Example_2.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2: Visualize the distribution with quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bw2data as bd\n",
    "from sankertainpy.bw_to_plotly import recursive_calculation_to_plotly\n",
    "import sankertainpy.sankertainpy as sp\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import plotly.figure_factory as ff"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose brightway2 activity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bd.projects.set_current('sankertainpy')\n",
    "ei=bd.Database('ecoinvent 3.8 cutoff')\n",
    "activity= ei.random()\n",
    "method= bd.methods.random()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the graph traversal LCA data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "result= recursive_calculation_to_plotly(activity, method, max_level=2, cutoff=0.05, mc=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the Sankey figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= sp.generate_sankey(result, type=1, cutoff=0.04)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjust the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.update_layout(width=1600)\n",
    "fig.update_layout(height=700)\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate distribution plot with marked quantiles from sankey diagram type 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=result['scores'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig2 = ff.create_distplot([data], ['ta'], bin_size=0.0007, show_hist=True, show_rug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = matplotlib.cm.get_cmap('RdYlGn')\n",
    "cmap_basic = matplotlib.cm.get_cmap('Blues')\n",
    "quantiles= np.arange(0.125,0.925,0.05) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_quant=0\n",
    "last_x=[]\n",
    "last_y=[]\n",
    "for qu in quantiles:\n",
    "    xl = np.quantile(data, old_quant)\n",
    "    xr = np.quantile(data, qu)\n",
    "    x1=last_x\n",
    "    y1=last_y\n",
    "\n",
    "    for i, xc in enumerate(fig2.data[0].x):\n",
    "        if xc >xl and xc <xr:\n",
    "            x1.append(xc)\n",
    "            y1.append(fig2.data[0].y[i]) \n",
    "        \n",
    "    if old_quant==0:\n",
    "        fig2.add_scatter(x=x1, y=y1,fill='tozeroy', mode='none' , fillcolor=str('rgba'+str(cmap_basic(0.5,1.0))))\n",
    "    else:\n",
    "        fig2.add_scatter(x=x1, y=y1,fill='tozeroy', mode='none' , fillcolor=str('rgba'+str(cmap(1-qu,0.9))))\n",
    "    last_x=[x1[len(x1)-1]]\n",
    "    last_y=[y1[len(x1)-1]]\n",
    "    old_quant = qu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
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            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y2",
         "domain": [
          0,
          1
         ],
         "zeroline": false
        },
        "yaxis": {
         "anchor": "free",
         "domain": [
          0,
          1
         ],
         "position": 0,
         "visible": true
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2.layout.paper_bgcolor='rgba(0,0,0,0)'\n",
    "fig2.update_yaxes(visible=True)\n",
    "fig2.update_layout(showlegend=False)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "visualization_contest",
   "language": "python",
   "name": "python3"
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