def getNormedDays(series, period=False, method='Arser', cheap=False):
if series.index.dtype == numpy.dtype('int64'):
series = pandas.Series(array(series), index=getTimePoints(series))
#series = rollingMeanScale(series, 24, plotAxis=axs[0,1])
arser = Arser(list(series.index), series)
if period:
stats = arser.evaluate(T_start =period, T_end=period)
period, phase = stats['period'][0], stats['phase'][0]
else:
if method=='Arser':
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
if int(period)==24:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
else:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
if not cheap:
series = rollingMeanScale(series, period)
else:
series = rollingMeanScale(series, period, gamma=.001)
normedDays, phase = amplitudeAdjust(series, period, plotAxis=False)
return normedDays, period, phase
def plotSeries2(series, method='Arser', period=False, figsize=(12,9)):
fig, axs = subplots(1,4, figsize=figsize)
if series.index.dtype == numpy.dtype('int64'):
series = pandas.Series(array(series), index=getTimePoints(series))
#series = rollingMeanScale(series, 24, plotAxis=axs[0,1])
arser = Arser(list(series.index), series)
if period:
stats = arser.evaluate(T_start =period, T_end=period)
period, phase = stats['period'][0], stats['phase'][0]
else:
if method=='Arser':
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
if int(period)==24:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
else:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
plotLucData(series, ax=axs[0])
series = rollingMeanScale(series, period, plotAxis=axs[1])
normedDays = amplitudeAdjust(series, phase, period, plotAxis=axs[2])
#axs[1,0].set_ylabel('Normalized Luminescence')
signal = getCharacteristicSignal(normedDays, phase,
period, plotAxis=axs[3])
for ax in axs[:3]: ax.set_xlabel('Hours in LL')
axs[3].set_xlabel('Hours Past Peak Expression')
for ax in axs[1:]: ax.set_yticks([])
axs[0].set_xbound(5,105)
axs[1].set_xbound(5,105)
axs[2].set_xbound(5,105)
fig.subplots_adjust(wspace=.05, top=.9, bottom=.2)
return signal, normedDays, period, phase
def plotSeries(series, method='Arser', period=False, figsize=(12,9)):
fig, axs = subplots(2,2, figsize=figsize)
if series.index.dtype == numpy.dtype('int64'):
series = pandas.Series(array(series), index=getTimePoints(series))
#series = rollingMeanScale(series, 24, plotAxis=axs[0,1])
arser = Arser(list(series.index), series)
if period:
stats = arser.evaluate(T_start =period, T_end=period)
period, phase = stats['period'][0], stats['phase'][0]
else:
if method=='Arser':
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
if int(period)==24:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
else:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
plotLucData(series, ax=axs[0,0])
series = rollingMeanScale(series, period, plotAxis=axs[0,1])
normedDays,phase = amplitudeAdjust(series, period, plotAxis=axs[1,0])
axs[1,0].set_ylabel('Normalized Luminescence')
signal = getCharacteristicSignal(normedDays, phase,
period, plotAxis=axs[1,1])
return signal, normedDays, period, phase
def getNormedDays(series, period=False, method='Arser', cheap=False):
if series.index.dtype == numpy.dtype('int64'):
series = pandas.Series(array(series), index=getTimePoints(series))
#series = rollingMeanScale(series, 24, plotAxis=axs[0,1])
arser = Arser(list(series.index), series)
if period:
stats = arser.evaluate(T_start=period, T_end=period)
period, phase = stats['period'][0], stats['phase'][0]
else:
if method == 'Arser':
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
if int(period) == 24:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
else:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
if not cheap:
series = rollingMeanScale(series, period)
else:
series = rollingMeanScale(series, period, gamma=.001)
normedDays, phase = amplitudeAdjust(series, period, plotAxis=False)
return normedDays, period, phase
def plotSeries(series, method='Arser', period=False, figsize=(12, 9)):
fig, axs = subplots(2, 2, figsize=figsize)
if series.index.dtype == numpy.dtype('int64'):
series = pandas.Series(array(series), index=getTimePoints(series))
#series = rollingMeanScale(series, 24, plotAxis=axs[0,1])
arser = Arser(list(series.index), series)
if period:
stats = arser.evaluate(T_start=period, T_end=period)
period, phase = stats['period'][0], stats['phase'][0]
else:
if method == 'Arser':
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
if int(period) == 24:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
else:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
plotLucData(series, ax=axs[0, 0])
series = rollingMeanScale(series, period, plotAxis=axs[0, 1])
normedDays, phase = amplitudeAdjust(series, period, plotAxis=axs[1, 0])
axs[1, 0].set_ylabel('Normalized Luminescence')
signal = getCharacteristicSignal(normedDays,
phase,
period,
plotAxis=axs[1, 1])
return signal, normedDays, period, phase
def plotSeries2(series, method='Arser', period=False, figsize=(12, 9)):
fig, axs = subplots(1, 4, figsize=figsize)
if series.index.dtype == numpy.dtype('int64'):
series = pandas.Series(array(series), index=getTimePoints(series))
#series = rollingMeanScale(series, 24, plotAxis=axs[0,1])
arser = Arser(list(series.index), series)
if period:
stats = arser.evaluate(T_start=period, T_end=period)
period, phase = stats['period'][0], stats['phase'][0]
else:
if method == 'Arser':
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
if int(period) == 24:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
else:
stats = arser.evaluateNew()
period, phase = stats['period'], stats['phase']
plotLucData(series, ax=axs[0])
series = rollingMeanScale(series, period, plotAxis=axs[1])
normedDays = amplitudeAdjust(series, phase, period, plotAxis=axs[2])
#axs[1,0].set_ylabel('Normalized Luminescence')
signal = getCharacteristicSignal(normedDays,
phase,
period,
plotAxis=axs[3])
for ax in axs[:3]:
ax.set_xlabel('Hours in LL')
axs[3].set_xlabel('Hours Past Peak Expression')
for ax in axs[1:]:
ax.set_yticks([])
axs[0].set_xbound(5, 105)
axs[1].set_xbound(5, 105)
axs[2].set_xbound(5, 105)
fig.subplots_adjust(wspace=.05, top=.9, bottom=.2)
return signal, normedDays, period, phase