{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c8e9d95",
   "metadata": {},
   "source": [
    "# SOAR Inventory Plots\n",
    "\n",
    "This script makes two TAP requests, one for the metadata of the descriptors for each instrument and level, then another for all the file start and end times for that descriptor. With TAP+ the results were limited to 2000 results, but PyVO doesn't appear to be as limited, so the single function can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cb3e8885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory '20240701' created.\n"
     ]
    }
   ],
   "source": [
    "#from my_utilities import TAPquery - see below, included so standalone\n",
    "import pyvo as vo\n",
    "\n",
    "# URL encoding\n",
    "import urllib.parse\n",
    "\n",
    "# General handling\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# For scaling the plots to number of descriptors\n",
    "import math \n",
    "\n",
    "# For making the results directory\n",
    "import os\n",
    "\n",
    "DataPath = '/Users/hmiddleton/Library/CloudStorage/OneDrive-ESA/SolarOrbiter/Data/Inventories/'\n",
    "ARCHIVE = 'https://soar.esac.esa.int/soar-sl-tap/tap'\n",
    "\n",
    "dt_today = dt.datetime.today()\n",
    "dir_today = dt_today.strftime('%Y%m%d')\n",
    "\n",
    "# Create the directory\n",
    "if not os.path.exists(DataPath + dir_today):\n",
    "    os.makedirs(DataPath + dir_today)\n",
    "    print(f\"Directory '{dir_today}' created.\")\n",
    "else:\n",
    "    print(f\"Directory '{dir_today}' already exists.\")\n",
    "\n",
    "ResultsPath = DataPath + dir_today + '/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b545056",
   "metadata": {},
   "outputs": [],
   "source": [
    "def TAPquery(ARCHIVE, query):\n",
    "    '''\n",
    "    Given the ARCHIVE (e.g., https://csa.esac.esa.int/csa-sl-tap/tap/) and the\n",
    "    ADQL query, fetch the results and return as an pandas dataframe.\n",
    "    '''\n",
    "    try:\n",
    "        SOAR = vo.dal.TAPService(ARCHIVE)\n",
    "        results = SOAR.search(query)\n",
    "        astropy_table = results.to_table()\n",
    "        return astropy_table.to_pandas()\n",
    "    except Exception as e:\n",
    "        print(f\"Error executing query: {e}\")\n",
    "        return None\n",
    "\n",
    "\n",
    "def getTAPtable(level):\n",
    "    table = 'v_sc_data_item'\n",
    "    if 'LL' in level: table = 'v_ll_data_item'\n",
    "    return table\n",
    "\n",
    "\n",
    "def get_descriptors(ARCHIVE, instru, level):\n",
    "    '''\n",
    "    Get all descriptors for the given instrument and level and return a list\n",
    "    '''\n",
    "    \n",
    "    table = getTAPtable(level)\n",
    "    \n",
    "    # ordered by reverse because the plot starts at the bottom\n",
    "    ADQL = (f\"SELECT DISTINCT descriptor FROM {table} \"\n",
    "            f\"WHERE instrument='{instru}' \"\n",
    "            f\"AND level='{level}' \"\n",
    "            f\"ORDER BY descriptor DESC\")\n",
    "    \n",
    "    result_df = TAPquery(ARCHIVE, ADQL)\n",
    "\n",
    "    if result_df is not None and len(result_df) > 0:\n",
    "        # Return that column as a list\n",
    "        return result_df['descriptor'].values.tolist()\n",
    "    else:        \n",
    "        return 'no_data'\n",
    "\n",
    "\n",
    "def get_data_np(dataset, level):\n",
    "    '''\n",
    "    Get the details of the files for one descriptor and return\n",
    "    numpy array with datetimes\n",
    "    '''\n",
    "\n",
    "    table = getTAPtable(level)\n",
    "\n",
    "    ADQL = (f\"SELECT begin_time, end_time, data_item_id, filesize \"\n",
    "                          f\" FROM {table} \"\n",
    "                          f\" WHERE descriptor='{dataset}' \"\n",
    "                          f\" AND level='{level}' \"\n",
    "                          f\" AND (file_format='CDF' OR file_format='FITS' OR file_format='JP2') \"\n",
    "                          f\" AND is_active='True' \"\n",
    "                          f\" ORDER BY begin_time\")\n",
    "\n",
    "    df = TAPquery(ARCHIVE, ADQL)\n",
    "    \n",
    "    # Convert the times from strings to datetimes\n",
    "    df['begin_time'] = pd.to_datetime(df['begin_time'])\n",
    "    df['end_time'] = pd.to_datetime(df['end_time'])\n",
    "    \n",
    "    # Return numpy array with datetimes\n",
    "    return df.to_numpy()\n",
    "\n",
    "\n",
