def case_unselect_one_record():
fit_data = FitData(COLUMNS)
fit_data.unselect_record(2)
return (
fit_data,
[1] + list(range(3, NUMBER_OF_RECORDS + 1)),
)
def test_x_larger_than_size(column):
with pytest.raises(
FitDataColumnIndexError,
match=
"^No column number 11 in data. index should be between 1 and 10$",
):
FitData(COLUMNS, **{column: 11})
def test_x_zero_index(column):
with pytest.raises(
FitDataColumnIndexError,
match=
"^No column number 0 in data. index should be between 1 and 10$",
):
FitData(COLUMNS, **{column: 0})
def case_select_all_records():
fit_data = FitData(COLUMNS)
fit_data.unselect_all_records()
fit_data.select_record(2)
fit_data.select_record(5)
fit_data.select_all_records()
return fit_data, list(range(1, NUMBER_OF_RECORDS + 1))
def case_fitting(case_path):
with open(str(case_path), mode="r") as json_file:
json_obj = json.load(json_file)
func_name = json_obj["fit_function"]
func = FitFunctionsRegistry.load(func_name)
fix = json_obj.get("fix", None)
if fix is not None:
for index, value in fix:
func.fix(index, value)
data_dict = json_obj["data"]
data = FitData(
OrderedDict(
[
("x", data_dict["x"]),
("xerr", data_dict["xerr"]),
("y", data_dict["y"]),
("yerr", data_dict["yerr"]),
]
)
)
a0 = json_obj.get("a0", None)
result = fit_to_data(data=data, func=func, a0=a0)
decimal = json_obj.get("decimal", 2)
inp = dict(actual_result=result, a0=a0, func=func, delta=np.power(10.0, -decimal))
expected_result = json_obj["result"]
return inp, expected_result
def test_exception_risen_because_of_columns_length():
data = deepcopy(COLUMNS)
data["a"] = data["a"][:-2]
with pytest.raises(
FitDataColumnsLengthError,
match="^All columns in FitData should have the same length$",
):
FitData(data=data)
def case_select_multiple_records():
fit_data = FitData(COLUMNS)
fit_data.unselect_all_records()
fit_data.select_record(2)
fit_data.select_record(5)
fit_data.select_record(3)
fit_data.select_record(10)
return fit_data, [2, 3, 5, 10]
def case_jumbled_columns():
fit_data = FitData(COLUMNS,
x_column=3,
xerr_column=1,
y_column="b",
yerr_column=9)
expected_columns = Columns(x="c", xerr="a", y="b", yerr="i")
return fit_data, expected_columns
def case_unselect_multiple():
fit_data = FitData(COLUMNS)
fit_data.unselect_record(2)
fit_data.unselect_record(5)
fit_data.unselect_record(3)
fit_data.unselect_record(10)
return fit_data, [1, 4, 6, 7, 8, 9] + list(range(11,
NUMBER_OF_RECORDS + 1))
def case_string_xerr_column():
fit_data = FitData(COLUMNS, xerr_column="d")
expected_columns = Columns(
x="a",
xerr="d",
y="e",
yerr="f",
)
return fit_data, expected_columns
def case_int_x_column():
fit_data = FitData(COLUMNS, x_column=3)
expected_columns = Columns(
x="c",
xerr="d",
y="e",
yerr="f",
)
return fit_data, expected_columns
def test_set_selection_with_different_size():
fit_data = FitData(COLUMNS)
def set_records_indices():
fit_data.records_indices = [False, False, True]
with pytest.raises(
FitDataColumnsSelectionError,
match=
f"^Should select {NUMBER_OF_RECORDS} records, only 3 selected.$",
):
set_records_indices()
def test_set_selection_with_non_boolean_values():
fit_data = FitData(COLUMNS)
def set_records_indices():
fit_data.records_indices = [False, False, "dummy"
] + [True] * (NUMBER_OF_RECORDS - 3)
with pytest.raises(
FitDataColumnsSelectionError,
match=
"^When setting record indices, all values should be booleans.$",
):
set_records_indices()
def case_select_two_records():
fit_data = FitData(COLUMNS)
fit_data.unselect_all_records()
fit_data.select_record(2)
fit_data.select_record(5)
return fit_data, [2, 5]
def case_select_one_records():
fit_data = FitData(COLUMNS)
fit_data.unselect_all_records()
fit_data.select_record(2)
return fit_data, [2]
def case_unselect_all():
fit_data = FitData(COLUMNS)
fit_data.unselect_all_records()
return fit_data, []
def case_unselect_two_records():
fit_data = FitData(COLUMNS)
fit_data.unselect_record(2)
fit_data.unselect_record(5)
return fit_data, [1, 3, 4] + list(range(6, NUMBER_OF_RECORDS + 1))
def test_x_not_existing(column):
with pytest.raises(FitDataColumnExistenceError,
match='^Could not find column "r" in data$'):
FitData(COLUMNS, **{column: "r"})
def case_int_y_column():
fit_data = FitData(COLUMNS, y_column=5)
expected_columns = Columns(x="a", xerr="b", y="e", yerr="f")
return fit_data, expected_columns
def case_x_and_y_column():
fit_data = FitData(COLUMNS, x_column=3, y_column="h")
expected_columns = Columns(x="c", xerr="d", y="h", yerr="i")
return fit_data, expected_columns
def case_string_yerr_column():
fit_data = FitData(COLUMNS, yerr_column="f")
expected_columns = Columns(x="a", xerr="b", y="c", yerr="f")
return fit_data, expected_columns
def case_set_selected_records():
fit_data = FitData(COLUMNS)
fit_data.records_indices = [False, True, False, False, True
] + [False] * (NUMBER_OF_RECORDS - 5)
return fit_data, [2, 5]
def case_default():
fit_data = FitData(COLUMNS)
expected_columns = Columns(x="a", xerr="b", y="c", yerr="d")
return fit_data, expected_columns