lsst.jointcal.utils Namespace Reference

Classes
class	JointcalStatistics

Functions
def	plot_flux_distributions (plt, old_mag, new_mag, old_weighted_rms, new_weighted_rms, faint, bright, old_PA1, new_PA1, name='', outdir='.plots')
def	plot_all_wcs_deltas (plt, data_refs, visits, old_wcs_list, per_ccd_plot=False, name='', outdir='.plots')
def	make_xy_wcs_grid (x_dim, y_dim, wcs1, wcs2, num=50)
def	wcs_convert (xv, yv, wcs)
def	plot_all_wcs_quivers (plt, data_refs, visits, old_wcs_list, name, outdir='.plots')
def	plot_wcs_quivers (ax, wcs1, wcs2, x_dim, y_dim)
def	plot_wcs_magnitude (plt, data_refs, visits, old_wcs_list, name, outdir='.plots')
def	plot_wcs (plt, wcs1, wcs2, x_dim, y_dim, center=(0, 0), name="", outdir='.plots')
def	plot_rms_histogram (plt, old_rms_relative, old_rms_absolute, new_rms_relative, new_rms_absolute, old_rel_total, old_abs_total, new_rel_total, new_abs_total, name="", outdir='.plots')

Variables
	MatchDict = collections.namedtuple('MatchDict', ['relative', 'absolute'])

Function Documentation

make_xy_wcs_grid()

def lsst.jointcal.utils.make_xy_wcs_grid	(		x_dim,
			y_dim,
			wcs1,
			wcs2,
			num = 50
	)

Return num x/y grid coordinates for wcs1 and wcs2.

Definition at line 530 of file utils.py.

def make_xy_wcs_grid(x_dim, y_dim, wcs1, wcs2, num=50):
    """Return num x/y grid coordinates for wcs1 and wcs2."""
    x = np.linspace(0, x_dim, num)
    y = np.linspace(0, y_dim, num)
    x1, y1 = wcs_convert(x, y, wcs1)
    x2, y2 = wcs_convert(x, y, wcs2)
    return x1, y1, x2, y2

plot_all_wcs_deltas()

def lsst.jointcal.utils.plot_all_wcs_deltas	(		plt,
			data_refs,
			visits,
			old_wcs_list,
			per_ccd_plot = False,
			name = '',
			outdir = '.plots'
	)

Various plots of the difference between old and new Wcs.

Parameters
----------
plt : matplotlib.pyplot instance
    pyplot instance to plot with.
data_refs : list of lsst.daf.persistence.butlerSubset.ButlerDataRef
    A list of data refs to plot.
visits : list of visit id (usually int)
    list of visit identifiers, one-to-one correspondent with catalogs.
old_wcs_list : list of lsst.afw.image.wcs.Wcs
    A list of the old (pre-jointcal) WCSs, one-to-one corresponding to data_refs.
per_ccd_plot : bool, optional
    Make per-ccd plots of the "wcs different" (warning: slow!)
name : str
    Name to include in plot titles and save files.
outdir : str, optional
    Directory to write the saved plots to.

Definition at line 495 of file utils.py.

                        name='', outdir='.plots'):
    """
    Various plots of the difference between old and new Wcs.

    Parameters
    ----------
    plt : matplotlib.pyplot instance
        pyplot instance to plot with.
    data_refs : list of lsst.daf.persistence.butlerSubset.ButlerDataRef
        A list of data refs to plot.
    visits : list of visit id (usually int)
        list of visit identifiers, one-to-one correspondent with catalogs.
    old_wcs_list : list of lsst.afw.image.wcs.Wcs
        A list of the old (pre-jointcal) WCSs, one-to-one corresponding to data_refs.
    per_ccd_plot : bool, optional
        Make per-ccd plots of the "wcs different" (warning: slow!)
    name : str
        Name to include in plot titles and save files.
    outdir : str, optional
        Directory to write the saved plots to.
    """

    plot_wcs_magnitude(plt, data_refs, visits, old_wcs_list, name, outdir=outdir)
    plot_all_wcs_quivers(plt, data_refs, visits, old_wcs_list, name, outdir=outdir)

    if per_ccd_plot:
        for i, ref in enumerate(data_refs):
            md = ref.get('calexp_md')
            dims = lsst.afw.image.bboxFromMetadata(md).getDimensions()
            plot_wcs(plt, old_wcs_list[i], ref.get('jointcal_wcs'),
                     dims.getX(), dims.getY(),
                     center=(md.getScalar('CRVAL1'), md.getScalar('CRVAL2')), name='dataRef %d'%i,
                     outdir=outdir)

plot_all_wcs_quivers()

def lsst.jointcal.utils.plot_all_wcs_quivers	(		plt,
			data_refs,
			visits,
			old_wcs_list,
			name,
			outdir = '.plots'
	)

Make quiver plots of the WCS deltas for each CCD in each visit.

