notebooks/create_panels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6270ff49",
   "metadata": {},
   "source": [
    "# Creating multi-panel figures\n",
    "\n",
    "You can create yourself multi-panel figures by directly using the underlying Matplotlib objects. We however also provide a simplified way to create such panels by assembling multiple plot or animation objects. This is particularly helpful to create complex animations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4eab0013",
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "#%load_ext autoreload\n",
    "#%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2635d5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import skimage.io\n",
    "from microfilm import microplot\n",
    "from microfilm import microanim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6e45a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = skimage.io.imread('../demodata/coli_nucl_ori_ter.tif')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "271f6ffb",
   "metadata": {},
   "source": [
    "## Micropanel\n",
    "\n",
    "First we create two microimages that we can adjust as we like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b1f58f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 165.927x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "microim1 = microplot.microshow(images=[image[0, 10, :, :], image[1, 10, :, :]],\n",
    "                               cmaps=['Greys', 'pure_magenta'], flip_map=[False, False],\n",
    "                               label_text='A', label_color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a764800",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 165.927x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "microim2 = microplot.microshow(images=[image[0, 10, :, :], image[2, 10, :, :]],\n",
    "                               cmaps=['Greys', 'pure_cyan'], flip_map=[False, False],\n",
    "                               label_text='B', label_color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d317040",
   "metadata": {},
   "source": [
    "Then we create a Micropanel object as aa grid and just add our two microimages. Those are then exactly reproduced, including annotations in the panels. Note that we can use any option used in ```matplotlib.pyplot.subplots()``` when creating the figure. Also the indices used to place the plots (e.g.g ```pos=[0,0]```) correspond to the xy position in the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b23afa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 331.855x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "micropanel = microplot.Micropanel(rows=1, cols=2, figscaling=3)\n",
    "\n",
    "micropanel.add_element(pos=[0,0], microim=microim1)\n",
    "micropanel.add_element(pos=[0,1], microim=microim2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ffa6fd9",
   "metadata": {},
   "source": [
    "By default, the panel size is adjusted to optimall fit the image size. **Note that for this adjustment we assume that all images in the panel of the same size.** As shown above, you can adjust the general size of the image with the ```figscaling``` parameter. If you want, you can also explicitly specify a figure size in which case no adjustments are done:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76146d87-df6e-4ebf-9e73-36ae6dab9c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "micropanel_size = microplot.Micropanel(rows=1, cols=2, figsize=[8,3], figscaling=3)\n",
    "\n",
    "micropanel_size.add_element(pos=[0,0], microim=microim1)\n",
    "micropanel_size.add_element(pos=[0,1], microim=microim2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eff5eb4-9d65-490b-b534-621bd61b23ac",
   "metadata": {},
   "source": [
    "In the end you can save the panel with the regular ```savefig``` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75c48705",
   "metadata": {},
   "outputs": [],
   "source": [
    "micropanel.savefig('../illustrations/panel.png', bbox_inches = 'tight', pad_inches = 0, dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaeb1e89-d70a-41e2-b2f1-5d8a92776182",
   "metadata": {},
   "source": [
    "## Avoiding double display\n",
    "\n",
    "When you create a panel, you first create each element and then add it to the panel. When using ```microshow``` for plot creation, you will therefore see the plots first individually upon creation and then again in the panel. If you create plots for exports, this is not an issue, but if you want to display a panale as a result in a notebook, you might want to avoid this double display. In that case you can create each plot directly as ```Microimage``` objects, which won't display them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "28a305d2-8eb4-47f6-b5d1-b83517cbd453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 331.855x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "microim1 = microplot.Microimage(images=[image[0, 10, :, :], image[1, 10, :, :]],\n",
