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diff --git a/docs/jupyter_execute/rst_files/array_to_image/index.ipynb b/docs/jupyter_execute/rst_files/array_to_image/index.ipynb
new file mode 100644
index 0000000..bd6c44a
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/array_to_image/index.ipynb
@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c760f72c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-31T18:59:26.347758Z",
+     "iopub.status.busy": "2024-12-31T18:59:26.347330Z",
+     "iopub.status.idle": "2024-12-31T18:59:26.820488Z",
+     "shell.execute_reply": "2024-12-31T18:59:26.819994Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 100)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lexi_bu.lexi import array_to_image\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Create a 2D array\n",
+    "input_array = np.random.rand(100, 100)\n",
+    "\n",
+    "# Print the shape of the input array\n",
+    "# The shape should be (100, 100)\n",
+    "print(input_array.shape)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/jupyter_execute/rst_files/array_to_image/index.py b/docs/jupyter_execute/rst_files/array_to_image/index.py
new file mode 100644
index 0000000..3e7d7ad
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/array_to_image/index.py
@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from lexi_bu.lexi import array_to_image
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Create a 2D array
+input_array = np.random.rand(100, 100)
+
+# Print the shape of the input array
+# The shape should be (100, 100)
+print(input_array.shape)
+
diff --git a/docs/jupyter_execute/rst_files/get_exposure_maps/index.ipynb b/docs/jupyter_execute/rst_files/get_exposure_maps/index.ipynb
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index 0000000..65e1e15
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_exposure_maps/index.ipynb
@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "10ae02f8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-31T18:59:28.063842Z",
+     "iopub.status.busy": "2024-12-31T18:59:28.063403Z",
+     "iopub.status.idle": "2024-12-31T18:59:45.528350Z",
+     "shell.execute_reply": "2024-12-31T18:59:45.527804Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vetinari/Desktop/git/Lexi-Bu/lexi/lexi_bu/lexi.py:802: UserWarning: Ephemeris data do not cover the full time range requested.End regions will be forward/backfilled.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exposure map not found, computing now. This may take a while \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3000x3000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/vetinari/Desktop/git/Lexi-Bu/lexi/docs/_build/jupyter_execute/rst_files/get_exposure_maps/index_0_2.