plot-results.ipynb 176 KB
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{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": 1,
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   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
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    "import numpy as np\n",
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    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "from itertools import combinations \n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# render figures in notebook\n",
    "pio.renderers.default = \"notebook_connected\"\n",
    "\n",
    "# templates figures\n",
    "px.defaults.template = \"simple_white\"\n",
    "pio.templates.default = \"simple_white\"\n",
    "\n",
    "# layout for all figures\n",
    "LAYOUT_FIGURES = dict(\n",
    "    autosize=False,\n",
    "    width = 500,\n",
    "    height = 500,\n",
    "    xaxis = dict(\n",
    "      constrain=\"domain\",\n",
    "      mirror=True,\n",
    "      showexponent=\"all\",\n",
    "      exponentformat=\"power\"\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "      scaleanchor = \"x\",\n",
    "      scaleratio = 1,\n",
    "      mirror=True,\n",
    "      showexponent=\"all\",\n",
    "      exponentformat=\"power\"\n",
    "    ),\n",
    "    title = dict(\n",
    "      y = 0.9,\n",
    "      x = 0.5,\n",
    "      xanchor = 'center',\n",
    "      yanchor = 'top'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auxiliary Functions"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {},
   "outputs": [],
   "source": [
    "def create_folder(path):\n",
    "    \"\"\"Creates a folder if it does not exist\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    path : str\n",
    "        Path of the new folder\n",
    "    \n",
    "    Examples\n",
    "    --------\n",
    "    \n",
    "    >>> create_folder('./results')\n",
    "    \"\"\"\n",
    "    if not os.path.exists(path):\n",
    "        os.makedirs(path)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {},
   "outputs": [],
   "source": [
    "def create_figure(df, model):\n",
    "    \"\"\"Creates a scatter figure showing the time taken by each tool to verify each property of a model\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pandas.Dataframe\n",
    "        Dataframe containing the results of the experiments\n",
    "    model : string\n",
    "        model to be plotted\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    plotly.graph_objects.Figure\n",
    "        Scatter figure\n",
    "    \n",
    "    Examples\n",
    "    --------\n",
    "    \n",
    "    >>> import os\n",
    "    >>> import pandas as pd\n",
    "    >>> csv_file = os.path.join(\"results\", \"output.csv\")\n",
    "    >>> df = pd.read_csv(csv_file)\n",
    "    >>> fig = create_figure(df, 'philo10')\n",
    "    \"\"\"\n",
    "    model_df = df[df.model == model]\n",
    "\n",
    "    figure = px.scatter(model_df, \n",
    "                        x=\"formula\", y=\"time\",\n",
    "                        title=model, \n",
    "                        color=\"tool\", \n",
    "                        symbol_sequence=['x'])\n",
    "\n",
    "    figure.update_layout(yaxis_title=\"time (s)\", title=LAYOUT_FIGURES['title'])\n",
    "    return figure"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
   "outputs": [],
   "source": [
    "def get_axis_title(experiment, show_strategy=True):\n",
    "    \"\"\"Get the axis title of a figure depending on the experiment being plotted\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    experiment : str\n",
    "        String with the experiment information\n",
    "    show_strategy : bool, optional\n",
    "        Flag to show the information related to the strategy used by the tool\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    str\n",
    "        axis title\n",
    "        \n",
    "    Examples\n",
    "    --------\n",
    "    \n",
    "    >>> get_axis_title('pmc-sog_otfL_couv99-default_1_1', True)\n",
    "    pmc-sog (Lace, strategy: couv99-default, # cores: 1)\n",
    "    \"\"\"\n",
    "    information = experiment.split('_')\n",
    "    tool_name = information[0]\n",
    "    \n",
    "    info = []\n",
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    "    library_dic = {\n",
    "        'otfL': 'Lace',\n",
    "        'otfP': 'Pthreads',\n",
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    "        'otfC': 'Cthreads',\n",
