{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "39d16ccb",
   "metadata": {},
   "source": [
    "# Multiblock Arrays\n",
    "\n",
    "A `WolfArray` object stores data in a matrix format, leveraging Numpy arrays for efficient numerical operations.\n",
    "\n",
    "A `WolfArrayMB` object organizes data as a dictionary of `WolfArray` objects, effectively creating a tiled structure of multiple `WolfArray` instances. \n",
    "This structure allows for seamless handling of datasets that are too large to fit into a single array, enabling efficient data manipulation and analysis.\n",
    "\n",
    "Each block can be parameterized independently, offering flexibility in data representation.\n",
    "\n",
    "The WOLF CPU code is designed to work with multiblock models. \n",
    "The `WolfArrayMB` class ensures compatibility with the Fortran-based WOLF code, \n",
    "maintaining consistency and interoperability between the two implementations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ece70414",
   "metadata": {},
   "outputs": [],
   "source": [
    "import _add_path # add parent directory to path - debug mode only\n",
    "from wolfhece.wolf_array import WolfArray, WolfArrayMB, header_wolf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518bf5ef",
   "metadata": {},
   "source": [
    "## Creating a Multiblock Array from scratch\n",
    "\n",
    "You need multiple `WolfArray` objects to create a `WolfArrayMB` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c1d2bd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgrammationGitLab\\HECEPython\\docs\\source\\tutorials\\../../..\\wolfhece\\wolf_array.py:10215: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n",
      "D:\\ProgrammationGitLab\\HECEPython\\docs\\source\\tutorials\\../../..\\wolfhece\\wolf_array.py:10215: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 2 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h1 = header_wolf()\n",
    "h1.shape = (10,10)       # 10x10 pixels\n",
    "h1.set_resolution(1.,1.) # 1x1 meters per pixel\n",
    "h1.set_origin(-1.,-1.)     # origin at (-1.,-1.) meters\n",
    "\n",
    "h2 = header_wolf()\n",
    "h2.shape = (5,5)           # 5x5 pixels\n",
    "h2.set_resolution(0.5,0.5) # 0.5x0.5 meters per pixel\n",
    "h2.set_origin(15.,15.)     # origin at (15,15) meters\n",
    "\n",
    "block1 = WolfArray(srcheader=h1)\n",
    "block2 = WolfArray(srcheader=h2)\n",
    "\n",
    "block1.array[:,:] = np.arange(100).reshape((10,10)) # Populate with 0-99\n",
    "block2.array[:,:] = np.arange(25).reshape((5,5))    # Populate with 0-24\n",
    "\n",
    "# Print the blocks\n",
    "block1.plot_matplotlib(with_legend=True)\n",
    "block2.plot_matplotlib(with_legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc268999",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape  : 1 x 1 \n",
      "Resolution  : 18.5 x 18.5 \n",
      "Spatial extent : \n",
      "   - Origin : (-1.0 ; -1.0) \n",
      "   - End : (17.5 ; 17.5) \n",
      "   - Width x Height : 18.5 x 18.5 \n",
      "   - Translation : (0.0 ; 0.0)\n",
      "Null value : 0\n",
      "\n",
      "Number of blocks : 2\n",
      "\n",
      "Block block1 : \n",
      "\n",
      "Shape  : 10 x 10 \n",
      "Resolution  : 1.0 x 1.0 \n",
      "Spatial extent : \n",
      "   - Origin : (-1.0 ; -1.0) \n",
      "   - End : (9.0 ; 9.0) \n",
      "   - Width x Height : 10.0 x 10.0 \n",
      "   - Translation : (0.0 ; 0.0)\n",
      "Null value : 0.0\n",
      "\n",
      "Block block2 : \n",
      "\n",
      "Shape  : 5 x 5 \n",
      "Resolution  : 0.5 x 0.5 \n",
      "Spatial extent : \n",
      "   - Origin : (15.0 ; 15.0) \n",
      "   - End : (17.5 ; 17.5) \n",
      "   - Width x Height : 2.5 x 2.5 \n",
      "   - Translation : (0.0 ; 0.0)\n",
      "Null value : 0.0\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mb = WolfArrayMB() # a void WolfArrayMB()\n",
    "mb.add_block(block1, force_idx=True) # add the first block - by reference\n",
    "mb.add_block(block2, force_idx=True) # add the second block - by reference\n",
    "mb.set_header_from_added_blocks() # construct the header from the added blocks\n",
    "mb.plot_matplotlib()\n",
    "\n",
    "print(mb)\n",
    "\n",
    "# In the mb header :\n",
    "#  - The shape os the mb is (1,1)\n",
    "#  - The origin is the lower-left corner of the added blocks\n",
