diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 192c4d2..206f0e2 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -26,4 +26,4 @@ jobs:
       # renovate: datasource=conda depName=conda-forge/python
       python-version: "3.12.4"
       # renovate: datasource=pypi depName=ruff
-      ruff-version: "0.4.10"
+      ruff-version: "0.7.4"
diff --git a/extcats/CatalogQuery.py b/extcats/CatalogQuery.py
index 0c151fc..31452f6 100755
--- a/extcats/CatalogQuery.py
+++ b/extcats/CatalogQuery.py
@@ -721,7 +721,7 @@ def test_queries(
         for pp in tqdm.tqdm(points):
             buff = qfunc(pp[0], pp[1], rs_arcsec, method, **qfunc_args)
             if (
-                (type(buff) == tuple and buff == (None, None))
+                (isinstance(buff, tuple) and buff == (None, None))
                 or (buff is None)
                 or (not buff)
             ):
diff --git a/notebooks/example_ingest_multiproc.ipynb b/notebooks/example_ingest_multiproc.ipynb
index 0fad6ba..4ca2fad 100644
--- a/notebooks/example_ingest_multiproc.ipynb
+++ b/notebooks/example_ingest_multiproc.ipynb
@@ -30,77 +30,87 @@
     "from healpy import ang2pix\n",
     "\n",
     "import importlib\n",
+    "\n",
     "importlib.reload(CatalogPusher)\n",
     "\n",
     "# build the pusher object and point it to the raw files.\n",
     "ps1p = CatalogPusher.CatalogPusher(\n",
-    "    catalog_name = 'ps1_test',                    # short name of the catalog\n",
-    "    data_source = '../testdata/PS1DR1_test/',     # where to find the data (other options are possible)\n",
-    "    file_type = '*.csv.gz'                        # filter files (there is col definition file in data_source)\n",
-    "    )\n",
+    "    catalog_name=\"ps1_test\",  # short name of the catalog\n",
+    "    data_source=\"../testdata/PS1DR1_test/\",  # where to find the data (other options are possible)\n",
+    "    file_type=\"*.csv.gz\",  # filter files (there is col definition file in data_source)\n",
+    ")\n",
     "\n",
     "# define the reader for the raw files (import column names from file.)\n",
-    "headfile = '../testdata/PS1DR1_test/column_headings.csv'\n",
-    "with open(headfile, 'r') as header:\n",
-    "    catcols=[c.strip() for c in header.readline().split(',')]\n",
+    "headfile = \"../testdata/PS1DR1_test/column_headings.csv\"\n",
+    "with open(headfile, \"r\") as header:\n",
+    "    catcols = [c.strip() for c in header.readline().split(\",\")]\n",
     "\n",
     "# skimm out some columns\n",
-    "bad = ['projectionID', 'skyCellID']\n",
-    "usecols = [c for c in catcols if (not c in bad) or ('gNpt' in c)]\n",
+    "bad = [\"projectionID\", \"skyCellID\"]\n",
+    "usecols = [c for c in catcols if (c not in bad) or (\"gNpt\" in c)]\n",
     "\n",
     "# specify some data types to save up on the storage\n",
     "# See https://outerspace.stsci.edu/display/PANSTARRS/PS1+MeanObject+table+fields\n",
     "types = {}\n",
     "for c in usecols:\n",
     "    types[c] = np.float16\n",
-    "    if c == 'objID':\n",
+    "    if c == \"objID\":\n",
     "        types[c] = np.int32\n",
-    "    if 'Flags' in c:\n",
+    "    if \"Flags\" in c:\n",
     "        types[c] = np.int16\n",
-    "    if ('ra' in c) or ('dec' in c):\n",
+    "    if (\"ra\" in c) or (\"dec\" in c):\n",
     "        types[c] = np.float32\n",
     "\n",
     "ps1p.assign_file_reader(\n",
-    "    reader_func = pd.read_csv,           # callable to use to read the raw_files. \n",
-    "    read_chunks = True,                  # weather or not the reader process each file into smaller chunks.\n",
-    "    names=catcols,                       # All other arguments are passed directly to this function.\n",
+    "    reader_func=pd.read_csv,  # callable to use to read the raw_files.\n",
+    "    read_chunks=True,  # weather or not the reader process each file into smaller chunks.\n",
+    "    names=catcols,  # All other arguments are passed directly to this function.\n",
     "    usecols=usecols,\n",
-    "    dtype = types,\n",
-    "    na_values = -999,\n",
+    "    dtype=types,\n",
+    "    na_values=-999,\n",
     "    chunksize=50000,\n",
-    "    engine='c')\n",
+    "    engine=\"c\",\n",
+    ")\n",
     "\n",
     "# define modifier. This time the healpix grid is finer (an orer 16 corresponds to 3\")\n",
-    "hp_nside16=2**16\n",
+    "hp_nside16 = 2**16\n",
+    "\n",
+    "\n",
     "def ps1_modifier(srcdict):\n",
-    "    srcdict['_id'] = srcdict.pop('objID')\n",
-    "    srcdict['hpxid_16']=int(\n",
-    "        ang2pix(hp_nside16, srcdict['raMean'], srcdict['decMean'], lonlat = True, nest = True))\n",
+    "    srcdict[\"_id\"] = srcdict.pop(\"objID\")\n",
+    "    srcdict[\"hpxid_16\"] = int(\n",
+    "        ang2pix(\n",
+    "            hp_nside16, srcdict[\"raMean\"], srcdict[\"decMean\"], lonlat=True, nest=True\n",
+    "        )\n",
+    "    )\n",
     "    return srcdict\n",
+    "\n",
+    "\n",
     "ps1p.assign_dict_modifier(ps1_modifier)\n",
     "\n",
-    "# wrap up the file pushing function so that we can \n",
+    "# wrap up the file pushing function so that we can\n",
     "# use multiprocessing to speed up the catalog ingestion\n",
     "\n",
+    "\n",
     "def pushfiles(filerange):\n",
     "    # push stuff\n",
     "    ps1p.push_to_db(\n",
-    "        coll_name = 'srcs',\n",
