_cell=None, raw_map=None,
periodic=False,
positive_iso_level=None,
iso_level_positive_range_fraction=None,
negative_iso_level=None,
iso_level_negative_range_fraction=None,
wires=True,
**kwds):
if fft_map is not None:
unit_cell = fft_map.unit_cell()
super(map_viewer, self).__init__(unit_cell=unit_cell,
light_position=(-1, 1, 1, 0),
**kwds)
assert (fft_map is not None
or (unit_cell is not None and raw_map is not None))
assert (positive_iso_level is not None
or iso_level_positive_range_fraction is not None)
assert (bool(negative_iso_level is not None)
^ bool(iso_level_negative_range_fraction is None))
if fft_map is not None:
self.rho = fft_map.real_map()
else:
self.rho = raw_map
density_stats = maptbx.statistics(self.rho)
self.min_density = density_stats.min()
self.max_density = density_stats.max()
if positive_iso_level is not None:
self.positive_iso_level = positive_iso_level
else:
self.positive_iso_level = (
iso_level_positive_range_fraction*self.max_density)
if (negative_iso_level is None
and iso_level_negative_range_fraction is not None):
negative_iso_level = (
iso_level_negative_range_fraction*self.min_density)
self.negative_iso_level = negative_iso_level
self.periodic = periodic
self.wires = wires
self.compute_triangulation()
def exercise(args):
exercise_with_tst_input_map()
for file_name in args:
print file_name
m = iotbx.ccp4_map.map_reader(file_name=file_name)
print "header_min: ", m.header_min
print "header_max: ", m.header_max
print "header_mean:", m.header_mean
print "header_rms: ", m.header_rms
print "unit cell grid:", m.unit_cell_grid
print "unit cell parameters:", m.unit_cell_parameters
print "space group number: ", m.space_group_number
print "map origin:", m.data.origin()
print "map grid: ", m.data.all()
map_stats = maptbx.statistics(m.data)
assert approx_equal(map_stats.min(), m.header_min)
assert approx_equal(map_stats.max(), m.header_max)
assert approx_equal(map_stats.mean(), m.header_mean)
if (m.header_rms != 0):
assert approx_equal(map_stats.sigma(), m.header_rms)
print
def exercise(args):
exercise_with_tst_input_map()
for file_name in args:
print file_name
m = iotbx.ccp4_map.map_reader(file_name=file_name)
print "header_min: ", m.header_min
print "header_max: ", m.header_max
print "header_mean:", m.header_mean
print "header_rms: ", m.header_rms
print "unit cell grid:", m.unit_cell_grid
print "unit cell parameters:", m.unit_cell_parameters
print "space group number: ", m.space_group_number
print "map origin:", m.data.origin()
print "map grid: ", m.data.all()
map_stats = maptbx.statistics(m.data)
assert approx_equal(map_stats.min(), m.header_min)
assert approx_equal(map_stats.max(), m.header_max)
assert approx_equal(map_stats.mean(), m.header_mean)
if (m.header_rms != 0):
assert approx_equal(map_stats.sigma(), m.header_rms)
print
def recycle():
for n,first,last in [[(5,3,4),(0,0,0),(3,5,6)],
[(4,3,5),(-1,-3,4),(6,4,5)],
[(3,4,5),(-2,3,0),(-2,3,0)],
[(3,4,5),(-2,3,0),(-2,3,3)],
[(3,4,5),(-2,3,0),(-2,8,0)],
[(3,4,5),(-2,3,0),(-2,9,0)],
[(3,4,5),(-2,3,0),(3,3,0)],
[(3,4,5),(-2,3,0),(4,3,0)]]:
gridding = iotbx.xplor.map.gridding(
n=n, first=first, last=last)
flex_grid = gridding.as_flex_grid()
data = 20000*flex.random_double(size=flex_grid.size_1d())-10000
data.resize(flex_grid)
stats = maptbx.statistics(data)
iotbx.xplor.map.writer(
file_name="tmp.map",
title_lines=["regression test"],
unit_cell=uctbx.unit_cell((10,20,30,80,90,100)),
gridding=gridding,
data=data,
average=stats.mean(),
standard_deviation=stats.sigma())
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["regression test"]
assert read.gridding.n == n
assert read.gridding.first == first
assert read.gridding.last == last
assert read.unit_cell.is_similar_to(
uctbx.unit_cell((10,20,30,80,90,100)))
assert eps_eq(read.average, stats.mean(), eps=1.e-4)
assert eps_eq(read.standard_deviation, stats.sigma(), eps=1.e-4)
assert read.data.origin() == first
assert read.data.last(False) == last
assert read.data.focus() == data.focus()
assert eps_eq(read.data, data, eps=1.e-4)
def run_fast_terms(structure_fixed, structure_p1,
f_obs, f_calc_fixed, f_calc_p1,
symmetry_flags, gridding, grid_tags,
n_sample_grid_points=10,
test_origin=False,
verbose=0):
if (f_calc_fixed is None):
f_part = flex.complex_double()
else:
f_part = f_calc_fixed.data()
m = flex.double()
for i in xrange(f_obs.indices().size()):
m.append(random.random())
assert f_obs.anomalous_flag() == f_calc_p1.anomalous_flag()
fast_terms = translation_search.fast_terms(
gridding=gridding,
anomalous_flag=f_obs.anomalous_flag(),
miller_indices_p1_f_calc=f_calc_p1.indices(),
p1_f_calc=f_calc_p1.data())
for squared_flag in (False, True):
map = fast_terms.summation(
space_group=f_obs.space_group(),
miller_indices_f_obs=f_obs.indices(),
m=m,
f_part=f_part,
squared_flag=squared_flag).fft().accu_real_copy()
assert map.all() == gridding
map_stats = maptbx.statistics(map)
if (0 or verbose):
map_stats.show_summary()
grid_tags.build(f_obs.space_group_info().type(), symmetry_flags)
assert grid_tags.n_grid_misses() == 0
assert grid_tags.verify(map)
for i_sample in xrange(n_sample_grid_points):
run_away_counter = 0
while 1:
run_away_counter += 1
assert run_away_counter < 1000
if (i_sample == 0 and test_origin):
grid_point = [0,0,0]
else:
grid_point = [random.randrange(g) for g in gridding]
grid_site = [float(x)/g for x,g in zip(grid_point,gridding)]
structure_shifted = structure_fixed.deep_copy_scatterers()
assert structure_shifted.special_position_indices().size() == 0
structure_shifted.add_scatterers(
scatterers=structure_p1.apply_shift(grid_site).scatterers())
if (structure_shifted.special_position_indices().size() == 0):
break
if (test_origin):
assert i_sample != 0
i_grid = flex.norm(f_obs.structure_factors_from_scatterers(
xray_structure=structure_shifted, algorithm="direct").f_calc().data())
if (squared_flag): p = 4
else: p = 2
map_value = map[grid_point] * f_obs.space_group().n_ltr()**p
if (not squared_flag):
sum_m_i_grid = flex.sum(m * i_grid)
else:
sum_m_i_grid = flex.sum(m * flex.pow2(i_grid))
assert "%.6g" % sum_m_i_grid == "%.6g" % map_value, (
sum_m_i_grid, map_value)
def __init__(self,
map_1,
xray_structure,
fft_map,
atom_radius,
hydrogen_atom_radius,
model_i,
number_previous_scatters,
ignore_hd=False,
residue_detail=True,
selection=None,
pdb_hierarchy=None):
self.xray_structure = xray_structure
self.selection = selection
self.pdb_hierarchy = pdb_hierarchy
self.result = []
self.map_1_size = map_1.size()
self.map_1_stat = maptbx.statistics(map_1)
self.atoms_with_labels = None
self.residue_detail = residue_detail
self.model_i = model_i
if (pdb_hierarchy is not None):
self.atoms_with_labels = list(pdb_hierarchy.atoms_with_labels())
scatterers = self.xray_structure.scatterers()
sigma_occ = flex.double()
if (self.selection is None):
self.selection = flex.bool(scatterers.size(), True)
real_map_unpadded = fft_map.real_map_unpadded()
