{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducing 3D Interactive Figure of DIB SNR Inside/Outside the Local Bubble in Saydjari+2023"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup the plotly template options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:45:42.250125Z",
     "start_time": "2022-12-08T22:45:22.617038Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from astropy.table import Table\n",
    "from plotly.offline import plot\n",
    "import glob\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import Normalize\n",
    "import matplotlib\n",
    "from astropy.table import vstack\n",
    "import os\n",
    "import h5py\n",
    "\n",
    "DEFAULT_FONT = 'Arial, sans-serif'\n",
    "\n",
    "import plotly.io as plt_io\n",
    "\n",
    "# create our custom_dark theme from the plotly_dark template\n",
    "plt_io.templates[\"custom_dark\"] = plt_io.templates[\"plotly_dark\"]\n",
    "\n",
    "# set the paper_bgcolor and the plot_bgcolor to a new color\n",
    "plt_io.templates[\"custom_dark\"]['layout']['paper_bgcolor'] = 'black'\n",
    "plt_io.templates[\"custom_dark\"]['layout']['plot_bgcolor'] = 'black'\n",
    "\n",
    "# you may also want to change gridline colors if you are modifying background\n",
    "plt_io.templates['custom_dark']['layout']['yaxis']['gridcolor'] = 'black'\n",
    "plt_io.templates['custom_dark']['layout']['xaxis']['gridcolor'] = 'black'\n",
    "\n",
    "data = []\n",
    "\n",
    "# make figure\n",
    "fig_dict = {\n",
    "    \"data\": [],\n",
    "    \"layout\": {}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup the plotly layout parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:52:21.055017Z",
     "start_time": "2022-12-08T22:52:21.037405Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_dict['layout'] = go.Layout(\n",
    "    margin=dict(r=50, l=50, b=50, t=50),  # noqa\n",
    "    width=1400,\n",
    "    scene=dict(\n",
    "        xaxis=dict(\n",
    "            title='X [pc]',\n",
    "            titlefont=dict(\n",
    "                family=DEFAULT_FONT,\n",
    "                size=20,\n",
    "                color='#686868'\n",
    "            ),\n",
    "            showspikes=False,\n",
    "            showgrid=True,\n",
    "            showline=True,\n",
    "            backgroundcolor='black',\n",
    "            gridcolor='#303030',\n",
    "            zerolinecolor='#303030',\n",
    "            showticklabels=True,\n",
    "            tickfont=dict(\n",
    "                family=DEFAULT_FONT,\n",
    "                size=12,\n",
    "                color='#686868'),\n",
    "            range=[-400, 400]),\n",
    "        yaxis=dict(\n",
    "            title='Y [pc]',\n",
    "            titlefont=dict(\n",
    "                family=DEFAULT_FONT,\n",
    "                size=20,\n",
    "                color='#686868'), #'#a9a9a9'\n",
    "            showspikes=False,\n",
    "            showgrid=True,\n",
    "            showline=False,\n",
    "            zerolinecolor='#303030',\n",
    "            backgroundcolor='black',\n",
    "            gridcolor='#303030',\n",
    "            range=[-400, 400],\n",
    "            showticklabels=True,\n",
    "            tickfont=dict(\n",
    "                family=DEFAULT_FONT,\n",
    "                size=12,\n",
    "                color='#686868'),\n",
    "        ),\n",
    "        zaxis=dict(\n",
    "            title='Z [pc]',\n",
    "            titlefont=dict(\n",
    "                family=DEFAULT_FONT,\n",
    "                size=20,\n",
    "                color='#686868'),\n",
    "            showspikes=False,\n",
    "            showgrid=True,\n",
    "            showline=False,\n",
    "            zerolinecolor='gold',\n",
    "            zerolinewidth=5,\n",
    "            backgroundcolor='black',\n",
    "            gridcolor='#303030',\n",
    "            range=[-400, 400],\n",
    "            showticklabels=True,\n",
    "            tickfont=dict(\n",
    "                family=DEFAULT_FONT,\n",
    "                size=12,\n",
    "                color='#686868'),\n",
    "        ),\n",
    "        aspectratio=dict(x=1*1.65, y=1*1.65,z=1*1.65),))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Leike+2020 3D dust data with Densities > 10 cm^-3 as a 3D point cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:52:30.460544Z",
     "start_time": "2022-12-08T22:52:30.010269Z"
    }
   },
   "outputs": [],
   "source": [
    "tl = Table.read(\"../support/3D_Dust_10cc_Leike2020.fits\")\n",
    "tl.sort('ys')\n",
    "dustlayer = go.Scatter3d(x = tl['xs'][::20],\n",
    "                            y = tl['ys'][::20],\n",
    "                            z = tl['zs'][::20],\n",
    "                            mode=\"markers\",\n",
    "                            marker = dict(size = 6, color = 'gray'),\n",
    "                            opacity=0.015,\n",
    "                            showlegend=True,\n",
    "                            visible = True,\n",
    "                            legendgroup='dust',\n",
    "                            hoverinfo='skip',\n",
    "                            name='3D Dust (Leike+2020)')\n",
    "fig_dict['data'].append(dustlayer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a 3D model for the Bubble's Surface from Pelgrims+2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:52:38.837804Z",
     "start_time": "2022-12-08T22:52:38.705951Z"
    }
   },
   "outputs": [],
   "source": [
    "tb = Table.read(\"../support/Local_Bubble_Pelgrims2020.fits\")\n",
    "lbubble = go.Scatter3d(x = tb['x'][::10],\n",
