def tst_copy(self):
import copy
from dials.array_family import flex
# Create a table
table = flex.reflection_table([
('col1', flex.int(range(10)))])
# Make a shallow copy of the table
shallow = copy.copy(table)
shallow['col2'] = flex.double(range(10))
assert(table.ncols() == 2)
assert(table.is_consistent())
print 'OK'
# Make a deep copy of the table
deep = copy.deepcopy(table)
deep['col3'] = flex.std_string(10)
assert(table.ncols() == 2)
assert(deep.ncols() == 3)
assert(table.is_consistent())
assert(deep.is_consistent())
table2 = table.copy()
table2['col3'] = flex.std_string(10)
assert(table.ncols() == 2)
assert(table2.ncols() == 3)
assert(table.is_consistent())
assert(table2.is_consistent())
print 'OK'
def tst_select(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Select some columns
new_table = table.select(('col1', 'col2'))
assert(new_table.nrows() == 10)
assert(new_table.ncols() == 2)
assert(all(a == b for a, b in zip(new_table['col1'], c1)))
assert(all(a == b for a, b in zip(new_table['col2'], c2)))
print 'OK'
# Select some columns
new_table = table.select(flex.std_string(['col1', 'col2']))
assert(new_table.nrows() == 10)
assert(new_table.ncols() == 2)
assert(all(a == b for a, b in zip(new_table['col1'], c1)))
assert(all(a == b for a, b in zip(new_table['col2'], c2)))
print 'OK'
# Select some rows
index = flex.size_t([0, 1, 5, 8, 9])
cc1 = [c1[i] for i in index]
cc2 = [c2[i] for i in index]
cc3 = [c3[i] for i in index]
new_table = table.select(index)
assert(new_table.nrows() == 5)
assert(new_table.ncols() == 3)
assert(all(a == b for a, b in zip(new_table['col1'], cc1)))
assert(all(a == b for a, b in zip(new_table['col2'], cc2)))
assert(all(a == b for a, b in zip(new_table['col3'], cc3)))
print 'OK'
# Select some rows
index = flex.bool([True, True, False, False, False,
True, False, False, True, True])
new_table = table.select(index)
assert(new_table.nrows() == 5)
assert(new_table.ncols() == 3)
assert(all(a == b for a, b in zip(new_table['col1'], cc1)))
assert(all(a == b for a, b in zip(new_table['col2'], cc2)))
assert(all(a == b for a, b in zip(new_table['col3'], cc3)))
print 'OK'
def tst_updating(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table0 = flex.reflection_table()
table1 = flex.reflection_table()
table2 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table2['col3'] = flex.std_string(c3)
# Update from zero columns
table0.update(table1)
assert(table0.is_consistent())
assert(table0.nrows() == 10)
assert(table0.ncols() == 2)
print 'OK'
# Update table1 with table2 columns
table1.update(table2)
assert(table1.is_consistent())
assert(table1.nrows() == 10)
assert(table1.ncols() == 3)
assert(table2.is_consistent())
assert(table2.nrows() == 10)
assert(table2.ncols() == 1)
print 'OK'
# Update trable1 with invalid table
c3 = ['a', 'b', 'c']
# Create a table with some elements
table2 = flex.reflection_table()
table2['col3'] = flex.std_string(c3)
try:
table1.update(table2)
assert(False)
except Exception:
pass
assert(table1.is_consistent())
assert(table1.nrows() == 10)
assert(table1.ncols() == 3)
assert(table2.is_consistent())
assert(table2.nrows() == 3)
assert(table2.ncols() == 1)
print 'OK'
def tst_serialize(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Pickle, then unpickle
import cPickle as pickle
obj = pickle.dumps(table)
new_table = pickle.loads(obj)
assert(new_table.is_consistent())
assert(new_table.nrows() == 10)
assert(new_table.ncols() == 3)
assert(all(a == b for a, b in zip(new_table['col1'], c1)))
assert(all(a == b for a, b in zip(new_table['col2'], c2)))
assert(all(a == b for a, b in zip(new_table['col3'], c3)))
print 'OK'
def tst_init(self):
from dials.array_family import flex
# test default
table = flex.reflection_table()
assert(table.is_consistent())
assert(table.nrows() == 0)
assert(table.ncols() == 0)
assert(table.empty())
print 'Ok'
# test with nrows
table = flex.reflection_table(10)
assert(table.is_consistent())
assert(table.nrows() == 10)
assert(table.ncols() == 0)
assert(table.empty())
print 'OK'
# test with valid columns
table = flex.reflection_table([
('col1', flex.int(10)),
('col2', flex.double(10)),
('col3', flex.std_string(10))])
assert(table.is_consistent())
assert(table.nrows() == 10)
assert(table.ncols() == 3)
assert(not table.empty())
print 'OK'
# test with invalid columns
try:
table = flex.reflection_table([
('col1', flex.int(10)),
('col2', flex.double(20)),
('col3', flex.std_string(10))])
assert(false)
except Exception:
pass
print 'OK'
def tst_iteration(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Try iterating keys
k = []
for key in table.keys():
k.append(key)
assert(len(k) == 3)
assert(k.count('col1') == 1)
assert(k.count('col2') == 1)
assert(k.count('col3') == 1)
print 'OK'
# Try iterating columns
k = []
c = []
for key, col in table.cols():
k.append(key)
c.append(col)
assert(len(k) == 3)
assert(k.count('col1') == 1)
assert(k.count('col2') == 1)
assert(k.count('col3') == 1)
print 'OK'
# Try iterating rows
for row1, row2 in zip(table.rows(), zip(c1, c2, c3)):
assert(row1['col1'] == row2[0])
assert(row1['col2'] == row2[1])
assert(row1['col3'] == row2[2])
print 'OK'
def tst_row_operations(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Extend the table
table.extend(table)
c1 = c1 * 2
c2 = c2 * 2
c3 = c3 * 2
assert(table.nrows() == 20)
assert(table.ncols() == 3)
assert(table.is_consistent())
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Append some rows to the table
row = { 'col1' : 10 }
c1 = c1 + [10]
c2 = c2 + [0]
c3 = c3 + ['']
table.append(row)
assert(table.nrows() == 21)
assert(table.ncols() == 3)
assert(table.is_consistent())
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
row = { 'col2' : 11 }
c1 = c1 + [0]
c2 = c2 + [11]
c3 = c3 + ['']
table.append(row)
assert(table.nrows() == 22)
assert(table.ncols() == 3)
assert(table.is_consistent())
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
row = { 'col1' : 12, 'col2' : 12, 'col3' : 'l' }
c1 = c1 + [12]
c2 = c2 + [12]
c3 = c3 + ['l']
table.append(row)
assert(table.nrows() == 23)
assert(table.ncols() == 3)
assert(table.is_consistent())
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Try inserting some rows
row = { 'col1' : -1 }
c1.insert(5, -1)
c2.insert(5, 0)
c3.insert(5, '')
table.insert(5, row)
assert(table.nrows() == 24)
assert(table.ncols() == 3)
assert(table.is_consistent())
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
row = { 'col1' : -2, 'col2' : -3, 'col3' : 'abc' }
c1.insert(2, -2)
c2.insert(2, -3)
c3.insert(2, 'abc')
table.insert(2, row)
assert(table.nrows() == 25)
assert(table.ncols() == 3)
assert(table.is_consistent())
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Try iterating through table rows
for i in range(table.nrows()):
row = table[i]
assert(row['col1'] == c1[i])
assert(row['col2'] == c2[i])
assert(row['col3'] == c3[i])
print 'OK'
# Trying setting some rows
row = { 'col1' : 100 }
table[2] = row
assert(table[2]['col1'] == 100)
assert(table[2]['col2'] == c2[2])
assert(table[2]['col3'] == c3[2])
row = { 'col1' : 1000, 'col2' : 2000, 'col3' : 'hello' }
table[10] = row
assert(table[10]['col1'] == 1000)
assert(table[10]['col2'] == 2000)
assert(table[10]['col3'] == 'hello')
print 'OK'
def test_to_from_msgpack(tmpdir):
from dials.model.data import Shoebox
def gen_shoebox():
shoebox = Shoebox(0, (0, 4, 0, 3, 0, 1))
shoebox.allocate()
for k in range(1):
for j in range(3):
for i in range(4):
shoebox.data[k, j, i] = i + j + k + 0.1
shoebox.mask[k, j, i] = i % 2
shoebox.background[k, j, i] = i * j + 0.2
return shoebox
def compare(a, b):
assert a.is_consistent()
assert b.is_consistent()
assert a.panel == b.panel
assert a.bbox == b.bbox
for aa, bb in zip(a.data, b.data):
if abs(aa - bb) > 1e-9:
return False
for aa, bb in zip(a.background, b.background):
if abs(aa - bb) > 1e-9:
return False
for aa, bb in zip(a.mask, b.mask):
if aa != bb:
return False
return True
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ["a", "b", "c", "d", "e", "f", "g", "i", "j", "k"]
c4 = [True, False, True, False, True] * 2
c5 = list(range(10))
c6 = [(i + 1, i + 2) for i in range(10)]
c7 = [(i + 1, i + 2, i + 3) for i in range(10)]
c8 = [tuple(i + j for j in range(9)) for i in range(10)]
c9 = [tuple(i + j for j in range(6)) for i in range(10)]
c10 = [(i + 1, i + 2, i + 3) for i in range(10)]
c11 = [gen_shoebox() for i in range(10)]
# Create a table with some elements
table = flex.reflection_table()
table["col1"] = flex.int(c1)
table["col2"] = flex.double(c2)
table["col3"] = flex.std_string(c3)
table["col4"] = flex.bool(c4)
table["col5"] = flex.size_t(c5)
table["col6"] = flex.vec2_double(c6)
table["col7"] = flex.vec3_double(c7)
table["col8"] = flex.mat3_double(c8)
table["col9"] = flex.int6(c9)
table["col10"] = flex.miller_index(c10)
table["col11"] = flex.shoebox(c11)
obj = table.as_msgpack()
new_table = flex.reflection_table.from_msgpack(obj)
assert new_table.is_consistent()
assert new_table.nrows() == 10
assert new_table.ncols() == 11
assert all(tuple(a == b for a, b in zip(new_table["col1"], c1)))
assert all(tuple(a == b for a, b in zip(new_table["col2"], c2)))
assert all(tuple(a == b for a, b in zip(new_table["col3"], c3)))
assert all(tuple(a == b for a, b in zip(new_table["col4"], c4)))
assert all(tuple(a == b for a, b in zip(new_table["col5"], c5)))
assert all(tuple(a == b for a, b in zip(new_table["col6"], c6)))
assert all(tuple(a == b for a, b in zip(new_table["col7"], c7)))
assert all(tuple(a == b for a, b in zip(new_table["col8"], c8)))
assert all(tuple(a == b for a, b in zip(new_table["col9"], c9)))
assert all(tuple(a == b for a, b in zip(new_table["col10"], c10)))
assert all(tuple(compare(a, b) for a, b in zip(new_table["col11"], c11)))
table.as_msgpack_file(tmpdir.join("reflections.mpack").strpath)
new_table = flex.reflection_table.from_msgpack_file(
tmpdir.join("reflections.mpack").strpath)
assert new_table.is_consistent()
assert new_table.nrows() == 10
assert new_table.ncols() == 11
assert all(tuple(a == b for a, b in zip(new_table["col1"], c1)))
assert all(tuple(a == b for a, b in zip(new_table["col2"], c2)))
assert all(tuple(a == b for a, b in zip(new_table["col3"], c3)))
assert all(tuple(a == b for a, b in zip(new_table["col4"], c4)))
assert all(tuple(a == b for a, b in zip(new_table["col5"], c5)))
assert all(tuple(a == b for a, b in zip(new_table["col6"], c6)))
assert all(tuple(a == b for a, b in zip(new_table["col7"], c7)))
assert all(tuple(a == b for a, b in zip(new_table["col8"], c8)))
assert all(tuple(a == b for a, b in zip(new_table["col9"], c9)))
assert all(tuple(a == b for a, b in zip(new_table["col10"], c10)))
assert all(tuple(compare(a, b) for a, b in zip(new_table["col11"], c11)))
def tst_del_selected(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
# Del selected columns
table1.del_selected(('col3', 'col2'))
assert (table1.nrows() == 10)
assert (table1.ncols() == 1)
assert ("col1" in table1)
assert ("col2" not in table1)
assert ("col3" not in table1)
assert (all(a == b for a, b in zip(table1['col1'], c1)))
print 'OK'
# Del selected columns
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
table1.del_selected(flex.std_string(['col3', 'col2']))
assert (table1.nrows() == 10)
assert (table1.ncols() == 1)
assert ("col1" in table1)
assert ("col2" not in table1)
assert ("col3" not in table1)
assert (all(a == b for a, b in zip(table1['col1'], c1)))
print 'OK'
# Del selected rows
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
index = flex.size_t([0, 1, 5, 8, 9])
index2 = range(10)
for i in index:
index2.remove(i)
ccc1 = [c1[i] for i in index2]
ccc2 = [c2[i] for i in index2]
ccc3 = [c3[i] for i in index2]
table1.del_selected(index)
assert (table1.nrows() == len(ccc1))
assert (all(a == b for a, b in zip(table1['col1'], ccc1)))
assert (all(a == b for a, b in zip(table1['col2'], ccc2)))
assert (all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
# Del selected rows
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
flags = flex.bool(
[True, True, False, False, False, True, False, False, True, True])
table1.del_selected(index)
assert (table1.nrows() == len(ccc1))
assert (all(a == b for a, b in zip(table1['col1'], ccc1)))
assert (all(a == b for a, b in zip(table1['col2'], ccc2)))
assert (all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
def tst_slicing(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Try forward slicing
new_table = table[2:7:2]
assert (new_table.ncols() == 3)
assert (new_table.nrows() == 3)
assert (new_table.is_consistent())
c11 = c1[2:7:2]
c22 = c2[2:7:2]
c33 = c3[2:7:2]
assert (all(a == b for a, b in zip(new_table['col1'], c11)))
assert (all(a == b for a, b in zip(new_table['col2'], c22)))