-    "        index_on = ['hpxid_16'],\n",
-    "        filerange = filerange,\n",
-    "        overwrite_coll = False,\n",
-    "        dry = False, \n",
-    "        fillna_val = None)\n",
+    "        coll_name=\"srcs\",\n",
+    "        index_on=[\"hpxid_16\"],\n",
+    "        filerange=filerange,\n",
+    "        overwrite_coll=False,\n",
+    "        dry=False,\n",
+    "        fillna_val=None,\n",
+    "    )\n",
     "    # add metadata to direct queries\n",
-    "    ps1p.healpix_meta(\n",
-    "        healpix_id_key = 'hpxid_16', \n",
-    "        order = 16, is_indexed = True, nest = True)\n",
+    "    ps1p.healpix_meta(healpix_id_key=\"hpxid_16\", order=16, is_indexed=True, nest=True)\n",
+    "\n",
     "\n",
     "# each job will run on a subgroup of all the files\n",
     "file_groups = ps1p.file_groups(group_size=1)\n",
-    "with concurrent.futures.ProcessPoolExecutor(max_workers = 2) as executor:\n",
-    "    executor.map(pushfiles, file_groups)   \n",
-    "print (\"done! Enjoy your PS1_test database.\")"
+    "with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executor:\n",
+    "    executor.map(pushfiles, file_groups)\n",
+    "print(\"done! Enjoy your PS1_test database.\")"
    ]
   },
   {
diff --git a/notebooks/insert_allWISE.ipynb b/notebooks/insert_allWISE.ipynb
index 4381751..cd22e14 100644
--- a/notebooks/insert_allWISE.ipynb
+++ b/notebooks/insert_allWISE.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -78,27 +78,26 @@
    ],
    "source": [
     "import numpy as np\n",
+    "import pandas as pd\n",
     "from healpy import ang2pix\n",
     "from extcats import CatalogPusher\n",
     "\n",
     "# build the pusher object and point it to the raw files.\n",
     "wisep = CatalogPusher.CatalogPusher(\n",
-    "    catalog_name = 'wise',\n",
-    "    data_source = '../testdata/AllWISE/',\n",
-    "    file_type = \".bz2\")\n",
+    "    catalog_name=\"wise\", data_source=\"../testdata/AllWISE/\", file_type=\".bz2\"\n",
+    ")\n",
     "\n",
     "\n",
     "# read column names and types from schema file\n",
     "schema_file = \"../testdata/AllWISE/wise-allwise-cat-schema.txt\"\n",
     "names, types = [], {}\n",
     "with open(schema_file) as schema:\n",
-    "    for l in schema:\n",
-    "        if \"#\" in l or (not l.strip()):\n",
+    "    for line in schema:\n",
+    "        if \"#\" in line or (not line.strip()):\n",
     "            continue\n",
-    "        name, dtype = zip(\n",
-    "            [p.strip() for p in l.strip().split(\" \") if not p in [\"\"]])\n",
+    "        name, dtype = zip([p.strip() for p in line.strip().split(\" \") if p not in [\"\"]])\n",
     "        name, dtype = name[0], dtype[0]\n",
-    "        #print (name, dtype)\n",
+    "        # print (name, dtype)\n",
     "        names.append(name)\n",
     "        # convert the data type\n",
     "        if \"char\" in dtype:\n",
@@ -114,60 +113,65 @@
     "        elif dtype == \"int8\":\n",
     "            types[name] = np.int8\n",
     "        else:\n",
-    "            print(\"unknown data type: %s\"%dtype)\n",
+    "            print(\"unknown data type: %s\" % dtype)\n",
     "\n",
     "# select the columns you want to use.\n",
     "use_cols = []\n",
-    "select = [\"Basic Position and Identification Information\", \n",
-    "         \"Primary Photometric Information\", \n",
-    "         \"Measurement Quality and Source Reliability Information\",\n",
-    "         \"2MASS PSC Association Information\"]\n",
+    "select = [\n",
+    "    \"Basic Position and Identification Information\",\n",
+    "    \"Primary Photometric Information\",\n",
+    "    \"Measurement Quality and Source Reliability Information\",\n",
+    "    \"2MASS PSC Association Information\",\n",
+    "]\n",
     "with open(schema_file) as schema:\n",
     "    blocks = schema.read().split(\"#\")\n",
     "    for block in blocks:\n",
     "        if any([k in block for k in select]):\n",
-    "            for l in block.split(\"\\n\")[1:]:\n",
-    "                if \"#\" in l or (not l.strip()):\n",
+    "            for line in block.split(\"\\n\")[1:]:\n",
+    "                if \"#\" in line or (not line.strip()):\n",
     "                    continue\n",
     "                name, dtype = zip(\n",
-    "                    [p.strip() for p in l.strip().split(\" \") if not p in [\"\"]])\n",
+    "                    [p.strip() for p in line.strip().split(\" \") if p not in [\"\"]]\n",
+    "                )\n",
     "                use_cols.append(name[0])\n",
-    "print(\"we will be using %d columns out of %d\"%(len(use_cols), len(names)))\n",
+    "print(\"we will be using %d columns out of %d\" % (len(use_cols), len(names)))\n",
     "\n",
     "# now assign the reader to the catalog pusher object\n",
-    "import pandas as pd\n",
+    "\n",
     "wisep.assign_file_reader(\n",
-    "        reader_func = pd.read_csv, \n",
-    "        read_chunks = True,\n",
-    "        names = names,\n",
-    "        usecols = lambda x : x in use_cols,\n",
-    "        #dtype = types,    #this mess up with NaN values\n",
-    "        chunksize=5000,\n",
-    "        header=None,\n",
-    "        engine='c',\n",
-    "        sep='|',\n",
-    "        na_values = 'nnnn')\n",
+    "    reader_func=pd.read_csv,\n",
+    "    read_chunks=True,\n",
