def case_select_all_records():
fitting_data = FittingData(COLUMNS)
fitting_data.unselect_all_records()
fitting_data.select_record(2)
fitting_data.select_record(5)
fitting_data.select_all_records()
return fitting_data, list(range(1, NUMBER_OF_RECORDS + 1))
def case_unselect_one_record():
fitting_data = FittingData(COLUMNS)
fitting_data.unselect_record(2)
return (
fitting_data,
[1] + list(range(3, NUMBER_OF_RECORDS + 1)),
)
def test_save_and_read_csv_without_name(tmpdir):
original_fitting_data = FittingData(data=RAW_DATA)
original_fitting_data.save_csv(tmpdir)
loaded_fitting_data = FittingData.read_from_csv(
Path(tmpdir / "fitting_data.csv"))
assert_fitting_data_are_equal(original_fitting_data, loaded_fitting_data)
def test_x_larger_than_size(column):
with pytest.raises(
FittingDataColumnIndexError,
match=
"^No column number 11 in data. index should be between 1 and 10$",
):
FittingData(COLUMNS, **{column: 11})
def case_fitting(case_path):
with open(str(case_path), mode="r") as json_file:
json_obj = json.load(json_file)
func_name = json_obj["fit_function"]
func = FittingFunctionsRegistry.load(func_name)
fix = json_obj.get("fix", None)
if fix is not None:
for index, value in fix:
func.fix(index, value)
data_dict = json_obj["data"]
data = FittingData(
OrderedDict([
("x", data_dict["x"]),
("xerr", data_dict["xerr"]),
("y", data_dict["y"]),
("yerr", data_dict["yerr"]),
]))
a0 = json_obj.get("a0", None)
result = fit(data=data, func=func, a0=a0)
decimal = json_obj.get("decimal", 2)
inp = dict(actual_result=result,
a0=a0,
func=func,
delta=np.power(10.0, -decimal))
expected_result = json_obj["result"]
return inp, expected_result
def test_x_zero_index(column):
with pytest.raises(
FittingDataColumnIndexError,
match=
"^No column number 0 in data. index should be between 1 and 10$",
):
FittingData(COLUMNS, **{column: 0})
def test_set_cell_not_allowed_because_of_non_existing_row():
fitting_data = FittingData(COLUMNS)
value = np.random.uniform(0, 10)
column = np.random.choice(COLUMNS_NAMES)
with pytest.raises(FittingDataSetError, match="^Record number 13 does not exists$"):
fitting_data.set_cell(NUMBER_OF_RECORDS + 1, column, value)
def case_unselect_multiple():
fitting_data = FittingData(COLUMNS)
fitting_data.unselect_record(2)
fitting_data.unselect_record(5)
fitting_data.unselect_record(3)
fitting_data.unselect_record(10)
return fitting_data, [1, 4, 6, 7, 8, 9] + list(range(11, NUMBER_OF_RECORDS + 1))
def test_save_and_read_excel_without_name(tmpdir):
original_fitting_data = FittingData(data=RAW_DATA)
original_fitting_data.save_excel(tmpdir, sheet=SHEET_NAME)
loaded_fitting_data = FittingData.read_from_excel(
Path(tmpdir / "fitting_data.xlsx"), SHEET_NAME)
assert_fitting_data_are_equal(original_fitting_data, loaded_fitting_data)
def case_jumbled_columns():
fitting_data = FittingData(COLUMNS,
x_column=3,
xerr_column=1,
y_column="b",
yerr_column=9)
expected_columns = Columns(x="c", xerr="a", y="b", yerr="i")
return fitting_data, expected_columns
def test_exception_risen_because_of_columns_length():
data = deepcopy(COLUMNS)
data["a"] = data["a"][:-2]
with pytest.raises(
FittingDataColumnsLengthError,
match="^All columns in FittingData should have the same length$",
):
FittingData(data=data)
def test_set_header_with_same_header_does_not_change_anything(header_name):
fitting_data = FittingData(COLUMNS)
fitting_data.set_header(header_name, header_name)
for header in COLUMNS_NAMES:
assert fitting_data.data[header] == pytest.approx(
COLUMNS[header]
), f'Header "{header}" has changed unexpectedly'
def test_set_cell_not_allowed_because_of_non_existing_column():
fitting_data = FittingData(COLUMNS)
value = np.random.uniform(0, 10)
with pytest.raises(
FittingDataSetError, match='^Column name "I do not exist" does not exists$'
):
fitting_data.set_cell(0, "I do not exist", value)
def case_select_multiple_records():
fitting_data = FittingData(COLUMNS)
fitting_data.unselect_all_records()
fitting_data.select_record(2)
fitting_data.select_record(5)
fitting_data.select_record(3)
fitting_data.select_record(10)
return fitting_data, [2, 3, 5, 10]
def test_header_cannot_already_exist(header_name):
fitting_data = FittingData(COLUMNS)
new_header = np.random.choice(
[header for header in COLUMNS_NAMES if header != header_name]
)
with pytest.raises(
FittingDataSetError, match=f'^The column name:"{new_header}" is already used.$'
):
fitting_data.set_header(header_name, new_header)
def test_set_selection_with_different_size():
fitting_data = FittingData(COLUMNS)
def set_records_indices():
fitting_data.records_indices = [False, False, True]