    "                               cmaps=['Greys', 'pure_magenta'], flip_map=[False, False],\n",
    "                               label_text='A', label_color='black')\n",
    "\n",
    "microim2 = microplot.Microimage(images=[image[0, 10, :, :], image[2, 10, :, :]],\n",
    "                               cmaps=['Greys', 'pure_cyan'], flip_map=[False, False],\n",
    "                               label_text='B', label_color='black')\n",
    "\n",
    "micropanel = microplot.Micropanel(rows=1, cols=2, figscaling=3)\n",
    "\n",
    "micropanel.add_element(pos=[0,0], microim=microim1)\n",
    "micropanel.add_element(pos=[0,1], microim=microim2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b822f5",
   "metadata": {},
   "source": [
    "## Usage with options\n",
    "\n",
    "Creation of panels of course works with all the options available in Microimage. For example we add here a label, channel names and a scalebar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0c200e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "microim1 = microplot.Microimage(images=[image[0, 10, :, :], image[1, 10, :, :]],\n",
    "                               cmaps=['Greys', 'pure_magenta'], flip_map=[False, False],\n",
    "                               label_text='A', label_color='black', channel_label_show=True,\n",
    "                               channel_names=['nucleoid', 'ori'], scalebar_size_in_units=3,\n",
    "                               scalebar_unit_per_pix=0.05, scalebar_color='black', scalebar_font_size=20,\n",
    "                               unit='um', fig_scaling=4)\n",
    "\n",
    "microim2 = microplot.Microimage(images=[image[0, 10, :, :], image[2, 10, :, :]],\n",
    "                               cmaps=['Greys', 'pure_cyan'], flip_map=[False, False],\n",
    "                               label_text='B', label_color='black', channel_label_show=True,\n",
    "                               channel_names=['nucleoid', 'ter'], scalebar_size_in_units=3,\n",
    "                               scalebar_unit_per_pix=0.05, scalebar_color='black', scalebar_font_size=20,\n",
    "                               unit='um', fig_scaling=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "392bb4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 499.994x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "micropanel = microplot.Micropanel(rows=1, cols=2)\n",
    "\n",
    "micropanel.add_element(pos=[0,0], microim=microim1)\n",
    "micropanel.add_element(pos=[0,1], microim=microim2)\n",
    "\n",
    "micropanel.add_channel_label(channel_label_size=0.04)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65ef8f6-d64e-4d80-a6cf-bb100f2e00c1",
   "metadata": {},
   "source": [
    "## Adjusting size: accomodate colorbar\n",
    "\n",
    "Note that in certain cases, you may want to control the space between plots. You can do this with the ```margin``` option which represents the fraction of the figure to use for that space. This is particularly useful if your images have colorbars, which are not fully visible with the default margin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c38ceb53-2e4d-4911-90a4-cec6e8b8b81c",
   "metadata": {},
   "outputs": [],
   "source": [
    "microim1 = microplot.Microimage(images=[image[1, 10, :, :]], show_colorbar=True,\n",
    "                               cmaps=['pure_magenta'], flip_map=[False],\n",
    "                               label_text='A', label_color='black', channel_label_show=True,\n",
    "                               channel_names=['ori'], scalebar_size_in_units=3,\n",
    "                               scalebar_unit_per_pix=0.05, scalebar_color='white', unit='um', fig_scaling=4)\n",
    "\n",
    "microim2 = microplot.Microimage(images=[image[2, 10, :, :]], show_colorbar=True,\n",
    "                               cmaps=['pure_cyan'], flip_map=[False],\n",
    "                               label_text='B', label_color='black', channel_label_show=True,\n",
    "                               channel_names=['ter'], scalebar_size_in_units=3,\n",
    "                               scalebar_unit_per_pix=0.05, scalebar_color='white', unit='um', fig_scaling=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a5deef88-f11b-495e-bae4-ef2c2b609c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 526.543x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "micropanel = microplot.Micropanel(rows=1, cols=2, margin=0.2)\n",
    "\n",
    "micropanel.add_element(pos=[0,0], microim=microim1)\n",
    "micropanel.add_element(pos=[0,1], microim=microim2)\n",
    "\n",
    "micropanel.add_channel_label(channel_label_size=0.04)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff775c2",