+    "    names=names,\n",
+    "    usecols=lambda x: x in use_cols,\n",
+    "    # dtype = types,    #this mess up with NaN values\n",
+    "    chunksize=5000,\n",
+    "    header=None,\n",
+    "    engine=\"c\",\n",
+    "    sep=\"|\",\n",
+    "    na_values=\"nnnn\",\n",
+    ")\n",
     "\n",
     "\n",
     "# define the dictionary modifier that will act on the single entries\n",
     "def modifier(srcdict):\n",
-    "    srcdict['hpxid_16'] = int(\n",
-    "        ang2pix(2**16, srcdict['ra'], srcdict['dec'], lonlat = True, nest = True))\n",
-    "    #srcdict['_id'] = srcdict.pop('source_id')   doesn't work, seems it is not unique\n",
+    "    srcdict[\"hpxid_16\"] = int(\n",
+    "        ang2pix(2**16, srcdict[\"ra\"], srcdict[\"dec\"], lonlat=True, nest=True)\n",
+    "    )\n",
+    "    # srcdict['_id'] = srcdict.pop('source_id')   doesn't work, seems it is not unique\n",
     "    return srcdict\n",
+    "\n",
+    "\n",
     "wisep.assign_dict_modifier(modifier)\n",
     "\n",
     "\n",
     "# finally push it in the databse\n",
     "wisep.push_to_db(\n",
-    "    coll_name = 'srcs', \n",
-    "    index_on = \"hpxid_16\",\n",
-    "    overwrite_coll = True, \n",
-    "    append_to_coll = False)\n",
+    "    coll_name=\"srcs\", index_on=\"hpxid_16\", overwrite_coll=True, append_to_coll=False\n",
+    ")\n",
     "\n",
     "\n",
     "# if needed print extensive info on database\n",
-    "#wisep.info()"
+    "# wisep.info()"
    ]
   },
   {
@@ -210,53 +214,57 @@
     }
    ],
    "source": [
-    "# now test the database for query performances. We use \n",
+    "# now test the database for query performances. We use\n",
     "# a sample of randomly distributed points on a sphere\n",
-    "# as targets. \n",
+    "# as targets.\n",
     "\n",
     "# define the funtion to test coordinate based queries:\n",
-    "from healpy import ang2pix, get_all_neighbours\n",
+    "from healpy import get_all_neighbours\n",
     "from astropy.table import Table\n",
     "from astropy.coordinates import SkyCoord\n",
     "\n",
-    "return_fields = ['designation', 'ra', 'dec']\n",
+    "return_fields = [\"designation\", \"ra\", \"dec\"]\n",
     "project = {}\n",
-    "for field in return_fields: project[field] = 1\n",
-    "print (project)\n",
+    "for field in return_fields:\n",
+    "    project[field] = 1\n",
+    "print(project)\n",
+    "\n",
+    "\n",
+    "hp_order, rs_arcsec = 16, 30.0\n",
     "\n",
     "\n",
-    "hp_order, rs_arcsec = 16, 30.\n",
     "def test_query(ra, dec, coll):\n",
     "    \"\"\"query collection for points within rs of target ra, dec.\n",
     "    The results as returned as an astropy Table.\"\"\"\n",
-    "    \n",
-    "    # find the index of the target pixel and its neighbours \n",
-    "    target_pix = int( ang2pix(2**hp_order, ra, dec, nest = True, lonlat = True) )\n",
-    "    neighbs = get_all_neighbours(2**hp_order, ra, dec, nest = True, lonlat = True)\n",
+    "\n",
+    "    # find the index of the target pixel and its neighbours\n",
+    "    target_pix = int(ang2pix(2**hp_order, ra, dec, nest=True, lonlat=True))\n",
+    "    neighbs = get_all_neighbours(2**hp_order, ra, dec, nest=True, lonlat=True)\n",
     "\n",
     "    # remove non-existing neigbours (in case of E/W/N/S) and add center pixel\n",
     "    pix_group = [int(pix_id) for pix_id in neighbs if pix_id != -1] + [target_pix]\n",
-    "    \n",
+    "\n",
     "    # query the database for sources in these pixels\n",
-    "    qfilter = { 'hpxid_%d'%hp_order: { '$in': pix_group } }\n",
+    "    qfilter = {\"hpxid_%d\" % hp_order: {\"$in\": pix_group}}\n",
     "    qresults = [o for o in coll.find(qfilter)]\n",
-    "    if len(qresults)==0:\n",
+    "    if len(qresults) == 0:\n",
     "        return None\n",
-    "    \n",
+    "\n",
     "    # then use astropy to find the closest match\n",
     "    tab = Table(qresults)\n",
-    "    target = SkyCoord(ra, dec, unit = 'deg')\n",
-    "    matches_pos = SkyCoord(tab['ra'], tab['dec'], unit = 'deg')\n",
+    "    target = SkyCoord(ra, dec, unit=\"deg\")\n",
+    "    matches_pos = SkyCoord(tab[\"ra\"], tab[\"dec\"], unit=\"deg\")\n",
     "    d2t = target.separation(matches_pos).arcsecond\n",
     "    match_id = np.argmin(d2t)\n",
     "\n",
     "    # if it's too far away don't use it\n",
-    "    if d2t[match_id]>rs_arcsec:\n",
+    "    if d2t[match_id] > rs_arcsec:\n",
     "        return None\n",
     "    return tab[match_id]\n",
     "\n",
+    "\n",
     "# run the test\n",
-    "wisep.run_test(test_query, npoints = 10000)\n"
+    "wisep.run_test(test_query, npoints=10000)"
    ]
   },
   {
@@ -274,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -290,13 +298,14 @@
     }
    ],
    "source": [
-    "mqp.healpix_meta(healpix_id_key = 'hpxid_16', order = 16, is_indexed = True, nest = True)\n",
-    "mqp.coord_meta(ra = 'ra', dec = 'dec')\n",
-    "mqp.science_meta(\n",
-    "    contact =  'C. Norris', \n",
-    "    email = 'chuck.norris@desy.de', \n",
-    "    description = 'allWISE infrared catalog',\n",
-    "    reference = 'http://wise2.ipac.caltech.edu/docs/release/allwise/')"
+    "wisep.healpix_meta(healpix_id_key=\"hpxid_16\", order=16, is_indexed=True, nest=True)\n",
+    "wisep.coord_meta(ra=\"ra\", dec=\"dec\")\n",
+    "wisep.science_meta(\n",
