density_on_sky.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import healpy as hp\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import multiprocessing as mproc\n",
    "from lsst.daf.persistence import Butler\n",
    "repo_dir = '/datasets/hsc/repo/rerun/DM-13666/WIDE'\n",
    "butler = Butler(repo_dir)\n",
    "#/datasets/hsc/repo/rerun/DM-13666/DEEP/deepCoadd-results/HSC-R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below defines a function that can be used to parse the catalog in parallel using python's `multiprocessing` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pixel_counts(butler, catalog_name, dataId_list, nside,\n",
    "                     i_process, out_dict):\n",
    "    \"\"\"\n",
    "    Takes a butler and a list of dataIds and figurs out how many\n",
    "    stars appear in each healpix\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    butler -- an instance of lsst.daf.persistence.Butler\n",
    "    \n",
    "    catalog_name -- the dataset to query (i.e. 'deepCoadd_ref')\n",
    "    \n",
    "    dataId_list -- a list of dataIds to process\n",
    "    \n",
    "    nside -- the nside of the healpixel grid\n",
    "    \n",
    "    i_process -- a unique identifier for this process as called\n",
    "    from multiprocessing\n",
    "    \n",
    "    out_dict -- a multiprocessing.Manager().dict() where we will\n",
    "    store the output\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Nothing\n",
    "    \n",
    "    A numpy array corresponding to the number of stars in each\n",
    "    healpixel is stored in out_dict[i_process]\n",
    "    \"\"\"\n",
    "    \n",
    "    npix = hp.nside2npix(nside)\n",
    "    pixel_counts = np.zeros(npix, dtype=int)\n",
    "    ct = 0\n",
    "    t_start = time.time()\n",
    "    for dataId in dataId_list:\n",
    "        ref_cat = butler.get(catalog_name, dataId=dataId)\n",
    "        is_primary = ref_cat['detect_isPrimary']\n",
    "        is_star = ref_cat['base_ClassificationExtendedness_value'] < 0.99\n",
    "        valid = is_primary & is_star\n",
    "        data = ref_cat[valid]\n",
    "        pixel_number = hp.ang2pix(nside,\n",
    "                                  np.degrees(data['coord_ra']),\n",
    "                                  np.degrees(data['coord_dec']),\n",
    "                                  lonlat=True)\n",
    "\n",
    "        l_pixel_dexes, l_pixel_counts = np.unique(pixel_number,\n",
    "                                                  return_counts=True)\n",
    "        pixel_counts[l_pixel_dexes]+=l_pixel_counts\n",
    "        ct += 1\n",
    "        if ct %10 ==0:\n",
    "            duration = time.time()-t_start\n",
    "            predict = len(dataId_list)*duration/ct\n",
    "            print('ran %d of %d dataIds; took %.2e sec; total should take %.2e hrs' %\n",
    "                  (ct, len(dataId_list), duration, predict/3600.0))\n",
    "    out_dict[i_process] = pixel_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resolution 1.311396e-02 sq degrees\n"
     ]
    }
   ],
   "source": [
    "nside = 512\n",
    "npix = hp.nside2npix(nside)\n",
    "print('resolution %e sq degrees' % (4.0*np.pi*(180.0/np.pi)**2/npix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_counts = np.zeros(npix, dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "sky_map = butler.get('deepCoadd_skyMap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog_name = 'deepCoadd_forced_src'\n",
    "ref_name = 'deepCoadd_ref'\n",
    "filter_name = 'HSC-R'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "tract_root = os.path.join(repo_dir, 'deepCoadd-results', filter_name)\n",
    "tract_list = os.listdir(tract_root)\n",
    "tract_list = np.array(tract_list).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find all of the tracts and patches that actually exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_data_id = []\n",
    "for tract_id in tract_list:\n",
    "    tract = sky_map[tract_id]\n",
    "    patches = tract.getNumPatches()\n",
    "    for ip1 in range(patches[0]):\n",
    "        for ip2 in range(patches[1]):\n",
    "            dataId = {'filter':filter_name,\n",
    "                      'tract':tract_id,\n",
    "                      'patch':'%d,%d' % (ip1, ip2)}\n",
    "            if not butler.datasetExists(catalog_name, dataId=dataId):\n",
    "                continue\n",
    "            else:\n",
    "                valid_data_id.append(dataId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6965\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "print(len(valid_data_id))\n",
    "valid_data_id_true = copy.deepcopy(valid_data_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `multiprocessing` to assembled density maps for each tract, patch combination that is valid.  This takes about 2 hours to run on 4 cores, using the WIDE survey, which, as you will see in the plot below, is still a very small fraction of the sky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_frac 1741 len 6965\n",
      "0 1741 1741\n",
      "1741 3482 1741\n",
      "3482 5223 1741\n",
      "5223 6965 1742\n",
      "ran 10 of 1741 dataIds; took 2.84e+01 sec; total should take 1.37e+00 hrs\n",
      "ran 10 of 1741 dataIds; took 3.38e+01 sec; total should take 1.63e+00 hrs\n",
      "ran 10 of 1741 dataIds; took 3.68e+01 sec; total should take 1.78e+00 hrs\n",
      "ran 10 of 1742 dataIds; took 4.89e+01 sec; total should take 2.36e+00 hrs\n",
      "ran 20 of 1741 dataIds; took 6.20e+01 sec; total should take 1.50e+00 hrs\n",
      "ran 20 of 1741 dataIds; took 6.58e+01 sec; total should take 1.59e+00 hrs\n",
      "ran 20 of 1741 dataIds; took 7.60e+01 sec; total should take 1.84e+00 hrs\n",
      "ran 30 of 1741 dataIds; took 9.59e+01 sec; total should take 1.55e+00 hrs\n",
      "ran 20 of 1742 dataIds; took 9.93e+01 sec; total should take 2.40e+00 hrs\n",
      "ran 30 of 1741 dataIds; took 1.02e+02 sec; total should take 1.65e+00 hrs\n",
      "ran 30 of 1741 dataIds; took 1.08e+02 sec; total should take 1.75e+00 hrs\n",
      "ran 40 of 1741 dataIds; took 1.31e+02 sec; total should take 1.58e+00 hrs\n",
      "ran 40 of 1741 dataIds; took 1.41e+02 sec; total should take 1.70e+00 hrs\n",
      "ran 40 of 1741 dataIds; took 1.48e+02 sec; total should take 1.78e+00 hrs\n",
      "ran 30 of 1742 dataIds; took 1.54e+02 sec; total should take 2.49e+00 hrs\n",
      "ran 50 of 1741 dataIds; took 1.64e+02 sec; total should take 1.58e+00 hrs\n",
      "ran 50 of 1741 dataIds; took 1.86e+02 sec; total should take 1.80e+00 hrs\n",
      "ran 50 of 1741 dataIds; took 1.86e+02 sec; total should take 1.80e+00 hrs\n",
      "ran 60 of 1741 dataIds; took 1.99e+02 sec; total should take 1.60e+00 hrs\n",
      "ran 40 of 1742 dataIds; took 2.01e+02 sec; total should take 2.43e+00 hrs\n",
      "ran 60 of 1741 dataIds; took 2.19e+02 sec; total should take 1.77e+00 hrs\n",
      "ran 60 of 1741 dataIds; took 2.20e+02 sec; total should take 1.78e+00 hrs\n",
      "ran 70 of 1741 dataIds; took 2.27e+02 sec; total should take 1.57e+00 hrs\n",
      "ran 50 of 1742 dataIds; took 2.49e+02 sec; total should take 2.41e+00 hrs\n",
      "ran 70 of 1741 dataIds; took 2.53e+02 sec; total should take 1.75e+00 hrs\n",
      "ran 70 of 1741 dataIds; took 2.55e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 80 of 1741 dataIds; took 2.64e+02 sec; total should take 1.60e+00 hrs\n",
      "ran 80 of 1741 dataIds; took 2.88e+02 sec; total should take 1.74e+00 hrs\n",
      "ran 80 of 1741 dataIds; took 2.92e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 60 of 1742 dataIds; took 2.93e+02 sec; total should take 2.36e+00 hrs\n",
      "ran 90 of 1741 dataIds; took 3.03e+02 sec; total should take 1.63e+00 hrs\n",
      "ran 90 of 1741 dataIds; took 3.27e+02 sec; total should take 1.75e+00 hrs\n",
      "ran 90 of 1741 dataIds; took 3.31e+02 sec; total should take 1.78e+00 hrs\n",
      "ran 100 of 1741 dataIds; took 3.39e+02 sec; total should take 1.64e+00 hrs\n",
      "ran 70 of 1742 dataIds; took 3.40e+02 sec; total should take 2.35e+00 hrs\n",
      "ran 100 of 1741 dataIds; took 3.63e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 100 of 1741 dataIds; took 3.65e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 110 of 1741 dataIds; took 3.76e+02 sec; total should take 1.65e+00 hrs\n",
      "ran 80 of 1742 dataIds; took 3.84e+02 sec; total should take 2.33e+00 hrs\n",
      "ran 110 of 1741 dataIds; took 3.96e+02 sec; total should take 1.74e+00 hrs\n",
