[pysal] 01/06: New upstream version 1.14.2
Bas Couwenberg
sebastic at debian.org
Sun Sep 24 09:45:25 UTC 2017
This is an automated email from the git hooks/post-receive script.
sebastic pushed a commit to branch master
in repository pysal.
commit 361549145c6e203c12d30182b99178c4fcf47372
Author: Bas Couwenberg <sebastic at xs4all.nl>
Date: Sun Sep 24 11:14:30 2017 +0200
New upstream version 1.14.2
---
.coveragerc | 3 +
.github/ISSUE_TEMPLATE.md | 4 +-
.travis.yml | 2 +-
CHANGELOG.txt | 153 +++
README.rst | 44 +-
THANKS.txt | 15 +-
doc/source/conf.py | 6 +-
doc/source/developers/docs/index.rst | 4 +-
doc/source/developers/guidelines.rst | 2 +-
doc/source/index.rst | 2 +-
doc/source/library/weights/Contiguity.rst | 9 +
doc/source/users/tutorials/autocorrelation.rst | 4 +-
doc/source/users/tutorials/fileio.rst | 32 +-
doc/source/users/tutorials/region.rst | 2 +-
doc/source/users/tutorials/weights.rst | 98 +-
pysal/cg/kdtree.py | 22 +-
pysal/cg/segmentLocator.py | 6 +-
pysal/contrib/geotable/tests/test_utils.py | 61 +-
pysal/contrib/geotable/utils.py | 46 +-
pysal/contrib/glm/GLM_validate_estimation.ipynb | 444 +++++++++
pysal/contrib/glm/glm.py | 58 +-
pysal/contrib/glm/iwls.py | 34 +-
pysal/contrib/glm/utils.py | 59 ++
pysal/contrib/gwr/__init__.py | 4 +
pysal/contrib/gwr/diagnostics.py | 81 ++
pysal/contrib/gwr/gwr.py | 882 ++++++++++++++++
pysal/contrib/gwr/kernels.py | 124 +++
.../gwr/notebooks/GWR_Georgia_Example.ipynb | 354 +++++++
.../gwr/notebooks/GWR_Georgia_Prediction.ipynb | 1049 ++++++++++++++++++++
pysal/contrib/gwr/search.py | 143 +++
pysal/contrib/gwr/sel_bw.py | 256 +++++
pysal/contrib/gwr/tests/test_gwr.py | 849 ++++++++++++++++
pysal/contrib/gwr/tests/test_kernels.py | 84 ++
pysal/contrib/gwr/tests/test_sel_bw.py | 130 +++
pysal/contrib/network/klincs.py | 2 +-
pysal/contrib/spint/gravity.py | 20 +-
pysal/contrib/spint/tests/test_gravity.py | 15 +-
pysal/contrib/viz/mapping.py | 10 +-
pysal/core/IOHandlers/arcgis_swm.py | 123 ++-
pysal/core/util/shapefile.py | 7 +
pysal/esda/geary.py | 18 +-
pysal/esda/mapclassify.py | 2 +-
pysal/esda/moran.py | 36 +-
pysal/esda/smoothing.py | 96 +-
pysal/esda/tests/test_geary.py | 16 +-
pysal/esda/tests/test_moran.py | 8 +
pysal/esda/tests/test_smoothing.py | 104 +-
pysal/examples/clearwater/clearwater_BS_F.ctl | 42 +
.../clearwater/clearwater_BS_F_listwise.csv | 240 +++++
.../clearwater/clearwater_BS_F_summary.txt | 173 ++++
pysal/examples/clearwater/clearwater_BS_NN.ctl | 42 +
.../clearwater/clearwater_BS_NN_listwise.csv | 240 +++++
.../clearwater/clearwater_BS_NN_summary.txt | 179 ++++
pysal/examples/clearwater/clearwater_GS_F.ctl | 42 +
.../clearwater/clearwater_GS_F_listwise.csv | 240 +++++
.../clearwater/clearwater_GS_F_summary.txt | 175 ++++
pysal/examples/clearwater/clearwater_GS_NN.ctl | 42 +
.../clearwater/clearwater_GS_NN_listwise.csv | 240 +++++
.../clearwater/clearwater_GS_NN_summary.txt | 174 ++++
pysal/examples/clearwater/landslides.csv | 240 +++++
pysal/examples/clearwater/quads.dbf | Bin 0 -> 1788 bytes
pysal/examples/clearwater/quads.shp | Bin 0 -> 2004 bytes
pysal/examples/clearwater/quads.shx | Bin 0 -> 212 bytes
pysal/examples/clearwater/streams.dbf | Bin 0 -> 1437154 bytes
pysal/examples/clearwater/streams.shp | Bin 0 -> 2130324 bytes
pysal/examples/clearwater/streams.shx | Bin 0 -> 55348 bytes
pysal/examples/georgia/FB.p | 94 ++
pysal/examples/georgia/GData_utm.csv | 160 +++
pysal/examples/georgia/G_utm.dbf | Bin 0 -> 47706 bytes
pysal/examples/georgia/G_utm.sbn | Bin 0 -> 1964 bytes
pysal/examples/georgia/G_utm.sbx | Bin 0 -> 340 bytes
pysal/examples/georgia/G_utm.shp | Bin 0 -> 243584 bytes
pysal/examples/georgia/G_utm.shx | Bin 0 -> 1476 bytes
pysal/examples/georgia/XB.p | 37 +
pysal/examples/georgia/err.p | 37 +
pysal/examples/georgia/georgia_BS_F.ctl | 45 +
pysal/examples/georgia/georgia_BS_F_listwise.csv | 160 +++
pysal/examples/georgia/georgia_BS_F_summary.txt | 170 ++++
pysal/examples/georgia/georgia_BS_NN.ctl | 45 +
pysal/examples/georgia/georgia_BS_NN_listwise.csv | 160 +++
pysal/examples/georgia/georgia_BS_NN_summary.txt | 170 ++++
pysal/examples/georgia/georgia_GS_F.ctl | 45 +
pysal/examples/georgia/georgia_GS_F_listwise.csv | 160 +++
pysal/examples/georgia/georgia_GS_F_summary.txt | 168 ++++
pysal/examples/georgia/georgia_GS_NN.ctl | 45 +
pysal/examples/georgia/georgia_GS_NN_listwise.csv | 160 +++
pysal/examples/georgia/georgia_GS_NN_summary.txt | 165 +++
pysal/examples/londonhp/londonhp.dbf | Bin 0 -> 81885 bytes
pysal/examples/londonhp/londonhp.shp | Bin 0 -> 8948 bytes
pysal/examples/londonhp/londonhp.shx | Bin 0 -> 2628 bytes
pysal/examples/tokyo/Readme_tokyomortality.txt | 55 +
pysal/examples/tokyo/SampleTokyoMortalityGWPR.ctl | 40 +
pysal/examples/tokyo/Tokyomortality.csv | 1 +
pysal/examples/tokyo/Tokyomortality.txt | 263 +++++
pysal/examples/tokyo/tokyo_BS_F.ctl | 41 +
pysal/examples/tokyo/tokyo_BS_F_listwise.csv | 263 +++++
pysal/examples/tokyo/tokyo_BS_F_summary.txt | 183 ++++
pysal/examples/tokyo/tokyo_BS_NN.ctl | 41 +
pysal/examples/tokyo/tokyo_BS_NN_OFF.ctl | 40 +
pysal/examples/tokyo/tokyo_BS_NN_OFF_listwise.csv | 263 +++++
pysal/examples/tokyo/tokyo_BS_NN_OFF_summary.txt | 148 +++
pysal/examples/tokyo/tokyo_BS_NN_listwise.csv | 263 +++++
pysal/examples/tokyo/tokyo_BS_NN_summary.txt | 162 +++
pysal/examples/tokyo/tokyo_GS_F.ctl | 41 +
pysal/examples/tokyo/tokyo_GS_F_listwise.csv | 263 +++++
pysal/examples/tokyo/tokyo_GS_F_summary.txt | 183 ++++
pysal/examples/tokyo/tokyo_GS_NN.ctl | 41 +
pysal/examples/tokyo/tokyo_GS_NN_listwise.csv | 263 +++++
pysal/examples/tokyo/tokyo_GS_NN_summary.txt | 162 +++
pysal/examples/tokyo/tokyomet262.dbf | Bin 0 -> 8514 bytes
pysal/examples/tokyo/tokyomet262.prj | 1 +
pysal/examples/tokyo/tokyomet262.shp | Bin 0 -> 184536 bytes
pysal/examples/tokyo/tokyomet262.shp.xml | 2 +
pysal/examples/tokyo/tokyomet262.shx | Bin 0 -> 2196 bytes
pysal/examples/wmat/us48_CONTIGUITY_EDGES_ONLY.swm | Bin 0 -> 2350 bytes
pysal/examples/wmat/us48_INVERSE_DISTANCE.swm | Bin 0 -> 3654 bytes
pysal/network/network.py | 78 +-
pysal/network/tests/test_network.py | 5 +
pysal/network/util.py | 49 +-
pysal/spatial_dynamics/util.py | 8 +-
pysal/spreg/error_sp_hom.py | 4 +-
pysal/spreg/summary_output.py | 2 +-
pysal/spreg/tests/test_error_sp_het_regimes.py | 2 +-
pysal/spreg/tests/test_error_sp_hom_regimes.py | 2 +-
pysal/spreg/tests/test_error_sp_regimes.py | 2 +-
pysal/spreg/utils.py | 2 +-
pysal/version.py | 4 +-
pysal/weights/Contiguity.py | 62 +-
pysal/weights/Distance.py | 8 +-
pysal/weights/spatial_lag.py | 15 +-
pysal/weights/tests/test_Contiguity.py | 7 +
pysal/weights/tests/test_Distance.py | 18 +-
pysal/weights/tests/test_spatial_lag.py | 4 +-
pysal/weights/tests/test_swm_IO.py | 42 +
pysal/weights/util.py | 17 +-
requirements_dev.txt | 2 +-
rtd.txt | 1 +
setup.py | 4 +-
138 files changed, 12091 insertions(+), 379 deletions(-)
diff --git a/.coveragerc b/.coveragerc
index 351f12c..bae9cce 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -8,3 +8,6 @@ omit =
__init__.py
*/contrib/*
*/test/*
+exclude_lines =
+ pragma: no cover
+ if __name__ == .__main__.:
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
index 5391dac..6394144 100644
--- a/.github/ISSUE_TEMPLATE.md
+++ b/.github/ISSUE_TEMPLATE.md
@@ -8,7 +8,7 @@ Feel free to delete the commands after you've filled out each bullet.
- Platform information:
```python
->>> import os; print(os.name, os.sys.platform);print(os.uname())
+>>> import os; print(os.name, os.sys.platform)
```
- Python version:
```python
@@ -16,7 +16,7 @@ Feel free to delete the commands after you've filled out each bullet.
```
- SciPy version:
```python
->>> import scpiy; print(scipy.__version__)
+>>> import scipy; print(scipy.__version__)
```
- NumPy version:
```python
diff --git a/.travis.yml b/.travis.yml
index 3dadf8b..dac6dd6 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -34,7 +34,7 @@ install:
script:
- python setup.py sdist >/dev/null
- echo "check_stable=False" >pysal/config.py
- - nosetests --with-coverage --cover-package=pysal;
+ - nosetests --verbose --with-coverage --cover-package=pysal;
#- cd doc; make pickle; make doctest
notifications:
email:
diff --git a/CHANGELOG.txt b/CHANGELOG.txt
index faa5d4e..4635e57 100644
--- a/CHANGELOG.txt
+++ b/CHANGELOG.txt
@@ -1,3 +1,156 @@
+v<1.14.2>, 2017-09-23
+This is a bug fix release for the 1.14 Python 2.x. series of PySAL.
+
+We closed a total of 8 issues, 3 pull requests and 5 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (3):
+
+* :ghpull:`975`: Correction of Geary's C under randomization
+* :ghpull:`972`: Bug Fix for #970
+* :ghpull:`973`: Passing p_thres in plot_lisa_cluster. This fixes #971
+
+Issues (5):
+
+* :ghissue:`974`: Re: #979
+* :ghissue:`975`: Correction of Geary's C under randomization
+* :ghissue:`972`: Bug Fix for #970
+* :ghissue:`973`: Passing p_thres in plot_lisa_cluster. This fixes #971
+* :ghissue:`968`: find_bin does not include top value
+
+
+v<1.14.0>, 2017-07-13
+
+This is the last version of PySAL that will officially support Python 2.x.
+This version will also receive only bug fixes going forward. All new PySAL
+functionality will be added using the new structure documented at pysal.org.
+
+
+We closed a total of 114 issues, 36 pull requests and 78 regular issues;
+this is the full list (generated with the script
+:file:`tools/github_stats.py`):
+
+Pull Requests (36):
+
+* :ghpull:`915`: Network dev
+* :ghpull:`911`: coverage change
+* :ghpull:`931`: Fix for negative values of ebi_v in assancao_rate smoother function
+* :ghpull:`955`: Minor corrections to issue template
+* :ghpull:`888`: Fix unusual interpreter path.
