From c7f4a9203a809a0ee78ddd7288380c51ed556a87 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Fri, 16 Jul 2021 14:50:19 +0200
Subject: [PATCH] cleanup, remove commented code
---
mlair/plotting/postprocessing_plotting.py | 3 ---
mlair/run_modules/post_processing.py | 14 ++------------
2 files changed, 2 insertions(+), 15 deletions(-)
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index e5080f6e..eef9208a 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -741,7 +741,6 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
data = self._data
self.raise_error_if_separate_vars_do_not_exist(data, separate_vars, self._x_name)
all_variables = self._get_unique_values_from_column_of_df(data, self._x_name)
- # remaining_vars = helpers.list_pop(all_variables, separate_vars) #remove_items
remaining_vars = helpers.remove_items(all_variables, separate_vars)
data_first = self._select_data(df=data, variables=separate_vars, column_name=self._x_name)
data_second = self._select_data(df=data, variables=remaining_vars, column_name=self._x_name)
@@ -945,8 +944,6 @@ class PlotTimeSeries:
def _plot_obs(self, ax, data):
ahead = 1
obs_data = data.sel(type="obs", ahead=ahead).shift(index=ahead)
- # index = data.index + np.timedelta64(1, self._sampling)
- # ax.plot(index, obs_data.values, color=matplotlib.colors.cnames["green"], label="obs")
ax.plot(obs_data, color=matplotlib.colors.cnames["green"], label="obs")
@staticmethod
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 0c530400..c4ce0088 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -185,7 +185,6 @@ class PostProcessing(RunEnvironment):
number_of_bootstraps = self.data_store.get("number_of_bootstraps", "postprocessing")
dims = ["index", self.ahead_dim, "type"]
for station in self.test_data:
- # logging.info(str(station))
X, Y = None, None
bootstraps = BootStraps(station, number_of_bootstraps, bootstrap_type=bootstrap_type,
bootstrap_method=bootstrap_method)
@@ -252,7 +251,6 @@ class PostProcessing(RunEnvironment):
boot_var = boot_set if isinstance(boot_set, str) else f"{boot_set[0]}_{boot_set[1]}"
file_name = os.path.join(forecast_path,
f"bootstraps_{station}_{boot_var}_{bootstrap_type}_{bootstrap_method}.nc")
- # boot_data = xr.open_dataarray(file_name)
with xr.open_dataarray(file_name) as da:
boot_data = da.load()
boot_data = boot_data.combine_first(labels).combine_first(orig)
@@ -271,14 +269,12 @@ class PostProcessing(RunEnvironment):
if prediction_name is None:
prediction_name = self.forecast_indicator
file = os.path.join(path, file_name)
- # prediction = xr.open_dataarray(file).sel(type=prediction_name).squeeze()
with xr.open_dataarray(file) as da:
prediction = da.load().sel(type=prediction_name).squeeze()
return self.repeat_data(prediction, number_of_bootstraps)
- # vals = np.tile(prediction.data, (number_of_bootstraps, 1))
- # return vals[~np.isnan(vals).any(axis=1), :]
- def repeat_data(self, data, number_of_repetition):
+ @staticmethod
+ def repeat_data(data, number_of_repetition):
if isinstance(data, xr.DataArray):
data = data.data
vals = np.tile(data, (number_of_repetition, 1))
@@ -349,9 +345,6 @@ class PostProcessing(RunEnvironment):
model_setup=self.forecast_indicator, sampling=self._sampling,
ahead_dim=self.ahead_dim, separate_vars=to_list(self.target_var),
bootstrap_type=boot_type, bootstrap_method=boot_method)
- # PlotBootstrapSkillScore(self.bootstrap_skill_scores, plot_folder=self.plot_path,
- # model_setup=self.forecast_indicator, sampling=self._sampling,
- # ahead_dim=self.ahead_dim, separate_vars=to_list(self.target_var))
except Exception as e:
logging.error(f"Could not create plot PlotBootstrapSkillScore due to the following error: {e}")
@@ -530,7 +523,6 @@ class PostProcessing(RunEnvironment):
file = os.path.join(path, f"forecasts_{station_name}_test.nc")
with xr.open_dataarray(file) as da:
data = da.load()
- # data = xr.open_dataarray(file)
forecast = data.sel(type=[self.forecast_indicator])
forecast.coords["type"] = [competitor_name]
return forecast
@@ -688,7 +680,6 @@ class PostProcessing(RunEnvironment):
file = os.path.join(path, f"forecasts_{str(station)}_train_val.nc")
with xr.open_dataarray(file) as da:
return da.load()
- # return xr.open_dataarray(file)
except (IndexError, KeyError, FileNotFoundError):
return None
@@ -705,7 +696,6 @@ class PostProcessing(RunEnvironment):
file = os.path.join(path, f"forecasts_{str(station)}_test.nc")
with xr.open_dataarray(file) as da:
return da.load()
- # return xr.open_dataarray(file)
except (IndexError, KeyError, FileNotFoundError):
return None
--
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