def project_nmx(kt, ax, bx, ageCutoff, kt_resid=None, bx_resid=None, numAgeWidths=LCFIT_DEFAULT_NO_AGEWIDTHS): """ Convert 2-d array of kt forward innovations into 3-d array of nmxs To get a particular age profile after running the function, need to pick a run and a timestep, eg self.nmx_projected[10, 12, :] gives 10th run, t=12, all the age spec rates. 'Kx' and 'Bx' are for coherent. """ # Make sure kt_resid and bx_resid are either both None or are the # right dimensions for the residual (Coherent) process if kt_resid is None and bx_resid is None: # Normal case without coherent isCoherent = False kt_resid = N.zeros_like(kt) bx_resid = N.zeros_like(bx) pass elif (kt_resid.shape == kt.shape) and (bx_resid.shape == bx.shape): # Coherent isCoherent = True pass else: raise "Weird mix of kt's and bx's. \n\nkt: %s. \n\nkt_resid: %s \n\nbx: %s \n\nbx_resid: %s\n" % \ (kt, kt_resid, bx, bx_resid) # Fill 3d matrix of nmx if get 2-d matrix of kt (corresponding to run by year if len(kt.shape) == 2: numRuns, stepsForward = kt.shape[0], kt.shape[1] nmx_projected = N.zeros([numRuns, stepsForward, numAgeWidths], N.float64) # 3-d matrix to fill in for runIndex in range(0,numRuns): for stepIndex in range(0,stepsForward): nmxTmp = N.zeros((1,numAgeWidths), N.float64).ravel() nmxTmp[0:len(bx)] = N.exp(kt[runIndex,stepIndex]*bx + kt_resid[runIndex,stepIndex]*bx_resid + ax) nmx_projected[runIndex, stepIndex, :] = LcExtension.extendMx(nmxTmp, ageCutoff=ageCutoff) pass pass return nmx_projected # Fill 2d matrix of nmx if have 1-d matrix of kt elif len(kt.shape) == 1: # nmx_projected=N.zeros((kt.shape[0], numAgeWidths), N.float64) for yearIndex in range(0, kt.shape[0]): nmxTmp = N.zeros((1,numAgeWidths), N.float64).ravel() nmxTmp[0:len(bx)] = N.exp(kt[yearIndex]*bx + kt_resid[yearIndex]*bx_resid + ax) nmxTmpExtended = LcExtension.extendMx(nmxTmp, ageCutoff=ageCutoff) nmx_projected[yearIndex, :] = nmxTmpExtended return nmx_projected # Fill 1d matrix of mx for single kt number elif len(kt.shape) == 0 and kt.size == 1: nmx_projected=N.zeros(numAgeWidths, N.float64) nmxTmp = N.zeros(numAgeWidths, N.float64) nmxTmp[0:len(bx)] = N.exp(kt*bx + kt_resid*bx_resid + ax) nmxTmpExtended = LcExtension.extendMx(nmxTmp, ageCutoff=ageCutoff) nmx_projected[:] = nmxTmpExtended return nmx_projected else: raise LcException, "Unexpected kt.shape: %r. kt: %r. kt.size: %r" % ([kt.shape],kt, kt.size)
def _dealWithRates(self, rates, **kwargs):
"""Parses the rates. Sets up datastructures for missing data."""
# Save code version info with instance. Here because I think
# it is screwing up based on # some weird scoping thing in MF
self.LcObjectINFO = LcObjectINFO
assert type(rates) == types.StringType, \
AssertionError("Rates should be as string for input. Instead: %s." % type(rates))
# Get rates from text, extend ...
self.rates_text = rates
self.nmx = LcUtil.parseRates(self.rates_text)
# ... handle missing data: if there are nans after parsing,
# determine which years (rows) they are in ...
self.nmxNans = N.isnan(self.nmx)
self.goodRowsBool = (~self.nmxNans).any(axis=1)
self.goodRowsNum = N.where(self.goodRowsBool)[0].tolist() # weird numpy.where() return
self.yearIndices = N.arange(1, self.nmx.shape[0]+1, dtype=N.int0) # XXX 1 indexed for missing data formulas
# ... extend everything ...
self.nmxExtended = LcUtil.emptyLikeWithNans(self.nmx)
tmp = LcExtension.extendMx(self.nmx[self.goodRowsNum,:], ageCutoff=self.ageCutoff)
assert self.nmxExtended[self.goodRowsNum,:].shape == tmp.shape, \
AssertionError("orig shape: %s. extended shape: %s" \
% (tmp.shape, self.nmxExtended[self.goodRowsNum,:].shape))
self.nmxExtended[self.goodRowsNum,:] = tmp
assert (self.nmxExtended[self.goodRowsNum,:] > 0.0).all() and (N.isfinite(self.nmxExtended[self.goodRowsNum,:])).all(), \
AssertionError("%s" % self.nmxExtended)
# ... asserts, and hopefully fall of the end (only update object state).
self.nmxInfs = N.isinf(self.nmx)
assert not self.nmxInfs.any(), \
AssertionError("why are there infs in nmx???:\n %r" % N.round_(self.nmx, 2))
return None
def multiKt2e0(kt, ax, bx, lifeTableParams, numAgeWidths = LCFIT_DEFAULT_NO_AGEWIDTHS): e0s = [0] * len(kt) for i, k in enumerate(kt): nmxTmp = N.zeros((1,numAgeWidths), N.float64).ravel() nmxTmp[0:len(bx)] = N.exp(k * bx + ax) nmxTmp = LcExtension.extendMx(nmxTmp, ageCutoff=lifeTableParams['ageCutoff']) e0s[i] = lifeTable(nmxTmp, **lifeTableParams) del nmxTmp return e0s
def multiKt2e0(kt,
ax,
bx,
lifeTableParams,
numAgeWidths=LCFIT_DEFAULT_NO_AGEWIDTHS):
e0s = [0] * len(kt)
for i, k in enumerate(kt):
nmxTmp = N.zeros((1, numAgeWidths), N.float64).ravel()
nmxTmp[0:len(bx)] = N.exp(k * bx + ax)
nmxTmp = LcExtension.extendMx(nmxTmp,
ageCutoff=lifeTableParams['ageCutoff'])
e0s[i] = lifeTable(nmxTmp, **lifeTableParams)
del nmxTmp
return e0s
def lifeTable(nmxp,
ageCutoff=None,
extensionMethod=LCFIT_DEFAULT_EXTENSION_METHOD,
ltFuncType='ex',
beginFuncParam=0,
endFuncParam=0,
numAgeWidths=LCFIT_DEFAULT_NO_AGEWIDTHS,
gender='combined',
ax_calc_alg='graduated',
ageWidth=5,
qxMax=.7,
qx0_intercept=0.93,
qx0_slope=1.7):
'''
From a given nMx schedule, calc and return a functional or the
full LT. The columns for a full LT are nmx, nqx, lx, nLx, Ex.
extensionMethod=[LCFIT_DEFAULT_EXTENSION_METHOD]
ltFuncType=["ex", "lx", "lxPercent", "depRatio", "full"]
closeRate must be an age with "ex" and "lx"; a percentile with "lxPercent",
a pair of ages (young and old) with "depRatio".
XXX Full of magic numbers!
