From 00c609e6491f2ec065de2cadd5c211b9f17676fd Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Tue, 7 Apr 2026 18:18:38 +0200
Subject: [PATCH 01/63] Start IFU mode, image slicer

---
 MICADO/MICADO_IFU.yaml | 32 ++++++++++++++++++++++++++++++++
 MICADO/default.yaml    | 14 ++++++++++++++
 2 files changed, 46 insertions(+)
 create mode 100644 MICADO/MICADO_IFU.yaml

diff --git a/MICADO/MICADO_IFU.yaml b/MICADO/MICADO_IFU.yaml
new file mode 100644
index 00000000..8ab1693b
--- /dev/null
+++ b/MICADO/MICADO_IFU.yaml
@@ -0,0 +1,32 @@
+---
+### MICADO IFU mode
+object: instrument
+alias: INST
+name: MICADO_IFU
+description: additional effects for the IFU mode
+date_modified: 2026-04-07
+changes:
+  - 2026-04-07 (OC) include image slicer
+
+properties:
+  pixel_scale: 0.004          # arcsec / pixel
+  plate_scale: 0.266666666666 # arcsec / pixel
+  decouple_detector_from_sky_headers: True   # needed for spectroscopy
+
+effects:
+  - name: image_slicer
+    description: field of view of the image slicer
+    class: MetisLMSImageSlicer
+    kwargs:
+      filename: MICADO_IFU_Apertures.fits
+      ext_id: "Aperture List"
+
+---
+### set observation mode
+object: observation
+alias: OBS
+name: MICADO_IFU
+
+properties:
+  obs_mode: "IFU"
+  fits_ins_mode: "IFU"
diff --git a/MICADO/default.yaml b/MICADO/default.yaml
index 665174d2..f1d42e3c 100644
--- a/MICADO/default.yaml
+++ b/MICADO/default.yaml
@@ -106,6 +106,20 @@ mode_yamls :
   yamls :
   - MICADO_IMG_HCI.yaml
 
+- object: observation
+  alias: OBS
+  name: IFU
+  description: "Integral-field spectroscopy"
+  status: experimental
+  yamls:
+  - MICADO_IFU.yaml
+  properties:
+    trace_file: TRACE_MICADO.fits
+    filter_name_fw1: Spec_HK
+    filter_name_fw2: open
+    filter_name_pupil: open
+    fits_ins_filt: Spec_HK
+
 - object : observation
   alias: OBS
   name : SPEC_15000x20

From 44ed9ab06d4cc0f40c989c46eb5f94eaa956d00a Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Wed, 8 Apr 2026 16:58:01 +0200
Subject: [PATCH 02/63] Preliminary introduction of SpectralTraceList

---
 MICADO/MICADO_IFU.yaml | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/MICADO/MICADO_IFU.yaml b/MICADO/MICADO_IFU.yaml
index 8ab1693b..08b60f10 100644
--- a/MICADO/MICADO_IFU.yaml
+++ b/MICADO/MICADO_IFU.yaml
@@ -21,6 +21,15 @@ effects:
       filename: MICADO_IFU_Apertures.fits
       ext_id: "Aperture List"
 
+  - name: spectral_traces
+    description: spectral order trace geometry on the focal plane
+    class: SpectralTraceList
+    kwargs:
+      filename: "!OBS.trace_file"
+      wave_colname: "wavelength"
+      s_colname: "xi"
+      col_number_start: 1
+
 ---
 ### set observation mode
 object: observation

From 11d077d67563dde228775378ccc8ebd47d0496ae Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Thu, 7 May 2026 16:16:46 +0200
Subject: [PATCH 03/63] Redefine offset via meta keywords

---
 MICADO/MASK_slit_Short_offset.dat | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/MICADO/MASK_slit_Short_offset.dat b/MICADO/MASK_slit_Short_offset.dat
index 252042d0..bc0c3680 100644
--- a/MICADO/MASK_slit_Short_offset.dat
+++ b/MICADO/MASK_slit_Short_offset.dat
@@ -7,14 +7,18 @@
 # type : aperture:slit_geometry
 # x_unit : arcsec
 # y_unit : arcsec
+# offset_type : detector
+# offset_x : 0
+# offset_y : 4.5
 # changes :
 # - 2019-07-10 (KL) Created the file
 # - 2020-03-24 (KL) Changed geometry to 3000x20mas
 # - 2023-07-13 (OC) Changed geometry to 3000x16mas
 # - 2026-02-03 (OC) Initialised from MASK_slit_3000x16.dat
+# - 2026-05-07 (OC) Put offset into meta data
 #
 x       y
--1.5    1.192
-1.5     1.192
-1.5     1.208
--1.5    1.208
+-1.5    -0.008
++1.5     -0.008
++1.5     +0.008
+-1.5    +0.008

From 0490febb1e37f8db137a2c50e038c3ab677455c6 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Sat, 16 May 2026 18:25:12 +0200
Subject: [PATCH 04/63] Use merged aperture/trace file

---
 MICADO/MICADO_IFU.yaml | 4 ++--
 MICADO/default.yaml    | 3 ++-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/MICADO/MICADO_IFU.yaml b/MICADO/MICADO_IFU.yaml
index 08b60f10..82ef2467 100644
--- a/MICADO/MICADO_IFU.yaml
+++ b/MICADO/MICADO_IFU.yaml
@@ -18,12 +18,12 @@ effects:
     description: field of view of the image slicer
     class: MetisLMSImageSlicer
     kwargs:
-      filename: MICADO_IFU_Apertures.fits
+      filename: "!OBS.trace_file"
       ext_id: "Aperture List"
 
   - name: spectral_traces
     description: spectral order trace geometry on the focal plane
-    class: SpectralTraceList
+    class: MicadoIFUSpectralTraceList
     kwargs:
       filename: "!OBS.trace_file"
       wave_colname: "wavelength"
diff --git a/MICADO/default.yaml b/MICADO/default.yaml
index 9293f0c9..cc9c9ae2 100644
--- a/MICADO/default.yaml
+++ b/MICADO/default.yaml
@@ -133,10 +133,11 @@ mode_yamls :
   yamls:
   - MICADO_IFU.yaml
   properties:
-    trace_file: TRACE_MICADO.fits
+    trace_file: TRACE_MICADO_IFU.fits
     filter_name_fw1: Spec_HK
     filter_name_fw2: open
     filter_name_pupil: open
+    fits_ins_mode: IFU
     fits_ins_filt: Spec_HK
 
 #### Deprecated modes

From d3abde185e370f62238dec1b909664bebe641f65 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Wed, 20 May 2026 15:34:32 +0200
Subject: [PATCH 05/63] IFU trace file

---
 MICADO/MICADO_IFU.yaml       |   1 +
 MICADO/TRACE_MICADO_IFU.fits | Bin 0 -> 80640 bytes
 2 files changed, 1 insertion(+)
 create mode 100644 MICADO/TRACE_MICADO_IFU.fits

diff --git a/MICADO/MICADO_IFU.yaml b/MICADO/MICADO_IFU.yaml
index 82ef2467..2155fc7a 100644
--- a/MICADO/MICADO_IFU.yaml
+++ b/MICADO/MICADO_IFU.yaml
@@ -29,6 +29,7 @@ effects:
       wave_colname: "wavelength"
       s_colname: "xi"
       col_number_start: 1
+      slice_samples: 5  # number of samples along slice width
 
 ---
 ### set observation mode
diff --git a/MICADO/TRACE_MICADO_IFU.fits b/MICADO/TRACE_MICADO_IFU.fits
new file mode 100644
index 0000000000000000000000000000000000000000..e8bc6fe0138ed4a0bc01cb3badf0b452a98a542e
GIT binary patch
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From 069c87a60cca0f8e6820b48d173c3e8d847ab031 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Fri, 22 May 2026 17:17:16 +0200
Subject: [PATCH 06/63] Example notebook for MICADO_IFU

---
 .../docs/example_notebooks/MICADO_IFU.ipynb   | 604 ++++++++++++++++++
 1 file changed, 604 insertions(+)
 create mode 100644 MICADO/docs/example_notebooks/MICADO_IFU.ipynb

diff --git a/MICADO/docs/example_notebooks/MICADO_IFU.ipynb b/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
new file mode 100644
index 00000000..a16e061d
--- /dev/null
+++ b/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
@@ -0,0 +1,604 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fe82dad8-f30b-4d6b-a8a4-70b322955831",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates the ScopeSim implementation of the MICADO IFU mode. The IFU mode has only recently been added to MICADO. It will consist of an image slicer with 32 slices with an on-sky width of 0.012 arcsec and a length of 0.45 arcsec. The slices are rearranged before the light is passed through the standard MICADO spectrograph, which results in 32 non-overlapping traces for each spectral order. The IFU will be offered in HK only, so that there are two orders (ORDER_3_1 and ORDER_4_1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "74ef81df-7c46-49f1-8ec2-5ccf1037b844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from astropy import units as u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f040e328-20d2-4223-b393-bf446a93f0c3",
+   "metadata": {},
+   "source": [
+    "We start by importing ScopeSim and pointing it to the relevant instrument packages. Most users should download the latest version of the instrument packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5dcc145d-0616-45fa-baa3-25b99d399967",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scopesim as sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a2f6d6a9-8bda-4744-8cad-aab2405f42ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Edit this path if you have a custom install directory, otherwise comment it out. [For ReadTheDocs only]\n",
+    "sim.link_irdb(\"../../../../\")\n",
+    "\n",
+    "# If you want/need to download the instrument packages uncomment the following line\n",
+    "#sim.download_packages([\"Armazones\", \"ELT\", \"METIS\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c490464-f6e3-402b-bf80-647f4049bdfa",
+   "metadata": {},
+   "source": [
+    "The following command gives the versions of python packages that are used by Scopesim as well as of instrument packages (called IRDB packages). We ask to include the output of this command in any communication with us, in particular in bug reports."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5f86135c-dd97-4647-af27-de85b69b92b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python:\n",
+      "3.12.9 (main, Feb  5 2025, 01:31:18) [GCC 14.2.0]\n",
+      "\n",
+      "Installed Python packages:\n",
+      "           anisocado: 0.5.0\n",
+      "         astar-utils: 0.5.2\n",
+      "             astropy: 6.1.7\n",
+      "      beautifulsoup4: 4.13.3\n",
+      "            docutils: 0.21.2\n",
+      "               httpx: 0.28.1\n",
+      "                lxml: 6.0.2\n",
+      "          matplotlib: 3.10.8\n",
+      "      more-itertools: 10.2.0\n",
+      "               numpy: 2.2.6\n",
+      "           packaging: 25.0\n",
+      "               pooch: 1.9.0\n",
+      "              pyyaml: 6.0.3\n",
+      "               scipy: 1.17.0\n",
+      "            scopesim: 0.11.4a7\n",
+      "       scopesim_data: could not be loaded.\n",
+      "  scopesim_templates: 0.8.2a2\n",
+      "         skycalc-ipy: 0.7.0\n",
+      "             synphot: 1.6.1\n",
+      "                tqdm: 4.67.3\n",
+      "\n",
+      "Installed IRDB packages:\n",
+      "       HARMONI: version number not available.\n",
+      "         HAWKI: version number not available.\n",
+      "           VLT: version number not available.\n",
+      "        OSIRIS: version number not available.\n",
+      "           ELT: version number not available.\n",
+      "       LaPalma: version number not available.\n",
+      "         VISIR: version number not available.\n",
+      "           GTC: version number not available.\n",
+      "          KMOS: version number not available.\n",
+      "         MCIFU: version number not available.\n",
+      "     Armazones: version number not available.\n",
+      "  test_package: version number not available.\n",
+      "       Paranal: version number not available.\n",
+      "        MICADO: version number not available.\n",
+      "      ViennaLT: version number not available.\n",
+      "          UVES: version number not available.\n",
+      "        MOSAIC: version number not available.\n",
+      "          ERIS: version number not available.\n",
+      "          FORS: version number not available.\n",
+      "          WFC3: version number not available.\n",
+      "           HST: version number not available.\n",
+      "          MUSE: version number not available.\n",
+      "         METIS: version number not available.\n",
+      "          NACO: version number not available.\n",
+      "        MORFEO: version number not available.\n",
+      "          LFOA: version number not available.\n",
+      "          JWST: version number not available.\n",
+      "        NIRCam: version number not available.\n",
+      "\n",
+      "Operating System info:\n",
+      "   System: Linux\n",
+      "  Release: 6.12.48+deb13-amd64\n",
+      "  Version: #1 SMP PREEMPT_DYNAMIC Debian 6.12.48-1 (2025-09-20)\n",
+      "  Machine: x86_64\n"
+     ]
+    }
+   ],
+   "source": [
+    "sim.bug_report()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9803afd8-3257-45c0-81b7-8568951e6302",
+   "metadata": {},
+   "source": [
+    "# Setting up the instrument\n",
+    "We set up MICADO to use the IFU in SCAO (stand-alone) mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3af4d5ec-1403-45aa-ac91-5bf4789c0134",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32mastar.scopesim.commands.user_commands - Mode 'IFU' is still in experimental stage, results may not be representative of physical instrument.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "cmds = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IFU\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1a382f8a-9d8c-42f5-acdc-a4fbaa30d844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado = sim.OpticalTrain(cmds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "488baefc-0567-4eaa-8992-47d78e9dd8ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=27</i>\n",
+       "<table id=\"table140558159944000-484738\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>idx</th><th>element</th><th>name</th><th>class</th><th>included</th></tr></thead>\n",
+       "<tr><td>0</td><td>armazones</td><td>skycalc_atmosphere</td><td>SkycalcTERCurve</td><td>True</td></tr>\n",
+       "<tr><td>1</td><td>ELT</td><td>telescope_reflection</td><td>SurfaceList</td><td>True</td></tr>\n",
+       "<tr><td>2</td><td>ELT</td><td>telescope_fits_keywords</td><td>ExtraFitsKeywords</td><td>True</td></tr>\n",
+       "<tr><td>3</td><td>MICADO</td><td>micado_static_surfaces</td><td>SurfaceList</td><td>True</td></tr>\n",
+       "<tr><td>4</td><td>MICADO</td><td>micado_ncpas_psf</td><td>NonCommonPathAberration</td><td>True</td></tr>\n",
+       "<tr><td>5</td><td>MICADO</td><td>slit_wheel : [False]</td><td>SlitWheel</td><td>False</td></tr>\n",
+       "<tr><td>6</td><td>MICADO</td><td>filter_wheel_1 : [Spec_HK]</td><td>FilterWheel</td><td>True</td></tr>\n",
+       "<tr><td>7</td><td>MICADO</td><td>filter_wheel_2 : [open]</td><td>FilterWheel</td><td>True</td></tr>\n",
+       "<tr><td>8</td><td>MICADO</td><td>pupil_wheel : [open]</td><td>FilterWheel</td><td>True</td></tr>\n",
+       "<tr><td>9</td><td>MICADO_DET</td><td>full_detector_array</td><td>DetectorList</td><td>False</td></tr>\n",
+       "<tr><td>10</td><td>MICADO_DET</td><td>detector_window</td><td>DetectorWindow</td><td>True</td></tr>\n",
+       "<tr><td>11</td><td>MICADO_DET</td><td>qe_curve</td><td>QuantumEfficiencyCurve</td><td>True</td></tr>\n",
+       "<tr><td>12</td><td>MICADO_DET</td><td>exposure_integration</td><td>ExposureIntegration</td><td>True</td></tr>\n",
+       "<tr><td>13</td><td>MICADO_DET</td><td>dark_current</td><td>DarkCurrent</td><td>True</td></tr>\n",
+       "<tr><td>14</td><td>MICADO_DET</td><td>shot_noise</td><td>ShotNoise</td><td>True</td></tr>\n",
+       "<tr><td>15</td><td>MICADO_DET</td><td>detector_linearity</td><td>LinearityCurve</td><td>True</td></tr>\n",
+       "<tr><td>16</td><td>MICADO_DET</td><td>border_reference_pixels</td><td>ReferencePixelBorder</td><td>False</td></tr>\n",
+       "<tr><td>17</td><td>MICADO_DET</td><td>readout_noise</td><td>PoorMansHxRGReadoutNoise</td><td>True</td></tr>\n",
+       "<tr><td>18</td><td>MICADO_DET</td><td>exposure_output : [sum]</td><td>ExposureOutput</td><td>True</td></tr>\n",
+       "<tr><td>19</td><td>MICADO_DET</td><td>ad_conversion</td><td>ADConversion</td><td>True</td></tr>\n",
+       "<tr><td>20</td><td>MICADO_DET</td><td>source_fits_keywords</td><td>SourceDescriptionFitsKeywords</td><td>False</td></tr>\n",
+       "<tr><td>21</td><td>MICADO_DET</td><td>extra_fits_keywords</td><td>ExtraFitsKeywords</td><td>True</td></tr>\n",
+       "<tr><td>22</td><td>default_ro</td><td>relay_psf</td><td>FieldConstantPSF</td><td>True</td></tr>\n",
+       "<tr><td>23</td><td>default_ro</td><td>relay_surface_list</td><td>SurfaceList</td><td>True</td></tr>\n",
+       "<tr><td>24</td><td>default_ro</td><td>extra_fits_keywords_ro</td><td>ExtraFitsKeywords</td><td>True</td></tr>\n",
+       "<tr><td>25</td><td>MICADO_IFU</td><td>image_slicer</td><td>MetisLMSImageSlicer</td><td>True</td></tr>\n",
+       "<tr><td>26</td><td>MICADO_IFU</td><td>spectral_traces</td><td>MicadoIFUSpectralTraceList</td><td>True</td></tr>\n",
+       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
+       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
+       "display: inline-block; margin-right: 1em; }\n",
+       ".paginate_button { margin-right: 5px; }\n",
+       "</style>\n",
+       "<script>\n",
+       "\n",
+       "var astropy_sort_num = function(a, b) {\n",
+       "    var a_num = parseFloat(a);\n",
+       "    var b_num = parseFloat(b);\n",
+       "\n",
+       "    if (isNaN(a_num) && isNaN(b_num))\n",
+       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
+       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
+       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
+       "    else\n",
+       "        return isNaN(a_num) ? -1 : 1;\n",
+       "}\n",
+       "\n",
+       "require.config({paths: {\n",
+       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
+       "}});\n",
+       "require([\"datatables\"], function(){\n",
+       "    console.log(\"$('#table140558159944000-484738').dataTable()\");\n",
+       "    \n",
+       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
+       "    \"optionalnum-asc\": astropy_sort_num,\n",
+       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
+       "});\n",
+       "\n",
+       "    $('#table140558159944000-484738').dataTable({\n",
+       "        order: [],\n",
+       "        pageLength: 50,\n",
+       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
+       "        pagingType: \"full_numbers\",\n",
+       "        columnDefs: [{targets: [0], type: \"optionalnum\"}]\n",
+       "    });\n",
+       "});\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micado.effects.show_in_notebook()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca723bb3-06d8-4c83-9423-1fb906f870c9",
+   "metadata": {},
+   "source": [
+    "# Define a source and simulate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "25f0c843-7f6e-4c89-97b8-6f9f19c227fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star = sim.source.source_templates.star(x=0, y=0, flux=15*u.mag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a714cce-36c9-49a4-ad64-d075d7c0887d",
+   "metadata": {},
+   "source": [
+    "To simulate the full MICADO field, we need to switch two effects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9734379c-515a-4825-987f-f9ea700a7dba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado[\"full_detector_array\"].include = True\n",
+    "micado[\"detector_window\"].include = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a0c00218-1784-453c-bc66-a34cd3f0da80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce3ff3e505534db582a79e9f30c12b84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " FOVs:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "378fbf552304465e899581316b1544dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " FOV effects:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32mastar.scopesim.effects.psfs - Interpolating PSF onto (3750, 193, 225) cube\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa483b6d2b70482d8c12ae392bd30461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " PSF slices:   0%|          | 0/3750 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e87a27d5736943ecab471fe1e460f5a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " Spectral Traces:   0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_01: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_02: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_03: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_04: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_05: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_06: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_07: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_08: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_09: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_10: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_11: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_12: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_13: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_14: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_15: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_16: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_17: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_18: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_19: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_20: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_21: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_22: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_23: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_24: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_25: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_26: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_27: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_28: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_29: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_30: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_31: footprint is outside FoV\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_32: footprint is outside FoV\u001b[0m\n",
+      "\u001b[35mastar.scopesim.optics.image_plane - WARNING: No BUNIT found in added HDU.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "324d3da330754bc0904d933fdeda9650",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " FOV effects:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32mastar.scopesim.effects.psfs - Interpolating PSF onto (6250, 193, 225) cube\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10dfcab95e204fd49207b8afec1e7da8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " PSF slices:   0%|          | 0/6250 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85e3d76138ad4e4c824885aaffc4fe7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " Spectral Traces:   0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
+      "\u001b[35mastar.scopesim.optics.image_plane - WARNING: No BUNIT found in added HDU.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "micado.observe(star)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "010296c5-d034-4f60-984c-58a435b5530e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32mastar.scopesim.detector.detector_manager - Extracting from 9 detectors...\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
+      "\u001b[35mastar.scopesim.effects.fits_headers - WARNING: #exposure_action.include not found\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "hdul = micado.readout(dit=100, ndit=10)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "69383a4c-5b91-4417-b9ad-5936b9bd55b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7fd6198c9b50>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(hdul[5].data, origin='lower', vmax=1000)\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "593afd4c-d040-4f07-9d39-62945d2ff88a",
+   "metadata": {},
+   "source": [
+    "To view the entire detector array we recommend saving to disk (`hdul.writeto(\"your_file.fits\")`) and using `ds9`. Open the file with `File -> Open as -> Mosaic WCS` and select `WCS d` to arrange the nine detectors correctly in the detector plane. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4c50800-909b-4527-84b5-4e8f78d9dffb",
+   "metadata": {},
+   "source": [
+    "# Rectification (not yet available)"
+   ]
+  }
+ ],
+ "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 03393684557f2e2d43c3a8f7e9c7da0a02896a49 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Fri, 22 May 2026 17:20:21 +0200
Subject: [PATCH 07/63] link_irdb not needed, apparently

---
 .../docs/example_notebooks/MICADO_IFU.ipynb   | 426 +-----------------
 1 file changed, 18 insertions(+), 408 deletions(-)

diff --git a/MICADO/docs/example_notebooks/MICADO_IFU.ipynb b/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
index a16e061d..310aa6c4 100644
--- a/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
+++ b/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "74ef81df-7c46-49f1-8ec2-5ccf1037b844",
    "metadata": {},
    "outputs": [],
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "5dcc145d-0616-45fa-baa3-25b99d399967",
    "metadata": {},
    "outputs": [],
@@ -39,14 +39,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "a2f6d6a9-8bda-4744-8cad-aab2405f42ad",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Edit this path if you have a custom install directory, otherwise comment it out. [For ReadTheDocs only]\n",
-    "sim.link_irdb(\"../../../../\")\n",
-    "\n",
     "# If you want/need to download the instrument packages uncomment the following line\n",
     "#sim.download_packages([\"Armazones\", \"ELT\", \"METIS\"])"
    ]
@@ -61,77 +58,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "5f86135c-dd97-4647-af27-de85b69b92b5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Python:\n",
-      "3.12.9 (main, Feb  5 2025, 01:31:18) [GCC 14.2.0]\n",
-      "\n",
-      "Installed Python packages:\n",
-      "           anisocado: 0.5.0\n",
-      "         astar-utils: 0.5.2\n",
-      "             astropy: 6.1.7\n",
-      "      beautifulsoup4: 4.13.3\n",
-      "            docutils: 0.21.2\n",
-      "               httpx: 0.28.1\n",
-      "                lxml: 6.0.2\n",
-      "          matplotlib: 3.10.8\n",
-      "      more-itertools: 10.2.0\n",
-      "               numpy: 2.2.6\n",
-      "           packaging: 25.0\n",
-      "               pooch: 1.9.0\n",
-      "              pyyaml: 6.0.3\n",
-      "               scipy: 1.17.0\n",
-      "            scopesim: 0.11.4a7\n",
-      "       scopesim_data: could not be loaded.\n",
-      "  scopesim_templates: 0.8.2a2\n",
-      "         skycalc-ipy: 0.7.0\n",
-      "             synphot: 1.6.1\n",
-      "                tqdm: 4.67.3\n",
-      "\n",
-      "Installed IRDB packages:\n",
-      "       HARMONI: version number not available.\n",
-      "         HAWKI: version number not available.\n",
-      "           VLT: version number not available.\n",
-      "        OSIRIS: version number not available.\n",
-      "           ELT: version number not available.\n",
-      "       LaPalma: version number not available.\n",
-      "         VISIR: version number not available.\n",
-      "           GTC: version number not available.\n",
-      "          KMOS: version number not available.\n",
-      "         MCIFU: version number not available.\n",
-      "     Armazones: version number not available.\n",
-      "  test_package: version number not available.\n",
-      "       Paranal: version number not available.\n",
-      "        MICADO: version number not available.\n",
-      "      ViennaLT: version number not available.\n",
-      "          UVES: version number not available.\n",
-      "        MOSAIC: version number not available.\n",
-      "          ERIS: version number not available.\n",
-      "          FORS: version number not available.\n",
-      "          WFC3: version number not available.\n",
-      "           HST: version number not available.\n",
-      "          MUSE: version number not available.\n",
-      "         METIS: version number not available.\n",
-      "          NACO: version number not available.\n",
-      "        MORFEO: version number not available.\n",
-      "          LFOA: version number not available.\n",
-      "          JWST: version number not available.\n",
-      "        NIRCam: version number not available.\n",
-      "\n",
-      "Operating System info:\n",
-      "   System: Linux\n",
-      "  Release: 6.12.48+deb13-amd64\n",
-      "  Version: #1 SMP PREEMPT_DYNAMIC Debian 6.12.48-1 (2025-09-20)\n",
-      "  Machine: x86_64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sim.bug_report()"
    ]
@@ -147,25 +77,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "3af4d5ec-1403-45aa-ac91-5bf4789c0134",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32mastar.scopesim.commands.user_commands - Mode 'IFU' is still in experimental stage, results may not be representative of physical instrument.\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cmds = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IFU\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "1a382f8a-9d8c-42f5-acdc-a4fbaa30d844",
    "metadata": {},
    "outputs": [],
@@ -175,92 +97,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "488baefc-0567-4eaa-8992-47d78e9dd8ed",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<i>Table length=27</i>\n",
-       "<table id=\"table140558159944000-484738\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>idx</th><th>element</th><th>name</th><th>class</th><th>included</th></tr></thead>\n",
-       "<tr><td>0</td><td>armazones</td><td>skycalc_atmosphere</td><td>SkycalcTERCurve</td><td>True</td></tr>\n",
-       "<tr><td>1</td><td>ELT</td><td>telescope_reflection</td><td>SurfaceList</td><td>True</td></tr>\n",
-       "<tr><td>2</td><td>ELT</td><td>telescope_fits_keywords</td><td>ExtraFitsKeywords</td><td>True</td></tr>\n",
-       "<tr><td>3</td><td>MICADO</td><td>micado_static_surfaces</td><td>SurfaceList</td><td>True</td></tr>\n",
-       "<tr><td>4</td><td>MICADO</td><td>micado_ncpas_psf</td><td>NonCommonPathAberration</td><td>True</td></tr>\n",
-       "<tr><td>5</td><td>MICADO</td><td>slit_wheel : [False]</td><td>SlitWheel</td><td>False</td></tr>\n",
-       "<tr><td>6</td><td>MICADO</td><td>filter_wheel_1 : [Spec_HK]</td><td>FilterWheel</td><td>True</td></tr>\n",
-       "<tr><td>7</td><td>MICADO</td><td>filter_wheel_2 : [open]</td><td>FilterWheel</td><td>True</td></tr>\n",
-       "<tr><td>8</td><td>MICADO</td><td>pupil_wheel : [open]</td><td>FilterWheel</td><td>True</td></tr>\n",
-       "<tr><td>9</td><td>MICADO_DET</td><td>full_detector_array</td><td>DetectorList</td><td>False</td></tr>\n",
-       "<tr><td>10</td><td>MICADO_DET</td><td>detector_window</td><td>DetectorWindow</td><td>True</td></tr>\n",
-       "<tr><td>11</td><td>MICADO_DET</td><td>qe_curve</td><td>QuantumEfficiencyCurve</td><td>True</td></tr>\n",
-       "<tr><td>12</td><td>MICADO_DET</td><td>exposure_integration</td><td>ExposureIntegration</td><td>True</td></tr>\n",
-       "<tr><td>13</td><td>MICADO_DET</td><td>dark_current</td><td>DarkCurrent</td><td>True</td></tr>\n",
-       "<tr><td>14</td><td>MICADO_DET</td><td>shot_noise</td><td>ShotNoise</td><td>True</td></tr>\n",
-       "<tr><td>15</td><td>MICADO_DET</td><td>detector_linearity</td><td>LinearityCurve</td><td>True</td></tr>\n",
-       "<tr><td>16</td><td>MICADO_DET</td><td>border_reference_pixels</td><td>ReferencePixelBorder</td><td>False</td></tr>\n",
-       "<tr><td>17</td><td>MICADO_DET</td><td>readout_noise</td><td>PoorMansHxRGReadoutNoise</td><td>True</td></tr>\n",
-       "<tr><td>18</td><td>MICADO_DET</td><td>exposure_output : [sum]</td><td>ExposureOutput</td><td>True</td></tr>\n",
-       "<tr><td>19</td><td>MICADO_DET</td><td>ad_conversion</td><td>ADConversion</td><td>True</td></tr>\n",
-       "<tr><td>20</td><td>MICADO_DET</td><td>source_fits_keywords</td><td>SourceDescriptionFitsKeywords</td><td>False</td></tr>\n",
-       "<tr><td>21</td><td>MICADO_DET</td><td>extra_fits_keywords</td><td>ExtraFitsKeywords</td><td>True</td></tr>\n",
-       "<tr><td>22</td><td>default_ro</td><td>relay_psf</td><td>FieldConstantPSF</td><td>True</td></tr>\n",
-       "<tr><td>23</td><td>default_ro</td><td>relay_surface_list</td><td>SurfaceList</td><td>True</td></tr>\n",
-       "<tr><td>24</td><td>default_ro</td><td>extra_fits_keywords_ro</td><td>ExtraFitsKeywords</td><td>True</td></tr>\n",
-       "<tr><td>25</td><td>MICADO_IFU</td><td>image_slicer</td><td>MetisLMSImageSlicer</td><td>True</td></tr>\n",
-       "<tr><td>26</td><td>MICADO_IFU</td><td>spectral_traces</td><td>MicadoIFUSpectralTraceList</td><td>True</td></tr>\n",
-       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
-       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
-       "display: inline-block; margin-right: 1em; }\n",
-       ".paginate_button { margin-right: 5px; }\n",
-       "</style>\n",
-       "<script>\n",
-       "\n",
-       "var astropy_sort_num = function(a, b) {\n",
-       "    var a_num = parseFloat(a);\n",
-       "    var b_num = parseFloat(b);\n",
-       "\n",
-       "    if (isNaN(a_num) && isNaN(b_num))\n",
-       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
-       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
-       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
-       "    else\n",
-       "        return isNaN(a_num) ? -1 : 1;\n",
-       "}\n",
-       "\n",
-       "require.config({paths: {\n",
-       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
-       "}});\n",
-       "require([\"datatables\"], function(){\n",
-       "    console.log(\"$('#table140558159944000-484738').dataTable()\");\n",
-       "    \n",
-       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
-       "    \"optionalnum-asc\": astropy_sort_num,\n",
-       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
-       "});\n",
-       "\n",
-       "    $('#table140558159944000-484738').dataTable({\n",
-       "        order: [],\n",
-       "        pageLength: 50,\n",
-       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
-       "        pagingType: \"full_numbers\",\n",
-       "        columnDefs: [{targets: [0], type: \"optionalnum\"}]\n",
-       "    });\n",
-       "});\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "micado.effects.show_in_notebook()"
    ]
@@ -275,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "25f0c843-7f6e-4c89-97b8-6f9f19c227fc",
    "metadata": {},
    "outputs": [],
@@ -293,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "9734379c-515a-4825-987f-f9ea700a7dba",
    "metadata": {},
    "outputs": [],
@@ -304,260 +144,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "a0c00218-1784-453c-bc66-a34cd3f0da80",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce3ff3e505534db582a79e9f30c12b84",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       " FOVs:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32mastar.scopesim.effects.psfs - Interpolating PSF onto (3750, 193, 225) cube\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       " PSF slices:   0%|          | 0/3750 [00:00<?, ?it/s]"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-       "version_major": 2,
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_01: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_02: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_03: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_04: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_05: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_06: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_07: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_08: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_09: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_10: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_11: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_12: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_13: footprint is outside FoV\u001b[0m\n",
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-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_15: footprint is outside FoV\u001b[0m\n",
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-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_17: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_18: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_19: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_20: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_21: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_22: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_23: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_24: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_25: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_26: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_27: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_28: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_29: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_30: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_31: footprint is outside FoV\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Spectral trace ORDER_3_1_32: footprint is outside FoV\u001b[0m\n",
-      "\u001b[35mastar.scopesim.optics.image_plane - WARNING: No BUNIT found in added HDU.\u001b[0m\n"
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-     "metadata": {},
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32mastar.scopesim.effects.psfs - Interpolating PSF onto (6250, 193, 225) cube\u001b[0m\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.spectral_trace_list_utils - WARNING: xlim_mm is None\u001b[0m\n",
-      "\u001b[35mastar.scopesim.optics.image_plane - WARNING: No BUNIT found in added HDU.\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "micado.observe(star)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "010296c5-d034-4f60-984c-58a435b5530e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32mastar.scopesim.detector.detector_manager - Extracting from 9 detectors...\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[32mastar.scopesim.effects.electronic.electrons - Applying gain 1\u001b[0m\n",
-      "\u001b[35mastar.scopesim.effects.fits_headers - WARNING: #exposure_action.include not found\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "hdul = micado.readout(dit=100, ndit=10)[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "id": "69383a4c-5b91-4417-b9ad-5936b9bd55b1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7fd6198c9b50>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.imshow(hdul[5].data, origin='lower', vmax=1000)\n",
     "plt.colorbar()"

From b0c5c1443bfa100977507e1a099a508d69cba27c Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Fri, 29 May 2026 15:14:50 +0200
Subject: [PATCH 08/63] Add missing TECH keywords to most modes

Using best-guess values for MICADO, for METIS taken from:
https://github.com/AstarVienna/irdb/issues/356#issuecomment-4553690985

Closes #356, except the LMS extended mode, which is not yet implemented
anyway (also set the status to concept for that).
---
 METIS/default.yaml                      | 27 ++++++++++++++++++++-----
 METIS/headers/FITS_img_lm_keywords.yaml |  2 +-
 METIS/headers/FITS_img_n_keywords.yaml  |  2 +-
 METIS/headers/FITS_lms_keywords.yaml    |  2 +-
 METIS/headers/FITS_lss_keywords.yaml    |  2 +-
 MICADO/default.yaml                     |  9 +++++++--
 6 files changed, 33 insertions(+), 11 deletions(-)

diff --git a/METIS/default.yaml b/METIS/default.yaml
index ae377944..95b958e5 100644
--- a/METIS/default.yaml
+++ b/METIS/default.yaml
@@ -7,7 +7,7 @@ name: METIS_default_configuration
 description: default parameters needed for a METIS simulation
 status: development
 needs_scopesim: "v0.11.3"
-date_modified: 2025-10-09
+date_modified: 2026-05-29
 changes:
   - 2021-12-16 (OC) chopnod defaults to perpendicular
   - 2021-12-16 (OC) default slits renamed
@@ -23,6 +23,7 @@ changes:
   - 2025-09-29 (OC) version bump
   - 2025-10-09 (OC) proper MJD value
   - 2026-03-31 (OC) set wave_min to 2.65um for bluest LMS settings
+  - 2026-05-29 (FH) added tech keyword to most modes
 
 packages:
   - Armazones
@@ -53,7 +54,6 @@ properties:
   #       corresponding yaml file with the keywords.
   tplname: "METIS_img_lm_obs_GenericOffset"
   catg: "SCIENCE"
-  tech: "IMAGE,LM"
   type: "OBJECT"
   # More default properties for the observation
   mjdobs: "61529.104"
@@ -82,6 +82,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: IMG_LM        # use as FITS header keyword
+      tech: IMAGE,LM
       psf_file: PSF_SCAO_9mag_06seeing.fits
       filter_name: Lp
       nd_filter_name: open
@@ -102,6 +103,7 @@ mode_yamls:
       - METIS_DET_IMG_N_GeoSnap.yaml
     properties:
       ins_mode: IMG_N         # use as FITS header keyword
+      tech: IMAGE,N
       psf_file: PSF_SCAO_9mag_06seeing.fits
       filter_name: N2
       nd_filter_name: open
@@ -125,6 +127,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: LSS_L         # use as FITS header keyword
+      tech: LSS,LM
       psf_file: PSF_LM_9mag_06seeing.fits
       trace_file: TRACE_LSS_L.fits
       efficiency_file: TER_grating_L.fits
@@ -150,6 +153,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: IMG_M         # use as FITS header keyword
+      tech: LSS,LM
       psf_file: PSF_LM_9mag_06seeing.fits
       trace_file: TRACE_LSS_M.fits
       efficiency_file: TER_grating_M.fits
@@ -175,6 +179,7 @@ mode_yamls:
       - METIS_DET_IMG_N_GeoSnap.yaml
     properties:
       ins_mode: IMG_N         # use as FITS header keyword
+      tech: LSS,N
       psf_file: PSF_N_9mag_06seeing.fits
       trace_file: TRACE_LSS_N.fits
       efficiency_file: TER_grating_N.fits
@@ -201,6 +206,7 @@ mode_yamls:
       - METIS_DET_IFU.yaml
     properties:
       ins_mode: LMS          # use as FITS header keyword
+      tech: IFU
       psf_file: PSF_LM_9mag_06seeing.fits
       slit: false
       adc: false
@@ -212,7 +218,7 @@ mode_yamls:
     alias: OBS
     name: lms_extended
     description: "METIS LM-band integral-field spectroscopy, extended mode"
-    status: experimental
+    status: concept
     yamls:
       - Armazones.yaml
       - ELT.yaml
@@ -238,6 +244,7 @@ mode_yamls:
       - METIS_DET_IFU_SMPL.yaml
     properties:
       ins_mode: LMS_cube          # use as FITS header keyword
+      tech: IFU
       psf_file: PSF_LM_9mag_06seeing.fits
       interp_psf: False
       slit: false
@@ -259,6 +266,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: IMG_LM        # use as FITS header keyword
+      tech: IMAGE,LM
       pupil_mask: PPS-LM
       psf_file: none
       psf_name: "!OBS.pupil_mask"
@@ -280,6 +288,7 @@ mode_yamls:
       - METIS_DET_IMG_N_GeoSnap.yaml
     properties:
       ins_mode: IMG_N          # use as FITS header keyword
+      tech: IMAGE,N
       pupil_mask: PPS-N
       psf_file: none
       psf_name: "!OBS.pupil_mask"
@@ -304,6 +313,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: LSS_L        # use as FITS header keyword
+      tech: LSS,LM
       pupil_mask: PPS-CFO2
       psf_file: none
       psf_name: "!OBS.pupil_mask"
@@ -330,6 +340,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: LSS_M        # use as FITS header keyword
+      tech: LSS,LM
       pupil_mask: PPS-CFO2
       psf_file: none
       psf_name: "!OBS.pupil_mask"
@@ -356,6 +367,7 @@ mode_yamls:
       - METIS_DET_IMG_N_GeoSnap.yaml
     properties:
       ins_mode: LSS_N        # use as FITS header keyword
+      tech: LSS,N
       pupil_mask: PPS-CFO2
       psf_file: none
       psf_name: "!OBS.pupil_mask"
@@ -383,6 +395,7 @@ mode_yamls:
       - METIS_DET_IFU.yaml
     properties:
       ins_mode: LMS          # use as FITS header keyword
+      tech: IFU
       pupil_mask: PPS-LMS
       psf_file: none
       psf_name: "!OBS.pupil_mask"
@@ -396,7 +409,7 @@ mode_yamls:
     alias: OBS
     name: wcu_lms_extended
     description: "METIS LM-band integral-field spectroscopy, extended mode with WCU"
-    status: experimental
+    status: concept
     yamls:
       - METIS_WCU.yaml
       - METIS.yaml
@@ -427,6 +440,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: LSS_L        # use as FITS header keyword
+      tech: LSS,LM
       psf_file: PSF_LM_9mag_06seeing.fits    # REPLACE!
       trace_file: TRACE_LSS_L.fits
       efficiency_file: TER_grating_L.fits
@@ -451,6 +465,7 @@ mode_yamls:
       - METIS_DET_IMG_LM.yaml
     properties:
       ins_mode: LSS_M        # use as FITS header keyword
+      tech: LSS,LM
       psf_file: PSF_LM_9mag_06seeing.fits    # REPLACE!
       trace_file: TRACE_LSS_M.fits
       efficiency_file: TER_grating_M.fits
@@ -475,6 +490,7 @@ mode_yamls:
       - METIS_DET_IMG_N_GeoSnap.yaml
     properties:
       ins_mode: LSS_N        # use as FITS header keyword
+      tech: LSS,N
       psf_file: PSF_N_9mag_06seeing.fits    # REPLACE!
       trace_file: TRACE_LSS_N.fits
       efficiency_file: TER_grating_N.fits
@@ -500,6 +516,7 @@ mode_yamls:
       - METIS_DET_IFU.yaml
     properties:
       ins_mode: LMS          # use as FITS header keyword
+      tech: IFU
       psf_file: PSF_LM_9mag_06seeing.fits    # REPLACE!
       slit: false
       adc: false
@@ -511,7 +528,7 @@ mode_yamls:
     alias: OBS
     name: leiden_lms_extended
     description: "METIS LM-band integral-field spectroscopy, extended mode with Leiden sky"
-    status: experimental
+    status: concept
     yamls:
       - Leiden_sky.yaml
       - METIS.yaml
diff --git a/METIS/headers/FITS_img_lm_keywords.yaml b/METIS/headers/FITS_img_lm_keywords.yaml
index 63e7ff31..e5f1b890 100644
--- a/METIS/headers/FITS_img_lm_keywords.yaml
+++ b/METIS/headers/FITS_img_lm_keywords.yaml
@@ -4,7 +4,7 @@
     HIERARCH:
       ESO:
         DPR:
-          TECH:   "IMAGE,LM"
+          TECH: "!OBS.tech"  # duplicate from FITS_common_keywords.yaml
         INS:
           OPTI4:                # LMS beamsplitter pickoff wheel
             DEVSIM: [true, "Device simulation flag"]
diff --git a/METIS/headers/FITS_img_n_keywords.yaml b/METIS/headers/FITS_img_n_keywords.yaml
index d28cbb62..baa285bd 100644
--- a/METIS/headers/FITS_img_n_keywords.yaml
+++ b/METIS/headers/FITS_img_n_keywords.yaml
@@ -4,7 +4,7 @@
     HIERARCH:
       ESO:
         DPR:
-          TECH:   "IMAGE,N"
+          TECH: "!OBS.tech"  # duplicate from FITS_common_keywords.yaml
         INS:
 
           OPTI4:                # LMS beamsplitter pickoff wheel
diff --git a/METIS/headers/FITS_lms_keywords.yaml b/METIS/headers/FITS_lms_keywords.yaml
index 6507841c..0435e71c 100644
--- a/METIS/headers/FITS_lms_keywords.yaml
+++ b/METIS/headers/FITS_lms_keywords.yaml
@@ -4,7 +4,7 @@
     HIERARCH:
       ESO:
         DPR:
-          TECH:   "IFU"
+          TECH: "!OBS.tech"  # duplicate from FITS_common_keywords.yaml
         INS:
           LMS:                  # LMS mode (Nominal or Extended)
             MODE: "Nominal"
diff --git a/METIS/headers/FITS_lss_keywords.yaml b/METIS/headers/FITS_lss_keywords.yaml
index 82059b3a..5b98a70a 100644
--- a/METIS/headers/FITS_lss_keywords.yaml
+++ b/METIS/headers/FITS_lss_keywords.yaml
@@ -4,7 +4,7 @@
     HIERARCH:
       ESO:
         DPR:
-          TECH:   "LSS"         # TODO: How to add the filter name?
+          TECH: "!OBS.tech"  # duplicate from FITS_common_keywords.yaml
         INS:
           OPTI4:                # LMS beamsplitter pickoff wheel
             DEVSIM: [true, "Device simulation flag"]
diff --git a/MICADO/default.yaml b/MICADO/default.yaml
index 6f5937f9..f3614ae8 100644
--- a/MICADO/default.yaml
+++ b/MICADO/default.yaml
@@ -7,7 +7,7 @@ name: MICADO_default_configuration
 description: default parameters needed for a MICADO simulation
 status: development
 needs_scopesim: "v0.11.3"
-date_modified: 2025-10-09
+date_modified: 2026-05-29
 changes:
   - 2023-07-13 (OC) add modes for FDR slits, deprecate pre-FDR slits
   - 2025-06-27 (FH) add needs_scopesim keyword
@@ -15,6 +15,7 @@ changes:
   - 2025-10-17 (JB) add parametes for FITS keywords, tplstart as MJD
   - 2026-01-02 (OC) random seed to None
   - 2026-04-09 (OC) new SPEC mode, rename slits
+  - 2026-05-29 (FH) added tech keyword to most modes
 
 packages:
 - Armazones
@@ -39,7 +40,6 @@ properties :
   dit : 60
   ndit : 1
   catg : SCIENCE
-  tech : IMAGE
   type : OBJECT
   # Should be datetimes, but is copied verbatim, so should work.
   mjdobs : 61529.104
@@ -85,6 +85,7 @@ mode_yamls :
     filter_name_fw1: open
     filter_name_fw2: Ks
     filter_name_pupil: open
+    tech: IMAGE
     fits_ins_filt: Ks
 
 - object : observation
@@ -98,6 +99,7 @@ mode_yamls :
     filter_name_fw1: open
     filter_name_fw2: Ks
     filter_name_pupil: open
+    tech: IMAGE
     fits_ins_filt: Ks
 
 - object : observation
@@ -139,6 +141,7 @@ mode_yamls :
     filter_name_fw1 : Spec_HK
     filter_name_fw2 : open
     filter_name_pupil : open
+    tech: SPEC
     fits_ins_filt : Spec_HK
 
 - object : observation
@@ -155,6 +158,7 @@ mode_yamls :
     filter_name_fw1 : Spec_HK
     filter_name_fw2 : open
     filter_name_pupil : open
+    tech: SPEC
     fits_ins_filt : Spec_HK
 
 - object : observation
@@ -171,6 +175,7 @@ mode_yamls :
     filter_name_fw1 : Spec_IJ
     filter_name_fw2 : open
     filter_name_pupil : open
+    tech: SPEC
     fits_ins_filt : Spec_IJ
 
 ---

From 5242440af1d83a07fa868548300c9cdf142c8e65 Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Mon, 17 Aug 2026 16:10:12 +0200
Subject: [PATCH 09/63] Convert METIS demo notebooks to MyST md format

---
 .../demos/demo_Leiden_sky.ipynb               | 391 -----------------
 .../demos/demo_Leiden_sky.md                  | 169 ++++++++
 .../demos/demo_adc_wheel.ipynb                | 187 --------
 .../example_notebooks/demos/demo_adc_wheel.md |  82 ++++
 .../demos/demo_auto_exposure.ipynb            | 378 ----------------
 .../demos/demo_auto_exposure.md               | 202 +++++++++
 .../demos/demo_chopping_and_nodding.ipynb     | 174 --------
 .../demos/demo_chopping_and_nodding.md        |  86 ++++
 .../demos/demo_detector_modes.ipynb           | 406 ------------------
 .../demos/demo_detector_modes.md              | 204 +++++++++
 .../demos/demo_filter_wheel.ipynb             | 370 ----------------
 .../demos/demo_filter_wheel.md                | 177 ++++++++
 .../demos/demo_grating_efficiency.ipynb       | 378 ----------------
 .../demos/demo_grating_efficiency.md          | 185 ++++++++
 .../demos/demo_inter_pixel_capacitance.ipynb  | 332 --------------
 .../demos/demo_inter_pixel_capacitance.md     | 152 +++++++
 .../demos/demo_lss_simple.ipynb               | 197 ---------
 .../demos/demo_lss_simple.md                  |  96 +++++
 .../demos/demo_metis_lms_efficiency.ipynb     | 145 -------
 .../demos/demo_metis_lms_efficiency.md        |  74 ++++
 .../demos/demo_metis_wcu_psfs.ipynb           | 311 --------------
 .../demos/demo_metis_wcu_psfs.md              | 130 ++++++
 .../demos/demo_rectify_traces.ipynb           | 290 -------------
 .../demos/demo_rectify_traces.md              | 139 ++++++
 .../demos/demo_reference_pixel_mask.ipynb     | 301 -------------
 .../demos/demo_reference_pixel_mask.md        | 136 ++++++
 .../demos/demo_skycalc.ipynb                  | 266 ------------
 .../example_notebooks/demos/demo_skycalc.md   | 133 ++++++
 .../demos/demo_slit_wheel.ipynb               | 182 --------
 .../demos/demo_slit_wheel.md                  |  94 ++++
 30 files changed, 2059 insertions(+), 4308 deletions(-)
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_Leiden_sky.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_adc_wheel.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_auto_exposure.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_detector_modes.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_filter_wheel.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_lss_simple.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_rectify_traces.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_skycalc.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_skycalc.md
 delete mode 100644 METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb
 create mode 100644 METIS/docs/example_notebooks/demos/demo_slit_wheel.md

diff --git a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.ipynb b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.ipynb
deleted file mode 100644
index a21fd97a..00000000
--- a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.ipynb
+++ /dev/null
@@ -1,391 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e50a4189-3f1d-423d-915a-97f322b0aa3d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "import numpy as np\n",
-    "from astropy import units as u"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61e31623-0ce1-44d1-a0fe-d46273016be9",
-   "metadata": {},
-   "source": [
-    "METIS AIT activities in Leiden will use bespoke relay optics to observe the sky above the integration hall with METIS. Scopesim can simulate these observations for the spectroscopic modes. Emission and transmission spectra for the sky above Leiden were calculated by Wolfgang Kausch for 50 values of precipital water vapour (PWV). The data are stored on the Astar server in Vienna and will be downloaded the first time you use them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "93347b06-0726-40c1-871d-ad0a6ca71ed5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scopesim as sim\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")\n",
-    "\n",
-    "# If you haven't got the instrument packages yet, uncomment the following line.\n",
-    "#sim.download_packages([\"METIS\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfb44995-bf3f-4958-bfaf-f44c0071229f",
-   "metadata": {},
-   "source": [
-    "The available modes using the Leiden sky are `leiden_lss_l`, `leiden_lss_m`, `leiden_lss_n` and `leiden_lms`. We will simulate an LSS_M observation, using a PWV value of 10 mm. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f42951c7-c651-457a-8a3a-b521c663aeae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=['leiden_lss_m'])\n",
-    "cmd[\"!ATMO.pwv\"] = 10       # could also use properties={\"!ATMO.pwv\": 10} in UserCommands"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b77fa0a2-d7dc-455c-a020-ff4afc9181fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95cb7c31-30ec-446a-8a1a-e9ef578e92ff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.effects.pprint_all()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "1af39ad0-7706-4615-a5cb-faeab1f83bed",
-   "metadata": {},
-   "source": [
-    "The `leiden_` modes replace the `ELT` and `Armazones` packages with two effects, `leiden_sky`, which creates the background source using the atmospheric spectra for the selected PWV, and `telescope`, which models the relay optics. \n",
-    "\n",
-    "`telescope` has no user-modifiable parameters; its configuration can be inspected as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9201c83-9966-4bb6-84ce-879e6b3108ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['telescope'].table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbe32a5a-9f11-4463-bd15-1a19c0230377",
-   "metadata": {},
-   "source": [
-    "The atmospheric emission is provided by the effect `leiden_sky`. It has one parameter, the precipitable water vapour:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce6e2317-d269-488f-83ce-193a9168ce2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis[\"leiden_sky\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49c58cb0-0374-4b41-9911-5b431723b706",
-   "metadata": {},
-   "source": [
-    "The transmission and emission spectra can be plotted as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "07305c38-57a8-40cb-b9b0-392563dbad29",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['leiden_sky'].plot(which=\"te\", wavelength=np.linspace(3, 6, 1001)*u.um);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80c3334c-0a97-4626-9b47-79c6bd4251c8",
-   "metadata": {},
-   "source": [
-    "An observation is done as usual with the `.observe()` method. No `Source` object is required."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4c26dab8-2453-4265-8213-f30b22fdfa3b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b9fd2bbb-5b1e-4a95-ae16-b26fe370bfd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(metis.image_planes[0].data, norm=\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e03e8caa-a004-4894-aca1-29a6181e3468",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(metis.image_planes[0].data[:, 1000])\n",
-    "plt.xlabel(\"Row number (~ wavelength)\")\n",
-    "plt.ylabel(\"Photons/second (ImagePlane)\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c885e30f-dd8d-4956-8822-c5c9262c8291",
-   "metadata": {},
-   "source": [
-    "The plots show the `ImagePlane`. After a `readout`, the `metis[spectral_traces].rectify_traces()` method could be used to show the simulation on a linear wavelength scale."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87f75df4-c0dd-49af-9ac1-b36ad39bcd87",
-   "metadata": {},
-   "source": [
-    "**Not yet**: The `leiden_sky` effect (or rather the underlying `AtmoLibraryTERCurve` effect) has an update method, which allows to change the PWV value. This does not work yet! We recommend creating a new `OpticalTrain` after setting `cmd[\"!ATMO.pwv\"]` to a new value. This will hopefully be fixed soon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d1cfdf4-d5fe-46f6-b0c0-239bf73944ce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['leiden_sky'].update(pwv=50)\n",
-    "metis['leiden_sky'].plot(which=\"te\", wavelength=np.linspace(3, 6, 1001)*u.um);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f66f972e-eeab-45e1-aa21-caee8eaa462e",
-   "metadata": {},
-   "source": [
-    "# Comparison to Armazones/ELT\n",
-    "The following compares the Leiden spectrum with a spectrum of the Armazones sky and METIS at the ELT."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "da3baa54-3d48-4e28-80b7-482920caa615",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd_elt = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_m\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4c558bce-64f2-466f-b881-40ed4365da76",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "elt = sim.OpticalTrain(cmd_elt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b102df8-0e50-4904-a836-0deeadc4633c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "elt.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2859605-935c-4590-93cd-05821d74e4b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(elt.image_planes[0].data[:, 1000]/(36.9**2 - 10.95**2) * (0.66**2 * np.cos(np.pi/4)), label=\"Armazones\")\n",
-    "#plt.plot(elt.image_planes[0].data[:, 1000] / 975.2347899768369 * 0.34, label=\"Armazones 2\")\n",
-    "plt.plot(metis.image_planes[0].data[:, 1000], label=\"Leiden\")\n",
-    "plt.xlabel(\"Row number (~ wavelength)\")\n",
-    "plt.ylabel(\"Photons/second (ImagePlane)\")\n",
-    "#plt.ylim(0, 10)\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "785b241d-cdcb-4a30-944f-dd5b03446f6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "leiden = metis['leiden_sky']\n",
-    "armazones = elt[\"skycalc_atmosphere\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "19ec7934-5a13-434b-9ef0-73b6c3ef5cd7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lam = np.linspace(4, 5, 1001)*u.um\n",
-    "f_leiden = leiden.emission(lam)\n",
-    "f_armazones = armazones.emission(lam)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b271a01a-ce69-43cf-b90c-699fdff0417b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(lam, f_armazones, label=\"Armazones\", lw=1)\n",
-    "plt.plot(lam, f_leiden, label=\"Leiden\", lw=1)\n",
-    "#plt.semilogy()\n",
-    "plt.xlabel(\"Wavelength [um]\")\n",
-    "plt.ylabel(\"PHOTLAM\")\n",
-    "plt.xlim(4.6, 4.8)\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9d196b7-64a6-4095-8a2c-7cd5696882e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lam_leiden = leiden.surface.emission.waveset.to(u.um)\n",
-    "lam_armazones = armazones.surface.emission.waveset.to(u.um)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "843e625d-12b2-4d95-8e98-4796f7e85c1f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(lam_armazones, armazones.surface.emission(lam_armazones), label=\"Armazones\")\n",
-    "plt.plot(lam_leiden, leiden.surface.emission(lam_leiden), label=\"Leiden\")\n",
-    "plt.xlim(3.2, 3.4)\n",
-    "plt.ylim(0, 0.5)\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b778f3f-ae11-4529-9b2b-1bbe591311a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rlam_leiden = lam_leiden[(lam_leiden > 4.6*u.um) * (lam_leiden < 4.8*u.um)] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "807c7b3f-8bab-41d2-aa2b-6eec26a7507a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "leiden.emission.integrate(wavelengths=rlam_leiden)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a2bffda-dcfa-49d3-91ce-a5875a01be83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rlam_arm = lam_armazones[(lam_armazones > 4.6*u.um) * (lam_armazones < 4.8*u.um)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "845ff1a9-d89b-4351-be02-a807ec3d146f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "armazones.emission.integrate(wavelengths=rlam_arm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3411e7b5-8661-461e-8056-b644c549dc94",
-   "metadata": {},
-   "source": [
-    "As expected, the Leiden sky is brighter (in the M band) than the sky above Armazones. Note that this is just an exemplary comparison, and we have not tried to match the atmospheric parameters in much detail. A similar exercise in the L band results in Armazones being brighter than Leiden. It should be borne in mind that the Leiden sky model only includes atmospheric emission and neglects scattered moon light, zodiacal light and other effects that are taken into account in the more sophisticated `skycalc` model used for Armazones."
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
new file mode 100644
index 00000000..f2149494
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
@@ -0,0 +1,169 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt
+import numpy as np
+from astropy import units as u
+```
+
+METIS AIT activities in Leiden will use bespoke relay optics to observe the sky above the integration hall with METIS. Scopesim can simulate these observations for the spectroscopic modes. Emission and transmission spectra for the sky above Leiden were calculated by Wolfgang Kausch for 50 values of precipital water vapour (PWV). The data are stored on the Astar server in Vienna and will be downloaded the first time you use them.
+
+```{code-cell} ipython3
+import scopesim as sim
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+
+# If you haven't got the instrument packages yet, uncomment the following line.
+#sim.download_packages(["METIS"])
+```
+
+The available modes using the Leiden sky are `leiden_lss_l`, `leiden_lss_m`, `leiden_lss_n` and `leiden_lms`. We will simulate an LSS_M observation, using a PWV value of 10 mm. 
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=['leiden_lss_m'])
+cmd["!ATMO.pwv"] = 10       # could also use properties={"!ATMO.pwv": 10} in UserCommands
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+metis.effects.pprint_all()
+```
+
+The `leiden_` modes replace the `ELT` and `Armazones` packages with two effects, `leiden_sky`, which creates the background source using the atmospheric spectra for the selected PWV, and `telescope`, which models the relay optics. 
+
+`telescope` has no user-modifiable parameters; its configuration can be inspected as follows:
+
+```{code-cell} ipython3
+metis['telescope'].table
+```
+
+The atmospheric emission is provided by the effect `leiden_sky`. It has one parameter, the precipitable water vapour:
+
+```{code-cell} ipython3
+print(metis["leiden_sky"])
+```
+
+The transmission and emission spectra can be plotted as follows:
+
+```{code-cell} ipython3
+metis['leiden_sky'].plot(which="te", wavelength=np.linspace(3, 6, 1001)*u.um);
+```
+
+An observation is done as usual with the `.observe()` method. No `Source` object is required.
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+plt.imshow(metis.image_planes[0].data, norm="log")
+```
+
+```{code-cell} ipython3
+plt.plot(metis.image_planes[0].data[:, 1000])
+plt.xlabel("Row number (~ wavelength)")
+plt.ylabel("Photons/second (ImagePlane)");
+```
+
+The plots show the `ImagePlane`. After a `readout`, the `metis[spectral_traces].rectify_traces()` method could be used to show the simulation on a linear wavelength scale.
+
++++
+
+**Not yet**: The `leiden_sky` effect (or rather the underlying `AtmoLibraryTERCurve` effect) has an update method, which allows to change the PWV value. This does not work yet! We recommend creating a new `OpticalTrain` after setting `cmd["!ATMO.pwv"]` to a new value. This will hopefully be fixed soon.
+
+```{code-cell} ipython3
+metis['leiden_sky'].update(pwv=50)
+metis['leiden_sky'].plot(which="te", wavelength=np.linspace(3, 6, 1001)*u.um);
+```
+
+# Comparison to Armazones/ELT
+The following compares the Leiden spectrum with a spectrum of the Armazones sky and METIS at the ELT.
+
+```{code-cell} ipython3
+cmd_elt = sim.UserCommands(use_instrument="METIS", set_modes=["lss_m"])
+```
+
+```{code-cell} ipython3
+elt = sim.OpticalTrain(cmd_elt)
+```
+
+```{code-cell} ipython3
+elt.observe()
+```
+
+```{code-cell} ipython3
+plt.plot(elt.image_planes[0].data[:, 1000]/(36.9**2 - 10.95**2) * (0.66**2 * np.cos(np.pi/4)), label="Armazones")
+#plt.plot(elt.image_planes[0].data[:, 1000] / 975.2347899768369 * 0.34, label="Armazones 2")
+plt.plot(metis.image_planes[0].data[:, 1000], label="Leiden")
+plt.xlabel("Row number (~ wavelength)")
+plt.ylabel("Photons/second (ImagePlane)")
+#plt.ylim(0, 10)
+plt.legend();
+```
+
+```{code-cell} ipython3
+leiden = metis['leiden_sky']
+armazones = elt["skycalc_atmosphere"]
+```
+
+```{code-cell} ipython3
+lam = np.linspace(4, 5, 1001)*u.um
+f_leiden = leiden.emission(lam)
+f_armazones = armazones.emission(lam)
+```
+
+```{code-cell} ipython3
+plt.plot(lam, f_armazones, label="Armazones", lw=1)
+plt.plot(lam, f_leiden, label="Leiden", lw=1)
+#plt.semilogy()
+plt.xlabel("Wavelength [um]")
+plt.ylabel("PHOTLAM")
+plt.xlim(4.6, 4.8)
+plt.legend();
+```
+
+```{code-cell} ipython3
+lam_leiden = leiden.surface.emission.waveset.to(u.um)
+lam_armazones = armazones.surface.emission.waveset.to(u.um)
+```
+
+```{code-cell} ipython3
+plt.plot(lam_armazones, armazones.surface.emission(lam_armazones), label="Armazones")
+plt.plot(lam_leiden, leiden.surface.emission(lam_leiden), label="Leiden")
+plt.xlim(3.2, 3.4)
+plt.ylim(0, 0.5)
+plt.legend()
+```
+
+```{code-cell} ipython3
+rlam_leiden = lam_leiden[(lam_leiden > 4.6*u.um) * (lam_leiden < 4.8*u.um)] 
+```
+
+```{code-cell} ipython3
+leiden.emission.integrate(wavelengths=rlam_leiden)
+```
+
+```{code-cell} ipython3
+rlam_arm = lam_armazones[(lam_armazones > 4.6*u.um) * (lam_armazones < 4.8*u.um)]
+```
+
+```{code-cell} ipython3
+armazones.emission.integrate(wavelengths=rlam_arm)
+```
+
+As expected, the Leiden sky is brighter (in the M band) than the sky above Armazones. Note that this is just an exemplary comparison, and we have not tried to match the atmospheric parameters in much detail. A similar exercise in the L band results in Armazones being brighter than Leiden. It should be borne in mind that the Leiden sky model only includes atmospheric emission and neglects scattered moon light, zodiacal light and other effects that are taken into account in the more sophisticated `skycalc` model used for Armazones.
diff --git a/METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb b/METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb
deleted file mode 100644
index c75cb634..00000000
--- a/METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb
+++ /dev/null
@@ -1,187 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test ADC wheel\n",
-    "\n",
-    "This notebook demonstrates how to use the various atmospheric dispersion correctors in METIS. Note that the action of an adc is currently restricted to a transmission loss. The implementation of the geometric differential refraction residuals will follow at a later stage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"]) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=['img_lm'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The ADC to use is defined by `\"!OBS.adc\"`. This can be set to `false` when no ADC is in the path. The default for LM band imaging is"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd[\"!OBS.adc\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The effect `metis['adc_wheel']` works the same way as e.g. `metis['filter_wheel']`. The following ADCs are now available (yes, there's only one) and can be selected with `metis['adc_wheel'].change_adc()` as demonstrated below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "metis['adc_wheel'].get_table()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run a simulation with the ADC in the path (`observe()` without a source observe empty sky):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()\n",
-    "implane_adc = metis.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now remove the ADC from the path by changing to `False`. Run the simulation without the ADC:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['adc_wheel'].change_adc(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()\n",
-    "implane_no_adc = metis.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare the image plane simulated with and without the ADC. The ratio should be equal to the throughput of the ADC (90 per cent):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "med_adc = np.median(implane_adc)\n",
-    "med_no_adc = np.median(implane_no_adc)\n",
-    "print(\"With ADC:    {:.1f}\".format(np.median(med_adc)))\n",
-    "print(\"Without ADC: {:.1f}\".format(np.median(med_no_adc)))\n",
-    "print(\"Ratio:       {:.1f}\".format(med_adc/med_no_adc))"
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_adc_wheel.md b/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
new file mode 100644
index 00000000..2f093ac8
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
@@ -0,0 +1,82 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Test ADC wheel
+
+This notebook demonstrates how to use the various atmospheric dispersion correctors in METIS. Note that the action of an adc is currently restricted to a transmission loss. The implementation of the geometric differential refraction residuals will follow at a later stage.
+
+```{code-cell} ipython3
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../") 
+```
+
+```{code-cell} ipython3
+import numpy as np
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"]) 
+```
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=['img_lm'])
+```
+
+The ADC to use is defined by `"!OBS.adc"`. This can be set to `false` when no ADC is in the path. The default for LM band imaging is
+
+```{code-cell} ipython3
+cmd["!OBS.adc"]
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+The effect `metis['adc_wheel']` works the same way as e.g. `metis['filter_wheel']`. The following ADCs are now available (yes, there's only one) and can be selected with `metis['adc_wheel'].change_adc()` as demonstrated below.
+
+```{code-cell} ipython3
+metis['adc_wheel'].get_table()
+```
+
+Run a simulation with the ADC in the path (`observe()` without a source observe empty sky):
+
+```{code-cell} ipython3
+metis.observe()
+implane_adc = metis.image_planes[0].data
+```
+
+Now remove the ADC from the path by changing to `False`. Run the simulation without the ADC:
+
+```{code-cell} ipython3
+metis['adc_wheel'].change_adc(False)
+```
+
+```{code-cell} ipython3
+metis.observe()
+implane_no_adc = metis.image_planes[0].data
+```
+
+Compare the image plane simulated with and without the ADC. The ratio should be equal to the throughput of the ADC (90 per cent):
+
+```{code-cell} ipython3
+med_adc = np.median(implane_adc)
+med_no_adc = np.median(implane_no_adc)
+print("With ADC:    {:.1f}".format(np.median(med_adc)))
+print("Without ADC: {:.1f}".format(np.median(med_no_adc)))
+print("Ratio:       {:.1f}".format(med_adc/med_no_adc))
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb b/METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb
deleted file mode 100644
index e8a2869c..00000000
--- a/METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Auto Exposure\n",
-    "\n",
-    "This is a setup/test/demonstration notebook for the `AutoExposure` effect in Scopesim. This effect splits the requested total exposure time into NDIT subexposures of integration time DIT such that the maximum counts in a single subexposure does not exceed a certain fill fraction of the detector full well. The final readout is the sum over the NDIT subexposures, i.e. corresponds to the total requested exposure time.\n",
-    "\n",
-    "The notebook uses the `irdb/METIS` configuration. The observed source is blank sky, except for the last example where a star of 0 mag is used (Vega)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from astropy import units as u\n",
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imaging LM-band"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_lm\"])\n",
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For `AutoExposure` to work the exposure time has to be given explicitely as a parameter to the `readout` method. If this is not done, the default values for `DIT` and `NDIT` will be used. The following is for an exposure time of 1 second. The resulting readout is divided by `NDIT` to produce the average over the `NDIT` subexposures. After application of the gain to convert from ADU to electrons this allows direct comparison to the detector full well. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outhdul = metis.readout(exptime=1)[0]\n",
-    "gain = outhdul[1].header[\"ESO DET1 CHIP GAIN\"] * u.electron / u.adu\n",
-    "full_well = outhdul[1].header[\"ESO DET1 CHIP FULLWELL\"] * u.electron\n",
-    "outimg = outhdul[1].data * u.adu * gain\n",
-    "fill_frac = outimg.max() / full_well << u.percent\n",
-    "\n",
-    "print(\"\\nResult\\n======\")\n",
-    "print(f\"Maximum value in readout: {outimg.max():7.1f} (per DIT)\")\n",
-    "print(f\"Detector full well: {full_well:13.0f}\")\n",
-    "print(f\"Fill fraction: {fill_frac:18.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The same with a much larger exposure time of 1000 seconds:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outhdul = metis.readout(exptime = 1000)[0]\n",
-    "gain = outhdul[1].header[\"ESO DET1 CHIP GAIN\"] * u.electron / u.adu\n",
-    "full_well = outhdul[1].header[\"ESO DET1 CHIP FULLWELL\"] * u.electron\n",
-    "outimg = outhdul[1].data * u.adu * gain\n",
-    "fill_frac = outimg.max() / full_well << u.percent\n",
-    "\n",
-    "print(\"\\nResult\\n======\")\n",
-    "print(f\"Maximum value in readout: {outimg.max():7.1f} (per DIT)\")\n",
-    "print(f\"Detector full well: {full_well:13.0f}\")\n",
-    "print(f\"Fill fraction: {fill_frac:18.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The desired fill fraction can be changed with the argument `fill_frac`. The default value of 75 per cent is a typical good value that keeps the detector counts within the linear regime."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outhdul = metis.readout(exptime = 1000, fill_frac=0.9)[0]\n",
-    "gain = outhdul[1].header[\"ESO DET1 CHIP GAIN\"] * u.electron / u.adu\n",
-    "full_well = outhdul[1].header[\"ESO DET1 CHIP FULLWELL\"] * u.electron\n",
-    "outimg = outhdul[1].data * u.adu * gain\n",
-    "fill_frac = outimg.max() / full_well << u.percent\n",
-    "\n",
-    "print(\"\\nResult\\n======\")\n",
-    "print(f\"Maximum value in readout: {outimg.max():7.1f} (per DIT)\")\n",
-    "print(f\"Detector full well: {full_well:13.0f}\")\n",
-    "print(f\"Fill fraction: {fill_frac:18.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imaging N-band"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_n\"])\n",
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outhdul = metis.readout(exptime=1)[0]\n",
-    "gain = outhdul[1].header[\"ESO DET2 CHIP GAIN\"] * u.electron / u.adu\n",
-    "full_well = outhdul[1].header[\"ESO DET2 CHIP FULLWELL\"] * u.electron\n",
-    "outimg = outhdul[1].data * u.adu * gain\n",
-    "fill_frac = outimg.max() / full_well << u.percent\n",
-    "\n",
-    "print(\"\\nResult\\n======\")\n",
-    "print(f\"Maximum value in readout: {outimg.max():7.1f} (per DIT)\")\n",
-    "print(f\"Detector full well: {full_well:13.0f}\")\n",
-    "print(f\"Fill fraction: {fill_frac:18.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Long-slit spectroscopy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"],\n",
-    "                       properties={\"!OBS.interp_psf\": False})\n",
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outhdul = metis.readout(exptime=3600.)[0]\n",
-    "gain = outhdul[1].header[\"ESO DET1 CHIP GAIN\"] * u.electron / u.adu\n",
-    "full_well = outhdul[1].header[\"ESO DET1 CHIP FULLWELL\"] * u.electron\n",
-    "outimg = outhdul[1].data * u.adu * gain\n",
-    "fill_frac = outimg.max() / full_well << u.percent\n",
-    "\n",
-    "print(\"\\nResult\\n======\")\n",
-    "print(f\"Maximum value in readout: {outimg.max():7.1f} (per DIT)\")\n",
-    "print(f\"Detector full well: {full_well:13.0f}\")\n",
-    "print(f\"Fill fraction: {fill_frac:18.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What happens when the source saturates the detector?\n",
-    "We take an N-band image of Vega. `DIT` is automatically set to the minimum value supported by the detector, but the centre of the star still saturates the detector. In the final image, the star's profile is capped at the full well of the detector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_n\"])\n",
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src = sim.source.source_templates.star()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outhdul = metis.readout(exptime=1)[0]\n",
-    "gain = outhdul[1].header[\"ESO DET2 CHIP GAIN\"] * u.electron / u.adu\n",
-    "full_well = outhdul[1].header[\"ESO DET2 CHIP FULLWELL\"] * u.electron\n",
-    "outimg = outhdul[1].data * u.adu * gain\n",
-    "fill_frac = outimg.max() / full_well << u.percent\n",
-    "\n",
-    "print(\"\\nResult\\n======\")\n",
-    "print(f\"Maximum value in readout: {outimg.max():7.1f} (per DIT)\")\n",
-    "print(f\"Detector full well: {full_well:13.0f}\")\n",
-    "print(f\"Fill fraction: {fill_frac:18.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot a cut through the star to show how its peak saturates the detector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(outimg[950:1100, 1024])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "npix = (outimg >= full_well).sum()\n",
-    "print(\"Number of saturated pixels:\", npix)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The default values for the detector full well in the various modes reflects our current best knowledge of the properties of the actual METIS detectors. These values can be changed as in the following example, but be aware that this makes the simulations unrealistic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**NB: There's something wrong with this example, please ignore for the time being.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_well = 1000 * metis.cmds[\"!DET.full_well\"] * u.electron\n",
-    "outhdul = metis.readout(exptime=1, full_well=full_well)[0]\n",
-    "gain = outhdul[1].header[\"ESO DET2 CHIP GAIN\"] * u.electron / u.adu\n",
-    "outimg = outhdul[1].data * u.adu * gain\n",
-    "fill_frac = outimg.max() / full_well << u.percent\n",
-    "\n",
-    "print(\"\\nResult\\n======\")\n",
-    "print(f\"Maximum value in readout: {outimg.max():7.1f} (per DIT)\")\n",
-    "print(f\"Detector full well: {full_well:13.0f}\")\n",
-    "print(f\"Fill fraction: {fill_frac:18.1f}\")"
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_auto_exposure.md b/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
new file mode 100644
index 00000000..4337a367
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
@@ -0,0 +1,202 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Auto Exposure
+
+This is a setup/test/demonstration notebook for the `AutoExposure` effect in Scopesim. This effect splits the requested total exposure time into NDIT subexposures of integration time DIT such that the maximum counts in a single subexposure does not exceed a certain fill fraction of the detector full well. The final readout is the sum over the NDIT subexposures, i.e. corresponds to the total requested exposure time.
+
+The notebook uses the `irdb/METIS` configuration. The observed source is blank sky, except for the last example where a star of 0 mag is used (Vega).
+
+```{code-cell} ipython3
+from astropy import units as u
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell. 
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+## Imaging LM-band
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+For `AutoExposure` to work the exposure time has to be given explicitely as a parameter to the `readout` method. If this is not done, the default values for `DIT` and `NDIT` will be used. The following is for an exposure time of 1 second. The resulting readout is divided by `NDIT` to produce the average over the `NDIT` subexposures. After application of the gain to convert from ADU to electrons this allows direct comparison to the detector full well. 
+
+```{code-cell} ipython3
+outhdul = metis.readout(exptime=1)[0]
+gain = outhdul[1].header["ESO DET1 CHIP GAIN"] * u.electron / u.adu
+full_well = outhdul[1].header["ESO DET1 CHIP FULLWELL"] * u.electron
+outimg = outhdul[1].data * u.adu * gain
+fill_frac = outimg.max() / full_well << u.percent
+
+print("\nResult\n======")
+print(f"Maximum value in readout: {outimg.max():7.1f} (per DIT)")
+print(f"Detector full well: {full_well:13.0f}")
+print(f"Fill fraction: {fill_frac:18.1f}")
+```
+
+The same with a much larger exposure time of 1000 seconds:
+
+```{code-cell} ipython3
+outhdul = metis.readout(exptime = 1000)[0]
+gain = outhdul[1].header["ESO DET1 CHIP GAIN"] * u.electron / u.adu
+full_well = outhdul[1].header["ESO DET1 CHIP FULLWELL"] * u.electron
+outimg = outhdul[1].data * u.adu * gain
+fill_frac = outimg.max() / full_well << u.percent
+
+print("\nResult\n======")
+print(f"Maximum value in readout: {outimg.max():7.1f} (per DIT)")
+print(f"Detector full well: {full_well:13.0f}")
+print(f"Fill fraction: {fill_frac:18.1f}")
+```
+
+The desired fill fraction can be changed with the argument `fill_frac`. The default value of 75 per cent is a typical good value that keeps the detector counts within the linear regime.
+
+```{code-cell} ipython3
+outhdul = metis.readout(exptime = 1000, fill_frac=0.9)[0]
+gain = outhdul[1].header["ESO DET1 CHIP GAIN"] * u.electron / u.adu
+full_well = outhdul[1].header["ESO DET1 CHIP FULLWELL"] * u.electron
+outimg = outhdul[1].data * u.adu * gain
+fill_frac = outimg.max() / full_well << u.percent
+
+print("\nResult\n======")
+print(f"Maximum value in readout: {outimg.max():7.1f} (per DIT)")
+print(f"Detector full well: {full_well:13.0f}")
+print(f"Fill fraction: {fill_frac:18.1f}")
+```
+
+## Imaging N-band
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_n"])
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+outhdul = metis.readout(exptime=1)[0]
+gain = outhdul[1].header["ESO DET2 CHIP GAIN"] * u.electron / u.adu
+full_well = outhdul[1].header["ESO DET2 CHIP FULLWELL"] * u.electron
+outimg = outhdul[1].data * u.adu * gain
+fill_frac = outimg.max() / full_well << u.percent
+
+print("\nResult\n======")
+print(f"Maximum value in readout: {outimg.max():7.1f} (per DIT)")
+print(f"Detector full well: {full_well:13.0f}")
+print(f"Fill fraction: {fill_frac:18.1f}")
+```
+
+## Long-slit spectroscopy
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["lss_l"],
+                       properties={"!OBS.interp_psf": False})
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+outhdul = metis.readout(exptime=3600.)[0]
+gain = outhdul[1].header["ESO DET1 CHIP GAIN"] * u.electron / u.adu
+full_well = outhdul[1].header["ESO DET1 CHIP FULLWELL"] * u.electron
+outimg = outhdul[1].data * u.adu * gain
+fill_frac = outimg.max() / full_well << u.percent
+
+print("\nResult\n======")
+print(f"Maximum value in readout: {outimg.max():7.1f} (per DIT)")
+print(f"Detector full well: {full_well:13.0f}")
+print(f"Fill fraction: {fill_frac:18.1f}")
+```
+
+## What happens when the source saturates the detector?
+We take an N-band image of Vega. `DIT` is automatically set to the minimum value supported by the detector, but the centre of the star still saturates the detector. In the final image, the star's profile is capped at the full well of the detector.
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_n"])
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+src = sim.source.source_templates.star()
+```
+
+```{code-cell} ipython3
+metis.observe(src)
+```
+
+```{code-cell} ipython3
+outhdul = metis.readout(exptime=1)[0]
+gain = outhdul[1].header["ESO DET2 CHIP GAIN"] * u.electron / u.adu
+full_well = outhdul[1].header["ESO DET2 CHIP FULLWELL"] * u.electron
+outimg = outhdul[1].data * u.adu * gain
+fill_frac = outimg.max() / full_well << u.percent
+
+print("\nResult\n======")
+print(f"Maximum value in readout: {outimg.max():7.1f} (per DIT)")
+print(f"Detector full well: {full_well:13.0f}")
+print(f"Fill fraction: {fill_frac:18.1f}")
+```
+
+Plot a cut through the star to show how its peak saturates the detector.
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt
+%matplotlib inline
+```
+
+```{code-cell} ipython3
+plt.plot(outimg[950:1100, 1024])
+```
+
+```{code-cell} ipython3
+npix = (outimg >= full_well).sum()
+print("Number of saturated pixels:", npix)
+```
+
+The default values for the detector full well in the various modes reflects our current best knowledge of the properties of the actual METIS detectors. These values can be changed as in the following example, but be aware that this makes the simulations unrealistic.
+
++++
+
+**NB: There's something wrong with this example, please ignore for the time being.**
+
+```{code-cell} ipython3
+full_well = 1000 * metis.cmds["!DET.full_well"] * u.electron
+outhdul = metis.readout(exptime=1, full_well=full_well)[0]
+gain = outhdul[1].header["ESO DET2 CHIP GAIN"] * u.electron / u.adu
+outimg = outhdul[1].data * u.adu * gain
+fill_frac = outimg.max() / full_well << u.percent
+
+print("\nResult\n======")
+print(f"Maximum value in readout: {outimg.max():7.1f} (per DIT)")
+print(f"Detector full well: {full_well:13.0f}")
+print(f"Fill fraction: {fill_frac:18.1f}")
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb
deleted file mode 100644
index 2e865af5..00000000
--- a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb
+++ /dev/null
@@ -1,174 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chopping and nodding in Scopesim\n",
-    "\n",
-    "This notebook demonstrates how to use the `ChopNodCombiner` effect in Scopesim. Both chopping and nodding are currently defined as two-point patterns, where the throw direction is given as a 2D vector (dx, dy) in `metis[\"chop_nod\"].meta[\"chop_offsets\"]` and `metis[\"chop_nod\"].meta[\"nod_offsets\"]`. For parallel nodding, the two vectors are parallel (typically nod_offset = - chop_offset, giving a three-point pattern), for perpendicular nodding, the vectors are orthogonal.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_n\"])\n",
-    "metis = sim.OpticalTrain(cmd)\n",
-    "metis[\"chop_nod\"].include = True\n",
-    "metis[\"reference_pixel_mask\"].include = False    # Hack: this would mess up the chop/nod results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The default is perpendicular nodding, with the chop throw in the x-direction and the nod throw in the y direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Chop offsets:\", metis.cmds[metis[\"chop_nod\"].meta[\"chop_offsets\"]])\n",
-    "print(\"Nod offsets: \", metis.cmds[metis[\"chop_nod\"].meta[\"nod_offsets\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src = sim.source.source_templates.star()\n",
-    "metis.observe(src)\n",
-    "imghdul = metis.readout(exptime=1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(imghdul[1].data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
-    "plt.colorbar();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For parallel nodding, turn the nod throw into the x-direction as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"chop_nod\"].meta[\"nod_offsets\"] = [-3, 0]\n",
-    "imghdu_par = metis.readout(exptime=1)[0][1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(imghdu_par.data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
-    "plt.colorbar();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Other four-point patterns are possible:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"chop_nod\"].meta[\"nod_offsets\"] = [-3, 3]\n",
-    "imghdu_3 = metis.readout(exptime=1)[0][1]\n",
-    "plt.imshow(imghdu_3.data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
-    "plt.colorbar();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"chop_nod\"].meta[\"chop_offsets\"] = [-3, 2]\n",
-    "metis[\"chop_nod\"].meta[\"nod_offsets\"] = [2, 3]\n",
-    "imghdu_4 = metis.readout(exptime=1)[0][1]\n",
-    "plt.imshow(imghdu_4.data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
-    "plt.colorbar();"
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
new file mode 100644
index 00000000..87d6fed5
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
@@ -0,0 +1,86 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Chopping and nodding in Scopesim
+
+This notebook demonstrates how to use the `ChopNodCombiner` effect in Scopesim. Both chopping and nodding are currently defined as two-point patterns, where the throw direction is given as a 2D vector (dx, dy) in `metis["chop_nod"].meta["chop_offsets"]` and `metis["chop_nod"].meta["nod_offsets"]`. For parallel nodding, the two vectors are parallel (typically nod_offset = - chop_offset, giving a three-point pattern), for perpendicular nodding, the vectors are orthogonal.  
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt
+
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell.
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_n"])
+metis = sim.OpticalTrain(cmd)
+metis["chop_nod"].include = True
+metis["reference_pixel_mask"].include = False    # Hack: this would mess up the chop/nod results
+```
+
+The default is perpendicular nodding, with the chop throw in the x-direction and the nod throw in the y direction.
+
+```{code-cell} ipython3
+print("Chop offsets:", metis.cmds[metis["chop_nod"].meta["chop_offsets"]])
+print("Nod offsets: ", metis.cmds[metis["chop_nod"].meta["nod_offsets"]])
+```
+
+```{code-cell} ipython3
+src = sim.source.source_templates.star()
+metis.observe(src)
+imghdul = metis.readout(exptime=1)[0]
+```
+
+```{code-cell} ipython3
+plt.imshow(imghdul[1].data, origin="lower", vmin=-3e3, vmax=3e3)
+plt.colorbar();
+```
+
+For parallel nodding, turn the nod throw into the x-direction as well.
+
+```{code-cell} ipython3
+metis["chop_nod"].meta["nod_offsets"] = [-3, 0]
+imghdu_par = metis.readout(exptime=1)[0][1]
+```
+
+```{code-cell} ipython3
+plt.imshow(imghdu_par.data, origin="lower", vmin=-3e3, vmax=3e3)
+plt.colorbar();
+```
+
+Other four-point patterns are possible:
+
+```{code-cell} ipython3
+metis["chop_nod"].meta["nod_offsets"] = [-3, 3]
+imghdu_3 = metis.readout(exptime=1)[0][1]
+plt.imshow(imghdu_3.data, origin="lower", vmin=-3e3, vmax=3e3)
+plt.colorbar();
+```
+
+```{code-cell} ipython3
+metis["chop_nod"].meta["chop_offsets"] = [-3, 2]
+metis["chop_nod"].meta["nod_offsets"] = [2, 3]
+imghdu_4 = metis.readout(exptime=1)[0][1]
+plt.imshow(imghdu_4.data, origin="lower", vmin=-3e3, vmax=3e3)
+plt.colorbar();
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb b/METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb
deleted file mode 100644
index ebf99a9a..00000000
--- a/METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb
+++ /dev/null
@@ -1,406 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Selecting detector modes in METIS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook demonstrates the effect `detector_readout_parameters`, which selects between the different detector readout modes. These are `fast` and `slow` for the HAWAII2RG detectors, and `high_capacity` and `low_capacity` for the Geosnap detector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from astropy import units as u\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_lm\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The default readout mode for `img_lm` is the fast mode:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd[\"!OBS.detector_readout_mode\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We build the optical train using the default mode, and then check that the relevant parameters (`mindit`, `full_well`, `readout_noise` and `dark_current`) are taken over correctly, first in the `cmds` property of the optical train, then - most importantly - into the parameters of the affected `Effect` objects (demonstrated for the `readout_noise` effect)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.cmds[\"!DET\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['readout_noise'].meta['noise_std']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The \"bang string\" here means that the value is taken from the `cmds` structure in the previous cell, i.e."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.cmds[metis['readout_noise'].meta['noise_std']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this stage, we have access to the available detector modes and the parameter values that are set by them:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis['detector_readout_parameters'].list_modes())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can switch to the `slow` mode in the existing optical train by doing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.cmds[\"!OBS.detector_readout_mode\"] = \"slow\"\n",
-    "metis.update()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.cmds[\"!DET\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.cmds[metis['readout_noise'].meta['noise_std']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test: detector noise level (LSS-L)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To investigate the behaviour of the detector readout modes, we look at the L-band long-slit mode where the areas of the detector outside the spectral trace contain only readout noise and dark current. The default mode for long-slit spectroscopy is the `slow` mode, and we'll switch to the `fast` mode afterwards."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"])\n",
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Detector mode:\", metis.cmds[\"!OBS.detector_readout_mode\"])\n",
-    "metis['psf'].include = False     # not needed for blank-sky observations\n",
-    "metis.observe()\n",
-    "hdul_slow = metis.readout(exptime=1000)[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We get the statistics in a strip at the left edge of the detector that is not covered by any source or background flux and compare to the expected values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndit_slow = metis.cmds[\"!OBS.ndit\"]\n",
-    "dit_slow = metis.cmds[\"!OBS.dit\"]\n",
-    "\n",
-    "bg_slow = hdul_slow[1].data[250:1750, 10:200].mean()\n",
-    "bg_slow_expected = dit_slow * ndit_slow * metis.cmds[\"!DET.dark_current\"]\n",
-    "\n",
-    "noise_slow = hdul_slow[1].data[250:1750, 10:200].std()\n",
-    "noise_slow_expected = np.sqrt(ndit_slow) * metis.cmds[\"!DET.readout_noise\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do the same for the `fast` mode."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hdul_fast = metis.readout(detector_readout_mode=\"fast\", exptime=1000)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndit_fast = metis.cmds[\"!OBS.ndit\"]\n",
-    "dit_fast = metis.cmds[\"!OBS.dit\"]\n",
-    "\n",
-    "bg_fast = hdul_fast[1].data[250:1750, 10:200].mean()\n",
-    "bg_fast_expected = dit_fast * ndit_fast * metis.cmds[\"!DET.dark_current\"]\n",
-    "\n",
-    "noise_fast = hdul_fast[1].data[250:1750, 10:200].std()\n",
-    "noise_fast_expected = np.sqrt(ndit_fast) * metis.cmds[\"!DET.readout_noise\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"\"\"\n",
-    "Fast: ndit  = {ndit_fast}     dit = {dit_fast:.2f}\n",
-    "      bg    = {bg_fast:5.1f}   expected: {bg_fast_expected:5.1f}\n",
-    "      noise = {noise_fast:5.1f}  expected: {noise_fast_expected:5.1f}\"\"\")\n",
-    "print(f\"\"\"\n",
-    "Slow: ndit  = {ndit_slow}     dit = {dit_slow:.2f}\n",
-    "      bg    = {bg_slow:5.1f}   expected: {bg_slow_expected:5.1f}   \n",
-    "      noise = {noise_slow:5.1f}   expected: {noise_slow_expected:.1f}\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can let Scopesim automatically select the \"best\" mode."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hdul_auto = metis.readout(detector_readout_mode=\"auto\", exptime=1000)[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The advantage of the \"slow\" mode of the H2RG detector is the low readout noise. The \"fast\" mode permit smaller DITs and would be selected for bright sources that would saturate the detector at the minimum DIT of the \"slow\" mode. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test: Full well (IMG-N) \n",
-    "This demonstrates the high- and low-capacity modes of the Geosnap detector. The setup uses a neutral-density filter to ensure that the background does not saturate the detector in the low-capacity mode. The source is a very bright star, which saturates in the low-capacity mode but does not in the high-capacity mode."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "star = sim.source.source_templates.star(flux=20 * u.Jy)\n",
-    "\n",
-    "cmd_n = sim.UserCommands(use_instrument=\"METIS\", set_modes=['img_n'],\n",
-    "                        properties={\"!OBS.filter_name\": \"N2\", \"!OBS.nd_filter_name\": \"ND_OD1\"})\n",
-    "metis_n = sim.OpticalTrain(cmd_n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_n.observe(star, update=True)\n",
-    "print(\"--- high-capacity mode ---\")\n",
-    "hdul_high = metis_n.readout(detector_readout_mode=\"high_capacity\", exptime=1)[0]\n",
-    "fullwell_high = metis_n.cmds[\"!DET.full_well\"]\n",
-    "gain_high = metis_n.cmds[\"!DET.gain\"]\n",
-    "ndit_high = metis_n.cmds[\"!OBS.ndit\"]\n",
-    "\n",
-    "print(\"--- low-capacity mode ---\")\n",
-    "hdul_low = metis_n.readout(detector_readout_mode=\"low_capacity\", exptime=1)[0]\n",
-    "fullwell_low = metis_n.cmds[\"!DET.full_well\"]\n",
-    "gain_low = metis_n.cmds[\"!DET.gain\"]\n",
-    "ndit_low = metis_n.cmds[\"!OBS.ndit\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We take the average over NDIT exposures (units: electrons per DIT) so that we can immediately compare the counts to the full wells for the two modes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detimg_high = hdul_high[1].data * gain_high\n",
-    "detimg_low = hdul_low[1].data * gain_low"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(12, 4))\n",
-    "plt.subplot(121)\n",
-    "plt.plot(detimg_high[1024, 974:1074])\n",
-    "plt.title(\"high-capacity mode\")\n",
-    "plt.hlines(fullwell_high, 0, 100, colors='k', linestyles='dashed')\n",
-    "plt.ylabel(\"Electrons per DIT\")\n",
-    "\n",
-    "plt.subplot(122)\n",
-    "plt.plot(detimg_low[1024, 974:1074])\n",
-    "plt.title(label=\"low-capacity mode\")\n",
-    "plt.hlines(fullwell_low, 0, 100, colors='k', linestyles=\"dashed\")\n",
-    "plt.ylabel(\"Electrons per DIT\");"
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_detector_modes.md b/METIS/docs/example_notebooks/demos/demo_detector_modes.md
new file mode 100644
index 00000000..cb5ed2a2
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_detector_modes.md
@@ -0,0 +1,204 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Selecting detector modes in METIS
+
++++
+
+This notebook demonstrates the effect `detector_readout_parameters`, which selects between the different detector readout modes. These are `fast` and `slow` for the HAWAII2RG detectors, and `high_capacity` and `low_capacity` for the Geosnap detector.
+
+```{code-cell} ipython3
+import numpy as np
+from astropy import units as u
+from matplotlib import pyplot as plt
+
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell.
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
+```
+
+The default readout mode for `img_lm` is the fast mode:
+
+```{code-cell} ipython3
+cmd["!OBS.detector_readout_mode"]
+```
+
+We build the optical train using the default mode, and then check that the relevant parameters (`mindit`, `full_well`, `readout_noise` and `dark_current`) are taken over correctly, first in the `cmds` property of the optical train, then - most importantly - into the parameters of the affected `Effect` objects (demonstrated for the `readout_noise` effect).
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+metis.cmds["!DET"]
+```
+
+```{code-cell} ipython3
+metis['readout_noise'].meta['noise_std']
+```
+
+The "bang string" here means that the value is taken from the `cmds` structure in the previous cell, i.e.
+
+```{code-cell} ipython3
+metis.cmds[metis['readout_noise'].meta['noise_std']]
+```
+
+At this stage, we have access to the available detector modes and the parameter values that are set by them:
+
+```{code-cell} ipython3
+print(metis['detector_readout_parameters'].list_modes())
+```
+
+We can switch to the `slow` mode in the existing optical train by doing
+
+```{code-cell} ipython3
+metis.cmds["!OBS.detector_readout_mode"] = "slow"
+metis.update()
+```
+
+```{code-cell} ipython3
+metis.cmds["!DET"]
+```
+
+```{code-cell} ipython3
+metis.cmds[metis['readout_noise'].meta['noise_std']]
+```
+
+# Test: detector noise level (LSS-L)
+
++++
+
+To investigate the behaviour of the detector readout modes, we look at the L-band long-slit mode where the areas of the detector outside the spectral trace contain only readout noise and dark current. The default mode for long-slit spectroscopy is the `slow` mode, and we'll switch to the `fast` mode afterwards.
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["lss_l"])
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+print("Detector mode:", metis.cmds["!OBS.detector_readout_mode"])
+metis['psf'].include = False     # not needed for blank-sky observations
+metis.observe()
+hdul_slow = metis.readout(exptime=1000)[0]
+```
+
+We get the statistics in a strip at the left edge of the detector that is not covered by any source or background flux and compare to the expected values.
+
+```{code-cell} ipython3
+ndit_slow = metis.cmds["!OBS.ndit"]
+dit_slow = metis.cmds["!OBS.dit"]
+
+bg_slow = hdul_slow[1].data[250:1750, 10:200].mean()
+bg_slow_expected = dit_slow * ndit_slow * metis.cmds["!DET.dark_current"]
+
+noise_slow = hdul_slow[1].data[250:1750, 10:200].std()
+noise_slow_expected = np.sqrt(ndit_slow) * metis.cmds["!DET.readout_noise"]
+```
+
+Do the same for the `fast` mode.
+
+```{code-cell} ipython3
+hdul_fast = metis.readout(detector_readout_mode="fast", exptime=1000)[0]
+```
+
+```{code-cell} ipython3
+ndit_fast = metis.cmds["!OBS.ndit"]
+dit_fast = metis.cmds["!OBS.dit"]
+
+bg_fast = hdul_fast[1].data[250:1750, 10:200].mean()
+bg_fast_expected = dit_fast * ndit_fast * metis.cmds["!DET.dark_current"]
+
+noise_fast = hdul_fast[1].data[250:1750, 10:200].std()
+noise_fast_expected = np.sqrt(ndit_fast) * metis.cmds["!DET.readout_noise"]
+```
+
+```{code-cell} ipython3
+print(f"""
+Fast: ndit  = {ndit_fast}     dit = {dit_fast:.2f}
+      bg    = {bg_fast:5.1f}   expected: {bg_fast_expected:5.1f}
+      noise = {noise_fast:5.1f}  expected: {noise_fast_expected:5.1f}""")
+print(f"""
+Slow: ndit  = {ndit_slow}     dit = {dit_slow:.2f}
+      bg    = {bg_slow:5.1f}   expected: {bg_slow_expected:5.1f}   
+      noise = {noise_slow:5.1f}   expected: {noise_slow_expected:.1f}""")
+```
+
+Finally, we can let Scopesim automatically select the "best" mode.
+
+```{code-cell} ipython3
+hdul_auto = metis.readout(detector_readout_mode="auto", exptime=1000)[0]
+```
+
+The advantage of the "slow" mode of the H2RG detector is the low readout noise. The "fast" mode permit smaller DITs and would be selected for bright sources that would saturate the detector at the minimum DIT of the "slow" mode. 
+
++++
+
+# Test: Full well (IMG-N) 
+This demonstrates the high- and low-capacity modes of the Geosnap detector. The setup uses a neutral-density filter to ensure that the background does not saturate the detector in the low-capacity mode. The source is a very bright star, which saturates in the low-capacity mode but does not in the high-capacity mode.
+
+```{code-cell} ipython3
+star = sim.source.source_templates.star(flux=20 * u.Jy)
+
+cmd_n = sim.UserCommands(use_instrument="METIS", set_modes=['img_n'],
+                        properties={"!OBS.filter_name": "N2", "!OBS.nd_filter_name": "ND_OD1"})
+metis_n = sim.OpticalTrain(cmd_n)
+```
+
+```{code-cell} ipython3
+metis_n.observe(star, update=True)
+print("--- high-capacity mode ---")
+hdul_high = metis_n.readout(detector_readout_mode="high_capacity", exptime=1)[0]
+fullwell_high = metis_n.cmds["!DET.full_well"]
+gain_high = metis_n.cmds["!DET.gain"]
+ndit_high = metis_n.cmds["!OBS.ndit"]
+
+print("--- low-capacity mode ---")
+hdul_low = metis_n.readout(detector_readout_mode="low_capacity", exptime=1)[0]
+fullwell_low = metis_n.cmds["!DET.full_well"]
+gain_low = metis_n.cmds["!DET.gain"]
+ndit_low = metis_n.cmds["!OBS.ndit"]
+```
+
+We take the average over NDIT exposures (units: electrons per DIT) so that we can immediately compare the counts to the full wells for the two modes.
+
+```{code-cell} ipython3
+detimg_high = hdul_high[1].data * gain_high
+detimg_low = hdul_low[1].data * gain_low
+```
+
+```{code-cell} ipython3
+plt.figure(figsize=(12, 4))
+plt.subplot(121)
+plt.plot(detimg_high[1024, 974:1074])
+plt.title("high-capacity mode")
+plt.hlines(fullwell_high, 0, 100, colors='k', linestyles='dashed')
+plt.ylabel("Electrons per DIT")
+
+plt.subplot(122)
+plt.plot(detimg_low[1024, 974:1074])
+plt.title(label="low-capacity mode")
+plt.hlines(fullwell_low, 0, 100, colors='k', linestyles="dashed")
+plt.ylabel("Electrons per DIT");
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb b/METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb
deleted file mode 100644
index 7a7d55d4..00000000
--- a/METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb
+++ /dev/null
@@ -1,370 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook demonstrates the use of the `FilterWheel` in Scopesim. The METIS configuration contains two instances of this effect, named `filter_wheel` (for science filters) and `nd_filter_wheel` (for neutral-density filters). Each filter wheel contains a number of predefined filters, with different filter sets for the LM- and N-band imagers. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_lm\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The filter to use is defined by setting `!OBS.filter_name`. In `img_lm` mode, it defaults to the Lp filter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd[\"!OBS.filter_name\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The METIS package defines the list of filters that are available in the real instrument:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"filter_wheel\"].filters.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "More information about the filters can be obtained with the `get_table()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"filter_wheel\"].get_table()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At any moment one of these filters is in the optical path and used for the simulation. Initially, this is the one set by `!OBS.filter_name`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis[\"filter_wheel\"].current_filter)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The current filter can be changed to any of the filters in the list:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"filter_wheel\"].change_filter(\"PAH_3.3\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis[\"filter_wheel\"].current_filter)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Observing the same source in different filters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src = sim.source.source_templates.empty_sky()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"filter_wheel\"].change_filter(\"Lp\")\n",
-    "\n",
-    "metis.observe(src)\n",
-    "img_Lp = metis.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"filter_wheel\"].change_filter(\"PAH_3.3\")\n",
-    "\n",
-    "metis.observe(src, update=True)\n",
-    "img_PAH = metis.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Background in Lp:      {:8.1f} counts/s\".format(np.median(img_Lp)))\n",
-    "print(\"Background in PAH_3.3: {:8.1f} counts/s\".format(np.median(img_PAH)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the neutral-density filter wheel\n",
-    "\n",
-    "METIS also has neutral-density filters that can be inserted and changed using the `nd_filter_wheel` effect. The transmission of the filter `ND_ODx` is $10^{-x}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"nd_filter_wheel\"].filters.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis[\"nd_filter_wheel\"].current_filter)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Observe a bright star (default arguments result in Vega at 0 mag) in the Lp filter. It will be found that the star saturates the detector in the open position, and requires the `ND_OD4` filter not to do so."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "star = sim.source.source_templates.star()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"filter_wheel\"].change_filter(\"Lp\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"nd_filter_wheel\"].change_filter(\"open\")\n",
-    "metis.observe(star, update=True)\n",
-    "hdu_open = metis.readout()[0][1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"nd_filter_wheel\"].change_filter(\"ND_OD3\")\n",
-    "metis.observe(star, update=True)\n",
-    "hdu_OD3 = metis.readout()[0][1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"nd_filter_wheel\"].change_filter(\"ND_OD4\")\n",
-    "metis.observe(star, update=True)\n",
-    "hdu_OD4 = metis.readout()[0][1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 4))\n",
-    "\n",
-    "im1 = ax1.imshow(hdu_open.data[700:1350, 700:1350], origin=\"lower\", vmin=1e-3, vmax=2e6, norm=\"log\")\n",
-    "ax1.set_title(\"ND filter: open\")\n",
-    "ax2.imshow(hdu_OD3.data[700:1350, 700:1350], origin=\"lower\", vmin=1e-3, vmax=2e6, norm=\"log\")\n",
-    "ax2.set_title(\"ND filter: 1e-3\")\n",
-    "ax3.imshow(hdu_OD4.data[700:1350, 700:1350], origin=\"lower\", vmin=1e-3, vmax=2e6, norm=\"log\")\n",
-    "ax3.set_title(\"ND filter: 1e-4\")\n",
-    "plt.colorbar(im1, ax=(ax1, ax2, ax3));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 4))\n",
-    "\n",
-    "ax1.plot(hdu_open.data[800:1250, 1024])\n",
-    "ax1.set_title(\"ND filter: open\")\n",
-    "ax2.plot(hdu_OD3.data[800:1250, 1024])\n",
-    "ax2.set_title(\"ND filter: 1e-3\")\n",
-    "ax3.plot(hdu_OD4.data[800:1250, 1024])\n",
-    "ax3.set_title(\"ND filter: 1e-4\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding a custom filter to the filter wheel\n",
-    "A custom filter that is not in the default filter set can be added to the wheel using the method `add_filter`. A \"filter\" is an object of class `TERCurve` (or one of its subclasses) and the various methods for instantiating such an object can be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newfilter = sim.effects.ter_curves.TopHatFilterCurve(\n",
-    "    transmission=0.9, blue_cutoff=3.8, red_cutoff=3.9, \n",
-    "    name=\"custom_tophat\")\n",
-    "metis[\"filter_wheel\"].add_filter(newfilter)\n",
-    "metis[\"filter_wheel\"].filters.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"filter_wheel\"].change_filter(\"custom_tophat\")\n",
-    "metis[\"filter_wheel\"].current_filter.plot();"
-   ]
-  }
- ],
- "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": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_filter_wheel.md b/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
new file mode 100644
index 00000000..6eb29550
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
@@ -0,0 +1,177 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+This notebook demonstrates the use of the `FilterWheel` in Scopesim. The METIS configuration contains two instances of this effect, named `filter_wheel` (for science filters) and `nd_filter_wheel` (for neutral-density filters). Each filter wheel contains a number of predefined filters, with different filter sets for the LM- and N-band imagers. 
+
+```{code-cell} ipython3
+import numpy as np
+from matplotlib import pyplot as plt
+
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell.
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
+```
+
+The filter to use is defined by setting `!OBS.filter_name`. In `img_lm` mode, it defaults to the Lp filter:
+
+```{code-cell} ipython3
+cmd["!OBS.filter_name"]
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+The METIS package defines the list of filters that are available in the real instrument:
+
+```{code-cell} ipython3
+metis["filter_wheel"].filters.keys()
+```
+
+More information about the filters can be obtained with the `get_table()` method:
+
+```{code-cell} ipython3
+metis["filter_wheel"].get_table()
+```
+
+At any moment one of these filters is in the optical path and used for the simulation. Initially, this is the one set by `!OBS.filter_name`:
+
+```{code-cell} ipython3
+print(metis["filter_wheel"].current_filter)
+```
+
+The current filter can be changed to any of the filters in the list:
+
+```{code-cell} ipython3
+metis["filter_wheel"].change_filter("PAH_3.3")
+```
+
+```{code-cell} ipython3
+print(metis["filter_wheel"].current_filter)
+```
+
+# Observing the same source in different filters
+
+```{code-cell} ipython3
+src = sim.source.source_templates.empty_sky()
+```
+
+```{code-cell} ipython3
+metis["filter_wheel"].change_filter("Lp")
+
+metis.observe(src)
+img_Lp = metis.image_planes[0].data
+```
+
+```{code-cell} ipython3
+metis["filter_wheel"].change_filter("PAH_3.3")
+
+metis.observe(src, update=True)
+img_PAH = metis.image_planes[0].data
+```
+
+```{code-cell} ipython3
+print("Background in Lp:      {:8.1f} counts/s".format(np.median(img_Lp)))
+print("Background in PAH_3.3: {:8.1f} counts/s".format(np.median(img_PAH)))
+```
+
+# Using the neutral-density filter wheel
+
+METIS also has neutral-density filters that can be inserted and changed using the `nd_filter_wheel` effect. The transmission of the filter `ND_ODx` is $10^{-x}$.
+
+```{code-cell} ipython3
+metis["nd_filter_wheel"].filters.keys()
+```
+
+```{code-cell} ipython3
+print(metis["nd_filter_wheel"].current_filter)
+```
+
+Observe a bright star (default arguments result in Vega at 0 mag) in the Lp filter. It will be found that the star saturates the detector in the open position, and requires the `ND_OD4` filter not to do so.
+
+```{code-cell} ipython3
+star = sim.source.source_templates.star()
+```
+
+```{code-cell} ipython3
+metis["filter_wheel"].change_filter("Lp")
+```
+
+```{code-cell} ipython3
+metis["nd_filter_wheel"].change_filter("open")
+metis.observe(star, update=True)
+hdu_open = metis.readout()[0][1]
+```
+
+```{code-cell} ipython3
+metis["nd_filter_wheel"].change_filter("ND_OD3")
+metis.observe(star, update=True)
+hdu_OD3 = metis.readout()[0][1]
+```
+
+```{code-cell} ipython3
+metis["nd_filter_wheel"].change_filter("ND_OD4")
+metis.observe(star, update=True)
+hdu_OD4 = metis.readout()[0][1]
+```
+
+```{code-cell} ipython3
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 4))
+
+im1 = ax1.imshow(hdu_open.data[700:1350, 700:1350], origin="lower", vmin=1e-3, vmax=2e6, norm="log")
+ax1.set_title("ND filter: open")
+ax2.imshow(hdu_OD3.data[700:1350, 700:1350], origin="lower", vmin=1e-3, vmax=2e6, norm="log")
+ax2.set_title("ND filter: 1e-3")
+ax3.imshow(hdu_OD4.data[700:1350, 700:1350], origin="lower", vmin=1e-3, vmax=2e6, norm="log")
+ax3.set_title("ND filter: 1e-4")
+plt.colorbar(im1, ax=(ax1, ax2, ax3));
+```
+
+```{code-cell} ipython3
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 4))
+
+ax1.plot(hdu_open.data[800:1250, 1024])
+ax1.set_title("ND filter: open")
+ax2.plot(hdu_OD3.data[800:1250, 1024])
+ax2.set_title("ND filter: 1e-3")
+ax3.plot(hdu_OD4.data[800:1250, 1024])
+ax3.set_title("ND filter: 1e-4");
+```
+
+## Adding a custom filter to the filter wheel
+A custom filter that is not in the default filter set can be added to the wheel using the method `add_filter`. A "filter" is an object of class `TERCurve` (or one of its subclasses) and the various methods for instantiating such an object can be used.
+
+```{code-cell} ipython3
+newfilter = sim.effects.ter_curves.TopHatFilterCurve(
+    transmission=0.9, blue_cutoff=3.8, red_cutoff=3.9, 
+    name="custom_tophat")
+metis["filter_wheel"].add_filter(newfilter)
+metis["filter_wheel"].filters.keys()
+```
+
+```{code-cell} ipython3
+metis["filter_wheel"].change_filter("custom_tophat")
+metis["filter_wheel"].current_filter.plot();
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb
deleted file mode 100644
index 427fe676..00000000
--- a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ed1256e8",
-   "metadata": {},
-   "source": [
-    "# Grating Efficiency\n",
-    "\n",
-    "This notebook demonstrates and tests the `SpectralEfficiency` effect for the METIS long-slit spectroscopic modes. Lacking real data, the grating effciencies used here (and available in the irdb) are pure fantasy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57fc6481",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from astropy.io import fits\n",
-    "from astropy.io import ascii as ioascii\n",
-    "from astropy import units as u\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from synphot import SourceSpectrum, Empirical1D\n",
-    "from scopesim_templates.micado import flatlamp\n",
-    "\n",
-    "import scopesim as sim\n",
-    "\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91e6eeb1",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62947951",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ff36c76",
-   "metadata": {},
-   "source": [
-    "## Source -- constant spectrum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4f632845",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lam = np.linspace(2.8, 18, 2048)\n",
-    "flux = 0.1 * np.ones_like(lam)\n",
-    "spec = SourceSpectrum(Empirical1D, points = lam * u.um, lookup_table=flux)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac741ecc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src = flatlamp()\n",
-    "src.fields[0].spectra[0] = spec  # NB: do not try to set src.spectra[0] directly, this has no effect"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07e9705d",
-   "metadata": {},
-   "source": [
-    "## L band"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bd88ee5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd_l = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60a42863",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_l = sim.OpticalTrain(cmd_l)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82c6319f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_l[\"psf\"].include = False                # PSF is not necessary for slit-filling source\n",
-    "metis_l[\"skycalc_atmosphere\"].include = False # sky lines obscure flat spectrum "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eab4c272",
-   "metadata": {},
-   "source": [
-    "Observe the source with and without the grating efficiency. The ratio between the two results (we look at the image plane, which is noise free) shows directly the efficiency.\n",
-    "As long as no realistic data are available, the efficiency effect is turned off by default and has to be included explicitely."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "903231ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_l[\"grating_efficiency\"].include  = True\n",
-    "metis_l.observe(src)\n",
-    "sim_l_with_effic = metis_l.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25c7d614",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_l[\"grating_efficiency\"].include = False\n",
-    "metis_l.observe(src, update=True)\n",
-    "sim_l_without_effic = metis_l.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "31e717b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ratio_l = sim_l_with_effic / sim_l_without_effic\n",
-    "plt.figure(figsize=(15, 5))\n",
-    "plt.subplot(131)\n",
-    "plt.imshow(sim_l_with_effic, origin=\"lower\")\n",
-    "plt.title(\"L band, with grating efficiency\")\n",
-    "plt.subplot(132)\n",
-    "plt.imshow(sim_l_without_effic, origin=\"lower\")\n",
-    "plt.title(\"L band, without grating efficiency\")\n",
-    "plt.subplot(133)\n",
-    "plt.imshow(ratio_l, origin=\"lower\")\n",
-    "plt.title(\"L band, ratio\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99af7463",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot((sim_l_with_effic / sim_l_without_effic)[:, 1000]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5924ad57",
-   "metadata": {},
-   "source": [
-    "## M band"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0baa31a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd_m = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_m\"])\n",
-    "metis_m = sim.OpticalTrain(cmd_m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2a46fd7e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_m[\"psf\"].include = False                # PSF is not necessary for slit-filling source\n",
-    "metis_m[\"skycalc_atmosphere\"].include = False # sky lines obscure flat spectrum "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f6e9b15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_m[\"grating_efficiency\"].include  = True\n",
-    "metis_m.observe(src, update=True)\n",
-    "sim_m_with_effic = metis_m.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7503615",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_m[\"grating_efficiency\"].include = False\n",
-    "metis_m.observe(src, update=True)\n",
-    "sim_m_without_effic = metis_m.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1d538045",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ratio_m = sim_m_with_effic / sim_m_without_effic\n",
-    "plt.figure(figsize=(15, 5))\n",
-    "plt.subplot(131)\n",
-    "plt.imshow(sim_m_with_effic, origin=\"lower\")\n",
-    "plt.title(\"M band, with grating efficiency\")\n",
-    "plt.subplot(132)\n",
-    "plt.imshow(sim_m_without_effic, origin=\"lower\")\n",
-    "plt.title(\"M band, with grating efficiency\")\n",
-    "plt.subplot(133)\n",
-    "plt.imshow(ratio_m, origin=\"lower\")\n",
-    "plt.title(\"M band, ratio\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8eff13df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(ratio_m[:, 1000]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "907e5766",
-   "metadata": {},
-   "source": [
-    "## N band"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60a1aa82",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd_n = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_n\"])\n",
-    "metis_n = sim.OpticalTrain(cmd_n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d52d1788",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_n[\"psf\"].include = False                # PSF is not necessary for slit-filling source\n",
-    "metis_n[\"skycalc_atmosphere\"].include = False # sky lines obscure flat spectrum "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e75cc80d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_n[\"grating_efficiency\"].include  = True\n",
-    "metis_n.observe(src, update=True)\n",
-    "sim_n_with_effic = metis_n.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17fddec7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis_n[\"grating_efficiency\"].include = False\n",
-    "metis_n.observe(src, update=True)\n",
-    "sim_n_without_effic = metis_n.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a4f11b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ratio_n = sim_n_with_effic / sim_n_without_effic\n",
-    "plt.figure(figsize=(15, 5))\n",
-    "plt.subplot(131)\n",
-    "plt.imshow(sim_n_with_effic, origin=\"lower\")\n",
-    "plt.title(\"N band, with grating efficiency\")\n",
-    "plt.subplot(132)\n",
-    "plt.imshow(sim_n_without_effic, origin=\"lower\")\n",
-    "plt.title(\"N band, with grating efficiency\")\n",
-    "plt.subplot(133)\n",
-    "plt.imshow(ratio_n, origin=\"lower\")\n",
-    "plt.title(\"N band, ratio\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f27b7712",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(ratio_n[:, 1000]);"
-   ]
-  }
- ],
- "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": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
new file mode 100644
index 00000000..83f0fa0e
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
@@ -0,0 +1,185 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Grating Efficiency
+
+This notebook demonstrates and tests the `SpectralEfficiency` effect for the METIS long-slit spectroscopic modes. Lacking real data, the grating effciencies used here (and available in the irdb) are pure fantasy.
+
+```{code-cell} ipython3
+import numpy as np
+from astropy.io import fits
+from astropy.io import ascii as ioascii
+from astropy import units as u
+from matplotlib import pyplot as plt
+
+from synphot import SourceSpectrum, Empirical1D
+from scopesim_templates.micado import flatlamp
+
+import scopesim as sim
+
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell.
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+## Source -- constant spectrum
+
+```{code-cell} ipython3
+lam = np.linspace(2.8, 18, 2048)
+flux = 0.1 * np.ones_like(lam)
+spec = SourceSpectrum(Empirical1D, points = lam * u.um, lookup_table=flux)
+```
+
+```{code-cell} ipython3
+src = flatlamp()
+src.fields[0].spectra[0] = spec  # NB: do not try to set src.spectra[0] directly, this has no effect
+```
+
+## L band
+
+```{code-cell} ipython3
+cmd_l = sim.UserCommands(use_instrument="METIS", set_modes=["lss_l"])
+```
+
+```{code-cell} ipython3
+metis_l = sim.OpticalTrain(cmd_l)
+```
+
+```{code-cell} ipython3
+metis_l["psf"].include = False                # PSF is not necessary for slit-filling source
+metis_l["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum 
+```
+
+Observe the source with and without the grating efficiency. The ratio between the two results (we look at the image plane, which is noise free) shows directly the efficiency.
+As long as no realistic data are available, the efficiency effect is turned off by default and has to be included explicitely.
+
+```{code-cell} ipython3
+metis_l["grating_efficiency"].include  = True
+metis_l.observe(src)
+sim_l_with_effic = metis_l.image_planes[0].data
+```
+
+```{code-cell} ipython3
+metis_l["grating_efficiency"].include = False
+metis_l.observe(src, update=True)
+sim_l_without_effic = metis_l.image_planes[0].data
+```
+
+```{code-cell} ipython3
+ratio_l = sim_l_with_effic / sim_l_without_effic
+plt.figure(figsize=(15, 5))
+plt.subplot(131)
+plt.imshow(sim_l_with_effic, origin="lower")
+plt.title("L band, with grating efficiency")
+plt.subplot(132)
+plt.imshow(sim_l_without_effic, origin="lower")
+plt.title("L band, without grating efficiency")
+plt.subplot(133)
+plt.imshow(ratio_l, origin="lower")
+plt.title("L band, ratio");
+```
+
+```{code-cell} ipython3
+plt.plot((sim_l_with_effic / sim_l_without_effic)[:, 1000]);
+```
+
+## M band
+
+```{code-cell} ipython3
+cmd_m = sim.UserCommands(use_instrument="METIS", set_modes=["lss_m"])
+metis_m = sim.OpticalTrain(cmd_m)
+```
+
+```{code-cell} ipython3
+metis_m["psf"].include = False                # PSF is not necessary for slit-filling source
+metis_m["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum 
+```
+
+```{code-cell} ipython3
+metis_m["grating_efficiency"].include  = True
+metis_m.observe(src, update=True)
+sim_m_with_effic = metis_m.image_planes[0].data
+```
+
+```{code-cell} ipython3
+metis_m["grating_efficiency"].include = False
+metis_m.observe(src, update=True)
+sim_m_without_effic = metis_m.image_planes[0].data
+```
+
+```{code-cell} ipython3
+ratio_m = sim_m_with_effic / sim_m_without_effic
+plt.figure(figsize=(15, 5))
+plt.subplot(131)
+plt.imshow(sim_m_with_effic, origin="lower")
+plt.title("M band, with grating efficiency")
+plt.subplot(132)
+plt.imshow(sim_m_without_effic, origin="lower")
+plt.title("M band, with grating efficiency")
+plt.subplot(133)
+plt.imshow(ratio_m, origin="lower")
+plt.title("M band, ratio");
+```
+
+```{code-cell} ipython3
+plt.plot(ratio_m[:, 1000]);
+```
+
+## N band
+
+```{code-cell} ipython3
+cmd_n = sim.UserCommands(use_instrument="METIS", set_modes=["lss_n"])
+metis_n = sim.OpticalTrain(cmd_n)
+```
+
+```{code-cell} ipython3
+metis_n["psf"].include = False                # PSF is not necessary for slit-filling source
+metis_n["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum 
+```
+
+```{code-cell} ipython3
+metis_n["grating_efficiency"].include  = True
+metis_n.observe(src, update=True)
+sim_n_with_effic = metis_n.image_planes[0].data
+```
+
+```{code-cell} ipython3
+metis_n["grating_efficiency"].include = False
+metis_n.observe(src, update=True)
+sim_n_without_effic = metis_n.image_planes[0].data
+```
+
+```{code-cell} ipython3
+ratio_n = sim_n_with_effic / sim_n_without_effic
+plt.figure(figsize=(15, 5))
+plt.subplot(131)
+plt.imshow(sim_n_with_effic, origin="lower")
+plt.title("N band, with grating efficiency")
+plt.subplot(132)
+plt.imshow(sim_n_without_effic, origin="lower")
+plt.title("N band, with grating efficiency")
+plt.subplot(133)
+plt.imshow(ratio_n, origin="lower")
+plt.title("N band, ratio");
+```
+
+```{code-cell} ipython3
+plt.plot(ratio_n[:, 1000]);
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.ipynb b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.ipynb
deleted file mode 100644
index 8300f254..00000000
--- a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.ipynb
+++ /dev/null
@@ -1,332 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f6b7e7d5-70b3-4a06-8380-80ba648170c5",
-   "metadata": {},
-   "source": [
-    "This notebook demonstrates the ScopeSim effect `InterPixelCapacitance`. Inter-pixel capacitance correlates the voltages or data values measured in adjacent pixels of an infrared detector. The effect implements the three-parameter model of Kannawadi et al. (2016) (PASP 128, 095001) and applies the following convolution kernel (in their notation) to the detector readout:\n",
-    "$$ K(\\alpha, \\alpha^\\prime, \\alpha_{+}) = \\begin{pmatrix}\n",
-    "   \\alpha^\\prime & \\alpha - \\alpha_{+} & \\alpha^\\prime \\\\\n",
-    "   \\alpha + \\alpha_{+} & 1 - 4(\\alpha + \\alpha^\\prime) & \\alpha + \\alpha_{+} \\\\\n",
-    "   \\alpha^\\prime & \\alpha - \\alpha_{+} & \\alpha^\\prime\n",
-    "   \\end{pmatrix}\n",
-    "$$\n",
-    "\n",
-    "The correspondence between these parameters and those of the Scopesim effect is as follows:\n",
-    "- $\\alpha$ -- ``alpha_edge``: gives the influence of the four pixels sharing an edge with the target pixel.\n",
-    "- $\\alpha^\\prime$ -- ``alpha_corner``: gives the influence of the four pixels sharing a corner with the target pixel.\n",
-    "- $\\alpha_{+}$ -- ``alpha_aniso``: gives a difference in the influence of neighbouring pixels along rows and along columns\n",
-    "\n",
-    "The default setting in the instrument package sets these parameter with literature values typical for HxRG detectors. \n",
-    "It is also possible to set the kernel directly in the yaml file. This allows using kernels that are larger than $3\\times 3$.\n",
-    "\n",
-    "When the $\\alpha$ parameters are used to define the IPC kernel it is automatically normalised to unit sum. When the kernel is provided it is normalised by ScopeSim when its sum is greater than one. When it is less than one (but larger than zero), it is accepted as is, which then leads to loss of flux -- it is the user's responsibility to ensure the kernel is normalised unless this flux loss is intended. Kernels with negative sum are rejected and raise a `ValueError`.   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9339c51b-9402-467c-91dd-57f90283101f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "from astropy import units as u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d2fb8c45-8d59-49bc-a940-fde3c99a147e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out. [For ReadTheDocs only]\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d073e7be-ffd4-4697-84e9-bc592d04df34",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell, which will install the packages into `./inst_pkgs`, a subdirectory of your current working directory. If you have already downloaded the packages but to a different location you can set\n",
-    "```python\n",
-    "sim.set_inst_pgks_path(\"/path/to/inst/pkgs\")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "702e1c8f-338f-4166-a3ad-ad58df89534d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_package([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55e7fa50-baf8-4410-8db9-eb9f30021075",
-   "metadata": {},
-   "source": [
-    "The `InterPixelCapacitance` effect will be demonstrated in the METIS IMG_LM mode. It will become active in the `readout()` step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe9690aa-19c5-46ff-8ff2-47d5cf5933cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_lm\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa6477aa-4f28-442c-8f3f-7aee4036b983",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aff0e33d-ce64-48fb-aeb4-57de2b9b6a3c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.effects.pprint_all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a5ffef65-8b14-4a84-8c0e-457bfdc4a83e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c8bd1828-de83-4b35-b8ef-16d61dc64d98",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis['ipc'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e495fc00-54c6-4c6f-aec6-836e2565f734",
-   "metadata": {},
-   "source": [
-    "The kernel can be replaced using the `update` method. Note that the parameters need to be specified in full, either as `kernel` or the three `alpha` parameters (`alpha` parameters that are not set explicitly default to zero):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc516e61-201e-4b3c-b1cb-df1068b2d327",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['ipc'].update(alpha_edge=0.02, alpha_corner=0.002, alpha_aniso=0.001)\n",
-    "print(metis['ipc'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eea214d5-8863-44cb-888b-23e358966832",
-   "metadata": {},
-   "source": [
-    "## Effect on noise\n",
-    "The IPC effect is applied after dark current and shot noise but before readout noise. Its main effect is to correlate the photon noise, which is for instance measurable as a decrease in the rms noise. To show this, we observe a piece of blank sky and read out with and without the IPC effect included."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ca7d8574-b87c-4b13-ba43-1c8432f51c48",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "87f64fb0-5352-49c9-9a58-7dde96190640",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['ipc'].include = True         # this is the default\n",
-    "with_ipc = metis.readout(exptime=10)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8f2428bc-abf4-45f2-9ada-64457866c752",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['ipc'].include = False\n",
-    "without_ipc = metis.readout(exptime=10)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec0d258d-7421-415e-89b6-ffccaeb6d4af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The edge rows and columns need to be discarded as they are incompletely covered by the IPC kernel\n",
-    "print(f\"RMS noise without IPC: {without_ipc[1].data[1:-1, 1:-1].std()}\")\n",
-    "print(f\"RMS noise with IPC:    {with_ipc[1].data[1:-1, 1:-1].std()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09c84f86-009a-4f2b-8709-fe1dd4bbad3f",
-   "metadata": {},
-   "source": [
-    "## Effect on image quality\n",
-    "The effect on the image quality is rather slight, in particular because the SCAO PSF in the L band is broad with a FWHM of about five pixels. We simulate a star and apply the default SCAO PSF."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b2cb9f2b-638a-4422-9793-cf43acac53e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "star = sim.source.source_templates.star(flux=0.01*u.Jy)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fcf5e4bf-9a88-4981-bf93-7a860fbf9812",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe(star)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65274649-0a13-486c-a4bf-a59ac8ddeec0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['ipc'].include = True\n",
-    "star_w_ipc = metis.readout(exptime=1)[0]\n",
-    "metis['ipc'].include = False\n",
-    "star_wo_ipc = metis.readout(exptime=1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b606d556-156a-4629-aa15-5c4ea0045755",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(np.arange(1010,1035), star_wo_ipc[1].data[1023, 1010:1035], label=\"without IPC\")\n",
-    "plt.plot(np.arange(1010, 1035), star_w_ipc[1].data[1023, 1010:1035], label=\"with IPC\")\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6ae04433-7442-45fc-8c75-705773b49f58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(np.arange(1010, 1035), (star_w_ipc[1].data - star_wo_ipc[1].data)[1023, 1010:1035], label=\"Difference (with_ipc - without_ipc)\")\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "718af26c-c72f-4667-9abb-3bf4a7e018d9",
-   "metadata": {},
-   "source": [
-    "The effect is also more pronounced with a different (though unrealistic) kernel:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "73e4df86-3089-4403-9cd3-291ffd3617b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['ipc'].update(kernel=[[1, 1, 1], [1, 1, 1], [1, 1, 1]])\n",
-    "print(metis['ipc'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65b9b44c-fbc0-41c5-af99-495e886628ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['ipc'].include = True\n",
-    "star_w_strong_ipc = metis.readout(exptime=1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b539e170-8d9e-4f44-ba05-84cf792a757a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(np.arange(1010,1035), star_wo_ipc[1].data[1023, 1010:1035], label=\"without IPC\")\n",
-    "plt.plot(np.arange(1010, 1035), star_w_strong_ipc[1].data[1023, 1010:1035], label=\"with strong IPC\")\n",
-    "plt.legend();"
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
new file mode 100644
index 00000000..41fcb53b
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
@@ -0,0 +1,152 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+This notebook demonstrates the ScopeSim effect `InterPixelCapacitance`. Inter-pixel capacitance correlates the voltages or data values measured in adjacent pixels of an infrared detector. The effect implements the three-parameter model of Kannawadi et al. (2016) (PASP 128, 095001) and applies the following convolution kernel (in their notation) to the detector readout:
+$$ K(\alpha, \alpha^\prime, \alpha_{+}) = \begin{pmatrix}
+   \alpha^\prime & \alpha - \alpha_{+} & \alpha^\prime \\
+   \alpha + \alpha_{+} & 1 - 4(\alpha + \alpha^\prime) & \alpha + \alpha_{+} \\
+   \alpha^\prime & \alpha - \alpha_{+} & \alpha^\prime
+   \end{pmatrix}
+$$
+
+The correspondence between these parameters and those of the Scopesim effect is as follows:
+- $\alpha$ -- ``alpha_edge``: gives the influence of the four pixels sharing an edge with the target pixel.
+- $\alpha^\prime$ -- ``alpha_corner``: gives the influence of the four pixels sharing a corner with the target pixel.
+- $\alpha_{+}$ -- ``alpha_aniso``: gives a difference in the influence of neighbouring pixels along rows and along columns
+
+The default setting in the instrument package sets these parameter with literature values typical for HxRG detectors. 
+It is also possible to set the kernel directly in the yaml file. This allows using kernels that are larger than $3\times 3$.
+
+When the $\alpha$ parameters are used to define the IPC kernel it is automatically normalised to unit sum. When the kernel is provided it is normalised by ScopeSim when its sum is greater than one. When it is less than one (but larger than zero), it is accepted as is, which then leads to loss of flux -- it is the user's responsibility to ensure the kernel is normalised unless this flux loss is intended. Kernels with negative sum are rejected and raise a `ValueError`.   
+
+```{code-cell} ipython3
+import numpy as np
+from matplotlib import pyplot as plt
+from astropy import units as u
+```
+
+```{code-cell} ipython3
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out. [For ReadTheDocs only]
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell, which will install the packages into `./inst_pkgs`, a subdirectory of your current working directory. If you have already downloaded the packages but to a different location you can set
+```python
+sim.set_inst_pgks_path("/path/to/inst/pkgs")
+```
+
+```{code-cell} ipython3
+# sim.download_package(["METIS", "ELT", "Armazones"])
+```
+
+The `InterPixelCapacitance` effect will be demonstrated in the METIS IMG_LM mode. It will become active in the `readout()` step.
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+```{code-cell} ipython3
+metis.effects.pprint_all()
+```
+
+```{code-cell} ipython3
+
+```
+
+```{code-cell} ipython3
+print(metis['ipc'])
+```
+
+The kernel can be replaced using the `update` method. Note that the parameters need to be specified in full, either as `kernel` or the three `alpha` parameters (`alpha` parameters that are not set explicitly default to zero):
+
+```{code-cell} ipython3
+metis['ipc'].update(alpha_edge=0.02, alpha_corner=0.002, alpha_aniso=0.001)
+print(metis['ipc'])
+```
+
+## Effect on noise
+The IPC effect is applied after dark current and shot noise but before readout noise. Its main effect is to correlate the photon noise, which is for instance measurable as a decrease in the rms noise. To show this, we observe a piece of blank sky and read out with and without the IPC effect included.
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+metis['ipc'].include = True         # this is the default
+with_ipc = metis.readout(exptime=10)[0]
+```
+
+```{code-cell} ipython3
+metis['ipc'].include = False
+without_ipc = metis.readout(exptime=10)[0]
+```
+
+```{code-cell} ipython3
+# The edge rows and columns need to be discarded as they are incompletely covered by the IPC kernel
+print(f"RMS noise without IPC: {without_ipc[1].data[1:-1, 1:-1].std()}")
+print(f"RMS noise with IPC:    {with_ipc[1].data[1:-1, 1:-1].std()}")
+```
+
+## Effect on image quality
+The effect on the image quality is rather slight, in particular because the SCAO PSF in the L band is broad with a FWHM of about five pixels. We simulate a star and apply the default SCAO PSF.
+
+```{code-cell} ipython3
+star = sim.source.source_templates.star(flux=0.01*u.Jy)
+```
+
+```{code-cell} ipython3
+metis.observe(star)
+```
+
+```{code-cell} ipython3
+metis['ipc'].include = True
+star_w_ipc = metis.readout(exptime=1)[0]
+metis['ipc'].include = False
+star_wo_ipc = metis.readout(exptime=1)[0]
+```
+
+```{code-cell} ipython3
+plt.plot(np.arange(1010,1035), star_wo_ipc[1].data[1023, 1010:1035], label="without IPC")
+plt.plot(np.arange(1010, 1035), star_w_ipc[1].data[1023, 1010:1035], label="with IPC")
+plt.legend();
+```
+
+```{code-cell} ipython3
+plt.plot(np.arange(1010, 1035), (star_w_ipc[1].data - star_wo_ipc[1].data)[1023, 1010:1035], label="Difference (with_ipc - without_ipc)")
+plt.legend();
+```
+
+The effect is also more pronounced with a different (though unrealistic) kernel:
+
+```{code-cell} ipython3
+metis['ipc'].update(kernel=[[1, 1, 1], [1, 1, 1], [1, 1, 1]])
+print(metis['ipc'])
+```
+
+```{code-cell} ipython3
+metis['ipc'].include = True
+star_w_strong_ipc = metis.readout(exptime=1)[0]
+```
+
+```{code-cell} ipython3
+plt.plot(np.arange(1010,1035), star_wo_ipc[1].data[1023, 1010:1035], label="without IPC")
+plt.plot(np.arange(1010, 1035), star_w_strong_ipc[1].data[1023, 1010:1035], label="with strong IPC")
+plt.legend();
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb b/METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb
deleted file mode 100644
index fceb9222..00000000
--- a/METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb
+++ /dev/null
@@ -1,197 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simulating long-slit spectroscopy in METIS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook shows the most basic setup for long-slit spectroscopy, using a star as the source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set up the instrument in lss_l mode:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The source is a star with Vega spectrum and apparent brightness of 12 mag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src = sim.source.source_templates.star(flux=12)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe(src, update=True)\n",
-    "result = metis.readout(detector_readout_mode=\"auto\", exptime=1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(12,10))\n",
-    "plt.imshow(result[1].data, origin=\"lower\", vmin=100, norm=\"log\")\n",
-    "plt.colorbar();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Rectifying the spectrum\n",
-    "The default configuration for METIS applies a non-linear mapping of the two-dimensional spectrum onto the detector as determined from ray-tracing simulations of the optical system. The mapping can be reversed to obtain a rectified version of the 2D spectrum that is linear in both wavelength and spatial position and can easily be analysed. Note that this is optimistic compared to an actual data reduction process, where the mapping parameters would have to be estimated from data with some uncertainty. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tracelist = metis[\"spectral_traces\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rectified = tracelist.rectify_traces(result, -4, 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`rectified` is again an `HDUList` with the data in the first extension. The header of this extension contains the WCS keywords needed to translate from pixels to wavelength and spatial position. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from astropy.wcs import WCS\n",
-    "from astropy import units as u\n",
-    "wcs = WCS(rectified[1].header)\n",
-    "naxis1, naxis2 = wcs._naxis\n",
-    "det_wave = wcs.all_pix2world(np.arange(naxis1), 1, 0)[0] * u.Unit(wcs.wcs.cunit[0])\n",
-    "det_xi   = wcs.all_pix2world(1, np.arange(naxis2), 0)[1] * u.Unit(wcs.wcs.cunit[1])\n",
-    "det_wave = det_wave.to(u.um).value   # ensure desired units and dismiss for plotting\n",
-    "det_xi   = det_xi.to(u.arcsec).value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(12,7))\n",
-    "plt.imshow(rectified[1].data, vmin=100, norm=\"log\",\n",
-    "           extent=(det_wave[0], det_wave[-1], det_xi[0], det_xi[-1]),\n",
-    "           origin=\"lower\", aspect=\"auto\")\n",
-    "plt.xlabel(r\"Wavelength [$\\mu$m]\")\n",
-    "plt.ylabel(\"Position along slit [arcsec]\")\n",
-    "plt.colorbar();"
-   ]
-  }
- ],
- "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": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_lss_simple.md b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
new file mode 100644
index 00000000..66ad5393
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
@@ -0,0 +1,96 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Simulating long-slit spectroscopy in METIS
+
++++
+
+This notebook shows the most basic setup for long-slit spectroscopy, using a star as the source.
+
+```{code-cell} ipython3
+import numpy as np
+from matplotlib import pyplot as plt
+
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell.
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+Set up the instrument in lss_l mode:
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["lss_l"])
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+The source is a star with Vega spectrum and apparent brightness of 12 mag.
+
+```{code-cell} ipython3
+src = sim.source.source_templates.star(flux=12)
+```
+
+```{code-cell} ipython3
+metis.observe(src, update=True)
+result = metis.readout(detector_readout_mode="auto", exptime=1)[0]
+```
+
+```{code-cell} ipython3
+plt.figure(figsize=(12,10))
+plt.imshow(result[1].data, origin="lower", vmin=100, norm="log")
+plt.colorbar();
+```
+
+# Rectifying the spectrum
+The default configuration for METIS applies a non-linear mapping of the two-dimensional spectrum onto the detector as determined from ray-tracing simulations of the optical system. The mapping can be reversed to obtain a rectified version of the 2D spectrum that is linear in both wavelength and spatial position and can easily be analysed. Note that this is optimistic compared to an actual data reduction process, where the mapping parameters would have to be estimated from data with some uncertainty. 
+
+```{code-cell} ipython3
+tracelist = metis["spectral_traces"]
+```
+
+```{code-cell} ipython3
+rectified = tracelist.rectify_traces(result, -4, 4)
+```
+
+`rectified` is again an `HDUList` with the data in the first extension. The header of this extension contains the WCS keywords needed to translate from pixels to wavelength and spatial position. 
+
+```{code-cell} ipython3
+from astropy.wcs import WCS
+from astropy import units as u
+wcs = WCS(rectified[1].header)
+naxis1, naxis2 = wcs._naxis
+det_wave = wcs.all_pix2world(np.arange(naxis1), 1, 0)[0] * u.Unit(wcs.wcs.cunit[0])
+det_xi   = wcs.all_pix2world(1, np.arange(naxis2), 0)[1] * u.Unit(wcs.wcs.cunit[1])
+det_wave = det_wave.to(u.um).value   # ensure desired units and dismiss for plotting
+det_xi   = det_xi.to(u.arcsec).value
+```
+
+```{code-cell} ipython3
+plt.figure(figsize=(12,7))
+plt.imshow(rectified[1].data, vmin=100, norm="log",
+           extent=(det_wave[0], det_wave[-1], det_xi[0], det_xi[-1]),
+           origin="lower", aspect="auto")
+plt.xlabel(r"Wavelength [$\mu$m]")
+plt.ylabel("Position along slit [arcsec]")
+plt.colorbar();
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.ipynb b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.ipynb
deleted file mode 100644
index 9e1b0969..00000000
--- a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.ipynb
+++ /dev/null
@@ -1,145 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# METIS LMS Efficiency\n",
-    "\n",
-    "This notebook demonstrates the effect `MetisLMSEfficiency`. Here we use it standalone to reproduce Figure 6 from E-REP-ATC-MET-1016 (v1.0) to show that the effect computes the efficiency correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from astropy import units as u\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "import scopesim as sim\n",
-    "from scopesim.effects.metis_lms_trace_list import MetisLMSEfficiency\n",
-    "\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument package yet, uncomment the following cell. The METIS package provides the spectral trace definition file. The ELT and Armazones packages are not needed for the purposes of this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages(\"METIS\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When simulating an LMS observation, the user selects a target wavelength by setting `cmd['!OBS.wavelen']`, e.g. 4.2 (microns). In normal use, the efficiency is instantiated as an effect within the `OpticalTrain`. Here, we instantiate the effect directly as"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eff = MetisLMSEfficiency(wavelen=4.2, filename=\"../../../TRACE_LMS.fits\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The effect automatically selects the echelle order for that wavelength and computes the grating efficiency."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(eff.meta[\"order\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eff.surface.transmission.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively, the order can be specified directly. This is used in the following to plot the efficiencies for all orders. The resulting figure can be compared to the original figure from E-REP-ATC-MET-1016."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.ylim(0, 1)\n",
-    "plt.xlim(3.0, 5.05)\n",
-    "plt.xlabel(r\"Wavelength [$\\mu$m]\")\n",
-    "plt.ylabel(\"Efficiency\")\n",
-    "plt.xticks(np.arange(3.0, 5.05, 0.2))\n",
-    "for order in np.arange(22, 37):\n",
-    "    eff = MetisLMSEfficiency(order=order, filename=\"../../../TRACE_LMS.fits\")\n",
-    "    lam = eff.surface.transmission.waveset\n",
-    "    effic = eff.surface.transmission(lam)\n",
-    "    lammax = lam[np.argmax(effic)]\n",
-    "    p = plt.plot(lam.to(u.um), eff.surface.transmission(lam))\n",
-    "    plt.text(lammax.to(u.um).value, 0.76, str(order), ha=\"center\", color=p[0].get_color())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
new file mode 100644
index 00000000..10846d88
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
@@ -0,0 +1,74 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# METIS LMS Efficiency
+
+This notebook demonstrates the effect `MetisLMSEfficiency`. Here we use it standalone to reproduce Figure 6 from E-REP-ATC-MET-1016 (v1.0) to show that the effect computes the efficiency correctly.
+
+```{code-cell} ipython3
+import numpy as np
+from astropy import units as u
+from matplotlib import pyplot as plt
+
+import scopesim as sim
+from scopesim.effects.metis_lms_trace_list import MetisLMSEfficiency
+
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument package yet, uncomment the following cell. The METIS package provides the spectral trace definition file. The ELT and Armazones packages are not needed for the purposes of this notebook.
+
+```{code-cell} ipython3
+# sim.download_packages("METIS")
+```
+
+When simulating an LMS observation, the user selects a target wavelength by setting `cmd['!OBS.wavelen']`, e.g. 4.2 (microns). In normal use, the efficiency is instantiated as an effect within the `OpticalTrain`. Here, we instantiate the effect directly as
+
+```{code-cell} ipython3
+eff = MetisLMSEfficiency(wavelen=4.2, filename="../../../TRACE_LMS.fits")
+```
+
+The effect automatically selects the echelle order for that wavelength and computes the grating efficiency.
+
+```{code-cell} ipython3
+print(eff.meta["order"])
+```
+
+```{code-cell} ipython3
+eff.surface.transmission.plot()
+```
+
+Alternatively, the order can be specified directly. This is used in the following to plot the efficiencies for all orders. The resulting figure can be compared to the original figure from E-REP-ATC-MET-1016.
+
+```{code-cell} ipython3
+plt.figure(figsize=(8, 6))
+plt.ylim(0, 1)
+plt.xlim(3.0, 5.05)
+plt.xlabel(r"Wavelength [$\mu$m]")
+plt.ylabel("Efficiency")
+plt.xticks(np.arange(3.0, 5.05, 0.2))
+for order in np.arange(22, 37):
+    eff = MetisLMSEfficiency(order=order, filename="../../../TRACE_LMS.fits")
+    lam = eff.surface.transmission.waveset
+    effic = eff.surface.transmission(lam)
+    lammax = lam[np.argmax(effic)]
+    p = plt.plot(lam.to(u.um), eff.surface.transmission(lam))
+    plt.text(lammax.to(u.um).value, 0.76, str(order), ha="center", color=p[0].get_color())
+```
+
+```{code-cell} ipython3
+
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.ipynb b/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.ipynb
deleted file mode 100644
index 0f1c3d5a..00000000
--- a/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.ipynb
+++ /dev/null
@@ -1,311 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8d5514ce-43fa-4bd2-a28e-c3a9842e4165",
-   "metadata": {},
-   "source": [
-    "This notebook is targeted at the AIT team who use Scopesim to simulate observations with the warm calibration unit (WCU). It demonstrates how to select point-spread functions that correspond to a pupil-mask that is inserted in the optical path. This is an interplay between two effects in ScopeSim, an instance of `PupilMaskWheel` and an instance of `FieldConstantPSF`. Note that the instrument configuration for ScopeSim currently has a single pupil-mask wheel which is placed in the WCU. This differs from the real instrument, which will have a pupil-mask wheel in both imager arms as well as one in the common fore optics (CFO), and means that the functionality presented here can only be used with the WCU modes. The sky modes still use the SCAO PSFs as they always did. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae5f7199-2437-424f-97d4-4b1e61a657cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scopesim as sim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "58b77812-09c7-4f64-879c-1f1bd12f144e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")\n",
-    "\n",
-    "# If you haven't got the instrument packages yet, uncomment the following line.\n",
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f3ae712-7d2d-40a7-a9e5-ce2cdd1c8244",
-   "metadata": {},
-   "source": [
-    "By default Scopesim loads one of the PPS masks with their corresponding PSF. The PSF files are stored on the Astar server in Vienna; they are automatically downloaded if they are not available locally (either in the download cache folder or in the subdirectory `./METIS_psfs`). For imaging in the LM band, do"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ea0cf94f-c640-4cac-989a-42139ee847c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"wcu_img_lm\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53ab2313-8e70-4704-abe2-9b08d0b84a05",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd436e2a-d681-4d31-bb5f-c7d3e1a11085",
-   "metadata": {},
-   "source": [
-    "We can inspect the two effects to check that they agree and to find out the location of the psf file that is used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4f5fa778-2bbd-4902-9f8f-1747f7796c2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis['pupil_masks'])\n",
-    "print(metis['psf'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79c3328c-7e31-406e-b8e9-7592347fee85",
-   "metadata": {},
-   "source": [
-    "Change the WCU focal-plane mask to a pinhole to check that the correct PSF is used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf70057b-a7f0-428a-a13e-299480a04252",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['wcu_source'].set_fpmask(\"pinhole_lm\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afd70da3-9969-45e1-b3e0-91d6b946fbe1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "beed9131-f049-4b52-8838-76497337022d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "260599fb-d096-4a38-afc0-3ea3ace9cf7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "implane = metis.image_planes[0].data\n",
-    "plt.imshow(implane - np.median(implane), norm='log', origin='lower')\n",
-    "plt.xlim(900, 1150)\n",
-    "plt.ylim(900, 1150);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b660de0c-2155-4376-a545-a9ca8aff9589",
-   "metadata": {},
-   "source": [
-    "## Changing the pupil mask\n",
-    "The pupil mask can be changed using the `change_mask` method of the pupil mask effect. Unfortunately, the PSF is not changed automatically due to a short-coming in ScopeSim (effects do not talk to each other...). ScopeSim does tell you, however, what you need to do:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "422cb6c4-6c66-4710-8b19-14c2715cb205",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['pupil_masks'].change_mask(\"APP-LM\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f491a53-a9fb-4bfa-b286-fe629358de8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['psf'].update(pupil_mask=\"APP-LM\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99f68e8b-ab60-497f-b6d3-ceffa59044ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(metis['pupil_masks'])\n",
-    "print(metis['psf'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25d678f0-b4b6-44d5-b20a-0ed3e11b885f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "df24914f-94c7-494f-8482-850671078ef6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "implane = metis.image_planes[0].data\n",
-    "plt.imshow(implane - np.median(implane), norm='log', origin='lower')\n",
-    "plt.xlim(900, 1150)\n",
-    "plt.ylim(900, 1150);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "355fe981-8c52-4c2e-b820-7058c2ed61df",
-   "metadata": {},
-   "source": [
-    "For each of the PPS PSFs there is a `WCU` variant, which does not include the ELT spiders:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af23ffe2-7cc0-4118-96d3-c4394d7e3cbb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['pupil_masks'].change_mask(\"PPS-LM\")\n",
-    "metis['psf'].update(pupil_mask=\"PPS-LM_WCU\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a10db4e3-389d-44e0-8bb6-25140a1bcbd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a51fd43d-c331-4852-b8ad-9b0987e23cc1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "implane = metis.image_planes[0].data\n",
-    "plt.imshow(implane - np.median(implane), norm='log', origin='lower')\n",
-    "plt.xlim(900, 1150)\n",
-    "plt.ylim(900, 1150);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e5c474e-2455-4cae-baec-8ee1bf2f1569",
-   "metadata": {},
-   "source": [
-    "## A problem with spectroscopy\n",
-    "The PSF files (as provided by Roy van Boekel) have many extensions with the PSF computed at various wavelengths. This has uncovered a problem in the spectroscopic modes which results in a number of dark lines and pixels at wavelengths where Scopesim transitions from one PSF extension to the next (the segmentation in wavelength is evident by the ample output in the simulation below). This will hopefully be fixed soon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e79a958-2c6e-4b08-85f2-95cd3b1b5db3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"wcu_lss_n\"])\n",
-    "metis = sim.OpticalTrain(cmd)\n",
-    "metis['wcu_source'].set_fpmask(\"pinhole_n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5eba59f2-216e-4954-9b9c-d265d208758e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d2e363dc-8eac-472e-9c13-8a169cf8532c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "implane = metis.image_planes[0].data\n",
-    "plt.imshow(implane, origin='lower');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "750b71d3-2438-4fad-b221-305386f7af4c",
-   "metadata": {},
-   "source": [
-    "Note that the trace of the pinhole is not visible in this figure due to the high background. Subtracting a WCU_OFF simulation (after setting `metis['wcu_source'].set_bb_aperture(0.)`) would make it visible."
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md b/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
new file mode 100644
index 00000000..ff65c110
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
@@ -0,0 +1,130 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+This notebook is targeted at the AIT team who use Scopesim to simulate observations with the warm calibration unit (WCU). It demonstrates how to select point-spread functions that correspond to a pupil-mask that is inserted in the optical path. This is an interplay between two effects in ScopeSim, an instance of `PupilMaskWheel` and an instance of `FieldConstantPSF`. Note that the instrument configuration for ScopeSim currently has a single pupil-mask wheel which is placed in the WCU. This differs from the real instrument, which will have a pupil-mask wheel in both imager arms as well as one in the common fore optics (CFO), and means that the functionality presented here can only be used with the WCU modes. The sky modes still use the SCAO PSFs as they always did. 
+
+```{code-cell} ipython3
+import scopesim as sim
+```
+
+```{code-cell} ipython3
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+
+# If you haven't got the instrument packages yet, uncomment the following line.
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+By default Scopesim loads one of the PPS masks with their corresponding PSF. The PSF files are stored on the Astar server in Vienna; they are automatically downloaded if they are not available locally (either in the download cache folder or in the subdirectory `./METIS_psfs`). For imaging in the LM band, do
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["wcu_img_lm"])
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+We can inspect the two effects to check that they agree and to find out the location of the psf file that is used.
+
+```{code-cell} ipython3
+print(metis['pupil_masks'])
+print(metis['psf'])
+```
+
+Change the WCU focal-plane mask to a pinhole to check that the correct PSF is used.
+
+```{code-cell} ipython3
+metis['wcu_source'].set_fpmask("pinhole_lm")
+```
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt
+import numpy as np
+```
+
+```{code-cell} ipython3
+implane = metis.image_planes[0].data
+plt.imshow(implane - np.median(implane), norm='log', origin='lower')
+plt.xlim(900, 1150)
+plt.ylim(900, 1150);
+```
+
+## Changing the pupil mask
+The pupil mask can be changed using the `change_mask` method of the pupil mask effect. Unfortunately, the PSF is not changed automatically due to a short-coming in ScopeSim (effects do not talk to each other...). ScopeSim does tell you, however, what you need to do:
+
+```{code-cell} ipython3
+metis['pupil_masks'].change_mask("APP-LM")
+```
+
+```{code-cell} ipython3
+metis['psf'].update(pupil_mask="APP-LM")
+```
+
+```{code-cell} ipython3
+print(metis['pupil_masks'])
+print(metis['psf'])
+```
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+implane = metis.image_planes[0].data
+plt.imshow(implane - np.median(implane), norm='log', origin='lower')
+plt.xlim(900, 1150)
+plt.ylim(900, 1150);
+```
+
+For each of the PPS PSFs there is a `WCU` variant, which does not include the ELT spiders:
+
+```{code-cell} ipython3
+metis['pupil_masks'].change_mask("PPS-LM")
+metis['psf'].update(pupil_mask="PPS-LM_WCU")
+```
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+implane = metis.image_planes[0].data
+plt.imshow(implane - np.median(implane), norm='log', origin='lower')
+plt.xlim(900, 1150)
+plt.ylim(900, 1150);
+```
+
+## A problem with spectroscopy
+The PSF files (as provided by Roy van Boekel) have many extensions with the PSF computed at various wavelengths. This has uncovered a problem in the spectroscopic modes which results in a number of dark lines and pixels at wavelengths where Scopesim transitions from one PSF extension to the next (the segmentation in wavelength is evident by the ample output in the simulation below). This will hopefully be fixed soon.
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["wcu_lss_n"])
+metis = sim.OpticalTrain(cmd)
+metis['wcu_source'].set_fpmask("pinhole_n")
+```
+
+```{code-cell} ipython3
+metis.observe()
+```
+
+```{code-cell} ipython3
+implane = metis.image_planes[0].data
+plt.imshow(implane, origin='lower');
+```
+
+Note that the trace of the pinhole is not visible in this figure due to the high background. Subtracting a WCU_OFF simulation (after setting `metis['wcu_source'].set_bb_aperture(0.)`) would make it visible.
diff --git a/METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb b/METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb
deleted file mode 100644
index 39f98ca6..00000000
--- a/METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb
+++ /dev/null
@@ -1,290 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fe7880aa",
-   "metadata": {},
-   "source": [
-    "# Rectification\n",
-    "\n",
-    "This notebook demonstrates how to rectify a detector readout from a spectroscopic simulation using Scopesim. \"Rectification\" means the transformation of a spectral trace from the detector onto a rectangular pixel grid of wavelength and spatial position along the slit. Wavelength calibration and rectification are major tasks of the spectroscopic data-reduction pipeline. For convenience, Scopesim includes functionality to perform these tasks by reversing the *known* mapping that was used for the simulation, resulting in easily analysable 2D spectra that include all the noise and background components but neglect the uncertainties of a wavelength calibration as it would be performed during the reduction of real data. \n",
-    "Rectification is demonstrated on a METIS long-slit simulation, but the procedure applies to MICADO spectroscopic simulations as well (but more expensive to simulate and rectify). METIS IFU simulations have to be treated differently. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b7e93cd-e320-431e-80e6-8686abf88c75",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from astropy import units as u\n",
-    "from astropy.io import fits\n",
-    "from astropy.wcs import WCS\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from synphot import SourceSpectrum, Empirical1D\n",
-    "from scopesim_templates.micado import flatlamp\n",
-    "import scopesim as sim\n",
-    "\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7b2a57b",
-   "metadata": {},
-   "source": [
-    "If you haven’t got the instrument packages yet, uncomment the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "40add2a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66118fe8",
-   "metadata": {},
-   "source": [
-    "## Creation of a source - lamp with equally spaced lines"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd09d911",
-   "metadata": {},
-   "source": [
-    "As an example, we use a calibration lamp with equally spaced and equally strong emission lines, covering the L band. The line list is turned into a spectrum by placing a narrow Gaussian at each line position. To simulate the lamp, we (ab)use the `flatlamp` function and replace the default spectrum (a black body) by the line spectrum."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2cd445b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lines = np.arange(2.8, 4.2, 0.1)\n",
-    "\n",
-    "wave = np.linspace(2.8, 4.2, 4096)\n",
-    "flux = np.zeros_like(wave)\n",
-    "sigma = 0.0005\n",
-    "for line in lines:\n",
-    "    flux += 0.0003 * np.exp(-(wave - line)**2 / (2 * sigma**2))\n",
-    "\n",
-    "spec = SourceSpectrum(Empirical1D, points=wave*u.um, lookup_table=flux)\n",
-    "\n",
-    "src_linelamp = flatlamp()\n",
-    "src_linelamp.fields[0].spectra[0] = spec     # NB: Do not try to set src_linelamp.spectra[0], this has no effect. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0adc8671",
-   "metadata": {},
-   "source": [
-    "## Simulation of an observation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d84af6f1",
-   "metadata": {},
-   "source": [
-    "We use METIS in the L-band long-slit spectroscopic mode, using a fairly narrow slit. We explicitely request the realistic spectral mapping with non-linear dispersion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "44adab0e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmds = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"],\n",
-    "                       properties={\"!OBS.trace_file\": \"TRACE_LSS_L.fits\",\n",
-    "                                   \"!OBS.slit\": \"B-28_6\"})\n",
-    "\n",
-    "metis = sim.OpticalTrain(cmds)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5732f705",
-   "metadata": {},
-   "source": [
-    "We exclude atmospheric emission (and absorption) and the telescope optics as is appropriate for a calibration-lamp observation. As the source fills the slit homogeneously a PSF convolution should have no effect on the result. Excluding PSF convolution cuts down significantly on computation time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d61f1c7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"skycalc_atmosphere\"].include = False\n",
-    "metis[\"telescope_reflection\"].include = False\n",
-    "metis[\"psf\"].include = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ccafc4c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis.observe(src_linelamp, update=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fd86db6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readout = metis.readout(exptime=1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54b7719d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(readout[1].data, origin=\"lower\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c035912b",
-   "metadata": {},
-   "source": [
-    "## Rectification of the spectrum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43f5145d",
-   "metadata": {},
-   "source": [
-    "The non-linearity in the dispersion in METIS is small and not readily apparent.Still, rectification is necessary to arrive at a 2D spectrum with well-defined wavelength and spatial coordinates. The method to use is `rectify_traces` and belongs to the `SpectralTraceList` effect, which is accessible in the METIS `OpticalTrain` as `\"spectral_traces\"` (in MICADO it would be `\"micado_spectral_traces\"`. Currently, it is necessary to specify the spatial extent of the slit when calling the method. The long slit in METIS has a length of 8 arcsec and extends from -4 arcsec to +4 arcsec."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c17c9c04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tracelist = metis[\"spectral_traces\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1649e402",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rectified = tracelist.rectify_traces(readout, -4.0, 4.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49469101",
-   "metadata": {},
-   "source": [
-    "`rectified` is a HDU list with one extension for each spectral trace - for METIS there's only one trace, for MICADO there would be several. Each extension has a WCS that translates pixel coordinates into wavelength and position along the slit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "df3b5a30",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wcs = WCS(rectified[1])\n",
-    "naxis1 = rectified[1].header[\"NAXIS1\"]\n",
-    "naxis2 = rectified[1].header[\"NAXIS2\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cbd79196",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lam = (wcs.all_pix2world(np.arange(naxis1), 800, 0)[0] * u.Unit(wcs.wcs.cunit[0])).to(u.um).value\n",
-    "xi = (wcs.all_pix2world(1000, np.arange(naxis2), 0)[1] * u.Unit(wcs.wcs.cunit[1])).to(u.arcsec).value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d9827b49",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(rectified[1].data, origin=\"lower\", extent=[lam[0], lam[-1], xi[0], xi[-1]])\n",
-    "plt.gca().set_aspect(\"auto\")\n",
-    "plt.xlabel(r\"Wavelength [$\\mu$m]\")\n",
-    "plt.ylabel(r\"Spatial position along slit [arcsec]\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76b8c5c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "i1, i2 = 120, 620\n",
-    "plt.figure(figsize=(15, 7))\n",
-    "plt.plot(lam[i1:i2], rectified[1].data[800, i1:i2], label=\"single row\")\n",
-    "plt.plot(lam[i1:i2], rectified[1].data.mean(axis=0)[i1:i2], label=\"average\")\n",
-    "plt.legend()\n",
-    "plt.xlabel(r\"Wavelength [$\\mu$m]\");"
-   ]
-  }
- ],
- "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": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_rectify_traces.md b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
new file mode 100644
index 00000000..77597e54
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
@@ -0,0 +1,139 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Rectification
+
+This notebook demonstrates how to rectify a detector readout from a spectroscopic simulation using Scopesim. "Rectification" means the transformation of a spectral trace from the detector onto a rectangular pixel grid of wavelength and spatial position along the slit. Wavelength calibration and rectification are major tasks of the spectroscopic data-reduction pipeline. For convenience, Scopesim includes functionality to perform these tasks by reversing the *known* mapping that was used for the simulation, resulting in easily analysable 2D spectra that include all the noise and background components but neglect the uncertainties of a wavelength calibration as it would be performed during the reduction of real data. 
+Rectification is demonstrated on a METIS long-slit simulation, but the procedure applies to MICADO spectroscopic simulations as well (but more expensive to simulate and rectify). METIS IFU simulations have to be treated differently. 
+
+```{code-cell} ipython3
+import numpy as np
+
+from astropy import units as u
+from astropy.io import fits
+from astropy.wcs import WCS
+from matplotlib import pyplot as plt
+
+from synphot import SourceSpectrum, Empirical1D
+from scopesim_templates.micado import flatlamp
+import scopesim as sim
+
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven’t got the instrument packages yet, uncomment the following cell.
+
+```{code-cell} ipython3
+#sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+## Creation of a source - lamp with equally spaced lines
+
++++
+
+As an example, we use a calibration lamp with equally spaced and equally strong emission lines, covering the L band. The line list is turned into a spectrum by placing a narrow Gaussian at each line position. To simulate the lamp, we (ab)use the `flatlamp` function and replace the default spectrum (a black body) by the line spectrum.
+
+```{code-cell} ipython3
+lines = np.arange(2.8, 4.2, 0.1)
+
+wave = np.linspace(2.8, 4.2, 4096)
+flux = np.zeros_like(wave)
+sigma = 0.0005
+for line in lines:
+    flux += 0.0003 * np.exp(-(wave - line)**2 / (2 * sigma**2))
+
+spec = SourceSpectrum(Empirical1D, points=wave*u.um, lookup_table=flux)
+
+src_linelamp = flatlamp()
+src_linelamp.fields[0].spectra[0] = spec     # NB: Do not try to set src_linelamp.spectra[0], this has no effect. 
+```
+
+## Simulation of an observation
+
++++
+
+We use METIS in the L-band long-slit spectroscopic mode, using a fairly narrow slit. We explicitely request the realistic spectral mapping with non-linear dispersion.
+
+```{code-cell} ipython3
+cmds = sim.UserCommands(use_instrument="METIS", set_modes=["lss_l"],
+                       properties={"!OBS.trace_file": "TRACE_LSS_L.fits",
+                                   "!OBS.slit": "B-28_6"})
+
+metis = sim.OpticalTrain(cmds)
+```
+
+We exclude atmospheric emission (and absorption) and the telescope optics as is appropriate for a calibration-lamp observation. As the source fills the slit homogeneously a PSF convolution should have no effect on the result. Excluding PSF convolution cuts down significantly on computation time.
+
+```{code-cell} ipython3
+metis["skycalc_atmosphere"].include = False
+metis["telescope_reflection"].include = False
+metis["psf"].include = False
+```
+
+```{code-cell} ipython3
+metis.observe(src_linelamp, update=True)
+```
+
+```{code-cell} ipython3
+readout = metis.readout(exptime=1)[0]
+```
+
+```{code-cell} ipython3
+plt.imshow(readout[1].data, origin="lower");
+```
+
+## Rectification of the spectrum
+
++++
+
+The non-linearity in the dispersion in METIS is small and not readily apparent.Still, rectification is necessary to arrive at a 2D spectrum with well-defined wavelength and spatial coordinates. The method to use is `rectify_traces` and belongs to the `SpectralTraceList` effect, which is accessible in the METIS `OpticalTrain` as `"spectral_traces"` (in MICADO it would be `"micado_spectral_traces"`. Currently, it is necessary to specify the spatial extent of the slit when calling the method. The long slit in METIS has a length of 8 arcsec and extends from -4 arcsec to +4 arcsec.
+
+```{code-cell} ipython3
+tracelist = metis["spectral_traces"]
+```
+
+```{code-cell} ipython3
+rectified = tracelist.rectify_traces(readout, -4.0, 4.0)
+```
+
+`rectified` is a HDU list with one extension for each spectral trace - for METIS there's only one trace, for MICADO there would be several. Each extension has a WCS that translates pixel coordinates into wavelength and position along the slit.
+
+```{code-cell} ipython3
+wcs = WCS(rectified[1])
+naxis1 = rectified[1].header["NAXIS1"]
+naxis2 = rectified[1].header["NAXIS2"]
+```
+
+```{code-cell} ipython3
+lam = (wcs.all_pix2world(np.arange(naxis1), 800, 0)[0] * u.Unit(wcs.wcs.cunit[0])).to(u.um).value
+xi = (wcs.all_pix2world(1000, np.arange(naxis2), 0)[1] * u.Unit(wcs.wcs.cunit[1])).to(u.arcsec).value
+```
+
+```{code-cell} ipython3
+plt.imshow(rectified[1].data, origin="lower", extent=[lam[0], lam[-1], xi[0], xi[-1]])
+plt.gca().set_aspect("auto")
+plt.xlabel(r"Wavelength [$\mu$m]")
+plt.ylabel(r"Spatial position along slit [arcsec]");
+```
+
+```{code-cell} ipython3
+i1, i2 = 120, 620
+plt.figure(figsize=(15, 7))
+plt.plot(lam[i1:i2], rectified[1].data[800, i1:i2], label="single row")
+plt.plot(lam[i1:i2], rectified[1].data.mean(axis=0)[i1:i2], label="average")
+plt.legend()
+plt.xlabel(r"Wavelength [$\mu$m]");
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.ipynb b/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.ipynb
deleted file mode 100644
index 66778ecc..00000000
--- a/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.ipynb
+++ /dev/null
@@ -1,301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1e941464-1404-46ab-a397-80a5ebee1cdf",
-   "metadata": {},
-   "source": [
-    "The METIS detectors have a number of masked rows and pixels around their borders. These pixels do not see any signal but do have dark current, readout noise and other detector effects. This notebook shows what the effect `ReferencePixelBorder` does and how it is set up for METIS. It also explains how the dimensions of the mask can be changed and of course how the mask can be switched off entirely."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4e714c2-78d5-4b59-ac37-b543361931f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a60f1fb-6829-432b-a175-f636f0a65306",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scopesim as sim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bd5bfa01-10a2-4b89-9ee6-0cf3803bb7a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Edit this path if you have a custom install directorym otherwise comment it out\n",
-    "sim.link_irdb(\"../../../../\")\n",
-    "\n",
-    "# If you haven't got the instrument packages yet, uncomment the following line\n",
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd1a598c-be59-4be9-99a4-f931de79593e",
-   "metadata": {},
-   "source": [
-    "## Imager detectors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f44877ee-ea96-4272-8675-414b4eefa44f",
-   "metadata": {},
-   "source": [
-    "The Imager detectors (H2RG for IMG-LM, Geosnap for IMG-N) have equal width masks all around, with 64 pixels for the H2RG and 28 pixels for the Geosnap. We look at the H2RG here and simulate a WCU flat field for simplicity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7e1959ef-5d74-4adc-a1fd-f4abb8c77aff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"wcu_img_lm\"])\n",
-    "metis = sim.OpticalTrain(cmd)\n",
-    "metis.observe()\n",
-    "readout = metis.readout(ndit=1, dit=1.)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db135971-904e-4efb-aa8c-dab38ecc1553",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
-    "ax1.imshow(readout[1].data)\n",
-    "ax1.set_title(\"IMG-LM flat field\")\n",
-    "ax2.plot(readout[1].data[250,])\n",
-    "ax2.set_title(\"Row 250\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9c3d3b2-6763-4603-ac7c-9819e92462ef",
-   "metadata": {},
-   "source": [
-    "The dimensions of the mask are recorded the header of the image extension of the readout (the order is **b**ottom, **l**eft, **t**op, **r**ight):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "487a48ed-07b0-4582-b36e-e125d11d02f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(readout[1].header[\"HIERARCH ESO DET1 CHIP REFPIX\"])               # keyword value itself\n",
-    "print(readout[1].header.comments[\"HIERARCH ESO DET1 CHIP REFPIX\"])      # keyword comment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6357f23-b282-4115-83c0-661715e64097",
-   "metadata": {},
-   "source": [
-    "To demonstrate that the masked pixels indeed get dark current, we increase the latter to an appreciable value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66c39cb3-c635-47c9-b45d-b6432cade4c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"dark_current\"].meta[\"value\"] = 10000.\n",
-    "readout = metis.readout(ndit=1, dit=1.)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "096020fc-1a2b-48b7-8174-28f62c784190",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
-    "ax1.imshow(readout[1].data)\n",
-    "ax1.set_title(\"IMG-LM flat field\")\n",
-    "ax2.plot(readout[1].data[250,])\n",
-    "ax2.set_ylim(0, 26000)\n",
-    "ax2.set_ylabel(\"ADU\")\n",
-    "ax2.set_title(\"Row 250\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1943d886-f425-4ec5-a44b-5a28a684a638",
-   "metadata": {},
-   "source": [
-    "Note that with a gain of 4 e/ADU, the reference level of 2500 ADU corresponds to 10000 electrons, as expected."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74d14fff-1335-49d8-a052-40c95ca984c1",
-   "metadata": {},
-   "source": [
-    "## LMS detector array\n",
-    "\n",
-    "The LMS detector array consists of 4 H2RG detectors. In the y-direction all detectors have 32 reference rows at the bottom and top. In the x-direction (the dispersion direction) only the outsides are masked with 64 columns, while there are no reference pixels on the inside. (The simulation takes several minutes.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b6048c19-cd3e-40a1-ac50-ea9a4ec31057",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"wcu_lms\"])\n",
-    "metis = sim.OpticalTrain(cmd)\n",
-    "metis['psf'].include = False    # saves some simulation time\n",
-    "metis.observe()\n",
-    "readout = metis.readout(ndit=1, dit=300)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "155d8739-8d6c-4f71-af4b-67a6cbc04242",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2, 2, layout=\"tight\", sharex=True, sharey=True)\n",
-    "ax1.imshow(readout[1].data)\n",
-    "ax2.imshow(readout[2].data)\n",
-    "ax3.imshow(readout[3].data)\n",
-    "ax4.imshow(readout[4].data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f459223-3ddf-4ecf-9606-a8b5aaaf337c",
-   "metadata": {},
-   "source": [
-    "## Modifying the behaviour of `ReferencePixelBorder`\n",
-    "When setting up the instrument, the detector mask can be defined with the parameter `!DET.border`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c002ffa9-5769-465f-8b05-064b2af4b683",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"wcu_img_lm\"])\n",
-    "cmd[\"!DET.border\"] = [0, 40, 80, 120]   # bottom left top right\n",
-    "metis = sim.OpticalTrain(cmd)\n",
-    "metis.observe()\n",
-    "readout = metis.readout(dit=1., ndit=1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad2a1ae9-1d6f-4cb8-94bd-776e79085836",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(readout[1].data, origin='lower')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "763cbbb5-bcd4-404f-94ff-1aaf6889b149",
-   "metadata": {},
-   "source": [
-    "When the `OpticalTrain` already exists and has `observe`d a scene, the mask can be modified by changing the effect parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "799e53a4-3f64-43bb-8d4d-5fa180f8d729",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['reference_pixel_mask'].meta[\"border\"] = [300, 300, 300, 300]\n",
-    "readout = metis.readout(dit=1, ndit=1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a67c02e-d7f4-4beb-b479-973de6624f22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(readout[1].data, origin='lower')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd892e89-05bf-4c53-8f74-d5dd72d5cb79",
-   "metadata": {},
-   "source": [
-    "As always, the mask can be switched off entirely by setting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "340dcb82-abda-44ca-b4da-4703c21a16a4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis['reference_pixel_mask'].include = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "49d8cd62-912c-4756-b8de-99bcd31f378f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readout = metis.readout(dit=1., ndit=1)[0]\n",
-    "plt.imshow(readout[1].data, origin='lower')"
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md b/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
new file mode 100644
index 00000000..8c115987
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
@@ -0,0 +1,136 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+The METIS detectors have a number of masked rows and pixels around their borders. These pixels do not see any signal but do have dark current, readout noise and other detector effects. This notebook shows what the effect `ReferencePixelBorder` does and how it is set up for METIS. It also explains how the dimensions of the mask can be changed and of course how the mask can be switched off entirely.
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt
+```
+
+```{code-cell} ipython3
+import scopesim as sim
+```
+
+```{code-cell} ipython3
+# Edit this path if you have a custom install directorym otherwise comment it out
+sim.link_irdb("../../../../")
+
+# If you haven't got the instrument packages yet, uncomment the following line
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+## Imager detectors
+
++++
+
+The Imager detectors (H2RG for IMG-LM, Geosnap for IMG-N) have equal width masks all around, with 64 pixels for the H2RG and 28 pixels for the Geosnap. We look at the H2RG here and simulate a WCU flat field for simplicity.
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["wcu_img_lm"])
+metis = sim.OpticalTrain(cmd)
+metis.observe()
+readout = metis.readout(ndit=1, dit=1.)[0]
+```
+
+```{code-cell} ipython3
+_, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+ax1.imshow(readout[1].data)
+ax1.set_title("IMG-LM flat field")
+ax2.plot(readout[1].data[250,])
+ax2.set_title("Row 250");
+```
+
+The dimensions of the mask are recorded the header of the image extension of the readout (the order is **b**ottom, **l**eft, **t**op, **r**ight):
+
+```{code-cell} ipython3
+print(readout[1].header["HIERARCH ESO DET1 CHIP REFPIX"])               # keyword value itself
+print(readout[1].header.comments["HIERARCH ESO DET1 CHIP REFPIX"])      # keyword comment
+```
+
+To demonstrate that the masked pixels indeed get dark current, we increase the latter to an appreciable value.
+
+```{code-cell} ipython3
+metis["dark_current"].meta["value"] = 10000.
+readout = metis.readout(ndit=1, dit=1.)[0]
+```
+
+```{code-cell} ipython3
+_, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+ax1.imshow(readout[1].data)
+ax1.set_title("IMG-LM flat field")
+ax2.plot(readout[1].data[250,])
+ax2.set_ylim(0, 26000)
+ax2.set_ylabel("ADU")
+ax2.set_title("Row 250");
+```
+
+Note that with a gain of 4 e/ADU, the reference level of 2500 ADU corresponds to 10000 electrons, as expected.
+
++++
+
+## LMS detector array
+
+The LMS detector array consists of 4 H2RG detectors. In the y-direction all detectors have 32 reference rows at the bottom and top. In the x-direction (the dispersion direction) only the outsides are masked with 64 columns, while there are no reference pixels on the inside. (The simulation takes several minutes.)
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["wcu_lms"])
+metis = sim.OpticalTrain(cmd)
+metis['psf'].include = False    # saves some simulation time
+metis.observe()
+readout = metis.readout(ndit=1, dit=300)[0]
+```
+
+```{code-cell} ipython3
+_, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2, 2, layout="tight", sharex=True, sharey=True)
+ax1.imshow(readout[1].data)
+ax2.imshow(readout[2].data)
+ax3.imshow(readout[3].data)
+ax4.imshow(readout[4].data)
+```
+
+## Modifying the behaviour of `ReferencePixelBorder`
+When setting up the instrument, the detector mask can be defined with the parameter `!DET.border`:
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["wcu_img_lm"])
+cmd["!DET.border"] = [0, 40, 80, 120]   # bottom left top right
+metis = sim.OpticalTrain(cmd)
+metis.observe()
+readout = metis.readout(dit=1., ndit=1)[0]
+```
+
+```{code-cell} ipython3
+plt.imshow(readout[1].data, origin='lower')
+```
+
+When the `OpticalTrain` already exists and has `observe`d a scene, the mask can be modified by changing the effect parameter:
+
+```{code-cell} ipython3
+metis['reference_pixel_mask'].meta["border"] = [300, 300, 300, 300]
+readout = metis.readout(dit=1, ndit=1)[0]
+```
+
+```{code-cell} ipython3
+plt.imshow(readout[1].data, origin='lower')
+```
+
+As always, the mask can be switched off entirely by setting
+
+```{code-cell} ipython3
+metis['reference_pixel_mask'].include = False
+```
+
+```{code-cell} ipython3
+readout = metis.readout(dit=1., ndit=1)[0]
+plt.imshow(readout[1].data, origin='lower')
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_skycalc.ipynb b/METIS/docs/example_notebooks/demos/demo_skycalc.ipynb
deleted file mode 100644
index 819c3465..00000000
--- a/METIS/docs/example_notebooks/demos/demo_skycalc.ipynb
+++ /dev/null
@@ -1,266 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "54686e5f-f108-4633-bf8d-d13205e07f92",
-   "metadata": {},
-   "source": [
-    "# The `SkycalcTERCurve` effect in ScopeSim\n",
-    "\n",
-    "This notebook demonstrates how to work with the skycalc effect, `SkycalcTERCurve`. This effect provides the atmospheric emission and transmission that are applied to a source object in ScopeSim. The effect is based on `skycalc_ipy`, a python wrapper to ESO’s SkyCalc tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c01ec5f-9e28-41f2-b047-8e4aef3e1b88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "from astropy import units as u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c64d3cf-75f7-4174-b1c7-6bb202b58fe6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scopesim as sim\n",
-    "\n",
-    "# If you have not done so already, please download the relevant instrument packages by uncommenting the following line:\n",
-    "# sim.download_packages([\"Armazones\", \"ELT\", \"METIS\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "431dfc2a-a822-43fc-8bdb-f7762b8b6583",
-   "metadata": {},
-   "source": [
-    "We use the `SkycalcTERCurve` in the context of METIS/LMS, although sky mode of any other instrument would do as well. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27271168-1d50-4fa2-8970-ae5caca56a87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lms\"])\n",
-    "cmd[\"!OBS.wavelen\"] = 3.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2f28719-eeb7-41f9-8ee7-d748d39ea5dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04d263d1-306a-4a5a-8625-34471fea65d8",
-   "metadata": {},
-   "source": [
-    "The METIS optical train includes the `SkycalcTERCurve` effect under the name `skycalc_atmosphere`. We assign it to a variable for convenience."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8d65b08f-5384-456e-b6da-696eb2c8740a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sky = metis['skycalc_atmosphere']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "991e9b1c-00e5-4622-a813-542c90887664",
-   "metadata": {},
-   "source": [
-    "## Inspecting and modifying sky parameters\n",
-    "\n",
-    "The list of parameters that are sent to the ESO skycalc server when requesting a sky spectrum can be obtained by simply printing the effect object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c8d3eac4-af5d-4cfc-aa15-20476fbb3ccf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(sky)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9136d40-f4ae-420c-bb48-3f12ab8215b7",
-   "metadata": {},
-   "source": [
-    "The emission and transmission spectra are returned as columns in `sky.skycalc_table` and can be plotted as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "85df87ea-d8a4-4bf9-8638-bec13981a99c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tbl = sky.skycalc_table\n",
-    "\n",
-    "# Extract columns and restrict to a smaller wavelength range\n",
-    "wavelength = tbl['wavelength'].to(u.um)\n",
-    "idx = (wavelength > 3.4*u.um) * (wavelength < 3.6*u.um)\n",
-    "transmission = tbl['transmission']\n",
-    "emission = tbl['emission']\n",
-    "\n",
-    "_, axes = plt.subplots(2, 1, figsize=(8, 8), sharex=True)\n",
-    "axes[0].plot(wavelength[idx], transmission[idx])\n",
-    "axes[0].set_ylabel(\"Transmission\")\n",
-    "axes[0].set_xlim(3.4, 3.6)\n",
-    "axes[1].plot(wavelength[idx], emission[idx])\n",
-    "axes[1].set_xlabel(f\"Wavelength [{wavelength.unit}]\")\n",
-    "axes[1].set_ylabel(f\"Radiance [{emission.unit}]\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5e5a8fc-175e-444a-a827-afdf4aa2f675",
-   "metadata": {},
-   "source": [
-    "The effect has an `update()` method that can be used to change parameter values. The names of the available parameters can be obtained from the list printed above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "099a72d2-09d6-4b0c-8e6f-73a54945adcd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sky.update(airmass=3., pwv=20.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f8b1fddf-ac55-4742-8391-34b1a0799153",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tbl2 = sky.skycalc_table\n",
-    "\n",
-    "# Extract columns and restrict to a smaller wavelength range\n",
-    "wavelength2 = tbl2['wavelength'].to(u.um)\n",
-    "idx2 = (wavelength2 > 3.4*u.um) * (wavelength2 < 3.6*u.um)\n",
-    "transmission2 = tbl2['transmission']\n",
-    "emission2 = tbl2['emission']\n",
-    "\n",
-    "_, axes = plt.subplots(2, 1, figsize=(8, 8), sharex=True)\n",
-    "axes[0].plot(wavelength[idx], transmission[idx], lw=1)\n",
-    "axes[0].plot(wavelength2[idx2], transmission2[idx2], lw=1)\n",
-    "axes[0].set_ylabel(\"Transmission\")\n",
-    "axes[0].set_xlim(3.4, 3.6)\n",
-    "axes[1].plot(wavelength[idx], emission[idx], lw=1)\n",
-    "axes[1].plot(wavelength2[idx2], emission2[idx2], lw=1)\n",
-    "axes[1].set_xlabel(f\"Wavelength [{wavelength.unit}]\")\n",
-    "axes[1].set_ylabel(f\"Radiance [{emission.unit}]\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9f76808-b785-4201-874c-3a74ff8c2431",
-   "metadata": {},
-   "source": [
-    "## Use in simulations\n",
-    "As an example of how the sky parameters affect observations we simulate METIS N-band images for a number of values of PWV (precipitable water vapour) and record the median background level in the (noiseless) `ImagePlane`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f4c1421-b576-4242-b342-c920e621beb7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_n\"])\n",
-    "metis = sim.OpticalTrain(cmd)\n",
-    "metis['detector_linearity'].include = False    # to avoid saturation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "547b5167-deb9-4611-b5d5-adfdfa59e160",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sky = metis['skycalc_atmosphere']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c736631-1935-4624-b2bc-89e5c1723502",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pwv_values = [0.5, 5, 10, 20]\n",
-    "bg_level = []\n",
-    "plt.figure()\n",
-    "lam = np.linspace(7, 12, 1001) * u.um\n",
-    "for pwv in pwv_values:\n",
-    "    sky.update(pwv=pwv)\n",
-    "    plt.plot(sky.surface.table['wavelength'].to(u.um), sky.surface.table['emission'], label=f\"pwv = {pwv}\", alpha=0.7) \n",
-    "    metis.observe()\n",
-    "    bg_level.append(np.median(metis.image_planes[0].data))\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e03144ee-156d-4150-849d-80889e1b83f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(pwv_values, bg_level, '-o')\n",
-    "plt.ylim(0, 3e8)\n",
-    "plt.xlabel(\"PWV [mm]\")\n",
-    "plt.ylabel(\"ImagePlane level [e/s]\");"
-   ]
-  }
- ],
- "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_skycalc.md b/METIS/docs/example_notebooks/demos/demo_skycalc.md
new file mode 100644
index 00000000..bab79421
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_skycalc.md
@@ -0,0 +1,133 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# The `SkycalcTERCurve` effect in ScopeSim
+
+This notebook demonstrates how to work with the skycalc effect, `SkycalcTERCurve`. This effect provides the atmospheric emission and transmission that are applied to a source object in ScopeSim. The effect is based on `skycalc_ipy`, a python wrapper to ESO’s SkyCalc tool.
+
+```{code-cell} ipython3
+import numpy as np
+from matplotlib import pyplot as plt
+from astropy import units as u
+```
+
+```{code-cell} ipython3
+import scopesim as sim
+
+# If you have not done so already, please download the relevant instrument packages by uncommenting the following line:
+# sim.download_packages(["Armazones", "ELT", "METIS"])
+```
+
+We use the `SkycalcTERCurve` in the context of METIS/LMS, although sky mode of any other instrument would do as well. 
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["lms"])
+cmd["!OBS.wavelen"] = 3.5
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+The METIS optical train includes the `SkycalcTERCurve` effect under the name `skycalc_atmosphere`. We assign it to a variable for convenience.
+
+```{code-cell} ipython3
+sky = metis['skycalc_atmosphere']
+```
+
+## Inspecting and modifying sky parameters
+
+The list of parameters that are sent to the ESO skycalc server when requesting a sky spectrum can be obtained by simply printing the effect object:
+
+```{code-cell} ipython3
+print(sky)
+```
+
+The emission and transmission spectra are returned as columns in `sky.skycalc_table` and can be plotted as follows:
+
+```{code-cell} ipython3
+tbl = sky.skycalc_table
+
+# Extract columns and restrict to a smaller wavelength range
+wavelength = tbl['wavelength'].to(u.um)
+idx = (wavelength > 3.4*u.um) * (wavelength < 3.6*u.um)
+transmission = tbl['transmission']
+emission = tbl['emission']
+
+_, axes = plt.subplots(2, 1, figsize=(8, 8), sharex=True)
+axes[0].plot(wavelength[idx], transmission[idx])
+axes[0].set_ylabel("Transmission")
+axes[0].set_xlim(3.4, 3.6)
+axes[1].plot(wavelength[idx], emission[idx])
+axes[1].set_xlabel(f"Wavelength [{wavelength.unit}]")
+axes[1].set_ylabel(f"Radiance [{emission.unit}]");
+```
+
+The effect has an `update()` method that can be used to change parameter values. The names of the available parameters can be obtained from the list printed above.
+
+```{code-cell} ipython3
+sky.update(airmass=3., pwv=20.)
+```
+
+```{code-cell} ipython3
+tbl2 = sky.skycalc_table
+
+# Extract columns and restrict to a smaller wavelength range
+wavelength2 = tbl2['wavelength'].to(u.um)
+idx2 = (wavelength2 > 3.4*u.um) * (wavelength2 < 3.6*u.um)
+transmission2 = tbl2['transmission']
+emission2 = tbl2['emission']
+
+_, axes = plt.subplots(2, 1, figsize=(8, 8), sharex=True)
+axes[0].plot(wavelength[idx], transmission[idx], lw=1)
+axes[0].plot(wavelength2[idx2], transmission2[idx2], lw=1)
+axes[0].set_ylabel("Transmission")
+axes[0].set_xlim(3.4, 3.6)
+axes[1].plot(wavelength[idx], emission[idx], lw=1)
+axes[1].plot(wavelength2[idx2], emission2[idx2], lw=1)
+axes[1].set_xlabel(f"Wavelength [{wavelength.unit}]")
+axes[1].set_ylabel(f"Radiance [{emission.unit}]");
+```
+
+## Use in simulations
+As an example of how the sky parameters affect observations we simulate METIS N-band images for a number of values of PWV (precipitable water vapour) and record the median background level in the (noiseless) `ImagePlane`.
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_n"])
+metis = sim.OpticalTrain(cmd)
+metis['detector_linearity'].include = False    # to avoid saturation
+```
+
+```{code-cell} ipython3
+sky = metis['skycalc_atmosphere']
+```
+
+```{code-cell} ipython3
+pwv_values = [0.5, 5, 10, 20]
+bg_level = []
+plt.figure()
+lam = np.linspace(7, 12, 1001) * u.um
+for pwv in pwv_values:
+    sky.update(pwv=pwv)
+    plt.plot(sky.surface.table['wavelength'].to(u.um), sky.surface.table['emission'], label=f"pwv = {pwv}", alpha=0.7) 
+    metis.observe()
+    bg_level.append(np.median(metis.image_planes[0].data))
+plt.legend();
+```
+
+```{code-cell} ipython3
+plt.plot(pwv_values, bg_level, '-o')
+plt.ylim(0, 3e8)
+plt.xlabel("PWV [mm]")
+plt.ylabel("ImagePlane level [e/s]");
+```
diff --git a/METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb b/METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb
deleted file mode 100644
index 45b932a7..00000000
--- a/METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb
+++ /dev/null
@@ -1,182 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Slit Wheel\n",
-    "\n",
-    "This notebook demonstrates how to use the various slits in METIS. They are defined in a `SlitWheel` effect, which works in the same way as `FilterWheel`. The notebook uses imaging mode to show the slits directly. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "import scopesim as sim\n",
-    "sim.bug_report()\n",
-    "\n",
-    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
-    "sim.link_irdb(\"../../../../\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't got the instrument packages yet, uncomment the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_lm\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In imaging mode, `\"!OBS.slit\"` is `false` by default, i.e. there is no slit in the path. However, slits can be used in imaging as well by setting `!OBS.slit` to one of the slits available in the METIS package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmd[\"!OBS.slit\"] = \"C-38_1\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis = sim.OpticalTrain(cmd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following slits are now available and can be selected with `metis[\"slit_wheel\"].change_slit()` as demonstrated below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"slit_wheel\"].get_table()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "implanes = dict()\n",
-    "for slit in metis[\"slit_wheel\"].slits:\n",
-    "    metis[\"slit_wheel\"].change_slit(slit)\n",
-    "    metis.observe()\n",
-    "    implanes[slit] = metis.image_planes[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(15, 5))\n",
-    "for i, slit in enumerate(metis[\"slit_wheel\"].slits):\n",
-    "    plt.subplot(2, 3, i+1)\n",
-    "    plt.imshow(implanes[slit][600:1450,], origin=\"lower\")\n",
-    "    plt.title(\"Slit \" + slit)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding a slit to the slit wheel\n",
-    "\n",
-    "The slit wheel holds a number of default slits (defined by the configuration for the instrument used). A custom slit can be added using the method `add_slit`. A \"slit\" is an object of class `ApertureMask` and the various methods for instantiating such an object can be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newslit = sim.effects.ApertureMask(\n",
-    "    name=\"Square\",\n",
-    "    array_dict={\n",
-    "        \"x\": [-1, 1, 1, -1],\n",
-    "        \"y\": [-1, -1, 1, 1]\n",
-    "    }, \n",
-    "    x_unit=\"arcsec\",\n",
-    "    y_unit=\"arcsec\",\n",
-    ")\n",
-    "metis[\"slit_wheel\"].add_slit(newslit)\n",
-    "metis[\"slit_wheel\"].get_table()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metis[\"slit_wheel\"].change_slit(\"Square\")\n",
-    "metis.observe()\n",
-    "implane = metis.image_planes[0].data\n",
-    "plt.imshow(implane[600:1450,], origin=\"lower\")\n",
-    "plt.title(\"Slit \" + metis[\"slit_wheel\"].current_slit.meta[\"name\"]);"
-   ]
-  }
- ],
- "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": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/METIS/docs/example_notebooks/demos/demo_slit_wheel.md b/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
new file mode 100644
index 00000000..7cb719c3
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
@@ -0,0 +1,94 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.19.5
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Slit Wheel
+
+This notebook demonstrates how to use the various slits in METIS. They are defined in a `SlitWheel` effect, which works in the same way as `FilterWheel`. The notebook uses imaging mode to show the slits directly. 
+
+```{code-cell} ipython3
+from matplotlib import pyplot as plt
+
+import scopesim as sim
+sim.bug_report()
+
+# Edit this path if you have a custom install directory, otherwise comment it out.
+sim.link_irdb("../../../../")
+```
+
+If you haven't got the instrument packages yet, uncomment the following cell.
+
+```{code-cell} ipython3
+# sim.download_packages(["METIS", "ELT", "Armazones"])
+```
+
+```{code-cell} ipython3
+cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
+```
+
+In imaging mode, `"!OBS.slit"` is `false` by default, i.e. there is no slit in the path. However, slits can be used in imaging as well by setting `!OBS.slit` to one of the slits available in the METIS package.
+
+```{code-cell} ipython3
+cmd["!OBS.slit"] = "C-38_1"
+```
+
+```{code-cell} ipython3
+metis = sim.OpticalTrain(cmd)
+```
+
+The following slits are now available and can be selected with `metis["slit_wheel"].change_slit()` as demonstrated below.
+
+```{code-cell} ipython3
+metis["slit_wheel"].get_table()
+```
+
+```{code-cell} ipython3
+implanes = dict()
+for slit in metis["slit_wheel"].slits:
+    metis["slit_wheel"].change_slit(slit)
+    metis.observe()
+    implanes[slit] = metis.image_planes[0].data
+```
+
+```{code-cell} ipython3
+plt.figure(figsize=(15, 5))
+for i, slit in enumerate(metis["slit_wheel"].slits):
+    plt.subplot(2, 3, i+1)
+    plt.imshow(implanes[slit][600:1450,], origin="lower")
+    plt.title("Slit " + slit)
+```
+
+## Adding a slit to the slit wheel
+
+The slit wheel holds a number of default slits (defined by the configuration for the instrument used). A custom slit can be added using the method `add_slit`. A "slit" is an object of class `ApertureMask` and the various methods for instantiating such an object can be used.
+
+```{code-cell} ipython3
+newslit = sim.effects.ApertureMask(
+    name="Square",
+    array_dict={
+        "x": [-1, 1, 1, -1],
+        "y": [-1, -1, 1, 1]
+    }, 
+    x_unit="arcsec",
+    y_unit="arcsec",
+)
+metis["slit_wheel"].add_slit(newslit)
+metis["slit_wheel"].get_table()
+```
+
+```{code-cell} ipython3
+metis["slit_wheel"].change_slit("Square")
+metis.observe()
+implane = metis.image_planes[0].data
+plt.imshow(implane[600:1450,], origin="lower")
+plt.title("Slit " + metis["slit_wheel"].current_slit.meta["name"]);
+```

