diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index d3f6a56a2ec42e41454f0ced8853cb8d1001e495..a41c636b5ab17d2039f7976fca625e9c8e11ce6e 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -50,8 +50,8 @@ class PlotMonthlySummary(RunEnvironment):
def _prepare_data(self, stations: List) -> xr.DataArray:
"""
- Pre-process data required to plot. For each station, load locally saved predictions, extract the CNN and orig
- prediction and group them into monthly bins (no aggregation, only sorting them).
+ Pre-process data required to plot. For each station, load locally saved predictions, extract the CNN prediction
+ and the observation and group them into monthly bins (no aggregation, only sorting them).
:param stations: all stations to plot
:return: The entire data set, flagged with the corresponding month.
"""
@@ -65,10 +65,10 @@ class PlotMonthlySummary(RunEnvironment):
if len(data_cnn.shape) > 1:
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_obs = data.sel(type="obs", ahead=1).squeeze()
+ data_obs.coords["ahead"] = "obs"
- data_concat = xr.concat([data_orig, data_cnn], dim="ahead")
+ data_concat = xr.concat([data_obs, 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
@@ -189,7 +189,7 @@ class PlotStationMap(RunEnvironment):
plt.close('all')
-def plot_conditional_quantiles(stations: list, plot_folder: str = ".", rolling_window: int = 3, ref_name: str = 'orig',
+def plot_conditional_quantiles(stations: list, plot_folder: str = ".", rolling_window: int = 3, ref_name: str = 'obs',
pred_name: str = 'CNN', season: str = "", forecast_path: str = None,
plot_name_affix: str = "", units: str = "ppb"):
"""
@@ -222,7 +222,7 @@ def plot_conditional_quantiles(stations: list, plot_folder: str = ".", rolling_w
for station in stations:
file = os.path.join(forecast_path, f"forecasts_{station}_test.nc")
data_tmp = xr.open_dataarray(file)
- data_collector.append(data_tmp.loc[:, :, ['CNN', 'orig', 'OLS']].assign_coords(station=station))
+ data_collector.append(data_tmp.loc[:, :, ['CNN', 'obs', 'OLS']].assign_coords(station=station))
return xr.concat(data_collector, dim='station').transpose('index', 'type', 'ahead', 'station')
def segment_data(data):
@@ -252,7 +252,7 @@ def plot_conditional_quantiles(stations: list, plot_folder: str = ".", rolling_w
def labels(plot_type, data_unit="ppb"):
names = (f"forecast concentration (in {data_unit})", f"observed concentration (in {data_unit})")
- if plot_type == "orig":
+ if plot_type == "obs":
return names
else:
return names[::-1]
@@ -515,7 +515,7 @@ class PlotTimeSeries(RunEnvironment):
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"])
+ return data.sel(type=["CNN", "obs"])
def _plot(self, plot_folder):
pdf_pages = self._create_pdf_pages(plot_folder)
@@ -529,7 +529,7 @@ class PlotTimeSeries(RunEnvironment):
for i_half_of_year in range(factor):
pos = factor * i_year + i_half_of_year
plot_data = self._create_plot_data(data_year, factor, i_half_of_year)
- self._plot_orig(axes[pos], plot_data)
+ self._plot_obs(axes[pos], plot_data)
self._plot_ahead(axes[pos], plot_data)
if np.isnan(plot_data.values).all():
nan_list.append(pos)
@@ -574,10 +574,10 @@ class PlotTimeSeries(RunEnvironment):
label = f"{ahead}{self._sampling}"
ax.plot(index, plot_data.values, color=color[ahead-1], label=label)
- def _plot_orig(self, ax, data):
- orig_data = data.sel(type="orig", ahead=1)
+ def _plot_obs(self, ax, data):
+ obs_data = data.sel(type="obs", ahead=1)
index = data.index + np.timedelta64(1, self._sampling)
- ax.plot(index, orig_data.values, color=matplotlib.colors.cnames["green"], label="orig")
+ ax.plot(index, obs_data.values, color=matplotlib.colors.cnames["green"], label="obs")
@staticmethod
def _get_time_range(data):
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index 3c50799b939da2bc8517d1e58dd2c73baebe367b..06203c879872891f57c719040482fe052824c65e 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -43,7 +43,7 @@ class PostProcessing(RunEnvironment):
with TimeTracking():
self.train_ols_model()
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.")
+ "skip train_ols_model() whenever it is possible to save time.")
with TimeTracking():
self.make_prediction()
logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
@@ -64,9 +64,9 @@ class PostProcessing(RunEnvironment):
logging.debug("Run plotting routines...")