+* :ghpull:`926`: DOC: fix a broken link (HTTP 404) in documentation
+* :ghpull:`951`: Order enforce
+* :ghpull:`953`: deprecating headbanging
+* :ghpull:`952`: fix for #932 coordinates need to be (lon, lat) not (lat, lon)
+* :ghpull:`946`: [WIP] add Distance bugfix
+* :ghpull:`942`: fixed the bug in reading the new ArcGIS .swm file
+* :ghpull:`947`: [WIP] fix bug #944
+* :ghpull:`950`: Lag cat fix
+* :ghpull:`928`: Fixed incorrect file path in fileio.rst
+* :ghpull:`927`: DOC: fix formatting of a code block in the documentation
+* :ghpull:`934`: Added String_Type values to unsupported file types
+* :ghpull:`929`: DOC: make code snippet in documentation compatible to Python 3
+* :ghpull:`924`: add pseudocode makefile
+* :ghpull:`916`: Util.compute length
+* :ghpull:`919`: Dijkstra mp
+* :ghpull:`921`: Mapping: allow tips parameter even if col not passed
+* :ghpull:`912`: adjust the formula for calculating local Moran's I in the docs
+* :ghpull:`913`: 0. Fixed docstring that was causing latex error
+* :ghpull:`910`: add new weights classes & methods to docs & tutorials
+* :ghpull:`908`: Geographically weighted regresson
+* :ghpull:`907`: Spint
+* :ghpull:`905`: Spint - fix local param indexing
+* :ghpull:`904`: Rc1.13.1
+* :ghpull:`903`: Fix for numpy bump induced breakage
+* :ghpull:`902`: Spreg fix
+* :ghpull:`901`: Badge for docs
+* :ghpull:`899`: fix inplace overwrite issue
+* :ghpull:`897`: fix minor typo
+* :ghpull:`887`: Dev version bump
+* :ghpull:`885`: Fixing doc builds
+* :ghpull:`883`: release 1.13.0
+
+Issues (78):
+
+* :ghissue:`892`: allow non-binary weights to propagate in w_subset
+* :ghissue:`915`: Network dev
+* :ghissue:`925`: Reorg
+* :ghissue:`911`: coverage change
+* :ghissue:`848`: Points contrib module
+* :ghissue:`931`: Fix for negative values of ebi_v in assancao_rate smoother function
+* :ghissue:`956`: Nose tests fail for python 3 in Windows
+* :ghissue:`917`: Code not updated in docs
+* :ghissue:`943`: mapclassify docs contain a long WARNING
+* :ghissue:`948`: test_Headbanging_Median_Rate fails with Python 3.6
+* :ghissue:`955`: Minor corrections to issue template
+* :ghissue:`958`: Estimate panel models in spreg
+* :ghissue:`949`: spreg request: Conley standard errors
+* :ghissue:`888`: Fix unusual interpreter path.
+* :ghissue:`926`: DOC: fix a broken link (HTTP 404) in documentation
+* :ghissue:`951`: Order enforce
+* :ghissue:`953`: deprecating headbanging
+* :ghissue:`313`: Add handler for topojson
+* :ghissue:`316`: Add support for geojson
+* :ghissue:`721`: Ability to specify categories / ranges in spatial markov method
+* :ghissue:`474`: Add contrib module to explore interface with geopandas
+* :ghissue:`651`: Design Point Pattern API
+* :ghissue:`648`: FileIO deprecation warning
+* :ghissue:`891`: Strange weights when I apply w_subset
+* :ghissue:`201`: Distance matrix builder
+* :ghissue:`872`: `weights.util.higher_order` warns when using a subclassed `W`
+* :ghissue:`923`: Arc_KDTree returned distances incorrect
+* :ghissue:`952`: fix for #932 coordinates need to be (lon, lat) not (lat, lon)
+* :ghissue:`945`: `DistanceBand` failing when `ids` passed
+* :ghissue:`946`: [WIP] add Distance bugfix
+* :ghissue:`939`: Compute spatial weight matrix from geopandas dataframe
+* :ghissue:`937`: Can't read ESRI spatial weights matrix
+* :ghissue:`942`: fixed the bug in reading the new ArcGIS .swm file
+* :ghissue:`729`: Update contrib docs
+* :ghissue:`947`: [WIP] fix bug #944
+* :ghissue:`950`: Lag cat fix
+* :ghissue:`944`: Higher Order Weights from Queen Contiguity Weights - Unsupported sparse argument
+* :ghissue:`922`: Weights, ordering, and from_dataframe
+* :ghissue:`940`: Add a Gitter chat badge to README.md
+* :ghissue:`928`: Fixed incorrect file path in fileio.rst
+* :ghissue:`906`: GSOC 2017
+* :ghissue:`938`: I use “Markov(class_ids, classes=None)” ,classes (array) – (k, 1), define k=4, but the result display error.
+* :ghissue:`927`: DOC: fix formatting of a code block in the documentation
+* :ghissue:`935`: Creating Network instance from road shapefile fails; ValueError: list.remove(x): x not in list
+* :ghissue:`932`: Opening PolyLineM Shapefile fails to return file type unsupported error
+* :ghissue:`934`: Added String_Type values to unsupported file types
+* :ghissue:`929`: DOC: make code snippet in documentation compatible to Python 3
+* :ghissue:`924`: add pseudocode makefile
+* :ghissue:`916`: Util.compute length
+* :ghissue:`919`: Dijkstra mp
+* :ghissue:`866`: Use install_requires to automatically install required packages if they are not present
+* :ghissue:`921`: Mapping: allow tips parameter even if col not passed
+* :ghissue:`914`: added wkt representation for chain/polygon
+* :ghissue:`918`: Python 3.5, pysal FileIO import error
+* :ghissue:`912`: adjust the formula for calculating local Moran's I in the docs
+* :ghissue:`913`: 0. Fixed docstring that was causing latex error
+* :ghissue:`910`: add new weights classes & methods to docs & tutorials
+* :ghissue:`908`: Geographically weighted regresson
+* :ghissue:`907`: Spint
+* :ghissue:`905`: Spint - fix local param indexing
+* :ghissue:`904`: Rc1.13.1
+* :ghissue:`896`: test_Headbanging_Median_Rate fails
+* :ghissue:`903`: Fix for numpy bump induced breakage
+* :ghissue:`902`: Spreg fix
+* :ghissue:`901`: Badge for docs
+* :ghissue:`899`: fix inplace overwrite issue
+* :ghissue:`610`: SpReg Deprecation Warning
+* :ghissue:`895`: Weights for rasters or arrays with missing data
+* :ghissue:`897`: fix minor typo
+* :ghissue:`881`: Readthedocs failing to build due to missing ingmath module
+* :ghissue:`893`: DistanceBand error (AttributeError: power not found)
+* :ghissue:`890`: no neighbors on threshold_continuousW, after w_subset
+* :ghissue:`887`: Dev version bump
+* :ghissue:`886`: Dev
+* :ghissue:`885`: Fixing doc builds
+* :ghissue:`884`: Rel/1.13.1
+* :ghissue:`883`: release 1.13.0
+* :ghissue:`876`: changing shp.next() to next(shp)
+
v<1.13.0>, 2016-11-24
We closed a total of 38 issues, 7 pull requests and 31 regular issues;
diff --git a/README.rst b/README.rst
index 1587a06..ef5c32d 100644
--- a/README.rst
+++ b/README.rst
@@ -10,6 +10,10 @@ Python Spatial Analysis Library
.. image:: https://badges.gitter.im/pysal/pysal.svg
:target: https://gitter.im/pysal/pysal
+.. image:: https://readthedocs.org/projects/pip/badge/?version=latest
+ :target: http://pysal.readthedocs.io/en/latest/index.html
+
+
PySAL_ is an open source cross-platform library of spatial analysis functions
written in Python. It is intended to support the development of high level
applications for spatial analysis.
@@ -25,46 +29,6 @@ Counties 1990.*
-It is important to underscore what PySAL is, and is not, designed to do. First
-and foremost, PySAL is a library in the fullest sense of the word. Developers
-looking for a suite of spatial analytical methods that they can incorporate
-into application development should feel at home using PySAL. Spatial analysts
-who may be carrying out research projects requiring customized scripting,
-extensive simulation analysis, or those seeking to advance the state of the art
-in spatial analysis should also find PySAL to be a useful foundation for their
-work.
-
-End users looking for a user friendly graphical user interface for spatial
-analysis should not turn to PySAL directly. Instead, we would direct them to
-projects like the GeoDaX_ suite of software products which wrap PySAL
-functionality in GUIs. At the same time, we expect that with developments such
-as the Python based plug-in architectures for QGIS, GRASS, and the toolbox
-extensions for ArcGIS, that end user access to PySAL functionality will be
-widening in the near future.
-
-.. _PySAL : https://github.com/pysal/pysal/
-.. _GeoDaX : https://geodacenter.asu.edu/software
-
-
-.. |build| image:: https://travis-ci.org/pysal/pysal.png
- :scale: 100%
- :align: middle
- :target: https://travis-ci.org/pysal/pysal
-.. |cover| image:: https://coveralls.io/repos/pysal/pysal/badge.svg?branch=master
- :scale: 50%
- :align: top
- :target: https://coveralls.io/r/pysal/pysal?branch=master
-.. |docs| image:: https://readthedocs.org/projects/pysal/badge/?verison=latest
- :scale: 50%
- :align: top
- :target: http://pysal.readthedocs.org/en/latest/
-.. |talk| image:: https://badges.gitter.im/Join%20Chat.svg
- :scale: 50%
- :align: top
- :target: https://gitter.im/pysal/pysal?
-
-
-
PySAL modules
=============
diff --git a/THANKS.txt b/THANKS.txt
index 883363d..6b2b267 100644
--- a/THANKS.txt
+++ b/THANKS.txt
@@ -11,6 +11,7 @@ Pedro Amaral
Luc Anselin
Jotham Apaloo
Daniel Arribas-Bel
+Roger Bivand
David C. Folch
Forest Gregg
Myunghwa Hwang
@@ -33,10 +34,22 @@ Andrew Winslow
Levi Wolf
Jing Yao
Xinyue Ye
+jo-tham
+sebastic
+andy-esch
+calebrob6
+fnorf
+mylons-tcc
+dnadai2
+MridulS
Funding from the following sources has supported PySAL development:
-Google Summer of Code 2016
+Google Summer of Code 2017
+
+Google Summer of Code 2016
+
+National Institutes of Health Award 7R01CA1266858-06, Geospatial Factors and Impacts II.
National Science Foundation New Approaches for Spatial Distribution Dynamics
diff --git a/doc/source/conf.py b/doc/source/conf.py
index cd630c2..e051b80 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -11,6 +11,8 @@ import os
# -- General configuration -----------------------------------------------------
+needs_sphinx = '1.4.3'
+
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.autodoc',
@@ -45,9 +47,9 @@ copyright = u'2014-, PySAL Developers; 2009-13 Sergio Rey'
# built documents.
#
# The short X.Y version.
-version = '1.13.0'
+version = '1.14.2'
# The full version, including alpha/beta/rc tags.
-release = '1.13.0'
+release = '1.14.2'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
diff --git a/doc/source/developers/docs/index.rst b/doc/source/developers/docs/index.rst
index 68238e4..cfb007e 100644
--- a/doc/source/developers/docs/index.rst
+++ b/doc/source/developers/docs/index.rst
@@ -47,11 +47,11 @@ If you're using Sphinx version 1.3 or newer, napoleon is included and should be
If you're using a version of Sphinx that does not ship with napoleon ( Sphinx < 1.3), you'll need napoleon version 0.2.4 or later and Sphinx version 1.0 or later to compile the documentation.
Both modules are available at the Python Package Index, and can be downloaded and installed
-from the command line using *pip* or *easy_install*.::
+from the command line using *pip* or *easy_install*::
$ easy_install sphinx
$ easy_install sphinxcontrib-napoleon
-or
+or::
$ pip sphinx
$ pip sphinxcontrib-napoleon
diff --git a/doc/source/developers/guidelines.rst b/doc/source/developers/guidelines.rst
index b7e4c5f..c494d79 100644
--- a/doc/source/developers/guidelines.rst
+++ b/doc/source/developers/guidelines.rst
@@ -9,7 +9,7 @@ PySAL is adopting many of the conventions in the larger scientific computing
in Python community and we ask that anyone interested in joining the project
please review the following documents:
- * `Documentation standards <http://projects.scipy.org/numpy/wiki/CodingStyleGuidelines>`_
+ * `Documentation standards <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_
* `Coding guidelines <http://www.python.org/dev/peps/pep-0008/>`_
* :doc:`Testing guidelines <testing>`
diff --git a/doc/source/index.rst b/doc/source/index.rst
index ead78ed..de9c263 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -20,7 +20,7 @@ PySAL
.. sidebar:: Releases
- - `Stable 1.13.0 (Released 2016-12-9) <users/installation.html>`_
+ - `Stable 1.14.2 (Released 2017-9-23) <users/installation.html>`_
- `Development <http://github.com/pysal/pysal/tree/dev>`_
PySAL is an open source library of spatial analysis functions written in
diff --git a/doc/source/library/weights/Contiguity.rst b/doc/source/library/weights/Contiguity.rst
index b35faa0..140329b 100644
--- a/doc/source/library/weights/Contiguity.rst
+++ b/doc/source/library/weights/Contiguity.rst
@@ -8,4 +8,13 @@ The :mod:`weights.Contiguity.` module provides for the construction and manipula
.. automodule:: pysal.weights.Contiguity
:synopsis: Spatial weights for PySAL: Python Spatial Analysis Library
:members:
+ :inherited-members:
+
+.. autoclass:: pysal.weights.Contiguity.Queen
+ :members:
+ :inherited-members:
+
+.. autoclass:: pysal.weights.Contiguity.Rook
+ :members:
+ :inherited-members:
diff --git a/doc/source/users/tutorials/autocorrelation.rst b/doc/source/users/tutorials/autocorrelation.rst
index 1b16c4c..94c82e0 100644
--- a/doc/source/users/tutorials/autocorrelation.rst
+++ b/doc/source/users/tutorials/autocorrelation.rst
@@ -617,7 +617,7 @@ which indicates that none of the C values from the permuted samples was as extre
Getis and Ord's G
-----------------
-The last statistic for global spatial autcorrelation implemented in PySAL is Getis and Ord's G:
+The last statistic for global spatial autocorrelation implemented in PySAL is Getis and Ord's G:
.. math::
@@ -676,7 +676,7 @@ PySAL implements local Moran's I as follows:
.. math::
- I_i = \sum_j z_i w_{i,j} z_j / \sum_i z_i z_i
+ I_i = \frac{(n-1) z_i \sum_j w_{i,j} z_j}{\sum_j z_j^2}
which results in :math:`n` values of local spatial autocorrelation, 1 for each spatial unit. Continuing on with the St. Louis example, the LISA statistics are obtained with:
diff --git a/doc/source/users/tutorials/fileio.rst b/doc/source/users/tutorials/fileio.rst
index a341799..0496107 100644
--- a/doc/source/users/tutorials/fileio.rst
+++ b/doc/source/users/tutorials/fileio.rst
@@ -14,10 +14,11 @@ An Overview of the FileIO system in PySAL.
