if len(jobCalibrations) > 0: jobSuccess = True
job = jobs[0]
time.sleep(4)
resultsURL = "http://nova.astrometry.net/api/jobs/%s/calibration" % (job)
print("Requesting results from ", resultsURL)
response = requests.post(resultsURL)
print(response.text)
wcsURL = "http://nova.astrometry.net/wcs_file/4014529"
print("Requesting results from ", wcsURL)
response = requests.post(wcsURL)
print(response.text)
# Use wget to grab the WCS file
wcsFilename = generallib.addSuffixToFilename(runObject.runID, "wcs")
wcsURL = "http://nova.astrometry.net/wcs_file/%s" % job
wcsCommand = ["wget"]
wcsCommand.append("-O")
wcsCommand.append(wcsFilename)
wcsCommand.append(wcsURL)
subprocess.call(wcsCommand)
runObject.addProperty("wcsFITS", wcsFilename)
runObject.save()
# Read the WCS solution and add this to the JSON object
wcsFile = fits.open(runObject.getProperty("wcsFITS"))
header = wcsFile[0].header
wcs = wcslib.wcsSolution()
color=filterColours[f],
fmt='.',
ecolor='gray',
capsize=0,
marker='.',
ms=4,
alpha=1.0)
axes = matplotlib.pyplot.gca()
matplotlib.rcParams['legend.loc'] = 'upper right'
axes.legend()
matplotlib.pyplot.draw()
if arg.save is not None:
savename = generallib.addSuffixToFilename(arg.save, "all")
print("Writing to file: %s" % savename)
matplotlib.pyplot.savefig(savename)
zoomPlot = matplotlib.pyplot.figure(figsize=(plotWidth, plotHeight * 1.4))
colours = ['g', 'r', 'purple', 'brown']
filterColours = {
'u': 'blue',
'g': 'green',
'r': 'red',
'i': 'purple',
'z': 'brown'
}
offset = 0.1
midDates = {}
startPhase = 0.9
vxy = float(hdulist[mainImage].header['VXY'])
hdulist.close()
vmin, vmax = numpy.median(data) - data.std(), numpy.median(data) + 4 * data.std()
contrastData = generallib.percentiles(data, percentileLower, percentileUpper)
matplotlib.pyplot.imshow(contrastData, origin='lower', cmap=colourMap, aspect='equal', extent=(-nx*vxy,nx*vxy, -ny*vxy,ny*vxy))
(vx, vy) = trm.roche.vlobe2(q, n=200)
matplotlib.pyplot.plot(vx*scale, vy*scale, color='g')
(vx, vy) = trm.roche.vlobe1(q, n=200)
matplotlib.pyplot.plot(vx*scale, vy*scale, ls="--", color='g')
(vx, vy) = trm.roche.vstream(q, step=0.01, vtype=1, n=70)
matplotlib.pyplot.plot(vx*scale, vy*scale, color='g')
matplotlib.pyplot.xlabel("$\mathrm{V}_\mathrm{x} (\mathrm{km}\,\mathrm{s}^{-1})$")
matplotlib.pyplot.ylabel("$\mathrm{V}_\mathrm{y} (\mathrm{km}\,\mathrm{s}^{-1})$")
axis = matplotlib.pyplot.gca()
print("Scale:", axis.get_xscale())
axis.set_autoscale_on(False)
axis.xaxis.set_label_position('top')
axis.xaxis.tick_top()
matplotlib.pyplot.draw()
if arg.save:
filename = generallib.addSuffixToFilename(arg.save, "map")
print("Writing file: %s"%filename)
matplotlib.pyplot.savefig(filename)
matplotlib.pyplot.show(block=arg.p)
if arg.json is not None:
t.writeToJSON(arg.json)
print(len(targets))
for object in targets:
photometryPlot = matplotlib.pyplot.figure(figsize=(plotWidth, plotHeight))
xValues = object.getColumn('HJD')
yValues = object.getColumn('mag')
yErrors = object.getColumn('err')
matplotlib.pyplot.errorbar(xValues, yValues, color='k', yerr=yErrors, fmt = '.', ecolor='gray', capsize=0)
matplotlib.pyplot.gca().invert_yaxis()
matplotlib.pyplot.xlabel('HJD')
matplotlib.pyplot.ylabel('CRTS magnitude')
matplotlib.pyplot.gca().set_xlim(left=min(xValues), right=max(xValues))
if arg.save is not None:
filename = generallib.addSuffixToFilename(arg.save, "full")
print("Writing to file: %s"%filename)
matplotlib.pyplot.savefig(filename)
matplotlib.pyplot.draw()
t = targets[0]
# Do a Periodogram
startFrequency = 1.1 # Cycles/day
stopFrequency = 20 # Cycles/day
spacing = 0.0001
numsamples = int((stopFrequency-startFrequency)/spacing)
f0,df,Nf = startFrequency, spacing, numsamples
startTime = numpy.min(xValues)
print("HDULength is %d thefore we have %d images"%(length, numImages))
for index in range(numImages):
num = index*2 + 1
data = hdulist[num].data
nx = int(hdulist[num].header['NAXIS1'])
ny = int(hdulist[num].header['NAXIS2'])
vxy = float(hdulist[num].header['VXY'])
vmin, vmax = numpy.median(data) - data.std(), numpy.median(data) + 4 * data.std()
contrastData = generallib.percentiles(data, 5, 98)
#plt.imshow(data, cmap='viridis', aspect='equal', vmin=vmin, vmax=vmax, extent=(-nx*vxy,nx*vxy, -ny*vxy,ny*vxy))
dopplerPlot = matplotlib.pyplot.figure(figsize=(plotHeight, plotHeight))
matplotlib.pyplot.imshow(contrastData, origin='lower', cmap='viridis', aspect='equal', extent=(-nx*vxy,nx*vxy, -ny*vxy,ny*vxy))
#matplotlib.pyplot.imshow(data, origin='lower', cmap='viridis', aspect='equal', vmin=vmin, vmax=vmax, extent=(-nx*vxy,nx*vxy, -ny*vxy,ny*vxy))
(vx, vy) = trm.roche.vlobe2(q, n=200)
matplotlib.pyplot.plot(vx*scale, vy*scale, color='g')
(vx, vy) = trm.roche.vlobe1(q, n=200)
matplotlib.pyplot.plot(vx*scale, vy*scale, ls="--", color='g')
(vx, vy) = trm.roche.vstream(q, step=0.01, vtype=1, n=80)
matplotlib.pyplot.plot(vx*scale, vy*scale, color='g')
matplotlib.pyplot.draw()
if arg.save:
if numImages>1: filename = generallib.addSuffixToFilename(arg.save, str(index))
else: filename = arg.save
print("Writing file: %s"%filename)
matplotlib.pyplot.savefig(filename)
matplotlib.pyplot.show(block=True)