    "        'otf': 'Hybrid'\n",
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    "    }\n",
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    "    \n",
    "    if (len(information) == 5):\n",
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    "        info.append(library_dic[information[1]])\n",
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    "\n",
    "    if (show_strategy):\n",
    "        info.append('strategy: {}'.format(information[-3]))\n",
    "\n",
    "    nb_nodes = int(information[-2])\n",
    "    if (nb_nodes > 1):\n",
    "        info.append('# nodes: {}'.format(nb_nodes))\n",
    "\n",
    "    info.append('# cores: {}'.format(information[-1]))\n",
    "\n",
    "    title = '{} ({})'.format(tool_name, ', '.join(info))\n",
    "    \n",
    "    return title"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "def create_log_figure(table, table_errors, model, tool_x, tool_y, show_strategy=True, callback=None):\n",
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    "    \"\"\"Creates a Scatter figure in logarithmic scale comparing the performance of two tools\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    table : pandas.Dataframe\n",
    "        Dataframe with the times of each experiment\n",
    "    table_errors : pandas.Dataframe\n",
    "        Dataframe with the errors of each experiment\n",
    "    model : string\n",
    "        Model to be analyzed\n",
    "    tool_x : string\n",
    "        Tool to be compared and plotted on the x-axis\n",
    "    tool_y : string\n",
    "        Tool to be compared and plotted on the y-axis\n",
    "    show_strategy : bool\n",
    "        Flag to show the stretagy used by the tools\n",
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    "    callback : function\n",
    "        Function to be called when clicking on a point\n",
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    "        \n",
    "    Returns\n",
    "    -------\n",
    "    plotly.graph_objects.Figure\n",
    "        Scatter figure\n",
    "        \n",
    "    Examples\n",
    "    --------\n",
    "    >>> import os\n",
    "    >>> import pandas as pd\n",
    "    >>> csv_file = os.path.join(\"results\", \"output.csv\")\n",
    "    >>> df = pd.read_csv(csv_file)\n",
    "    >>> table = df.set_index(['model', 'formula', 'tool'], drop=True).unstack('tool')\n",
    "    >>> fig = create_log_figure(table['time'], table['error'], 'philo10', 'pmc-sog_otfL_couv99-default_1_8', 'pmc-sog_otfP_couv99-default_1_8')\n",
    "    \"\"\"\n",
    "    try:\n",
    "        min_values = table.loc[model].min()\n",
    "        max_values = table.loc[model].max()\n",
    "\n",
    "        min_value = min(min_values[tool_x], min_values[tool_y])/2.\n",
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    "        min_value_log = np.log10(min_value)\n",
    "\n",
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    "        max_value = max(max_values[tool_x], max_values[tool_y])\n",
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    "        max_value_log = np.log10(max_value)\n",
    "\n",
    "        table_model = table.loc[model]\n",
    "        table_errors_model = table_error.loc[model]\n",
    "        \n",
    "        full_table_x = pd.concat([table_model[tool_x],table_model['property'], table_errors_model[tool_x]], axis=1)\n",
    "        full_table_x.columns = ['time', 'property', 'error']\n",
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    "\n",
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    "        full_table_y = pd.concat([table_model[tool_y],table_model['property'], table_errors_model[tool_y]], axis=1)\n",
    "        full_table_y.columns = ['time', 'property', 'error']\n",
    "\n",
    "        traces = [\n",
    "            {\"property\": 'T', \"color\":\"green\"},\n",
    "            {\"property\": 'F', \"color\":\"red\"},\n",
    "            {\"property\": 'U', \"color\":\"black\"}\n",
    "        ]\n",
    "\n",
    "        figures = []\n",
    "        for t in traces:\n",
    "            # filter by verification output\n",
    "            table_x = full_table_x[full_table_x.property == t['property']]\n",
    "            table_y = full_table_y[full_table_y.property == t['property']]\n",
    "\n",
    "            # custom data\n",
    "            custom_data = list(zip(table_x.index, table_x.error,table_y.error))\n",
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    "            \n",
    "            # tools\n",
