diff --git a/docs/ipynb/casestudies/transit.ipynb b/docs/ipynb/casestudies/transit.ipynb
index ea0ea0e5..99033f21 100644
--- a/docs/ipynb/casestudies/transit.ipynb
+++ b/docs/ipynb/casestudies/transit.ipynb
@@ -31,7 +31,7 @@
                 "darks = glob(\"/Users/lgrcia/data/WASP12-astrodennis/Darks/*.fit\")\n",
                 "bias = glob(\"/Users/lgrcia/data/WASP12-astrodennis/Bias/*.fit\")\n",
                 "flats = glob(\"/Users/lgrcia/data/WASP12-astrodennis/Flats/*.fit\")\n",
-                "sciences = sorted(glob(\"/Users/lgrcia/data/WASP12-astrodennis/ScienceImages/*.fit\"))\n"
+                "sciences = sorted(glob(\"/Users/lgrcia/data/WASP12-astrodennis/ScienceImages/*.fit\"))"
             ]
         },
         {
@@ -46,7 +46,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 3,
+            "execution_count": 2,
             "metadata": {
                 "tags": []
             },
@@ -54,7 +54,7 @@
                 {
                     "data": {
                         "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "949f03d414a24bbb887922ab99721442",
+                            "model_id": "5f32d1ce1fcc493797516089cdb4710e",
                             "version_major": 2,
                             "version_minor": 0
                         },
@@ -93,12 +93,17 @@
                 "        blocks.Cutouts(21),  # stars cutouts\n",
                 "        blocks.MedianEPSF(),  # building EPSF\n",
                 "        blocks.Gaussian2D(ref),  # modeling EPSF with initial guess\n",
-                "        blocks.ComputeTransformTwirl(ref), # compute alignment\n",
+                "        blocks.ComputeTransformTwirl(ref),  # compute alignment\n",
                 "        blocks.AlignReferenceSources(ref),  # alignment\n",
                 "        blocks.CentroidQuadratic(),  # centroiding\n",
                 "        blocks.AperturePhotometry(),  # aperture photometry\n",
                 "        blocks.AnnulusBackground(),  # annulus background\n",
-                "        blocks.GetFluxes(\"fwhm\", \"keyword:AIRMASS\"),\n",
+                "        blocks.GetFluxes(\n",
+                "            \"fwhm\",\n",
+                "            airmass=lambda im: im.header[\"AIRMASS\"],\n",
+                "            dx=lambda im: im.transform.translation[0],\n",
+                "            dy=lambda im: im.transform.translation[1],\n",
+                "        ),\n",
                 "    ]\n",
                 ")\n",
                 "\n",
@@ -115,7 +120,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 4,
+            "execution_count": 3,
             "metadata": {},
             "outputs": [
                 {
@@ -124,37 +129,37 @@
                             "╒═════════╤════════╤═══════════════════════╤════════════════╕\n",
                             "│   index │ name   │ type                  │ processing     │\n",
                             "╞═════════╪════════╪═══════════════════════╪════════════════╡\n",
-                            "│       0 │        │ Calibration           │ 2.315 s (4%)   │\n",
+                            "│       0 │        │ Calibration           │ 1.951 s (4%)   │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       1 │        │ PointSourceDetection  │ 25.323 s (41%) │\n",
+                            "│       1 │        │ PointSourceDetection  │ 19.651 s (40%) │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       2 │        │ Cutouts               │ 2.862 s (5%)   │\n",
+                            "│       2 │        │ Cutouts               │ 2.216 s (5%)   │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       3 │        │ MedianEPSF            │ 0.226 s (0%)   │\n",
+                            "│       3 │        │ MedianEPSF            │ 0.178 s (0%)   │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       4 │        │ Gaussian2D            │ 4.024 s (6%)   │\n",
+                            "│       4 │        │ Gaussian2D            │ 3.153 s (6%)   │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       5 │        │ ComputeTransformTwirl │ 0.645 s (1%)   │\n",
+                            "│       5 │        │ ComputeTransformTwirl │ 0.550 s (1%)   │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       6 │        │ AlignReferenceSources │ 0.179 s (0%)   │\n",
+                            "│       6 │        │ AlignReferenceSources │ 0.140 s (0%)   │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       7 │        │ CentroidQuadratic     │ 1.844 s (3%)   │\n",
+                            "│       7 │        │ CentroidQuadratic     │ 1.425 s (3%)   │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       8 │        │ AperturePhotometry    │ 17.158 s (28%) │\n",
+                            "│       8 │        │ AperturePhotometry    │ 13.455 s (28%) │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│       9 │        │ AnnulusBackground     │ 7.703 s (12%)  │\n",
+                            "│       9 │        │ AnnulusBackground     │ 5.958 s (12%)  │\n",
                             "├─────────┼────────┼───────────────────────┼────────────────┤\n",
-                            "│      10 │        │ GetFluxes             │ 0.032 s (0%)   │\n",
+                            "│      10 │        │ GetFluxes             │ 0.028 s (0%)   │\n",
                             "╘═════════╧════════╧═══════════════════════╧════════════════╛"
                         ]
                     },
-                    "execution_count": 4,
+                    "execution_count": 3,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
             ],
             "source": [
-                "photometry"
+                "photometry\n"
             ]
         },
         {
@@ -171,11 +176,13 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 5,
+            "execution_count": 4,
             "metadata": {},
             "outputs": [],
             "source": [
-                "fluxes = photometry[-1].fluxes\n"
+                "from prose import Fluxes\n",
+                "\n",
+                "fluxes: Fluxes = photometry[-1].fluxes\n"
             ]
         },
         {
@@ -188,7 +195,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 6,
+            "execution_count": 5,
             "metadata": {},
             "outputs": [
                 {
@@ -203,7 +210,7 @@
                 }
             ],
             "source": [
-                "ref.show()\n"
+                "ref.show()"
             ]
         },
         {
@@ -216,12 +223,12 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 7,
