diff --git a/examples/GPM_WRF24_JPDF_comparison.py b/examples/GPM_WRF24_JPDF_comparison.py
index 20b070e3..45eee894 100644
--- a/examples/GPM_WRF24_JPDF_comparison.py
+++ b/examples/GPM_WRF24_JPDF_comparison.py
@@ -15,6 +15,32 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    GPM_WRF24_JPDF_comparison.py
+
+    This is an example of calculating the joint probability distribution
+    function of rainfall intensity and duration for the Northern Great
+    Plains using GPM IMERG data for June/01/2015
+
+    In this example:
+
+    1. Load the GPM and WRF24 datasets with spatial filter.
+    2. Load the spatial filter (Bukovsky region mask).
+    3. Spatially subset the WRF data.
+    4. Analyze the wet spells.
+    5. Calculate the joint PDF(JPDF) of spell_duration and peak_rainfall.
+    6. Visualize the JPDF.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. dataset
+    3. dataset_processor
+    4. metrics
+    5. plotter
+
+"""
+
 from ocw.dataset import Bounds
 import ocw.data_source.local as local
 import ocw.dataset_processor as dsp
diff --git a/examples/draw_climatology_map_MISR_AOD.py b/examples/draw_climatology_map_MISR_AOD.py
index 979c0f56..c75d3b38 100644
--- a/examples/draw_climatology_map_MISR_AOD.py
+++ b/examples/draw_climatology_map_MISR_AOD.py
@@ -15,6 +15,32 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    draw_climatology_map_MISR_AOD.py
+
+    Use OCW to download an MISR dataset, subset the data, calculate the 16 and 5 year
+    mean and draw a countour map of the means and the current values.
+
+    In this example:
+
+    1. Download a dataset from https://dx.doi.org/10.6084/m9.figshare.3753321.v1.
+    ***  Note *** The dataset for this example is not downloaded as part of the example
+    and must be downloaded to examples directory before running the example.
+    *** Note *** Depending on the OS on which the example is being run, the download
+    may remove the - in the filename.  Rename the file appropriately.
+    2. Subset the data set (lat / lon / start date / end date).
+    3. Calculate the 16, 5 and 1 year mean.
+    4. Draw a three contour maps using the calculated means and current values.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. dataset
+    3. dataset_processor
+    4. plotter
+
+"""
+
 import ocw.dataset as ds
 import ocw.data_source.local as local
 import ocw.dataset_processor as dsp
diff --git a/examples/esgf_integration_example.py b/examples/esgf_integration_example.py
index 8247435f..e939927d 100644
--- a/examples/esgf_integration_example.py
+++ b/examples/esgf_integration_example.py
@@ -15,6 +15,21 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    esgf_integration_example.py
+
+    Use OCW to download an ESGF dataset into the common format of an OCW dataset object.
+
+    In this example:
+
+    1. Download an ESGF (https://esgf.llnl.gov/) dataset and load it into a OCW dataset object.
+
+    OCW modules demonstrated:
+
+    1. datasource/esgf
+
+"""
+
 import ocw.data_source.esgf as esgf
 from getpass import getpass
 import ssl
diff --git a/examples/knmi_to_cru31_full_bias.py b/examples/knmi_to_cru31_full_bias.py
index 95b64a8f..13b56864 100644
--- a/examples/knmi_to_cru31_full_bias.py
+++ b/examples/knmi_to_cru31_full_bias.py
@@ -15,6 +15,38 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    knmi_to_cru31_full_bias.py
+
+    Use OCW to download, evaluate and plot (contour map) a dataset
+    against a reference dataset and OCW standard metrics (bias).
+
+    In this example:
+
+    1. Download a netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
+    2. Load the local files into OCW dataset objects.
+    3. Interface with the Regional Climate Model Evalutaion Database (https://rcmes.jpl.nasa.gov/)
+       to load the CRU3.1 Daily-Max Temp dataset (https://rcmes.jpl.nasa.gov/content/cru31).
+    4. Process each dataset to the same same shape.
+    5. Temporally rebin the datasets to a single timestep.
+    6. Spatially regrid the dataset objects to a 1/2 degree grid.
+    7. Build a bias metric to use for evaluation use the standard OCW metric set.
+    8. Create an evaluation object using the datasets and metric.
+    9. Plot the results of the evaluation (contour map).
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. datasource/rcmed
+    3. dataset
+    4. dataset_processor
+    5. evaluation
+    6. metrics
+    7. plotter
+
+"""
+
 import datetime
 import urllib
 from os import path
diff --git a/examples/model_ensemble_to_rcmed.py b/examples/model_ensemble_to_rcmed.py
index e8e3dbe0..185aa2ec 100644
--- a/examples/model_ensemble_to_rcmed.py
+++ b/examples/model_ensemble_to_rcmed.py
@@ -15,6 +15,38 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    model_ensemble_to_rcmed.py
+
+    Use OCW to download, evaluate and plot (contour map) two datasets
+    against a reference dataset and OCW standard metrics (bias).
+
+    In this example:
+
+    1. Download two netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
+        AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc
+    2. Load the local files into OCW dataset objects.
+    3. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
+       to load the CRU3.1 Daily-Max Temp dataset (https://rcmes.jpl.nasa.gov/content/cru31).
+    4. Temporally rebin the datasets to annual.
+    5. Spatially regrid the dataset objects to a 1/2 degree grid.
+    6. Build a bias metric to use for evaluation use the standard OCW metric set.
+    7. Create an evaluation object using the datasets and metric.
+    8. Plot the results of the evaluation (contour map).
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. datasource/rcmed
+    3. dataset
+    4. dataset_processor
+    5. metrics
+    6. evaluation
+    7. plotter
+
+"""
+
 import datetime
 import math
 import urllib
diff --git a/examples/multi_model_evaluation.py b/examples/multi_model_evaluation.py
index 7756cc9f..ba6ad563 100644
--- a/examples/multi_model_evaluation.py
+++ b/examples/multi_model_evaluation.py
@@ -15,6 +15,42 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    multi_model_evaluation.py
+
+    Use OCW to download, evaluate and plot (contour map) two datasets
+    against a reference dataset and OCW standard metrics.
+
+    In this example:
+
+    1. Download two netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+        AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+    2. Load the local files into OCW dataset objects.
+    3. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
+       to load the CRU3.1 Daily Precipitation dataset (https://rcmes.jpl.nasa.gov/content/cru31).
+    4. Process each dataset to the same same shape.
+        a.) Restrict the datasets re: geographic and time boundaries.
+        b.) Convert the dataset water flux to common units.
+        c.) Normalize the dataset date / times to monthly.
+        d.) Spatially regrid each dataset.
+    5.  Calculate the mean annual value for each dataset.
+    6.  Evaluate the datasets against the reference data set and OCW standard metric and plot
+        a contour map.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. datasource/rcmed
+    3. dataset
+    4. dataset_processor
+    5. metrics
+    6. evaluation
+    7. plotter
+    8. utils
+
+"""
+
 import datetime
 import urllib
 import numpy as np
diff --git a/examples/multi_model_taylor_diagram.py b/examples/multi_model_taylor_diagram.py
index 31d4020c..8edee7b4 100644
--- a/examples/multi_model_taylor_diagram.py
+++ b/examples/multi_model_taylor_diagram.py
@@ -1,3 +1,54 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+    multi_model_taylor_diagram.py
+
+    Use OCW to download, normalize and evaluate three datasets
+    against a reference dataset and OCW standard metrics
+    drawing a Taylor diagram of the results of the evaluation.
+
+    In this example:
+
+    1. Download three netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+        AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc
+        AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+    2. Load the local files into OCW dataset objects.
+    3. Process each dataset to the same same shape.
+        a.) Restrict the datasets re: geographic and time boundaries.
+        b.) Temporally rebin the data (monthly).
+        c.) Spatially regrid each dataset.
+    4.  Extract the metrics used for the evaluation and evaluate
+        against a reference dataset and standard OCW metrics.
+    5.  Draw evaluation results Taylor diagram.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. dataset
+    3. dataset_processor
+    4. evaluation
+    5. metrics
+    6. plotter
+    7. utils
+
+"""
+
 # Apache OCW lib immports
 from ocw.dataset import Dataset, Bounds
 import ocw.data_source.local as local
diff --git a/examples/podaac_integration_example.py b/examples/podaac_integration_example.py
index 61663d71..be85884e 100644
--- a/examples/podaac_integration_example.py
+++ b/examples/podaac_integration_example.py
@@ -15,6 +15,27 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    podaac_integration_example.py
+
+    Use OCW to download a PODACC dataset, evaluate and plot (contour map).
+
+    In this example:
+
+    1. Download a remote PO.DAAC (https://podaac.jpl.nasa.gov/) dataset
+       and read it into an OCW dataset object.
+    2. Create a temporal STD metric using one of the OCW standard metrics.
+    3. Evaluate the dataset against the metric and plot a contour map.
+
+    OCW modules demonstrated:
+
+    1. datasource/podaac_datasource
+    2. metrics
+    3. evaluation
+    4. plotter
+
+"""
+
 import ocw.data_source.podaac_datasource as podaac
 import ocw.evaluation as evaluation
 import ocw.metrics as metrics
diff --git a/examples/simple_model_to_model_bias.py b/examples/simple_model_to_model_bias.py
index 8e834b67..ad1f29b0 100644
--- a/examples/simple_model_to_model_bias.py
+++ b/examples/simple_model_to_model_bias.py
@@ -15,6 +15,35 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    simple_model_to_model_bias.py
+
+    Use OCW to download, normalize and evaluate two datasets
+    against an OCW metric (bias) and plot the results of the
+    evaluation (contour map).
+
+    In this example:
+
+    1. Download two netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
+        AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc
+    2. Load the local files into OCW dataset objects.
+    3. Temporally rebin the data anually.
+    4. Spatially regrid the dataset objects to a 1 degree grid.
+    5. Build a bias metric to use for evaluation use the standard OCW metric set.
+    6. Create an evaluation object using the datasets and metric.
+    7. Plot the results of the evaluation (contour map).
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. dataset_processor
+    3. evaluation
+    4. metrics
+    5. plotter
+
+"""
+
 import datetime
 from os import path
 import sys
diff --git a/examples/simple_model_tstd.py b/examples/simple_model_tstd.py
index fb3ce48d..6412493f 100644
--- a/examples/simple_model_tstd.py
+++ b/examples/simple_model_tstd.py
@@ -15,6 +15,30 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    simple_model_tstd.py
+
+    Use OCW to download, evaluate against a OCW standard
+    metrics (temporal STD) and plot the results (contour map).
+
+    In this example:
+
+    1. Download a netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
+    2. Load the local file into OCW dataset objects.
+    3. Use the standard OCW metrics to build a metric against which to evaluation (temporal STD).
+    4. Create an evaluation object of the dataset vs. the metric.
+    5. Plot the results of the evaluation using a contour map.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. metrics
+    3. evaluation
+    4. plotter
+
+"""
+
 from os import path
 import urllib
 
