{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zones\n",
    "\n",
    "Here we illustrate how to define and work with zones, using the ``atomsmltr.environment.zones`` subpackage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to zones\n",
    "\n",
    "### Defining zone\n",
    "\n",
    "The ``Zone`` class allows to define zones in the position-velocity space, that are associated with actions. Currently, the only action that is implemented is \"stop\", and it means that the simulation will stop when an atom steps **outside** of the defined zone.\n",
    "\n",
    "When creating a zone, users should specify whether they apply to 'position' or 'speed' using the ``target`` property. Then, one can check whether a position or speed vector is inside the zone using the ``in_zone()`` method.\n",
    "\n",
    "Note that regular zones are designed to deal with only position or speed vector, and hence the ``in_zone()`` takes a vector of shape (,3) or (n,m,..,3) as an input. To work with position-velocity vectors of dimension (...,6), there is a special type of zone collection : ``SuperZone``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "───────────────\n",
      "| Upper Limit |\n",
      "───────────────\n",
      ". Parameters :\n",
      "  ├── type : 1D lower limit\n",
      "  ├── tag : positive x\n",
      "  ├── in_tag\n",
      "  ├── out_tag : positive x\n",
      "  ├── target : position\n",
      "  ├── action : stop\n",
      "  ├── value : 0.0\n",
      "  ├── axis : 0\n",
      "  └── inverted : False\n",
      "\n",
      "\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# - \"1D\" zones > limits\n",
    "from atomsmltr.environment import UpperLimit, LowerLimit, Limits \n",
    "\n",
    "# define a zone corresponding to x > 0\n",
    "x_pos = LowerLimit(value=0, axis=0, target=\"position\", action=\"stop\", tag=\"positive x\")\n",
    "x_pos.print_info()\n",
    "print(x_pos.get_value((1,0,0)))\n",
    "print(x_pos.get_value((-1,0,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# - other examples\n",
    "# zone -10 < y < 10\n",
    "y_limits = Limits(min=-10, max=10, axis=1, target=\"position\", tag=\"y limits\")\n",
    "# zone vz < 500\n",
    "vz_max = UpperLimit(value=500, axis=2, target=\"speed\", tag=\"vz cap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logic of a zone can be inverted using the ``inverted`` property :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "from atomsmltr.environment import UpperLimit\n",
    "\n",
    "xlim = UpperLimit(10, axis=0, target=\"position\")\n",
    "print(xlim.get_value((20, 0, 0)))\n",
    "xlim.inverted = True\n",
    "print(xlim.get_value((20, 0, 0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples of 3D zones\n",
    "\n",
    "Currently two kind of 3D zones are implemented: **Box** and **Cylinder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from atomsmltr.environment import Box\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# -- setup zone\n",
    "zone = Box(xmin=0, xmax=3, ymin=-5, ymax=8, zmin=-8, zmax=8, target=\"position\")\n",
    "\n",
    "# -- plot\n",
    "\n",
    "# prepare the grid\n",
    "grid = np.mgrid[-10:10:500j, -10:10:500j, 0:0:1j]\n",
    "grid = np.squeeze(grid)\n",
    "X, Y, _ = grid\n",
    "X, Y = X.T, Y.T\n",
    "pos = grid.T\n",
    "\n",
    "# compute result\n",
    "res = zone.get_value(pos)\n",
    "\n",
    "# plot\n",
    "plt.figure()\n",
    "plt.pcolormesh(X, Y, res, cmap=\"cividis\")\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from atomsmltr.environment import Cylinder\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# -- setup zone\n",
