def getPredRADs(N, S, Nmax):
PRED = []
# Predicted geometric series
predRAD = predRADs.get_GeomSeries(N, S, False) # False mean no zeros allowed
PRED.append(predRAD)
# Predicted log-series
logSeries = mete.get_mete_rad(S, N)
predRAD = logSeries[0]
PRED.append(predRAD)
# Predicted PLN
predRAD = pln.get_rad_from_obs(RAD, 'pln')
PRED.append(predRAD)
sample_size = 10
# Predicted from compositions (basically geometric series)
predRAD = getPred(N, S, maxn, 'compositions', sample_size)
PRED.append(predRAD)
# Predicted from Fraction 1: Power Fraction
predRAD = getPred(N, S, maxn, 'power fraction', sample_size)
PRED.append(predRAD)
# Predicted from Fraction 2: Random non-preemption
predRAD = getPred(N, S, maxn, 'random fraction non-preemption', sample_size)
PRED.append(predRAD)
# Predicted from Fraction 3: Random preemption
predRAD = getPred(N, S, maxn, 'random fraction', sample_size)
PRED.append(predRAD)
return PRED
def get_MLEs(RAC, sample_size):
sample = []
while len(sample) < sample_size:
RAC = pln.get_rad_from_obs(RAC, 'pln')
sample.append(np.var(RAC, ddof=1))
#print 'MLEs:', len(sample)
#plt.plot(sample)
#plt.show()
return sample
def get_LogNormMLE(RAC):
varlst = [1, 2, 3]
VarOfVars = 1
while VarOfVars > 0.01: # while the variance of varlst is greater than zero
RAC = pln.get_rad_from_obs(RAC, 'pln') #get RAC from pln
varlst.append(np.var(
RAC, ddof=1)) # add the variance of the end RAC to the list
varlst.pop(0) # remove the first number in the varlst
print varlst
VarOfVars = np.var(varlst, ddof=1)
break
return RAC
def plot_RADs_canonical(N, S):
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
# Predicted geometric series
print 'generating geometric series'
t0 = time.time()
predRAD = get_GeomSeries(N, S, False) # False mean no zeros allowed
t = time.time() - t0
print 'time for geometric series:',t
ranks = range(1, S+1)
plt.plot(ranks, predRAD, lw = 1, c='m')
# Predicted log-series
print 'generating log-series'
t0 = time.time()
logSeries = mete.get_mete_rad(S, N)
t = time.time() - t0
print 'time for log-series:',t
predRAD = logSeries[0]
ranks = range(1, S+1)
plt.plot(ranks, predRAD, lw = 1, c='c')
# Predicted PLN
print 'generating Poisson log-normal'
t0 = time.time()
predRAD = pln.get_rad_from_obs(predRAD, 'pln')
t = time.time() - t0
print 'time for log-normal:',t
ranks = range(1, len(predRAD)+1)
plt.plot(ranks, predRAD, lw = 1, c='gray')
plt.yscale('log')
plt.savefig('/Users/lisalocey/Desktop/RareBio/figs/GlobalRADs_N='+str(int(N))+'_S='+str(int(S))+'.png',dpi=600)
plt.show()
return
RAC.sort()
RAC.reverse()
sample.append(RAC)
return sample
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
S = 500
RAD = np.random.logseries(0.99, S)
RAD.tolist()
RAD = pln.get_rad_from_obs(RAD, 'pln')
print sum(RAD), len(RAD)
RADs = SimLogNorm(sum(RAD), len(RAD), 10000)
print sum(RADs[0]), len(RADs[0])
x = []
y = []
for RAD in RADs:
seed()
y.extend(np.log(RAD))
x.extend(range(len(RAD)))
plt.hexbin(x, y, mincnt=1, bins='log', cmap=plt.cm.jet)
plt.plot(np.log(RAD), color='k', lw=4)
plt.xlim(0, S)
