8.3 KiB
8.3 KiB
WCS and Other Astropy Modules
World Coordinate System (astropy.wcs)
The WCS module manages transformations between pixel coordinates in images and world coordinates (e.g., celestial coordinates).
Reading WCS from FITS
from astropy.wcs import WCS
from astropy.io import fits
# Read WCS from FITS header
with fits.open('image.fits') as hdul:
wcs = WCS(hdul[0].header)
Pixel to World Transformations
# Single pixel to world coordinates
world = wcs.pixel_to_world(100, 200) # Returns SkyCoord
print(f"RA: {world.ra}, Dec: {world.dec}")
# Arrays of pixels
import numpy as np
x_pixels = np.array([100, 200, 300])
y_pixels = np.array([150, 250, 350])
world_coords = wcs.pixel_to_world(x_pixels, y_pixels)
World to Pixel Transformations
from astropy.coordinates import SkyCoord
import astropy.units as u
# Single coordinate
coord = SkyCoord(ra=10.5*u.degree, dec=41.2*u.degree)
x, y = wcs.world_to_pixel(coord)
# Array of coordinates
coords = SkyCoord(ra=[10, 11, 12]*u.degree, dec=[41, 42, 43]*u.degree)
x_pixels, y_pixels = wcs.world_to_pixel(coords)
WCS Information
# Print WCS details
print(wcs)
# Access key properties
print(wcs.wcs.crpix) # Reference pixel
print(wcs.wcs.crval) # Reference value (world coords)
print(wcs.wcs.cd) # CD matrix
print(wcs.wcs.ctype) # Coordinate types
# Pixel scale
pixel_scale = wcs.proj_plane_pixel_scales() # Returns Quantity array
Creating WCS
from astropy.wcs import WCS
# Create new WCS
wcs = WCS(naxis=2)
wcs.wcs.crpix = [512.0, 512.0] # Reference pixel
wcs.wcs.crval = [10.5, 41.2] # RA, Dec at reference pixel
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN'] # Projection type
wcs.wcs.cdelt = [-0.0001, 0.0001] # Pixel scale (degrees/pixel)
wcs.wcs.cunit = ['deg', 'deg']
Footprint and Coverage
# Calculate image footprint (corner coordinates)
footprint = wcs.calc_footprint()
# Returns array of [RA, Dec] for each corner
NDData (astropy.nddata)
Container for n-dimensional datasets with metadata, uncertainty, and masking.
Creating NDData
from astropy.nddata import NDData
import numpy as np
import astropy.units as u
# Basic NDData
data = np.random.random((100, 100))
ndd = NDData(data)
# With units
ndd = NDData(data, unit=u.electron/u.s)
# With uncertainty
from astropy.nddata import StdDevUncertainty
uncertainty = StdDevUncertainty(np.sqrt(data))
ndd = NDData(data, uncertainty=uncertainty, unit=u.electron/u.s)
# With mask
mask = data < 0.1 # Mask low values
ndd = NDData(data, mask=mask)
# With WCS
from astropy.wcs import WCS
ndd = NDData(data, wcs=wcs)
CCDData for CCD Images
from astropy.nddata import CCDData
# Create CCDData
ccd = CCDData(data, unit=u.adu, meta={'object': 'M31'})
# Read from FITS
ccd = CCDData.read('image.fits', unit=u.adu)
# Write to FITS
ccd.write('output.fits', overwrite=True)
Modeling (astropy.modeling)
Framework for creating and fitting models to data.
Common Models
from astropy.modeling import models, fitting
import numpy as np
# 1D Gaussian
gauss = models.Gaussian1D(amplitude=10, mean=5, stddev=1)
x = np.linspace(0, 10, 100)
y = gauss(x)
# 2D Gaussian
gauss_2d = models.Gaussian2D(amplitude=10, x_mean=50, y_mean=50,
x_stddev=5, y_stddev=3)
# Polynomial
poly = models.Polynomial1D(degree=3)
# Power law
power_law = models.PowerLaw1D(amplitude=10, x_0=1, alpha=2)
Fitting Models to Data
# Generate noisy data
true_model = models.Gaussian1D(amplitude=10, mean=5, stddev=1)
x = np.linspace(0, 10, 100)
y_true = true_model(x)
y_noisy = y_true + np.random.normal(0, 0.5, x.shape)
# Fit model
fitter = fitting.LevMarLSQFitter()
initial_model = models.Gaussian1D(amplitude=8, mean=4, stddev=1.5)
fitted_model = fitter(initial_model, x, y_noisy)
print(f"Fitted amplitude: {fitted_model.amplitude.value}")
print(f"Fitted mean: {fitted_model.mean.value}")
print(f"Fitted stddev: {fitted_model.stddev.value}")
Compound Models
# Add models
double_gauss = models.Gaussian1D(amp=5, mean=3, stddev=1) + \
models.Gaussian1D(amp=8, mean=7, stddev=1.5)
# Compose models
composite = models.Gaussian1D(amp=10, mean=5, stddev=1) | \
models.Scale(factor=2) # Scale output
Visualization (astropy.visualization)
Tools for visualizing astronomical images and data.