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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SLF26NCoqKuKDDz6Iv/zlLzFo0KD0vHHjxsXgwYNjypQpNR5fLwBofH369ImioqIoKiqKbbfdNnbdddc45phj4qabbooZM2bE4Ycfnp47ZsyYGDlyZI3H8h4yAGSXwC7AJurWW2+NSy65JGPbDTfcEN/97nfrtP/6L4QqKirqtf768+vyYiuJNQCau4b2gw2ZOXNmDBs2LBYvXpzeNmTIkPj73/8eLVq0qNMx9AOAZDW0F1x22WUxc+bMiIjo0aNHjBo1qrFL1AsAmkBD+0F1v1uPO+64Ou17wgknZIxffvnlWL16dZV5+gFAshraC1KpVIwcOTLmzp2b3nbggQfG5MmT4/zzz48BAwZEu3btonXr1rHNNtvESSedFK+88kqcd9556fnz58+PESNG1Pj15HoBQNPq0qVL3HfffXHggQemt40dO7bOH7LzHjIANC2BXYBN0JdfYbKuUaNGVdlWm44dO2aMFyxYUOd9ly1bFsuWLUuP27VrV+3X5TZkjYi1f9ir7XgAzV1j9IPazJ07Nw4++OD45JNP0tsGDRoUDz30UBQWFtb5OPoBQHIa2gvefPPNuPbaa9Pjq6++us5fk1sfegFAspL4W1Hbtm1j5513rtO+W221VfTq1Ss9Li8vj9mzZ29wDf0AoPE0Ri/497//Ha+88kp63LNnz7jvvvtq/X2bn58fV199dQwbNiy9bfr06XHTTTdVO18vAGh6BQUFcf3112dsu+aaa6qd2xS/p/UCAKiZwC7AJuaxxx6LE088MSorK9PbLrjggrj44ovrdZztttsuYzxr1qw677v+3PWP1Rhr1GcdgOaosfpBTRYvXhyHHHJITJ8+Pb1txx13jEcffTQ6dOhQr2PpBwDJaIxe8Nvf/jZ9BcSBAwfG17/+9Zg5c2att3Wvuh6x9k2VdR//7LPPqqyjFwAkp7FeG2y//fYZ427dulX7Ae2a9OzZM2OsHwA0ncbqBWPHjs0Yn3HGGXX+O9AvfvGLjPHdd99d7Ty9ACA7dtpppxgwYEB6XFZWFosWLaoyryG/p1esWJERpu3UqVN06dKlUdeobr5eAEAuKch2AQD81/PPPx9HHXVUrFy5Mr3tBz/4QVx11VX1PtaOO+4YDz/8cHr8wQcf1HnfGTNmVDlWTWusqz5rrL9OXl5e7LDDDvXaHyBXNWY/qE55eXl885vfjLfeeiu97Stf+UqMGzcuunbtWu/j6QcAja+xesHy5cvT9994443YZptt6l3LT3/60/jpT3+aHv/kJz+J6667LmOOXgCQjMZ8bbDTTjtljFu3bl2v/defv2LFiipz9AOAxteYvWDSpEkZ42984xt13nfvvfeOVq1apeuYNGlSVFRUVPmWph122CHy8vIilUpFRMN6QUTN708AUNX2228fkydPjoiIVCoVM2fOjM6dO2fMaci/2df/HV3Tv9f1AgComSvsAmwiXn/99TjssMNi2bJl6W3HHnts/OlPf9qo4xUXF2eMX3755Trvu/7cmr4esV+/ftGqVav0+PXXX8/4o2FtPvzww5g7d256XFRUFG3atKlzjQC5qrH7wfpWrlwZ3/72t+PFF19Mb+vZs2eMHz8+evfuvVHH1A8AGlfSvSAJegFA42vsfrDu1bYiIj7//PN67b/+Fdi33HLLKnP0A4DG1di9YP3f/d27d6/zvgUFBRlXUUylUtVebb1t27bxla98JT2eM2dOfPTRR3VaY+XKlfHGG2+kx61atXJVRYB6aNmyZca4ug/ZNcV7yHoBANRMYBdgEzB16tQYNmxYxh/LDj300Lj77rvr9dWE6zrooIMyxhMmTKjzvuvPPeSQQ6qd16pVq9h///3T4xUrVsSrr77aqGsANCdJ9IN1rVmzJo4//vgYN25celvnzp3j8ccfj379+m30cfUDgMaTdC9Iil4A0LiS6Ad77LFHxtW15syZU+1X5FZn1apV8f7776fHeXl5sdVWW1WZpx8ANJ4kekGHDh0yxuXl5fXaf/357dq1q3bexr4/8corr2SEy77xjW9UCZ8BULOPP/44Y1zdBzP22muvjN/f6//urU19/s2uFwBA9Tbdd3oAmomPPvooDj744Jg/f3562/777x9jx45t0IuPXr16xW677ZYeT58+vU6fkPz444/jqaeeSo979OgRe+65Z43zjzjiiIzx3XffXaf61p+3/nEAmpuk+sGXUqlU/PCHP4z77rsvva1du3bxyCOPxC677NLg4+sHAA2XRC944IEHIpVK1et26aWXZhxj9OjRGY9fd9111a6lFwA0jqReGxQUFMS3vvWtjG1PPPFEnfadMGFCxpvmAwYMqBL6+pJ+ANBwSfWCrbfeOmP81ltv1XnfGTNmxJIlS9LjNm3a6AUAm5ClS5dmfFiusLAwevXqVWVe69atM4K25eXl8cADD2zw+BUVFTF27Nj0uGXLlnHooYfWOF8vAIDqCewCZNH8+fPjkEMOiVmzZqW3DR48OB5++OFG+cq/E044IWN8zTXXbHCfP/zhD1FZWZkeH3/88ZGXl1fj/KOPPjpat26dHv/tb3/L+PrC6kyaNCnj6o49evSIAw88cIO1AeSqpPtBRMQFF1wQo0ePTo8LCwvjwQcfrPVDGfWhHwA0TFP0gqTpBQANl3Q/+J//+Z+M8fXXX1+n/f7whz9kjL/5zW/WOFc/AGiYJHvBfvvtlzFe929FG7L+3H322afGuQcddFB069YtPX788cdj8uTJtR5/zpw5cc8996THhYWFcdRRR9W5PoDm7qqrroqVK1emxwceeGDGv8vXtf57yNddd12kUqlaj3/HHXfE4sWL0+PDDjssOnbsWON8vQAAqiewC5AlS5YsieHDh8fUqVPT24qLi+PRRx+t8VPp9XXaaadFjx490uN77703HnzwwRrnv/rqq3Httdemx23atIkLL7yw1jW23nrr+OEPf5geL1myJE4//fSM0O+6Kioq4oc//GHG4xdddFGNLxgBcl1T9INRo0ZlfGijoKAg7r333jjggAMa5fgR+gFAQzRFL2gKegFAwzRFPzj44INj//33T4+fffbZDYZ277zzznjooYfS41atWsXZZ59d43z9AGDjJd0LRowYEfn5/317+Omnn44bb7xxg/u99NJLcdVVV1U5Vk3Wf2+hsrIyfvjDH9b4leuVlZVx+umnZ1zB99RTT42ePXtusDaAXHP11VfHF198Ua997r333vjNb36Tse3HP/5xjfOPOuqojG/ee+mll+KPf/xjjfNnzpwZF198cXqcl5dX5Rua1qcXAED18lIb+pgMAI1u5cqVMWzYsHj66afT27p27RoPPfRQbLXVVvU6VteuXaNdu3Y1Pv6nP/0pTj311PS4devWceONN0ZJSUm0aNEiItZ+TfoDDzwQJ598cnz++efpuZdccklcccUVG6zh008/jeLi4li0aFF627HHHht//OMfMwLDH3zwQZxyyinxzDPPpLf169cvJk+e7E0YoFlqin5w6623VvnD3JVXXhnHHXdcvY5fWFi4wT+M6QcA9deUrw3q6rLLLovLL788PR49enSUlJTUaV+9AGDjNGU/ePXVV+PrX/96rFq1KiLWvtl+8cUXx0UXXRRt27ZNz1uxYkVce+21cckll8SaNWvS23/5y1/Gr371q1pr0A8A6q+pesH3vve9uPPOOzO2nXXWWfGLX/wi43d0RMTy5cvjtttui4suuiiWLVuW3t6nT5+YNm1arb+rly9fHgMGDIgPPvggvW3o0KFxxx13xDbbbJPeNmfOnDj77LPjH//4R3pbly5d4u23347u3bvX7YQBckinTp2iZcuWMXLkyDjuuONi8ODBUVBQUO3c1157La699tq4++67M7Yfdthh8a9//avWdR599NH45je/mb6ybl5eXowaNSrOPffcKCwsTM979tlnY+TIkTF79uz0thNPPDHuuuuuDZ6LXgAAVQnsAmTBzJkzM16ENERd3jw/+eSTo7S0NGNb7969Y9ddd40WLVrEpEmT4v333894fPjw4fGvf/0rHerdkCeeeCKGDx8eq1evTm8rLCyMvfbaK3r06BGzZs2Kl19+OeMNng4dOsRLL70UO+20U53WAMg1TdEPhg4dmvHm98YaMmRIxhtGNdEPAOqnqV8b1EVDArsRegHAxmjqflBaWhonn3xyxrYtttgi9tlnn+jevXt89tln8cILL2Rc3SoiYtiwYfGf//wn4+qMNdEPAOqnqXrB4sWLY5999ol33nknY3tBQUHsuuuuUVRUFC1atIhPP/00Xn311YygbsTaKyY+8cQTsffee2+wjsmTJ8fee++dcaXIgoKC2HPPPaN3794xZ86ceOmllzKuttiyZct4/PHHY+jQoXU/YYAc0qlTp4wLLBUWFkZxcXH07NkzOnXqFCtXrozPPvss3nrrrZg/f36V/ffYY48YP358tG/ffoNrXX755XHZZZdlbOvWrVsMHjw4tthii3j33XdjypQpGY8PHDgwnnvuuYwP+9VGLwCATNV/DAeAnPKnP/0p8vLyYvTo0elts2fPzvgk5LoOP/zwuOuuu+oc1o2IOPDAA2PMmDHx/e9/P/0isqKiosZwV69evWLMmDHegAHIMfoBAHoBwKavpKQkVqxYEeeee24sX748IiKWLVsW48ePr3Gfk046KW677bY6hXUj9AOATVWnTp1i/PjxceKJJ8ZTTz2V3r569ep49dVX49VXX61x3969e8fdd99dp7BuRMSAAQPi3//+dxx//PHxySefpNd5/vnna6ztjjvuENACWEdFRUVMnDhxg/Py8vLi1FNPjd/97nd1/gam//3f/42VK1fGb3/726isrIyIiPnz58cjjzxS7fx99tkn7r333jqHdSP0AgBYX93+sgbAZq1Vq1Zxxx13xNixY2P33Xevcd7OO+8ct912Wzz00EPRsWPHeq9z9NFHx5tvvhknnHBCtGnTpto5HTt2jFNPPTXefPPN2Hfffeu9BgCbPv0AAL0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+      "text/plain": [
+       "<Figure size 3000x3000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/vetinari/Desktop/git/Lexi-Bu/lexi/docs/_build/jupyter_execute/rst_files/get_exposure_maps/index_0_3.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['exposure_maps', 'ra_arr', 'dec_arr', 'time_range', 'time_integrate', 'ra_range', 'dec_range', 'ra_res', 'dec_res', 'start_time_arr', 'stop_time_arr'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lexi_bu.lexi import get_exposure_maps\n",
+    "\n",
+    "exposure_maps_dict = get_exposure_maps(\n",
+    "    time_range=[\"2025-03-02 08:04:00\", \"2025-03-08 23:43:00\"],\n",
+    "    ra_range=[190, 310],\n",
+    "    dec_range=[-33, 3],\n",
+    "    ra_res=0.5,\n",
+    "    dec_res=0.5,\n",
+    "    save_exposure_map_file=False,\n",
+    "    save_exposure_map_image=True,\n",
+    "    verbose=False,\n",
+    "    array_to_image_kwargs={\"display\": True}\n",
+    ")\n",
+    "\n",
