Example python code for circle packing chart using circlify with either matplotlib or plotly.

packed-circle-graph-with-circlify-and-plotly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc10b3e5",
   "metadata": {},
   "source": [
    "# Packed Circle Graph with Circlify, Matplotlib and Plotly\n",
    "\n",
    "[Plotly](https://plotly.com/python/) doesn't support Circle Packing charts out of the box. You can read about circle packing charts and see examples [here](https://datavizcatalogue.com/methods/circle_packing.html), [here](https://datavizproject.com/data-type/packed-circle-chart/) and [here](https://www.data-to-viz.com/graph/circularpacking.html).\n",
    "\n",
    "This notebook shows how to generate these plots using both Matplotlib and Plotly. The [circlify](https://github.com/elmotec/circlify) library is used to calculate the layout for circle packing.\n",
    "\n",
    "The code for Matplotlib is taken from this [example of Circle Packing using Matplotlib](https://www.python-graph-gallery.com/circular-packing-1-level-hierarchy).\n",
    "\n",
    "The parameters to draw a circle are slightly different for Plotly. I've adapted the Matplotlib example for Plotly using the documentation for [circles and other shapes](https://plotly.com/python/shapes/#circles-positioned-relative-to-the-axis-data)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155ea4c7",
   "metadata": {},
   "source": [
    "## Import libraries\n",
    "\n",
    "These obviously need to be installed. They can all be installed via pip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86576108",
   "metadata": {},
   "outputs": [],
   "source": [
    "import circlify\n",
    "import matplotlib.pyplot as plt # this is just to demonstrate with matplotlib\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8feda5a",
   "metadata": {},
   "source": [
    "## Calculate the layout\n",
    "\n",
    "See the [circlify](https://github.com/elmotec/circlify) page for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e48821ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "magnitudes = [2, 10, 12, 23, 65, 87]\n",
    "\n",
    "# compute circle positions:\n",
    "circles = circlify.circlify(\n",
    "    magnitudes, \n",
    "    show_enclosure=False, \n",
    "    target_enclosure=circlify.Circle(x=0, y=0, r=1)\n",
    ")\n",
    "\n",
    "# create some child circles based on same magnitudes above and using the circle x, y, and r calculated above\n",
    "child_circle_groups = []\n",
    "for i in range(len(magnitudes)):\n",
    "    child_circle_groups.append(circlify.circlify(\n",
    "        magnitudes, \n",
    "        show_enclosure=False, \n",
    "        target_enclosure=circlify.Circle(x=circles[i].x, y=circles[i].y, r=circles[i].r)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e632ec",
   "metadata": {},
   "source": [
    "## Example using Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b2f5744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create just a figure and only one subplot\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "# Remove axes\n",
    "ax.axis('off')\n",
    "\n",
    "# Find axis boundaries\n",
    "lim = max(\n",
    "    max(\n",
    "        abs(circle.x) + circle.r,\n",
    "        abs(circle.y) + circle.r,\n",
    "    )\n",
    "    for circle in circles\n",
    ")\n",
    "plt.xlim(-lim, lim)\n",
    "plt.ylim(-lim, lim)\n",
    "\n",
    "# add parent circles\n",
    "for circle in circles:\n",
    "    x, y, r = circle\n",
    "    ax.add_patch(plt.Circle((x, y), r, alpha=0.2, linewidth=2, fill=False)) \n",
    "\n",
    "# add child circles\n",
    "for child_circles in child_circle_groups:\n",
    "    for child_circle in child_circles:\n",
    "        x, y, r = child_circle\n",
    "        ax.add_patch(plt.Circle((x, y), r, alpha=0.2, linewidth=2, fill=False)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f317f09",
   "metadata": {},
   "source": [
    "## Example using Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f213803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [],
       "layout": {
        "height": 800,
        "plot_bgcolor": "white",
        "shapes": [
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.5271111672666338,
          "x1": -0.3644610120592211,
          "xref": "x",
          "y0": 0.45604194261178266,
          "y1": 0.6186920978191953,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": 0.16762938416600354,
          "x1": 0.5313261877606696,
          "xref": "x",
          "y0": 0.5272471319310098,
          "y1": 0.890943935525676,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.28644884276550686,
          "x1": 0.11196104407714236,
          "xref": "x",
          "y0": -0.6866710193443333,
          "y1": -0.28826113250168406,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.3686020934215285,
          "x1": 0.1829714187900782,
          "xref": "x",
          "y0": 0.31580673147405086,
          "y1": 0.8673802436856575,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -1,
