Resolve "release v1.4.0"

Closes #317 (closed)

mlair/plotting/abstract_plot_class.py

+"""Abstract plot class that should be used for preprocessing and postprocessing plots."""
+__author__ = "Lukas Leufen"
+__date__ = '2021-04-13'
+
+import logging
+import os
+
+from matplotlib import pyplot as plt
+
+
+class AbstractPlotClass:
+    """
+    Abstract class for all plotting routines to unify plot workflow.
+
+    Each inheritance requires a _plot method. Create a plot class like:
+
+    .. code-block:: python
+
+        class MyCustomPlot(AbstractPlotClass):
+
+            def __init__(self, plot_folder, *args, **kwargs):
+                super().__init__(plot_folder, "custom_plot_name")
+                self._data = self._prepare_data(*args, **kwargs)
+                self._plot(*args, **kwargs)
+                self._save()
+
+            def _prepare_data(*args, **kwargs):
+                <your custom data preparation>
+                return data
+
+            def _plot(*args, **kwargs):
+                <your custom plotting without saving>
+
+    The save method is already implemented in the AbstractPlotClass. If special saving is required (e.g. if you are
+    using pdfpages), you need to overwrite it. Plots are saved as .pdf with a resolution of 500dpi per default (can be
+    set in super class initialisation).
+
+    Methods like the shown _prepare_data() are optional. The only method required to implement is _plot.
+
+    If you want to add a time tracking module, just add the TimeTrackingWrapper as decorator around your custom plot
+    class. It will log the spent time if you call your plotting without saving the returned object.
+
+    .. code-block:: python
+
+        @TimeTrackingWrapper
+        class MyCustomPlot(AbstractPlotClass):
+            pass
+
+    Let's assume it takes a while to create this very special plot.
+
+    >>> MyCustomPlot()
+    INFO: MyCustomPlot finished after 00:00:11 (hh:mm:ss)
+
+    """
+
+    def __init__(self, plot_folder, plot_name, resolution=500, rc_params=None):
+        """Set up plot folder and name, and plot resolution (default 500dpi)."""
+        plot_folder = os.path.abspath(plot_folder)
+        if not os.path.exists(plot_folder):
+            os.makedirs(plot_folder)
+        self.plot_folder = plot_folder
+        self.plot_name = plot_name
+        self.resolution = resolution
+        if rc_params is None:
+            rc_params = {'axes.labelsize': 'large',
+                         'xtick.labelsize': 'large',
+                         'ytick.labelsize': 'large',
+                         'legend.fontsize': 'large',
+                         'axes.titlesize': 'large',
+                         }
+        self.rc_params = rc_params
+        self._update_rc_params()
+
+    def _plot(self, *args):
+        """Abstract plot class needs to be implemented in inheritance."""
+        raise NotImplementedError
+
+    def _save(self, **kwargs):
+        """Store plot locally. Name of and path to plot need to be set on initialisation."""
+        plot_name = os.path.join(self.plot_folder, f"{self.plot_name}.pdf")
+        logging.debug(f"... save plot to {plot_name}")
+        plt.savefig(plot_name, dpi=self.resolution, **kwargs)
+        plt.close('all')
+
+    def _update_rc_params(self):
+        plt.rcParams.update(self.rc_params)
+
+    @staticmethod
+    def _get_sampling(sampling):
+        if sampling == "daily":
+            return "D"
+        elif sampling == "hourly":
+            return "h"
+
+    @staticmethod
+    def get_dataset_colors():
+        """
+        Standard colors used for train-, val-, and test-sets during postprocessing
+        """
+        colors = {"train": "#e69f00", "val": "#009e73", "test": "#56b4e9", "train_val": "#000000"}  # hex code
+        return colors