python plot.ipynb

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     "text": [
      "/shared/silo_researcher/Matsen_F/MatsenGrp/working/cwarth/santa-dnds\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, show, ColumnDataSource\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.palettes import Spectral11\n",
    "from bokeh.models import NumeralTickFormatter\n",
    "from IPython.display import display\n",
    "import os\n",
    "output_notebook()\n",
    "\n",
    "print(os.getcwd())"
   ]
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>model</th>\n",
       "      <th>fitness</th>\n",
       "      <th>generation</th>\n",
       "      <th>replicate</th>\n",
       "      <th>omega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>build/empiricalvalues/fit_0.01/rep_0/gen_500/r...</td>\n",
       "      <td>empiricalvalues</td>\n",
       "      <td>0.01</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.78057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>build/empiricalvalues/fit_0.01/rep_0/gen_5000/...</td>\n",
       "      <td>empiricalvalues</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.87260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>build/empiricalvalues/fit_0.01/rep_0/gen_20000...</td>\n",
       "      <td>empiricalvalues</td>\n",
       "      <td>0.01</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.12662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>build/empiricalvalues/fit_0.01/rep_1/gen_20000...</td>\n",
       "      <td>empiricalvalues</td>\n",
       "      <td>0.01</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.78372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>build/empiricalvalues/fit_0.01/rep_1/gen_5000/...</td>\n",
       "      <td>empiricalvalues</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44114</td>\n",
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      "text/plain": [
       "                                                path            model  \\\n",
       "0  build/empiricalvalues/fit_0.01/rep_0/gen_500/r...  empiricalvalues   \n",
       "1  build/empiricalvalues/fit_0.01/rep_0/gen_5000/...  empiricalvalues   \n",
       "2  build/empiricalvalues/fit_0.01/rep_0/gen_20000...  empiricalvalues   \n",
       "3  build/empiricalvalues/fit_0.01/rep_1/gen_20000...  empiricalvalues   \n",
       "4  build/empiricalvalues/fit_0.01/rep_1/gen_5000/...  empiricalvalues   \n",
       "\n",
       "   fitness  generation  replicate    omega  \n",
       "0     0.01         500          0  0.78057  \n",
       "1     0.01        5000          0  0.87260  \n",
       "2     0.01       20000          0  1.12662  \n",
       "3     0.01       20000          1  0.78372  \n",
       "4     0.01        5000          1  0.44114  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_csv(\"build/results.csv\")\n",
    "df.head()"
   ]
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     "text": [
      "['empiricalvalues', 'homoresidue', 'noselection', 'purifyingchem']\n",
      "[ 0.01    0.015   0.0105  0.02    0.505   1.    ]\n"
     ]
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       "<p><code>&lt;Bokeh Notebook handle for <strong>In[15]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x7f01d119edd0>"
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     },
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   ],
   "source": [
    "from bokeh.models import Jitter\n",
    "from bokeh.models import HoverTool, BoxSelectTool\n",
    "\n",
    "\n",
    "\n",
    "df = df.query('omega<2')\n",
    "factors = list(df.model.unique())\n",
    "print(factors)\n",
    "print(df.fitness.unique())\n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"model\", \"$model\"),\n",
    "            (\"fitness\", \"$fitness\"),\n",
    "            (\"generation\", \"$generation\"),\n",
    "            (\"dN/dS\", \"$omega\"),\n",
    "        ]\n",
    "    )\n",
    "TOOLS = [BoxSelectTool(), hover]\n",
    "\n",
    "fig = figure(title=\"dN/dS ratios - colored according to fitness value\", x_range=factors, \n",
    "             width=900, height=450, \n",
    "             tools=TOOLS, toolbar_location=\"above\")\n",
    "\n",
    "fig.title.text_font_size='16pt'\n",
    "fig.xaxis.axis_label_text_font_size='16pt'\n",
    "fig.yaxis.axis_label_text_font_size='16pt'\n",
