Notebooks/FullExample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Science with `deeplenstronomy`!\n",
    "\n",
    "The notebooks up until this point have demonstrated the features of `deeplenstronomy` at their most basic level. This notebook will show how I would design a science-ready training set by putting some of those features to use.\n",
    "\n",
    "We'll be going through the configuration file `example.yaml`, which simulates a 4-class dataset consisting of galaxy-galay lensing, galaxy-galaxy lensing where the lens is a real DES galaxy, lensed quasars, and a galaxy by itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import deeplenstronomy.deeplenstronomy as dl\n",
    "from deeplenstronomy.visualize import view_image, view_image_rgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Level Dataset Properties\n",
    "\n",
    "Like in the other examples, we start by selecting the size of our dataset and telling `deeplenstronomy` where to save the images at the end of the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# A full example of using deeplenstronomy.\r\n",
      "# The science goal here will be the detection of galaxy-galaxy lensing and lensed quasars.\r\n",
      "# This file will contain all the information needed to describe the dataset.\r\n",
      "\r\n",
      "## We start at the top level with the DATASET section\r\n",
      "\r\n",
      "DATASET:\r\n",
      "    NAME: ExampleDataset # set a name, this value is only used if you request the h5 file format\r\n",
      "    PARAMETERS:\r\n",
      "        SIZE: 1000 # number of images in the full dataset, I'll keep it small for this example\r\n",
      "        OUTDIR: ExampleDataset # will be created on your system if your request to save images\r\n"
     ]
    }
   ],
   "source": [
    "! head -11 data/example.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next up is the cosmology section. Here I could also specify $T_{cmb}$, $N_{eff}$, and other parameters of the `astropy.cosmology.FlatLambdaCDM` class, but we can get away with just specifying $H_0$ and $\\Omega_m$ since that is technically all that is needed for the cosmology calculation performed internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Next we'll specify the COSMOLOGY to use, I'll keep it simple a just use basic values\r\n",
      "\r\n",
      "COSMOLOGY:\r\n",
      "    PARAMETERS:\r\n",
      "        H0: 74.0\r\n",
      "        Om0: 0.3\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -19 data/example.yaml | tail -7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Camera and Observing Conditions\n",
    "\n",
    "Now normally we would write the `IMAGE` and `SURVEY` sections next to describe the camera and observing conditions, but I will make use of `deeplenstronomy`'s built-in surveys functionality and just pass `survey='des'` when calling the `deeplenstronomy.make_dataset()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "## Next we technically need the IMAGE and SURVEY sections, but I will get around this using a built-in survey mode\r\n",
      "## by specifying survey='des' in deeplenstronomy.make_dataset(), these sections will be automatically written\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -22 data/example.yaml | tail -4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describing the Objects to Simulate\n",
    "\n",
    "Time to describe the objects we want to put in our dataset. Onward to the `SPECIES` section!\n",
    "\n",
    "I'll start with a galaxy. It's name will be \"SimulatedLens\" and I'll use this name to refer to it later on. I'll add to this galaxy a \"Light Profile\" to describe how it's light will appear and a \"Mass Profile\" to tell `deeplenstronomy` how it will affect the light of objects behind it.\n",
    "\n",
    "For the Light Profile, I'm choosing to call the `lenstronomy` \"SERSIC_ELLIPSE\" model, then I set its parameters:\n",
    "\n",
    "- I'll be giving this galaxy realistic color later on, so I'm going to put a fake value in for the `magnitude`\n",
    "- `center_x` and `center_y` are set to 0 so that this object is always in the exact center of the image;\n",
    "- The 4 other parameters are all drawn from distributions of physically-motivated values to give the simulated galaxies diversity\n",
    "\n",
    "For the Mass Profile, I'm choosing to call the `lenstronomy` \"SIE\" model, then I set its parameters:\n",
    "\n",
    "- I draw `theta_E`, `e1`, and `e2` from realistic distributions;\n",
    "- `center_x` and `center_y` are set to 0 for the same reason as the Light Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPECIES:\r\n",
      "    GALAXY_1:  # This will be the first type of lens galaxy, and it's properties will by fully simulated\r\n",
      "        NAME: SimulatedLens # name of this galaxy object\r\n",
      "        LIGHT_PROFILE_1: # we'll describe how the light of this galaxy looks\r\n",
      "            NAME: SERSIC_ELLIPSE # a good default lenstronomy light model\r\n",
      "            PARAMETERS:\r\n",
      "                magnitude: -5 # I'm setting this to a non-real value because I will overwrite it with a USERDIST later\r\n",
      "                center_x: 0.0 # set the lens galaxy to be at the center of the images\r\n",
      "                center_y: 0.0\r\n",
      "                R_sersic: # controls the angular size of the galaxy\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: normal  # use a normal distribution around 6\r\n",
      "                        PARAMETERS:\r\n",