    "def plot_inventory(results, level):\n",
    "    \n",
    "    # Scale the plot for the number of descriptors\n",
    "    ht = math.ceil(len(results.keys())/3)\n",
    "    fig, ax = plt.subplots(1,1, figsize=(12, ht))\n",
    "\n",
    "    #fig, ax = plt.subplots(figsize=(6, 4)) to adjust size, but need to take ht into account\n",
    "\n",
    "    # Dates that happen to work to list the descriptors\n",
    "    d0 = dt.datetime.strptime('2019-01-01', '%Y-%m-%d')\n",
    "    d1 = dt.datetime.strptime('2019-01-14', '%Y-%m-%d')\n",
    "    # End date, leaving a little room on the right\n",
    "    dnow = dt_today + dt.timedelta(days=7)\n",
    "    ax.set_xlim(d0,dnow)\n",
    "\n",
    "    # Default colours but cycling\n",
    "    colours = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9'] * 10\n",
    "    \n",
    "    j = 0 # y coord for lines and annotations\n",
    "    # For each descriptor:\n",
    "    for k, i in enumerate(list(results.keys())):\n",
    "        #print(i) # dataset\n",
    "        np = results[i] # records, i.e., files\n",
    "        st_times = np[:,0] # begin_time for files\n",
    "        y_pos = [j] * len(st_times) # make a list of 'j's needed\n",
    "        ax.plot(st_times, y_pos, '|', color=colours[k]) # plot them\n",
    "        # Annotate instead of y tick labels\n",
    "        ax.annotate(i, (d1,j+0.005), xycoords='data', color=colours[k])\n",
    "        j += 0.05\n",
    "\n",
    "    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%b'))\n",
    "\n",
    "    ax.set_yticklabels([]) # No y ticks\n",
    "    ax.set_title(f\"{instru}, {level}, {dir_today}\")\n",
    "    ax.set_ylim(-0.05, j)\n",
    "\n",
    "    # Rotates and right-aligns the x labels so they don't crowd each other.\n",
    "    for label in ax.get_xticklabels(which='major'):\n",
    "        label.set(rotation=30, horizontalalignment='right')\n",
    "    plt.grid()\n",
    "\n",
    "    # Save to file and plot here\n",
    "    plt.savefig(ResultsPath+instru+'_'+level+'.png', bbox_inches=\"tight\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ce87781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPD\n",
      "L1\n",
      "['epd-step-quicklook', 'epd-step-nom-far', 'epd-step-nom-close', 'epd-step-main-far', 'epd-step-main-close', 'epd-step-burst1-close', 'epd-step-aux', 'epd-sis-b-rates-slow', 'epd-sis-b-rates-medium', 'epd-sis-b-rates-fast', 'epd-sis-b-hehist', 'epd-sis-a-rates-slow', 'epd-sis-a-rates-medium', 'epd-sis-a-rates-fast', 'epd-sis-a-hehist', 'epd-epthet2-sc', 'epd-epthet2-quicklook', 'epd-epthet2-nom-far', 'epd-epthet2-nom-close', 'epd-epthet2-burst3-close', 'epd-epthet2-burst2-close', 'epd-epthet2-burst1-close', 'epd-epthet1-sc', 'epd-epthet1-quicklook', 'epd-epthet1-nom-far', 'epd-epthet1-nom-close', 'epd-epthet1-burst3-close', 'epd-epthet1-burst2-close', 'epd-epthet1-burst1-close']\n",
      "epd-step-quicklook; 1433; 1,524,754,775; 2024-04-01 00:00:00\n",
      "epd-step-nom-far; 96; 81,166,730; 2021-09-21 00:00:00\n",
      "epd-step-nom-close; 484; 1,731,403,863; 2021-10-23 00:00:00\n",
      "epd-step-main-far; 20; 1,129,821; 2024-01-18 00:00:00\n",
      "epd-step-main-close; 870; 12,293,144,789; 2024-04-01 00:00:00\n",
      "epd-step-burst1-close; 455; 34,827,639; 2021-10-21 16:37:28\n",
      "epd-step-aux; 870; 573,516,216; 2024-04-01 00:00:00\n",
      "epd-sis-b-rates-slow; 1386; 19,766,869; 2024-04-01 00:00:00\n",
      "epd-sis-b-rates-medium; 1381; 105,504,194; 2024-04-01 00:00:00\n",
      "epd-sis-b-rates-fast; 969; 411,309,675; 2024-04-01 00:00:00\n",
      "epd-sis-b-hehist; 1386; 9,139,854; 2024-04-01 00:00:00\n",
      "epd-sis-a-rates-slow; 1386; 20,236,612; 2024-04-01 00:00:00\n",
      "epd-sis-a-rates-medium; 1381; 112,157,575; 2024-04-01 00:00:00\n",
      "epd-sis-a-rates-fast; 969; 434,671,929; 2024-04-01 00:00:00\n",
      "epd-sis-a-hehist; 1386; 9,239,476; 2024-04-01 00:00:00\n",
      "epd-epthet2-sc; 1433; 114,284,339; 2024-04-01 00:00:00\n",
      "epd-epthet2-quicklook; 1433; 406,054,585; 2024-04-01 00:00:00\n",
      "epd-epthet2-nom-far; 110; 29,894,979; 2024-01-18 00:00:00\n",
      "epd-epthet2-nom-close; 1353; 2,986,273,695; 2024-04-01 00:00:00\n",
      "epd-epthet2-burst3-close; 1; 12,280; 2020-06-15 12:37:28\n",
      "epd-epthet2-burst2-close; 1; 22,588; 2020-06-15 12:37:28\n",
      "epd-epthet2-burst1-close; 1; 18,975; 2020-06-15 12:37:28\n",
      "epd-epthet1-sc; 1433; 114,056,961; 2024-04-01 00:00:00\n",
      "epd-epthet1-quicklook; 1433; 406,593,480; 2024-04-01 00:00:00\n",
