clean up and add notes to history #1327

HISTORY.rst

@@ -44,12 +44,16 @@ Unreleased Changes
     * Updated the package installation instructions in the API docs for clarity
       and also to highlight the ease of installation through ``conda-forge``.
       https://github.com/natcap/invest/issues/1256
-    * ``utils.build_lookup_from_csv`` now accepts kwargs for ``pandas.read_csv``
-      (`#1319 <https://github.com/natcap/invest/issues/1319>`_)
+    * ``utils.build_lookup_from_csv`` has been deprecated and its functionality
+      has been merged into ``utils.read_csv_to_dataframe``
+      (`#1319 <https://github.com/natcap/invest/issues/1319>`_),
+      (`#1327 <https://github.com/natcap/invest/issues/1327>`_)
 * Workbench
     * Fixed a bug where sampledata downloads failed silently (and progress bar
       became innacurate) if the Workbench did not have write permission to
       the download location. https://github.com/natcap/invest/issues/1070
+* Forest Carbon
+    * The biophysical table is now case-insensitive.
 * HRA
     * Fixed a bug in HRA where the model would error when all exposure and
       consequence criteria were skipped for a single habitat. The model now

src/natcap/invest/utils.py

@@ -598,8 +598,8 @@ def expand_path(path, base_path):
 
 
 def read_csv_to_dataframe(
-        path, index_col=None, usecols=None, cols_to_lower=True,
-        vals_to_lower=True, expand_path_cols=[], sep=None, engine='python',
+        path, index_col=False, usecols=None, cols_to_lower=True,
+        vals_to_lower=True, expand_path_cols=None, sep=None, engine='python',
         encoding='utf-8-sig', **kwargs):
     """Return a dataframe representation of the CSV.
 

src/natcap/invest/wave_energy.py

@@ -743,8 +743,7 @@ def execute(args):
     # arrays. Also store the amount of energy the machine produces
     # in a certain wave period/height state as a 2D array
     machine_perf_dict = {}
-    machine_perf_data = utils.read_csv_to_dataframe(
-        args['machine_perf_path'], cols_to_lower=False, vals_to_lower=False)
+    machine_perf_data = utils.read_csv_to_dataframe(args['machine_perf_path'])
     # Get the wave period fields, starting from the second column of the table
     machine_perf_dict['periods'] = machine_perf_data.columns.values[1:]
     # Build up the height field by taking the first column of the table