Bokeh-wedges.ipynb

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   "source": [
    "from bokeh.plotting import figure\n",
    "from bokeh.layouts import row, column, widgetbox, gridplot\n",
    "\n",
    "from bokeh.io import curdoc\n",
    "from bokeh.io import output_notebook, show, output_file\n",
    "\n",
    "from bokeh.models import ColumnDataSource, HoverTool, TapTool, Range1d, OpenURL\n",
    "from bokeh.models import LinearColorMapper , ColorBar\n",
    "from bokeh.models.widgets import Select, Slider\n",
    "\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def palette(name_of_mpl_palette):\n",
    "    \"\"\" Transforms a matplotlib palettes into a bokeh \n",
    "    palettes\n",
    "    \"\"\"\n",
    "    from matplotlib.colors import rgb2hex\n",
    "    import matplotlib.cm as cm\n",
    "    colormap =cm.get_cmap(name_of_mpl_palette) #choose any matplotlib colormap here\n",
    "    bokehpalette = [rgb2hex(m) for m in colormap(np.arange(colormap.N))]\n",
    "    return bokehpalette\n",
    "\n",
    "\n",
    "my_palette = palette(\"viridis\")\n",
    "\n",
    "\n",
    "html_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">XSigma: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@z1</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">RA: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@x1</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">DEC: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@y1</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\""
   ]
  },
  {
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   "execution_count": 4,
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Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"0f26c0e3-6c21-48b8-a6ba-fedf13cbb079\",\"elementid\":\"16b2c737-52ac-4a96-927f-c3969a55dce5\",\"modelid\":\"40c44ac2-1e22-492f-be4b-0e469eb689b4\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "\n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"16b2c737-52ac-4a96-927f-c3969a55dce5\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "\n",
       "    }\n",
       "\n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_exposure = '00000003'#args['exposure']\n",
    "selected_arm = 'r' #args['arm']\n",
    "selected_spectrograph =  4 #args['spectrograph']\n",
    "\n",
    "# determining the position of fibers:\n",
    "c1,c2 = selected_spectrograph*500, (selected_spectrograph+1)*500\n",
    "qlf_fiberid = np.arange(0,5000)[c1:c2] \n",
    "left, right, bottom, top =  331, 335, 13, 16.7\n",
    "\n",
    "my_hover = HoverTool(tooltips=html_tooltip)\n",
    "\n",
    "z1 = xwsigma['XSIGMA']\n",
    "source = ColumnDataSource(data={\n",
    "    'x1' : xwsigma['RA'][c1:c2],\n",
    "    'y1' : xwsigma['DEC'][c1:c2],\n",
    "    'z1' : xwsigma['XSIGMA'],\n",
    "    'QLF_FIBERID': qlf_fiberid,\n",
    "})\n",
    "\n",
    "source_comp = ColumnDataSource(data = {\n",
    "    'x1': xwsigma['RA'][:c1] + xwsigma['RA'][c2:],\n",
    "    'y1': xwsigma['DEC'][:c1] + xwsigma['DEC'][c2:],\n",
    "    'z1': ['']*4500\n",
    "})\n",
    "\n",
    "p = figure( title = 'XSIGMA', x_axis_label='RA', y_axis_label='DEC',\n",
    "           plot_width=700, plot_height=600\n",
    "           ,x_range=Range1d(left, right), y_range=Range1d(bottom, top)\n",
    "          , tools= [my_hover, \"pan,box_zoom,reset,crosshair\"])\n",
    "\n",
    "mapper = LinearColorMapper(palette= my_palette,\n",
    "                           low=0.98*np.min(z1), \n",
    "                           high=1.02*np.max(z1))#nipy_spectral)#Viridis256)#,RdYlBu11)#Viridis256)#, low=0, high=100)\n",
    "\n",
    "\n",
    "p.circle('x1','y1', source = source, name=\"data\", radius = 0.016,\n",
    "        fill_color={'field': 'z1', 'transform': mapper}, \n",
    "         line_color='black', line_width=0.1)\n",
    "\n",
    "p.circle('x1','y1', source = source_comp, radius = 0.015,\n",
    "         fill_color = 'lightgray', line_color='black', line_width=0.3)\n",
    "\n",
    "# bokeh.pydata.org/en/latest/docs/reference/models/annotations.html\n",
    "color_bar = ColorBar(color_mapper=mapper, label_standoff=-13,\n",
    "                     major_label_text_font_style=\"bold\", padding = 26,\n",
    "                     major_label_text_align='right',\n",
    "                     major_label_text_font_size=\"10pt\",\n",
    "                     location=(0, 0))\n",
    "\n",
    "p.add_layout(color_bar, 'left')\n",
    "#layout = row([color_bar, p])\n",
    "output_notebook()\n",
    "#output_file('output_XSigma.html')\n",
    "show(p)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"0bc6b8bb-dd81-4b75-b873-adc17b2a76cf\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "\n",
       "    var force = false;\n",
       "\n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "\n",