      "epd-epthet1-nom-far; 111; 29,671,854; 2024-01-18 00:00:00\n",
      "epd-epthet1-nom-close; 1353; 3,038,836,289; 2024-04-01 00:00:00\n",
      "epd-epthet1-burst3-close; 1; 12,208; 2020-06-15 12:37:28\n",
      "epd-epthet1-burst2-close; 1; 22,719; 2020-06-15 12:37:28\n",
      "epd-epthet1-burst1-close; 1; 18,720; 2020-06-15 12:37:28\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L2\n",
      "['epd-step-rates', 'epd-step-main', 'epd-step-hcad', 'epd-step-burst', 'epd-sis-b-rates-slow', 'epd-sis-b-rates-medium', 'epd-sis-b-rates-fast', 'epd-sis-b-hehist', 'epd-sis-a-rates-slow', 'epd-sis-a-rates-medium', 'epd-sis-a-rates-fast', 'epd-sis-a-hehist', 'epd-het-sun-rates', 'epd-het-sun-burst', 'epd-het-south-rates', 'epd-het-south-burst', 'epd-het-north-rates', 'epd-het-north-burst', 'epd-het-asun-rates', 'epd-het-asun-burst', 'epd-ept-sun-rates', 'epd-ept-sun-hcad', 'epd-ept-sun-burst-ion', 'epd-ept-sun-burst-ele-close', 'epd-ept-south-rates', 'epd-ept-south-hcad', 'epd-ept-south-burst-ion', 'epd-ept-south-burst-ele-close', 'epd-ept-north-rates', 'epd-ept-north-hcad', 'epd-ept-north-burst-ion', 'epd-ept-north-burst-ele-close', 'epd-ept-asun-rates', 'epd-ept-asun-hcad', 'epd-ept-asun-burst-ion', 'epd-ept-asun-burst-ele-close']\n",
      "epd-step-rates; 496; 2,107,645,977; 2021-10-23 00:00:00\n",
      "epd-step-main; 870; 46,578,936,664; 2024-04-01 00:00:00\n",
      "epd-step-hcad; 496; 2,912,728,171; 2021-10-23 00:00:00\n",
      "epd-step-burst; 442; 114,430,017; 2021-10-21 16:37:28\n",
      "epd-sis-b-rates-slow; 1409; 47,615,836; 2024-04-01 00:00:00\n",
      "epd-sis-b-rates-medium; 1404; 220,788,710; 2024-04-01 00:00:00\n",
      "epd-sis-b-rates-fast; 969; 722,760,161; 2024-04-01 00:00:00\n",
      "epd-sis-b-hehist; 1386; 9,451,509; 2024-04-01 00:00:00\n",
      "epd-sis-a-rates-slow; 1409; 49,632,347; 2024-04-01 00:00:00\n",
      "epd-sis-a-rates-medium; 1404; 243,207,769; 2024-04-01 00:00:00\n",
      "epd-sis-a-rates-fast; 969; 799,554,843; 2024-04-01 00:00:00\n",
      "epd-sis-a-hehist; 1386; 9,549,474; 2024-04-01 00:00:00\n",
      "epd-het-sun-rates; 1363; 937,322,497; 2024-04-01 00:00:00\n",
      "epd-het-sun-burst; 1; 21,625; 2020-06-15 12:37:28\n",
      "epd-het-south-rates; 1363; 931,661,225; 2024-04-01 00:00:00\n",
      "epd-het-south-burst; 1; 21,492; 2020-06-15 12:37:28\n",
      "epd-het-north-rates; 1363; 934,348,928; 2024-04-01 00:00:00\n",
      "epd-het-north-burst; 1; 21,658; 2020-06-15 12:37:28\n",
      "epd-het-asun-rates; 1363; 924,940,137; 2024-04-01 00:00:00\n",
      "epd-het-asun-burst; 1; 21,674; 2020-06-15 12:37:28\n",
      "epd-ept-sun-rates; 1363; 4,679,684,047; 2024-04-01 00:00:00\n",
      "epd-ept-sun-hcad; 290; 209,300,266; 2021-03-24 00:00:00\n",
      "epd-ept-sun-burst-ion; 1; 17,866; 2020-06-15 12:37:28\n",
      "epd-ept-sun-burst-ele-close; 1; 14,118; 2020-06-15 12:37:28\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[18], line 21\u001b[0m\n\u001b[1;32m     19\u001b[0m results\u001b[38;5;241m=\u001b[39m {} \u001b[38;5;66;03m# Store in this dictionary\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[0;32m---> 21\u001b[0m     ds_l_info_np \u001b[38;5;241m=\u001b[39m \u001b[43mget_data_np\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m \n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# Just for information, print the descriptor, the total number of files, \u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;66;03m# the size of all those files, and the latest end time\u001b[39;00m\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;66;03m# put comma 100 separators in to help with reading\u001b[39;00m\n\u001b[1;32m     26\u001b[0m     s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(ds_l_info_np[:,\u001b[38;5;241m3\u001b[39m])\n",
      "Cell \u001b[0;32mIn[17], line 60\u001b[0m, in \u001b[0;36mget_data_np\u001b[0;34m(dataset, level)\u001b[0m\n\u001b[1;32m     50\u001b[0m table \u001b[38;5;241m=\u001b[39m getTAPtable(level)\n\u001b[1;32m     52\u001b[0m ADQL \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSELECT begin_time, end_time, data_item_id, filesize \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     53\u001b[0m                       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m FROM \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     54\u001b[0m                       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m WHERE descriptor=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     