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"""
Metric Calculator Adapters change how metrics are calculated by calling
the `calc_metric` function of the adapted calculator instead of the unadapted
version.
"""
from pytesmo.time_series.grouping import YearlessDatetime, TsDistributor
from pytesmo.validation_framework.metric_calculators import (
PairwiseIntercomparisonMetrics, TripleCollocationMetrics)
import warnings
import numpy as np
from cadati.conv_doy import days_past
[docs]def days_in_month(month: int) -> int:
"""
Get number of days in this month (in a LEAP YEAR)
"""
return days_past[month] - days_past[month - 1]
[docs]class SubsetsMetricsAdapter:
"""
Adapt MetricCalculators to calculate metrics for groups of temporal
subsets (also across multiple years).
"""
_supported_metric_calculators = (
PairwiseIntercomparisonMetrics,
TripleCollocationMetrics,
)
def __init__(self, calculator, subsets, group_results='tuple'):
"""
Add functionality to a metric calculator to calculate validation
metrics for subsets of certain datetimes in a time series
(e.g. seasonal).
Parameters
----------
calculator : PairwiseIntercomparisonMetrics or TripleCollocationMetrics
A metric calculator to adapt. Preferably an instance of a metric
calculator listed in `_supported_metric_calculators`
subsets : dict[str, TsDistributor], optional (default: None)
Define subsets of data. With group names as key and a
data distributor as values.
group_results: str, optional (default: 'tuple')
How to group the results.
- 'tuple' will group the results by (group, metric)
- 'join' will join group and metric name with a '|'
"""
if not isinstance(calculator, self._supported_metric_calculators):
warnings.warn(f"Adapting {calculator.__class__} is not supported.")
self.cls = calculator
self.subsets = subsets
self.group_results = group_results
assert group_results in ('tuple', 'join'), \
f"Unknown group_results: {group_results}"
# metadata metrics and lon, lat, gpi are excluded from applying
# seasonally
self.non_seas_metrics = ["gpi", "lon", "lat"]
if hasattr(self.cls, 'metadata_template'):
if self.cls.metadata_template is not None:
self.non_seas_metrics += list(
self.cls.metadata_template.keys())
all_metrics = calculator.result_template
subset_metrics = {}
# for each subset create a copy of the metric template
for name in subsets.keys():
for k, v in all_metrics.items():
subset_metrics[self._genname(name, k)] = np.array(v)
self.result_template = subset_metrics
def _genname(self, setname: str, metric: str) -> str or tuple:
if metric in self.non_seas_metrics:
k = f"{metric}"
elif self.group_results == 'tuple':
k = (f"{setname}", *np.atleast_1d(metric))
elif self.group_results == 'join':
k = f"{setname}|{metric}"
else:
raise NotImplementedError(
f"Unknown group_results: {self.group_results}")
return k
[docs] def calc_metrics(self, data, gpi_info):
"""
Calculates the desired statistics, for each set that was defined.
Parameters
----------
data : pandas.DataFrame
with 2 columns, the first column is the reference dataset
named 'ref'
the second column the dataset to compare against named 'other'
gpi_info : tuple
Grid point info (i.e. gpi, lon, lat)
"""
dataset = self.result_template.copy()
for setname, distr in self.subsets.items():
df = distr.select(data)
ds = self.cls.calc_metrics(df, gpi_info=gpi_info)
for metric, res in ds.items():
k = self._genname(setname, metric)
dataset[k] = res
return dataset
[docs]class MonthsMetricsAdapter(SubsetsMetricsAdapter):
"""
Adapt MetricCalculators to calculate metrics for groups across months
"""
def __init__(self, calculator, month_subsets=None, group_results='tuple'):
"""
Add functionality to a metric calculator to calculate validation
metrics for subsets of certain months in a time series (e.g. seasonal).
Parameters
----------
calculator : PairwiseIntercomparisonMetrics or TripleCollocationMetrics
A metric calculator to adapt. Preferably an instance of a metric
calculator listed in `_supported_metric_calculators`
month_subsets : dict, optional (default: None)
Define groups of data. With group names as key and a list of
months (1-12) that belong to the group as values.
e.g. {'Group1': [4,5,6,7,8,9], 'Group2': [10,11,12,1,2,3]} will
split the data used by the metric calculator into 2 groups.
One using only observations made between April and September,
and one using observations from the rest of the year.
The name will be used in the results to distinguish between the
same metrics for different groups:
e.g. ('Group1', 'BIAS'): ..., ('Group2', 'BIAS'): ..., etc. or
'Group1|BIAS': ..., 'Group2|BIAS': ..., etc.
denpending on the chosen `group_results` parameter.
The default groups are based on 4 seasons plus one group that uses
all data (as the unadapted metric calculator would do):
{'DJF': [12,1,2], 'MAM': [3,4,5], 'JJA': [6, 7, 8],
'SON': [9, 10, 11], 'ALL': list(range(1, 13))}
group_results: str, optional (default: 'tuple')
How to group the results.
- 'tuple' will group the results by (group, metric)
- 'join' will join group and metric name with a '|'
"""
if month_subsets is None:
month_subsets = {
'DJF': [12, 1, 2],
'MAM': [3, 4, 5],
'JJA': [6, 7, 8],
'SON': [9, 10, 11],
'ALL': list(range(1, 13)),
}
for name, months in month_subsets.items():
distr = TsDistributor(yearless_date_ranges=[(
YearlessDatetime(m, 1, 0, 0, 0),
YearlessDatetime(m, days_in_month(m), 23, 59, 59))
for m in months])
month_subsets[name] = distr
super().__init__(calculator, subsets=month_subsets,
group_results=group_results)