uvotplot¶
helper functions¶
Ellipse()¶
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uvotpy.uvotplot.Ellipse(xxx_todo_changeme, xxx_todo_changeme1, angle=0.0, resolution=200, **kwargs)¶ plot an ellipse using an N-sided polygon
Parameters: (x,y) : float
centre ellipse
(rx,ry) : float
half axis ellipse
angle : float
angle in units of degrees
resolution : int
determines number of points to use
and additional kwargs for pyplot.plot()
binplot()¶
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uvotpy.uvotplot.binplot(*args, **kwargs)¶ Bin up the arrays with the keyword bin=<number> Same parameters as used by plot (pyplot)
contourpk()¶
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uvotpy.uvotplot.contourpk(x, y, f, levels=None, xb=None, xe=None, yb=None, ye=None, s=60, kx=1, ky=1, dolabels=True, **kwargs)¶ Make a contour plot with 1-D array inputs for X, Y and F. This is a wrapper to convert lists of points (X,Y,Z) in 2-D arrays, then calls contour()
Parameters: X, Y: ndarrays[:], 1D on a 2D plane
coordinates X, Y
Z : ndarray[:], 1D function on X,Y
maskEllipse()¶
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uvotpy.uvotplot.maskEllipse(maskimg, x, y, a, b, theta, test=0, chatter=1)¶ update a mask excluding ellipse region
Parameters: maskimg : ndarray, 2D, bool
boolean array to aplly mask to (i.e., numpy.ones( array([200,400]),dtype=bool) )
x,y : int, float
ellipse center coordinate x,y
a,b : float
ellipse major axis a; minor axis b;
theta : float
rotation angle theta counterclockwise in deg.
Returns: maskimg with all pixels inside the ellipse are set to False
plot_ellipsoid_regions()¶
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uvotpy.uvotplot.plot_ellipsoid_regions(Xim, Yim, Xa, Yb, Thet, b2mag, matched, ondetector, img_pivot, img_pivot_ori, img_size, limitMag, img_angle=0.0, lmap=False, makeplot=True, color='k', annulusmag=13.0, ax=None, chatter=1)¶ This routine is to plot ellipsoid regions on the grism image/graph, which may be a rotated, cropped part of the detector image
Returns: None or boolean map image, plots an ellipse on the current figure
waveAccPlot()¶
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uvotpy.uvotplot.waveAccPlot(wave_obs, pix_obs, wave_zmx, pix_zmx, disp_coef, obsid=None, acc=None, order=None, wheelpos=200, figureno=1, legloc=[1, 2])¶ Plots of the accuracy of the wavelength solution from zemax compared to the observed wavelengths.
Parameters: wave_obs, pix_obs : ndarray
observed wavelengths points (green circles)
wave_zmx ,pix_zmx : ndarray
calculated zemax points (or the interpolated solution (red crosses)
disp_coef : ndarray
dispersion coefficients
disp_coef : list
coefficients in reverse order: if p is of length N, this the polynomial
is as follows for coeff named p:
y(x) = p[0]*(x**N-1) + p[1]*(x**N-2) + ... + p[N-2]*x + p[N-1]
kwargs : dict
- acc : accuracy in wavelength
- order : order of polynomial disp_coef (default len(coef) )
- obsid : if given, append to title
Notes
Figure description
x-axis : pix - pixel number referenced to [260nm in first order]
Top panel only
y-axis: lambda - lambda_linear
linear term in the dispersion a linear term is fit to the wavelengths
$lambda_{lin}$ = coef[0]+coef[1]*pixBottom panel only
y-axis: residuals
wave_obs, pix_obs - wave(pix_obs) (green circles) wave_zmx, pix_zmx - wave(pix_zmx) (red crosses)