+    "    contact=\"C. Norris\",\n",
+    "    email=\"chuck.norris@desy.de\",\n",
+    "    description=\"allWISE infrared catalog\",\n",
+    "    reference=\"http://wise2.ipac.caltech.edu/docs/release/allwise/\",\n",
+    ")"
    ]
   },
   {
diff --git a/notebooks/insert_example.ipynb b/notebooks/insert_example.ipynb
index a1a3a67..8a48e89 100644
--- a/notebooks/insert_example.ipynb
+++ b/notebooks/insert_example.ipynb
@@ -48,6 +48,7 @@
    "source": [
     "# here we download the test data\n",
     "from extcats.testdata import MQC\n",
+    "\n",
     "MQC.download()"
    ]
   },
@@ -65,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -104,74 +105,97 @@
     }
    ],
    "source": [
+    "import pymongo\n",
+    "import pandas as pd\n",
+    "\n",
+    "from healpy import ang2pix\n",
+    "\n",
     "from extcats import CatalogPusher\n",
     "\n",
     "# build the pusher object and point it to the raw files.\n",
     "mqp = CatalogPusher.CatalogPusher(\n",
-    "    catalog_name = 'milliquas_old',             # short name of the catalog\n",
-    "    data_source = '../testdata/milliquas/', # where to find the data\n",
-    "    file_type = 'tdat.gz'\n",
-    "    )\n",
+    "    catalog_name=\"milliquas_old\",  # short name of the catalog\n",
+    "    data_source=\"../testdata/milliquas/\",  # where to find the data\n",
+    "    file_type=\"tdat.gz\",\n",
+    ")\n",
     "\n",
     "\n",
-    "# define the reader for the raw files. In this case the formatting or the raw file \n",
+    "# define the reader for the raw files. In this case the formatting or the raw file\n",
     "# is pretty ugly so we have to put quite a lot of information here.\n",
     "# the pandas package provides very efficient ways to read flat files and its use\n",
     "# is recommended. For this specific example see:\n",
     "# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html\n",
-    "catcols=['name', 'ra', 'dec', 'lii', 'bii', 'broad_type', 'rmag', \n",
-    "         'bmag', 'optical_flag', 'red_psf_flag', 'blue_psf_flag',\n",
-    "         'redshift', 'ref_name', 'ref_redshift', 'qso_prob', \n",
-    "         'radio_name', 'xray_name', 'alt_name_1', 'alt_name_2', 'class']\n",
-    "import pandas as pd\n",
-    "mqp.assign_file_reader(\n",
-    "        reader_func = pd.read_table,         # callable to use to read the raw_files. \n",
-    "        read_chunks = True,                  # weather or not the reader process each file into smaller chunks.\n",
-    "        names=catcols,                       # All other arguments are passed directly to this function.\n",
-    "        chunksize=50000,\n",
-    "        engine='c',\n",
-    "        skiprows=65,\n",
-    "        sep='|',\n",
-    "        index_col=False,\n",
-    "        comment='<')\n",
+    "catcols = [\n",
+    "    \"name\",\n",
+    "    \"ra\",\n",
+    "    \"dec\",\n",
+    "    \"lii\",\n",
+    "    \"bii\",\n",
+    "    \"broad_type\",\n",
+    "    \"rmag\",\n",
+    "    \"bmag\",\n",
+    "    \"optical_flag\",\n",
+    "    \"red_psf_flag\",\n",
+    "    \"blue_psf_flag\",\n",
+    "    \"redshift\",\n",
+    "    \"ref_name\",\n",
+    "    \"ref_redshift\",\n",
+    "    \"qso_prob\",\n",
+    "    \"radio_name\",\n",
+    "    \"xray_name\",\n",
+    "    \"alt_name_1\",\n",
+    "    \"alt_name_2\",\n",
+    "    \"class\",\n",
+    "]\n",
     "\n",
+    "mqp.assign_file_reader(\n",
+    "    reader_func=pd.read_table,  # callable to use to read the raw_files.\n",
+    "    read_chunks=True,  # weather or not the reader process each file into smaller chunks.\n",
+    "    names=catcols,  # All other arguments are passed directly to this function.\n",
+    "    chunksize=50000,\n",
+    "    engine=\"c\",\n",
+    "    skiprows=65,\n",
+    "    sep=\"|\",\n",
+    "    index_col=False,\n",
+    "    comment=\"<\",\n",
+    ")\n",
     "\n",
     "# now we have to define a modifier function that acts on the single documents\n",
     "# (dictionaries) and format them in the way they have to appear in the database.\n",
     "# in this case we format coordinates in the geoJSON type (this enables mongo to\n",
     "# support queries in spherical cooridnates), and we assign to each source its\n",
     "# healpix index on a grid of order 16, corresponding to ~3\" resolution.\n",
-    "from healpy import ang2pix\n",
+    "\n",
+    "\n",
     "def mqc_modifier(srcdict):\n",
-    "    \n",
     "    # format coordinates into geoJSON type (commented version uses 'legacy' pair):\n",
     "    # unfortunately mongo needs the RA to be folded into -180, +180\n",
-    "    ra=srcdict['ra'] if srcdict['ra']<180. else srcdict['ra']-360.\n",
-    "    srcdict['pos']={\n",
-    "            'type': 'Point', \n",
-    "            'coordinates': [ra, srcdict['dec']]\n",
-    "                    }\n",
-    "    #srcdict['pos']=[srcdict['ra'], srcdict['dec']] # This is the legacy coordinate format\n",
-    "    \n",
+    "    ra = srcdict[\"ra\"] if srcdict[\"ra\"] < 180.0 else srcdict[\"ra\"] - 360.0\n",
+    "    srcdict[\"pos\"] = {\"type\": \"Point\", \"coordinates\": [ra, srcdict[\"dec\"]]}\n",
+    "    # srcdict['pos']=[srcdict['ra'], srcdict['dec']] # This is the legacy coordinate format\n",
+    "\n",
     "    # add healpix index\n",
-    "    srcdict['hpxid_16']=int(\n",