assert (all(a == b for a, b in zip(new_table['col3'], c33)))
print 'OK'
# Try backward slicing
new_table = table[7:2:-2]
assert (new_table.ncols() == 3)
assert (new_table.nrows() == 3)
assert (new_table.is_consistent())
c11 = c1[7:2:-2]
c22 = c2[7:2:-2]
c33 = c3[7:2:-2]
assert (all(a == b for a, b in zip(new_table['col1'], c11)))
assert (all(a == b for a, b in zip(new_table['col2'], c22)))
assert (all(a == b for a, b in zip(new_table['col3'], c33)))
print 'OK'
# Try setting forward slicing
table[2:7:2] = new_table
assert (table.ncols() == 3)
assert (table.nrows() == 10)
assert (table.is_consistent())
c1[2:7:2] = c11
c2[2:7:2] = c22
c3[2:7:2] = c33
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Try setting backward slicing
table[7:2:-2] = new_table
assert (table.ncols() == 3)
assert (table.nrows() == 10)
assert (table.is_consistent())
c1[7:2:-2] = c11
c2[7:2:-2] = c22
c3[7:2:-2] = c33
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
def run(self, all_experiments, all_reflections):
""" Load all the data using MPI """
from dxtbx.model.experiment_list import ExperimentList
from dials.array_family import flex
# Both must be none or not none
test = [all_experiments is None, all_reflections is None].count(True)
assert test in [0, 2]
if test == 2:
all_experiments = ExperimentList()
all_reflections = flex.reflection_table()
starting_expts_count = starting_refls_count = 0
else:
starting_expts_count = len(all_experiments)
starting_refls_count = len(all_reflections)
# Generate and send a list of file paths to each worker
if self.mpi_helper.rank == 0:
file_list = self.get_list()
self.params.input.path = None # the input is already parsed
from xfel.merging.application.input.file_load_calculator import file_load_calculator
load_calculator = file_load_calculator(self.params, file_list)
calculated_file_list = load_calculator.calculate_file_load(
self.mpi_helper.size)
self.logger.log('Transmitting a list of %d lists of file pairs' %
(len(calculated_file_list)))
transmitted = calculated_file_list
else:
transmitted = None
self.logger.log_step_time("BROADCAST_FILE_LIST")
transmitted = self.mpi_helper.comm.bcast(transmitted, root=0)
new_file_list = transmitted[self.mpi_helper.rank]
self.logger.log("Received a list of %d file pairs" %
len(new_file_list))
self.logger.log_step_time("BROADCAST_FILE_LIST", True)
# Load the data
self.logger.log_step_time("LOAD")
for experiments_filename, reflections_filename in new_file_list:
experiments = ExperimentListFactory.from_json_file(
experiments_filename, check_format=False)
reflections = easy_pickle.load(reflections_filename)
for experiment_id, experiment in enumerate(experiments):
experiment.identifier = create_experiment_identifier(
experiment, experiments_filename, experiment_id)
all_experiments.append(experiment)
refls = reflections.select(reflections['id'] == experiment_id)
refls['exp_id'] = flex.std_string(len(refls),
experiment.identifier)
all_reflections.extend(refls)
self.logger.log_step_time("LOAD", True)
self.logger.log('Read %d experiments consisting of %d reflections' %
(len(all_experiments) - starting_expts_count,
len(all_reflections) - starting_refls_count))
self.logger.log(get_memory_usage())
# Count the loaded data
data_counter(self.params).count(all_experiments, all_reflections)
return all_experiments, all_reflections
def run(self, all_experiments, all_reflections):
""" Load all the data using MPI """
from dxtbx.model.experiment_list import ExperimentList
from dials.array_family import flex
# Both must be none or not none
test = [all_experiments is None, all_reflections is None].count(True)
assert test in [0, 2]
if test == 2:
all_experiments = ExperimentList()
all_reflections = flex.reflection_table()
starting_expts_count = starting_refls_count = 0
else:
starting_expts_count = len(all_experiments)
starting_refls_count = len(all_reflections)
self.logger.log(
"Initial number of experiments: %d; Initial number of reflections: %d"
% (starting_expts_count, starting_refls_count))
# Generate and send a list of file paths to each worker
if self.mpi_helper.rank == 0:
file_list = self.get_list()
self.logger.log(
"Built an input list of %d json/pickle file pairs" %
(len(file_list)))
self.params.input.path = None # Rank 0 has already parsed the input parameters
# optionally write a file list mapping to disk, useful in post processing if save_experiments_and_reflections=True
file_id_from_names = None
if self.params.output.expanded_bookkeeping:
apath = lambda x: os.path.abspath(x)
file_names_from_id = {
i_f: tuple(map(apath, exp_ref_pair))
for i_f, exp_ref_pair in enumerate(file_list)
}
with open(
os.path.join(self.params.output.output_dir,
"file_list_map.json"), "w") as o:
json.dump(file_names_from_id, o)
file_id_from_names = {
tuple(map(apath, exp_ref_pair)): i_f
for i_f, exp_ref_pair in enumerate(file_list)
}
per_rank_file_list = file_load_calculator(self.params, file_list, self.logger).\
calculate_file_load(available_rank_count = self.mpi_helper.size)
self.logger.log(
'Transmitting a list of %d lists of json/pickle file pairs' %
(len(per_rank_file_list)))
transmitted = per_rank_file_list, file_id_from_names
else:
transmitted = None
self.logger.log_step_time("BROADCAST_FILE_LIST")
new_file_list, file_names_mapping = self.mpi_helper.comm.bcast(
transmitted, root=0)
new_file_list = new_file_list[
self.mpi_helper.
rank] if self.mpi_helper.rank < len(new_file_list) else None
self.logger.log_step_time("BROADCAST_FILE_LIST", True)
# Load the data
self.logger.log_step_time("LOAD")
if new_file_list is not None:
self.logger.log("Received a list of %d json/pickle file pairs" %
len(new_file_list))
for experiments_filename, reflections_filename in new_file_list:
self.logger.log("Reading %s %s" %
(experiments_filename, reflections_filename))
experiments = ExperimentListFactory.from_json_file(
experiments_filename,
check_format=self.params.input.read_image_headers)
reflections = flex.reflection_table.from_file(
reflections_filename)
if self.params.output.expanded_bookkeeping:
# NOTE: these are un-prunable
reflections["input_refl_index"] = flex.int(
list(range(len(reflections))))
reflections["orig_exp_id"] = reflections['id']
assert file_names_mapping is not None
exp_ref_pair = os.path.abspath(
experiments_filename), os.path.abspath(
reflections_filename)
this_refl_fileMappings = [
file_names_mapping[exp_ref_pair]
] * len(reflections)
reflections["file_list_mapping"] = flex.int(
this_refl_fileMappings)
self.logger.log("Data read, prepping")
if 'intensity.sum.value' in reflections:
reflections[
'intensity.sum.value.unmodified'] = reflections[
'intensity.sum.value'] * 1
if 'intensity.sum.variance' in reflections:
reflections[
'intensity.sum.variance.unmodified'] = reflections[
'intensity.sum.variance'] * 1
new_ids = flex.int(len(reflections), -1)
new_identifiers = flex.std_string(len(reflections))
eid = reflections.experiment_identifiers()
for k in eid.keys():
del eid[k]
if self.params.output.expanded_bookkeeping:
preGen_experiment_identifiers(experiments,
experiments_filename)
for experiment_id, experiment in enumerate(experiments):
# select reflections of the current experiment
refls_sel = reflections['id'] == experiment_id
if refls_sel.count(True) == 0: continue
if experiment.identifier is None or len(
experiment.identifier) == 0:
experiment.identifier = create_experiment_identifier(
experiment, experiments_filename, experiment_id)
if not self.params.input.keep_imagesets:
experiment.imageset = None
all_experiments.append(experiment)
# Reflection experiment 'id' is unique within this rank; 'exp_id' (i.e. experiment identifier) is unique globally
new_identifiers.set_selected(refls_sel,
experiment.identifier)
new_id = len(all_experiments) - 1
eid[new_id] = experiment.identifier
new_ids.set_selected(refls_sel, new_id)
assert (new_ids < 0
).count(True) == 0, "Not all reflections accounted for"
reflections['id'] = new_ids
reflections['exp_id'] = new_identifiers
all_reflections.extend(reflections)
else:
self.logger.log("Received a list of 0 json/pickle file pairs")
self.logger.log_step_time("LOAD", True)
self.logger.log('Read %d experiments consisting of %d reflections' %
(len(all_experiments) - starting_expts_count,
len(all_reflections) - starting_refls_count))
self.logger.log("Memory usage: %d MB" % get_memory_usage())
all_reflections = self.prune_reflection_table_keys(all_reflections)
# Do we have any data?
from xfel.merging.application.utils.data_counter import data_counter
data_counter(self.params).count(all_experiments, all_reflections)
return all_experiments, all_reflections
def batch_plot_shapes_and_annotations(self):
light_grey = "#d3d3d3"
grey = "#808080"
shapes = []
annotations = []
batches = flex.int(self.batches)
text = flex.std_string(batches.size())
for i, batch in enumerate(self.batch_params):
fillcolor = [light_grey, grey][i % 2] # alternate colours
shapes.append({
"type":
"rect",
# x-reference is assigned to the x-values
"xref":
"x",
# y-reference is assigned to the plot paper [0,1]
"yref":
"paper",
"x0":
self._batch_increments[i],
"y0":
0,
"x1":
self._batch_increments[i] +
(batch["range"][1] - batch["range"][0]),
"y1":
1,
"fillcolor":
fillcolor,
"opacity":
0.2,
"line": {
"width": 0
},
})
annotations.append({
# x-reference is assigned to the x-values
"xref":
"x",
# y-reference is assigned to the plot paper [0,1]
"yref":
"paper",
"x":
self._batch_increments[i] +
(batch["range"][1] - batch["range"][0]) / 2,
"y":
1,
"text":
f"{batch['id']}",
"showarrow":
False,
"yshift":
20,
# 'arrowhead': 7,
# 'ax': 0,
# 'ay': -40
})
sel = (batches >= batch["range"][0]) & (batches <=
batch["range"][1])
text.set_selected(
sel,
flex.std_string([
f"{batch['id']}: {j - batch['range'][0] + 1}"
for j in batches.select(sel)
]),
)
return shapes, annotations, list(text)
def run_once(directory):
from dxtbx.serialize import load
sweep_dir = os.path.basename(directory)
print sweep_dir
datablock_name = os.path.join(directory, "datablock.json")
if not os.path.exists(datablock_name):
# this is what xia2 calls it:
datablock_name = os.path.join(directory, "datablock_import.json")
strong_spots_name = os.path.join(directory, "strong.pickle")
experiments_name = os.path.join(directory, "experiments.json")
indexed_spots_name = os.path.join(directory, "indexed.pickle")
unindexed_spots_name = os.path.join(directory, "unindexed.pickle")
if not (os.path.exists(datablock_name) and os.path.exists(strong_spots_name)):
return
datablock = load.datablock(datablock_name)
assert len(datablock) == 1
if len(datablock[0].extract_sweeps()) == 0:
print "Skipping %s" %directory
return
sweep = datablock[0].extract_sweeps()[0]
template = sweep.get_template()
strong_spots = easy_pickle.load(strong_spots_name)
n_strong_spots = len(strong_spots)
if os.path.exists(experiments_name):
experiments = load.experiment_list(experiments_name)
n_indexed_lattices = len(experiments)
else:
experiments = None
n_indexed_lattices = 0
g = glob.glob(os.path.join(directory, "xds*", "run_2", "INTEGRATE.HKL"))
n_integrated_lattices = len(g)
if os.path.exists(indexed_spots_name):
indexed_spots = easy_pickle.load(indexed_spots_name)
else:
indexed_spots = None
g = glob.glob(os.path.join(directory, "indexed_*.pickle"))
if len(g):
for path in g:
if indexed_spots is None:
indexed_spots = easy_pickle.load(path)
else:
indexed_spots.extend(easy_pickle.load(path))
if os.path.exists(unindexed_spots_name):
unindexed_spots = easy_pickle.load(unindexed_spots_name)
n_unindexed_spots = len(unindexed_spots)
else:
n_unindexed_spots = 0
# calculate estimated d_min for sweep based on 95th percentile
from dials.algorithms.indexing import indexer
detector = sweep.get_detector()
scan = sweep.get_scan()
beam = sweep.get_beam()
goniometer = sweep.get_goniometer()
if len(strong_spots) == 0:
d_strong_spots_99th_percentile = 0
d_strong_spots_95th_percentile = 0
d_strong_spots_50th_percentile = 0
n_strong_spots_dmin_4 = 0
else:
spots_mm = indexer.indexer_base.map_spots_pixel_to_mm_rad(
strong_spots, detector, scan)
indexer.indexer_base.map_centroids_to_reciprocal_space(
spots_mm, detector, beam, goniometer)
d_spacings = 1/spots_mm['rlp'].norms()
perm = flex.sort_permutation(d_spacings, reverse=True)
d_spacings_sorted = d_spacings.select(perm)
percentile_99th = int(math.floor(0.99 * len(d_spacings)))
percentile_95th = int(math.floor(0.95 * len(d_spacings)))
percentile_50th = int(math.floor(0.5 * len(d_spacings)))
d_strong_spots_99th_percentile = d_spacings_sorted[percentile_99th]
d_strong_spots_95th_percentile = d_spacings_sorted[percentile_95th]
d_strong_spots_50th_percentile = d_spacings_sorted[percentile_50th]
n_strong_spots_dmin_4 = (d_spacings >= 4).count(True)
cell_params = flex.sym_mat3_double()