      "ran 110 of 1741 dataIds; took 4.03e+02 sec; total should take 1.77e+00 hrs\n",
      "ran 120 of 1741 dataIds; took 4.10e+02 sec; total should take 1.65e+00 hrs\n",
      "ran 90 of 1742 dataIds; took 4.20e+02 sec; total should take 2.26e+00 hrs\n",
      "ran 120 of 1741 dataIds; took 4.35e+02 sec; total should take 1.75e+00 hrs\n",
      "ran 120 of 1741 dataIds; took 4.38e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 130 of 1741 dataIds; took 4.45e+02 sec; total should take 1.65e+00 hrs\n",
      "ran 100 of 1742 dataIds; took 4.58e+02 sec; total should take 2.22e+00 hrs\n",
      "ran 130 of 1741 dataIds; took 4.74e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 130 of 1741 dataIds; took 4.74e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 140 of 1741 dataIds; took 4.79e+02 sec; total should take 1.65e+00 hrs\n",
      "ran 110 of 1742 dataIds; took 4.99e+02 sec; total should take 2.19e+00 hrs\n",
      "ran 140 of 1741 dataIds; took 5.11e+02 sec; total should take 1.77e+00 hrs\n",
      "ran 150 of 1741 dataIds; took 5.16e+02 sec; total should take 1.66e+00 hrs\n",
      "ran 140 of 1741 dataIds; took 5.18e+02 sec; total should take 1.79e+00 hrs\n",
      "ran 120 of 1742 dataIds; took 5.40e+02 sec; total should take 2.18e+00 hrs\n",
      "ran 150 of 1741 dataIds; took 5.47e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 160 of 1741 dataIds; took 5.58e+02 sec; total should take 1.69e+00 hrs\n",
      "ran 150 of 1741 dataIds; took 5.67e+02 sec; total should take 1.83e+00 hrs\n",
      "ran 130 of 1742 dataIds; took 5.84e+02 sec; total should take 2.17e+00 hrs\n",
      "ran 160 of 1741 dataIds; took 5.89e+02 sec; total should take 1.78e+00 hrs\n",
      "ran 170 of 1741 dataIds; took 5.94e+02 sec; total should take 1.69e+00 hrs\n",
      "ran 160 of 1741 dataIds; took 6.07e+02 sec; total should take 1.83e+00 hrs\n",
      "ran 170 of 1741 dataIds; took 6.22e+02 sec; total should take 1.77e+00 hrs\n",
      "ran 140 of 1742 dataIds; took 6.24e+02 sec; total should take 2.16e+00 hrs\n",
      "ran 180 of 1741 dataIds; took 6.31e+02 sec; total should take 1.70e+00 hrs\n",
      "ran 170 of 1741 dataIds; took 6.42e+02 sec; total should take 1.83e+00 hrs\n",
      "ran 180 of 1741 dataIds; took 6.54e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 150 of 1742 dataIds; took 6.63e+02 sec; total should take 2.14e+00 hrs\n",
      "ran 190 of 1741 dataIds; took 6.68e+02 sec; total should take 1.70e+00 hrs\n",
      "ran 180 of 1741 dataIds; took 6.73e+02 sec; total should take 1.81e+00 hrs\n",
      "ran 190 of 1741 dataIds; took 6.93e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 200 of 1741 dataIds; took 7.02e+02 sec; total should take 1.70e+00 hrs\n",
      "ran 190 of 1741 dataIds; took 7.05e+02 sec; total should take 1.80e+00 hrs\n",
      "ran 160 of 1742 dataIds; took 7.06e+02 sec; total should take 2.13e+00 hrs\n",
      "ran 200 of 1741 dataIds; took 7.23e+02 sec; total should take 1.75e+00 hrs\n",
      "ran 200 of 1741 dataIds; took 7.35e+02 sec; total should take 1.78e+00 hrs\n",
      "ran 210 of 1741 dataIds; took 7.36e+02 sec; total should take 1.69e+00 hrs\n",
      "ran 170 of 1742 dataIds; took 7.49e+02 sec; total should take 2.13e+00 hrs\n",
      "ran 210 of 1741 dataIds; took 7.54e+02 sec; total should take 1.74e+00 hrs\n",
      "ran 220 of 1741 dataIds; took 7.66e+02 sec; total should take 1.68e+00 hrs\n",
      "ran 210 of 1741 dataIds; took 7.71e+02 sec; total should take 1.77e+00 hrs\n",
      "ran 220 of 1741 dataIds; took 7.86e+02 sec; total should take 1.73e+00 hrs\n",
      "ran 180 of 1742 dataIds; took 7.87e+02 sec; total should take 2.12e+00 hrs\n",
      "ran 230 of 1741 dataIds; took 8.02e+02 sec; total should take 1.69e+00 hrs\n",
      "ran 220 of 1741 dataIds; took 8.07e+02 sec; total should take 1.77e+00 hrs\n",
      "ran 230 of 1741 dataIds; took 8.16e+02 sec; total should take 1.72e+00 hrs\n",
      "ran 190 of 1742 dataIds; took 8.20e+02 sec; total should take 2.09e+00 hrs\n",
      "ran 240 of 1741 dataIds; took 8.34e+02 sec; total should take 1.68e+00 hrs\n",
      "ran 230 of 1741 dataIds; took 8.40e+02 sec; total should take 1.77e+00 hrs\n",
      "ran 240 of 1741 dataIds; took 8.48e+02 sec; total should take 1.71e+00 hrs\n",
      "ran 200 of 1742 dataIds; took 8.57e+02 sec; total should take 2.07e+00 hrs\n",
      "ran 240 of 1741 dataIds; took 8.70e+02 sec; total should take 1.75e+00 hrs\n",
      "ran 250 of 1741 dataIds; took 8.71e+02 sec; total should take 1.69e+00 hrs\n",
      "ran 250 of 1741 dataIds; took 8.77e+02 sec; total should take 1.70e+00 hrs\n",
      "ran 210 of 1742 dataIds; took 8.98e+02 sec; total should take 2.07e+00 hrs\n",
      "ran 250 of 1741 dataIds; took 9.08e+02 sec; total should take 1.76e+00 hrs\n",
      "ran 260 of 1741 dataIds; took 9.09e+02 sec; total should take 1.69e+00 hrs\n",
      "ran 260 of 1741 dataIds; took 9.11e+02 sec; total should take 1.69e+00 hrs\n",
      "ran 220 of 1742 dataIds; took 9.36e+02 sec; total should take 2.06e+00 hrs\n",
      "ran 270 of 1741 dataIds; took 9.40e+02 sec; total should take 1.68e+00 hrs\n",
      "ran 270 of 1741 dataIds; took 9.48e+02 sec; total should take 1.70e+00 hrs\n",
      "ran 260 of 1741 dataIds; took 9.55e+02 sec; total should take 1.78e+00 hrs\n",
      "ran 230 of 1742 dataIds; took 9.68e+02 sec; total should take 2.04e+00 hrs\n",
      "ran 280 of 1741 dataIds; took 9.72e+02 sec; total should take 1.68e+00 hrs\n",
      "ran 280 of 1741 dataIds; took 9.85e+02 sec; total should take 1.70e+00 hrs\n",
      "ran 270 of 1741 dataIds; took 9.96e+02 sec; total should take 1.78e+00 hrs\n",
      "ran 240 of 1742 dataIds; took 1.00e+03 sec; total should take 2.02e+00 hrs\n",
      "ran 290 of 1741 dataIds; took 1.00e+03 sec; total should take 1.67e+00 hrs\n",
      "ran 290 of 1741 dataIds; took 1.02e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 280 of 1741 dataIds; took 1.03e+03 sec; total should take 1.79e+00 hrs\n",
      "ran 250 of 1742 dataIds; took 1.04e+03 sec; total should take 2.00e+00 hrs\n",
      "ran 300 of 1741 dataIds; took 1.04e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 300 of 1741 dataIds; took 1.06e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 260 of 1742 dataIds; took 1.07e+03 sec; total should take 2.00e+00 hrs\n",
      "ran 290 of 1741 dataIds; took 1.07e+03 sec; total should take 1.79e+00 hrs\n",
      "ran 310 of 1741 dataIds; took 1.08e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 310 of 1741 dataIds; took 1.09e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 320 of 1741 dataIds; took 1.11e+03 sec; total should take 1.67e+00 hrs\n",
      "ran 270 of 1742 dataIds; took 1.11e+03 sec; total should take 1.99e+00 hrs\n",
      "ran 300 of 1741 dataIds; took 1.12e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 320 of 1741 dataIds; took 1.13e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 330 of 1741 dataIds; took 1.14e+03 sec; total should take 1.67e+00 hrs\n",
      "ran 280 of 1742 dataIds; took 1.14e+03 sec; total should take 1.97e+00 hrs\n",
      "ran 330 of 1741 dataIds; took 1.16e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 310 of 1741 dataIds; took 1.16e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 340 of 1741 dataIds; took 1.16e+03 sec; total should take 1.66e+00 hrs\n",
      "ran 290 of 1742 dataIds; took 1.18e+03 sec; total should take 1.97e+00 hrs\n",
      "ran 350 of 1741 dataIds; took 1.19e+03 sec; total should take 1.65e+00 hrs\n",
      "ran 320 of 1741 dataIds; took 1.19e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 340 of 1741 dataIds; took 1.20e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 300 of 1742 dataIds; took 1.22e+03 sec; total should take 1.96e+00 hrs\n",
      "ran 360 of 1741 dataIds; took 1.22e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 350 of 1741 dataIds; took 1.23e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 330 of 1741 dataIds; took 1.23e+03 sec; total should take 1.80e+00 hrs\n",
      "ran 370 of 1741 dataIds; took 1.25e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 310 of 1742 dataIds; took 1.25e+03 sec; total should take 1.96e+00 hrs\n",
      "ran 360 of 1741 dataIds; took 1.26e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 340 of 1741 dataIds; took 1.27e+03 sec; total should take 1.80e+00 hrs\n",
      "ran 380 of 1741 dataIds; took 1.28e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 320 of 1742 dataIds; took 1.29e+03 sec; total should take 1.95e+00 hrs\n",
