def record_correlation(s, t, overlap=300):
"""Return the correlation between the monthly anomalies of the two
records, where they have common months."""
assert s.first_year == t.first_year
a = list(s.series)
b = list(t.series)
series.anomalize(a)
series.anomalize(b)
common = [(u,v) for u,v in zip(a, b) if valid(u) and valid(v)]
if len(common) < overlap:
return None
return correlation.pearson(*zip(*common))
def asdict(arg, inp, mode):
"""`arg` should be a list of 11-digit station identifiers or
12-digit record identifiers. The records from `inp` are extracted
and returned as a dictionary (that maps identifiers to (data,begin)
pair). If `mode` is 'anom' then data are converted to monthly
anomalies.
"""
# Clear Climate Code, tool directory
import v2index
v2 = v2index.File(inp)
table = {}
for id in arg:
for id12, series in v2.get(id):
data, begin = from_lines(series)
if mode == 'anom':
# Clear Climate Code, code directory
from code.series import anomalize
anomalize(data, None)
table[id12] = (data, begin)
return table
def asdict(arg, inp, mode, axes, offset=None, scale=None):
"""`arg` should be a list of 11-digit station identifiers or
12-digit record identifiers. The records from `inp` are extracted
and returned as a dictionary (that maps identifiers to (data,begin)
pair). If `mode` is 'anom' then data are converted to monthly
anomalies; if `mode` is 'annual' then data are converted to annual
anomalies (using the GISTEMP algorithm that copes with missing
months). *offset* can be used to offset each station. The first
station in the *arg* list will have no offset, each subsequent
station will have its data biased by adding *offset* (the offset
increasing arithmetically for each station). All of the duplicates
for a given station will be offset by the same amount. The visual
effect is to displace stations upward (if the offset is positive).
"""
# Clear Climate Code, tool directory
import ghcnm_index
# Clear Climate Code
from code import series
v2 = ghcnm_index.File(inp)
table = {}
if not offset:
offset = [0.0] * len(arg)
for id,axis,off in zip(arg, axes, offset):
for id12,rows in v2.get(id):
data,begin = from_lines(rows, scale)
if mode == 'anom':
series.anomalize(data, None)
if mode == 'annual':
_, data = series.monthly_annual(data)
data = apply_data_offset(data, off)
table[id12] = (data,begin,axis)
return table
def asdict(arg, inp, mode, axes, offset=None, scale=0.1):
"""`arg` should be a list of 11-digit station identifiers or
12-digit record identifiers. The records from `inp` are extracted
and returned as a dictionary (that maps identifiers to (data,begin)
pair). If `mode` is 'anom' then data are converted to monthly
anomalies; if `mode` is 'annual' then data are converted to annual
anomalies (using the GISTEMP algorithm that copes with missing
months). *offset* can be used to offset each station. The first
station in the *arg* list will have no offset, each subsequent
station will have its data biased by adding *offset* (the offset
increasing arithmetically for each station). All of the duplicates
for a given station will be offset by the same amount. The visual
effect is to displace stations upward (if the offset is positive).
"""
# Clear Climate Code, tool directory
import v2index
# Clear Climate Code
from code import series
v2 = v2index.File(inp)
table = {}
if not offset:
offset = [0.0] * len(arg)
for id,axis,off in zip(arg, axes, offset):
for id12,rows in v2.get(id):
data,begin = from_lines(rows, scale)
if mode == 'anom':
series.anomalize(data, None)
if mode == 'annual':
_, data = series.monthly_annual(data)
data = apply_data_offset(data, off)
table[id12] = (data,begin,axis)
return table
def asdict(arg, inp, mode):
"""`arg` should be a list of 11-digit station identifiers or
12-digit record identifiers. The records from `inp` are extracted
and returned as a dictionary (that maps identifiers to (data,begin)
pair). If `mode` is 'anom' then data are converted to monthly
anomalies.
"""
# Clear Climate Code, tool directory
import v2index
v2 = v2index.File(inp)
table = {}
for id in arg:
for id12,series in v2.get(id):
data,begin = from_lines(series)
if mode == 'anom':
# Clear Climate Code, code directory
from code.series import anomalize
anomalize(data, None)
table[id12] = (data,begin)
return table