From cac85306c6e0051a3094121a13be533cf00522f7 Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Mon, 17 Aug 2026 17:36:16 +0200
Subject: [PATCH 10/63] Ensure exactly one (1) H1-level (`#`) in each notebook

The additional diff comes from stripped whitespace or newlines.
---
 .../example_notebooks/demos/demo_Leiden_sky.md   |  9 +++++----
 .../example_notebooks/demos/demo_adc_wheel.md    |  5 ++---
 .../demos/demo_auto_exposure.md                  |  7 ++-----
 .../demos/demo_chopping_and_nodding.md           |  3 +--
 .../demos/demo_detector_modes.md                 | 16 ++++------------
 .../example_notebooks/demos/demo_filter_wheel.md | 10 +++++-----
 .../demos/demo_grating_efficiency.md             |  7 +++----
 .../demos/demo_inter_pixel_capacitance.md        |  5 +++--
 .../example_notebooks/demos/demo_lss_simple.md   |  9 +++------
 .../demos/demo_metis_lms_efficiency.md           |  5 -----
 .../demos/demo_metis_wcu_psfs.md                 |  3 ++-
 .../demos/demo_rectify_traces.md                 | 16 +++-------------
 .../demos/demo_reference_pixel_mask.md           |  7 +------
 .../docs/example_notebooks/demos/demo_skycalc.md |  5 ++---
 .../example_notebooks/demos/demo_slit_wheel.md   |  5 ++---
 15 files changed, 38 insertions(+), 74 deletions(-)

diff --git a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
index f2149494..2288481e 100644
--- a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
+++ b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
@@ -11,6 +11,7 @@ kernelspec:
   name: python3
 ---
 
+# Leiden sky (AIT)
 ```{code-cell} ipython3
 from matplotlib import pyplot as plt
 import numpy as np
@@ -29,7 +30,7 @@ sim.link_irdb("../../../../")
 #sim.download_packages(["METIS"])
 ```
 
-The available modes using the Leiden sky are `leiden_lss_l`, `leiden_lss_m`, `leiden_lss_n` and `leiden_lms`. We will simulate an LSS_M observation, using a PWV value of 10 mm. 
+The available modes using the Leiden sky are `leiden_lss_l`, `leiden_lss_m`, `leiden_lss_n` and `leiden_lms`. We will simulate an LSS_M observation, using a PWV value of 10 mm.
 
 ```{code-cell} ipython3
 cmd = sim.UserCommands(use_instrument="METIS", set_modes=['leiden_lss_m'])
@@ -44,7 +45,7 @@ metis = sim.OpticalTrain(cmd)
 metis.effects.pprint_all()
 ```
 
-The `leiden_` modes replace the `ELT` and `Armazones` packages with two effects, `leiden_sky`, which creates the background source using the atmospheric spectra for the selected PWV, and `telescope`, which models the relay optics. 
+The `leiden_` modes replace the `ELT` and `Armazones` packages with two effects, `leiden_sky`, which creates the background source using the atmospheric spectra for the selected PWV, and `telescope`, which models the relay optics.
 
 `telescope` has no user-modifiable parameters; its configuration can be inspected as follows:
 
@@ -91,7 +92,7 @@ metis['leiden_sky'].update(pwv=50)
 metis['leiden_sky'].plot(which="te", wavelength=np.linspace(3, 6, 1001)*u.um);
 ```
 
-# Comparison to Armazones/ELT
+## Comparison to Armazones/ELT
 The following compares the Leiden spectrum with a spectrum of the Armazones sky and METIS at the ELT.
 
 ```{code-cell} ipython3
@@ -151,7 +152,7 @@ plt.legend()
 ```
 
 ```{code-cell} ipython3
-rlam_leiden = lam_leiden[(lam_leiden > 4.6*u.um) * (lam_leiden < 4.8*u.um)] 
+rlam_leiden = lam_leiden[(lam_leiden > 4.6*u.um) * (lam_leiden < 4.8*u.um)]
 ```
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_adc_wheel.md b/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
index 2f093ac8..b44c8dea 100644
--- a/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
+++ b/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
@@ -12,7 +12,6 @@ kernelspec:
 ---
 
 # Test ADC wheel
-
 This notebook demonstrates how to use the various atmospheric dispersion correctors in METIS. Note that the action of an adc is currently restricted to a transmission loss. The implementation of the geometric differential refraction residuals will follow at a later stage.
 
 ```{code-cell} ipython3
@@ -20,7 +19,7 @@ import scopesim as sim
 sim.bug_report()
 
 # Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../") 
+sim.link_irdb("../../../../")
 ```
 
 ```{code-cell} ipython3
@@ -30,7 +29,7 @@ import numpy as np
 If you haven't got the instrument packages yet, uncomment the following cell
 
 ```{code-cell} ipython3
-# sim.download_packages(["METIS", "ELT", "Armazones"]) 
+# sim.download_packages(["METIS", "ELT", "Armazones"])
 ```
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_auto_exposure.md b/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
index 4337a367..c48ad1f5 100644
--- a/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
+++ b/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
@@ -12,7 +12,6 @@ kernelspec:
 ---
 
 # Auto Exposure
-
 This is a setup/test/demonstration notebook for the `AutoExposure` effect in Scopesim. This effect splits the requested total exposure time into NDIT subexposures of integration time DIT such that the maximum counts in a single subexposure does not exceed a certain fill fraction of the detector full well. The final readout is the sum over the NDIT subexposures, i.e. corresponds to the total requested exposure time.
 
 The notebook uses the `irdb/METIS` configuration. The observed source is blank sky, except for the last example where a star of 0 mag is used (Vega).
@@ -26,7 +25,7 @@ sim.bug_report()
 sim.link_irdb("../../../../")
 ```
 
-If you haven't got the instrument packages yet, uncomment the following cell. 
+If you haven't got the instrument packages yet, uncomment the following cell.
 
 ```{code-cell} ipython3
 # sim.download_packages(["METIS", "ELT", "Armazones"])
@@ -43,7 +42,7 @@ metis = sim.OpticalTrain(cmd)
 metis.observe()
 ```
 
-For `AutoExposure` to work the exposure time has to be given explicitely as a parameter to the `readout` method. If this is not done, the default values for `DIT` and `NDIT` will be used. The following is for an exposure time of 1 second. The resulting readout is divided by `NDIT` to produce the average over the `NDIT` subexposures. After application of the gain to convert from ADU to electrons this allows direct comparison to the detector full well. 
+For `AutoExposure` to work the exposure time has to be given explicitely as a parameter to the `readout` method. If this is not done, the default values for `DIT` and `NDIT` will be used. The following is for an exposure time of 1 second. The resulting readout is divided by `NDIT` to produce the average over the `NDIT` subexposures. After application of the gain to convert from ADU to electrons this allows direct comparison to the detector full well.
 
 ```{code-cell} ipython3
 outhdul = metis.readout(exptime=1)[0]
@@ -184,8 +183,6 @@ print("Number of saturated pixels:", npix)
 
 The default values for the detector full well in the various modes reflects our current best knowledge of the properties of the actual METIS detectors. These values can be changed as in the following example, but be aware that this makes the simulations unrealistic.
 
-+++
-
 **NB: There's something wrong with this example, please ignore for the time being.**
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
index 87d6fed5..1a92d390 100644
--- a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
+++ b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
@@ -12,8 +12,7 @@ kernelspec:
 ---
 
 # Chopping and nodding in Scopesim
-
-This notebook demonstrates how to use the `ChopNodCombiner` effect in Scopesim. Both chopping and nodding are currently defined as two-point patterns, where the throw direction is given as a 2D vector (dx, dy) in `metis["chop_nod"].meta["chop_offsets"]` and `metis["chop_nod"].meta["nod_offsets"]`. For parallel nodding, the two vectors are parallel (typically nod_offset = - chop_offset, giving a three-point pattern), for perpendicular nodding, the vectors are orthogonal.  
+This notebook demonstrates how to use the `ChopNodCombiner` effect in Scopesim. Both chopping and nodding are currently defined as two-point patterns, where the throw direction is given as a 2D vector (dx, dy) in `metis["chop_nod"].meta["chop_offsets"]` and `metis["chop_nod"].meta["nod_offsets"]`. For parallel nodding, the two vectors are parallel (typically nod_offset = - chop_offset, giving a three-point pattern), for perpendicular nodding, the vectors are orthogonal.
 
 ```{code-cell} ipython3
 from matplotlib import pyplot as plt
diff --git a/METIS/docs/example_notebooks/demos/demo_detector_modes.md b/METIS/docs/example_notebooks/demos/demo_detector_modes.md
index cb5ed2a2..36aa2d75 100644
--- a/METIS/docs/example_notebooks/demos/demo_detector_modes.md
+++ b/METIS/docs/example_notebooks/demos/demo_detector_modes.md
@@ -12,9 +12,6 @@ kernelspec:
 ---
 
 # Selecting detector modes in METIS
-
-+++
-
 This notebook demonstrates the effect `detector_readout_parameters`, which selects between the different detector readout modes. These are `fast` and `slow` for the HAWAII2RG detectors, and `high_capacity` and `low_capacity` for the Geosnap detector.
 
 ```{code-cell} ipython3
@@ -86,10 +83,7 @@ metis.cmds["!DET"]
 metis.cmds[metis['readout_noise'].meta['noise_std']]
 ```
 
-# Test: detector noise level (LSS-L)
-
-+++
-
+## Test: detector noise level (LSS-L)
 To investigate the behaviour of the detector readout modes, we look at the L-band long-slit mode where the areas of the detector outside the spectral trace contain only readout noise and dark current. The default mode for long-slit spectroscopy is the `slow` mode, and we'll switch to the `fast` mode afterwards.
 
 ```{code-cell} ipython3
@@ -141,7 +135,7 @@ Fast: ndit  = {ndit_fast}     dit = {dit_fast:.2f}
       noise = {noise_fast:5.1f}  expected: {noise_fast_expected:5.1f}""")
 print(f"""
 Slow: ndit  = {ndit_slow}     dit = {dit_slow:.2f}
-      bg    = {bg_slow:5.1f}   expected: {bg_slow_expected:5.1f}   
+      bg    = {bg_slow:5.1f}   expected: {bg_slow_expected:5.1f}
       noise = {noise_slow:5.1f}   expected: {noise_slow_expected:.1f}""")
 ```
 
@@ -151,11 +145,9 @@ Finally, we can let Scopesim automatically select the "best" mode.
 hdul_auto = metis.readout(detector_readout_mode="auto", exptime=1000)[0]
 ```
 
-The advantage of the "slow" mode of the H2RG detector is the low readout noise. The "fast" mode permit smaller DITs and would be selected for bright sources that would saturate the detector at the minimum DIT of the "slow" mode. 
-
-+++
+The advantage of the "slow" mode of the H2RG detector is the low readout noise. The "fast" mode permit smaller DITs and would be selected for bright sources that would saturate the detector at the minimum DIT of the "slow" mode.
 
-# Test: Full well (IMG-N) 
+## Test: Full well (IMG-N)
 This demonstrates the high- and low-capacity modes of the Geosnap detector. The setup uses a neutral-density filter to ensure that the background does not saturate the detector in the low-capacity mode. The source is a very bright star, which saturates in the low-capacity mode but does not in the high-capacity mode.
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_filter_wheel.md b/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
index 6eb29550..92e14c79 100644
--- a/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
+++ b/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
@@ -11,7 +11,8 @@ kernelspec:
   name: python3
 ---
 
-This notebook demonstrates the use of the `FilterWheel` in Scopesim. The METIS configuration contains two instances of this effect, named `filter_wheel` (for science filters) and `nd_filter_wheel` (for neutral-density filters). Each filter wheel contains a number of predefined filters, with different filter sets for the LM- and N-band imagers. 
+# Filter Wheel
+This notebook demonstrates the use of the `FilterWheel` in Scopesim. The METIS configuration contains two instances of this effect, named `filter_wheel` (for science filters) and `nd_filter_wheel` (for neutral-density filters). Each filter wheel contains a number of predefined filters, with different filter sets for the LM- and N-band imagers.
 
 ```{code-cell} ipython3
 import numpy as np
@@ -72,7 +73,7 @@ metis["filter_wheel"].change_filter("PAH_3.3")
 print(metis["filter_wheel"].current_filter)
 ```
 
-# Observing the same source in different filters
+## Observing the same source in different filters
 
 ```{code-cell} ipython3
 src = sim.source.source_templates.empty_sky()
@@ -97,8 +98,7 @@ print("Background in Lp:      {:8.1f} counts/s".format(np.median(img_Lp)))
 print("Background in PAH_3.3: {:8.1f} counts/s".format(np.median(img_PAH)))
 ```
 
-# Using the neutral-density filter wheel
-
+## Using the neutral-density filter wheel
 METIS also has neutral-density filters that can be inserted and changed using the `nd_filter_wheel` effect. The transmission of the filter `ND_ODx` is $10^{-x}$.
 
 ```{code-cell} ipython3
@@ -165,7 +165,7 @@ A custom filter that is not in the default filter set can be added to the wheel
 
 ```{code-cell} ipython3
 newfilter = sim.effects.ter_curves.TopHatFilterCurve(
-    transmission=0.9, blue_cutoff=3.8, red_cutoff=3.9, 
+    transmission=0.9, blue_cutoff=3.8, red_cutoff=3.9,
     name="custom_tophat")
 metis["filter_wheel"].add_filter(newfilter)
 metis["filter_wheel"].filters.keys()
diff --git a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
index 83f0fa0e..51c21fc4 100644
--- a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
+++ b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
@@ -12,7 +12,6 @@ kernelspec:
 ---
 
 # Grating Efficiency
-
 This notebook demonstrates and tests the `SpectralEfficiency` effect for the METIS long-slit spectroscopic modes. Lacking real data, the grating effciencies used here (and available in the irdb) are pure fantasy.
 
 ```{code-cell} ipython3
@@ -64,7 +63,7 @@ metis_l = sim.OpticalTrain(cmd_l)
 
 ```{code-cell} ipython3
 metis_l["psf"].include = False                # PSF is not necessary for slit-filling source
-metis_l["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum 
+metis_l["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum
 ```
 
 Observe the source with and without the grating efficiency. The ratio between the two results (we look at the image plane, which is noise free) shows directly the efficiency.
@@ -109,7 +108,7 @@ metis_m = sim.OpticalTrain(cmd_m)
 
 ```{code-cell} ipython3
 metis_m["psf"].include = False                # PSF is not necessary for slit-filling source
-metis_m["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum 
+metis_m["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum
 ```
 
 ```{code-cell} ipython3
@@ -151,7 +150,7 @@ metis_n = sim.OpticalTrain(cmd_n)
 
 ```{code-cell} ipython3
 metis_n["psf"].include = False                # PSF is not necessary for slit-filling source
-metis_n["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum 
+metis_n["skycalc_atmosphere"].include = False # sky lines obscure flat spectrum
 ```
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
index 41fcb53b..8ad64641 100644
--- a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
+++ b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
@@ -11,6 +11,7 @@ kernelspec:
   name: python3
 ---
 
+# Inter-Pixel Capacitance
 This notebook demonstrates the ScopeSim effect `InterPixelCapacitance`. Inter-pixel capacitance correlates the voltages or data values measured in adjacent pixels of an infrared detector. The effect implements the three-parameter model of Kannawadi et al. (2016) (PASP 128, 095001) and applies the following convolution kernel (in their notation) to the detector readout:
 $$ K(\alpha, \alpha^\prime, \alpha_{+}) = \begin{pmatrix}
    \alpha^\prime & \alpha - \alpha_{+} & \alpha^\prime \\
@@ -24,10 +25,10 @@ The correspondence between these parameters and those of the Scopesim effect is
 - $\alpha^\prime$ -- ``alpha_corner``: gives the influence of the four pixels sharing a corner with the target pixel.
 - $\alpha_{+}$ -- ``alpha_aniso``: gives a difference in the influence of neighbouring pixels along rows and along columns
 
-The default setting in the instrument package sets these parameter with literature values typical for HxRG detectors. 
+The default setting in the instrument package sets these parameter with literature values typical for HxRG detectors.
 It is also possible to set the kernel directly in the yaml file. This allows using kernels that are larger than $3\times 3$.
 
-When the $\alpha$ parameters are used to define the IPC kernel it is automatically normalised to unit sum. When the kernel is provided it is normalised by ScopeSim when its sum is greater than one. When it is less than one (but larger than zero), it is accepted as is, which then leads to loss of flux -- it is the user's responsibility to ensure the kernel is normalised unless this flux loss is intended. Kernels with negative sum are rejected and raise a `ValueError`.   
+When the $\alpha$ parameters are used to define the IPC kernel it is automatically normalised to unit sum. When the kernel is provided it is normalised by ScopeSim when its sum is greater than one. When it is less than one (but larger than zero), it is accepted as is, which then leads to loss of flux -- it is the user's responsibility to ensure the kernel is normalised unless this flux loss is intended. Kernels with negative sum are rejected and raise a `ValueError`.
 
 ```{code-cell} ipython3
 import numpy as np
diff --git a/METIS/docs/example_notebooks/demos/demo_lss_simple.md b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
index 66ad5393..1c1122fa 100644
--- a/METIS/docs/example_notebooks/demos/demo_lss_simple.md
+++ b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
@@ -12,9 +12,6 @@ kernelspec:
 ---
 
 # Simulating long-slit spectroscopy in METIS
-
-+++
-
 This notebook shows the most basic setup for long-slit spectroscopy, using a star as the source.
 
 ```{code-cell} ipython3
@@ -61,8 +58,8 @@ plt.imshow(result[1].data, origin="lower", vmin=100, norm="log")
 plt.colorbar();
 ```
 
-# Rectifying the spectrum
-The default configuration for METIS applies a non-linear mapping of the two-dimensional spectrum onto the detector as determined from ray-tracing simulations of the optical system. The mapping can be reversed to obtain a rectified version of the 2D spectrum that is linear in both wavelength and spatial position and can easily be analysed. Note that this is optimistic compared to an actual data reduction process, where the mapping parameters would have to be estimated from data with some uncertainty. 
+## Rectifying the spectrum
+The default configuration for METIS applies a non-linear mapping of the two-dimensional spectrum onto the detector as determined from ray-tracing simulations of the optical system. The mapping can be reversed to obtain a rectified version of the 2D spectrum that is linear in both wavelength and spatial position and can easily be analysed. Note that this is optimistic compared to an actual data reduction process, where the mapping parameters would have to be estimated from data with some uncertainty.
 
 ```{code-cell} ipython3
 tracelist = metis["spectral_traces"]
@@ -72,7 +69,7 @@ tracelist = metis["spectral_traces"]
 rectified = tracelist.rectify_traces(result, -4, 4)
 ```
 
-`rectified` is again an `HDUList` with the data in the first extension. The header of this extension contains the WCS keywords needed to translate from pixels to wavelength and spatial position. 
+`rectified` is again an `HDUList` with the data in the first extension. The header of this extension contains the WCS keywords needed to translate from pixels to wavelength and spatial position.
 
 ```{code-cell} ipython3
 from astropy.wcs import WCS
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
index 10846d88..47032130 100644
--- a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
+++ b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
@@ -12,7 +12,6 @@ kernelspec:
 ---
 
 # METIS LMS Efficiency
-
 This notebook demonstrates the effect `MetisLMSEfficiency`. Here we use it standalone to reproduce Figure 6 from E-REP-ATC-MET-1016 (v1.0) to show that the effect computes the efficiency correctly.
 
 ```{code-cell} ipython3
@@ -68,7 +67,3 @@ for order in np.arange(22, 37):
     p = plt.plot(lam.to(u.um), eff.surface.transmission(lam))
     plt.text(lammax.to(u.um).value, 0.76, str(order), ha="center", color=p[0].get_color())
 ```
-
-```{code-cell} ipython3
-
-```
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md b/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
index ff65c110..851e1f02 100644
--- a/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
+++ b/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
@@ -11,7 +11,8 @@ kernelspec:
   name: python3
 ---
 
-This notebook is targeted at the AIT team who use Scopesim to simulate observations with the warm calibration unit (WCU). It demonstrates how to select point-spread functions that correspond to a pupil-mask that is inserted in the optical path. This is an interplay between two effects in ScopeSim, an instance of `PupilMaskWheel` and an instance of `FieldConstantPSF`. Note that the instrument configuration for ScopeSim currently has a single pupil-mask wheel which is placed in the WCU. This differs from the real instrument, which will have a pupil-mask wheel in both imager arms as well as one in the common fore optics (CFO), and means that the functionality presented here can only be used with the WCU modes. The sky modes still use the SCAO PSFs as they always did. 
+# WCU (AIT)
+This notebook is targeted at the AIT team who use Scopesim to simulate observations with the warm calibration unit (WCU). It demonstrates how to select point-spread functions that correspond to a pupil-mask that is inserted in the optical path. This is an interplay between two effects in ScopeSim, an instance of `PupilMaskWheel` and an instance of `FieldConstantPSF`. Note that the instrument configuration for ScopeSim currently has a single pupil-mask wheel which is placed in the WCU. This differs from the real instrument, which will have a pupil-mask wheel in both imager arms as well as one in the common fore optics (CFO), and means that the functionality presented here can only be used with the WCU modes. The sky modes still use the SCAO PSFs as they always did.
 
 ```{code-cell} ipython3
 import scopesim as sim
diff --git a/METIS/docs/example_notebooks/demos/demo_rectify_traces.md b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
index 77597e54..3e108a5a 100644
--- a/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
+++ b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
@@ -12,9 +12,8 @@ kernelspec:
 ---
 
 # Rectification
-
-This notebook demonstrates how to rectify a detector readout from a spectroscopic simulation using Scopesim. "Rectification" means the transformation of a spectral trace from the detector onto a rectangular pixel grid of wavelength and spatial position along the slit. Wavelength calibration and rectification are major tasks of the spectroscopic data-reduction pipeline. For convenience, Scopesim includes functionality to perform these tasks by reversing the *known* mapping that was used for the simulation, resulting in easily analysable 2D spectra that include all the noise and background components but neglect the uncertainties of a wavelength calibration as it would be performed during the reduction of real data. 
-Rectification is demonstrated on a METIS long-slit simulation, but the procedure applies to MICADO spectroscopic simulations as well (but more expensive to simulate and rectify). METIS IFU simulations have to be treated differently. 
+This notebook demonstrates how to rectify a detector readout from a spectroscopic simulation using Scopesim. "Rectification" means the transformation of a spectral trace from the detector onto a rectangular pixel grid of wavelength and spatial position along the slit. Wavelength calibration and rectification are major tasks of the spectroscopic data-reduction pipeline. For convenience, Scopesim includes functionality to perform these tasks by reversing the *known* mapping that was used for the simulation, resulting in easily analysable 2D spectra that include all the noise and background components but neglect the uncertainties of a wavelength calibration as it would be performed during the reduction of real data.
+Rectification is demonstrated on a METIS long-slit simulation, but the procedure applies to MICADO spectroscopic simulations as well (but more expensive to simulate and rectify). METIS IFU simulations have to be treated differently.
 
 ```{code-cell} ipython3
 import numpy as np
@@ -41,9 +40,6 @@ If you haven’t got the instrument packages yet, uncomment the following cell.
 ```
 
 ## Creation of a source - lamp with equally spaced lines
-
-+++
-
 As an example, we use a calibration lamp with equally spaced and equally strong emission lines, covering the L band. The line list is turned into a spectrum by placing a narrow Gaussian at each line position. To simulate the lamp, we (ab)use the `flatlamp` function and replace the default spectrum (a black body) by the line spectrum.
 
 ```{code-cell} ipython3
@@ -58,13 +54,10 @@ for line in lines:
 spec = SourceSpectrum(Empirical1D, points=wave*u.um, lookup_table=flux)
 
 src_linelamp = flatlamp()
-src_linelamp.fields[0].spectra[0] = spec     # NB: Do not try to set src_linelamp.spectra[0], this has no effect. 
+src_linelamp.fields[0].spectra[0] = spec     # NB: Do not try to set src_linelamp.spectra[0], this has no effect.
 ```
 
 ## Simulation of an observation
-
-+++
-
 We use METIS in the L-band long-slit spectroscopic mode, using a fairly narrow slit. We explicitely request the realistic spectral mapping with non-linear dispersion.
 
 ```{code-cell} ipython3
@@ -96,9 +89,6 @@ plt.imshow(readout[1].data, origin="lower");
 ```
 
 ## Rectification of the spectrum
-
-+++
-
 The non-linearity in the dispersion in METIS is small and not readily apparent.Still, rectification is necessary to arrive at a 2D spectrum with well-defined wavelength and spatial coordinates. The method to use is `rectify_traces` and belongs to the `SpectralTraceList` effect, which is accessible in the METIS `OpticalTrain` as `"spectral_traces"` (in MICADO it would be `"micado_spectral_traces"`. Currently, it is necessary to specify the spatial extent of the slit when calling the method. The long slit in METIS has a length of 8 arcsec and extends from -4 arcsec to +4 arcsec.
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md b/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
index 8c115987..d9927c10 100644
--- a/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
+++ b/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
@@ -11,6 +11,7 @@ kernelspec:
   name: python3
 ---
 
+# Reference pixel mask
 The METIS detectors have a number of masked rows and pixels around their borders. These pixels do not see any signal but do have dark current, readout noise and other detector effects. This notebook shows what the effect `ReferencePixelBorder` does and how it is set up for METIS. It also explains how the dimensions of the mask can be changed and of course how the mask can be switched off entirely.
 
 ```{code-cell} ipython3
@@ -30,9 +31,6 @@ sim.link_irdb("../../../../")
 ```
 
 ## Imager detectors
-
-+++
-
 The Imager detectors (H2RG for IMG-LM, Geosnap for IMG-N) have equal width masks all around, with 64 pixels for the H2RG and 28 pixels for the Geosnap. We look at the H2RG here and simulate a WCU flat field for simplicity.
 
 ```{code-cell} ipython3
@@ -76,10 +74,7 @@ ax2.set_title("Row 250");
 
 Note that with a gain of 4 e/ADU, the reference level of 2500 ADU corresponds to 10000 electrons, as expected.
 
-+++
-
 ## LMS detector array
-
 The LMS detector array consists of 4 H2RG detectors. In the y-direction all detectors have 32 reference rows at the bottom and top. In the x-direction (the dispersion direction) only the outsides are masked with 64 columns, while there are no reference pixels on the inside. (The simulation takes several minutes.)
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_skycalc.md b/METIS/docs/example_notebooks/demos/demo_skycalc.md
index bab79421..a7153f84 100644
--- a/METIS/docs/example_notebooks/demos/demo_skycalc.md
+++ b/METIS/docs/example_notebooks/demos/demo_skycalc.md
@@ -12,7 +12,6 @@ kernelspec:
 ---
 
 # The `SkycalcTERCurve` effect in ScopeSim
-
 This notebook demonstrates how to work with the skycalc effect, `SkycalcTERCurve`. This effect provides the atmospheric emission and transmission that are applied to a source object in ScopeSim. The effect is based on `skycalc_ipy`, a python wrapper to ESO’s SkyCalc tool.
 
 ```{code-cell} ipython3
@@ -28,7 +27,7 @@ import scopesim as sim
 # sim.download_packages(["Armazones", "ELT", "METIS"])
 ```
 
-We use the `SkycalcTERCurve` in the context of METIS/LMS, although sky mode of any other instrument would do as well. 
+We use the `SkycalcTERCurve` in the context of METIS/LMS, although sky mode of any other instrument would do as well.
 
 ```{code-cell} ipython3
 cmd = sim.UserCommands(use_instrument="METIS", set_modes=["lms"])
@@ -119,7 +118,7 @@ plt.figure()
 lam = np.linspace(7, 12, 1001) * u.um
 for pwv in pwv_values:
     sky.update(pwv=pwv)
-    plt.plot(sky.surface.table['wavelength'].to(u.um), sky.surface.table['emission'], label=f"pwv = {pwv}", alpha=0.7) 
+    plt.plot(sky.surface.table['wavelength'].to(u.um), sky.surface.table['emission'], label=f"pwv = {pwv}", alpha=0.7)
     metis.observe()
     bg_level.append(np.median(metis.image_planes[0].data))
 plt.legend();
diff --git a/METIS/docs/example_notebooks/demos/demo_slit_wheel.md b/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
index 7cb719c3..27a6ebcc 100644
--- a/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
+++ b/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
@@ -12,8 +12,7 @@ kernelspec:
 ---
 
 # Slit Wheel
-
-This notebook demonstrates how to use the various slits in METIS. They are defined in a `SlitWheel` effect, which works in the same way as `FilterWheel`. The notebook uses imaging mode to show the slits directly. 
+This notebook demonstrates how to use the various slits in METIS. They are defined in a `SlitWheel` effect, which works in the same way as `FilterWheel`. The notebook uses imaging mode to show the slits directly.
 
 ```{code-cell} ipython3
 from matplotlib import pyplot as plt
@@ -77,7 +76,7 @@ newslit = sim.effects.ApertureMask(
     array_dict={
         "x": [-1, 1, 1, -1],
         "y": [-1, -1, 1, 1]
-    }, 
+    },
     x_unit="arcsec",
     y_unit="arcsec",
 )

From 0ba0384c48124b9b437f79110109907771c703d4 Mon Sep 17 00:00:00 2001
From: jbrink <janus.brink27@gmail.com>
Date: Tue, 1 Sep 2026 14:37:55 +0200
Subject: [PATCH 11/63] Added MICADO verification notebook for
 scopesim_targets' flat lamp; added TEL.area default to MICADO MCA mode.

---
 MICADO/MICADO_Calibration.yaml                |   6 +
 .../MICADO_MCA verification.ipynb             | 951 ++++++++++++++++++
 2 files changed, 957 insertions(+)
 create mode 100644 MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb

diff --git a/MICADO/MICADO_Calibration.yaml b/MICADO/MICADO_Calibration.yaml
index 1c37f4e6..0bdd72c8 100644
--- a/MICADO/MICADO_Calibration.yaml
+++ b/MICADO/MICADO_Calibration.yaml
@@ -77,3 +77,9 @@ properties:
   psf:
     interp_psf: True
     interp_order: 1
+---
+# Required parameters to replace telescope
+alias: TEL
+properties:
+  area: 975.23478998
+  telescope: none (MCA)
\ No newline at end of file
diff --git a/MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb b/MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb
new file mode 100644
index 00000000..db75ff29
--- /dev/null
+++ b/MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb	
@@ -0,0 +1,951 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8d031126-6b96-4e80-93f5-749c1c78d32d",
+   "metadata": {},
+   "source": [
+    "# Verification of the MICADO Calibration Assembly (MCA)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f21c5249-7d0c-438a-a35b-b538d354265a",
+   "metadata": {},
+   "source": [
+    "This notebook describes how to simulate calibration exposures using the MICADO Calibration Assembly with ScopeSim. For this purpose a new submode `CALIB` has been introduced, which replaces the `SCAO` and `MCAO` submodes that are used for on-sky observations. It can be combined with all instrument modes, i.e. `IMG_4mas`, `SPEC`, etc. \n",
+    "`CALIB` does not include the Armazones (atmosphere) and ELT effects. It currently includes the following to describe the MCA:\n",
+    "- `mca_mirror`: a single deployable mirror that is unique to the MCA. As with all mirrors the effect describes throughput (reflectivity) as well as thermal emission.\n",
+    "- `relay_surface_list`: This is the list of mirrors in the relay optics that is used in stand-alone mode, identical to the mirror list in the `SCAO` submode. Note that MORFEO is not yet supported for MCA simulations.\n",
+    "- `air_transmission`: The optical path from the MCA to the entrance window of MICADO has a length of about 14 metres through air, which therefore imprints an absorption signal on the input (continuum) spectrum. The effect uses a library of transmission spectra for various values of relative humidity (see below for details).\n",
+    "- `psf`: Very simplistically, the instrumental PSF (imprinted on observations using a pinhole mask) is modeled as Gaussian PSF of FWHM = 0.02 arcsec. This can be made more realistic in the future."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e96f6127-7dda-4320-b023-66ae4f0421f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scopesim as sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3651e77-db22-4022-8f35-06280590106d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.link_irdb(\"../../../\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75e62ece-9745-445e-a0f8-84c9b9aeee56",
+   "metadata": {},
+   "source": [
+    "If you have not done so already, please download the relevant instrument packages using the following code in a new cell:\n",
+    "\n",
+    "```sim.download_packages([\"MICADO\"])```\n",
+    "\n",
+    "Alternatively, if you would like to keep the instrument packages in a separate directory, you can set the following config value:\n",
+    "\n",
+    "```sim.set_inst_pkgs_path(\"path/to/packages\")```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a677b5fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sim.set_inst_pkgs_path(\"/Users/user/path/inst_pkgs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1943890b-30c5-439b-98b5-fda6d6b91100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from astropy import units as u\n",
+    "from astropy.wcs import WCS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8da54c14-a202-4ebe-8f95-7e19b947d1e1",
+   "metadata": {},
+   "source": [
+    "## Setting up the optical train\n",
+    "We first set up MICADO for the nominal imaging mode, and fix the \"telescope area\" to the MCA deployable mirror area:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ea94b35-dcd9-4696-838a-1ab528816a6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"IMG_4mas\"]) #, properties={\"!TEL.area\": 0.20029616662043426})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d29a9929",
+   "metadata": {},
+   "source": [
+    "Uncomment to include debug messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3a42b53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from scopesim import set_console_log_level\n",
+    "# set_console_log_level(\"DEBUG\")\n",
+    "# from scopesim.optics.image_plane_utils import logger\n",
+    "# logger.setLevel(\"DEBUG\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2d449fc",
+   "metadata": {},
+   "source": [
+    "Instatiate the optical train and confirm the TEL.area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e1dc88e-e5d7-4992-8e50-31a770947ea2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado = sim.OpticalTrain(cmd)\n",
+    "micado[\"filter_wheel_2\"].change_filter(\"Ks\")\n",
+    "# micado[\"filter_wheel_2\"].change_filter(\"open\")\n",
+    "micado.cmds[\"!TEL.area\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0604a8c5",
+   "metadata": {},
+   "source": [
+    "### Check mirror areas\n",
+    "A snippet to print all the mirror areas in the effects list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "672a6825",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# micado.cmds[\"!TEL\"]\n",
+    "# from scopesim.effects import SurfaceList\n",
+    "# list = micado.optics_manager.get_all(SurfaceList)\n",
+    "# for s in list:\n",
+    "#     print(s.display_name, s.area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b698f5a2-f488-40d0-9744-08fb8df7d9d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado.effects.pprint_all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f186aa16-4f7a-45fb-b740-e36e5a6bf671",
+   "metadata": {},
+   "source": [
+    "## Imaging \n",
+    "The MICADO calibration mode needs to use a `Source` object (unlike the METIS WCU mode). We use a flat field from `Scopesim_Targets`.\n",
+    "\n",
+    "First, we define the pixel scale of the *source* and its angular extent in *mas*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5dcfc9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from synphot import SourceSpectrum, units\n",
+    "from synphot.models import Empirical1D\n",
+    "from scopesim_targets.extended_source import Flat\n",
+    "from astropy.table import QTable, unique\n",
+    "from astropy.io import fits\n",
+    "\n",
+    "PIXSCALE = 64.0 # pixel scale in mas\n",
+    "NX = int(1024 * 4 // PIXSCALE) # size of flat lamp in pixels\n",
+    "NY = int(1024 * 4 // PIXSCALE) # size of flat lamp in pixels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd15300",
+   "metadata": {},
+   "source": [
+    "Read flux definition of the flat lamp from file..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d91c314",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# COLUMN = \"5V\"\n",
+    "# ROOT = \"../../../../flatcube/\"\n",
+    "\n",
+    "# tbl = unique(QTable.read(ROOT+\"rad.ecsv\"), \"wavelength\")\n",
+    "# wave = tbl[\"wavelength\"]\n",
+    "# wave = wave.value.astype(\"float32\") * wave.unit # convert to float32\n",
+    "# flux = tbl[COLUMN]\n",
+    "# flux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17a98c4c",
+   "metadata": {},
+   "source": [
+    "...or define it as a black-body spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a361d011",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.modeling.physical_models import BlackBody\n",
+    "from astropy import constants as c\n",
+    "\n",
+    "bb = BlackBody(temperature = 2000*u.K)\n",
+    "wave = np.linspace(0.7, 2.5, 10000)*u.um\n",
+    "wave <<= u.nm\n",
+    "energy_flux_nu = bb(wave)\n",
+    "\n",
+    "photon_energy = (c.h * c.c / wave).to(u.erg)\n",
+    "\n",
+    "# Photon flux per Hz: (Energy Flux) / (Energy per Photon)\n",
+    "# Units: photon / (cm2 s Hz sr)\n",
+    "photon_flux_nu = (energy_flux_nu / photon_energy) * u.photon\n",
+    "photon_flux_lam = photon_flux_nu.to(\n",
+    "    u.photon / (u.cm**2 * u.s * u.AA * u.sr), \n",
+    "    equivalencies=u.spectral_density(wave)\n",
+    ")\n",
+    "# arbitrary scaling factor\n",
+    "flux = photon_flux_lam * 1e-5 # * (1*u.arcsec**2)\n",
+    "flux <<= u.Unit(\"ph / (nm s sr cm2)\")\n",
+    "flux "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c393b11a",
+   "metadata": {},
+   "source": [
+    "### 2D source definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a3f8a9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spec = SourceSpectrum(Empirical1D, points=wave, lookup_table=flux * (1*u.arcsec**2))\n",
+    "\n",
+    "ref_wave = 1.75*u.um\n",
+    "ref_flux = spec(ref_wave).to(u.Jy, u.spectral_density(ref_wave))\n",
+    "\n",
+    "tg = Flat(spectrum=spec, brightness=(ref_wave, ref_flux/u.arcsec**2))\n",
+    "\n",
+    "grid = {\n",
+    "    \"pixel_scale\": (PIXSCALE * u.mas/u.pix) << u.arcsec/u.pix,\n",
+    "    \"width\": NX,\n",
+    "    \"height\": NY,\n",
+    "}\n",
+    "\n",
+    "flat_2d = tg.to_source(grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d6d4390",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# flux at reference wavelength\n",
+    "spec(ref_wave)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e57e0d6",
+   "metadata": {},
+   "source": [
+    "### Cube source definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d151baf",
+   "metadata": {},
+   "source": [
+    "Re-read the table from file, or comment out to use the previous flux definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecef1ae0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tbl = QTable.read(ROOT+\"rad.ecsv\")\n",
+    "# wave = tbl[\"wavelength\"]\n",
+    "# flux = tbl[COLUMN]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecd330b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dwave = np.diff(wave)\n",
+    "delta_wave = dwave[0]\n",
+    "n_wave = len(wave)\n",
+    "pixel_scale = PIXSCALE * u.mas\n",
+    "\n",
+    "cube = np.broadcast_to(\n",
+    "    flux.value.astype(\"float32\")[:, None, None], (n_wave, NY, NX)\n",
+    ").copy()\n",
+    "\n",
+    "w = WCS(naxis=3)\n",
+    "w.wcs.ctype = [\"LINEAR\", \"LINEAR\", \"WAVE\"]\n",
+    "w.wcs.cunit = [\"deg\", \"deg\", \"nm\"]\n",
+    "w.wcs.crpix = [NX / 2 + 0.5, NY / 2 + 0.5, 1]\n",
+    "w.wcs.crval = [0.0, 0.0, wave[0].value]\n",
+    "pix_scale_deg = pixel_scale.to(u.deg).value\n",
+    "w.wcs.cdelt = [pix_scale_deg, pix_scale_deg, delta_wave.value]\n",
+    "\n",
+    "header = w.to_header()\n",
+    "header[\"BUNIT\"] = flux.unit.to_string()\n",
+    "print(f\"{header[\"BUNIT\"] = }\")\n",
+    "\n",
+    "hdu = fits.PrimaryHDU(data=cube, header=header)\n",
+    "flat_cube = sim.Source(cube=hdu)\n",
+    "\n",
+    "print(f\"delta_wave: {delta_wave}\")\n",
+    "print(f\"pixel_area: {pixel_scale**2}\")\n",
+    "print(f\"field_area: {NX*NY*pixel_scale**2 << u.arcsec**2}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a907b82e",
+   "metadata": {},
+   "source": [
+    "## Back to the simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87bb0d01",
+   "metadata": {},
+   "source": [
+    "Plot the current filter's transmission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6775726b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado[\"filter_wheel_2\"].current_filter.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a78f795-8b86-469a-b2e0-f748a9b15f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Observe either the 2d or cube flat lamp source\n",
+    "micado.observe(flat_2d)\n",
+    "\n",
+    "# Reference values for the BB spectrum\n",
+    "\n",
+    "# 2D flat @ 16 mas pixels\n",
+    "# canvas_image_hdu.data.mean() = np.float64(55542064166.866264)\n",
+    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888673.0266698446)\n",
+    "\n",
+    "# 2D flat @ 32 mas pixels\n",
+    "# canvas_image_hdu.data.mean() = np.float64(55542064166.86521)\n",
+    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888673.026669826)\n",
+    "\n",
+    "# 3D flat @ 16 mas pixels\n",
+    "# canvas_image_hdu.data.mean() = np.float64(55541849853.236404)\n",
+    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888669.5976517651)\n",
+    "\n",
+    "# 3D flat @ 32 mas pixels\n",
+    "# canvas_image_hdu.data.mean() = np.float64(55541849853.236404)\n",
+    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888669.5976517651)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b1594f3",
+   "metadata": {},
+   "source": [
+    "### Flat souces comparison\n",
+    "\n",
+    "First - inspect the properties of the 2D collapsed image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7086b0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scopesim.source.source_fields import (\n",
+    "    HDUSourceField,\n",
+    "    ImageSourceField,\n",
+    "    CubeSourceField,\n",
+    ")\n",
+    "\n",
+    "def flux_image(field: HDUSourceField):\n",
+    "    \"\"\"Return 2D image in ph/s/cm2.\n",
+    "\n",
+    "    Spectrum is integrated over whole range.\n",
+    "    Cubes are flattened and spectrally integrated.\n",
+    "\n",
+    "    # usage e.g.:\n",
+    "    flximg = flux_image(micado._last_source.fields[0])\n",
+    "    \"\"\"\n",
+    "    if isinstance(field, ImageSourceField):\n",
+    "        return field.data * field.spectrum.integrate()\n",
+    "    if isinstance(field, CubeSourceField):\n",
+    "        dlam = field.header[\"CDELT3\"] * u.Unit(field.header[\"CUNIT3\"])\n",
+    "        print(f\"{field.bunit = }, {dlam = }, field.pixel_area = {field.pixel_area << u.mas**2 }\")\n",
+    "        img = field.data.sum(axis=0) * field.bunit * dlam\n",
+    "        if field.is_bunit_spatially_differential:\n",
+    "            img *= field.pixel_area\n",
+    "        return img.to(u.ph/u.s/u.cm**2)\n",
+    "    raise TypeError(f\"{type(field)} unsupported\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "113fe548",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inspect either the source object or the source as ingested into the optical train\n",
+    "\n",
+    "flximg = flux_image(flat_2d.fields[0])\n",
+    "print(f\"flat_2d img shape {flximg.shape}\")\n",
+    "print(f\"flat_2d img mean {flximg.mean()}\")\n",
+    "\n",
+    "flximg = flux_image(flat_cube.fields[0])\n",
+    "print(f\"flat_cube img shape {flximg.shape}\")\n",
+    "print(f\"flat_cube img mean {flximg.mean()}\")\n",
+    "\n",
+    "# flximg = flux_image(micado._last_source.fields[0])\n",
+    "\n",
+    "# Referene values\n",
+    "# flat_2d: 142.18657012921608 ph / (s cm2) \n",
+    "# flat_cube: 142.19518660314446 ph / (s cm2) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f21af4e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the header of the ingested source\n",
+    "# micado._last_source.fields[0].header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd3cc32",
+   "metadata": {},
+   "source": [
+    "### Compare the spectra of the 2D and cube flat sources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20b9ae63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cube_field = flat_cube.fields[0]\n",
+    "flux = cube_field.data.mean(axis=(1,2)) * cube_field.bunit * cube_field.pixel_area << u.Unit(\"photlam\")\n",
+    "print(f\"{cube_field.bunit = }\")\n",
+    "waveset = cube_field.waveset\n",
+    "spec = SourceSpectrum(Empirical1D, points=waveset, lookup_table=flux)\n",
+    "plt.plot(waveset, spec(waveset), label = \"cube\")\n",
+    "\n",
+    "waveset_2d = flat_2d.fields[0].spectrum.waveset << u.um\n",
+    "plt.plot(waveset_2d, flat_2d.fields[0].spectrum(waveset_2d) / (NX*NY), label=\"2d\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07d060e0",
+   "metadata": {},
+   "source": [
+    "### Image plane check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81145758",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = micado.image_planes[0].data\n",
+    "print(\"Sum:     \", im.sum() * u.Unit(\"ph/s\"))\n",
+    "print(\"Max:     \", im.max() * u.Unit(\"ph/s/pix\"))\n",
+    "print(\"Mean:     \", im.mean() * u.Unit(\"ph/s/pix\"))\n",
+    "# plt.imshow(im)\n",
+    "# plt.colorbar();\n",
+    "\n",
+    "# Reference values from BB source\n",
+    "# 2D_Flat @ 16 mas source pixel size\n",
+    "\n",
+    "\n",
+    "# 2D @ 32\n",
+    "\n",
+    "\n",
+    "# 3D @ 16\n",
+    "\n",
+    "\n",
+    "# 3D @ 32\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "452d0f40",
+   "metadata": {},
+   "source": [
+    "### Readout check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd5145ed-eb05-4da9-b0c3-5e7c7b8e4126",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "readout = micado.readout(dit=1, ndit=1)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25a083cf-f17a-4a89-a4a1-6f8e289515ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Sum:     \", readout[1].data.sum())\n",
+    "print(\"Mean:     \", readout[1].data.mean())\n",
+    "print(\"Std. dev.:\", readout[1].data.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bb25d42-4c7a-49d4-9adb-0236d8e2e2e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(readout[1].data.ravel(), bins=100);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79e9618c-39e6-4bc5-b2fb-82c064fac095",
+   "metadata": {},
+   "source": [
+    "Also from scopesim_templates, a pinhole mask, which we shall set up as a regular grid for imaging (blindly taken from the documentation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08f89ccc-db86-455d-bb1f-6874f20a01d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scopesim_templates.micado.pinhole_masks import pinhole_mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eefcc929-52a8-43cb-8175-02eed087869c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr = np.arange(-5, 6, 0.5)      # [arcsec]\n",
+    "x, y = np.meshgrid(dr, dr)\n",
+    "x, y = x.flatten(), y.flatten()\n",
+    "waves = np.arange(0.7, 2.5, 0.001) * u.um\n",
+    "pinh = pinhole_mask(x, y, waves, sum_factor=9001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4cde4ec-5685-4903-850f-f17869102330",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# micado.observe(pinh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c94b54a6-287e-4072-8478-8816efe61d59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read_pinh = micado.readout(dit=1, ndit=1)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86ea0e0a-f96e-4022-bb87-3a15e5162f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.imshow(read_pinh[1].data)\n",
+    "# plt.title(\"Imaging, 4 mas\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7efa2af3-31be-49d5-8e8a-def0868dab48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"IMG_1.5mas\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbcf8295-66bb-45d1-a2f4-e2d6ee76e2f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# micado = sim.OpticalTrain(cmd)\n",
+    "# micado.observe(pinh)\n",
+    "# read_pinh_zoom = micado.readout(dit=1, ndit=1)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a78e2430-3cac-4d46-beda-0199b43aa37f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.imshow(read_pinh_zoom[1].data)\n",
+    "# plt.title(\"Imaging, 1.5 mas\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "705d55b9-58ce-4d6c-975c-5c38a8e7be7d",
+   "metadata": {},
+   "source": [
+    "# Spectroscopy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab69ff63-b887-421e-b256-b1088691706d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"SPEC\"])\n",
+    "micado = sim.OpticalTrain(cmd)\n",
+    "cmd[\"!TEL.area\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e8e4330-78e5-4636-8261-ade7ad35ac60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado.effects.pprint_all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "867deabf-0500-481e-80ce-194be4f71b55",
+   "metadata": {},
+   "source": [
+    "We will observe the flat lamp, which allows us to switch off the psf effects. We'll try to simulate a full field of view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1328dcd-c64f-4654-b734-40e8eee1e1b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado['psf'].include = False\n",
+    "micado['micado_ncpas_psf'].include = False\n",
+    "micado['filter_wheel_1'].change_filter(\"Spec_HK\")\n",
+    "micado['detector_window'].include = False\n",
+    "micado['full_detector_array'].include = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "113aa70e-3ff3-434b-ba7b-2a907bcec544",
+   "metadata": {},
+   "source": [
+    "The transmission of the 14 meter air column in the MCA and relay optics is provided by the `air_transmission` effect. This is a library of transmission spectra for relative humidities between 5 and 95 per cent, available in steps of 5 per cent. The default is 10 per cent, which can be changed with the `update()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2aed29dd-770d-4f89-8ad1-28d584de8cc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air = micado['air_transmission']\n",
+    "print(\"Default humidity:\", air.meta['relH'], \"(per cent)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c97cea0-6e40-4c17-934b-e3ec70957c2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air.update(relH=21)\n",
+    "print(\"Current humidity:\", air.meta['relH'], \"(per cent)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a401844-c89f-487c-a161-7380207380f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91d97c4c-998a-44e9-aebb-d27fa30b7dd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado.observe(flat_cube)\n",
+    "\n",
+    "# 2d @ 32, FOV1:\n",
+    "# 3D FOV make_imagefields: field_hdu.data.mean() = np.float64(0.059604644775390514) weightmap\n",
+    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133029222573.6335)\n",
+    "\n",
+    "# 2d @ 64, FOV1:\n",
+    "# 3D FOV make_imagefields: field_cube.mean() = <Quantity 1.36404382 PHOTLAM>\n",
+    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133029222573.63048)\n",
+    "\n",
+    "# 3d @ 32, FOV1:\n",
+    "# 3D FOV make_cubefields: field_data.mean() = np.float64(1.3640210615398614) ph s-1 arcsec-2\n",
+    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133027002625.97089)\n",
+    "\n",
+    "# 3d @ 64, FOV1:\n",
+    "# 3D FOV make_cubefields: field_data.mean() = np.float64(1.3640210615398596) PHOTLAM arcsec-2\n",
+    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133027002625.97089)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c318f367",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hdu = micado.fov_manager.fovs[0].view()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8195fe94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hdu.writeto(\"spec_cube.fits\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cea7a5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"{micado.image_planes[0].data.max() = }\")\n",
+    "# cube @32: 275.9863315908688 ph/s/pix\n",
+    "# cube @64: 275.9863315908688\n",
+    "# 2d @32: 275.98635377691744 ph/s/pix\n",
+    "# 2d @64: 275.98635377691744\n",
+    "\n",
+    "# MCA 3d @64: 153.86593589531887"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d592302f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micado.image_planes[0].hdu.writeto(\"spec_implane.fits\", overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fb99fb8-6b6c-429a-a739-d67f49ba47f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "readout = micado.readout(dit=60, ndit=1, filename=\"spec_readout.fits\")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "006b4c00-0b75-4ff2-b285-beadb7c7d230",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "plt.imshow(readout[5].data)\n",
+    "# plt.imshow(micado.image_planes[0].data)\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a891916-29a1-4be4-84da-71c19f1339ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#rect = micado['micado_spectral_traces'].rectify_traces(readout, -1.5, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58422392-95af-49f2-861f-c6ae107c9f6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.imshow(rect[1].data); \n",
+    "#plt.xlim(500, 4000);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "021f891e-765b-4a1a-b390-6c8c9605f79f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# j = np.arange(rect[2].data.shape[1])\n",
+    "# wcs = WCS(rect[2].header).spectral\n",
+    "# lam = wcs.all_pix2world(j, 0)[0]\n",
+    "# lam = (lam * wcs.wcs.cunit[0]).to(u.um)\n",
+    "# plt.plot(lam, rect[2].data[300, ])\n",
+    "# plt.title(rect[2].header[\"EXTNAME\"])\n",
+    "# plt.xlabel(\"Wavelength [um]\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "scopesim_dev",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 4565612a057693a7d27b345c4fe1e75b5b93ca6c Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Thu, 10 Sep 2026 15:33:09 +0200
Subject: [PATCH 12/63] Add flag keywords for MICADO detector window

These allow the "full detector array" mode to be a "first-order-
configurable", i.e. can be set via `properties`, instead of having to
switch the `.include` on the two effects after instantiating the optical
train. The latter still works, so this is fully backwards compatible.
---
 MICADO/MICADO_H4RG.yaml | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/MICADO/MICADO_H4RG.yaml b/MICADO/MICADO_H4RG.yaml
index 97fbb658..ecda4997 100644
--- a/MICADO/MICADO_H4RG.yaml
+++ b/MICADO/MICADO_H4RG.yaml
@@ -14,13 +14,15 @@ properties:
     x: 0
     y: 0
     gain: 1  # should be taken from FPA layout??
-    include_ipc: false     # inter-pixel capacitance
+    include_ipc: False    # inter-pixel capacitance
+    include_full: False   # full 9 detectors array
+    include_window: True  # 1k x 1k detector window
 
 effects:
   - name: full_detector_array
     description : The full 3x3 MICADO detector array list
     class: DetectorList
-    include: False
+    include: "!DET.include_full"
     kwargs:
         filename : FPA_array_layout.dat
         active_detectors : "all"
@@ -28,7 +30,7 @@ effects:
   - name: detector_window
     class: DetectorWindow
     description: Cut-out of the focal plane image with custom dimensions and coordinates
-    include: True
+    include: "!DET.include_window"
     kwargs:
         image_plane_id: 0
         pixel_size: 0.015

From 806f31886ba9feba271143950757ce2c33849f8f Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Wed, 29 Oct 2025 11:14:02 +0100
Subject: [PATCH 13/63] Reduce mIFU bundles to 169

---
 MOSAIC/TRACE_mIFU-HR-H.fits | Bin 2649600 -> 996480 bytes
 MOSAIC/TRACE_mIFU-LR-H.fits | Bin 2649600 -> 996480 bytes
 MOSAIC/TRACE_mIFU-LR-J.fits | Bin 2649600 -> 996480 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/MOSAIC/TRACE_mIFU-HR-H.fits b/MOSAIC/TRACE_mIFU-HR-H.fits
index de05772d8f5b9f79b03c4678d9926e7d05db49e5..aa926723cf52510746ebe75c54d9f79d800943f2 100644
GIT binary patch
delta 8608
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From 73d1fd75f09b5494c05a0d03b2d0806dbdbccb2f Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Thu, 10 Sep 2026 21:19:04 +0200
Subject: [PATCH 14/63] Reference border not needed at this stage

---
 MOSAIC/MOSAIC_DET_NIR.yaml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/MOSAIC/MOSAIC_DET_NIR.yaml b/MOSAIC/MOSAIC_DET_NIR.yaml
index fedb8a3a..d8cb20fd 100644
--- a/MOSAIC/MOSAIC_DET_NIR.yaml
+++ b/MOSAIC/MOSAIC_DET_NIR.yaml
@@ -68,12 +68,6 @@ effects:
     kwargs:
       filename: "!DET.linearity.file_name"
 
-  - name: border_reference_pixels
-    description: Blanks the signal on N edge row and column pixels
-    class: ReferencePixelBorder
-    kwargs:
-      border: [0, 0, 0, 0]
-
   - name: readout_noise
     description: Readout noise frames
     class: BasicReadoutNoise

From 96682c92d6d3d96e42f23dea605e2decf86ed374 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Thu, 10 Sep 2026 22:19:07 +0200
Subject: [PATCH 15/63] tests and fix filename error

---
 MOSAIC/default.yaml         |   7 +-
 MOSAIC/tests/test_mosaic.py | 235 ++++++++++++++++++++++++++++++++++++
 2 files changed, 239 insertions(+), 3 deletions(-)
 create mode 100644 MOSAIC/tests/test_mosaic.py

diff --git a/MOSAIC/default.yaml b/MOSAIC/default.yaml
index 8c57d911..c69a93d1 100644
--- a/MOSAIC/default.yaml
+++ b/MOSAIC/default.yaml
@@ -5,10 +5,11 @@ alias : OBS
 name : MOSAIC_default_configuration
 description : default parameters needed for a MOSAIC simulation
 needs_scopesim: "v0.11.0"
-date_modified: 2026-08-06
+date_modified: 2026-09-10
 changes:
   - 2025-09-01 (OC) adjusted resolutions for modes
   - 2026-08-06 (OC) set output format per mode
+  - 2026-09-10 (KL) fix trace and spectrograph TER filenames for mIFU-HR-H
 
 packages :
   - Armazones
@@ -229,8 +230,8 @@ mode_yamls :
     properties:
       ins_mode: mIFU-HR-H
       fibre_ter_file: TER_mIFU_HR-H.dat
-      spec_ter_file:  TER_spec_NIR_HR_H.dat
-      trace_file: TRACE_mIFU_NIR-HR-H.fits
+      spec_ter_file:  TER_spec_HR_H.dat
+      trace_file: TRACE_mIFU-HR-H.fits
       dispersion: 2.4e-5    # um/pixel on detector
       resolution: 72000     # resolution of sky spectrum
       output_format: table
diff --git a/MOSAIC/tests/test_mosaic.py b/MOSAIC/tests/test_mosaic.py
new file mode 100644
index 00000000..99020915
--- /dev/null
+++ b/MOSAIC/tests/test_mosaic.py
@@ -0,0 +1,235 @@
+"""Basic unit tests for irdb/MOSAIC"""
+# pylint: disable=missing-class-docstring
+# pylint: disable=missing-function-docstring
+
+import os
+from glob import glob
+
+import pytest
+
+import numpy as np
+#from astropy.io.fits import HDUList
+from astropy import units as u
+from matplotlib import pyplot as plt
+from matplotlib.colors import LogNorm
+
+import scopesim
+from scopesim.source.source_templates import star_field
+#import scopesim_templates as sim_tp
+
+PLOTS = False
+PKGS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
+scopesim.rc.__config__["!SIM.file.local_packages_path"] = PKGS_DIR
+
+
+class TestLoads:
+    """Test that irdb/MOSAIC is loaded and an OpticalTrain is produced"""
+
+    @pytest.mark.parametrize("themode",
+                            ["MOS-LR-B", "MOS-HR-B1", "MOS-HR-B2", "MOS-LR-R",
+                             "MOS-HR-R1", "MOS-HR-R2", "MOS-LR-J", "MOS-LR-H",
+                             "MOS-HR-H", "mIFU-LR-J", "mIFU-LR-H", "mIFU-HR-H"])
+    def test_scopesim_loads_package(self, themode):
+        """Load the configuration for all supported modes"""
+        cmd = scopesim.UserCommands(use_instrument="MOSAIC",
+                                    set_modes=[themode])
+        assert isinstance(cmd, scopesim.UserCommands)
+
+        mosaic = scopesim.OpticalTrain(cmd)
+        assert isinstance(mosaic, scopesim.OpticalTrain)
+
+
+YAML_LIST = glob(os.path.join(PKGS_DIR, "MOSAIC/*.yaml"))
+@pytest.fixture(name="yaml_list", scope="class", params=YAML_LIST)
+def fixture_yaml_list(request):
+    return scopesim.commands.user_commands.load_yaml_dicts(request.param)
+
+class TestYAML:
+    """Test that yaml files result in correct lists"""
+
+    def test_yaml_read_okay(self, yaml_list):
+        """yaml file is read correctly and gives list"""
+        assert isinstance(yaml_list, list)
+
+    def test_yaml_length_not_zero(self, yaml_list):
+        """yaml_list has entries"""
+        assert len(yaml_list) > 0
+
+    def test_yaml_entries_are_dicts(self, yaml_list):
+        """yaml list entries are dictionaries"""
+        for yaml_entry in yaml_list:
+            assert isinstance(yaml_entry, dict)
+
+
+QE_LIST = glob(os.path.join(PKGS_DIR, "MOSAIC/QE_detector_*.dat"))
+@pytest.fixture(name="qe_curve", scope="class", params=QE_LIST)
+def fixture_qe_curve(request):
+    return scopesim.effects.QuantumEfficiencyCurve(filename=request.param)
+
+class TestQuantumEfficiency:
+    """Test that QE files result in correct QuantumEfficiencyCurves"""
+
+    def test_qe_read_okay(self, qe_curve):
+        """qe file is read correctly and gives QuantumEfficiencyCurve"""
+        assert isinstance(qe_curve, scopesim.effects.QuantumEfficiencyCurve)
+
+    def test_qe_table_not_zero(self, qe_curve):
+        """Table attribute shall not be empty"""
+        assert len(qe_curve.table) > 0
+
+    def test_qe_has_wavelength_unit(self, qe_curve):
+        """qe file specifies a wavelength unit"""
+        assert 'wavelength_unit' in qe_curve.meta
+
+    def test_qe_wavelength_parses_correctly(self, qe_curve):
+        """wavelength unit is parsed by astropy.units"""
+        wunit = qe_curve.meta['wavelength_unit']
+        assert isinstance(u.Unit(wunit), u.Unit)
+
+
+TER_LIST = glob(os.path.join(PKGS_DIR, "METIS/TER_*.dat"))
+@pytest.fixture(name="ter_curve", scope="class", params=TER_LIST)
+def fixture_ter_curve(request):
+    return scopesim.effects.TERCurve(filename=request.param)
+
+class TestTERCurve:
+    """Test that TER files result in correct TERCurves"""
+
+    def test_ter_read_okay(self, ter_curve):
+        """ter file is read correctly and gives TERCurve"""
+        assert isinstance(ter_curve, scopesim.effects.TERCurve)
+
+    def test_ter_table_not_zero(self, ter_curve):
+        """Table attribute shall not be empty"""
+        assert len(ter_curve.table) > 0
+
+    def test_ter_has_wavelength_unit(self, ter_curve):
+        """ter file specifies a wavelength unit"""
+        assert 'wavelength_unit' in ter_curve.meta
+
+    def test_ter_wavelength_parses_correctly(self, ter_curve):
+        """wavelength unit is parsed by astropy.units"""
+        wunit = ter_curve.meta['wavelength_unit']
+        assert isinstance(u.Unit(wunit), u.Unit)
+
+
+FPA_LIST = glob(os.path.join(PKGS_DIR, "MOSAIC/FPA_*_layout.dat"))
+@pytest.fixture(name="det_list", scope="class", params=FPA_LIST)
+def fixture_det_list(request):
+    return scopesim.effects.DetectorList(filename=request.param)
+
+class TestFPALayout:
+    """Test that FPA files result in correct DetectorLists"""
+    # Do we need explicit tests for units?
+    def test_fpa_read_okay(self, det_list):
+        """fpa layout is read correctly and gives DetectorList"""
+        assert isinstance(det_list, scopesim.effects.DetectorList)
+
+    def test_fpa_table_not_zero(self, det_list):
+        """Table attribute shall not be empty"""
+        assert len(det_list.table) > 0
+
+
+LIN_LIST = glob(os.path.join(PKGS_DIR, "MOSAIC/FPA_linearity_*.dat"))
+@pytest.fixture(name="lin_curve", scope="class", params=LIN_LIST)
+def fixture_lin_curve(request):
+    return scopesim.effects.LinearityCurve(filename=request.param)
+
+class TestLinearityCurve:
+    """Test that linearity files result in correct LinearityCurves"""
+
+    def test_lin_read_okay(self, lin_curve):
+        """linearity curve is read correctly and gives LinearityCurve"""
+        assert isinstance(lin_curve, scopesim.effects.LinearityCurve)
+
+    def test_lin_table_not_zero(self, lin_curve):
+        """Table attribute shall not be empty"""
+        assert len(lin_curve.table) > 0
+
+
+TRACE_LIST = glob(os.path.join(PKGS_DIR, "MOSAIC/TRACE*.fits"))
+@pytest.fixture(name="trace_list", scope="class", params=TRACE_LIST)
+def fixture_trace_list(request):
+    return scopesim.effects.MosaicSpectralTraceList(filename=request.param)
+
+class TestTraceFile:
+    """Test that trace files result in correct MosaicSpectralTraces
+    """
+
+    def test_tracelist_read_okay(self, trace_list):
+        """Trace file is read correctly and gives SpectralTrace"""
+        assert isinstance(trace_list, scopesim.effects.MosaicSpectralTraceList)
+
+    def test_tracelist_has_table(self, trace_list):
+        """Trace list has a table with at least one entry"""
+        assert len(trace_list.data) > 0
+
+    def test_tracelist_has_traces(self, trace_list):
+        """SpectralTraceList contains at least one SpectralTrace"""
+        for trace in trace_list.spectral_traces:
+            assert isinstance(trace_list.spectral_traces[trace],
+                              scopesim.effects.SpectralTrace)
+
+
+@pytest.mark.slow
+class TestObserves:
+    """Test basic observations for the main instrument modes"""
+    def test_something_comes_out_mos_lr_r(self):
+        """Basic test for MOS-LR-R"""
+        src = star_field(100, 0, 10, width=10, use_grid=True)
+        # convert to single star
+
+        cmds = scopesim.UserCommands(use_instrument="MOSAIC",
+                                     set_modes=['MOS-LR-R'])
+        mosaic = scopesim.OpticalTrain(cmds)
+        #mosaic['detector_linearity'].include = False
+
+        mosaic.observe(src)
+        hdus = mosaic.readout()
+
+        im = mosaic.image_planes[0].data
+        mx, med, std = np.max(im), np.median(im), np.std(im)
+
+        if PLOTS:
+            for i, img in enumerate([mosaic.image_planes[0].data,
+                                     hdus[0][1].data]):
+                plt.subplot(1, 2, i+1)
+                med = np.median(img)
+                plt.imshow(img, norm=LogNorm(), vmin=0.7*med, vmax=1.3*med)
+                plt.title("MOS-LR-R Test")
+                plt.colorbar()
+            plt.show()
+
+        assert mx > med + 3 * std
+
+    def test_something_comes_out_mifu_lr_h(self):
+        """Basic test for mIFU-LR-H"""
+        src = star_field(100, 0, 10, width=10, use_grid=True)
+
+        cmds = scopesim.UserCommands(use_instrument="MOSAIC",
+                                     set_modes=["mIFU-LR-H"])
+
+        mosaic = scopesim.OpticalTrain(cmds)
+        #mosaic['detector_linearity'].include = False
+
+        mosaic.observe(src)
+        hdus = mosaic.readout()
+
+        im = mosaic.image_planes[0].data
+        mx, med, std = np.max(im), np.median(im), np.std(im)
+
+        if PLOTS:
+            for i, img in enumerate([mosaic.image_planes[0].data,
+                                     hdus[0][1].data]):
+                plt.subplot(1, 2, i+1)
+                med = np.median(img)
+                plt.imshow(img, vmin=0.7*med, vmax=1.3*med, norm=LogNorm())
+                plt.title("mIFU-LR-H Test")
+                plt.colorbar()
+            plt.show()
+
+        assert mx > med + 3 * std
+
+        # This should not be here, but putting it into a separate test would work
+        # better if the simulation were done in a fixture
+        assert isinstance(hdus[0][1].header["INHERIT"], bool)

From c42f2a39d88facf24bf3e713b0e1fd33936c2a82 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Mon, 14 Sep 2026 17:11:07 +0200
Subject: [PATCH 16/63] Minor changes to notebook

---
 MOSAIC/docs/example_notebooks/MOSAIC_demo.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/MOSAIC/docs/example_notebooks/MOSAIC_demo.ipynb b/MOSAIC/docs/example_notebooks/MOSAIC_demo.ipynb
index a700f67e..4a4c3d25 100644
--- a/MOSAIC/docs/example_notebooks/MOSAIC_demo.ipynb
+++ b/MOSAIC/docs/example_notebooks/MOSAIC_demo.ipynb
@@ -5,6 +5,8 @@
    "id": "612ecdf2-d7da-4a1f-87da-ff42d5a7e222",
    "metadata": {},
    "source": [
+    "# MOSAIC demo\n",
+    "\n",
     "This is a simple demo of the \"ETC\" mode for MOSAIC. To use this you need the `oc/mosaic` branch of Scopesim and the `oc/mosaic` branch of the irdb. The implementation creates one-dimensional spectra for the output (summed over the fibres in a bundle for the MOS modes). The first sections demonstrate how to use Scopesim for the MOS and mIFU modes. The final section provides a more detailed description of the implementation and instrument definition that is currently used."
    ]
   },
@@ -635,7 +637,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.imshow(mosaic.image_planes[0].data[900:1100, 1900:2100], norm='log', origin='lower')\n",
+    "plt.imshow(mosaic.image_planes[0].data[1900:2100, 1900:2100], origin='lower')\n",
     "plt.colorbar();"
    ]
   },

From 7c3a99244c331d0ccfe6856ebdca8e1a8f9e951c Mon Sep 17 00:00:00 2001
From: jbrink <janus.brink27@gmail.com>
Date: Tue, 15 Sep 2026 14:37:36 +0200
Subject: [PATCH 17/63] Fixed typo and updates to reference values in the
 comments. Updated plots in spectroscopy section.

