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7
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def test_td64arr_add_datetime64_nat(self, box_with_array): # GH#23215 other = np.datetime64('NaT') tdi = timedelta_range('1 day', periods=3) expected = pd.DatetimeIndex(["NaT", "NaT", "NaT"]) tdser = tm.box_expected(tdi, box_with_array) expected = tm.box_expected(expected, box_with_array) tm.assert_equal(tdser + other, expected) tm.assert_equal(other + tdser, expected)
15,602
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9
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def test_td64arr_add_sub_numeric_scalar_invalid(self, box_with_array, scalar): box = box_with_array tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') tdser = tm.box_expected(tdser, box) err = TypeError if box in [pd.Index, tm.to_array] and not isinstance(scalar, float): err = NullFrequencyError with pytest.raises(err): tdser + scalar with pytest.raises(err): scalar + tdser with pytest.raises(err): tdser - scalar with pytest.raises(err): scalar - tdser
15,603
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{"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "other", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Index", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "err", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "raises", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": null}}], "type_comment": null}}, {"_type": "With", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Sub", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "other", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Index", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "err", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "raises", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": null}}], "type_comment": null}}], "name": "test_td64arr_add_intlike", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_td64arr_add_intlike(self, box_with_array): # GH#19123 tdi = TimedeltaIndex(['59 days', '59 days', 'NaT']) ser = tm.box_expected(tdi, box_with_array) err = TypeError if box_with_array in [pd.Index, tm.to_array]: err = NullFrequencyError other = Series([20, 30, 40], dtype='uint8') # TODO: separate/parametrize with pytest.raises(err): ser + 1 with pytest.raises(err): ser - 1 with pytest.raises(err): ser + other with pytest.raises(err): ser - other with pytest.raises(err): ser + np.array(other) with pytest.raises(err): ser - np.array(other) with pytest.raises(err): ser + pd.Index(other) with pytest.raises(err): ser - pd.Index(other)
15,604
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"withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "err", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "raises", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": null}}], "type_comment": null}}, {"_type": "With", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Sub", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "vector", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "tdser", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "err", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "raises", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": null}}], "type_comment": null}}], "name": "test_td64arr_add_sub_numeric_arr_invalid", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_td64arr_add_sub_numeric_arr_invalid(self, box, vec, dtype): tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') tdser = tm.box_expected(tdser, box) err = TypeError if box is pd.Index and not dtype.startswith('float'): err = NullFrequencyError vector = vec.astype(dtype) with pytest.raises(err): tdser + vector with pytest.raises(err): vector + tdser with pytest.raises(err): tdser - vector with pytest.raises(err): vector - tdser
15,605
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9
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def test_operators_timedelta64_with_timedelta(self, scalar_td): # smoke tests td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan td1 + scalar_td scalar_td + td1 td1 - scalar_td scalar_td - td1 td1 / scalar_td scalar_td / td1
15,606
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10
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def test_timedelta64_operations_with_timedeltas(self): # td operate with td td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td2 = timedelta(minutes=5, seconds=4) result = td1 - td2 expected = (Series([timedelta(seconds=0)] * 3) - Series([timedelta(seconds=1)] * 3)) assert result.dtype == 'm8[ns]' tm.assert_series_equal(result, expected) result2 = td2 - td1 expected = (Series([timedelta(seconds=1)] * 3) - Series([timedelta(seconds=0)] * 3)) tm.assert_series_equal(result2, expected) # roundtrip tm.assert_series_equal(result + td2, td1) # Now again, using pd.to_timedelta, which should build # a Series or a scalar, depending on input. td1 = Series(pd.to_timedelta(['00:05:03'] * 3)) td2 = pd.to_timedelta('00:05:04') result = td1 - td2 expected = (Series([timedelta(seconds=0)] * 3) - Series([timedelta(seconds=1)] * 3)) assert result.dtype == 'm8[ns]' tm.assert_series_equal(result, expected) result2 = td2 - td1 expected = (Series([timedelta(seconds=1)] * 3) - Series([timedelta(seconds=0)] * 3)) tm.assert_series_equal(result2, expected) # roundtrip tm.assert_series_equal(result + td2, td1)
15,607
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8
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def test_td64arr_add_td64_array(self, box): dti = pd.date_range('2016-01-01', periods=3) tdi = dti - dti.shift(1) tdarr = tdi.values expected = 2 * tdi tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, box) result = tdi + tdarr tm.assert_equal(result, expected) result = tdarr + tdi tm.assert_equal(result, expected)
15,608
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8
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def test_td64arr_sub_td64_array(self, box): dti = pd.date_range('2016-01-01', periods=3) tdi = dti - dti.shift(1) tdarr = tdi.values expected = 0 * tdi tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, box) result = tdi - tdarr tm.assert_equal(result, expected) result = tdarr - tdi tm.assert_equal(result, expected)
15,609
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9
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def test_td64arr_add_sub_tdi(self, box, names): # GH#17250 make sure result dtype is correct # GH#19043 make sure names are propagated correctly if box is pd.DataFrame and names[1] == 'Venkman': pytest.skip("Name propagation for DataFrame does not behave like " "it does for Index/Series") tdi = TimedeltaIndex(['0 days', '1 day'], name=names[0]) ser = Series([Timedelta(hours=3), Timedelta(hours=4)], name=names[1]) expected = Series([Timedelta(hours=3), Timedelta(days=1, hours=4)], name=names[2]) ser = tm.box_expected(ser, box) expected = tm.box_expected(expected, box) result = tdi + ser tm.assert_equal(result, expected) if box is not pd.DataFrame: assert result.dtype == 'timedelta64[ns]' else: assert result.dtypes[0] == 'timedelta64[ns]' result = ser + tdi tm.assert_equal(result, expected) if box is not pd.DataFrame: assert result.dtype == 'timedelta64[ns]' else: assert result.dtypes[0] == 'timedelta64[ns]' expected = Series([Timedelta(hours=-3), Timedelta(days=1, hours=-4)], name=names[2]) expected = tm.box_expected(expected, box) result = tdi - ser tm.assert_equal(result, expected) if box is not pd.DataFrame: assert result.dtype == 'timedelta64[ns]' else: assert result.dtypes[0] == 'timedelta64[ns]' result = ser - tdi tm.assert_equal(result, -expected) if box is not pd.DataFrame: assert result.dtype == 'timedelta64[ns]' else: assert result.dtypes[0] == 'timedelta64[ns]'
15,610
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8
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def test_td64arr_sub_NaT(self, box): # GH#18808 ser = Series([NaT, Timedelta('1s')]) expected = Series([NaT, NaT], dtype='timedelta64[ns]') ser = tm.box_expected(ser, box) expected = tm.box_expected(expected, box) res = ser - pd.NaT tm.assert_equal(res, expected)
15,611
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def test_td64arr_add_sub_td64_nat(self, box): # GH#23320 special handling for timedelta64("NaT") tdi = pd.TimedeltaIndex([NaT, Timedelta('1s')]) other = np.timedelta64("NaT") expected = pd.TimedeltaIndex(["NaT"] * 2) obj = tm.box_expected(tdi, box) expected = tm.box_expected(expected, box) result = obj + other tm.assert_equal(result, expected) result = other + obj tm.assert_equal(result, expected) result = obj - other tm.assert_equal(result, expected) result = other - obj tm.assert_equal(result, expected)
15,612
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7
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def test_td64arr_sub_timedeltalike(self, two_hours, box): # only test adding/sub offsets as - is now numeric rng = timedelta_range('1 days', '10 days') expected = timedelta_range('0 days 22:00:00', '9 days 22:00:00') rng = tm.box_expected(rng, box) expected = tm.box_expected(expected, box) result = rng - two_hours tm.assert_equal(result, expected)
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7
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def test_td64arr_add_timedeltalike(self, two_hours, box): # only test adding/sub offsets as + is now numeric rng = timedelta_range('1 days', '10 days') expected = timedelta_range('1 days 02:00:00', '10 days 02:00:00', freq='D') rng = tm.box_expected(rng, box) expected = tm.box_expected(expected, box) result = rng + two_hours tm.assert_equal(result, expected)
15,614
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10
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def test_td64arr_add_offset_index(self, names, box): # GH#18849, GH#19744 if box is pd.DataFrame and names[1] == 'bar': pytest.skip("Name propagation for DataFrame does not behave like " "it does for Index/Series") tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00'], name=names[0]) other = pd.Index([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)], name=names[1]) expected = TimedeltaIndex([tdi[n] + other[n] for n in range(len(tdi))], freq='infer', name=names[2]) tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, box) # The DataFrame operation is transposed and so operates as separate # scalar operations, which do not issue a PerformanceWarning warn = PerformanceWarning if box is not pd.DataFrame else None with tm.assert_produces_warning(warn): res = tdi + other tm.assert_equal(res, expected) with tm.assert_produces_warning(warn): res2 = other + tdi tm.assert_equal(res2, expected)
15,615
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def test_timedelta64_operations_with_DateOffset(self): # GH#10699 td = Series([timedelta(minutes=5, seconds=3)] * 3) result = td + pd.offsets.Minute(1) expected = Series([timedelta(minutes=6, seconds=3)] * 3) tm.assert_series_equal(result, expected) result = td - pd.offsets.Minute(1) expected = Series([timedelta(minutes=4, seconds=3)] * 3) tm.assert_series_equal(result, expected) with tm.assert_produces_warning(PerformanceWarning): result = td + Series([pd.offsets.Minute(1), pd.offsets.Second(3), pd.offsets.Hour(2)]) expected = Series([timedelta(minutes=6, seconds=3), timedelta(minutes=5, seconds=6), timedelta(hours=2, minutes=5, seconds=3)]) tm.assert_series_equal(result, expected) result = td + pd.offsets.Minute(1) + pd.offsets.Second(12) expected = Series([timedelta(minutes=6, seconds=15)] * 3) tm.assert_series_equal(result, expected) # valid DateOffsets for do in ['Hour', 'Minute', 'Second', 'Day', 'Micro', 'Milli', 'Nano']: op = getattr(pd.offsets, do) td + op(5) op(5) + td td - op(5) op(5) - td
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10
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def test_td64arr_add_offset_array(self, box): # GH#18849 tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00']) other = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)]) expected = TimedeltaIndex([tdi[n] + other[n] for n in range(len(tdi))], freq='infer') tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, box) # The DataFrame operation is transposed and so operates as separate # scalar operations, which do not issue a PerformanceWarning warn = PerformanceWarning if box is not pd.DataFrame else None with tm.assert_produces_warning(warn): res = tdi + other tm.assert_equal(res, expected) with tm.assert_produces_warning(warn): res2 = other + tdi tm.assert_equal(res2, expected)
15,617
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10
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def test_td64arr_sub_offset_index(self, names, box): # GH#18824, GH#19744 if box is pd.DataFrame and names[1] == 'bar': pytest.skip("Name propagation for DataFrame does not behave like " "it does for Index/Series") tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00'], name=names[0]) other = pd.Index([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)], name=names[1]) expected = TimedeltaIndex([tdi[n] - other[n] for n in range(len(tdi))], freq='infer', name=names[2]) tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, box) # The DataFrame operation is transposed and so operates as separate # scalar operations, which do not issue a PerformanceWarning warn = PerformanceWarning if box is not pd.DataFrame else None with tm.assert_produces_warning(warn): res = tdi - other tm.assert_equal(res, expected)
15,618
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10
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def test_td64arr_sub_offset_array(self, box_with_array): # GH#18824 tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00']) other = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)]) expected = TimedeltaIndex([tdi[n] - other[n] for n in range(len(tdi))], freq='infer') tdi = tm.box_expected(tdi, box_with_array) expected = tm.box_expected(expected, box_with_array) # The DataFrame operation is transposed and so operates as separate # scalar operations, which do not issue a PerformanceWarning warn = None if box_with_array is pd.DataFrame else PerformanceWarning with tm.assert_produces_warning(warn): res = tdi - other tm.assert_equal(res, expected)
15,619
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10
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"Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Sub", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "other", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "res3", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "PerformanceWarning", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_produces_warning", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": null}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "res3", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected_sub", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_td64arr_with_offset_series", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_td64arr_with_offset_series(self, names, box_df_fail): # GH#18849 box = box_df_fail box2 = Series if box in [pd.Index, tm.to_array] else box tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00'], name=names[0]) other = Series([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)], name=names[1]) expected_add = Series([tdi[n] + other[n] for n in range(len(tdi))], name=names[2]) tdi = tm.box_expected(tdi, box) expected_add = tm.box_expected(expected_add, box2) with tm.assert_produces_warning(PerformanceWarning): res = tdi + other tm.assert_equal(res, expected_add) with tm.assert_produces_warning(PerformanceWarning): res2 = other + tdi tm.assert_equal(res2, expected_add) # TODO: separate/parametrize add/sub test? expected_sub = Series([tdi[n] - other[n] for n in range(len(tdi))], name=names[2]) expected_sub = tm.box_expected(expected_sub, box2) with tm.assert_produces_warning(PerformanceWarning): res3 = tdi - other tm.assert_equal(res3, expected_sub)
15,620
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10
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def test_td64arr_addsub_anchored_offset_arraylike(self, obox, box_with_array): # GH#18824 tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00']) tdi = tm.box_expected(tdi, box_with_array) anchored = obox([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)]) # addition/subtraction ops with anchored offsets should issue # a PerformanceWarning and _then_ raise a TypeError. with pytest.raises(TypeError): with tm.assert_produces_warning(PerformanceWarning): tdi + anchored with pytest.raises(TypeError): with tm.assert_produces_warning(PerformanceWarning): anchored + tdi with pytest.raises(TypeError): with tm.assert_produces_warning(PerformanceWarning): tdi - anchored with pytest.raises(TypeError): with tm.assert_produces_warning(PerformanceWarning): anchored - tdi
15,621
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12
