suncasa.utils.stackplotX

Attributes

sunpy1

Classes

LightCurveBuilder
SpaceTimeSlitBuilder
CutslitBuilder
Stackplot

Functions

aiaprep(sunpymap)
resettable(f)
b_filter(data, lowcut, highcut, fs, ix)
runningmean(data, window, mode, 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 values at a slice
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.stackplotX.sunpy1

suncasa.utils.stackplotX.aiaprep(sunpymap)

suncasa.utils.stackplotX.resettable(f)

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

suncasa.utils.stackplotX.runningmean(data, window, mode, ix)

Parameters:

• data

• window

• ix

• mode – available options are ratio and diff

Returns:

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

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

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

suncasa.utils.stackplotX.MakeSlit(pointDF)

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

Get values at a slice

Inputs:

data: input image data. Dimension: (ny, nx) or (ny, nx, nwv). nwv is the number of wavelengths/frequencies cutslit: cutslit generated from CutslitBuilder().cutslitplt xrange: [min(xs), max(xs)], where xs is the x coordinate values of the input image data.

If None (default), assume pixel coordinate values in cutslit

yrange: [min(ys), max(ys)], where ys is the y coordinate values of the input image data.

If None (default), assume pixel coordinate values in cutslit

get_peak: If True, return the peak of all pixels across the slit within the slit width.

If False (default), return the average value.

verbose: If True, print out more details in command line. Default is False

Returns:

distance from min(cutslit[‘xcen’]), min(cutslit[‘ycen’])

’y’: value on the cut, the shape is (len(cutslit[‘xcen’], [nwv])}

Return type:

A dictionary of {‘x’

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

Convert 3 column data to matplotlib grid

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

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

dspec

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()

select_distance_along_a_slice(ixx)

select points more accurately by doing it on a distance-flux plot

spacetimeslits_tofile(outfile=None, spacetimeslits=None)

spacetimeslits_fromfile(infile, color=None)

class suncasa.utils.stackplotX.CutslitBuilder(axes, cutwidth=5.0, cutlength=150, cutang=0.0, cutsmooth=10.0, scale=1.0)

axes

cutlength = 150

cutwidth = 5.0

cutang = 0.0

cutsmooth = 10.0

scale = 1.0

xx

yy

cid

__call__(event)

update(mask=None)

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

instrum_meta

aia_lvl1

suncasadb

fitsdir

mapseq = None

mapseq_diff = None

mapseq_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

fig_mapseq = None

pixscale = 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_mapseq(trange, outfile=None, fov=None, wavelength='171', binpix=1, dt_data=1, derotate=False, tosave=True, hdf5=False, superpixel=False, aia_prep=False, mapinterp=False, overwrite=False, dtype=None, normalize=True)

mapseq_fromfile(infile)

mapseq_tofile(outfile=None, mapseq=None, hdf5=False)

mapseq_drot()

mapseq_resample(binpix=1)

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

mapseq_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, dtype=None, hdf5=False, normalize=True)

Parameters:

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

• 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_mapseq(mapseq=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:

• mapseq

• 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(mapseq, frm_range=[], threshold=None, gamma=1.0, get_peak=False, trackslit_diffrot=False, negval=False, movingcut=[])

movingcut: [x,y]. x and y are an array of offset in X and Y direction, respectively. the length of x/y is nframes

stackplt_wrap()

stackplt_tofile(outfile=None, stackplt=None)

stackplt_fromfile(infile, doplot=False, **kwargs)

plot_stackplot(mapseq=None, fov=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, trackslit_diffrot=False, negval=False, movingcut=[])

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

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

mapseq_info(mapseq=None)

property cutslit

property tplt

classmethod set_fits_dir(fitsdir)