sites_cart = self.xray_structure.sites_cart()
if not self.residue_detail:
self.gifes = [
None,
] * scatterers.size()
self._result = [
None,
] * scatterers.size()
#
atom_radii = flex.double(scatterers.size(), atom_radius)
for i_seq, sc in enumerate(scatterers):
if (self.selection[i_seq]):
if (sc.element_symbol().strip().lower() in ["h", "d"]):
atom_radii[i_seq] = hydrogen_atom_radius
#
for i_seq, site_cart in enumerate(sites_cart):
if (self.selection[i_seq]):
sel = maptbx.grid_indices_around_sites(
unit_cell=self.xray_structure.unit_cell(),
fft_n_real=real_map_unpadded.focus(),
fft_m_real=real_map_unpadded.all(),
sites_cart=flex.vec3_double([site_cart]),
site_radii=flex.double([atom_radii[i_seq]]))
self.gifes[i_seq] = sel
m1 = map_1.select(sel)
ed1 = map_1.eight_point_interpolation(
scatterers[i_seq].site)
sigma_occ.append(ed1)
a = None
if (self.atoms_with_labels is not None):
a = self.atoms_with_labels[i_seq]
self._result[i_seq] = group_args(atom=a,
m1=m1,
ed1=ed1,
xyz=site_cart)
self.xray_structure.set_occupancies(sigma_occ)
### For testing other residue averaging options
residues = self.extract_residues(
model_i=model_i,
number_previous_scatters=number_previous_scatters)
self.xray_structure.residue_selections = residues
# Residue detail
if self.residue_detail:
assert self.pdb_hierarchy is not None
residues = self.extract_residues(
model_i=model_i,
number_previous_scatters=number_previous_scatters)
self.gifes = [
None,
] * len(residues)
self._result = [
None,
] * len(residues)
for i_seq, residue in enumerate(residues):
residue_sites_cart = sites_cart.select(residue.selection)
if 0: print(i_seq, list(residue.selection)) # DEBUG
sel = maptbx.grid_indices_around_sites(
unit_cell=self.xray_structure.unit_cell(),
fft_n_real=real_map_unpadded.focus(),
fft_m_real=real_map_unpadded.all(),
sites_cart=residue_sites_cart,
site_radii=flex.double(residue.selection.size(),
atom_radius))
self.gifes[i_seq] = sel
m1 = map_1.select(sel)
ed1 = flex.double()
for i_seq_r in residue.selection:
ed1.append(
map_1.eight_point_interpolation(
scatterers[i_seq_r].site))
self._result[i_seq] = \
group_args(residue = residue, m1 = m1, ed1 = flex.mean(ed1),
xyz=residue_sites_cart.mean(), n_atoms=residue_sites_cart.size())
residue_scatterers = scatterers.select(residue.selection)
residue_ed1 = flex.double()
for n, scatter in enumerate(residue_scatterers):
if ignore_hd:
if scatter.element_symbol() not in ['H', 'D']:
residue_ed1.append(ed1[n])
else:
residue_ed1.append(ed1[n])
for x in range(ed1.size()):
sigma_occ.append(flex.mean(residue_ed1))
self.xray_structure.set_occupancies(sigma_occ)
self.xray_structure.residue_selections = residues
del map_1
def run(self, args, command_name, out=sys.stdout):
command_line = (iotbx_option_parser(
usage="%s [options]" % command_name,
description='Example: %s data.mtz data.mtz ref_model.pdb'%command_name)
.option(None, "--show_defaults",
action="store_true",
help="Show list of parameters.")
).process(args=args)
cif_file = None
processed_args = utils.process_command_line_args(
args = args,
log = sys.stdout,
master_params = master_phil)
params = processed_args.params
if(params is None): params = master_phil
self.params = params.extract().ensemble_probability
pdb_file_names = processed_args.pdb_file_names
if len(pdb_file_names) != 1 :
raise Sorry("Only one PDB structure may be used")
pdb_file = file_reader.any_file(pdb_file_names[0])
self.log = multi_out()
self.log.register(label="stdout", file_object=sys.stdout)
self.log.register(
label="log_buffer",
file_object=StringIO(),
atexit_send_to=None)
sys.stderr = self.log
log_file = open(pdb_file_names[0].split('/')[-1].replace('.pdb','') + '_pensemble.log', "w")
self.log.replace_stringio(
old_label="log_buffer",
new_label="log",
new_file_object=log_file)
utils.print_header(command_name, out = self.log)
params.show(out = self.log)
#
f_obs = None
r_free_flags = None
reflection_files = processed_args.reflection_files
if self.params.fobs_vs_fcalc_post_nll:
if len(reflection_files) == 0:
raise Sorry("Fobs from input MTZ required for fobs_vs_fcalc_post_nll")
if len(reflection_files) > 0:
crystal_symmetry = processed_args.crystal_symmetry
print >> self.log, 'Reflection file : ', processed_args.reflection_file_names[0]
utils.print_header("Model and data statistics", out = self.log)
rfs = reflection_file_server(
crystal_symmetry = crystal_symmetry,
reflection_files = processed_args.reflection_files,
log = self.log)
parameters = utils.data_and_flags_master_params().extract()
determine_data_and_flags_result = utils.determine_data_and_flags(
reflection_file_server = rfs,
parameters = parameters,
data_parameter_scope = "refinement.input.xray_data",
flags_parameter_scope = "refinement.input.xray_data.r_free_flags",
data_description = "X-ray data",
keep_going = True,
log = self.log)
f_obs = determine_data_and_flags_result.f_obs
number_of_reflections = f_obs.indices().size()
r_free_flags = determine_data_and_flags_result.r_free_flags
test_flag_value = determine_data_and_flags_result.test_flag_value
if(r_free_flags is None):
r_free_flags=f_obs.array(data=flex.bool(f_obs.data().size(), False))
# process PDB
pdb_file.assert_file_type("pdb")
#
pdb_in = hierarchy.input(file_name=pdb_file.file_name)
ens_pdb_hierarchy = pdb_in.construct_hierarchy()
ens_pdb_hierarchy.atoms().reset_i_seq()
ens_pdb_xrs_s = pdb_in.input.xray_structures_simple()
number_structures = len(ens_pdb_xrs_s)
print >> self.log, 'Number of structure in ensemble : ', number_structures
# Calculate sigmas from input map only
if self.params.assign_sigma_from_map and self.params.ensemble_sigma_map_input is not None:
# process MTZ
input_file = file_reader.any_file(self.params.ensemble_sigma_map_input)
if input_file.file_type == "hkl" :
if input_file.file_object.file_type() != "ccp4_mtz" :
raise Sorry("Only MTZ format accepted for map input")
else:
mtz_file = input_file
else:
raise Sorry("Only MTZ format accepted for map input")
miller_arrays = mtz_file.file_server.miller_arrays
map_coeffs_1 = miller_arrays[0]
#
xrs_list = []
for n, ens_pdb_xrs in enumerate(ens_pdb_xrs_s):
# get sigma levels from ensemble fc for each structure
xrs = get_map_sigma(ens_pdb_hierarchy = ens_pdb_hierarchy,
ens_pdb_xrs = ens_pdb_xrs,
map_coeffs_1 = map_coeffs_1,
residue_detail = self.params.residue_detail,
ignore_hd = self.params.ignore_hd,
log = self.log)
xrs_list.append(xrs)
# write ensemble pdb file, occupancies as sigma level
filename = pdb_file_names[0].split('/')[-1].replace('.pdb','') + '_vs_' + self.params.ensemble_sigma_map_input.replace('.mtz','') + '_pensemble.pdb'
write_ensemble_pdb(filename = filename,
xrs_list = xrs_list,
ens_pdb_hierarchy = ens_pdb_hierarchy
)
# Do full analysis vs Fobs
else:
model_map_coeffs = []
fmodel = None
# Get <fcalc>
for model, ens_pdb_xrs in enumerate(ens_pdb_xrs_s):
ens_pdb_xrs.set_occupancies(1.0)
if model == 0:
# If mtz not supplied get fobs from xray structure...
# Use input Fobs for scoring against nll
if self.params.fobs_vs_fcalc_post_nll:
dummy_fobs = f_obs
else:
if f_obs == None:
if self.params.fcalc_high_resolution == None:
raise Sorry("Please supply high resolution limit or input mtz file.")
dummy_dmin = self.params.fcalc_high_resolution
dummy_dmax = self.params.fcalc_low_resolution
else:
print >> self.log, 'Supplied mtz used to determine high and low resolution cuttoffs'
dummy_dmax, dummy_dmin = f_obs.d_max_min()
#
dummy_fobs = abs(ens_pdb_xrs.structure_factors(d_min = dummy_dmin).f_calc())
dummy_fobs.set_observation_type_xray_amplitude()
# If mtz supplied, free flags are over written to prevent array size error
r_free_flags = dummy_fobs.array(data=flex.bool(dummy_fobs.data().size(),False))
#
fmodel = utils.fmodel_simple(
scattering_table = "wk1995",
xray_structures = [ens_pdb_xrs],
f_obs = dummy_fobs,
target_name = 'ls',
bulk_solvent_and_scaling = False,
r_free_flags = r_free_flags
)
f_calc_ave = fmodel.f_calc().array(data = fmodel.f_calc().data()*0).deep_copy()
# XXX Important to ensure scale is identical for each model and <model>
fmodel.set_scale_switch = 1.0
f_calc_ave_total = fmodel.f_calc().data().deep_copy()
else:
fmodel.update_xray_structure(xray_structure = ens_pdb_xrs,
update_f_calc = True,
update_f_mask = False)
f_calc_ave_total += fmodel.f_calc().data().deep_copy()
print >> self.log, 'Model :', model+1
print >> self.log, "\nStructure vs real Fobs (no bulk solvent or scaling)"
print >> self.log, 'Rwork : %5.4f '%fmodel.r_work()
print >> self.log, 'Rfree : %5.4f '%fmodel.r_free()
print >> self.log, 'K1 : %5.4f '%fmodel.scale_k1()
fcalc_edm = fmodel.electron_density_map()
fcalc_map_coeffs = fcalc_edm.map_coefficients(map_type = 'Fc')
fcalc_mtz_dataset = fcalc_map_coeffs.as_mtz_dataset(column_root_label ='Fc')
if self.params.output_model_and_model_ave_mtz:
fcalc_mtz_dataset.mtz_object().write(file_name = str(model+1)+"_Fc.mtz")
model_map_coeffs.append(fcalc_map_coeffs.deep_copy())
fmodel.update(f_calc = f_calc_ave.array(f_calc_ave_total / number_structures))
print >> self.log, "\nEnsemble vs real Fobs (no bulk solvent or scaling)"
print >> self.log, 'Rwork : %5.4f '%fmodel.r_work()
print >> self.log, 'Rfree : %5.4f '%fmodel.r_free()
print >> self.log, 'K1 : %5.4f '%fmodel.scale_k1()
# Get <Fcalc> map
fcalc_ave_edm = fmodel.electron_density_map()
fcalc_ave_map_coeffs = fcalc_ave_edm.map_coefficients(map_type = 'Fc').deep_copy()
fcalc_ave_mtz_dataset = fcalc_ave_map_coeffs.as_mtz_dataset(column_root_label ='Fc')
if self.params.output_model_and_model_ave_mtz:
fcalc_ave_mtz_dataset.mtz_object().write(file_name = "aveFc.mtz")
fcalc_ave_map_coeffs = fcalc_ave_map_coeffs.fft_map()
fcalc_ave_map_coeffs.apply_volume_scaling()
fcalc_ave_map_data = fcalc_ave_map_coeffs.real_map_unpadded()
fcalc_ave_map_stats = maptbx.statistics(fcalc_ave_map_data)
print >> self.log, "<Fcalc> Map Stats :"
fcalc_ave_map_stats.show_summary(f = self.log)
offset = fcalc_ave_map_stats.min()
model_neg_ll = []
number_previous_scatters = 0
# Run through structure list again and get probability
xrs_list = []
for model, ens_pdb_xrs in enumerate(ens_pdb_xrs_s):
if self.params.verbose:
print >> self.log, '\n\nModel : ', model+1
# Get model atom sigmas vs Fcalc
fcalc_map = model_map_coeffs[model].fft_map()
fcalc_map.apply_volume_scaling()
fcalc_map_data = fcalc_map.real_map_unpadded()
fcalc_map_stats = maptbx.statistics(fcalc_map_data)
if self.params.verbose:
print >> self.log, "Fcalc map stats :"
fcalc_map_stats.show_summary(f = self.log)
xrs = get_map_sigma(ens_pdb_hierarchy = ens_pdb_hierarchy,
ens_pdb_xrs = ens_pdb_xrs,
fft_map_1 = fcalc_map,
model_i = model,
residue_detail = self.params.residue_detail,
ignore_hd = self.params.ignore_hd,
number_previous_scatters = number_previous_scatters,
log = self.log)
fcalc_sigmas = xrs.scatterers().extract_occupancies()
del fcalc_map
# Get model atom sigmas vs <Fcalc>
xrs = get_map_sigma(ens_pdb_hierarchy = ens_pdb_hierarchy,
ens_pdb_xrs = ens_pdb_xrs,
fft_map_1 = fcalc_ave_map_coeffs,
model_i = model,
residue_detail = self.params.residue_detail,
ignore_hd = self.params.ignore_hd,
number_previous_scatters = number_previous_scatters,
log = self.log)
### For testing other residue averaging options
#print xrs.residue_selections
fcalc_ave_sigmas = xrs.scatterers().extract_occupancies()
# Probability of model given <model>
prob = fcalc_ave_sigmas / fcalc_sigmas
# XXX debug option
if False:
for n,p in enumerate(prob):
print >> self.log, ' {0:5d} {1:5.3f}'.format(n,p)
# Set probabilty between 0 and 1
# XXX Make Histogram / more stats
prob_lss_zero = flex.bool(prob <= 0)
prob_grt_one = flex.bool(prob > 1)
prob.set_selected(prob_lss_zero, 0.001)
prob.set_selected(prob_grt_one, 1.0)
xrs.set_occupancies(prob)
xrs_list.append(xrs)
sum_neg_ll = sum(-flex.log(prob))
model_neg_ll.append((sum_neg_ll, model))
if self.params.verbose:
print >> self.log, 'Model probability stats :'
print >> self.log, prob.min_max_mean().show()
print >> self.log, ' Count < 0.0 : ', prob_lss_zero.count(True)
print >> self.log, ' Count > 1.0 : ', prob_grt_one.count(True)
# For averaging by residue
number_previous_scatters += ens_pdb_xrs.sites_cart().size()
# write ensemble pdb file, occupancies as sigma level
write_ensemble_pdb(filename = pdb_file_names[0].split('/')[-1].replace('.pdb','') + '_pensemble.pdb',
