Image

class ehtim.image.Image(image, psize, ra, dec, pa=0.0, polrep='stokes', pol_prim=None, rf=230000000000.0, pulse=<function trianglePulse2D>, source='SgrA', mjd=51544, time=0.0)

A polarimetric image (in units of Jy/pixel).

pulse

The function convolved with the pixel values for continuous image.

Type

function

psize

The pixel dimension in radians

Type

float

xdim

The number of pixels along the x dimension

Type

int

ydim

The number of pixels along the y dimension

Type

int

mjd

The integer MJD of the image

Type

int

time

The observing time of the image (UTC hours)

Type

float

source

The astrophysical source name

Type

str

ra

The source Right Ascension in fractional hours

Type

float

dec

The source declination in fractional degrees

Type

float

rf

The image frequency in Hz

Type

float

polrep

polarization representation, either ‘stokes’ or ‘circ’

Type

str

pol_prim

The default image: I,Q,U or V for Stokes, or RR,LL,LR,RL for Circular

Type

str

_imdict

The dictionary with the polarimetric images

Type

dict

_mflist

List of spectral index images (and higher order terms)

Type

list

add_const_mf(alpha, beta=0.0)

Add a constant spectral index and curvature term

Parameters

• alpha (float) – spectral index (with no - sign)

• beta (float) – curvature

Returns

output image with constant mf information added

Return type

(Image)

add_const_pol(mag, angle, cmag=0, csign=1)

Return an with constant fractional linear and circular polarization

Parameters

• mag (float) – constant polarization fraction to add to the image

• angle (float) – constant EVPA

• cmag (int) – constant circular polarization fraction to add to the image

• cmag – constant circular polarization sign +/- 1

Returns

output image

Return type

(Image)

add_crescent(flux, Rp, Rn, a, b, x=0, y=0, pol=None)

Add a crescent to an image; see Kamruddin & Dexter (2013).

Parameters

• flux (float) – the total flux contained in the crescent in Jy

• Rp (float) – the larger radius in radians

• Rn (float) – the smaller radius in radians

• a (float) – the relative x offset of smaller disk in radians

• b (float) – the relative y offset of smaller disk in radians

• x (float) – the center x coordinate of the larger disk in radians

• y (float) – the center y coordinate of the larger disk in radians

• pol (str) – the polarization to add the flux to. None defaults to pol_prim.

Returns

output image add_gaus

Return type

(Image)

add_flat(flux, pol=None)

Add a flat background flux to the main polarization image.

Parameters

• flux (float) – total flux to add to image

• pol (str) – the polarization to add the flux to. None defaults to pol_prim.

Returns

output image

Return type

(Image)

add_gauss(flux, beamparams, pol=None)

Add a gaussian to an image.

Parameters

• flux (float) – the total flux contained in the Gaussian in Jy

• beamparams (list) – [fwhm_maj, fwhm_min, theta, x, y], all in radians

• pol (str) – the polarization to add the flux to. None defaults to pol_prim.

Returns

output image

Return type

(Image)

add_pol_image(image, pol)

Add another image polarization.

Parameters

• image (list) – 2D image frame (possibly complex) in a Jy/pixel array

• pol (str) – The image type: ‘I’,’Q’,’U’,’V’ for stokes, ‘RR’,’LL’,’RL’,’LR’ for circ

add_qu(qimage, uimage)

Add Stokes Q and U images. self.polrep must be ‘stokes’

Parameters

• qimage (numpy.array) – The 2D Stokes Q values in Jy/pixel array

• uimage (numpy.array) – The 2D Stokes U values in Jy/pixel array

Returns:

add_random_pol(mag, corr, cmag=0.0, ccorr=0.0, seed=0)

Return an image random linear and circular polarizations with certain correlation lengths

Parameters

• mag (float) – linear polarization fraction

• corr (float) – EVPA correlation length (radians)

• cmag (float) – circular polarization fraction

• ccorr (float) – CP correlation length (radians)

• seed (int) – Seed for random number generation

Returns

output image

Return type

(Image)

add_ring_m1(I0, I1, r0, phi, sigma, x=0, y=0, pol=None)

Add a ring to an image with an m=1 mode

Parameters

• I0 (float) –

• I1 (float) –

• r0 (float) – the radius

• phi (float) – angle of m1 mode

• sigma (float) – the blurring size

• x (float) – the center x coordinate of the larger disk in radians

• y (float) – the center y coordinate of the larger disk in radians

• pol (str) – the polarization to add the flux to. None defaults to pol_prim.

Returns

output image add_gaus

Return type

(Image)

add_tophat(flux, radius, pol=None)

Add centered tophat flux to the Stokes I image inside a given radius.

Parameters

• flux (float) – total flux to add to image

• radius (float) – radius of top hat flux in radians

• pol (str) – the polarization to add the flux to. None defaults to pol_prim

Returns

output image

Return type

(Image)

add_v(vimage)

Add Stokes V image. self.polrep must be ‘stokes’

Parameters

vimage (numpy.array) – The 2D Stokes Q values in Jy/pixel array

add_zblterm(obs, uv_min, zblval=None, new_fov=False, gauss_sz=False, gauss_sz_factor=0.75, debias=True)

Add a large Gaussian term to account for missing flux in the zero baseline.