    "            metainfo = {\n",
    "                'model': model, \n",
    "                'tools': {'x': tool_x, 'y': tool_y},\n",
    "                'folder': os.path.join(os.path.abspath(os.pardir), \"results\")\n",
    "            }\n",
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    "\n",
    "            figures.append(go.Scatter(x=table_x.time,\n",
    "                                      y=table_y.time,\n",
    "                                      name=t['property'],\n",
    "                                      mode='markers',\n",
    "                                      marker_symbol='circle-open',\n",
    "                                      marker_color=t['color'],\n",
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    "                                      meta = metainfo,\n",
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    "                                      customdata=custom_data,\n",
    "                                      hovertemplate =\n",
    "                                        '<b>Formula # %{customdata[0]}</b><br>' +\n",
    "                                        '<br><b>Times:</b><br>' +\n",
    "                                        '<b>x:</b> %{x} s' +\n",
    "                                        '<br><b>y:</b> %{y} s<br>' +\n",
    "                                        '<br><b>Errors:</b><br>' +\n",
    "                                        '<b>x:</b> %{customdata[1]}<br>' +\n",
    "                                        '<b>y:</b> %{customdata[2]}',\n",
    "                                        ))    \n",
    "\n",
    "        # Line\n",
    "        figures.append(go.Scatter(x=[min_value, max_value], \n",
    "                                    y=[min_value, max_value],\n",
    "                                    mode='lines', showlegend=False,\n",
    "                                    line=dict(color='black', width=1)))\n",
    "\n",
    "        # Create figure\n",
    "        figure = go.FigureWidget(figures)\n",
    "        figure.update_layout(LAYOUT_FIGURES,\n",
    "                             title_text=model,\n",
    "                             hoverlabel=dict(bgcolor=\"white\", align='auto'),\n",
    "                             legend_title_text='property',\n",
    "                             xaxis=dict(type='log', autorange=False, range=[min_value_log, max_value_log]),\n",
    "                             yaxis=dict(type='log', autorange=False, range=[min_value_log, max_value_log]),\n",
    "                             xaxis_title=get_axis_title(tool_x, show_strategy),\n",
    "                             yaxis_title=get_axis_title(tool_y, show_strategy))\n",
    "\n",
    "        # Add event\n",
    "        if callback is not None:\n",
    "            for i in range(len(figure.data)):\n",
    "                figure.data[i].on_click(callback)\n",
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    "    \n",
    "        return figure\n",
    "    except Exception as e:\n",
    "        print(\"Error when ploting model: {} - tool_x: {} - tool_y: {}\".format(model, tool_x, tool_y))\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "def get_filename(base_path, tool, model, model_instance, formula):\n",
    "    \"\"\"Returns the absolute path of the experiment log\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    base_path : string\n",
    "        Path of the folder where logs are saved\n",
    "    tool : string\n",
    "        Tool name\n",
    "    model : string\n",
    "        Model name\n",
    "    model_instance : string\n",
    "        Name of the model instance\n",
    "    formula : string\n",
    "        Identifier of the formula\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    string\n",
    "        Absolute path of the log file\n",
    "    \n",
    "    \"\"\"\n",
    "    information = tool.split('_')\n",
    "    \n",
    "    tool_name = information[0]\n",
    "    tool_configuration = '_'.join(information[:-2])\n",
    "    nb_nodes = information[-2]\n",
    "    nb_cores = information[-1]\n",
    "    \n",
    "    experiment_folder = os.path.join(base_path, tool_name, tool_configuration, model, model_instance)\n",
    "    filename = f'{tool_name}_{model_instance}-n{nb_nodes}-th{nb_cores}-f{formula}'\n",
    "    absolute_path = os.path.join(experiment_folder, filename)\n",
    "    \n",
    "    return absolute_path\n",
    "\n",
    "def open_logs_callback(trace, points, selector):\n",
