def plot_variances_subplot(chemnames, logprior):
times, bestfit, var = variances(chemnames, logprior)
nallplots = len(chemnames)
# 9 at a time
nfigs = nallplots / 9 # integer division -- no fractional part
for figno in range(1, nfigs + 1):
Plotting.figure()
for i in range(0, 9):
Plotting.subplot(3, 3, i + 1)
chemind = i + (figno - 1) * 9
Plotting.plot(times, bestfit[chemnames[chemind]])
Plotting.hold(True)
Plotting.plot(
times, bestfit[chemnames[chemind]] +
scipy.sqrt(var[chemnames[chemind]]), 'r-')
Plotting.plot(
times, bestfit[chemnames[chemind]] -
scipy.sqrt(var[chemnames[chemind]]), 'r-')
yt = Plotting.yticks()
Plotting.axis([0, 100.0, yt[0], yt[-1]])
Plotting.title(chemnames[chemind])
Plotting.xlabel('time')
Plotting.ylabel('arb. units')
xt = Plotting.xticks()
Plotting.xticks([xt[0], xt[-1]])
Plotting.savefig('./figs/variance_wt_' + i.__str__() + '.ps')
Plotting.show()
def plot_variances_subplot(chemnames,logprior) :
times, bestfit, var = variances(chemnames,logprior)
nallplots = len(chemnames)
# 9 at a time
nfigs = nallplots/9 # integer division -- no fractional part
for figno in range(1,nfigs+1) :
Plotting.figure()
for i in range(0,9) :
Plotting.subplot(3,3,i+1)
chemind = i+(figno-1)*9
Plotting.plot(times,bestfit[chemnames[chemind]])
Plotting.hold(True)
Plotting.plot(times,bestfit[chemnames[chemind]]
+ scipy.sqrt(var[chemnames[chemind]]),'r-')
Plotting.plot(times,bestfit[chemnames[chemind]]
- scipy.sqrt(var[chemnames[chemind]]),'r-')
yt = Plotting.yticks()
Plotting.axis([0,100.0,yt[0],yt[-1]])
Plotting.title(chemnames[chemind])
Plotting.xlabel('time')
Plotting.ylabel('arb. units')
xt = Plotting.xticks()
Plotting.xticks([xt[0],xt[-1]])
Plotting.savefig('./figs/variance_wt_'+i.__str__()+'.ps')
Plotting.show()
def plot_variance_newpoint(chemnames,sensvect_design,logprior=1.0e20,
return_data = True) :
"""
chemnames: list of chemical names
sensvect_design: a sensivity vector of a quantity that is
measurable
This will plot the old and new variances of the chemicals in
chemnames, given a new measurement that has sensitivity vector
sensvect_design
"""
times,bestfit,var = variances(chemnames,logprior)
times,varchange = variance_change(chemnames,sensvect_design,logprior)
for key in bestfit.keys() :
Plotting.figure()
Plotting.plot(times,bestfit[key])
Plotting.hold(True)
Plotting.plot(times,bestfit[key] + scipy.sqrt(var[key]),'r-')
Plotting.plot(times,bestfit[key] - scipy.sqrt(var[key]),'r-')
Plotting.plot(times,bestfit[key] + scipy.sqrt(var[key]+varchange[key]),'k--')
Plotting.plot(times,bestfit[key] - scipy.sqrt(var[key]+varchange[key]),'k--')
Plotting.title(key,fontsize=14)
Plotting.xlabel('time')
Plotting.ylabel('arb. units')
Plotting.axis([0.0,40.0,-.01,1.2e4])
Plotting.show()
if return_data :
newvar = {}
for ky in var.keys() :
newvar[ky] = var[key] + varchange[key]
return times,bestfit,newvar
def plot_variance_newweights(weights,
chemnames,
sensarray_design,
logprior=1.0e20,
scale=1.0,
return_data=True):
"""
weights : a proposed set of weights for each of the row vectors in
sensarray_design
chemnames : a list of chemicals for which we will plot the variance
logprior : as before
This will plot the old and new variances on chemnames, similar to
above.
NOTE: the weights that are passed in do not necessarily have to sum to
one. e.g. if the weights are normalized such that max(weights) = 1, then
by scaling all the weights by 1/sigma, you are then assuming that
the most accurate measurement has an error of size sigma. sigma for
example could be 20% of the maximum value of a trajectory.