      "                            mean: 6.0\r\n",
      "                            std: 1.5\r\n",
      "                n_sersic: # controls the slope of the light profile\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: normal  # use a normal distribution around 4\r\n",
      "                        PARAMETERS:\r\n",
      "                            mean: 4.0\r\n",
      "                            std: 0.8\r\n",
      "                e1: # controls half of the eccentricity of the galaxy\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -0.3\r\n",
      "                            maximum: 0.3\r\n",
      "                e2: # controls other half of the eccentricity of the galaxy\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -0.3\r\n",
      "                            maximum: 0.3\r\n",
      "\r\n",
      "        MASS_PROFILE_1: # now let's describe how the mass will be distributed in this galaxy\r\n",
      "            NAME: SIE # a good default lenstronomy mass model\r\n",
      "            PARAMETERS:\r\n",
      "                theta_E: # the Einstein radius of the lensing\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution from 0.5 to 10\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.5\r\n",
      "                            maximum: 3\r\n",
      "                e1: # controls half of the eccentricity of the galaxy's mass distribution\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -0.3\r\n",
      "                            maximum: 0.3\r\n",
      "                e2: # controls other half of the eccentricity of the galaxy's mass distribution\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -0.3\r\n",
      "                            maximum: 0.3\r\n",
      "                center_x: 0.0 # center the mass distribution since the light was centered as well\r\n",
      "                center_y: 0.0 \r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -83 data/example.yaml | tail -58"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I'll write the properties for the galaxy I plan to use as a source galaxy in the gravitational lensing systems. I'll name it \"SimulatedSource\". I'll use a similar Light Profile, and I can get away without a Mass Profile because the source galaxy's mass will not affect the lensing.\n",
    "\n",
    "The parameters will be defined in a similar way to the Light Profile of SimulatedLens, but this time I will draw `center_x` and `center_y` from narrow distributions to account for not-exactly-alligned gravitational lensing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    GALAXY_2: # now we'll do a source galaxy, and no need for a mass profile since only the light matters in the lensing\r\n",
      "        NAME: SimulatedSource\r\n",
      "        LIGHT_PROFILE_1:\r\n",
      "            NAME: SERSIC_ELLIPSE\r\n",
      "            PARAMETERS:\r\n",
      "                magnitude: -5 # again, we'll overwrite this value with a USERDIST\r\n",
      "                center_x:\r\n",
      "                    DISTRIBUTION: #draw source position from a distribution to allow for small misalignments\r\n",
      "                        NAME: normal\r\n",
      "                        PARAMETERS:\r\n",
      "                            mean: 0.0\r\n",
      "                            std: 0.5\r\n",
      "                center_y:\r\n",
      "                    DISTRIBUTION: #draw source position from a distribution to allow for small misalignments\r\n",
      "                        NAME: normal\r\n",
      "                        PARAMETERS:\r\n",
      "                            mean: 0.0\r\n",
      "                            std: 0.5\r\n",
      "                R_sersic: # controls the angular size of the galaxy\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: normal  # use a normal distribution around 6\r\n",
      "                        PARAMETERS:\r\n",
      "                            mean: 6.0\r\n",
      "                            std: 1.5\r\n",
      "                n_sersic: # controls the slope of the light profile\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: normal  # use a normal distribution around 4\r\n",
      "                        PARAMETERS:\r\n",
      "                            mean: 4.0\r\n",
      "                            std: 0.8\r\n",
      "                e1: # controls half of the eccentricity of the galaxy\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -0.3\r\n",
      "                            maximum: 0.3\r\n",
      "                e2: # controls other half of the eccentricity of the galaxy\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -1.0\r\n",
      "                            maximum: 1.0\r\n"
     ]
    }
   ],
   "source": [
    "! head -126 data/example.yaml | tail -42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To finish off the galaxies I'll define in this configuration file, I will make a third galaxy called \"DarkLens\". The reason for the name is I am going to simulate **no light at all** coming from this galaxy, and then I am going to use a real DES image of a lens galaxy in its place.\n",
    "\n",
    "The Light Profile here is very simple because I'm intending it to be meaningless. I set the magnitude to 100 so that the light will be several dozens of orders of magnitude too faint to detect, and then I add values to all the other parameters to satisfy both `deeplenstronomy` and `lenstronomy`.\n",
    "\n",
