diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml new file mode 100644 index 0000000..0a6dbf8 --- /dev/null +++ b/.github/workflows/run_tests.yml @@ -0,0 +1,57 @@ +name: test + +on: + push: + branches: + - "main" + - "master" + - "dev" + paths: + - "**.py" + - ".github/workflows/*test*.yml" + - "pyproject.toml" + - "poetry.lock" + - "requirements/requirements*.txt" + pull_request: + paths: + - "**.py" + - ".github/workflows/*test*.yml" + - "pyproject.toml" + - "poetry.lock" + - "requirements/requirements*.txt" + +jobs: + test: + name: "test Python ${{ matrix.python }} on ${{ matrix.os }}" + runs-on: "${{ matrix.os }}" + strategy: + fail-fast: false + matrix: + os: ["ubuntu-latest"] + python: ["3.9", "3.10", "3.11", "3.12"] + include: + - os: "macos-latest" + python: "3.10" + defaults: + run: + shell: "bash -eo pipefail {0}" + + steps: + - uses: "actions/checkout@v2" + - uses: "actions/setup-python@v2" + with: + python-version: "${{ matrix.python }}" + - uses: "actions/cache@v2" + id: "cache" + with: + path: "${{ env.pythonLocation }}" + key: "test-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml', 'requirements/*') }}" + - run: "python --version" + - run: "python -mpip install -U pip" + - run: "python -mpip --version" + - run: "python -mpip install -r requirements/requirements-dev.txt" + - run: "python -mpip install ./" + - name: "Run tests" + run: "pytest tests/" + env: + PYTHONPATH: ${{ github.workspace }}/tests diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..221990c --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,91 @@ +ci: + autofix_prs: false +default_language_version: + python: "python3" +fail_fast: false + +repos: + - repo: "https://github.com/pre-commit/pre-commit-hooks" + rev: "v4.5.0" + hooks: + - id: "check-added-large-files" + args: ["--maxkb=40000"] + - id: "check-ast" + - id: "check-byte-order-marker" + - id: "check-docstring-first" + - id: "check-executables-have-shebangs" + - id: "check-json" + - id: "check-symlinks" + - id: "check-merge-conflict" + - id: "check-vcs-permalinks" + - id: "check-xml" + - id: "check-yaml" + - id: "debug-statements" + - id: "end-of-file-fixer" + exclude: ".+\\.ipynb" + - id: "name-tests-test" + - id: "trailing-whitespace" + + - repo: "https://github.com/pre-commit/pygrep-hooks" + rev: "v1.10.0" + hooks: + - id: "python-check-blanket-noqa" + - id: "python-check-blanket-type-ignore" + - id: "python-use-type-annotations" + + - repo: "https://github.com/asottile/reorder_python_imports" + rev: "v3.12.0" + hooks: + - id: "reorder-python-imports" + args: + - "--py38-plus" + + - repo: "https://github.com/psf/black" + rev: "23.11.0" + hooks: + - id: "black" + + - repo: "https://github.com/astral-sh/ruff-pre-commit" + # Ruff version. + rev: "v0.1.7" + hooks: + - id: "ruff" + + - repo: "https://github.com/kynan/nbstripout" + rev: "0.6.1" + hooks: + - id: "nbstripout" + + - repo: "https://github.com/python-poetry/poetry" + rev: "1.7.0" + hooks: + - id: "poetry-check" + name: "poetry check" + - id: "poetry-check" + name: "poetry check --lock" + args: ["--lock"] + - id: "poetry-lock" + name: "poetry lock --no-update" + args: ["--no-update"] + - id: "poetry-export" + name: "poetry export main" + args: + [ + "--only", + "main", + "-f", + "requirements.txt", + "-o", + "requirements/requirements.txt", + ] + - id: "poetry-export" + name: "poetry export dev" + args: + [ + "--with", + "dev", + "-f", + "requirements.txt", + "-o", + "requirements/requirements-dev.txt", + ] diff --git a/README.md b/README.md index 4730311..571beec 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ # SeaStats -Simple package to compare and anylyse 2 time series. We use the following conventionn in this repo: +Simple package to compare and anylyse 2 time series. We use the following conventionn in this repo: * `sim`: modelled surge time series * `mod`: observed surge time series -## Metrics available: +## Metrics available: -These are following metrics available in this repository: +These are following metrics available in this repository: We need metrics to assess the quality of the model. We define the most important ones, as stated on this [Stats wiki](https://cirpwiki.info/wiki/Statistics): @@ -33,8 +33,42 @@ with : $$\lambda = 1 - \frac{\sum{(x_c - x_m)^2}}{\sum{(x_m - \overline{x}_m)^2} + \sum{(x_c - \overline{x}_c)^2} + n(\overline{x}_m - \overline{x}_c)^2 + \kappa}$$ * with `kappa` $$2 \cdot \left| \sum{((x_m - \overline{x}_m) \cdot (x_c - \overline{x}_c))} \right|$$ -### ADD STORM SELECTION PROCESS -from methodology explained in this [notebook](https://tomsail.github.io/static/Model_metrics.html) -### ADD "Slope" parameters -more info can be found [in this preprint](https://doi.org/10.5194/egusphere-2024-1415) from Campos-Caba et. al. -""" \ No newline at end of file +### Storm metrics: +The metrics are returned by the function +```python +storm_metrics(sim: pd.Series, obs: pd.Series, quantile: float, cluster_duration:int = 72) +``` +which returns this dictionary: +``` +"db_match" : val, +"R1_norm": val, +"R1": val, +"R3_norm": val, +"R3": val, +"error": val, +"error_metric": val +``` +we defined the following metrics for the storms events: + +* `R1`/`R3`/`error_metric`: we select the biggest observated storms, and then calculate error (so the absolute value of differenc between the model and the observed peaks) + * `R1` is the error for the biggest storm + * `R3` is the mean error for the 3 biggest storms + * `error_metric` is the mean error for all the storms above the threshold. + +* `R1_norm`/`R3_norm`/`error`: Same methodology, but values are in normalised (in %) by the observed peaks. + +### case of NaNs +The `storm_metrics()` might return: +``` +"db_match" : np.nan, +"R1_norm": np.nan, +"R1": np.nan, +"R3_norm": np.nan, +"R3": np.nan, +"error": np.nan, +"error_metric": np.nan +``` + +this happens when the function `storms/match_extremes.py` couldn't finc concomitent storms for the observed and modeled time series. + +## Usage diff --git a/notebooks/example_abed.ipynb b/notebooks/example_abed.ipynb new file mode 100644 index 0000000..63cc69e --- /dev/null +++ b/notebooks/example_abed.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import holoviews as hv\n", + "import hvplot.pandas\n", + "\n", + "from seastats.stats import get_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## load files " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sim = pd.read_parquet('../tests/data/abed_sim.parquet')\n", + "obs = pd.read_parquet('../tests/data/abed_obs.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# sim and obs need to be Series\n", + "obs = obs[obs.columns[0]]\n", + "sim = sim[sim.columns[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "obs.hvplot(line_dash = \"dashed\") * sim.hvplot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stats = get_stats(obs, sim)\n", + "stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## storm metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we defined the following metrics for the storms events: \n", + "\n", + "* `R1`/`R3`/`error_metric`: we select the biggest observated storms, and then calculate error (so the absolute value of differenc between the model and the observed peaks)\n", + " * `R1` is the error for the biggest storm\n", + " * `R3` is the mean error for the 3 biggest storms\n", + " * `error_metric` is the mean error for all the storms above the threshold.