+    "print(exposure_maps_dict.keys())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/jupyter_execute/rst_files/get_exposure_maps/index.py b/docs/jupyter_execute/rst_files/get_exposure_maps/index.py
new file mode 100644
index 0000000..037516c
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_exposure_maps/index.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from lexi_bu.lexi import get_exposure_maps
+
+exposure_maps_dict = get_exposure_maps(
+    time_range=["2025-03-02 08:04:00", "2025-03-08 23:43:00"],
+    ra_range=[190, 310],
+    dec_range=[-33, 3],
+    ra_res=0.5,
+    dec_res=0.5,
+    save_exposure_map_file=False,
+    save_exposure_map_image=True,
+    verbose=False,
+    array_to_image_kwargs={"display": True}
+)
+
+print(exposure_maps_dict.keys())
+
diff --git a/docs/jupyter_execute/rst_files/get_exposure_maps/index_0_2.png b/docs/jupyter_execute/rst_files/get_exposure_maps/index_0_2.png
new file mode 100644
index 0000000..7cfca95
Binary files /dev/null and b/docs/jupyter_execute/rst_files/get_exposure_maps/index_0_2.png differ
diff --git a/docs/jupyter_execute/rst_files/get_exposure_maps/index_0_3.png b/docs/jupyter_execute/rst_files/get_exposure_maps/index_0_3.png
new file mode 100644
index 0000000..8502f87
Binary files /dev/null and b/docs/jupyter_execute/rst_files/get_exposure_maps/index_0_3.png differ
diff --git a/docs/jupyter_execute/rst_files/get_lexi_data/index.ipynb b/docs/jupyter_execute/rst_files/get_lexi_data/index.ipynb
new file mode 100644
index 0000000..bfe6861
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_lexi_data/index.ipynb
@@ -0,0 +1,68 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "100d4fb3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-31T18:59:46.742015Z",
+     "iopub.status.busy": "2024-12-31T18:59:46.741642Z",
+     "iopub.status.idle": "2024-12-31T18:59:48.289041Z",
+     "shell.execute_reply": "2024-12-31T18:59:48.288104Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading files from \u001b[95mdata/level_1c/cdf/1.0.0\u001b[00m on branch \u001b[92mstable\u001b[00m:\n",
+      "A total of \u001b[1;92m1917\u001b[0m files found\n",
+      "\n",
+      "Files to download: \u001b[1;92m9\u001b[0m\n",
+      "\n",
+      "                           Epoch_unix  ra_J2000_deg  dec_J2000_deg\n",
+      "Epoch_utc                                                         \n",
+      "2025-03-02 08:50:00+00:00  1740905400    204.737595     -10.064720\n",
+      "2025-03-02 08:50:00+00:00  1740905400    199.864853      -7.779826\n",
+      "2025-03-02 08:50:00+00:00  1740905400    200.145035     -15.348747\n",
+      "2025-03-02 08:50:00+00:00  1740905400    199.899582      -7.916301\n",
+      "2025-03-02 08:50:00+00:00  1740905400    205.031219     -12.977372\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lexi_bu.lexi import get_lexi_data\n",
+    "\n",
+    "df_lexi = get_lexi_data(\n",
+    "    time_range=[\"2025-03-02 08:50:00\", \"2025-03-02 09:23:00\"],\n",
+    "    verbose=False\n",
+    ")\n",
+    "\n",