Image Normalization
from astropy.visualization import simple_norm
import matplotlib.pyplot as plt
# Load image
from astropy.io import fits
data = fits.getdata('image.fits')
# Normalize for display
norm = simple_norm(data, 'sqrt', percent=99)
# Display
plt.imshow(data, norm=norm, cmap='gray', origin='lower')
plt.colorbar()
plt.show()
Stretching and Intervals
from astropy.visualization import (MinMaxInterval, AsinhStretch,
ImageNormalize, ZScaleInterval)
# Z-scale interval
interval = ZScaleInterval()
vmin, vmax = interval.get_limits(data)
# Asinh stretch
stretch = AsinhStretch()
norm = ImageNormalize(data, interval=interval, stretch=stretch)
plt.imshow(data, norm=norm, cmap='gray', origin='lower')
PercentileInterval
from astropy.visualization import PercentileInterval
# Show data between 5th and 95th percentiles
interval = PercentileInterval(90) # 90% of data
vmin, vmax = interval.get_limits(data)
plt.imshow(data, vmin=vmin, vmax=vmax, cmap='gray', origin='lower')
Constants (astropy.constants)
Physical and astronomical constants with units.
from astropy import constants as const
# Speed of light
c = const.c
print(f"c = {c}")
print(f"c in km/s = {c.to(u.km/u.s)}")
# Gravitational constant
G = const.G
# Astronomical constants
M_sun = const.M_sun # Solar mass
R_sun = const.R_sun # Solar radius
L_sun = const.L_sun # Solar luminosity
au = const.au # Astronomical unit
pc = const.pc # Parsec
# Fundamental constants
h = const.h # Planck constant
hbar = const.hbar # Reduced Planck constant
k_B = const.k_B # Boltzmann constant
m_e = const.m_e # Electron mass
m_p = const.m_p # Proton mass
e = const.e # Elementary charge
N_A = const.N_A # Avogadro constant
Using Constants in Calculations
# Calculate Schwarzschild radius
M = 10 * const.M_sun
r_s = 2 * const.G * M / const.c**2
print(f"Schwarzschild radius: {r_s.to(u.km)}")
# Calculate escape velocity
M = const.M_earth
R = const.R_earth
v_esc = np.sqrt(2 * const.G * M / R)
print(f"Earth escape velocity: {v_esc.to(u.km/u.s)}")
Convolution (astropy.convolution)
Convolution kernels for image processing.
from astropy.convolution import Gaussian2DKernel, convolve
# Create Gaussian kernel
kernel = Gaussian2DKernel(x_stddev=2)
# Convolve image
smoothed_image = convolve(data, kernel)
# Handle NaNs
from astropy.convolution import convolve_fft
smoothed = convolve_fft(data, kernel, nan_treatment='interpolate')
Stats (astropy.stats)
Statistical functions for astronomical data.
from astropy.stats import sigma_clip, sigma_clipped_stats
# Sigma clipping
clipped_data = sigma_clip(data, sigma=3, maxiters=5)
# Get statistics with sigma clipping
mean, median, std = sigma_clipped_stats(data, sigma=3.0)
# Robust statistics
from astropy.stats import mad_std, biweight_location, biweight_scale
robust_std = mad_std(data)
robust_mean = biweight_location(data)
robust_scale = biweight_scale(data)
Utils
Data Downloads
from astropy.utils.data import download_file
# Download file (caches locally)
url = 'https://example.com/data.fits'
local_file = download_file(url, cache=True)
Progress Bars
from astropy.utils.console import ProgressBar
with ProgressBar(len(data_list)) as bar:
for item in data_list:
# Process item
bar.update()
SAMP (Simple Application Messaging Protocol)
Interoperability with other astronomy tools.
from astropy.samp import SAMPIntegratedClient
# Connect to SAMP hub
client = SAMPIntegratedClient()
client.connect()
# Broadcast table to other applications
message = {
'samp.mtype': 'table.load.votable',
'samp.params': {
'url': 'file:///path/to/table.xml',
'table-id': 'my_table',
'name': 'My Catalog'
}
}
client.notify_all(message)
# Disconnect
client.disconnect()