\n", + "\n", + "* `R1_norm`/`R3_norm`/`error`: Same methodology, but values are in normalised (in %) by the observed peaks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from seastats.storms import storm_metrics\n", + "from pyextremes import get_extremes\n", + "\n", + "quantile = 0.99\n", + "cluster_duration = 72" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# storm metrics\n", + "metric99 = storm_metrics(sim, obs, quantile=quantile, cluster_duration=cluster_duration)\n", + "metric99" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "threshold = sim.quantile(quantile)\n", + "ext_ = get_extremes(sim, \"POT\", threshold=threshold, r=f\"{cluster_duration}h\")\n", + "extremes1 = pd.DataFrame({\"modeled\" : ext_, \"time_model\" : ext_.index}, index=ext_.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "storm_plot = sim.hvplot() * hv.Scatter((extremes1.index, extremes1.modeled)).opts(color=\"red\")\n", + "storm_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "threshold = obs.quantile(quantile)\n", + "ext_ = get_extremes(obs, \"POT\", threshold=threshold, r=f\"{cluster_duration}h\")\n", + "extremes2 = pd.DataFrame({\"modeled\" : ext_, \"time_model\" : ext_.index}, index=ext_.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "storm_plot = obs.hvplot(line_dash = \"dashed\") * hv.Scatter((extremes2.index, extremes2.modeled)).opts(color=\"red\")\n", + "storm_plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "complete the stats dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metric99 = storm_metrics(sim, obs, quantile=0.99, cluster_duration=72)\n", + "metric95 = storm_metrics(sim, obs, quantile=0.95, cluster_duration=72)\n", + "# Create a dictionary for the current version's statistics\n", + "stats[\"R1\"] = metric99[\"R1\"]\n", + "stats[\"R1_norm\"] = metric99[\"R1_norm\"]\n", + "stats[\"R3\"] = metric99[\"R3\"]\n", + "stats[\"R3_norm\"] = metric99[\"R3_norm\"]\n", + "stats[\"error99\"] = metric99[\"error\"]\n", + "stats[\"error99m\"] = metric99[\"error_metric\"]\n", + "stats[\"error95\"] = metric95[\"error\"]\n", + "stats[\"error95m\"] = metric95[\"error_metric\"]\n", + "stats" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff 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--hash=sha256:fecb2508f0f2b71c819aecf5df2c03cef001c56a4b49302e640f3b34710d25e4 +zipp==3.19.2 ; python_version >= "3.9" and python_version < "3.10" \ + --hash=sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19 \ + --hash=sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c diff --git a/seastats/stats.py b/seastats/stats.py index cc9423c..f545cb8 100644 --- a/seastats/stats.py +++ b/seastats/stats.py @@ -1,102 +1,110 @@ +from typing import Dict +from typing import Tuple + import numpy as np import pandas as pd -from scipy.stats import linregress -from typing import Tuple, Dict -def get_bias(df1: pd.Series, df2: pd.Series, round:int = 3)->float: - bias = df1.mean() - df2.mean() +def get_bias(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + bias = sim.mean() - obs.mean() return np.round(bias, round) -def get_mse(df1: pd.Series, df2: pd.Series, round: int = 3)->float: - mse = np.square(np.subtract(df2, df1)).mean() +def get_mse(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + mse = np.square(np.subtract(obs, sim)).mean() return np.round(mse, round) -def get_rmse(df1: pd.Series, df2: pd.Series, round:int = 3)->float: - rmse = np.sqrt(get_mse(df1, df2, 10)) +def get_rmse(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + rmse = np.sqrt(get_mse(sim, obs, 10)) return np.round(rmse, round) -def get_mae(df1: pd.Series, df2: pd.Series, round:int = 3)->float: - mae = np.abs(np.subtract(df2, df1)).mean() +def get_mae(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + mae = np.abs(np.subtract(obs, sim)).mean() return np.round(mae, round) -def get_mad(df1: pd.Series, df2: pd.Series, round:int = 3)->float: - mae = np.abs(np.subtract(df2, df1)).std() + +def get_mad(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + mae = np.abs(np.subtract(obs, sim)).std() return np.round(mae, round) -def get_madp(df1: pd.Series, df2: pd.Series, round:int = 3)->float: - pc1, pc2 = get_percentiles(df1, df2) +def get_madp(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + pc1, pc2 = get_percentiles(sim, obs) return get_mad(pc1, pc2, round) -def get_madc(df1: pd.Series, df2: pd.Series, round:int = 3)->float: - madp = get_madp(df1, df2, round) - return get_mad(df1, df2, round) + madp +def get_madc(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + madp = get_madp(sim, obs, round) + return get_mad(sim, obs, round) + madp -def get_rms(df1: pd.Series, df2: pd.Series, round:int = 3)->float: - crmsd = ((df1 - df1.mean()) - (df2 - df2.mean()))**2 - return np.round(np.sqrt(crmsd.mean()), round) +def get_rms(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + crmsd = ((sim - sim.mean()) - (obs - obs.mean())) ** 2 + return np.round(np.sqrt(crmsd.mean()), round) -def get_corr(df1: pd.Series, df2: pd.Series, round: int = 3)->float: - corr = df1.corr(df2) +def get_corr(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + corr = sim.corr(obs) return np.round(corr, round) -def get_rms_quantile(df1: pd.Series, df2: pd.Series, quantile: float = 0.95, round:int = 3)->float: - df1_95 = df1[df1 > df1.quantile(quantile)] - df2_95 = df2[df2 > df2.quantile(quantile)] - crmsd = ((df1_95 - df1_95.mean()) - (df2_95 - df2_95.mean()))**2 - return np.round(np.sqrt(crmsd.mean()), round) +def get_rms_quantile( + sim: pd.Series, obs: pd.Series, quantile: float = 0.95, round: int = 3 +) -> float: + df1_95 = sim[sim > sim.quantile(quantile)] + df2_95 = obs[obs > obs.quantile(quantile)] + crmsd = ((df1_95 - df1_95.mean()) - (df2_95 - df2_95.mean())) ** 2 + return np.round(np.sqrt(crmsd.mean()), round) -def get_corr_quantile(df1: pd.Series, df2: pd.Series, quantile: float = 0.95, round: int = 3)->float: - df1_95 = df1[df1 > df1.quantile(quantile)] - df2_95 = df2[df2 > df2.quantile(quantile)] +def get_corr_quantile( + sim: pd.Series, obs: pd.Series, quantile: float = 0.95, round: int = 3 +) -> float: + df1_95 = sim[sim > sim.quantile(quantile)] + df2_95 = obs[obs > obs.quantile(quantile)] corr = df1_95.corr(df2_95) return np.round(corr, round) -def get_nse(df1: pd.Series, df2: pd.Series, round: int = 3)->float: - nse = 1 - np.nansum(np.subtract(df2, df1) ** 2) / np.nansum((df2 - np.nanmean(df2)) ** 2) +def get_nse(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + nse = 1 - np.nansum(np.subtract(obs, sim) ** 2) / np.nansum( + (obs - np.nanmean(obs)) ** 2 + ) return np.round(nse, round) -def get_lambda(df1: pd.Series, df2: pd.Series, round: int = 3)->float: - Xmean = np.nanmean(df2) - Ymean = np.nanmean(df1) - nObs = len(df2) - corr = get_corr(df1, df2, 10) +def get_lambda(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + Xmean = np.nanmean(obs) + Ymean = np.nanmean(sim) + nObs = len(obs) + corr = get_corr(sim, obs, 10) if corr >= 0: kappa = 0 else: - kappa = 2 * abs(np.nansum((df2 - Xmean) * (df1 - Ymean))) + kappa = 2 * abs(np.nansum((obs - Xmean) * (sim - Ymean))) - Nomin = np.nansum((df2 - df1) ** 2) + Nomin = np.nansum((obs - sim) ** 2) Denom = ( - np.nansum((df2 - Xmean) ** 2) - + np.nansum((df1 - Ymean) ** 2) + np.nansum((obs - Xmean) ** 2) + + np.nansum((sim - Ymean) ** 2) + nObs * ((Xmean - Ymean) ** 2) + kappa ) lambda_index = 1 - Nomin / Denom return np.round(lambda_index, round) - -def get_kge(df1: pd.Series, df2: pd.Series, round: int =3)->float: - corr = get_corr(df1, df2, 10) - b = (df1.mean() - df2.mean())/df2.std() - g = df1.std()/df2.std() - kge = 1 - np.sqrt((corr-1)**2 + b**2 + (g-1)**2) + +def get_kge(sim: pd.Series, obs: pd.Series, round: int = 3) -> float: + corr = get_corr(sim, obs, 10) + b = (sim.mean() - obs.mean()) / obs.std() + g = sim.std() / obs.std() + kge = 1 - np.sqrt((corr - 1) ** 2 + b**2 + (g - 1) ** 2) return np.round(kge, round) -def truncate_seconds(ts: pd.Series)->pd.Series: +def truncate_seconds(ts: pd.Series) -> pd.Series: df = pd.DataFrame({"time": ts.index, "value": ts.values}) df = df.assign(time=df.time.dt.floor("min")) if df.time.duplicated().any(): @@ -106,46 +114,69 @@ def truncate_seconds(ts: pd.Series)->pd.Series: df = df.iloc[df.time.drop_duplicates().index].reset_index(drop=True) df.index = df.time df = df.drop("time", axis=1) - ts = pd.Series(index=df.index, data=df.value) + ts = pd.Series(index=df.index, data=df.value) return ts -def align_ts(df1: pd.Series, df2: pd.Series, resample_to_model: bool = True)->Tuple[pd.Series, pd.Series]: - # requisite: df2 is the observation time series - df2 = df2.dropna() - df2 = truncate_seconds(df2) +def align_ts( + sim: pd.Series, obs: pd.Series, resample_to_model: bool = True +) -> Tuple[pd.Series, pd.Series]: + # requisite: obs is the observation time series + obs = obs.dropna() + obs = truncate_seconds(obs) if resample_to_model: - freq = df1.index.to_series().diff().median() - df2 = df2.resample(pd.Timedelta(freq)).mean() - sim, obs = df1.align(df2, axis = 0) - nan_mask1 = pd.isna(sim) - nan_mask2 = pd.isna(obs) + freq = sim.index.to_series().diff().median() + obs = obs.resample(pd.Timedelta(freq)).mean() + sim_, obs_ = sim.align(obs, axis=0) + nan_mask1 = pd.isna(sim_) + nan_mask2 = pd.isna(obs_) nan_mask = np.logical_or(nan_mask1.values, nan_mask2.values) - sim = sim[~nan_mask] - obs = obs[~nan_mask] - return sim, obs + sim_ = sim_[~nan_mask] + obs_ = obs_[~nan_mask] + return sim_, obs_ -def get_percentiles(df1: pd.Series, df2: pd.Series, higher_tail:bool = False) -> Tuple[pd.Series, pd.Series]: +def get_percentiles( + sim: pd.Series, obs: pd.Series, higher_tail: bool = False +) -> Tuple[pd.Series, pd.Series]: x = np.arange(0, 0.99, 0.01) if higher_tail: x = np.hstack([x, np.arange(0.99, 1, 0.001)]) - pc1 = np.zeros(len(x)) - pc2 = np.zeros(len(x)) + pc_sim = np.zeros(len(x)) + pc_obs = np.zeros(len(x)) for it, thd in enumerate(x): - pc1[it] = df1.quantile(thd) - pc2[it] = df2.quantile(thd) - return pd.Series(pc1), pd.Series(pc2) + pc_sim[it] = sim.quantile(thd) + pc_obs[it] = obs.quantile(thd) + return pd.Series(pc_sim), pd.Series(pc_obs) + + +def get_slope_intercept( + sim: pd.Series, obs: pd.Series, round: int = 3 +) -> Tuple[float, float]: + # Calculate means of x and y + x_mean = np.mean(obs) + y_mean = np.mean(sim) + + # Calculate the terms needed for the numerator and denominator of the slope + numerator = np.sum((obs - x_mean) * (sim - y_mean)) + denominator = np.sum((obs - x_mean) ** 2) + + # Calculate slope (beta1) and intercept (beta0) + slope = numerator / denominator + intercept = y_mean - slope * x_mean + return np.round(slope, round), np.round(intercept, round) -def get_slope_intercept_pp(df1: pd.Series, df2: pd.Series, round: int = 3)->Tuple[float,float]: - pc1, pc2 = get_percentiles(df1, df2) - slope, intercept, _, _, _ = linregress(pc1, pc2) +def get_slope_intercept_pp( + sim: pd.Series, obs: pd.Series, round: int = 3 +) -> Tuple[float, float]: + pc1, pc2 = get_percentiles(sim, obs) + slope, intercept = get_slope_intercept(pc1, pc2) return np.round(slope, round), np.round(intercept, round) -def get_stats(sim: pd.Series, obs: pd.Series)->Dict[str, float]: - slope, intercept, _, _, _ = linregress(sim, obs) +def get_stats(sim: pd.Series, obs: pd.Series) -> Dict[str, float]: + slope, intercept = get_slope_intercept(sim, obs) slopepp, interceptpp = get_slope_intercept_pp(sim, obs) version_stat = { "bias": get_bias(sim, obs), @@ -164,9 +195,9 @@ def get_stats(sim: pd.Series, obs: pd.Series)->Dict[str, float]: "intercept": intercept, "slope_pp": slopepp, "intercept_pp": interceptpp, - "mad" : get_mad(sim, obs), - "madp" : get_madp(sim, obs), - "madc" : get_madc(sim, obs), - "kge": get_kge(sim, obs) + "mad": get_mad(sim, obs), + "madp": get_madp(sim, obs), + "madc": get_madc(sim, obs), + "kge": get_kge(sim, obs), } return version_stat diff --git a/seastats/storms.py b/seastats/storms.py index c0231f9..bf06346 100644 --- a/seastats/storms.py +++ b/seastats/storms.py @@ -1,71 +1,81 @@ +from typing import Dict -import pandas as pd import numpy as np +import pandas as pd from pyextremes import get_extremes -from typing import Dict -def get_extremes_ts(ts1: pd.Series, ts2: pd.Series, quantile: float, cluster_duration:int = 72 -)-> pd.DataFrame: + +def get_extremes_ts( + sim: pd.Series, obs: pd.Series, quantile: float, cluster_duration: int = 72 +) -> pd.DataFrame: # first ts - threshold = ts1.quantile(quantile) - ext_ = get_extremes(ts1, "POT", threshold=threshold, r=f"{cluster_duration}h") - extremes1 = pd.DataFrame({"modeled" : ext_, "time_model" : ext_.index}, index=ext_.index) + threshold = sim.quantile(quantile) + ext_ = get_extremes(sim, "POT", threshold=threshold, r=f"{cluster_duration}h") + extremes1 = pd.DataFrame( + {"modeled": ext_, "time_model": ext_.index}, index=ext_.index + ) # second ts - threshold = ts2.quantile(quantile) - ext_ = get_extremes(ts2, "POT", threshold=threshold, r=f"{cluster_duration}h") - extremes2 = pd.DataFrame({"observed" : ext_, "time_obs" : ext_.index}, index=ext_.index) + threshold = obs.quantile(quantile) + ext_ = get_extremes(obs, "POT", threshold=threshold, r=f"{cluster_duration}h") + extremes2 = pd.DataFrame( + {"observed": ext_, "time_obs": ext_.index}, index=ext_.index + ) extremes = pd.concat([extremes1, extremes2], axis=1) if extremes.empty: return pd.DataFrame() - else: - extremes = extremes.groupby(pd.Grouper(freq='2D')).mean().dropna(how='all') + else: + extremes = extremes.groupby(pd.Grouper(freq="2D")).mean().dropna(how="all") return extremes -def match_extremes(extremes: pd.DataFrame)-> pd.DataFrame: +def match_extremes(extremes: pd.DataFrame) -> pd.DataFrame: if extremes.empty: return pd.DataFrame() - extremes_match = extremes.groupby(pd.Grouper(freq='2D')).mean().dropna() + extremes_match = extremes.groupby(pd.Grouper(freq="2D")).mean().dropna() if len(extremes_match) == 0: return pd.DataFrame() - else: - extremes_match['difference'] = extremes_match['observed'] - extremes_match['modeled'] - extremes_match['error'] = np.abs(extremes_match['difference']/extremes_match['observed']) - extremes_match['error_m'] = extremes_match["error"] * extremes_match['observed'] + else: + extremes_match["difference"] = ( + extremes_match["observed"] - extremes_match["modeled"] + ) + extremes_match["error"] = np.abs( + extremes_match["difference"] / extremes_match["observed"] + ) + extremes_match["error_m"] = extremes_match["error"] * extremes_match["observed"] return extremes_match -def storm_metrics(ts1: pd.Series, ts2: pd.Series, quantile: float, cluster_duration:int = 72 -)->Dict[str, float]: - extremes = get_extremes_ts(ts1, ts2, quantile, cluster_duration) +def storm_metrics( + sim: pd.Series, obs: pd.Series, quantile: float, cluster_duration: int = 72 +) -> Dict[str, float]: + extremes = get_extremes_ts(sim, obs, quantile, cluster_duration) extremes_match = match_extremes(extremes) if extremes_match.empty: return { - "db_match" : np.nan, + "db_match": np.nan, "R1_norm": np.nan, "R1": np.nan, "R3_norm": np.nan, "R3": np.nan, "error": np.nan, - "error_metric": np.nan + "error_metric": np.nan, } - else: + else: # R1: diff for the biggest storm in each dataset - idx_max = extremes_match['observed'].idxmax() - R1_norm = extremes_match['error'][idx_max] - R1 = extremes_match['error_m'][idx_max] + idx_max = extremes_match["observed"].idxmax() + R1_norm = extremes_match["error"][idx_max] + R1 = extremes_match["error_m"][idx_max] # R3: Difference between observed and modelled for the biggest storm - idx_max = extremes_match['observed'].nlargest(3).index - R3_norm = extremes_match['error'][idx_max].mean() - R3 = extremes_match['error_m'][idx_max].mean() + idx_max = extremes_match["observed"].nlargest(3).index + R3_norm = extremes_match["error"][idx_max].mean() + R3 = extremes_match["error_m"][idx_max].mean() metrics = { - "db_match" : len(extremes_match)/len(extremes), - "R1_norm": R1_norm, - "R1": R1, - "R3_norm": R3_norm, - "R3": R3, - "error": extremes_match['error'].mean(), - "error_metric": extremes_match['error_m'].mean() + "db_match": len(extremes_match) / len(extremes), + "R1_norm": R1_norm, + "R1": R1, + "R3_norm": R3_norm, + "R3": R3, + "error": extremes_match["error"].mean(), + "error_metric": extremes_match["error_m"].mean(), } return metrics - \ No newline at end of file diff --git a/tests/compute_stats_test.py b/tests/compute_stats_test.py new file mode 100644 index 0000000..185a872 --- /dev/null +++ b/tests/compute_stats_test.py @@ -0,0 +1,24 @@ +import pandas as pd + +from seastats.stats import get_stats +from seastats.storms import storm_metrics + + +def test_get_stats(): + sim = pd.read_parquet("tests/data/abed_sim.parquet") + obs = pd.read_parquet("tests/data/abed_obs.parquet") + # sim and obs need to be Series + obs = obs[obs.columns[0]] + sim = sim[sim.columns[0]] + stats = get_stats(sim, obs) + metric99 = storm_metrics(sim, obs, quantile=0.99, cluster_duration=72) + metric95 = storm_metrics(sim, obs, quantile=0.95, cluster_duration=72) + # Create a dictionary for the current version's statistics + stats["R1"] = metric99["R1"] + stats["R1_norm"] = metric99["R1_norm"] + stats["R3"] = metric99["R3"] + stats["R3_norm"] = metric99["R3_norm"] + stats["error99"] = metric99["error"] + stats["error99m"] = metric99["error_metric"] + stats["error95"] = metric95["error"] + stats["error95m"] = metric95["error_metric"] diff --git a/tests/data/abed_obs.parquet b/tests/data/abed_obs.parquet new file mode 100644 index 0000000..16e2012 Binary files /dev/null and b/tests/data/abed_obs.parquet differ diff --git a/tests/data/abed_sim.parquet b/tests/data/abed_sim.parquet new file mode 100644 index 0000000..0858310 Binary files /dev/null and b/tests/data/abed_sim.parquet differ