miniep5/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import glob\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prep_json(d):\n",
    "    d = d['results'][0]\n",
    "    if sum(d['exit_codes']) != 0:\n",
    "        raise ValueError('exit_codes != 0')\n",
    "    d = {'num_ifs': params[0],\n",
    "        'num_threads': params[1],\n",
    "        'array_size': params[2],\n",
    "        'n': len(d['times'])} | d\n",
    "    d['ci'] = 1.960 * d['stddev'] / (d['n']**0.5)\n",
    "    _ = d.pop('command')\n",
    "    _ = d.pop('times')\n",
    "    _ = d.pop('exit_codes')\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_ifs</th>\n",
       "      <th>num_threads</th>\n",
       "      <th>array_size</th>\n",
       "      <th>n</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>median</th>\n",
       "      <th>user</th>\n",
       "      <th>system</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>ci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.002824</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.002852</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.003385</td>\n",
       "      <td>0.000044</td>\n",
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       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
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       "      <td>100</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.002729</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.003462</td>\n",
       "      <td>0.000041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0.002754</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.002768</td>\n",
       "      <td>0.000973</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>0.002219</td>\n",
       "      <td>0.003369</td>\n",
       "      <td>0.000043</td>\n",
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       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.003173</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>0.000766</td>\n",
       "      <td>0.002706</td>\n",
       "      <td>0.003788</td>\n",
       "      <td>0.000037</td>\n",
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       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.007088</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.007080</td>\n",
       "      <td>0.005165</td>\n",
       "      <td>0.000793</td>\n",
       "      <td>0.006622</td>\n",
       "      <td>0.007852</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>10000</td>\n",
       "      <td>4096</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0.193100</td>\n",
       "      <td>0.002592</td>\n",
       "      <td>0.193009</td>\n",
       "      <td>0.028526</td>\n",
       "      <td>0.318859</td>\n",
       "      <td>0.186807</td>\n",
       "      <td>0.199367</td>\n",
       "      <td>0.000508</td>\n",
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       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>10000</td>\n",
       "      <td>4096</td>\n",
       "      <td>1000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.194090</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.193790</td>\n",
       "      <td>0.029979</td>\n",
       "      <td>0.321537</td>\n",
       "      <td>0.187868</td>\n",
       "      <td>0.201080</td>\n",
       "      <td>0.000466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>10000</td>\n",
       "      <td>4096</td>\n",
       "      <td>10000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.197888</td>\n",
       "      <td>0.003360</td>\n",
       "      <td>0.197353</td>\n",
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       "      <td>0.191389</td>\n",
       "      <td>0.210862</td>\n",
       "      <td>0.000658</td>\n",
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       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>10000</td>\n",
       "      <td>4096</td>\n",
       "      <td>100000</td>\n",
       "      <td>100</td>\n",
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       "      <td>0.203274</td>\n",
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       "      <td>0.196746</td>\n",
       "      <td>0.214141</td>\n",
       "      <td>0.000628</td>\n",
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       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>10000</td>\n",
       "      <td>4096</td>\n",
       "      <td>1000000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.250761</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.251942</td>\n",
       "      <td>0.637051</td>\n",
       "      <td>0.386394</td>\n",
       "      <td>0.235086</td>\n",
       "      <td>0.264447</td>\n",
       "      <td>0.001388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>546 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num_ifs  num_threads  array_size    n      mean    stddev    median  \\\n",
       "198        0            1           1  100  0.002824  0.000227  0.002852   \n",
       "155        0            1          10  100  0.002715  0.000210  0.002729   \n",
       "495        0            1         100  100  0.002754  0.000221  0.002768   \n",
       "383        0            1        1000  100  0.003173  0.000190  0.003188   \n",
       "310        0            1       10000  100  0.007088  0.000187  0.007080   \n",
       "..       ...          ...         ...  ...       ...       ...       ...   \n",
       "219    10000         4096         100  100  0.193100  0.002592  0.193009   \n",
       "443    10000         4096        1000  100  0.194090  0.002379  0.193790   \n",
       "327    10000         4096       10000  100  0.197888  0.003360  0.197353   \n",
       "212    10000         4096      100000  100  0.203358  0.003205  0.203274   \n",
       "368    10000         4096     1000000  100  0.250761  0.007082  0.251942   \n",
       "\n",
       "         user    system       min       max        ci  \n",
       "198  0.000968  0.000871  0.002248  0.003385  0.000044  \n",
       "155  0.000932  0.000755  0.002178  0.003462  0.000041  \n",