   "metadata": {},
   "source": [
    "## Micropanel animations\n",
    "\n",
    "The same principle applies for animations. We first create them separately to have full control over creation and tehn integrate them in the panel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "993c6d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da7c3866",
   "metadata": {},
   "outputs": [],
   "source": [
    "microanim1 = microanim.Microanim(data=image[[0,1],::], cmaps=['Greys', 'pure_magenta'],\n",
    "                                 flip_map=[False, False], label_text='A', label_color='black',\n",
    "                                channel_label_show=True, channel_names=['nucleoid', 'ori'])\n",
    "microanim2 = microanim.Microanim(data=image[[0,2],::], cmaps=['Greys', 'pure_cyan'],\n",
    "                                 flip_map=[False, False], label_text='B', label_color='black',\n",
    "                                channel_label_show=True, channel_names=['nucleoid', 'ter'])\n",
    "\n",
    "microanim1.add_time_stamp(unit='MM', unit_per_frame=3, location='lower-right', timestamp_color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4a2791f2",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3c31b05421348cbbdd04b0eead114b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': \"Canvas(toolbar=Toolbar(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "microanim1.ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd8de7d6",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_1.png\" width=\"300\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_1.png',width=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d36b186c",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b9685beac464401a9af39a964f1d804",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': \"Canvas(toolbar=Toolbar(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "microanim2.ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ff70a62",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_2.png\" width=\"300\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_2.png',width=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4914f48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "animpanel = microanim.Microanimpanel(rows=1, cols=2,)\n",
    "animpanel.add_element(pos=[0,0], microanim=microanim1)\n",
    "animpanel.add_element(pos=[0,1], microanim=microanim2)\n",
    "\n",
    "animpanel.add_channel_label()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a511d6e5",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "671e5d37cc5c49ec919e89c2dd37f8d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': \"Canvas(toolbar=Toolbar(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "animpanel.ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "680f56a4",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_3.gif\" width=\"750\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_3.gif',width=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f674c256",
   "metadata": {},
   "source": [
    "Exporting the panel works the same way as for the simple animations. For example the above animation can be saved with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc5b031f",
   "metadata": {},
   "outputs": [],
   "source": [
    "animpanel.save_movie('../illustrations/panel.gif')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb2efea",
   "metadata": {},
   "source": [
    "You get access to the underlying objects in the panel via the ```microanims``` parameter. Hence you can still update elements after assembling them. For example you could add a timer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0a3637c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "animpanel.microanims[0,1].add_time_stamp(unit='MM',unit_per_frame=3, location='lower_left', timestamp_color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72a3151a",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "671e5d37cc5c49ec919e89c2dd37f8d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': \"Canvas(toolbar=Toolbar(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "animpanel.ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1fa4a880",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_4.png\" width=\"750\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://github.com/guiwitz/microfilm/raw/master/illustrations/create_panels_4.png',width=750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb95e7b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "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": "3.9.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "008abe021cc34364a179cd8da13a980f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_c0518b074809434692297bb5726b7216",
       "outputs": [