      "ran 370 of 1741 dataIds; took 1.30e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 350 of 1741 dataIds; took 1.30e+03 sec; total should take 1.80e+00 hrs\n",
      "ran 390 of 1741 dataIds; took 1.31e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 330 of 1742 dataIds; took 1.31e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 360 of 1741 dataIds; took 1.33e+03 sec; total should take 1.79e+00 hrs\n",
      "ran 400 of 1741 dataIds; took 1.34e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 380 of 1741 dataIds; took 1.34e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 340 of 1742 dataIds; took 1.34e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 410 of 1741 dataIds; took 1.37e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 370 of 1741 dataIds; took 1.37e+03 sec; total should take 1.79e+00 hrs\n",
      "ran 350 of 1742 dataIds; took 1.37e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 390 of 1741 dataIds; took 1.38e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 420 of 1741 dataIds; took 1.40e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 380 of 1741 dataIds; took 1.40e+03 sec; total should take 1.79e+00 hrs\n",
      "ran 360 of 1742 dataIds; took 1.41e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 400 of 1741 dataIds; took 1.41e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 430 of 1741 dataIds; took 1.43e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 390 of 1741 dataIds; took 1.44e+03 sec; total should take 1.78e+00 hrs\n",
      "ran 370 of 1742 dataIds; took 1.45e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 410 of 1741 dataIds; took 1.45e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 440 of 1741 dataIds; took 1.46e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 400 of 1741 dataIds; took 1.47e+03 sec; total should take 1.78e+00 hrs\n",
      "ran 420 of 1741 dataIds; took 1.48e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 380 of 1742 dataIds; took 1.48e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 450 of 1741 dataIds; took 1.49e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 410 of 1741 dataIds; took 1.51e+03 sec; total should take 1.78e+00 hrs\n",
      "ran 430 of 1741 dataIds; took 1.52e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 390 of 1742 dataIds; took 1.52e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 460 of 1741 dataIds; took 1.53e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 400 of 1742 dataIds; took 1.55e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 440 of 1741 dataIds; took 1.55e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 420 of 1741 dataIds; took 1.55e+03 sec; total should take 1.78e+00 hrs\n",
      "ran 470 of 1741 dataIds; took 1.56e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 450 of 1741 dataIds; took 1.58e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 410 of 1742 dataIds; took 1.58e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 480 of 1741 dataIds; took 1.59e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 430 of 1741 dataIds; took 1.59e+03 sec; total should take 1.79e+00 hrs\n",
      "ran 420 of 1742 dataIds; took 1.61e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 460 of 1741 dataIds; took 1.61e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 490 of 1741 dataIds; took 1.62e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 440 of 1741 dataIds; took 1.64e+03 sec; total should take 1.80e+00 hrs\n",
      "ran 430 of 1742 dataIds; took 1.65e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 470 of 1741 dataIds; took 1.65e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 500 of 1741 dataIds; took 1.66e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 440 of 1742 dataIds; took 1.68e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 450 of 1741 dataIds; took 1.68e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 480 of 1741 dataIds; took 1.69e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 510 of 1741 dataIds; took 1.70e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 450 of 1742 dataIds; took 1.71e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 490 of 1741 dataIds; took 1.72e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 460 of 1741 dataIds; took 1.72e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 520 of 1741 dataIds; took 1.73e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 500 of 1741 dataIds; took 1.74e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 530 of 1741 dataIds; took 1.75e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 460 of 1742 dataIds; took 1.75e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 510 of 1741 dataIds; took 1.76e+03 sec; total should take 1.67e+00 hrs\n",
      "ran 470 of 1741 dataIds; took 1.76e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 540 of 1741 dataIds; took 1.77e+03 sec; total should take 1.59e+00 hrs\n",
      "ran 470 of 1742 dataIds; took 1.79e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 520 of 1741 dataIds; took 1.79e+03 sec; total should take 1.66e+00 hrs\n",
      "ran 550 of 1741 dataIds; took 1.80e+03 sec; total should take 1.58e+00 hrs\n",
      "ran 480 of 1741 dataIds; took 1.81e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 530 of 1741 dataIds; took 1.81e+03 sec; total should take 1.65e+00 hrs\n",
      "ran 560 of 1741 dataIds; took 1.82e+03 sec; total should take 1.57e+00 hrs\n",
      "ran 480 of 1742 dataIds; took 1.83e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 540 of 1741 dataIds; took 1.84e+03 sec; total should take 1.65e+00 hrs\n",
      "ran 490 of 1741 dataIds; took 1.84e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 570 of 1741 dataIds; took 1.85e+03 sec; total should take 1.57e+00 hrs\n",
      "ran 550 of 1741 dataIds; took 1.87e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 490 of 1742 dataIds; took 1.88e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 580 of 1741 dataIds; took 1.88e+03 sec; total should take 1.57e+00 hrs\n",
      "ran 500 of 1741 dataIds; took 1.90e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 590 of 1741 dataIds; took 1.92e+03 sec; total should take 1.57e+00 hrs\n",
      "ran 560 of 1741 dataIds; took 1.92e+03 sec; total should take 1.66e+00 hrs\n",
      "ran 500 of 1742 dataIds; took 1.93e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 510 of 1741 dataIds; took 1.95e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 600 of 1741 dataIds; took 1.96e+03 sec; total should take 1.58e+00 hrs\n",
      "ran 570 of 1741 dataIds; took 1.96e+03 sec; total should take 1.67e+00 hrs\n",
      "ran 510 of 1742 dataIds; took 1.99e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 610 of 1741 dataIds; took 1.99e+03 sec; total should take 1.58e+00 hrs\n",
      "ran 520 of 1741 dataIds; took 2.00e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 580 of 1741 dataIds; took 2.01e+03 sec; total should take 1.67e+00 hrs\n",
      "ran 620 of 1741 dataIds; took 2.04e+03 sec; total should take 1.59e+00 hrs\n",
      "ran 520 of 1742 dataIds; took 2.04e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 590 of 1741 dataIds; took 2.05e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 530 of 1741 dataIds; took 2.06e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 630 of 1741 dataIds; took 2.08e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 530 of 1742 dataIds; took 2.10e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 600 of 1741 dataIds; took 2.10e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 540 of 1741 dataIds; took 2.11e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 640 of 1741 dataIds; took 2.12e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 610 of 1741 dataIds; took 2.14e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 540 of 1742 dataIds; took 2.15e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 550 of 1741 dataIds; took 2.16e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 650 of 1741 dataIds; took 2.16e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 620 of 1741 dataIds; took 2.19e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 660 of 1741 dataIds; took 2.21e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 550 of 1742 dataIds; took 2.21e+03 sec; total should take 1.95e+00 hrs\n",
      "ran 560 of 1741 dataIds; took 2.22e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 670 of 1741 dataIds; took 2.24e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 630 of 1741 dataIds; took 2.24e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 560 of 1742 dataIds; took 2.25e+03 sec; total should take 1.95e+00 hrs\n",