diff --git a/examples/subregions_portrait_diagram.py b/examples/subregions_portrait_diagram.py
index e85286f5..3e6785c8 100644
--- a/examples/subregions_portrait_diagram.py
+++ b/examples/subregions_portrait_diagram.py
@@ -1,5 +1,67 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+    subregions_portrait_diagram.py
+
+    Use OCW to download, normalize, evaluate and plot (portrait diagram)
+    three local datasets against a reference dataset.
+
+    In this example:
+
+    1. Download three netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+        AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc
+        AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+    2. Load the local files into OCW dataset objects.
+    3. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
+       to load the CRU3.1 Daily Precipitation dataset (https://rcmes.jpl.nasa.gov/content/cru31).
+    4. Process each dataset to the same same shape.
+        a.) Restrict the datasets re: geographic and time boundaries.
+        b.) Convert the dataset water flux to common units.
+        c.) Normalize the dataset date / times to monthly.
+        d.) Spatially regrid each dataset.
+    5.  Calculate the mean annual value for each dataset.
+    6.  Separate each dataset into 13 subregions.
+    7.  Extract the metrics used for the evaluation and evaluate
+        against a reference dataset.
+    8.  Create a portrait diagram of the results of the evaluation.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. datasource/rcmed
+    3. dataset
+    4. dataset_processor
+    5. metrics
+    6. evaluation
+    7. plotter
+    8. utils
+
+"""
+
+from os import path
+import urllib
+import ssl
+import datetime
+import numpy as np
+
 # Apache OCW lib immports
-from ocw.dataset import Dataset, Bounds
+from ocw.dataset import Bounds
 import ocw.data_source.local as local
 import ocw.data_source.rcmed as rcmed
 import ocw.dataset_processor as dsp
@@ -8,24 +70,17 @@
 import ocw.plotter as plotter
 import ocw.utils as utils
 
