diff --git a/config/data_set.toml b/config/data_set.toml
index d1f77c5..246fe03 100644
--- a/config/data_set.toml
+++ b/config/data_set.toml
@@ -1,24 +1,26 @@
 [sampling_options]
 mode = "basic"
-layout = "vlba"
-img_size = 64
-fov_center_ra = [00:00:00.0, 23:59:59.59]
-fov_center_dec = [00:00:00.0, 11:59:59.59]
-fov_size = 0.0064 # max res 0.1
-corr_int_time = 10.0
-scan_start = ["01-01-2020 00:00:01", "31-12-2021 23:59:59"]
-scan_duration = [50, 300]
-scans = [30, 72]
-interval_length = 1200
+layout = "vla"
+img_size = 128
+fov_center_ra = [100, 110]
+fov_center_dec = [30, 40]
+fov_size = 100 # max res 0.1
+corr_int_time = 30.0
+scan_start = ["16-01-2020 00:04:01", "16-01-2020 08:59:59"]
+scan_duration = [60, 90]
+scans = [1, 2]
+interval_length = 360
 base_freq = 15.21e9
 frequsel = [0e8, 0.8e8, 1.44e8, 2.08e8]
 bandwidths = [6.4e7, 6.4e7, 6.4e7, 6.4e7]
+corrupted = true
 
 [bundle_options]
-num_bundles = 5
-size_bundles = 10
-in_path = "../../../test_radiosim/build/test_data"
-out_path = "./build"
-
-[cluster_options]
-num_jobs = 4
\ No newline at end of file
+in_path = "build/skies/"
+out_path_fits = "build/uvfits"
+out_path_gridded = "build/gridded"
+num_test_images = 500
+bundle_size = 100
+train_valid_split = 0.2
+grid_size = 128
+amp_phase = true
diff --git a/examples/01_layouts.ipynb b/examples/01_layouts.ipynb
new file mode 100644
index 0000000..75efda4
--- /dev/null
+++ b/examples/01_layouts.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5b65f63b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyvisgen.layouts.layouts import get_array_layout, Stations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dae31ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vlba_layout = get_array_layout(\"vlba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "48586eb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vla_layout = get_array_layout(\"vla\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1813010a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eht_layout = get_array_layout(\"eht\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1398748a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyvisgen.simulation.utils import calc_ref_elev\n",
+    "from datetime import datetime\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import astropy.units as un\n",
+    "from astropy.time import Time\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ae966407",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime(2021, 6, 22, 18, 0, 1)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "start_time_l = datetime.strptime(\"01-01-2020 00:00:01\", \"%d-%m-%Y %H:%M:%S\")\n",
+    "start_time_h = datetime.strptime(\"31-12-2021 23:59:59\", \"%d-%m-%Y %H:%M:%S\")\n",
+    "start_times = pd.date_range(\n",
+    "    start_time_l,\n",
+    "    start_time_h,\n",
+    "    freq=\"1h\",\n",
+    ").strftime(\"%d-%m-%Y %H:%M:%S\")\n",
+    "scan_start = np.random.choice(\n",
+    "    [datetime.strptime(time, \"%d-%m-%Y %H:%M:%S\") for time in start_times]\n",
+    ")\n",
+    "scan_start"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "15c4d443",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39,)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "start_time = Time(scan_start.isoformat(), format=\"isot\")\n",
+    "interval = 3600\n",
+    "num_scans = 3\n",
+    "scan_duration = 360\n",
+    "int_time = 30\n",
+    "\n",
+    "time_lst = [\n",
+    "    start_time + interval * i * un.second + j * int_time * un.second\n",
+    "    for i in range(num_scans)\n",
+    "    for j in range(int(scan_duration / int_time) + 1)\n",
+    "]\n",
+    "# +1 because t_1 is the stop time of t_0\n",
+    "# in order to save computing power we take one time more to complete interval\n",
+    "time = Time(time_lst)\n",
+    "time.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ae6e0fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fc0bcc36bb0>]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "plt.plot(time.datetime, np.ones(time.shape), marker=\".\", linestyle=\"none\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b164b0bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "src_crd = SkyCoord(\n",
+    "    ra=240,\n",
+    "    dec=50,\n",
+    "    unit=(un.deg, un.deg),\n",
+    ")\n",
+    "\n",
+    "array_layout = get_array_layout(\"vlba\")\n",
+    "\n",
+    "_, el_st_all = calc_ref_elev(src_crd, time, array_layout)\n",
+    "\n",
+    "el_min = 15\n",
+    "el_max = 85\n",
+    "\n",
+    "valid = np.where((el_st_all >= el_min) & (el_st_all <= el_max), np.zeros(el_st_all.shape), 1)\n",
+    "print(valid)\n",
+    "telescopes = valid * (np.arange(10) + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bf876195",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Time')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "plt.plot(time.datetime, telescopes, color=\"darkorange\", marker=\".\", linestyle=\"none\")\n",
+    "\n",
+    "plt.ylabel(\"Telescope\")\n",
+    "plt.xlabel(\"Time\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f13d9598",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c955440f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76bee522",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d837c212",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/02_times.ipynb b/examples/02_times.ipynb
new file mode 100644
index 0000000..d828788
--- /dev/null
+++ b/examples/02_times.ipynb
@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "384f9bbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyvisgen.simulation.utils import calc_ref_elev\n",
+    "from datetime import datetime\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import astropy.units as un\n",
+    "from astropy.time import Time\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pyvisgen.layouts.layouts import get_array_layout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f5d84015",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# try different observation dates\n",
+    "scan_start = datetime.strptime(\"18-1-2021 6:0:1\", \"%d-%m-%Y %H:%M:%S\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "16b44ffe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25,)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "start_time = Time(scan_start.isoformat(), format=\"isot\")\n",
+    "\n",
+    "# try different intervals and integration times / units are seconds\n",
+    "interval = 360\n",
+    "num_scans = 1\n",
+    "scan_duration = 360\n",
+    "int_time = 15\n",
+    "\n",
+    "time_lst = [\n",
+    "    start_time + interval * i * un.second + j * int_time * un.second\n",
+    "    for i in range(num_scans)\n",
+    "    for j in range(int(scan_duration / int_time) + 1)\n",
+    "]\n",
+    "time = Time(time_lst)\n",
+    "time.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c748d88e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f017ec26e80>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot time steps\n",
+    "plt.figure(figsize=(12,8))\n",
+    "plt.plot(time.datetime, np.ones(time.shape), marker=\".\", linestyle=\"none\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6588f902",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[59.45441696 59.42492996 59.43560109 59.44401955 59.4355367  59.41870372\n",
+      " 59.43547195 59.43044743 59.43735472 59.45068632 59.43911547 59.45855358\n",
+      " 59.42215729 59.43549136 59.41492211 59.43553383 59.43512177 59.43560022\n",
+      " 59.43613891 59.44134061 59.43403561 59.43552479 59.43557475 59.43555851\n",
+      " 59.43554835 59.428052   59.43245418 59.44713497]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# try different source coordinates\n",
+    "src_crd = SkyCoord(\n",
+    "    ra=137,\n",
+    "    dec=34,\n",
+    "    unit=(un.deg, un.deg),\n",
+    ")\n",
+    "\n",
+    "array_layout = get_array_layout(\"vla\")\n",
+    "\n",
+    "_, el_st_all = calc_ref_elev(src_crd, time, array_layout)\n",
+    "print(el_st_all[0])\n",
+    "\n",
+    "# this is fixed for the telescopes\n",
+    "el_min = 15\n",
+    "el_max = 75\n",
+    "\n",
+    "valid = np.where((el_st_all >= el_min) & (el_st_all <= el_max), np.ones(el_st_all.shape), 0)\n",
+    "telescopes = valid * (np.arange(28) + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cc3eef79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([70.8706793 , 70.93335042, 70.99602154, 71.05869265, 71.12136377,\n",
+       "       71.18403488, 71.246706  , 71.30937712, 71.37204823, 71.43471935,\n",
+       "       71.49739046, 71.56006158, 71.6227327 , 71.68540381, 71.74807493,\n",
+       "       71.81074604, 71.87341716, 71.93608828, 71.99875939, 72.06143051,\n",
+       "       72.12410162, 72.18677274, 72.24944386, 72.31211497, 72.37478609])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astropy.coordinates import EarthLocation, AltAz, Angle\n",
+    "ha_all = Angle(\n",
+    "    [t.sidereal_time(\"apparent\", \"greenwich\") - src_crd.ra for t in time]\n",
+    ")\n",
+    "ha_all.deg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7dd1602e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fc657749",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 29.0)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "plt.plot(time.datetime, telescopes, color=\"darkorange\", marker=\".\", linestyle=\"none\")\n",
+    "\n",
+    "plt.ylabel(\"Telescope\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylim(0, 29)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a9907a67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "import geoplot\n",
+    "import geoplot.crs as gcrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cdc8f9b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "world = geopandas.read_file(\n",
+    "    geopandas.datasets.get_path('naturalearth_lowres')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "df5c6d32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "world[\"gdp_pp\"] = world[\"gdp_md_est\"] / world[\"pop_est\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9f70660c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from shapely.geometry import Point\n",
+    "\n",
+    "d = {'col1': ['ant1', 'ant2', 'ant3', 'ant4', 'ant5', 'ant6', 'ant7', 'ant8', 'ant9', 'ant10'],\n",
+    "     'geometry': [\n",
+    "    Point(array_layout.x[0], array_layout.y[0], array_layout.z[0]),\n",
+    "    Point(array_layout.x[1], array_layout.y[1], array_layout.z[1]),\n",
+    "    Point(array_layout.x[2], array_layout.y[2], array_layout.z[2]),\n",
+    "    Point(array_layout.x[3], array_layout.y[3], array_layout.z[3]),\n",
+    "    Point(array_layout.x[4], array_layout.y[4], array_layout.z[4]),\n",
+    "    Point(array_layout.x[5], array_layout.y[5], array_layout.z[5]),\n",
+    "    Point(array_layout.x[6], array_layout.y[6], array_layout.z[6]),\n",
+    "    Point(array_layout.x[7], array_layout.y[7], array_layout.z[7]),\n",
+    "    Point(array_layout.x[8], array_layout.y[8], array_layout.z[8]),\n",
+    "    Point(array_layout.x[9], array_layout.y[9], array_layout.z[9]),\n",
+    "]}\n",
+    "gdf = geopandas.GeoDataFrame(d, crs=4328)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "51ef9600",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3069209/1818496961.py:5: DeprecationWarning: The outline_patch property is deprecated. Use GeoAxes.spines['geo'] or the default Axes properties instead.\n",
+      "  ax.outline_patch.set_visible(True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = geoplot.polyplot(\n",
+    "    world, projection=geoplot.crs.Orthographic(central_longitude=360-ha_all.deg[0], central_latitude=37), figsize=(8, 4)\n",
+    ")\n",
+    "geoplot.pointplot(gdf.to_crs(\"epsg:4326\"), projection=geoplot.crs.Orthographic(central_longitude=360-137, central_latitude=37), ax=ax)\n",
+    "ax.outline_patch.set_visible(True)\n",
+    "ax.set_global()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64d7ea96",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbe6c876",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6faad99",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98fa3f95",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/0X_simulation_chain.ipynb b/examples/0X_simulation_chain.ipynb
new file mode 100644
index 0000000..f594388
--- /dev/null
+++ b/examples/0X_simulation_chain.ipynb
@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "42e51422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyvisgen.layouts.layouts import get_array_layout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "719cee21",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7135e0e8",
+   "metadata": {},
+   "source": [
+    "### Define radio interferometer array layout\n",
+    "\n",
+    "Available layouts:\n",
+    "* vla\n",
+    "* vlba\n",
+    "* eht"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fb73812f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array_layout = get_array_layout(\"vlba\") # in rc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a4f1f2bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Stations(st_num=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), name=array(['MKO', 'OVRO', 'BR', 'NL', 'HC', 'KPN', 'PT', 'FD', 'LA', 'SC'],\n",
+       "      dtype=object), x=array([-5464075.238656, -2409150.471188, -2112065.261576,  -130872.556729,\n",
+       "        1446374.806874, -1995678.891541, -1640953.992891, -1324009.374466,\n",
+       "       -1449752.637707,  2607848.664542]), y=array([-2495247.871441, -4478573.093114, -3705356.502894, -4762317.087045,\n",
+       "       -4447939.68308 , -5037317.693848, -5014816.024485, -5332181.952906,\n",
+       "       -4975298.573645, -5488069.500452]), z=array([2148297.485741, 3838617.326057, 4726813.649034, 4226850.993404,\n",
+       "       4322306.19968 , 3357328.002045, 3575411.772132, 3231962.377936,\n",
+       "       3709123.828301, 1932739.778597]), diam=array([25, 25, 25, 25, 25, 25, 25, 25, 25, 25]), el_low=array([15, 15, 15, 15, 15, 15, 15, 15, 15, 15]), el_high=array([85, 85, 85, 85, 85, 85, 85, 85, 85, 85]), sefd=array([  110, 11900,   560,  4900,  2900,  1600,  7300,  4744,  4744,\n",
+       "        4744]), altitude=array([5030, 3185, 4640, 4205, 2850, 2550, 2800, 3210, 3210, 3210]))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array_layout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6cace9fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from pyvisgen.utils.data import data_handler\n",
+    "from pyvisgen.simulation.data_set import create_sampling_rc\n",
+    "from pyvisgen.simulation.visibility import vis_loop\n",
+    "from pyvisgen.utils.config import read_data_set_conf\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e2d892fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = \"test_conf.toml\"\n",
+    "conf = read_data_set_conf(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fb870908",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data_handler(conf[\"in_path\"])\n",
+    "samp_ops = create_sampling_rc(conf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ff3e2b78",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(16245,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "SI = torch.tensor(data[0][0][0], dtype=torch.cdouble)\n",
+    "vis_data = vis_loop(samp_ops, SI)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb6d8ab3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyvisgen/fits/data.py b/pyvisgen/fits/data.py
index 83acb8c..2a315e2 100644
--- a/pyvisgen/fits/data.py
+++ b/pyvisgen/fits/data.py
@@ -30,7 +30,11 @@ def __getitem__(self, i):
         return self.open_file(i)
 