n_indexed = flex.double()
d_min_indexed = flex.double()
rmsds = flex.vec3_double()
sweep_dir_cryst = flex.std_string()
if experiments is not None:
for i, experiment in enumerate(experiments):
sweep_dir_cryst.append(sweep_dir)
crystal_model = experiment.crystal
unit_cell = crystal_model.get_unit_cell()
space_group = crystal_model.get_space_group()
crystal_symmetry = crystal.symmetry(unit_cell=unit_cell,
space_group=space_group)
cb_op_reference_setting = crystal_symmetry.change_of_basis_op_to_reference_setting()
crystal_symmetry_reference_setting = crystal_symmetry.change_basis(
cb_op_reference_setting)
cell_params.append(crystal_symmetry_reference_setting.unit_cell().parameters())
spots_mm = indexed_spots.select(indexed_spots['id'] == i)
n_indexed.append(len(spots_mm))
if len(spots_mm) == 0:
d_min_indexed.append(0)
else:
indexer.indexer_base.map_centroids_to_reciprocal_space(
spots_mm, detector, beam, goniometer)
d_spacings = 1/spots_mm['rlp'].norms()
perm = flex.sort_permutation(d_spacings, reverse=True)
d_min_indexed.append(d_spacings[perm[-1]])
try:
rmsds.append(get_rmsds_obs_pred(spots_mm, experiment))
except Exception, e:
print e
rmsds.append((-1,-1,-1))
continue
def test_del_selected():
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ["a", "b", "c", "d", "e", "f", "g", "i", "j", "k"]
# Create a table with some elements
table1 = flex.reflection_table()
table1["col1"] = flex.int(c1)
table1["col2"] = flex.double(c2)
table1["col3"] = flex.std_string(c3)
# Del selected columns
table1.del_selected(("col3", "col2"))
assert table1.nrows() == 10
assert table1.ncols() == 1
assert "col1" in table1
assert "col2" not in table1
assert "col3" not in table1
assert all(a == b for a, b in zip(table1["col1"], c1))
# Del selected columns
table1 = flex.reflection_table()
table1["col1"] = flex.int(c1)
table1["col2"] = flex.double(c2)
table1["col3"] = flex.std_string(c3)
table1.del_selected(flex.std_string(["col3", "col2"]))
assert table1.nrows() == 10
assert table1.ncols() == 1
assert "col1" in table1
assert "col2" not in table1
assert "col3" not in table1
assert all(a == b for a, b in zip(table1["col1"], c1))
# Del selected rows
table1 = flex.reflection_table()
table1["col1"] = flex.int(c1)
table1["col2"] = flex.double(c2)
table1["col3"] = flex.std_string(c3)
index = flex.size_t([0, 1, 5, 8, 9])
index2 = list(range(10))
for i in index:
index2.remove(i)
ccc1 = [c1[i] for i in index2]
ccc2 = [c2[i] for i in index2]
ccc3 = [c3[i] for i in index2]
table1.del_selected(index)
assert table1.nrows() == len(ccc1)
assert all(a == b for a, b in zip(table1["col1"], ccc1))
assert all(a == b for a, b in zip(table1["col2"], ccc2))
assert all(a == b for a, b in zip(table1["col3"], ccc3))
# Del selected rows
table1 = flex.reflection_table()
table1["col1"] = flex.int(c1)
table1["col2"] = flex.double(c2)
table1["col3"] = flex.std_string(c3)
table1.del_selected(index)
assert table1.nrows() == len(ccc1)
assert all(a == b for a, b in zip(table1["col1"], ccc1))
assert all(a == b for a, b in zip(table1["col2"], ccc2))
assert all(a == b for a, b in zip(table1["col3"], ccc3))
def tst_del_selected(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
# Del selected columns
table1.del_selected(('col3', 'col2'))
assert(table1.nrows() == 10)
assert(table1.ncols() == 1)
assert("col1" in table1)
assert("col2" not in table1)
assert("col3" not in table1)
assert(all(a == b for a, b in zip(table1['col1'], c1)))
print 'OK'
# Del selected columns
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
table1.del_selected(flex.std_string(['col3', 'col2']))
assert(table1.nrows() == 10)
assert(table1.ncols() == 1)
assert("col1" in table1)
assert("col2" not in table1)
assert("col3" not in table1)
assert(all(a == b for a, b in zip(table1['col1'], c1)))
print 'OK'
# Del selected rows
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
index = flex.size_t([0, 1, 5, 8, 9])
index2 = range(10)
for i in index:
index2.remove(i)
ccc1 = [c1[i] for i in index2]
ccc2 = [c2[i] for i in index2]
ccc3 = [c3[i] for i in index2]
table1.del_selected(index)
assert(table1.nrows() == len(ccc1))
assert(all(a == b for a, b in zip(table1['col1'], ccc1)))
assert(all(a == b for a, b in zip(table1['col2'], ccc2)))
assert(all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
# Del selected rows
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1['col2'] = flex.double(c2)
table1['col3'] = flex.std_string(c3)
flags = flex.bool([True, True, False, False, False,
True, False, False, True, True])
table1.del_selected(index)
assert(table1.nrows() == len(ccc1))
assert(all(a == b for a, b in zip(table1['col1'], ccc1)))
assert(all(a == b for a, b in zip(table1['col2'], ccc2)))
assert(all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
def tst_set_selected(self):
from dials.array_family import flex
from copy import deepcopy
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table1 = flex.reflection_table()
table2 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table2['col2'] = flex.double(c2)
table2['col3'] = flex.std_string(c3)
# Set selected columns
table1.set_selected(('col3', 'col2'), table2)
assert(table1.nrows() == 10)
assert(table1.ncols() == 3)
assert(all(a == b for a, b in zip(table1['col1'], c1)))
assert(all(a == b for a, b in zip(table1['col2'], c2)))
assert(all(a == b for a, b in zip(table1['col3'], c3)))
print 'OK'
# Set selected columns
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1.set_selected(flex.std_string(['col3', 'col2']), table2)
assert(table1.nrows() == 10)
assert(table1.ncols() == 3)
assert(all(a == b for a, b in zip(table1['col1'], c1)))
assert(all(a == b for a, b in zip(table1['col2'], c2)))
assert(all(a == b for a, b in zip(table1['col3'], c3)))
print 'OK'
cc1 = list(range(10, 15))
cc2 = list(range(10, 15))
cc3 = ['l', 'm', 'n', 'o', 'p']
# Set selected rows
table2 = flex.reflection_table()
table2['col1'] = flex.int(cc1)
table2['col2'] = flex.double(cc2)
table2['col3'] = flex.std_string(cc3)
index = flex.size_t([0, 1, 5, 8, 9])
ccc1 = deepcopy(c1)
ccc2 = deepcopy(c2)
ccc3 = deepcopy(c3)
for j, i in enumerate(index):
ccc1[i] = cc1[j]
ccc2[i] = cc2[j]
ccc3[i] = cc3[j]
table1.set_selected(index, table2)
assert(all(a == b for a, b in zip(table1['col1'], ccc1)))
assert(all(a == b for a, b in zip(table1['col2'], ccc2)))
assert(all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
# Set selected rows
table2 = flex.reflection_table()
table2['col1'] = flex.int(cc1)
table2['col2'] = flex.double(cc2)
table2['col3'] = flex.std_string(cc3)
flags = flex.bool([True, True, False, False, False,
True, False, False, True, True])
table1.set_selected(index, table2)
assert(all(a == b for a, b in zip(table1['col1'], ccc1)))
assert(all(a == b for a, b in zip(table1['col2'], ccc2)))
assert(all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
def tst_slicing(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Try forward slicing
new_table = table[2:7:2]
assert(new_table.ncols() == 3)
assert(new_table.nrows() == 3)
assert(new_table.is_consistent())
c11 = c1[2:7:2]
c22 = c2[2:7:2]
c33 = c3[2:7:2]
assert(all(a == b for a, b in zip(new_table['col1'], c11)))
assert(all(a == b for a, b in zip(new_table['col2'], c22)))
assert(all(a == b for a, b in zip(new_table['col3'], c33)))
print 'OK'
# Try backward slicing
new_table = table[7:2:-2]
assert(new_table.ncols() == 3)
assert(new_table.nrows() == 3)
assert(new_table.is_consistent())
c11 = c1[7:2:-2]
c22 = c2[7:2:-2]
c33 = c3[7:2:-2]
assert(all(a == b for a, b in zip(new_table['col1'], c11)))
assert(all(a == b for a, b in zip(new_table['col2'], c22)))
assert(all(a == b for a, b in zip(new_table['col3'], c33)))
print 'OK'
# Try setting forward slicing
table[2:7:2] = new_table
assert(table.ncols() == 3)
assert(table.nrows() == 10)
assert(table.is_consistent())
c1[2:7:2] = c11
c2[2:7:2] = c22
c3[2:7:2] = c33
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Try setting backward slicing
table[7:2:-2] = new_table
assert(table.ncols() == 3)
assert(table.nrows() == 10)
assert(table.is_consistent())
c1[7:2:-2] = c11
c2[7:2:-2] = c22
c3[7:2:-2] = c33
assert(all(a == b for a, b in zip(table['col1'], c1)))
assert(all(a == b for a, b in zip(table['col2'], c2)))
assert(all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
def run(self, experiments, reflections):
self.logger.log_step_time("POSTREFINEMENT")
if (not self.params.postrefinement.enable) or (self.params.scaling.algorithm != "mark0"): # mark1 implies no scaling/post-refinement
self.logger.log("No post-refinement was done")
if self.mpi_helper.rank == 0:
self.logger.main_log("No post-refinement was done")
return experiments, reflections
target_symm = symmetry(unit_cell = self.params.scaling.unit_cell, space_group_info = self.params.scaling.space_group)
i_model = self.params.scaling.i_model
miller_set = self.params.scaling.miller_set
# Ensure that match_multi_indices() will return identical results
# when a frame's observations are matched against the
# pre-generated Miller set, self.miller_set, and the reference
# data set, self.i_model. The implication is that the same match
# can be used to map Miller indices to array indices for intensity
# accumulation, and for determination of the correlation
# coefficient in the presence of a scaling reference.
assert len(i_model.indices()) == len(miller_set.indices())
assert (i_model.indices() == miller_set.indices()).count(False) == 0
new_experiments = ExperimentList()
new_reflections = flex.reflection_table()
experiments_rejected_by_reason = {} # reason:how_many_rejected
for experiment in experiments:
exp_reflections = reflections.select(reflections['exp_id'] == experiment.identifier)
# Build a miller array for the experiment reflections with original miller indexes
exp_miller_indices_original = miller.set(target_symm, exp_reflections['miller_index'], not self.params.merging.merge_anomalous)
observations_original_index = miller.array(exp_miller_indices_original,
exp_reflections['intensity.sum.value'],
flex.double(flex.sqrt(exp_reflections['intensity.sum.variance'])))
assert exp_reflections.size() == exp_miller_indices_original.size()
assert observations_original_index.size() == exp_miller_indices_original.size()
# Build a miller array for the experiment reflections with asu miller indexes
exp_miller_indices_asu = miller.set(target_symm, exp_reflections['miller_index_asymmetric'], True)
observations = miller.array(exp_miller_indices_asu, exp_reflections['intensity.sum.value'], flex.double(flex.sqrt(exp_reflections['intensity.sum.variance'])))
matches = miller.match_multi_indices(miller_indices_unique = miller_set.indices(), miller_indices = observations.indices())
pair1 = flex.int([pair[1] for pair in matches.pairs()]) # refers to the observations
pair0 = flex.int([pair[0] for pair in matches.pairs()]) # refers to the model
assert exp_reflections.size() == exp_miller_indices_original.size()
assert observations_original_index.size() == exp_miller_indices_original.size()
# narrow things down to the set that matches, only
observations_pair1_selected = observations.customized_copy(indices = flex.miller_index([observations.indices()[p] for p in pair1]),
data = flex.double([observations.data()[p] for p in pair1]),
sigmas = flex.double([observations.sigmas()[p] for p in pair1]))
observations_original_index_pair1_selected = observations_original_index.customized_copy(indices = flex.miller_index([observations_original_index.indices()[p] for p in pair1]),
data = flex.double([observations_original_index.data()[p] for p in pair1]),
sigmas = flex.double([observations_original_index.sigmas()[p] for p in pair1]))
I_observed = observations_pair1_selected.data()
MILLER = observations_original_index_pair1_selected.indices()
ORI = crystal_orientation(experiment.crystal.get_A(), basis_type.reciprocal)
Astar = matrix.sqr(ORI.reciprocal_matrix())
Astar_from_experiment = matrix.sqr(experiment.crystal.get_A())
assert Astar == Astar_from_experiment
WAVE = experiment.beam.get_wavelength()
BEAM = matrix.col((0.0,0.0,-1./WAVE))
BFACTOR = 0.
MOSAICITY_DEG = experiment.crystal.get_half_mosaicity_deg()
DOMAIN_SIZE_A = experiment.crystal.get_domain_size_ang()
# calculation of correlation here
I_reference = flex.double([i_model.data()[pair[0]] for pair in matches.pairs()])
I_invalid = flex.bool([i_model.sigmas()[pair[0]] < 0. for pair in matches.pairs()])
use_weights = False # New facility for getting variance-weighted correlation
if use_weights:
# variance weighting
I_weight = flex.double([1./(observations_pair1_selected.sigmas()[pair[1]])**2 for pair in matches.pairs()])
else:
I_weight = flex.double(len(observations_pair1_selected.sigmas()), 1.)
I_weight.set_selected(I_invalid, 0.)
