histograms_bokeh.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scalar_metrics_v02 import LoadMetrics\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countpix loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-countpix-r3-00000003.yaml\n",
      "getbias loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-getbias-r3-00000003.yaml\n",
      "getrms loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-getrms-r3-00000003.yaml\n",
      "xwsigma loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-xwsigma-r3-00000003.yaml\n",
      "countbins loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-countbins-r3-00000003.yaml\n",
      "integ loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-integ-r3-00000003.yaml\n",
      "skycont loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-skycont-r3-00000003.yaml\n",
      "skypeak loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-skypeak-r3-00000003.yaml\n",
      "skyresid loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-skyresid-r3-00000003.yaml\n",
      "snr loaded\n",
      "/home/foliveira/quicklook/spectro/redux/exposures/20190101/00000003/ql-snr-r3-00000003.yaml\n"
     ]
    }
   ],
   "source": [
    "cam, exp, night = 'r3', '00000003', '20190101'\n",
    "lm = LoadMetrics(cam, exp, night)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "metrics, tests  = lm.metrics, lm.tests # Load_metrics_n_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": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hist_bk(metric, key, loghist=False):\n",
    "    from bokeh.models import HoverTool, ColumnDataSource\n",
    "    bins = np.sqrt(len(metric[key]))/1.5\n",
    "    bins = int(bins)\n",
    "    hist, edges = np.histogram(metric[key], bins = bins)\n",
    "    \n",
    "    logcount = []\n",
    "    for xi in hist:\n",
    "        if xi >0: \n",
    "            logcount.append(np.log10(xi))\n",
    "        else:\n",
    "            logcount.append(xi)\n",
    "            \n",
    "    source = ColumnDataSource(data=dict(\n",
    "        counts = list(hist),\n",
    "        logcounts = logcount,\n",
    "        left = edges[:-1],\n",
    "        right = edges[1:],\n",
    "        bottom = [0]*len(hist),\n",
    "        ))\n",
    "\n",
    "    hover = HoverTool(tooltips=[\n",
    "         (\"counts\", \"@counts\"),\n",
    "         ])\n",
    "    \n",
    "    p = figure(title=key,tools=[hover],\n",
    "            background_fill_color=\"white\")\n",
    "    if loghist:\n",
    "        p.quad(top=\"logcounts\", source=source, bottom=\"bottom\", left=\"left\", right=\"right\",\n",
    "                fill_color=\"dodgerblue\", line_color=\"black\")\n",
    "        p.yaxis.axis_label = 'Log10(COUNTS)'       \n",
    "    else:\n",
    "        p.quad(top=\"logcounts\", source=source, bottom=\"bottom\", left=\"left\", right=\"right\",\n",
    "                fill_color=\"dodgerblue\", line_color=\"black\")\n",
    "        p.yaxis.axis_label = 'COUNTS'\n",
    "\n",
    "\n",
    "    p.legend.location = \"top_left\"\n",