Parameters

• obs – an Obsdata object to determine min non-zero baseline and 0-bl flux

• uv_min (float) – The cutoff in Glambada used to determine what is a 0-bl

• new_fov (rad) – The size of the padded image once the Gaussian is added (if False it will be set to 3 x the gaussian fwhm)

• gauss_sz (rad) – The size of the Gaussian added to add flux to the 0-bl. (if False it is computed from the min non-zero baseline)

• gauss_sz_factor (float) – The fraction of the min non-zero baseline used to caluclate the Gaussian FWHM.

• debias (bool) – True if you use debiased amplitudes to caluclate the 0-bl flux in Jy

Returns

a padded image with a large Gaussian component

Return type

(Image)

align_images(im_list, pol=None, shift=True, final_fov=False, scale='lin', gamma=0.5, dynamic_range=[1000.0])

Align all the images in im_list to the current image (self) Aligns all images by comparison of the primary pol image.

Parameters

• im_list (list) – list of images to align to the current image

• shift (list) – list of manual image shifts, otherwise use the shift from maximum cross-correlation

• pol (str) – which polarization image to compare. Default is self.pol_prim

• final_fov (float) – fov of the comparison image (rad). If False it is the largestinput image fov

• scale (str) – compare images in ‘log’,’lin’,or ‘gamma’ scale

• gamma (float) – exponent for gamma scale comparison

• dynamic_range (float) – dynamic range for log and gamma scale comparisons

Returns

(im_list_shift, shifts, im0_pad)

Return type

(tuple)

apply_mask(mask_im, fill_val=0.0)

Apply a mask to the image

Parameters

• mask_im (Image) – a mask image with the same dimensions as the Image

• fill_val (float) – masked pixels of all polarizations are set to this value

Returns

the masked image

Return type

(Image)

blur_circ(fwhm_i, fwhm_pol=0, filttype='gauss')

Apply a circular gaussian filter to the image, with FWHM in radians.

Parameters

• fwhm_i (float) – circular beam size for Stokes I blurring in radian

• fwhm_pol (float) – circular beam size for Stokes Q,U,V blurring in radian

• filttype (str) – “gauss” or “butter”

Returns

output image

Return type

(Image)

blur_gauss(beamparams, frac=1.0, frac_pol=0)

Blur image with a Gaussian beam w/ beamparams [fwhm_max, fwhm_min, theta] in radians.

Parameters

• beamparams (list) – [fwhm_maj, fwhm_min, theta, x, y] in radians

• frac (float) – fractional beam size for blurring the main image

• frac_pol (float) – fractional beam size for blurring the other polarizations

Returns

output image

Return type

(Image)

blur_mf(freqs, fwhm, fit_order=1, filttype='gauss')

Blur image correctly across multiple frequencies WARNING: does not currently do polarization correctly!

Parameters

• freqs (float) – Frequencies to include in the blurring & spectral index fit

• fwhm (float) – circular beam size

• fit_order (int) – how many orders to fit spectrum: 1 or 2

• filttype (str) – “gauss” or “butter”

Returns

output image

Return type

(Image)

property bvec

Return the B mode image vector

center(pol=None)

Center the image based on the coordinates of the centroid(). A non-integer shift is used, which wraps the image when rotating.

Parameters

pol (str) – The polarization for which to find the image centroid

Returns

centroid positions (x0,y0) in radians

Return type

(np.array)

centroid(pol=None)

Compute the location of the image centroid (corresponding to the polarization pol)

Parameters

pol (str) – The polarization for which to find the image centroid

Returns

centroid positions (x0,y0) in radians

Return type

(np.array)

property chivec

Return the fractional polarization angle for each pixel

circ_polfrac()

Return the total fractional circular polarized flux

Args:

Returns

image fractional circular polarized flux

Return type

(float)

compare_images(im_compare, pol=None, psize=None, target_fov=None, blur_frac=0.0, beamparams=[1.0, 1.0, 1.0], metric=['nxcorr', 'nrmse', 'rssd'], blursmall=False, shift=True)

Compare to another image by computing normalized cross correlation, normalized root mean squared error, or square root of the sum of squared differences. Returns metrics only for the primary polarization imvec!

Parameters

• im_compare (Image) – the image to compare to

• pol (str) – which polarization image to compare. Default is self.pol_prim

• psize (float) – pixel size of comparison image (rad). If None it is the smallest of the input image pizel sizes

• target_fov (float) – fov of the comparison image (rad). If None it is twice the largest fov of the input images

• beamparams (list) – the nominal Gaussian beam parameters [fovx, fovy, position angle]

• blur_frac (float) – fractional beam to blur each image to before comparison

• metric (list) – a list of fidelity metrics from [‘nxcorr’,’nrmse’,’rssd’]

• blursmall (bool) – True to blur the unpadded image rather than the large image.