    "    \"\"\"Callback that open the log files when clicking on a point of the figure\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    trace : plotly.graph_objects.Figure\n",
    "        the figure to attach the callback\n",
    "    points : plotly.callbacks.Points \n",
    "        points of the figure selected\n",
    "    selector: plotly.callbacks.InputDeviceState \n",
    "        Device information \n",
    "    \"\"\"\n",
    "    inds = points.point_inds\n",
    "    if (inds):\n",
    "        index = inds[0]\n",
    "\n",
    "        formula, error_x, error_y = trace['customdata'][index]\n",
    "        model_instance = trace['meta']['model']\n",
    "        model = ''.join(c for c in model_instance if not c.isdigit())\n",
    "        tools = trace['meta']['tools']\n",
    "        logs_folder = trace['meta']['folder']\n",
    "\n",
    "        filename_x = get_filename(logs_folder, tools['x'], model, model_instance, formula)\n",
    "        filename_y = get_filename(logs_folder, tools['y'], model, model_instance, formula)\n",
    "\n",
    "        for f in [filename_x, filename_y]:\n",
    "            webbrowser.open(f'file://{f}.err')\n",
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    "            webbrowser.open(f'file://{f}.out')\n",
    "\n",
    "OPEN_LOGS_CALLBACK_JS = \"\"\"\n",
    "function get_filename (base_path, tool, model_instance, formula) {\n",
    "  const information = tool.split('_');\n",
    "  const size = information.length;\n",
    "\n",
    "  const tool_name = information[0];\n",
    "  const tool_configuration = information.slice(0, size - 2).join('_');\n",
    "  const nb_nodes = information[size - 2];\n",
    "  const nb_cores = information[size - 1];\n",
    "\n",
    "  const model = model_instance.replace(/[0-9]/g, '');\n",
    "\n",
    "  const experiment_folder = `${base_path}/${tool_name}/${tool_configuration}/${model}/${model_instance}`;\n",
    "  const filename = `${tool_name}_${model_instance}-n${nb_nodes}-th${nb_cores}-f${formula}`;\n",
    "\n",
    "  return `${experiment_folder}/${filename}`;\n",
    "}\n",
    "\n",
    "const plots = document.getElementsByClassName(\"plotly-graph-div js-plotly-plot\");\n",
    "const myPlot = plots[0];\n",
    "\n",
    "myPlot.on('plotly_click', function(data){\n",
    "    const points = data.points;\n",
    "    if (points.length != 1) {return ;}\n",
    "    \n",
    "    const myPoint = points[0];\n",
    "    const formula = myPoint.customdata[0];\n",
    "    const meta = myPoint.data.meta;\n",
    "    \n",
    "    const href = window.location.href.split('/');\n",
    "    const base_path = href.splice(0,href.length-2).join('/');\n",
    "    const results_path = `${base_path}/results`;\n",
    "    \n",
    "    const filename_x = get_filename(results_path, meta.tools.x, meta.model, formula);\n",
    "    const filename_y = get_filename(results_path, meta.tools.y, meta.model, formula);\n",
    "    \n",
    "    console.log('x: ' + filename_x);\n",
    "    window.open(`${filename_x}.err`);\n",
    "    window.open(`${filename_x}.out`);\n",
    "    \n",
    "    console.log('y: ' + filename_y);\n",
    "    window.open(`${filename_y}.err`);\n",
    "    window.open(`${filename_y}.out`);\n",
    "});\n",
    "\"\"\"\n"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "metadata": {},
   "outputs": [],
   "source": [
    "def create_figure_explored_states(table_explored_states, model):\n",
    "    \"\"\"Creates figure showing the number of explorated states during the verification \n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    table_explored_states : pandas.Dataframe\n",
    "        Dataframe with the explorated states of each experiment\n",
    "    model : string\n",
    "        Model to be analyzed\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    plotly.graph_objects.Figure\n",
    "        Scatter figure\n",
    "    \"\"\"\n",
    "    colors={'T': 'green', 'F': 'red'}\n",
    "    float_formatter = \"{:.2E}\".format\n",
    "\n",
    "    table_model = table_explored_states[table_explored_states.property != 'U']\n",
    "    table_model = table_model[table_model.model == model]\n",
    "\n",
    "    table_stats = table_model.groupby(['property']).agg(['mean']) \n",
    "\n",
    "    fig = go.Figure()\n",
    "    for p in table_stats.index:\n",
    "        data = table_model[table_model.property==p]\n",
    "        stats = table_stats.loc[p]\n",
    "        \n",
    "        x_axis = np.arange(1, data['formula'].count()+1, 1)\n",
