Add support for dataframes

scaling/froude.py

@@ -28,23 +28,24 @@ def _convert(x, length_scale_factor, input_unit, target_unit):
     # Convert to output units
     x_scaled *= unit_conversion_factor
 
-    # Scale time (dataframe or series only)
-    try:
-        x_scaled.index *= length_scale_factor**TIME_EXPONENT
-    except (AttributeError, TypeError):
-        pass
-
     return x_scaled
 
 
-def proto_to_model(x_proto, length_scale, input_unit, target_unit):
+def proto_to_model(x_proto,
+                   length_scale,
+                   input_unit,
+                   target_unit,
+                   index_input_unit=None,
+                   index_target_unit=None):
     """Convert prototype value(s) to model value(s) in specified units.
 
     Args:
         x_proto:            prototype values (array_like, or pandas dataframe)
-        length_scale:  ratio between prototype and model dimensions (float)
+        length_scale:       ratio between proto and model dimensions (float)
         input_unit:         unit of input (string)
         target_unit:        unit of output (string)
+        index_input_unit:   unit of input index (dataframe only)
+        index_target_unit:  unit of output index (dataframe only)
 
     Returns:
         input values in model scale
@@ -52,17 +53,36 @@ def proto_to_model(x_proto, length_scale, input_unit, target_unit):
 
     length_scale_factor = 1 / length_scale
 
-    return _convert(x_proto, length_scale_factor, input_unit, target_unit)
+    # Convert values
+    x_model = _convert(x_proto, length_scale_factor, input_unit, target_unit)
+
+    # Convert index (dataframe or series only)
+    if (index_input_unit is not None) and (index_target_unit is not None):
+        if type(x_model).__name__ in ['DataFrame', 'Series']:
+            x_model.index = _convert(x_model.index, length_scale_factor,
+                                     index_input_unit, index_target_unit)
+        else:
+            raise ValueError("'index_input_unit' and 'index_target_unit' "
+                             "can only be used when input is dataframe")
+
+    return x_model
 
 
-def model_to_proto(x_model, length_scale, input_unit, target_unit):
+def model_to_proto(x_model,
+                   length_scale,
+                   input_unit,
+                   target_unit,
+                   index_input_unit=None,
+                   index_target_unit=None):
     """Convert model value(s) to prototype value(s) in specified units.
 
     Args:
         x_model:            model values (array_like, or pandas dataframe)
-        length_scale:  ratio between prototype and model dimensions (float)
+        length_scale:       ratio between proto and model dimensions (float)
         input_unit:         unit of input (string)
         target_unit:        unit of output (string)
+        index_input_unit:   unit of input index (dataframe only)
+        index_target_unit:  unit of output index (dataframe only)
 
     Returns:
         input values in prototype scale
@@ -70,7 +90,19 @@ def model_to_proto(x_model, length_scale, input_unit, target_unit):
 
     length_scale_factor = length_scale
 
-    return _convert(x_model, length_scale_factor, input_unit, target_unit)
+    # Convert values
+    x_proto = _convert(x_model, length_scale_factor, input_unit, target_unit)
+
+    # Convert index (dataframe or series only)
+    if (index_input_unit is not None) and (index_target_unit is not None):
+        if type(x_proto).__name__ in ['DataFrame', 'Series']:
+            x_proto.index = _convert(x_proto.index, length_scale_factor,
+                                     index_input_unit, index_target_unit)
+        else:
+            raise ValueError("'index_input_unit' and 'index_target_unit' "
+                             "can only be used when input is dataframe")
+
+    return x_proto
 
 
 def dimensions(unit):