-import datetime
-import numpy as np
-import numpy.ma as ma
-
-from os import path
-import urllib
-import ssl
 if hasattr(ssl, '_create_unverified_context'):
     ssl._create_default_https_context = ssl._create_unverified_context
 
 # File URL leader
-FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"
+FILE_LEADER = 'http://zipper.jpl.nasa.gov/dist/'
 # Three Local Model Files
-FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
-FILE_2 = "AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc"
-FILE_3 = "AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
+FILE_1 = 'AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc'
+FILE_2 = 'AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc'
+FILE_3 = 'AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc'
 # Filename for the output image/plot (without file extension)
-OUTPUT_PLOT = "portrait_diagram"
+OUTPUT_PLOT = 'portrait_diagram'
 
 # Spatial and temporal configurations
 LAT_MIN = -45.0
@@ -34,7 +89,8 @@
 LON_MAX = 60.0
 START = datetime.datetime(2000, 01, 1)
 END = datetime.datetime(2007, 12, 31)
-EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
+EVAL_BOUNDS = Bounds(lat_min=LAT_MIN, lat_max=LAT_MAX, lon_min=LON_MIN,
+                     lon_max=LON_MAX, start=START, end=END)
 
 # variable that we are analyzing
 varName = 'pr'
@@ -58,32 +114,32 @@
 if not path.exists(FILE_3):
     urllib.urlretrieve(FILE_LEADER + FILE_3, FILE_3)
 
-""" Step 1: Load Local NetCDF File into OCW Dataset Objects and store in list"""
-target_datasets.append(local.load_file(FILE_1, varName, name="KNMI"))
-target_datasets.append(local.load_file(FILE_2, varName, name="REGCM"))
-target_datasets.append(local.load_file(FILE_3, varName, name="UCT"))
+# Step 1: Load Local NetCDF File into OCW Dataset Objects and store in list
+target_datasets.append(local.load_file(FILE_1, varName, name='KNMI'))
+target_datasets.append(local.load_file(FILE_2, varName, name='REGCM'))
+target_datasets.append(local.load_file(FILE_3, varName, name='UCT'))
 
-""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
-print("Working with the rcmed interface to get CRU3.1 Monthly Mean Precipitation")
+# Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module
+print('Working with the rcmed interface to get CRU3.1 Monthly Mean Precipitation')
 # the dataset_id and the parameter id were determined from
 # https://rcmes.jpl.nasa.gov/content/data-rcmes-database
 CRU31 = rcmed.parameter_dataset(
     10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
 
-""" Step 3: Processing Datasets so they are the same shape """
-print("Processing datasets ...")
+# Step 3: Processing Datasets so they are the same shape
+print('Processing datasets ...')
 CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
-print("... on units")
+print('... on units')
 CRU31 = dsp.water_flux_unit_conversion(CRU31)
 
 for member, each_target_dataset in enumerate(target_datasets):
     target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
-    target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[
-                                                             member])
+    target_datasets[member] = \
+        dsp.water_flux_unit_conversion(target_datasets[member])
     target_datasets[member] = dsp.normalize_dataset_datetimes(
         target_datasets[member], 'monthly')
 
-print("... spatial regridding")
+print('... spatial regridding')
 new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
 new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
 CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
@@ -97,12 +153,12 @@
 _, CRU31.values = utils.calc_climatology_year(CRU31)
 
 for member, each_target_dataset in enumerate(target_datasets):
-    _, target_datasets[member].values = utils.calc_climatology_year(target_datasets[
-                                                                    member])
+    _, target_datasets[member].values = \
+        utils.calc_climatology_year(target_datasets[member])
 
 # make the model ensemble
 target_datasets_ensemble = dsp.ensemble(target_datasets)
-target_datasets_ensemble.name = "ENS"
+target_datasets_ensemble.name = 'ENS'
 
 # append to the target_datasets for final analysis
 target_datasets.append(target_datasets_ensemble)
@@ -111,21 +167,21 @@
     allNames.append(target.name)
 
 list_of_regions = [
-    Bounds(-10.0, 0.0, 29.0, 36.5),
-    Bounds(0.0, 10.0,  29.0, 37.5),
-    Bounds(10.0, 20.0, 25.0, 32.5),
-    Bounds(20.0, 33.0, 25.0, 32.5),
-    Bounds(-19.3, -10.2, 12.0, 20.0),
-    Bounds(15.0, 30.0, 15.0, 25.0),
-    Bounds(-10.0, 10.0, 7.3, 15.0),
-    Bounds(-10.9, 10.0, 5.0, 7.3),
-    Bounds(33.9, 40.0,  6.9, 15.0),
-    Bounds(10.0, 25.0,  0.0, 10.0),
-    Bounds(10.0, 25.0, -10.0,  0.0),
-    Bounds(30.0, 40.0, -15.0,  0.0),
-    Bounds(33.0, 40.0, 25.0, 35.00)]
-
-region_list = ["R" + str(i + 1) for i in xrange(13)]
+    Bounds(lat_min=-10.0, lat_max=0.0, lon_min=29.0, lon_max=36.5),
+    Bounds(lat_min=0.0, lat_max=10.0, lon_min=29.0, lon_max=37.5),
+    Bounds(lat_min=10.0, lat_max=20.0, lon_min=25.0, lon_max=32.5),
+    Bounds(lat_min=20.0, lat_max=33.0, lon_min=25.0, lon_max=32.5),
+    Bounds(lat_min=-19.3, lat_max=-10.2, lon_min=12.0, lon_max=20.0),
+    Bounds(lat_min=15.0, lat_max=30.0, lon_min=15.0, lon_max=25.0),
+    Bounds(lat_min=-10.0, lat_max=10.0, lon_min=7.3, lon_max=15.0),
+    Bounds(lat_min=-10.9, lat_max=10.0, lon_min=5.0, lon_max=7.3),
+    Bounds(lat_min=33.9, lat_max=40.0, lon_min=6.9, lon_max=15.0),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=0.0, lon_max=10.0),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=-10.0, lon_max=0.0),
+    Bounds(lat_min=30.0, lat_max=40.0, lon_min=-15.0, lon_max=0.0),
+    Bounds(lat_min=33.0, lat_max=40.0, lon_min=25.0, lon_max=35.00)]
+
+region_list = ['R' + str(i + 1) for i in xrange(13)]
 