xrs_list = xrs_list,
ens_pdb_hierarchy = ens_pdb_hierarchy
)
# XXX Test ordering models by nll
# XXX Test removing nth percentile atoms
if self.params.sort_ensemble_by_nll_score or self.params.fobs_vs_fcalc_post_nll:
for percentile in [1.0,0.975,0.95,0.9,0.8,0.6,0.2]:
model_neg_ll = sorted(model_neg_ll)
f_calc_ave_total_reordered = None
print_list = []
for i_neg_ll in model_neg_ll:
xrs = xrs_list[i_neg_ll[1]]
nll_occ = xrs.scatterers().extract_occupancies()
# Set q=0 nth percentile atoms
sorted_nll_occ = sorted(nll_occ, reverse=True)
number_atoms = len(sorted_nll_occ)
percentile_prob_cutoff = sorted_nll_occ[int(number_atoms * percentile)-1]
cutoff_selections = flex.bool(nll_occ < percentile_prob_cutoff)
cutoff_nll_occ = flex.double(nll_occ.size(), 1.0).set_selected(cutoff_selections, 0.0)
#XXX Debug
if False:
print '\nDebug'
for x in xrange(len(cutoff_selections)):
print cutoff_selections[x], nll_occ[x], cutoff_nll_occ[x]
print percentile
print percentile_prob_cutoff
print cutoff_selections.count(True)
print cutoff_selections.size()
print cutoff_nll_occ.count(0.0)
print 'Count q = 1 : ', cutoff_nll_occ.count(1.0)
print 'Count scatterers size : ', cutoff_nll_occ.size()
xrs.set_occupancies(cutoff_nll_occ)
fmodel.update_xray_structure(xray_structure = xrs,
update_f_calc = True,
update_f_mask = True)
if f_calc_ave_total_reordered == None:
f_calc_ave_total_reordered = fmodel.f_calc().data().deep_copy()
f_mask_ave_total_reordered = fmodel.f_masks()[0].data().deep_copy()
cntr = 1
else:
f_calc_ave_total_reordered += fmodel.f_calc().data().deep_copy()
f_mask_ave_total_reordered += fmodel.f_masks()[0].data().deep_copy()
cntr+=1
fmodel.update(f_calc = f_calc_ave.array(f_calc_ave_total_reordered / cntr).deep_copy(),
f_mask = f_calc_ave.array(f_mask_ave_total_reordered / cntr).deep_copy()
)
# Update solvent and scale
# XXX Will need to apply_back_trace on latest version
fmodel.set_scale_switch = 0
fmodel.update_all_scales()
# Reset occ for outout
xrs.set_occupancies(nll_occ)
# k1 updated vs Fobs
if self.params.fobs_vs_fcalc_post_nll:
print_list.append([cntr, i_neg_ll[0], i_neg_ll[1], fmodel.r_work(), fmodel.r_free()])
# Order models by nll and print summary
print >> self.log, '\nModels ranked by nll <Fcalc> R-factors recalculated'
print >> self.log, 'Percentile cutoff : {0:5.3f}'.format(percentile)
xrs_list_sorted_nll = []
print >> self.log, ' | NLL <Rw> <Rf> Ens Model'
for info in print_list:
print >> self.log, ' {0:4d} | {1:8.1f} {2:8.4f} {3:8.4f} {4:12d}'.format(
info[0],
info[1],
info[3],
info[4],
info[2]+1,
)
xrs_list_sorted_nll.append(xrs_list[info[2]])
# Output nll ordered ensemble
write_ensemble_pdb(filename = 'nll_ordered_' + pdb_file_names[0].split('/')[-1].replace('.pdb','') + '_pensemble.pdb',
xrs_list = xrs_list_sorted_nll,
ens_pdb_hierarchy = ens_pdb_hierarchy
)
def __init__(self, map_1,
xray_structure,
fft_map,
atom_radius,
hydrogen_atom_radius,
model_i,
number_previous_scatters,
ignore_hd = False,
residue_detail = True,
selection = None,
pdb_hierarchy = None):
self.xray_structure = xray_structure
self.selection = selection
self.pdb_hierarchy = pdb_hierarchy
self.result = []
self.map_1_size = map_1.size()
self.map_1_stat = maptbx.statistics(map_1)
self.atoms_with_labels = None
self.residue_detail = residue_detail
self.model_i = model_i
if(pdb_hierarchy is not None):
self.atoms_with_labels = list(pdb_hierarchy.atoms_with_labels())
scatterers = self.xray_structure.scatterers()
sigma_occ = flex.double()
if(self.selection is None):
self.selection = flex.bool(scatterers.size(), True)
real_map_unpadded = fft_map.real_map_unpadded()
sites_cart = self.xray_structure.sites_cart()
if not self.residue_detail:
self.gifes = [None,]*scatterers.size()
self._result = [None,]*scatterers.size()
#
atom_radii = flex.double(scatterers.size(), atom_radius)
for i_seq, sc in enumerate(scatterers):
if(self.selection[i_seq]):
if(sc.element_symbol().strip().lower() in ["h","d"]):
atom_radii[i_seq] = hydrogen_atom_radius
#
for i_seq, site_cart in enumerate(sites_cart):
if(self.selection[i_seq]):
sel = maptbx.grid_indices_around_sites(
unit_cell = self.xray_structure.unit_cell(),
fft_n_real = real_map_unpadded.focus(),
fft_m_real = real_map_unpadded.all(),
sites_cart = flex.vec3_double([site_cart]),
site_radii = flex.double([atom_radii[i_seq]]))
self.gifes[i_seq] = sel
m1 = map_1.select(sel)
ed1 = map_1.eight_point_interpolation(scatterers[i_seq].site)
sigma_occ.append(ed1)
a = None
if(self.atoms_with_labels is not None):
a = self.atoms_with_labels[i_seq]
self._result[i_seq] = group_args(atom = a, m1 = m1, ed1 = ed1,
xyz=site_cart)
self.xray_structure.set_occupancies(sigma_occ)
### For testing other residue averaging options
residues = self.extract_residues(model_i = model_i,
number_previous_scatters = number_previous_scatters)
self.xray_structure.residue_selections = residues
# Residue detail
if self.residue_detail:
assert self.pdb_hierarchy is not None
residues = self.extract_residues(model_i = model_i,
number_previous_scatters = number_previous_scatters)
self.gifes = [None,]*len(residues)
self._result = [None,]*len(residues)
for i_seq, residue in enumerate(residues):
residue_sites_cart = sites_cart.select(residue.selection)
if 0: print i_seq, list(residue.selection) # DEBUG
sel = maptbx.grid_indices_around_sites(
unit_cell = self.xray_structure.unit_cell(),
