Add Contour and Filled Contour Plotting Capabilities (JCSDA-internal#216

)

## Description

This PR adds the ability to plot contour and filled contour plots from
EMCPy's plotting capabilities. The appropriate driver scripts and
configuration yamls have been created.

## Dependencies
None.

## Impact

Please list the other repositories (if any) that this PR will require
changes in (example below):

None.

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

@@ -0,0 +1,99 @@
+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 numpy as np
+
+from abc import ABC, abstractmethod
+
+# --------------------------------------------------------------------------------------------------
+
+
+class ContourPlot(ABC):
+
+    """Base class for creating Contour plots."""
+
+    def __init__(self, config, logger, dataobj):
+
+        """
+        Creates a Contour plot abstract class based on the provided configuration.
+
+        Args:
+            config (dict): A dictionary containing the configuration for the contour plot on a map.
+            logger (Logger): An instance of the logger for logging messages.
+            dataobj: An instance of the data object containing input data.
+
+
+        Example:
+
+            ::
+
+                    config = {
+                        "x": {"variable": "collection::group::variable"},
+                        "y": {"variable": "collection::group::variable"},
+                        "z": {"variable": "collection::group::variable"},
+                        "plot_property": "property_value",
+                        "plot_option": "option_value",
+                        "schema": "path_to_schema_file.yaml"
+                    }
+                    logger = Logger()
+                    contour_plot = ContourPlot(config, logger, None)
+        """
+
+        self.config = config
+        self.logger = logger
+        self.dataobj = dataobj
+        self.xdata = []
+        self.ydata = []
+        self.zdata = []
+        self.plotobj = None
+
+# --------------------------------------------------------------------------------------------------
+
+    def data_prep(self):
+        """ Preparing data for configure_plot  """
+
+        # Get the data to plot from the data_collection
+        # ---------------------------------------------
+        var0 = self.config['x']['variable']
+        var1 = self.config['y']['variable']
+        var2 = self.config['z']['variable']
+
+        var0_cgv = var0.split('::')
+        var1_cgv = var1.split('::')
+        var2_cgv = var2.split('::')
+
+        if len(var0_cgv) != 3:
+            self.logger.abort('Contour: comparison first var \'var0\' does not appear to ' +
+                              'be in the required format of collection::group::variable.')
+        if len(var1_cgv) != 3:
+            self.logger.abort('Contour: comparison second var \'var1\' does not appear to ' +
+                              'be in the required format of collection::group::variable.')
+        if len(var2_cgv) != 3:
+            self.logger.abort('Contour: comparison second var \'var2\' does not appear to ' +
+                              'be in the required format of collection::group::variable.')
+
+        # Optionally get the channel to plot
+        channel = None
+        if 'channel' in self.config:
+            channel = self.config.get('channel')
+
+        xdata = self.dataobj.get_variable_data(var0_cgv[0], var0_cgv[1], var0_cgv[2], channel)
+        ydata = self.dataobj.get_variable_data(var1_cgv[0], var1_cgv[1], var1_cgv[2], channel)
+        zdata = self.dataobj.get_variable_data(var2_cgv[0], var2_cgv[1], var2_cgv[2], channel)
+
+        # see if we need to slice data
+        xdata = slice_var_from_str(self.config['x'], xdata, self.logger)
+        ydata = slice_var_from_str(self.config['y'], ydata, self.logger)
+        zdata = slice_var_from_str(self.config['z'], zdata, self.logger)
+
+        # contour data should be flattened
+        xdata = xdata.flatten()
+        ydata = ydata.flatten()
+        zdata = zdata.flatten()
+
+    @abstractmethod
+    def configure_plot(self):
+        """ Virtual method for configuring plot based on selected backend  """
+        pass
+
+# --------------------------------------------------------------------------------------------------

