def test_dataframemodel_set_data_complex(monkeypatch):
"""
Test that editing complex dtypes is handled gracefully in df editor.
Unit regression test for issue #6115 .
"""
MockQMessageBox = Mock()
attr_to_patch = ('spyder_modelx.widgets' +
'.mxdataview.QMessageBox')
monkeypatch.setattr(attr_to_patch, MockQMessageBox)
test_params = [(1, numpy.complex128), (2, numpy.complex64), (3, complex)]
for count, complex_type in test_params:
test_df = DataFrame(numpy.arange(10, 15), dtype=complex_type)
model = DataFrameModel(test_df.copy())
index = model.createIndex(2, 0)
assert not model.setData(index, '42')
MockQMessageBox.critical.assert_called_with(ANY, "Error", ANY)
assert MockQMessageBox.critical.call_count == count
try:
assert numpy.sum(test_df[0].values ==
model.df.values) == len(test_df)
except AttributeError:
assert numpy.sum(test_df[0].as_matrix() ==
model.df.as_matrix()) == len(test_df)
def test_dataframemodel_set_data_overflow(monkeypatch):
"""
Test that entry of an overflowing integer is caught and handled properly.
Unit regression test for issue #6114 .
"""
MockQMessageBox = Mock()
attr_to_patch = ('spyder_modelx.widgets' +
'.mxdataview.QMessageBox')
monkeypatch.setattr(attr_to_patch, MockQMessageBox)
# Numpy doesn't raise the OverflowError for ints smaller than 64 bits
if not os.name == 'nt':
int32_bit_exponent = 66
else:
int32_bit_exponent = 34
test_parameters = [(1, numpy.int32, int32_bit_exponent),
(2, numpy.int64, 66)]
for idx, int_type, bit_exponent in test_parameters:
test_df = DataFrame(numpy.arange(7, 11), dtype=int_type)
model = DataFrameModel(test_df.copy())
index = model.createIndex(2, 0)
assert not model.setData(index, str(int(2 ** bit_exponent)))
MockQMessageBox.critical.assert_called_with(ANY, "Error", ANY)
assert MockQMessageBox.critical.call_count == idx
try:
assert numpy.sum(test_df[0].values ==
model.df.values) == len(test_df)
except AttributeError:
assert numpy.sum(test_df[0].as_matrix() ==
model.df.as_matrix()) == len(test_df)
def test_dataframeeditor_with_OutOfBoundsDatetime(): # Test for #6177
df = DataFrame([{'DATETIME': datetime.strptime("9999-1-1T00:00",
"%Y-%m-%dT%H:%M")}])
model = DataFrameModel(df)
try:
model.get_value(0, 0)
except Exception:
assert False
def test_dataframemodel_sort():
"""Validate the data in the model."""
df = DataFrame({'colA': [1, 3], 'colB': ['c', 'a']})
dfm = DataFrameModel(df)
dfm.sort(1)
assert data(dfm, 0, 0) == '3'
assert data(dfm, 1, 0) == '1'
assert data(dfm, 0, 1) == 'a'
assert data(dfm, 1, 1) == 'c'
def test_dataframemodel_basic():
df = DataFrame({'colA': [1, 3], 'colB': ['c', 'a']})
dfm = DataFrameModel(df)
assert dfm.rowCount() == 2
assert dfm.columnCount() == 2
assert data(dfm, 0, 0) == '1'
assert data(dfm, 0, 1) == 'c'
assert data(dfm, 1, 0) == '3'
assert data(dfm, 1, 1) == 'a'
def test_dataframemodel_sort_is_stable(): # cf. issue 3010
"""Validate the sort function."""
df = DataFrame([[2,14], [2,13], [2,16], [1,3], [2,9], [1,15], [1,17],
[2,2], [2,10], [1,6], [2,5], [2,8], [1,11], [1,1],
[1,12], [1,4], [2,7]])
dfm = DataFrameModel(df)
dfm.sort(1)
dfm.sort(0)
col2 = [data(dfm, i, 1) for i in range(len(df))]
assert col2 == [str(x) for x in [1, 3, 4, 6, 11, 12, 15, 17,
2, 5, 7, 8, 9, 10, 13, 14, 16]]
def test_dataframemodel_set_data_bool(monkeypatch):
"""Test that bools are editible in df and false-y strs are detected."""
MockQMessageBox = Mock()
attr_to_patch = ('spyder_modelx.widgets' +
'.mxdataview.QMessageBox')
monkeypatch.setattr(attr_to_patch, MockQMessageBox)
test_params = [numpy.bool_, numpy.bool, bool]
test_strs = ['foo', 'false', 'f', '0', '0.', '0.0', '', ' ']
expected_df = DataFrame([1, 0, 0, 0, 0, 0, 0, 0, 0], dtype=bool)
for bool_type in test_params:
test_df = DataFrame([0, 1, 1, 1, 1, 1, 1, 1, 0], dtype=bool_type)
model = DataFrameModel(test_df.copy())
for idx, test_str in enumerate(test_strs):
assert model.setData(model.createIndex(idx, 0), test_str)
assert not MockQMessageBox.critical.called
try:
assert numpy.sum(expected_df[0].values ==
model.df.values[:, 0]) == len(expected_df)
except AttributeError:
assert numpy.sum(expected_df[0].as_matrix() ==
model.df.as_matrix()[:, 0]) == len(expected_df)