diff --git a/examples/Basic use Gaia Calibrated.ipynb b/examples/Basic use Gaia Calibrated.ipynb
new file mode 100644
index 0000000..6501757
--- /dev/null
+++ b/examples/Basic use Gaia Calibrated.ipynb	
@@ -0,0 +1,612 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6f32dd30-8516-4543-909f-c45df019d0d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mphot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "549e5434-ce34-424a-9fb2-e941d9c555e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "`/Users/peter/Github/mphot/src/mphot/datafiles/system_responses/speculoos_Andor_iKon-L-936_-60_I+z_instrument_system_response.csv` has been generated and saved!\n"
+     ]
+    }
+   ],
+   "source": [
+    "instrument_efficiency_path = \"../resources/systems/speculoos_Andor_iKon-L-936_-60.csv\"  # index in microns, efficiency of telescope+instrument as fraction\n",
+    "filter_path = (\n",
+    "    \"../resources/filters/I+z.csv\"  # index in microns, filter efficiency as fraction\n",
+    ")\n",
+    "\n",
+    "name, system_response = mphot.generate_system_response(\n",
+    "    instrument_efficiency_path, filter_path\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ba5f2fbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'speculoos_Andor_iKon-L-936_-60_I+z'}, xlabel='Wavelength (microns)', ylabel='System Response'>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system_response.plot(\n",
+    "    xlabel=\"Wavelength (microns)\", ylabel=\"System Response\", title=name, ylim=(0, 1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0feee513-9586-40b8-81e3-d0a05723a2d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sky properties\n",
+    "props_sky = {\n",
+    "    \"pwv\": 2.5,  # PWV [mm]\n",
+    "    \"airmass\": 1.1,  # airmass\n",
+    "    \"seeing\": 1.35,  # seeing (==FWHM) [\"]\n",
+    "}\n",
+    "\n",
+    "# instrument properties\n",
+    "props_instrument = {\n",
+    "    \"name\": name,  # name to get SR/precision grid from file\n",
+    "    \"plate_scale\": 0.35,  # pixel plate scale [\"]\n",
+    "    \"N_dc\": 0.2,  # dark current [e/pix/s]\n",
+    "    \"N_rn\": 6.328,  # read noise [e_rms/pix]\n",
+    "    \"well_depth\": 64000,  # well depth [e/pix]\n",
+    "    \"well_fill\": 0.7,  # fractional value to fill central target pixel, assuming gaussian (width function of seeing^)\n",
+    "    \"read_time\": 10.5,  # read time between images [s]\n",
+    "    \"r0\": 0.5,  # radius of telescope's primary mirror [m]\n",
+    "    \"r1\": 0.14,  # radius of telescope's secondary mirror [m]\n",
+    "    # \"min_exp\" : 0,          # optional, minimum exposure time [s]\n",
+    "    # \"max_exp\" : 120,        # optional, maximum exposure time [s]\n",
+    "    # \"ap_rad\" : 3            # optional, aperture radius [FWHM, seeing] for photometry -- 3 default == 7 sigma of Gaussian\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43ff22f5-b58d-44c4-ae15-2bcf5703ff07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:astroquery:Query finished.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>single frame [ppt]</th>\n",
+       "      <th>10 minute binned [ppt]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>speculoos_Andor_iKon-L-936_-60_I+z</th>\n",
+       "      <th>speculoos_Andor_iKon-L-936_-60_I+z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>1.48</td>\n",
+       "      <td>0.482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Star</th>\n",
+       "      <td>1.14</td>\n",
+       "      <td>0.373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scintillation</th>\n",
+       "      <td>0.574</td>\n",
+       "      <td>0.187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sky</th>\n",
+       "      <td>0.717</td>\n",
+       "      <td>0.234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dark current</th>\n",
+       "      <td>0.0875</td>\n",
+       "      <td>0.0285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Read noise</th>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.0553</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              single frame [ppt]  \\\n",
+       "              speculoos_Andor_iKon-L-936_-60_I+z   \n",
+       "All                                         1.48   \n",
+       "Star                                        1.14   \n",
+       "Scintillation                              0.574   \n",
+       "Sky                                        0.717   \n",
+       "Dark current                              0.0875   \n",
+       "Read noise                                 0.169   \n",
+       "\n",
+       "                          10 minute binned [ppt]  \n",
+       "              speculoos_Andor_iKon-L-936_-60_I+z  \n",
+       "All                                        0.482  \n",
+       "Star                                       0.373  \n",
+       "Scintillation                              0.187  \n",
+       "Sky                                        0.234  \n",
+       "Dark current                              0.0285  \n",
+       "Read noise                                0.0553  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>speculoos_Andor_iKon-L-936_-60_I+z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Teff [K]</th>\n",
+       "      <td>2.56e+3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>distance [pc]</th>\n",
+       "      <td>12.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N_star [e/s]</th>\n",
+       "      <td>1.44e+4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>star_flux [e/m2/s]</th>\n",
+       "      <td>2.17e+4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>scn [e_rms]</th>\n",
+       "      <td>440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pixels in aperture [pix]</th>\n",
+       "      <td>421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ap_radius [pix]</th>\n",
+       "      <td>11.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N_sky [e/pix/s]</th>\n",
+       "      <td>13.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sky_radiance [e/m2/arcsec2/s]</th>\n",
+       "      <td>152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>seeing [arcsec]</th>\n",
+       "      <td>1.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pwv [mm]</th>\n",
+       "      <td>2.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>airmass</th>\n",