+            "execution_count": 6,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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gq868tvv19Jzf2Q7PvggroTDpLGuAAkTugxUWFmbtE6RkG8cPk5KaECUfarkPqq++UJ35BRIKVa1EFLr81RUhPT1dcjt3HZfb29tVP86Wrya3Dfb54hiAAkTqAwcAR44csf4/MzPTaZvExESnbSxfRKXVp3IfdiUfVKVfqOPHjyMxMRH19fV2z6F2v0qer7W11ePHEhG54s+uCNXV1UhPT0d1dbX1NjUdl225epxUf0lPwoq7fpfB3i+TASiAjEajy/tPnjyJ3Nxc5Obmoq2tDWFhYXbhx7KN5YuopiZE7sOuZC4hqfAUFRXldGz19fXIycmB2Wx2+wXw9BeIyWSyO1lYpKenB+UXjohCi69qmOX2Gx0dbT3nq+24DABZWVmIiYnxqK+lXH8h2yBjOfbW1lanoGZbKxYK/TIZgAJISXNSW1ubdfLCM2fOyG6j1+tVN5l5OoxeKjw1NzdLbms2mxEbG+vxft1xdRIhIuosT7oidGa/lv152vQfERHh1dFe7ga9SO0nFIbAA+wEHVBKmmgs25hMJtTW1kpu097eDpPJJNnpzNJkJrdqvKccZ5V214SltAOh1GzVrh4bCiMNiCh0+eq82tlOwp6c+9R2llY66MWirq7Oo+cJFNYABZBcPyAL2y+Dqw+OZaV3x3kf3DWZAd5ro21qapI9fl8Ob3fVdBbs7c9EFBrUnlc92a/a85Tc9aOpqUn2/OqtwSlyamtrkZycHDI/TBmAAszxCwBA8sug5IPjOO+DXLOUpcnMXbhQGiDkfiVERUWhvr7e46pQpdWoUieRUGh/JqLQofS8qoTjudXd+VXuPCzVj9QyD5tU30u1fS09CSyWbhuOz5Oenm4NVMHyg5QBKAg4fgGkPhzuaossLly4gISEBJd9ggwGg2y4sHxx3HVwsyX3K8HxV5LjY7w5vN32NQyV9mciCj2drd2oqalxOrfK1QK5+yGn5NzruH+1tU4pKSmy3S+kWF4H2+dRcz3xJwagEOKuGhYAzpw5gzNnzlg/YHLrj124cEHyOeRCi6sA4cmvhLAw++5nUr9yPD3RcF4gIvIVT0asWs5vTU1NqKmpsbvP8cea5dyt5Iec0pYBqf27Oxc6hq+UlBQkJyejvr5ecuStZd+O3SvCwsKctg+WH6QMQCHE3UJ3tiwfMHfrj6khFyCU1k7ZOnLkCFJSUpCQkIALFy7Y/cKw/YK6OtHIVQ2HSvszEQUvV01PampRlI6isrCcu+V+yNXV1SEjIwOAZ+deT5fWqK2tRUJCgmT46d27N7p164aYmBgAysocDD9IGYBChKuF7i5cuCA5RN7yAbP8U9uj35GrYZ+2JwSpXzlSamtrJatWbb+gjrVeluY5udBk4VhtG2xTsBNR8FLSh1DJUHVPz7m2fUIdWToaS01gqPTc6y58yIUvuSWOTp8+bf2/40SOcoLhB6nqYfDr16+XvP3y5csoLi7u7PGQBFdrXen1eiQkJEg+zvEDJvehzsjIQE5OjstjUDLs09LvSOoLqLa9t62tzTr83fK35fmPHDniFJwsr8f58+dx8OBB6/0pKSnIzc116tztizV9iCj0eWNtQcs5Ri4wuGP5kZmSkiJ5v+350XIdkDv3yu3fk/stNTyuKAk/cuXyN9U1QDNnzsTWrVvx6quvWi+8lZWV+NnPfobvvvsOJSUlXj9IrXPXp0Vpm7Tch9rSaVqq1350dDQ6Ojpw/Phxu8fIVaPKHWtsbKyquTKU/pKx1dLSIlltm5ycbP2bo8OIyBV3c9jY1kZLjdxV0+SVnp6Obt26yZ67k5OTJWvJW1tbcfToUbvHyJ3fPakNd3U9UNvkZpGTk2Otubf8C/T5V3UAOnDgAB588EHk5uZi3bp1OHr0KObOnYvx48fjj3/8oy+OUfOU9GlR0iYtF5QAoLm5GUaj0WkKdldfZqlqVFe/HJR+ceLj41WHH1csbeYcHUZE7sidwxxDhxQlzT+pqakwGo12IUpu6Qu5ICLVqViuFj85ORnJycmKR32ZTCa0tLQgLCwMqamp1nNxdXU1unXrpmgwjqPMzExERERI1twH8vyrOgBlZ2fjs88+w+zZs3HHHXcgPDwcGzZswAMPPOCL4yMoH3WgpE3aXadod4ur2mptbYXBYHCaz0LuWC3PXVdXJ/mrxtIpWskXylFmZqZs9aylFshVu3ZiYqLq5ySirkfqHJaamqqoaUdum6SkJGsNeE1NDUwmk9NC0XI1IZbzpqU57fLly5Lbmc1ml9cJd2t8Kam9cpxrzvIcco/JyMiwtjB4Y/4kb/OoE/TWrVuxceNG5Ofn4+jRo3j99ddxyy23IC0tzdvHpxnuJh3szIyhjlx1ilayuKpFdXW15NwOro5Vr9cjIyPDGkhsfwW5+pLISUpKQq9evazPITdnhatOhVVVVTCbzR5NAklEoUvue+5qDhtPODb/24YfwH1NSFNTk9vac4PBgNjYWI9HpyntvGw71xxw5bWSWgwbgN12wTg6V3Un6Mcffxw//elPMW/ePOzatQtfffUV9Ho9cnNzsWnTJl8cY5dn6bjrbl0Zvd55nazOcFUj0tzc7DRXjzu2HQXdHavl/ujoaEVriqWnpyM3NxcDBgxAamqq9fbz58/bLcNh29/Hlu3UAXLH3traiubmZtTU1Ch6P4godLk771o6FqsNP+np6R4dj+P52NLJubW11W34cazpcXedkPrxq7ScZ86ccXq9LH2D5I7JclydWfvMF1TXAH322WfYs2cPBg8eDOBK1eCHH36IlStX4uGHH8Z9993n9YPsygLZL8VVjYinOlOdafk15vhLJD093S70SE0kZlst66oaOCkpCWFhYZJl9GQSSCIKPXLn3bCwMMTExFi/62rXwrLUgicmJqKlpUXVudT2fKx27iCpJTFc8caipI7nRSWtFEajEVlZWQBg9zoHiuoAtG/fPskL5/Tp01FQUOCVg9ISb89arKbpxpNJtNzxtDpTqjo2OjraqRxKXi93X0QlQzld7Z+IQpvc8HTHhaXlzmfZ2dkIDw+33m/Zn+XcotfrERERIfv8iYmJTn2AXC3l447adcja29sl75NqBktPT4dOp3M515yFY3BUs6xHIKgOQK4ucFdffXWnDkaLvNku6skHzDYstLe3u/3FkpWVhYiICGsHajXTwcuRq47Nzc1VPMrM8XZXHcI9CX7BMGkXEXWektoV29oNqRrl+Ph42f25C085OTmIjo62Lg6q9EeeK0rPT67K7lh7BfynlsZkMkkGIMfnlXstgnUEruoAlJWVBZ1OJ3v/iRMnOnVAWuPJujJSOvMBs+0U7Y5ttWVnO2Zbaqvkfo3ILXrqjddLzVDOQLdTE5F3qKldsQQRg8GAnJwcydXV3Z13pc5V0dHRAOR/pMn1vbQcg+McaUrPT3Jlz8rKsjuv6/V6p1GxSs67rl6LYF2fUXUAmj17tt3f7e3tOHDgALZt24Ynn3zSW8elKd4Y4eWND5i7mhFPh95LUfIrzPbXhW3TnuPrBUB2iQ5XXA3llGuCI6LQpaZ2RWqywdjYWEX7s5x31Z7b5c6LtsEpNjYWPXv2VH29kDvWiIgIRftwVxZXr0UwjgADPAhAs2bNkrx95cqV2Lt3b6cPSKtsL8aeDMH21gdMKlx4ezi4kl9htmFLrlpVat4KT9uVvTnNABEFJ7nzYXx8PBoaGqx/S837I1Wj7mrSREtYUvojUe68aGkys+XJD09vXCNcPa+r/Xur5t7bVA+DlzN69Gi899573tqdZikdEu/Im0MMbYdRenvoPSD/SyErKwsDBgywW7vL1bo83lizx5YvykpEwUOv10sOU29oaHA50tTC8dwlt7/q6mrV5yG586LZbFa1HzmurhHeWB/R3TUoKSnJOpWJ4/qMgeK11eD/7//+j7PpdlJnO4qFSi2Gq+Uy1FSrygl0uzJ5jhNQkje4+hw51qZYKFl+R+rcJbc/techfzQTSV0jvDk6y901yNMuE76iOgANGTLErhO0EAI1NTU4d+4c1wJTSO7L6a1+PO4mwAr0BUZNdagnJwXH+5SUORheF60LxmGyFHrcfY48DRRqz1FhYWGq+ib6q5nIsbuFt0dnBVvIcUV1ABo/frzd32FhYejZsyduvfVW2cXY6D9cfTk9/QWg9OIdTBcYpbVV7k4K7k4YSsocTK+LVgXrMFkKLUo+R1LnFLllIORGf9mS2l9iYqLdyFKl5xR/1+IHcnRWMPzoVB2AFi5c6Ivj0AR3X05PfgEovXgH4wVG6S8FVycFV/cpKbPSbQL9Re3qgnWYLIUWpZ8jqfNGt27dZIesu+NuWg0151p/1qAEanRWsPzoVBSAbNdackftlNxa4o1ZjG25u3jbXrhD/QLj6qQgd5+SMrvbJli+qF1dsA6TpdCi5nPkeN7obO2LZX/BuOq5nECMzgqmH+OKAlB8fLzLyQ+BK32BdDodOjo6vHJgXZE3ZjG25eri7ThLs9wCfV35AqPk9Xa1TTB9Ubu6YB0mS6EnJSUFtbW11r/VfI4s517LqChPglCohXktNbs5UhSAPv74Y18fhyZIneSBKzVsntQquOp8J7W0hGM7d1e/wCi5qFqGsUq9LqHyS66rNNGFyihGCg6On3vH2tqUlBQkJyer/hx1ttY3FMO8FprdpCgKQMuXL8f69ethNBrxxhtvYMKECUGbZoOdVBOhp7UKcl80uXkjoqOjkZubq6kLjLuL6vnz551WnnfXKd12dAfg/Yki1ehqTXSuTsRdJehR50ktnuzYibm2thbJycl2t7n7DHmj1tdkMrlcPiOQguE7FEwBUVEA+uCDD9Da2gqj0YgpU6bgjjvucPpgkTLerv6TusDLTWZluT9Yvoz+IldmuUVYExMTZTulO47usOXv8KGlJrquFvTIc3LfWym251Uln6HOnp+lnsNx+YxAcbVQqb9DUbDU9ioKQDk5OSguLsaoUaMghMCmTZtkOztPmjTJqwfY1fii+s/xAh9MCTuYqe2U7m7RVH+Hj2BqS/clLQU9ck/Nel6W86rSz1Bnzs/B/DmVO7bLly87Nf/764dFMPwYV7QUxurVq3HkyBE88cQT0Ol0WLBgAWbNmuX0z3GhVHc+/fRTjB07FmlpadDpdNiyZYvbx5SVlWHo0KEwGAzo378/1q9f77TNypUr0bdvX0RGRiIvLw9ffPGFquPyJUs4seWLcBKM044HGzWd0mNjYxVNSa/m5NxZwdSW7kuezAbuC95YLoA6T+7z7TjQw/a8qvQz1Jnzc7B8TtUcg9R6Z1r6fCuqARo5ciQ+//xzAFf6Pxw9etQrTWCtra0YPHgwHn74Ydxzzz1ut6+qqsKdd96JadOm4e2330ZpaSkeffRR9OrVC4WFhQCAd999F0VFRVi9ejXy8vLw8ssvo7CwEJWVlUHTbOev6r9gSNjBTG1NmZJg4c/w4euavmDoLwAER9BjE1zwkJvIMDo6WrbfjZrPkKfn52D4nDqyfIfDwpQv+9nVapBd0QkhhJoHnDx5En369HE7LF71geh02Lx5s9NM07bmzZuHrVu34tChQ9bb7r//fjQ0NGDbtm0AgLy8PFx//fVYsWIFgCsLyfXu3Ru/+tWvMH/+fEXH0tTUhLi4ODQ2NnJeIw1Qc6F3vBDaCtRF0RdBxVsjarwlkAHEZDLh4MGDTrfn5uZq5kIRjCyf+9bWVtlmHNvvRn19vc+be4IpKDseS2JiIurr661/y81+HeqfazXXb9UzQTtWD/pTeXk5CgoK7G4rLCy0Nr2ZTCbs27cPxcXF1vvDwsJQUFCA8vJy2f22tbXZVRGqmfiRQp+amjLHX4dAYEeBAd6v6ZPqL1BbW4va2tqAndAD2WlSK32tQo3ltT969Kjd7ZZ+N45zodmyHe3pTcHSuVfqO1xfX+9UQyY1+7WWPtNeWw3eH2pqapCSkmJ3W0pKCpqamnDp0iVcuHABHR0dktu46rxaUlKC559/3ifHTF2PVKfzzgqW5ibAdZ+FQHbqDFSTbjA2bdAVcp/VlpYW2fAD2I/29LZg6Hog97qYzWa7UWnBEtgCRXnDYBdWXFyMxsZG67/Tp08H+pBIQ2pqanDw4EEcPXoUBw8exPnz5wN6PO4u7Eo7dXaVTsP+GrhA6nUmhAZD52RfUbskSGxsrCY/zyFVA5Sammo3xTlwpWreaDQiKioK4eHhCA8Pl9wmNTVVdr8Gg4G/5iggampqJEdiREVFyU6i5uvaIrkZyy2UfFeCqS+EN2j9l3IwkPrcyw0EiImJcbu/rnzO51QoyoRUAMrPz8eHH35od9uOHTuQn58P4MqbPmzYMJSWllo7U5vNZpSWlmLGjBn+PlwKUf5qjjKZTLITuNk22dqGB6kZcKOjoxUdq5pyWS74dXV1dj8oHJuX5Z4nWOdD6YxgaNrwhWBqfpXjKlDLhVNXIV4LYYCh3T2vBaCnnnoKNTU1WLt2reLHtLS04NixY9a/q6qqUFFRgcTERPTp0wfFxcWorq7GG2+8AQCYNm0aVqxYgblz5+Lhhx/Gzp07sWnTJmzdutW6j6KiIkyePBnDhw/HiBEj8PLLL6O1tRVTpkzxVlH9IhROSl2RL2ZLlXus0ip4S3iw/N+W0lEtntTI6PV6ZGRkIDk52RqElHSGZqfh0OHucxEM5yElgVoqnAbjgAV/66qh3Vu8FoCqq6tV953Zu3cvRo0aZf27qKgIADB58mSsX78eZ8+exalTp6z3Z2VlYevWrZgzZw6WL1+OjIwMvPbaa9Y5gABgwoQJOHfuHJ599lnU1NTguuuuw7Zt2xT9cg0WXa35IFTInWjb2tpQU1NjvU3N++HqvVRTBa8kLMnVsnijRsaxWdnV49lpODS4+1wEy3moM4HaFwMWqOtQFIASExNx9OhRJCUl4eGHH8by5cud1jfZsGGD6ie/9dZb4WoaIqlZnm+99VYcOHDA5X5nzJgRsk1eXbX5IBTInWhtww+g/P1w915KtdOnpqY6PR+gPDxIXRQ6WyOj9vFdpf+Bq9oPb9WMBHI/7mYuDpbzEAM1+YqiAGQymdDU1ISkpCRs2LABv/3tb4Nmgbeuhs0HgaO2Rsbd+6F2rTHLxctgMMiGh5SUFKfaGHdl6OwFxJPHh3r/A1e1H96qGQn0fly9r8F0HpLrlG+5JhF5SlEAys/Px/jx4zFs2DAIITBz5kxERUVJbqumDxA546+dwHE3+slWZ5bFkFprzPai4io8JCcnywYguVqWztbIePr4UO1/4KrmzvJ/qfvUlNVbNb2d2Y8n72ugzkNSM/qyZpw6S1EAeuutt/DSSy/h+PHjAIDGxkZ8//33Pj0wreoqzQehKikpCVFRUS4nzgSU/frszHspFx6k9qlkmYrO1siEeo2OGp4saqm2ZsRbNSyd3Y/RaERWVhYAICYmxu3wcql9+qOjdDDVSFHXoSgApaSkYMmSJQCudER+88030aNHD58emJZp6WITjKKjo93WBCn99emL91Jun+4uRJ2tkQnVGh21PKmFVVsz4q2a3s7sx92IR6PRiNzcXJefKU+a32wX6JSb60ppeVgzTp2huhP0qFGjNHESDDStXGyClW3IaG9vR1VVldM2Sn99+uK9dNyn1PxArib/DEbBMOQacF/74Y0aWm/1a/G0llGu6ezy5cuKp1ZobW1V3fwmt5iwu1pMuZpPos5QtBp8TEwMvvrqK/Tr1w/h4eGoqalBz549/XF8AcHV4MmWJ6uBO17MfXlxlzu+UApBwTLk2pavR4F5a5V5k8mElpYWAPbNWK40Nzc7LSIqR+p45IIMAAwYMEBykIxceW25e99NJpPT5JxyjwmWQE3+5fXV4NkJmrRM7a9sx4tDYmIi6uvr7R6r9uLu6mQu1z/C3YKPwXKBCNapH1zV3Hlaq2f7mnujX4vjUiqdHQHm6nhsm65cNQ/L7dsS0lw5efIkwsLCXAY5uTmpLMdqMBicVoIPhkBNwUd1J2idTsdO0KQ5UrPKNjc3O4UHqYu5bfgB1F/cHQOVY3OBq4uZ3MW0MzNee7t2q6t2cHV8XaSaKaUoDSdy68g5hgGlnenT09Mll2YxGAwua3xsyf0wUPp4ANbmZqnQIvdZOXv2rMtFhIMhUFPwYSdoIoUsv/pdNdco+ZULABcuXEBCQoLbUTWA87Brx+Uo9Hq9y4uX1P497f/hi9otb3ZwVRPGlG7rScCTCjuO7091dbXT7ZZV56XCteMxya0jp7SJSKozfbdu3Zw+24DzZ1BKTk4OoqOjJY9VafixJVUbJPeZcBV+LEI9UJP3qV4KQ6ozKJFWuGqucax2d+XMmTM4c+aM08VJqrZHju2vWktfH8eLqdQJ31WTmdz+AeW1W1FRUZIXQjnemvpBTT8ix9qTpKQkxMbGOjW9eDrKydWabbaio6PtRlo1NTXZ9ZOx9ONyDGGuhuSrWbZEyRxUzc3NLssLXHld5MLPhQsXJB/Tu3dvmEwmlxN7Wq43tov+Kp2ryxFHjJEjRQHoD3/4Ax577DFERkbiD3/4g8ttZ86c6ZUDIwpGcheelpYW2ZOyYy2JLdtfuZa/bbm6OFiOx3IBS01NRWJiotvaCk9nvFa6eOuRI0dUrVIPKG9ilKOmH5FU09H58+ettQi2zYFS+4yKinI5fFtpLSAA6z4sTYlSwamxsdFunykpKUhISJDcX0JCgmTgcKz9cFWr5RiK5D4vOTk5kq+DZd+tra2ywQ8A4uPjodfrkZycjJaWFpc/rh2DfVZWlqof4+np6dbPL2uByEJRAHrppZcwceJEREZG4qWXXpLdTqfTMQBRl6b2V2RWVhYSExORnp6OCxcu4MyZM07bWE7kcrU9chc1qeOxvXjJXeRcNZk5am1ttY7oUVN2TzrmKmlilCuT0n5ErpqOLCwhRy7I2E6S6Xhsjs1PtqSau5SUwfE4LE2gllpHi/j4eLefEzWjqCzkhuxfunTJbROpHNuy6/V6JCYmKnpvgCvvT05Ojtv9WwK1YxBjh2iyUBSAbJM2m8BIy+Saayw1OI4st+v1eiQkJEgGIAu5C6flohYTE2N3MVQzEs3xpK+0icp2JJlU2V3Vblko7ZgLqGtitC2T0n5EamqxlFDa/GmpEZOrNZE6Vncs4cdSIyR3zJbaj/r6esmAoaSDsJKlKJT09cnIyLDr+6a0tsiR2WyW7MQtVevoONyfHaLJQnUfICKtk+onYTKZnBYqdQwocr+kbbla7LSlpQWpqakwGo0ehwglI8ccuVu8NTk52W1gUFrrIBdQGhoacPr0adkyKe1H1Nra6rqwHnDV/Alcec1sL+62o788rZmzVVtbi+7du0vel5CQoGh/7joIK6lhUxIubcOPmpFhjgwGA2JjY93Osq6mZjAYpoQg/1IUgIqKihTvcNmyZR4fDFGosG1qctWh1pElQMj1eUhOTkZycrJsc5llEtLOXqyUhDEL27AkdaFQsnSI0o65csHMMfxYNDQ0ICoqCgaDwe2yI0qbWKRkZGQgJiZGcc2QLccRSq6aY6Q6s3eGXJOYI3eBWEkNm7t92AZSJbVFWVlZiIiIkGzCsv0cu/ouKDnuYJyEk/xDUQA6cOCA3d/79+/H5cuXcfXVVwO4UsUYHh6OYcOGef8IiYKY0g61tix9Hsxms2yNhavmMne/1pU2B1kCg1zYcjwmVxcKVzOuytVqWYKabWBRE8wA+2BkOy2ALUtoa29vl9xHVlYWmpubXQ6lttRcqGn+BFzX6AHSQdDSmd2x1syxz4+tmJgYj0dHKRlxp6SGTW5uIalmKSW1Rbavq6umw84cd7BOwkn+oSgAffzxx9b/L1u2DLGxsdiwYYN1JMKFCxcwZcoU3HTTTb45SqIgpLRDrdzJ1FWNhdq5fWypGVbuqm9STk4OIiIi0NzcLDn7r23Z5C5oWVlZiImJkQwBra2tdv0zLAEmKSkJYWFhqvsbSr3WSppZDAaDy+eyfe3k3jOpdaqSk5MBqBvJZ6HX65GRkYHk5GSnplap5kS9Xq9o/TpH6enpsrUdjrV9Shb2Vbr4r5LaIqk+X1JLbLjj6pi66iScpIzqPkBLly7F3/72N7thmAkJCfjNb36D22+/Hf/93//t1QMkClZKO9RKnUxtLy5yJ3U1c/s4UrMKvVxgunTpktsmH0vZ5C5olnl1lMw6bBtgXNWqKDkeQFkzS2ZmJsxms+R9jh12LaSaXYxGI7KysgA4r8flrmbGVRhwfC65YOS4vclkkt2nLcflUuQ6JdvWrimpLfKkRsm2tgiA07phnamZkTsmrjKvbaoDUFNTE86dO+d0+7lz5xRNmEXUVSg9Sba3t8NkMilqSnKkdG4fKbYXQ9s5daT68UjNw+Nu4UrgP6+Bu1onx/27++WttinMwva1dlUrFRERYfd6SJGbqduR1PtpW1bbsrvqz6KGu5Ch5vWzvOauasuUhA+1HYldhXS5a4m3a2bU1JZS16M6AN19992YMmUKli5dihEjRgAA9uzZgyeffBL33HOP1w+QKFhJnTzj4+PR0NBgt53t2kZGo1F1nwMlv6jlOF7UHI/PNnzZPo/S2X9tj8tdrZOSctiGyqSkJERFRanqeOz4Wss9h+00AFLvo6sZuG3J9SGxZVt7Ehsbi8TERLvV233F9v0ICwtDW1ubZLOYwWBQVFvW0tKCxMREyfs87UgcDDUzampLqWvRCSGEmgdcvHgRTzzxBNauXWvtVNitWzc88sgjePHFF1VNgR+smpqaEBcXh8bGRpedO4kA6cVB5UZ5yc1gO2DAAI/6N7g6nrCwMEXhITc3V7KJTqoGSG1nVFe1Akovmq5qWFxd2HNycnDhwgXJPjhSz+XJJIHNzc1O88xIsfSFkqpp8eeoI7nnVloOx9oty+dd6rNi+Vw5rm2ndLFdTmBInlBz/VZdA9S9e3f88Y9/xIsvvojjx48DALKzs7tE8CHyhFQ/jYiICFX78NYvW0/mVpHrhCvVNKDme+7uQq9k2HpbWxuMRqPdelm2Q6AByPbhcRX+5Grd1KyjBSh/32zXtHK35povyb3mSudHcvxspaenQ6fTSW7b0tKCixcvynYCV7LYrtolVYjU8HgixOjoaAwaNMibx0LUZbiqwnc3YaKnPF11W+5YO9M0oHR4sVwTiJpaEk/Do2Pw82REkNywdDlyowa92bfFXV8cx9dcbjSjqyVYLFyNgnQ3Ck3JTNLV1dWSNZRE3hAW6AMg6oosNSi2EhMTceTIEWv4SUlJQW5urteq9ZWOSrOVnp7utjNtbGys5DaWztVSnYhdhQl35MKTXGdlqddaCcfg5Cq0SpVVLnDazvTs6bF46vz58zh48CCOHj2KgwcPupzbyELuPYmPj/fKMSl97s58Zog8waUwiHzEsROqY5NMbW2tda4Yi85Mye9u1W7HPhXp6enWofZquauhUduJ1bbcntTEuJth25FUrZtcs5/cGmRyxxkdHY3c3FzZY3G3KKqnPJ3Uz9UUBp5OrqiUkpmkOSSdfIUBiMiHLM0NSob1drZzrLt+O5YRSJ0d7aLkQqtmZJVUvw8pSiaAVNr3Sq5zpJLpACxldXXB1uvlZ/tOSkryyvvgyNNJ/VwNBXd8Pdwt+qqGkpmkOSSdfIkBiMgP3P269daU/N4Yiu6O3IW2rq4OGRkZTsdiGVll+WcJAZbRclL9PpTUkkjVlimtLXAVCpRMB9DW1obY2Fi3F2y590OqH05nA1FnalDczUouNZ+TY41iYmIi6uvrJfdvOzO2N2aSJvIGBiAiP3D369abU/J7I+S4IndBtTTpKRlZdfnyZZcdaC3NSGqH0CudAFBpUHIXKpRcsN29H2pr/uTCUmdrUJR+bizbSdUopqenux3y7u45fP35JbJgACLyE1cXy0D2f1Bb+6DX610ucKpkZJW7NdRsF0eVOl5XtWXumm3UhgIli4D6sjnRVmenFvA2qSkg1IQdokBiACLyI7mLZaD6P3ja7yg5OVkyACkdWeWKu3IrqS2Ta7bxJBT4MlQoKYvtxJadmVqAiOwxABEFCX//eu9MvyOlgU1qO6nlQgD72ZJd8aS2rLOhwFehwl1ZlExsyZXLiTzDAEQURPz5672z/Y6UBjZ30wEAV0Z+ya0z5UgqVFn6nljuDxWugqTSiS05TJzIMwxARBrljX5HajvOyo0SUruUjrsV1kNpzSi5IKlkAkAOEyfyHAMQkUb5u9+Rq+YcT2oxLMfpuIinP9fW8hapIOluYksOEyfqHAYgIg3zV78jV805nQld3pw+INi4m9iSiDqHAYioC1MyxN0f/Y7kgkpWVpbivj9SuvryCZwYkMh3GICIuqjOLq3hTa7Wm+oMLSyfwGHtRL7BAETUBXlraQ1v8WVQCVQtiTeWryCiwGEAIuqCgrFvjC+Dir9rSYKpdo2IPBMW6AMgIu8L1r4xljWkQrnGRK52zWQyBeiIiMgTDEBEXZClyclWV+sbEyiuatc6y2Qyobm5mWGKyA/YBEbURXEEkW/4qnaNzWpE/sUaIKIurCs0OQUbX9SusVmNyP9YA0REpJK3a9eCsdM6UVfHAERE5AFvjjwL1k7rRF0Zm8CIiAKMndaJ/I81QEREQYCd1on8iwGIiChIcNkLIv9hExgRERFpDgMQERERaU7AA9DKlSvRt29fREZGIi8vD1988YXstu3t7Vi0aBGys7MRGRmJwYMHY9u2bXbbdHR04JlnnkFWVhaioqKQnZ2NX//61xBC+LooREREFCICGoDeffddFBUVYeHChdi/fz8GDx6MwsJC1NXVSW6/YMECrFmzBq+88goOHz6MadOm4e6778aBAwes2/z2t7/FqlWrsGLFCvzrX//Cb3/7W/zud7/DK6+84q9iERERUZDTiQBWjeTl5eH666/HihUrAABmsxm9e/fGr371K8yfP99p+7S0NDz99NOYPn269bZ7770XUVFReOuttwAA//Vf/4WUlBS8/vrrstu409TUhLi4ODQ2NsJoNHamiEREROQnaq7fAasBMplM2LdvHwoKCv5zMGFhKCgoQHl5ueRj2traEBkZaXdbVFQUdu/ebf175MiRKC0txdGjRwEA//znP7F7926MHj3aB6UgIiKiUBSwYfDnz59HR0cHUlJS7G5PSUnBkSNHJB9TWFiIZcuW4eabb0Z2djZKS0vx/vvvo6Ojw7rN/Pnz0dTUhJycHISHh6OjowMvvPACJk6cKHssbW1tdlPRNzU1dbJ0REREFMwC3glajeXLl+Oqq65CTk4O9Ho9ZsyYgSlTpiAs7D/F2LRpE95++22888472L9/PzZs2IDf//732LBhg+x+S0pKEBcXZ/3Xu3dvfxSHiIiIAiRgASgpKQnh4eGora21u722thapqamSj+nZsye2bNmC1tZWnDx5EkeOHEFMTAz69etn3ebJJ5/E/Pnzcf/99yM3Nxc///nPMWfOHJSUlMgeS3FxMRobG63