diff --git a/mlair/configuration/defaults.py b/mlair/configuration/defaults.py
index 5d70d9f0d76817591b92cdb5ccddd95ad0d5d006..4d762581ecc64ba4f4335d77a48bd1c1d913e01c 100644
--- a/mlair/configuration/defaults.py
+++ b/mlair/configuration/defaults.py
@@ -25,6 +25,7 @@ DEFAULT_PERMUTE_DATA = False
DEFAULT_BATCH_SIZE = int(256 * 2)
DEFAULT_EPOCHS = 20
DEFAULT_TARGET_VAR = "o3"
+DEFAULT_TARGET_VAR_UNIT = "ppb"
DEFAULT_TARGET_DIM = "variables"
DEFAULT_WINDOW_LEAD_TIME = 3
DEFAULT_WINDOW_DIM = "window"
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 4a8196056ddcfe856c279fd7108436deeba160a5..0d81764541eda54697248989aa9e55ae66ff6a5d 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -5,6 +5,7 @@ __date__ = '2020-11-23'
import logging
import math
import os
+import sys
import warnings
from typing import Dict, List, Tuple
@@ -171,11 +172,13 @@ class PlotConditionalQuantiles(AbstractPlotClass): # pragma: no cover
warnings.filterwarnings("ignore", message="Attempted to set non-positive bottom ylim on a log-scaled axis.")
def __init__(self, stations: List, data_pred_path: str, plot_folder: str = ".", plot_per_seasons=True,
- rolling_window: int = 3, model_name: str = "nn", obs_name: str = "obs", **kwargs):
+ rolling_window: int = 3, model_name: str = "nn", obs_name: str = "obs", target_var_unit: str = "ppb",
+ **kwargs):
"""Initialise."""
super().__init__(plot_folder, "conditional_quantiles")
self._data_pred_path = data_pred_path
self._stations = stations
+ self.target_var_unit = target_var_unit
self._rolling_window = rolling_window
self._model_name = model_name
self._obs_name = obs_name
@@ -185,14 +188,13 @@ class PlotConditionalQuantiles(AbstractPlotClass): # pragma: no cover
self._bins = self._get_bins_from_rage_of_data()
self._plot()
- @staticmethod
- def _get_opts(kwargs):
+ def _get_opts(self, kwargs):
"""Extract options from kwargs."""
return {"q": kwargs.get("q", [.1, .25, .5, .75, .9]),
"linetype": kwargs.get("linetype", [':', '-.', '--', '-.', ':']),
"legend": kwargs.get("legend", ['.10th and .90th quantile', '.25th and .75th quantile',
'.50th quantile', 'reference 1:1']),
- "data_unit": kwargs.get("data_unit", "ppb"), }
+ "data_unit": kwargs.get("data_unit", self.target_var_unit), }
def _load_data(self) -> xr.DataArray:
"""
@@ -312,10 +314,20 @@ class PlotConditionalQuantiles(AbstractPlotClass): # pragma: no cover
def _plot_seasons(self):
"""Create seasonal plots."""
for season in self._seasons:
- self._plot_base(data=self._data.where(self._data['index.season'] == season), x_model=self._model_name,
- y_model=self._obs_name, plot_name_affix="cali-ref", season=season)
- self._plot_base(data=self._data.where(self._data['index.season'] == season), x_model=self._obs_name,
- y_model=self._model_name, plot_name_affix="like-base", season=season)
+ try:
+ self._plot_base(data=self._data.where(self._data['index.season'] == season), x_model=self._model_name,
+ y_model=self._obs_name, plot_name_affix="cali-ref", season=season)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotConditionalQuantiles._plot_seasons: {season}, cali-ref"
+ f" due to the following error:"
+ f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
+ try:
+ self._plot_base(data=self._data.where(self._data['index.season'] == season), x_model=self._obs_name,
+ y_model=self._model_name, plot_name_affix="like-base", season=season)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotConditionalQuantiles._plot_seasons: {season}, like-base"
+ f" due to the following error:"
+ f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
def _plot_all(self):
"""Plot overall conditional quantiles on full data."""