    "        mean = stats['explored_states','mean']\n",
    "        \n",
    "        figure = px.scatter(data, \n",
    "                            x=x_axis, \n",
    "                            y=\"explored_states\",\n",
    "                            title=model, \n",
    "                            color='property',\n",
    "                            color_discrete_map=colors,\n",
    "                            symbol_sequence=[\"circle\"])\n",
    "\n",
    "        line = go.Scatter(x=[x_axis[0], x_axis[-1]], \n",
    "                          y=[mean, mean],\n",
    "                          mode='lines', showlegend=False,                          \n",
    "                          line=dict(color=colors[p], width=1.5))\n",
    "\n",
    "        fig.add_trace(figure['data'][0])\n",
    "        fig.add_trace(line)\n",
    "        \n",
    "        fig.add_annotation(x=1, \n",
    "                           y=mean,\n",
    "                           font=dict(color=colors[p]),\n",
    "                           text=f\"mean = {float_formatter(mean)}\")\n",
    "\n",
    "    fig.update_layout(title_text=model, title=LAYOUT_FIGURES['title'], \n",
    "                      width = 500, height = 500, margin=dict(r=110))\n",
    "    \n",
    "    fig.update_xaxes(title=\"formula\", range=[x_axis[0]-1, x_axis[-1]+1])\n",
    "    fig.update_yaxes(title=\"# explored states\")\n",
    "    \n",
    "    fig.update_annotations(dict(\n",
    "        showarrow=False,\n",
    "        xanchor=\"left\",\n",
    "        yanchor=\"middle\",\n",
    "        xref='paper'))\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment filters\n",
    "\n",
    "def versus_dfs(experiments):\n",
    "    \"\"\"Selects only experiments using DFS strategy\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    strategy_exp1= exp1.split('_')[1]\n",
    "    strategy_exp2= exp2.split('_')[1]\n",
    "    \n",
    "    return strategy_exp1 == 'dfs' or strategy_exp2 == 'dfs'\n",
    "\n",
    "def versus_sequential(experiments):\n",
    "    \"\"\"Selects only experiments run sequentially \"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    nodes_exp1, threads_exp1 = exp1.split('_')[-2:]\n",
    "    nodes_exp2, threads_exp2 = exp2.split('_')[-2:]\n",
    "\n",
    "    return (nodes_exp1 == '1' and nodes_exp2 == '1') and \\\n",
    "            (threads_exp1 == '1' or threads_exp2 == '1')\n",
    "\n",
    "def same_tool(experiments, tool):\n",
    "    \"\"\"Selects only experiments comparing the same tool\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    tool_exp1= exp1.split('_')[0]\n",
    "    tool_exp2= exp2.split('_')[0]\n",
    "    return tool_exp1.startswith(tool) and tool_exp2.startswith(tool)\n",
    "\n",
    "def same_number_threads(experiments):\n",
    "    \"\"\"Selects only experiments comparing the same number of processes and cores\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    nodes_exp1, threads_exp1 = exp1.split('_')[-2:]\n",
    "    nodes_exp2, threads_exp2 = exp2.split('_')[-2:]\n",
    "    return (nodes_exp1 == nodes_exp2) and (threads_exp1 == threads_exp2) \n",
    "\n",
    "def same_thread_library(experiments):\n",
    "    \"\"\"Selects only experiments comparing the same parallelization library\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    library_exp1 = exp1.split('_')[1]\n",
    "    library_exp2 = exp2.split('_')[1]\n",
    "    return library_exp1 == library_exp2\n",
    "\n",
    "def same_strategy(experiments):\n",
    "    \"\"\"Selects only experiments comparing the same strategy\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    strategy_exp1 = exp1.split('_')[2]\n",
    "    strategy_exp2 = exp2.split('_')[2]\n",
    "    return strategy_exp1 == strategy_exp2\n",
    "\n",
    "def only_couvreur_strategy(experiments):\n",
    "    \"\"\"Selects only experiments comparing couvreur emptiness check algorithm\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    strategy_exp1 = exp1.split('_')[2]\n",
    "    strategy_exp2 = exp2.split('_')[2]\n",
    "    return strategy_exp1.startswith('couv99') and strategy_exp2.startswith('couv99')\n",
    "\n",
    "def compare_threads_library(experiments):\n",
    "    \"\"\"Compares parallization libraries used in pmc-sog. \n",
    "    \n",
    "    It selects experiments where the tool is only pmc-sog and the strategy, number of threads, \n",
    "    number of processus are the same.\n",
    "    \"\"\"\n",