Parameters
----------
plt : matplotlib.pyplot instance
    pyplot instance to plot with.
data_refs : list of lsst.daf.persistence.butlerSubset.ButlerDataRef
    A list of data refs to plot.
visits : list of visit id (usually int)
    list of visit identifiers, one-to-one correspondent with catalogs.
old_wcs_list : list of lsst.afw.image.wcs.Wcs
    A list of the old (pre-jointcal) WCSs, one-to-one corresponding to data_refs.
name : str
    Name to include in plot titles and save files.
outdir : str, optional
    Directory to write the saved plots to.

Definition at line 551 of file utils.py.

def plot_all_wcs_quivers(plt, data_refs, visits, old_wcs_list, name, outdir='.plots'):
    """
    Make quiver plots of the WCS deltas for each CCD in each visit.

    Parameters
    ----------
    plt : matplotlib.pyplot instance
        pyplot instance to plot with.
    data_refs : list of lsst.daf.persistence.butlerSubset.ButlerDataRef
        A list of data refs to plot.
    visits : list of visit id (usually int)
        list of visit identifiers, one-to-one correspondent with catalogs.
    old_wcs_list : list of lsst.afw.image.wcs.Wcs
        A list of the old (pre-jointcal) WCSs, one-to-one corresponding to data_refs.
    name : str
        Name to include in plot titles and save files.
    outdir : str, optional
        Directory to write the saved plots to.
    """

    for visit in visits:
        fig = plt.figure()
        # fig.set_tight_layout(True)
        ax = fig.add_subplot(111)
        for old_wcs, ref in zip(old_wcs_list, data_refs):
            if ref.dataId['visit'] != visit:
                continue
            md = ref.get('calexp_md')
            dims = lsst.afw.image.bboxFromMetadata(md).getDimensions()
            Q = plot_wcs_quivers(ax, old_wcs, ref.get('jointcal_wcs'),
                                 dims.getX(), dims.getY())
            # TODO: add CCD bounding boxes to plot once DM-5503 is finished.
            # TODO: add a circle for the full focal plane.
        length = (0.1*u.arcsecond).to(u.radian).value
        ax.quiverkey(Q, 0.9, 0.95, length, '0.1 arcsec', coordinates='figure', labelpos='W')
        plt.xlabel('RA')
        plt.ylabel('Dec')
        plt.title('visit: {}'.format(visit))
        filename = os.path.join(outdir, '{}-{}-quivers.pdf')
        plt.savefig(filename.format(name, visit))

plot_flux_distributions()

def lsst.jointcal.utils.plot_flux_distributions	(		plt,
			old_mag,
			new_mag,
			old_weighted_rms,
			new_weighted_rms,
			faint,
			bright,
			old_PA1,
			new_PA1,
			name = '',
			outdir = '.plots'
	)

Plot various distributions of fluxes and magnitudes.

Parameters
----------
plt : matplotlib.pyplot instance
    pyplot instance to plot with
old_mag : np.array
    old magnitudes
new_mag : np.array
    new magnitudes
old_weighted_rms : np.array
    old rms weighted by the mean (rms(data)/mean(data))
new_weighted_rms : np.array
    old rms weighted by the mean (rms(data)/mean(data))
faint : float
    Faint end of range that PA1 was computed from.
bright : float
    Bright end of range that PA1 was computed from.
old_PA1 : float
    Old value of PA1, to plot as horizontal line.
new_PA1 : float
    New value of PA1, to plot as horizontal line.
name : str
    Name to include in plot titles and save files.
outdir : str, optional
    Directory to write the saved plots to.

Definition at line 434 of file utils.py.

                            name='', outdir='.plots'):
    """Plot various distributions of fluxes and magnitudes.