"""
times, bestfit, var = variances(chemnames, logprior)
times, varchange = var_change_weighted(weights, chemnames,
sensarray_design, logprior)
for key in bestfit.keys():
Plotting.figure()
Plotting.plot(times, scale * bestfit[key])
Plotting.hold(True)
Plotting.plot(times,
scale * bestfit[key] + scale * scipy.sqrt(var[key]),
'r-')
Plotting.plot(times,
scale * bestfit[key] - scale * scipy.sqrt(var[key]),
'r-')
Plotting.plot(
times, scale * bestfit[key] +
scale * scipy.sqrt(var[key] + varchange[key]), 'k--')
Plotting.plot(
times, scale * bestfit[key] -
scale * scipy.sqrt(var[key] + varchange[key]), 'k--')
Plotting.title(key, fontsize=14)
Plotting.xlabel('time')
Plotting.ylabel('arb. units')
Plotting.axis([0.0, 40.0, -.01, 1.2e4])
Plotting.show()
if return_data:
newvar = {}
for ky in var.keys():
newvar[ky] = var[key] + varchange[key]
return times, bestfit, newvar
def plot_variance_newpoint(chemnames,
sensvect_design,
logprior=1.0e20,
return_data=True):
"""
chemnames: list of chemical names
sensvect_design: a sensivity vector of a quantity that is
measurable
This will plot the old and new variances of the chemicals in
chemnames, given a new measurement that has sensitivity vector
sensvect_design
"""
times, bestfit, var = variances(chemnames, logprior)
times, varchange = variance_change(chemnames, sensvect_design, logprior)
for key in bestfit.keys():
Plotting.figure()
Plotting.plot(times, bestfit[key])
Plotting.hold(True)
Plotting.plot(times, bestfit[key] + scipy.sqrt(var[key]), 'r-')
Plotting.plot(times, bestfit[key] - scipy.sqrt(var[key]), 'r-')
Plotting.plot(times,
bestfit[key] + scipy.sqrt(var[key] + varchange[key]),
'k--')
Plotting.plot(times,
bestfit[key] - scipy.sqrt(var[key] + varchange[key]),
'k--')
Plotting.title(key, fontsize=14)
Plotting.xlabel('time')
Plotting.ylabel('arb. units')
Plotting.axis([0.0, 40.0, -.01, 1.2e4])
Plotting.show()
if return_data:
newvar = {}
for ky in var.keys():
newvar[ky] = var[key] + varchange[key]
return times, bestfit, newvar
def plot_variance_newweights(weights,chemnames,sensarray_design,logprior=1.0e20,scale=1.0,return_data = True) :
"""
weights : a proposed set of weights for each of the row vectors in
sensarray_design
chemnames : a list of chemicals for which we will plot the variance
logprior : as before
This will plot the old and new variances on chemnames, similar to
above.
NOTE: the weights that are passed in do not necessarily have to sum to
one. e.g. if the weights are normalized such that max(weights) = 1, then
by scaling all the weights by 1/sigma, you are then assuming that
the most accurate measurement has an error of size sigma. sigma for
example could be 20% of the maximum value of a trajectory.
"""
times,bestfit,var = variances(chemnames,logprior)
times,varchange = var_change_weighted(weights,chemnames,sensarray_design,logprior)
for key in bestfit.keys() :
Plotting.figure()
Plotting.plot(times,scale*bestfit[key])
Plotting.hold(True)
Plotting.plot(times,scale*bestfit[key] + scale*scipy.sqrt(var[key]),'r-')
Plotting.plot(times,scale*bestfit[key] - scale*scipy.sqrt(var[key]),'r-')
Plotting.plot(times,scale*bestfit[key] + scale*scipy.sqrt(var[key]+varchange[key]),'k--')
Plotting.plot(times,scale*bestfit[key] - scale*scipy.sqrt(var[key]+varchange[key]),'k--')
Plotting.title(key,fontsize=14)
Plotting.xlabel('time')
Plotting.ylabel('arb. units')
Plotting.axis([0.0,40.0,-.01,1.2e4])
Plotting.show()
if return_data :
newvar = {}
for ky in var.keys() :
newvar[ky] = var[key] + varchange[key]
return times,bestfit,newvar