    "\n",
    "fitness = df.fitness.unique()\n",
    "colormap = dict(zip(fitness,[Spectral11[i] for i in range(len(fitness))]))\n",
    "\n",
    "for i,(k,grp) in enumerate(df.groupby(\"model\")):\n",
    "    colors = [colormap[x] for x in grp.fitness]\n",
    "    fig.circle(x={'value':i+1, 'transform': Jitter(width=0.1)}, y=\"omega\", \n",
    "               source=ColumnDataSource(data=grp),\n",
    "               color=colors, alpha=0.5, size=10)\n",
    "    \n",
    "\n",
    "show(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['noselection', 'purifyingchem', 'empiricalvalues']\n",
      "[ 0.0104  0.01  ]\n"
     ]
    },
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       "            var render_items = [{\"docid\":\"4508bf1a-cfa0-4e19-8716-23725b55d5ab\",\"elementid\":\"1742fe78-6c89-4951-851e-f3d83db4878c\",\"modelid\":\"7777726d-dab1-4225-9f21-a5155223be0c\",\"notebook_comms_target\":\"7b2eed2c-552d-4bb3-89fa-1a34bf0146f2\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\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"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[16]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fe6e5a27f10>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bokeh.models import Jitter\n",
    "from bokeh.models import HoverTool, BoxSelectTool\n",
    "\n",
    "\n",
    "\n",
    "factors = list(df.model.unique())\n",
    "print(factors)\n",
    "print(df.fitness.unique())\n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"model\", \"$model\"),\n",
    "            (\"fitness\", \"$fitness\"),\n",
    "            (\"generation\", \"$generation\"),\n",
    "            (\"dN/dS\", \"$omega\"),\n",
    "        ]\n",
    "    )\n",
    "TOOLS = [BoxSelectTool(), hover]\n",
    "\n",
    "fig = figure(title=\"dN/dS ratios - colored according to fitness value\", x_range=factors, \n",
    "             width=900, height=450, \n",
    "             tools=TOOLS, toolbar_location=\"above\")\n",
    "\n",
    "fig.title.text_font_size='16pt'\n",
    "fig.xaxis.axis_label_text_font_size='16pt'\n",
    "fig.yaxis.axis_label_text_font_size='16pt'\n",
    "\n",
    "fitness = df.fitness.unique()\n",
    "colormap = dict(zip(fitness,[Spectral11[i*9] for i in range(len(fitness))]))\n",
    "\n",
    "for i,(k,grp) in enumerate(df.groupby(\"model\")):\n",
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     "text": [
      "['empiricalvalues', 'homoresidue', 'noselection', 'purifyingchem']\n",
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0,4,4,4,0,0,0,2,2,2,1,1,1,3,3,3]}},\"id\":\"34f9ea3e-f0a4-4c46-a0a8-e119c967ddfd\",\"type\":\"ColumnDataSource\"},{\"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\":\"a47fd12d-230f-4d70-8e3d-67be1460721b\",\"type\":\"BoxAnnotation\"}],\"root_ids\":[\"b6a2ef95-22ee-4140-a6e2-1f7fddf911a6\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.1\"}};\n",
       "            var render_items = [{\"docid\":\"c62e9c72-bcc0-41b3-82b5-ae9871f647a5\",\"elementid\":\"c65215be-bfae-4feb-8e1a-a835ebd6011a\",\"modelid\":\"b6a2ef95-22ee-4140-a6e2-1f7fddf911a6\",\"notebook_comms_target\":\"18292f0c-15ee-4c2d-8d17-8d5052c1950c\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\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"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[7]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7f01d119e5d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bokeh.models import Jitter\n",
    "from bokeh.models import HoverTool, BoxSelectTool\n",
    "\n",
    "\n",
    "\n",
    "factors = list(df.model.unique())\n",
    "print(factors)\n",
    "print(df.fitness.unique())\n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"model\", \"$model\"),\n",
    "            (\"fitness\", \"$fitness\"),\n",
    "            (\"generation\", \"$generation\"),\n",
    "            (\"dN/dS\", \"$omega\"),\n",
    "        ]\n",
    "    )\n",
    "TOOLS = [BoxSelectTool(), hover]\n",
    "\n",
    "fig = figure(title=\"dN/dS ratios - colored according to fitness value\", x_range=factors, \n",
    "             width=900, height=450, \n",
    "             tools=TOOLS, toolbar_location=\"above\")\n",
    "\n",
    "fig.title.text_font_size='16pt'\n",
    "fig.xaxis.axis_label_text_font_size='16pt'\n",
