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feat: streamlined API, tests and README file
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,196 @@ | ||
| from seastats.stats import GENERAL_METRICS | ||
| from seastats.stats import GENERAL_METRICS_ALL | ||
| from seastats.storms import STORM_METRICS | ||
| from seastats.storms import STORM_METRICS_ALL | ||
| from __future__ import annotations | ||
|
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| import logging | ||
| from collections.abc import Sequence | ||
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| import numpy as np | ||
| import pandas as pd | ||
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| from seastats.stats import get_bias | ||
| from seastats.stats import get_corr | ||
| from seastats.stats import get_kge | ||
| from seastats.stats import get_lambda | ||
| from seastats.stats import get_mad | ||
| from seastats.stats import get_madc | ||
| from seastats.stats import get_madp | ||
| from seastats.stats import get_mae | ||
| from seastats.stats import get_mse | ||
| from seastats.stats import get_nse | ||
| from seastats.stats import get_rms | ||
| from seastats.stats import get_rmse | ||
| from seastats.stats import get_slope_intercept | ||
| from seastats.stats import get_slope_intercept_pp | ||
| from seastats.storms import match_extremes | ||
|
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| logger = logging.getLogger(__name__) | ||
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| GENERAL_METRICS_ALL = [ | ||
| "bias", | ||
| "rmse", | ||
| "rms", | ||
| "rms_qm", | ||
| "sim_mean", | ||
| "obs_mean", | ||
| "sim_std", | ||
| "obs_std", | ||
| "mae", | ||
| "mse", | ||
| "nse", | ||
| "lamba", | ||
| "cr", | ||
| "cr_qm", | ||
| "slope", | ||
| "intercept", | ||
| "slope_pp", | ||
| "intercept_pp", | ||
| "mad", | ||
| "madp", | ||
| "madc", | ||
| "kge", | ||
| ] | ||
| GENERAL_METRICS = ["bias", "rms", "rmse", "cr", "nse", "kge"] | ||
| STORM_METRICS = ["R1", "R3", "error"] | ||
| STORM_METRICS_ALL = ["R1", "R1_norm", "R3", "R3_norm", "error" "error_norm"] | ||
|
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| SUGGESTED_METRICS = sorted(GENERAL_METRICS + STORM_METRICS) | ||
| SUPPORTED_METRICS = sorted(GENERAL_METRICS_ALL + STORM_METRICS_ALL) | ||
|
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|
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| def get_stats( | ||
| sim: pd.Series, | ||
| obs: pd.Series, | ||
| metrics: Sequence[str] = None, | ||
| quantile: float = 0, | ||
| cluster: int = 72, | ||
| round: int = -1, | ||
| ) -> dict[str, float]: | ||
| """ | ||
| Calculates various statistical metrics between the simulated and observed time series data. | ||
| :param pd.Series sim: The simulated time series data. | ||
| :param pd.Series obs: The observed time series data. | ||
| :param list[str] metrics: (Optional) The list of statistical metrics to calculate. If None, all items in SUGGESTED_METRICS will be calculated. If ["all"], all items in SUPPORTED_METRICS will be calculated. Default is None. | ||
| :param float quantile: (Optional) Quantile used to calculate the metrics. Default is 0 (no selection) | ||
| :param int cluster: (Optional) Cluster duration for grouping storm events. Default is 72 hours. | ||
| :param int round: (Optional) Apply rounding to the results to. Default is no rounding (value is -1) | ||
| :return dict stats: dictionary containing the calculated metrics. | ||
| The dictionary contains the following keys and their corresponding values: | ||
| - `bias`: The bias between the simulated and observed time series data. | ||
| - `rmse`: The Root Mean Square Error between the simulated and observed time series data. | ||
| - `mae`: The Mean Absolute Error the simulated and observed time series data. | ||
| - `mse`: The Mean Square Error the simulated and observed time series data. | ||
| - `rms`: The raw mean square error between the simulated and observed time series data. | ||
| - `sim_mean`: The mean of the simulated time series data. | ||
| - `obs_mean`: The mean of the observed time series data. | ||
| - `sim_std`: The standard deviation of the simulated time series data. | ||
| - `obs_std`: The standard deviation of the observed time series data. | ||
| - `nse`: The Nash-Sutcliffe efficiency between the simulated and observed time series data. | ||
| - `lamba`: The lambda statistic between the simulated and observed time series data. | ||
| - `cr`: The correlation coefficient between the simulated and observed time series data. | ||
| - `slope`: The slope of the linear regression between the simulated and observed time series data. | ||
| - `intercept`: The intercept of the linear regression between the simulated and observed time series data. | ||
| - `slope_pp`: The slope of the linear regression between the percentiles of the simulated and observed time series data. | ||
| - `intercept_pp`: The intercept of the linear regression between the percentiles of the simulated and observed time series data. | ||
| - `mad`: The median absolute deviation of the simulated time series data from its median. | ||
| - `madp`: The median absolute deviation of the simulated time series data from its median, calculated using the percentiles of the observed time series data. | ||
| - `madc`: The median absolute deviation of the simulated time series data from its median, calculated by adding `mad` to `madp` | ||
| - `kge`: The Kling-Gupta efficiency between the simulated and observed time series data. | ||
| - `R1`: Difference between observed and modelled for the biggest storm | ||
| - `R1_norm`: Normalized R1 (R1 divided by observed value) | ||
| - `R3`: Average difference between observed and modelled for the three biggest storms | ||
| - `R3_norm`: Normalized R3 (R3 divided by observed value) | ||
| - `error`: Average difference between observed and modelled for all storms | ||
| - `error_norm`: Normalized error (error divided by observed value) | ||
| """ | ||
|
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| if metrics is None: | ||
| metrics = SUGGESTED_METRICS | ||
| elif metrics == ["all"]: | ||
| metrics = SUPPORTED_METRICS | ||
| else: | ||
| if not isinstance(metrics, list): | ||
| raise ValueError( | ||
| "metrics must be a list of supported variables e.g. SUGGESTED_METRICS, or None, or ['all']" | ||
| ) | ||
|
|
||
| if quantile > 0: # signal subsetting is only to be done on general metrics | ||
| sim_ = sim[sim > sim.quantile(quantile)] | ||
| obs_ = obs[obs > obs.quantile(quantile)] | ||
| else: | ||
| sim_ = sim | ||
| obs_ = obs | ||
|
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| stats = {} | ||
|
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| for metric in metrics: | ||
| match metric: | ||
| case "bias": | ||
| stats["bias"] = get_bias(sim_, obs_) | ||
| case "rmse": | ||
| stats["rmse"] = get_rmse(sim_, obs_) | ||
| case "rms": | ||
| stats["rms"] = get_rms(sim_, obs_) | ||
| case "sim_mean": | ||
| stats["sim_mean"] = sim_.mean() | ||
| case "obs_mean": | ||
| stats["obs_mean"] = obs_.mean() | ||
| case "sim_std": | ||
| stats["sim_std"] = sim_.std() | ||
| case "obs_std": | ||
| stats["obs_std"] = obs_.std() | ||
| case "mae": | ||
| stats["mae"] = get_mae(sim_, obs_) | ||
| case "mse": | ||
| stats["mse"] = get_mse(sim_, obs_) | ||
| case "nse": | ||
| stats["nse"] = get_nse(sim_, obs_) | ||
| case "lamba": | ||
| stats["lamba"] = get_lambda(sim_, obs_) | ||
| case "cr": | ||
| stats["cr"] = get_corr(sim_, obs_) | ||
| case "slope": | ||
| stats["slope"], _ = get_slope_intercept(sim_, obs_) | ||
| case "intercept": | ||
| _, stats["intercept"] = get_slope_intercept(sim_, obs_) | ||
| case "slope_pp": | ||
| stats["slope_pp"], _ = get_slope_intercept_pp(sim_, obs_) | ||
| case "intercept_pp": | ||
| _, stats["intercept_pp"] = get_slope_intercept_pp(sim_, obs_) | ||
| case "mad": | ||
| stats["mad"] = get_mad(sim_, obs_) | ||
| case "madp": | ||
| stats["madp"] = get_madp(sim_, obs_) | ||
| case "madc": | ||
| stats["madc"] = get_madc(sim_, obs_) | ||
| case "kge": | ||
| stats["kge"] = get_kge(sim_, obs_) | ||
|
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| # Storm metrics part with PoT Selection | ||
| if np.any([m in STORM_METRICS_ALL for m in metrics]): | ||
| df = match_extremes(sim, obs, quantile=quantile, cluster=cluster) | ||
|
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| for metric in metrics: | ||
| match metric: | ||
| case "R1": | ||
| stats["R1"] = df["error"].iloc[0] | ||
| case "R1_norm": | ||
| stats["R1_norm"] = df["error_norm"].iloc[0] | ||
| case "R3": | ||
| stats["R3"] = df["error"].iloc[0:3].mean() | ||
| case "R3_norm": | ||
| stats["R3_norm"] = df["error_norm"].iloc[0:3].mean() | ||
| case "error": | ||
| stats["error"] = df["error"].mean() | ||
| case "error_norm": | ||
| stats["error_norm"] = df["error_norm"].mean() | ||
| else: | ||
| logger.info("no storm metric specified") | ||
|
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| if round > 0: | ||
| for metric in metrics: | ||
| stats[metric] = np.round(stats[metric]) | ||
|
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| return stats |
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