diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index 469b00d7da8954d1e96ab10f5372b338f9fa8b3e..03f0c5c5c12fd22e7bcb349662adc6c3e3154549 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -7,6 +7,7 @@ import math
import warnings
from src import helpers
from src.helpers import TimeTracking
+from src.run_modules.run_environment import RunEnvironment
import numpy as np
import xarray as xr
@@ -24,59 +25,76 @@ from typing import Dict, List
logging.getLogger('matplotlib').setLevel(logging.WARNING)
-def plot_monthly_summary(stations: List, data_path: str, name: str, target_var: str, window_lead_time: int = None,
- plot_folder: str = "."):
+class PlotMonthlySummary(RunEnvironment):
"""
Show a monthly summary over all stations for each lead time ("ahead") as box and whiskers plot. The plot is saved
in data_path with name monthly_summary_box_plot.pdf and 500dpi resolution.
- :param stations: all stations to plot
- :param data_path: path, where the data is located
- :param name: full name of the local files with a % as placeholder for the station name
- :param target_var: display name of the target variable on plot's axis
- :param window_lead_time: lead time to plot, if window_lead_time is higher than the available lead time or not given
- the maximum lead time from data is used. (default None -> use maximum lead time from data).
- :param plot_folder: path to save the plot (default: current directory)
"""
- logging.debug("run plot_monthly_summary()")
- forecasts = None
-
- for station in stations:
- logging.debug(f"... preprocess station {station}")
- file_name = os.path.join(data_path, name % station)
- data = xr.open_dataarray(file_name)
-
- data_cnn = data.sel(type="CNN").squeeze()
- data_cnn.coords["ahead"].values = [f"{days}d" for days in data_cnn.coords["ahead"].values]
-
- data_orig = data.sel(type="orig", ahead=1).squeeze()
- data_orig.coords["ahead"] = "orig"
-
- data_concat = xr.concat([data_orig, data_cnn], dim="ahead")
- data_concat = data_concat.drop("type")
-
- data_concat.index.values = data_concat.index.values.astype("datetime64[M]").astype(int) % 12 + 1
- data_concat = data_concat.clip(min=0)
-
- forecasts = xr.concat([forecasts, data_concat], 'index') if forecasts is not None else data_concat
-
- ahead_steps = len(forecasts.ahead)
- if window_lead_time is None:
- window_lead_time = ahead_steps
- window_lead_time = min(ahead_steps, window_lead_time)
-
- forecasts = forecasts.to_dataset(name='values').to_dask_dataframe()
- logging.debug("... start plotting")
- ax = sns.boxplot(x='index', y='values', hue='ahead', data=forecasts.compute(), whis=1.,
- palette=[matplotlib.colors.cnames["green"]] + sns.color_palette("Blues_d",
- window_lead_time).as_hex(),
- flierprops={'marker': '.', 'markersize': 1}, showmeans=True,
- meanprops={'markersize': 1, 'markeredgecolor': 'k'})
- ax.set(xlabel='month', ylabel=f'{target_var}')
- plt.tight_layout()
- plot_name = os.path.join(os.path.abspath(plot_folder), 'monthly_summary_box_plot.pdf')
- logging.debug(f"... save plot to {plot_name}")
- plt.savefig(plot_name, dpi=500)
- plt.close('all')
+ def __init__(self, stations: List, data_path: str, name: str, target_var: str, window_lead_time: int = None,
+ plot_folder: str = "."):
+ """
+ Sets attributes and create plot
+ :param stations: all stations to plot
+ :param data_path: path, where the data is located
+ :param name: full name of the local files with a % as placeholder for the station name
+ :param target_var: display name of the target variable on plot's axis
+ :param window_lead_time: lead time to plot, if window_lead_time is higher than the available lead time or not given
+ the maximum lead time from data is used. (default None -> use maximum lead time from data).