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"BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__rmul__", "value": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_td64arr_mul_int_series", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Call", 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{}}}}}}}}, {"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}, "value": {"_type": "Name", "_fields": {"id": "names", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "IfExp", "_fields": {"body": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 2}}, "value": {"_type": "Name", "_fields": {"id": "names", "ctx": {"_type": "Load", "_fields": {}}}}}}, "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "IsNot", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "box", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "to_array", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}, "value": {"_type": "Name", "_fields": {"id": "names", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "xname", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "ListComp", "_fields": {"elt": {"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "n", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "n", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "range", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "target": {"_type": "Name", "_fields": {"id": "n", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Name", "_fields": {"id": "Series", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "dtype", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "timedelta64[ns]"}}}}, {"_type": "keyword", "_fields": {"arg": "name", "value": {"_type": "Name", "_fields": {"id": "xname", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Name", "_fields": {"id": "box", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": "Name", "_fields": {"id": "xbox", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Name", "_fields": {"id": "Series", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": "Name", "_fields": {"id": "xbox", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": 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"Is", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Name", "_fields": {"id": "NotImplemented", "ctx": {"_type": "Load", "_fields": {}}}}]}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Is", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "box", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "DataFrame", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}], "name": "test_float_series_rdiv_td64arr", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "names"}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": null}}, {"_type": "Constant", "_fields": {"kind": null, "value": null}}, {"_type": "Constant", "_fields": {"kind": null, "value": null}}]}}, {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "Egon"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "Venkman"}}, {"_type": "Constant", "_fields": {"kind": null, "value": null}}]}}, {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "NCC1701D"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "NCC1701D"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "NCC1701D"}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "parametrize", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "mark", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}]}}], "name": "TestTimedeltaArraylikeMulDivOps", "bases": [], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestTimedeltaArraylikeMulDivOps: # Tests for timedelta64[ns] # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__ # TODO: Moved from tests.series.test_operators; needs cleanup @pytest.mark.parametrize("m", [1, 3, 10]) @pytest.mark.parametrize("unit", ['D', 'h', 'm', 's', 'ms', 'us', 'ns']) def test_timedelta64_conversions(self, m, unit): startdate = Series(pd.date_range('2013-01-01', '2013-01-03')) enddate = Series(pd.date_range('2013-03-01', '2013-03-03')) ser = enddate - startdate ser[2] = np.nan # op expected = Series([x / np.timedelta64(m, unit) for x in ser]) result = ser / np.timedelta64(m, unit) tm.assert_series_equal(result, expected) # reverse op expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in ser]) result = np.timedelta64(m, unit) / ser tm.assert_series_equal(result, expected) # ------------------------------------------------------------------ # Multiplication # organized with scalar others first, then array-like def test_td64arr_mul_int(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) result = idx * 1 tm.assert_equal(result, idx) result = 1 * idx tm.assert_equal(result, idx) def test_td64arr_mul_tdlike_scalar_raises(self, two_hours, box_with_array): rng = timedelta_range('1 days', '10 days', name='foo') rng = tm.box_expected(rng, box_with_array) with pytest.raises(TypeError): rng * two_hours def test_tdi_mul_int_array_zerodim(self, box_with_array): rng5 = np.arange(5, dtype='int64') idx = TimedeltaIndex(rng5) expected = TimedeltaIndex(rng5 * 5) idx = tm.box_expected(idx, box_with_array) expected = tm.box_expected(expected, box_with_array) result = idx * np.array(5, dtype='int64') tm.assert_equal(result, expected) def test_tdi_mul_int_array(self, box_with_array): rng5 = np.arange(5, dtype='int64') idx = TimedeltaIndex(rng5) expected = TimedeltaIndex(rng5 ** 2) idx = tm.box_expected(idx, box_with_array) expected = tm.box_expected(expected, box_with_array) result = idx * rng5 tm.assert_equal(result, expected) def test_tdi_mul_int_series(self, box_with_array): box = box_with_array xbox = pd.Series if box in [pd.Index, tm.to_array] else box idx = TimedeltaIndex(np.arange(5, dtype='int64')) expected = TimedeltaIndex(np.arange(5, dtype='int64') ** 2) idx = tm.box_expected(idx, box) expected = tm.box_expected(expected, xbox) result = idx * pd.Series(np.arange(5, dtype='int64')) tm.assert_equal(result, expected) def test_tdi_mul_float_series(self, box_with_array): box = box_with_array xbox = pd.Series if box in [pd.Index, tm.to_array] else box idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box) rng5f = np.arange(5, dtype='float64') expected = TimedeltaIndex(rng5f * (rng5f + 1.0)) expected = tm.box_expected(expected, xbox) result = idx * Series(rng5f + 1.0) tm.assert_equal(result, expected) # TODO: Put Series/DataFrame in others? @pytest.mark.parametrize('other', [ np.arange(1, 11), pd.Int64Index(range(1, 11)), pd.UInt64Index(range(1, 11)), pd.Float64Index(range(1, 11)), pd.RangeIndex(1, 11) ], ids=lambda x: type(x).__name__) def test_tdi_rmul_arraylike(self, other, box_with_array): box = box_with_array xbox = get_upcast_box(box, other) tdi = TimedeltaIndex(['1 Day'] * 10) expected = timedelta_range('1 days', '10 days') expected._data.freq = None tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, xbox) result = other * tdi tm.assert_equal(result, expected) commute = tdi * other tm.assert_equal(commute, expected) # ------------------------------------------------------------------ # __div__, __rdiv__ def test_td64arr_div_nat_invalid(self, box_with_array): # don't allow division by NaT (maybe could in the future) rng = timedelta_range('1 days', '10 days', name='foo') rng = tm.box_expected(rng, box_with_array) with pytest.raises(TypeError, match="'?true_divide'? cannot use operands"): rng / pd.NaT with pytest.raises(TypeError, match='Cannot divide NaTType by'): pd.NaT / rng def test_td64arr_div_td64nat(self, box_with_array): # GH#23829 rng = timedelta_range('1 days', '10 days',) rng = tm.box_expected(rng, box_with_array) other = np.timedelta64('NaT') expected = np.array([np.nan] * 10) expected = tm.box_expected(expected, box_with_array) result = rng / other tm.assert_equal(result, expected) result = other / rng tm.assert_equal(result, expected) def test_td64arr_div_int(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) result = idx / 1 tm.assert_equal(result, idx) with pytest.raises(TypeError, match='Cannot divide'): # GH#23829 1 / idx def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array): # GH#20088, GH#22163 ensure DataFrame returns correct dtype rng = timedelta_range('1 days', '10 days', name='foo') expected = pd.Float64Index((np.arange(10) + 1) * 12, name='foo') rng = tm.box_expected(rng, box_with_array) expected = tm.box_expected(expected, box_with_array) result = rng / two_hours tm.assert_equal(result, expected) result = two_hours / rng expected = 1 / expected tm.assert_equal(result, expected) def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array): rng = TimedeltaIndex(['1 days', pd.NaT, '2 days'], name='foo') expected = pd.Float64Index([12, np.nan, 24], name='foo') rng = tm.box_expected(rng, box_with_array) expected = tm.box_expected(expected, box_with_array) result = rng / two_hours tm.assert_equal(result, expected) result = two_hours / rng expected = 1 / expected tm.assert_equal(result, expected) def test_td64arr_div_td64_ndarray(self, box_with_array): # GH#22631 rng = TimedeltaIndex(['1 days', pd.NaT, '2 days']) expected = pd.Float64Index([12, np.nan, 24]) rng = tm.box_expected(rng, box_with_array) expected = tm.box_expected(expected, box_with_array) other = np.array([2, 4, 2], dtype='m8[h]') result = rng / other tm.assert_equal(result, expected) result = rng / tm.box_expected(other, box_with_array) tm.assert_equal(result, expected) result = rng / other.astype(object) tm.assert_equal(result, expected) result = rng / list(other) tm.assert_equal(result, expected) # reversed op expected = 1 / expected result = other / rng tm.assert_equal(result, expected) result = tm.box_expected(other, box_with_array) / rng tm.assert_equal(result, expected) result = other.astype(object) / rng tm.assert_equal(result, expected) result = list(other) / rng tm.assert_equal(result, expected) def test_tdarr_div_length_mismatch(self, box_with_array): rng = TimedeltaIndex(['1 days', pd.NaT, '2 days']) mismatched = [1, 2, 3, 4] rng = tm.box_expected(rng, box_with_array) for obj in [mismatched, mismatched[:2]]: # one shorter, one longer for other in [obj, np.array(obj), pd.Index(obj)]: with pytest.raises(ValueError): rng / other with pytest.raises(ValueError): other / rng # ------------------------------------------------------------------ # __floordiv__, __rfloordiv__ def test_td64arr_floordiv_tdscalar(self, box_with_array, scalar_td): # GH#18831 td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan expected = Series([0, 0, np.nan]) td1 = tm.box_expected(td1, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) result = td1 // scalar_td tm.assert_equal(result, expected) def test_td64arr_rfloordiv_tdscalar(self, box_with_array, scalar_td): # GH#18831 td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan expected = Series([1, 1, np.nan]) td1 = tm.box_expected(td1, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) result = scalar_td // td1 tm.assert_equal(result, expected) def test_td64arr_rfloordiv_tdscalar_explicit(self, box_with_array, scalar_td): # GH#18831 td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan expected = Series([1, 1, np.nan]) td1 = tm.box_expected(td1, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) # We can test __rfloordiv__ using this syntax, # see `test_timedelta_rfloordiv` result = td1.__rfloordiv__(scalar_td) tm.assert_equal(result, expected) def test_td64arr_floordiv_int(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) result = idx // 1 tm.assert_equal(result, idx) pattern = ('floor_divide cannot use operands|' 'Cannot divide int by Timedelta*') with pytest.raises(TypeError, match=pattern): 1 // idx def test_td64arr_floordiv_tdlike_scalar(self, two_hours, box_with_array): tdi = timedelta_range('1 days', '10 days', name='foo') expected = pd.Int64Index((np.arange(10) + 1) * 12, name='foo') tdi = tm.box_expected(tdi, box_with_array) expected = tm.box_expected(expected, box_with_array) result = tdi // two_hours tm.assert_equal(result, expected) # TODO: Is this redundant with test_td64arr_floordiv_tdlike_scalar? @pytest.mark.parametrize('scalar_td', [ timedelta(minutes=10, seconds=7), Timedelta('10m7s'), Timedelta('10m7s').to_timedelta64() ], ids=lambda x: type(x).__name__) def test_td64arr_rfloordiv_tdlike_scalar(self, scalar_td, box_with_array): # GH#19125 tdi = TimedeltaIndex(['00:05:03', '00:05:03', pd.NaT], freq=None) expected = pd.Index([2.0, 2.0, np.nan]) tdi = tm.box_expected(tdi, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) res = tdi.__rfloordiv__(scalar_td) tm.assert_equal(res, expected) expected = pd.Index([0.0, 0.0, np.nan]) expected = tm.box_expected(expected, box_with_array, transpose=False) res = tdi // (scalar_td) tm.assert_equal(res, expected) # ------------------------------------------------------------------ # mod, divmod # TODO: operations with timedelta-like arrays, numeric arrays, # reversed ops def test_td64arr_mod_tdscalar(self, box_with_array, three_days): tdi = timedelta_range('1 Day', '9 days') tdarr = tm.box_expected(tdi, box_with_array) expected = TimedeltaIndex(['1 Day', '2 Days', '0 Days'] * 3) expected = tm.box_expected(expected, box_with_array) result = tdarr % three_days tm.assert_equal(result, expected) if box_with_array is pd.DataFrame: pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__") result = divmod(tdarr, three_days) tm.assert_equal(result[1], expected) tm.assert_equal(result[0], tdarr // three_days) def test_td64arr_mod_int(self, box_with_array): tdi = timedelta_range('1 ns', '10 ns', periods=10) tdarr = tm.box_expected(tdi, box_with_array) expected = TimedeltaIndex(['1 ns', '0 ns'] * 5) expected = tm.box_expected(expected, box_with_array) result = tdarr % 2 tm.assert_equal(result, expected) with pytest.raises(TypeError): 2 % tdarr if box_with_array is pd.DataFrame: pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__") result = divmod(tdarr, 2) tm.assert_equal(result[1], expected) tm.assert_equal(result[0], tdarr // 2) def test_td64arr_rmod_tdscalar(self, box_with_array, three_days): tdi = timedelta_range('1 Day', '9 days') tdarr = tm.box_expected(tdi, box_with_array) expected = ['0 Days', '1 Day', '0 Days'] + ['3 Days'] * 6 expected = TimedeltaIndex(expected) expected = tm.box_expected(expected, box_with_array) result = three_days % tdarr tm.assert_equal(result, expected) if box_with_array is pd.DataFrame: pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__") result = divmod(three_days, tdarr) tm.assert_equal(result[1], expected) tm.assert_equal(result[0], three_days // tdarr) # ------------------------------------------------------------------ # Operations with invalid others def test_td64arr_mul_tdscalar_invalid(self, box_with_array, scalar_td): td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan td1 = tm.box_expected(td1, box_with_array) # check that we are getting a TypeError # with 'operate' (from core/ops.py) for the ops that are not # defined pattern = 'operate|unsupported|cannot|not supported' with pytest.raises(TypeError, match=pattern): td1 * scalar_td with pytest.raises(TypeError, match=pattern): scalar_td * td1 def test_td64arr_mul_too_short_raises(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) with pytest.raises(TypeError): idx * idx[:3] with pytest.raises(ValueError): idx * np.array([1, 2]) def test_td64arr_mul_td64arr_raises(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) with pytest.raises(TypeError): idx * idx # ------------------------------------------------------------------ # Operations with numeric others @pytest.mark.parametrize('one', [1, np.array(1), 1.0, np.array(1.0)]) def test_td64arr_mul_numeric_scalar(self, box_with_array, one): # GH#4521 # divide/multiply by integers tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') expected = Series(['-59 Days', '-59 Days', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, box_with_array) result = tdser * (-one) tm.assert_equal(result, expected) result = (-one) * tdser tm.assert_equal(result, expected) expected = Series(['118 Days', '118 Days', 'NaT'], dtype='timedelta64[ns]') expected = tm.box_expected(expected, box_with_array) result = tdser * (2 * one) tm.assert_equal(result, expected) result = (2 * one) * tdser tm.assert_equal(result, expected) @pytest.mark.parametrize('two', [2, 2.0, np.array(2), np.array(2.0)]) def test_td64arr_div_numeric_scalar(self, box_with_array, two): # GH#4521 # divide/multiply by integers tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') expected = Series(['29.5D', '29.5D', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, box_with_array) result = tdser / two tm.assert_equal(result, expected) with pytest.raises(TypeError, match='Cannot divide'): two / tdser @pytest.mark.parametrize('dtype', ['int64', 'int32', 'int16', 'uint64', 'uint32', 'uint16', 'uint8', 'float64', 'float32', 'float16']) @pytest.mark.parametrize('vector', [np.array([20, 30, 40]), pd.Index([20, 30, 40]), Series([20, 30, 40])], ids=lambda x: type(x).__name__) def test_td64arr_rmul_numeric_array(self, box_with_array, vector, dtype): # GH#4521 # divide/multiply by integers xbox = get_upcast_box(box_with_array, vector) tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') vector = vector.astype(dtype) expected = Series(['1180 Days', '1770 Days', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, xbox) result = tdser * vector tm.assert_equal(result, expected) result = vector * tdser tm.assert_equal(result, expected) @pytest.mark.parametrize('dtype', ['int64', 'int32', 'int16', 'uint64', 'uint32', 'uint16', 'uint8', 'float64', 'float32', 'float16']) @pytest.mark.parametrize('vector', [np.array([20, 30, 40]), pd.Index([20, 30, 40]), Series([20, 30, 40])], ids=lambda x: type(x).