tutorials/developers-tutorial1/developers-help-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyDMD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Developers Tutorial 1: Extending PyDMD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contrary to other **PyDMD** tutorials, this notebook illustrates the general structure of the basic classes, aiming to help developers to <u>extend</u> the package functionality in a smooth way.\n",
    "\n",
    "In this tutorial we will indeed create from scratch a new version of the original DMD; such extension will be totally useless from a scientific perspective, the only purpose is showing the suggested steps to implement new variants inside **PyDMD**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Back-end API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The necessary bricks for building the new DMD version are:\n",
    "\n",
    "- [`DMDBase`](https://pydmd.github.io/PyDMD/dmdbase.html), the actual backbone of all the different implemented versions;\n",
    "- [`DMDTimeDict`](https://github.com/PyDMD/PyDMD/blob/7ac9f3fa855e9a5e7008daad9906eaa6e59ba80a/pydmd/dmdbase.py#L759), the class that manages the time window;\n",
    "- [`DMDOperator`](https://pydmd.github.io/PyDMD/dmdoperator.html), the class that manages the so-called DMD operator;\n",
    "\n",
    "The following schematic outlines the general structure of every `DMDBase` module.\n",
    "![](PyDMD-structure.png)\n",
    "In general, all `PyDMD` modules should be capable of the following tasks:\n",
    "1) Accepting and storing DMD algorithm parameters.\n",
    "2) Performing DMD given snapshot data $\\mathbf{X}$ via the `fit` method, which often does the following:\n",
    "    - prepares and stores the input data,\n",
    "    - sets the `DMDTimeDict`s,\n",
    "    - computes the DMD operator and its eigendecomposition, and\n",
    "        - (this is done by invoking the `DMDOperator`'s `compute_operator` function)\n",
    "    - computes the DMD amplitudes using the computed operator.\n",
    "3) Fetching and utilizing DMD results, such as:\n",
    "    - the reduced DMD operator $\\tilde{\\mathbf{A}}$\n",
    "    - the spatial modes $\\mathbf{\\Phi}$ (the eigenvectors of $\\mathbf{A}$)\n",
    "    - the temporal frequencies $\\mathbf{\\Lambda}$ (the eigenvalues of $\\mathbf{A}$)\n",
    "    - the spatiotemporal mode amplitudes $\\mathbf{b}$\n",
    "\n",
    "Hence different modules implement different DMD variants, where any number of these steps may be performed differently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a New PyDMD Module\n",
    "\n",
    "We start the new module by importing all these classes and the usual math environment (`matplotlib`+`numpy`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from pydmd import DMDBase, DMD\n",
    "from pydmd.utils import compute_tlsq\n",
    "from pydmd.snapshots import Snapshots\n",
    "\n",
    "import matplotlib.colors as colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now able to create a new class inheriting from `DMDBase`: in this way we just need to implement the new `fit()` method. We can also overload one or more attributes/methods, depending on the algorithm which we are going to follow.\n",
    "\n",
    "In this case, we also overload the constructor (i.e. the `__init__` method) to present a non-trivial case. \n",
    "\n",
    "### Our simple algorithm\n",
    "The new functionality we are going to implement is basically a simple DMD which maps the input snapshots using a custom function, provided by the user before of the construction of the operator.\n",
    "\n",
    "Since the (quite) high number of parameters in the constructor, we suggest to pass explicitly all of them instead of using `args` and `kwargs`. It's redundant, but also much more readable! We can call the `DMDBase` constructor thanks to `super` keyword (more info on `super` [here](https://docs.python.org/3.9/library/functions.html?highlight=super#super))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyFancyNameDMD(DMDBase):\n",
    "    \"\"\"\n",
    "    The MyFancyNameDMD class.\n",
    "    A dummy DMD variant which, useless outside this tutorial.\n",
    "    Make sure to write an exhaustive docstring if you want to\n",
    "    distribute your code, or use it again in some years.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, func, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.__func = func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just specify that the last line of constructor saves the passed function in the object, giving us the possibility to recall it later.\n",
    "\n",
    "We are now ready to implement the `fit` method. Define the custom mapping as $f: \\mathbb R^n \\to \\mathbb R^n$ acting on the snapshots $\\{\\mathbf x_i \\in \\mathbb R^n\\}_{i=1}^m$. Then we just need to pre-process the snapshots in `fit()` and build the DMD operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(self, X):\n",
    "    \"\"\"\n",
    "    Compute the DMD to the input data.\n",
    "\n",
    "    :param X: the input snapshots.\n",
    "    :type X: numpy.ndarray or iterable\n",
    "    \"\"\"\n",
    "\n",
    "    # adjust the shape of the snapshots\n",
    "    input_snapshots = Snapshots(X).snapshots\n",
    "\n",
    "    # apply the mapping\n",
    "    mapped_snapshots = np.apply_along_axis(self.__func, 0, input_snapshots)\n",
    "\n",
    "    # store the snapshots\n",
    "    self._snapshots_holder = Snapshots(mapped_snapshots)\n",
    "\n",
    "    # build the matrices X and Y\n",
    "    n_samples = self.snapshots.shape[-1]\n",
    "    X = self.snapshots[:, :-1]\n",
    "    Y = self.snapshots[:, 1:]\n",
    "    X, Y = compute_tlsq(X, Y, self._tlsq_rank)\n",
    "\n",
    "    # compute the DMD operator\n",
    "    self._svd_modes, _, _ = self.operator.compute_operator(X, Y)\n",
    "\n",
    "    # Default timesteps\n",
    "    self._set_initial_time_dictionary({\"t0\": 0, \"tend\": n_samples - 1, \"dt\": 1})\n",
    "\n",
    "    # compute DMD amplitudes\n",
    "    self._b = self._compute_amplitudes()\n",
    "\n",