Introduction
============
-PySAL contains a new file input-output API that should be used for all file IO
-operations. The goal is to abstract file handling and return native PySAL data
-types when reading from known file types. A list of known extensions can be
-found by issuing the following command::
+PySAL contains a file input-output API that stands as a reference pure python
+implementation for spatial IO. The design goal for this API is to abstract
+file handling and return native PySAL data types when reading from known
+file types. A list of known extensions can be found by issuing the
+following command::
pysal.open.check()
@@ -28,6 +29,8 @@ if has the necessary header information and dispatch accordingly. In the event
that pysal does not understand your file IO operations will be dispatched to
python's internal open.
+PySAL can also fully leverage `Geopandas <http://geopandas.org>`_ in analyses. It also provides an alternative, tabular data IO system, ``pdio``.
+
Examples: Reading files
=======================
@@ -37,7 +40,7 @@ Shapefiles
.. doctest::
>>> import pysal
- >>> shp = pysal.open('../pysal/examples/10740.shp')
+ >>> shp = pysal.open(pysal.examples.get_path('10740.shp'))
>>> poly = shp.next()
>>> type(poly)
<class 'pysal.cg.shapes.Polygon'>
@@ -55,7 +58,7 @@ DBF Files
.. doctest::
>>> import pysal
- >>> db = pysal.open('../pysal/examples/10740.dbf','r')
+ >>> db = pysal.open(pysal.examples.get_path('10740.dbf'),'r')
>>> db.header
['GIST_ID', 'FIPSSTCO', 'TRT2000', 'STFID', 'TRACTID']
>>> db.field_spec
@@ -427,3 +430,20 @@ From GAL to ArcGIS SWM format
For further details see the :doc:`FileIO API <../../library/core/FileIO>`.
+
+Alternative Tabular API
+========================
+
+For shapefile input and output, the dataframe API constructs a dataframe similar to that constructed by Geopandas, but populated by PySAL's own shape classes. This is provided as a convenience method for users who have shapefile-heavy workflows and would like to get Geopandas-style interaction. This API is only a frontend to the existing PySAL api documented above, and users who have heavier spatial data needs may find Geopandas useful.
+
+.. doctest::
+ >>> df = pysal.pdio.read_files(ps.examples.get_path('columbus.shp'))
+ >>> df.head().NEIG
+ 0 5
+ 1 1
+ 2 6
+ 3 2
+ 4 7
+ Name: NEIG, dtype: int64
+ >>> df['new_data'] = np.random.normal(0,1,shape=df.shape[0])
+ >>> ps.pdio.write_files(df, './test_out.shp')
diff --git a/doc/source/users/tutorials/region.rst b/doc/source/users/tutorials/region.rst
index f31dd28..57f65ff 100644
--- a/doc/source/users/tutorials/region.rst
+++ b/doc/source/users/tutorials/region.rst
@@ -145,7 +145,7 @@ constraints.
>>> import pysal
>>> from pysal.region import Random_Region
>>> nregs = 13
- >>> cards = range(2,14) + [10]
+ >>> cards = list(range(2,14)) + [10]
>>> w = pysal.lat2W(10,10,rook = False)
>>> ids = w.id_order
>>>
diff --git a/doc/source/users/tutorials/weights.rst b/doc/source/users/tutorials/weights.rst
index 3ee6dbe..e3ee068 100644
--- a/doc/source/users/tutorials/weights.rst
+++ b/doc/source/users/tutorials/weights.rst
@@ -26,7 +26,8 @@ matrices across three different general types:
* Distance Based Weights
* Kernel Weights
-These different types of weights are implemented as instances of the PySAL weights class
+These different types of weights are implemented as instances or subclasses of
+the PySAL weights class
:class:`~pysal.weights.W`.
In what follows, we provide a high level overview of spatial weights in PySAL, starting with the three different types of weights, followed by
@@ -105,7 +106,6 @@ of the lattice to define contiguities:
>>> wq = pysal.lat2W(rook = False)
>>> wq.neighbors[0]
[5, 1, 6]
- >>>
The bishop criterion, which designates pairs of cells as neighbors if they share
only a vertex, is yet a third alternative for contiguity weights. A bishop matrix
@@ -116,12 +116,13 @@ requiring a regular grid. For empirical research, a common use case is to have
a shapefile, which is a nontopological vector data structure, and a need
to carry out some form of spatial analysis that requires spatial weights. Since
topology is not stored in the underlying file there is a need to construct
-the spatial weights prior to carrying out the analysis. In PySAL spatial
-weights can be obtained directly from shapefiles:
+the spatial weights prior to carrying out the analysis.
+
+In PySAL, weights are constructed by default from any contiguity graph representation. Most users will find the ``.from_shapefile`` methods most useful:
.. doctest::
- >>> w = pysal.rook_from_shapefile("../pysal/examples/columbus.shp")
+ >>> w = pysal.weights.Rook.from_shapefile("../pysal/examples/columbus.shp")
>>> w.n
49
>>> print "%.4f"%w.pct_nonzero
@@ -133,13 +134,32 @@ If queen, rather than rook, contiguity is required then the following would work
.. doctest::
- >>> w = pysal.queen_from_shapefile("../pysal/examples/columbus.shp")
+ >>> w = pysal.weights.Queen.from_shapefile("../pysal/examples/columbus.shp")
>>> print "%.4f"%w.pct_nonzero
0.0983
>>> w.histogram
[(2, 5), (3, 9), (4, 12), (5, 5), (6, 9), (7, 3), (8, 4), (9, 1), (10, 1)]
-
+In addition to these methods, contiguity weights can be built from dataframes with a geometry column. This includes dataframes built from geopandas or from the PySAL pandas IO extension, pdio. For instance:
+
+ >>> import geopandas as gpd
+ >>> test = gpd.read_file(pysal.examples.get_path('south.shp'))
+ >>> W = pysal.weights.Queen.from_dataframe(test)
+ >>> Wrook = pysal.weights.Rook.from_dataframe(test, idVariable='CNTY_FIPS')
+ >>> pdiodf = pysal.pdio.read_files(pysal.examples.get_path('south.shp'))
+ >>> W = pysal.weights.Rook.from_dataframe(pdiodf)
+
+Or, weights can be constructed directly from an interable of shapely objects:
+
+ >>> import geopandas as gpd
+ >>> shapelist = gpd.read_file(pysal.examples.get_path('columbus.shp')).geometry.tolist()
+ >>> W = pysal.weights.Queen.from_iterable(shapelist)
+
+The ``.from_file`` method on contigutiy weights simply passes down to the parent class's ``.from_file`` method, so the returned object is of instance ``W``, not ``Queen`` or ``Rook``. This occurs because the weights cannot be verified *as* contiguity weights without the original shapes.
+
+ >>> W = pysal.weights.Rook.from_file(pysal.examples.get_path('columbus.gal')
+ >>> type(W)
+ pysal.weights.weights.W
Distance Based Weights
----------------------
@@ -158,8 +178,7 @@ k-nearest neighbor weights
The neighbors for a given observations can be defined using a k-nearest neighbor criterion.
For example we could use the the centroids of our
5x5 lattice as point locations to measure the distances. First, we import numpy to
-create the coordinates as a 25x2 numpy array named data (numpy arrays are the only
-form of input supported at this point):
+create the coordinates as a 25x2 numpy array named ``data``:
.. doctest::
@@ -170,16 +189,24 @@ form of input supported at this point):
>>> data=np.hstack([x,y])
-then define the knn set as:
+then define the KNN weight as:
.. doctest::
- >>> wknn3 = pysal.knnW(data, k = 3)
+ >>> wknn3 = pysal.weights.KNN(data, k = 3)
>>> wknn3.neighbors[0]
[1, 5, 6]
>>> wknn3.s0
75.0
- >>> w4 = pysal.knnW(data, k = 4)
+
+For efficiency, a KDTree is constructed to compute efficient nearest neighbor queries. To construct many K-Nearest neighbor weights from the same data, a convenience method is provided that prevents re-constructing the KDTree while letting the user change aspects of the weight object. By default, the reweight method operates in place:
+
+ >>> w4 = wknn3.reweight(k=4, inplace=False)
+ >>> w4.neighbors[0]
+ [1,5,6,2]
+ >>> l1norm = wknn3.reweight(p=1, inplace=False)
+ >>> l1norm.neighbors
+ [1,5,2]
>>> set(w4.neighbors[0]) == set([1, 5, 6, 2])
True
>>> w4.s0
@@ -192,10 +219,16 @@ shapefile:
.. doctest::
- >>> wknn5 = pysal.knnW_from_shapefile(pysal.examples.get_path('columbus.shp'), k=5)
+ >>> wknn5 = pysal.weights.KNN.from_shapefile(pysal.examples.get_path('columbus.shp'), k=5)
>>> wknn5.neighbors[0]
[2, 1, 3, 7, 4]
+Or from a dataframe:
+
+ >>> import geopandas as gpd
+ >>> df = gpd.read_file(ps.examples.get_path('baltim.shp'))
+ >>> k5 = pysal.weights.KNN.from_dataframe(df, k=5)
+
Distance band weights
---------------------
@@ -206,7 +239,7 @@ falling within a threshold distance of the focal unit:
.. doctest::
- >>> wthresh = pysal.threshold_binaryW_from_array(data, 2)
+ >>> wthresh = pysal.weights.DistanceBand.from_array(data, 2)
>>> set(wthresh.neighbors[0]) == set([1, 2, 5, 6, 10])
True
>>> set(wthresh.neighbors[1]) == set( [0, 2, 5, 6, 7, 11, 3])
@@ -221,6 +254,12 @@ As can be seen in the above example, the number of neighbors is likely to vary
across observations with distance band weights in contrast to what holds for
knn weights.
+In addition to constructing these from the helper function, Distance Band weights. For example, a threshold binary W can be constructed from a dataframe:
+
+ >>> import geopandas as gpd
+ >>> df = gpd.read_file(ps.examples.get_path('baltim.shp'))
+ >>> ps.weights.DistanceBand.from_dataframe(df, threshold=6, binary=True)
+
Distance band weights can be generated for shapefiles as well as arrays of points. [#]_ First, the
minimum nearest neighbor distance should be determined so that each unit is assured of at least one
neighbor:
@@ -235,7 +274,7 @@ with this threshold in hand, the distance band weights are obtained as:
.. doctest::
- >>> wt = pysal.threshold_binaryW_from_shapefile("../pysal/examples/columbus.shp", thresh)
+ >>> wt = pysal.weights.DistanceBand.from_shapefile("../pysal/examples/columbus.shp", threshold=thresh, binary=True)
>>> wt.min_neighbors
1
>>> wt.histogram
@@ -253,7 +292,7 @@ points:
.. doctest::
>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
- >>> wid = pysal.threshold_continuousW_from_array(points,14.2)
+ >>> wid = pysal.weights.DistanceBand.from_array(points,14.2,binary=False)
>>> wid.weights[0]
[0.10000000000000001, 0.089442719099991588]
@@ -261,7 +300,7 @@ If we change the distance decay exponent to -2.0 the result is so called gravity
.. doctest::
- >>> wid2 = pysal.threshold_continuousW_from_array(points,14.2,alpha = -2.0)
+ >>> wid2 = pysal.weights.DistanceBand.from_array(points,14.2,alpha = -2.0, binary=False)
>>> wid2.weights[0]
[0.01, 0.0079999999999999984]
@@ -365,8 +404,31 @@ Finally, the kernel function could be changed (with endogenous adaptive bandwidt
More details on kernel weights can be found in
-:class:`~pysal.weights.Distance.Kernel`.
+:class:`~pysal.weights.Distance.Kernel`. All kernel methods also support construction from shapefiles with ``Kernel.from_shapefile`` and from dataframes with ``Kernel.from_dataframe``.