• shift (int) – manual image shift, otherwise use shift from maximum cross-correlation

Returns

[errormetric, im1_pad, im2_shift]

Return type

(tuple)

contour(contour_levels=[0.1, 0.25, 0.5, 0.75], contour_cfun=None, color='w', legend=True, show_im=True, cfun='afmhot', scale='lin', interp='gaussian', gamma=0.5, dynamic_range=1000.0, plotp=False, nvec=20, pcut=0.01, mcut=0.1, label_type='ticks', has_title=True, has_cbar=True, cbar_lims=(), cbar_unit='Jy', 'pixel', contour_im=False, power=0, beamcolor='w', export_pdf='', show=True, beamparams=None, cbar_orientation='vertical', scale_lw=1, beam_lw=1, cbar_fontsize=12, axis=None, scale_fontsize=12)

Display the image in a contour plot.

Parameters

• contour_levels (arr) – the fractional contour levels relative to the max flux plotted

• contour_cfun (pyplot colormap function) – the function used to get the RGB colors

• legend (bool) – True to show a legend that says what each contour line corresponds to

• cfun (str) – matplotlib.pyplot color function

• scale (str) – image scaling in [‘log’,’gamma’,’lin’]

• interp (str) – image interpolation ‘gauss’ or ‘lin’

• gamma (float) – index for gamma scaling

• dynamic_range (float) – dynamic range for log and gamma scaling

• plotp (bool) – True to plot linear polarimetic image

• nvec (int) – number of polarimetric vectors to plot

• pcut (float) – minimum stokes P value for displaying polarimetric vectors as fraction of maximum Stokes I pixel

• mcut (float) – minimum fractional polarization for plotting vectors

• label_type (string) – specifies the type of axes labeling: ‘ticks’, ‘scale’, ‘none’

• has_title (bool) – True if you want a title on the plot

• has_cbar (bool) – True if you want a colorbar on the plot

• cbar_lims (tuple) – specify the lower and upper limit of the colorbar

• cbar_unit (tuple of strings) – the unit of each pixel for the colorbar: ‘Jy’, ‘m-Jy’, ‘$mu$Jy’

• export_pdf (str) – path to exported PDF with plot

• show (bool) – Display the plot if true

• show_im (bool) – Display the image with the contour plot if True

Returns

figure object with image

Return type

(matplotlib.figure.Figure)

copy()

Return a copy of the Image object.

Args:

Returns

copy of the Image.

Return type

(Image)

copy_pol_images(old_image)

Copy polarization images from old_image over to self.

Parameters

old_image (Image) – image object to copy from

display(pol=None, cfun=False, interp='gaussian', scale='lin', gamma=0.5, dynamic_range=1000.0, plotp=False, plot_stokes=False, nvec=20, vec_cfun=None, vec_cbar_lims=(), scut=0, pcut=0.1, mcut=0.01, scale_ticks=False, log_offset=False, label_type='ticks', has_title=True, alpha=1, has_cbar=True, only_cbar=False, cbar_lims=(), cbar_unit='Jy', 'pixel', export_pdf='', pdf_pad_inches=0.0, show=True, beamparams=None, cbar_orientation='vertical', scinot=False, scale_lw=1, beam_lw=1, cbar_fontsize=12, axis=None, scale_fontsize=12, power=0, beamcolor='w', beampos='right', scalecolor='w', dpi=500)

Display the image.

Parameters

• pol (str) – which polarization image to plot. Default is self.pol_prim pol=’spec’ will plot spectral index pol=’curv’ will plot spectral curvature

• cfun (str) – matplotlib.pyplot color function. False changes with ‘pol’, but is ‘afmhot’ for most

• interp (str) – image interpolation ‘gauss’ or ‘lin’

• scale (str) – image scaling in [‘log’,’gamma’,’lin’]

• gamma (float) – index for gamma scaling

• dynamic_range (float) – dynamic range for log and gamma scaling

• plotp (bool) – True to plot linear polarimetic image

• plot_stokes (bool) – True to plot stokes subplots along with plotp

• nvec (int) – number of polarimetric vectors to plot

• vec_cfun (str) – color function for vectors colored by lin pol frac

• vec_cbar_lims (tuple) – lower and upper limit of the fractional polarization colormap

• scut (float) – minimum fractional stokes I value for displaying spectral index

• pcut (float) – minimum fractional stokes I value for displaying polarimetric vectors

• mcut (float) – minimum fractional polarization value for displaying vectors

• scale_ticks (bool) – if True, scale polarization ticks by linear polarization magnitude

• label_type (string) – specifies the type of axes labeling: ‘ticks’, ‘scale’, ‘none’

• has_title (bool) – True if you want a title on the plot

• has_cbar (bool) – True if you want a colorbar on the plot

• cbar_lims (tuple) – specify the lower and upper limit of the colorbar

• cbar_unit (tuple) – specifies the unit of the colorbar: e.g., (‘Jy’,’pixel’),(‘m-Jy’,’$mu$as$^2$’),[‘Tb’]

• beamparams (list) – [fwhm_maj, fwhm_min, theta], set to plot beam contour

• export_pdf (str) – path to exported PDF with plot

• show (bool) – Display the plot if true

• scinot (bool) – Display numbers/units in scientific notation

• scale_lw (float) – Linewidth of the scale overlay

• beam_lw (float) – Linewidth of the beam overlay

• cbar_fontsize (float) – Fontsize of the text elements of the colorbar

• axis (matplotlib.axes.Axes) – An axis object

• scale_fontsize (float) – Fontsize of the scale label

• power (float) – Passed to colorbar for division of ticks by 1e(power)

