From e684af40b358aa1ba0180ccdba39fcb9289871b8 Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Mon, 20 May 2019 15:28:57 -0400 Subject: [PATCH 1/9] Initial commit of photutils notebooks --- .../01_photutils_background_estimation.ipynb | 619 ++++++++++ .../01_background_estimation/requirements.txt | 4 + .../02_photutils_source_detection.ipynb | 1100 +++++++++++++++++ .../02_source_detection/requirements.txt | 4 + .../03_photutils_aperture_photometry.ipynb | 978 +++++++++++++++ .../requirements.txt | 4 + .../04_photutils_psf_photometry.ipynb | 253 ++++ .../04_psf_photometry/requirements.txt | 4 + .../photutils_notebook_style.mplstyle | 20 + 9 files changed, 2986 insertions(+) create mode 100644 notebooks/photutils/01_background_estimation/01_photutils_background_estimation.ipynb create mode 100644 notebooks/photutils/01_background_estimation/requirements.txt create mode 100644 notebooks/photutils/02_source_detection/02_photutils_source_detection.ipynb create mode 100644 notebooks/photutils/02_source_detection/requirements.txt create mode 100644 notebooks/photutils/03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb create mode 100644 notebooks/photutils/03_photutils_aperture_photometry/requirements.txt create mode 100644 notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb create mode 100644 notebooks/photutils/04_psf_photometry/requirements.txt create mode 100644 notebooks/photutils/photutils_notebook_style.mplstyle diff --git a/notebooks/photutils/01_background_estimation/01_photutils_background_estimation.ipynb b/notebooks/photutils/01_background_estimation/01_photutils_background_estimation.ipynb new file mode 100644 index 00000000..46991a0e --- /dev/null +++ b/notebooks/photutils/01_background_estimation/01_photutils_background_estimation.ipynb @@ -0,0 +1,619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
![photutils logo](photutils_banner.svg)
\n", + "\n", + "# Background Estimation with `photutils`\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### What is background estimation?\n", + "In order to most accurately do photometric analysis of celestial sources in image data, it is important to estimate and subtract the image background. Any astronomical image will have background noise, due to both detector effects and background emission from the night sky. This noise can be modeled as uniform, or as varying with position on the detector. \n", + "\n", + "The `photutils` package provides tools for estimating 2-dimensional background noise, which can then be subtracted from an image to ensure the most accurate photometry possible.\n", + "\n", + "##### What does this tutorial include?\n", + "This tutorial covers the basics of background estimation and subtraction, including the following methods:\n", + "- Scalar Background Estimation\n", + "- 2-D Background Estimation\n", + "\n", + "##### Which data are used in this tutorial?\n", + "We will be manipulating Hubble eXtreme Deep Field (XDF) data, which was collected using the Advanced Camera for Surveys (ACS) on Hubble between 2002 and 2012. The image we use here is the result of 1.8 million seconds (500 hours!) of exposure time, and includes some of the faintest and most distant galaxies that have ever been observed. \n", + "\n", + "Background subtraction is essential for accurate photometric analysis of astronomical data like the XDF.\n", + "\n", + "##### The methods demonstrated here are available in narrative form within the `photutils.background` [documentation](http://photutils.readthedocs.io/en/stable/background.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
**Note:** This notebook focuses on global background estimation. Local background subtraction with annulus apertures is demonstrated in the [aperture photometry notebook](03_photutils_aperture_photometry.ipynb).
\n", + "\n", + "
**Important:** Before proceeding, please be sure to update your versions of `astropy`, `matplotlib`, and `photutils`, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the [AstroConda](https://astroconda.readthedocs.io) distribution.
\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import necessary packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import packages that we will use to perform arithmetic functions and visualize data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from astropy.io import fits\n", + "import astropy.units as u\n", + "from astropy.stats import sigma_clipped_stats, SigmaClip\n", + "from astropy.visualization import ImageNormalize, LogStretch\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import LogLocator\n", + "\n", + "# Show plots in the notebook\n", + "% matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [shared style file](photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('photutils_notebook_style.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", + "\n", + "(Generally, the best package for web queries of astronomical data is `astroquery`; however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with `astroquery`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://archive.stsci.edu/pub/hlsp/xdf/hlsp_xdf_hst_acswfc-60mas_hudf_f435w_v1_sci.fits'\n", + "with fits.open(url) as hdulist:\n", + " hdulist.info()\n", + " data = hdulist[0].data\n", + " header = hdulist[0].header" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Modifying data\n", + "For the purposes of this notebook example, we're going to add a background effect to this data, but don't worry about this. (Pay no attention to that man behind the curtain!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "mask = data == 0\n", + "n_data_pixels = len(data[~mask])\n", + "background = np.linspace(-1e-4, 5e-4, num=n_data_pixels)\n", + "\n", + "modified_data = np.copy(data)\n", + "modified_data[~mask] += background[:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data representation\n", + "\n", + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons (counts) per second." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.nddata import CCDData\n", + "unit = u.ct / u.s\n", + "xdf_image = CCDData(modified_data, unit=unit, meta=header)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "xdf_image_clipped = np.clip(xdf_image, 1e-4, None) # clip to plot with logarithmic stretch\n", + "fitsplot = ax1.imshow(xdf_image_clipped, norm=norm_image)\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Note: Double-click on any inline plot to zoom in.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mask data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You probably noticed that a large portion of the data is equal to zero. The data we are using is a reduced mosaic that combines many different exposures, and that has been rotated such that not all of the array holds data. \n", + "\n", + "We want to **mask** out the non-data, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the mask\n", + "xdf_image.mask = xdf_image.data == 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", + "plt.tight_layout()\n", + "\n", + "# Plot the mask\n", + "ax1.imshow(xdf_image.mask, cmap='Greys')\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Mask')\n", + "\n", + "# Plot the masked data\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "fitsplot = ax2.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar_ax = fig.add_axes([1, 0.09, 0.03, 0.87])\n", + "cbar = fig.colorbar(fitsplot, cbar_ax, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax2.set_xlabel('X (pixels)')\n", + "ax2.set_title('Masked Data')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform scalar background estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the data are properly masked, we can calculate some basic statistical values to do a scalar estimation of the image background. \n", + "\n", + "By \"scalar estimation\", we mean the calculation of a single value (such as the mean or median) to represent the value of the background for our entire two-dimensional dataset. This is in contrast to a two-dimensional background, where the estimated background is represented as an array of values that can vary spatially with the dataset. We will calculate a 2D background in the upcoming section.\n", + "\n", + "Here we will calculate the mean, median, and mode using sigma clipping. With sigma clipping, the data is iteratively clipped to exclude data points outside of a certain sigma (standard deviation), thus removing some of the noise from the data before determining statistical values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, iters=5, mask=xdf_image.mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what difference does this sigma clipping make? And how important is masking, anyway? Let's visualize these statistics to get an idea:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the data without masking\n", + "stats_nomask = sigma_clipped_stats(xdf_image.data, sigma=3.0, iters=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n", + "\n", + "# Plot histograms of the data\n", + "flux_range = (-.5e-3, 1.5e-3)\n", + "ax1.hist(xdf_image[~xdf_image.mask], bins=100, range=flux_range)\n", + "ax2.hist(xdf_image[~xdf_image.mask], bins=100, range=flux_range)\n", + "\n", + "# Plot lines for each kind of mean\n", + "ax1.axvline(mean, label='Masked \\& Clipped', c='C1', lw=3)\n", + "ax1.axvline(np.average(xdf_image[~xdf_image.mask]), label='Masked', c='C2', ls='--', lw=3)\n", + "ax1.axvline(stats_nomask[0], label='Clipped', c='C3', ls='-.', lw=3)\n", + "ax1.axvline(np.average(xdf_image), label='Neither', c='C6', ls=':', lw=3)\n", + "\n", + "ax1.set_xlim(flux_range)\n", + "ax1.set_xlabel(r'Flux Count Rate ($e^{-1}/s$)', fontsize=14)\n", + "ax1.set_ylabel('Frequency', fontsize=14)\n", + "ax1.set_title('Effect of Sigma-Clipping and Masking on Mean', fontsize=16)\n", + "ax1.legend(fontsize=11)\n", + "\n", + "\n", + "# Plot lines for each kind of median\n", + "# Note: use np.ma.median rather than np.median for masked arrays\n", + "ax2.axvline(median, label='Masked \\& Clipped', c='C1', lw=3)\n", + "ax2.axvline(np.ma.median(xdf_image[~xdf_image.mask]), label='Masked', c='C2', ls='--', lw=3)\n", + "ax2.axvline(stats_nomask[1], label='Clipped', c='C3', ls='-.', lw=3)\n", + "ax2.axvline(np.ma.median(xdf_image), label='Neither', c='C6', ls=':', lw=3)\n", + "\n", + "ax2.set_xlim(flux_range)\n", + "ax2.set_xlabel(r'Flux Count Rate ($e^{-1}/s$)', fontsize=14)\n", + "ax2.set_title('Effect of Sigma-Clipping and Masking on Median', fontsize=16)\n", + "ax2.legend(fontsize=11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just from simply looking at the distribution of the data, it is pretty easy to see how sigma-clipping and masking improve the calculation of the mean and median.\n", + "\n", + "But enough looking at numbers, let's actually remove the background from the data. By using the `subtract()` method of the `CCDData` class, we can subtract the mean background while maintaining the metadata and mask of our original CCDData object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the scalar background subtraction, maintaining metadata, unit, and mask\n", + "xdf_scalar_bkgdsub = xdf_image.subtract(mean * u.ct / u.s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", + "plt.tight_layout()\n", + "\n", + "# Define the normalization\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "xdf_scalar_bkgdsub_clipped = np.clip(xdf_scalar_bkgdsub, 1e-4, None) # clip to plot with logarithmic stretch\n", + "\n", + "# Plot the original data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Original Data')\n", + "\n", + "# Plot the subtracted data\n", + "fitsplot = ax2.imshow(np.ma.masked_where(xdf_scalar_bkgdsub.mask, xdf_scalar_bkgdsub_clipped), norm=norm_image)\n", + "ax2.set_xlabel('X (pixels)')\n", + "ax2.set_title('Scalar Background-Subtracted Data')\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar_ax = fig.add_axes([1, 0.09, 0.03, 0.87])\n", + "cbar = fig.colorbar(fitsplot, cbar_ax, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that both plots above use the same normalization scheme, represented by the colorbar at right. That is to say, if two pixels have the same color in both arrays, they have the same value.\n", + "\n", + "That looks better! You can tell that the background is darker, especially in the top corner. However, the background still does not seem to be completely removed. In this case, the background varies spatially; it is two-dimensional. Thankfully, `photutils` includes functions to remove background like this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

**Exercises:**


\n", + "Perform a median scalar background subtraction on our sigma-clipped data. Plot it and visually inspect it. How does it compare to the original data?\n", + "

\n", + "Compare the median background subtraction to the mean background subtraction. Which is better?\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform 2-D background estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Background2D` class allows users to model 2-dimensional backgrounds, by evaluating the background signal in small boxes, and smoothing these boxes to reconstruct a continuous 2D background. The class includes the following arguments/attributes:\n", + "* **`box_size`** - the size of the boxes used to calculate the background. This should be larger than individual sources, yet still small enough to encompass changes in the background.\n", + "* **`filter_size`** - the size of the median filter used to smooth the final 2D background.\n", + "* **`filter_threshold`** - threshold below which the smoothing median filter will not be applied.\n", + "* **`sigma_clip`** - an ` astropy.stats.SigmaClip` object that is used to specify the sigma and number of iterations used to sigma-clip the data before background calculations are performed.\n", + "* **`bkg_estimator`** - the method used to perform the background calculation in each box (mean, median, SExtractor algorithm, etc.).\n", + "\n", + "For this example, we will use the `MeanBackground` estimator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils.background import Background2D, MeanBackground" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma_clip = SigmaClip(sigma=3., iters=5)\n", + "bkg_estimator = MeanBackground()\n", + "bkg = Background2D(xdf_image, box_size=200, filter_size=(10, 10), mask=xdf_image.mask,\n", + " sigma_clip=sigma_clip, bkg_estimator=bkg_estimator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, what does this 2D background look like? Where were the boxes placed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "background_clipped = np.clip(bkg.background, 1e-4, None) # clip to plot with logarithmic stretch\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, background_clipped), norm=norm_image)\n", + "\n", + "# Plot the meshes\n", + "bkg.plot_meshes(outlines=True, color='lightgrey')\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('2D Estimated Background')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And how does the data look if we use this background subtraction method (again maintaining the attributes of the CCDData object)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the 2D background subtraction, maintaining metadata, unit, and mask\n", + "xdf_2d_bkgdsub = xdf_image.subtract(bkg.background * u.ct / u.s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", + "plt.tight_layout()\n", + "\n", + "# Define the normalization\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "xdf_2d_bkgdsub_clipped = np.clip(xdf_2d_bkgdsub, 1e-4, None) # clip to plot with logarithmic stretch\n", + "\n", + "# Plot the scalar-subtracted data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_scalar_bkgdsub.mask, xdf_scalar_bkgdsub_clipped), norm=norm_image)\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_title('Scalar Background-Subtracted Data')\n", + "\n", + "# Plot the 2D-subtracted data\n", + "fitsplot = ax2.imshow(np.ma.masked_where(xdf_2d_bkgdsub.mask, xdf_2d_bkgdsub_clipped), norm=norm_image)\n", + "ax2.set_xlabel('X (pixels)')\n", + "ax2.set_title('2D Background-Subtracted Data')\n", + "\n", + "# Plot the colorbar\n", + "cbar_ax = fig.add_axes([1, 0.09, 0.03, 0.87])\n", + "cbar = fig.colorbar(fitsplot, cbar_ax, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how much more even the 2D background-subtracted image looks; especially the difference between these two images in the bottom corner and top corner. This makes sense, as the background that `Background2D` identified was a gradient from the top corner down to the bottom!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

**Exercises:**


\n", + "Calculate the standard deviation (with sigma-clipping and masking!) for the original data, the scalar background-subtracted data, and the 2D background-subtracted data. How do the values compare? Which has the smallest standard deviation?

\n", + "\n", + "Notice that the difference between each dataset's standard deviation is small - why might this be?\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "The `photutils` package provides a powerful tool in the `Background2D` class, allowing users to easily estimate and subtract spatially variant background signals from their data.\n", + "\n", + "**To continue with this `photutils` tutorial, go on to the [source detection notebook](02_photutils_source_detection.ipynb).**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "September 2018\n", + "\n", + "Author: Lauren Chambers (lchambers@stsci.edu), Erik Tollerud (etollerud@stsci.edu)\n", + "\n", + "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." + ] + } + ], + "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": "3.5.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/photutils/01_background_estimation/requirements.txt b/notebooks/photutils/01_background_estimation/requirements.txt new file mode 100644 index 00000000..a0d3505e --- /dev/null +++ b/notebooks/photutils/01_background_estimation/requirements.txt @@ -0,0 +1,4 @@ +astropy>=3.1.2 +matplotlib>2.2.2 +numpy>=1.13.3 +photutils>=0.4 diff --git a/notebooks/photutils/02_source_detection/02_photutils_source_detection.ipynb b/notebooks/photutils/02_source_detection/02_photutils_source_detection.ipynb new file mode 100644 index 00000000..ead20219 --- /dev/null +++ b/notebooks/photutils/02_source_detection/02_photutils_source_detection.ipynb @@ -0,0 +1,1100 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "[\n", + "\n", + "](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", + "\n", + "# Source Detection with `photutils`\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### What is source detection?\n", + "In order to do photometry on astronomical image data, one must first determine the locations of the sources in the image. Source detection methods find sources algorithmically, by looking for regions of an image where the signal from a source is statistically higher than the signal from background noise. Some algorithms search for sources whose profiles match specific data models, such as a 2-D Gaussian, while others simply look for local maxima.\n", + "\n", + "The `photutils` package provides a variety of tools that use different detection algorithms to locate sources in an image.\n", + "\n", + "##### What does this tutorial include?\n", + "This tutorial covers different tools for source detection with `photutils`, including the following methods:\n", + "* Source Detection with `DAOStarFinder`\n", + "* Source Detection with `IRAFStarFinder`\n", + "* Source Detection with `find_peaks`\n", + "* Image Segmentation\n", + "\n", + "##### Which data are used in this tutorial?\n", + "We will be manipulating Hubble eXtreme Deep Field (XDF) data, which was collected using the Advanced Camera for Surveys (ACS) on Hubble between 2002 and 2012. The image we use here is the result of 1.8 million seconds (500 hours!) of exposure time, and includes some of the faintest and most distant galaxies that have ever been observed. \n", + "\n", + "##### The methods demonstrated here are available in narrative form within the `photutils.detection` [documentation](http://photutils.readthedocs.io/en/stable/detection.html) and `photutils.segmentation` [documentation](http://photutils.readthedocs.io/en/stable/segmentation.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**Important:** Before proceeding, please be sure to update your versions of `astropy`, `matplotlib`, and `photutils`, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the [AstroConda](https://astroconda.readthedocs.io) distribution.\n", + "\n", + "
\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import necessary packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import packages that we will use to perform arithmetic functions and visualize data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "import astropy.units as u\n", + "from astropy.nddata import CCDData\n", + "from astropy.stats import sigma_clipped_stats, SigmaClip\n", + "from astropy.visualization import ImageNormalize, LogStretch\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import LogLocator\n", + "import numpy as np\n", + "from photutils.background import Background2D, MeanBackground\n", + "\n", + "# Show plots in the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [style file shared with the other photutils tutorials](photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('photutils_notebook_style.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", + "\n", + "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://archive.stsci.edu/pub/hlsp/xdf/hlsp_xdf_hst_acswfc-60mas_hudf_f435w_v1_sci.fits'\n", + "with fits.open(url) as hdulist:\n", + " hdulist.info()\n", + " data = hdulist[0].data\n", + " header = hdulist[0].header" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As explained in the [previous notebook](01_photutils_background_estimation.ipynb) on background estimation, it is important to **mask** these data, as a large portion of the values are equal to zero. We will mask out the non-data portions of the image array, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the mask\n", + "mask = data == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons (counts) per second." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "unit = u.electron/ u.s\n", + "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Set up the normalization and colormap\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch(), clip=False)\n", + "cmap = plt.get_cmap('viridis')\n", + "cmap.set_over(cmap.colors[-1])\n", + "cmap.set_under(cmap.colors[0])\n", + "cmap.set_bad('white') # Show masked data as white\n", + "xdf_image_clipped = np.clip(xdf_image, 1e-4, None) # clip to plot with logarithmic stretch\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Tip: Double-click on any inline plot to zoom in.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Source Detection with `DAOStarFinder`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the `DAOStarFinder` [class](http://photutils.readthedocs.io/en/stable/api/photutils.DAOStarFinder.html), `photutils` provides users with an easy application of the popular [DAOFIND](http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?daofind) algorithm ([Stetson 1987, PASP 99, 191](http://adsabs.harvard.edu/abs/1987PASP...99..191S)), originally developed at the Dominion Astrophysical Observatory. \n", + "\n", + "This algorithm detects sources by:\n", + "* Searching for local maxima\n", + "* Selecting only sources with peak amplitude above a defined threshold\n", + "* Selecting sources with sizes and shapes that match a 2-D Gaussian kernel (circular or elliptical)\n", + "\n", + "It returns:\n", + "* Location of the source centroid\n", + "* Parameters reflecting the source's sharpness and roundness\n", + "\n", + "Generally, the threshold that source detection algorithms use is defined as a multiple of the standard deviation. So first, we need to calculate statistics for the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5, mask=xdf_image.mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's run the `DAOStarFinder` algorithm on our data and see what it finds. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import DAOStarFinder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "daofind = DAOStarFinder(fwhm=5.0, threshold=20.*std)\n", + "sources_dao = daofind(xdf_image * ~xdf_image.mask) \n", + "print(sources_dao)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "ax1.scatter(sources_dao['xcentroid'], sources_dao['ycentroid'], s=30, marker='o', \n", + " lw=1, alpha=0.7, facecolor='None', edgecolor='r')\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('DAOFind Sources')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's randomly pull out some of these sources to get a closer look at them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(3,3, figsize=(3, 3))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.1)\n", + "\n", + "cutout_size = 20\n", + "\n", + "srcs = np.random.permutation(sources_dao)[:axs.size]\n", + "for ax, src in zip(axs.ravel(), srcs):\n", + " slc = (slice(int(src['ycentroid'] - cutout_size), int(src['ycentroid'] + cutout_size)),\n", + " slice(int(src['xcentroid'] - cutout_size), int(src['xcentroid'] + cutout_size)))\n", + " ax.imshow(xdf_image_clipped[slc], norm=norm_image)\n", + " ax.text(2, 2, str(src['id']), color='w', va='top')\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

