def blsSummaryPlot(clipList, num=None):
mags = np.loadtxt("kees-c5.mags", delimiter="|")
nPad = mags.shape[1]
# epic, blsArray = gather.gatherValue(clipList, 'bls.convolved_bls')
epic, blsArray = gather.gatherFunction(clipList, getBls)
#Strip out occasional bls spectrum with non standard length
lengths = np.array(map(lambda x: len(x), blsArray))
typicalLength = int(np.median(lengths))
idx = lengths == typicalLength
epic = np.array(epic)[idx]
blsArray = np.array(blsArray)[idx]
obj = np.column_stack([epic, blsArray])
obj2 = join(mags, 0, None, obj, 0, None, dtype=object)
nPad += 1
# print obj2.shape
magCol = 3
idx = np.argsort(obj2[:, magCol])
obj2 = obj2[idx]
mag = obj2[:,magCol]
blsArray = np.vstack(obj2[:, -1])
print blsArray.shape
# return obj2
mp.clf()
mp.imshow(blsArray, interpolation="nearest", origin="bottom",\
aspect="auto", cmap=mp.cm.YlGnBu_r)
mp.colorbar()
return blsArray
def blsSummaryPlot(clipList, num=None):
mags = np.loadtxt("kees-c5.mags", delimiter="|")
nPad = mags.shape[1]
# epic, blsArray = gather.gatherValue(clipList, 'bls.convolved_bls')
epic, blsArray = gather.gatherFunction(clipList, getBls)
#Strip out occasional bls spectrum with non standard length
lengths = np.array(map(lambda x: len(x), blsArray))
typicalLength = int(np.median(lengths))
idx = lengths == typicalLength
epic = np.array(epic)[idx]
blsArray = np.array(blsArray)[idx]
obj = np.column_stack([epic, blsArray])
obj2 = join(mags, 0, None, obj, 0, None, dtype=object)
nPad += 1
# print obj2.shape
magCol = 3
idx = np.argsort(obj2[:, magCol])
obj2 = obj2[idx]
mag = obj2[:, magCol]
blsArray = np.vstack(obj2[:, -1])
print blsArray.shape
# return obj2
mp.clf()
mp.imshow(blsArray, interpolation="nearest", origin="bottom",\
aspect="auto", cmap=mp.cm.YlGnBu_r)
mp.colorbar()
return blsArray
def skyLinePlot(clipList):
"""A plot of which cadences contribute to the most transits
Based on similar plot created by Jessie Christiansen for the
SOC pipeline.
Inputs:
-----------
clipList
(list) list of filenames of clips to process
"""
epic, vals = gather.gatherFunction(clipList, getPeriodEpochDuration)
clip = dpc.loadClipboard(clipList[0])
clip = pl.serveTask(clip)
time = clip['serve.time']
flags = clip['detrend.flags']
period = np.array(map(lambda x: x[0], vals))
epoch = np.array(map(lambda x: x[1], vals))
duration_days = np.array(map(lambda x: x[2], vals)) / 24.
isCand = np.array(map(lambda x: x[3], vals))
skyLine = time * 0
candSkyLine = time * 0
for i in range(len(period)):
idx = kplrfits.markTransitCadences(time, period[i], epoch[i], \
duration_days[i], flags=flags)
skyLine[idx] += 1
if isCand[i]:
candSkyLine[idx] += 1
mp.clf()
mp.step(time[~flags], skyLine[~flags], 'b-', lw=2, \
label="All targets")
mp.step(time[~flags], candSkyLine[~flags], 'r-', lw=2, \
label="Candidates")
mp.xlabel("Time (BKJD)")
mp.ylabel("Number of Transits on Cadence")
return mp.gcf()
def wedgePlot(clipList):
"""Create a wedge plot by plotting period and epoch of all events.
Based on similar diagnostic plot used by SOC pipeline.
Inputs:
-----------
clipList
(list) list of filenames of clips to process
Returns:
-----------
A figure handle for the plot created.
Notes:
----------
Each target is represented by a semi-transparent grey dot
indicating the period and epoch of that target. Those targets
with the value of disposition.isCandidate are marked in red.
"""
epic, vals = gather.gatherFunction(clipList, getPeriodEpochDuration)
period = np.array(map(lambda x: x[0], vals))
epoch = np.array(map(lambda x: x[1], vals))
isCand = np.array(map(lambda x: x[3], vals))
mp.clf()
mp.plot(period, epoch, 'ko', alpha=.4, label="All Targets")
mp.plot(period[isCand], epoch[isCand], 'ro', \
ms=10, label="Candidates")
mp.xlabel("Period (days)")
mp.ylabel("Epoch (BKJD)")
mp.legend(loc=0)
return mp.gcf()