• beamcolor (str) – color of the beam overlay

• scalecolor (str) – color of the scale label overlay

Returns

figure object with image

Return type

(matplotlib.figure.Figure)

property evec

Return the E mode image vector

evpa()

Return the total evpa

Args:

Returns

image average evpa (E of N) in radian

Return type

(float)

property evpavec

Return the fractional polarization angle for each pixel

find_shift(im_compare, pol=None, psize=None, target_fov=None, beamparams=[1.0, 1.0, 1.0], blur_frac=0.0, blursmall=False, scale='lin', gamma=0.5, dynamic_range=1000.0)

Find image shift that maximizes normalized cross correlation with a second image im2. Finds shift only by comparison of the primary pol image.

Parameters

• im_compare (Image) – image with respect with to switch

• pol (str) – which polarization image to compare. Default is self.pol_prim

• psize (float) – pixel size of comparison image (rad). If None it is the smallest of the input image pizel sizes

• target_fov (float) – fov of the comparison image (rad). If None it is twice the largest fov of the input images

• beamparams (list) – the nominal Gaussian beam parameters [fovx, fovy, position angle]

• blur_frac (float) – fractional beam to blur each image to before comparison

• blursmall (bool) – True to blur the unpadded image rather than the large image.

• scale (str) – compare images in ‘log’,’lin’,or ‘gamma’ scale

• gamma (float) – exponent for gamma scale comparison

• dynamic_range (float) – dynamic range for log and gamma scale comparisons

Returns

(errormetric, im1_pad, im2_shift)

Return type

(tuple)

fit_gauss(units='rad')

Determine the Gaussian parameters that short baselines would measure for the source by diagonalizing the image covariance matrix. Returns parameters only for the primary polarization!

Parameters

units (string) – ‘rad’ returns values in radians, ‘natural’ returns FWHM in uas and PA in degrees

Returns

a tuple (fwhm_maj, fwhm_min, theta) of the fit Gaussian parameters

Return type

(tuple)

fit_gauss_empirical(paramguess=None)

Determine the Gaussian parameters that short baselines would measure Returns parameters only for the primary polarization!

Parameters

paramguess (tuple) – Initial guess (fwhm_maj, fwhm_min, theta) of fit parameters

Returns

a tuple (fwhm_maj, fwhm_min, theta) of the fit Gaussian parameters.

Return type

(tuple)

fovx()

Return the image fov in x direction in radians.

Args:

Returns

image fov in x direction (radian)

Return type

(float)

fovy()

Returns the image fov in y direction in radians.

Args:

Returns

image fov in y direction (radian)

Return type

(float)

get_image_mf(nu)

Get image at a given frequency given the spectral information in self._mflist

Parameters

nu (float) – frequency in Hz

Returns

image at the desired frequency

Return type

(Image)

get_polvec(pol)

Get the imvec corresponding to the chosen polarization

grad(gradtype='abs')

Return the gradient image

Parameters

gradtype (str) – ‘x’,’y’,or ‘abs’ for the image gradient dimension

Returns

an image object containing the gradient image

Return type

Image

image_args()

Copy arguments for making a new Image into a list and dictonary

imarr(pol=None)

Return the 2D image array of a given pol parameter.

Parameters

pol (str) – I,Q,U or V for Stokes, or RR,LL,LR,RL for Circ

Returns

2D image array of dimension (ydim, xdim)

Return type

(numpy.array)

lin_polfrac()

Return the total fractional linear polarized flux

Args:

Returns

image fractional linear polarized flux

Return type

(float)

property lrvec

Return the imvec of LR

mask(cutoff=0.05, beamparams=None, frac=0.0)

Produce an image mask that shows all pixels above the specified cutoff frac of the max Works off the primary image

Parameters

• cutoff (float) – mask pixels with intensities greater than cuttoff * max

• beamparams (list) – either [fwhm_maj, fwhm_min, pos_ang] or a single fwhm

• frac (float) – the fraction of nominal beam to blur with

Returns

output mask image

Return type

(Image)

property mvec

Return the fractional polarization for each pixel

observe(array, tint, tadv, tstart, tstop, bw, mjd=None, timetype='UTC', polrep_obs=None, elevmin=10.0, elevmax=85.0, no_elevcut_space=False, ttype='nfft', fft_pad_factor=2, fix_theta_GMST=False, sgrscat=False, add_th_noise=True, jones=False, inv_jones=False, opacitycal=True, ampcal=True, phasecal=True, frcal=True, dcal=True, rlgaincal=True, stabilize_scan_phase=False, stabilize_scan_amp=False, neggains=False, tau=0.1, taup=0.1, gain_offset=0.1, gainp=0.1, phase_std=- 1, dterm_offset=0.05, rlratio_std=0.0, rlphase_std=0.0, sigmat=None, phasesigmat=None, rlgsigmat=None, rlpsigmat=None, caltable_path=None, seed=False, verbose=True)

Generate baselines from an array object and observe the image.