    "#  - The resolution is the footprint of the added blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41050f8a",
   "metadata": {},
   "source": [
    "## By Reference or By Value?\n",
    "\n",
    "When adding a block to a `WolfArrayMB` object, you can choose to add it either by reference or by value.\n",
    "\n",
    "- **By Reference**: By default, the `add_block` method does not create a copy of the `WolfArray` object. This means that any modifications to the `WolfArrayMB` object will directly affect the original `WolfArray` object, and vice versa.\n",
    "\n",
    "- **By Value**: If you want to create a copy of the `WolfArray` object while adding it, you can set the `copy` parameter to `True`. This ensures that the original `WolfArray` object remains unchanged, even if the `WolfArrayMB` object is modified.\n",
    "\n",
    "Key Considerations:\n",
    "\n",
    "- Adding by reference is memory-efficient but links the objects, so changes in one will reflect in the other.\n",
    "- Adding by value ensures independence between the objects but requires additional memory for the copy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "acf84fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "block2.array[:,:] = 5.\n",
    "mb.plot_matplotlib() # plot the mb again"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ae88a6",
   "metadata": {},
   "source": [
    "## Accessing the Blocks\n",
    "\n",
    "Blocks within a `WolfArrayMB` object are stored in the `myblocks` dictionary and can be accessed using their keys.\n",
    "\n",
    "### Key Format\n",
    "\n",
    "The keys follow the format `blockn`, where `n` is the block number (1-based index). For example:\n",
    "- The first block has the key `block1`.\n",
    "- The second block has the key `block2`.\n",
    "\n",
    "### Access Methods\n",
    "\n",
    "You can access blocks in two ways:\n",
    "\n",
    "1. **Using Keys**: Access blocks directly using their string keys, e.g., `mb.myblocks['block1']`.\n",
    "2. **Using Indexing**: The `__getitem__` method allows you to use Python's 0-based indexing convention, e.g., `mb[0]` for the first block.\n",
    "\n",
    "This flexibility makes it easy to retrieve and manipulate individual blocks within the multiblock array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e4e91f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['block1', 'block2'])\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# keys\n",
    "print(mb.myblocks.keys()) # get the keys of the blocks\n",
    "\n",
    "ref1 = mb[0] # get the first block\n",
    "ref2 = mb['block1']\n",
    "\n",
    "print(ref1.is_like(block1)) # check\n",
    "print(ref1.is_like(ref2)) # check"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2bedae",
   "metadata": {},
   "source": [
    "## Convert to WolfArray \"single\" block\n",
    "\n",
    "The `WolfArrayMB` object can be converted to a single `WolfArray` object using the `as_WolfArray` method. This method concatenates all blocks  and rebin if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3c78266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape  : 37 x 37 \n",
      "Resolution  : 0.5 x 0.5 \n",
      "Spatial extent : \n",
      "   - Origin : (-1.0 ; -1.0) \n",
      "   - End : (17.5 ; 17.5) \n",
      "   - Width x Height : 18.5 x 18.5 \n",
      "   - Translation : (0.0 ; 0.0)\n",
      "Null value : 0.0\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "single = mb.as_WolfArray() # convert to a single WolfArray\n",
    "\n",
    "print(single)\n",
    "\n",
    "single.plot_matplotlib() # plot the single WolfArray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0491901",
   "metadata": {},
   "source": [
    "## And More...\n",
    "\n",
    "`WolfArrayMB` philosophy plays a crucial role in the WOLF code, especially within the `Wolfresults_2D` class, where the same idea is used to store and manage the results of 2D simulations. \n",
    "The ability to handle large datasets efficiently makes it indispensable for complex numerical workflows.\n",
    "\n",
    "To gain a deeper understanding of how `WolfArrayMB` is utilized in the WOLF codebase, explore the source code and documentation. \n",
    "This will provide valuable insights into its integration and application in real-world scenarios."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3.11",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}