       "\n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "\n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "\n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        document.getElementById(\"0bc6b8bb-dd81-4b75-b873-adc17b2a76cf\").textContent = \"BokehJS successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "\n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"0bc6b8bb-dd81-4b75-b873-adc17b2a76cf\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0bc6b8bb-dd81-4b75-b873-adc17b2a76cf' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "\n",
       "    var js_urls = [];\n",
       "\n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = {\"0ac1af8e-1f1a-4166-8082-74fc16180750\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"1326d584-5075-40f8-942b-3b7cd5214ab1\",\"type\":\"BasicTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"xs_units\":\"screen\",\"ys_units\":\"screen\"},\"id\":\"1ffbe416-1196-453e-969d-ab7692266b40\",\"type\":\"PolyAnnotation\"},{\"attributes\":{},\"id\":\"825b58b0-614b-485d-906d-9983f3593cac\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"b5826efe-9066-47e2-a3d9-de5f0fa3a8c5\",\"type\":\"HoverTool\"},{\"id\":\"dbd9c270-ff27-494c-9d77-1cedc7c0bbb6\",\"type\":\"PanTool\"},{\"id\":\"9a7f8e7f-e9f9-47dc-a1c1-9995e7ff22d5\",\"type\":\"BoxZoomTool\"},{\"id\":\"5d28f2f9-cafa-4c88-8997-cf56c19b6f0b\",\"type\":\"ResetTool\"},{\"id\":\"a88f5a35-30d6-4682-af76-28f3333f0d88\",\"type\":\"CrosshairTool\"},{\"id\":\"8a5abc73-1774-4947-aacf-4828489ad11b\",\"type\":\"LassoSelectTool\"}]},\"id\":\"cc723313-986b-4f75-abdd-1301c6247966\",\"type\":\"Toolbar\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"wl\"},\"y\":{\"field\":\"wavg_resid\"}},\"id\":\"81dfd3ff-9759-4a72-b120-56484c00ab03\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"871e2cae-6bab-4fe3-ae55-1d503180371f\",\"type\":\"BasicTicker\"},{\"attributes\":{\"plot\":{\"id\":\"69f10bc7-f0fd-4c00-ae78-defb6cf91655\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"dbd9c270-ff27-494c-9d77-1cedc7c0bbb6\",\"type\":\"PanTool\"},{\"attributes\":{\"callback\":null},\"id\":\"a9255d89-3418-42d0-9a02-c4cdd7645990\",\"type\":\"DataRange1d\"},{\"attributes\":{\"data_source\":{\"id\":\"79250d8e-8fa3-404e-b25e-9a396cb5645f\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6df25c43-cb3c-4725-9060-eee1106367fa\",\"type\":\"Line\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"c0458305-4af9-454b-8fa1-3dd7a7253461\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"7ef1bda2-b742-4d43-b666-e07146707519\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"below\":[{\"id\":\"8be8c91c-1877-4de9-9717-2ff1ddab618d\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"7ec3ea8f-bd60-4901-8530-eedfe4f01bfb\",\"type\":\"LinearAxis\"}],\"plot_height\":240,\"plot_width\":720,\"renderers\":[{\"id\":\"8be8c91c-1877-4de9-9717-2ff1ddab618d\",\"type\":\"LinearAxis\"},{\"id\":\"e4a6e17a-0a38-495a-a3bb-fec01b6ed533\",\"type\":\"Grid\"},{\"id\":\"7ec3ea8f-bd60-4901-8530-eedfe4f01bfb\",\"type\":\"LinearAxis\"},{\"id\":\"97d6910e-8fee-477b-8b5e-20175ca0912b\",\"type\":\"Grid\"},{\"id\":\"cd09817c-b7c4-42bf-8157-1028f2364ec3\",\"type\":\"BoxAnnotation\"},{\"id\":\"1ffbe416-1196-453e-969d-ab7692266b40\",\"type\":\"PolyAnnotation\"},{\"id\":\"7ef1bda2-b742-4d43-b666-e07146707519\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"44bcf8ea-f489-4d81-933a-a31022596a9e\",\"type\":\"Title\"},\"tool_events\":{\"id\":\"96c6f9b2-4982-4520-a9a5-00573771c0b4\",\"type\":\"ToolEvents\"},\"toolbar\":{\"id\":\"36dc7687-61d4-4bb6-b88b-04541d878be4\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"b6bac794-0920-4aa1-a350-30b21f9432c2\",\"type\":\"DataRange1d\"},\"y_range\":{\"id\":\"a9255d89-3418-42d0-9a02-c4cdd7645990\",\"type\":\"DataRange1d\"}},\"id\":\"762249a1-ed4a-43d3-927d-8c2555318eb3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"plot\":null,\"text\":\"WAVG_RESID_WAVE\"},\"id\":\"05b127f3-d174-441c-bd7e-13ffd54daa35\",\"type\":\"Title\"},{\"attributes\":{\"axis_label\":\"Angstrom\",\"formatter\":{\"id\":\"654f9074-a48f-4775-acd8-70f04e4795fb\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"762249a1-ed4a-43d3-927d-8c2555318eb3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1326d584-5075-40f8-942b-3b7cd5214ab1\",\"type\":\"BasicTicker\"}},\"id\":\"8be8c91c-1877-4de9-9717-2ff1ddab618d\",\"type\":\"LinearAxis\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"cd09817c-b7c4-42bf-8157-1028f2364ec3\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"wl\"},\"y\":{\"field\":\"wavg_resid\"}},\"id\":\"615b71d1-641d-4502-a2f1-9e797f653d0d\",\"type\":\"Line\"},{\"attributes\":{\"plot\":{\"id\":\"762249a1-ed4a-43d3-927d-8c2555318eb3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1326d584-5075-40f8-942b-3b7cd5214ab1\",\"type\":\"BasicTicker\"}},\"id\":\"e4a6e17a-0a38-495a-a3bb-fec01b6ed533\",\"type\":\"Grid\"},{\"attributes\":{\"bottom_un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   <div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">Wavelength: </span>\\n            <span style=\\\"font-size: 13px; color: #515151\\\">@wl &#8491</span>\\n        </div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">y: </span>\\n            <span style=\\\"font-size: 13px; color: #515151;\\\">@wavg_resid</span>\\n        </div>\\n    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    "skr_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">Wavelength: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@wl &#8491</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">y: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@med_resid</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\"\n",
    "\n",
    "wavg_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">Wavelength: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@wl &#8491</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">y: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@wavg_resid</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\"\n",
    "\n",
    "skr_hover=HoverTool(tooltips=skr_tooltip, mode='vline')\n",
    "wavg_hover=HoverTool(tooltips=wavg_tooltip, mode='vline')\n",
    "\n",
    "\n",
    "skyres_source = ColumnDataSource(\n",
    "                data={'wl': skyresid['WAVELENGTH'],\n",
    "                      'med_resid' : skyresid['MED_RESID_WAVE'],\n",
    "                      'wavg_resid':  skyresid['WAVG_RES_WAVE']\n",
    "                     })\n",
    "\n",
    "p1 = figure(title= 'MED_RESID_WAVE', \n",
    "            x_axis_label='Angstrom',\n",
    "            plot_width = 720, plot_height = 240,\n",
    "          tools=[skr_hover,\"pan,box_zoom,reset,crosshair, lasso_select\" ])\n",
    "p1.line('wl', 'med_resid', source=skyres_source)\n",
    "\n",
    "p2 = figure(title= 'WAVG_RESID_WAVE', \n",
    "            x_axis_label='Angstrom',\n",
    "            plot_width = 720, plot_height = 240,\n",
    "          tools=[wavg_hover,\"pan,box_zoom,reset,crosshair, lasso_select\" ])\n",
    "p2.line('wl', 'wavg_resid', source=skyres_source)\n",
    "\n",
    "p1.x_range = p2.x_range\n",
    "layout=column(p1,p2)\n",
    "\n",
    "# output_file('output_res_spectrum1.html')\n",
    "show(layout)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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   <div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">Wavelength: </span>\\n            <span style=\\\"font-size: 13px; color: #515151\\\">@wl &#8491</span>\\n        </div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">y: </span>\\n            <span style=\\\"font-size: 13px; color: #515151;\\\">@wavg_resid</span>\\n        </div>\\n    </div>\\n\"},\"id\":\"7e9ea2ca-1271-4ec0-98f1-095828a8a5e9\",\"type\":\"HoverTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"7e9ea2ca-1271-4ec0-98f1-095828a8a5e9\",\"type\":\"HoverTool\"},{\"id\":\"a685665d-67f7-416e-8e54-f6004fdf7b0c\",\"type\":\"PanTool\"},{\"id\":\"c845a152-1c78-4bf7-99ba-225f45debdbf\",\"type\":\"BoxZoomTool\"},{\"id\":\"4530cdf2-38d5-45d1-a3fe-b4b4d29d9543\",\"type\":\"ResetTool\"},{\"id\":\"13381daa-e927-4b66-be95-2836fdb0d976\",\"type\":\"CrosshairTool\"}]},\"id\":\"66695dfd-1263-433e-a43e-0ea88a681048\",\"type\":\"Toolbar\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"9e287219-103d-4ee6-8b30-168efd0da495\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"children\":[{\"id\":\"afdaa690-5674-4c28-a7d6-7fd9a1283eb6\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"77b86b26-7e46-4a17-ae6c-590d6ff88ce6\",\"subtype\":\"Figure\",\"type\":\"Plot\"}]},\"id\":\"555cedc2-4663-47fe-96d2-f545434f5a19\",\"type\":\"Column\"},{\"attributes\":{\"bottom\":{\"field\":\"bottom\"},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"wavg_resid\"},\"width\":{\"field\":\"wd\"},\"x\":{\"field\":\"wl\"}},\"id\":\"80c0ae54-9e4b-4cff-9556-2568b0d83bfc\",\"type\":\"VBar\"},{\"attributes\":{\"bottom\":{\"field\":\"bottom\"},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"med_resid\"},\"width\":{\"field\":\"wd\"},\"x\":{\"field\":\"wl\"}},\"id\":\"9cb7e6c2-fdff-4577-a9fc-9cada26ee0dc\",\"type\":\"VBar\"},{\"attributes\":{\"overlay\":{\"id\":\"9e287219-103d-4ee6-8b30-168efd0da495\",\"type\":\"BoxAnnotation\"},\"plot\":{\"id\":\"77b86b26-7e46-4a17-ae6c-590d6ff88ce6\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"c845a152-1c78-4bf7-99ba-225f45debdbf\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"de10819b-87f5-4be8-8d3d-61e0659d9916\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"plot\":{\"id\":\"afdaa690-5674-4c28-a7d6-7fd9a1283eb6\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"tooltips\":\"\\n    <div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">Wavelength: </span>\\n            <span style=\\\"font-size: 13px; color: #515151\\\">@wl &#8491</span>\\n        </div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">y: </span>\\n            <span style=\\\"font-size: 13px; color: #515151;\\\">@med_resid</span>\\n        </div>\\n    </div>\\n\"},\"id\":\"fbe2b910-552a-4471-a3dd-7887f46fd6a4\",\"type\":\"HoverTool\"},{\"attributes\":{\"data_source\":{\"id\":\"cbfa367a-5347-4f31-a62c-f9de7922535c\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"9cb7e6c2-fdff-4577-a9fc-9cada26ee0dc\",\"type\":\"VBar\"},\"hover_glyph\":{\"id\":\"09d24cb0-c888-4c33-86e0-35d8bd4696f9\",\"type\":\"VBar\"},\"nonselection_glyph\":{\"id\":\"2a1a7cee-03eb-404a-bcd4-8e60529b3319\",\"type\":\"VBar\"},\"selection_glyph\":null},\"id\":\"77bc91cc-909c-41de-b89d-98b7565ed67e\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"555cedc2-4663-47fe-96d2-f545434f5a19\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"5ef7eeca-fecd-43e5-813c-279f23ec3237\",\"elementid\":\"223e4ad4-8096-4c54-853f-5305cccfc2d2\",\"modelid\":\"555cedc2-4663-47fe-96d2-f545434f5a19\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "\n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"223e4ad4-8096-4c54-853f-5305cccfc2d2\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "\n",
       "    }\n",
       "\n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skr_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">Wavelength: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@wl &#8491</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">y: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@med_resid</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\"\n",
    "\n",
    "wavg_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">Wavelength: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@wl &#8491</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">y: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@wavg_resid</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\" \n",
    "skr_hover=HoverTool(tooltips=skr_tooltip)\n",
    "wavg_hover=HoverTool(tooltips=wavg_tooltip)\n",
    "\n",
    "\n",
    "skyres_source = ColumnDataSource(\n",
    "                data={'wl': skyresid['WAVELENGTH'],\n",
    "                      'med_resid' : skyresid['MED_RESID_WAVE'],\n",
    "                      'wavg_resid':  skyresid['WAVG_RES_WAVE'],\n",
    "                       'wd': [1] *len(skyresid['WAVELENGTH']),\n",
    "                      'bottom':[0.] *len(skyresid['WAVELENGTH'])\n",
    "                     })\n",
    "\n",
    "p1 = figure(title= 'MED_RESID_WAVE', \n",
    "            x_axis_label='Angstrom',\n",
    "            plot_width = 720, plot_height = 240,\n",
    "          tools=[skr_hover,\"pan,box_zoom,reset, crosshair\" ])\n",
    "\n",
    "p1.vbar(x='wl',width='wd', top='med_resid',  bottom='bottom', source=skyres_source\n",
    "       , hover_color='red')\n",
    "\n",
    "p2 = figure(title= 'WAVG_RESID_WAVE', \n",
    "            x_axis_label='Angstrom',\n",
    "            plot_width = 720, plot_height = 240,\n",
    "          tools=[wavg_hover,\"pan,box_zoom,reset, crosshair\"])\n",
    "\n",
    "p2.vbar(x='wl',width='wd', top='wavg_resid',  bottom='bottom', source=skyres_source\n",
    "       ,hover_color='red')\n",
    "\n",
    "p1.x_range = p2.x_range\n",
    "layout=column(p1,p2)\n",
    "\n",
    "#output_file('output_res_spectrum2.html')\n",
    "show(layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"a6f93c4e-8705-4806-be3d-3c3c32bd1a8f\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "\n",
       "    var force = false;\n",
       "\n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "\n",
       "\n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "\n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "\n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        document.getElementById(\"a6f93c4e-8705-4806-be3d-3c3c32bd1a8f\").textContent = \"BokehJS successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "\n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"a6f93c4e-8705-4806-be3d-3c3c32bd1a8f\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a6f93c4e-8705-4806-be3d-3c3c32bd1a8f' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "\n",
       "    var js_urls = [];\n",
       "\n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