Parameters

• array (Array) – an array object containing sites with which to generate baselines

• tint (float) – the scan integration time in seconds

• tadv (float) – the uniform cadence between scans in seconds

• tstart (float) – the start time of the observation in hours

• tstop (float) – the end time of the observation in hours

• bw (float) – the observing bandwidth in Hz

• mjd (int) – the mjd of the observation, if set as different from the image mjd

• timetype (str) – how to interpret tstart and tstop; either ‘GMST’ or ‘UTC’

• polrep_obs (str) – ‘stokes’ or ‘circ’ sets the data polarimetric representation

• elevmin (float) – station minimum elevation in degrees

• elevmax (float) – station maximum elevation in degrees

• no_elevcut_space (bool) – if True, do not apply elevation cut to orbiters

• ttype (str) – “fast”, “nfft” or “dtft”

• fft_pad_factor (float) – zero pad the image to fft_pad_factor * image size in the FFT

• fix_theta_GMST (bool) – if True, stops earth rotation to sample fixed u,v

• sgrscat (bool) – if True, the visibilites will be blurred by the Sgr A* kernel

• add_th_noise (bool) – if True, baseline-dependent thermal noise is added

• jones (bool) – if True, uses Jones matrix to apply mis-calibration effects otherwise uses old formalism without D-terms

• inv_jones (bool) – if True, applies estimated inverse Jones matrix (not including random terms) to calibrate data

• opacitycal (bool) – if False, time-dependent gaussian errors are added to opacities

• ampcal (bool) – if False, time-dependent gaussian errors are added to station gains

• phasecal (bool) – if False, time-dependent station-based random phases are added

• frcal (bool) – if False, feed rotation angle terms are added to Jones matrix.

• dcal (bool) – if False, time-dependent gaussian errors added to Jones matrix D-terms.

• rlgaincal (bool) – if False, time-dependent gains are not equal for R and L pol

• stabilize_scan_phase (bool) – if True, random phase errors are constant over scans

• stabilize_scan_amp (bool) – if True, random amplitude errors are constant over scans

• neggains (bool) – if True, force the applied gains to be <1

• tau (float) – the base opacity at all sites, or a dict giving one opacity per site

• taup (float) – the fractional std. dev. of the random error on the opacities

• gainp (float) – the fractional std. dev. of the random error on the gains or a dict giving one std. dev. per site

• gain_offset (float) – the base gain offset at all sites, or a dict giving one gain offset per site

• phase_std (float) – std. dev. of LCP phase, or a dict giving one std. dev. per site a negative value samples from uniform

• dterm_offset (float) – the base std. dev. of random additive error at all sites, or a dict giving one std. dev. per site

• rlratio_std (float) – the fractional std. dev. of the R/L gain offset or a dict giving one std. dev. per site

• rlphase_std (float) – std. dev. of R/L phase offset, or a dict giving one std. dev. per site a negative value samples from uniform

• sigmat (float) – temporal std for a Gaussian Process used to generate gains. If sigmat=None then an iid gain noise is applied.

• phasesigmat (float) – temporal std for a Gaussian Process used to generate phases. If phasesigmat=None then an iid gain noise is applied.

• rlgsigmat (float) – temporal std deviation for a Gaussian Process used to generate R/L gain ratios. If rlgsigmat=None then an iid gain noise is applied.

• rlpsigmat (float) – temporal std deviation for a Gaussian Process used to generate R/L phase diff. If rlpsigmat=None then an iid gain noise is applied.

• caltable_path (string) – If not None, path and prefix for saving the applied caltable

• seed (int) – seeds the random component of the noise terms. DO NOT set to 0!

• verbose (bool) – print updates and warnings

Returns

an observation object

Return type

(Obsdata)

observe_same(obs_in, ttype='nfft', fft_pad_factor=2, sgrscat=False, add_th_noise=True, jones=False, inv_jones=False, opacitycal=True, ampcal=True, phasecal=True, frcal=True, dcal=True, rlgaincal=True, stabilize_scan_phase=False, stabilize_scan_amp=False, neggains=False, taup=0.1, gain_offset=0.1, gainp=0.1, phase_std=- 1, dterm_offset=0.05, rlratio_std=0.0, rlphase_std=0.0, sigmat=None, phasesigmat=None, rlgsigmat=None, rlpsigmat=None, caltable_path=None, seed=False, verbose=True)

Observe the image on the same baselines as an existing observation object and add noise.

Parameters

• obs_in (Obsdata) – the existing observation

• ttype (str) – “fast” or “nfft” or “direct”

• fft_pad_factor (float) – zero pad the image to fft_pad_factor * image size in FFT

• sgrscat (bool) – if True, the visibilites will be blurred by the Sgr A* kernel

• add_th_noise (bool) – if True, baseline-dependent thermal noise is added

• jones (bool) – if True, uses Jones matrix to apply mis-calibration effects

• inv_jones (bool) – if True, applies estimated inverse Jones matrix (not including random terms) to a priori calibrate data

• opacitycal (bool) – if False, time-dependent gaussian errors are added to opacities

• ampcal (bool) – if False, time-dependent gaussian errors are added to station gains

• phasecal (bool) – if False, time-dependent station-based random phases are added

• frcal (bool) – if False, feed rotation angle terms are added to Jones matrices.