+       "      <td>1.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plate_scale [\"/pix]</th>\n",
+       "      <td>0.350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N_dc [e/pix/s]</th>\n",
+       "      <td>0.200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N_rn [e_rms/pix]</th>\n",
+       "      <td>6.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A [m2]</th>\n",
+       "      <td>0.724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r0 [m]</th>\n",
+       "      <td>0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r1 [m]</th>\n",
+       "      <td>0.140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t [s]</th>\n",
+       "      <td>53.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>well_depth [e/pix]</th>\n",
+       "      <td>6.40e+4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>peak well_fill</th>\n",
+       "      <td>0.700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>binning [mins]</th>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>read_time [s]</th>\n",
+       "      <td>10.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>binned images</th>\n",
+       "      <td>9.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>altitude [m]</th>\n",
+       "      <td>2.44e+3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gaia-BP weight</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gaia-G weight</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gaia-RP weight</th>\n",
+       "      <td>0.530</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              speculoos_Andor_iKon-L-936_-60_I+z\n",
+       "Teff [K]                                                 2.56e+3\n",
+       "distance [pc]                                               12.5\n",
+       "N_star [e/s]                                             1.44e+4\n",
+       "star_flux [e/m2/s]                                       2.17e+4\n",
+       "scn [e_rms]                                                  440\n",
+       "pixels in aperture [pix]                                     421\n",
+       "ap_radius [pix]                                             11.6\n",
+       "N_sky [e/pix/s]                                             13.4\n",
+       "sky_radiance [e/m2/arcsec2/s]                                152\n",
+       "seeing [arcsec]                                             1.35\n",
+       "pwv [mm]                                                    2.50\n",
+       "airmass                                                     1.10\n",
+       "plate_scale [\"/pix]                                        0.350\n",
+       "N_dc [e/pix/s]                                             0.200\n",
+       "N_rn [e_rms/pix]                                            6.33\n",
+       "A [m2]                                                     0.724\n",
+       "r0 [m]                                                     0.500\n",
+       "r1 [m]                                                     0.140\n",
+       "t [s]                                                       53.4\n",
+       "well_depth [e/pix]                                       6.40e+4\n",
+       "peak well_fill                                             0.700\n",
+       "binning [mins]                                              10.0\n",
+       "read_time [s]                                               10.5\n",
+       "binned images                                               9.39\n",
+       "altitude [m]                                             2.44e+3\n",
+       "Gaia-BP weight                                              0.00\n",
+       "Gaia-G weight                                               0.00\n",
+       "Gaia-RP weight                                             0.530"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>speculoos_Andor_iKon-L-936_-60_I+z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>star [mag]</th>\n",
+       "      <td>13.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sky [mag/arcsec2]</th>\n",
+       "      <td>18.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vega_flux [e/s]</th>\n",
+       "      <td>3.62e+9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  speculoos_Andor_iKon-L-936_-60_I+z\n",
+       "star [mag]                                      13.5\n",
+       "sky [mag/arcsec2]                               18.8\n",
+       "vega_flux [e/s]                              3.62e+9"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the precision and components used to calculate it (generates grid if not already present)\n",
+    "result = mphot.get_precision_gaia(\n",
+    "    props_instrument, props_sky, source_id=2635476908753563008, Teff=2556\n",
+    ")\n",
+    "\n",
+    "mphot.display_results(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c73c5df6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract data\n",
+    "image_precision, binned_precision, components = result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1d4f2607",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'All': np.float64(0.0014784305097078603),\n",
+       " 'Star': np.float64(0.00114248800541316),\n",
+       " 'Scintillation': np.float64(0.0005742753870924696),\n",
+       " 'Sky': np.float64(0.0007171732908190986),\n",
+       " 'Dark current': np.float64(8.746087490519473e-05),\n",
+       " 'Read noise': np.float64(0.0001694071297154272)}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_precision  # precision budget as fraction, x1000 for ppt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f42e28b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'All': np.float64(0.00048234881838114764),\n",
+       " 'Star': np.float64(0.00037274510760371523),\n",
+       " 'Scintillation': np.float64(0.00018736156523458422),\n",
+       " 'Sky': np.float64(0.0002339830564437331),\n",
+       " 'Dark current': np.float64(2.8534753164312186e-05),\n",
+       " 'Read noise': np.float64(5.5270320997180155e-05)}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "binned_precision  # precision budget as fraction, x1000 for ppt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "859da728",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'speculoos_Andor_iKon-L-936_-60_I+z',\n",
+       " 'Teff [K]': 2555,\n",
