Dura mat mountain pref

pvdeg/__init__.py

@@ -12,7 +12,7 @@
 from . import humidity
 from . import letid
 from . import montecarlo
-from . import scenario
+from .scenario import Scenario, GeospatialScenario
 from . import spectral
 from . import symbolic
 from . import standards

pvdeg/geospatial.py

@@ -898,7 +898,7 @@ def elevation_stochastic_downselect(
         Options : `'mean'`, `'sum'`, `'median'`
     normalization : str, (default = 'linear')
         function to apply when normalizing weights. Logarithmic uses log_e/ln
-        options : `'linear'`, `'logarithmic'`, '`exponential'`
+        options : `'linear'`, `'log'`, '`exp'`, `'invert-linear'`
 
     Returns:
     --------
@@ -927,14 +927,17 @@ def elevation_stochastic_downselect(
 
 
 def interpolate_analysis(
-    result: xr.Dataset, data_var: str, method="nearest"
+    result: xr.Dataset, data_var: str, method="nearest", resolution=100j,
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     Interpolate sparse spatial result data against DataArray coordinates.
     Takes DataArray instead of Dataset, index one variable of a dataset to get a dataarray.
 
     Parameters:
     -----------
+    resolution: complex
+        Change the amount the input is interpolated.
+        For more interpolation set higher (200j is more than 100j)
 
     Result:
     -------
@@ -951,22 +954,53 @@ def interpolate_analysis(
     )  # probably a nicer way to do this
 
     grid_lat, grid_lon = np.mgrid[
-        df["latitude"].min() : df["latitude"].max() : 100j,
-        df["longitude"].min() : df["longitude"].max() : 100j,
+        df["latitude"].min() : df["latitude"].max() : resolution,
+        df["longitude"].min() : df["longitude"].max() : resolution,
     ]
 
     grid_z = griddata(data[:, 0:2], data[:, 2], xi=(grid_lat, grid_lon), method=method)
 
     return grid_z, grid_lat, grid_lon
 
 
-def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") -> None:
+# api could be updated to match that of plot_USA
+def plot_sparse_analysis(
+    result: xr.Dataset, 
+    data_var: str, 
+    method="nearest", 
+    resolution:complex=100j,
+    figsize:tuple=(10,8),
+    show_plot:bool=False,
+) -> None:
+    """
+    Plot the output of a sparse geospatial analysis using interpolation.
+
+    Parameters
+    -----------
+    result: xr.Dataset
+        xarray dataset in memory containing coordinates['longitude', 'latitude'] and at least one datavariable.
+    data_var: str
+        name of datavariable to plot from result
+    method: str
+        interpolation method. 
+        Options: `'nearest', 'linear', 'cubic'`
+        See [`scipy.interpolate.griddata`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html)
+    resolution: complex
+        Change the amount the input is interpolated.
+        For more interpolation set higher (200j is more than 100j)
+
+    Returns
+    -------
+    fig, ax: tuple
+        matplotlib figure and axes of plot
+    """
+
     grid_values, lat, lon = interpolate_analysis(
-        result=result, data_var=data_var, method=method
+        result=result, data_var=data_var, method=method, resolution=resolution
     )
 
-    fig = plt.figure()
-    ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False)
+    fig = plt.figure(figsize=figsize)
+    ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) # these should be the same ccrs
     ax.patch.set_visible(False)
 
     extent = [lon.min(), lon.max(), lat.min(), lat.max()]
@@ -976,8 +1010,8 @@ def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") ->
         extent=extent,
         origin="lower",
         cmap="viridis",
-        transform=ccrs.PlateCarree(),
-    )  # should this be trnsposed
+        transform=ccrs.PlateCarree(), # why are ccrs different
+    )
 
     shapename = "admin_1_states_provinces_lakes"
     states_shp = shpreader.natural_earth(
@@ -994,7 +1028,73 @@ def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") ->
     cbar = plt.colorbar(img, ax=ax, orientation="vertical", fraction=0.02, pad=0.04)
     cbar.set_label("Value")
 
-    plt.title("Interpolated Heatmap")
+    plt.title(f"Interpolated Sparse Analysis, {data_var}")
     plt.xlabel("Longitude")
     plt.ylabel("Latitude")
-    plt.show()
+
+    if show_plot:
+        plt.show()
+
+    return fig, ax
+
+def plot_sparse_analysis_land(
+    result: xr.Dataset, 
+    data_var: str, 
+    method="nearest", 
+    resolution:complex=100j,
+    figsize:tuple=(10,8),
+    show_plot:bool=False,
+    proj=ccrs.PlateCarree(),
+):
+
+    import matplotlib.path as mpath
+    from cartopy.mpl.patch import geos_to_path
+
+    grid_values, lat, lon = interpolate_analysis(
+        result=result, data_var=data_var, method=method, resolution=resolution
+    )
+
+    fig = plt.figure(figsize=figsize)
+    ax = fig.add_axes([0, 0, 1, 1], projection=proj, frameon=False)
+    ax.patch.set_visible(False)
+
+    extent = [lon.min(), lon.max(), lat.min(), lat.max()]
+    ax.set_extent(extent, crs=proj)
+
+    mesh = ax.pcolormesh(lon, lat, grid_values, transform=proj, cmap='viridis')
+
+    land_path = geos_to_path(list(cfeature.LAND.geometries()))
+    land_path = mpath.Path.make_compound_path(*land_path)
+    plate_carre_data_transform = proj._as_mpl_transform(ax)
+    mesh.set_clip_path(land_path, plate_carre_data_transform)
+
+    shapename = "admin_1_states_provinces_lakes"
+    states_shp = shpreader.natural_earth(
+        resolution="110m", category="cultural", name=shapename
+    )
+
+    ax.add_geometries(
+        shpreader.Reader(states_shp).geometries(),
+        proj,
+        facecolor="none",
+        edgecolor="black",
+        linestyle=':'
+    )
+
+    cbar = plt.colorbar(mesh, ax=ax, orientation="vertical", fraction=0.02, pad=0.04)
+    cbar.set_label("Value")
+
+    utilities._add_cartopy_features(
+        ax=ax, 
+        features = [
+            cfeature.BORDERS,
+            cfeature.COASTLINE,
+            cfeature.LAND,
+            cfeature.OCEAN,
+        ],
+    )
+
+    if show_plot:
+        plt.show()
+
+    return fig, ax