      "ran 570 of 1741 dataIds; took 2.27e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 680 of 1741 dataIds; took 2.28e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 640 of 1741 dataIds; took 2.29e+03 sec; total should take 1.73e+00 hrs\n",
      "ran 570 of 1742 dataIds; took 2.30e+03 sec; total should take 1.95e+00 hrs\n",
      "ran 580 of 1741 dataIds; took 2.31e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 690 of 1741 dataIds; took 2.32e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 580 of 1742 dataIds; took 2.34e+03 sec; total should take 1.95e+00 hrs\n",
      "ran 650 of 1741 dataIds; took 2.34e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 590 of 1741 dataIds; took 2.35e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 700 of 1741 dataIds; took 2.35e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 660 of 1741 dataIds; took 2.37e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 590 of 1742 dataIds; took 2.38e+03 sec; total should take 1.95e+00 hrs\n",
      "ran 600 of 1741 dataIds; took 2.39e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 710 of 1741 dataIds; took 2.39e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 670 of 1741 dataIds; took 2.41e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 600 of 1742 dataIds; took 2.41e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 610 of 1741 dataIds; took 2.42e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 720 of 1741 dataIds; took 2.42e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 680 of 1741 dataIds; took 2.44e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 610 of 1742 dataIds; took 2.45e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 730 of 1741 dataIds; took 2.45e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 620 of 1741 dataIds; took 2.46e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 690 of 1741 dataIds; took 2.48e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 620 of 1742 dataIds; took 2.48e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 740 of 1741 dataIds; took 2.49e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 630 of 1741 dataIds; took 2.50e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 630 of 1742 dataIds; took 2.51e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 700 of 1741 dataIds; took 2.52e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 750 of 1741 dataIds; took 2.53e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 640 of 1741 dataIds; took 2.53e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 640 of 1742 dataIds; took 2.55e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 710 of 1741 dataIds; took 2.55e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 760 of 1741 dataIds; took 2.56e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 650 of 1741 dataIds; took 2.57e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 650 of 1742 dataIds; took 2.59e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 720 of 1741 dataIds; took 2.59e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 770 of 1741 dataIds; took 2.60e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 660 of 1741 dataIds; took 2.61e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 660 of 1742 dataIds; took 2.63e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 730 of 1741 dataIds; took 2.63e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 780 of 1741 dataIds; took 2.63e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 670 of 1741 dataIds; took 2.65e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 790 of 1741 dataIds; took 2.66e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 740 of 1741 dataIds; took 2.67e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 670 of 1742 dataIds; took 2.68e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 680 of 1741 dataIds; took 2.68e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 800 of 1741 dataIds; took 2.70e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 750 of 1741 dataIds; took 2.70e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 680 of 1742 dataIds; took 2.72e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 690 of 1741 dataIds; took 2.72e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 810 of 1741 dataIds; took 2.74e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 760 of 1741 dataIds; took 2.74e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 700 of 1741 dataIds; took 2.76e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 690 of 1742 dataIds; took 2.77e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 820 of 1741 dataIds; took 2.77e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 770 of 1741 dataIds; took 2.78e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 710 of 1741 dataIds; took 2.80e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 830 of 1741 dataIds; took 2.81e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 700 of 1742 dataIds; took 2.81e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 780 of 1741 dataIds; took 2.82e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 720 of 1741 dataIds; took 2.85e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 840 of 1741 dataIds; took 2.85e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 710 of 1742 dataIds; took 2.86e+03 sec; total should take 1.95e+00 hrs\n",
      "ran 790 of 1741 dataIds; took 2.86e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 730 of 1741 dataIds; took 2.88e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 850 of 1741 dataIds; took 2.89e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 720 of 1742 dataIds; took 2.89e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 800 of 1741 dataIds; took 2.90e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 740 of 1741 dataIds; took 2.90e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 860 of 1741 dataIds; took 2.93e+03 sec; total should take 1.65e+00 hrs\n",
      "ran 730 of 1742 dataIds; took 2.93e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 810 of 1741 dataIds; took 2.93e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 750 of 1741 dataIds; took 2.94e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 870 of 1741 dataIds; took 2.96e+03 sec; total should take 1.65e+00 hrs\n",
      "ran 740 of 1742 dataIds; took 2.96e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 820 of 1741 dataIds; took 2.96e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 760 of 1741 dataIds; took 2.97e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 880 of 1741 dataIds; took 2.99e+03 sec; total should take 1.65e+00 hrs\n",
      "ran 830 of 1741 dataIds; took 3.00e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 750 of 1742 dataIds; took 3.00e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 770 of 1741 dataIds; took 3.01e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 890 of 1741 dataIds; took 3.02e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 840 of 1741 dataIds; took 3.03e+03 sec; total should take 1.75e+00 hrs\n",
      "ran 760 of 1742 dataIds; took 3.04e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 900 of 1741 dataIds; took 3.05e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 780 of 1741 dataIds; took 3.05e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 850 of 1741 dataIds; took 3.07e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 910 of 1741 dataIds; took 3.07e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 770 of 1742 dataIds; took 3.08e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 790 of 1741 dataIds; took 3.09e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 920 of 1741 dataIds; took 3.09e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 860 of 1741 dataIds; took 3.10e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 930 of 1741 dataIds; took 3.12e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 780 of 1742 dataIds; took 3.12e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 800 of 1741 dataIds; took 3.12e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 870 of 1741 dataIds; took 3.13e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 940 of 1741 dataIds; took 3.14e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 810 of 1741 dataIds; took 3.15e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 790 of 1742 dataIds; took 3.16e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 950 of 1741 dataIds; took 3.17e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 880 of 1741 dataIds; took 3.17e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 820 of 1741 dataIds; took 3.19e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 800 of 1742 dataIds; took 3.20e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 960 of 1741 dataIds; took 3.20e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 890 of 1741 dataIds; took 3.21e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 830 of 1741 dataIds; took 3.23e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 810 of 1742 dataIds; took 3.24e+03 sec; total should take 1.94e+00 hrs\n",