    Parameters
    ----------
    plt : matplotlib.pyplot instance
        pyplot instance to plot with
    old_mag : np.array
        old magnitudes
    new_mag : np.array
        new magnitudes
    old_weighted_rms : np.array
        old rms weighted by the mean (rms(data)/mean(data))
    new_weighted_rms : np.array
        old rms weighted by the mean (rms(data)/mean(data))
    faint : float
        Faint end of range that PA1 was computed from.
    bright : float
        Bright end of range that PA1 was computed from.
    old_PA1 : float
        Old value of PA1, to plot as horizontal line.
    new_PA1 : float
        New value of PA1, to plot as horizontal line.
    name : str
        Name to include in plot titles and save files.
    outdir : str, optional
        Directory to write the saved plots to.
    """

    import seaborn
    seaborn.set_style('whitegrid')
    import scipy.stats

    old_color = 'blue'
    new_color = 'red'
    plt.figure()
    plt.plot(old_mag, old_weighted_rms, '.', color=old_color, label='old')
    plt.plot(new_mag, new_weighted_rms, '.', color=new_color, label='new')
    plt.axvline(faint, ls=':', color=old_color)
    plt.axvline(bright, ls=':', color=old_color)
    plt.axhline(old_PA1, ls='--', color=old_color)
    plt.axhline(new_PA1, ls='--', color=new_color)
    plt.legend(loc='upper left')
    plt.title('Where is the systematic flux rms limit?')
    plt.xlabel('magnitude')
    plt.ylabel('rms/mean per source')
    filename = os.path.join(outdir, '{}-photometry-PA1.pdf')
    plt.savefig(filename.format(name))

    plt.figure()
    seaborn.distplot(old_weighted_rms, fit=scipy.stats.lognorm, kde=False, label="old", color=old_color)
    seaborn.distplot(new_weighted_rms, fit=scipy.stats.lognorm, kde=False, label="new", color=new_color)
    plt.title('Source RMS pre/post-jointcal')
    plt.xlabel('rms(flux)/mean(flux)')
    plt.ylabel('number')
    plt.legend(loc='upper right')
    filename = os.path.join(outdir, '{}-photometry-rms.pdf')
    plt.savefig(filename.format(name))

plot_rms_histogram()

def lsst.jointcal.utils.plot_rms_histogram	(		plt,
			old_rms_relative,
			old_rms_absolute,
			new_rms_relative,
			new_rms_absolute,
			old_rel_total,
			old_abs_total,
			new_rel_total,
			new_abs_total,
			name = "",
			outdir = '.plots'
	)

Plot histograms of the source separations and their RMS values.

Parameters
----------
plt : matplotlib.pyplot instance
    pyplot instance to plot with.
old_rms_relative : np.array
    old relative rms/star
old_rms_absolute : np.array
    old absolute rms/star
new_rms_relative : np.array
    new relative rms/star
new_rms_absolute : np.array
    new absolute rms/star
old_rel_total : float
    old relative rms over all stars
old_abs_total : float
    old absolute rms over all stars
new_rel_total : float
    new relative rms over all stars
new_abs_total : float
    new absolute rms over all stars
name : str
    Name to include in plot titles and save files.
outdir : str, optional
    Directory to write the saved plots to.

Definition at line 714 of file utils.py.

                       name="", outdir='.plots'):
    """Plot histograms of the source separations and their RMS values.

    Parameters
    ----------
    plt : matplotlib.pyplot instance
        pyplot instance to plot with.
    old_rms_relative : np.array
        old relative rms/star
    old_rms_absolute : np.array
        old absolute rms/star
    new_rms_relative : np.array
        new relative rms/star
    new_rms_absolute : np.array
        new absolute rms/star
    old_rel_total : float
        old relative rms over all stars
    old_abs_total : float
        old absolute rms over all stars
    new_rel_total : float
        new relative rms over all stars
    new_abs_total : float
        new absolute rms over all stars
    name : str
        Name to include in plot titles and save files.
    outdir : str, optional
        Directory to write the saved plots to.
    """
    plt.figure()

    color_rel = 'black'
    ls_old = 'dotted'
    color_abs = 'green'
    ls_new = 'dashed'
    plotOptions = {'lw': 2, 'range': (0, 0.1)*u.arcsecond, 'normed': True,
                   'bins': 30, 'histtype': 'step'}

    plt.title('relative vs. absolute: %d vs. %d'%(len(old_rms_relative), len(old_rms_absolute)))

    plt.hist(old_rms_absolute, color=color_abs, ls=ls_old, label='old abs', **plotOptions)
    plt.hist(new_rms_absolute, color=color_abs, ls=ls_new, label='new abs', **plotOptions)

    plt.hist(old_rms_relative, color=color_rel, ls=ls_old, label='old rel', **plotOptions)
    plt.hist(new_rms_relative, color=color_rel, ls=ls_new, label='new rel', **plotOptions)

