def SBBXtoBX(data):
"""Simultaneously combine the land series and the ocean series and
combine subboxes into boxes. *data* should be an iterator of
(land, ocean) subbox series pairs. Returns an iterator of box data.
"""
# First item from iterator is normally a pair of metadataobjects,
# one for land, one for ocean. If we are piping step3 straight into
# step5 then it is not a pair. In that case we synthesize missing
# ocean data.
meta = data.next()
try:
land_meta, ocean_meta = meta
except (TypeError, ValueError):
# Use the land meta object for both land and ocean data
land_meta, ocean_meta = meta, meta
print "No ocean data; using land data only"
data = blank_ocean_data(data)
# number of subboxes within each box
nsubbox = 100
# TODO: Formalise use of only monthlies, see step 3.
assert land_meta.mavg == 6
NYRSIN = land_meta.monm / 12
combined_year_beg = min(land_meta.yrbeg, ocean_meta.yrbeg)
# Index into the combined array of the first year of the land data.
land_offset = 12 * (land_meta.yrbeg - combined_year_beg)
# As land_offset but for ocean data.
ocean_offset = 12 * (ocean_meta.yrbeg - combined_year_beg)
combined_n_months = max(land_meta.monm + land_offset,
land_meta.monm + ocean_offset)
info = [
land_meta.mo1, land_meta.kq, land_meta.mavg, land_meta.monm,
land_meta.monm4, combined_year_beg, land_meta.missing_flag,
land_meta.precipitation_flag
]
info[4] = 2 * land_meta.monm + 5
yield (info, land_meta.title)
for box_number, box in enumerate(eqarea.grid()):
# Averages for the land and ocean (one series per subbox)...
avg = []
wgtc = []
# Eat the records from land and ocean 100 (nsubbox) at a time.
# In other words, all 100 subboxes for the box.
landsub, oceansub = zip(*itertools.islice(data, nsubbox))
# :todo: combine below zip with above zip?
for i, l, o in zip(range(nsubbox), landsub, oceansub):
a = [MISSING] * combined_n_months
if (o.good_count < parameters.subbox_min_valid
or l.d < parameters.subbox_land_range):
# use land series for this subbox
a[land_offset:land_offset + len(l.series)] = l.series
wgtc.append(l.good_count)
else:
# use ocean series for this subbox
a[ocean_offset:ocean_offset + len(o.series)] = o.series
wgtc.append(o.good_count)
avg.append(a)
# GISTEMP sort.
# We want to end up with IORDR, the permutation array that
# represents the sorter order. IORDR[0] is the index (into the
# *wgtc* array) of the longest record, IORDR[1] the index of the
# next longest record, and so on. We do that by decorating the
# *wgtc* array with indexes 0 to 99, and then extracting the
# (permuted) indexes into IORDR.
# :todo: should probably import from a purpose built module.
from step3 import sort
IORDR = range(nsubbox)
sort(IORDR, lambda x, y: wgtc[y] - wgtc[x])
# From here to the "for" loop over the cells (below) we are
# initialising data for the loop. Primarily the AVGR and WTR
# arrays.
nc = IORDR[0]
# Weights for the box's record.
wtr = [a != MISSING for a in avg[nc]]
# Box record
avgr = avg[nc][:]
# Loop over the remaining cells.
for nc in IORDR[1:]:
if wgtc[nc] >= parameters.subbox_min_valid:
series.combine(avgr, wtr, avg[nc], 1, 0,
combined_n_months / 12,
parameters.box_min_overlap)
series.anomalize(avgr, parameters.subbox_reference_period,
combined_year_beg)
ngood = sum(valid(a) for a in avgr)
yield (avgr, wtr, ngood, box)
# We've now consumed all 8000 input boxes and yielded 80 boxes. We
# need to tickle the input to check that it is exhausted and to
# cause it to run the final tail of its generator.
# We expect the call to .next() to raise StopIteration, which is
# just what we want.
data.next()
# Ordinarily we never reach here.
assert 0, "Too many input records"
def SBBXtoBX(data):
"""Simultaneously combine the land series and the ocean series and
combine subboxes into boxes. *data* should be an iterator of
(land, ocean) subbox series pairs. Returns an iterator of box data.