 # metrics
 pattern_correlation = metrics.PatternCorrelation()
diff --git a/examples/subregions_rectangular_boundaries.py b/examples/subregions_rectangular_boundaries.py
index 7fcf0e88..cf396bce 100644
--- a/examples/subregions_rectangular_boundaries.py
+++ b/examples/subregions_rectangular_boundaries.py
@@ -1,16 +1,43 @@
-# Apache OCW lib immports
-from ocw.dataset import Dataset, Bounds
-import ocw.data_source.local as local
-import ocw.data_source.rcmed as rcmed
-import ocw.dataset_processor as dsp
-import ocw.evaluation as evaluation
-import ocw.metrics as metrics
-import ocw.plotter as plotter
-import ocw.utils as utils
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+    subregions_rectangular_boundaries.py
+
+    Use OCW to define a set a sub regions and draw the sub regions.
+
+    In this example:
+
+    1. Subset the data set (lat / lon / start date / end date).
+    2. Draw each sub region.
+
+    OCW modules demonstrated:
+
+    1. dataset (Bounds)
+    2. plotter
+
+"""
 
 import datetime
 import numpy as np
-import numpy.ma as ma
+
+# Apache OCW lib immports
+from ocw.dataset import Bounds
+import ocw.plotter as plotter
 
 OUTPUT_PLOT = "subregions"
 
@@ -32,19 +59,19 @@
 new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
 
 list_of_regions = [
-    Bounds(-10.0, 0.0, 29.0, 36.5, START_SUB, END_SUB),
-    Bounds(0.0, 10.0,  29.0, 37.5, START_SUB, END_SUB),
-    Bounds(10.0, 20.0, 25.0, 32.5, START_SUB, END_SUB),
-    Bounds(20.0, 33.0, 25.0, 32.5, START_SUB, END_SUB),
-    Bounds(-19.3, -10.2, 12.0, 20.0, START_SUB, END_SUB),
-    Bounds(15.0, 30.0, 15.0, 25.0, START_SUB, END_SUB),
-    Bounds(-10.0, 10.0, 7.3, 15.0, START_SUB, END_SUB),
-    Bounds(-10.9, 10.0, 5.0, 7.3,  START_SUB, END_SUB),
-    Bounds(33.9, 40.0,  6.9, 15.0, START_SUB, END_SUB),
-    Bounds(10.0, 25.0,  0.0, 10.0, START_SUB, END_SUB),
-    Bounds(10.0, 25.0, -10.0,  0.0, START_SUB, END_SUB),
-    Bounds(30.0, 40.0, -15.0,  0.0, START_SUB, END_SUB),
-    Bounds(33.0, 40.0, 25.0, 35.0, START_SUB, END_SUB)]
+    Bounds(lat_min=-10.0, lat_max=0.0, lon_min=29.0, lon_max=36.5, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=0.0, lat_max=10.0, lon_min=29.0, lon_max=37.5, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=10.0, lat_max=20.0, lon_min=25.0, lon_max=32.5, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=20.0, lat_max=33.0, lon_min=25.0, lon_max=32.5, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=-19.3, lat_max=-10.2, lon_min=12.0, lon_max=20.0, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=15.0, lat_max=30.0, lon_min=15.0, lon_max=25.0, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=-10.0, lat_max=10.0, lon_min=7.3, lon_max=15.0, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=-10.9, lat_max=10.0, lon_min=5.0, lon_max=7.3, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=33.9, lat_max=40.0, lon_min=6.9, lon_max=15.0, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=0.0, lon_max=10.0, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=-10.0, lon_max=0.0, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=30.0, lat_max=40.0, lon_min=-15.0, lon_max=0.0, start=START_SUB, end=END_SUB),
+    Bounds(lat_min=33.0, lat_max=40.0, lon_min=25.0, lon_max=35.0, start=START_SUB, end=END_SUB)]
 
 # for plotting the subregions
 plotter.draw_subregions(list_of_regions, new_lats,
diff --git a/examples/subset_TRMM_data_for_NCA_regions.py b/examples/subset_TRMM_data_for_NCA_regions.py
index 90b752be..4ae4300d 100644
--- a/examples/subset_TRMM_data_for_NCA_regions.py
+++ b/examples/subset_TRMM_data_for_NCA_regions.py
@@ -15,9 +15,32 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    subset_TRMM_data_for_NCA_regions.py
+
+    Use OCW to subset TRMM data (https://pmm.nasa.gov/trmm) for NCA regions and draw
+    a contour map for the U.S. (CA', 'NV', 'UT', 'AZ', 'NM', 'CO'), Cuba and
+    the Bahamas (https://scenarios.globalchange.gov/regions_nca4).
+
+    In this example:
+
+    1. Interface with the Regional Climate Model Evaluation Database (https://rcmes.jpl.nasa.gov/)
+       to load the TRMM dataset.
+    2. Define the bounds for the U.S. (CA', 'NV', 'UT', 'AZ', 'NM', 'CO'), Cuba and the Bahamas and
+       the start date / end date.
+    3. Create a contour map of the TRMM data for the U.S., Cuba, and Bahamas.
+
+    OCW modules demonstrated:
+
+    1. datasource/rcmed
+    2. dataset (Bounds)
+    3. dataset_processor
+    4. plotter
+
+"""
+
 # Apache OCW lib immports
 import ocw.dataset_processor as dsp
-import ocw.utils as utils
 from ocw.dataset import Bounds
 import ocw.data_source.rcmed as rcmed
 import ocw.plotter as plotter
diff --git a/examples/taylor_diagram_example.py b/examples/taylor_diagram_example.py
index 8d5bbf0d..8f683c10 100644
--- a/examples/taylor_diagram_example.py
+++ b/examples/taylor_diagram_example.py
@@ -15,6 +15,37 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    taylor_diagram_example.py
+
+    Use OCW to download, normalize and evaluate two datasets
+    drawing a Taylor diagram of the results of the evaluation.
+
+    In this example:
+
+    1. Download two netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc
+        AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc
+    2. Load the local files into OCW dataset objects.
+    3. Process each dataset to the same same shape.
+        a.) Restrict the datasets re: geographic and time boundaries.
+        b.) Temporally rebin the data (monthly).
+        c.) Spatially regrid each dataset.
+    4.  Extract the metrics used for the evaluation and evaluate
+        against a reference dataset.
+    5.  Draw evaluation results Taylor diagram.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. dataset
+    3. dataset_processor
+    4. evaluation
+    5. metrics
+    6. plotter
+
+"""
+
 import datetime
 import sys
 from os import path
diff --git a/examples/temperature_trends_over_CONUS.py b/examples/temperature_trends_over_CONUS.py
index 54f58432..8510294d 100644
--- a/examples/temperature_trends_over_CONUS.py
+++ b/examples/temperature_trends_over_CONUS.py
@@ -15,6 +15,35 @@
 # specific language governing permissions and limitations
 # under the License.
 