__name__) def test_td64arr_div_numeric_array(self, box_with_array, vector, dtype): # GH#4521 # divide/multiply by integers xbox = get_upcast_box(box_with_array, vector) tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') vector = vector.astype(dtype) expected = Series(['2.95D', '1D 23H 12m', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, xbox) result = tdser / vector tm.assert_equal(result, expected) pattern = ('true_divide cannot use operands|' 'cannot perform __div__|' 'cannot perform __truediv__|' 'unsupported operand|' 'Cannot divide') with pytest.raises(TypeError, match=pattern): vector / tdser if not isinstance(vector, pd.Index): # Index.__rdiv__ won't try to operate elementwise, just raises result = tdser / vector.astype(object) if box_with_array is pd.DataFrame: expected = [tdser.iloc[0, n] / vector[n] for n in range(len(vector))] else: expected = [tdser[n] / vector[n] for n in range(len(tdser))] expected = tm.box_expected(expected, xbox) tm.assert_equal(result, expected) with pytest.raises(TypeError, match=pattern): vector.astype(object) / tdser @pytest.mark.parametrize('names', [(None, None, None), ('Egon', 'Venkman', None), ('NCC1701D', 'NCC1701D', 'NCC1701D')]) def test_td64arr_mul_int_series(self, box_df_fail, names): # GH#19042 test for correct name attachment box = box_df_fail # broadcasts along wrong axis, but doesn't raise tdi = TimedeltaIndex(['0days', '1day', '2days', '3days', '4days'], name=names[0]) # TODO: Should we be parametrizing over types for `ser` too? ser = Series([0, 1, 2, 3, 4], dtype=np.int64, name=names[1]) expected = Series(['0days', '1day', '4days', '9days', '16days'], dtype='timedelta64[ns]', name=names[2]) tdi = tm.box_expected(tdi, box) box = Series if (box is pd.Index and type(ser) is Series) else box expected = tm.box_expected(expected, box) result = ser * tdi tm.assert_equal(result, expected) # The direct operation tdi * ser still needs to be fixed. result = ser.__rmul__(tdi) tm.assert_equal(result, expected) # TODO: Should we be parametrizing over types for `ser` too? @pytest.mark.parametrize('names', [(None, None, None), ('Egon', 'Venkman', None), ('NCC1701D', 'NCC1701D', 'NCC1701D')]) def test_float_series_rdiv_td64arr(self, box_with_array, names): # GH#19042 test for correct name attachment # TODO: the direct operation TimedeltaIndex / Series still # needs to be fixed. box = box_with_array tdi = TimedeltaIndex(['0days', '1day', '2days', '3days', '4days'], name=names[0]) ser = Series([1.5, 3, 4.5, 6, 7.5], dtype=np.float64, name=names[1]) xname = names[2] if box is not tm.to_array else names[1] expected = Series([tdi[n] / ser[n] for n in range(len(ser))], dtype='timedelta64[ns]', name=xname) xbox = box if box in [pd.Index, tm.to_array] and type(ser) is Series: xbox = Series tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, xbox) result = ser.__rdiv__(tdi) if box is pd.DataFrame: # TODO: Should we skip this case sooner or test something else? assert result is NotImplemented else: tm.assert_equal(result, expected)
15,622
[ -0.0013255304656922817, -0.014561050571501255, 0.014696712605655193, 0.019897088408470154, 0.01753430813550949, -0.024848749861121178, 0.02021363191306591, 0.0053190793842077255, 0.032875414937734604, 0.024780917912721634, 0.008563661016523838, 0.007314440794289112, 0.020100580528378487, -0.012051303870975971, 0.0096772201359272, 0.016562065109610558, -0.02231639251112938, 0.014289726503193378, -0.007370966486632824, 0.020439734682440758, -0.0314057432115078, 0.02351474016904831, 0.0012633519945666194, -0.0020306899677962065, 0.027335885912179947, -0.03780446574091911, -0.018789181485772133, -0.023333856835961342, 0.012458289973437786, 0.0013191712787374854, -0.013396618887782097, -0.03662873059511185, 0.02414782904088497, -0.007088337559252977, -0.007704468909651041, -0.01934313401579857, 0.0205075666308403, 0.044926717877388, -0.009405896067619324, -0.021106740459799767, -0.048159997910261154, -0.04770778864622116, -0.028036804869771004, 0.009564167819917202, 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8
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def test_td64arr_mul_int(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) result = idx * 1 tm.assert_equal(result, idx) result = 1 * idx tm.assert_equal(result, idx)
15,623
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11
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{"_type": "Name", "_fields": {"id": "np", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "Timedelta", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "right": {"_type": "Name", "_fields": {"id": "x", "ctx": {"_type": "Load", "_fields": {}}}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Name", "_fields": {"id": "x", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Name", "_fields": {"id": "Series", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "m", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "unit", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "timedelta64", "value": {"_type": "Name", "_fields": {"id": "np", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "right": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_series_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_timedelta64_conversions", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_timedelta64_conversions(self, m, unit): startdate = Series(pd.date_range('2013-01-01', '2013-01-03')) enddate = Series(pd.date_range('2013-03-01', '2013-03-03')) ser = enddate - startdate ser[2] = np.nan # op expected = Series([x / np.timedelta64(m, unit) for x in ser]) result = ser / np.timedelta64(m, unit) tm.assert_series_equal(result, expected) # reverse op expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in ser]) result = np.timedelta64(m, unit) / ser tm.assert_series_equal(result, expected)
15,624
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8
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def test_tdi_mul_int_array_zerodim(self, box_with_array): rng5 = np.arange(5, dtype='int64') idx = TimedeltaIndex(rng5) expected = TimedeltaIndex(rng5 * 5) idx = tm.box_expected(idx, box_with_array) expected = tm.box_expected(expected, box_with_array) result = idx * np.array(5, dtype='int64') tm.assert_equal(result, expected)
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7
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def test_tdi_mul_int_array(self, box_with_array): rng5 = np.arange(5, dtype='int64') idx = TimedeltaIndex(rng5) expected = TimedeltaIndex(rng5 ** 2) idx = tm.box_expected(idx, box_with_array) expected = tm.box_expected(expected, box_with_array) result = idx * rng5 tm.assert_equal(result, expected)
15,626
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9
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def test_tdi_mul_int_series(self, box_with_array): box = box_with_array xbox = pd.Series if box in [pd.Index, tm.to_array] else box idx = TimedeltaIndex(np.arange(5, dtype='int64')) expected = TimedeltaIndex(np.arange(5, dtype='int64') ** 2) idx = tm.box_expected(idx, box) expected = tm.box_expected(expected, xbox) result = idx * pd.Series(np.arange(5, dtype='int64')) tm.assert_equal(result, expected)
15,627
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9
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def test_tdi_mul_float_series(self, box_with_array): box = box_with_array xbox = pd.Series if box in [pd.Index, tm.to_array] else box idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box) rng5f = np.arange(5, dtype='float64') expected = TimedeltaIndex(rng5f * (rng5f + 1.0)) expected = tm.box_expected(expected, xbox) result = idx * Series(rng5f + 1.0) tm.assert_equal(result, expected)
15,628
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7
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def test_tdi_rmul_arraylike(self, other, box_with_array): box = box_with_array xbox = get_upcast_box(box, other) tdi = TimedeltaIndex(['1 Day'] * 10) expected = timedelta_range('1 days', '10 days') expected._data.freq = None tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, xbox) result = other * tdi tm.assert_equal(result, expected) commute = tdi * other tm.assert_equal(commute, expected)
15,629
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8
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def test_td64arr_div_nat_invalid(self, box_with_array): # don't allow division by NaT (maybe could in the future) rng = timedelta_range('1 days', '10 days', name='foo') rng = tm.box_expected(rng, box_with_array) with pytest.raises(TypeError, match="'?true_divide'? cannot use operands"): rng / pd.NaT with pytest.raises(TypeError, match='Cannot divide NaTType by'): pd.NaT / rng
15,630
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def test_td64arr_div_int(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) result = idx / 1 tm.assert_equal(result, idx) with pytest.raises(TypeError, match='Cannot divide'): # GH#23829 1 / idx
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9
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def test_td64arr_div_td64nat(self, box_with_array): # GH#23829 rng = timedelta_range('1 days', '10 days',) rng = tm.box_expected(rng, box_with_array) other = np.timedelta64('NaT') expected = np.array([np.nan] * 10) expected = tm.box_expected(expected, box_with_array) result = rng / other tm.assert_equal(result, expected) result = other / rng tm.assert_equal(result, expected)
15,632
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8
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def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array): rng = TimedeltaIndex(['1 days', pd.NaT, '2 days'], name='foo') expected = pd.Float64Index([12, np.nan, 24], name='foo') rng = tm.box_expected(rng, box_with_array) expected = tm.box_expected(expected, box_with_array) result = rng / two_hours tm.assert_equal(result, expected) result = two_hours / rng expected = 1 / expected tm.assert_equal(result, expected)
15,633
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10
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def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array): # GH#20088, GH#22163 ensure DataFrame returns correct dtype rng = timedelta_range('1 days', '10 days', name='foo') expected = pd.Float64Index((np.arange(10) + 1) * 12, name='foo') rng = tm.box_expected(rng, box_with_array) expected = tm.box_expected(expected, box_with_array) result = rng / two_hours tm.assert_equal(result, expected) result = two_hours / rng expected = 1 / expected tm.assert_equal(result, expected)
15,634
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def test_td64arr_div_td64_ndarray(self, box_with_array): # GH#22631 rng = TimedeltaIndex(['1 days', pd.NaT, '2 days']) expected = pd.Float64Index([12, np.nan, 24]) rng = tm.box_expected(rng, box_with_array) expected = tm.box_expected(expected, box_with_array) other = np.array([2, 4, 2], dtype='m8[h]') result = rng / other tm.assert_equal(result, expected) result = rng / tm.box_expected(other, box_with_array) tm.assert_equal(result, expected) result = rng / other.astype(object) tm.assert_equal(result, expected) result = rng / list(other) tm.assert_equal(result, expected) # reversed op expected = 1 / expected result = other / rng tm.assert_equal(result, expected) result = tm.box_expected(other, box_with_array) / rng tm.assert_equal(result, expected) result = other.astype(object) / rng tm.assert_equal(result, expected) result = list(other) / rng tm.assert_equal(result, expected)
15,635
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10
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def test_tdarr_div_length_mismatch(self, box_with_array): rng = TimedeltaIndex(['1 days', pd.NaT, '2 days']) mismatched = [1, 2, 3, 4] rng = tm.box_expected(rng, box_with_array) for obj in [mismatched, mismatched[:2]]: # one shorter, one longer for other in [obj, np.array(obj), pd.Index(obj)]: with pytest.raises(ValueError): rng / other with pytest.raises(ValueError): other / rng
15,636
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9
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def test_td64arr_floordiv_tdscalar(self, box_with_array, scalar_td): # GH#18831 td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan expected = Series([0, 0, np.nan]) td1 = tm.box_expected(td1, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) result = td1 // scalar_td tm.assert_equal(result, expected)
15,637
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"_fields": {"id": "box_with_array", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "box_expected", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "transpose", "value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "td1", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "box_with_array", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "box_expected", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "transpose", "value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "FloorDiv", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "scalar_td", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "td1", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_td64arr_rfloordiv_tdscalar", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_td64arr_rfloordiv_tdscalar(self, box_with_array, scalar_td): # GH#18831 td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan expected = Series([1, 1, np.nan]) td1 = tm.box_expected(td1, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) result = scalar_td // td1 tm.assert_equal(result, expected)
15,638
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9
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def test_td64arr_rfloordiv_tdscalar_explicit(self, box_with_array, scalar_td): # GH#18831 td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan expected = Series([1, 1, np.nan]) td1 = tm.box_expected(td1, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) # We can test __rfloordiv__ using this syntax, # see `test_timedelta_rfloordiv` result = td1.__rfloordiv__(scalar_td) tm.assert_equal(result, expected)
15,639
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8
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def test_td64arr_floordiv_int(self, box_with_array): idx = TimedeltaIndex(np.arange(5, dtype='int64')) idx = tm.box_expected(idx, box_with_array) result = idx // 1 tm.assert_equal(result, idx) pattern = ('floor_divide cannot use operands|' 'Cannot divide int by Timedelta*') with pytest.raises(TypeError, match=pattern): 1 // idx
15,640
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10
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def test_td64arr_floordiv_tdlike_scalar(self, two_hours, box_with_array): tdi = timedelta_range('1 days', '10 days', name='foo') expected = pd.Int64Index((np.arange(10) + 1) * 12, name='foo') tdi = tm.box_expected(tdi, box_with_array) expected = tm.box_expected(expected, box_with_array) result = tdi // two_hours tm.assert_equal(result, expected)
15,641
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def test_td64arr_rfloordiv_tdlike_scalar(self, scalar_td, box_with_array): # GH#19125 tdi = TimedeltaIndex(['00:05:03', '00:05:03', pd.NaT], freq=None) expected = pd.Index([2.0, 2.0, np.nan]) tdi = tm.box_expected(tdi, box_with_array, transpose=False) expected = tm.box_expected(expected, box_with_array, transpose=False) res = tdi.__rfloordiv__(scalar_td) tm.assert_equal(res, expected) expected = pd.Index([0.0, 0.0, np.nan]) expected = tm.box_expected(expected, box_with_array, transpose=False) res = tdi // (scalar_td) tm.assert_equal(res, expected)
15,642
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8
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def test_td64arr_mod_int(self, box_with_array): tdi = timedelta_range('1 ns', '10 ns', periods=10) tdarr = tm.box_expected(tdi, box_with_array) expected = TimedeltaIndex(['1 ns', '0 ns'] * 5) expected = tm.box_expected(expected, box_with_array) result = tdarr % 2 tm.assert_equal(result, expected) with pytest.raises(TypeError): 2 % tdarr if box_with_array is pd.DataFrame: pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__") result = divmod(tdarr, 2) tm.assert_equal(result[1], expected) tm.assert_equal(result[0], tdarr // 2)
15,643
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8
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def test_td64arr_mod_tdscalar(self, box_with_array, three_days): tdi = timedelta_range('1 Day', '9 days') tdarr = tm.box_expected(tdi, box_with_array) expected = TimedeltaIndex(['1 Day', '2 Days', '0 Days'] * 3) expected = tm.box_expected(expected, box_with_array) result = tdarr % three_days tm.assert_equal(result, expected) if box_with_array is pd.DataFrame: pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__") result = divmod(tdarr, three_days) tm.assert_equal(result[1], expected) tm.assert_equal(result[0], tdarr // three_days)
15,644
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def test_td64arr_mul_tdscalar_invalid(self, box_with_array, scalar_td): td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan td1 = tm.box_expected(td1, box_with_array) # check that we are getting a TypeError # with 'operate' (from core/ops.py) for the ops that are not # defined pattern = 'operate|unsupported|cannot|not supported' with pytest.raises(TypeError, match=pattern): td1 * scalar_td with pytest.raises(TypeError, match=pattern): scalar_td * td1