"""
# First item from iterator is normally a pair of metadataobjects,
# one for land, one for ocean. If we are piping step3 straight into
# step5 then it is not a pair. In that case we synthesize missing
# ocean data.
meta = data.next()
try:
land_meta, ocean_meta = meta
except (TypeError, ValueError):
# Use the land meta object for both land and ocean data
land_meta,ocean_meta = meta, meta
print "No ocean data; using land data only"
data = blank_ocean_data(data)
# number of subboxes within each box
nsubbox = 100
# TODO: Formalise use of only monthlies, see step 3.
assert land_meta.mavg == 6
NYRSIN = land_meta.monm/12
combined_year_beg = min(land_meta.yrbeg, ocean_meta.yrbeg)
# Index into the combined array of the first year of the land data.
land_offset = 12*(land_meta.yrbeg-combined_year_beg)
# As land_offset but for ocean data.
ocean_offset = 12*(ocean_meta.yrbeg-combined_year_beg)
combined_n_months = max(land_meta.monm + land_offset,
land_meta.monm + ocean_offset)
info = [land_meta.mo1, land_meta.kq, land_meta.mavg, land_meta.monm,
land_meta.monm4, combined_year_beg, land_meta.missing_flag,
land_meta.precipitation_flag]
info[4] = 2 * land_meta.monm + 5
yield(info, land_meta.title)
for box_number,box in enumerate(eqarea.grid()):
# Averages for the land and ocean (one series per subbox)...
avg = []
wgtc = []
# Eat the records from land and ocean 100 (nsubbox) at a time.
# In other words, all 100 subboxes for the box.
landsub,oceansub = zip(*itertools.islice(data, nsubbox))
# :todo: combine below zip with above zip?
for i, l, o in zip(range(nsubbox), landsub, oceansub):
a = [MISSING]*combined_n_months
if (o.good_count < parameters.subbox_min_valid
or l.d < parameters.subbox_land_range):
# use land series for this subbox
a[land_offset:land_offset+len(l.series)] = l.series
wgtc.append(l.good_count)
else:
# use ocean series for this subbox
a[ocean_offset:ocean_offset+len(o.series)] = o.series
wgtc.append(o.good_count)
avg.append(a)
# GISTEMP sort.
# We want to end up with IORDR, the permutation array that
# represents the sorter order. IORDR[0] is the index (into the
# *wgtc* array) of the longest record, IORDR[1] the index of the
# next longest record, and so on. We do that by decorating the
# *wgtc* array with indexes 0 to 99, and then extracting the
# (permuted) indexes into IORDR.
# :todo: should probably import from a purpose built module.
from step3 import sort
IORDR = range(nsubbox)
sort(IORDR, lambda x,y: wgtc[y] - wgtc[x])
# From here to the "for" loop over the cells (below) we are
# initialising data for the loop. Primarily the AVGR and WTR
# arrays.
nc = IORDR[0]
# Weights for the box's record.
wtr = [a != MISSING for a in avg[nc]]
# Box record
avgr = avg[nc][:]
# Loop over the remaining cells.
for nc in IORDR[1:]:
if wgtc[nc] >= parameters.subbox_min_valid:
series.combine(avgr, wtr, avg[nc], 1, 0,
combined_n_months/12, parameters.box_min_overlap)
series.anomalize(avgr, parameters.subbox_reference_period,
combined_year_beg)
ngood = sum(valid(a) for a in avgr)
yield (avgr, wtr, ngood, box)
# We've now consumed all 8000 input boxes and yielded 80 boxes. We
# need to tickle the input to check that it is exhausted and to
# cause it to run the final tail of its generator.
# We expect the call to .next() to raise StopIteration, which is
# just what we want.
data.next()
# Ordinarily we never reach here.
assert 0, "Too many input records"
def subbox_to_box(meta, cells, celltype='BOX'):
"""Aggregate the subboxes (aka cells, typically 8000 per globe)
into boxes (typically 80 boxes per globe), and combine records to
produce one time series per box.
*celltype* is used for logging, using a distinct (3 character) code
will allow the log output for the land, ocean, and land--ocean
analyses to be separated.
*meta* specifies the meta data and is used to determine the first
year (meta.yrbeg) and length (meta.monm) for all the resulting
series.
Returns an iterator of box data: for each box a quadruple of
(*anom*, *weight*, *ngood*, *box*) is yielded. *anom* is the
temperature anomaly series, *weight* is the weights for the series
(number of cells contributing for each month), *ngood* is total
number of valid data in the series, *box* is a 4-tuple that
describes the regions bounds: (southern, northern, western, eastern).
"""
# The (80) large boxes.
boxes = list(eqarea.grid())
# For each box, make a list of contributors (cells that contribute
# to the box time series); initially empty.
contributordict = dict((box, []) for box in boxes)
# Partition the cells into the boxes.
for cell in cells:
box = whichbox(boxes, cell.box)
contributordict[box].append(cell)
def padded_series(s):
"""Produce a series, that is padded to start in meta.yrbeg and
is of length meta.monm months.
