def filter2(network, **hom_params):
"""Reconciles the detected undocumented changepoints with documented ones,
if available, by "absorbing" detected changepoints near these known breaks
in the data.
TODO: implement this functionality
:Param network:
The network object containing the station/series data and metadata, and
a record of suspect, undocumented changepoints and their approximate
amplitude:
:Ivar hits_array:
A numpy array of dimensions [number of stations, number of months]
containing the timing of the set of undocumented changepoints
filtered so far.
:Ivar amps_array:
A numpy array of dimensions [number of stations, number of months]
containing the approximate amplitudes of the undocumented
changepoints filtered so far.
:Param hom_params:
The parameters object containing important parameter settings for this
analysis:
<none so far>
:Return:
does nothing right now other than print out some summary information.
"""
################################################################################
## FILTER 2
## won't actually do anything but print things to console for now
## Use station history and metadata to absorb undocumented changepoints
## to known ones where possible
station_list = network.stations.keys()
ids = station_list
(num_stations, num_months) = network.amps_array.shape
for station_index in range(num_stations):
station_id = station_list[station_index]
station_series = network.raw_series[station_id]
data = station_series.series
scale_series(data, 0.1, station_series.MISSING_VAL)
stdk = compute_monthly_avg_std(data)
for month in range(num_months):
if network.hits_array[station_index, month] > 0:
ahigh = network.amps_array[station_index, month]
jsum = network.hits_array[station_index, month]
astd = abs(ahigh)
iy, im = imo2iym(month)
## These are PRE-DEFINED in inhomog.parm.system.mthly.incl. They
## will need to be re-factored later in to a more logical place
## (Parameters maybe?)
arange = [0.4, 0.6, 0.8, 1.0, 1.5, 3.0, 5.0]
mrgyr = [36, 18, 12, 8, 6, 5, 5]
nrange = len(arange)
# Create search bracket for looking at station history files
for irange in range(len(arange)):
if astd < arange[irange]: break
ibracket = mrgyr[irange]*2 + 1
print "ASTD: ",station_index,station_id,"1",iy,im,astd,ahigh,stdk,jsum,ibracket
def splitmerge(network, pairs=None, beg_year=1, end_year=2, **kwargs):
## EXPERIMENTAL PLACEHOLDERS - will eventually be replaced with a master
## loop to do all the id pairs.
id_list = network.stations.keys()
pair_results = dict()
def dict_to_tuples(d):
keys = d.keys()
return [(key, d[key]) for key in keys]
## Generate station pairs for use in splitmerge by iteratively going through the
## station_list and adding stations in order of decreasing correlation. Skip a
## neighbor if the pair is already present; want 20 stations or until all the
## correlated neighbors are used up.
# pairs = []
# for id1 in id_list:
# neighbors = dict_to_tuples(network.correlations[id1])
# sorted_neighbors = sorted(neighbors, key=operator.itemgetter(1))
# added_pairs = 0
# while sorted_neighbors and (added_pairs < 5):
# id2, _ = sorted_neighbors.pop()
# ordered_pair = tuple(sorted((id1, id2)))
# if not ordered_pair in pairs:
# pairs.append(ordered_pair)
# added_pairs += 1
for (id1, id2) in pairs:
print "Pair %s with %s" % (id1, id2)
pair_str = "%6s-%6s" % (id1, id2)
#if pair_str != "051528-298107":
# continue
raw_series = network.raw_series
stations = network.stations
series_copy = deepcopy(raw_series)
min_ann = 5
num_years = end_year - beg_year
num_months = num_years*12
for s in series_copy.itervalues():
data = s.series
scaled = scale_series(data, 0.1, s.MISSING_VAL)
anomalies = compute_monthly_anomalies(scaled, s.MISSING_VAL)
s.set_series(anomalies, s.years)
## Retrieve the data for each of the stations.
station1 = stations[id1]
series1 = series_copy[id1]
data1 = series1.monthly_series
station2 = stations[id2]
series2 = series_copy[id2]
data2 = series2.monthly_series
#print data1[:50]
#print data2[:50]
#print "################################################################"
## Compute the difference series
diff_data = diff(data1, data2)
