| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # |
| |
| import numpy as np |
| from iotdb.utils.IoTDBConstants import TSDataType |
| from iotdb.utils.NumpyTablet import NumpyTablet |
| from iotdb.utils.Tablet import Tablet |
| |
| |
| def test_numpy_tablet_serialization(): |
| |
| measurements_ = ["s_01", "s_02", "s_03", "s_04", "s_05", "s_06"] |
| data_types_ = [ |
| TSDataType.BOOLEAN, |
| TSDataType.INT32, |
| TSDataType.INT64, |
| TSDataType.FLOAT, |
| TSDataType.DOUBLE, |
| TSDataType.TEXT, |
| ] |
| values_ = [ |
| [False, 10, 11, 1.1, 10011.1, "test01"], |
| [True, 100, 11111, 1.25, 101.0, "test02"], |
| [False, 100, 1, 188.1, 688.25, "test03"], |
| [True, 0, 0, 0, 6.25, "test04"], |
| ] |
| timestamps_ = [16, 17, 18, 19] |
| tablet_ = Tablet( |
| "root.sg_test_01.d_01", measurements_, data_types_, values_, timestamps_ |
| ) |
| np_values_ = [ |
| np.array([False, True, False, True], np.dtype(">?")), |
| np.array([10, 100, 100, 0], np.dtype(">i4")), |
| np.array([11, 11111, 1, 0], np.dtype(">i8")), |
| np.array([1.1, 1.25, 188.1, 0], np.dtype(">f4")), |
| np.array([10011.1, 101.0, 688.25, 6.25], np.dtype(">f8")), |
| np.array(["test01", "test02", "test03", "test04"]), |
| ] |
| np_timestamps_ = np.array([16, 17, 18, 19], np.dtype(">i8")) |
| np_tablet_ = NumpyTablet( |
| "root.sg_test_01.d_01", measurements_, data_types_, np_values_, np_timestamps_ |
| ) |
| assert tablet_.get_binary_timestamps() == np_tablet_.get_binary_timestamps() |
| assert tablet_.get_binary_values() == np_tablet_.get_binary_values() |
| |
| |
| def test_sort_numpy_tablet(): |
| |
| measurements_ = ["s_01", "s_02", "s_03", "s_04", "s_05", "s_06"] |
| data_types_ = [ |
| TSDataType.BOOLEAN, |
| TSDataType.INT32, |
| TSDataType.INT64, |
| TSDataType.FLOAT, |
| TSDataType.DOUBLE, |
| TSDataType.TEXT, |
| ] |
| values_ = [ |
| [True, 10000, 11111, 8.999, 776, "test05"], |
| [True, 1000, 1111, 0, 6.25, "test06"], |
| [False, 100, 111, 188.1, 688.25, "test07"], |
| [False, 10, 11, 1.25, 101.0, "test08"], |
| [False, 0, 1, 1.1, 10011.1, "test09"], |
| ] |
| timestamps_ = [5, 6, 7, 8, 9] |
| tablet_ = Tablet( |
| "root.sg_test_01.d_01", measurements_, data_types_, values_, timestamps_ |
| ) |
| np_values_unsorted = [ |
| np.array([False, False, False, True, True], np.dtype(">?")), |
| np.array([0, 10, 100, 1000, 10000], np.dtype(">i4")), |
| np.array([1, 11, 111, 1111, 11111], np.dtype(">i8")), |
| np.array([1.1, 1.25, 188.1, 0, 8.999], np.dtype(">f4")), |
| np.array([10011.1, 101.0, 688.25, 6.25, 776], np.dtype(">f8")), |
| np.array(["test09", "test08", "test07", "test06", "test05"]), |
| ] |
| np_timestamps_unsorted = np.array([9, 8, 7, 6, 5], np.dtype(">i8")) |
| np_tablet_ = NumpyTablet( |
| "root.sg_test_01.d_01", |
| measurements_, |
| data_types_, |
| np_values_unsorted, |
| np_timestamps_unsorted, |
| ) |
| assert tablet_.get_binary_timestamps() == np_tablet_.get_binary_timestamps() |
| assert tablet_.get_binary_values() == np_tablet_.get_binary_values() |
| |
| |
| def test_numpy_tablet_auto_correct_datatype(): |
| |
| measurements_ = ["s_01", "s_02", "s_03", "s_04", "s_05", "s_06"] |
| data_types_ = [ |
| TSDataType.BOOLEAN, |
| TSDataType.INT32, |
| TSDataType.INT64, |
| TSDataType.FLOAT, |
| TSDataType.DOUBLE, |
| TSDataType.TEXT, |
| ] |
| values_ = [ |
| [True, 10000, 11111, 8.999, 776, "test05"], |
| [True, 1000, 1111, 0, 6.25, "test06"], |
| [False, 100, 111, 188.1, 688.25, "test07"], |
| [False, 10, 11, 1.25, 101.0, "test08"], |
| [False, 0, 1, 1.1, 10011.1, "test09"], |
| ] |
| timestamps_ = [5, 6, 7, 8, 9] |
| tablet_ = Tablet( |
| "root.sg_test_01.d_01", measurements_, data_types_, values_, timestamps_ |
| ) |
| np_values_unsorted = [ |
| np.array([False, False, False, True, True]), |
| np.array([0, 10, 100, 1000, 10000]), |
| np.array([1, 11, 111, 1111, 11111]), |
| np.array([1.1, 1.25, 188.1, 0, 8.999]), |
| np.array([10011.1, 101.0, 688.25, 6.25, 776]), |
| np.array(["test09", "test08", "test07", "test06", "test05"]), |
| ] |
| np_timestamps_unsorted = np.array([9, 8, 7, 6, 5]) |
| # numpy.dtype of int and float should be little endian by default |
| assert np_timestamps_unsorted.dtype != np.dtype(">i8") |
| for i in range(1, 4): |
| assert np_values_unsorted[i].dtype != data_types_[i].np_dtype() |
| np_tablet_ = NumpyTablet( |
| "root.sg_test_01.d_01", |
| measurements_, |
| data_types_, |
| np_values_unsorted, |
| np_timestamps_unsorted, |
| ) |
| assert tablet_.get_binary_timestamps() == np_tablet_.get_binary_timestamps() |
| assert tablet_.get_binary_values() == np_tablet_.get_binary_values() |