fft_n_real = real_map_unpadded.focus(),
fft_m_real = real_map_unpadded.all(),
sites_cart = residue_sites_cart,
site_radii = flex.double(residue.selection.size(), atom_radius))
self.gifes[i_seq] = sel
m1 = map_1.select(sel)
ed1 = flex.double()
for i_seq_r in residue.selection:
ed1.append(map_1.eight_point_interpolation(scatterers[i_seq_r].site))
self._result[i_seq] = \
group_args(residue = residue, m1 = m1, ed1 = flex.mean(ed1),
xyz=residue_sites_cart.mean(), n_atoms=residue_sites_cart.size())
residue_scatterers = scatterers.select(residue.selection)
residue_ed1 = flex.double()
for n,scatter in enumerate(residue_scatterers):
if ignore_hd:
if scatter.element_symbol() not in ['H', 'D']:
residue_ed1.append(ed1[n])
else:
residue_ed1.append(ed1[n])
for x in range(ed1.size()):
sigma_occ.append(flex.mean(residue_ed1))
self.xray_structure.set_occupancies(sigma_occ)
self.xray_structure.residue_selections = residues
del map_1
def run(self, args, command_name, out=sys.stdout):
command_line = (iotbx_option_parser(
usage="%s [options]" % command_name,
description='Example: %s data.mtz data.mtz ref_model.pdb' %
command_name).option(
None,
"--show_defaults",
action="store_true",
help="Show list of parameters.")).process(args=args)
cif_file = None
processed_args = utils.process_command_line_args(
args=args, log=sys.stdout, master_params=master_phil)
params = processed_args.params
if (params is None): params = master_phil
self.params = params.extract().ensemble_probability
pdb_file_names = processed_args.pdb_file_names
if len(pdb_file_names) != 1:
raise Sorry("Only one PDB structure may be used")
pdb_file = file_reader.any_file(pdb_file_names[0])
self.log = multi_out()
self.log.register(label="stdout", file_object=sys.stdout)
self.log.register(label="log_buffer",
file_object=StringIO(),
atexit_send_to=None)
sys.stderr = self.log
log_file = open(
pdb_file_names[0].split('/')[-1].replace('.pdb', '') +
'_pensemble.log', "w")
self.log.replace_stringio(old_label="log_buffer",
new_label="log",
new_file_object=log_file)
utils.print_header(command_name, out=self.log)
params.show(out=self.log)
#
f_obs = None
r_free_flags = None
reflection_files = processed_args.reflection_files
if self.params.fobs_vs_fcalc_post_nll:
if len(reflection_files) == 0:
raise Sorry(
"Fobs from input MTZ required for fobs_vs_fcalc_post_nll")
if len(reflection_files) > 0:
crystal_symmetry = processed_args.crystal_symmetry
print('Reflection file : ',
processed_args.reflection_file_names[0],
file=self.log)
utils.print_header("Model and data statistics", out=self.log)
rfs = reflection_file_server(
crystal_symmetry=crystal_symmetry,
reflection_files=processed_args.reflection_files,
log=self.log)
parameters = extract_xtal_data.data_and_flags_master_params(
).extract()
determine_data_and_flags_result = extract_xtal_data.run(
reflection_file_server=rfs,
parameters=parameters,
data_parameter_scope="refinement.input.xray_data",
flags_parameter_scope="refinement.input.xray_data.r_free_flags",
data_description="X-ray data",
keep_going=True,
log=self.log)
f_obs = determine_data_and_flags_result.f_obs
number_of_reflections = f_obs.indices().size()
r_free_flags = determine_data_and_flags_result.r_free_flags
test_flag_value = determine_data_and_flags_result.test_flag_value
if (r_free_flags is None):
r_free_flags = f_obs.array(
data=flex.bool(f_obs.data().size(), False))
# process PDB
pdb_file.assert_file_type("pdb")
#
pdb_in = hierarchy.input(file_name=pdb_file.file_name)
ens_pdb_hierarchy = pdb_in.construct_hierarchy()
ens_pdb_hierarchy.atoms().reset_i_seq()
ens_pdb_xrs_s = pdb_in.input.xray_structures_simple()
number_structures = len(ens_pdb_xrs_s)
print('Number of structure in ensemble : ',
number_structures,
file=self.log)
# Calculate sigmas from input map only
if self.params.assign_sigma_from_map and self.params.ensemble_sigma_map_input is not None:
# process MTZ
input_file = file_reader.any_file(
self.params.ensemble_sigma_map_input)
if input_file.file_type == "hkl":
if input_file.file_object.file_type() != "ccp4_mtz":
raise Sorry("Only MTZ format accepted for map input")
else:
mtz_file = input_file
else:
raise Sorry("Only MTZ format accepted for map input")
miller_arrays = mtz_file.file_server.miller_arrays
map_coeffs_1 = miller_arrays[0]
#
xrs_list = []
for n, ens_pdb_xrs in enumerate(ens_pdb_xrs_s):
# get sigma levels from ensemble fc for each structure
xrs = get_map_sigma(ens_pdb_hierarchy=ens_pdb_hierarchy,
ens_pdb_xrs=ens_pdb_xrs,
map_coeffs_1=map_coeffs_1,
residue_detail=self.params.residue_detail,
ignore_hd=self.params.ignore_hd,
log=self.log)
xrs_list.append(xrs)
# write ensemble pdb file, occupancies as sigma level
filename = pdb_file_names[0].split('/')[-1].replace(
'.pdb',
'') + '_vs_' + self.params.ensemble_sigma_map_input.replace(
'.mtz', '') + '_pensemble.pdb'
write_ensemble_pdb(filename=filename,
xrs_list=xrs_list,
ens_pdb_hierarchy=ens_pdb_hierarchy)
# Do full analysis vs Fobs
else:
model_map_coeffs = []
fmodel = None
# Get <fcalc>
for model, ens_pdb_xrs in enumerate(ens_pdb_xrs_s):
ens_pdb_xrs.set_occupancies(1.0)
if model == 0:
# If mtz not supplied get fobs from xray structure...
# Use input Fobs for scoring against nll
if self.params.fobs_vs_fcalc_post_nll:
dummy_fobs = f_obs
else:
if f_obs == None:
if self.params.fcalc_high_resolution == None:
raise Sorry(
"Please supply high resolution limit or input mtz file."
