autorange pop-up plot - Githubissues

Create a pop-up plot for choosing autorange parameters.

The component parts of the 'autorange' algorithm can be accessed using the new 'autorange_components; function in latools.processes.signal_id. This returns all the data that autorange considers when identifying signal and background regions.

This can be used to re-create a plot that highlights the meaning of all the autorange parameters. for example:

d = la.analyse('path/to/test/data/')
d.despike()
# select a single sample
di = d.data['STD-1']

# get time and data to use
t = di.Time
sig = di.data['total_counts']

# get autorange components
t, sig, sigs, tsig, tsigs, kde_x, yd, g, trans, thresh = autorange_components(t, sig, # data to consider
                                                                              transform='log',  # data transformation
                                                                              swin=7,  # signal-smoothing window (n-point running mean)
                                                                              gwin=7,  # gradient calculation window (n-point running gradient)
                                                                              win=20,  # data region to consider in peak fitting (n points either side of rough center)
                                                                              on_mult=(1.5, 1.0), off_mult=(1.0, 1.5))  # FWHM multiples used to exclude transitions

# make a plot
fig, axs = plt.subplots(2, 1, sharex=True, figsize=[12, 7])

sax, gax = axs

# plot raw signal and smoothed signal
sax.plot(t, tsig, c='k', alpha=0.3, label='raw data')  # plot raw data
sax.plot(t, tsigs, c='k', label='smoothed data')  # plot smoothed data

# plot gradient
gax.plot(t, g, c='k', label='absolute gradient')

# y axis labels
sax.set_ylabel('counts')
sax.set_yticklabels([])
gax.set_ylabel('|gradient|')
gax.set_yticklabels([])
gax.set_xlim(0, max(t))

gax.set_xlabel('Time (s)')

# figure layout
fig.tight_layout()

# shrink axes
for ax in axs:
    p = ax.axes.get_position()
    pn = [p.x0, p.y0, p.width * 0.8, p.height]

# create axis for kernel density (kde) estimator plot
p = sax.axes.get_position()
hax = fig.add_axes((p.x0 + p.width + 0.02, p.y0, p.width * 0.18 / 0.8, p.height))
hax.set_yticklabels([])
hax.set_xticklabels([])
hax.set_ylim(sax.get_ylim())

# plot kde
hax.fill_betweenx(kde_x, yd)
hax.set_title('Data Density', loc='left')

# add in threshold calculated from kde
for ax in [sax, hax]:
    ax.axhline(thresh, ls='dashed', c=(0,0,0,0.3))

# plot transitions
i = 1  # index of highlight transition

# draw guide lines on plot
for n in range(len(trans['zeros'])):
    # change colour of highlighted plot
    if n == i:
        c = 'b'
    else:
        c = 'r'

    # Draw 'firt guess' positions
    gax.axvline(t[trans['zeros'][n]], c=c, alpha=0.3, ls='dashed')
    sax.axvline(t[trans['zeros'][n]], c=c, alpha=0.6, ls='dashed')

    # draw regions considered in peak fits
    gax.axvspan(*t[trans['lohi'][n]], ls='dashed', facecolor=(0,0,0,0.05), edgecolor=(0,0,0,0.8))

    # draw transition centres from peak fits
    gax.axvline(trans['pgs'][n][1], c=c, alpha=0.8)

    # shade excluded transition regions
    gax.axvspan(*trans['excl'][n], color=c, alpha=0.1, lw=0)

# draw close-up of highlighted transition in panel
p = gax.axes.get_position()
pax = fig.add_axes((p.x0 + p.width + 0.02, p.y0, p.width * 0.18 / 0.8, p.height))

pax.set_yticklabels([])
pax.set_ylim(gax.get_ylim())
pax.set_xlabel('Time (s)')

# draw data
pax.scatter(trans['xs'][i], trans['ys'][i], c='k', s=10, label='data')
# draw fitted peak
pax.plot(trans['xs'][i], gauss(trans['xs'][i], *trans['pgs'][i]), c='r', label='fit')

# draw transition info
pax.axvline(t[trans['zeros'][i]], c='b', alpha=0.3, ls='dashed', label='centre (rough)')   # rough centre
pax.axvline(trans['pgs'][i][1], c='b', alpha=0.8, label='centre (fitted)')  # fitted centre
pax.axvspan(*trans['excl'][i], color='b', alpha=0.1, lw=0, label='excluded transition')  # shade transition
pax.axvspan(*t[trans['lohi'][i]], ls='dashed', facecolor=(0,0,0,0.05), edgecolor=(0,0,0,0.8))  # shade total considered region

# legends
sax.legend(loc='upper right')
pax.legend(loc='upper right')

Each of the autorange parameters should be available alongside a plot like this, and the plot should update when the parameters are changed. Those parameters are:

transform: a 'log' on/off check box. Changes whether the data are log-transformed. This will alter whether the data in the top left panel are on a log scale or not, and will change the shape of the data distribution in the top right panel.
swin: width of the data smoothing window (n-point running mean). This will alter the difference between the black and grey lines in the top left panel, and may alter the data distribution in the top right panel.
gwin: width of the window used to calculate the gradient (n-point running gradient). This will alter the shape of the line in the bottom left panel.
win: the number of data points either side of the rough transition to include in peak fitting (n data points). This will alter the width of the grey-shaded regions with dashed lines on the sides in the bottom left panel.
on_mult and off_mult: the multiples of the peak full-width-at-half-maximum (FWHM) to use when excluding the transition window. This will alter the coloured shaded regions in the bottom panels. 'on_mult' is applied to 'laser on' transitions, and 'off_mult' is applied to 'laser off' transitions. In each case the first number affects the amount of data excluded before the transition, and the second affects the amount of data excluded after the transition. For example (2, 1) will exclude twice as much data before the transition than after the transition.

If the plot were formatted like this, you should be able to switch which transition you were looking at in the bottom right panel by clicking on one of the transition regions, and be able to switch which sample you were looking at.

oscarbranson / latools_gui

autorange pop-up plot #84