Exercises:


\n", + "\n", + "Re-run the `DAOStarFinder` algorithm with a smaller threshold (like 5σ), and plot the sources that it finds. Do the same, but with a larger threshold (like 100σ). How did changing the threshold affect the results?\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source Detection with `IRAFStarFinder`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly to `DAOStarFinder`, `IRAFStarFinder` is a class that implements a pre-existing algorithm that is widely used within the astronomical community. This class uses the `starfind` [algorithm](http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?starfind) that was originally part of IRAF.\n", + "\n", + "`IRAFStarFinder` is fundamentally similar to `DAOStarFinder` in that it detects sources by finding local maxima above a certain threshold that match a Gaussian kernel. However, `IRAFStarFinder` differs in the following ways:\n", + "* Does not allow users to specify an elliptical Gaussian kernel\n", + "* Uses image moments to calculate the centroids, roundness, and sharpness of objects\n", + "\n", + "Let's run the `IRAFStarFinder` algorithm on our data, with the same FWHM and threshold, and see how its results differ from `DAOStarFinder`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import IRAFStarFinder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iraffind = IRAFStarFinder(fwhm=5.0, threshold=20.*std)\n", + "sources_iraf = iraffind(xdf_image * ~xdf_image.mask) \n", + "print(sources_iraf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "ax1.scatter(sources_iraf['xcentroid'], sources_iraf['ycentroid'], s=30, marker='o', \n", + " lw=1, alpha=0.7, facecolor='None', edgecolor='r')\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('IRAFFind Sources')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, let's randomly select some sources for a closer look:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(3,3, figsize=(3, 3))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.1)\n", + "\n", + "cutout_size = 20\n", + "\n", + "srcs = np.random.permutation(sources_iraf)[:axs.size]\n", + "for ax, src in zip(axs.ravel(), srcs):\n", + " slc = (slice(int(src['ycentroid'] - cutout_size), int(src['ycentroid'] + cutout_size)),\n", + " slice(int(src['xcentroid'] - cutout_size), int(src['xcentroid'] + cutout_size)))\n", + " ax.imshow(xdf_image_clipped[slc], norm=norm_image)\n", + " ax.text(2, 2, str(src['id']), color='w', va='top')\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

Exercises:


\n", + "\n", + "Re-run the `IRAFStarFinder` algorithm with a smaller full-width-half-max (FWHM) – try 3 pixels – and plot the sources that it finds. Do the same, but with a larger FWHM (like 10 pixels). How did changing the FWHM affect the results? What astronomical objects might be better captures by smaller FWHM? Larger?\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note: Comparing `DAOStarFinder` and `IRAFStarFinder`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have noticed that the `IRAFStarFinder` algorithm only found 211 sources in our data - 14% of what `DAOStarFinder` found. Why is this?\n", + "\n", + "The answer comes down to the default settings for the two algorithms: (1) there are differences in the upper and lower bounds on the requirements for source roundness and sharpness, and (2) `IRAFStarFinder` includes a minimum separation between sources that `DAOStarFinder` does not have:\n", + "\n", + "| | `IRAFStarFinder` | `DAOStarFinder` |\n", + "|------|------|------|\n", + "| sharplo | 0.5 | 0.2 |\n", + "| sharphi | 2.0 | 1.0 |\n", + "| roundlo | 0.0 | -1.0 |\n", + "| roundhi | 0.2 | 1.0 |\n", + "| minsep_fwhm | 1.5 * FWHM | N/A |\n", + "\n", + "Thinking about this, *it then makes sense* that `IRAFStarFinder` would find fewer sources. It has stricter restrictions on source roundness, meaning that it eliminates more elliptical galactic sources (this is the eXtreme Deep Field, after all!), and the minimum separation requirement further rules out sources that are too close to one another.\n", + "\n", + "If we set all these parameters to be equivalent, though, we should find much better agreement between the two methods:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iraffind_match = IRAFStarFinder(fwhm=5.0, threshold=20.*std, \n", + " sharplo=0.2, sharphi=1.0, \n", + " roundlo=-1.0, roundhi=1.0,\n", + " minsep_fwhm=0.0)\n", + "sources_iraf_match = iraffind_match(xdf_image * ~xdf_image.mask) \n", + "print(sources_iraf_match)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number of detected sources are in much better agreement now - 1415 versus 1470 - but the improved agreement can also be seen by plotting the location of these sources:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 6))\n", + "plt.tight_layout()\n", + "\n", + "# Plot the DAOStarFinder data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "ax1.scatter(sources_dao['xcentroid'], sources_dao['ycentroid'], s=30, marker='o', \n", + " lw=1, alpha=0.7, facecolor='None', edgecolor='r')\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('DAOStarFinder Sources')\n", + "\n", + "# Plot the IRAFStarFinder data\n", + "fitsplot = ax2.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "ax2.scatter(sources_iraf_match['xcentroid'], sources_iraf_match['ycentroid'], \n", + " s=30, marker='o', lw=1, alpha=0.7, facecolor='None', edgecolor='r')\n", + "ax2.set_xlabel('X (pixels)')\n", + "ax2.set_title('IRAFStarFinder Sources')\n", + "\n", + "# Define the colorbar\n", + "cbar_ax = fig.add_axes([1, 0.09, 0.03, 0.87])\n", + "cbar = plt.colorbar(fitsplot, cbar_ax, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take this example as reminder to be mindful when selecting a source detection algorithm, and when defining algorithm parameters! Don't be afraid to play around with the parameters and investigate how that affects your results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source Detection with `find_peaks`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more general source detection cases that do not require comparison with models, `photutils` offers the `find_peaks` function. \n", + "\n", + "This function simply finds sources by identifying local maxima above a given threshold and separated by a given distance, rather than trying to fit data to a given model. Unlike the previous detection algorithms, `find_peaks` does not necessarily calculate objects' centroids. Unless the `subpixel` argument is set to `True`, `find_peaks` will return just the integer value of the peak pixel for each source.\n", + "\n", + "This algorithm is particularly useful for identifying non-stellar sources or heavily distorted sources in image data.\n", + "\n", + "Let's see how it does:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import find_peaks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sources_findpeaks = find_peaks(xdf_image.data, mask=xdf_image.mask, \n", + " threshold=20.*std, box_size=30, subpixel=True) \n", + "print(sources_findpeaks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "ax1.scatter(sources_findpeaks['x_peak'], sources_findpeaks['y_peak'], s=30, marker='o', \n", + " lw=1, alpha=0.7, facecolor='None', edgecolor='r')\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('find\\_peaks Sources')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And a closer look:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(3,3, figsize=(3, 3))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.1)\n", + "\n", + "cutout_size = 20\n", + "\n", + "srcs = np.random.permutation(sources_findpeaks)[:axs.size]\n", + "for ax, src in zip(axs.ravel(), srcs):\n", + " slc = (slice(int(src['y_peak'] - cutout_size), int(src['y_peak'] + cutout_size)),\n", + " slice(int(src['x_peak'] - cutout_size), int(src['x_peak'] + cutout_size)))\n", + " ax.imshow(xdf_image_clipped[slc], norm=norm_image)\n", + " src_id = np.where((sources_findpeaks['x_peak'] == src['x_peak']) & \n", + " (sources_findpeaks['y_peak'] == src['y_peak']))[0][0]\n", + " ax.text(2, 2, str(src_id), color='w', va='top')\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Detection Methods\n", + "\n", + "Let's compare how each of these different strategies did.\n", + "\n", + "First, how many sources did each method find?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('''DAOStarFinder: {} sources\n", + "IRAFStarFinder: {} sources\n", + "find_peaks: {} sources'''.format(len(sources_dao), len(sources_iraf), len(sources_findpeaks)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, how many of these sources match? We can answer this question by using [sets](https://docs.python.org/3/library/stdtypes.html#set-types-set-frozenset) to compare the centroids of the different sources (rounding to the first decimal place)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Make lists of centroid coordinates\n", + "centroids_dao = [(x, y) for x, y in sources_dao['xcentroid', 'ycentroid']]\n", + "centroids_iraf = [(x, y) for x, y in sources_iraf['xcentroid', 'ycentroid']]\n", + "centroids_findpeaks = [(x, y) for x, y in sources_findpeaks['x_centroid', 'y_centroid']]\n", + "\n", + "# Round those coordinates to the first decimal place and convert them to be sets\n", + "rounded_centroids_dao = set([(round(x, 1), round(y, 1)) for x, y in centroids_dao])\n", + "rounded_centroids_iraf = set([(round(x, 1), round(y, 1)) for x, y in centroids_iraf])\n", + "rounded_centroids_findpeaks = set([(round(x, 1), round(y, 1)) for x, y in centroids_findpeaks])\n", + "\n", + "# Examine the intersections of different sets to determine which sources are shared\n", + "all_match = rounded_centroids_dao.intersection(rounded_centroids_iraf).intersection(rounded_centroids_findpeaks)\n", + "dao_iraf_match = rounded_centroids_dao.intersection(rounded_centroids_iraf)\n", + "dao_findpeaks_match = rounded_centroids_dao.intersection(rounded_centroids_findpeaks)\n", + "iraf_findpeaks_match = rounded_centroids_iraf.intersection(rounded_centroids_findpeaks)\n", + "\n", + "print('''Matching sources found by:\n", + " All methods: {}\n", + " DAOStarFinder & IRAFStarFinder: {}\n", + " DAOStarFinder & find_peaks: {}\n", + " IRAFStarFinder & find_peaks: {}'''\n", + " .format(len(all_match), len(dao_iraf_match), \n", + " len(dao_findpeaks_match), len(iraf_findpeaks_match)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And just for fun, let's plot these matching sources. (The colors chosen to represent different sets are from [Paul Tol's guide for accessible color schemes](https://personal.sron.nl/~pault/).)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "ax1.scatter([x for x, y in list(dao_findpeaks_match)], [y for x, y in list(dao_findpeaks_match)],\n", + " s=30, marker='s', lw=1.5, facecolor='None', edgecolor='#EE7733',\n", + " label='Found by DAO \\& find\\_peaks')\n", + "ax1.scatter([x for x, y in list(dao_iraf_match)], [y for x, y in list(dao_iraf_match)],\n", + " s=30, marker='D', lw=1.5, facecolor='None', edgecolor='#EE3377',\n", + " label='Found by DAO \\& IRAF')\n", + "ax1.scatter([x for x, y in list(iraf_findpeaks_match)], [y for x, y in list(iraf_findpeaks_match)],\n", + " s=30, marker='o', lw=1.5, facecolor='None', edgecolor='#0077BB',\n", + " label='Found by IRAF \\& find\\_peaks')\n", + "ax1.scatter([x for x, y in list(all_match)], [y for x, y in list(all_match)],\n", + " s=30, marker='o', lw=1.2, linestyle=':',facecolor='None', edgecolor='#BBBBBB',\n", + " label='Found by all methods')\n", + "ax1.legend()\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Sources Found by Different Methods')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Remember that you can double-click on the plot to zoom in and look around!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Detection Algorithms\n", + "\n", + "If none of the algorithms we've reviewed above do exactly what you need, `photutils` also provides infrastructure for you to generate and use your own source detection algorithm: the `StarFinderBase` object can be inherited and used to develop new star-finding classes. Take a look at the [documentation](https://photutils.readthedocs.io/en/latest/api/photutils.detection.StarFinderBase.html#photutils.detection.StarFinderBase) for more information.\n", + "\n", + "If you do go that route, remember that `photutils` is open-developed; you would be very welcome to [open a pull request](https://github.com/astropy/photutils/blob/master/CONTRIBUTING.rst) and incorporate your new star finder into the `photutils` source code - for everyone to use!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Image Segmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beyond traditional source detection methods, an additional option for identifying sources in image data is a process called **image segmentation**. This method identifies and labels contiguous (connected) objects within an image. \n", + "\n", + "You might have noticed that, in the previous source detection algorithms, large and extended sources are often incorrectly identified as more than one source. Segmentation would label all the pixels within a large galaxy as belonging to the same object, and would allow the user to then measure the photometry, centroid, and morphology of the entire object at once.\n", + "\n", + "#### Creating a `SegmentationImage`\n", + "\n", + "In `photutils`, image segmentation maps are created using the threshold method in the `detect_sources()` function. This method identifies all of the objects in the data that have signals above a determined **`threshold`** (usually defined as a multiple of the standard deviation) and that have more than a defined number of adjoining pixels, **`npixels`**. The data can also optionally be smoothed using a kernel, **`filter_kernel`**, before applying the threshold cut.\n", + "\n", + "The `detect_sources()` function returns a `SegmentationImage` object: an array in which each object is labeled with an integer. As a simple example, a segmentation map containing two distinct sources might look like this:\n", + "\n", + "```\n", + "0 0 0 0 0 0 0 0 0 0\n", + "0 1 1 0 0 0 0 0 0 0\n", + "1 1 1 1 1 0 0 0 2 0\n", + "1 1 1 1 0 0 0 2 2 2\n", + "1 1 1 0 0 0 2 2 2 2\n", + "1 1 1 1 0 0 0 2 2 0\n", + "1 1 0 0 0 0 2 2 0 0\n", + "0 1 0 0 0 0 2 0 0 0\n", + "0 0 0 0 0 0 0 0 0 0\n", + "```\n", + "where all of the pixels labeled `1` belong to the first source, all those labeled `2` belong to the second, and all null pixels are designated to be background.\n", + "\n", + "Let's see what the segmentation map for our XDF data will look like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import detect_sources\n", + "from photutils.utils import random_cmap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define threshold and minimum object size\n", + "threshold = 5. * std\n", + "npixels = 15\n", + "\n", + "# Create a segmentation image\n", + "segm = detect_sources(xdf_image.data, threshold, npixels)\n", + "\n", + "print('Found {} sources'.format(segm.max_label))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 6))\n", + "plt.tight_layout()\n", + "\n", + "# Plot the original data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Original Data')\n", + "\n", + "# Plot the segmentation image\n", + "rand_cmap = random_cmap(random_state=12345)\n", + "rand_cmap.set_under('black')\n", + "segplot = ax2.imshow(np.ma.masked_where(xdf_image.mask, segm), vmin=1, cmap=rand_cmap)\n", + "ax2.set_xlabel('X (pixels)')\n", + "ax2.set_title('Segmentation Map')\n", + "\n", + "# Define the colorbar\n", + "cbar_ax = fig.add_axes([1, 0.09, 0.03, 0.87])\n", + "cbar = plt.colorbar(segplot, cbar_ax)\n", + "cbar.set_label('Object Label', rotation=270, labelpad=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the sources in original data to those in the segmentation image. Each color in the segmentation map denotes a separate source.\n", + "\n", + "You can easily see that larger galaxies are shown in the segmentation map as contiguous objects of the same color - for example, the two yellow and pink galaxies near (1200, 2500). Each pixel containing light from the same galaxy has been labeled as belonging to the same object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a closer look, let's see what the sources we found with `find_peaks` look like in this segmentation map:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(3,3, figsize=(3, 3))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.1)\n", + "\n", + "cutout_size = 20\n", + "\n", + "srcs = np.random.permutation(sources_findpeaks)[:axs.size]\n", + "for ax, src in zip(axs.ravel(), srcs):\n", + " slc = (slice(int(src['y_peak'] - cutout_size), int(src['y_peak'] + cutout_size)),\n", + " slice(int(src['x_peak'] - cutout_size), int(src['x_peak'] + cutout_size)))\n", + " ax.imshow(segm.data[slc], cmap=rand_cmap, vmin=1, vmax=len(sources_findpeaks))\n", + " src_id = np.where((sources_findpeaks['x_peak'] == src['x_peak']) & \n", + " (sources_findpeaks['y_peak'] == src['y_peak']))[0][0]\n", + " ax.text(2, 2, str(src_id), color='w', va='top')\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