'''
#lcfitlogger.debug( 'gender: %s' % gender)
assert N.isfinite(nmxp).all(), AssertionError("%s" %
pprint.pformat(locals()))
assert len(nmxp.shape) == 1, AssertionError(
"Bad shape for nmxp: %s. Should be %s" % (str(nmxp.shape), '(x,)'))
if gender not in ('combined', 'male', 'female'):
raise LcException("Bad value for gender in lifeTable(): %s" % gender)
if N.isnan(nmxp).any() or N.isinf(nmxp).any():
raise "Bad mx: %s" % nmxp
# Copy nmx to avoid modifying a reference
nmx = nmxp.copy()
# set nmx's that creep in as inf to something big
nmx[N.isinf(nmx)] = LCFIT_INF_NMX_REPLACEMENT
assert N.isfinite(nmx).all(), AssertionError("%s" %
pprint.pformat(locals()))
# extend nmx out to appropriate age widths. Filling with stuff to
# make it break if they are not replaced in the Kannisto
# extension thing
neededSlots = numAgeWidths - nmx.shape[0]
if neededSlots > 0:
nmx.resize(numAgeWidths)
# Set up data structures
nqx = N.array([1.0] * numAgeWidths, N.float64)
lx = N.array([0.0] * numAgeWidths, N.float64)
nLx = N.array([0.0] * numAgeWidths, N.float64)
ndx = N.array([0.0] * numAgeWidths, N.float64)
Ex = N.array([0.0] * numAgeWidths, N.float64)
Tx = N.array([0.0] * numAgeWidths, N.float64)
ax = N.array([0.0] * numAgeWidths, N.float64)
# Extend bad data ages
if ageCutoff:
nmxOld = copy.copy(nmx)
nmx = LcExtension.extendMx(nmx,
extName=extensionMethod,
ageCutoff=ageCutoff)
assert N.isfinite(nmx).all() and (nmx>0).all(), \
AssertionError("%s" % pprint.pformat(locals()))
# Graduate and get important stuff
(ax, qx, dx, lx) = adjustAx(nmx, gender=gender)
#raise pprint.pformat((ax, qx, dx, lx))
# Everything -> nLx
if ax_calc_alg == 'LiNan': # Default
for i in range(len(nLx) - 1):
nLx[i] = (lx[i] - lx[i + 1]) / nmx[i]
pass
nLx[-1] = lx[-1] / nmx[-1]
pass
elif ax_calc_alg == 'graduated': # Not usually entered
nLx[0] = lx[0] - ax[0] * ndx[0]
nLx[1] = lx[1] - ax[1] * ndx[1]
nLx[2:-1] = (lx[2:-1] * 5) - (
ndx[2:-1] * ax[2:-1]
) # Magic numbers 5, 2.5: age width and multiplier
nLx[-1] = lx[-1] / nmx[-1]
nLx[N.where(abs(lx) < .0001)] = 0.0 # coarse
elif ax_calc_alg == 'empirical':
pass
else:
raise Exception('Unknown ax_calc_alg: %s' % ax_calc_ag)
# Do strange adjustment for lx where x >= 10, from lfexpt.m
# Set up datastructures ...
if False:
c = N.zeros_like(nLx)
nLLx = N.zeros_like(nLx)
llx = lx.copy()
qqx = N.zeros_like(nLx)
# ... compute adjustment "c" for each age ...
for age in range(3, len(nLLx) - 1):
if nLx[age] > 0.000001:
c[age] = (1/(48*nLx[age])) * \
(nLx[age-1]-nLx[age+1]) * \
(nmx[age+1] - nmx[age-1])
else:
c[age] = c[age - 1]
pass
pass
# ... recalculate lx with adj and mort rates ...
for age in range(3, len(nLLx) - 1):
llx[age + 1] = llx[age] * N.exp(-5 * (nmx[age] + c[age]))
# ... recalculate Lx and qx ...
for age in range(0, len(nLLx) - 1):
nLLx[age] = (llx[age] - llx[age + 1]) / nmx[age]
qqx[age] = (llx[age] - llx[age + 1]) / llx[age]
qqx[-1] = 1.0
nLLx[-1] = nLLx[-2]
# ... copy to lx and nLx, then Finis.
lx = llx.copy()
nLx = nLLx.copy()
pass
# nLx -> Tx
Tx = N.cumsum(nLx[::-1])[::-1] # Double reverse by indexing stride
# Tx + lx -> ex
Ex = Tx / lx
Ex[N.isnan(Ex)] = 0
Ex[-1] = 1 / nmx[-1]
# Sanity
if not (10 < Ex[0] < 100):
raise LcException("Unreasonable LifeExp: %s. %s" %
(Ex[0], pprint.pformat(locals())))