"""Explanation of 'include_negatives' semantics as originally implemented in cxi.merge postrefinement:
include_negatives = True
+ and - reflections both used for Rh distribution for initial estimate of RS parameter
+ and - reflections both used for calc/obs correlation slope for initial estimate of G parameter
+ and - reflections both passed to the refinery and used in the target function (makes sense if
you look at it from a certain point of view)
include_negatives = False
+ and - reflections both used for Rh distribution for initial estimate of RS parameter
+ reflections only used for calc/obs correlation slope for initial estimate of G parameter
+ and - reflections both passed to the refinery and used in the target function (makes sense if
you look at it from a certain point of view)
"""
# RB: By design, for MPI-Merge "include negatives" is implicitly True
SWC = simple_weighted_correlation(I_weight, I_reference, I_observed)
if self.params.output.log_level == 0:
self.logger.log("Old correlation is: %f"%SWC.corr)
if self.params.postrefinement.algorithm == "rs":
Rhall = flex.double()
for mill in MILLER:
H = matrix.col(mill)
Xhkl = Astar*H
Rh = ( Xhkl + BEAM ).length() - (1./WAVE)
Rhall.append(Rh)
Rs = math.sqrt(flex.mean(Rhall*Rhall))
RS = 1./10000. # reciprocal effective domain size of 1 micron
RS = Rs # try this empirically determined approximate, monochrome, a-mosaic value
current = flex.double([SWC.slope, BFACTOR, RS, 0., 0.])
parameterization_class = rs_parameterization
refinery = rs_refinery(ORI=ORI, MILLER=MILLER, BEAM=BEAM, WAVE=WAVE, ICALCVEC = I_reference, IOBSVEC = I_observed)
elif self.params.postrefinement.algorithm == "eta_deff":
eta_init = 2. * MOSAICITY_DEG * math.pi/180.
D_eff_init = 2. * DOMAIN_SIZE_A
current = flex.double([SWC.slope, BFACTOR, eta_init, 0., 0., D_eff_init])
parameterization_class = eta_deff_parameterization
refinery = eta_deff_refinery(ORI=ORI, MILLER=MILLER, BEAM=BEAM, WAVE=WAVE, ICALCVEC = I_reference, IOBSVEC = I_observed)
func = refinery.fvec_callable(parameterization_class(current))
functional = flex.sum(func * func)
if self.params.output.log_level == 0:
self.logger.log("functional: %f"%functional)
self.current = current;
self.parameterization_class = parameterization_class
self.refinery = refinery;
self.observations_pair1_selected = observations_pair1_selected;
self.observations_original_index_pair1_selected = observations_original_index_pair1_selected
error_detected = False
try:
self.run_plain()
result_observations_original_index, result_observations, result_matches = self.result_for_cxi_merge()
assert result_observations_original_index.size() == result_observations.size()
assert result_matches.pairs().size() == result_observations_original_index.size()
except (AssertionError, ValueError, RuntimeError) as e:
error_detected = True
reason = repr(e)
if not reason:
reason = "Unknown error"
if not reason in experiments_rejected_by_reason:
experiments_rejected_by_reason[reason] = 1
else:
experiments_rejected_by_reason[reason] += 1
if not error_detected:
new_experiments.append(experiment)
new_exp_reflections = flex.reflection_table()
new_exp_reflections['miller_index_asymmetric'] = flex.miller_index(result_observations.indices())
new_exp_reflections['intensity.sum.value'] = flex.double(result_observations.data())
new_exp_reflections['intensity.sum.variance'] = flex.double(flex.pow(result_observations.sigmas(),2))
new_exp_reflections['exp_id'] = flex.std_string(len(new_exp_reflections), experiment.identifier)
new_reflections.extend(new_exp_reflections)
'''
# debugging
elif reason.startswith("ValueError"):
self.logger.log("Rejected b/c of value error exp id: %s; unit cell: %s"%(exp_id, str(experiment.crystal.get_unit_cell())) )
'''
# report rejected experiments, reflections
experiments_rejected_by_postrefinement = len(experiments) - len(new_experiments)
reflections_rejected_by_postrefinement = reflections.size() - new_reflections.size()
self.logger.log("Experiments rejected by post-refinement: %d"%experiments_rejected_by_postrefinement)
self.logger.log("Reflections rejected by post-refinement: %d"%reflections_rejected_by_postrefinement)
all_reasons = []
for reason, count in six.iteritems(experiments_rejected_by_reason):
self.logger.log("Experiments rejected due to %s: %d"%(reason,count))
all_reasons.append(reason)
comm = self.mpi_helper.comm
MPI = self.mpi_helper.MPI
# Collect all rejection reasons from all ranks. Use allreduce to let each rank have all reasons.
all_reasons = comm.allreduce(all_reasons, MPI.SUM)
all_reasons = set(all_reasons)
# Now that each rank has all reasons from all ranks, we can treat the reasons in a uniform way.
total_experiments_rejected_by_reason = {}
for reason in all_reasons:
rejected_experiment_count = 0
if reason in experiments_rejected_by_reason:
rejected_experiment_count = experiments_rejected_by_reason[reason]
total_experiments_rejected_by_reason[reason] = comm.reduce(rejected_experiment_count, MPI.SUM, 0)
total_accepted_experiment_count = comm.reduce(len(new_experiments), MPI.SUM, 0)
# how many reflections have we rejected due to post-refinement?
rejected_reflections = len(reflections) - len(new_reflections);
total_rejected_reflections = self.mpi_helper.sum(rejected_reflections)
if self.mpi_helper.rank == 0:
for reason, count in six.iteritems(total_experiments_rejected_by_reason):
self.logger.main_log("Total experiments rejected due to %s: %d"%(reason,count))
self.logger.main_log("Total experiments accepted: %d"%total_accepted_experiment_count)
self.logger.main_log("Total reflections rejected due to post-refinement: %d"%total_rejected_reflections)
self.logger.log_step_time("POSTREFINEMENT", True)
return new_experiments, new_reflections
def tst_resizing(self):
from dials.array_family import flex
# Create a table with 2 empty columns
table = flex.reflection_table()
assert(table.empty())
table['col1'] = flex.int()
table['col2'] = flex.double()
assert(table.nrows() == 0)
assert(table.ncols() == 2)
assert(not table.empty())
assert('col1' in table)
assert('col2' in table)
assert('col3' not in table)
print 'OK'
# Create a table with 2 columns and 10 rows
table = flex.reflection_table()
table['col1'] = flex.int(10)
table['col2'] = flex.double(10)
assert(table.nrows() == 10)
assert(table.ncols() == 2)
print 'OK'
# Add an extra column with the wrong size (throw)
try:
table['col3'] = flex.std_string(20)
assert(False)
except Exception:
pass
assert(table.nrows() == 10)
assert(table.ncols() == 2)
assert(table.is_consistent())
assert(len(table['col1']) == 10)
assert(len(table['col2']) == 10)
assert len(table) == table.size()
print 'OK'
# Resize the table (should resize all columns)
table.resize(50)
assert(table.nrows() == 50)
assert(table.ncols() == 2)
assert(table.is_consistent())
assert(len(table['col1']) == 50)
assert(len(table['col2']) == 50)
print 'OK'
# Make the table inconsistent
table['col1'].resize(40)
assert(not table.is_consistent())
assert_exception(lambda: table.nrows())
assert_exception(lambda: table.ncols())
print 'OK'
# Clear the table
table.clear()
assert(table.is_consistent())
assert(table.empty())
assert(table.nrows() == 0)
assert(table.ncols() == 0)
print 'OK'
def run(args):
sweep_directories = []
templates = []
n_strong_spots = flex.int()
n_strong_spots_dmin_4 = flex.int()
d_strong_spots_99th_percentile = flex.double()
d_strong_spots_95th_percentile = flex.double()
d_strong_spots_50th_percentile = flex.double()
n_unindexed_spots = flex.int()
n_indexed_lattices = flex.int()
n_integrated_lattices = flex.int()
sweep_dir_cryst = flex.std_string()
orig_dir = os.path.abspath(os.curdir)
rmsds = flex.vec3_double()
cell_params = flex.sym_mat3_double()
n_indexed = flex.double()
d_min_indexed = flex.double()
rmsds = flex.vec3_double()
nproc = easy_mp.get_processes(libtbx.Auto)
#nproc = 1
results = easy_mp.parallel_map(
func=run_once,
iterable=args,
processes=nproc,
method="multiprocessing",
preserve_order=True,
asynchronous=True,
preserve_exception_message=True,
)
for result in results:
if result is None: continue
sweep_directories.append(result.sweep_dir)
templates.append(result.template)
n_strong_spots.append(result.n_strong_spots)
n_strong_spots_dmin_4.append(result.n_strong_spots_dmin_4)
n_unindexed_spots.append(result.n_unindexed_spots)
n_indexed_lattices.append(result.n_indexed_lattices)
n_integrated_lattices.append(result.n_integrated_lattices)
d_strong_spots_50th_percentile.append(result.d_strong_spots_50th_percentile)
d_strong_spots_95th_percentile.append(result.d_strong_spots_95th_percentile)
d_strong_spots_99th_percentile.append(result.d_strong_spots_99th_percentile)
cell_params.extend(result.cell_params)
n_indexed.extend(result.n_indexed)
d_min_indexed.extend(result.d_min_indexed)
rmsds.extend(result.rmsds)
sweep_dir_cryst.extend(result.sweep_dir_cryst)
table_data = [('sweep_dir', 'template', '#strong_spots', '#unindexed_spots', '#lattices',
'd_spacing_50th_percentile', 'd_spacing_95th_percentile',
'd_spacing_99th_percentile',)]
for i in range(len(sweep_directories)):
table_data.append((sweep_directories[i],
templates[i],
str(n_strong_spots[i]),
str(n_unindexed_spots[i]),
str(n_indexed_lattices[i]),
str(d_strong_spots_50th_percentile[i]),
str(d_strong_spots_95th_percentile[i]),
str(d_strong_spots_99th_percentile[i]),
))
with open('results.txt', 'wb') as f:
print >> f, table_utils.format(
table_data, has_header=True, justify='right')
table_data = [('sweep_dir', 'cell_a', 'cell_b', 'cell_c', 'alpha', 'beta', 'gamma',
'#indexed_reflections', 'd_min_indexed',
'rmsd_x', 'rmsd_y', 'rmsd_phi')]
for i in range(len(cell_params)):
table_data.append((sweep_dir_cryst[i],
str(cell_params[i][0]),
str(cell_params[i][1]),
str(cell_params[i][2]),
str(cell_params[i][3]),
str(cell_params[i][4]),
str(cell_params[i][5]),
str(n_indexed[i]),
str(d_min_indexed[i]),
str(rmsds[i][0]),
str(rmsds[i][1]),
str(rmsds[i][2]),
))
with open('results_indexed.txt', 'wb') as f:
print >> f, table_utils.format(
table_data, has_header=True, justify='right')
cell_a = flex.double([params[0] for params in cell_params])
cell_b = flex.double([params[1] for params in cell_params])
cell_c = flex.double([params[2] for params in cell_params])
cell_alpha = flex.double([params[3] for params in cell_params])
cell_beta = flex.double([params[4] for params in cell_params])
cell_gamma = flex.double([params[5] for params in cell_params])
from matplotlib import pyplot
from matplotlib.backends.backend_pdf import PdfPages
pyplot.rc('font', family='serif')
pyplot.rc('font', serif='Times New Roman')
red, blue = '#B2182B', '#2166AC'
hist = flex.histogram(n_strong_spots_dmin_4.as_double(), n_slots=20)
hist.show()
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.bar(hist.slot_centers(), hist.slots(), width=0.75*hist.slot_width(),
color=blue, edgecolor=blue)
ax.set_xlabel('Spot count')
ax.set_ylabel('Frequency')
pdf = PdfPages("spot_count_histogram.pdf")
pdf.savefig(fig)
pdf.close()
#pyplot.show()
hist = flex.histogram(n_indexed_lattices.as_double(),
n_slots=flex.max(n_indexed_lattices))
hist.show()
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.bar(range(int(hist.data_max())), hist.slots(),
width=0.75*hist.slot_width(), align='center',
color=blue, edgecolor=blue)
ax.set_xlim(-0.5, hist.data_max()-0.5)
ax.set_xticks(range(0,int(hist.data_max())))
ax.set_xlabel('Number of indexed lattices')
ax.set_ylabel('Frequency')
pdf = PdfPages("n_indexed_lattices_histogram.pdf")
pdf.savefig(fig)
pdf.close()
#pyplot.show()
if flex.max(n_integrated_lattices) > 0:
hist = flex.histogram(n_integrated_lattices.as_double(),
n_slots=flex.max(n_integrated_lattices))
hist.show()
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.bar(range(int(hist.data_max())), hist.slots(),
width=0.75*hist.slot_width(),
align='center', color=blue, edgecolor=blue)
ax.set_xlim(-0.5, hist.data_max()-0.5)
ax.set_xticks(range(0,int(hist.data_max())))
ax.set_xlabel('Number of integrated lattices')
ax.set_ylabel('Frequency')
pdf = PdfPages("n_integrated_lattices_histogram.pdf")
pdf.savefig(fig)
pdf.close()
#pyplot.show()
fig, axes = pyplot.subplots(nrows=2, ncols=3, squeeze=False)
for i, cell_param in enumerate(
(cell_a, cell_b, cell_c, cell_alpha, cell_beta, cell_gamma)):
ax = axes.flat[i]
flex.min_max_mean_double(cell_param).show()
print flex.median(cell_param)
hist = flex.histogram(cell_param, n_slots=20)
hist.show()
ax.bar(hist.slot_centers(), hist.slots(), width=0.75*hist.slot_width(),
color=blue, edgecolor=blue)
ax.set_xlabel('Cell parameter')
ax.set_ylabel('Frequency')
pyplot.tight_layout()
pdf = PdfPages("cell_parameters.pdf")
pdf.savefig(fig)
pdf.close()
def export_mtz(observed_hkls, experiment, filename):
if experiment.goniometer:
axis = experiment.goniometer.get_rotation_axis()
else:
axis = 0.0, 0.0, 0.0
s0 = experiment.beam.get_s0()
wavelength = experiment.beam.get_wavelength()
from scitbx import matrix
panel = experiment.detector[0]
pixel_size = panel.get_pixel_size()
cb_op_to_ref = experiment.crystal.get_space_group().info(
).change_of_basis_op_to_reference_setting()
experiment.crystal = experiment.crystal.change_basis(cb_op_to_ref)
from iotbx import mtz
from scitbx.array_family import flex
import itertools
m = mtz.object()
m.set_title('from dials.scratch.mg.strategy_i19')
m.set_space_group_info(experiment.crystal.get_space_group().info())
nrefcount = sum(observed_hkls.itervalues())
nref = max(observed_hkls.itervalues())
for batch in range(1, nref+1):
o = m.add_batch().set_num(batch).set_nbsetid(1).set_ncryst(1)
o.set_time1(0.0).set_time2(0.0).set_title('Batch %d' % batch)
o.set_ndet(1).set_theta(flex.float((0.0, 0.0))).set_lbmflg(0)
o.set_alambd(wavelength).set_delamb(0.0).set_delcor(0.0)
o.set_divhd(0.0).set_divvd(0.0)
o.set_so(flex.float(s0)).set_source(flex.float((0, 0, -1)))
o.set_bbfac(0.0).set_bscale(1.0)
o.set_sdbfac(0.0).set_sdbscale(0.0).set_nbscal(0)
_unit_cell = experiment.crystal.get_unit_cell()
_U = experiment.crystal.get_U()
o.set_cell(flex.float(_unit_cell.parameters()))
o.set_lbcell(flex.int((-1, -1, -1, -1, -1, -1)))
o.set_umat(flex.float(_U.transpose().elems))
mosaic = experiment.crystal.get_mosaicity()
o.set_crydat(flex.float([mosaic, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))
o.set_lcrflg(0)
o.set_datum(flex.float((0.0, 0.0, 0.0)))
# detector size, distance
o.set_detlm(flex.float([0.0, panel.get_image_size()[0],
0.0, panel.get_image_size()[1],
0, 0, 0, 0]))
o.set_dx(flex.float([panel.get_directed_distance(), 0.0]))
# goniometer axes and names, and scan axis number, and number of axes, missets
o.set_e1(flex.float(axis))
o.set_e2(flex.float((0.0, 0.0, 0.0)))
o.set_e3(flex.float((0.0, 0.0, 0.0)))
o.set_gonlab(flex.std_string(('AXIS', '', '')))
o.set_jsaxs(1)
o.set_ngonax(1)
o.set_phixyz(flex.float((0.0, 0.0, 0.0, 0.0, 0.0, 0.0)))
phi_start, phi_range = 0.0, 0.0
o.set_phistt(phi_start)
o.set_phirange(phi_range)
o.set_phiend(phi_start + phi_range)
o.set_scanax(flex.float(axis))
# number of misorientation angles
o.set_misflg(0)