      "ran 970 of 1741 dataIds; took 3.24e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 900 of 1741 dataIds; took 3.25e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 840 of 1741 dataIds; took 3.26e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 980 of 1741 dataIds; took 3.27e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 820 of 1742 dataIds; took 3.28e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 910 of 1741 dataIds; took 3.28e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 850 of 1741 dataIds; took 3.29e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 990 of 1741 dataIds; took 3.30e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 830 of 1742 dataIds; took 3.32e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 920 of 1741 dataIds; took 3.32e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 860 of 1741 dataIds; took 3.33e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1000 of 1741 dataIds; took 3.33e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 840 of 1742 dataIds; took 3.35e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 930 of 1741 dataIds; took 3.36e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 1010 of 1741 dataIds; took 3.37e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 870 of 1741 dataIds; took 3.37e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 940 of 1741 dataIds; took 3.39e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 850 of 1742 dataIds; took 3.39e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 1020 of 1741 dataIds; took 3.40e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 880 of 1741 dataIds; took 3.40e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 950 of 1741 dataIds; took 3.42e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 860 of 1742 dataIds; took 3.42e+03 sec; total should take 1.93e+00 hrs\n",
      "ran 1030 of 1741 dataIds; took 3.43e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 890 of 1741 dataIds; took 3.45e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 870 of 1742 dataIds; took 3.46e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 960 of 1741 dataIds; took 3.46e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 1040 of 1741 dataIds; took 3.47e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 900 of 1741 dataIds; took 3.48e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 880 of 1742 dataIds; took 3.49e+03 sec; total should take 1.92e+00 hrs\n",
      "ran 970 of 1741 dataIds; took 3.49e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 1050 of 1741 dataIds; took 3.50e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 910 of 1741 dataIds; took 3.51e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 890 of 1742 dataIds; took 3.52e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 980 of 1741 dataIds; took 3.53e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 1060 of 1741 dataIds; took 3.53e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 920 of 1741 dataIds; took 3.54e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 900 of 1742 dataIds; took 3.55e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 990 of 1741 dataIds; took 3.56e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 930 of 1741 dataIds; took 3.57e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1070 of 1741 dataIds; took 3.57e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 910 of 1742 dataIds; took 3.59e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1000 of 1741 dataIds; took 3.60e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 1080 of 1741 dataIds; took 3.60e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 940 of 1741 dataIds; took 3.60e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 920 of 1742 dataIds; took 3.62e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1010 of 1741 dataIds; took 3.63e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 1090 of 1741 dataIds; took 3.63e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 950 of 1741 dataIds; took 3.64e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 930 of 1742 dataIds; took 3.65e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1020 of 1741 dataIds; took 3.67e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 1100 of 1741 dataIds; took 3.67e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 960 of 1741 dataIds; took 3.67e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 940 of 1742 dataIds; took 3.69e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1110 of 1741 dataIds; took 3.70e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1030 of 1741 dataIds; took 3.70e+03 sec; total should take 1.74e+00 hrs\n",
      "ran 970 of 1741 dataIds; took 3.71e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1040 of 1741 dataIds; took 3.73e+03 sec; total should take 1.73e+00 hrs\n",
      "ran 950 of 1742 dataIds; took 3.73e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1120 of 1741 dataIds; took 3.73e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 980 of 1741 dataIds; took 3.75e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1050 of 1741 dataIds; took 3.76e+03 sec; total should take 1.73e+00 hrs\n",
      "ran 960 of 1742 dataIds; took 3.77e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1130 of 1741 dataIds; took 3.77e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1060 of 1741 dataIds; took 3.79e+03 sec; total should take 1.73e+00 hrs\n",
      "ran 990 of 1741 dataIds; took 3.80e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1140 of 1741 dataIds; took 3.80e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 970 of 1742 dataIds; took 3.80e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1070 of 1741 dataIds; took 3.81e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1080 of 1741 dataIds; took 3.83e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1150 of 1741 dataIds; took 3.83e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 980 of 1742 dataIds; took 3.84e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1000 of 1741 dataIds; took 3.84e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1090 of 1741 dataIds; took 3.86e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1160 of 1741 dataIds; took 3.87e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 990 of 1742 dataIds; took 3.87e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1010 of 1741 dataIds; took 3.89e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1100 of 1741 dataIds; took 3.89e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1170 of 1741 dataIds; took 3.90e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1000 of 1742 dataIds; took 3.91e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1020 of 1741 dataIds; took 3.93e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1110 of 1741 dataIds; took 3.93e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1180 of 1741 dataIds; took 3.94e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1010 of 1742 dataIds; took 3.95e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1190 of 1741 dataIds; took 3.97e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1030 of 1741 dataIds; took 3.97e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1120 of 1741 dataIds; took 3.97e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1020 of 1742 dataIds; took 3.98e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1200 of 1741 dataIds; took 4.00e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1130 of 1741 dataIds; took 4.00e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1040 of 1741 dataIds; took 4.01e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1210 of 1741 dataIds; took 4.03e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1030 of 1742 dataIds; took 4.04e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1140 of 1741 