          "x1": -0.07275145071973754,
          "xref": "x",
          "y0": -0.46362427464013123,
          "y1": 0.46362427464013123,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.07275145071973754,
          "x1": 1,
          "xref": "x",
          "y0": -0.5363757253598688,
          "y1": 0.5363757253598688,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.4886534462466667,
          "x1": -0.475425909752169,
          "xref": "x",
          "y0": 0.5744546665889372,
          "y1": 0.587682203083435,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.4321536169869657,
          "x1": -0.40257594621040954,
          "xref": "x",
          "y0": 0.5802454341361098,
          "y1": 0.6098231049126661,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.46908156403032414,
          "x1": -0.43668084906476184,
          "xref": "x",
          "y0": 0.48152344627909494,
          "y1": 0.5139241612446572,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.47576268351532186,
          "x1": -0.43090592483056406,
          "xref": "x",
          "y0": 0.5630500271603891,
          "y1": 0.6079067858451468,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.5271111672666338,
          "x1": -0.4517026070384923,
          "xref": "x",
          "y0": 0.49966274010141826,
          "y1": 0.5750713003295598,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": -0.4517026070384923,
          "x1": -0.3644610120592211,
          "xref": "x",
          "y0": 0.4937462227258534,
          "y1": 0.5809878177051246,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": 0.25362346262637253,
          "x1": 0.2832011334029287,
          "xref": "x",
          "y0": 0.7920260321448466,
          "y1": 0.8216037029214028,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": 0.3799609215681955,
          "x1": 0.44609860404068413,
          "xref": "x",
          "y0": 0.8049745820222246,
          "y1": 0.8711122644947132,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": 0.29738752170973365,
          "x1": 0.3698377228923257,
          "xref": "x",
          "y0": 0.5842255063000303,
          "y1": 0.6566757074826224,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": 0.2824480843754804,
          "x1": 0.38275084604490284,
          "xref": "x",
          "y0": 0.7665244831237386,
          "y1": 0.8668272447931611,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": 0.16762938416600354,
          "x1": 0.33624805092151494,
          "xref": "x",
          "y0": 0.6247862003505872,
          "y1": 0.7934048671060986,
          "yref": "y"
         },
         {
          "line": {
           "color": "LightSeaGreen",
           "width": 2
          },
          "type": "circle",
          "x0": 0.33624805092151494,
          "x1": 0.5313261877606698,
          "xref": "x",
          "y0": 0.6115564653087655,
          "y1": 0.8066346021479203,
          "yref": "y"
         },
         {
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\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"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}}},\"width\":800,\"xaxis\":{\"range\":[-1.05,1.05],\"showgrid\":false,\"showticklabels\":false,\"zeroline\":false},\"yaxis\":{\"range\":[-1.05,1.05],\"showgrid\":false,\"showticklabels\":false,\"zeroline\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('df00d526-7833-451d-b947-ff5066c81bb2');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "# Set axes properties\n",
    "fig.update_xaxes(\n",
    "    range=[-1.05, 1.05], # making slightly wider axes than -1 to 1 so no edge of circles cut-off\n",
    "    showticklabels=False,\n",
    "    showgrid=False,\n",
    "    zeroline=False\n",
    ")\n",
    "\n",
    "fig.update_yaxes(\n",
    "    range=[-1.05, 1.05],\n",
    "    showticklabels=False,\n",
    "    showgrid=False,\n",
    "    zeroline=False,\n",
    ")\n",
    "\n",
    "# add parent circles\n",
    "for circle in circles:\n",
    "    x, y, r = circle\n",
    "    fig.add_shape(type=\"circle\",\n",
    "        xref=\"x\", yref=\"y\",\n",
    "        x0=x-r, y0=y-r, x1=x+r, y1=y+r,\n",
    "        # fillcolor=\"PaleTurquoise\", # fill color if needed\n",
    "        line_color=\"LightSeaGreen\",\n",
    "        line_width=2,\n",
    "    )\n",
    "\n",
    "# add child circles\n",
    "for child_circles in child_circle_groups:\n",
    "    for child_circle in child_circles:\n",
    "        x, y, r = child_circle\n",
    "        fig.add_shape(type=\"circle\",\n",
    "            xref=\"x\", yref=\"y\",\n",
    "            x0=x-r, y0=y-r, x1=x+r, y1=y+r,\n",
    "            # fillcolor=\"PaleTurquoise\", # fill color if needed\n",
    "            line_color=\"LightSeaGreen\",\n",
    "            line_width=2,\n",
    "        )\n",
    "    \n",
    "# Set figure size\n",
    "fig.update_layout(width=800, height=800, plot_bgcolor=\"white\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf51ab86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

This is what Plotly output looks like: