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MonthMetricsAdapter for new metrics calculators (#246)
* MonthMetricsAdapter for new metrics calculators * Cleanup * Add option for custom filename to results manager * Update docstrings and tests * Remove unnecessary f-strings
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src/pytesmo/validation_framework/metric_calculators_adapters.py
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| # Copyright (c) 2020, TU Wien, Department of Geodesy and Geoinformation | ||
| # All rights reserved. | ||
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| # Redistribution and use in source and binary forms, with or without | ||
| # modification, are permitted provided that the following conditions are met: | ||
| # * Redistributions of source code must retain the above copyright | ||
| # notice, this list of conditions and the following disclaimer. | ||
| # * Redistributions in binary form must reproduce the above copyright | ||
| # notice, this list of conditions and the following disclaimer in the | ||
| # documentation and/or other materials provided with the distribution. | ||
| # * Neither the name of the TU Wien, Department of Geodesy and | ||
| # Geoinformation nor the names of its contributors may be used to endorse | ||
| # or promote products derived from this software without specific prior | ||
| # written permission. | ||
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| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
| # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | ||
| # ARE DISCLAIMED. IN NO EVENT SHALL VIENNA UNIVERSITY OF TECHNOLOGY, | ||
| # DEPARTMENT OF GEODESY AND GEOINFORMATION BE LIABLE FOR ANY | ||
| # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES | ||
| # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; | ||
| # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND | ||
| # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF | ||
| # THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
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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. | ||
| """ | ||
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| from pytesmo.validation_framework.metric_calculators import ( | ||
| PairwiseIntercomparisonMetrics, | ||
| TripleCollocationMetrics | ||
| ) | ||
| import warnings | ||
| import numpy as np | ||
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| class MonthsMetricsAdapter(object): | ||
| """ | ||
| Adapt MetricCalculators to calculate metrics for groups across months | ||
| """ | ||
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| _supported_metric_calculators = ( | ||
| PairwiseIntercomparisonMetrics, | ||
| TripleCollocationMetrics, | ||
| ) | ||
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| def __init__(self, calculator, sets=None): | ||
| """ | ||
| 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` | ||
| sets : 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. | ||
| 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))} | ||
| """ | ||
| if not isinstance(calculator, self._supported_metric_calculators): | ||
| warnings.warn(f"Adapting {calculator.__class__} is not supported.") | ||
| self.cls = calculator | ||
| if sets is None: | ||
| sets = { | ||
| "DJF": [12, 1, 2], | ||
| "MAM": [3, 4, 5], | ||
| "JJA": [6, 7, 8], | ||
| "SON": [9, 10, 11], | ||
| "ALL": list(range(1, 13)), | ||
| } | ||
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| self.sets = sets | ||
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| # metadata metrics and lon, lat, gpi are excluded from applying | ||
| # seasonally | ||
| self.non_seas_metrics = ["gpi", "lon", "lat"] | ||
| if self.cls.metadata_template is not None: | ||
| self.non_seas_metrics += list(self.cls.metadata_template.keys()) | ||
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| all_metrics = calculator.result_template | ||
| subset_metrics = {} | ||
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| # for each subset create a copy of the metric template | ||
| for name in sets.keys(): | ||
| for k, v in all_metrics.items(): | ||
| if k in self.non_seas_metrics: | ||
| subset_metrics[k] = np.array(v) | ||
| else: | ||
| subset_metrics[(name, k)] = np.array(v) | ||
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| self.result_template = subset_metrics | ||
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| @staticmethod | ||
| def filter_months(df, months, dropna=False): | ||
| """ | ||
| Select only entries of a time series that are within certain month(s) | ||
| Parameters | ||
| ---------- | ||
| df : pd.DataFrame | ||
| Time series (index.month must exist) that is split up into the | ||
| selected groups. | ||
| months : list | ||
| Months for which data is kept, e.g. [12,1,2] to keep data for | ||
| winter | ||
| dropna : bool, optional (default: False) | ||
| Drop lines for months that are not to be kept, if this is false, | ||
| the original index is not changed, but filtered values are replaced | ||
| with nan. | ||
| Returns | ||
| ------- | ||
| df_filtered : pd.DataFrame | ||
| The filtered series | ||
| """ | ||
| dat = df.copy(True) | ||
| dat["__index_month"] = dat.index.month | ||
| cond = ["__index_month == {}".format(m) for m in months] | ||
| selection = dat.query(" | ".join(cond)).index | ||
| dat.drop("__index_month", axis=1, inplace=True) | ||
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| if dropna: | ||
| return dat.loc[selection] | ||
| else: | ||
| dat.loc[dat.index.difference(selection)] = np.nan | ||
| return dat | ||
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| 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() | ||
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| for setname, months in self.sets.items(): | ||
| df = self.filter_months(data, months=months, dropna=True) | ||
| ds = self.cls.calc_metrics(df, gpi_info=gpi_info) | ||
| for metric, res in ds.items(): | ||
| if metric in self.non_seas_metrics: | ||
| k = f"{metric}" | ||
| else: | ||
| k = (f"{setname}", *np.atleast_1d(metric)) | ||
| dataset[k] = res | ||
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| return dataset |
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