     def get_files(self, path):
-        return np.sort(np.array([x for x in path.iterdir()]))
+        import natsort
+
+        fits_files = [str(x) for x in path.iterdir()]
+        fits_files_sorted = natsort.natsorted(fits_files)
+        return fits_files_sorted
 
     def get_uv_data(self, i):
         with fits.open(self.files[i]) as hdul:
diff --git a/pyvisgen/fits/writer.py b/pyvisgen/fits/writer.py
index 9f04990..083122e 100644
--- a/pyvisgen/fits/writer.py
+++ b/pyvisgen/fits/writer.py
@@ -15,9 +15,7 @@ def create_vis_hdu(data, conf, layout="vlba", source_name="sim-source-0"):
 
     w = data.w
 
-    DATE = data.date - int(
-        data.date.min()
-    ) 
+    DATE = data.date - int(data.date.min())
 
     _DATE = data._date  # central time in the integration period
 
diff --git a/pyvisgen/gridding/alt_gridder.py b/pyvisgen/gridding/alt_gridder.py
index 7f95954..8038e3a 100644
--- a/pyvisgen/gridding/alt_gridder.py
+++ b/pyvisgen/gridding/alt_gridder.py
@@ -98,11 +98,11 @@ def ms2dirty_python_fast(
             # xkernel = kernel(pos - ratposx + xle)
             # ykernel = kernel(pos - ratposy + yle)
             for xx in range(supp):
-                foo = vis #* xkernel[xx]
+                foo = vis  # * xkernel[xx]
                 myxpos = (xle + xx) % ng[0]
                 for yy in range(supp):
                     myypos = (yle + yy) % ng[1]
-                    grid[myxpos, myypos] += foo #* ykernel[yy]
+                    grid[myxpos, myypos] += foo  # * ykernel[yy]
     #     loopim = np.fft.fftshift(np.fft.ifft2(grid)*np.prod(ng))
     #     loopim = loopim[slc0, slc1]
     #     if do_wgridding:
@@ -116,9 +116,9 @@ def ms2dirty_python_fast(
 
 
 def get_npixdirty(uvw, freq, fov_deg, mask):
-    speedOfLight = 299792458.
-    bl = np.sqrt(uvw[:,0]**2+uvw[:,1]**2+uvw[:,2]**2)
-    bluvw = bl.reshape((-1,1))*freq.reshape((1,-1))/speedOfLight
-    maxbluvw = np.max(bluvw*mask)
-    minsize = int((2*fov_deg*np.pi/180*maxbluvw)) + 1
-    return minsize+(minsize%2)  # make even
+    speedOfLight = 299792458.0
+    bl = np.sqrt(uvw[:, 0] ** 2 + uvw[:, 1] ** 2 + uvw[:, 2] ** 2)
+    bluvw = bl.reshape((-1, 1)) * freq.reshape((1, -1)) / speedOfLight
+    maxbluvw = np.max(bluvw * mask)
+    minsize = int((2 * fov_deg * np.pi / 180 * maxbluvw)) + 1
+    return minsize + (minsize % 2)  # make even
diff --git a/pyvisgen/gridding/gridder.py b/pyvisgen/gridding/gridder.py
index b6033f5..9820489 100644
--- a/pyvisgen/gridding/gridder.py
+++ b/pyvisgen/gridding/gridder.py
@@ -1,24 +1,27 @@
-import cv2
 import numpy as np
 from tqdm import tqdm
 from pathlib import Path
-from radiosim.data import radiosim_data
 from pyvisgen.fits.data import fits_data
 from pyvisgen.utils.config import read_data_set_conf
+from pyvisgen.utils.data import load_bundles, open_bundles
 from radionets.dl_framework.data import save_fft_pair
 import astropy.constants as const
-from pyvisgen.gridding.alt_gridder import ms2dirty_python_fast, get_npixdirty
+from pyvisgen.gridding.alt_gridder import ms2dirty_python_fast
+import os
+
+os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
 
 
 def create_gridded_data_set(config):
     conf = read_data_set_conf(config)
-    out_path_fits = Path(conf["out_path"])
-    out_path = out_path_fits.parent / "gridded/"
+    out_path_fits = Path(conf["out_path_fits"])
+    out_path = Path(conf["out_path_gridded"])
     out_path.mkdir(parents=True, exist_ok=True)
 