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

@@ -0,0 +1,100 @@
+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 numpy as np
+
+from abc import ABC, abstractmethod
+
+# --------------------------------------------------------------------------------------------------
+
+
+class FilledContourPlot(ABC):
+
+    """Base class for creating Filled Contour plots."""
+
+    def __init__(self, config, logger, dataobj):
+
+        """
+        Creates a Filled Contour plot abstract class based on the provided configuration.
+
+        Args:
+            config (dict): A dictionary containing the configuration for the filled contour
+                           plot on a map.
+            logger (Logger): An instance of the logger for logging messages.
+            dataobj: An instance of the data object containing input data.
+
+
+        Example:
+
+            ::
+
+                    config = {
+                        "x": {"variable": "collection::group::variable"},
+                        "y": {"variable": "collection::group::variable"},
+                        "z": {"variable": "collection::group::variable"},
+                        "plot_property": "property_value",
+                        "plot_option": "option_value",
+                        "schema": "path_to_schema_file.yaml"
+                    }
+                    logger = Logger()
+                    filled_contour_plot = FilledContourPlot(config, logger, None)
+        """
+
+        self.config = config
+        self.logger = logger
+        self.dataobj = dataobj
+        self.xdata = []
+        self.ydata = []
+        self.zdata = []
+        self.plotobj = None
+
+# --------------------------------------------------------------------------------------------------
+
+    def data_prep(self):
+        """ Preparing data for configure_plot  """
+
+        # Get the data to plot from the data_collection
+        # ---------------------------------------------
+        var0 = self.config['x']['variable']
+        var1 = self.config['y']['variable']
+        var2 = self.config['z']['variable']
+
+        var0_cgv = var0.split('::')
+        var1_cgv = var1.split('::')
+        var2_cgv = var2.split('::')
+
+        if len(var0_cgv) != 3:
+            self.logger.abort('Contour: comparison first var \'var0\' does not appear to ' +
+                              'be in the required format of collection::group::variable.')
+        if len(var1_cgv) != 3:
+            self.logger.abort('Contour: comparison second var \'var1\' does not appear to ' +
+                              'be in the required format of collection::group::variable.')
+        if len(var2_cgv) != 3:
+            self.logger.abort('Contour: comparison third var \'var2\' does not appear to ' +
+                              'be in the required format of collection::group::variable.')
+
+        # Optionally get the channel to plot
+        channel = None
+        if 'channel' in self.config:
+            channel = self.config.get('channel')
+
+        xdata = self.dataobj.get_variable_data(var0_cgv[0], var0_cgv[1], var0_cgv[2], channel)
+        ydata = self.dataobj.get_variable_data(var1_cgv[0], var1_cgv[1], var1_cgv[2], channel)
+        zdata = self.dataobj.get_variable_data(var2_cgv[0], var2_cgv[1], var2_cgv[2], channel)
+
+        # see if we need to slice data
+        xdata = slice_var_from_str(self.config['x'], xdata, self.logger)
+        ydata = slice_var_from_str(self.config['y'], ydata, self.logger)
+        zdata = slice_var_from_str(self.config['z'], zdata, self.logger)
+
+        # contour data should be flattened
+        xdata = xdata.flatten()
+        ydata = ydata.flatten()
+        zdata = zdata.flatten()
+
+    @abstractmethod
+    def configure_plot(self):
+        """ Virtual method for configuring plot based on selected backend  """
+        pass
+
+# --------------------------------------------------------------------------------------------------