    "    p.xaxis.axis_label = key\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'figure' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-b19c7cab8019>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist_bk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcountbins\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"NBINSLOW\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloghist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mp2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist_bk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcountbins\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"NBINSHIGH\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloghist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mp3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist_bk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxwsigma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"XSIGMA\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mp4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist_bk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxwsigma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"WSIGMA\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloghist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-1f13e4de1db1>\u001b[0m in \u001b[0;36mhist_bk\u001b[0;34m(metric, key, loghist)\u001b[0m\n\u001b[1;32m     24\u001b[0m          ])\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     p = figure(title=key,tools=[hover],\n\u001b[0m\u001b[1;32m     27\u001b[0m             background_fill_color=\"white\")\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mloghist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'figure' is not defined"
     ]
    }
   ],
   "source": [
    "p1 = hist_bk(countbins,\"NBINSLOW\", loghist=True);\n",
    "p2 = hist_bk(countbins,\"NBINSHIGH\", loghist=True);\n",
    "p3 = hist_bk(xwsigma,\"XSIGMA\");\n",
    "p4 = hist_bk(xwsigma,\"WSIGMA\", loghist=True)\n",
    "\n",
    "\n",
    "# xwsigma, \"XSIGMA\", \"WSIGMA\"\n",
    "# countbins['NBINSHIGH'], bins=12,\n",
    "# countbins['NBINSLOW'], bins=12, \n",
    "# countbins['NBINSMED']\n",
    "#show(gridplot(p1,p2, ncols=2, plot_width=350, plot_height=350, toolbar_location=\"left\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"d89ff598-f299-4570-a315-42656b9b2cc3\",\"elementid\":\"3fe31777-43c7-41e8-8e27-433c614defdc\",\"modelid\":\"60bb4467-c61d-4042-84a5-3b0edd1f5ed6\"}];\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(\"3fe31777-43c7-41e8-8e27-433c614defdc\")).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": [
    "show(gridplot(p1,p2, p3, p4, ncols=1))#, plot_width= 350, plot_height=300))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4907119849998598"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(500)/15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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       "\n",
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       "        console.warn(\"failed to load library \" + url);\n",
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       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "\n",
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       "\n",
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       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
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       "  ];\n",
       "\n",
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       "\n",
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       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