+       " 'distance [pc]': 12.46691341544997,\n",
+       " 'N_star [e/s]': np.float64(14355.86910074321),\n",
+       " 'star_flux [e/m2/s]': np.float64(21704.56590523942),\n",
+       " 'scn [e_rms]': np.float64(439.96375976405676),\n",
+       " 'pixels in aperture [pix]': 420.6528449020946,\n",
+       " 'ap_radius [pix]': 11.571428571428573,\n",
+       " 'N_sky [e/pix/s]': np.float64(13.447779358663627),\n",
+       " 'sky_radiance [e/m2/arcsec2/s]': np.float64(151.66387177866176),\n",
+       " 'seeing [arcsec]': 1.35,\n",
+       " 'pwv [mm]': 2.5,\n",
+       " 'airmass': 1.1,\n",
+       " 'plate_scale [\"/pix]': 0.35,\n",
+       " 'N_dc [e/pix/s]': 0.2,\n",
+       " 'N_rn [e_rms/pix]': 6.328,\n",
+       " 'A [m2]': 0.7238229473870883,\n",
+       " 'r0 [m]': 0.5,\n",
+       " 'r1 [m]': 0.14,\n",
+       " 't [s]': np.float64(53.3663145608113),\n",
+       " 'well_depth [e/pix]': 64000,\n",
+       " 'peak well_fill': 0.7,\n",
+       " 'binning [mins]': 10,\n",
+       " 'read_time [s]': 10.5,\n",
+       " 'binned images': np.float64(9.394623818925714),\n",
+       " 'altitude [m]': 2440,\n",
+       " 'Gaia-BP weight': np.float64(0.0),\n",
+       " 'Gaia-G weight': np.float64(0.0),\n",
+       " 'Gaia-RP weight': np.float64(0.5298431381539679)}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "components  # dictionary of components from calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e5d67d65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(53.3663145608113)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extract exposure time\n",
+    "exposure_time = components[\"t [s]\"]\n",
+    "exposure_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42db7d66",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mphot_pip",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/Basic use.ipynb b/examples/Basic use.ipynb
index 8a89196..dbdc771 100644
--- a/examples/Basic use.ipynb	
+++ b/examples/Basic use.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "id": "6f32dd30-8516-4543-909f-c45df019d0d7",
    "metadata": {},
    "outputs": [],
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "id": "549e5434-ce34-424a-9fb2-e941d9c555e8",
    "metadata": {},
    "outputs": [
@@ -25,15 +25,19 @@
     }
    ],
    "source": [
-    "instrument_efficiency_path = '../resources/systems/speculoos_Andor_iKon-L-936_-60.csv' # index in microns, efficiency of telescope+instrument as fraction\n",
-    "filter_path = '../resources/filters/I+z.csv' # index in microns, filter efficiency as fraction\n",
+    "instrument_efficiency_path = \"../resources/systems/speculoos_Andor_iKon-L-936_-60.csv\"  # index in microns, efficiency of telescope+instrument as fraction\n",
+    "filter_path = (\n",
+    "    \"../resources/filters/I+z.csv\"  # index in microns, filter efficiency as fraction\n",
+    ")\n",
     "\n",
-    "name, system_response = mphot.generate_system_response(instrument_efficiency_path, filter_path)"
+    "name, system_response = mphot.generate_system_response(\n",
+    "    instrument_efficiency_path, filter_path\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "id": "ba5f2fbd",
    "metadata": {},
    "outputs": [
@@ -43,13 +47,13 @@
        "<Axes: title={'center': 'speculoos_Andor_iKon-L-936_-60_I+z'}, xlabel='Wavelength (microns)', ylabel='System Response'>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -59,50 +63,59 @@
     }
    ],
    "source": [
-    "system_response.plot(xlabel='Wavelength (microns)', ylabel='System Response', title=name, ylim=(0, 1))"
+    "system_response.plot(\n",
+    "    xlabel=\"Wavelength (microns)\", ylabel=\"System Response\", title=name, ylim=(0, 1)\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "id": "0feee513-9586-40b8-81e3-d0a05723a2d0",
    "metadata": {},
    "outputs": [],
    "source": [
     "# sky properties\n",
     "props_sky = {\n",
-    "    \"pwv\" : 2.5,        # PWV [mm]\n",
-    "    \"airmass\" : 1.1,    # airmass\n",
-    "    \"seeing\" : 1.35     # seeing (==FWHM) [\"]\n",
+    "    \"pwv\": 2.5,  # PWV [mm]\n",
+    "    \"airmass\": 1.1,  # airmass\n",
+    "    \"seeing\": 1.35,  # seeing (==FWHM) [\"]\n",
     "}\n",
     "\n",
     "# instrument properties\n",
     "props_instrument = {\n",
-    "    \"name\" : name,          # name to get SR/precision grid from file\n",
-    "    \"plate_scale\" : 0.35,   # pixel plate scale [\"]\n",
-    "    \"N_dc\" : 0.2,           # dark current [e/pix/s]\n",
-    "    \"N_rn\" : 6.328,         # read noise [e_rms/pix]\n",
-    "    \"well_depth\" : 64000,   # well depth [e/pix]\n",
-    "    \"well_fill\" : 0.7,      # fractional value to fill central target pixel, assuming gaussian (width function of seeing^)\n",
-    "    \"read_time\" : 10.5,     # read time between images [s]\n",
-    "    \"r0\" : 0.5,             # radius of telescope's primary mirror [m]\n",
-    "    \"r1\" : 0.14,            # radius of telescope's secondary mirror [m]\n",
+    "    \"name\": name,  # name to get SR/precision grid from file\n",
+    "    \"plate_scale\": 0.35,  # pixel plate scale [\"]\n",
+    "    \"N_dc\": 0.2,  # dark current [e/pix/s]\n",
+    "    \"N_rn\": 6.328,  # read noise [e_rms/pix]\n",
+    "    \"well_depth\": 64000,  # well depth [e/pix]\n",
+    "    \"well_fill\": 0.7,  # fractional value to fill central target pixel, assuming gaussian (width function of seeing^)\n",
+    "    \"read_time\": 10.5,  # read time between images [s]\n",
+    "    \"r0\": 0.5,  # radius of telescope's primary mirror [m]\n",
+    "    \"r1\": 0.14,  # radius of telescope's secondary mirror [m]\n",
     "    # \"min_exp\" : 0,          # optional, minimum exposure time [s]\n",
     "    # \"max_exp\" : 120,        # optional, maximum exposure time [s]\n",
     "    # \"ap_rad\" : 3            # optional, aperture radius [FWHM, seeing] for photometry -- 3 default == 7 sigma of Gaussian\n",
     "}\n",
     "\n",
     "# target properties\n",
-    "Teff = 2566       # [K]\n",
-    "distance = 12.5   # [pc]\n"
+    "Teff = 2566  # [K]\n",
+    "distance = 12.5  # [pc]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "43ff22f5-b58d-44c4-ae15-2bcf5703ff07",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progress: [####################] 100.0%\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -136,33 +149,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
