✨ (analysis) adjust grid + generate container replica graph

loadtesting/notebooks/locust.ipynb

@@ -10,7 +10,7 @@
     "import pytz\n",
     "import plotly.graph_objects as go\n",
     "import os\n",
-    "from matplotlib.ticker import ScalarFormatter, LogLocator"
+    "import matplotlib.dates as mdates"
    ],
    "id": "f546290dcd056f13",
    "outputs": [],
@@ -21,7 +21,7 @@
    "id": "3d6de811535875d3",
    "metadata": {},
    "source": [
-    "basedir = \"../data/baseapp/2024-07-15/16-18/\"\n",
+    "basedir = \"../data/baseapp/2024-07-15/10-12/\"\n",
     "outputdir = basedir+\"output/\"\n",
     "os.makedirs(outputdir, exist_ok=True)\n",
     "locust_data_history_file = basedir + \"data_stats_history.csv\"\n",
@@ -153,6 +153,8 @@
     "ax1.tick_params(axis='y')\n",
     "ax1.legend(loc='upper left')\n",
     "ax1.grid(True)\n",
+    "ax1.xaxis.set_major_locator(mdates.MinuteLocator(interval=5))\n",
+    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
     "\n",
     "# Secondary Y-Axis\n",
     "ax2 = ax1.twinx()\n",
@@ -182,6 +184,8 @@
     "ax1.tick_params(axis='y')\n",
     "ax1.legend(loc='upper left')\n",
     "ax1.grid(True)\n",
+    "ax1.xaxis.set_major_locator(mdates.MinuteLocator(interval=5))\n",
+    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
     "\n",
     "# Secondary Y-Axis\n",
     "ax2 = ax1.twinx()\n",
@@ -250,6 +254,56 @@
    "outputs": [],
    "execution_count": null
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "# Load data from CSV\n",
+    "containers_data_file = \"../data/baseapp/2024-07-15/containers.csv\"\n",
+    "containers_df = pd.read_csv(containers_data_file)\n",
+    "\n",
+    "# Parse the time column and set it as the index\n",
+    "containers_df['Time'] = pd.to_datetime(containers_df['Time'])\n",
+    "containers_df.set_index('Time', inplace=True)\n",
+    "\n",
+    "# Format column names to display only container names\n",
+    "containers_df.columns = [col.split('=')[1].strip(' \"{}') for col in containers_df.columns]\n",
+    "\n",
+    "def plot_data(start_time, end_time):\n",
+    "    # Filter data within the specified time range\n",
+    "    mask = (containers_df.index >= start_time) & (containers_df.index <= end_time)\n",
+    "    filtered_data = containers_df.loc[mask]\n",
+    "    \n",
+    "    # Plotting\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    \n",
+    "    for column in filtered_data.columns:\n",
+    "        if column.endswith(\"service\") or column == \"gateway\":\n",
+    "            plt.plot(filtered_data.index, filtered_data[column], label=column, linewidth=2.0)\n",
+    "        else:\n",
+    "            plt.plot(filtered_data.index, filtered_data[column], label='_nolegend_', linestyle='--')  \n",
+    "    \n",
+    "    plt.xlabel('Time')\n",
+    "    plt.ylabel('Value')\n",
+    "    plt.title('Container Replicas Over Time')\n",
+    "\n",
+    "    # Add legend for high variability columns only\n",
+    "    plt.legend(loc='best', bbox_to_anchor=(1.05, 1), borderaxespad=0.)\n",
+    "    \n",
+    "    plt.grid(True)\n",
+    "    xticks = pd.date_range(start=start_time, end=end_time, freq='5min') \n",
+    "    plt.xticks(ticks=xticks, labels=[tick.strftime(\"%H:%M\") for tick in xticks], rotation=45)\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(outputdir+'plt_container_replicas.png', bbox_inches='tight')\n",
+    "    plt.show()\n",
+    "\n",
+    "# Plot data within the specified time range\n",
+    "plot_data(start_time, end_time)"
+   ],
+   "id": "f072d2575bf7a131",
+   "outputs": [],
+   "execution_count": null
+  },
   {
    "cell_type": "code",
    "id": "11e7a30b897c45e7",