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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,7 @@ | ||
| time window: | ||
| begin: '1900-01-01T00:00:00Z' | ||
| end: '2035-01-01T00:00:00Z' | ||
| bound to include: begin | ||
|
|
||
| obs spaces: | ||
| {{ obs_spaces }} |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| #!/usr/bin/env python3 | ||
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| # creates figures of timeseries from the csv outputs computed by gdassoca_obsstats.x | ||
| import argparse | ||
| from itertools import product | ||
| import os | ||
| import glob | ||
| import pandas as pd | ||
| import matplotlib.pyplot as plt | ||
| import matplotlib.dates as mdates | ||
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| class ObsStats: | ||
| def __init__(self): | ||
| self.data = pd.DataFrame() | ||
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| def read_csv(self, filepaths): | ||
| # Iterate through the list of file paths and append their data | ||
| for filepath in filepaths: | ||
| new_data = pd.read_csv(filepath) | ||
| # Convert date to datetime for easier plotting | ||
| new_data['date'] = pd.to_datetime(new_data['date'], format='%Y%m%d%H') | ||
| self.data = pd.concat([self.data, new_data], ignore_index=True) | ||
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| def plot_timeseries(self, ocean, variable): | ||
| # Filter data for the given ocean and variable | ||
| filtered_data = self.data[(self.data['Ocean'] == ocean) & (self.data['Variable'] == variable)] | ||
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| if filtered_data.empty: | ||
| print("No data available for the given ocean and variable combination.") | ||
| return | ||
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| # Get unique experiments | ||
| experiments = filtered_data['Exp'].unique() | ||
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| # Plot settings | ||
| fig, axs = plt.subplots(3, 1, figsize=(10, 15), sharex=True) | ||
| fig.suptitle(f'{variable} statistics, {ocean} ocean', fontsize=16, fontweight='bold') | ||
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| for exp in experiments: | ||
| exp_data = filtered_data[filtered_data['Exp'] == exp] | ||
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| # Plot RMSE | ||
| axs[0].plot(exp_data['date'], exp_data['RMSE'], marker='o', linestyle='-', label=exp) | ||
| axs[0].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H')) | ||
| axs[0].xaxis.set_major_locator(mdates.DayLocator()) | ||
| axs[0].tick_params(labelbottom=False) | ||
| axs[0].set_ylabel('RMSE') | ||
| axs[0].legend() | ||
| axs[0].grid(True) | ||
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| # Plot Bias | ||
| axs[1].plot(exp_data['date'], exp_data['Bias'], marker='o', linestyle='-', label=exp) | ||
| axs[1].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H')) | ||
| axs[1].xaxis.set_major_locator(mdates.DayLocator()) | ||
| axs[1].tick_params(labelbottom=False) | ||
| axs[1].set_ylabel('Bias') | ||
| axs[1].legend() | ||
| axs[1].grid(True) | ||
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| # Plot Count | ||
| axs[2].plot(exp_data['date'], exp_data['Count'], marker='o', linestyle='-', label=exp) | ||
| axs[2].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H')) | ||
| axs[2].xaxis.set_major_locator(mdates.DayLocator()) | ||
| axs[2].set_ylabel('Count') | ||
| axs[2].legend() | ||
| axs[2].grid(True) | ||
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| # Improve layout and show plot | ||
| plt.tight_layout(rect=[0, 0.03, 1, 0.95]) | ||
| plt.savefig(f'{ocean}_test.png') | ||
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| if __name__ == "__main__": | ||
| epilog = ["Usage examples: ./gdassoca_obsstats.py --exp gdas.*.csv"] | ||
| parser = argparse.ArgumentParser(description="Observation space RMSE's and BIAS's", | ||
| formatter_class=argparse.RawDescriptionHelpFormatter, | ||
| epilog=os.linesep.join(epilog)) | ||
| parser.add_argument("--exps", nargs='+', required=True, | ||
| help="Path to the experiment's COMROOT") | ||
| parser.add_argument("--variable", required=True, help="ombg_qc or ombg_noqc") | ||
| parser.add_argument("--figname", required=True, help="The name of the output figure") | ||
| args = parser.parse_args() | ||
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| flist = [] | ||
| for exp in args.exps: | ||
| print(exp) | ||
| wc = exp + '/*.*/??/analysis/ocean/*.t??z.stats.csv' | ||
| flist.append(glob.glob(wc)) | ||
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| flist = sum(flist, []) | ||
| obsStats = ObsStats() | ||
| obsStats.read_csv(flist) | ||
| for var, ocean in product(['ombg_noqc', 'ombg_qc'], | ||
| ['Atlantic', 'Pacific', 'Indian', 'Arctic', 'Southern']): | ||
| obsStats.plot_timeseries(ocean, var) |
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