-    "        ang2pix(2**16, srcdict['ra'], srcdict['dec'], lonlat = True, nest = True))\n",
-    "    \n",
+    "    srcdict[\"hpxid_16\"] = int(\n",
+    "        ang2pix(2**16, srcdict[\"ra\"], srcdict[\"dec\"], lonlat=True, nest=True)\n",
+    "    )\n",
+    "\n",
     "    return srcdict\n",
     "\n",
+    "\n",
     "mqp.assign_dict_modifier(mqc_modifier)\n",
     "\n",
     "# fill in the database, creting indexes on the position and healpix ID.\n",
-    "import pymongo\n",
+    "\n",
     "mqp.push_to_db(\n",
-    "    coll_name = 'srcs', \n",
-    "    index_on = ['hpxid_16', [('pos', pymongo.GEOSPHERE)] ] ,\n",
-    "    index_args = [{}, {}], # specify arguments for the index creation\n",
-    "    overwrite_coll = False, \n",
-    "    append_to_coll = False)\n",
+    "    coll_name=\"srcs\",\n",
+    "    index_on=[\"hpxid_16\", [(\"pos\", pymongo.GEOSPHERE)]],\n",
+    "    index_args=[{}, {}],  # specify arguments for the index creation\n",
+    "    overwrite_coll=False,\n",
+    "    append_to_coll=False,\n",
+    ")\n",
     "\n",
     "# now print some info on the database\n",
-    "#mqp.info()"
+    "# mqp.info()"
    ]
   },
   {
@@ -208,80 +232,84 @@
    "source": [
     "# define the funtion to test coordinate based queries:\n",
     "import numpy as np\n",
-    "from healpy import ang2pix, get_all_neighbours\n",
+    "from healpy import get_all_neighbours\n",
     "from astropy.table import Table\n",
     "from astropy.coordinates import SkyCoord\n",
     "from math import radians\n",
+    "\n",
     "# define your search radius\n",
-    "rs_arcsec = 10.\n",
+    "rs_arcsec = 10.0\n",
+    "\n",
     "\n",
     "def test_query_healpix(ra, dec, coll):\n",
-    "    \"\"\"query collection for the closest point within \n",
+    "    \"\"\"query collection for the closest point within\n",
     "    rs_arcsec of target ra, dec. It uses healpix ID\n",
     "    to perform the search.\n",
-    "    \n",
-    "    The results as returned as an astropy Table. \"\"\"\n",
-    "    \n",
-    "    # find the index of the target pixel and its neighbours \n",
-    "    target_pix = int( ang2pix(2**16, ra, dec, nest = True, lonlat = True) )\n",
-    "    neighbs = get_all_neighbours(2*16, ra, dec, nest = True, lonlat = True)\n",
+    "\n",
+    "    The results as returned as an astropy Table.\"\"\"\n",
+    "\n",
+    "    # find the index of the target pixel and its neighbours\n",
+    "    target_pix = int(ang2pix(2**16, ra, dec, nest=True, lonlat=True))\n",
+    "    neighbs = get_all_neighbours(2 * 16, ra, dec, nest=True, lonlat=True)\n",
     "\n",
     "    # remove non-existing neigbours (in case of E/W/N/S) and add center pixel\n",
     "    pix_group = [int(pix_id) for pix_id in neighbs if pix_id != -1] + [target_pix]\n",
-    "    \n",
+    "\n",
     "    # query the database for sources in these pixels\n",
-    "    qfilter = { 'hpxid_16': { '$in': pix_group } }\n",
+    "    qfilter = {\"hpxid_16\": {\"$in\": pix_group}}\n",
     "    qresults = [o for o in coll.find(qfilter)]\n",
-    "    if len(qresults)==0:\n",
+    "    if len(qresults) == 0:\n",
     "        return None\n",
-    "    \n",
+    "\n",
     "    # then use astropy to find the closest match\n",
     "    tab = Table(qresults)\n",
-    "    target = SkyCoord(ra, dec, unit = 'deg')\n",
-    "    matches_pos = SkyCoord(tab['ra'], tab['dec'], unit = 'deg')\n",
+    "    target = SkyCoord(ra, dec, unit=\"deg\")\n",
+    "    matches_pos = SkyCoord(tab[\"ra\"], tab[\"dec\"], unit=\"deg\")\n",
     "    d2t = target.separation(matches_pos).arcsecond\n",
     "    match_id = np.argmin(d2t)\n",
     "\n",
     "    # if it's too far away don't use it\n",
-    "    if d2t[match_id]>rs_arcsec:\n",
+    "    if d2t[match_id] > rs_arcsec:\n",
     "        return None\n",
     "    return tab[match_id]\n",
     "\n",
     "\n",
     "def test_query_2dsphere(ra, dec, coll):\n",
-    "    \"\"\"query collection for the closest point within \n",
+    "    \"\"\"query collection for the closest point within\n",
     "    rs_arcsec of target ra, dec. It uses mondod spherical\n",
     "    geometry queries.\n",
-    "    \n",
-    "    The results as returned as an astropy Table. \"\"\"\n",
-    "    \n",
-    "    \n",
+    "\n",
+    "    The results as returned as an astropy Table.\"\"\"\n",
+    "\n",
     "    # fold the RA between -180 and 180.\n",
     "    if ra > 180:\n",
-    "        ra = ra - 360.\n",
-    "    \n",
+    "        ra = ra - 360.0\n",
+    "\n",
     "    # query and return\n",
-    "    geowithin={\"$geoWithin\": { \"$centerSphere\": [[ra, dec], radians(rs_arcsec/3600.)]}}\n",
+    "    geowithin = {\n",
+    "        \"$geoWithin\": {\"$centerSphere\": [[ra, dec], radians(rs_arcsec / 3600.0)]}\n",
+    "    }\n",
     "    qresults = [o for o in coll.find({\"pos\": geowithin})]\n",
-    "    if len(qresults)==0:\n",
+    "    if len(qresults) == 0:\n",
     "        return None\n",
-    "    \n",
+    "\n",
     "    # then use astropy to find the closest match\n",
     "    tab = Table(qresults)\n",
-    "    target = SkyCoord(ra, dec, unit = 'deg')\n",
-    "    matches_pos = SkyCoord(tab['ra'], tab['dec'], unit = 'deg')\n",
+    "    target = SkyCoord(ra, dec, unit=\"deg\")\n",
+    "    matches_pos = SkyCoord(tab[\"ra\"], tab[\"dec\"], unit=\"deg\")\n",
     "    d2t = target.separation(matches_pos).arcsecond\n",