    "    return self"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By recalling the already implemented methods, we just need few lines of code to complete the new version. Of course, in case of doubts, the [documentation](https://mathlab.github.io/PyDMD/index.html) is the best starting point to better understand classes and methods.\n",
    "\n",
    "We merge the two cells above in the next one in order to have the entire class implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyFancyNameDMD(DMDBase):\n",
    "    \"\"\"\n",
    "    The MyFancyNameDMD class.\n",
    "    A dummy DMD variant which, useless outside this tutorial.\n",
    "    Make sure to write an exhaustive docstring if you want to\n",
    "    distribute your code, or use it again in some years.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, func, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.__func = func\n",
    "\n",
    "    def fit(self, X):\n",
    "        \"\"\"\n",
    "        Compute the DMD to the input data.\n",
    "\n",
    "        :param X: the input snapshots.\n",
    "        :type X: numpy.ndarray or iterable\n",
    "        \"\"\"\n",
    "\n",
    "        # adjust the shape of the snapshots\n",
    "        input_snapshots = Snapshots(X).snapshots\n",
    "\n",
    "        # apply the mapping\n",
    "        mapped_snapshots = np.apply_along_axis(self.__func, 0, input_snapshots)\n",
    "\n",
    "        # store the snapshots\n",
    "        self._snapshots_holder = Snapshots(mapped_snapshots)\n",
    "\n",
    "        # build the matrices X and Y\n",
    "        n_samples = self.snapshots.shape[-1]\n",
    "        X = self.snapshots[:, :-1]\n",
    "        Y = self.snapshots[:, 1:]\n",
    "        X, Y = compute_tlsq(X, Y, self._tlsq_rank)\n",
    "\n",
    "        # compute the DMD operator\n",
    "        self._svd_modes, _, _ = self.operator.compute_operator(X, Y)\n",
    "\n",
    "        # Default timesteps\n",
    "        self._set_initial_time_dictionary(\n",
    "            {\"t0\": 0, \"tend\": n_samples - 1, \"dt\": 1}\n",
    "        )\n",
    "\n",
    "        # compute DMD amplitudes\n",
    "        self._b = self._compute_amplitudes()\n",
    "\n",
    "        return self"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, we're done!\n",
    "\n",
    "Our new class is implemented, inheriting all the methods to the base class. We have nothing to do but try it on a simple example. First of all, we instantiate a new object, passing as function a simple normalizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myfunc(snapshot):\n",
    "    return snapshot + np.mean(snapshot) * np.random.rand(len(snapshot))\n",
    "\n",
    "\n",
    "my_dmd = MyFancyNameDMD(func=myfunc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recycle the dataset from [tutorial 1](https://mathlab.github.io/PyDMD/tutorial1dmd.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f1(x, t):\n",
    "    return 1.0 / np.cosh(x + 3) * np.exp(2.3j * t)\n",
    "\n",
    "\n",
    "def f2(x, t):\n",
    "    return 2.0 / np.cosh(x) * np.tanh(x) * np.exp(2.8j * t)\n",
    "\n",
    "\n",
    "x = np.linspace(-5, 5, 65)\n",
    "t = np.linspace(0, 4 * np.pi, 129)\n",
    "\n",
    "xgrid, tgrid = np.meshgrid(x, t)\n",
    "\n",
    "X1 = f1(xgrid, tgrid)\n",
    "X2 = f2(xgrid, tgrid)\n",
    "X = X1 + X2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fit` method we have implemented is now called to perform our algorithm to the input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/francescoandreuzzi/code/sissa/pydmd/venv/lib/python3.9/site-packages/pydmd/snapshots.py:72: UserWarning: Input data condition number 2.381641522890837e+18. Consider preprocessing data, passing in augmented data\n",
      "matrix, or regularization methods.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.MyFancyNameDMD at 0x10a84cc40>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dmd.fit(X.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check that everything works as expected we compare the results obtained with the new version with respect to the original standard approach. We import the `DMD` class and fit over the same data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pydmd.dmd.DMD at 0x120390c40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmd = DMD()\n",
    "dmd.fit(X.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have performed both the algorithms, we can now look at the reconstructed system using the `reconstructed_data` attribute. We note here that, as the other methods and attributes, `reconstructed_data` is inherited from `DMDBase`, giving us the possibility to call it without having implemented explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.pcolormesh(my_dmd.reconstructed_data.real)\n",
    "plt.title(\"MyFancyNameDMD\")\n",
    "plt.xlabel(\"t\")\n",
    "plt.ylabel(\"x\")\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "Z = np.abs(my_dmd.reconstructed_data.real - dmd.reconstructed_data.real)\n",
    "pcm = plt.pcolormesh(\n",
    "    Z,\n",
    "    norm=colors.LogNorm(vmin=max(1.0e-16, Z.min()), vmax=max(1.0e-15, Z.max())),\n",
    ")\n",
    "plt.title(\"|MyFancyNameDMD - DMD|\")\n",
    "plt.xlabel(\"t\")\n",
    "plt.ylabel(\"x\")\n",
    "plt.colorbar(pcm, extend=\"max\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reconstruction looks fine! We are able to note that, since the normalization of the snapshots, the two approaches returns similar results, just a bit rescaled, that is what we expected by preprocessing in this way the input data.\n",
    "\n",
    "But what about if we want to rescale back the reconstruction (or the modes) in the new version?\n",
    "Simple, we have just to overload the `reconstructed_data` or `modes` attributes. \n",
    "\n",
    "This is the basic guide to implement a new version of DMD. For more complex changes to the algorithm, we recommend to carefully read the documentation of the package, and maybe reporting in the [Github issues page](https://github.com/mathLab/PyDMD/issues) the extension you are going to implement."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}