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def test_td64arr_rmod_tdscalar(self, box_with_array, three_days): tdi = timedelta_range('1 Day', '9 days') tdarr = tm.box_expected(tdi, box_with_array) expected = ['0 Days', '1 Day', '0 Days'] + ['3 Days'] * 6 expected = TimedeltaIndex(expected) expected = tm.box_expected(expected, box_with_array) result = three_days % tdarr tm.assert_equal(result, expected) if box_with_array is pd.DataFrame: pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__") result = divmod(three_days, tdarr) tm.assert_equal(result[1], expected) tm.assert_equal(result[0], three_days // tdarr)
15,646
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8
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def test_td64arr_div_numeric_scalar(self, box_with_array, two): # GH#4521 # divide/multiply by integers tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') expected = Series(['29.5D', '29.5D', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, box_with_array) result = tdser / two tm.assert_equal(result, expected) with pytest.raises(TypeError, match='Cannot divide'): two / tdser
15,647
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7
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def test_td64arr_mul_numeric_scalar(self, box_with_array, one): # GH#4521 # divide/multiply by integers tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') expected = Series(['-59 Days', '-59 Days', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, box_with_array) result = tdser * (-one) tm.assert_equal(result, expected) result = (-one) * tdser tm.assert_equal(result, expected) expected = Series(['118 Days', '118 Days', 'NaT'], dtype='timedelta64[ns]') expected = tm.box_expected(expected, box_with_array) result = tdser * (2 * one) tm.assert_equal(result, expected) result = (2 * one) * tdser tm.assert_equal(result, expected)
15,648
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"Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "xbox", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "box_expected", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "tdser", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "vector", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "vector", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "tdser", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_td64arr_rmul_numeric_array", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_td64arr_rmul_numeric_array(self, box_with_array, vector, dtype): # GH#4521 # divide/multiply by integers xbox = get_upcast_box(box_with_array, vector) tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') vector = vector.astype(dtype) expected = Series(['1180 Days', '1770 Days', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, xbox) result = tdser * vector tm.assert_equal(result, expected) result = vector * tdser tm.assert_equal(result, expected)
15,649
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"_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "vector", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Index", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "isinstance", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, "orelse": []}}, {"_type": "With", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "astype", "value": {"_type": "Name", "_fields": {"id": "vector", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "right": {"_type": "Name", "_fields": {"id": "tdser", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "TypeError", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "raises", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "match", "value": {"_type": "Name", "_fields": {"id": "pattern", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "optional_vars": null}}], "type_comment": null}}], "name": "test_td64arr_div_numeric_array", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_td64arr_div_numeric_array(self, box_with_array, vector, dtype): # GH#4521 # divide/multiply by integers xbox = get_upcast_box(box_with_array, vector) tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]') vector = vector.astype(dtype) expected = Series(['2.95D', '1D 23H 12m', 'NaT'], dtype='timedelta64[ns]') tdser = tm.box_expected(tdser, box_with_array) expected = tm.box_expected(expected, xbox) result = tdser / vector tm.assert_equal(result, expected) pattern = ('true_divide cannot use operands|' 'cannot perform __div__|' 'cannot perform __truediv__|' 'unsupported operand|' 'Cannot divide') with pytest.raises(TypeError, match=pattern): vector / tdser if not isinstance(vector, pd.Index): # Index.__rdiv__ won't try to operate elementwise, just raises result = tdser / vector.astype(object) if box_with_array is pd.DataFrame: expected = [tdser.iloc[0, n] / vector[n] for n in range(len(vector))] else: expected = [tdser[n] / vector[n] for n in range(len(tdser))] expected = tm.box_expected(expected, xbox) tm.assert_equal(result, expected) with pytest.raises(TypeError, match=pattern): vector.astype(object) / tdser
15,650
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"Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Index", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "to_array", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}]}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "Is", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "type", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Name", "_fields": {"id": "Series", "ctx": {"_type": "Load", "_fields": {}}}}]}}]}}, "orelse": []}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "box", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "box_expected", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "xbox", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "box_expected", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__rdiv__", "value": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Assert", "_fields": {"msg": null, "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Is", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Name", "_fields": {"id": "NotImplemented", "ctx": {"_type": "Load", "_fields": {}}}}]}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Is", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "box", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "DataFrame", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}], "name": "test_float_series_rdiv_td64arr", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_float_series_rdiv_td64arr(self, box_with_array, names): # GH#19042 test for correct name attachment # TODO: the direct operation TimedeltaIndex / Series still # needs to be fixed. box = box_with_array tdi = TimedeltaIndex(['0days', '1day', '2days', '3days', '4days'], name=names[0]) ser = Series([1.5, 3, 4.5, 6, 7.5], dtype=np.float64, name=names[1]) xname = names[2] if box is not tm.to_array else names[1] expected = Series([tdi[n] / ser[n] for n in range(len(ser))], dtype='timedelta64[ns]', name=xname) xbox = box if box in [pd.Index, tm.to_array] and type(ser) is Series: xbox = Series tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, xbox) result = ser.__rdiv__(tdi) if box is pd.DataFrame: # TODO: Should we skip this case sooner or test something else? assert result is NotImplemented else: tm.assert_equal(result, expected)
15,651
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"_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tdi", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__rmul__", "value": {"_type": "Name", "_fields": {"id": "ser", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_td64arr_mul_int_series", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_td64arr_mul_int_series(self, box_df_fail, names): # GH#19042 test for correct name attachment box = box_df_fail # broadcasts along wrong axis, but doesn't raise tdi = TimedeltaIndex(['0days', '1day', '2days', '3days', '4days'], name=names[0]) # TODO: Should we be parametrizing over types for `ser` too? ser = Series([0, 1, 2, 3, 4], dtype=np.int64, name=names[1]) expected = Series(['0days', '1day', '4days', '9days', '16days'], dtype='timedelta64[ns]', name=names[2]) tdi = tm.box_expected(tdi, box) box = Series if (box is pd.Index and type(ser) is Series) else box expected = tm.box_expected(expected, box) result = ser * tdi tm.assert_equal(result, expected) # The direct operation tdi * ser still needs to be fixed. result = ser.__rmul__(tdi) tm.assert_equal(result, expected)
15,652
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class TestTimedeltaArraylikeInvalidArithmeticOps: def test_td64arr_pow_invalid(self, scalar_td, box_with_array): td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan td1 = tm.box_expected(td1, box_with_array) # check that we are getting a TypeError # with 'operate' (from core/ops.py) for the ops that are not # defined pattern = 'operate|unsupported|cannot|not supported' with pytest.raises(TypeError, match=pattern): scalar_td ** td1 with pytest.raises(TypeError, match=pattern): td1 ** scalar_td
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9
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def test_td64arr_pow_invalid(self, scalar_td, box_with_array): td1 = Series([timedelta(minutes=5, seconds=3)] * 3) td1.iloc[2] = np.nan td1 = tm.box_expected(td1, box_with_array) # check that we are getting a TypeError # with 'operate' (from core/ops.py) for the ops that are not # defined pattern = 'operate|unsupported|cannot|not supported' with pytest.raises(TypeError, match=pattern): scalar_td ** td1 with pytest.raises(TypeError, match=pattern): td1 ** scalar_td
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8
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def capture_warnings(*args, **kw): orig_warn(*args, **kw) popwarn = warnings.pop(0) canary.append(popwarn) if regex: assert re.match(popwarn, args[0]) else: eq_(args[0], popwarn)
15,655
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{"id": "ty", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__types", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}], "name": "get_type", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "prefixes"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__send_request", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "prefixes", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Name", "_fields": {"id": "property", "ctx": {"_type": "Load", "_fields": {}}}}]}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "prop", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "prop", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "paths/{}"}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__send_request", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "paths"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "get_property_paths", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "Fountain", "bases": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class Fountain(object): def __init__(self, host='localhost', port=5002): self.__fountain_url = 'http://{}:{}'.format(host, port) self.__types = {} self.__properties = {} def __send_request(self, path): try: response = requests.get(urlparse.urljoin(self.__fountain_url, path)) if response.status_code != 200: raise IOError(response.json()) return response.json() except requests.ConnectionError: message = 'A fountain is not available' log.error(message) raise EnvironmentError(message) @property def types(self): response = self.__send_request('types') return response.get('types') @property def properties(self): response = self.__send_request('properties') return response.get('properties') def get_type_seeds(self, ty): response = self.__send_request('seeds/{}'.format(ty)) return response.get('seeds') def get_property(self, prop): if prop not in self.__properties: self.__properties[prop] = self.__send_request('properties/{}'.format(prop)) return self.__properties[prop] def get_type(self, ty): if ty not in self.__types: self.__types[ty] = self.__send_request('types/{}'.format(ty)) return self.__types[ty] @property def prefixes(self): response = self.__send_request('prefixes') return response def get_property_paths(self, prop): response = self.__send_request('paths/{}'.format(prop)) return response.get("paths")
15,656
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"Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_symbol_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "target": {"_type": "Name", "_fields": {"id": "code", "ctx": {"_type": "Store", "_fields": {}}}}}}], "iter": {"_type": "Name", "_fields": {"id": "string", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "c", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "code", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "get_string_code", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "BNPVirtKeyboard", "bases": [{"_type": "Name", "_fields": {"id": "VirtKeyboard", "ctx": {"_type": "Load", 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class BNPVirtKeyboard(VirtKeyboard): symbols={'0': '9cc4789a2cb223e8f2d5e676e90264b5', '1': 'e10b58fc085f9683052d5a63c96fc912', '2': '04ec647e7b3414bcc069f0c54eb55a4c', '3': 'fde84fd9bac725db8463554448f1e469', '4': '2359eea8671bf112b58264bec0294f71', '5': '82b55b63480114f04fad8c5c4fa5673a', '6': 'e074864faeaeabb3be3d118192cd8879', '7': 'af5740e4ca71fadc6f4ae1412d864a1c', '8': 'cab759c574038ad89a0e35cc76ab7214', '9': '828cf0faf86ac78e7f43208907620527' } url="/NSImgGrille?timestamp=%d" color=27 def __init__(self, page): coords = {} size = 136 x, y, width, height = (0, 0, size/5, size/5) for a in page.document.xpath('//div[@id="secret-nbr-keyboard"]/a'): code = a.attrib['ondblclick'] coords[code] = (x+1, y+1, x+height-2, y+height-2) if (x + width + 1) >= size: y += height x = 0 else: x += width VirtKeyboard.__init__(self, page.browser.openurl(self.url % time.time()), coords, self.color) self.check_symbols(self.symbols, page.browser.responses_dirname) def get_symbol_code(self, md5sum): code = VirtKeyboard.get_symbol_code(self, md5sum) return re.sub(u'[^\d]', '', code) def get_string_code(self, string): code = '' for c in string: code += self.get_symbol_code(self.symbols[c]) return code
15,657
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10
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "path", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Try", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__fountain_url", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "urljoin", "value": {"_type": "Name", "_fields": {"id": "urlparse", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Name", "_fields": {"id": "requests", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "json", "value": {"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "IOError", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "status_code", "value": {"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 200}}]}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "json", "value": {"_type": "Name", "_fields": {"id": "response", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "orelse": [], "handlers": [{"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "A fountain is not available"}}, "targets": [{"_type": "Name", "_fields": {"id": "message", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "message", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "error", "value": {"_type": "Name", "_fields": {"id": "log", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "message", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "EnvironmentError", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "name": null, "type": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "ConnectionError", "value": {"_type": "Name", "_fields": {"id": "requests", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "finalbody": []}}], "name": "__send_request", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def __send_request(self, path): try: response = requests.get(urlparse.urljoin(self.__fountain_url, path)) if response.status_code != 200: raise IOError(response.json()) return response.json() except requests.ConnectionError: message = 'A fountain is not available' log.error(message) raise EnvironmentError(message)
15,658