     "    match_id = np.argmin(d2t)\n",
     "\n",
     "    # if it's too far away don't use it\n",
-    "    if d2t[match_id]>rs_arcsec:\n",
+    "    if d2t[match_id] > rs_arcsec:\n",
     "        return None\n",
     "    return tab[match_id]\n",
     "\n",
-    "# run the test. Here we compare queries based on the \n",
+    "\n",
+    "# run the test. Here we compare queries based on the\n",
     "# healpix index with those based on the 2dsphere mongod support.\n",
-    "mqp.run_test(test_query_healpix, npoints = 1000)\n",
-    "mqp.run_test(test_query_2dsphere, npoints = 1000)"
+    "mqp.run_test(test_query_healpix, npoints=1000)\n",
+    "mqp.run_test(test_query_2dsphere, npoints=1000)"
    ]
   },
   {
@@ -313,14 +341,15 @@
     }
    ],
    "source": [
-    "mqp.healpix_meta(healpix_id_key = 'hpxid_16', order = 16, is_indexed = True, nest = True)\n",
-    "mqp.sphere2d_meta(sphere2d_key = 'pos', is_indexed = True, pos_format = 'geoJSON')\n",
-    "mqp.coord_meta(ra = 'ra', dec = 'dec')\n",
+    "mqp.healpix_meta(healpix_id_key=\"hpxid_16\", order=16, is_indexed=True, nest=True)\n",
+    "mqp.sphere2d_meta(sphere2d_key=\"pos\", is_indexed=True, pos_format=\"geoJSON\")\n",
+    "mqp.coord_meta(ra=\"ra\", dec=\"dec\")\n",
     "mqp.science_meta(\n",
-    "    contact =  'C. Norris', \n",
-    "    email = 'chuck.norris@desy.de', \n",
-    "    description = 'compilation of AGN and Quasar',\n",
-    "    reference = 'http://quasars.org/milliquas.htm')"
+    "    contact=\"C. Norris\",\n",
+    "    email=\"chuck.norris@desy.de\",\n",
+    "    description=\"compilation of AGN and Quasar\",\n",
+    "    reference=\"http://quasars.org/milliquas.htm\",\n",
+    ")"
    ]
   },
   {
diff --git a/notebooks/query_example.ipynb b/notebooks/query_example.ipynb
index 03299e7..67a2d4a 100644
--- a/notebooks/query_example.ipynb
+++ b/notebooks/query_example.ipynb
@@ -49,15 +49,16 @@
     "\n",
     "# initialize the CatalogQuery object pointing it to an existsing database\n",
     "mqc_query = CatalogQuery.CatalogQuery(\n",
-    "        cat_name = 'milliquas',           # name of the database\n",
-    "        coll_name = 'srcs',               # name of the collection with the sources\n",
-    "        dbclient = None)\n",
+    "    cat_name=\"milliquas\",  # name of the database\n",
+    "    coll_name=\"srcs\",  # name of the collection with the sources\n",
+    "    dbclient=None,\n",
+    ")\n",
     "\n",
     "\n",
-    "# specify target position (same format as the 'ra_key' and \n",
+    "# specify target position (same format as the 'ra_key' and\n",
     "# 'dec_key specified at initilization) and serach radius\n",
-    "target_ra, target_dec, rs = 5.458082, 16.035756, 100.\n",
-    "target_ra, target_dec, rs = 321.6639722, -89.48325, 100.\n"
+    "target_ra, target_dec, rs = 5.458082, 16.035756, 100.0\n",
+    "target_ra, target_dec, rs = 321.6639722, -89.48325, 100.0"
    ]
   },
   {
@@ -74,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -111,64 +112,101 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "# the 'raw' method does not require any pre-formatting of the catalog.\n",
     "# It first selects points within a box of radius 'box_scale' times larger than the\n",
     "# search radius using $gte and $lte operators, then uses the $where expression\n",
     "# to compute the angular distance of the sources in the box from the target.\n",
-    "out_raw = mqc_query.findwithin(target_ra, target_dec, rs, method = 'raw', box_scale = 2.5)\n",
-    "if not out_raw is None:\n",
-    "    print (\"%d sources found around target position using the 'raw' method.\"%len(out_raw))\n",
+    "out_raw = mqc_query.findwithin(target_ra, target_dec, rs, method=\"raw\", box_scale=2.5)\n",
+    "if out_raw is not None:\n",
+    "    print(\n",
+    "        \"%d sources found around target position using the 'raw' method.\" % len(out_raw)\n",
+    "    )\n",
     "\n",
-    "# the '2dsphere' method uses instead the use mongodb searches in \n",
+    "# the '2dsphere' method uses instead the use mongodb searches in\n",
     "# spherical geometry using \"$geoWithin\" and \"$centerSphere\" operators.\n",
-    "# it requires the catalog documents to have been assigned a geoJSON \n",
+    "# it requires the catalog documents to have been assigned a geoJSON\n",
     "# or 'legacy pair' field of type 'Point' (see insert_example notebook).\n",
-    "out_2dsphere = mqc_query.findwithin(target_ra, target_dec, rs, method = '2dsphere')\n",
-    "if not out_2dsphere is None:\n",
-    "    print (\"%d sources found around target position using the '2dsphere' method.\"%len(out_2dsphere))\n",
+    "out_2dsphere = mqc_query.findwithin(target_ra, target_dec, rs, method=\"2dsphere\")\n",
+    "if out_2dsphere is not None:\n",
+    "    print(\n",
+    "        \"%d sources found around target position using the '2dsphere' method.\"\n",
+    "        % len(out_2dsphere)\n",
+    "    )\n",
     "\n",
     "\n",
-    "# finally, the healpix method can be used to speed up queries using a \n",
-    "# spatial prepartinioning of the data based on a HEALPix grid. In this \n",
+    "# finally, the healpix method can be used to speed up queries using a\n",
+    "# spatial prepartinioning of the data based on a HEALPix grid. In this\n",
     "# case, the sources in the catalog should be assigned a field containing\n",
