live matplotlib views with jupyter-scatter

live-mpl.ipynb

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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mass</th>\n",
       "      <th>speed</th>\n",
       "      <th>pval</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.843422</td>\n",
       "      <td>0.939455</td>\n",
       "      <td>0.828880</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.956246</td>\n",
       "      <td>0.708909</td>\n",
       "      <td>0.820758</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.588491</td>\n",
       "      <td>0.356468</td>\n",
       "      <td>0.664645</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.441721</td>\n",
       "      <td>0.405219</td>\n",
       "      <td>0.382708</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.598023</td>\n",
       "      <td>0.943465</td>\n",
       "      <td>0.397512</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       mass     speed      pval group\n",
       "0  0.843422  0.939455  0.828880     B\n",
       "1  0.956246  0.708909  0.820758     A\n",
       "2  0.588491  0.356468  0.664645     A\n",
       "3  0.441721  0.405219  0.382708     A\n",
       "4  0.598023  0.943465  0.397512     A"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import jscatter\n",
    "import traitlets\n",
    "import ipywidgets\n",
    "\n",
    "# Create some example data\n",
    "\n",
    "data = np.random.rand(500, 4)\n",
    "df = pd.DataFrame(data, columns=['mass', 'speed', 'pval', 'group'])\n",
    "df['group'] = df['group'].map(lambda c: chr(65 + round(c)), na_action=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2cd08802-0951-4092-a023-f0ffd82b2b70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create a scatter plot\n",
    "scatter = jscatter.Scatter(x=\"mass\", y=\"speed\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c80934c7-896c-4e31-8f12-1b8bdfecdfd4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<traitlets.traitlets.directional_link at 0x10e0fe790>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wire up the Scatter plot with additional \"pieces\" of derived state\n",
    "\n",
    "# create a new piece of state for the Jupyter Scatter widget\n",
    "scatter.widget.add_traits(\n",
    "    selection_df=traitlets.Any(allow_none=True),\n",
    "    selection_summary=traitlets.Any(allow_none=True),\n",
    ")\n",
    "\n",
    "# specify a directional link such that any time `selection` changes (the indices)\n",
    "# we update this summary\n",
    "traitlets.dlink(\n",
    "    source=(scatter.widget, \"selection\"),\n",
    "    target=(scatter.widget, \"selection_df\"),\n",
    "    transform=lambda sel: scatter._data.iloc[sel]\n",
    ")"
   ]
  },
  {
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   "execution_count": 10,
   "id": "c70d5f89-d9a6-41d8-8df6-68acb11d8870",
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    {
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       "model_id": "6d0d3d28cb7342f8b0edc0a10d013091",
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      "text/plain": [
       "HBox(children=(HBox(children=(VBox(children=(Button(icon='arrows', layout=Layout(width='36px'), style=ButtonSt…"
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   ],
   "source": [
    "class ECDF(traitlets.HasTraits):\n",
    "    data = traitlets.Any(allow_none=True)\n",
    "    \n",
    "    def __init__(self, x: str):\n",
    "        self.x = x\n",
    "        with plt.ioff():\n",
    "            self.fig, self.ax = plt.subplots(1, 1) \n",
    "            self.fig.canvas.header_visible = False\n",
    "        super().__init__()\n",
    "         \n",
    "    @traitlets.observe(\"data\")\n",
    "    def _on_data_change(self, change):\n",
    "        self.ax.clear()\n",
    "        sns.ecdfplot(x=self.x, data=change[\"new\"], ax=self.ax)\n",
    "        self.fig.canvas.draw_idle()\n",
    "        self.fig.canvas.flush_events()\n",
    "\n",
    "        \n",
    "ecdf_mass = ECDF(x=\"mass\")\n",
    "ecdf_speed = ECDF(x=\"speed\")\n",
    "traitlets.dlink((scatter.widget, \"selection_df\"), (ecdf_mass, \"data\"))\n",
    "traitlets.dlink((scatter.widget, \"selection_df\"), (ecdf_speed, \"data\"))\n",
    "\n",
    "ecdf_mass.fig.canvas.layout.width = \"30%\"\n",
    "ecdf_speed.fig.canvas.layout.width = \"30%\"\n",
    "\n",
    "ipywidgets.HBox([\n",
    "    scatter.show(),\n",
    "    ecdf_mass.fig.canvas,\n",
    "    ecdf_speed.fig.canvas,\n",
    "])"
   ]
  },
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