---
 .../MICADO_MCA verification.ipynb             | 305 +++++++++---------
 1 file changed, 150 insertions(+), 155 deletions(-)

diff --git a/MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb b/MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb
index db75ff29..70f86867 100644
--- a/MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb	
+++ b/MICADO/docs/extra_notebooks/MICADO_MCA verification.ipynb	
@@ -94,7 +94,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"IMG_4mas\"]) #, properties={\"!TEL.area\": 0.20029616662043426})"
+    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"IMG_4mas\"])"
    ]
   },
   {
@@ -113,7 +113,7 @@
    "outputs": [],
    "source": [
     "# from scopesim import set_console_log_level\n",
-    "# set_console_log_level(\"DEBUG\")\n",
+    "# sim.set_console_log_level(\"DEBUG\")\n",
     "# from scopesim.optics.image_plane_utils import logger\n",
     "# logger.setLevel(\"DEBUG\")"
    ]
@@ -123,7 +123,7 @@
    "id": "d2d449fc",
    "metadata": {},
    "source": [
-    "Instatiate the optical train and confirm the TEL.area"
+    "Instantiate the optical train and confirm the TEL.area"
    ]
   },
   {
@@ -134,8 +134,12 @@
    "outputs": [],
    "source": [
     "micado = sim.OpticalTrain(cmd)\n",
-    "micado[\"filter_wheel_2\"].change_filter(\"Ks\")\n",
-    "# micado[\"filter_wheel_2\"].change_filter(\"open\")\n",
+    "micado[\"pupil_wheel\"].change_filter(\"open\") # ND1\n",
+    "micado[\"filter_wheel_2\"].change_filter(\"open\") # Ks\n",
+    "micado[\"filter_wheel_1\"].change_filter(\"Spec_IJ\") # Spec_IJ\n",
+    "micado['slit_wheel'].change_slit(\"Short\")\n",
+    "# micado['micado_wide_field_mirror_list'].include = False\n",
+    "# micado['air_transmission'].include = False\n",
     "micado.cmds[\"!TEL.area\"]"
    ]
   },
@@ -216,7 +220,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# COLUMN = \"5V\"\n",
+    "# COLUMN = \"9V\"\n",
     "# ROOT = \"../../../../flatcube/\"\n",
     "\n",
     "# tbl = unique(QTable.read(ROOT+\"rad.ecsv\"), \"wavelength\")\n",
@@ -303,7 +307,8 @@
    "outputs": [],
    "source": [
     "# flux at reference wavelength\n",
-    "spec(ref_wave)"
+    "spec(ref_wave)\n",
+    "# ref value: 2.5045721 photlam"
    ]
   },
   {
@@ -393,7 +398,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "micado[\"filter_wheel_2\"].current_filter.plot();"
+    "micado[\"filter_wheel_1\"].current_filter.plot();"
    ]
   },
   {
@@ -406,23 +411,25 @@
     "# Observe either the 2d or cube flat lamp source\n",
     "micado.observe(flat_2d)\n",
     "\n",
-    "# Reference values for the BB spectrum\n",
+    "# Reference values for the BB spectrum, Spec_IJ filter, short slit\n",
     "\n",
-    "# 2D flat @ 16 mas pixels\n",
-    "# canvas_image_hdu.data.mean() = np.float64(55542064166.866264)\n",
-    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888673.0266698446)\n",
+    "# 2D flat @ 64 mas pixels\n",
+    "# astar.scopesim.optics.fov - 2D FOV make_hdu: canvas_image_hdu.data.mean() = 1146701.811449 ph/s/pix\n",
     "\n",
     "# 2D flat @ 32 mas pixels\n",
-    "# canvas_image_hdu.data.mean() = np.float64(55542064166.86521)\n",
-    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888673.026669826)\n",
+    "# astar.scopesim.optics.fov - 2D FOV make_hdu: canvas_image_hdu.data.mean() = 1146701.811449 ph/s/pix\n",
     "\n",
-    "# 3D flat @ 16 mas pixels\n",
-    "# canvas_image_hdu.data.mean() = np.float64(55541849853.236404)\n",
-    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888669.5976517651)\n",
+    "# 3D flat @ 64 mas pixels\n",
+    "# astar.scopesim.optics.fov - 2D FOV make_hdu: canvas_image_hdu.data.mean() = 1147022.169664 ph/s/pix\n",
     "\n",
     "# 3D flat @ 32 mas pixels\n",
-    "# canvas_image_hdu.data.mean() = np.float64(55541849853.236404)\n",
-    "# After scaling: canvas_image_hdu.data.mean() = np.float64(888669.5976517651)"
+    "# astar.scopesim.optics.fov - 2D FOV make_hdu: canvas_image_hdu.data.mean() = 1147022.169664 ph/s/pix\n",
+    "\n",
+    "# Ks, short slit, no air, no wide-field-surf-list:\n",
+    "# astar.scopesim.optics.fov - 2D FOV make_hdu: canvas_image_hdu.data.mean() = 888745.590001 ph/s/pix\n",
+    "\n",
+    "# MCA source, Spec_IJ, short slit:\n",
+    "# astar.scopesim.optics.fov - 2D FOV make_hdu: canvas_image_hdu.data.mean() = 655077.538545 ph/s/pix"
    ]
   },
   {
@@ -519,15 +526,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "plt.figure(figsize=(6,3))\n",
     "cube_field = flat_cube.fields[0]\n",
     "flux = cube_field.data.mean(axis=(1,2)) * cube_field.bunit * cube_field.pixel_area << u.Unit(\"photlam\")\n",
     "print(f\"{cube_field.bunit = }\")\n",
-    "waveset = cube_field.waveset\n",
+    "waveset = cube_field.waveset << u.Angstrom\n",
     "spec = SourceSpectrum(Empirical1D, points=waveset, lookup_table=flux)\n",
     "plt.plot(waveset, spec(waveset), label = \"cube\")\n",
     "\n",
-    "waveset_2d = flat_2d.fields[0].spectrum.waveset << u.um\n",
+    "waveset_2d = flat_2d.fields[0].spectrum.waveset << u.Angstrom\n",
     "plt.plot(waveset_2d, flat_2d.fields[0].spectrum(waveset_2d) / (NX*NY), label=\"2d\")\n",
+    "plt.xlabel(\"wavelength [A]\")\n",
+    "plt.ylabel(\"flux [ph / (s cm^2 A)]\")\n",
     "plt.legend();"
    ]
   },
@@ -553,173 +563,140 @@
     "# plt.imshow(im)\n",
     "# plt.colorbar();\n",
     "\n",
-    "# Reference values from BB source\n",
-    "# 2D_Flat @ 16 mas source pixel size\n",
-    "\n",
+    "# Reference values from BB source, Spec_IJ filter, Short slit\n",
+    "# 2D_Flat @ 64 mas source pixel size\n",
+    "# Sum:      3440105437.5108867 ph / s\n",
+    "# Max:      1146701.8125036291 ph / (pix s)\n",
     "\n",
     "# 2D @ 32\n",
+    "# Sum:      3440105437.510886 ph / s\n",
+    "# Max:      1146701.812503629 ph / (pix s)\n",
     "\n",
+    "# 3D @ 64\n",
+    "# Sum:      3441064137.2765436 ph / s\n",
+    "# Max:      1147021.3790921816 ph / (pix s)\n",
     "\n",
-    "# 3D @ 16\n",
+    "# 3D @ 32\n",
+    "# Sum:      3441064137.276469 ph / s\n",
+    "# Max:      1147021.379092156 ph / (pix s)\n",
     "\n",
+    "# Reference values from radiometry file\n",
+    "# 5V 2D @ 64, Ks, open - Mean:      87709.80445358888 ph / (pix s)\n",
+    "# 5V 2D @ 64, Ks, ND1  - Mean:      8770.980445358895 ph / (pix s)\n",
+    "# 5V 2D @ 64, Ks, ND3  - Mean:      87.70980445358875 ph / (pix s)\n",
     "\n",
-    "# 3D @ 32\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "452d0f40",
-   "metadata": {},
-   "source": [
-    "### Readout check"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bd5145ed-eb05-4da9-b0c3-5e7c7b8e4126",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readout = micado.readout(dit=1, ndit=1)[0]"
+    "# BB, Ks, short_slit: total\n",
+    "# Sum:      2712488860.1544886 ph / s (BB, Ks, no air, no wide-img-surfaces)\n",
+    "#           1965232618.795486 (MCA, Spec_IJ)\n",
+    "#           2705219519 (spec total, BB, Ks, no air, no grating_eff)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25a083cf-f17a-4a89-a4a1-6f8e289515ef",
+   "id": "5d436a33",
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\"Sum:     \", readout[1].data.sum())\n",
-    "print(\"Mean:     \", readout[1].data.mean())\n",
-    "print(\"Std. dev.:\", readout[1].data.std())"
+    "micado.image_planes[0].hdu.writeto(\"./out/img_implane.fits\", overwrite=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1bb25d42-4c7a-49d4-9adb-0236d8e2e2e7",
+   "id": "d27ed332",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.hist(readout[1].data.ravel(), bins=100);"
+    "plt.imshow(im)\n",
+    "plt.colorbar();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "79e9618c-39e6-4bc5-b2fb-82c064fac095",
-   "metadata": {},
-   "source": [
-    "Also from scopesim_templates, a pinhole mask, which we shall set up as a regular grid for imaging (blindly taken from the documentation)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08f89ccc-db86-455d-bb1f-6874f20a01d2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scopesim_templates.micado.pinhole_masks import pinhole_mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eefcc929-52a8-43cb-8175-02eed087869c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dr = np.arange(-5, 6, 0.5)      # [arcsec]\n",
-    "x, y = np.meshgrid(dr, dr)\n",
-    "x, y = x.flatten(), y.flatten()\n",
-    "waves = np.arange(0.7, 2.5, 0.001) * u.um\n",
-    "pinh = pinhole_mask(x, y, waves, sum_factor=9001)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d4cde4ec-5685-4903-850f-f17869102330",
+   "id": "452d0f40",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# micado.observe(pinh)"
+    "### Readout check"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c94b54a6-287e-4072-8478-8816efe61d59",
+   "id": "bd5145ed-eb05-4da9-b0c3-5e7c7b8e4126",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# read_pinh = micado.readout(dit=1, ndit=1)[0]"
+    "readout = micado.readout(dit=1, ndit=1)[0]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "86ea0e0a-f96e-4022-bb87-3a15e5162f80",
+   "id": "25a083cf-f17a-4a89-a4a1-6f8e289515ef",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# plt.imshow(read_pinh[1].data)\n",
-    "# plt.title(\"Imaging, 4 mas\");"
+    "print(\"Sum:     \", readout[1].data.sum())\n",
+    "print(\"Max:     \", readout[1].data.max())\n",
+    "print(\"Mean:     \", readout[1].data.mean())\n",
+    "print(\"Std. dev.:\", readout[1].data.std())"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7efa2af3-31be-49d5-8e8a-def0868dab48",
+   "id": "1bb25d42-4c7a-49d4-9adb-0236d8e2e2e7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"IMG_1.5mas\"])"
+    "mask = readout[1].data > 50\n",
+    "plt.hist(readout[1].data[mask], bins=100);"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bbcf8295-66bb-45d1-a2f4-e2d6ee76e2f2",
+   "cell_type": "markdown",
+   "id": "705d55b9-58ce-4d6c-975c-5c38a8e7be7d",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# micado = sim.OpticalTrain(cmd)\n",
-    "# micado.observe(pinh)\n",
-    "# read_pinh_zoom = micado.readout(dit=1, ndit=1)[0]"
+    "# Spectroscopy"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a78e2430-3cac-4d46-beda-0199b43aa37f",
+   "id": "ab69ff63-b887-421e-b256-b1088691706d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# plt.imshow(read_pinh_zoom[1].data)\n",
-    "# plt.title(\"Imaging, 1.5 mas\");"
+    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"SPEC\"])\n",
+    "micado = sim.OpticalTrain(cmd)\n",
+    "cmd[\"!TEL.area\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "705d55b9-58ce-4d6c-975c-5c38a8e7be7d",
+   "id": "867deabf-0500-481e-80ce-194be4f71b55",
    "metadata": {},
    "source": [
-    "# Spectroscopy"
+    "We will observe the flat lamp, which allows us to switch off the psf effects. We'll try to simulate a full field of view."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ab69ff63-b887-421e-b256-b1088691706d",
+   "id": "c1328dcd-c64f-4654-b734-40e8eee1e1b0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"CALIB\", \"SPEC\"])\n",
-    "micado = sim.OpticalTrain(cmd)\n",
-    "cmd[\"!TEL.area\"]"
+    "micado['psf'].include = False\n",
+    "micado['micado_ncpas_psf'].include = False\n",
+    "micado['filter_wheel_1'].change_filter(\"Spec_IJ\")\n",
+    "micado['filter_wheel_2'].change_filter(\"open\")\n",
+    "micado['detector_window'].include = False\n",
+    "micado['full_detector_array'].include = True\n",
+    "micado['grating_efficiency'].include = True\n",
+    "micado['air_transmission'].include = True"
    ]
   },
   {
@@ -734,32 +711,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "867deabf-0500-481e-80ce-194be4f71b55",
+   "id": "113aa70e-3ff3-434b-ba7b-2a907bcec544",
    "metadata": {},
    "source": [
-    "We will observe the flat lamp, which allows us to switch off the psf effects. We'll try to simulate a full field of view."
+    "The transmission of the 14 meter air column in the MCA and relay optics is provided by the `air_transmission` effect. This is a library of transmission spectra for relative humidities between 5 and 95 per cent, available in steps of 5 per cent. The default is 10 per cent, which can be changed with the `update()` method:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c1328dcd-c64f-4654-b734-40e8eee1e1b0",
+   "id": "17c96de9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "micado['psf'].include = False\n",
-    "micado['micado_ncpas_psf'].include = False\n",
-    "micado['filter_wheel_1'].change_filter(\"Spec_HK\")\n",
-    "micado['detector_window'].include = False\n",
-    "micado['full_detector_array'].include = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "113aa70e-3ff3-434b-ba7b-2a907bcec544",
-   "metadata": {},
-   "source": [
-    "The transmission of the 14 meter air column in the MCA and relay optics is provided by the `air_transmission` effect. This is a library of transmission spectra for relative humidities between 5 and 95 per cent, available in steps of 5 per cent. The default is 10 per cent, which can be changed with the `update()` method:"
+    "air = micado['air_transmission']\n",
+    "air.update(relH=21)"
    ]
   },
   {
@@ -791,7 +757,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "air.plot();"
+    "waveset = air.throughput.waveset\n",
+    "t_air = air.throughput\n",
+    "plt.figure(figsize=(6,2))\n",
+    "plt.plot(waveset, t_air(waveset))\n",
+    "plt.xlabel(\"Wavelength [A]\")\n",
+    "plt.ylabel(\"Throughput [normalised]\");"
    ]
   },
   {
@@ -801,23 +772,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "micado.observe(flat_cube)\n",
+    "micado.observe(flat_2d)\n",
     "\n",
-    "# 2d @ 32, FOV1:\n",
-    "# 3D FOV make_imagefields: field_hdu.data.mean() = np.float64(0.059604644775390514) weightmap\n",
-    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133029222573.6335)\n",
+    "# Reference values with BB, Spec_IJ, short slit, air with RH=21%\n",
+    "# 2d @ 32, FOV1, ORDER_3_1:\n",
+    "# astar.scopesim.optics.fov - 3D FOV make_imagefields: field_cube.mean() = <Quantity 0.55063668 PHOTLAM>\n",
+    "# astar.scopesim.optics.fov - 3D FOV make_hdu: canvas_cube_hdu.data.mean() = 53700005148.001999\n",
     "\n",
     "# 2d @ 64, FOV1:\n",
-    "# 3D FOV make_imagefields: field_cube.mean() = <Quantity 1.36404382 PHOTLAM>\n",
-    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133029222573.63048)\n",
+    "# astar.scopesim.optics.fov - 3D FOV make_imagefields: field_cube.mean() = <Quantity 0.55063668 PHOTLAM>\n",
+    "# astar.scopesim.optics.fov - 3D FOV make_hdu: canvas_cube_hdu.data.mean() = 53700005148.001999\n",
     "\n",
     "# 3d @ 32, FOV1:\n",
-    "# 3D FOV make_cubefields: field_data.mean() = np.float64(1.3640210615398614) ph s-1 arcsec-2\n",
-    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133027002625.97089)\n",
+    "# astar.scopesim.optics.fov - 3D FOV make_cubefields: field_data.mean() = 0.550609 [PHOTLAM / arcsec2]\n",
+    "# astar.scopesim.optics.fov - 3D FOV make_hdu: canvas_cube_hdu.data.mean() = 53697302115.919312\n",
     "\n",
     "# 3d @ 64, FOV1:\n",
-    "# 3D FOV make_cubefields: field_data.mean() = np.float64(1.3640210615398596) PHOTLAM arcsec-2\n",
-    "# 3D FOV make_hdu: canvas_cube_hdu.data.mean() = np.float64(133027002625.97089)\n"
+    "# astar.scopesim.optics.fov - 3D FOV make_cubefields: field_data.mean() = 0.550609 [PHOTLAM / arcsec2]\n",
+    "# astar.scopesim.optics.fov - 3D FOV make_hdu: canvas_cube_hdu.data.mean() = 53697302115.921593\n"
    ]
   },
   {
@@ -837,7 +809,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hdu.writeto(\"spec_cube.fits\")"
+    "hdu.writeto(\"./out/spec_cube.fits\", overwrite=True)"
    ]
   },
   {
@@ -848,12 +820,15 @@
    "outputs": [],
    "source": [
     "print(f\"{micado.image_planes[0].data.max() = }\")\n",
-    "# cube @32: 275.9863315908688 ph/s/pix\n",
-    "# cube @64: 275.9863315908688\n",
-    "# 2d @32: 275.98635377691744 ph/s/pix\n",
-    "# 2d @64: 275.98635377691744\n",
     "\n",
-    "# MCA 3d @64: 153.86593589531887"
+    "# MCA flux, Spec_IJ, short-slit\n",
+    "# max:\n",
+    "# 3d 5V @64: \n",
+    "# 2d 9V @64: 133.34228423529976\n",
+    "\n",
+    "# BB, Ks, short-slit, no air, no grating_eff\n",
+    "# sum (for spec total flux)\n",
+    "# 2d #64: 2705219519.086759\n"
    ]
   },
   {
@@ -863,7 +838,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "micado.image_planes[0].hdu.writeto(\"spec_implane.fits\", overwrite=True)"
+    "micado.image_planes[0].hdu.writeto(\"./out/spec_implane.fits\", overwrite=True)"
    ]
   },
   {
@@ -873,7 +848,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "readout = micado.readout(dit=60, ndit=1, filename=\"spec_readout.fits\")[0]"
+    "readout = micado.readout(dit=60, ndit=1, filename=\"./out/spec_readout.fits\")[0]"
    ]
   },
   {
@@ -896,7 +871,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#rect = micado['micado_spectral_traces'].rectify_traces(readout, -1.5, 1.5)"
+    "rect = micado['micado_spectral_traces'].rectify_traces(readout, -1.5, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "732e497e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"{len(rect) = }\")\n",
+    "print(f\"{rect[3].header[\"EXTNAME\"]}\")"
    ]
   },
   {
@@ -906,8 +892,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#plt.imshow(rect[1].data); \n",
-    "#plt.xlim(500, 4000);"
+    "plt.figure(figsize=(12,2))\n",
+    "plt.imshow(rect[3].data); \n",
+    "plt.xlim(0, rect[3].data.shape[1]);\n",
+    "plt.xlabel(\"pixel\");"
    ]
   },
   {
@@ -917,19 +905,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# j = np.arange(rect[2].data.shape[1])\n",
-    "# wcs = WCS(rect[2].header).spectral\n",
-    "# lam = wcs.all_pix2world(j, 0)[0]\n",
-    "# lam = (lam * wcs.wcs.cunit[0]).to(u.um)\n",
-    "# plt.plot(lam, rect[2].data[300, ])\n",
-    "# plt.title(rect[2].header[\"EXTNAME\"])\n",
-    "# plt.xlabel(\"Wavelength [um]\");"
+    "plt.figure(figsize=(15,2))\n",
+    "j = np.arange(rect[3].data.shape[1])\n",
+    "wcs = WCS(rect[3].header).spectral\n",
+    "lam = wcs.all_pix2world(j, 0)[0]\n",
+    "lam = (lam * wcs.wcs.cunit[0]).to(u.um)\n",
+    "L = spec(lam) / spec(lam).max()\n",
+    "plt.plot(lam, rect[3].data[300:500, ].mean(axis=0), label=\"spec_flat\")\n",
+    "plt.plot(lam, np.divide(rect[3].data[300:500, ].mean(axis=0), L), label=\"lamp-normalised response\")\n",
+    "# mask = air.throughput(lam) > 0.8\n",
+    "# data = rect[3].data[:, mask] / L[mask]\n",
+    "# plt.plot(lam[mask], np.divide(data[300:301,].mean(axis=0), air.throughput(lam)[mask]), label=\"normalised response\")\n",
+    "plt.title(rect[3].header[\"EXTNAME\"])\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Wavelength [um]\");"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "scopesim_dev",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

From 48bd457fd0349c3a3e6656a6a7496d30ddd9a5d9 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Thu, 17 Sep 2026 16:14:54 +0200
Subject: [PATCH 18/63] Include dark current in AutoExposure

---
 METIS/METIS_DET_IFU.yaml            | 9 ++++++---
 METIS/METIS_DET_IFU_SMPL.yaml       | 5 ++++-
 METIS/METIS_DET_IMG_LM.yaml         | 3 ++-
 METIS/METIS_DET_IMG_N_Aquarius.yaml | 7 +++++--
 METIS/METIS_DET_IMG_N_GeoSnap.yaml  | 3 ++-
 MOSAIC/MOSAIC_DET_NIR.yaml          | 1 +
 VLT/VLT.yaml                        | 2 +-
 7 files changed, 21 insertions(+), 9 deletions(-)

diff --git a/METIS/METIS_DET_IFU.yaml b/METIS/METIS_DET_IFU.yaml
index 69539f10..7e38defd 100644
--- a/METIS/METIS_DET_IFU.yaml
+++ b/METIS/METIS_DET_IFU.yaml
@@ -17,7 +17,7 @@ properties:
   dit: "!OBS.dit"
   ndit: "!OBS.ndit"
   mindit: 1.3               # seconds, Roy van Boekel, pers. communication
-  include_prnu: False      
+  include_prnu: False
   full_well: 1.e+5   # electrons, E-TNT-MPIA-1004, v1-0
   gain:
     1: 2.0            # electrons/ADU, email B.Serra 2024-10-09
@@ -58,6 +58,9 @@ effects:
     include: True
     kwargs:
       fill_frac: "!OBS.auto_exposure.fill_frac"
+      full_well: "!DET.full_well"
+      dark_current: "!DET.dark_current"
+      mindit: "!DET.mindit"
 
   - name: reference_pixel_mask
     description: Mask for reference pixels
@@ -71,11 +74,11 @@ effects:
     class: ExposureIntegration
 
   - name: prnu
-    description: Pixel-to-pixel response non-uniformity 
+    description: Pixel-to-pixel response non-uniformity
     class: PixelResponseNonUniformity
     include: "!DET.include_prnu"
     kwargs:
-      prnu_std: 0.005 
+      prnu_std: 0.005
       prnu_seed: 42
 
   - name: dark_current
diff --git a/METIS/METIS_DET_IFU_SMPL.yaml b/METIS/METIS_DET_IFU_SMPL.yaml
index 9cf7820c..0b4e9ca7 100644
--- a/METIS/METIS_DET_IFU_SMPL.yaml
+++ b/METIS/METIS_DET_IFU_SMPL.yaml
@@ -15,7 +15,7 @@ properties:
   temperature: -233
   dit: "!OBS.dit"
   ndit: "!OBS.ndit"
-  mindit: 1.3               # seconds, Roy van Boekel, pers. communication    
+  mindit: 1.3               # seconds, Roy van Boekel, pers. communication
   full_well: 1.e+5   # electrons, E-TNT-MPIA-1004, v1-0
   gain: 2
   dark_current: 0.1         # [e-/s]
@@ -46,6 +46,9 @@ effects:
     include: True
     kwargs:
       fill_frac: "!OBS.auto_exposure.fill_frac"
+      full_well: "!DET.full_well"
+      dark_current: "!DET.dark_current"
+      mindit: "!DET.mindit"
 
   - name: exposure_action
     description: Summing up sky signal for all DITs and NDITs
diff --git a/METIS/METIS_DET_IMG_LM.yaml b/METIS/METIS_DET_IMG_LM.yaml
index 6a5aa872..8e62f3b3 100644
--- a/METIS/METIS_DET_IMG_LM.yaml
+++ b/METIS/METIS_DET_IMG_LM.yaml
@@ -19,7 +19,7 @@ properties:
   temperature: -230
   dit: "!OBS.dit"
   ndit: "!OBS.ndit"
-  include_prnu: False     
+  include_prnu: False
   layout:
     file_name: "FPA_metis_img_lm_layout.dat"
   qe_curve:
@@ -70,6 +70,7 @@ effects:
     kwargs:
       fill_frac: "!OBS.auto_exposure.fill_frac"
       full_well: "!DET.full_well"
+      dark_current: "!DET.dark_current"
       mindit: "!DET.mindit"
 
   - name: reference_pixel_mask
diff --git a/METIS/METIS_DET_IMG_N_Aquarius.yaml b/METIS/METIS_DET_IMG_N_Aquarius.yaml
index 4c8394a7..3bd92d04 100644
--- a/METIS/METIS_DET_IMG_N_Aquarius.yaml
+++ b/METIS/METIS_DET_IMG_N_Aquarius.yaml
@@ -16,7 +16,7 @@ properties:
   temperature: -265
   dit: "!OBS.dit"
   ndit: "!OBS.ndit"
-  include_prnu: False      
+  include_prnu: False
   mindit: 0.008            # seconds, E-REP-NOVA-MET-1191, v2-0
   full_well: 1.e+6  # electrons, low-capacity mode
   dark_current: 13         # [e-/s] E-REP-NOVA-MET-1191, v2-0
@@ -65,13 +65,16 @@ effects:
     include: true
     kwargs:
       fill_frac: "!OBS.auto_exposure.fill_frac"
+      full_well: "!DET.full_well"
+      dark_current: "!DET.dark_current"
+      mindit: "!DET.mindit"
 
   - name: exposure_integration
     description: Summing up sky signal for all DITs and NDITs
     class: ExposureIntegration
 
   - name: prnu
-    description: Pixel-to-pixel response non-uniformity 
+    description: Pixel-to-pixel response non-uniformity
     class: PixelResponseNonUniformity
     include: "!DET.include_prnu"
     kwargs:
diff --git a/METIS/METIS_DET_IMG_N_GeoSnap.yaml b/METIS/METIS_DET_IMG_N_GeoSnap.yaml
index 5f5eb98f..79784095 100644
--- a/METIS/METIS_DET_IMG_N_GeoSnap.yaml
+++ b/METIS/METIS_DET_IMG_N_GeoSnap.yaml
@@ -21,7 +21,7 @@ properties:
   temperature: -240
   dit: "!OBS.dit"
   ndit: "!OBS.ndit"
-  include_prnu: False      
+  include_prnu: False
   border: [28, 28, 28, 28]
   layout:
     file_name: "FPA_metis_img_n_geosnap_layout.dat"
@@ -74,6 +74,7 @@ effects:
     kwargs:
       fill_frac: "!OBS.auto_exposure.fill_frac"
       full_well: "!DET.full_well"
+      dark_current: "!DET.dark_current"
       mindit: "!DET.mindit"
 
   - name: reference_pixel_mask
diff --git a/MOSAIC/MOSAIC_DET_NIR.yaml b/MOSAIC/MOSAIC_DET_NIR.yaml
index d8cb20fd..a6cbbce9 100644
--- a/MOSAIC/MOSAIC_DET_NIR.yaml
+++ b/MOSAIC/MOSAIC_DET_NIR.yaml
@@ -46,6 +46,7 @@ effects:
     kwargs:
       fill_frac: 0.75     #"!OBS.auto_exposure.fill_frac"
       full_well: "!DET.full_well"
+      dark_current: "!DET.dark_current"
       mindit: "!DET.mindit"
 
   - name: exposure_integration
diff --git a/VLT/VLT.yaml b/VLT/VLT.yaml
index 9b41d8a0..4fae4bd0 100644
--- a/VLT/VLT.yaml
+++ b/VLT/VLT.yaml
@@ -5,7 +5,7 @@ name: VLT
 description : The very large telescope UT4
 
 properties:
-  temperture: "!ATMO.temperature"
+  temperature: "!ATMO.temperature"
 
 effects:
 - name: vlt_mirror_list

From 8365b06a4a9debb459cdd1064fa4cddd3136ccac Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Thu, 17 Sep 2026 17:22:54 +0200
Subject: [PATCH 19/63] Provisional: offsets in both table and meta

---
 MICADO/MASK_slit_Long_offset.dat  |  7 +++++--
 MICADO/MASK_slit_Short_offset.dat | 15 +++++++--------
 MICADO/MASK_slit_Wide_offset.dat  | 11 +++++++----
 3 files changed, 19 insertions(+), 14 deletions(-)

diff --git a/MICADO/MASK_slit_Long_offset.dat b/MICADO/MASK_slit_Long_offset.dat
index d1ec03b6..9ce702ad 100644
--- a/MICADO/MASK_slit_Long_offset.dat
+++ b/MICADO/MASK_slit_Long_offset.dat
@@ -2,17 +2,20 @@
 # author : Kieran Leschinski, Oliver Czoske
 # source : ELT-TRE-MCD-56300-0014_3.0
 # date_created  : 2026-02-03
-# date_modified : 2026-02-03
+# date_modified : 2026-09-17
 # status : FDR design
-# type : aperture:slit_geometry
 # x_unit : arcsec
 # y_unit : arcsec
+# offset_type : detector
+# offset_x : 0
+# offset_y : 1.2
 # changes :
 # - 2019-07-10 (KL) Created the file
 # - 2020-03-24 (KL) Changed geometry to 15000x50mas
 # - 2023-07-13 (OC) Changed geometry to 15000x20mas
 # - 2023-07-24 (OC) Position in x corrected
 # - 2026-02-03 (OC) Initialised from MASK_slit_15000x20.dat
+# - 2026-09-17 (OC) Put offset into meta data
 #
 x       y
 -5.0    1.190
diff --git a/MICADO/MASK_slit_Short_offset.dat b/MICADO/MASK_slit_Short_offset.dat
index bc0c3680..5ff5d993 100644
--- a/MICADO/MASK_slit_Short_offset.dat
+++ b/MICADO/MASK_slit_Short_offset.dat
@@ -2,23 +2,22 @@
 # author : Kieran Leschinski, Oliver Czoske
 # source : ELT-TRE-MCD-56300-0014_3.0
 # date_created  : 2026-02-03
-# date_modified : 2026-02-03
+# date_modified : 2026-09-17
 # status : FDR design
-# type : aperture:slit_geometry
 # x_unit : arcsec
 # y_unit : arcsec
 # offset_type : detector
 # offset_x : 0
-# offset_y : 4.5
+# offset_y : 1.2
 # changes :
 # - 2019-07-10 (KL) Created the file
 # - 2020-03-24 (KL) Changed geometry to 3000x20mas
 # - 2023-07-13 (OC) Changed geometry to 3000x16mas
 # - 2026-02-03 (OC) Initialised from MASK_slit_3000x16.dat
-# - 2026-05-07 (OC) Put offset into meta data
+# - 2026-09-17 (OC) Put offset into meta data
 #
 x       y
--1.5    -0.008
-+1.5     -0.008
-+1.5     +0.008
--1.5    +0.008
+-1.5    +1.192
++1.5    +1.192
++1.5    +1.208
+-1.5    +1.208
diff --git a/MICADO/MASK_slit_Wide_offset.dat b/MICADO/MASK_slit_Wide_offset.dat
index 450b4b98..b4d1d795 100644
--- a/MICADO/MASK_slit_Wide_offset.dat
+++ b/MICADO/MASK_slit_Wide_offset.dat
@@ -2,19 +2,22 @@
 # author : Kieran Leschinski, Oliver Czoske
 # source : ELT-TRE-MCD-56300-0014_3.0
 # date_created  : 2026-02-03
-# date_modified : 2026-02-03
+# date_modified : 2026-09-17
 # status : FDR design
-# type : aperture:slit_geometry
 # x_unit : arcsec
 # y_unit : arcsec
+# offset_type : detector
+# offset_x : 0
+# offset_y : 1.2
 # changes :
 # - 2019-07-10 (KL) Created the file
 # - 2020-03-24 (KL) Changed geometry to 3000x50mas
 # - 2023-07-13 (OC) Changed geometry to 3000x48mas
 # - 2026-02-03 (OC) Initialised from MASK_slit_3000x48.dat
+# - 2026-09-17 (OC) Put offset into meta data
 #
 x       y
 -1.5    1.176
-1.5     1.176
-1.5     1.224
++1.5    1.176
++1.5    1.224
 -1.5    1.224

From aea1e98d11425b909a58901e721f4825a5fa5ce1 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Fri, 18 Sep 2026 20:07:15 +0200
Subject: [PATCH 20/63] Restructure default.yaml

---
 MICADO/default.yaml                            |  2 ++
 MICADO/docs/example_notebooks/MICADO_IFU.ipynb | 10 +++++++++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/MICADO/default.yaml b/MICADO/default.yaml
index 259de6f5..2c993f4c 100644
--- a/MICADO/default.yaml
+++ b/MICADO/default.yaml
@@ -145,7 +145,9 @@ mode_yamls :
   description: "Integral-field spectroscopy"
   status: experimental
   yamls:
+  - MICADO.yaml
   - MICADO_IFU.yaml
+  - MICADO_H4RG.yaml
   properties:
     trace_file: TRACE_MICADO_IFU.fits
     filter_name_fw1: Spec_HK
diff --git a/MICADO/docs/example_notebooks/MICADO_IFU.ipynb b/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
index 310aa6c4..69284037 100644
--- a/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
+++ b/MICADO/docs/example_notebooks/MICADO_IFU.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "302bd8b6-7ad0-4b12-be41-cd6daab1db6a",
+   "metadata": {},
+   "source": [
+    "# MICADO IFU"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "fe82dad8-f30b-4d6b-a8a4-70b322955831",
@@ -102,7 +110,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "micado.effects.show_in_notebook()"
+    "micado.effects.pprint_all()"
    ]
   },
   {

From 39c72c0c388ad58778a40a8e2579a847d4bc80d4 Mon Sep 17 00:00:00 2001
From: "Fabian H." <73600109+teutoburg@users.noreply.github.com>
Date: Fri, 18 Sep 2026 21:08:16 +0200
Subject: [PATCH 21/63] Exclude 'lms_extended' mode for now

Doesn't work yet anyway...
---
 METIS/tests/test_modes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/METIS/tests/test_modes.py b/METIS/tests/test_modes.py
index 9843083d..35613d2d 100644
--- a/METIS/tests/test_modes.py
+++ b/METIS/tests/test_modes.py
@@ -14,7 +14,7 @@
 PKGS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
 scopesim.rc.__config__["!SIM.file.local_packages_path"] = PKGS_DIR
 
-ELT_MODES = ["img_lm", "img_n", "lss_l", "lss_m", "lss_n", "lms", "lms_extended"]
+ELT_MODES = ["img_lm", "img_n", "lss_l", "lss_m", "lss_n", "lms"]  # , "lms_extended"]
 # Generate WCU_MODES automatically to ensure that each elt mode has a corresponding wcu mode
 WCU_MODES = [("wcu_" + mode) for mode in ELT_MODES]
 

From eba1d1994e0db172278464941ca94fe6ceeb23cb Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Fri, 18 Sep 2026 21:27:16 +0200
Subject: [PATCH 22/63] Remove deprecated SPEC_..x.. modes

---
 MICADO/test_micado/test_micado_compiles.py | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

diff --git a/MICADO/test_micado/test_micado_compiles.py b/MICADO/test_micado/test_micado_compiles.py
index 4305ce74..137bf329 100644
--- a/MICADO/test_micado/test_micado_compiles.py
+++ b/MICADO/test_micado/test_micado_compiles.py
@@ -5,17 +5,14 @@
 Comments
 --------
 - 2022-03-18 (KL) Green locally
-  ! SPEC mode is missing from the tests.
-
-.. todo:: Add SPEC modes to these tests
 
 """
 
 # integration test using everything and the MICADO package
+import os
 from pathlib import Path
 import pytest
 from pytest import approx
-import os
 
 import numpy as np
 from astropy.io import fits
@@ -31,6 +28,8 @@
 rc.__config__["!SIM.file.local_packages_path"] = str(PATH_IRDB)
 PLOTS = False
 
+# pylint: disable=missing-class-docstring,
+# pylint: disable=missing-function-docstring
 
 class TestInit:
     def test_all_packages_are_available(self):
@@ -57,14 +56,14 @@ def test_user_commands_loads_mode_files(self):
 
     def test_user_commands_can_change_modes(self):
         cmd = scopesim.UserCommands(use_instrument="MICADO")
-        cmd.set_modes("MCAO", "SPEC_3000x48")
+        cmd.set_modes("MCAO", "SPEC")
 
         assert "MORFEO" in [yd["name"] for yd in cmd.yaml_dicts]
         assert "MICADO_SPEC" in [yd["name"] for yd in cmd.yaml_dicts]
 
     def test_user_commands_can_change_modes_via_init(self):
         cmd = scopesim.UserCommands(
-            use_instrument="MICADO", set_modes=["MCAO", "SPEC_3000x48"]
+            use_instrument="MICADO", set_modes=["MCAO", "SPEC"]
         )
 
         assert "MORFEO" in [yd["name"] for yd in cmd.yaml_dicts]
@@ -81,8 +80,7 @@ def test_works_seamlessly_for_micado_wide_mode(self):
         opt = scopesim.OpticalTrain(cmd)
         assert isinstance(opt, scopesim.OpticalTrain)
 
-        src = scopesim.source.source_templates.empty_sky()
-        opt.observe(src)
+        opt.observe()
         hdu_list = opt.readout()[0]
 
         assert isinstance(hdu_list, fits.HDUList)
@@ -99,8 +97,7 @@ def test_works_seamlessly_for_micado_zoom_mode(self):
         micado = scopesim.OpticalTrain(cmd)
         assert isinstance(micado, scopesim.OpticalTrain)
 
-        src = scopesim.source.source_templates.empty_sky()
-        micado.observe(src)
+        micado.observe()
         hdu_list = micado.readout()[0]
 
         assert isinstance(hdu_list, fits.HDUList)
@@ -117,8 +114,7 @@ def test_returns_ndit_dit_scaled_image(self, ndit, dit):
         micado["skycalc_atmosphere"].include = False
         micado["detector_linearity"].include = False
 
-        src = scopesim.source.source_templates.empty_sky()
-        micado.observe(src)
+        micado.observe()
         implane_image = micado.image_planes[0].data
 
         hdus = micado.readout()

From 41fdc1065180da3a41e9d009c2ef14e57f3dd761 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Fri, 18 Sep 2026 21:33:18 +0200
Subject: [PATCH 23/63] Replace None with Null in yamls

---
 MICADO/MICADO_IMG_HCI.yaml       | 4 ++--
 MICADO/MICADO_Standalone_RO.yaml | 2 +-
 MICADO/default.yaml              | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/MICADO/MICADO_IMG_HCI.yaml b/MICADO/MICADO_IMG_HCI.yaml
index 7aebcd2b..1bcfc547 100644
--- a/MICADO/MICADO_IMG_HCI.yaml
+++ b/MICADO/MICADO_IMG_HCI.yaml
@@ -24,7 +24,7 @@ effects :
     description: alterations to the PSF due to the coronagraph
     class: CoronagraphPSF
     kwargs:
-        filename: None
+        filename: Null
 
 ### set observation mode
 ---
@@ -36,4 +36,4 @@ properties :
     obs_mode: "IMG_HCI"
     fits_ins_mode: "LRI"
     fits_ins_coro: "CLC-15"
-    fits_ins_mask: "HighContrast"
\ No newline at end of file
+    fits_ins_mask: "HighContrast"
diff --git a/MICADO/MICADO_Standalone_RO.yaml b/MICADO/MICADO_Standalone_RO.yaml
index 2e578496..9c4e73a2 100644
--- a/MICADO/MICADO_Standalone_RO.yaml
+++ b/MICADO/MICADO_Standalone_RO.yaml
@@ -39,7 +39,7 @@ effects :
 #    class : FieldVaryingPSF
 #    include : False
 #    kwargs:
-#        filename : None
+#        filename : Null
 
 ### set instrument mode
 ---
diff --git a/MICADO/default.yaml b/MICADO/default.yaml
index 2c993f4c..690543f0 100644
--- a/MICADO/default.yaml
+++ b/MICADO/default.yaml
@@ -13,7 +13,7 @@ changes:
   - 2025-06-27 (FH) add needs_scopesim keyword
   - 2025-10-09 (OC) proper MJD value
   - 2025-10-17 (JB) add parametes for FITS keywords, tplstart as MJD
-  - 2026-01-02 (OC) random seed to None
+  - 2026-01-02 (OC) random seed to Null (i.e. None)
   - 2026-04-09 (OC) new SPEC mode, rename slits
   - 2026-07-28 (OC) new CALIB mode, full yaml list in mode definitions
 
@@ -220,7 +220,7 @@ description : RC simulation parameters which need to change for a MICADO run
 
 properties :
   random :
-    seed : None                 # 9001
+    seed : Null                 # 9001
 
   spectral :
     wave_min : 0.7

From 68b767de3cd57992be40402551bf7a406886c5f5 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Fri, 18 Sep 2026 21:44:39 +0200
Subject: [PATCH 24/63] Seeds None -> Null

---
 HAWKI/default.yaml    | 4 ++--
 LFOA/default.yaml     | 2 +-
 METIS/default.yaml    | 2 +-
 MOSAIC/default.yaml   | 2 +-
 NIRCam/default.yaml   | 2 +-
 OSIRIS/default.yaml   | 2 +-
 ViennaLT/default.yaml | 2 +-
 WFC3/default.yaml     | 2 +-
 8 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/HAWKI/default.yaml b/HAWKI/default.yaml
index 5dc7f076..f758f031 100644
--- a/HAWKI/default.yaml
+++ b/HAWKI/default.yaml
@@ -7,7 +7,7 @@ status: experimental
 date_modified: 2022-02-22
 changes:
   - 2022-02-22 (MV) formatting
-  - 2026-01-02 (OC) random seed to None
+  - 2026-01-02 (OC) random seed to Null
 
 packages:
   - Paranal
@@ -40,7 +40,7 @@ description : RC simulation parameters that need to change for a HAWKI run
 
 properties :
   random :
-    seed : None             # 9001
+    seed: Null
 
   spectral :
     wave_min : 0.85
diff --git a/LFOA/default.yaml b/LFOA/default.yaml
index 79213f09..319a3bd3 100644
--- a/LFOA/default.yaml
+++ b/LFOA/default.yaml
@@ -34,7 +34,7 @@ description : RC simulation parameters
 
 properties :
   random :
-    seed : None           # 9001
+    seed : Null
 
   spectral :
     wave_min : 0.3
diff --git a/METIS/default.yaml b/METIS/default.yaml
index ae377944..48b02f1e 100644
--- a/METIS/default.yaml
+++ b/METIS/default.yaml
@@ -532,7 +532,7 @@ description: RC simulation parameters which need to change for a METIS run
 
 properties:
   random:
-    seed: None                         # 9001
+    seed: Null                         # 9001
 
   spectral:
     wave_min: 2.65
diff --git a/MOSAIC/default.yaml b/MOSAIC/default.yaml
index c69a93d1..d6378a7d 100644
--- a/MOSAIC/default.yaml
+++ b/MOSAIC/default.yaml
@@ -246,7 +246,7 @@ description : RC simulation paramters which need to change for a MOSAIC run
 
 properties :
   random :
-    seed : None
+    seed : Null
 
   spectral :
     wave_min : 0.35
diff --git a/NIRCam/default.yaml b/NIRCam/default.yaml
index c360b261..48de3aec 100644
--- a/NIRCam/default.yaml
+++ b/NIRCam/default.yaml
@@ -59,7 +59,7 @@ alias: SIM
 
 properties:
   random:
-    seed: None                         # 9001
+    seed: Null
 
   spectral:
     wave_min: 0.6
diff --git a/OSIRIS/default.yaml b/OSIRIS/default.yaml
index 288298ae..b54fe6cf 100644
--- a/OSIRIS/default.yaml
+++ b/OSIRIS/default.yaml
@@ -85,7 +85,7 @@ description: RC simulation parameters which need to change for a OSIRIS run
 
 properties:
   random:
-    seed: None                         # 9001
+    seed: Null
 
   spectral:   # in [um]
     wave_min: 0.3001
diff --git a/ViennaLT/default.yaml b/ViennaLT/default.yaml
index 424b6153..025ae606 100644
--- a/ViennaLT/default.yaml
+++ b/ViennaLT/default.yaml
@@ -36,7 +36,7 @@ description : RC simulation parameters
 
 properties :
   random :
-    seed : None               # 9001
+    seed : Null
 
   spectral :
     wave_min : 0.3
diff --git a/WFC3/default.yaml b/WFC3/default.yaml
index 3ff7470a..114e620a 100644
--- a/WFC3/default.yaml
+++ b/WFC3/default.yaml
@@ -58,7 +58,7 @@ description: RC simulation parameters that need to change for a WFC3
 
 properties:
   random:
-    seed: None         # 9001
+    seed: Null
 
   spectral:
     wave_min: 0.8

From 819ea81e38d0511eca149334c2cf2006f6107e0c Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Sun, 20 Sep 2026 17:21:52 +0200
Subject: [PATCH 25/63] Largely cosmetic updates to notebooks (Scopesim #1034)

---
 .../example_notebooks/IMG_L_N-examples.ipynb  |   2 +-
 .../Introduction_to_Scopesim_for_METIS.ipynb  |  21 ++++++----
 .../Introduction_to_Scopesim_for_METIS.pdf    | Bin 41197 -> 41697 bytes
 .../LSS-YSO_model_simulation.ipynb            |   7 ++++
 .../LSS_AGN-01_preparation.ipynb              |  18 ++++++---
 .../LSS_AGN-02_simulation.ipynb               |  36 +++++++++++++++---
 .../METIS_IFU-01-Source_cube.ipynb            |  12 +++++-
 .../METIS_IFU-02-Simulation.ipynb             |  16 ++++++--
 8 files changed, 86 insertions(+), 26 deletions(-)

diff --git a/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb b/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb
index 6f59aca7..6f5e9e4e 100644
--- a/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb
+++ b/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Imaging observations of HL Tau"
+    "# L- and N-band Imaging Observations of HL Tau"
    ]
   },
   {
diff --git a/METIS/docs/example_notebooks/Introduction_to_Scopesim_for_METIS.ipynb b/METIS/docs/example_notebooks/Introduction_to_Scopesim_for_METIS.ipynb
index 7b450b3c..fcb30d74 100644
--- a/METIS/docs/example_notebooks/Introduction_to_Scopesim_for_METIS.ipynb
+++ b/METIS/docs/example_notebooks/Introduction_to_Scopesim_for_METIS.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to ScopeSim for METIS"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -11,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Installation and prerequisites\n",
+    "## Installation and prerequisites\n",
     "\n",
     "The easiest way to install Scopesim is to use `pip` from the command line:\n",
     "```sh\n",
@@ -42,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Instrument packages\n",
+    "## Instrument packages\n",
     "Scopesim is a general simulation framework. In order to simulate observations with any particular instrument, you will have to load an instrument package, as well as packages describing the telescope and observatory location (the latter is important for the atmospheric conditions). In the case of METIS, the packages can be downloaded with \n",
     "```python\n",
     "sim.download_packages(['METIS', 'telescopes/ELT', 'locations/Armazones'])\n",
@@ -65,7 +72,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Setting up the instrument\n",
+    "## Setting up the instrument\n",
     "The instrument is configured using the `sim.UserCommands()` class. A basic setup for METIS could be\n",
     "```python\n",
     "cmd = sim.UserCommands(use_instrument='METIS', set_modes=['img_lm'])\n",
@@ -110,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Building and modifying the instrument\n",
+    "## Building and modifying the instrument\n",
     "The combination of atmosphere, telescope and instrument is represented in scopesim as an `OpticalTrain` object, which is instantiated as\n",
     "```python\n",
     "metis = sim.OpticalTrain(cmd)\n",
@@ -139,7 +146,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Defining and observing the source\n",
+    "## Defining and observing the source\n",
     "The `observe()` method of an `OpticalTrain` transmits a source object through the atmosphere, telescope and instrument into the detector plane but does not yet include the detector itself (with the exception of the quantum efficiency, which is treated as a transmission effect). The result is an ideal noise-free image in units of electrons per second (electrons because of the inclusion of QE). \n",
     "\n",
     "The definition of `Source` objects is described in other notebooks. The observation is done by\n",
@@ -172,7 +179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Creating detector images\n",
+    "## Creating detector images\n",
     "The `readout()` method applies photon noise and detector noise to the image and creates detector images with a given exposure time. The result of `readout()` is a list of FITS HDULists (essentially FITS \"files\" in memory). For METIS only a single HDULIST is created, so a command like\n",
     "```python\n",
     "result = metis.readout()[0]\n",
@@ -218,7 +225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/METIS/docs/example_notebooks/Introduction_to_Scopesim_for_METIS.pdf b/METIS/docs/example_notebooks/Introduction_to_Scopesim_for_METIS.pdf
index ec70892a464476e046fb655d5cc348f852ccba99..1cc95371a5447c950c9f377913c02ade38626a2d 100644
GIT binary patch
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diff --git a/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb b/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb
index 782aa319..4cd5baf8 100644
--- a/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb
+++ b/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# METIS Long-Slit Spectroscopy of a Young Stellar Object"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb b/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb
index 43cddaae..db1cbc0c 100644
--- a/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb
+++ b/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# METIS LSS Observation of an AGN. Part 1: Source Preparation"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -21,8 +28,7 @@
     "from astropy.wcs import WCS\n",
     "\n",
     "from matplotlib import pyplot as plt\n",
-    "from matplotlib.colors import LogNorm\n",
-    "%matplotlib inline"
+    "from matplotlib.colors import LogNorm"
    ]
   },
   {
@@ -44,7 +50,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Preparing the data cube for Scopesim\n",
+    "## Preparing the data cube for Scopesim\n",
     "\n",
     "The data used in this notebook have to be downloaded from the Scopesim server. If you already have them, make sure that the files are in the current working directory; you can then skip the download step.\n",
     "\n",
@@ -112,7 +118,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tabular WCS"
+    "## Tabular WCS"
    ]
   },
   {
@@ -214,7 +220,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Visual inspection of the cube\n",
+    "## Visual inspection of the cube\n",
     "We first look at a plane of cube somewhere in the N-band, then produce a through-slit image in the N2-band using the widest slit available in METIS."
    ]
   },
@@ -280,7 +286,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Rotating the field\n",
+    "## Rotating the field\n",
     "The METIS slit is always aligned with the x-axis. For more interesting slit orientations, we actually have to rotate the input cube. We pick an angle of 50 degrees (counterclockwise from the positive x-direction)."
    ]
   },
diff --git a/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb b/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb
index 7eedd976..61536873 100644
--- a/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb
+++ b/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# METIS LSS Observation of an AGN. Part 2: Simulation with ScopeSim"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -15,8 +22,7 @@
    "source": [
     "from matplotlib import pyplot as plt\n",
     "from matplotlib.colors import LogNorm\n",
-    "from astropy.io import fits\n",
-    "%matplotlib inline"
+    "from astropy.io import fits"
    ]
   },
   {
@@ -101,6 +107,24 @@
     "hdul_01 = metis.readout(detector_readout_mode=\"auto\")[0]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make the readouts comparable we record DIT and NDIT from `hdul_01` (we could have obtained it from the optical train `metis` as well) and apply the same readout parameters for the second as well as the sky observation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dit = hdul_01[0].header[\"HIERARCH ESO DET DIT\"]\n",
+    "ndit = hdul_01[0].header[\"HIERARCH ESO DET NDIT\"]\n",
+    "print(f\"DIT = {dit:04f}, NDIT = {ndit}\") "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -108,7 +132,7 @@
    "outputs": [],
    "source": [
     "metis.observe(src_02, update=True)\n",
-    "hdul_02 = metis.readout(detector_readout_mode=\"auto\")[0]"
+    "hdul_02 = metis.readout(dit=dit, ndit=ndit)[0]"
    ]
   },
   {
@@ -118,7 +142,7 @@
    "outputs": [],
    "source": [
     "metis.observe(sky, update=True)\n",
-    "hdul_sky = metis.readout(detector_readout_mode=\"auto\")[0]"
+    "hdul_sky = metis.readout(dit=dit, ndit=ndit)[0]"
    ]
   },
   {
@@ -136,9 +160,9 @@
    "source": [
     "plt.figure(figsize=(16, 8))\n",
     "plt.subplot(121)\n",
-    "plt.imshow(hdul_01[1].data - hdul_sky[1].data + 1, origin='lower', norm=\"log\")\n",
+    "plt.imshow(hdul_01[1].data - hdul_sky[1].data, origin='lower', norm=\"log\")\n",
     "plt.subplot(122)\n",
-    "plt.imshow(hdul_02[1].data - hdul_sky[1].data + 1, origin='lower', norm=\"log\")"
+    "plt.imshow(hdul_02[1].data - hdul_sky[1].data, origin='lower', norm=\"log\")"
    ]
   },
   {
diff --git a/METIS/docs/example_notebooks/METIS_IFU-01-Source_cube.ipynb b/METIS/docs/example_notebooks/METIS_IFU-01-Source_cube.ipynb
index 46e8daee..8c120db8 100644
--- a/METIS/docs/example_notebooks/METIS_IFU-01-Source_cube.ipynb
+++ b/METIS/docs/example_notebooks/METIS_IFU-01-Source_cube.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7eba959c-8980-4cb1-a975-36d9d900e659",
+   "metadata": {},
+   "source": [
+    "# METIS IFU. Part 1: Creation of a Source Cube"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0011d317-3bf9-4959-bd77-4c3af2b87f80",
@@ -48,7 +56,7 @@
    "id": "8ad081b7-cf7e-4ceb-ab08-5e98ca9c781a",
    "metadata": {},
    "source": [
-    "The idea is to use an (ALMA) image of HL Tau and turn it into a spectral cube including Doppler shifts due to disk rotation. The image is taken for our server."
+    "The idea is to use an (ALMA) image of HL Tau and turn it into a spectral cube including Doppler shifts due to disk rotation. The image is taken from our server."
    ]
   },
   {
@@ -78,7 +86,7 @@
    "id": "e2a8958d-16c5-449f-976b-4098e7da45f5",
    "metadata": {},
    "source": [
-    "Any fits file that is to be used as source for Scopesim needs to have `BUNIT` keyword, which specifies the units of the data values, as well as the WCS keywords, from which Scopesim takes the pixel scale."
+    "Any fits file that is to be used as source for Scopesim needs to have the `BUNIT` keyword, which specifies the units of the data values, as well as the WCS keywords, from which Scopesim takes the pixel scale."
    ]
   },
   {
diff --git a/METIS/docs/example_notebooks/METIS_IFU-02-Simulation.ipynb b/METIS/docs/example_notebooks/METIS_IFU-02-Simulation.ipynb
index e6c44de2..fd1376f9 100644
--- a/METIS/docs/example_notebooks/METIS_IFU-02-Simulation.ipynb
+++ b/METIS/docs/example_notebooks/METIS_IFU-02-Simulation.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ec06eac8-435a-4cb9-9600-faeac05b42cd",
+   "metadata": {},
+   "source": [
+    "# METIS IFU. Part 2: Simulation"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d6459b98-1019-4152-9b28-12ffc312b3ed",
@@ -78,7 +86,7 @@
    "id": "00144738-6b36-4d08-b1c2-b3b448a37c9f",
    "metadata": {},
    "source": [
-    "# Setting up the instrument\n",
+    "## Setting up the instrument\n",
     "\n",
     "The first step is to set up the instrument in the desired configuration. There are currently two modes that use the LM spectrograph and that are of interest for simulating science observations:\n",
     "- `lms`: This is the nominal lms/ifu mode that produces raw two-dimensional detector images in the way that METIS will do. To analyse these data requires a data reduction pipeline, which is not currently available. Scopesim includes a function that allows rectifying these images into a data cube with well-defined spatial and spectral dimensions as we shall demonstrate later in this notebook. This cube corresponds to a single exposure and under-samples the spatial PSF in the across-slice direction. \n",
@@ -94,7 +102,7 @@
    "id": "7c81a867-2ac1-48ea-a533-d38615665f19",
    "metadata": {},
    "source": [
-    "# Using the nominal LMS mode"
+    "## Using the nominal LMS mode"
    ]
   },
   {
@@ -168,7 +176,7 @@
    "id": "9315931c-e959-413b-8b00-c537ba5ade65",
    "metadata": {},
    "source": [
-    "## Observation of blank sky\n",
+    "### Observation of blank sky\n",
     "We start by simply observing a piece of blank sky. This way we don't have to worry about setting up an astronomical source, which we will however do shortly. IFU simulations are fairly complex and therefore need some time. Much of that time is spent on PSF convolution, which has no effect when the target fills the field of view homogeneously as blank sky does. One could turn off PSF convolution entirely by doing `metis['psf'].include = False` but one needs to be careful to turn it back on again when observing an actual source. Another way to speed things up is not to scale the PSF by wavelength - this is indeed admissable in LMS observations which cover a short wavelength range (one should not do in long-slit simulations where the PSF at the red end is nearly twice as large as at the blue end). We have already switched off PSF scaling (except to the central wavelength) above. "
    ]
   },
@@ -253,7 +261,7 @@
    "id": "4b117404-1e3e-49cc-ad9e-02326f8e8335",
    "metadata": {},
    "source": [
-    "## Observation of a source\n",
+    "### Observation of a source\n",
     "\n",
     "We take the source from the fits file that we created in the previous notebook, `METIS_IFU-01-Source_cube.ipynb`."
    ]

From d948d40d919748ff1b88ecf1e45c82d0bdbb7c82 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Sun, 20 Sep 2026 18:25:12 +0200
Subject: [PATCH 26/63] Update first batch of MICADO notebooks

---
 .../1_scopesim_MCAO_4mas_galaxy-TLDR.ipynb    |  47 +----
 .../1_scopesim_MCAO_4mas_galaxy.ipynb         |  86 ++------
 .../2_scopesim_SCAO_1.5mas_astrometry.ipynb   | 188 +++---------------
 .../4_scopesim_SPEC_pipe_galaxy.ipynb         | 112 ++---------
 4 files changed, 68 insertions(+), 365 deletions(-)

diff --git a/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy-TLDR.ipynb b/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy-TLDR.ipynb
index 1aa93dce..3f711ff0 100644
--- a/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy-TLDR.ipynb
+++ b/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy-TLDR.ipynb
@@ -21,18 +21,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "cc2e788f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://scopesim.univie.ac.at/scopesim_templates/spiral_two_component.fits\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import datetime\n",
     "from astropy import units as u\n",
@@ -64,45 +56,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "1698c2ba",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f818d47edd0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import LogNorm\n",
     "\n",
-    "plt.figure(figsize=(10,10))\n",
-    "plt.imshow(hdus[0][1].data, norm=LogNorm(vmin=1e3))\n",
-    "plt.colorbar()"
+    "plt.imshow(hdus[0][1].data, norm=LogNorm(vmin=1e3), origin='lower')\n",
+    "plt.colorbar();"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d7007a3",
+   "id": "cec6c28c-12ab-44e6-8fb9-8419797b7766",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -124,7 +93,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy.ipynb b/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy.ipynb
index e9987ce1..abb80ed9 100644
--- a/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy.ipynb
+++ b/MICADO/docs/example_notebooks/1_scopesim_MCAO_4mas_galaxy.ipynb
@@ -13,12 +13,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "standard-origin",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import os\n",
     "import datetime\n",
     "import numpy as np\n",
@@ -62,18 +61,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "expanded-collins",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://scopesim.univie.ac.at/scopesim_templates/spiral_two_component.fits\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "gal = sim_tp.extragalactic.galaxies.spiral_two_component(extent=16*u.arcsec, fluxes=(15, 15)*u.mag)"
    ]
@@ -88,31 +79,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "medieval-associate",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f4e2b3ca950>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1500x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "wave = np.linspace(0.3, 2.5, 2201) * u.um\n",
     "plt.figure(figsize=(15, 10))\n",
@@ -145,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "distant-brand",
    "metadata": {},
    "outputs": [],
@@ -166,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "floral-screw",
    "metadata": {},
    "outputs": [],
@@ -205,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "second-works",
    "metadata": {},
    "outputs": [],
@@ -223,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "acting-strengthening",
    "metadata": {},
    "outputs": [],
@@ -250,44 +220,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "addressed-permit",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f4e2b438990>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.figure(figsize=(10,10))\n",
     "plt.imshow(hdus[0][1].data, norm=LogNorm(vmin=1e3))\n",
-    "plt.colorbar()"
+    "plt.colorbar();"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f213c77",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -306,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/MICADO/docs/example_notebooks/2_scopesim_SCAO_1.5mas_astrometry.ipynb b/MICADO/docs/example_notebooks/2_scopesim_SCAO_1.5mas_astrometry.ipynb
index 970d6b25..5e202707 100644
--- a/MICADO/docs/example_notebooks/2_scopesim_SCAO_1.5mas_astrometry.ipynb
+++ b/MICADO/docs/example_notebooks/2_scopesim_SCAO_1.5mas_astrometry.ipynb
@@ -15,21 +15,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "dangerous-civilization",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\ghost\\AppData\\Local\\Temp\\ipykernel_18404\\2370635355.py:9: DeprecationWarning: In a future version top level function calls will be removed. Always use this syntax: from module.submodule import function\n",
-      "  import scopesim_templates as sim_tp\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import datetime\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -71,42 +61,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "appreciated-english",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['A0V']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x12737344a90>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "stars = sim_tp.star_grid(n=4, mmin=15, mmax=15, filter_name=\"Ks\", separation=0.032)\n",
     "\n",
-    "plt.scatter(stars.fields[0][\"x\"], stars.fields[0][\"y\"])"
+    "plt.scatter(stars.fields[0][\"x\"], stars.fields[0][\"y\"]);"
    ]
   },
   {
@@ -131,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "freelance-custody",
    "metadata": {},
    "outputs": [],
@@ -176,11 +138,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "informative-marathon",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "micado = sim.OpticalTrain(cmd)\n",
@@ -200,34 +160,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "suited-destination",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x127376cb910>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10,10))\n",
-    "plt.imshow(hdus[0][1].data, norm=LogNorm(), origin=\"lower\")"
+    "plt.imshow(hdus[0][1].data, norm=LogNorm(), origin=\"lower\");"
    ]
   },
   {
@@ -242,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "strange-delhi",
    "metadata": {},
    "outputs": [],
@@ -261,11 +200,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "cubic-integer",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "micado.observe(stars)\n",
@@ -282,34 +219,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "structural-wound",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1273748bf90>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10,10))\n",
-    "plt.imshow(hdus2[0][1].data, norm=LogNorm(), origin=\"lower\")"
+    "plt.imshow(hdus2[0][1].data, norm=LogNorm(), origin=\"lower\");"
    ]
   },
   {
@@ -332,31 +248,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "mechanical-oxygen",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x12737365790>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from copy import deepcopy\n",
     "t0_cluster = sim_tp.cluster(mass=10, core_radius=0.002, distance=8000)\n",
@@ -369,7 +264,7 @@
     "\n",
     "plt.scatter(t0_cluster.fields[0][\"x\"], t0_cluster.fields[0][\"y\"], c=\"b\", label=\"t0\")\n",
     "plt.scatter(t1_cluster.fields[0][\"x\"], t1_cluster.fields[0][\"y\"], c=\"r\", label=\"t1\")\n",
-    "plt.legend(loc=2)"
+    "plt.legend(loc=2);"
    ]
   },
   {
@@ -387,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "driving-prayer",
    "metadata": {},
    "outputs": [],
@@ -430,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "vocational-juice",
    "metadata": {},
    "outputs": [],
@@ -450,52 +345,23 @@
     "In contrast to the grid of stars being observed with the wide-field mode, the zoom-mode enables us to see much more of the PSF structure. \n",
     "The shift in star positions is also quite obvious.\n",
     "\n",
-    "Each ``fits.HDUList`` could also ahve been saved to disk as a regular FITS file by passing a ``filename=`` keyword to the ``readout``method call.\n",
+    "Each ``fits.HDUList`` could also have been saved to disk as a regular FITS file by passing a ``filename=`` keyword to the ``readout``method call.\n",
     "Alternatively we could use the standard ``astropy.fits`` functionality (i.e. the ``writeto()`` method) to save the ``HDUList``s to disk."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "experienced-karaoke",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x12738721810>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(15,8))\n",
     "plt.subplot(121)\n",
     "plt.imshow(t0_hdus[0][1].data, norm=LogNorm(), origin=\"lower\")\n",
     "plt.subplot(122)\n",
-    "plt.imshow(t1_hdus[0][1].data, norm=LogNorm(), origin=\"lower\")"
+    "plt.imshow(t1_hdus[0][1].data, norm=LogNorm(), origin=\"lower\");"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "grave-emission",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -514,7 +380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/MICADO/docs/example_notebooks/4_scopesim_SPEC_pipe_galaxy.ipynb b/MICADO/docs/example_notebooks/4_scopesim_SPEC_pipe_galaxy.ipynb
index 16da3c51..42f8818e 100644
--- a/MICADO/docs/example_notebooks/4_scopesim_SPEC_pipe_galaxy.ipynb
+++ b/MICADO/docs/example_notebooks/4_scopesim_SPEC_pipe_galaxy.ipynb
@@ -13,21 +13,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "fifty-beginning",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\ghost\\AppData\\Local\\Temp\\ipykernel_2940\\97098804.py:9: DeprecationWarning: In a future version top level function calls will be removed. Always use this syntax: from module.submodule import function\n",
-      "  import scopesim_templates as sim_tp\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import os\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -74,18 +64,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "asian-cisco",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://scopesim.univie.ac.at/scopesim_templates/spiral_two_component.fits\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "gal = sim_tp.extragalactic.galaxies.spiral_two_component(extent=16*u.arcsec, fluxes=(15, 15)*u.mag)\n",
     "gal.shift(dy=-30*0.004)"
@@ -104,21 +86,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "polish-testimony",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "wave = np.linspace(0.7, 2.2, 1001) * u.um\n",
     "\n",
@@ -127,7 +98,7 @@
     "    plt.subplot(2,2,2*i+1)\n",
     "    plt.imshow(gal.fields[i].data, origin=\"lower\")\n",
     "    plt.subplot(2,2,2*i+2)\n",
-    "    plt.plot(wave, gal.spectra[i](wave), c=c)"
+    "    plt.plot(wave, gal.spectra[i](wave), c=c);"
    ]
   },
   {
@@ -140,34 +111,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "imperial-aquatic",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1a23c788190>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(15,3))\n",
-    "plt.imshow(gal.fields[1].data[625:635, 400:800], origin=\"lower\")"
+    "plt.imshow(gal.fields[1].data[625:635, 400:800], origin=\"lower\");"
    ]
   },
   {
@@ -187,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "recognized-switch",
    "metadata": {},
    "outputs": [],
@@ -209,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "0466008b",
    "metadata": {},
    "outputs": [],
@@ -230,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "28dc2278",
    "metadata": {},
    "outputs": [],
@@ -248,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "reported-judge",
    "metadata": {},
    "outputs": [],
@@ -271,37 +221,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "molecular-grill",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Slit position')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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/3ANQlgAKAAAAgKIEUAAAAAAUZQg5AAAAUFRvkp4az5vrHegCdgM6oAAA4E3OEHIAShNAAQDAm5wh5ACUJoACAAAAoCgzoAAAAICiejMovTXuganz2VrFdwgAAACAogRQAAAAABQlgAIAAACgKAEUAAAAAEUZQg4AAAAU1VMNSk9V3x6YOp+tVXyHAAAAAChKAAUAAABAUQIoAAAAAIoyAwoAAAAoqjeN9KYx0GUUU+eztYoOKAAAAACKEkABAMCbXG/lX+4BKEsABQAAb3KDGtVAlwBAzQmgAAAAACjKEHIAAACgqJ5qUHqq+vbA1PlsreI7BAAAAEBRAigAAAAAihJAAQAAAFCUGVAAAABAUT0ZlJ4a98DU+Wyt4jsEAAAAQFECKAAAeJPbPuPpLPrpOwa6DABqTAAFAABvcr3PP5+eNAa6DABqTAAFAACkt/KnAQDlGEIOAAAAFNVbNdJb1bfTss5naxX/zAEAAABAUQIoAAAAAIoSQAEAAABQlBlQAABABjV6B7oEoMZ6Myg9Ne6B6a3x2VrFdwgAAACAogRQAABAeit/GgBQjt8yAACAR/AAKEoABQAA6IACoChDyAEAAICieqtBtQ6663y2VvEdAgAAAKAoARQAAGAGFABFCaAAAAAAKEoABQAAmF8CQFGGkAMAAABF9aSRnjQGuoxi6ny2VvHPHAAAgBlQABQlgAIAADyCB0BRfssAAAA6oAAoygwoAABABxRQVG81qNb/P1Pns7WK7xAAAAAARQmgAAAAj+ABUJQACgAA8PgIAEX5LQMAAABAUYaQAwAAHsEDiupJ0pPGQJdRTM9AF7Ab0AEFAAAAQFECKAAAAACKEkABAACGkANQlBlQAACAGVBAUb3VoFoH3XU+W6v4DgEAAP54AqAov2UAAAAAKEoABQAAAEBRAigAAAAAijKEHAAAACiqpxqUnhrPmqvz2VrFdwgAAACAogRQAAAAABQlgAIAADKo0TvQJQBQY2ZAAQAA6TW/BCioSiO9aQx0GcVUNT5bq/gtAwAAAEBRAigAAAAAihJAAQAAZkABUJQACgAAAICiDCEHAAAAiuqpBqWnxm92UOeztYrvEAAA4F3wAF5Hd9xxR44//vh0dnam0Wjk61//evPe9u3b85nPfCaTJ0/OiBEj0tnZmT/8wz/Mj3/84z6fY9u2bTn99NMzduzYjBgxIjNnzswTTzzRZ8+mTZsye/bstLe3p729PbNnz87TTz/dZ8/atWtz/PHHZ8SIERk7dmzOOOOMdHd399lz//33Z9q0aRk+fHje+ta35vzzz09VVbt0Zr9lAAAAAF5HW7Zsya//+q9n8eLFO9177rnnct999+Vzn/tc7rvvvvz93/99HnnkkcycObPPvnnz5uWWW27JkiVLsmLFijz77LM57rjj0tPT09wza9asrF69OkuXLs3SpUuzevXqzJ49u3m/p6cnxx57bLZs2ZIVK1ZkyZIl+drXvpb58+c392zevDlHHXVUOjs7s3LlylxxxRW55JJLsmjRol06s0fwAAAAQ8gBXkfHHHNMjjnmmJe9197enuXLl/e5dsUVV+Q3f/M3s3bt2rztbW9LV1dXrr322txwww058sgjkyQ33nhjJkyYkNtuuy0zZszIQw89lKVLl+buu+/OwQcfnCS55pprMnXq1Dz88MPZb7/9smzZsjz44INZt25dOjs7kySXXnppTj755FxwwQUZNWpUbrrppjz//PO57rrr0tbWlkmTJuWRRx7JokWLcuaZZ6bRaPTrzDqgAAAAj+ABRfVWjdqvkrq6utJoNPKWt7wlSbJq1aps374906dPb+7p7OzMpEmTcueddyZJ7rrrrrS3tzfDpyR5//vfn/b29j57Jk2a1AyfkmTGjBnZtm1bVq1a1dwzbdq0tLW19dnz4x//OI899li/z+C3DAAAAEALbN68uc/atm3ba/6czz//fD772c9m1qxZGTVqVJJkw4YNGTZsWEaPHt1n7/jx47Nhw4bmnnHjxu30+caNG9dnz/jx4/vcHz16dIYNG/aKe178+MU9/SGAAgAAAGiBCRMmNAd+t7e3Z+HCha/p823fvj0f/ehH09vbm7/+67/+hfurqurzSNzLPR7Xij0vDiDv7+N3iRlQAAAAAC2xbt26ZpdSkj6Pre2q7du358QTT8yjjz6af/mXf+nzeTs6OtLd3Z1Nmzb16YLauHFjDjnkkOaen/zkJzt93qeeeqrZwdTR0ZF77rmnz/1NmzZl+/btffa8tNNp48aNSbJTZ9Qr0QEFAAAA0AKjRo3qs15tAPVi+PSDH/wgt912W/baa68+96dMmZKhQ4f2GVa+fv36rFmzphlATZ06NV1dXbn33nube+655550dXX12bNmzZqsX7++uWfZsmVpa2vLlClTmnvuuOOOdHd399nT2dmZffbZp99n0gEFAAB4FzygqJ4MSk+Ne2B29WzPPvtsfvjDHzY/fvTRR7N69eqMGTMmnZ2d+Z//83/mvvvuyz/+4z+mp6en2YE0ZsyYDBs2LO3t7TnllFMyf/787LXXXhkzZkzOOuusTJ48ufmuePvvv3+OPvrozJkzJ1dddVWS5BOf+ESOO+647LfffkmS6dOn54ADDsjs2bNz8cUX56c//WnOOuuszJkzp9lxNWvWrHzxi1/MySefnD/7sz/LD37wgyxYsCCf//znPYIHAADsGu+CB/D6+d73vpfDDz+8+fGZZ56ZJPnYxz6W8847L9/4xjeSJO9973v7vO473/lODjvssCTJZZddliFDhuTEE0/M1q1bc8QRR+S6667L4MGDm/tvuummnHHGGc13y5s5c2YWL17cvD948OB861vfyty5c3PooYdm+PDhmTVrVi655JLmnvb29ixfvjynnXZaDjrooIwePTpnnnlms+b+alQvTo56E9u8eXPa29tzWE7IkMbQgS4HAABed799/5Z8Zq8fDHQZ8Kax+ZnejH7Xj9LV1dVntk/dvPj39rx/m5m2X6rv39vbnt2eyw/9Ru1/nq+Ff+YAAAA8ggdAUR7BAwAAPIIHFNVbNdJb9X9e0O6mzmdrFb9lAAAAAChKAAUAAABAUQIoAAAgPR7BA6Agv2UAAIDc/p7hufv5noEuA4CaMoQcAAAAKKo3g9Jb4x6YOp+tVXyHAAAAAChKAAUAAABAUQIoAAAAAIoyAwoAAAAoqqdqpKdqDHQZxdT5bK2iAwoAAACAogRQAAAAABQlgAIAAACgKAEUAAAAAEUZQg4AAAAU1Vs10lvjQd11Plur6IACAAAAoCgBFAAAAABFCaAAAAAAKMoMKAAAAKCoqhqU3qq+PTBVjc/WKr5DAAAAABQlgAIAAACgKAEUAAAAAEUJoAAAAAAoyhByAAAAoKieNNKTxkCXUUydz9YqOqAAAAAAKEoABQAAAEBRAigAAAAAijIDCgAAACiqt0p6q/rOSeqtBrqCNz4dUAAAAAAUJYACAAAAoCgBFAAAAABFCaAAAAAAKMoQcgAAAKCo3mpQeqv69sDU+Wyt4jsEAAAAQFECKAAAAACKEkABAAAAUJQZUAAAAEBRvWmkN42BLqOYOp+tVXRAAQAAAFCUAAoAAACAogRQAAAAABQlgAIAAACgKEPIAQAAgKJ6qkZ6qvoO6q7z2VpFBxQAAAAARQmgAAAAAChKAAUAAABAUWZAAQAAAEX1VoPSW9W3B6bOZ2sV3yEAAAAAihJAAQAAAFCUAAoAAACAogRQAAAAABRlCDkAAABQVG8a6a0aA11GMb2p79laRQcUAAAAAEUJoAAAAAAoSgAFAAAAQFFmQAEAAABFVWnUek5SVeOztYoOKAAAAACKEkABAAAAUJQACgAAAICiBFAAAAAAFGUIOQAAAFBUb9VIb1XfQd11Plur6IACAAAAoCgBFAAAAABFDWgAtXDhwrzvfe/LyJEjM27cuHzoQx/Kww8/3GdPVVU577zz0tnZmeHDh+ewww7LAw880GfPtm3bcvrpp2fs2LEZMWJEZs6cmSeeeOL1PAoAAAAAP8eABlC33357TjvttNx9991Zvnx5fvazn2X69OnZsmVLc89FF12URYsWZfHixVm5cmU6Ojpy1FFH5ZlnnmnumTdvXm655ZYsWbIkK1asyLPPPpvjjjsuPT09A3EsAADYLXVn8ECXANRUbzWo9otX1qiqqhroIl701FNPZdy4cbn99tvzwQ9+MFVVpbOzM/PmzctnPvOZJDu6ncaPH58LL7wwp556arq6uvLLv/zLueGGG/KRj3wkSfLjH/84EyZMyD/90z9lxowZv/Drbt68Oe3t7TksJ2RIY2jRMwIAwBvZknV3ZvTgPQe6DKi9zc/0ZvS7fpSurq6MGjVqoMsp5sW/t//H8j/K0BHDBrqcYrZv6c4tR3259j/P1+INFdF1dXUlScaMGZMkefTRR7Nhw4ZMnz69uaetrS3Tpk3LnXfemSRZtWpVtm/f3mdPZ2dnJk2a1NzzUtu2bcvmzZv7LAAAAADKeMMEUFVV5cwzz8wHPvCBTJo0KUmyYcOGJMn48eP77B0/fnzz3oYNGzJs2LCMHj365+55qYULF6a9vb25JkyY0OrjAAAAAPCCN0wA9alPfSr/8R//ka9+9as73Ws0Gn0+rqpqp2sv9Up7zjnnnHR1dTXXunXrXn3hAAAAALyiIQNdQJKcfvrp+cY3vpE77rgjv/Irv9K83tHRkWRHl9Pee+/dvL5x48ZmV1RHR0e6u7uzadOmPl1QGzduzCGHHPKyX6+trS1tbW0ljgIAAAC8RG/VSG/1yo0ku7M6n61VBrQDqqqqfOpTn8rf//3f51/+5V+y77779rm/7777pqOjI8uXL29e6+7uzu23394Ml6ZMmZKhQ4f22bN+/fqsWbPm5wZQAAAAALx+BrQD6rTTTsvNN9+cf/iHf8jIkSObM5va29szfPjwNBqNzJs3LwsWLMjEiRMzceLELFiwIHvuuWdmzZrV3HvKKadk/vz52WuvvTJmzJicddZZmTx5co488siBPB4AAAAAGeAA6sorr0ySHHbYYX2uf/nLX87JJ5+cJDn77LOzdevWzJ07N5s2bcrBBx+cZcuWZeTIkc39l112WYYMGZITTzwxW7duzRFHHJHrrrsugwcPfr2OAgAAAMDP0aiqqhroIgba5s2b097ensNyQoY0hg50OQAAMGCWrLszowfvOdBlQO1tfqY3o9/1o3R1dWXUqFEDXU4xL/69ffyyUzJ0xLCBLqeY7Vu6883p19b+5/lavGHeBQ8AAACAehJAAQAAAFCUAAoAAACAogRQAAAAABQ1oO+CBwAAANRfb9VIb9UY6DKKqfPZWkUHFAAAAABFCaAAAAAAKEoABQAAAEBRZkABAAAARZkBhQ4oAAAAAIoSQAEAAABQlAAKAAAAgKIEUAAAAAAUZQg5AAAAUJQh5OiAAgAAAKAoARQAAAAARQmgAAAAACjKDCgAAACgKDOg0AEFAAAAQFECKAAAAACKEkABAAAAUJQACgAAAICiDCEHAAAAiqqS9Ka+g7qrgS5gN6ADCgAAAICiBFAAAAAAFCWAAgAAAKAoM6AAAACAonqrRnqr+s6AqvPZWkUHFAAAAABFCaAAAAAAKEoABQAAAEBRAigAAAAAijKEHAAAACjKEHJ0QAEAAABQlAAKAAAAgKIEUAAAAAAUZQYUAAAAUJQZUOiAAgAAAKAoARQAAAAARQmgAAAAAChKAAUAAABAUYaQAwAAAEUZQo4OKAAAAACKEkABAAAAUJQACgAAAICizIACAAAAiqqqRqoaz0mq89laRQcUAAAAAEUJoAAAAAAoSgAFAAAAQFECKAAAAACKMoQcAAAAKKo3jfSmvoO663y2VtEBBQAAAEBRAigAAKDp4e1tA10CADUkgAIAAJrOe9fBA10CADVkBhQAAABQVG/VSG9V3zlJdT5bq+iAAgAAAKAoARQAAAAARQmgAAAAAChKAAUAAABAUYaQAwAAAEVVVSNVjQd11/lsraIDCgAAAICiBFAAAAAAFCWAAgAAAKAoM6AAAACAonqrRnprPCepzmdrFR1QAAAAABQlgAIAAACgKAEUAAAAAEUJoAAAAAAoyhByAAAAoKiqaqSq8aDuOp+tVXRAAQAAAFCUAAoAAACAogRQAAAAAK+jO+64I8cff3w6OzvTaDTy9a9/vc/9qqpy3nnnpbOzM8OHD89hhx2WBx54oM+ebdu25fTTT8/YsWMzYsSIzJw5M0888USfPZs2bcrs2bPT3t6e9vb2zJ49O08//XSfPWvXrs3xxx+fESNGZOzYsTnjjDPS3d3dZ8/999+fadOmZfjw4XnrW9+a888/P1VV7dKZBVAAAABAUVXVSG+N167OgNqyZUt+/dd/PYsXL37Z+xdddFEWLVqUxYsXZ+XKleno6MhRRx2VZ555prln3rx5ueWWW7JkyZKsWLEizz77bI477rj09PQ098yaNSurV6/O0qVLs3Tp0qxevTqzZ89u3u/p6cmxxx6bLVu2ZMWKFVmyZEm+9rWvZf78+c09mzdvzlFHHZXOzs6sXLkyV1xxRS655JIsWrRol85sCDkAAADA6+iYY47JMccc87L3qqrK5ZdfnnPPPTcf/vCHkyTXX399xo8fn5tvvjmnnnpqurq6cu211+aGG27IkUcemSS58cYbM2HChNx2222ZMWNGHnrooSxdujR33313Dj744CTJNddck6lTp+bhhx/Ofvvtl2XLluXBBx/MunXr0tnZmSS59NJLc/LJJ+eCCy7IqFGjctNNN+X555/Pddddl7a2tkyaNCmPPPJIFi1alDPPPDONRv/CNx1QAAAAAC2wefPmPmvbtm27/DkeffTRbNiwIdOnT29ea2try7Rp03LnnXcmSVatWpXt27f32dPZ2ZlJkyY199x1111pb29vhk9J8v73vz/t7e199kyaNKkZPiXJjBkzsm3btqxataq5Z9q0aWlra+uz58c//nEee+yxfp9LAAUAAADQAhMmTGjOW2pvb8/ChQt3+XNs2LAhSTJ+/Pg+18ePH9+8t2HDhgwbNiyjR49+xT3jxo3b6fOPGzeuz56Xfp3Ro0dn2LBhr7jnxY9f3NMfHsEDAAAAaIF169Zl1KhRzY//e9fQrnrpo21VVf3Cx91euufl9rdiz4sDyPv7+F2iAwoAAAAorEpSVTVeL5xz1KhRfdarCaA6OjqS7NxdtHHjxmbnUUdHR7q7u7Np06ZX3POTn/xkp8//1FNP9dnz0q+zadOmbN++/RX3bNy4McnOXVqvRAAFAAAA8Aax7777pqOjI8uXL29e6+7uzu23355DDjkkSTJlypQMHTq0z57169dnzZo1zT1Tp05NV1dX7r333uaee+65J11dXX32rFmzJuvXr2/uWbZsWdra2jJlypTmnjvuuCPd3d199nR2dmafffbp97kEUAAAAACvo2effTarV6/O6tWrk+wYPL569eqsXbs2jUYj8+bNy4IFC3LLLbdkzZo1Ofnkk7Pnnntm1qxZSZL29vaccsopmT9/fv75n/853//+9/MHf/AHmTx5cvNd8fbff/8cffTRmTNnTu6+++7cfffdmTNnTo477rjst99+SZLp06fngAMOyOzZs/P9738///zP/5yzzjorc+bMaT5KOGvWrLS1teXkk0/OmjVrcsstt2TBggW79A54iRlQAAAAAK+r733vezn88MObH5955plJko997GO57rrrcvbZZ2fr1q2ZO3duNm3alIMPPjjLli3LyJEjm6+57LLLMmTIkJx44onZunVrjjjiiFx33XUZPHhwc89NN92UM844o/lueTNnzszixYub9wcPHpxvfetbmTt3bg499NAMHz48s2bNyiWXXNLc097enuXLl+e0007LQQcdlNGjR+fMM89s1txfjerFyVFvYps3b057e3sOywkZ0hg60OUAAMCAaQwZkqVrvzfQZUDtbX6mN6Pf9aN0dXX1GVpdNy/+vX3g352ZwXu++oHcb3Q9z23L9//notr/PF8LHVAAAABAUb1ppJH+P661u+mt8dlaxQwoAAAAAIoSQAEAAABQlAAKAAAAgKLMgAIAAACKqqpGqqq+c5LqfLZW0QEFAAAAQFECKAAAAACKEkABAAAAUJQACgAAAICiDCEHAAAAiuqtGmnUeFB3b43P1io6oAAAAAAoSgAFAAAAQFECKAAAAACKMgMKAAAAKKqqdqy6qvPZWkUHFAAAAABFCaAAAAAAKEoABQAAAEBRAigAAAAAijKEHAAAACiqqhqpqsZAl1FMnc/WKjqgAAAAAChKAAUAAABAUQIoAAAAAIoyAwoAAAAoygwodEABAAAAUJQACgAAAICiBFAAAAAAFCWAAgAAAKAoQ8gBAACAonqrRho1HtTdW+OztYoOKAAAAACKEkABAAAAUJQACgAAaKp6qzyyfctAlwFAzQigAACA/6u3J/MO+tBAVwHUTFXVf/HKBFAAAAAAFCWAAgAAAKAoARQAAAAARQmgAAAAAChqyEAXAAAAANTbjkHdjYEuoxhDyH8xHVAAAAAAFCWAAgAAAKAoARQAAAAARZkBBQAAABRVVY2az4Cq79laRQcUAAAAAEUJoAAAAAAoSgAFAAAAQFECKAAAAACKMoQcAAAAKKp6YdVVnc/WKjqgAAAAAChKAAUAAABAUQIoAAAAAIoyAwoAAAAoqqoaqarGQJdRTJ3P1io6oAAAAAAoSgAFAAAAQFECKAAAAACKEkABAAAAUJQh5AAAAEBZ1Qurrup8thbRAQUAAABAUQIoAAAAAIoSQAEAAABQlBlQAAAAQFlVI1XVGOgqyqnz2VpEBxQAAAAARQmgAAAAAChKAAUAAABAUQIoAAAAAIoyhBwAAAAoqqp2rLqq89laRQcUAAAAAEUJoAAAAAAoSgAFAAAAQFFmQAEAAABFVVUjVdUY6DKKqfPZWkUHFAAAAABFCaAAAAAAKEoABQAAAEBRAigAAAAAijKEHAAAACirauxYdVXns7WIDigAAAAAihJAAQAAAFCUAAoAAACAosyAAgAAAIqqqh2rrup8tlbRAQUAAABAUQIoAAAAAIoSQAEAAABQlAAKAAAAgKIMIQcAAADKql5YdVXns7WIDigAAAAAihJAAQAAAFCUAAoAAACAosyAAgAAAIqqqkaqqjHQZRRT57O1ig4oAAAAAIoSQAEAAABQlAAKAAAAgKIEUAAAAAAU9YYJoBYuXJhGo5F58+Y1r1VVlfPOOy+dnZ0ZPnx4DjvssDzwwAN9Xrdt27acfvrpGTt2bEaMGJGZM2fmiSeeeJ2rBwAAAF5RVePFL/SGCKBWrlyZq6++Ou95z3v6XL/ooouyaNGiLF68OCtXrkxHR0eOOuqoPPPMM8098+bNyy233JIlS5ZkxYoVefbZZ3Pcccelp6fn9T4GAADUQ29P/r9n2we6CgBqZMADqGeffTYnnXRSrrnmmowePbp5vaqqXH755Tn33HPz4Q9/OJMmTcr111+f5557LjfffHOSpKurK9dee20uvfTSHHnkkTnwwANz44035v77789tt902UEcCAIDdWs9//TRf/r3fGegyAKiRAQ+gTjvttBx77LE58sgj+1x/9NFHs2HDhkyfPr15ra2tLdOmTcudd96ZJFm1alW2b9/eZ09nZ2cmTZrU3PNytm3bls2bN/dZAAAAAJQxZCC/+JIlS3Lfffdl5cqVO93bsGFDkmT8+PF9ro8fPz6PP/54c8+wYcP6dE69uOfF17+chQsX5otf/OJrLR8AAADoh6pqpKoaA11GMXU+W6sMWAfUunXr8id/8ie58cYbs8cee/zcfY1G3x9iVVU7XXupX7TnnHPOSVdXV3OtW7du14oHAAAAoN8GLIBatWpVNm7cmClTpmTIkCEZMmRIbr/99vzVX/1VhgwZ0ux8emkn08aNG5v3Ojo60t3dnU2bNv3cPS+nra0to0aN6rMAAAAAKGPAAqgjjjgi999/f1avXt1cBx10UE466aSsXr0673jHO9LR0ZHly5c3X9Pd3Z3bb789hxxySJJkypQpGTp0aJ8969evz5o1a5p7AAAAABhYAzYDauTIkZk0aVKfayNGjMhee+3VvD5v3rwsWLAgEydOzMSJE7NgwYLsueeemTVrVpKkvb09p5xySubPn5+99torY8aMyVlnnZXJkyfvNNQcAAAAgIExoEPIf5Gzzz47W7duzdy5c7Np06YcfPDBWbZsWUaOHNncc9lll2XIkCE58cQTs3Xr1hxxxBG57rrrMnjw4AGsHAAAAGiqXlh1VeeztUijqqo3/bdp8+bNaW9vz2E5IUMaQwe6HAAAGHCDfn3/fPvbXx3oMqC2Nj/Tm9Hv+lG6urpqPZf4xb+3J3zpCxk0/Oe/Adnurnfr81n3yS/W/uf5WgzYDCgAAAAA3hwEUAAAAAAU9YaeAQUAAADUQeOFVVd1Pltr6IACAAAAoCgBFAAAAABFCaAAAAAAKEoABQAAAEBRhpADAAAAZVUvrLqq89laRAcUAAAAAEUJoAAAAAAoSgAFAAAAQFFmQAEAAABlmQH1pqcDCgAAAICiBFAAAAAAFCWAAgAAAKAoARQAAAAARRlCDgAAAJRVNXasuqrz2VpEBxQAAAAARQmgAAAAAChKAAUAAABAUWZAAQAAAEVV1Y5VV3U+W6vogAIAAACgKAEUAAAAAEUJoAAAAAAoSgAFAAAAQFGGkAMAAABlVS+suqrz2VpEBxQAAAAARQmgAAAAAChKAAUAAABAUWZAAQAAAGVVjR2rrup8thbRAQUAAABAUQIoAAAAAIoSQAEAAABQlAAKAAAAgKIMIQcAAACKalQ7Vl3V+WytogMKAAAAgKIEUAAAAAAUJYACAAAAoCgzoAAAAICyqhdWXdX5bC2iAwoAAACAogRQAAAAABQlgAIAAACgKAEUAAAAwOvoZz/7Wf78z/88++67b4YPH553vOMdOf/889Pb29vcU1VVzjvvvHR2dmb48OE57LDD8sADD/T5PNu2bcvpp5+esWPHZsSIEZk5c2aeeOKJPns2bdqU2bNnp729Pe3t7Zk9e3aefvrpPnvWrl2b448/PiNGjMjYsWNzxhlnpLu7u6VnFkABAAAAZVWN+q9dcOGFF+ZLX/pSFi9enIceeigXXXRRLr744lxxxRXNPRdddFEWLVqUxYsXZ+XKleno6MhRRx2VZ555prln3rx5ueWWW7JkyZKsWLEizz77bI477rj09PQ098yaNSurV6/O0qVLs3Tp0qxevTqzZ89u3u/p6cmxxx6bLVu2ZMWKFVmyZEm+9rWvZf78+a/hB74z74IHAAAA8Dq66667csIJJ+TYY49Nkuyzzz756le/mu9973tJdnQ/XX755Tn33HPz4Q9/OEly/fXXZ/z48bn55ptz6qmnpqurK9dee21uuOGGHHnkkUmSG2+8MRMmTMhtt92WGTNm5KGHHsrSpUtz99135+CDD06SXHPNNZk6dWoefvjh7Lffflm2bFkefPDBrFu3Lp2dnUmSSy+9NCeffHIuuOCCjBo1qiVnflUB1NNPP5177703Gzdu7NMeliR/+Id/2JLCAAAAAHYnmzdv7vNxW1tb2tradtr3gQ98IF/60pfyyCOP5F3velf+/d//PStWrMjll1+eJHn00UezYcOGTJ8+vc/nmjZtWu68886ceuqpWbVqVbZv395nT2dnZyZNmpQ777wzM2bMyF133ZX29vZm+JQk73//+9Pe3p4777wz++23X+66665MmjSpGT4lyYwZM7Jt27asWrUqhx9+eEu+N7scQH3zm9/MSSedlC1btmTkyJFpNP5vm1mj0RBAAQAAAG9KEyZM6PPxF77whZx33nk77fvMZz6Trq6u/Nqv/VoGDx6cnp6eXHDBBfn93//9JMmGDRuSJOPHj+/zuvHjx+fxxx9v7hk2bFhGjx69054XX79hw4aMGzdup68/bty4Pnte+nVGjx6dYcOGNfe0wi4HUPPnz8/HP/7xLFiwIHvuuWfLCgEAAABqqnph1dULZ1u3bl2fR9ZervspSf72b/82N954Y26++ea8+93vzurVqzNv3rx0dnbmYx/7WHPff2/6SXY8mvfSazuV8pI9L7f/1ex5rXY5gHryySdzxhlnCJ8AAAAA/ptRo0b1a2bSpz/96Xz2s5/NRz/60STJ5MmT8/jjj2fhwoX52Mc+lo6OjiQ7upP23nvv5us2btzY7Fbq6OhId3d3Nm3a1KcLauPGjTnkkEOae37yk5/s9PWfeuqpPp/nnnvu6XN/06ZN2b59+06dUa/FLr8L3owZM5pDsQAAAADYNc8991wGDeobyQwePLg5Z3vfffdNR0dHli9f3rzf3d2d22+/vRkuTZkyJUOHDu2zZ/369VmzZk1zz9SpU9PV1ZV77723ueeee+5JV1dXnz1r1qzJ+vXrm3uWLVuWtra2TJkypWVn7lcH1De+8Y3mfx977LH59Kc/nQcffDCTJ0/O0KFD++ydOXNmy4oDAAAAqJvjjz8+F1xwQd72trfl3e9+d77//e9n0aJF+fjHP55kxyNx8+bNy4IFCzJx4sRMnDixOQpp1qxZSZL29vaccsopmT9/fvbaa6+MGTMmZ511ViZPntx8V7z9998/Rx99dObMmZOrrroqSfKJT3wixx13XPbbb78kyfTp03PAAQdk9uzZufjii/PTn/40Z511VubMmdOyd8BL+hlAfehDH9rp2vnnn7/TtUajkZ6entdcFAAAMLAaW57Pn2+cnL8Yd/9AlwJQO1dccUU+97nPZe7cudm4cWM6Oztz6qmn5vOf/3xzz9lnn52tW7dm7ty52bRpUw4++OAsW7YsI0eObO657LLLMmTIkJx44onZunVrjjjiiFx33XUZPHhwc89NN92UM844o/lueTNnzszixYub9wcPHpxvfetbmTt3bg499NAMHz48s2bNyiWXXNLSMzeqqqrzGLB+2bx5c9rb23NYTsiQxtBf/AIAAHgT2Hbs+/Lda64Z6DKgljY/05vR7/pRurq6Wtpl8kbz4t/bEy79Xxk0fI+BLqeY3q3PZ938z9X+5/la7PIMqK985SvZtm3bTte7u7vzla98pSVFAQAAAFAfuxxA/dEf/VG6urp2uv7MM8/kj/7oj1pSFAAAAAD1scsBVFVVaTQaO11/4okn0t7e3pKiAAAAAKiPfg0hT5IDDzwwjUYjjUYjRxxxRIYM+b8v7enpyaOPPpqjjz66SJEAAADAbqx6YdVVnc/WIv0OoF58J7zVq1dnxowZ+aVf+qXmvWHDhmWfffbJ7/7u77a8QAAAAAB2b/0OoL7whS8kSfbZZ5985CMfyR571Hd6PQAAAACt0+8A6kUf+9jHStQBAAAAQE3tcgA1evTolx1C3mg0sscee+Sd73xnTj75ZO+IBwAAAECSVxFAff7zn88FF1yQY445Jr/5m7+ZqqqycuXKLF26NKeddloeffTR/PEf/3F+9rOfZc6cOSVqBgAAAHYnVWPHqqs6n61FdjmAWrFiRf7iL/4in/zkJ/tcv+qqq7Js2bJ87Wtfy3ve85781V/9lQAKAAAAgAza1RfceuutOfLII3e6fsQRR+TWW29NkvzO7/xOfvSjH7326gAAAADY7e1yADVmzJh885vf3On6N7/5zYwZMyZJsmXLlowcOfK1VwcAAADAbm+XH8H73Oc+lz/+4z/Od77znfzmb/5mGo1G7r333vzTP/1TvvSlLyVJli9fnmnTprW8WAAAAGD306h2rLqq89laZZcDqDlz5uSAAw7I4sWL8/d///epqiq/9mu/lttvvz2HHHJIkmT+/PktLxQAAACA3dMuB1BJcuihh+bQQw9tdS0AAAAA1NCrCqB6e3vzwx/+MBs3bkxvb2+fex/84AdbUhgAAAAA9bDLAdTdd9+dWbNm5fHHH09V9X3IsdFopKenp2XFAQAAALD72+UA6pOf/GQOOuigfOtb38ree++dRqNRoi4AAACgLqoXVl3V+WwtsssB1A9+8IP83d/9Xd75zneWqAcAAACAmhm0qy84+OCD88Mf/rBELQAAAADU0C53QJ1++umZP39+NmzYkMmTJ2fo0KF97r/nPe9pWXEAAAAA7P52OYD63d/93STJxz/+8ea1RqORqqoMIQcAAABgJ7scQD366KMl6gAAAACgpnY5gHr7299eog4AAAAAamqXh5AnyQ033JBDDz00nZ2defzxx5Mkl19+ef7hH/6hpcUBAAAAsPvb5QDqyiuvzJlnnpnf+Z3fydNPP92c+fSWt7wll19+eavrAwAAAGA3t8sB1BVXXJFrrrkm5557bgYPHty8ftBBB+X+++9vaXEAAADA7q+RpFHVeA30N3g3sMsB1KOPPpoDDzxwp+ttbW3ZsmVLS4oCAAAAoD52OYDad999s3r16p2uf/vb384BBxzQipoAAAAAqJFdfhe8T3/60znttNPy/PPPp6qq3HvvvfnqV7+ahQsX5m/+5m9K1AgAAADAbmyXA6g/+qM/ys9+9rOcffbZee655zJr1qy89a1vzV/+5V/mox/9aIkaAQAAgN1Z1dix6qrOZ2uRXQ6gkmTOnDmZM2dO/vM//zO9vb0ZN25cq+sCAAAAoCZeVQD1orFjx7aqDgAAAABqql8B1IEHHphGo3/tZPfdd99rKggAAACAeulXAPWhD32ocBkAAAAA1FW/AqgvfOELpesAAAAA6qp6YdVVnc/WIoNezYuefvrp/M3f/E3OOeec/PSnP02y49G7J598sqXFAQAAALD72+Uh5P/xH/+RI488Mu3t7XnssccyZ86cjBkzJrfccksef/zxfOUrXylRJwAAAAC7qV3ugDrzzDNz8skn5wc/+EH22GOP5vVjjjkmd9xxR0uLAwAAAGD3t8sB1MqVK3PqqafudP2tb31rNmzY0JKiAAAAAKiPXX4Eb4899sjmzZt3uv7www/nl3/5l1tSFAAAAFAjhpC/6e1yB9QJJ5yQ888/P9u3b0+SNBqNrF27Np/97Gfzu7/7uy0vEAAAAIDd2y4HUJdcckmeeuqpjBs3Llu3bs20adPyzne+MyNHjswFF1xQokYAAAAAdmO7/AjeqFGjsmLFivzLv/xL7rvvvvT29uY3fuM3cuSRR5aoDwAAAIDd3C4HUI899lj22Wef/PZv/3Z++7d/u0RNAAAAQI00qh2rrup8tlbZ5Ufw3vGOd+QDH/hArrrqqvz0pz8tURMAAAAANbLLAdT3vve9TJ06NX/xF3+Rzs7OnHDCCfk//+f/ZNu2bSXqAwAAAGA3t8sB1G/8xm/k4osvztq1a/Ptb38748aNy6mnnppx48bl4x//eIkaAQAAANiN7XIA9aJGo5HDDz8811xzTW677ba84x3vyPXXX9/K2gAAAACogVcdQK1bty4XXXRR3vve9+Z973tfRowYkcWLF7eyNgAAAKAOqjfB4hXt8rvgXX311bnpppvyb//2b9lvv/1y0kkn5etf/3r22WefAuUBAAAAsLvb5QDqf/2v/5WPfvSj+cu//Mu8973vLVASAAAAAHWyywHU2rVr02g0StQCAAAAQA3tcgD1Yvj03HPPZe3atenu7u5z/z3veU9rKgMAAADqoe5zkup8thbZ5QDqqaeeysknn5ylS5e+7P2enp7XXBQAAAAA9bHL74I3b968PP3007n77rszfPjwLF26NNdff30mTpyYb3zjGyVqBAAAAGA3tssdUP/yL/+Sf/iHf8j73ve+DBo0KG9/+9tz1FFHZdSoUVm4cGGOPfbYEnUCAAAAsJva5Q6oLVu2ZNy4cUmSMWPG5KmnnkqSTJ48Offdd19rqwMAAABgt7fLAdR+++2Xhx9+OEny3ve+N1dddVWefPLJfOlLX8ree+/d8gIBAACA3Vujqv/ilfU7gPrhD3+YZMcMqPXr1ydJvvCFL2Tp0qV529velr/6q7/KggULylQJAAC87vb48XP5o7W/NdBlAFAD/Q6g3vWud2XChAm59dZbU1VVHnvssRx44IF57LHHsnLlyqxbty4f+chHStYKAAC8jqrvP5DVX5k80GUAUAP9HkJ+++235/bbb893v/vdfOpTn8rzzz+ft73tbfnt3/7tHH744Rk/fnzJOgEAAADYTfU7gPqt3/qt/NZv/Vb+/M//PNu3b89dd92V7373u/nud7+br371q9m2bVve+c53NudDAQAAACRJqsaOVVd1PluL9DuA+u+GDh2aD37wg3nf+96XqVOn5tZbb80111zTnBMFAAAAAC/apQDq+eefz5133pnvfOc7+e53v5uVK1dm3333zbRp03LllVdm2rRppeoEAAAAYDfV7wBq2rRpWblyZX71V381H/zgB3P66adn2rRpZj8BAAAA8Ir6HUDdeeed2XvvvXP44YfnsMMOywc/+MGMHTu2ZG0AAAAA1MCg/m58+umnc/XVV2fPPffMhRdemLe+9a2ZPHlyPvWpT+Xv/u7v8tRTT5WsEwAAANhdVW+CxSvqdwfUiBEjcvTRR+foo49OkjzzzDNZsWJFvvOd7+Siiy7KSSedlIkTJ2bNmjXFigUAAABg99PvDqiXGjFiRMaMGZMxY8Zk9OjRGTJkSB566KFW1gYAAABADfS7A6q3tzff+9738t3vfjff+c538m//9m/ZsmVL3vrWt+bwww/P//7f/zuHH354yVoBAAAA2A31O4B6y1veki1btmTvvffOYYcdlkWLFuXwww/Pr/7qr5asDwAAANjNNaodq67qfLZW6XcAdfHFF+fwww/Pu971rpL1AAAAAFAz/Q6gTj311JJ1AAAAAFBTr3oIOQAAAAD0hwAKAAAAgKL6/QgeAAAAwKtSvbDqqs5naxEdUAAAAAAUJYACAAAAoCgBFAAAAABFmQEFAAAAlFUljTrPSarz2VpEBxQAAAAARQmgAAAAAChKAAUAAABAUQIoAAAAAIoyhBwAAAAoq0q9B3XX+WwtogMKAAAAgKIEUAAAAAAUJYACAAAAoCgzoAAAAICyzIB609MBBQAAAEBRAigAAAAAihJAAQAAAFCUAAoAAACAogwhBwAAAIpqVDtWXdX5bK2iAwoAAACAogRQAAAAABQlgAIAAACgKAEUAAAAAEUJoAAAAAAoSgAFAAAAQFECKAAAAACKEkABAAAAUNSQgS4AAAAAqLnqhVVXdT5bi+iAAgAAAKAoARQAAAAARQmgAAAAACjKDCgAAACgqEa1Y9VVnc/WKjqgAAAAAChKAAUAAABAUQIoAAAAAIoSQAEAAABQlCHkAAAAQHkGdb+p6YACAAAAoCgBFAAAAABFCaAAAAAAKMoMKAAAAKCsKvWeAVXns7XIgHdAPfnkk/mDP/iD7LXXXtlzzz3z3ve+N6tWrWrer6oq5513Xjo7OzN8+PAcdthheeCBB/p8jm3btuX000/P2LFjM2LEiMycOTNPPPHE630UAAAAAF7GgAZQmzZtyqGHHpqhQ4fm29/+dh588MFceumlectb3tLcc9FFF2XRokVZvHhxVq5cmY6Ojhx11FF55plnmnvmzZuXW265JUuWLMmKFSvy7LPP5rjjjktPT88AnAoAAACA/25AH8G78MILM2HChHz5y19uXttnn32a/11VVS6//PKce+65+fCHP5wkuf766zN+/PjcfPPNOfXUU9PV1ZVrr702N9xwQ4488sgkyY033pgJEybktttuy4wZM17XMwEAQJ2M/mF3PvOT9+bC8asHuhQAdmMD2gH1jW98IwcddFB+7/d+L+PGjcuBBx6Ya665pnn/0UcfzYYNGzJ9+vTmtba2tkybNi133nlnkmTVqlXZvn17nz2dnZ2ZNGlScw8AAPDqDF32vdxy69SBLgOA3dyABlA/+tGPcuWVV2bixIm59dZb88lPfjJnnHFGvvKVryRJNmzYkCQZP358n9eNHz++eW/Dhg0ZNmxYRo8e/XP3vNS2bduyefPmPgsAAAAoo1HVf/HKBvQRvN7e3hx00EFZsGBBkuTAAw/MAw88kCuvvDJ/+Id/2NzXaDT6vK6qqp2uvdQr7Vm4cGG++MUvvsbqAQAAAOiPAe2A2nvvvXPAAQf0ubb//vtn7dq1SZKOjo4k2amTaePGjc2uqI6OjnR3d2fTpk0/d89LnXPOOenq6mqudevWteQ8AAAAAOxsQAOoQw89NA8//HCfa4888kje/va3J0n23XffdHR0ZPny5c373d3duf3223PIIYckSaZMmZKhQ4f22bN+/fqsWbOmueel2traMmrUqD4LAAAAgDIG9BG8P/3TP80hhxySBQsW5MQTT8y9996bq6++OldffXWSHY/ezZs3LwsWLMjEiRMzceLELFiwIHvuuWdmzZqVJGlvb88pp5yS+fPnZ6+99sqYMWNy1llnZfLkyc13xQMAAAAGUPXCqqs6n61FBjSAet/73pdbbrkl55xzTs4///zsu+++ufzyy3PSSSc195x99tnZunVr5s6dm02bNuXggw/OsmXLMnLkyOaeyy67LEOGDMmJJ56YrVu35ogjjsh1112XwYMHD8SxAAAAAPhvGlVVvelzus2bN6e9vT2H5YQMaQwd6HIAAOAN5dH/d2oe+cMrB7oMqJXNz/Rm9Lt+lK6urlqPhXnx7+2Jn16QwW17DHQ5xfRsez4/uPjPav/zfC0GdAYUAAAAAPUngAIAAACgqAGdAQUAAADUX6PaseqqzmdrFR1QAAAAABQlgAIAAACgKAEUAAAAAEWZAQUAAACUVb2w6qrOZ2sRHVAAAAAAFCWAAgAAAKAoARQAAAAARQmgAAAAACjKEHIAAACgLEPI3/R0QAEAAABQlAAKAAAAgKIEUAAAAAAUJYACAAAAimpU9V+76sknn8wf/MEfZK+99sqee+6Z9773vVm1alXzflVVOe+889LZ2Znhw4fnsMMOywMPPNDnc2zbti2nn356xo4dmxEjRmTmzJl54okn+uzZtGlTZs+enfb29rS3t2f27Nl5+umn++xZu3Ztjj/++IwYMSJjx47NGWecke7u7l0/1CsQQAEAAAC8jjZt2pRDDz00Q4cOzbe//e08+OCDufTSS/OWt7ylueeiiy7KokWLsnjx4qxcuTIdHR056qij8swzzzT3zJs3L7fcckuWLFmSFStW5Nlnn81xxx2Xnp6e5p5Zs2Zl9erVWbp0aZYuXZrVq1dn9uzZzfs9PT059thjs2XLlqxYsSJLlizJ1772tcyfP7+lZ/YueAAAAACvowsvvDATJkzIl7/85ea1ffbZp/nfVVXl8ssvz7nnnpsPf/jDSZLrr78+48ePz80335xTTz01XV1dufbaa3PDDTfkyCOPTJLceOONmTBhQm677bbMmDEjDz30UJYuXZq77747Bx98cJLkmmuuydSpU/Pwww9nv/32y7Jly/Lggw9m3bp16ezsTJJceumlOfnkk3PBBRdk1KhRLTmzDigAAACA19E3vvGNHHTQQfm93/u9jBs3LgceeGCuueaa5v1HH300GzZsyPTp05vX2traMm3atNx5551JklWrVmX79u199nR2dmbSpEnNPXfddVfa29ub4VOSvP/97097e3ufPZMmTWqGT0kyY8aMbNu2rc8jga+VAAoAAACgBTZv3txnbdu27WX3/ehHP8qVV16ZiRMn5tZbb80nP/nJnHHGGfnKV76SJNmwYUOSZPz48X1eN378+Oa9DRs2ZNiwYRk9evQr7hk3btxOX3/cuHF99rz064wePTrDhg1r7mkFARQAAABQVvUmWEkmTJjQHPbd3t6ehQsXvuy3o7e3N7/xG7+RBQsW5MADD8ypp56aOXPm5Morr+yzr9Fo9P02VtVO13b6Vr9kz8vtfzV7XisBFAAAAEALrFu3Ll1dXc11zjnnvOy+vffeOwcccECfa/vvv3/Wrl2bJOno6EiSnTqQNm7c2OxW6ujoSHd3dzZt2vSKe37yk5/s9PWfeuqpPnte+nU2bdqU7du379QZ9VoIoAAAAABaYNSoUX1WW1vby+479NBD8/DDD/e59sgjj+Ttb397kmTfffdNR0dHli9f3rzf3d2d22+/PYccckiSZMqUKRk6dGifPevXr8+aNWuae6ZOnZqurq7ce++9zT333HNPurq6+uxZs2ZN1q9f39yzbNmytLW1ZcqUKa/l29GHd8EDAAAAeB396Z/+aQ455JAsWLAgJ554Yu69995cffXVufrqq5PseCRu3rx5WbBgQSZOnJiJEydmwYIF2XPPPTNr1qwkSXt7e0455ZTMnz8/e+21V8aMGZOzzjorkydPbr4r3v7775+jjz46c+bMyVVXXZUk+cQnPpHjjjsu++23X5Jk+vTpOeCAAzJ79uxcfPHF+elPf5qzzjorc+bMadk74CUCKAAAAKC0/zYnqZZ28Wzve9/7csstt+Scc87J+eefn3333TeXX355TjrppOaes88+O1u3bs3cuXOzadOmHHzwwVm2bFlGjhzZ3HPZZZdlyJAhOfHEE7N169YcccQRue666zJ48ODmnptuuilnnHFG893yZs6cmcWLFzfvDx48ON/61rcyd+7cHHrooRk+fHhmzZqVSy655FV+M15eo6qqOv9PoF82b96c9vb2HJYTMqQxdKDLAQCAN5RH/9+peeQPr/zFG4F+2/xMb0a/60fp6upqaZfJG82Lf2/v9ycLMrhtj4Eup5iebc/n4b/8s9r/PF8LM6AAAAAAKEoABQAAAEBRAigAAAAAijKEHAAAACiqUe1YdVXns7WKDigAAAAAihJAAQAAAFCUAAoAAACAosyAAgAAAMqqXlh1VeeztYgOKAAAAACKEkABAAAAUJQACgAAAICiBFAAAAAAFGUIOQAAAFBUo9qx6qrOZ2sVHVAAAAAAFCWAAgAAAKAoARQAAAAARZkBBQAAAJRVvbDqqs5naxEdUAAAAAAUJYACAAAAoCgBFAAAAABFCaAAAAAAKMoQcgAAAKAsQ8jf9HRAAQAAAFCUAAoAAACAogRQAAAAABRlBhQAAABQVOOFVVd1Plur6IACAABeUeftP8tnfvLegS4DgN2YAAoAAHhFbd9emb9/6L0DXQYAuzEBFAAAAABFCaAAAAAAKMoQcgAAAKCs6oVVV3U+W4vogAIAAACgKAEUAAAAAEUJoAAAAAAoygwoAAAAoKhGtWPVVZ3P1io6oAAAAAAoSgAFAAAAQFECKAAAAACKEkABAAAAUJQh5AAAAEBZ1Qurrup8thbRAQUAAABAUQIoAAAAAIoSQAEAAABQlBlQAAAAQHnmJL2p6YACAAAAoCgBFAAAAABFCaAAAAAAKEoABQAAAEBRhpADAAAARTWqHauu6ny2VtEBBQAAAEBRAigAAAAAihJAAQAAAFCUGVAAAABAWdULq67qfLYW0QEFAAAAQFECKAAAAACKEkABAAAAUJQACgAAAICiDCEHAAAAimpUO1Zd1flsraIDCgAAAICiBFAAAAAAFCWAAgAAAKAoM6AAAACAsqoXVl3V+WwtogMKAAAAgKIEUAAAAAAUJYACAAAAoCgBFAAAAABFGUIOAAAAFNWodqy6qvPZWkUHFAAAAABFCaAAAAAAKEoABQAAAEBRAigAAAAAijKEHAAAACiremHVVZ3P1iI6oAAAAAAoSgAFAAAAQFECKAAAAACKMgMKAAAAKMsMqDc9HVAAAAAAFCWAAgAAAKAoARQAAAAARQmgAAAAACjKEHIAAACgqEa1Y9VVnc/WKjqgAAAAAChKAAUAAABAUQIoAAAAAIoyAwoAAAAoq3ph1VWdz9YiOqAAAAAAKEoABQAAAEBRAigAAOAXetu1g7PgP/cb6DIA2E0JoAAAgF9oyD+vyt2b9h3oMgDYTRlCDgAAABTVqKo0qvpO6q7z2VpFBxQAANAvvVVjoEsAYDclgAIAAPplUMO/8APw6gigAAAAACjKDCgAAACgrOqFVVd1PluL6IACAAAAoCgBFAAA0C+GkAPwagmgAAAAAChKAAUAAPSLd8ED4NUyhBwAAAAoqlHtWHVV57O1ig4oAAAAAIoSQAEAAABQlAAKAAAAgKLMgAIAAADKql5YdVXns7WIDigAAAAAihJAAQAA/dJbNQa6BAB2UwIoAACgXwZ5n3EAXiUBFAAAAABFGUIOAAAAFNWodqy6qvPZWkUHFAAAAABFCaAAAAAAKEoABQAA9It3wQPg1TIDCgAA6Bfvgge8atULq67qfLYW0QEFAAAAQFECKAAAAACKEkABAAD9YgYUAK+WAAoAAOgXM6AAeLUMIQcAAACKalQ7Vl3V+WytogMKAAAAgKIEUAAAAAAUJYACAAD6xRByAF4tM6AAAACAsqoXVl3V+WwtogMKAADoF++CB8CrJYACAAAAoCgBFAAAAABFCaAAAAAAKMoQcgAAAKA4Y+Te3HRAAQAA/dJbNQa6BAB2UwIoAACgX7wLHgCvlgAKAADoFx1QALxaZkABAAAAZVXVjlVXdT5bi+iAAgAA+sUjeAC8WgIoAAAAAIoSQAEAAABQlAAKAAAAgKIMIQcAAACKalQ7Vl3V+WytogMKAAAAgKIEUAAAQL/0Vo2BLgGA3ZQACgAA6JdBnjEB4FUyAwoAAAAoq3ph1VWdz9YiOqAAAAAAKEoABQAAAEBRAigAAAAAihJAAQAAAFCUAAoAAOiX3qox0CUAu6lGb/3Xq7Vw4cI0Go3Mmzevea2qqpx33nnp7OzM8OHDc9hhh+WBBx7o87pt27bl9NNPz9ixYzNixIjMnDkzTzzxRJ89mzZtyuzZs9Pe3p729vbMnj07Tz/9dJ89a9euzfHHH58RI0Zk7NixOeOMM9Ld3f3qD/RzCKAAAIB+GdTwNk8ArbRy5cpcffXVec973tPn+kUXXZRFixZl8eLFWblyZTo6OnLUUUflmWeeae6ZN29ebrnllixZsiQrVqzIs88+m+OOOy49PT3NPbNmzcrq1auzdOnSLF26NKtXr87s2bOb93t6enLsscdmy5YtWbFiRZYsWZKvfe1rmT9/fsvPOqAB1M9+9rP8+Z//efbdd98MHz4873jHO3L++eent/f/RoetSv0AAAAA3iieffbZnHTSSbnmmmsyevTo5vWqqnL55Zfn3HPPzYc//OFMmjQp119/fZ577rncfPPNSZKurq5ce+21ufTSS3PkkUfmwAMPzI033pj7778/t912W5LkoYceytKlS/M3f/M3mTp1aqZOnZprrrkm//iP/5iHH344SbJs2bI8+OCDufHGG3PggQfmyCOPzKWXXpprrrkmmzdvbul5BzSAuvDCC/OlL30pixcvzkMPPZSLLrooF198ca644ormnlalfgAAAABvFKeddlqOPfbYHHnkkX2uP/roo9mwYUOmT5/evNbW1pZp06blzjvvTJKsWrUq27dv77Ons7MzkyZNau6566670t7enoMPPri55/3vf3/a29v77Jk0aVI6Ozube2bMmJFt27Zl1apVLT3vkJZ+tl1011135YQTTsixxx6bJNlnn33y1a9+Nd/73veS7Jz6Jcn111+f8ePH5+abb86pp57aTP1uuOGG5g/txhtvzIQJE3LbbbdlxowZA3M4AAAAYIfqhVVXL5ztpV1DbW1taWtr22n7kiVLct9992XlypU73duwYUOSZPz48X2ujx8/Po8//nhzz7Bhw/p0Tr2458XXb9iwIePGjdvp848bN67Pnpd+ndGjR2fYsGHNPa0yoB1QH/jAB/LP//zPeeSRR5Ik//7v/54VK1bkd37nd5K0LvV7qW3btmXz5s19FgAA8Mq2ffqXc9Mzew10GQBvWBMmTGgO/G5vb8/ChQt32rNu3br8yZ/8SW688cbsscceP/dzNRp93/ihqqqdrr3US/e83P5Xs6cVBrQD6jOf+Uy6urrya7/2axk8eHB6enpywQUX5Pd///eTtC71e6mFCxfmi1/8YquPAwAA9Xbv/Xmie0yS/xroSgDekNatW5dRo0Y1P3657qdVq1Zl48aNmTJlSvNaT09P7rjjjixevLg5n2nDhg3Ze++9m3s2btzYzEc6OjrS3d2dTZs29clDNm7cmEMOOaS55yc/+clOX/+pp57q83nuueeePvc3bdqU7du375TFvFYD2gH1t3/7t7nxxhtz880357777sv111+fSy65JNdff32ffa1I/f67c845J11dXc21bt2613YQAAAA4E1v1KhRfdbLBVBHHHFE7r///qxevbq5DjrooJx00klZvXp13vGOd6SjoyPLly9vvqa7uzu33357M1yaMmVKhg4d2mfP+vXrs2bNmuaeqVOnpqurK/fee29zzz333JOurq4+e9asWZP169c39yxbtixtbW19ArJWGNAOqE9/+tP57Gc/m49+9KNJksmTJ+fxxx/PwoUL87GPfSwdHR1JXnvq91I/7xlMAAAAgJJGjhyZSZMm9bk2YsSI7LXXXs3r8+bNy4IFCzJx4sRMnDgxCxYsyJ577plZs2YlSdrb23PKKadk/vz52WuvvTJmzJicddZZmTx5cnM+9v7775+jjz46c+bMyVVXXZUk+cQnPpHjjjsu++23X5Jk+vTpOeCAAzJ79uxcfPHF+elPf5qzzjorc+bM6dPJ1QoD2gH13HPPZdCgviUMHjw4vb29SZJ99923JakfAAAAMHAaVf1XK5199tmZN29e5s6dm4MOOihPPvlkli1blpEjRzb3XHbZZfnQhz6UE088MYceemj23HPPfPOb38zgwYObe2666aZMnjw506dPz/Tp0/Oe97wnN9xwQ/P+4MGD861vfSt77LFHDj300Jx44on50Ic+lEsuuaS1B0rSqKpqwObQn3zyybntttty1VVX5d3vfne+//3v5xOf+EQ+/vGP58ILL0ySXHjhhVm4cGG+/OUvN1O/7373u3n44Yeb3/g//uM/zj/+4z/muuuua6Z+//Vf/5VVq1b1+cb/PJs3b057e3sOywkZ0hha9MwAALA7++37t+Qze/1goMuA3d7mZ3oz+l0/SldXV8s7Td5IXvx7+zd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-      "text/plain": [
-       "<Figure size 1500x1500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.figure(figsize=(15,15))\n",
     "plt.imshow(hdus[0][1].data)\n",
     "plt.colorbar()\n",
     "plt.ylabel(\"Wavelength\")\n",
-    "plt.xlabel(\"Slit position\")"
+    "plt.xlabel(\"Slit position\");"
    ]
   },
   {
@@ -316,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "junior-density",
    "metadata": {},
    "outputs": [],
@@ -327,7 +255,7 @@
     "    flux = hdus[0][1].data[10:, 400:410].sum(axis=1)\n",
     "\n",
     "    plt.figure(figsize=(15, 5))\n",
-    "    plt.plot(waves[9:], flux)"
+    "    plt.plot(waves[9:], flux);"
    ]
   }
  ],
@@ -347,7 +275,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,

From c6d4ec33b8664f037765e7f5d6547a99d9cedc69 Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Sun, 20 Sep 2026 17:54:28 +0200
Subject: [PATCH 27/63] Import myst nb tests from scopesim-targets

