9. Tips and Tricks, code snippets ¶

Contents

Tips and Tricks, code snippets

9.1. Which PyCS am I using ?¶

If you have several copies, from SVN or installed…

import pycs
print pycs.__file__

9.2. “Faking” PyCS delay or error measurements ¶

This is useful if you want to include measurments from non-pycs techniques on the typical pycs plots

# To fake a PyCS delay measurement :
data = [{"label":"AB", "mean":-118.6, "med":0.0, "std":0.0}]

# To fake a PyCS errorbar :
"""
data = [{
"label":"AB",
"sys":3.0,
"ran":4.0,
"tot":5.0,
"bias":-3.0
}]
"""
outname = "sims_copies_runresults_rathna_May07_delays.pkl"
dc = pycs.sim.plot.delaycontainer(data = data, name = "Difference-smoothing technique", plotcolour = "darkorange", objects=["A", "B"])
pycs.gen.util.writepickle(dc, outname)

9.3. Full example to generate a nice big light curve plot ¶

This was used for Fig. 4 of the RXJ1131 paper :

# Loading the lists of lightcurves
EulerC2 = pycs.gen.util.readpickle("data/EulerC2_matched.pkl")
ECAM = pycs.gen.util.readpickle("data/ECAM_matched.pkl")
SMARTS = pycs.gen.util.readpickle("data/SMARTS_matched.pkl")
Mercator = pycs.gen.util.readpickle("data/Mercator_matched.pkl")


# Shifting the curves so to get reasonable mag scales :
tels = [ECAM, SMARTS, Mercator, EulerC2]
for tel in tels:
        for l in tel:
                l.shiftmag(14.5)
        tel[1].shiftmag(0.3)
        tel[3].shiftmag(0.3)


# Tweaking the jdrange and magrange below so that it looks nice
xw = 1700
x1 = 52700
x2 = 54400

yw = 5
y1= 0.85
y2= -0.2

# Text to be written on the plot :
text = [
(0.504, 0.89, r"$\mathrm{RX\,J1131-1231}$", {"fontsize":24, "horizontalalignment":"center"}),
(0.03, 0.8, "A", {"fontsize":18}),
(0.03, 0.67, "B", {"fontsize":18}),
(0.03, 0.55, "C", {"fontsize":18}),
(0.03, 0.20, "D", {"fontsize":18}),
(0.052, 0.67, "+0.3 mag", {"fontsize":10}),
(0.052, 0.20, "+0.3 mag", {"fontsize":10}),
(0.052, 0.47, "Euler C2 : 265 epochs", {"fontsize":10, "color":"red"}),
(0.052, 0.42, "Euler ECAM : 76 epochs", {"fontsize":10, "color":"#FF7700"}),
(0.052, 0.37, "SMARTS 1.3-m : 288 epochs", {"fontsize":10, "color":"green"}),
(0.052, 0.32, "Mercator : 78 epochs", {"fontsize":10, "color":"blue"})

]

pycs.gen.lc.display(ECAM + SMARTS + Mercator + EulerC2,
nicefont=False, showdelays=False, showlegend=False, showdates=True, errorbarcolour="#777777",
figsize=(10,4), plotsize=(0.05, 0.97, 0.05, 0.93), jdrange=(x1, x1+xw), magrange=(y1+yw, y1),
filename="fig_lcpart1.pdf", markersize=3.0, capsize=0, text=text, jdmintickstep=50, magmintickstep=0.2, showgrid=True
)

pycs.gen.lc.display(ECAM + SMARTS + Mercator + EulerC2,
nicefont=False, showdelays=False, showlegend=False, showdates=True, errorbarcolour="#777777",
figsize=(10,4.25), plotsize=(0.05, 0.97, 0.11, 0.94), jdrange=(x2, x2+xw), magrange=(y2+yw, y2),
filename="fig_lcpart2.pdf", markersize=3.0, capsize=0, jdmintickstep=50, magmintickstep=0.2, showgrid=True
)

9.4. Building scrolling plots for long curves ¶

# Animated plot for talk :

startjd = 52900.0
width = 1000.0
endjd = 55800.0
n = 1000

for i in range(n):

        a = startjd + i* (endjd - width - startjd)/(n-1)
        b = a + width

        filename = "mov/%i.png" % (i)
        pycs.gen.lc.display(lcs, nicefont=True, showdelays=False, showlegend=False, showdates=True, showgrid=True, magrange=(4.3, 0), jdrange=(a, b), filename=filename)

And then use ffmpeg (or any other similar tool) to turn this into a movie.

9.5. Tweaking magnitudes for individual seasons ¶

For a lightcurve l, l.mags is just a numpy array. To lower the third season by 0.03 mags :

seasons = pycs.gen.sea.autofactory(l)
l.mags[seasons[2].indices] += 0.03

9.6. Playing with custom properties ¶

You can perfectly create your own properties. It’s just a list of dicts …

for i in range(len(l)):
        l.properties[i]["my_new_prop"] = "brocoli"

# To see what properties a curve has :
print l.longinfo()

“Common” properties are properties that all points of the curve have (this is usually the case). Only those “common” properties can be exported as columns in rdb files, for insance.

9.7. Splitting a curve by properties ¶

def splitbyprop(l, prop = "telescope"):
        """
        kills mask ...
        """

        vals = sorted(list(set([l.properties[i][prop] for i in range(len(l))])))

        out = []
        for val in vals:
                lcp = l.copy()
                lcp.mask = np.array([l.properties[i][prop] == val for i in range(len(l))])
                lcp.cutmask()
                lcp.telescopename = val
                out.append(lcp)

        #pycs.gen.mrg.colourise(out)
        return out

9.9. Plotting a structure function ¶

April 2012, implemented to see how it looks on residuals. Ugly. But might be intresting for curves ?

(lcs, spline) = pycs.gen.util.readpickle("optspl.pkl")

rls = pycs.gen.stat.subtract(lcs, spline)

pycs.gen.lc.display(rls)

pycs.gen.stat.sf(rls[0])

9.10. Playing with power spectra ¶

It’s what tweakml does :

(lcs, spline) = pycs.gen.util.readpickle("optspl.pkl")
#pycs.gen.lc.display(lcs, [spline])

spline = l.ml.spline.copy()
source = pycs.sim.src.Source(spline, sampling = 0.2)

source.addplaw2(beta=-2.0, sigma=0.01, flux=False, fmin=1/1000.0, fmax=1/10.0, hann=False, seed=None)
pycs.sim.src.sourceplot([source], filename=None, figsize=(12, 8), showlegend=True, marker=None)

newspline = source.spline()
l.ml.replacespline(newspline)