Change viz scripts to call functions in fsate; add consistent plot st…

…yle theme (#33) * Create custom matplotlib stylesheet for RaFTS plots * Flip axes on scatter; change perf to pred for clarity * Change perf to pred for clarity * Read in mplstyle file directly from fs_algo * incorporate plotting functions into fs_perf_viz.py * Use functions for creating file output paths * Change perf_map to pred_map --------- Co-authored-by: glitt13 <[email protected]>
NOAA-OWP · Dec 19, 2024 · c6cf6cb · c6cf6cb
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diff --git a/pkg/fs_algo/fs_algo/RaFTS_theme.mplstyle b/pkg/fs_algo/fs_algo/RaFTS_theme.mplstyle
@@ -0,0 +1,19 @@
+# Style theme for RaFTS data visualizations
+
+axes.labelsize : 12
+lines.linewidth : 2
+xtick.labelsize : 11
+ytick.labelsize : 11
+legend.fontsize : 11
+font.family : Arial
+
+# viridis color codes: https://waldyrious.net/viridis-palette-generator/
+# viridis with a slightly lighter purple:
+axes.prop_cycle: cycler('color', ['7e3b8a', '21918c', 'fde725', '3b528b', '5ec962'])
+
+# Other odd options -------
+# viridis: 
+# axes.prop_cycle: cycler('color', ['440154', '21918c', 'fde725', '3b528b', '5ec962'])
+
+# viridis plasma: 
+# axes.prop_cycle: cycler('color', ['f89540', 'cc4778', '7e03a8', '0d0887', 'f0f921'])
diff --git a/pkg/fs_algo/fs_algo/fs_algo_train_eval.py b/pkg/fs_algo/fs_algo/fs_algo_train_eval.py
@@ -1547,11 +1547,11 @@ def plot_pred_vs_obs_regr(y_pred, y_obs, ds:str, metr:str):
     # Adapted from plot in bolotinl's fs_perf_viz.py
 
     # Plot the observed vs. predicted module performance
-    plt.scatter(x=y_obs,y=y_pred, c='teal')
+    plt.scatter(x=y_obs,y=y_pred)
     plt.axline((0, 0), (1, 1), color='black', linestyle='--')
     plt.ylabel('Predicted {}'.format(metr))
     plt.xlabel('Actual {}'.format(metr))
-    plt.title('Observed vs. Predicted Performance: {}'.format(ds))
+    plt.title('Observed vs. RaFTS Predicted Performance: {}'.format(ds))
     fig = plt.gcf()
     return fig
 
@@ -1567,15 +1567,15 @@ def plot_pred_vs_obs_wrap(y_pred, y_obs, dir_out_viz_base:str|os.PathLike,
     plt.clf()
     plt.close()
 
-#%% Performance map visualization, adapted from plot in bolotinl's fs_perf_viz.py
-def std_map_perf_path(dir_out_viz_base:str|os.PathLike, ds:str,
+#%% Prediction map visualization, adapted from plot in bolotinl's fs_perf_viz.py
+def std_map_pred_path(dir_out_viz_base:str|os.PathLike, ds:str,
                       metr:str,algo_str:str,
                       split_type:str='') -> pathlib.PosixPath:
 
     # Generate a filepath of the feature_importance plot:
-    path_perf_map_plot = Path(f"{dir_out_viz_base}/{ds}/performance_map_{ds}_{metr}_{algo_str}_{split_type}.png")
-    path_perf_map_plot.parent.mkdir(parents=True,exist_ok=True)
-    return path_perf_map_plot
+    path_pred_map_plot = Path(f"{dir_out_viz_base}/{ds}/prediction_map_{ds}_{metr}_{algo_str}_{split_type}.png")
+    path_pred_map_plot.parent.mkdir(parents=True,exist_ok=True)
+    return path_pred_map_plot
 
 def gen_conus_basemap(dir_out_basemap, # This should be the data_visualizations directory
                     url = 'https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_500k.zip',
@@ -1607,14 +1607,15 @@ def gen_conus_basemap(dir_out_basemap, # This should be the data_visualizations
 #     geo_df['performance'] = data['prediction'].values
 #     geo_df.crs = ("EPSG:4326")
 