MISS = series1.MISSING_VAL # Missing value placeholder
## Quickly pass through the data to find where it starts. We need to do this
## because it's possible that beg_year is earlier than the first year of
## valid data in either data1 or data2. Furthermore, the original PHA code
## deliberately clipped off the first year of good data, so emulate that
## effect here as well.
##
## Ultimately, we save the extreme early and extreme late month with valid
## data to use as our first guess at the undocumented changepoints.
first = 0
first_set = False
last = 0
for (i, d1, d2) in zip(xrange(num_months), data1, data2):
if d1!=MISS and d2!=MISS:
if first < 12:
first = i
#first_set = True
#if not first_set:
# first = i
# first_set = True
last = i
## Set the initial breakpoints and the list of already-found, homogenous
## segments.
breakpoints = [first, last, ]
homog_segs = []
#####################################################################
## BEGIN SPLITMERGE PROCESS TO GENERATE FIRST GUESS AT UNDOCUMENTED
## CHANGEPOINTS
iter = 0 # counts how many times we've repeated the splitmerge process
enter_BIC = False # break out of iterations into the BIC process?
last_breakpoints = []
while (iter < 10) and not enter_BIC:
seg_bounds = zip(breakpoints[:-1], breakpoints[1:])
last_breakpoints = deepcopy(breakpoints)
new_breakpoints = deepcopy(breakpoints)
new_homog_segs = []
print "Parse segments (isplit = 1), ipass: "******"Too short: ", imo2iym(l), imo2iym(r)
continue
## If we've previously found that this segment is homogenous (has no
## potential changepoint), then we can skip it as well and proceed to
## the next one.
# Set the within() method to check if this segment is within any
# previously found homogenous ones. Use lambda, since we can't pass
# keyword or positional arguments to map().
within_this_seg = lambda seg: within((l, r), seg)
within_stable_segs = map(within_this_seg, homog_segs)
if any(within_stable_segs):
print "Stable segment: ", imo2iym(l), imo2iym(r)
if l == first:
new_breakpoints.append(first)
continue
## The standard normal homogeneity test - which is the statistical test
## we'll use to see if there is a potential changepoint in this segment
## - requires us to normalize our paired difference series. We can do
## that in snht(), but we'll do it right now so we can inspect those
## standardized values later.
z = standardize(segment, MISS)
## Apply standard normal homogeneity test.
## For mechanics, see Alexandersson and Moberg 1997, Int'l Jrnl of
## Climatology (pp 25-34)
likelihood_ratios = snht(z, MISS, standardized=True)
z_count = len(get_valid_data(z))
## We're left with the likelihood ratio for each value being a potential
## changepoint. Find the max ratio, and if that value is significant, let
## it be the newest potential changepoint.
ind_max_ratio = 0
max_ratio = 0.0
clip_ratios = likelihood_ratios[2:-2] # clip the beginning and end,
# they can't be changepoints.
for (ind, ratio) in zip(xrange(len(clip_ratios)), clip_ratios):
if ratio > max_ratio:
ind_max_ratio = ind
max_ratio = ratio
## Now we find the critical value for this data set, and check our max
## likelihood ratio against it
crit_val = lrt_lookup(z_count)
# The possible changepoint is the index of the max ratio we found.
# We have to shift it the following ways to align it to the original
# data -
# 1) shift by 2 re-aligns it from clip_ratios to likelihood_ratios
# 2) shift by adjust re-aligns it to this segment in diff_data
# 3) shift by l re-aligns it to the first index in diff_data
possible_changepoint = l + ind_max_ratio + 2 + adjust
y_new, m_new = imo2iym(possible_changepoint) # year, month
## If this is the first iteration, we indicate as such, and add the new
## changepoint
if iter == 0:
print "%6s-%6s MD FIRST series %4d %2d to %4d %2d | at %4d %2d ts: %4.2f limit >: %3.2f" % (id1,id2,y1,m1,y2,m2,y_new,m_new,max_ratio,crit_val)
breakpoints.append(possible_changepoint)
breakpoints = sorted(breakpoints)
else:
## Else, if we found a new possible changepoint, add it to our list.
if max_ratio > crit_val:
print "%6s-%6s MD Inhomogenity for series %4d %2d to %4d %2d | at %4d %2d ts: %4.2f limit >: %3.2f %4d" % (id1,id2,y1,m1,y2,m2,y_new,m_new,max_ratio,crit_val,z_count)
new_breakpoints.append(possible_changepoint)