)
dummy_dmin = self.params.fcalc_high_resolution
dummy_dmax = self.params.fcalc_low_resolution
else:
print(
'Supplied mtz used to determine high and low resolution cuttoffs',
file=self.log)
dummy_dmax, dummy_dmin = f_obs.d_max_min()
#
dummy_fobs = abs(
ens_pdb_xrs.structure_factors(
d_min=dummy_dmin).f_calc())
dummy_fobs.set_observation_type_xray_amplitude()
# If mtz supplied, free flags are over written to prevent array size error
r_free_flags = dummy_fobs.array(
data=flex.bool(dummy_fobs.data().size(), False))
#
fmodel = utils.fmodel_simple(
scattering_table="wk1995",
xray_structures=[ens_pdb_xrs],
f_obs=dummy_fobs,
target_name='ls',
bulk_solvent_and_scaling=False,
r_free_flags=r_free_flags)
f_calc_ave = fmodel.f_calc().array(
data=fmodel.f_calc().data() * 0).deep_copy()
# XXX Important to ensure scale is identical for each model and <model>
fmodel.set_scale_switch = 1.0
f_calc_ave_total = fmodel.f_calc().data().deep_copy()
else:
fmodel.update_xray_structure(xray_structure=ens_pdb_xrs,
update_f_calc=True,
update_f_mask=False)
f_calc_ave_total += fmodel.f_calc().data().deep_copy()
print('Model :', model + 1, file=self.log)
print("\nStructure vs real Fobs (no bulk solvent or scaling)",
file=self.log)
print('Rwork : %5.4f ' % fmodel.r_work(),
file=self.log)
print('Rfree : %5.4f ' % fmodel.r_free(),
file=self.log)
print('K1 : %5.4f ' % fmodel.scale_k1(),
file=self.log)
fcalc_edm = fmodel.electron_density_map()
fcalc_map_coeffs = fcalc_edm.map_coefficients(map_type='Fc')
fcalc_mtz_dataset = fcalc_map_coeffs.as_mtz_dataset(
column_root_label='Fc')
if self.params.output_model_and_model_ave_mtz:
fcalc_mtz_dataset.mtz_object().write(
file_name=str(model + 1) + "_Fc.mtz")
model_map_coeffs.append(fcalc_map_coeffs.deep_copy())
fmodel.update(f_calc=f_calc_ave.array(f_calc_ave_total /
number_structures))
print("\nEnsemble vs real Fobs (no bulk solvent or scaling)",
file=self.log)
print('Rwork : %5.4f ' % fmodel.r_work(), file=self.log)
print('Rfree : %5.4f ' % fmodel.r_free(), file=self.log)
print('K1 : %5.4f ' % fmodel.scale_k1(), file=self.log)
# Get <Fcalc> map
fcalc_ave_edm = fmodel.electron_density_map()
fcalc_ave_map_coeffs = fcalc_ave_edm.map_coefficients(
map_type='Fc').deep_copy()
fcalc_ave_mtz_dataset = fcalc_ave_map_coeffs.as_mtz_dataset(
column_root_label='Fc')
if self.params.output_model_and_model_ave_mtz:
fcalc_ave_mtz_dataset.mtz_object().write(file_name="aveFc.mtz")
fcalc_ave_map_coeffs = fcalc_ave_map_coeffs.fft_map()
fcalc_ave_map_coeffs.apply_volume_scaling()
fcalc_ave_map_data = fcalc_ave_map_coeffs.real_map_unpadded()
fcalc_ave_map_stats = maptbx.statistics(fcalc_ave_map_data)
print("<Fcalc> Map Stats :", file=self.log)
fcalc_ave_map_stats.show_summary(f=self.log)
offset = fcalc_ave_map_stats.min()
model_neg_ll = []
number_previous_scatters = 0
# Run through structure list again and get probability
xrs_list = []
for model, ens_pdb_xrs in enumerate(ens_pdb_xrs_s):
if self.params.verbose:
print('\n\nModel : ',
model + 1,
file=self.log)
# Get model atom sigmas vs Fcalc
fcalc_map = model_map_coeffs[model].fft_map()
fcalc_map.apply_volume_scaling()
fcalc_map_data = fcalc_map.real_map_unpadded()
fcalc_map_stats = maptbx.statistics(fcalc_map_data)
if self.params.verbose:
print("Fcalc map stats :", file=self.log)
fcalc_map_stats.show_summary(f=self.log)
xrs = get_map_sigma(
ens_pdb_hierarchy=ens_pdb_hierarchy,
ens_pdb_xrs=ens_pdb_xrs,
fft_map_1=fcalc_map,
model_i=model,
residue_detail=self.params.residue_detail,
ignore_hd=self.params.ignore_hd,
number_previous_scatters=number_previous_scatters,
log=self.log)
fcalc_sigmas = xrs.scatterers().extract_occupancies()
del fcalc_map
# Get model atom sigmas vs <Fcalc>
xrs = get_map_sigma(
ens_pdb_hierarchy=ens_pdb_hierarchy,
ens_pdb_xrs=ens_pdb_xrs,
fft_map_1=fcalc_ave_map_coeffs,
model_i=model,
residue_detail=self.params.residue_detail,
ignore_hd=self.params.ignore_hd,
number_previous_scatters=number_previous_scatters,
log=self.log)
### For testing other residue averaging options
#print xrs.residue_selections
fcalc_ave_sigmas = xrs.scatterers().extract_occupancies()
# Probability of model given <model>
prob = fcalc_ave_sigmas / fcalc_sigmas
# XXX debug option
if False:
for n, p in enumerate(prob):
print(' {0:5d} {1:5.3f}'.format(n, p), file=self.log)
# Set probabilty between 0 and 1
# XXX Make Histogram / more stats
prob_lss_zero = flex.bool(prob <= 0)
prob_grt_one = flex.bool(prob > 1)
prob.set_selected(prob_lss_zero, 0.001)
prob.set_selected(prob_grt_one, 1.0)
xrs.set_occupancies(prob)
xrs_list.append(xrs)
sum_neg_ll = sum(-flex.log(prob))
model_neg_ll.append((sum_neg_ll, model))
if self.params.verbose:
print('Model probability stats :', file=self.log)
print(prob.min_max_mean().show(), file=self.log)
print(' Count < 0.0 : ',
prob_lss_zero.count(True),
file=self.log)
print(' Count > 1.0 : ',
prob_grt_one.count(True),
file=self.log)
# For averaging by residue
number_previous_scatters += ens_pdb_xrs.sites_cart().size()
# write ensemble pdb file, occupancies as sigma level
write_ensemble_pdb(
filename=pdb_file_names[0].split('/')[-1].replace('.pdb', '') +
'_pensemble.pdb',
xrs_list=xrs_list,
ens_pdb_hierarchy=ens_pdb_hierarchy)
# XXX Test ordering models by nll
# XXX Test removing nth percentile atoms
if self.params.sort_ensemble_by_nll_score or self.params.fobs_vs_fcalc_post_nll:
for percentile in [1.0, 0.975, 0.95, 0.9, 0.8, 0.6, 0.2]:
model_neg_ll = sorted(model_neg_ll)
f_calc_ave_total_reordered = None
print_list = []
for i_neg_ll in model_neg_ll:
xrs = xrs_list[i_neg_ll[1]]
nll_occ = xrs.scatterers().extract_occupancies()
# Set q=0 nth percentile atoms
sorted_nll_occ = sorted(nll_occ, reverse=True)
number_atoms = len(sorted_nll_occ)
percentile_prob_cutoff = sorted_nll_occ[
int(number_atoms * percentile) - 1]
cutoff_selections = flex.bool(
nll_occ < percentile_prob_cutoff)
cutoff_nll_occ = flex.double(nll_occ.size(),
1.0).set_selected(
cutoff_selections,
0.0)
#XXX Debug
if False:
print('\nDebug')
for x in range(len(cutoff_selections)):