+"""
+    temperature_trends_over_CONUS.py
+
+    Use OCW to plot the temperature trends over CONUS using the nClimDiv reference data.
+
+    In this example:
+
+    1. Load the local file nClimDiv/nClimDiv_tave_1895-2005.nc into OCW Dataset Objects.
+    *** Note *** It is assume this file exists locally in a subdirectory nClimDiv located
+    *** Note *** The files can be downloaded from :
+    https://rcmes.jpl.nasa.gov/RCMES_Turtorial_data/NCA-CMIP_examples.tar.gz
+    *** Note *** Additional information about the file content can be found here:
+    https://rcmes.jpl.nasa.gov/content/nca-cmip-analysis-using-rcmes
+    in the same directory as the example.
+    2. Load the CMIP5 simulations into a list of OCW Dataset Objects.
+    3. Spatially subset the observed dataset into state and regional boundaries.
+    4. Temporally subset the observed and model datasets.
+    5. Calculate and plot the temperature trend for each region.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. dataset
+    3. dataset_processor
+    4. plotter
+    5. utlis
+
+"""
+
 import numpy as np
 import datetime
 
@@ -26,8 +55,8 @@
 import ocw.plotter as plotter
 import ocw.utils as utils
 
-# nClimDiv observation file
-file_obs = 'nClimDiv/nClimDiv_tave_1895-2005.nc'
+# nClimGrid observation file
+file_obs = 'nClimGrid/nClimGrid_tave_1895-2005.nc'
 
 # CMIP5 simulations
 model_file_path = 'CMIP5_historical'
@@ -40,7 +69,7 @@
 start_date = datetime.datetime(1979, 12, 1)
 end_date = datetime.datetime(2005, 8, 31)
 
-nyear = 26                             
+nyear = 26
 
 month_start = 6 # June
 month_end = 8   # August
@@ -60,39 +89,39 @@
 n_region = 7 # number of regions
 
 # CONUS regional boundaries
-NW_bounds = Bounds(boundary_type='us_states', 
+NW_bounds = Bounds(boundary_type='us_states',
                    us_states=regions[0])
-SW_bounds = Bounds(boundary_type='us_states', 
+SW_bounds = Bounds(boundary_type='us_states',
                    us_states=regions[1])
-NGP_bounds = Bounds(boundary_type='us_states', 
+NGP_bounds = Bounds(boundary_type='us_states',
                    us_states=regions[2])
-SGP_bounds = Bounds(boundary_type='us_states', 
+SGP_bounds = Bounds(boundary_type='us_states',
                    us_states=regions[3])
-MW_bounds = Bounds(boundary_type='us_states', 
+MW_bounds = Bounds(boundary_type='us_states',
                    us_states=regions[4])
-NE_bounds = Bounds(boundary_type='us_states', 
+NE_bounds = Bounds(boundary_type='us_states',
                    us_states=regions[5])
-SE_bounds = Bounds(boundary_type='us_states', 
+SE_bounds = Bounds(boundary_type='us_states',
                    us_states=regions[6])
 
 regional_bounds = [NW_bounds, SW_bounds, NGP_bounds,
                    SGP_bounds, MW_bounds, NE_bounds, SE_bounds]
 
-""" Load nClimDiv file into OCW Dataset """
-obs_dataset = local.load_file(file_obs, variable_name='tave') 
+""" Load nClimGrid file into OCW Dataset """
+obs_dataset = local.load_file(file_obs, variable_name='tave')
 
 """ Load CMIP5 simulations into a list of OCW Datasets"""
 model_dataset = local.load_multiple_files(file_path=model_file_path, variable_name='tas',
-                                          dataset_name=dataset_name, variable_unit='K') 
+                                          dataset_name=dataset_name, variable_unit='K')
 
 """ Temporal subset of obs_dataset """
-obs_dataset_subset = dsp.temporal_slice(obs_dataset, 
+obs_dataset_subset = dsp.temporal_slice(obs_dataset,
                   start_time=start_date, end_time=end_date)
 obs_dataset_season = dsp.temporal_subset(obs_dataset_subset, month_start, month_end,
                       average_each_year=True)
 
 """ Temporal subset of model_dataset """
-model_dataset_subset = [dsp.temporal_slice(dataset,start_time=start_date, end_time=end_date) 
+model_dataset_subset = [dsp.temporal_slice(dataset,start_time=start_date, end_time=end_date)
                         for dataset in model_dataset]
 model_dataset_season = [dsp.temporal_subset(dataset, month_start, month_end,
                       average_each_year=True) for dataset in model_dataset_subset]
@@ -104,7 +133,7 @@
 
 for iregion in np.arange(n_region):
     obs_timeseries[:, iregion] = utils.calc_time_series(
-                         dsp.subset(obs_dataset_season, regional_bounds[iregion])) 
+                         dsp.subset(obs_dataset_season, regional_bounds[iregion]))
     for imodel in np.arange(nmodel):
         model_timeseries[imodel, :, iregion] = utils.calc_time_series(
                          dsp.subset(model_dataset_season[imodel], regional_bounds[iregion]))
@@ -125,20 +154,20 @@
         regional_trends_model[imodel, iregion], regional_trends_model_error[iregion] = utils.calculate_temporal_trend_of_time_series(
                                                                                        year, model_timeseries[imodel, :, iregion])
     regional_trends_ens[iregion], regional_trends_ens_error[iregion] = utils.calculate_ensemble_temporal_trends(
-                                                                       model_timeseries[:, :, iregion]) 
+                                                                       model_timeseries[:, :, iregion])
 