dataIds; took 4.05e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1050 of 1741 dataIds; took 4.06e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1220 of 1741 dataIds; took 4.07e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1040 of 1742 dataIds; took 4.09e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1150 of 1741 dataIds; took 4.09e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1230 of 1741 dataIds; took 4.10e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1060 of 1741 dataIds; took 4.10e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1160 of 1741 dataIds; took 4.13e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1240 of 1741 dataIds; took 4.13e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1050 of 1742 dataIds; took 4.13e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1070 of 1741 dataIds; took 4.15e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1250 of 1741 dataIds; took 4.15e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1170 of 1741 dataIds; took 4.16e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1260 of 1741 dataIds; took 4.18e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 1060 of 1742 dataIds; took 4.18e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1080 of 1741 dataIds; took 4.18e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1180 of 1741 dataIds; took 4.20e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1270 of 1741 dataIds; took 4.21e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 1070 of 1742 dataIds; took 4.22e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1090 of 1741 dataIds; took 4.22e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1190 of 1741 dataIds; took 4.23e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1280 of 1741 dataIds; took 4.24e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 1100 of 1741 dataIds; took 4.26e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1080 of 1742 dataIds; took 4.26e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1200 of 1741 dataIds; took 4.27e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1290 of 1741 dataIds; took 4.28e+03 sec; total should take 1.60e+00 hrs\n",
      "ran 1210 of 1741 dataIds; took 4.30e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1090 of 1742 dataIds; took 4.30e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1110 of 1741 dataIds; took 4.30e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1300 of 1741 dataIds; took 4.32e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1220 of 1741 dataIds; took 4.33e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1100 of 1742 dataIds; took 4.34e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1120 of 1741 dataIds; took 4.34e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1230 of 1741 dataIds; took 4.36e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1310 of 1741 dataIds; took 4.36e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1110 of 1742 dataIds; took 4.38e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1130 of 1741 dataIds; took 4.38e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1240 of 1741 dataIds; took 4.40e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1320 of 1741 dataIds; took 4.40e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1120 of 1742 dataIds; took 4.42e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1140 of 1741 dataIds; took 4.43e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1250 of 1741 dataIds; took 4.44e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1330 of 1741 dataIds; took 4.44e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1130 of 1742 dataIds; took 4.45e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1150 of 1741 dataIds; took 4.46e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1260 of 1741 dataIds; took 4.48e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1340 of 1741 dataIds; took 4.49e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1160 of 1741 dataIds; took 4.49e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1140 of 1742 dataIds; took 4.49e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1270 of 1741 dataIds; took 4.51e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1350 of 1741 dataIds; took 4.52e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1170 of 1741 dataIds; took 4.52e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1150 of 1742 dataIds; took 4.53e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1280 of 1741 dataIds; took 4.54e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1360 of 1741 dataIds; took 4.54e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1180 of 1741 dataIds; took 4.55e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1160 of 1742 dataIds; took 4.57e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1290 of 1741 dataIds; took 4.57e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1370 of 1741 dataIds; took 4.57e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1190 of 1741 dataIds; took 4.59e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1380 of 1741 dataIds; took 4.60e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1300 of 1741 dataIds; took 4.61e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1170 of 1742 dataIds; took 4.61e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1200 of 1741 dataIds; took 4.64e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1390 of 1741 dataIds; took 4.64e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1180 of 1742 dataIds; took 4.65e+03 sec; total should take 1.91e+00 hrs\n",
      "ran 1310 of 1741 dataIds; took 4.65e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1400 of 1741 dataIds; took 4.67e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1210 of 1741 dataIds; took 4.67e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1190 of 1742 dataIds; took 4.68e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1320 of 1741 dataIds; took 4.69e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1410 of 1741 dataIds; took 4.70e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1220 of 1741 dataIds; took 4.71e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1200 of 1742 dataIds; took 4.71e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1330 of 1741 dataIds; took 4.72e+03 sec; total should take 1.72e+00 hrs\n",
      "ran 1420 of 1741 dataIds; took 4.73e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1340 of 1741 dataIds; took 4.75e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1230 of 1741 dataIds; took 4.75e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1210 of 1742 dataIds; took 4.75e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1430 of 1741 dataIds; took 4.76e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1350 of 1741 dataIds; took 4.78e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1220 of 1742 dataIds; took 4.79e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1240 of 1741 dataIds; took 4.79e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1440 of 1741 dataIds; took 4.80e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1360 of 1741 dataIds; took 4.81e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1230 of 1742 dataIds; took 4.82e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1250 of 1741 dataIds; took 4.82e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1450 of 1741 dataIds; took 4.83e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1370 of 1741 dataIds; took 4.84e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1240 of 1742 dataIds; took 4.86e+03 sec; total should take 1.90e+00 hrs\n",
      "ran 1260 of 1741 dataIds; took 4.86e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1460 of 1741 dataIds; took 4.87e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1380 of 1741 dataIds; took 4.88e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1250 of 1742 dataIds; took 4.89e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1270 of 1741 dataIds; took 4.89e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1470 of 1741 dataIds; took 4.90e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1390 of 1741 dataIds; took 4.91e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1260 of 1742 dataIds; took 4.93e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1280 of 1741 dataIds; took 4.93e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1400 of 1741 dataIds; took 4.94e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1480 of 1741 dataIds; took 4.94e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1410 of 1741 dataIds; took 4.96e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 1270 of 1742 dataIds; took 4.96e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1290 of 1741 dataIds; took 4.96e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1490 of 1741 dataIds; took 4.97e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1420 of 1741 dataIds; took 4.99e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 1280 of 1742 dataIds; took 5.00e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1300 of 1741 dataIds; took 5.00e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1500 of 1741 dataIds; took 5.01e+03 sec; total should take 1.61e+00 hrs\n",