    plt.axvline(x=old_abs_total.value, linewidth=1.5, color=color_abs, ls=ls_old)
    plt.axvline(x=new_abs_total.value, linewidth=1.5, color=color_abs, ls=ls_new)
    plt.axvline(x=old_rel_total.value, linewidth=1.5, color=color_rel, ls=ls_old)
    plt.axvline(x=new_rel_total.value, linewidth=1.5, color=color_rel, ls=ls_new)

    plt.xlim(plotOptions['range'])
    plt.xlabel('arcseconds')
    plt.legend(loc='best')
    filename = os.path.join(outdir, '{}-histogram.pdf')
    plt.savefig(filename.format(name))

plot_wcs()

def lsst.jointcal.utils.plot_wcs	(		plt,
			wcs1,
			wcs2,
			x_dim,
			y_dim,
			center = (0, 0),
			name = "",
			outdir = '.plots'
	)

Plot the "distortion map": wcs1-wcs2 delta of points in the CCD grid.

Parameters
----------
plt : matplotlib.pyplot instance
    pyplot instance to plot with.
wcs1 : lsst.afw.image.wcs.Wcs
    First WCS to compare.
wcs2 : lsst.afw.image.wcs.Wcs
    Second WCS to compare.
x_dim : int
    Size of array in X-coordinate to make the grid over.
y_dim : int
    Size of array in Y-coordinate to make the grid over.
center : tuple, optional
    Center of the data, in on-chip coordinates.
name : str
    Name to include in plot titles and save files.
outdir : str, optional
    Directory to write the saved plots to.

Definition at line 677 of file utils.py.

def plot_wcs(plt, wcs1, wcs2, x_dim, y_dim, center=(0, 0), name="", outdir='.plots'):
    """Plot the "distortion map": wcs1-wcs2 delta of points in the CCD grid.

    Parameters
    ----------
    plt : matplotlib.pyplot instance
        pyplot instance to plot with.
    wcs1 : lsst.afw.image.wcs.Wcs
        First WCS to compare.
    wcs2 : lsst.afw.image.wcs.Wcs
        Second WCS to compare.
    x_dim : int
        Size of array in X-coordinate to make the grid over.
    y_dim : int
        Size of array in Y-coordinate to make the grid over.
    center : tuple, optional
        Center of the data, in on-chip coordinates.
    name : str
        Name to include in plot titles and save files.
    outdir : str, optional
        Directory to write the saved plots to.
    """

    plt.figure()

    x1, y1, x2, y2 = make_xy_wcs_grid(x_dim, y_dim, wcs1, wcs2, num=50)
    plt.plot((x1 - x2) + center[0], (y1 - y2) + center[1], '-')
    plt.xlabel('delta RA (arcsec)')
    plt.ylabel('delta Dec (arcsec)')
    plt.title(name)
    filename = os.path.join(outdir, '{}-wcs.pdf')
    plt.savefig(filename.format(name))

plot_wcs_magnitude()

def lsst.jointcal.utils.plot_wcs_magnitude	(		plt,
			data_refs,
			visits,
			old_wcs_list,
			name,
			outdir = '.plots'
	)

Plot the magnitude of the WCS change between old and new visits as a heat map.

Parameters
----------
plt : matplotlib.pyplot instance
    pyplot instance to plot with.
data_refs : list of lsst.daf.persistence.butlerSubset.ButlerDataRef
    A list of data refs to plot.
visits : list of visit id (usually int)
    list of visit identifiers, one-to-one correspondent with catalogs.
old_wcs_list : list of lsst.afw.image.wcs.Wcs
    A list of the old (pre-jointcal) WCSs, one-to-one corresponding to data_refs.
name : str
    Name to include in plot titles and save files.
outdir : str, optional
    Directory to write the saved plots to.

Definition at line 617 of file utils.py.

def plot_wcs_magnitude(plt, data_refs, visits, old_wcs_list, name, outdir='.plots'):
    """Plot the magnitude of the WCS change between old and new visits as a heat map.