Exercises:


\n", + "\n", + "Recompute the `SegmentationImage`, but alter the threshold and the minimum number of pixels in a source. How does changing the threshold affect the results? What about changing the number of pixels?\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Analyzing `source_properties`\n", + "\n", + "Once we have a `SegmentationImage` object, `photutils` provides many powerful tools to manipulate and analyze the identified objects. \n", + "\n", + "Individual objects within the segmentation map can be altered using methods such as `relabel` to change the labels of objects, `remove_labels` to remove objects, or `deblend_sources` to separating overlapping sources that were incorrectly labeled as one source.\n", + "\n", + "However, perhaps the most powerful aspect of the `SegmentationImage` is the ability to create a catalog using `source_properties` to measure the centroids, photometry, and morphology of the detected objects:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import source_properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "catalog = source_properties(xdf_image.data, segm)\n", + "table = catalog.to_table()\n", + "print(table)\n", + "print(table.colnames)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Creating apertures from segmentation data\n", + "\n", + "We can use this information to create isophotal ellipses for each identified source. These ellipses can also later be used as photometric apertures!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import EllipticalAperture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the approximate isophotal extent\n", + "r = 4.\n", + "\n", + "# Create the apertures\n", + "apertures = []\n", + "for obj in catalog:\n", + " position = (obj.xcentroid.value, obj.ycentroid.value)\n", + " a = obj.semimajor_axis_sigma.value * r\n", + " b = obj.semiminor_axis_sigma.value * r\n", + " theta = obj.orientation.value\n", + " apertures.append(EllipticalAperture(position, a, b, theta=theta))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "\n", + "# Plot the apertures\n", + "for aperture in apertures:\n", + " aperture.plot(color='red', lw=1, alpha=0.7, ax=ax1)\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Segmentation Image Apertures')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

Exercises:


\n", + "\n", + "Play with the isophotal extent of the elliptical apertures (defined above as `r`). Observe how changing this value affects the apertures that are created.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear that using `photutils` for image segmentation can allow users to generate highly customized apertures - great for complex data that contain many different kinds of celestial sources." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Conclusions\n", + "\n", + "The `photutils` package provides users with a variety of methods for detecting sources in their data, from familar algorithms such as `DAOFind` and `starfind`, to more complex and customizable image segmentation algorithms. These methods allow for easy creation of a diverse array of apertures that can be used for photometric analysis.\n", + "\n", + "**To continue with this `photutils` tutorial, go on to the [aperture photometry](03_photutils_aperture_photometry.ipynb) or [PSF photometry notebook](04_photutils_psf_photometry.ipynb).**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Additional Resources\n", + "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## About this Notebook\n", + "**Authors:** Lauren Chambers (lchambers@stsci.edu), Erik Tollerud (etollerud@stsci.edu), Tom Wilson (towilson@stsci.edu)\n", + "
**Updated:** May 2019" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#title_ID)\n", + "\"STScI" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/photutils/02_source_detection/requirements.txt b/notebooks/photutils/02_source_detection/requirements.txt new file mode 100644 index 00000000..a0d3505e --- /dev/null +++ b/notebooks/photutils/02_source_detection/requirements.txt @@ -0,0 +1,4 @@ +astropy>=3.1.2 +matplotlib>2.2.2 +numpy>=1.13.3 +photutils>=0.4 diff --git a/notebooks/photutils/03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb b/notebooks/photutils/03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb new file mode 100644 index 00000000..0368dbe4 --- /dev/null +++ b/notebooks/photutils/03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb @@ -0,0 +1,978 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "[\n", + "\n", + "](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", + "# Aperture Photometry with `photutils`\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### What is aperture photometry?\n", + "The most common method to measure the flux of a celestial source is aperture photometry. This kind of photometry measures the amount of flux within a region of an astronomical image of defined shape and size (an aperture) surrounding a source. The ideal aperture would capture all of the flux emitted by a desired source, and none of the flux emitted by the surrounding sky or nearby sources. Especially when performing photometry on image data that includes a number of sources with varying size and shape, it is important to perform aperture corrections to account for imperfect apertures and better constrain photometric errors.\n", + "\n", + "The `photutils` package provides tools for performing photometry with apertures of various shapes.\n", + "\n", + "##### What does this tutorial include?\n", + "This tutorial covers how to perform aperture photometry with `photutils`, including the following methods:\n", + "* Creating Apertures\n", + " * Circular Apertures\n", + " * Elliptical Apertures\n", + " * Sky Apertures with WCS\n", + "* Performing Aperture Photometry\n", + "* Calculating Aperture Corrections with Local Background Subtraction\n", + "\n", + "##### Which data are used in this tutorial?\n", + "We will be manipulating Hubble eXtreme Deep Field (XDF) data, which was collected using the Advanced Camera for Surveys (ACS) on Hubble between 2002 and 2012. The image we use here is the result of 1.8 million seconds (500 hours!) of exposure time, and includes some of the faintest and most distant galaxies that have ever been observed. \n", + "\n", + "##### The methods demonstrated here are available in narrative form within the `photutils.aperture` [documentation](http://photutils.readthedocs.io/en/stable/aperture.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**Important:** Before proceeding, please be sure to install or update your [AstroConda](https://astroconda.readthedocs.io) distribution. This notebook may not work properly with older versions of AstroConda.\n", + "\n", + "
\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import necessary packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import packages that we will use to perform arithmetic functions and visualize data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "import astropy.units as u\n", + "from astropy.nddata import CCDData\n", + "from astropy.stats import sigma_clipped_stats, SigmaClip\n", + "from astropy.visualization import ImageNormalize, LogStretch\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import LogLocator\n", + "import numpy as np\n", + "from photutils.background import Background2D, MeanBackground\n", + "\n", + "# Show plots in the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [shared style file](photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('photutils_notebook_style.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", + "\n", + "(Generally, the best package for web queries of astronomical data is `astroquery`; however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with `astroquery`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://archive.stsci.edu/pub/hlsp/xdf/hlsp_xdf_hst_acswfc-60mas_hudf_f435w_v1_sci.fits'\n", + "with fits.open(url) as hdulist:\n", + " hdulist.info()\n", + " data = hdulist[0].data\n", + " header = hdulist[0].header" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As explained in the [previous notebook](01_photutils_background_estimation.ipynb) on background estimation, it is important to **mask** these data, as a large portion of the values are equal to zero. We will mask out the non-data, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the mask\n", + "mask = data == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons (counts) per second." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "unit = u.ct / u.s\n", + "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "xdf_image_clipped = np.clip(xdf_image, 1e-4, None) # clip to plot with logarithmic stretch\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Tip: Double-click on any inline plot to zoom in.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Creating Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `photutils`, users can create apertures with the following shapes:\n", + "\"Examples\n", + "\n", + "Each of these can be defined either in pixel coordinates or in celestial coordinates (using a WCS transformation).\n", + "\n", + "It is also possible for users to create custom aperture shapes.\n", + "\n", + "Any aperture object is created by defining its position and size (and, if applicable, orientation). Let's use the `find_peaks` method that we learned about in a [previous notebook](02_photutils_source_detection.ipynb) to get the positions of sources in our data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import find_peaks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate statistics\n", + "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, iters=5, mask=xdf_image.mask)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sources_findpeaks = find_peaks(xdf_image.data, mask=xdf_image.mask, \n", + " threshold=20.*std, box_size=30, subpixel=True) \n", + "# Display the table\n", + "sources_findpeaks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And let's plot the centroids of each of these sources:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "ax1.scatter(sources_findpeaks['x_centroid'], sources_findpeaks['y_centroid'], s=10, marker='.', \n", + " lw=1, alpha=0.7, color='r')#facecolor='None', edgecolor='r')\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('find\\_peaks Sources')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So thanks to `find_peaks`, we now we know the positions of all our sources. Next, we need to define apertures for each source. First, as the simplest example, let's try using circular apertures of a fixed size.\n", + "\n", + "### Circular Apertures" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import CircularAperture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define the aperture\n", + "position = (sources_findpeaks['x_centroid'], sources_findpeaks['y_centroid'])\n", + "radius = 10.\n", + "circular_aperture = CircularAperture(position, r=radius)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "\n", + "# Plot the apertures\n", + "circular_aperture.plot(color='red', alpha=0.7)\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Circular Apertures')\n", + "\n", + "# Crop to show an inset of the data\n", + "ax1.set_xlim(2000, 3000)\n", + "ax1.set_ylim(2000, 1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, these circular apertures don't fit our data very well. After all, this is the Hubble eXtreme Deep Field, so there aren't any nice, round, nearby Milky Way stars in this image! \n", + "\n", + "Let's use ellipses instead, to better match the morphology of the galactic blobs in our image." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Elliptical Apertures" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import (detect_sources, source_properties, \\\n", + " EllipticalAnnulus, EllipticalAperture)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a [previous notebook](02_photutils_source_detection.ipynb), we showed how you can use the `photutils.detect_sources` feature to generate segmentation maps, which identify and label contiguous (connected) objects within an image. Then, with `source_properties`, you can access descriptive properties for each unique object - not just their centroid positions, but also their pixel areas, eccentricities, orientations with respect to the coordinate frame of the image, and more.\n", + "\n", + "Here we'll use the centroid, semimajor axis, semiminor axis, and orientation values from `source_properties` to generate elliptical apertures for each of the sources in our image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Define threshold and minimum object size\n", + "threshold = 5. * std\n", + "npixels = 15\n", + "\n", + "# Create a segmentation image\n", + "segm = detect_sources(xdf_image.data, threshold, npixels)\n", + "\n", + "# Create a catalog using source properties\n", + "catalog = source_properties(xdf_image.data, segm)\n", + "table = catalog.to_table()\n", + "\n", + "# Display the table\n", + "table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r = 3. # approximate isophotal extent of semimajor axis\n", + "\n", + "# Create the apertures\n", + "elliptical_apertures = []\n", + "for obj in catalog:\n", + " position = (obj.xcentroid.value, obj.ycentroid.value)\n", + " a = obj.semimajor_axis_sigma.value * r\n", + " b = obj.semiminor_axis_sigma.value * r\n", + " theta = obj.orientation.value\n", + " elliptical_apertures.append(EllipticalAperture(position, a, b, theta=theta))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "\n", + "# Plot the apertures\n", + "for aperture in elliptical_apertures:\n", + " aperture.plot(color='red', alpha=0.7, ax=ax1)\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Elliptical Apertures')\n", + "\n", + "# Crop to show an inset of the data\n", + "ax1.set_xlim(2000, 3000)\n", + "ax1.set_ylim(2000, 1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, these custom-made elliptical apertures fit our XDF galaxies much better than the one-size-fits-all circular apertures from before." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sky Coordinates & Apertures\n", + "\n", + "At the moment, the positions of our apertures are in pixels, relative to our data array. However, if you need aperture positions in terms of celestial coordinates, `photutils` also includes aperture objects that can be integrated with Astropy's `SkyCoords`.\n", + "\n", + "Fortunately this is extremely easy when we use the [World Coordinate System (WCS)](http://docs.astropy.org/en/stable/wcs/) to produce a WCS object from the header of the FITS file containing our image, and then the `to_sky()` method to transform our `EllipticalAperture` objects into `SkyEllipticalAperture` objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.wcs import WCS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wcs = WCS(header)\n", + "sky_elliptical_apertures = [ap.to_sky(wcs) for ap in elliptical_apertures]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we wanted to generate `SkyEllipticalAperture` objects from the get-go, we could have used that WCS object in the following way:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import SkyEllipticalAperture\n", + "from astropy.coordinates import SkyCoord\n", + "\n", + "r = 3. # approximate isophotal extent of semimajor axis\n", + "\n", + "# Create the apertures\n", + "sky_elliptical_apertures = []\n", + "for obj in catalog:\n", + " # Convert the centroids into RA/Dec using WCS\n", + " ra, dec = wcs.all_pix2world(obj.xcentroid.value, obj.ycentroid.value, 0)\n", + " # Convert the positions to an Astropy SkyCoord object, with units!\n", + " sky_position = SkyCoord(ra, dec, unit=u.deg)\n", + " \n", + " # Define the elliptical parameters, now with units\n", + " a = obj.semimajor_axis_sigma.value * r * u.pix\n", + " b = obj.semiminor_axis_sigma.value * r * u.pix\n", + " theta = obj.orientation.value * u.rad\n", + " \n", + " # Convert the theta from radians from X axis to the radians from North \n", + " x_to_north_angle = (90. + header['ORIENTAT']) * u.deg\n", + " x_to_north_angle_rad = x_to_north_angle.to_value(u.rad) * u.rad\n", + " theta -= x_to_north_angle_rad\n", + " \n", + " # Define the apertures\n", + " ap = SkyEllipticalAperture(sky_position, a, b, theta=theta)\n", + " sky_elliptical_apertures.append(ap)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, you can't plot SkyApertures. However, you can use them just as easily to perform aperture photometry!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## Performing Aperture Photometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have aperture objects that fit our data reasonably well, we can finally perform photometry with the `aperture_photometry` function. This function takes the following arguments:\n", + "\n", + "* **`data`** - the background-subtracted data array on which to perform photometry.\n", + "* **`apertures`** - an aperture object containing the aperture(s) to use for the photometry.\n", + "* **`error`** (optional) - an array of values that represent the pixel-wise Gaussian 1-sigma errors of the input data.\n", + "* **`mask`** (optional) - a mask for the `data` to exclude certain pixels from calculations.\n", + "* **`method`** (optional) - how to place the aperture(s) onto the pixel grid (see below).\n", + "* **`unit`** (optional) - unit of `data` and `error`.\n", + "* **`wcs`** (optional) - the WCS transformation to use if `apertures` is a `SkyAperture` object. \n", + "* **`subpixels`** (optional) - the factor by which pixels are resampled (see below).\n", + "\n", + "The following methods are the options for how to place apertures onto the data pixel grid:\n", + "\n", + "* **exact** (default) - calculate the exact fractional overlap of each aperture for each overlapping pixel. This method is the most accurate, but will also take the longest. \n", + "* **center** - a pixel is either entirely in or entirely out of the aperture, depending on whether the pixel center is inside or outside of the aperture.\n", + "* **subpixel** - a pixel is divided into `subpixels` x `subpixels` subpixels, each of which are considered to be entirely in or out of the aperture depending on whether its center is in or out of the aperture. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import aperture_photometry\n", + "from astropy.table import QTable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see what this looks like using the first aperture in our image:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The CCDData mask will be automatically applied\n", + "phot_datum = aperture_photometry(xdf_image, elliptical_apertures[0]) \n", + "phot_datum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `aperture_sum` value is what reports the number of counts within the aperture: 3.47 ct/s.\n", + "\n", + "And, just as a check, to make sure our sky apertures give basically the same answer..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The CCDData mask will be automatically applied\n", + "sky_phot_datum = aperture_photometry(xdf_image, sky_elliptical_apertures[0], wcs=wcs)\n", + "sky_phot_datum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Woohoo!\n", + "\n", + "Unfortunately for our purposes, the `aperture_photometry` function can be only used alone for one of the two cases:\n", + "* Identical apertures at distinct positions (e.g. circular apertures with `r = 3` for many sources)\n", + "* Distinct apertures at identical positions (e.g. two circular apertures with `r = 3` and `r = 5` for one source)\n", + "\n", + "Since our elliptical apertures are distinct apertures at distinct positions, we need to do a little more work to get a single table of photometric values.\n", + "\n", + "(This step will take a while, almost 5 minutes, so hang tight!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The CCDData mask will be automatically applied\n", + "phot_table = aperture_photometry(xdf_image, elliptical_apertures[0])\n", + "id = 1\n", + "for aperture in elliptical_apertures[1:]:\n", + " id += 1\n", + " phot_row = aperture_photometry(xdf_image, aperture)[0]\n", + " phot_row[0] = id\n", + " phot_table.add_row(phot_row)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at all these apertures we've made:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display the table\n", + "phot_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's only so much you can learn from looking at a table of numbers, so let's explore alternate ways to examine these data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 5))\n", + "\n", + "values = [phot.value for phot in phot_table['aperture_sum']]\n", + "logbins=bins = 10.**(np.linspace(-1, 2, 100))\n", + "plt.hist(values, bins=logbins)\n", + "\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.title('Histogram of Source Photometry')\n", + "plt.xlabel('Count Rate [ct/s]')\n", + "plt.ylabel('Number of Sources')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 5))\n", + "\n", + "plt.scatter(table['area'], values, alpha=0.5)\n", + "\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.title('Count Rate v. Aperture Area')\n", + "plt.xlabel('Aperture Area [pixels$^2$]')\n", + "plt.ylabel('Count Rate [ct/s]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

Exercise:


\n", + "\n", + "Re-calculate the photometry for these elliptical apertures - or just a subset of them - using the `subpixel` aperture placement method instead of the default `exact` method. How does this affect the count sum calculated for those apertures?\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "# Aperture Corrections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've done photometry with some lovely apertures, but unfortunately even using elliptical apertures with unique sizes and orientations does not account for an important source of extraneous flux: the sky background.\n", + "\n", + "## Local Background Subtraction\n", + "\n", + "In the [background estimation notebook](01_photutils_background_estimation.ipynb), we explored how to perform global background subtraction of image data with `photutils`. However, you can also use `photutils` to perform local background estimations for aperture corrections.\n", + "\n", + "To estimate the local background for each aperture, measure the counts within annulus apertures around (but not including!) each source. In our example, we defined elliptical apertures with `r = 3` to measure the counts within each source. To calculate the background for each source, let's measure the counts elliptical annuli between `r = 3.5` and `r = 5`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r_in = 3.5 # approximate isophotal extent of inner semimajor axis\n", + "r_out = 5. # approximate isophotal extent of inner semimajor axis\n", + "\n", + "# Create the apertures\n", + "elliptical_annuli = []\n", + "for obj in catalog:\n", + " position = (obj.xcentroid.value, obj.ycentroid.value)\n", + " a_in = obj.semimajor_axis_sigma.value * r_in\n", + " a_out = obj.semimajor_axis_sigma.value * r_out\n", + " b_out = obj.semiminor_axis_sigma.value * r_out\n", + " theta = obj.orientation.value\n", + " elliptical_annuli.append(EllipticalAnnulus(position, a_in, a_out, b_out, theta=theta))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image)\n", + "\n", + "# Plot the apertures\n", + "for aperture in elliptical_annuli:\n", + " aperture.plot(color='red', alpha=0.4, ax=ax1, fill=True)\n", + "for aperture in elliptical_apertures:\n", + " aperture.plot(color='white', alpha=0.7, ax=ax1)\n", + "\n", + "# Define the colorbar\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')\n", + "ax1.set_title('Elliptical Annuli')\n", + "\n", + "# Crop to show an inset of the data\n", + "ax1.set_xlim(2000, 3000)\n", + "ax1.set_ylim(2000, 1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating Aperture Corrections\n", + "\n", + "Now that our apertures have been defined, we can do photometry with them to estimate and account for the background. The aperture correction is calculated by:\n", + "- Calculating the count rate within each annulus using `aperture_photometry`\n", + "- Dividing each annulus' count rate by each annulus' area to get the mean background value for each annulus\n", + "- Taking the mean of those annulus means to get a mean background value for the entire image\n", + "- Multiplying the global background mean value times the area of each elliptical photometric aperture, to get the estimated background count rate within each aperture\n", + "- Subtracting the estimated background count rate from the photometric count rate for each aperture\n", + "\n", + "(Just like when we did photometry with the elliptical apertures above, the below step will take almost 5 minutes.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The CCDData mask will be automatically applied\n", + "bkg_phot_table = aperture_photometry(xdf_image, elliptical_annuli[0])\n", + "id = 1\n", + "for aperture in elliptical_annuli[1:]:\n", + " id += 1\n", + " phot_row = aperture_photometry(xdf_image, aperture)[0]\n", + " phot_row[0] = id\n", + " bkg_phot_table.add_row(phot_row)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display table\n", + "bkg_phot_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have noticed that these background count rates are *really* small. In this case, this is to be expected - since our example XDF data is a high-level science product (HLSP) that already has already been background-subtracted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the mean background level (per pixel) in the annuli \n", + "bkg_area = [annulus.area() for annulus in elliptical_annuli]\n", + "bkg_mean_per_aperture = bkg_phot_table['aperture_sum'].value / bkg_area\n", + "bkg_mean = np.average(bkg_mean_per_aperture) * (u.ct / u.s)\n", + "print('Background mean:', bkg_mean)\n", + "\n", + "# Calculate the total background within each elliptical aperture\n", + "bkg_sum = bkg_mean * table['area'].value\n", + "\n", + "# Subtract the background from the original photometry\n", + "flux_bkgsub = phot_table['aperture_sum'] - bkg_sum\n", + "\n", + "# Add this as a column to the original photometry table\n", + "phot_table['aperture_sum_bkgsub'] = flux_bkgsub" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's see the difference between our original count rates and our background-subtracted count rates (it should be small for us!):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display table\n", + "phot_table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 5))\n", + "\n", + "values = [phot.value for phot in phot_table['aperture_sum']]\n", + "values_bkgsub = [phot.value for phot in phot_table['aperture_sum_bkgsub']]\n", + "logbins=bins = 10.**(np.linspace(-1, 2, 100))\n", + "plt.hist(values, bins=logbins, alpha=0.7, label='Original photometry')\n", + "plt.hist(values_bkgsub, bins=logbins, alpha=0.7, label='Background-subtracted')\n", + "\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.title('Histogram of Source Photometry')\n", + "plt.xlabel('Count Rate [ct/s]')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "# Conclusions\n", + "\n", + "The `photutils` package provides a comprehensive toolkit for astronomers to perform aperture photometry, including customizable aperture shapes that allow for more precise photometry and easy photometric correction.\n", + "\n", + "**To continue with this `photutils` tutorial, go on to the [PSF photometry notebook](04_photutils_psf_photometry.ipynb).**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Additional Resources\n", + "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## About this Notebook\n", + "**Authors:** Lauren Chambers (lchambers@stsci.edu), Erik Tollerud (etollerud@stsci.edu), Clare Shanahan (cshanahan@stsci.edu)\n", + "
**Updated:** May 2019" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#title_ID)\n", + "\"STScI" + ] + } + ], + "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": "3.5.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/photutils/03_photutils_aperture_photometry/requirements.txt b/notebooks/photutils/03_photutils_aperture_photometry/requirements.txt new file mode 100644 index 00000000..a0d3505e --- /dev/null +++ b/notebooks/photutils/03_photutils_aperture_photometry/requirements.txt @@ -0,0 +1,4 @@ +astropy>=3.1.2 +matplotlib>2.2.2 +numpy>=1.13.3 +photutils>=0.4 diff --git a/notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb b/notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb new file mode 100644 index 00000000..0a6aa615 --- /dev/null +++ b/notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", + "# PSF Photometry with `photutils`\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### What is PSF photometry?\n", + "A more specific form of photometry than aperture photometry, PSF photometry takes into account the shape of a source's point spread function (PSF). The PSF is a model that represents the distribution of light from a point source as it falls onto a detector. An example of a basic PSF is simply a 2-D Gaussian, while more complex PSFs can include distortion, diffraction, or interference effects associated with a particular telescope. For instance, the PSFs from the Hubble Space Telescope and the James Webb Space Telescope have been meticulously modeled, and can be simulated with the [Tiny Tim](http://www.stsci.edu/hst/observatory/focus/TinyTim) and [WebbPSF](https://github.com/mperrin/webbpsf) software packages, respectively. However, for datasets that do not have readily available PSF models, such models can be statistically generated by analyzing the image itself.\n", + "\n", + "The `photutils` package provides tools that combine background estimation, source detection, and model-fitting to perform PSF photometry on image data.\n", + "\n", + "##### What does this tutorial include?\n", + "This tutorial covers how to perform PSF photometry with `photutils`, including the following methods:\n", + "* Gaussian PSF Photometry\n", + "* Iterative Subtraction\n", + "* Point Response Function (PRF) Photometry\n", + "\n", + "The methods demonstrated here are available in narrative form within the `photutils.psf` [documentation](http://photutils.readthedocs.io/en/stable/psf.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
**Warning:** The PSF photometry API is currently considered experimental and may change in the future. The photutils development team will aim to keep compatibility where practical, but will not finalize the API until sufficient user feedback has been accumulated.
**Important:** Before proceeding, please be sure to install or update your [AstroConda](https://astroconda.readthedocs.io) distribution. This notebook may not work properly with older versions of AstroConda.
\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import necessary packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import packages that we will use to perform arithmetic functions and visualize data:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from astropy.io import fits\n", + "from astropy.stats import sigma_clipped_stats, SigmaClip\n", + "from astropy.visualization import ZScaleInterval, ImageNormalize\n", + "from photutils import make_source_mask\n", + "from photutils.background import Background2D, MedianBackground\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm\n", + "% matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also define some `matplotlib` parameters, to make sure our plots look nice. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "matplotlib.rc('font', family='serif', weight='light', size=12)\n", + "matplotlib.rc('mathtext', bf='serif:normal')\n", + "matplotlib.rc('axes', titlesize=18, titlepad=12, labelsize=16)\n", + "matplotlib.rc('xtick', labelsize=14)\n", + "matplotlib.rc('ytick', labelsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have place the data for this tutorial in the github repository, for easy access. The data were originally retrieved from the STScI archive: https://archive.stsci.edu/prepds/udf/udf_hlsp.html." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "with fits.open('data/h_udf_wfc_v_drz_img.fits') as hdulist:\n", + " v_data = hdulist[0].data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "clim = (0, 1e-2)\n", + "\n", + "v_data_plot = np.copy(v_data)\n", + "v_data_plot[v_data_plot <= 0] = 1e-10\n", + "\n", + "norm_image = ImageNormalize(v_data, interval=ZScaleInterval())\n", + "fitsplot = ax1.imshow(v_data, norm=norm_image)#, clim=clim)\n", + "\n", + "plt.colorbar(fitsplot, fraction=0.046, pad=0.04)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Circular Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performing Aperture Photometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Aperture Corrections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elliptical Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performing Aperture Photometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Aperture Corrections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Exercises" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "May 2018\n", + "\n", + "Author: Lauren Chambers (lchambers@stsci.edu)\n", + "\n", + "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." + ] + } + ], + "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": "3.5.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/photutils/04_psf_photometry/requirements.txt b/notebooks/photutils/04_psf_photometry/requirements.txt new file mode 100644 index 00000000..a0d3505e --- /dev/null +++ b/notebooks/photutils/04_psf_photometry/requirements.txt @@ -0,0 +1,4 @@ +astropy>=3.1.2 +matplotlib>2.2.2 +numpy>=1.13.3 +photutils>=0.4 diff --git a/notebooks/photutils/photutils_notebook_style.mplstyle b/notebooks/photutils/photutils_notebook_style.mplstyle new file mode 100644 index 00000000..2f1f6450 --- /dev/null +++ b/notebooks/photutils/photutils_notebook_style.mplstyle @@ -0,0 +1,20 @@ +# ---- Matplotlib formatting ---- + +font.family : serif +font.weight : light +mathtext.bf : serif:normal + +font.size : 12 +axes.titlesize : 20 +axes.titlepad : 12 +axes.labelsize : 18 +xtick.labelsize : 16 +ytick.labelsize : 16 + +text.usetex : True + +figure.subplot.bottom : 0.15 +figure.dpi : 200 + +savefig.dpi : 300 +savefig.transparent : True From 907b6370c5ac435cb255b780fc795ad5c14932e7 Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Mon, 20 May 2019 16:38:36 -0400 Subject: [PATCH 2/9] Rename notebooks, and implement previous edits across all notebooks --- ...n.ipynb => 01_background_estimation.ipynb} | 122 ++++--- ...ection.ipynb => 02_source_detection.ipynb} | 52 +-- .../03_aperture_photometry.ipynb} | 84 +++-- .../requirements.txt | 0 .../04_photutils_psf_photometry.ipynb | 253 ------------- .../04_psf_photometry/04_psf_photometry.ipynb | 333 ++++++++++++++++++ 6 files changed, 491 insertions(+), 353 deletions(-) rename notebooks/photutils/01_background_estimation/{01_photutils_background_estimation.ipynb => 01_background_estimation.ipynb} (82%) rename notebooks/photutils/02_source_detection/{02_photutils_source_detection.ipynb => 02_source_detection.ipynb} (93%) rename notebooks/photutils/{03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb => 03_aperture_photometry/03_aperture_photometry.ipynb} (88%) rename notebooks/photutils/{03_photutils_aperture_photometry => 03_aperture_photometry}/requirements.txt (100%) delete mode 100644 notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb create mode 100644 notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb diff --git a/notebooks/photutils/01_background_estimation/01_photutils_background_estimation.ipynb b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb similarity index 82% rename from notebooks/photutils/01_background_estimation/01_photutils_background_estimation.ipynb rename to notebooks/photutils/01_background_estimation/01_background_estimation.ipynb index 46991a0e..c02e4078 100644 --- a/notebooks/photutils/01_background_estimation/01_photutils_background_estimation.ipynb +++ b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb @@ -4,7 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
![photutils logo](photutils_banner.svg)
\n", + "\n", + "\n", + "[\n", + "\n", + "](http://photutils.readthedocs.io/en/stable/index.html)\n", "\n", "# Background Estimation with `photutils`\n", "---" @@ -29,16 +33,24 @@ "\n", "Background subtraction is essential for accurate photometric analysis of astronomical data like the XDF.\n", "\n", - "##### The methods demonstrated here are available in narrative form within the `photutils.background` [documentation](http://photutils.readthedocs.io/en/stable/background.html)." + "*The methods demonstrated here are available in narrative form within the `photutils.background` [documentation](http://photutils.readthedocs.io/en/stable/background.html).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "
**Note:** This notebook focuses on global background estimation. Local background subtraction with annulus apertures is demonstrated in the [aperture photometry notebook](03_photutils_aperture_photometry.ipynb).
\n", + "
\n", + "\n", + "**Note:** This notebook focuses on global background estimation. Local background subtraction with **annulus apertures** is demonstrated in the [aperture photometry notebook](../03_aperture_photometry/03_aperture_photometry.ipynb).\n", + "\n", + "
\n", "\n", - "
**Important:** Before proceeding, please be sure to update your versions of `astropy`, `matplotlib`, and `photutils`, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the [AstroConda](https://astroconda.readthedocs.io) distribution.
\n", + "
\n", + " \n", + " **Important:** Before proceeding, please be sure to update your versions of `astropy`, `matplotlib`, and `photutils`, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the [AstroConda](https://astroconda.readthedocs.io) distribution.\n", + " \n", + "
\n", "\n", "---" ] @@ -73,14 +85,14 @@ "from matplotlib.ticker import LogLocator\n", "\n", "# Show plots in the notebook\n", - "% matplotlib inline" + "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [shared style file](photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [style file shared with the other photutils tutorials](../photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" ] }, { @@ -89,7 +101,7 @@ "metadata": {}, "outputs": [], "source": [ - "plt.style.use('photutils_notebook_style.mplstyle')" + "plt.style.use('../photutils_notebook_style.mplstyle')" ] }, { @@ -105,7 +117,7 @@ "source": [ "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", "\n", - "(Generally, the best package for web queries of astronomical data is `astroquery`; however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with `astroquery`.)" + "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)" ] }, { @@ -183,10 +195,17 @@ "# Set up the figure with subplots\n", "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", "\n", - "# Plot the data\n", - "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "# Set up the normalization and colormap\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch(), clip=False)\n", + "cmap = plt.get_cmap('viridis')\n", + "cmap.set_over(cmap.colors[-1])\n", + "cmap.set_under(cmap.colors[0])\n", + "cmap.set_bad('white') # Show masked data as white\n", "xdf_image_clipped = np.clip(xdf_image, 1e-4, None) # clip to plot with logarithmic stretch\n", - "fitsplot = ax1.imshow(xdf_image_clipped, norm=norm_image)\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", "\n", "# Define the colorbar and fix the labels\n", "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", @@ -194,7 +213,8 @@ "cbar.ax.set_yticklabels(labels)\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')" ] @@ -203,7 +223,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "*Note: Double-click on any inline plot to zoom in.*" + "*Tip: Double-click on any inline plot to zoom in.*" ] }, { @@ -219,7 +239,7 @@ "source": [ "You probably noticed that a large portion of the data is equal to zero. The data we are using is a reduced mosaic that combines many different exposures, and that has been rotated such that not all of the array holds data. \n", "\n", - "We want to **mask** out the non-data, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data." + "We want to **mask** out the non-data portions of the image array,, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data." ] }, { @@ -251,15 +271,16 @@ "ax1.set_title('Mask')\n", "\n", "# Plot the masked data\n", - "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", - "fitsplot = ax2.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "fitsplot = ax2.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", "\n", "# Define the colorbar and fix the labels\n", "cbar_ax = fig.add_axes([1, 0.09, 0.03, 0.87])\n", "cbar = fig.colorbar(fitsplot, cbar_ax, ticks=LogLocator(subs=range(10)))\n", "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", "cbar.ax.set_yticklabels(labels)\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax2.set_xlabel('X (pixels)')\n", "ax2.set_title('Masked Data')" ] @@ -288,7 +309,7 @@ "metadata": {}, "outputs": [], "source": [ - "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, iters=5, mask=xdf_image.mask)" + "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5, mask=xdf_image.mask)" ] }, { @@ -305,7 +326,7 @@ "outputs": [], "source": [ "# Calculate the data without masking\n", - "stats_nomask = sigma_clipped_stats(xdf_image.data, sigma=3.0, iters=5)" + "stats_nomask = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5)" ] }, { @@ -329,7 +350,7 @@ "ax1.axvline(np.average(xdf_image), label='Neither', c='C6', ls=':', lw=3)\n", "\n", "ax1.set_xlim(flux_range)\n", - "ax1.set_xlabel(r'Flux Count Rate ($e^{-1}/s$)', fontsize=14)\n", + "ax1.set_xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), fontsize=14)\n", "ax1.set_ylabel('Frequency', fontsize=14)\n", "ax1.set_title('Effect of Sigma-Clipping and Masking on Mean', fontsize=16)\n", "ax1.legend(fontsize=11)\n", @@ -343,7 +364,7 @@ "ax2.axvline(np.ma.median(xdf_image), label='Neither', c='C6', ls=':', lw=3)\n", "\n", "ax2.set_xlim(flux_range)\n", - "ax2.set_xlabel(r'Flux Count Rate ($e^{-1}/s$)', fontsize=14)\n", + "ax2.set_xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), fontsize=14)\n", "ax2.set_title('Effect of Sigma-Clipping and Masking on Median', fontsize=16)\n", "ax2.legend(fontsize=11)" ] @@ -377,10 +398,6 @@ "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", "plt.tight_layout()\n", "\n", - "# Define the normalization\n", - "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", - "xdf_scalar_bkgdsub_clipped = np.clip(xdf_scalar_bkgdsub, 1e-4, None) # clip to plot with logarithmic stretch\n", - "\n", "# Plot the original data\n", "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", "ax1.set_xlabel('X (pixels)')\n", @@ -388,6 +405,7 @@ "ax1.set_title('Original Data')\n", "\n", "# Plot the subtracted data\n", + "xdf_scalar_bkgdsub_clipped = np.clip(xdf_scalar_bkgdsub, 1e-4, None) # clip to plot with logarithmic stretch\n", "fitsplot = ax2.imshow(np.ma.masked_where(xdf_scalar_bkgdsub.mask, xdf_scalar_bkgdsub_clipped), norm=norm_image)\n", "ax2.set_xlabel('X (pixels)')\n", "ax2.set_title('Scalar Background-Subtracted Data')\n", @@ -397,7 +415,8 @@ "cbar = fig.colorbar(fitsplot, cbar_ax, ticks=LogLocator(subs=range(10)))\n", "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", "cbar.ax.set_yticklabels(labels)\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)" + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)" ] }, { @@ -414,10 +433,13 @@ "metadata": {}, "source": [ "
\n", - "

**Exercises:**


\n", + " \n", + "

Exercises:


\n", + "\n", "Perform a median scalar background subtraction on our sigma-clipped data. Plot it and visually inspect it. How does it compare to the original data?\n", "

\n", "Compare the median background subtraction to the mean background subtraction. Which is better?\n", + "\n", "
" ] }, @@ -480,7 +502,6 @@ "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", "\n", "# Plot the data\n", - "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", "background_clipped = np.clip(bkg.background, 1e-4, None) # clip to plot with logarithmic stretch\n", "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, background_clipped), norm=norm_image)\n", "\n", @@ -493,7 +514,8 @@ "cbar.ax.set_yticklabels(labels)\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", "ax1.set_title('2D Estimated Background')" @@ -529,12 +551,12 @@ "plt.tight_layout()\n", "\n", "# Define the normalization\n", - "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", "xdf_2d_bkgdsub_clipped = np.clip(xdf_2d_bkgdsub, 1e-4, None) # clip to plot with logarithmic stretch\n", "\n", "# Plot the scalar-subtracted data\n", "fitsplot = ax1.imshow(np.ma.masked_where(xdf_scalar_bkgdsub.mask, xdf_scalar_bkgdsub_clipped), norm=norm_image)\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_ylabel('Y (pixels)')\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_title('Scalar Background-Subtracted Data')\n", @@ -549,7 +571,8 @@ "cbar = fig.colorbar(fitsplot, cbar_ax, ticks=LogLocator(subs=range(10)))\n", "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", "cbar.ax.set_yticklabels(labels)\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)" + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)" ] }, { @@ -564,10 +587,13 @@ "metadata": {}, "source": [ "
\n", - "

**Exercises:**


\n", + " \n", + "

Exercises:


\n", + "\n", "Calculate the standard deviation (with sigma-clipping and masking!) for the original data, the scalar background-subtracted data, and the 2D background-subtracted data. How do the values compare? Which has the smallest standard deviation?