    "    return same_tool(experiments, 'pmc-sog') and \\\n",
    "            same_strategy(experiments) and \\\n",
    "            same_number_threads(experiments) and \\\n",
    "            not same_thread_library(experiments)\n",
    "\n",
    "def compare_couvreur_strategies(experiments):\n",
    "    \"\"\"Compares couvreurs strategies used in pmc-sog. \n",
    "    \n",
    "    It selects experiments where the tool is only pmc-sog, the strategy is couvreur, and \n",
    "    the parallelization library, number of threads, number of processus are the same.\n",
    "    \"\"\"\n",
    "    return only_couvreur_strategy(experiments) and \\\n",
    "            same_thread_library(experiments) and \\\n",
    "            same_number_threads(experiments)\n",
    "\n",
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    "def same_distributed_number_threads(experiments):\n",
    "    \"\"\"Selects only experiments where the multiplication of theirs nodes with cores are the same.\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    nodes_exp1, threads_exp1 = exp1.split('_')[-2:]\n",
    "    nodes_exp2, threads_exp2 = exp2.split('_')[-2:]\n",
    "    return (int(nodes_exp1) * int(threads_exp1)) == (int(nodes_exp2) * int(threads_exp2))\n",
    "\n",
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    "def compare_tools(experiments):\n",
    "    \"\"\"Compares pmc-sog and pnml2lts-mc using the DFS algorithm. \n",
    "    \n",
    "    It selects experiments where the tools are not the same, the exploration algorithm is DFS and \n",
    "    the number of processus and cores are the same.\n",
    "    \"\"\"\n",
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    "    return not (same_tool(experiments, 'pmc-sog') or same_tool(experiments,'pnml2lts-mc')) and \\\n",
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    "            versus_dfs(experiments)\n",
    "\n",
    "def compare_multithreading(experiments):\n",
    "    \"\"\"Compares the sequential and multi-core version of pmc-sog. \n",
    "    \n",
    "    It selects experiments where the tools is pmc-sog, the parallelization library, the emptiness check \n",
    "    strategy are the same. Here the number of processus and cores are different.\n",
    "    \"\"\"\n",
    "    return same_tool(experiments, 'pmc-sog') and \\\n",
    "            same_thread_library(experiments) and \\\n",
    "            same_strategy(experiments) and \\\n",
    "            versus_sequential(experiments)\n",
    "\n",
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    "def against_hybrid(experiments):\n",
    "    \"\"\"Selects only experiments comparing with hybrid mode\"\"\"\n",
    "    exp1, exp2 = experiments\n",
    "    library_exp1 = exp1.split('_')[1]\n",
    "    library_exp2 = exp2.split('_')[1]\n",
    "    return (library_exp1 == 'otf') or (library_exp2 == 'otf')\n",
    "\n",
    "\n",
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    "def compare_distributed(experiments):\n",
    "    \"\"\"Compares the hybrid version of pmc-sog\"\"\"\n",
    "    return same_tool(experiments, 'pmc-sog') and \\\n",
    "        same_strategy(experiments) and \\\n",
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    "        same_distributed_number_threads(experiments) and \\\n",
    "        against_hybrid(experiments)\n",
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    "\n",
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    "# Plots to be created\n",
    "plots = {\n",
    "    'compare_thread_library': compare_threads_library,\n",
    "    'compare_couvreur_algorithm': compare_couvreur_strategies,\n",
    "    'compare_tools': compare_tools,\n",
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    "    'compare_multicore': compare_multithreading,\n",
    "    'compare_distributed': compare_distributed\n",
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    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 9,
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Root folder\n",
    "PROJECT_FOLDER = os.path.abspath(os.pardir)\n",
    "\n",
    "# csv file with the output\n",
    "csv_file = os.path.join(PROJECT_FOLDER, \"results\", \"output.csv\")\n",
    "\n",
    "# Output folder\n",
    "OUTPUT_FOLDER = os.path.join(PROJECT_FOLDER,\"results\", \"figures\")\n",
    "create_folder(OUTPUT_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 10,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>formula</th>\n",
       "      <th>tool</th>\n",
       "      <th>time</th>\n",