    "The Mass Profile here is important. To make the training dataset realistic, I want to make sure the the real image of a galaxy that I use has its properties encoded into the lensing calculations. Specifically, I will use the measured velocity dispersion of the DES galaxy in each image in the simulation. Thus, I am specifying `sigma_v` instead of `theta_E` in this case.\n",
    "\n",
    "- It's important when using `deeplenstronomy`'s `BACKGROUNDS` functionality to draw the parameters you are associating with the images from distributions. Internally, this is not a straightforward overwriting. Rather, the distribution is sampled first, and then the image with the closest velocity dispersion to each drawn value is utilized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    GALAXY_3: # I'll use this galaxy as a completely dark object so that a real image of a lens galaxy can be used\r\n",
      "        NAME: DarkLens\r\n",
      "        LIGHT_PROFILE_1:\r\n",
      "            NAME: SERSIC_ELLIPSE\r\n",
      "            PARAMETERS:\r\n",
      "                magnitude: 100 # so that the light is essentially zero\r\n",
      "                center_x: 0.0 # doesn't matter, just need a valid number\r\n",
      "                center_y: 0.0 # doesn't matter, just need a valid number\r\n",
      "                R_sersic: 1.0 # doesn't matter, just need a valid number\r\n",
      "                n_sersic: 1.0 # doesn't matter, just need a valid number\r\n",
      "                e1: 0.5 # doesn't matter, just need a valid number\r\n",
      "                e2: 0.5 # doesn't matter, just need a valid number\r\n",
      "        MASS_PROFILE_1:\r\n",
      "            NAME: SIE\r\n",
      "            PARAMETERS:\r\n",
      "                sigma_v: # velocity dispersion of the lens galaxy, this will be linked to the lens galaxy image used\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 150\r\n",
      "                            maximum: 400\r\n",
      "                e1: # controls half of the eccentricity of the galaxy's mass distribution\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -0.3\r\n",
      "                            maximum: 0.3\r\n",
      "                e2: # controls other half of the eccentricity of the galaxy's mass distribution\r\n",
      "                    DISTRIBUTION: # draw from a distribution to give the dataset diversity\r\n",
      "                        NAME: uniform # use a uniform distribution on (-1,1)\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: -0.3\r\n",
      "                            maximum: 0.3\r\n",
      "                center_x: 0.0 # center the mass distribution since the light was centered as well\r\n",
      "                center_y: 0.0 \r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -163 data/example.yaml | tail -36"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Closing out the `SPECIES` section, I'll include a quasar so that we can simulated lensed quasars along with the other cases of galaxy-galaxy lensing.\n",
    "\n",
    "I'll name this object \"Quasar\" and specify that I want the `POINTSOURCE` to be located within the galaxy that I named SimulatedSource earlier. Since a quasar is always at the center of a galaxy, I will leave off the `sep`, `sep_unit` and `angle` parameters, in which case `deeplenstronomy` will place the `POINTSOURCE` at the center of its `HOST`. Lastly, I'll plan to overwrite the magnitude with a realistic, color-dependent, distribution using `deeplenstronomy`'s `USERDIST` functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    \r\n",
      "    POINTSOURCE_1: # add a point source for a quasar\r\n",
      "        NAME: Quasar\r\n",
      "        HOST: SimulatedSource # put the quasar in the simulated source galaxy\r\n",
      "        PARAMETERS: # no need for sep, angle, or sep_unit parameters since the point source will be at the center of the host\r\n",
      "            magnitude: -5 # We'll overwrite this with a realistic quasar color distribution as a USERDIST\r\n",
      "        \r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -171 data/example.yaml | tail -8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object Placements\n",
    "\n",
    "Now I'll fill in the `GEOMETRY` section to tell `deeplenstronomy` how I want the objects oriented. Let's start with a straightforward galaxy-galaxy lensing system. Each system I choose to create will be called a `CONFIGURATION`. I've named this one \"GalaxyGalaxySimulated\" so I can keep track that I'm doing galaxy-galaxy lensing and using all simulated light. I also specify `FRACTION: 0.25` so that this type of system will make up 1/4 of the total images that get produced.\n",
    "\n",
    "Because I want to put one galaxy behind another one, I need two `PLANE`s, in each plane I write the name of the objects I want and I set the redshift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Lastly, we'll specify how we want the GEOMETRY of the systems simulated\r\n",
      "\r\n",
      "GEOMETRY:\r\n",
      "    CONFIGURATION_1: \r\n",
      "        NAME: GalaxyGalaxySimulated # describe what's in this configuration\r\n",
      "        FRACTION: 0.25 # images from this configuration will make up 1/4 of the dataset\r\n",
      "        PLANE_1: # add all objects in the first plane\r\n",
      "            OBJECT_1: SimulatedLens # name of the object to put in this plane\r\n",
      "            PARAMETERS:\r\n",
      "                REDSHIFT:\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.05\r\n",