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "77b9c401fc16459388d9a4e561dbded1",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "035ce2dabe164a97987e2a51df797b8b": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "ToolbarModel",
      "state": {
       "layout": "IPY_MODEL_3fccd556023b4004b9b73a2ede7d1dcf",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis, CTRL fixes aspect",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "082651fb2d684cd3a7670f1bece3dffa": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_716965c43d6d4f0d884e69ce6560824f",
        "IPY_MODEL_17d4272ddcf345d48991031d937f1f79"
       ],
       "layout": "IPY_MODEL_4d15f9fa511b4beb925a2ca3b7e2213c"
      }
     },
     "09c4d48d4b584c59a4b271bdd3affcb1": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_figure_label": "Figure 2",
       "_height": 216,
       "_width": 165,
       "layout": "IPY_MODEL_e6a7112cd2cf4cd4af449560bebecefb",
       "toolbar": "IPY_MODEL_bd9d4004630b4a698c221c8313f62129",
       "toolbar_position": "left"
      }
     },
     "11677788b34b4dc69422e9216096677a": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "ToolbarModel",
      "state": {
       "layout": "IPY_MODEL_8a2009714e8e4ea09c0c18d8d6c00537",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis, CTRL fixes aspect",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "17b78ce2bc354fbd994e782c1a599f55": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "17d4272ddcf345d48991031d937f1f79": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_43b7a04e5c694c2692e8fe5fd408e7f8",
       "max": 29,
       "style": "IPY_MODEL_8b81e40f8f05468ea4ecd23fbec76c8d"
      }
     },
     "25aae11794fd49d886ef936decccecda": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "27d3469015ba4d99931a6a068174255f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_3ae31805ec15464f9aae0db6d2714cea",
       "max": 29,
       "style": "IPY_MODEL_d8939a2da2224a83aebc7364ff3a28bc"
      }
     },
     "2814fc1dc4c741009bff12f9ec352086": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_cd7a0bba01d04481904e73a8303caed9",
       "outputs": [
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "f45162d9a2814e18a1bca89284e787df",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "2a47a786c9b64864b056e98f9fdd3363": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "2bde2061fcea4fe9ae11eb4fe3acff44": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_54ee91056e824f87bc1b600987d63e68",
       "max": 29,
       "style": "IPY_MODEL_ef2e39092ecb4dbc89763e79f21789d1"
      }
     },
     "3373d4be85604817a22f08d124706353": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "37151c98b03f4559b16be5fe796e5324": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_c76cc3ffdb0a4b6695f1a22c2655a674",
       "outputs": [
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "09c4d48d4b584c59a4b271bdd3affcb1",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "37bac5ee9b434a72b2281479d118fdc4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_62e85071903341ee922929036b0f66e7",
       "max": 29,
       "style": "IPY_MODEL_90a6efa382864f3eb586cd36316cb496"
      }
     },
     "3ae31805ec15464f9aae0db6d2714cea": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3af907635f6148f585dae0bb7e648b36": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3b0ca17be4884dca977a6a65648d6c86": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3fccd556023b4004b9b73a2ede7d1dcf": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "41412a5ab8024ab899df6a06ac575fb1": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_figure_label": "Figure 1",
       "_height": 216,
       "_width": 165,
       "layout": "IPY_MODEL_17b78ce2bc354fbd994e782c1a599f55",
       "toolbar": "IPY_MODEL_11677788b34b4dc69422e9216096677a",
       "toolbar_position": "left"
      }
     },
     "4387e6f234d746da95380acd29f20b28": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "43b7a04e5c694c2692e8fe5fd408e7f8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "449a2cbf41b144bbb0fab01819857d61": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4d15f9fa511b4beb925a2ca3b7e2213c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "54ee91056e824f87bc1b600987d63e68": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "56ccec9fcc6342688fc483bed2590565": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "58f244009143497695cc134c8b054357": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5c83b475783e41f3ae5895144e135c5e": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "ToolbarModel",
      "state": {
       "layout": "IPY_MODEL_5ceaebb5e9b74ab8bea3f956ea6445a3",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis, CTRL fixes aspect",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "5ceaebb5e9b74ab8bea3f956ea6445a3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "62e85071903341ee922929036b0f66e7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "671e5d37cc5c49ec919e89c2dd37f8d5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_862b9204d9644d6d8acd2d74a23302a2",