-       "      <td>1.50</td>\n",
-       "      <td>0.537</td>\n",
+       "      <td>1.48</td>\n",
+       "      <td>0.475</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Star</th>\n",
        "      <td>1.14</td>\n",
-       "      <td>0.409</td>\n",
+       "      <td>0.367</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scintillation</th>\n",
-       "      <td>0.516</td>\n",
-       "      <td>0.184</td>\n",
+       "      <td>0.584</td>\n",
+       "      <td>0.188</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sky</th>\n",
-       "      <td>0.802</td>\n",
-       "      <td>0.286</td>\n",
+       "      <td>0.705</td>\n",
+       "      <td>0.227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dark current</th>\n",
-       "      <td>0.0977</td>\n",
-       "      <td>0.0349</td>\n",
+       "      <td>0.0859</td>\n",
+       "      <td>0.0276</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Read noise</th>\n",
-       "      <td>0.170</td>\n",
-       "      <td>0.0608</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.0545</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -171,21 +184,21 @@
       "text/plain": [
        "                              single frame [ppt]  \\\n",
        "              speculoos_Andor_iKon-L-936_-60_I+z   \n",
-       "All                                         1.50   \n",
+       "All                                         1.48   \n",
        "Star                                        1.14   \n",
-       "Scintillation                              0.516   \n",
-       "Sky                                        0.802   \n",
-       "Dark current                              0.0977   \n",
-       "Read noise                                 0.170   \n",
+       "Scintillation                              0.584   \n",
+       "Sky                                        0.705   \n",
+       "Dark current                              0.0859   \n",
+       "Read noise                                 0.169   \n",
        "\n",
        "                          10 minute binned [ppt]  \n",
        "              speculoos_Andor_iKon-L-936_-60_I+z  \n",
-       "All                                        0.537  \n",
-       "Star                                       0.409  \n",
-       "Scintillation                              0.184  \n",
-       "Sky                                        0.286  \n",
-       "Dark current                              0.0349  \n",
-       "Read noise                                0.0608  "
+       "All                                        0.475  \n",
+       "Star                                       0.367  \n",
+       "Scintillation                              0.188  \n",
+       "Sky                                        0.227  \n",
+       "Dark current                              0.0276  \n",
+       "Read noise                                0.0545  "
       ]
      },
      "metadata": {},
@@ -226,15 +239,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N_star [e/s]</th>\n",
-       "      <td>1.16e+4</td>\n",
+       "      <td>1.49e+4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>star_flux [e/m2/s]</th>\n",
-       "      <td>1.77e+4</td>\n",
+       "      <td>2.27e+4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>scn [e_rms]</th>\n",
-       "      <td>394</td>\n",
+       "      <td>448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pixels in aperture [pix]</th>\n",
@@ -246,7 +259,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N_sky [e/pix/s]</th>\n",
-       "      <td>13.5</td>\n",
+       "      <td>13.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sky_radiance [e/m2/arcsec2/s]</th>\n",
@@ -290,7 +303,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t [s]</th>\n",
-       "      <td>66.1</td>\n",
+       "      <td>51.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>well_depth [e/pix]</th>\n",
@@ -310,7 +323,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>binned images</th>\n",
-       "      <td>7.84</td>\n",
+       "      <td>9.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>altitude [m]</th>\n",
+       "      <td>2.44e+3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -320,12 +337,12 @@
        "                              speculoos_Andor_iKon-L-936_-60_I+z\n",
        "Teff [K]                                                 2.57e+3\n",
        "distance [pc]                                               12.5\n",
-       "N_star [e/s]                                             1.16e+4\n",
-       "star_flux [e/m2/s]                                       1.77e+4\n",
-       "scn [e_rms]                                                  394\n",
+       "N_star [e/s]                                             1.49e+4\n",
+       "star_flux [e/m2/s]                                       2.27e+4\n",
+       "scn [e_rms]                                                  448\n",
        "pixels in aperture [pix]                                     421\n",
        "ap_radius [pix]                                             11.6\n",
-       "N_sky [e/pix/s]                                             13.5\n",
+       "N_sky [e/pix/s]                                             13.4\n",
        "sky_radiance [e/m2/arcsec2/s]                                152\n",
        "seeing [arcsec]                                             1.35\n",
        "pwv [mm]                                                    2.50\n",
@@ -336,12 +353,13 @@
        "A [m2]                                                     0.724\n",
        "r0 [m]                                                     0.500\n",
        "r1 [m]                                                     0.140\n",
-       "t [s]                                                       66.1\n",
+       "t [s]                                                       51.6\n",
        "well_depth [e/pix]                                       6.40e+4\n",
        "peak well_fill                                             0.700\n",
        "binning [mins]                                              10.0\n",
        "read_time [s]                                               10.5\n",
-       "binned images                                               7.84"