# crystal axis closest to rotation axis (why do I want this?)
o.set_jumpax(0)
# type of data - 1; 2D, 2; 3D, 3; Laue
o.set_ldtype(2)
# now create the actual data structures - first keep a track of the columns
# H K L M/ISYM BATCH I SIGI IPR SIGIPR FRACTIONCALC XDET YDET ROT WIDTH
# LP MPART FLAG BGPKRATIOS
from cctbx.array_family import flex as cflex # implicit import
# now go for it and make an MTZ file...
x = m.add_crystal('XTAL', 'DIALS', unit_cell.parameters())
d = x.add_dataset('FROMDIALS', wavelength)
# now add column information...
type_table = {'IPR': 'J', 'BGPKRATIOS': 'R', 'WIDTH': 'R', 'I': 'J',
'H': 'H', 'K': 'H', 'MPART': 'I', 'L': 'H', 'BATCH': 'B',
'M_ISYM': 'Y', 'SIGI': 'Q', 'FLAG': 'I', 'XDET': 'R', 'LP': 'R',
'YDET': 'R', 'SIGIPR': 'Q', 'FRACTIONCALC': 'R', 'ROT': 'R'}
m.adjust_column_array_sizes(nrefcount)
m.set_n_reflections(nrefcount)
# assign H, K, L, M_ISYM space
for column in 'H', 'K', 'L', 'M_ISYM':
d.add_column(column, type_table[column]).set_values(flex.float(nrefcount, 0.0))
batchnums = ( _ for (x, n) in observed_hkls.iteritems() for _ in range(1, n+1) )
d.add_column('BATCH', type_table['BATCH']).set_values(flex.float(batchnums))
d.add_column('FRACTIONCALC', type_table['FRACTIONCALC']).set_values(flex.float(nrefcount, 3.0))
m.replace_original_index_miller_indices(cb_op_to_ref.apply(
cflex.miller_index([ _ for (x, n) in observed_hkls.iteritems() for _ in itertools.repeat(x, n) ])
))
m.write(filename)
return m
def run(self, all_experiments, all_reflections):
""" Load all the data using MPI """
from dxtbx.model.experiment_list import ExperimentList
from dials.array_family import flex
# Both must be none or not none
test = [all_experiments is None, all_reflections is None].count(True)
assert test in [0, 2]
if test == 2:
all_experiments = ExperimentList()
all_reflections = flex.reflection_table()
starting_expts_count = starting_refls_count = 0
else:
starting_expts_count = len(all_experiments)
starting_refls_count = len(all_reflections)
self.logger.log(
"Initial number of experiments: %d; Initial number of reflections: %d"
% (starting_expts_count, starting_refls_count))
# Generate and send a list of file paths to each worker
if self.mpi_helper.rank == 0:
file_list = self.get_list()
self.logger.log(
"Built an input list of %d json/pickle file pairs" %
(len(file_list)))
self.params.input.path = None # Rank 0 has already parsed the input parameters
per_rank_file_list = file_load_calculator(self.params, file_list, self.logger).\
calculate_file_load(available_rank_count = self.mpi_helper.size)
self.logger.log(
'Transmitting a list of %d lists of json/pickle file pairs' %
(len(per_rank_file_list)))
transmitted = per_rank_file_list
else:
transmitted = None
self.logger.log_step_time("BROADCAST_FILE_LIST")
transmitted = self.mpi_helper.comm.bcast(transmitted, root=0)
new_file_list = transmitted[
self.mpi_helper.
rank] if self.mpi_helper.rank < len(transmitted) else None
self.logger.log_step_time("BROADCAST_FILE_LIST", True)
# Load the data
self.logger.log_step_time("LOAD")
if new_file_list is not None:
self.logger.log("Received a list of %d json/pickle file pairs" %
len(new_file_list))
for experiments_filename, reflections_filename in new_file_list:
experiments = ExperimentListFactory.from_json_file(
experiments_filename, check_format=False)
reflections = flex.reflection_table.from_file(
reflections_filename)
for experiment_id, experiment in enumerate(experiments):
if experiment.identifier is None or len(
experiment.identifier) == 0:
experiment.identifier = create_experiment_identifier(
experiment, experiments_filename, experiment_id)
all_experiments.append(experiment)
#experiment.identifier = "%d"%(len(all_experiments) - 1)
# select reflections of the current experiment
refls = reflections.select(
reflections['id'] == experiment_id)
# Reflection experiment 'id' is supposed to be unique within this rank; 'exp_id' (i.e. experiment identifier) is supposed to be unique globally
#refls['id'] = flex.size_t(len(refls), len(all_experiments)-1)
refls['exp_id'] = flex.std_string(len(refls),
experiment.identifier)
all_reflections.extend(refls)
else:
self.logger.log("Received a list of 0 json/pickle file pairs")
self.logger.log_step_time("LOAD", True)
self.logger.log('Read %d experiments consisting of %d reflections' %
(len(all_experiments) - starting_expts_count,
len(all_reflections) - starting_refls_count))
self.logger.log("Memory usage: %d MB" % get_memory_usage())
from xfel.merging.application.reflection_table_utils import reflection_table_utils
all_reflections = reflection_table_utils.prune_reflection_table_keys(
reflections=all_reflections,
keys_to_keep=[
'intensity.sum.value', 'intensity.sum.variance',
'miller_index', 'miller_index_asymmetric', 'exp_id', 's1'
])
self.logger.log("Pruned reflection table")
self.logger.log("Memory usage: %d MB" % get_memory_usage())
# Do we have any data?
from xfel.merging.application.utils.data_counter import data_counter
data_counter(self.params).count(all_experiments, all_reflections)
return all_experiments, all_reflections
def run():
# The script usage
usage = "usage: xia2.multi_crystal_scale_and_merge [options] [param.phil] " \
"experiments1.json experiments2.json reflections1.pickle " \
"reflections2.pickle..."
# Create the parser
parser = OptionParser(
usage=usage,
phil=phil_scope,
read_reflections=True,
read_experiments=True,
check_format=False,
epilog=help_message)
# Parse the command line
params, options = parser.parse_args(show_diff_phil=True)
# Configure the logging
for name in ('xia2', 'dials'):
log.config(
info=params.output.log,
debug=params.output.debug_log,
name=name)
from dials.util.version import dials_version
logger.info(dials_version())
# Try to load the models and data
if len(params.input.experiments) == 0:
logger.info("No Experiments found in the input")
parser.print_help()
return
if len(params.input.reflections) == 0:
logger.info("No reflection data found in the input")
parser.print_help()
return
try:
assert len(params.input.reflections) == len(params.input.experiments)
except AssertionError:
raise Sorry("The number of input reflections files does not match the "
"number of input experiments")
if params.seed is not None:
import random
flex.set_random_seed(params.seed)
random.seed(params.seed)
expt_filenames = OrderedDict((e.filename, e.data) for e in params.input.experiments)
refl_filenames = OrderedDict((r.filename, r.data) for r in params.input.reflections)
experiments = flatten_experiments(params.input.experiments)
reflections = flatten_reflections(params.input.reflections)
reflections_all = flex.reflection_table()
assert len(reflections) == 1 or len(reflections) == len(experiments)
if len(reflections) > 1:
for i, (expt, refl) in enumerate(zip(experiments, reflections)):
expt.identifier = '%i' % i
refl['identifier'] = flex.std_string(refl.size(), expt.identifier)
refl['id'] = flex.int(refl.size(), i)
reflections_all.extend(refl)
reflections_all.experiment_identifiers()[i] = expt.identifier
else:
reflections_all = reflections[0]
assert 'identifier' in reflections_all
assert len(set(reflections_all['identifier'])) == len(experiments)
assert reflections_all.are_experiment_identifiers_consistent(experiments)
if params.identifiers is not None:
identifiers = []
for identifier in params.identifiers:
identifiers.extend(identifier.split(','))
params.identifiers = identifiers
scaled = ScaleAndMerge.MultiCrystalScale(experiments, reflections_all, params)
def _add_batch(
mtz,
experiment,
wavelength,
dataset_id,
batch_number,
image_number,
force_static_model,
):
"""Add a single image's metadata to an mtz file.
Returns the batch object.
"""
assert batch_number > 0
# Recalculate useful numbers and references here
# We ignore panels beyond the first one, at the moment
panel = experiment.detector[0]
if experiment.goniometer:
axis = matrix.col(experiment.goniometer.get_rotation_axis())
else:
axis = 0.0, 0.0, 0.0
U = matrix.sqr(experiment.crystal.get_U())
if experiment.goniometer is not None:
F = matrix.sqr(experiment.goniometer.get_fixed_rotation())
else:
F = matrix.sqr((1, 0, 0, 0, 1, 0, 0, 0, 1))
# Create the batch object and start configuring it
o = mtz.add_batch().set_num(batch_number).set_nbsetid(
dataset_id).set_ncryst(1)
o.set_time1(0.0).set_time2(0.0).set_title("Batch {}".format(batch_number))
o.set_ndet(1).set_theta(flex.float((0.0, 0.0))).set_lbmflg(0)
o.set_alambd(wavelength).set_delamb(0.0).set_delcor(0.0)
o.set_divhd(0.0).set_divvd(0.0)
# FIXME hard-coded assumption on indealized beam vector below... this may be
# broken when we come to process data from a non-imgCIF frame
s0n = matrix.col(experiment.beam.get_s0()).normalize().elems
o.set_so(flex.float(s0n)).set_source(flex.float((0, 0, -1)))
# these are probably 0, 1 respectively, also flags for how many are set, sd
o.set_bbfac(0.0).set_bscale(1.0)
o.set_sdbfac(0.0).set_sdbscale(0.0).set_nbscal(0)
# unit cell (this is fine) and the what-was-refined-flags FIXME hardcoded
# take time-varying parameters from the *end of the frame* unlikely to
# be much different at the end - however only exist if scan-varying
# refinement was used
if not force_static_model and experiment.crystal.num_scan_points > 0:
# Get the index of the image in the sequence e.g. first => 0, second => 1
image_index = image_number - experiment.scan.get_image_range()[0]
_unit_cell = experiment.crystal.get_unit_cell_at_scan_point(
image_index)
_U = matrix.sqr(experiment.crystal.get_U_at_scan_point(image_index))
else:
_unit_cell = experiment.crystal.get_unit_cell()