\n", "\n", "Notice that the difference between each dataset's standard deviation is small - why might this be?\n", + "\n", "
" ] }, @@ -575,11 +601,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "---\n", "## Conclusions\n", "\n", "The `photutils` package provides a powerful tool in the `Background2D` class, allowing users to easily estimate and subtract spatially variant background signals from their data.\n", "\n", - "**To continue with this `photutils` tutorial, go on to the [source detection notebook](02_photutils_source_detection.ipynb).**" + "**To continue with this `photutils` tutorial, go on to the [source detection notebook](../02_source_detection/02_source_detection.ipynb).**" ] }, { @@ -587,11 +614,26 @@ "metadata": {}, "source": [ "---\n", - "September 2018\n", - "\n", - "Author: Lauren Chambers (lchambers@stsci.edu), Erik Tollerud (etollerud@stsci.edu)\n", - "\n", - "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." + "## Additional Resources\n", + "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## About this Notebook\n", + "**Authors:** Lauren Chambers (lchambers@stsci.edu), Erik Tollerud (etollerud@stsci.edu)\n", + "
**Updated:** May 2019" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#title_ID)\n", + "\"STScI" ] } ], @@ -611,7 +653,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.5" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/notebooks/photutils/02_source_detection/02_photutils_source_detection.ipynb b/notebooks/photutils/02_source_detection/02_source_detection.ipynb similarity index 93% rename from notebooks/photutils/02_source_detection/02_photutils_source_detection.ipynb rename to notebooks/photutils/02_source_detection/02_source_detection.ipynb index ead20219..b40fad3d 100644 --- a/notebooks/photutils/02_source_detection/02_photutils_source_detection.ipynb +++ b/notebooks/photutils/02_source_detection/02_source_detection.ipynb @@ -34,7 +34,7 @@ "##### Which data are used in this tutorial?\n", "We will be manipulating Hubble eXtreme Deep Field (XDF) data, which was collected using the Advanced Camera for Surveys (ACS) on Hubble between 2002 and 2012. The image we use here is the result of 1.8 million seconds (500 hours!) of exposure time, and includes some of the faintest and most distant galaxies that have ever been observed. \n", "\n", - "##### The methods demonstrated here are available in narrative form within the `photutils.detection` [documentation](http://photutils.readthedocs.io/en/stable/detection.html) and `photutils.segmentation` [documentation](http://photutils.readthedocs.io/en/stable/segmentation.html)." + "*The methods demonstrated here are available in narrative form within the `photutils.detection` [documentation](http://photutils.readthedocs.io/en/stable/detection.html) and `photutils.segmentation` [documentation](http://photutils.readthedocs.io/en/stable/segmentation.html).*" ] }, { @@ -88,7 +88,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [style file shared with the other photutils tutorials](photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [style file shared with the other photutils tutorials](../photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" ] }, { @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "plt.style.use('photutils_notebook_style.mplstyle')" + "plt.style.use('../photutils_notebook_style.mplstyle')" ] }, { @@ -133,7 +133,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As explained in the [previous notebook](01_photutils_background_estimation.ipynb) on background estimation, it is important to **mask** these data, as a large portion of the values are equal to zero. We will mask out the non-data portions of the image array, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data. " + "As explained in a [previous notebook](../01_background_estimation/01_background_estimation.ipynb) on background estimation, it is important to **mask** these data, as a large portion of the values are equal to zero. We will mask out the non-data portions of the image array, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data. " ] }, { @@ -465,15 +465,15 @@ "\n", "The answer comes down to the default settings for the two algorithms: (1) there are differences in the upper and lower bounds on the requirements for source roundness and sharpness, and (2) `IRAFStarFinder` includes a minimum separation between sources that `DAOStarFinder` does not have:\n", "\n", - "| | `IRAFStarFinder` | `DAOStarFinder` |\n", - "|------|------|------|\n", - "| sharplo | 0.5 | 0.2 |\n", - "| sharphi | 2.0 | 1.0 |\n", - "| roundlo | 0.0 | -1.0 |\n", - "| roundhi | 0.2 | 1.0 |\n", + "|   | `IRAFStarFinder` | `DAOStarFinder` |\n", + "|----------------|-------|------|\n", + "| sharplo | 0.5 | 0.2 |\n", + "| sharphi | 2.0 | 1.0 |\n", + "| roundlo | 0.0 | -1.0 |\n", + "| roundhi | 0.2 | 1.0 |\n", "| minsep_fwhm | 1.5 * FWHM | N/A |\n", "\n", - "Thinking about this, *it then makes sense* that `IRAFStarFinder` would find fewer sources. It has stricter restrictions on source roundness, meaning that it eliminates more elliptical galactic sources (this is the eXtreme Deep Field, after all!), and the minimum separation requirement further rules out sources that are too close to one another.\n", + "Thinking about this, *it then makes sense* that `IRAFStarFinder` would find fewer sources. It has tighter restrictions on source roundness and ``sharplo``, meaning that it eliminates more elliptical galactic sources (this is the eXtreme Deep Field, after all!), and the minimum separation requirement further rules out sources that are too close to one another.\n", "\n", "If we set all these parameters to be equivalent, though, we should find much better agreement between the two methods:" ] @@ -555,7 +555,7 @@ "source": [ "For more general source detection cases that do not require comparison with models, `photutils` offers the `find_peaks` function. \n", "\n", - "This function simply finds sources by identifying local maxima above a given threshold and separated by a given distance, rather than trying to fit data to a given model. Unlike the previous detection algorithms, `find_peaks` does not necessarily calculate objects' centroids. Unless the `subpixel` argument is set to `True`, `find_peaks` will return just the integer value of the peak pixel for each source.\n", + "This function simply finds sources by identifying local maxima above a given threshold and separated by a given distance, rather than trying to fit data to a given model. Unlike the previous detection algorithms, `find_peaks` does not necessarily calculate objects' centroids. Unless the `centroid_func` argument is passed a function like `photutils.centroids.centroid_2dg` that can handle source position centroiding, `find_peaks` will return just the integer value of the peak pixel for each source.\n", "\n", "This algorithm is particularly useful for identifying non-stellar sources or heavily distorted sources in image data.\n", "\n", @@ -568,7 +568,8 @@ "metadata": {}, "outputs": [], "source": [ - "from photutils import find_peaks" + "from photutils import find_peaks\n", + "from photutils.centroids import centroid_2dg" ] }, { @@ -578,7 +579,8 @@ "outputs": [], "source": [ "sources_findpeaks = find_peaks(xdf_image.data, mask=xdf_image.mask, \n", - " threshold=20.*std, box_size=30, subpixel=True) \n", + " threshold=20.*std, box_size=30, \n", + " centroid_func=centroid_2dg) \n", "print(sources_findpeaks)" ] }, @@ -659,7 +661,7 @@ "source": [ "print('''DAOStarFinder: {} sources\n", "IRAFStarFinder: {} sources\n", - "find_peaks: {} sources'''.format(len(sources_dao), len(sources_iraf), len(sources_findpeaks)))" + "find_peaks: {} sources'''.format(len(sources_dao), len(sources_iraf_match), len(sources_findpeaks)))" ] }, { @@ -679,13 +681,13 @@ "source": [ "# Make lists of centroid coordinates\n", "centroids_dao = [(x, y) for x, y in sources_dao['xcentroid', 'ycentroid']]\n", - "centroids_iraf = [(x, y) for x, y in sources_iraf['xcentroid', 'ycentroid']]\n", + "centroids_iraf = [(x, y) for x, y in sources_iraf_match['xcentroid', 'ycentroid']]\n", "centroids_findpeaks = [(x, y) for x, y in sources_findpeaks['x_centroid', 'y_centroid']]\n", "\n", - "# Round those coordinates to the first decimal place and convert them to be sets\n", - "rounded_centroids_dao = set([(round(x, 1), round(y, 1)) for x, y in centroids_dao])\n", - "rounded_centroids_iraf = set([(round(x, 1), round(y, 1)) for x, y in centroids_iraf])\n", - "rounded_centroids_findpeaks = set([(round(x, 1), round(y, 1)) for x, y in centroids_findpeaks])\n", + "# Round those coordinates to the ones place and convert them to be sets\n", + "rounded_centroids_dao = set([(round(x, 0), round(y, 0)) for x, y in centroids_dao])\n", + "rounded_centroids_iraf = set([(round(x, 0), round(y, 0)) for x, y in centroids_iraf])\n", + "rounded_centroids_findpeaks = set([(round(x, 0), round(y, 0)) for x, y in centroids_findpeaks])\n", "\n", "# Examine the intersections of different sets to determine which sources are shared\n", "all_match = rounded_centroids_dao.intersection(rounded_centroids_iraf).intersection(rounded_centroids_findpeaks)\n", @@ -721,18 +723,18 @@ "# Plot the data\n", "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", "ax1.scatter([x for x, y in list(dao_findpeaks_match)], [y for x, y in list(dao_findpeaks_match)],\n", - " s=30, marker='s', lw=1.5, facecolor='None', edgecolor='#EE7733',\n", + " s=30, marker='s', lw=1, facecolor='None', edgecolor='#EE7733',\n", " label='Found by DAO \\& find\\_peaks')\n", "ax1.scatter([x for x, y in list(dao_iraf_match)], [y for x, y in list(dao_iraf_match)],\n", - " s=30, marker='D', lw=1.5, facecolor='None', edgecolor='#EE3377',\n", + " s=30, marker='D', lw=1, facecolor='None', edgecolor='#EE3377',\n", " label='Found by DAO \\& IRAF')\n", "ax1.scatter([x for x, y in list(iraf_findpeaks_match)], [y for x, y in list(iraf_findpeaks_match)],\n", - " s=30, marker='o', lw=1.5, facecolor='None', edgecolor='#0077BB',\n", + " s=30, marker='o', lw=1, facecolor='None', edgecolor='#0077BB',\n", " label='Found by IRAF \\& find\\_peaks')\n", "ax1.scatter([x for x, y in list(all_match)], [y for x, y in list(all_match)],\n", " s=30, marker='o', lw=1.2, linestyle=':',facecolor='None', edgecolor='#BBBBBB',\n", " label='Found by all methods')\n", - "ax1.legend()\n", + "ax1.legend(ncol=2)\n", "\n", "# Define the colorbar\n", "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", @@ -1045,7 +1047,7 @@ "\n", "The `photutils` package provides users with a variety of methods for detecting sources in their data, from familar algorithms such as `DAOFind` and `starfind`, to more complex and customizable image segmentation algorithms. These methods allow for easy creation of a diverse array of apertures that can be used for photometric analysis.\n", "\n", - "**To continue with this `photutils` tutorial, go on to the [aperture photometry](03_photutils_aperture_photometry.ipynb) or [PSF photometry notebook](04_photutils_psf_photometry.ipynb).**" + "**To continue with this `photutils` tutorial, go on to the [aperture photometry](../03_aperture_photometry/03_aperture_photometry.ipynb) or [PSF photometry notebook](../04_psf_photometry/04_psf_photometry.ipynb).**" ] }, { diff --git a/notebooks/photutils/03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb similarity index 88% rename from notebooks/photutils/03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb rename to notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb index 0368dbe4..2363a079 100644 --- a/notebooks/photutils/03_photutils_aperture_photometry/03_photutils_aperture_photometry.ipynb +++ b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb @@ -35,7 +35,7 @@ "##### Which data are used in this tutorial?\n", "We will be manipulating Hubble eXtreme Deep Field (XDF) data, which was collected using the Advanced Camera for Surveys (ACS) on Hubble between 2002 and 2012. The image we use here is the result of 1.8 million seconds (500 hours!) of exposure time, and includes some of the faintest and most distant galaxies that have ever been observed. \n", "\n", - "##### The methods demonstrated here are available in narrative form within the `photutils.aperture` [documentation](http://photutils.readthedocs.io/en/stable/aperture.html)." + "*The methods demonstrated here are available in narrative form within the `photutils.aperture` [documentation]( http://photutils.readthedocs.io/en/stable/aperture.html).*" ] }, { @@ -89,7 +89,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [shared style file](photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [style file shared with the other photutils tutorials](../photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" ] }, { @@ -98,7 +98,7 @@ "metadata": {}, "outputs": [], "source": [ - "plt.style.use('photutils_notebook_style.mplstyle')" + "plt.style.use('../photutils_notebook_style.mplstyle')" ] }, { @@ -114,7 +114,7 @@ "source": [ "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", "\n", - "(Generally, the best package for web queries of astronomical data is `astroquery`; however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with `astroquery`.)" + "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)" ] }, { @@ -134,7 +134,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As explained in the [previous notebook](01_photutils_background_estimation.ipynb) on background estimation, it is important to **mask** these data, as a large portion of the values are equal to zero. We will mask out the non-data, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data. " + "As explained in a [previous notebook](../01_background_estimation/01_background_estimation.ipynb) on background estimation, it is important to **mask** these data, as a large portion of the values are equal to zero. We will mask out the non-data portions of the image array, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data. " ] }, { @@ -160,7 +160,7 @@ "metadata": {}, "outputs": [], "source": [ - "unit = u.ct / u.s\n", + "unit = u.electron / u.s\n", "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" ] }, @@ -182,10 +182,17 @@ "# Set up the figure with subplots\n", "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", "\n", - "# Plot the data\n", - "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch())\n", + "# Set up the normalization and colormap\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch(), clip=False)\n", + "cmap = plt.get_cmap('viridis')\n", + "cmap.set_over(cmap.colors[-1])\n", + "cmap.set_under(cmap.colors[0])\n", + "cmap.set_bad('white') # Show masked data as white\n", "xdf_image_clipped = np.clip(xdf_image, 1e-4, None) # clip to plot with logarithmic stretch\n", - "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", "\n", "# Define the colorbar and fix the labels\n", "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", @@ -193,7 +200,8 @@ "cbar.ax.set_yticklabels(labels)\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')" ] @@ -224,7 +232,7 @@ "\n", "It is also possible for users to create custom aperture shapes.\n", "\n", - "Any aperture object is created by defining its position and size (and, if applicable, orientation). Let's use the `find_peaks` method that we learned about in a [previous notebook](02_photutils_source_detection.ipynb) to get the positions of sources in our data:" + "Any aperture object is created by defining its position and size (and, if applicable, orientation). Let's use the `find_peaks` method that we learned about in a [previous notebook](../02_source_detection/02_source_detection.ipynb) to get the positions of sources in our data:" ] }, { @@ -233,7 +241,8 @@ "metadata": {}, "outputs": [], "source": [ - "from photutils import find_peaks" + "from photutils import find_peaks\n", + "from photutils.centroids import centroid_2dg" ] }, { @@ -243,7 +252,7 @@ "outputs": [], "source": [ "# Calculate statistics\n", - "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, iters=5, mask=xdf_image.mask)" + "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5, mask=xdf_image.mask)" ] }, { @@ -253,7 +262,8 @@ "outputs": [], "source": [ "sources_findpeaks = find_peaks(xdf_image.data, mask=xdf_image.mask, \n", - " threshold=20.*std, box_size=30, subpixel=True) \n", + " threshold=20.*std, box_size=30, \n", + " centroid_func=centroid_2dg) \n", "# Display the table\n", "sources_findpeaks" ] @@ -285,7 +295,8 @@ "cbar.ax.set_yticklabels(labels)\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", "ax1.set_title('find\\_peaks Sources')" @@ -342,7 +353,8 @@ "cbar.ax.set_yticklabels(labels)\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", "ax1.set_title('Circular Apertures')\n", @@ -382,7 +394,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In a [previous notebook](02_photutils_source_detection.ipynb), we showed how you can use the `photutils.detect_sources` feature to generate segmentation maps, which identify and label contiguous (connected) objects within an image. Then, with `source_properties`, you can access descriptive properties for each unique object - not just their centroid positions, but also their pixel areas, eccentricities, orientations with respect to the coordinate frame of the image, and more.\n", + "In a [previous notebook](../02_source_detection/02_source_detection.ipynb), we showed how you can use the `photutils.detect_sources` [feature](https://photutils.readthedocs.io/en/stable/api/photutils.detect_sources.html) to generate segmentation maps, which identify and label contiguous (connected) objects within an image. Then, with `source_properties` [feature](https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.source_properties.html?highlight=source_properties), you can access descriptive properties for each unique object - not just their centroid positions, but also their pixel areas, eccentricities, orientations with respect to the coordinate frame of the image, and more.\n", "\n", "Here we'll use the centroid, semimajor axis, semiminor axis, and orientation values from `source_properties` to generate elliptical apertures for each of the sources in our image." ] @@ -450,7 +462,8 @@ "cbar.ax.set_yticklabels(labels)\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", "ax1.set_title('Elliptical Apertures')\n", @@ -607,7 +620,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `aperture_sum` value is what reports the number of counts within the aperture: 3.47 ct/s.\n", + "The `aperture_sum` value is what reports the number of electron counts within the aperture: 3.47 e–/s.\n", "\n", "And, just as a check, to make sure our sky apertures give basically the same answer..." ] @@ -693,7 +706,7 @@ "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.title('Histogram of Source Photometry')\n", - "plt.xlabel('Count Rate [ct/s]')\n", + "plt.xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')))\n", "plt.ylabel('Number of Sources')" ] }, @@ -713,7 +726,7 @@ "plt.xscale('log')\n", "plt.title('Count Rate v. Aperture Area')\n", "plt.xlabel('Aperture Area [pixels$^2$]')\n", - "plt.ylabel('Count Rate [ct/s]')" + "plt.xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')))" ] }, { @@ -745,7 +758,7 @@ "\n", "## Local Background Subtraction\n", "\n", - "In the [background estimation notebook](01_photutils_background_estimation.ipynb), we explored how to perform global background subtraction of image data with `photutils`. However, you can also use `photutils` to perform local background estimations for aperture corrections.\n", + "In the [background estimation notebook](../01_background_estimation/01_background_estimation.ipynb), we explored how to perform global background subtraction of image data with `photutils`. However, you can also use `photutils` to perform local background estimations for aperture corrections.\n", "\n", "To estimate the local background for each aperture, measure the counts within annulus apertures around (but not including!) each source. In our example, we defined elliptical apertures with `r = 3` to measure the counts within each source. To calculate the background for each source, let's measure the counts elliptical annuli between `r = 3.5` and `r = 5`." ] @@ -795,7 +808,8 @@ "cbar.ax.set_yticklabels(labels)\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ($e^{-1}/s$)', rotation=270, labelpad=30)\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", "ax1.set_title('Elliptical Annuli')\n", @@ -847,13 +861,6 @@ "bkg_phot_table" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might have noticed that these background count rates are *really* small. In this case, this is to be expected - since our example XDF data is a high-level science product (HLSP) that already has already been background-subtracted." - ] - }, { "cell_type": "code", "execution_count": null, @@ -863,7 +870,7 @@ "# Calculate the mean background level (per pixel) in the annuli \n", "bkg_area = [annulus.area() for annulus in elliptical_annuli]\n", "bkg_mean_per_aperture = bkg_phot_table['aperture_sum'].value / bkg_area\n", - "bkg_mean = np.average(bkg_mean_per_aperture) * (u.ct / u.s)\n", + "bkg_mean = np.average(bkg_mean_per_aperture) * (u.electron / u.s)\n", "print('Background mean:', bkg_mean)\n", "\n", "# Calculate the total background within each elliptical aperture\n", @@ -876,6 +883,13 @@ "phot_table['aperture_sum_bkgsub'] = flux_bkgsub" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have noticed that these background count rates are *really* small. In this case, this is to be expected - since our example XDF data is a high-level science product (HLSP) that already has already been background-subtracted." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -910,7 +924,7 @@ "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.title('Histogram of Source Photometry')\n", - "plt.xlabel('Count Rate [ct/s]')\n", + "plt.xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')))\n", "plt.legend()" ] }, @@ -923,7 +937,7 @@ "\n", "The `photutils` package provides a comprehensive toolkit for astronomers to perform aperture photometry, including customizable aperture shapes that allow for more precise photometry and easy photometric correction.\n", "\n", - "**To continue with this `photutils` tutorial, go on to the [PSF photometry notebook](04_photutils_psf_photometry.ipynb).**" + "**To continue with this `photutils` tutorial, go on to the [PSF photometry notebook](../04_psf_photometry/04_psf_photometry.ipynb).**" ] }, { @@ -941,7 +955,7 @@ "source": [ "---\n", "## About this Notebook\n", - "**Authors:** Lauren Chambers (lchambers@stsci.edu), Erik Tollerud (etollerud@stsci.edu), Clare Shanahan (cshanahan@stsci.edu)\n", + "**Authors:** Lauren Chambers (lchambers@stsci.edu), Erik Tollerud (etollerud@stsci.edu), Tom Wilson (towilson@stsci.edu) Clare Shanahan (cshanahan@stsci.edu)\n", "
**Updated:** May 2019" ] }, @@ -970,7 +984,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.5" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/notebooks/photutils/03_photutils_aperture_photometry/requirements.txt b/notebooks/photutils/03_aperture_photometry/requirements.txt similarity index 100% rename from notebooks/photutils/03_photutils_aperture_photometry/requirements.txt rename to notebooks/photutils/03_aperture_photometry/requirements.txt diff --git a/notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb b/notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb deleted file mode 100644 index 0a6aa615..00000000 --- a/notebooks/photutils/04_psf_photometry/04_photutils_psf_photometry.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[](http://photutils.readthedocs.io/en/stable/index.html)\n", - "\n", - "# PSF Photometry with `photutils`\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### What is PSF photometry?\n", - "A more specific form of photometry than aperture photometry, PSF photometry takes into account the shape of a source's point spread function (PSF). The PSF is a model that represents the distribution of light from a point source as it falls onto a detector. An example of a basic PSF is simply a 2-D Gaussian, while more complex PSFs can include distortion, diffraction, or interference effects associated with a particular telescope. For instance, the PSFs from the Hubble Space Telescope and the James Webb Space Telescope have been meticulously modeled, and can be simulated with the [Tiny Tim](http://www.stsci.edu/hst/observatory/focus/TinyTim) and [WebbPSF](https://github.com/mperrin/webbpsf) software packages, respectively. However, for datasets that do not have readily available PSF models, such models can be statistically generated by analyzing the image itself.\n", - "\n", - "The `photutils` package provides tools that combine background estimation, source detection, and model-fitting to perform PSF photometry on image data.\n", - "\n", - "##### What does this tutorial include?\n", - "This tutorial covers how to perform PSF photometry with `photutils`, including the following methods:\n", - "* Gaussian PSF Photometry\n", - "* Iterative Subtraction\n", - "* Point Response Function (PRF) Photometry\n", - "\n", - "The methods demonstrated here are available in narrative form within the `photutils.psf` [documentation](http://photutils.readthedocs.io/en/stable/psf.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
**Warning:** The PSF photometry API is currently considered experimental and may change in the future. The photutils development team will aim to keep compatibility where practical, but will not finalize the API until sufficient user feedback has been accumulated.
**Important:** Before proceeding, please be sure to install or update your [AstroConda](https://astroconda.readthedocs.io) distribution. This notebook may not work properly with older versions of AstroConda.
\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import necessary packages" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's import packages that we will use to perform arithmetic functions and visualize data:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from astropy.io import fits\n", - "from astropy.stats import sigma_clipped_stats, SigmaClip\n", - "from astropy.visualization import ZScaleInterval, ImageNormalize\n", - "from photutils import make_source_mask\n", - "from photutils.background import Background2D, MedianBackground\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.colors import LogNorm\n", - "% matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also define some `matplotlib` parameters, to make sure our plots look nice. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "matplotlib.rc('font', family='serif', weight='light', size=12)\n", - "matplotlib.rc('mathtext', bf='serif:normal')\n", - "matplotlib.rc('axes', titlesize=18, titlepad=12, labelsize=16)\n", - "matplotlib.rc('xtick', labelsize=14)\n", - "matplotlib.rc('ytick', labelsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have place the data for this tutorial in the github repository, for easy access. The data were originally retrieved from the STScI archive: https://archive.stsci.edu/prepds/udf/udf_hlsp.html." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "with fits.open('data/h_udf_wfc_v_drz_img.fits') as hdulist:\n", - " v_data = hdulist[0].data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", - "\n", - "clim = (0, 1e-2)\n", - "\n", - "v_data_plot = np.copy(v_data)\n", - "v_data_plot[v_data_plot <= 0] = 1e-10\n", - "\n", - "norm_image = ImageNormalize(v_data, interval=ZScaleInterval())\n", - "fitsplot = ax1.imshow(v_data, norm=norm_image)#, clim=clim)\n", - "\n", - "plt.colorbar(fitsplot, fraction=0.046, pad=0.04)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Circular Apertures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Apertures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Performing Aperture Photometry" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculating Aperture Corrections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Elliptical Apertures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Apertures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Performing Aperture Photometry" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculating Aperture Corrections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## Exercises" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "May 2018\n", - "\n", - "Author: Lauren Chambers (lchambers@stsci.edu)\n", - "\n", - "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." - ] - } - ], - "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": "3.5.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb new file mode 100644 index 00000000..27e39968 --- /dev/null +++ b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "[\n", + "\n", + "](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", + "# PSF Photometry with `photutils`\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### What is PSF photometry?\n", + "A more specific form of photometry than aperture photometry, PSF photometry takes into account the shape of a source's point spread function (PSF). The PSF is a model that represents the distribution of light from a point source as it falls onto a detector. An example of a basic PSF is simply a 2-D Gaussian, while more complex PSFs can include distortion, diffraction, or interference effects associated with a particular telescope. For instance, the PSFs from the Hubble Space Telescope and the James Webb Space Telescope have been meticulously modeled, and can be simulated with the [Tiny Tim](http://www.stsci.edu/hst/observatory/focus/TinyTim) and [WebbPSF](https://github.com/mperrin/webbpsf) software packages, respectively. However, for datasets that do not have readily available PSF models, such models can be statistically generated by analyzing the image itself.\n", + "\n", + "The `photutils` package provides tools that combine background estimation, source detection, and model-fitting to perform PSF photometry on image data.\n", + "\n", + "##### What does this tutorial include?\n", + "This tutorial covers how to perform PSF photometry with `photutils`, including the following methods:\n", + "* Gaussian PSF Photometry\n", + "* Iterative Subtraction\n", + "* Point Response Function (PRF) Photometry\n", + "\n", + "##### Which data are used in this tutorial?\n", + "We will be manipulating Hubble eXtreme Deep Field (XDF) data, which was collected using the Advanced Camera for Surveys (ACS) on Hubble between 2002 and 2012. The image we use here is the result of 1.8 million seconds (500 hours!) of exposure time, and includes some of the faintest and most distant galaxies that have ever been observed. \n", + "\n", + "*The methods demonstrated here are available in narrative form within the `photutils.psf` [documentation](http://photutils.readthedocs.io/en/stable/psf.html).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "**Warning:** The PSF photometry API is currently considered experimental and may change in the future. The photutils development team will aim to keep compatibility where practical, but will not finalize the API until sufficient user feedback has been accumulated.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "**Important:** Before proceeding, please be sure to install or update your [AstroConda](https://astroconda.readthedocs.io) distribution. This notebook may not work properly with older versions of AstroConda.\n", + "\n", + "
\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import necessary packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import packages that we will use to perform arithmetic functions and visualize data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "import astropy.units as u\n", + "from astropy.nddata import CCDData\n", + "# from astropy.stats import sigma_clipped_stats\n", + "from astropy.visualization import ImageNormalize, LogStretch\n", + "import matplotlib\n", + "from matplotlib.colors import LogNorm\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import LogLocator\n", + "import numpy as np\n", + "\n", + "# Show plots in the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also define some `matplotlib` parameters, such as title font size and the dpi, to make sure our plots look nice. To make it quick, we'll do this by loading a [style file shared with the other photutils tutorials](../photutils_notebook_style.mplstyle) into `pyplot`. We will use this style file for all the notebook tutorials. (See [here](https://matplotlib.org/users/customizing.html) to learn more about customizing `matplotlib`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('../photutils_notebook_style.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", + "\n", + "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://archive.stsci.edu/pub/hlsp/xdf/hlsp_xdf_hst_acswfc-60mas_hudf_f435w_v1_sci.fits'\n", + "with fits.open(url) as hdulist:\n", + " hdulist.info()\n", + " data = hdulist[0].data\n", + " header = hdulist[0].header" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As explained in a [previous notebook](../01_background_estimation/01_background_estimation.ipynb) on background estimation, it is important to **mask** these data, as a large portion of the values are equal to zero. We will mask out the non-data portions of the image array, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the mask\n", + "mask = data == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons (counts) per second." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "unit = u.electron / u.s\n", + "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Set up the figure with subplots\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", + "\n", + "# Set up the normalization and colormap\n", + "norm_image = ImageNormalize(vmin=1e-4, vmax=5e-2, stretch=LogStretch(), clip=False)\n", + "cmap = plt.get_cmap('viridis')\n", + "cmap.set_over(cmap.colors[-1])\n", + "cmap.set_under(cmap.colors[0])\n", + "cmap.set_bad('white') # Show masked data as white\n", + "xdf_image_clipped = np.clip(xdf_image, 1e-4, None) # clip to plot with logarithmic stretch\n", + "\n", + "# Plot the data\n", + "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), \n", + " norm=norm_image, cmap=cmap)\n", + "\n", + "# Define the colorbar and fix the labels\n", + "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", + "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "# Define labels\n", + "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", + " rotation=270, labelpad=30)\n", + "ax1.set_xlabel('X (pixels)')\n", + "ax1.set_ylabel('Y (pixels)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Tip: Double-click on any inline plot to zoom in.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Circular Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performing Aperture Photometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Aperture Corrections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elliptical Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Apertures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performing Aperture Photometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Aperture Corrections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Exercises" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Additional Resources\n", + "For more examples and details, please visit the [photutils](http://photutils.readthedocs.io/en/stable/index.html) documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## About this Notebook\n", + "**Authors:** Lauren Chambers (lchambers@stsci.edu)\n", + "
**Updated:** May 2019" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#title_ID)\n", + "\"STScI" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ef462b5daea49dc53fcae39759d93f43a5e23d62 Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Mon, 20 May 2019 16:58:30 -0400 Subject: [PATCH 3/9] Fixes so HTML renders correctly --- .../01_background_estimation.ipynb | 8 +++----- .../02_source_detection.ipynb | 18 ++++++++---------- .../03_aperture_photometry.ipynb | 13 ++++++------- .../04_psf_photometry/04_psf_photometry.ipynb | 10 ++++------ 4 files changed, 21 insertions(+), 28 deletions(-) diff --git a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb index c02e4078..9136a730 100644 --- a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb +++ b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb @@ -6,9 +6,7 @@ "source": [ "\n", "\n", - "[\n", - "\n", - "](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", "\n", "# Background Estimation with `photutils`\n", "---" @@ -42,13 +40,13 @@ "source": [ "
\n", "\n", - "**Note:** This notebook focuses on global background estimation. Local background subtraction with **annulus apertures** is demonstrated in the [aperture photometry notebook](../03_aperture_photometry/03_aperture_photometry.ipynb).\n", + "Note: This notebook focuses on global background estimation. Local background subtraction with annulus apertures is demonstrated in the aperture photometry notebook.\n", "\n", "
\n", "\n", "
\n", " \n", - " **Important:** Before proceeding, please be sure to update your versions of `astropy`, `matplotlib`, and `photutils`, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the [AstroConda](https://astroconda.readthedocs.io) distribution.\n", + "Important: Before proceeding, please be sure to update your versions of astropy, matplotlib, and photutils, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the AstroConda distribution.\n", " \n", "
\n", "\n", diff --git a/notebooks/photutils/02_source_detection/02_source_detection.ipynb b/notebooks/photutils/02_source_detection/02_source_detection.ipynb index b40fad3d..2de6f6da 100644 --- a/notebooks/photutils/02_source_detection/02_source_detection.ipynb +++ b/notebooks/photutils/02_source_detection/02_source_detection.ipynb @@ -6,9 +6,7 @@ "source": [ "\n", "\n", - "[\n", - "\n", - "](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", "\n", "\n", "# Source Detection with `photutils`\n", @@ -42,9 +40,9 @@ "metadata": {}, "source": [ "
\n", - "\n", - "**Important:** Before proceeding, please be sure to update your versions of `astropy`, `matplotlib`, and `photutils`, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the [AstroConda](https://astroconda.readthedocs.io) distribution.\n", - "\n", + " \n", + "Important: Before proceeding, please be sure to update your versions of astropy, matplotlib, and photutils, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the AstroConda distribution.\n", + " \n", "
\n", "\n", "---" @@ -336,7 +334,7 @@ "\n", "