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       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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       "}(this));"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/foliveira/miniconda3/envs/quicklook/lib/python3.5/site-packages/ipykernel_launcher.py:41: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/home/foliveira/miniconda3/envs/quicklook/lib/python3.5/site-packages/ipykernel_launcher.py:41: RuntimeWarning: divide by zero encountered in log\n",
      "/home/foliveira/miniconda3/envs/quicklook/lib/python3.5/site-packages/ipykernel_launcher.py:41: RuntimeWarning: invalid value encountered in multiply\n",
      "/home/foliveira/miniconda3/envs/quicklook/lib/python3.5/site-packages/ipykernel_launcher.py:42: RuntimeWarning: divide by zero encountered in log\n"
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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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\",\"dty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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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1GjiK6IbuP0ySq3qbi+4/l4YXY0CQ7j/NfIhp15TuP8CNs7Ngme4/wur2Ztyd7j8PY1qoSqLuP7Xpj5yrpu4/6Rv0Z/+q7j/Tx44uRq/uP6tzExSAs+4/PeXhO6237j+3qQbJzbvuP8WdO97hv+4/6XXonenD7j8WRyMq5cfuP3MPsaTUy+4/UD8GL7jP7j9BQkfqj9PuP1EISfdb1+4/T4+Rdhzb7j8qbFiI0d7uP1tUh0x74u4/S6e64hnm7j+890FqrenuPyyVIAI27e4/HRUOybPw7j9e3HbdJvTuPyOofF2P9+4/Exf3Zu367j8iMnQXQf7uP031OIyKAe8/IthB4skE7z8YVkM2/wfvP6p2qqQqC+8/PlWdSUwO7z/GqPtAZBHvPxtLX6ZyFO8/FMAclXcX7z9HvEMocxrvP4Wrn3plHe8/8ja4pk4g7z/UytHGLiPvP/Ib7vQFJu8/q6zMStQo7z+OUevhmSvvP6O1htNWLu8/Pd6aOAsx7z9druMptzPvP6xp3b9aNu8//TbFEvY47z9aopk6iTvvP5ceG08UPu8/Z4bMZ5dA7z/5nPObEkPvPwyOmQKGRe8/iG2LsvFH7z+FtlrCVUrvP9vJXUiyTO8/FmywWgdP7z/sQjQPVVHvPxhSkXubU+8/sXc2tdpV7z/m51nREljvPymo+eRDWu8/wQncBG5c7z/KI5BFkV7vP5pMbrutYO8/hZKYesNi7z8PNPuW0mTvP3gXTSTbZu8/rUEQNt1o7z+aTJLf2GrvP9Lc7DPObO8/oxYGRr1u7z93EpEopnDvP5dQDu6Icu8/TizMqGV07z9WTudqPHbvP6seS0YNeO8/sjWyTNh57z+szKaPnXvvP5ItgyBdfe8/NSJyEBd/7z+4Ym9wy4DvP2QDSFF6gu8/weGawyOE7z8UEdnXx4XvPxxGRp5mh+8/MEL5JgCJ7z+mPdyBlIrvP45Rrb4jjO8/uOD+7K2N7z8YADgcM4/vP2belFuzkO8/Iisnui6S7z/ZfNZGpZPvP7+2YBAXle8/m21aJYSW7z/9Sy+U7JfvP8V1ImtQme8//+pOuK+a7z8B6qeJCpzvP+ZQ+exgne8/VP7n77Ke7z+KMfKfAKDvP8rpbwpKoe8/CUWTPI+i7z/03WhD0KPvP0Up2CsNpe8/YdKjAkam7z9XF2rUeqfvPxskpa2rqO8/JG2rmtip7z9RCbCnAavvPyULw+AmrO8/VNnRUUit7z+ghqcGZq7vPw4p7QqAr+8/ajAqapaw7z8kvMQvqbHvP3nwAWe4su8//koGG8Sz7z9y9tVWzLTvP+8dVSXRte8/cj9IkdK27z+zfVSl0LfvP17x/2vLuO8/m/mx78K57z/0i7M6t7rvP5aDL1eou+8/6O8yT5a87z9+Yq0sgb3vP2g8cflovu8/3vozv02/7z9Gg46HL8DvP5tu/VsOwe8/K1ThRerB7z/CE39Ow8LvPyAfAH+Zw+8/5sJy4GzE7z/Rbsp7PcXvP2H931kLxu8/3/pxg9bG7z/L6yQBn8fvP6aSg9tkyO8/JzX/GijJ7z/U4O/H6MnvPwOvlOqmyu8/PggUi2LL7z8Q53uxG8zvP0UawmXSzO8/gIbEr4bN7z9QZ0mXOM7vP6KP/yPozu8/qKl+XZXP7z8mdkdLQNDvPzILxPTo0O8/YxJIYY/R7z9qBhGYM9LvPyRwRqDV0u8/EyP6gHXT7z9WeShBE9TvPwSPuOeu1O8/DX18e0jV7z+CkzED4NXvP1eTgIV11u8/n+f9CAnX7z853imUmtfvP/3fcC0q2O8/W6gr27fY7z97fJ+jQ9nvP9Bh/ozN2e8/LlRnnVXa7z9be+ba29rvPxxgdUtg2+8/wSD79OLb7z8xpUzdY9zvP3nSLArj3O8/1r1MgWDd7z9J30tI3N3vP6ZDuGRW3u8/Lr4O3M7e7z+sGbuzRd/vPxFJGPG63+8/o5dwmS7g7z+p2P2xoODvP6SW6T8R4e8/EkJNSIDh7z+4XzLQ7eHvP3m2ktxZ4u8/u3xYcsTi7z9VhV6WLePvPw1scE2V4+8/osFKnPvj7z9sN5uHYOTvP4PKABTE5O8/gu4LRibl7z/Ptz4ih+XvP4QFDa3m5e8/4Krc6kTm7z9UmAXgoebvPyUE0pD95u8/npJ+AVjn7z/jfTo2sefvP1O9JzMJ6O8/iSxb/F/o7z/1sdyVtejvPw5lpwMK6e8/IrSpSV3p7z/BicVrr+nvP8Vx0G0A6u8/+L2TU1Dq7z9Zqswgn+rvPwKBLNns6u8/q71YgDnr7z