Move notebook tests to separate conftest
---
 .github/workflows/notebooktests.yml |  19 +-
 METIS/docs/conftest.py              | 340 ++++++++++++++++++++++++++++
 conftest.py                         |   2 +
 requirements.github_actions.txt     |   4 +
 4 files changed, 354 insertions(+), 11 deletions(-)
 create mode 100644 METIS/docs/conftest.py

diff --git a/.github/workflows/notebooktests.yml b/.github/workflows/notebooktests.yml
index a8420b07..cce96499 100644
--- a/.github/workflows/notebooktests.yml
+++ b/.github/workflows/notebooktests.yml
@@ -45,6 +45,9 @@ on:
         required: false
         default: main
 
+env:
+  MPLBACKEND: Agg  # headless matplotlib
+
 jobs:
   micado_notebooks:
     name: Run MICADO Notebooks
@@ -158,11 +161,13 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v7
+
       - name: Set up Python
         uses: actions/setup-python@v7
         with:
           # No matrix is used since this is a time-consuming task.
           python-version: 3.13
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
@@ -175,14 +180,6 @@ jobs:
           pip uninstall -y scopesim scopesim_templates
           pip install --pre git+https://github.com/AstarVienna/ScopeSim_Templates.git@${{ inputs.ScopeSim_Templates }}
           pip install --pre git+https://github.com/AstarVienna/ScopeSim.git@${{ inputs.ScopeSim }}
-      - name: Run Notebooks
-        env:
-          PYDEVD_DISABLE_FILE_VALIDATION: 1
-        run: |
-          echo "## METIS Demo Notebooks tested" >> $GITHUB_STEP_SUMMARY
-          for fn in METIS/docs/example_notebooks/demos/*.ipynb
-          do
-            echo "${fn}"
-            echo "- ${fn}" >> $GITHUB_STEP_SUMMARY
-            /usr/bin/time -v jupytext --execute --update "${fn}"
-          done
+
+      - name: Execute MyST notebooks
+        run: pytest METIS/docs/example_notebooks/demos --notebooks
diff --git a/METIS/docs/conftest.py b/METIS/docs/conftest.py
new file mode 100644
index 00000000..02a1aac1
--- /dev/null
+++ b/METIS/docs/conftest.py
@@ -0,0 +1,340 @@
+# -*- coding: utf-8 -*-
+"""Pytest plugin to execute the MyST text-notebooks in this directory.
+
+Code below is copied 1:1 from scopesim-targets/docs/conf.py
+See also scopesim-targets/docs/notebook_testing.md for more info.
+Toasted and by no means final...
+"""
+
+import asyncio
+import sys
+from graphlib import CycleError, TopologicalSorter
+from pathlib import Path
+
+import pytest
+import yaml
+
+
+TIMEOUT = 600  # seconds per notebook
+STARTUP_TIMEOUT = 120  # seconds to wait for the kernel to come up
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--notebooks",
+        action="store_true",
+        default=False,
+        help="Collect and execute the MyST notebooks in docs/.",
+    )
+    parser.addoption(
+        "--notebook-setup",
+        default=None,
+        metavar="PATH",
+        help=(
+            "Python file executed in each notebook's kernel before its own "
+            "cells. Defaults to _notebook_setup.py next to this conftest, if "
+            "present. Use for test-only environment wiring that must not "
+            "appear in the published docs."
+        ),
+    )
+
+
+def pytest_collect_file(file_path: Path, parent):
+    if file_path.suffix != ".md":
+        return None
+    if ".ipynb_checkpoints" in file_path.parts:
+        return None
+    if not parent.config.getoption("--notebooks"):
+        return None
+    if not _is_myst_notebook(_front_matter(file_path.read_text(encoding="utf-8"))):
+        return None
+    return MystNotebookFile.from_parent(parent, path=file_path)
+
+
+# --- front matter ----------------------------------------------------------
+
+def _split_front_matter(text: str):
+    """Return (front_matter_block, rest) or (None, text)."""
+    if not text.startswith("---"):
+        return None, text
+    _, _, after = text.partition("---\n")
+    block, sep, rest = after.partition("\n---")
+    if not sep:
+        return None, text
+    return block, rest.partition("\n")[2]
+
+
+def _front_matter(text: str) -> dict:
+    block, _ = _split_front_matter(text)
+    if block is None:
+        return {}
+    try:
+        return yaml.safe_load(block) or {}
+    except yaml.YAMLError:
+        return {}
+
+
+def _with_front_matter(text: str, meta: dict) -> str:
+    """Splice *meta* back in as the front matter of *text*."""
+    _, body = _split_front_matter(text)
+    return "---\n" + yaml.safe_dump(meta, sort_keys=False) + "---\n" + body
+
+
+def _is_myst_notebook(meta: dict) -> bool:
+    if meta.get("file_format") == "mystnb":
+        return True
+    text_repr = meta.get("jupytext", {}).get("text_representation", {})
+    return text_repr.get("format_name") == "myst"
+
+
+def _complete_kernelspec(meta: dict) -> bool:
+    """Fill in kernelspec fields nbformat requires. True if anything changed.
+
+    A short ``kernelspec: {name: python3}`` is enough for MyST-NB but not for
+    nbformat, which requires ``display_name`` too.
+    """
+    kernelspec = meta.get("kernelspec")
+    if not isinstance(kernelspec, dict):
+        return False
+
+    name = kernelspec.setdefault("name", "python3")
+    changed = False
+
+    if "display_name" not in kernelspec:
+        kernelspec["display_name"] = name
+        changed = True
+
+    if "language" not in kernelspec:
+        kernelspec["language"] = "python" if name.startswith("python") else name
+        changed = True
+
+    return changed
+
+
+def _needs(meta: dict) -> list[str]:
+    """Dependencies as ``myst_test: {needs: [...]}`` in front matter."""
+    raw = (meta.get("myst_test") or {}).get("needs") or []
+    return [raw] if isinstance(raw, str) else list(raw)
+
+
+def _read_notebook(path: Path):
+    import jupytext  # needs to be imported lazily
+
+    text = path.read_text(encoding="utf-8")
+    meta = _front_matter(text)
+
+    if _complete_kernelspec(meta):
+        text = _with_front_matter(text, meta)
+
+    return jupytext.reads(text, fmt="md:myst")
+
+
+def _setup_source(config) -> str | None:
+    """Test-only preamble, executed in the kernel but never written to disk."""
+    override = config.getoption("--notebook-setup")
+    path = Path(override) if override else Path(__file__).parent / "_notebook_setup.py"
+    if override and not path.exists():
+        raise pytest.UsageError(f"--notebook-setup: {path} does not exist")
+    return path.read_text(encoding="utf-8") if path.exists() else None
+
+
+# --- ordering --------------------------------------------------------------
+
+def _components(deps: dict) -> dict:
+    """Map each notebook to an id shared by everything it is connected to."""
+    parent = {path: path for path in deps}
+
+    def find(path):
+        while parent[path] != path:
+            parent[path] = parent[parent[path]]
+            path = parent[path]
+        return path
+
+    for path, targets in deps.items():
+        for target in targets:
+            root_a, root_b = find(path), find(target)
+            if root_a != root_b:
+                parent[root_a] = root_b
+
+    return {path: find(path) for path in deps}
+
+
+# tryfirst is load-bearing: xdist rewrites nodeids to carry the group suffix
+# in its own pytest_collection_modifyitems, so a marker added after that hook
+# runs is silently ignored and the chain gets scattered across workers.
+@pytest.hookimpl(tryfirst=True)
+def pytest_collection_modifyitems(config, items):
+    slots = [i for i, item in enumerate(items) if isinstance(item, MystNotebookItem)]
+    if not slots:
+        return
+    by_path = {items[i].path.resolve(): items[i] for i in slots}
+
+    deps = {}
+    for path, item in by_path.items():
+        targets = []
+        for raw in item.needs:
+            target = (path.parent / raw).resolve()
+            if not target.exists():
+                raise pytest.UsageError(
+                    f"{item.nodeid}: 'needs' entry {raw!r} does not exist"
+                )
+            if target in by_path:
+                targets.append(target)
+            else:
+                # Exists, but not part of this run: the notebook cannot
+                # legitimately run in isolation, so say so rather than
+                # relying on leftovers from a previous run.
+                item.unmet.append(raw)
+        deps[path] = targets
+
+    try:
+        order = list(TopologicalSorter(
+            {path: set(targets) for path, targets in deps.items()}
+        ).static_order())
+    except CycleError as exc:
+        raise pytest.UsageError(
+            f"circular 'needs' between notebooks: {' -> '.join(str(p) for p in exc.args[1])}"
+        ) from exc
+
+    # Pin each chain to one xdist worker; independent notebooks stay free to
+    # be distributed. Only add the marker when xdist is actually installed,
+    # or --strict-markers rejects it.
+    if config.pluginmanager.hasplugin("xdist"):
+        groups = _components(deps)
+        sizes = {}
+        for root in groups.values():
+            sizes[root] = sizes.get(root, 0) + 1
+        for path, root in groups.items():
+            if sizes[root] > 1:
+                by_path[path].add_marker(pytest.mark.xdist_group(root.stem))
+
+    for slot, path in zip(slots, [p for p in order if p in by_path]):
+        items[slot] = by_path[path]
+
+
+# --- collection / execution ------------------------------------------------
+
+class MystNotebookFile(pytest.File):
+    def collect(self):
+        item = MystNotebookItem.from_parent(self, name="notebook")
+        item.needs = _needs(_front_matter(self.path.read_text(encoding="utf-8")))
+        item.unmet = []
+        # Warnings raised in *this* process by zmq/nbclient plumbing must not
+        # be turned into errors by the project-wide filterwarnings=error:
+        # an exception thrown inside the event loop strands a live kernel.
+        # (Warnings from notebook code itself happen in the kernel process.)
+        item.add_marker(pytest.mark.filterwarnings("default::RuntimeWarning"))
+        item.add_marker(pytest.mark.filterwarnings("default::DeprecationWarning"))
+        yield item
+
+
+def _run(coro):
+    """Run *coro* on a loop we fully control, then tear it down.
+
+    On Windows the default Proactor loop cannot do ``add_reader``, which zmq
+    needs; pyzmq works around it with an extra tornado selector thread, and
+    that combination is what deadlocks past Ctrl+C. A SelectorEventLoop avoids
+    the whole mess. Kernels are launched with ``subprocess.Popen`` by
+    jupyter_client, not via asyncio, so the selector loop's lack of subprocess
+    support is not a problem here.
+    """
+    if sys.platform == "win32":
+        loop = asyncio.SelectorEventLoop()
+    else:
+        loop = asyncio.new_event_loop()
+
+    asyncio.set_event_loop(loop)
+
+    try:
+        return loop.run_until_complete(coro)
+    finally:
+        try:
+            pending = asyncio.all_tasks(loop)
+            for task in pending:
+                task.cancel()
+
+            if pending:
+                loop.run_until_complete(
+                    asyncio.gather(*pending, return_exceptions=True)
+                )
+            loop.run_until_complete(loop.shutdown_asyncgens())
+
+        finally:
+            asyncio.set_event_loop(None)
+            loop.close()
+
+
+class MystNotebookItem(pytest.Item):
+    def runtest(self):
+        from nbclient import NotebookClient # needs to be imported lazily
+
+        if self.unmet:
+            pytest.skip(
+                f"needs {', '.join(self.unmet)}, not collected in this run"
+            )
+
+        notebook = _read_notebook(self.path)
+
+        if notebook.metadata.get("mystnb", {}).get("execution_mode") == "off":
+            pytest.skip("execution_mode: off in front matter")
+        if not any(cell.cell_type == "code" for cell in notebook.cells):
+            pytest.skip("no code cells")
+
+        client = NotebookClient(
+            notebook,
+            timeout=TIMEOUT,
+            startup_timeout=STARTUP_TIMEOUT,
+            kernel_name=notebook.metadata["kernelspec"]["name"],
+            # run with the notebook's own directory as cwd, like Sphinx does
+            resources={"metadata": {"path": str(self.path.parent)}},
+        )
+
+        setup = None
+        if notebook.metadata.get("myst_test", {}).get("setup") is not False:
+            setup = _setup_source(self.config)
+
+        _run(_execute_cells(client, setup=setup))
+
+    def reportinfo(self):
+        return self.path, 0, f"myst notebook: {self.path.name}"
+
+
+async def _execute_cells(client, *, setup: str | None):
+    """Execute code cells one at a time, failing at the first error."""
+    # need to be imported lazily
+    import nbformat
+    from nbclient.exceptions import CellExecutionError
+
+    client.reset_execution_trackers()
+
+    async with client.async_setup_kernel():
+        if setup:
+            cell = nbformat.v4.new_code_cell(setup)
+            client.nb.cells.append(cell)
+            try:
+                await client.async_execute_cell(cell, len(client.nb.cells) - 1)
+            except CellExecutionError as exc:
+                pytest.fail(f"notebook setup failed:\n{exc}", pytrace=False)
+            finally:
+                client.nb.cells.pop()
+
+        number = 0
+        for index, cell in enumerate(client.nb.cells):
+            if cell.cell_type != "code":
+                continue
+            number += 1
+
+            try:
+                await client.async_execute_cell(cell, index)
+            except CellExecutionError as exc:
+                pytest.fail(
+                    f"code cell {number}: {_short_error(exc)}\n\n{exc}",
+                    pytrace=False,
+                )
+
+
+def _short_error(exc) -> str:
+    """One-line summary for pytest's short summary: the error, not the cell."""
+    ename = getattr(exc, "ename", None) or type(exc).__name__
+    evalue = (getattr(exc, "evalue", "") or "").strip().splitlines()
+    return f"{ename}: {evalue[0]}" if evalue else ename
diff --git a/conftest.py b/conftest.py
index 3e512832..6696dde3 100644
--- a/conftest.py
+++ b/conftest.py
@@ -1,3 +1,5 @@
+# -*- coding: utf-8 -*-
+
 import pytest
 
 
diff --git a/requirements.github_actions.txt b/requirements.github_actions.txt
index 971af25a..afacd163 100644
--- a/requirements.github_actions.txt
+++ b/requirements.github_actions.txt
@@ -11,3 +11,7 @@ jupytext
 ipykernel
 nbconvert
 astar-utils
+jupytext
+myst-nb
+seaborn
+pandas

From 2fcacfe80fc114f3f1dc7b25550b1f007c2ae9cc Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Sun, 20 Sep 2026 22:22:11 +0200
Subject: [PATCH 28/63] Move IRDB linking to setup cell

---
 .../example_notebooks/demos/_notebook_setup.py     |  7 +++++++
 .../example_notebooks/demos/demo_Leiden_sky.md     |  3 ---
 .../docs/example_notebooks/demos/demo_adc_wheel.md |  9 ++-------
 .../example_notebooks/demos/demo_auto_exposure.md  |  4 +---
 .../demos/demo_chopping_and_nodding.md             |  3 ---
 .../example_notebooks/demos/demo_detector_modes.md |  3 ---
 .../example_notebooks/demos/demo_filter_wheel.md   |  3 ---
 .../demos/demo_grating_efficiency.md               |  3 ---
 .../demos/demo_inter_pixel_capacitance.md          | 14 +-------------
 .../example_notebooks/demos/demo_lss_simple.md     |  6 ------
 .../demos/demo_metis_lms_efficiency.md             |  3 ---
 .../example_notebooks/demos/demo_metis_wcu_psfs.md |  8 --------
 .../example_notebooks/demos/demo_rectify_traces.md |  3 ---
 .../demos/demo_reference_pixel_mask.md             |  7 -------
 .../example_notebooks/demos/demo_slit_wheel.md     |  2 --
 15 files changed, 11 insertions(+), 67 deletions(-)
 create mode 100644 METIS/docs/example_notebooks/demos/_notebook_setup.py

diff --git a/METIS/docs/example_notebooks/demos/_notebook_setup.py b/METIS/docs/example_notebooks/demos/_notebook_setup.py
new file mode 100644
index 00000000..b8661aed
--- /dev/null
+++ b/METIS/docs/example_notebooks/demos/_notebook_setup.py
@@ -0,0 +1,7 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Set IRDB path for notebooks, executed before every MyST notebook."""
+
+from scopesim import link_irdb
+
+link_irdb("../../../../")
diff --git a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
index 2288481e..24638f0d 100644
--- a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
+++ b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
@@ -23,9 +23,6 @@ METIS AIT activities in Leiden will use bespoke relay optics to observe the sky
 ```{code-cell} ipython3
 import scopesim as sim
 
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
-
 # If you haven't got the instrument packages yet, uncomment the following line.
 #sim.download_packages(["METIS"])
 ```
diff --git a/METIS/docs/example_notebooks/demos/demo_adc_wheel.md b/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
index b44c8dea..d7e10259 100644
--- a/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
+++ b/METIS/docs/example_notebooks/demos/demo_adc_wheel.md
@@ -15,15 +15,10 @@ kernelspec:
 This notebook demonstrates how to use the various atmospheric dispersion correctors in METIS. Note that the action of an adc is currently restricted to a transmission loss. The implementation of the geometric differential refraction residuals will follow at a later stage.
 
 ```{code-cell} ipython3
+import numpy as np
 import scopesim as sim
-sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
-```
 
-```{code-cell} ipython3
-import numpy as np
+sim.bug_report()
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell
diff --git a/METIS/docs/example_notebooks/demos/demo_auto_exposure.md b/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
index c48ad1f5..7582d74c 100644
--- a/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
+++ b/METIS/docs/example_notebooks/demos/demo_auto_exposure.md
@@ -19,10 +19,8 @@ The notebook uses the `irdb/METIS` configuration. The observed source is blank s
 ```{code-cell} ipython3
 from astropy import units as u
 import scopesim as sim
-sim.bug_report()
 
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
+sim.bug_report()
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell.
diff --git a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
index 1a92d390..4c3115a5 100644
--- a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
+++ b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.md
@@ -19,9 +19,6 @@ from matplotlib import pyplot as plt
 
 import scopesim as sim
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell.
diff --git a/METIS/docs/example_notebooks/demos/demo_detector_modes.md b/METIS/docs/example_notebooks/demos/demo_detector_modes.md
index 36aa2d75..0a22012b 100644
--- a/METIS/docs/example_notebooks/demos/demo_detector_modes.md
+++ b/METIS/docs/example_notebooks/demos/demo_detector_modes.md
@@ -21,9 +21,6 @@ from matplotlib import pyplot as plt
 
 import scopesim as sim
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell.
diff --git a/METIS/docs/example_notebooks/demos/demo_filter_wheel.md b/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
index 92e14c79..1a2f58e8 100644
--- a/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
+++ b/METIS/docs/example_notebooks/demos/demo_filter_wheel.md
@@ -20,9 +20,6 @@ from matplotlib import pyplot as plt
 
 import scopesim as sim
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell.
diff --git a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
index 51c21fc4..eeb7cb71 100644
--- a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
+++ b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.md
@@ -27,9 +27,6 @@ from scopesim_templates.micado import flatlamp
 import scopesim as sim
 
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell.
diff --git a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
index 8ad64641..44269ca6 100644
--- a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
+++ b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
@@ -34,21 +34,12 @@ When the $\alpha$ parameters are used to define the IPC kernel it is automatical
 import numpy as np
 from matplotlib import pyplot as plt
 from astropy import units as u
-```
 
-```{code-cell} ipython3
 import scopesim as sim
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out. [For ReadTheDocs only]
-sim.link_irdb("../../../../")
-```
-
-If you haven't got the instrument packages yet, uncomment the following cell, which will install the packages into `./inst_pkgs`, a subdirectory of your current working directory. If you have already downloaded the packages but to a different location you can set
-```python
-sim.set_inst_pgks_path("/path/to/inst/pkgs")
 ```
 
+If you haven't got the instrument packages yet, uncomment the following cell, which will install the packages into `./inst_pkgs`, a subdirectory of your current working directory.
 ```{code-cell} ipython3
 # sim.download_package(["METIS", "ELT", "Armazones"])
 ```
@@ -57,9 +48,6 @@ The `InterPixelCapacitance` effect will be demonstrated in the METIS IMG_LM mode
 
 ```{code-cell} ipython3
 cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
-```
-
-```{code-cell} ipython3
 metis = sim.OpticalTrain(cmd)
 ```
 
diff --git a/METIS/docs/example_notebooks/demos/demo_lss_simple.md b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
index 1c1122fa..95c317d1 100644
--- a/METIS/docs/example_notebooks/demos/demo_lss_simple.md
+++ b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
@@ -20,9 +20,6 @@ from matplotlib import pyplot as plt
 
 import scopesim as sim
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell.
@@ -35,9 +32,6 @@ Set up the instrument in lss_l mode:
 
 ```{code-cell} ipython3
 cmd = sim.UserCommands(use_instrument="METIS", set_modes=["lss_l"])
-```
-
-```{code-cell} ipython3
 metis = sim.OpticalTrain(cmd)
 ```
 
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
index 47032130..5ab7d038 100644
--- a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
+++ b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
@@ -23,9 +23,6 @@ import scopesim as sim
 from scopesim.effects.metis_lms_trace_list import MetisLMSEfficiency
 
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven't got the instrument package yet, uncomment the following cell. The METIS package provides the spectral trace definition file. The ELT and Armazones packages are not needed for the purposes of this notebook.
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md b/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
index 851e1f02..08ab43ee 100644
--- a/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
+++ b/METIS/docs/example_notebooks/demos/demo_metis_wcu_psfs.md
@@ -16,11 +16,6 @@ This notebook is targeted at the AIT team who use Scopesim to simulate observati
 
 ```{code-cell} ipython3
 import scopesim as sim
-```
-
-```{code-cell} ipython3
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 
 # If you haven't got the instrument packages yet, uncomment the following line.
 # sim.download_packages(["METIS", "ELT", "Armazones"])
@@ -30,9 +25,6 @@ By default Scopesim loads one of the PPS masks with their corresponding PSF. The
 
 ```{code-cell} ipython3
 cmd = sim.UserCommands(use_instrument="METIS", set_modes=["wcu_img_lm"])
-```
-
-```{code-cell} ipython3
 metis = sim.OpticalTrain(cmd)
 ```
 
diff --git a/METIS/docs/example_notebooks/demos/demo_rectify_traces.md b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
index 3e108a5a..a67ed740 100644
--- a/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
+++ b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
@@ -28,9 +28,6 @@ from scopesim_templates.micado import flatlamp
 import scopesim as sim
 
 sim.bug_report()
-
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven’t got the instrument packages yet, uncomment the following cell.
diff --git a/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md b/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
index d9927c10..26f64779 100644
--- a/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
+++ b/METIS/docs/example_notebooks/demos/demo_reference_pixel_mask.md
@@ -16,15 +16,8 @@ The METIS detectors have a number of masked rows and pixels around their borders
 
 ```{code-cell} ipython3
 from matplotlib import pyplot as plt
-```
 
-```{code-cell} ipython3
 import scopesim as sim
-```
-
-```{code-cell} ipython3
-# Edit this path if you have a custom install directorym otherwise comment it out
-sim.link_irdb("../../../../")
 
 # If you haven't got the instrument packages yet, uncomment the following line
 # sim.download_packages(["METIS", "ELT", "Armazones"])
diff --git a/METIS/docs/example_notebooks/demos/demo_slit_wheel.md b/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
index 27a6ebcc..94899c7d 100644
--- a/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
+++ b/METIS/docs/example_notebooks/demos/demo_slit_wheel.md
@@ -20,8 +20,6 @@ from matplotlib import pyplot as plt
 import scopesim as sim
 sim.bug_report()
 
-# Edit this path if you have a custom install directory, otherwise comment it out.
-sim.link_irdb("../../../../")
 ```
 
 If you haven't got the instrument packages yet, uncomment the following cell.

From ae7c62a5229e240ad3c29ee5048e6c25f1ea4581 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Sun, 20 Sep 2026 23:02:15 +0200
Subject: [PATCH 29/63] two more MICADO notebooks

---
 .../6_basic_spectroscopy.ipynb                | 87 +++++++++--------
 .../7_dithered_cluster.ipynb                  | 97 ++++---------------
 2 files changed, 66 insertions(+), 118 deletions(-)

diff --git a/MICADO/docs/example_notebooks/6_basic_spectroscopy.ipynb b/MICADO/docs/example_notebooks/6_basic_spectroscopy.ipynb
index 977473ea..603343bd 100644
--- a/MICADO/docs/example_notebooks/6_basic_spectroscopy.ipynb
+++ b/MICADO/docs/example_notebooks/6_basic_spectroscopy.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Basic usage of the MICADO spectroscopy mode in ScopeSim\n",
     "\n",
-    "<h1 id=\"tocheading\">Table of Contents</h1>\n",
+    "<h2 id=\"tocheading\">Table of Contents</h2>\n",
     "<div id=\"toc\"><br></div>"
    ]
   },
@@ -18,7 +18,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%javascript\n",
+    "%%javascript \n",
     "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')"
    ]
   },
@@ -49,10 +49,7 @@
     "\n",
     "import scopesim as sim\n",
     "from scopesim.source import source_templates as src_tp\n",
-    "import scopesim_templates as sim_tp\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# sim.rc.__config__[\"!SIM.file.local_packages_path\"] = \"inst_simulations/inst_pkgs\""
+    "import scopesim_templates as sim_tp"
    ]
   },
   {
@@ -104,26 +101,26 @@
     "Here we also specify which modes are to be used. \n",
     "To start with, we will use the 15 arcsec slit in the ``Spec_IJ`` filter.\n",
     "\n",
-    "The ``set_modes`` parameter takes a list of two mode keywords: ``[<AO mode>, <slit>]``. \n",
+    "The ``set_modes`` parameter takes a list of two mode keywords: ``[<AO mode>, <insmode>]``. \n",
     "- AO modes affect total system transmisison and which PSF file is used.\n",
     "  - Possible values are ``SCAO``, ``MCAO``\n",
-    "- The slit modes difine the slit dimensions (and add the grating losses to the total transmission).\n",
-    "  The mode name reflects the dimensions of the slit in  [mas]:\n",
-    "  - Possible values are ``SPEC_3000x16``, ``SPEC_3000x48``, ``SPEC_15000x20``\n",
-    "\n",
-    "Some possible combinations would be:\n",
+    "- The second keyword sets the instrument mode\n",
+    "  - Possible values are ``IMG_4mas``, ``IMG_1.5mas``, ``SPEC`` or ``IFU``\n",
     "\n",
-    "    set_modes=[\"SCAO\", \"SPEC_3000x16\"]\n",
-    "    set_modes=[\"MCAO\", \"SPEC_3000x48\"]\n",
-    "    set_modes=[\"SCAO\", \"SPEC_15000x20\"]\n",
+    "This notebook demonstrates the long-slit spectroscopy mode, ``SPEC``.\n",
+    "The slit to use is set by the parameter ``!OBS.slit``. The available slits are\n",
+    "The slit name correspondes to the dimensions of the slit in  [mas]:\n",
+    "  - ``Short``: ``3000x16``\n",
+    "  - ``Wide``: ``3000x48``\n",
+    "  - ``Long``: ``15000x20``\n",
     "\n",
     "Possible combinations of filter and slit are:\n",
     "\n",
-    "    Spec_IJ, SPEC_3000x48\n",
-    "    Spec_IJ, SPEC_3000x16\n",
-    "    Spec_J, SPEC_15000x20\n",
-    "    Spec_HK, SPEC_3000x48\n",
-    "    Spec_HK, SPEC_1500020\n"
+    "    Spec_IJ, Wide\n",
+    "    Spec_IJ, Short\n",
+    "    Spec_J, Long\n",
+    "    Spec_HK, Wide\n",
+    "    Spec_HK, Long\n"
    ]
   },
   {
@@ -144,10 +141,11 @@
    "outputs": [],
    "source": [
     "AO_MODE = \"SCAO\"\n",
-    "SLIT = \"SPEC_3000x48\"\n",
+    "SLIT = \"Wide\"\n",
     "FILT = \"Spec_IJ\"\n",
     "\n",
-    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[AO_MODE, SLIT])\n",
+    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[AO_MODE, \"SPEC\"],\n",
+    "                      properties={\"!OBS.slit\": SLIT})\n",
     "\n",
     "# we can swap to imaging mode just to check what the field looks like\n",
     "# cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IMG_4mas\"])\n",
@@ -178,9 +176,9 @@
     "    } # in microns\n",
     "\n",
     "R = {\n",
-    "    \"SPEC_3000x16\": 10000,\n",
-    "    \"SPEC_3000x48\": 3300,\n",
-    "    \"SPEC_15000x20\": 8000,\n",
+    "    \"Short\": 10000,\n",
+    "    \"Wide\": 3300,\n",
+    "    \"Long\": 8000,\n",
     "}\n",
     "\n",
     "def spec_res(slit, filter):\n",
@@ -205,7 +203,7 @@
     "cmd[\"!SIM.spectral.spectral_bin_width\"] = spec_res(SLIT, FILT)\n",
     "\n",
     "# settings to speed up the simulation\n",
-    "# cmd[\"!SIM.spectral.spectral_bin_width\"] = 5e-4\n",
+    "cmd[\"!SIM.spectral.spectral_bin_width\"] = 5e-4\n",
     "cmd[\"!OBS.interp_psf\"] = False\n",
     "\n",
     "print(f\"Spectral bin width set to {cmd['!SIM.spectral.spectral_bin_width']:.2e} microns\")\n",
@@ -318,9 +316,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(15,15))\n",
-    "plt.imshow(micado.image_planes[0].data, norm=LogNorm(), origin=\"lower\")\n",
-    "plt.colorbar()"
+    "plt.imshow(micado.image_planes[0].data, norm=\"log\", origin=\"lower\")\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c183dcf5-090b-4d90-876b-2e927d8f3756",
+   "metadata": {},
+   "source": [
+    "Note the clearly visible band of width around 250 pixels around the main trace of the star. This band is due to the finite extent of the PSF kernel used by ScopeSim. The appearance would be improved by using a larger PSF file with an extent of, say, 1024 pixels. This would, however, considerably increase the computing time for PSF convolution."
    ]
   },
   {
@@ -380,14 +385,13 @@
     "    9: 9,\n",
     "}\n",
     "\n",
-    "plt.figure(figsize=(15,15))\n",
     "if USE_FULL_DETECTOR:\n",
     "    for i in range(1,10):\n",
     "        plt.subplot(3,3,i)\n",
-    "        plt.imshow(hdul[0][hdu_layout[i]].data, norm=LogNorm(), origin=\"lower\")\n",
+    "        plt.imshow(hdul[0][hdu_layout[i]].data, norm=\"log\", origin=\"lower\");\n",
     "else:\n",
-    "    plt.imshow(hdul[0][1].data, norm=LogNorm(), origin=\"lower\")\n",
-    "    plt.colorbar()"
+    "    plt.imshow(hdul[0][1].data, norm=\"log\", origin=\"lower\")\n",
+    "    plt.colorbar();"
    ]
   },
   {
@@ -410,9 +414,10 @@
    "outputs": [],
    "source": [
     "FILT = \"Spec_HK\"\n",
-    "SLIT = \"SPEC_15000x20\"\n",
+    "SLIT = \"Long\"\n",
     "\n",
-    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", SLIT])\n",
+    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"SPEC\"])\n",
+    "cmd[\"!OBS.slit\"] = SLIT\n",
     "cmd[\"!OBS.filter_name_fw1\"] = FILT\n",
     "cmd[\"!OBS.filter_name_fw2\"] = \"open\"\n",
     "\n",
@@ -465,8 +470,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(15,15))\n",
-    "plt.imshow(micado.image_planes[0].data, norm=LogNorm(), origin=\"lower\")"
+    "plt.imshow(micado.image_planes[0].data, norm=\"log\", origin=\"lower\");"
    ]
   },
   {
@@ -504,16 +508,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(15,15))\n",
     "if USE_FULL_DETECTOR:\n",
     "    for i in range(1,10):\n",
     "        plt.subplot(3,3,i)\n",
     "\n",
     "        im = hdul[0][hdu_layout[i]].data\n",
-    "        plt.imshow(im, norm=LogNorm(vmin=0.99*np.median(im), vmax=1.1*np.median(im)), origin=\"lower\")\n",
+    "        plt.imshow(im, norm=LogNorm(vmin=0.99*np.median(im), vmax=1.1*np.median(im)), origin=\"lower\");\n",
     "else:\n",
     "    im = hdul[0][1].data\n",
-    "    plt.imshow(im, norm=LogNorm(vmin=0.99*np.median(im), vmax=1.1*np.median(im)), origin=\"lower\")"
+    "    plt.imshow(im, norm=LogNorm(vmin=0.99*np.median(im), vmax=1.1*np.median(im)), origin=\"lower\");"
    ]
   },
   {
@@ -540,7 +543,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "scopesim_dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -554,7 +557,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/MICADO/docs/example_notebooks/7_dithered_cluster.ipynb b/MICADO/docs/example_notebooks/7_dithered_cluster.ipynb
index f0a682fc..db1d4cb7 100644
--- a/MICADO/docs/example_notebooks/7_dithered_cluster.ipynb
+++ b/MICADO/docs/example_notebooks/7_dithered_cluster.ipynb
@@ -1,13 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9e467944-3e58-43a0-89b2-34f287021f86",
+   "metadata": {},
+   "source": [
+    "# Imaging with Dithering"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "f768a8f1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "from matplotlib import pyplot as plt\n",
     "from matplotlib.colors import LogNorm\n",
     "import datetime\n",
@@ -19,19 +26,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "ffbdc64d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO - sample_imf: Setting maximum allowed mass to 10000\n",
-      "INFO - sample_imf: Loop 0 added 1.01e+04 Msun to previous total of 0.00e+00 Msun\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "src = sim_tp.micado.cluster()\n",
     "\n",
@@ -43,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "e3f2c0e0",
    "metadata": {},
    "outputs": [],
@@ -81,44 +79,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "b95f46b3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "len(hdus[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "04863ddb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(20, 20))\n",
     "for i in range(1, 10):\n",
@@ -128,46 +102,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "50c31fef",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SIMPLE  =                    T / conforms to FITS standard                      \n",
-       "BITPIX  =                    8 / array data type                                \n",
-       "NAXIS   =                    0 / number of array dimensions                     \n",
-       "EXTEND  =                    T                                                  \n",
-       "MJD-OBS = '2022-01-01T02:30:00'                                                 \n",
-       "RA      = 0.000277777777777777 / [deg]                                          \n",
-       "DEC     = 0.000555555555555555 / [deg]                                          \n",
-       "HIERARCH ESO DPR CATG = 'SCIENCE '                                              \n",
-       "HIERARCH ESO DPR TYPE = 'OBJECT  '                                              \n",
-       "HIERARCH ESO DPR TECH = 'IMAGE   '                                              \n",
-       "HIERARCH ESO DET DIT = 3600                                                     \n",
-       "HIERARCH ESO DET NDIT = 1                                                       \n",
-       "HIERARCH ESO DET READOUT = 'CDS     '                                           \n",
-       "HIERARCH ESO INS FILT1 NAME = 'open    '                                        \n",
-       "HIERARCH ESO INS FILT2 NAME = 'H       '                                        \n",
-       "HIERARCH ESO INS STOP NAME = 'H       '                                         \n",
-       "HIERARCH ESO INS DROT POSANGLE = 0                                              \n",
-       "HIERARCH ESO TPL START = '2022-05-04T02:30:00'                                  \n",
-       "HIERARCH ESO TPL EXPNO = 2                                                      "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "hdus[0][0].header"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "80727be5",
    "metadata": {},
    "outputs": [],
@@ -178,7 +123,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -192,7 +137,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,

From 83fb62b9ce8474c84305e1a0b2ad1ca5f60cac89 Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Mon, 21 Sep 2026 13:43:17 +0200
Subject: [PATCH 30/63] Update needs-scopesim keywords to v0.12.0rc2

---
 METIS/default.yaml  | 2 +-
 MICADO/default.yaml | 2 +-
 MOSAIC/default.yaml | 2 +-
 NIRCam/default.yaml | 2 +-
 WFC3/default.yaml   | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/METIS/default.yaml b/METIS/default.yaml
index c0bba3e6..b3d98603 100644
--- a/METIS/default.yaml
+++ b/METIS/default.yaml
@@ -6,7 +6,7 @@ alias: OBS
 name: METIS_default_configuration
 description: default parameters needed for a METIS simulation
 status: development
-needs_scopesim: "v0.11.3"
+needs_scopesim: "v0.12.0rc2"
 date_modified: 2026-05-29
 changes:
   - 2021-12-16 (OC) chopnod defaults to perpendicular
diff --git a/MICADO/default.yaml b/MICADO/default.yaml
index 975a9575..786a6289 100644
--- a/MICADO/default.yaml
+++ b/MICADO/default.yaml
@@ -6,7 +6,7 @@ alias: OBS
 name: MICADO_default_configuration
 description: default parameters needed for a MICADO simulation
 status: development
-needs_scopesim: "v0.11.3"
+needs_scopesim: "v0.12.0rc2"
 date_modified: 2026-07-28
 changes:
   - 2023-07-13 (OC) add modes for FDR slits, deprecate pre-FDR slits
diff --git a/MOSAIC/default.yaml b/MOSAIC/default.yaml
index d6378a7d..2d1c32c5 100644
--- a/MOSAIC/default.yaml
+++ b/MOSAIC/default.yaml
@@ -4,7 +4,7 @@ object : configuration
 alias : OBS
 name : MOSAIC_default_configuration
 description : default parameters needed for a MOSAIC simulation
-needs_scopesim: "v0.11.0"
+needs_scopesim: "v0.12.0rc2"
 date_modified: 2026-09-10
 changes:
   - 2025-09-01 (OC) adjusted resolutions for modes
diff --git a/NIRCam/default.yaml b/NIRCam/default.yaml
index 48de3aec..453de3d9 100644
--- a/NIRCam/default.yaml
+++ b/NIRCam/default.yaml
@@ -3,7 +3,7 @@ object: configuration
 alias: OBS
 name: NIRCam
 status: experimental
-needs_scopesim: "v0.11.1"
+needs_scopesim: "v0.12.0rc2"
 date_modified: 2025-12-04
 changes:
   - 2025-12-04 (FH) initial version created
diff --git a/WFC3/default.yaml b/WFC3/default.yaml
index 114e620a..ad30d4ee 100644
--- a/WFC3/default.yaml
+++ b/WFC3/default.yaml
@@ -8,7 +8,7 @@ alias: OBS
 name: HST_WFC3_default_config
 description: defatult configuration for HST WFC3 UVIS and NIR imaging
 status: experimental
-needs_scopesim: "v0.11.2a2"
+needs_scopesim: "v0.12.0rc2"
 date_modified: 2025-12-05
 changes:
   - 2019-11-20 (KL) initial version

From 2e4e6b0b045bcdb18400fc80a07528a8dd1f550e Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Mon, 21 Sep 2026 15:32:32 +0200
Subject: [PATCH 31/63] Small cleanup in some notebooks

---
 .../example_notebooks/demos/demo_Leiden_sky.md    |  2 +-
 .../demos/demo_inter_pixel_capacitance.md         |  6 +-----
 .../example_notebooks/demos/demo_lss_simple.md    |  6 +++---
 .../demos/demo_metis_lms_efficiency.md            |  4 ++--
 .../demos/demo_rectify_traces.md                  | 15 ++++++++++-----
 5 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
index 24638f0d..c3481f21 100644
--- a/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
+++ b/METIS/docs/example_notebooks/demos/demo_Leiden_sky.md
@@ -39,7 +39,7 @@ metis = sim.OpticalTrain(cmd)
 ```
 
 ```{code-cell} ipython3
-metis.effects.pprint_all()
+metis.effects
 ```
 
 The `leiden_` modes replace the `ELT` and `Armazones` packages with two effects, `leiden_sky`, which creates the background source using the atmospheric spectra for the selected PWV, and `telescope`, which models the relay optics.
diff --git a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
index 44269ca6..21d76070 100644
--- a/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
+++ b/METIS/docs/example_notebooks/demos/demo_inter_pixel_capacitance.md
@@ -52,11 +52,7 @@ metis = sim.OpticalTrain(cmd)
 ```
 
 ```{code-cell} ipython3
-metis.effects.pprint_all()
-```
-
-```{code-cell} ipython3
-
+metis.effects
 ```
 
 ```{code-cell} ipython3
diff --git a/METIS/docs/example_notebooks/demos/demo_lss_simple.md b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
index 95c317d1..7a47b068 100644
--- a/METIS/docs/example_notebooks/demos/demo_lss_simple.md
+++ b/METIS/docs/example_notebooks/demos/demo_lss_simple.md
@@ -16,6 +16,8 @@ This notebook shows the most basic setup for long-slit spectroscopy, using a sta
 
 ```{code-cell} ipython3
 import numpy as np
+from astropy import units as u
+from astropy.wcs import WCS
 from matplotlib import pyplot as plt
 
 import scopesim as sim
@@ -66,8 +68,6 @@ rectified = tracelist.rectify_traces(result, -4, 4)
 `rectified` is again an `HDUList` with the data in the first extension. The header of this extension contains the WCS keywords needed to translate from pixels to wavelength and spatial position.
 
 ```{code-cell} ipython3
-from astropy.wcs import WCS
-from astropy import units as u
 wcs = WCS(rectified[1].header)
 naxis1, naxis2 = wcs._naxis
 det_wave = wcs.all_pix2world(np.arange(naxis1), 1, 0)[0] * u.Unit(wcs.wcs.cunit[0])
@@ -81,7 +81,7 @@ plt.figure(figsize=(12,7))
 plt.imshow(rectified[1].data, vmin=100, norm="log",
            extent=(det_wave[0], det_wave[-1], det_xi[0], det_xi[-1]),
            origin="lower", aspect="auto")
-plt.xlabel(r"Wavelength [$\mu$m]")
+plt.xlabel(f"Wavelength [{u.um.to_string('latex')}]")
 plt.ylabel("Position along slit [arcsec]")
 plt.colorbar();
 ```
diff --git a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
index 5ab7d038..b0b442c6 100644
--- a/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
+++ b/METIS/docs/example_notebooks/demos/demo_metis_lms_efficiency.md
@@ -50,10 +50,10 @@ eff.surface.transmission.plot()
 Alternatively, the order can be specified directly. This is used in the following to plot the efficiencies for all orders. The resulting figure can be compared to the original figure from E-REP-ATC-MET-1016.
 
 ```{code-cell} ipython3
-plt.figure(figsize=(8, 6))
+plt.figure(figsize=(10, 5))
 plt.ylim(0, 1)
 plt.xlim(3.0, 5.05)
-plt.xlabel(r"Wavelength [$\mu$m]")
+plt.xlabel(f"Wavelength [{u.um.to_string('latex')}]")
 plt.ylabel("Efficiency")
 plt.xticks(np.arange(3.0, 5.05, 0.2))
 for order in np.arange(22, 37):
diff --git a/METIS/docs/example_notebooks/demos/demo_rectify_traces.md b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
index a67ed740..0f6d25c6 100644
--- a/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
+++ b/METIS/docs/example_notebooks/demos/demo_rectify_traces.md
@@ -58,9 +58,14 @@ src_linelamp.fields[0].spectra[0] = spec     # NB: Do not try to set src_linelam
 We use METIS in the L-band long-slit spectroscopic mode, using a fairly narrow slit. We explicitely request the realistic spectral mapping with non-linear dispersion.
 
 ```{code-cell} ipython3
-cmds = sim.UserCommands(use_instrument="METIS", set_modes=["lss_l"],
-                       properties={"!OBS.trace_file": "TRACE_LSS_L.fits",
-                                   "!OBS.slit": "B-28_6"})
+cmds = sim.UserCommands(
+    use_instrument="METIS",
+    set_modes=["lss_l"],
+    properties={
+        "!OBS.trace_file": "TRACE_LSS_L.fits",
+        "!OBS.slit": "B-28_6",
+    },
+)
 
 metis = sim.OpticalTrain(cmds)
 ```
@@ -112,7 +117,7 @@ xi = (wcs.all_pix2world(1000, np.arange(naxis2), 0)[1] * u.Unit(wcs.wcs.cunit[1]
 ```{code-cell} ipython3
 plt.imshow(rectified[1].data, origin="lower", extent=[lam[0], lam[-1], xi[0], xi[-1]])
 plt.gca().set_aspect("auto")
-plt.xlabel(r"Wavelength [$\mu$m]")
+plt.xlabel(f"Wavelength [{u.um.to_string('latex')}]")
 plt.ylabel(r"Spatial position along slit [arcsec]");
 ```
 
@@ -122,5 +127,5 @@ plt.figure(figsize=(15, 7))
 plt.plot(lam[i1:i2], rectified[1].data[800, i1:i2], label="single row")
 plt.plot(lam[i1:i2], rectified[1].data.mean(axis=0)[i1:i2], label="average")
 plt.legend()
-plt.xlabel(r"Wavelength [$\mu$m]");
+plt.xlabel(f"Wavelength [{u.um.to_string('latex')}]");
 ```

From ea0b68b96748a27d859d96f10e3c580a3e98dd55 Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Mon, 21 Sep 2026 15:32:52 +0200
Subject: [PATCH 32/63] Update Ubuntu and Python for RTD build

---
 .readthedocs.yaml | 4 ++--
 conf.py           | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 35efb924..1583d976 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -5,9 +5,9 @@
 version: 2
 
 build:
-  os: "ubuntu-24.04"
+  os: "ubuntu-26.04"
   tools:
-    python: "3.11"
+    python: "3.13"
   jobs:
     post_install:
       - pytest ./MICADO/test_micado/ -m validation --junit-xml=./MICADO/test_micado/validation_results.xml --tb=line -o junit_suite_name=MICADO || true
diff --git a/conf.py b/conf.py
index 51292183..949c3572 100644
--- a/conf.py
+++ b/conf.py
@@ -88,4 +88,4 @@ def remove_inst_pkgs_symlink():
             path.unlink()
 
 
-remove_inst_pkgs_symlink()
+# remove_inst_pkgs_symlink()

From 0309fe9e5933ab3dcb3b2ece526677a152f0f0ce Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Mon, 21 Sep 2026 18:07:46 +0200
Subject: [PATCH 33/63] Keep symlinks away from RTD

Otherwise build hangs...
---
 conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conf.py b/conf.py
index 949c3572..51292183 100644
--- a/conf.py
+++ b/conf.py
@@ -88,4 +88,4 @@ def remove_inst_pkgs_symlink():
             path.unlink()
 
 
-# remove_inst_pkgs_symlink()
+remove_inst_pkgs_symlink()

From ab283d8a628847b4a3629f65c4212858f02b8e71 Mon Sep 17 00:00:00 2001
From: oczoske <ollicz68@yahoo.com>
Date: Mon, 21 Sep 2026 21:25:54 +0200
Subject: [PATCH 34/63] Increase offset back to 3 arcsec; last batch of nbs

---
 MICADO/MASK_slit_Long_offset.dat              |  10 +-
 MICADO/MASK_slit_Short_offset.dat             |  10 +-
 MICADO/MASK_slit_Wide_offset.dat              |  10 +-
 .../example_notebooks/MICADO_4_stars.ipynb    | 256 ------------------
 .../docs/example_notebooks/MICADO_FAQs.ipynb  |   4 +-
 .../MICADO_rectify_traces.ipynb               |  76 ++----
 .../docs/extra_notebooks/MICADO_4_stars.ipynb | 234 ++++++++++++++++
 MICADO/docs/extra_notebooks/MICADO_MCA.ipynb  |   5 +-
 .../extra_notebooks/MICADO_spectro_demo.ipynb |  58 ++--
 9 files changed, 323 insertions(+), 340 deletions(-)
 delete mode 100644 MICADO/docs/example_notebooks/MICADO_4_stars.ipynb
 create mode 100644 MICADO/docs/extra_notebooks/MICADO_4_stars.ipynb

diff --git a/MICADO/MASK_slit_Long_offset.dat b/MICADO/MASK_slit_Long_offset.dat
index 9ce702ad..e847fa6b 100644
--- a/MICADO/MASK_slit_Long_offset.dat
+++ b/MICADO/MASK_slit_Long_offset.dat
@@ -8,7 +8,7 @@
 # y_unit : arcsec
 # offset_type : detector
 # offset_x : 0
-# offset_y : 1.2
+# offset_y : 3.
 # changes :
 # - 2019-07-10 (KL) Created the file
 # - 2020-03-24 (KL) Changed geometry to 15000x50mas
@@ -18,7 +18,7 @@
 # - 2026-09-17 (OC) Put offset into meta data
 #
 x       y
--5.0    1.190
-10.0    1.190
-10.0    1.210
--5.0    1.210
+-5.0    2.990
+10.0    2.990
+10.0    3.010
+-5.0    3.010
diff --git a/MICADO/MASK_slit_Short_offset.dat b/MICADO/MASK_slit_Short_offset.dat
index 5ff5d993..a75b823a 100644
--- a/MICADO/MASK_slit_Short_offset.dat
+++ b/MICADO/MASK_slit_Short_offset.dat
@@ -8,7 +8,7 @@
 # y_unit : arcsec
 # offset_type : detector
 # offset_x : 0
-# offset_y : 1.2
+# offset_y : 3.
 # changes :
 # - 2019-07-10 (KL) Created the file
 # - 2020-03-24 (KL) Changed geometry to 3000x20mas
@@ -17,7 +17,7 @@
 # - 2026-09-17 (OC) Put offset into meta data
 #
 x       y
--1.5    +1.192
-+1.5    +1.192
-+1.5    +1.208
--1.5    +1.208
+-1.5    +2.992
++1.5    +2.992
++1.5    +3.008
+-1.5    +3.008
diff --git a/MICADO/MASK_slit_Wide_offset.dat b/MICADO/MASK_slit_Wide_offset.dat
index b4d1d795..266272fb 100644
--- a/MICADO/MASK_slit_Wide_offset.dat
+++ b/MICADO/MASK_slit_Wide_offset.dat
@@ -8,7 +8,7 @@
 # y_unit : arcsec
 # offset_type : detector
 # offset_x : 0
-# offset_y : 1.2
+# offset_y : 3.
 # changes :
 # - 2019-07-10 (KL) Created the file
 # - 2020-03-24 (KL) Changed geometry to 3000x50mas
@@ -17,7 +17,7 @@
 # - 2026-09-17 (OC) Put offset into meta data
 #
 x       y
--1.5    1.176
-+1.5    1.176
-+1.5    1.224
--1.5    1.224
+-1.5    2.976
++1.5    2.976
++1.5    3.024
+-1.5    3.024
diff --git a/MICADO/docs/example_notebooks/MICADO_4_stars.ipynb b/MICADO/docs/example_notebooks/MICADO_4_stars.ipynb
deleted file mode 100644
index 35d65ef5..00000000
--- a/MICADO/docs/example_notebooks/MICADO_4_stars.ipynb
+++ /dev/null
@@ -1,256 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c91d6ba9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.colors import LogNorm\n",
-    "from astropy import units as u\n",
-    "\n",
-    "import scopesim_templates as sim_tp\n",
-    "import scopesim as sim"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04224d82",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e962001c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# These notebooks should run on anyones computer.\n",
-    "\n",
-    "# Gijs's local computer\n",
-    "# sim.download_packages([\"Armazones\", \"ELT\", \"MORFEO\", \"MICADO\"])\n",
-    "\n",
-    "# Kieran's computer -> redirects ScopeSim to folder containing IRDB \n",
-    "# MicadoWise could do this too and clone daily from github to save running time\n",
-    "#sim.rc.__config__[\"!SIM.file.local_packages_path\"] = \"F:\\Work\\irdb\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eaf2e74a",
-   "metadata": {},
-   "source": [
-    "# Make the MICADO optical train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "418bfe52",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmds = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IMG_1.5mas\"])\n",
-    "\n",
-    "# EXPTIME = 3600 = ndit * dit\n",
-    "cmds[\"!DET.dit\"] = 30\n",
-    "cmds[\"!DET.ndit\"] = 120\n",
-    "\n",
-    "micado = sim.OpticalTrain(cmds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "00a30f92",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check the status of the effect objects -> mainly to see which detector (full or windowed) is selected\n",
-    "# micado.effects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47b7244d",
-   "metadata": {},
-   "source": [
-    "# Create the on-sky scene with 3+1 stars"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "11aa3b4e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['A0V']\n",
-      "['A0V']\n"
-     ]
-    }
-   ],
-   "source": [
-    "fixed_stars = sim_tp.stellar.stars(filter_name=\"H\", \n",
-    "                                   amplitudes=[20, 21, 22]*u.mag,       # [u.mag, u.ABmag, u.Jy]\n",
-    "                                   spec_types=[\"A0V\", \"A0V\", \"A0V\"],    \n",
-    "                                   x=[0, 0.5, 0.5], y=[0.5, 0, 0.5])    # [u.arcsec]\n",
-    "moving_star = sim_tp.stellar.star(filter_name=\"H\", \n",
-    "                                  amplitude=23*u.mag, \n",
-    "                                  spec_type=\"A0V\", \n",
-    "                                  x=-0.0, y=0.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51660ce7",
-   "metadata": {},
-   "source": [
-    "# Move one of the stars around"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "656ec7e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "moving_star.shift(dx=0.1, dy=-0.1)    # dx, dy added to latest position, NOT original positions from previous cell"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9cf9b358",
-   "metadata": {},
-   "source": [
-    "# Observe the stars"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fd8555fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scene = fixed_stars + moving_star\n",
-    "micado.observe(scene)\n",
-    "hdu = micado.readout()[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "90fd996b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f863b57ce50>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "photon_map = micado.image_planes[0].data\n",
-    "readout_image = hdu[1].data\n",
-    "\n",
-    "im = readout_image\n",
-    "\n",
-    "plt.figure(figsize=(12,12))\n",
-    "norm = LogNorm(vmax=1.1 * np.median(im), vmin=0.9 * np.median(im))\n",
-    "plt.imshow(im, norm=norm)\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b64f8781",
-   "metadata": {},
-   "source": [
-    "# Use the whole MICADO detector array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "8d109645",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# micado.effects    # list the names of all the MICADO effects\n",
-    "# micado[\"full_detector_array\"].include = True\n",
-    "# micado[\"detector_window\"].include = False\n",
-    "\n",
-    "# scene = fixed_stars + moving_star\n",
-    "\n",
-    "# micado.observe(scene) \n",
-    "# hdu_full = micado.readout()[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49e4ac18",
-   "metadata": {},
-   "source": [
-    "# Write out FITS to disk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "852d0f86",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hdu.writeto(\"test_4_stars_windowed.fits\")\n",
-    "# hdu_full.writeto(\"test_4_stars_all_detector.fits\")"
-   ]
-  }
- ],
- "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": "3.11.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/MICADO/docs/example_notebooks/MICADO_FAQs.ipynb b/MICADO/docs/example_notebooks/MICADO_FAQs.ipynb
index 77a32c37..f230e488 100644
--- a/MICADO/docs/example_notebooks/MICADO_FAQs.ipynb
+++ b/MICADO/docs/example_notebooks/MICADO_FAQs.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "excellent-sewing",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/MICADO/docs/example_notebooks/MICADO_rectify_traces.ipynb b/MICADO/docs/example_notebooks/MICADO_rectify_traces.ipynb
index a4cb3ae8..4b122245 100644
--- a/MICADO/docs/example_notebooks/MICADO_rectify_traces.ipynb
+++ b/MICADO/docs/example_notebooks/MICADO_rectify_traces.ipynb
@@ -5,7 +5,7 @@
    "id": "fe7880aa",
    "metadata": {},
    "source": [
-    "# Rectify Traces\n",
+    "# Rectification of Spectral Traces\n",
     "This notebook demonstrates how to rectify a detector readout from a spectroscopic simulation using Scopesim. \"Rectification\" means the transformation of a spectral trace from the detector onto a rectangular pixel grid of wavelength and spatial position along the slit. Wavelength calibration and rectification are major tasks of the spectroscopic data-reduction pipeline. For convenience, Scopesim includes functionality to perform these tasks by reversing the *known* mapping that was used for the simulation, resulting in easily analysable 2D spectra that include all the noise and background components but neglect the uncertainties of a wavelength calibration as it would be performed during the reduction of real data. \n",
     "Rectification is demonstrated on a MICADO long-slit simulation."
    ]
@@ -35,8 +35,7 @@
    "outputs": [],
    "source": [
     "from matplotlib import pyplot as plt\n",
-    "from matplotlib.colors import LogNorm\n",
-    "%matplotlib inline"
+    "from matplotlib.colors import LogNorm"
    ]
   },
   {
@@ -116,29 +115,6 @@
     "src_linelamp.fields[0].spectra[0] = spec     # NB: Do not try to set src_linelamp.spectra[0], this has no effect. "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "5f539847",
-   "metadata": {},
-   "source": [
-    "We can also replace the test spectrum with an actual line-list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59938957",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OPTIONAL: import line list from file, or comment out to keep the test spectrum\n",
-    "# llist = line_list(unit_flux=1e-12,\n",
-    "#                   dwave=0.01,\n",
-    "#                   smoothing_fwhm=0.03)\n",
-    "\n",
-    "# src_linelamp.fields[0].spectra = llist.spectra"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -150,7 +126,7 @@
     "plt.imshow(src_linelamp.fields[0].data)\n",
     "plt.colorbar()\n",
     "\n",
-    "src_linelamp.spectra[0].plot()"
+    "src_linelamp.spectra[0].plot();"
    ]
   },
   {
@@ -172,11 +148,11 @@
     "\n",
     "Possible combinations of filter and slit are:\n",
     "\n",
-    "    Spec_IJ, SPEC_3000x48\n",
-    "    Spec_IJ, SPEC_3000x16\n",
-    "    Spec_J, SPEC_15000x20\n",
-    "    Spec_HK, SPEC_3000x48\n",
-    "    Spec_HK, SPEC_1500020"
+    "    Spec_IJ, Wide\n",
+    "    Spec_IJ, Short\n",
+    "    Spec_J, Long\n",
+    "    Spec_HK, Wide\n",
+    "    Spec_HK, Long"
    ]
   },
   {
@@ -191,9 +167,9 @@
     "# FILT = \"Spec_HK\"\n",
     "# FILT = \"J\"\n",
     "\n",
-    "# SLIT = \"SPEC_3000x16\"\n",
-    "SLIT = \"SPEC_3000x48\"\n",
-    "# SLIT = \"SPEC_15000x20\"\n",
+    "# SLIT = \"Short\"\n",
+    "SLIT = \"Wide\"\n",
+    "# SLIT = \"Long\"\n",
     "\n",
     "min_wavelen = {\n",
     "    \"Spec_IJ\": 0.82,\n",
@@ -202,9 +178,9 @@
     "    } # in microns\n",
     "\n",
     "R = {\n",
-    "    \"SPEC_3000x16\": 10000,\n",
-    "    \"SPEC_3000x48\": 3300,\n",
-    "    \"SPEC_15000x20\": 8000,\n",
+    "    \"Short\": 10000,\n",
+    "    \"Wide\": 3300,\n",
+    "    \"Long\": 8000,\n",
     "}\n",
     "\n",
     "# estimate spectral resolution in microns\n",
@@ -224,11 +200,12 @@
    "outputs": [],
    "source": [
     "# Set up ScopeSim parameters\n",
-    "cmds = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", f\"{SLIT}\"])\n",
+    "cmds = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"SPEC\"])\n",
     "cmds[\"!OBS.dit\"] = 100\n",
     "cmds[\"!OBS.ndit\"] = 1\n",
     "cmds[\"!OBS.filter_name_fw1\"] = f\"{FILT}\"     # Spec_IJ, J, Spec_HK\n",
     "cmds[\"!OBS.filter_name_fw2\"] = \"open\"\n",
+    "cmds[\"!OBS.slit\"] = f\"{SLIT}\"                # Short, Wide, Long\n",
     "cmds[\"!SIM.spectral.spectral_bin_width\"] = spec_res_um\n",
     "cmds[\"!DET.width\"] = 4096\n",
     "cmds[\"!DET.height\"] = 4096\n",
@@ -266,7 +243,7 @@
     "# print(cmds.list_modes())\n",
     "# micado.effects.pprint_all()\n",
     "# micado[\"pupil_wheel\"].meta\n",
-    "micado[\"filter_wheel_1\"].current_filter.plot()"
+    "micado[\"filter_wheel_1\"].current_filter.plot();"
    ]
   },
   {
@@ -288,9 +265,9 @@
    "outputs": [],
    "source": [
     "# plot the image plane using a logarithmic intensity scale\n",
-    "plt.figure(figsize=(15,15))\n",
-    "plt.imshow(micado.image_planes[0].data, norm=LogNorm())\n",
-    "plt.colorbar()"
+    "# (only every other pixel in each dimension, otherwise very large)\n",
+    "plt.imshow(micado.image_planes[0].data[::2, ::2], norm=\"log\")\n",
+    "plt.colorbar();"
    ]
   },
   {
@@ -331,7 +308,8 @@
    "source": [
     "if USE_FULL_DETECTOR:\n",
     "    # plot the 3x3 detector array\n",
-    "    fig, axs = plt.subplots(3, 3, figsize=(10,10), layout='constrained')\n",
+    "    fig, axs = plt.subplots(3, 3, figsize=(10,10), layout='constrained',\n",
+    "                           sharex=True, sharey=True)\n",
     "    for i, hdu in enumerate(ro[1:]):\n",
     "        row = int(i/3)\n",
     "        col = i%3\n",
@@ -414,7 +392,7 @@
     "\n",
     "    # axs[i].set_ylabel(r\"Spatial position along slit [arcsec]\");\n",
     "\n",
-    "axs[-1].set_xlabel(r\"Wavelength [$\\mu$m]\")\n"
+    "axs[-1].set_xlabel(r\"Wavelength [$\\mu$m]\");\n"
    ]
   },
   {
@@ -435,15 +413,15 @@
     "    axs[i].plot(lam, trc.data.mean(axis=0), label=\"average\")\n",
     "    axs[i].set_ylabel(trc.header[\"EXTNAME\"])\n",
     "\n",
-    "axs[-1].set_xlabel(r\"Wavelength [$\\mu$m]\")\n",
-    "plt.show()\n",
+    "axs[-1].set_xlabel(r\"Wavelength [$\\mu$m]\");\n",
+    "\n",
     "    "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "scopesim_dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -457,7 +435,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/MICADO/docs/extra_notebooks/MICADO_4_stars.ipynb b/MICADO/docs/extra_notebooks/MICADO_4_stars.ipynb
new file mode 100644
index 00000000..e10c056c
--- /dev/null
+++ b/MICADO/docs/extra_notebooks/MICADO_4_stars.ipynb
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1909027e-ca6b-4561-b981-44ec40c423c8",
+   "metadata": {},
+   "source": [
+    "# MICADO imaging of some stars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c91d6ba9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import LogNorm\n",
+    "from astropy import units as u\n",
+    "\n",
+    "import scopesim_templates as sim_tp\n",
+    "import scopesim as sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04224d82",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e962001c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These notebooks should run on anyones computer.\n",
+    "\n",
+    "# Gijs's local computer\n",
+    "# sim.download_packages([\"Armazones\", \"ELT\", \"MORFEO\", \"MICADO\"])\n",
+    "\n",
+    "# Kieran's computer -> redirects ScopeSim to folder containing IRDB \n",
+    "# MicadoWise could do this too and clone daily from github to save running time\n",
+    "#sim.rc.__config__[\"!SIM.file.local_packages_path\"] = \"F:\\Work\\irdb\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eaf2e74a",
+   "metadata": {},
+   "source": [
+    "## Make the MICADO optical train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "418bfe52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmds = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IMG_1.5mas\"])\n",
+    "\n",
+    "# EXPTIME = 3600 = ndit * dit\n",
+    "cmds[\"!DET.dit\"] = 30\n",
+    "cmds[\"!DET.ndit\"] = 120\n",
+    "\n",
+    "micado = sim.OpticalTrain(cmds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00a30f92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check the status of the effect objects -> mainly to see which detector (full or windowed) is selected\n",
+    "# micado.effects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47b7244d",
+   "metadata": {},
+   "source": [
+    "## Create the on-sky scene with 3+1 stars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11aa3b4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fixed_stars = sim_tp.stellar.stars(filter_name=\"H\", \n",
+    "                                   amplitudes=[20, 21, 22]*u.mag,       # [u.mag, u.ABmag, u.Jy]\n",
+    "                                   spec_types=[\"A0V\", \"A0V\", \"A0V\"],    \n",
+    "                                   x=[0, 0.5, 0.5], y=[0.5, 0, 0.5])    # [u.arcsec]\n",
+    "moving_star = sim_tp.stellar.star(filter_name=\"H\", \n",
+    "                                  amplitude=23*u.mag, \n",
+    "                                  spec_type=\"A0V\", \n",
+    "                                  x=-0.0, y=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51660ce7",
+   "metadata": {},
+   "source": [
+    "## Move one of the stars around"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "656ec7e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moving_star.shift(dx=0.1, dy=-0.1)    # dx, dy added to latest position, NOT original positions from previous cell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cf9b358",
+   "metadata": {},
+   "source": [
+    "## Observe the stars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd8555fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene = fixed_stars + moving_star\n",
+    "micado.observe(scene)\n",
+    "hdu = micado.readout()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90fd996b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "photon_map = micado.image_planes[0].data\n",
+    "readout_image = hdu[1].data\n",
+    "\n",
+    "im = readout_image\n",
+    "\n",
+    "plt.figure(figsize=(12,12))\n",
+    "norm = LogNorm(vmax=1.1 * np.median(im), vmin=0.9 * np.median(im))\n",
+    "plt.imshow(im, norm=norm)\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b64f8781",
+   "metadata": {},
+   "source": [
+    "## Use the whole MICADO detector array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d109645",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# micado.effects    # list the names of all the MICADO effects\n",
+    "# micado[\"full_detector_array\"].include = True\n",
+    "# micado[\"detector_window\"].include = False\n",
+    "\n",
+    "# scene = fixed_stars + moving_star\n",
+    "\n",
+    "# micado.observe(scene) \n",
+    "# hdu_full = micado.readout()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49e4ac18",
+   "metadata": {},
+   "source": [
+    "## Write out FITS to disk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "852d0f86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hdu.writeto(\"test_4_stars_windowed.fits\")\n",
+    "# hdu_full.writeto(\"test_4_stars_all_detector.fits\")"
+   ]
+  }
+ ],
+ "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/MICADO/docs/extra_notebooks/MICADO_MCA.ipynb b/MICADO/docs/extra_notebooks/MICADO_MCA.ipynb
index f4f40fa1..8c9cf7fe 100644
--- a/MICADO/docs/extra_notebooks/MICADO_MCA.ipynb
+++ b/MICADO/docs/extra_notebooks/MICADO_MCA.ipynb
@@ -381,7 +381,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "readout = micado.readout(dit=600, ndit=1)[0]"
+    "readout = micado.readout(dit=1, ndit=600)[0]"
    ]
   },
   {
@@ -391,7 +391,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.imshow(readout[5].data)"
+    "plt.imshow(readout[5].data, origin='lower')\n",
+    "plt.colorbar();"
    ]
   },
   {
diff --git a/MICADO/docs/extra_notebooks/MICADO_spectro_demo.ipynb b/MICADO/docs/extra_notebooks/MICADO_spectro_demo.ipynb
index 92998dfb..55dca33d 100644
--- a/MICADO/docs/extra_notebooks/MICADO_spectro_demo.ipynb
+++ b/MICADO/docs/extra_notebooks/MICADO_spectro_demo.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dafc556a-3a58-43d5-a5db-c8acf310e63c",
+   "metadata": {},
+   "source": [
+    "# MICADO Long-Slit Spectroscopy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -54,6 +62,8 @@
    "outputs": [],
    "source": [
     "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IMG_4mas\"])\n",
+    "cmd[\"!DET.width\"] = 2048\n",
+    "cmd[\"!DET.height\"] = 2048\n",
     "mcd = sim.OpticalTrain(cmd)"
    ]
   },
@@ -72,7 +82,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mcd.effects.show_in_notebook()"
+    "mcd.effects.pprint_all()"
    ]
   },
   {
@@ -115,7 +125,7 @@
     "    mcd['slit_wheel'].change_slit(slit)\n",
     "    mcd.observe()\n",
     "    ax.imshow(mcd.image_planes[0].data, origin='lower')\n",
-    "    ax.text(1000, 1000, slit, va='top', ha='right', c='white') "
+    "    ax.text(2000, 2000, slit, va='top', ha='right', c='white') "
    ]
   },
   {
@@ -123,7 +133,7 @@
    "id": "c87edabd-5dec-427b-b1c0-3aac27011a0b",
    "metadata": {},
    "source": [
-    "Note that these simulations used a detector window of 1024x1024 pixels rather than the full MICADO array. The long slit extends over 15000mas/4mas = 3750 pixels in is therefore cut in these image."
+    "Note that these simulations used a detector window of 1024x1024 pixels rather than the full MICADO array. The long slit extends over 15000mas/4mas = 3750 pixels and is therefore cut in these image."
    ]
   },
   {
@@ -152,7 +162,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mcd['slit_wheel'].change_slit(\"3000x48\")\n",
+    "mcd['slit_wheel'].change_slit(\"Wide\")\n",
     "mcd.observe(star)"
    ]
   },
@@ -164,8 +174,8 @@
    "outputs": [],
    "source": [
     "plt.imshow(mcd.image_planes[0].data, norm='log', origin='lower')\n",
-    "plt.xlim(480, 550)\n",
-    "plt.ylim(500, 526);"
+    "plt.xlim(992, 1062)\n",
+    "plt.ylim(1010, 1035);"
    ]
   },
   {
@@ -183,7 +193,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "star.shift(dy=1.2)"
+    "star.shift(dy=3.)"
    ]
   },
   {
@@ -193,7 +203,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mcd['slit_wheel'].change_slit(\"3000x48_offset\")\n",
+    "mcd['slit_wheel'].change_slit(\"Wide_offset\")\n",
     "mcd.observe(star)"
    ]
   },
@@ -205,8 +215,8 @@
    "outputs": [],
    "source": [
     "plt.imshow(mcd.image_planes[0].data, norm='log', origin='lower')\n",
-    "plt.xlim(480, 550)\n",
-    "plt.ylim(800, 826);     # shift by 300 pixels (=1.2 arcsec)"
+    "plt.xlim(992, 1062)\n",
+    "plt.ylim(1760, 1786);     # shift by 750 pixels (=3 arcsec)"
    ]
   },
   {
@@ -249,7 +259,7 @@
     "mcd['relay_psf'].include = False\n",
     "mcd['micado_ncpas_psf'].include = False\n",
     "\n",
-    "mcd['slit_wheel'].change_slit(\"3000x16\")\n",
+    "mcd['slit_wheel'].change_slit(\"Short\")\n",
     "mcd['filter_wheel_1'].change_filter(\"Spec_HK\")"
    ]
   },
@@ -260,7 +270,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mcd.effects.show_in_notebook()"
+    "mcd.effects.pprint_all()"
    ]
   },
   {
@@ -319,7 +329,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mcd['slit_wheel'].change_slit(\"3000x16_offset\")\n",
+    "mcd['slit_wheel'].change_slit(\"Short_offset\")\n",
     "mcd.observe()"
    ]
   },
@@ -342,7 +352,7 @@
    "outputs": [],
    "source": [
     "with fits.open(\"readout_offset.fits\") as readout_offset:\n",
-    "    rectified = mcd['micado_spectral_traces'].rectify_traces(readout_offset, -1.5, 1.5)\n",
+    "    rectified = mcd['micado_spectral_traces'].rectify_traces(readout_offset, -1.5, 1.5, bin_width=3.1798928e-5)\n",
     "    rectified.writeto(\"rect_offset.fits\", overwrite=True)"
    ]
   },
@@ -389,8 +399,14 @@
     "        wcs_off = WCS(rect_offset[2].header).spectral\n",
     "        lam_nom = wcs_nom.all_pix2world(np.arange(rect_nominal[2].data.shape[1]), 0)[0]\n",
     "        lam_off = wcs_off.all_pix2world(np.arange(rect_offset[2].data.shape[1]), 0)[0]\n",
-    "        spec_nom = rect_nominal[2].data[350,]\n",
-    "        spec_off = rect_offset[2].data[350,]"
+    "        common_nom = (lam_nom > 1.42e-6) * (lam_nom < 1.85e-6)\n",
+    "        common_off = (lam_off > 1.42e-6) * (lam_off < 1.85e-6)\n",
+    "        lam_nom = lam_nom[common_nom]\n",
+    "        lam_off = lam_off[common_off]\n",
+    "        print(np.diff(lam_nom))\n",
+    "        print(np.diff(lam_off))\n",
+    "        spec_nom = rect_nominal[2].data[350, common_nom]\n",
+    "        spec_off = rect_offset[2].data[350, common_off]"
    ]
   },
   {
@@ -428,6 +444,16 @@
     "mspec_off = np.ma.masked_array(spec_off, mask=mask_off)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0920f404-4cee-46cd-8c37-63e2d0e46b7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spec_off.shape"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From a8a71ae32e31b4ec4dd18afc7fdc569e0dc093f9 Mon Sep 17 00:00:00 2001
From: teutoburg <ghost@instruct.at>
Date: Tue, 22 Sep 2026 13:49:29 +0200
Subject: [PATCH 35/63] Update badge reports in preparation for release

---
 _REPORTS/badge_report_log.txt | 744 +++++++++++++++----------------
 _REPORTS/badges.md            | 810 +++++++++++++++++-----------------
 _REPORTS/badges.yaml          | 802 ++++++++++++++++-----------------
 3 files changed, 1185 insertions(+), 1171 deletions(-)

diff --git a/_REPORTS/badge_report_log.txt b/_REPORTS/badge_report_log.txt
index b73fa2ed..1add71a6 100644
--- a/_REPORTS/badge_report_log.txt
+++ b/_REPORTS/badge_report_log.txt
@@ -1,369 +1,375 @@
-ERROR::Armazones\TER_armazones_default_FULL_IMG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::Armazones\TER_armazones_default_MIR_IMG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::Armazones\TER_armazones_default_NIR_IMG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::Armazones\TER_armazones_default_OPT_IMG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['Armazones\\TER_armazones_default_FULL_IMG.dat', 'Armazones\\TER_armazones_default_MIR_IMG.dat', 'Armazones\\TER_armazones_default_NIR_IMG.dat', 'Armazones\\TER_armazones_default_OPT_IMG.dat']}
-ERROR::ELT\TER_ELT_5_mirror.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ELT\TER_ELT_5_mirror_clean.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ELT\TER_ELT_6_mirror_field_track.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ELT\TER_ELT_6_mirror_field_track_clean.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ELT\TER_ELT_6_mirror_pupil_track.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ELT\TER_ELT_6_mirror_pupil_track_clean.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ELT\TER_ELT_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ELT\TER_ELT_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['ELT\\TER_ELT_5_mirror.dat', 'ELT\\TER_ELT_5_mirror_clean.dat', 'ELT\\TER_ELT_6_mirror_field_track.dat', 'ELT\\TER_ELT_6_mirror_field_track_clean.dat', 'ELT\\TER_ELT_6_mirror_pupil_track.dat', 'ELT\\TER_ELT_6_mirror_pupil_track_clean.dat', 'ELT\\TER_ELT_mirror_aluminium.dat', 'ELT\\TER_ELT_mirror_mgf2agal.dat']}
-ERROR::GTC\reflectivity_GTC.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::GTC\unity.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['GTC\\reflectivity_GTC.dat', 'GTC\\unity.dat']}
-ERROR::HAWKI\FPA_hawki_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\FPA_linearity.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\LIST_HAWKI_mirrors.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\QE_detector_H2RG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\TC_dichroic.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\TER_window.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_BrGamma.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_CH4.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_H2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_J.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_Ks.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_NB1060.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_NB1190.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_NB2090.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\filters\TC_filter_Y.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\BG_total_BrGamma.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\BG_total_J.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\BG_total_Ks.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\ESO_ETC_bg_fluxes.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\TER_system_BrGamma.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\TER_system_CH4.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\TER_system_H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\TER_system_J.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\TER_system_Ks.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HAWKI\test_hawki\hawki_eso_etc\TER_system_Y.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['HAWKI\\FPA_hawki_layout.dat', 'HAWKI\\FPA_linearity.dat', 'HAWKI\\LIST_HAWKI_mirrors.dat', 'HAWKI\\QE_detector_H2RG.dat', 'HAWKI\\TC_dichroic.dat', 'HAWKI\\TER_mirror_gold.dat', 'HAWKI\\TER_window.dat', 'HAWKI\\filters\\TC_filter_BrGamma.dat', 'HAWKI\\filters\\TC_filter_CH4.dat', 'HAWKI\\filters\\TC_filter_H.dat', 'HAWKI\\filters\\TC_filter_H2.dat', 'HAWKI\\filters\\TC_filter_J.dat', 'HAWKI\\filters\\TC_filter_Ks.dat', 'HAWKI\\filters\\TC_filter_NB1060.dat', 'HAWKI\\filters\\TC_filter_NB1190.dat', 'HAWKI\\filters\\TC_filter_NB2090.dat', 'HAWKI\\filters\\TC_filter_Y.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\BG_total_BrGamma.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\BG_total_J.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\BG_total_Ks.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\ESO_ETC_bg_fluxes.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\TER_system_BrGamma.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\TER_system_CH4.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\TER_system_H.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\TER_system_J.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\TER_system_Ks.dat', 'HAWKI\\test_hawki\\hawki_eso_etc\\TER_system_Y.dat']}
-ERROR::HST\TER_ELT_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HST\wfc3_ir_primary_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::HST\wfc3_ir_secondary_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['HST\\TER_ELT_mirror_mgf2agal.dat', 'HST\\wfc3_ir_primary_001_syn.dat', 'HST\\wfc3_ir_secondary_001_syn.dat']}
-ERROR::JWST\TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['JWST\\TER_mirror_gold.dat']}
-ERROR::LaPalma\orm_extinct.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['LaPalma\\orm_extinct.dat']}
-ERROR::LFOA\LIST_LFOA_mirrors_static.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\QE_SBIG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\TER_atmosphere.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\TER_focal_reducer.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\TER_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\B.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\Halpha_narrow.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\Halpha_wide.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\Hbeta.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\I.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\OIII.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\R.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\SII.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\sloan_g.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\sloan_i.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\sloan_r.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\sloan_u.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\sloan_z.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\U.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\V.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\old_filters\TC_filter_Halpha_narrow_old.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\old_filters\TC_filter_Halpha_wide_old.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\old_filters\TC_filter_Hbeta_old.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\old_filters\TC_filter_OIII_old.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::LFOA\filters\old_filters\TC_filter_SII_old.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['LFOA\\LIST_LFOA_mirrors_static.dat', 'LFOA\\QE_SBIG.dat', 'LFOA\\TER_atmosphere.dat', 'LFOA\\TER_focal_reducer.dat', 'LFOA\\TER_mirror_aluminium.dat', 'LFOA\\filters\\B.dat', 'LFOA\\filters\\Halpha_narrow.dat', 'LFOA\\filters\\Halpha_wide.dat', 'LFOA\\filters\\Hbeta.dat', 'LFOA\\filters\\I.dat', 'LFOA\\filters\\OIII.dat', 'LFOA\\filters\\R.dat', 'LFOA\\filters\\SII.dat', 'LFOA\\filters\\sloan_g.dat', 'LFOA\\filters\\sloan_i.dat', 'LFOA\\filters\\sloan_r.dat', 'LFOA\\filters\\sloan_u.dat', 'LFOA\\filters\\sloan_z.dat', 'LFOA\\filters\\U.dat', 'LFOA\\filters\\V.dat', 'LFOA\\filters\\old_filters\\TC_filter_Halpha_narrow_old.dat', 'LFOA\\filters\\old_filters\\TC_filter_Halpha_wide_old.dat', 'LFOA\\filters\\old_filters\\TC_filter_Hbeta_old.dat', 'LFOA\\filters\\old_filters\\TC_filter_OIII_old.dat', 'LFOA\\filters\\old_filters\\TC_filter_SII_old.dat']}
-ERROR::METIS\FPA_linearity_GeoSnap_high_capacity.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\FPA_linearity_GeoSnap_low_capacity.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\FPA_linearity_HxRG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\FPA_metis_img_lm_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\FPA_metis_img_nq_aquarius_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\FPA_metis_img_n_geosnap_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\FPA_metis_lms_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\FPA_metis_lms_smpl_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\LIST_ifu_apertures.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\LIST_METIS_mirrors_cfo.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\LIST_METIS_mirrors_img_lm.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\LIST_METIS_mirrors_img_n.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\LIST_METIS_mirrors_lms.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\LIST_METIS_mirrors_wcu.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\MASK_slit_A-19_0.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\MASK_slit_B-28_6.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\MASK_slit_C-38_1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\MASK_slit_D-57_1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\MASK_slit_E-114_2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\QE_detector_geosnap.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\QE_detector_H2RG_METIS.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_ADC_const_90.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_entrance_window.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_img_dichroic.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_lms_grism_max.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_lms_grism_min.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_lms_predisperser_prism.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\TER_sca_dichroic.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\code\2023-06-20-spectra_Grism-L_band.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\code\2023-06-20-spectra_Grism-M_band.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\code\2023-06-20-spectra_Grism-N_band.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_Br_alpha.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_Br_alpha_ref.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_CO_1-0_ice.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_CO_ref.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_H2O-ice.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_HCI_L_long.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_HCI_L_short.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_HCI_M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_IB_4.05.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_L.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_Lp.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_L_spec.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_Mp.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_M_spec.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_N1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_N2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_N3.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_ND_OD1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_ND_OD2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_ND_OD3.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_ND_OD4.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_ND_OD5.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_Ne_II.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_Ne_II_ref.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_N_spec.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_open.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_PAH_11.25.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_PAH_11.25_ref.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_PAH_3.3.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_PAH_3.3_ref.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_PAH_8.6.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_PAH_8.6_ref.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_Q1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_short-L.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_S_IV.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\filters\TC_filter_S_IV_ref.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\wcu\fp_mask_grid_lm.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\wcu\fp_mask_pinhole_lm.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::METIS\wcu\fp_mask_pinhole_n.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['METIS\\FPA_linearity_GeoSnap_high_capacity.dat', 'METIS\\FPA_linearity_GeoSnap_low_capacity.dat', 'METIS\\FPA_linearity_HxRG.dat', 'METIS\\FPA_metis_img_lm_layout.dat', 'METIS\\FPA_metis_img_nq_aquarius_layout.dat', 'METIS\\FPA_metis_img_n_geosnap_layout.dat', 'METIS\\FPA_metis_lms_layout.dat', 'METIS\\FPA_metis_lms_smpl_layout.dat', 'METIS\\LIST_ifu_apertures.dat', 'METIS\\LIST_METIS_mirrors_cfo.dat', 'METIS\\LIST_METIS_mirrors_img_lm.dat', 'METIS\\LIST_METIS_mirrors_img_n.dat', 'METIS\\LIST_METIS_mirrors_lms.dat', 'METIS\\LIST_METIS_mirrors_wcu.dat', 'METIS\\MASK_slit_A-19_0.dat', 'METIS\\MASK_slit_B-28_6.dat', 'METIS\\MASK_slit_C-38_1.dat', 'METIS\\MASK_slit_D-57_1.dat', 'METIS\\MASK_slit_E-114_2.dat', 'METIS\\QE_detector_geosnap.dat', 'METIS\\QE_detector_H2RG_METIS.dat', 'METIS\\TER_ADC_const_90.dat', 'METIS\\TER_entrance_window.dat', 'METIS\\TER_img_dichroic.dat', 'METIS\\TER_lms_grism_max.dat', 'METIS\\TER_lms_grism_min.dat', 'METIS\\TER_lms_predisperser_prism.dat', 'METIS\\TER_mirror_aluminium.dat', 'METIS\\TER_mirror_gold.dat', 'METIS\\TER_sca_dichroic.dat', 'METIS\\code\\2023-06-20-spectra_Grism-L_band.dat', 'METIS\\code\\2023-06-20-spectra_Grism-M_band.dat', 'METIS\\code\\2023-06-20-spectra_Grism-N_band.dat', 'METIS\\filters\\TC_filter_Br_alpha.dat', 'METIS\\filters\\TC_filter_Br_alpha_ref.dat', 'METIS\\filters\\TC_filter_CO_1-0_ice.dat', 'METIS\\filters\\TC_filter_CO_ref.dat', 'METIS\\filters\\TC_filter_H2O-ice.dat', 'METIS\\filters\\TC_filter_HCI_L_long.dat', 'METIS\\filters\\TC_filter_HCI_L_short.dat', 'METIS\\filters\\TC_filter_HCI_M.dat', 'METIS\\filters\\TC_filter_IB_4.05.dat', 'METIS\\filters\\TC_filter_L.dat', 'METIS\\filters\\TC_filter_Lp.dat', 'METIS\\filters\\TC_filter_L_spec.dat', 'METIS\\filters\\TC_filter_Mp.dat', 'METIS\\filters\\TC_filter_M_spec.dat', 'METIS\\filters\\TC_filter_N1.dat', 'METIS\\filters\\TC_filter_N2.dat', 'METIS\\filters\\TC_filter_N3.dat', 'METIS\\filters\\TC_filter_ND_OD1.dat', 'METIS\\filters\\TC_filter_ND_OD2.dat', 'METIS\\filters\\TC_filter_ND_OD3.dat', 'METIS\\filters\\TC_filter_ND_OD4.dat', 'METIS\\filters\\TC_filter_ND_OD5.dat', 'METIS\\filters\\TC_filter_Ne_II.dat', 'METIS\\filters\\TC_filter_Ne_II_ref.dat', 'METIS\\filters\\TC_filter_N_spec.dat', 'METIS\\filters\\TC_filter_open.dat', 'METIS\\filters\\TC_filter_PAH_11.25.dat', 'METIS\\filters\\TC_filter_PAH_11.25_ref.dat', 'METIS\\filters\\TC_filter_PAH_3.3.dat', 'METIS\\filters\\TC_filter_PAH_3.3_ref.dat', 'METIS\\filters\\TC_filter_PAH_8.6.dat', 'METIS\\filters\\TC_filter_PAH_8.6_ref.dat', 'METIS\\filters\\TC_filter_Q1.dat', 'METIS\\filters\\TC_filter_short-L.dat', 'METIS\\filters\\TC_filter_S_IV.dat', 'METIS\\filters\\TC_filter_S_IV_ref.dat', 'METIS\\wcu\\fp_mask_grid_lm.dat', 'METIS\\wcu\\fp_mask_pinhole_lm.dat', 'METIS\\wcu\\fp_mask_pinhole_n.dat']}
-ERROR::MICADO\FPA_array_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\FPA_linearity.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\INST_MICADO_wavefront_error_budget.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\LIST_MICADO_mirrors_spec.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\LIST_MICADO_mirrors_static.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\LIST_MICADO_mirrors_wide.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\LIST_MICADO_mirrors_zoom.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\LIST_RO_SCAO_mirrors.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\MASK_slit_15000x20.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\MASK_slit_3000x16.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\MASK_slit_3000x48.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\QE_detector_H2RG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\QE_detector_H4RG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\TER_coating_antireflection.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\TER_entrance_window.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\TER_full_adc.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\TER_grating.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\TER_MICADO_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\TER_SCAO_dichroic.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_blank.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_block.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_Br-gamma.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_FeII.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_H-cont.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_H-long.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_H-short.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_H2_1-0S1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_He-I.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_I-long.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_J-long.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_J-short.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_J.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_K-cont.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_K-long.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_K-mid.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_K-short.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_Ks.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_Ks2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_ND1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_ND3.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_open.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_Pa-beta.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_Spec_HK.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_Spec_IJ.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_stop_IJH.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_stop_K.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xH1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xH2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xI1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xI2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xJ1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xJ2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xK1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xK2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xY1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_xY2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MICADO\filters\TC_filter_Y.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['MICADO\\FPA_array_layout.dat', 'MICADO\\FPA_linearity.dat', 'MICADO\\INST_MICADO_wavefront_error_budget.dat', 'MICADO\\LIST_MICADO_mirrors_spec.dat', 'MICADO\\LIST_MICADO_mirrors_static.dat', 'MICADO\\LIST_MICADO_mirrors_wide.dat', 'MICADO\\LIST_MICADO_mirrors_zoom.dat', 'MICADO\\LIST_RO_SCAO_mirrors.dat', 'MICADO\\MASK_slit_15000x20.dat', 'MICADO\\MASK_slit_3000x16.dat', 'MICADO\\MASK_slit_3000x48.dat', 'MICADO\\QE_detector_H2RG.dat', 'MICADO\\QE_detector_H4RG.dat', 'MICADO\\TER_coating_antireflection.dat', 'MICADO\\TER_entrance_window.dat', 'MICADO\\TER_full_adc.dat', 'MICADO\\TER_grating.dat', 'MICADO\\TER_MICADO_mirror_mgf2agal.dat', 'MICADO\\TER_mirror_gold.dat', 'MICADO\\TER_SCAO_dichroic.dat', 'MICADO\\filters\\TC_filter_blank.dat', 'MICADO\\filters\\TC_filter_block.dat', 'MICADO\\filters\\TC_filter_Br-gamma.dat', 'MICADO\\filters\\TC_filter_FeII.dat', 'MICADO\\filters\\TC_filter_H-cont.dat', 'MICADO\\filters\\TC_filter_H-long.dat', 'MICADO\\filters\\TC_filter_H-short.dat', 'MICADO\\filters\\TC_filter_H.dat', 'MICADO\\filters\\TC_filter_H2_1-0S1.dat', 'MICADO\\filters\\TC_filter_He-I.dat', 'MICADO\\filters\\TC_filter_I-long.dat', 'MICADO\\filters\\TC_filter_J-long.dat', 'MICADO\\filters\\TC_filter_J-short.dat', 'MICADO\\filters\\TC_filter_J.dat', 'MICADO\\filters\\TC_filter_K-cont.dat', 'MICADO\\filters\\TC_filter_K-long.dat', 'MICADO\\filters\\TC_filter_K-mid.dat', 'MICADO\\filters\\TC_filter_K-short.dat', 'MICADO\\filters\\TC_filter_Ks.dat', 'MICADO\\filters\\TC_filter_Ks2.dat', 'MICADO\\filters\\TC_filter_ND1.dat', 'MICADO\\filters\\TC_filter_ND3.dat', 'MICADO\\filters\\TC_filter_open.dat', 'MICADO\\filters\\TC_filter_Pa-beta.dat', 'MICADO\\filters\\TC_filter_Spec_HK.dat', 'MICADO\\filters\\TC_filter_Spec_IJ.dat', 'MICADO\\filters\\TC_filter_stop_IJH.dat', 'MICADO\\filters\\TC_filter_stop_K.dat', 'MICADO\\filters\\TC_filter_xH1.dat', 'MICADO\\filters\\TC_filter_xH2.dat', 'MICADO\\filters\\TC_filter_xI1.dat', 'MICADO\\filters\\TC_filter_xI2.dat', 'MICADO\\filters\\TC_filter_xJ1.dat', 'MICADO\\filters\\TC_filter_xJ2.dat', 'MICADO\\filters\\TC_filter_xK1.dat', 'MICADO\\filters\\TC_filter_xK2.dat', 'MICADO\\filters\\TC_filter_xY1.dat', 'MICADO\\filters\\TC_filter_xY2.dat', 'MICADO\\filters\\TC_filter_Y.dat']}
-ERROR::MORFEO\TER_mirror_silver.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MORFEO\TER_MORFEO_entrance_window.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MORFEO\TER_MORFEO_lgs_dichroic.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MORFEO\TER_MORFEO_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MORFEO\TER_MORFEO_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MORFEO\TER_MORFEO_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MORFEO\TER_MORFEO_mirror_silver.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['MORFEO\\TER_mirror_silver.dat', 'MORFEO\\TER_MORFEO_entrance_window.dat', 'MORFEO\\TER_MORFEO_lgs_dichroic.dat', 'MORFEO\\TER_MORFEO_mirror_aluminium.dat', 'MORFEO\\TER_MORFEO_mirror_gold.dat', 'MORFEO\\TER_MORFEO_mirror_mgf2agal.dat', 'MORFEO\\TER_MORFEO_mirror_silver.dat']}
-ERROR::MOSAIC\FPA_linearity_HxRG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\FPA_mosaic_NIR_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\FPA_mosaic_VIS_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\QE_detector_NIR.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\QE_VIS_ML2_depleted.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\QE_VIS_ML2_standard.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_grating_VIS_LR_B.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_mIFU_HR-H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_mIFU_LR-H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_mIFU_LR-J.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_mos_HR-H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_mos_LR-H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_mos_LR-J.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_spec_HR-H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_spec_LR-H.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_spec_LR-J.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_sys_MOS-HR-B1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_sys_MOS-HR-B2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_sys_MOS-HR-R1.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_sys_MOS-HR-R2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_sys_MOS-LR-B.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::MOSAIC\TER_sys_MOS-LR-R.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['MOSAIC\\FPA_linearity_HxRG.dat', 'MOSAIC\\FPA_mosaic_NIR_layout.dat', 'MOSAIC\\FPA_mosaic_VIS_layout.dat', 'MOSAIC\\QE_detector_NIR.dat', 'MOSAIC\\QE_VIS_ML2_depleted.dat', 'MOSAIC\\QE_VIS_ML2_standard.dat', 'MOSAIC\\TER_grating_VIS_LR_B.dat', 'MOSAIC\\TER_mIFU_HR-H.dat', 'MOSAIC\\TER_mIFU_LR-H.dat', 'MOSAIC\\TER_mIFU_LR-J.dat', 'MOSAIC\\TER_mos_HR-H.dat', 'MOSAIC\\TER_mos_LR-H.dat', 'MOSAIC\\TER_mos_LR-J.dat', 'MOSAIC\\TER_spec_HR-H.dat', 'MOSAIC\\TER_spec_LR-H.dat', 'MOSAIC\\TER_spec_LR-J.dat', 'MOSAIC\\TER_sys_MOS-HR-B1.dat', 'MOSAIC\\TER_sys_MOS-HR-B2.dat', 'MOSAIC\\TER_sys_MOS-HR-R1.dat', 'MOSAIC\\TER_sys_MOS-HR-R2.dat', 'MOSAIC\\TER_sys_MOS-LR-B.dat', 'MOSAIC\\TER_sys_MOS-LR-R.dat']}
-ERROR::NACO\filters\H_CONICA.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.00.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.03.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.06.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.09.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.12.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.15.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.18.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.21.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.24.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.27.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.30.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.33.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.36.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.39.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.42.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.45.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\IB_2.48.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\J_CONICA.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\Ks_CONICA.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\L_prime.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\M_prime.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.04.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.08.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.09.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.24.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.26.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.28.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.64.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_1.75.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_2.12.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_2.17.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_3.74.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NACO\filters\NB_4.05.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['NACO\\filters\\H_CONICA.dat', 'NACO\\filters\\IB_2.00.dat', 'NACO\\filters\\IB_2.03.dat', 'NACO\\filters\\IB_2.06.dat', 'NACO\\filters\\IB_2.09.dat', 'NACO\\filters\\IB_2.12.dat', 'NACO\\filters\\IB_2.15.dat', 'NACO\\filters\\IB_2.18.dat', 'NACO\\filters\\IB_2.21.dat', 'NACO\\filters\\IB_2.24.dat', 'NACO\\filters\\IB_2.27.dat', 'NACO\\filters\\IB_2.30.dat', 'NACO\\filters\\IB_2.33.dat', 'NACO\\filters\\IB_2.36.dat', 'NACO\\filters\\IB_2.39.dat', 'NACO\\filters\\IB_2.42.dat', 'NACO\\filters\\IB_2.45.dat', 'NACO\\filters\\IB_2.48.dat', 'NACO\\filters\\J_CONICA.dat', 'NACO\\filters\\Ks_CONICA.dat', 'NACO\\filters\\L_prime.dat', 'NACO\\filters\\M_prime.dat', 'NACO\\filters\\NB_1.04.dat', 'NACO\\filters\\NB_1.08.dat', 'NACO\\filters\\NB_1.09.dat', 'NACO\\filters\\NB_1.24.dat', 'NACO\\filters\\NB_1.26.dat', 'NACO\\filters\\NB_1.28.dat', 'NACO\\filters\\NB_1.64.dat', 'NACO\\filters\\NB_1.75.dat', 'NACO\\filters\\NB_2.12.dat', 'NACO\\filters\\NB_2.17.dat', 'NACO\\filters\\NB_3.74.dat', 'NACO\\filters\\NB_4.05.dat']}
-ERROR::NIRCam\DET_LW_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\DET_LW_layout_simple.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\DET_SW_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F250M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F277W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F300M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F322W2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F323N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F335M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F356W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F360M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F405N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F410M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F430M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F444W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F460M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F466N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F470N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\long_wl\TC_filter_F480M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F070W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F090W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F115W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F140M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F150W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F150W2.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F162M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F164N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F182M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F187N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F200W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F210M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::NIRCam\filters\short_wl\TC_filter_F212N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['NIRCam\\DET_LW_layout.dat', 'NIRCam\\DET_LW_layout_simple.dat', 'NIRCam\\DET_SW_layout.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F250M.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F277W.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F300M.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F322W2.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F323N.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F335M.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F356W.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F360M.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F405N.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F410M.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F430M.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F444W.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F460M.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F466N.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F470N.dat', 'NIRCam\\filters\\long_wl\\TC_filter_F480M.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F070W.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F090W.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F115W.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F140M.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F150W.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F150W2.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F162M.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F164N.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F182M.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F187N.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F200W.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F210M.dat', 'NIRCam\\filters\\short_wl\\TC_filter_F212N.dat']}
-ERROR::OSIRIS\GTC_im_efficiency.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\MAAT_APERTURES.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\QE_OSIRIS_new_detector.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\QE_OSIRIS_old_detector_plus_M3.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\unity.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\code\OSIRIS_stitchedArc.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R1000B.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R1000R.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R2000B.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R2500I.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R2500R.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R2500U.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R2500V.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R300B.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R300R.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R500B.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\traces\TRACE_R500R.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\tests\test_data\fg191b2b.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::OSIRIS\tests\test_data\OSIRIS_stitchedArc.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['OSIRIS\\GTC_im_efficiency.dat', 'OSIRIS\\MAAT_APERTURES.dat', 'OSIRIS\\QE_OSIRIS_new_detector.dat', 'OSIRIS\\QE_OSIRIS_old_detector_plus_M3.dat', 'OSIRIS\\unity.dat', 'OSIRIS\\code\\OSIRIS_stitchedArc.dat', 'OSIRIS\\traces\\TRACE_R1000B.dat', 'OSIRIS\\traces\\TRACE_R1000R.dat', 'OSIRIS\\traces\\TRACE_R2000B.dat', 'OSIRIS\\traces\\TRACE_R2500I.dat', 'OSIRIS\\traces\\TRACE_R2500R.dat', 'OSIRIS\\traces\\TRACE_R2500U.dat', 'OSIRIS\\traces\\TRACE_R2500V.dat', 'OSIRIS\\traces\\TRACE_R300B.dat', 'OSIRIS\\traces\\TRACE_R300R.dat', 'OSIRIS\\traces\\TRACE_R500B.dat', 'OSIRIS\\traces\\TRACE_R500R.dat', 'OSIRIS\\tests\\test_data\\fg191b2b.dat', 'OSIRIS\\tests\\test_data\\OSIRIS_stitchedArc.dat']}
-ERROR::Paranal\TER_paranal_default_NIR_IMG.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['Paranal\\TER_paranal_default_NIR_IMG.dat']}
-ERROR::test_package\TC_filter_Ks.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['test_package\\TC_filter_Ks.dat']}
-ERROR::ViennaLT\FPA_ViLT_layout.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::ViennaLT\LIST_ViLT_mirrors_static.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['ViennaLT\\FPA_ViLT_layout.dat', 'ViennaLT\\LIST_ViLT_mirrors_static.dat']}
-ERROR::VLT\LIST_VLT_mirrors.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::VLT\TC_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['VLT\\LIST_VLT_mirrors.dat', 'VLT\\TC_mirror_aluminium.dat']}
-ERROR::WFC3\FPA_linearity.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\LIST_WFC3_mirrors_IR.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_cor_004_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_csm_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_dn_002_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_fold_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_mask_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_mir1_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_mir2_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_qe_003_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_rcp_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\wfc3_ir_win_001_syn.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F098M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F105W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F110W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F125W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F126N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F127M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F128N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F130N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F132N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F139M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F140W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F153M.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F160W.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F164N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-ERROR::WFC3\filters\TER_filter_F167N.dat Unexpected Exception <class 'AttributeError'> 'WindowsPath' object has no attribute 'lower'
-INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['WFC3\\FPA_linearity.dat', 'WFC3\\LIST_WFC3_mirrors_IR.dat', 'WFC3\\wfc3_ir_cor_004_syn.dat', 'WFC3\\wfc3_ir_csm_001_syn.dat', 'WFC3\\wfc3_ir_dn_002_syn.dat', 'WFC3\\wfc3_ir_fold_001_syn.dat', 'WFC3\\wfc3_ir_mask_001_syn.dat', 'WFC3\\wfc3_ir_mir1_001_syn.dat', 'WFC3\\wfc3_ir_mir2_001_syn.dat', 'WFC3\\wfc3_ir_qe_003_syn.dat', 'WFC3\\wfc3_ir_rcp_001_syn.dat', 'WFC3\\wfc3_ir_win_001_syn.dat', 'WFC3\\filters\\TER_filter_F098M.dat', 'WFC3\\filters\\TER_filter_F105W.dat', 'WFC3\\filters\\TER_filter_F110W.dat', 'WFC3\\filters\\TER_filter_F125W.dat', 'WFC3\\filters\\TER_filter_F126N.dat', 'WFC3\\filters\\TER_filter_F127M.dat', 'WFC3\\filters\\TER_filter_F128N.dat', 'WFC3\\filters\\TER_filter_F130N.dat', 'WFC3\\filters\\TER_filter_F132N.dat', 'WFC3\\filters\\TER_filter_F139M.dat', 'WFC3\\filters\\TER_filter_F140W.dat', 'WFC3\\filters\\TER_filter_F153M.dat', 'WFC3\\filters\\TER_filter_F160W.dat', 'WFC3\\filters\\TER_filter_F164N.dat', 'WFC3\\filters\\TER_filter_F167N.dat']}
+ERROR::ELT/TER_ELT_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ELT/TER_ELT_6_mirror_pupil_track_clean.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ELT/TER_ELT_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ELT/TER_ELT_6_mirror_field_track.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ELT/TER_ELT_6_mirror_pupil_track.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ELT/TER_ELT_5_mirror_clean.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ELT/TER_ELT_6_mirror_field_track_clean.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ELT/TER_ELT_5_mirror.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['ELT/TER_ELT_mirror_aluminium.dat', 'ELT/TER_ELT_6_mirror_pupil_track_clean.dat', 'ELT/TER_ELT_mirror_mgf2agal.dat', 'ELT/TER_ELT_6_mirror_field_track.dat', 'ELT/TER_ELT_6_mirror_pupil_track.dat', 'ELT/TER_ELT_5_mirror_clean.dat', 'ELT/TER_ELT_6_mirror_field_track_clean.dat', 'ELT/TER_ELT_5_mirror.dat']}
+ERROR::Armazones/TER_armazones_default_FULL_IMG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::Armazones/TER_armazones_default_OPT_IMG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::Armazones/TER_armazones_default_MIR_IMG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::Armazones/TER_armazones_default_NIR_IMG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['Armazones/TER_armazones_default_FULL_IMG.dat', 'Armazones/TER_armazones_default_OPT_IMG.dat', 'Armazones/TER_armazones_default_MIR_IMG.dat', 'Armazones/TER_armazones_default_NIR_IMG.dat']}
+ERROR::MOSAIC/TER_mos_HR-H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_mos_LR-J.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_sys_MOS-HR-R1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_grating_VIS_LR_B.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_spec_LR-J.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_sys_MOS-HR-B2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_mIFU_LR-H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/QE_VIS_ML2_standard.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_mos_LR-H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/FPA_mosaic_NIR_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_mIFU_LR-J.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_mIFU_HR-H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/QE_detector_NIR.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_spec_LR-H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/FPA_linearity_HxRG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_spec_HR-H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_sys_MOS-HR-B1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_sys_MOS-LR-B.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/QE_VIS_ML2_depleted.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/FPA_mosaic_VIS_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_sys_MOS-LR-R.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MOSAIC/TER_sys_MOS-HR-R2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['MOSAIC/TER_mos_HR-H.dat', 'MOSAIC/TER_mos_LR-J.dat', 'MOSAIC/TER_sys_MOS-HR-R1.dat', 'MOSAIC/TER_grating_VIS_LR_B.dat', 'MOSAIC/TER_spec_LR-J.dat', 'MOSAIC/TER_sys_MOS-HR-B2.dat', 'MOSAIC/TER_mIFU_LR-H.dat', 'MOSAIC/QE_VIS_ML2_standard.dat', 'MOSAIC/TER_mos_LR-H.dat', 'MOSAIC/FPA_mosaic_NIR_layout.dat', 'MOSAIC/TER_mIFU_LR-J.dat', 'MOSAIC/TER_mIFU_HR-H.dat', 'MOSAIC/QE_detector_NIR.dat', 'MOSAIC/TER_spec_LR-H.dat', 'MOSAIC/FPA_linearity_HxRG.dat', 'MOSAIC/TER_spec_HR-H.dat', 'MOSAIC/TER_sys_MOS-HR-B1.dat', 'MOSAIC/TER_sys_MOS-LR-B.dat', 'MOSAIC/QE_VIS_ML2_depleted.dat', 'MOSAIC/FPA_mosaic_VIS_layout.dat', 'MOSAIC/TER_sys_MOS-LR-R.dat', 'MOSAIC/TER_sys_MOS-HR-R2.dat']}
+ERROR::WFC3/wfc3_ir_dn_002_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_rcp_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/LIST_WFC3_mirrors_IR.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/FPA_linearity.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_csm_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_qe_003_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_mask_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_mir1_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_fold_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_cor_004_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_mir2_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/wfc3_ir_win_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F132N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F125W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F110W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F153M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F126N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F139M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F160W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F127M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F128N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F130N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F164N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F167N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F140W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F098M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::WFC3/filters/TER_filter_F105W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['WFC3/wfc3_ir_dn_002_syn.dat', 'WFC3/wfc3_ir_rcp_001_syn.dat', 'WFC3/LIST_WFC3_mirrors_IR.dat', 'WFC3/FPA_linearity.dat', 'WFC3/wfc3_ir_csm_001_syn.dat', 'WFC3/wfc3_ir_qe_003_syn.dat', 'WFC3/wfc3_ir_mask_001_syn.dat', 'WFC3/wfc3_ir_mir1_001_syn.dat', 'WFC3/wfc3_ir_fold_001_syn.dat', 'WFC3/wfc3_ir_cor_004_syn.dat', 'WFC3/wfc3_ir_mir2_001_syn.dat', 'WFC3/wfc3_ir_win_001_syn.dat', 'WFC3/filters/TER_filter_F132N.dat', 'WFC3/filters/TER_filter_F125W.dat', 'WFC3/filters/TER_filter_F110W.dat', 'WFC3/filters/TER_filter_F153M.dat', 'WFC3/filters/TER_filter_F126N.dat', 'WFC3/filters/TER_filter_F139M.dat', 'WFC3/filters/TER_filter_F160W.dat', 'WFC3/filters/TER_filter_F127M.dat', 'WFC3/filters/TER_filter_F128N.dat', 'WFC3/filters/TER_filter_F130N.dat', 'WFC3/filters/TER_filter_F164N.dat', 'WFC3/filters/TER_filter_F167N.dat', 'WFC3/filters/TER_filter_F140W.dat', 'WFC3/filters/TER_filter_F098M.dat', 'WFC3/filters/TER_filter_F105W.dat']}
+ERROR::JWST/TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['JWST/TER_mirror_gold.dat']}
+ERROR::MORFEO/TER_MORFEO_entrance_window.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MORFEO/TER_MORFEO_lgs_dichroic.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MORFEO/TER_MORFEO_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MORFEO/TER_MORFEO_mirror_silver.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MORFEO/TER_MORFEO_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MORFEO/TER_MORFEO_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MORFEO/TER_mirror_silver.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['MORFEO/TER_MORFEO_entrance_window.dat', 'MORFEO/TER_MORFEO_lgs_dichroic.dat', 'MORFEO/TER_MORFEO_mirror_mgf2agal.dat', 'MORFEO/TER_MORFEO_mirror_silver.dat', 'MORFEO/TER_MORFEO_mirror_gold.dat', 'MORFEO/TER_MORFEO_mirror_aluminium.dat', 'MORFEO/TER_mirror_silver.dat']}
+ERROR::MICADO/QE_detector_H2RG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/TER_full_adc.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/TER_SCAO_dichroic.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/INST_MICADO_wavefront_error_budget.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/TER_MICADO_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/FPA_array_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/TER_coating_antireflection.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/LIST_MICADO_mirrors_zoom.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/QE_detector_H4RG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/MASK_slit_Short_offset.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/FPA_linearity.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/MASK_slit_Wide_offset.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/TER_grating.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/LIST_MCA_mirrors.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/MASK_slit_Short.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/MASK_slit_Long_offset.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/LIST_MICADO_mirrors_wide.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/MASK_slit_Long.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/LIST_MICADO_mirrors_static.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/MASK_slit_Wide.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/LIST_MICADO_mirrors_spec.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/TER_entrance_window.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/LIST_RO_SCAO_mirrors.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_K-mid.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xH1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_K-long.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xK2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xJ1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_stop_IJH.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_H-long.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_ND3.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_blank.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xH2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xJ2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xY1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_Spec_HK.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_block.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_J.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_He-I.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_J-long.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_FeII.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_open.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_Br-gamma.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_I-long.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_Pa-beta.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_Ks.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xI1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_K-short.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_Y.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_Ks2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xI2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_H2_1-0S1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_stop_K.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_J-short.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_K-cont.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xY2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_H-short.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_xK1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_H-cont.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_Spec_IJ.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::MICADO/filters/TC_filter_ND1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['MICADO/QE_detector_H2RG.dat', 'MICADO/TER_full_adc.dat', 'MICADO/TER_SCAO_dichroic.dat', 'MICADO/INST_MICADO_wavefront_error_budget.dat', 'MICADO/TER_MICADO_mirror_mgf2agal.dat', 'MICADO/FPA_array_layout.dat', 'MICADO/TER_coating_antireflection.dat', 'MICADO/LIST_MICADO_mirrors_zoom.dat', 'MICADO/QE_detector_H4RG.dat', 'MICADO/MASK_slit_Short_offset.dat', 'MICADO/FPA_linearity.dat', 'MICADO/MASK_slit_Wide_offset.dat', 'MICADO/TER_grating.dat', 'MICADO/TER_mirror_gold.dat', 'MICADO/LIST_MCA_mirrors.dat', 'MICADO/MASK_slit_Short.dat', 'MICADO/MASK_slit_Long_offset.dat', 'MICADO/LIST_MICADO_mirrors_wide.dat', 'MICADO/MASK_slit_Long.dat', 'MICADO/LIST_MICADO_mirrors_static.dat', 'MICADO/MASK_slit_Wide.dat', 'MICADO/LIST_MICADO_mirrors_spec.dat', 'MICADO/TER_entrance_window.dat', 'MICADO/LIST_RO_SCAO_mirrors.dat', 'MICADO/filters/TC_filter_K-mid.dat', 'MICADO/filters/TC_filter_xH1.dat', 'MICADO/filters/TC_filter_H.dat', 'MICADO/filters/TC_filter_K-long.dat', 'MICADO/filters/TC_filter_xK2.dat', 'MICADO/filters/TC_filter_xJ1.dat', 'MICADO/filters/TC_filter_stop_IJH.dat', 'MICADO/filters/TC_filter_H-long.dat', 'MICADO/filters/TC_filter_ND3.dat', 'MICADO/filters/TC_filter_blank.dat', 'MICADO/filters/TC_filter_xH2.dat', 'MICADO/filters/TC_filter_xJ2.dat', 'MICADO/filters/TC_filter_xY1.dat', 'MICADO/filters/TC_filter_Spec_HK.dat', 'MICADO/filters/TC_filter_block.dat', 'MICADO/filters/TC_filter_J.dat', 'MICADO/filters/TC_filter_He-I.dat', 'MICADO/filters/TC_filter_J-long.dat', 'MICADO/filters/TC_filter_FeII.dat', 'MICADO/filters/TC_filter_open.dat', 'MICADO/filters/TC_filter_Br-gamma.dat', 'MICADO/filters/TC_filter_I-long.dat', 'MICADO/filters/TC_filter_Pa-beta.dat', 'MICADO/filters/TC_filter_Ks.dat', 'MICADO/filters/TC_filter_xI1.dat', 'MICADO/filters/TC_filter_K-short.dat', 'MICADO/filters/TC_filter_Y.dat', 'MICADO/filters/TC_filter_Ks2.dat', 'MICADO/filters/TC_filter_xI2.dat', 'MICADO/filters/TC_filter_H2_1-0S1.dat', 'MICADO/filters/TC_filter_stop_K.dat', 'MICADO/filters/TC_filter_J-short.dat', 'MICADO/filters/TC_filter_K-cont.dat', 'MICADO/filters/TC_filter_xY2.dat', 'MICADO/filters/TC_filter_H-short.dat', 'MICADO/filters/TC_filter_xK1.dat', 'MICADO/filters/TC_filter_H-cont.dat', 'MICADO/filters/TC_filter_Spec_IJ.dat', 'MICADO/filters/TC_filter_ND1.dat']}
+ERROR::METIS/FPA_metis_img_n_geosnap_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/LIST_METIS_mirrors_cfo.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_sca_dichroic.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/FPA_metis_lms_smpl_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/FPA_metis_img_lm_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_img_dichroic.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/MASK_slit_D-57_1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/QE_detector_geosnap.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_ADC_const_90.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/MASK_slit_B-28_6.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/FPA_linearity_GeoSnap_high_capacity.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/LIST_METIS_mirrors_wcu.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_lms_grism_max.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/QE_detector_H2RG_METIS.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/LIST_METIS_mirrors_img_lm.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/FPA_linearity_GeoSnap_low_capacity.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/FPA_linearity_HxRG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/LIST_METIS_mirrors_lms.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/FPA_metis_lms_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/MASK_slit_C-38_1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_lms_grism_min.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/MASK_slit_A-19_0.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/MASK_slit_E-114_2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_lms_predisperser_prism.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/FPA_metis_img_nq_aquarius_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/LIST_METIS_mirrors_img_n.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/TER_entrance_window.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_IB_4.05.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_S_IV.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_PAH_8.6.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_ND-1.4.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_short-L.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_PAH_11.25_ref.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_L.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_PAH_11.25.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_HCI_M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_H2O-ice.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_ND_OD5.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_PAH_3.3_ref.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_PAH_3.3.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_ND_OD1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_ND_OD2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_Br_alpha_ref.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_Br_alpha.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_open.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_Q1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_PAH_8.6_ref.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_Ne_II_ref.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_S_IV_ref.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_ND_OD3.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_M_spec.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_CO_1-0_ice.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_Mp.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_L_spec.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_CO_ref.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_N2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_HCI_L_short.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_N3.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_closed.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_Ne_II.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_N_spec.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_HCI_L_long.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_ND_OD4.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_ND-2.8.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_Lp.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/filters/TC_filter_N1.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/code/2023-06-20-spectra_Grism-L_band.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/code/2023-06-20-spectra_Grism-M_band.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/code/2023-06-20-spectra_Grism-N_band.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/wcu/fp_mask_pinhole_lm.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/wcu/fp_mask_grid_lm.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::METIS/wcu/fp_mask_pinhole_n.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['METIS/FPA_metis_img_n_geosnap_layout.dat', 'METIS/LIST_METIS_mirrors_cfo.dat', 'METIS/TER_sca_dichroic.dat', 'METIS/FPA_metis_lms_smpl_layout.dat', 'METIS/FPA_metis_img_lm_layout.dat', 'METIS/TER_img_dichroic.dat', 'METIS/MASK_slit_D-57_1.dat', 'METIS/QE_detector_geosnap.dat', 'METIS/TER_ADC_const_90.dat', 'METIS/TER_mirror_aluminium.dat', 'METIS/MASK_slit_B-28_6.dat', 'METIS/FPA_linearity_GeoSnap_high_capacity.dat', 'METIS/LIST_METIS_mirrors_wcu.dat', 'METIS/TER_lms_grism_max.dat', 'METIS/TER_mirror_gold.dat', 'METIS/QE_detector_H2RG_METIS.dat', 'METIS/LIST_METIS_mirrors_img_lm.dat', 'METIS/FPA_linearity_GeoSnap_low_capacity.dat', 'METIS/FPA_linearity_HxRG.dat', 'METIS/LIST_METIS_mirrors_lms.dat', 'METIS/FPA_metis_lms_layout.dat', 'METIS/MASK_slit_C-38_1.dat', 'METIS/TER_lms_grism_min.dat', 'METIS/MASK_slit_A-19_0.dat', 'METIS/MASK_slit_E-114_2.dat', 'METIS/TER_lms_predisperser_prism.dat', 'METIS/FPA_metis_img_nq_aquarius_layout.dat', 'METIS/LIST_METIS_mirrors_img_n.dat', 'METIS/TER_entrance_window.dat', 'METIS/filters/TC_filter_IB_4.05.dat', 'METIS/filters/TC_filter_S_IV.dat', 'METIS/filters/TC_filter_PAH_8.6.dat', 'METIS/filters/TC_filter_ND-1.4.dat', 'METIS/filters/TC_filter_short-L.dat', 'METIS/filters/TC_filter_PAH_11.25_ref.dat', 'METIS/filters/TC_filter_L.dat', 'METIS/filters/TC_filter_PAH_11.25.dat', 'METIS/filters/TC_filter_HCI_M.dat', 'METIS/filters/TC_filter_H2O-ice.dat', 'METIS/filters/TC_filter_ND_OD5.dat', 'METIS/filters/TC_filter_PAH_3.3_ref.dat', 'METIS/filters/TC_filter_PAH_3.3.dat', 'METIS/filters/TC_filter_ND_OD1.dat', 'METIS/filters/TC_filter_ND_OD2.dat', 'METIS/filters/TC_filter_Br_alpha_ref.dat', 'METIS/filters/TC_filter_Br_alpha.dat', 'METIS/filters/TC_filter_open.dat', 'METIS/filters/TC_filter_Q1.dat', 'METIS/filters/TC_filter_PAH_8.6_ref.dat', 'METIS/filters/TC_filter_Ne_II_ref.dat', 'METIS/filters/TC_filter_S_IV_ref.dat', 'METIS/filters/TC_filter_ND_OD3.dat', 'METIS/filters/TC_filter_M_spec.dat', 'METIS/filters/TC_filter_CO_1-0_ice.dat', 'METIS/filters/TC_filter_Mp.dat', 'METIS/filters/TC_filter_L_spec.dat', 'METIS/filters/TC_filter_CO_ref.dat', 'METIS/filters/TC_filter_N2.dat', 'METIS/filters/TC_filter_HCI_L_short.dat', 'METIS/filters/TC_filter_N3.dat', 'METIS/filters/TC_filter_closed.dat', 'METIS/filters/TC_filter_Ne_II.dat', 'METIS/filters/TC_filter_N_spec.dat', 'METIS/filters/TC_filter_HCI_L_long.dat', 'METIS/filters/TC_filter_ND_OD4.dat', 'METIS/filters/TC_filter_ND-2.8.dat', 'METIS/filters/TC_filter_Lp.dat', 'METIS/filters/TC_filter_N1.dat', 'METIS/code/2023-06-20-spectra_Grism-L_band.dat', 'METIS/code/2023-06-20-spectra_Grism-M_band.dat', 'METIS/code/2023-06-20-spectra_Grism-N_band.dat', 'METIS/wcu/fp_mask_pinhole_lm.dat', 'METIS/wcu/fp_mask_grid_lm.dat', 'METIS/wcu/fp_mask_pinhole_n.dat']}
+ERROR::NACO/filters/IB_2.24.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.33.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.08.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.30.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.09.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_3.74.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.64.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.18.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.24.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.45.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_2.12.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.06.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/M_prime.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.00.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_4.05.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.39.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/J_CONICA.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.28.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_2.17.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.21.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/L_prime.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.15.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/H_CONICA.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.27.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.26.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.48.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.03.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.09.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.75.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/NB_1.04.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.42.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.36.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/IB_2.12.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NACO/filters/Ks_CONICA.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['NACO/filters/IB_2.24.dat', 'NACO/filters/IB_2.33.dat', 'NACO/filters/NB_1.08.dat', 'NACO/filters/IB_2.30.dat', 'NACO/filters/IB_2.09.dat', 'NACO/filters/NB_3.74.dat', 'NACO/filters/NB_1.64.dat', 'NACO/filters/IB_2.18.dat', 'NACO/filters/NB_1.24.dat', 'NACO/filters/IB_2.45.dat', 'NACO/filters/NB_2.12.dat', 'NACO/filters/IB_2.06.dat', 'NACO/filters/M_prime.dat', 'NACO/filters/IB_2.00.dat', 'NACO/filters/NB_4.05.dat', 'NACO/filters/IB_2.39.dat', 'NACO/filters/J_CONICA.dat', 'NACO/filters/NB_1.28.dat', 'NACO/filters/NB_2.17.dat', 'NACO/filters/IB_2.21.dat', 'NACO/filters/L_prime.dat', 'NACO/filters/IB_2.15.dat', 'NACO/filters/H_CONICA.dat', 'NACO/filters/IB_2.27.dat', 'NACO/filters/NB_1.26.dat', 'NACO/filters/IB_2.48.dat', 'NACO/filters/IB_2.03.dat', 'NACO/filters/NB_1.09.dat', 'NACO/filters/NB_1.75.dat', 'NACO/filters/NB_1.04.dat', 'NACO/filters/IB_2.42.dat', 'NACO/filters/IB_2.36.dat', 'NACO/filters/IB_2.12.dat', 'NACO/filters/Ks_CONICA.dat']}
+ERROR::OSIRIS/QE_OSIRIS_new_detector.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/unity.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/QE_OSIRIS_old_detector_plus_M3.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/GTC_im_efficiency.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/MAAT_APERTURES.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/code/OSIRIS_stitchedArc.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R2500U.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R2500V.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R2500R.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R500R.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R300R.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R1000R.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R2000B.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R2500I.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R1000B.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R300B.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/traces/TRACE_R500B.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/tests/test_data/OSIRIS_stitchedArc.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::OSIRIS/tests/test_data/fg191b2b.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['OSIRIS/QE_OSIRIS_new_detector.dat', 'OSIRIS/unity.dat', 'OSIRIS/QE_OSIRIS_old_detector_plus_M3.dat', 'OSIRIS/GTC_im_efficiency.dat', 'OSIRIS/MAAT_APERTURES.dat', 'OSIRIS/code/OSIRIS_stitchedArc.dat', 'OSIRIS/traces/TRACE_R2500U.dat', 'OSIRIS/traces/TRACE_R2500V.dat', 'OSIRIS/traces/TRACE_R2500R.dat', 'OSIRIS/traces/TRACE_R500R.dat', 'OSIRIS/traces/TRACE_R300R.dat', 'OSIRIS/traces/TRACE_R1000R.dat', 'OSIRIS/traces/TRACE_R2000B.dat', 'OSIRIS/traces/TRACE_R2500I.dat', 'OSIRIS/traces/TRACE_R1000B.dat', 'OSIRIS/traces/TRACE_R300B.dat', 'OSIRIS/traces/TRACE_R500B.dat', 'OSIRIS/tests/test_data/OSIRIS_stitchedArc.dat', 'OSIRIS/tests/test_data/fg191b2b.dat']}
+ERROR::VLT/LIST_VLT_mirrors.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::VLT/TC_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['VLT/LIST_VLT_mirrors.dat', 'VLT/TC_mirror_aluminium.dat']}
+ERROR::ViennaLT/LIST_ViLT_mirrors_static.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::ViennaLT/FPA_ViLT_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['ViennaLT/LIST_ViLT_mirrors_static.dat', 'ViennaLT/FPA_ViLT_layout.dat']}
+ERROR::Paranal/TER_paranal_default_NIR_IMG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['Paranal/TER_paranal_default_NIR_IMG.dat']}
+ERROR::test_package/TC_filter_Ks.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['test_package/TC_filter_Ks.dat']}
+ERROR::HST/wfc3_ir_primary_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HST/wfc3_ir_secondary_001_syn.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HST/TER_ELT_mirror_mgf2agal.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['HST/wfc3_ir_primary_001_syn.dat', 'HST/wfc3_ir_secondary_001_syn.dat', 'HST/TER_ELT_mirror_mgf2agal.dat']}
+ERROR::HAWKI/QE_detector_H2RG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/FPA_hawki_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/FPA_linearity.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/LIST_HAWKI_mirrors.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/TER_mirror_gold.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/TC_dichroic.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/TER_window.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_CH4.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_J.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_NB1190.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_H2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_NB1060.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_Ks.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_Y.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_BrGamma.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/filters/TC_filter_NB2090.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/BG_total_Ks.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/BG_total_BrGamma.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/BG_total_J.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/TER_system_J.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/TER_system_CH4.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/TER_system_BrGamma.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/ESO_ETC_bg_fluxes.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/TER_system_Ks.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/TER_system_H.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::HAWKI/test_hawki/hawki_eso_etc/TER_system_Y.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['HAWKI/QE_detector_H2RG.dat', 'HAWKI/FPA_hawki_layout.dat', 'HAWKI/FPA_linearity.dat', 'HAWKI/LIST_HAWKI_mirrors.dat', 'HAWKI/TER_mirror_gold.dat', 'HAWKI/TC_dichroic.dat', 'HAWKI/TER_window.dat', 'HAWKI/filters/TC_filter_CH4.dat', 'HAWKI/filters/TC_filter_H.dat', 'HAWKI/filters/TC_filter_J.dat', 'HAWKI/filters/TC_filter_NB1190.dat', 'HAWKI/filters/TC_filter_H2.dat', 'HAWKI/filters/TC_filter_NB1060.dat', 'HAWKI/filters/TC_filter_Ks.dat', 'HAWKI/filters/TC_filter_Y.dat', 'HAWKI/filters/TC_filter_BrGamma.dat', 'HAWKI/filters/TC_filter_NB2090.dat', 'HAWKI/test_hawki/hawki_eso_etc/BG_total_Ks.dat', 'HAWKI/test_hawki/hawki_eso_etc/BG_total_BrGamma.dat', 'HAWKI/test_hawki/hawki_eso_etc/BG_total_J.dat', 'HAWKI/test_hawki/hawki_eso_etc/TER_system_J.dat', 'HAWKI/test_hawki/hawki_eso_etc/TER_system_CH4.dat', 'HAWKI/test_hawki/hawki_eso_etc/TER_system_BrGamma.dat', 'HAWKI/test_hawki/hawki_eso_etc/ESO_ETC_bg_fluxes.dat', 'HAWKI/test_hawki/hawki_eso_etc/TER_system_Ks.dat', 'HAWKI/test_hawki/hawki_eso_etc/TER_system_H.dat', 'HAWKI/test_hawki/hawki_eso_etc/TER_system_Y.dat']}
+ERROR::NIRCam/DET_LW_layout_simple.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/DET_LW_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/DET_SW_layout.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F322W2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F466N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F300M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F360M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F405N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F444W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F323N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F430M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F410M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F277W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F335M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F470N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F480M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F356W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F250M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/long_wl/TC_filter_F460M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F070W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F187N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F210M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F115W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F162M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F200W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F212N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F150W2.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F140M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F150W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F164N.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F090W.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::NIRCam/filters/short_wl/TC_filter_F182M.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['NIRCam/DET_LW_layout_simple.dat', 'NIRCam/DET_LW_layout.dat', 'NIRCam/DET_SW_layout.dat', 'NIRCam/filters/long_wl/TC_filter_F322W2.dat', 'NIRCam/filters/long_wl/TC_filter_F466N.dat', 'NIRCam/filters/long_wl/TC_filter_F300M.dat', 'NIRCam/filters/long_wl/TC_filter_F360M.dat', 'NIRCam/filters/long_wl/TC_filter_F405N.dat', 'NIRCam/filters/long_wl/TC_filter_F444W.dat', 'NIRCam/filters/long_wl/TC_filter_F323N.dat', 'NIRCam/filters/long_wl/TC_filter_F430M.dat', 'NIRCam/filters/long_wl/TC_filter_F410M.dat', 'NIRCam/filters/long_wl/TC_filter_F277W.dat', 'NIRCam/filters/long_wl/TC_filter_F335M.dat', 'NIRCam/filters/long_wl/TC_filter_F470N.dat', 'NIRCam/filters/long_wl/TC_filter_F480M.dat', 'NIRCam/filters/long_wl/TC_filter_F356W.dat', 'NIRCam/filters/long_wl/TC_filter_F250M.dat', 'NIRCam/filters/long_wl/TC_filter_F460M.dat', 'NIRCam/filters/short_wl/TC_filter_F070W.dat', 'NIRCam/filters/short_wl/TC_filter_F187N.dat', 'NIRCam/filters/short_wl/TC_filter_F210M.dat', 'NIRCam/filters/short_wl/TC_filter_F115W.dat', 'NIRCam/filters/short_wl/TC_filter_F162M.dat', 'NIRCam/filters/short_wl/TC_filter_F200W.dat', 'NIRCam/filters/short_wl/TC_filter_F212N.dat', 'NIRCam/filters/short_wl/TC_filter_F150W2.dat', 'NIRCam/filters/short_wl/TC_filter_F140M.dat', 'NIRCam/filters/short_wl/TC_filter_F150W.dat', 'NIRCam/filters/short_wl/TC_filter_F164N.dat', 'NIRCam/filters/short_wl/TC_filter_F090W.dat', 'NIRCam/filters/short_wl/TC_filter_F182M.dat']}
+ERROR::LaPalma/orm_extinct.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['LaPalma/orm_extinct.dat']}
+ERROR::GTC/reflectivity_GTC.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::GTC/unity.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['GTC/reflectivity_GTC.dat', 'GTC/unity.dat']}
+ERROR::LFOA/LIST_LFOA_mirrors_static.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/QE_SBIG.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/TER_mirror_aluminium.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/TER_focal_reducer.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/TER_atmosphere.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/U.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/SII.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/sloan_i.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/B.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/R.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/OIII.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/sloan_g.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/I.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/Halpha_wide.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/sloan_z.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/V.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/Hbeta.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/Halpha_narrow.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/sloan_r.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/sloan_u.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/old_filters/TC_filter_Halpha_wide_old.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/old_filters/TC_filter_Halpha_narrow_old.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/old_filters/TC_filter_Hbeta_old.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/old_filters/TC_filter_OIII_old.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+ERROR::LFOA/filters/old_filters/TC_filter_SII_old.dat Unexpected Exception <class 'AttributeError'> 'PosixPath' object has no attribute 'lower'
+INFO::{'inconsistent_table_error': [], 'value_error': [], 'unexpected_error': ['LFOA/LIST_LFOA_mirrors_static.dat', 'LFOA/QE_SBIG.dat', 'LFOA/TER_mirror_aluminium.dat', 'LFOA/TER_focal_reducer.dat', 'LFOA/TER_atmosphere.dat', 'LFOA/filters/U.dat', 'LFOA/filters/SII.dat', 'LFOA/filters/sloan_i.dat', 'LFOA/filters/B.dat', 'LFOA/filters/R.dat', 'LFOA/filters/OIII.dat', 'LFOA/filters/sloan_g.dat', 'LFOA/filters/I.dat', 'LFOA/filters/Halpha_wide.dat', 'LFOA/filters/sloan_z.dat', 'LFOA/filters/V.dat', 'LFOA/filters/Hbeta.dat', 'LFOA/filters/Halpha_narrow.dat', 'LFOA/filters/sloan_r.dat', 'LFOA/filters/sloan_u.dat', 'LFOA/filters/old_filters/TC_filter_Halpha_wide_old.dat', 'LFOA/filters/old_filters/TC_filter_Halpha_narrow_old.dat', 'LFOA/filters/old_filters/TC_filter_Hbeta_old.dat', 'LFOA/filters/old_filters/TC_filter_OIII_old.dat', 'LFOA/filters/old_filters/TC_filter_SII_old.dat']}
diff --git a/_REPORTS/badges.md b/_REPORTS/badges.md
index 755dc41b..185dd11d 100644
--- a/_REPORTS/badges.md
+++ b/_REPORTS/badges.md
@@ -3,17 +3,6 @@
 For details on errors and conflicts, see badge report log file in this directory.
 
