Feature/time series dataframe

tests/test_timeseries_dataframe.py

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+# Copyright (c) 2024, Zong-han, Xie <[email protected]>
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# - Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+# - Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# - Neither the name of the copyright holder nor the names of its contributors
+#   may be used to endorse or promote products derived from this software
+#   without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+import os
+import contextlib
+from io import StringIO
+import unittest
+
+import numpy as np
+
+from modmesh import SimpleArrayUint64, SimpleArrayFloat64
+
+
+class TimeSeriesDataFrame(object):
+
+    def __init__(self):
+        self._init_members()
+
+    def _init_members(self):
+        self._columns = list()
+        self._index_data = None
+        self._index_name = None
+        self._data = list()
+
+    def read_from_text_file(
+        self,
+        fname,
+        delimiter=',',
+        timestamp_in_file=True,
+        timestamp_column=None
+    ):
+        """
+        Generate dataframe from a text file.
+
+        :param fname: path to the text file.
+        :type fname: str | Iterable[str] | io.StringIO
+        :param delimiter: delimiter.
+        :type delimiter: str
+        :param timestamp_in_file: If the text file containing index column,
+                   data in this column expected to be integer.
+        :type timestamp_in_file: bool
+        :prarm timestamp_column: Column which stores timestamp data.
+        :type timestamp_column: str
+        :return: None
+        """
+
+        if isinstance(fname, str):
+            if not os.path.exists(fname):
+                raise Exception("Text file '{}' does not exist".format(fname))
+            fid = open(fname, 'rt')
+            fid_ctx = contextlib.closing(fid)
+        else:
+            fid = fname
+            fid_ctx = contextlib.nullcontext(fid)
+
+        with fid_ctx:
+            fhd = iter(fid)
+
+            idx_col_num = 0 if timestamp_in_file else None
+
+            table_header = [
+                x.strip() for x in next(fhd).strip().split(delimiter)
+            ]
+            nd_arr = np.genfromtxt(fhd, delimiter=delimiter)
+
+            self._init_members()
+
+            if timestamp_in_file:
+                if timestamp_column in table_header:
+                    idx_col_num = table_header.index(timestamp_column)
+                self._index_data = SimpleArrayUint64(
+                    array=nd_arr[:, idx_col_num].astype(np.uint64)
+                )
+                self._index_name = table_header[idx_col_num]
+            else:
+                self._index_data = SimpleArrayUint64(
+                    array=np.arange(nd_arr.shape[0]).astype(np.uint64)
+                )
+                self._index_name = "Index"
+
+            self._columns = table_header
+            if idx_col_num is not None:
+                self._columns.pop(idx_col_num)
+
+            for i in range(nd_arr.shape[1]):
+                if i != idx_col_num:
+                    self._data.append(
+                        SimpleArrayFloat64(array=nd_arr[:, i].copy())
+                    )
+
+    def __getitem__(self, name):
+        if name not in self._columns:
+            raise Exception("Column '{}' does not exist".format(name))
+        return self._data[self._columns.index(name)].ndarray
+
+    @property
+    def columns(self):
+        return self._columns
+
+    @property
+    def shape(self):
+        return (self._index_data.ndarray.shape[0], len(self._data))
+
+    @property
+    def index(self):
+        return self._index_data.ndarray
+
+
+class TimeSeriesDataFrameTC(unittest.TestCase):
+
+    col_sol = ['DELTA_VEL[1]', 'DELTA_VEL[2]', 'DELTA_VEL[3]']
+    col_sol2 = ['DELTA_VEL[1]', 'DELTA_VEL[2]', 'EPOCH', 'DELTA_VEL[3]']