# Decide what to return. For the functionals, use the parameters
# to figure out what parts of the lifetable want.
if ltFuncType == 'ex':
xTmp = LCFIT_AGE_INDICES[beginFuncParam]
assert 0 <= xTmp <= max(LCFIT_AGES), "Bad age: %s" % beginFuncParam
return (float(Ex[xTmp]))
elif ltFuncType == 'lx':
xTmp = LCFIT_AGE_INDICES[beginFuncParam]
assert 0 <= xTmp <= max(LCFIT_AGES), "Bad age: %s" % beginFuncParam
return (float(lx[xTmp]))
elif ltFuncType == 'lxPercent':
lxTmp = beginFuncParam / 100.0
assert 0.0 < lxTmp <= 1.0, \
AssertionError("Bad percentile: %s." % beginFuncParam)
lxInterp = scipy.interpolate.interp1d(
lx[::-1], LCFIT_AGES[::-1]) # both x and lx reversed
return lxInterp(lxTmp).ravel()[0]
elif ltFuncType == 'depRatio':
xTmpYoung = LCFIT_AGE_INDICES[beginFuncParam]
xTmpOld = LCFIT_AGE_INDICES[endFuncParam]
assert 0 <= xTmpYoung < xTmpOld <= max(LCFIT_AGES), \
AssertionError ("Bad ages: Young: %s, Old: %s" % (beginFuncParam, endFuncParam))
ppyYoung = N.sum(nLx[0:xTmpYoung]) # Total person years
ppyWorking = N.sum(nLx[xTmpYoung:xTmpOld])
ppyOld = N.sum(nLx[xTmpOld:])
return ppyWorking / (ppyYoung + ppyOld)
elif ltFuncType == 'full':
return (S.array(zip(nmx, nqx, lx, nLx, Ex)))
else:
raise Exception, "unknown ltFuncType: %s" % str(locals())
#!/usr/bin/python
'''
This module provides the home for LcSinglePopObject, which takes data
in the form of text, runs an Lee-Carter infeence procedure on it, does
some forecasting, and makes some nice pictures.
This module also contains a number of module functions to support
this.
'''
## Imports ################################################
from LcConfig import * # Imports numpy and friends
from LcLog import lcfitlogger
from LcAnnotation import *
import LcExtension
LcExtension.setExtensionName(LCFIT_DEFAULT_EXTENSION_METHOD)
import LcUtil
from LcUtil import Diagnose as D
from LcUtil import listTypes as LT
sys.path.append('./TESTING') # For test data
## Module Variables ######
LcObjectINFO ='Single Population object' # Project/misc information
# Image constants
LC_IMAGE_NAME = 'lc-model.png' # Image with kt, ax, bx, e0u
FC_IMAGE_NAME = 'lc-forecast.png' # Image with forecasted stuff
LNMX_IMAGE_NAME = 'lc-lognmx.png' # Emprical log nmx at various ages
MORTP_IMAGE_NAME = 'lc-mortp.png' # Mortality profiles at begin, end, end proj
def lifeTable (nmxp, ageCutoff=None, extensionMethod=LCFIT_DEFAULT_EXTENSION_METHOD,
ltFuncType='ex', beginFuncParam=0, endFuncParam=0,
numAgeWidths = LCFIT_DEFAULT_NO_AGEWIDTHS,
gender='combined', ax_calc_alg='graduated',
ageWidth=5, qxMax=.7, qx0_intercept=0.93, qx0_slope=1.7):
'''
From a given nMx schedule, calc and return a functional or the
full LT. The columns for a full LT are nmx, nqx, lx, nLx, Ex.
extensionMethod=[LCFIT_DEFAULT_EXTENSION_METHOD]
ltFuncType=["ex", "lx", "lxPercent", "depRatio", "full"]
closeRate must be an age with "ex" and "lx"; a percentile with "lxPercent",
a pair of ages (young and old) with "depRatio".
XXX Full of magic numbers!