## If not, record that we found a homogeneous segment.
else:
print "%6s-%6s MD Homogeneous series %4d %2d to %4d %2d | at %4d %2d ts: %4.2f limit >: %3.2f %4d" % (id1,id2,y1,m1,y2,m2,y_new,m_new,max_ratio,crit_val,z_count)
new_homog_segs.append((l, r))
## Now we need to update our account of which segments were homogeneous,
## because we need to know during the next iteration. We will do this,
## as well as condense stable segments that lie adjacent to each other
## i.e, if we have the segments [(1,5), (5, 10,),, (12, 15)], then we
## really have [(1,10), (12, 15)].
homog_segs.extend(new_homog_segs)
if homog_segs:
homog_segs = sorted(homog_segs, key=operator.itemgetter(0))
final_homog_segs = [homog_segs[0], ] # this will be like a stack
for seg in homog_segs[1:]:
last_seg = final_homog_segs[-1]
if last_seg[1] == seg[0]:
new_seg = (last_seg[0], seg[1])
final_homog_segs.pop()
final_homog_segs.append(new_seg)
else:
final_homog_segs.append(seg)
homog_segs = final_homog_segs
## So we have new segments that can be generated from these new
## breakpoints. Now, the PHA routine enters a "merge" process
## to see whether or not to keep these newly found changepoints or throw
## them out as false alarms.
##
## We do this by "leapfrogging" every other breakpoint. This gives us
## a set of segments that all have another breakpoint in them. We want
## to see if these segments are homogeneous, because if they are, it
## means that the breakpoint we previously found in the segment has
## been superseded.
new_breakpoints = sorted(new_breakpoints)
seg_bounds = zip(new_breakpoints[:-2], new_breakpoints[2:])
remove_breakpoints = set()
merged_breakpoints = set()
if iter > 0:
print "Merge segments (isplit = 0), ipass: "******"Stable segment: ", imo2iym(l), imo2iym(r)
# if l == first:
# new_breakpoints.append(first)
# seg_lookup.append(((l, r), 'stable'))
# continue
# Set the within() method to check if this segment is within any
# previously found homogenous ones. Use lambda, since we can't pass
# keyword or positional arguments to map().
within_this_seg = lambda seg: within((l, r), seg)
within_stable_segs = map(within_this_seg, homog_segs)
if any(within_stable_segs):
print "Stable segment: ", imo2iym(l), imo2iym(r)
#if l == first:
# new_breakpoints.append(first)
merged_breakpoints.update([l, r])
continue
## Apply the same adjustments and the same standard normal homogeneity
## test that we did in the previous splitting process. There is no
## difference here until we consider what to do if we find a new
## homogeneous segment.
adjust = int(seg_bounds.index((l, r)) > 0)
segment = diff_data[l+adjust:r+1]
z = standardize(segment, MISS)
likelihood_ratios = snht(z, MISS, standardized=True)
z_count = len(get_valid_data(z))
ind_max_ratio = 0
max_ratio = 0.0
clip_ratios = likelihood_ratios[2:-2] # We clip the beginning and end
for (ind, ratio) in zip(xrange(len(clip_ratios)), clip_ratios):
if ratio > max_ratio:
ind_max_ratio = ind
max_ratio = ratio
crit_val = lrt_lookup(z_count)
possible_changepoint = l + ind_max_ratio + 2 + adjust
y_new, m_new = imo2iym(possible_changepoint)
if z_count < 2:
y1, m1 = imo2iym(l)
y2, m2 = imo2iym(r)
print "%6s-%6s MD No found peaks %4d %2d to %4d %2d" % (id1,id2,y1,m1,y2,m2)
print "%6s-%6s MD Compress 1 out peak at %4d %2d" % (id1,id2,y_new,m_new)
#remove_breakpoints.add_
## If we found a new breakpoint that is statistically significant, then
## great! Let's keep it.
if max_ratio > crit_val:
print "%6s-%6s MD Peak kept in merge at %4d %2d | ts: %4.2f limit >: %3.2f" % (id1,id2,y_new,m_new,max_ratio,crit_val)
merged_breakpoints.add(l)
merged_breakpoints.add(new_bp)
merged_breakpoints.add(r)
## If not, then this segment was homogeneous, so the breakpoint which
## already exists in it is no good.
else:
print "%6s-%6s MD Compress 2 out peak at %4d %2d | ts: %4.2f limit >: %3.2f" % (id1,id2,y_new,m_new,max_ratio,crit_val)
