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 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 plotSeries(series):
fig, axs = subplots(2,2, figsize=(12,9))
arser = Arser(list(series.index), series)
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
plotLucData(series, ax=axs[0,0])
series = rollingMeanScale(series, period, plotAxis=axs[0,1])
normedDays = amplitudeAdjust(series, phase, period, plotAxis=axs[1,0])
signal = getCharacteristicSignal(normedDays, phase, period, plotAxis=axs[1,1])
def plotSeriesOld(series):
times = arange(5, 106, 2.5)
numDays = 4
colors = ['b', 'g', 'r', 'c']
fig, axs = subplots(1, 2, figsize=(12, 4))
arser = Arser(list(series.index), series)
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
dayBreaks = [phase + period * day for day in range(numDays + 1)]
timePoints = []
for i in range(numDays):
values = map(
float,
list(series.index[(series.index > (dayBreaks[i] - .5))
& (series.index < (dayBreaks[i + 1] + .5))]))
timePoints.append(values)
axs[0].plot(times, array(series), '--', label='normal', color='black')
axs[0].plot(times,
array(series),
'o',
label='normal',
color='black',
alpha=.3)
axs[0].set_title('Raw Data')
normedDays = [
cycle_adjust(array(series.ix[timePoints[i]])) for i in range(numDays)
]
t0 = array([[t] for day in timePoints for t in day])
t0 = (t0 - phase) % period
t0 = [[t] for t in array([t0 - period, t0, t0 + period]).flatten()]
normedSeries = [e for day in normedDays for e in day]
normedSeries = array([normedSeries, normedSeries, normedSeries]).flatten()
for i in range(numDays):
axs[1].plot(array(timePoints[i] - phase) % period,
normedDays[i],
'o',
label=str(i),
color=colors[i])
axs[1].set_title('Raw Data Phase Shifted')
axs[1].legend(loc='best')
svr_rbf = SVR(kernel='rbf', C=1e4, gamma=.03, epsilon=.01, scale_C=True)
y_rbf = svr_rbf.fit(t0, list(normedSeries))
t1 = [[t] for t in arange(0, period, period / 100.)]
axs[1].plot(t1, y_rbf.predict(t1))
axs[1].set_xbound(0, period)
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 plotSeriesOld(series):
times = arange(5,106,2.5)
numDays=4
colors = ['b','g','r','c']
fig, axs = subplots(1,2,figsize=(12,4))
arser = Arser(list(series.index), series)
stats = arser.evaluate()
period, phase = stats['period'][0], stats['phase'][0]
dayBreaks = [phase+period*day for day in range(numDays+1)]
timePoints = []
for i in range(numDays):
values = map(float, list(series.index[ (series.index > (dayBreaks[i]-.5))
& (series.index < (dayBreaks[i+1]+.5))]))
timePoints.append(values)
axs[0].plot(times, array(series), '--',label='normal',color='black')
axs[0].plot(times, array(series), 'o',label='normal',color='black', alpha=.3)
axs[0].set_title('Raw Data')
normedDays = [cycle_adjust(array(series.ix[timePoints[i]])) for i in range(numDays)]
t0 = array([[t] for day in timePoints for t in day])
t0 = (t0 - phase) % period
t0 = [[t] for t in array([t0-period,t0,t0+period]).flatten()]
normedSeries = [e for day in normedDays for e in day]
normedSeries = array([normedSeries,normedSeries,normedSeries]).flatten()
for i in range(numDays):
axs[1].plot(array(timePoints[i]-phase) % period, normedDays[i], 'o',
label=str(i),color=colors[i])
axs[1].set_title('Raw Data Phase Shifted')
axs[1].legend(loc='best')
svr_rbf = SVR(kernel='rbf', C=1e4, gamma=.03, epsilon=.01)
y_rbf = svr_rbf.fit(t0, list(normedSeries))
t1 = [[t] for t in arange(0,period, period/100.)]
axs[1].plot(t1, y_rbf.predict(t1));
axs[1].set_xbound(0,period)
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