+       "binned images                                               9.66\n",
+       "altitude [m]                                             2.44e+3"
       ]
      },
      "metadata": {},
@@ -374,7 +392,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>star [mag]</th>\n",
-       "      <td>13.7</td>\n",
+       "      <td>13.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sky [mag/arcsec2]</th>\n",
@@ -382,7 +400,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>vega_flux [e/s]</th>\n",
-       "      <td>3.64e+9</td>\n",
+       "      <td>3.62e+9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -390,9 +408,9 @@
       ],
       "text/plain": [
        "                  speculoos_Andor_iKon-L-936_-60_I+z\n",
-       "star [mag]                                      13.7\n",
+       "star [mag]                                      13.5\n",
        "sky [mag/arcsec2]                               18.8\n",
-       "vega_flux [e/s]                              3.64e+9"
+       "vega_flux [e/s]                              3.62e+9"
       ]
      },
      "metadata": {},
@@ -408,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 16,
    "id": "c73c5df6",
    "metadata": {},
    "outputs": [],
@@ -419,76 +437,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "id": "1d4f2607",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'All': np.float64(0.0018594013224931464),\n",
-       " 'Star': np.float64(0.0015193997816438612),\n",
-       " 'Scintillation': np.float64(0.0006850773409832677),\n",
-       " 'Sky': np.float64(0.0007880245951457461),\n",
-       " 'Dark current': np.float64(9.605090685296679e-05),\n",
-       " 'Read noise': np.float64(0.00022194161222999516)}"
+       "{'All': np.float64(0.00147594836851001),\n",
+       " 'Star': np.float64(0.0011421734515731407),\n",
+       " 'Scintillation': np.float64(0.000584092870418665),\n",
+       " 'Sky': np.float64(0.0007047307620578089),\n",
+       " 'Dark current': np.float64(8.594348090094706e-05),\n",
+       " 'Read noise': np.float64(0.0001693138590163)}"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "image_precision # precision budget as fraction, x1000 for ppt"
+    "image_precision  # precision budget as fraction, x1000 for ppt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 18,
    "id": "f42e28b3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'All': np.float64(0.0005259167624883394),\n",
-       " 'Star': np.float64(0.00042975005149302546),\n",
-       " 'Scintillation': np.float64(0.0001937686355632719),\n",
-       " 'Sky': np.float64(0.00022288644136519523),\n",
-       " 'Dark current': np.float64(2.7167229234003913e-05),\n",
-       " 'Read noise': np.float64(6.277440633899071e-05)}"
+       "{'All': np.float64(0.00047478539015359647),\n",
+       " 'Star': np.float64(0.0003674161504549647),\n",
+       " 'Scintillation': np.float64(0.00018789191226764728),\n",
+       " 'Sky': np.float64(0.00022669889879321268),\n",
+       " 'Dark current': np.float64(2.76464339683557e-05),\n",
+       " 'Read noise': np.float64(5.446514818984999e-05)}"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "binned_precision # precision budget as fraction, x1000 for ppt"
+    "binned_precision  # precision budget as fraction, x1000 for ppt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "id": "859da728",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'name': 'andorSPC_-60_I+z',\n",
+       "{'name': 'speculoos_Andor_iKon-L-936_-60_I+z',\n",
        " 'Teff [K]': 2566,\n",
        " 'distance [pc]': 12.5,\n",
-       " 'N_star [e/s]': np.float64(11551.209732244162),\n",
-       " 'star_flux [e/m2/s]': np.float64(17667.026959303585),\n",
-       " 'scn [e_rms]': np.float64(296.75325008140226),\n",
-       " 'npix': 230.81088883516844,\n",
-       " 'ap_radius [pix]': 8.571428571428571,\n",
-       " 'N_sky [e/pix/s]': np.float64(13.461907485031265),\n",
-       " 'sky_radiance [e/m2/arcsec2/s]': np.float64(151.82320859470744),\n",
-       " 'seeing [arcsec]': 1,\n",
+       " 'N_star [e/s]': np.float64(14859.085084107135),\n",
+       " 'star_flux [e/m2/s]': np.float64(22726.265287930593),\n",
+       " 'scn [e_rms]': np.float64(447.7316365611539),\n",
+       " 'pixels in aperture [pix]': 420.6528449020946,\n",
+       " 'ap_radius [pix]': 11.571428571428573,\n",
+       " 'N_sky [e/pix/s]': np.float64(13.447779358663627),\n",
+       " 'sky_radiance [e/m2/arcsec2/s]': np.float64(151.66387177866176),\n",
+       " 'seeing [arcsec]': 1.35,\n",
        " 'pwv [mm]': 2.5,\n",
        " 'airmass': 1.1,\n",
        " 'plate_scale [\"/pix]': 0.35,\n",
@@ -497,43 +515,44 @@
        " 'A [m2]': 0.7238229473870883,\n",
        " 'r0 [m]': 0.5,\n",
        " 'r1 [m]': 0.14,\n",
-       " 't [s]': np.float64(37.49975336520353),\n",
+       " 't [s]': np.float64(51.587419939677886),\n",
        " 'well_depth [e/pix]': 64000,\n",
-       " 'well_fill': 0.7,\n",
+       " 'peak well_fill': 0.7,\n",
        " 'binning [mins]': 10,\n",
        " 'read_time [s]': 10.5,\n",
-       " 'nImages': np.float64(12.500064228141598)}"
+       " 'binned images': np.float64(9.663793415525085),\n",
+       " 'altitude [m]': 2440}"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "components # dictionary of components from calculation"
+    "components  # dictionary of components from calculation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "id": "e5d67d65",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "np.float64(37.49975336520353)"
+       "np.float64(51.587419939677886)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# extract exposure time\n",
-    "exposure_time = components['t [s]']\n",
+    "exposure_time = components[\"t [s]\"]\n",
     "exposure_time"
    ]
   },
@@ -548,7 +567,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "astronomy",
+   "display_name": "mphot_pip",
    "language": "python",
    "name": "python3"
   },
@@ -562,7 +581,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index 2a836a3..b04d5e7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "mphot"
-version = "1.0.1"
+version = "1.1.1"
 authors = [
   {name = "Peter Pedersen"},
 ]
diff --git a/src/mphot/core.py b/src/mphot/core.py
index b87ccf3..963c8bf 100644
--- a/src/mphot/core.py
+++ b/src/mphot/core.py
@@ -1,18 +1,21 @@
 import math
 import os
 from pathlib import Path
+import logging
 
