whenxuan: create the notebook and upload the data for memd

Author: wwhenxuan

examples/image/bmemd.ipynb

@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "37c1435e",
    "metadata": {},
    "outputs": [
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "3bbca5cb",
    "metadata": {},
    "outputs": [
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "a6387137",
    "metadata": {},
    "outputs": [
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "fae64ede",
    "metadata": {},
    "outputs": [
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "17e70079",
    "metadata": {},
    "outputs": [
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "bf82caba",
    "metadata": {},
    "outputs": [
@@ -469,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "0f906ca7",
    "metadata": {},
    "outputs": [

examples/memd/memd.ipynb

@@ -39,6 +39,10 @@
     "load_single_nsignal",
     "load_map2",
     "load_wind_demo",
+    "load_memd_syn_12channel",
+    "load_memd_syn_16channel",
+    "load_memd_syn_hex",
+    "load_memd_taichi_hex",
 ]
 
 # A series of functions for generating 1D NumPy signals
@@ -117,4 +121,8 @@
     load_single_nsignal,
     load_map2,
     load_wind_demo,
+    load_memd_syn_12channel,
+    load_memd_syn_16channel,
+    load_memd_syn_hex,
+    load_memd_taichi_hex,
 )

pysdkit/data/__init__.py

@@ -140,6 +140,56 @@ def test_fmd() -> Tuple[np.ndarray, np.ndarray, float]:
     return t, x, fs
 
 
+def _load_memd_multichannel(name: str) -> Dict[str, np.ndarray]:
+    """Load a packaged MEMD demo array stored as ``(n_channels, n_samples)``."""
+    signal = np.asarray(np.load(data_file(name)), dtype=np.float64)
+    if signal.ndim != 2:
+        raise ValueError("{} must be a 2-D array".format(name))
+    t = np.arange(signal.shape[1], dtype=float)
+    return {"signal": signal, "t": t}
+
+
+def load_memd_syn_12channel() -> Dict[str, np.ndarray]:
+    """
+    Load the packaged MATLAB ``syn_12channel_inp.mat`` demo (``s12``).
+
+    Twelve-channel synthetic mixture of five tones plus noise on some
+    channels, length 1001, from the MEMD toolbox of Rehman & Mandic.
+    """
+    return _load_memd_multichannel("memd_syn_12channel.npy")
+
+
+def load_memd_syn_16channel() -> Dict[str, np.ndarray]:
+    """
+    Load the packaged MATLAB ``syn_16channel_inp.mat`` demo (``s16``).
+
+    Sixteen-channel synthetic mixture of six tones plus noise on some
+    channels, length 1001, from the MEMD toolbox of Rehman & Mandic.
+    """
+    return _load_memd_multichannel("memd_syn_16channel.npy")
+
+
+def load_memd_syn_hex() -> Dict[str, np.ndarray]:
+    """
+    Load the packaged MATLAB ``syn_hex_inp.mat`` demo (``s6``).
+
+    Hexavariate synthetic series used in Rehman & Mandic, Proc. R. Soc. A
+    466:1291–1302 (2010), Figure 3 (four sinusoids with a common scale).
+    """
+    return _load_memd_multichannel("memd_syn_hex.npy")
+
+
+def load_memd_taichi_hex() -> Dict[str, np.ndarray]:
+    """
+    Load the packaged MATLAB ``taichi_hex_inp.mat`` demo (``taichi_final``).
+
+    Real-world hexavariate Tai-chi recording: two 3-D inertial body-sensor
+    streams (left wrist and left ankle) stacked into six channels of length
+    800. Stored as ``(6, 800)``.
+    """
+    return _load_memd_multichannel("memd_taichi_hex.npy")
+
+
 def test_vmd() -> Tuple[np.ndarray, np.ndarray, float]:
     """
     Load the packaged VMD demo signal (``vmd_example.npy``).