'''
#lcfitlogger.debug( 'gender: %s' % gender)
assert N.isfinite(nmxp).all(), AssertionError("%s" % pprint.pformat(locals()))
assert len(nmxp.shape) == 1, AssertionError(
"Bad shape for nmxp: %s. Should be %s" % (str(nmxp.shape), '(x,)'))
if gender not in ('combined', 'male', 'female'):
raise LcException("Bad value for gender in lifeTable(): %s" % gender)
if N.isnan(nmxp).any() or N.isinf(nmxp).any():
raise "Bad mx: %s" % nmxp
# Copy nmx to avoid modifying a reference
nmx = nmxp.copy()
# set nmx's that creep in as inf to something big
nmx[N.isinf(nmx)] = LCFIT_INF_NMX_REPLACEMENT
assert N.isfinite(nmx).all(), AssertionError("%s" % pprint.pformat(locals()))
# extend nmx out to appropriate age widths. Filling with stuff to
# make it break if they are not replaced in the Kannisto
# extension thing
neededSlots = numAgeWidths - nmx.shape[0]
if neededSlots > 0:
nmx.resize(numAgeWidths)
# Set up data structures
nqx = N.array([1.0] * numAgeWidths, N.float64)
lx = N.array([0.0] * numAgeWidths, N.float64)
nLx = N.array([0.0] * numAgeWidths, N.float64)
ndx = N.array([0.0] * numAgeWidths, N.float64)
Ex = N.array([0.0] * numAgeWidths, N.float64)
Tx = N.array([0.0] * numAgeWidths, N.float64)
ax = N.array([0.0] * numAgeWidths, N.float64)
# Extend bad data ages
if ageCutoff:
nmxOld = copy.copy(nmx)
nmx = LcExtension.extendMx(nmx, extName=extensionMethod, ageCutoff=ageCutoff)
assert N.isfinite(nmx).all() and (nmx>0).all(), \
AssertionError("%s" % pprint.pformat(locals()))
# Graduate and get important stuff
(ax, qx, dx, lx) = adjustAx(nmx, gender=gender)
#raise pprint.pformat((ax, qx, dx, lx))
# Everything -> nLx
if ax_calc_alg == 'LiNan': # Default
for i in range(len(nLx)-1):
nLx[i] = (lx[i] - lx[i+1])/nmx[i]
pass
nLx[-1] = lx[-1]/nmx[-1]
pass
elif ax_calc_alg == 'graduated': # Not usually entered
nLx[0] = lx[0] - ax[0] * ndx[0]
nLx[1] = lx[1] - ax[1] * ndx[1]
nLx[2:-1] = (lx[2:-1] * 5) - (ndx[2:-1] * ax[2:-1]) # Magic numbers 5, 2.5: age width and multiplier
nLx[-1] = lx[-1] / nmx[-1]
nLx[N.where(abs(lx)<.0001)] = 0.0 # coarse
elif ax_calc_alg == 'empirical':
pass
else:
raise Exception ('Unknown ax_calc_alg: %s' % ax_calc_ag)
# Do strange adjustment for lx where x >= 10, from lfexpt.m
# Set up datastructures ...
if False:
c = N.zeros_like(nLx)
nLLx = N.zeros_like(nLx)
llx = lx.copy()
qqx = N.zeros_like(nLx)
# ... compute adjustment "c" for each age ...
for age in range(3, len(nLLx)-1):
if nLx[age] > 0.000001:
c[age] = (1/(48*nLx[age])) * \
(nLx[age-1]-nLx[age+1]) * \
(nmx[age+1] - nmx[age-1])
else:
c[age] = c[age-1]
pass
pass
# ... recalculate lx with adj and mort rates ...
for age in range(3, len(nLLx)-1):
llx[age+1] = llx[age]*N.exp(-5*(nmx[age]+c[age]))
# ... recalculate Lx and qx ...
for age in range(0, len(nLLx)-1):
nLLx[age] = (llx[age] - llx[age+1]) / nmx[age]
qqx[age] = (llx[age] - llx[age+1]) / llx[age]
qqx[-1] = 1.0
nLLx[-1] = nLLx[-2]
# ... copy to lx and nLx, then Finis.
lx = llx.copy()
nLx = nLLx.copy()
pass
# nLx -> Tx
Tx = N.cumsum(nLx[::-1])[::-1] # Double reverse by indexing stride
# Tx + lx -> ex
Ex = Tx/lx
Ex[N.isnan(Ex)] = 0
Ex[-1] = 1/nmx[-1]
# Sanity
if not (10 < Ex[0] < 100):
raise LcException ("Unreasonable LifeExp: %s. %s" % (Ex[0], pprint.pformat(locals())))