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   <div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">Wavelength: </span>\\n            <span style=\\\"font-size: 13px; color: #515151\\\">@wl &#8491</span>\\n        </div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">y: </span>\\n            <span style=\\\"font-size: 13px; color: #515151;\\\">@wavg_resid</span>\\n        </div>\\n    </div>\\n\"},\"id\":\"4557553c-c5e7-40a1-9ed2-7bc88dffc627\",\"type\":\"HoverTool\"},{\"attributes\":{\"plot\":{\"id\":\"a1764ad7-dcc6-4616-ab8b-22e1a69777eb\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"1b59710a-3ec1-46eb-ba64-6cbf6073f088\",\"type\":\"PanTool\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"190cdd03-5127-4a24-8b61-34f0782b038e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"6ceec873-b3a4-4772-98ce-8a323b884d96\",\"type\":\"BasicTicker\"}},\"id\":\"86214890-17de-4c97-96b8-f10c880669b8\",\"type\":\"Grid\"},{\"attributes\":{\"plot\":{\"id\":\"a1764ad7-dcc6-4616-ab8b-22e1a69777eb\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"d3fade7f-011c-4a3b-8254-b096192292ad\",\"type\":\"ResetTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"63b6c4d2-06b5-4d56-adda-7e7ec36ab218\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"75ab0f2b-2bcf-4304-8ffd-4da804c3f2e0\",\"type\":\"BasicTicker\"},{\"attributes\":{\"plot\":{\"id\":\"a1764ad7-dcc6-4616-ab8b-22e1a69777eb\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"75ab0f2b-2bcf-4304-8ffd-4da804c3f2e0\",\"type\":\"BasicTicker\"}},\"id\":\"6b97bd35-73b7-4fbd-a79a-c34e831e1df5\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"2549e7fe-209d-4e23-b938-046f49da3270\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"ecbaa3e4-1a78-4069-9e3a-be2786bf6081\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"6ceec873-b3a4-4772-98ce-8a323b884d96\",\"type\":\"BasicTicker\"},{\"attributes\":{\"axis_label\":\"Angstrom\",\"formatter\":{\"id\":\"ecbaa3e4-1a78-4069-9e3a-be2786bf6081\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"a1764ad7-dcc6-4616-ab8b-22e1a69777eb\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"75ab0f2b-2bcf-4304-8ffd-4da804c3f2e0\",\"type\":\"BasicTicker\"}},\"id\":\"3a868dac-2cd0-418d-8ccc-4452a68b0f9b\",\"type\":\"LinearAxis\"},{\"attributes\":{\"callback\":null},\"id\":\"461a8923-73b9-42e9-b93e-c697a0bbfdf1\",\"type\":\"DataRange1d\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"4557553c-c5e7-40a1-9ed2-7bc88dffc627\",\"type\":\"HoverTool\"},{\"id\":\"c586b264-2741-4544-94ca-1db1c4410796\",\"type\":\"PanTool\"},{\"id\":\"a96ca571-7433-4705-85c8-633ed870b0a2\",\"type\":\"BoxZoomTool\"},{\"id\":\"42ddc014-023c-4d7b-bba6-c2e41363d95f\",\"type\":\"ResetTool\"},{\"id\":\"ab487d24-d471-4b25-bbdd-b49d955863ca\",\"type\":\"CrosshairTool\"},{\"id\":\"703293a6-84fd-44ba-9b76-be1e69624b2f\",\"type\":\"LassoSelectTool\"}]},\"id\":\"69e184c2-7870-4b8c-ab24-afa78f1f17bd\",\"type\":\"Toolbar\"},{\"attributes\":{\"axis_label\":\"Angstrom\",\"formatter\":{\"id\":\"e78b0823-f5a5-4d12-9902-75bc65d16429\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"190cdd03-5127-4a24-8b61-34f0782b038e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"2549e7fe-209d-4e23-b938-046f49da3270\",\"type\":\"BasicTicker\"}},\"id\":\"1d111a14-6dfd-4566-a318-f9f27a097e9a\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"a6021b8a-8721-4f5f-b593-606166eada04\",\"type\":\"ToolEvents\"},{\"attributes\":{},\"id\":\"a38ab756-46bb-48e6-ac7e-194c4f647d1c\",\"type\":\"ToolEvents\"},{\"attributes\":{\"callback\":null,\"mode\":\"vline\",\"plot\":{\"id\":\"a1764ad7-dcc6-4616-ab8b-22e1a69777eb\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"tooltips\":\"\\n    <div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">Wavelength: </span>\\n            <span style=\\\"font-size: 13px; color: #515151\\\">@wl &#8491</span>\\n        </div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">y: </span>\\n            <span style=\\\"font-size: 13px; color: #515151;\\\">@med_resid</span>\\n        </div>\\n    </div>\\n\"},\"id\":\"2cce713f-16e9-49bb-876d-9368da988490\",\"type\":\"HoverTool\"},{\"attributes\":{\"plot\":null,\"text\":\"MED_RESID_WAVE\"},\"id\":\"6e367f99-47ea-4d04-b6ec-246305d3a146\",\"type\":\"Title\"},{\"attributes\":{\"children\":[{\"id\":\"a1764ad7-dcc6-4616-ab8b-22e1a69777eb\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"190cdd03-5127-4a24-8b61-34f0782b038e\",\"subtype\":\"Figure\",\"type\":\"Plot\"}]},\"id\":\"e541eb3a-244b-4ec5-95a3-bba152cb0977\",\"type\":\"Column\"},{\"attributes\":{},\"id\":\"7e9c9bf6-b06f-4300-9d3c-6e68c0434647\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"plot\":null,\"text\":\"WAVG_RESID_WAVE\"},\"id\":\"798fe790-a3ef-48b0-ab44-486d92e41c1c\",\"type\":\"Title\"}],\"root_ids\":[\"e541eb3a-244b-4ec5-95a3-bba152cb0977\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"289843e0-8411-48fa-88c0-b26b29f62bf3\",\"elementid\":\"a6f93c4e-8705-4806-be3d-3c3c32bd1a8f\",\"modelid\":\"e541eb3a-244b-4ec5-95a3-bba152cb0977\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "\n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"a6f93c4e-8705-4806-be3d-3c3c32bd1a8f\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "\n",
       "    }\n",
       "\n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skr_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">Wavelength: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@wl &#8491</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">y: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@med_resid</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\"\n",