 import numpy as np
 import pandas as pd
 from IPython.display import clear_output, display
 from scipy.integrate import simpson as simps
+
 from scipy.interpolate import griddata
 from scipy.optimize import minimize
 from astroquery.gaia import Gaia
 
-grid_flux_ingredients_name = "pre_grid_2400m_flux.pkl"
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
 grid_flux_ingredients_name_extended = "pre_grid_03_to_3_microns_2400m_flux.pkl"
-grid_radiance_ingredients_name = "pre_grid_2400m_radiance.pkl"
 grid_radiance_ingredients_name_extended = "pre_grid_03_to_3_microns_2400m_radiance.pkl"
 vega_file = "vega.csv"
 vega_file_extended = "vega_03_to_3_microns.csv"
@@ -87,7 +90,7 @@ def generate_system_response(
 
     dfSR.to_csv(SRFile, header=False)
 
-    print(f"`{SRFile}` has been generated and saved!")
+    logger.info(f"`{SRFile}` has been generated and saved!")
 
     return name, dfSR
 
@@ -124,10 +127,6 @@ def generate_flux_grid(
         gridIngredients = pd.read_pickle(
             Path(__file__).parent / "datafiles" / grid_flux_ingredients_name_extended
         )
-    else:
-        gridIngredients = pd.read_pickle(
-            Path(__file__).parent / "datafiles" / grid_flux_ingredients_name
-        )
 
     rsr = pd.read_csv(sResponse, header=None, index_col=0)
 
@@ -621,10 +620,7 @@ def integration_time(
     return t
 
 
-def convert_airmass(
-    airmass: float,
-    h: float
-) -> float:
+def convert_airmass(airmass: float, h: float) -> float:
     """
     Convert airmass at the observatory to an equivalent airmass at Paranal Observatory, assuming an isothermal atmospheric model.
 
@@ -642,9 +638,13 @@ def convert_airmass(
     return airmass * np.exp((2440 - h) / 8000)
 
 
-
 def scintillation_noise(
-    r: float, t: float, N_star: float, h: float = 2440, C: float = 1.56, airmass: float = 1.5
+    r: float,
+    t: float,
+    N_star: float,
+    h: float = 2440,
+    C: float = 1.56,
+    airmass: float = 1.5,
 ) -> float:
     """
     Calculate the scintillation noise for a given set of parameters.
@@ -742,10 +742,10 @@ def get_precision(
 
         scn (float, optional):
             Scintillation noise, calculated if None. Default is None.
-        
+
         h (float, optional):
             Altitude of the observatory in meters. Default is 2440 for Paranal Observatory.
-        
+
         C (float, optional):
             Empirical coefficient used in the calculation of scn. Default is 1.56, optimized for the 20-cm NGTS telescopes at Paranal Observatory.
 
@@ -937,7 +937,9 @@ def get_precision(
     npix = np.pi * ap**2
 
     if scn is None:
-        scn = scintillation_noise(r0, t, N_star, h=h, C=C, airmass=airmass) # use unconverted airmass here
+        scn = scintillation_noise(
+            r0, t, N_star, h=h, C=C, airmass=airmass
+        )  # use unconverted airmass here
 
     precision = np.sqrt(
         N_star * t + scn**2 + npix * (N_sky * t + N_dc * t + N_rn**2)
@@ -982,7 +984,7 @@ def get_precision(
         "sky_radiance [e/m2/arcsec2/s]": radiance,
         "seeing [arcsec]": fwhm,
         "pwv [mm]": pwv,
-        "airmass": airmass, # unconverted airmass
+        "airmass": airmass,  # unconverted airmass
         'plate_scale ["/pix]': plate_scale,
         "N_dc [e/pix/s]": N_dc,
         "N_rn [e_rms/pix]": N_rn,  # not sure of units
@@ -1002,9 +1004,7 @@ def get_precision(
 
 
 def best_gaia_filters(
-    system_name: str,
-    min_weight_sum: float = 0,
-    support_points: int = 8000
+    system_name: str, min_weight_sum: float = 0, support_points: int = 8000
 ) -> np.ndarray:
     """
     Determine the weights of the linear combination of Gaia filters that best resembles the instrument system response.
@@ -1012,10 +1012,10 @@ def best_gaia_filters(
     Args:
         system_name (str):
             Name of the instrument.
-        
+
         min_weight_sum (float, optional):
             The minimum sum of weights in the linear combination of Gaia filters. Default is 0.
-        
+
         support_points (int, optional):
             The number of support points in the wavelength spectrum between 0.3 and 3.0 microns used to interpolate the instrument system response and Gaia filter transmission curves. Default is 8000.
 
@@ -1030,42 +1030,65 @@ def best_gaia_filters(
     """
 
     # Load transmission curves
-    path = Path(__file__).parent / "datafiles" / "system_responses" / f"{system_name}_instrument_system_response.csv"
-    trans = np.loadtxt(str(path), delimiter=',')
-    
-    gaia_filters = ['bp', 'g', 'rp'] 
+    path = (
+        Path(__file__).parent
+        / "datafiles"
+        / "system_responses"
+        / f"{system_name}_instrument_system_response.csv"
+    )
+    trans = np.loadtxt(str(path), delimiter=",")
+
+    gaia_filters = ["bp", "g", "rp"]
     N = len(gaia_filters)
-    gaia_paths = [Path(__file__).parent / "datafiles" / "flux_calibration" / f"gaia_{gaia_filter}.csv" for gaia_filter in gaia_filters]
+    gaia_paths = [
+        Path(__file__).parent
+        / "datafiles"
+        / "flux_calibration"
+        / f"gaia_{gaia_filter}.csv"
+        for gaia_filter in gaia_filters
+    ]
     gaia_trans = []
 
     for gaia_path in gaia_paths:
-        g_trans = np.loadtxt(str(gaia_path), delimiter=',')
+        g_trans = np.loadtxt(str(gaia_path), delimiter=",")
         gaia_trans.append(g_trans)
 