_U = U
# apply the fixed rotation to this to unify matrix definitions - F * U
# was what was used in the actual prediction: U appears to be stored
# as the transpose?! At least is for Mosflm...
#
# FIXME Do we need to apply the setting rotation here somehow? i.e. we have
# the U.B. matrix assuming that the axis is equal to S * axis_datum but
# here we are just giving the effective axis so at scan angle 0 this will
# not be correct... FIXME 2 not even sure we can express the stack of
# matrices S * R * F * U * B in MTZ format?... see [=A=] below
_U = matrix.sqr(dials_u_to_mosflm(F * _U, _unit_cell))
# FIXME need to get what was refined and what was constrained from the
# crystal model - see https://github.com/dials/dials/issues/355
o.set_cell(flex.float(_unit_cell.parameters()))
o.set_lbcell(flex.int((-1, -1, -1, -1, -1, -1)))
o.set_umat(flex.float(_U.transpose().elems))
# get the mosaic spread though today it may not actually be set - should
# this be in the BATCH headers?
try:
mosaic = experiment.crystal.get_mosaicity()
except AttributeError:
mosaic = 0
o.set_crydat(
flex.float(
[mosaic, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))
o.set_lcrflg(0)
o.set_datum(flex.float((0.0, 0.0, 0.0)))
# detector size, distance
o.set_detlm(
flex.float([
0.0,
panel.get_image_size()[0], 0.0,
panel.get_image_size()[1], 0, 0, 0, 0
]))
o.set_dx(flex.float([panel.get_directed_distance(), 0.0]))
# goniometer axes and names, and scan axis number, and num axes, missets
# [=A=] should we be using this to unroll the setting matrix etc?
o.set_e1(flex.float(axis))
o.set_e2(flex.float((0.0, 0.0, 0.0)))
o.set_e3(flex.float((0.0, 0.0, 0.0)))
o.set_gonlab(flex.std_string(("AXIS", "", "")))
o.set_jsaxs(1)
o.set_ngonax(1)
o.set_phixyz(flex.float((0.0, 0.0, 0.0, 0.0, 0.0, 0.0)))
# scan ranges, axis
if experiment.scan:
phi_start, phi_range = experiment.scan.get_image_oscillation(
image_number)
else:
phi_start, phi_range = 0.0, 0.0
o.set_phistt(phi_start)
o.set_phirange(phi_range)
o.set_phiend(phi_start + phi_range)
o.set_scanax(flex.float(axis))
# number of misorientation angles
o.set_misflg(0)
# crystal axis closest to rotation axis (why do I want this?)
o.set_jumpax(0)
# type of data - 1; 2D, 2; 3D, 3; Laue
o.set_ldtype(2)
return o
def run(self, all_experiments, all_reflections):
""" Load all the data using MPI """
from dxtbx.model.experiment_list import ExperimentList
from dials.array_family import flex
# Both must be none or not none
test = [all_experiments is None, all_reflections is None].count(True)
assert test in [0,2]
if test == 2:
all_experiments = ExperimentList()
all_reflections = flex.reflection_table()
starting_expts_count = starting_refls_count = 0
else:
starting_expts_count = len(all_experiments)
starting_refls_count = len(all_reflections)
self.logger.log("Initial number of experiments: %d; Initial number of reflections: %d"%(starting_expts_count, starting_refls_count))
# Generate and send a list of file paths to each worker
if self.mpi_helper.rank == 0:
file_list = self.get_list()
self.logger.log("Built an input list of %d json/pickle file pairs"%(len(file_list)))
self.params.input.path = None # Rank 0 has already parsed the input parameters
per_rank_file_list = file_load_calculator(self.params, file_list, self.logger).\
calculate_file_load(available_rank_count = self.mpi_helper.size)
self.logger.log('Transmitting a list of %d lists of json/pickle file pairs'%(len(per_rank_file_list)))
transmitted = per_rank_file_list
else:
transmitted = None
self.logger.log_step_time("BROADCAST_FILE_LIST")
transmitted = self.mpi_helper.comm.bcast(transmitted, root = 0)
new_file_list = transmitted[self.mpi_helper.rank] if self.mpi_helper.rank < len(transmitted) else None
self.logger.log_step_time("BROADCAST_FILE_LIST", True)
# Load the data
self.logger.log_step_time("LOAD")
if new_file_list is not None:
self.logger.log("Received a list of %d json/pickle file pairs"%len(new_file_list))
for experiments_filename, reflections_filename in new_file_list:
self.logger.log("Reading %s %s"%(experiments_filename, reflections_filename))
experiments = ExperimentListFactory.from_json_file(experiments_filename, check_format = False)
reflections = flex.reflection_table.from_file(reflections_filename)
self.logger.log("Data read, prepping")
if 'intensity.sum.value' in reflections:
reflections['intensity.sum.value.unmodified'] = reflections['intensity.sum.value'] * 1
if 'intensity.sum.variance' in reflections:
reflections['intensity.sum.variance.unmodified'] = reflections['intensity.sum.variance'] * 1
new_ids = flex.int(len(reflections), -1)
new_identifiers = flex.std_string(len(reflections))
eid = reflections.experiment_identifiers()
for k in eid.keys():
del eid[k]
for experiment_id, experiment in enumerate(experiments):
# select reflections of the current experiment
refls_sel = reflections['id'] == experiment_id
if refls_sel.count(True) == 0: continue
if experiment.identifier is None or len(experiment.identifier) == 0:
experiment.identifier = create_experiment_identifier(experiment, experiments_filename, experiment_id)
all_experiments.append(experiment)
# Reflection experiment 'id' is unique within this rank; 'exp_id' (i.e. experiment identifier) is unique globally
new_identifiers.set_selected(refls_sel, experiment.identifier)
new_id = len(all_experiments)-1
eid[new_id] = experiment.identifier
new_ids.set_selected(refls_sel, new_id)
assert (new_ids < 0).count(True) == 0, "Not all reflections accounted for"
reflections['id'] = new_ids
reflections['exp_id'] = new_identifiers
all_reflections.extend(reflections)
else:
self.logger.log("Received a list of 0 json/pickle file pairs")
self.logger.log_step_time("LOAD", True)
self.logger.log('Read %d experiments consisting of %d reflections'%(len(all_experiments)-starting_expts_count, len(all_reflections)-starting_refls_count))
self.logger.log("Memory usage: %d MB"%get_memory_usage())
all_reflections = self.prune_reflection_table_keys(all_reflections)
# Do we have any data?
from xfel.merging.application.utils.data_counter import data_counter
data_counter(self.params).count(all_experiments, all_reflections)
return all_experiments, all_reflections
def tst_row_operations(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Extend the table
table.extend(table)
c1 = c1 * 2
c2 = c2 * 2
c3 = c3 * 2
assert (table.nrows() == 20)
assert (table.ncols() == 3)
assert (table.is_consistent())
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Append some rows to the table
row = {'col1': 10}
c1 = c1 + [10]
c2 = c2 + [0]
c3 = c3 + ['']
table.append(row)
assert (table.nrows() == 21)
assert (table.ncols() == 3)
assert (table.is_consistent())
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
row = {'col2': 11}
c1 = c1 + [0]
c2 = c2 + [11]
c3 = c3 + ['']
table.append(row)
assert (table.nrows() == 22)
assert (table.ncols() == 3)
assert (table.is_consistent())
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
row = {'col1': 12, 'col2': 12, 'col3': 'l'}
c1 = c1 + [12]
c2 = c2 + [12]
c3 = c3 + ['l']
table.append(row)
assert (table.nrows() == 23)
assert (table.ncols() == 3)
assert (table.is_consistent())
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Try inserting some rows
row = {'col1': -1}
c1.insert(5, -1)
c2.insert(5, 0)
c3.insert(5, '')
table.insert(5, row)
assert (table.nrows() == 24)
assert (table.ncols() == 3)
assert (table.is_consistent())
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
row = {'col1': -2, 'col2': -3, 'col3': 'abc'}
c1.insert(2, -2)
c2.insert(2, -3)
c3.insert(2, 'abc')
table.insert(2, row)
assert (table.nrows() == 25)
assert (table.ncols() == 3)
assert (table.is_consistent())
assert (all(a == b for a, b in zip(table['col1'], c1)))
assert (all(a == b for a, b in zip(table['col2'], c2)))
assert (all(a == b for a, b in zip(table['col3'], c3)))
print 'OK'
# Try iterating through table rows
for i in range(table.nrows()):
row = table[i]
assert (row['col1'] == c1[i])
assert (row['col2'] == c2[i])
assert (row['col3'] == c3[i])
print 'OK'
# Trying setting some rows
row = {'col1': 100}
table[2] = row
assert (table[2]['col1'] == 100)
assert (table[2]['col2'] == c2[2])
assert (table[2]['col3'] == c3[2])
row = {'col1': 1000, 'col2': 2000, 'col3': 'hello'}
table[10] = row
assert (table[10]['col1'] == 1000)
assert (table[10]['col2'] == 2000)
assert (table[10]['col3'] == 'hello')
print 'OK'
def run(self, all_experiments, all_reflections):
""" Load all the data using MPI """
from dxtbx.model.experiment_list import ExperimentList
from dials.array_family import flex
# Both must be none or not none
test = [all_experiments is None, all_reflections is None].count(True)
assert test in [0, 2]
if test == 2:
all_experiments = ExperimentList()
all_reflections = flex.reflection_table()
starting_expts_count = starting_refls_count = 0
else:
starting_expts_count = len(all_experiments)
starting_refls_count = len(all_reflections)
self.logger.log(
"Initial number of experiments: %d; Initial number of reflections: %d"
% (starting_expts_count, starting_refls_count))
# Generate and send a list of file paths to each worker
if self.mpi_helper.rank == 0:
file_list = self.get_list()
self.logger.log(
"Built an input list of %d json/pickle file pairs" %
(len(file_list)))
self.params.input.path = None # Rank 0 has already parsed the input parameters
per_rank_file_list = file_load_calculator(self.params, file_list, self.logger).\
calculate_file_load(available_rank_count = self.mpi_helper.size)
self.logger.log(
'Transmitting a list of %d lists of json/pickle file pairs' %
(len(per_rank_file_list)))
transmitted = per_rank_file_list
else:
transmitted = None
self.logger.log_step_time("BROADCAST_FILE_LIST")
transmitted = self.mpi_helper.comm.bcast(transmitted, root=0)
new_file_list = transmitted[
self.mpi_helper.
rank] if self.mpi_helper.rank < len(transmitted) else None
self.logger.log_step_time("BROADCAST_FILE_LIST", True)
# Load the data
self.logger.log_step_time("LOAD")
if new_file_list is not None:
self.logger.log("Received a list of %d json/pickle file pairs" %
len(new_file_list))
for experiments_filename, reflections_filename in new_file_list:
experiments = ExperimentListFactory.from_json_file(
experiments_filename, check_format=False)
reflections = flex.reflection_table.from_file(
reflections_filename)
# NOTE: had to use slicing below because it selection no longer works...
reflections.sort("id")
unique_refl_ids = set(reflections['id'])
assert len(unique_refl_ids) == len(
experiments
), "refl table and experiment list should contain data on same experiment " # TODO: decide if this is true
assert min(
reflections["id"]
) >= 0, "No more -1 in the id column, ideally it should be the numerical index of experiment, but beware that this is not enforced anywhere in the upstream code base"
if 'intensity.sum.value' in reflections:
reflections[
'intensity.sum.value.unmodified'] = reflections[
'intensity.sum.value'] * 1
if 'intensity.sum.variance' in reflections:
reflections[
'intensity.sum.variance.unmodified'] = reflections[
'intensity.sum.variance'] * 1
for experiment_id, experiment in enumerate(experiments):
if experiment.identifier is None or len(
experiment.identifier) == 0:
experiment.identifier = create_experiment_identifier(
experiment, experiments_filename, experiment_id)
all_experiments.append(experiment)
# select reflections of the current experiment
# FIXME the selection was broke for me, it raised
# RuntimeError: boost::bad_get: failed value get using boost::get
#refls = reflections.select(reflections['id'] == experiment_id)
# NOTE: this is a hack due to the broken expereimnt_id selection above
exp_id_pos = np.where(
reflections['id'] == experiment_id)[0]
assert exp_id_pos.size, "no refls in this experiment" # NOTE: maybe we can relax this assertion ?
refls = reflections[exp_id_pos[0]:exp_id_pos[-1] + 1]
#FIXME: how will this work if reading in multiple composite mode experiment jsons?
