suncasa.utils.stackplot

Classes

LightCurveBuilder
SpaceTimeSlitBuilder
CutslitBuilder
Stackplot

Functions

resettable(f)
b_filter(data, lowcut, highcut, fs, ix)
runningmean(data, window, ix)
c_correlate(a, v[, returnx])
XCorrMap(data[, refpix])
XCorrStackplt(z, x, y[, doxscale])	get the cross correlation map along y axis
FitSlit(xx, yy, cutwidth, cutang, cutlength[, s, ...])
MakeSlit(pointDF)
getimprofile(data, cutslit[, xrange, yrange, get_peak])
smooth(x[, window_len, window])	smooth the data using a window with requested size.
grid(x, y, z[, resX, resY])	Convert 3 column data to matplotlib grid
polyfit(x, y, length, deg)

Module Contents

suncasa.utils.stackplot.resettable(f)

suncasa.utils.stackplot.b_filter(data, lowcut, highcut, fs, ix)

suncasa.utils.stackplot.runningmean(data, window, ix)

suncasa.utils.stackplot.c_correlate(a, v, returnx=False)

suncasa.utils.stackplot.XCorrMap(data, refpix=[0, 0])

suncasa.utils.stackplot.XCorrStackplt(z, x, y, doxscale=True)

get the cross correlation map along y axis :param z: data :param x: x axis :param y: y axis :return:

suncasa.utils.stackplot.FitSlit(xx, yy, cutwidth, cutang, cutlength, s=None, method='Polyfit', ascending=True)

suncasa.utils.stackplot.MakeSlit(pointDF)

suncasa.utils.stackplot.getimprofile(data, cutslit, xrange=None, yrange=None, get_peak=False)

suncasa.utils.stackplot.smooth(x, window_len=11, window='hanning')

smooth the data using a window with requested size.

This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the begining and end part of the output signal.

input:

x: the input signal window_len: the dimension of the smoothing window; should be an odd integer window: the type of window from ‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’

flat window will produce a moving average smoothing.

output:

the smoothed signal

example:

t=linspace(-2,2,0.1) x=sin(t)+randn(len(t))*0.1 y=smooth(x)

see also:

numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve scipy.signal.lfilter

NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.

suncasa.utils.stackplot.grid(x, y, z, resX=20, resY=40)

Convert 3 column data to matplotlib grid

suncasa.utils.stackplot.polyfit(x, y, length, deg)

class suncasa.utils.stackplot.LightCurveBuilder(stackplt, axes, scale=1.0, color='white')

stackplt

axes_dspec

lghtcurvlines = []

scale = 1.0

color = 'white'

xx

yy

cid

bcut_curvsav

bcut_curvdel

lightcurves = []

clickedpoints_text = []

vmax

vmin

ymax

ymin

__call__(event)

update(mask=None)

save(event)

delete(event)

delete_byindex(index)

update_text()

lightcurves_tofile(outfile=None, lightcurves=None)

lightcurves_fromfile(infile, color=None)

class suncasa.utils.stackplot.SpaceTimeSlitBuilder(axes, cutlength=80, cutsmooth=10.0, scale=1.0, color='white')

cutlength = 80

scale = 1.0

color = 'white'

xx

yy

cid

bcut_cutsav

bcut_cutdel

cutsmooth = 10.0

spacetimeslits = []

slitlines = []

slitlines_text = []

__call__(event)

FitSlit(xx, yy, cutlength, method='Polyfit', s=0, ascending=True)

polynomial fit

update(mask=None)

save(event)

delete(event)

delete_byindex(index)

update_text()

spacetimeslits_tofile(outfile=None, spacetimeslits=None)

spacetimeslits_fromfile(infile, color=None)

class suncasa.utils.stackplot.CutslitBuilder(axes, cutwidth=5, cutang=0, cutlength=80, cutsmooth=10.0, scale=1.0)

axes

cutlength = 80

cutwidth = 5

cutang = 0

cutsmooth = 10.0

scale = 1.0

xx

yy

cid

__call__(event)

update(mask=None)

class suncasa.utils.stackplot.Stackplot(infile=None)

instrum_meta

aia_lvl1

suncasadb

fitsdir

mapcube = None

mapcube_diff = None

mapcube_plot = None

cutslitbd = None

stackplt = None

trange = None

wavelength = None

fitsfile = None

exptime_orig = None

fov = None

binpix = None

dt_data = None

divider_im = None

divider_dspec = None

sCutwdth = None

sCutang = None

sCutlngth = None

fig_mapcube = None

get_plot_title(smap, title)

plot_map(smap, dspec=None, diff=False, norm=None, cmap=None, SymLogNorm=False, linthresh=0.5, returnImAx=False, layout_vert=False, uni_cm=False, draw_limb=False, draw_grid=False, colortitle=None, title=['observatory', 'detector', 'wavelength', 'time'], fov=fov, *args, **kwargs)

make_mapcube(trange, outfile=None, fov=None, wavelength='171', binpix=1, dt_data=1, derotate=False, tosave=True, superpixel=False, aia_prep=False, mapinterp=False)

mapcube_fromfile(infile)

mapcube_tofile(outfile=None, mapcube=None)

mapcube_drot()

mapcube_resample(binpix=1)

mapcube_diff_denoise(log=False, vmax=None, vmin=None)

mapcube_mkdiff(mode='rdiff', dt=36.0, medfilt=None, gaussfilt=None, bfilter=False, lowcut=1 / 10 / 60.0, highcut=1 / 1 / 60.0, window=[None, None], outfile=None, tosave=False)

Parameters:

• mode – accept modes: rdiff, rratio, bdiff, bratio, dtrend

• dt – time difference in second between frames when [rdiff, rratio, bdiff, bratio] is invoked

• medfilt

• gaussfilt

• bfilter – do butter bandpass filter

• lowcut – low cutoff frequency in Hz

• highcut – high cutoff frequency in Hz

• outfile

• tosave

Returns:

plot_mapcube(mapcube=None, hdr=False, norm=None, vmax=None, vmin=None, cmap=None, diff=False, sav_img=False, out_dir=None, dpi=100, anim=False, silent=False, draw_limb=False, draw_grid=False, colortitle=None, title=['observatory', 'detector', 'wavelength', 'time'], fov=[], fps=15)

Parameters:

• mapcube

• hdr

• vmax

• vmin

• diff

• sav_img

• out_dir

• dpi

• anim

Returns:

cutslit_fromfile(infile, color=None, mask=None)

cutslit_tofile(outfile=None, cutslit=None)

make_stackplot(mapcube, frm_range=[], threshold=None, gamma=1.0, get_peak=False)

stackplt_wrap()

stackplt_tofile(outfile=None, stackplt=None)

stackplt_fromfile(infile, **kwargs)

plot_stackplot(mapcube=None, hdr=False, norm=None, vmax=None, vmin=None, cmap=None, layout_vert=False, diff=False, uni_cm=True, sav_img=False, out_dir=None, dpi=100, anim=False, frm_range=[], cutslitplt=None, silent=False, refresh=True, threshold=None, gamma=1.0, get_peak=False)

stackplt_traject_fromfile(infile, frm_range=[], cmap='inferno', vmax=None, vmin=None, gamma=1.0)

stackplt_lghtcurv_fromfile(infile, frm_range=[], cmap='inferno', vmax=None, vmin=None, gamma=1.0, log=False, axes=None)

mapcube_info(mapcube=None)

property cutslit

property tplt

classmethod set_fits_dir(fitsdir)