       "      <th>property</th>\n",
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       "      <th>explored_states</th>\n",
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       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>philo10</td>\n",
       "      <td>1</td>\n",
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       "      <td>pmc-sog_otfL_couv99-default_1_16</td>\n",
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       "      <td>6.572</td>\n",
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       "      <td>F</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>philo10</td>\n",
       "      <td>1</td>\n",
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       "      <td>pmc-sog_otfL_couv99-shy_1_16</td>\n",
       "      <td>5.926</td>\n",
       "      <td>F</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>philo10</td>\n",
       "      <td>1</td>\n",
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       "      <td>pmc-sog_otfP_couv99-default_1_16</td>\n",
       "      <td>6.811</td>\n",
       "      <td>F</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>philo10</td>\n",
       "      <td>1</td>\n",
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       "      <td>pmc-sog_otfP_couv99-shy_1_16</td>\n",
       "      <td>6.862</td>\n",
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       "      <td>F</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>philo10</td>\n",
       "      <td>1</td>\n",
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       "      <td>pmc-sog_otf_couv99-default_2_8</td>\n",
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       "      <td>5.705</td>\n",
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       "      <td>F</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>OK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
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       "     model  formula                              tool   time property  \\\n",
       "0  philo10        1  pmc-sog_otfL_couv99-default_1_16  6.572        F   \n",
       "1  philo10        1      pmc-sog_otfL_couv99-shy_1_16  5.926        F   \n",
       "2  philo10        1  pmc-sog_otfP_couv99-default_1_16  6.811        F   \n",
       "3  philo10        1      pmc-sog_otfP_couv99-shy_1_16  6.862        F   \n",
       "4  philo10        1    pmc-sog_otf_couv99-default_2_8  5.705        F   \n",
       "\n",
       "   explored_states error  \n",
       "0              NaN    OK  \n",
       "1              NaN    OK  \n",
       "2              NaN    OK  \n",
       "3              NaN    OK  \n",
       "4              NaN    OK  "
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      ]
     },
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     "execution_count": 10,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data\n",
    "df = pd.read_csv(csv_file)\n",
    "\n",
    "# merge the information related to the experiment (# nodes, # threads, strategy) to the tool column\n",
    "df['tool'] = df[['tool', 'strategy', 'num_nodes', 'num_threads']].astype(str).apply('_'.join, axis=1)\n",
    "df = df.drop(columns=['strategy', 'num_nodes', 'num_threads'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>property</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>formula</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">philo10</th>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                property\n",
       "model   formula         \n",
       "philo10 1              F\n",
       "        2              F\n",
       "        3              F\n",
       "        4              F\n",
       "        5              F"
      ]
     },
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     "execution_count": 11,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ground truth for properties\n",
    "p_df = pd.read_csv(csv_file)\n",
    "p_df =p_df[\n",
    "    (p_df.tool=='pnml2lts-mc') & \n",
    "    (p_df.strategy == 'ndfs') & \n",
    "    (p_df.num_nodes == 1) & \n",
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    "    (p_df.num_threads == 16)]\n",
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    "\n",
    "# only property column is needed\n",
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    "p_df = p_df.drop(columns=['tool', 'strategy', 'num_nodes', 'num_threads', 'time', 'explored_states', 'error'])\n",
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    "p_df.fillna('U', inplace=True)\n",