/TMOsZhevvP4UicqnP6+8/xnRwMhns7z+hxV24YezvP9+Qpj6p7O8/WFGsyO/s7z/7ocVZNe3vP2tePvV57e8/UMNXnr3t7z9MjkhYAO7vP5gdPSZC7u8/T49XC4Pu7z9d4K8Kw+7vPyMLVCcC7+8/vCVIZEDv7z/5f4bEfe/vPwPB/0q67+8/sQSb+vXv7z+L+DXWMPDvP3z4pOBq8O8/NSuzHKTw7z9CniKN3PDvP9NhrDQU8e8/NKQAFkvx7z/6zMYzgfHvP+eXnZC28e8/fC8bL+vx7z9LR80RH/LvP/Q1OTtS8u8/4g7crYTy7z+7uypstvLvP4sVknjn8u8/ov121Rfz7z84djaFR/PvP766JYp28+8/8VeS5qTz7z+pQ8Kc0vPvP2P0867/8+8/hnheHyz07z9rjTHwV/TvPx22lSOD9O8/3VGsu6307z9kso+61/TvP+ExUyIB9e8/w0gD9Sn17z81o6U0UvXvP2s2OeN59e8/p1W2AqH17z8Fxw6Vx/XvPxDYLZzt9e8/EXL4GRP27z8qLk0QOPbvPzdpBIFc9u8/dVfwbYD27z/oF93Yo/bvP5jHkMPG9u8/hZTLL+n27z9u0EcfC/fvP2ADupMs9+8/C/7Qjk337z/m6zUSbvfvPxVljB+O9+8/KYByuK337z+c44DezPfvPyLXSpPr9+8/yFRe2An47z/dGUSvJ/jvP6a3fxlF+O8/6aOPGGL47z8/Se2tfvjvPzsXDdua+O8/XJJeobb47z/UY0wC0vjvPxtpPP/s+O8/XMOPmQf57z+o5qLSIfnvPwipzas7+e8/VlFjJlX57z/ypbJDbvnvP0j7BQWH+e8/KkKja5/57z/+Fcx4t/nvP8XKvS3P+e8/8Xqxi+b57z8YFdyT/fnvP3ppbkcU+u8/XzeVpyr67z9JOnm1QPrvPwg3P3JW+u8/lwgI32v67z/irPD8gPrvP1ZREs2V+u8/WF+CUKr67z+OiFKIvvrvPwHTkHXS+u8/IqVHGeb67z+j0X10+frvPy+jNogM++8//OdxVR/77z87/SvdMfvvP2TaXSBE++8/YBz9H1b77z+SEPzcZ/vvP7e/SVh5++8/r/jRkor77z8aW32Nm/vvP95hMUms++8/hW3Qxrz77z97zjkHzfvvPzXPSQvd++8/K77Z0+z77z+/979h/PvvP//vz7UL/O8/RTza0Br87z/HnKyzKfzvP/UFEl84/O8/zqnS00b87z8GAbQSVfzvPx3UeBxj/O8/U0Th8XD87z991KqTfvzvP8lxkAKM/O8/WHxKP5n87z/Kz45KpvzvP6XLECWz/O8/rFuBz7/87z8NAI9KzPzvP4XV5ZbY/O8/YJ0vteT87z9hxROm8PzvP5dvN2r8/O8/Dno9Agj97z90hsZuE/3vP54BcbAe/e8/9SrZxyn97z/OG5m1NP3vP63OSHo//e8/aCZ+Fkr97z8+9cyKVP3vP84Dx9de/e8//hf8/Wj97z/L+/n9cv3vP/yDTNh8/e8/zpZ9jYb97z95MhUekP3vP69zmYqZ/e8//5uO06L97z8gGHf5q/3vPzOG0/y0/e8/5Lsi3r397z+AzOGdxv3vP/QOjDzP/e8/uSObutf97z+s+oYY4P3vP9XYxVbo/e8/GF7MdfD97z/big12+P3vP4zF+lcA/u8/JOADHAj+7z+PHZfCD/7vPwQ3IUwX/u8/T2ENuR7+7z8EUsUJJv7vP6REsT4t/u8/tP83WDT+7z+/2b5WO/7vP0m+qTpC/u8/sjJbBEn+7z8IWzS0T/7vP83+lEpW/u8/po3bx1z+7z8EJGUsY/7vP7WPjXhp/u8/a1SvrG/+7z8wsCPJdf7vP9SfQs57/u8/QeNivIH+7z/HAdqTh/7vP1pO/FSN/u8/wescAJP+7z+10I2VmP7vP/jLnxWe/u8/VIiigKP+7z+ckOTWqP7vP5BTsxiu/u8/vidbRrP+7z9UTydguP7vP+X7YWa9/u8/HFJUWcL+7z9ubUY5x/7vP7ZjfwbM/u8/yUhFwdD+7z/8Md1p1f7vP6I5iwDa/u8/eYKShd7+7z8POzX54v7vPx6htFvn/u8/1QRRrev+7z8dzEnu7/7vP9F13R70/u8/6ZxJP/j+7z+d+8pP/P7vP39unVAA/+8/g/f7QQT/7z8GwSAkCP/vP8QgRfcL/+8/y5qhuw//7z9Z5G1xE//vP7vm4BgX/+8/IMIwshr/7z9d0JI9Hv/vP6unO7sh/+8/Xx1fKyX/7z+WSDCOKP/vP9OE4eMr/+8/nnSkLC//7z8QBKpoMv/vP2FrIpg1/+8/YTE9uzj/7z/1LSnSO//vP4CMFN0+/+8/T84s3EH/7z/xzJ7PRP/vP5G8lrdH/+8/RS5AlEr/7z9PEsZlTf/vP1+6UixQ/+8/zNsP6FL/7z+7kSaZVf/vP05fvz9Y/+8/wzEC3Fr/7z+IYhZuXf/vP1K5IvZf/+8/JG5NdGL/7z9VK7zoZP/vP4kPlFNn/+8/q6/5tGn/7z/ZGBENbP/vP0zS/Vtu/+8/Ot/ioXD/7z+0wOLecv/vP3R3HxN1/+8/s4W6Pnf/7z/r8NRhef/vP51Dj3x7/+8/CY8Jj33/7z/pbGOZf//vPxkBvJuB/+8/SvsxloP/7z+fmOOIhf/vP06l7nOH/+8/On5wV4n/7z+GEoYzi//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\",\"dtype\":\"float64\",\"shape\":[1000]}}},\"id\":\"0cc1581e-2f7b-48b0-81a4-48c40a803a10\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"bottom\":{\"value\":0},\"fill_color\":{\"value\":\"#036564\"},\"left\":{\"field\":\"left\"},\"line_color\":{\"value\":\"#033649\"},\"right\":{\"field\":\"right\"},\"top\":{\"field\":\"top\"}},\"id\":\"40d5a693-51e4-4fbb-aab7-1638c584248b\",\"type\":\"Quad\"}],\"root_ids\":[\"fd3c081a-d001-4b60-a8fb-5a006271b2ec\"]},\"title\":\"Bok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Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"be7a0050-b126-412d-8e1a-d91e1da3193c\",\"elementid\":\"946bd820-b779-4338-b642-5b7b564af50e\",\"modelid\":\"fd3c081a-d001-4b60-a8fb-5a006271b2ec\"}];\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(\"946bd820-b779-4338-b642-5b7b564af50e\")).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": [