+
+Weights from other python objects
+==================================
+
+PySAL weights can also be constructed easily from many other objects. Most importantly, all weight types can be constructed directly from ``geopandas`` geodataframes using the ``.from_dataframe`` method. For distance and kernel weights, underlying features should typically be points. But, if polygons are supplied, the centroids of the polygons will be used by default:
+
+ >>> import geopandas as gpd
+ >>> df = gpd.read_file(pysal.examples.get_path('columbus.shp'))
+ >>> kw = pysal.weights.Kernel.from_dataframe(df)
+ >>> dbb = pysal.weights.DistanceBand.from_dataframe(df, threshold=.9, binary=False)
+ >>> dbc = pysal.weights.DistanceBand.from_dataframe(df, threshold=.9, binary=True)
+ >>> q = pysal.weights.Queen.from_dataframe(df)
+ >>> r = pysal.weights.Rook.from_dataframe(df)
+
+This also applies to dynamic views of the dataframe:
+
+ >>> q2 = pysal.weights.Queen.from_dataframe(df.query('DISCBD < 2'))
+
+Weights can also be constructed from NetworkX objects. This is easiest to construct using a sparse weight, but that can be converted to a full dense PySAL weight easily:
+ >>> import networkx as nx
+ >>> G = nx.random_lobster(50,.2,.5)
+ >>> sparse_lobster = ps.weights.WSP(nx.adj_matrix(G))
+ >>> dense_lobster = sparse_lobster.to_W()
A Closer look at W
==================
diff --git a/pysal/cg/kdtree.py b/pysal/cg/kdtree.py
index f9655e1..278c320 100644
--- a/pysal/cg/kdtree.py
+++ b/pysal/cg/kdtree.py
@@ -19,23 +19,23 @@ DISTANCE_METRICS = ['Euclidean', 'Arc']
FLOAT_EPS = numpy.finfo(float).eps
-def KDTree(data, leafsize=10, distance_metric='Euclidean',
+def KDTree(data, leafsize=10, distance_metric='Euclidean',
radius=RADIUS_EARTH_KM):
"""
kd-tree built on top of kd-tree functionality in scipy. If using scipy 0.12
or greater uses the scipy.spatial.cKDTree, otherwise uses
scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance.
Note that Arc distance is only appropriate when points in latitude and
- longitude, and the radius set to meaningful value (see docs below).
+ longitude, and the radius set to meaningful value (see docs below).
Parameters
----------
data : array
- The data points to be indexed. This array is not copied,
+ The data points to be indexed. This array is not copied,
and so modifying this data will result in bogus results.
Typically nx2.
leafsize : int
- The number of points at which the algorithm switches over
+ The number of points at which the algorithm switches over
to brute-force. Has to be positive. Optional, default is 10.
distance_metric : string
Options: "Euclidean" (default) and "Arc".
@@ -56,7 +56,7 @@ def KDTree(data, leafsize=10, distance_metric='Euclidean',
return Arc_KDTree(data, leafsize, radius)
-# internal hack for the Arc_KDTree class inheritance
+# internal hack for the Arc_KDTree class inheritance
if int(scipy.version.version.split(".")[1]) < 12:
temp_KDTree = scipy.spatial.KDTree
else:
@@ -164,10 +164,16 @@ class Arc_KDTree(temp_KDTree):
return sphere.linear2arcdist(d, r), i
if dims == 1:
#TODO: implement linear2arcdist on numpy arrays
- d = [sphere.linear2arcdist(x, r) for x in d]
+ d1 = [sphere.linear2arcdist(dx, r) for dx in d]
elif dims == 2:
- d = [[sphere.linear2arcdist(x, r) for x in row] for row in d]
- return numpy.array(d), i
+ d1 = [[sphere.linear2arcdist(dx, r) for dx in row] for row in d]
+ d = numpy.array(d1)
+ dinf = d == numpy.inf
+ if dinf.sum() > 0:
+ i = i.astype(float)
+ i[dinf] = numpy.nan
+
+ return d, i
def query_ball_point(self, x, r, p=2, eps=0):
"""
diff --git a/pysal/cg/segmentLocator.py b/pysal/cg/segmentLocator.py
index 07c4c64..fe17a60 100644
--- a/pysal/cg/segmentLocator.py
+++ b/pysal/cg/segmentLocator.py
@@ -114,19 +114,19 @@ class SegmentGrid(object):
raise Exception('Invalid arguments for SegmentGrid(): (' + str(self.x_range) + ', ' + str(self.y_range) + ', ' + str(self.res) + ')')
@property
def hashKeys(self):
- if self._hashKeys == None:
+ if self._hashKeys is None:
self._hashKeys = numpy.array(self.hash.keys(),dtype=float)
return self._hashKeys
@property
def kd(self):
- if self._kd == None:
+ if self._kd is None:
self._kd = scipy.spatial.cKDTree(self.hashKeys)
return self._kd
@property
def kd2(self):
- if self._kd2 == None:
+ if self._kd2 is None:
self._kd2 = scipy.spatial.KDTree(self.hashKeys)
return self._kd2
diff --git a/pysal/contrib/geotable/tests/test_utils.py b/pysal/contrib/geotable/tests/test_utils.py
index bb4ee59..bba20df 100644
--- a/pysal/contrib/geotable/tests/test_utils.py
+++ b/pysal/contrib/geotable/tests/test_utils.py
@@ -1,13 +1,66 @@
from .. import utils
-from ...pdio import read_files as rf
+from ...pdio import read_files
+import pysal as ps
import unittest as ut
+try:
+ import geopandas as gpd
+except ImportError:
+ gpd = None
+MISSING_GEOPANDAS = gpd is None
+
+ at ut.skipIf(MISSING_GEOPANDAS, 'geopandas is missing...')
class Test_Utils(ut.TestCase):
+ def setUp(self):
+ self.df = read_files(ps.examples.get_path('columbus.shp'))
+ self.gdf = gpd.read_file(ps.examples.get_path('columbus.shp'))
+
def test_converters(self):
- ## make a round trip to geodataframe and back
- raise
+ import shapely.geometry as shgeom
+ import pandas as pd
+
+ from_df = utils.to_gdf(self.df)
+ from_gdf = utils.to_df(self.gdf)
+
+ self.assertIsInstance(from_df, type(self.gdf))
+ self.assertIsInstance(from_gdf, type(self.df))
+
+ self.assertIsInstance(from_gdf.iloc[0].geometry,
+ ps.cg.Polygon)
+ self.assertIsInstance(from_df.iloc[0].geometry,
+ shgeom.base.BaseGeometry)
+
+ self.assertIsInstance(self.df, pd.DataFrame)
+ self.assertIsInstance(self.gdf,gpd.GeoDataFrame)
+
+ self.assertIsInstance(self.df.iloc[0].geometry,
+ ps.cg.Polygon)
+ self.assertIsInstance(self.gdf.iloc[0].geometry,
+ shgeom.base.BaseGeometry)
+
def test_insert_metadata(self):
## add an attribute to a dataframe and see
## if it is pervasive over copies
- raise
+ W = ps.weights.Queen.from_dataframe(self.gdf)
+ new = utils.insert_metadata(self.df, W, 'W', inplace=False)
+ utils.insert_metadata(self.gdf, W, 'W', inplace=True)
+ assert hasattr(new, 'W')
+ self.assertIsInstance(new.W, ps.weights.W)
+
+ assert hasattr(new.copy(), 'W')
+
+ assert hasattr(new.copy(deep=True), 'W')
+
+ assert hasattr(self.gdf, 'W')
+
+ try:
+ utils.insert_metadata(self.gdf, W, 'W',
+ inplace=True, overwrite=False)
+ raise UserWarning('Did not raise an exception when'
+ 'overwriting and overwrite=False')
+ except Exception:
+ pass
+
+if __name__ == "__main__":
+ ut.main()
diff --git a/pysal/contrib/geotable/utils.py b/pysal/contrib/geotable/utils.py
index ae2fbc4..541db46 100644
--- a/pysal/contrib/geotable/utils.py
+++ b/pysal/contrib/geotable/utils.py
@@ -6,7 +6,7 @@ from warnings import warn
def to_df(df, geom_col='geometry', **kw):
"""
Convert a Geopandas dataframe into a normal pandas dataframe with a column
- containing PySAL shapes.
+ containing PySAL shapes. Always returns a copy.
Arguments
---------
@@ -24,15 +24,14 @@ def to_df(df, geom_col='geometry', **kw):
"""
import pandas as pd
from geopandas import GeoDataFrame, GeoSeries
- df[geom_col] = df[geom_col].apply(pShape)
- if isinstance(df, (GeoDataFrame, GeoSeries)):
- df = pd.DataFrame(df, **kw)
- return df
+ out = df.copy(deep=True)
+ out[geom_col] = out[geom_col].apply(pShape)
+ return pd.DataFrame(out, **kw)
@_requires('geopandas')
def to_gdf(df, geom_col='geometry', **kw):
"""
- Convert a pandas dataframe with geometry column to a GeoPandas dataframe
+ Convert a pandas dataframe with geometry column to a GeoPandas dataframe. Returns a copy always.
Arguments
---------
@@ -50,13 +49,40 @@ def to_gdf(df, geom_col='geometry', **kw):
"""
from geopandas import GeoDataFrame
from shapely.geometry import asShape as sShape
- df[geom_col] = df[geom_col].apply(sShape)
- return GeoDataFrame(df, geometry=geom_col, **kw)
+ out = df.copy(deep=True)
+ out[geom_col] = out[geom_col].apply(sShape)
+ out = GeoDataFrame(out, geometry=geom_col, **kw)
+ return out
-def insert_metadata(df, obj, name=None, inplace=True, overwrite=False):
+def insert_metadata(df, obj, name=None, inplace=False, overwrite=False):
+ """
+ Insert an object into a dataframe's metadata with a given key.
+
+ Arguments
+ ------------
+ df : pd.DataFrame
+ dataframe to insert into the metadata
+ obj : object
+ object desired to insert into the dataframe
+ name : string
+ key of the object to use. Will be available as
+ an attribute of the dataframe.
+ inplace : bool
+ flag to denote whether to operate on a copy
+ of the dataframe or not.
+ overwrite : bool
+ flag to denote whether to replace existing entry
+ in metadata or not.
+
+ Returns
+ --------
+ If inplace, changes dataframe implicitly.
+ Else, returns a new dataframe with added metadata.