     "# the ID of the healpix that contains it.\n",
-    "out_healpix = mqc_query.findwithin(target_ra, target_dec, rs, method = 'healpix')\n",
-    "if not out_healpix is None:\n",
-    "    print (\"%d sources found around target position using the 'healpix' method.\"%len(out_healpix))\n",
-    "\n",
-    "out_healpix_square = mqc_query.findwithin(target_ra, target_dec, rs, method = 'healpix', circular = False)\n",
-    "if not out_healpix_square is None:\n",
-    "    print (\"%d sources found around target position using the 'healpix' (square) method.\"%len(out_healpix_square))\n",
+    "out_healpix = mqc_query.findwithin(target_ra, target_dec, rs, method=\"healpix\")\n",
+    "if out_healpix is not None:\n",
+    "    print(\n",
+    "        \"%d sources found around target position using the 'healpix' method.\"\n",
+    "        % len(out_healpix)\n",
+    "    )\n",
+    "\n",
+    "out_healpix_square = mqc_query.findwithin(\n",
+    "    target_ra, target_dec, rs, method=\"healpix\", circular=False\n",
+    ")\n",
+    "if out_healpix_square is not None:\n",
+    "    print(\n",
+    "        \"%d sources found around target position using the 'healpix' (square) method.\"\n",
+    "        % len(out_healpix_square)\n",
+    "    )\n",
     "\n",
     "\n",
     "# ======================================== #\n",
     "#    make a plot with the query results    #\n",
     "# ======================================== #\n",
     "%matplotlib notebook\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "# get a random sample from the catalog\n",
-    "cat_pos=[[o['ra'], o['dec']] for o in \n",
-    "         mqc_query.src_coll.aggregate([{ '$sample': { 'size': 5000 }}])]\n",
+    "cat_pos = [\n",
+    "    [o[\"ra\"], o[\"dec\"]]\n",
+    "    for o in mqc_query.src_coll.aggregate([{\"$sample\": {\"size\": 5000}}])\n",
+    "]\n",
     "cat_ra, cat_dec = zip(*cat_pos)\n",
     "\n",
     "\n",
-    "fig=plt.figure()\n",
-    "ax=fig.add_subplot(111)#, projection=\"aitoff\")\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)  # , projection=\"aitoff\")\n",
     "\n",
     "ax.scatter(cat_ra, cat_dec, label=\"random sample\", c=\"k\", s=50, marker=\"o\", zorder=1)\n",
-    "ax.scatter(out_raw['ra'], out_raw['dec'], label=\"matches (RAW)\", c=\"r\", s=100, marker=\"+\")\n",
-    "ax.scatter(out_2dsphere['ra'], out_2dsphere['dec'], label=\"matches (2D sphere)\", c=\"b\", s=100, marker=\"x\")\n",
-    "ax.scatter(out_healpix['ra'], out_healpix['dec'], label=\"matches (HEALPix)\", c=\"m\", s=100, marker=\"v\")\n",
     "ax.scatter(\n",
-    "out_healpix_square['ra'], out_healpix_square['dec'], label=\"matches (HEALPix square)\", c=\"g\", s=50, marker=\"v\")\n",
-    "\n",
-    "ax.scatter(target_ra, target_dec, label='target', s=200, c='y', marker='*', zorder=0)\n",
-    "ax.set_xlim(target_ra-2, target_ra+2)\n",
-    "ax.set_ylim(target_dec-3, target_dec+3)\n",
-    "ax.legend(loc='best')\n",
-    "fig.show()\n",
-    "    "
+    "    out_raw[\"ra\"], out_raw[\"dec\"], label=\"matches (RAW)\", c=\"r\", s=100, marker=\"+\"\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    out_2dsphere[\"ra\"],\n",
+    "    out_2dsphere[\"dec\"],\n",
+    "    label=\"matches (2D sphere)\",\n",
+    "    c=\"b\",\n",
+    "    s=100,\n",
+    "    marker=\"x\",\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    out_healpix[\"ra\"],\n",
+    "    out_healpix[\"dec\"],\n",
+    "    label=\"matches (HEALPix)\",\n",
+    "    c=\"m\",\n",
+    "    s=100,\n",
+    "    marker=\"v\",\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    out_healpix_square[\"ra\"],\n",
+    "    out_healpix_square[\"dec\"],\n",
+    "    label=\"matches (HEALPix square)\",\n",
+    "    c=\"g\",\n",
+    "    s=50,\n",
+    "    marker=\"v\",\n",
+    ")\n",
+    "\n",
+    "ax.scatter(target_ra, target_dec, label=\"target\", s=200, c=\"y\", marker=\"*\", zorder=0)\n",
+    "ax.set_xlim(target_ra - 2, target_ra + 2)\n",
+    "ax.set_ylim(target_dec - 3, target_dec + 3)\n",
+    "ax.legend(loc=\"best\")\n",
+    "fig.show()"
    ]
   },
   {
@@ -200,16 +238,16 @@
     }
    ],
    "source": [
-    "rawcp, rawcp_dist = mqc_query.findclosest(target_ra, target_dec, rs, method = 'raw')\n",
-    "s2dcp, s2d_dist = mqc_query.findclosest(target_ra, target_dec, rs, method = '2dsphere')\n",
-    "hpcp, hpcp_dist = mqc_query.findclosest(target_ra, target_dec, rs, method = 'healpix')\n",
+    "rawcp, rawcp_dist = mqc_query.findclosest(target_ra, target_dec, rs, method=\"raw\")\n",
+    "s2dcp, s2d_dist = mqc_query.findclosest(target_ra, target_dec, rs, method=\"2dsphere\")\n",
+    "hpcp, hpcp_dist = mqc_query.findclosest(target_ra, target_dec, rs, method=\"healpix\")\n",
     "\n",
     "# here we verify that all the counterparts are actually the same\n",
-    "print ('      Database ID        |   cp-dist [\"]')\n",
-    "print (\"------------------------------------------\")\n",
-    "print (rawcp['_id'], \"|\", rawcp_dist)\n",
-    "print (s2dcp['_id'], \"|\", s2d_dist)\n",
-    "print (hpcp['_id'], \"|\", hpcp_dist)\n"
+    "print('      Database ID        |   cp-dist [\"]')\n",
+    "print(\"------------------------------------------\")\n",
+    "print(rawcp[\"_id\"], \"|\", rawcp_dist)\n",
+    "print(s2dcp[\"_id\"], \"|\", s2d_dist)\n",
+    "print(hpcp[\"_id\"], \"|\", hpcp_dist)"
    ]
   },
   {