      "ran 1430 of 1741 dataIds; took 5.01e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 1290 of 1742 dataIds; took 5.03e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1440 of 1741 dataIds; took 5.04e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 1310 of 1741 dataIds; took 5.04e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1510 of 1741 dataIds; took 5.04e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1450 of 1741 dataIds; took 5.06e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 1320 of 1741 dataIds; took 5.07e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1300 of 1742 dataIds; took 5.07e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1520 of 1741 dataIds; took 5.08e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1460 of 1741 dataIds; took 5.09e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1330 of 1741 dataIds; took 5.11e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1310 of 1742 dataIds; took 5.11e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1530 of 1741 dataIds; took 5.12e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1470 of 1741 dataIds; took 5.12e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1340 of 1741 dataIds; took 5.14e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1320 of 1742 dataIds; took 5.15e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1480 of 1741 dataIds; took 5.15e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1540 of 1741 dataIds; took 5.15e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1490 of 1741 dataIds; took 5.17e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1350 of 1741 dataIds; took 5.18e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1330 of 1742 dataIds; took 5.18e+03 sec; total should take 1.89e+00 hrs\n",
      "ran 1550 of 1741 dataIds; took 5.19e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1500 of 1741 dataIds; took 5.21e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1360 of 1741 dataIds; took 5.22e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1340 of 1742 dataIds; took 5.22e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1560 of 1741 dataIds; took 5.23e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1370 of 1741 dataIds; took 5.25e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1510 of 1741 dataIds; took 5.25e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1350 of 1742 dataIds; took 5.25e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1570 of 1741 dataIds; took 5.26e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1380 of 1741 dataIds; took 5.28e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1360 of 1742 dataIds; took 5.29e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1520 of 1741 dataIds; took 5.29e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1580 of 1741 dataIds; took 5.30e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1390 of 1741 dataIds; took 5.31e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1370 of 1742 dataIds; took 5.31e+03 sec; total should take 1.88e+00 hrs\n",
      "ran 1530 of 1741 dataIds; took 5.33e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1590 of 1741 dataIds; took 5.33e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1380 of 1742 dataIds; took 5.34e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1400 of 1741 dataIds; took 5.35e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1540 of 1741 dataIds; took 5.36e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1600 of 1741 dataIds; took 5.36e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1390 of 1742 dataIds; took 5.36e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1410 of 1741 dataIds; took 5.38e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1550 of 1741 dataIds; took 5.39e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1400 of 1742 dataIds; took 5.40e+03 sec; total should take 1.87e+00 hrs\n",
      "ran 1610 of 1741 dataIds; took 5.40e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1420 of 1741 dataIds; took 5.41e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1560 of 1741 dataIds; took 5.43e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1410 of 1742 dataIds; took 5.43e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1620 of 1741 dataIds; took 5.45e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1430 of 1741 dataIds; took 5.45e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1420 of 1742 dataIds; took 5.45e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1570 of 1741 dataIds; took 5.46e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1440 of 1741 dataIds; took 5.48e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1630 of 1741 dataIds; took 5.49e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1430 of 1742 dataIds; took 5.49e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1580 of 1741 dataIds; took 5.50e+03 sec; total should take 1.68e+00 hrs\n",
      "ran 1450 of 1741 dataIds; took 5.52e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1440 of 1742 dataIds; took 5.53e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1640 of 1741 dataIds; took 5.53e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1590 of 1741 dataIds; took 5.55e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 1460 of 1741 dataIds; took 5.55e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1450 of 1742 dataIds; took 5.57e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1650 of 1741 dataIds; took 5.57e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1470 of 1741 dataIds; took 5.59e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1600 of 1741 dataIds; took 5.59e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 1460 of 1742 dataIds; took 5.61e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1660 of 1741 dataIds; took 5.62e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 1480 of 1741 dataIds; took 5.62e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1610 of 1741 dataIds; took 5.63e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 1470 of 1742 dataIds; took 5.65e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1490 of 1741 dataIds; took 5.65e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1670 of 1741 dataIds; took 5.66e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 1620 of 1741 dataIds; took 5.67e+03 sec; total should take 1.69e+00 hrs\n",
      "ran 1680 of 1741 dataIds; took 5.68e+03 sec; total should take 1.64e+00 hrs\n",
      "ran 1500 of 1741 dataIds; took 5.69e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1480 of 1742 dataIds; took 5.69e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1690 of 1741 dataIds; took 5.71e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1630 of 1741 dataIds; took 5.72e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 1510 of 1741 dataIds; took 5.72e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1700 of 1741 dataIds; took 5.73e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1490 of 1742 dataIds; took 5.74e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1520 of 1741 dataIds; took 5.75e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1710 of 1741 dataIds; took 5.76e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1640 of 1741 dataIds; took 5.76e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 1500 of 1742 dataIds; took 5.78e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1530 of 1741 dataIds; took 5.78e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1720 of 1741 dataIds; took 5.78e+03 sec; total should take 1.63e+00 hrs\n",
      "ran 1650 of 1741 dataIds; took 5.81e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 1730 of 1741 dataIds; took 5.81e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1510 of 1742 dataIds; took 5.81e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1540 of 1741 dataIds; took 5.82e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1740 of 1741 dataIds; took 5.84e+03 sec; total should take 1.62e+00 hrs\n",