    "\n",
    "wavg_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">Wavelength: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@wl &#8491</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">y: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">@wavg_resid</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\"\n",
    "\n",
    "skr_hover=HoverTool(tooltips=skr_tooltip, mode='vline')\n",
    "wavg_hover=HoverTool(tooltips=wavg_tooltip, mode='vline')\n",
    "\n",
    "\n",
    "skyres_source = ColumnDataSource(\n",
    "                data={'wl': skyresid['WAVELENGTH'],\n",
    "                      'med_resid' : skyresid['MED_RESID_WAVE'],\n",
    "                      'wavg_resid':  skyresid['WAVG_RES_WAVE']\n",
    "                     })\n",
    "\n",
    "p1 = figure(title= 'MED_RESID_WAVE', \n",
    "            x_axis_label='Angstrom',\n",
    "            plot_width = 720, plot_height = 340,\n",
    "          tools=[skr_hover,\"pan,box_zoom,reset,crosshair, lasso_select\" ])\n",
    "\n",
    "p1.line('wl', 'med_resid', source=skyres_source)\n",
    "\n",
    "p1.circle('wl', 'med_resid', source=skyres_source, alpha = 0, size=1,\n",
    "          hover_alpha=1,\n",
    "         hover_fill_color='orange', hover_line_color='red') \n",
    "\n",
    "\n",
    "p2 = figure(title= 'WAVG_RESID_WAVE', \n",
    "            x_axis_label='Angstrom',\n",
    "            plot_width = 720, plot_height = 340,\n",
    "          tools=[wavg_hover,\"pan,box_zoom,reset,crosshair, lasso_select\" ])\n",
    "\n",
    "p2.line('wl', 'wavg_resid', source=skyres_source)\n",
    "p2.circle('wl', 'wavg_resid', source=skyres_source, alpha=0, size=1,\n",
    "         hover_alpha=1,\n",
    "          hover_fill_color='orange', hover_line_color='red') \n",
    "\n",
    "\n",
    "\n",
    "p1.x_range = p2.x_range\n",
    "layout=column(p1,p2)\n",
    "#output_file('output_res_spectrum3.html')\n",
    "show(layout)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countpix loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-countpix-r4-00000003.yaml\n",
      "getbias loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-getbias-r4-00000003.yaml\n",
      "getrms loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-getrms-r4-00000003.yaml\n",
      "xwsigma loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-xwsigma-r4-00000003.yaml\n",
      "countbins loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-countbins-r4-00000003.yaml\n",
      "integ loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-integ-r4-00000003.yaml\n",
      "skycont loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-skycont-r4-00000003.yaml\n",
      "skypeak loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-skypeak-r4-00000003.yaml\n",
      "skyresid loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-skyresid-r4-00000003.yaml\n",
      "snr loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-snr-r4-00000003.yaml\n"
     ]
    }
   ],
   "source": [
    "# Pre Load this!\n",
    "selected_exposure = '00000003'#args['exposure']\n",
    "selected_arm = 'r' #args['arm']\n",
    "selected_spectrograph =  4 #args['spectrograph']\n",
    "\n",
    "from scalar_metrics_v02 import LoadMetrics\n",
    "\n",
    "cam = selected_arm+str(selected_spectrograph)\n",
    "exp, night = '00000003', '20190101'\n",
    "lm = LoadMetrics(cam, exp, night);\n",
    "metrics, tests  = lm.metrics, lm.tests \n",
    "\n",
    "countpix  = metrics['countpix']\n",
    "getbias   = metrics['getbias']\n",
    "getrms    = metrics['getrms']\n",
    "xwsigma   = metrics['xwsigma']\n",
    "countbins = metrics['countbins']\n",
    "integ     = metrics['integ']\n",
    "skycont   = metrics['skycont']\n",
    "skypeak   = metrics['skypeak']\n",
    "skyresid  = metrics['skyresid']\n",
    "snr       = metrics['snr']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bokeh.plotting import output_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "#from bokeh.plotting import output_file\n",
    "from bokeh.plotting import output_notebook\n",
    "import numpy as np\n",
    "# from bokeh.io import output_file # Outra aba\n",
    "\n",
    "import getpass\n",
    "usr = getpass.getuser()\n",
    "print( usr)\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import scalar_metrics_v02\n",
    "importlib.reload(scalar_metrics_v02)\n",
    "from scalar_metrics_v02 import LoadMetrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cam, exp, night = 'r3', '00000003', '20190101'\n",
    "\n",
    "print (\"\\n\\nTests for the available scalars and using yaml output\\n\", \"=\"*50)\n",
    "print (\"TO DO:\\n *docs of functions \\n *what more else?\")\n",
    "\n",