+ """
if not inplace:
new = df.copy(deep=True)
- insert_metadata(new, obj, name=name, inplace=True)
+ insert_metadata(new, obj, name=name,
+ inplace=True, overwrite=overwrite)
return new
if name is None:
name = type(obj).__name__
diff --git a/pysal/contrib/glm/GLM_validate_estimation.ipynb b/pysal/contrib/glm/GLM_validate_estimation.ipynb
new file mode 100644
index 0000000..1b17831
--- /dev/null
+++ b/pysal/contrib/glm/GLM_validate_estimation.ipynb
@@ -0,0 +1,444 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Import GLM and pysal\n",
+ "import os\n",
+ "import numpy as np\n",
+ "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/glm')\n",
+ "from glm import GLM\n",
+ "import pysal\n",
+ "import pandas as pd\n",
+ "import statsmodels.formula.api as smf\n",
+ "import statsmodels.api as sm\n",
+ "from family import Gaussian, Binomial, Poisson, QuasiPoisson\n",
+ "\n",
+ "from statsmodels.api import families"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Prepare some test data - columbus example\n",
+ "db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')\n",
+ "y = np.array(db.by_col(\"HOVAL\"))\n",
+ "y = np.reshape(y, (49,1))\n",
+ "X = []\n",
+ "#X.append(np.ones(len(y)))\n",
+ "X.append(db.by_col(\"INC\"))\n",
+ "X.append(db.by_col(\"CRIME\"))\n",
+ "X = np.array(X).T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 46.42818268]\n",
+ " [ 0.62898397]\n",
+ " [ -0.48488854]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#First fit pysal OLS model\n",
+ "from pysal.spreg import ols\n",
+ "OLS = ols.OLS(y, X)\n",
+ "print OLS.betas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'family.Gaussian'>\n",
+ "<class 'family.Gaussian'>\n",
+ "<class 'family.Gaussian'>\n",
+ "[ 46.42818268 0.62898397 -0.48488854]\n",
+ "[ 46.42818268 0.62898397 -0.48488854]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Then fit Gaussian GLM\n",
+ "\n",
+ "#create Gaussian GLM model object\n",
+ "model = GLM(y, X, Gaussian())\n",
+ "model\n",
+ "\n",
+ "#Fit model to estimate coefficients and return GLMResults object\n",
+ "results = model.fit()\n",
+ "\n",
+ "#Check coefficients - R betas [46.4282, 0.6290, -0.4849]\n",
+ "print results.params\n",
+ "\n",
+ "# Gaussian GLM results from statsmodels\n",
+ "sm_model = smf.GLM(y, sm.add_constant(X), family=families.Gaussian())\n",
+ "sm_results = sm_model.fit()\n",
+ "print sm_results.params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2 2\n",
+ "<class 'family.Gaussian'>\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "<class 'family.Gaussian'>\n",
+ "<class 'family.Gaussian'>\n",
+ "<class 'family.Gaussian'>\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n"
+ ]
+ }
+ ],
+ "source": [
+ "print results.df_model, sm_results.df_model\n",
+ "print np.allclose(results.aic, sm_results.aic)\n",
+ "print np.allclose(results.bic, sm_results.bic)\n",
+ "print np.allclose(results.deviance, sm_results.deviance)\n",
+ "print np.allclose(results.df_model, sm_results.df_model)\n",
+ "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+ "print np.allclose(results.llf, sm_results.llf)\n",
+ "print np.allclose(results.mu, sm_results.mu)\n",
+ "print np.allclose(results.n, sm_results.nobs)\n",
+ "print np.allclose(results.null, sm_results.null)\n",
+ "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+ "print np.allclose(results.params, sm_results.params)\n",
+ "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+ "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+ "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+ "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+ "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+ "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+ "print np.allclose(results.scale, sm_results.scale)\n",
+ "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+ "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+ "print np.allclose(results.bse, sm_results.bse)\n",
+ "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+ "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+ "print np.allclose(results.tvalues, sm_results.tvalues)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'family.Poisson'>\n",
+ "<class 'family.Poisson'>\n",
+ "<class 'family.Poisson'>\n",
+ "[ 3.92159085 0.01183491 -0.01371397]\n",
+ "[ 3.92159085 0.01183491 -0.01371397]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Now fit a Poisson GLM \n",
+ "\n",
+ "poisson_y = np.round(y).astype(int)\n",
+ "\n",
+ "#create Poisson GLM model object\n",
+ "model = GLM(poisson_y, X, Poisson())\n",
+ "model\n",
+ "\n",
+ "#Fit model to estimate coefficients and return GLMResults object\n",
+ "results = model.fit()\n",
+ "\n",
+ "#Check coefficients - R betas [3.91926, 0.01198, -0.01371]\n",
+ "print results.params.T\n",
+ "\n",
+ "# Poisson GLM results from statsmodels\n",
+ "sm_results = smf.GLM(poisson_y, sm.add_constant(X), family=families.Poisson()).fit()\n",
+ "print sm_results.params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'family.Poisson'>\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "<class 'family.Poisson'>\n",
+ "<class 'family.Poisson'>\n",
+ "<class 'family.Poisson'>\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "[ 0.13049161 0.00511599 0.00193769] [ 0.13049161 0.00511599 0.00193769]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print np.allclose(results.aic, sm_results.aic)\n",
+ "print np.allclose(results.bic, sm_results.bic)\n",
+ "print np.allclose(results.deviance, sm_results.deviance)\n",
+ "print np.allclose(results.df_model, sm_results.df_model)\n",
+ "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+ "print np.allclose(results.llf, sm_results.llf)\n",
+ "print np.allclose(results.mu, sm_results.mu)\n",
+ "print np.allclose(results.n, sm_results.nobs)\n",
+ "print np.allclose(results.null, sm_results.null)\n",
+ "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+ "print np.allclose(results.params, sm_results.params)\n",
+ "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+ "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+ "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+ "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+ "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+ "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+ "print np.allclose(results.scale, sm_results.scale)\n",
+ "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+ "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+ "print np.allclose(results.bse, sm_results.bse)\n",
+ "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+ "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+ "print np.allclose(results.tvalues, sm_results.tvalues)\n",
+ "print results.bse, sm_results.bse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-5.33638276 0.0287754 ]\n",
+ "[-5.33638276 0.0287754 ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Now fit a binomial GLM\n",
+ "londonhp = pd.read_csv('/Users/toshan/projects/londonhp.csv')\n",
+ "#londonhp = pd.read_csv('/Users/qszhao/Dropbox/pysal/pysal/contrib/gwr/londonhp.csv')\n",
+ "y = londonhp['BATH2'].values\n",
+ "y = np.reshape(y, (316,1))\n",
+ "X = londonhp['FLOORSZ'].values\n",
+ "X = np.reshape(X, (316,1))\n",
+ "\n",
+ "#create logistic GLM model object\n",
+ "model = GLM(y, X, Binomial())\n",
+ "model\n",
+ "\n",
+ "#Fit model to estimate coefficients and return GLMResults object\n",
+ "results = model.fit()\n",
+ "\n",
+ "#Check coefficients - R betas [-5.33638, 0.02878]\n",
+ "print results.params.T\n",
+ "\n",
+ "# Logistic GLM results from statsmodels\n",
+ "sm_results = smf.GLM(y, sm.add_constant(X), family=families.Binomial()).fit()\n",
+ "print sm_results.params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 1\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n",
+ "True\n"
+ ]
+ }
+ ],
+ "source": [
+ "print results.df_model, sm_results.df_model\n",
+ "print np.allclose(results.aic, sm_results.aic)\n",
+ "print np.allclose(results.bic, sm_results.bic)\n",
+ "print np.allclose(results.deviance, sm_results.deviance)\n",
+ "print np.allclose(results.df_model, sm_results.df_model)\n",
+ "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+ "print np.allclose(results.llf, sm_results.llf)\n",
+ "print np.allclose(results.mu, sm_results.mu)\n",
+ "print np.allclose(results.n, sm_results.nobs)\n",
+ "print np.allclose(results.null, sm_results.null)\n",
+ "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+ "print np.allclose(results.params, sm_results.params)\n",
+ "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+ "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+ "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+ "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+ "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+ "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+ "print np.allclose(results.scale, sm_results.scale)\n",
+ "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+ "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+ "print np.allclose(results.bse, sm_results.bse)\n",
+ "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+ "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+ "print np.allclose(results.tvalues, sm_results.tvalues)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'family.QuasiPoisson'>\n",
+ "<class 'family.QuasiPoisson'>\n",
+ "<class 'family.QuasiPoisson'>\n"
+ ]
+ }
+ ],
+ "source": [
+ "#create QUasiPoisson GLM model object\n",
+ "model = GLM(poisson_y, X, QuasiPoisson())\n",
+ "model\n",
+ "\n",
+ "#Fit model to estimate coefficients and return GLMResults object\n",
+ "results = model.fit()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/glm/glm.py b/pysal/contrib/glm/glm.py
index 5ec4229..9d14145 100644
--- a/pysal/contrib/glm/glm.py
+++ b/pysal/contrib/glm/glm.py
@@ -87,8 +87,6 @@ class GLM(RegressionPropsY):
self.X = X
self.family = family
self.k = self.X.shape[1]
- self.df_model = self.X.shape[1] - 1
- self.df_resid = self.n - self.df_model - 1
if offset is None:
self.offset = np.ones(shape=(self.n,1))
else:
@@ -129,6 +127,13 @@ class GLM(RegressionPropsY):
self.fit_params['n_iter'] = n_iter
return GLMResults(self, params.flatten(), predy, w)
+ @cache_readonly
+ def df_model(self):
+ return self.X.shape[1] - 1
+
+ @cache_readonly
+ def df_resid(self):
+ return self.n - self.df_model - 1
class GLMResults(LikelihoodModelResults):
"""
@@ -195,19 +200,19 @@ class GLMResults(LikelihoodModelResults):
llf : float
value of the loglikelihood function evalued at params;
see family.py for distribution-specific loglikelihoods
- llnull : float
+ llnull : float
value of log-likelihood function evaluated at null
- aic : float
+ aic : float
AIC
- bic : float
+ bic : float
BIC
D2 : float
percent deviance explained
adj_D2 : float
adjusted percent deviance explained
- pseudo_R2 : float
- McFadden's pseudo R2 (coefficient of determination)
- adj_pseudoR2 : float
+ pseudo_R2 : float
+ McFadden's pseudo R2 (coefficient of determination)
+ adj_pseudoR2 : float
adjusted McFadden's pseudo R2
resid_response : array
response residuals; defined as y-mu
@@ -221,15 +226,15 @@ class GLMResults(LikelihoodModelResults):
derivatives of the link functions.
resid_anscombe : array
- Anscombe residuals; see family.py for
- distribution-specific Anscombe residuals.
-
+ Anscombe residuals; see family.py for
+ distribution-specific Anscombe residuals.
+
resid_deviance : array
- deviance residuals; see family.py for
- distribution-specific deviance residuals.
+ deviance residuals; see family.py for
+ distribution-specific deviance residuals.
pearson_chi2 : float
- chi-Squared statistic is defined as the sum
+ chi-Squared statistic is defined as the sum
of the squares of the Pearson residuals
normalized_cov_params : array
@@ -265,15 +270,24 @@ class GLMResults(LikelihoodModelResults):
self.X = model.X
self.k = model.k
self.offset = model.offset
- self.df_model = model.df_model
- self.df_resid = model.df_resid
self.family = model.family
self.fit_params = model.fit_params
self.params = params
self.w = w
self.mu = mu.flatten()
self._cache = {}
- self.normalized_cov_params = la.inv(spdot(self.w.T, self.w))
+
+ @cache_readonly
+ def df_model(self):
+ return self.model.df_model
+
+ @cache_readonly
+ def df_resid(self):
+ return self.model.df_resid
+
+ @cache_readonly
+ def normalized_cov_params(self):
+ return la.inv(spdot(self.w.T, self.w))
@cache_readonly
def resid_response(self):
@@ -309,7 +323,7 @@ class GLMResults(LikelihoodModelResults):
X = np.ones((len(y), 1))
null_mod = GLM(y, X, family=self.family, offset=self.offset, constant=False)
return null_mod.fit().mu
-
+
@cache_readonly
def scale(self):
if isinstance(self.family, (family.Binomial, family.Poisson)):
@@ -325,7 +339,7 @@ class GLMResults(LikelihoodModelResults):
@cache_readonly
def null_deviance(self):
return self.family.deviance(self.y, self.null)
-
+
@cache_readonly
def llnull(self):
return self.family.loglike(self.y, self.null, scale=self.scale)
@@ -354,12 +368,12 @@ class GLMResults(LikelihoodModelResults):
@cache_readonly
def adj_D2(self):
return 1.0 - (float(self.n) - 1.0)/(float(self.n) - float(self.k)) * (1.0-self.D2)
-
+
@cache_readonly
def pseudoR2(self):
return 1 - (self.llf/self.llnull)
-
+
@cache_readonly
def adj_pseudoR2(self):
return 1 - ((self.llf-self.k)/self.llnull)
-
+
diff --git a/pysal/contrib/glm/iwls.py b/pysal/contrib/glm/iwls.py
index 29803e3..082fd05 100644
--- a/pysal/contrib/glm/iwls.py
+++ b/pysal/contrib/glm/iwls.py
@@ -3,7 +3,7 @@ import numpy.linalg as la
from scipy import sparse as sp
from scipy.sparse import linalg as spla
from pysal.spreg.utils import spdot, spmultiply
-from family import Binomial
+from family import Binomial, Poisson
def _compute_betas(y, x):
"""
@@ -29,7 +29,7 @@ def _compute_betas_gwr(y, x, wi):
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
Geographically weighted regression: the analysis of spatially varying relationships.
"""
- xT = np.dot(x.T, wi)
+ xT = (x * wi).T
xtx = np.dot(xT, x)
xtx_inv = la.inv(xtx)
xtx_inv_xt = np.dot(xtx_inv, xT)
@@ -41,27 +41,33 @@ def iwls(y, x, family, offset, y_fix,
"""
Iteratively re-weighted least squares estimation routine
"""
- #spx = sp.csr_matrix(x)
- #dx = np.float(spx.nnz)/np.float(np.multiply(*spx.shape))
n_iter = 0
diff = 1.0e6
- if isinstance(family, Binomial):
- y = family.link._clean(y)
if ini_betas is None:
- betas = np.zeros((x.shape[1], 1), np.float)
+ betas = np.zeros((x.shape[1], 1), np.float)
else:
betas = ini_betas
- mu = family.starting_mu(y)
- v = family.predict(mu)
+
+ if isinstance(family, Binomial):
+ y = family.link._clean(y)
+ if isinstance(family, Poisson):
+ y_off = y/offset
+ y_off = family.starting_mu(y_off)
+ v = family.predict(y_off)
+ mu = family.starting_mu(y)
+ else:
+ mu = family.starting_mu(y)
+ v = family.predict(mu)
+
while diff > tol and n_iter < max_iter:
n_iter += 1
w = family.weights(mu)
z = v + (family.link.deriv(mu)*(y-mu))
w = np.sqrt(w)
if type(x) != np.ndarray:
- w = sp.csr_matrix(w)
- z = sp.csr_matrix(z)
+ w = sp.csr_matrix(w)
+ z = sp.csr_matrix(z)
wx = spmultiply(x, w, array_out=False)
wz = spmultiply(z, w, array_out=False)
if wi is None:
@@ -70,9 +76,13 @@ def iwls(y, x, family, offset, y_fix,
n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
v = spdot(x, n_betas)
mu = family.fitted(v)
+
+ if isinstance(family, Poisson):
+ mu = mu * offset
+
diff = min(abs(n_betas-betas))
betas = n_betas
-
+
if wi is None:
return betas, mu, wx, n_iter
else:
diff --git a/pysal/contrib/glm/utils.py b/pysal/contrib/glm/utils.py
index 0789675..f7ec321 100644
--- a/pysal/contrib/glm/utils.py
+++ b/pysal/contrib/glm/utils.py
@@ -153,6 +153,65 @@ except ImportError:
return "NumpyVersion(%s)" % self.vstring
+
+class ResettableCache(dict):
+ """
+ Dictionary whose elements mey depend one from another.
+ If entry `B` depends on entry `A`, changing the values of entry `A` will
+ reset the value of entry `B` to a default (None); deleteing entry `A` will
+ delete entry `B`. The connections between entries are stored in a
+ `_resetdict` private attribute.
+ Parameters
+ ----------
+ reset : dictionary, optional
+ An optional dictionary, associated a sequence of entries to any key
+ of the object.
