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 extend 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; we specify that of course such extension will be totally useless from a scientific perspective, the only purpose is showing the suggested steps to implement any variants inside **PyDMD**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The necessary bricks for building the new DMD version are:\n",
    "\n",
    "- [`DMDBase`](https://mathlab.github.io/PyDMD/dmdbase.html), the actual backbone of all the different implemented versions;\n",
    "- `DMDTimeDict`, the class that manages the time window;\n",
    "- `DMDOperator`, the class that manages the so-called DMD operator;\n",
    "\n",
    "We start the new module by importing all these classes and the usual math environment (`matplotlib`+`scipy`+`numpy`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "from pydmd import DMDBase\n",
    "from pydmd.dmdoperator import DMDOperator\n",
    "from pydmd.dmdbase import DMDTimeDict\n",
    "from pydmd.utils import compute_tlsq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now able to create a new class inheriting the `DMDBase` one: in this way we have just to implement the new `fit` method. We can also overload one or more attributes/methods, depending by the algorithm of the new version.\n",
    "\n",
    "In this case, we also overload the constructor (i.e. the `__init__` method) to present a very realistic case. The new functionality we are going to implement is basically a simple DMD where the input snapshots are evaluated by a custom function, passed 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 ([here](https://docs.python.org/3.9/library/functions.html?highlight=super#super) there are more information)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyFancyNameDMD(DMDBase):\n",
    "    \"\"\"\n",
    "    The MyFancyNameDMD class.\n",
    "    \n",
    "    It is just a dummy version for this tutorials. But ensure in production to put an ehxaustive docstring.\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "        self,\n",
    "        svd_rank=0,\n",
    "        tlsq_rank=0,\n",
    "        exact=False,\n",
    "        opt=False,\n",
    "        rescale_mode=None,\n",
    "        forward_backward=False,\n",
    "        sorted_eigs=False,\n",
    "        func=None\n",
    "    ):\n",
    "        super().__init__(svd_rank=svd_rank, tlsq_rank=tlsq_rank, exact=exact,\n",
    "            opt=opt, rescale_mode=rescale_mode, forward_backward=forward_backward,\n",
    "            sorted_eigs=sorted_eigs)\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. Defining the passed function as $f: \\mathbb R^n \\to \\mathbb R^n$ acting on the snapshots $\\{\\mathbf x_i \\in \\mathbb R^n\\}_{i=1}^m$, we can write our dummy DMD simply by pre-processing the snapshots and then building the operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "    def fit(self, X):\n",
    "        \"\"\"\n",
    "        Compute the Dynamic Modes Decomposition to the input data.\n",
    "        \n",
    "        :param X: the input snapshots.\n",
    "        :type X: numpy.ndarray or iterable\n",
    "        \"\"\"\n",
    "        self._snapshots, self._snapshots_shape = self._col_major_2darray(X)\n",
    "\n",
    "        for i_snap, snap in enumerate(self._snapshots.T):\n",
    "            self._snapshots[:, i_snap] = self.__func(snap)\n",
    "\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",
    "        self._svd_modes, _, _ = self.operator.compute_operator(X,Y)\n",
    "        \n",
    "        # Default timesteps\n",
    "        self.original_time = DMDTimeDict({'t0': 0, 'tend': n_samples - 1, 'dt': 1})\n",
    "        self.dmd_time = DMDTimeDict({'t0': 0, 'tend': n_samples - 1, 'dt': 1})\n",
    "\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",
    "    \n",
    "    It is just a dummy version for this tutorials. But ensure in production to put an ehxaustive docstring.\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "        self,\n",
    "        svd_rank=0,\n",
    "        tlsq_rank=0,\n",
    "        exact=False,\n",
    "        opt=False,\n",
    "        rescale_mode=None,\n",
    "        forward_backward=False,\n",
    "        sorted_eigs=False,\n",
    "        func=None\n",
    "    ):\n",
    "        super().__init__(svd_rank=svd_rank, tlsq_rank=tlsq_rank, exact=exact,\n",
    "            opt=opt, rescale_mode=rescale_mode, forward_backward=forward_backward,\n",
    "            sorted_eigs=sorted_eigs)\n",
    "        self.__func = func\n",
    "        \n",
    "    def fit(self, X):\n",
    "        \"\"\"\n",
    "        Compute the Dynamic Modes Decomposition to the input data.\n",
    "        \n",
    "        :param X: the input snapshots.\n",
    "        :type X: numpy.ndarray or iterable\n",
    "        \"\"\"\n",
    "        self._snapshots, self._snapshots_shape = self._col_major_2darray(X)\n",
    "\n",
    "        for i_snap, snap in enumerate(self._snapshots.T):\n",
    "            self._snapshots[:, i_snap] = self.__func(snap)\n",
    "\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",
    "        self._svd_modes, _, _ = self.operator.compute_operator(X,Y)\n",
    "        \n",
    "        # Default timesteps\n",
    "        self.original_time = DMDTimeDict({'t0': 0, 'tend': n_samples - 1, 'dt': 1})\n",
    "        self.dmd_time = DMDTimeDict({'t0': 0, 'tend': n_samples - 1, 'dt': 1})\n",
    "\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.linalg.norm(snapshot)\n",
    "\n",
    "my_dmd = MyFancyNameDMD(func=myfunc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recover 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./np.cosh(x+3)*np.exp(2.3j*t)\n",
    "\n",
    "def f2(x,t):\n",
    "    return 2./np.cosh(x)*np.tanh(x)*np.exp(2.8j*t)\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": [
      "/u/n/ndemo/.local/lib/python3.7/site-packages/pydmd-0.3.3-py3.7.egg/pydmd/dmdbase.py:351: UserWarning: Input data matrix X has condition number 2.568344092956508e+18. Consider preprocessing data, passing in augmented data matrix, or regularization methods.\n",
      "  cond_number\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.MyFancyNameDMD at 0x7f26a2338b38>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dmd.fit(X.T.copy())"
   ]
  },
  {
   "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 0x7f26b8d64ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pydmd import DMD\n",
    "dmd = DMD()\n",
    "dmd.fit(X.T.copy())"
   ]
  },
  {
   "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": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.pcolor(my_dmd.reconstructed_data.real)\n",
    "plt.title('MyFancyNameDMD')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('DMD')\n",
    "plt.pcolor(dmd.reconstructed_data.real)\n",
    "plt.colorbar()\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",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}