    "                            y = tb['y'][::10],\n",
    "                            z = tb['z'][::10],\n",
    "                            marker = dict(size = 6, colorscale =[[0.0, 'rgb(0, 0, 0)'], [0.5, 'rgb(103, 100, 193)'],[1.0, 'rgb(0, 0, 0)']], color =tb['z'][::10],cmin=-300,cmax=300),\n",
    "                            opacity=0.05,\n",
    "                            showlegend=True,\n",
    "                            visible = True,\n",
    "                            legendgroup='bubble',\n",
    "                            hoverinfo='skip',\n",
    "                            name='Local Bubble (Pelgrims+2020)')\n",
    "fig_dict['data'].append(lbubble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Current Sun Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:52:41.251558Z",
     "start_time": "2022-12-08T22:52:41.221121Z"
    }
   },
   "outputs": [],
   "source": [
    "sun_current = go.Scatter3d(x=[0], \n",
    "                  y=[0], \n",
    "                  z=[0], \n",
    "                  mode='markers',\n",
    "                  marker = dict(size = 3 , color = 'yellow', symbol='x'),\n",
    "                  hoverinfo='text',\n",
    "                  hovertext=['Sun'],\n",
    "                  opacity=1,\n",
    "                  name='Sun',\n",
    "                  showlegend=True,\n",
    "                  legendgroup='sun_current',\n",
    "                  visible=True)\n",
    "fig_dict['data'].append(sun_current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:53:04.148555Z",
     "start_time": "2022-12-08T22:52:56.850137Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.colors as mcolors\n",
    "from matplotlib import colormaps as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import colorcet as cc\n",
    "\n",
    "f = h5py.File(\"../dib_explore_v14.h5\",'r')\n",
    "\n",
    "#generate colormap\n",
    "colorlist = plt.cm.get_cmap(\"cet_CET_L6_r\",512)(np.linspace(0.9,0.5,256))\n",
    "colorlist = np.append(colorlist,plt.cm.get_cmap(\"cet_CET_L4_r\",512)(np.linspace(0.5,0,256)),axis=0)\n",
    "cmap_split = mcolors.LinearSegmentedColormap.from_list('mycmap', colorlist, N=512)\n",
    "\n",
    "#generate colormap normalization\n",
    "vmin=np.min(f['dib_snr'][:])\n",
    "vcenter=5\n",
    "vmax=10\n",
    "norm = colors.TwoSlopeNorm(vmin=vmin,vcenter=vcenter,vmax=vmax)\n",
    "\n",
    "#grab RGBA colors of all the detections and convert them to strings to faciltate plotly 3D scatter plotting\n",
    "rgbas = cmap_split(norm(f['dib_snr'][:]))\n",
    "rgb_str = [r'{}'.format(mcolors.rgb2hex((rgba[0], rgba[1], rgba[2]))) for rgba in rgbas]\n",
    "\n",
    "\n",
    "dibs = go.Scatter3d(x = f['X'][:]*1000,\n",
    "                    y = f['Y'][:]*1000,\n",
    "                    z = f['Z'][:]*1000,\n",
    "                    opacity=0.85,\n",
    "                    mode = 'markers',\n",
    "                    hoverinfo='text',\n",
    "                    hovertext=['DIB SNR: {:.1f}<br>EW: {:.2f}<br>VLSR: {:.1f}<br>Sigma: {:.2f}'.format(f['dib_snr'][:][k],f['ew'][:][k],f['v_dust_lsr'][:][k],f['sig'][:][k]) for k in range(0,len(f['X']))],\n",
    "                    marker = dict(showscale=False,size = 5, color = rgb_str,symbol='circle'),\n",
    "                    name='DIB Catalog (Saydjari+2022)',\n",
    "                    showlegend=True,\n",
    "                    legendgroup='dibs',\n",
    "                    visible = True)\n",
    "\n",
    "fig_dict['data'].append(dibs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output to File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:53:09.449438Z",
     "start_time": "2022-12-08T22:53:08.004836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../figures/DIB_Local_Bubble_SNR.html'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = go.Figure(fig_dict)\n",
    "\n",
    "fig.update_layout(showlegend = True, template = 'custom_dark')\n",
    "fig.layout.legend.font.size = 14\n",
    "fig.layout.legend.font.color = 'white'\n",
    "fig.layout.legend.title='Click to Show/Hide:'\n",
    "fig.layout.legend.title.side='top'\n",
    "fig.layout.legend.title.font.size=22\n",
    "fig.update_layout(legend= {'itemsizing': 'constant'})\n",
    "\n",
    "camera = dict(\n",
    "    up=dict(x=0, y=0, z=1),\n",
    "    eye = {'x': -1.25, 'y': -2.0, 'z': 1.25},\n",
    "    projection=dict(type='orthographic')\n",
    ")\n",
    "\n",
    "fig.update_layout(scene_camera=camera)\n",
    "\n",
    "plot(fig, filename='../figures/DIB_Local_Bubble_SNR.html', auto_open=False, auto_play=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Colorbar to Accompany Plotly Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-08T22:54:12.273310Z",
     "start_time": "2022-12-08T22:54:12.091129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10))\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.scatter(f['X'][:]*1000,f['Y'][:]*1000,c = f['dib_snr'][:],cmap=cmap_split,norm=norm)\n",
    "plt.xlim(-500,500)\n",
    "plt.ylim(-500,500)\n",
    "plt.gca().set_visible(False)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticks([ 4.,  5.,  6.,  7.,  8.,  9., 10.])\n",
    "cbar.set_label('DIB S/N', rotation=270,fontsize=20,labelpad=30)\n",
    "cbar.ax.tick_params(labelsize=15)\n",
    "plt.savefig(\"../figures/dib_snr_cbar.png\",dpi=200,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-dibs]",
   "language": "python",
   "name": "conda-env-.conda-dibs-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "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": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}