    "import numpy as np\n",
    "import scipy.special\n",
    "\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "p1 = figure(title=\"Normal Distribution (μ=0, σ=0.5)\",tools=\"save\",\n",
    "            background_fill_color=\"#E8DDCB\")\n",
    "\n",
    "mu, sigma = 0, 0.5\n",
    "\n",
    "measured = np.random.normal(mu, sigma, 1000)\n",
    "hist, edges = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(-2, 2, 1000)\n",
    "pdf = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2 / (2*sigma**2))\n",
    "cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2\n",
    "\n",
    "p1.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "        fill_color=\"#036564\", line_color=\"#033649\")\n",
    "p1.line(x, pdf, line_color=\"#D95B43\", line_width=8, alpha=0.7, legend=\"PDF\")\n",
    "p1.line(x, cdf, line_color=\"white\", line_width=2, alpha=0.7, legend=\"CDF\")\n",
    "\n",
    "p1.legend.location = \"top_left\"\n",
    "p1.xaxis.axis_label = 'x'\n",
    "p1.yaxis.axis_label = 'Pr(x)'\n",
    "\n",
    "\n",
    "\n",
    "p2 = figure(title=\"Log Normal Distribution (μ=0, σ=0.5)\", tools=\"save\",\n",
    "            background_fill_color=\"#E8DDCB\")\n",
    "\n",
    "mu, sigma = 0, 0.5\n",
    "\n",
    "measured = np.random.lognormal(mu, sigma, 1000)\n",
    "hist, edges = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 8.0, 1000)\n",
    "pdf = 1/(x* sigma * np.sqrt(2*np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2*sigma**2))\n",
    "cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2\n",
    "\n",
    "p2.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "        fill_color=\"#036564\", line_color=\"#033649\")\n",
    "p2.line(x, pdf, line_color=\"#D95B43\", line_width=8, alpha=0.7, legend=\"PDF\")\n",
    "p2.line(x, cdf, line_color=\"white\", line_width=2, alpha=0.7, legend=\"CDF\")\n",
    "\n",
    "p2.legend.location = \"bottom_right\"\n",
    "p2.xaxis.axis_label = 'x'\n",
    "p2.yaxis.axis_label = 'Pr(x)'\n",
    "\n",
    "\n",
    "\n",
    "p3 = figure(title=\"Gamma Distribution (k=1, θ=2)\", tools=\"save\",\n",
    "            background_fill_color=\"#E8DDCB\")\n",
    "\n",
    "k, theta = 1.0, 2.0\n",
    "\n",
    "measured = np.random.gamma(k, theta, 1000)\n",
    "hist, edges = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 20.0, 1000)\n",
    "pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))\n",
    "cdf = scipy.special.gammainc(k, x/theta) / scipy.special.gamma(k)\n",
    "\n",
    "p3.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "        fill_color=\"#036564\", line_color=\"#033649\")\n",
    "p3.line(x, pdf, line_color=\"#D95B43\", line_width=8, alpha=0.7, legend=\"PDF\")\n",
    "p3.line(x, cdf, line_color=\"white\", line_width=2, alpha=0.7, legend=\"CDF\")\n",
    "\n",
    "p3.legend.location = \"top_left\"\n",
    "p3.xaxis.axis_label = 'x'\n",
    "p3.yaxis.axis_label = 'Pr(x)'\n",
    "\n",
    "\n",
    "\n",
    "p4 = figure(title=\"Beta Distribution (α=2, β=2)\", tools=\"save\",\n",
    "            background_fill_color=\"#E8DDCB\")\n",
    "\n",
    "alpha, beta = 2.0, 2.0\n",
    "\n",