 """ Generate plots """
 
-plotter.fill_US_states_with_color(regions, 'nClimDiv_tave_trends_JJA_1980-2005', 
+plotter.fill_US_states_with_color(regions, 'nClimGrid_tave_trends_JJA_1980-2005',
                                   values=regional_trends_obs,
                                   region_names=['%.3f' %(10*i) for i in regional_trends_obs])
 
-plotter.fill_US_states_with_color(regions, 'CMIP5_ENS_tave_trends_JJA_1980-2005', 
+plotter.fill_US_states_with_color(regions, 'CMIP5_ENS_tave_trends_JJA_1980-2005',
                                   values=regional_trends_ens,
                                   region_names=['%.3f' %(10*i) for i in regional_trends_ens])
 
 bias_ens = regional_trends_ens - regional_trends_obs
-plotter.fill_US_states_with_color(regions, 'CMIP5_ENS_tave_trends_bias_from_nClimDiv_JJA_1980-2005', 
+plotter.fill_US_states_with_color(regions, 'CMIP5_ENS_tave_trends_bias_from_nClimGrid_JJA_1980-2005',
                                   values=bias_ens,
                                   region_names=['%.3f' %(10*i) for i in bias_ens])
 
@@ -146,7 +175,7 @@
 ens_data = np.vstack([regional_trends_ens, regional_trends_ens_error])
 
 plotter.draw_plot_to_compare_trends(obs_data, ens_data, regional_trends_model,
-                                    fname='Trends_comparison_btn_CMIP5_and_nClimDiv',
+                                    fname='Trends_comparison_btn_CMIP5_and_nClimGrid',
                                     data_labels=['NW','SW','NGP','SGP','MW','NE','SE'],
                                     xlabel='NCA regions', ylabel='tas trend [K/year]')
 
diff --git a/examples/time_series_with_regions.py b/examples/time_series_with_regions.py
index 3bb133c1..d92599b6 100644
--- a/examples/time_series_with_regions.py
+++ b/examples/time_series_with_regions.py
@@ -1,19 +1,69 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+    time_series_with_regions.py
+
+    Use OCW to download and plot (time series) three local datasets against a reference dataset.
+
+    In this example:
+
+    1. Download three netCDF files from a local site.
+        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+        AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc
+        AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
+    2. Load the local files into OCW dataset objects.
+    3. Interface with the Regional Climate Model Evalutaion Database (https://rcmes.jpl.nasa.gov/)
+       to load the CRU3.1 Daily Precipitation dataset (https://rcmes.jpl.nasa.gov/content/cru31).
+    4. Process each dataset to the same same shape.
+        a.) Restrict the datasets re: geographic and time boundaries.
+        b.) Convert the dataset water flux to common units.
+        c.) Normalize the dataset date / times to monthly.
+        d.) Spatially regrid each dataset.
+    5.  Calculate the mean monthly value for each dataset.
+    6.  Separate each dataset into 13 subregions.
+    7.  Create a time series for each dataset in each subregion.
+
+    OCW modules demonstrated:
+
+    1. datasource/local
+    2. datasource/rcmed
+    3. dataset
+    4. dataset_processor
+    5. plotter
+
+"""
+
+import sys
+import datetime
+from os import path
+from calendar import monthrange
+import ssl
+
+import numpy as np
+
 # Apache OCW lib immports
-from ocw.dataset import Dataset, Bounds
+from ocw.dataset import Bounds
 import ocw.data_source.local as local
 import ocw.data_source.rcmed as rcmed
 import ocw.dataset_processor as dsp
-import ocw.evaluation as evaluation
-import ocw.metrics as metrics
 import ocw.plotter as plotter
 import ocw.utils as utils
 
-import datetime
-import numpy as np
-import numpy.ma as ma
-from os import path
-import sys
-
 if sys.version_info[0] >= 3:
     from urllib.request import urlretrieve
 else:
@@ -21,17 +71,17 @@
     # But note that this might need an update when Python 4
     # might be around one day
     from urllib import urlretrieve
-import ssl
+
 if hasattr(ssl, '_create_unverified_context'):
     ssl._create_default_https_context = ssl._create_unverified_context
 
-
 # File URL leader
-FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"
+FILE_LEADER = 'http://zipper.jpl.nasa.gov/dist/'
+
 # Three Local Model Files
-FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
-FILE_2 = "AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc"
-FILE_3 = "AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
+FILE_1 = 'AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc'
+FILE_2 = 'AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc'
+FILE_3 = 'AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc'
 
 LAT_MIN = -45.0
 LAT_MAX = 42.24
@@ -56,97 +106,99 @@
 
 # Download necessary NetCDF file if not present
 if not path.exists(FILE_1):
-    print("Downloading %s" % (FILE_LEADER + FILE_1))
+    print('Downloading %s' % (FILE_LEADER + FILE_1))
     urlretrieve(FILE_LEADER + FILE_1, FILE_1)
 
 if not path.exists(FILE_2):
-    print("Downloading %s" % (FILE_LEADER + FILE_2))
+    print('Downloading %s' % (FILE_LEADER + FILE_2))
     urlretrieve(FILE_LEADER + FILE_2, FILE_2)
 
 if not path.exists(FILE_3):
-    print("Downloading %s" % (FILE_LEADER + FILE_3))
+    print('Downloading %s' % (FILE_LEADER + FILE_3))
     urlretrieve(FILE_LEADER + FILE_3, FILE_3)
 
-""" Step 1: Load Local NetCDF File into OCW Dataset Objects and store in list"""
-target_datasets.append(local.load_file(FILE_1, varName, name="KNMI"))
-target_datasets.append(local.load_file(FILE_2, varName, name="REGCM"))
-target_datasets.append(local.load_file(FILE_3, varName, name="UCT"))
-
+# Step 1: Load Local NetCDF File into OCW Dataset Objects and store in list
+target_datasets.append(local.load_file(FILE_1, varName, name='KNMI'))
+target_datasets.append(local.load_file(FILE_2, varName, name='REGCM'))
+target_datasets.append(local.load_file(FILE_3, varName, name='UCT'))
 
-""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
-print("Working with the rcmed interface to get CRU3.1 Daily Precipitation")
+# Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module
+print('Working with the rcmed interface to get CRU3.1 Daily Precipitation')
 # the dataset_id and the parameter id were determined from
 # https://rcmes.jpl.nasa.gov/content/data-rcmes-database
 CRU31 = rcmed.parameter_dataset(
     10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
 