        "IPY_MODEL_cedf683ad804480c9f1dbc51fc24238f"
       ],
       "layout": "IPY_MODEL_97907c5aa195423ba64ead4170331e91"
      }
     },
     "6749a264155040c49df194efdcae2755": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "6c32af550424402ab63e4191ce52227a": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_figure_label": "Figure 5",
       "_height": 500,
       "_width": 768,
       "layout": "IPY_MODEL_ac28bdca8ecb4da5af8f8681b5979cf3",
       "toolbar": "IPY_MODEL_f707eb1f35064e4aae6fc54fdc36f2ea",
       "toolbar_position": "left"
      }
     },
     "6e7f0941ac474d188b8cd101bfb5aa22": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_25aae11794fd49d886ef936decccecda"
      }
     },
     "70b1523330a9421bbe83afd83acd5684": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_56ccec9fcc6342688fc483bed2590565",
       "max": 29,
       "style": "IPY_MODEL_6749a264155040c49df194efdcae2755"
      }
     },
     "7106ab12da37400db232d28dcdc99807": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_figure_label": "Figure 6",
       "_height": 500,
       "_message": "x=9.0 y=215.6\n[0.994, 0.994, 0.994, 1]",
       "_width": 676,
       "layout": "IPY_MODEL_b5e201c0c32e44ff98aa290bb3fd5c48",
       "toolbar": "IPY_MODEL_5c83b475783e41f3ae5895144e135c5e",
       "toolbar_position": "left"
      }
     },
     "71085f590a914a3d932be72c3e52d218": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "716965c43d6d4f0d884e69ce6560824f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3b0ca17be4884dca977a6a65648d6c86"
      }
     },
     "77b9c401fc16459388d9a4e561dbded1": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_figure_label": "Figure 3",
       "_height": 216,
       "_message": "x=6. y=217.\n[0.994, 0.994, 0.994, 1]",
       "_width": 165,
       "layout": "IPY_MODEL_3373d4be85604817a22f08d124706353",
       "toolbar": "IPY_MODEL_035ce2dabe164a97987e2a51df797b8b",
       "toolbar_position": "left"
      }
     },
     "7b9685beac464401a9af39a964f1d804": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_2814fc1dc4c741009bff12f9ec352086",
        "IPY_MODEL_b4c059e010cd4ca590af0f169b86c8ca"
       ],
       "layout": "IPY_MODEL_71085f590a914a3d932be72c3e52d218"
      }
     },
     "81e0e61ef15447a9ad6f11a3f4766ea0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "84777c63b037448e9d892eecb71a3402": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_c180bb6149e0423c91e0fa0533ef3928",
        "IPY_MODEL_2bde2061fcea4fe9ae11eb4fe3acff44"
       ],
       "layout": "IPY_MODEL_b6608f652b4049b99a7e6f3f72273275"
      }
     },
     "862b9204d9644d6d8acd2d74a23302a2": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_dcbcca11d45d4957976d53132e33d1ba",
       "outputs": [
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "7106ab12da37400db232d28dcdc99807",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "88c85488f2cd48688984e44e73efc3da": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9d588676e1bd445ab4324e49995bc5ba"
      }
     },
     "8a2009714e8e4ea09c0c18d8d6c00537": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8b81e40f8f05468ea4ecd23fbec76c8d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "90a6efa382864f3eb586cd36316cb496": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "913fd4d4def54f84b30e1901f9ac0289": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_f49bab06a84749a8805eb23c5e1f9057",
       "max": 29,
       "style": "IPY_MODEL_e3c98c7f3b54418aa0f708de8416b73b"
      }
     },
     "92618d0d6ab14a9b81e45d8868090a21": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "952cb2ae0f29467a8694097752ec75b2": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_449a2cbf41b144bbb0fab01819857d61"
      }
     },
     "95ef092925564c468d290d5f4b280b92": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_6e7f0941ac474d188b8cd101bfb5aa22",
        "IPY_MODEL_37bac5ee9b434a72b2281479d118fdc4"
       ],
       "layout": "IPY_MODEL_c5dc96726bac4f9d9477b32e972c7249"
      }
     },
     "97907c5aa195423ba64ead4170331e91": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9d588676e1bd445ab4324e49995bc5ba": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a3c31b05421348cbbdd04b0eead114b0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_008abe021cc34364a179cd8da13a980f",
        "IPY_MODEL_ba811cc9c786458c8e78c982827fdaba"
       ],
       "layout": "IPY_MODEL_2a47a786c9b64864b056e98f9fdd3363"
      }
     },
     "ac28bdca8ecb4da5af8f8681b5979cf3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b4c059e010cd4ca590af0f169b86c8ca": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_b62cf05c2c024f4c9ee0ef7285c836be",
       "max": 29,
       "style": "IPY_MODEL_ea85d21595604c20b418c94c5046a36a"