• dcal (bool) – if False, time-dependent gaussian errors added to D-terms.

• rlgaincal (bool) – if False, time-dependent gains are not equal for R and L pol

• stabilize_scan_phase (bool) – if True, random phase errors are constant over scans

• stabilize_scan_amp (bool) – if True, random amplitude errors are constant over scans

• neggains (bool) – if True, force the applied gains to be <1

• taup (float) – the fractional std. dev. of the random error on the opacities

• gainp (float) – the fractional std. dev. of the random error on the gains or a dict giving one std. dev. per site

• gain_offset (float) – the base gain offset at all sites, or a dict giving one gain offset per site

• phase_std (float) – std. dev. of LCP phase, or a dict giving one std. dev. per site a negative value samples from uniform

• dterm_offset (float) – the base std. dev. of random additive error at all sites, or a dict giving one std. dev. per site

• rlratio_std (float) – the fractional std. dev. of the R/L gain offset or a dict giving one std. dev. per site

• rlphase_std (float) – std. dev. of R/L phase offset, or a dict giving one std. dev. per site a negative value samples from uniform

• sigmat (float) – temporal std for a Gaussian Process used to generate gains. If sigmat=None then an iid gain noise is applied.

• phasesigmat (float) – temporal std for a Gaussian Process used to generate phases. If phasesigmat=None then an iid gain noise is applied.

• rlgsigmat (float) – temporal std deviation for a Gaussian Process used to generate R/L gain ratios. If rlgsigmat=None then an iid gain noise is applied.

• rlpsigmat (float) – temporal std deviation for a Gaussian Process used to generate R/L phase diff. If rlpsigmat=None then an iid gain noise is applied.

• caltable_path (string) – If not None, path and prefix for saving the applied caltable

• seed (int) – seeds the random component of the noise terms. DO NOT set to 0!

• verbose (bool) – print updates and warnings

Returns

an observation object

Return type

(Obsdata)

observe_same_nonoise(obs, sgrscat=False, ttype='nfft', cache=False, fft_pad_factor=2, zero_empty_pol=True, verbose=True)

Observe the image on the same baselines as an existing observation without noise.

Parameters

• obs (Obsdata) – the existing observation

• sgrscat (bool) – if True, the visibilites will be blurred by the Sgr A* kernel

• ttype (str) – “fast” or “nfft” or “direct”

• cache (bool) – Use cached fft for ‘fast’ mode – deprecated, use nfft instead!

• fft_pad_factor (float) – zero pad the image to fft_pad_factor * image size in FFT

• zero_empty_pol (bool) – if True, returns zero vec if the polarization doesn’t exist. Otherwise return None

• verbose (bool) – Boolean value controls output prints.

Returns

an observation object with no noise

Return type

(Obsdata)

observe_vex(vex, source, t_int=0.0, tight_tadv=False, polrep_obs=None, ttype='nfft', fft_pad_factor=2, fix_theta_GMST=False, sgrscat=False, add_th_noise=True, jones=False, inv_jones=False, opacitycal=True, ampcal=True, phasecal=True, frcal=True, dcal=True, rlgaincal=True, stabilize_scan_phase=False, stabilize_scan_amp=False, neggains=False, tau=0.1, taup=0.1, gain_offset=0.1, gainp=0.1, phase_std=- 1, dterm_offset=0.05, rlratio_std=0.0, rlphase_std=0.0, sigmat=None, phasesigmat=None, rlgsigmat=None, rlpsigmat=None, caltable_path=None, seed=False, verbose=True)

Generate baselines from a vex file and observes the image.

Parameters

• vex (Vex) – an vex object containing sites and scan information

• source (str) – the source to observe

• t_int (float) – if not zero, overrides the vex scan lengths

• tight_tadv (float) – if True, advance right after each integration, otherwise advance after 2x the scan length

• polrep_obs (str) – ‘stokes’ or ‘circ’ sets the data polarimetric representation

• ttype (str) – “fast” or “nfft” or “dtft”

• fft_pad_factor (float) – zero pad the image to fft_pad_factor * image size in FFT

• fix_theta_GMST (bool) – if True, stops earth rotation to sample fixed u,v

• sgrscat (bool) – if True, the visibilites will be blurred by the Sgr A* kernel

• add_th_noise (bool) – if True, baseline-dependent thermal noise is added

• jones (bool) – if True, uses Jones matrix to apply mis-calibration effects otherwise uses old formalism without D-terms

• inv_jones (bool) – if True, applies estimated inverse Jones matrix (not including random terms) to calibrate data

• opacitycal (bool) – if False, time-dependent gaussian errors are added to opacities

• ampcal (bool) – if False, time-dependent gaussian errors are added to station gains

• phasecal (bool) – if False, time-dependent station-based random phases are added

• frcal (bool) – if False, feed rotation angle terms are added to Jones matrix.

• dcal (bool) – if False, time-dependent gaussian errors added to Jones matrix D-terms.

