plot_generic_histogram.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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# =============================================\n",
    "# THIS comes from INTERFACE\n",
    "#\n",
    "selected_exposure = '00000003'# args['exposure']\n",
    "selected_arm = 'r' # args['arm']\n",
    "selected_spectrograph =  4 # args['spectrograph']\n",
    "\n",
    "# =============================================\n",
    "# THIS comes from QLF.CFG\n",
    "#\n",
    "night = '20190101'\n",
    "\n",
    "# ============================================\n",
    "#  THIS READ yaml files\n",
    "#\n",
    "from scalar_metrics_v02 import LoadMetrics\n",
    "\n",
    "cam = selected_arm+str(selected_spectrograph)\n",
    "exp = selected_exposure # intentionaly redundant\n",
    "lm = LoadMetrics(cam, exp, night);\n",
    "metrics, tests  = lm.metrics, lm.tests \n",
    "\n",
    "# =============================================\n",
    "# THIS is only to simplify the code understanding\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']"
   ]
  },
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   <div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">Frequency: </span>\\n            <span style=\\\"font-size: 13px; color: #515151\\\">@hist</span>\\n        </div>\\n        <div>\\n            <span style=\\\"font-size: 12px; font-weight: bold; color: #303030;\\\">sigma: </span>\\n            <span style=\\\"font-size: 13px; color: #515151;\\\">[@left, @right]</span>\\n        </div>\\n    </div>\\n\"},\"id\":\"cfc97157-9b47-40b9-890b-ded1a4c3a825\",\"type\":\"HoverTool\"},{\"attributes\":{\"axis_label\":\"Frequency\",\"formatter\":{\"id\":\"1ab16caf-c472-4a85-84d7-4b8066cc8fc6\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"b7d6fb77-9a46-4bb7-aaa5-f85e896fecfe\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"acbc6b1c-e0fa-4126-af69-420d19af0cdf\",\"type\":\"BasicTicker\"}},\"id\":\"d01c97ea-6f65-4526-81de-1cfb81302cc2\",\"type\":\"LinearAxis\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"cfc97157-9b47-40b9-890b-ded1a4c3a825\",\"type\":\"HoverTool\"},{\"id\":\"743bbf29-ce94-4f6d-a4d0-92721793018e\",\"type\":\"PanTool\"},{\"id\":\"590264e3-5778-46c9-bc17-c500de045ab8\",\"type\":\"WheelZoomTool\"},{\"id\":\"2efd379a-ea4b-4f9c-8e53-b67f45e40e43\",\"type\":\"BoxZoomTool\"},{\"id\":\"46ce89cc-7eb4-4b6f-a4de-7afbe338df06\",\"type\":\"ResetTool\"}]},\"id\":\"af430625-071b-409c-9cad-289e5f6955b8\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null},\"id\":\"405b1994-4812-4bf0-8309-9cd9d88bd123\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"acbc6b1c-e0fa-4126-af69-420d19af0cdf\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1ab16caf-c472-4a85-84d7-4b8066cc8fc6\",\"type\":\"BasicTickFormatter\"},{\"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\":\"e4a2e701-0518-472c-b945-f3ebd1522433\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"callback\":null},\"id\":\"7912331e-3b48-465d-9ddc-35ce0e357bb6\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":null,\"text\":\"XSIGMA\"},\"id\":\"66ba7a32-8d65-42cf-9c99-85259a820813\",\"type\":\"Title\"}],\"root_ids\":[\"b7d6fb77-9a46-4bb7-aaa5-f85e896fecfe\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"839e1b63-f197-4a72-907e-711d57e136c7\",\"elementid\":\"1dce0040-a8e8-4a5c-a376-732acdb4b2bb\",\"modelid\":\"b7d6fb77-9a46-4bb7-aaa5-f85e896fecfe\"}];\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(\"1dce0040-a8e8-4a5c-a376-732acdb4b2bb\")).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": [
    "hist_tooltip = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">Frequency: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151\">@hist</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">sigma: </span>\n",
    "            <span style=\"font-size: 13px; color: #515151;\">[@left, @right]</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\"\n",
    "sname = 'XSIGMA'\n",
    "\n",
    "def bins_doane(data):\n",
    "    # Dane's rule of thumb for bins\n",
    "    ndata = len(data)\n",
    "    mean = np.mean(data)\n",
    "    sigma = np.std(data)\n",
    "    b = sum([ (x - mean)**3 for x in xwsigma[sname]])\n",
    "    b = b/sum([ (x - mean)**2 for x in xwsigma[sname]])**(1.5)\n",
    "    return int(round(np.log2(ndata) + 1 + np.log2((1.+b)/(sigma*b))))\n",
    "\n",
    "bins = bins_doane(xwsigma[sname]) # formely: 17\n",
    "hover = HoverTool(tooltips=hist_tooltip)\n",
    "\n",
    "\n",
    "hist, edges = np.histogram(xwsigma[sname], bins = bins)\n",
    "\n",
    "source = ColumnDataSource(data={\n",
    "    'hist':hist,\n",
    "    'bottom':[0] *len(hist),\n",
    "    'left':edges[:-1],\n",
    "    'right':edges[1:]\n",
    "})\n",
    "\n",
    "p = figure(title=sname,tools=[hover,\"pan,wheel_zoom,box_zoom,reset\"],\n",
    "           y_axis_label='Frequency', x_axis_label=sname,\n",
    "           #y_axis_type='log',\n",
    "            background_fill_color=\"white\")\n",
    "\n",
    "p.quad(top='hist', bottom='bottom', left='left', right='right',\n",
    "       source=source, \n",
    "        fill_color=\"dodgerblue\", line_color=\"black\", alpha=0.8,\n",
    "       hover_fill_color='blue', hover_line_color='black', hover_alpha=0.8)\n",
    "p.legend.location = \"top_left\"\n",
    "output_notebook()\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bins_doane(data):\n",
    "    # Doane's rule of thumb for bins\n",
    "    ndata = len(data)\n",
    "    mean = np.mean(data)\n",
    "    sigma = np.std(data)\n",
    "    b = sum([ (x - mean)**3 for x in xwsigma['XSIGMA']])\n",
    "    b = b/sum([ (x - mean)**2 for x in xwsigma['XSIGMA']])**(1.5)\n",
    "    return int(round(np.log2(ndata) + 1 + np.log2((1.+b)/(sigma*b))))\n",
    "    \n",
    "ndata = len(xwsigma['XSIGMA'])\n",
    "mean = np.mean(xwsigma['XSIGMA'])\n",
    "sigma = np.std(xwsigma['XSIGMA'])\n",
    "\n",
    "b = sum([ (x - mean)**3 for x in xwsigma['XSIGMA']])\n",
    "b = b/sum([ (x - mean)**2 for x in xwsigma['XSIGMA']])**(1.5)\n",
    "danesrule= int(round(np.log2(ndata) + 1 + np.log2((1.+b)/(sigma*b))))\n",
    "sturgesrule = int(round(1.+3.322 *np.log10(500) ))\n",
    "scottsrule= int(round(3.49 *sigma/(ndata)**(1./3.)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1 + 3.322 *np.log10(55) #= 6.781 ≅ 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(danesrule, sturgesrule, scottsrule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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