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from __future__ import annotations
import json
import pathlib
import pytest
from dials.algorithms.correlation.analysis import CorrelationMatrix
from dials.command_line.correlation_matrix import phil_scope
from dials.util.multi_dataset_handling import (
assign_unique_identifiers,
parse_multiple_datasets,
)
from dials.util.options import ArgumentParser, reflections_and_experiments_from_files
@pytest.fixture()
def proteinase_k(dials_data):
mcp = dials_data("vmxi_proteinase_k_sweeps", pathlib=True)
params = phil_scope.extract()
input_data = []
parser = ArgumentParser(
usage=" ",
phil=phil_scope,
read_reflections=True,
read_experiments=True,
check_format=False,
epilog=" ",
)
for i in [0, 1, 2, 3]:
input_data.append(str(mcp / f"experiments_{i}.expt"))
input_data.append(str(mcp / f"reflections_{i}.refl"))
params, options, args = parser.parse_args(
args=input_data, show_diff_phil=False, return_unhandled=True
)
params.output.json = "dials.correlation_matrix.json"
reflections, experiments = reflections_and_experiments_from_files(
params.input.reflections, params.input.experiments
)
reflections = parse_multiple_datasets(reflections)
assert len(experiments) == len(reflections)
assert len(experiments) > 1
experiments, reflections = assign_unique_identifiers(experiments, reflections)
yield experiments, reflections, params
def test_corr_mat(proteinase_k, run_in_tmp_path):
experiments, reflections, params = proteinase_k
matrices = CorrelationMatrix(experiments, reflections, params)
matrices.calculate_matrices()
matrices.output_json()
assert pathlib.Path("dials.correlation_matrix.json").is_file()
def test_filtered_corr_mat(proteinase_k, run_in_tmp_path):
experiments, reflections, params = proteinase_k
ids_to_identifiers_map = {}
for table in reflections:
ids_to_identifiers_map.update(table.experiment_identifiers())
# Simulate filtered dataset by multiplex
id_to_remove = [ids_to_identifiers_map[2]]
ids_to_identifiers_map.pop(2)
reflections.pop(2)
experiments.remove_on_experiment_identifiers(id_to_remove)
matrices = CorrelationMatrix(
experiments, reflections, params, ids_to_identifiers_map
)
matrices.calculate_matrices()
matrices.output_json()
assert pathlib.Path("dials.correlation_matrix.json").is_file()
expected_ids = [[0, 3], [0, 1, 3]]
# Check main algorithm correct with filtering
for i, j in zip(matrices.correlation_clusters, expected_ids):
assert i.labels == j
# Check json output also correct
with open(pathlib.Path("dials.correlation_matrix.json")) as f:
data = json.load(f)
assert len(data["correlation_matrix_clustering"]) == len(expected_ids)
for i, j in zip(data["correlation_matrix_clustering"], expected_ids):
assert len(data["correlation_matrix_clustering"][i]["datasets"]) == len(j)
for a, e in zip(data["correlation_matrix_clustering"][i]["datasets"], j):
assert a == ids_to_identifiers_map[e]
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