+
+    dlc_data = """EPOCH ,DELTA_VEL[1] ,DELTA_VEL[2] ,DELTA_VEL[3]
+1.6025960102293e+18,-0.18792724609375,-0.00048828125,-0.0478515625
+1.60259601024931e+18,-0.1903076171875,-0.0009765625,-0.0489501953125
+1.60259601026931e+18,-0.18743896484375,0.0006103515625,-0.0498046875
+1.60259601028932e+18,-0.18927001953125,-0.0009765625,-0.04840087890625
+1.60259601030931e+18,-0.188720703125,-0.00103759765625,-0.0504150390625
+1.60259601032931e+18,-0.18951416015625,-0.000732421875,-0.0489501953125
+1.60259601034931e+18,-0.18902587890625,-0.000732421875,-0.0489501953125
+1.6025960103693e+18,-0.1895751953125,-0.00128173828125,-0.04925537109375
+1.60259601038931e+18,-0.18841552734375,6.103515625e-05,-0.0489501953125
+1.60259601040931e+18,-0.1884765625,-0.00042724609375,-0.04840087890625
+"""
+    modified_dlc_data = """DELTA_VEL[1] ,DELTA_VEL[2] ,EPOCH ,DELTA_VEL[3]
+-0.18792724609375,-0.00048828125,1602596010229299968,-0.0478515625
+-0.1903076171875,-0.0009765625,1602596010249309952,-0.0489501953125
+-0.18743896484375,0.0006103515625,1602596010269309952,-0.0498046875
+-0.18927001953125,-0.0009765625,1602596010289319936,-0.04840087890625
+-0.188720703125,-0.00103759765625,1602596010309309952,-0.0504150390625
+-0.18951416015625,-0.000732421875,1602596010329309952,-0.0489501953125
+-0.18902587890625,-0.000732421875,1602596010349309952,-0.0489501953125
+-0.1895751953125,-0.00128173828125,1602596010369299968,-0.04925537109375
+-0.18841552734375,6.103515625e-05,1602596010389309952,-0.0489501953125
+-0.1884765625,-0.00042724609375,1602596010409309952,-0.04840087890625
+"""
+
+    def test_read_from_text_file_basic(self):
+        tsdf = TimeSeriesDataFrame()
+
+        tsdf.read_from_text_file(StringIO(self.dlc_data))
+        self.assertEqual(tsdf._columns, self.col_sol)
+        self.assertEqual(len(tsdf._columns), 3)
+        for i in range(len(tsdf._columns)):
+            self.assertEqual(tsdf._data[i].ndarray.shape[0], 10)
+        self.assertEqual(tsdf._index_name, 'EPOCH')
+
+        tsdf.read_from_text_file(
+            StringIO(self.modified_dlc_data),
+            delimiter=',',
+            timestamp_column='EPOCH'
+        )
+
+        self.assertEqual(tsdf._columns, self.col_sol)
+        self.assertEqual(len(tsdf._columns), 3)
+        for i in range(len(tsdf._columns)):
+            self.assertEqual(tsdf._data[i].ndarray.shape[0], 10)
+        self.assertEqual(tsdf._index_name, 'EPOCH')
+
+        tsdf.read_from_text_file(
+            StringIO(self.modified_dlc_data),
+            delimiter=',',
+            timestamp_in_file=False
+        )
+        self.assertEqual(tsdf._columns, self.col_sol2)
+        self.assertEqual(len(tsdf._columns), 4)
+        for i in range(len(tsdf._columns)):
+            self.assertEqual(tsdf._data[i].ndarray.shape[0], 10)
+        self.assertEqual(tsdf._index_name, 'Index')
+
+    def test_dataframe_attribute_columns(self):
+        tsdf = TimeSeriesDataFrame()
+        tsdf.read_from_text_file(StringIO(self.dlc_data))
+        self.assertEqual(tsdf.columns, self.col_sol)
+
+    def test_dataframe_attribute_shape(self):
+        tsdf = TimeSeriesDataFrame()
+        tsdf.read_from_text_file(StringIO(self.dlc_data))
+        self.assertEqual(tsdf.shape, (10, 3))
+
+    def test_dataframe_attribute_index(self):
+        tsdf = TimeSeriesDataFrame()
+        tsdf.read_from_text_file(StringIO(self.dlc_data))
+
+        nd_arr = np.genfromtxt(StringIO(self.dlc_data), delimiter=',')[1:]
+
+        self.assertEqual(
+            list(tsdf.index), list(nd_arr[:, 0].astype(np.uint64))
+        )
+
+    def test_dataframe_get_column(self):
+        tsdf = TimeSeriesDataFrame()
+        tsdf.read_from_text_file(StringIO(self.dlc_data))
+
+        col_data = tsdf['DELTA_VEL[1]']
+
+        nd_arr = np.genfromtxt(StringIO(self.dlc_data), delimiter=',')[1:]
+
+        self.assertEqual(list(col_data), list(nd_arr[:, 1]))