+    "print(df_lexi.head())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/jupyter_execute/rst_files/get_lexi_data/index.py b/docs/jupyter_execute/rst_files/get_lexi_data/index.py
new file mode 100644
index 0000000..5f017b1
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_lexi_data/index.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from lexi_bu.lexi import get_lexi_data
+
+df_lexi = get_lexi_data(
+    time_range=["2025-03-02 08:50:00", "2025-03-02 09:23:00"],
+    verbose=False
+)
+
+print(df_lexi.head())
+
diff --git a/docs/jupyter_execute/rst_files/get_lexi_images/index.ipynb b/docs/jupyter_execute/rst_files/get_lexi_images/index.ipynb
new file mode 100644
index 0000000..f3a19fd
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_lexi_images/index.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9dc4cd83",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-31T18:59:49.604915Z",
+     "iopub.status.busy": "2024-12-31T18:59:49.603469Z",
+     "iopub.status.idle": "2024-12-31T18:59:51.766504Z",
+     "shell.execute_reply": "2024-12-31T18:59:51.765902Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading files from \u001b[95mdata/level_1c/cdf/1.0.0\u001b[00m on branch \u001b[92mstable\u001b[00m:\n",
+      "A total of \u001b[1;92m1917\u001b[0m files found\n",
+      "\n",
+      "Files to download: \u001b[1;92m8\u001b[0m\n",
+      "\n",
+      "[Timestamp('2025-03-04 08:53:41+0000', tz='UTC')]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3000x3000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/vetinari/Desktop/git/Lexi-Bu/lexi/docs/_build/jupyter_execute/rst_files/get_lexi_images/index_0_1.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['lexi_images', 'ra_arr', 'dec_arr', 'time_range', 'time_integrate', 'ra_range', 'dec_range', 'ra_res', 'dec_res', 'start_time_arr', 'stop_time_arr'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lexi_bu.lexi import get_lexi_images\n",
+    "\n",
+    "lexi_images_dict = get_lexi_images(\n",
+    "    time_range=[\"2025-03-04 08:53:41\", \"2025-03-04 09:23:41\"],\n",
+    "    ra_range=[220, 240],\n",
+    "    dec_range=[-30, -15],\n",
+    "    ra_res=1,\n",
+    "    dec_res=1,\n",
+    "    background_correction_on=False,\n",
+    "    save_exposure_map_file=False,\n",
+    "    save_sky_backgrounds_file=False,\n",
+    "    save_exposure_map_image=False,\n",
+    "    save_sky_backgrounds_image=False,\n",
+    "    save_lexi_images=True,\n",
+    "    verbose=False,\n",
+    "    array_to_image_kwargs={\"display\": True}\n",
+    ")\n",
+    "\n",
+    "print(lexi_images_dict.keys())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/jupyter_execute/rst_files/get_lexi_images/index.py b/docs/jupyter_execute/rst_files/get_lexi_images/index.py
new file mode 100644
index 0000000..fb4dc69
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_lexi_images/index.py
@@ -0,0 +1,26 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from lexi_bu.lexi import get_lexi_images
+