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"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "//form[@name=\"logincanalnet\"]"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "xpath", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "document", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "form", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "action"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "attrib", "value": {"_type": "Name", "_fields": {"id": "form", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "url", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [], "values": []}}, "targets": [{"_type": "Name", "_fields": {"id": "params", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "value"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "attrib", "value": {"_type": "Name", "_fields": {"id": "ctrl", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "name"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "attrib", "value": {"_type": "Name", "_fields": {"id": "ctrl", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "value": {"_type": "Name", "_fields": {"id": "params", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "input"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "findall", "value": {"_type": "Name", "_fields": {"id": "form", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "ctrl", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "iso-8859-1"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "encode", "value": {"_type": "Name", "_fields": {"id": "login", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "ch1"}}, "value": {"_type": "Name", "_fields": {"id": "params", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "password", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_string_code", "value": {"_type": "Name", "_fields": {"id": "vk", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "ch5"}}, "value": {"_type": "Name", "_fields": {"id": "params", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "url", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "params", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "urlencode", "value": {"_type": "Name", "_fields": {"id": "urllib", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "location", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "browser", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "login", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "LoginPage", "bases": [{"_type": "Name", "_fields": {"id": "Page", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class LoginPage(Page): def on_loaded(self): for td in self.document.getroot().cssselect('td.LibelleErreur'): if td.text is None: continue msg = td.text.strip() if 'indisponible' in msg: raise BrowserUnavailable(msg) def login(self, login, password): try: vk=BNPVirtKeyboard(self) except VirtKeyboardError as err: self.logger.error("Error: %s"%err) return False # Mechanize does not recognize the form.. form = self.document.xpath('//form[@name="logincanalnet"]')[0] url = form.attrib['action'] params = {} for ctrl in form.findall('input'): params[ctrl.attrib['name']] = ctrl.attrib['value'] params['ch1'] = login.encode('iso-8859-1') params['ch5'] = vk.get_string_code(password) self.browser.location(url, urllib.urlencode(params))
15,659
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"_fields": {}}, "attr": "time", "value": {"_type": "Name", "_fields": {"id": "time", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "openurl", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "browser", "value": {"_type": "Name", "_fields": {"id": "page", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "coords", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "color", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__init__", "value": {"_type": "Name", "_fields": {"id": "VirtKeyboard", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "symbols", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "responses_dirname", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "browser", "value": {"_type": "Name", "_fields": {"id": "page", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "check_symbols", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "__init__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def __init__(self, page): coords = {} size = 136 x, y, width, height = (0, 0, size/5, size/5) for a in page.document.xpath('//div[@id="secret-nbr-keyboard"]/a'): code = a.attrib['ondblclick'] coords[code] = (x+1, y+1, x+height-2, y+height-2) if (x + width + 1) >= size: y += height x = 0 else: x += width VirtKeyboard.__init__(self, page.browser.openurl(self.url % time.time()), coords, self.color) self.check_symbols(self.symbols, page.browser.responses_dirname)
15,660
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10
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def login(self, login, password): try: vk=BNPVirtKeyboard(self) except VirtKeyboardError as err: self.logger.error("Error: %s"%err) return False # Mechanize does not recognize the form.. form = self.document.xpath('//form[@name="logincanalnet"]')[0] url = form.attrib['action'] params = {} for ctrl in form.findall('input'): params[ctrl.attrib['name']] = ctrl.attrib['value'] params['ch1'] = login.encode('iso-8859-1') params['ch5'] = vk.get_string_code(password) self.browser.location(url, urllib.urlencode(params))
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12
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class ConfirmPage(Page): def get_error(self): for td in self.document.xpath('//td[@class="hdvon1"]'): if td.text: return td.text.strip() return None def get_relocate_url(self): script = self.document.xpath('//script')[0] m = re.match('document.location.replace\("(.*)"\)', script.text[script.text.find('document.location.replace'):]) if m: return m.group(1)
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9
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def testRowLengths(self, rt_input, expected, axis=1, ragged_rank=None, expected_ragged_rank=None): rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank) lengths = rt.row_lengths(axis) self.assertAllEqual(lengths, expected) if expected_ragged_rank is not None: if isinstance(lengths, ragged_tensor.RaggedTensor): self.assertEqual(lengths.ragged_rank, expected_ragged_rank) else: self.assertEqual(0, expected_ragged_rank)
15,663
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11
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"value": 3}}}}}}, {"_type": "keyword", "_fields": {"arg": "exception", "value": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "ValueError", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "InvalidArgumentError", "value": {"_type": "Name", "_fields": {"id": "errors", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "parameters", "value": {"_type": "Name", "_fields": {"id": "parameterized", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}], "name": "RaggedRowLengthsOp", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TensorFlowTestCase", "value": {"_type": "Name", "_fields": {"id": "test_util", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TestCase", "value": {"_type": "Name", "_fields": {"id": "parameterized", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class RaggedRowLengthsOp(test_util.TensorFlowTestCase, parameterized.TestCase): @parameterized.parameters([ # Docstring Example dict( rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []], expected=[2, 0, 2, 1, 0]), dict( rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []], axis=2, expected=[[3, 1], [], [2, 1], [1], []]), # 2D Tensor (1 ragged dimension) dict( rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']], expected=[1, 3, 1, 0, 1]), dict( rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']], axis=0, expected=5), dict( rt_input=[['a', 'b', 'c', 'd', 'e', 'f', 'g']], expected=[7]), dict( rt_input=[[], ['a', 'b', 'c', 'd', 'e', 'f', 'g'], []], expected=[0, 7, 0]), dict( rt_input=[], ragged_rank=1, expected=[]), dict( rt_input=[], ragged_rank=1, axis=0, expected=0), # 3D Tensor (1 ragged dimension) dict( rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]], ragged_rank=1, axis=0, expected=2), dict( rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]], ragged_rank=1, axis=1, expected=[3, 2]), dict( rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]], ragged_rank=1, axis=2, expected=[[2, 2, 2], [2, 2]], expected_ragged_rank=1), # 3D Tensor (2 ragged dimensions) dict( rt_input=[[[1, 2], [3, 4, 5], []], [[6, 7, 8, 9], [10]]], axis=0, expected=2), dict( rt_input=[[[1, 2], [3, 4, 5], []], [[6, 7, 8, 9], [10]]], axis=-3, expected=2), dict( rt_input=[[[1, 2], [3, 4, 5], []], [[6, 7, 8, 9], [10]]], axis=1, expected=[3, 2]), dict( rt_input=[[[1, 2], [3, 4, 5], []], [[6, 7, 8, 9], [10]]], axis=-2, expected=[3, 2]), dict( rt_input=[[[1, 2], [3, 4, 5], []], [[6, 7, 8, 9], [10]]], axis=2, expected=[[2, 3, 0], [4, 1]], expected_ragged_rank=1), dict( rt_input=[[[1, 2], [3, 4, 5], []], [[6, 7, 8, 9], [10]]], axis=-1, expected=[[2, 3, 0], [4, 1]], expected_ragged_rank=1), ]) # pyformat: disable def testRowLengths(self, rt_input, expected, axis=1, ragged_rank=None, expected_ragged_rank=None): rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank) lengths = rt.row_lengths(axis) self.assertAllEqual(lengths, expected) if expected_ragged_rank is not None: if isinstance(lengths, ragged_tensor.RaggedTensor): self.assertEqual(lengths.ragged_rank, expected_ragged_rank) else: self.assertEqual(0, expected_ragged_rank) @parameterized.parameters([ dict( # axis=2 out of bounds: expected -2<=axis<2. rt_input=[[10, 20], [30]], axis=2, exception=(ValueError, errors.InvalidArgumentError)), dict( # axis=-3 out of bounds: expected -2<=axis<2. rt_input=[[2, 3, 0], [4, 1, 2]], axis=-3, exception=(ValueError, errors.InvalidArgumentError)), ]) def testErrors(self, rt_input, exception, message=None, axis=1): rt = ragged_factory_ops.constant(rt_input) with self.assertRaisesRegexp(exception, message): rt.row_lengths(axis)
15,664
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def process(self, msg, kwargs): # NOTE(mrodden): catch any Message/other object and # coerce to unicode before they can get # to the python logging and possibly # cause string encoding trouble if not isinstance(msg, six.string_types): msg = six.text_type(msg) if 'extra' not in kwargs: kwargs['extra'] = {} extra = kwargs['extra'] context = kwargs.pop('context', None) if not context: context = getattr(local.store, 'context', None) if context: extra.update(_dictify_context(context)) instance = kwargs.pop('instance', None) instance_uuid = (extra.get('instance_uuid') or kwargs.pop('instance_uuid', None)) instance_extra = '' if instance: instance_extra = CONF.instance_format % instance elif instance_uuid: instance_extra = (CONF.instance_uuid_format % {'uuid': instance_uuid}) extra['instance'] = instance_extra extra.setdefault('user_identity', kwargs.pop('user_identity', None)) extra['project'] = self.project extra['version'] = self.version extra['extra'] = extra.copy() return msg, kwargs
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def _setup_logging_from_conf(project, version): log_root = getLogger(None).logger for handler in log_root.handlers: log_root.removeHandler(handler) if CONF.use_syslog: facility = _find_facility_from_conf() # TODO(bogdando) use the format provided by RFCSysLogHandler # after existing syslog format deprecation in J if CONF.use_syslog_rfc_format: syslog = RFCSysLogHandler(address='/dev/log', facility=facility) else: syslog = logging.handlers.SysLogHandler(address='/dev/log', facility=facility) log_root.addHandler(syslog) logpath = _get_log_file_path() if logpath: filelog = logging.handlers.WatchedFileHandler(logpath) log_root.addHandler(filelog) if CONF.use_stderr: streamlog = ColorHandler() log_root.addHandler(streamlog) elif not logpath: # pass sys.stdout as a positional argument # python2.6 calls the argument strm, in 2.7 it's stream streamlog = logging.StreamHandler(sys.stdout) log_root.addHandler(streamlog) if CONF.publish_errors: handler = importutils.import_object( "fuel_agent.openstack.common.log_handler.PublishErrorsHandler", logging.ERROR) log_root.addHandler(handler) datefmt = CONF.log_date_format for handler in log_root.handlers: # NOTE(alaski): CONF.log_format overrides everything currently. This # should be deprecated in favor of context aware formatting. if CONF.log_format: handler.setFormatter(logging.Formatter(fmt=CONF.log_format, datefmt=datefmt)) log_root.info('Deprecated: log_format is now deprecated and will ' 'be removed in the next release') else: handler.setFormatter(ContextFormatter(project=project, version=version, datefmt=datefmt)) if CONF.debug: log_root.setLevel(logging.DEBUG) elif CONF.verbose: log_root.setLevel(logging.INFO) else: log_root.setLevel(logging.WARNING) for pair in CONF.default_log_levels: mod, _sep, level_name = pair.partition('=') logger = logging.getLogger(mod) # NOTE(AAzza) in python2.6 Logger.setLevel doesn't convert string name # to integer code. if sys.version_info < (2, 7): level = logging.getLevelName(level_name) logger.setLevel(level) else: logger.setLevel(level_name)
15,666
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16
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def main(argv): parser = OptionParser(usage=("Usage: %prog [options] modulename\n" "Utility script to create a basic template for a new ns-3 module")) (options, args) = parser.parse_args() if len(args) != 1: parser.print_help() return 1 modname = args[0].lower() if False in [word.isalnum() for word in modname.split("-")]: print >> sys.stderr, "Module name should only contain alphanumeric characters and dashes" return 2 assert os.path.sep not in modname moduledir = os.path.join(os.path.dirname(__file__), modname) if os.path.exists(moduledir): print >> sys.stderr, "Module %r already exists" % (modname,) return 2 print("Creating module %r, " "run './waf configure' to include it in the build" % (modname,)) os.mkdir(moduledir) wscript = file(os.path.join(moduledir, "wscript"), "wt") wscript.write(WSCRIPT_TEMPLATE % dict(MODULE=modname)) wscript.close() # # model # modeldir = os.path.join(moduledir, "model") os.mkdir(modeldir) model_cc = file(os.path.join(moduledir, "model", "%s.cc" % modname), "wt") model_cc.write(MODEL_CC_TEMPLATE % dict(MODULE=modname)) model_cc.close() model_h = file(os.path.join(moduledir, "model", "%s.h" % modname), "wt") model_h.write(MODEL_H_TEMPLATE % dict(MODULE=modname, INCLUDE_GUARD="%s_H" % (modname.replace("-", "_").upper()),)) model_h.close() # # test # testdir = os.path.join(moduledir, "test") os.mkdir(testdir) test_cc = file(os.path.join(moduledir, "test", "%s-test-suite.cc" % modname), "wt") test_cc.write(TEST_CC_TEMPLATE % dict(MODULE=modname, CAPITALIZED=''.join([word.capitalize() for word in modname.split('-')]), COMPOUND=''.join([modname.split('-')[0]] + [word.capitalize() for word in modname.split('-')[1:]]), )) test_cc.close() # # helper # helperdir = os.path.join(moduledir, "helper") os.mkdir(helperdir) helper_cc = file(os.path.join(moduledir, "helper", "%s-helper.cc" % modname), "wt") helper_cc.write(HELPER_CC_TEMPLATE % dict(MODULE=modname)) helper_cc.close() helper_h = file(os.path.join(moduledir, "helper", "%s-helper.h" % modname), "wt") helper_h.write(HELPER_H_TEMPLATE % dict(MODULE=modname, INCLUDE_GUARD="%s_HELPER_H" % (modname.replace("-", "_").upper()),)) helper_h.close() # # examples # examplesdir = os.path.join(moduledir, "examples") os.mkdir(examplesdir) examples_wscript = file(os.path.join(examplesdir, "wscript"), "wt") examples_wscript.write(EXAMPLES_WSCRIPT_TEMPLATE % dict(MODULE=modname)) examples_wscript.close() example_cc = file(os.path.join(moduledir, "examples", "%s-example.cc" % modname), "wt") example_cc.write(EXAMPLE_CC_TEMPLATE % dict(MODULE=modname)) example_cc.close() # # doc # docdir = os.path.join(moduledir, "doc") os.mkdir(docdir) doc_rst = file(os.path.join(moduledir, "doc", "%s.rst" % modname), "wt") doc_rst.write(DOC_RST_TEMPLATE % dict(MODULE=modname)) doc_rst.close() return 0
15,667
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11
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"decorator_list": []}}], "type_ignores": []}}
class Migration(migrations.Migration): dependencies = [ ('socialaccount', '0002_token_max_lengths'), ('oauth', '0004_drop_github_and_bitbucket_models'), ] operations = [ migrations.AddField( model_name='remoteorganization', name='account', field=models.ForeignKey(related_name='remote_organizations', verbose_name='Connected account', blank=True, to='socialaccount.SocialAccount', null=True, on_delete=models.CASCADE), ), migrations.AddField( model_name='remoterepository', name='account', field=models.ForeignKey(related_name='remote_repositories', verbose_name='Connected account', blank=True, to='socialaccount.SocialAccount', null=True, on_delete=models.CASCADE), ), ]
15,668
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class Tag(models.Model): name = models.CharField(max_length=100) tagged_type = models.ForeignKey(ContentType, models.CASCADE, related_name="fixtures_tag_set") tagged_id = models.PositiveIntegerField(default=0) tagged = GenericForeignKey(ct_field='tagged_type', fk_field='tagged_id') def __str__(self): return '<%s: %s> tagged "%s"' % (self.tagged.__class__.__name__, self.tagged, self.name)
15,669
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7
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class Blog(models.Model): name = models.CharField(max_length=100) featured = models.ForeignKey(Article, models.CASCADE, related_name='fixtures_featured_set') articles = models.ManyToManyField(Article, blank=True, related_name='fixtures_articles_set') def __str__(self): return self.name
15,670
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9
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"UniverseSettings", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "FilterETFs", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "ETF", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Universe", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "AddUniverse", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "Initialize", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def Initialize(self): self.SetStartDate(2011, 2, 1) self.SetEndDate(2011, 4, 4) self.SetCash(100000) self.filterDateConstituentSymbolCount = {} self.constituentDataEncountered = {} self.constituentSymbols = [] self.mappingEventOccurred = False self.UniverseSettings.Resolution = Resolution.Hour self.aapl = Symbol.Create("AAPL", SecurityType.Equity, Market.USA) self.qqq = self.AddEquity("QQQ", Resolution.Daily).Symbol self.AddUniverse(self.Universe.ETF(self.qqq, self.UniverseSettings, self.FilterETFs))