    "p_df.set_index(['model', 'formula'], inplace=True)\n",
    "p_df.sort_index(inplace=True)\n",
    "\n",
    "p_df.head()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">time</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>tool</th>\n",
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       "      <th>pmc-sog_otfL_couv99-default_1_1</th>\n",
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       "      <th>pmc-sog_otfL_couv99-default_1_16</th>\n",
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913
       "      <th>pmc-sog_otfL_couv99-shy_1_1</th>\n",
914
       "      <th>pmc-sog_otfL_couv99-shy_1_16</th>\n",
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       "      <th>pmc-sog_otfP_couv99-default_1_1</th>\n",
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       "      <th>pmc-sog_otfP_couv99-default_1_16</th>\n",
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       "      <th>pmc-sog_otfP_couv99-shy_1_1</th>\n",
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       "      <th>pmc-sog_otfP_couv99-shy_1_16</th>\n",
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       "      <th>pmc-sog_otf_couv99-default_2_16</th>\n",
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       "      <th>pmc-sog_otf_couv99-default_2_8</th>\n",
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921
       "      <th>...</th>\n",
922
       "      <th>pmc-sog_otf_couv99-default_8_2</th>\n",
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923
       "      <th>pmc-sog_otf_couv99-shy_2_16</th>\n",
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       "      <th>pmc-sog_otf_couv99-shy_2_8</th>\n",
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       "      <th>pmc-sog_otf_couv99-shy_4_16</th>\n",
       "      <th>pmc-sog_otf_couv99-shy_4_4</th>\n",
927
       "      <th>pmc-sog_otf_couv99-shy_8_2</th>\n",
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Jaime Arias committed
928
       "      <th>pnml2lts-mc_dfs_1_1</th>\n",
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929
       "      <th>pnml2lts-mc_dfs_1_16</th>\n",
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Jaime Arias committed
930
       "      <th>pnml2lts-mc_ndfs_1_1</th>\n",
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931
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       "      <th>pnml2lts-mc_ndfs_1_16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>formula</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">philo10</th>\n",
       "      <th>1</th>\n",
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963
       "      <td>NaN</td>\n",
964
       "      <td>6.572</td>\n",
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965
       "      <td>NaN</td>\n",
966
       "      <td>5.926</td>\n",
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967
       "      <td>NaN</td>\n",
968
       "      <td>6.811</td>\n",
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969
       "      <td>NaN</td>\n",
970
       "      <td>6.862</td>\n",
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       "      <td>5.688</td>\n",
       "      <td>5.705</td>\n",
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       "      <td>...</td>\n",
       "      <td>OK</td>\n",
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975
       "      <td>TIME LIMIT</td>\n",
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       "      <td>OK</td>\n",
       "      <td>OK</td>\n",
       "      <td>OK</td>\n",
       "      <td>OK</td>\n",
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980
       "      <td>NaN</td>\n",
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981
       "      <td>OK</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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987
       "      <td>NaN</td>\n",
988
       "      <td>5.179</td>\n",
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989
       "      <td>NaN</td>\n",
990
       "      <td>5.090</td>\n",
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991
       "      <td>NaN</td>\n",
992
       "      <td>4.575</td>\n",
993
       "      <td>NaN</td>\n",
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       "      <td>4.350</td>\n",
       "      <td>5.206</td>\n",
       "      <td>6.592</td>\n",
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       "      <td>...</td>\n",
       "      <td>OK</td>\n",
       "      <td>OK</td>\n",
       "      <td>OK</td>\n",