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Merge pull request #107 from holukas/diel-cycle-plot
Diel cycle plot
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| Original file line number | Diff line number | Diff line change |
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| import calendar | ||
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| import matplotlib.pyplot as plt | ||
| from pandas import Series, DataFrame | ||
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| import diive.core.plotting.styles.LightTheme as theme | ||
| from diive.core.plotting.plotfuncs import set_fig | ||
| from diive.core.times.resampling import diel_cycle | ||
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| class DielCycle: | ||
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| def __init__(self, series: Series): | ||
| """Plot diel cycles of time series. | ||
| Args: | ||
| series: Time series with datetime index. | ||
| The index must contain date and time info. | ||
| """ | ||
| self.series = series | ||
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| self.var = self.series.name | ||
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| self.fig = None | ||
| self.ax = None | ||
| self.showplot = False | ||
| self.title = None | ||
| self.txt_ylabel_units = None | ||
| self._diel_cycles_df = None | ||
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| def get_data(self) -> DataFrame: | ||
| return self.diel_cycles_df | ||
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| @property | ||
| def diel_cycles_df(self): | ||
| """Return dataframe containing diel cycle aggregates.""" | ||
| if not isinstance(self._diel_cycles_df, DataFrame): | ||
| raise Exception(f'No diel cycles dataframe available. Please run .plot() first.') | ||
| return self._diel_cycles_df | ||
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| def plot(self, | ||
| ax: plt.Axes = None, | ||
| title: str = None, | ||
| color: str = None, | ||
| txt_ylabel_units: str = None, | ||
| mean: bool = True, | ||
| std: bool = True, | ||
| each_month: bool = False, | ||
| legend_n_col: int = 1, | ||
| **kwargs): | ||
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| self.title = title | ||
| self.txt_ylabel_units = txt_ylabel_units | ||
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| # Resample | ||
| self._diel_cycles_df = diel_cycle(series=self.series, | ||
| mean=mean, | ||
| std=std, | ||
| each_month=each_month) | ||
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| months = set(self.diel_cycles_df.index.get_level_values(0).tolist()) | ||
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| self.fig, self.ax, self.showplot = set_fig(ax=ax) | ||
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| counter_plotted = -1 | ||
| n_months = len(months) | ||
| alpha = 0.05 if n_months > 10 else 0.1 | ||
| auto_color = True if not color else False | ||
| for counter, month in enumerate(months): | ||
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| means = self.diel_cycles_df.loc[month]['mean'] | ||
| if means.isnull().all(): | ||
| continue | ||
| else: | ||
| counter_plotted += 1 | ||
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| means_add_sd = self.diel_cycles_df.loc[month]['mean+sd'] | ||
| means_sub_sd = self.diel_cycles_df.loc[month]['mean-sd'] | ||
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| if auto_color: | ||
| color = theme.colors_12_months()[counter_plotted] | ||
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| # monthstr = calendar.month_name[month] if each_month else 'Mittelwert' | ||
| monthstr = calendar.month_abbr[month] if each_month else 'mean' | ||
| means.plot(ax=self.ax, label=f'{monthstr}', color=color, zorder=99, lw=2, **kwargs) | ||
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| # label = "Mittelwert ± 1 Standardabweichung" | ||
| label = None | ||
| # label = "mean±1sd" if counter == 0 else "" | ||
| self.ax.fill_between(means.index.values, | ||
| means_add_sd.values, | ||
| means_sub_sd.values, | ||
| alpha=alpha, zorder=0, color=color, edgecolor='none', | ||
| label=label) | ||
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| self._format(title=title, legend_n_col=legend_n_col) | ||
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| if self.showplot: | ||
| self.fig.show() | ||
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| def _format(self, title, legend_n_col): | ||
| # title = self.title if self.title else f"{self.series.name} in {self.series.index.freqstr} time resolution" | ||
| from diive.core.plotting.plotfuncs import default_format, format_spines, default_legend, add_zeroline_y | ||
| if title: | ||
| self.ax.set_title(title, color='black', fontsize=24) | ||
| # ax_xlabel_txt = "Uhrzeit" | ||
| ax_xlabel_txt = "Time (hours of day)" | ||
| default_format(ax=self.ax, ax_xlabel_txt=ax_xlabel_txt, ax_ylabel_txt=self.series.name, | ||
| txt_ylabel_units=self.txt_ylabel_units, | ||
| ticks_direction='in', ticks_length=8, ticks_width=2, | ||
| ax_labels_fontsize=20, | ||
| ticks_labels_fontsize=20) | ||
| format_spines(ax=self.ax, color='black', lw=1) | ||
| default_legend(ax=self.ax, ncol=legend_n_col) | ||
| add_zeroline_y(ax=self.ax, data=self.diel_cycles_df['mean']) | ||
| self.ax.set_xticks(['3:00', '6:00', '9:00', '12:00', '15:00', '18:00', '21:00']) | ||
| # self.ax.set_xticklabels(['3:00', '6:00', '9:00', '12:00', '15:00', '18:00', '21:00']) | ||
| self.ax.set_xticklabels([3, 6, 9, 12, 15, 18, 21]) | ||
| self.ax.set_xlim(['0:00', '23:59:59']) | ||
| if self.showplot: | ||
| self.fig.tight_layout() | ||
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| def example(): | ||
| from diive.configs.exampledata import load_exampledata_parquet | ||
| df = load_exampledata_parquet() | ||
| series = df['NEE_CUT_REF_f'].copy() | ||
| dc = DielCycle(series=series) | ||
| title = r'$\mathrm{Mean\ CO_2\ flux\ (2013-2024)}$' | ||
| units = r'($\mathrm{µmol\ CO_2\ m^{-2}\ s^{-1}}$)' | ||
| dc.plot(ax=None, title=title, txt_ylabel_units=units, | ||
| each_month=True, legend_n_col=2) | ||
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| # from diive.core.io.filereader import search_files, MultiDataFileReader | ||
| # filepaths = search_files(searchdirs=fr"F:\CURRENT\HON\2-FLUXRUN\out", pattern='*.csv') | ||
| # orig = MultiDataFileReader(filepaths=filepaths, filetype='EDDYPRO-FLUXNET-CSV-30MIN', output_middle_timestamp=True) | ||
| # origdf = orig.data_df | ||
| # origmeta = orig.metadata_df | ||
| # origfreq = origdf.index.freq # Original frequency | ||
| # from diive.core.io.files import save_parquet | ||
| # save_parquet(filename="merged", data=origdf, outpath=r"F:\CURRENT\HON\2-FLUXRUN\out") | ||
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| # import numpy as np | ||
| # from diive.core.io.files import load_parquet | ||
| # df = load_parquet(filepath=r"L:\Sync\luhk_work\CURRENT\HON\2-FLUXRUN\out\merged.parquet") | ||
| # | ||
| # # CO2 | ||
| # co2 = df['FC'].copy() | ||
| # keep = (df['USTAR'] > 0.07) & (df['FC_SSITC_TEST'] <= 1) | ||
| # co2[~keep] = np.nan | ||
| # keep = (co2 < 50) & (co2 > -50) | ||
| # co2[~keep] = np.nan | ||
| # from diive.pkgs.outlierdetection.zscore import zScoreDaytimeNighttime | ||
| # zs = zScoreDaytimeNighttime(series=co2, | ||
| # lat=47.418861, | ||
| # lon=8.491361, | ||
| # utc_offset=1, | ||
| # thres_zscore=3, | ||
| # showplot=False) | ||
| # zs.calc() | ||
| # flag = zs.get_flag() | ||
| # remove = flag == 2 | ||
| # co2[remove] = np.nan | ||
| # keep = (co2.index.month >= 3) & (co2.index.month <= 5) | ||
| # co2[~keep] = np.nan | ||
| # title = r'$\mathrm{CO_2\ flux}$ (Mar-May 2024)' | ||
| # units = r'($\mathrm{µmol\ CO_2\ m^{-2}\ s^{-1}}$)' | ||
| # | ||
| # h2o = df['FH2O'].copy() | ||
| # keep = df['FH2O_SSITC_TEST'] <= 1 | ||
| # h2o[~keep] = np.nan | ||
| # keep = (h2o < 50) & (h2o > -50) | ||
| # h2o[~keep] = np.nan | ||
| # | ||
| # from diive.pkgs.outlierdetection.zscore import zScoreDaytimeNighttime | ||
| # zs = zScoreDaytimeNighttime(series=h2o, | ||
| # lat=47.418861, | ||
| # lon=8.491361, | ||
| # utc_offset=1, | ||
| # thres_zscore=3, | ||
| # showplot=False) | ||
| # zs.calc() | ||
| # flag = zs.get_flag() | ||
| # remove = flag == 2 | ||
| # h2o[remove] = np.nan | ||
| # keep = (h2o.index.month >= 3) & (h2o.index.month <= 5) | ||
| # h2o[~keep] = np.nan | ||
| # | ||
| # import matplotlib.gridspec as gridspec | ||
| # fig = plt.figure(facecolor='white', figsize=(14, 14), dpi=200) | ||
| # gs = gridspec.GridSpec(2, 1) # rows, cols | ||
| # gs.update(wspace=0.3, hspace=0.4, left=0.08, right=0.93, top=0.93, bottom=0.07) | ||
| # ax = fig.add_subplot(gs[0, 0]) | ||
| # ax2 = fig.add_subplot(gs[1, 0]) | ||
| # dc_co2 = DielCycle(series=co2) | ||
| # title = r'$\mathrm{CO_{2}\text{-}Austausch\ (März-Mai\ 2024)}$' | ||
| # units = r'($\mathrm{µmol\ CO_2\ m^{-2}\ s^{-1}}$)' | ||
| # dc_co2.plot(ax=ax, color='#388E3C', title=title, txt_ylabel_units=units) | ||
| # dc_h2o = DielCycle(series=h2o) | ||
| # title = r'$\mathrm{Wasserdampf\text{-}Austausch\ (März-Mai\ 2024)}$' | ||
| # units = r'($\mathrm{mmol\ H_2O\ m^{-2}\ s^{-1}}$)' | ||
| # dc_h2o.plot(ax=ax2, color='#1976D2', title=title, txt_ylabel_units=units) | ||
| # fig.show() | ||
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| if __name__ == '__main__': | ||
| example() |
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