+ items : var, optional
+ An optional dictionary used to initialize the dictionary
+ Examples
+ --------
+ >>> reset = dict(a=('b',), b=('c',))
+ >>> cache = resettable_cache(a=0, b=1, c=2, reset=reset)
+ >>> assert_equal(cache, dict(a=0, b=1, c=2))
+ >>> print("Try resetting a")
+ >>> cache['a'] = 1
+ >>> assert_equal(cache, dict(a=1, b=None, c=None))
+ >>> cache['c'] = 2
+ >>> assert_equal(cache, dict(a=1, b=None, c=2))
+ >>> cache['b'] = 0
+ >>> assert_equal(cache, dict(a=1, b=0, c=None))
+ >>> print("Try deleting b")
+ >>> del(cache['a'])
+ >>> assert_equal(cache, {})
+ """
+
+ def __init__(self, reset=None, **items):
+ self._resetdict = reset or {}
+ dict.__init__(self, **items)
+
+ def __setitem__(self, key, value):
+ dict.__setitem__(self, key, value)
+ # if hasattr needed for unpickling with protocol=2
+ if hasattr(self, '_resetdict'):
+ for mustreset in self._resetdict.get(key, []):
+ self[mustreset] = None
+
+ def __delitem__(self, key):
+ dict.__delitem__(self, key)
+ for mustreset in self._resetdict.get(key, []):
+ del(self[mustreset])
+
+# def __getstate__(self):
+# print('pickling wrapper', self.__dict__)
+# return self.__dict__
+#
+# def __setstate__(self, dict_):
+# print('unpickling wrapper', dict_)
+# self.__dict__.update(dict_)
+
+
+resettable_cache = ResettableCache
+
def _next_regular(target):
"""
Find the next regular number greater than or equal to target.
diff --git a/pysal/contrib/gwr/__init__.py b/pysal/contrib/gwr/__init__.py
new file mode 100644
index 0000000..eeb63b3
--- /dev/null
+++ b/pysal/contrib/gwr/__init__.py
@@ -0,0 +1,4 @@
+import gwr
+import sel_bw
+import diagnostics
+import kernels
diff --git a/pysal/contrib/gwr/diagnostics.py b/pysal/contrib/gwr/diagnostics.py
new file mode 100644
index 0000000..44a75d5
--- /dev/null
+++ b/pysal/contrib/gwr/diagnostics.py
@@ -0,0 +1,81 @@
+"""
+Diagnostics for estimated gwr modesl
+"""
+__author__ = "Taylor Oshan tayoshan at gmail.com"
+
+import numpy as np
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial
+
+def get_AICc(gwr):
+ """
+ Get AICc value
+
+ Gaussian: p61, (2.33), Fotheringham, Brunsdon and Charlton (2002)
+
+ GWGLM: AICc=AIC+2k(k+1)/(n-k-1), Nakaya et al. (2005): p2704, (36)
+
+ """
+ n = gwr.n
+ k = gwr.tr_S
+ if isinstance(gwr.family, Gaussian):
+ aicc = -2.0*gwr.llf + 2.0*n*(k + 1.0)/(n-k-2.0)
+ elif isinstance(gwr.family, (Poisson, Binomial)):
+ aicc = get_AIC(gwr) + 2.0 * k * (k+1.0) / (n - k - 1.0)
+ return aicc
+
+def get_AIC(gwr):
+ """
+ Get AIC calue
+
+ Gaussian: p96, (4.22), Fotheringham, Brunsdon and Charlton (2002)
+
+ GWGLM: AIC(G)=D(G) + 2K(G), where D and K denote the deviance and the effective
+ number of parameters in the model with bandwidth G, respectively.
+
+ """
+ k = gwr.tr_S
+ #deviance = -2*log-likelihood
+ y = gwr.y
+ mu = gwr.mu
+ if isinstance(gwr.family, Gaussian):
+ aic = -2.0 * gwr.llf + 2.0 * (k+1)
+ elif isinstance(gwr.family, (Poisson, Binomial)):
+ aic = np.sum(gwr.family.resid_dev(y, mu)**2) + 2.0 * k
+ return aic
+
+def get_BIC(gwr):
+ """
+ Get BIC value
+
+ Gaussian: p61 (2.34), Fotheringham, Brunsdon and Charlton (2002)
+ BIC = -2log(L)+klog(n)
+
+ GWGLM: BIC = dev + tr_S * log(n)
+
+ """
+ n = gwr.n # (scalar) number of observations
+ k = gwr.tr_S
+ y = gwr.y
+ mu = gwr.mu
+ if isinstance(gwr.family, Gaussian):
+ bic = -2.0 * gwr.llf + (k+1) * np.log(n)
+ elif isinstance(gwr.family, (Poisson, Binomial)):
+ bic = np.sum(gwr.family.resid_dev(y, mu)**2) + k * np.log(n)
+ return bic
+
+def get_CV(gwr):
+ """
+ Get CV value
+
+ Gaussian only
+
+ Methods: p60, (2.31) or p212 (9.4)
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying relationships.
+ Modification: sum of residual squared is divided by n according to GWR4 results
+
+ """
+ aa = gwr.resid_response.reshape((-1,1))/(1.0-gwr.influ)
+ cv = np.sum(aa**2)/gwr.n
+ return cv
+
diff --git a/pysal/contrib/gwr/gwr.py b/pysal/contrib/gwr/gwr.py
new file mode 100644
index 0000000..9fd423b
--- /dev/null
+++ b/pysal/contrib/gwr/gwr.py
@@ -0,0 +1,882 @@
+#Main GWR classes
+
+#Offset does not yet do anyhting and needs to be implemented
+
+__author__ = "Taylor Oshan Tayoshan at gmail.com"
+
+import numpy as np
+import numpy.linalg as la
+from scipy.stats import t
+from kernels import *
+from diagnostics import get_AIC, get_AICc, get_BIC
+import pysal.spreg.user_output as USER
+from pysal.contrib.glm.family import Gaussian, Binomial, Poisson
+from pysal.contrib.glm.glm import GLM, GLMResults
+from pysal.contrib.glm.iwls import iwls
+from pysal.contrib.glm.utils import cache_readonly
+
+fk = {'gaussian': fix_gauss, 'bisquare': fix_bisquare, 'exponential': fix_exp}
+ak = {'gaussian': adapt_gauss, 'bisquare': adapt_bisquare, 'exponential': adapt_exp}
+
+class GWR(GLM):
+ """
+ Geographically weighted regression. Can currently estimate Gaussian,
+ Poisson, and logistic models(built on a GLM framework). GWR object prepares
+ model input. Fit method performs estimation and returns a GWRResults object.
+
+ Parameters
+ ----------
+ coords : array-like
+ n*2, collection of n sets of (x,y) coordinates of
+ observatons; also used as calibration locations is
+ 'points' is set to None
+
+ y : array
+ n*1, dependent variable
+
+ X : array
+ n*k, independent variable, exlcuding the constant
+
+ bw : scalar
+ bandwidth value consisting of either a distance or N
+ nearest neighbors; user specified or obtained using
+ Sel_BW
+
+ family : family object
+ underlying probability model; provides
+ distribution-specific calculations
+
+ offset : array
+ n*1, the offset variable at the ith location. For Poisson model
+ this term is often the size of the population at risk or
+ the expected size of the outcome in spatial epidemiology
+ Default is None where Ni becomes 1.0 for all locations;
+ only for Poisson models
+
+ sigma2_v1 : boolean
+ specify sigma squared, True to use n as denominator;
+ default is False which uses n-k
+
+ kernel : string
+ type of kernel function used to weight observations;
+ available options:
+ 'gaussian'
+ 'bisquare'
+ 'exponential'
+
+ fixed : boolean
+ True for distance based kernel function and False for
+ adaptive (nearest neighbor) kernel function (default)
+
+ constant : boolean
+ True to include intercept (default) in model and False to exclude
+ intercept.
+
+ Attributes
+ ----------
+ coords : array-like
+ n*2, collection of n sets of (x,y) coordinates used for
+ calibration locations
+
+ y : array
+ n*1, dependent variable
+
+ X : array
+ n*k, independent variable, exlcuding the constant
+
+ bw : scalar
+ bandwidth value consisting of either a distance or N
+ nearest neighbors; user specified or obtained using
+ Sel_BW
+
+ family : family object
+ underlying probability model; provides
+ distribution-specific calculations
+
+ offset : array
+ n*1, the offset variable at the ith location. For Poisson model
+ this term is often the size of the population at risk or
+ the expected size of the outcome in spatial epidemiology
+ Default is None where Ni becomes 1.0 for all locations
+
+ sigma2_v1 : boolean
+ specify sigma squared, True to use n as denominator;
+ default is False which uses n-k
+
+ kernel : string
+ type of kernel function used to weight observations;
+ available options:
+ 'gaussian'
+ 'bisquare'
+ 'exponential'
+
+ fixed : boolean
+ True for distance based kernel function and False for
+ adaptive (nearest neighbor) kernel function (default)
+
+ constant : boolean
+ True to include intercept (default) in model and False to exclude
+ intercept
+
+ n : integer
+ number of observations
+
+ k : integer
+ number of independent variables
+
+ mean_y : float
+ mean of y
+
+ std_y : float
+ standard deviation of y
+
+ fit_params : dict
+ parameters passed into fit method to define estimation
+ routine
+
+ W : array
+ n*n, spatial weights matrix for weighting all
+ observations from each calibration point
+ points : array-like
+ n*2, collection of n sets of (x,y) coordinates used for
+ calibration locations instead of all observations;
+ defaults to None unles specified in predict method
+ P : array
+ n*k, independent variables used to make prediction;
+ exlcuding the constant; default to None unless specified
+ in predict method
+ exog_scale : scalar
+ estimated scale using sampled locations; defualt is None
+ unless specified in predict method
+ exog_resid : array-like
+ estimated residuals using sampled locations; defualt is None
+ unless specified in predict method
+
+ Examples
+ --------
+ #basic model calibration
+
+ >>> import pysal
+ >>> from pysal.contrib.gwr.gwr import GWR
+ >>> data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+ >>> coords = zip(data.bycol('X'), data.by_col('Y'))
+ >>> y = np.array(data.by_col('PctBach')).reshape((-1,1))
+ >>> rural = np.array(data.by_col('PctRural')).reshape((-1,1))
+ >>> pov = np.array(data.by_col('PctPov')).reshape((-1,1))
+ >>> african_amer = np.array(data.by_col('PctBlack')).reshape((-1,1))
+ >>> X = np.hstack([rural, pov, african_amer])
+ >>> model = GWR(coords, y, X, bw=90.000, fixed=False, kernel='bisquare')
+ >>> results = model.fit()
+ >>> print results.params.shape
+ (159, 4)
+
+ #predict at unsampled locations
+
+ >>> index = np.arange(len(self.y))
+ >>> test = index[-10:]
+ >>> X_test = X[test]
+ >>> coords_test = list(coords[test])
+ >>> model = GWR(coords, y, X, bw=94, fixed=False, kernel='bisquare')
+ >>> results = model.predict(coords_test, X_test)
+ >>> print results.params.shape
+ (10, 4)
+
+ """
+ def __init__(self, coords, y, X, bw, family=Gaussian(), offset=None,
+ sigma2_v1=False, kernel='bisquare', fixed=False, constant=True):
+ """
+ Initialize class
+ """
+ GLM.__init__(self, y, X, family, constant=constant)
+ self.constant = constant
+ self.sigma2_v1 = sigma2_v1
+ self.coords = coords
+ self.bw = bw
+ self.kernel = kernel
+ self.fixed = fixed
+ if offset is None:
+ self.offset = np.ones((self.n, 1))
+ else:
+ self.offset = offset * 1.0
+ self.fit_params = {}
+ self.W = self._build_W(fixed, kernel, coords, bw)
+ self.points = None
+ self.exog_scale = None
+ self.exog_resid = None
+ self.P = None
+
+ def _build_W(self, fixed, kernel, coords, bw, points=None):
+ if fixed:
+ try:
+ W = fk[kernel](coords, bw, points)
+ except:
+ raise TypeError('Unsupported kernel function ', kernel)
+ else:
+ try:
+ W = ak[kernel](coords, bw, points)
+ except:
+ raise TypeError('Unsupported kernel function ', kernel)
+
+ return W
+
+ def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'):
+ """
+ Method that fits a model with a particular estimation routine.
+
+ Parameters
+ ----------
+
+ ini_betas : array
+ k*1, initial coefficient values, including constant.
+ Default is None, which calculates initial values during
+ estimation
+ tol: float
+ Tolerence for estimation convergence
+ max_iter : integer
+ Maximum number of iterations if convergence not
+ achieved
+ solve : string
+ Technique to solve MLE equations.