-    sky_dist = radiosim_data(conf["in_path"])
+    sky_dist = load_bundles(conf["in_path"])
     fits_files = fits_data(out_path_fits)
     size = len(fits_files)
+    print(size)
 
     ###################
     # test
@@ -41,14 +44,14 @@ def create_gridded_data_set(config):
                 truth_amp_phase_test = convert_amp_phase(truth_fft_test, sky_sim=True)
             assert gridded_data_test.shape[1] == 2
 
-
             out = out_path / Path("samp_test" + str(i) + ".h5")
             save_fft_pair(out, gridded_data_test, truth_amp_phase_test)
     #
     ###################
 
-    size_valid = conf["train_valid_split"] * size
-    size_train = size - size_valid
+    size_train = int(size // (1 + conf["train_valid_split"]))
+    size_valid = size - size_train
+    print(f"Training size: {size_train}, Validation size: {size_valid}")
     bundle_train = int(size_train // conf["bundle_size"])
     bundle_valid = int(size_valid // conf["bundle_size"])
 
@@ -105,38 +108,33 @@ def create_gridded_data_set(config):
 
 
 def open_data(fits_files, sky_dist, conf, i):
-    uv_data = np.array(
-        [
-            fits_files.get_uv_data(n).copy()
-            for n in np.arange(
-                i * conf["bundle_size"],
-                (i * conf["bundle_size"]) + conf["bundle_size"],
-            )
-        ],
-        dtype="object",
-    )
+    uv_data = [
+        fits_files.get_uv_data(n).copy()
+        for n in np.arange(
+            i * conf["bundle_size"], (i * conf["bundle_size"]) + conf["bundle_size"]
+        )
+    ]
     freq_data = np.array(
         [
             fits_files.get_freq_data(n)
             for n in np.arange(
-                i * conf["bundle_size"],
-                (i * conf["bundle_size"]) + conf["bundle_size"],
+                i * conf["bundle_size"], (i * conf["bundle_size"]) + conf["bundle_size"]
             )
         ],
         dtype="object",
     )
     gridded_data = np.array(
-        [
-            ducc0_gridding(data, freq).copy()
-            for data, freq in tqdm(zip(uv_data, freq_data))
-        ]
+        [grid_data(data, freq, conf).copy() for data, freq in zip(uv_data, freq_data)]
     )
+    bundle = np.floor_divide(i * conf["bundle_size"], len(open_bundles(sky_dist[0])))
     gridded_truth = np.array(
         [
-            sky_dist[int(n)][0][0]
+            open_bundles(sky_dist[bundle])[n]
             for n in np.arange(
-                i * conf["bundle_size"],
-                (i * conf["bundle_size"]) + conf["bundle_size"],
+                i * conf["bundle_size"] - bundle * len(open_bundles(sky_dist[0])),
+                (i * conf["bundle_size"])
+                + conf["bundle_size"]
+                - bundle * len(open_bundles(sky_dist[0])),
             )
         ]
     )
@@ -147,8 +145,7 @@ def calc_truth_fft(sky_dist):
     # norm = np.sum(np.sum(sky_dist_test, keepdims=True, axis=1), axis=2)
     # sky_dist_test = np.expand_dims(sky_dist_test, -1) / norm[:, None, None]
     truth_fft = np.fft.fftshift(
-        np.fft.fft2(np.fft.fftshift(sky_dist, axes=(1, 2)), axes=(1, 2)),
-        axes=(1, 2),
+        np.fft.fft2(np.fft.fftshift(sky_dist, axes=(1, 2)), axes=(1, 2)), axes=(1, 2)
     )
     return truth_fft
 
@@ -182,22 +179,22 @@ def ducc0_gridding(uv_data, freq_data):
     mask[wgt == 0] = False
 
     DEG2RAD = np.pi / 180
-    nthreads = 4
+    # nthreads = 4
     epsilon = 1e-4
-    do_wgridding = False
-    verbosity = 1
+    # do_wgridding = False
+    # verbosity = 1
 
-    do_sycl = False  # True
-    do_cng = False  # True
+    # do_sycl = False  # True
+    # do_cng = False  # True
 
-    ntries = 1
+    # ntries = 1
 
-    fov_deg = 0.02 # 1e-5  # 3.3477833333331884e-5
+    fov_deg = 0.02  # 1e-5  # 3.3477833333331884e-5
 
     npixdirty = 64  # get_npixdirty(uvw, freq, fov_deg, mask)
     pixsize = fov_deg / npixdirty * DEG2RAD
 
-    mintime = 1e300
+    # mintime = 1e300
 
     grid = ms2dirty_python_fast(
         uvw, freq, vis, npixdirty, npixdirty, pixsize, pixsize, epsilon, False
@@ -209,18 +206,21 @@ def ducc0_gridding(uv_data, freq_data):
 
 def grid_data(uv_data, freq_data, conf):
     cmplx = uv_data["DATA"]
-    real = np.squeeze(cmplx[..., 0, 0]).ravel()
-    imag = np.squeeze(cmplx[..., 0, 1]).ravel()
+    real = np.squeeze(cmplx[..., 0, 0, 0])  # .ravel()
+    imag = np.squeeze(cmplx[..., 0, 0, 1])  # .ravel()
     # weight = np.squeeze(cmplx[..., 0, 2])
 
     freq = freq_data[1]
     IF_bands = (freq_data[0]["IF FREQ"] + freq).reshape(-1, 1)
 
-    u = np.repeat([uv_data["UU--"]], np.squeeze(cmplx[..., 0, 0]).shape[1], axis=0)
-    v = np.repeat([uv_data["VV--"]], np.squeeze(cmplx[..., 0, 0]).shape[1], axis=0)
+    u = np.repeat([uv_data["UU--"]], real.shape[1], axis=0)
+    v = np.repeat([uv_data["VV--"]], real.shape[1], axis=0)
     u = (u * IF_bands).T.ravel()
     v = (v * IF_bands).T.ravel()
 
+    real = real.ravel()
+    imag = imag.ravel()
+
     samps = np.array(
         [
             np.append(u, -u),
@@ -229,7 +229,6 @@ def grid_data(uv_data, freq_data, conf):
             np.append(imag, -imag),
         ]
     )
-
     # Generate Mask
     N = conf["grid_size"]  # image size
     fov = (
@@ -242,9 +241,6 @@ def grid_data(uv_data, freq_data, conf):
 
     mask, *_ = np.histogram2d(samps[0], samps[1], bins=[bins, bins], normed=False)
     mask[mask == 0] = 1
-    # flip or rot90? Stefan used flip -> write test!!!
-    # mask = np.flip(mask, [1])
-    mask = np.rot90(mask, 1)
 
     mask_real, x_edges, y_edges = np.histogram2d(
         samps[0], samps[1], bins=[bins, bins], weights=samps[2], normed=False
@@ -253,12 +249,12 @@ def grid_data(uv_data, freq_data, conf):
         samps[0], samps[1], bins=[bins, bins], weights=samps[3], normed=False
     )
 
-    mask_real = np.rot90(mask_real, 1)
-    mask_imag = np.rot90(mask_imag, 1)
-
     mask_real /= mask
     mask_imag /= mask
 
+    mask_real = np.rot90(mask_real, 1)
+    mask_imag = np.rot90(mask_imag, 1)
+
     gridded_vis = np.zeros((2, N, N))
     gridded_vis[0] = mask_real
     gridded_vis[1] = mask_imag
@@ -271,17 +267,18 @@ def convert_amp_phase(data, sky_sim=False, rescale=False):
         phase = np.angle(data)
         # get rescale clear
         # amp = (np.log10(amp + 1e-10) / 10) + 1
-        if rescale:
-            amp = np.array([cv2.resize(a, (128, 128)) for a in amp])
-            phase = np.array([cv2.resize(p, (128, 128)) for p in phase])
+        # if rescale:
+        #     amp = np.array([cv2.resize(a, (128, 128)) for a in amp])
+        #     phase = np.array([cv2.resize(p, (128, 128)) for p in phase])
         data = np.stack((amp, phase), axis=1)
     else:
-        amp = np.abs(data)
-        phase = np.angle(data)
+        test = data[:, 0] + 1j * data[:, 1]
+        amp = np.abs(test)
+        phase = np.angle(test)
         # amp = (np.log10(amp + 1e-10) / 10) + 1
-        if rescale:
-            amp = np.array([cv2.resize(a, (128, 128)) for a in amp])
-            phase = np.array([cv2.resize(p, (128, 128)) for p in phase])
+        # if rescale:
+        #     amp = np.array([cv2.resize(a, (128, 128)) for a in amp])
+        #     phase = np.array([cv2.resize(p, (128, 128)) for p in phase])
         data = np.stack((amp, phase), axis=1)
     return data
 
