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Fix photutils 2.0 deprecation removals
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@larrybradley
larrybradley committed Oct 28, 2024
1 parent 3e01e5a commit 8a4a8fc
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85 changes: 41 additions & 44 deletions notebooks/ACS/acs_cte_forward_model/acs_cte_forward_model_example.ipynb
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Expand Up @@ -89,7 +89,6 @@
"- *os* for setting environment variables\n",
"- *shutil* for managing directories\n",
"- *numpy* for math and array calculations\n",
"- *collections OrderedDict* for making dictionaries easily\n",
"- *matplotlib pyplot* for plotting\n",
"- *matplotlib.colors LogNorm* for scaling images\n",
"- *astropy.io fits* for working with FITS files\n",
Expand All @@ -110,10 +109,10 @@
"import os\n",
"import shutil\n",
"import numpy as np\n",
"from collections import OrderedDict\n",
"import matplotlib.pyplot as plt\n",
"from astropy.io import fits\n",
"from photutils import datasets\n",
"from astropy.modeling.models import Gaussian2D\n",
"from photutils.datasets import make_noise_image, make_model_params, make_model_image\n",
"from astroquery.mast import Observations\n",
"from acstools import acsccd\n",
"from acstools import acscte\n",
Expand Down Expand Up @@ -298,7 +297,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we generate a table of random Gaussian sources of typical brightness for our 47 Tuc field with $\\mathrm{FWHM}\\sim2.5$ pixels. Because $\\mathrm{FWHM} = 2.355\\sigma$, we will generate Gaussian sources with $\\sigma \\sim 1.06$ pixels in both $x$ and $y$. "
"First, we generate a table of random Gaussian sources of typical brightness for our 47 Tuc field with $\\mathrm{FWHM}\\sim2.5$ pixels. Because $\\mathrm{FWHM} = 2.355\\sigma$, we will generate Gaussian sources with $\\sigma \\sim 1.06$ pixels in both $x$ and $y$. We get use the shape of one of the flc image SCI extensions for creating the (x, y) coordinates of the sources."
]
},
{
Expand All @@ -307,27 +306,31 @@
"metadata": {},
"outputs": [],
"source": [
"n_sources = 300\n",
"param_ranges = [('amplitude', [500, 30000]),\n",
" ('x_mean', [0, 4095]),\n",
" ('y_mean', [0, 2047]),\n",
" ('x_stddev', [1.05, 1.07]),\n",
" ('y_stddev', [1.05, 1.07]),\n",
" ('theta', [0, np.pi])]\n",
"wfc2 = fits.getdata('jd0q14ctq_flc.fits', ext=1)\n",
"\n",
"param_ranges = OrderedDict(param_ranges)\n",
"shape = wfc2.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n_sources = 300\n",
"\n",
"sources = datasets.make_random_gaussians_table(n_sources, param_ranges, \n",
" seed=12345)\n",
"sources = make_model_params(shape, n_sources, x_name='x_mean', y_name='y_mean',\n",
" amplitude=(500, 30000), x_stddev=(1.05, 1.07), \n",
" y_stddev=(1.05, 1.07), theta=(0, np.pi), seed=12345)\n",
"\n",
"print(sources)"
"sources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we get the shape of one of the `flc` image `SCI` extensions and make an image from the table of Gaussian sources. Note that this step may take a few minutes. Finally, we run the synthetic image through a Poisson sampler in order to simulate the Poisson noise of the scene."
"Next, we make an image from the table of Gaussian sources. Finally, we run the synthetic image through a Poisson sampler in order to simulate the Poisson noise of the scene."