print(cutoff_selections[x], nll_occ[x],
cutoff_nll_occ[x])
print(percentile)
print(percentile_prob_cutoff)
print(cutoff_selections.count(True))
print(cutoff_selections.size())
print(cutoff_nll_occ.count(0.0))
print('Count q = 1 : ',
cutoff_nll_occ.count(1.0))
print('Count scatterers size : ',
cutoff_nll_occ.size())
xrs.set_occupancies(cutoff_nll_occ)
fmodel.update_xray_structure(xray_structure=xrs,
update_f_calc=True,
update_f_mask=True)
if f_calc_ave_total_reordered == None:
f_calc_ave_total_reordered = fmodel.f_calc().data(
).deep_copy()
f_mask_ave_total_reordered = fmodel.f_masks(
)[0].data().deep_copy()
cntr = 1
else:
f_calc_ave_total_reordered += fmodel.f_calc().data(
).deep_copy()
f_mask_ave_total_reordered += fmodel.f_masks(
)[0].data().deep_copy()
cntr += 1
fmodel.update(
f_calc=f_calc_ave.array(
f_calc_ave_total_reordered / cntr).deep_copy(),
f_mask=f_calc_ave.array(
f_mask_ave_total_reordered / cntr).deep_copy())
# Update solvent and scale
# XXX Will need to apply_back_trace on latest version
fmodel.set_scale_switch = 0
fmodel.update_all_scales()
# Reset occ for outout
xrs.set_occupancies(nll_occ)
# k1 updated vs Fobs
if self.params.fobs_vs_fcalc_post_nll:
print_list.append([
cntr, i_neg_ll[0], i_neg_ll[1],
fmodel.r_work(),
fmodel.r_free()
])
# Order models by nll and print summary
print(
'\nModels ranked by nll <Fcalc> R-factors recalculated',
file=self.log)
print('Percentile cutoff : {0:5.3f}'.format(percentile),
file=self.log)
xrs_list_sorted_nll = []
print(' | NLL <Rw> <Rf> Ens Model',
file=self.log)
for info in print_list:
print(' {0:4d} | {1:8.1f} {2:8.4f} {3:8.4f} {4:12d}'.
format(
info[0],
info[1],
info[3],
info[4],
info[2] + 1,
),
file=self.log)
xrs_list_sorted_nll.append(xrs_list[info[2]])
# Output nll ordered ensemble
write_ensemble_pdb(
filename='nll_ordered_' +
pdb_file_names[0].split('/')[-1].replace('.pdb', '') +
'_pensemble.pdb',
xrs_list=xrs_list_sorted_nll,
ens_pdb_hierarchy=ens_pdb_hierarchy)
def exercise_under_sampled(space_group_info,
anomalous_flag,
conjugate_flag,
under_sampling,
d_min=2.,
resolution_factor=0.5,
max_prime=5,
verbose=0):
structure_factors = random_structure.xray_structure(
space_group_info,
elements=("N", "C", "C", "O"),
random_f_prime_d_min=1,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
random_u_iso=True,
random_occupancy=True).structure_factors(anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct")
f_calc = structure_factors.f_calc()
n_real = maptbx.crystal_gridding(unit_cell=f_calc.unit_cell(),
d_min=d_min,
resolution_factor=resolution_factor,
max_prime=max_prime,
mandatory_factors=(under_sampling, ) *
3).n_real()
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
map = maptbx.structure_factors.to_map(space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
f_calc_p1 = f_calc.expand_to_p1()
map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
assert flex.max(
flex.abs(map_p1.complex_map() - map.complex_map())) < 1.e-10
if (not anomalous_flag):
real_map = rfft.backward(map.complex_map())
assert real_map.all() == rfft.m_real()
else:
real_map = cfft.backward(map.complex_map())
assert not real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(real_map).show_summary()
maptbx.statistics(real_map).show_summary()
else:
maptbx.statistics(flex.real(real_map)).show_summary()
maptbx.statistics(flex.imag(real_map)).show_summary()
n_real_under_sampled = [n // under_sampling for n in n_real]
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = cfft.n()
under_sampled_map = maptbx.structure_factors.to_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
under_sampled_map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
assert flex.max(
flex.abs(under_sampled_map_p1.complex_map() -
under_sampled_map.complex_map())) < 1.e-10
if (not anomalous_flag):
under_sampled_map_before_fft = under_sampled_map.complex_map(
).deep_copy()
under_sampled_real_map = rfft.backward(under_sampled_map.complex_map())
assert under_sampled_real_map.all() == rfft.m_real()
else:
under_sampled_real_map = cfft.backward(under_sampled_map.complex_map())
assert not under_sampled_real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(under_sampled_real_map).show_summary()
maptbx.statistics(under_sampled_real_map).show_summary()
else:
maptbx.statistics(flex.real(under_sampled_real_map)).show_summary()
maptbx.statistics(flex.imag(under_sampled_real_map)).show_summary()
if (0 or verbose):
print(real_map.all(), n_complex)
print(under_sampled_real_map.all(), n_complex_under_sampled)
if (not anomalous_flag):
x_source = real_map
y_source = under_sampled_real_map
else:
x_source = flex.real(real_map)
y_source = flex.real(under_sampled_real_map)
x = flex.double()
n = x_source.focus()
for i in range(0, n[0], under_sampling):
for j in range(0, n[1], under_sampling):
for k in range(0, n[2], under_sampling):
x.append(x_source[(i, j, k)])
y = maptbx.copy(y_source, flex.grid(y_source.focus())).as_1d()
if (0 or verbose):
print("x:", tuple(x))
print("y:", tuple(y))
assert flex.max(flex.abs(x-y)) \
< (flex.max(flex.abs(x))+flex.max(flex.abs(y)))/2*1.e-6
if (under_sampling == 1):
x = maptbx.copy(x_source, flex.grid(x_source.focus())).as_1d()
c = flex.linear_correlation(x, y)
assert c.coefficient() >= 0.9999
def exercise_under_sampled(space_group_info, anomalous_flag, conjugate_flag,
under_sampling,
d_min=2., resolution_factor=0.5, max_prime=5,
verbose=0):
structure_factors = random_structure.xray_structure(
space_group_info,
elements=("N", "C", "C", "O"),
random_f_prime_d_min=1,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
random_u_iso=True,
random_occupancy=True
).structure_factors(
anomalous_flag=anomalous_flag, d_min=d_min, algorithm="direct")
f_calc = structure_factors.f_calc()
n_real = maptbx.crystal_gridding(
unit_cell=f_calc.unit_cell(),
d_min=d_min,
resolution_factor=resolution_factor,
max_prime=max_prime,
mandatory_factors=(under_sampling,)*3).n_real()
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real)