• rlgaincal (bool) – if False, time-dependent gains are not equal for R and L pol

• stabilize_scan_phase (bool) – if True, random phase errors are constant over scans

• stabilize_scan_amp (bool) – if True, random amplitude errors are constant over scans

• neggains (bool) – if True, force the applied gains to be <1

• tau (float) – the base opacity at all sites, or a dict giving one opacity per site

• taup (float) – the fractional std. dev. of the random error on the opacities

• gainp (float) – the fractional std. dev. of the random error on the gains or a dict giving one std. dev. per site

• gain_offset (float) – the base gain offset at all sites, or a dict giving one gain offset per site

• phase_std (float) – std. dev. of LCP phase, or a dict giving one std. dev. per site a negative value samples from uniform

• dterm_offset (float) – the base std. dev. of random additive error at all sites, or a dict giving one std. dev. per site

• rlratio_std (float) – the fractional std. dev. of the R/L gain offset or a dict giving one std. dev. per site

• rlphase_std (float) – std. dev. of R/L phase offset, or a dict giving one std. dev. per site a negative value samples from uniform

• sigmat (float) – temporal std for a Gaussian Process used to generate gains. If sigmat=None then an iid gain noise is applied.

• phasesigmat (float) – temporal std for a Gaussian Process used to generate phases. If phasesigmat=None then an iid gain noise is applied.

• rlgsigmat (float) – temporal std deviation for a Gaussian Process used to generate R/L gain ratios. If rlgsigmat=None then an iid gain noise is applied.

• rlpsigmat (float) – temporal std deviation for a Gaussian Process used to generate R/L phase diff. If rlpsigmat=None then an iid gain noise is applied.

• caltable_path (string) – If not None, path and prefix for saving the applied caltable

• seed (int) – seeds the random component of the noise terms. DO NOT set to 0!

• verbose (bool) – print updates and warnings

Returns

an observation object

Return type

(Obsdata)

orth_chi()

Rotate the EVPA 90 degrees

Args:

Returns

image with rotated EVPA

Return type

(Image)

overlay_display(im_list, color_coding=array([[1, 0, 1], [0, 1, 0]]), export_pdf='', show=True, f=False, shift=[0, 0], final_fov=False, interp='gaussian', scale='lin', gamma=0.5, dynamic_range=[1000.0], rescale=True)

Overlay primary polarization images of a list of images to compare structures.

Parameters

• im_list (list) – list of images to align to the current image

• color_coding (numpy.array) – Color coding of each image in the composite

• f (matplotlib.pyplot.figure) – Figure to overlay on top of

• export_pdf (str) – path to exported PDF with plot

• show (bool) – Display the plot if true

• shift (list) – list of manual image shifts, otherwise use the shift from maximum cross-correlation

• final_fov (float) – fov of the comparison image (rad). If False it is the largestinput image fov

• scale (str) – compare images in ‘log’,’lin’,or ‘gamma’ scale

• gamma (float) – exponent for gamma scale comparison

• dynamic_range (float) – dynamic range for log and gamma scale comparisons

Returns

figure object with image

Return type

(matplotlib.figure.Figure)

pad(fovx, fovy)

Pad an image to new fov_x by fov_y in radian. :param fovx: new fov in x dimension (rad) :type fovx: float :param fovy: new fov in y dimension (rad) :type fovy: float

Returns

padded image

Return type

im_pad (Image)

property pvec

Return the polarization magnitude for each pixel

regrid_image(targetfov, npix, interp='linear')

Resample the image to new (square) dimensions.

Parameters

• targetfov (float) – new field of view (radian)

• npix (int) – new pixel dimension

• interp ('linear', 'cubic', 'quintic') – type of interpolation. default is linear

Returns

resampled image

Return type

(Image)

resample_square(xdim_new, ker_size=5)

Exactly resample a square image to new dimensions using the pulse function.

Parameters

• xdim_new (int) – new pixel dimension

• ker_size (int) – kernel size for resampling

Returns

resampled image

Return type

im_resampled (Image)

rotate(angle, interp='cubic')

Rotate the image counterclockwise by the specified angle.

Parameters

• angle (float) – CCW angle to rotate the image (radian)

• interp ('linear', 'cubic', 'quintic') – type of interpolation. default is cubic

Returns

resampled image

Return type

(Image)

sample_uv(uv, polrep_obs='stokes', sgrscat=False, ttype='nfft', cache=False, fft_pad_factor=2, zero_empty_pol=True, verbose=True)

Sample the image on the selected uv points without creating an Obsdata object.

Parameters

• uv (ndarray) – an array of uv points

• polrep_obs (str) – ‘stokes’ or ‘circ’ sets the data polarimetric representation

• sgrscat (bool) – if True, the visibilites will be blurred by the Sgr A* kernel

• ttype (str) – “fast” or “nfft” or “direct”

• cache (bool) – Use cached fft for ‘fast’ mode – deprecated, use nfft instead!

• fft_pad_factor (float) – zero pad the image to fft_pad_factor * image size in FFT

• zero_empty_pol (bool) – if True, returns zero vec if the polarization doesn’t exist. Otherwise return None

• verbose (bool) – Boolean value controls output prints.

Returns

a list of [I,Q,U,V] visibilities

Return type

(list)

save_fits(fname)

Save image data to a fits file.