 
-## Armazones
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
-### Structure
-* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
-* [![](https://img.shields.io/badge/no_missing_files-green)]()
-### Contents
-* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/TER_armazones_default_FULL_IMG.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_armazones_default_MIR_IMG.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_armazones_default_NIR_IMG.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_armazones_default_OPT_IMG.dat-error-red)]()
 ## ELT
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
@@ -21,89 +10,97 @@ For details on errors and conflicts, see badge report log file in this directory
 * [![](https://img.shields.io/badge/#telescope_reflection.filename-missing-red)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/TER_ELT_5_mirror.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_ELT_5_mirror_clean.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_ELT_6_mirror_field_track.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_ELT_6_mirror_field_track_clean.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_ELT_6_mirror_pupil_track.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_ELT_6_mirror_pupil_track_clean.dat-error-red)]()
 * [![](https://img.shields.io/badge/TER_ELT_mirror_aluminium.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_ELT_6_mirror_pupil_track_clean.dat-error-red)]()
 * [![](https://img.shields.io/badge/TER_ELT_mirror_mgf2agal.dat-error-red)]()
-## ERIS
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
-### Structure
-* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
-* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
-### Contents
-* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-## FORS
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
-### Structure
-* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
-* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
-### Contents
-* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-## GTC
+* [![](https://img.shields.io/badge/TER_ELT_6_mirror_field_track.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_ELT_6_mirror_pupil_track.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_ELT_5_mirror_clean.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_ELT_6_mirror_field_track_clean.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_ELT_5_mirror.dat-error-red)]()
+## Armazones
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/reflectivity_GTC.dat-error-red)]()
-* [![](https://img.shields.io/badge/unity.dat-error-red)]()
-## HARMONI
+* [![](https://img.shields.io/badge/TER_armazones_default_FULL_IMG.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_armazones_default_OPT_IMG.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_armazones_default_MIR_IMG.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_armazones_default_NIR_IMG.dat-error-red)]()
+## MCIFU
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
 * [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
 * [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
 ### Contents
 * [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-## HAWKI
+## MOSAIC
 [![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
 * [![](https://img.shields.io/badge/default_yaml-OK-green)]()
-* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
+* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/FPA_hawki_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_linearity.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_HAWKI_mirrors.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_detector_H2RG.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_dichroic.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mirror_gold.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_window.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_BrGamma.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_CH4.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_J.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Ks.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_NB1060.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_NB1190.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_NB2090.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Y.dat-error-red)]()
-* [![](https://img.shields.io/badge/BG_total_BrGamma.dat-error-red)]()
-* [![](https://img.shields.io/badge/BG_total_J.dat-error-red)]()
-* [![](https://img.shields.io/badge/BG_total_Ks.dat-error-red)]()
-* [![](https://img.shields.io/badge/ESO_ETC_bg_fluxes.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_system_BrGamma.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_system_CH4.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_system_H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_system_J.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_system_Ks.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_system_Y.dat-error-red)]()
-## HST
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+* [![](https://img.shields.io/badge/TER_mos_HR--H.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mos_LR--J.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_sys_MOS--HR--R1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_grating_VIS_LR_B.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_spec_LR--J.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_sys_MOS--HR--B2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mIFU_LR--H.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_VIS_ML2_standard.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mos_LR--H.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_mosaic_NIR_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mIFU_LR--J.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mIFU_HR--H.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_detector_NIR.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_spec_LR--H.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_linearity_HxRG.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_spec_HR--H.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_sys_MOS--HR--B1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_sys_MOS--LR--B.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_VIS_ML2_depleted.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_mosaic_VIS_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_sys_MOS--LR--R.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_sys_MOS--HR--R2.dat-error-red)]()
+## WFC3
+[![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
+* [![](https://img.shields.io/badge/default_yaml-OK-green)]()
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/TER_ELT_mirror_mgf2agal.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_primary_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_secondary_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_dn_002_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_rcp_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_WFC3_mirrors_IR.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_linearity.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_csm_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_qe_003_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_mask_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_mir1_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_fold_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_cor_004_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_mir2_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_win_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F132N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F125W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F110W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F153M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F126N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F139M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F160W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F127M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F128N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F130N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F164N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F167N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F140W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F098M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_filter_F105W.dat-error-red)]()
 ## JWST
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
@@ -112,143 +109,282 @@ For details on errors and conflicts, see badge report log file in this directory
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
 * [![](https://img.shields.io/badge/TER_mirror_gold.dat-error-red)]()
-## KMOS
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
-### Structure
-* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
-* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
-### Contents
-* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-## LaPalma
+## MORFEO
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/orm_extinct.dat-error-red)]()
-## LFOA
+* [![](https://img.shields.io/badge/TER_MORFEO_entrance_window.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_MORFEO_lgs_dichroic.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_MORFEO_mirror_mgf2agal.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_MORFEO_mirror_silver.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_MORFEO_mirror_gold.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_MORFEO_mirror_aluminium.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mirror_silver.dat-error-red)]()
+## MICADO
 [![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
 * [![](https://img.shields.io/badge/default_yaml-OK-green)]()
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
-* [![](https://img.shields.io/badge/no_missing_files-green)]()
+* [![](https://img.shields.io/badge/MICADO_relay_air_trans.fits-missing-red)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/LIST_LFOA_mirrors_static.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_SBIG.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_atmosphere.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_focal_reducer.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mirror_aluminium.dat-error-red)]()
-* [![](https://img.shields.io/badge/B.dat-error-red)]()
-* [![](https://img.shields.io/badge/Halpha_narrow.dat-error-red)]()
-* [![](https://img.shields.io/badge/Halpha_wide.dat-error-red)]()
-* [![](https://img.shields.io/badge/Hbeta.dat-error-red)]()
-* [![](https://img.shields.io/badge/I.dat-error-red)]()
-* [![](https://img.shields.io/badge/OIII.dat-error-red)]()
-* [![](https://img.shields.io/badge/R.dat-error-red)]()
-* [![](https://img.shields.io/badge/SII.dat-error-red)]()
-* [![](https://img.shields.io/badge/sloan_g.dat-error-red)]()
-* [![](https://img.shields.io/badge/sloan_i.dat-error-red)]()
-* [![](https://img.shields.io/badge/sloan_r.dat-error-red)]()
-* [![](https://img.shields.io/badge/sloan_u.dat-error-red)]()
-* [![](https://img.shields.io/badge/sloan_z.dat-error-red)]()
-* [![](https://img.shields.io/badge/U.dat-error-red)]()
-* [![](https://img.shields.io/badge/V.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Halpha_narrow_old.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Halpha_wide_old.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Hbeta_old.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_OIII_old.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_SII_old.dat-error-red)]()
-## MCIFU
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
-### Structure
-* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
-* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
-### Contents
-* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
+* [![](https://img.shields.io/badge/QE_detector_H2RG.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_full_adc.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_SCAO_dichroic.dat-error-red)]()
+* [![](https://img.shields.io/badge/INST_MICADO_wavefront_error_budget.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_MICADO_mirror_mgf2agal.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_array_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_coating_antireflection.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_zoom.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_detector_H4RG.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_Short_offset.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_linearity.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_Wide_offset.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_grating.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mirror_gold.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_MCA_mirrors.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_Short.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_Long_offset.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_wide.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_Long.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_static.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_Wide.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_spec.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_entrance_window.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_RO_SCAO_mirrors.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_K--mid.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xH1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_K--long.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xK2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xJ1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_stop_IJH.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H--long.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_ND3.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_blank.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xH2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xJ2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xY1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Spec_HK.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_block.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_J.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_He--I.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_J--long.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_FeII.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_open.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Br--gamma.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_I--long.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Pa--beta.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Ks.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xI1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_K--short.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Y.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Ks2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xI2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H2_1--0S1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_stop_K.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_J--short.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_K--cont.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xY2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H--short.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_xK1.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H--cont.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Spec_IJ.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_ND1.dat-error-red)]()
 ## METIS
 [![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
 * [![](https://img.shields.io/badge/default_yaml-OK-green)]()
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
-* [![](https://img.shields.io/badge/TER_ELT_6_mirror_field_track.dat-missing-red)]()
-* [![](https://img.shields.io/badge/Leiden_atmo_ter.fits-missing-red)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/FPA_linearity_GeoSnap_high_capacity.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_linearity_GeoSnap_low_capacity.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_linearity_HxRG.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_metis_img_lm_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_metis_img_nq_aquarius_layout.dat-error-red)]()
 * [![](https://img.shields.io/badge/FPA_metis_img_n_geosnap_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_metis_lms_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_metis_lms_smpl_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_ifu_apertures.dat-error-red)]()
 * [![](https://img.shields.io/badge/LIST_METIS_mirrors_cfo.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_METIS_mirrors_img_lm.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_METIS_mirrors_img_n.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_METIS_mirrors_lms.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_METIS_mirrors_wcu.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_A--19_0.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_B--28_6.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_C--38_1.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_D--57_1.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_E--114_2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_sca_dichroic.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_metis_lms_smpl_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_metis_img_lm_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_img_dichroic.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_D--57_1.dat-error-red)]()
 * [![](https://img.shields.io/badge/QE_detector_geosnap.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_detector_H2RG_METIS.dat-error-red)]()
 * [![](https://img.shields.io/badge/TER_ADC_const_90.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_entrance_window.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_img_dichroic.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mirror_aluminium.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_B--28_6.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_linearity_GeoSnap_high_capacity.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_METIS_mirrors_wcu.dat-error-red)]()
 * [![](https://img.shields.io/badge/TER_lms_grism_max.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mirror_gold.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_detector_H2RG_METIS.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_METIS_mirrors_img_lm.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_linearity_GeoSnap_low_capacity.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_linearity_HxRG.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_METIS_mirrors_lms.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_metis_lms_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_C--38_1.dat-error-red)]()
 * [![](https://img.shields.io/badge/TER_lms_grism_min.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_A--19_0.dat-error-red)]()
+* [![](https://img.shields.io/badge/MASK_slit_E--114_2.dat-error-red)]()
 * [![](https://img.shields.io/badge/TER_lms_predisperser_prism.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mirror_aluminium.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mirror_gold.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_sca_dichroic.dat-error-red)]()
-* [![](https://img.shields.io/badge/2023--06--20--spectra_Grism--L_band.dat-error-red)]()
-* [![](https://img.shields.io/badge/2023--06--20--spectra_Grism--M_band.dat-error-red)]()
-* [![](https://img.shields.io/badge/2023--06--20--spectra_Grism--N_band.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Br_alpha.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Br_alpha_ref.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_CO_1--0_ice.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_CO_ref.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H2O--ice.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_HCI_L_long.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_HCI_L_short.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_HCI_M.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_metis_img_nq_aquarius_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_METIS_mirrors_img_n.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_entrance_window.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_IB_4.05.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_S_IV.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_PAH_8.6.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_ND--1.4.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_short--L.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_PAH_11.25_ref.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_L.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Lp.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_L_spec.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Mp.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_M_spec.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_N1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_N2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_N3.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_PAH_11.25.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_HCI_M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H2O--ice.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_ND_OD5.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_PAH_3.3_ref.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_PAH_3.3.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_ND_OD1.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_ND_OD2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_ND_OD3.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_ND_OD4.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_ND_OD5.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Ne_II.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Ne_II_ref.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_N_spec.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Br_alpha_ref.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Br_alpha.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_open.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_PAH_11.25.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_PAH_11.25_ref.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_PAH_3.3.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_PAH_3.3_ref.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_PAH_8.6.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_PAH_8.6_ref.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_Q1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_short--L.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_S_IV.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_PAH_8.6_ref.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Ne_II_ref.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_S_IV_ref.dat-error-red)]()
-* [![](https://img.shields.io/badge/fp_mask_grid_lm.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_ND_OD3.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_M_spec.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_CO_1--0_ice.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Mp.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_L_spec.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_CO_ref.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_N2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_HCI_L_short.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_N3.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_closed.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Ne_II.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_N_spec.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_HCI_L_long.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_ND_OD4.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_ND--2.8.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Lp.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_N1.dat-error-red)]()
+* [![](https://img.shields.io/badge/2023--06--20--spectra_Grism--L_band.dat-error-red)]()
+* [![](https://img.shields.io/badge/2023--06--20--spectra_Grism--M_band.dat-error-red)]()
+* [![](https://img.shields.io/badge/2023--06--20--spectra_Grism--N_band.dat-error-red)]()
 * [![](https://img.shields.io/badge/fp_mask_pinhole_lm.dat-error-red)]()
+* [![](https://img.shields.io/badge/fp_mask_grid_lm.dat-error-red)]()
 * [![](https://img.shields.io/badge/fp_mask_pinhole_n.dat-error-red)]()
-## MICADO
+## NACO
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+### Structure
+* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
+* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
+### Contents
+* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
+* [![](https://img.shields.io/badge/IB_2.24.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.33.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.08.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.30.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.09.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_3.74.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.64.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.18.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.24.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.45.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_2.12.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.06.dat-error-red)]()
+* [![](https://img.shields.io/badge/M_prime.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.00.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_4.05.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.39.dat-error-red)]()
+* [![](https://img.shields.io/badge/J_CONICA.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.28.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_2.17.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.21.dat-error-red)]()
+* [![](https://img.shields.io/badge/L_prime.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.15.dat-error-red)]()
+* [![](https://img.shields.io/badge/H_CONICA.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.27.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.26.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.48.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.03.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.09.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.75.dat-error-red)]()
+* [![](https://img.shields.io/badge/NB_1.04.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.42.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.36.dat-error-red)]()
+* [![](https://img.shields.io/badge/IB_2.12.dat-error-red)]()
+* [![](https://img.shields.io/badge/Ks_CONICA.dat-error-red)]()
+## UVES
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+### Structure
+* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
+* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
+### Contents
+* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
+## HARMONI
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+### Structure
+* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
+* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
+### Contents
+* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
+## OSIRIS
+[![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
+### Structure
+* [![](https://img.shields.io/badge/default_yaml-OK-green)]()
+* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
+* [![](https://img.shields.io/badge/no_missing_files-green)]()
+### Contents
+* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
+* [![](https://img.shields.io/badge/QE_OSIRIS_new_detector.dat-error-red)]()
+* [![](https://img.shields.io/badge/unity.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_OSIRIS_old_detector_plus_M3.dat-error-red)]()
+* [![](https://img.shields.io/badge/GTC_im_efficiency.dat-error-red)]()
+* [![](https://img.shields.io/badge/MAAT_APERTURES.dat-error-red)]()
+* [![](https://img.shields.io/badge/OSIRIS_stitchedArc.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R2500U.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R2500V.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R2500R.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R500R.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R300R.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R1000R.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R2000B.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R2500I.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R1000B.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R300B.dat-error-red)]()
+* [![](https://img.shields.io/badge/TRACE_R500B.dat-error-red)]()
+* [![](https://img.shields.io/badge/fg191b2b.dat-error-red)]()
+## VLT
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+### Structure
+* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
+* [![](https://img.shields.io/badge/no_missing_files-green)]()
+### Contents
+* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
+* [![](https://img.shields.io/badge/LIST_VLT_mirrors.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_mirror_aluminium.dat-error-red)]()
+## ViennaLT
+[![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
+### Structure
+* [![](https://img.shields.io/badge/default_yaml-OK-green)]()
+* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
+* [![](https://img.shields.io/badge/TER_atmosphere.dat-missing-red)]()
+* [![](https://img.shields.io/badge/QE_SBIG.dat-missing-red)]()
+### Contents
+* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
+* [![](https://img.shields.io/badge/LIST_ViLT_mirrors_static.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_ViLT_layout.dat-error-red)]()
+## Paranal
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+### Structure
+* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
+* [![](https://img.shields.io/badge/no_missing_files-green)]()
+### Contents
+* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
+* [![](https://img.shields.io/badge/TER_paranal_default_NIR_IMG.dat-error-red)]()
+## test_package
 [![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
 * [![](https://img.shields.io/badge/default_yaml-OK-green)]()
@@ -256,109 +392,66 @@ For details on errors and conflicts, see badge report log file in this directory
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/FPA_array_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_linearity.dat-error-red)]()
-* [![](https://img.shields.io/badge/INST_MICADO_wavefront_error_budget.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_spec.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_static.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_wide.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_MICADO_mirrors_zoom.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_RO_SCAO_mirrors.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_15000x20.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_3000x16.dat-error-red)]()
-* [![](https://img.shields.io/badge/MASK_slit_3000x48.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_detector_H2RG.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_detector_H4RG.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_coating_antireflection.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_entrance_window.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_full_adc.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_grating.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_MICADO_mirror_mgf2agal.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mirror_gold.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_SCAO_dichroic.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_blank.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_block.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Br--gamma.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_FeII.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H--cont.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H--long.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H--short.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_H2_1--0S1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_He--I.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_I--long.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_J--long.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_J--short.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_J.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_K--cont.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_K--long.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_K--mid.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_K--short.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_Ks.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Ks2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_ND1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_ND3.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_open.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Pa--beta.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Spec_HK.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Spec_IJ.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_stop_IJH.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_stop_K.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xH1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xH2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xI1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xI2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xJ1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xJ2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xK1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xK2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xY1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_xY2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_Y.dat-error-red)]()
-## MORFEO
+## KMOS
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+### Structure
+* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
+* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
+### Contents
+* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
+## VISIR
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
+### Structure
+* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
+* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
+### Contents
+* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
+## HST
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/TER_mirror_silver.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_MORFEO_entrance_window.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_MORFEO_lgs_dichroic.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_MORFEO_mirror_aluminium.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_MORFEO_mirror_gold.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_MORFEO_mirror_mgf2agal.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_MORFEO_mirror_silver.dat-error-red)]()
-## MOSAIC
+* [![](https://img.shields.io/badge/wfc3_ir_primary_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/wfc3_ir_secondary_001_syn.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_ELT_mirror_mgf2agal.dat-error-red)]()
+## HAWKI
 [![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
 * [![](https://img.shields.io/badge/default_yaml-OK-green)]()
-* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
+* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/FPA_linearity_HxRG.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_mosaic_NIR_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/FPA_mosaic_VIS_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_detector_NIR.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_VIS_ML2_depleted.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_VIS_ML2_standard.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_grating_VIS_LR_B.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mIFU_HR--H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mIFU_LR--H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mIFU_LR--J.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mos_HR--H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mos_LR--H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_mos_LR--J.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_spec_HR--H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_spec_LR--H.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_spec_LR--J.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_sys_MOS--HR--B1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_sys_MOS--HR--B2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_sys_MOS--HR--R1.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_sys_MOS--HR--R2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_sys_MOS--LR--B.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_sys_MOS--LR--R.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_detector_H2RG.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_hawki_layout.dat-error-red)]()
+* [![](https://img.shields.io/badge/FPA_linearity.dat-error-red)]()
+* [![](https://img.shields.io/badge/LIST_HAWKI_mirrors.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mirror_gold.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_dichroic.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_window.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_CH4.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_J.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_NB1190.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_H2.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_NB1060.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Ks.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Y.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_BrGamma.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_NB2090.dat-error-red)]()
+* [![](https://img.shields.io/badge/BG_total_Ks.dat-error-red)]()
+* [![](https://img.shields.io/badge/BG_total_BrGamma.dat-error-red)]()
+* [![](https://img.shields.io/badge/BG_total_J.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_system_J.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_system_CH4.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_system_BrGamma.dat-error-red)]()
+* [![](https://img.shields.io/badge/ESO_ETC_bg_fluxes.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_system_Ks.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_system_H.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_system_Y.dat-error-red)]()
 ## MUSE
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
@@ -366,47 +459,6 @@ For details on errors and conflicts, see badge report log file in this directory
 * [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
 ### Contents
 * [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-## NACO
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
-### Structure
-* [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
-* [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
-### Contents
-* [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-* [![](https://img.shields.io/badge/H_CONICA.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.00.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.03.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.06.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.09.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.12.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.15.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.18.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.21.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.24.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.27.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.30.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.33.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.36.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.39.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.42.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.45.dat-error-red)]()
-* [![](https://img.shields.io/badge/IB_2.48.dat-error-red)]()
-* [![](https://img.shields.io/badge/J_CONICA.dat-error-red)]()
-* [![](https://img.shields.io/badge/Ks_CONICA.dat-error-red)]()
-* [![](https://img.shields.io/badge/L_prime.dat-error-red)]()
-* [![](https://img.shields.io/badge/M_prime.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.04.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.08.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.09.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.24.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.26.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.28.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.64.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_1.75.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_2.12.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_2.17.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_3.74.dat-error-red)]()
-* [![](https://img.shields.io/badge/NB_4.05.dat-error-red)]()
 ## NIRCam
 [![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
@@ -415,116 +467,70 @@ For details on errors and conflicts, see badge report log file in this directory
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/DET_LW_layout.dat-error-red)]()
 * [![](https://img.shields.io/badge/DET_LW_layout_simple.dat-error-red)]()
+* [![](https://img.shields.io/badge/DET_LW_layout.dat-error-red)]()
 * [![](https://img.shields.io/badge/DET_SW_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F250M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F277W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F300M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F322W2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F323N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F335M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F356W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F466N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F300M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F360M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F405N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F410M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F430M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F444W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F460M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F466N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F323N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F430M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F410M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F277W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F335M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F470N.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F480M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F356W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F250M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F460M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F070W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F090W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F187N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F210M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F115W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F162M.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F200W.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F212N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F150W2.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F140M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F150W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F150W2.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F162M.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F164N.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_F090W.dat-error-red)]()
 * [![](https://img.shields.io/badge/TC_filter_F182M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F187N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F200W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F210M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_filter_F212N.dat-error-red)]()
-## OSIRIS
-[![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
-### Structure
-* [![](https://img.shields.io/badge/default_yaml-OK-green)]()
-* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
-* [![](https://img.shields.io/badge/no_missing_files-green)]()
-### Contents
-* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/GTC_im_efficiency.dat-error-red)]()
-* [![](https://img.shields.io/badge/MAAT_APERTURES.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_OSIRIS_new_detector.dat-error-red)]()
-* [![](https://img.shields.io/badge/QE_OSIRIS_old_detector_plus_M3.dat-error-red)]()
-* [![](https://img.shields.io/badge/unity.dat-error-red)]()
-* [![](https://img.shields.io/badge/OSIRIS_stitchedArc.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R1000B.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R1000R.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R2000B.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R2500I.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R2500R.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R2500U.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R2500V.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R300B.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R300R.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R500B.dat-error-red)]()
-* [![](https://img.shields.io/badge/TRACE_R500R.dat-error-red)]()
-* [![](https://img.shields.io/badge/fg191b2b.dat-error-red)]()
-## Paranal
-[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
-### Structure
-* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
-* [![](https://img.shields.io/badge/no_missing_files-green)]()
-### Contents
-* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/TER_paranal_default_NIR_IMG.dat-error-red)]()
-## test_package
-[![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
-### Structure
-* [![](https://img.shields.io/badge/default_yaml-OK-green)]()
-* [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
-* [![](https://img.shields.io/badge/no_missing_files-green)]()
-### Contents
-* [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/TC_filter_Ks.dat-error-red)]()
-## UVES
+## ERIS
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
 * [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
 * [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
 ### Contents
 * [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-## ViennaLT
-[![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
+## LaPalma
+[![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
-* [![](https://img.shields.io/badge/default_yaml-OK-green)]()
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
-* [![](https://img.shields.io/badge/TER_atmosphere.dat-missing-red)]()
-* [![](https://img.shields.io/badge/QE_SBIG.dat-missing-red)]()
+* [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/FPA_ViLT_layout.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_ViLT_mirrors_static.dat-error-red)]()
-## VISIR
+* [![](https://img.shields.io/badge/orm_extinct.dat-error-red)]()
+## FORS
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
 * [![](https://img.shields.io/badge/self_named_yaml-not_found-red)]()
 * [![](https://img.shields.io/badge/no_files_referenced-yellowgreen)]()
 ### Contents
 * [![](https://img.shields.io/badge/no_yaml_files-yellowgreen)]()
-## VLT
+## GTC
 [![](https://img.shields.io/badge/package_type-support-deepskyblue)]()
 ### Structure
 * [![](https://img.shields.io/badge/self_named_yaml-found-green)]()
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/LIST_VLT_mirrors.dat-error-red)]()
-* [![](https://img.shields.io/badge/TC_mirror_aluminium.dat-error-red)]()
-## WFC3
+* [![](https://img.shields.io/badge/reflectivity_GTC.dat-error-red)]()
+* [![](https://img.shields.io/badge/unity.dat-error-red)]()
+## LFOA
 [![](https://img.shields.io/badge/package_type-observation-blueviolet)]()
 ### Structure
 * [![](https://img.shields.io/badge/default_yaml-OK-green)]()
@@ -532,30 +538,28 @@ For details on errors and conflicts, see badge report log file in this directory
 * [![](https://img.shields.io/badge/no_missing_files-green)]()
 ### Contents
 * [![](https://img.shields.io/badge/all_yamls_readable-green)]()
-* [![](https://img.shields.io/badge/FPA_linearity.dat-error-red)]()
-* [![](https://img.shields.io/badge/LIST_WFC3_mirrors_IR.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_cor_004_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_csm_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_dn_002_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_fold_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_mask_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_mir1_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_mir2_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_qe_003_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_rcp_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/wfc3_ir_win_001_syn.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F098M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F105W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F110W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F125W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F126N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F127M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F128N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F130N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F132N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F139M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F140W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F153M.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F160W.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F164N.dat-error-red)]()
-* [![](https://img.shields.io/badge/TER_filter_F167N.dat-error-red)]()
\ No newline at end of file
+* [![](https://img.shields.io/badge/LIST_LFOA_mirrors_static.dat-error-red)]()
+* [![](https://img.shields.io/badge/QE_SBIG.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_mirror_aluminium.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_focal_reducer.dat-error-red)]()
+* [![](https://img.shields.io/badge/TER_atmosphere.dat-error-red)]()
+* [![](https://img.shields.io/badge/U.dat-error-red)]()
+* [![](https://img.shields.io/badge/SII.dat-error-red)]()
+* [![](https://img.shields.io/badge/sloan_i.dat-error-red)]()
+* [![](https://img.shields.io/badge/B.dat-error-red)]()
+* [![](https://img.shields.io/badge/R.dat-error-red)]()
+* [![](https://img.shields.io/badge/OIII.dat-error-red)]()
+* [![](https://img.shields.io/badge/sloan_g.dat-error-red)]()
+* [![](https://img.shields.io/badge/I.dat-error-red)]()
+* [![](https://img.shields.io/badge/Halpha_wide.dat-error-red)]()
+* [![](https://img.shields.io/badge/sloan_z.dat-error-red)]()
+* [![](https://img.shields.io/badge/V.dat-error-red)]()
+* [![](https://img.shields.io/badge/Hbeta.dat-error-red)]()
+* [![](https://img.shields.io/badge/Halpha_narrow.dat-error-red)]()
+* [![](https://img.shields.io/badge/sloan_r.dat-error-red)]()
+* [![](https://img.shields.io/badge/sloan_u.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Halpha_wide_old.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Halpha_narrow_old.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_Hbeta_old.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_OIII_old.dat-error-red)]()
+* [![](https://img.shields.io/badge/TC_filter_SII_old.dat-error-red)]()
\ No newline at end of file
diff --git a/_REPORTS/badges.yaml b/_REPORTS/badges.yaml
index f4cf880d..e5c02a3d 100644
--- a/_REPORTS/badges.yaml
+++ b/_REPORTS/badges.yaml
@@ -1,14 +1,3 @@
-Armazones:
-  package_type: support
-  structure:
-    self_named_yaml: found
-    no_missing_files: '!OK'
-  contents:
-    all_yamls_readable: '!OK'
-    TER_armazones_default_FULL_IMG.dat: error
-    TER_armazones_default_MIR_IMG.dat: error
-    TER_armazones_default_NIR_IMG.dat: error
-    TER_armazones_default_OPT_IMG.dat: error
 ELT:
   package_type: support
   structure:
@@ -16,89 +5,97 @@ ELT:
     '#telescope_reflection.filename': missing
   contents:
     all_yamls_readable: '!OK'
-    TER_ELT_5_mirror.dat: error
-    TER_ELT_5_mirror_clean.dat: error
-    TER_ELT_6_mirror_field_track.dat: error
-    TER_ELT_6_mirror_field_track_clean.dat: error
-    TER_ELT_6_mirror_pupil_track.dat: error
-    TER_ELT_6_mirror_pupil_track_clean.dat: error
     TER_ELT_mirror_aluminium.dat: error
+    TER_ELT_6_mirror_pupil_track_clean.dat: error
     TER_ELT_mirror_mgf2agal.dat: error
-ERIS:
-  package_type: support
-  structure:
-    self_named_yaml: not found
-    no_files_referenced: '!NONE'
-  contents:
-    no_yaml_files: '!NONE'
-FORS:
-  package_type: support
-  structure:
-    self_named_yaml: not found
-    no_files_referenced: '!NONE'
-  contents:
-    no_yaml_files: '!NONE'
-GTC:
+    TER_ELT_6_mirror_field_track.dat: error
+    TER_ELT_6_mirror_pupil_track.dat: error
+    TER_ELT_5_mirror_clean.dat: error
+    TER_ELT_6_mirror_field_track_clean.dat: error
+    TER_ELT_5_mirror.dat: error
+Armazones:
   package_type: support
   structure:
     self_named_yaml: found
     no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    reflectivity_GTC.dat: error
-    unity.dat: error
-HARMONI:
+    TER_armazones_default_FULL_IMG.dat: error
+    TER_armazones_default_OPT_IMG.dat: error
+    TER_armazones_default_MIR_IMG.dat: error
+    TER_armazones_default_NIR_IMG.dat: error
+MCIFU:
   package_type: support
   structure:
     self_named_yaml: not found
     no_files_referenced: '!NONE'
   contents:
     no_yaml_files: '!NONE'
-HAWKI:
+MOSAIC:
   package_type: observation
   structure:
     default_yaml: OK
-    self_named_yaml: found
+    self_named_yaml: not found
     no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    FPA_hawki_layout.dat: error
-    FPA_linearity.dat: error
-    LIST_HAWKI_mirrors.dat: error
-    QE_detector_H2RG.dat: error
-    TC_dichroic.dat: error
-    TER_mirror_gold.dat: error
-    TER_window.dat: error
-    TC_filter_BrGamma.dat: error
-    TC_filter_CH4.dat: error
-    TC_filter_H.dat: error
-    TC_filter_H2.dat: error
-    TC_filter_J.dat: error
-    TC_filter_Ks.dat: error
-    TC_filter_NB1060.dat: error
-    TC_filter_NB1190.dat: error
-    TC_filter_NB2090.dat: error
-    TC_filter_Y.dat: error
-    BG_total_BrGamma.dat: error
-    BG_total_J.dat: error
-    BG_total_Ks.dat: error
-    ESO_ETC_bg_fluxes.dat: error
-    TER_system_BrGamma.dat: error
-    TER_system_CH4.dat: error
-    TER_system_H.dat: error
-    TER_system_J.dat: error
-    TER_system_Ks.dat: error
-    TER_system_Y.dat: error
-HST:
-  package_type: support
+    TER_mos_HR-H.dat: error
+    TER_mos_LR-J.dat: error
+    TER_sys_MOS-HR-R1.dat: error
+    TER_grating_VIS_LR_B.dat: error
+    TER_spec_LR-J.dat: error
+    TER_sys_MOS-HR-B2.dat: error
+    TER_mIFU_LR-H.dat: error
+    QE_VIS_ML2_standard.dat: error
+    TER_mos_LR-H.dat: error
+    FPA_mosaic_NIR_layout.dat: error
+    TER_mIFU_LR-J.dat: error
+    TER_mIFU_HR-H.dat: error
+    QE_detector_NIR.dat: error
+    TER_spec_LR-H.dat: error
+    FPA_linearity_HxRG.dat: error
+    TER_spec_HR-H.dat: error
+    TER_sys_MOS-HR-B1.dat: error
+    TER_sys_MOS-LR-B.dat: error
+    QE_VIS_ML2_depleted.dat: error
+    FPA_mosaic_VIS_layout.dat: error
+    TER_sys_MOS-LR-R.dat: error
+    TER_sys_MOS-HR-R2.dat: error
+WFC3:
+  package_type: observation
   structure:
+    default_yaml: OK
     self_named_yaml: found
     no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    TER_ELT_mirror_mgf2agal.dat: error
-    wfc3_ir_primary_001_syn.dat: error
-    wfc3_ir_secondary_001_syn.dat: error
+    wfc3_ir_dn_002_syn.dat: error
+    wfc3_ir_rcp_001_syn.dat: error
+    LIST_WFC3_mirrors_IR.dat: error
+    FPA_linearity.dat: error
+    wfc3_ir_csm_001_syn.dat: error
+    wfc3_ir_qe_003_syn.dat: error
+    wfc3_ir_mask_001_syn.dat: error
+    wfc3_ir_mir1_001_syn.dat: error
+    wfc3_ir_fold_001_syn.dat: error
+    wfc3_ir_cor_004_syn.dat: error
+    wfc3_ir_mir2_001_syn.dat: error
+    wfc3_ir_win_001_syn.dat: error
+    TER_filter_F132N.dat: error
+    TER_filter_F125W.dat: error
+    TER_filter_F110W.dat: error
+    TER_filter_F153M.dat: error
+    TER_filter_F126N.dat: error
+    TER_filter_F139M.dat: error
+    TER_filter_F160W.dat: error
+    TER_filter_F127M.dat: error
+    TER_filter_F128N.dat: error
+    TER_filter_F130N.dat: error
+    TER_filter_F164N.dat: error
+    TER_filter_F167N.dat: error
+    TER_filter_F140W.dat: error
+    TER_filter_F098M.dat: error
+    TER_filter_F105W.dat: error
 JWST:
   package_type: support
   structure:
@@ -107,260 +104,172 @@ JWST:
   contents:
     all_yamls_readable: '!OK'
     TER_mirror_gold.dat: error
-KMOS:
-  package_type: support
-  structure:
-    self_named_yaml: not found
-    no_files_referenced: '!NONE'
-  contents:
-    no_yaml_files: '!NONE'
-LaPalma:
+MORFEO:
   package_type: support
   structure:
     self_named_yaml: found
     no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    orm_extinct.dat: error
-LFOA:
+    TER_MORFEO_entrance_window.dat: error
+    TER_MORFEO_lgs_dichroic.dat: error
+    TER_MORFEO_mirror_mgf2agal.dat: error
+    TER_MORFEO_mirror_silver.dat: error
+    TER_MORFEO_mirror_gold.dat: error
+    TER_MORFEO_mirror_aluminium.dat: error
+    TER_mirror_silver.dat: error
+MICADO:
   package_type: observation
   structure:
     default_yaml: OK
     self_named_yaml: found
-    no_missing_files: '!OK'
+    MICADO_relay_air_trans.fits: missing
   contents:
     all_yamls_readable: '!OK'
-    LIST_LFOA_mirrors_static.dat: error
-    QE_SBIG.dat: error
-    TER_atmosphere.dat: error
-    TER_focal_reducer.dat: error
-    TER_mirror_aluminium.dat: error
-    B.dat: error
-    Halpha_narrow.dat: error
-    Halpha_wide.dat: error
-    Hbeta.dat: error
-    I.dat: error
-    OIII.dat: error
-    R.dat: error
-    SII.dat: error
-    sloan_g.dat: error
-    sloan_i.dat: error
-    sloan_r.dat: error
-    sloan_u.dat: error
-    sloan_z.dat: error
-    U.dat: error
-    V.dat: error
-    TC_filter_Halpha_narrow_old.dat: error
-    TC_filter_Halpha_wide_old.dat: error
-    TC_filter_Hbeta_old.dat: error
-    TC_filter_OIII_old.dat: error
-    TC_filter_SII_old.dat: error
-MCIFU:
-  package_type: support
-  structure:
-    self_named_yaml: not found
-    no_files_referenced: '!NONE'
-  contents:
-    no_yaml_files: '!NONE'
+    QE_detector_H2RG.dat: error
+    TER_full_adc.dat: error
+    TER_SCAO_dichroic.dat: error
+    INST_MICADO_wavefront_error_budget.dat: error
+    TER_MICADO_mirror_mgf2agal.dat: error
+    FPA_array_layout.dat: error
+    TER_coating_antireflection.dat: error
+    LIST_MICADO_mirrors_zoom.dat: error
+    QE_detector_H4RG.dat: error
+    MASK_slit_Short_offset.dat: error
+    FPA_linearity.dat: error
+    MASK_slit_Wide_offset.dat: error
+    TER_grating.dat: error
+    TER_mirror_gold.dat: error
+    LIST_MCA_mirrors.dat: error
+    MASK_slit_Short.dat: error
+    MASK_slit_Long_offset.dat: error
+    LIST_MICADO_mirrors_wide.dat: error
+    MASK_slit_Long.dat: error
+    LIST_MICADO_mirrors_static.dat: error
+    MASK_slit_Wide.dat: error
+    LIST_MICADO_mirrors_spec.dat: error
+    TER_entrance_window.dat: error
+    LIST_RO_SCAO_mirrors.dat: error
+    TC_filter_K-mid.dat: error
+    TC_filter_xH1.dat: error
+    TC_filter_H.dat: error
+    TC_filter_K-long.dat: error
+    TC_filter_xK2.dat: error
+    TC_filter_xJ1.dat: error
+    TC_filter_stop_IJH.dat: error
+    TC_filter_H-long.dat: error
+    TC_filter_ND3.dat: error
+    TC_filter_blank.dat: error
+    TC_filter_xH2.dat: error
+    TC_filter_xJ2.dat: error
+    TC_filter_xY1.dat: error
+    TC_filter_Spec_HK.dat: error
+    TC_filter_block.dat: error
+    TC_filter_J.dat: error
+    TC_filter_He-I.dat: error
+    TC_filter_J-long.dat: error
+    TC_filter_FeII.dat: error
+    TC_filter_open.dat: error
+    TC_filter_Br-gamma.dat: error
+    TC_filter_I-long.dat: error
+    TC_filter_Pa-beta.dat: error
+    TC_filter_Ks.dat: error
+    TC_filter_xI1.dat: error
+    TC_filter_K-short.dat: error
+    TC_filter_Y.dat: error
+    TC_filter_Ks2.dat: error
+    TC_filter_xI2.dat: error
+    TC_filter_H2_1-0S1.dat: error
+    TC_filter_stop_K.dat: error
+    TC_filter_J-short.dat: error
+    TC_filter_K-cont.dat: error
+    TC_filter_xY2.dat: error
+    TC_filter_H-short.dat: error
+    TC_filter_xK1.dat: error
+    TC_filter_H-cont.dat: error
+    TC_filter_Spec_IJ.dat: error
+    TC_filter_ND1.dat: error
 METIS:
   package_type: observation
   structure:
     default_yaml: OK
     self_named_yaml: found
-    TER_ELT_6_mirror_field_track.dat: missing
-    Leiden_atmo_ter.fits: missing
   contents:
     all_yamls_readable: '!OK'
-    FPA_linearity_GeoSnap_high_capacity.dat: error
-    FPA_linearity_GeoSnap_low_capacity.dat: error
-    FPA_linearity_HxRG.dat: error
-    FPA_metis_img_lm_layout.dat: error
-    FPA_metis_img_nq_aquarius_layout.dat: error
     FPA_metis_img_n_geosnap_layout.dat: error
-    FPA_metis_lms_layout.dat: error
-    FPA_metis_lms_smpl_layout.dat: error
-    LIST_ifu_apertures.dat: error
     LIST_METIS_mirrors_cfo.dat: error
-    LIST_METIS_mirrors_img_lm.dat: error
-    LIST_METIS_mirrors_img_n.dat: error
-    LIST_METIS_mirrors_lms.dat: error
-    LIST_METIS_mirrors_wcu.dat: error
-    MASK_slit_A-19_0.dat: error
-    MASK_slit_B-28_6.dat: error
-    MASK_slit_C-38_1.dat: error
+    TER_sca_dichroic.dat: error
+    FPA_metis_lms_smpl_layout.dat: error
+    FPA_metis_img_lm_layout.dat: error
+    TER_img_dichroic.dat: error
     MASK_slit_D-57_1.dat: error
-    MASK_slit_E-114_2.dat: error
     QE_detector_geosnap.dat: error
-    QE_detector_H2RG_METIS.dat: error
     TER_ADC_const_90.dat: error
-    TER_entrance_window.dat: error
-    TER_img_dichroic.dat: error
+    TER_mirror_aluminium.dat: error
+    MASK_slit_B-28_6.dat: error
+    FPA_linearity_GeoSnap_high_capacity.dat: error
+    LIST_METIS_mirrors_wcu.dat: error
     TER_lms_grism_max.dat: error
+    TER_mirror_gold.dat: error
+    QE_detector_H2RG_METIS.dat: error
+    LIST_METIS_mirrors_img_lm.dat: error
+    FPA_linearity_GeoSnap_low_capacity.dat: error
+    FPA_linearity_HxRG.dat: error
+    LIST_METIS_mirrors_lms.dat: error
+    FPA_metis_lms_layout.dat: error
+    MASK_slit_C-38_1.dat: error
     TER_lms_grism_min.dat: error
+    MASK_slit_A-19_0.dat: error
+    MASK_slit_E-114_2.dat: error
     TER_lms_predisperser_prism.dat: error
-    TER_mirror_aluminium.dat: error
-    TER_mirror_gold.dat: error
-    TER_sca_dichroic.dat: error
-    2023-06-20-spectra_Grism-L_band.dat: error
-    2023-06-20-spectra_Grism-M_band.dat: error
-    2023-06-20-spectra_Grism-N_band.dat: error
-    TC_filter_Br_alpha.dat: error
-    TC_filter_Br_alpha_ref.dat: error
-    TC_filter_CO_1-0_ice.dat: error
-    TC_filter_CO_ref.dat: error
-    TC_filter_H2O-ice.dat: error
-    TC_filter_HCI_L_long.dat: error
-    TC_filter_HCI_L_short.dat: error
-    TC_filter_HCI_M.dat: error
+    FPA_metis_img_nq_aquarius_layout.dat: error
+    LIST_METIS_mirrors_img_n.dat: error
+    TER_entrance_window.dat: error
     TC_filter_IB_4.05.dat: error
+    TC_filter_S_IV.dat: error
+    TC_filter_PAH_8.6.dat: error
+    TC_filter_ND-1.4.dat: error
+    TC_filter_short-L.dat: error
+    TC_filter_PAH_11.25_ref.dat: error
     TC_filter_L.dat: error
-    TC_filter_Lp.dat: error
-    TC_filter_L_spec.dat: error
-    TC_filter_Mp.dat: error
-    TC_filter_M_spec.dat: error
-    TC_filter_N1.dat: error
-    TC_filter_N2.dat: error
-    TC_filter_N3.dat: error
+    TC_filter_PAH_11.25.dat: error
+    TC_filter_HCI_M.dat: error
+    TC_filter_H2O-ice.dat: error
+    TC_filter_ND_OD5.dat: error
+    TC_filter_PAH_3.3_ref.dat: error
+    TC_filter_PAH_3.3.dat: error
     TC_filter_ND_OD1.dat: error
     TC_filter_ND_OD2.dat: error
-    TC_filter_ND_OD3.dat: error
-    TC_filter_ND_OD4.dat: error
-    TC_filter_ND_OD5.dat: error
-    TC_filter_Ne_II.dat: error
-    TC_filter_Ne_II_ref.dat: error
-    TC_filter_N_spec.dat: error
+    TC_filter_Br_alpha_ref.dat: error
+    TC_filter_Br_alpha.dat: error
     TC_filter_open.dat: error
-    TC_filter_PAH_11.25.dat: error
-    TC_filter_PAH_11.25_ref.dat: error
-    TC_filter_PAH_3.3.dat: error
-    TC_filter_PAH_3.3_ref.dat: error
-    TC_filter_PAH_8.6.dat: error
-    TC_filter_PAH_8.6_ref.dat: error
     TC_filter_Q1.dat: error
-    TC_filter_short-L.dat: error
-    TC_filter_S_IV.dat: error
+    TC_filter_PAH_8.6_ref.dat: error
+    TC_filter_Ne_II_ref.dat: error
     TC_filter_S_IV_ref.dat: error
-    fp_mask_grid_lm.dat: error
+    TC_filter_ND_OD3.dat: error
+    TC_filter_M_spec.dat: error
+    TC_filter_CO_1-0_ice.dat: error
+    TC_filter_Mp.dat: error
+    TC_filter_L_spec.dat: error
+    TC_filter_CO_ref.dat: error
+    TC_filter_N2.dat: error
+    TC_filter_HCI_L_short.dat: error
+    TC_filter_N3.dat: error
+    TC_filter_closed.dat: error
+    TC_filter_Ne_II.dat: error
+    TC_filter_N_spec.dat: error
+    TC_filter_HCI_L_long.dat: error
+    TC_filter_ND_OD4.dat: error
+    TC_filter_ND-2.8.dat: error
+    TC_filter_Lp.dat: error
+    TC_filter_N1.dat: error
+    2023-06-20-spectra_Grism-L_band.dat: error
+    2023-06-20-spectra_Grism-M_band.dat: error
+    2023-06-20-spectra_Grism-N_band.dat: error
     fp_mask_pinhole_lm.dat: error
+    fp_mask_grid_lm.dat: error
     fp_mask_pinhole_n.dat: error
-MICADO:
-  package_type: observation
-  structure:
-    default_yaml: OK
-    self_named_yaml: found
-    no_missing_files: '!OK'
-  contents:
-    all_yamls_readable: '!OK'
-    FPA_array_layout.dat: error
-    FPA_linearity.dat: error
-    INST_MICADO_wavefront_error_budget.dat: error
-    LIST_MICADO_mirrors_spec.dat: error
-    LIST_MICADO_mirrors_static.dat: error
-    LIST_MICADO_mirrors_wide.dat: error
-    LIST_MICADO_mirrors_zoom.dat: error
-    LIST_RO_SCAO_mirrors.dat: error
-    MASK_slit_15000x20.dat: error
-    MASK_slit_3000x16.dat: error
-    MASK_slit_3000x48.dat: error
-    QE_detector_H2RG.dat: error
-    QE_detector_H4RG.dat: error
-    TER_coating_antireflection.dat: error
-    TER_entrance_window.dat: error
-    TER_full_adc.dat: error
-    TER_grating.dat: error
-    TER_MICADO_mirror_mgf2agal.dat: error
-    TER_mirror_gold.dat: error
-    TER_SCAO_dichroic.dat: error
-    TC_filter_blank.dat: error
-    TC_filter_block.dat: error
-    TC_filter_Br-gamma.dat: error
-    TC_filter_FeII.dat: error
-    TC_filter_H-cont.dat: error
-    TC_filter_H-long.dat: error
-    TC_filter_H-short.dat: error
-    TC_filter_H.dat: error
-    TC_filter_H2_1-0S1.dat: error
-    TC_filter_He-I.dat: error
-    TC_filter_I-long.dat: error
-    TC_filter_J-long.dat: error
-    TC_filter_J-short.dat: error
-    TC_filter_J.dat: error
-    TC_filter_K-cont.dat: error
-    TC_filter_K-long.dat: error
-    TC_filter_K-mid.dat: error
-    TC_filter_K-short.dat: error
-    TC_filter_Ks.dat: error
-    TC_filter_Ks2.dat: error
-    TC_filter_ND1.dat: error
-    TC_filter_ND3.dat: error
-    TC_filter_open.dat: error
-    TC_filter_Pa-beta.dat: error
-    TC_filter_Spec_HK.dat: error
-    TC_filter_Spec_IJ.dat: error
-    TC_filter_stop_IJH.dat: error
-    TC_filter_stop_K.dat: error
-    TC_filter_xH1.dat: error
-    TC_filter_xH2.dat: error
-    TC_filter_xI1.dat: error
-    TC_filter_xI2.dat: error
-    TC_filter_xJ1.dat: error
-    TC_filter_xJ2.dat: error
-    TC_filter_xK1.dat: error
-    TC_filter_xK2.dat: error
-    TC_filter_xY1.dat: error
-    TC_filter_xY2.dat: error
-    TC_filter_Y.dat: error
-MORFEO:
-  package_type: support
-  structure:
-    self_named_yaml: found
-    no_missing_files: '!OK'
-  contents:
-    all_yamls_readable: '!OK'
-    TER_mirror_silver.dat: error
-    TER_MORFEO_entrance_window.dat: error
-    TER_MORFEO_lgs_dichroic.dat: error
-    TER_MORFEO_mirror_aluminium.dat: error
-    TER_MORFEO_mirror_gold.dat: error
-    TER_MORFEO_mirror_mgf2agal.dat: error
-    TER_MORFEO_mirror_silver.dat: error
-MOSAIC:
-  package_type: observation
-  structure:
-    default_yaml: OK
-    self_named_yaml: not found
-    no_missing_files: '!OK'
-  contents:
-    all_yamls_readable: '!OK'
-    FPA_linearity_HxRG.dat: error
-    FPA_mosaic_NIR_layout.dat: error
-    FPA_mosaic_VIS_layout.dat: error
-    QE_detector_NIR.dat: error
-    QE_VIS_ML2_depleted.dat: error
-    QE_VIS_ML2_standard.dat: error
-    TER_grating_VIS_LR_B.dat: error
-    TER_mIFU_HR-H.dat: error
-    TER_mIFU_LR-H.dat: error
-    TER_mIFU_LR-J.dat: error
-    TER_mos_HR-H.dat: error
-    TER_mos_LR-H.dat: error
-    TER_mos_LR-J.dat: error
-    TER_spec_HR-H.dat: error
-    TER_spec_LR-H.dat: error
-    TER_spec_LR-J.dat: error
-    TER_sys_MOS-HR-B1.dat: error
-    TER_sys_MOS-HR-B2.dat: error
-    TER_sys_MOS-HR-R1.dat: error
-    TER_sys_MOS-HR-R2.dat: error
-    TER_sys_MOS-LR-B.dat: error
-    TER_sys_MOS-LR-R.dat: error
-MUSE:
-  package_type: support
-  structure:
-    self_named_yaml: not found
-    no_files_referenced: '!NONE'
-  contents:
-    no_yaml_files: '!NONE'
 NACO:
   package_type: support
   structure:
@@ -368,80 +277,54 @@ NACO:
     no_files_referenced: '!NONE'
   contents:
     no_yaml_files: '!NONE'
-    H_CONICA.dat: error
-    IB_2.00.dat: error
-    IB_2.03.dat: error
-    IB_2.06.dat: error
-    IB_2.09.dat: error
-    IB_2.12.dat: error
-    IB_2.15.dat: error
-    IB_2.18.dat: error
-    IB_2.21.dat: error
     IB_2.24.dat: error
-    IB_2.27.dat: error
-    IB_2.30.dat: error
     IB_2.33.dat: error
-    IB_2.36.dat: error
-    IB_2.39.dat: error
-    IB_2.42.dat: error
+    NB_1.08.dat: error
+    IB_2.30.dat: error
+    IB_2.09.dat: error
+    NB_3.74.dat: error
+    NB_1.64.dat: error
+    IB_2.18.dat: error
+    NB_1.24.dat: error
     IB_2.45.dat: error
-    IB_2.48.dat: error
+    NB_2.12.dat: error
+    IB_2.06.dat: error
+    M_prime.dat: error
+    IB_2.00.dat: error
+    NB_4.05.dat: error
+    IB_2.39.dat: error
     J_CONICA.dat: error
-    Ks_CONICA.dat: error
+    NB_1.28.dat: error
+    NB_2.17.dat: error
+    IB_2.21.dat: error
     L_prime.dat: error
-    M_prime.dat: error
-    NB_1.04.dat: error
-    NB_1.08.dat: error
-    NB_1.09.dat: error
-    NB_1.24.dat: error
+    IB_2.15.dat: error
+    H_CONICA.dat: error
+    IB_2.27.dat: error
     NB_1.26.dat: error
-    NB_1.28.dat: error
-    NB_1.64.dat: error
+    IB_2.48.dat: error
+    IB_2.03.dat: error
+    NB_1.09.dat: error
     NB_1.75.dat: error
-    NB_2.12.dat: error
-    NB_2.17.dat: error
-    NB_3.74.dat: error
-    NB_4.05.dat: error
-NIRCam:
-  package_type: observation
+    NB_1.04.dat: error
+    IB_2.42.dat: error
+    IB_2.36.dat: error
+    IB_2.12.dat: error
+    Ks_CONICA.dat: error
+UVES:
+  package_type: support
+  structure:
+    self_named_yaml: not found
+    no_files_referenced: '!NONE'
+  contents:
+    no_yaml_files: '!NONE'
+HARMONI:
+  package_type: support
   structure:
-    default_yaml: OK
-    self_named_yaml: found
-    no_missing_files: '!OK'
+    self_named_yaml: not found
+    no_files_referenced: '!NONE'
   contents:
-    all_yamls_readable: '!OK'
-    DET_LW_layout.dat: error
-    DET_LW_layout_simple.dat: error
-    DET_SW_layout.dat: error
-    TC_filter_F250M.dat: error
-    TC_filter_F277W.dat: error
-    TC_filter_F300M.dat: error
-    TC_filter_F322W2.dat: error
-    TC_filter_F323N.dat: error
-    TC_filter_F335M.dat: error
-    TC_filter_F356W.dat: error
-    TC_filter_F360M.dat: error
-    TC_filter_F405N.dat: error
-    TC_filter_F410M.dat: error
-    TC_filter_F430M.dat: error
-    TC_filter_F444W.dat: error
-    TC_filter_F460M.dat: error
-    TC_filter_F466N.dat: error
-    TC_filter_F470N.dat: error
-    TC_filter_F480M.dat: error
-    TC_filter_F070W.dat: error
-    TC_filter_F090W.dat: error
-    TC_filter_F115W.dat: error
-    TC_filter_F140M.dat: error
-    TC_filter_F150W.dat: error
-    TC_filter_F150W2.dat: error
-    TC_filter_F162M.dat: error
-    TC_filter_F164N.dat: error
-    TC_filter_F182M.dat: error
-    TC_filter_F187N.dat: error
-    TC_filter_F200W.dat: error
-    TC_filter_F210M.dat: error
-    TC_filter_F212N.dat: error
+    no_yaml_files: '!NONE'
 OSIRIS:
   package_type: observation
   structure:
@@ -450,24 +333,44 @@ OSIRIS:
     no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    GTC_im_efficiency.dat: error
-    MAAT_APERTURES.dat: error
     QE_OSIRIS_new_detector.dat: error
-    QE_OSIRIS_old_detector_plus_M3.dat: error
     unity.dat: error
+    QE_OSIRIS_old_detector_plus_M3.dat: error
+    GTC_im_efficiency.dat: error
+    MAAT_APERTURES.dat: error
     OSIRIS_stitchedArc.dat: error
-    TRACE_R1000B.dat: error
+    TRACE_R2500U.dat: error
+    TRACE_R2500V.dat: error
+    TRACE_R2500R.dat: error
+    TRACE_R500R.dat: error
+    TRACE_R300R.dat: error
     TRACE_R1000R.dat: error
     TRACE_R2000B.dat: error
     TRACE_R2500I.dat: error
-    TRACE_R2500R.dat: error
-    TRACE_R2500U.dat: error
-    TRACE_R2500V.dat: error
+    TRACE_R1000B.dat: error
     TRACE_R300B.dat: error
-    TRACE_R300R.dat: error
     TRACE_R500B.dat: error
-    TRACE_R500R.dat: error
     fg191b2b.dat: error
+VLT:
+  package_type: support
+  structure:
+    self_named_yaml: found
+    no_missing_files: '!OK'
+  contents:
+    all_yamls_readable: '!OK'
+    LIST_VLT_mirrors.dat: error
+    TC_mirror_aluminium.dat: error
+ViennaLT:
+  package_type: observation
+  structure:
+    default_yaml: OK
+    self_named_yaml: found
+    TER_atmosphere.dat: missing
+    QE_SBIG.dat: missing
+  contents:
+    all_yamls_readable: '!OK'
+    LIST_ViLT_mirrors_static.dat: error
+    FPA_ViLT_layout.dat: error
 Paranal:
   package_type: support
   structure:
@@ -485,41 +388,144 @@ test_package:
   contents:
     all_yamls_readable: '!OK'
     TC_filter_Ks.dat: error
-UVES:
+KMOS:
   package_type: support
   structure:
     self_named_yaml: not found
     no_files_referenced: '!NONE'
   contents:
     no_yaml_files: '!NONE'
-ViennaLT:
+VISIR:
+  package_type: support
+  structure:
+    self_named_yaml: not found
+    no_files_referenced: '!NONE'
+  contents:
+    no_yaml_files: '!NONE'
+HST:
+  package_type: support
+  structure:
+    self_named_yaml: found
+    no_missing_files: '!OK'
+  contents:
+    all_yamls_readable: '!OK'
+    wfc3_ir_primary_001_syn.dat: error
+    wfc3_ir_secondary_001_syn.dat: error
+    TER_ELT_mirror_mgf2agal.dat: error
+HAWKI:
   package_type: observation
   structure:
     default_yaml: OK
     self_named_yaml: found
-    TER_atmosphere.dat: missing
-    QE_SBIG.dat: missing
+    no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    FPA_ViLT_layout.dat: error
-    LIST_ViLT_mirrors_static.dat: error
-VISIR:
+    QE_detector_H2RG.dat: error
+    FPA_hawki_layout.dat: error
+    FPA_linearity.dat: error
+    LIST_HAWKI_mirrors.dat: error
+    TER_mirror_gold.dat: error
+    TC_dichroic.dat: error
+    TER_window.dat: error
+    TC_filter_CH4.dat: error
+    TC_filter_H.dat: error
+    TC_filter_J.dat: error
+    TC_filter_NB1190.dat: error
+    TC_filter_H2.dat: error
+    TC_filter_NB1060.dat: error
+    TC_filter_Ks.dat: error
+    TC_filter_Y.dat: error
+    TC_filter_BrGamma.dat: error
+    TC_filter_NB2090.dat: error
+    BG_total_Ks.dat: error
+    BG_total_BrGamma.dat: error
+    BG_total_J.dat: error
+    TER_system_J.dat: error
+    TER_system_CH4.dat: error
+    TER_system_BrGamma.dat: error
+    ESO_ETC_bg_fluxes.dat: error
+    TER_system_Ks.dat: error
+    TER_system_H.dat: error
+    TER_system_Y.dat: error
+MUSE:
   package_type: support
   structure:
     self_named_yaml: not found
     no_files_referenced: '!NONE'
   contents:
     no_yaml_files: '!NONE'
-VLT:
+NIRCam:
+  package_type: observation
+  structure:
+    default_yaml: OK
+    self_named_yaml: found
+    no_missing_files: '!OK'
+  contents:
+    all_yamls_readable: '!OK'
+    DET_LW_layout_simple.dat: error
+    DET_LW_layout.dat: error
+    DET_SW_layout.dat: error
+    TC_filter_F322W2.dat: error
+    TC_filter_F466N.dat: error
+    TC_filter_F300M.dat: error
+    TC_filter_F360M.dat: error
+    TC_filter_F405N.dat: error
+    TC_filter_F444W.dat: error
+    TC_filter_F323N.dat: error
+    TC_filter_F430M.dat: error
+    TC_filter_F410M.dat: error
+    TC_filter_F277W.dat: error
+    TC_filter_F335M.dat: error
+    TC_filter_F470N.dat: error
+    TC_filter_F480M.dat: error
+    TC_filter_F356W.dat: error
+    TC_filter_F250M.dat: error
+    TC_filter_F460M.dat: error
+    TC_filter_F070W.dat: error
+    TC_filter_F187N.dat: error
+    TC_filter_F210M.dat: error
+    TC_filter_F115W.dat: error
+    TC_filter_F162M.dat: error
+    TC_filter_F200W.dat: error
+    TC_filter_F212N.dat: error
+    TC_filter_F150W2.dat: error
+    TC_filter_F140M.dat: error
+    TC_filter_F150W.dat: error
+    TC_filter_F164N.dat: error
+    TC_filter_F090W.dat: error
+    TC_filter_F182M.dat: error
+ERIS:
+  package_type: support
+  structure:
+    self_named_yaml: not found
+    no_files_referenced: '!NONE'
+  contents:
+    no_yaml_files: '!NONE'
+LaPalma:
   package_type: support
   structure:
     self_named_yaml: found
     no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    LIST_VLT_mirrors.dat: error
-    TC_mirror_aluminium.dat: error
-WFC3:
+    orm_extinct.dat: error
+FORS:
+  package_type: support
+  structure:
+    self_named_yaml: not found
+    no_files_referenced: '!NONE'
+  contents:
+    no_yaml_files: '!NONE'
+GTC:
+  package_type: support
+  structure:
+    self_named_yaml: found
+    no_missing_files: '!OK'
+  contents:
+    all_yamls_readable: '!OK'
+    reflectivity_GTC.dat: error
+    unity.dat: error
+LFOA:
   package_type: observation
   structure:
     default_yaml: OK
@@ -527,30 +533,28 @@ WFC3:
     no_missing_files: '!OK'
   contents:
     all_yamls_readable: '!OK'
-    FPA_linearity.dat: error
-    LIST_WFC3_mirrors_IR.dat: error
-    wfc3_ir_cor_004_syn.dat: error
-    wfc3_ir_csm_001_syn.dat: error
-    wfc3_ir_dn_002_syn.dat: error
-    wfc3_ir_fold_001_syn.dat: error
-    wfc3_ir_mask_001_syn.dat: error
-    wfc3_ir_mir1_001_syn.dat: error
-    wfc3_ir_mir2_001_syn.dat: error
-    wfc3_ir_qe_003_syn.dat: error
-    wfc3_ir_rcp_001_syn.dat: error
-    wfc3_ir_win_001_syn.dat: error
-    TER_filter_F098M.dat: error
-    TER_filter_F105W.dat: error
-    TER_filter_F110W.dat: error
-    TER_filter_F125W.dat: error
-    TER_filter_F126N.dat: error
-    TER_filter_F127M.dat: error
-    TER_filter_F128N.dat: error
-    TER_filter_F130N.dat: error
-    TER_filter_F132N.dat: error
-    TER_filter_F139M.dat: error
-    TER_filter_F140W.dat: error
-    TER_filter_F153M.dat: error
-    TER_filter_F160W.dat: error
-    TER_filter_F164N.dat: error
-    TER_filter_F167N.dat: error
+    LIST_LFOA_mirrors_static.dat: error
+    QE_SBIG.dat: error
+    TER_mirror_aluminium.dat: error
+    TER_focal_reducer.dat: error
+    TER_atmosphere.dat: error
+    U.dat: error
+    SII.dat: error
+    sloan_i.dat: error
+    B.dat: error
+    R.dat: error
+    OIII.dat: error
+    sloan_g.dat: error
+    I.dat: error
+    Halpha_wide.dat: error
+    sloan_z.dat: error
+    V.dat: error
+    Hbeta.dat: error
+    Halpha_narrow.dat: error
+    sloan_r.dat: error
+    sloan_u.dat: error
+    TC_filter_Halpha_wide_old.dat: error
+    TC_filter_Halpha_narrow_old.dat: error
+    TC_filter_Hbeta_old.dat: error
+    TC_filter_OIII_old.dat: error
+    TC_filter_SII_old.dat: error

From 1677e74cef39468085d3fb477464f676761dbf9f Mon Sep 17 00:00:00 2001
From: "Fabian H." <73600109+teutoburg@users.noreply.github.com>
Date: Tue, 22 Sep 2026 14:03:35 +0200
Subject: [PATCH 36/63] Correct contact email addresses in docs

---
 docs/ScopeSim_guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/ScopeSim_guide.md b/docs/ScopeSim_guide.md
index 9eb81bc7..41c30c1f 100644
--- a/docs/ScopeSim_guide.md
+++ b/docs/ScopeSim_guide.md
@@ -52,4 +52,4 @@ jupyter notebook <Notebook.ipynb>
 
 - [ScopeSim Slack](https://join.slack.com/t/scopesim/shared_invite/zt-143s42izo-LnyqoG7gH5j~aGn51Z~4IA)
 - [GitHub Issues](https://github.com/AstarVienna/irdb/issues)
-- Email: scopesim@univie.ac.at · kieran.leschinski@univie.ac.at
+- Email: astar.astro@univie.ac.at · kieran.leschinski@univie.ac.at

From 3a4cb99af60eb90630bb576f00a5c96b6c0b2e5d Mon Sep 17 00:00:00 2001
From: Yashvi-Sharma <yashuvatsas@gmail.com>
Date: Wed, 14 Oct 2026 23:01:03 -0700
Subject: [PATCH 37/63] stubbed out zimager mode

---
 ZShooter_v2/ZShooter_IMAGER.yaml              |  75 ++++++
 ZShooter_v2/ZShooter_IMDET.yaml               | 214 ++++++++++++++++++
 ZShooter_v2/default.yaml                      |  14 ++
 .../dichroics/zimage_dichroic_tree.dat        |   8 +
 4 files changed, 311 insertions(+)
 create mode 100755 ZShooter_v2/ZShooter_IMAGER.yaml
 create mode 100644 ZShooter_v2/ZShooter_IMDET.yaml
 create mode 100644 ZShooter_v2/dichroics/zimage_dichroic_tree.dat

diff --git a/ZShooter_v2/ZShooter_IMAGER.yaml b/ZShooter_v2/ZShooter_IMAGER.yaml
new file mode 100755
index 00000000..dc9992de
--- /dev/null
+++ b/ZShooter_v2/ZShooter_IMAGER.yaml
@@ -0,0 +1,75 @@
+object: instrument
+alias: INST
+name: ZIMAGE
+description: Effects from the ZIMAGE optics
+
+#properties:
+
+# There are 3 image planes in ZIMAGE: 3 filters. We use the DichroicTree effect to establish the branching
+# in the beginning of the flow, using aperture_id as the identifying key. Though physically there may be other
+# optical elements in between dichroics, here all dichroics are considered together in a tree. Other effects that
+# act selectively on different arms can be orchestrated using SelectorWheel effects.
+
+effects:
+ - name: dichroic_tree
+   class: DichroicTree
+   include: True
+   kwargs:
+     tree_filename: "dichroics/zimage_dichroic_tree.dat"
+     dichroic_names:
+       - A-BC     # First dichroic, splits beam into A and BC
+       - B-C      # Next dichroic in BC arm, splits B from C
+     filename_format: "dichroics/TER_dichroic_{}.dat"
+
+ - name: channel_optics_selector
+   description: ZIMAGE channel optics
+   class: SelectorWheel
+   include: True
+   kwargs:
+     z_order: [610]
+     selector_key: "aperture_id"
+     wheel:
+       - selector_value: [0]
+         effect_class: SurfaceList
+         effect_kwargs:
+           filename: "optics/LIST_zimage_filter_A_optics.dat"
+
+       - selector_value: [1]
+         effect_class: SurfaceList
+         effect_kwargs:
+           filename: "optics/LIST_zimage_filter_B_optics.dat"
+
+       - selector_value: [2]
+         effect_class: SurfaceList
+         effect_kwargs:
+           filename: "optics/LIST_zimage_filter_C_optics.dat"
+
+
+# - name: ir_blocking_filter_selector
+#   description: nIR blocking filter
+#   class: SelectorWheel
+#   include: False
+#   kwargs:
+#     z_order: [610]
+#     selector_key: "aperture_id"
+#     allowed_missing_selector_values: [ 0, 1, 2 ]
+#     emission_phase: post_disperser
+#     wheel:
+#       - selector_value: [ 3 ]
+#         effect_class: TERCurve
+#         effect_kwargs:
+#           temperature: -198
+#           temperature_unit: deg_D
+#           filename: "optics/TER_nir_blocking_filtYJ.dat"
+#       - selector_value: [ 4 ]
+#         effect_class: TERCurve
+#         effect_kwargs:
+#           temperature: -198
+#           temperature_unit: deg_D
+#           filename: "optics/TER_nir_blocking_filtH.dat"
+#       - selector_value: [ 5 ]
+#         effect_class: TERCurve
+#         effect_kwargs:
+#           temperature: -198
+#           temperature_unit: deg_D
+#           filename: "optics/TER_nir_blocking_filtK.dat"
\ No newline at end of file
diff --git a/ZShooter_v2/ZShooter_IMDET.yaml b/ZShooter_v2/ZShooter_IMDET.yaml
new file mode 100644
index 00000000..62b9d4a0
--- /dev/null
+++ b/ZShooter_v2/ZShooter_IMDET.yaml
@@ -0,0 +1,214 @@
+### ZShooter_v2 DETECTORs for ZIMAGE
+object : detector
+alias : DET
+name : zimage_detectors
+description : 3 CMOS
+
+properties :
+    temperature : -120
+    binx : 1
+    biny: 1
+    full_well: 64000
+    # Per-image-plane scales. These currently match the global ZShooter
+    # values in ZShooter.yaml, but live here so each camera can diverge when
+    # the final channel optics are available.
+    pixel_scale_A: 0.1570545
+    pixel_scale_B: 0.1570545
+    pixel_scale_C: 0.1570545
+    plate_scale_A: 10.4703
+    plate_scale_B: 10.4703
+    plate_scale_C: 10.4703
+
+effects :
+  - name: detector_A
+    include: True
+    class: DetectorList
+    kwargs:
+        image_plane_id : 0
+        pixel_scale: "!DET.pixel_scale_A"
+        plate_scale: "!DET.plate_scale_A"
+        array_dict:
+          id: [0]
+          x_cen: [0.0]
+          y_cen: [0.0]
+#          x_size: [4096]
+#          y_size: [4096]
+#          pixel_size: [0.015]
+          angle: [0.0]
+          gain: [1.0]
+        x_cen_unit: mm
+        y_cen_unit: mm
+        x_size_unit: pixel
+        y_size_unit: pixel
+        pixel_size_unit: mm
+        angle_unit: deg
+        gain_unit: electron/adu
+        detector: "CMOS_A"
+
+  - name: detector_B
+    include: True
+    class: DetectorList
+    kwargs:
+        image_plane_id : 1
+        pixel_scale: "!DET.pixel_scale_B"
+        plate_scale: "!DET.plate_scale_B"
+        array_dict:
+          id: [1]
+          x_cen: [0.0]
+          y_cen: [0.0]
+#          x_size: [2048]
+#          y_size: [4096]
+#          pixel_size: [0.015]
+          angle: [0.0]
+          gain: [1.0]
+        x_cen_unit: mm
+        y_cen_unit: mm
+        x_size_unit: pixel
+        y_size_unit: pixel
+        pixel_size_unit: mm
+        angle_unit: deg
+        gain_unit: electron/adu
+        detector: "CMOS_B"
+
+  - name: detector_C
+    include: True
+    class: DetectorList
+    kwargs:
+        image_plane_id : 2
+        pixel_scale: "!DET.pixel_scale_C"
+        plate_scale: "!DET.plate_scale_C"
+        array_dict:
+          id: [2]
+          x_cen: [0.0]
+          y_cen: [0.0]
+#          x_size: [2048]
+#          y_size: [4096]
+#          pixel_size: [0.015]
+          angle: [0.0]
+          gain: [1.0]
+        x_cen_unit: mm
+        y_cen_unit: mm
+        x_size_unit: pixel
+        y_size_unit: pixel
+        pixel_size_unit: mm
+        angle_unit: deg
+        gain_unit: electron/adu
+        detector: "CMOS_C"
+
+  - name: exposure_integration_selector
+    description: Summing up sky signal for all DITs and NDITs for different detectors
+    class: SelectorWheel
+    include: True
+    kwargs:
+      selector_key: "detector_id" # not really used as the Detector object's id is stored as just "id", but kept for consistency
+      wheel:
+        - selector_value: [0]
+          effect_class: ExposureIntegration
+          effect_kwargs:
+            dit: "!OBS.imager_dit_a"
+            ndit: "!OBS.imager_ndit_a"
+        - selector_value: [1]
+          effect_class: ExposureIntegration
+          effect_kwargs:
+            dit: "!OBS.imager_dit_b"
+            ndit: "!OBS.imager_ndit_b"
+        - selector_value: [2]
+          effect_class: ExposureIntegration
+          effect_kwargs:
+            dit: "!OBS.imager_dit_c"
+            ndit: "!OBS.imager_ndit_c"
+
+
+  - name: shot_noise
+    description : apply poisson shot noise to images
+    class: ShotNoise
+
+  - name: dark_current_selector
+    description: Selector Wheel for dark current levels for CMOS
+    class: SelectorWheel
+    include: True
+    kwargs:
+      selector_key: "detector_id"
+      wheel:
+        - selector_value: [0]
+          effect_class: DarkCurrent
+          effect_kwargs:
+            value: 0.0001    # [e-/s] level of dark current
+            dit: "!OBS.imager_dit_a"
+            ndit: "!OBS.imager_ndit_a"
+        - selector_value: [1]
+          effect_class: DarkCurrent
+          effect_kwargs:
+            value: 0.0001    # [e-/s] level of dark current
+            dit: "!OBS.imager_dit_b"
+            ndit: "!OBS.imager_ndit_b"
+        - selector_value: [2]
+          effect_class: DarkCurrent
+          effect_kwargs:
+            value: 0.0001     # [e-/s] level of dark current
+            dit: "!OBS.imager_dit_c"
+            ndit: "!OBS.imager_ndit_c"
+
+  - name: detector_linearity_selector
+    description: Selector Wheel for linearity characteristics for CMOS
+    class: SelectorWheel
+    include: False
+    kwargs:
+      selector_key: "detector_id" # not really used as the Detector object's id is stored as just "id", but kept for consistency
+      wheel:
+        - selector_value: [0]
+          effect_class: LinearityCurve
+          effect_kwargs:
+            array_dict:
+              incident: [0, 64e3]
+              measured: [0, 64e3]
+            ndit: "!OBS.imager_ndit_a"
+        - selector_value: [1]
+          effect_class: LinearityCurve
+          effect_kwargs:
+            array_dict:
+              incident: [0, 64e3]
+              measured: [0, 64e3]
+            ndit: "!OBS.imager_ndit_b"
+        - selector_value: [2]
+          effect_class: LinearityCurve
+          effect_kwargs:
+            array_dict:
+              incident: [0, 64e3]
+              measured: [0, 64e3]
+            ndit: "!OBS.imager_ndit_c"
+
+  - name: readout_noise_selector
+    description: Selector Wheel for readout noise levels for CMOS
+    class: SelectorWheel
+    include: True
+    kwargs:
+      selector_key: "detector_id" # not really used as the Detector object's id is stored as just "id", but kept for consistency
+      wheel:
+        - selector_value: [0]
+          effect_class: BasicReadoutNoise
+          effect_kwargs:
+            noise_std: 0.1
+            ndit: "!OBS.imager_ndit_a"
+        - selector_value: [1]
+          effect_class: BasicReadoutNoise
+          effect_kwargs:
+            noise_std: 0.1
+            ndit: "!OBS.imager_ndit_b"
+        - selector_value: [2]
+          effect_class: BasicReadoutNoise
+          effect_kwargs:
+            noise_std: 0.1
+            ndit: "!OBS.imager_ndit_c"
+
+  - name: bias_selector
+    description: Selector Wheel for bias levels for CMOS
+    class: SelectorWheel
+    include: True
+    kwargs:
+      selector_key: "detector_id" # not really used as the Detector object's id is stored as just "id", but kept for consistency
+      wheel:
+        - selector_value: [0,1,2]   # CCD detectors
+          effect_class: Bias
+          effect_kwargs:
+            bias: 1040    # CCD bias level in electrons
diff --git a/ZShooter_v2/default.yaml b/ZShooter_v2/default.yaml
index 4aae908a..95b475fc 100755
--- a/ZShooter_v2/default.yaml
+++ b/ZShooter_v2/default.yaml
@@ -40,6 +40,12 @@ properties:
   ndit_h: 1                # number of exposures for H arms
   dit_k: 300.0              # exposure time for K arms
   ndit_k: 1                # number of exposures for K arms
+  imager_dit_a: 10.
+  imager_ndit_a: 1.
+  imager_dit_b: 10.
+  imager_ndit_b: 1.
+  imager_dit_c: 10.
+  imager_ndit_c: 1.
 
 mode_yamls:
 - object: instrument
@@ -50,6 +56,14 @@ mode_yamls:
     - ZShooter_SPEC.yaml
     - ZShooter_DET.yaml
 
+- object: instrument
+  alias: OBS
+  name: IMAGER
+  description: "imaging only mode"
+  yamls:
+    - ZShooter_IMAGER.yaml
+    - ZShooter_IMDET.yaml
+
 ---
 ### default simulation parameters needed for a MICADO simulation
 object : simulation
diff --git a/ZShooter_v2/dichroics/zimage_dichroic_tree.dat b/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
new file mode 100644
index 00000000..f090ef7f
--- /dev/null
+++ b/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
@@ -0,0 +1,8 @@
+# name : ZShooter_v2 dichroic tree
+# author : Y. Sharma
+# date_created : 2026-10-14
+# description : Image plane ID mapping 0->A, 1->B, 2->C
+aperture_id        A-BC     B-C
+0                    R       X
+1                    T       R
+2                    T       T

From f3e2dd4cdc37cab9979e6feca5bb07aba44d522f Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Thu, 15 Oct 2026 13:31:08 -0700
Subject: [PATCH 38/63] **Add ZImager optics and dichroics, update detector
 configurations**

- Introduced detailed optics files for ZImager `u`, `v`, and `z` bands with updated throughput lists.
- Added dichroic tree for ZImager to split beams for `u-vz` and `v-z` transitions, including the associated spectral curves and parameters.
- Updated `ZShooter_IMAGER.yaml` to include new dichroics, optics, and detector throughput selection.
- Revised `ZShooter_IMDET.yaml` to adjust temperature, binning, pixel scales, and plate scales for `CMOS` detectors.
- Adjusted `make_dichroics.py` to generate new dichroic configuration files for `u`, `v`, and `z` bands.
---
 ZShooter_v2/ZShooter_IMAGER.yaml              |   57 +-
 ZShooter_v2/ZShooter_IMDET.yaml               |  123 +-
 ZShooter_v2/detector_specs/quest2_TER.dat     |  212 ++
 ZShooter_v2/dichroics/TER_dichroic_u-v.dat    | 2209 +++++++++++++++++
 ZShooter_v2/dichroics/TER_dichroic_v-z.dat    | 2209 +++++++++++++++++
 ZShooter_v2/dichroics/make_dichroics.py       |   32 +-
 .../dichroics/zimage_dichroic_tree.dat        |    4 +-
 .../optics/LIST_zimage_common_optics.dat      |   18 +
 ZShooter_v2/optics/LIST_zimage_u_optics.dat   |   18 +
 ZShooter_v2/optics/LIST_zimage_v_optics.dat   |   19 +
 ZShooter_v2/optics/LIST_zimage_z_optics.dat   |   18 +
 11 files changed, 4818 insertions(+), 101 deletions(-)
 create mode 100644 ZShooter_v2/detector_specs/quest2_TER.dat
 create mode 100644 ZShooter_v2/dichroics/TER_dichroic_u-v.dat
 create mode 100644 ZShooter_v2/dichroics/TER_dichroic_v-z.dat
 create mode 100644 ZShooter_v2/optics/LIST_zimage_common_optics.dat
 create mode 100644 ZShooter_v2/optics/LIST_zimage_u_optics.dat
 create mode 100644 ZShooter_v2/optics/LIST_zimage_v_optics.dat
 create mode 100644 ZShooter_v2/optics/LIST_zimage_z_optics.dat

diff --git a/ZShooter_v2/ZShooter_IMAGER.yaml b/ZShooter_v2/ZShooter_IMAGER.yaml
index dc9992de..20e40712 100755
--- a/ZShooter_v2/ZShooter_IMAGER.yaml
+++ b/ZShooter_v2/ZShooter_IMAGER.yaml
@@ -11,14 +11,20 @@ description: Effects from the ZIMAGE optics
 # act selectively on different arms can be orchestrated using SelectorWheel effects.
 
 effects:
+ - name: preoptics
+   class: SurfaceList
+   include: True
+   kwargs:
+     filename: "optics/LIST_zimage_common_optics.dat"
+
  - name: dichroic_tree
    class: DichroicTree
    include: True
    kwargs:
      tree_filename: "dichroics/zimage_dichroic_tree.dat"
      dichroic_names:
-       - A-BC     # First dichroic, splits beam into A and BC
-       - B-C      # Next dichroic in BC arm, splits B from C
+       - u-vz     # First dichroic, splits beam into u and red
+       - v-z     # Next dichroic, splits z band light
      filename_format: "dichroics/TER_dichroic_{}.dat"
 
  - name: channel_optics_selector
@@ -32,44 +38,27 @@ effects:
        - selector_value: [0]
          effect_class: SurfaceList
          effect_kwargs:
-           filename: "optics/LIST_zimage_filter_A_optics.dat"
+           filename: "optics/LIST_zimage_u_optics.dat"
 
        - selector_value: [1]
          effect_class: SurfaceList
          effect_kwargs:
-           filename: "optics/LIST_zimage_filter_B_optics.dat"
+           filename: "optics/LIST_zimage_v_optics.dat"
 
        - selector_value: [2]
          effect_class: SurfaceList
          effect_kwargs:
-           filename: "optics/LIST_zimage_filter_C_optics.dat"
+           filename: "optics/LIST_zimage_z_optics.dat"
 
-
-# - name: ir_blocking_filter_selector
-#   description: nIR blocking filter
-#   class: SelectorWheel
-#   include: False
-#   kwargs:
-#     z_order: [610]
-#     selector_key: "aperture_id"
-#     allowed_missing_selector_values: [ 0, 1, 2 ]
-#     emission_phase: post_disperser
-#     wheel:
-#       - selector_value: [ 3 ]
-#         effect_class: TERCurve
-#         effect_kwargs:
-#           temperature: -198
-#           temperature_unit: deg_D
-#           filename: "optics/TER_nir_blocking_filtYJ.dat"
-#       - selector_value: [ 4 ]
-#         effect_class: TERCurve
-#         effect_kwargs:
-#           temperature: -198
-#           temperature_unit: deg_D
-#           filename: "optics/TER_nir_blocking_filtH.dat"
-#       - selector_value: [ 5 ]
-#         effect_class: TERCurve
-#         effect_kwargs:
-#           temperature: -198
-#           temperature_unit: deg_D
-#           filename: "optics/TER_nir_blocking_filtK.dat"
\ No newline at end of file
+ - name: detector_qe_selector
+   description: Throughput-only QE curves for different detectors
+   class: SelectorWheel
+   include: True
+   kwargs:
+     z_order: [610]
+     selector_key: "aperture_id"
+     wheel:
+       - selector_value: [0, 1, 2]
+         effect_class: SpectralQuantumEfficiency
+         effect_kwargs:
+           filename: "detector_specs/quest2_TER.dat"
\ No newline at end of file
diff --git a/ZShooter_v2/ZShooter_IMDET.yaml b/ZShooter_v2/ZShooter_IMDET.yaml
index 62b9d4a0..6c5cf5cd 100644
--- a/ZShooter_v2/ZShooter_IMDET.yaml
+++ b/ZShooter_v2/ZShooter_IMDET.yaml
@@ -5,35 +5,32 @@ name : zimage_detectors
 description : 3 CMOS
 
 properties :
-    temperature : -120
+    temperature : -35
     binx : 1
     biny: 1
-    full_well: 64000
-    # Per-image-plane scales. These currently match the global ZShooter
-    # values in ZShooter.yaml, but live here so each camera can diverge when
-    # the final channel optics are available.
-    pixel_scale_A: 0.1570545
-    pixel_scale_B: 0.1570545
-    pixel_scale_C: 0.1570545
-    plate_scale_A: 10.4703
-    plate_scale_B: 10.4703
-    plate_scale_C: 10.4703
+    full_well: 7000
+    pixel_scale_A: 0.049922  #"/pix
+    pixel_scale_B: 0.049922
+    pixel_scale_C: 0.049922
+    plate_scale_A: 10.851 #"/mm
+    plate_scale_B: 10.851
+    plate_scale_C: 10.851
 
 effects :
-  - name: detector_A
+  - name: detector_u
     include: True
     class: DetectorList
     kwargs:
         image_plane_id : 0
-        pixel_scale: "!DET.pixel_scale_A"
-        plate_scale: "!DET.plate_scale_A"
+        pixel_scale: "!DET.pixel_scale_u"
+        plate_scale: "!DET.plate_scale_u"
         array_dict:
           id: [0]
           x_cen: [0.0]
           y_cen: [0.0]
-#          x_size: [4096]
-#          y_size: [4096]
-#          pixel_size: [0.015]
+          x_size: [xxx]
+          y_size: [2304]
+          pixel_size: [0.0046]
           angle: [0.0]
           gain: [1.0]
         x_cen_unit: mm
@@ -43,22 +40,22 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "CMOS_A"
+        detector: "CMOS_u"
 
-  - name: detector_B
+  - name: detector_v
     include: True
     class: DetectorList
     kwargs:
         image_plane_id : 1
-        pixel_scale: "!DET.pixel_scale_B"
-        plate_scale: "!DET.plate_scale_B"
+        pixel_scale: "!DET.pixel_scale_v"
+        plate_scale: "!DET.plate_scale_v"
         array_dict:
           id: [1]
           x_cen: [0.0]
           y_cen: [0.0]
-#          x_size: [2048]
-#          y_size: [4096]
-#          pixel_size: [0.015]
+          x_size: [xxx]
+          y_size: [2304]
+          pixel_size: [0.0046]
           angle: [0.0]
           gain: [1.0]
         x_cen_unit: mm
@@ -68,22 +65,22 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "CMOS_B"
+        detector: "CMOS_v"
 
-  - name: detector_C
+  - name: detector_z
     include: True
     class: DetectorList
     kwargs:
         image_plane_id : 2
-        pixel_scale: "!DET.pixel_scale_C"
-        plate_scale: "!DET.plate_scale_C"
+        pixel_scale: "!DET.pixel_scale_z"
+        plate_scale: "!DET.plate_scale_z"
         array_dict:
           id: [2]
           x_cen: [0.0]
           y_cen: [0.0]
-#          x_size: [2048]
-#          y_size: [4096]
-#          pixel_size: [0.015]
+          x_size: [xxx]
+          y_size: [2304]
+          pixel_size: [0.0046]
           angle: [0.0]
           gain: [1.0]
         x_cen_unit: mm
@@ -93,7 +90,7 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "CMOS_C"
+        detector: "CMOS_z"
 
   - name: exposure_integration_selector
     description: Summing up sky signal for all DITs and NDITs for different detectors
@@ -105,18 +102,18 @@ effects :
         - selector_value: [0]
           effect_class: ExposureIntegration
           effect_kwargs:
-            dit: "!OBS.imager_dit_a"
-            ndit: "!OBS.imager_ndit_a"
+            dit: "!OBS.imager_dit_u"
+            ndit: "!OBS.imager_ndit_u"
         - selector_value: [1]
           effect_class: ExposureIntegration
           effect_kwargs:
-            dit: "!OBS.imager_dit_b"
-            ndit: "!OBS.imager_ndit_b"
+            dit: "!OBS.imager_dit_v"
+            ndit: "!OBS.imager_ndit_v"
         - selector_value: [2]
           effect_class: ExposureIntegration
           effect_kwargs:
-            dit: "!OBS.imager_dit_c"
-            ndit: "!OBS.imager_ndit_c"
+            dit: "!OBS.imager_dit_z"
+            ndit: "!OBS.imager_ndit_z"
 
 
   - name: shot_noise
@@ -133,21 +130,21 @@ effects :
         - selector_value: [0]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.0001    # [e-/s] level of dark current
-            dit: "!OBS.imager_dit_a"
-            ndit: "!OBS.imager_ndit_a"
+            value: 0.006    # [e-/s] level of dark current
+            dit: "!OBS.imager_dit_u"
+            ndit: "!OBS.imager_ndit_u"
         - selector_value: [1]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.0001    # [e-/s] level of dark current
-            dit: "!OBS.imager_dit_b"
-            ndit: "!OBS.imager_ndit_b"
+            value: 0.006    # [e-/s] level of dark current
+            dit: "!OBS.imager_dit_v"
+            ndit: "!OBS.imager_ndit_v"
         - selector_value: [2]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.0001     # [e-/s] level of dark current
-            dit: "!OBS.imager_dit_c"
-            ndit: "!OBS.imager_ndit_c"
+            value: 0.006     # [e-/s] level of dark current
+            dit: "!OBS.imager_dit_z"
+            ndit: "!OBS.imager_ndit_z"
 
   - name: detector_linearity_selector
     description: Selector Wheel for linearity characteristics for CMOS
@@ -160,23 +157,23 @@ effects :
           effect_class: LinearityCurve
           effect_kwargs:
             array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.imager_ndit_a"
+              incident: [0, 7e3]
+              measured: [0, 7e3]
+            ndit: "!OBS.imager_ndit_u"
         - selector_value: [1]
           effect_class: LinearityCurve
           effect_kwargs:
             array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.imager_ndit_b"
+              incident: [0, 7e3]
+              measured: [0, 7e3]
+            ndit: "!OBS.imager_ndit_v"
         - selector_value: [2]
           effect_class: LinearityCurve
           effect_kwargs:
             array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.imager_ndit_c"
+              incident: [0, 7e3]
+              measured: [0, 7e3]
+            ndit: "!OBS.imager_ndit_z"
 
   - name: readout_noise_selector
     description: Selector Wheel for readout noise levels for CMOS
@@ -188,18 +185,18 @@ effects :
         - selector_value: [0]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.1
-            ndit: "!OBS.imager_ndit_a"
+            noise_std: 0.3
+            ndit: "!OBS.imager_ndit_u"
         - selector_value: [1]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.1
-            ndit: "!OBS.imager_ndit_b"
+            noise_std: 0.3
+            ndit: "!OBS.imager_ndit_v"
         - selector_value: [2]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.1
-            ndit: "!OBS.imager_ndit_c"
+            noise_std: 0.3
+            ndit: "!OBS.imager_ndit_z"
 
   - name: bias_selector
     description: Selector Wheel for bias levels for CMOS
@@ -211,4 +208,4 @@ effects :
         - selector_value: [0,1,2]   # CCD detectors
           effect_class: Bias
           effect_kwargs:
-            bias: 1040    # CCD bias level in electrons
+            bias: 0    # CCD bias level in electrons
diff --git a/ZShooter_v2/detector_specs/quest2_TER.dat b/ZShooter_v2/detector_specs/quest2_TER.dat
new file mode 100644
index 00000000..5a489332
--- /dev/null
+++ b/ZShooter_v2/detector_specs/quest2_TER.dat
@@ -0,0 +1,212 @@
+# author : Jeb Bailey
+# sources : Quest2 Datasheet plot
+# date_created  : 2025-12-07
+# date_modified : 2025-12-07
+# type : detector:quantum_efficiency
+# status : estimate
+# description : Hamamatsu Orca-Quest2 QE (includes window)
+# action : transmission
+# wavelength_unit : nm
+# notes :
+# - Extracted programatically
+wavelength  transmission
+250.0 0.02492054071900562
+254.2713567839196 0.034248942566097678
+258.5427135678392 0.035989663125943148
+262.8140703517588 0.039500750443898904
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diff --git a/ZShooter_v2/dichroics/TER_dichroic_u-v.dat b/ZShooter_v2/dichroics/TER_dichroic_u-v.dat
new file mode 100644
index 00000000..ba5f3717
--- /dev/null
+++ b/ZShooter_v2/dichroics/TER_dichroic_u-v.dat
@@ -0,0 +1,2209 @@
+# name : zimager_u_dichroic
+# author : unknown
+# date_created : 2026-10-15T12:00:00
+# date_modified : 2026-10-15T19:57:00.562
+# description : Transmits red, reflects u
+# wavelength_unit : um
+# changes :
+#
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diff --git a/ZShooter_v2/dichroics/TER_dichroic_v-z.dat b/ZShooter_v2/dichroics/TER_dichroic_v-z.dat
new file mode 100644
index 00000000..807586d7
--- /dev/null
+++ b/ZShooter_v2/dichroics/TER_dichroic_v-z.dat
@@ -0,0 +1,2209 @@
+# name : zimager_z_dichroic
+# author : unknown
+# date_created : 2026-10-15T12:00:00
+# date_modified : 2026-10-15T19:57:00.562
+# description : Logpass at z, reflects shorter wavelengths 
+# wavelength_unit : um
+# changes :
+#
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+2.452 0.000000 0.000000
+2.453 0.000000 0.000000
+2.454 0.000000 0.000000
+2.455 0.000000 0.000000
+2.456 0.000000 0.000000
+2.457 0.000000 0.000000
+2.458 0.000000 0.000000
+2.459 0.000000 0.000000
+2.460 0.000000 0.000000
+2.461 0.000000 0.000000
+2.462 0.000000 0.000000
+2.463 0.000000 0.000000
+2.464 0.000000 0.000000
+2.465 0.000000 0.000000
+2.466 0.000000 0.000000
+2.467 0.000000 0.000000
+2.468 0.000000 0.000000
+2.469 0.000000 0.000000
+2.470 0.000000 0.000000
+2.471 0.000000 0.000000
+2.472 0.000000 0.000000
+2.473 0.000000 0.000000
+2.474 0.000000 0.000000
+2.475 0.000000 0.000000
+2.476 0.000000 0.000000
+2.477 0.000000 0.000000
+2.478 0.000000 0.000000
+2.479 0.000000 0.000000
+2.480 0.000000 0.000000
+2.481 0.000000 0.000000
+2.482 0.000000 0.000000
+2.483 0.000000 0.000000
+2.484 0.000000 0.000000
+2.485 0.000000 0.000000
+2.486 0.000000 0.000000
+2.487 0.000000 0.000000
+2.488 0.000000 0.000000
+2.489 0.000000 0.000000
+2.490 0.000000 0.000000
+2.491 0.000000 0.000000
+2.492 0.000000 0.000000
+2.493 0.000000 0.000000
+2.494 0.000000 0.000000
+2.495 0.000000 0.000000
+2.496 0.000000 0.000000
+2.497 0.000000 0.000000
+2.498 0.000000 0.000000
+2.499 0.000000 0.000000
diff --git a/ZShooter_v2/dichroics/make_dichroics.py b/ZShooter_v2/dichroics/make_dichroics.py
index 9f846ad9..c0064f85 100644
--- a/ZShooter_v2/dichroics/make_dichroics.py
+++ b/ZShooter_v2/dichroics/make_dichroics.py
@@ -96,6 +96,8 @@ def split(name: str,
         "yj-hk": split("yj-hk", ("yj",), ("h", "k")),
         "g-r": split("g-r", ("g",), ("r",)),
         "h-k": split("h-k", ("h",), ("k",)),
+        "u-vz": split("b-gr", ("u",), ("v", "z")),
+        "v-z": split("v-z", ("v",), ("z",)),
     }
 
 
@@ -136,17 +138,43 @@ def read_channel_ranges(trace_parameters_path: Path) -> dict[str, u.Quantity]:
              'b-gr': 10*u.nm,
              'g-r': 12 * u.nm,
              'yj-hk': 30 * u.nm,
-             'h-k': 40 * u.nm}
+             'h-k': 40 * u.nm,
+             'u-vz': 10 * u.nm,
+             'v-z': 12 * u.nm}
 
 TRACE_PARAMETERS_PATH = DICHROIC_DIR.parent / "traces" / "echelle_trace_parameters.dat"
 
 
 
 channel_range = read_channel_ranges(TRACE_PARAMETERS_PATH)
-
+channel_range['u']=u.Quantity([300, 400])*u.nm
+channel_range['v']=u.Quantity([400, 850])*u.nm
+channel_range['z']=u.Quantity([850, 1100])*u.nm
 dichroics = make_zshooter_dichroics(channel_range, DICHROIC_DOMAIN, DICHROIC_TRANS, DICHROIC_REFL, DICHROIC_PAD,
                                     transition_widths=DICHROIC_TRANS_WID)
 
+
+
+##  ZImager dichroics:
+hdr_zi_u = f"""# name : zimager_u_dichroic
+# author : unknown
+# date_created : 2026-10-15T12:00:00
+# date_modified : {Time.now().isot}
+# description : Transmits red, reflects u
+# wavelength_unit : um
+# changes :
+#"""
+hdr_zi_z = f"""# name : zimager_z_dichroic
+# author : unknown
+# date_created : 2026-10-15T12:00:00
+# date_modified : {Time.now().isot}
+# description : Logpass at z, reflects shorter wavelengths 
+# wavelength_unit : um
+# changes :
+#"""
+write_dichroic_curves(DICHROIC_DIR / 'TER_dichroic_u-v.dat', dichroics['u-vz'][1]._model, dichroics['u-vz'][0]._model, header=hdr_zi_u)
+write_dichroic_curves(DICHROIC_DIR / 'TER_dichroic_v-z.dat', dichroics['v-z'][1]._model, dichroics['v-z'][0]._model, header=hdr_zi_z)
+
 ## First dichroic: I, splits BGR from YJHK
 hdr_i = f"""# name : first_dichroic
 # author : unknown
diff --git a/ZShooter_v2/dichroics/zimage_dichroic_tree.dat b/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
index f090ef7f..201fdf0c 100644
--- a/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
+++ b/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
@@ -1,8 +1,8 @@
 # name : ZShooter_v2 dichroic tree
 # author : Y. Sharma
 # date_created : 2026-10-14
-# description : Image plane ID mapping 0->A, 1->B, 2->C
-aperture_id        A-BC     B-C
+# description : Image plane ID mapping 0->u, 1->v, 2->z
+aperture_id        u-vz     v-z
 0                    R       X
 1                    T       R
 2                    T       T
diff --git a/ZShooter_v2/optics/LIST_zimage_common_optics.dat b/ZShooter_v2/optics/LIST_zimage_common_optics.dat
new file mode 100644
index 00000000..1204a203
--- /dev/null
+++ b/ZShooter_v2/optics/LIST_zimage_common_optics.dat
@@ -0,0 +1,18 @@
+# author : Jeb Bailey
+# date_created  : 2026-10-15
+# date_modified : 2026-10-15
+# status : ZShooter ZImager common optics throughput list
+# type : mirror:list
+# outer_unit : m
+# inner_unit : m
+# angle_unit : degree
+# temperature_unit  : deg_C
+# changes :
+# - 2026-10-15 : created
+#
+name                  outer   inner   angle   temperature   action         filename
+VIS_Fold              0.09    0.0     0       0             reflection     optics/TER_vis_armirror.dat
+VIS_field             0.09    0.0     0       0             transmission   optics/TER_vis_arcoat.dat
+VIS_Fold_2            0.09    0.0     0       0             reflection     optics/TER_vis_armirror.dat
+VIS_ADC_12            0.09    0.0     0       0             transmission   optics/TER_vis_arcoat.dat
+VIS_ADC_34            0.09    0.0     0       0             transmission   optics/TER_vis_arcoat.dat
diff --git a/ZShooter_v2/optics/LIST_zimage_u_optics.dat b/ZShooter_v2/optics/LIST_zimage_u_optics.dat
new file mode 100644
index 00000000..b31fcbc5
--- /dev/null
+++ b/ZShooter_v2/optics/LIST_zimage_u_optics.dat
@@ -0,0 +1,18 @@
+# author : Jeb Bailey
+# date_created  : 2026-10-15
+# date_modified : 2026-10-15
+# status : ZShooter ZImager u optics throughput list
+# type : mirror:list
+# outer_unit : m
+# inner_unit : m
+# angle_unit : degree
+# temperature_unit  : deg_C
+# changes :
+# - 2026-10-15 : created
+#
+name                  outer   inner   angle   temperature   action         filename
+u_element_1           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+u_element_2           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+u_element_3           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+u_element_4           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+u_element_5           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
diff --git a/ZShooter_v2/optics/LIST_zimage_v_optics.dat b/ZShooter_v2/optics/LIST_zimage_v_optics.dat
new file mode 100644
index 00000000..611b9f9c
--- /dev/null
+++ b/ZShooter_v2/optics/LIST_zimage_v_optics.dat
@@ -0,0 +1,19 @@
+# author : Jeb Bailey
+# date_created  : 2026-10-15
+# date_modified : 2026-10-15
+# status : ZShooter ZImager v optics throughput list
+# type : mirror:list
+# outer_unit : m
+# inner_unit : m
+# angle_unit : degree
+# temperature_unit  : deg_C
+# changes :
+# - 2026-10-15 : created
+#
+name                  outer   inner   angle   temperature   action         filename
+v_element_1           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+v_element_2           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+v_element_3           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+v_element_4           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+v_element_5           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+v_element_6           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
diff --git a/ZShooter_v2/optics/LIST_zimage_z_optics.dat b/ZShooter_v2/optics/LIST_zimage_z_optics.dat
new file mode 100644
index 00000000..09e8bc77
--- /dev/null
+++ b/ZShooter_v2/optics/LIST_zimage_z_optics.dat
@@ -0,0 +1,18 @@
+# author : Jeb Bailey
+# date_created  : 2026-10-15
+# date_modified : 2026-10-15
+# status : ZShooter ZImager z optics throughput list
+# type : mirror:list
+# outer_unit : m
+# inner_unit : m
+# angle_unit : degree
+# temperature_unit  : deg_C
+# changes :
+# - 2026-10-15 : created
+#
+name                  outer   inner   angle   temperature   action         filename
+z_element_1           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+z_element_2           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+z_element_3           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+z_element_4           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+z_element_5           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat

From 331bfc63f4dfb7db8c7fb02437e87288b4ba0520 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Thu, 15 Oct 2026 15:29:48 -0700
Subject: [PATCH 39/63] **Update ZShooter_IMDET.yaml: adjust detector scales,
 sizes, and biases**

- Updated pixel and plate scales for all detectors to improve accuracy.
- Set detector `x_size` values to 4096 pixels for all detectors.
- Modified bias level to 200 electrons for better alignment with detector specs.
---
 ZShooter_v2/ZShooter_IMDET.yaml | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/ZShooter_v2/ZShooter_IMDET.yaml b/ZShooter_v2/ZShooter_IMDET.yaml
index 6c5cf5cd..8c8fbcde 100644
--- a/ZShooter_v2/ZShooter_IMDET.yaml
+++ b/ZShooter_v2/ZShooter_IMDET.yaml
@@ -9,12 +9,12 @@ properties :
     binx : 1
     biny: 1
     full_well: 7000
-    pixel_scale_A: 0.049922  #"/pix
-    pixel_scale_B: 0.049922
-    pixel_scale_C: 0.049922
-    plate_scale_A: 10.851 #"/mm
-    plate_scale_B: 10.851
-    plate_scale_C: 10.851
+    pixel_scale_A: 0.0511  #"/pix
+    pixel_scale_B: 0.0511
+    pixel_scale_C: 0.0511
+    plate_scale_A: 11.1087 #"/mm
+    plate_scale_B: 11.1087
+    plate_scale_C: 11.1087
 
 effects :
   - name: detector_u
@@ -28,7 +28,7 @@ effects :
           id: [0]
           x_cen: [0.0]
           y_cen: [0.0]
-          x_size: [xxx]
+          x_size: [4096]
           y_size: [2304]
           pixel_size: [0.0046]
           angle: [0.0]
@@ -53,7 +53,7 @@ effects :
           id: [1]
           x_cen: [0.0]
           y_cen: [0.0]
-          x_size: [xxx]
+          x_size: [4096]
           y_size: [2304]
           pixel_size: [0.0046]
           angle: [0.0]
@@ -78,7 +78,7 @@ effects :
           id: [2]
           x_cen: [0.0]
           y_cen: [0.0]
-          x_size: [xxx]
+          x_size: [4096]
           y_size: [2304]
           pixel_size: [0.0046]
           angle: [0.0]
@@ -205,7 +205,7 @@ effects :
     kwargs:
       selector_key: "detector_id" # not really used as the Detector object's id is stored as just "id", but kept for consistency
       wheel:
-        - selector_value: [0,1,2]   # CCD detectors
+        - selector_value: [0,1,2]
           effect_class: Bias
           effect_kwargs:
-            bias: 0    # CCD bias level in electrons
+            bias: 200    #  bias level in electrons

From b60c2a53a17dfdfddc8d09448e84e17294f70c79 Mon Sep 17 00:00:00 2001
From: Yashvi-Sharma <yashuvatsas@gmail.com>
Date: Thu, 15 Oct 2026 17:49:05 -0700
Subject: [PATCH 40/63] tested zimager mode, fixed minor typos, small change in
 dichroictree effect to set the arm id as FOV attribute so that image_plane_id
 can correctly get propagated for fov to plane mapping

---
 ZShooter_v2/ZShooter_IMDET.yaml                      | 12 ++++++------
 ZShooter_v2/default.yaml                             | 12 ++++++------
 .../{TER_dichroic_u-v.dat => TER_dichroic_u-vz.dat}  |  0
 ZShooter_v2/dichroics/make_dichroics.py              |  2 +-
 ZShooter_v2/dichroics/zimage_dichroic_tree.dat       |  2 +-
 5 files changed, 14 insertions(+), 14 deletions(-)
 rename ZShooter_v2/dichroics/{TER_dichroic_u-v.dat => TER_dichroic_u-vz.dat} (100%)

diff --git a/ZShooter_v2/ZShooter_IMDET.yaml b/ZShooter_v2/ZShooter_IMDET.yaml
index 8c8fbcde..55324a57 100644
--- a/ZShooter_v2/ZShooter_IMDET.yaml
+++ b/ZShooter_v2/ZShooter_IMDET.yaml
@@ -9,12 +9,12 @@ properties :
     binx : 1
     biny: 1
     full_well: 7000
-    pixel_scale_A: 0.0511  #"/pix
-    pixel_scale_B: 0.0511
-    pixel_scale_C: 0.0511
-    plate_scale_A: 11.1087 #"/mm
-    plate_scale_B: 11.1087
-    plate_scale_C: 11.1087
+    pixel_scale_u: 0.0511  #"/pix
+    pixel_scale_v: 0.0511
+    pixel_scale_z: 0.0511
+    plate_scale_u: 11.1087 #"/mm
+    plate_scale_v: 11.1087
+    plate_scale_z: 11.1087
 
 effects :
   - name: detector_u
diff --git a/ZShooter_v2/default.yaml b/ZShooter_v2/default.yaml
index 95b475fc..bf134053 100755
--- a/ZShooter_v2/default.yaml
+++ b/ZShooter_v2/default.yaml
@@ -40,12 +40,12 @@ properties:
   ndit_h: 1                # number of exposures for H arms
   dit_k: 300.0              # exposure time for K arms
   ndit_k: 1                # number of exposures for K arms
-  imager_dit_a: 10.
-  imager_ndit_a: 1.
-  imager_dit_b: 10.
-  imager_ndit_b: 1.
-  imager_dit_c: 10.
-  imager_ndit_c: 1.
+  imager_dit_u: 10.
+  imager_ndit_u: 1.
+  imager_dit_v: 10.
+  imager_ndit_v: 1.
+  imager_dit_z: 10.
+  imager_ndit_z: 1.
 
 mode_yamls:
 - object: instrument
diff --git a/ZShooter_v2/dichroics/TER_dichroic_u-v.dat b/ZShooter_v2/dichroics/TER_dichroic_u-vz.dat
similarity index 100%
rename from ZShooter_v2/dichroics/TER_dichroic_u-v.dat
rename to ZShooter_v2/dichroics/TER_dichroic_u-vz.dat
diff --git a/ZShooter_v2/dichroics/make_dichroics.py b/ZShooter_v2/dichroics/make_dichroics.py
index c0064f85..635eb059 100644
--- a/ZShooter_v2/dichroics/make_dichroics.py
+++ b/ZShooter_v2/dichroics/make_dichroics.py
@@ -172,7 +172,7 @@ def read_channel_ranges(trace_parameters_path: Path) -> dict[str, u.Quantity]:
 # wavelength_unit : um
 # changes :
 #"""
-write_dichroic_curves(DICHROIC_DIR / 'TER_dichroic_u-v.dat', dichroics['u-vz'][1]._model, dichroics['u-vz'][0]._model, header=hdr_zi_u)
+write_dichroic_curves(DICHROIC_DIR / 'TER_dichroic_u-vz.dat', dichroics['u-vz'][1]._model, dichroics['u-vz'][0]._model, header=hdr_zi_u)
 write_dichroic_curves(DICHROIC_DIR / 'TER_dichroic_v-z.dat', dichroics['v-z'][1]._model, dichroics['v-z'][0]._model, header=hdr_zi_z)
 
 ## First dichroic: I, splits BGR from YJHK
diff --git a/ZShooter_v2/dichroics/zimage_dichroic_tree.dat b/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
index 201fdf0c..99344e1e 100644
--- a/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
+++ b/ZShooter_v2/dichroics/zimage_dichroic_tree.dat
@@ -2,7 +2,7 @@
 # author : Y. Sharma
 # date_created : 2026-10-14
 # description : Image plane ID mapping 0->u, 1->v, 2->z
-aperture_id        u-vz     v-z
+image_plane_id        u-vz     v-z
 0                    R       X
 1                    T       R
 2                    T       T

From 69e39b78cc92e73685bddf6f9089c1c483625970 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Fri, 16 Oct 2026 01:21:49 -0700
Subject: [PATCH 41/63] **Add Sloan filter data files**

---
 ZShooter_v2/filters/sloan_g.dat | 1813 +++++++++++++++++++++++++++++++
 ZShooter_v2/filters/sloan_i.dat |  913 ++++++++++++++++
 ZShooter_v2/filters/sloan_r.dat |  913 ++++++++++++++++
 ZShooter_v2/filters/sloan_u.dat | 1813 +++++++++++++++++++++++++++++++
 ZShooter_v2/filters/sloan_z.dat | 1813 +++++++++++++++++++++++++++++++
 5 files changed, 7265 insertions(+)
 create mode 100644 ZShooter_v2/filters/sloan_g.dat
 create mode 100644 ZShooter_v2/filters/sloan_i.dat
 create mode 100644 ZShooter_v2/filters/sloan_r.dat
 create mode 100644 ZShooter_v2/filters/sloan_u.dat
 create mode 100644 ZShooter_v2/filters/sloan_z.dat

diff --git a/ZShooter_v2/filters/sloan_g.dat b/ZShooter_v2/filters/sloan_g.dat
new file mode 100644
index 00000000..3056ea7d
--- /dev/null
+++ b/ZShooter_v2/filters/sloan_g.dat
@@ -0,0 +1,1813 @@
+# manufacturer : Chroma Technology Corp.
+# filter : 27056 Sloan-g
+# description : Representative catalogue transmission of the filter alone; excludes atmosphere, telescope optics and detector QE.
+# source : https://www.chroma.com/products/parts/27056-sloan-g/
+# data_source : https://www.chroma.com/api/sv/plot/spectrum
+# data_request : 'POST {"id":7908,"type":"filters"}'
+# incidence_angle : 0 deg
+# wavelength_unit : nm
+# transmission_unit : ""
+# conversion : Vendor wavelength in nm; vendor transmission in percent divided by 100. Samples are otherwise unchanged.
+# download_date : 2026-10-16
+wavelength transmission
+300.5 9.5000001465451004e-07
+301 9.5000001465451004e-07
+301.5 9.5000001465451004e-07
+302 9.5000001465451004e-07
+302.5 9.5000001465451004e-07
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+304 9.5000001465451004e-07
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+306 9.5000001465451004e-07
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+334 1.0974899851134999e-06
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+336 1.1205099781363999e-06
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diff --git a/ZShooter_v2/filters/sloan_i.dat b/ZShooter_v2/filters/sloan_i.dat
new file mode 100644
index 00000000..6bd8d230
--- /dev/null
+++ b/ZShooter_v2/filters/sloan_i.dat
@@ -0,0 +1,913 @@
+# manufacturer : Chroma Technology Corp.
+# filter : 27058 Sloan-i
+# description : Representative catalogue transmission of the filter alone; excludes atmosphere, telescope optics and detector QE.
+# source : https://www.chroma.com/products/parts/27058-sloan-i/
+# data_source : https://www.chroma.com/api/sv/plot/spectrum
+# data_request : 'POST {"id":7910,"type":"filters"}'
+# incidence_angle : 0 deg
+# wavelength_unit : nm
+# transmission_unit : ""
+# conversion : Vendor wavelength in nm; vendor transmission in percent divided by 100. Samples are otherwise unchanged.
+# download_date : 2026-10-16
+wavelength transmission
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diff --git a/ZShooter_v2/filters/sloan_r.dat b/ZShooter_v2/filters/sloan_r.dat
new file mode 100644
index 00000000..0053efb0
--- /dev/null
+++ b/ZShooter_v2/filters/sloan_r.dat
@@ -0,0 +1,913 @@
+# manufacturer : Chroma Technology Corp.
+# filter : 27057 Sloan-r
+# description : Representative catalogue transmission of the filter alone; excludes atmosphere, telescope optics and detector QE.
+# source : https://www.chroma.com/products/parts/27057-sloan-r/
+# data_source : https://www.chroma.com/api/sv/plot/spectrum
+# data_request : 'POST {"id":7909,"type":"filters"}'
+# incidence_angle : 0 deg
+# wavelength_unit : nm
+# transmission_unit : ""
+# conversion : Vendor wavelength in nm; vendor transmission in percent divided by 100. Samples are otherwise unchanged.
+# download_date : 2026-10-16
+wavelength transmission
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diff --git a/ZShooter_v2/filters/sloan_u.dat b/ZShooter_v2/filters/sloan_u.dat
new file mode 100644
index 00000000..1534a13c
--- /dev/null
+++ b/ZShooter_v2/filters/sloan_u.dat
@@ -0,0 +1,1813 @@
+# manufacturer : Chroma Technology Corp.
+# filter : 27055 Sloan-u
+# description : Representative catalogue transmission of the filter alone; excludes atmosphere, telescope optics and detector QE.
+# source : https://www.chroma.com/products/parts/27055-sloan-u/
+# data_source : https://www.chroma.com/api/sv/plot/spectrum
+# data_request : 'POST {"id":7907,"type":"filters"}'
+# incidence_angle : 0 deg
+# wavelength_unit : nm
+# transmission_unit : ""
+# conversion : Vendor wavelength in nm; vendor transmission in percent divided by 100. Samples are otherwise unchanged.
+# download_date : 2026-10-16
+wavelength transmission
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diff --git a/ZShooter_v2/filters/sloan_z.dat b/ZShooter_v2/filters/sloan_z.dat
new file mode 100644
index 00000000..2d00b7b2
--- /dev/null
+++ b/ZShooter_v2/filters/sloan_z.dat
@@ -0,0 +1,1813 @@
+# manufacturer : Chroma Technology Corp.
+# filter : 27059 Sloan-z
+# description : Representative catalogue transmission of the filter alone; excludes atmosphere, telescope optics and detector QE.
+# source : https://www.chroma.com/products/parts/27059-sloan-z/
+# data_source : https://www.chroma.com/api/sv/plot/spectrum
+# data_request : 'POST {"id":7911,"type":"filters"}'
+# incidence_angle : 0 deg
+# wavelength_unit : nm
+# transmission_unit : ""
+# conversion : Vendor wavelength in nm; vendor transmission in percent divided by 100. Samples are otherwise unchanged.
+# download_date : 2026-10-16
+wavelength transmission
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From 1fca110723e8d3f2d2774053c27fecc0382e9818 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Fri, 16 Oct 2026 01:23:26 -0700
Subject: [PATCH 42/63] **Add Sloan filters to ZImager optics and update
 ZShooter_IMAGER.yaml configurations**

- Included `sloan_u.dat` and `sloan_z.dat` filters in ZImager optics files.
- Added filter wheel selector with support for `g`, `r`, `i` filters in `ZShooter_IMAGER.yaml`.
- Updated dichroic tree structure with revised `z_order` values.
- Changed selector key for wheels and quantum efficiency curves to `image_plane_id`.
---
 ZShooter_v2/ZShooter_IMAGER.yaml            | 36 +++++++++++++++------
 ZShooter_v2/optics/LIST_zimage_u_optics.dat |  1 +
 ZShooter_v2/optics/LIST_zimage_z_optics.dat |  1 +
 3 files changed, 29 insertions(+), 9 deletions(-)

diff --git a/ZShooter_v2/ZShooter_IMAGER.yaml b/ZShooter_v2/ZShooter_IMAGER.yaml
index 20e40712..ed34e00d 100755
--- a/ZShooter_v2/ZShooter_IMAGER.yaml
+++ b/ZShooter_v2/ZShooter_IMAGER.yaml
@@ -3,10 +3,11 @@ alias: INST
 name: ZIMAGE
 description: Effects from the ZIMAGE optics
 
-#properties:
+properties:
+  imager_filter_v: g
 
 # There are 3 image planes in ZIMAGE: 3 filters. We use the DichroicTree effect to establish the branching
-# in the beginning of the flow, using aperture_id as the identifying key. Though physically there may be other
+# in the beginning of the flow, using image_plane_id as the identifying key. Though physically there may be other
 # optical elements in between dichroics, here all dichroics are considered together in a tree. Other effects that
 # act selectively on different arms can be orchestrated using SelectorWheel effects.
 
