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import pickle
from collections import UserDict
import numpy as np
import pytest
from MDAnalysis.analysis import results as results_module
from numpy.testing import assert_equal
from itertools import cycle
class Test_Results:
@pytest.fixture
def results(self):
return results_module.Results(a=1, b=2)
def test_get(self, results):
assert results.a == results["a"] == 1
def test_no_attr(self, results):
msg = "'Results' object has no attribute 'c'"
with pytest.raises(AttributeError, match=msg):
results.c
def test_set_attr(self, results):
value = [1, 2, 3, 4]
results.c = value
assert results.c is results["c"] is value
def test_set_key(self, results):
value = [1, 2, 3, 4]
results["c"] = value
assert results.c is results["c"] is value
@pytest.mark.parametrize("key", dir(UserDict) + ["data"])
def test_existing_dict_attr(self, results, key):
msg = f"'{key}' is a protected dictionary attribute"
with pytest.raises(AttributeError, match=msg):
results[key] = None
@pytest.mark.parametrize("key", dir(UserDict) + ["data"])
def test_wrong_init_type(self, key):
msg = f"'{key}' is a protected dictionary attribute"
with pytest.raises(AttributeError, match=msg):
results_module.Results(**{key: None})
@pytest.mark.parametrize("key", ("0123", "0j", "1.1", "{}", "a b"))
def test_weird_key(self, results, key):
msg = f"'{key}' is not a valid attribute"
with pytest.raises(ValueError, match=msg):
results[key] = None
def test_setattr_modify_item(self, results):
mylist = [1, 2]
mylist2 = [3, 4]
results.myattr = mylist
assert results.myattr is mylist
results["myattr"] = mylist2
assert results.myattr is mylist2
mylist2.pop(0)
assert len(results.myattr) == 1
assert results.myattr is mylist2
def test_setitem_modify_item(self, results):
mylist = [1, 2]
mylist2 = [3, 4]
results["myattr"] = mylist
assert results.myattr is mylist
results.myattr = mylist2
assert results.myattr is mylist2
mylist2.pop(0)
assert len(results["myattr"]) == 1
assert results["myattr"] is mylist2
def test_delattr(self, results):
assert hasattr(results, "a")
delattr(results, "a")
assert not hasattr(results, "a")
def test_missing_delattr(self, results):
assert not hasattr(results, "d")
msg = "'Results' object has no attribute 'd'"
with pytest.raises(AttributeError, match=msg):
delattr(results, "d")
def test_pop(self, results):
assert hasattr(results, "a")
results.pop("a")
assert not hasattr(results, "a")
def test_update(self, results):
assert not hasattr(results, "spudda")
results.update({"spudda": "fett"})
assert results.spudda == "fett"
def test_update_data_fail(self, results):
msg = f"'data' is a protected dictionary attribute"
with pytest.raises(AttributeError, match=msg):
results.update({"data": 0})
def test_pickle(self, results):
results_p = pickle.dumps(results)
results_new = pickle.loads(results_p)
@pytest.mark.parametrize(
"args, kwargs, length",
[
(({"darth": "tater"},), {}, 1),
([], {"darth": "tater"}, 1),
(({"darth": "tater"},), {"yam": "solo"}, 2),
(({"darth": "tater"},), {"darth": "vader"}, 1),
],
)
def test_initialize_arguments(self, args, kwargs, length):
results = results_module.Results(*args, **kwargs)
ref = dict(*args, **kwargs)
assert ref == results
assert len(results) == length
def test_different_instances(self, results):
new_results = results_module.Results(darth="tater")
assert new_results.data is not results.data
class Test_ResultsGroup:
@pytest.fixture
def results_0(self):
return results_module.Results(
sequence=[0],
ndarray_mean=np.array([0, 0, 0]),
ndarray_sum=np.array([0, 0, 0, 0]),
float=0.0,
float_sum=0.0,
)
@pytest.fixture
def results_1(self):
return results_module.Results(
sequence=[1],
ndarray_mean=np.array([1, 1, 1]),
ndarray_sum=np.array([1, 1, 1, 1]),
float=1.0,
float_sum=1.0,
)
@pytest.fixture
def merger(self):
RG = results_module.ResultsGroup
lookup = {
"sequence": RG.flatten_sequence,
"ndarray_mean": RG.ndarray_mean,
"ndarray_sum": RG.ndarray_sum,
"float": RG.float_mean,
"float_sum": lambda floats: sum(floats),
}
return RG(lookup=lookup)
@pytest.mark.parametrize("n", [1, 2, 5, 14])
def test_all_results(self, results_0, results_1, merger, n):
objects = [
obj for obj, _ in zip(cycle([results_0, results_1]), range(n))
]
arr = [i for _, i in zip(range(n), cycle([0, 1]))]
answers = {
"sequence": arr,
"ndarray_mean": [np.mean(arr) for _ in range(3)],
"ndarray_sum": [np.sum(arr) for _ in range(4)],
"float": np.mean(arr),
"float_sum": np.sum(arr),
}
results = merger.merge(objects)
for attr, merged_value in results.items():
assert_equal(
merged_value,
answers.get(attr),
err_msg=f"{attr=}, {merged_value=}, {arr=}, {objects=}",
)
def test_missing_aggregator(self, results_0, results_1, merger):
original_float_lookup = merger._lookup.get("float")
merger._lookup["float"] = None
with pytest.raises(
ValueError, match="No aggregation function for key='float'"
):
merger.merge([results_0, results_1], require_all_aggregators=True)
merger._lookup["float"] = original_float_lookup
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