    "fig.yaxis.axis_label_text_font_size='16pt'\n",
    "\n",
    "fitness = df.fitness.unique()\n",
    "colormap = dict(zip(fitness,[Spectral11[i] for i in range(len(fitness))]))\n",
    "\n",
    "for i,(k,grp) in enumerate(df.groupby(\"model\")):\n",
    "    colors = [colormap[x] for x in grp.fitness]\n",
    "    fig.circle(x={'value':i+1, 'transform': Jitter(width=0.1)}, y=\"omega\", \n",
    "               source=ColumnDataSource(data=grp),\n",
    "               color=colors, alpha=0.5, size=10)\n",
    "    \n",
    "\n",
    "show(fig)\n"
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       "            var render_items = [{\"docid\":\"e7dd4a0f-0fa3-43cd-b127-72a36296551d\",\"elementid\":\"0d833734-292f-484d-9827-501dd96f99d9\",\"modelid\":\"79da977e-b0d5-4141-aad8-6b6188d3c8a6\",\"notebook_comms_target\":\"b5d3a6cf-6c09-4ec4-a895-d4cf4e57f3a6\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\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"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[75]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fe6e57fe8d0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of jitter usage posted to Google groups\n",
    "# \n",
    "\n",
    "\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.layouts import row\n",
    "from bokeh.models.sources import ColumnDataSource\n",
    "from bokeh.models.transforms import Jitter\n",
    "from bokeh.models import Jitter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame.from_dict({\n",
    "        \"model\" : np.random.choice(['a', 'b', 'c', 'd'], 50), \n",
    "        \"omega\" : range(50)\n",
    "        })\n",
    "factors = list(df.model.unique())\n",
    "\n",
    "# without jitter.\n",
    "fig = figure(width=250, plot_height=250, title=\"no jitter plot\", x_range=factors)\n",
    "fig.toolbar_location = None\n",
    "fig.circle(x=\"model\", y=\"omega\", source=ColumnDataSource(data=df),\n",
    "             alpha=0.3, size=5)\n",
    "\n",
    "\n",
    "# with jitter\n",
    "jitfig = figure(width=250, plot_height=250, title=\"jitter plot\", x_range=factors)\n",
    "jitfig.toolbar_location = None\n",
    "for i,(k,grp) in enumerate(df.groupby(\"model\")):\n",
    "    print(k)\n",
    "    jitfig.circle(x={\"values\":i+1}, y=\"omega\", \n",
    "               source=ColumnDataSource(data=grp),\n",
    "               alpha=0.3, size=5)\n",
    "\n",
    "p = row(fig, jitfig)\n",
    "\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GlyphRenderer, ViewModel:GlyphRenderer, ref _id: 48446d4a-509d-4c78-bcb0-1faa3128cbed\n",
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       "            var render_items = [{\"docid\":\"5562c3ac-c212-4a7c-b347-3a15ed3e6f83\",\"elementid\":\"3a09af1d-8f06-4b2d-8199-26e9e23804c3\",\"modelid\":\"52c052e2-28f2-4adf-915f-61235c2f6228\",\"notebook_comms_target\":\"d86a271a-d8e4-4df0-abc5-a0ee5f448ddb\"}];\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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       "<p><code>&lt;Bokeh Notebook handle for <strong>In[45]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x7fe6e63b8390>"
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   ],
   "source": [
    "# example of jitter usage posted to Google groups\n",
    "# \n",
    "\n",
    "\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.layouts import row\n",
    "from bokeh.models.sources import ColumnDataSource\n",
    "from bokeh.models.transforms import Jitter\n",
    "from bokeh.models import Jitter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame.from_dict({\n",
    "        \"model\" : np.random.choice(['a', 'b', 'c', 'd'], 50), \n",
    "        \"omega\" : range(50)\n",
    "        })\n",
    "factors = list(df.model.unique())\n",
    "\n",
    "# without jitter.\n",
    "fig = figure(width=250, plot_height=250, title=\"no jitter plot\", x_range=factors)\n",
    "fig.toolbar_location = None\n",
    "c = fig.circle(x=\"model\", y=\"omega\", source=ColumnDataSource(data=df),\n",
    "             alpha=0.3, size=5)\n",
    "print(c)\n",
    "print(c.__dict__)\n",
    "print(c.__dict__['_property_values']['data_source'])\n",
    "ds = c.__dict__['_property_values']['data_source']\n",
    "print(ds.__dict__)\n",
    "\n",
    "show(fig)\n"
   ]
  }
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