Exercises:


\n", "\n", - "Re-run the `DAOStarFinder` algorithm with a smaller threshold (like 5σ), and plot the sources that it finds. Do the same, but with a larger threshold (like 100σ). How did changing the threshold affect the results?\n", + "Re-run the DAOStarFinder algorithm with a smaller threshold (like 5σ), and plot the sources that it finds. Do the same, but with a larger threshold (like 100σ). How did changing the threshold affect the results?\n", "\n", "" ] @@ -445,7 +443,7 @@ "\n", "

Exercises:


\n", "\n", - "Re-run the `IRAFStarFinder` algorithm with a smaller full-width-half-max (FWHM) – try 3 pixels – and plot the sources that it finds. Do the same, but with a larger FWHM (like 10 pixels). How did changing the FWHM affect the results? What astronomical objects might be better captures by smaller FWHM? Larger?\n", + "Re-run the IRAFStarFinder algorithm with a smaller full-width-half-max (FWHM) – try 3 pixels – and plot the sources that it finds. Do the same, but with a larger FWHM (like 10 pixels). How did changing the FWHM affect the results? What astronomical objects might be better captures by smaller FWHM? Larger?\n", "\n", "" ] @@ -910,7 +908,7 @@ "\n", "

Exercises:


\n", "\n", - "Recompute the `SegmentationImage`, but alter the threshold and the minimum number of pixels in a source. How does changing the threshold affect the results? What about changing the number of pixels?\n", + "Recompute the SegmentationImage, but alter the threshold and the minimum number of pixels in a source. How does changing the threshold affect the results? What about changing the number of pixels?\n", "\n", "" ] @@ -1026,7 +1024,7 @@ "\n", "

Exercises:


\n", "\n", - "Play with the isophotal extent of the elliptical apertures (defined above as `r`). Observe how changing this value affects the apertures that are created.\n", + "Play with the isophotal extent of the elliptical apertures (defined above as r). Observe how changing this value affects the apertures that are created.\n", "\n", "" ] diff --git a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb index 2363a079..17ec279c 100644 --- a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb +++ b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb @@ -6,9 +6,7 @@ "source": [ "\n", "\n", - "[\n", - "\n", - "](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", "\n", "# Aperture Photometry with `photutils`\n", "---" @@ -43,9 +41,9 @@ "metadata": {}, "source": [ "
\n", - "\n", - "**Important:** Before proceeding, please be sure to install or update your [AstroConda](https://astroconda.readthedocs.io) distribution. This notebook may not work properly with older versions of AstroConda.\n", - "\n", + " \n", + "Important: Before proceeding, please be sure to update your versions of astropy, matplotlib, and photutils, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the AstroConda distribution.\n", + " \n", "
\n", "\n", "---" @@ -226,6 +224,7 @@ "metadata": {}, "source": [ "With `photutils`, users can create apertures with the following shapes:\n", + "\n", "\"Examples\n", "\n", "Each of these can be defined either in pixel coordinates or in celestial coordinates (using a WCS transformation).\n", @@ -737,7 +736,7 @@ "\n", "

Exercise:


\n", "\n", - "Re-calculate the photometry for these elliptical apertures - or just a subset of them - using the `subpixel` aperture placement method instead of the default `exact` method. How does this affect the count sum calculated for those apertures?\n", + "Re-calculate the photometry for these elliptical apertures - or just a subset of them - using the subpixel aperture placement method instead of the default exact method. How does this affect the count sum calculated for those apertures?\n", "\n", "" ] diff --git a/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb index 27e39968..7ebdfd1d 100644 --- a/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb +++ b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb @@ -6,9 +6,7 @@ "source": [ "\n", "\n", - "[\n", - "\n", - "](http://photutils.readthedocs.io/en/stable/index.html)\n", + "\n", "\n", "# PSF Photometry with `photutils`\n", "---" @@ -46,9 +44,9 @@ "\n", "\n", "
\n", - "\n", - "**Important:** Before proceeding, please be sure to install or update your [AstroConda](https://astroconda.readthedocs.io) distribution. This notebook may not work properly with older versions of AstroConda.\n", - "\n", + " \n", + "Important: Before proceeding, please be sure to update your versions of astropy, matplotlib, and photutils, or this notebook may not work properly. Or, if you don't want to handle packages individually, you can always use (and keep updated!) the AstroConda distribution.\n", + " \n", "
\n", "\n", "---" From d0a8698573075b3cf909cb917a584f1359c6387d Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Mon, 20 May 2019 17:03:01 -0400 Subject: [PATCH 4/9] Add .gitignore from #99 and add apertures PNG --- notebooks/.gitignore | 39 ++++++++++++++++++ .../03_aperture_photometry.ipynb | 2 +- .../03_aperture_photometry/apertures.png | Bin 0 -> 76112 bytes 3 files changed, 40 insertions(+), 1 deletion(-) create mode 100644 notebooks/.gitignore create mode 100644 notebooks/photutils/03_aperture_photometry/apertures.png diff --git a/notebooks/.gitignore b/notebooks/.gitignore new file mode 100644 index 00000000..27a68f88 --- /dev/null +++ b/notebooks/.gitignore @@ -0,0 +1,39 @@ +# Files generated by convert.py +# ----------------------------- +exec_*.ipynb +*.html +*.fits +*.coo +*.match +*.log +*.png +*.list +*.txt +*.cat +*.jpg +*.gif +*.zip +DrizzlePac/Initialization/reference_files/ +DrizzlePac/drizzle_wfpc2/reference_files/ +MAST/Kepler/Kepler_Lightcurve/mastDownload/ +MAST/Kepler/Kepler_TPF/mastDownload/ + +# Exceptions committed to repo +# ---------------------------- +!DrizzlePac/align_sparse_fields/input_flc.list +!DrizzlePac/mask_satellite/sat.jpeg +!DrizzlePac/sky_matching/drz.list +!DrizzlePac/sky_matching/labeled_local_globalmatch_match.gif +!DrizzlePac/sky_matching/MDRIZSKY_Values.png +!MAST/HSC/HSC_TAP/smc_colormag.png +!MAST/Kepler/Kepler_FFI/ffi_tic_plot.png +!MAST/Kepler/Kepler_TPF/tpf_fluxplot0.png +!MAST/Kepler/Kepler_TPF/tpf_fluxplot_28-29.png +!MAST/Kepler/Kepler_Lightcurve/light_curve_tres2.png +!MAST/K2/K2_FFI/kepler_focal_plane_layout_channels_color.png +!MAST/PanSTARRS/PS1_DR2_TAP/stsci_pri_combo_mark_horizonal_white_bkgd.png +!photutils/03_aperture_photometry/apertures.png +!**/requirements.txt +!**/skyfile.txt +!**/exclusions.txt +!**/inclusions*.txt diff --git a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb index 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zupe?<;~~39YuJK%jrwu_mc*`3%t7h#=un2#F?DQ#5OJ4g65 z;;`@U2o_3bb2@}9Dlo4uRKQ6Io$8e($myQytbu> zPF44*T}DW`kQCX9_qpgU;u{1CfMGk-i+k}5Bgy)wDM$T1k-d|IqMM?plJS~s!9pgd zuGvyf?5fch;Eae9yLI3BUSi7Ln4Y8FhqrN=2giGOn37_Rs0D@v|rJY{sRy+RR zKfxgixrVc)Vu>o|s=tj9he%lc62xTbeaAmW9eu5_ez=6NbNg=>$pgNCzO?OQ zKV2!Ig3GPHU)(nFQrNfFYY{kq4VjPCAD&}P9yt~8=h1b+6NtjXZ>HXdk%uhG{r%$o zV&HK@S=sUa8uC+xOQ)J$Oq`W?f5u$E?$7$Xn@K>q>MCH`=ND2&m{=XSb(MWhqLg3#A^4Apo;|G6+2R^0I!!F1{hlbN% z5BAr;{I!CA{WJhOF=HETT|E!~`siP-_V?d}@PNr$LN9ZQ{x!#cJ^$a=`ycmsfL$Yp zgQb<9|M=)Xjkq-mteXqmHIn`xCi%}-`)k&*=2DLS?dtZQZu^gWEGdHpsFD%yB>hj% z^4C57SngAdSGD;6I8iQlfGrpy8CKr?<39gkAh!3xL>2gcn`Zc*CaSTD5}0KnG|W~0 zf13TWcra1AvL;Ss|IKug&q#i~f&U!CIA7rc=AaDX%=+^s@SoCS^+$zrX0QJjXfC1R literal 0 HcmV?d00001 From fe62145c40864dfee1baedb9d64ca17d7ddc8387 Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Mon, 20 May 2019 17:05:41 -0400 Subject: [PATCH 5/9] Maybe adding a tags will help...? --- .../03_aperture_photometry/03_aperture_photometry.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb index ef35021a..6dc76b08 100644 --- a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb +++ b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb @@ -225,7 +225,7 @@ "source": [ "With `photutils`, users can create apertures with the following shapes:\n", "\n", - "\"Examples\n", + "\"Examples\n", "\n", "Each of these can be defined either in pixel coordinates or in celestial coordinates (using a WCS transformation).\n", "\n", From 4f0b732e76f90eaf3186cd5af5a84c0aea40dee1 Mon Sep 17 00:00:00 2001 From: obviousrebel Date: Tue, 21 May 2019 11:04:42 -0400 Subject: [PATCH 6/9] attempting to fix ci errors with latex --- notebooks/photutils/photutils_notebook_style.mplstyle | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/photutils/photutils_notebook_style.mplstyle b/notebooks/photutils/photutils_notebook_style.mplstyle index 2f1f6450..7b9b10d6 100644 --- a/notebooks/photutils/photutils_notebook_style.mplstyle +++ b/notebooks/photutils/photutils_notebook_style.mplstyle @@ -11,7 +11,7 @@ axes.labelsize : 18 xtick.labelsize : 16 ytick.labelsize : 16 -text.usetex : True + figure.subplot.bottom : 0.15 figure.dpi : 200 From dc17e7c78b9fcec1a4824a0733a8ee6185376f50 Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Wed, 31 Jul 2019 10:17:48 -0400 Subject: [PATCH 7/9] First round of edits --- .../01_background_estimation.ipynb | 8 +++++--- .../02_source_detection/02_source_detection.ipynb | 8 +++++--- .../03_aperture_photometry/03_aperture_photometry.ipynb | 3 +++ .../photutils/04_psf_photometry/04_psf_photometry.ipynb | 3 +++ 4 files changed, 16 insertions(+), 6 deletions(-) diff --git a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb index 9136a730..5037a5c1 100644 --- a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb +++ b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
Made possible by the Astropy Project and ScienceBetter Consulting through financial support from the Community Software Initiative at the Space Telescope Science Institute.
\n", + "\n", "\n", "\n", "\n", @@ -136,7 +138,7 @@ "metadata": {}, "source": [ "#### Modifying data\n", - "For the purposes of this notebook example, we're going to add a background effect to this data, but don't worry about this. (Pay no attention to that man behind the curtain!)" + "For the purposes of this notebook example, we're going to add a linear background effect from the top to the bottom of these data. But don't worry about this (pay no attention to that man behind the curtain!)." ] }, { @@ -171,7 +173,7 @@ "outputs": [], "source": [ "from astropy.nddata import CCDData\n", - "unit = u.ct / u.s\n", + "unit = u.electron / u.s\n", "xdf_image = CCDData(modified_data, unit=unit, meta=header)" ] }, @@ -452,7 +454,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `Background2D` class allows users to model 2-dimensional backgrounds, by evaluating the background signal in small boxes, and smoothing these boxes to reconstruct a continuous 2D background. The class includes the following arguments/attributes:\n", + "The `Background2D` class allows users to model 2-dimensional backgrounds, by calculating the mean or median in small boxes, and smoothing these boxes to reconstruct a continuous 2D background. The class includes the following arguments/attributes:\n", "* **`box_size`** - the size of the boxes used to calculate the background. This should be larger than individual sources, yet still small enough to encompass changes in the background.\n", "* **`filter_size`** - the size of the median filter used to smooth the final 2D background.\n", "* **`filter_threshold`** - threshold below which the smoothing median filter will not be applied.\n", diff --git a/notebooks/photutils/02_source_detection/02_source_detection.ipynb b/notebooks/photutils/02_source_detection/02_source_detection.ipynb index 2de6f6da..20e658c1 100644 --- a/notebooks/photutils/02_source_detection/02_source_detection.ipynb +++ b/notebooks/photutils/02_source_detection/02_source_detection.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
Made possible by the Astropy Project and ScienceBetter Consulting through financial support from the Community Software Initiative at the Space Telescope Science Institute.
\n", + "\n", "\n", "\n", "\n", @@ -148,7 +150,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons (counts) per second." + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons per second." ] }, { @@ -157,7 +159,7 @@ "metadata": {}, "outputs": [], "source": [ - "unit = u.electron/ u.s\n", + "unit = u.electron / u.s\n", "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" ] }, @@ -452,7 +454,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Note: Comparing `DAOStarFinder` and `IRAFStarFinder`" + "## Note: Comparing `DAOStarFinder` and `IRAFStarFinder`" ] }, { diff --git a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb index 6dc76b08..e430a215 100644 --- a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb +++ b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb @@ -4,10 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
Made possible by the Astropy Project and ScienceBetter Consulting through financial support from the Community Software Initiative at the Space Telescope Science Institute.
\n", + "\n", "\n", "\n", "\n", "\n", + "\n", "# Aperture Photometry with `photutils`\n", "---" ] diff --git a/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb index 7ebdfd1d..3d4c77fa 100644 --- a/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb +++ b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb @@ -4,10 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
Made possible by the Astropy Project and ScienceBetter Consulting through financial support from the Community Software Initiative at the Space Telescope Science Institute.
\n", + "\n", "\n", "\n", "\n", "\n", + "\n", "# PSF Photometry with `photutils`\n", "---" ] From 852a3f27eac99c6059d9984d6dace3af25010c88 Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Wed, 31 Jul 2019 10:22:15 -0400 Subject: [PATCH 8/9] More fixes --- .../01_background_estimation.ipynb | 2 +- .../03_aperture_photometry.ipynb | 16 ++++++++-------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb index 5037a5c1..f39076cc 100644 --- a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb +++ b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb @@ -239,7 +239,7 @@ "source": [ "You probably noticed that a large portion of the data is equal to zero. The data we are using is a reduced mosaic that combines many different exposures, and that has been rotated such that not all of the array holds data. \n", "\n", - "We want to **mask** out the non-data portions of the image array,, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data." + "We want to **mask** out the non-data portions of the image array, so all of those pixels that have a value of zero don't interfere with our statistics and analyses of the data." ] }, { diff --git a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb index e430a215..3a27a501 100644 --- a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb +++ b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb @@ -301,7 +301,7 @@ " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", - "ax1.set_title('find\\_peaks Sources')" + "ax1.set_title('find_peaks Sources')" ] }, { @@ -661,11 +661,11 @@ "source": [ "# The CCDData mask will be automatically applied\n", "phot_table = aperture_photometry(xdf_image, elliptical_apertures[0])\n", - "id = 1\n", + "idx = 1\n", "for aperture in elliptical_apertures[1:]:\n", - " id += 1\n", + " idx += 1\n", " phot_row = aperture_photometry(xdf_image, aperture)[0]\n", - " phot_row[0] = id\n", + " phot_row[0] = idx\n", " phot_table.add_row(phot_row)" ] }, @@ -728,7 +728,7 @@ "plt.xscale('log')\n", "plt.title('Count Rate v. Aperture Area')\n", "plt.xlabel('Aperture Area [pixels$^2$]')\n", - "plt.xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')))" + "plt.ylabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')))" ] }, { @@ -845,11 +845,11 @@ "source": [ "# The CCDData mask will be automatically applied\n", "bkg_phot_table = aperture_photometry(xdf_image, elliptical_annuli[0])\n", - "id = 1\n", + "idx = 1\n", "for aperture in elliptical_annuli[1:]:\n", - " id += 1\n", + " idx += 1\n", " phot_row = aperture_photometry(xdf_image, aperture)[0]\n", - " phot_row[0] = id\n", + " phot_row[0] = idx\n", " bkg_phot_table.add_row(phot_row)" ] }, From b19ad49f10fd2df7f38582e850e6cc9610f0a49c Mon Sep 17 00:00:00 2001 From: Lauren Chambers Date: Wed, 31 Jul 2019 16:11:40 -0400 Subject: [PATCH 9/9] More edits from @mwcraig --- .../01_background_estimation.ipynb | 69 +++++++++++-------- .../02_source_detection.ipynb | 62 ++++++----------- .../03_aperture_photometry.ipynb | 60 +++++----------- .../04_psf_photometry/04_psf_photometry.ipynb | 29 ++------ 4 files changed, 84 insertions(+), 136 deletions(-) diff --git a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb index f39076cc..60d47c41 100644 --- a/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb +++ b/notebooks/photutils/01_background_estimation/01_background_estimation.ipynb @@ -285,6 +285,15 @@ "ax2.set_title('Masked Data')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the left we have plotted this mask, which has a value of 1 (or True) shown in black where the data is bad, and 0 (or False) shown in white where the data is good. \n", + "\n", + "After the mask is applied to the data - on the right above - the data values \"behind\" the masked values are shown in white." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -300,6 +309,8 @@ "\n", "By \"scalar estimation\", we mean the calculation of a single value (such as the mean or median) to represent the value of the background for our entire two-dimensional dataset. This is in contrast to a two-dimensional background, where the estimated background is represented as an array of values that can vary spatially with the dataset. We will calculate a 2D background in the upcoming section.\n", "\n", + "### Calculate scalar background value\n", + "\n", "Here we will calculate the mean, median, and mode using sigma clipping. With sigma clipping, the data is iteratively clipped to exclude data points outside of a certain sigma (standard deviation), thus removing some of the noise from the data before determining statistical values." ] }, @@ -309,7 +320,11 @@ "metadata": {}, "outputs": [], "source": [ - "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5, mask=xdf_image.mask)" + "# Calculate statistics with masking\n", + "mean, median, std = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5, mask=xdf_image.mask)\n", + "\n", + "# Calculate statistics without masking\n", + "stats_nomask = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5)" ] }, { @@ -319,16 +334,6 @@ "But what difference does this sigma clipping make? And how important is masking, anyway? Let's visualize these statistics to get an idea:" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the data without masking\n", - "stats_nomask = sigma_clipped_stats(xdf_image.data, sigma=3.0, maxiters=5)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -344,36 +349,38 @@ "ax2.hist(xdf_image[~xdf_image.mask], bins=100, range=flux_range)\n", "\n", "# Plot lines for each kind of mean\n", - "ax1.axvline(mean, label='Masked \\& Clipped', c='C1', lw=3)\n", - "ax1.axvline(np.average(xdf_image[~xdf_image.mask]), label='Masked', c='C2', ls='--', lw=3)\n", - "ax1.axvline(stats_nomask[0], label='Clipped', c='C3', ls='-.', lw=3)\n", - "ax1.axvline(np.average(xdf_image), label='Neither', c='C6', ls=':', lw=3)\n", + "ax1.axvline(mean, label='Masked & Clipped', c='C1', ls='-.', lw=3)\n", + "ax1.axvline(np.average(xdf_image[~xdf_image.mask]), label='Masked', c='C2', lw=3)\n", + "ax1.axvline(stats_nomask[0], label='Clipped', c='C3', ls=':', lw=3)\n", + "ax1.axvline(np.average(xdf_image), label='Neither', c='C5', ls='--', lw=3)\n", "\n", "ax1.set_xlim(flux_range)\n", "ax1.set_xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), fontsize=14)\n", "ax1.set_ylabel('Frequency', fontsize=14)\n", - "ax1.set_title('Effect of Sigma-Clipping and Masking on Mean', fontsize=16)\n", - "ax1.legend(fontsize=11)\n", - "\n", + "ax1.set_title('Effect of Sigma-Clipping \\n and Masking on Mean', fontsize=16)\n", "\n", "# Plot lines for each kind of median\n", "# Note: use np.ma.median rather than np.median for masked arrays\n", - "ax2.axvline(median, label='Masked \\& Clipped', c='C1', lw=3)\n", - "ax2.axvline(np.ma.median(xdf_image[~xdf_image.mask]), label='Masked', c='C2', ls='--', lw=3)\n", - "ax2.axvline(stats_nomask[1], label='Clipped', c='C3', ls='-.', lw=3)\n", - "ax2.axvline(np.ma.median(xdf_image), label='Neither', c='C6', ls=':', lw=3)\n", + "ax2.axvline(median, label='Masked & Clipped', c='C1', ls='-.', lw=3)\n", + "ax2.axvline(np.ma.median(xdf_image[~xdf_image.mask]), label='Masked', c='C2', lw=3)\n", + "ax2.axvline(stats_nomask[1], label='Clipped', c='C3', ls=':', lw=3)\n", + "ax2.axvline(np.ma.median(xdf_image), label='Neither', c='C5', ls='--', lw=3)\n", "\n", "ax2.set_xlim(flux_range)\n", "ax2.set_xlabel(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), fontsize=14)\n", - "ax2.set_title('Effect of Sigma-Clipping and Masking on Median', fontsize=16)\n", - "ax2.legend(fontsize=11)" + "ax2.set_title('Effect of Sigma-Clipping \\n and Masking on Median', fontsize=16)\n", + "\n", + "# Add legend\n", + "ax1.legend(fontsize=11, loc='lower center', bbox_to_anchor=(1.1, -0.45), ncol=2, handlelength=6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Just from simply looking at the distribution of the data, it is pretty easy to see how sigma-clipping and masking improve the calculation of the mean and median.