    Parameters
    ----------
    plt : matplotlib.pyplot instance
        pyplot instance to plot with.
    data_refs : list of lsst.daf.persistence.butlerSubset.ButlerDataRef
        A list of data refs to plot.
    visits : list of visit id (usually int)
        list of visit identifiers, one-to-one correspondent with catalogs.
    old_wcs_list : list of lsst.afw.image.wcs.Wcs
        A list of the old (pre-jointcal) WCSs, one-to-one corresponding to data_refs.
    name : str
        Name to include in plot titles and save files.
    outdir : str, optional
        Directory to write the saved plots to.
    """
    for visit in visits:
        fig = plt.figure()
        fig.set_tight_layout(True)
        ax = fig.add_subplot(111)
        # Start min/max at the "opposite" ends so they always get the first valid value.
        xmin = np.inf
        ymin = np.inf
        xmax = -np.inf
        ymax = -np.inf
        for old_wcs, ref in zip(old_wcs_list, data_refs):
            if ref.dataId['visit'] != visit:
                continue
            md = ref.get('calexp_md')
            dims = lsst.afw.image.bboxFromMetadata(md).getDimensions()
            x1, y1, x2, y2 = make_xy_wcs_grid(dims.getX(), dims.getY(),
                                              old_wcs, ref.get('jointcal_wcs'))
            uu = x2 - x1
            vv = y2 - y1
            extent = (x1[0, 0], x1[-1, -1], y1[0, 0], y1[-1, -1])
            xmin = min(x1.min(), xmin)
            ymin = min(y1.min(), ymin)
            xmax = max(x1.max(), xmax)
            ymax = max(y1.max(), ymax)
            magnitude = (np.linalg.norm((uu, vv), axis=0)*u.radian).to(u.arcsecond).value
            img = ax.imshow(magnitude, vmin=0, vmax=0.3,
                            aspect='auto', extent=extent, cmap=plt.get_cmap('magma'))
            # TODO: add CCD bounding boxes to the plot once DM-5503 is finished.
            # TODO: add a circle for the full focal plane.

        # We're reusing only one of the returned images here for colorbar scaling,
        # but it doesn't matter because we set vmin/vmax so they are all scaled the same.
        cbar = plt.colorbar(img)
        cbar.ax.set_ylabel('distortion (arcseconds)')
        plt.xlim(xmin, xmax)
        plt.ylim(ymin, ymax)
        plt.xlabel('RA')
        plt.ylabel('Dec')
        plt.title('visit: {}'.format(visit))
        filename = os.path.join(outdir, '{}-{}-heatmap.pdf')
        plt.savefig(filename.format(name, visit))

plot_wcs_quivers()

def lsst.jointcal.utils.plot_wcs_quivers	(		ax,
			wcs1,
			wcs2,
			x_dim,
			y_dim
	)

Plot the delta between wcs1 and wcs2 as vector arrows.

Parameters
----------
ax : matplotlib.axis
    Matplotlib axis instance to plot to.
wcs1 : lsst.afw.image.wcs.Wcs
    First WCS to compare.
wcs2 : lsst.afw.image.wcs.Wcs
    Second WCS to compare.
x_dim : int
    Size of array in X-coordinate to make the grid over.
y_dim : int
    Size of array in Y-coordinate to make the grid over.

Definition at line 593 of file utils.py.

def plot_wcs_quivers(ax, wcs1, wcs2, x_dim, y_dim):
    """
    Plot the delta between wcs1 and wcs2 as vector arrows.

    Parameters
    ----------
    ax : matplotlib.axis
        Matplotlib axis instance to plot to.
    wcs1 : lsst.afw.image.wcs.Wcs
        First WCS to compare.
    wcs2 : lsst.afw.image.wcs.Wcs
        Second WCS to compare.
    x_dim : int
        Size of array in X-coordinate to make the grid over.
    y_dim : int
        Size of array in Y-coordinate to make the grid over.
    """

    x1, y1, x2, y2 = make_xy_wcs_grid(x_dim, y_dim, wcs1, wcs2)
    uu = x2 - x1
    vv = y2 - y1
    return ax.quiver(x1, y1, uu, vv, units='x', pivot='tail', scale=1e-3, width=1e-5)

wcs_convert()

def lsst.jointcal.utils.wcs_convert	(		xv,
			yv,
			wcs
	)

Convert two arrays of x/y points into an on-sky grid.

Definition at line 539 of file utils.py.

def wcs_convert(xv, yv, wcs):
    """Convert two arrays of x/y points into an on-sky grid."""
    xout = np.zeros((xv.shape[0], yv.shape[0]))
    yout = np.zeros((xv.shape[0], yv.shape[0]))
    for i, x in enumerate(xv):
        for j, y in enumerate(yv):
            sky = wcs.pixelToSky(x, y)
            xout[i, j] = sky.getRa()
            yout[i, j] = sky.getDec()
    return xout, yout

Variable Documentation

MatchDict

lsst.jointcal.utils.MatchDict = collections.namedtuple('MatchDict', ['relative', 'absolute'])

Definition at line 46 of file utils.py.

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