path = self.data_store.get("forecast_path", "general")
- plot_conditional_quantiles(self.test_data.stations, pred_name="CNN", ref_name="orig",
+ plot_conditional_quantiles(self.test_data.stations, pred_name="CNN", ref_name="obs",
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",
+ plot_conditional_quantiles(self.test_data.stations, pred_name="obs", 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", self.target_var,
@@ -113,15 +113,15 @@ class PostProcessing(RunEnvironment):
# ols
ols_prediction = self._create_ols_forecast(input_data, ols_prediction, mean, std, transformation_method)
- # orig pred
- orig_pred = self._create_orig_forecast(data, None, mean, std, transformation_method)
+ # observation
+ observation = self._create_observation(data, None, mean, std, transformation_method)
# merge all predictions
full_index = self.create_fullindex(data.data.indexes['datetime'], self._get_frequency())
all_predictions = self.create_forecast_arrays(full_index, list(data.label.indexes['window']),
CNN=nn_prediction,
persi=persistence_prediction,
- orig=orig_pred,
+ obs=observation,
OLS=ols_prediction)
# save all forecasts locally
@@ -134,7 +134,7 @@ class PostProcessing(RunEnvironment):
return getter.get(self._sampling, None)
@staticmethod
- def _create_orig_forecast(data, _, mean, std, transformation_method):
+ def _create_observation(data, _, mean, std, transformation_method):
return statistics.apply_inverse_transformation(data.label.copy(), mean, std, transformation_method)
def _create_ols_forecast(self, input_data, ols_prediction, mean, std, transformation_method):
@@ -227,7 +227,7 @@ class PostProcessing(RunEnvironment):
try:
data = self.train_val_data.get_data_generator(station)
mean, std, transformation_method = data.get_transformation_information(variable=self.target_var)
- external_data = self._create_orig_forecast(data, None, mean, std, transformation_method)
+ external_data = self._create_observation(data, None, mean, std, transformation_method)
external_data = external_data.squeeze("Stations").sel(window=1).drop(["window", "Stations", "variables"])
return external_data.rename({'datetime': 'index'})
except KeyError:
diff --git a/src/statistics.py b/src/statistics.py
index df73784df830d5f7b96bf0fcd18a65d362516f12..e3481d0e0f0561ac8a903648a69e92c6d6acc40d 100644
--- a/src/statistics.py
+++ b/src/statistics.py
@@ -126,12 +126,12 @@ class SkillScores(RunEnvironment):
return skill_score
- def _climatological_skill_score(self, data, mu_type=1, observation_name="orig", forecast_name="CNN", external_data=None):
+ def _climatological_skill_score(self, data, mu_type=1, observation_name="obs", forecast_name="CNN", external_data=None):
kwargs = {"external_data": external_data} if external_data is not None else {}
return self.__getattribute__(f"skill_score_mu_case_{mu_type}")(data, observation_name, forecast_name, **kwargs)
@staticmethod
- def general_skill_score(data, observation_name="orig", forecast_name="CNN", reference_name="persi"):
+ def general_skill_score(data, observation_name="obs", forecast_name="CNN", reference_name="persi"):
data = data.dropna("index")
observation = data.sel(type=observation_name)
forecast = data.sel(type=forecast_name)
@@ -159,12 +159,12 @@ class SkillScores(RunEnvironment):
suffix = {"mean": mean, "sigma": sigma, "r": r, "p": p}
return AI, BI, CI, data, suffix
- def skill_score_mu_case_1(self, data, observation_name="orig", forecast_name="CNN"):
+ def skill_score_mu_case_1(self, data, observation_name="obs", forecast_name="CNN"):
AI, BI, CI, data, _ = self.skill_score_pre_calculations(data, observation_name, forecast_name)
skill_score = np.array(AI - BI - CI)
return pd.DataFrame({"skill_score": [skill_score], "AI": [AI], "BI": [BI], "CI": [CI]}).to_xarray().to_array()
- def skill_score_mu_case_2(self, data, observation_name="orig", forecast_name="CNN"):
+ def skill_score_mu_case_2(self, data, observation_name="obs", forecast_name="CNN"):
AI, BI, CI, data, suffix = self.skill_score_pre_calculations(data, observation_name, forecast_name)
monthly_mean = self.create_monthly_mean_from_daily_data(data)
data = xr.concat([data, monthly_mean], dim="type")
@@ -177,14 +177,14 @@ class SkillScores(RunEnvironment):
skill_score = np.array((AI - BI - CI - AII + BII) / (1 - AII + BII))
return pd.DataFrame({"skill_score": [skill_score], "AII": [AII], "BII": [BII]}).to_xarray().to_array()
- def skill_score_mu_case_3(self, data, observation_name="orig", forecast_name="CNN", external_data=None):
+ def skill_score_mu_case_3(self, data, observation_name="obs", forecast_name="CNN", external_data=None):
AI, BI, CI, data, suffix = self.skill_score_pre_calculations(data, observation_name, forecast_name)
mean, sigma = suffix["mean"], suffix["sigma"]
AIII = (((external_data.mean().values - mean.loc[observation_name]) / sigma.loc[observation_name])**2).values
skill_score = np.array((AI - BI - CI + AIII) / 1 + AIII)
return pd.DataFrame({"skill_score": [skill_score], "AIII": [AIII]}).to_xarray().to_array()
- def skill_score_mu_case_4(self, data, observation_name="orig", forecast_name="CNN", external_data=None):
+ def skill_score_mu_case_4(self, data, observation_name="obs", forecast_name="CNN", external_data=None):
AI, BI, CI, data, suffix = self.skill_score_pre_calculations(data, observation_name, forecast_name)
monthly_mean_external = self.create_monthly_mean_from_daily_data(external_data, columns=data.type.values, index=data.index)
data = xr.concat([data, monthly_mean_external], dim="type")