# Reflection experiment 'id' is supposed to be unique within this rank; 'exp_id' (i.e. experiment identifier) is supposed to be unique globally
refls['exp_id'] = flex.std_string(len(refls),
experiment.identifier)
new_id = 0
if len(all_reflections) > 0:
new_id = max(all_reflections['id']) + 1
# FIXME: it is hard to interperet that a function call returning a changeable property
eid = refls.experiment_identifiers()
for k in eid.keys():
del eid[k]
eid[new_id] = experiment.identifier
refls['id'] = flex.int(len(refls), new_id)
all_reflections.extend(refls)
else:
self.logger.log("Received a list of 0 json/pickle file pairs")
self.logger.log_step_time("LOAD", True)
self.logger.log('Read %d experiments consisting of %d reflections' %
(len(all_experiments) - starting_expts_count,
len(all_reflections) - starting_refls_count))
self.logger.log("Memory usage: %d MB" % get_memory_usage())
from xfel.merging.application.reflection_table_utils import reflection_table_utils
all_reflections = reflection_table_utils.prune_reflection_table_keys(
reflections=all_reflections,
keys_to_keep=[
'intensity.sum.value', 'intensity.sum.variance',
'miller_index', 'miller_index_asymmetric', 'exp_id', 's1',
'intensity.sum.value.unmodified',
'intensity.sum.variance.unmodified'
])
self.logger.log("Pruned reflection table")
self.logger.log("Memory usage: %d MB" % get_memory_usage())
# Do we have any data?
from xfel.merging.application.utils.data_counter import data_counter
data_counter(self.params).count(all_experiments, all_reflections)
return all_experiments, all_reflections
def tst_set_selected(self):
from dials.array_family import flex
from copy import deepcopy
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table1 = flex.reflection_table()
table2 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table2['col2'] = flex.double(c2)
table2['col3'] = flex.std_string(c3)
# Set selected columns
table1.set_selected(('col3', 'col2'), table2)
assert (table1.nrows() == 10)
assert (table1.ncols() == 3)
assert (all(a == b for a, b in zip(table1['col1'], c1)))
assert (all(a == b for a, b in zip(table1['col2'], c2)))
assert (all(a == b for a, b in zip(table1['col3'], c3)))
print 'OK'
# Set selected columns
table1 = flex.reflection_table()
table1['col1'] = flex.int(c1)
table1.set_selected(flex.std_string(['col3', 'col2']), table2)
assert (table1.nrows() == 10)
assert (table1.ncols() == 3)
assert (all(a == b for a, b in zip(table1['col1'], c1)))
assert (all(a == b for a, b in zip(table1['col2'], c2)))
assert (all(a == b for a, b in zip(table1['col3'], c3)))
print 'OK'
cc1 = list(range(10, 15))
cc2 = list(range(10, 15))
cc3 = ['l', 'm', 'n', 'o', 'p']
# Set selected rows
table2 = flex.reflection_table()
table2['col1'] = flex.int(cc1)
table2['col2'] = flex.double(cc2)
table2['col3'] = flex.std_string(cc3)
index = flex.size_t([0, 1, 5, 8, 9])
ccc1 = deepcopy(c1)
ccc2 = deepcopy(c2)
ccc3 = deepcopy(c3)
for j, i in enumerate(index):
ccc1[i] = cc1[j]
ccc2[i] = cc2[j]
ccc3[i] = cc3[j]
table1.set_selected(index, table2)
assert (all(a == b for a, b in zip(table1['col1'], ccc1)))
assert (all(a == b for a, b in zip(table1['col2'], ccc2)))
assert (all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
# Set selected rows
table2 = flex.reflection_table()
table2['col1'] = flex.int(cc1)
table2['col2'] = flex.double(cc2)
table2['col3'] = flex.std_string(cc3)
flags = flex.bool(
[True, True, False, False, False, True, False, False, True, True])
table1.set_selected(index, table2)
assert (all(a == b for a, b in zip(table1['col1'], ccc1)))
assert (all(a == b for a, b in zip(table1['col2'], ccc2)))
assert (all(a == b for a, b in zip(table1['col3'], ccc3)))
print 'OK'
def test_row_operations():
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ["a", "b", "c", "d", "e", "f", "g", "i", "j", "k"]
# Create a table with some elements
table = flex.reflection_table()
table["col1"] = flex.int(c1)
table["col2"] = flex.double(c2)
table["col3"] = flex.std_string(c3)
# Extend the table
table.extend(table)
c1 = c1 * 2
c2 = c2 * 2
c3 = c3 * 2
assert table.nrows() == 20
assert table.ncols() == 3
assert table.is_consistent()
assert all(a == b for a, b in zip(table["col1"], c1))
assert all(a == b for a, b in zip(table["col2"], c2))
assert all(a == b for a, b in zip(table["col3"], c3))
# Append some rows to the table
row = {"col1": 10}
c1 = c1 + [10]
c2 = c2 + [0]
c3 = c3 + [""]
table.append(row)
assert table.nrows() == 21
assert table.ncols() == 3
assert table.is_consistent()
assert all(a == b for a, b in zip(table["col1"], c1))
assert all(a == b for a, b in zip(table["col2"], c2))
assert all(a == b for a, b in zip(table["col3"], c3))
row = {"col2": 11}
c1 = c1 + [0]
c2 = c2 + [11]
c3 = c3 + [""]
table.append(row)
assert table.nrows() == 22
assert table.ncols() == 3
assert table.is_consistent()
assert all(a == b for a, b in zip(table["col1"], c1))
assert all(a == b for a, b in zip(table["col2"], c2))
assert all(a == b for a, b in zip(table["col3"], c3))
row = {"col1": 12, "col2": 12, "col3": "l"}
c1 = c1 + [12]
c2 = c2 + [12]
c3 = c3 + ["l"]
table.append(row)
assert table.nrows() == 23
assert table.ncols() == 3
assert table.is_consistent()
assert all(a == b for a, b in zip(table["col1"], c1))
assert all(a == b for a, b in zip(table["col2"], c2))
assert all(a == b for a, b in zip(table["col3"], c3))
# Try inserting some rows
row = {"col1": -1}
c1.insert(5, -1)
c2.insert(5, 0)
c3.insert(5, "")
table.insert(5, row)
assert table.nrows() == 24
assert table.ncols() == 3
assert table.is_consistent()
assert all(a == b for a, b in zip(table["col1"], c1))
assert all(a == b for a, b in zip(table["col2"], c2))
assert all(a == b for a, b in zip(table["col3"], c3))
row = {"col1": -2, "col2": -3, "col3": "abc"}
c1.insert(2, -2)
c2.insert(2, -3)
c3.insert(2, "abc")
table.insert(2, row)
assert table.nrows() == 25
assert table.ncols() == 3
assert table.is_consistent()
assert all(a == b for a, b in zip(table["col1"], c1))
assert all(a == b for a, b in zip(table["col2"], c2))
assert all(a == b for a, b in zip(table["col3"], c3))
# Try iterating through table rows
for i in range(table.nrows()):
row = table[i]
assert row["col1"] == c1[i]
assert row["col2"] == c2[i]
assert row["col3"] == c3[i]
# Trying setting some rows
row = {"col1": 100}
table[2] = row
assert table[2]["col1"] == 100
assert table[2]["col2"] == c2[2]
assert table[2]["col3"] == c3[2]
row = {"col1": 1000, "col2": 2000, "col3": "hello"}
table[10] = row
assert table[10]["col1"] == 1000
assert table[10]["col2"] == 2000
assert table[10]["col3"] == "hello"
def tst_resizing(self):
from dials.array_family import flex
# Create a table with 2 empty columns
table = flex.reflection_table()
assert (table.empty())
table['col1'] = flex.int()
table['col2'] = flex.double()
assert (table.nrows() == 0)
assert (table.ncols() == 2)
assert (not table.empty())
assert ('col1' in table)
assert ('col2' in table)
assert ('col3' not in table)
print 'OK'
# Create a table with 2 columns and 10 rows
table = flex.reflection_table()
table['col1'] = flex.int(10)
table['col2'] = flex.double(10)
assert (table.nrows() == 10)
assert (table.ncols() == 2)
print 'OK'
# Add an extra column with the wrong size (throw)
try:
table['col3'] = flex.std_string(20)
assert (False)
except Exception:
pass
assert (table.nrows() == 10)
assert (table.ncols() == 2)
assert (table.is_consistent())
assert (len(table['col1']) == 10)
assert (len(table['col2']) == 10)
assert len(table) == table.size()
print 'OK'
# Resize the table (should resize all columns)
table.resize(50)
assert (table.nrows() == 50)
assert (table.ncols() == 2)
assert (table.is_consistent())
assert (len(table['col1']) == 50)
assert (len(table['col2']) == 50)
print 'OK'
# Make the table inconsistent
table['col1'].resize(40)
assert (not table.is_consistent())
assert_exception(lambda: table.nrows())
assert_exception(lambda: table.ncols())
print 'OK'
# Clear the table
table.clear()
assert (table.is_consistent())
assert (table.empty())
assert (table.nrows() == 0)
assert (table.ncols() == 0)
print 'OK'
def test_set_selected():
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ["a", "b", "c", "d", "e", "f", "g", "i", "j", "k"]
# Create a table with some elements
table1 = flex.reflection_table()
table2 = flex.reflection_table()
table1["col1"] = flex.int(c1)
table2["col2"] = flex.double(c2)
table2["col3"] = flex.std_string(c3)
# Set selected columns
table1.set_selected(("col3", "col2"), table2)
assert table1.nrows() == 10
assert table1.ncols() == 3
assert all(a == b for a, b in zip(table1["col1"], c1))
assert all(a == b for a, b in zip(table1["col2"], c2))
assert all(a == b for a, b in zip(table1["col3"], c3))
# Set selected columns
table1 = flex.reflection_table()
table1["col1"] = flex.int(c1)
table1.set_selected(flex.std_string(["col3", "col2"]), table2)
assert table1.nrows() == 10
assert table1.ncols() == 3
assert all(a == b for a, b in zip(table1["col1"], c1))
assert all(a == b for a, b in zip(table1["col2"], c2))
assert all(a == b for a, b in zip(table1["col3"], c3))
cc1 = list(range(10, 15))
cc2 = list(range(10, 15))
cc3 = ["l", "m", "n", "o", "p"]
# Set selected rows
table2 = flex.reflection_table()
table2["col1"] = flex.int(cc1)
table2["col2"] = flex.double(cc2)
table2["col3"] = flex.std_string(cc3)
index = flex.size_t([0, 1, 5, 8, 9])
ccc1 = copy.deepcopy(c1)
ccc2 = copy.deepcopy(c2)
ccc3 = copy.deepcopy(c3)
for j, i in enumerate(index):
ccc1[i] = cc1[j]
ccc2[i] = cc2[j]
ccc3[i] = cc3[j]
table1.set_selected(index, table2)
assert all(a == b for a, b in zip(table1["col1"], ccc1))
assert all(a == b for a, b in zip(table1["col2"], ccc2))
assert all(a == b for a, b in zip(table1["col3"], ccc3))
# Set selected rows
table2 = flex.reflection_table()
table2["col1"] = flex.int(cc1)
table2["col2"] = flex.double(cc2)
table2["col3"] = flex.std_string(cc3)
table1.set_selected(index, table2)
assert all(a == b for a, b in zip(table1["col1"], ccc1))
assert all(a == b for a, b in zip(table1["col2"], ccc2))
assert all(a == b for a, b in zip(table1["col3"], ccc3))
def export_mtz(
integrated_data,
experiment_list,
hklout,
ignore_panels=False,
include_partials=False,
keep_partials=False,
min_isigi=None,
force_static_model=False,
filter_ice_rings=False,
):
"""Export data from integrated_data corresponding to experiment_list to an
MTZ file hklout."""
from dials.array_family import flex
# for the moment assume (and assert) that we will convert data from exactly
# one lattice...
# FIXME allow for more than one experiment in here: this is fine just add
# multiple MTZ data sets (DIALS1...DIALSN) and multiple batch headers: one
# range of batches for each experiment
assert len(experiment_list) == 1
# select reflections that are assigned to an experiment (i.e. non-negative id)
integrated_data = integrated_data.select(integrated_data["id"] >= 0)
assert max(integrated_data["id"]) == 0
# strip out negative variance reflections: these should not really be there
# FIXME Doing select on summation results. Should do on profile result if
# present? Yes
if "intensity.prf.variance" in integrated_data:
selection = integrated_data.get_flags(integrated_data.flags.integrated, all=True)
else:
selection = integrated_data.get_flags(integrated_data.flags.integrated_sum)
integrated_data = integrated_data.select(selection)
selection = integrated_data["intensity.sum.variance"] <= 0
if selection.count(True) > 0:
integrated_data.del_selected(selection)
logger.info("Removing %d reflections with negative variance" % selection.count(True))
if "intensity.prf.variance" in integrated_data:
selection = integrated_data["intensity.prf.variance"] <= 0
if selection.count(True) > 0:
integrated_data.del_selected(selection)
logger.info("Removing %d profile reflections with negative variance" % selection.count(True))
if filter_ice_rings:
selection = integrated_data.get_flags(integrated_data.flags.in_powder_ring)
integrated_data.del_selected(selection)
logger.info("Removing %d reflections in ice ring resolutions" % selection.count(True))
if min_isigi is not None:
selection = (
integrated_data["intensity.sum.value"] / flex.sqrt(integrated_data["intensity.sum.variance"])
) < min_isigi
integrated_data.del_selected(selection)
logger.info("Removing %d reflections with I/Sig(I) < %s" % (selection.count(True), min_isigi))
if "intensity.prf.variance" in integrated_data:
selection = (
integrated_data["intensity.prf.value"] / flex.sqrt(integrated_data["intensity.prf.variance"])
) < min_isigi
integrated_data.del_selected(selection)
logger.info("Removing %d profile reflections with I/Sig(I) < %s" % (selection.count(True), min_isigi))
# FIXME in here work on including partial reflections => at this stage best
# to split off the partial refections into a different selection & handle
# gracefully... better to work on a short list as will need to "pop" them &
# find matching parts to combine.
if include_partials:
integrated_data = sum_partial_reflections(integrated_data)
integrated_data = scale_partial_reflections(integrated_data)
if "partiality" in integrated_data:
selection = integrated_data["partiality"] < 0.99
if selection.count(True) > 0 and not keep_partials:
integrated_data.del_selected(selection)
logger.info("Removing %d incomplete reflections" % selection.count(True))
