bamford/sersic_gals_asym.ipynb

## sersic_gals_asym.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Creating varied, asymmetrical 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\nfrom astropy.nddata import block_reduce\nfrom scipy.special import gammaincinv, gammainc, gamma\nfrom math import pi\nfrom vae_stn_test import plot_examples, asinh_scale\nfrom tqdm import tqdm",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Tensorflow using CPU\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from astropy.modeling.parameters import Parameter\n\nclass Sersic2DAsym(Sersic2D):\n    r\"\"\"\n    Two dimensional Sersic profile with asymmetry.\n\n    Parameters are same as Sersic2D, plus:\n    ----------\n    asym_strength : float, optional\n        Strength of asymmetry.\n    asym_angle : float, optional\n        Position angle of maximum asymmetry.\n\n\n    Notes\n    -----\n    Asymmetry introduced by multiplying Sersic2D profile by\n    (1 - asym_strength * cosine(azimuthal angle - asym_angle)\n    \"\"\"\n\n    asym_strength = Parameter(default=0)\n    asym_angle = Parameter(default=0)\n\n    @classmethod\n    def evaluate(cls, x, y, amplitude, r_eff, n, x_0, y_0, ellip, theta,\n                 asym_strength, asym_angle):\n        \"\"\"Two dimensional Sersic profile function with asymmetry.\"\"\"\n\n        if cls._gammaincinv is None:\n            try:\n                from scipy.special import gammaincinv\n                cls._gammaincinv = gammaincinv\n            except ValueError:\n                raise ImportError('Sersic2D model requires scipy > 0.11.')\n\n        bn = cls._gammaincinv(2. * n, 0.5)\n        a, b = r_eff, (1 - ellip) * r_eff\n        cos_theta, sin_theta = np.cos(theta), np.sin(theta)\n        x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta\n        x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta\n        z = np.sqrt((x_maj / a) ** 2 + (x_min / b) ** 2)\n        eps = 1e-32\n        angle = np.arctan(x_maj/(x_min + eps))\n        angle += np.pi * (x_min < 0) - np.pi/2\n        angle[np.isnan(angle)] = 0\n        asym = (1 - asym_strength * np.cos(theta - asym_angle - angle))\n        return amplitude * asym * np.exp(-bn * (z ** (1 / n) - 1))\n\n\n    def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):\n        par_unit = super()._parameter_units_for_data_units(inputs_unit, outputs_unit)\n        return par_unit + {'asym_angle': u.rad}",
      "execution_count": 2,
      "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 = Sersic2DAsym(amplitude=1, r_eff=15, n=4, x_0=50, y_0=50,\n               ellip=0.5, theta=-np.pi/4, asym_strength=0.5, asym_angle=-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": 3,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 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)\nngal = 100\nelip = np.round(np.random.uniform(low=0.0, high=0.8, size=(ngal,)), decimals=2)\nPAs = np.round(np.random.uniform(low=0.0, high=np.pi, size=(ngal,)), decimals=2)\nReff = np.round(np.random.lognormal(2.3, 0.3, size=(ngal,)), decimals=2)\nsersic_low = np.round(np.random.lognormal(0.2, 0.2, size=(ngal//2,)), decimals=2)\nsersic_high = np.round(np.random.lognormal(1.3, 0.15, size=(ngal//2,)), decimals=2)\nsersic = np.concatenate((sersic_low, sersic_high))\nnp.random.shuffle(sersic)\nasym_low = np.round(np.random.beta(0.5, 10, size=(ngal//2,)), decimals=2)\nasym_high = np.round(np.random.beta(10, 20, size=(ngal//2,)), decimals=2)\nasym = np.concatenate((asym_low, asym_high))\nnp.random.shuffle(asym)\nasym_angle = np.round(np.random.uniform(low=0.0, high=2*np.pi, size=(ngal,)), decimals=2)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(Reff);",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(asym);",
      "execution_count": 6,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(sersic);",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def oversamp_factor(Reff, sersic, elip, factor=25, minoversamp=5, maxoversamp=50, unbinned=False):\n    oversamp = factor * sersic / (Reff * (1-elip))\n    oversamp = np.minimum(oversamp, maxoversamp)\n    oversamp = np.maximum(oversamp, minoversamp)\n    if not unbinned:\n        oversamp = np.ceil(oversamp).astype('int')\n    return oversamp",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(oversamp_factor(Reff, sersic, elip), bins=100)\nplt.hist(oversamp_factor(Reff, sersic, elip, unbinned=True), bins=100, alpha=0.5);",
      "execution_count": 9,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def b(n):\n    # Normalisation constant\n    return gammaincinv(2*n, 0.5)\n\ndef sersic_lum(Ie, re, n):\n    bn = b(n)\n    g2n = gamma(2*n)\n    return Ie * re**2 * 2*pi*n * np.exp(bn)/(bn**(2*n)) * g2n\n\ndef sersic_enc_lum(r, Ie, re, n):\n    x = b(n) * (r/re)**(1.0/n)\n    return sersic_lum(Ie, re, n) * gammainc(2*n, x)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def make_gals(el, pa, re, sersic, asym, asym_angle, size=(60, 60),\n              oversamp=None):\n    if oversamp is None:\n        oversamp = oversamp_factor(re, sersic, el)\n    else:\n        oversamp = oversamp * np.ones(len(re), dtype='int')\n\n    #amp = np.exp(-b(sersic))\n    #amp = 0.16 / sersic_enc_lum(0.3, 1, re, sersic)\n    amp = 1 / sersic_enc_lum(1.333, 1, re, sersic) / 0.195\n    \n    gal_array = []\n    for i in tqdm(range(len(el))):\n        this_size = [x * oversamp[i] for x in size]\n        this_re = re[i] * oversamp[i]\n        mod = Sersic2DAsym(amplitude=amp[i], r_eff=this_re, n=sersic[i],\n                       x_0=(this_size[0]-1)/2.0, y_0=(this_size[1]-1)/2.0,\n                       ellip=el[i], theta=pa[i],\n                       asym_strength=asym[i], asym_angle=asym_angle[i])\n        x,y = np.meshgrid(np.arange(this_size[0]), np.arange(this_size[1]))\n        img = mod(x, y)\n        if oversamp[i] != 1:\n            img = asinh_scale(img)\n            img = block_reduce(img, oversamp[i], np.mean)\n            img = asinh_scale(img, inverse=True)\n        #norm = img / np.max(img)\n        norm = img\n        gal_array.append(norm)\n    return np.array(gal_array)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%time\ngal_array = make_gals(elip, PAs, Reff, sersic, asym*0, asym_angle, oversamp=1)",