+ :param plot_folder: path to save the plot (default: current directory)
+ """
+ super().__init__()
+ self._data_path = data_path
+ self._data_name = name
+ self._data = self._get_data(stations)
+ self._window_lead_time = self._get_window_lead_time(window_lead_time)
+ self._plot(target_var, plot_folder)
+
+ def _get_data(self, stations):
+ """
+ pre-process data
+ :param stations:
+ :return:
+ """
+ forecasts = None
+ for station in stations:
+ logging.debug(f"... preprocess station {station}")
+ file_name = os.path.join(self._data_path, self._data_name % station)
+ data = xr.open_dataarray(file_name)
+
+ data_cnn = data.sel(type="CNN").squeeze()
+ data_cnn.coords["ahead"].values = [f"{days}d" for days in data_cnn.coords["ahead"].values]
+
+ data_orig = data.sel(type="orig", ahead=1).squeeze()
+ data_orig.coords["ahead"] = "orig"
+
+ data_concat = xr.concat([data_orig, data_cnn], dim="ahead")
+ data_concat = data_concat.drop("type")
+
+ data_concat.index.values = data_concat.index.values.astype("datetime64[M]").astype(int) % 12 + 1
+ data_concat = data_concat.clip(min=0)
+
+ forecasts = xr.concat([forecasts, data_concat], 'index') if forecasts is not None else data_concat
+ return forecasts
+
+ def _get_window_lead_time(self, window_lead_time):
+ ahead_steps = len(self._data.ahead)
+ if window_lead_time is None:
+ window_lead_time = ahead_steps
+ return min(ahead_steps, window_lead_time)
+
+ def _plot(self, target_var, plot_folder):
+ data = self._data.to_dataset(name='values').to_dask_dataframe()
+ logging.debug("... start plotting")
+ color_palette = [matplotlib.colors.cnames["green"]] + sns.color_palette("Blues_d", self._window_lead_time).as_hex()
+ ax = sns.boxplot(x='index', y='values', hue='ahead', data=data.compute(), whis=1., palette=color_palette,
+ flierprops={'marker': '.', 'markersize': 1}, showmeans=True,
+ meanprops={'markersize': 1, 'markeredgecolor': 'k'})
+ ax.set(xlabel='month', ylabel=f'{target_var}')
+ plt.tight_layout()
+ plot_name = os.path.join(os.path.abspath(plot_folder), 'monthly_summary_box_plot.pdf')
+ logging.debug(f"... save plot to {plot_name}")
+ plt.savefig(plot_name, dpi=500)
+ plt.close('all')
def plot_station_map(generators: Dict, plot_folder: str = "."):
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index ecbc415f8b463690dcc75d7731d8ec859d74da33..c8fde35cb39df00e94aeeb2ca218e6c65c248b54 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -15,8 +15,8 @@ from src.data_handling.data_distributor import Distributor
from src.data_handling.data_generator import DataGenerator
from src.model_modules.linear_model import OrdinaryLeastSquaredModel
from src import statistics
-from src.plotting.postprocessing_plotting import plot_monthly_summary, plot_station_map, plot_conditional_quantiles, \
- plot_climatological_skill_score, plot_competitive_skill_score
+from src.plotting.postprocessing_plotting import plot_station_map, plot_conditional_quantiles, \
+ plot_climatological_skill_score, plot_competitive_skill_score, PlotMonthlySummary
from src.datastore import NameNotFoundInDataStore
@@ -62,8 +62,8 @@ class PostProcessing(RunEnvironment):
plot_conditional_quantiles(self.test_data.stations, pred_name="orig", ref_name="CNN",
forecast_path=path, plot_name_affix="like-bas", plot_folder=self.plot_path)
plot_station_map(generators={'b': self.test_data}, plot_folder=self.plot_path)
- plot_monthly_summary(self.test_data.stations, path, r"forecasts_%s_test.nc", target_var,
- plot_folder=self.plot_path)
+ PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", target_var,
+ plot_folder=self.plot_path)
#
plot_climatological_skill_score(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
plot_climatological_skill_score(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
diff --git a/src/statistics.py b/src/statistics.py
index fd8491748f7ebd30d7056af9cc1ce162c5743881..df73784df830d5f7b96bf0fcd18a65d362516f12 100644
--- a/src/statistics.py
+++ b/src/statistics.py
@@ -136,7 +136,7 @@ class SkillScores(RunEnvironment):
observation = data.sel(type=observation_name)
forecast = data.sel(type=forecast_name)
reference = data.sel(type=reference_name)
- mse = statistics.mean_squared_error
+ mse = mean_squared_error
skill_score = 1 - mse(observation, forecast) / mse(observation, reference)
return skill_score.values
@@ -216,4 +216,4 @@ class SkillScores(RunEnvironment):
for month in mu.month:
monthly_mean[monthly_mean.index.dt.month == month, :] = mu[mu.month == month].values
- return monthly_mean
\ No newline at end of file
+ return monthly_mean