From f051bffdc1a81988b131d500e9ffbe7a8603854e Mon Sep 17 00:00:00 2001
From: Futuer <57895730+futuer-szd@users.noreply.github.com>
Date: Fri, 14 Jun 2024 10:42:52 +0100
Subject: [PATCH] [DOC,MNT,BUG] Documentation and testing for downsample.py
 (#45)

* 1.Fixes a bug in downsample.py when
n_timepoints / new_ratio is an integer
2.complete a testing for downsample.py
in transformations/tests/test_downsample.py
3.write documentation for transformations/downsample.py

* 1.Fixes a bug in downsample.py
when n_timepoints / new_ratio is an integer
2.complete a testing for downsample.py
in transformations/tests/test_downsample.py
3.write documentation for transformations/downsample.py

* modify the variable names
both in the code and comments

---------

Co-authored-by: futuer <barryszd@outlook.com>
---
 aeon_neuro/transformations/downsample.py      | 29 ++++++++++--
 aeon_neuro/transformations/tests/__init__.py  |  1 +
 .../transformations/tests/test_downsample.py  | 46 +++++++++++++++++++
 3 files changed, 71 insertions(+), 5 deletions(-)
 create mode 100644 aeon_neuro/transformations/tests/__init__.py
 create mode 100644 aeon_neuro/transformations/tests/test_downsample.py

diff --git a/aeon_neuro/transformations/downsample.py b/aeon_neuro/transformations/downsample.py
index 39d9d75..f31aaff 100644
--- a/aeon_neuro/transformations/downsample.py
+++ b/aeon_neuro/transformations/downsample.py
@@ -1,13 +1,32 @@
+"""Downsample series by frequency."""
+
 import numpy as np
 
 
 def downsample_series(X, sfreq, target_sample_rate):
+    """Downsample a time series.
+
+    Parameters
+    ----------
+    X : a numpy array of shape = [n_cases, n_channels, n_timepoints]
+        The input time series data to be downsampled.
+    sfreq : int
+        The original sampling frequency of the time series data.
+    target_sample_rate : int
+        The target sampling frequency after downsampling.
+
+    Returns
+    -------
+    downsampled : a numpy array of
+        shape = [n_cases, n_channels, updated_timepoints]
+        The downsampled time series data.
+    """
     new_ratio = int(sfreq / target_sample_rate)
-    n_instances, n_dimensions, n_timepoints = np.shape(X)
-    updated_timepoints = int(n_timepoints / new_ratio)
-    downsampled_data = np.zeros((n_instances, n_dimensions, updated_timepoints + 1))
-    for i in range(n_instances):
-        for j in range(n_dimensions):
+    n_cases, n_channels, n_timepoints = np.shape(X)
+    updated_timepoints = int(np.ceil(n_timepoints / new_ratio))
+    downsampled_data = np.zeros((n_cases, n_channels, updated_timepoints))
+    for i in range(n_cases):
+        for j in range(n_channels):
             updated_index = 0
             for k in range(0, n_timepoints, new_ratio):
                 downsampled_data[i][j][updated_index] = X[i][j][k]
diff --git a/aeon_neuro/transformations/tests/__init__.py b/aeon_neuro/transformations/tests/__init__.py
new file mode 100644
index 0000000..664b70c
--- /dev/null
+++ b/aeon_neuro/transformations/tests/__init__.py
@@ -0,0 +1 @@
+"""Unit tests of transformations."""
diff --git a/aeon_neuro/transformations/tests/test_downsample.py b/aeon_neuro/transformations/tests/test_downsample.py
new file mode 100644
index 0000000..537bdbe
--- /dev/null
+++ b/aeon_neuro/transformations/tests/test_downsample.py
@@ -0,0 +1,46 @@
+"""Tests for downsample_series."""
+
+import numpy as np
+import pytest
+
+from aeon_neuro.transformations.downsample import downsample_series
+
+X = np.array(
+    [
+        [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]],
+        [
+            [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
+            [100, 90, 80, 70, 60, 50, 40, 30, 20, 10],
+        ],
+    ]
+)
+
+testdata = [
+    (
+        X,
+        10,
+        5,
+        np.array(
+            [
+                [[1, 3, 5, 7, 9], [10, 8, 6, 4, 2]],
+                [[10, 30, 50, 70, 90], [100, 80, 60, 40, 20]],
+            ]
+        ),
+    ),
+    (
+        X,
+        10,
+        3,
+        np.array(
+            [[[1, 4, 7, 10], [10, 7, 4, 1]], [[10, 40, 70, 100], [100, 70, 40, 10]]]
+        ),
+    ),
+    (X, 10, 2, np.array([[[1, 6], [10, 5]], [[10, 60], [100, 50]]])),
+]
+
+
+@pytest.mark.parametrize("X, sfreq, target_sample_rate, expected", testdata)
+def test_downsample_series(X, sfreq, target_sample_rate, expected):
+    """Test the downsampling of a time series."""
+    result = downsample_series(X, sfreq, target_sample_rate)
+    np.testing.assert_array_equal(result, expected)