diff --git a/mlair/run_modules/experiment_setup.py b/mlair/run_modules/experiment_setup.py
index 251cf9ac0dcbd524ae4ba6bd9d3910fbee2accb8..9f9e9bc02132991986802ee2ec75891e1910dbaf 100644
--- a/mlair/run_modules/experiment_setup.py
+++ b/mlair/run_modules/experiment_setup.py
@@ -23,7 +23,7 @@ from mlair.configuration.defaults import DEFAULT_STATIONS, DEFAULT_VAR_ALL_DICT,
DEFAULT_USE_MULTIPROCESSING, DEFAULT_USE_MULTIPROCESSING_ON_DEBUG, DEFAULT_MAX_NUMBER_MULTIPROCESSING, \
DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_TYPE, DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_METHOD, DEFAULT_OVERWRITE_LAZY_DATA, \
DEFAULT_UNCERTAINTY_ESTIMATE_BLOCK_LENGTH, DEFAULT_UNCERTAINTY_ESTIMATE_EVALUATE_COMPETITORS, \
- DEFAULT_UNCERTAINTY_ESTIMATE_N_BOOTS, DEFAULT_DO_UNCERTAINTY_ESTIMATE
+ DEFAULT_UNCERTAINTY_ESTIMATE_N_BOOTS, DEFAULT_DO_UNCERTAINTY_ESTIMATE, DEFAULT_TARGET_VAR_UNIT
# <<<<<<< HEAD
# DEFAULT_BOOTSTRAP_TYPE, DEFAULT_BOOTSTRAP_METHOD, DEFAULT_OVERWRITE_LAZY_DATA
#
@@ -232,7 +232,7 @@ class ExperimentSetup(RunEnvironment):
max_number_multiprocessing: int = None, start_script: Union[Callable, str] = None,
overwrite_lazy_data: bool = None, uncertainty_estimate_block_length: str = None,
uncertainty_estimate_evaluate_competitors: bool = None, uncertainty_estimate_n_boots: int = None,
- do_uncertainty_estimate: bool = None, **kwargs):
+ do_uncertainty_estimate: bool = None, target_var_unit: str = None, **kwargs):
# create run framework
super().__init__()
@@ -329,6 +329,7 @@ class ExperimentSetup(RunEnvironment):
self._set_param("target_var", target_var, default=DEFAULT_TARGET_VAR)
self._set_param("target_dim", target_dim, default=DEFAULT_TARGET_DIM)
self._set_param("window_lead_time", window_lead_time, default=DEFAULT_WINDOW_LEAD_TIME)
+ self._set_param("target_var_unit", target_var_unit, default=DEFAULT_TARGET_VAR_UNIT)
# interpolation
self._set_param("dimensions", dimensions, default=DEFAULT_DIMENSIONS)
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 7c42b9a34c75e332bb18b8ac465486f4eaa189a7..e148c9f1bef8b0d8b5fd14686dc10588d01c8b58 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -81,6 +81,7 @@ class PostProcessing(RunEnvironment):
self.train_val_data = self.data_store.get("data_collection", "train_val")
self.plot_path: str = self.data_store.get("plot_path")
self.target_var = self.data_store.get("target_var")
+ self.target_var_unit = self.data_store.get("target_var_unit")
self._sampling = self.data_store.get("sampling")
self.window_lead_time = extract_value(self.data_store.get("output_shape", "model"))
self.skill_scores = None
@@ -484,7 +485,8 @@ class PostProcessing(RunEnvironment):
try:
if "PlotConditionalQuantiles" in plot_list:
- PlotConditionalQuantiles(self.test_data.keys(), data_pred_path=path, plot_folder=self.plot_path)
+ PlotConditionalQuantiles(self.test_data.keys(), data_pred_path=path, plot_folder=self.plot_path,
+ target_var_unit=self.target_var_unit)
except Exception as e:
logging.error(f"Could not create plot PlotConditionalQuantiles due to the following error:"
f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
@@ -492,7 +494,7 @@ class PostProcessing(RunEnvironment):
try:
if "PlotMonthlySummary" in plot_list:
PlotMonthlySummary(self.test_data.keys(), path, r"forecasts_%s_test.nc", self.target_var,
- plot_folder=self.plot_path)
+ plot_folder=self.plot_path, target_var_unit=self.target_var_unit)
except Exception as e:
logging.error(f"Could not create plot PlotMonthlySummary due to the following error:"
f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
@@ -585,7 +587,7 @@ class PostProcessing(RunEnvironment):
PlotSampleUncertaintyFromBootstrap(
data=self.uncertainty_estimate, plot_folder=self.plot_path, model_type_dim=self.model_type_dim,
dim_name_boots=self.uncertainty_estimate_boot_dim, error_measure="mean squared error",
- error_unit=r"ppb$^2$", block_length=block_length)
+ error_unit=fr"{self.target_var_unit}$^2$", block_length=block_length)
except Exception as e:
logging.error(f"Could not create plot PlotSampleUncertaintyFromBootstrap due to the following error: {e}"
f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")