Here are the examples of the python api numpy.average taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : 06.py
Copyright MIT License
Author : swharden
Copyright MIT License
Author : swharden
def yyyyzeSweep(abf,plotToo=True,color=None,label=None):
Y=abf.sweepYsmartbase()[abf.pointsPerSec*.5:]
AV,SD=np.average(Y),np.std(Y)
dev=5 # number of stdevs from the avg to set the range
R1,R2=[(AV-SD)*dev,(AV+SD)*dev]
nBins=1000
hist,bins=np.histogram(Y,bins=nBins,range=[R1,R2],density=True)
histSmooth=abf.convolve(hist,cm.kernel_gaussian(nBins/5))
peakI=np.where(histSmooth==max(histSmooth))[0][0]
peakX=bins[peakI]
hist,histSmooth=hist/max(histSmooth),histSmooth/max(histSmooth) # normalize height to 1
if plotToo:
plt.plot(bins[1:],hist,'.',color=color,alpha=.2,ms=10)
plt.plot(bins[1:],histSmooth,'-',color=color,lw=5,alpha=.5,label=label)
return
3
View Complete Implementation : 05.py
Copyright MIT License
Author : swharden
Copyright MIT License
Author : swharden
def yyyyzeSweep(abf,plotToo=True,color=None,label=None):
Y=abf.sweepYsmartbase()[abf.pointsPerSec*.5:]
AV,SD=np.average(Y),np.std(Y)
dev=5 # number of stdevs from the avg to set the range
R1,R2=[(AV-SD)*dev,(AV+SD)*dev]
nBins=1000
hist,bins=np.histogram(Y,bins=nBins,range=[R1,R2],density=True)
histSmooth=abf.convolve(hist,cm.kernel_gaussian(nBins/5))
if plotToo:
plt.plot(bins[1:],hist,'.',color=color,alpha=.2,ms=10)
plt.plot(bins[1:],histSmooth,'-',color=color,lw=5,alpha=.5,label=label)
return
3
View Complete Implementation : 03b.py
Copyright MIT License
Author : swharden
Copyright MIT License
Author : swharden
def yyyyzeSweep(abf,plotColor=None):
Y=abf.sweepYsmartbase()
Y=Y[abf.pointsPerSec*.5:]
# create a 1 Kbin histogram with bins centered around 3x the SD from the mean
AV,SD=np.average(Y),np.std(Y)
B1,B2=AV-SD*3,AV+SD*3
nBins=1000
hist, bin_edges = np.histogram(Y, density=True, bins=nBins, range=(B1,B2))
histSmooth=np.convolve(hist,kernel_gaussian(nBins/2),mode='same')
if plotColor:
plt.plot(bin_edges[:-1],histSmooth,'-',alpha=.3,color=plotColor,lw=2)
return
3
View Complete Implementation : 2016-12-17 04 simplify.py
Copyright MIT License
Author : swharden
Copyright MIT License
Author : swharden
def phasicTonic(self,m1=None,m2=None):
m1=0 if m1 is None else m1*self.pointsPerSec
m2=len(abf.sweepY) if m2 is None else m2*self.pointsPerSec
m1,m2=int(m1),int(m2)
Y=self.sweepY[m1:m2]
Y=Y-np.average(Y)
return [np.average(Y),np.median(Y),np.var(Y),np.std(Y)]
3
View Complete Implementation : statistics.py
Copyright MIT License
Author : justthetips
Copyright MIT License
Author : justthetips
def sharpe_ratio(returns, rf):
"""
calculate the sharpe ratio
:param returns: the returns
:param rf: the risk free rates
:return: the sharpe ratio
"""
return (np.average(returns - rf)) / vol(returns)
3
View Complete Implementation : statistics.py
Copyright MIT License
Author : justthetips
Copyright MIT License
Author : justthetips
def treynor_ratio(returns, market, rf):
"""
calculate the treynor ratio
:param returns: returns
:param market: market returns
:param rf: risk free returns
:return: the treynor ratio
"""
return (np.average(returns - rf)) / beta(returns, market)
3
View Complete Implementation : statistics.py
Copyright MIT License
Author : justthetips
Copyright MIT License
Author : justthetips
def sterling_ratio(returns, rf, periods):
"""
calculare the sterling ratio
:param returns: the returns
:param rf: the risk free rate
:param periods: the number of periods
:return: the sterling ratio
"""
return (np.average(returns - rf)) / average_dd(returns, periods)
3
View Complete Implementation : statistics.py
Copyright MIT License
Author : justthetips
Copyright MIT License
Author : justthetips
def burke_ratio(returns, rf, periods):
"""
calculate the burke ratio
:param returns: the returns
:param rf: the risk free rate
:param periods: the number of periods
:return: the burke ratio
"""
return (np.average(returns - rf)) / math.sqrt(average_dd_squared(returns, periods))
3
View Complete Implementation : statistics.py
Copyright MIT License
Author : justthetips
Copyright MIT License
Author : justthetips
def kappa_three_ratio(returns, rf, target=0):
"""
calculate the kappa three ratio
:param returns: the returns
:param rf: the risk free rate
:param target: the return target
:return: the ktr
"""
return (np.average(returns - rf)) / math.pow(lpm(returns, target, 3), float(1 / 3))
3
View Complete Implementation : statistics.py
Copyright MIT License
Author : justthetips
Copyright MIT License
Author : justthetips
def calmar_ratio(returns, rf):
"""
calculate the calmar ratio
:param returns: the returns
:param rf: the risk free rate
:return: the calmar ratio
"""
return (np.average(returns - rf)) / max_dd(returns)