Merge pull request #45 from wwhenxuan/master

Update PySDKit 0.4.23 for FMD

Author: wwhenxuan

example/data_generator.ipynb

@@ -291,7 +291,7 @@
    "cell_type": "code",
    "source": [
     "# Adjust the visualization method through the `view` variable\n",
-    "plot_IMFs(signal, IMFs, view='3d')"
+    "plot_IMFs(signal, IMFs, view=\"3d\")"
    ],
    "id": "f95df9777178ea16",
    "outputs": [
@@ -375,7 +375,7 @@
    "cell_type": "code",
    "source": [
     "# Adjust the visualization via the `view` variable\n",
-    "plot_IMFs(signal, IMFs, view='3d')"
+    "plot_IMFs(signal, IMFs, view=\"3d\")"
    ],
    "id": "8f17486b6e647d2",
    "outputs": [

example/demo.ipynb

@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:57.173409Z",
-     "start_time": "2025-08-13T03:30:55.759152Z"
+     "end_time": "2025-09-25T12:27:56.007889Z",
+     "start_time": "2025-09-25T12:27:54.104682Z"
     }
    },
    "source": [
@@ -21,20 +21,20 @@
   },
   {
    "cell_type": "code",
+   "id": "1af048fdecdd07b5",
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2025-09-25T12:27:56.892626Z",
+     "start_time": "2025-09-25T12:27:56.799160Z"
+    }
+   },
    "source": [
     "# create an example signal\n",
     "t, signal = test_emd()\n",
     "# print(signal.shape)\n",
     "fig = plot_signal(t, signal)"
    ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:57.352329Z",
-     "start_time": "2025-08-13T03:30:57.193032Z"
-    }
-   },
-   "id": "1af048fdecdd07b5",
    "outputs": [
     {
      "data": {
@@ -51,20 +51,20 @@
   },
   {
    "cell_type": "code",
+   "id": "65e9ea5b377df1a4",
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2025-09-25T12:28:03.022235Z",
+     "start_time": "2025-09-25T12:28:02.833098Z"
+    }
+   },
    "source": [
     "# create an instance for signal decomposition\n",
     "emd = EMD()\n",
     "IMFs = emd.fit_transform(signal, max_imfs=2)\n",
     "plot_IMFs(signal, IMFs)"
    ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:57.878889Z",
-     "start_time": "2025-08-13T03:30:57.554232Z"
-    }
-   },
-   "id": "65e9ea5b377df1a4",
    "outputs": [
     {
      "data": {
@@ -77,24 +77,24 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 3
+   "execution_count": 4
   },
   {
+   "cell_type": "code",
+   "id": "da12b15cebe128fb",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:58.355629Z",
-     "start_time": "2025-08-13T03:30:57.933150Z"
+     "end_time": "2025-09-25T12:28:03.722792Z",
+     "start_time": "2025-09-25T12:28:03.499695Z"
     }
    },
-   "cell_type": "code",
    "source": [
     "from pysdkit import EWT\n",
     "\n",
     "ewt = EWT(K=3)\n",
     "IMFs = ewt.fit_transform(signal)\n",
     "plot_IMFs(signal, IMFs)"
    ],
-   "id": "da12b15cebe128fb",
    "outputs": [
     {
      "data": {
@@ -107,10 +107,18 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "cell_type": "code",
+   "id": "36cd37054ec6ba22",
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2025-09-25T12:28:04.805946Z",
+     "start_time": "2025-09-25T12:28:04.239604Z"
+    }
+   },
    "source": [
     "from pysdkit import VMD\n",
     "\n",
@@ -124,14 +132,6 @@
     "\n",
     "vmd.plot_IMFs(save_figure=True, dpi=600)"
    ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:59.181836Z",
-     "start_time": "2025-08-13T03:30:58.370269Z"
-    }
-   },
-   "id": "36cd37054ec6ba22",
    "outputs": [
     {
      "name": "stdout",
@@ -151,24 +151,24 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 5
+   "execution_count": 6
   },
   {
    "cell_type": "code",
-   "source": [
-    "from pysdkit.plot import plot_IMFs_amplitude_spectra\n",
-    "\n",
-    "# frequency domain visualization\n",
-    "plot_IMFs_amplitude_spectra(IMFs, smooth=\"exp\")   # use exp smooth"
-   ],
+   "id": "d066e800d5dd681a",
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:59.532683Z",
-     "start_time": "2025-08-13T03:30:59.197549Z"
+     "end_time": "2025-09-25T12:28:05.776299Z",
+     "start_time": "2025-09-25T12:28:05.482323Z"
     }
    },
-   "id": "d066e800d5dd681a",
+   "source": [
+    "from pysdkit.plot import plot_IMFs_amplitude_spectra\n",
+    "\n",
+    "# frequency domain visualization\n",
+    "plot_IMFs_amplitude_spectra(IMFs, smooth=\"exp\")  # use exp smooth"
+   ],
    "outputs": [
     {
      "name": "stdout",
@@ -188,60 +188,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 6
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:59.542167Z",
-     "start_time": "2025-08-13T03:30:59.539689Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "90b80872d654572e",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-13T03:30:59.577504Z",
-     "start_time": "2025-08-13T03:30:59.575177Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "3413958f0630f43a",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "8c71740dde571d35"
+   "execution_count": 7
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "pysdkit",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,