*s* should be a giss_data.Series instance.
"""
result = [MISSING] * meta.monm
offset = 12 * (s.first_year - meta.yrbeg)
result[offset:offset + len(s)] = s.series
return result
# For each box, sort and combine the contributing cells, and output
# the result (by yielding it).
for box in boxes:
contributors = contributordict[box]
# :todo: should probably import from a purpose built module.
from step3 import sort
sort(contributors, lambda x, y: y.good_count - x.good_count)
best = contributors[0]
box_series = padded_series(best)
box_weight = [float(valid(a)) for a in box_series]
# Start the *contributed* list with this cell.
l = [any(valid(v) for v in box_series[i::12]) for i in range(12)]
s = ''.join('01'[x] for x in l)
contributed = [[best.uid, 1.0, s]]
# Loop over the remaining contributors.
for cell in contributors[1:]:
if cell.good_count >= parameters.subbox_min_valid:
addend_series = padded_series(cell)
weight = 1.0
station_months = series.combine(box_series, box_weight,
addend_series, weight,
parameters.box_min_overlap)
s = ''.join('01'[bool(x)] for x in station_months)
else:
weight = 0.0
s = '0' * 12
contributed.append([cell.uid, weight, s])
box_first_year = meta.yrbeg
series.anomalize(box_series, parameters.subbox_reference_period,
box_first_year)
uid = giss_data.boxuid(box, celltype=celltype)
log.write("%s cells %s\n" % (uid, asjson(contributed)))
ngood = sum(valid(a) for a in box_series)
yield (box_series, box_weight, ngood, box)
def subbox_to_box(meta, cells, celltype='BOX'):
"""Aggregate the subboxes (aka cells, typically 8000 per globe)
into boxes (typically 80 boxes per globe), and combine records to
produce one time series per box.
*celltype* is used for logging, using a distinct (3 character) code
will allow the log output for the land, ocean, and land--ocean
analyses to be separated.
*meta* specifies the meta data and is used to determine the first
year (meta.yrbeg) and length (meta.monm) for all the resulting
series.
Returns an iterator of box data: for each box a quadruple of
(*anom*, *weight*, *ngood*, *box*) is yielded. *anom* is the
temperature anomaly series, *weight* is the weights for the series
(number of cells contributing for each month), *ngood* is total
number of valid data in the series, *box* is a 4-tuple that
describes the regions bounds: (southern, northern, western, eastern).
"""
# The (80) large boxes.
boxes = list(eqarea.grid())
# For each box, make a list of contributors (cells that contribute
# to the box time series); initially empty.
contributordict = dict((box, []) for box in boxes)
# Partition the cells into the boxes.
for cell in cells:
box = whichbox(boxes, cell.box)
contributordict[box].append(cell)
def padded_series(s):
"""Produce a series, that is padded to start in meta.yrbeg and
is of length meta.monm months.
*s* should be a giss_data.Series instance.
"""
result = [MISSING] * meta.monm
offset = 12 * (s.first_year - meta.yrbeg)
result[offset:offset+len(s)] = s.series
return result
# For each box, sort and combine the contributing cells, and output
# the result (by yielding it).
for box in boxes:
contributors = contributordict[box]
# :todo: should probably import from a purpose built module.
from step3 import sort
sort(contributors, lambda x,y: y.good_count - x.good_count)
best = contributors[0]
box_series = padded_series(best)
box_weight = [float(valid(a)) for a in box_series]
# Start the *contributed* list with this cell.
l = [any(valid(v) for v in box_series[i::12]) for i in range(12)]
s = ''.join('01'[x] for x in l)
contributed = [[best.uid, 1.0, s]]
# Loop over the remaining contributors.
for cell in contributors[1:]:
if cell.good_count >= parameters.subbox_min_valid:
addend_series = padded_series(cell)
weight = 1.0
station_months = series.combine(box_series, box_weight,
addend_series, weight, parameters.box_min_overlap)
s = ''.join('01'[bool(x)] for x in station_months)
else:
weight = 0.0
s = '0'*12
contributed.append([cell.uid, weight, s])
box_first_year = meta.yrbeg
series.anomalize(box_series, parameters.subbox_reference_period,
box_first_year)
uid = giss_data.boxuid(box, celltype=celltype)
log.write("%s cells %s\n" % (uid, asjson(contributed)))
ngood = sum(valid(a) for a in box_series)
yield (box_series, box_weight, ngood, box)