15,671
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{"_type": "FormattedValue", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "filterDateConstituentSymbolCount", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "conversion": -1, "format_spec": null}}]}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "filterDateConstituentSymbolCount", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 2}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "No mapping/SymbolChangedEvent occurred. Expected for QQQ to be mapped from QQQQ -> QQQ"}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "mappingEventOccurred", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "JoinedStr", "_fields": {"values": [{"_type": "Constant", "_fields": {"kind": null, "value": "Expected 25 or more constituents in filter function on "}}, {"_type": "FormattedValue", "_fields": {"value": {"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Load", "_fields": {}}}}, "conversion": -1, "format_spec": null}}, {"_type": "Constant", "_fields": {"kind": null, "value": ", found "}}, {"_type": "FormattedValue", "_fields": {"value": {"_type": "Name", "_fields": {"id": "constituentsCount", "ctx": {"_type": "Load", "_fields": {}}}}, "conversion": -1, "format_spec": null}}]}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Lt", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "constituentsCount", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 25}}]}}, "orelse": []}}], "iter": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "items", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "filterDateConstituentSymbolCount", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "constituentsCount", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "JoinedStr", "_fields": {"values": [{"_type": "Constant", "_fields": {"kind": null, "value": "Received data in OnData(...) but it did not contain any constituent data on "}}, {"_type": "FormattedValue", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "%Y-%m-%d %H:%M:%S.%f"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "strftime", "value": {"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "conversion": -1, "format_spec": null}}]}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Name", "_fields": {"id": "constituentEncountered", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}], "iter": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "items", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "constituentDataEncountered", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "constituentEncountered", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}], "name": "OnEndOfAlgorithm", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "ETFConstituentUniverseFilterFunctionRegressionAlgorithm", "bases": [{"_type": "Name", "_fields": {"id": "QCAlgorithm", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class ETFConstituentUniverseFilterFunctionRegressionAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2011, 2, 1) self.SetEndDate(2011, 4, 4) self.SetCash(100000) self.filterDateConstituentSymbolCount = {} self.constituentDataEncountered = {} self.constituentSymbols = [] self.mappingEventOccurred = False self.UniverseSettings.Resolution = Resolution.Hour self.aapl = Symbol.Create("AAPL", SecurityType.Equity, Market.USA) self.qqq = self.AddEquity("QQQ", Resolution.Daily).Symbol self.AddUniverse(self.Universe.ETF(self.qqq, self.UniverseSettings, self.FilterETFs)) def FilterETFs(self, constituents): constituentSymbols = [i.Symbol for i in constituents] if self.aapl not in constituentSymbols: raise Exception("AAPL not found in QQQ constituents") self.filterDateConstituentSymbolCount[self.UtcTime.date()] = len(constituentSymbols) for symbol in constituentSymbols: self.constituentSymbols.append(symbol) self.constituentSymbols = list(set(self.constituentSymbols)) return constituentSymbols def OnData(self, data): if len(data.SymbolChangedEvents) != 0: for symbolChanged in data.SymbolChangedEvents.Values: if symbolChanged.Symbol != self.qqq: raise Exception(f"Mapped symbol is not QQQ. Instead, found: {symbolChanged.Symbol}") if symbolChanged.OldSymbol != "QQQQ": raise Exception(f"Old QQQ Symbol is not QQQQ. Instead, found: {symbolChanged.OldSymbol}") if symbolChanged.NewSymbol != "QQQ": raise Exception(f"New QQQ Symbol is not QQQ. Instead, found: {symbolChanged.NewSymbol}") self.mappingEventOccurred = True if self.qqq in data and len([i for i in data.Keys]) == 1: return if self.UtcTime.date() not in self.constituentDataEncountered: self.constituentDataEncountered[self.UtcTime.date()] = False if len([i for i in data.Keys if i in self.constituentSymbols]) != 0: self.constituentDataEncountered[self.UtcTime.date()] = True if not self.Portfolio.Invested: self.SetHoldings(self.aapl, 0.5) def OnEndOfAlgorithm(self): if len(self.filterDateConstituentSymbolCount) != 2: raise Exception(f"ETF constituent filtering function was not called 2 times (actual: {len(self.filterDateConstituentSymbolCount)}") if not self.mappingEventOccurred: raise Exception("No mapping/SymbolChangedEvent occurred. Expected for QQQ to be mapped from QQQQ -> QQQ") for constituentDate, constituentsCount in self.filterDateConstituentSymbolCount.items(): if constituentsCount < 25: raise Exception(f"Expected 25 or more constituents in filter function on {constituentDate}, found {constituentsCount}") for constituentDate, constituentEncountered in self.constituentDataEncountered.items(): if not constituentEncountered: raise Exception(f"Received data in OnData(...) but it did not contain any constituent data on {constituentDate.strftime('%Y-%m-%d %H:%M:%S.%f')}")
15,672
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9
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def FilterETFs(self, constituents): constituentSymbols = [i.Symbol for i in constituents] if self.aapl not in constituentSymbols: raise Exception("AAPL not found in QQQ constituents") self.filterDateConstituentSymbolCount[self.UtcTime.date()] = len(constituentSymbols) for symbol in constituentSymbols: self.constituentSymbols.append(symbol) self.constituentSymbols = list(set(self.constituentSymbols)) return constituentSymbols
15,673
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{"ctx": {"_type": "Load", "_fields": {}}, "attr": "constituentDataEncountered", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "date", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "UtcTime", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "constituentDataEncountered", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "type_comment": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "ListComp", "_fields": {"elt": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Load", "_fields": {}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [{"_type": "Compare", "_fields": {"ops": [{"_type": "In", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "constituentSymbols", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}], "iter": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Keys", "value": {"_type": "Name", "_fields": {"id": "data", "ctx": {"_type": "Load", "_fields": {}}}}}}, "target": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "aapl", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": 0.5}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "SetHoldings", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Invested", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Portfolio", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "orelse": []}}], "name": "OnData", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def OnData(self, data): if len(data.SymbolChangedEvents) != 0: for symbolChanged in data.SymbolChangedEvents.Values: if symbolChanged.Symbol != self.qqq: raise Exception(f"Mapped symbol is not QQQ. Instead, found: {symbolChanged.Symbol}") if symbolChanged.OldSymbol != "QQQQ": raise Exception(f"Old QQQ Symbol is not QQQQ. Instead, found: {symbolChanged.OldSymbol}") if symbolChanged.NewSymbol != "QQQ": raise Exception(f"New QQQ Symbol is not QQQ. Instead, found: {symbolChanged.NewSymbol}") self.mappingEventOccurred = True if self.qqq in data and len([i for i in data.Keys]) == 1: return if self.UtcTime.date() not in self.constituentDataEncountered: self.constituentDataEncountered[self.UtcTime.date()] = False if len([i for i in data.Keys if i in self.constituentSymbols]) != 0: self.constituentDataEncountered[self.UtcTime.date()] = True if not self.Portfolio.Invested: self.SetHoldings(self.aapl, 0.5)
15,674
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10
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def get_button(self, main_dialog, text): buttonBox = main_dialog.findChild(QDialogButtonBox, "buttonBox") self.assertIsNotNone(buttonBox) button = None for button in buttonBox.buttons(): if str(button.text()) == text: return button self.assertIsNotNone(None) return None
15,675
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12
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "JoinedStr", "_fields": {"values": [{"_type": "Constant", "_fields": {"kind": null, "value": "ETF constituent filtering function was not called 2 times (actual: "}}, {"_type": "FormattedValue", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "filterDateConstituentSymbolCount", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "conversion": -1, "format_spec": null}}]}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "filterDateConstituentSymbolCount", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 2}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "No mapping/SymbolChangedEvent occurred. Expected for QQQ to be mapped from QQQQ -> QQQ"}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "mappingEventOccurred", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "JoinedStr", "_fields": {"values": [{"_type": "Constant", "_fields": {"kind": null, "value": "Expected 25 or more constituents in filter function on "}}, {"_type": "FormattedValue", "_fields": {"value": {"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Load", "_fields": {}}}}, "conversion": -1, "format_spec": null}}, {"_type": "Constant", "_fields": {"kind": null, "value": ", found "}}, {"_type": "FormattedValue", "_fields": {"value": {"_type": "Name", "_fields": {"id": "constituentsCount", "ctx": {"_type": "Load", "_fields": {}}}}, "conversion": -1, "format_spec": null}}]}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Lt", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "constituentsCount", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 25}}]}}, "orelse": []}}], "iter": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "items", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "filterDateConstituentSymbolCount", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "constituentsCount", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "JoinedStr", "_fields": {"values": [{"_type": "Constant", "_fields": {"kind": null, "value": "Received data in OnData(...) but it did not contain any constituent data on "}}, {"_type": "FormattedValue", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "%Y-%m-%d %H:%M:%S.%f"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "strftime", "value": {"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "conversion": -1, "format_spec": null}}]}}], "func": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Name", "_fields": {"id": "constituentEncountered", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}], "iter": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "items", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "constituentDataEncountered", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "constituentDate", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "constituentEncountered", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}], "name": "OnEndOfAlgorithm", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def OnEndOfAlgorithm(self): if len(self.filterDateConstituentSymbolCount) != 2: raise Exception(f"ETF constituent filtering function was not called 2 times (actual: {len(self.filterDateConstituentSymbolCount)}") if not self.mappingEventOccurred: raise Exception("No mapping/SymbolChangedEvent occurred. Expected for QQQ to be mapped from QQQQ -> QQQ") for constituentDate, constituentsCount in self.filterDateConstituentSymbolCount.items(): if constituentsCount < 25: raise Exception(f"Expected 25 or more constituents in filter function on {constituentDate}, found {constituentsCount}") for constituentDate, constituentEncountered in self.constituentDataEncountered.items(): if not constituentEncountered: raise Exception(f"Received data in OnData(...) but it did not contain any constituent data on {constituentDate.strftime('%Y-%m-%d %H:%M:%S.%f')}")
15,676
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10
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def get_memory(self, number): _mem = self._memobj.memory[number] _flg = self._memobj.flags[number] _name = self._memobj.names[number] mem = chirp_common.Memory() mem.number = number if _flg.empty: mem.empty = True return mem mem.freq = _get_freq(_mem.freq) mem.offset = _get_freq(_mem.offset) mem.rtone = chirp_common.TONES[_mem.rtone - 1] mem.ctone = chirp_common.TONES[_mem.ctone - 1] mem.duplex = DUPLEX[_flg.duplex] mem.tmode = TMODES[_flg.tmode] mem.skip = _flg.skip and "S" or "" if _name.name.get_raw() != "\xFF\xFF\xFF\xFF": mem.name = str(_name.name).rstrip() return mem
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10
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1}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "itemText", "value": {"_type": "Name", "_fields": {"id": "mFunctionsCombo", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Constant", "_fields": {"kind": null, "value": "abs(param: int): int"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "QComboBox", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "mSpatialPredicatesCombo"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "findChild", "value": {"_type": "Name", "_fields": {"id": "dialog", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "mSpatialPredicatesCombo", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "mSpatialPredicatesCombo", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIsNotNone", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "itemText", "value": {"_type": "Name", "_fields": {"id": "mSpatialPredicatesCombo", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Constant", "_fields": {"kind": null, "value": "ST_Disjoint(geometry, geometry): boolean"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "QTextEdit", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "mWhereEditor"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "findChild", "value": {"_type": "Name", "_fields": {"id": "dialog", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "mWhereEditor", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "mWhereEditor", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIsNotNone", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "1 = 1"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "setText", "value": {"_type": "Name", "_fields": {"id": "mWhereEditor", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "accept", "value": {"_type": "Name", "_fields": {"id": "dialog", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "dialog", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "wait_object_destruction", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "dialog", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "main_dialog", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_button_add", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "buttonAdd", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "isEnabled", "value": {"_type": "Name", "_fields": {"id": "buttonAdd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertTrue", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "addWfsLayer_uri", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "addWfsLayer_layer_name", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "slotAddWfsLayer", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "connect", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "addWfsLayer", "value": {"_type": "Name", "_fields": {"id": "main_dialog", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "buttonAdd", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "LeftButton", "value": {"_type": "Name", "_fields": {"id": "Qt", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "mouseClick", "value": {"_type": "Name", "_fields": {"id": "QTest", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "addWfsLayer_uri", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": " restrictToRequestBBOX='1' srsname='EPSG:4326' typename='my:typename' url='"}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "http://"}}}}, "right": {"_type": "Name", "_fields": {"id": "expected_endpoint", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "' version='auto' table=\"\" sql=SELECT * FROM typename WHERE 1 = 1"}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "addWfsLayer_layer_name", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "my:typename"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test(self): # This