/Tp8+7Z1CEhERUVAKWADS6/UYNmwYSktLrbeZzWaUlpYiPz/f5WMjIyORnp6Oy5cv47333sO4ceOs9128eNGuRggAwsPDYTabZfdnMBhgNBrt/hEREVHXFdClMIqKijB58mQMHz4cI0aMwMsvv4zW1lZMmTIFADBp0iSkp6dba2/27NmD6upqXHfddaiursZzzz0Hs9mMuXPnWvc5duxYvPDCC+jTpw9+8IMf4MCBA1i2bBkefvjhgJSRiIiIgk9AA9CECRNw7tw5PPvss6ipqcF1112Hbdu2WTtGnzp1yq425/vvv8eCBQtw4sQJxMTEYMyYMXjzzTcRHx9v3eaVV17BM888g1/+8peoq6tDWloaHn/8cTz77LP+Lh4REREFqYDOAxSsOA8QERFR6AmJeYCIiIiIAoUBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0J+ABaOXKlejbty8iIyORl5eHL774Qnbb9vZ2LFq0CNnZ2YiMjMTgwYOxbds2p+2qq6vx4IMPokePHoiKikJubi727t3ry2IQERFRCAloAHr33XdRVFSEhQsXYv/+/Rg8eDAKCwtRV1cnuf2CBQuwZs0avPLKKzh8+DCmTZuGu+++GwcOHLBuc+HCBdx4442IiIjARx99hMOHD2Pp0qVISEjwV7GIiIgoyOmEECJQT56Xl4frr78eK1asAACYzWb07t0bv/rVrzB//nyn7dPS0vD0009j+vTp1tvuvfdeREVF4a233gIAzJ8/H5999hl27drl8XE1NTUhLi4OjY2NMBqNHu+HiIiI/EfN9TtgNUAmkwn79u1DQUHBfw4mLAwFBQUoLy+XfExbWxsiIyPtbouKisLu3butf//lL3/B8OHD8dOf/hTJyckYMmQI/vSnP7k8lra2NjQ1Ndn9IyIioq4rYAHo/Pnz6OjoQEpKit3tKSkpqKmpkXxMYWEhli1bhm+++QZmsxk7duzA+++/j7Nnz1q3OXHiBFatWoWrrroK27dvxy9+8QvMnDkTGzZskD2WkpISxMXFWf/17t3bO4UkIiKioBTwTtBqLF++HFdddRVycnKg1+sxY8YMTJkyBWFh/ymG2WzG0KFDsXjxYgwZMgSPPfYYpk6ditWrV8vut7i4GI2NjdZ/p0+f9kdxiIiIKEACFoCSkpIQHh6O2tpau9tra2uRmpoq+ZiePXtiy5YtaG1txcmTJ3HkyBHExMSgX79+1m169eqFa665xu5xAwcOxKlTp2SPxWAwwGg02v0jIiKiritgAUiv12PYsGEoLS213mY2m1FaWor8/HyXj42MjER6ejouX76M9957D+PGjbPed+ONN6KystJu+6NHjyIzM9O7BSAiIqKQ1S2QT15UVITJkydj+PDhGDFiBF5++WW0trZiypQpAIBJkyYhPT0dJSUlAIA9e/aguroa1113Haqrq/Hcc8/BbDZj7ty51n3OmTMHI0eOxOLFi3Hffffhiy++wKuvvopXX301IGUkIiKi4BPQADRhwgScO3cOzz77LGpqanDddddh27Zt1o7Rp06dsuvf8/3332PBggU4ceIEYmJiMGbMGLz55puIj4+3bnP99ddj8+bNKC4uxqJFi5CVlYWXX34ZEydO9HfxiIiIKEgFdB6gYMV5gIiIiEJPSMwDRERERBQoDEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDkMQERERKQ5DEBERESkOQxAREREpDlBEYBWrlyJvn37IjIyEnl5efjiiy9kt21vb8eiRYuQnZ2NyMhIDB48GNu2bZPdfsmSJdDpdJg9e7YPjpyIiIhCUcAD0LvvvouioiIsXLgQ+/fvx+DBg1FYWIi6ujrJ7RcsWIA1a9bglVdeweHDhzFt2jTcfffdOHDggNO2X375JdasWYNBgwb5uhhEREQUQgIegJYtW4apU6diypQpuOaaa7B69Wp0794da9euldz+zTffxFNPPYUxY8agX79++MUvfoExY8Zg6dKldtu1tLRg4sSJ+NOf/oSEhAR/FIWIiIhCRLdAPrnJZMK+fftQXFxsvS0sLAwFBQUoLy+XfExbWxsiIyPtbouKisLu3bvtbps+fTruvPNOFBQU4De/+Y3L42hra0NbW5v178bGRgBAU1OTqvIQERFR4Fiu20IIt9sGNACdP38eHR0dSElJsbs9JSUFR44ckXxMYWEhli1bhptvvhnZ2dkoLS3F+++/j46ODus2GzduxP79+/Hll18qOo6SkhI8//zzTrf37t1bRWmIiIgoGDQ3NyMuLs7lNgENQJ5Yvnw5pk6dipycHOh0OmRnZ2PKlCnWJrPTp09j1qxZ2LFjh1NNkZzi4mIUFRVZ/zabzaivr0ePHj2g0+l8Ug5Xmpqa0Lt3b5w+fRpGo9Hvz+8vWiknoJ2yaqWcgHbKynJ2PV25rEIINDc3Iy0tze22AQ1ASUlJCA8PR21trd3ttbW1SE1NlXxMz549sWXLFnz//ff47rvvkJaWhvnz56Nfv34AgH379qGurg5Dhw61PqajowOffvopVqxYgba2NoSHh9vt02AwwGAw2N0WHx/vhRJ2jtFo7HIfTilaKSegnbJqpZyAdsrKcnY9XbWs7mp+LALaCVqv12PYsGEoLS213mY2m1FaWor8/HyXj42MjER6ejouX76M9957D+PGjQMA3HbbbTh48CAqKiqs/4YPH46JEyeioqLCKfwQERGR9gS8CayoqAiTJ0/G8OHDMWLECLz88stobW3FlClTAACTJk1Ceno6SkpKAAB79uxBdXU1rrvuOlRXV+O5556D2WzG3LlzAQCxsbG49tpr7Z4jOjoaPXr0cLqdiIiItCngAWjChAk4d+4cnn32WdTU1OC6667Dtm3brB2jT506hbCw/1RUff/991iwYAFOnDiBmJgYjBkzBm+++WZQNFl5i8FgwMKFC52a5boarZQT0E5ZtVJOQDtlZTm7Hi2V1RWdUDJWjIiIiKgLCfhEiERERET+xgBEREREmsMARERERJrDAERERESawwDkAytXrkTfvn0RGRmJvLw8fPHFF7Lbrl+/Hjqdzu6f4wzWQgg8++yz6NWrF6KiolBQUIBvvvnGbpv6+npMnDgRRqMR8fHxeOSRR9DS0uKT8tnyZlnb29sxb9485ObmIjo6GmlpaZg0aRL+/e9/2+2nb9++TvtZsmSJz8oIeP89feihh5y2ueOOO+y2CcR76u1yOt5v+ffiiy9atwnE+wmoKysANDQ0YPr06ejVqxcMBgMGDBiADz/8UNU+v//+e0yfPh09evRATEwM7r33XqeJYL3N2+UsKSnB9ddfj9jYWCQnJ2P8+PGorKy028ett97q9J5OmzbNJ+Wz5e2yPvfcc07lyMnJsdtHV3hPpb6DOp0O06dPt24TqPfUpwR51caNG4Verxdr164VX3/9tZg6daqIj48XtbW1ktuvW7dOGI1GcfbsWeu/mpoau22WLFki4uLixJYtW8Q///lPcdddd4msrCxx6dIl6zZ33HGHGDx4sPj888/Frl27RP/+/cUDDzwQUmVtaGgQBQUF4t133xVHjhwR5eXlYsSIEWLYsGF2+8nMzBSLFi2y209LS0vIlFMIISZPnizuuOMOu23q6+vttvH3e+qLctred/bsWbF27Vqh0+nE8ePHrdv4+/30pKxtbW1i+PDhYsyYMWL37t2iqqpKlJWViYqKClX7nDZtmujdu7coLS0Ve/fuFTfccIMYOXJkSJWzsLBQrFu3Thw6dEhUVFSIMWPGiD59+ti9Z7fccouYOnWq3Xva2Njos3L6qqwLFy4UP/jBD+zKce7cObv9dIX3tK6uzq6MO3bsEADExx9/bN0mEO+przEAedmIESPE9OnTrX93dHSItLQ0UVJSIrn9unXrRFxcnOz+zGazSE1NFS+++KL1toaGBmEwGMT//M//CCGEOHz4sAAgvvzyS+s2H330kdDpdKK6urqTJZLn7bJK+eKLLwQAcfLkSettmZmZ4qWXXvLkkD3ii3JOnjxZjBs3Tvb+QLyn/ng/x40bJ370ox/Z3ebv91MI9WVdtWqV6NevnzCZTB7vs6GhQURERIj//d//tW7zr3/9SwAQ5eXlnS2SR8fkSEk5HdXV1QkA4pNPPrHedsstt4hZs2Z5fNye8EVZFy5cKAYPHix7f1d9T2fNmiWys7OF2Wy23haI99TX2ATmRSaTCfv27UNBQYH1trCwMBQUFKC8vFz2cS0tLcjMzETv3r0xbtw4fP3119b7qqqqUFNTY7fPuLg45OXlWfdZXl6O+Ph4DB8+3LpNQUEBwsLCsGfPHm8W0coXZZXS2NgInU7nNNHlkiVL0KNHDwwZMgQvvvgiLl++3KnyyPFlOcvKypCcnIyrr74av/jFL/Ddd99Z7/P3e+qP97O2thZbt27FI4884nSfv95PwLOy/uUvf0F+fj6mT5+OlJQUXHvttVi8eDE6OjoU73Pfvn1ob2+32yYnJwd9+vRx+RoHUzmlNDY2AgASExPtbn/77beRlJSEa6+9FsXFxbh48aIXSiXNl2X95ptvkJaWhn79+mHixIk4deqU9b6u+J6aTCa89dZbePjhh50WA/fne+oPAZ8Juis5f/48Ojo6rLNYW6SkpODIkSOSj7n66quxdu1aDBo0CI2Njfj973+PkSNH4uuvv0ZGRgZqamqs+3Dcp+W+mpoaJCcn293frVs3JCYmWrfxNl+U1dH333+PefPm4YEHHrBbsG/mzJkYOnQoEhMT8Y9//APFxcU4e/Ysli1b5t1CwnflvOOOO3DPPfcgKysLx48fx1NPPYXRo0ejvLwc4eHhfn9P/fF+btiwAbGxsbjnnnvsbvfn+wl4VtYTJ05g586dmDhxIj788EMcO3YMv/zlL9He3o6FCxcq2mdNTQ30er1TmLf9LnuTL8rpyGw2Y/bs2bjxxhvtlhr62c9+hszMTKSlpeGrr77CvHnzUFlZiffff9+7hfz/fFXWvLw8rF+/HldffTXOnj2L559/HjfddBMOHTqE2NjYLvmebtmyBQ0NDXjooYfsbvf3e+oPDEABlp+fb7fw68iRIzFw4ECsWbMGv/71rwN4ZN6npqzt7e247777IITAqlWr7O4rKiqy/n/QoEHQ6/V4/PHHUVJSEhRTuysp5/3332+9Pzc3F4MGDUJ2djbKyspw2223+f2YPaH2s7t27VpMnDjRqaN0sL+fwJULfXJyMl599VWEh4dj2LBhqK6uxosvvih5EQlVass5ffp0HDp0CLt377a7/bHHHrP+Pzc3F7169cJtt92G48ePIzs72+flUEJJWUePHm3dftCgQcjLy0NmZiY2bdokWZMZjNS+p6+//jpGjx6NtLQ0u9tD4T1Vi01gXpSUlITw8HCnEQC1tbVITU1VtI+IiAgMGTIEx44dAwDr41ztMzU1FXV1dXb3X758GfX19YqfVy1flNXCEn5OnjyJHTt22NX+SMnLy8Ply5fx7bffqiqDEr4sp61+/fohKSnJ7n3353vq63Lu2rULlZWVePTRR93ux5fvJ+BZWXv16oUBAwYgPDzcetvAgQNRU1MDk8mkaJ+pqakwmUxoaGhQ/Lyd4Yty2poxYwY++OADfPzxx5I1frby8vIAwOV3oDN8XVaL+Ph4DBgwwO572pXe05MnT+Lvf/+74u8p4Lv31B8YgLxIr9dj2LBhKC0ttd5mNptRWlpq90vZlY6ODhw8eBC9evUCAGRlZSE1NdVun01NTdizZ491n/n5+WhoaMC+ffus2+zcuRNms9n6IfU2X5QV+E/4+eabb/D3v/8dPXr0cLufiooKhIWFOTUZeYOvyunozJkz+O6776zb+Ps99XU5X3/9dQwbNgyDBw92ux9fvp+AZ2W98cYbcezYMZjNZuttR48eRa9evaDX6xXtc9iwYYiIiLDbprKyEqdOnVL8Gqvhi3ICV6blmDFjBjZv3oydO3ciKyvL7bFUVFQAgMvvQGf4qqyOWlpacPz4cWs5usp7arFu3TokJyfjzjvvdHssvn5P/SLQvbC7mo0bNwqDwSDWr18vDh8+LB577DERHx9vHR7885//XMyfP9+6/fPPPy+2b98ujh8/Lvbt2yfuv/9+ERkZKb7++mvrNkuWLBHx8fHiz3/+s/jqq6/EuHHjJIfBDxkyROzZs0fs3r1bXHXVVX4ZBu/NsppMJnHXXXeJjIwMUVFRYTfcsq2tTQghxD/+8Q/x0ksviYqKCnH8+HHx1ltviZ49e4pJkyaFTDmbm5vFE088IcrLy0VVVZX4+9//LoYOHSquuuoq8f3331v34+/31BefXSGEaGxsFN27dxerVq1yes5AvJ+elPXUqVMiNjZWzJgxQ1RWVooPPvhAJCcni9/85jeK9ynElSHTffr0ETt37hR79+4V+fn5Ij8/P6TK+Ytf/ELExcWJsrIyu+/oxYsXhRBCHDt2TCxatEjs3btXVFVViT//+c+iX79+4uabb/ZZOX1V1v/+7/8WZWVloqqqSnz22WeioKBAJCUlibq6Ous2XeE9FeLKaLI+ffqIefPmOT1noN5TX2MA8oFXXnlF9OnTR+j1ejFixAjx+eefW++75ZZbxOTJk61/z54927ptSkqKGDNmjNi/f7/d/sxms3jmmWdESkqKMBgM4rbbbhOVlZV223z33XfigQceEDExMcJoNIopU6aI5uZmn5ZTCO+WtaqqSgCQ/GeZj2Lfvn0iLy9PxMXFicjISDFw4ECxePFiu+AQ7OW8ePGiuP3220XPnj1FRESEyMzMFFOnTnWaQycQ76m3P7tCCLFmzRoRFRUlGhoanO4L1PsphLqyCnElrOXl5QmDwSD69esnXnjhBXH58mXF+xRCiEuXLolf/vKXIiEhQXTv3l3cfffd4uzZsz4ro7tj8qScct/RdevWCSGuXHBvvvlmkZiYKAwGg+jfv7948skn/TJnjLfLOmHCBNGrVy+h1+tFenq6mDBhgjh27JjdPrrCeyqEENu3bxcAnK4tQgT2PfUlnRBCBKTqiYiIiChA2AeIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiIiDSHAYiIiIg0hwGIiIiINIcBiIiCSllZGXQ6ndP6Sv5SWlqKgQMHoqOjo9P7uv/++7F06VIvHBV1dd9++y0eeeQRZGVlISoqCtnZ2Vi4cKHsumSOhBAYPXo0dDodtmzZYnefTqdz+rdx40br/Q899JDkNj/4wQ+s26xatQqDBg2C0WiE0WhEfn4+PvroI7vnOX78OO6++2707NkTRqMR9913n9O6ZXfddRf69OmDyMhI9OrVCz//+c/x73//W+WrBfzrX//CXXfdhbi4OERHR+P666/HqVOnVO2DAYiIAubWW2/F7Nmz7W4bOXIkzp49i7i4uIAc09y5c7FgwQLr4pHr169HfHy89f7169dbLxDh4eFISEhAXl4eFi1ahMbGRrt9LViwAC+88ILT7aRdt956K9avX+90+5EjR2A2m7FmzRp8/fXXeOmll7B69Wo89dRTivb78ssvQ6fTyd6/bt06nD171vpv/Pjx1vuWL19ud9/p06eRmJiIn/70p9ZtMjIysGTJEuzbtw979+7Fj370I4wbNw5ff/01AKC1tRW33347dDoddu7cic8++wwmkwljx461W4ds1KhR2LRpEyorK/Hee+/h+PHj+MlPfqKojBbHjx/HD3/4Q+Tk5KCsrAxfffUVnnnmGURGRqraD5fCIKKAueWWW8SsWbMCfRhWu3btEnFxcXbr7K1bt07ExcXZ/W00GsXZs2fFv//9b3H48GHx2muviezsbNG3b19RXV1tt8/hw4eLFStW+KsIFORuueUW67Ih7vzud78TWVlZbrc7cOCASE9PF2fPnhUAxObNm+3ul7rNlc2bNwudTie+/fZbl9slJCSI1157TQhxZSmNsLAwu+UxGhoahE6nEzt27JDdx5///Geh0+mEyWSy3rZr1y7xwx/+UERGRoqMjAzxq1/9SrS0tFjvnzBhgnjwwQcVl0cOa4CIKCAeeughfPLJJ1i+fLm1RuXbb791agKz1MB88MEHuPrqq9G9e3f85Cc/wcWLF7Fhwwb07dsXCQkJmDlzpl2zVVtbG5544gmkp6cjOjoaeXl5KCsrc3lMGzduxI9//GO3vyR1Oh1SU1PRq1cvDBw4EI888gj+8Y9/oKWlBXPnzrXbduzYsXbNDURKNTY2IjEx0eU2Fy9exM9+9jOsXLkSqampsttNnz4dSUlJGDFiBNauXQvhYhWs119/HQUFBcjMzJS8v6OjAxs3bkRra6t1Ffq2tjbodDoYDAbrdpGRkQgLC8Pu3bsl91NfX4+3334bI0eOREREBIArtTt33HEH7r33Xnz11Vd49913sXv3bsyYMQMAYDabsXXrVgwYMACFhYVITk5GXl6eU7OfIp2OUEREHmhoaBD5+fli6tSp1hXFL1++LD7++GMBQFy4cEEIcaXGJSIiQvz4xz8W+/fvF5988ono0aOHuP3228V9990nvv76a/HXv/5V6PV6sXHjRuv+H330UTFy5Ejx6aefimPHjokXX3xRGAwGcfToUdljGjRokFiyZIndbVI1QLZ/25o1a5aIjY21W2jyo48+Enq93i8LvFLwU1oD9M033wij0SheffVVl9s99thj4pFHHrH+DYnankWLFondu3eL/fv3iyVLlgiDwSCWL18uub/q6moRHh4u3n33Xaf7vvrqKxEdHS3Cw8NFXFyc2Lp1q/W+uro6YTQaxaxZs0Rra6toaWkRM2bMEADEY489ZrefuXPniu7duwsA4oYbbhDnz5+33vfII484bb9r1y4RFhYmLl26ZK3l6t69u1i2bJk4cOCAKCkpETqdTpSVlbl8rRwxABFRwEg1gUkFIAB2q3A//vjjonv37qK5udl6W2FhoXj88ceFEEKcPHlShIeHOzVH3XbbbaK4uFj2eOLi4sQbb7xhd5uaALRq1SoBQNTW1lpv++c//ykAuG1OoK7phRdeENHR0dZ/YWFhwmAw2N128uRJu8ecOXNGZGdn2wUbKX/+859F//797b4HUgHI0TPPPCMyMjIk71u8eLHo0aOHaGtrc7qvra1NfPPNN2Lv3r1i/vz5IikpSXz99dfW+7dv3y769esndDqdCA8PFw8++KAYOnSomDZtmt1+zp07JyorK8Xf/vY3ceONN4oxY8YIs9kshLjSZKzX6+1eH0tYOnz4sKiurhYAxAMPPGC3z7Fjx4r777/fZbkddVNfZ0RE5F/du3dHdna29e+UlBT07dsXMTExdrfV1dUBAA4ePIiOjg4MGDDAbj9tbW3o0aOH7PNcunRJfUdKG+L/NyvYdkaNiooCcKWpgrRn2rRpuO+++6x/T5w4Effeey/uuece621paWnW///73//GqFGjMHLkSLz66qsu971z504cP37crpM+ANx777246aabZJt88/Ly8Otf/xptbW12TVZCCKxduxY///nPodfrnR6n1+vRv39/AMCwYcPw5ZdfYvny5VizZg0A4Pbbb8fx48dx/vx5dOvWDfHx8UhNTUW/fv3s9pOUlISkpCQMGDAAAwcORO/evfH5558jPz8fLS0tePzxxzFz5kyn5+/Tpw8AoFu3brjmmmvs7hs4cKBsU5scBiAiCnqW/gEWOp1O8jbLaJOWlhaEh4dj37591tFcFrahyVFSUhIuXLjg8XH+61//gtFotAtZ9fX1AICePXt6vF8KXYmJiXb9eKKiopCcnGwNEraqq6sxatQoDBs2DOvWrUNYmOtuuvPnz8ejjz5qd1tubi5eeukljB07VvZxFRUVSEhIsAs/APDJJ5/g2LFjeOSRR5QUDWazGW1tbU63JyUlAbgS0Orq6nDXXXe53AcA636GDh2Kw4cPS74+Ftdffz0qKyvtbjt69KhsnyU5DEBEFDB6vd4r8+04GjJkCDo6OlBXV4ebbrpJ1eMOHz7s0XPW1dXhnXfewfjx4+0uXIcOHUJGRob1okAkpbq6GrfeeisyMzPx+9//HufOnbPeZ+ncXF1djdtuuw1vvPEGRowYgdTUVMmOz3369EFWVhYA4K9//Stqa2txww03IDIyEjt27MDixYvxxBNPOD3u9ddfR15eHq699lqn+4qLizF69Gj06dMHzc3NeOedd1BWVobt27dbt1m3bh0GDhyInj17ory8HLNmzcKcOXNw9dVXAwD27NmDL7/8Ej/84Q+RkJCA48eP45lnnkF2dra1M/W8efNwww03YMaMGXj00UcRHR2Nw4cPY8eOHVixYgUA4Mknn8SECRNw8803Y9SoUdi2bRv++te/uh3k4IgBiIgCpm/fvtizZw++/fZbxMTEuB3xotSAAQMwceJETJo0CUuXLsWQIUNw7tw5lJaWYtCgQbjzzjslH1dYWIgNGza43b8QAjU1NRBCoKGhAeXl5Vi8eDHi4uKwZMkSu2137dqF22+/3Svloq5rx44dOHbsGI4dO4aMjAy7+yxNq+3t7aisrFTVnBoREYGVK1dizpw5EEKgf//+WLZsGaZOnWq3XWNjI9577z0sX75ccj91dXWYNGmSdY6uQYMGYfv27fjxj39s3aayshLFxcWor69H37598fTTT2POnDnW+7t37473338fCxcuRGtrK3r16oU77rgDCxYssNZGDRo0CJ988gmefvpp3HTTTRBCIDs7GxMmTLDu5+6778bq1atRUlKCmTNn4uqrr8Z7772HH/7wh4pfFwAcBUZEgVNZWSluuOEGERUVJQCIqqoqyU7Qjp2OFy5cKAYPHmx32+TJk8W4ceOsf5tMJvHss8+Kvn37ioiICNGrVy9x9913i6+++kr2eL777jsRGRkpjhw5Yr3t9ddfFz169LD+bemUDUDodDoRFxcnRowYIRYtWmQ3B4oQQly6dEnExcWJ8vJydS8MEfmcTggXkwEQEWnMk08+iaamJmvHziVLluCtt97CoUOHVO9r1apV2Lx5M/72t795+zCJqJM4ESIRkY2nn34amZmZuHjxIvbv349169ahoKDAo31FRETglVde8fIREpE3sAaIiEjCyy+/jEWLFqGgoACrV6/2Wv8kIgoODEBERESkOWwCIyIiIs1hACIiIiLNYQAiIiIizWEAIiIiIs1hACIiIiLNYQAiIiIizWEAIiIiIs1hACIiIiLNYQAiIiIizfl/N2fkFcOlUyQAAAAASUVORK5CYII=",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -240,7 +247,52 @@
                 "\n",
                 "# plotting\n",
                 "ax = plt.subplot(xlabel=\"time (JD)\", ylabel=\"diff. flux\", ylim=(0.94, 1.06))\n",
-                "diff.plot()\n"
+                "diff.plot()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "And here is our planetary transit. To validate the differential photometry and the automatic choice of comparison stars, we can plot their light curves along the target light curve"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 500x700 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.figure(None, (5, 7))\n",
+                "ax = plt.subplot(xlabel=\"time (JD)\", ylabel=\"diff. flux (abritrary units)\")\n",
+                "\n",
+                "\n",
+                "# plotting only the first five comparisons\n",
+                "for j, i in enumerate([diff.target, *diff.comparisons[0:5]]):\n",
+                "    y = diff.fluxes[diff.aperture, i].copy()\n",
+                "    y = (y - np.mean(y)) / np.std(y) + 8 * j\n",
+                "    plt.text(\n",
+                "        diff.time.max(), np.mean(y) + 4, i if i != diff.target else \"target\", ha=\"right\"\n",
+                "    )\n",
+                "    plt.plot(diff.time, y, \".\", c=\"0.8\" if i != diff.target else \"k\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Explanatory measurements"
             ]
         },
         {
@@ -248,7 +300,7 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "And here is our planetary transit. To help modeling the light curve, some explanatory measurements have been stored in"
+                "To help modeling the light curve, some explanatory measurements have been stored in"
             ]
         },
         {
@@ -278,8 +330,10 @@
                             "    <tr style=\"text-align: right;\">\n",
                             "      <th></th>\n",
                             "      <th>bkg</th>\n",
-                            "      <th>fwhm</th>\n",
                             "      <th>airmass</th>\n",
+                            "      <th>dx</th>\n",
+                            "      <th>dy</th>\n",