      }
     },
     "b5e201c0c32e44ff98aa290bb3fd5c48": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b62cf05c2c024f4c9ee0ef7285c836be": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b6608f652b4049b99a7e6f3f72273275": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ba811cc9c786458c8e78c982827fdaba": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_3af907635f6148f585dae0bb7e648b36",
       "max": 29,
       "style": "IPY_MODEL_d27439759fe44c358980ee80204b6d1f"
      }
     },
     "bd9d4004630b4a698c221c8313f62129": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "ToolbarModel",
      "state": {
       "layout": "IPY_MODEL_d90aba1198964a3a906cbf5fc4fcbfe1",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis, CTRL fixes aspect",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "c0518b074809434692297bb5726b7216": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c180bb6149e0423c91e0fa0533ef3928": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_cd62152efac64e3bb3c1996c56473e6d",
       "outputs": [
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "c48f1971ec5149cda5d88816ee9c16eb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c5dc96726bac4f9d9477b32e972c7249": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c76cc3ffdb0a4b6695f1a22c2655a674": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cb717ad93d474fac9d115058981caa12": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cd62152efac64e3bb3c1996c56473e6d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cd7a0bba01d04481904e73a8303caed9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cedf683ad804480c9f1dbc51fc24238f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "Time",
       "layout": "IPY_MODEL_d2d4bf42b087492baf83bef1095ee38c",
       "max": 29,
       "style": "IPY_MODEL_fab6f0500e054b90aefa9c08be17f590"
      }
     },
     "d27439759fe44c358980ee80204b6d1f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "d2d4bf42b087492baf83bef1095ee38c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d8939a2da2224a83aebc7364ff3a28bc": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "d90aba1198964a3a906cbf5fc4fcbfe1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "dc46765f0c054bdabf8e215555736aa9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_37151c98b03f4559b16be5fe796e5324",
        "IPY_MODEL_27d3469015ba4d99931a6a068174255f"
       ],
       "layout": "IPY_MODEL_81e0e61ef15447a9ad6f11a3f4766ea0"
      }
     },
     "dcbcca11d45d4957976d53132e33d1ba": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e3c98c7f3b54418aa0f708de8416b73b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "e4a479e01e6c44249c13e4daae0c029b": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "ToolbarModel",
      "state": {
       "layout": "IPY_MODEL_c48f1971ec5149cda5d88816ee9c16eb",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis, CTRL fixes aspect",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "e6a7112cd2cf4cd4af449560bebecefb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e9d2cbfeb25347db9c41c8177d323c99": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_cb717ad93d474fac9d115058981caa12"
      }
     },
     "ea85d21595604c20b418c94c5046a36a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "ebc97c6c4ff24991aa39c229b19e85fd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_88c85488f2cd48688984e44e73efc3da",
        "IPY_MODEL_913fd4d4def54f84b30e1901f9ac0289"
       ],
       "layout": "IPY_MODEL_92618d0d6ab14a9b81e45d8868090a21"
      }
     },
     "edd54922789c48d49d28a5bb8fbedbf7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_952cb2ae0f29467a8694097752ec75b2",
        "IPY_MODEL_70b1523330a9421bbe83afd83acd5684"
       ],
       "layout": "IPY_MODEL_f71a4c2d2b8f4f39aece5e610e77586e"
      }
     },
     "ef2e39092ecb4dbc89763e79f21789d1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "f45162d9a2814e18a1bca89284e787df": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_figure_label": "Figure 4",
       "_height": 216,
       "_width": 165,
       "layout": "IPY_MODEL_58f244009143497695cc134c8b054357",
       "toolbar": "IPY_MODEL_e4a479e01e6c44249c13e4daae0c029b",
       "toolbar_position": "left"
      }
     },
     "f49bab06a84749a8805eb23c5e1f9057": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f707eb1f35064e4aae6fc54fdc36f2ea": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.9.0",
      "model_name": "ToolbarModel",
      "state": {
       "layout": "IPY_MODEL_4387e6f234d746da95380acd29f20b28",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis, CTRL fixes aspect",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "f71a4c2d2b8f4f39aece5e610e77586e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "fab6f0500e054b90aefa9c08be17f590": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}