-
-""" Step 3: Processing datasets so they are the same shape ... """
-print("Processing datasets so they are the same shape")
+# Step 3: Processing datasets so they are the same shape
+print('Processing datasets so they are the same shape')
 CRU31 = dsp.water_flux_unit_conversion(CRU31)
 CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
 
 for member, each_target_dataset in enumerate(target_datasets):
     target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
-    target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[
-                                                             member])
+    target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member])
     target_datasets[member] = dsp.normalize_dataset_datetimes(
         target_datasets[member], 'monthly')
 
-print("... spatial regridding")
+print('... spatial regridding')
 new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
 new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
 CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
 
-
 for member, each_target_dataset in enumerate(target_datasets):
-    target_datasets[member] = dsp.spatial_regrid(
-        target_datasets[member], new_lats, new_lons)
+    target_datasets[member] =\
+        dsp.spatial_regrid(target_datasets[member], new_lats, new_lons)
 
-# find climatology monthly for obs and models
+# Find climatology monthly for obs and models.
 CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
+# Shift the day of the month to the end of the month as matplotlib does not handle
+# the xticks elegantly when the first date is the epoch and tries to determine
+# the start of the xticks based on a value < 1.
+for index, item in enumerate(CRU31.times):
+    CRU31.times[index] = \
+        datetime.date(item.year, item.month, monthrange(item.year, item.month)[1])
 
 for member, each_target_dataset in enumerate(target_datasets):
-    target_datasets[member].values, target_datasets[
-        member].times = utils.calc_climatology_monthly(target_datasets[member])
+    target_datasets[member].values, target_datasets[member].times = \
+        utils.calc_climatology_monthly(target_datasets[member])
 
 # make the model ensemble
 target_datasets_ensemble = dsp.ensemble(target_datasets)
-target_datasets_ensemble.name = "ENS"
+target_datasets_ensemble.name = 'ENS'
 
 # append to the target_datasets for final analysis
 target_datasets.append(target_datasets_ensemble)
 
-""" Step 4: Subregion stuff """
+# Step 4: Subregion stuff
 list_of_regions = [
-    Bounds(-10.0, 0.0, 29.0, 36.5),
-    Bounds(0.0, 10.0,  29.0, 37.5),
-    Bounds(10.0, 20.0, 25.0, 32.5),
-    Bounds(20.0, 33.0, 25.0, 32.5),
-    Bounds(-19.3, -10.2, 12.0, 20.0),
-    Bounds(15.0, 30.0, 15.0, 25.0),
-    Bounds(-10.0, 10.0, 7.3, 15.0),
-    Bounds(-10.9, 10.0, 5.0, 7.3),
-    Bounds(33.9, 40.0,  6.9, 15.0),
-    Bounds(10.0, 25.0,  0.0, 10.0),
-    Bounds(10.0, 25.0, -10.0,  0.0),
-    Bounds(30.0, 40.0, -15.0,  0.0),
-    Bounds(33.0, 40.0, 25.0, 35.0)]
-
-region_list = [["R" + str(i + 1)] for i in xrange(13)]
+    Bounds(lat_min=-10.0, lat_max=0.0, lon_min=29.0, lon_max=36.5),
+    Bounds(lat_min=0.0, lat_max=10.0, lon_min=29.0, lon_max=37.5),
+    Bounds(lat_min=10.0, lat_max=20.0, lon_min=25.0, lon_max=32.5),
+    Bounds(lat_min=20.0, lat_max=33.0, lon_min=25.0, lon_max=32.5),
+    Bounds(lat_min=-19.3, lat_max=-10.2, lon_min=12.0, lon_max=20.0),
+    Bounds(lat_min=15.0, lat_max=30.0, lon_min=15.0, lon_max=25.0),
+    Bounds(lat_min=-10.0, lat_max=10.0, lon_min=7.3, lon_max=15.0),
+    Bounds(lat_min=-10.9, lat_max=10.0, lon_min=5.0, lon_max=7.3),
+    Bounds(lat_min=33.9, lat_max=40.0, lon_min=6.9, lon_max=15.0),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=0.0, lon_max=10.0),
+    Bounds(lat_min=10.0, lat_max=25.0, lon_min=-10.0, lon_max=0.0),
+    Bounds(lat_min=30.0, lat_max=40.0, lon_min=-15.0, lon_max=0.0),
+    Bounds(lat_min=33.0, lat_max=40.0, lon_min=25.0, lon_max=35.0)]
+
+region_list = [['R' + str(i + 1)] for i in xrange(13)]
 
 for regions in region_list:
     firstTime = True
-    subset_name = regions[0] + "_CRU31"
-    # labels.append(subset_name) #for legend, uncomment this line
+    subset_name = regions[0] + '_CRU31'
+    labels.append(subset_name)
     subset = dsp.subset(CRU31, list_of_regions[region_counter], subset_name)
     tSeries = utils.calc_time_series(subset)
     results.append(tSeries)
     tSeries = []
     firstTime = False
     for member, each_target_dataset in enumerate(target_datasets):
-        subset_name = regions[0] + "_" + target_datasets[member].name
-        # labels.append(subset_name) #for legend, uncomment this line
+        subset_name = regions[0] + '_' + target_datasets[member].name
+        labels.append(subset_name)
         subset = dsp.subset(target_datasets[member],
                             list_of_regions[region_counter],
                             subset_name)
@@ -154,8 +206,8 @@
         results.append(tSeries)
         tSeries = []
 
