bamford/sersic_gals.ipynb

## sersic_gals.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Creating simple images to test STN "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np \nimport matplotlib.pyplot as plt \nfrom astropy.modeling.models import Sersic2D",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Test - generate an image "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "x,y = np.meshgrid(np.arange(100), np.arange(100))\n\nmod = Sersic2D(amplitude=1, r_eff=15, n=4, x_0=50, y_0=50,\n               ellip=.5, theta=-np.pi/4)\nimg = mod(x, y)\nlog_img = np.log10(img)\n\nplt.figure()\nplt.imshow(log_img, origin='lower', interpolation='nearest',\n           vmin=-1, vmax=2)\nplt.xlabel('x')\nplt.ylabel('y')\ncbar = plt.colorbar()\ncbar.set_label('Log Brightness', rotation=270, labelpad=25)\ncbar.set_ticks([-1, 0, 1, 2], update_ticks=True)\nplt.show()",
      "execution_count": 2,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Generate array of images"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "np.random.seed(2345)\nelip = np.round(np.random.uniform(low=0.0, high=0.8, size=(30000,)), decimals=2)\nPAs = np.round(np.random.uniform(low=0.0, high=np.pi, size=(30000,)), decimals=2)\nReff = np.round(np.random.lognormal(2.3, 0.3, size=(30000,)), decimals=2)\n#sersic = np.ones(30000)\nsersic = np.round(np.random.lognormal(0.5, 0.5, size=(30000,)), decimals=2)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def make_gals(el, pa, re, sersic, size=(60, 60)):\n    x,y = np.meshgrid(np.arange(size[0]), np.arange(size[1]))\n    gal_array = []\n    for i in range(len(el)):\n        mod = Sersic2D(amplitude=1, r_eff=re[i], n=sersic[i],\n                       x_0=(size[0]-1)/2.0, y_0=(size[1]-1)/2.0,\n                       ellip=el[i], theta=pa[i])\n        img = mod(x, y)\n        norm = img / np.max(img)\n        gal_array.append(norm)\n    return np.array(gal_array)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "gal_array = make_gals(elip, PAs, Reff, sersic)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "gal_array.shape",
      "execution_count": 6,
      "outputs": [
        {
          "data": {
            "text/plain": "(30000, 60, 60)"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(30, 35))\nfor i in range(25):\n    plt.subplot(5, 5, i+1)\n    plt.imshow(gal_array[i,...], cmap='inferno',\n               origin='lower', interpolation='nearest',\n               vmin=0, vmax=1)",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 2160x2520 with 25 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "np.save('sersic_gals_n', gal_array)",
      "execution_count": 8,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "conda-env-CB-py",
      "display_name": "Python [conda env:CB]",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.7",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "hide_input": false,
    "gist": {
      "id": "",
      "data": {
        "description": "Create Sersic gals",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}