       "495  0.000973  0.000770  0.002219  0.003369  0.000043  \n",
       "383  0.001369  0.000766  0.002706  0.003788  0.000037  \n",
       "310  0.005165  0.000793  0.006622  0.007852  0.000037  \n",
       "..        ...       ...       ...       ...       ...  \n",
       "219  0.028526  0.318859  0.186807  0.199367  0.000508  \n",
       "443  0.029979  0.321537  0.187868  0.201080  0.000466  \n",
       "327  0.037236  0.326702  0.191389  0.210862  0.000658  \n",
       "212  0.077805  0.346546  0.196746  0.214141  0.000628  \n",
       "368  0.637051  0.386394  0.235086  0.264447  0.001388  \n",
       "\n",
       "[546 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = 'out/20220731101132/'\n",
    "data = []\n",
    "for fpath in glob.iglob(out+'hf/*'):\n",
    "    fname, fext = fpath.split('/')[-1].split('.')\n",
    "    if fext == 'json':\n",
    "        params = [int(x) for x in fname.split('_')]\n",
    "        with open(fpath, 'r') as f:\n",
    "            data.append(prep_json(json.load(f)))\n",
    "df = pd.DataFrame(data).sort_values(['num_ifs','num_threads','array_size'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def line(error_y_mode=None, **kwargs):\n",
    "    \"\"\"Extension of `plotly.express.line` to use error bands.\n",
    "    Source: https://stackoverflow.com/a/69594497\"\"\"\n",
    "    ERROR_MODES = {'bar','band','bars','bands',None}\n",
    "    if error_y_mode not in ERROR_MODES:\n",
    "        raise ValueError(f\"'error_y_mode' must be one of {ERROR_MODES}, received {repr(error_y_mode)}.\")\n",
    "    if error_y_mode in {'bar','bars',None}:\n",
    "        fig = px.line(**kwargs)\n",
    "    elif error_y_mode in {'band','bands'}:\n",
    "        if 'error_y' not in kwargs:\n",
    "            raise ValueError(f\"If you provide argument 'error_y_mode' you must also provide 'error_y'.\")\n",
    "        figure_with_error_bars = px.line(**kwargs)\n",
    "        fig = px.line(**{arg: val for arg,val in kwargs.items() if arg != 'error_y'})\n",
    "        for data in figure_with_error_bars.data:\n",
    "            x = list(data['x'])\n",
    "            y_upper = list(data['y'] + data['error_y']['array'])\n",
    "            y_lower = list(data['y'] - data['error_y']['array'] if data['error_y']['arrayminus'] is None else data['y'] - data['error_y']['arrayminus'])\n",
    "            color = f\"rgba({tuple(int(data['line']['color'].lstrip('#')[i:i+2], 16) for i in (0, 2, 4))},.3)\".replace('((','(').replace('),',',').replace(' ','')\n",
    "            fig.add_trace(\n",
    "                go.Scatter(\n",
    "                    x = x+x[::-1],\n",
    "                    y = y_upper+y_lower[::-1],\n",
    "                    fill = 'toself',\n",
    "                    fillcolor = color,\n",
    "                    line = dict(\n",
    "                        color = 'rgba(255,255,255,0)'\n",
    "                    ),\n",
    "                    hoverinfo = \"skip\",\n",
    "                    showlegend = False,\n",
    "                    legendgroup = data['legendgroup'],\n",
    "                    xaxis = data['xaxis'],\n",
    "                    yaxis = data['yaxis'],\n",
    "                )\n",
    "            )\n",
    "        # Reorder data as said here: https://stackoverflow.com/a/66854398/8849755\n",
    "        reordered_data = []\n",
    "        for i in range(int(len(fig.data)/2)):\n",
    "            reordered_data.append(fig.data[i+int(len(fig.data)/2)])\n",
    "            reordered_data.append(fig.data[i])\n",
    "        fig.data = tuple(reordered_data)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#f0f921',\n",
       " '#fad625',\n",
       " '#fcb430',\n",
       " '#f89640',\n",
       " '#ed7953',\n",
       " '#dd5f65',\n",
       " '#cb4778',\n",
       " '#b52f8c',\n",
       " '#9c179e',\n",
       " '#7c06a6',\n",
       " '#5c02a3',\n",
       " '#380499',\n",
       " '#0d0887']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rgb_to_hex(rgb):\n",
    "    return '%02x%02x%02x' % rgb\n",
    "\n",
    "n_colors = 13\n",
    "colors = px.colors.sample_colorscale(\n",
    "    'plasma_r', \n",
    "    [n/(n_colors -1) for n in range(n_colors)])\n",
    "colors = ['#'+rgb_to_hex(eval(c.lstrip('rgb'))) for c in colors]\n",
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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        {
         "hovertemplate": "num_threads=1<br>num_ifs=%{x}<br>mean=%{y}<extra></extra>",
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         "line": {
          "color": "#f0f921",
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         "hovertemplate": "num_threads=4<br>num_ifs=%{x}<br>mean=%{y}<extra></extra>",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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          },
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           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
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            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Array size = 1000000"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "num_ifs"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "mean"
         },
         "type": "log"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for array_size in df['array_size'].unique():\n",
    "    fig = line(\n",
    "        data_frame=df[df['array_size']==array_size],\n",
    "        title=f'Array size = {array_size}',\n",
    "        x='num_ifs',\n",
    "        y='mean',\n",
    "        log_y=True,\n",
    "        error_y = 'ci',\n",
    "        error_y_mode = 'band',\n",
    "        color='num_threads',\n",
    "        color_discrete_sequence=colors)\n",
    "    fig.update_xaxes(type='category')\n",
    "    fig.update_layout(margin=dict(l=0, r=120, t=50, b=0))\n",
    "    fig.write_image(out+f'{array_size}.png', scale=2)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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