    "zone = Cylinder(origin=(1,3,0), direction=(0,0,1), radius=5, target=\"position\")\n",
    "\n",
    "# -- plot\n",
    "\n",
    "# prepare the grid\n",
    "grid = np.mgrid[-10:10:500j, -10:10:500j, 0:0:1j]\n",
    "grid = np.squeeze(grid)\n",
    "X, Y, _ = grid\n",
    "X, Y = X.T, Y.T\n",
    "pos = grid.T\n",
    "\n",
    "# compute result\n",
    "res = zone.get_value(pos)\n",
    "\n",
    "# plot\n",
    "plt.figure()\n",
    "plt.pcolormesh(X, Y, res, cmap=\"cividis\")\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zone collections\n",
    "\n",
    "### Create a collection\n",
    "\n",
    "Simple zones can be combined using the logical operators ``&`` (and), ``|`` (or) , ``^`` (xor) to create zone collections. \n",
    "\n",
    "Hence, there are three kind of zone collections : ``ANDCollection``, ``ORCollection``, ``XORCollection``.\n",
    "\n",
    "A zone collection contains a list of zones, and the result is evaluated by combining the result of each zone and \"adding\" them using the logical operator. It is possible to add zones to the list using the ``+=`` operator. Note that only zones collections of the same kind (or, and, xor) can be added using the ``+=`` operator ; in this case, the lists of zones are merged.\n",
    "\n",
    "Finally, note that a ``ZoneCollection`` is still a ``Zone`` , so it is possible to combine them with other ``Zone`` objects to create new collections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "───────────────────────\n",
      "| AND Zone Collection |\n",
      "───────────────────────\n",
      ". Parameters :\n",
      "  ├── type : AND Zone Collection\n",
      "  ├── tag : xarovo\n",
      "  ├── in_tag\n",
      "  ├── out_tag\n",
      "  ├── target : position\n",
      "  ├── action : stop\n",
      "  ├── zones : ['x limits', 'y limits']\n",
      "  └── inverted : False\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from atomsmltr.environment import Limits\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# -- define y and y limits\n",
    "xlim = Limits(0, 10, axis=0, tag=\"x limits\")\n",
    "ylim = Limits(-5, 5, axis=1, tag=\"y limits\")\n",
    "\n",
    "# -- try collections\n",
    "and_coll = xlim & ylim \n",
    "or_coll = xlim | ylim\n",
    "xor_coll = xlim ^ ylim\n",
    "\n",
    "and_coll.print_info()\n",
    "\n",
    "# -- plot\n",
    "\n",
    "# prepare the grid\n",
    "grid = np.mgrid[-10:10:500j, -10:10:500j, 0:0:1j]\n",
    "grid = np.squeeze(grid)\n",
    "X, Y, _ = grid\n",
    "X, Y = X.T, Y.T\n",
    "pos = grid.T\n",
    "\n",
    "# compute result\n",
    "res_and = and_coll.get_value(pos)\n",
    "res_or = or_coll.get_value(pos)\n",
    "res_xor = xor_coll.get_value(pos)\n",
    "\n",
    "# plot\n",
    "fig, axes = plt.subplots(1,3,figsize=(8,3), tight_layout=True)\n",
    "axes[0].pcolormesh(X, Y, res_and, cmap=\"cividis\")\n",
    "axes[0].set_title(\"and\")\n",
    "axes[1].pcolormesh(X, Y, res_or, cmap=\"cividis\")\n",
    "axes[1].set_title(\"or\")\n",
    "axes[2].pcolormesh(X, Y, res_xor, cmap=\"cividis\")\n",
    "axes[2].set_title(\"xor\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x_max', 'x_min']\n",
      "['y_max', 'y_min']\n",
      "['x_max', 'x_min', 'y_max', 'y_min']\n",
      "['xlim', 'ylim']\n",
      "['y_max', 'y_min', 'x_max', 'x_min', 'z_min', 'z_max']\n"
     ]
    }
   ],
   "source": [
    "from atomsmltr.environment import UpperLimit, LowerLimit\n",
    "\n",
    "# -- define limits\n",
    "x_min = LowerLimit(0, axis=0, tag=\"x_min\")\n",
    "y_min = LowerLimit(-1, axis=1, tag=\"y_min\")\n",