Parameters

fname (str) – path to output fits file

Returns:

save_txt(fname)

Save image data to text file.

Parameters

fname (str) – path to output text file

Returns:

shift(shiftidx)

Shift the image by a given number of pixels.

Parameters

shiftidx (list) – pixel offsets [x_offset, y_offset] for the image shift

Returns

shifted images

Return type

(Image)

shift_fft(shift)

Shift the image by a given vector in radians.

This allows non-integer pixel shifts, via FFT.

Parameters

shift (list) – offsets [x_offset, y_offset] for the image shift in radians

Returns

shifted image

Return type

(Image)

sourcevec()

Return the source position vector in geocentric coordinates at 0h GMST.

Args:

Returns

normal vector pointing to source in geocentric coordinates (m)

Return type

(numpy.array)

switch_polrep(polrep_out='stokes', pol_prim_out=None)

Return a new image with the polarization representation changed :param polrep_out: the polrep of the output data :type polrep_out: str :param pol_prim_out: The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for circ :type pol_prim_out: str

Returns

new Image object with potentially different polrep

Return type

(Image)

threshold(cutoff=0.05, beamparams=None, frac=0.0, fill_val=None)

Apply a hard threshold to the primary polarization image. Leave other polarizations untouched.

Parameters

• cutoff (float) – Mask pixels with intensities greater than cuttoff * max

• beamparams (list) – either [fwhm_maj, fwhm_min, pos_ang] or a single fwhm

• frac (float) – the fraction of nominal beam to blur with

• fill_val (float) – masked pixels are set to this value. If fill_val==None, they are set to the min unmasked value

Returns

output mask image

Return type

(Image)

total_flux()

Return the total flux of the image in Jy.

Args:

Returns

image total flux (Jy)

Return type

(float)

ehtim.image.avg_imlist(imlist)

Average a list of images.

Parameters

imlist (list) – list of image objects

Returns

average image object

Return type

(Image)

ehtim.image.blur_mf(im, freqs, kernel, fit_order=2)

blur multifrequncy images with the same beam

ehtim.image.get_specim(imlist, reffreq, fit_order=2)

get the spectral index/curvature from a list of images

ehtim.image.load_fits(fname, aipscc=False, pulse=<function trianglePulse2D>, polrep='stokes', pol_prim=None, zero_pol=False)

Read in an image from a FITS file.

Parameters

• fname (str) – path to input fits file

• aipscc (bool) – if True, then AIPS CC table will be loaded

• pulse (function) – The function convolved with the pixel values for continuous image.

• polrep (str) – polarization representation, either ‘stokes’ or ‘circ’

• pol_prim (str) – The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular

• zero_pol (bool) – If True, loads any missing polarizations as zeros

Returns

loaded image object

Return type

(Image)

ehtim.image.load_image(image, display=False, aipscc=False)

Read in an image from a text, .fits, .h5, or ehtim.image.Image object

Parameters

• image (str/Image) – path to input file

• display (boolean) – determine whether to display the image default

• aipscc (boolean) – if True, then AIPS CC table will be loaded instead of the original brightness distribution.

Returns

loaded image object (boolean): False if the image cannot be read

Return type

(Image)

ehtim.image.load_txt(fname, polrep='stokes', pol_prim=None, pulse=<function trianglePulse2D>, zero_pol=True)

Read in an image from a text file.

Parameters

• fname (str) – path to input text file

• pulse (function) – The function convolved with the pixel values for continuous image.

• polrep (str) – polarization representation, either ‘stokes’ or ‘circ’

• pol_prim (str) – The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular

• zero_pol (bool) – If True, loads any missing polarizations as zeros

Returns

loaded image object

Return type

(Image)

ehtim.image.make_empty(npix, fov, ra, dec, rf=230000000000.0, source='SgrA', polrep='stokes', pol_prim=None, pulse=<function trianglePulse2D>, mjd=51544, time=0.0)

Make an empty square image.

Parameters

• npix (int) – the pixel size of each axis

• fov (float) – the field of view of each axis in radians

• ra (float) – The source Right Ascension in fractional hours

• dec (float) – The source declination in fractional degrees

• rf (float) – The image frequency in Hz

• source (str) – The source name

• polrep (str) – polarization representation, either ‘stokes’ or ‘circ’

• pol_prim (str) – The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular

• pulse (function) – The function convolved with the pixel values for continuous image.

• mjd (int) – The integer MJD of the image

• time (float) – The observing time of the image (UTC hours)

Returns

an image object

Return type

(Image)

ehtim.image.make_square(obs, npix, fov, pulse=<function trianglePulse2D>, polrep='stokes', pol_prim=None)

Make an empty square image.

Parameters

• obs (Obsdata) – an obsdata object with the image metadata

• npix (int) – the pixel size of each axis

• fov (float) – the field of view of each axis in radians

• pulse (function) – the function convolved with the pixel values for continuous image

• polrep (str) – polarization representation, either ‘stokes’ or ‘circ’

• pol_prim (str) – The default image: I,Q,U or V for Stokes, or RR,LL,LR,RL for Circular

Returns

an image object

Return type

(Image)