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

@@ -0,0 +1,75 @@
+from eva.eva_path import return_eva_path
+from eva.utilities.utils import get_schema, update_object, slice_var_from_str
+
+from abc import ABC, abstractmethod
+
+# --------------------------------------------------------------------------------------------------
+
+
+class MapContour(ABC):
+
+    """Base class for creating map contour plots."""
+
+    def __init__(self, config, logger, dataobj):
+
+        """
+        Creates a contour map plot abstract class based on the provided configuration.
+
+        Args:
+            config (dict): A dictionary containing the configuration for the contour map plot.
+            logger (Logger): An instance of the logger for logging messages.
+            dataobj: An instance of the data object containing input data.
+
+        Example:
+
+            ::
+
+                    config = {
+                        "longitude": {"variable": "collection::group::variable"},
+                        "latitude": {"variable": "collection::group::variable"},
+                        "data": {"variable": "collection::group::variable"},
+                        "plot_property": "property_value",
+                        "plot_option": "option_value",
+                        "schema": "path_to_schema_file.yaml"
+                    }
+                    logger = Logger()
+                    map_plot = MapContour(config, logger, None)
+        """
+        self.collection = None
+        self.datavar_name = None
+
+        self.config = config
+        self.logger = logger
+        self.dataobj = dataobj
+        self.lonvar = []
+        self.latvar = []
+        self.datavar = []
+        self.plotobj = None
+
+# --------------------------------------------------------------------------------------------------
+
+    def data_prep(self):
+        """ Preparing data for configure_plot  """
+
+        # prepare data based on config
+        lonvar_cgv = self.config['longitude']['variable'].split('::')
+        self.collection = lonvar_cgv[0]
+        self.lonvar = self.dataobj.get_variable_data(lonvar_cgv[0], lonvar_cgv[1],
+                                                     lonvar_cgv[2], None)
+        self.lonvar = slice_var_from_str(self.config['longitude'], self.lonvar, self.logger)
+        latvar_cgv = self.config['latitude']['variable'].split('::')
+        self.latvar = self.dataobj.get_variable_data(latvar_cgv[0], latvar_cgv[1],
+                                                     latvar_cgv[2], None)
+        self.latvar = slice_var_from_str(self.config['latitude'], self.latvar, self.logger)
+        datavar_cgv = self.config['data']['variable'].split('::')
+        self.datavar_name = datavar_cgv[1] + '::' + datavar_cgv[2]
+        self.datavar = self.dataobj.get_variable_data(datavar_cgv[0], datavar_cgv[1],
+                                                      datavar_cgv[2], None)
+        self.datavar = slice_var_from_str(self.config['data'], self.datavar, self.logger)
+
+# --------------------------------------------------------------------------------------------------
+
+    @abstractmethod
+    def configure_plot(self):
+        """ Virtual method for configuring plot based on selected backend  """
+        pass

src/eva/plotting/batch/emcpy/defaults/contour_plot.yaml

@@ -0,0 +1,9 @@
+# Contour default yaml
+corner_mask: False
+colors: 'black'
+alpha:
+cmap:
+norm:
+vmin:
+vmax:
+levels:

src/eva/plotting/batch/emcpy/defaults/filled_contour_plot.yaml

@@ -0,0 +1,13 @@
+# Filled Contour default yaml
+corner_mask: False
+colors:
+alpha:
+cmap: 'viridis'
+norm:
+vmin:
+vmax:
+origin:
+extent:
+locator:
+extend:
+colorbar: True

src/eva/plotting/batch/emcpy/defaults/map_contour.yaml

@@ -0,0 +1,11 @@
+# MapContour class default schema
+levels:
+clabel: False
+colors: 'black'
+linewidths: 1.5
+linestyles: '-'
+cmap:
+vmin:
+vmax:
+alpha:
+colorbar: False

src/eva/plotting/batch/emcpy/diagnostics/emcpy_contour_plot.py

@@ -0,0 +1,50 @@
+from eva.eva_path import return_eva_path
+from eva.utilities.config import get
+from eva.utilities.utils import get_schema, update_object
+import emcpy.plots.plots
+import os
+
+from eva.plotting.batch.base.diagnostics.contour_plot import ContourPlot
+
+# --------------------------------------------------------------------------------------------------
+
+
+class EmcpyContourPlot(ContourPlot):
+
+    """
+    EmcpyContourPlot class is a subclass of the ContourPlot class, designed for configuring
+    and plotting contour plot visualizations using the emcpy library.
+
+    Attributes:
+        Inherits attributes from the ContourPlot class.
+
+    Methods:
+        configure_plot(): Configures the plotting settings for the contour plot.
+    """
+
+    def configure_plot(self):
+
+        """
+        Configures the plotting settings for the contour plot.
+
+        Returns:
+            plotobj: The configured plot object for emcpy contour plots.
+        """
+
+        # Create declarative plotting ContourPlot object
+        # -------------------------------------------
+        self.plotobj = emcpy.plots.plots.ContourPlot(self.xdata, self.ydata, self.z)
+
+        # Get defaults from schema
+        # ------------------------
+        layer_schema = self.config.get('schema', os.path.join(return_eva_path(), 'plotting',
+                                       'batch', 'emcpy', 'defaults', 'contour_plot.yaml'))
+        new_config = get_schema(layer_schema, self.config, self.logger)
+        delvars = ['x', 'y', 'z', 'type', 'schema']
+        for d in delvars:
+            new_config.pop(d, None)
+        self.plotobj = update_object(self.plotobj, new_config, self.logger)
+
+        return self.plotobj
+
+# --------------------------------------------------------------------------------------------------