+                            "      <th>fwhm</th>\n",
                             "      <th>time</th>\n",
                             "      <th>flux</th>\n",
                             "    </tr>\n",
@@ -288,42 +342,52 @@
                             "    <tr>\n",
                             "      <th>0</th>\n",
                             "      <td>261.908217</td>\n",
-                            "      <td>4.823861</td>\n",
                             "      <td>1.878414</td>\n",
+                            "      <td>-0.314490</td>\n",
+                            "      <td>0.566460</td>\n",
+                            "      <td>4.823861</td>\n",
                             "      <td>2.457393e+06</td>\n",
-                            "      <td>1.014314</td>\n",
+                            "      <td>1.011125</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>1</th>\n",
-                            "      <td>263.317344</td>\n",
-                            "      <td>4.473168</td>\n",
+                            "      <td>263.309778</td>\n",
                             "      <td>1.870291</td>\n",
+                            "      <td>-1.088033</td>\n",
+                            "      <td>0.466066</td>\n",
+                            "      <td>4.473168</td>\n",
                             "      <td>2.457393e+06</td>\n",
-                            "      <td>1.003261</td>\n",
+                            "      <td>0.999756</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>2</th>\n",
                             "      <td>261.167119</td>\n",
-                            "      <td>4.369253</td>\n",
                             "      <td>1.862163</td>\n",
+                            "      <td>-0.868474</td>\n",
+                            "      <td>0.423316</td>\n",
+                            "      <td>4.369253</td>\n",
                             "      <td>2.457393e+06</td>\n",
-                            "      <td>1.002052</td>\n",
+                            "      <td>1.001618</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>3</th>\n",
                             "      <td>252.933837</td>\n",
-                            "      <td>4.677914</td>\n",
                             "      <td>1.854302</td>\n",
+                            "      <td>-0.953985</td>\n",
+                            "      <td>0.551378</td>\n",
+                            "      <td>4.677914</td>\n",
                             "      <td>2.457393e+06</td>\n",
-                            "      <td>1.012743</td>\n",
+                            "      <td>1.012742</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>4</th>\n",
                             "      <td>252.841623</td>\n",
-                            "      <td>4.735902</td>\n",
                             "      <td>1.846374</td>\n",
+                            "      <td>-1.025708</td>\n",
+                            "      <td>0.448884</td>\n",
+                            "      <td>4.735902</td>\n",
                             "      <td>2.457393e+06</td>\n",
-                            "      <td>1.011300</td>\n",
+                            "      <td>1.012824</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>...</th>\n",
@@ -332,67 +396,92 @@
                             "      <td>...</td>\n",
                             "      <td>...</td>\n",
                             "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>327</th>\n",
                             "      <td>157.205167</td>\n",
-                            "      <td>4.130921</td>\n",
                             "      <td>1.013443</td>\n",
+                            "      <td>1.007175</td>\n",
+                            "      <td>0.613441</td>\n",
+                            "      <td>4.130921</td>\n",
                             "      <td>2.457394e+06</td>\n",
-                            "      <td>1.013381</td>\n",
+                            "      <td>1.015685</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>328</th>\n",
                             "      <td>157.248958</td>\n",
-                            "      <td>4.012928</td>\n",
                             "      <td>1.013356</td>\n",
+                            "      <td>1.577194</td>\n",
+                            "      <td>0.623359</td>\n",
+                            "      <td>4.012928</td>\n",
                             "      <td>2.457394e+06</td>\n",
-                            "      <td>1.009544</td>\n",
+                            "      <td>1.010365</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>329</th>\n",
                             "      <td>156.650647</td>\n",
-                            "      <td>4.491135</td>\n",
                             "      <td>1.013278</td>\n",
+                            "      <td>0.737424</td>\n",
+                            "      <td>0.519273</td>\n",
+                            "      <td>4.491135</td>\n",
                             "      <td>2.457394e+06</td>\n",
-                            "      <td>1.005749</td>\n",
+                            "      <td>1.007430</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>330</th>\n",
                             "      <td>156.423297</td>\n",
-                            "      <td>4.576204</td>\n",
                             "      <td>1.013210</td>\n",
+                            "      <td>0.700936</td>\n",
+                            "      <td>0.711975</td>\n",
+                            "      <td>4.576204</td>\n",
                             "      <td>2.457394e+06</td>\n",
-                            "      <td>1.007899</td>\n",
+                            "      <td>1.007304</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>331</th>\n",
-                            "      <td>157.139672</td>\n",
-                            "      <td>4.015622</td>\n",
+                            "      <td>157.131704</td>\n",
                             "      <td>1.013150</td>\n",
+                            "      <td>0.977755</td>\n",
+                            "      <td>0.784270</td>\n",
+                            "      <td>4.015622</td>\n",
                             "      <td>2.457394e+06</td>\n",
-                            "      <td>1.002996</td>\n",
+                            "      <td>1.005600</td>\n",
                             "    </tr>\n",
                             "  </tbody>\n",
                             "</table>\n",
-                            "<p>332 rows × 5 columns</p>\n",
+                            "<p>332 rows × 7 columns</p>\n",