      "                            maximum: 0.3\r\n",
      "        PLANE_2: # add all objects in the second plane\r\n",
      "            OBJECT_1: SimulatedSource\r\n",
      "            PARAMETERS:\r\n",
      "                REDSHIFT:\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.5\r\n",
      "                            maximum: 1.0\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -228 data/example.yaml | tail -25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let's put in a second configuration where I keep everything the same, except I'll switch out \"SimulatedLens\" for \"DarkLens\". This will be the configuration where I put in the real DES images later on. This one I'll name \"GalaxyGalaxyReal\" to indicate the use of real images while still doing galax-galaxy lensing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    CONFIGURATION_2:\r\n",
      "        NAME: GalaxyGalaxyReal # describe what's in this configuration\r\n",
      "        FRACTION: 0.25 # images from this configuration will make up 1/4 of the dataset\r\n",
      "        PLANE_1: # add all objects in the first plane\r\n",
      "            OBJECT_1: DarkLens # name of the object to put in this plane\r\n",
      "            PARAMETERS:\r\n",
      "                REDSHIFT:\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.05\r\n",
      "                            maximum: 0.3\r\n",
      "        PLANE_2: # add all objects in the second plane\r\n",
      "            OBJECT_1: SimulatedSource\r\n",
      "            PARAMETERS:\r\n",
      "                REDSHIFT:\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.5\r\n",
      "                            maximum: 1.0\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -250 data/example.yaml | tail -22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let's make the dataset a little more fun and put in a lensed quasar system. I'll call this configuration \"GalaxyQuasarSimulated\". Everything is the same as `CONFIGURATION_1`, but I have added `OBJECT_2: Quasar` to `PLANE_2`. This line tells `deeplenstronomy` to include the object in it the `SPECIES` section named \"Quasar\". Recall that \"Quasar\" has a `HOST` of \"SimulatedSource\", so `deeplenstronomy` is already prepared to put this object in that galaxy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    CONFIGURATION_3:\r\n",
      "        NAME: GalaxyQuasarSimulated # describe what's in this configuration\r\n",
      "        FRACTION: 0.25 # images from this configuration will make up 1/4 of the dataset\r\n",
      "        PLANE_1: # add all objects in the first plane\r\n",
      "            OBJECT_1: SimulatedLens # name of the object to put in this plane\r\n",
      "            PARAMETERS:\r\n",
      "                REDSHIFT:\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.05\r\n",
      "                            maximum: 0.3\r\n",
      "        PLANE_2: # add all objects in the second plane\r\n",
      "            OBJECT_1: SimulatedSource\r\n",
      "            OBJECT_2: Quasar # now we add the quasar on top of the source galaxy\r\n",
      "            PARAMETERS:\r\n",
      "                REDSHIFT:\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.5\r\n",
      "                            maximum: 1.0\r\n"
     ]
    }
   ],
   "source": [
    "! head -272 data/example.yaml | tail -22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, I'll include a configuration where I only simulate a single galaxy. The motivation here is to simulate a background class for a neural network to train on. Presumably the largest background you'll face will be individual galaxies, but you can get as detailed as you want. To make \"JustAGalaxy\" I will copy `CONFIGURATION_1`, but I will remove the second plane that contained the background galaxy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    CONFIGURATION_4:\r\n",
      "        NAME: JustAGalaxy # describe what's in this configuration\r\n",
      "        FRACTION: 0.25 # images from this configuration will make up 1/4 of the dataset\r\n",
      "        PLANE_1: # add all objects in the first plane\r\n",
      "            OBJECT_1: SimulatedLens # name of the object to put in this plane\r\n",
      "            PARAMETERS:\r\n",
      "                REDSHIFT:\r\n",
      "                    DISTRIBUTION:\r\n",
      "                        NAME: uniform\r\n",
      "                        PARAMETERS:\r\n",
      "                            minimum: 0.05\r\n",
      "                            maximum: 0.3\r\n"
     ]
    }
   ],
   "source": [
    "! head -285 data/example.yaml | tail -12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Including the real galaxy images\n",
    "\n",
    "Recall that in `CONFIGURATION_2` I've utilized the object we aptly named \"DarkLens\" and that we effectively gave this object zero apparent luminosity. The reasoning here is that we wanted to have a mass profile, but utilize the light from a real image. To do that, I'll use the `BACKGROUNDS` feature of `deeplenstronomy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Now we'll point toward the background images we want to use, which contain DES galaxies at z < 0.3\r\n",
      "\r\n",
      "BACKGROUNDS: \r\n",
      "    PATH: data/example_background_images\r\n",
      "    CONFIGURATIONS: ['CONFIGURATION_2']\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -203 data/example.yaml | tail -6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `CONFIGURATIONS` argument is a list containing only `CONFIGURATION_2` which specifies that I only want these background images to be utilized for `CONFIGURATION_2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y.fits  g.fits  i.fits  map.txt r.fits  z.fits\r\n"