+lexi_images_dict = get_lexi_images(
+    time_range=["2025-03-04 08:53:41", "2025-03-04 09:23:41"],
+    ra_range=[220, 240],
+    dec_range=[-30, -15],
+    ra_res=1,
+    dec_res=1,
+    background_correction_on=False,
+    save_exposure_map_file=False,
+    save_sky_backgrounds_file=False,
+    save_exposure_map_image=False,
+    save_sky_backgrounds_image=False,
+    save_lexi_images=True,
+    verbose=False,
+    array_to_image_kwargs={"display": True}
+)
+
+print(lexi_images_dict.keys())
+
diff --git a/docs/jupyter_execute/rst_files/get_lexi_images/index_0_1.png b/docs/jupyter_execute/rst_files/get_lexi_images/index_0_1.png
new file mode 100644
index 0000000..5fc97fb
Binary files /dev/null and b/docs/jupyter_execute/rst_files/get_lexi_images/index_0_1.png differ
diff --git a/docs/jupyter_execute/rst_files/get_sky_backgrounds/index.ipynb b/docs/jupyter_execute/rst_files/get_sky_backgrounds/index.ipynb
new file mode 100644
index 0000000..2c15eee
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_sky_backgrounds/index.ipynb
@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "caef1bbc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-31T18:59:52.980310Z",
+     "iopub.status.busy": "2024-12-31T18:59:52.979770Z",
+     "iopub.status.idle": "2024-12-31T19:00:14.847266Z",
+     "shell.execute_reply": "2024-12-31T19:00:14.846651Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vetinari/Desktop/git/Lexi-Bu/lexi/lexi_bu/lexi.py:802: UserWarning: Ephemeris data do not cover the full time range requested.End regions will be forward/backfilled.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exposure map not found, computing now. This may take a while \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3000x3000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/vetinari/Desktop/git/Lexi-Bu/lexi/docs/_build/jupyter_execute/rst_files/get_sky_backgrounds/index_0_2.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3000x3000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/vetinari/Desktop/git/Lexi-Bu/lexi/docs/_build/jupyter_execute/rst_files/get_sky_backgrounds/index_0_3.png"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sky background not found, computing now. This may take a while \n",
+      "\n",
+      "dict_keys(['sky_backgrounds', 'ra_arr', 'dec_arr', 'time_range', 'time_integrate', 'ra_range', 'dec_range', 'ra_res', 'dec_res', 'start_time_arr', 'stop_time_arr'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lexi_bu.lexi import get_sky_backgrounds\n",
+    "\n",
+    "sky_background_dict = get_sky_backgrounds(\n",
+    "    time_range=[\"2025-03-02 08:04:00\", \"2025-03-08 23:43:00\"],\n",
+    "    ra_range=[190, 310],\n",
+    "    dec_range=[-33, 3],\n",
+    "    ra_res=0.5,\n",
+    "    dec_res=0.5,\n",
+    "    save_exposure_map_file=False,\n",
+    "    save_sky_backgrounds_file=False,\n",
+    "    save_exposure_map_image=True,\n",
+    "    save_sky_backgrounds_image=False,\n",
+    "    verbose=False,\n",
