beckermr/jax_grads.ipynb

## jax_grads.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import jax\nimport jax.numpy as jnp\n\nimport proplot as pplt\nimport numpy as np\nimport galsim\n\n%matplotlib inline",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "obj = galsim.Exponential(half_light_radius=0.5/0.2)\nim = obj.drawImage(nx=23, ny=23, scale=1).array\n\nfig, axs = pplt.subplots()\naxs.imshow(im, origin=\"lower\", cmap=\"rocket\")",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x112d61240>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Figure(nrows=1, ncols=1, refwidth=2.5)",
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 284,
              "height": 282
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def _adaptive_shape_kern(im, sigma2):\n    half = (23-1)//2\n    y, x = jnp.mgrid[:23, :23]\n    y = y - half\n    x = x - half\n    \n    kern = jnp.exp(\n        -0.5 * x * x / sigma2\n        -0.5 * y * y / sigma2\n    )\n    \n    norm = jnp.sum(im * kern)\n    mx2 = jnp.sum(x * x * kern * im) / norm\n    my2 = jnp.sum(y * y * kern * im) / norm\n\n    \n    # adaptive moments has this deweighting thing\n    # I got this snippet out of ngmix\n    idetm = 1.0 / (mx2 * my2)\n    idetw = 1.0 / sigma2 / sigma2\n    Nrr = mx2*idetm - sigma2*idetw  # Nrr = Icc*idetm - Wcc*idetw\n    Ncc = my2*idetm - sigma2*idetw  # Ncc = Irr*idetm - Wrr*idetw\n    detn = Nrr*Ncc\n\n    # now set from the inverted matrix\n    idetn = 1.0/detn\n    mx2 = Ncc*idetn\n    my2 = Nrr*idetn\n    \n    new_sigma2 = (mx2 + my2)/2.0\n    \n    return new_sigma2\n\n\ndef dumb_adaptive_thing(im):\n    sigma2 = (2.0/0.2/2.355)**2\n    for _ in range(10):\n        sigma2 = _adaptive_shape_kern(im, sigma2)\n    return sigma2",
      "execution_count": 52,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dumb_adaptive_thing(im)",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 53,
          "data": {
            "text/plain": "Array(3.1479647, dtype=float32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vg = jax.jit(jax.value_and_grad(dumb_adaptive_thing, 0))",
      "execution_count": 55,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "sigma2, dsigma2_dimg = vg(im)",
      "execution_count": 61,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, axs = pplt.subplots(ncols=2, nrows=1)\n\naxs[0].imshow(im, origin=\"lower\", cmap=\"rocket\")\naxs[0].set_title(\"object\")\nm = axs[1].imshow(dsigma2_dimg, origin=\"lower\", cmap=\"rocket\")\naxs[1].set_title(\"derivative of adaptive moments sigma2 wrt to image\")\naxs[1].colorbar(m, loc=\"r\")",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 67,
          "data": {
            "text/plain": "<matplotlib.colorbar.Colorbar at 0x14d23b910>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Figure(nrows=1, ncols=2, refwidth=2.5)",
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 670,
              "height": 300
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "conda-env-work-py",
      "display_name": "Python [conda env:work]",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.9",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "jax adaptive moments",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}