     # Get best linear combination of Gaia filters
     lam = np.linspace(0.3, 3, support_points)
     trans_interp = np.interp(lam, trans[:, 0], trans[:, 1], left=0, right=0)
-    gaia_trans_interp = np.column_stack([np.interp(lam, g_trans[:, 0], g_trans[:, 1], left=0, right=0) for g_trans in gaia_trans])
+    gaia_trans_interp = np.column_stack(
+        [
+            np.interp(lam, g_trans[:, 0], g_trans[:, 1], left=0, right=0)
+            for g_trans in gaia_trans
+        ]
+    )
 
     def squared_err(weights):
         err = gaia_trans_interp @ weights - trans_interp
 
         return err @ err
-    
+
     def jacobian(weights):
         err = gaia_trans_interp @ weights - trans_interp
 
         return 2 * gaia_trans_interp.T @ err
-    
+
     bounds = [(0, None)] * N
-    cons = ({'type': 'ineq', 'fun': lambda weights: np.sum(weights) - min_weight_sum},)
+    cons = ({"type": "ineq", "fun": lambda weights: np.sum(weights) - min_weight_sum},)
     x0 = np.full(N, max(1.0, min_weight_sum / N))
 
-    res = minimize(squared_err, x0, jac=jacobian, bounds=bounds, constraints=cons, options={'maxiter': 2000})
+    res = minimize(
+        squared_err,
+        x0,
+        jac=jacobian,
+        bounds=bounds,
+        constraints=cons,
+        options={"maxiter": 2000},
+    )
 
     if not res.success:
         raise RuntimeError(f"Optimization failed: {res.message}")
-    
+
     return res.x
 
 
@@ -1083,7 +1106,9 @@ def get_precision_gaia(
     scn: float | None = None,
     h: float = 2440,
     C: float = 1.56,
-    exp_time: float | None = None
+    exp_time: float | None = None,
+    Teff: float | None = None,
+    distance: float | None = None,
 ) -> dict:
     """
     Calculate the precision of astronomical observations based on various parameters and perform calibration of fluxes to Gaia fluxes.
@@ -1141,16 +1166,22 @@ def get_precision_gaia(
 
         scn (float, optional):
             Scintillation noise, calculated if None. Default is None.
-        
+
         h (float, optional):
             Altitude of the observatory in meters. Default is 2440 for Paranal Observatory.
-        
+
         C (float, optional):
             Empirical coefficient used in the calculation of scn. Default is 1.56, optimized for the 20-cm NGTS telescopes at Paranal Observatory.
 
         exp_time (float, optional):
             Exposure time in seconds, calculated if None. Default is None.
 
+        Teff (float, optional):
+            Effective temperature of the star in Kelvin. If None, it will be fetched from the Gaia catalog. Default is None.
+
+        distance (float, optional):
+            Distance to the star in parsecs. If None, it will be calculated from the parallax fetched from the Gaia catalog. Default is None.
+
     Returns:
         tuple: A tuple containing:
             image_precision : dict
@@ -1163,7 +1194,7 @@ def get_precision_gaia(
     name = props["name"]
     r0 = props["r0"]
     r1 = props["r1"]
-    
+
     adql = f"""
     SELECT *
         FROM gaiadr3.gaia_source AS gaia
@@ -1172,10 +1203,21 @@ def get_precision_gaia(
     job = Gaia.launch_job_async(adql)
     params = job.get_results().to_pandas()
 
-    Teff = float(params['teff_gspphot'].iloc[0])
-    distance = float(params['distance_gspphot'].iloc[0])
-
-    gaia_filters = np.array(['bp', 'g', 'rp'])
+    if Teff is None:
+        Teff = float(params["teff_gspphot"].iloc[0])
+    if distance is None:
+        distance = 1 / (float(params["parallax"].iloc[0]) * 1e-3)  # pc
+
+    if np.isnan(Teff):
+        logger.warning(f"Teff value for source_id {source_id} is NaN.")
+        Teff = 3000  # K
+        logger.warning(f"Setting Teff to {Teff} K.")
+    if np.isnan(distance):
+        logger.warning(f"Distance value for source_id {source_id} is NaN.")
+        distance = 10  # pc
+        logger.warning(f"Setting distance to {distance} pc.")
+
+    gaia_filters = np.array(["bp", "g", "rp"])
     gaia_fluxes = np.array([])
     mphot_fluxes = np.array([])
 
@@ -1188,41 +1230,41 @@ def get_precision_gaia(
         weights = 1
         gaia_filters = np.array([gaia_filter])
     else:
-        weights = best_gaia_filters(name, min_weight_sum=min_weight_sum, support_points=support_points)
+        weights = best_gaia_filters(
+            name, min_weight_sum=min_weight_sum, support_points=support_points
+        )
         weights_vec = weights
 
-
     for g_filter in gaia_filters:
-        gaia_str = f'phot_{g_filter}_mean_flux'
+        gaia_str = f"phot_{g_filter}_mean_flux"
         gaia_flux = float(params[gaia_str].iloc[0] / 0.7278)
         gaia_fluxes = np.append(gaia_fluxes, gaia_flux)
 
-
         # Get simulated Gaia flux
         props_instrument_gaia = props.copy()
-        props_instrument_gaia['name'] = f'gaia_{g_filter}_inverse_atmosphere_paranal'
+        props_instrument_gaia["name"] = f"gaia_{g_filter}_inverse_atmosphere_paranal"
 
         ## Ideal conditions
-        props_sky_gaia = { 
+        props_sky_gaia = {
             "pwv": 0.05,
-            "airmass": 1,  
-            "seeing": props['plate_scale'],  
+            "airmass": 1,
+            "seeing": props["plate_scale"],
         }
 