test is quite fragile as it depends on windows manager behaviour # regarding focus, so not surprising it doesn't pass # on other platforms than Linux. #if 'TRAVIS_OS_NAME' in os.environ and os.environ['TRAVIS_OS_NAME'] == 'osx': # return main_dialog = QgsProviderRegistry.instance().selectWidget("WFS") main_dialog.setProperty("hideDialogs", True) self.assertIsNotNone(main_dialog) # Create new connection btnNew = main_dialog.findChild(QWidget, "btnNew") self.assertIsNotNone(btnNew) QTest.mouseClick(btnNew, Qt.LeftButton) new_conn = find_window('QgsNewHttpConnectionBase') self.assertIsNotNone(new_conn) txtName = new_conn.findChild(QLineEdit, "txtName") self.assertIsNotNone(txtName) txtName.setText("test_connection") txtUrl = new_conn.findChild(QLineEdit, "txtUrl") self.assertIsNotNone(txtUrl) txtUrl.setText("test_url") new_conn.accept() # Wait for object to be destroyed new_conn = self.wait_object_destruction(new_conn) # Try to connect btnConnect = main_dialog.findChild(QWidget, "btnConnect") self.assertIsNotNone(btnConnect) QTest.mouseClick(btnConnect, Qt.LeftButton) # Depends on asynchronous signal QApplication.processEvents() error_box = find_window('WFSCapabilitiesErrorBox') self.assertIsNotNone(error_box) # Close error box error_box.accept() # Wait for object to be destroyed error_box = self.wait_object_destruction(error_box) # Edit connection btnEdit = main_dialog.findChild(QWidget, "btnEdit") self.assertIsNotNone(btnEdit) QTest.mouseClick(btnEdit, Qt.LeftButton) new_conn = find_window('QgsNewHttpConnectionBase',) self.assertIsNotNone(new_conn) txtName = new_conn.findChild(QLineEdit, "txtName") self.assertIsNotNone(txtName) txtName.setText("test_connection") txtUrl = new_conn.findChild(QLineEdit, "txtUrl") self.assertIsNotNone(txtUrl) endpoint = self.basetestpath + '/fake_qgis_http_endpoint' expected_endpoint = endpoint if sys.platform == 'win32' and expected_endpoint[1] == ':': expected_endpoint = expected_endpoint[0] + expected_endpoint[2:] with open(sanitize(endpoint, '?SERVICE=WFS?REQUEST=GetCapabilities?ACCEPTVERSIONS=2.0.0,1.1.0,1.0.0'), 'wb') as f: f.write(""" <wfs:WFS_Capabilities version="2.0.0" xmlns="http://www.opengis.net/wfs/2.0" xmlns:wfs="http://www.opengis.net/wfs/2.0" xmlns:ows="http://www.opengis.net/ows/1.1" xmlns:gml="http://schemas.opengis.net/gml/3.2" xmlns:fes="http://www.opengis.net/fes/2.0"> <FeatureTypeList> <FeatureType> <Name>my:typename</Name> <Title>Title</Title> <Abstract>Abstract</Abstract> <DefaultCRS>urn:ogc:def:crs:EPSG::4326</DefaultCRS> <ows:WGS84BoundingBox> <ows:LowerCorner>-71.123 66.33</ows:LowerCorner> <ows:UpperCorner>-65.32 78.3</ows:UpperCorner> </ows:WGS84BoundingBox> </FeatureType> </FeatureTypeList> <fes:Filter_Capabilities> <fes:Spatial_Capabilities> <fes:GeometryOperands> <fes:GeometryOperand name="gml:Envelope"/> <fes:GeometryOperand name="gml:Point"/> <fes:GeometryOperand name="gml:MultiPoint"/> <fes:GeometryOperand name="gml:LineString"/> <fes:GeometryOperand name="gml:MultiLineString"/> <fes:GeometryOperand name="gml:Polygon"/> <fes:GeometryOperand name="gml:MultiPolygon"/> <fes:GeometryOperand name="gml:MultiGeometry"/> </fes:GeometryOperands> <fes:SpatialOperators> <fes:SpatialOperator name="Disjoint"/> <fes:SpatialOperator name="Equals"/> <fes:SpatialOperator name="DWithin"/> <fes:SpatialOperator name="Beyond"/> <fes:SpatialOperator name="Intersects"/> <fes:SpatialOperator name="Touches"/> <fes:SpatialOperator name="Crosses"/> <fes:SpatialOperator name="Within"/> <fes:SpatialOperator name="Contains"/> <fes:SpatialOperator name="Overlaps"/> <fes:SpatialOperator name="BBOX"/> </fes:SpatialOperators> </fes:Spatial_Capabilities> <fes:Functions> <fes:Function name="abs"> <fes:Returns>xs:int</fes:Returns> <fes:Arguments> <fes:Argument name="param"> <fes:Type>xs:int</fes:Type> </fes:Argument> </fes:Arguments> </fes:Function> </fes:Functions> </fes:Filter_Capabilities> </wfs:WFS_Capabilities>""".encode('UTF-8')) txtUrl.setText("http://" + endpoint) new_conn.accept() # Wait for object to be destroyed new_conn = self.wait_object_destruction(new_conn) # Try to connect btnConnect = main_dialog.findChild(QWidget, "btnConnect") self.assertIsNotNone(btnConnect) QTest.mouseClick(btnConnect, Qt.LeftButton) buttonAdd = self.get_button_add(main_dialog) for i in range(10): QApplication.processEvents() if buttonAdd.isEnabled(): break self.assertTrue(buttonAdd.isEnabled()) # Add layer self.addWfsLayer_uri = None self.addWfsLayer_layer_name = None main_dialog.addWfsLayer.connect(self.slotAddWfsLayer) QTest.mouseClick(buttonAdd, Qt.LeftButton) self.assertEqual(self.addWfsLayer_uri, ' restrictToRequestBBOX=\'1\' srsname=\'EPSG:4326\' typename=\'my:typename\' url=\'' + "http://" + expected_endpoint + '\' version=\'auto\' table="" sql=') self.assertEqual(self.addWfsLayer_layer_name, 'my:typename') # Click on Build Query buttonBuildQuery = self.get_button_build_query(main_dialog) self.assertTrue(buttonBuildQuery.isEnabled()) QTest.mouseClick(buttonBuildQuery, Qt.LeftButton) error_box = find_window('WFSFeatureTypeErrorBox') self.assertIsNotNone(error_box) # Close error box error_box.accept() # Wait for object to be destroyed error_box = self.wait_object_destruction(error_box) # Click again but with valid DescribeFeatureType with open(sanitize(endpoint, '?SERVICE=WFS&REQUEST=DescribeFeatureType&VERSION=2.0.0&TYPENAME=my:typename'), 'wb') as f: f.write(""" <xsd:schema xmlns:my="http://my" xmlns:gml="http://www.opengis.net/gml/3.2" xmlns:xsd="http://www.w3.org/2001/XMLSchema" elementFormDefault="qualified" targetNamespace="http://my"> <xsd:import namespace="http://www.opengis.net/gml/3.2"/> <xsd:complexType name="typenameType"> <xsd:complexContent> <xsd:extension base="gml:AbstractFeatureType"> <xsd:sequence> <xsd:element maxOccurs="1" minOccurs="0" name="intfield" nillable="true" type="xsd:int"/> <xsd:element maxOccurs="1" minOccurs="0" name="geometryProperty" nillable="true" type="gml:PolygonPropertyType"/> </xsd:sequence> </xsd:extension> </xsd:complexContent> </xsd:complexType> <xsd:element name="typename" substitutionGroup="gml:_Feature" type="my:typenameType"/> </xsd:schema> """.encode('UTF-8')) QTest.mouseClick(buttonBuildQuery, Qt.LeftButton) # Check that the combos are properly initialized dialog = find_window('QgsSQLComposerDialogBase') self.assertIsNotNone(dialog) mTablesCombo = dialog.findChild(QComboBox, "mTablesCombo") self.assertIsNotNone(mTablesCombo) self.assertEqual(mTablesCombo.itemText(1), 'typename (Title)') mColumnsCombo = dialog.findChild(QComboBox, "mColumnsCombo") self.assertIsNotNone(mColumnsCombo) self.assertEqual(mColumnsCombo.itemText(1), 'intfield (int)') self.assertEqual(mColumnsCombo.itemText(mColumnsCombo.count() - 2), 'geometryProperty (geometry)') self.assertEqual(mColumnsCombo.itemText(mColumnsCombo.count() - 1), '*') mFunctionsCombo = dialog.findChild(QComboBox, "mFunctionsCombo") self.assertIsNotNone(mFunctionsCombo) self.assertEqual(mFunctionsCombo.itemText(1), 'abs(param: int): int') mSpatialPredicatesCombo = dialog.findChild(QComboBox, "mSpatialPredicatesCombo") self.assertIsNotNone(mSpatialPredicatesCombo) self.assertEqual(mSpatialPredicatesCombo.itemText(1), 'ST_Disjoint(geometry, geometry): boolean') mWhereEditor = dialog.findChild(QTextEdit, "mWhereEditor") self.assertIsNotNone(mWhereEditor) mWhereEditor.setText('1 = 1') dialog.accept() # Wait for object to be destroyed dialog = self.wait_object_destruction(dialog) # Add layer buttonAdd = self.get_button_add(main_dialog) self.assertTrue(buttonAdd.isEnabled()) self.addWfsLayer_uri = None self.addWfsLayer_layer_name = None main_dialog.addWfsLayer.connect(self.slotAddWfsLayer) QTest.mouseClick(buttonAdd, Qt.LeftButton) self.assertEqual(self.addWfsLayer_uri, ' restrictToRequestBBOX=\'1\' srsname=\'EPSG:4326\' typename=\'my:typename\' url=\'' + "http://" + expected_endpoint + '\' version=\'auto\' table="" sql=SELECT * FROM typename WHERE 1 = 1') self.assertEqual(self.addWfsLayer_layer_name, 'my:typename')
15,678
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11
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"_fields": {"id": "cpu", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TYPE_SPPZ", "value": {"_type": "Name", "_fields": {"id": "AwlInsn", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "rawInsn", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__init__", "value": {"_type": "Name", "_fields": {"id": "AwlInsn", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertOpCount", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "If", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "Jump instruction expects label operand"}}], "func": {"_type": "Name", "_fields": {"id": "AwlSimError", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "type", "value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 0}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "ops", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "LBL_REF", "value": {"_type": "Name", "_fields": {"id": "AwlOperator", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": []}}], "name": "__init__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "statusWord", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "cpu", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "labelIndex", "value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 0}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "ops", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "jumpToLabel", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "cpu", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "A0", "value": {"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}, "orelse": []}}], "name": "run", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "AwlInsn_SPPZ", "bases": [{"_type": "Name", "_fields": {"id": "AwlInsn", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class AwlInsn_SPPZ(AwlInsn): #+cdef __slots__ = () def __init__(self, cpu, rawInsn): AwlInsn.__init__(self, cpu, AwlInsn.TYPE_SPPZ, rawInsn) self.assertOpCount(1) if self.ops[0].type != AwlOperator.LBL_REF: raise AwlSimError("Jump instruction expects label operand") def run(self): #@cy cdef S7StatusWord s s = self.cpu.statusWord if s.A0 == 0: self.cpu.jumpToLabel(self.ops[0].labelIndex)
15,679
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class Migration(SchemaMigration): def forwards(self, orm): # Adding field 'Action.deleted' db.add_column(u'Action', 'deleted', self.gf('django.db.models.fields.BooleanField')(default=False), keep_default=False) def backwards(self, orm): # Deleting field 'Action.deleted' db.delete_column(u'Action', 'deleted') models = { u'discount.action': { 'Meta': {'ordering': "[u'-created_at']", 'object_name': 'Action', 'db_table': "u'Action'"}, 'auto_del': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'auto_del_action_price': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'auto_end': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'auto_start': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'datetime_end': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2014, 8, 24, 0, 0)'}), 'datetime_start': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2014, 8, 17, 0, 0)'}), 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'default': "u'\\u0410\\u043a\\u0446\\u0438\\u044f \\u043e\\u0442 2014-08-17 15:03:28.195409'", 'max_length': '256'}), 'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) } } complete_apps = ['discount']
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9
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def _run_scheduler_job(args): job = SchedulerJob( subdir=process_subdir(args.subdir), num_runs=args.num_runs, do_pickle=args.do_pickle, ) enable_health_check = conf.getboolean("scheduler", "ENABLE_HEALTH_CHECK") with _serve_logs(args.skip_serve_logs), _serve_health_check(enable_health_check): job.run()
15,681
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7
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def parse_arguments(): parser = argparse.ArgumentParser(description='Flatten and roll up Jstacks suitable for making flamegraphs') parser.add_argument('file', help='File to process') parser.add_argument( '--include_threadname', dest='include_threadname', action='store_true', help='Whether or nor to include threadnames in flat stacks' ) parser.add_argument( '--thread_states', type=tuple, help='Thread states to be included in flat stacks' ) parser.add_argument('--print_type', help='Type of stacks to print', choices=('rolled', 'flat', 'full') ) parser.set_defaults(include_thread_name=False, thread_states=('RUNNABLE',), print_type='rolled') return parser.parse_args()
15,682
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8
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def __init__(self, filename, include_threadname=False, thread_states=('RUNNABLE',)): self._include_threadname = include_threadname self._thread_states = thread_states self._stacks = [] self._flat_stacks = [] self._rolled_stacks = [] with open(filename) as stack_file: self.original_file = stack_file.read()
15,683
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12
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{"_type": "Name", "_fields": {"id": "line", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "escape", "value": {"_type": "Name", "_fields": {"id": "cgi", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "out", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "mode", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "warning"}}]}}, "orelse": []}}], "iter": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "log", "value": {"_type": "Name", "_fields": {"id": "c", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "mode", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "line", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "_", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "getvalue", "value": {"_type": "Name", "_fields": {"id": "out", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "dest", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "pisaDocument", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "raise_exception", "value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}]}}}}], "name": "pisaErrorDocument", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def pisaErrorDocument(dest, c): out = pisaTempFile(capacity=c.capacity) out.write("<p style='background-color:red;'><strong>%d error(s) occured:</strong><p>" % c.err) for mode, line, msg, _ in c.log: if mode=="error": out.write("<pre>%s in line %d: %s</pre>" % (mode, line, cgi.escape(msg))) out.write("<p><strong>%d warning(s) occured:</strong><p>" % c.warn) for mode, line, msg, _ in c.log: if mode=="warning": out.write("<p>%s in line %d: %s</p>" % (mode, line, cgi.escape(msg))) return pisaDocument(out.getvalue(), dest, raise_exception=False)
15,684
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"Load", "_fields": {}}, "attr": "story", "value": {"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "indexing_story", "value": {"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "story", "value": {"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "test": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "indexing_story", "value": {"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "link", "value": {"_type": "Name", "_fields": {"id": "frag", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "NotIn", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "anchor", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "anchorName", "value": {"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": []}}], "iter": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "anchorFrag", "value": {"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "frag", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "anchor", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "pisaStory", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def pisaStory(src, path=None, link_callback=None, debug=0, default_css=None, xhtml=False, encoding=None, context=None, xml_output=None, **kw): # Prepare Context if not context: context = pisaContext(path, debug=debug) context.pathCallback = link_callback # Use a default set of CSS definitions to get an expected output if default_css is None: default_css = DEFAULT_CSS # Parse and fill the story pisaParser(src, context, default_css, xhtml, encoding, xml_output) # Avoid empty documents if not context.story: context.story = [Spacer(1,1)] if context.indexing_story: context.story.append(context.indexing_story) # Remove anchors if they do not exist (because of a bug in Reportlab) for frag, anchor in context.anchorFrag: if anchor not in context.anchorName: frag.link = None return context
15,685
