synthesizer.imaging.image¶
A module containing the definition of an image.
This module contains the definition of an image.
An image can be generated from particle based data with or without smoothing, and from parametric data with smoothing defined by a morphology derived density grid.
- Example Usage:
# Particle based case img = Image(resolution=0.1 * kpc, fov=1.0 * kpc) img.get_img(signal, coordinates * kpc, smoothing_lengths * kpc, kernel)
# Parametric case img = Image(resolution=0.1 * kpc, fov=1.0 * kpc) img.get_img(signal, density_grid)
Classes
- class synthesizer.imaging.image.Image(resolution, fov, img=None)[source]¶
A class for generating images.
This class is used to generate images from particle based data with or without smoothing, and from parametric data with smoothing defined by a morphology derived density grid.
This can be used in isolation to generate singular images or generated by an ImageCollection to generate a collection of images in various filters.
- resolution¶
The resolution of the image.
- Type:
unyt_quantity, float
- fov¶
The field of view of the image.
- Type:
unyt_quantity, float
- npix¶
The number of pixels in the image.
- Type:
tuple
- arr¶
The array containing the image.
- Type:
array_like, float
- units¶
The units of the image.
- Type:
unyt.Units
- apply_noise_array(noise_arr)[source]¶
Apply a noise array.
- Parameters:
noise_arr (np.ndarray) – The noise array to add to the image.
- Returns:
- Image
The image including the noise array
- np.ndarray
The weight map, derived from 1 / std **2
- apply_noise_from_snr(snr, depth, aperture_radius=None)[source]¶
Apply noise derived from a SNR and depth.
This can either be for a point source or an aperture if aperture_radius is passed.
This assumes the SNR is defined as SNR = S / sqrt(noise_std)
Args:
- Returns:
- Image
The image including the noise array
- np.ndarray
The noise array.
- np.ndarray
The weight map.
- apply_noise_from_std(noise_std)[source]¶
Apply noise derived from a standard deviation.
This creates noise with a normal distribution centred on 0 with the passed standard deviation.
- Parameters:
noise_std (float) – The standard deviation of the noise to add to the image.
- Returns:
- Image
The image including the noise array
- np.ndarray
The weight map.
- apply_psf(psf)[source]¶
Apply a Point Spread Function to this image.
- Parameters:
psf (np.ndarray) – An array describing the point spread function.
- Returns:
- Image
The image convolved with the psf.
- get_img_hist(signal, coordinates=None)[source]¶
Calculate an image with no smoothing.
This is only applicable to particle based images and is just a wrapper for numpy.histogram2d.
- Parameters:
signal (array_like, float) – The signal to be sorted into the image.
coordinates (unyt_array, float) – The coordinates of the particles.
- Returns:
- img (array_like, float)
A 2D array containing the pixel values sorted into the image. (npix, npix)
- get_img_smoothed(signal, coordinates=None, smoothing_lengths=None, kernel=None, kernel_threshold=1, density_grid=None)[source]¶
Calculate a smoothed image.
In the particle case this smooths each particle’s signal over the SPH kernel defined by their smoothing length. This uses C extensions to calculate the image for each particle efficiently.
In the parametric case the signal is smoothed over a density grid. This density grid is an array defining the weight in each pixel.
- Parameters:
signal (array_like, float) – The signal to be sorted into the image.
coordinates (unyt_array, float) – The coordinates of the particles. (particle case only)
smoothing_lengths (unyt_array, float) – The smoothing lengths of the particles. (particle case only)
kernel (str) – The kernel to use for smoothing. (particle case only)
kernel_threshold (float) – The threshold for the kernel. (particle case only)
density_grid (array_like, float) – The density grid to smooth over. (parametric case only)
- Returns:
- array_like (float)
A 2D array containing particles sorted into an image. (npix[0], npix[1])
- Return type:
img
- Raises:
InconsistentArguments – If conflicting particle and parametric arguments are passed or any arguments are missing an error is raised.
- plot_img(show=False, cmap='Greys_r', norm=None)[source]¶
Plot an image.
- Parameters:
show (bool) – Whether to show the plot or not (Default False).
cmap (str) – The name of the matplotlib colormap for image plotting. Can be any valid string that can be passed to the cmap argument of imshow. Defaults to “Greys_r”.
norm (function) – A normalisation function. This can be custom made or one of matplotlib’s normalisation functions. It must take an array and return the same array after normalisation.
tick_formatter (matplotlib.ticker.FuncFormatter) – An instance of the tick formatter for formatting the colorbar ticks.
- Returns:
- matplotlib.pyplot.figure
The figure object containing the plot
- matplotlib.pyplot.figure.axis
The axis object containing the image.
- plot_map(show=False, extent=None, cmap='Greys_r', cbar_label=None, norm=None, tick_formatter=None)[source]¶
Plot a map.
Unlike an image we want a colorbar and axes for a map.
- Parameters:
show (bool) – Whether to show the plot or not (Default False).
extent (array_like) – The extent of the x and y axes.
cmap (str) – The name of the matplotlib colormap for image plotting. Can be any valid string that can be passed to the cmap argument of imshow. Defaults to “Greys_r”.
cbar_label (str) – The label for the colorbar.
norm (function) – A normalisation function. This can be custom made or one of matplotlib’s normalisation functions. It must take an array and return the same array after normalisation.
tick_formatter (matplotlib.ticker.FuncFormatter) – An instance of the tick formatter for formatting the colorbar ticks.
- Returns:
- matplotlib.pyplot.figure
The figure object containing the plot
- matplotlib.pyplot.figure.axis
The axis object containing the image.
- plot_unknown_pleasures(constrast=100, target_lines=50, figsize=(8, 8), title='SYNTHESIZER')[source]¶
Create a representation of an image similar in style to Joy Division’s seminal 1979 album Unknown Pleasures.
Borrows some code from this matplotlib examples: https://matplotlib.org/stable/gallery/animation/unchained.html
- Arguments
- constrast (float)
The contrast.
- target_lines (int)
The target number of individual lines to use.