+    "    array_to_image_kwargs={\"display\": True}\n",
+    ")\n",
+    "\n",
+    "print(sky_background_dict.keys())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/jupyter_execute/rst_files/get_sky_backgrounds/index.py b/docs/jupyter_execute/rst_files/get_sky_backgrounds/index.py
new file mode 100644
index 0000000..b874684
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_sky_backgrounds/index.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from lexi_bu.lexi import get_sky_backgrounds
+
+sky_background_dict = get_sky_backgrounds(
+    time_range=["2025-03-02 08:04:00", "2025-03-08 23:43:00"],
+    ra_range=[190, 310],
+    dec_range=[-33, 3],
+    ra_res=0.5,
+    dec_res=0.5,
+    save_exposure_map_file=False,
+    save_sky_backgrounds_file=False,
+    save_exposure_map_image=True,
+    save_sky_backgrounds_image=False,
+    verbose=False,
+    array_to_image_kwargs={"display": True}
+)
+
+print(sky_background_dict.keys())
+
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diff --git a/docs/jupyter_execute/rst_files/get_sky_backgrounds/index_0_3.png b/docs/jupyter_execute/rst_files/get_sky_backgrounds/index_0_3.png
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diff --git a/docs/jupyter_execute/rst_files/get_spc_prams/index.ipynb b/docs/jupyter_execute/rst_files/get_spc_prams/index.ipynb
new file mode 100644
index 0000000..a3b9ca4
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_spc_prams/index.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a6015423",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-31T19:00:16.126658Z",
+     "iopub.status.busy": "2024-12-31T19:00:16.126372Z",
+     "iopub.status.idle": "2024-12-31T19:00:16.721277Z",
+     "shell.execute_reply": "2024-12-31T19:00:16.720700Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                 RA       DEC\n",
+      "epoch_utc                                    \n",
+      "2025-03-02 08:50:00+00:00  3.518914 -0.197321\n",
+      "2025-03-02 08:50:05+00:00  3.518928 -0.197328\n",
+      "2025-03-02 08:50:10+00:00  3.518941 -0.197334\n",
+      "2025-03-02 08:50:15+00:00  3.518955 -0.197341\n",
+      "2025-03-02 08:50:20+00:00  3.518969 -0.197348\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lexi_bu.lexi import get_spc_prams\n",
+    "\n",
+    "df_spc = get_spc_prams(\n",
+    "    time_range=[\"2025-03-02 08:50:00\", \"2025-03-02 09:23:00\"],\n",
+    "    verbose=False\n",
+    ")\n",
+    "\n",
+    "print(df_spc.head())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/jupyter_execute/rst_files/get_spc_prams/index.py b/docs/jupyter_execute/rst_files/get_spc_prams/index.py
new file mode 100644
index 0000000..3871245
--- /dev/null
+++ b/docs/jupyter_execute/rst_files/get_spc_prams/index.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from lexi_bu.lexi import get_spc_prams
+
+df_spc = get_spc_prams(
+    time_range=["2025-03-02 08:50:00", "2025-03-02 09:23:00"],
+    verbose=False
+)
+
+print(df_spc.head())
+