From ce73ab9b7e21ca07c507cf0c44d2fe2fb03346ad Mon Sep 17 00:00:00 2001 From: TannedCung Date: Wed, 12 Jun 2024 14:46:18 +0700 Subject: [PATCH] update --- cope2n-api/EDA/processing_time.ipynb | 109 ++++----------------------- 1 file changed, 16 insertions(+), 93 deletions(-) diff --git a/cope2n-api/EDA/processing_time.ipynb b/cope2n-api/EDA/processing_time.ipynb index 64e9c39..ee77961 100644 --- a/cope2n-api/EDA/processing_time.ipynb +++ b/cope2n-api/EDA/processing_time.ipynb @@ -466,96 +466,19 @@ "plot_trend(frame_by_file.index, frame_by_file[\"queue\"], \"AI queuing Trend\")\n" ] }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def split_timestamps_by_day(timestamps):\n", - " \"\"\"\n", - " Splits a list of timestamps into a list of lists, where each inner list contains the timestamps for a single day.\n", - " \n", - " Args:\n", - " timestamps (list): A list of timestamp values.\n", - " \n", - " Returns:\n", - " list: A list of lists, where each inner list contains the timestamps for a single day.\n", - " \"\"\"\n", - " # Convert timestamps to datetime objects\n", - " datetimes = [pd.Timestamp(t, unit='s') for t in timestamps]\n", - " \n", - " # Create a DataFrame with the datetime objects\n", - " df = pd.DataFrame({'timestamp': datetimes})\n", - " \n", - " # Group the DataFrame by day and collect the timestamps for each day\n", - " timestamps_by_day = []\n", - " for _, group in df.groupby(pd.Grouper(key='timestamp', freq='D')):\n", - " day_timestamps = [t.timestamp() for t in group['timestamp']]\n", - " timestamps_by_day.append(day_timestamps)\n", - " \n", - " return timestamps_by_day" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "def visualize_requests_by_time(timestamps_by_day):\n", - " \"\"\"\n", - " Generates a histogram to visualize the number of requests received by time.\n", - " \n", - " Args:\n", - " timestamps (list): A list of timestamps (in seconds) representing the time when each request was received.\n", - " \"\"\"\n", - " num_days = len(timestamps_by_day)\n", - " fig, axes = plt.subplots(nrows=int(np.ceil(num_days / 2)), ncols=2, figsize=(90, 450))\n", - " for i, day_timestamps in enumerate(timestamps_by_day):\n", - " row = i // 2\n", - " col = i % 2\n", - " \n", - " day_timestamps = [pd.Timestamp(t, unit='s') for t in day_timestamps]\n", - " # Get the current axis\n", - " ax = axes[row, col] if num_days > 1 else axes\n", - " \n", - " # Plot the histogram for the current day\n", - " ax.hist(pd.to_datetime(day_timestamps), bins=60*24, edgecolor='black')\n", - " # ax.xticks(rotation=45)\n", - " ax.set_title(f\"Day {i+1}\")\n", - " ax.set_xlabel(\"Time\")\n", - " ax.set_ylabel(\"Count\")\n", - "\n", - " ax2 = ax.twinx()\n", - " ax2.set_ylabel(\"Time Process (s)\")\n", - " ax2.set_ylim(min(day_timestamps), max(day_timestamps))\n", - " # Convert timestamps to datetime objects\n", - " # timestamps = [pd.Timestamp(t, unit='s') for t in timestamps]\n", - " \n", - " # # Create a histogram with 24 bins (one for each hour of the day)\n", - " # plt.figure(figsize=(12, 6))\n", - " # # plt.hist(pd.to_datetime([t.hour for t in timestamps], unit='h'), bins=24, edgecolor='black')\n", - " # plt.hist(pd.to_datetime(timestamps), bins=1000, edgecolor='black')\n", - " \n", - " # Set x-axis labels to show the hour of the day\n", - " # x_labels = [f'{i:02d}:00' for i in range(24)]\n", - " # plt.xticks(np.arange(0, 24), x_labels, rotation=45)\n", - " # plt.xticks(rotation=45)\n", - " # plt.xlabel('Hour of the Day')\n", - " # plt.ylabel('Number of Requests')\n", - " # plt.title('Requests Received by Time of Day')\n", - " # plt.show()\n", - " plt.suptitle(\"Distribution of Requests by Time\")\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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