enable forecast records plot

Author: rileyhales

docs/api-documentation/plots.rst

@@ -4,4 +4,4 @@ geoglows.plots
 
 .. automodule:: geoglows.plots
 	:members:
-		forecast, forecast_stats, forecast_ensembles, retrospective, annual_averages, monthly_averages, daily_averages, flow_duration_curve, corrected_retrospective, corrected_month_average, corrected_day_average, corrected_scatterplots
+		forecast, forecast_stats, forecast_ensembles, forecast_records, retrospective, annual_averages, monthly_averages, daily_averages, flow_duration_curve, corrected_retrospective, corrected_month_average, corrected_day_average, corrected_scatterplots

geoglows/__init__.py

@@ -12,6 +12,6 @@
     'bias', 'plots', 'data', 'analyze', 'streams', 'tables', 'streamflow',
     'METADATA_TABLE_PATH'
 ]
-__version__ = '1.2.1'
+__version__ = '1.3.0'
 __author__ = 'Riley Hales'
 __license__ = 'BSD 3-Clause Clear License'

geoglows/_plots/__init__.py

@@ -2,6 +2,7 @@
     forecast,
     forecast_stats,
     forecast_ensembles,
+    forecast_records,
     retrospective,
     daily_averages,
     monthly_averages,
@@ -17,6 +18,7 @@
     'forecast',
     'forecast_stats',
     'forecast_ensembles',
+    'forecast_records',
     'retrospective',
     'daily_averages',
     'monthly_averages',

geoglows/_plots/plotly_forecasts.py

@@ -259,27 +259,27 @@ def forecast_ensembles(df: pd.DataFrame, *, rp_df: pd.DataFrame = None, plot_tit
     return go.Figure(scatter_plots, layout=layout)
 
 
-def forecast_records(recs: pd.DataFrame, *, rp_df: pd.DataFrame = None, plot_titles: list = False, ) -> go.Figure:
+def forecast_records(df: pd.DataFrame, *, rp_df: pd.DataFrame = None, plot_titles: list = False, ) -> go.Figure:
     """
     Makes the streamflow saved forecast data and metadata into a plotly plot
 
     Args:
-        recs: the csv response from forecast_records
+        df: the csv response from forecast_records
         rp_df: the csv response from return_periods
         plot_titles: a list of strings to place in the figure title. each list item will be on a new line.
 
     Return:
          plotly.GraphObject: plotly object, especially for use with python notebooks and the .show() method
     """
     # Start processing the inputs
-    dates = recs.index.tolist()
+    dates = df.index.tolist()
     startdate = dates[0]
     enddate = dates[-1]
 
     plot_data = {
         'x_records': dates,
-        'recorded_flows': recs.dropna(axis=0).values.flatten(),
-        'y_max': max(recs.values),
+        'recorded_flows': df.dropna(axis=0).values.flatten(),
+        'y_max': np.nanmax(df.values),
     }
     if rp_df is not None:
         plot_data.update(rp_df.to_dict(orient='index').items())

geoglows/_plots/plots.py

@@ -2,10 +2,17 @@
 import plotly.graph_objects as go
 
 from .format_tools import plotly_figure_to_html_plot
+from .plotly_bias_corrected import (
+    corrected_retrospective as plotly_corrected_retrospective,
+    corrected_month_average as plotly_corrected_month_average,
+    corrected_day_average as plotly_corrected_day_average,
+    corrected_scatterplots as plotly_corrected_scatterplots,
+)
 from .plotly_forecasts import (
     forecast as plotly_forecast,
     forecast_stats as plotly_forecast_stats,
-    forecast_ensembles as plotly_forecast_ensembles
+    forecast_ensembles as plotly_forecast_ensembles,
+    forecast_records as plotly_forecast_records,
 )
 from .plotly_retrospective import (
     retrospective as plotly_retrospective,
@@ -14,17 +21,12 @@
     annual_averages as plotly_annual_averages,
     flow_duration_curve as plotly_flow_duration_curve,
 )
-from .plotly_bias_corrected import (
-    corrected_retrospective as plotly_corrected_retrospective,
-    corrected_month_average as plotly_corrected_month_average,
-    corrected_day_average as plotly_corrected_day_average,
-    corrected_scatterplots as plotly_corrected_scatterplots,
-)
 
 __all__ = [
     'forecast',
     'forecast_stats',
     'forecast_ensembles',
+    'forecast_records',
 
     'retrospective',
     'daily_averages',
@@ -105,6 +107,29 @@ def forecast_ensembles(df: pd.DataFrame, *,
     raise NotImplementedError(f'Plot type "{plot_type}" is not supported.')
 
 
+def forecast_records(df: pd.DataFrame, *,
+                     plot_type: str = 'plotly',
+                     rp_df: pd.DataFrame = None,
+                     plot_titles: list = None, ) -> go.Figure:
+    """
+    Plots forecasted streamflow and optional return periods
+    Args:
+        df:
+        plot_type:
+        rp_df:
+        plot_titles:
+
+    Returns:
+        go.Figure
+    """
+    if plot_type in ('plotly', 'html'):
+        figure = plotly_forecast_records(df, rp_df=rp_df, plot_titles=plot_titles)
+        if plot_type == 'html':
+            return plotly_figure_to_html_plot(figure)
+        return figure
+    raise NotImplementedError(f'Plot type "{plot_type}" is not supported.')
+
+
 def retrospective(df: pd.DataFrame, *,
                   plot_type: str = 'plotly',
                   rp_df: pd.DataFrame = None,