with pytest.raises(
FittingDataColumnsSelectionError,
match=f"^Should select {NUMBER_OF_RECORDS} records, only 3 selected.$",
):
set_records_indices()
def test_set_selection_with_non_boolean_values():
fitting_data = FittingData(COLUMNS)
def set_records_indices():
fitting_data.records_indices = [False, False, "dummy"] + [True] * (
NUMBER_OF_RECORDS - 3
)
with pytest.raises(
FittingDataColumnsSelectionError,
match="^When setting record indices, all values should be booleans.$",
):
set_records_indices()
def test_set_cell_not_allowed_because_of_non_float_value():
fitting_data = FittingData(COLUMNS)
column = np.random.choice(COLUMNS_NAMES)
record = np.random.randint(1, NUMBER_OF_RECORDS + 1)
value = "I'm not a float"
with pytest.raises(
FittingDataSetError,
match=(
f'^The cell at record number:"{record}", column:"{column}"'
f" has invalid syntax: I'm not a float.$"
),
):
fitting_data.set_cell(record, column, value)
def test_set_cell_allowed(record_number, column_name):
fitting_data = FittingData(deepcopy(COLUMNS))
value = np.random.uniform(0, 10)
fitting_data.set_cell(
record_number=record_number,
column_name=column_name,
value=value,
)
for column in COLUMNS_NAMES:
for i in range(NUMBER_OF_RECORDS):
if column == column_name and i + 1 == record_number:
assert fitting_data.data[column][i] == value, "New value was not set"
else:
assert (
fitting_data.data[column][i] == COLUMNS[column][i]
), "Data was change unexpectedly"
def test_set_header_allowed(i):
old_header = COLUMNS_NAMES[i]
new_header = "new_header"
fitting_data = FittingData(deepcopy(COLUMNS))
fitting_data.set_header(old_header, new_header)
constant_headers = [header for header in COLUMNS_NAMES if header != old_header]
for header in constant_headers:
assert fitting_data.data[header] == pytest.approx(
COLUMNS[header]
), f'Header "{header}" has changed unexpectedly'
assert fitting_data.data[new_header] == pytest.approx(
COLUMNS[old_header]
), f'Header "{new_header}" has changed unexpectedly'
assert set(fitting_data.all_columns) == set(
constant_headers + [new_header]
), "Did not update all columns"
def case_default():
fitting_data = FittingData(COLUMNS)
expected_columns = Columns(x="a", xerr="b", y="c", yerr="d")
return fitting_data, expected_columns
def test_header_cannot_be_set_to_be_empty(header_name):
fitting_data = FittingData(COLUMNS)
with pytest.raises(
FittingDataSetError, match="^Cannot set new header to be empty$"
):
fitting_data.set_header(header_name, "")
def case_unselect_two_records():
fitting_data = FittingData(COLUMNS)
fitting_data.unselect_record(2)
fitting_data.unselect_record(5)
return fitting_data, [1, 3, 4] + list(range(6, NUMBER_OF_RECORDS + 1))
from eddington import FittingData
from tests.fitting_data import (
COLUMNS,
COLUMNS_NAMES,
CONTENT,
DEFAULT_SHEET,
NUMBER_OF_RECORDS,
)
DECIMAL = 5
DIRECTORY_PATH = Path("/path/to/directory")
EPSILON = 1e-5
FIT_DATA = FittingData(COLUMNS)
def assert_workbook_calls(workbook,
sheet_name=DEFAULT_SHEET,
name="fitting_data"):
worksheet = workbook.active
assert worksheet.title == sheet_name
assert (worksheet.append.call_count == NUMBER_OF_RECORDS +
1), "worksheet append called unexpected number of times"
assert worksheet.append.call_args_list[0] == call(
COLUMNS_NAMES), "worksheet append called unexpected number of times"
for i in range(NUMBER_OF_RECORDS):
assert worksheet.append.call_args_list[i + 1][0][0] == pytest.approx(
CONTENT[i],
rel=EPSILON), "worksheet append called unexpected number of times"
def case_set_selected_records():
fitting_data = FittingData(COLUMNS)
fitting_data.records_indices = [False, True, False, False, True] + [False] * (
NUMBER_OF_RECORDS - 5
)
return fitting_data, [2, 5]
def case_string_yerr_column():
fitting_data = FittingData(COLUMNS, yerr_column="f")
expected_columns = Columns(x="a", xerr="b", y="c", yerr="f")
return fitting_data, expected_columns
def case_select_two_records():
fitting_data = FittingData(COLUMNS)
fitting_data.unselect_all_records()
fitting_data.select_record(2)
fitting_data.select_record(5)
return fitting_data, [2, 5]
def case_x_and_y_column():
fitting_data = FittingData(COLUMNS, x_column=3, y_column="h")
expected_columns = Columns(x="c", xerr="d", y="h", yerr="i")
return fitting_data, expected_columns
def case_unselect_all():
fitting_data = FittingData(COLUMNS)
fitting_data.unselect_all_records()
return fitting_data, []
def case_int_xerr_column():
fitting_data = FittingData(COLUMNS, xerr_column=4)
expected_columns = Columns(x="a", xerr="d", y="e", yerr="f")
return fitting_data, expected_columns