Adding the hvplot backend (JCSDA-internal#186)

## Description

This PR expands upon the hvplot backend introduced in JCSDA-internal#158. All other
diagnostic types are reformatted to have a base class and child classes
for each backend. Each base class includes two methods: `data_prep` and
`configure_plot`. `configure_plot` is a virtual method that is resolved
once a child class for either emcpy or hvplot is instantiated.

`figure_driver` is updated to reflect these changes

requirements.txt

@@ -22,3 +22,4 @@ hvplot
 nbconvert
 bokeh
 geopandas
+geoviews

src/eva/plotting/batch/base/diagnostics/density.py

@@ -1,32 +1,27 @@
 from eva.eva_path import return_eva_path
 from eva.utilities.config import get
 from eva.utilities.utils import get_schema, update_object, slice_var_from_str
-import emcpy.plots.plots
-import os
 import numpy as np
 
+from abc import ABC, abstractmethod
 
 # --------------------------------------------------------------------------------------------------
 
 
-class Density():
+class Density(ABC):
 
     """Base class for creating density plots."""
 
     def __init__(self, config, logger, dataobj):
 
         """
-        Creates a density plot based on the provided configuration and data.
+        Creates a density plot abstract class based on the provided configuration and data.
 
         Args:
             config (dict): A dictionary containing the configuration for the density plot.
             logger (Logger): An instance of the logger for logging messages.
             dataobj: An instance of the data object containing input data.
 
-        This class initializes and configures a density plot based on the provided configuration and
-        data. The density plot is created using a declarative plotting library from EMCPy
-        (https://github.com/NOAA-EMC/emcpy).
-
         Example:
 
             ::
@@ -46,45 +41,49 @@ def __init__(self, config, logger, dataobj):
                     density_plot = Density(config, logger, dataobj)
         """
 
+        self.config = config
+        self.logger = logger
+        self.dataobj = dataobj
+        self.plotobj = None
+        self.data = None
+
+# --------------------------------------------------------------------------------------------------
+
+    def data_prep(self):
+
+        """ Preparing data for configure_plot  """
+
         # Get the data to plot from the data_collection
         # ---------------------------------------------
-        varstr = config['data']['variable']
+        varstr = self.config['data']['variable']
         var_cgv = varstr.split('::')
 
         if len(var_cgv) != 3:
-            logger.abort('In Density the variable \'var_cgv\' does not appear to ' +
-                         'be in the required format of collection::group::variable.')
+            self.logger.abort('In Density the variable \'var_cgv\' does not appear to ' +
+                              'be in the required format of collection::group::variable.')
 
         # Optionally get the channel to plot
         channel = None
-        if 'channel' in config['data']:
-            channel = config['data'].get('channel')
+        if 'channel' in self.config['data']:
+            channel = self.config['data'].get('channel')
 
-        data = dataobj.get_variable_data(var_cgv[0], var_cgv[1], var_cgv[2], channel)
+        data = self.dataobj.get_variable_data(var_cgv[0], var_cgv[1], var_cgv[2], channel)
 
         # See if we need to slice data
-        data = slice_var_from_str(config['data'], data, logger)
+        data = slice_var_from_str(self.config['data'], data, self.logger)
 
         # Density data should be flattened
         data = data.flatten()
 
         # Missing data should also be removed
         mask = ~np.isnan(data)
-        data = data[mask]
-
-        # Create declarative plotting density object
-        # --------------------------------------------
-        self.plotobj = emcpy.plots.plots.Density(data)
-
-        # Get defaults from schema
-        # ------------------------
-        layer_schema = config.get('schema', os.path.join(return_eva_path(), 'plotting',
-                                                         'emcpy', 'defaults', 'density.yaml'))
-        config = get_schema(layer_schema, config, logger)
-        delvars = ['type', 'schema', 'data']
-        for d in delvars:
-            config.pop(d, None)
-        self.plotobj = update_object(self.plotobj, config, logger)
+        self.data = data[mask]
+
+# --------------------------------------------------------------------------------------------------
 
+    @abstractmethod
+    def configure_plot(self):
+        """ Virtual method for configuring plot based on selected backend  """
+        pass
 
 # --------------------------------------------------------------------------------------------------

src/eva/plotting/batch/base/diagnostics/histogram.py

@@ -1,31 +1,27 @@
 from eva.eva_path import return_eva_path
 from eva.utilities.config import get
 from eva.utilities.utils import get_schema, update_object, slice_var_from_str
-import emcpy.plots.plots
-import os
 import numpy as np
 
+from abc import ABC, abstractmethod
 
 # --------------------------------------------------------------------------------------------------
 
 
-class Histogram():
+class Histogram(ABC):
 
     """Base class for creating histogram plots."""
 
     def __init__(self, config, logger, dataobj):
 
         """
-        Creates a histogram plot based on the provided configuration and data.
+        Creates a histogram plot abstract class based on the provided configuration and data.
 
         Args:
             config (dict): A dictionary containing the configuration for the histogram plot.
             logger (Logger): An instance of the logger for logging messages.
             dataobj: An instance of the data object containing input data.
 