-def plot_map_perf(geo_df, states,title,metr,colname_data='performance'):
+def plot_map_pred(geo_df, states,title,metr,colname_data='performance'):
     fig, ax = plt.subplots(1, 1, figsize=(20, 24))
     base = states.boundary.plot(ax=ax,color="#555555", linewidth=1)
     # Points
     geo_df.plot(column=colname_data, ax=ax, markersize=150, cmap='viridis', legend=False, zorder=2) # delete zorder to plot points behind states boundaries
     # States
     states.boundary.plot(ax=ax, color="#555555", linewidth=1, zorder=1)  # Plot states boundary again with lower zorder
-
+
+    # TODO: need to customize the colorbar min and max based on the metric
     ## cbar = plt.cm.ScalarMappable(norm=matplotlib.colors.Normalize(vmin=0,vmax = 1), cmap='viridis')
     cbar = plt.cm.ScalarMappable(cmap='viridis')
     ax.tick_params(axis='x', labelsize= 24)
@@ -1630,26 +1631,26 @@ def plot_map_perf(geo_df, states,title,metr,colname_data='performance'):
     fig = plt.gcf()
     return fig
 
-def plot_map_perf_wrap(test_gdf,dir_out_viz_base, ds,
+def plot_map_pred_wrap(test_gdf,dir_out_viz_base, ds,
                       metr,algo_str,
                       split_type='test',
                       colname_data='performance'):
 
-    path_perf_map_plot = std_map_perf_path(dir_out_viz_base,ds,metr,algo_str,split_type)
-    dir_out_basemap = path_perf_map_plot.parent.parent
+    path_pred_map_plot = std_map_pred_path(dir_out_viz_base,ds,metr,algo_str,split_type)
+    dir_out_basemap = path_pred_map_plot.parent.parent
     states = gen_conus_basemap(dir_out_basemap = dir_out_basemap)
 
     # Ensure the gdf matches the 4326 epsg used for states:
     test_gdf = test_gdf.to_crs(4326)
 
     # Generate the map
     plot_title = f"Predicted Performance: {metr} - {ds}"
-    plot_perf_map = plot_map_perf(geo_df=test_gdf, states=states,title=plot_title,
+    plot_pred_map = plot_map_pred(geo_df=test_gdf, states=states,title=plot_title,
                                   metr=metr,colname_data=colname_data)
 
     # Save the plot as a .png file
-    plot_perf_map.savefig(path_perf_map_plot, dpi=300, bbox_inches='tight')
-    print(f"Wrote performance map to \n{path_perf_map_plot}")
+    plot_pred_map.savefig(path_pred_map_plot, dpi=300, bbox_inches='tight')
+    print(f"Wrote performance map to \n{path_pred_map_plot}")
     plt.clf()
     plt.close()
 

diff --git a/pkg/fs_algo/fs_algo/fs_perf_viz.py b/pkg/fs_algo/fs_algo/fs_perf_viz.py
@@ -25,6 +25,7 @@
 import xarray as xr
 import urllib.request
 import zipfile
+import pkg_resources
 
 
 if __name__ == "__main__":
@@ -101,6 +102,11 @@
 
     # Location for accessing existing outputs and saving plots
     dir_out = fsate.fs_save_algo_dir_struct(dir_base).get('dir_out')
+    dir_out_viz_base = Path(dir_out/Path("data_visualizations"))
+
+    # Enforce style
+    style_path = pkg_resources.resource_filename('fs_algo', 'RaFTS_theme.mplstyle')
+    plt.style.use(style_path)
 
     # Loop through all datasets
     for ds in datasets:
@@ -120,20 +126,8 @@
                 # data.to_csv(f'{dir_out}/data_visualizations/{ds}_{algo}_{metric}_data.csv')
 
                 # Does the user want a scatter plot comparing the observed module performance and the predicted module performance by RaFTS?
-                if 'perf_map' in true_keys:
-                    url = 'https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_500k.zip'
-                    zip_filename = f'{dir_out}/data_visualizations/cb_2018_us_state_500k.zip'
-                    filename = f'{dir_out}/data_visualizations/cb_2018_us_state_500k.shp'
-
-                    if not Path(zip_filename).exists():
-                        print('Downloading shapefile...')
-                        urllib.request.urlretrieve(url, zip_filename)
-                    if not Path(filename).exists():
-                        with zipfile.ZipFile(zip_filename, 'r') as zip_ref:
-                            zip_ref.extractall(f'{dir_out}/data_visualizations')
-
-                    states = gpd.read_file(filename)
-                    states = states.to_crs("EPSG:4326")
+                if 'pred_map' in true_keys:
+                    states = fsate.gen_conus_basemap(f'{dir_out}/data_visualizations/')
 