    "measured = np.random.beta(alpha, beta, 1000)\n",
    "hist, edges = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 1, 1000)\n",
    "pdf = x**(alpha-1) * (1-x)**(beta-1) / scipy.special.beta(alpha, beta)\n",
    "cdf = scipy.special.btdtr(alpha, beta, x)\n",
    "\n",
    "p4.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "        fill_color=\"#036564\", line_color=\"#033649\")\n",
    "p4.line(x, pdf, line_color=\"#D95B43\", line_width=8, alpha=0.7, legend=\"PDF\")\n",
    "p4.line(x, cdf, line_color=\"white\", line_width=2, alpha=0.7, legend=\"CDF\")\n",
    "\n",
    "p4.xaxis.axis_label = 'x'\n",
    "p4.yaxis.axis_label = 'Pr(x)'\n",
    "\n",
    "\n",
    "\n",
    "p5 = figure(title=\"Weibull Distribution (λ=1, k=1.25)\", tools=\"save\",\n",
    "            background_fill_color=\"#E8DDCB\")\n",
    "\n",
    "lam, k = 1, 1.25\n",
    "\n",
    "measured = lam*(-np.log(np.random.uniform(0, 1, 1000)))**(1/k)\n",
    "hist, edges = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 8, 1000)\n",
    "pdf = (k/lam)*(x/lam)**(k-1) * np.exp(-(x/lam)**k)\n",
    "cdf = 1 - np.exp(-(x/lam)**k)\n",
    "\n",
    "p5.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "       fill_color=\"#036564\", line_color=\"#033649\")\n",
    "p5.line(x, pdf, line_color=\"#D95B43\", line_width=8, alpha=0.7, legend=\"PDF\")\n",
    "p5.line(x, cdf, line_color=\"white\", line_width=2, alpha=0.7, legend=\"CDF\")\n",
    "\n",
    "p5.legend.location = \"top_left\"\n",
    "p5.xaxis.axis_label = 'x'\n",
    "p5.yaxis.axis_label = 'Pr(x)'\n",
    "\n",
    "\n",
    "\n",
    "#output_file('histogram.html', title=\"histogram.py example\")\n",
    "\n",
    "show(gridplot(p1,p2,p3,p4,p5, ncols=2, plot_width=400, plot_height=400, toolbar_location=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "    \n",
    "    \n",
    "hist, edges = np.histogram(xwsigma['XSIGMA'], bins = 17)\n",
    "\n",
    "p = figure(title=\"XSIGMA\",tools=\"hover\",\n",
    "            background_fill_color=\"white\")\n",
    "\n",
    "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "        fill_color=\"dodgerblue\", line_color=\"black\")\n",
    "\n",
    "p.legend.location = \"top_left\"\n",
    "p.xaxis.axis_label = 'XSIGMA'\n",
    "p.yaxis.axis_label = 'COUNTS'\n",
    "show(p)\n",
    "\n",
    "print( hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "p1 = figure(title=\"Normal Distribution (μ=0, σ=0.5)\",tools=\"save\",\n",
    "            background_fill_color=\"white\")\n",
    "\n",
    "mu, sigma = 0.7, 0.5\n",
    "\n",
    "measured = np.random.normal(mu, sigma, 1000)\n",
    "hist, edges = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(-2, 2, 1000)\n",
    "pdf = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2 / (2*sigma**2))\n",
    "cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2\n",
    "\n",
    "p1.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "        fill_color=\"lightgreen\", line_color=\"black\")\n",
    "#p1.line(x, pdf, line_color=\"#D95B43\", line_width=8, alpha=0.7, legend=\"PDF\")\n",
    "#p1.line(x, cdf, line_color=\"white\", line_width=2, alpha=0.7, legend=\"CDF\")\n",
    "\n",
    "p1.legend.location = \"top_left\"\n",
    "p1.xaxis.axis_label = 'x'\n",
    "p1.yaxis.axis_label = 'Pr(x)'\n",
    "\n",
    "show(p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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