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From good...@apache.org
Subject [1/4] climate git commit: CLIMATE-465 Add class level documentation and Apache license to examples
Date Sat, 02 Dec 2017 08:16:38 GMT
Repository: climate
Updated Branches:
  refs/heads/master 8b5de9c41 -> 2d30ef8ed


CLIMATE-465 Add class level documentation and Apache license to examples


Project: http://git-wip-us.apache.org/repos/asf/climate/repo
Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/7d6f5ae8
Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/7d6f5ae8
Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/7d6f5ae8

Branch: refs/heads/master
Commit: 7d6f5ae8abc9d37cfdace59080da1c54ff7f71e4
Parents: d41041f
Author: Michael Anderson <michaelanderson@Michaels-iMac.local>
Authored: Sun Nov 26 16:30:10 2017 -0500
Committer: Michael Anderson <michaelanderson@Michaels-iMac.local>
Committed: Sun Nov 26 16:30:10 2017 -0500

----------------------------------------------------------------------
 examples/GPM_WRF24_JPDF_comparison.py         | 26 +++++++++++
 examples/draw_climatology_map_MISR_AOD.py     | 26 +++++++++++
 examples/esgf_integration_example.py          | 15 +++++++
 examples/knmi_to_cru31_full_bias.py           | 32 ++++++++++++++
 examples/model_ensemble_to_rcmed.py           | 32 ++++++++++++++
 examples/multi_model_evaluation.py            | 36 +++++++++++++++
 examples/multi_model_taylor_diagram.py        | 51 ++++++++++++++++++++++
 examples/podaac_integration_example.py        | 21 +++++++++
 examples/simple_model_to_model_bias.py        | 29 ++++++++++++
 examples/simple_model_tstd.py                 | 24 ++++++++++
 examples/subregions_portrait_diagram.py       | 39 +++++++++++++++++
 examples/subregions_rectangular_boundaries.py | 17 ++++++++
 examples/subset_TRMM_data_for_NCA_regions.py  | 25 ++++++++++-
 examples/taylor_diagram_example.py            | 31 +++++++++++++
 examples/temperature_trends_over_CONUS.py     | 25 +++++++++++
 examples/time_series_with_regions.py          | 47 ++++++++++++++++----
 16 files changed, 466 insertions(+), 10 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/GPM_WRF24_JPDF_comparison.py
----------------------------------------------------------------------
diff --git a/examples/GPM_WRF24_JPDF_comparison.py b/examples/GPM_WRF24_JPDF_comparison.py
index 20b070e..45eee89 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/draw_climatology_map_MISR_AOD.py
----------------------------------------------------------------------
diff --git a/examples/draw_climatology_map_MISR_AOD.py b/examples/draw_climatology_map_MISR_AOD.py
index 979c0f5..c75d3b3 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/esgf_integration_example.py
----------------------------------------------------------------------
diff --git a/examples/esgf_integration_example.py b/examples/esgf_integration_example.py
index 8247435..e939927 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/knmi_to_cru31_full_bias.py
----------------------------------------------------------------------
diff --git a/examples/knmi_to_cru31_full_bias.py b/examples/knmi_to_cru31_full_bias.py
index 95b64a8..13b5686 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/model_ensemble_to_rcmed.py
----------------------------------------------------------------------
diff --git a/examples/model_ensemble_to_rcmed.py b/examples/model_ensemble_to_rcmed.py
index e8e3dbe..185aa2e 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/multi_model_evaluation.py
----------------------------------------------------------------------
diff --git a/examples/multi_model_evaluation.py b/examples/multi_model_evaluation.py
index 7756cc9..ba6ad56 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/multi_model_taylor_diagram.py
----------------------------------------------------------------------
diff --git a/examples/multi_model_taylor_diagram.py b/examples/multi_model_taylor_diagram.py
index 31d4020..8edee7b 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/podaac_integration_example.py
----------------------------------------------------------------------
diff --git a/examples/podaac_integration_example.py b/examples/podaac_integration_example.py
index 61663d7..be85884 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/simple_model_to_model_bias.py
----------------------------------------------------------------------
diff --git a/examples/simple_model_to_model_bias.py b/examples/simple_model_to_model_bias.py
index 8e834b6..ad1f29b 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/simple_model_tstd.py
----------------------------------------------------------------------
diff --git a/examples/simple_model_tstd.py b/examples/simple_model_tstd.py
index fb3ce48..6412493 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
 

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/subregions_portrait_diagram.py
----------------------------------------------------------------------
diff --git a/examples/subregions_portrait_diagram.py b/examples/subregions_portrait_diagram.py
index a6d3c14..3e6785c 100644
--- a/examples/subregions_portrait_diagram.py
+++ b/examples/subregions_portrait_diagram.py
@@ -15,6 +15,45 @@
 # 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/subregions_rectangular_boundaries.py
----------------------------------------------------------------------
diff --git a/examples/subregions_rectangular_boundaries.py b/examples/subregions_rectangular_boundaries.py
index 37eb884..cf396bc 100644
--- a/examples/subregions_rectangular_boundaries.py
+++ b/examples/subregions_rectangular_boundaries.py
@@ -15,6 +15,23 @@
 # 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
 

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/subset_TRMM_data_for_NCA_regions.py
----------------------------------------------------------------------
diff --git a/examples/subset_TRMM_data_for_NCA_regions.py b/examples/subset_TRMM_data_for_NCA_regions.py
index 90b752b..4ae4300 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/taylor_diagram_example.py
----------------------------------------------------------------------
diff --git a/examples/taylor_diagram_example.py b/examples/taylor_diagram_example.py
index 8d5bbf0..8f683c1 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

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/temperature_trends_over_CONUS.py
----------------------------------------------------------------------
diff --git a/examples/temperature_trends_over_CONUS.py b/examples/temperature_trends_over_CONUS.py
index 54f5843..028c8a2 100644
--- a/examples/temperature_trends_over_CONUS.py
+++ b/examples/temperature_trends_over_CONUS.py
@@ -15,6 +15,31 @@
 # 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
+    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
 

http://git-wip-us.apache.org/repos/asf/climate/blob/7d6f5ae8/examples/time_series_with_regions.py
----------------------------------------------------------------------
diff --git a/examples/time_series_with_regions.py b/examples/time_series_with_regions.py
index 0799cf0..d92599b 100644
--- a/examples/time_series_with_regions.py
+++ b/examples/time_series_with_regions.py
@@ -15,10 +15,38 @@
 # specific language governing permissions and limitations
 # under the License.
 
-'''
-    Download three netCDF files, process the files to be the same shape,
-    divide the data into subregions and plot a monthly time series for each sub region.
-'''
+"""
+    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
@@ -118,8 +146,8 @@ 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.
 CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
@@ -127,11 +155,12 @@ CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
 # 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])
+    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)


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