@@ -21,6 +22,7 @@ effects:
    class: DichroicTree
    include: True
    kwargs:
+     z_order: [117, 217, 517]
      tree_filename: "dichroics/zimage_dichroic_tree.dat"
      dichroic_names:
        - u-vz     # First dichroic, splits beam into u and red
@@ -32,8 +34,8 @@ effects:
    class: SelectorWheel
    include: True
    kwargs:
-     z_order: [610]
-     selector_key: "aperture_id"
+     z_order: [220, 520]
+     selector_key: "image_plane_id"
      wheel:
        - selector_value: [0]
          effect_class: SurfaceList
@@ -50,15 +52,31 @@ effects:
          effect_kwargs:
            filename: "optics/LIST_zimage_z_optics.dat"
 
+ - name: filter_wheel_selector
+   description: ZIMAGE v-channel filter wheel
+   class: SelectorWheel
+   include: True
+   kwargs:
+     z_order: [224, 524]
+     selector_key: "image_plane_id"
+     allowed_missing_selector_values: [0, 2]
+     wheel:
+       - selector_value: [1]
+         effect_class: FilterWheel
+         effect_kwargs:
+           filter_names: [g, r, i]
+           filename_format: "filters/sloan_{}.dat"
+           current_filter: "!INST.imager_filter_v"
+
  - name: detector_qe_selector
-   description: Throughput-only QE curves for different detectors
+   description: ZIMAGE detector quantum efficiency
    class: SelectorWheel
    include: True
    kwargs:
-     z_order: [610]
-     selector_key: "aperture_id"
+     z_order: [513]
+     selector_key: "image_plane_id"
      wheel:
        - selector_value: [0, 1, 2]
-         effect_class: SpectralQuantumEfficiency
+         effect_class: QuantumEfficiencyCurve
          effect_kwargs:
-           filename: "detector_specs/quest2_TER.dat"
\ No newline at end of file
+           filename: "detector_specs/quest2_TER.dat"
diff --git a/ZShooter_v2/optics/LIST_zimage_u_optics.dat b/ZShooter_v2/optics/LIST_zimage_u_optics.dat
index b31fcbc5..c2a6bcec 100644
--- a/ZShooter_v2/optics/LIST_zimage_u_optics.dat
+++ b/ZShooter_v2/optics/LIST_zimage_u_optics.dat
@@ -16,3 +16,4 @@ u_element_2           0.09    0.0     0       0           transmission   optics/
 u_element_3           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
 u_element_4           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
 u_element_5           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+u_filter              0.09    0.0     0       0           transmission   filters/sloan_u.dat
diff --git a/ZShooter_v2/optics/LIST_zimage_z_optics.dat b/ZShooter_v2/optics/LIST_zimage_z_optics.dat
index 09e8bc77..263795de 100644
--- a/ZShooter_v2/optics/LIST_zimage_z_optics.dat
+++ b/ZShooter_v2/optics/LIST_zimage_z_optics.dat
@@ -16,3 +16,4 @@ z_element_2           0.09    0.0     0       0           transmission   optics/
 z_element_3           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
 z_element_4           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
 z_element_5           0.09    0.0     0       0           transmission   optics/TER_vis_arcoat.dat
+z_filter              0.09    0.0     0       0           transmission   filters/sloan_z.dat

From 8005be84b4e793a50c1ca9bc991710c6ea4246ef Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Fri, 16 Oct 2026 20:44:20 -0700
Subject: [PATCH 43/63] Add Paranal spectroscopy atmosphere and UT3 Cassegrain
 optical path

---
 Paranal/Paranal_spectroscopy.yaml       | 39 +++++++++++++++++++++++++
 VLT/LIST_VLT_UT3_Cassegrain_mirrors.dat |  9 ++++++
 VLT/VLT_UT3_Cassegrain.yaml             | 12 ++++++++
 3 files changed, 60 insertions(+)
 create mode 100644 Paranal/Paranal_spectroscopy.yaml
 create mode 100644 VLT/LIST_VLT_UT3_Cassegrain_mirrors.dat
 create mode 100644 VLT/VLT_UT3_Cassegrain.yaml

diff --git a/Paranal/Paranal_spectroscopy.yaml b/Paranal/Paranal_spectroscopy.yaml
new file mode 100644
index 00000000..b6a5463c
--- /dev/null
+++ b/Paranal/Paranal_spectroscopy.yaml
@@ -0,0 +1,39 @@
+object: atmosphere
+alias: ATMO
+name: paranal_spectroscopy
+description: Paranal sky and wavelength-dependent seeing for slit spectroscopy
+properties:
+  altitude: 2635
+  longitude: -70.4042
+  latitude: -24.6272
+  temperature: 9
+  humidity: 0.15
+  pressure: 0.75
+  pwv: 2.5
+  airmass: '!OBS.airmass'
+  moon_sun_sep: 0
+  local_sky_file: false
+  seeing: '!OBS.seeing'
+effects:
+  - name: skycalc_atmosphere
+    class: SkycalcTERCurve
+    kwargs:
+      observatory: paranal
+      airmass: '!OBS.airmass'
+      pwv: '!ATMO.pwv'
+      moon_sun_sep: '!ATMO.moon_sun_sep'
+      wmin: 300
+      wmax: 2500
+      wunit: nm
+      wgrid_mode: fixed_spectral_resolution
+      wres: '!SIM.spectral.spectral_resolution'
+      use_local_skycalc_file: '!ATMO.local_sky_file'
+  - name: seeing_psf
+    class: MoffatPSF
+    kwargs:
+      alpha: 4.765
+      fwhm:
+        seeing: '!OBS.seeing'
+        seeing_unit: arcsec
+        pivot_wave: 500
+        pivot_wave_unit: nm
diff --git a/VLT/LIST_VLT_UT3_Cassegrain_mirrors.dat b/VLT/LIST_VLT_UT3_Cassegrain_mirrors.dat
new file mode 100644
index 00000000..2f907697
--- /dev/null
+++ b/VLT/LIST_VLT_UT3_Cassegrain_mirrors.dat
@@ -0,0 +1,9 @@
+# description: UT3 Cassegrain path; M2 obscuration projected onto the entrance pupil
+# source: https://www.eso.org/sci/facilities/paranal/telescopes/ut/m1unit.html
+# outer_unit: m
+# inner_unit: m
+# angle_unit: degree
+# temperature_description: Resolved from !ATMO.temperature in degrees Celsius
+name outer inner angle temperature action filename
+M1 8.2 1.116 0.0 !ATMO.temperature reflection TC_mirror_aluminium.dat
+M2 1.116 0.05 0.0 !ATMO.temperature reflection TC_mirror_aluminium.dat
diff --git a/VLT/VLT_UT3_Cassegrain.yaml b/VLT/VLT_UT3_Cassegrain.yaml
new file mode 100644
index 00000000..8e0a3f9a
--- /dev/null
+++ b/VLT/VLT_UT3_Cassegrain.yaml
@@ -0,0 +1,12 @@
+object: telescope
+alias: TEL
+name: VLT_UT3_Cassegrain
+description: UT3 M1/M2 path to the Cassegrain focus
+properties:
+  name: VLT UT3
+  temperature: '!ATMO.temperature'
+effects:
+  - name: vlt_mirror_list
+    class: SurfaceList
+    kwargs:
+      filename: LIST_VLT_UT3_Cassegrain_mirrors.dat

From cdcfb853c1822a0c9fa5aca31d68cfcea1f0b822 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Fri, 16 Oct 2026 20:44:20 -0700
Subject: [PATCH 44/63] Pin attributed X-shooter calibrations and reproducible
 preparation scripts

---
 XSHOOTER/calibration/ESO_COPYING              |  340 +++++
 XSHOOTER/calibration/eso_etc/NIR_fudge.dat    |   18 +
 XSHOOTER/calibration/eso_etc/UVB_fudge.dat    |   14 +
 XSHOOTER/calibration/eso_etc/VIS_fudge.dat    |   17 +
 XSHOOTER/calibration/eso_etc/ccd926.dat       |  176 +++
 XSHOOTER/calibration/eso_etc/ccd927.dat       |  125 ++
 XSHOOTER/calibration/eso_etc/ccd928.dat       |  153 ++
 XSHOOTER/calibration/eso_etc/instr_NIR.dat    |  663 +++++++++
 XSHOOTER/calibration/eso_etc/instr_UVB.dat    |  129 ++
 XSHOOTER/calibration/eso_etc/instr_VIS.dat    |  217 +++
 XSHOOTER/calibration/eso_etc/nir_dit_ron.dat  |   27 +
 XSHOOTER/calibration/eso_model.c              | 1290 +++++++++++++++++
 XSHOOTER/calibration/fit_lsf.py               |   76 +
 XSHOOTER/calibration/measure_standard.py      |  126 ++
 XSHOOTER/calibration/model_nir.fits           |    7 +
 XSHOOTER/calibration/model_uvb.fits           |    4 +
 XSHOOTER/calibration/model_vis.fits           |   10 +
 XSHOOTER/calibration/optical_response.fits    |  Bin 0 -> 547200 bytes
 XSHOOTER/calibration/physical_model.py        |   52 +
 XSHOOTER/calibration/slit_resolution.ecsv     |   24 +
 .../calibration/standard_measurements.fits    |  Bin 0 -> 230400 bytes
 .../calibration/standard_repeatability.ecsv   |   36 +
 XSHOOTER/calibration/trace_residuals.ecsv     |   54 +
 XSHOOTER/prepare_calibrations.py              |  171 +++
 24 files changed, 3729 insertions(+)
 create mode 100644 XSHOOTER/calibration/ESO_COPYING
 create mode 100644 XSHOOTER/calibration/eso_etc/NIR_fudge.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/UVB_fudge.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/VIS_fudge.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/ccd926.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/ccd927.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/ccd928.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/instr_NIR.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/instr_UVB.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/instr_VIS.dat
 create mode 100644 XSHOOTER/calibration/eso_etc/nir_dit_ron.dat
 create mode 100644 XSHOOTER/calibration/eso_model.c
 create mode 100644 XSHOOTER/calibration/fit_lsf.py
 create mode 100644 XSHOOTER/calibration/measure_standard.py
 create mode 100644 XSHOOTER/calibration/model_nir.fits
 create mode 100644 XSHOOTER/calibration/model_uvb.fits
 create mode 100644 XSHOOTER/calibration/model_vis.fits
 create mode 100644 XSHOOTER/calibration/optical_response.fits
 create mode 100644 XSHOOTER/calibration/physical_model.py
 create mode 100644 XSHOOTER/calibration/slit_resolution.ecsv
 create mode 100644 XSHOOTER/calibration/standard_measurements.fits
 create mode 100644 XSHOOTER/calibration/standard_repeatability.ecsv
 create mode 100644 XSHOOTER/calibration/trace_residuals.ecsv
 create mode 100644 XSHOOTER/prepare_calibrations.py

diff --git a/XSHOOTER/calibration/ESO_COPYING b/XSHOOTER/calibration/ESO_COPYING
new file mode 100644
index 00000000..b9bd89f5
--- /dev/null
+++ b/XSHOOTER/calibration/ESO_COPYING
@@ -0,0 +1,340 @@
+		    GNU GENERAL PUBLIC LICENSE
+		       Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc.
+     51 Franklin St, Fifth Floor, Boston, MA  02111-1307  USA
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+			    Preamble
+
+  The licenses for most software are designed to take away your
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+License is intended to guarantee your freedom to share and change free
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+Foundation's software and to any other program whose authors commit to
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+your programs, too.
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+
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+   TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
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+
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diff --git a/XSHOOTER/calibration/eso_etc/NIR_fudge.dat b/XSHOOTER/calibration/eso_etc/NIR_fudge.dat
new file mode 100644
index 00000000..92aa13fb
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/NIR_fudge.dat
@@ -0,0 +1,18 @@
+900 0.142
+1005.77 0.142
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diff --git a/XSHOOTER/calibration/eso_etc/UVB_fudge.dat b/XSHOOTER/calibration/eso_etc/UVB_fudge.dat
new file mode 100644
index 00000000..6eb9934d
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/UVB_fudge.dat
@@ -0,0 +1,14 @@
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diff --git a/XSHOOTER/calibration/eso_etc/VIS_fudge.dat b/XSHOOTER/calibration/eso_etc/VIS_fudge.dat
new file mode 100644
index 00000000..0999bc9e
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/VIS_fudge.dat
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diff --git a/XSHOOTER/calibration/eso_etc/ccd926.dat b/XSHOOTER/calibration/eso_etc/ccd926.dat
new file mode 100644
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--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/ccd926.dat
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diff --git a/XSHOOTER/calibration/eso_etc/ccd927.dat b/XSHOOTER/calibration/eso_etc/ccd927.dat
new file mode 100644
index 00000000..688663d0
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/ccd927.dat
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diff --git a/XSHOOTER/calibration/eso_etc/ccd928.dat b/XSHOOTER/calibration/eso_etc/ccd928.dat
new file mode 100644
index 00000000..bf6e91bb
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/ccd928.dat
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diff --git a/XSHOOTER/calibration/eso_etc/instr_NIR.dat b/XSHOOTER/calibration/eso_etc/instr_NIR.dat
new file mode 100644
index 00000000..3640bb2b
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/instr_NIR.dat
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diff --git a/XSHOOTER/calibration/eso_etc/instr_UVB.dat b/XSHOOTER/calibration/eso_etc/instr_UVB.dat
new file mode 100644
index 00000000..1ddb6385
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/instr_UVB.dat
@@ -0,0 +1,129 @@
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diff --git a/XSHOOTER/calibration/eso_etc/instr_VIS.dat b/XSHOOTER/calibration/eso_etc/instr_VIS.dat
new file mode 100644
index 00000000..a0a04ef3
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/instr_VIS.dat
@@ -0,0 +1,217 @@
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diff --git a/XSHOOTER/calibration/eso_etc/nir_dit_ron.dat b/XSHOOTER/calibration/eso_etc/nir_dit_ron.dat
new file mode 100644
index 00000000..085b5869
--- /dev/null
+++ b/XSHOOTER/calibration/eso_etc/nir_dit_ron.dat
@@ -0,0 +1,27 @@
+0 25.6
+1 25.6
+2 24.4
+3 23.1
+4 20.9
+5 19.9
+6 18.4
+7 17.9
+8 16.8
+16 13
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+1e+10 10
diff --git a/XSHOOTER/calibration/eso_model.c b/XSHOOTER/calibration/eso_model.c
new file mode 100644
index 00000000..5ec129aa
--- /dev/null
+++ b/XSHOOTER/calibration/eso_model.c
@@ -0,0 +1,1290 @@
+/* Mathematical kernel from ESO X-shooter pipeline 3.8.12.
+ * Copyright (C) 2006 European Southern Observatory.
+ * GPL-2.0-or-later; see ESO_COPYING.
+ * Source: xsh/xsh_model_kernel.c, xsh_model_kernel.h, xsh_model_ref_ind.h.
+ * https://ftp.eso.org/pub/dfs/pipelines/instruments/xshooter/xshoo-kit-3.8.12-3.tar.gz
+ * Mathematical function bodies are unchanged. CPL I/O and unused functions
+ * are omitted; the last four functions expose the kernel for offline table
+ * preparation only. This file is not part of the ScopeSim runtime.
+ */
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#define NIR_FIX 36
+#define mm2um 1000.0
+#define mm2nm 1000000.0
+#define XSH_TRACE_MSG(x)
+static const int vectordim=4;
+typedef double DOUBLE;
+
+
+typedef DOUBLE vec[4];
+typedef DOUBLE cvec[4];
+typedef DOUBLE mat[4][4];
+
+typedef int detloc[3];
+typedef int XY[3];
+
+typedef struct {
+  DOUBLE best,min,max;
+  int flag;
+  char  name[20];
+} ann_all_par;
+
+typedef struct
+  {
+    int counter;
+    double x;
+    double y;
+    int arm;
+    double wave;
+    double flux;
+    int slit_pos;
+    int order;
+  } coord ;
+
+struct xs_3
+{
+
+  int arm;
+  double chipxpix,chipypix;
+  double xsize_corr, ysize_corr;
+  int ASIZE, BSIZE, SIZE;
+  int morder, morder_min, morder_max;
+  DOUBLE blaze_pad;
+  DOUBLE temper, t_ir_p2, t_ir_p3;
+  DOUBLE mues, nues, taues, slit_scale, es_s, es_w, es_x, es_xf, es_y, es_y_tot;
+  DOUBLE fcol;
+  DOUBLE mup1, cmup1, nup1, taup1;
+  DOUBLE rind, rind2, rind3, rind_a1, rind_a2, rind_a3, rind_b1, rind_b2, rind_lm1, rind_lm2, rind_lm3, rind_wta0, rind_wta1, rind_wta2, rind_wtb0, rind_wtb1, rind_wtb2;
+  DOUBLE rind_delta;
+  DOUBLE mup2, nup2, taup2;
+  DOUBLE mup3, nup3, taup3;
+  DOUBLE mup4, nup4, taup4;
+  DOUBLE mup5, nup5, taup5;
+  DOUBLE mup6, nup6, taup6;
+
+  DOUBLE mug, nug, taug, sg, grat_alpha, grat_beta;
+  DOUBLE mud, nud, taud, pix, pix_X, pix_Y, offx, offy, flipx, flipy, xpospix, ypospix, fdet;
+
+  detloc chippix;
+  double xdet,ydet;
+
+  double chipx, chipy, chipxup, chipyup, chipxdown, chipydown, chiprot;
+
+
+  double pc_x_xx, pc_x_x1, pc_x_yy, pc_x_y1, pc_x_xy, pc_x_x3, pc_x_x2y, pc_x_y2x, pc_x_y3;
+  double pc_y_xx, pc_y_x1, pc_y_yy, pc_y_y1, pc_y_xy, pc_y_x3, pc_y_x2y, pc_y_y2x, pc_y_y3;
+
+  double pc4_x_xy3,pc4_x_x3y,pc4_x_x2y2,pc4_x_x4,pc4_x_y4;
+  double pc4_y_xy3,pc4_y_x3y,pc4_y_x2y2,pc4_y_x4,pc4_y_y4;
+
+
+  double d2_x3, d2_x2, d2_x1;
+  double d2_y3x3, d2_y3x2, d2_y3x1,d2_y3x0,d2_y2x3, d2_y2x2, d2_y2x1,d2_y2x0,d2_y1x3, d2_y1x2, d2_y1x1,d2_y1x0;
+
+  double ca_x0,ca_x1,ca_y0,ca_y1;
+
+
+  mat e_slit;
+  mat mup_ir_cor, mup_ir_cor_out, toprism1, toprism2, toprism3, toprism4, toprism5, toprism6, prism_out, ret_prism2, ret_prism1, ret_prism_out1,ret_prism4, ret_prism3, ret_prism_out2,ret_prism6, ret_prism5, ret_prism_out3,todetector;
+  mat tograt, grat_out;
+  DOUBLE slit[10];
+
+
+  double config_mjd;
+};
+typedef struct xs_3 xsh_xs_3;
+/* $Id: xsh_model_ref_ind.h,v 1.4 2010-07-28 11:17:30 amodigli Exp $
+ *
+ *   This file is part of the ESO X-shooter Pipeline
+ *   Copyright (C) 2006 European Southern Observatory
+ *
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 2 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software
+ * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+ */
+
+
+
+#ifndef XSH_MODEL_REF_IND_H
+#define XSH_MODEL_REF_IND_H
+
+static double schott_sf6_manu [6][7] = {
+  {1.72618475,0.392383376,1.05068036,0.0134871947,0.0569318095,118.557185,260.0},
+  {1.72618475,0.392383376,1.05068036,0.0134871947,0.0569318095,118.557185,270.0},
+  {1.72618475,0.392383376,1.05068036,0.0134871947,0.0569318095,118.557185,280.0},
+  {1.72618475,0.392383376,1.05068036,0.0134871947,0.0569318095,118.557185,290.0},
+  {1.72618475,0.392383376,1.05068036,0.0134871947,0.0569318095,118.557185,300.0},
+  {1.72618475,0.392383376,1.05068036,0.0134871947,0.0569318095,118.557185,310.0}
+  };
+
+static double silica_zemax [6][7] = {
+  {0.6961663,0.4079426,0.8974794,0.004679148,0.01351206,97.934,260.0},
+  {0.6961663,0.4079426,0.8974794,0.004679148,0.01351206,97.934,270.0},
+  {0.6961663,0.4079426,0.8974794,0.004679148,0.01351206,97.934,280.0},
+  {0.6961663,0.4079426,0.8974794,0.004679148,0.01351206,97.934,290.0},
+  {0.6961663,0.4079426,0.8974794,0.004679148,0.01351206,97.934,300.0},
+  {0.6961663,0.4079426,0.8974794,0.004679148,0.01351206,97.934,310.0}};
+
+static double znse [6][7] = {
+  {4.43472613286,0.437534439541,2.94296707239,0.20047046355,0.383301991611,47.9937969433,95.0},
+  {4.43542230714,0.438059580351,2.9265319319,0.200506374102,0.383453912766,47.8502966353,100.0},
+  {4.43612895652,0.438586230317,2.91141326949,0.200541768113,0.383612197906,47.7172014285,105.0},
+  {4.43604895636,0.439902006841,2.88537551027,0.200541339195,0.38368776333,47.4933353795,110.0},
+  {4.43675967885,0.440440921835,2.87838606989,0.200575497514,0.383853032135,47.4256517629,115.0},
+  {4.4374705012,0.440979834519,2.87139508026,0.200609780065,0.384017392306,47.3579544185,120.0}};
+#endif
+
+
+
+void xsh_3_detpix(struct xs_3 *p_xs_3)
+;
+void xsh_3_eval(DOUBLE  lambda,
+	       int morder,
+	       DOUBLE** ref_ind,
+	       struct xs_3* p_xs_3)
+;
+void xsh_3_init(struct xs_3 * p_xs_3)
+;
+static void xsh_copyvector(vec a, vec b)
+;
+void xsh_matrixforvector(vec a,mat B,vec c)
+;
+double xsh_model_sellmeier_ext(int arm, double temper, double lam_sqr)
+;
+void xsh_multiplymatrix(mat A, mat B, mat C)
+;
+void xsh_normall(vec a)
+;
+void xsh_normz(vec a)
+;
+static void xsh_nullmatrix(mat A)
+;
+static void xsh_nullvector(vec a)
+;
+void xsh_ref_ind_read(int arm, DOUBLE** ref_ind_T,DOUBLE temper)
+;
+void xsh_refract(vec b, DOUBLE rind, vec a)
+;
+void
+xsh_rotationmatrix(mat A, const char axis, const DOUBLE angle)
+;
+void
+xsh_rotin(mat A,const DOUBLE x_angle, const DOUBLE y_angle,const DOUBLE z_angle)
+;
+void xsh_subtractvectors(vec a, vec b)
+;
+void xsh_transpose(mat A ,mat B)
+;
+void xsh_3_detpix(struct xs_3 *p_xs_3)
+{
+
+  double xdet2, ydet2;
+
+  double xdet_c, xdet_c2, xdet_c3, ydet_c, ydet_c2, ydet_c3;
+  double d2_y1=0.0;
+  double d2_y2=0.0;
+  double d2_y3=0.0;
+  double pc_x, pc_y, dstn_x, dstn_y;
+  double xdet_c4, ydet_c4;
+
+  int array_size_fix=0;
+
+
+
+
+  p_xs_3->xpospix=0.0;
+  p_xs_3->ypospix=0.0;
+  p_xs_3->chippix[0]=-1;
+  p_xs_3->chippix[1]=0;
+  p_xs_3->chippix[2]=0;
+
+#ifndef FCCDD_FLAG
+
+
+
+  ydet2=p_xs_3->ydet*p_xs_3->ydet;
+  double ydet3=ydet2*p_xs_3->ydet;
+  xdet2=p_xs_3->xdet*p_xs_3->xdet;
+  double xdet3=xdet2*p_xs_3->xdet;
+  xdet_c=p_xs_3->xdet-p_xs_3->chipx;
+
+  xdet_c2=xdet_c*xdet_c;
+  xdet_c3=xdet_c2*xdet_c;
+  xdet_c4=xdet_c3*xdet_c;
+  ydet_c=p_xs_3->ydet-p_xs_3->chipy;
+
+  ydet_c2=ydet_c*ydet_c;
+  ydet_c3=ydet_c2*ydet_c;
+  ydet_c4=ydet_c3*ydet_c;
+
+
+  pc_x=p_xs_3->chipx+
+    (p_xs_3->pc_x_xx*xdet_c2)+
+    (p_xs_3->pc_x_x1*xdet_c)+
+    (p_xs_3->pc_x_yy*ydet_c2)+
+    (p_xs_3->pc_x_y1*ydet_c)+
+    (p_xs_3->pc_x_xy*xdet_c*ydet_c)+
+    (p_xs_3->pc_x_x3*xdet_c3)+
+    (p_xs_3->pc_x_x2y*xdet_c2*ydet_c)+
+    (p_xs_3->pc_x_y2x*xdet_c*ydet_c2)+
+    (p_xs_3->pc_x_y3*ydet_c3)+
+    (p_xs_3->pc4_x_xy3*xdet_c*ydet_c3)+
+    (p_xs_3->pc4_x_x3y*xdet_c3*ydet_c)+
+    (p_xs_3->pc4_x_x2y2*xdet_c2*ydet_c2)+
+    (p_xs_3->pc4_x_x4*xdet_c4)+
+    (p_xs_3->pc4_x_y4*ydet_c4);
+  pc_y=p_xs_3->chipy+
+    (p_xs_3->pc_y_xx*xdet_c2)+
+    (p_xs_3->pc_y_x1*xdet_c)+
+    (p_xs_3->pc_y_yy*ydet_c2)+
+    (p_xs_3->pc_y_y1*ydet_c)+
+    (p_xs_3->pc_y_xy*xdet_c*ydet_c)+
+    (p_xs_3->pc_y_x3*xdet_c3)+
+    (p_xs_3->pc_y_x2y*xdet_c2*ydet_c)+
+    (p_xs_3->pc_y_y2x*xdet_c*ydet_c2)+
+    (p_xs_3->pc_y_y3*ydet_c3)+
+    (p_xs_3->pc4_y_xy3*xdet_c*ydet_c3)+
+    (p_xs_3->pc4_y_x3y*xdet_c3*ydet_c)+
+    (p_xs_3->pc4_y_x2y2*xdet_c2*ydet_c2)+
+    (p_xs_3->pc4_y_x4*xdet_c4)+
+    (p_xs_3->pc4_y_y4*ydet_c4);
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  p_xs_3->xdet=pc_x;
+  p_xs_3->ydet=pc_y;
+
+
+  if (p_xs_3->arm<2) {
+    dstn_y=(p_xs_3->d2_x3*ydet_c3)+(p_xs_3->d2_x2*ydet_c2)+(p_xs_3->d2_x1*ydet_c);
+
+    p_xs_3->ydet=p_xs_3->ydet+dstn_y;
+
+
+
+
+
+
+
+  }
+  else if (p_xs_3->arm==2) {
+
+    if (p_xs_3->d2_x3!=0.0) {
+      dstn_x=(p_xs_3->d2_x3*xdet_c3)+(p_xs_3->d2_x2*xdet_c2)+(p_xs_3->d2_x1*xdet_c);
+
+      p_xs_3->xdet=p_xs_3->xdet+dstn_x;
+      d2_y3=(p_xs_3->d2_y3x3*xdet_c3)+(p_xs_3->d2_y3x2*xdet_c2)+(p_xs_3->d2_y3x1*xdet_c)+p_xs_3->d2_y3x0;
+      d2_y2=(p_xs_3->d2_y2x3*xdet_c3)+(p_xs_3->d2_y2x2*xdet_c2)+(p_xs_3->d2_y2x1*xdet_c)+p_xs_3->d2_y2x0;
+      d2_y1=(p_xs_3->d2_y1x3*xdet_c3)+(p_xs_3->d2_y1x2*xdet_c2)+(p_xs_3->d2_y1x1*xdet_c)+p_xs_3->d2_y1x0;
+
+
+
+
+      dstn_y=(d2_y3*ydet_c3)+(d2_y2*ydet_c2)+(d2_y1*ydet_c);
+      p_xs_3->ydet=p_xs_3->ydet+dstn_y;
+    }
+
+    else {
+      dstn_y=(p_xs_3->ca_x1*(xdet_c-p_xs_3->ca_x0)+p_xs_3->ca_y1*(ydet_c-p_xs_3->ca_y0))*p_xs_3->es_y_tot;
+      p_xs_3->ydet=p_xs_3->ydet+dstn_y;
+    }
+  }
+#endif
+#ifdef CCCDD_FLAG
+
+  h_det=sqrt(p_xs_3->xdet*p_xs_3->xdet+p_xs_3->xdet*p_xs_3->xdet);
+
+  p_xs_3->xdet=(p_xs_3->fdet*p_xs_3->xdet/h_det)*sin(h_det/p_xs_3->fdet);
+  p_xs_3->ydet=(p_xs_3->fdet*p_xs_3->ydet/h_det)*sin(h_det/p_xs_3->fdet);
+#endif
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    p_xs_3->chippix[0]=1;
+
+
+    if(p_xs_3->arm!=2) {
+      p_xs_3->xpospix=0.5*(p_xs_3->chipxpix+p_xs_3->xsize_corr)-(p_xs_3->ydet-p_xs_3->chipy)/p_xs_3->pix_X;
+      p_xs_3->ypospix=0.5*(p_xs_3->chipypix+p_xs_3->ysize_corr)-(p_xs_3->xdet-p_xs_3->chipx)/p_xs_3->pix_Y;
+    }
+    else {
+      p_xs_3->xpospix=0.5*(p_xs_3->chipxpix+p_xs_3->xsize_corr)+(p_xs_3->xdet-p_xs_3->chipx)/p_xs_3->pix_X;
+      p_xs_3->ypospix=0.5*(p_xs_3->chipypix+p_xs_3->ysize_corr)+(p_xs_3->ydet-p_xs_3->chipy)/p_xs_3->pix_Y;
+      array_size_fix=NIR_FIX;
+    }
+
+
+    if (p_xs_3->pix_X>0.029) {
+      p_xs_3->xpospix=p_xs_3->xpospix+0.25;
+    }
+    if (p_xs_3->pix_Y>0.029) {
+      p_xs_3->ypospix=p_xs_3->ypospix+0.25;
+    }
+
+    if (p_xs_3->xpospix>p_xs_3->chipxpix-1+array_size_fix) {
+      p_xs_3->xpospix=-1.0;
+      p_xs_3->ypospix=-1.0;
+      p_xs_3->chippix[0]=6;
+    }
+    if (p_xs_3->xpospix<0) {
+      p_xs_3->xpospix=-1.0;
+      p_xs_3->ypospix=-1.0;
+      p_xs_3->chippix[0]=5;
+    }
+    if (p_xs_3->ypospix<0) {
+      p_xs_3->xpospix=-1.0;
+      p_xs_3->ypospix=-1.0;
+      p_xs_3->chippix[0]=8;
+    }
+    if (p_xs_3->ypospix>p_xs_3->chipypix-1) {
+      p_xs_3->xpospix=-1.0;
+      p_xs_3->ypospix=-1.0;
+      p_xs_3->chippix[0]=7;
+    }
+
+      p_xs_3->chippix[1]=(int)(p_xs_3->xpospix-0.5)+1;
+      p_xs_3->chippix[2]=(int)(p_xs_3->ypospix-0.5)+1;
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  return;
+}
+void xsh_3_eval(DOUBLE  lambda,
+	       int morder,
+	       DOUBLE** ref_ind,
+	       struct xs_3* p_xs_3)
+{
+  DOUBLE lambda_um,lamf,rind_up,rind_down,xx,yy,dndT;
+  vec vres,slitpos2,slitpos,detproj;
+
+  mat mech={ {1,0,0,0},
+	     {morder*(p_xs_3->sg),1,0,0},
+	     {0,0,1,0},
+	     {0,0,0,-1}};
+  mat chrom_corr={ {1,0,0,0},
+		   {0,1,0,0},
+		   {0,0,1,0},
+		   {0,0,0,1}};
+
+
+
+  vec vres1={lambda,
+	      0,
+	      0,
+	      -p_xs_3->fcol};
+
+
+  XSH_TRACE_MSG(vres1)
+
+
+  slitpos[0]=0.0;
+  slitpos[1]=1.0*p_xs_3->es_x;
+
+  if (p_xs_3->config_mjd>2455409.0) {
+
+    if (p_xs_3->arm==0) {
+      if (lambda>0.000425) {
+	slitpos[2]=-1.0*p_xs_3->es_y_tot*(1.0+(lambda-0.000425)*p_xs_3->offx);
+      }
+      else{
+	slitpos[2]=-1.0*p_xs_3->es_y_tot*(1.0+(lambda-0.000425)*p_xs_3->offy);
+      }
+    }
+    else if (p_xs_3->arm==1) {
+      if (lambda>0.000650) {
+	slitpos[2]=1.0*(p_xs_3->es_y_tot)*(1.0+(lambda-0.000900)*p_xs_3->offx);
+      }
+      else{
+	slitpos[2]=1.0*(p_xs_3->es_y_tot)*(1.0+(lambda-0.000018)*p_xs_3->offy);
+      }
+    }
+    else if (p_xs_3->arm==2) {
+      if (lambda>0.001400) {
+	slitpos[2]=1.0*(p_xs_3->es_y_tot-p_xs_3->es_y)*(1.0+(lambda-0.001600)*p_xs_3->offx)+p_xs_3->es_y;
+      }
+      else{
+	slitpos[2]=1.0*(p_xs_3->es_y_tot)*(1.0+(lambda-0.000990)*p_xs_3->offy);
+      }
+    }
+  }
+  else {
+    if (p_xs_3->arm==0) {
+      slitpos[2]=-1.0*p_xs_3->es_y_tot;
+    }
+    else {
+      slitpos[2]=1.0*p_xs_3->es_y_tot;
+    }
+  }
+  slitpos[3]=0.0;
+  xsh_matrixforvector(slitpos2,p_xs_3->e_slit,slitpos);
+  xsh_subtractvectors(vres1,slitpos2);
+  xsh_copyvector(vres,vres1);
+  xsh_normall(vres);
+  if (p_xs_3->arm!=2 && p_xs_3->config_mjd<2455409.0) {
+
+    xsh_rotin(chrom_corr,vres[2]*(lambda-p_xs_3->offy)*p_xs_3->offx,0.0,0.0);
+    xsh_matrixforvector(vres,chrom_corr,vres);
+  }
+
+  XSH_TRACE_MSG(vres)
+
+
+
+  if (p_xs_3->arm==2) xsh_matrixforvector(vres,p_xs_3->mup_ir_cor,vres);
+
+  xsh_matrixforvector(vres,p_xs_3->toprism1,vres);
+
+
+  lambda_um=lambda* mm2um;
+  lamf=lambda_um*lambda_um;
+
+  if (p_xs_3->arm==1) {
+
+
+    p_xs_3->rind=sqrt(ref_ind[0][0]*lamf/(lamf-ref_ind[0][3])+ref_ind[0][1]*lamf/(lamf-ref_ind[0][4])+ref_ind[0][2]*lamf/(lamf-ref_ind[0][5])+1.0);
+
+
+    double lam_ig2=0.04963984;
+    double H=71.0867E-06;
+    double G=-50.2451E-06;
+    double R;
+
+    R=lamf/(lamf-lam_ig2);
+    dndT=(G*R+H*R*R)/(2.0*p_xs_3->rind);
+
+
+
+
+    p_xs_3->rind+=dndT*(p_xs_3->temper-293.15);
+
+  }
+  else {
+
+    p_xs_3->rind=xsh_model_sellmeier_ext(p_xs_3->arm,p_xs_3->temper,lamf);
+  }
+
+
+
+
+
+
+
+  xsh_refract(vres,p_xs_3->rind,vres);
+
+
+
+  xsh_matrixforvector(vres,p_xs_3->toprism2,vres);
+
+
+
+  xsh_refract(vres,1.0/(p_xs_3->rind),vres);
+
+
+
+
+  xsh_matrixforvector(vres,p_xs_3->toprism1,vres);
+
+
+
+
+ XSH_TRACE_MSG(vres)
+  if (p_xs_3->arm==2) {
+ XSH_TRACE_MSG(vres)
+
+
+    xsh_matrixforvector(vres,p_xs_3->toprism3,vres);
+
+
+
+
+    rind_up=sqrt(ref_ind[1][0]*lamf/(lamf-ref_ind[1][3]*ref_ind[1][3])+ref_ind[1][1]*lamf/(lamf-ref_ind[1][4]*ref_ind[1][4])+ref_ind[1][2]*lamf/(lamf-ref_ind[1][5]*ref_ind[1][5])+1.0);
+
+
+    rind_down=sqrt(ref_ind[0][0]*lamf/(lamf-ref_ind[0][3]*ref_ind[0][3])+ref_ind[0][1]*lamf/(lamf-ref_ind[0][4]*ref_ind[0][4])+ref_ind[0][2]*lamf/(lamf-ref_ind[0][5]*ref_ind[0][5])+1.0);
+
+
+    p_xs_3->rind2=rind_down+((p_xs_3->t_ir_p2-ref_ind[0][6])/(ref_ind[1][6]-ref_ind[0][6]))*(rind_up-rind_down);
+
+
+    xsh_refract(vres,p_xs_3->rind2,vres);
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->toprism4,vres);
+
+
+
+    xsh_refract(vres,1.0/(p_xs_3->rind2),vres);
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->toprism3,vres);
+
+
+ XSH_TRACE_MSG(vres)
+
+
+    xsh_matrixforvector(vres,p_xs_3->toprism5,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+    rind_up=sqrt(ref_ind[1][0]*lamf/(lamf-ref_ind[1][3]*ref_ind[1][3])+ref_ind[1][1]*lamf/(lamf-ref_ind[1][4]*ref_ind[1][4])+ref_ind[1][2]*lamf/(lamf-ref_ind[1][5]*ref_ind[1][5])+1.0);
+
+
+    rind_down=sqrt(ref_ind[0][0]*lamf/(lamf-ref_ind[0][3]*ref_ind[0][3])+ref_ind[0][1]*lamf/(lamf-ref_ind[0][4]*ref_ind[0][4])+ref_ind[0][2]*lamf/(lamf-ref_ind[0][5]*ref_ind[0][5])+1.0);
+
+
+    p_xs_3->rind3=rind_down+((p_xs_3->t_ir_p3-ref_ind[0][6])/(ref_ind[1][6]-ref_ind[0][6]))*(rind_up-rind_down);
+
+
+    xsh_refract(vres,p_xs_3->rind3,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->toprism6,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+    xsh_refract(vres,1.0/(p_xs_3->rind3),vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->toprism5,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+  }
+
+
+
+
+
+
+
+
+
+  xsh_matrixforvector(vres,p_xs_3->tograt,vres);
+ XSH_TRACE_MSG(vres)
+
+
+  p_xs_3->grat_alpha=atan(vres[1]/vres[3]);
+
+
+
+  xsh_matrixforvector(vres,mech,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+  xsh_normz(vres);
+ XSH_TRACE_MSG(vres)
+
+
+  p_xs_3->grat_beta=atan(vres[1]/vres[3]);
+
+
+
+  xsh_matrixforvector(vres,p_xs_3->grat_out,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+  if (p_xs_3->arm==2) {
+
+    xsh_matrixforvector(vres,p_xs_3->ret_prism6,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+
+    xsh_refract(vres,p_xs_3->rind3,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->ret_prism5,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+
+    xsh_refract(vres,1.0/(p_xs_3->rind3),vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->ret_prism6,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->ret_prism4,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+    xsh_refract(vres,p_xs_3->rind2,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->ret_prism3,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+    xsh_refract(vres,1.0/(p_xs_3->rind2),vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+    xsh_matrixforvector(vres,p_xs_3->ret_prism4,vres);
+ XSH_TRACE_MSG(vres)
+
+
+  }
+
+
+  xsh_matrixforvector(vres,p_xs_3->ret_prism2,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+  xsh_refract(vres,p_xs_3->rind,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+  xsh_matrixforvector(vres,p_xs_3->ret_prism1,vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+  xsh_refract(vres,1.0/(p_xs_3->rind),vres);
+ XSH_TRACE_MSG(vres)
+
+
+
+
+  xsh_matrixforvector(vres,p_xs_3->ret_prism2,vres);
+ XSH_TRACE_MSG(vres)
+
+
+  if (p_xs_3->arm==2) xsh_matrixforvector(vres,p_xs_3->mup_ir_cor_out,vres);
+ XSH_TRACE_MSG(vres)
+
+
+#ifdef FCCDD_FLAG
+  xsh_matrixforvector(vres,p_xs_3->todetector,vres);
+ XSH_TRACE_MSG(vres)
+
+#endif
+
+
+  xx=(p_xs_3->flipx)*(p_xs_3->fdet)/((vres[3]/vres[1])*cos(p_xs_3->nud)-sin(p_xs_3->nud));
+  yy=(p_xs_3->flipy)*(p_xs_3->fdet)/((vres[3]/vres[2])*cos(p_xs_3->mud)-sin(p_xs_3->mud));
+
+
+
+
+#ifdef CCCDD_FLAG
+
+    xx=(p_xs_3->flipx)*(p_xs_3->fdet)*vres[1];
+    yy=(p_xs_3->flipy)*(p_xs_3->fdet)*vres[2];
+#endif
+
+#ifdef XCCCDD_FLAG
+
+    xx=(p_xs_3->flipx)*(p_xs_3->fdet)*2.0*vres[1];
+    yy=(p_xs_3->flipy)*(p_xs_3->fdet)*2.0*vres[2];
+#endif
+
+
+  detproj[0]=0.0;
+  detproj[1]=xx*cos(-p_xs_3->taud)+yy*sin(-p_xs_3->taud);
+  detproj[2]=-xx*sin(-p_xs_3->taud)+yy*cos(-p_xs_3->taud);
+  detproj[3]=0.0;
+  xsh_copyvector(vres,detproj);
+ XSH_TRACE_MSG(vres)
+
+
+  p_xs_3->morder=(float)(morder);
+
+  p_xs_3->xdet=vres[1];
+  p_xs_3->ydet=vres[2];
+}
+void xsh_3_init(struct xs_3 * p_xs_3)
+{
+
+#ifdef FCCDD_FLAG
+  xsh_rotin(p_xs_3->todetector,-0.062,0.01,0.0);
+#endif
+
+
+
+ xsh_rotin(p_xs_3->e_slit,p_xs_3->mues,p_xs_3->nues,p_xs_3->taues);
+
+
+ xsh_rotin(p_xs_3->mup_ir_cor,p_xs_3->cmup1,0.0,0.0);
+ xsh_rotin(p_xs_3->mup_ir_cor_out,-p_xs_3->cmup1,0.0,0.0);
+ xsh_rotin(p_xs_3->toprism1,p_xs_3->mup1,p_xs_3->nup1,p_xs_3->taup1);
+ xsh_rotin(p_xs_3->toprism2,p_xs_3->mup2,p_xs_3->nup2,p_xs_3->taup2);
+
+
+
+
+
+
+
+ xsh_rotin(p_xs_3->toprism3,p_xs_3->mup3,p_xs_3->nup3,p_xs_3->taup3);
+ xsh_rotin(p_xs_3->toprism4,p_xs_3->mup4,p_xs_3->nup4,p_xs_3->taup4);
+
+
+
+
+
+
+ xsh_rotin(p_xs_3->toprism5,p_xs_3->mup5,p_xs_3->nup5,p_xs_3->taup5);
+ xsh_rotin(p_xs_3->toprism6,p_xs_3->mup6,p_xs_3->nup6,p_xs_3->taup6);
+
+
+
+
+
+
+ xsh_rotin(p_xs_3->tograt,p_xs_3->mug,p_xs_3->nug,p_xs_3->taug);
+ xsh_transpose(p_xs_3->grat_out,p_xs_3->tograt);
+
+
+
+
+
+ xsh_transpose(p_xs_3->ret_prism2,p_xs_3->toprism1);
+ xsh_transpose(p_xs_3->ret_prism1,p_xs_3->toprism2);
+ xsh_multiplymatrix(p_xs_3->ret_prism_out1,p_xs_3->toprism2,p_xs_3->toprism1);
+ xsh_transpose(p_xs_3->ret_prism4,p_xs_3->toprism3);
+ xsh_transpose(p_xs_3->ret_prism3,p_xs_3->toprism4);
+ xsh_multiplymatrix(p_xs_3->ret_prism_out2,p_xs_3->toprism4,p_xs_3->toprism3);
+ xsh_transpose(p_xs_3->ret_prism6,p_xs_3->toprism5);
+ xsh_transpose(p_xs_3->ret_prism5,p_xs_3->toprism6);
+ xsh_multiplymatrix(p_xs_3->ret_prism_out3,p_xs_3->toprism6,p_xs_3->toprism5);
+
+
+ if (p_xs_3->arm!=2) {
+   p_xs_3->chipxup=p_xs_3->chipx+p_xs_3->pix_Y*(0.5*p_xs_3->chipypix);
+   p_xs_3->chipxdown=p_xs_3->chipx-p_xs_3->pix_Y*(0.5*p_xs_3->chipypix);
+   p_xs_3->chipyup=p_xs_3->chipy+p_xs_3->pix_X*(0.5*p_xs_3->chipxpix);
+   p_xs_3->chipydown=p_xs_3->chipy-p_xs_3->pix_X*(0.5*p_xs_3->chipxpix);
+ }
+ else {
+   p_xs_3->chipxup=p_xs_3->chipx+p_xs_3->pix_X*(0.5*p_xs_3->chipxpix+NIR_FIX);
+   p_xs_3->chipxdown=p_xs_3->chipx-p_xs_3->pix_X*(0.5*p_xs_3->chipxpix);
+   p_xs_3->chipyup=p_xs_3->chipy+p_xs_3->pix_Y*(+0.5*p_xs_3->chipypix);
+   p_xs_3->chipydown=p_xs_3->chipy-p_xs_3->pix_Y*(0.5*p_xs_3->chipypix);
+ }
+}
+static void xsh_copyvector(vec a, vec b)
+{
+  int i;
+  for(i=0; i<vectordim; i++) a[i]=b[i];
+
+}
+void xsh_matrixforvector(vec a,mat B,vec c)
+{
+  register int i,j;
+  vec temp;
+  xsh_nullvector(temp);
+  for(j=0; j<vectordim; j++) {
+    double c_j = c[j];
+    for(i=0; i<vectordim; i++) {
+        temp[i] +=B[i][j]*c_j;
+    }
+  }
+  xsh_copyvector(a,temp);
+}
+double xsh_model_sellmeier_ext(int arm, double temper, double lam_sqr)
+{
+  typedef DOUBLE sellmeier_mat[3][5];
+  int ii, jj;
+  double ref_ind,top,bottom;
+  double T[5];
+  sellmeier_mat S_UVB={{1.10127,     -4.94251E-05, 5.27414E-07,  -1.59700E-09, 1.75949E-12},
+		       {1.78752E-05, 4.76391E-05,  -4.49019E-07, 1.44546E-09,  -1.57223E-12},
+		       {0.793552,    -1.27815E-03, 1.84595E-05,  -9.20275E-08, 1.48829E-10}};
+  sellmeier_mat L_UVB={{-0.08906,    9.08730E-06,  -6.53638E-08, 7.77072E-11,  6.84605E-14},
+		       {0.297562,    -859578E-04,  6.59069E-06,  -1.09482E-08, 7.85145E-13},
+		       {9.34454,     -7.09788E-03, 1.01968E-04,  -5.07660E-07, 8.21348E-10}};
+
+  sellmeier_mat S_NIR={{0.104940144,5.40E-06,3.23E-08,1.83E-13,-3.60E-14},
+		   {0.996335571,-3.29E-06,1.48E-08,-3.01E-11,4.99E-14},
+		   {0.832484961,6.38E-04,-2.40E-06,6.10E-10,4.77E-12}};
+  sellmeier_mat L_NIR={{-3.07E-03,-2.56E-05,6.21E-07,-2.54E-09,2.88E-12},
+		   {9.40E-02,-1.59E-06,1.28E-08,1.82E-12,-3.01E-14},
+		   {9.598633568,3.15E-03,-1.22E-05,5.48E-09,1.96E-11}};
+  T[0]=1.0;
+  T[1]=temper;
+  T[2]=temper*temper;
+  T[3]=T[2]*temper;
+  T[4]=T[3]*temper;
+  ref_ind=1.0;
+  if (arm==0) {
+    for (ii=0;ii<3;ii++) {
+      top=bottom=0.0;
+      for (jj=0;jj<5;jj++) {
+	top+=S_UVB[ii][jj]*T[jj];
+	bottom+=L_UVB[ii][jj]*T[jj];
+      }
+      ref_ind+=(top*lam_sqr)/(lam_sqr-(bottom*bottom));
+    }
+  }
+  else if (arm==2) {
+    for (ii=0;ii<3;ii++) {
+      top=bottom=0.0;
+      for (jj=0;jj<5;jj++) {
+	top+=S_NIR[ii][jj]*T[jj];
+	bottom+=L_NIR[ii][jj]*T[jj];
+      }
+      ref_ind+=(top*lam_sqr)/(lam_sqr-(bottom*bottom));
+    }
+  }
+  return sqrt(ref_ind);
+}
+void xsh_multiplymatrix(mat A, mat B, mat C)
+{
+  register int i,j,k;
+  xsh_nullmatrix(A);
+  for(i=0; i<vectordim; i++) {
+    for(j=0; j<vectordim; j++) {
+      for(k=0; k<vectordim; k++){
+	  A[i][j] +=B[i][k]*C[k][j];
+	}
+    }
+  }
+}
+void xsh_normall(vec a)
+{
+   DOUBLE mag;
+   mag=1./sqrt(a[1]*a[1]+a[2]*a[2]+a[3]*a[3]);
+   a[1]=a[1]*mag;
+   a[2]=a[2]*mag;
+   a[3]=a[3]*mag;
+}
+void xsh_normz(vec a)
+{
+  double x2_sqr = a[2]*a[2];
+  if (x2_sqr > 1.0) {
+    a[1]=0.0;
+    a[2]=1.0;
+    a[3]=0.0;
+    return;
+  }
+  double x1_sqr = a[1]*a[1];
+  if (x1_sqr > 1.0) {
+    a[1]=1.0;
+    a[2]=0.0;
+    a[3]=0.0;
+    return;
+  }
+  double sum_sqrs = x1_sqr + x2_sqr;
+  if (sum_sqrs > 1.0) {
+    double norm = 1./sqrt (sum_sqrs);
+    a[1] *=norm;
+    a[2] *=norm;
+    a[3]=0.0;
+  }
+  else{
+    if (a[3]>=0) a[3]=sqrt(1.0- sum_sqrs);
+    else a[3]=-sqrt(1.0-sum_sqrs);
+  }
+
+  return;
+}
+static void xsh_nullmatrix(mat A)
+{
+
+  register int i,j;
+   for(i=0; i<vectordim; i++) {
+    for(j=0; j<vectordim; j++) {
+      A[i][j]=0.0;
+    }
+   }
+
+
+
+}
+static void xsh_nullvector(vec a)
+{
+
+  int i;
+  for(i=0; i<vectordim; i++) a[i]=0.0;
+
+
+}
+void xsh_ref_ind_read(int arm, DOUBLE** ref_ind_T,DOUBLE temper)
+{
+  int ii,jj,flag;
+
+
+
+  ii=2;
+  flag=0;
+  for (ii=2; ii<8; ii+=1) {
+    for (jj=0;jj<7;jj+=1){
+      if (arm==0) {
+	ref_ind_T[ii][jj]=silica_zemax[ii-2][jj];
+      }
+      else if (arm==1) {
+	ref_ind_T[ii][jj]=schott_sf6_manu[ii-2][jj];
+      }
+      else {
+	ref_ind_T[ii][jj]=znse[ii-2][jj];
+      }
+    }
+
+    if (temper>=ref_ind_T[ii][6])
+    {
+
+      flag=1;
+      for (jj=0; jj<7; jj++) {
+	ref_ind_T[0][jj]=ref_ind_T[ii][jj];
+      }
+    }
+    else if (temper<ref_ind_T[ii][6] && flag==1)
+    {
+
+      flag=2;
+      for (jj=0; jj<7; jj++) {
+	ref_ind_T[1][jj]=ref_ind_T[ii][jj];
+      }
+    }
+  }
+
+  if (flag!=2) {
+    printf("******* Temperature out of range! ******* %lf \n", temper);
+    for (jj=0; jj<7; jj++) {
+      ref_ind_T[1][jj]=ref_ind_T[7][jj];
+    }
+  }
+  return;
+}
+void xsh_refract(vec b, DOUBLE rind, vec a)
+{
+  DOUBLE phi, pheta, y_signflag, z_signflag, sin_refrangle;
+  y_signflag=1.0;
+  z_signflag=1.0;
+  phi=atan(sqrt(a[1]*a[1]+a[2]*a[2])/a[3]);
+  pheta=atan(a[1]/a[2]);
+  if (a[2]<0.0) y_signflag=-1.0;
+  if (a[3]<0.0) z_signflag=-1.0;
+
+  sin_refrangle = sin(phi)/rind;
+  b[0]=a[0];
+  b[1]=z_signflag*y_signflag*sin(pheta)*sin_refrangle;
+  b[2]=z_signflag*y_signflag*cos(pheta)*sin_refrangle;
+  b[3]=z_signflag*cos(asin(sin_refrangle));
+  return;
+}
+void
+xsh_rotationmatrix(mat A, const char axis, const DOUBLE angle)
+{
+  xsh_nullmatrix(A);
+  double sin_a=sin(angle);
+  double cos_a=cos(angle);
+
+
+  switch(axis){
+
+  case 'x':{
+
+  A[0][0]=A[1][1]=1;
+  A[2][2]=A[3][3]=cos_a;
+  A[2][3]=sin_a;
+  A[3][2]= -A[2][3];
+  break;
+  }
+
+
+  case 'y':{
+
+  A[0][0]=A[2][2]=1;
+  A[1][1]=A[3][3]=cos_a;
+  A[1][3]=-sin_a;
+  A[3][1]=-A[1][3];
+  break;
+  }
+
+
+  case 'z':{
+  A[0][0]=A[3][3]=1;
+  A[1][1]=A[2][2]=cos_a;
+  A[1][2]=sin_a;
+  A[2][1]=-A[1][2];
+  break;
+  }
+  printf("Error on creating rotation matrix\n");
+ }
+
+}
+void
+xsh_rotin(mat A,const DOUBLE x_angle, const DOUBLE y_angle,const DOUBLE z_angle)
+{
+  mat temp;
+  mat X,Y,Z;
+  xsh_rotationmatrix(X,'x',x_angle);
+  xsh_rotationmatrix(Y,'y',y_angle);
+  xsh_rotationmatrix(Z,'z',z_angle);
+
+  xsh_multiplymatrix(temp,Y,X);
+  xsh_multiplymatrix(A,Z,temp);
+}
+void xsh_subtractvectors(vec a, vec b)
+{
+  int i;
+  for(i=0; i<vectordim; i++) a[i]-=b[i];
+}
+void xsh_transpose(mat A ,mat B)
+{
+   register int i,j;
+   for(i=0; i<vectordim; i++) {
+    for(j=0; j<vectordim; j++) {
+      A[i][j]=B[j][i];
+    }
+   }
+}
+static struct xs_3 model;
+static double indices[8][7];
+static double *index_rows[8];
+void reset_model(void) {memset(&model, 0, sizeof(model));}
+int set_parameter(const char *name, double value) {struct xs_3 *p=&model;
+if (!strcmp(name, "arm")) {p->arm=value; return 1;}
+if (!strcmp(name, "ca_x0")) {p->ca_x0=value; return 1;}
+if (!strcmp(name, "ca_x1")) {p->ca_x1=value; return 1;}
+if (!strcmp(name, "ca_y0")) {p->ca_y0=value; return 1;}
+if (!strcmp(name, "ca_y1")) {p->ca_y1=value; return 1;}
+if (!strcmp(name, "chiprot")) {p->chiprot=value; return 1;}
+if (!strcmp(name, "chipx")) {p->chipx=value; return 1;}
+if (!strcmp(name, "chipy")) {p->chipy=value; return 1;}
+if (!strcmp(name, "cmup1")) {p->cmup1=value; return 1;}
+if (!strcmp(name, "config_mjd")) {p->config_mjd=value; return 1;}
+if (!strcmp(name, "d2_x1")) {p->d2_x1=value; return 1;}
+if (!strcmp(name, "d2_x2")) {p->d2_x2=value; return 1;}
+if (!strcmp(name, "d2_x3")) {p->d2_x3=value; return 1;}
+if (!strcmp(name, "d2_y1x0")) {p->d2_y1x0=value; return 1;}
+if (!strcmp(name, "d2_y1x1")) {p->d2_y1x1=value; return 1;}
+if (!strcmp(name, "d2_y1x2")) {p->d2_y1x2=value; return 1;}
+if (!strcmp(name, "d2_y1x3")) {p->d2_y1x3=value; return 1;}
+if (!strcmp(name, "d2_y2x0")) {p->d2_y2x0=value; return 1;}
+if (!strcmp(name, "d2_y2x1")) {p->d2_y2x1=value; return 1;}
+if (!strcmp(name, "d2_y2x2")) {p->d2_y2x2=value; return 1;}
+if (!strcmp(name, "d2_y2x3")) {p->d2_y2x3=value; return 1;}
+if (!strcmp(name, "d2_y3x0")) {p->d2_y3x0=value; return 1;}
+if (!strcmp(name, "d2_y3x1")) {p->d2_y3x1=value; return 1;}
+if (!strcmp(name, "d2_y3x2")) {p->d2_y3x2=value; return 1;}
+if (!strcmp(name, "d2_y3x3")) {p->d2_y3x3=value; return 1;}
+if (!strcmp(name, "es_s")) {p->es_s=value; return 1;}
+if (!strcmp(name, "es_w")) {p->es_w=value; return 1;}
+if (!strcmp(name, "es_x")) {p->es_x=value; return 1;}
+if (!strcmp(name, "es_y")) {p->es_y=value; return 1;}
+if (!strcmp(name, "fcol")) {p->fcol=value; return 1;}
+if (!strcmp(name, "fdet")) {p->fdet=value; return 1;}
+if (!strcmp(name, "flipx")) {p->flipx=value; return 1;}
+if (!strcmp(name, "flipy")) {p->flipy=value; return 1;}
+if (!strcmp(name, "mud")) {p->mud=value; return 1;}
+if (!strcmp(name, "mues")) {p->mues=value; return 1;}
+if (!strcmp(name, "mug")) {p->mug=value; return 1;}
+if (!strcmp(name, "mup1")) {p->mup1=value; return 1;}
+if (!strcmp(name, "mup2")) {p->mup2=value; return 1;}
+if (!strcmp(name, "mup3")) {p->mup3=value; return 1;}
+if (!strcmp(name, "mup4")) {p->mup4=value; return 1;}
+if (!strcmp(name, "mup5")) {p->mup5=value; return 1;}
+if (!strcmp(name, "mup6")) {p->mup6=value; return 1;}
+if (!strcmp(name, "nud")) {p->nud=value; return 1;}
+if (!strcmp(name, "nues")) {p->nues=value; return 1;}
+if (!strcmp(name, "nug")) {p->nug=value; return 1;}
+if (!strcmp(name, "nup1")) {p->nup1=value; return 1;}
+if (!strcmp(name, "nup2")) {p->nup2=value; return 1;}
+if (!strcmp(name, "nup3")) {p->nup3=value; return 1;}
+if (!strcmp(name, "nup4")) {p->nup4=value; return 1;}
+if (!strcmp(name, "nup5")) {p->nup5=value; return 1;}
+if (!strcmp(name, "nup6")) {p->nup6=value; return 1;}
+if (!strcmp(name, "offx")) {p->offx=value; return 1;}
+if (!strcmp(name, "offy")) {p->offy=value; return 1;}
+if (!strcmp(name, "pc4_x_x2y2")) {p->pc4_x_x2y2=value; return 1;}
+if (!strcmp(name, "pc4_x_x3y")) {p->pc4_x_x3y=value; return 1;}
+if (!strcmp(name, "pc4_x_x4")) {p->pc4_x_x4=value; return 1;}
+if (!strcmp(name, "pc4_x_xy3")) {p->pc4_x_xy3=value; return 1;}
+if (!strcmp(name, "pc4_x_y4")) {p->pc4_x_y4=value; return 1;}
+if (!strcmp(name, "pc4_y_x2y2")) {p->pc4_y_x2y2=value; return 1;}
+if (!strcmp(name, "pc4_y_x3y")) {p->pc4_y_x3y=value; return 1;}
+if (!strcmp(name, "pc4_y_x4")) {p->pc4_y_x4=value; return 1;}
+if (!strcmp(name, "pc4_y_xy3")) {p->pc4_y_xy3=value; return 1;}
+if (!strcmp(name, "pc4_y_y4")) {p->pc4_y_y4=value; return 1;}
+if (!strcmp(name, "pc_x_x1")) {p->pc_x_x1=value; return 1;}
+if (!strcmp(name, "pc_x_x2y")) {p->pc_x_x2y=value; return 1;}
+if (!strcmp(name, "pc_x_x3")) {p->pc_x_x3=value; return 1;}
+if (!strcmp(name, "pc_x_xx")) {p->pc_x_xx=value; return 1;}
+if (!strcmp(name, "pc_x_xy")) {p->pc_x_xy=value; return 1;}
+if (!strcmp(name, "pc_x_y1")) {p->pc_x_y1=value; return 1;}
+if (!strcmp(name, "pc_x_y2x")) {p->pc_x_y2x=value; return 1;}
+if (!strcmp(name, "pc_x_y3")) {p->pc_x_y3=value; return 1;}
+if (!strcmp(name, "pc_x_yy")) {p->pc_x_yy=value; return 1;}
+if (!strcmp(name, "pc_y_x1")) {p->pc_y_x1=value; return 1;}
+if (!strcmp(name, "pc_y_x2y")) {p->pc_y_x2y=value; return 1;}
+if (!strcmp(name, "pc_y_x3")) {p->pc_y_x3=value; return 1;}
+if (!strcmp(name, "pc_y_xx")) {p->pc_y_xx=value; return 1;}
+if (!strcmp(name, "pc_y_xy")) {p->pc_y_xy=value; return 1;}
+if (!strcmp(name, "pc_y_y1")) {p->pc_y_y1=value; return 1;}
+if (!strcmp(name, "pc_y_y2x")) {p->pc_y_y2x=value; return 1;}
+if (!strcmp(name, "pc_y_y3")) {p->pc_y_y3=value; return 1;}
+if (!strcmp(name, "pc_y_yy")) {p->pc_y_yy=value; return 1;}
+if (!strcmp(name, "pix")) {p->pix=value; return 1;}
+if (!strcmp(name, "sg")) {p->sg=value; return 1;}
+if (!strcmp(name, "slit[0]")) {p->slit[0]=value; return 1;}
+if (!strcmp(name, "slit[1]")) {p->slit[1]=value; return 1;}
+if (!strcmp(name, "slit[2]")) {p->slit[2]=value; return 1;}
+if (!strcmp(name, "slit[3]")) {p->slit[3]=value; return 1;}
+if (!strcmp(name, "slit[4]")) {p->slit[4]=value; return 1;}
+if (!strcmp(name, "slit[5]")) {p->slit[5]=value; return 1;}
+if (!strcmp(name, "slit[6]")) {p->slit[6]=value; return 1;}
+if (!strcmp(name, "slit[7]")) {p->slit[7]=value; return 1;}
+if (!strcmp(name, "slit[8]")) {p->slit[8]=value; return 1;}
+if (!strcmp(name, "slit_scale")) {p->slit_scale=value; return 1;}
+if (!strcmp(name, "t_ir_p2")) {p->t_ir_p2=value; return 1;}
+if (!strcmp(name, "t_ir_p3")) {p->t_ir_p3=value; return 1;}
+if (!strcmp(name, "taud")) {p->taud=value; return 1;}
+if (!strcmp(name, "taues")) {p->taues=value; return 1;}
+if (!strcmp(name, "taug")) {p->taug=value; return 1;}
+if (!strcmp(name, "taup1")) {p->taup1=value; return 1;}
+if (!strcmp(name, "taup2")) {p->taup2=value; return 1;}
+if (!strcmp(name, "taup3")) {p->taup3=value; return 1;}
+if (!strcmp(name, "taup4")) {p->taup4=value; return 1;}
+if (!strcmp(name, "taup5")) {p->taup5=value; return 1;}
+if (!strcmp(name, "taup6")) {p->taup6=value; return 1;}
+if (!strcmp(name, "temper")) {p->temper=value; return 1;}
+return 0;}
+
+void initialize_model(int nx, int ny, double xcorr, double ycorr) {
+    model.ASIZE=nx; model.BSIZE=ny; model.SIZE=ny;
+    model.chipxpix=nx; model.chipypix=ny;
+    model.xsize_corr=xcorr; model.ysize_corr=ycorr;
+    model.pix_X=model.pix; model.pix_Y=model.pix;
+    for (int i=0; i<8; i++) index_rows[i]=indices[i];
+    double temperature = model.arm==2 ? model.t_ir_p2 : model.temper;
+    xsh_ref_ind_read(model.arm, index_rows, temperature);
+    xsh_3_init(&model);
+}
+void evaluate(int n, double *wave_nm, double *slit_as, double eta_as, int order, double *x, double *y) {
+    double original_es_x=model.es_x;
+    model.morder=order;
+    model.es_x=original_es_x + eta_as * model.slit_scale;
+    for (int i=0; i<n; i++) {
+        model.es_y_tot=model.es_y+slit_as[i]*model.slit_scale;
+        xsh_3_eval(wave_nm[i]/1000000.0, order, index_rows, &model);
+        xsh_3_detpix(&model);
+        // Unclipped version of ESO's pixel transformation, pixel centers at 0.5.
+        if (model.arm != 2) {
+            x[i]=0.5*(model.chipxpix+model.xsize_corr)-(model.ydet-model.chipy)/model.pix_X;
+            y[i]=0.5*(model.chipypix+model.ysize_corr)-(model.xdet-model.chipx)/model.pix_Y;
+        } else {
+            x[i]=0.5*(model.chipxpix+model.xsize_corr)+(model.xdet-model.chipx)/model.pix_X;
+            y[i]=0.5*(model.chipypix+model.ysize_corr)+(model.ydet-model.chipy)/model.pix_Y;
+        }
+    }
+    model.es_x=original_es_x;
+}
diff --git a/XSHOOTER/calibration/fit_lsf.py b/XSHOOTER/calibration/fit_lsf.py
new file mode 100644
index 00000000..3291a29a
--- /dev/null
+++ b/XSHOOTER/calibration/fit_lsf.py
@@ -0,0 +1,76 @@
+"""Fit optical Gaussian blur to the manual's slit-limited arc-line resolution.
+
+Uniform slit illumination and the physical detector pixel are integrated before
+fitting a Gaussian, as in an arc-line resolution measurement. Each arm uses the
+mean slit dispersion and pixel sampling at its modeled order centers. This is
+an arm-level approximation; it does not impose constant final resolving power.
+"""
+from pathlib import Path
+
+import numpy as np
+from astropy.io import fits
+from astropy.table import Table
+from scipy.optimize import curve_fit, least_squares
+from scipy.special import erf
+from scopesim.effects.spectral_trace_list_utils import SpectralTrace
+
+ROOT = Path(__file__).resolve().parents[1]
+# X-shooter User Manual v10, Table 13 (slit width [arcsec], resolving power).
+REFERENCE = {
+    'UVB': ([.5, .8, 1., 1.3, 1.6], [9700, 6700, 5400, 4100, 3200]),
+    'VIS': ([.4, .7, .9, 1.2, 1.5], [18400, 11400, 8900, 6500, 5000]),
+    'NIR': ([.4, .6, .9, 1.2], [11600, 8100, 5600, 4300]),
+}
+
+
+def gaussian(wave, amplitude, sigma):
+    return amplitude*np.exp(-.5*(wave/sigma)**2)
+
+
+def slit_line_resolving_power(width, r_slit, r_pixel, r_optical):
+    # Fractional wavelength coordinate; both slit and detector pixel widths
+    # are obtained from the configured optical model, not numerical sky pixels.
+    sigma = 1/r_optical/np.sqrt(8*np.log(2))
+    slit_width = width/r_slit
+    wave = np.linspace(-3*slit_width-8*sigma, 3*slit_width+8*sigma, 1601)
+    pixel = np.linspace(-.5/r_pixel, .5/r_pixel, 31)
+    pos = wave[:, None] + pixel
+    line = np.mean(erf((pos+slit_width/2)/(np.sqrt(2)*sigma)) - erf((pos-slit_width/2)/(np.sqrt(2)*sigma)), axis=1)
+    parameters, _ = curve_fit(gaussian, wave, line, p0=[line.max(), slit_width/2.355])
+    return 1/(abs(parameters[1])*np.sqrt(8*np.log(2)))
+
+
+def resolution_table():
+    rows = []
+    with fits.open(ROOT / 'traces' / 'traces.fits') as hdul:
+        for arm, (widths, reference) in REFERENCE.items():
+            slit_sampling, pixel_sampling = [], []
+            traces = [SpectralTrace(hdu) for hdu in hdul[2:] if hdu.name.startswith(arm)]
+            for trace in traces:
+                wave = (trace.wave_min + trace.wave_max)/2
+                x, y = trace.xilam2x(0, wave), trace.xilam2y(0, wave)
+                gx, gy = trace.xy2lam.gradient()
+                slit_sampling.append(wave/abs(trace.slit_dispersion(wave)))
+                pixel_sampling.append(wave/(np.hypot(gx(x, y), gy(x, y))*trace.meta['pixel_size']))
+            r_slit, r_pixel = np.mean(slit_sampling), np.mean(pixel_sampling)
+            configured_r = traces[0].meta['optical_resolving_power']
+
+            def residual(log_r):
+                modeled = [slit_line_resolving_power(width, r_slit, r_pixel, np.exp(log_r[0])) for width in widths]
+                return np.array(modeled)/reference - 1
+
+            fit = least_squares(residual, [np.log(configured_r)])
+            fitted_r = np.exp(fit.x[0])
+            print(f'{arm}: fitted optical R = {fitted_r:.0f}; configured R = {configured_r}')
+            for width, published_r in zip(widths, reference):
+                modeled_r = slit_line_resolving_power(width, r_slit, r_pixel, configured_r)
+                rows.append((arm, width, published_r, modeled_r, modeled_r/published_r - 1))
+    table = Table(rows=rows, names=['arm', 'slit_arcsec', 'reference_R', 'model_R', 'fractional_error'])
+    table['slit_arcsec'].unit = 'arcsec'
+    return table
+
+
+if __name__ == '__main__':
+    table = resolution_table()
+    table.write(ROOT / 'calibration' / 'slit_resolution.ecsv', overwrite=True)
+    print(table)
diff --git a/XSHOOTER/calibration/measure_standard.py b/XSHOOTER/calibration/measure_standard.py
new file mode 100644
index 00000000..d1ca5130
--- /dev/null
+++ b/XSHOOTER/calibration/measure_standard.py
@@ -0,0 +1,126 @@
+"""Calibrate optical order response using the 2024-01-01 EG21 nod pair.
+
+Run after prepare_calibrations.py, with the raw/reference files listed in
+README.md in --data-dir. The first nod calibrates throughput; the second nod
+checks repeatability. NIR measurements are retained for validation but are not
+used to adjust the ETC response because of telluric and detector residuals.
+"""
+from pathlib import Path
+import argparse
+import numpy as np
+from astropy import units as u, constants as c
+from astropy.io import fits
+from astropy.table import Table
+from scipy.interpolate import interp1d, PchipInterpolator
+from scopesim.effects.spectral_trace_list_utils import SpectralTrace
+
+ROOT = Path(__file__).parent.parent
+
+
+def order_rate(trace, difference, slit_position, pixel, gain, exposure, arm):
+    """Sum a 3.6 arcsec spatial aperture in native detector rows or columns."""
+    ny, nx = difference.shape
+    wavelengths = np.linspace(trace.wave_min, trace.wave_max, 5000)
+    slit = np.full_like(wavelengths, slit_position)
+    x = trace.xilam2x(slit, wavelengths)/pixel + nx/2 - .5
+    y = trace.xilam2y(slit, wavelengths)/pixel + ny/2 - .5
+    dispersion, cross = (x, y) if arm == 'nir' else (y, x)
+    order = np.argsort(dispersion)
+    dp = np.arange(nx if arm == 'nir' else ny)
+    covered = (dp > dispersion.min()+1) & (dp < dispersion.max()-1)
+    dp = dp[covered]
+    wave = interp1d(dispersion[order], wavelengths[order])(dp)
+    cp = np.interp(dp, dispersion[order], cross[order])
+    cross_pixels = np.rint(cp).astype(int)[:, None] + np.arange(-30, 31)
+    on_detector = (cross_pixels >= 0) & (cross_pixels < (ny if arm == 'nir' else nx))
+    cross_pixels = np.clip(cross_pixels, 0, (ny if arm == 'nir' else nx)-1)
+    xx, yy = (dp[:, None]+np.zeros_like(cross_pixels), cross_pixels) if arm == 'nir' else (cross_pixels, dp[:, None]+np.zeros_like(cross_pixels))
+    xmm, ymm = (xx+.5-nx/2)*pixel, (yy+.5-ny/2)*pixel
+    s = trace.xy2xi(xmm, ymm)
+    gx, gy = trace.xy2xi.gradient()
+    ds = abs((gy if arm == 'nir' else gx)(xmm, ymm))*pixel
+    weight = np.clip((1.8-abs(s-slit_position))/ds+.5, 0, 1)*on_detector
+    rate = np.sum(difference[yy, xx]*weight, axis=1)*gain/exposure
+    return wave, rate, abs(np.gradient(wave))
+
+
+def measure(data_dir):
+    traces = fits.open(ROOT/'traces/traces.fits')
+    extinction = fits.getdata(data_dir/'atmos-ext-uvb.fits', 1)
+    with fits.open(data_dir/'sky-default-raw.fits') as sky_file:
+        sky = sky_file[1].data
+        sky_wave = (sky['lam']*u.Unit(sky_file[1].columns['lam'].unit)).to_value(u.um)
+    mirrors = Table.read(ROOT.parent/'VLT/TC_mirror_aluminium.dat', format='ascii')
+    area = np.pi/4*(8.2**2-1.116**2)*u.m**2
+    corrections, measurements, checks = [fits.PrimaryHDU()], [fits.PrimaryHDU()], []
+    for arm_id, arm in enumerate(['uvb', 'vis', 'nir']):
+        first = fits.open(data_dir/f'standard-{arm}.fits')
+        second = fits.open(data_dir/f'standard-b-{arm}.fits')
+        data_a, data_b = first[0].data, second[0].data
+        if arm != 'nir':
+            start = first[0].header['ESO DET OUT1 PRSCX']
+            data_a, data_b = data_a[:, start:start+2048], data_b[:, start:start+2048]
+        difference = data_a.astype('float64')-data_b
+        reference = fits.getdata(data_dir/f'response-{arm}.fits', 1)
+        qe = Table.read(ROOT/f'throughput/qe_{arm}.dat', format='ascii')
+        for row in traces[1].data:
+            if row['aperture_id'] != arm_id:
+                continue
+            trace = SpectralTrace(traces[row['extension_id']])
+            measured = []
+            for slit_position, sign, header in [(-2.5, 1, first[0].header), (2.5, -1, second[0].header)]:
+                wave, rate, dwave = order_rate(trace, sign*difference, slit_position, trace.meta['pixel_size'], [.63, .64, 2.29][arm_id], header['EXPTIME'], arm)
+                use = (wave*1000 >= reference['LAMBDA'][0]) & (wave*1000 <= reference['LAMBDA'][-1])
+                wave, rate, dwave = wave[use], rate[use], dwave[use]
+                flux = np.interp(wave*1000, reference['LAMBDA'], reference['REF'])*u.erg/u.cm**2/u.s/u.AA
+                incident = (flux*area*(dwave*u.um)/(c.h*c.c/(wave*u.um))).to_value(1/u.s)
+                airmass = header['ESO TEL AIRM START']
+                if arm == 'nir':
+                    transmission = np.interp(wave, sky_wave, sky['trans'])**(airmass/1.3)
+                else:
+                    transmission = 10**(-.4*airmass*np.interp(wave*1000, extinction['LAMBDA'], extinction['EXTINCTION']))
+                telescope = np.interp(wave, mirrors['wavelength'], mirrors['reflection'])**2
+                quantum_efficiency = np.interp(wave, qe['wavelength'], qe['transmission'])
+                throughput = rate/(incident*transmission*telescope*quantum_efficiency)
+                order = np.argsort(wave)
+                measured.append((wave[order], throughput[order]))
+            wave, eta_a = measured[0]
+            wave_b, eta_b = measured[1]
+            overlap = (wave >= wave_b[0]) & (wave <= wave_b[-1])
+            wave, eta_a = wave[overlap], eta_a[overlap]
+            eta_b = np.interp(wave, wave_b, eta_b)
+            # Exclude molecular absorption and use robust 32-pixel continuum bins
+            # to suppress cosmic rays and fixed-pattern structure in unflattened data.
+            molecular = np.interp(wave, sky_wave, sky['trans_ma'])
+            rows = []
+            for start in range(0, len(wave)-31, 32):
+                index = slice(start, start+32)
+                if np.min(molecular[index]) < .97 or np.min(eta_a[index]) <= 0 or np.min(eta_b[index]) <= 0:
+                    continue
+                rows.append((np.mean(wave[index]), np.median(eta_a[index]), np.median(eta_b[index])))
+            table = Table(rows=rows, names=['wavelength', 'throughput_a', 'throughput_b'])
+            table['wavelength'].unit = u.um
+            measurements.append(fits.table_to_hdu(table))
+            measurements[-1].name = trace.trace_id
+            if arm == 'nir':
+                continue
+            # Calibrate the smooth order response directly in physical throughput.
+            # Outside the sampled interval, retain its endpoint value explicitly.
+            # Molecular gaps are interpolated; they are not independent calibrations.
+            profile = PchipInterpolator(table['wavelength'], table['throughput_a'])
+            ew = np.linspace(trace.wave_min, trace.wave_max, 1025)
+            efficiency = profile(np.clip(ew, table['wavelength'][0], table['wavelength'][-1]))
+            output = fits.table_to_hdu(Table([ew*u.um, efficiency], names=['wavelength', 'efficiency']))
+            output.name = trace.trace_id
+            corrections.append(output)
+            residual = table['throughput_b']/profile(table['wavelength'])-1
+            checks.append((trace.trace_id, len(table), np.median(residual), np.sqrt(np.mean(residual**2))))
+    fits.HDUList(corrections).writeto(ROOT/'calibration/optical_response.fits', overwrite=True, checksum=True)
+    fits.HDUList(measurements).writeto(ROOT/'calibration/standard_measurements.fits', overwrite=True, checksum=True)
+    Table(rows=checks, names=['order', 'continuum_bins', 'nod_b_fractional_bias', 'nod_b_fractional_rms']).write(ROOT/'calibration/standard_repeatability.ecsv', overwrite=True)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--data-dir', required=True, type=Path)
+    measure(parser.parse_args().data_dir)
diff --git a/XSHOOTER/calibration/model_nir.fits b/XSHOOTER/calibration/model_nir.fits
new file mode 100644
index 00000000..a26cb962
--- /dev/null
+++ b/XSHOOTER/calibration/model_nir.fits
@@ -0,0 +1,7 @@
+SIMPLE  =                    T / file does conform to FITS standard             BITPIX  =                   16 / number of bits per data pixel                  NAXIS   =                    0 / number of data axes                            EXTEND  =                    T / FITS dataset may contain extensions            COMMENT   FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H DATE    = '2024-01-02T10:24:22' / file creation date (YYYY-MM-DDThh:mm:ss UT)   ORIGIN  = 'ESO     '           / European Southern Observatory                  TELESCOP= 'Tel name not set'   / ESO Telescope Name                             INSTRUME= 'XSHOOTER'           / Instrument used.                               OBJECT  = 'LAMP,WAVE'          / Original target.                               MJD-OBS =       60311.41700462 / Obs start 2024-01-02T10:00:29.199              DATE-OBS= '2024-01-02T10:00:29.1992' / Observing date                           PI-COI  = 'XSHOOTER Operation Team' / PI-COI name.                              ARCFILE = 'M.XSHOOTER.2024-01-03T13:03:56.276.fits' / Archive File Name         DATAMD5 = '9441962672f0b9b0d2775a1aba15d358' / MD5 checksum                     PIPEFILE= 'r.XSHOO.2024-01-02T10:00:29.199_tpl_0007.fits' / no comment          HIERARCH ESO OBS DID = 'ESO-VLT-DIC.OBS-2.2' / OBS Dictionary                   HIERARCH ESO OBS GRP = '0       ' / linked blocks                               HIERARCH ESO OBS ID = 200189624 / Observation block ID                          HIERARCH ESO OBS NAME = 'Calibration' / OB name                                 HIERARCH ESO OBS NTPL =     18 / Number of templates within OB                  HIERARCH ESO OBS PI-COI ID = 52022 / ESO internal PI-COI ID                     HIERARCH ESO OBS PI-COI NAME = 'XSHOOTER Operation Team' / PI-COI name          HIERARCH ESO OBS PROG ID = '60.A-9022(C)' / ESO program identification          HIERARCH ESO OBS START = '2024-01-02T09:10:44' / OB start time                  HIERARCH ESO OBS TPLNO =     8 / Template number within OB                      HIERARCH ESO TPL DID = 'ESO-VLT-DIC.TPL-1.9' / Data dictionary for TPL          HIERARCH ESO TPL EXPNO =     1 / Exposure number within template                HIERARCH ESO TPL ID = 'XSHOOTER_slt_cal_NIRArcsMultiplePinhole' / Templ         HIERARCH ESO TPL NAME = 'Stand-alone Daytime Calibration Template' / Temp       HIERARCH ESO TPL NEXP =      2 / Number of exposures within templat             HIERARCH ESO TPL PRESEQ = 'XSHOOTER_slt_cal_NIRArcsMultiplePinhole.seq' / S     HIERARCH ESO TPL START = '2024-01-02T09:59:46' / TPL start time                 HIERARCH ESO TPL VERSION = '$Revision: 192749 $' / Version of the template      HIERARCH ESO INS ADC1 DEC = -235528.29476 / Telescope desclination [deg].       HIERARCH ESO INS ADC1 ENC END = 14980 / Encoder position at end [Enc]           HIERARCH ESO INS ADC1 ENC START = 14980 / Encoder position at start [Enc]       HIERARCH ESO INS ADC1 END = 72.4951 / Position angle at end [deg].              HIERARCH ESO INS ADC1 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC1 RA = 120829.236997 / Telescope right ascension [deg].     HIERARCH ESO INS ADC1 START = 72.495 / ADC position at start [deg].             HIERARCH ESO INS ADC2 DEC = -235528.29476 / Telescope desclination [deg].       HIERARCH ESO INS ADC2 ENC END = 56459 / Encoder position at end [Enc]           HIERARCH ESO INS ADC2 ENC START = 56459 / Encoder position at start [Enc]       HIERARCH ESO INS ADC2 END = 273.731 / Position angle at end [deg].              HIERARCH ESO INS ADC2 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC2 RA = 120829.236997 / Telescope right ascension [deg].     HIERARCH ESO INS ADC2 START = 273.731 / ADC position at start [deg].            HIERARCH ESO INS ADC3 DEC = -235528.29476 / Telescope desclination [deg].       HIERARCH ESO INS ADC3 ENC END = 14501 / Encoder position at end [Enc]           HIERARCH ESO INS ADC3 ENC START = 14501 / Encoder position at start [Enc]       HIERARCH ESO INS ADC3 END = 70.177 / Position angle at end [deg].               HIERARCH ESO INS ADC3 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC3 RA = 120829.236997 / Telescope right ascension [deg].     HIERARCH ESO INS ADC3 START = 70.177 / ADC position at start [deg].             HIERARCH ESO INS ADC4 DEC = -235528.29476 / Telescope desclination [deg].       HIERARCH ESO INS ADC4 ENC END = 56765 / Encoder position at end [Enc]           HIERARCH ESO INS ADC4 ENC START = 56765 / Encoder position at start [Enc]       HIERARCH ESO INS ADC4 END = 275.212 / Position angle at end [deg].              HIERARCH ESO INS ADC4 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC4 RA = 120829.236997 / Telescope right ascension [deg].     HIERARCH ESO INS ADC4 START = 275.212 / ADC position at start [deg].            HIERARCH ESO INS DATE = '2005-04-01' / Instrument release date (yyyy-mm-d       HIERARCH ESO INS DID = 'ESO-VLT-DIC.XSHOOTER_ICS-363028' / Data dictiona        HIERARCH ESO INS FOCU1 ENC = 27082 / Absolute position [Enc].                   HIERARCH ESO INS FOCU1 POS = 15.6 / Position [C].                               HIERARCH ESO INS FOCU2 ENC = 11750 / Absolute position [Enc].                   HIERARCH ESO INS FOCU2 POS = 0. / Position [C].                                 HIERARCH ESO INS ID = 'XSHOOTER/176642' / Instrument ID.                        HIERARCH ESO INS LAMP2 ID = 'SPL2    ' / Lamp ID.                               HIERARCH ESO INS LAMP2 NAME = 'ArHgNeXe_Lamp' / Lamp name.                      HIERARCH ESO INS LAMP2 ST =  T / Lamp activated.                                HIERARCH ESO INS MIRR1 ID = '        ' / Mirror unique ID.                      HIERARCH ESO INS MIRR1 NAME = '        ' / Mirror name.                         HIERARCH ESO INS MIRR1 NO = -1 / Mirror slide position.                         HIERARCH ESO INS MODE = 'SLITSPEC' / Instrument mode used.                      HIERARCH ESO INS OPTI2 ID = 'SLOT    ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI2 NAME = 'SLOT    ' / OPTIi name.                          HIERARCH ESO INS OPTI2 NO =  3 / OPTIi slot number.                             HIERARCH ESO INS OPTI2 TYPE = 'MIRROR  ' / OPTIi element.                       HIERARCH ESO INS OPTI3 ID = 'PS4     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI3 NAME = 'Pin_row ' / OPTIi name.                          HIERARCH ESO INS OPTI3 NO =  4 / OPTIi slot number.                             HIERARCH ESO INS OPTI3 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS OPTI4 ID = 'PS4     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI4 NAME = 'Pin_row ' / OPTIi name.                          HIERARCH ESO INS OPTI4 NO =  4 / OPTIi slot number.                             HIERARCH ESO INS OPTI4 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS OPTI5 ID = 'PS11    ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI5 NAME = 'Pin_row ' / OPTIi name.                          HIERARCH ESO INS OPTI5 NO = 11 / OPTIi slot number.                             HIERARCH ESO INS OPTI5 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS PATH = 'SLITSPEC' / Optical path used.                         HIERARCH ESO INS SENS21 ID = 'CA1F1   ' / sensor ID.                            HIERARCH ESO INS SENS21 NAME = 'Cab.1 Flow rate' / sensor common name.          HIERARCH ESO INS SENS21 VAL = 0. / Cab. 1 Flow rate [l/h]                       HIERARCH ESO INS SENS60 ID = 'NPM     ' / sensor ID.                            HIERARCH ESO INS SENS60 NAME = 'NIR cryo. P' / sensor common name.              HIERARCH ESO INS SENS60 VAL = 4.88E-05 / NIR Pressure [mbar]                    HIERARCH ESO INS SENS81 ID = 'RANGE   ' / sensor ID.                            HIERARCH ESO INS SENS81 NAME = 'Heater Range' / sensor common name.             HIERARCH ESO INS SENS81 VAL = 4. / Lake 340 Heater Range                        HIERARCH ESO INS SENS82 ID = 'HEATR   ' / sensor ID.                            HIERARCH ESO INS SENS82 NAME = 'Heater output' / sensor common name.            HIERARCH ESO INS SENS82 VAL = 52.6 / Lake 340 Heater output                     HIERARCH ESO INS SENSOR4 SWSIM = T / If T, function is software simulat         HIERARCH ESO INS SHUT1 ID = 'INSH    ' / Shutter ID.                            HIERARCH ESO INS SHUT1 NAME = 'Instrument shutter' / Shutter name.              HIERARCH ESO INS SHUT1 ST =  F / Shutter open.                                  HIERARCH ESO INS SWSIM = 'NORMAL  ' / Software simulation.                      HIERARCH ESO INS TEMP1 ID = 'TMUC    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP1 NAME = 'UVB Camera temp.' / Temperature sensor name.     HIERARCH ESO INS TEMP1 VAL = 15.33 / UVB Camera temp. [C]                       HIERARCH ESO INS TEMP10 ID = 'TMA     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP10 NAME = 'Ambient temp.' / Temperature sensor name.       HIERARCH ESO INS TEMP10 VAL = 15.11 / Ambient temp. [C]                         HIERARCH ESO INS TEMP11 ID = 'TMADC   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP11 NAME = 'BB UVB ADC temp.' / Temperature sensor name.    HIERARCH ESO INS TEMP11 VAL = 17.51 / BB UVB ADC temp. [C]                      HIERARCH ESO INS TEMP2 ID = 'TMUP    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP2 NAME = 'UVB Prism temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP2 VAL = 15.11 / UVB Prism temp. [C]                        HIERARCH ESO INS TEMP21 ID = 'CA1OT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP21 NAME = 'Cab.1 outlet temp.' / Temperature sensor name.  HIERARCH ESO INS TEMP21 VAL = 0. / Cab. 1 outlet temp. [C]                      HIERARCH ESO INS TEMP22 ID = 'CA1IT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP22 NAME = 'Cab.1 inlet temp.' / Temperature sensor name.   HIERARCH ESO INS TEMP22 VAL = 0. / Cab. 1 inlet temp. [C]                       HIERARCH ESO INS TEMP23 ID = 'CA1CT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP23 NAME = 'Cab.1 temp.' / Temperature sensor name.         HIERARCH ESO INS TEMP23 VAL = 0. / Cab. 1 temp. [C]                             HIERARCH ESO INS TEMP24 ID = 'CA1AT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP24 NAME = 'Cab.1 Amb. temp.' / Temperature sensor name.    HIERARCH ESO INS TEMP24 VAL = 0. / Cab. 1 Amb. temp.  [C]                       HIERARCH ESO INS TEMP3 ID = 'TMUB    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP3 NAME = 'UVB bench temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP3 VAL = 15.28 / UVB bench temp. [C]                        HIERARCH ESO INS TEMP4 ID = 'TMVC    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP4 NAME = 'VIS Camera temp.' / Temperature sensor name.     HIERARCH ESO INS TEMP4 VAL = 14.13 / VIS Camera temp. [C]                       HIERARCH ESO INS TEMP5 ID = 'TMVP    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP5 NAME = 'VIS Prism temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP5 VAL = 14.86 / VIS Prism temp. [C]                        HIERARCH ESO INS TEMP6 ID = 'TMVB    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP6 NAME = 'VIS bench temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP6 VAL = 14.3 / VIS bench temp. [C]                         HIERARCH ESO INS TEMP7 ID = 'TMUCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP7 NAME = 'UVB cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP7 VAL = 10.49 / UVB cryo. temp. [C]                        HIERARCH ESO INS TEMP8 ID = 'TMVCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP8 NAME = 'VIS cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP8 VAL = 12.12 / VIS cryo. temp. [C]                        HIERARCH ESO INS TEMP80 ID = 'OBCT    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP80 NAME = 'OB Cover Tel.' / Temperature sensor name.       HIERARCH ESO INS TEMP80 VAL = 103.91 / OB Cover Telescope [K]                   HIERARCH ESO INS TEMP81 ID = 'OBCB    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP81 NAME = 'OB Cover Tank' / Temperature sensor name.       HIERARCH ESO INS TEMP81 VAL = 104.09 / OB Cover Tank [K]                        HIERARCH ESO INS TEMP82 ID = 'OBPR    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP82 NAME = 'OB Prism1' / Temperature sensor name.           HIERARCH ESO INS TEMP82 VAL = 103.25 / OB Prism1 [K]                            HIERARCH ESO INS TEMP83 ID = 'OBCL    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP83 NAME = 'OB Corrector Lens' / Temperature sensor name.   HIERARCH ESO INS TEMP83 VAL = 104.15 / OB Corrector Lens [K]                    HIERARCH ESO INS TEMP84 ID = 'CP      ' / Temperature sensor ID.                HIERARCH ESO INS TEMP84 NAME = 'Cryo. Cold Plate' / Temperature sensor name.    HIERARCH ESO INS TEMP84 VAL = 86.759 / Cryostat Cold Plate [K]                  HIERARCH ESO INS TEMP85 ID = 'RS      ' / Temperature sensor ID.                HIERARCH ESO INS TEMP85 NAME = 'Cryo. rad. shield' / Temperature sensor name.   HIERARCH ESO INS TEMP85 VAL = 98.822 / Cryostat rad. shield [K]                 HIERARCH ESO INS TEMP86 ID = 'DCB     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP86 NAME = 'CCD Copper bar' / Temperature sensor name.      HIERARCH ESO INS TEMP86 VAL = 76.597 / CCD Copper bar [K]                       HIERARCH ESO INS TEMP9 ID = 'TMNCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP9 NAME = 'NIR cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP9 VAL = 21.57 / NIR cryo. temp. [C]                        HIERARCH ESO INS TEMP90 ID = 'NHT     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP90 NAME = 'NIR Head temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP90 VAL = 79. / NIR Head temp. [K]                          HIERARCH ESO INS TILT1 AFCX = 1.148 / Flexure corr. in X [volt].                HIERARCH ESO INS TILT1 AFCY = -0.171 / Flexure corr. in Y [volt].               HIERARCH ESO INS TILT1 CONVX = 0.208 / VoltToMarcsec conversion in X.           HIERARCH ESO INS TILT1 CONVY = 0.269 / VoltToMarcsec conversion in Y.           HIERARCH ESO INS TILT1 ENGX = 6.183 / Piezo voltage at end in X [volt].         HIERARCH ESO INS TILT1 ENGY = 4.82 / Piezo voltage at end in Y [volt].          HIERARCH ESO INS TILT1 ID = 'afcs1   ' / AFCS ID.                               HIERARCH ESO INS TILT1 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT1 NAME = 'Flexure comp. UVB' / AFCS description.           HIERARCH ESO INS TILT1 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT1 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT1 POSXEND = 1.183 / Piezo position at end [volt].          HIERARCH ESO INS TILT1 POSXSTART = 1.183 / Piezo position at start in X [volt   HIERARCH ESO INS TILT1 POSYEND = -0.18 / Piezo position at end [volt].          HIERARCH ESO INS TILT1 POSYSTART = -0.18 / Piezo position at start in Y [volt   HIERARCH ESO INS TILT1 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT1 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT1 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT1 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO INS TILT2 AFCX = -0.098 / Flexure corr. in X [volt].               HIERARCH ESO INS TILT2 AFCY = -0.135 / Flexure corr. in Y [volt].               HIERARCH ESO INS TILT2 CONVX = 0.291 / VoltToMarcsec conversion in X.           HIERARCH ESO INS TILT2 CONVY = 0.384 / VoltToMarcsec conversion in Y.           HIERARCH ESO INS TILT2 ENGX = 4.9 / Piezo voltage at end in X [volt].           HIERARCH ESO INS TILT2 ENGY = 4.952 / Piezo voltage at end in Y [volt].         HIERARCH ESO INS TILT2 ID = 'afcs2   ' / AFCS ID.                               HIERARCH ESO INS TILT2 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT2 NAME = 'Flexure comp. VIS' / AFCS description.           HIERARCH ESO INS TILT2 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT2 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT2 POSXEND = -0.1 / Piezo position at end [volt].           HIERARCH ESO INS TILT2 POSXSTART = -0.1 / Piezo position at start in X [volt    HIERARCH ESO INS TILT2 POSYEND = -0.047 / Piezo position at end [volt].         HIERARCH ESO INS TILT2 POSYSTART = -0.047 / Piezo position at start in Y [volt  HIERARCH ESO INS TILT2 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT2 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT2 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT2 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO INS TILT3 AFCX = 0.868 / Flexure corr. in X [volt].                HIERARCH ESO INS TILT3 AFCY = 0.38 / Flexure corr. in Y [volt].                 HIERARCH ESO INS TILT3 CONVX = 0.58 / VoltToMarcsec conversion in X.            HIERARCH ESO INS TILT3 CONVY = 0.56 / VoltToMarcsec conversion in Y.            HIERARCH ESO INS TILT3 ENGX = 5.952 / Piezo voltage at end in X [volt].         HIERARCH ESO INS TILT3 ENGY = 5.384 / Piezo voltage at end in Y [volt].         HIERARCH ESO INS TILT3 ID = 'afcs3   ' / AFCS ID.                               HIERARCH ESO INS TILT3 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT3 NAME = 'Flexure comp. NIR' / AFCS description.           HIERARCH ESO INS TILT3 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT3 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT3 POSXEND = 0.952 / Piezo position at end [volt].          HIERARCH ESO INS TILT3 POSXSTART = 0.952 / Piezo position at start in X [volt   HIERARCH ESO INS TILT3 POSYEND = 0.381 / Piezo position at end [volt].          HIERARCH ESO INS TILT3 POSYSTART = 0.381 / Piezo position at start in Y [volt   HIERARCH ESO INS TILT3 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT3 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT3 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT3 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO DET CHIP GAIN = 2.29 / Gain in e-/ADU                              HIERARCH ESO DET CHIP ID = 'ESO-Hawaii2RG' / Detector ID                        HIERARCH ESO DET CHIP NAME = 'Hawaii2RG' / Detector name                        HIERARCH ESO DET CHIP NX = 2048 / Pixels in X                                   HIERARCH ESO DET CHIP NY = 1100 / Pixels in Y                                   HIERARCH ESO DET CHIP PXSPACE = 1.8E-05 / Pixel-Pixel Spacing                   HIERARCH ESO DET CHIP RON = 0. / Read-out noise in e                            HIERARCH ESO DET CHIP TYPE = 'IR      ' / The Type of Det Chip                  HIERARCH ESO DET CHOP FREQ = 0 / Chopping Frequency                             HIERARCH ESO DET CON OPMODE = 'NORMAL  ' / Operational Mode                     HIERARCH ESO DET DID = 'ESO-VLT-DIC.IRACE-1.48' / Dictionary Name and Re        HIERARCH ESO DET DIT =  0.6651 / Integration Time                               HIERARCH ESO DET DITDELAY = 0. / Pause Between DITs                             HIERARCH ESO DET EXP NAME = 'XSHOOTER_SLT_WAVE_NIR_002_0001' / Exposure Name    HIERARCH ESO DET EXP NO = 63159 / Exposure number                               HIERARCH ESO DET EXP UTC = '2024-01-02T10:00:52.1513' / File Creation Time      HIERARCH ESO DET FILE CUBE ST = F / Data Cube On/Off                            HIERARCH ESO DET FRAM NO =   1 / Frame number                                   HIERARCH ESO DET FRAM TYPE = 'INT     ' / Frame type                            HIERARCH ESO DET FRAM UTC = '2024-01-02T10:00:51.3301' / Time Recv Frame        HIERARCH ESO DET IRACE ADC1 DELAY = 7 / ADC Delay Adjustment                    HIERARCH ESO DET IRACE ADC1 ENABLE = 1 / Enable ADC Board (0/1)                 HIERARCH ESO DET IRACE ADC1 FILTER1 = 0 / ADC Filter1 Adjustment                HIERARCH ESO DET IRACE ADC1 FILTER2 = 0 / ADC Filter2 Adjustment                HIERARCH ESO DET IRACE ADC1 HEADER = 1 / Header of ADC Board                    HIERARCH ESO DET IRACE ADC1 NAME = 'ADC-RG  ' / Name for ADC Board              HIERARCH ESO DET IRACE ADC2 DELAY = 7 / ADC Delay Adjustment                    HIERARCH ESO DET IRACE ADC2 ENABLE = 1 / Enable ADC Board (0/1)                 HIERARCH ESO DET IRACE ADC2 FILTER1 = 0 / ADC Filter1 Adjustment                HIERARCH ESO DET IRACE ADC2 FILTER2 = 0 / ADC Filter2 Adjustment                HIERARCH ESO DET IRACE ADC2 HEADER = 1 / Header of ADC Board                    HIERARCH ESO DET IRACE ADC2 NAME = 'ADC-RG  ' / Name for ADC Board              HIERARCH ESO DET IRACE SEQCONT = F / Sequencer Continuous Mode                  HIERARCH ESO DET MINDIT = 0.6651 / Minimum DIT                                  HIERARCH ESO DET MODE NAME = '        ' / DCS Detector Mode                     HIERARCH ESO DET NCORRS =    4 / Read-Out Mode                                  HIERARCH ESO DET NCORRS NAME = 'NonDest ' / Read-Out Mode Name                  HIERARCH ESO DET NDIT =     10 / # of Sub-Integrations                          HIERARCH ESO DET NDITSKIP =  0 / DITs skipped at 1st.INT                        HIERARCH ESO DET NDSAMPLES = 2 / # of Non-Dest. Samples                         HIERARCH ESO DET NDSKIP =    1 / Samples skipped per DIT                        HIERARCH ESO DET RSPEED =    6 / Read-Speed Factor                              HIERARCH ESO DET RSPEEDADD = 0 / Read-Speed Add                                 HIERARCH ESO DET SATLEVEL = 42000 / Saturation Level                            HIERARCH ESO DET VOLT1 CLKHI1 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI10 = 3.3 / Set Value High-Clock                     HIERARCH ESO DET VOLT1 CLKHI11 = 3.3 / Set Value High-Clock                     HIERARCH ESO DET VOLT1 CLKHI12 = 3.3 / Set Value High-Clock                     HIERARCH ESO DET VOLT1 CLKHI13 = 3.3 / Set Value High-Clock                     HIERARCH ESO DET VOLT1 CLKHI14 = 3.3 / Set Value High-Clock                     HIERARCH ESO DET VOLT1 CLKHI15 = 0. / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI16 = 3.3 / Set Value High-Clock                     HIERARCH ESO DET VOLT1 CLKHI2 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI3 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI4 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI5 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI6 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI7 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI8 = 3.3 / Set Value High-Clock                      HIERARCH ESO DET VOLT1 CLKHI9 = 0. / Set Value High-Clock                       HIERARCH ESO DET VOLT1 CLKHINM1 = 'clk1Hi FSYNCB' / Name of High-Clock          HIERARCH ESO DET VOLT1 CLKHINM10 = 'clk10Hi MODECTRL1' / Name of High-Clock     HIERARCH ESO DET VOLT1 CLKHINM11 = 'clk11Hi MODECTRL2' / Name of High-Clock     HIERARCH ESO DET VOLT1 CLKHINM12 = 'clock12Hi HORIWMEN' / Name of High-Clock    HIERARCH ESO DET VOLT1 CLKHINM13 = 'clock13Hi VERTWMEN' / Name of High-Clock    HIERARCH ESO DET VOLT1 CLKHINM14 = 'clock14Hi CSB' / Name of High-Clock         HIERARCH ESO DET VOLT1 CLKHINM15 = 'clock15Hi DATACLK' / Name of High-Clock     HIERARCH ESO DET VOLT1 CLKHINM16 = 'clock16Hi DATAIN' / Name of High-Clock      HIERARCH ESO DET VOLT1 CLKHINM2 = 'clk2Hi VCLK' / Name of High-Clock            HIERARCH ESO DET VOLT1 CLKHINM3 = 'clk3Hi LSYNCB' / Name of High-Clock          HIERARCH ESO DET VOLT1 CLKHINM4 = 'clk4Hi HCLK' / Name of High-Clock            HIERARCH ESO DET VOLT1 CLKHINM5 = 'clk5Hi READEN' / Name of High-Clock          HIERARCH ESO DET VOLT1 CLKHINM6 = 'clk6Hi RESETEN' / Name of High-Clock         HIERARCH ESO DET VOLT1 CLKHINM7 = 'clk7Hi MAINRESETB' / Name of High-Clock      HIERARCH ESO DET VOLT1 CLKHINM8 = 'clk8Hi BUFDISABLE' / Name of High-Clock      HIERARCH ESO DET VOLT1 CLKHINM9 = 'clk9Hi FASTENPAD' / Name of High-Clock       HIERARCH ESO DET VOLT1 CLKHIT1 = 3.3252 / Tel Value High-Clock                  HIERARCH ESO DET VOLT1 CLKHIT10 = 3.3301 / Tel Value High-Clock                 HIERARCH ESO DET VOLT1 CLKHIT11 = 3.3398 / Tel Value High-Clock                 HIERARCH ESO DET VOLT1 CLKHIT12 = 3.3447 / Tel Value High-Clock                 HIERARCH ESO DET VOLT1 CLKHIT13 = 3.3398 / Tel Value High-Clock                 HIERARCH ESO DET VOLT1 CLKHIT14 = 3.3252 / Tel Value High-Clock                 HIERARCH ESO DET VOLT1 CLKHIT15 = 0.0439 / Tel Value High-Clock                 HIERARCH ESO DET VOLT1 CLKHIT16 = 3.3398 / Tel Value High-Clock                 HIERARCH ESO DET VOLT1 CLKHIT2 = 3.335 / Tel Value High-Clock                   HIERARCH ESO DET VOLT1 CLKHIT3 = 3.3301 / Tel Value High-Clock                  HIERARCH ESO DET VOLT1 CLKHIT4 = 3.335 / Tel Value High-Clock                   HIERARCH ESO DET VOLT1 CLKHIT5 = 3.3301 / Tel Value High-Clock                  HIERARCH ESO DET VOLT1 CLKHIT6 = 3.3252 / Tel Value High-Clock                  HIERARCH ESO DET VOLT1 CLKHIT7 = 3.3301 / Tel Value High-Clock                  HIERARCH ESO DET VOLT1 CLKHIT8 = 3.3252 / Tel Value High-Clock                  HIERARCH ESO DET VOLT1 CLKHIT9 = 0.0439 / Tel Value High-Clock                  HIERARCH ESO DET VOLT1 CLKLO1 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLO10 = 3.3 / Set value Low-Clock                      HIERARCH ESO DET VOLT1 CLKLO11 = 3.3 / Set value Low-Clock                      HIERARCH ESO DET VOLT1 CLKLO12 = 0. / Set value Low-Clock                       HIERARCH ESO DET VOLT1 CLKLO13 = 0. / Set value Low-Clock                       HIERARCH ESO DET VOLT1 CLKLO14 = 0. / Set value Low-Clock                       HIERARCH ESO DET VOLT1 CLKLO15 = 0. / Set value Low-Clock                       HIERARCH ESO DET VOLT1 CLKLO16 = 3.3 / Set value Low-Clock                      HIERARCH ESO DET VOLT1 CLKLO2 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLO3 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLO4 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLO5 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLO6 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLO7 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLO8 = 3.3 / Set value Low-Clock                       HIERARCH ESO DET VOLT1 CLKLO9 = 0. / Set value Low-Clock                        HIERARCH ESO DET VOLT1 CLKLONM1 = 'clk1Lo FSYNCB' / Name of Low-Clock           HIERARCH ESO DET VOLT1 CLKLONM10 = 'clk10Lo MODECTRL1' / Name of Low-Clock      HIERARCH ESO DET VOLT1 CLKLONM11 = 'clk11Lo MODECTRL2' / Name of Low-Clock      HIERARCH ESO DET VOLT1 CLKLONM12 = 'clock12Lo HORIWMEN' / Name of Low-Clock     HIERARCH ESO DET VOLT1 CLKLONM13 = 'clock13Lo VERTWMEN' / Name of Low-Clock     HIERARCH ESO DET VOLT1 CLKLONM14 = 'clock14Lo CSB' / Name of Low-Clock          HIERARCH ESO DET VOLT1 CLKLONM15 = 'clock15Lo DATACLK' / Name of Low-Clock      HIERARCH ESO DET VOLT1 CLKLONM16 = 'clock16Lo DATAIN' / Name of Low-Clock       HIERARCH ESO DET VOLT1 CLKLONM2 = 'clk2Lo VCLK' / Name of Low-Clock             HIERARCH ESO DET VOLT1 CLKLONM3 = 'clk3Lo LSYNCB' / Name of Low-Clock           HIERARCH ESO DET VOLT1 CLKLONM4 = 'clk4Lo HCLK' / Name of Low-Clock             HIERARCH ESO DET VOLT1 CLKLONM5 = 'clk5Lo READEN' / Name of Low-Clock           HIERARCH ESO DET VOLT1 CLKLONM6 = 'clk6Lo RESETEN' / Name of Low-Clock          HIERARCH ESO DET VOLT1 CLKLONM7 = 'clk7Lo MAINRESETB' / Name of Low-Clock       HIERARCH ESO DET VOLT1 CLKLONM8 = 'clk8Lo BUFDISABLE' / Name of Low-Clock       HIERARCH ESO DET VOLT1 CLKLONM9 = 'clk9Lo FASTENPAD' / Name of Low-Clock        HIERARCH ESO DET VOLT1 CLKLOT1 = 0.0439 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT10 = 3.3398 / Tel Value Low-Clock                  HIERARCH ESO DET VOLT1 CLKLOT11 = 3.335 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT12 = 0.0391 / Tel Value Low-Clock                  HIERARCH ESO DET VOLT1 CLKLOT13 = 0.0537 / Tel Value Low-Clock                  HIERARCH ESO DET VOLT1 CLKLOT14 = 0.0391 / Tel Value Low-Clock                  HIERARCH ESO DET VOLT1 CLKLOT15 = 0.0293 / Tel Value Low-Clock                  HIERARCH ESO DET VOLT1 CLKLOT16 = 3.3398 / Tel Value Low-Clock                  HIERARCH ESO DET VOLT1 CLKLOT2 = 0.0537 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT3 = 0.0439 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT4 = 0.0488 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT5 = 0.0488 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT6 = 0.0391 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT7 = 0.0488 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT8 = 3.3301 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 CLKLOT9 = 0.0391 / Tel Value Low-Clock                   HIERARCH ESO DET VOLT1 DC1 = 3.3 / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC10 = 0. / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC11 = 0. / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC12 = 0. / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC13 = 0. / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC14 = 0. / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC15 = 0. / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC2 = 3.3 / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC3 = 3.3 / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC4 = 1.5 / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC5 = 0.5 / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC6 = 2.3 / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC7 = 3.5 / Set value DC-Voltage                         HIERARCH ESO DET VOLT1 DC8 = 3. / Set value DC-Voltage                          HIERARCH ESO DET VOLT1 DC9 = 0. / Set value DC-Voltage                          HIERARCH ESO DET VOLT1 DCNM1 = 'DC1 VDD ' / Name of DC-voltage                  HIERARCH ESO DET VOLT1 DCNM10 = 'DC10    ' / Name of DC-voltage                 HIERARCH ESO DET VOLT1 DCNM11 = 'DC11    ' / Name of DC-voltage                 HIERARCH ESO DET VOLT1 DCNM12 = 'DC12    ' / Name of DC-voltage                 HIERARCH ESO DET VOLT1 DCNM13 = 'DC13    ' / Name of DC-voltage                 HIERARCH ESO DET VOLT1 DCNM14 = 'DC14    ' / Name of DC-voltage                 HIERARCH ESO DET VOLT1 DCNM15 = 'DC15    ' / Name of DC-voltage                 HIERARCH ESO DET VOLT1 DCNM2 = 'DC2 VDDA' / Name of DC-voltage                  HIERARCH ESO DET VOLT1 DCNM3 = 'DC3 VBIASPOWER' / Name of DC-voltage            HIERARCH ESO DET VOLT1 DCNM4 = 'DC4 DSUB' / Name of DC-voltage                  HIERARCH ESO DET VOLT1 DCNM5 = 'DC5 VRESET' / Name of DC-voltage                HIERARCH ESO DET VOLT1 DCNM6 = 'DC6 BIASGATE' / Name of DC-voltage              HIERARCH ESO DET VOLT1 DCNM7 = 'DC7 EXTREF' / Name of DC-voltage                HIERARCH ESO DET VOLT1 DCNM8 = 'DC8 VLOAD' / Name of DC-voltage                 HIERARCH ESO DET VOLT1 DCNM9 = 'DC9     ' / Name of DC-voltage                  HIERARCH ESO DET VOLT1 DCTA1 = 3.3105 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA10 = 0.0049 / Tel Value 1 for DC                     HIERARCH ESO DET VOLT1 DCTA11 = 0.0098 / Tel Value 1 for DC                     HIERARCH ESO DET VOLT1 DCTA12 = 0.0049 / Tel Value 1 for DC                     HIERARCH ESO DET VOLT1 DCTA13 = 0.0049 / Tel Value 1 for DC                     HIERARCH ESO DET VOLT1 DCTA14 = 0.0049 / Tel Value 1 for DC                     HIERARCH ESO DET VOLT1 DCTA15 = 0.0049 / Tel Value 1 for DC                     HIERARCH ESO DET VOLT1 DCTA2 = 3.3105 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA3 = 3.3105 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA4 = 1.5088 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA5 = 0.5029 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA6 = 2.3145 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA7 = 3.5107 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA8 = 3.0127 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTA9 = 0.0049 / Tel Value 1 for DC                      HIERARCH ESO DET VOLT1 DCTB1 = 3.3008 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB10 = 0.0049 / Tel Value 2 for DC                     HIERARCH ESO DET VOLT1 DCTB11 = 0.0098 / Tel Value 2 for DC                     HIERARCH ESO DET VOLT1 DCTB12 = 0.0049 / Tel Value 2 for DC                     HIERARCH ESO DET VOLT1 DCTB13 = 0.0049 / Tel Value 2 for DC                     HIERARCH ESO DET VOLT1 DCTB14 = 0.0049 / Tel Value 2 for DC                     HIERARCH ESO DET VOLT1 DCTB15 = 0.0049 / Tel Value 2 for DC                     HIERARCH ESO DET VOLT1 DCTB2 = 3.3057 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB3 = 3.3008 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB4 = 1.5039 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB5 = 0.5029 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB6 = 2.2656 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB7 = 3.4912 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB8 = 3.0078 / Tel Value 2 for DC                      HIERARCH ESO DET VOLT1 DCTB9 = 0.0049 / Tel Value 2 for DC                      HIERARCH ESO DET WIN NX = 2048 / # of Pixels in X                               HIERARCH ESO DET WIN NY = 1100 / # of Pixels in Y                               HIERARCH ESO DET WIN STARTX = 1 / Lower left X ref                              HIERARCH ESO DET WIN STARTY = 1 / Lower left Y ref                              HIERARCH ESO DET WIN TYPE =  0 / Win-Type: 0=SW/1=HW                            HIERARCH ESO PRO CATG = 'XSH_MOD_CFG_OPT_2D_NIR' / Category of pipeline product HIERARCH ESO PRO ANCESTOR = 'XSHOO.2024-01-02T10:00:29.199.fits'                HIERARCH ESO PRO DID = 'PRO-1.15' / Data dictionary for PRO                     HIERARCH ESO PRO TYPE = 'REDUCED ' / Product type                               HIERARCH ESO PRO TECH = 'ECHELLE,MULTI-PINHOLE' / Observation technique         HIERARCH ESO PRO SCIENCE =   F / Scientific product if T                        HIERARCH ESO PRO REC1 ID = 'xsh_2dmap' / Pipeline recipe (unique) identifier    HIERARCH ESO PRO REC1 DRS ID = 'cpl-7.3.1' / Data Reduction System identifier   HIERARCH ESO PRO REC1 PIPE ID = 'xshoo/3.6.5a' / Pipeline (unique) identifier   HIERARCH ESO PRO REC1 RAW1 NAME = 'XSHOO.2024-01-02T10:00:29.199.fits' / File naHIERARCH ESO PRO REC1 RAW1 CATG = 'WAVE_NIR_ON' / Category of raw frame         HIERARCH ESO PRO REC1 RAW2 NAME = 'XSHOO.2024-01-02T10:00:57.308.fits' / File naHIERARCH ESO PRO REC1 RAW2 CATG = 'WAVE_NIR_OFF' / Category of raw frame        HIERARCH ESO PRO DATANCOM =  2 / Number of combined frames                      HIERARCH ESO PRO REC1 CAL1 NAME= 'r.XSHOO.2024-01-01T11:45:10.320_tpl_0000.fits'HIERARCH ESO PRO REC1 CAL1 CATG = 'ORDER_TAB_EDGES_SLIT_NIR' / Category of calibHIERARCH ESO PRO REC1 CAL1 DATAMD5 = 'e28e745f99323529e636dd8e02eaf478' / MD5 siHIERARCH ESO PRO REC1 CAL2 NAME= 'r.XSHOO.2024-01-02T09:48:52.875_tpl_0005.fits'HIERARCH ESO PRO REC1 CAL2 CATG = 'XSH_MOD_CFG_OPT_FMT_NIR' / Category of calibrHIERARCH ESO PRO REC1 CAL2 DATAMD5 = '7f4ae607818fdf1504d52d41d397594b' / MD5 siHIERARCH ESO PRO REC1 CAL3 NAME = 'XS_GALL_091001B_NIR.fits' / File name of caliHIERARCH ESO PRO REC1 CAL3 CATG = 'ARC_LINE_LIST_NIR' / Category of calibration HIERARCH ESO PRO REC1 CAL3 DATAMD5 = '332aac0c7b705dde0ff0d1fbd4c45218' / MD5 siHIERARCH ESO PRO REC1 CAL4 NAME = 'XS_GALM_091001B_NIR.fits' / File name of caliHIERARCH ESO PRO REC1 CAL4 CATG = 'ARC_LINE_LIST_INTMON_NIR' / Category of calibHIERARCH ESO PRO REC1 CAL4 DATAMD5 = 'c4667a4a1bfca324eb87d0cc1f2b203e' / MD5 siHIERARCH ESO PRO REC1 CAL5 NAME = 'XS_GSFT_090930A_NIR_9p.fits' / File name of cHIERARCH ESO PRO REC1 CAL5 CATG = 'SPECTRAL_FORMAT_TAB_NIR' / Category of calibrHIERARCH ESO PRO REC1 CAL6 NAME = 'XS_GBPM_091001A_NIR.fits' / File name of caliHIERARCH ESO PRO REC1 CAL6 CATG = 'BP_MAP_RP_NIR' / Category of calibration framHIERARCH ESO PRO REC1 CAL6 DATAMD5 = '6f99f13903272739f959c96eda6d7e1b' / MD5 siHIERARCH ESO PRO REC1 PARAM1 NAME = 'keep-temp' / If 'no', temporary files are dHIERARCH ESO PRO REC1 PARAM1 VALUE = 'no      ' / Default: 'no'                 HIERARCH ESO PRO REC1 PARAM2 NAME = 'debug-level' / Additional xshooter debug leHIERARCH ESO PRO REC1 PARAM2 VALUE = 'none    ' / Default: 'none'               HIERARCH ESO PRO REC1 PARAM3 NAME = 'time-stamp' / Add timestamp to product fileHIERARCH ESO PRO REC1 PARAM3 VALUE = 'false   ' / Default: false                HIERARCH ESO PRO REC1 PARAM4 NAME = 'decode-bp' / Integer representation of the HIERARCH ESO PRO REC1 PARAM4 VALUE = '2144337919' / Default: 2144337919         HIERARCH ESO PRO REC1 PARAM5 NAME = 'pre-overscan-corr' / pre-overscan correctioHIERARCH ESO PRO REC1 PARAM5 VALUE = '0       ' / Default: 1                    HIERARCH ESO PRO REC1 PARAM6 NAME = 'detectarclines-fit-win-hsize' / Half windowHIERARCH ESO PRO REC1 PARAM6 VALUE = '6       ' / Default: 6                    HIERARCH ESO PRO REC1 PARAM7 NAME = 'detectarclines-search-win-hsize' / Half winHIERARCH ESO PRO REC1 PARAM7 VALUE = '3       ' / Default: 3                    HIERARCH ESO PRO REC1 PARAM8 NAME = 'detectarclines-running-median-hsize' / HalfHIERARCH ESO PRO REC1 PARAM8 VALUE = '0       ' / Default: 0                    HIERARCH ESO PRO REC1 PARAM9 NAME = 'detectarclines-wavesol-deg-lambda' / DegreeHIERARCH ESO PRO REC1 PARAM9 VALUE = '5       ' / Default: 5                    HIERARCH ESO PRO REC1 PARAM10 NAME = 'detectarclines-wavesol-deg-order' / DegreeHIERARCH ESO PRO REC1 PARAM10 VALUE = '4       ' / Default: 4                   HIERARCH ESO PRO REC1 PARAM11 NAME = 'detectarclines-wavesol-deg-slit' / Degree HIERARCH ESO PRO REC1 PARAM11 VALUE = '1       ' / Default: 1                   HIERARCH ESO PRO REC1 PARAM12 NAME = 'detectarclines-min-sn' / Minimum signal-toHIERARCH ESO PRO REC1 PARAM12 VALUE = '5       ' / Default: 5                   HIERARCH ESO PRO REC1 PARAM13 NAME = 'detectarclines-find-lines-center' / MethodHIERARCH ESO PRO REC1 PARAM13 VALUE = 'gaussian' / Default: 'gaussian'          HIERARCH ESO PRO REC1 PARAM14 NAME = 'detectarclines-clip-sigma' / Kappa value iHIERARCH ESO PRO REC1 PARAM14 VALUE = '2       ' / Default: 2                   HIERARCH ESO PRO REC1 PARAM15 NAME = 'detectarclines-clip-niter' / Number of iteHIERARCH ESO PRO REC1 PARAM15 VALUE = '0       ' / Default: 0                   HIERARCH ESO PRO REC1 PARAM16 NAME = 'detectarclines-clip-frac' / Minimal fractiHIERARCH ESO PRO REC1 PARAM16 VALUE = '0.7     ' / Default: 0.7                 HIERARCH ESO PRO REC1 PARAM17 NAME = 'dispersol-deg-x' / Degree in X in the polyHIERARCH ESO PRO REC1 PARAM17 VALUE = '4       ' / Default: 4                   HIERARCH ESO PRO REC1 PARAM18 NAME = 'dispersol-deg-y' / Degree in Y in the polyHIERARCH ESO PRO REC1 PARAM18 VALUE = '5       ' / Default: 5                   HIERARCH ESO PRO REC1 PARAM19 NAME = 'model-maxit' / Number/10 of annealing iterHIERARCH ESO PRO REC1 PARAM19 VALUE = '1000    ' / Default: 500                 HIERARCH ESO PRO REC1 PARAM20 NAME = 'model-anneal-factor' / Multiplier applied HIERARCH ESO PRO REC1 PARAM20 VALUE = '0.5     ' / Default: 1                   HIERARCH ESO PRO REC1 PARAM21 NAME = 'model-scenario' / selects preset flag and HIERARCH ESO PRO REC1 PARAM21 VALUE = '8       ' / Default: 4                   HIERARCH ESO QC MODEL NDAT = 1853                                               HIERARCH ESO QC MODEL PREDICT RESX_MIN = -0.69963247751275                      HIERARCH ESO QC MODEL PREDICT RESY_MIN = -0.937204397815094                     HIERARCH ESO QC MODEL PREDICT RESX_MAX = 0.0862843649647402                     HIERARCH ESO QC MODEL PREDICT RESY_MAX = 1.33783324590775                       HIERARCH ESO QC MODEL PREDICT RESX_AVG = -0.255528861570653                     HIERARCH ESO QC MODEL PREDICT RESY_AVG = -0.176023905886889                     HIERARCH ESO QC MODEL PREDICT RESX_MED = -0.254650389236758                     HIERARCH ESO QC MODEL PREDICT RESY_MED = -0.1744031969547                       HIERARCH ESO QC MODEL PREDICT RESX_RMS = 0.102157474669703                      HIERARCH ESO QC MODEL PREDICT RESY_RMS = 0.190269829587982                      HIERARCH ESO QC MODEL ANNEAL RESX_MIN = -0.391566407941923                      HIERARCH ESO QC MODEL ANNEAL RESY_MIN = -0.621655643923646                      HIERARCH ESO QC MODEL ANNEAL RESX_MAX = 0.341120439861243                       HIERARCH ESO QC MODEL ANNEAL RESY_MAX = 1.64348900278458                        HIERARCH ESO QC MODEL ANNEAL RESX_AVG = 0.000269880599610766                    HIERARCH ESO QC MODEL ANNEAL RESY_AVG = 0.000942639519733203                    HIERARCH ESO QC MODEL ANNEAL RESX_MED = 0.00305695071540413                     HIERARCH ESO QC MODEL ANNEAL RESY_MED = 0.00535539916495509                     HIERARCH ESO QC MODEL ANNEAL RESX_RMS = 0.101804827980034                       HIERARCH ESO QC MODEL ANNEAL RESY_RMS = 0.168675418717308                       HIERARCH ESO OCS DET3 IMGNAME = 'XSHOOTER_SLT_WAVE_NIR_' / Data File Name.      HIERARCH ESO SEQ ARM = 'NIR     ' / Instrument Arm                              ORIGFILE= 'XS_PMC2_240101A_NIR_H2.fits' / Original File Name                    CHECKSUM= 'EaDAGVA5EaAAEUA3'   / HDU checksum updated 2024-01-03T13:03:56       DATASUM = '         0'         / data unit checksum updated 2024-01-03T13:03:56 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / 8-bit bytes                                    NAXIS   =                    2 / 2-dimensional binary table                     NAXIS1  =                   42 / width of table in bytes                        NAXIS2  =                   91 / number of rows in table                        PCOUNT  =                    0 / size of special data area                      GCOUNT  =                    1 / one data group (required keyword)              TFIELDS =                    6 / number of fields in each row                   TTYPE1  = 'Best_Guess'         / label for field   1                            TFORM1  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE2  = 'Low_Limit'          / label for field   2                            TFORM2  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE3  = 'High_Limit'         / label for field   3                            TFORM3  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE4  = 'Compute_Flag'       / label for field   4                            TFORM4  = '1J      '           / data format of field: 4-byte INTEGER           TTYPE5  = 'Parameter_Name'     / label for field   5                            TFORM5  = '10A     '           / data format of field: ASCII Character          TTYPE6  = 'Parameter_Units'    / label for field   6                            TFORM6  = '4A      '           / data format of field: ASCII Character          CHECKSUM= 'UOXXaNWVVNWVaNWV'   / HDU checksum updated 2024-01-03T13:03:56       DATASUM = '2473409829'         / data unit checksum updated 2024-01-03T13:03:56 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             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\ No newline at end of file
diff --git a/XSHOOTER/calibration/model_uvb.fits b/XSHOOTER/calibration/model_uvb.fits
new file mode 100644
index 00000000..b3c7e513
--- /dev/null
+++ b/XSHOOTER/calibration/model_uvb.fits
@@ -0,0 +1,4 @@
+SIMPLE  =                    T / file does conform to FITS standard             BITPIX  =                   16 / number of bits per data pixel                  NAXIS   =                    0 / number of data axes                            EXTEND  =                    T / FITS dataset may contain extensions            COMMENT   FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H DATE    = '2024-01-02T10:25:37' / file creation date (YYYY-MM-DDThh:mm:ss UT)   ORIGIN  = 'ESO     '           / European Southern Observatory                  TELESCOP= 'Tel name not set'   / ESO Telescope Name                             INSTRUME= 'XSHOOTER'           / Instrument used.                               OBJECT  = 'LAMP,WAVE'          / Original target.                               MJD-OBS =      60311.415838525 / MJD start (2024-01-02T09:58:48.449)            DATE-OBS= '2024-01-02T09:58:48.448' / Date of observation                       PI-COI  = 'XSHOOTER Operation Team' / PI-COI name.                              ARCFILE = 'M.XSHOOTER.2024-01-03T13:03:57.940.fits' / Archive File Name         DATAMD5 = '9c6f68da55e20fd03fb60dad5a4a77f7' / MD5 checksum                     PIPEFILE= 'r.XSHOO.2024-01-02T09:58:48.448_0007.fits' / no comment              HIERARCH ESO OBS DID = 'ESO-VLT-DIC.OBS-2.2' / OBS Dictionary                   HIERARCH ESO OBS GRP = '0       ' / linked blocks                               HIERARCH ESO OBS ID = 200189624 / Observation block ID                          HIERARCH ESO OBS NAME = 'Calibration' / OB name                                 HIERARCH ESO OBS NTPL =     18 / Number of templates within OB                  HIERARCH ESO OBS PI-COI ID = 52022 / ESO internal PI-COI ID                     HIERARCH ESO OBS PI-COI NAME = 'XSHOOTER Operation Team' / PI-COI name          HIERARCH ESO OBS PROG ID = '60.A-9022(C)' / ESO program identification          HIERARCH ESO OBS START = '2024-01-02T09:10:44' / OB start time                  HIERARCH ESO OBS TPLNO =     7 / Template number within OB                      HIERARCH ESO TPL DID = 'ESO-VLT-DIC.TPL-1.9' / Data dictionary for TPL          HIERARCH ESO TPL EXPNO =     1 / Exposure number within template                HIERARCH ESO TPL ID = 'XSHOOTER_slt_cal_UVBVisArcsMultiplePinhole' / Te         HIERARCH ESO TPL NAME = 'Stand-alone Daytime Calibration Template' / Temp       HIERARCH ESO TPL NEXP =      2 / Number of exposures within templat             HIERARCH ESO TPL PRESEQ = 'XSHOOTER_slt_cal_UVBVisArcsMultiplePinhole.seq'      HIERARCH ESO TPL START = '2024-01-02T09:58:04' / TPL start time                 HIERARCH ESO TPL VERSION = '$Revision: 192749 $' / Version of the template      HIERARCH ESO INS ADC1 DEC = -235528.30025 / Telescope desclination [deg].       HIERARCH ESO INS ADC1 ENC END = 14980 / Encoder position at end [Enc]           HIERARCH ESO INS ADC1 ENC START = 14980 / Encoder position at start [Enc]       HIERARCH ESO INS ADC1 END = 72.4951 / Position angle at end [deg].              HIERARCH ESO INS ADC1 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC1 RA = 120658.990189 / Telescope right ascension [deg].     HIERARCH ESO INS ADC1 START = 72.495 / ADC position at start [deg].             HIERARCH ESO INS ADC2 DEC = -235528.30025 / Telescope desclination [deg].       HIERARCH ESO INS ADC2 ENC END = 56459 / Encoder position at end [Enc]           HIERARCH ESO INS ADC2 ENC START = 56459 / Encoder position at start [Enc]       HIERARCH ESO INS ADC2 END = 273.731 / Position angle at end [deg].              HIERARCH ESO INS ADC2 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC2 RA = 120658.990189 / Telescope right ascension [deg].     HIERARCH ESO INS ADC2 START = 273.731 / ADC position at start [deg].            HIERARCH ESO INS ADC3 DEC = -235528.30025 / Telescope desclination [deg].       HIERARCH ESO INS ADC3 ENC END = 14501 / Encoder position at end [Enc]           HIERARCH ESO INS ADC3 ENC START = 14501 / Encoder position at start [Enc]       HIERARCH ESO INS ADC3 END = 70.177 / Position angle at end [deg].               HIERARCH ESO INS ADC3 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC3 RA = 120658.990189 / Telescope right ascension [deg].     HIERARCH ESO INS ADC3 START = 70.177 / ADC position at start [deg].             HIERARCH ESO INS ADC4 DEC = -235528.30025 / Telescope desclination [deg].       HIERARCH ESO INS ADC4 ENC END = 56765 / Encoder position at end [Enc]           HIERARCH ESO INS ADC4 ENC START = 56765 / Encoder position at start [Enc]       HIERARCH ESO INS ADC4 END = 275.212 / Position angle at end [deg].              HIERARCH ESO INS ADC4 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC4 RA = 120658.990189 / Telescope right ascension [deg].     HIERARCH ESO INS ADC4 START = 275.212 / ADC position at start [deg].            HIERARCH ESO INS DATE = '2005-04-01' / Instrument release date (yyyy-mm-d       HIERARCH ESO INS DID = 'ESO-VLT-DIC.XSHOOTER_ICS-363028' / Data dictiona        HIERARCH ESO INS FOCU1 ENC = 27082 / Absolute position [Enc].                   HIERARCH ESO INS FOCU1 POS = 15.6 / Position [C].                               HIERARCH ESO INS FOCU2 ENC = 11750 / Absolute position [Enc].                   HIERARCH ESO INS FOCU2 POS = 0. / Position [C].                                 HIERARCH ESO INS ID = 'XSHOOTER/176642' / Instrument ID.                        HIERARCH ESO INS LAMP1 ID = 'SPL1    ' / Lamp ID.                               HIERARCH ESO INS LAMP1 NAME = 'ThAr_Lamp' / Lamp name.                          HIERARCH ESO INS LAMP1 ST =  T / Lamp activated.                                HIERARCH ESO INS MIRR1 ID = 'ThAr_D2 ' / Mirror unique ID.                      HIERARCH ESO INS MIRR1 NAME = 'ThAr_D2 ' / Mirror name.                         HIERARCH ESO INS MIRR1 NO =  2 / Mirror slide position.                         HIERARCH ESO INS MODE = 'SLITSPEC' / Instrument mode used.                      HIERARCH ESO INS OPTI2 ID = 'SLOT    ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI2 NAME = 'SLOT    ' / OPTIi name.                          HIERARCH ESO INS OPTI2 NO =  3 / OPTIi slot number.                             HIERARCH ESO INS OPTI2 TYPE = 'MIRROR  ' / OPTIi element.                       HIERARCH ESO INS OPTI3 ID = 'PS4     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI3 NAME = 'Pin_row ' / OPTIi name.                          HIERARCH ESO INS OPTI3 NO =  4 / OPTIi slot number.                             HIERARCH ESO INS OPTI3 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS OPTI4 ID = 'PS4     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI4 NAME = 'Pin_row ' / OPTIi name.                          HIERARCH ESO INS OPTI4 NO =  4 / OPTIi slot number.                             HIERARCH ESO INS OPTI4 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS OPTI5 ID = 'PS8     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI5 NAME = 'Blind   ' / OPTIi name.                          HIERARCH ESO INS OPTI5 NO =  8 / OPTIi slot number.                             HIERARCH ESO INS OPTI5 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS PATH = 'SLITSPEC' / Optical path used.                         HIERARCH ESO INS SENS21 ID = 'CA1F1   ' / sensor ID.                            HIERARCH ESO INS SENS21 NAME = 'Cab.1 Flow rate' / sensor common name.          HIERARCH ESO INS SENS21 VAL = 0. / Cab. 1 Flow rate [l/h]                       HIERARCH ESO INS SENS60 ID = 'NPM     ' / sensor ID.                            HIERARCH ESO INS SENS60 NAME = 'NIR cryo. P' / sensor common name.              HIERARCH ESO INS SENS60 VAL = 4.84E-05 / NIR Pressure [mbar]                    HIERARCH ESO INS SENS81 ID = 'RANGE   ' / sensor ID.                            HIERARCH ESO INS SENS81 NAME = 'Heater Range' / sensor common name.             HIERARCH ESO INS SENS81 VAL = 4. / Lake 340 Heater Range                        HIERARCH ESO INS SENS82 ID = 'HEATR   ' / sensor ID.                            HIERARCH ESO INS SENS82 NAME = 'Heater output' / sensor common name.            HIERARCH ESO INS SENS82 VAL = 52.7 / Lake 340 Heater output                     HIERARCH ESO INS SENSOR4 SWSIM = T / If T, function is software simulat         HIERARCH ESO INS SHUT1 ID = 'INSH    ' / Shutter ID.                            HIERARCH ESO INS SHUT1 NAME = 'Instrument shutter' / Shutter name.              HIERARCH ESO INS SHUT1 ST =  F / Shutter open.                                  HIERARCH ESO INS SWSIM = 'NORMAL  ' / Software simulation.                      HIERARCH ESO INS TEMP1 ID = 'TMUC    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP1 NAME = 'UVB Camera temp.' / Temperature sensor name.     HIERARCH ESO INS TEMP1 VAL = 15.33 / UVB Camera temp. [C]                       HIERARCH ESO INS TEMP10 ID = 'TMA     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP10 NAME = 'Ambient temp.' / Temperature sensor name.       HIERARCH ESO INS TEMP10 VAL = 15.11 / Ambient temp. [C]                         HIERARCH ESO INS TEMP11 ID = 'TMADC   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP11 NAME = 'BB UVB ADC temp.' / Temperature sensor name.    HIERARCH ESO INS TEMP11 VAL = 17.51 / BB UVB ADC temp. [C]                      HIERARCH ESO INS TEMP2 ID = 'TMUP    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP2 NAME = 'UVB Prism temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP2 VAL = 15.11 / UVB Prism temp. [C]                        HIERARCH ESO INS TEMP21 ID = 'CA1OT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP21 NAME = 'Cab.1 outlet temp.' / Temperature sensor name.  HIERARCH ESO INS TEMP21 VAL = 0. / Cab. 1 outlet temp. [C]                      HIERARCH ESO INS TEMP22 ID = 'CA1IT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP22 NAME = 'Cab.1 inlet temp.' / Temperature sensor name.   HIERARCH ESO INS TEMP22 VAL = 0. / Cab. 1 inlet temp. [C]                       HIERARCH ESO INS TEMP23 ID = 'CA1CT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP23 NAME = 'Cab.1 temp.' / Temperature sensor name.         HIERARCH ESO INS TEMP23 VAL = 0. / Cab. 1 temp. [C]                             HIERARCH ESO INS TEMP24 ID = 'CA1AT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP24 NAME = 'Cab.1 Amb. temp.' / Temperature sensor name.    HIERARCH ESO INS TEMP24 VAL = 0. / Cab. 1 Amb. temp.  [C]                       HIERARCH ESO INS TEMP3 ID = 'TMUB    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP3 NAME = 'UVB bench temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP3 VAL = 15.28 / UVB bench temp. [C]                        HIERARCH ESO INS TEMP4 ID = 'TMVC    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP4 NAME = 'VIS Camera temp.' / Temperature sensor name.     HIERARCH ESO INS TEMP4 VAL = 14.13 / VIS Camera temp. [C]                       HIERARCH ESO INS TEMP5 ID = 'TMVP    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP5 NAME = 'VIS Prism temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP5 VAL = 14.86 / VIS Prism temp. [C]                        HIERARCH ESO INS TEMP6 ID = 'TMVB    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP6 NAME = 'VIS bench temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP6 VAL = 14.21 / VIS bench temp. [C]                        HIERARCH ESO INS TEMP7 ID = 'TMUCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP7 NAME = 'UVB cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP7 VAL = 10.45 / UVB cryo. temp. [C]                        HIERARCH ESO INS TEMP8 ID = 'TMVCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP8 NAME = 'VIS cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP8 VAL = 12.12 / VIS cryo. temp. [C]                        HIERARCH ESO INS TEMP80 ID = 'OBCT    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP80 NAME = 'OB Cover Tel.' / Temperature sensor name.       HIERARCH ESO INS TEMP80 VAL = 103.92 / OB Cover Telescope [K]                   HIERARCH ESO INS TEMP81 ID = 'OBCB    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP81 NAME = 'OB Cover Tank' / Temperature sensor name.       HIERARCH ESO INS TEMP81 VAL = 104.1 / OB Cover Tank [K]                         HIERARCH ESO INS TEMP82 ID = 'OBPR    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP82 NAME = 'OB Prism1' / Temperature sensor name.           HIERARCH ESO INS TEMP82 VAL = 103.25 / OB Prism1 [K]                            HIERARCH ESO INS TEMP83 ID = 'OBCL    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP83 NAME = 'OB Corrector Lens' / Temperature sensor name.   HIERARCH ESO INS TEMP83 VAL = 104.13 / OB Corrector Lens [K]                    HIERARCH ESO INS TEMP84 ID = 'CP      ' / Temperature sensor ID.                HIERARCH ESO INS TEMP84 NAME = 'Cryo. Cold Plate' / Temperature sensor name.    HIERARCH ESO INS TEMP84 VAL = 86.728 / Cryostat Cold Plate [K]                  HIERARCH ESO INS TEMP85 ID = 'RS      ' / Temperature sensor ID.                HIERARCH ESO INS TEMP85 NAME = 'Cryo. rad. shield' / Temperature sensor name.   HIERARCH ESO INS TEMP85 VAL = 98.813 / Cryostat rad. shield [K]                 HIERARCH ESO INS TEMP86 ID = 'DCB     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP86 NAME = 'CCD Copper bar' / Temperature sensor name.      HIERARCH ESO INS TEMP86 VAL = 76.593 / CCD Copper bar [K]                       HIERARCH ESO INS TEMP9 ID = 'TMNCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP9 NAME = 'NIR cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP9 VAL = 21.53 / NIR cryo. temp. [C]                        HIERARCH ESO INS TEMP90 ID = 'NHT     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP90 NAME = 'NIR Head temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP90 VAL = 79. / NIR Head temp. [K]                          HIERARCH ESO INS TILT1 AFCX = 1.148 / Flexure corr. in X [volt].                HIERARCH ESO INS TILT1 AFCY = -0.171 / Flexure corr. in Y [volt].               HIERARCH ESO INS TILT1 CONVX = 0.208 / VoltToMarcsec conversion in X.           HIERARCH ESO INS TILT1 CONVY = 0.269 / VoltToMarcsec conversion in Y.           HIERARCH ESO INS TILT1 ENGX = 6.182 / Piezo voltage at end in X [volt].         HIERARCH ESO INS TILT1 ENGY = 4.819 / Piezo voltage at end in Y [volt].         HIERARCH ESO INS TILT1 ID = 'afcs1   ' / AFCS ID.                               HIERARCH ESO INS TILT1 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT1 NAME = 'Flexure comp. UVB' / AFCS description.           HIERARCH ESO INS TILT1 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT1 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT1 POSXEND = 1.18 / Piezo position at end [volt].           HIERARCH ESO INS TILT1 POSXSTART = 1.18 / Piezo position at start in X [volt    HIERARCH ESO INS TILT1 POSYEND = -0.18 / Piezo position at end [volt].          HIERARCH ESO INS TILT1 POSYSTART = -0.18 / Piezo position at start in Y [volt   HIERARCH ESO INS TILT1 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT1 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT1 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT1 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO INS TILT2 AFCX = -0.098 / Flexure corr. in X [volt].               HIERARCH ESO INS TILT2 AFCY = -0.135 / Flexure corr. in Y [volt].               HIERARCH ESO INS TILT2 CONVX = 0.291 / VoltToMarcsec conversion in X.           HIERARCH ESO INS TILT2 CONVY = 0.384 / VoltToMarcsec conversion in Y.           HIERARCH ESO INS TILT2 ENGX = 4.901 / Piezo voltage at end in X [volt].         HIERARCH ESO INS TILT2 ENGY = 4.954 / Piezo voltage at end in Y [volt].         HIERARCH ESO INS TILT2 ID = 'afcs2   ' / AFCS ID.                               HIERARCH ESO INS TILT2 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT2 NAME = 'Flexure comp. VIS' / AFCS description.           HIERARCH ESO INS TILT2 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT2 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT2 POSXEND = -0.099 / Piezo position at end [volt].         HIERARCH ESO INS TILT2 POSXSTART = -0.099 / Piezo position at start in X [volt  HIERARCH ESO INS TILT2 POSYEND = -0.047 / Piezo position at end [volt].         HIERARCH ESO INS TILT2 POSYSTART = -0.047 / Piezo position at start in Y [volt  HIERARCH ESO INS TILT2 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT2 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT2 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT2 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO INS TILT3 AFCX = 0.868 / Flexure corr. in X [volt].                HIERARCH ESO INS TILT3 AFCY = 0.38 / Flexure corr. in Y [volt].                 HIERARCH ESO INS TILT3 CONVX = 0.58 / VoltToMarcsec conversion in X.            HIERARCH ESO INS TILT3 CONVY = 0.56 / VoltToMarcsec conversion in Y.            HIERARCH ESO INS TILT3 ENGX = 5.952 / Piezo voltage at end in X [volt].         HIERARCH ESO INS TILT3 ENGY = 5.383 / Piezo voltage at end in Y [volt].         HIERARCH ESO INS TILT3 ID = 'afcs3   ' / AFCS ID.                               HIERARCH ESO INS TILT3 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT3 NAME = 'Flexure comp. NIR' / AFCS description.           HIERARCH ESO INS TILT3 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT3 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT3 POSXEND = 0.951 / Piezo position at end [volt].          HIERARCH ESO INS TILT3 POSXSTART = 0.951 / Piezo position at start in X [volt   HIERARCH ESO INS TILT3 POSYEND = 0.382 / Piezo position at end [volt].          HIERARCH ESO INS TILT3 POSYSTART = 0.382 / Piezo position at start in Y [volt   HIERARCH ESO INS TILT3 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT3 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT3 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT3 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO DET BITS =     16 / Bits per pixel readout                         HIERARCH ESO DET CHIP1 DATE = '2005-04-22' / Date of installation [YYYY-MM-DD]  HIERARCH ESO DET CHIP1 ID = 'Pisces Austr II' / Detector chip identification    HIERARCH ESO DET CHIP1 INDEX = 1 / Chip index                                   HIERARCH ESO DET CHIP1 NAME = 'e2v CC44-82' / Detector chip name                HIERARCH ESO DET CHIP1 NX = 2048 / # of pixels along X                          HIERARCH ESO DET CHIP1 NY = 4102 / # of pixels along Y                          HIERARCH ESO DET CHIP1 PSZX = 15. / Size of pixel in X                          HIERARCH ESO DET CHIP1 PSZY = 15. / Size of pixel in Y                          HIERARCH ESO DET CHIP1 X =   1 / X location in array                            HIERARCH ESO DET CHIP1 XGAP = 0. / Gap between chips along x                    HIERARCH ESO DET CHIP1 Y =   1 / Y location in array                            HIERARCH ESO DET CHIP1 YGAP = 0. / Gap between chips along y                    HIERARCH ESO DET CHIPS =     1 / # of chips in detector array                   HIERARCH ESO DET DATE = '2005-04-22' / Installation date                        HIERARCH ESO DET DEC =      0. / Apparent 00:00:00.0 DEC at start               HIERARCH ESO DET DID = 'ESO-VLT-DIC.XSHOOT_CCD,ESO-VLT-DIC.FCDDCS' / Dic        HIERARCH ESO DET EXP NO =  461 / Unique exposure ID number                      HIERARCH ESO DET EXP RDTTIME = 16.553 / image readout time                      HIERARCH ESO DET EXP TYPE = 'Normal  ' / Exposure type                          HIERARCH ESO DET EXP XFERTIM = 16.571 / image transfer time                     HIERARCH ESO DET FRAM ID =   1 / Image sequencial number                        HIERARCH ESO DET FRAM TYPE = 'Normal  ' / Type of frame                         HIERARCH ESO DET ID = 'CCD FIERA - Rev: 238334' / Detector system Id            HIERARCH ESO DET NAME = 'shdetb - shdetb' / Name of detector system             HIERARCH ESO DET OUT1 CHIP = 1 / Chip to which the output belongs               HIERARCH ESO DET OUT1 CONAD = 1.83 / Conversion from ADUs to electrons          HIERARCH ESO DET OUT1 GAIN = 0.55 / Conversion from electrons to ADU            HIERARCH ESO DET OUT1 ID = 'R       ' / Output ID as from manufacturer          HIERARCH ESO DET OUT1 INDEX = 2 / Output index                                  HIERARCH ESO DET OUT1 NAME = 'R       ' / Description of output                 HIERARCH ESO DET OUT1 NX = 2048 / valid pixels along X                          HIERARCH ESO DET OUT1 NY = 3000 / valid pixels along Y                          HIERARCH ESO DET OUT1 OVSCX = 48 / Overscan region in X                         HIERARCH ESO DET OUT1 OVSCY = 0 / Overscan region in Y                          HIERARCH ESO DET OUT1 PRSCX = 48 / Prescan region in X                          HIERARCH ESO DET OUT1 PRSCY = 0 / Prescan region in Y                           HIERARCH ESO DET OUT1 RON = 4.6 / [e-] Readout noise per output (ele            HIERARCH ESO DET OUT1 X = 2048 / X location of output                           HIERARCH ESO DET OUT1 Y =    1 / Y location of output                           HIERARCH ESO DET OUTPUTS =   1 / # of outputs                                   HIERARCH ESO DET OUTREF =    0 / reference output                               HIERARCH ESO DET RA =       0. / Apparent 00:00:00.0 RA at start                HIERARCH ESO DET READ CLOCK = '400k/1pt/lg' / Readout clock pattern used        HIERARCH ESO DET READ MODE = 'normal  ' / Readout method                        HIERARCH ESO DET READ NFRAM = 1 / Number of readouts buffered in sin            HIERARCH ESO DET READ SPEED = '1pt/400k/lg' / Readout speed                     HIERARCH ESO DET SHUT ID = 'VA 25mm BistableCCD Sensor1' / Shutter unique id    HIERARCH ESO DET SHUT TMCLOS = 0. / Time taken to close shutter                 HIERARCH ESO DET SHUT TMOPEN = 0. / Time taken to open shutter                  HIERARCH ESO DET SHUT TYPE = 'Iris on Slit' / type of shutter                   HIERARCH ESO DET SOFW MODE = 'Normal  ' / CCD sw operational mode               HIERARCH ESO DET TELE INT = 600. / Interval between two successive te           HIERARCH ESO DET TELE NO =   4 / # of sources active                            HIERARCH ESO DET TLM1 END = 153. / Telemetry value at read completion           HIERARCH ESO DET TLM1 ID = 'CCD Sensor1' / ID of telemetry sensor               HIERARCH ESO DET TLM1 NAME = 'CCD T1  ' / Description of telemetry param.       HIERARCH ESO DET TLM1 START = 153. / Telemetry value at read start              HIERARCH ESO DET TLM2 END = 154.6 / Telemetry value at read completion          HIERARCH ESO DET TLM2 ID = 'CCD Sensor2' / ID of telemetry sensor               HIERARCH ESO DET TLM2 NAME = 'CCD T2  ' / Description of telemetry param.       HIERARCH ESO DET TLM2 START = 154.6 / Telemetry value at read start             HIERARCH ESO DET TLM3 END = 284.5 / Telemetry value at read completion          HIERARCH ESO DET TLM3 ID = 'Box Temp' / ID of telemetry sensor                  HIERARCH ESO DET TLM3 NAME = 'EBOX T  ' / Description of telemetry param.       HIERARCH ESO DET TLM3 START = 284.5 / Telemetry value at read start             HIERARCH ESO DET TLM4 END = 8.8E-07 / Telemetry value at read completion        HIERARCH ESO DET TLM4 ID = 'Vacuum  ' / ID of telemetry sensor                  HIERARCH ESO DET TLM4 NAME = 'Vacuum  ' / Description of telemetry param.       HIERARCH ESO DET TLM4 START = 8.8E-07 / Telemetry value at read start           HIERARCH ESO DET WIN1 BINX = 1 / Binning factor along X                         HIERARCH ESO DET WIN1 BINY = 1 / Binning factor along Y                         HIERARCH ESO DET WIN1 DIT1 = 14.999996 / actual subintegration time             HIERARCH ESO DET WIN1 DKTM = 15.3908 / Dark current time                        HIERARCH ESO DET WIN1 NDIT = 1 / # of subintegrations                           HIERARCH ESO DET WIN1 NX = 2144 / # of pixels along X                           HIERARCH ESO DET WIN1 NY = 3000 / # of pixels along Y                           HIERARCH ESO DET WIN1 ST =   T / If T, window enabled                           HIERARCH ESO DET WIN1 STRX = 1 / Lower left pixel in X                          HIERARCH ESO DET WIN1 STRY = 1 / Lower left pixel in Y                          HIERARCH ESO DET WIN1 UIT1 = 15. / user defined subintegration time             HIERARCH ESO DET WINDOWS =   1 / # of windows readout                           HIERARCH ESO PRO CATG = 'XSH_MOD_CFG_OPT_2D_UVB' / Category of pipeline product HIERARCH ESO PRO ANCESTOR = 'XSHOO.2024-01-02T09:58:48.448.fits'                HIERARCH ESO PRO DID = 'PRO-1.15' / Data dictionary for PRO                     HIERARCH ESO PRO TYPE = 'REDUCED ' / Product type                               HIERARCH ESO PRO TECH = 'ECHELLE,MULTI-PINHOLE' / Observation technique         HIERARCH ESO PRO SCIENCE =   F / Scientific product if T                        HIERARCH ESO PRO REC1 ID = 'xsh_2dmap' / Pipeline recipe (unique) identifier    HIERARCH ESO PRO REC1 DRS ID = 'cpl-7.3.1' / Data Reduction System identifier   HIERARCH ESO PRO REC1 PIPE ID = 'xshoo/3.6.5a' / Pipeline (unique) identifier   HIERARCH ESO PRO REC1 RAW1 NAME = 'XSHOO.2024-01-02T09:58:48.448.fits' / File naHIERARCH ESO PRO REC1 RAW1 CATG = 'WAVE_UVB' / Category of raw frame            HIERARCH ESO PRO DATANCOM =  1 / Number of combined frames                      HIERARCH ESO PRO REC1 CAL1 NAME= 'r.XSHOO.2024-01-02T09:37:29.725_tpl_0000.fits'HIERARCH ESO PRO REC1 CAL1 CATG = 'MASTER_BIAS_UVB' / Category of calibration frHIERARCH ESO PRO REC1 CAL1 DATAMD5 = '61264eba4ec6b8abda4248c5e783911d' / MD5 siHIERARCH ESO PRO REC1 CAL2 NAME= 'r.XSHOO.2024-01-02T10:03:48.425_tpl_0000.fits'HIERARCH ESO PRO REC1 CAL2 CATG = 'ORDER_TAB_EDGES_SLIT_UVB' / Category of calibHIERARCH ESO PRO REC1 CAL2 DATAMD5 = '3d946121c92877366a588157d462f4f1' / MD5 siHIERARCH ESO PRO REC1 CAL3 NAME = 'r.XSHOO.2024-01-02T09:47:07.356_0005.fits'   HIERARCH ESO PRO REC1 CAL3 CATG = 'XSH_MOD_CFG_OPT_FMT_UVB' / Category of calibrHIERARCH ESO PRO REC1 CAL3 DATAMD5 = '84a5d03245111296b9a29a87e1c7aaae' / MD5 siHIERARCH ESO PRO REC1 CAL4 NAME = 'XS_GALL_091001B_UVB.fits' / File name of caliHIERARCH ESO PRO REC1 CAL4 CATG = 'ARC_LINE_LIST_UVB' / Category of calibration HIERARCH ESO PRO REC1 CAL4 DATAMD5 = '716325feebc0d8f958a249a4e3c603d1' / MD5 siHIERARCH ESO PRO REC1 CAL5 NAME = 'XS_GALM_091001B_UVB.fits' / File name of caliHIERARCH ESO PRO REC1 CAL5 CATG = 'ARC_LINE_LIST_INTMON_UVB' / Category of calibHIERARCH ESO PRO REC1 CAL5 DATAMD5 = 'c662ce5d543847a01f1448a2ca208856' / MD5 siHIERARCH ESO PRO REC1 CAL6 NAME = 'XS_GSFT_091001A_UVB.fits' / File name of caliHIERARCH ESO PRO REC1 CAL6 CATG = 'SPECTRAL_FORMAT_TAB_UVB' / Category of calibrHIERARCH ESO PRO REC1 CAL6 DATAMD5 = 'cb66fee5070f774602aa02e63fb0d6d5' / MD5 siHIERARCH ESO PRO REC1 PARAM1 NAME = 'keep-temp' / If 'no', temporary files are dHIERARCH ESO PRO REC1 PARAM1 VALUE = 'no      ' / Default: 'no'                 HIERARCH ESO PRO REC1 PARAM2 NAME = 'debug-level' / Additional xshooter debug leHIERARCH ESO PRO REC1 PARAM2 VALUE = 'none    ' / Default: 'none'               HIERARCH ESO PRO REC1 PARAM3 NAME = 'time-stamp' / Add timestamp to product fileHIERARCH ESO PRO REC1 PARAM3 VALUE = 'false   ' / Default: false                HIERARCH ESO PRO REC1 PARAM4 NAME = 'decode-bp' / Integer representation of the HIERARCH ESO PRO REC1 PARAM4 VALUE = '2144337919' / Default: 2144337919         HIERARCH ESO PRO REC1 PARAM5 NAME = 'pre-overscan-corr' / pre-overscan correctioHIERARCH ESO PRO REC1 PARAM5 VALUE = '1       ' / Default: 1                    HIERARCH ESO PRO REC1 PARAM6 NAME = 'detectarclines-fit-win-hsize' / Half windowHIERARCH ESO PRO REC1 PARAM6 VALUE = '6       ' / Default: 6                    HIERARCH ESO PRO REC1 PARAM7 NAME = 'detectarclines-search-win-hsize' / Half winHIERARCH ESO PRO REC1 PARAM7 VALUE = '3       ' / Default: 3                    HIERARCH ESO PRO REC1 PARAM8 NAME = 'detectarclines-running-median-hsize' / HalfHIERARCH ESO PRO REC1 PARAM8 VALUE = '0       ' / Default: 0                    HIERARCH ESO PRO REC1 PARAM9 NAME = 'detectarclines-wavesol-deg-lambda' / DegreeHIERARCH ESO PRO REC1 PARAM9 VALUE = '5       ' / Default: 5                    HIERARCH ESO PRO REC1 PARAM10 NAME = 'detectarclines-wavesol-deg-order' / DegreeHIERARCH ESO PRO REC1 PARAM10 VALUE = '4       ' / Default: 4                   HIERARCH ESO PRO REC1 PARAM11 NAME = 'detectarclines-wavesol-deg-slit' / Degree HIERARCH ESO PRO REC1 PARAM11 VALUE = '1       ' / Default: 1                   HIERARCH ESO PRO REC1 PARAM12 NAME = 'detectarclines-min-sn' / Minimum signal-toHIERARCH ESO PRO REC1 PARAM12 VALUE = '5       ' / Default: 5                   HIERARCH ESO PRO REC1 PARAM13 NAME = 'detectarclines-find-lines-center' / MethodHIERARCH ESO PRO REC1 PARAM13 VALUE = 'gaussian' / Default: 'gaussian'          HIERARCH ESO PRO REC1 PARAM14 NAME = 'detectarclines-clip-sigma' / Kappa value iHIERARCH ESO PRO REC1 PARAM14 VALUE = '2       ' / Default: 2                   HIERARCH ESO PRO REC1 PARAM15 NAME = 'detectarclines-clip-niter' / Number of iteHIERARCH ESO PRO REC1 PARAM15 VALUE = '0       ' / Default: 0                   HIERARCH ESO PRO REC1 PARAM16 NAME = 'detectarclines-clip-frac' / Minimal fractiHIERARCH ESO PRO REC1 PARAM16 VALUE = '0.7     ' / Default: 0.7                 HIERARCH ESO PRO REC1 PARAM17 NAME = 'dispersol-deg-x' / Degree in X in the polyHIERARCH ESO PRO REC1 PARAM17 VALUE = '4       ' / Default: 4                   HIERARCH ESO PRO REC1 PARAM18 NAME = 'dispersol-deg-y' / Degree in Y in the polyHIERARCH ESO PRO REC1 PARAM18 VALUE = '5       ' / Default: 5                   HIERARCH ESO PRO REC1 PARAM19 NAME = 'model-maxit' / Number/10 of annealing iterHIERARCH ESO PRO REC1 PARAM19 VALUE = '500     ' / Default: 500                 HIERARCH ESO PRO REC1 PARAM20 NAME = 'model-anneal-factor' / Multiplier applied HIERARCH ESO PRO REC1 PARAM20 VALUE = '1       ' / Default: 1                   HIERARCH ESO PRO REC1 PARAM21 NAME = 'model-scenario' / selects preset flag and HIERARCH ESO PRO REC1 PARAM21 VALUE = '8       ' / Default: 4                   HIERARCH ESO QC MODEL NDAT = 3316                                               HIERARCH ESO QC MODEL PREDICT RESX_MIN = 0.139217878265526                      HIERARCH ESO QC MODEL PREDICT RESY_MIN = -6.16755101790636                      HIERARCH ESO QC MODEL PREDICT RESX_MAX = 1.01794232115162                       HIERARCH ESO QC MODEL PREDICT RESY_MAX = 3.46260388926248                       HIERARCH ESO QC MODEL PREDICT RESX_AVG = 0.49962757389755                       HIERARCH ESO QC MODEL PREDICT RESY_AVG = 0.0983453301308799                     HIERARCH ESO QC MODEL PREDICT RESX_MED = 0.503840149517259                      HIERARCH ESO QC MODEL PREDICT RESY_MED = 0.109003961921985                      HIERARCH ESO QC MODEL PREDICT RESX_RMS = 0.0651088248575295                     HIERARCH ESO QC MODEL PREDICT RESY_RMS = 0.30940075128134                       HIERARCH ESO QC MODEL ANNEAL RESX_MIN = -0.360006542611927                      HIERARCH ESO QC MODEL ANNEAL RESY_MIN = -6.2529548597654                        HIERARCH ESO QC MODEL ANNEAL RESX_MAX = 0.514386831571755                       HIERARCH ESO QC MODEL ANNEAL RESY_MAX = 3.36765606128301                        HIERARCH ESO QC MODEL ANNEAL RESX_AVG = -0.000595469216278192                   HIERARCH ESO QC MODEL ANNEAL RESY_AVG = -0.00178212842889225                    HIERARCH ESO QC MODEL ANNEAL RESX_MED = 0.00420440279810919                     HIERARCH ESO QC MODEL ANNEAL RESY_MED = 0.0098243463497738                      HIERARCH ESO QC MODEL ANNEAL RESX_RMS = 0.0651661124124859                      HIERARCH ESO QC MODEL ANNEAL RESY_RMS = 0.309020095948337                       HIERARCH ESO OCS DET1 IMGNAME = 'XSHOOTER_SLT_WAVE_UVB_' / Data File Name.      HIERARCH ESO SEQ ARM = 'UVB     ' / Instrument Arm                              ORIGFILE= 'XS_PMC2_240101A_1x1_UVB_Ps.fits' / Original File Name                CHECKSUM= 'GFQRJ9POGCPOG9PO'   / HDU checksum updated 2024-01-03T13:03:58       DATASUM = '         0'         / data unit checksum updated 2024-01-03T13:03:58 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / 8-bit bytes                                    NAXIS   =                    2 / 2-dimensional binary table                     NAXIS1  =                   42 / width of table in bytes                        NAXIS2  =                   91 / number of rows in table                        PCOUNT  =                    0 / size of special data area                      GCOUNT  =                    1 / one data group (required keyword)              TFIELDS =                    6 / number of fields in each row                   TTYPE1  = 'Best_Guess'         / label for field   1                            TFORM1  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE2  = 'Low_Limit'          / label for field   2                            TFORM2  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE3  = 'High_Limit'         / label for field   3                            TFORM3  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE4  = 'Compute_Flag'       / label for field   4                            TFORM4  = '1J      '           / data format of field: 4-byte INTEGER           TTYPE5  = 'Parameter_Name'     / label for field   5                            TFORM5  = '10A     '           / data format of field: ASCII Character          TTYPE6  = 'Parameter_Units'    / label for field   6                            TFORM6  = '4A      '           / data format of field: ASCII Character          CHECKSUM= '3WclAUbi4UbiAUbi'   / HDU checksum updated 2024-01-03T13:03:58       DATASUM = '1833495973'         / data unit checksum updated 2024-01-03T13:03:58 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             arm          @r(õÂ�\@r(õÂ�\@r(õÂ�\temper    K   	º€	º€	º€t_ir_p2   K   t_ir_p3   K   ?Òí1©±0…?Òí1©±0…?Òí1©±0…es_x      mm  ¿` Nµ ‚´¿` Nµ ‚´¿` Nµ ‚´es_y      mm  mues      deg nues      deg ?Õ 0Ý?‘?Õ 0Ý?‘?Õ 0Ý?‘taues     deg ?Ï'kâ­?Ï'kâ­?Ï'kâ­slit_scalemm/"@záG®{@záG®{@záG®{es_s      mm  ?Õ�$Ý/?Õ�$Ý/?Õ�$Ý/es_w      mm  @ƒ¾v
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\ No newline at end of file
diff --git a/XSHOOTER/calibration/model_vis.fits b/XSHOOTER/calibration/model_vis.fits
new file mode 100644
index 00000000..0f274dcb
--- /dev/null
+++ b/XSHOOTER/calibration/model_vis.fits
@@ -0,0 +1,10 @@
+SIMPLE  =                    T / file does conform to FITS standard             BITPIX  =                   16 / number of bits per data pixel                  NAXIS   =                    0 / number of data axes                            EXTEND  =                    T / FITS dataset may contain extensions            COMMENT   FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H DATE    = '2024-01-02T10:25:21' / file creation date (YYYY-MM-DDThh:mm:ss UT)   ORIGIN  = 'ESO     '           / European Southern Observatory                  TELESCOP= 'Tel name not set'   / ESO Telescope Name                             INSTRUME= 'XSHOOTER'           / Instrument used.                               OBJECT  = 'LAMP,WAVE'          / Original target.                               MJD-OBS =      60311.415897712 / MJD start (2024-01-02T09:58:53.562)            DATE-OBS= '2024-01-02T09:58:53.562' / Date of observation                       PI-COI  = 'XSHOOTER Operation Team' / PI-COI name.                              ARCFILE = 'M.XSHOOTER.2024-01-03T13:03:59.573.fits' / Archive File Name         DATAMD5 = '5870997b4c52d7d1d0ac719412ed562b' / MD5 checksum                     PIPEFILE= 'r.XSHOO.2024-01-02T09:58:53.562_0007.fits' / no comment              HIERARCH ESO OBS DID = 'ESO-VLT-DIC.OBS-2.2' / OBS Dictionary                   HIERARCH ESO OBS GRP = '0       ' / linked blocks                               HIERARCH ESO OBS ID = 200189624 / Observation block ID                          HIERARCH ESO OBS NAME = 'Calibration' / OB name                                 HIERARCH ESO OBS NTPL =     18 / Number of templates within OB                  HIERARCH ESO OBS PI-COI ID = 52022 / ESO internal PI-COI ID                     HIERARCH ESO OBS PI-COI NAME = 'XSHOOTER Operation Team' / PI-COI name          HIERARCH ESO OBS PROG ID = '60.A-9022(C)' / ESO program identification          HIERARCH ESO OBS START = '2024-01-02T09:10:44' / OB start time                  HIERARCH ESO OBS TPLNO =     7 / Template number within OB                      HIERARCH ESO TPL DID = 'ESO-VLT-DIC.TPL-1.9' / Data dictionary for TPL          HIERARCH ESO TPL EXPNO =     2 / Exposure number within template                HIERARCH ESO TPL ID = 'XSHOOTER_slt_cal_UVBVisArcsMultiplePinhole' / Te         HIERARCH ESO TPL NAME = 'Stand-alone Daytime Calibration Template' / Temp       HIERARCH ESO TPL NEXP =      2 / Number of exposures within templat             HIERARCH ESO TPL PRESEQ = 'XSHOOTER_slt_cal_UVBVisArcsMultiplePinhole.seq'      HIERARCH ESO TPL START = '2024-01-02T09:58:04' / TPL start time                 HIERARCH ESO TPL VERSION = '$Revision: 192749 $' / Version of the template      HIERARCH ESO INS ADC1 DEC = -235528.30059 / Telescope desclination [deg].       HIERARCH ESO INS ADC1 ENC END = 14980 / Encoder position at end [Enc]           HIERARCH ESO INS ADC1 ENC START = 14980 / Encoder position at start [Enc]       HIERARCH ESO INS ADC1 END = 72.4951 / Position angle at end [deg].              HIERARCH ESO INS ADC1 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC1 RA = 120643.949096 / Telescope right ascension [deg].     HIERARCH ESO INS ADC1 START = 72.495 / ADC position at start [deg].             HIERARCH ESO INS ADC2 DEC = -235528.30059 / Telescope desclination [deg].       HIERARCH ESO INS ADC2 ENC END = 56459 / Encoder position at end [Enc]           HIERARCH ESO INS ADC2 ENC START = 56459 / Encoder position at start [Enc]       HIERARCH ESO INS ADC2 END = 273.731 / Position angle at end [deg].              HIERARCH ESO INS ADC2 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC2 RA = 120643.949096 / Telescope right ascension [deg].     HIERARCH ESO INS ADC2 START = 273.731 / ADC position at start [deg].            HIERARCH ESO INS ADC3 DEC = -235528.30059 / Telescope desclination [deg].       HIERARCH ESO INS ADC3 ENC END = 14501 / Encoder position at end [Enc]           HIERARCH ESO INS ADC3 ENC START = 14501 / Encoder position at start [Enc]       HIERARCH ESO INS ADC3 END = 70.177 / Position angle at end [deg].               HIERARCH ESO INS ADC3 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC3 RA = 120643.949096 / Telescope right ascension [deg].     HIERARCH ESO INS ADC3 START = 70.177 / ADC position at start [deg].             HIERARCH ESO INS ADC4 DEC = -235528.30059 / Telescope desclination [deg].       HIERARCH ESO INS ADC4 ENC END = 56765 / Encoder position at end [Enc]           HIERARCH ESO INS ADC4 ENC START = 56765 / Encoder position at start [Enc]       HIERARCH ESO INS ADC4 END = 275.212 / Position angle at end [deg].              HIERARCH ESO INS ADC4 MODE = 'OFF     ' / ADC mode.                             HIERARCH ESO INS ADC4 RA = 120643.949096 / Telescope right ascension [deg].     HIERARCH ESO INS ADC4 START = 275.212 / ADC position at start [deg].            HIERARCH ESO INS DATE = '2005-04-01' / Instrument release date (yyyy-mm-d       HIERARCH ESO INS DID = 'ESO-VLT-DIC.XSHOOTER_ICS-363028' / Data dictiona        HIERARCH ESO INS FOCU1 ENC = 27082 / Absolute position [Enc].                   HIERARCH ESO INS FOCU1 POS = 15.6 / Position [C].                               HIERARCH ESO INS FOCU2 ENC = 11750 / Absolute position [Enc].                   HIERARCH ESO INS FOCU2 POS = 0. / Position [C].                                 HIERARCH ESO INS ID = 'XSHOOTER/176642' / Instrument ID.                        HIERARCH ESO INS LAMP1 ID = 'SPL1    ' / Lamp ID.                               HIERARCH ESO INS LAMP1 NAME = 'ThAr_Lamp' / Lamp name.                          HIERARCH ESO INS LAMP1 ST =  T / Lamp activated.                                HIERARCH ESO INS MIRR1 ID = 'ThAr_D2 ' / Mirror unique ID.                      HIERARCH ESO INS MIRR1 NAME = 'ThAr_D2 ' / Mirror name.                         HIERARCH ESO INS MIRR1 NO =  2 / Mirror slide position.                         HIERARCH ESO INS MODE = 'SLITSPEC' / Instrument mode used.                      HIERARCH ESO INS OPTI2 ID = 'SLOT    ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI2 NAME = 'SLOT    ' / OPTIi name.                          HIERARCH ESO INS OPTI2 NO =  3 / OPTIi slot number.                             HIERARCH ESO INS OPTI2 TYPE = 'MIRROR  ' / OPTIi element.                       HIERARCH ESO INS OPTI3 ID = 'PS4     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI3 NAME = 'Pin_row ' / OPTIi name.                          HIERARCH ESO INS OPTI3 NO =  4 / OPTIi slot number.                             HIERARCH ESO INS OPTI3 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS OPTI4 ID = 'PS4     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI4 NAME = 'Pin_row ' / OPTIi name.                          HIERARCH ESO INS OPTI4 NO =  4 / OPTIi slot number.                             HIERARCH ESO INS OPTI4 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS OPTI5 ID = 'PS8     ' / OPTIi unique ID.                       HIERARCH ESO INS OPTI5 NAME = 'Blind   ' / OPTIi name.                          HIERARCH ESO INS OPTI5 NO =  8 / OPTIi slot number.                             HIERARCH ESO INS OPTI5 TYPE = 'SLIT    ' / OPTIi element.                       HIERARCH ESO INS PATH = 'SLITSPEC' / Optical path used.                         HIERARCH ESO INS SENS21 ID = 'CA1F1   ' / sensor ID.                            HIERARCH ESO INS SENS21 NAME = 'Cab.1 Flow rate' / sensor common name.          HIERARCH ESO INS SENS21 VAL = 0. / Cab. 1 Flow rate [l/h]                       HIERARCH ESO INS SENS60 ID = 'NPM     ' / sensor ID.                            HIERARCH ESO INS SENS60 NAME = 'NIR cryo. P' / sensor common name.              HIERARCH ESO INS SENS60 VAL = 4.83E-05 / NIR Pressure [mbar]                    HIERARCH ESO INS SENS81 ID = 'RANGE   ' / sensor ID.                            HIERARCH ESO INS SENS81 NAME = 'Heater Range' / sensor common name.             HIERARCH ESO INS SENS81 VAL = 4. / Lake 340 Heater Range                        HIERARCH ESO INS SENS82 ID = 'HEATR   ' / sensor ID.                            HIERARCH ESO INS SENS82 NAME = 'Heater output' / sensor common name.            HIERARCH ESO INS SENS82 VAL = 52.7 / Lake 340 Heater output                     HIERARCH ESO INS SENSOR4 SWSIM = T / If T, function is software simulat         HIERARCH ESO INS SHUT1 ID = 'INSH    ' / Shutter ID.                            HIERARCH ESO INS SHUT1 NAME = 'Instrument shutter' / Shutter name.              HIERARCH ESO INS SHUT1 ST =  F / Shutter open.                                  HIERARCH ESO INS SWSIM = 'NORMAL  ' / Software simulation.                      HIERARCH ESO INS TEMP1 ID = 'TMUC    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP1 NAME = 'UVB Camera temp.' / Temperature sensor name.     HIERARCH ESO INS TEMP1 VAL = 15.33 / UVB Camera temp. [C]                       HIERARCH ESO INS TEMP10 ID = 'TMA     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP10 NAME = 'Ambient temp.' / Temperature sensor name.       HIERARCH ESO INS TEMP10 VAL = 15.11 / Ambient temp. [C]                         HIERARCH ESO INS TEMP11 ID = 'TMADC   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP11 NAME = 'BB UVB ADC temp.' / Temperature sensor name.    HIERARCH ESO INS TEMP11 VAL = 17.51 / BB UVB ADC temp. [C]                      HIERARCH ESO INS TEMP2 ID = 'TMUP    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP2 NAME = 'UVB Prism temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP2 VAL = 15.11 / UVB Prism temp. [C]                        HIERARCH ESO INS TEMP21 ID = 'CA1OT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP21 NAME = 'Cab.1 outlet temp.' / Temperature sensor name.  HIERARCH ESO INS TEMP21 VAL = 0. / Cab. 1 outlet temp. [C]                      HIERARCH ESO INS TEMP22 ID = 'CA1IT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP22 NAME = 'Cab.1 inlet temp.' / Temperature sensor name.   HIERARCH ESO INS TEMP22 VAL = 0. / Cab. 1 inlet temp. [C]                       HIERARCH ESO INS TEMP23 ID = 'CA1CT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP23 NAME = 'Cab.1 temp.' / Temperature sensor name.         HIERARCH ESO INS TEMP23 VAL = 0. / Cab. 1 temp. [C]                             HIERARCH ESO INS TEMP24 ID = 'CA1AT   ' / Temperature sensor ID.                HIERARCH ESO INS TEMP24 NAME = 'Cab.1 Amb. temp.' / Temperature sensor name.    HIERARCH ESO INS TEMP24 VAL = 0. / Cab. 1 Amb. temp.  [C]                       HIERARCH ESO INS TEMP3 ID = 'TMUB    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP3 NAME = 'UVB bench temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP3 VAL = 15.28 / UVB bench temp. [C]                        HIERARCH ESO INS TEMP4 ID = 'TMVC    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP4 NAME = 'VIS Camera temp.' / Temperature sensor name.     HIERARCH ESO INS TEMP4 VAL = 14.13 / VIS Camera temp. [C]                       HIERARCH ESO INS TEMP5 ID = 'TMVP    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP5 NAME = 'VIS Prism temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP5 VAL = 14.86 / VIS Prism temp. [C]                        HIERARCH ESO INS TEMP6 ID = 'TMVB    ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP6 NAME = 'VIS bench temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP6 VAL = 14.21 / VIS bench temp. [C]                        HIERARCH ESO INS TEMP7 ID = 'TMUCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP7 NAME = 'UVB cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP7 VAL = 10.45 / UVB cryo. temp. [C]                        HIERARCH ESO INS TEMP8 ID = 'TMVCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP8 NAME = 'VIS cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP8 VAL = 12.12 / VIS cryo. temp. [C]                        HIERARCH ESO INS TEMP80 ID = 'OBCT    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP80 NAME = 'OB Cover Tel.' / Temperature sensor name.       HIERARCH ESO INS TEMP80 VAL = 103.91 / OB Cover Telescope [K]                   HIERARCH ESO INS TEMP81 ID = 'OBCB    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP81 NAME = 'OB Cover Tank' / Temperature sensor name.       HIERARCH ESO INS TEMP81 VAL = 104.1 / OB Cover Tank [K]                         HIERARCH ESO INS TEMP82 ID = 'OBPR    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP82 NAME = 'OB Prism1' / Temperature sensor name.           HIERARCH ESO INS TEMP82 VAL = 103.25 / OB Prism1 [K]                            HIERARCH ESO INS TEMP83 ID = 'OBCL    ' / Temperature sensor ID.                HIERARCH ESO INS TEMP83 NAME = 'OB Corrector Lens' / Temperature sensor name.   HIERARCH ESO INS TEMP83 VAL = 104.14 / OB Corrector Lens [K]                    HIERARCH ESO INS TEMP84 ID = 'CP      ' / Temperature sensor ID.                HIERARCH ESO INS TEMP84 NAME = 'Cryo. Cold Plate' / Temperature sensor name.    HIERARCH ESO INS TEMP84 VAL = 86.723 / Cryostat Cold Plate [K]                  HIERARCH ESO INS TEMP85 ID = 'RS      ' / Temperature sensor ID.                HIERARCH ESO INS TEMP85 NAME = 'Cryo. rad. shield' / Temperature sensor name.   HIERARCH ESO INS TEMP85 VAL = 98.81 / Cryostat rad. shield [K]                  HIERARCH ESO INS TEMP86 ID = 'DCB     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP86 NAME = 'CCD Copper bar' / Temperature sensor name.      HIERARCH ESO INS TEMP86 VAL = 76.597 / CCD Copper bar [K]                       HIERARCH ESO INS TEMP9 ID = 'TMNCR   ' / Temperature sensor ID.                 HIERARCH ESO INS TEMP9 NAME = 'NIR cryo. temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP9 VAL = 21.53 / NIR cryo. temp. [C]                        HIERARCH ESO INS TEMP90 ID = 'NHT     ' / Temperature sensor ID.                HIERARCH ESO INS TEMP90 NAME = 'NIR Head temp.' / Temperature sensor name.      HIERARCH ESO INS TEMP90 VAL = 79.001 / NIR Head temp. [K]                       HIERARCH ESO INS TILT1 AFCX = 1.148 / Flexure corr. in X [volt].                HIERARCH ESO INS TILT1 AFCY = -0.171 / Flexure corr. in Y [volt].               HIERARCH ESO INS TILT1 CONVX = 0.208 / VoltToMarcsec conversion in X.           HIERARCH ESO INS TILT1 CONVY = 0.269 / VoltToMarcsec conversion in Y.           HIERARCH ESO INS TILT1 ENGX = 6.183 / Piezo voltage at end in X [volt].         HIERARCH ESO INS TILT1 ENGY = 4.82 / Piezo voltage at end in Y [volt].          HIERARCH ESO INS TILT1 ID = 'afcs1   ' / AFCS ID.                               HIERARCH ESO INS TILT1 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT1 NAME = 'Flexure comp. UVB' / AFCS description.           HIERARCH ESO INS TILT1 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT1 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT1 POSXEND = 1.18 / Piezo position at end [volt].           HIERARCH ESO INS TILT1 POSXSTART = 1.18 / Piezo position at start in X [volt    HIERARCH ESO INS TILT1 POSYEND = -0.18 / Piezo position at end [volt].          HIERARCH ESO INS TILT1 POSYSTART = -0.18 / Piezo position at start in Y [volt   HIERARCH ESO INS TILT1 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT1 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT1 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT1 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO INS TILT2 AFCX = -0.098 / Flexure corr. in X [volt].               HIERARCH ESO INS TILT2 AFCY = -0.135 / Flexure corr. in Y [volt].               HIERARCH ESO INS TILT2 CONVX = 0.291 / VoltToMarcsec conversion in X.           HIERARCH ESO INS TILT2 CONVY = 0.384 / VoltToMarcsec conversion in Y.           HIERARCH ESO INS TILT2 ENGX = 4.9 / Piezo voltage at end in X [volt].           HIERARCH ESO INS TILT2 ENGY = 4.953 / Piezo voltage at end in Y [volt].         HIERARCH ESO INS TILT2 ID = 'afcs2   ' / AFCS ID.                               HIERARCH ESO INS TILT2 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT2 NAME = 'Flexure comp. VIS' / AFCS description.           HIERARCH ESO INS TILT2 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT2 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT2 POSXEND = -0.099 / Piezo position at end [volt].         HIERARCH ESO INS TILT2 POSXSTART = -0.099 / Piezo position at start in X [volt  HIERARCH ESO INS TILT2 POSYEND = -0.047 / Piezo position at end [volt].         HIERARCH ESO INS TILT2 POSYSTART = -0.047 / Piezo position at start in Y [volt  HIERARCH ESO INS TILT2 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT2 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT2 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT2 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO INS TILT3 AFCX = 0.868 / Flexure corr. in X [volt].                HIERARCH ESO INS TILT3 AFCY = 0.38 / Flexure corr. in Y [volt].                 HIERARCH ESO INS TILT3 CONVX = 0.58 / VoltToMarcsec conversion in X.            HIERARCH ESO INS TILT3 CONVY = 0.56 / VoltToMarcsec conversion in Y.            HIERARCH ESO INS TILT3 ENGX = 5.952 / Piezo voltage at end in X [volt].         HIERARCH ESO INS TILT3 ENGY = 5.384 / Piezo voltage at end in Y [volt].         HIERARCH ESO INS TILT3 ID = 'afcs3   ' / AFCS ID.                               HIERARCH ESO INS TILT3 MODE = 'STAT    ' / AFCS mode.                           HIERARCH ESO INS TILT3 NAME = 'Flexure comp. NIR' / AFCS description.           HIERARCH ESO INS TILT3 OFFSETX = 5. / Offset voltage in X [volt].               HIERARCH ESO INS TILT3 OFFSETY = 5. / Offset voltage in Y [volt].               HIERARCH ESO INS TILT3 POSXEND = 0.951 / Piezo position at end [volt].          HIERARCH ESO INS TILT3 POSXSTART = 0.951 / Piezo position at start in X [volt   HIERARCH ESO INS TILT3 POSYEND = 0.382 / Piezo position at end [volt].          HIERARCH ESO INS TILT3 POSYSTART = 0.382 / Piezo position at start in Y [volt   HIERARCH ESO INS TILT3 REFRACXEND = -9999. / Atm. refr. corr. end in X [volt]   HIERARCH ESO INS TILT3 REFRACXST = -9999. / Atm. refr. corr. start in X [volt   HIERARCH ESO INS TILT3 REFRACYEND = -9999. / Atm. refr. corr. end in Y [volt]   HIERARCH ESO INS TILT3 REFRACYST = -9999. / Atm. refr. corr. start in Y [volt   HIERARCH ESO DET BITS =     16 / Bits per pixel readout                         HIERARCH ESO DET CHIP1 DATE = '2005-04-22' / Date of installation [YYYY-MM-DD]  HIERARCH ESO DET CHIP1 ID = 'Catherine' / Detector chip identification          HIERARCH ESO DET CHIP1 INDEX = 1 / Chip index                                   HIERARCH ESO DET CHIP1 NAME = 'MIT/LL CCID-20' / Detector chip name             HIERARCH ESO DET CHIP1 NX = 2048 / # of pixels along X                          HIERARCH ESO DET CHIP1 NY = 4102 / # of pixels along Y                          HIERARCH ESO DET CHIP1 PSZX = 15. / Size of pixel in X                          HIERARCH ESO DET CHIP1 PSZY = 15. / Size of pixel in Y                          HIERARCH ESO DET CHIP1 X =   1 / X location in array                            HIERARCH ESO DET CHIP1 XGAP = 0. / Gap between chips along x                    HIERARCH ESO DET CHIP1 Y =   1 / Y location in array                            HIERARCH ESO DET CHIP1 YGAP = 0. / Gap between chips along y                    HIERARCH ESO DET CHIPS =     1 / # of chips in detector array                   HIERARCH ESO DET DATE = '2005-04-22' / Installation date                        HIERARCH ESO DET DEC =      0. / Apparent 00:00:00.0 DEC at start               HIERARCH ESO DET DID = 'ESO-VLT-DIC.XSHOOT_CCD,ESO-VLT-DIC.FCDDCS' / Dic        HIERARCH ESO DET EXP NO =  431 / Unique exposure ID number                      HIERARCH ESO DET EXP RDTTIME = 21.075 / image readout time                      HIERARCH ESO DET EXP TYPE = 'Normal  ' / Exposure type                          HIERARCH ESO DET EXP XFERTIM = 36.874 / image transfer time                     HIERARCH ESO DET FRAM ID =   1 / Image sequencial number                        HIERARCH ESO DET FRAM TYPE = 'Normal  ' / Type of frame                         HIERARCH ESO DET ID = 'CCD FIERA - Rev: 238334' / Detector system Id            HIERARCH ESO DET NAME = 'shdetv - shdetv' / Name of detector system             HIERARCH ESO DET OUT1 CHIP = 1 / Chip to which the output belongs               HIERARCH ESO DET OUT1 CONAD = 1.64 / Conversion from ADUs to electrons          HIERARCH ESO DET OUT1 GAIN = 0.61 / Conversion from electrons to ADU            HIERARCH ESO DET OUT1 ID = 'A       ' / Output ID as from manufacturer          HIERARCH ESO DET OUT1 INDEX = 1 / Output index                                  HIERARCH ESO DET OUT1 NAME = 'A       ' / Description of output                 HIERARCH ESO DET OUT1 NX = 2048 / valid pixels along X                          HIERARCH ESO DET OUT1 NY = 4000 / valid pixels along Y                          HIERARCH ESO DET OUT1 OVSCX = 48 / Overscan region in X                         HIERARCH ESO DET OUT1 OVSCY = 0 / Overscan region in Y                          HIERARCH ESO DET OUT1 PRSCX = 10 / Prescan region in X                          HIERARCH ESO DET OUT1 PRSCY = 0 / Prescan region in Y                           HIERARCH ESO DET OUT1 RON = 6.6 / [e-] Readout noise per output (ele            HIERARCH ESO DET OUT1 X =    1 / X location of output                           HIERARCH ESO DET OUT1 Y =    1 / Y location of output                           HIERARCH ESO DET OUTPUTS =   1 / # of outputs                                   HIERARCH ESO DET OUTREF =    0 / reference output                               HIERARCH ESO DET RA =       0. / Apparent 00:00:00.0 RA at start                HIERARCH ESO DET READ CLOCK = '400k/1pt/lg' / Readout clock pattern used        HIERARCH ESO DET READ MODE = 'normal  ' / Readout method                        HIERARCH ESO DET READ NFRAM = 1 / Number of readouts buffered in sin            HIERARCH ESO DET READ SPEED = '1pt/400k/lg' / Readout speed                     HIERARCH ESO DET SHUT ID = 'VA 25mm BistableCCD Sensor1' / Shutter unique id    HIERARCH ESO DET SHUT TMCLOS = 0. / Time taken to close shutter                 HIERARCH ESO DET SHUT TMOPEN = 0. / Time taken to open shutter                  HIERARCH ESO DET SHUT TYPE = 'Iris on Slit' / type of shutter                   HIERARCH ESO DET SOFW MODE = 'Normal  ' / CCD sw operational mode               HIERARCH ESO DET TELE INT = 600. / Interval between two successive te           HIERARCH ESO DET TELE NO =   4 / # of sources active                            HIERARCH ESO DET TLM1 END = 135. / Telemetry value at read completion           HIERARCH ESO DET TLM1 ID = 'CCD Sensor1' / ID of telemetry sensor               HIERARCH ESO DET TLM1 NAME = 'CCD T1  ' / Description of telemetry param.       HIERARCH ESO DET TLM1 START = 135. / Telemetry value at read start              HIERARCH ESO DET TLM2 END = 142.8 / Telemetry value at read completion          HIERARCH ESO DET TLM2 ID = 'CCD Sensor2' / ID of telemetry sensor               HIERARCH ESO DET TLM2 NAME = 'CCD T2  ' / Description of telemetry param.       HIERARCH ESO DET TLM2 START = 142.8 / Telemetry value at read start             HIERARCH ESO DET TLM3 END = 0. / Telemetry value at read completion             HIERARCH ESO DET TLM3 ID = 'Box Temp' / ID of telemetry sensor                  HIERARCH ESO DET TLM3 NAME = 'EBOX T  ' / Description of telemetry param.       HIERARCH ESO DET TLM3 START = 0. / Telemetry value at read start                HIERARCH ESO DET TLM4 END = 1.5E-05 / Telemetry value at read completion        HIERARCH ESO DET TLM4 ID = 'Vacuum  ' / ID of telemetry sensor                  HIERARCH ESO DET TLM4 NAME = 'Vacuum  ' / Description of telemetry param.       HIERARCH ESO DET TLM4 START = 1.5E-05 / Telemetry value at read start           HIERARCH ESO DET WIN1 BINX = 1 / Binning factor along X                         HIERARCH ESO DET WIN1 BINY = 1 / Binning factor along Y                         HIERARCH ESO DET WIN1 DIT1 = 10.000004 / actual subintegration time             HIERARCH ESO DET WIN1 DKTM = 10.4004 / Dark current time                        HIERARCH ESO DET WIN1 NDIT = 1 / # of subintegrations                           HIERARCH ESO DET WIN1 NX = 2106 / # of pixels along X                           HIERARCH ESO DET WIN1 NY = 4000 / # of pixels along Y                           HIERARCH ESO DET WIN1 ST =   T / If T, window enabled                           HIERARCH ESO DET WIN1 STRX = 1 / Lower left pixel in X                          HIERARCH ESO DET WIN1 STRY = 1 / Lower left pixel in Y                          HIERARCH ESO DET WIN1 UIT1 = 10. / user defined subintegration time             HIERARCH ESO DET WINDOWS =   1 / # of windows readout                           HIERARCH ESO PRO CATG = 'XSH_MOD_CFG_OPT_2D_VIS' / Category of pipeline product HIERARCH ESO PRO ANCESTOR = 'XSHOO.2024-01-02T09:58:53.562.fits'                HIERARCH ESO PRO DID = 'PRO-1.15' / Data dictionary for PRO                     HIERARCH ESO PRO TYPE = 'REDUCED ' / Product type                               HIERARCH ESO PRO TECH = 'ECHELLE,MULTI-PINHOLE' / Observation technique         HIERARCH ESO PRO SCIENCE =   F / Scientific product if T                        HIERARCH ESO PRO REC1 ID = 'xsh_2dmap' / Pipeline recipe (unique) identifier    HIERARCH ESO PRO REC1 DRS ID = 'cpl-7.3.1' / Data Reduction System identifier   HIERARCH ESO PRO REC1 PIPE ID = 'xshoo/3.6.5a' / Pipeline (unique) identifier   HIERARCH ESO PRO REC1 RAW1 NAME = 'XSHOO.2024-01-02T09:58:53.562.fits' / File naHIERARCH ESO PRO REC1 RAW1 CATG = 'WAVE_VIS' / Category of raw frame            HIERARCH ESO PRO DATANCOM =  1 / Number of combined frames                      HIERARCH ESO PRO REC1 CAL1 NAME= 'r.XSHOO.2024-01-02T09:37:53.277_tpl_0000.fits'HIERARCH ESO PRO REC1 CAL1 CATG = 'MASTER_BIAS_VIS' / Category of calibration frHIERARCH ESO PRO REC1 CAL1 DATAMD5 = '8a967f6adfea9fa522d1060c93a0ffbf' / MD5 siHIERARCH ESO PRO REC1 CAL2 NAME= 'r.XSHOO.2024-01-01T12:40:00.568_tpl_0000.fits'HIERARCH ESO PRO REC1 CAL2 CATG = 'ORDER_TAB_EDGES_SLIT_VIS' / Category of calibHIERARCH ESO PRO REC1 CAL2 DATAMD5 = '3bc1f0d8a6cc9c0a3d1d49d92f4b404c' / MD5 siHIERARCH ESO PRO REC1 CAL3 NAME = 'r.XSHOO.2024-01-02T09:47:12.537_0005.fits'   HIERARCH ESO PRO REC1 CAL3 CATG = 'XSH_MOD_CFG_OPT_FMT_VIS' / Category of calibrHIERARCH ESO PRO REC1 CAL3 DATAMD5 = 'b68ea06c8684360784b200ce1fb350fa' / MD5 siHIERARCH ESO PRO REC1 CAL4 NAME = 'XS_GALL_091001B_VIS.fits' / File name of caliHIERARCH ESO PRO REC1 CAL4 CATG = 'ARC_LINE_LIST_VIS' / Category of calibration HIERARCH ESO PRO REC1 CAL4 DATAMD5 = 'd8f02f22cfe364fa306a5b82cbe5320b' / MD5 siHIERARCH ESO PRO REC1 CAL5 NAME = 'XS_GALM_091001A_VIS.fits' / File name of caliHIERARCH ESO PRO REC1 CAL5 CATG = 'ARC_LINE_LIST_INTMON_VIS' / Category of calibHIERARCH ESO PRO REC1 CAL5 DATAMD5 = 'd23a9f0b11b8fa5cf29d4a9f2c67c727' / MD5 siHIERARCH ESO PRO REC1 CAL6 NAME = 'XS_GSFT_091001B_VIS.fits' / File name of caliHIERARCH ESO PRO REC1 CAL6 CATG = 'SPECTRAL_FORMAT_TAB_VIS' / Category of calibrHIERARCH ESO PRO REC1 CAL6 DATAMD5 = '9b447dab81b23bf0fbccda78f6fdc79d' / MD5 siHIERARCH ESO PRO REC1 PARAM1 NAME = 'keep-temp' / If 'no', temporary files are dHIERARCH ESO PRO REC1 PARAM1 VALUE = 'no      ' / Default: 'no'                 HIERARCH ESO PRO REC1 PARAM2 NAME = 'debug-level' / Additional xshooter debug leHIERARCH ESO PRO REC1 PARAM2 VALUE = 'none    ' / Default: 'none'               HIERARCH ESO PRO REC1 PARAM3 NAME = 'time-stamp' / Add timestamp to product fileHIERARCH ESO PRO REC1 PARAM3 VALUE = 'false   ' / Default: false                HIERARCH ESO PRO REC1 PARAM4 NAME = 'decode-bp' / Integer representation of the HIERARCH ESO PRO REC1 PARAM4 VALUE = '2144337919' / Default: 2144337919         HIERARCH ESO PRO REC1 PARAM5 NAME = 'pre-overscan-corr' / pre-overscan correctioHIERARCH ESO PRO REC1 PARAM5 VALUE = '3       ' / Default: 1                    HIERARCH ESO PRO REC1 PARAM6 NAME = 'detectarclines-fit-win-hsize' / Half windowHIERARCH ESO PRO REC1 PARAM6 VALUE = '6       ' / Default: 6                    HIERARCH ESO PRO REC1 PARAM7 NAME = 'detectarclines-search-win-hsize' / Half winHIERARCH ESO PRO REC1 PARAM7 VALUE = '3       ' / Default: 3                    HIERARCH ESO PRO REC1 PARAM8 NAME = 'detectarclines-running-median-hsize' / HalfHIERARCH ESO PRO REC1 PARAM8 VALUE = '0       ' / Default: 0                    HIERARCH ESO PRO REC1 PARAM9 NAME = 'detectarclines-wavesol-deg-lambda' / DegreeHIERARCH ESO PRO REC1 PARAM9 VALUE = '5       ' / Default: 5                    HIERARCH ESO PRO REC1 PARAM10 NAME = 'detectarclines-wavesol-deg-order' / DegreeHIERARCH ESO PRO REC1 PARAM10 VALUE = '4       ' / Default: 4                   HIERARCH ESO PRO REC1 PARAM11 NAME = 'detectarclines-wavesol-deg-slit' / Degree HIERARCH ESO PRO REC1 PARAM11 VALUE = '1       ' / Default: 1                   HIERARCH ESO PRO REC1 PARAM12 NAME = 'detectarclines-min-sn' / Minimum signal-toHIERARCH ESO PRO REC1 PARAM12 VALUE = '5       ' / Default: 5                   HIERARCH ESO PRO REC1 PARAM13 NAME = 'detectarclines-find-lines-center' / MethodHIERARCH ESO PRO REC1 PARAM13 VALUE = 'gaussian' / Default: 'gaussian'          HIERARCH ESO PRO REC1 PARAM14 NAME = 'detectarclines-clip-sigma' / Kappa value iHIERARCH ESO PRO REC1 PARAM14 VALUE = '2       ' / Default: 2                   HIERARCH ESO PRO REC1 PARAM15 NAME = 'detectarclines-clip-niter' / Number of iteHIERARCH ESO PRO REC1 PARAM15 VALUE = '0       ' / Default: 0                   HIERARCH ESO PRO REC1 PARAM16 NAME = 'detectarclines-clip-frac' / Minimal fractiHIERARCH ESO PRO REC1 PARAM16 VALUE = '0.7     ' / Default: 0.7                 HIERARCH ESO PRO REC1 PARAM17 NAME = 'dispersol-deg-x' / Degree in X in the polyHIERARCH ESO PRO REC1 PARAM17 VALUE = '4       ' / Default: 4                   HIERARCH ESO PRO REC1 PARAM18 NAME = 'dispersol-deg-y' / Degree in Y in the polyHIERARCH ESO PRO REC1 PARAM18 VALUE = '5       ' / Default: 5                   HIERARCH ESO PRO REC1 PARAM19 NAME = 'model-maxit' / Number/10 of annealing iterHIERARCH ESO PRO REC1 PARAM19 VALUE = '500     ' / Default: 500                 HIERARCH ESO PRO REC1 PARAM20 NAME = 'model-anneal-factor' / Multiplier applied HIERARCH ESO PRO REC1 PARAM20 VALUE = '1       ' / Default: 1                   HIERARCH ESO PRO REC1 PARAM21 NAME = 'model-scenario' / selects preset flag and HIERARCH ESO PRO REC1 PARAM21 VALUE = '8       ' / Default: 4                   HIERARCH ESO QC MODEL NDAT = 4104                                               HIERARCH ESO QC MODEL PREDICT RESX_MIN = -1.00540638591667                      HIERARCH ESO QC MODEL PREDICT RESY_MIN = -1.30308463205688                      HIERARCH ESO QC MODEL PREDICT RESX_MAX = 0.79123421012855                       HIERARCH ESO QC MODEL PREDICT RESY_MAX = 3.52496588103259                       HIERARCH ESO QC MODEL PREDICT RESX_AVG = 0.0571981803822093                     HIERARCH ESO QC MODEL PREDICT RESY_AVG = -0.0477794258609724                    HIERARCH ESO QC MODEL PREDICT RESX_MED = 0.061085484833427                      HIERARCH ESO QC MODEL PREDICT RESY_MED = -0.0527939029663003                    HIERARCH ESO QC MODEL PREDICT RESX_RMS = 0.112183495381328                      HIERARCH ESO QC MODEL PREDICT RESY_RMS = 0.200250130004924                      HIERARCH ESO QC MODEL ANNEAL RESX_MIN = -1.0491927560364                        HIERARCH ESO QC MODEL ANNEAL RESY_MIN = -1.25889109675791                       HIERARCH ESO QC MODEL ANNEAL RESX_MAX = 0.739817072330652                       HIERARCH ESO QC MODEL ANNEAL RESY_MAX = 3.57272567960854                        HIERARCH ESO QC MODEL ANNEAL RESX_AVG = 0.00101235127262206                     HIERARCH ESO QC MODEL ANNEAL RESY_AVG = 0.00104462085298337                     HIERARCH ESO QC MODEL ANNEAL RESX_MED = 0.00364133199730077                     HIERARCH ESO QC MODEL ANNEAL RESY_MED = -0.00551640284936639                    HIERARCH ESO QC MODEL ANNEAL RESX_RMS = 0.10927191093365                        HIERARCH ESO QC MODEL ANNEAL RESY_RMS = 0.199876964897261                       HIERARCH ESO OCS DET2 IMGNAME = 'XSHOOTER_SLT_WAVE_VIS_' / Data File Name.      HIERARCH ESO SEQ ARM = 'VIS     ' / Instrument Arm                              ORIGFILE= 'XS_PMC2_240101A_1x1_VIS_Ct.fits' / Original File Name                CHECKSUM= 'TXFaUU9WTUCaTU9W'   / HDU checksum updated 2024-01-03T13:03:59       DATASUM = '         0'         / data unit checksum updated 2024-01-03T13:03:59 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / 8-bit bytes                                    NAXIS   =                    2 / 2-dimensional binary table                     NAXIS1  =                   42 / width of table in bytes                        NAXIS2  =                   91 / number of rows in table                        PCOUNT  =                    0 / size of special data area                      GCOUNT  =                    1 / one data group (required keyword)              TFIELDS =                    6 / number of fields in each row                   TTYPE1  = 'Best_Guess'         / label for field   1                            TFORM1  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE2  = 'Low_Limit'          / label for field   2                            TFORM2  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE3  = 'High_Limit'         / label for field   3                            TFORM3  = '1D      '           / data format of field: 8-byte DOUBLE            TTYPE4  = 'Compute_Flag'       / label for field   4                            TFORM4  = '1J      '           / data format of field: 4-byte INTEGER           TTYPE5  = 'Parameter_Name'     / label for field   5                            TFORM5  = '10A     '           / data format of field: ASCII Character          TTYPE6  = 'Parameter_Units'    / label for field   6                            TFORM6  = '4A      '           / data format of field: ASCII Character          CHECKSUM= 'i2Ahi14fi19fi19f'   / HDU checksum updated 2024-01-03T13:03:59       DATASUM = '250737898'          / data unit checksum updated 2024-01-03T13:03:59 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ?ð?ð?ðarm          @r(õÂ�\@r(õÂ�\@r(õÂ�\temper    K   t_ir_p2   K   t_ir_p3   K   ¿± 9ÄšZ¿± 9ÄšZ¿± 9ÄšZes_x      mm  ?k"�”ÈLƒ?k"�”ÈLƒ?k"�”ÈLƒes_y      mm  mues      deg nues      deg ?³ð|³ÝØ?³ð|³ÝØ?³ð|³ÝØtaues     deg ?Ð.Íh³Eg?Ð.Íh³Eg?Ð.Íh³Egslit_scalemm/"@záG®{@záG®{@záG®{es_s      mm  ?ÐQë…¸R?ÐQë…¸R?ÐQë…¸Res_w      mm  @ƒkM7ïß@ƒkM7ïß@ƒkM7ïßfcol      mm  À3õlwD™À3õlwD™À3õlwD™cmup1     deg ÀHd€�–ÀHd€�–ÀHd€�–mup1      deg ?Äþ·0?Äþ·0?Äþ·0nup1      deg ¿Á<Á´¾OL¿Á<Á´¾OL¿Á<Á´¾OLtaup1     deg @H¥ø
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+:¾pc_x_y3      ?H�ùfÖ2?H�ùfÖ2?H�ùfÖ2pc_y_xx      ¿��oÄ骿��oÄ骿��oÄéªpc_y_x1      ?‰Håž‹A?‰Håž‹A?‰Håž‹Apc_y_yy      ?ïÃÆ«?ïÃÆ«?ïÃÆ«pc_y_y1      >õ¦,~Û¤»>õ¦,~Û¤»>õ¦,~Û¤»pc_y_xy      >Ž®îØø
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pc_y_x3      ¾áSÝ„µ®¾áSÝ„µ®¾áSÝ„µ®pc_y_x2y     >£yõïL�>£yõïL�>£yõïL�pc_y_y2x     ¿
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+‡ê$†>pc_y_y3      d2_y1x0      d2_y1x1      d2_y1x2      d2_y1x3      d2_y2x0      d2_y2x1      d2_y2x2      d2_y2x3      d2_y3x0      d2_y3x1      d2_y3x2      d2_y3x3      @>@>@>offx      pix À$À$À$offy      pix ¿Ÿ11Î*óñ¿Ÿ11Î*óñ¿Ÿ11Î*óñd2_x1        ¿Qý3H™&¿Qý3H™&¿Qý3H™&d2_x2        ?î–»ØØÂ?î–»ØØÂ?î–»ØØÂd2_x3        ?ð?ð?ðflipx        ?ð?ð?ðflipy        ÀS÷ÎÙ‡ÀS÷ÎÙ‡ÀS÷ÎÙ‡slit[0]   "   À~ùÛ"ÐåÀ~ùÛ"ÐåÀ~ùÛ"Ðåslit[1]   "   ÀS÷ÎÙ‡ÀS÷ÎÙ‡ÀS÷ÎÙ‡slit[2]   "   ¿÷S÷ÎÙ‡¿÷S÷ÎÙ‡¿÷S÷ÎÙ‡slit[3]   "   slit[4]   "   ?÷S÷ÎÙ‡?÷S÷ÎÙ‡?÷S÷ÎÙ‡slit[5]   "   @S÷ÎÙ‡@S÷ÎÙ‡@S÷ÎÙ‡slit[6]   "   @~ùÛ"Ðå@~ùÛ"Ðå@~ùÛ"Ðåslit[7]   "   @S÷ÎÙ‡@S÷ÎÙ‡@S÷ÎÙ‡slit[8]   "   AB»¹AB»¹AB»¹config_mjddays
\ No newline at end of file
diff --git a/XSHOOTER/calibration/optical_response.fits b/XSHOOTER/calibration/optical_response.fits
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diff --git a/XSHOOTER/calibration/physical_model.py b/XSHOOTER/calibration/physical_model.py
new file mode 100644
index 00000000..b529d584
--- /dev/null
+++ b/XSHOOTER/calibration/physical_model.py
@@ -0,0 +1,52 @@
+"""ESO's calibrated optical kernel, used only to prepare ScopeSim trace tables.
+
+The library holds one active arm at a time, matching the preparation loop.
+Simulation uses the prepared FITS files and requires neither this module nor a
+C compiler. Internal model coordinates are converted separately by the caller.
+"""
+from pathlib import Path
+import ctypes
+import subprocess
+import numpy as np
+from astropy.table import Table
+
+ROOT = Path(__file__).parent
+
+
+def build_kernel(directory):
+    directory.mkdir(parents=True, exist_ok=True)
+    library = directory / 'eso_model.so'
+    subprocess.run(['cc', '-shared', '-fPIC', '-O2', str(ROOT / 'eso_model.c'), '-o', str(library), '-lm'], check=True)
+    return library
+
+
+class PhysicalModel:
+    def __init__(self, library, arm):
+        self.library = ctypes.CDLL(str(library))
+        self.library.set_parameter.argtypes = [ctypes.c_char_p, ctypes.c_double]
+        self.library.reset_model.restype = None
+        self.library.initialize_model.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_double, ctypes.c_double]
+        self.library.initialize_model.restype = None
+        array = np.ctypeslib.ndpointer(dtype=np.float64, ndim=1, flags='C_CONTIGUOUS')
+        self.library.evaluate.argtypes = [ctypes.c_int, array, array, ctypes.c_double, ctypes.c_int, array, array]
+        self.library.evaluate.restype = None
+        parameters = Table.read(ROOT / f'model_{arm}.fits', hdu=1)
+        self.parameters = dict(zip(np.char.strip(parameters['Parameter_Name']), parameters['Best_Guess']))
+        self.library.reset_model()
+        angular = {'mues', 'nues', 'taues', 'cmup1', 'mug', 'nug', 'taug', 'mud', 'nud', 'taud', 'chiprot'}
+        angular.update(f'{prefix}{i}' for prefix in ['mup', 'nup', 'taup'] for i in range(1, 7))
+        for name, value in self.parameters.items():
+            if name in angular:
+                value = np.deg2rad(value)
+            if self.library.set_parameter(name.encode(), value) != 1:
+                raise KeyError(name)
+        # xsh_data_instrument.c and xsh_model_arm_constants.h: internal model sizes.
+        dimensions = {'uvb': (2048, 3000, 96., 0.), 'vis': (2048, 4000, 96., 0.), 'nir': (1020, 2040, 4., 8.)}
+        self.library.initialize_model(*dimensions[arm])
+
+    def __call__(self, wavelength_nm, slit_arcsec, order, eta_arcsec=0):
+        wavelength_nm = np.ascontiguousarray(wavelength_nm, dtype=np.float64)
+        slit_arcsec = np.ascontiguousarray(slit_arcsec, dtype=np.float64)
+        x, y = np.empty_like(wavelength_nm), np.empty_like(wavelength_nm)
+        self.library.evaluate(len(x), wavelength_nm, slit_arcsec, eta_arcsec, order, x, y)
+        return x, y
diff --git a/XSHOOTER/calibration/slit_resolution.ecsv b/XSHOOTER/calibration/slit_resolution.ecsv
new file mode 100644
index 00000000..8e5a532a
--- /dev/null
+++ b/XSHOOTER/calibration/slit_resolution.ecsv
@@ -0,0 +1,24 @@
+# %ECSV 1.0
+# ---
+# datatype:
+# - {name: arm, datatype: string}
+# - {name: slit_arcsec, unit: arcsec, datatype: float64}
+# - {name: reference_R, datatype: int64}
+# - {name: model_R, datatype: float64}
+# - {name: fractional_error, datatype: float64}
+# schema: astropy-2.0
+arm slit_arcsec reference_R model_R fractional_error
+UVB 0.5 9700 9672.854097593941 -0.002798546639799837
+UVB 0.8 6700 6608.218215626431 -0.013698773787099938
+UVB 1.0 5400 5383.711782743362 -0.003016336529007013
+UVB 1.3 4100 4191.229862225606 0.022251185908684246
+UVB 1.6 3200 3423.998745372947 0.06999960792904592
+VIS 0.4 18400 18457.764179838072 0.003139357599895254
+VIS 0.7 11400 11142.772052916074 -0.02256385500736191
+VIS 0.9 8900 8743.243314949963 -0.017613110679779442
+VIS 1.2 6500 6593.128801162627 0.014327507871173317
+VIS 1.5 5000 5287.0667197038365 0.05741334394076736
+NIR 0.4 11600 11704.553663281138 0.009013246834580935
+NIR 0.6 8100 8074.551839337022 -0.0031417482299972432
+NIR 0.9 5600 5452.1280937236115 -0.026405697549355067
+NIR 1.2 4300 4105.5319594358725 -0.045225125712587744
diff --git a/XSHOOTER/calibration/standard_measurements.fits b/XSHOOTER/calibration/standard_measurements.fits
new file mode 100644
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diff --git a/XSHOOTER/calibration/standard_repeatability.ecsv b/XSHOOTER/calibration/standard_repeatability.ecsv
new file mode 100644
index 00000000..a9b4da4a
--- /dev/null
+++ b/XSHOOTER/calibration/standard_repeatability.ecsv
@@ -0,0 +1,36 @@
+# %ECSV 1.0
+# ---
+# datatype:
+# - {name: order, datatype: string}
+# - {name: continuum_bins, datatype: int64}
+# - {name: nod_b_fractional_bias, datatype: float64}
+# - {name: nod_b_fractional_rms, datatype: float64}
+# schema: astropy-2.0
+order continuum_bins nod_b_fractional_bias nod_b_fractional_rms
+UVB_13 86 -0.032353523186082145 0.16421993248200176
+UVB_14 93 -0.007037596421887504 0.010632669540307714
+UVB_15 93 -0.007287429033590698 0.01172829422236263
+UVB_16 93 -0.009256545659129145 0.012832761538475379
+UVB_17 93 -0.010764927708111882 0.027796997341089973
+UVB_18 93 -0.009293256128322547 0.016025473382567487
+UVB_19 93 -0.009083939213707426 0.013998354184808997
+UVB_20 93 -0.005065724106676828 0.01844406683740275
+UVB_21 93 -0.00658418587016496 0.015684797413889895
+UVB_22 93 -0.006196489407747308 0.021486874409469593
+UVB_23 93 -0.010638978488790363 0.04439461669769377
+UVB_24 67 -0.0265543074054585 0.03635922748573164
+VIS_16 46 0.008513969863966908 0.021410247684132216
+VIS_17 4 0.029693000771252476 0.02972321528278055
+VIS_18 47 0.007682975081807486 0.012106532201232808
+VIS_19 83 0.016081758671578017 0.018819832008178654
+VIS_20 43 0.009378535036832947 0.014278088802941503
+VIS_21 83 0.014206633625650555 0.015114403445250087
+VIS_22 66 0.01589314821066634 0.02130639413122634
+VIS_23 43 0.009727092471127463 0.011441761577916569
+VIS_24 79 0.01092420823966167 0.03353395735680118
+VIS_25 96 0.011517459882217862 0.0181266211181002
+VIS_26 110 0.009125442661444771 0.013764814099190538
+VIS_27 110 0.009682939764955378 0.014006839365794523
+VIS_28 105 0.008101474855620294 0.015871887012680313
+VIS_29 102 0.0002626654512207782 0.02062652222720522
+VIS_30 69 -0.009606199255197456 0.04148529442253448
diff --git a/XSHOOTER/calibration/trace_residuals.ecsv b/XSHOOTER/calibration/trace_residuals.ecsv
new file mode 100644
index 00000000..d777d28a
--- /dev/null
+++ b/XSHOOTER/calibration/trace_residuals.ecsv
@@ -0,0 +1,54 @@
+# %ECSV 1.0
+# ---
+# datatype:
+# - {name: order, datatype: string}
+# - {name: degree, datatype: int64}
+# - {name: forward_rms_pix, datatype: float64}
+# - {name: forward_max_pix, datatype: float64}
+# - {name: inverse_rms_pix, datatype: float64}
+# - {name: inverse_max_pix, datatype: float64}
+# schema: astropy-2.0
+order degree forward_rms_pix forward_max_pix inverse_rms_pix inverse_max_pix
+UVB_13 5 0.0015613122775746252 0.0033238821088239488 0.0014664621824980106 0.0032314438546241714
+UVB_14 5 0.0015726015028583616 0.0033414178515767286 0.0014864863124297936 0.003273347240142329
+UVB_15 5 0.00158881355179112 0.0033692846562521795 0.0015104866790106985 0.0033259879188343587
+UVB_16 5 0.0016196313390047271 0.0034279764705356596 0.0015491216248893895 0.003410065794523795
+UVB_17 5 0.0018345157238968696 0.0051301032890032185 0.0016848851657405878 0.0049002277279650746
+UVB_18 5 0.0017164107506668525 0.0043379322603179496 0.0016654134389651107 0.003770558219291858
+UVB_19 5 0.0016849134206980627 0.0035433905249550807 0.0016372260510381332 0.0035901046193586765
+UVB_20 5 0.001707971638927708 0.003583217598324744 0.0016668921706800884 0.003648716225432205
+UVB_21 5 0.0017316272586883309 0.0036362101465484075 0.001696695620575159 0.003705673970068731
+UVB_22 5 0.0017484520495781974 0.0036810573864979094 0.001718824084072264 0.0037453083946888876
+UVB_23 5 0.0017796441367959924 0.0037563065909514987 0.001754810771795242 0.003814389180360988
+UVB_24 5 0.0018176095269595972 0.0038454783499170028 0.001796795759356058 0.003896433869553864
+VIS_16 5 0.00697420856797676 0.016047156998642217 0.0069725304793512915 0.015730184945277115
+VIS_17 5 0.006957167955874303 0.015984533127698 0.006955502319991808 0.0156731473228958
+VIS_18 5 0.007011719546370871 0.01608790158198494 0.0070100876289279305 0.015780272601935545
+VIS_19 5 0.007051274933294316 0.016156246503538432 0.007049672269500947 0.01585279276879307
+VIS_20 5 0.0071490695677086874 0.016358720081784055 0.007147512470190443 0.0160581593339575
+VIS_21 5 0.007197130052666208 0.016444936391381924 0.0071956096920596175 0.016148878072938775
+VIS_22 5 0.007259852536930956 0.01656509673870744 0.007258367927621719 0.01627307817960287
+VIS_23 5 0.007306209194968138 0.01664689104599917 0.007304756666967063 0.016359477249783747
+VIS_24 5 0.008094999537878693 0.02684213559703867 0.007571694965771207 0.02756860296757416
+VIS_25 5 0.008809365434239914 0.02897365207622402 0.007953722919459384 0.01901876120309902
+VIS_26 5 0.007506447231580508 0.017026128070632276 0.007505104314388203 0.016753070380355607
+VIS_27 5 0.007604698520623842 0.017224518836344536 0.0076034035734484995 0.01695612874498093
+VIS_28 5 0.007680484872243018 0.017370949179827587 0.007679235773515046 0.017108168070583147
+VIS_29 5 0.007762239727235426 0.017530256855612283 0.007761045265790401 0.017273820830678688
+VIS_30 5 0.005023018554825261 0.011312918866030735 0.005020381402458403 0.01113808924635065
+NIR_11 4 0.01605046138277653 0.04051648637127759 0.017217546489214818 0.05809218475799039
+NIR_12 4 0.017236064439017058 0.043966105578366256 0.018302667029282405 0.060704952798875056
+NIR_13 4 0.01878948687391842 0.048417471436947655 0.01976485704496851 0.06433263638945308
+NIR_14 4 0.020068239257445924 0.05216484906089178 0.020970467743638293 0.06709243446527392
+NIR_15 4 0.021753914159366974 0.05699995460171866 0.022586079611140418 0.07088343715279662
+NIR_16 4 0.02377258753749477 0.06274103604234192 0.024537481184586592 0.07548136785802421
+NIR_17 4 0.025942234361506767 0.06887803746765357 0.026647157047143894 0.08032583819094813
+NIR_18 4 0.028540778033685387 0.07616955946660549 0.029188475133096893 0.08617288304507992
+NIR_19 5 0.004809040401623709 0.012776817016073326 0.004830904397266779 0.013396439852376556
+NIR_20 5 0.0053009761527131865 0.01423532029474809 0.005318272027599554 0.014879014012637441
+NIR_21 5 0.005868771888665359 0.015923431140351253 0.0058819678623419 0.01659089046954467
+NIR_22 5 0.00658427991556698 0.018037555837960236 0.006593706669000218 0.018730394296657897
+NIR_23 5 0.007386266199772169 0.020441538420264155 0.007392449206361206 0.02116226129030952
+NIR_24 5 0.00831216225488845 0.023234905693340437 0.008315514000648169 0.023982825092284502
+NIR_25 5 0.0065170316469920385 0.01850006561416726 0.006521170238417572 0.019134293862418952
+NIR_26 4 0.0166574251482539 0.04433605693962299 0.017305450753652803 0.04927016348385945
diff --git a/XSHOOTER/prepare_calibrations.py b/XSHOOTER/prepare_calibrations.py
new file mode 100644
index 00000000..df7dc6d0
--- /dev/null
+++ b/XSHOOTER/prepare_calibrations.py
@@ -0,0 +1,171 @@
+"""Prepare ScopeSim tables from the calibrated ESO X-shooter optical model.
+
+All inputs are pinned in calibration/. The attributed ESO mathematical kernel
+is compiled only for preparation. Simulation needs the generated FITS tables,
+not an ESO installation or a compiler. Run this script from any directory.
+"""
+from pathlib import Path
+import argparse
+import tempfile
+
+import numpy as np
+from astropy.io import fits
+from astropy.table import Table
+from astropy import units as u
+from scopesim.effects.spectral_trace_list_utils import SpectralTrace
+from calibration.physical_model import PhysicalModel, build_kernel
+
+ROOT = Path(__file__).parent
+# Full standard readout science geometry (overscan omitted for CCDs).
+ARM = {'uvb': (2048, 3000, .015), 'vis': (2048, 4000, .015), 'nir': (2048, 1100, .018)}
+# Gaussian optical blur fitted to Table 13 after slit and pixel integration.
+OPTICAL_R = {'uvb': 17000, 'vis': 43000, 'nir': 46000}
+# User Manual v10, Table 14, NIR orders 11--26; wavelengths in nm.
+NIR_BLAZE = [2377.28, 2179.17, 2011.54, 1867.86, 1743.33, 1634.38, 1538.23, 1452.78, 1376.31, 1307.5, 1245.24, 1188.64, 1136.96, 1089.58, 1046.0, 1005.77]
+
+
+def focal_plane(arm, xp, yp):
+    """ESO internal half-pixel-centered coordinates -> raw-oriented focal mm.
+
+    CCDs: undo pipeline UVB rotation, with prescan/overscan already removed.
+    NIR: undo CPL's positive quarter-turn and the crop of raw columns 4:2044
+    and rows 20:1076 (xsh_data_pre.c). Standard raw readout is 2048 x 1100.
+    """
+    nx, ny, pixel = ARM[arm]
+    if arm == 'uvb':
+        x, y = nx - xp, ny - yp
+    elif arm == 'vis':
+        x, y = xp, yp
+    else:
+        x, y = 2044 - yp, xp + 20
+    return (x - nx / 2) * pixel, (y - ny / 2) * pixel
+
+
+def prepare(build):
+    library = build_kernel(build)
+    optical_response = fits.open(ROOT / 'calibration' / 'optical_response.fits')
+    catalog, traces, efficiencies, checks = [], [], [], []
+    cache = ROOT / 'calibration' / 'eso_etc'
+    for arm_id, arm in enumerate(ARM):
+        nx, ny, pixel = ARM[arm]
+        model = PhysicalModel(library, arm)
+        orders = {'uvb': range(13, 25), 'vis': range(16, 31), 'nir': range(11, 27)}[arm]
+        if arm == 'nir':
+            optics = np.loadtxt(cache / 'instr_NIR.dat')
+            correction = np.loadtxt(cache / 'NIR_fudge.dat')
+        qe = np.loadtxt(cache / f'ccd{926 + arm_id}.dat')
+        for index, order in enumerate(orders):
+            blaze_nm = 2 * np.sin(np.deg2rad(-model.parameters['nug'])) / (order * model.parameters['sg']) * 1e6
+            scan = np.linspace(blaze_nm * (1 - 2 / order), blaze_nm * (1 + 2 / order), 2049)
+            covered = []
+            for s in [-5.5, 0, 5.5]:
+                xp, yp = model(scan, np.full_like(scan, s), order)
+                x, y = focal_plane(arm, xp, yp)
+                inside = (abs(x) <= nx * pixel / 2) & (abs(y) <= ny * pixel / 2)
+                covered.extend(scan[inside])
+            # One scan interval beyond the detector intersection keeps edge pixels
+            # covered. The physical model, rather than an extrapolated inverse,
+            # provides the coordinates outside the illuminated detector footprint.
+            limits = [min(covered) - (scan[1] - scan[0]), max(covered) + (scan[1] - scan[0])]
+            wave, slit = np.meshgrid(np.linspace(*limits, 193), np.linspace(-5.5, 5.5, 21))
+            xp, yp = model(wave.ravel(), slit.ravel(), order)
+            x, y = focal_plane(arm, xp, yp)
+            table = Table([wave.ravel() / 1000 * u.um, slit.ravel() * u.arcsec, x * u.mm, y * u.mm], names=['wavelength', 's', 'x', 'y'])
+            name = f'{arm.upper()}_{order}'
+            hdu = fits.table_to_hdu(table)
+            hdu.name = name
+            hdu.header['DISPDIR'] = 'x' if arm == 'nir' else 'y'
+            hdu.header['PIXSIZE'] = (pixel, 'Physical unbinned pixel size [mm]')
+            wv, sv = np.meshgrid(np.linspace(*limits, 90)[1:-1], np.linspace(-5.4, 5.4, 12))
+            xv, yv = model(wv.ravel(), sv.ravel(), order)
+            xv, yv = focal_plane(arm, xv, yv)
+            for degree in range(4, 13):
+                hdu.header['FITDEG'] = degree
+                trace = SpectralTrace(hdu, trace_flux_jacobian=True)
+                dx = trace.xilam2x(sv.ravel(), wv.ravel() / 1000) - xv
+                dy = trace.xilam2y(sv.ravel(), wv.ravel() / 1000) - yv
+                residual = np.hypot(dx, dy) / pixel
+                si, wi = trace.xy2xi(xv, yv), trace.xy2lam(xv, yv)
+                inverse = np.hypot(trace.xilam2x(si, wi) - xv, trace.xilam2y(si, wi) - yv) / pixel
+                if max(np.sqrt(np.mean(residual**2)), np.sqrt(np.mean(inverse**2))) < .03 and max(residual.max(), inverse.max()) < .1:
+                    break
+            else:
+                raise ValueError(f'{name}: forward/inverse mapping fails the subpixel requirement')
+            checks.append((name, degree, np.sqrt(np.mean(residual**2)), residual.max(), np.sqrt(np.mean(inverse**2)), inverse.max()))
+            # The cross-slit wavelength shift comes from the same calibrated
+            # optical model, including prism anamorphism. It is independent of
+            # the numerical sky-grid spacing and the requested on-chip binning.
+            xp, yp = model(wave.ravel(), slit.ravel(), order, eta_arcsec=.01)
+            xm, ym = model(wave.ravel(), slit.ravel(), order, eta_arcsec=-.01)
+            xp, yp = focal_plane(arm, xp, yp)
+            xm, ym = focal_plane(arm, xm, ym)
+            table['dlam_deta'] = (trace.xy2lam(xp, yp) - trace.xy2lam(xm, ym)) / .02 * u.um / u.arcsec
+            header = hdu.header.copy()
+            hdu = fits.table_to_hdu(table)
+            hdu.header.extend(header, update=True)
+            hdu.header['SLITCOL'] = 'dlam_deta'
+            hdu.header['LSFRES'] = OPTICAL_R[arm]
+            print(name, 'degree', degree, 'RMS [pix]', round(checks[-1][2], 5), flush=True)
+            catalog.append((name, len(traces) + 2, arm_id, arm_id))
+            traces.append(hdu)
+            if arm == 'nir':
+                # ETC envelope and scalar diffraction blaze are an explicit
+                # approximation to the NIR order response.
+                ew = np.linspace(*limits, 1025)
+                blaze = np.sinc(order * (NIR_BLAZE[index] / ew - 1))**2
+                throughput = np.interp(ew, optics[:, 0], optics[:, 1], left=0, right=0)
+                throughput *= np.interp(ew, correction[:, 0], correction[:, 1]) * blaze
+                eff = fits.table_to_hdu(Table([ew / 1000 * u.um, throughput], names=['wavelength', 'efficiency']))
+                eff.name = name
+            else:
+                eff = optical_response[name].copy()
+            efficiencies.append(eff)
+        with (ROOT / 'throughput' / f'qe_{arm}.dat').open('w') as output:
+            output.write('# wavelength_unit: um\n# Source: ESO ETC ccd' + str(926 + arm_id) + '.dat\nwavelength transmission\n')
+            np.savetxt(output, np.column_stack([qe[:, 0] / 1000, qe[:, 1]]), fmt='%.9g')
+    optical_response.close()
+    cat = fits.table_to_hdu(Table(rows=catalog, names=['description', 'extension_id', 'aperture_id', 'image_plane_id']))
+    primary = fits.PrimaryHDU()
+    primary.header['ECAT'] = 1
+    primary.header['EDATA'] = 2
+    primary.header['INSTRUME'] = 'XSHOOTER'
+    fits.HDUList([primary, cat] + traces).writeto(ROOT / 'traces' / 'traces.fits', overwrite=True, checksum=True)
+    fits.HDUList([primary.copy(), cat.copy()] + efficiencies).writeto(ROOT / 'throughput' / 'orders.fits', overwrite=True, checksum=True)
+    # Table 12 measures the warm stray light at four wavelengths, separately
+    # from dark current and sky. Approximate its detector variation by a smooth
+    # cross-dispersion profile, constant outside the measured positions. No
+    # structure along dispersion is constrained by these four measurements.
+    measurements = np.array([1.048, 1.238, 1.300, 1.682])
+    positions = []
+    nir_traces = [SpectralTrace(hdu) for hdu in traces if hdu.name.startswith('NIR')]
+    for wave in measurements:
+        trace = min(nir_traces, key=lambda tr: abs((tr.wave_min + tr.wave_max) / 2 - wave))
+        positions.append(trace.xilam2y(0, wave) / ARM['nir'][2] + ARM['nir'][1] / 2 - .5)
+    index = np.argsort(positions)
+    positions = np.array(positions)[index]
+    unfiltered = np.array([.06, .10, .13, .15])[index]
+    filtered = np.array([.02, .03, .04, .04])[index]
+    background = [fits.PrimaryHDU()]
+    for slit_name in ['0.4', '0.6', '0.9', '1.2', '5.0', '0.6JH', '0.9JH']:
+        aperture = Table.read(ROOT / 'slits' / f'slit_{slit_name}.dat', format='ascii')
+        slit_width = np.ptp(aperture['y'])
+        rates = filtered if slit_name.endswith('JH') else unfiltered
+        row_rate = np.interp(np.arange(ARM['nir'][1]), positions, rates) * slit_width
+        rate_map = np.broadcast_to(row_rate[:, None], (ARM['nir'][1], ARM['nir'][0])).astype('float32')
+        hdu = fits.CompImageHDU(rate_map, name=slit_name, compression_type='GZIP_1', quantize_level=0)
+        hdu.header['BUNIT'] = 'electron / s'
+        background.append(hdu)
+    fits.HDUList(background).writeto(ROOT / 'detector' / 'nir_background.fits', overwrite=True, checksum=True)
+    Table(rows=checks, names=['order', 'degree', 'forward_rms_pix', 'forward_max_pix', 'inverse_rms_pix', 'inverse_max_pix']).write(ROOT / 'calibration' / 'trace_residuals.ecsv', overwrite=True)
+    with (ROOT / 'detector' / 'nir_read_noise.dat').open('w') as output:
+        output.write('# dit_unit: s\n# read_noise_unit: electron\n# Source: ESO ETC nir_dit_ron.dat\ndit read_noise\n')
+        read_noise = np.loadtxt(cache / 'nir_dit_ron.dat')
+        measured = (read_noise[:, 0] >= 1) & (read_noise[:, 0] <= 1800)
+        np.savetxt(output, read_noise[measured], fmt='%.9g')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.parse_args()
+    with tempfile.TemporaryDirectory(prefix='xshooter-model-') as directory:
+        prepare(Path(directory))

