diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index 97d326bc87c72142ab20ea95effbd88f490f2937..65b7fc0f6dd8a537fa8ded2dbc632e4668eaf0d3 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -477,3 +477,57 @@ class PlotCompetitiveSkillScore(RunEnvironment):
logging.debug(f"... save plot to {plot_name}")
plt.savefig(plot_name, dpi=500)
plt.close()
+
+
+class PlotTimeSeries(RunEnvironment):
+
+ def __init__(self, stations: List, data_path: str, name: str, window_lead_time: int = None, plot_folder: str = "."):
+ super().__init__()
+ self._data_path = data_path
+ self._data_name = name
+ self._stations = stations
+ self._window_lead_time = self._get_window_lead_time(window_lead_time)
+ self._plot(plot_folder)
+
+ def _get_window_lead_time(self, window_lead_time: int):
+ """
+ Extract the lead time from data and arguments. If window_lead_time is not given, extract this information from
+ data itself by the number of ahead dimensions. If given, check if data supports the give length. If the number
+ of ahead dimensions in data is lower than the given lead time, data's lead time is used.
+ :param window_lead_time: lead time from arguments to validate
+ :return: validated lead time, comes either from given argument or from data itself
+ """
+ ahead_steps = len(self._load_data(self._stations[0]).ahead)
+ if window_lead_time is None:
+ window_lead_time = ahead_steps
+ return min(ahead_steps, window_lead_time)
+
+ def _load_data(self, station):
+ logging.debug(f"... preprocess station {station}")
+ file_name = os.path.join(self._data_path, self._data_name % station)
+ data = xr.open_dataarray(file_name)
+ return data.sel(type=["CNN", "orig"])
+
+ def _plot(self, plot_folder):
+ f, axes = plt.subplots(len(self._stations), sharex="all")
+ color_palette = [matplotlib.colors.cnames["green"]] + sns.color_palette("Blues_d", self._window_lead_time).as_hex()
+ for pos, station in enumerate(self._stations):
+ data = self._load_data(station)
+ axes[pos].plot(data.index+ np.timedelta64(1, "D"), data.sel(type="CNN", ahead=1).values, color=color_palette[0])
+ for ahead in data.coords["ahead"].values:
+ plot_data = data.sel(type="CNN", ahead=ahead).drop(["type", "ahead"]).squeeze()
+ axes[pos].plot(plot_data.index + np.timedelta64(int(ahead), "D"), plot_data.values, color=color_palette[ahead])
+ self._save(plot_folder)
+
+ @staticmethod
+ def _save(plot_folder):
+ """
+ Standard save method to store plot locally. The name of this plot is static.
+ :param plot_folder: path to save the plot
+ """
+ plot_name = os.path.join(os.path.abspath(plot_folder), 'test_timeseries_plot.pdf')
+ logging.debug(f"... save plot to {plot_name}")
+ plt.savefig(plot_name, dpi=500)
+ plt.close('all')
+
+
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index e6f271ce3cc6cf2548ff5b06ba40e2fd509f8c8d..1db58b8f3c26e76e816ac5c8059d1fc9b2d20e8a 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -16,7 +16,8 @@ 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_conditional_quantiles
-from src.plotting.postprocessing_plotting import PlotMonthlySummary, PlotStationMap, PlotClimatologicalSkillScore, PlotCompetitiveSkillScore
+from src.plotting.postprocessing_plotting import PlotMonthlySummary, PlotStationMap, PlotClimatologicalSkillScore, \
+ PlotCompetitiveSkillScore, PlotTimeSeries
from src.datastore import NameNotFoundInDataStore
from src.helpers import TimeTracking
@@ -42,10 +43,10 @@ class PostProcessing(RunEnvironment):
logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
"skip make_prediction() whenever it is possible to save time.")
with TimeTracking():
- preds_for_all_stations = self.make_prediction()
+ self.make_prediction()
logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
"skip make_prediction() whenever it is possible to save time.")
- self.skill_scores = self.calculate_skill_scores()
+ # self.skill_scores = self.calculate_skill_scores()
self.plot()
def _load_model(self):
@@ -64,17 +65,18 @@ class PostProcessing(RunEnvironment):
path = self.data_store.get("forecast_path", "general")
target_var = self.data_store.get("target_var", "general")
- plot_conditional_quantiles(self.test_data.stations, pred_name="CNN", ref_name="orig",
- forecast_path=path, plot_name_affix="cali-ref", plot_folder=self.plot_path)
- 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)
- PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)
- PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", target_var,
- plot_folder=self.plot_path)
- PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
- PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
- extra_name_tag="all_terms_", model_setup="CNN")
- PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path, model_setup="CNN")
+ # plot_conditional_quantiles(self.test_data.stations, pred_name="CNN", ref_name="orig",
+ # forecast_path=path, plot_name_affix="cali-ref", plot_folder=self.plot_path)
+ # 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)
+ # PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)
+ # PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", target_var,
+ # plot_folder=self.plot_path)
+ # PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, model_setup="CNN")
+ # PlotClimatologicalSkillScore(self.skill_scores[1], plot_folder=self.plot_path, score_only=False,
+ # extra_name_tag="all_terms_", model_setup="CNN")
+ # PlotCompetitiveSkillScore(self.skill_scores[0], plot_folder=self.plot_path, model_setup="CNN")
+ PlotTimeSeries(self.test_data.stations, path, r"forecasts_%s_test.nc", plot_folder=self.plot_path)
def calculate_test_score(self):
test_score = self.model.evaluate_generator(generator=self.test_data_distributed.distribute_on_batches(),
@@ -93,12 +95,11 @@ class PostProcessing(RunEnvironment):
def make_prediction(self, freq="1D"):
logging.debug("start make_prediction")
- nn_prediction_all_stations = []
- for i, v in enumerate(self.test_data):
+ for i, _ in enumerate(self.test_data):
data = self.test_data.get_data_generator(i)
nn_prediction, persistence_prediction, ols_prediction = self._create_empty_prediction_arrays(data, count=3)
- input_data = self.test_data[i][0]
+ input_data = data.get_transposed_history()
# get scaling parameters
mean, std, transformation_method = data.get_transformation_information(variable='o3')
@@ -129,10 +130,6 @@ class PostProcessing(RunEnvironment):
file = os.path.join(path, f"forecasts_{data.station[0]}_test.nc")
all_predictions.to_netcdf(file)
- # save nn forecast to return variable
- nn_prediction_all_stations.append(nn_prediction)
- return nn_prediction_all_stations
-
@staticmethod
def _create_orig_forecast(data, _, mean, std, transformation_method):
return statistics.apply_inverse_transformation(data.label.copy(), mean, std, transformation_method)