57\u001b[0m                       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m AND is_active=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     58\u001b[0m                       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m ORDER BY begin_time\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 60\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mTAPquery\u001b[49m\u001b[43m(\u001b[49m\u001b[43mARCHIVE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mADQL\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;66;03m# Convert the times from strings to datetimes\u001b[39;00m\n\u001b[1;32m     63\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbegin_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbegin_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "Cell \u001b[0;32mIn[17], line 8\u001b[0m, in \u001b[0;36mTAPquery\u001b[0;34m(ARCHIVE, query)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m      7\u001b[0m     SOAR \u001b[38;5;241m=\u001b[39m vo\u001b[38;5;241m.\u001b[39mdal\u001b[38;5;241m.\u001b[39mTAPService(ARCHIVE)\n\u001b[0;32m----> 8\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[43mSOAR\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m     astropy_table \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mto_table()\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m astropy_table\u001b[38;5;241m.\u001b[39mto_pandas()\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/pyvo/dal/tap.py:278\u001b[0m, in \u001b[0;36mTAPService.run_sync\u001b[0;34m(self, query, language, maxrec, uploads, **keywords)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun_sync\u001b[39m(\n\u001b[1;32m    250\u001b[0m         \u001b[38;5;28mself\u001b[39m, query, language\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mADQL\u001b[39m\u001b[38;5;124m\"\u001b[39m, maxrec\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, uploads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    251\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkeywords):\n\u001b[1;32m    252\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    253\u001b[0m \u001b[38;5;124;03m    runs sync query and returns its result\u001b[39;00m\n\u001b[1;32m    254\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[38;5;124;03m    TAPResults\u001b[39;00m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlanguage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlanguage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxrec\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxrec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muploads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muploads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 278\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/pyvo/dal/tap.py:1117\u001b[0m, in \u001b[0;36mTAPQuery.execute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexecute\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1104\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1105\u001b[0m \u001b[38;5;124;03m    submit the query and return the results as a TAPResults instance\u001b[39;00m\n\u001b[1;32m   1106\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1115\u001b[0m \u001b[38;5;124;03m       for errors parsing the VOTable response\u001b[39;00m\n\u001b[1;32m   1116\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TAPResults(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_votable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, url\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqueryurl, session\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session)\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/pyvo/dal/query.py:244\u001b[0m, in \u001b[0;36mDALQuery.execute_votable\u001b[0;34m(self, post)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;124;03mSubmit the query and return the results as an AstroPy votable instance.