n_complex = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real)
n_complex = cfft.n()
map = maptbx.structure_factors.to_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
f_calc_p1 = f_calc.expand_to_p1()
map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real,
map_grid=flex.grid(n_complex),
conjugate_flag=conjugate_flag)
assert flex.max(flex.abs(map_p1.complex_map() - map.complex_map())) < 1.e-10
if (not anomalous_flag):
real_map = rfft.backward(map.complex_map())
assert real_map.all() == rfft.m_real()
else:
real_map = cfft.backward(map.complex_map())
assert not real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(real_map).show_summary()
maptbx.statistics(real_map).show_summary()
else:
maptbx.statistics(flex.real(real_map)).show_summary()
maptbx.statistics(flex.imag(real_map)).show_summary()
n_real_under_sampled = [n//under_sampling for n in n_real]
if (not anomalous_flag):
rfft = fftpack.real_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = rfft.n_complex()
else:
cfft = fftpack.complex_to_complex_3d(n_real_under_sampled)
n_complex_under_sampled = cfft.n()
under_sampled_map = maptbx.structure_factors.to_map(
space_group=f_calc.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc.indices(),
structure_factors=f_calc.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
under_sampled_map_p1 = maptbx.structure_factors.to_map(
space_group=f_calc_p1.space_group(),
anomalous_flag=anomalous_flag,
miller_indices=f_calc_p1.indices(),
structure_factors=f_calc_p1.data(),
n_real=n_real_under_sampled,
map_grid=flex.grid(n_complex_under_sampled),
conjugate_flag=conjugate_flag)
assert flex.max(flex.abs(under_sampled_map_p1.complex_map()
- under_sampled_map.complex_map())) < 1.e-10
if (not anomalous_flag):
under_sampled_map_before_fft = under_sampled_map.complex_map().deep_copy()
under_sampled_real_map = rfft.backward(under_sampled_map.complex_map())
assert under_sampled_real_map.all() == rfft.m_real()
else:
under_sampled_real_map = cfft.backward(under_sampled_map.complex_map())
assert not under_sampled_real_map.is_padded()
if (0 or verbose):
if (not anomalous_flag):
maptbx.statistics(under_sampled_real_map).show_summary()
maptbx.statistics(under_sampled_real_map).show_summary()
else:
maptbx.statistics(flex.real(under_sampled_real_map)).show_summary()
maptbx.statistics(flex.imag(under_sampled_real_map)).show_summary()
if (0 or verbose):
print real_map.all(), n_complex
print under_sampled_real_map.all(), n_complex_under_sampled
if (not anomalous_flag):
x_source = real_map
y_source = under_sampled_real_map
else:
x_source = flex.real(real_map)
y_source = flex.real(under_sampled_real_map)
x = flex.double()
n = x_source.focus()
for i in xrange(0, n[0], under_sampling):
for j in xrange(0, n[1], under_sampling):
for k in xrange(0, n[2], under_sampling):
x.append(x_source[(i,j,k)])
y = maptbx.copy(y_source, flex.grid(y_source.focus())).as_1d()
if (0 or verbose):
print "x:", tuple(x)
print "y:", tuple(y)
assert flex.max(flex.abs(x-y)) \
< (flex.max(flex.abs(x))+flex.max(flex.abs(y)))/2*1.e-6
if (under_sampling == 1):
x = maptbx.copy(x_source, flex.grid(x_source.focus())).as_1d()
c = flex.linear_correlation(x, y)
assert c.coefficient() >= 0.9999
def statistics(self):
from cctbx import maptbx
return maptbx.statistics(self.map_data())
def exercise_statistics():
import scitbx.math
for flex_type in flex_types():
a = flex_type(flex.grid((3,5)))
s = maptbx.statistics(a)
assert s.min() == 0
assert s.max() == 0
assert s.mean() == 0
assert s.mean_sq() == 0
assert s.sigma() == 0
a = flex_type([random.random() for i in xrange(3*5)])
a.resize(flex.grid((3,5)))
s = maptbx.statistics(a)
assert approx_equal(flex.min(a), s.min())
assert approx_equal(flex.max(a), s.max())
assert approx_equal(flex.mean(a), s.mean())
assert approx_equal(flex.mean_sq(a), s.mean_sq())
assert approx_equal(flex.mean_sq(a)-flex.mean(a)**2, s.sigma()**2)
b = flex_type(flex.grid((4,6)).set_focus((3,5)))
for i in xrange(3):
for j in xrange(5):
b[(i,j)] = a[(i,j)]
b[(3,5)] = -1
b[(2,5)] = 2
b.resize(flex.grid((-2,3), (2,9)).set_focus((1,8)))
t = maptbx.statistics(b)
assert not_approx_equal(flex.min(b), t.min())
assert not_approx_equal(flex.max(b), t.max())
assert not_approx_equal(flex.mean(b), t.mean())
assert not_approx_equal(flex.mean_sq(b), t.mean_sq())
assert not_approx_equal(flex.mean_sq(b)-flex.mean(b)**2, t.sigma()**2)
assert approx_equal(s.min(), t.min())
assert approx_equal(s.max(), t.max())
assert approx_equal(s.mean(), t.mean())
assert approx_equal(s.mean_sq(), t.mean_sq())
assert approx_equal(s.sigma(), t.sigma())
a = flex.double(flex.grid(5,3))
s = maptbx.more_statistics(a)
assert s.min() == 0
assert s.max() == 0
assert s.mean() == 0
assert s.mean_sq() == 0
assert s.sigma() == 0
assert s.skewness() == 0
assert s.kurtosis() == 0
a = flex.random_double(5*3)
reference = scitbx.math.basic_statistics(a)
a.resize(flex.grid(5,3))
s = maptbx.more_statistics(a)
assert approx_equal(s.min(), reference.min)
assert approx_equal(s.max(), reference.max)
assert approx_equal(s.mean(), reference.mean)
assert approx_equal(s.sigma(), reference.biased_standard_deviation)
assert approx_equal(s.skewness(), reference.skew)
assert approx_equal(s.kurtosis(), reference.kurtosis)
b = flex.double(flex.grid((6,4)).set_focus((5,3)))
for i in xrange(5):
for j in xrange(3):
b[(i,j)] = a[(i,j)]
b[(5,3)] = -1
b[(5,2)] = 2
b.resize(flex.grid((3,-2), (9,2)).set_focus((8,1)))
t = maptbx.statistics(b)
assert approx_equal(s.min(), reference.min)
assert approx_equal(s.max(), reference.max)
assert approx_equal(s.mean(), reference.mean)
assert approx_equal(s.sigma(), reference.biased_standard_deviation)
assert approx_equal(s.skewness(), reference.skew)
assert approx_equal(s.kurtosis(), reference.kurtosis)
m = flex.double(flex.grid((6,4,8)).set_focus((5,3,7)))
def statistics (self) : from cctbx import maptbx return maptbx.statistics(self.data)
def sigma(self):
return maptbx.statistics(self.real_map()).sigma()
def sigma(self): return maptbx.statistics(self.real_map()).sigma()