-    plotter.draw_time_series(np.array(results), CRU31.times, labels, regions[
-                             0], ptitle=regions[0], fmt='png')
+    plotter.draw_time_series(np.array(results), CRU31.times, labels, regions[0],
+                             label_month=True, ptitle=regions[0], fmt='png')
     results = []
     tSeries = []
     labels = []
diff --git a/ocw-ui/frontend/app/scripts/directives/predictivefilebrowserinput.js b/ocw-ui/frontend/app/scripts/directives/predictivefilebrowserinput.js
index 7142c15f..6fe21183 100644
--- a/ocw-ui/frontend/app/scripts/directives/predictivefilebrowserinput.js
+++ b/ocw-ui/frontend/app/scripts/directives/predictivefilebrowserinput.js
@@ -29,9 +29,16 @@ angular.module('ocwUiApp')
 .directive('predictiveFileBrowserInput', function() {
 	var link = function($scope, $elem, $attrs) {
 		$scope.autocomplete = [];
-		
+
 		// Set id to use this directive correctly in multiple places
-		$scope.id = 'autoCompletePath'+ $elem.context.id
+		/*
+		This had been written as $elem.context.id, but $elem is an object (jQuery.fn.init)
+		and the object did not have a context or id attribute. This was
+		throwing an error to the console and the list of files was not being displayed.
+		Replaced with $attrs.id.
+		*/
+		$scope.id = 'autoCompletePath' + $attrs.id;
+
 		/*
 		 * We need a place to dump our auto-completion options
 		 */
@@ -43,8 +50,8 @@ angular.module('ocwUiApp')
 			var val = $(e.target).text();
 			$($elem).val(val);
 			// Need to trigger the input box's "input" event so Angular updates the model!
-			$elem.trigger('input'); 
-			
+			$elem.trigger('input');
+
 			// If the user selected a directory, find more results..
 			if (val[val.length - 1] == '/') {
 				$scope.fetchFiles($($elem).val());
@@ -152,7 +159,7 @@ angular.module('ocwUiApp')
 			$scope.possibleCompletes = $scope.autocomplete;
 		};
 
-		/* 
+		/*
 		 * Handle <TAB> presses.
 		 *
 		 * Attempt to auto-complete options when the user presses <TAB>.
@@ -220,7 +227,7 @@ angular.module('ocwUiApp')
 			}
 		};
 
-		/* 
+		/*
 		 * Handle all other key presses in the input box
 		 *
 		 * Filter the auto-complete options as the user types to ensure that only options
@@ -236,7 +243,7 @@ angular.module('ocwUiApp')
 			$scope.updateAutoComplete();
 		};
 
-		/* 
+		/*
 		 * When a path is auto-completed with <TAB> we need to check to see if it points
 		 * to a directory. If it does, we still need to fetch results!
 		 */
@@ -247,7 +254,7 @@ angular.module('ocwUiApp')
 			}
 		};
 
-		/* 
+		/*
 		 * Calculate the greatest common prefix of the passed options.
 		 *
 		 * Params:
@@ -275,7 +282,7 @@ angular.module('ocwUiApp')
 			return longestString.slice(0, index - 1);
 		};
 
-		/* 
+		/*
 		 * Filter the auto-complete options based on the current input.
 		 */
 		$scope.filterResults = function() {
diff --git a/ocw/dataset_processor.py b/ocw/dataset_processor.py
index 160ffb72..cf2e90e2 100755
--- a/ocw/dataset_processor.py
+++ b/ocw/dataset_processor.py
@@ -867,9 +867,13 @@ def mask_missing_data(dataset_array):
 
     mask_array = np.zeros(dataset_array[0].values.shape)
     for dataset in dataset_array:
-        index = np.where(dataset.values.mask == True)
-        if index[0].size > 0:
-            mask_array[index] = 1
+        # CLIMATE-797 - Not every array passed in will be a masked array.
+        # For those that are, action based on the mask passed in.
+        # For those that are not, take no action (else AttributeError).
+        if hasattr(dataset.values, 'mask'):
+            index = np.where(dataset.values.mask == True)
+            if index[0].size > 0:
+                mask_array[index] = 1
     masked_array = []
     for dataset in dataset_array:
         dataset.values = ma.array(dataset.values, mask=mask_array)
diff --git a/ocw/plotter.py b/ocw/plotter.py
index 7f9b0920..f0af03fe 100755
--- a/ocw/plotter.py
+++ b/ocw/plotter.py
@@ -367,9 +367,24 @@ def draw_subregions(subregions, lats, lons, fname, fmt='png', ptitle='',
     fig.clf()
 
 
+def _get_colors(num_colors):
+    """
+    matplotlib will recycle colors after a certain number.  This can make
+    line type charts confusing as colors will be reused.  This function
+    provides a distribution of colors across the default color map
+    to better approximate uniqueness.
+
+    :param num_colors: The number of unique colors to generate.
+    :return: A color map with num_colors.
+    """
+    cmap = plt.get_cmap()
+    return [cmap(1. * i / num_colors) for i in range(num_colors)]
+
+
 def draw_time_series(results, times, labels, fname, fmt='png', gridshape=(1, 1),
                      xlabel='', ylabel='', ptitle='', subtitles=None,
-                     label_month=False, yscale='linear', aspect=None):
+                     label_month=False, yscale='linear', aspect=None,
+                     cycle_colors=True, cmap=None):
     ''' Draw a time series plot.
 
     :param results: 3D array of time series data.
@@ -415,7 +430,22 @@ def draw_time_series(results, times, labels, fname, fmt='png', gridshape=(1, 1),
     :param aspect: (Optional) approximate aspect ratio of each subplot
         (width / height). Default is 8.5 / 5.5
     :type aspect: :class:`float`
+
+    :param cycle_colors: (Optional) flag to toggle whether to allow matlibplot
+        to re-use colors when plotting or force an evenly distributed range.
+    :type cycle_colors: :class:`bool`
+
+    :param cmap: (Optional) string or :class:`matplotlib.colors.LinearSegmentedColormap`
+        instance denoting the colormap. This must be able to be recognized by
+        `Matplotlib's get_cmap function <http://matplotlib.org/api/cm_api.html#matplotlib.cm.get_cmap>`_.
+        Maps like rainbow and spectral with wide spectrum of colors are nice choices when used with
+        the cycle_colors option. tab20, tab20b, and tab20c are good if the plot has less than 20 datasets.
+    :type cmap: :mod:`string` or :class:`matplotlib.colors.LinearSegmentedColormap`
+
     '''
+    if cmap is not None:
+        set_cmap(cmap)
+
     # Handle the single plot case.
     if results.ndim == 2:
         results = results.reshape(1, *results.shape)
@@ -448,6 +478,10 @@ def draw_time_series(results, times, labels, fname, fmt='png', gridshape=(1, 1),
     # Make the plots
     for i, ax in enumerate(grid):
         data = results[i]
+
+        if not cycle_colors:
+            ax.set_prop_cycle('color', _get_colors(data.shape[0]))
+
         if label_month:
             xfmt = mpl.dates.DateFormatter('%b')
             xloc = mpl.dates.MonthLocator()