                     # Plot performance on map
                     lat = data['Y']
@@ -143,27 +137,14 @@
                     geo_df['performance'] = data['prediction'].values
                     geo_df.crs = ("EPSG:4326")
 
-                    fig, ax = plt.subplots(1, 1, figsize=(20, 24))
-                    base = states.boundary.plot(ax=ax,color="#555555", linewidth=1)
-                    # Points
-                    geo_df.plot(column="performance", ax=ax, markersize=150, cmap='viridis', legend=False, zorder=2) # delete zorder to plot points behind states boundaries
-                    # States
-                    states.boundary.plot(ax=ax, color="#555555", linewidth=1, zorder=1)  # Plot states boundary again with lower zorder
-
-                    cbar = plt.cm.ScalarMappable(norm=matplotlib.colors.Normalize(vmin=-0.41,vmax = 1), cmap='viridis')
-                    ax.tick_params(axis='x', labelsize= 24)
-                    ax.tick_params(axis='y', labelsize= 24)
-                    plt.xlabel('Latitude',fontsize = 26)
-                    plt.ylabel('Longitude',fontsize = 26)
-                    cbar_ax = plt.colorbar(cbar, ax=ax,fraction=0.02, pad=0.04)
-                    cbar_ax.set_label(label=metric,size=24)
-                    cbar_ax.ax.tick_params(labelsize=24)  # Set colorbar tick labels size
-                    plt.title("Predicted Performance: {}".format(ds), fontsize = 28)
-                    ax.set_xlim(-126, -66)
-                    ax.set_ylim(24, 50)
+                    fsate.plot_map_pred(geo_df=geo_df, states=states, 
+                                        title=f'RaFTS Predicted Performance Map: {ds}', 
+                                        metr=metric, colname_data='performance')
 
                     # Save the plot as a .png file
-                    output_path = f'{dir_out}/data_visualizations/{ds}_{algo}_{metric}_performance_map.png'
+                    output_path = fsate.std_map_pred_path(dir_out_viz_base=dir_out_viz_base,
+                                                          ds=ds, metr=metric, algo_str=algo,
+                                                          split_type='prediction')
                     plt.savefig(output_path, dpi=300, bbox_inches='tight')
                     plt.clf()
                     plt.close()
@@ -196,14 +177,13 @@
                     data = pd.merge(data, obs, how = 'inner', on = 'identifier')
 
                     # Plot the observed vs. predicted module performance
-                    plt.scatter(data['prediction'], data[metric], c='teal')
-                    plt.axline((0, 0), (1, 1), color='black', linestyle='--')
-                    plt.xlabel('Predicted {}'.format(metric))
-                    plt.ylabel('Actual {}'.format(metric))
-                    plt.title('Observed vs. Predicted Performance: {}'.format(ds))
+                    fsate.plot_pred_vs_obs_regr(y_pred=data['prediction'], y_obs=data[metric], 
+                                                ds = ds, metr=metric)
 
                     # Save the plot as a .png file
-                    output_path = f'{dir_out}/data_visualizations/{ds}_{algo}_{metric}_obs_vs_sim_scatter.png'
+                    output_path = fsate.std_regr_pred_obs_path(dir_out_viz_base=dir_out_viz_base,
+                                                               ds=ds, metr=metric, algo_str=algo,
+                                                            split_type='prediction')
                     plt.savefig(output_path, dpi=300, bbox_inches='tight')
                     plt.clf()
                     plt.close()

diff --git a/scripts/eval_ingest/xssa/xssa_viz_config.yaml b/scripts/eval_ingest/xssa/xssa_viz_config.yaml
@@ -7,5 +7,5 @@ metrics: # All option could pull from the pred config file
   - 'NSE'
 plot_types:
   - obs_vs_sim_scatter: True # NOTE: These plots can only be created if observed (actual) model performance values are available
-  - perf_map: True
+  - pred_map: True