{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `presamples` tagged graph issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem\n",
    "`bw2analyzer.recurse_tagged_database` doesn't work with LCA objects initialised with presamples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo\n",
    "### Import the necessary stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from brightway2 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import presamples as ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bw2analyzer.tagged import recurse_tagged_database, aggregate_tagged_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set  up the brightway2 project\n",
    "\n",
    "> Note - rather than run bw2setup() this takes a library project with ecoinvent 3.3 in it and copies it. \n",
    "\n",
    "> To recreate this notebook either do something similar or run bw2setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'presamples_test' in projects:\n",
    "    projects.set_current('presamples_test')\n",
    "else:\n",
    "    projects.set_current('Ecoinvent3_3')\n",
    "    projects.copy_project('presamples_test', switch=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the simple system\n",
    "\n",
    "Below is a picture of the system to be modelled. Basically it's set up so `input_1` has a known climate change impact of `1`, `input_2` has an impact of `2` and `input_3` has an impact of `3`. \n",
    "\n",
    "`a`, `b`, and `c` in the diagram are the exchange amounts of each input to `FU`. These all default to `1` (total impact of `6`). \n",
    "\n",
    "We're going to try and use `presamples` to run 3 different analyses where only `a`, `b` or `c` is set to `1` (others set to `0` sequentially, so the expected total impacts are `1`, `2` and `3` accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/presamples_flow_chart.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CO2 = ('biosphere3', 'f9749677-9c9f-4678-ab55-c607dfdc2cb9')      # Carbon dioxide, fossil, kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    ('test', 'FU'):{\n",
    "        'exchanges': [{\n",
    "            'input':('test', 'input_1'),\n",
    "            'amount':1,\n",
    "            'formula':'p_input_1',\n",
    "            'type': 'technosphere'\n",
    "        },{\n",
    "            'input':('test', 'input_2'),\n",
    "            'amount':1,\n",
    "            'formula':'p_input_2',\n",
    "            'type': 'technosphere'\n",
    "        },{\n",
    "            'input':('test', 'input_3'),\n",
    "            'amount':1,\n",
    "            'formula':'p_input_3',\n",
    "            'type': 'technosphere'\n",
    "        },\n",
    "        ],\n",
    "        'name': 'FU',\n",
    "        'type': 'process'\n",
    "    },\n",
    "    ('test', 'input_1'):{\n",
    "        'exchanges': [{\n",
    "            'amount': 1,\n",
    "            'input': CO2,\n",
    "            'type': 'biosphere'\n",
    "        }],\n",
    "        'name': 'input_1',\n",
    "        'type': 'process'\n",
    "    },\n",
    "    ('test', 'input_2'):{\n",
    "        'exchanges': [{\n",
    "            'amount': 2,\n",
    "            'input': CO2,\n",
    "            'type': 'biosphere'\n",
    "        }],\n",
    "        'name': 'input_2',\n",
    "        'type': 'process'\n",
    "    },\n",
    "    ('test', 'input_3'):{\n",
    "        'exchanges': [{\n",
    "            'amount': 3,\n",
    "            'input': CO2,\n",
    "            'type': 'biosphere'\n",
    "        }],\n",
    "        'name': 'input_3',\n",
    "        'type': 'process'\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and write the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = Database(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing activities to SQLite3 database:\n",
      "0%  100%\n",
      "[####] | ETA: 00:00:00\n",
      "Total time elapsed: 00:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title: Writing activities to SQLite3 database:\n",
      "  Started: 05/30/2018 15:12:12\n",
      "  Finished: 05/30/2018 15:12:12\n",
      "  Total time elapsed: 00:00:00\n",
      "  CPU %: 97.50\n",
      "  Memory %: 0.81\n"
     ]
    }
   ],
   "source": [
    "db.write(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the functional unit and method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fu = db.search('FU')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = ('IPCC 2013', 'climate change', 'GTP 100a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the default lca score (expected = `6`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lca = LCA({fu:1},m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lca.lci()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lca.lcia()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lca.score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok that works - next to try it with `presamples`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate the `presamples` package\n",