From 299b3a7fdaf5e7b33c6ccef558bb197b79c63116 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Fri, 16 Oct 2026 20:44:20 -0700
Subject: [PATCH 45/63] Configure the three X-shooter arms with calibrated
 orders and slit options

---
 XSHOOTER/XSHOOTER.yaml               |  11 ++
 XSHOOTER/XSHOOTER_SPEC.yaml          |  96 ++++++++++++++
 XSHOOTER/default.yaml                |  47 +++++++
 XSHOOTER/slits/slit_0.4.dat          |   8 ++
 XSHOOTER/slits/slit_0.5.dat          |   8 ++
 XSHOOTER/slits/slit_0.6.dat          |   8 ++
 XSHOOTER/slits/slit_0.6JH.dat        |   8 ++
 XSHOOTER/slits/slit_0.7.dat          |   8 ++
 XSHOOTER/slits/slit_0.8.dat          |   8 ++
 XSHOOTER/slits/slit_0.9.dat          |   8 ++
 XSHOOTER/slits/slit_0.9JH.dat        |   8 ++
 XSHOOTER/slits/slit_1.0.dat          |   8 ++
 XSHOOTER/slits/slit_1.2.dat          |   8 ++
 XSHOOTER/slits/slit_1.3.dat          |   8 ++
 XSHOOTER/slits/slit_1.5.dat          |   8 ++
 XSHOOTER/slits/slit_1.6.dat          |   8 ++
 XSHOOTER/slits/slit_5.0.dat          |   8 ++
 XSHOOTER/throughput/branches.dat     |   5 +
 XSHOOTER/throughput/clear.dat        |   4 +
 XSHOOTER/throughput/filter_0.4.dat   |   4 +
 XSHOOTER/throughput/filter_0.6.dat   |   4 +
 XSHOOTER/throughput/filter_0.6JH.dat |  34 +++++
 XSHOOTER/throughput/filter_0.9.dat   |   4 +
 XSHOOTER/throughput/filter_0.9JH.dat |  34 +++++
 XSHOOTER/throughput/filter_1.2.dat   |   4 +
 XSHOOTER/throughput/filter_5.0.dat   |   4 +
 XSHOOTER/throughput/jh_filter.dat    |  34 +++++
 XSHOOTER/throughput/orders.fits      | Bin 0 -> 875520 bytes
 XSHOOTER/throughput/qe_nir.dat       | 156 +++++++++++++++++++++++
 XSHOOTER/throughput/qe_uvb.dat       | 179 +++++++++++++++++++++++++++
 XSHOOTER/throughput/qe_vis.dat       | 128 +++++++++++++++++++
 XSHOOTER/traces/traces.fits          | Bin 0 -> 7191360 bytes
 32 files changed, 860 insertions(+)
 create mode 100644 XSHOOTER/XSHOOTER.yaml
 create mode 100644 XSHOOTER/XSHOOTER_SPEC.yaml
 create mode 100644 XSHOOTER/default.yaml
 create mode 100644 XSHOOTER/slits/slit_0.4.dat
 create mode 100644 XSHOOTER/slits/slit_0.5.dat
 create mode 100644 XSHOOTER/slits/slit_0.6.dat
 create mode 100644 XSHOOTER/slits/slit_0.6JH.dat
 create mode 100644 XSHOOTER/slits/slit_0.7.dat
 create mode 100644 XSHOOTER/slits/slit_0.8.dat
 create mode 100644 XSHOOTER/slits/slit_0.9.dat
 create mode 100644 XSHOOTER/slits/slit_0.9JH.dat
 create mode 100644 XSHOOTER/slits/slit_1.0.dat
 create mode 100644 XSHOOTER/slits/slit_1.2.dat
 create mode 100644 XSHOOTER/slits/slit_1.3.dat
 create mode 100644 XSHOOTER/slits/slit_1.5.dat
 create mode 100644 XSHOOTER/slits/slit_1.6.dat
 create mode 100644 XSHOOTER/slits/slit_5.0.dat
 create mode 100644 XSHOOTER/throughput/branches.dat
 create mode 100644 XSHOOTER/throughput/clear.dat
 create mode 100644 XSHOOTER/throughput/filter_0.4.dat
 create mode 100644 XSHOOTER/throughput/filter_0.6.dat
 create mode 100644 XSHOOTER/throughput/filter_0.6JH.dat
 create mode 100644 XSHOOTER/throughput/filter_0.9.dat
 create mode 100644 XSHOOTER/throughput/filter_0.9JH.dat
 create mode 100644 XSHOOTER/throughput/filter_1.2.dat
 create mode 100644 XSHOOTER/throughput/filter_5.0.dat
 create mode 100644 XSHOOTER/throughput/jh_filter.dat
 create mode 100644 XSHOOTER/throughput/orders.fits
 create mode 100644 XSHOOTER/throughput/qe_nir.dat
 create mode 100644 XSHOOTER/throughput/qe_uvb.dat
 create mode 100644 XSHOOTER/throughput/qe_vis.dat
 create mode 100644 XSHOOTER/traces/traces.fits

diff --git a/XSHOOTER/XSHOOTER.yaml b/XSHOOTER/XSHOOTER.yaml
new file mode 100644
index 00000000..4297034a
--- /dev/null
+++ b/XSHOOTER/XSHOOTER.yaml
@@ -0,0 +1,11 @@
+object: instrument
+alias: INST
+name: XSHOOTER
+properties:
+  # Sky sampling is numerical. Detector pixel sizes and slit dispersion live in the traces.
+  pixel_scale: 0.05
+  plate_scale: 3.333333333333333
+  decouple_detector_from_sky_headers: true
+  uvb_curr_slit: '1.0'
+  vis_curr_slit: '0.9'
+  nir_curr_slit: '0.9'
diff --git a/XSHOOTER/XSHOOTER_SPEC.yaml b/XSHOOTER/XSHOOTER_SPEC.yaml
new file mode 100644
index 00000000..f38509ae
--- /dev/null
+++ b/XSHOOTER/XSHOOTER_SPEC.yaml
@@ -0,0 +1,96 @@
+object: instrument
+alias: INST
+name: XSHOOTER_spectrographs
+effects:
+  - name: arm_routing
+    class: DichroicTree
+    kwargs:
+      tree_filename: throughput/branches.dat
+      dichroic_names: []
+      filename_format: '{}'
+  - name: slits
+    class: SelectorWheel
+    kwargs:
+      selector_key: aperture_id
+      wheel:
+        - selector_value: [0]
+          effect_class: SlitWheel
+          effect_kwargs:
+            slit_names: ['0.5', '0.8', '1.0', '1.3', '1.6', '5.0']
+            filename_format: 'slits/slit_{}.dat'
+            current_slit: '!INST.uvb_curr_slit'
+        - selector_value: [1]
+          effect_class: SlitWheel
+          effect_kwargs:
+            slit_names: ['0.4', '0.7', '0.9', '1.2', '1.5', '5.0']
+            filename_format: 'slits/slit_{}.dat'
+            current_slit: '!INST.vis_curr_slit'
+        - selector_value: [2]
+          effect_class: SlitWheel
+          effect_kwargs:
+            slit_names: ['0.4', '0.6', '0.9', '1.2', '5.0', '0.6JH', '0.9JH']
+            filename_format: 'slits/slit_{}.dat'
+            current_slit: '!INST.nir_curr_slit'
+  - name: order_throughput
+    class: SpectralEfficiency
+    kwargs:
+      filename: throughput/orders.fits
+  - name: nir_order_blocking
+    class: SelectorWheel
+    kwargs:
+      selector_key: aperture_id
+      allowed_missing_selector_values: [0, 1]
+      wheel:
+        - selector_value: [2]
+          effect_class: FilterWheel
+          effect_kwargs:
+            filter_names: ['0.4', '0.6', '0.9', '1.2', '5.0', '0.6JH', '0.9JH']
+            filename_format: 'throughput/filter_{}.dat'
+            current_filter: '!INST.nir_curr_slit'
+  - name: detector_qe
+    class: SelectorWheel
+    kwargs:
+      selector_key: aperture_id
+      wheel:
+        - selector_value: [0]
+          effect_class: SpectralQuantumEfficiency
+          effect_kwargs: {filename: throughput/qe_uvb.dat}
+        - selector_value: [1]
+          effect_class: SpectralQuantumEfficiency
+          effect_kwargs: {filename: throughput/qe_vis.dat}
+        - selector_value: [2]
+          effect_class: SpectralQuantumEfficiency
+          effect_kwargs: {filename: throughput/qe_nir.dat}
+  - name: atmospheric_dispersion
+    description: UVB/VIS ADCs assumed ideal; NIR has no ADC
+    class: SelectorWheel
+    kwargs:
+      selector_key: aperture_id
+      z_order: [651]
+      allowed_missing_selector_values: [0, 1]
+      wheel:
+        - selector_value: [2]
+          effect_class: ADShift
+          effect_kwargs:
+            temperature: '!ATMO.temperature'
+            pressure: '!ATMO.pressure'
+            humidity: '!ATMO.humidity'
+            wave_ref_um: 1.31
+            method: map_coords
+  - name: spectral_traces
+    class: SpectralTraceList
+    kwargs:
+      filename: traces/traces.fits
+      trace_flux_jacobian: true
+  - name: nir_stray_light
+    class: SelectorWheel
+    kwargs:
+      selector_key: image_plane_id
+      allowed_missing_selector_values: [0, 1]
+      wheel:
+        - selector_value: [2]
+          effect_class: ImagePlaneBackground
+          effect_kwargs:
+            include: true
+            filename: detector/nir_background.fits
+            extname: '!INST.nir_curr_slit'
diff --git a/XSHOOTER/default.yaml b/XSHOOTER/default.yaml
new file mode 100644
index 00000000..0100cd41
--- /dev/null
+++ b/XSHOOTER/default.yaml
@@ -0,0 +1,47 @@
+object: configuration
+alias: OBS
+name: XSHOOTER_default_configuration
+description: VLT UT3 X-shooter simultaneous slit spectroscopy
+status: development
+version: 0.1
+packages: [Paranal, VLT, XSHOOTER]
+yamls: [Paranal_spectroscopy.yaml, VLT_UT3_Cassegrain.yaml, XSHOOTER.yaml]
+properties:
+  instrument: XSHOOTER
+  modes: [SPEC]
+  object: Test
+  brightness: dark
+  mjdobs: 60310.25  # 2024-01-01 06:00 UTC; geometry calibration epoch
+  pupil_angle: 0
+  az: 0
+  airmass: 1.3
+  seeing: 0.7
+  dit_uvb: 300.0
+  dit_vis: 300.0
+  dit_nir: 300.0
+  ndit_uvb: 1
+  ndit_vis: 1
+  ndit_nir: 1
+mode_yamls:
+  - name: SPEC
+    object: instrument
+    alias: OBS
+    description: Three simultaneous 11 arcsec slits; full science readout regions
+    yamls: [XSHOOTER_SPEC.yaml, XSHOOTER_DET.yaml]
+---
+object: simulation
+alias: SIM
+name: XSHOOTER_simulation
+properties:
+  spectral:
+    wave_unit: um
+    wave_min: 0.3
+    wave_mid: 1.0
+    wave_max: 2.48
+    spectral_bin_width: 0.000005
+    spectral_resolution: 200000
+    trace_flux_jacobian: true
+  sub_pixel:
+    flag: true
+  computing:
+    preload_field_of_views: false
diff --git a/XSHOOTER/slits/slit_0.4.dat b/XSHOOTER/slits/slit_0.4.dat
new file mode 100644
index 00000000..660c23d2
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.4.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.2
+5.5 -0.2
+5.5 0.2
+-5.5 0.2
diff --git a/XSHOOTER/slits/slit_0.5.dat b/XSHOOTER/slits/slit_0.5.dat
new file mode 100644
index 00000000..9c16b123
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.5.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.25
+5.5 -0.25
+5.5 0.25
+-5.5 0.25
diff --git a/XSHOOTER/slits/slit_0.6.dat b/XSHOOTER/slits/slit_0.6.dat
new file mode 100644
index 00000000..f2d049ef
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.6.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.3
+5.5 -0.3
+5.5 0.3
+-5.5 0.3
diff --git a/XSHOOTER/slits/slit_0.6JH.dat b/XSHOOTER/slits/slit_0.6JH.dat
new file mode 100644
index 00000000..f2d049ef
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.6JH.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.3
+5.5 -0.3
+5.5 0.3
+-5.5 0.3
diff --git a/XSHOOTER/slits/slit_0.7.dat b/XSHOOTER/slits/slit_0.7.dat
new file mode 100644
index 00000000..35524b62
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.7.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.35
+5.5 -0.35
+5.5 0.35
+-5.5 0.35
diff --git a/XSHOOTER/slits/slit_0.8.dat b/XSHOOTER/slits/slit_0.8.dat
new file mode 100644
index 00000000..a2073b4f
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.8.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.4
+5.5 -0.4
+5.5 0.4
+-5.5 0.4
diff --git a/XSHOOTER/slits/slit_0.9.dat b/XSHOOTER/slits/slit_0.9.dat
new file mode 100644
index 00000000..640d36b0
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.9.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.45
+5.5 -0.45
+5.5 0.45
+-5.5 0.45
diff --git a/XSHOOTER/slits/slit_0.9JH.dat b/XSHOOTER/slits/slit_0.9JH.dat
new file mode 100644
index 00000000..640d36b0
--- /dev/null
+++ b/XSHOOTER/slits/slit_0.9JH.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.45
+5.5 -0.45
+5.5 0.45
+-5.5 0.45
diff --git a/XSHOOTER/slits/slit_1.0.dat b/XSHOOTER/slits/slit_1.0.dat
new file mode 100644
index 00000000..fb6963d7
--- /dev/null
+++ b/XSHOOTER/slits/slit_1.0.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.5
+5.5 -0.5
+5.5 0.5
+-5.5 0.5
diff --git a/XSHOOTER/slits/slit_1.2.dat b/XSHOOTER/slits/slit_1.2.dat
new file mode 100644
index 00000000..487288fd
--- /dev/null
+++ b/XSHOOTER/slits/slit_1.2.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.6
+5.5 -0.6
+5.5 0.6
+-5.5 0.6
diff --git a/XSHOOTER/slits/slit_1.3.dat b/XSHOOTER/slits/slit_1.3.dat
new file mode 100644
index 00000000..92867fea
--- /dev/null
+++ b/XSHOOTER/slits/slit_1.3.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.65
+5.5 -0.65
+5.5 0.65
+-5.5 0.65
diff --git a/XSHOOTER/slits/slit_1.5.dat b/XSHOOTER/slits/slit_1.5.dat
new file mode 100644
index 00000000..71c4407b
--- /dev/null
+++ b/XSHOOTER/slits/slit_1.5.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.75
+5.5 -0.75
+5.5 0.75
+-5.5 0.75
diff --git a/XSHOOTER/slits/slit_1.6.dat b/XSHOOTER/slits/slit_1.6.dat
new file mode 100644
index 00000000..fe0fd76e
--- /dev/null
+++ b/XSHOOTER/slits/slit_1.6.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -0.8
+5.5 -0.8
+5.5 0.8
+-5.5 0.8
diff --git a/XSHOOTER/slits/slit_5.0.dat b/XSHOOTER/slits/slit_5.0.dat
new file mode 100644
index 00000000..7cb44cc3
--- /dev/null
+++ b/XSHOOTER/slits/slit_5.0.dat
@@ -0,0 +1,8 @@
+# x_unit: arcsec
+# y_unit: arcsec
+# description: 11 arcsec long X-shooter slit
+x y
+-5.5 -2.5
+5.5 -2.5
+5.5 2.5
+-5.5 2.5
diff --git a/XSHOOTER/throughput/branches.dat b/XSHOOTER/throughput/branches.dat
new file mode 100644
index 00000000..26c4cd55
--- /dev/null
+++ b/XSHOOTER/throughput/branches.dat
@@ -0,0 +1,5 @@
+# description: Routing only; dichroic transmission is already in the ESO instrument response
+aperture_id
+0
+1
+2
diff --git a/XSHOOTER/throughput/clear.dat b/XSHOOTER/throughput/clear.dat
new file mode 100644
index 00000000..aaab927f
--- /dev/null
+++ b/XSHOOTER/throughput/clear.dat
@@ -0,0 +1,4 @@
+# wavelength_unit: um
+wavelength transmission
+0.3 1
+2.5 1
diff --git a/XSHOOTER/throughput/filter_0.4.dat b/XSHOOTER/throughput/filter_0.4.dat
new file mode 100644
index 00000000..aaab927f
--- /dev/null
+++ b/XSHOOTER/throughput/filter_0.4.dat
@@ -0,0 +1,4 @@
+# wavelength_unit: um
+wavelength transmission
+0.3 1
+2.5 1
diff --git a/XSHOOTER/throughput/filter_0.6.dat b/XSHOOTER/throughput/filter_0.6.dat
new file mode 100644
index 00000000..aaab927f
--- /dev/null
+++ b/XSHOOTER/throughput/filter_0.6.dat
@@ -0,0 +1,4 @@
+# wavelength_unit: um
+wavelength transmission
+0.3 1
+2.5 1
diff --git a/XSHOOTER/throughput/filter_0.6JH.dat b/XSHOOTER/throughput/filter_0.6JH.dat
new file mode 100644
index 00000000..3c57c395
--- /dev/null
+++ b/XSHOOTER/throughput/filter_0.6JH.dat
@@ -0,0 +1,34 @@
+# wavelength_unit: um
+# source: ESO X-shooter User Manual v10, Figure 13
+# description: Graph-digitized cutoff; 0.945 passband continuation below plotted 2 um
+wavelength transmission
+0.3 0.945
+1.9 0.945
+2.000 0.945
+2.010 0.935
+2.020 0.925
+2.030 0.929
+2.040 0.925
+2.045 0.921
+2.050 0.910
+2.052 0.890
+2.054 0.865
+2.056 0.825
+2.058 0.780
+2.060 0.725
+2.062 0.650
+2.064 0.565
+2.066 0.475
+2.068 0.385
+2.070 0.300
+2.072 0.235
+2.074 0.175
+2.076 0.130
+2.078 0.098
+2.080 0.072
+2.085 0.035
+2.090 0.019
+2.100 0.006
+2.120 0.002
+2.150 0.002
+2.500 0.002
diff --git a/XSHOOTER/throughput/filter_0.9.dat b/XSHOOTER/throughput/filter_0.9.dat
new file mode 100644
index 00000000..aaab927f
--- /dev/null
+++ b/XSHOOTER/throughput/filter_0.9.dat
@@ -0,0 +1,4 @@
+# wavelength_unit: um
+wavelength transmission
+0.3 1
+2.5 1
diff --git a/XSHOOTER/throughput/filter_0.9JH.dat b/XSHOOTER/throughput/filter_0.9JH.dat
new file mode 100644
index 00000000..3c57c395
--- /dev/null
+++ b/XSHOOTER/throughput/filter_0.9JH.dat
@@ -0,0 +1,34 @@
+# wavelength_unit: um
+# source: ESO X-shooter User Manual v10, Figure 13
+# description: Graph-digitized cutoff; 0.945 passband continuation below plotted 2 um
+wavelength transmission
+0.3 0.945
+1.9 0.945
+2.000 0.945
+2.010 0.935
+2.020 0.925
+2.030 0.929
+2.040 0.925
+2.045 0.921
+2.050 0.910
+2.052 0.890
+2.054 0.865
+2.056 0.825
+2.058 0.780
+2.060 0.725
+2.062 0.650
+2.064 0.565
+2.066 0.475
+2.068 0.385
+2.070 0.300
+2.072 0.235
+2.074 0.175
+2.076 0.130
+2.078 0.098
+2.080 0.072
+2.085 0.035
+2.090 0.019
+2.100 0.006
+2.120 0.002
+2.150 0.002
+2.500 0.002
diff --git a/XSHOOTER/throughput/filter_1.2.dat b/XSHOOTER/throughput/filter_1.2.dat
new file mode 100644
index 00000000..aaab927f
--- /dev/null
+++ b/XSHOOTER/throughput/filter_1.2.dat
@@ -0,0 +1,4 @@
+# wavelength_unit: um
+wavelength transmission
+0.3 1
+2.5 1
diff --git a/XSHOOTER/throughput/filter_5.0.dat b/XSHOOTER/throughput/filter_5.0.dat
new file mode 100644
index 00000000..aaab927f
--- /dev/null
+++ b/XSHOOTER/throughput/filter_5.0.dat
@@ -0,0 +1,4 @@
+# wavelength_unit: um
+wavelength transmission
+0.3 1
+2.5 1
diff --git a/XSHOOTER/throughput/jh_filter.dat b/XSHOOTER/throughput/jh_filter.dat
new file mode 100644
index 00000000..3c57c395
--- /dev/null
+++ b/XSHOOTER/throughput/jh_filter.dat
@@ -0,0 +1,34 @@
+# wavelength_unit: um
+# source: ESO X-shooter User Manual v10, Figure 13
+# description: Graph-digitized cutoff; 0.945 passband continuation below plotted 2 um
+wavelength transmission
+0.3 0.945
+1.9 0.945
+2.000 0.945
+2.010 0.935
+2.020 0.925
+2.030 0.929
+2.040 0.925
+2.045 0.921
+2.050 0.910
+2.052 0.890
+2.054 0.865
+2.056 0.825
+2.058 0.780
+2.060 0.725
+2.062 0.650
+2.064 0.565
+2.066 0.475
+2.068 0.385
+2.070 0.300
+2.072 0.235
+2.074 0.175
+2.076 0.130
+2.078 0.098
+2.080 0.072
+2.085 0.035
+2.090 0.019
+2.100 0.006
+2.120 0.002
+2.150 0.002
+2.500 0.002
diff --git a/XSHOOTER/throughput/orders.fits b/XSHOOTER/throughput/orders.fits
new file mode 100644
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diff --git a/XSHOOTER/throughput/qe_nir.dat b/XSHOOTER/throughput/qe_nir.dat
new file mode 100644
index 00000000..d55aa700
--- /dev/null
+++ b/XSHOOTER/throughput/qe_nir.dat
@@ -0,0 +1,156 @@
+# wavelength_unit: um
+# Source: ESO ETC ccd928.dat
+wavelength transmission
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diff --git a/XSHOOTER/throughput/qe_uvb.dat b/XSHOOTER/throughput/qe_uvb.dat
new file mode 100644
index 00000000..cdd20bad
--- /dev/null
+++ b/XSHOOTER/throughput/qe_uvb.dat
@@ -0,0 +1,179 @@
+# wavelength_unit: um
+# Source: ESO ETC ccd926.dat
+wavelength transmission
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diff --git a/XSHOOTER/throughput/qe_vis.dat b/XSHOOTER/throughput/qe_vis.dat
new file mode 100644
index 00000000..61936dc2
--- /dev/null
+++ b/XSHOOTER/throughput/qe_vis.dat
@@ -0,0 +1,128 @@
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HcmV?d00001


From b966cdda999d2891967227894f0cab7473d031d7 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Fri, 16 Oct 2026 20:44:20 -0700
Subject: [PATCH 46/63] Implement full-frame X-shooter readouts with CCD
 binning and measured noise