# Crap, if there are any potential breakpoints in this segment,
# we need to remove them because this segment is homogeneous. Let's
# remember this homogeneous segment for now and come back once
# we've found all of them.
merged_breakpoints.update([l, r])
remove_breakpoints.add(new_bp)
## At this point, we have a set of all the breakpoints we've accumulated
## during this iteration of split/merge, as well as a set of breakpoints
## which we've found to be of no further use. We can difference update
## our set of breakpoints to remove these guys, and let those merged
## breakpoints be the set of newest breakpoints for the next splitmerge
## iteration.
merged_breakpoints.difference_update(remove_breakpoints)
breakpoints = list(merged_breakpoints)
breakpoints = sorted(breakpoints)
## Did we actually find new breakpoints? If not, then we're done
## with splitmerge and can move on to the BIC process.
enter_BIC = (breakpoints == last_breakpoints)
iter = iter + 1
## Okay wow, we've potentially made it to the BIC stage now... !
if first not in breakpoints:
breakpoints.insert(0, first)
ym_breakpoints = map(imo2iym, breakpoints)
#print ym_breakpoints
## ENTERING MINBIC
bp_dictionary = dict()
####################################
##### MULTIPROCESS
from multiprocessing import Pool
global counter
multi_bp_dict = {}
counter = 0
def cb(r):
global counter
#print counter, r
counter += 1
start = time.clock()
po = Pool(processes=4)
for left,bp,right in zip(breakpoints[0:], breakpoints[1:], breakpoints[2:]):
if left != first:
left = left + 1
# recall that we only consider data after the first full year. we will be
# computing regressions with the independent variable indexed from this
# starting point, so we need to shift these indices. we also need to shift them
# by +1 if this is any segment beyond the first one, so that we don't include
# changepoints in more than one analysis.
# TOTAL_SHIFT = -12 + 1 = -11
#
# However, this shift is only necessary while looking at the array indices that
# we generate using range(). the data should already be aligned correctly.
total_shift = -12 + 1
left_shift, bp_shift, right_shift = left+total_shift, bp+total_shift, right+total_shift
y1, m1 = imo2iym(left)
yb, mb = imo2iym(bp)
y2, m2 = imo2iym(right)
#print "Entering MINBIC - %4d %2d %4d %2d %4d %2d" % (y1, m1, yb,
# mb, y2, m2)
(seg_x, seg_data) = range(left_shift, right_shift+1), diff_data[left:right+1]
bp_index = bp-left
#print len(seg_x), len(seg_data), bp_index
#bp_analysis = minbic(seg_x, seg_data, bp_index, MISS)
multi_bp_dict[bp] = po.apply_async(minbic,(seg_x,seg_data,bp_index,MISS,),callback=cb)
po.close()
po.join()
for bp in multi_bp_dict:
r = multi_bp_dict[bp]
multi_bp_dict[bp] = r.get()
#print "counter - %d" % counter
elapsed = (time.clock() - start)