\n", + "Just from simply looking at the distribution of the data, it is pretty easy to see how sigma-clipping and masking improve the calculation of the mean and median: the masked & sigma-clipped values are closest to the center of the distribution in both cases. It's also worthwhile to note that the median does a better job even without masking or clipping!\n", + "\n", + "### Subtract scalar background value\n", "\n", "But enough looking at numbers, let's actually remove the background from the data. By using the `subtract()` method of the `CCDData` class, we can subtract the mean background while maintaining the metadata and mask of our original CCDData object:" ] @@ -385,7 +392,7 @@ "outputs": [], "source": [ "# Calculate the scalar background subtraction, maintaining metadata, unit, and mask\n", - "xdf_scalar_bkgdsub = xdf_image.subtract(mean * u.ct / u.s)" + "xdf_scalar_bkgdsub = xdf_image.subtract(mean * u.electron / u.s)" ] }, { @@ -495,13 +502,15 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "# Set up the figure with subplots\n", "fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))\n", "\n", - "# Plot the data\n", + "# Plot the background\n", "background_clipped = np.clip(bkg.background, 1e-4, None) # clip to plot with logarithmic stretch\n", "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, background_clipped), norm=norm_image)\n", "\n", @@ -525,6 +534,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "You might notice that not all areas of the background array have mesh boxes over them (look for those boxes that do not have a `+`). If you compare this background array with the original data, you'll see that these un-boxed areas contain particularly bright sources, and thus are not being included in the background estimate .\n", + "\n", "And how does the data look if we use this background subtraction method (again maintaining the attributes of the CCDData object)?" ] }, @@ -535,7 +546,7 @@ "outputs": [], "source": [ "# Calculate the 2D background subtraction, maintaining metadata, unit, and mask\n", - "xdf_2d_bkgdsub = xdf_image.subtract(bkg.background * u.ct / u.s)" + "xdf_2d_bkgdsub = xdf_image.subtract(bkg.background * u.electron / u.s)" ] }, { diff --git a/notebooks/photutils/02_source_detection/02_source_detection.ipynb b/notebooks/photutils/02_source_detection/02_source_detection.ipynb index 20e658c1..ee395477 100644 --- a/notebooks/photutils/02_source_detection/02_source_detection.ipynb +++ b/notebooks/photutils/02_source_detection/02_source_detection.ipynb @@ -113,7 +113,9 @@ "source": [ "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", "\n", - "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)" + "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)\n", + "\n", + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons per second." ] }, { @@ -123,10 +125,7 @@ "outputs": [], "source": [ "url = 'https://archive.stsci.edu/pub/hlsp/xdf/hlsp_xdf_hst_acswfc-60mas_hudf_f435w_v1_sci.fits'\n", - "with fits.open(url) as hdulist:\n", - " hdulist.info()\n", - " data = hdulist[0].data\n", - " header = hdulist[0].header" + "xdf_image = CCDData.read(url, unit=u.electron / u.s)" ] }, { @@ -143,24 +142,8 @@ "outputs": [], "source": [ "# Define the mask\n", - "mask = data == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons per second." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "unit = u.electron / u.s\n", - "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" + "mask = xdf_image.data == 0\n", + "xdf_image.mask = mask" ] }, { @@ -267,8 +250,8 @@ "metadata": {}, "outputs": [], "source": [ - "daofind = DAOStarFinder(fwhm=5.0, threshold=20.*std)\n", - "sources_dao = daofind(xdf_image * ~xdf_image.mask) \n", + "daofind = DAOStarFinder(fwhm=5.0, threshold=20. * std)\n", + "sources_dao = daofind(np.ma.masked_where(xdf_image.mask, xdf_image)) \n", "print(sources_dao)" ] }, @@ -376,8 +359,8 @@ "metadata": {}, "outputs": [], "source": [ - "iraffind = IRAFStarFinder(fwhm=5.0, threshold=20.*std)\n", - "sources_iraf = iraffind(xdf_image * ~xdf_image.mask) \n", + "iraffind = IRAFStarFinder(fwhm=5.0, threshold=20. * std)\n", + "sources_iraf = iraffind(np.ma.masked_where(xdf_image.mask, xdf_image)) \n", "print(sources_iraf)" ] }, @@ -484,11 +467,11 @@ "metadata": {}, "outputs": [], "source": [ - "iraffind_match = IRAFStarFinder(fwhm=5.0, threshold=20.*std, \n", + "iraffind_match = IRAFStarFinder(fwhm=5.0, threshold=20. * std, \n", " sharplo=0.2, sharphi=1.0, \n", " roundlo=-1.0, roundhi=1.0,\n", " minsep_fwhm=0.0)\n", - "sources_iraf_match = iraffind_match(xdf_image * ~xdf_image.mask) \n", + "sources_iraf_match = iraffind_match(np.ma.masked_where(xdf_image.mask, xdf_image)) \n", "print(sources_iraf_match)" ] }, @@ -579,7 +562,7 @@ "outputs": [], "source": [ "sources_findpeaks = find_peaks(xdf_image.data, mask=xdf_image.mask, \n", - " threshold=20.*std, box_size=30, \n", + " threshold=20. * std, box_size=30, \n", " centroid_func=centroid_2dg) \n", "print(sources_findpeaks)" ] @@ -609,7 +592,7 @@ " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", - "ax1.set_title('find\\_peaks Sources')" + "ax1.set_title('find_peaks Sources')" ] }, { @@ -724,26 +707,21 @@ "fitsplot = ax1.imshow(np.ma.masked_where(xdf_image.mask, xdf_image_clipped), norm=norm_image)\n", "ax1.scatter([x for x, y in list(dao_findpeaks_match)], [y for x, y in list(dao_findpeaks_match)],\n", " s=30, marker='s', lw=1, facecolor='None', edgecolor='#EE7733',\n", - " label='Found by DAO \\& find\\_peaks')\n", + " label='Found by DAO & find_peaks')\n", "ax1.scatter([x for x, y in list(dao_iraf_match)], [y for x, y in list(dao_iraf_match)],\n", " s=30, marker='D', lw=1, facecolor='None', edgecolor='#EE3377',\n", - " label='Found by DAO \\& IRAF')\n", + " label='Found by DAO & IRAF')\n", "ax1.scatter([x for x, y in list(iraf_findpeaks_match)], [y for x, y in list(iraf_findpeaks_match)],\n", " s=30, marker='o', lw=1, facecolor='None', edgecolor='#0077BB',\n", - " label='Found by IRAF \\& find\\_peaks')\n", + " label='Found by IRAF & find_peaks')\n", "ax1.scatter([x for x, y in list(all_match)], [y for x, y in list(all_match)],\n", " s=30, marker='o', lw=1.2, linestyle=':',facecolor='None', edgecolor='#BBBBBB',\n", " label='Found by all methods')\n", - "ax1.legend(ncol=2)\n", "\n", - "# Define the colorbar\n", - "cbar = plt.colorbar(fitsplot, fraction=0.046, pad=0.04, ticks=LogLocator(subs=range(10)))\n", - "labels = ['$10^{-4}$'] + [''] * 8 + ['$10^{-3}$'] + [''] * 8 + ['$10^{-2}$']\n", - "cbar.ax.set_yticklabels(labels)\n", + "# Add legend\n", + "ax1.legend(ncol=2, loc='lower center', bbox_to_anchor=(0.5, -0.25))\n", "\n", "# Define labels\n", - "cbar.set_label(r'Flux Count Rate ({})'.format(xdf_image.unit.to_string('latex')), \n", - " rotation=270, labelpad=30)\n", "ax1.set_xlabel('X (pixels)')\n", "ax1.set_ylabel('Y (pixels)')\n", "ax1.set_title('Sources Found by Different Methods')" @@ -976,7 +954,7 @@ "outputs": [], "source": [ "# Define the approximate isophotal extent\n", - "r = 4.\n", + "r = 4. # pixels\n", "\n", "# Create the apertures\n", "apertures = []\n", diff --git a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb index 3a27a501..3f585cce 100644 --- a/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb +++ b/notebooks/photutils/03_aperture_photometry/03_aperture_photometry.ipynb @@ -115,7 +115,9 @@ "source": [ "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", "\n", - "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)" + "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)\n", + "\n", + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons per second." ] }, { @@ -125,10 +127,7 @@ "outputs": [], "source": [ "url = 'https://archive.stsci.edu/pub/hlsp/xdf/hlsp_xdf_hst_acswfc-60mas_hudf_f435w_v1_sci.fits'\n", - "with fits.open(url) as hdulist:\n", - " hdulist.info()\n", - " data = hdulist[0].data\n", - " header = hdulist[0].header" + "xdf_image = CCDData.read(url, unit=u.electron / u.s)" ] }, { @@ -145,24 +144,8 @@ "outputs": [], "source": [ "# Define the mask\n", - "mask = data == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons (counts) per second." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "unit = u.electron / u.s\n", - "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" + "mask = xdf_image.data == 0\n", + "xdf_image.mask = mask" ] }, { @@ -308,7 +291,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So thanks to `find_peaks`, we now we know the positions of all our sources. Next, we need to define apertures for each source. First, as the simplest example, let's try using circular apertures of a fixed size.\n", + "So thanks to `find_peaks`, we now we know the positions of all our sources. Next, we need to define apertures for each source. First, as the simplest example, let's try using circular apertures of a fixed radius: 10 pixels.\n", "\n", "### Circular Apertures" ] @@ -328,9 +311,9 @@ "metadata": {}, "outputs": [], "source": [ - "# define the aperture\n", + "# Define the aperture\n", "position = (sources_findpeaks['x_centroid'], sources_findpeaks['y_centroid'])\n", - "radius = 10.\n", + "radius = 10. # pixels\n", "circular_aperture = CircularAperture(position, r=radius)" ] }, @@ -430,7 +413,7 @@ "metadata": {}, "outputs": [], "source": [ - "r = 3. # approximate isophotal extent of semimajor axis\n", + "r = 3. # pixels; approximate isophotal extent of semimajor axis\n", "\n", "# Create the apertures\n", "elliptical_apertures = []\n", @@ -490,16 +473,9 @@ "\n", "At the moment, the positions of our apertures are in pixels, relative to our data array. However, if you need aperture positions in terms of celestial coordinates, `photutils` also includes aperture objects that can be integrated with Astropy's `SkyCoords`.\n", "\n", - "Fortunately this is extremely easy when we use the [World Coordinate System (WCS)](http://docs.astropy.org/en/stable/wcs/) to produce a WCS object from the header of the FITS file containing our image, and then the `to_sky()` method to transform our `EllipticalAperture` objects into `SkyEllipticalAperture` objects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from astropy.wcs import WCS" + "Fortunately this is extremely easy when we use the [World Coordinate System (WCS)](http://docs.astropy.org/en/stable/wcs/) to decipher a WCS object from the header of the FITS file containing our image, and then the `to_sky()` method to transform our `EllipticalAperture` objects into `SkyEllipticalAperture` objects. \n", + "\n", + "And, better yet, our `CCDData` object has held onto its WCS information ever since we created it!" ] }, { @@ -508,7 +484,7 @@ "metadata": {}, "outputs": [], "source": [ - "wcs = WCS(header)\n", + "wcs = xdf_image.wcs\n", "sky_elliptical_apertures = [ap.to_sky(wcs) for ap in elliptical_apertures]" ] }, @@ -528,7 +504,7 @@ "from photutils import SkyEllipticalAperture\n", "from astropy.coordinates import SkyCoord\n", "\n", - "r = 3. # approximate isophotal extent of semimajor axis\n", + "r = 3. # pixels; approximate isophotal extent of semimajor axis\n", "\n", "# Create the apertures\n", "sky_elliptical_apertures = []\n", @@ -645,8 +621,8 @@ "Woohoo!\n", "\n", "Unfortunately for our purposes, the `aperture_photometry` function can be only used alone for one of the two cases:\n", - "* Identical apertures at distinct positions (e.g. circular apertures with `r = 3` for many sources)\n", - "* Distinct apertures at identical positions (e.g. two circular apertures with `r = 3` and `r = 5` for one source)\n", + "* Identical apertures at distinct positions (e.g. circular apertures with `r = 3` pixels for many sources)\n", + "* Distinct apertures at identical positions (e.g. two circular apertures with `r = 3` pixels and `r = 5` pixels for one source)\n", "\n", "Since our elliptical apertures are distinct apertures at distinct positions, we need to do a little more work to get a single table of photometric values.\n", "\n", @@ -762,7 +738,7 @@ "\n", "In the [background estimation notebook](../01_background_estimation/01_background_estimation.ipynb), we explored how to perform global background subtraction of image data with `photutils`. However, you can also use `photutils` to perform local background estimations for aperture corrections.\n", "\n", - "To estimate the local background for each aperture, measure the counts within annulus apertures around (but not including!) each source. In our example, we defined elliptical apertures with `r = 3` to measure the counts within each source. To calculate the background for each source, let's measure the counts elliptical annuli between `r = 3.5` and `r = 5`." + "To estimate the local background for each aperture, measure the counts within annulus apertures around (but not including!) each source. In our example, we defined elliptical apertures with `r = 3` pixels to measure the counts within each source. To calculate the background for each source, let's measure the counts elliptical annuli between `r = 3.5` pixels and `r = 5` pixels." ] }, { diff --git a/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb index 3d4c77fa..1a6687f3 100644 --- a/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb +++ b/notebooks/photutils/04_psf_photometry/04_psf_photometry.ipynb @@ -119,7 +119,9 @@ "source": [ "As described in the introduction, we will be using Hubble eXtreme Deep Field (XDF) data. Since this file is too large to store on GitHub, we will just use `astropy` to directly download the file from the STScI archive: https://archive.stsci.edu/prepds/xdf/ \n", "\n", - "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)" + "(Generally, the best package for web queries of astronomical data is [Astroquery](https://astroquery.readthedocs.io/en/latest/); however, the dataset we are using is a High Level Science Product (HLSP) and thus is not located within a catalog that could be queried with Astroquery.)\n", + "\n", + "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons per second." ] }, { @@ -129,10 +131,7 @@ "outputs": [], "source": [ "url = 'https://archive.stsci.edu/pub/hlsp/xdf/hlsp_xdf_hst_acswfc-60mas_hudf_f435w_v1_sci.fits'\n", - "with fits.open(url) as hdulist:\n", - " hdulist.info()\n", - " data = hdulist[0].data\n", - " header = hdulist[0].header" + "xdf_image = CCDData.read(url, unit=u.electron / u.s)" ] }, { @@ -149,24 +148,8 @@ "outputs": [], "source": [ "# Define the mask\n", - "mask = data == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Throughout this notebook, we are going to store our images in Python using a `CCDData` object (see [Astropy documentation](http://docs.astropy.org/en/stable/nddata/index.html#ccddata-class-for-images)), which contains a `numpy` array in addition to metadata such as uncertainty, masks, or units. In this case, our data is in electrons (counts) per second." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "unit = u.electron / u.s\n", - "xdf_image = CCDData(data, unit=unit, meta=header, mask=mask)" + "mask = xdf_image.data == 0\n", + "xdf_image.mask = mask" ] }, {