         _, _, components_gaia = get_precision(
-            props_instrument_gaia, 
-            props_sky_gaia, 
-            Teff, 
-            distance, 
-            binning=binning,  
+            props_instrument_gaia,
+            props_sky_gaia,
+            Teff,
+            distance,
+            binning=binning,
             override_grid=override_grid,
-            SPCcorrection=SPCcorrection, 
+            SPCcorrection=SPCcorrection,
             N_sky=N_sky,
             scn=scn,
             h=h,
             C=C,
             exp_time=exp_time,
-            extended=True
+            extended=True,
         )
 
         mphot_flux = components_gaia["N_star [e/s]"] / (np.pi * (r0**2 - r1**2))
@@ -1230,46 +1272,45 @@ def get_precision_gaia(
 
     factor = np.sum(weights * gaia_fluxes) / np.sum(weights * mphot_fluxes)
 
-
     # Calibrate simulated flux
     _, _, components = get_precision(
-        props, 
-        props_sky, 
-        Teff, 
-        distance, 
-        binning=binning,  
+        props,
+        props_sky,
+        Teff,
+        distance,
+        binning=binning,
         override_grid=override_grid,
-        SPCcorrection=SPCcorrection, 
+        SPCcorrection=SPCcorrection,
         N_sky=N_sky,
         scn=scn,
         h=h,
         C=C,
         exp_time=exp_time,
-        extended=True
+        extended=True,
     )
 
     N_star_cal = components["N_star [e/s]"] * factor
 
     image_precision, binned_precision, components_final = get_precision(
-        props, 
-        props_sky, 
-        Teff, 
-        distance, 
-        binning=binning,  
+        props,
+        props_sky,
+        Teff,
+        distance,
+        binning=binning,
         override_grid=False,
-        SPCcorrection=SPCcorrection, 
+        SPCcorrection=SPCcorrection,
         N_star=N_star_cal,
         N_sky=N_sky,
         scn=scn,
         h=h,
         C=C,
         exp_time=exp_time,
-        extended=True
+        extended=True,
     )
 
-    components_final['Gaia-BP weight'] = weights_vec[0]
-    components_final['Gaia-G weight'] = weights_vec[1]
-    components_final['Gaia-RP weight'] = weights_vec[2]
+    components_final["Gaia-BP weight"] = weights_vec[0]
+    components_final["Gaia-G weight"] = weights_vec[1]
+    components_final["Gaia-RP weight"] = weights_vec[2]
 
     return image_precision, binned_precision, components_final
 
@@ -1319,16 +1360,6 @@ def vega_mag(
             header=None,
             index_col=0,
         )
-    else:
-        gridIngredients = pd.read_pickle(
-            Path(__file__).parent / "datafiles" / grid_flux_ingredients_name
-        )
-
-        vega = pd.read_csv(
-            Path(__file__).parent / "datafiles" / vega_file,
-            header=None,
-            index_col=0,
-        )
 
     rsr = pd.read_csv(SRFile, header=None, index_col=0)
     rsr = rsr[1].rename("rsr")
@@ -1561,7 +1592,7 @@ def display_results(r1: tuple, r2: tuple = None, extended: bool = True) -> None:
         components1["N_star [e/s]"],
         components1["sky_radiance [e/m2/arcsec2/s]"],
         components1["A [m2]"],
-        extended=extended
+        extended=extended,
     )
 
     if r2 is not None:
@@ -1576,7 +1607,9 @@ def display_results(r1: tuple, r2: tuple = None, extended: bool = True) -> None:
 
         props_sky2 = {
             "pwv": components2["pwv [mm]"],
-            "airmass": convert_airmass(components2["airmass"], components2["altitude [m]"]),
+            "airmass": convert_airmass(
+                components2["airmass"], components2["altitude [m]"]
+            ),
             "seeing": components2["seeing [arcsec]"],
         }
 
@@ -1593,7 +1626,7 @@ def display_results(r1: tuple, r2: tuple = None, extended: bool = True) -> None:
             components2["N_star [e/s]"],
             components2["sky_radiance [e/m2/arcsec2/s]"],
             components2["A [m2]"],
-            extended=extended
+            extended=extended,
         )
 
         columns = [
diff --git a/src/mphot/datafiles/pre_grid_2400m_flux.pkl b/src/mphot/datafiles/pre_grid_2400m_flux.pkl
deleted file mode 100644
index 99df9c6..0000000
Binary files a/src/mphot/datafiles/pre_grid_2400m_flux.pkl and /dev/null differ
diff --git a/src/mphot/datafiles/pre_grid_2400m_radiance.pkl b/src/mphot/datafiles/pre_grid_2400m_radiance.pkl
deleted file mode 100644
index 36a2b28..0000000
Binary files a/src/mphot/datafiles/pre_grid_2400m_radiance.pkl and /dev/null differ
diff --git a/src/mphot/datafiles/system_responses/speculoos_Andor_iKon-L-936_-60_I+z_instrument_system_response.csv b/src/mphot/datafiles/system_responses/speculoos_Andor_iKon-L-936_-60_I+z_instrument_system_response.csv
new file mode 100644
index 0000000..714d557
--- /dev/null
+++ b/src/mphot/datafiles/system_responses/speculoos_Andor_iKon-L-936_-60_I+z_instrument_system_response.csv
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diff --git a/src/mphot/grids/.gitignore b/src/mphot/grids/.gitignore
index 3387cba..6e61f2c 100644
--- a/src/mphot/grids/.gitignore
+++ b/src/mphot/grids/.gitignore
@@ -1 +1,13 @@
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\ No newline at end of file
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\ No newline at end of file
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