print "ELAPSED TIME - %2.3e" % elapsed
#print new_bp_dict
####################################
##### NORMAL
# start = time.clock()
# for left,bp,right in zip(breakpoints[0:], breakpoints[1:], breakpoints[2:]):
#
# if left != first:
# left = left + 1
# # recall that we only consider data after the first full year. we will be
# # computing regressions with the independent variable indexed from this
# # starting point, so we need to shift these indices. we also need to shift them
# # by +1 if this is any segment beyond the first one, so that we don't include
# # changepoints in more than one analysis.
# # TOTAL_SHIFT = -12 + 1 = -11
# #
# # However, this shift is only necessary while looking at the array indices that
# # we generate using range(). the data should already be aligned correctly.
# total_shift = -12 + 1
# left_shift, bp_shift, right_shift = left+total_shift, bp+total_shift, right+total_shift
# y1, m1 = imo2iym(left)
# yb, mb = imo2iym(bp)
# y2, m2 = imo2iym(right)
# print "Entering MINBIC - %4d %2d %4d %2d %4d %2d" % (y1, m1, yb,
# mb, y2, m2)
# (seg_x, seg_data) = range(left_shift, right_shift+1), diff_data[left:right+1]
# bp_index = bp-left
# #print len(seg_x), len(seg_data), bp_index
# bp_analysis = minbic(seg_x, seg_data, bp_index, MISS)
#
# bp_dictionary[bp] = bp_analysis
# elapsed2 = (time.clock() - start)
# print "ELAPSED TIME = %3.2e" % elapsed2
##################################3
## Print the adjustment summaries
bp_dictionary = multi_bp_dict
sorted_bps = sorted(bp_dictionary.keys())
ndelete = []
valid_bps = {}
for bp in sorted_bps:
stats = bp_dictionary[bp]
cmodel=stats['cmodel']
iqtype=stats['iqtype']
asigx=stats['offset']
azscr=stats['offset_z']
rslp=stats['slopes']
end1 = bp
y_end1, m_end1 = imo2iym(end1)
beg2 = bp+1
y_beg2, m_beg2 = imo2iym(beg2)
# If cmodel is *SLR*, then there is no breakpoint
if 'SLR' in cmodel:
print ("%s-%s -- -- MD TESTSEG SKIP: %7.2f %5d %5d %3d %5d %5d %3d" %
(id1, id2, asigx, end1, y_end1, m_end1, beg2, y_beg2, m_beg2))
# Don't store it!
else:
print ("%6s-%6s -- -- MD TESTSEG ADJ: %7.2f %7.2f %8.4f %8.4f %5d %5d %3d %5d %5d %3d %2d" %
(id1,id2, asigx, azscr, rslp[0], rslp[1], end1, y_end1, m_end1, beg2, y_beg2, m_beg2, iqtype))
# Store it!
valid_bps[bp] = stats
###############################
## Go back and see if we can get rid of some of the change points.
## If 2 or more of the chgpts are within MINLEN,
## a) if the chgpt estimates are the same sign, then test each
## singly with same endpoints and keep lowest BIC
## b) if not the same sign,
## retain earliest changepoint
# add the first, last to valid_bps
interior_bps = valid_bps.keys()
# Add first, last if not already in interior_bps
for bp in [first, last]:
if bp not in interior_bps:
interior_bps.append(bp)
sorted_bps = sorted(interior_bps)
for left in sorted_bps:
print sorted_bps, left
## We're looking for the next interim breakpoint that satisfies two
## conditions:
## 1) at least MINLEN valid data (non-missing to the right)
## 2) has at least one breakpoint between 'left' and it
right = 0
close_bps = []
for right in sorted_bps:
if right <= left: continue
if not close_bps:
close_bps.append(right)
else:
valid_between_bps = diff_data[close_bps[-1]:right]
valid_length = len(get_valid_data(valid_between_bps, MISS))
print imo2iym(close_bps[-1]),valid_length,imo2iym(right)
if valid_length > MINLEN:
break
close_bps.append(right)
# We could actually run out of things in sorted_bps, and wind up with
# right == close_bps[-1]. Detect that and break out of this analysis
# if that happens.
if close_bps[-1]==right: break
if left != first:
left = left + 1
close_bp_results = {}
for bp in close_bps:
# # recall that we only consider data after the first full year. we will be
# # computing regressions with the independent variable indexed from this
# # starting point, so we need to shift these indices. we also need to shift them
# # by +1 if this is any segment beyond the first one, so that we don't include
# # changepoints in more than one analysis.
# # TOTAL_SHIFT = -12 + 1 = -11
# #
# # However, this shift is only necessary while looking at the array indices that
# # we generate using range(). the data should already be aligned correctly.
total_shift = -12 + 1
left_shift, bp_shift, right_shift = left+total_shift, bp+total_shift, right+total_shift
y1, m1 = imo2iym(left)
yb, mb = imo2iym(bp)
y2, m2 = imo2iym(right)
print ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>"
print y1,m1,"-",yb,mb,"-",y2,m2
print "<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<"
(seg_x, seg_data) = range(left_shift, right_shift+1), diff_data[left:right+1]
bp_index = bp-left
bp_analysis = minbic(seg_x, seg_data, bp_index, MISS, kthslr0_on=True)
cmodel=bp_analysis['cmodel']
iqtype= bp_analysis['iqtype']
offset= bp_analysis['offset']
rslp= bp_analysis['slopes']
crit_val = bp_analysis['crit_val']
test_stat = bp_analysis['test_stat']
bic = bp_analysis['bic']
print ("Interim chgpt: %s %4d %2d %4d %2d %4d %2d %8.2f %8.2f %8.2f %8.2f %7.3f %7.3f %2d" %
(pair_str, y1, m1, yb, mb, y2, m2, bic, test_stat, crit_val, offset, rslp[0], rslp[1], iqtype))
close_bp_results[bp] = bp_analysis
# Now we have a small problem... we might have more than one breakpoint,
# so we need to choose which one is best. We will check the sign of
# the breakpoint amplitude changes:
sign_of_amps = map(sign, [close_bp_results[bp]['offset'] for bp in close_bps])
positive = lambda x: sign(x) >= 0
negative = lambda x: sign(x) <= 0
zero = lambda x: sign(x) == 0
print "------------>",[close_bp_results[bp]['offset'] for bp in close_bps]
if (all(map(positive, sign_of_amps)) or
all(map(negative, sign_of_amps))):
# Pick the best (minimum BIC)
bics = [(bp, close_bp_results[bp]['bic']) for bp in close_bps]
sorted_bics = sorted(bics, key=operator.itemgetter(1))
smallest_bp = sorted_bics[0][0]
# Remove this smallest-bic bp from the in-interval bps
close_bps.remove(smallest_bp)
valid_bps[smallest_bp] = close_bp_results[smallest_bp]
#print "leftovers",close_bps
for bp in close_bps: # The remaining bps which we will reject
sorted_bps.remove(bp) # Remove them from this loop
del valid_bps[bp] # Remove them as valid
yb, mb = imo2iym(smallest_bp)
print ("Same domain - Lowest Interim: %s %4d %2d" %
(pair_str, yb, mb))
elif (all(map(zero, sign_of_amps))):
# Choose the earliest changepoint; the rest of these have
# amplitude changes which are 0.
first_bp, last_bp = close_bps[0], close_bps[-1]
# Remove the first interim bp and update valid_bps with this new
# computation.
close_bps.remove(first_bp)
valid_bps[first_bp] = close_bp_results[first_bp]
# Reject remaining interim bps
for bp in close_bps:
sorted_bps.remove(bp)
del valid_bps[bp]
yb, mb = imo2iym(first_bp)
print ("Null domain - Earliest Interim : %s %4d %2d" %
(pair_str, yb, mb))
else:
# We'll use the earliest interim changepoint, but we need
# to get rid of bad data. Replace all the data between the
# interim changepoints as missing and re-compute BIC.
first_bp, last_bp = close_bps[0], close_bps[-1]
first_bp_index = first_bp-left
last_bp_index = last_bp-left
print len(seg_x), len(seg_data)
print first_bp_index+1, last_bp_index+1
print left, bp, right
for i in range(first_bp_index+1, last_bp_index+1):
print i, imo2iym(i), i+left, imo2iym(i+left)
seg_x[i] = MISS
seg_data[i] = MISS
# Recall that seg_data[0] == diff_data[left]. ndelete records
# the *true month where there is unviable data*, so it needs to
# point back to the original element in diff_data we are
# worried about.
ndelete.append(i+left)
bp_analysis = minbic(seg_x, seg_data, first_bp_index, MISS, kthslr0_on=True)
# Remove the first interim bp and update valid_bps with this new
# computation.
close_bps.remove(first_bp)
valid_bps[first_bp] = bp_analysis
# Reject remaining interim bps
for bp in close_bps:
sorted_bps.remove(bp)
del valid_bps[bp]
yb, mb = imo2iym(first_bp)
print ("Diff domain - Earliest Interim : %s %4d %2d" %
(pair_str, yb, mb))
## Remove changepoints which are an SLR model.
nspan = [0]*num_months
bp_count = 1
for bp in sorted(valid_bps.keys()):
bp_analysis = valid_bps[bp]
if "SLR" in bp_analysis['cmodel']:
del valid_bps[bp]
continue
print " IN: ",bp
nspan[bp] = bp_count
## If adjacent months are missing next to this breakpoint, then
## assume that those could be a breakpoint as well and copy this
## breakpoint's analysis results for them.
for month in range(bp+1, last):
if (month in ndelete) or (diff_data[month] == MISS):
nspan[month] = bp_count
print " IN: ",month
valid_bps[month] = bp_analysis
else:
break
bp_count += 1
valid_bps['del'] = ndelete
valid_bps['nspan'] = nspan
pair_results[pair_str] = valid_bps
#print "ELAPSED TIMES = %3.2e %3.2e" % (elapsed1, elapsed2)
print "done"
##
import pickle
f = open("pair_results", 'w')
pickle.dump(pair_results, f)
return pair_results