                             "</div>"
                         ],
                         "text/plain": [
-                            "            bkg      fwhm   airmass          time      flux\n",
-                            "0    261.908217  4.823861  1.878414  2.457393e+06  1.014314\n",
-                            "1    263.317344  4.473168  1.870291  2.457393e+06  1.003261\n",
-                            "2    261.167119  4.369253  1.862163  2.457393e+06  1.002052\n",
-                            "3    252.933837  4.677914  1.854302  2.457393e+06  1.012743\n",
-                            "4    252.841623  4.735902  1.846374  2.457393e+06  1.011300\n",
-                            "..          ...       ...       ...           ...       ...\n",
-                            "327  157.205167  4.130921  1.013443  2.457394e+06  1.013381\n",
-                            "328  157.248958  4.012928  1.013356  2.457394e+06  1.009544\n",
-                            "329  156.650647  4.491135  1.013278  2.457394e+06  1.005749\n",
-                            "330  156.423297  4.576204  1.013210  2.457394e+06  1.007899\n",
-                            "331  157.139672  4.015622  1.013150  2.457394e+06  1.002996\n",
+                            "            bkg   airmass        dx        dy      fwhm          time  \\\n",
+                            "0    261.908217  1.878414 -0.314490  0.566460  4.823861  2.457393e+06   \n",
+                            "1    263.309778  1.870291 -1.088033  0.466066  4.473168  2.457393e+06   \n",
+                            "2    261.167119  1.862163 -0.868474  0.423316  4.369253  2.457393e+06   \n",
+                            "3    252.933837  1.854302 -0.953985  0.551378  4.677914  2.457393e+06   \n",
+                            "4    252.841623  1.846374 -1.025708  0.448884  4.735902  2.457393e+06   \n",
+                            "..          ...       ...       ...       ...       ...           ...   \n",
+                            "327  157.205167  1.013443  1.007175  0.613441  4.130921  2.457394e+06   \n",
+                            "328  157.248958  1.013356  1.577194  0.623359  4.012928  2.457394e+06   \n",
+                            "329  156.650647  1.013278  0.737424  0.519273  4.491135  2.457394e+06   \n",
+                            "330  156.423297  1.013210  0.700936  0.711975  4.576204  2.457394e+06   \n",
+                            "331  157.131704  1.013150  0.977755  0.784270  4.015622  2.457394e+06   \n",
+                            "\n",
+                            "         flux  \n",
+                            "0    1.011125  \n",
+                            "1    0.999756  \n",
+                            "2    1.001618  \n",
+                            "3    1.012742  \n",
+                            "4    1.012824  \n",
+                            "..        ...  \n",
+                            "327  1.015685  \n",
+                            "328  1.010365  \n",
+                            "329  1.007430  \n",
+                            "330  1.007304  \n",
+                            "331  1.005600  \n",
                             "\n",
-                            "[332 rows x 5 columns]"
+                            "[332 rows x 7 columns]"
                         ]
                     },
                     "execution_count": 8,
@@ -401,7 +490,41 @@
                 }
             ],
             "source": [
-                "diff.dataframe\n"
+                "diff.dataframe"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "that we can plot to check for any correlation with the differential flux"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 14,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 500x700 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.figure(None, (5, 7))\n",
+                "ax = plt.subplot(xlabel=\"time (JD)\", ylabel=\"scaled signals (abritrary units)\")\n",
+                "\n",
+                "for i, name in enumerate([\"flux\", \"fwhm\", \"airmass\", \"bkg\", \"dx\", \"dy\"]):\n",
+                "    y = diff.df[name].copy()\n",
+                "    y = (y - np.mean(y)) / np.std(y) + 8 * i\n",
+                "    plt.text(diff.time.max(), np.mean(y) + 4, name, ha=\"right\")\n",
+                "    plt.plot(diff.time, y, \".\", c=\"0.8\" if name != \"flux\" else \"k\")\n"
             ]
         },
         {
@@ -411,18 +534,18 @@
             "source": [
                 "## Multiprocessing alternative\n",
                 "\n",
-                "For those in a hurry"
+                "For those in a hurry, the photometry sequence can be made parallel"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 9,
+            "execution_count": 10,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "577a16681be9499da9d9406b449cbc79",
+                            "model_id": "5e6f577f7c1e4230ad8ed608db25c144",
                             "version_major": 2,
                             "version_minor": 0
                         },
@@ -446,14 +569,19 @@
                 "        blocks.Cutouts(21),  # stars cutouts\n",
                 "        blocks.MedianEPSF(),  # building EPSF\n",
                 "        blocks.Gaussian2D(ref),  # modeling EPSF with initial guess\n",
-                "        blocks.ComputeTransformTwirl(ref), # compute alignment\n",
+                "        blocks.ComputeTransformTwirl(ref),  # compute alignment\n",
                 "        blocks.AlignReferenceSources(ref),  # align sources\n",
                 "        blocks.CentroidQuadratic(),  # centroiding\n",
                 "        blocks.AperturePhotometry(),  # aperture photometry\n",
                 "        blocks.AnnulusBackground(),  # annulus background\n",
                 "    ],\n",
                 "    [\n",
-                "        blocks.GetFluxes(\"fwhm\", \"keyword:AIRMASS\"),\n",
+                "        blocks.GetFluxes(\n",
+                "            \"fwhm\",\n",
+                "            airmass=lambda im: im.header[\"AIRMASS\"],\n",
+                "            dx=lambda im: im.transform.translation[0],\n",
+                "            dy=lambda im: im.transform.translation[1],\n",
+                "        ),\n",
                 "    ],\n",
                 ")\n",
                 "\n",
@@ -504,4 +632,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/prose/fluxes.py b/prose/fluxes.py
index b869e0a4..71827614 100644
--- a/prose/fluxes.py
+++ b/prose/fluxes.py
@@ -2,6 +2,7 @@
 import warnings
 from copy import deepcopy
 from dataclasses import asdict, dataclass
+from functools import partial
 from pathlib import Path
 from typing import Union
 