      "execution_count": 12,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "100%|██████████| 100/100 [00:00<00:00, 782.57it/s]"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "CPU times: user 127 ms, sys: 6.07 ms, total: 133 ms\nWall time: 133 ms\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(sersic, gal_array.max(-1).max(-1), c=elip)",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.collections.PathCollection at 0x7f8bceecaa90>"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%time\ngal_array_oversamp5 = make_gals(elip, PAs, Reff, sersic, asym, asym_angle, oversamp=5)",
      "execution_count": 14,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "100%|██████████| 100/100 [00:01<00:00, 84.38it/s]"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "CPU times: user 1.02 s, sys: 31.7 ms, total: 1.05 s\nWall time: 1.19 s\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%time\ngal_array_oversamp10 = make_gals(elip, PAs, Reff, sersic, asym, asym_angle, oversamp=10)",
      "execution_count": 15,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "100%|██████████| 100/100 [00:03<00:00, 25.73it/s]"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "CPU times: user 3.62 s, sys: 283 ms, total: 3.9 s\nWall time: 3.89 s\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%time\ngal_array_oversamp50 = make_gals(elip, PAs, Reff, sersic, asym, asym_angle, oversamp=50)",
      "execution_count": 16,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "100%|██████████| 100/100 [01:49<00:00,  1.10s/it]"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "CPU times: user 1min 39s, sys: 10.4 s, total: 1min 49s\nWall time: 1min 49s\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%time\ngal_array_oversamp = make_gals(elip, PAs, Reff, sersic, asym, asym_angle, oversamp=None)",
      "execution_count": 17,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "100%|██████████| 100/100 [00:17<00:00,  5.60it/s]"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "CPU times: user 14.2 s, sys: 3.63 s, total: 17.9 s\nWall time: 17.9 s\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "gal_array.shape, gal_array_oversamp5.shape, gal_array_oversamp.shape",
      "execution_count": 18,
      "outputs": [
        {
          "data": {
            "text/plain": "((100, 60, 60), (100, 60, 60), (100, 60, 60))"
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "np.save('sersic_gals_asym', gal_array)",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "np.save('sersic_gals_asym_oversamp', gal_array_oversamp)",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims(gal_array, -1))",
      "execution_count": 21,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims(gal_array_oversamp10, -1))",
      "execution_count": 22,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims(gal_array_oversamp, -1))",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims(gal_array-gal_array_oversamp50, -1),\n                           diff=True, vlimits=(-0.1, 0.1))",
      "execution_count": 24,
      "outputs": [
        {
          "data": {
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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist((abs(gal_array-gal_array_oversamp50)/gal_array_oversamp50).max(-1).max(-1),\n         bins=100);",
      "execution_count": 25,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims(gal_array_oversamp5-gal_array_oversamp50, -1),\n                           diff=True, vlimits=(-0.01, 0.01))",
      "execution_count": 26,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist((abs(gal_array_oversamp5-gal_array_oversamp50)/gal_array_oversamp50).max(-1).max(-1),\n         bins=100);",
      "execution_count": 27,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims(gal_array_oversamp10-gal_array_oversamp50, -1),\n                           diff=True, vlimits=(-0.01, 0.01))",
      "execution_count": 28,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist((abs(gal_array_oversamp10-gal_array_oversamp50)/gal_array_oversamp50).max(-1).max(-1),\n         bins=100);",
      "execution_count": 29,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims(gal_array_oversamp-gal_array_oversamp50, -1),\n                           diff=True, vlimits=(-0.01, 0.01))",
      "execution_count": 30,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_examples(np.expand_dims((gal_array_oversamp-gal_array_oversamp50)/gal_array_oversamp50, -1),\n                           diff=True, vlimits=(-0.01, 0.01))",
      "execution_count": 31,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1800 with 25 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist((abs(gal_array_oversamp-gal_array_oversamp50)/gal_array_oversamp50).max(-1).max(-1),\n         bins=100);",
      "execution_count": 32,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    }
  ],
  "metadata": {
    "hide_input": false,
    "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"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Create asymmetric Sersic gals ",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}