Use widgets for plots and scopesimple interface for examples

docs/source/examples/1_scopesim_intro.ipynb

@@ -27,9 +27,9 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
+    "%matplotlib widget\n",
     "\n",
-    "import scopesim as sim\n",
+    "from scopesim import Simulation\n",
     "from scopesim_templates.stellar.clusters import cluster"
    ]
   },
@@ -38,7 +38,7 @@
    "id": "heard-motel",
    "metadata": {},
    "source": [
-    "Now, create a star cluster as the source using the ``scopesim_templates`` package. You can ignore the output that is sometimes printed. The `seed` argument is used to control the random number generation that creates the stars in the cluster. If this number is kept the same, the output will be consistent with each run, otherwise the position and brightness of the stars is randomised every time."
+    "Now, create a star cluster as the source using the `scopesim_templates` package. You can ignore the output that is sometimes printed. The `seed` argument is used to control the random number generation that creates the stars in the cluster. If this number is kept the same, the output will be consistent with each run, otherwise the position and brightness of the stars is randomised every time."
    ]
   },
   {
@@ -61,9 +61,9 @@
    "id": "finite-linux",
    "metadata": {},
    "source": [
-    "Next, make the MICADO optical system model with ``OpticalTrain``. Observe the cluster ``Source`` object with the ``.observe()`` method and read out the MICADO detectors with ``.readout()``. This may take a few moments on slower machines.\n",
+    "Next, create the MICADO optical system model with the new simplified `Simulation` interface. Observe the cluster `Source` object simply by calling the simulation instance, which returns the resulting FITS HDUL. This may take a few moments on slower machines.\n",
     "\n",
-    "The resulting FITS file can either be returned as an ``astropy.fits.HDUList`` object, or saved to disk using the optional ``filename`` parameter."
+    "The resulting FITS file can either be returned as an `astropy.fits.HDUList` object, which can be saved to disk using the `writeto` method."
    ]
   },
   {
@@ -73,36 +73,35 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "micado = sim.OpticalTrain(\"MICADO\")\n",
-    "micado.observe(source)\n",
-    "hdul = micado.readout()\n",
-    "# micado.readout(filename=\"TEST.fits\")"
+    "simulation = Simulation(\"MICADO\", [\"SCAO\", \"IMG_4mas\"])\n",
+    "hdul = simulation(source)\n",
+    "# hdul.writeto(\"TEST.fits\")"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "latest-ranking",
    "metadata": {},
    "source": [
-    "Display the contents the first HDU"
+    "Plot the results of the simulation. The `fig_kwargs` are passed to matplotlib's figure creation."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "undefined-flush",
+   "id": "a4a2e783-8459-4be9-87dd-77b8625446e1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(10,8))\n",
-    "plt.imshow(hdul[0][1].data, norm=\"log\", vmin=3e3, vmax=3e4, cmap=\"hot\")\n",
-    "plt.colorbar();"
+    "simulation.plot(norm=\"log\", vmin=3e3, vmax=3e4, cmap=\"hot\",\n",
+    "                fig_kwargs={\"figsize\": (8, 8), \"layout\": \"tight\"})\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a4a2e783-8459-4be9-87dd-77b8625446e1",
+   "id": "02cd19cd-f99f-4800-9212-5655c1fbdaca",
    "metadata": {},
    "outputs": [],
    "source": []

docs/source/examples/2_multiple_telescopes.ipynb

@@ -20,9 +20,9 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
+    "%matplotlib widget\n",
     "\n",
-    "import scopesim as sim\n",
+    "from scopesim import Simulation\n",
     "from scopesim_templates.stellar.clusters import cluster"
    ]
   },
@@ -70,11 +70,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "lfoa = sim.OpticalTrain(\"LFOA\")\n",
-    "lfoa.observe(source,\n",
-    "             properties={\"!OBS.ndit\": 10, \"!OBS.ndit\": 360},\n",
-    "             update=True)\n",
-    "data_lfoa = lfoa.readout()[0][1].data"
+    "lfoa = Simulation(\"LFOA\")\n",
+    "data_lfoa = lfoa(source, dit=10, ndit=360)[1].data"
    ]
   },
   {
@@ -94,17 +91,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "micado = sim.OpticalTrain(\"MICADO\")\n",
+    "micado = Simulation(\"MICADO\")\n",
     "# Use the central detector of the full MICADO array\n",
-    "micado[\"detector_window\"].include = False\n",
-    "micado[\"full_detector_array\"].include = True\n",
-    "micado[\"full_detector_array\"].meta[\"active_detectors\"] = [5]\n",
-    "micado.cmds[\"!OBS.dit\"] = 10\n",
-    "micado.cmds[\"!OBS.ndit\"] = 360\n",
-    "micado.update()\n",
-    "\n",
-    "micado.observe(source)\n",
-    "data_micado = micado.readout()[0][1].data"
+    "micado.optical_train[\"detector_window\"].include = False\n",
+    "micado.optical_train[\"full_detector_array\"].include = True\n",
+    "micado.optical_train[\"full_detector_array\"].meta[\"active_detectors\"] = [5]\n",
+    "\n",
+    "data_micado = micado(source, dit=10, ndit=360)[1].data"
    ]
   },
   {
@@ -152,8 +145,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pixel_scale_lfoa = lfoa.cmds[\"!INST.pixel_scale\"]\n",
-    "pixel_scale_micado = micado.cmds[\"!INST.pixel_scale\"]\n",
+    "pixel_scale_lfoa = lfoa.settings[\"!INST.pixel_scale\"]\n",
+    "pixel_scale_micado = micado.settings[\"!INST.pixel_scale\"]\n",
     "scale_factor = pixel_scale_lfoa / pixel_scale_micado\n",
     "\n",
     "size_lfoa_x = data_micado.shape[0] / scale_factor /2\n",
@@ -191,7 +184,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2cfbf286-fed2-4c26-a09c-f637ac35fb7d",
+   "id": "e4c30619-8205-49f9-94d6-97a3e1e3f40c",
    "metadata": {},
    "outputs": [],
    "source": []

docs/source/examples/3_custom_effects.ipynb

@@ -26,11 +26,31 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "%matplotlib widget\n",
     "\n",
     "import scopesim as sim\n",
     "from scopesim_templates.stellar import stars, star_grid"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5b93a241-6994-4308-a940-03c9470e0300",
+   "metadata": {},
+   "source": [
+    "In this examples, we will use the \"advanced\" interface of ScopeSim, which involves multiple steps to create and run the simulation, but allows for more (and easier) cutomisation of the optical train model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdeee262-51f5-4522-8226-0f84bea9b8f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IMG_1.5mas\"])\n",
+    "micado = sim.OpticalTrain(cmd)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "loving-skill",
@@ -42,13 +62,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "celtic-fluid",
+   "id": "98032a6d-b720-4db2-90a6-e4a530c71cb0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IMG_1.5mas\"])\n",
-    "micado = sim.OpticalTrain(cmd)\n",
-    "\n",
     "micado.effects"
    ]
   },

pyproject.toml

@@ -46,6 +46,7 @@ optional = true
 jupyter = "^1.0"
 jupytext = "^1.10.0"
 ipykernel = "^6.24.0"
+ipympl = "^0.9.4"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^7.4.3"
@@ -67,6 +68,7 @@ nbsphinx = "^0.9.3"
 numpydoc = "^1.6.0"
 scopesim_templates = ">=0.6.0"
 ipykernel = "^6.24.0"
+ipympl = "^0.9.4"
 
 [tool.poetry.urls]
 "Bug Tracker" = "https://github.com/AstarVienna/ScopeSim/issues"