    "z_min = LowerLimit(-10, axis=2, tag=\"z_min\")\n",
    "x_max = UpperLimit(0, axis=0, tag=\"x_max\")\n",
    "y_max = UpperLimit(-1, axis=1, tag=\"y_max\")\n",
    "z_max = UpperLimit(-10, axis=2, tag=\"z_max\")\n",
    "\n",
    "\n",
    "# -- play with collections\n",
    "# combine x_min and x_max\n",
    "xlim = x_max & x_min\n",
    "xlim.tag = \"xlim\"\n",
    "# now xlim is a collection of two zones\n",
    "print([z.tag for z in xlim.zones])    \n",
    "# same with y\n",
    "ylim = y_max & y_min\n",
    "ylim.tag = \"ylim\"\n",
    "print([z.tag for z in ylim.zones])\n",
    "\n",
    "# How to combine them ?\n",
    "# option 1 : \"merge\"\n",
    "xylim = xlim + ylim  # addition works since both are ANDCollections\n",
    "print([z.tag for z in xylim.zones])\n",
    "\n",
    "# option 2 : combine them in a new collection\n",
    "xylim = xlim & ylim\n",
    "print([z.tag for z in xylim.zones])  # then the new collection has only two zones\n",
    "\n",
    "# increment also works\n",
    "all_lim = ylim + xlim\n",
    "all_lim += z_min\n",
    "all_lim += z_max\n",
    "print([z.tag for z in all_lim.zones])  # then the new collection has only two zones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few examples of zones collections\n",
    "\n",
    "### using OR collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from atomsmltr.environment.zones import Cylinder\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# -- generate the zone\n",
    "\n",
    "# create two cylinders\n",
    "cyl_1 = Cylinder()\n",
    "cyl_1.origin = (0, 0, 0)\n",
    "cyl_1.direction = (1, 1, 1)\n",
    "cyl_1.radius = 5\n",
    "\n",
    "# the second one is inverted\n",
    "cyl_2 = cyl_1.copy()\n",
    "cyl_2.radius = 4.7\n",
    "cyl_2.inverted = True\n",
    "\n",
    "# the union of the tw0\n",
    "orbit = cyl_1 & cyl_2\n",
    "\n",
    "# add another cylinder\n",
    "nucleus = Cylinder()\n",
    "nucleus.origin = (0, 0, 0)\n",
    "nucleus.direction = (0, 0, 1)\n",
    "nucleus.radius = 2\n",
    "\n",
    "# and a last one\n",
    "electron = Cylinder()\n",
    "electron.origin = (5.5, 6.3, 0)\n",
    "electron.direction = (0, 0, 1)\n",
    "electron.radius = 1\n",
    "\n",
    "# combine them in a \"OR\" collection\n",
    "coll = orbit | nucleus | electron\n",
    "\n",
    "# -- plot\n",
    "\n",
    "# prepare the grid\n",
    "grid = np.mgrid[-10:10:500j, -10:10:500j, 0:0:1j]\n",
    "grid = np.squeeze(grid)\n",
    "X, Y, _ = grid\n",
    "X, Y = X.T, Y.T\n",
    "pos = grid.T\n",
    "\n",
    "# compute result\n",
    "res = coll.get_value(pos)\n",
    "\n",
    "# plot\n",
    "plt.figure()\n",
    "plt.pcolormesh(X, Y, res, cmap=\"cividis\")\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### playing with XOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# - One step further\n",
    "from atomsmltr.environment import LowerLimit\n",
    "x_positive = LowerLimit(value=0, axis=0)\n",
    "coll_2 = coll ^ x_positive  # xor\n",
    "\n",
    "# compute result\n",
    "res = coll_2.get_value(pos)\n",
    "\n",
    "# plot\n",
    "plt.figure()\n",
    "plt.pcolormesh(X, Y, res, cmap=\"cividis\")\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# - Go on with that ?\n",
    "y_positive = LowerLimit(value=0, axis=1)\n",
    "coll_3 = coll_2 ^ y_positive\n",
    "\n",
    "# compute result\n",
    "res = coll_3.get_value(pos)\n",
    "\n",
    "# plot\n",
    "plt.figure()\n",
    "plt.pcolormesh(X, Y, res, cmap=\"cividis\")\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "atomsmltr-KmviZRuT-py3.12",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}