voyager/core.py
def plot(self, trajectory_file = None, **kwargs):
        """Utility function to statically visualize the calculated trajectories

        Args:
            trajectory_file (str, optional): The GeoJSON files with trajectories. Defaults to None.

        Returns:
            fig, ax: Matplotlib figure and axis tuples
        """
        
        # Create matplotlib figure objects
        fig, ax = plt.subplots(subplot_kw={'projection': cartopy.crs.PlateCarree()}, figsize=(20,10))

        # Choose coastline resolution
        ax.coastlines('50m')

        # Limit map to bounding box
        ax.set_extent([self.bbox[0], self.bbox[2], self.bbox[1], self.bbox[3]], cartopy.crs.PlateCarree())

        # Add ocean and land features, for visuals
        ax.add_feature(cartopy.feature.OCEAN, zorder=0)
        ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black')

        # Adds gridds to visual
        ax.gridlines(crs=cartopy.crs.PlateCarree(), draw_labels=True,
                  linewidth=2, color='gray', alpha=0.2, linestyle='--')

        # Use geopandas built-in GeoJSON processing and visualization
        df = geopandas.read_file(trajectory_file)
        df.plot(ax=ax, zorder=10, **kwargs)

        return fig, ax
Utility function to statically visualize the calculated trajectories

        Args:
            trajectory_file (str, optional): The GeoJSON files with trajectories. Defaults to None.

        Returns:
            fig, ax: Matplotlib figure and axis tuples
        
Utility function to statically visualize the calculated trajectories
Utility function to statically visualize the calculated trajectories
def plot(self, trajectory_file = None, **kwargs):
        fig, ax = plt.subplots(subplot_kw={'projection': cartopy.crs.PlateCarree()}, figsize=(20,10))
        ax.coastlines('50m')
        ax.set_extent([self.bbox[0], self.bbox[2], self.bbox[1], self.bbox[3]], cartopy.crs.PlateCarree())
        ax.add_feature(cartopy.feature.OCEAN, zorder=0)
        ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black')
        ax.gridlines(crs=cartopy.crs.PlateCarree(), draw_labels=True,
                  linewidth=2, color='gray', alpha=0.2, linestyle='--')
        df = geopandas.read_file(trajectory_file)
        df.plot(ax=ax, zorder=10, **kwargs)
        return fig, ax
def plot ( self , trajectory_file = None , ** kwargs ) : fig , ax = plt . subplots ( subplot_kw = { 'projection' : cartopy . crs . PlateCarree ( ) } , figsize = ( 20 , 10 ) ) ax . coastlines ( '50m' ) ax . set_extent ( [ self . bbox [ 0 ] , self . bbox [ 2 ] , self . bbox [ 1 ] , self . bbox [ 3 ] ] , cartopy . crs . PlateCarree ( ) ) ax . add_feature ( cartopy . feature . OCEAN , zorder = 0 ) ax . add_feature ( cartopy . feature . LAND , zorder = 0 , edgecolor = 'black' ) ax . gridlines ( crs = cartopy . crs . PlateCarree ( ) , draw_labels = True , linewidth = 2 , color = 'gray' , alpha = 0.2 , linestyle = '--' ) df = geopandas . read_file ( trajectory_file ) df . plot ( ax = ax , zorder = 10 , ** kwargs ) return fig , ax
Utility function to statically visualize the calculated trajectories
Utility function to statically visualize the calculated trajectories
"""Utility function to statically visualize the calculated trajectories

        Args:
            trajectory_file (str, optional): The GeoJSON files with trajectories. Defaults to None.

        Returns:
            fig, ax: Matplotlib figure and axis tuples
        """ # Create matplotlib figure objects # Choose coastline resolution # Limit map to bounding box # Add ocean and land features, for visuals # Adds gridds to visual # Use geopandas built-in GeoJSON processing and visualization
fig, ax: Matplotlib figure and axis tuples
fig ax : Matplotlib figure and axis tuples
self
trajectory_file
The GeoJSON files with trajectories. Defaults to None.
The GeoJSON files with trajectories . Defaults to None .
None