\u001b[39;00m\n\u001b[1;32m    221\u001b[0m \u001b[38;5;124;03mAs this is the level where qualified error messages are available,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[38;5;124;03mDALQueryError\u001b[39;00m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    243\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 244\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m votableparse(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_stream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpost\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mread)\n\u001b[1;32m    245\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    246\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraise_if_error()\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/pyvo/dal/tap.py:1101\u001b[0m, in \u001b[0;36mTAPQuery.execute_stream\u001b[0;34m(self, post)\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msync\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   1098\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m DALServiceError(\n\u001b[1;32m   1099\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot execute a non-synchronous query. Use submit instead\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_stream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpost\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/pyvo/utils/decorators.py:9\u001b[0m, in \u001b[0;36mstream_decode_content.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m----> 9\u001b[0m     raw \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m     raw\u001b[38;5;241m.\u001b[39mread \u001b[38;5;241m=\u001b[39m partial(raw\u001b[38;5;241m.\u001b[39mread, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m raw\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/pyvo/dal/query.py:193\u001b[0m, in \u001b[0;36mDALQuery.execute_stream\u001b[0;34m(self, post)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;129m@stream_decode_content\u001b[39m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexecute_stream\u001b[39m(\u001b[38;5;28mself\u001b[39m, post\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    187\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;124;03m    Submit the query and return the raw response as a file stream.\u001b[39;00m\n\u001b[1;32m    189\u001b[0m \n\u001b[1;32m    190\u001b[0m \u001b[38;5;124;03m    No exceptions are raised here because non-2xx responses might still\u001b[39;00m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;124;03m    contain payload. They can be raised later by calling ``raise_if_error``\u001b[39;00m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 193\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpost\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    196\u001b[0m         response\u001b[38;5;241m.\u001b[39mraise_for_status()\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/pyvo/dal/tap.py:1134\u001b[0m, in \u001b[0;36mTAPQuery.submit\u001b[0;34m(self, post)\u001b[0m\n\u001b[1;32m   1126\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqueryurl\n\u001b[1;32m   1128\u001b[0m files \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m   1129\u001b[0m     upload\u001b[38;5;241m.\u001b[39mname: upload\u001b[38;5;241m.\u001b[39mfileobj()\n\u001b[1;32m   1130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m upload \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_uploads\n\u001b[1;32m   1131\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m upload\u001b[38;5;241m.\u001b[39mis_inline\n\u001b[1;32m   1132\u001b[0m }\n\u001b[0;32m-> 1134\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1135\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1136\u001b[0m \u001b[38;5;66;03m# requests doesn't decode the content by default\u001b[39;00m\n\u001b[1;32m   1137\u001b[0m response\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mread \u001b[38;5;241m=\u001b[39m partial(response\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mread, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/requests/sessions.py:637\u001b[0m, in \u001b[0;36mSession.post\u001b[0;34m(self, url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    627\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[1;32m    628\u001b[0m \n\u001b[1;32m    629\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 637\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    664\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    682\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/urllib3/connectionpool.py:793\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    790\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    792\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 793\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    805\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    806\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    808\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m    809\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/urllib3/connectionpool.py:467\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    465\u001b[0m     \u001b[38;5;66;03m# Trigger any extra validation we need to do.