Close #220

Update Travis to Python 3.6-3.8: Dropped support for Python 3.5.

.travis.yml

@@ -8,16 +8,9 @@
 
 language: python
 python:
-  - "3.5"
   - "3.6"
-
-# Enable 3.7 without globally enabling sudo and dist: xenial for other build jobs
-# This configuration is described in https://github.com/travis-ci/travis-ci/issues/9815#issue-336465122
-matrix:
-  include:
-    - python: 3.7
-      dist: xenial
-      sudo: true
+  - "3.7"
+  - "3.8"
 
 # Download and set up anaconda
 before_install:
@@ -34,7 +27,7 @@ before_install:
 
 # Install packages
 install: 
-  - conda create --yes -n testenv python=$TRAVIS_PYTHON_VERSION numpy=1.15 scipy flake8 requests
+  - conda create --yes -n testenv python=$TRAVIS_PYTHON_VERSION numpy>=1.16 scipy flake8 requests
   - source activate testenv
 
 # Run test(s)

com_mseg.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import numpy as np
+import numpy.lib.recfunctions as recfn
 import re
 import csv
 import json
@@ -357,10 +358,12 @@ def sd_mseg_percent(sd_array, sel, yrs):
         entries = filtered[filtered['Description'] == name]
 
         # Calculate the sum of all year columns and write it to the
-        # appropriate row in the tval array (note that the .view()
-        # function converts the structured array into a standard
-        # numpy array, which allows the use of the .sum() function)
-        tval[idx, ] = np.sum(entries[yrs].view(('<f8', len(yrs))), axis=0)
+        # appropriate row in the tval array (note that the recfn module
+        # introduces the structured_to_unstructured function to
+        # convert the structured array into a standard numpy array,
+        # which allows the use of the .sum() function)
+        tval[idx, ] = np.sum(recfn.structured_to_unstructured(
+            entries[yrs], dtype='<f8'), axis=0)
 
     # If at least one entry in tval is non-zero (tval.any() == True),
     # suppress any divide by zero warnings and calculate the percentage

com_mseg_tech.py

@@ -5,6 +5,7 @@
 import com_mseg as cm
 
 import numpy as np
+import numpy.lib.recfunctions as recfn
 import re
 import warnings
 import json
@@ -46,7 +47,6 @@ class UsefulVars(object):
         eu_map (dict): Mapping between end use names in cost conversion JSON
             and end use numbers in EIA raw technology cose data.
         cconv (dict): Factors for converting from unit costs to $/ft^2.
-
     """
 
     def __init__(self):
@@ -84,7 +84,7 @@ def __init__(self):
 
 
 class UsefulDicts(object):
-    """Set up class for dicts to relate diferent data file formats.
+    """Set up class for dicts to relate different data file formats.
 
     Attributes:
         kprem_endusedict (dict): Keys are the strings found in the time
@@ -214,8 +214,8 @@ def sd_data_selector(sd_data, sel, years):
         # appropriate row in the sd array (note that the .view()
         # function converts the structured array into a standard
         # numpy array, which allows the use of the .sum() function)
-        sd[idx, ] = np.sum(
-            entries[list(map(str, years))].view(('<f8', len(years))), axis=0)
+        sd[idx, ] = np.sum(recfn.structured_to_unstructured(
+            entries[list(map(str, years))], dtype='<f8'), axis=0)
 
     # Note that each row in sd corresponds to a single performance
     # level for a single technology and the rows are in the same order