diff --git a/pyvisgen/layouts/layouts.py b/pyvisgen/layouts/layouts.py
index 915a723..58c2264 100644
--- a/pyvisgen/layouts/layouts.py
+++ b/pyvisgen/layouts/layouts.py
@@ -2,6 +2,7 @@
 import pandas as pd
 from dataclasses import dataclass
 import numpy as np
+from astropy.coordinates import EarthLocation
 
 
 file_dir = Path(__file__).parent.resolve()
@@ -73,6 +74,12 @@ def get_array_layout(array_name, writer=False):
     """
     f = array_name + ".txt"
     array = pd.read_csv(file_dir / f, sep=" ")
+    if array_name == "vla":
+        loc = EarthLocation.of_site("VLA")
+        array["X"] += loc.value[0]
+        array["Y"] += loc.value[1]
+        array["Z"] += loc.value[2]
+
     stations = Stations(
         np.arange(len(array)),
         array["station_name"].values,
diff --git a/pyvisgen/layouts/vla.txt b/pyvisgen/layouts/vla.txt
index fae9dd9..580255b 100644
--- a/pyvisgen/layouts/vla.txt
+++ b/pyvisgen/layouts/vla.txt
@@ -1,9 +1,8 @@
 station_name X Y Z dish_dia el_low el_high SEFD altitude
 W32 1640.02760601 -4329.92665085 -2416.70580433 25 15 85 110 5030
 N24 -1660.49128127 -259.39960505 2454.41532549 25 15 85 110 5030
-OUT 0. 0. 0. 25 15 85 110 5030
 W20 733.34798474 -1932.98013829 -1078.10923116 25 15 85 110 5030
-E24 765.21097819 2889.44852033 -1108.8777769  25 15 85 110 5030
+E24 765.21097819 2889.44852033 -1108.8777769 25 15 85 110 5030
 N32 -2629.09218269 -410.65158466 3885.60273242 25 15 85 110 5030
 E36 1534.56120139 5793.91275687 -2223.48335235 25 15 85 110 5030
 N16 -801.39819671 -124.9731867 1182.11742906 25 15 85 110 5030
diff --git a/pyvisgen/simulation/data_set.py b/pyvisgen/simulation/data_set.py
index bf5adde..7ee6290 100644
--- a/pyvisgen/simulation/data_set.py
+++ b/pyvisgen/simulation/data_set.py
@@ -5,22 +5,28 @@
 from pathlib import Path
 from datetime import datetime
 from pyvisgen.utils.config import read_data_set_conf
+from pyvisgen.utils.data import load_bundles, open_bundles
 from pyvisgen.simulation.visibility import vis_loop
 import pyvisgen.fits.writer as writer
-from radiosim.data import radiosim_data
+import pyvisgen.layouts.layouts as layouts
+from astropy import units as un
+from astropy.coordinates import SkyCoord
+from pyvisgen.simulation.utils import calc_ref_elev, calc_time_steps
 
 
 def simulate_data_set(config, slurm=False, job_id=None, n=None):
-    np.random.seed(1)
     conf = read_data_set_conf(config)
-    out_path = Path(conf["out_path"])
+    out_path = Path(conf["out_path_fits"])
     out_path.mkdir(parents=True, exist_ok=True)
 
     if slurm:
-        job_id = int(job_id + n * 1000)
-        data = radiosim_data(conf["in_path"])
+        job_id = int(job_id + n * 500)
+        data = load_bundles(conf["in_path"])
         out = out_path / Path("vis_" + str(job_id) + ".fits")
-        SI = torch.tensor(data[job_id][0][0], dtype=torch.cdouble)
+        imgs_bundle = len(open_bundles(data[0]))
+        bundle = torch.div(job_id, imgs_bundle, rounding_mode="floor")
+        image = job_id - bundle * imgs_bundle
+        SI = torch.tensor(open_bundles(data[bundle])[image], dtype=torch.cdouble)
 
         samp_ops = create_sampling_rc(conf)
         vis_data = vis_loop(samp_ops, SI)
@@ -31,70 +37,49 @@ def simulate_data_set(config, slurm=False, job_id=None, n=None):
         hdu_list.writeto(out, overwrite=True)
 
     else:
-        data = radiosim_data(conf["in_path"])
-        for i in tqdm(range(len(data))):
-            out = out_path / Path("vis_" + str(i) + ".fits")
-            SI = torch.tensor(data[i][0][0], dtype=torch.cdouble)
-
-            samp_ops = create_sampling_rc(conf)
-            vis_data = vis_loop(samp_ops, SI)
-            while vis_data == 0:
+        data = load_bundles(conf["in_path"])
+        for i in range(len(data)):
+            SIs = open_bundles(data[i])
+            for j, SI in enumerate(tqdm(SIs)):
+                out = out_path / Path("vis_" + str(j) + ".fits")
                 samp_ops = create_sampling_rc(conf)
                 vis_data = vis_loop(samp_ops, SI)
-            hdu_list = writer.create_hdu_list(vis_data, samp_ops)
-            hdu_list.writeto(out, overwrite=True)
+                while vis_data == 0:
+                    samp_ops = create_sampling_rc(conf)
+                    vis_data = vis_loop(samp_ops, SI)
+                hdu_list = writer.create_hdu_list(vis_data, samp_ops)
+                hdu_list.writeto(out, overwrite=True)
 
 
 def create_sampling_rc(conf):
-    sampling_opts = draw_sampling_opts(conf)
-    samp_ops = {
-        "mode": sampling_opts[0],
-        "layout": sampling_opts[1],
-        "img_size": sampling_opts[2],
-        "fov_center_ra": sampling_opts[3],
-        "fov_center_dec": sampling_opts[4],
-        "fov_size": sampling_opts[5],
-        "corr_int_time": sampling_opts[6],
-        "scan_start": sampling_opts[7],
-        "scan_duration": sampling_opts[8],
-        "scans": sampling_opts[9],
-        "interval_length": sampling_opts[10],
-        "base_freq": sampling_opts[11],
-        "frequsel": sampling_opts[12],
-        "bandwidths": sampling_opts[13],
-    }
+    samp_ops = draw_sampling_opts(conf)
+
+    while test_opts(samp_ops) <= 5:
+        samp_ops = draw_sampling_opts(conf)
+
     return samp_ops
 
 
 def draw_sampling_opts(conf):
-    date_str_ra = pd.date_range(
-        conf["fov_center_ra"][0][0].strftime("%H:%M:%S"),
-        conf["fov_center_ra"][0][1].strftime("%H:%M:%S"),
-        freq="1min",
-    ).strftime("%H:%M:%S")
-    times_ra = [
-        datetime.time(datetime.strptime(date, "%H:%M:%S")) for date in date_str_ra
-    ]
-    fov_center_ra = np.random.choice(times_ra)
+    angles_ra = np.arange(
+        conf["fov_center_ra"][0][0], conf["fov_center_ra"][0][1], step=0.1
+    )
+    fov_center_ra = np.random.choice(angles_ra)
+
     angles_dec = np.arange(
-        conf["fov_center_dec"][0][0],
-        conf["fov_center_dec"][0][1],
-        step=0.1,
+        conf["fov_center_dec"][0][0], conf["fov_center_dec"][0][1], step=0.1
     )
     fov_center_dec = np.random.choice(angles_dec)
     start_time_l = datetime.strptime(conf["scan_start"][0], "%d-%m-%Y %H:%M:%S")
     start_time_h = datetime.strptime(conf["scan_start"][1], "%d-%m-%Y %H:%M:%S")
-    start_times = pd.date_range(
-        start_time_l,
-        start_time_h,
-        freq="1h",
-    ).strftime("%d-%m-%Y %H:%M:%S")
+    start_times = pd.date_range(start_time_l, start_time_h, freq="1h").strftime(
+        "%d-%m-%Y %H:%M:%S"
+    )
     scan_start = np.random.choice(
         [datetime.strptime(time, "%d-%m-%Y %H:%M:%S") for time in start_times]
     )
-    scan_duration = (
-        np.random.randint(conf["scan_duration"][0], conf["scan_duration"][1])
-        * conf["corr_int_time"]
+    scan_duration = np.random.randint(
+        conf["scan_duration"][0], conf["scan_duration"][1]
     )
     scans = np.random.randint(conf["scans"][0], conf["scans"][1])
     opts = np.array(
@@ -113,11 +98,44 @@ def draw_sampling_opts(conf):
             conf["base_freq"],
             conf["frequsel"],
             conf["bandwidths"],
+            conf["corrupted"],
         ],
         dtype="object",
     )
-    return opts
+    samp_ops = {
+        "mode": opts[0],
+        "layout": opts[1],
+        "img_size": opts[2],
+        "fov_center_ra": opts[3],
+        "fov_center_dec": opts[4],
+        "fov_size": opts[5],
+        "corr_int_time": opts[6],
+        "scan_start": opts[7],
+        "scan_duration": opts[8],
+        "scans": opts[9],
+        "interval_length": opts[10],
+        "base_freq": opts[11],
+        "frequsel": opts[12],
+        "bandwidths": opts[13],
+        "corrupted": opts[14],
+    }
+    return samp_ops
+
+
+def test_opts(rc):
+    array_layout = layouts.get_array_layout(rc["layout"])
+    src_crd = SkyCoord(
+        ra=rc["fov_center_ra"], dec=rc["fov_center_dec"], unit=(un.deg, un.deg)
+    )
+    time = calc_time_steps(rc)
+    _, el_st_0 = calc_ref_elev(src_crd, time[0], array_layout)
+    _, el_st_1 = calc_ref_elev(src_crd, time[1], array_layout)
+    el_min = 15
+    el_max = 85
+    active_telescopes_0 = np.sum((el_st_0 >= el_min) & (el_st_0 <= el_max))
+    active_telescopes_1 = np.sum((el_st_1 >= el_min) & (el_st_1 <= el_max))
+    return min(active_telescopes_0, active_telescopes_1)
 