    "lm = LoadMetrics(cam, exp, night);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics, tests  = lm.metrics, lm.tests # Load_metrics_n_tests()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "countpix  = metrics['countpix']\n",
    "getbias   = metrics['getbias']\n",
    "getrms    = metrics['getrms']\n",
    "xwsigma   = metrics['xwsigma']\n",
    "countbins = metrics['countbins']\n",
    "integ     = metrics['integ']\n",
    "skycont   = metrics['skycont']\n",
    "skypeak   = metrics['skypeak']\n",
    "skyresid  = metrics['skyresid']\n",
    "snr       = metrics['snr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models import ColumnDataSource\n",
    "import bokeh.plotting as bk\n",
    "bk.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating my new bokeh colormap palette\n",
    "\n",
    "def palette(name_of_mpl_palette):\n",
    "    \"\"\" Transforms a matplotlib palettes into a bokeh \n",
    "    palettes\n",
    "    \"\"\"\n",
    "    from matplotlib.colors import rgb2hex\n",
    "    import matplotlib.cm as cm\n",
    "    colormap =cm.get_cmap(name_of_mpl_palette) #choose any matplotlib colormap here\n",
    "    bokehpalette = [rgb2hex(m) for m in colormap(np.arange(colormap.N))]\n",
    "    return bokehpalette\n",
    "\n",
    "import bokeh.palettes as bp\n",
    "\n",
    "cmap_all = bp.__palettes__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "maps = [\"viridis\",\"coolwarm_r\",\"RdYlBu_r\", \"bwr\",\"BrBG_r\", \"jet\"\n",
    "       , \"cool\", \"spring\", \"winter\"]\n",
    "my_palette = palette(\"viridis\")\n",
    "CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a',\n",
    "                  '#f781bf', '#a65628', '#984ea3',\n",
    "'#999999', '#e41a1c', '#dede00']\n",
    "my_palette = palette(\"BrBG_r\")\n",
    "my_palette = palette(maps[8])\n",
    "my_palette = CB_color_cycle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XWSIGMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from bokeh.models import Range1d\n",
    "from bokeh.models import ColumnDataSource, HoverTool, TapTool #, OpenURL\n",
    "from bokeh.models import LinearColorMapper ,    ColorBar\n",
    "#from bokeh.palettes import (Colorblind3,RdYlBu11, YlGnBu9,Spectral3, \n",
    "#                    Plasma256, RdYlBu,Colorblind, Viridis3, Viridis256)\n",
    "\n",
    "\n",
    "camnum =  [ int(s) for s in cam if s.isdigit()][0]\n",
    "c1,c2  = camnum*500, (camnum+1)*500\n",
    "qlf_fiberid = np.arange(0,5000)[c1:c2] # Evaluate inside QLF\n",
    "\n",
    "z = np.array(xwsigma['WSIGMA'])\n",
    "\n",
    "#colors = [\n",
    "#    \"#%02x%02x%02x\" % (int(r), int(g), int(b)) \n",
    "#    for r, g, b, _ in 255*mpl.cm.nipy_spectral(mpl.colors.Normalize()(z))\n",
    "#]\n",
    "\n",
    "source = ColumnDataSource(data={\n",
    "    'x1' : xwsigma['RA'][c1:c2],\n",
    "    'y1' : xwsigma['DEC'][c1:c2],\n",
    "    'z'  : z,\n",
    "    'QLF_FIBERID': qlf_fiberid,\n",
    "})\n",
    "\n",
    "source_comp = ColumnDataSource(data = {\n",
    "    'x_comp': xwsigma['RA'][:c1] + xwsigma['RA'][c2:],\n",
    "    'y_comp': xwsigma['DEC'][:c1] + xwsigma['DEC'][c2:],   \n",
    "})\n",
    "\n",
    "left, right, bottom, top =  331, 335, 13, 16.7\n",
    "hover = HoverTool(tooltips=[\n",
    "         (\"FIBER ID\", \"@QLF_FIBERID\"),\n",
    "         (\"RA\", \"$x\"),\n",
    "         (\"DEC\", \"$y\"),\n",
    "         (\"WSIGMA\", \"@z\"),\n",
    "          (\"fc\",\"$color[ swatch]:fill_color\")\n",
    "         ])\n",
    "\n",
    "#hover = HoverTool(names=[\"data\"]) \n",
    "p = figure( plot_width=700, plot_height=600\n",
    "           ,x_range=Range1d(left, right), y_range=Range1d(bottom, top)\n",
    "          , tools= [hover, \"pan,box_zoom,reset\"])\n",
    "\n",
    "mapper = LinearColorMapper(palette= my_palette, low=0.98*np.min(z), high=1.02*np.max(z))#nipy_spectral)#Viridis256)#,RdYlBu11)#Viridis256)#, low=0, high=100)\n",
    "\n",
    "\n",
    "p.circle('x1','y1', source = source, name=\"data\", radius = 0.015,\n",
    "        fill_color={'field': 'z', 'transform': mapper}, # line_color=None,\n",
    "         line_color='black', line_width=0.1)#\"\"\")# , source=source)\n",
    "\n",
    "p.circle('x_comp','y_comp', source = source_comp, radius = 0.015, fill_color = 'lightgray'\n",
    "          , line_color='black', line_width=0.3)#\"\"\")# , source=source)\n",
    "\n",
    "# bokeh.pydata.org/en/latest/docs/reference/models/annotations.html\n",
    "color_bar = ColorBar(color_mapper=mapper, label_standoff=-14,\n",
    "                    major_label_text_font_style=\"bold\", padding = 25,\n",
    "                      major_label_text_align ='right',\n",
    "                     major_label_text_font_size=\"10pt\",\n",
    "                     location=(0, 0))\n",
    "\n",
    "p.add_layout(color_bar, 'left')\n",
    "\n",