    "\n",
    "#### Get a list of the inputs to FU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "FU_inputs = [exc for exc in fu.technosphere()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure out and store the matrix indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('test', 'input_1'), ('test', 'FU'), 'technosphere'),\n",
       " (('test', 'input_2'), ('test', 'FU'), 'technosphere'),\n",
       " (('test', 'input_3'), ('test', 'FU'), 'technosphere')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FU_indices = [(exc['input'], exc['output'], 'technosphere')\n",
    "                        for exc in FU_inputs\n",
    "                       ]\n",
    "FU_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up the value matrix (3x3 identity matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_by_one = np.eye(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the `presamples` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "presamples_one_by_one = [(\n",
    "    one_by_one,\n",
    "    FU_indices,\n",
    "    'technosphere'\n",
    ")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the `presamples` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id, test_fp = ps.create_presamples_package(\n",
    "    matrix_data=presamples_one_by_one,\n",
    "    name='presamples_test_one_by_one',\n",
    "    seed = 'sequential'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "\n",
    "- Set up the LCA\n",
    "- Run the tagged graph (via `recurse_tagged_database` so we can keep our `LCA` object)\n",
    "- Aggregate the tagged graph\n",
    "- Sum the aggregated graph to get the total impact\n",
    "- compare to `lca.score`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Run 1: Expected result = 1.0 kg-CO2e\n",
      "--------------------------------------------\n",
      "Total impact from LCA score:\t1.0 kg-CO2e\n",
      "Total impact from graph:\t6.0 kg-CO2e\n",
      "--------------------------------------------\n",
      "\n",
      "Run 2: Expected result = 2.0 kg-CO2e\n",
      "--------------------------------------------\n",
      "Total impact from LCA score:\t2.0 kg-CO2e\n",
      "Total impact from graph:\t6.0 kg-CO2e\n",
      "--------------------------------------------\n",
      "\n",
      "Run 3: Expected result = 3.0 kg-CO2e\n",
      "--------------------------------------------\n",
      "Total impact from LCA score:\t3.0 kg-CO2e\n",
      "Total impact from graph:\t6.0 kg-CO2e\n",
      "--------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "one_by_one_lca = LCA({fu:1}, m, presamples=[test_fp])\n",
    "\n",
    "graphs = []\n",
    "\n",
    "for i, k in enumerate(FU_inputs):\n",
    "\n",
    "    if i==0:\n",
    "        one_by_one_lca.lci()\n",
    "        one_by_one_lca.lcia()\n",
    "    else:\n",
    "        one_by_one_lca.presamples.update_matrices(one_by_one_lca)\n",
    "        one_by_one_lca.redo_lci()\n",
    "        one_by_one_lca.redo_lcia()\n",
    "\n",
    "    # Note: this is code excised from bw2analyzer.tagged.traverse_tagged_databases\n",
    "    \n",
    "    method_dict = {o[0]: o[1] for o in Method(m).load()}\n",
    "    \n",
    "    label = \"name\" # aggregate on name - i.e. traverse foreground model for contributions\n",
    "    default_tag = \"other\"\n",
    "    \n",
    "    cfu = {fu:1} # change format of fu to include amount\n",
    "    \n",
    "    graph = [recurse_tagged_database(key, amount, method_dict, one_by_one_lca, label, default_tag)\n",
    "                  for key, amount in cfu.items()]\n",
    "    \n",
    "    result = aggregate_tagged_graph(graph) # aggregate the tags\n",
    "    \n",
    "    graph_result = sum([v for k, v in result.items()]) # calculate the sum of the impacts\n",
    "    \n",
    "    # print the results\n",
    "    \n",
    "    print(\"\\nRun {}: Expected result = {:.1f} kg-CO2e\\n{}\".format(i+1,i+1, \"-\"*44))\n",
    "    print(\"Total impact from LCA score:\\t{} kg-CO2e\".format(one_by_one_lca.score))\n",
    "    print(\"Total impact from graph:\\t{} kg-CO2e\\n{}\".format(graph_result, \"-\"*44))\n",
    "    \n",
    "    graphs.append(graph) # create a list of the graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result\n",
    "\n",
    "`presamples` works as expected for LCA scores, but the tagged graph results reflect the original database\n",
    "\n",
    "All of the tagged graphs are the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphs[0] == graphs [1] == graphs[2]"
   ]
  }
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