+ 'iwls' = iteratively (re)weighted least squares (default)
+ """
+ self.fit_params['ini_params'] = ini_params
+ self.fit_params['tol'] = tol
+ self.fit_params['max_iter'] = max_iter
+ self.fit_params['solve']= solve
+ if solve.lower() == 'iwls':
+ m = self.W.shape[0]
+ params = np.zeros((m, self.k))
+ predy = np.zeros((m, 1))
+ v = np.zeros((m, 1))
+ w = np.zeros((m, 1))
+ z = np.zeros((m, self.n))
+ S = np.zeros((m, self.n))
+ R = np.zeros((m, self.n))
+ CCT = np.zeros((m, self.k))
+ #f = np.zeros((n, n))
+ p = np.zeros((m, 1))
+ for i in range(m):
+ wi = self.W[i].reshape((-1,1))
+ rslt = iwls(self.y, self.X, self.family, self.offset, None,
+ ini_params, tol, max_iter, wi=wi)
+ params[i,:] = rslt[0].T
+ predy[i] = rslt[1][i]
+ v[i] = rslt[2][i]
+ w[i] = rslt[3][i]
+ z[i] = rslt[4].flatten()
+ R[i] = np.dot(self.X[i], rslt[5])
+ ri = np.dot(self.X[i], rslt[5])
+ S[i] = ri*np.reshape(rslt[4].flatten(), (1,-1))
+ #dont need unless f is explicitly passed for
+ #prediction of non-sampled points
+ #cf = rslt[5] - np.dot(rslt[5], f)
+ #CCT[i] = np.diag(np.dot(cf, cf.T/rslt[3]))
+ CCT[i] = np.diag(np.dot(rslt[5], rslt[5].T))
+ S = S * (1.0/z)
+ return GWRResults(self, params, predy, S, CCT, w)
+
+ def predict(self, points, P, exog_scale=None, exog_resid=None, fit_params={}):
+ """
+ Method that predicts values of the dependent variable at un-sampled
+ locations
+
+ Parameters
+ ----------
+ points : array-like
+ n*2, collection of n sets of (x,y) coordinates used for
+ calibration prediction locations
+ P : array
+ n*k, independent variables used to make prediction;
+ exlcuding the constant
+ exog_scale : scalar
+ estimated scale using sampled locations; defualt is None
+ which estimates a model using points from "coords"
+ exog_resid : array-like
+ estimated residuals using sampled locations; defualt is None
+ which estimates a model using points from "coords"; if
+ given it must be n*1 where n is the length of coords
+ fit_params : dict
+ key-value pairs of parameters that will be passed into fit
+ method to define estimation routine; see fit method for more details
+
+ """
+ if (exog_scale is None) & (exog_resid is None):
+ train_gwr = self.fit(**fit_params)
+ self.exog_scale = train_gwr.scale
+ self.exog_resid = train_gwr.resid_response
+ elif (exog_scale is not None) & (exog_resid is not None):
+ self.exog_scale = exog_scale
+ self.exog_resid = exog_resid
+ else:
+ raise InputError('exog_scale and exog_resid must both either be'
+ 'None or specified')
+ self.points = points
+ if self.constant:
+ P = np.hstack([np.ones((len(P),1)), P])
+ self.P = P
+ else:
+ self.P = P
+ self.W = self._build_W(self.fixed, self.kernel, self.coords, self.bw, points)
+ gwr = self.fit(**fit_params)
+
+ return gwr
+
+ @cache_readonly
+ def df_model(self):
+ raise NotImplementedError('Only computed for fitted model in GWRResults')
+
+ @cache_readonly
+ def df_resid(self):
+ raise NotImplementedError('Only computed for fitted model in GWRResults')
+
+class GWRResults(GLMResults):
+ """
+ Basic class including common properties for all GWR regression models
+
+ Parameters
+ ----------
+ model : GWR object
+ pointer to GWR object with estimation parameters
+
+ params : array
+ n*k, estimated coefficients
+
+ predy : array
+ n*1, predicted y values
+
+ w : array
+ n*1, final weight used for iteratively re-weighted least
+ sqaures; default is None
+
+ S : array
+ n*n, hat matrix
+
+ CCT : array
+ n*k, scaled variance-covariance matrix
+
+ Attributes
+ ----------
+ model : GWR Object
+ points to GWR object for which parameters have been
+ estimated
+
+ params : array
+ n*k, parameter estimates
+
+ predy : array
+ n*1, predicted value of y
+
+ y : array
+ n*1, dependent variable
+
+ X : array
+ n*k, independent variable, including constant
+
+ family : family object
+ underlying probability model; provides
+ distribution-specific calculations
+
+ n : integer
+ number of observations
+
+ k : integer
+ number of independent variables
+
+ df_model : integer
+ model degrees of freedom
+
+ df_resid : integer
+ residual degrees of freedom
+
+ offset : array
+ n*1, the offset variable at the ith location.
+ For Poisson model this term is often the size of
+ the population at risk or the expected size of
+ the outcome in spatial epidemiology; Default is
+ None where Ni becomes 1.0 for all locations
+
+ scale : float
+ sigma squared used for subsequent computations
+
+ w : array
+ n*1, final weights from iteratively re-weighted least
+ sqaures routine
+
+ resid_response : array
+ n*1, residuals of the repsonse
+
+ resid_ss : scalar
+ residual sum of sqaures
+
+ W : array
+ n*n; spatial weights for each observation from each
+ calibration point
+
+ S : array
+ n*n, hat matrix
+
+ CCT : array
+ n*k, scaled variance-covariance matrix
+
+ tr_S : float
+ trace of S (hat) matrix
+
+ tr_STS : float
+ trace of STS matrix
+
+ tr_SWSTW : float
+ trace of weighted STS matrix; weights are those output
+ from iteratively weighted least sqaures (not spatial
+ weights)
+
+ y_bar : array
+ n*1, weighted mean value of y
+
+ TSS : array
+ n*1, geographically weighted total sum of squares
+
+ RSS : array
+ n*1, geographically weighted residual sum of squares
+
+ localR2 : array
+ n*1, local R square
+
+ sigma2_v1 : float
+ sigma squared, use (n-v1) as denominator
+
+ sigma2_v1v2 : float
+ sigma squared, use (n-2v1+v2) as denominator
+
+ sigma2_ML : float
+ sigma squared, estimated using ML
+
+ std_res : array
+ n*1, standardised residuals
+
+ bse : array
+ n*k, standard errors of parameters (betas)
+
+ influ : array
+ n*1, leading diagonal of S matrix
+
+ CooksD : array
+ n*1, Cook's D
+
+ tvalues : array
+ n*k, local t-statistics
+
+ adj_alpha : array
+ 3*1, corrected alpha values to account for multiple
+ hypothesis testing for the 90%, 95%, and 99% confidence
+ levels; tvalues with an absolute value larger than the
+ corrected alpha are considered statistically
+ significant.
+
+ deviance : array
+ n*1, local model deviance for each calibration point
+
+ resid_deviance : array
+ n*1, local sum of residual deviance for each
+ calibration point
+
+ llf : scalar
+ log-likelihood of the full model; see
+ pysal.contrib.glm.family for damily-sepcific
+ log-likelihoods
+
+ pDev : float
+ local percent of deviation accounted for; analogous to
+ r-squared for GLM's
+
+ mu : array
+ n*, flat one dimensional array of predicted mean
+ response value from estimator
+
+ fit_params : dict
+ parameters passed into fit method to define estimation
+ routine
+ """
+ def __init__(self, model, params, predy, S, CCT, w=None):
+ GLMResults.__init__(self, model, params, predy, w)
+ self.W = model.W
+ self.offset = model.offset
+ if w is not None:
+ self.w = w
+ self.predy = predy
+ self.S = S
+ self.CCT = self.cov_params(CCT, model.exog_scale)
+ self._cache = {}
+
+ @cache_readonly
+ def resid_ss(self):
+ if self.model.points is not None:
+ raise NotImplementedError('Not available for GWR prediction')
+ else:
+ u = self.resid_response.flatten()
+ return np.dot(u, u.T)
+
+ @cache_readonly
+ def scale(self, scale=None):
+ if isinstance(self.family, Gaussian):
+ if self.model.sigma2_v1:
+ scale = self.sigma2_v1
+ else:
+ scale = self.sigma2_v1v2
+ else:
+ scale = 1.0
+ return scale
+
+ def cov_params(self, cov, exog_scale=None):
+ """
+ Returns scaled covariance parameters
+ Parameters
+ ----------
+ cov : array
+ estimated covariance parameters
+
+ Returns
+ -------
+ Scaled covariance parameters
+
+ """
+ if exog_scale is not None:
+ return cov*exog_scale
+ else:
+ return cov*self.scale
+
+ @cache_readonly
+ def tr_S(self):
+ """
+ trace of S (hat) matrix
+ """
+ return np.trace(self.S*self.w)
+
+ @cache_readonly
+ def tr_STS(self):
+ """
+ trace of STS matrix
+ """
+ return np.trace(np.dot(self.S.T*self.w,self.S*self.w))
+
+ @cache_readonly
+ def y_bar(self):
+ """
+ weighted mean of y
+ """
+ if self.model.points is not None:
+ n = len(self.model.points)
+ else:
+ n = self.n
+ off = self.offset.reshape((-1,1))
+ arr_ybar = np.zeros(shape=(self.n,1))
+ for i in range(n):
+ w_i= np.reshape(np.array(self.W[i]), (-1, 1))
+ sum_yw = np.sum(self.y.reshape((-1,1)) * w_i)
+ arr_ybar[i] = 1.0 * sum_yw / np.sum(w_i*off)
+ return arr_ybar
+
+ @cache_readonly
+ def TSS(self):
+ """
+ geographically weighted total sum of squares
+
+ Methods: p215, (9.9)
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+
+ """
+ if self.model.points is not None:
+ n = len(self.model.points)
+ else:
+ n = self.n
+ TSS = np.zeros(shape=(n,1))
+ for i in range(n):
+ TSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1)) *
+ (self.y.reshape((-1,1)) - self.y_bar[i])**2)
+ return TSS
+
+ @cache_readonly
+ def RSS(self):
+ """
+ geographically weighted residual sum of squares
+
+ Methods: p215, (9.10)
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+ """
+ if self.model.points is not None:
+ n = len(self.model.points)
+ resid = self.model.exog_resid.reshape((-1,1))
+ else:
+ n = self.n
+ resid = self.resid_response.reshape((-1,1))
+ RSS = np.zeros(shape=(n,1))
+ for i in range(n):
+ RSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1))
+ * resid**2)
+ return RSS
+
+ @cache_readonly
+ def localR2(self):
+ """
+ local R square
+
+ Methods: p215, (9.8)
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+ """
+ if isinstance(self.family, Gaussian):
+ return (self.TSS - self.RSS)/self.TSS
+ else:
+ raise NotImplementedError('Only applicable to Gaussian')
+
+ @cache_readonly
+ def sigma2_v1(self):
+ """
+ residual variance
+
+ Methods: p214, (9.6),
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+
+ only use v1
+ """
+ return (self.resid_ss/(self.n-self.tr_S))
+
+ @cache_readonly
+ def sigma2_v1v2(self):
+ """
+ residual variance
+
+ Methods: p55 (2.16)-(2.18)
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+
+ use v1 and v2 #used in GWR4
+ """
+ if isinstance(self.family, (Poisson, Binomial)):
+ return self.resid_ss/(self.n - 2.0*self.tr_S +
+ self.tr_STS) #could be changed to SWSTW - nothing to test against
+ else:
+ return self.resid_ss/(self.n - 2.0*self.tr_S +
+ self.tr_STS) #could be changed to SWSTW - nothing to test against
+ @cache_readonly
+ def sigma2_ML(self):
+ """
+ residual variance
+
+ Methods: maximum likelihood
+ """
+ return self.resid_ss/self.n
+
+ @cache_readonly
+ def std_res(self):
+ """
+ standardized residuals
+
+ Methods: p215, (9.7)
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+ """
+ return self.resid_response.reshape((-1,1))/(np.sqrt(self.scale * (1.0 - self.influ)))
+
+ @cache_readonly
+ def bse(self):
+ """
+ standard errors of Betas
+
+ Methods: p215, (2.15) and (2.21)
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+ """
+ return np.sqrt(self.CCT)
+
+ @cache_readonly
+ def influ(self):
+ """
+ Influence: leading diagonal of S Matrix
+ """
+ return np.reshape(np.diag(self.S),(-1,1))
+
+ @cache_readonly
+ def cooksD(self):
+ """
+ Influence: leading diagonal of S Matrix
+
+ Methods: p216, (9.11),
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying
+ relationships.
+ Note: in (9.11), p should be tr(S), that is, the effective number of parameters
+ """
+ return self.std_res**2 * self.influ / (self.tr_S * (1.0-self.influ))
+
+ @cache_readonly
+ def deviance(self):
+ off = self.offset.reshape((-1,1)).T
+ y = self.y
+ ybar = self.y_bar
+ if isinstance(self.family, Gaussian):
+ raise NotImplementedError('deviance not currently used for Gaussian')
+ elif isinstance(self.family, Poisson):
+ dev = np.sum(2.0*self.W*(y*np.log(y/(ybar*off))-(y-ybar*off)),axis=1)
+ elif isinstance(self.family, Binomial):
+ dev = self.family.deviance(self.y, self.y_bar, self.W, axis=1)
+ return dev.reshape((-1,1))
+
+ @cache_readonly
+ def resid_deviance(self):
+ if isinstance(self.family, Gaussian):
+ raise NotImplementedError('deviance not currently used for Gaussian')
+ else:
+ off = self.offset.reshape((-1,1)).T
+ y = self.y
+ ybar = self.y_bar
+ global_dev_res = ((self.family.resid_dev(self.y, self.mu))**2)
+ dev_res = np.repeat(global_dev_res.flatten(),self.n)
+ dev_res = dev_res.reshape((self.n, self.n))
+ dev_res = np.sum(dev_res * self.W.T, axis=0)
+ return dev_res.reshape((-1,1))
+
+ @cache_readonly
+ def pDev(self):
+ """
+ Local percentage of deviance accounted for. Described in the GWR4
+ manual. Equivalent to 1 - (deviance/null deviance)
+ """
+ if isinstance(self.family, Gaussian):
+ raise NotImplementedError('Not implemented for Gaussian')
+ else:
+ return 1.0 - (self.resid_deviance/self.deviance)
+
+ @cache_readonly
+ def adj_alpha(self):
+ """
+ Corrected alpha (critical) values to account for multiple testing during hypothesis
+ testing. Includes corrected value for 90% (.1), 95% (.05), and 99%
+ (.01) confidence levels. Correction comes from:
+
+ da Silva, A. R., & Fotheringham, A. S. (2015). The Multiple Testing Issue in
+ Geographically Weighted Regression. Geographical Analysis.