 
 if __name__ == "__main__":
-    simulate_data_set("/net/big-tank/POOL/projects/radio/test_rime/create_dataset.toml")
+    simulate_data_set()
diff --git a/pyvisgen/simulation/scan.py b/pyvisgen/simulation/scan.py
index 643b746..9b319e8 100644
--- a/pyvisgen/simulation/scan.py
+++ b/pyvisgen/simulation/scan.py
@@ -1,10 +1,10 @@
 from dataclasses import dataclass
 from astropy import units as un
-from astropy.coordinates import EarthLocation, AltAz, Angle
+from astropy.coordinates import EarthLocation
 import numpy as np
 from scipy.special import j1
 from astroplan import Observer
-from pyvisgen.simulation.utils import single_occurance, get_pairs
+from pyvisgen.simulation.utils import calc_ref_elev, Array, calc_direction_cosines
 import torch
 import itertools
 import numexpr as ne
@@ -72,108 +72,42 @@ def get_baselines(src_crd, time, array_layout):
     Returns
     -------
     dataclass object
-        baselines between telescopes with visinility flags
+        baselines between telescopes with visibility flags
     """
     # Calculate for all times
     # calculate GHA, Greenwich as reference for EHT
-    ha_all = Angle(
-        [t.sidereal_time("apparent", "greenwich") - src_crd.ra for t in time]
-    )
+    ha_all, el_st_all = calc_ref_elev(src_crd, time, array_layout)
 
-    # calculate elevations
-    el_st_all = src_crd.transform_to(
-        AltAz(
-            obstime=time.reshape(len(time), -1),
-            location=EarthLocation.from_geocentric(
-                np.repeat([array_layout.x], len(time), axis=0),
-                np.repeat([array_layout.y], len(time), axis=0),
-                np.repeat([array_layout.z], len(time), axis=0),
-                unit=un.m,
-            ),
-        )
-    ).alt.degree
-
-    # fails for 1 timestep
-    assert len(ha_all.value) == len(el_st_all)
-
-    # always the same
-    delta_x, delta_y, delta_z = get_pairs(array_layout)
-    indices = single_occurance(delta_x)
-    delta_x = delta_x[indices]
-    delta_y = delta_y[indices]
-    delta_z = delta_z[indices]
-    mask = [i * len(array_layout.x) + i for i in range(len(array_layout.x))]
-    pairs = np.delete(
-        np.array(np.meshgrid(array_layout.name, array_layout.name)).T.reshape(-1, 2),
-        mask,
-        axis=0,
-    )[indices]
-
-    st_nums = np.delete(
-        np.array(np.meshgrid(array_layout.st_num, array_layout.st_num)).T.reshape(
-            -1, 2
-        ),
-        mask,
-        axis=0,
-    )[indices]
-
-    els_low = np.delete(
-        np.array(np.meshgrid(array_layout.el_low, array_layout.el_low)).T.reshape(
-            -1, 2
-        ),
-        mask,
-        axis=0,
-    )[indices]
-
-    els_high = np.delete(
-        np.array(np.meshgrid(array_layout.el_high, array_layout.el_high)).T.reshape(
-            -1, 2
-        ),
-        mask,
-        axis=0,
-    )[indices]
+    ar = Array(array_layout)
+    delta_x, delta_y, delta_z, indices = ar.calc_relative_pos
+    mask = ar.get_baseline_mask
+    antenna_pairs, st_num_pairs, els_low_pairs, els_high_pairs = ar.calc_ant_pair_vals
+    names = antenna_pairs[:, 0] + "-" + antenna_pairs[:, 1]
 
     # Loop over ha and el_st
     baselines = Baselines([], [], [], [], [], [], [])
     for ha, el_st in zip(ha_all, el_st_all):
-        u = np.sin(ha) * delta_x + np.cos(ha) * delta_y
-        v = (
-            -np.sin(src_crd.ra) * np.cos(ha) * delta_x
-            + np.sin(src_crd.ra) * np.sin(ha) * delta_y
-            + np.cos(src_crd.ra) * delta_z
-        )
-        w = (
-            np.cos(src_crd.ra) * np.cos(ha) * delta_x
-            - np.cos(src_crd.ra) * np.sin(ha) * delta_y
-            + np.sin(src_crd.ra) * delta_z
-        )
-        assert u.shape == v.shape == w.shape
+        u, v, w = calc_direction_cosines(ha, el_st, delta_x, delta_y, delta_z, src_crd)
 
+        # calc current elevations
         els_st = np.delete(
             np.array(np.meshgrid(el_st, el_st)).T.reshape(-1, 2),
             mask,
             axis=0,
         )[indices]
 
+        # calc valid baselines
         valid = np.ones(u.shape).astype(bool)
-
-        m1 = (els_st < els_low).any(axis=1)
-        m2 = (els_st > els_high).any(axis=1)
+        m1 = (els_st < els_low_pairs).any(axis=1)
+        m2 = (els_st > els_high_pairs).any(axis=1)
         valid_mask = np.ma.mask_or(m1, m2)
         valid[valid_mask] = False
 
-        names = pairs[:, 0] + "-" + pairs[:, 1]
-
-        u = u.reshape(-1)
-        v = v.reshape(-1)
-        w = w.reshape(-1)
-        valid = valid.reshape(-1)
-
         # collect baselines
         base = Baselines(
             names,
-            array_layout[st_nums[:, 0]],
-            array_layout[st_nums[:, 1]],
+            array_layout[st_num_pairs[:, 0]],
+            array_layout[st_num_pairs[:, 1]],
             u,
             v,
             w,
@@ -183,7 +117,7 @@ def get_baselines(src_crd, time, array_layout):
     return baselines
 
 
-def rd_grid(fov, samples, src_crd):
+def create_rd_grid(fov, samples, src_crd):
     """Calculates RA and Dec values for a given fov around a source position
 
     Parameters
@@ -200,6 +134,10 @@ def rd_grid(fov, samples, src_crd):
     3d array
         Returns a 3d array with every pixel containing a RA and Dec value
     """
+    # transform to rad
+    fov *= np.pi / (3600 * 180)
+
+    # define resolution
     res = fov / samples
 
     rd_grid = np.zeros((samples, samples, 2))
@@ -210,11 +148,10 @@ def rd_grid(fov, samples, src_crd):
         rd_grid[:, i, 1] = np.array(
             [-(i - samples / 2) * res + src_crd.dec.rad for i in range(samples)]
         )
-
     return rd_grid
 
 
-def lm_grid(rd_grid, src_crd):
+def create_lm_grid(rd_grid, src_crd):
     """Calculates sine projection for fov
 
     Parameters
@@ -335,17 +272,17 @@ def corrupted(lm, baselines, wave, time, src_crd, array_layout, SI, rd):
     B = np.zeros((lm.shape[0], lm.shape[1], 1), dtype=complex)
 
     B[:, :, 0] = SI + SI
-    # B[:, :, 0, 0] = I[:, :, 0] + I[:, :, 1]
-    # B[:, :, 0, 1] = I[:, :, 2] + 1j * I[:, :, 3]
-    # B[:, :, 1, 0] = I[:, :, 2] - 1j * I[:, :, 3]
-    # B[:, :, 1, 1] = I[:, :, 0] - I[:, :, 1]
+    # # only calculate without polarization for the moment
+    # B[:, :, 0, 0] = SI[:, :, 0] + SI[:, :, 1]
+    # B[:, :, 0, 1] = SI[:, :, 2] + 1j * SI[:, :, 3]
+    # B[:, :, 1, 0] = SI[:, :, 2] - 1j * SI[:, :, 3]
+    # B[:, :, 1, 1] = SI[:, :, 0] - SI[:, :, 1]
 
-    # coherency
     X = torch.einsum("lmi,lmb->lmbi", torch.tensor(B), K)
     # X = np.einsum('lmij,lmb->lmbij', B, K, optimize=True)
     # X = torch.tensor(B)[:,:,None,:,:] * K[:,:,:,None,None]
 
-    del K
+    del K, B
 
     # telescope response
     E_st = getE(rd, array_layout, wave, src_crd)
@@ -356,7 +293,7 @@ def corrupted(lm, baselines, wave, time, src_crd, array_layout, SI, rd):
 
     EX = torch.einsum("lmb,lmbi->lmbi", E1, X)
 