      "ran 1520 of 1742 dataIds; took 5.85e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1550 of 1741 dataIds; took 5.86e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1660 of 1741 dataIds; took 5.86e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1530 of 1742 dataIds; took 5.88e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1560 of 1741 dataIds; took 5.89e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1670 of 1741 dataIds; took 5.89e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1540 of 1742 dataIds; took 5.92e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1680 of 1741 dataIds; took 5.92e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1570 of 1741 dataIds; took 5.93e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1550 of 1742 dataIds; took 5.95e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1580 of 1741 dataIds; took 5.96e+03 sec; total should take 1.83e+00 hrs\n",
      "ran 1690 of 1741 dataIds; took 5.97e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1560 of 1742 dataIds; took 5.99e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1590 of 1741 dataIds; took 6.00e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 1700 of 1741 dataIds; took 6.00e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1570 of 1742 dataIds; took 6.02e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1600 of 1741 dataIds; took 6.03e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 1710 of 1741 dataIds; took 6.03e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1610 of 1741 dataIds; took 6.05e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 1580 of 1742 dataIds; took 6.06e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1720 of 1741 dataIds; took 6.06e+03 sec; total should take 1.71e+00 hrs\n",
      "ran 1620 of 1741 dataIds; took 6.09e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 1730 of 1741 dataIds; took 6.09e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 1590 of 1742 dataIds; took 6.10e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1630 of 1741 dataIds; took 6.12e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 1740 of 1741 dataIds; took 6.13e+03 sec; total should take 1.70e+00 hrs\n",
      "ran 1600 of 1742 dataIds; took 6.14e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1640 of 1741 dataIds; took 6.16e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 1610 of 1742 dataIds; took 6.17e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1650 of 1741 dataIds; took 6.19e+03 sec; total should take 1.82e+00 hrs\n",
      "ran 1620 of 1742 dataIds; took 6.21e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1660 of 1741 dataIds; took 6.23e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1630 of 1742 dataIds; took 6.25e+03 sec; total should take 1.86e+00 hrs\n",
      "ran 1670 of 1741 dataIds; took 6.26e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1640 of 1742 dataIds; took 6.29e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1680 of 1741 dataIds; took 6.29e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1650 of 1742 dataIds; took 6.32e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1690 of 1741 dataIds; took 6.33e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1660 of 1742 dataIds; took 6.35e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1700 of 1741 dataIds; took 6.36e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1670 of 1742 dataIds; took 6.38e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1710 of 1741 dataIds; took 6.40e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1680 of 1742 dataIds; took 6.41e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1720 of 1741 dataIds; took 6.43e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1690 of 1742 dataIds; took 6.44e+03 sec; total should take 1.85e+00 hrs\n",
      "ran 1730 of 1741 dataIds; took 6.46e+03 sec; total should take 1.81e+00 hrs\n",
      "ran 1700 of 1742 dataIds; took 6.47e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1740 of 1741 dataIds; took 6.48e+03 sec; total should take 1.80e+00 hrs\n",
      "ran 1710 of 1742 dataIds; took 6.50e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1720 of 1742 dataIds; took 6.54e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1730 of 1742 dataIds; took 6.57e+03 sec; total should take 1.84e+00 hrs\n",
      "ran 1740 of 1742 dataIds; took 6.60e+03 sec; total should take 1.83e+00 hrs\n"
     ]
    }
   ],
   "source": [
    "nside = 512\n",
    "n_proc = 4\n",
    "valid_data_id = copy.deepcopy(valid_data_id_true)\n",
    "manager = mproc.Manager()\n",
    "pixel_dict = manager.dict()\n",
    "\n",
    "# just take a random subset\n",
    "#valid_data_id = np.array(valid_data_id)\n",
    "#rng = np.random.RandomState(9912)\n",
    "#rng.shuffle(valid_data_id)\n",
    "#valid_data_id = valid_data_id[:400]\n",
    "\n",
    "valid_id_arr = []\n",
    "butler_arr = []\n",
    "\n",
    "n_frac = len(valid_data_id)//n_proc\n",
    "\n",
    "print('n_frac %d len %d' % (n_frac,len(valid_data_id)))\n",
    "\n",
    "for ii in range(n_proc):\n",
    "    i_start = ii*n_frac\n",
    "    i_end = (ii+1)*n_frac\n",
    "    if ii == n_proc-1:\n",
    "        i_end=len(valid_data_id)\n",
    "    local_id_list = valid_data_id[i_start:i_end]\n",
    "    print(i_start,i_end,len(local_id_list))\n",
    "    valid_id_arr.append(local_id_list)\n",
    "    butler_arr.append(Butler(repo_dir))\n",
    "\n",
    "    #butler, catalog_name, dataId_list, nside,\n",
    "    #                 i_process, out_dict\n",
    "    \n",
    "job_list = []\n",
    "for ii in range(n_proc):\n",
    "    p = mproc.Process(target=get_pixel_counts,\n",
    "                      args=(butler_arr[ii],\n",
    "                            'deepCoadd_ref',\n",
    "                            valid_id_arr[ii],\n",
    "                            nside, ii, pixel_dict))\n",
    "\n",
    "    p.start()\n",
    "    job_list.append(p)\n",
    "\n",
    "for p in job_list:\n",
    "    p.join()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collate our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4341662\n",
      "4085879\n",
      "4269221\n",
      "4485262\n",
      "17182024\n",
      "0.013113963206424483\n"
     ]
    }
   ],
   "source": [
    "for ii in range(n_proc):\n",
    "    assert len(pixel_dict[ii])==len(pixel_dict[0])\n",
    "\n",
    "pixel_counts = np.zeros(len(pixel_dict[0]), dtype=int)\n",
    "for ii in range(n_proc):\n",
    "    pixel_counts += pixel_dict[ii]\n",
    "    print(pixel_dict[ii].sum())\n",
    "\n",
    "npix = hp.nside2npix(nside)\n",
    "sq_degrees = 4.0*np.pi*(180.0/np.pi)**2/npix\n",
    "print(pixel_counts.sum())\n",
    "pixel_counts = pixel_counts.astype(float)/sq_degrees\n",
    "print(sq_degrees)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the density map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_pixels = np.where(pixel_counts>0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_arr, dec_arr = hp.pix2ang(nside, range(len(pixel_counts)),lonlat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_ra = ra_arr[valid_pixels]\n",
    "valid_dec = dec_arr[valid_pixels]\n",
    "valid_counts = pixel_counts[valid_pixels]\n",
    "\n",
    "ra_min = valid_ra.min()\n",
    "ra_max = valid_ra.max()\n",
    "dec_min = valid_dec.min()\n",
    "dec_max = valid_dec.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.003906249999996 341.455078125\n",
      "-7.331168113595439 53.09094829504232\n"
     ]
    }
   ],
   "source": [
    "print(ra_min,ra_max)\n",
    "print(dec_min,dec_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 180.0 -180.0 180.0\n",
      "The interval between parallels is 30 deg -0.00'.\n",
      "The interval between meridians is 30 deg -0.00'.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa179dbc8d0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa180dddc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hp.mollview(pixel_counts, cbar=False,\n",
    "                        flip='astro',\n",
    "                        return_projected_map=True)\n",
    "hp.graticule()\n",
    "ax = plt.gca()\n",
    "im = ax.get_images()[0]\n",
    "plt.colorbar(im,label='stars per sq degree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LSST",
   "language": "python",
   "name": "lsst"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}