# Decide what to return. For the functionals, use the parameters
# to figure out what parts of the lifetable want.
if ltFuncType == 'ex':
xTmp = LCFIT_AGE_INDICES[beginFuncParam]
assert 0 <= xTmp <= max(LCFIT_AGES), "Bad age: %s" % beginFuncParam
return (float(Ex[xTmp]))
elif ltFuncType == 'lx':
xTmp = LCFIT_AGE_INDICES[beginFuncParam]
assert 0 <= xTmp <= max(LCFIT_AGES), "Bad age: %s" % beginFuncParam
return (float(lx[xTmp]))
elif ltFuncType == 'lxPercent':
lxTmp = beginFuncParam / 100.0
assert 0.0 < lxTmp <= 1.0, \
AssertionError("Bad percentile: %s." % beginFuncParam)
lxInterp = scipy.interpolate.interp1d(lx[::-1], LCFIT_AGES[::-1]) # both x and lx reversed
return lxInterp(lxTmp).ravel()[0]
elif ltFuncType == 'depRatio':
xTmpYoung = LCFIT_AGE_INDICES[beginFuncParam]
xTmpOld = LCFIT_AGE_INDICES[endFuncParam]
assert 0 <= xTmpYoung < xTmpOld <= max(LCFIT_AGES), \
AssertionError ("Bad ages: Young: %s, Old: %s" % (beginFuncParam, endFuncParam))
ppyYoung = N.sum(nLx[0:xTmpYoung]) # Total person years
ppyWorking = N.sum(nLx[xTmpYoung:xTmpOld])
ppyOld = N.sum(nLx[xTmpOld:])
return ppyWorking / (ppyYoung + ppyOld)
elif ltFuncType == 'full': return (S.array(zip( nmx, nqx, lx, nLx, Ex)))
else: raise Exception, "unknown ltFuncType: %s" % str(locals())
def _dealWithRates(self, populations, mortRates, labels='', **kwargs):
""" Store the population and rate data. Create the combined rates matrix. """
# Save code version info with instance. Here because I think
# it is screwing up based on # some weird scoping thing in MF
self.LcObjectINFO = LcObjectINFO
# Age cutoff
self.ageCutoffIndex = LCFIT_AGE_INDICES[self.ageCutoff] + 1
# Population data
self.populationText = re.sub('\n', '\n', populations)
self.populationText = re.sub('\r', '', self.populationText).strip()
self.populationText = re.sub('\n+$', '', self.populationText)
self.populationText = re.sub('^\n+', '', self.populationText)
if LCFIT_EMPTY_ALL_RE.search(self.populationText):
self.useWeightedMx = False
self.populationTextList = []
self.populationList = []
else:
self.useWeightedMx = True
self.populationTextList = re.split('\n\n+', self.populationText)
self.populationList = [LcUtil.parseRates(pops) for pops in self.populationTextList]
# Mortality data
self.mortRatesText = re.sub('\n', '\n', mortRates)
self.mortRatesText = re.sub('\r', '', mortRates)
self.mortRatesText = re.sub('\n+$', '', self.mortRatesText)
self.mortRatesText = re.sub('^\n+', '', self.mortRatesText)
if LCFIT_EMPTY_ALL_RE.search(self.mortRatesText):
raise LcException("empty rates data")
self.mortRatesTextList = re.split('\n\n+', self.mortRatesText) #
self.mortRatesList = [LcUtil.parseRates(rates) for rates in self.mortRatesTextList]