-    del E1, X
+    del E1, X, E_st
     # EXE = torch.einsum('lmbij,lmbjk->lmbik',EX,torch.transpose(torch.conj(E2),3,4))
     EXE = torch.einsum("lmbi,lmb->lmbi", EX, E2)
     del EX, E2
@@ -380,12 +317,10 @@ def corrupted(lm, baselines, wave, time, src_crd, array_layout, SI, rd):
     P1 = torch.tensor(getP(b1), dtype=torch.cdouble)
     P2 = torch.tensor(getP(b2), dtype=torch.cdouble)
 
-    print("P", P1.shape)
-
-    PEXE = torch.einsum("bi,lmbj->lmbi", P1, EXE)
-    del EXE
-    PEXEP = torch.einsum("lmbi,bk->lmbik", PEXE, torch.transpose(torch.conj(P2), 1, 2))
-    del PEXE
+    PEXE = torch.einsum("bi,lmbi->lmbi", P1, EXE)
+    del EXE, P1, beta, tsob
+    PEXEP = torch.einsum("lmbi,bi->lmbi", PEXE, torch.conj(P2))
+    del PEXE, P2
 
     return PEXEP
 
@@ -511,7 +446,6 @@ def getE(rd, array_layout, wave, src_crd):
     # calculate matrix E for every point in grid
     # E = np.zeros((rd.shape[0], rd.shape[1], array_layout.st_num.shape[0], 2, 2))
     E = np.zeros((rd.shape[0], rd.shape[1], array_layout.st_num.shape[0]))
-    # print("E", E.shape)
 
     # get diameters of all stations and do vectorizing stuff
     diameters = array_layout.diam
@@ -546,7 +480,7 @@ def angularDistance(rd, src_crd):
     r = rd[:, :, 0] - src_crd.ra.rad
     d = rd[:, :, 1] - src_crd.dec.rad
 
-    theta = np.arcsin(np.sqrt(r ** 2 + d ** 2))
+    theta = np.arcsin(np.sqrt(r**2 + d**2))
 
     return theta
 
@@ -615,7 +549,7 @@ def getK(baselines, lm, wave, base_num):
 
     l = torch.tensor(lm[:, :, 0])
     m = torch.tensor(lm[:, :, 1])
-    n = torch.sqrt(1 - l ** 2 - m ** 2)
+    n = torch.sqrt(1 - l**2 - m**2)
 
     ul = torch.einsum("b,ij->ijb", torch.tensor(u_cmplt), l)
     vm = torch.einsum("b,ij->ijb", torch.tensor(v_cmplt), m)
@@ -640,10 +574,6 @@ def jinc(x):
     array
         value of jinc function at x
     """
-    # if x == 0:
-    #     return 1
-    # jinc = 2 * j1(x) / x
-    # jinc[x == 0] = 1
     jinc = np.ones(x.shape)
     jinc[x != 0] = 2 * j1(x[x != 0]) / x[x != 0]
     return jinc
diff --git a/pyvisgen/simulation/scripts/create_dataset.py b/pyvisgen/simulation/scripts/create_dataset.py
index 622b7fa..606b028 100644
--- a/pyvisgen/simulation/scripts/create_dataset.py
+++ b/pyvisgen/simulation/scripts/create_dataset.py
@@ -5,8 +5,8 @@
 
 @click.command()
 @click.argument("configuration_path", type=click.Path(exists=True, dir_okay=False))
-@click.option('--job_id', required=False, type=int, default=None)
-@click.option('--n', required=False, type=int, default=None)
+@click.option("--job_id", required=False, type=int, default=None)
+@click.option("--n", required=False, type=int, default=None)
 @click.option(
     "--mode",
     type=click.Choice(
diff --git a/pyvisgen/simulation/utils.py b/pyvisgen/simulation/utils.py
index cf591e9..dcb5264 100644
--- a/pyvisgen/simulation/utils.py
+++ b/pyvisgen/simulation/utils.py
@@ -4,6 +4,9 @@
 import numpy as np
 from astropy.time import Time
 from datetime import datetime
+import astropy.constants as const
+from astropy.coordinates import EarthLocation, AltAz, Angle
+from astropy.utils.decorators import lazyproperty
 
 
 def read_config(conf):
@@ -40,6 +43,7 @@ def read_config(conf):
 
 
 def single_occurance(array):
+    # only calc one half of visibility because of Fourier symmetry
     vals, index = np.unique(np.abs(array), return_index=True)
     return index
 
@@ -72,13 +76,12 @@ def get_pairs(array_layout):
 def calc_time_steps(conf):
     start_time = Time(conf["scan_start"].isoformat(), format="isot")
     interval = conf["interval_length"]
-    integration_time = conf["corr_int_time"]
     num_scans = conf["scans"]
     scan_duration = conf["scan_duration"]
     int_time = conf["corr_int_time"]
 
     time_lst = [
-        start_time + interval * i * un.second + j * integration_time * un.second
+        start_time + interval * i * un.second + j * int_time * un.second
         for i in range(num_scans)
         for j in range(int(scan_duration / int_time) + 1)
     ]
@@ -87,3 +90,133 @@ def calc_time_steps(conf):
     time = Time(time_lst)
 
     return time
+
+
+def get_IFs(rc):
+    IFs = np.array(
+        [
+            const.c / ((rc["base_freq"] + float(freq)) / un.second) / un.meter
+            for freq in rc["frequsel"]
+        ]
+    )
+    return IFs
+
+
+def calc_ref_elev(src_crd, time, array_layout):
+    if time.shape == ():
+        time = time[None]
+    # Calculate for all times
+    # calculate GHA, Greenwich as reference for EHT
+    ha_all = Angle(
+        [t.sidereal_time("apparent", "greenwich") - src_crd.ra for t in time]
+    )
+
+    # calculate elevations
+    el_st_all = src_crd.transform_to(
+        AltAz(
+            obstime=time.reshape(len(time), -1),
+            location=EarthLocation.from_geocentric(
+                np.repeat([array_layout.x], len(time), axis=0),
+                np.repeat([array_layout.y], len(time), axis=0),
+                np.repeat([array_layout.z], len(time), axis=0),
+                unit=un.m,
+            ),
+        )
+    ).alt.degree
+    assert len(ha_all.value) == len(el_st_all)
+    return ha_all, el_st_all
+
+
+class Array:
+    def __init__(self, array_layout):
+        self.array_layout = array_layout
+
+    @lazyproperty
+    def calc_relative_pos(self):
+        # from geocentric coordinates to relative coordinates inside array
+        delta_x, delta_y, delta_z = get_pairs(self.array_layout)
+        self.indices = single_occurance(delta_x)
+        delta_x = delta_x[self.indices]
+        delta_y = delta_y[self.indices]
+        delta_z = delta_z[self.indices]
+        return delta_x, delta_y, delta_z, self.indices
+
+    @lazyproperty
+    def get_baseline_mask(self):
+        # mask baselines between the same telescope
+        self.mask = [
+            i * len(self.array_layout.x) + i for i in range(len(self.array_layout.x))
+        ]
+        return self.mask
+
+    @lazyproperty
+    def calc_ant_pair_vals(self):
+        antenna_pairs = np.delete(
+            np.array(
+                np.meshgrid(self.array_layout.name, self.array_layout.name)
+            ).T.reshape(-1, 2),
+            self.mask,
+            axis=0,
+        )[self.indices]
+
+        st_num_pairs = np.delete(
+            np.array(
+                np.meshgrid(self.array_layout.st_num, self.array_layout.st_num)
+            ).T.reshape(-1, 2),
+            self.mask,
+            axis=0,
+        )[self.indices]
+
+        els_low_pairs = np.delete(
+            np.array(
+                np.meshgrid(self.array_layout.el_low, self.array_layout.el_low)
+            ).T.reshape(-1, 2),
+            self.mask,
+            axis=0,
+        )[self.indices]
+
+        els_high_pairs = np.delete(
+            np.array(
+                np.meshgrid(self.array_layout.el_high, self.array_layout.el_high)
+            ).T.reshape(-1, 2),
+            self.mask,
+            axis=0,
+        )[self.indices]
+        return antenna_pairs, st_num_pairs, els_low_pairs, els_high_pairs
+
+
+def calc_direction_cosines(ha, el_st, delta_x, delta_y, delta_z, src_crd):
+    u = (np.sin(ha) * delta_x + np.cos(ha) * delta_y).reshape(-1)
+    v = (
+        -np.sin(src_crd.ra) * np.cos(ha) * delta_x
+        + np.sin(src_crd.ra) * np.sin(ha) * delta_y
+        + np.cos(src_crd.ra) * delta_z
+    ).reshape(-1)
+    w = (
+        np.cos(src_crd.ra) * np.cos(ha) * delta_x
+        - np.cos(src_crd.ra) * np.sin(ha) * delta_y
+        + np.sin(src_crd.ra) * delta_z
+    ).reshape(-1)
+    assert u.shape == v.shape == w.shape
+    return u, v, w
+
+
+def calc_valid_baselines(baselines, base_num, t, rc):
+    valid = baselines.valid.reshape(-1, base_num)
+    mask = np.array(valid[:-1]).astype(bool) & np.array(valid[1:]).astype(bool)
+    u = baselines.u.reshape(-1, base_num)
+    v = baselines.v.reshape(-1, base_num)
+    w = baselines.w.reshape(-1, base_num)
+    base_valid = np.arange(len(baselines.u)).reshape(-1, base_num)[:-1][mask]
+    u_valid = (u[:-1][mask] + u[1:][mask]) / 2
+    v_valid = (v[:-1][mask] + v[1:][mask]) / 2
+    w_valid = (w[:-1][mask] + w[1:][mask]) / 2
+    date = np.repeat(
+        (t[:-1] + rc["corr_int_time"] * un.second / 2).jd.reshape(-1, 1),
+        base_num,
+        axis=1,
+    )[mask]
+
+    _date = np.zeros(len(u_valid))
+    assert u_valid.shape == v_valid.shape == w_valid.shape
+    return base_valid, u_valid, v_valid, w_valid, date, _date
diff --git a/pyvisgen/simulation/visibility.py b/pyvisgen/simulation/visibility.py
index e624d82..98c40a8 100644
--- a/pyvisgen/simulation/visibility.py
+++ b/pyvisgen/simulation/visibility.py
@@ -1,8 +1,7 @@
 import numpy as np
 from dataclasses import dataclass
-import pyvisgen.simulation.utils as ut
+from pyvisgen.simulation.utils import get_IFs, calc_valid_baselines, calc_time_steps
 import pyvisgen.layouts.layouts as layouts
-import astropy.constants as const
 from astropy import units as un
 import pyvisgen.simulation.scan as scan
 import torch
@@ -15,12 +14,12 @@ class Visibilities:
     SQ: [complex]
     SU: [complex]
     SV: [complex]
-    num: [int]
-    scan: [int]
-    base_num: [int]
-    u: [float]
-    v: [float]
-    w: [float]
+    num: [float]
+    scan: [float]
+    base_num: [float]
+    u: [un]
+    v: [un]
+    w: [un]
     date: [float]
     _date: [float]
 