     ]
    }
   ],
   "source": [
    "! ls data/example_background_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've supplied a directory `example_background_images` of 46 DES images of galaxies (and the galaxies also overlap with SDSS) split into each band that I'm simulating in this dataset. I also give a `map.txt` file, which tells `deeplenstronomy` the properties of the galaxies in the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION_2-PLANE_1-OBJECT_1-MASS_PROFILE_1-sigma_v-g CONFIGURATION_2-PLANE_1-OBJECT_1-MASS_PROFILE_1-sigma_v-r CONFIGURATION_2-PLANE_1-OBJECT_1-MASS_PROFILE_1-sigma_v-i CONFIGURATION_2-PLANE_1-OBJECT_1-MASS_PROFILE_1-sigma_v-z CONFIGURATION_2-PLANE_1-OBJECT_1-MASS_PROFILE_1-sigma_v-Y\r\n",
      "196.737 196.737 196.737 196.737 196.737\r\n",
      "255.839 255.839 255.839 255.839 255.839\r\n",
      "211.848 211.848 211.848 211.848 211.848\r\n",
      "241.311 241.311 241.311 241.311 241.311\r\n",
      "193.026 193.026 193.026 193.026 193.026\r\n",
      "241.299 241.299 241.299 241.299 241.299\r\n",
      "183.588 183.588 183.588 183.588 183.588\r\n",
      "271.999 271.999 271.999 271.999 271.999\r\n",
      "235.621 235.621 235.621 235.621 235.621\r\n"
     ]
    }
   ],
   "source": [
    "! head data/example_background_images/map.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of the galaxies here have a measure velocity dispersion, so in the `map.txt` file I am telling `deeplenstronomy` about the velocity dispersion of each galaxy (each galaxy is one row and the rows are aligned with the index of the image in the FITS files). \n",
    "\n",
    "Then, with the header row, I am telling `deeplenstronomy` which properties these velocity dispersions map to in the simualtions. Since the configuration with \"DarkLens\" is `CONFIGURATION_2` and \"DarkLens\" is `OBJECT_1` in `PLANE_1`, and since the velocity dispersion is characterized by the `sigma_v` parameter of \"DarkLens\"'s `MASS_PROFILE_1`, we can create a header string of:\n",
    "\n",
    "`CONFIGURATION_2-PLANE_1-OBJECT_1-MASS_PROFILE_1-sigma_v-`\n",
    "\n",
    "and finally since the velocity dispersion is not a color-dependent quantity, we make the velocity dispersion the same for each band."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Including Realistic Object Colors\n",
    "\n",
    "In the same vein of simulating a dark galaxy knowing we would include real images, recall that we also left flag values of -5 in the `LIGHT_PROFILE_1-magnitude` parameter for \"SimulatedLens\", \"SimulatedSource\", and \"Quasar\". In this section we will finally give these objects physically meaningful brightnesses and colors.\n",
    "\n",
    "I'll do this by utilizing `USERDIST`s. In total there are 7 `USERDIST`s that I am utilizing. For each one I specify it's filename and the mode I will be using. In this case I've chosen to use the `sample` mode, which draws from the raw points in the file rather than interpolate a grid of points. This mode is **much** more efficient when correlating more than a couple parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DISTRIBUTIONS:\r\n",
      "    USERDIST_1:\r\n",
      "        FILENAME: data/low_z_galaxy_colors_config_1.txt  # contains redshift and g,r,i,z,y mag for DES galaxies at z < 0.3\r\n",
      "        MODE: sample # sample from all the rows specified in the file\r\n",
      "    USERDIST_2:\r\n",
      "        FILENAME: data/low_z_galaxy_colors_config_3.txt  # redshift and g,r,i,z,y mag for DES galaxies at z<0.3\r\n",
      "        MODE: sample # sample from all the rows specified in the file\r\n",
      "    USERDIST_3:\r\n",
      "        FILENAME: data/low_z_galaxy_colors_config_4.txt  # redshift and g,r,i,z,y mag for DES galaxies at z<0.3\r\n",
      "        MODE: sample # sample from all the rows specified in the file\r\n",
      "    USERDIST_4:\r\n",
      "        FILENAME: data/quasar_colors.txt # contains fake quasar colors to make them blueish\r\n",
      "        MODE: sample   \r\n",
      "    USERDIST_5: \r\n",
      "        FILENAME: data/high_z_galaxy_colors_config_1.txt # redshift and g,r,i,z,y for DES galaxies at 0.5<z<1.0\r\n",
      "        MODE: sample\r\n",
      "    USERDIST_6: \r\n",
      "        FILENAME: data/high_z_galaxy_colors_config_2.txt # redshift and g,r,i,z,y for DES galaxies at 0.5<z<1.0\r\n",
      "        MODE: sample\r\n",
      "    USERDIST_7: \r\n",
      "        FILENAME: data/high_z_galaxy_colors_config_3.txt # redshift and g,r,i,z,y for DES galaxies at 0.5<z<1.0\r\n",
      "        MODE: sample\r\n",
      "   \r\n"
     ]
    }
   ],
   "source": [
    "! head -196 data/example.yaml | tail -23"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with the first 3 `USERDIST`s: filenames `low_z_galaxy_colors_config_*.txt`. These three files are identical with the exception of the header row, which I will cover next. The columns in the files weree produced with the following query:\n",
    "\n",
    "```python\n",
    "SELECT DNF_ZMEAN_SOF, MAG_PSF_G, MAG_PSF_R, MAG_PSF_I, MAG_PSF_Z, MAG_PSF_Y \n",
    "FROM Y3_GOLD_2_2 \n",
    "WHERE EXTENDED_CLASS_COADD = 3 and DEC > -44 and DEC < -40 and DNF_ZMEAN_SOF < 0.3 and ROWNUM < 101;\n",
    "```\n",
    "\n",