# Go over each data matrix, check input: no weird numbers, same size.
shapeList = []
shapeShape = self.mortRatesList[0].shape
for i, data in enumerate(self.populationList + self.mortRatesList):
if data.shape != shapeShape:
raise LcException("Inconsistent rate matrix shapes. First matrix shape: %r, current matrix [%r] shape: %r\nData: %r" % \
(shapeShape, i, data.shape, data[0,:].tolist()))
shapeList.append(data.shape)
pass
if self.useWeightedMx:
if len(self.populationList) != len(self.mortRatesList):
raise LcException("Must have pop and mx of same length. Pop = %i, Mort = %i." % \
(len(self.populationList), len(self.mortRatesList)))
# CG and takes logs of each of the rates
self.mortRatesListLog = []
for i, mxMatrix in enumerate(self.mortRatesList):
self.mortRatesList[i] = LcExtension.extendMx(mxData=mxMatrix, ageCutoff=LCFIT_DEFAULT_AGE_CUTOFF)
self.mortRatesListLog.append(N.log(self.mortRatesList[i]))
# Labels
if labels == '':
self.labels = map(str,range(1, len(self.mortRatesListLog)+1))
else:
self.labels = re.split('\s+', labels.strip())
if len(self.labels) < len(self.mortRatesListLog):
labelExtraNumbers = range(len(self.labels)+1, len(self.mortRatesListLog)+1)
labelExtra = map(str, labelExtraNumbers)
self.labels = self.labels + labelExtra
elif len(self.labels) > len(self.mortRatesListLog):
self.labels = self.labels[0:(len(self.mortRatesListLog))]
# Years
self.yearIndices = N.arange(1, self.mortRatesList[0].shape[0]+1, dtype=N.int0)
years_end = self.start_year + self.mortRatesList[-1].shape[0]
self.years = N.array(range(self.start_year, years_end))
assert len(self.years) >= 1, AssertionError("years: %s" % self.years)
self.years_fcst = N.array(range(years_end-1, years_end + self.stepsForward))
# Create an average mx matrix. If populations empty, no
# weights; if there is data for populations (1) check for
# reasonableness in size and number and (2) use population as
# weights and do the averaging.
if self.useWeightedMx:
self.totalWeightedMx = N.zeros_like(self.mortRatesList[0])
self.totalPop = N.zeros_like(self.populationList[0])
for (mx, pop) in zip(self.mortRatesList, self.populationList):
self.totalWeightedMx = self.totalWeightedMx + (mx*pop)
self.totalPop = self.totalPop + pop
pass
self.averagedMx = self.totalWeightedMx/self.totalPop
else:
temp_mx = N.zeros_like(self.mortRatesList[0])
for mx in self.mortRatesList:
temp_mx += mx
self.averagedMx = temp_mx / len(self.mortRatesList)
self.averagedMx = LcExtension.extendMx(mxData=self.averagedMx, ageCutoff=LCFIT_DEFAULT_AGE_CUTOFF)
self.averagedMxLog = N.log(self.averagedMx)
# Check data for ok-ness in mortality (not population, since
# that might be non-CG'ed and have nans).
for i, data in enumerate(self.mortRatesList + self.mortRatesListLog + [self.averagedMxLog, self.averagedMx]):
assert N.isfinite(data).all(), \
AssertionError("Bad data in mx[%r]:\n %r" % (N.round_(data, 2), i))
# Check for weird (ie > 1.0) numbers
for i, data in enumerate(self.mortRatesList + [self.averagedMx]):
if not (data<1.2).all():
raise LcException("Weird mx: \n%s\n%s" % (data[data<1.2], i))
kt against the combined kt.
'''
## Imports ###############################################
from LcLog import lcfitlogger
from LcConfig import * # This imports numpy and friends
from LcAnnotation import *
from LcSinglePopObject import *
sys.path.append('./TESTING') # For test data
import LcUtil
from LcUtil import Diagnose as D
import LcExtension
LcExtension.setExtensionName(LCFIT_DEFAULT_EXTENSION_METHOD)
## Module Variables ######
LcObjectINFO ='LcCoherentPopObject' # Project/misc information
# Image constants
FC_IMAGE_NAME = 'lc-forecast.png' # Image with forecasted stuff
E0S_IMAGE_NAME = 'lc-e0s.png' # Empirical e0s for each subpopulation
E0S_FCST_IMAGE_NAME = 'lc-e0s-fcst.png' # Forecasted e0s for each subpop and combined
LNMX_IMAGE_NAME = 'lc-lognmx.png' # Emprical log nmx at various ages
LC_IMAGE_NAME = 'lc-model.png' # Image with kt, ax, bx, e0u
IMGH = 150 # Height of all images
IMGW = 200 # Width of all images
############################################################################################