\u001b[39;00m\n\u001b[1;32m    466\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 467\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    469\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mconn\u001b[38;5;241m.\u001b[39mtimeout)\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/urllib3/connectionpool.py:1099\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[38;5;66;03m# Force connect early to allow us to validate the connection.\u001b[39;00m\n\u001b[1;32m   1098\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_closed:\n\u001b[0;32m-> 1099\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1101\u001b[0m \u001b[38;5;66;03m# TODO revise this, see https://github.com/urllib3/urllib3/issues/2791\u001b[39;00m\n\u001b[1;32m   1102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mproxy_is_verified:\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/urllib3/connection.py:616\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    614\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    615\u001b[0m     sock: socket\u001b[38;5;241m.\u001b[39msocket \u001b[38;5;241m|\u001b[39m ssl\u001b[38;5;241m.\u001b[39mSSLSocket\n\u001b[0;32m--> 616\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m sock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    617\u001b[0m     server_hostname: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n\u001b[1;32m    618\u001b[0m     tls_in_tls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/urllib3/connection.py:198\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    193\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Establish a socket connection and set nodelay settings on it.\u001b[39;00m\n\u001b[1;32m    194\u001b[0m \n\u001b[1;32m    195\u001b[0m \u001b[38;5;124;03m:return: New socket connection.\u001b[39;00m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 198\u001b[0m     sock \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    201\u001b[0m \u001b[43m        \u001b[49m\u001b[43msource_address\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[43m        \u001b[49m\u001b[43msocket_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msocket_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    203\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    205\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NameResolutionError(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost, \u001b[38;5;28mself\u001b[39m, e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/soar_env/lib/python3.12/site-packages/urllib3/util/connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m source_address:\n\u001b[1;32m     72\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 73\u001b[0m \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n\u001b[1;32m     75\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "instru_list = ['EPD', 'EUI', 'MAG', 'METIS', 'PHI', 'RPW', 'SOLOHI', 'SPICE', 'STIX', 'SWA']\n",
    "level_list = ['LL01', 'LL02', 'L0', 'L1', 'L2', 'L3']\n",
    "level_list = ['L1', 'L2', 'L3']\n",
    "\n",
    "\n",
    "for instru in instru_list:\n",
    "    print(instru)\n",
    "\n",
    "    for level in level_list:\n",
    "        print(level)\n",
    "\n",
    "        # Get datasets for instrument\n",
    "        datasets = get_descriptors(ARCHIVE, instru, level)\n",
    "\n",
    "        if datasets == 'no_data': continue\n",
    "        print(datasets)\n",
    "\n",
    "        # Get results for level\n",
    "        results= {} # Store in this dictionary\n",
    "        for ds in datasets:\n",
    "            ds_l_info_np = get_data_np(ds, level) \n",
    "\n",
    "        # Just for information, print the descriptor, the total number of files, \n",
    "        # the size of all those files, and the latest end time\n",
    "            # put comma 100 separators in to help with reading\n",
    "            s = sum(ds_l_info_np[:,3])\n",
    "            # end time to seconds level\n",
    "            et = ds_l_info_np[-1, 1]\n",
    "            #print(f\"{ds}; {len(ds_l_info_np)}; {s:,}; {et}\")\n",
    "\n",
    "            results[ds] = ds_l_info_np \n",
    "\n",
    "        # Plot!\n",
    "        plot_inventory(results, level)"
   ]
  },
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   "source": []
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