    "Ignoring some of the DES-specific column and table names, what I've done is select the magnitude in each band and the redshift of 100 galaxies in a random patch of sky for low redshift galaxies. I've then put the query results directly into the `USERDIST` files with updated column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION_1-PLANE_1-OBJECT_1-REDSHIFT CONFIGURATION_1-PLANE_1-OBJECT_1-LIGHT_PROFILE_1-magnitude-g CONFIGURATION_1-PLANE_1-OBJECT_1-LIGHT_PROFILE_1-magnitude-r CONFIGURATION_1-PLANE_1-OBJECT_1-LIGHT_PROFILE_1-magnitude-i CONFIGURATION_1-PLANE_1-OBJECT_1-LIGHT_PROFILE_1-magnitude-z CONFIGURATION_1-PLANE_1-OBJECT_1-LIGHT_PROFILE_1-magnitude-Y WEIGHT\r\n",
      "0.12649094 19.87325859 19.09209251 18.70531082 18.48275948 18.42626953 1.0\r\n",
      "0.25748792 21.79298973 21.43421555 21.26678276 21.34543800 21.33299065 1.0\r\n",
      "0.21782839 21.72157860 21.09894562 20.83550835 20.69699860 20.75214195 1.0\r\n",
      "0.17391822 22.20806122 21.85676384 21.70260429 21.67228889 21.56722069 1.0\r\n"
     ]
    }
   ],
   "source": [
    "! head -5 data/low_z_galaxy_colors_config_1.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column names match how `deeplenstronomy` will track the object. It can take some getting used to. I've also added a `WEIGHT` column. These weights represent the frequency at which each row is used. They are defined relative to each other, so it's not necessary thaty they sum to one.\n",
    "\n",
    "As I mentioned earlier, the only difference between `low_z_galaxy_colors_config_1.txt`, `low_z_galaxy_colors_config_3.txt`, and `low_z_galaxy_colors_config_4.txt` is the header row. In these other files `CONFIGURATION_1` has been replaced with `CONFIGURATION_3` and `CONFIGURATION_4` respectively. Thus, I am still using the same galaxy colors and redshfits, but I attributing it to different object in the simulation. These files are used for `CONFIGURATION_1`, `CONFIGURATION_3`, and `CONFIGURATION_4` because they target \"SimulatedLens\" in each case.\n",
    "\n",
    "Similarly, the files `high_z_galaxy_colors_config_1.txt`, `high_z_galaxy_colors_config_2.txt`, and `high_z_galaxy_colors_config_3.txt` were produced with the query:\n",
    "\n",
    "```python\n",
    "SELECT DNF_ZMEAN_SOF, MAG_PSF_G, MAG_PSF_R, MAG_PSF_I, MAG_PSF_Z, MAG_PSF_Y \n",
    "FROM Y3_GOLD_2_2 \n",
    "WHERE EXTENDED_CLASS_COADD = 3 and DEC > -44 and DEC < -40 and DNF_ZMEAN_SOF > 0.5 and ROWNUM < 101;\n",
    "```\n",
    "\n",
    "and these files target the redshift and magnitude of \"SimulatedSource\" everywhere it appears in the simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, I use the file `quasar_colors.txt` to put in **fake** quasar colors where I have created the distribution of magnitudes to make the objects appear blue and to be bright at the redshifts they will be place at in the simulation. I would recommed using real AGN observations if you are working with lensed quasars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFIGURATION_3-PLANE_2-OBJECT_2-magnitude-g CONFIGURATION_3-PLANE_2-OBJECT_2-magnitude-r CONFIGURATION_3-PLANE_2-OBJECT_2-magnitude-i CONFIGURATION_3-PLANE_2-OBJECT_2-magnitude-z CONFIGURATION_3-PLANE_2-OBJECT_2-magnitude-Y WEIGHT\r\n",
      "21.88 22.04 22.35 22.52 22.55 0.05\r\n",
      "21.93 22.32 22.33 22.34 22.35 0.05\r\n",
      "21.88 22.21 22.28 22.31 22.70 0.05\r\n",
      "21.40 21.61 21.66 21.75 21.76 0.05\r\n",
      "21.40 21.62 22.01 22.08 22.48 0.05\r\n",
      "21.96 22.06 22.30 22.49 22.71 0.05\r\n",
      "21.51 21.70 22.05 22.18 22.37 0.05\r\n",
      "21.51 21.58 21.97 22.29 22.45 0.05\r\n",
      "21.68 21.95 22.07 22.39 22.68 0.05\r\n"
     ]
    }
   ],
   "source": [
    "! head data/quasar_colors.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "That is all the information in the file `example.yaml`. We have utilized built-in astronomical surveys, realistic galaxy colors, real images of galaxies, and physically motivated distributions of all parameters to give us a rich training set ready for a neural network. At this point, we're ready to call `deeplenstronomy.make_dataset()` and simualted our images.\n",
    "\n",
    "### The Simulated Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering main organization loop\n",
      "Organizing CONFIGURATION_1\n",
      "Organizing CONFIGURATION_2\n",
      "Organizing CONFIGURATION_3\n",
      "Organizing CONFIGURATION_4\n",
      "Generating images for CONFIGURATION_1\n",
      "\tProgress: 100.0 %  ---  Elapsed Time: 0 H 0 M 9 S\n",
      "Generating images for CONFIGURATION_2\n",
      "\tProgress: 100.0 %  ---  Elapsed Time: 0 H 0 M 9 S\n",
      "Generating images for CONFIGURATION_3\n",
      "\tProgress: 100.0 %  ---  Elapsed Time: 0 H 0 M 44 S\n",
      "Generating images for CONFIGURATION_4\n",
      "\tProgress: 100.0 %  ---  Elapsed Time: 0 H 0 M 6 S\n"
     ]
    }
   ],
   "source": [
    "dataset = dl.make_dataset('data/example.yaml', survey='des', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect the images using the `deeplenstronomy` visualization features.\n",
    "\n",
    "Here's `CONFIGURATION_1`, which was galaxy-galaxy lensing with both the source and lens being simulated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    view_image(dataset.CONFIGURATION_1_images[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the metadata for these images is stored here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJID-g</th>\n",
       "      <th>H0-g</th>\n",
       "      <th>Om0-g</th>\n",
       "      <th>exposure_time-g</th>\n",
       "      <th>numPix-g</th>\n",
       "      <th>pixel_scale-g</th>\n",