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{"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "P"}}, "value": {"_type": "Name", "_fields": {"id": "data", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "P"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "has_key", "value": {"_type": "Name", "_fields": {"id": "data", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "V"}}, "value": {"_type": "Name", "_fields": {"id": "data", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "ver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "V"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "has_key", "value": {"_type": "Name", "_fields": {"id": "data", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "C"}}, "value": {"_type": "Name", "_fields": {"id": "data", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "int", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "way", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "C"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "has_key", "value": {"_type": "Name", "_fields": {"id": "data", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": []}}], "name": "__init__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def __init__(self, address=None, Body=None): self.address = address if not Body: self.rawBody = '{}' else: self.rawBody = Body self.cmdid = None self.timestamp = int(time.time()) self.params = dict() self.way = None self.ver = None try: data = json.loads(self.rawBody)#create a dict except: baseLogger.info(msg=("[Request]:RawData format is error data :",rawBody)) self.cmdid = constant.INVALID_CMDID if data.has_key('F'): self.cmdid = int(data['F']) if data.has_key('T'): self.timestamp = int(data['T']) if data.has_key('P'): self.params = data['P'] if data.has_key('V'): self.ver = data['V'] if data.has_key('C'): self.way = int(data['C'])
15,686
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def pisaDocument(src, dest=None, path=None, link_callback=None, debug=0, default_css=None, xhtml=False, encoding=None, xml_output=None, raise_exception=True, capacity=100*1024, **kw): log.debug("pisaDocument options:\n src = %r\n dest = %r\n path = %r\n link_callback = %r\n xhtml = %r", src, dest, path, link_callback, xhtml) # Prepare simple context context = pisaContext(path, debug=debug, capacity=capacity) context.pathCallback = link_callback # Build story context = pisaStory(src, path, link_callback, debug, default_css, xhtml, encoding, context=context, xml_output=xml_output) # Buffer PDF into memory out = pisaTempFile(capacity=context.capacity) doc = PmlBaseDoc( out, pagesize=context.pageSize, author=context.meta["author"].strip(), subject=context.meta["subject"].strip(), keywords=[x.strip() for x in context.meta["keywords"].strip().split(",") if x], title=context.meta["title"].strip(), showBoundary=0, allowSplitting=1) # Prepare templates and their frames if "body" in context.templateList: body = context.templateList["body"] del context.templateList["body"] else: x, y, w, h = getBox("1cm 1cm -1cm -1cm", context.pageSize) body = PmlPageTemplate( id="body", frames=[ Frame(x, y, w, h, id="body", leftPadding=0, rightPadding=0, bottomPadding=0, topPadding=0)], pagesize = context.pageSize) doc.addPageTemplates([body] + context.templateList.values()) # Use multibuild e.g. if a TOC has to be created if context.multiBuild: doc.multiBuild(context.story) else: doc.build(context.story) # Add watermarks if pyPdf: for bgouter in context.pisaBackgroundList: # If we have at least one background, then lets do it if bgouter: istream = out output = pyPdf.PdfFileWriter() input1 = pyPdf.PdfFileReader(istream) ctr = 0 # TODO: Why do we loop over the same list again? # see bgouter at line 137 for bg in context.pisaBackgroundList: page = input1.getPage(ctr) if (bg and not bg.notFound() and (bg.mimetype=="application/pdf")): bginput = pyPdf.PdfFileReader(bg.getFile()) pagebg = bginput.getPage(0) pagebg.mergePage(page) page = pagebg else: log.warn(context.warning( "Background PDF %s doesn't exist.", bg)) output.addPage(page) ctr += 1 out = pisaTempFile(capacity=context.capacity) output.write(out) # data = sout.getvalue() # Found a background? So leave loop after first occurence break else: log.warn(context.warning("pyPDF not installed!")) # Get the resulting PDF and write it to the file object # passed from the caller if dest is None: # No output file was passed - Let's use a pisaTempFile dest = pisaTempFile(capacity=context.capacity) context.dest = dest data = out.getvalue() # TODO: That load all the tempfile in RAM - Why bother with a swapping tempfile then? context.dest.write(data) # TODO: context.dest is a tempfile as well... return context
15,687
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8
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "node_in_file", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "In", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "node_in_file", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "[deleted]"}}]}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "In", "_fields": {}}], "left": {"_type": "Constant", "_fields": {"kind": null, "value": "bot"}}, "comparators": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lower", "value": {"_type": "Name", "_fields": {"id": "node_in_file", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}], "name": "is_valid_author", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def is_valid_author(node_in_file): if node_in_file in ['[deleted]']: return False if 'bot' in node_in_file.lower(): return False return True
15,688
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9
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class OptimizedPickleTests(AbstractPickleTests, AbstractPickleModuleTests): def dumps(self, arg, proto=0, fast=0): return pickletools.optimize(pickle.dumps(arg, proto)) def loads(self, buf): return pickle.loads(buf) module = pickle error = KeyError
15,689
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8
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def __init__( self, rate, device ): gr.hier_block2.__init__(self, "output", gr.io_signature(1,1,gr.sizeof_float), gr.io_signature(0,0,0)) self.vol = blocks.multiply_const_ff( 0.1 ) self.out = audio.sink( int(rate), device ) self.connect( self, self.vol, self.out )
15,690
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9
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class output( gr.hier_block2 ): def __init__( self, rate, device ): gr.hier_block2.__init__(self, "output", gr.io_signature(1,1,gr.sizeof_float), gr.io_signature(0,0,0)) self.vol = blocks.multiply_const_ff( 0.1 ) self.out = audio.sink( int(rate), device ) self.connect( self, self.vol, self.out ) def set( self, val ): self.vol.set_k( val )
15,691
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9
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def setUp(self): super(ScheduleSendEmailTestMixin, self).setUp() site = SiteFactory.create() self.site_config = SiteConfigurationFactory.create(site=site) ScheduleConfigFactory.create(site=self.site_config.site) DynamicUpgradeDeadlineConfiguration.objects.create(enabled=True) CommerceConfiguration.objects.create(checkout_on_ecommerce_service=True) self._courses_with_verified_modes = set()
15,692
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{"_type": "Load", "_fields": {}}, "attr": "timedelta", "value": {"_type": "Name", "_fields": {"id": "datetime", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "days", "value": {"_type": "Constant", "_fields": {"kind": null, "value": 7}}}}]}}}}, "targets": [{"_type": "Name", "_fields": {"id": "upgrade_deadline", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}]}}, {"_type": "Return", "_fields": {"value": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "current_day", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "offset", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "target_day", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "upgrade_deadline", "ctx": {"_type": "Load", "_fields": {}}}}]}}}}], "name": "_get_dates", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _get_dates(self, offset=None): current_day = _get_datetime_beginning_of_day(datetime.datetime.now(pytz.UTC)) offset = offset or self.expected_offsets[0] target_day = current_day + datetime.timedelta(days=offset) if self.resolver.schedule_date_field == 'upgrade_deadline': upgrade_deadline = target_day else: upgrade_deadline = current_day + datetime.timedelta(days=7) return current_day, offset, target_day, upgrade_deadline
15,693
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11
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def test_handle(self): with patch.object(self.command, 'async_send_task') as mock_send: test_day = datetime.datetime(2017, 8, 1, tzinfo=pytz.UTC) self.command().handle(date='2017-08-01', site_domain_name=self.site_config.site.domain) for offset in self.expected_offsets: mock_send.enqueue.assert_any_call( self.site_config.site, test_day, offset, None )
15,694
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def _schedule_factory(self, offset=None, **factory_kwargs): _, _, target_day, upgrade_deadline = self._get_dates(offset=offset) factory_kwargs.setdefault('start', target_day) factory_kwargs.setdefault('upgrade_deadline', upgrade_deadline) factory_kwargs.setdefault('enrollment__course__self_paced', True) if hasattr(self, 'experience_type'): factory_kwargs.setdefault('experience__experience_type', self.experience_type) schedule = ScheduleFactory(**factory_kwargs) course_id = schedule.enrollment.course_id if course_id not in self._courses_with_verified_modes: CourseModeFactory( course_id=course_id, mode_slug=CourseMode.VERIFIED, expiration_datetime=datetime.datetime.now(pytz.UTC) + datetime.timedelta(days=30), ) self._courses_with_verified_modes.add(course_id) return schedule
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10
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def test_resolver_send(self, mock_ace): current_day, offset, target_day, _ = self._get_dates() with patch.object(self.task, 'apply_async') as mock_apply_async: self.task.enqueue(self.site_config.site, current_day, offset) mock_apply_async.assert_any_call( (self.site_config.site.id, serialize(target_day), offset, 0, None), retry=False, ) mock_apply_async.assert_any_call( (self.site_config.site.id, serialize(target_day), offset, self.task.num_bins - 1, None), retry=False, ) self.assertFalse(mock_ace.send.called)
15,696
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{"_type": "Load", "_fields": {}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "b", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "call_count", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "apply_async", "value": {"_type": "Name", "_fields": {"id": "mock_schedule_send", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "schedule_count", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "called", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "send", "value": {"_type": "Name", "_fields": {"id": "mock_ace", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertFalse", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "task", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "async_send_task"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "object", "value": {"_type": "Name", "_fields": {"id": "patch", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": {"_type": "Name", "_fields": {"id": "mock_schedule_send", "ctx": {"_type": "Store", "_fields": {}}}}}}], "type_comment": null}}], "name": "test_schedule_bin", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_schedule_bin(self, schedule_count, mock_metric, mock_ace): with patch.object(self.task, 'async_send_task') as mock_schedule_send: current_day, offset, target_day, upgrade_deadline = self._get_dates() schedules = [ self._schedule_factory() for _ in range(schedule_count) ] bins_in_use = frozenset((self._calculate_bin_for_user(s.enrollment.user)) for s in schedules) is_first_match = True target_day_str = serialize(target_day) for b in range(self.task.num_bins): LOG.debug('Checking bin %d', b) expected_queries = NUM_QUERIES_SITE_SCHEDULES if b in bins_in_use: if is_first_match: expected_queries = ( # Since this is the first match, we need to cache all of the config models, so we run a # query for each of those... NUM_QUERIES_FIRST_MATCH + COURSE_MODES_QUERY # to cache the course modes for this course ) is_first_match = False with self.assertNumQueries(expected_queries, table_blacklist=WAFFLE_TABLES): self.task().apply(kwargs=dict( site_id=self.site_config.site.id, target_day_str=target_day_str, day_offset=offset, bin_num=b, )) num_schedules = mock_metric.call_args[0][1] if b in bins_in_use: self.assertGreater(num_schedules, 0) else: self.assertEqual(num_schedules, 0) self.assertEqual(mock_schedule_send.apply_async.call_count, schedule_count) self.assertFalse(mock_ace.send.called)
15,697
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9
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"_fields": {}}, "attr": "from_string", "value": {"_type": "Name", "_fields": {"id": "mock_message", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "Mock", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "mock_msg", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "id", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "site", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "site_config", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, {"_type": "Name", "_fields": {"id": "mock_msg", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "deliver_task", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "called", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "send", "value": {"_type": "Name", "_fields": {"id": "mock_ace", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertTrue", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Name", "_fields": {"id": "is_enabled", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "called", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "send", "value": {"_type": "Name", "_fields": {"id": "mock_ace", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertFalse", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}], "name": "test_deliver_config", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_deliver_config(self, is_enabled, mock_message, mock_ace): user = UserFactory.create() schedule_config_kwargs = { 'site': self.site_config.site, self.deliver_config: is_enabled, } self._update_schedule_config(schedule_config_kwargs) mock_message.from_string.return_value.recipient.username = user.username mock_msg = Mock() self.deliver_task(self.site_config.site.id, mock_msg) if is_enabled: self.assertTrue(mock_ace.send.called) else: self.assertFalse(mock_ace.send.called)
15,698
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14
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def test_no_course_overview(self): current_day, offset, target_day, upgrade_deadline = self._get_dates() # Don't use CourseEnrollmentFactory since it creates a course overview enrollment = CourseEnrollment.objects.create( course_id=CourseKey.from_string('edX/toy/Not_2012_Fall'), user=UserFactory.create(), ) self._schedule_factory(enrollment=enrollment) with patch.object(self.task, 'async_send_task') as mock_schedule_send: for bin_num in range(self.task().num_bins): self.task().apply(kwargs=dict( site_id=self.site_config.site.id, target_day_str=serialize(target_day), day_offset=offset, bin_num=bin_num, )) # There is no database constraint that enforces that enrollment.course_id points # to a valid CourseOverview object. However, in that case, schedules isn't going # to attempt to address it, and will instead simply skip those users. # This happens 'transparently' because django generates an inner-join between # enrollment and course_overview, and thus will skip any rows where course_overview # is null. self.assertEqual(mock_schedule_send.apply_async.call_count, 0)
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9
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def test_enqueue_config(self, is_enabled): schedule_config_kwargs = { 'site': self.site_config.site, self.enqueue_config: is_enabled, } self._update_schedule_config(schedule_config_kwargs) current_datetime = datetime.datetime(2017, 8, 1, tzinfo=pytz.UTC) with patch.object(self.task, 'apply_async') as mock_apply_async: self.task.enqueue(self.site_config.site, current_datetime, 3) if is_enabled: self.assertTrue(mock_apply_async.called) else: self.assertFalse(mock_apply_async.called)
15,700
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12
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def test_site_config(self, this_org_list, other_org_list, expected_message_count, mock_ace): filtered_org = 'filtered_org' unfiltered_org = 'unfiltered_org' this_config = SiteConfigurationFactory.create(values={'course_org_filter': this_org_list}) other_config = SiteConfigurationFactory.create(values={'course_org_filter': other_org_list}) for config in (this_config, other_config): ScheduleConfigFactory.create(site=config.site) user1 = UserFactory.create(id=self.task.num_bins) user2 = UserFactory.create(id=self.task.num_bins * 2) current_day, offset, target_day, upgrade_deadline = self._get_dates() self._schedule_factory( enrollment__course__org=filtered_org, enrollment__user=user1, ) self._schedule_factory( enrollment__course__org=unfiltered_org, enrollment__user=user1, ) self._schedule_factory( enrollment__course__org=unfiltered_org, enrollment__user=user2, ) with patch.object(self.task, 'async_send_task') as mock_schedule_send: self.task().apply(kwargs=dict( site_id=this_config.site.id, target_day_str=serialize(target_day), day_offset=offset, bin_num=0 )) self.assertEqual(mock_schedule_send.apply_async.call_count, expected_message_count) self.assertFalse(mock_ace.send.called)