# FIXME TODO for more than one experiment into an MTZ file:
#
# - add an epoch (or recover an epoch) from the scan and add this as an extra
# column to the MTZ file for scaling, so we know that the two lattices were
# integrated at the same time
# - decide a sensible BATCH increment to apply to the BATCH value between
# experiments and add this
#
# At the moment this is probably enough to be working on.
experiment = experiment_list[0]
# also only work with one panel(for the moment)
if not ignore_panels:
assert len(experiment.detector) == 1
from scitbx import matrix
if experiment.goniometer:
axis = matrix.col(experiment.goniometer.get_rotation_axis())
else:
axis = 0.0, 0.0, 0.0
s0 = experiment.beam.get_s0()
wavelength = experiment.beam.get_wavelength()
panel = experiment.detector[0]
origin = matrix.col(panel.get_origin())
fast = matrix.col(panel.get_fast_axis())
slow = matrix.col(panel.get_slow_axis())
pixel_size = panel.get_pixel_size()
fast *= pixel_size[0]
slow *= pixel_size[1]
cb_op_to_ref = experiment.crystal.get_space_group().info().change_of_basis_op_to_reference_setting()
experiment.crystal = experiment.crystal.change_basis(cb_op_to_ref)
U = experiment.crystal.get_U()
if experiment.goniometer is not None:
F = matrix.sqr(experiment.goniometer.get_fixed_rotation())
else:
F = matrix.sqr((1, 0, 0, 0, 1, 0, 0, 0, 1))
unit_cell = experiment.crystal.get_unit_cell()
from iotbx import mtz
from scitbx.array_family import flex
from math import floor, sqrt
m = mtz.object()
m.set_title("from dials.export_mtz")
m.set_space_group_info(experiment.crystal.get_space_group().info())
if experiment.scan:
image_range = experiment.scan.get_image_range()
else:
image_range = 1, 1
# pointless (at least) doesn't like batches starting from zero
b_incr = max(image_range[0], 1)
for b in range(image_range[0], image_range[1] + 1):
o = m.add_batch().set_num(b + b_incr).set_nbsetid(1).set_ncryst(1)
o.set_time1(0.0).set_time2(0.0).set_title("Batch %d" % (b + b_incr))
o.set_ndet(1).set_theta(flex.float((0.0, 0.0))).set_lbmflg(0)
o.set_alambd(wavelength).set_delamb(0.0).set_delcor(0.0)
o.set_divhd(0.0).set_divvd(0.0)
# FIXME hard-coded assumption on indealized beam vector below... this may be
# broken when we come to process data from a non-imgCIF frame
o.set_so(flex.float(s0)).set_source(flex.float((0, 0, -1)))
# these are probably 0, 1 respectively, also flags for how many are set, sd
o.set_bbfac(0.0).set_bscale(1.0)
o.set_sdbfac(0.0).set_sdbscale(0.0).set_nbscal(0)
# unit cell (this is fine) and the what-was-refined-flags FIXME hardcoded
# take time-varying parameters from the *end of the frame* unlikely to
# be much different at the end - however only exist if time-varying refinement
# was used
if not force_static_model and experiment.crystal.num_scan_points > 0:
_unit_cell = experiment.crystal.get_unit_cell_at_scan_point(b - image_range[0])
_U = experiment.crystal.get_U_at_scan_point(b - image_range[0])
else:
_unit_cell = unit_cell
_U = U
# apply the fixed rotation to this to unify matrix definitions - F * U
# was what was used in the actual prediction: U appears to be stored
# as the transpose?! At least is for Mosflm...
#
# FIXME Do we need to apply the setting rotation here somehow? i.e. we have
# the U.B. matrix assuming that the axis is equal to S * axis_datum but
# here we are just giving the effective axis so at scan angle 0 this will
# not be correct... FIXME 2 not even sure we can express the stack of
# matrices S * R * F * U * B in MTZ format?...
_U = dials_u_to_mosflm(F * _U, _unit_cell)
# FIXME need to get what was refined and what was constrained from the
# crystal model
o.set_cell(flex.float(_unit_cell.parameters()))
o.set_lbcell(flex.int((-1, -1, -1, -1, -1, -1)))
o.set_umat(flex.float(_U.transpose().elems))
# get the mosaic spread though today it may not actually be set
mosaic = experiment.crystal.get_mosaicity()
o.set_crydat(flex.float([mosaic, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))
o.set_lcrflg(0)
o.set_datum(flex.float((0.0, 0.0, 0.0)))
# detector size, distance
o.set_detlm(flex.float([0.0, panel.get_image_size()[0], 0.0, panel.get_image_size()[1], 0, 0, 0, 0]))
o.set_dx(flex.float([panel.get_directed_distance(), 0.0]))
# goniometer axes and names, and scan axis number, and number of axes, missets
o.set_e1(flex.float(axis))
o.set_e2(flex.float((0.0, 0.0, 0.0)))
o.set_e3(flex.float((0.0, 0.0, 0.0)))
o.set_gonlab(flex.std_string(("AXIS", "", "")))
o.set_jsaxs(1)
o.set_ngonax(1)
o.set_phixyz(flex.float((0.0, 0.0, 0.0, 0.0, 0.0, 0.0)))
# scan ranges, axis
if experiment.scan:
phi_start, phi_range = experiment.scan.get_image_oscillation(b)
else:
phi_start, phi_range = 0.0, 0.0
o.set_phistt(phi_start)
o.set_phirange(phi_range)
o.set_phiend(phi_start + phi_range)
o.set_scanax(flex.float(axis))
# number of misorientation angles
o.set_misflg(0)
# crystal axis closest to rotation axis (why do I want this?)
o.set_jumpax(0)
# type of data - 1; 2D, 2; 3D, 3; Laue
o.set_ldtype(2)
# now create the actual data structures - first keep a track of the columns
# H K L M/ISYM BATCH I SIGI IPR SIGIPR FRACTIONCALC XDET YDET ROT WIDTH
# LP MPART FLAG BGPKRATIOS
from cctbx.array_family import flex as cflex # implicit import
from cctbx.miller import map_to_asu_isym # implicit import
# gather the required information for the reflection file
nref = len(integrated_data["miller_index"])
x_px, y_px, z_px = integrated_data["xyzcal.px"].parts()
xdet = flex.double(x_px)
ydet = flex.double(y_px)
zdet = flex.double(z_px)
# compute ROT values
if experiment.scan:
rot = flex.double([experiment.scan.get_angle_from_image_index(z) for z in zdet])
else:
rot = zdet
# compute BATCH values
batch = flex.floor(zdet).iround() + 1 + b_incr
# we're working with full reflections so...
fractioncalc = flex.double(nref, 1.0)
# now go for it and make an MTZ file...
x = m.add_crystal("XTAL", "DIALS", unit_cell.parameters())
d = x.add_dataset("FROMDIALS", wavelength)
# now add column information...
# FIXME add DIALS_FLAG which can include e.g. was partial etc.
type_table = {
"H": "H",
"K": "H",
"L": "H",
"I": "J",
"SIGI": "Q",
"IPR": "J",
"SIGIPR": "Q",
"BG": "R",
"SIGBG": "R",
"XDET": "R",
"YDET": "R",
"BATCH": "B",
"BGPKRATIOS": "R",
"WIDTH": "R",
"MPART": "I",
"M_ISYM": "Y",
"FLAG": "I",
"LP": "R",
"FRACTIONCALC": "R",
"ROT": "R",
"DQE": "R",
}
# derive index columns from original indices with
#
# from m.replace_original_index_miller_indices
#
# so all that is needed now is to make space for the reflections - fill with
# zeros...
m.adjust_column_array_sizes(nref)
m.set_n_reflections(nref)
# assign H, K, L, M_ISYM space
for column in "H", "K", "L", "M_ISYM":
d.add_column(column, type_table[column]).set_values(flex.double(nref, 0.0).as_float())
m.replace_original_index_miller_indices(cb_op_to_ref.apply(integrated_data["miller_index"]))
d.add_column("BATCH", type_table["BATCH"]).set_values(batch.as_double().as_float())
if "lp" in integrated_data:
lp = integrated_data["lp"]
else:
lp = flex.double(nref, 1.0)
if "dqe" in integrated_data:
dqe = integrated_data["dqe"]
else:
dqe = flex.double(nref, 1.0)
I_profile = None
V_profile = None
I_sum = None
V_sum = None
# FIXME errors in e.g. LP correction need to be propogated here
scl = lp / dqe
if "intensity.prf.value" in integrated_data:
I_profile = integrated_data["intensity.prf.value"] * scl
V_profile = integrated_data["intensity.prf.variance"] * scl * scl
# Trap negative variances
assert V_profile.all_gt(0)
d.add_column("IPR", type_table["I"]).set_values(I_profile.as_float())
d.add_column("SIGIPR", type_table["SIGI"]).set_values(flex.sqrt(V_profile).as_float())
if "intensity.sum.value" in integrated_data:
I_sum = integrated_data["intensity.sum.value"] * scl
V_sum = integrated_data["intensity.sum.variance"] * scl * scl
# Trap negative variances
assert V_sum.all_gt(0)
d.add_column("I", type_table["I"]).set_values(I_sum.as_float())
d.add_column("SIGI", type_table["SIGI"]).set_values(flex.sqrt(V_sum).as_float())
if "background.sum.value" in integrated_data and "background.sum.variance" in integrated_data:
bg = integrated_data["background.sum.value"]
varbg = integrated_data["background.sum.variance"]
assert (varbg >= 0).count(False) == 0
sigbg = flex.sqrt(varbg)
d.add_column("BG", type_table["BG"]).set_values(bg.as_float())
d.add_column("SIGBG", type_table["SIGBG"]).set_values(sigbg.as_float())
d.add_column("FRACTIONCALC", type_table["FRACTIONCALC"]).set_values(fractioncalc.as_float())
d.add_column("XDET", type_table["XDET"]).set_values(xdet.as_float())
d.add_column("YDET", type_table["YDET"]).set_values(ydet.as_float())
d.add_column("ROT", type_table["ROT"]).set_values(rot.as_float())
d.add_column("LP", type_table["LP"]).set_values(lp.as_float())
d.add_column("DQE", type_table["DQE"]).set_values(dqe.as_float())
m.write(hklout)
return m
def export_mtz(observed_hkls, experiment, filename):
if experiment.goniometer:
axis = experiment.goniometer.get_rotation_axis()
else:
axis = 0.0, 0.0, 0.0
s0 = experiment.beam.get_s0()
wavelength = experiment.beam.get_wavelength()
from scitbx import matrix
panel = experiment.detector[0]
pixel_size = panel.get_pixel_size()
cb_op_to_ref = (experiment.crystal.get_space_group().info().
change_of_basis_op_to_reference_setting())
experiment.crystal = experiment.crystal.change_basis(cb_op_to_ref)
from iotbx import mtz
from scitbx.array_family import flex
import itertools
m = mtz.object()
m.set_title("from dials.scratch.mg.strategy_i19")
m.set_space_group_info(experiment.crystal.get_space_group().info())
nrefcount = sum(observed_hkls.itervalues())
nref = max(observed_hkls.itervalues())
for batch in range(1, nref + 1):
o = m.add_batch().set_num(batch).set_nbsetid(1).set_ncryst(1)
o.set_time1(0.0).set_time2(0.0).set_title("Batch %d" % batch)
o.set_ndet(1).set_theta(flex.float((0.0, 0.0))).set_lbmflg(0)
o.set_alambd(wavelength).set_delamb(0.0).set_delcor(0.0)
o.set_divhd(0.0).set_divvd(0.0)
o.set_so(flex.float(s0)).set_source(flex.float((0, 0, -1)))
o.set_bbfac(0.0).set_bscale(1.0)
o.set_sdbfac(0.0).set_sdbscale(0.0).set_nbscal(0)
_unit_cell = experiment.crystal.get_unit_cell()
_U = experiment.crystal.get_U()
o.set_cell(flex.float(_unit_cell.parameters()))
o.set_lbcell(flex.int((-1, -1, -1, -1, -1, -1)))
o.set_umat(flex.float(_U.transpose().elems))
mosaic = experiment.crystal.get_mosaicity()
o.set_crydat(
flex.float([
mosaic, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
]))
o.set_lcrflg(0)
o.set_datum(flex.float((0.0, 0.0, 0.0)))
# detector size, distance
o.set_detlm(
flex.float([
0.0,
panel.get_image_size()[0],
0.0,
panel.get_image_size()[1],
0,
0,
0,
0,
]))
o.set_dx(flex.float([panel.get_directed_distance(), 0.0]))
# goniometer axes and names, and scan axis number, and number of axes, missets
o.set_e1(flex.float(axis))
o.set_e2(flex.float((0.0, 0.0, 0.0)))
o.set_e3(flex.float((0.0, 0.0, 0.0)))
o.set_gonlab(flex.std_string(("AXIS", "", "")))
o.set_jsaxs(1)
o.set_ngonax(1)
o.set_phixyz(flex.float((0.0, 0.0, 0.0, 0.0, 0.0, 0.0)))
phi_start, phi_range = 0.0, 0.0
o.set_phistt(phi_start)
o.set_phirange(phi_range)
o.set_phiend(phi_start + phi_range)
o.set_scanax(flex.float(axis))
# number of misorientation angles
o.set_misflg(0)
# crystal axis closest to rotation axis (why do I want this?)
o.set_jumpax(0)
# type of data - 1; 2D, 2; 3D, 3; Laue
o.set_ldtype(2)
# now create the actual data structures - first keep a track of the columns
# H K L M/ISYM BATCH I SIGI IPR SIGIPR FRACTIONCALC XDET YDET ROT WIDTH
# LP MPART FLAG BGPKRATIOS
from cctbx.array_family import flex as cflex # implicit import
# now go for it and make an MTZ file...
x = m.add_crystal("XTAL", "DIALS", unit_cell.parameters())
d = x.add_dataset("FROMDIALS", wavelength)
# now add column information...
type_table = {
"IPR": "J",
"BGPKRATIOS": "R",
"WIDTH": "R",
"I": "J",
"H": "H",
"K": "H",
"MPART": "I",
"L": "H",
"BATCH": "B",
"M_ISYM": "Y",
"SIGI": "Q",
"FLAG": "I",
"XDET": "R",
"LP": "R",
"YDET": "R",
"SIGIPR": "Q",
"FRACTIONCALC": "R",
"ROT": "R",
}
m.adjust_column_array_sizes(nrefcount)
m.set_n_reflections(nrefcount)
# assign H, K, L, M_ISYM space
for column in "H", "K", "L", "M_ISYM":
d.add_column(column, type_table[column]).set_values(
flex.float(nrefcount, 0.0))
batchnums = (_ for (x, n) in observed_hkls.iteritems()
for _ in range(1, n + 1))
d.add_column("BATCH",
type_table["BATCH"]).set_values(flex.float(batchnums))
d.add_column("FRACTIONCALC",
type_table["FRACTIONCALC"]).set_values(
flex.float(nrefcount, 3.0))
m.replace_original_index_miller_indices(
cb_op_to_ref.apply(
cflex.miller_index([
_ for (x, n) in observed_hkls.iteritems()
for _ in itertools.repeat(x, n)
])))
m.write(filename)
return m