@@ -65,53 +64,45 @@ class Vis:
     SQ: complex
     SU: complex
     SV: complex
-    num: int
-    scan: int
-    base_num: int
-    u: float
-    v: float
-    w: float
+    num: float
+    scan: float
+    base_num: float
+    u: un
+    v: un
+    w: un
     date: float
     _date: float
 
 
-def vis_loop(rc, SI, num_threads=48):
-    # torch.set_num_threads(num_threads)
+def vis_loop(rc, SI, num_threads=10):
+    torch.set_num_threads(num_threads)
 
-    # read config
+    # define array, source coords, and IFs
     array_layout = layouts.get_array_layout(rc["layout"])
     src_crd = SkyCoord(
-        ra=rc["fov_center_ra"].strftime("%H:%M:%S"),
-        dec=rc["fov_center_dec"],
-        unit=(un.hourangle, un.deg),
-    )
-    rc["fov_center_ra"] = src_crd.ra.value
-    rc["fov_center_dec"] = src_crd.dec.value
-
-    waves = np.array(
-        [
-            const.c / ((rc["base_freq"] + float(freq)) / un.second) / un.meter
-            for freq in rc["frequsel"]
-        ]
+        ra=rc["fov_center_ra"], dec=rc["fov_center_dec"], unit=(un.deg, un.deg)
     )
 
-    # calculate rd, lm
-    rd = scan.rd_grid(rc["fov_size"] * np.pi / (3600 * 180), rc["img_size"], src_crd)
-    lm = scan.lm_grid(rd, src_crd)
+    # define IFs
+    IFs = get_IFs(rc)
 
     # calculate time steps
-    time = ut.calc_time_steps(rc)
+    time = calc_time_steps(rc)
+
+    # calculate rd, lm
+    rd = scan.create_rd_grid(rc["fov_size"], rc["img_size"], src_crd)
+    lm = scan.create_lm_grid(rd, src_crd)
 
-    # number statiosn, number baselines
+    # def number stations and number baselines
     stat_num = array_layout.st_num.shape[0]
     base_num = int(stat_num * (stat_num - 1) / 2)
 
     # calculate vis
     visibilities = Visibilities(
-        np.empty(shape=[0] + [len(waves)]),
-        np.empty(shape=[0] + [len(waves)]),
-        np.empty(shape=[0] + [len(waves)]),
-        np.empty(shape=[0] + [len(waves)]),
+        np.empty(shape=[0] + [len(IFs)]),
+        np.empty(shape=[0] + [len(IFs)]),
+        np.empty(shape=[0] + [len(IFs)]),
+        np.empty(shape=[0] + [len(IFs)]),
         [],
         [],
         [],
@@ -128,39 +119,28 @@ def vis_loop(rc, SI, num_threads=48):
 
         baselines = scan.get_baselines(src_crd, t, array_layout)
 
-        valid = baselines.valid.reshape(-1, base_num)
-        mask = np.array(valid[:-1]).astype(bool) & np.array(valid[1:]).astype(bool)
-        u = baselines.u.reshape(-1, base_num)
-        v = baselines.v.reshape(-1, base_num)
-        w = baselines.w.reshape(-1, base_num)
-        base_valid = np.arange(len(baselines.u)).reshape(-1, base_num)[:-1][mask]
-        u_valid = u[:-1][mask]
-        v_valid = v[:-1][mask]
-        w_valid = w[:-1][mask]
-        date = np.repeat(
-            (t[:-1] + rc["corr_int_time"] * un.second / 2).jd.reshape(-1, 1),
-            base_num,
-            axis=1,
-        )[mask]
-
-        _date = np.zeros(len(u_valid))
-
-        int_values = np.array(
-            [
-                calc_vis(
-                    lm,
-                    baselines,
-                    wave,
-                    t,
-                    src_crd,
-                    array_layout,
-                    SI,
-                    rd,
-                    vis_num,
-                )
-                for wave in waves
-            ]
+        base_valid, u_valid, v_valid, w_valid, date, _date = calc_valid_baselines(
+            baselines, base_num, t, rc
         )
+
+        int_values = []
+        for IF in IFs:
+            val_i = calc_vis(
+                lm,
+                baselines,
+                IF,
+                t,
+                src_crd,
+                array_layout,
+                SI,
+                rd,
+                vis_num,
+                corrupted=rc["corrupted"],
+            )
+            int_values.append(val_i)
+            del val_i
+
+        int_values = np.array(int_values)
         if int_values.dtype != np.complex128:
             continue
         int_values = np.swapaxes(int_values, 0, 1)
@@ -182,24 +162,29 @@ def vis_loop(rc, SI, num_threads=48):
         )
 
         visibilities.add(vis)
-    # workaround to guarantee min number of visibilities
-    # when num vis is below N sampling is redone
-    # if visibilities.get_values().shape[1] < 3500:
-    #     return 0
+        # workaround to guarantee min number of visibilities
+        # when num vis is below N sampling is redone
+        # if visibilities.get_values().shape[1] < 3500:
+        #     return 0
+        del int_values
     return visibilities
 
 
-def calc_vis(lm, baselines, wave, t, src_crd, array_layout, SI, rd, vis_num):
-    X1 = scan.uncorrupted(
-        lm, baselines, wave, t, src_crd, array_layout, SI#, rd
-    )
-    if X1.shape[0] == 1:
-        return -1
-    X2 = scan.uncorrupted(
-        lm, baselines, wave, t, src_crd, array_layout, SI#, rd
-    )
+def calc_vis(
+    lm, baselines, wave, t, src_crd, array_layout, SI, rd, vis_num, corrupted=True
+):
+    if corrupted:
+        X1 = scan.corrupted(lm, baselines, wave, t, src_crd, array_layout, SI, rd)
+        if X1.shape[0] == 1:
+            return -1
+        X2 = scan.corrupted(lm, baselines, wave, t, src_crd, array_layout, SI, rd)
+    else:
+        X1 = scan.uncorrupted(lm, baselines, wave, t, src_crd, array_layout, SI)
+        if X1.shape[0] == 1:
+            return -1
+        X2 = scan.uncorrupted(lm, baselines, wave, t, src_crd, array_layout, SI)
 
     int_values = scan.integrate(X1, X2).numpy()
-    del X1, X2
+    del X1, X2, SI
     int_values = int_values
     return int_values
diff --git a/pyvisgen/utils/config.py b/pyvisgen/utils/config.py
index 3eea4ad..fa2883a 100644
--- a/pyvisgen/utils/config.py
+++ b/pyvisgen/utils/config.py
@@ -33,18 +33,16 @@ def read_data_set_conf(conf_toml):
     conf["base_freq"] = config["sampling_options"]["base_freq"]
     conf["frequsel"] = config["sampling_options"]["frequsel"]
     conf["bandwidths"] = config["sampling_options"]["bandwidths"]
+    conf["corrupted"] = config["sampling_options"]["corrupted"]
 