---
 XSHOOTER/.gitignore                   |   3 +
 XSHOOTER/FITS_extra_keywords.yaml     |  49 ++++++
 XSHOOTER/README.md                    | 102 +++++++++++
 XSHOOTER/XSHOOTER_DET.yaml            | 198 +++++++++++++++++++++
 XSHOOTER/detector/nir_background.fits | Bin 0 -> 483840 bytes
 XSHOOTER/detector/nir_read_noise.dat  |  29 ++++
 XSHOOTER/tests/test_xshooter.py       | 237 ++++++++++++++++++++++++++
 7 files changed, 618 insertions(+)
 create mode 100644 XSHOOTER/.gitignore
 create mode 100644 XSHOOTER/FITS_extra_keywords.yaml
 create mode 100644 XSHOOTER/README.md
 create mode 100644 XSHOOTER/XSHOOTER_DET.yaml
 create mode 100644 XSHOOTER/detector/nir_background.fits
 create mode 100644 XSHOOTER/detector/nir_read_noise.dat
 create mode 100644 XSHOOTER/tests/test_xshooter.py

diff --git a/XSHOOTER/.gitignore b/XSHOOTER/.gitignore
new file mode 100644
index 00000000..e29d8a50
--- /dev/null
+++ b/XSHOOTER/.gitignore
@@ -0,0 +1,3 @@
+calibration/downloads/
+__pycache__/
+.pytest_cache/
diff --git a/XSHOOTER/FITS_extra_keywords.yaml b/XSHOOTER/FITS_extra_keywords.yaml
new file mode 100644
index 00000000..bfc21b71
--- /dev/null
+++ b/XSHOOTER/FITS_extra_keywords.yaml
@@ -0,0 +1,49 @@
+- ext_type: PrimaryHDU
+  keywords:
+    INSTRUME: XSHOOTER
+    TELESCOP: '!TEL.name'
+    OBJECT: '!OBS.object'
+    HIERARCH:
+      ESO:
+        TEL: {AIRM: {START: '!OBS.airmass'}}
+- ext_type: ImageHDU
+  keywords:
+    BUNIT: adu
+- detector_id: 0
+  ext_number: [0, 1]
+  keywords:
+    ARM: UVB
+    DIT: '!OBS.dit_uvb'
+    NDIT: '!OBS.ndit_uvb'
+    GAIN: '!DET.gain_uvb'
+    RDNOISE: '!DET.ron_uvb'
+    BINX: '!DET.binx_uvb'
+    BINY: '!DET.biny_uvb'
+    SLIT: '!INST.uvb_curr_slit'
+    READMODE: '!DET.readout_mode_uvb'
+    COMBINE: sum
+- detector_id: 1
+  ext_number: [0, 1]
+  keywords:
+    ARM: VIS
+    DIT: '!OBS.dit_vis'
+    NDIT: '!OBS.ndit_vis'
+    GAIN: '!DET.gain_vis'
+    RDNOISE: '!DET.ron_vis'
+    BINX: '!DET.binx_vis'
+    BINY: '!DET.biny_vis'
+    SLIT: '!INST.vis_curr_slit'
+    READMODE: '!DET.readout_mode_vis'
+    COMBINE: sum
+- detector_id: 2
+  ext_number: [0, 1]
+  keywords:
+    ARM: NIR
+    DIT: '!OBS.dit_nir'
+    NDIT: '!OBS.ndit_nir'
+    GAIN: '!DET.gain_nir'
+    BINX: 1
+    BINY: 1
+    SLIT: '!INST.nir_curr_slit'
+    READMODE: nondestructive
+    COMBINE: average
diff --git a/XSHOOTER/README.md b/XSHOOTER/README.md
new file mode 100644
index 00000000..f33a3e06
--- /dev/null
+++ b/XSHOOTER/README.md
@@ -0,0 +1,102 @@
+# XSHOOTER
+
+VLT UT3 / Paranal slit spectroscopy for ScopeSim, with simultaneous UVB, VIS, and NIR detector images. The package requires the accompanying `xshooter` ScopeSim changes. It is a development model: five-percent end-to-end accuracy is not established, particularly in the NIR, telluric bands, order edges, and wide optical slits.
+
+The companion notebook and full model/accuracy notes are in the zs-scopesim workspace:
+
+- `notebooks/01_Kilonovae_XSHOOTER.ipynb`
+- `docs/xshooter.md`
+
+## Configuration
+
+```python
+cmds = scopesim.UserCommands(use_instrument='XSHOOTER', set_modes=['SPEC'])
+train = scopesim.OpticalTrain(cmds)
+train.observe(source)
+uvb, vis, nir = train.readout()
+```
+
+Set the properties before constructing the optical train:
+
+| Property | Values |
+|---|---|
+| `!INST.uvb_curr_slit` | `'0.5', '0.8', '1.0', '1.3', '1.6', '5.0'` |
+| `!INST.vis_curr_slit` | `'0.4', '0.7', '0.9', '1.2', '1.5', '5.0'` |
+| `!INST.nir_curr_slit` | `'0.4', '0.6', '0.9', '1.2', '5.0', '0.6JH', '0.9JH'` |
+| `!OBS.dit_uvb`, `!OBS.dit_vis`, `!OBS.dit_nir` | Seconds per integration; NIR noise table covers 1–1800 s |
+| `!OBS.ndit_uvb`, `!OBS.ndit_vis`, `!OBS.ndit_nir` | Number of integrations |
+| `!DET.binx_uvb`, `!DET.biny_uvb`; likewise `_vis` | Pairs `(1, 1)`, `(1, 2)`, `(2, 2)` in FITS column/row order |
+| `!DET.readout_mode_uvb`, `!DET.readout_mode_vis` | `'slow'` (100 kHz high gain), `'fast'` (400 kHz low gain) |
+| `!OBS.seeing`, `!OBS.airmass`, `!ATMO.pwv` | Zenith seeing at 500 nm in arcsec, airmass, and PWV in mm |
+| `!ATMO.local_sky_file` | `false` for online SkyCalc, or a physical ESO SkyCalc FITS file matching the conditions |
+
+NIR has no on-chip binning. Slits are 11 arcsec long. Native arrays are UVB `(3000, 2048)`, VIS `(4000, 2048)`, and NIR `(1100, 2048)` in NumPy y/x order. These are complete standard science readout regions; optical prescan/overscan is omitted. No optional windows or IFU are implemented.
+
+The three returned HDULists each contain an empty primary HDU and a detector ImageHDU. Images are bias-subtracted floating ADU. Optical NDITs sum; NIR NDITs average. `DIT`, `NDIT`, `EXPTIME`, `COMBINE`, gain, slit, arm, and binning are recorded. Readout overheads, saturation/TLI recovery, persistence, nonlinear ramp processing, bad-pixel maps, and fringing realizations are outside this version. Use exposures below saturation.
+
+`Paranal_spectroscopy.yaml` and `VLT_UT3_Cassegrain.yaml` are separate components. A telescope substitution must replace the latter, not append a second telescope. The optical response calibration is conditional on the adopted telescope reflectivity and collecting area.
+
+## Calibrations
+
+`prepare_calibrations.py` builds the runtime trace, response, QE, NIR background, and NIR noise files from pinned inputs. Runtime simulation does not compile or load the ESO kernel.
+
+```bash
+conda run -n zssim python XSHOOTER/prepare_calibrations.py
+conda run -n zssim python XSHOOTER/calibration/fit_lsf.py
+conda run -n zssim python -m pytest XSHOOTER/tests
+```
+
+The trace tables contain 12 UVB, 15 VIS, and 16 NIR orders. Each has wavelength in micrometres, along-slit position in arcseconds, physical raw-oriented focal coordinates in millimetres, and a cross-slit wavelength derivative in micrometres/arcsecond. `FITDEG` specifies the fitted polynomial degree; `SLITCOL` selects the derivative; `LSFRES` supplies optical Gaussian resolving power. Slit integration, optical blur, and detector sampling remain distinct calculations.
+
+`calibration/fit_lsf.py` reproduces the arm-level optical blur fit against User Manual Table 13 and writes `slit_resolution.ecsv`. The default-slit estimates differ from the published values by 0.3%, 1.8%, and 2.6%; the widest tabulated UVB/VIS slits differ by 7.0%/5.7%. These are uniformly illuminated slit estimates, not a point-source resolution guarantee. The mapping tests independently check photon conservation, centroid, and line variance through the configured traces at two numerical sky-grid spacings.
+
+`calibration/eso_model.c` contains the mathematical functions from the ESO X-shooter 3.8.12 physical-model kernel, plus a small C interface. Its source and GPL-2.0-or-later license are identified in the file and `calibration/ESO_COPYING`. The pinned optimized model parameters are from the following public ESO archive products:
+
+| Arm | Physical model product |
+|---|---|
+| UVB | `M.XSHOOTER.2024-01-03T13:03:57.940` |
+| VIS | `M.XSHOOTER.2024-01-03T13:03:59.573` |
+| NIR | `M.XSHOOTER.2024-01-03T13:03:56.276` |
+
+Internal ESO model coordinates are not raw detector pixels. Preparation undoes the UVB half-turn and NIR CPL quarter-turn/crop. In NIR, raw half-pixel-centered coordinates are `(2044 - model_y, model_x + 20)`. The raw image retains 2048 columns and 1100 rows. Physical focal coordinates are centered on each array. The polynomial fit is checked against the optical kernel on independent wavelength/slit samples; every order meets 0.03 pixel RMS and 0.1 pixel maximum forward/inverse residual. `calibration/trace_residuals.ecsv` records the actual results.
+
+The optical response uses the public EG21 (`CPD-69 177`) standard-star nod pair below. Rates are measured in a 3.6 arcsec spatial aperture after nod subtraction. The response divides observed electron rate by reference photon rate, atmospheric transmission, two adopted aluminium mirror reflectivities, and detector QE. Continuum bins span 32 native dispersion pixels. The A nod supplies the response; B supplies the repeatability check. The normalization retains finite-aperture losses and any residual transparency error in that observation. This is not an independent absolute-throughput validation.
+
+| Local input filename | ESO product ID |
+|---|---|
+| `standard-uvb.fits` | `XSHOO.2024-01-01T01:14:01.322` |
+| `standard-b-uvb.fits` | `XSHOO.2024-01-01T01:18:24.675` |
+| `standard-vis.fits` | `XSHOO.2024-01-01T01:14:06.522` |
+| `standard-b-vis.fits` | `XSHOO.2024-01-01T01:18:29.906` |
+| `standard-nir.fits` | `XSHOO.2024-01-01T01:14:09.698` |
+| `standard-b-nir.fits` | `XSHOO.2024-01-01T01:18:32.604` |
+| `response-uvb.fits` | `M.XSHOOTER.2024-01-02T18:39:02.690` |
+| `response-vis.fits` | `M.XSHOOTER.2024-01-02T18:39:10.320` |
+| `response-nir.fits` | `M.XSHOOTER.2024-01-02T18:39:01.123` |
+| `atmos-ext-uvb.fits` | `M.XSHOOTER.2019-04-04T09:50:14.503` |
+
+Fetch each at `https://dataportal.eso.org/dataPortal/file/<product ID>`. Raw downloads use UNIX compress (`.Z`); decompress with `gzip -dc` before giving them the `.fits` names above. The response products contain the pipeline's `REF` standard-star flux in erg/cm2/s/Angstrom; the `RESPONSE` column itself is not used as raw instrumental throughput.
+
+`sky-default-raw.fits` is an ESO SkyCalc spectrum with observatory Paranal, airmass 1.3, PWV 2.5 mm, new Moon (`moon_sun_sep=0`), wavelength range 300–2500 nm, fixed spectral resolution 200000, and no LSF. Other sky parameters use the SkyCalc defaults. Its molecular transmission selects clear continuum intervals; its total transmission is used only for the exploratory NIR measurements. Optical normalization uses the archived extinction curve. The pipeline reference flux products and raw inputs are not required at runtime.
+
+To repeat the standard calibration after downloading those inputs into an explicit directory:
+
+```bash
+conda run -n zssim python XSHOOTER/calibration/measure_standard.py --data-dir /path/to/inputs
+conda run -n zssim python XSHOOTER/prepare_calibrations.py
+```
+
+`optical_response.fits` is the frozen optical calibration. `standard_measurements.fits` retains the binned A and B measurements. `standard_repeatability.ecsv` reports the B/A bias and RMS. The optical second-nod RMS is below 5% for 26/27 orders; UVB order 13 has about 16% RMS including low-response edges. VIS order 17 has only four continuum bins. Interpolation over absorption regions and endpoint continuation are explicit approximations.
+
+NIR remains based on the ESO ETC instrument envelope, empirical correction, QE, and a scalar sinc-squared blaze centered on the manual's Table 14. The standard measurements show that this cannot currently support a general 5% claim. The original ETC ASCII curves are frozen in `calibration/eso_etc`; they can be retrieved through the [ESO calibration database](https://www.eso.org/observing/etc/bin/simu/calibdisp?INS.NAME=XSHOOTER+INS.MODE%3Dnone).
+
+`nir_background.fits` uses Table 12's four warm-background measurements, including the JH-filter reduction. It is a smooth cross-dispersion approximation derived from the configured traces and slit widths, with endpoint continuation, not a measured 2D stray-light map. Sky, telescope emission, and detector dark current are separate. Temperature variation of this empirical component is not calibrated. The graph-digitized JH cutoff has a stated passband continuation; localized leaks and fringes are omitted.
+
+## References
+
+- [ESO documentation](https://www.eso.org/sci/facilities/paranal/instruments/xshooter/doc.html)
+- [User Manual v10 / P114](https://www.eso.org/sci/facilities/paranal/instruments/xshooter/doc/VLT-MAN-ESO-14650-4942_P114v1.pdf), tables 6–14 and Figure 13
+- [Vernet et al. 2011](https://www.eso.org/sci/facilities/paranal/instruments/xshooter/doc/xshooter_Vernet2011.pdf)
+- [Pipeline manual 3.8.12](https://ftp.eso.org/pub/dfs/pipelines/instruments/xshooter/xshoo-pipeline-manual-3.8.12.pdf)
+- [ESO pipeline source kit 3.8.12](https://ftp.eso.org/pub/dfs/pipelines/instruments/xshooter/xshoo-kit-3.8.12-3.tar.gz)
+- [ESO SkyCalc](https://www.eso.org/observing/etc/skycalc/)
diff --git a/XSHOOTER/XSHOOTER_DET.yaml b/XSHOOTER/XSHOOTER_DET.yaml
new file mode 100644
index 00000000..38426ff1
--- /dev/null
+++ b/XSHOOTER/XSHOOTER_DET.yaml
@@ -0,0 +1,198 @@
+object: detector
+alias: DET
+name: XSHOOTER_detectors
+properties:
+  binx_uvb: 1
+  biny_uvb: 1
+  binx_vis: 1
+  biny_vis: 1
+  readout_mode_uvb: slow
+  readout_mode_vis: slow
+  gain_nir: 2.29
+  dark_uvb: 0.0005555555555555556
+  dark_vis: 0.0005555555555555556
+  dark_nir: 0.02
+  # Numerical sky sampling / physical detector pitch, not an optical plate scale.
+  plate_scale_ccd: 3.333333333333333
+  plate_scale_nir: 2.777777777777778
+
+effects:
+  - name: uvb_readout_parameters
+    class: DetectorModePropertiesSetter
+    kwargs:
+      mode_key: '!DET.readout_mode_uvb'
+      mode_properties:
+        slow: {'!DET.gain_uvb': 0.63, '!DET.ron_uvb': 2.4}
+        fast: {'!DET.gain_uvb': 1.83, '!DET.ron_uvb': 4.6}
+  - name: vis_readout_parameters
+    class: DetectorModePropertiesSetter
+    kwargs:
+      mode_key: '!DET.readout_mode_vis'
+      mode_properties:
+        slow: {'!DET.gain_vis': 0.64, '!DET.ron_vis': 3.4}
+        fast: {'!DET.gain_vis': 1.64, '!DET.ron_vis': 6.6}
+
+  - name: detector_uvb
+    class: DetectorList
+    kwargs:
+      image_plane_id: 0
+      pixel_scale: '!INST.pixel_scale'
+      plate_scale: '!DET.plate_scale_ccd'
+      array_dict:
+        id: [0]
+        x_cen: [0.0]
+        y_cen: [0.0]
+        x_size: [2048]
+        y_size: [3000]
+        pixel_size: [0.015]
+        angle: [0.0]
+        gain: [1.0]
+      x_cen_unit: mm
+      y_cen_unit: mm
+      x_size_unit: pixel
+      y_size_unit: pixel
+      pixel_size_unit: mm
+      angle_unit: deg
+      gain_unit: electron/adu
+  - name: detector_vis
+    class: DetectorList
+    kwargs:
+      image_plane_id: 1
+      pixel_scale: '!INST.pixel_scale'
+      plate_scale: '!DET.plate_scale_ccd'
+      array_dict:
+        id: [1]
+        x_cen: [0.0]
+        y_cen: [0.0]
+        x_size: [2048]
+        y_size: [4000]
+        pixel_size: [0.015]
+        angle: [0.0]
+        gain: [1.0]
+      x_cen_unit: mm
+      y_cen_unit: mm
+      x_size_unit: pixel
+      y_size_unit: pixel
+      pixel_size_unit: mm
+      angle_unit: deg
+      gain_unit: electron/adu
+  - name: detector_nir
+    class: DetectorList
+    kwargs:
+      image_plane_id: 2
+      pixel_scale: '!INST.pixel_scale'
+      plate_scale: '!DET.plate_scale_nir'
+      array_dict:
+        id: [2]
+        x_cen: [0.0]
+        y_cen: [0.0]
+        x_size: [2048]
+        y_size: [1100]
+        pixel_size: [0.018]
+        angle: [0.0]
+        gain: [1.0]
+      x_cen_unit: mm
+      y_cen_unit: mm
+      x_size_unit: pixel
+      y_size_unit: pixel
+      pixel_size_unit: mm
+      angle_unit: deg
+      gain_unit: electron/adu
+
+  # Photon rate -> accumulated charge -> charge binning -> shot noise ->
+  # one amplifier read per binned pixel -> NIR average -> gain conversion.
+  - name: exposure_integration
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      z_order: [805]
+      wheel:
+        - selector_value: [0]
+          effect_class: ExposureIntegration
+          effect_kwargs: {dit: '!OBS.dit_uvb', ndit: '!OBS.ndit_uvb'}
+        - selector_value: [1]
+          effect_class: ExposureIntegration
+          effect_kwargs: {dit: '!OBS.dit_vis', ndit: '!OBS.ndit_vis'}
+        - selector_value: [2]
+          effect_class: ExposureIntegration
+          effect_kwargs: {dit: '!OBS.dit_nir', ndit: '!OBS.ndit_nir'}
+  - name: dark_current
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      z_order: [810]
+      wheel:
+        - selector_value: [0]
+          effect_class: DarkCurrent
+          effect_kwargs: {value: '!DET.dark_uvb', dit: '!OBS.dit_uvb', ndit: '!OBS.ndit_uvb'}
+        - selector_value: [1]
+          effect_class: DarkCurrent
+          effect_kwargs: {value: '!DET.dark_vis', dit: '!OBS.dit_vis', ndit: '!OBS.ndit_vis'}
+        - selector_value: [2]
+          effect_class: DarkCurrent
+          effect_kwargs: {value: '!DET.dark_nir', dit: '!OBS.dit_nir', ndit: '!OBS.ndit_nir'}
+  - name: on_chip_binning
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      z_order: [815]
+      allowed_missing_selector_values: [2]
+      wheel:
+        - selector_value: [0]
+          effect_class: OnChipBinning
+          effect_kwargs:
+            binx: '!DET.binx_uvb'
+            biny: '!DET.biny_uvb'
+            allowed_bins: [[1, 1], [1, 2], [2, 2]]
+        - selector_value: [1]
+          effect_class: OnChipBinning
+          effect_kwargs:
+            binx: '!DET.binx_vis'
+            biny: '!DET.biny_vis'
+            allowed_bins: [[1, 1], [1, 2], [2, 2]]
+  - name: shot_noise
+    class: ShotNoise
+    kwargs: {z_order: [820]}
+  - name: readout_noise
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      z_order: [825]
+      wheel:
+        - selector_value: [0]
+          effect_class: BasicReadoutNoise
+          effect_kwargs: {noise_std: '!DET.ron_uvb', ndit: '!OBS.ndit_uvb'}
+        - selector_value: [1]
+          effect_class: BasicReadoutNoise
+          effect_kwargs: {noise_std: '!DET.ron_vis', ndit: '!OBS.ndit_vis'}
+        - selector_value: [2]
+          effect_class: ReadoutNoiseFromTable
+          effect_kwargs: {filename: detector/nir_read_noise.dat, dit: '!OBS.dit_nir', ndit: '!OBS.ndit_nir'}
+  - name: nir_exposure_average
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      z_order: [830]
+      allowed_missing_selector_values: [0, 1]
+      wheel:
+        - selector_value: [2]
+          effect_class: ExposureOutput
+          effect_kwargs: {mode: average, dit: '!OBS.dit_nir', ndit: '!OBS.ndit_nir'}
+  - name: gain_conversion
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      z_order: [835]
+      wheel:
+        - selector_value: [0]
+          effect_class: ADConversion
+          effect_kwargs: {gain: '!DET.gain_uvb', dtype: float32}
+        - selector_value: [1]
+          effect_class: ADConversion
+          effect_kwargs: {gain: '!DET.gain_vis', dtype: float32}
+        - selector_value: [2]
+          effect_class: ADConversion
+          effect_kwargs: {gain: '!DET.gain_nir', dtype: float32}
+  - name: xshooter_fits_keywords
+    class: ExtraFitsKeywords
+    kwargs: {filename: FITS_extra_keywords.yaml}
diff --git a/XSHOOTER/detector/nir_background.fits b/XSHOOTER/detector/nir_background.fits
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diff --git a/XSHOOTER/detector/nir_read_noise.dat b/XSHOOTER/detector/nir_read_noise.dat
new file mode 100644
index 00000000..9af7064e
--- /dev/null
+++ b/XSHOOTER/detector/nir_read_noise.dat
@@ -0,0 +1,29 @@
+# dit_unit: s
+# read_noise_unit: electron
+# Source: ESO ETC nir_dit_ron.dat
+dit read_noise
+1 25.6
+2 24.4
+3 23.1
+4 20.9
+5 19.9
+6 18.4
+7 17.9
+8 16.8
+16 13
+32 10.2
+64 8.1
+100 7.4
+150 6.9
+200 6.8
+250 6.6
+300 6.7
+360 6.8
+475 7
+600 7.2
+700 7.5
+800 7.8
+900 8
+1200 8.6
+1500 9.2
+1800 9.9
diff --git a/XSHOOTER/tests/test_xshooter.py b/XSHOOTER/tests/test_xshooter.py
new file mode 100644
index 00000000..3e3b15a0
--- /dev/null
+++ b/XSHOOTER/tests/test_xshooter.py
@@ -0,0 +1,237 @@
+"""Physical checks against the configured, full-frame X-shooter model."""
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+from astropy import units as u
+from astropy.io import fits
+from astropy.table import Table
+from astropy.wcs import WCS
+
+import scopesim as sim
+from scopesim.detector import Detector
+from scopesim.optics.image_plane_utils import header_from_list_of_xy
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+@pytest.fixture
+def train(monkeypatch):
+    monkeypatch.setitem(sim.rc.__config__, '!SIM.file.local_packages_path', str(ROOT.parent))
+    cmds = sim.UserCommands(use_instrument='XSHOOTER', ignore_effects=['skycalc_atmosphere'])
+    return sim.OpticalTrain(cmds)
+
+
+def test_native_geometry_and_orders(train):
+    assert [plane.data.shape for plane in train.image_planes] == [(3000, 2048), (4000, 2048), (1100, 2048)]
+    traces = train['spectral_traces'].spectral_traces
+    assert len(traces) == 43
+    for arm, count, pitch in [('UVB', 12, .015), ('VIS', 15, .015), ('NIR', 16, .018)]:
+        selected = [trace for name, trace in traces.items() if name.startswith(arm)]
+        assert len(selected) == count
+        assert all(trace.meta['pixel_size'] == pitch for trace in selected)
+    assert train.cmds['!TEL.name'] == 'VLT UT3'
+
+
+@pytest.mark.parametrize('bx,by', [(1, 1), (1, 2), (2, 2)])
+def test_full_readout_charge_headers_and_repeat(train, bx, by):
+    for arm in ['uvb', 'vis']:
+        train.cmds[f'!DET.binx_{arm}'] = bx
+        train.cmds[f'!DET.biny_{arm}'] = by
+    for arm in ['uvb', 'vis', 'nir']:
+        train.cmds[f'!OBS.dit_{arm}'] = 30.
+        train.cmds[f'!OBS.ndit_{arm}'] = 3
+    for name in ['shot_noise', 'readout_noise']:
+        train[name].include = False
+    for plane in train.image_planes:
+        plane.data[:] = 2.
+    for repeat in range(2):
+        outputs = train.readout(reset=False)
+        for index, arm in enumerate(['uvb', 'vis', 'nir']):
+            hdu = outputs[index][1]
+            header = hdu.header
+            binx, biny = (bx, by) if arm != 'nir' else (1, 1)
+            ny, nx = train.image_planes[index].data.shape
+            gain = train.cmds[f'!DET.gain_{arm}']
+            dark = train.cmds[f'!DET.dark_{arm}']
+            integrations = 1 if arm == 'nir' else 3
+            expected = (2 + dark)*30*integrations*binx*biny/gain
+            assert hdu.data.shape == (ny//biny, nx//binx)
+            np.testing.assert_allclose(hdu.data, expected, rtol=2e-6)
+            assert outputs[index][0].header['ARM'] == arm.upper()
+            assert header['DET_ID'] == index
+            assert header['GAIN'] == gain
+            assert header['BUNIT'] == 'adu'
+            assert header['EXPTIME'] == 90
+            assert header['COMBINE'] == ('average' if arm == 'nir' else 'sum')
+            native = WCS(train.image_planes[index].header, key='D')
+            actual = WCS(header, key='D').all_pix2world(13, 17, 0)
+            np.testing.assert_allclose(actual, native.all_pix2world(13.5*binx-.5, 17.5*biny-.5, 0), atol=1e-10)
+
+
+@pytest.mark.parametrize('arm,mode,gain,ron', [('uvb', 'slow', .63, 2.4), ('uvb', 'fast', 1.83, 4.6), ('vis', 'slow', .64, 3.4), ('vis', 'fast', 1.64, 6.6)])
+def test_detector_modes(train, arm, mode, gain, ron):
+    train.cmds[f'!DET.readout_mode_{arm}'] = mode
+    train[f'{arm}_readout_parameters'].apply_to(None)
+    assert train.cmds[f'!DET.gain_{arm}'] == gain
+    assert train.cmds[f'!DET.ron_{arm}'] == ron
+
+
+def test_all_slits_and_jh_background(train):
+    for aid, arm in enumerate(['uvb', 'vis', 'nir']):
+        wheel = train['slits'].wheel_effects[aid]
+        for slit in wheel.meta['slit_names']:
+            train.cmds[f'!INST.{arm}_curr_slit'] = slit
+            aperture = wheel.current_slit.table
+            assert np.ptp(aperture['x']) == 11
+            assert np.ptp(aperture['y']) == pytest.approx(float(slit.removesuffix('JH')))
+            if arm == 'nir':
+                plane = train.image_planes[2]
+                plane.data[:] = 0
+                train['nir_stray_light'].apply_to(plane)
+                width = np.ptp(aperture['y'])
+                limits = (.02, .04) if slit.endswith('JH') else (.06, .15)
+                assert plane.data.min()/width == pytest.approx(limits[0])
+                assert plane.data.max()/width == pytest.approx(limits[1])
+                curve = train['nir_order_blocking'].wheel_effects[2].current_filter.surface.transmission
+                if slit.endswith('JH'):
+                    assert curve(22000).value < .01
+                    assert curve(15000).value == pytest.approx(.945)
+                else:
+                    assert curve(22000).value == 1
+
+
+@pytest.mark.parametrize('aid,ndit', [(0, 1), (0, 4), (1, 4), (2, 1), (2, 4)])
+def test_configured_noise_sequence(train, aid, ndit):
+    arm = ['uvb', 'vis', 'nir'][aid]
+    dit, rate = 100., .3
+    train.cmds[f'!OBS.dit_{arm}'] = dit
+    train.cmds[f'!OBS.ndit_{arm}'] = ndit
+    train.cmds['!SIM.random.seed'] = 312
+    if aid != 2:
+        train[f'{arm}_readout_parameters'].apply_to(None)
+        train.cmds[f'!DET.binx_{arm}'] = 2
+        train.cmds[f'!DET.biny_{arm}'] = 2
+    # Use a smaller detector only for statistical sample size; all effects and
+    # arm parameters come from the actual full-frame train.
+    header = header_from_list_of_xy([-6., 6.], [-4.5, 4.5], .015, 'D')
+    det = Detector(header, cmds=train.cmds, id=aid)
+    det.data[:] = rate
+    for effect in train.optics_manager.detector_effects:
+        effect.apply_to(det)
+    gain, dark = train.cmds[f'!DET.gain_{arm}'], train.cmds[f'!DET.dark_{arm}']
+    ron = det.header['RDNOISE'] if aid == 2 else train.cmds[f'!DET.ron_{arm}']
+    area = 1 if aid == 2 else 4
+    divisor = ndit if aid == 2 else 1
+    mean = (rate + dark)*dit*ndit*area/divisor/gain
+    variance = ((rate + dark)*dit*area + ron**2)*ndit/(divisor*gain)**2
+    assert det.data.mean() == pytest.approx(mean, rel=.003)
+    assert det.data.var() == pytest.approx(variance, rel=.025)
+
+
+def test_unsupported_bin_fails(train):
+    train.cmds['!DET.binx_uvb'], train.cmds['!DET.biny_uvb'] = 2, 1
+    with pytest.raises(ValueError, match='Unsupported'):
+        train.readout(reset=False)
+
+
+def test_trace_fit_bounds_and_finite_physical_calibrations():
+    residuals = Table.read(ROOT / 'calibration' / 'trace_residuals.ecsv')
+    assert len(residuals) == 43
+    for column in ['forward_rms_pix', 'inverse_rms_pix']:
+        assert np.all(residuals[column] < .03)
+    for column in ['forward_max_pix', 'inverse_max_pix']:
+        assert np.all(residuals[column] < .1)
+    with fits.open(ROOT / 'throughput' / 'orders.fits') as response:
+        for hdu in response[2:]:
+            assert np.all(np.isfinite(hdu.data['efficiency']))
+            assert np.all(hdu.data['efficiency'] >= 0)
+            assert np.all(hdu.data['efficiency'] <= 1)
+
+
+def test_slit_resolving_power_against_manual():
+    from XSHOOTER.calibration.fit_lsf import resolution_table
+    resolution = resolution_table()
+    assert np.all(abs(resolution['fractional_error']) < .08)
+    for arm, width in [('UVB', 1.), ('VIS', .9), ('NIR', .9)]:
+        selected = (resolution['arm'] == arm) & (resolution['slit_arcsec'] == width)
+        assert abs(resolution['fractional_error'][selected][0]) < .03
+
+
+@pytest.mark.parametrize('aid,name', [(0, 'UVB_18'), (1, 'VIS_23'), (2, 'NIR_16')])
+def test_mapped_line_photons_centroid_and_variance(train, aid, name):
+    trace = train['spectral_traces'].spectral_traces[name]
+    center = (trace.wave_min + trace.wave_max)/2
+    for step in [.1, .05]:
+        wave = np.linspace(center*.999, center*1.001, 2001)
+        xi = np.arange(-2, 2+step/2, step)
+        eta = np.arange(-.5+step/2, .5, step)
+        line = np.exp(-.5*((wave-center)/(center/100000))**2)
+        illumination = np.exp(-.5*(eta/.2)**2)
+        cube = line[:, None, None]*illumination[None, :, None]*np.exp(-.5*(xi/.35)**2)[None, None, :]
+        header = fits.Header({'CTYPE1': 'LINEAR', 'CTYPE2': 'LINEAR', 'CTYPE3': 'WAVE', 'CUNIT1': 'arcsec', 'CUNIT2': 'arcsec', 'CUNIT3': 'um', 'CDELT1': step, 'CDELT2': step, 'CDELT3': wave[1]-wave[0], 'CRPIX1': 1., 'CRPIX2': 1., 'CRPIX3': 1., 'CRVAL1': xi[0], 'CRVAL2': eta[0], 'CRVAL3': wave[0]})
+        metadata = {'wave_min': wave[0]*u.um, 'wave_max': wave[-1]*u.um, 'xi_min': xi[0]*u.arcsec, 'xi_max': xi[-1]*u.arcsec}
+        fov = SimpleNamespace(cube=fits.ImageHDU(cube, header), header=header, detector_header=train.image_planes[aid].header, meta=metadata, trace_id=name)
+        mapped = trace.map_spectra_to_focal_plane(fov)
+        # Integrate source radiance over wavelength and both sky coordinates.
+        photons = cube.sum()*step**2*(wave[1]-wave[0])
+        assert mapped.data.sum() == pytest.approx(photons, rel=.005)
+        yy, xx = np.indices(mapped.data.shape)
+        xmm, ymm = WCS(mapped.header, key='D').all_pix2world(xx, yy, 0)
+        detector_wave = trace.xy2lam(xmm, ymm)
+        mean = (mapped.data*detector_wave).sum()/mapped.data.sum()
+        variance = (mapped.data*(detector_wave-mean)**2).sum()/mapped.data.sum()
+        eta_variance = (eta**2*illumination).sum()/illumination.sum()
+        optical_sigma = center/trace.meta['optical_resolving_power']/np.sqrt(8*np.log(2))
+        expected_variance = (center/100000)**2 + optical_sigma**2 + eta_variance*trace.slit_dispersion(center)**2
+        assert abs(mean-center) < abs(trace.dlam_per_pix(center))*.02
+        assert variance == pytest.approx(expected_variance, rel=.02)
+
+
+@pytest.mark.parametrize('aid,center', [(0, .438), (1, .711), (2, 1.634)])
+@pytest.mark.parametrize('point_source', [False, True])
+def test_end_to_end_photon_rate(monkeypatch, aid, center, point_source):
+    from synphot import SourceSpectrum
+    from synphot.models import ConstFlux1D
+    from synphot.units import PHOTLAM
+    from scopesim.source.source_templates import uniform_source
+    from scipy.integrate import quad
+    from scipy.special import gamma
+
+    monkeypatch.setitem(sim.rc.__config__, '!SIM.file.local_packages_path', str(ROOT.parent))
+    lower, upper = center*.9995, center*1.0005
+    ignore = ['skycalc_atmosphere', 'atmospheric_dispersion', 'nir_stray_light']
+    if not point_source:
+        ignore.append('seeing_psf')
+    cmds = sim.UserCommands(use_instrument='XSHOOTER', ignore_effects=ignore, properties={'!SIM.spectral.wave_min': lower, '!SIM.spectral.wave_max': upper})
+    train = sim.OpticalTrain(cmds)
+    spectrum = SourceSpectrum(ConstFlux1D, amplitude=.01*PHOTLAM)
+    source = sim.Source(x=[0], y=[0], ref=[0], weight=[1], spectra=[spectrum]) if point_source else uniform_source(spectrum, extent=20)
+    train.observe(source)
+    wave = np.linspace(lower, upper, 2001)*u.um
+    throughput = np.zeros(len(wave))
+    for name, trace in train['spectral_traces'].spectral_traces.items():
+        if trace.meta['image_plane_id'] != aid:
+            continue
+        covered = (wave.value >= trace.wave_min) & (wave.value <= trace.wave_max)
+        throughput[covered] += train['order_throughput'].efficiencies[name].throughput(wave[covered]).value
+    qe = train['detector_qe'].wheel_effects[aid].throughput(wave).value
+    telescope = train.optics_manager.get_all(sim.effects.SurfaceList)[0].throughput(wave).value
+    aperture = train['slits'].wheel_effects[aid].current_slit.table
+    if point_source:
+        psf = train['seeing_psf']
+        beta = psf.alpha
+        scale = psf.fwhm(center*u.um).to_value(u.arcsec)/(2*np.sqrt(2**(1/beta)-1))
+        norm = gamma(beta-.5)/(scale*np.sqrt(np.pi)*gamma(beta-1))
+        width = np.ptp(aperture['y'])
+        # The marginal Moffat profile integrates over the along-slit coordinate;
+        # the 11 arcsec slit loses negligible flux at its ends for this seeing.
+        slit_weight = quad(lambda eta: norm*(1+(eta/scale)**2)**(.5-beta), -width/2, width/2)[0]
+    else:
+        slit_weight = np.ptp(aperture['x'])*np.ptp(aperture['y'])
+    density = spectrum(wave).to(u.photon/u.s/u.m**2/u.um)
+    rate = np.trapezoid((density*train.optics_manager.area).value*throughput*qe*telescope, wave.value)*slit_weight
+    # Finite sky and detector cells straddle the aperture/band edges. The source
+    # is bright enough that telescope thermal emission is negligible here.
+    assert train.image_planes[aid].data.sum() == pytest.approx(rate, rel=.01 if point_source else .025)

From 0efb498c8dfbe2c5a34276e4c6ce04cedeef1947 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sat, 17 Oct 2026 02:45:34 -0700
Subject: [PATCH 47/63] Use physical ZShooter dispersion and include
 dark-current shot noise

---
 XSHOOTER/tests/test_xshooter.py               |  2 +-
 ZShooter_v2/ZShooter_DET.yaml                 |  8 +-
 ZShooter_v2/ZShooter_SPEC.yaml                |  1 +
 ZShooter_v2/default.yaml                      |  1 +
 ZShooter_v2/tests/test_fidelity_candidates.py | 92 +++++++++++++++++++
 5 files changed, 99 insertions(+), 5 deletions(-)
 create mode 100644 ZShooter_v2/tests/test_fidelity_candidates.py

diff --git a/XSHOOTER/tests/test_xshooter.py b/XSHOOTER/tests/test_xshooter.py
index 3e3b15a0..1a2c7c4d 100644
--- a/XSHOOTER/tests/test_xshooter.py
+++ b/XSHOOTER/tests/test_xshooter.py
@@ -171,7 +171,7 @@ def test_mapped_line_photons_centroid_and_variance(train, aid, name):
         illumination = np.exp(-.5*(eta/.2)**2)
         cube = line[:, None, None]*illumination[None, :, None]*np.exp(-.5*(xi/.35)**2)[None, None, :]
         header = fits.Header({'CTYPE1': 'LINEAR', 'CTYPE2': 'LINEAR', 'CTYPE3': 'WAVE', 'CUNIT1': 'arcsec', 'CUNIT2': 'arcsec', 'CUNIT3': 'um', 'CDELT1': step, 'CDELT2': step, 'CDELT3': wave[1]-wave[0], 'CRPIX1': 1., 'CRPIX2': 1., 'CRPIX3': 1., 'CRVAL1': xi[0], 'CRVAL2': eta[0], 'CRVAL3': wave[0]})
-        metadata = {'wave_min': wave[0]*u.um, 'wave_max': wave[-1]*u.um, 'xi_min': xi[0]*u.arcsec, 'xi_max': xi[-1]*u.arcsec}
+        metadata = {'wave_min': wave[0]*u.um, 'wave_max': wave[-1]*u.um, 'xi_min': xi[0]*u.arcsec, 'xi_max': xi[-1]*u.arcsec, 'eta_min': -.5*u.arcsec, 'eta_max': .5*u.arcsec}
         fov = SimpleNamespace(cube=fits.ImageHDU(cube, header), header=header, detector_header=train.image_planes[aid].header, meta=metadata, trace_id=name)
         mapped = trace.map_spectra_to_focal_plane(fov)
         # Integrate source radiance over wavelength and both sky coordinates.
diff --git a/ZShooter_v2/ZShooter_DET.yaml b/ZShooter_v2/ZShooter_DET.yaml
index 76057c73..ee3516d7 100644
--- a/ZShooter_v2/ZShooter_DET.yaml
+++ b/ZShooter_v2/ZShooter_DET.yaml
@@ -258,10 +258,6 @@ effects :
             dit: "!OBS.dit_k"
             ndit: "!OBS.ndit_k"
 
-  - name: shot_noise
-    description : apply poisson shot noise to images
-    class: ShotNoise
-
   - name: dark_current_selector
     description: Selector Wheel for dark current levels for CCDs and APDs
     class: SelectorWheel
@@ -306,6 +302,10 @@ effects :
             dit: "!OBS.dit_k"
             ndit: "!OBS.ndit_k"
 
+  - name: shot_noise
+    description: Poisson statistics of integrated source, background and dark charge
+    class: ShotNoise
+
   - name: detector_linearity_selector
     description: Selector Wheel for linearity characteristics for CCDs and APDs
     class: SelectorWheel
diff --git a/ZShooter_v2/ZShooter_SPEC.yaml b/ZShooter_v2/ZShooter_SPEC.yaml
index 9cc71239..64eec4b9 100755
--- a/ZShooter_v2/ZShooter_SPEC.yaml
+++ b/ZShooter_v2/ZShooter_SPEC.yaml
@@ -254,6 +254,7 @@ effects:
    include: True
    kwargs:
      filename: "traces/echelle_trace_parameters.dat"
+     physical_slit_dispersion: true
      wave_colname: "wavelength"
      s_colname: "s"
      col_number_start: 1
diff --git a/ZShooter_v2/default.yaml b/ZShooter_v2/default.yaml
index 4aae908a..70f39bd6 100755
--- a/ZShooter_v2/default.yaml
+++ b/ZShooter_v2/default.yaml
@@ -70,6 +70,7 @@ properties :
     wave_max : 2.5
     spectral_bin_width: 0.00001 # in um, may want 0.000005
     spectral_resolution: 40000
+    trace_flux_jacobian: true
 
   computing :
     preload_field_of_views : True
diff --git a/ZShooter_v2/tests/test_fidelity_candidates.py b/ZShooter_v2/tests/test_fidelity_candidates.py
new file mode 100644
index 00000000..a237319c
--- /dev/null
+++ b/ZShooter_v2/tests/test_fidelity_candidates.py
@@ -0,0 +1,92 @@
+"""Physical checks using the configured ZShooter prescription and detector effects."""
+from pathlib import Path
+import numpy as np
+import pytest
+import yaml
+from astropy import units as u
+from astropy.io import fits
+from types import SimpleNamespace
+import scopesim as sim
+from scopesim.effects import EchelleSpectralTraceList
+from scopesim.effects.effects_utils import make_effect
+from scopesim.effects.spectral_trace_list_utils import XiLamImage
+from scopesim.detector import Detector
+from scopesim.optics.image_plane_utils import header_from_list_of_xy
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+@pytest.fixture(scope='module')
+def traces():
+    config = yaml.safe_load((ROOT / 'ZShooter_SPEC.yaml').read_text())
+    effect = next(item for item in config['effects'] if item['name'] == 'trace_list_analytical')
+    kwargs = dict(effect['kwargs'])
+    kwargs['filename'] = str(ROOT / kwargs['filename'])
+    simulation = next(item for item in yaml.safe_load_all((ROOT / 'default.yaml').read_text()) if item['alias'] == 'SIM')
+    kwargs['trace_flux_jacobian'] = simulation['properties']['spectral']['trace_flux_jacobian']
+    return EchelleSpectralTraceList(**kwargs).spectral_traces
+
+
+@pytest.mark.parametrize('channel', ['b', 'g', 'r', 'yj', 'h', 'k'])
+def test_physical_slit_dispersion_across_sky_grids(traces, channel):
+    trace = next(trace for name, trace in traces.items() if name.startswith(channel + '_'))
+    assert 'optical_resolving_power' not in trace.meta
+    wave0 = (trace.wave_min + trace.wave_max) / 2
+    derivative = trace.slit_dispersion(wave0)
+    assert derivative < 0
+    sigma_wave, sigma_eta = wave0 / 100000, .65 / np.sqrt(8*np.log(2))
+    wave = np.linspace(wave0 - .001*wave0, wave0 + .001*wave0, 2001)
+    eta_ref = np.linspace(-.35, .35, 10001)
+    illumination = np.exp(-.5*(eta_ref/sigma_eta)**2)
+    eta_variance = np.trapezoid(eta_ref**2 * illumination, eta_ref) / np.trapezoid(illumination, eta_ref)
+    expected_variance = sigma_wave**2 + derivative**2 * eta_variance
+    for rows in [5, 10, 20]:
+        step = .7 / rows
+        eta = (np.arange(rows) + .5) * step - .35
+        cube = np.exp(-.5*((wave-wave0)/sigma_wave)**2)[:, None, None] * np.exp(-.5*(eta/sigma_eta)**2)[None, :, None] * np.ones((1, 1, 3))
+        header = fits.Header({'CTYPE1': 'LINEAR', 'CTYPE2': 'LINEAR', 'CTYPE3': 'WAVE', 'CUNIT1': 'arcsec', 'CUNIT2': 'arcsec', 'CUNIT3': 'um', 'CDELT1': .1, 'CDELT2': step, 'CDELT3': wave[1]-wave[0], 'CRPIX1': 1., 'CRPIX2': 1., 'CRPIX3': 1., 'CRVAL1': 0., 'CRVAL2': eta[0], 'CRVAL3': wave[0]})
+        fov = SimpleNamespace(cube=fits.ImageHDU(cube, header), meta={'xi_min': 0*u.arcsec, 'eta_min': -.35*u.arcsec, 'eta_max': .35*u.arcsec})
+        image = XiLamImage(fov, lambda wave: 0., slit_dispersion=trace.slit_dispersion)
+        flux = np.trapezoid(image.image[1], wave)
+        variance = np.trapezoid((wave-wave0)**2 * image.image[1], wave) / flux
+        # Midpoint quadrature across a seeing-limited 0.7 arcsec slit, five or more rows.
+        assert variance == pytest.approx(expected_variance, rel=.03)
+        assert flux == pytest.approx(np.sqrt(2*np.pi)*sigma_wave*np.trapezoid(illumination, eta_ref), rel=.015)
+
+
+@pytest.mark.parametrize('detector_id,arm,dark,ron', [(0, 'blue', .000166, .1), (1, 'green', .000166, .1), (2, 'red', .000166, .1), (3, 'yj', .0001, .5), (4, 'h', .0001, .5), (5, 'k', .0001, .5)])
+def test_dark_current_participates_in_poisson_statistics(detector_id, arm, dark, ron):
+    config = yaml.safe_load((ROOT / 'ZShooter_DET.yaml').read_text())
+    names = ['exposure_integration_selector', 'dark_current_selector', 'shot_noise', 'readout_noise_selector']
+    effects = [item for item in config['effects'] if item['name'] in names]
+    assert len(effects) == 4
+    cmds = sim.UserCommands(properties={f'!OBS.dit_{arm}': 3600., f'!OBS.ndit_{arm}': 1, '!SIM.random.seed': 126})
+    header = header_from_list_of_xy([-4.5, 4.5], [-4.5, 4.5], .015, 'D')
+    detector = Detector(header, cmds=cmds, id=detector_id)
+    for item in effects:
+        make_effect(item, cmds).apply_to(detector)
+    assert detector.data.mean() == pytest.approx(dark*3600, abs=.005)
+    assert detector.data.var() == pytest.approx(dark*3600 + ron**2, rel=.02)
+
+
+@pytest.mark.parametrize('channel', ['b', 'g', 'r', 'yj', 'h', 'k'])
+def test_native_trace_photon_normalization_is_independent_of_sky_grid(traces, channel):
+    trace = next(trace for name, trace in traces.items() if name.startswith(channel + '_'))
+    center = (trace.wave_min + trace.wave_max) / 2
+    # Native detector geometry comes from the same IRDB prescription as the trace.
+    config = yaml.safe_load((ROOT / 'ZShooter_DET.yaml').read_text())
+    detector_effect = next(item for item in config['effects'] if item['class'] == 'DetectorList' and item['kwargs']['image_plane_id'] == trace.meta['image_plane_id'])
+    detector_header = make_effect(detector_effect).image_plane_header
+    for step in [.1, .05, .025]:
+        wave = np.linspace(center*.998, center*1.002, 2001)
+        xi = np.arange(-2, 2 + step/2, step)
+        rows = round(.7 / step)
+        eta_step = .7 / rows
+        eta = (np.arange(rows) + .5) * eta_step - .35
+        cube = np.exp(-.5*((wave-center)/(center/15000))**2)[:, None, None] * np.exp(-.5*(eta/.2)**2)[None, :, None] * np.exp(-.5*(xi/.35)**2)[None, None, :]
+        header = fits.Header({'CTYPE1': 'LINEAR', 'CTYPE2': 'LINEAR', 'CTYPE3': 'WAVE', 'CUNIT1': 'arcsec', 'CUNIT2': 'arcsec', 'CUNIT3': 'um', 'CDELT1': step, 'CDELT2': eta_step, 'CDELT3': wave[1]-wave[0], 'CRPIX1': 1., 'CRPIX2': 1., 'CRPIX3': 1., 'CRVAL1': xi[0], 'CRVAL2': eta[0], 'CRVAL3': wave[0]})
+        metadata = {'wave_min': wave[0]*u.um, 'wave_max': wave[-1]*u.um, 'xi_min': xi[0]*u.arcsec, 'xi_max': xi[-1]*u.arcsec, 'eta_min': -.35*u.arcsec, 'eta_max': .35*u.arcsec}
+        fov = SimpleNamespace(cube=fits.ImageHDU(cube, header), header=header, detector_header=detector_header, meta=metadata, trace_id=trace.trace_id)
+        image = trace.map_spectra_to_focal_plane(fov)
+        expected = cube.sum() * step * eta_step * (wave[1]-wave[0])
+        assert image.data.sum() == pytest.approx(expected, rel=.01)

From a31dc90b875f7b32fe93eb6cf876f767fa41ab0b Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sat, 17 Oct 2026 03:11:45 -0700
Subject: [PATCH 48/63] Configure independent on-chip binning for ZShooter
 visible CCDs

---
 ZShooter_v2/ZShooter_DET.yaml                 | 26 ++++++++++++++++++-
 ZShooter_v2/tests/test_fidelity_candidates.py | 18 +++++++++++++
 2 files changed, 43 insertions(+), 1 deletion(-)

diff --git a/ZShooter_v2/ZShooter_DET.yaml b/ZShooter_v2/ZShooter_DET.yaml
index ee3516d7..e1592c1b 100644
--- a/ZShooter_v2/ZShooter_DET.yaml
+++ b/ZShooter_v2/ZShooter_DET.yaml
@@ -8,6 +8,12 @@ properties :
     temperature : -120
     binx : 1
     biny: 1
+    binx_blue: 1
+    biny_blue: 1
+    binx_green: 1
+    biny_green: 1
+    binx_red: 1
+    biny_red: 1
     full_well: 64000
     # Per-image-plane scales. These currently match the global ZShooter
     # values in ZShooter.yaml, but live here so each camera can diverge when
@@ -302,6 +308,24 @@ effects :
             dit: "!OBS.dit_k"
             ndit: "!OBS.ndit_k"
 
+  - name: ccd_charge_binning
+    description: On-chip charge summation for the three visible CCD design channels
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      z_order: [815]
+      allowed_missing_selector_values: [3, 4, 5]
+      wheel:
+        - selector_value: [0]
+          effect_class: OnChipBinning
+          effect_kwargs: {binx: '!DET.binx_blue', biny: '!DET.biny_blue'}
+        - selector_value: [1]
+          effect_class: OnChipBinning
+          effect_kwargs: {binx: '!DET.binx_green', biny: '!DET.biny_green'}
+        - selector_value: [2]
+          effect_class: OnChipBinning
+          effect_kwargs: {binx: '!DET.binx_red', biny: '!DET.biny_red'}
+
   - name: shot_noise
     description: Poisson statistics of integrated source, background and dark charge
     class: ShotNoise
@@ -403,7 +427,7 @@ effects :
             ndit: "!OBS.ndit_k"
 
   - name: visible_binning_selector
-    description: Applies Binning in Visible
+    description: Software summation of visible pixels after read noise; separate from CCD charge binning
     class: SelectorWheel
     include: True
     kwargs:
diff --git a/ZShooter_v2/tests/test_fidelity_candidates.py b/ZShooter_v2/tests/test_fidelity_candidates.py
index a237319c..2a9e22c3 100644
--- a/ZShooter_v2/tests/test_fidelity_candidates.py
+++ b/ZShooter_v2/tests/test_fidelity_candidates.py
@@ -69,6 +69,24 @@ def test_dark_current_participates_in_poisson_statistics(detector_id, arm, dark,
     assert detector.data.var() == pytest.approx(dark*3600 + ron**2, rel=.02)
 
 
+@pytest.mark.parametrize('detector_id,arm,bx,by', [(0, 'blue', 1, 2), (1, 'green', 2, 1), (2, 'red', 2, 2)])
+def test_visible_ccd_charge_binning_is_independent_per_arm(detector_id, arm, bx, by):
+    config = yaml.safe_load((ROOT / 'ZShooter_DET.yaml').read_text())
+    configured = next(item for item in config['effects'] if item['name'] == 'ccd_charge_binning')
+    properties = {f'!DET.bin{axis}_{channel}': 1 for channel in ('blue', 'green', 'red') for axis in ('x', 'y')}
+    properties.update({f'!DET.binx_{arm}': bx, f'!DET.biny_{arm}': by})
+    cmds = sim.UserCommands(properties=properties)
+    effect = make_effect(configured, cmds)
+    for identity in range(6):
+        header = header_from_list_of_xy([-.3, .3], [-.3, .3], .015, 'D')
+        detector = Detector(header, cmds=cmds, id=identity)
+        detector.data[:] = 7.
+        effect.apply_to(detector)
+        expected_shape = (40//by, 40//bx) if identity == detector_id else (40, 40)
+        assert detector.data.shape == expected_shape
+        assert detector.data.sum() == pytest.approx(7*40*40)
+
+
 @pytest.mark.parametrize('channel', ['b', 'g', 'r', 'yj', 'h', 'k'])
 def test_native_trace_photon_normalization_is_independent_of_sky_grid(traces, channel):
     trace = next(trace for name, trace in traces.items() if name.startswith(channel + '_'))

From fc3e2b9a2824bf57b6e1aa6dd33ffdc26229be00 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sat, 17 Oct 2026 03:48:56 -0700
Subject: [PATCH 49/63] Check observed ZShooter flux convergence on three
 spatial grids

---
 ZShooter_v2/tests/test_fidelity_candidates.py | 33 +++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/ZShooter_v2/tests/test_fidelity_candidates.py b/ZShooter_v2/tests/test_fidelity_candidates.py
index 2a9e22c3..82be6cd6 100644
--- a/ZShooter_v2/tests/test_fidelity_candidates.py
+++ b/ZShooter_v2/tests/test_fidelity_candidates.py
@@ -108,3 +108,36 @@ def test_native_trace_photon_normalization_is_independent_of_sky_grid(traces, ch
         image = trace.map_spectra_to_focal_plane(fov)
         expected = cube.sum() * step * eta_step * (wave[1]-wave[0])
         assert image.data.sum() == pytest.approx(expected, rel=.01)
+
+
+@pytest.mark.parametrize('channel,center', [('b', .365), ('g', .53), ('r', .75), ('yj', 1.2), ('h', 1.65), ('k', 2.2)])
+def test_observed_point_flux_converges_with_spatial_grid(monkeypatch, channel, center):
+    from synphot import SourceSpectrum, ConstFlux1D
+    from synphot.units import PHOTLAM
+
+    monkeypatch.setitem(sim.rc.__config__, '!SIM.file.local_packages_path', str(ROOT.parent))
+    # Keep the prescribed optics, effective PSF and native detector pitch.
+    # Sky and ADC are excluded to isolate spatial integration rather than
+    # changing a quantized atmospheric displacement between grids.
+    ignore = ['atmo_transmission', 'continuum_emission', 'airglow_and_interline_continuum', 'adc_residuals']
+    source = sim.Source(x=[.037], y=[.13], ref=[0], weight=[1.], spectra=[SourceSpectrum(ConstFlux1D, amplitude=.01*PHOTLAM)])
+    rates = []
+    sky_steps = []
+    for step in [.1570545, .07852725, .039263625]:
+        properties = {'!INST.pixel_scale': step, '!SIM.spectral.wave_min': center*.999, '!SIM.spectral.wave_max': center*1.001, '!OBS.seeing': .7, '!OBS.mjdobs': 60310.25, '!INST.vis_curr_slit': .7, '!INST.nir_curr_slit': .7, '!INST.include_diffuse_emissivity': False}
+        properties.update({f'!DET.pixel_scale_{arm}': step for arm in ('b', 'g', 'r', 'yj', 'h', 'k')})
+        commands = sim.UserCommands(use_instrument='ZShooter_v2', properties=properties)
+        for document in commands.yaml_dicts:
+            if 'effects' in document:
+                document['effects'] = [effect for effect in document['effects'] if effect['name'] not in ignore]
+        train = sim.OpticalTrain(commands)
+        sky_steps.append(abs(train.fov_manager.fovs[0].header['CDELT1'])*3600)
+        assert all(manager[0]._native_header['CDELT1D'] == .015 for manager in train.detector_managers)
+        train.observe(None)
+        sky = sum(plane.data.sum() for plane in train.image_planes)
+        train.observe(source)
+        rates.append(sum(plane.data.sum() for plane in train.image_planes) - sky)
+    print(f'{channel}: source e/s {rates}; relative to finest {np.array(rates)/rates[-1]}')
+    assert sky_steps[0] > sky_steps[1] > sky_steps[2]
+    assert np.all(np.array(rates) > 0)
+    np.testing.assert_allclose(np.array(rates)/rates[-1], 1., rtol=.02)

From cf8859530eb709e0ee668ac201ed970eef9268d9 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 00:17:46 -0700
Subject: [PATCH 50/63] Restore prescribed 350 grooves/mm YJ cross disperser

---
 ZShooter_v2/traces/echelle_trace_parameters.dat | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ZShooter_v2/traces/echelle_trace_parameters.dat b/ZShooter_v2/traces/echelle_trace_parameters.dat
index 85c7bb73..7f97c3a4 100644
--- a/ZShooter_v2/traces/echelle_trace_parameters.dat
+++ b/ZShooter_v2/traces/echelle_trace_parameters.dat
@@ -28,6 +28,6 @@ prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design
 b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       2000          10            x               0               -6
 g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       1100          10            x               0               -6
 r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       625           10            x               0               -2
-yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       325           10            x               0               -4
+yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x               0               -4
 h        4              4                51    11   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x               0               -3
 k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x               0               -7

From aefd5271c30de1e0de06e848694974f60827c13c Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 00:24:27 -0700
Subject: [PATCH 51/63] Retune ZShooter detector rotations for full-slit band
 coverage

---
 ZShooter_v2/traces/echelle_trace_parameters.dat | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/ZShooter_v2/traces/echelle_trace_parameters.dat b/ZShooter_v2/traces/echelle_trace_parameters.dat
index 7f97c3a4..1b6155c3 100644
--- a/ZShooter_v2/traces/echelle_trace_parameters.dat
+++ b/ZShooter_v2/traces/echelle_trace_parameters.dat
@@ -22,12 +22,13 @@
 # geometry_note : focal_length is the physical camera focal length used for camera and cross-dispersion geometry. dispersion_focal_length is the effective focal length used for the echelle/main-dispersion detector mapping; it may include prescription-specific relay magnification, but that relay is not modeled by the analytical echelle code. plate_scale maps on-sky slit coordinate to detector/image-plane mm for the analytical trace generator only. detector_angle rotates the analytical trace footprint before detector placement. detector_pad is legacy padding metadata and is not optical trace geometry. nominal_slit_width and fwhm document the slit-width-to-detector-FWHM consistency point used by the analytical model and validation diagnostics.
 # future_trace_note : These analytical consistency columns are not a replacement for future Zemax-export trace tables. Zemax-backed traces should carry their own image-plane geometry and validation metadata.
 # changes :
+# - 2026-10-18 : Detector rotations maximize configured-band coverage of the full 10 arcsec slit with ScopeSim's corrected fixed-groove angle estimator (acfa5a71). The planar approximation, band limits and orders are retained. YJ ruling restored to 350 grooves/mm in cf88595. Validation in zs-scopesim/docs/zshooter-xdisp-geometry.md.
 # - 2025-10-20 : Initial creation of the file with trace parameters
 #
 prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle
-b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       2000          10            x               0               -6
-g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       1100          10            x               0               -6
-r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       625           10            x               0               -2
-yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x               0               -4
-h        4              4                51    11   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x               0               -3
-k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x               0               -7
+b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       2000          10            x               0               -6.86
+g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       1100          10            x               0               -5.46
+r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       625           10            x               0               -3.35
+yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x               0               8.56
+h        4              4                51    11   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x               0               -9.48
+k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x               0               4.49

From cf9110a55eb504a397f8e8d9b849bbeecc329a18 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 00:29:42 -0700
Subject: [PATCH 52/63] Revert "Retune ZShooter detector rotations for
 full-slit band coverage"

This reverts commit aefd5271c30de1e0de06e848694974f60827c13c.
---
 ZShooter_v2/traces/echelle_trace_parameters.dat | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/ZShooter_v2/traces/echelle_trace_parameters.dat b/ZShooter_v2/traces/echelle_trace_parameters.dat
index 1b6155c3..7f97c3a4 100644
--- a/ZShooter_v2/traces/echelle_trace_parameters.dat
+++ b/ZShooter_v2/traces/echelle_trace_parameters.dat
@@ -22,13 +22,12 @@
 # geometry_note : focal_length is the physical camera focal length used for camera and cross-dispersion geometry. dispersion_focal_length is the effective focal length used for the echelle/main-dispersion detector mapping; it may include prescription-specific relay magnification, but that relay is not modeled by the analytical echelle code. plate_scale maps on-sky slit coordinate to detector/image-plane mm for the analytical trace generator only. detector_angle rotates the analytical trace footprint before detector placement. detector_pad is legacy padding metadata and is not optical trace geometry. nominal_slit_width and fwhm document the slit-width-to-detector-FWHM consistency point used by the analytical model and validation diagnostics.
 # future_trace_note : These analytical consistency columns are not a replacement for future Zemax-export trace tables. Zemax-backed traces should carry their own image-plane geometry and validation metadata.
 # changes :
-# - 2026-10-18 : Detector rotations maximize configured-band coverage of the full 10 arcsec slit with ScopeSim's corrected fixed-groove angle estimator (acfa5a71). The planar approximation, band limits and orders are retained. YJ ruling restored to 350 grooves/mm in cf88595. Validation in zs-scopesim/docs/zshooter-xdisp-geometry.md.
 # - 2025-10-20 : Initial creation of the file with trace parameters
 #
 prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle
-b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       2000          10            x               0               -6.86
-g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       1100          10            x               0               -5.46
-r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       625           10            x               0               -3.35
-yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x               0               8.56
-h        4              4                51    11   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x               0               -9.48
-k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x               0               4.49
+b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       2000          10            x               0               -6
+g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       1100          10            x               0               -6
+r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       625           10            x               0               -2
+yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x               0               -4
+h        4              4                51    11   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x               0               -3
+k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x               0               -7

From 9fa8317c2209a72fea7a6e07e291840d0babe93a Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 01:33:17 -0700
Subject: [PATCH 53/63] Adopt verified ZShooter six-channel grating
 prescription

---
 ZShooter_v2/traces/echelle_trace_parameters.dat | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/ZShooter_v2/traces/echelle_trace_parameters.dat b/ZShooter_v2/traces/echelle_trace_parameters.dat
index 7f97c3a4..538a22b4 100644
--- a/ZShooter_v2/traces/echelle_trace_parameters.dat
+++ b/ZShooter_v2/traces/echelle_trace_parameters.dat
@@ -21,13 +21,15 @@
 # description : Table that defines the parameters for the analytical echelle trace generator used by EchelleSpectralTraceList effect. m0 = maximum order number; n = number of orders
 # geometry_note : focal_length is the physical camera focal length used for camera and cross-dispersion geometry. dispersion_focal_length is the effective focal length used for the echelle/main-dispersion detector mapping; it may include prescription-specific relay magnification, but that relay is not modeled by the analytical echelle code. plate_scale maps on-sky slit coordinate to detector/image-plane mm for the analytical trace generator only. detector_angle rotates the analytical trace footprint before detector placement. detector_pad is legacy padding metadata and is not optical trace geometry. nominal_slit_width and fwhm document the slit-width-to-detector-FWHM consistency point used by the analytical model and validation diagnostics.
 # future_trace_note : These analytical consistency columns are not a replacement for future Zemax-export trace tables. Zemax-backed traces should carry their own image-plane geometry and validation metadata.
+# prescription : 20260610B_ZS_6CH_Complete_Spectrograph.zar; echelle/CX rulings and order limits from surfaces 61/88. Nominal focal lengths and 15 um production detector pixels are retained.
+# angle_note : xbeta_center is the explicit unfolded CX incidence/central angle, in degrees. For local propagation sign s, alpha = -s * surface-85 Tilt-X; s is negative for B/G/YJ/H and positive for R/K. detector_angle is the negative of surface-166 coordinate Tilt-Z because it actively rotates points rather than coordinate axes. Neither angle is fitted to detector coverage.
 # changes :
 # - 2025-10-20 : Initial creation of the file with trace parameters
 #
 prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle
-b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       2000          10            x               0               -6
-g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       1100          10            x               0               -6
-r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.000       625           10            x               0               -2
-yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x               0               -4
-h        4              4                51    11   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x               0               -3
-k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x               0               -7
+b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       2000          10            x              21.20            -9.50
+g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       1100          10            x              16.75            -7.00
+r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       625           10            x              14.10            -6.00
+yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x              10.90            -5.00
+h        4              4                51    10   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x              13.70            -6.10
+k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x              16.80            -6.50

From 2e9a0ae07f270a797242ffb9bebef44f319ed792 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 03:47:55 -0700
Subject: [PATCH 54/63] Place selected ZShooter prescription and exclude YJ
 order 57

Keep the explicit Zemax grating angles and detector rotations. Record fixed positions in mm: R moves left 150 pixels, H left 130, and YJ up 50 relative to comparison row 2; B, G and K keep their positions. Document the temporary YJ order-57 exclusion without recentering retained orders.

Validation: all 23 retained YJ orders and all 10 H orders fit their full FSR and slit ends; 17 of 20 R orders fit completely.
---
 .../traces/echelle_trace_parameters.dat        | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/ZShooter_v2/traces/echelle_trace_parameters.dat b/ZShooter_v2/traces/echelle_trace_parameters.dat
index 538a22b4..5b5cb603 100644
--- a/ZShooter_v2/traces/echelle_trace_parameters.dat
+++ b/ZShooter_v2/traces/echelle_trace_parameters.dat
@@ -18,18 +18,22 @@
 # xdisp_freq_unit : mm
 # slitlength_unit : arcsec
 # detector_angle_unit : deg
+# detector_offset_x_unit : mm
+# detector_offset_y_unit : mm
 # description : Table that defines the parameters for the analytical echelle trace generator used by EchelleSpectralTraceList effect. m0 = maximum order number; n = number of orders
 # geometry_note : focal_length is the physical camera focal length used for camera and cross-dispersion geometry. dispersion_focal_length is the effective focal length used for the echelle/main-dispersion detector mapping; it may include prescription-specific relay magnification, but that relay is not modeled by the analytical echelle code. plate_scale maps on-sky slit coordinate to detector/image-plane mm for the analytical trace generator only. detector_angle rotates the analytical trace footprint before detector placement. detector_pad is legacy padding metadata and is not optical trace geometry. nominal_slit_width and fwhm document the slit-width-to-detector-FWHM consistency point used by the analytical model and validation diagnostics.
 # future_trace_note : These analytical consistency columns are not a replacement for future Zemax-export trace tables. Zemax-backed traces should carry their own image-plane geometry and validation metadata.
 # prescription : 20260610B_ZS_6CH_Complete_Spectrograph.zar; echelle/CX rulings and order limits from surfaces 61/88. Nominal focal lengths and 15 um production detector pixels are retained.
 # angle_note : xbeta_center is the explicit unfolded CX incidence/central angle, in degrees. For local propagation sign s, alpha = -s * surface-85 Tilt-X; s is negative for B/G/YJ/H and positive for R/K. detector_angle is the negative of surface-166 coordinate Tilt-Z because it actively rotates points rather than coordinate axes. Neither angle is fitted to detector coverage.
+# placement_note : detector_offset_x/y locate the rotated analytical camera origin relative to detector center, positive right/up. These fixed positions preserve comparison row 2 with R shifted left 150 pixels, H left 130 pixels and YJ up 50 pixels; B/G/K are unchanged. They replace automatic centering, not offsets added after centering.
+# yj_order_note : Temporarily exclude reddest YJ order 57 (approximately 1320-1343 nm FSR) for detector coverage; retain orders 58-80. Placement was fixed before removing this order so the retained orders do not recenter.
 # changes :
 # - 2025-10-20 : Initial creation of the file with trace parameters
 #
-prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle
-b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       2000          10            x              21.20            -9.50
-g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       1100          10            x              16.75            -7.00
-r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       625           10            x              14.10            -6.00
-yj       3              3                80    24   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x              10.90            -5.00
-h        4              4                51    10   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x              13.70            -6.10
-k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x              16.80            -6.50
+prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle    detector_offset_x    detector_offset_y
+b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       2000          10            x              21.20            -9.50    -0.679871            -3.156243
+g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       1100          10            x              16.75            -7.00    -0.569446            -4.041061
+r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       625           10            x              14.10            -6.00    -2.867205            -5.372810
+yj       3              3                80    23   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x              10.90            -5.00    -0.441923            -3.921145
+h        4              4                51    10   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x              13.70            -6.10    -2.474361            -4.610933
+k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x              16.80            -6.50    -0.621554            -5.307695

From 72d360ad1a4777ce5ea87f956909ba476381330a Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 06:15:50 -0700
Subject: [PATCH 55/63] Make arm exposure defaults follow global settings and
 verify configuration reuse

---
 HAWKI/default.yaml                            |  1 +
 XSHOOTER/default.yaml                         | 14 ++++----
 XSHOOTER/tests/test_xshooter.py               | 18 ++++++++++
 ZShooter_v2/default.yaml                      | 27 +++++++-------
 ZShooter_v2/tests/test_fidelity_candidates.py | 36 +++++++++++++++++++
 5 files changed, 78 insertions(+), 18 deletions(-)

diff --git a/HAWKI/default.yaml b/HAWKI/default.yaml
index f758f031..d16a7c1c 100644
--- a/HAWKI/default.yaml
+++ b/HAWKI/default.yaml
@@ -22,6 +22,7 @@ yamls:
 
 properties :
   instrument: HAWKI
+  modes: []  # Imaging configuration has no selectable observing modes.
   filter_name : Ks
   airmass : 1.2
   declination : -30
diff --git a/XSHOOTER/default.yaml b/XSHOOTER/default.yaml
index 0100cd41..28d5c51f 100644
--- a/XSHOOTER/default.yaml
+++ b/XSHOOTER/default.yaml
@@ -16,12 +16,14 @@ properties:
   az: 0
   airmass: 1.3
   seeing: 0.7
-  dit_uvb: 300.0
-  dit_vis: 300.0
-  dit_nir: 300.0
-  ndit_uvb: 1
-  ndit_vis: 1
-  ndit_nir: 1
+  dit: 300.0
+  ndit: 1
+  dit_uvb: '!OBS.dit'
+  dit_vis: '!OBS.dit'
+  dit_nir: '!OBS.dit'
+  ndit_uvb: '!OBS.ndit'
+  ndit_vis: '!OBS.ndit'
+  ndit_nir: '!OBS.ndit'
 mode_yamls:
   - name: SPEC
     object: instrument
diff --git a/XSHOOTER/tests/test_xshooter.py b/XSHOOTER/tests/test_xshooter.py
index 1a2c7c4d..dab39140 100644
--- a/XSHOOTER/tests/test_xshooter.py
+++ b/XSHOOTER/tests/test_xshooter.py
@@ -34,6 +34,24 @@ def test_native_geometry_and_orders(train):
     assert train.cmds['!TEL.name'] == 'VLT UT3'
 
 
+def test_readout_exposure_changes_reach_each_arm(train):
+    train['shot_noise'].include = False
+    train['readout_noise'].include = False
+    for plane in train.image_planes:
+        plane.data.fill(2.)
+    for seconds in (3600., 300., 3600.):
+        exposure = {'dit': seconds, 'ndit': 1, '!OBS.dit_vis': seconds / 2, '!OBS.ndit_vis': 2}
+        outputs = train.readout(**exposure)
+        for index, arm in enumerate(('uvb', 'vis', 'nir')):
+            hdu = outputs[index][1]
+            dit, ndit = (seconds / 2, 2) if arm == 'vis' else (seconds, 1)
+            assert hdu.header['DIT'] == dit
+            assert hdu.header['NDIT'] == ndit
+            assert hdu.header['EXPTIME'] == seconds
+            charge = hdu.data * train.cmds[f'!DET.gain_{arm}']
+            np.testing.assert_allclose(charge, (2 + train.cmds[f'!DET.dark_{arm}']) * seconds, rtol=2e-6)
+
+
 @pytest.mark.parametrize('bx,by', [(1, 1), (1, 2), (2, 2)])
 def test_full_readout_charge_headers_and_repeat(train, bx, by):
     for arm in ['uvb', 'vis']:
diff --git a/ZShooter_v2/default.yaml b/ZShooter_v2/default.yaml
index 70f39bd6..a312095a 100755
--- a/ZShooter_v2/default.yaml
+++ b/ZShooter_v2/default.yaml
@@ -28,18 +28,21 @@ properties:
   airmass : 1.0          # altitude, if set and different from airmass will override airmass
   seeing: 0.7
   brightness : "dark"    # Sky brightness, can be "dark", "grey", or "bright"
-  dit_blue : 300.0              # exposure time for B arm
-  ndit_blue : 1                # number of exposures for B arm
-  dit_green : 300.0              # exposure time for G arm
-  ndit_green : 1                # number of exposures for G arm
-  dit_red : 300.0              # exposure time for R arms
-  ndit_red : 1                # number of exposures for R arms
-  dit_yj: 300.0              # exposure time for YJ arms
-  ndit_yj: 1                # number of exposures for YJ arms
-  dit_h: 300.0              # exposure time for H arms
-  ndit_h: 1                # number of exposures for H arms
-  dit_k: 300.0              # exposure time for K arms
-  ndit_k: 1                # number of exposures for K arms
+  dit: 300.0
+  ndit: 1
+  # Arm settings follow the global exposure unless explicitly overridden.
+  dit_blue: '!OBS.dit'
+  ndit_blue: '!OBS.ndit'
+  dit_green: '!OBS.dit'
+  ndit_green: '!OBS.ndit'
+  dit_red: '!OBS.dit'
+  ndit_red: '!OBS.ndit'
+  dit_yj: '!OBS.dit'
+  ndit_yj: '!OBS.ndit'
+  dit_h: '!OBS.dit'
+  ndit_h: '!OBS.ndit'
+  dit_k: '!OBS.dit'
+  ndit_k: '!OBS.ndit'
 
 mode_yamls:
 - object: instrument
diff --git a/ZShooter_v2/tests/test_fidelity_candidates.py b/ZShooter_v2/tests/test_fidelity_candidates.py
index 82be6cd6..1a74f235 100644
--- a/ZShooter_v2/tests/test_fidelity_candidates.py
+++ b/ZShooter_v2/tests/test_fidelity_candidates.py
@@ -1,5 +1,6 @@
 """Physical checks using the configured ZShooter prescription and detector effects."""
 from pathlib import Path
+from copy import deepcopy
 import numpy as np
 import pytest
 import yaml
@@ -16,6 +17,41 @@
 ROOT = Path(__file__).resolve().parents[1]
 
 
+def test_reused_configuration_matches_fresh_slits_and_exposures(monkeypatch):
+    from synphot import SourceSpectrum, ConstFlux1D
+    from synphot.units import PHOTLAM
+
+    monkeypatch.setitem(sim.rc.__config__, '!SIM.file.local_packages_path', str(ROOT.parent))
+    properties = {'!SIM.spectral.wave_min': .549, '!SIM.spectral.wave_max': .551, '!OBS.mjdobs': 60310.25, '!INST.include_diffuse_emissivity': False}
+    commands = sim.UserCommands(use_instrument='ZShooter_v2', properties=properties)
+    ignore = ['atmo_transmission', 'continuum_emission', 'airglow_and_interline_continuum', 'adc_residuals', 'shot_noise', 'readout_noise_selector']
+    for document in commands.yaml_dicts:
+        if 'effects' in document:
+            document['effects'] = [effect for effect in document['effects'] if effect['name'] not in ignore]
+    train = sim.OpticalTrain(commands)
+    source = sim.Source(x=[0.], y=[.05], ref=[0], weight=[1.], spectra=[SourceSpectrum(ConstFlux1D, amplitude=.01*PHOTLAM)])
+    for slit, seconds in ((.7, 3600.), (.33, 300.), (.7, 3600.)):
+        settings = {'!INST.vis_curr_slit': slit, '!INST.nir_curr_slit': slit, '!OBS.dit': seconds, '!OBS.ndit': 1}
+        train.cmds.update(properties=settings.copy())
+        train.update(migrate_cmds=True)
+        fresh_commands = deepcopy(commands)
+        fresh_commands.update(properties=settings)
+        fresh = sim.OpticalTrain(fresh_commands)
+        train.observe(source, update=False)
+        fresh.observe(source, update=False)
+        for actual, expected in zip(train.image_planes, fresh.image_planes):
+            np.testing.assert_allclose(actual.data, expected.data, rtol=1e-6, atol=1e-12, err_msg=f'slit={slit}, DIT={seconds}')
+        for arm in range(6):
+            aperture = train['slitwheel_selector'].get_effect(arm).current_slit.table
+            assert np.ptp(aperture['y']) == pytest.approx(slit)
+        readouts = train.readout(dit=seconds, ndit=1, dit_green=seconds/2, ndit_green=2)
+        for index, readout in enumerate(readouts):
+            header = readout[1].header
+            assert header['DIT'] == (seconds/2 if index == 1 else seconds)
+            assert header['NDIT'] == (2 if index == 1 else 1)
+            assert header['EXPTIME'] == seconds
+
+
 @pytest.fixture(scope='module')
 def traces():
     config = yaml.safe_load((ROOT / 'ZShooter_SPEC.yaml').read_text())

From 75f5f1b09ff06028e0c8eb60b4173a57c90f9b48 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 06:19:44 -0700
Subject: [PATCH 56/63] Specify the ZShooter entrance slit scale and asymmetric
 collimators

---
 .../traces/echelle_trace_parameters.dat       | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/ZShooter_v2/traces/echelle_trace_parameters.dat b/ZShooter_v2/traces/echelle_trace_parameters.dat
index 5b5cb603..4e1cdaad 100644
--- a/ZShooter_v2/traces/echelle_trace_parameters.dat
+++ b/ZShooter_v2/traces/echelle_trace_parameters.dat
@@ -6,6 +6,9 @@
 # max_wave_unit : nm
 # echelle_blaze_unit : deg
 # focal_length_unit : mm
+# collimator1_focal_length_unit : mm
+# collimator2_focal_length_unit : mm
+# entrance_slit_scale_unit : mm/arcsec
 # dispersion_focal_length_unit : mm
 # fwhm_unit : pixel
 # nominal_slit_width_unit : arcsec
@@ -21,7 +24,7 @@
 # detector_offset_x_unit : mm
 # detector_offset_y_unit : mm
 # description : Table that defines the parameters for the analytical echelle trace generator used by EchelleSpectralTraceList effect. m0 = maximum order number; n = number of orders
-# geometry_note : focal_length is the physical camera focal length used for camera and cross-dispersion geometry. dispersion_focal_length is the effective focal length used for the echelle/main-dispersion detector mapping; it may include prescription-specific relay magnification, but that relay is not modeled by the analytical echelle code. plate_scale maps on-sky slit coordinate to detector/image-plane mm for the analytical trace generator only. detector_angle rotates the analytical trace footprint before detector placement. detector_pad is legacy padding metadata and is not optical trace geometry. nominal_slit_width and fwhm document the slit-width-to-detector-FWHM consistency point used by the analytical model and validation diagnostics.
+# geometry_note : The two collimators and entrance_slit_scale determine the slit angle and pupil relay. dispersion_focal_length=270 mm and fwhm=4.46 pixels record rounded derived nominal values; the trace generator calculates them from the physical inputs. plate_scale is the along-slit sky-to-image scale. detector_angle rotates the trace before fixed placement. detector_pad is legacy metadata and is not optical geometry.
 # future_trace_note : These analytical consistency columns are not a replacement for future Zemax-export trace tables. Zemax-backed traces should carry their own image-plane geometry and validation metadata.
 # prescription : 20260610B_ZS_6CH_Complete_Spectrograph.zar; echelle/CX rulings and order limits from surfaces 61/88. Nominal focal lengths and 15 um production detector pixels are retained.
 # angle_note : xbeta_center is the explicit unfolded CX incidence/central angle, in degrees. For local propagation sign s, alpha = -s * surface-85 Tilt-X; s is negative for B/G/YJ/H and positive for R/K. detector_angle is the negative of surface-166 coordinate Tilt-Z because it actively rotates points rather than coordinate axes. Neither angle is fitted to detector coverage.
@@ -30,10 +33,10 @@
 # changes :
 # - 2025-10-20 : Initial creation of the file with trace parameters
 #
-prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle    detector_offset_x    detector_offset_y
-b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       2000          10            x              21.20            -9.50    -0.679871            -3.156243
-g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       1100          10            x              16.75            -7.00    -0.569446            -4.041061
-r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       625           10            x              14.10            -6.00    -2.867205            -5.372810
-yj       3              3                80    23   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x              10.90            -5.00    -0.441923            -3.921145
-h        4              4                51    10   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x              13.70            -6.10    -2.474361            -4.610933
-k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x              16.80            -6.50    -0.621554            -5.307695
+prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle    detector_offset_x    detector_offset_y    collimator1_focal_length    collimator2_focal_length    entrance_slit_scale
+b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       2000          10            x              21.20            -9.50    -0.679871            -3.156243    1500    1250    0.5306
+g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       1100          10            x              16.75            -7.00    -0.569446            -4.041061    1500    1250    0.5306
+r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       625           10            x              14.10            -6.00    -2.867205            -5.372810    1500    1250    0.5306
+yj       3              3                80    23   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x              10.90            -5.00    -0.441923            -3.921145    1500    1250    0.5306
+h        4              4                51    10   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x              13.70            -6.10    -2.474361            -4.610933    1500    1250    0.5306
+k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x              16.80            -6.50    -0.621554            -5.307695    1500    1250    0.5306

From c5766a99daf6407666d3fb62c5bf69b7e81a8cb5 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 06:56:04 -0700
Subject: [PATCH 57/63] Include slit-cell and pixel response in X-shooter line
 moments

---
 XSHOOTER/tests/test_xshooter.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/XSHOOTER/tests/test_xshooter.py b/XSHOOTER/tests/test_xshooter.py
index dab39140..fe214a57 100644
--- a/XSHOOTER/tests/test_xshooter.py
+++ b/XSHOOTER/tests/test_xshooter.py
@@ -200,9 +200,12 @@ def test_mapped_line_photons_centroid_and_variance(train, aid, name):
         detector_wave = trace.xy2lam(xmm, ymm)
         mean = (mapped.data*detector_wave).sum()/mapped.data.sum()
         variance = (mapped.data*(detector_wave-mean)**2).sum()/mapped.data.sum()
-        eta_variance = (eta**2*illumination).sum()/illumination.sum()
+        eta_variance = (eta**2*illumination).sum()/illumination.sum() + step**2/12
         optical_sigma = center/trace.meta['optical_resolving_power']/np.sqrt(8*np.log(2))
-        expected_variance = (center/100000)**2 + optical_sigma**2 + eta_variance*trace.slit_dispersion(center)**2
+        # Independent uniform slit cells and detector pixels contribute their
+        # second moments in addition to the input and optical line profiles.
+        pixel_variance = trace.dlam_per_pix(center)**2/12
+        expected_variance = (center/100000)**2 + optical_sigma**2 + eta_variance*trace.slit_dispersion(center)**2 + pixel_variance
         assert abs(mean-center) < abs(trace.dlam_per_pix(center))*.02
         assert variance == pytest.approx(expected_variance, rel=.02)
 

From f53d82c6fe3e906cb0824630347ac5be50fc1a12 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 10:44:58 -0700
Subject: [PATCH 58/63] Verify independent sky-subtraction variance for
 X-shooter readouts

---
 XSHOOTER/tests/test_xshooter.py | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/XSHOOTER/tests/test_xshooter.py b/XSHOOTER/tests/test_xshooter.py
index fe214a57..b8bc9110 100644
--- a/XSHOOTER/tests/test_xshooter.py
+++ b/XSHOOTER/tests/test_xshooter.py
@@ -154,6 +154,36 @@ def test_unsupported_bin_fails(train):
         train.readout(reset=False)
 
 
+@pytest.mark.parametrize('aid,ndit', [(0, 1), (0, 4), (1, 1), (1, 4), (2, 1), (2, 4)])
+def test_independent_sky_subtraction_variance(train, aid, ndit):
+    arm = ['uvb', 'vis', 'nir'][aid]
+    dit, source_rate, sky_rate = 300., .03, .3
+    train.cmds[f'!OBS.dit_{arm}'] = dit
+    train.cmds[f'!OBS.ndit_{arm}'] = ndit
+    train.cmds['!SIM.random.seed'] = 62419
+    if aid != 2:
+        train[f'{arm}_readout_parameters'].apply_to(None)
+        train.cmds[f'!DET.binx_{arm}'] = 2
+        train.cmds[f'!DET.biny_{arm}'] = 2
+    header = header_from_list_of_xy([-6., 6.], [-4.5, 4.5], .015, 'D')
+    frames = []
+    for roid, rate in enumerate((source_rate+sky_rate, sky_rate)):
+        train.cmds['!OBS.roid'] = roid
+        detector = Detector(header, cmds=train.cmds, id=aid)
+        detector.data.fill(rate)
+        for effect in train.optics_manager.detector_effects:
+            effect.apply_to(detector)
+        frames.append(detector.data.copy())
+    gain, dark = train.cmds[f'!DET.gain_{arm}'], train.cmds[f'!DET.dark_{arm}']
+    ron = detector.header['RDNOISE'] if aid == 2 else train.cmds[f'!DET.ron_{arm}']
+    area, average = (1, ndit) if aid == 2 else (4, 1)
+    difference = (frames[0]-frames[1])*gain*average
+    signal = source_rate*dit*ndit*area
+    variance = signal+2*((sky_rate+dark)*dit*area+ron**2)*ndit
+    assert abs(difference.mean()-signal) < 4*np.sqrt(variance/difference.size)
+    assert difference.var() == pytest.approx(variance, rel=.025)
+
+
 def test_trace_fit_bounds_and_finite_physical_calibrations():
     residuals = Table.read(ROOT / 'calibration' / 'trace_residuals.ecsv')
     assert len(residuals) == 43

From 8c01711834fe54963c349619308e67b44efbdf81 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Sun, 18 Oct 2026 13:16:08 -0700
Subject: [PATCH 59/63] test(detectors): verify noisy science defaults and
 changed source reuse

---
 XSHOOTER/tests/test_xshooter.py               | 33 +++++++++++++
 ZShooter_v2/tests/test_fidelity_candidates.py | 48 +++++++++++++++++--
 2 files changed, 78 insertions(+), 3 deletions(-)

diff --git a/XSHOOTER/tests/test_xshooter.py b/XSHOOTER/tests/test_xshooter.py
index b8bc9110..54d38f6e 100644
--- a/XSHOOTER/tests/test_xshooter.py
+++ b/XSHOOTER/tests/test_xshooter.py
@@ -34,6 +34,39 @@ def test_native_geometry_and_orders(train):
     assert train.cmds['!TEL.name'] == 'VLT UT3'
 
 
+@pytest.mark.parametrize('aid', [0, 1, 2])
+def test_science_defaults_draw_nonuniform_source_and_sky(train, aid):
+    assert train['shot_noise'].include
+    assert train['readout_noise'].include
+    arm = ['uvb', 'vis', 'nir'][aid]
+    train.cmds[f'!OBS.dit_{arm}'], train.cmds[f'!OBS.ndit_{arm}'] = 300., 2
+    train.cmds['!SIM.random.seed'] = 19734
+    if aid != 2:
+        train[f'{arm}_readout_parameters'].apply_to(None)
+    source = np.repeat([0., .003, 3.], 100)[None, :]
+    sky = np.repeat([0., .03, 5.], 100)[:, None]
+    header = header_from_list_of_xy([-2.25, 2.25], [-2.25, 2.25], .015, 'D')
+    frames = []
+    for roid in (0, 1):
+        train.cmds['!OBS.roid'] = roid
+        detector = Detector(header, cmds=train.cmds, id=aid)
+        detector.data[:] = source+sky
+        for effect in train.optics_manager.detector_effects:
+            effect.apply_to(detector)
+        gain, dark = train.cmds[f'!DET.gain_{arm}'], train.cmds[f'!DET.dark_{arm}']
+        ron = detector.header['RDNOISE'] if aid == 2 else train.cmds[f'!DET.ron_{arm}']
+        expected = (source+sky+dark)*600
+        accumulated = detector.data*gain*(2 if aid == 2 else 1)
+        residual = (accumulated-expected)/np.sqrt(expected+2*ron**2)
+        for yy in (0, 100, 200):
+            for xx in (0, 100, 200):
+                region = residual[yy:yy+100, xx:xx+100]
+                assert abs(region.mean()) < .05
+                assert region.var() == pytest.approx(1., abs=.06)
+        frames.append(accumulated)
+    assert not np.array_equal(*frames)
+
+
 def test_readout_exposure_changes_reach_each_arm(train):
     train['shot_noise'].include = False
     train['readout_noise'].include = False
diff --git a/ZShooter_v2/tests/test_fidelity_candidates.py b/ZShooter_v2/tests/test_fidelity_candidates.py
index 1a74f235..84a955b3 100644
--- a/ZShooter_v2/tests/test_fidelity_candidates.py
+++ b/ZShooter_v2/tests/test_fidelity_candidates.py
@@ -17,8 +17,46 @@
 ROOT = Path(__file__).resolve().parents[1]
 
 
+def test_science_defaults_draw_source_sky_and_dark_per_pixel(monkeypatch):
+    monkeypatch.setitem(sim.rc.__config__, '!SIM.file.local_packages_path', str(ROOT.parent))
+    ignore = ['atmo_transmission', 'continuum_emission', 'airglow_and_interline_continuum', 'adc_residuals']
+    cmds = sim.UserCommands(use_instrument='ZShooter_v2', properties={'!OBS.mjdobs': 60310.25, '!SIM.random.seed': 38291})
+    for document in cmds.yaml_dicts:
+        if 'effects' in document:
+            document['effects'] = [effect for effect in document['effects'] if effect['name'] not in ignore]
+    train = sim.OpticalTrain(cmds)
+    assert train['shot_noise'].include
+    assert train['readout_noise_selector'].include
+    # Different source and sky levels in nine regions, including pure dark,
+    # sky-only and source-only pixels. No stochastic effect is disabled.
+    source = np.repeat([0., .003, 3.], 100)[None, :]
+    sky = np.repeat([0., .03, 5.], 100)[:, None]
+    header = header_from_list_of_xy([-2.25, 2.25], [-2.25, 2.25], .015, 'D')
+    for aid, arm in enumerate(('blue', 'green', 'red', 'yj', 'h', 'k')):
+        train.cmds[f'!OBS.dit_{arm}'], train.cmds[f'!OBS.ndit_{arm}'] = 300., 2
+        dark = train['dark_current_selector'].get_effect(aid).meta['value']
+        ron = train['readout_noise_selector'].get_effect(aid).meta['noise_std']
+        bias = train['bias_selector'].get_effect(aid).meta['bias']
+        expected = (source+sky+dark)*600
+        frames = []
+        for roid in (0, 1):
+            train.cmds['!OBS.roid'] = roid
+            detector = Detector(header, cmds=train.cmds, id=aid)
+            detector.data[:] = source+sky
+            for effect in train.optics_manager.detector_effects:
+                effect.apply_to(detector)
+            residual = (detector.data-bias-expected)/np.sqrt(expected+2*ron**2)
+            for yy in (0, 100, 200):
+                for xx in (0, 100, 200):
+                    region = residual[yy:yy+100, xx:xx+100]
+                    assert abs(region.mean()) < .05
+                    assert region.var() == pytest.approx(1., abs=.06)
+            frames.append(detector.data.copy())
+        assert not np.array_equal(*frames)
+
+
 def test_reused_configuration_matches_fresh_slits_and_exposures(monkeypatch):
-    from synphot import SourceSpectrum, ConstFlux1D
+    from synphot import SourceSpectrum, Empirical1D
     from synphot.units import PHOTLAM
 
     monkeypatch.setitem(sim.rc.__config__, '!SIM.file.local_packages_path', str(ROOT.parent))
@@ -29,8 +67,12 @@ def test_reused_configuration_matches_fresh_slits_and_exposures(monkeypatch):
         if 'effects' in document:
             document['effects'] = [effect for effect in document['effects'] if effect['name'] not in ignore]
     train = sim.OpticalTrain(commands)
-    source = sim.Source(x=[0.], y=[.05], ref=[0], weight=[1.], spectra=[SourceSpectrum(ConstFlux1D, amplitude=.01*PHOTLAM)])
-    for slit, seconds in ((.7, 3600.), (.33, 300.), (.7, 3600.)):
+    wave = np.linspace(5400., 5600., 4001)*u.AA
+    spectrum = SourceSpectrum(Empirical1D, points=wave, lookup_table=.01*np.exp(-.5*((wave.value-5500)/1.)**2)*PHOTLAM)
+    source = sim.Source(x=[0.], y=[.05], ref=[0], weight=[1.], spectra=[spectrum])
+    for slit, seconds, redshift, weight in ((.7, 3600., 0., 1.), (.33, 300., .0003, 2.), (.7, 3600., 0., 1.)):
+        source.spectra[0].z = redshift
+        source.fields[0].field['weight'][0] = weight
         settings = {'!INST.vis_curr_slit': slit, '!INST.nir_curr_slit': slit, '!OBS.dit': seconds, '!OBS.ndit': 1}
         train.cmds.update(properties=settings.copy())
         train.update(migrate_cmds=True)

From 6cb21a38c514d482007b54215cb03983af3831c3 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Tue, 20 Oct 2026 12:57:55 -0700
Subject: [PATCH 60/63] feat: add SPEC_ALTDETECT with physical QE and
 per-detector operating points

---
 XSHOOTER/XSHOOTER_DET.yaml                    |   3 +
 ZShooter_v2/README.md                         |  16 ++
 ZShooter_v2/ZShooter_ALTDETECT.yaml           |  40 +++
 ZShooter_v2/ZShooter_DET.yaml                 | 262 +++++++++++++-----
 ZShooter_v2/ZShooter_SPEC.yaml                | 165 ++---------
 ZShooter_v2/default.yaml                      |   9 +
 .../traces/echelle_trace_parameters.dat       |  12 +-
 7 files changed, 290 insertions(+), 217 deletions(-)
 create mode 100644 ZShooter_v2/README.md
 create mode 100644 ZShooter_v2/ZShooter_ALTDETECT.yaml

diff --git a/XSHOOTER/XSHOOTER_DET.yaml b/XSHOOTER/XSHOOTER_DET.yaml
index 38426ff1..674d7b58 100644
--- a/XSHOOTER/XSHOOTER_DET.yaml
+++ b/XSHOOTER/XSHOOTER_DET.yaml
@@ -47,6 +47,7 @@ effects:
         pixel_size: [0.015]
         angle: [0.0]
         gain: [1.0]
+        exnoise: [1.0]
       x_cen_unit: mm
       y_cen_unit: mm
       x_size_unit: pixel
@@ -69,6 +70,7 @@ effects:
         pixel_size: [0.015]
         angle: [0.0]
         gain: [1.0]
+        exnoise: [1.0]
       x_cen_unit: mm
       y_cen_unit: mm
       x_size_unit: pixel
@@ -91,6 +93,7 @@ effects:
         pixel_size: [0.018]
         angle: [0.0]
         gain: [1.0]
+        exnoise: [1.0]
       x_cen_unit: mm
       y_cen_unit: mm
       x_size_unit: pixel
diff --git a/ZShooter_v2/README.md b/ZShooter_v2/README.md
new file mode 100644
index 00000000..936a4efc
--- /dev/null
+++ b/ZShooter_v2/README.md
@@ -0,0 +1,16 @@
+# ZShooter detector engineering configurations
+
+`SPEC` selects tapered qCCDs in B/G/R and 15 um LmAPDs in YJ/H/K.
+`SPEC_ALTDETECT` selects untapered CCDs in B/G/R, the same YJ LmAPD, and 18 um H2RGs in H/K. Detector dimensions remain 2048 x 4096 in B/G/R and 2048 x 2048 in YJ/H/K. Optical prescription, focal-plane offsets and selected orders are shared.
+
+Select one mode when constructing `UserCommands`, then construct an `OpticalTrain`. Reconstruct the train when changing the detector package or its initialization parameters, including pitch and QE. Exposure parameters can be supplied independently per arm through `readout(dit_blue=..., ndit_blue=..., ...)`.
+
+The `!DET` operating points use input-referred electrons. LmAPDs have avalanche gain 10, usable well 15000 e, effective read noise 0.5 e RMS and photon-variance multiplier F=1.2. Their physical QE uses `H2RG_TER.dat`, the existing provisional NIR QE estimate; `APD_TER.dat` is the historical QE/F proxy and is no longer active. The adopted dark term remains independent of F. Changing F does not change the configured usable well or divide the already input-referred read noise again.
+
+Other usable wells default to 60000 e. Read noise is 0.1 e for qCCDs, 2.5 e for CCDs and 6 e for H2RGs per independent integration. Dark rates are 0.5976, 2, 0.36 and 23 e/hour/pixel for qCCD, CCD, LmAPD and H2RG respectively. These are engineering specifications. The settings in `ZShooter_DET.yaml` and `ZShooter_ALTDETECT.yaml` are individually editable per detector.
+
+DIT/NDIT are observing settings: no effect changes them automatically. `ExposureLimit` warns if the expected source+sky+thermal+dark charge per integration exceeds the usable well, after CCD charge summation and before random noise or software binning. It records `PEAKDIT` and `WELLDEP` without clipping. An integration means an independent exposure or completed ramp, not a nondestructive sample. The current CCD charge-packet limit is conservatively the same 60000 e as the native-pixel limit.
+
+Native headers record `DETMODE`, `DETTYPE`, `PIXEL_SIZE` (mm), `EXNOISE` (variance factor), `AVGAIN` and `WELLDEP`, alongside actual exposure and detector WCS. Image charge stays input-referred; avalanche gain is distinct from the electronic electron/ADU conversion.
+
+Analytical traces resolve their pixel pitch from the same detector commands. Seven along-slit samples and a sixth-degree transform fit keep the common physical footprint consistent across the two detector sizes. The larger H/K area changes coverage and sampling without retuning optics or reinstating the omitted YJ order.
diff --git a/ZShooter_v2/ZShooter_ALTDETECT.yaml b/ZShooter_v2/ZShooter_ALTDETECT.yaml
new file mode 100644
index 00000000..defb4cf4
--- /dev/null
+++ b/ZShooter_v2/ZShooter_ALTDETECT.yaml
@@ -0,0 +1,40 @@
+# Alternative detector engineering package; optical prescription is unchanged.
+object: detector
+alias: DET
+name: zshooter_alternative_detectors
+properties:
+  detector_mode: SPEC_ALTDETECT
+  detector_type_b: CCD
+  ron_b: 2.5
+  dark_b: 0.0005555555555555556  # 2 e/hour/pixel
+  qe_class_b: SpectralQuantumEfficiency
+  qe_kwargs_b:
+    filename: detector_specs/ccd_uv_enhanced_TER.dat
+  detector_type_g: CCD
+  ron_g: 2.5
+  dark_g: 0.0005555555555555556  # 2 e/hour/pixel
+  qe_class_g: SpectralQuantumEfficiency
+  qe_kwargs_g:
+    filename: detector_specs/ccd_ml_uv_enhanced_TER.dat
+  detector_type_r: CCD
+  ron_r: 2.5
+  dark_r: 0.0005555555555555556  # 2 e/hour/pixel
+  qe_class_r: SpectralQuantumEfficiency
+  qe_kwargs_r:
+    filename: detector_specs/ccd_ml2_uv_enhanced_hirho_TER.dat
+  detector_type_h: H2RG
+  pixel_size_h: 0.018
+  pixel_scale_h: 0.1884654  # Same 10.4703 arcsec/mm camera plate scale
+  usable_well_h: 60000
+  ron_h: 6.0
+  dark_h: 0.006388888888888889  # 23 e/hour/pixel
+  excess_noise_factor_h: 1.0
+  avalanche_gain_h: 1
+  detector_type_k: H2RG
+  pixel_size_k: 0.018
+  pixel_scale_k: 0.1884654  # Same 10.4703 arcsec/mm camera plate scale
+  usable_well_k: 60000
+  ron_k: 6.0
+  dark_k: 0.006388888888888889  # 23 e/hour/pixel
+  excess_noise_factor_k: 1.0
+  avalanche_gain_k: 1
diff --git a/ZShooter_v2/ZShooter_DET.yaml b/ZShooter_v2/ZShooter_DET.yaml
index e1592c1b..83b67a62 100644
--- a/ZShooter_v2/ZShooter_DET.yaml
+++ b/ZShooter_v2/ZShooter_DET.yaml
@@ -14,7 +14,101 @@ properties :
     biny_green: 1
     binx_red: 1
     biny_red: 1
-    full_well: 64000
+    detector_mode: SPEC
+    # Input-referred engineering operating points; F multiplies photon variance.
+    pixel_size_b: 0.015
+    usable_well_b: 60000
+    ron_b: 0.1
+    dark_b: 0.000166
+    excess_noise_factor_b: 1.0
+    avalanche_gain_b: 1
+    detector_type_b: qCCD
+    qe_class_b: TaperedQuantumEfficiency
+    qe_kwargs_b:
+      axis: y
+      position_unit: pix
+      position_min: 0
+      position_max: 4096
+      wave_unit: nm
+      center_wave_min: '!INST.tapered_qe_b_center_wave_min'
+      center_wave_max: '!INST.tapered_qe_b_center_wave_max'
+      flat_width: '!INST.tapered_qe_b_flat_width'
+      transition_width: '!INST.tapered_qe_b_transition_width'
+      peak: '!INST.tapered_qe_peak'
+      floor: '!INST.tapered_qe_floor'
+      clip_position: True
+      diffuse_position_samples: '!INST.tapered_qe_diffuse_position_samples'
+    pixel_size_g: 0.015
+    usable_well_g: 60000
+    ron_g: 0.1
+    dark_g: 0.000166
+    excess_noise_factor_g: 1.0
+    avalanche_gain_g: 1
+    detector_type_g: qCCD
+    qe_class_g: TaperedQuantumEfficiency
+    qe_kwargs_g:
+      axis: y
+      position_unit: pix
+      position_min: 0
+      position_max: 4096
+      wave_unit: nm
+      center_wave_min: '!INST.tapered_qe_g_center_wave_min'
+      center_wave_max: '!INST.tapered_qe_g_center_wave_max'
+      flat_width: '!INST.tapered_qe_g_flat_width'
+      transition_width: '!INST.tapered_qe_g_transition_width'
+      peak: '!INST.tapered_qe_peak'
+      floor: '!INST.tapered_qe_floor'
+      clip_position: True
+      diffuse_position_samples: '!INST.tapered_qe_diffuse_position_samples'
+    pixel_size_r: 0.015
+    usable_well_r: 60000
+    ron_r: 0.1
+    dark_r: 0.000166
+    excess_noise_factor_r: 1.0
+    avalanche_gain_r: 1
+    detector_type_r: qCCD
+    qe_class_r: TaperedQuantumEfficiency
+    qe_kwargs_r:
+      axis: y
+      position_unit: pix
+      position_min: 0
+      position_max: 4096
+      wave_unit: nm
+      center_wave_min: '!INST.tapered_qe_r_center_wave_min'
+      center_wave_max: '!INST.tapered_qe_r_center_wave_max'
+      flat_width: '!INST.tapered_qe_r_flat_width'
+      transition_width: '!INST.tapered_qe_r_transition_width'
+      peak: '!INST.tapered_qe_peak'
+      floor: '!INST.tapered_qe_floor'
+      clip_position: True
+      diffuse_position_samples: '!INST.tapered_qe_diffuse_position_samples'
+    pixel_size_yj: 0.015
+    usable_well_yj: 15000
+    ron_yj: 0.5
+    dark_yj: 0.0001
+    excess_noise_factor_yj: 1.2
+    avalanche_gain_yj: 10
+    detector_type_yj: LmAPD
+    qe_class_yj: SpectralQuantumEfficiency
+    qe_kwargs_yj: {filename: detector_specs/H2RG_TER.dat}
+    pixel_size_h: 0.015
+    usable_well_h: 15000
+    ron_h: 0.5
+    dark_h: 0.0001
+    excess_noise_factor_h: 1.2
+    avalanche_gain_h: 10
+    detector_type_h: LmAPD
+    qe_class_h: SpectralQuantumEfficiency
+    qe_kwargs_h: {filename: detector_specs/H2RG_TER.dat}
+    pixel_size_k: 0.015
+    usable_well_k: 15000
+    ron_k: 0.5
+    dark_k: 0.0001
+    excess_noise_factor_k: 1.2
+    avalanche_gain_k: 10
+    detector_type_k: LmAPD
+    qe_class_k: SpectralQuantumEfficiency
+    qe_kwargs_k: {filename: detector_specs/H2RG_TER.dat}
     # Per-image-plane scales. These currently match the global ZShooter
     # values in ZShooter.yaml, but live here so each camera can diverge when
     # the final channel optics are available.
@@ -84,9 +178,14 @@ effects :
           y_cen: [0.0]
           x_size: [2048]
           y_size: [4096]
-          pixel_size: [0.015]
+          pixel_size: ['!DET.pixel_size_b']
           angle: [0.0]
           gain: [1.0]
+          detmode: ['!DET.detector_mode']
+          dettype: ['!DET.detector_type_b']
+          exnoise: ['!DET.excess_noise_factor_b']
+          avgain: ['!DET.avalanche_gain_b']
+          welldep: ['!DET.usable_well_b']
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -94,7 +193,7 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "CCD_B"
+        detector: "!DET.detector_type_b"
 
   - name: detector_g
     description: Single 2k x 2k detector for ZShooter_v2 G arm
@@ -110,9 +209,14 @@ effects :
           y_cen: [0.0]
           x_size: [2048]
           y_size: [4096]
-          pixel_size: [0.015]
+          pixel_size: ['!DET.pixel_size_g']
           angle: [0.0]
           gain: [1.0]
+          detmode: ['!DET.detector_mode']
+          dettype: ['!DET.detector_type_g']
+          exnoise: ['!DET.excess_noise_factor_g']
+          avgain: ['!DET.avalanche_gain_g']
+          welldep: ['!DET.usable_well_g']
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -120,7 +224,7 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "CCD_G"
+        detector: "!DET.detector_type_g"
 
   - name: detector_r
     description: Single 2k x 2k detector for ZShooter_v2 R arm
@@ -136,9 +240,14 @@ effects :
           y_cen: [0.0]
           x_size: [2048]
           y_size: [4096]
-          pixel_size: [0.015]
+          pixel_size: ['!DET.pixel_size_r']
           angle: [0.0]
           gain: [1.0]
+          detmode: ['!DET.detector_mode']
+          dettype: ['!DET.detector_type_r']
+          exnoise: ['!DET.excess_noise_factor_r']
+          avgain: ['!DET.avalanche_gain_r']
+          welldep: ['!DET.usable_well_r']
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -146,7 +255,7 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "CCD_R"
+        detector: "!DET.detector_type_r"
 
   - name: detector_yj
     description: Single 2k x 2k detector for ZShooter_v2 YJ arm
@@ -162,9 +271,14 @@ effects :
           y_cen: [0.0]
           x_size: [2048]
           y_size: [2048]
-          pixel_size: [0.015]
+          pixel_size: ['!DET.pixel_size_yj']
           angle: [0.0]
           gain: [1.0]
+          detmode: ['!DET.detector_mode']
+          dettype: ['!DET.detector_type_yj']
+          exnoise: ['!DET.excess_noise_factor_yj']
+          avgain: ['!DET.avalanche_gain_yj']
+          welldep: ['!DET.usable_well_yj']
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -172,7 +286,7 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "APD_YJ"
+        detector: "!DET.detector_type_yj"
 
   - name: detector_h
     description: Single 2k x 2k detector for ZShooter_v2 H arm
@@ -188,9 +302,14 @@ effects :
           y_cen: [0.0]
           x_size: [2048]
           y_size: [2048]
-          pixel_size: [0.015]
+          pixel_size: ['!DET.pixel_size_h']
           angle: [0.0]
           gain: [1.0]
+          detmode: ['!DET.detector_mode']
+          dettype: ['!DET.detector_type_h']
+          exnoise: ['!DET.excess_noise_factor_h']
+          avgain: ['!DET.avalanche_gain_h']
+          welldep: ['!DET.usable_well_h']
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -198,7 +317,7 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "APD_H"
+        detector: "!DET.detector_type_h"
 
   - name: detector_k
     description: Single 2k x 2k detector for ZShooter_v2 K arm
@@ -214,9 +333,14 @@ effects :
           y_cen: [0.0]
           x_size: [2048]
           y_size: [2048]
-          pixel_size: [0.015]
+          pixel_size: ['!DET.pixel_size_k']
           angle: [0.0]
           gain: [1.0]
+          detmode: ['!DET.detector_mode']
+          dettype: ['!DET.detector_type_k']
+          exnoise: ['!DET.excess_noise_factor_k']
+          avgain: ['!DET.avalanche_gain_k']
+          welldep: ['!DET.usable_well_k']
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -224,7 +348,7 @@ effects :
         pixel_size_unit: mm
         angle_unit: deg
         gain_unit: electron/adu
-        detector: "APD_K"
+        detector: "!DET.detector_type_k"
 
   - name: exposure_integration_selector
     description: Summing up sky signal for all DITs and NDITs for different detectors
@@ -274,37 +398,37 @@ effects :
         - selector_value: [0]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.000166    # [e-/s] level of dark current
+            value: "!DET.dark_b"
             dit: "!OBS.dit_blue"
             ndit: "!OBS.ndit_blue"
         - selector_value: [1]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.000166    # [e-/s] level of dark current
+            value: "!DET.dark_g"
             dit: "!OBS.dit_green"
             ndit: "!OBS.ndit_green"
         - selector_value: [2]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.000166     # [e-/s] level of dark current
+            value: "!DET.dark_r"
             dit: "!OBS.dit_red"
             ndit: "!OBS.ndit_red"
         - selector_value: [3]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.0001    # [e-/s] level of dark current
+            value: "!DET.dark_yj"
             dit: "!OBS.dit_yj"
             ndit: "!OBS.ndit_yj"
         - selector_value: [4]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.0001    # [e-/s] level of dark current
+            value: "!DET.dark_h"
             dit: "!OBS.dit_h"
             ndit: "!OBS.ndit_h"
         - selector_value: [5]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.0001     # [e-/s] level of dark current
+            value: "!DET.dark_k"
             dit: "!OBS.dit_k"
             ndit: "!OBS.ndit_k"
 
@@ -326,60 +450,48 @@ effects :
           effect_class: OnChipBinning
           effect_kwargs: {binx: '!DET.binx_red', biny: '!DET.biny_red'}
 
-  - name: shot_noise
-    description: Poisson statistics of integrated source, background and dark charge
-    class: ShotNoise
-
-  - name: detector_linearity_selector
-    description: Selector Wheel for linearity characteristics for CCDs and APDs
+  - name: exposure_limit
+    description: Warn about per-integration charge; never change DIT/NDIT or clip data
     class: SelectorWheel
-    include: False
     kwargs:
-      selector_key: "detector_id" # not really used as the Detector object's id is stored as just "id", but kept for consistency
+      selector_key: detector_id
       wheel:
-        - selector_value: [0]   # CCD_B detectors
-          effect_class: LinearityCurve
-          effect_kwargs:
-            array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.ndit_blue"
-        - selector_value: [1]   # CCD_G detector
-          effect_class: LinearityCurve
-          effect_kwargs:
-            array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.ndit_green"
-        - selector_value: [2] # CCD_R detectors
-          effect_class: LinearityCurve
-          effect_kwargs:
-            array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.ndit_red"
-        - selector_value: [3]   # APD detectors
-          effect_class: LinearityCurve
-          effect_kwargs:
-            array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.ndit_yj"
-        - selector_value: [4]   # APD detectors
-          effect_class: LinearityCurve
-          effect_kwargs:
-            array_dict:
-              incident: [0, 64e3]
-              measured: [0, 64e3]
-            ndit: "!OBS.ndit_h"
-        - selector_value: [5 ]   # APD detectors
-          effect_class: LinearityCurve
-          effect_kwargs:
-            array_dict:
-              incident: [ 0, 64e3 ]
-              measured: [ 0, 64e3 ]
-            ndit: "!OBS.ndit_k"
+        - selector_value: [0]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.usable_well_b', ndit: '!OBS.ndit_blue'}
+        - selector_value: [1]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.usable_well_g', ndit: '!OBS.ndit_green'}
+        - selector_value: [2]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.usable_well_r', ndit: '!OBS.ndit_red'}
+        - selector_value: [3]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.usable_well_yj', ndit: '!OBS.ndit_yj'}
+        - selector_value: [4]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.usable_well_h', ndit: '!OBS.ndit_h'}
+        - selector_value: [5]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.usable_well_k', ndit: '!OBS.ndit_k'}
 
+  - name: shot_noise
+    description: Photon and dark statistics, with LmAPD excess photon variance
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      wheel:
+        - selector_value: [0, 1, 2]
+          effect_class: ShotNoise
+        - selector_value: [3]
+          effect_class: AvalancheShotNoise
+          effect_kwargs: {excess_noise_factor: '!DET.excess_noise_factor_yj', dark_current: '!DET.dark_yj', dit: '!OBS.dit_yj', ndit: '!OBS.ndit_yj'}
+        - selector_value: [4]
+          effect_class: AvalancheShotNoise
+          effect_kwargs: {excess_noise_factor: '!DET.excess_noise_factor_h', dark_current: '!DET.dark_h', dit: '!OBS.dit_h', ndit: '!OBS.ndit_h'}
+        - selector_value: [5]
+          effect_class: AvalancheShotNoise
+          effect_kwargs: {excess_noise_factor: '!DET.excess_noise_factor_k', dark_current: '!DET.dark_k', dit: '!OBS.dit_k', ndit: '!OBS.ndit_k'}
 
 #  - name: border_reference_pixels
 #    description: Blanks the signal on N edge row and column pixels
@@ -398,32 +510,32 @@ effects :
         - selector_value: [0]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.1    # CCD readout noise std dev in electrons
+            noise_std: "!DET.ron_b"
             ndit: "!OBS.ndit_blue"
         - selector_value: [1]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.1    # CCD readout noise std dev in electrons
+            noise_std: "!DET.ron_g"
             ndit: "!OBS.ndit_green"
         - selector_value: [2]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.1    # CCD readout noise std dev in electrons
+            noise_std: "!DET.ron_r"
             ndit: "!OBS.ndit_red"
         - selector_value: [3]   # APD detectors
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: .5    # APD readout noise std dev in electrons
+            noise_std: "!DET.ron_yj"
             ndit: "!OBS.ndit_yj"
         - selector_value: [ 4 ]   # APD detectors
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: .5     # APD readout noise std dev in electrons
+            noise_std: "!DET.ron_h"
             ndit: "!OBS.ndit_h"
         - selector_value: [ 5 ]   # APD detectors
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: .5     # APD readout noise std dev in electrons
+            noise_std: "!DET.ron_k"
             ndit: "!OBS.ndit_k"
 
   - name: visible_binning_selector
diff --git a/ZShooter_v2/ZShooter_SPEC.yaml b/ZShooter_v2/ZShooter_SPEC.yaml
index 64eec4b9..ea802d7f 100755
--- a/ZShooter_v2/ZShooter_SPEC.yaml
+++ b/ZShooter_v2/ZShooter_SPEC.yaml
@@ -158,95 +158,30 @@ effects:
      filename: "traces/echelle_trace_parameters.dat"
 
  - name: detector_qe_selector
-   description: Throughput-only QE curves for different detectors
+   description: Physical detector QE selected by the configured detector package
    class: SelectorWheel
-   include: False
    kwargs:
      z_order: [610]
-     selector_key: "aperture_id"
+     selector_key: aperture_id
      wheel:
-       - selector_value: [0] # CCD_B, e2v UV coating, UV enhanced
-         effect_class: SpectralQuantumEfficiency
-         effect_kwargs:
-           filename: "detector_specs/ccd_uv_enhanced_TER.dat"
-       - selector_value: [1] # CCD_G, placeholder inherited from old CCD_B curve
-         effect_class: SpectralQuantumEfficiency
-         effect_kwargs:
-           filename: "detector_specs/ccd_ml_uv_enhanced_TER.dat"
-       - selector_value: [2] # CCD_R, e2v ML coating, UV enhanced
-         effect_class: SpectralQuantumEfficiency
-         effect_kwargs:
-           filename: "detector_specs/ccd_ml2_uv_enhanced_hirho_TER.dat"
-       - selector_value: [3, 4, 5] # APD_H, APD_K
-         effect_class: SpectralQuantumEfficiency
-         effect_kwargs:
-           filename: "detector_specs/APD_TER.dat"
-
- - name: detector_qe_selector
-   description: >
-     Active detector QE selector. Visible channels use detector-axis tapered
-     flattop QE coatings; NIR channels use spectral APD QE. The flattop
-     bandpass peak is 95%, with broad cosine-edged transitions to a 30% floor.
-   class: SelectorWheel
-   include: True
-   kwargs:
-     z_order: [610]
-     selector_key: "aperture_id"
-     wheel:
-       - selector_value: [0] # B channel: 310-420 nm, flat width = 100 nm
-         effect_class: TaperedQuantumEfficiency
-         effect_kwargs:
-           axis: y
-           position_unit: pix
-           position_min: 0
-           position_max: 4096
-           wave_unit: nm
-           center_wave_min: "!INST.tapered_qe_b_center_wave_min"
-           center_wave_max: "!INST.tapered_qe_b_center_wave_max"
-           flat_width: "!INST.tapered_qe_b_flat_width"
-           transition_width: "!INST.tapered_qe_b_transition_width"
-           peak: "!INST.tapered_qe_peak"
-           floor: "!INST.tapered_qe_floor"
-           clip_position: True
-           diffuse_position_samples: "!INST.tapered_qe_diffuse_position_samples"
-       - selector_value: [1] # G channel: 400-600 nm, flat width = 100 nm
-         effect_class: TaperedQuantumEfficiency
-         effect_kwargs:
-           axis: y
-           position_unit: pix
-           position_min: 0
-           position_max: 4096
-           wave_unit: nm
-           center_wave_min: "!INST.tapered_qe_g_center_wave_min"
-           center_wave_max: "!INST.tapered_qe_g_center_wave_max"
-           flat_width: "!INST.tapered_qe_g_flat_width"
-           transition_width: "!INST.tapered_qe_g_transition_width"
-           peak: "!INST.tapered_qe_peak"
-           floor: "!INST.tapered_qe_floor"
-           clip_position: True
-           diffuse_position_samples: "!INST.tapered_qe_diffuse_position_samples"
-       - selector_value: [2] # R channel: 580-980 nm, flat width = 240 nm
-         effect_class: TaperedQuantumEfficiency
-         effect_kwargs:
-           axis: y
-           position_unit: pix
-           position_min: 0
-           position_max: 4096
-           wave_unit: nm
-           center_wave_min: "!INST.tapered_qe_r_center_wave_min"
-           center_wave_max: "!INST.tapered_qe_r_center_wave_max"
-           flat_width: "!INST.tapered_qe_r_flat_width"
-           transition_width: "!INST.tapered_qe_r_transition_width"
-           peak: "!INST.tapered_qe_peak"
-           floor: "!INST.tapered_qe_floor"
-           clip_position: True
-           diffuse_position_samples: "!INST.tapered_qe_diffuse_position_samples"
-       - selector_value: [3, 4, 5] # APD_H, APD_K
-         effect_class: SpectralQuantumEfficiency
-         effect_kwargs:
-           filename: "detector_specs/APD_TER.dat"
-
-
+       - selector_value: [0]
+         effect_class: '!DET.qe_class_b'
+         effect_kwargs: '!DET.qe_kwargs_b'
+       - selector_value: [1]
+         effect_class: '!DET.qe_class_g'
+         effect_kwargs: '!DET.qe_kwargs_g'
+       - selector_value: [2]
+         effect_class: '!DET.qe_class_r'
+         effect_kwargs: '!DET.qe_kwargs_r'
+       - selector_value: [3]
+         effect_class: '!DET.qe_class_yj'
+         effect_kwargs: '!DET.qe_kwargs_yj'
+       - selector_value: [4]
+         effect_class: '!DET.qe_class_h'
+         effect_kwargs: '!DET.qe_kwargs_h'
+       - selector_value: [5]
+         effect_class: '!DET.qe_class_k'
+         effect_kwargs: '!DET.qe_kwargs_k'
 
  - name: trace_list_analytical
    description: list of spectral order trace geometry on the focal plane calculated from analytical model for Echelle
@@ -255,6 +190,9 @@ effects:
    kwargs:
      filename: "traces/echelle_trace_parameters.dat"
      physical_slit_dispersion: true
+     # Converged over the wider 18 um detector footprint as well as 15 um.
+     fit_degree: 6
+     slit_samples: 7
      wave_colname: "wavelength"
      s_colname: "s"
      col_number_start: 1
@@ -271,83 +209,38 @@ effects:
          effect_class: PostDisperserDiffuseBackground
          effect_kwargs:
            filename: "optics/LIST_zspec_vis_optics.dat"
-           detector_qe:
-             class: TaperedQuantumEfficiency
-             kwargs:
-               axis: y
-               position_unit: pix
-               position_min: 0
-               position_max: 4096
-               wave_unit: nm
-               center_wave_min: "!INST.tapered_qe_b_center_wave_min"
-               center_wave_max: "!INST.tapered_qe_b_center_wave_max"
-               flat_width: "!INST.tapered_qe_b_flat_width"
-               transition_width: "!INST.tapered_qe_b_transition_width"
-               peak: "!INST.tapered_qe_peak"
-               floor: "!INST.tapered_qe_floor"
-               clip_position: True
-               diffuse_position_samples: "!INST.tapered_qe_diffuse_position_samples"
+           detector_qe: {class: '!DET.qe_class_b', kwargs: '!DET.qe_kwargs_b'}
            wave_bin: 0.001
        - selector_value: [1] # CCD_G, placeholder inherited from old CCD_B curve
          effect_class: PostDisperserDiffuseBackground
          effect_kwargs:
            filename: "optics/LIST_zspec_vis_optics.dat"
-           detector_qe:
-             class: TaperedQuantumEfficiency
-             kwargs:
-               axis: y
-               position_unit: pix
-               position_min: 0
-               position_max: 4096
-               wave_unit: nm
-               center_wave_min: "!INST.tapered_qe_g_center_wave_min"
-               center_wave_max: "!INST.tapered_qe_g_center_wave_max"
-               flat_width: "!INST.tapered_qe_g_flat_width"
-               transition_width: "!INST.tapered_qe_g_transition_width"
-               peak: "!INST.tapered_qe_peak"
-               floor: "!INST.tapered_qe_floor"
-               clip_position: True
-               diffuse_position_samples: "!INST.tapered_qe_diffuse_position_samples"
+           detector_qe: {class: '!DET.qe_class_g', kwargs: '!DET.qe_kwargs_g'}
            wave_bin: 0.001
        - selector_value: [2] # CCD_R, e2v ML coating, UV enhanced
          effect_class: PostDisperserDiffuseBackground
          effect_kwargs:
            filename: "optics/LIST_zspec_vis_optics.dat"
-           detector_qe:
-             class: TaperedQuantumEfficiency
-             kwargs:
-               axis: y
-               position_unit: pix
-               position_min: 0
-               position_max: 4096
-               wave_unit: nm
-               center_wave_min: "!INST.tapered_qe_r_center_wave_min"
-               center_wave_max: "!INST.tapered_qe_r_center_wave_max"
-               flat_width: "!INST.tapered_qe_r_flat_width"
-               transition_width: "!INST.tapered_qe_r_transition_width"
-               peak: "!INST.tapered_qe_peak"
-               floor: "!INST.tapered_qe_floor"
-               clip_position: True
-               diffuse_position_samples: "!INST.tapered_qe_diffuse_position_samples"
+           detector_qe: {class: '!DET.qe_class_r', kwargs: '!DET.qe_kwargs_r'}
            wave_bin: 0.001
        - selector_value: [3] # APD_YJ
          effect_class: PostDisperserDiffuseBackground
          effect_kwargs:
            filename: "optics/LIST_zspec_nir_optics.dat"
-           detector_qe_filename: "detector_specs/APD_TER.dat"
+           detector_qe: {class: '!DET.qe_class_yj', kwargs: '!DET.qe_kwargs_yj'}
            downstream_throughput_filename: "optics/TER_nir_blocking_filtYJ.dat"
            wave_bin: 0.001
        - selector_value: [4] # APD_H
          effect_class: PostDisperserDiffuseBackground
          effect_kwargs:
            filename: "optics/LIST_zspec_nir_optics.dat"
-           detector_qe_filename: "detector_specs/APD_TER.dat"
+           detector_qe: {class: '!DET.qe_class_h', kwargs: '!DET.qe_kwargs_h'}
            downstream_throughput_filename: "optics/TER_nir_blocking_filtH.dat"
            wave_bin: 0.001
        - selector_value: [5] # APD_K
          effect_class: PostDisperserDiffuseBackground
          effect_kwargs:
            filename: "optics/LIST_zspec_nir_optics.dat"
-           detector_qe_filename: "detector_specs/APD_TER.dat"
+           detector_qe: {class: '!DET.qe_class_k', kwargs: '!DET.qe_kwargs_k'}
            downstream_throughput_filename: "optics/TER_nir_blocking_filtK.dat"
            wave_bin: 0.001
diff --git a/ZShooter_v2/default.yaml b/ZShooter_v2/default.yaml
index a312095a..93a4521e 100755
--- a/ZShooter_v2/default.yaml
+++ b/ZShooter_v2/default.yaml
@@ -53,6 +53,15 @@ mode_yamls:
     - ZShooter_SPEC.yaml
     - ZShooter_DET.yaml
 
+- object: instrument
+  alias: OBS
+  name: SPEC_ALTDETECT
+  description: "Untapered CCDs in B/G/R, LmAPD in YJ, 18 um H2RGs in H/K"
+  yamls:
+    - ZShooter_SPEC.yaml
+    - ZShooter_DET.yaml
+    - ZShooter_ALTDETECT.yaml
+
 ---
 ### default simulation parameters needed for a MICADO simulation
 object : simulation
diff --git a/ZShooter_v2/traces/echelle_trace_parameters.dat b/ZShooter_v2/traces/echelle_trace_parameters.dat
index 4e1cdaad..0d0a382a 100644
--- a/ZShooter_v2/traces/echelle_trace_parameters.dat
+++ b/ZShooter_v2/traces/echelle_trace_parameters.dat
@@ -34,9 +34,9 @@
 # - 2025-10-20 : Initial creation of the file with trace parameters
 #
 prefix   aperture_id   image_plane_id    m0    n    min_wave    max_wave  design_res  echelle_blaze    focal_length    dispersion_focal_length    fwhm    nominal_slit_width    plate_scale    detector_pad    pixel_size  n_disp  n_xdisp     disp_freq    xdisp_freq    slitlength    dispdir    xbeta_center    detector_angle    detector_offset_x    detector_offset_y    collimator1_focal_length    collimator2_focal_length    entrance_slit_scale
-b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       2000          10            x              21.20            -9.50    -0.679871            -3.156243    1500    1250    0.5306
-g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       1100          10            x              16.75            -7.00    -0.569446            -4.041061    1500    1250    0.5306
-r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    4096        50.150       625           10            x              14.10            -6.00    -2.867205            -5.372810    1500    1250    0.5306
-yj       3              3                80    23   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       350           10            x              10.90            -5.00    -0.441923            -3.921145    1500    1250    0.5306
-h        4              4                51    10   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       300           10            x              13.70            -6.10    -2.474361            -4.610933    1500    1250    0.5306
-k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              0.015       2048    2048        24.000       270           10            x              16.80            -6.50    -0.621554            -5.307695    1500    1250    0.5306
+b        0              0                118   34   310         430       18000       65.6               225           270                        4.46     0.7                   10.4703       10              !DET.pixel_size_b       2048    4096        50.150       2000          10            x              21.20            -9.50    -0.679871            -3.156243    1500    1250    0.5306
+g        1              1                85    30   430         650       18000       65.6               225           270                        4.46     0.7                   10.4703       10              !DET.pixel_size_g       2048    4096        50.150       1100          10            x              16.75            -7.00    -0.569446            -4.041061    1500    1250    0.5306
+r        2              2                56    20   650         980       18000       65.6               225           270                        4.46     0.7                   10.4703       10              !DET.pixel_size_r       2048    4096        50.150       625           10            x              14.10            -6.00    -2.867205            -5.372810    1500    1250    0.5306
+yj       3              3                80    23   960         1340      18000       65.6               225           270                        4.46     0.7                   10.4703       10              !DET.pixel_size_yj       2048    2048        24.000       350           10            x              10.90            -5.00    -0.441923            -3.921145    1500    1250    0.5306
+h        4              4                51    10   1470        1830      18000       65.6               225           270                        4.46     0.7                   10.4703       10              !DET.pixel_size_h       2048    2048        24.000       300           10            x              13.70            -6.10    -2.474361            -4.610933    1500    1250    0.5306
+k        5              5                38    8    1990        2450      18000       65.6               225           270                        4.46     0.7                   10.4703       10              !DET.pixel_size_k       2048    2048        24.000       270           10            x              16.80            -6.50    -0.621554            -5.307695    1500    1250    0.5306

From 9884ea7429a480dfd63672c5b6e408f4d431f921 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Tue, 20 Oct 2026 17:03:24 -0700
Subject: [PATCH 61/63] Use YJ H2RG in the editable alternate detector package

---
 ZShooter_v2/README.md               | 6 +++---
 ZShooter_v2/ZShooter_ALTDETECT.yaml | 8 ++++++++
 ZShooter_v2/default.yaml            | 2 +-
 3 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/ZShooter_v2/README.md b/ZShooter_v2/README.md
index 936a4efc..ccd2f791 100644
--- a/ZShooter_v2/README.md
+++ b/ZShooter_v2/README.md
@@ -1,7 +1,7 @@
 # ZShooter detector engineering configurations
 
-`SPEC` selects tapered qCCDs in B/G/R and 15 um LmAPDs in YJ/H/K.
-`SPEC_ALTDETECT` selects untapered CCDs in B/G/R, the same YJ LmAPD, and 18 um H2RGs in H/K. Detector dimensions remain 2048 x 4096 in B/G/R and 2048 x 2048 in YJ/H/K. Optical prescription, focal-plane offsets and selected orders are shared.
+`SPEC` loads the baseline package in `ZShooter_DET.yaml`; `SPEC_ALTDETECT` adds the editable overrides in `ZShooter_ALTDETECT.yaml`. These names select engineering configurations, not permanent detector identities. The current baseline uses tapered qCCDs in B/G/R and 15 um LmAPDs in YJ/H/K; the current alternative uses untapered CCDs and 18 um H2RGs in YJ/H/K.
+Detector dimensions remain 2048 x 4096 in B/G/R and 2048 x 2048 in YJ/H/K. Optical prescription, focal-plane offsets and selected orders are shared. Use the configured effects and output headers as the source of detector properties when comparing a revised package.
 
 Select one mode when constructing `UserCommands`, then construct an `OpticalTrain`. Reconstruct the train when changing the detector package or its initialization parameters, including pitch and QE. Exposure parameters can be supplied independently per arm through `readout(dit_blue=..., ndit_blue=..., ...)`.
 
@@ -13,4 +13,4 @@ DIT/NDIT are observing settings: no effect changes them automatically. `Exposure
 
 Native headers record `DETMODE`, `DETTYPE`, `PIXEL_SIZE` (mm), `EXNOISE` (variance factor), `AVGAIN` and `WELLDEP`, alongside actual exposure and detector WCS. Image charge stays input-referred; avalanche gain is distinct from the electronic electron/ADU conversion.
 
-Analytical traces resolve their pixel pitch from the same detector commands. Seven along-slit samples and a sixth-degree transform fit keep the common physical footprint consistent across the two detector sizes. The larger H/K area changes coverage and sampling without retuning optics or reinstating the omitted YJ order.
+Analytical traces resolve their pixel pitch from the same detector commands. Seven along-slit samples and a sixth-degree transform fit keep the common physical footprint consistent across detector pitches. The current larger YJ/H/K area changes coverage and sampling without retuning optics or reinstating the omitted YJ order.
diff --git a/ZShooter_v2/ZShooter_ALTDETECT.yaml b/ZShooter_v2/ZShooter_ALTDETECT.yaml
index defb4cf4..e856c006 100644
--- a/ZShooter_v2/ZShooter_ALTDETECT.yaml
+++ b/ZShooter_v2/ZShooter_ALTDETECT.yaml
@@ -22,6 +22,14 @@ properties:
   qe_class_r: SpectralQuantumEfficiency
   qe_kwargs_r:
     filename: detector_specs/ccd_ml2_uv_enhanced_hirho_TER.dat
+  detector_type_yj: H2RG
+  pixel_size_yj: 0.018
+  pixel_scale_yj: 0.1884654  # Same 10.4703 arcsec/mm camera plate scale
+  usable_well_yj: 60000
+  ron_yj: 6.0
+  dark_yj: 0.006388888888888889  # 23 e/hour/pixel
+  excess_noise_factor_yj: 1.0
+  avalanche_gain_yj: 1
   detector_type_h: H2RG
   pixel_size_h: 0.018
   pixel_scale_h: 0.1884654  # Same 10.4703 arcsec/mm camera plate scale
diff --git a/ZShooter_v2/default.yaml b/ZShooter_v2/default.yaml
index 93a4521e..b91ca426 100755
--- a/ZShooter_v2/default.yaml
+++ b/ZShooter_v2/default.yaml
@@ -56,7 +56,7 @@ mode_yamls:
 - object: instrument
   alias: OBS
   name: SPEC_ALTDETECT
-  description: "Untapered CCDs in B/G/R, LmAPD in YJ, 18 um H2RGs in H/K"
+  description: "Editable alternative detector engineering package"
   yamls:
     - ZShooter_SPEC.yaml
     - ZShooter_DET.yaml

From 8be3e2c9fd394c7fa375fb472962475ae63aa705 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Tue, 20 Oct 2026 23:38:31 -0700
Subject: [PATCH 62/63] Write MOSAIC instrument identity to readout primary
 headers

---
 MOSAIC/default.yaml | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/MOSAIC/default.yaml b/MOSAIC/default.yaml
index 2d1c32c5..e56bc0b3 100644
--- a/MOSAIC/default.yaml
+++ b/MOSAIC/default.yaml
@@ -267,3 +267,13 @@ description: instrument parameters common to all MOSAIC modes
 
 properties:
   decouple_detector_from_sky_headers: True
+
+effects:
+  - name: mosaic_fits_keywords
+    description: Instrument identity for all MOSAIC output formats
+    class: ExtraFitsKeywords
+    kwargs:
+      header_dict:
+        - ext_type: PrimaryHDU
+          keywords:
+            INSTRUME: MOSAIC

From e332b9e63d34ea3c379fe82f1738b37ad04ee819 Mon Sep 17 00:00:00 2001
From: Jeb Bailey <jibailey@caltech.edu>
Date: Wed, 21 Oct 2026 20:11:59 -0700
Subject: [PATCH 63/63] Align ZImager dark statistics and exposure warnings
 with shared readout effects

---
 ZShooter_v2/ZShooter_IMDET.yaml         | 62 ++++++++++++++++++-------
 ZShooter_v2/dichroics/make_dichroics.py |  2 +-
 2 files changed, 46 insertions(+), 18 deletions(-)

diff --git a/ZShooter_v2/ZShooter_IMDET.yaml b/ZShooter_v2/ZShooter_IMDET.yaml
index 55324a57..d1971d02 100644
--- a/ZShooter_v2/ZShooter_IMDET.yaml
+++ b/ZShooter_v2/ZShooter_IMDET.yaml
@@ -9,6 +9,9 @@ properties :
     binx : 1
     biny: 1
     full_well: 7000
+    dark_current: 0.006  # electron/s/pixel
+    read_noise: 0.3     # electron RMS/integration
+    bias: 200          # electron
     pixel_scale_u: 0.0511  #"/pix
     pixel_scale_v: 0.0511
     pixel_scale_z: 0.0511
@@ -33,6 +36,9 @@ effects :
           pixel_size: [0.0046]
           angle: [0.0]
           gain: [1.0]
+          bunit: [electron]
+          detmode: [IMAGER]
+          dettype: [CMOS]
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -58,6 +64,9 @@ effects :
           pixel_size: [0.0046]
           angle: [0.0]
           gain: [1.0]
+          bunit: [electron]
+          detmode: [IMAGER]
+          dettype: [CMOS]
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -83,6 +92,9 @@ effects :
           pixel_size: [0.0046]
           angle: [0.0]
           gain: [1.0]
+          bunit: [electron]
+          detmode: [IMAGER]
+          dettype: [CMOS]
         x_cen_unit: mm
         y_cen_unit: mm
         x_size_unit: pixel
@@ -116,10 +128,6 @@ effects :
             ndit: "!OBS.imager_ndit_z"
 
 
-  - name: shot_noise
-    description : apply poisson shot noise to images
-    class: ShotNoise
-
   - name: dark_current_selector
     description: Selector Wheel for dark current levels for CMOS
     class: SelectorWheel
@@ -130,22 +138,42 @@ effects :
         - selector_value: [0]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.006    # [e-/s] level of dark current
+            value: "!DET.dark_current"
             dit: "!OBS.imager_dit_u"
             ndit: "!OBS.imager_ndit_u"
         - selector_value: [1]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.006    # [e-/s] level of dark current
+            value: "!DET.dark_current"
             dit: "!OBS.imager_dit_v"
             ndit: "!OBS.imager_ndit_v"
         - selector_value: [2]
           effect_class: DarkCurrent
           effect_kwargs:
-            value: 0.006     # [e-/s] level of dark current
+            value: "!DET.dark_current"
             dit: "!OBS.imager_dit_z"
             ndit: "!OBS.imager_ndit_z"
 
+  - name: exposure_limit
+    description: Warn about per-integration charge without changing DIT/NDIT
+    class: SelectorWheel
+    kwargs:
+      selector_key: detector_id
+      wheel:
+        - selector_value: [0]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.full_well', ndit: '!OBS.imager_ndit_u'}
+        - selector_value: [1]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.full_well', ndit: '!OBS.imager_ndit_v'}
+        - selector_value: [2]
+          effect_class: ExposureLimit
+          effect_kwargs: {usable_well: '!DET.full_well', ndit: '!OBS.imager_ndit_z'}
+
+  - name: shot_noise
+    description: Poisson statistics of source, sky and accumulated dark charge
+    class: ShotNoise
+
   - name: detector_linearity_selector
     description: Selector Wheel for linearity characteristics for CMOS
     class: SelectorWheel
@@ -157,22 +185,22 @@ effects :
           effect_class: LinearityCurve
           effect_kwargs:
             array_dict:
-              incident: [0, 7e3]
-              measured: [0, 7e3]
+              incident: [0, "!DET.full_well"]
+              measured: [0, "!DET.full_well"]
             ndit: "!OBS.imager_ndit_u"
         - selector_value: [1]
           effect_class: LinearityCurve
           effect_kwargs:
             array_dict:
-              incident: [0, 7e3]
-              measured: [0, 7e3]
+              incident: [0, "!DET.full_well"]
+              measured: [0, "!DET.full_well"]
             ndit: "!OBS.imager_ndit_v"
         - selector_value: [2]
           effect_class: LinearityCurve
           effect_kwargs:
             array_dict:
-              incident: [0, 7e3]
-              measured: [0, 7e3]
+              incident: [0, "!DET.full_well"]
+              measured: [0, "!DET.full_well"]
             ndit: "!OBS.imager_ndit_z"
 
   - name: readout_noise_selector
@@ -185,17 +213,17 @@ effects :
         - selector_value: [0]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.3
+            noise_std: "!DET.read_noise"
             ndit: "!OBS.imager_ndit_u"
         - selector_value: [1]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.3
+            noise_std: "!DET.read_noise"
             ndit: "!OBS.imager_ndit_v"
         - selector_value: [2]
           effect_class: BasicReadoutNoise
           effect_kwargs:
-            noise_std: 0.3
+            noise_std: "!DET.read_noise"
             ndit: "!OBS.imager_ndit_z"
 
   - name: bias_selector
@@ -208,4 +236,4 @@ effects :
         - selector_value: [0,1,2]
           effect_class: Bias
           effect_kwargs:
-            bias: 200    #  bias level in electrons
+            bias: "!DET.bias"
diff --git a/ZShooter_v2/dichroics/make_dichroics.py b/ZShooter_v2/dichroics/make_dichroics.py
index 635eb059..8e68d266 100644
--- a/ZShooter_v2/dichroics/make_dichroics.py
+++ b/ZShooter_v2/dichroics/make_dichroics.py
@@ -96,7 +96,7 @@ def split(name: str,
         "yj-hk": split("yj-hk", ("yj",), ("h", "k")),
         "g-r": split("g-r", ("g",), ("r",)),
         "h-k": split("h-k", ("h",), ("k",)),
-        "u-vz": split("b-gr", ("u",), ("v", "z")),
+        "u-vz": split("u-vz", ("u",), ("v", "z")),
         "v-z": split("v-z", ("v",), ("z",)),
     }