@@ -236,12 +274,12 @@
     }
    ],
    "source": [
-    "raw_bool = mqc_query.binaryserach(target_ra, target_dec, rs, method = 'raw')\n",
-    "s2d_bool = mqc_query.binaryserach(target_ra, target_dec, rs, method = '2dsphere')\n",
-    "hp_bool = mqc_query.binaryserach(target_ra, target_dec, rs, method = 'healpix')\n",
+    "raw_bool = mqc_query.binaryserach(target_ra, target_dec, rs, method=\"raw\")\n",
+    "s2d_bool = mqc_query.binaryserach(target_ra, target_dec, rs, method=\"2dsphere\")\n",
+    "hp_bool = mqc_query.binaryserach(target_ra, target_dec, rs, method=\"healpix\")\n",
     "\n",
     "# here we verify that all the counterparts are actually the same\n",
-    "print (raw_bool, s2d_bool, hp_bool)"
+    "print(raw_bool, s2d_bool, hp_bool)"
    ]
   },
   {
@@ -304,17 +342,21 @@
    ],
    "source": [
     "# test te three main types of queries with the healpix method\n",
-    "mqc_query.test_queries(query_type = 'within', method = 'healpix', rs_arcsec = 3, npoints=1e4)\n",
+    "mqc_query.test_queries(query_type=\"within\", method=\"healpix\", rs_arcsec=3, npoints=1e4)\n",
     "\n",
     "# here we don't seed the rng, to avoid mongo using some cached results\n",
-    "mqc_query.test_queries(query_type = 'within', method = 'healpix', rs_arcsec = 3, npoints=1e4, rnd_seed = None)\n",
+    "mqc_query.test_queries(\n",
+    "    query_type=\"within\", method=\"healpix\", rs_arcsec=3, npoints=1e4, rnd_seed=None\n",
+    ")\n",
     "\n",
-    "mqc_query.test_queries(query_type = 'closest', method = 'healpix', rs_arcsec = 3, npoints=1e4)\n",
-    "mqc_query.test_queries(query_type = 'binary', method = 'healpix', rs_arcsec = 3, npoints=1e4)\n",
+    "mqc_query.test_queries(query_type=\"closest\", method=\"healpix\", rs_arcsec=3, npoints=1e4)\n",
+    "mqc_query.test_queries(query_type=\"binary\", method=\"healpix\", rs_arcsec=3, npoints=1e4)\n",
     "\n",
     "# and the other query methods as well (they are much slower, since there are not indexes to support them)\n",
-    "mqc_query.test_queries(query_type = 'closest', method ='raw', rs_arcsec = 3, npoints=10)\n",
-    "mqc_query.test_queries(query_type = 'closest', method ='2dsphere', rs_arcsec = 3, npoints=100)"
+    "mqc_query.test_queries(query_type=\"closest\", method=\"raw\", rs_arcsec=3, npoints=10)\n",
+    "mqc_query.test_queries(\n",
+    "    query_type=\"closest\", method=\"2dsphere\", rs_arcsec=3, npoints=100\n",
+    ")"
    ]
   },
   {
diff --git a/poetry.lock b/poetry.lock
index fbfbdc3..a0ba7fc 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -192,28 +192,28 @@ files = [
 
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+version = "1.17.3"
 description = "Healpix tools package for Python"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -237,47 +237,53 @@ files = [
 
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 name = "mypy"
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+version = "1.13.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = ">=4.1.0"
+typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
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 [package.dependencies]
@@ -466,71 +485,70 @@ test = ["pytest", "pytest-doctestplus (>=0.7)"]
 
 [[package]]
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+    {file = "pymongo-4.10.1.tar.gz", hash = "sha256:a9de02be53b6bb98efe0b9eda84ffa1ec027fcb23a2de62c4f941d9a2f2f3330"},
 ]
 
 [package.dependencies]
@@ -538,22 +556,23 @@ dnspython = ">=1.16.0,<3.0.0"
 
 [package.extras]
 aws = ["pymongo-auth-aws (>=1.1.0,<2.0.0)"]
-encryption = ["certifi", "pymongo-auth-aws (>=1.1.0,<2.0.0)", "pymongocrypt (>=1.6.0,<2.0.0)"]
+docs = ["furo (==2023.9.10)", "readthedocs-sphinx-search (>=0.3,<1.0)", "sphinx (>=5.3,<8)", "sphinx-autobuild (>=2020.9.1)", "sphinx-rtd-theme (>=2,<3)", "sphinxcontrib-shellcheck (>=1,<2)"]
+encryption = ["certifi", "pymongo-auth-aws (>=1.1.0,<2.0.0)", "pymongocrypt (>=1.10.0,<2.0.0)"]
 gssapi = ["pykerberos", "winkerberos (>=0.5.0)"]
 ocsp = ["certifi", "cryptography (>=2.5)", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
 snappy = ["python-snappy"]
-test = ["pytest (>=7)"]
+test = ["pytest (>=8.2)", "pytest-asyncio (>=0.24.0)"]
 zstd = ["zstandard"]
 
 [[package]]
 name = "pytest"
-version = "8.2.2"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.2.2-py3-none-any.whl", hash = "sha256:c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343"},
-    {file = "pytest-8.2.2.tar.gz", hash = "sha256:de4bb8104e201939ccdc688b27a89a7be2079b22e2bd2b07f806b6ba71117977"},
+    {file = "pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2"},
+    {file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"},
 ]
 
 [package.dependencies]
@@ -561,7 +580,7 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.5,<2.0"
+pluggy = ">=1.5,<2"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
@@ -694,13 +713,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.4"
+version = "4.67.0"
 description = "Fast, Extensible Progress Meter"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
-    {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
+    {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
+    {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
 ]
 
 [package.dependencies]
@@ -708,6 +727,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
 dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]