-        This class initializes and configures a histogram plot based on the provided configuration
-        and data. The histogram plot is created using a declarative plotting library from EMCPy
-        (https://github.com/NOAA-EMC/emcpy).
 
         Example:
 
@@ -46,9 +42,20 @@ def __init__(self, config, logger, dataobj):
                     histogram_plot = Histogram(config, logger, dataobj)
         """
 
+        self.config = config
+        self.logger = logger
+        self.dataobj = dataobj
+        self.data = None
+        self.plotobj = None
+
+# --------------------------------------------------------------------------------------------------
+
+    def data_prep(self):
+        """ Preparing data for configure_plot  """
+
         # Get the data to plot from the data_collection
         # ---------------------------------------------
-        varstr = config['data']['variable']
+        varstr = self.config['data']['variable']
         var_cgv = varstr.split('::')
 
         if len(var_cgv) != 3:
@@ -57,34 +64,26 @@ def __init__(self, config, logger, dataobj):
 
         # Optionally get the channel to plot
         channel = None
-        if 'channel' in config['data']:
-            channel = config['data'].get('channel')
+        if 'channel' in self.config['data']:
+            channel = self.config['data'].get('channel')
 
-        data = dataobj.get_variable_data(var_cgv[0], var_cgv[1], var_cgv[2], channel)
+        data = self.dataobj.get_variable_data(var_cgv[0], var_cgv[1], var_cgv[2], channel)
 
         # See if we need to slice data
-        data = slice_var_from_str(config['data'], data, logger)
+        data = slice_var_from_str(self.config['data'], data, self.logger)
 
         # Histogram data should be flattened
         data = data.flatten()
 
         # Missing data should also be removed
         mask = ~np.isnan(data)
-        data = data[mask]
-
-        # Create declarative plotting histogram object
-        # --------------------------------------------
-        self.plotobj = emcpy.plots.plots.Histogram(data)
-
-        # Get defaults from schema
-        # ------------------------
-        layer_schema = config.get('schema', os.path.join(return_eva_path(), 'plotting',
-                                                         'emcpy', 'defaults', 'histogram.yaml'))
-        config = get_schema(layer_schema, config, logger)
-        delvars = ['type', 'schema', 'data']
-        for d in delvars:
-            config.pop(d, None)
-        self.plotobj = update_object(self.plotobj, config, logger)
+        self.data = data[mask]
+
+# --------------------------------------------------------------------------------------------------
 
+    @abstractmethod
+    def configure_plot(self):
+        """ Virtual method for configuring plot based on selected backend  """
+        pass
 
 # --------------------------------------------------------------------------------------------------

src/eva/plotting/batch/base/diagnostics/horizontal_line.py

@@ -1,9 +1,7 @@
 from eva.eva_path import return_eva_path
 from eva.utilities.utils import get_schema, update_object
-import emcpy.plots.plots
-import os
-
 
+from abc import ABC, abstractmethod
 # --------------------------------------------------------------------------------------------------
 
 
@@ -14,15 +12,12 @@ class HorizontalLine():
     def __init__(self, config, logger, dataobj):
 
         """
-        Creates a horizontal line plot based on the provided configuration.
+        Creates a horizontal line plot abstract class based on the provided configuration.
 
         Args:
             config (dict): A dictionary containing the configuration for the horizontal line plot.
             logger (Logger): An instance of the logger for logging messages.
-
-        This class initializes and configures a horizontal line plot based on the provided
-        configuration. The horizontal line plot is created using a declarative plotting library from
-        EMCPy (https://github.com/NOAA-EMC/emcpy).
+            dataobj: An instance of the data object containing input data.
 
         Example:
 
@@ -38,24 +33,22 @@ def __init__(self, config, logger, dataobj):
                     horizontal_line_plot = HorizontalLine(config, logger)
         """
 
-        # Get the y value to plot
-        # -----------------------
-        yval = config['y']
+        self.logger = logger
+        self.config = config
+        self.yval = None
 
-        # Create declarative plotting HorizontalLine object
-        # -------------------------------------------
-        self.plotobj = emcpy.plots.plots.HorizontalLine(yval)
+# --------------------------------------------------------------------------------------------------
 
-        # Get defaults from schema
-        # ------------------------
-        layer_schema = config.get('schema', os.path.join(return_eva_path(), 'plotting',
-                                                         'emcpy', 'defaults',
-                                                         'horizontal_line.yaml'))
-        config = get_schema(layer_schema, config, logger)
-        delvars = ['type', 'schema']
-        for d in delvars:
-            config.pop(d, None)
-        self.plotobj = update_object(self.plotobj, config, logger)
+    def data_prep(self):
+        """ Preparing data for configure_plot  """
 
+        # Get the y value to plot
+        # -----------------------
+        self.yval = self.config['y']
 
 # --------------------------------------------------------------------------------------------------
+
+    @abstractmethod
+    def configure_plot(self):
+        """ Virtual method for configuring plot based on selected backend  """
+        pass