@@ -12,21 +13,6 @@
 from prose import utils
 
 
-def binned_white_function(x, bins: int = 12):
-    # set binning idxs for white noise evaluation
-    bins = np.min([x.shape[-1], bins])
-    n = x.shape[-1] // bins
-    idxs = np.arange(n * bins)
-
-    def compute(f):
-        return np.nanmean(
-            np.nanstd(np.array(np.split(f.take(idxs, axis=-1), n, axis=-1)), axis=-1),
-            axis=0,
-        )
-
-    return compute
-
-
 def weights(
     fluxes: np.ndarray, tolerance: float = 1e-3, max_iteration: int = 200, bins: int = 5
 ):
@@ -51,7 +37,9 @@ def weights(
 
     # normalize
     dfluxes = fluxes / np.expand_dims(np.nanmean(fluxes, -1), -1)
-    binned_white = binned_white_function(fluxes, bins=bins)
+
+    def weight_function(fluxes):
+        return 1 / np.std(fluxes, axis=-1)
 
     i = 0
     evolution = 1e25
@@ -63,23 +51,24 @@ def weights(
     # --------------------------------------------------
     while evolution > tolerance and i < max_iteration:
         if i == 0:
-            weights = 1 / binned_white(dfluxes)
+            weights = weight_function(dfluxes)
             mask = np.where(~np.isfinite(weights))
         else:
             # This metric is preferred from std to optimize over white noise and not red noise
-            std = binned_white(lcs)
-            weights = 1 / std
+            weights = weight_function(lcs)
 
         weights[~np.isfinite(weights)] = 0
 
-        evolution = np.abs(np.nanmean(weights, axis=-1) - np.nanmean(last_weights, axis=-1))
+        evolution = np.abs(
+            np.nanmean(weights, axis=-1) - np.nanmean(last_weights, axis=-1)
+        )
 
         last_weights = weights
         lcs = diff(dfluxes, weights=weights)
 
         i += 1
 
-    weights[0,mask] = 0
+    weights[0, mask] = 0
 
     return weights[0]
 
@@ -119,7 +108,7 @@ def auto_diff_1d(fluxes, i=None):
     w = weights(dfluxes)
     if i is not None:
         idxs = np.argsort(w)[::-1]
-        white_noise = binned_white_function(dfluxes)
+        white_noise = utils.binned_nanstd(dfluxes)
         last_white_noise = 1e10
 
         def best_weights(j):
@@ -167,7 +156,7 @@ def optimal_flux(diff_fluxes, method="stddiff", sigma=4):
         ),
     ]
     if method == "binned":
-        white_noise = binned_white_function(fluxes)
+        white_noise = utils.binned_nanstd(fluxes)
         criterion = white_noise(fluxes)
     elif method == "stddiff":
         criterion = utils.std_diff_metric(fluxes)
@@ -279,6 +268,18 @@ def ndim(self):
         """Number of dimensions of fluxes"""
         return self.fluxes.ndim
 
+    @property
+    def comparisons(self):
+        """Comparison stars indices ordered from most to less weighted"""
+        if self.weights is None:
+            return None
+        else:
+            if self.aperture is None:
+                raise ValueError("aperture must be set")
+
+            idxs = np.argsort(self.weights[self.aperture])[::-1]
+            return idxs[np.flatnonzero(self.weights[self.aperture][idxs] > 0.0)]
+
     def vander(consant=True, **kwargs):
         pass
 
diff --git a/prose/utils.py b/prose/utils.py
index 7fea2637..ed9a3a7f 100644
--- a/prose/utils.py
+++ b/prose/utils.py
@@ -520,3 +520,18 @@ def get_all_blocks():
     ]
 
     return blocks
+
+
+def binned_nanstd(x, bins: int = 12):
+    # set binning idxs for white noise evaluation
+    bins = np.min([x.shape[-1], bins])
+    n = x.shape[-1] // bins
+    idxs = np.arange(n * bins)
+
+    def compute(f):
+        return np.nanmean(
+            np.nanstd(np.array(np.split(f.take(idxs, axis=-1), n, axis=-1)), axis=-1),
+            axis=0,
+        )
+
+    return compute
diff --git a/pyproject.toml b/pyproject.toml
index 5305a73c..ce10ff47 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "prose"
-version = "3.2.2"
+version = "3.2.3"
 description = "Modular image processing pipelines for Astronomy"
 authors = ["Lionel Garcia"]
 license = "MIT"