       "      <th>psf_type-g</th>\n",
       "      <th>read_noise-g</th>\n",
       "      <th>ccd_gain-g</th>\n",
       "      <th>seeing-g</th>\n",
       "      <th>...</th>\n",
       "      <th>PLANE_1-REDSHIFT-g</th>\n",
       "      <th>PLANE_2-REDSHIFT-g</th>\n",
       "      <th>PLANE_1-REDSHIFT-r</th>\n",
       "      <th>PLANE_2-REDSHIFT-r</th>\n",
       "      <th>PLANE_1-REDSHIFT-i</th>\n",
       "      <th>PLANE_2-REDSHIFT-i</th>\n",
       "      <th>PLANE_1-REDSHIFT-z</th>\n",
       "      <th>PLANE_2-REDSHIFT-z</th>\n",
       "      <th>PLANE_1-REDSHIFT-Y</th>\n",
       "      <th>PLANE_2-REDSHIFT-Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.263</td>\n",
       "      <td>GAUSSIAN</td>\n",
       "      <td>7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>0.921</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.263</td>\n",
       "      <td>GAUSSIAN</td>\n",
       "      <td>7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>1.719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165344</td>\n",
       "      <td>0.541347</td>\n",
       "      <td>0.165344</td>\n",
       "      <td>0.541347</td>\n",
       "      <td>0.165344</td>\n",
       "      <td>0.541347</td>\n",
       "      <td>0.165344</td>\n",
       "      <td>0.541347</td>\n",
       "      <td>0.165344</td>\n",
       "      <td>0.541347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.263</td>\n",
       "      <td>GAUSSIAN</td>\n",
       "      <td>7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>1.040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.237946</td>\n",
       "      <td>0.961559</td>\n",
       "      <td>0.237946</td>\n",
       "      <td>0.961559</td>\n",
       "      <td>0.237946</td>\n",
       "      <td>0.961559</td>\n",
       "      <td>0.237946</td>\n",
       "      <td>0.961559</td>\n",
       "      <td>0.237946</td>\n",
       "      <td>0.961559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.263</td>\n",
       "      <td>GAUSSIAN</td>\n",
       "      <td>7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>0.902</td>\n",
       "      <td>...</td>\n",
       "      <td>0.195374</td>\n",
       "      <td>0.799212</td>\n",
       "      <td>0.195374</td>\n",
       "      <td>0.799212</td>\n",
       "      <td>0.195374</td>\n",
       "      <td>0.799212</td>\n",
       "      <td>0.195374</td>\n",
       "      <td>0.799212</td>\n",
       "      <td>0.195374</td>\n",
       "      <td>0.799212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.263</td>\n",
       "      <td>GAUSSIAN</td>\n",
       "      <td>7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>0.962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254798</td>\n",
       "      <td>0.861982</td>\n",
       "      <td>0.254798</td>\n",
       "      <td>0.861982</td>\n",
       "      <td>0.254798</td>\n",
       "      <td>0.861982</td>\n",
       "      <td>0.254798</td>\n",
       "      <td>0.861982</td>\n",
       "      <td>0.254798</td>\n",
       "      <td>0.861982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 280 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJID-g  H0-g  Om0-g  exposure_time-g  numPix-g  pixel_scale-g psf_type-g  \\\n",
       "0        0  74.0    0.3             90.0       100          0.263   GAUSSIAN   \n",
       "1        1  74.0    0.3             90.0       100          0.263   GAUSSIAN   \n",
       "2        2  74.0    0.3             90.0       100          0.263   GAUSSIAN   \n",
       "3        3  74.0    0.3             90.0       100          0.263   GAUSSIAN   \n",
       "4        4  74.0    0.3             90.0       100          0.263   GAUSSIAN   \n",
       "\n",
       "   read_noise-g  ccd_gain-g  seeing-g  ...  PLANE_1-REDSHIFT-g  \\\n",
       "0             7       6.083     0.921  ...                 NaN   \n",
       "1             7       6.083     1.719  ...            0.165344   \n",
       "2             7       6.083     1.040  ...            0.237946   \n",
       "3             7       6.083     0.902  ...            0.195374   \n",
       "4             7       6.083     0.962  ...            0.254798   \n",
       "\n",
       "   PLANE_2-REDSHIFT-g  PLANE_1-REDSHIFT-r  PLANE_2-REDSHIFT-r  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "1            0.541347            0.165344            0.541347   \n",
       "2            0.961559            0.237946            0.961559   \n",
       "3            0.799212            0.195374            0.799212   \n",
       "4            0.861982            0.254798            0.861982   \n",
       "\n",
       "  PLANE_1-REDSHIFT-i PLANE_2-REDSHIFT-i  PLANE_1-REDSHIFT-z  \\\n",
       "0                NaN                NaN                 NaN   \n",
       "1           0.165344           0.541347            0.165344   \n",
       "2           0.237946           0.961559            0.237946   \n",
       "3           0.195374           0.799212            0.195374   \n",
       "4           0.254798           0.861982            0.254798   \n",
       "\n",
       "   PLANE_2-REDSHIFT-z PLANE_1-REDSHIFT-Y  PLANE_2-REDSHIFT-Y  \n",
       "0                 NaN                NaN                 NaN  \n",
       "1            0.541347           0.165344            0.541347  \n",
       "2            0.961559           0.237946            0.961559  \n",
       "3            0.799212           0.195374            0.799212  \n",
       "4            0.861982           0.254798            0.861982  \n",