]
},
{
Expand All @@ -336,11 +339,9 @@
"metadata": {},
"outputs": [],
"source": [
"wfc2 = fits.getdata('jd0q14ctq_flc.fits', ext=1)\n",
"\n",
"shape = wfc2.shape\n",
"\n",
"synth_stars_image = datasets.make_gaussian_sources_image(shape, sources)\n",
"model = Gaussian2D()\n",
"synth_stars_image = make_model_image(shape, model, sources, \n",
" x_name='x_mean', y_name='y_mean', progress_bar=True)\n",
"\n",
"synth_stars_image = np.random.poisson(synth_stars_image)"
]
Expand Down Expand Up @@ -382,7 +383,7 @@
"ax.imshow(flc, vmin=0, vmax=200, interpolation='nearest', cmap='Greys_r', origin='lower')\n",
"\n",
"ax.set_xlim(2000, 2800)\n",
"ax.set_ylim(1200, 1700)"
"ax.set_ylim(800, 1300)"
]
},
{
Expand Down Expand Up @@ -442,7 +443,7 @@
" markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
"\n",
"ax.set_xlim(2000, 2800)\n",
"ax.set_ylim(1200, 1700)"
"ax.set_ylim(800, 1300)"
]
},
{
Expand Down Expand Up @@ -722,9 +723,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"metadata": {},
"outputs": [],
"source": [
"flt_stars = fits.getdata('jd0q14ctq_stars_ctefmod_flt.fits', ext=1)\n",
Expand All @@ -737,15 +736,13 @@
" markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
"\n",
"ax.set_xlim(2000, 2800)\n",
"ax.set_ylim(1200, 1700)"
"ax.set_ylim(800, 1300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"metadata": {},
"outputs": [],
"source": [
"flc_stars = fits.getdata('jd0q14ctq_stars_ctefmod_flc.fits', ext=1)\n",
Expand All @@ -758,7 +755,7 @@
" markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
"\n",
"ax.set_xlim(2000, 2800)\n",
"ax.set_ylim(1200, 1700)"
"ax.set_ylim(800, 1300)"
]
},
{
Expand Down Expand Up @@ -829,7 +826,7 @@
"metadata": {},
"outputs": [],
"source": [
"noise_image = datasets.make_noise_image(shape, distribution='poisson', mean=40, seed=12345)\n",
"noise_image = make_noise_image(shape, distribution='poisson', mean=40, seed=12345)\n",
"\n",
"wfc1 += noise_image + synth_stars_image\n",
"wfc2 += noise_image + synth_stars_image\n",
Expand Down Expand Up @@ -860,7 +857,7 @@
" markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
"\n",
"ax.set_xlim(2000, 2800)\n",
"ax.set_ylim(1200, 1700)"
"ax.set_ylim(800, 1300)"
]
},
{
Expand Down Expand Up @@ -986,14 +983,14 @@
"rn_C = hdr['READNSEC']\n",
"rn_D = hdr['READNSED']\n",
"\n",
"img_rn_A = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_A)\n",
"img_rn_B = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_B)\n",
"img_rn_C = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_C)\n",
"img_rn_D = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_D)\n",
"img_rn_A = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_A)\n",
"img_rn_B = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_B)\n",
"img_rn_C = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_C)\n",
"img_rn_D = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
" mean=0., stddev=rn_D)\n",
"\n",
"wfc1_rn = np.hstack((img_rn_A, img_rn_B))\n",
"wfc2_rn = np.hstack((img_rn_C, img_rn_D))"
Expand Down Expand Up @@ -1290,7 +1287,7 @@
" markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
"\n",
"ax.set_xlim(2000, 2800)\n",
"ax.set_ylim(1200, 1700)"
"ax.set_ylim(800, 1300)"
]
},
{
Expand All @@ -1309,7 +1306,7 @@
" markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
"\n",
"ax.set_xlim(2000, 2800)\n",
"ax.set_ylim(1200, 1700)"
"ax.set_ylim(800, 1300)"
]
},
{
Expand Down Expand Up @@ -1365,7 +1362,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
"version": "3.12.7"
}
},
"nbformat": 4,
Expand Down
4 changes: 2 additions & 2 deletions notebooks/ACS/acs_cte_forward_model/requirements.txt
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Expand Up @@ -3,6 +3,6 @@ astropy>=5.3.3
astroquery>=0.4.6
matplotlib>=3.7.0
numpy>=1.23.4
photutils>=1.6.0
photutils>=2.0.2
crds>=11.17.7
stsci.tools>=4.1.0
stsci.tools>=4.1.0

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