    "\"\"\",# radius=radii/radii,\n",
    "fill_color=colors, fill_alpha=1.,\n",
    "line_color=None)\"\"\"\n",
    "\n",
    "\n",
    "show(p)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(xwsigma['RA'][c1:c2], xwsigma['DEC'][c1:c2], marker='.', c= xwsigma['XSIGMA'])#, c= mym['XSIGMA'])#, mym['WSIGMA'], edgecolor = 'k')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.xlim(331,335)\n",
    "plt.ylim(13,16.7)\n",
    "plt.scatter(xwsigma['RA'][c1:c2], xwsigma['DEC'][c1:c2], marker='.', c= xwsigma['WSIGMA'])\n",
    "plt.colorbar()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qlf_fiberid = np.arange(0,5000)[c1:c2]\n",
    "print (qlf_fiberid[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "​\n",
    "\n",
    "## AUXILIAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from bokeh.io import show\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.models import (\n",
    "    ColumnDataSource,\n",
    "    ColorBar,\n",
    "    LinearColorMapper,\n",
    "    LogColorMapper,\n",
    ")\n",
    "\n",
    "from bokeh.palettes import Viridis3, Viridis256\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "x = np.random.random(2500) * 140 - 20\n",
    "y = np.random.normal(size=2500) * 2 + 5\n",
    "\n",
    "source = ColumnDataSource(dict(x=x, y=y))\n",
    "opts = dict(x='x', line_color=None, source=source)\n",
    "\n",
    "\n",
    "cmap_list = [Category10_10,Accent8,BuPu5,\n",
    "                    Colorblind3,RdYlBu11, YlGnBu9,Spectral3, \n",
    "                    Plasma256, RdYlBu,Colorblind, Viridis3, Viridis256]\n",
    "\n",
    "def make_plot(mapper, title):\n",
    "    mapper.low_color = 'blue' # cor abaixo de low no range do LinearColorMapper\n",
    "    mapper.high_color = 'red' # \n",
    "    p = figure(toolbar_location=None, tools='', title=title)\n",
    "    color_bar = ColorBar(color_mapper=mapper, location=(0, 0))\n",
    "    p.circle(\n",
    "        x='x', y='y',\n",
    "        fill_color={'field': 'x', 'transform': mapper}, line_color=None,\n",
    "        source=source\n",
    "    )\n",
    "\n",
    "    p.add_layout(color_bar, 'right')\n",
    "    return p\n",
    "\n",
    "p1 = make_plot(LinearColorMapper(palette=Viridis256, low=0, high=100), title='Viridis256 - Linear, low/high = blue/red')\n",
    "p2 = make_plot(LogColorMapper(palette=Viridis256, low=0, high=100), title='Viridis256 - Log, low/high = blue/red')\n",
    "p3 = make_plot(LinearColorMapper(palette=Viridis3, low=0, high=100), title='Viridis3 - Linear, low/high = blue/red')\n",
    "p4 = make_plot(LogColorMapper(palette=Viridis3, low=0, high=100), title='Viridis3 - Log, low/high =, blue/red')\n",
    "\n",
    "show(gridplot([p1, p2, p3, p4], ncols=2, plot_width=400, plot_height=300, toolbar_location=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print( len(cmap_all))\n",
    "\n",
    "for i in range(1,187):\n",
    "\n",
    "    if 187%i == 0:\n",
    "\n",
    "        print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=[]\n",
    "for i in range(10):\n",
    "    p0 = make_plot(LinearColorMapper(palette=cmap_all[i], low=-20, high=120), title='%d'%(i))\n",
    "    p.append(p0)\n",
    "    \n",
    "show(gridplot(p, ncols=2, plot_width=400, plot_height=300, toolbar_location=None))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "p =[]\n",
    "\n",
    "cmap_list = [Category10_10, Accent8, BuPu5, Colorblind3\n",
    "             ,RdYlBu11, YlGnBu9,Spectral3, Plasma256\n",
    "             , RdYlBu11, Colorblind3, Viridis3, Viridis256]\n",
    "\n",
    "j = 8\n",
    "\n",
    "for i in range(j,j+4):\n",
    "    p0 = make_plot(LinearColorMapper(palette=cmap_list[i], low=-20, high=120), title='%d'%(i))\n",
    "    p.append(p0)\n",
    "\n",
    "show(gridplot([p[0], p[1], p[2], p[3]], ncols=2, plot_width=400, plot_height=300, toolbar_location=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  astropy.io  import fits\n",
    "import matplotlib.pyplot as plt\n",
    "imf = '/home/felipe/quicklook/spectro/redux/exposures/20190101/00000004/sframe-r0-00000004.fits'\n",
    "image_data = fits.getdata(imf, ext=0)\n",
    "#print(fits.info(imf), '\\n\\n=====\\n',fits.Header(imf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize =(18,4))\n",
    "plt.imshow(image_data)\n",
    "plt.show();plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''desi-00000003.fits.fz  fibermap-00000003.fits\n",
    "desi-00000004.fits.fz  fibermap-00000004.fits\n",
    "'''\n",
    "\n",
    "imfolder = '/home/felipe/quicklook/spectro/data/20190101/'\n",
    "imf  = imfolder+ 'fibermap-00000004.fits'\n",
    "image_data = fits.getdata(imf, ext=0)\n",
    "\n",
    "fits.info(imf)\n",
    "#plt.figure(figsize =(10,10))\n",
    "#plt.imshow(image_data)\n",
    "#plt.show()\n",
    "#plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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