       "\n",
       "[5 rows x 280 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.CONFIGURATION_1_metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's `CONFIGURATION_2` which was real DES images of the lens galaxy and a simulated source behind it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    view_image(dataset.CONFIGURATION_2_images[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is `CONFIGURATION_3` which was the same as `CONFIGURATION_1` but with a quasar in the source galaxy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    view_image(dataset.CONFIGURATION_3_images[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally here is `CONFIGURATION_4` which is just a galaxy by itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XTnzaGO068Lcm8C4Kq0lYPikU0zF6HaWIicBeRbhPY1Az1rPxLlVxubOtrm7ioPH+aOUHdDdERvre/VXEDacx9jwC4QDlSj+k747JpirexOT21uKrIbW8blxhmwTYUYG9YPjp5+/hmqU4yAj/9sn7SL5iiO8k2FHqKagmAZwIUUMy9KKdMvR+XhlSgQiAHhXoywg//fI93LAj9jLGv//mfcRPGeJbBXqQADczpNzAVQ0iv5FE2ljtV0FqEYruAP7xQ/zN1R/j/pAg+fAau68F6EEAXOTJtBRvqzdNU6M83pUyM3SBDz95jL+L/xB3PMEnv3wLj75SiO5NbNoZZ/KU0wI2EpedUDb81gAoLULxLfDxr97Cj5I/wm2a4IuPHuHRUwW2188/EjszsoLPi1hJohOyZKgAoUCPEslLhesnFL9OHuMfPnsdKqWIP4/x8FOF3XMJuheaYC6KQVvlqnwQpCmgtEtiR4nkJcX1byg+Sn4LH3/2CJJT0M8SvPIJwe65ACvxqcqVlF2vb8vJtYspMM/e0YPA1TOB13/BcHjyJpIUuPlM4urLFPQ+1bOjnIFfeHcZcLkxCZzEFeES0b3E1RcK/GdX+K9ffxeUA689Ibj5XCB6mYIeuU6mQuRuvqWt74URHRvWXHIJtpe4/kJC/CzBh5++B5oSvP5E4cFnAtFtqh/Ql1b4ZdGMdonTOiNwRo44X6BGakkppWDWLtAwxBIhwA4CyQuKmycE7EjBr69AOZB8rXD9pZ7Ky+5TwCYBG7RDnNMEJC8KJQGwfDkTLsHuNZ/XTwhoGkHsIhAJJM8Vrr8QSJ5rPonLZw/Bv4hEYAVbSIALsPsUyTMKIhRuPicgHIhvOaIXBz3bTJilo2SeUL2NqQmgjHmyK5lEtxzXXxJQziB2euZe8lJg9zQFu0uBY6qr0qxt6nC6UDJdBaTUM6AtlAIRmlN2x3H1FQXlAP8NAeUSyQuJ5NkR9O6YcarcytQcYymsr4Iybp0YYsH1ygbxSwIoIL7X8/eJUIjuFeKXHNHLI8gh1W6fi4Lbdx1AwVVdUtnv3PMgqdB8vqAg0vDJACqAaC8Rfy0QvTyC7rkWqCxgS47qEpAtl+XAvoaAmDiV5tmRAwUjBDQV+iFdLvVzZPdHkMNR82jbpXX3Smbq64+Cse6V2lc92PgUeqUIeqefd2IHmT2Uz/YC7PaoOU05wHk7p5cKy4my456D3etYZgdnJZ69ALs7guyNIc2MVKlycjFj7lyfQAHZArCQDEg5yJ7qlSK4QnRHc3KPAvSegx5SkINxVC7BNmgbbvx5kQwGQIu8XkU7S6RCQqUcdE8QQT/7EN1RvQ6sBOhRgO056L1xqC6XNhHog3cX/K06VcAsVEoATkEOKaiQwMGsEyl1ZUVSDqQcivOsesoMlOXwEp0+kLt9u+K74QypnrhDpATdm6nPQumW8zHNOZWyyKk5ZlVcbnWcN0LJLEYJ1QtBMilB9wygZrJJKgDLKRdQtioVojVO5+B0UYGyiTNf+Vnpu3n6BS96gBMCkFRXVFyCRmZfodt/JNUBDSFyR2VK/oty+02wQm35PBCdAIQE21uB0vySox78herJTQIX0N5TUp1MlMhi1bx1NBN/sxAsYJaJ4c4K8Fz39BXXbh/CVlHLutI1ocCllHlFakwRYU5uECLrkvQ1osWTboRrp8LXt0iQ/1yIT6HXLrTVlM23SuVvLeAiN1FWnPpUpBMZqfkFqqptAuRuqkws0XemlJQgnOWJw7YEbMByrtVfuoErcXFuv0bwlSTZ+50IoIMzFXmrxlmOpxCwbhKw+219ckQZUuUPQ9qBSwjsOjCK65ln4M5jEyb2tCA1xGSTwG8lkZZQa0zN6x2I+R0RpfiUSo9ze1/UTJI4cfrZiXry53tMu++9M23obOxznk3oKY95CGNEC+O9Jk5nrkiXq6DcQW+gAxc1xFKzgCHJA8kuFCkcp2+SqRu0hXO659uA23ehlMqn60upVy+wb8q0bVNAD34TzFUvKsxcfzkJ1FVPfQV/jagzTnW7u7EKgez1LtS5Qa9k1i6BVFBCbCeRKtXvPlSDMS3EqRUpu9ips18m+Ep2i8vs1Cvga0ScVPh1udRZnkjZZ/EEyUVM75ibqKbc2XxB536lWpwvUB0DtaptksFVfltF2U4JjDtVTLf+3PNKaYRJ5QRXBG2VA6i4wA5fdkHUDXD7+4ogBZC7U+TvziGUAtJZlNf8vtB7tuLktvZaqqfOiWANCbYrym0UJ1YVpToJKLtqh8z/Blagsmef5Omg33giBTok08xs6hjNuCbGkJb5tGPZHedjdEm2gnJ82lxKS+/IA3IurOi7sWpwVvU0wjhffJJEwfXbf0toJ2p5JDT7snbfjLiyS73kZGpRlUTBYN/oql9IJk+WR8k4tWKvZH7PpC65+pYI+jp/CzexlviFUCDE8uEkVKBSnDZhmM5EpdgXxr3MVykHinw1jfOhou/jOAcKBlXR0oIHUppqVJ2aqBKfwGmsrmHW8zICVeX6m4KVKh2o7qrFbkB2daobSgZ1FWlZ8E9FSsG+yFG5FZnrTqv4dI5fdz3dLtyPRNDKr1v1A1qogOLrHlzuWsRpa/xVosO4z8S+/KqHchLV/6gWpw2N8wwdW9AF4TdxmuXT3PEXhPxknPcR/Ik5XbyCAkpJ1QadS6p1peWECuSJFOjsVDfXSikN/JMghdOTpkS396CA8qszypy2Be3WEkHHNuqJSAH5/T33HilQjEWgWzz6yp9FicdBYl/1XidXmMy/e/Fae73+5YMCp03jH8gqKdSZKOA0bwLnjfOROJ1WoLoEahWpjkgB7q2n4jtJTpbhqCK5yzXWblsgcIe2oKpQMfALTqp8bwA4FXvz8yiJwDO0JQHATC1vO85Y4uRhIj1BF7Gv47SqGgW2Jfpjj3+gntdzjdQMWK6Cappu7ogUgKL6u/tlxzojcH1Ck+B3HPi1Tsps18eaKBH4nmBdjoGCUAGl+6MuaqaOex2LQK9k2kvsy2PdonzzHjh/jPsUkz3HP4A8B1jUiFVlS28F43wcgZojUIeovz2Hc+7SxTRcqEeBW0adSJUrzjJ6Oipv0RSvbVW/u90RKn3YBmGyn3WOW3nupmv2CV2n7Tv3owrxWnXIOn7bYnILMdsFDQbKffwk232okdrUUkdjBWpDBVUgeqtutQaVgg9UixRQFKoy2jgtBealCH6jSAGnQnR6gJPjte1T3OYvdy4aYxU4qUgr0RJzm4/JgQaqkde+Rqrx+sbldJEWX59ArVL+7DjntFLWHLg9HH7j9rZyv/LU7WKv/7nhRFDBcWUisKibWFF56J6x6DPaeLTfuzz+LdxnoKqOffIrT+87ldHzPtTZBsr9XOm4bfsUt40/zucRqC6BWia0rm1ShT6JtGIf39HqovpwaT9TPMHJ+bxHWxKoiVkApxN9OsRTLWet7amVcz3gpn6t2J+sHD9QmGr2K25fOa9N6GOgOr3TbCROJ8B4AnVuoJ7jpvIDnhy/bZ/T7R4EbtcAreKydjWPiZKBD3zWoaZarRWqykN0j9fT7R5zZ9HA4RChrzpO5TkbP+QBrwMN1Lmcrs1Izdfi63ovqo+bKn+m8CvPA3eMAK3icmC5b4/fZT9v0MVUNcTtWZWkT7HYhgGxqn/dsPxZ2ymHJtKNo495qvpczcaWD08Xq+MK1JRtE/u5FrLW5gDmRqOLajMIfe6bNOyfb98Gp53NVddjXSJaqlGgPam2GoIu3G4lJoHO5qmO104Ga+ExvprXbbS6qSn7/GvF2M50rHK/y7F8SQRdW9N9evpNn+96Tb7hzGpUb56wIgX847Urp8D4vK4kZ44vUGcE6pDy9OJd1Yhclj/beM7G7Z7x2ef+aV+h6jvQfeOuL8asRu3xOu3nKa99DNQYvK7MSK1vJQmc6aTK52jdZ+WBO+J9kiaxGqXc9xl9J/lMwcXaY7ENc1Wj7jE67es5r11xDq8rNVLTCNTcql913E77bShwO3A5eQsF2Banc2FLnK2pGrXX4zvOMU9tz0wOvZ6ZsPxq5mO4qfKxOu3rUeDO6UzrjtlpX484rcKYC3X2OeelY6rKfEvcDo3NsbmdmdMJyheDvl9EyTMUvednfQzcPtc8RlBeAqdVUGq+77IVzspYw/dawzWMjSW/05zjwsHEr9sYoPp108vrtg+5pktA1zK/7jO9zrVBTqesprbIVxn2O4aKdFxcWJU/w2KxZxI6Zonqe/CupczPjus5n20YO8luna8qzJlQL4XfC+J0prX4FlD9qmvYAtbApb2OS8E5QnVJPNVh6pi9RI6nrlBXwumMSx0tlFhXQvSoWFqktshpF5S/d9Xf4FK5acMUCTVwvfkqf95ZfHMn1pWRPSqW6PFvmc8hCHz0xxhxG3g/xUar/AWWOpohsa6Y8NExl+hfEqcB06NLNVq3b0A9NsbVgitJTJBYN/bH6Yww4yzAd4Q4C6gA6fwSOwCEkM8BfDzd5XiLbyulvtH3Q4HPWgQ+x0Xgc1wM4hMInDagktNeAhUQEBAQEDAXpltJIiAgICAg4AwEgQoICAgIWCWCQAUEBAQErBJBoAICAgICVokgUAEBAQEBq0QQqICAgICAVSIIVEBAQEDAKhEEKiAgICBglQgCFRAQEBCwSvw/Ugf71j6h65gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    view_image(dataset.CONFIGURATION_4_images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}