     conf["num_test_images"] = config["bundle_options"]["num_test_images"]
     conf["bundle_size"] = config["bundle_options"]["bundle_size"]
     conf["train_valid_split"] = config["bundle_options"]["train_valid_split"]
     conf["grid_size"] = config["bundle_options"]["grid_size"]
     conf["amp_phase"] = config["bundle_options"]["amp_phase"]
-    conf["target"] = config["bundle_options"]["target"]
     conf["in_path"] = config["bundle_options"]["in_path"]
-    conf["out_path"] = config["bundle_options"]["out_path"]
-
-    conf["num_jobs"] = config["cluster_options"]["num_jobs"]
-
+    conf["out_path_fits"] = config["bundle_options"]["out_path_fits"]
+    conf["out_path_gridded"] = config["bundle_options"]["out_path_gridded"]
     return conf
 
 
diff --git a/pyvisgen/utils/data.py b/pyvisgen/utils/data.py
new file mode 100644
index 0000000..3219161
--- /dev/null
+++ b/pyvisgen/utils/data.py
@@ -0,0 +1,24 @@
+import h5py
+import numpy as np
+import re
+from pathlib import Path
+from natsort import natsorted
+
+
+def load_bundles(data_path):
+    bundle_paths = get_bundles(data_path)
+    bundles = natsorted([path for path in bundle_paths if re.findall("fft_", path.name)])
+    print(bundles)
+    return bundles
+
+
+def get_bundles(path):
+    data_path = Path(path)
+    bundles = np.array([x for x in data_path.iterdir()])
+    return bundles
+
+
+def open_bundles(path):
+    f = h5py.File(path, "r")
+    bundle_y = np.array(f["y"])
+    return bundle_y
diff --git a/setup.py b/setup.py
index 19791a9..aa180f2 100644
--- a/setup.py
+++ b/setup.py
@@ -2,8 +2,8 @@
 
 setup(
     name="pyvisgen",
-    version="0.0.4",
-    description="Simulate radio interferometer observations and visibility generation.",
+    version="0.1.0",
+    description="Simulate radio interferometer observations and visibility generation with the RIME formalism.",
     url="https://github.com/radionets-project/pyvisgen",
     author="Kevin Schmidt, Felix Geyer, Stefan Fröse",
     author_email="kevin3.schmidt@tu-dortmund.de",
@@ -23,6 +23,11 @@
         "astroplan",
         "torch",
         "tqdm",
+        "numexpr",
+        "opencv-python",
+        "click",
+        "h5py",
+        "natsort",
     ],
     setup_requires=["pytest-runner"],
     tests_require=["pytest"],
@@ -33,7 +38,7 @@
         ],
     },
     classifiers=[
-        "Development Status :: 3 - Alpha",
+        "Development Status :: 4 - Beta",
         "Intended Audience :: Science/Research",
         "License :: OSI Approved :: MIT License",
         "Natural Language :: English",
@@ -45,7 +50,6 @@
         "Programming Language :: Python :: 3 :: Only",
         "Topic :: Scientific/Engineering :: Astronomy",
         "Topic :: Scientific/Engineering :: Physics",
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Scientific/Engineering :: Information Analysis",
     ],
 )
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..15130eb
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,12 @@
+import pytest
+import shutil
+
+
+@pytest.fixture(autouse=True, scope='session')
+def test_suite_cleanup_thing():
+    yield
+
+    build = "./tests/build/"
+    print("Cleaning up tests.")
+
+    shutil.rmtree(build)
diff --git a/tests/data/fft_train0.h5 b/tests/data/fft_train0.h5
new file mode 100644
index 0000000..ba35a8c
Binary files /dev/null and b/tests/data/fft_train0.h5 differ
diff --git a/tests/test_conf.toml b/tests/test_conf.toml
new file mode 100644
index 0000000..5b7e6c9
--- /dev/null
+++ b/tests/test_conf.toml
@@ -0,0 +1,32 @@
+[sampling_options]
+mode = "basic"
+layout = "vlba"
+img_size = 128
+fov_center_ra = [90, 140]
+fov_center_dec = [-20, 50]
+fov_size = 0.0064 # max res 0.1
+corr_int_time = 10.0
+scan_start = ["01-01-2020 00:00:01", "31-12-2021 23:59:59"]
+scan_duration = [0, 50]
+scans = [1, 10]
+interval_length = 1200
+base_freq = 15.21e9
+frequsel = [0e8, 0.8e8, 1.44e8, 2.08e8]
+bandwidths = [6.4e7, 6.4e7, 6.4e7, 6.4e7]
+corrupted = true
+
+[bundle_options]
+num_bundles = 5
+size_bundles = 10
+in_path = "./tests/data"
+out_path_fits = "./tests/build/fits"
+out_path_gridded = "./tests/build/gridded"
+num_test_images = 1000
+bundle_size = 10
+train_valid_split = 0.2
+grid_size = 5
+amp_phase = true
+target = 5
+
+[cluster_options]
+num_jobs = 4
diff --git a/tests/test_layouts.py b/tests/test_layouts.py
index 41b69e0..5509aa3 100644
--- a/tests/test_layouts.py
+++ b/tests/test_layouts.py
@@ -6,7 +6,7 @@ def test_get_array_layout():
 
     layout = get_array_layout("eht")
 
-    assert len(layout.name) == 8
+    assert len(layout.st_num) == 8
     assert type(layout[0].name) == str
     assert type(layout[0].x) == np.float64
     assert type(layout[0].y) == np.float64
@@ -16,3 +16,12 @@ def test_get_array_layout():
     assert type(layout[0].el_high) == np.int64
     assert type(layout[0].sefd) == np.int64
     assert type(layout[0].altitude) == np.int64
+
+    layout = get_array_layout("vlba")
+
+    assert len(layout.st_num) == 10
+    assert layout.get_station("MKO").st_num == 0
+
+    layout = get_array_layout("vla")
+
+    assert len(layout.st_num) == 27
diff --git a/tests/test_simulation.py b/tests/test_simulation.py
new file mode 100644
index 0000000..53d59c8
--- /dev/null
+++ b/tests/test_simulation.py
@@ -0,0 +1,61 @@
+import numpy as np
+from pyvisgen.utils.config import read_data_set_conf
+from pathlib import Path
+
+np.random.seed(1)
+config = "tests/test_conf.toml"
+conf = read_data_set_conf(config)
+out_path = Path(conf["out_path_fits"])
+out_path.mkdir(parents=True, exist_ok=True)
+
+
+def test_get_data():
+    from pyvisgen.utils.data import load_bundles
+
+    data = load_bundles(conf["in_path"])
+    assert len(data) > 0
+
+
+def test_create_sampling_rc():
+    from pyvisgen.simulation.data_set import test_opts, create_sampling_rc
+
+    samp_ops = create_sampling_rc(conf)
+    assert len(samp_ops) == 15
+
+    test_opts(samp_ops)
+
+
+def test_vis_loop():
+    import torch
+    from pyvisgen.utils.data import load_bundles, open_bundles
+    from pyvisgen.simulation.data_set import create_sampling_rc, test_opts
+    from pyvisgen.simulation.visibility import vis_loop
+    from astropy import units as un
+
+    bundles = load_bundles(conf["in_path"])
+    samp_ops = create_sampling_rc(conf)
+    num_active_telescopes = test_opts(samp_ops)
+    data = open_bundles(bundles[0])
+    SI = torch.tensor(data[0], dtype=torch.cdouble)
+    vis_data = vis_loop(samp_ops, SI)
+
+    assert type(vis_data[0].SI[0]) == np.complex128
+    assert type(vis_data[0].SQ[0]) == np.complex128
+    assert type(vis_data[0].SU[0]) == np.complex128
+    assert type(vis_data[0].SV[0]) == np.complex128
+    assert type(vis_data[0].num) == np.float64
+    assert type(vis_data[0].scan) == np.float64
+    assert type(vis_data[0].base_num) == np.float64
+    assert type(vis_data[0].u) == un.Quantity
+    assert type(vis_data[0].v) == un.Quantity
+    assert type(vis_data[0].w) == un.Quantity
+    assert type(vis_data[0].date) == np.float64
+    assert type(vis_data[0]._date) == np.float64
+
+    # test num vis for time step 0
+    num_vis_theory = num_active_telescopes * (num_active_telescopes - 1) / 2
+    num_vis_calsc = vis_data.base_num[
+        vis_data.date == np.unique(vis_data.date)[0]
+    ].shape[0]
+
+    assert num_vis_theory == num_vis_calsc
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 4e9cd4a..f0dbcdf 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -48,3 +48,27 @@ def test_calc_time_steps():
     time = calc_time_steps(conf)
 
     assert time.shape == (2232,)
+
+
+def test_Array():
+    from pyvisgen.simulation.utils import Array
+    from pyvisgen.layouts.layouts import get_array_layout
+
+    array_layout = get_array_layout("vlba")
+    ar = Array(array_layout)
+    delta_x, delta_y, delta_z, indices = ar.calc_relative_pos
+
+    mask = ar.get_baseline_mask
+
+    antenna_pairs, st_num_pairs, els_low_pairs, els_high_pairs = ar.calc_ant_pair_vals
+
+    assert delta_x.shape == delta_y.shape == delta_z.shape == (45, 1)
+    assert indices.shape == (45,)
+    assert len(mask) == 10
+    assert (
+        antenna_pairs.shape
+        == st_num_pairs.shape
+        == els_low_pairs.shape
+        == els_high_pairs.shape
+        == (45, 2)
+    )