example/example_zh/data_generator.ipynb

@@ -290,7 +290,7 @@
    "cell_type": "code",
    "source": [
     "# 通过`view`变量来调整可视化的方式\n",
-    "plot_IMFs(signal, IMFs, view='3d')"
+    "plot_IMFs(signal, IMFs, view=\"3d\")"
    ],
    "id": "f95df9777178ea16",
    "outputs": [
@@ -374,7 +374,7 @@
    "cell_type": "code",
    "source": [
     "# 通过`view`变量调整可视化方式\n",
-    "plot_IMFs(signal, IMFs, view='3d')"
+    "plot_IMFs(signal, IMFs, view=\"3d\")"
    ],
    "id": "8f17486b6e647d2",
    "outputs": [

example/example_zh/models/PCA.ipynb

@@ -206,12 +206,10 @@
    "source": [
     "def draw_vector(v0, v1, ax=None):\n",
     "    ax = ax or plt.gca()\n",
-    "    arrowprops = dict(arrowstyle=\"->\",\n",
-    "                      linewidth=2,\n",
-    "                      shrinkA=0,\n",
-    "                      shrinkB=0)\n",
+    "    arrowprops = dict(arrowstyle=\"->\", linewidth=2, shrinkA=0, shrinkB=0)\n",
     "    ax.annotate(\"\", v1, v0, arrowprops=arrowprops)\n",
     "\n",
+    "\n",
     "plt.scatter(X[:, 0], X[:, 1], color=\"royalblue\")\n",
     "for length, vector in zip(pca.explained_variance_ratio_, pca.components_):\n",
     "    v = np.array(vector) * 3 * np.sqrt(length)\n",

example/example_zh/models/knn.ipynb

@@ -55,8 +55,9 @@
     "    return X_rot\n",
     "\n",
     "\n",
-    "def test_knn_data(n_classes: int = 5, number: int = 10, length: int = 256, random_state: int = 42) -> Tuple[\n",
-    "    np.ndarray, np.ndarray]:\n",
+    "def test_knn_data(\n",
+    "    n_classes: int = 5, number: int = 10, length: int = 256, random_state: int = 42\n",
+    ") -> Tuple[np.ndarray, np.ndarray]:\n",
     "    \"\"\"\n",
     "    生成用于测试K近邻算法的函数\n",
     "\n",
@@ -86,7 +87,9 @@
     "print(\"training y\", labels.shape)\n",
     "\n",
     "# 生成用于测试的数据\n",
-    "test_samples, test_labels = test_knn_data(n_classes=2, number=1, length=128, random_state=42)\n",
+    "test_samples, test_labels = test_knn_data(\n",
+    "    n_classes=2, number=1, length=128, random_state=42\n",
+    ")\n",
     "# 打印测试数据的形状\n",
     "print(\"testing X\", test_samples.shape)\n",
     "print(\"testing y\", test_labels.shape)"

pysdkit/__init__.py

@@ -76,6 +76,9 @@
 # Jump Plus AM-FM Mode Decomposition
 from ._jmd import JMD
 
+# Feature Mode Decomposition
+from ._fmd import FMD
+
 # Moving Average Decomposition
 from .tsa import Moving_Decomp
 
@@ -132,6 +135,7 @@ def print_functions():
 Iterative Nonlinear Chirp Mode Decomposition    |    INCMD
 Empirical Wavelet Transform                     |    EWT
 Jump Plus AM-FM Mode Decomposition              |    JMD
+Feature Mode Decomposition                      |    FMD
 Moving Average Decomposition                    |    Moving
 Seasonal-Trend decomposition using LOESS        |    STL
 Hilbert-Huang Transform                         |    HHT
@@ -167,6 +171,7 @@ def print_functions():
     "ewt",
     "EWT",
     "JMD",
+    "FMD",
     "Moving_Decomp",
     "STL",
     "models",

pysdkit/_ewt/ewt.py

@@ -251,9 +251,9 @@ def EWT_Boundaries_Detect(
 
     elif reg == "gaussian":
         regFilter = np.zeros(lengthFilter)
-        regFilter[
-            regFilter.size // 2
-        ] = 1  # Ensure center is set for Gaussian filtering
+        regFilter[regFilter.size // 2] = (
+            1  # Ensure center is set for Gaussian filtering
+        )
         presig = np.convolve(
             ff, gaussian_filter(regFilter, sigmaFilter), mode="same"
         )  # Gaussian filter

pysdkit/_fmd/__init__.py

@@ -4,3 +4,4 @@
 @author: Whenxuan Wang
 @email: wwhenxuan@gmail.com
 """
+from .fmd import FMD