+
+ """
+ alpha = np.array([.1, .05, .001])
+ pe = (2.0 * self.tr_S) - self.tr_STS
+ p = self.k
+ return (alpha*p)/pe
+
+ def filter_tvals(self, alpha):
+ """
+ Utility function to set tvalues with an absolute value smaller than the
+ absolute value of the alpha (critical) value to 0
+
+ Parameters
+ ----------
+ alpha : scalar
+ critical value to determine which tvalues are
+ associated with statistically significant parameter
+ estimates
+
+ Returns
+ -------
+ filtered : array
+ n*k; new set of n tvalues for each of k variables
+ where absolute tvalues less than the absolute value of
+ alpha have been set to 0.
+ """
+ alpha = np.abs(alpha)/2.0
+ n = self.n
+ critical = t.ppf(1-alpha, n-1)
+ subset = (self.tvalues < critical) & (self.tvalues > -1.0*critical)
+ tvalues = self.tvalues.copy()
+ tvalues[subset] = 0
+ return tvalues
+
+ @cache_readonly
+ def df_model(self):
+ return self.n - self.tr_S
+
+ @cache_readonly
+ def df_resid(self):
+ return self.n - 2.0*self.tr_S + self.tr_STS
+
+ @cache_readonly
+ def normalized_cov_params(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def resid_pearson(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def resid_working(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def resid_anscombe(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def pearson_chi2(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def null(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def llnull(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def null_deviance(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def aic(self):
+ return get_AIC(self)
+
+ @cache_readonly
+ def aicc(self):
+ return get_AICc(self)
+
+ @cache_readonly
+ def bic(self):
+ return get_BIC(self)
+
+ @cache_readonly
+ def D2(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def adj_D2(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def pseudoR2(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def adj_pseudoR2(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def pvalues(self):
+ raise NotImplementedError('Not implemented for GWR')
+
+ @cache_readonly
+ def predictions(self):
+ P = self.model.P
+ if P is None:
+ raise NotImplementedError('predictions only avaialble if predict'
+ 'method called on GWR model')
+ else:
+ predictions = np.sum(P*self.params, axis=1).reshape((-1,1))
+ return predictions
diff --git a/pysal/contrib/gwr/kernels.py b/pysal/contrib/gwr/kernels.py
new file mode 100644
index 0000000..d5b371b
--- /dev/null
+++ b/pysal/contrib/gwr/kernels.py
@@ -0,0 +1,124 @@
+# GWR kernel function specifications
+
+__author__ = "Taylor Oshan tayoshan at gmail.com"
+
+#from pysal.weights.Distance import Kernel
+import scipy
+from scipy.spatial.kdtree import KDTree
+import numpy as np
+
+#adaptive specifications should be parameterized with nn-1 to match original gwr
+#implementation. That is, pysal counts self neighbors with knn automatically.
+
+def fix_gauss(coords, bw, points=None):
+ w = _Kernel(coords, function='gwr_gaussian', bandwidth=bw,
+ truncate=False, points=points)
+ return w.kernel
+
+def adapt_gauss(coords, nn, points=None):
+ w = _Kernel(coords, fixed=False, k=nn-1, function='gwr_gaussian',
+ truncate=False, points=points)
+ return w.kernel
+
+def fix_bisquare(coords, bw, points=None):
+ w = _Kernel(coords, function='bisquare', bandwidth=bw, points=points)
+ return w.kernel
+
+def adapt_bisquare(coords, nn, points=None):
+ w = _Kernel(coords, fixed=False, k=nn-1, function='bisquare', points=points)
+ return w.kernel
+
+def fix_exp(coords, bw, points=None):
+ w = _Kernel(coords, function='exponential', bandwidth=bw,
+ truncate=False, points=points)
+ return w.kernel
+
+def adapt_exp(coords, nn, points=None):
+ w = _Kernel(coords, fixed=False, k=nn-1, function='exponential',
+ truncate=False, points=points)
+ return w.kernel
+
+from scipy.spatial.distance import cdist
+
+#Customized Kernel class user for GWR because the default PySAL kernel class
+#favors memory optimization over speed optimizations and GWR often needs the
+#speed optimization since it is not always assume points far awar are truncated
+#to zero
+class _Kernel(object):
+ """
+
+ """
+ def __init__(self, data, bandwidth=None, fixed=True, k=None,
+ function='triangular', eps=1.0000001, ids=None, truncate=True,
+ points=None): #Added truncate flag
+ if issubclass(type(data), scipy.spatial.KDTree):
+ self.data = data.data
+ data = self.data
+ else:
+ self.data = data
+ if k is not None:
+ self.k = int(k) + 1
+ else:
+ self.k = k
+ if points is None:
+ self.dmat = cdist(self.data, self.data)
+ else:
+ self.points = points
+ self.dmat = cdist(self.points, self.data)
+ self.function = function.lower()
+ self.fixed = fixed
+ self.eps = eps
+ self.trunc = truncate
+ if bandwidth:
+ try:
+ bandwidth = np.array(bandwidth)
+ bandwidth.shape = (len(bandwidth), 1)
+ except:
+ bandwidth = np.ones((len(data), 1), 'float') * bandwidth
+ self.bandwidth = bandwidth
+ else:
+ self._set_bw()
+ self.kernel = self._kernel_funcs(self.dmat/self.bandwidth)
+
+ if self.trunc:
+ mask = np.repeat(self.bandwidth, len(self.data), axis=1)
+ self.kernel[(self.dmat >= mask)] = 0
+
+ def _set_bw(self):
+ if self.k is not None:
+ dmat = np.sort(self.dmat)[:,:self.k]
+ else:
+ dmat = self.dmat
+ if self.fixed:
+ # use max knn distance as bandwidth
+ bandwidth = dmat.max() * self.eps
+ n = len(self.data)
+ self.bandwidth = np.ones((n, 1), 'float') * bandwidth
+ else:
+ # use local max knn distance
+ self.bandwidth = dmat.max(axis=1) * self.eps
+ self.bandwidth.shape = (self.bandwidth.size, 1)
+
+
+ def _kernel_funcs(self, zs):
+ # functions follow Anselin and Rey (2010) table 5.4
+ if self.function == 'triangular':
+ return 1 - zs
+ elif self.function == 'uniform':
+ return np.ones(zi.shape) * 0.5
+ elif self.function == 'quadratic':
+ return (3. / 4) * (1 - zs ** 2)
+ elif self.function == 'quartic':
+ return (15. / 16) * (1 - zs ** 2) ** 2
+ elif self.function == 'gaussian':
+ c = np.pi * 2
+ c = c ** (-0.5)
+ return c * np.exp(-(zs ** 2) / 2.)
+ elif self.function == 'gwr_gaussian':
+ return np.exp(-0.5*(zs)**2)
+ elif self.function == 'bisquare':
+ return (1-(zs)**2)**2
+ elif self.function =='exponential':
+ return np.exp(-zs)
+ else:
+ print('Unsupported kernel function', self.function)
diff --git a/pysal/contrib/gwr/notebooks/GWR_Georgia_Example.ipynb b/pysal/contrib/gwr/notebooks/GWR_Georgia_Example.ipynb
new file mode 100644
index 0000000..f5874ee
--- /dev/null
+++ b/pysal/contrib/gwr/notebooks/GWR_Georgia_Example.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import geopandas as gp\n",
+ "import pysal as ps\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "%pylab inline\n",
+ "from sel_bw import Sel_BW\n",
+ "from gwr import GWR\n",
+ "from pysal.contrib.glm.family import Gaussian"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.colorbar.Colorbar at 0x1256533d0>"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAHqCAYAAADsw4zSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8lEUTgJ+9npBG6L0jHUIVFKRI7yK9KUVBsFGkSBX4\nAEUQFRGRXgSk995BqtJEBJXeISQh9e5y+/1xlxgg5S53h4j7+Ltf3tt3d2bek2Rud2dnhJQShUKh\nUCgUzyeaf9oAhUKhUCgU3kM5eoVCoVAonmOUo1coFAqF4jlGOXqFQqFQKJ5jlKNXKBQKheI5Rjl6\nhUKhUCieY5SjVyj+pQghXhFCXP2n7XgcIUR3IcSuf9oOhUJhRzl6hSIFhBCXhBDRQogIIcRNIcQc\nIYRvGmOecL5CiJFCCLNDToQQ4lchxGseMtOpRBhCiDNJ9FuFEDFCiIeO94M9ZIvLdikUCu+jHL1C\nkTIS [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x102804090>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Data\n",
+ "\n",
+ "data = ps.open(ps.examples.get_path('GData_utm.csv'))\n",
+ "shp = gp.read_file('/Users/toshan/dev/pysal/pysal/examples/georgia/G_utm.shp')\n",
+ "vmin, vmax = np.min(shp['PctBach']), np.max(shp['PctBach']) \n",
+ "ax = shp.plot('PctBach', vmin=vmin, vmax=vmax, figsize=(8,8), cmap='Reds')\n",
+ "ax.set_title('PctBach' + ' T-vals')\n",
+ "fig = ax.get_figure()\n",
+ "cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
+ "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='Reds')\n",
+ "sm._A = []\n",
+ "fig.colorbar(sm, cax=cax)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Prep data into design matrix and coordinates\n",
+ "\n",
+ "#Dependent variable\n",
+ "y = shp.PctBach.reshape((-1,1))\n",
+ "\n",
+ "#Design matrix - covariates - intercept added automatically\n",
+ "pov = shp.PctPov.reshape((-1,1))\n",
+ "rural = shp.PctRural.reshape((-1,1))\n",
+ "blk = shp.PctBlack.reshape((-1,1))\n",
+ "X = np.hstack([pov, rural, blk])\n",
+ "labels = ['Intercept', 'PctPov', 'PctRural', 'PctAfricanAmer']\n",
+ "\n",
+ "#Coordinates for calibration points\n",
+ "u = shp.X\n",
+ "v = shp.Y\n",
+ "coords = zip(u,v)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "100.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Find optimal bandwidth using golden section search to minimize AICc\n",
+ "\n",
+ "#Instantiate bandwidth selection class - bisquare NN (adaptive)\n",
+ "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n",
+ "\n",
+ "#Find optimal bandwidth by minimizing AICc using golden section search algorithm\n",
+ "bw = bw.search(search='golden_section', criterion='AICc')\n",
+ "print bw"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Instantiate GWR model and then estimate parameters and diagnostics using fit method\n",
+ "model = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n",
+ "results = model.fit()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(172, 4)"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Results in a set of mappable results \n",
+ "results.params.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAHqCAYAAACZeE2AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnWd4VEUXgN+5WxPSSAgQaugQWgIICCJFpQlib6Ai2BVF\nsaBYaH52UBRUVKSIgChFlN4EBBGQ3pEeIJSEFFK2zfdjbyBAyrYAwrzPs8/ezJ05M/cGMmfOnHNG\nSClRKBQKhUJx7aJd7gEoFAqFQqG4vChlQKFQKBSKaxylDCgUCoVCcY2jlAGFQqFQKK5xlDKgUCgU\nCsU1jlIGFAqFQqG4xlHKgEKhOA8hxBYhxI2XexwKheLSoZQBxVWNEGKfEKKdh3WXCCF6FfeYvEUI\nUVkI4RJCFPj/VQjxjhDCJoRI0z/pQohkD2R/L4QYnLdMSllPSrksEGO/oK/WQohDgZarUCj8RykD\nCkWA [...]
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+ ]
+ },
+ "metadata": {},
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+ },
+ {
+ "data": {
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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1291355d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAHqCAYAAABocxYNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4lEUTwH97PQmpECB06Z2EJiAKKEgRKTaqWBALAiJF\ninRBOioKKEpHEZCONKU3kRYQRHovARISQtrd5fb74y4QQspd7oLysb/nuefu9t2d2ffQ7LyzM7NC\nSolCoVAoFApFZmj+7QkoFAqFQqH476MMBoVCoVAoFFmiDAaFQqFQKBRZogwGhUKhUCgUWaIMBoVC\noVAoFFmiDAaFQqFQKBRZogwGheIxQwgxQAgx/d+eh0KheLRQBoPisUUIcU4IES+EuC2EuCqEmCWE\n8M5iTD0hxMU0bUOFEGaHnCghxA4hRC0Pz7WoEMLm0HFbCBHreH/V1flKKUdLKd/15PxS6TsrhHg2\nJ2Qr [...]
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+ "<matplotlib.figure.Figure at 0x127892190>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "<matplotlib.figure.Figure at 0x12a733390>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHqCAYAAABRFKdWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnWd4VEUXgN/ZnkoIvYbeS0JXaaL0LqAUBRTBLiiIgKAg\nIAKCBUWRzgcI0hHp0gSkdwTpvRNCSN3d7Hw/9iaEmLKb3YDKvM+zz947d+bMmQvZe+6ZM2eElBKF\nQqFQKBSKzKB71AooFAqFQqH496IMCYVCoVAoFJlGGRIKhUKhUCgyjTIkFAqFQqFQZBplSCgUCoVC\nocg0ypBQKBQKhUKRaZQhoVD8gxFChAghHEKILP1bFULUFkIcy8o+FArFfxNlSCgeC4QQ54QQMUKI\nSCHEVSHEdCGEbwZt6gkhLqYo+0QIYdXkhAshtgohamWt9qSZ7CXFuO5p399kJFAzTooldSDlVill\nWW8p [...]
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+ "<matplotlib.figure.Figure at 0x12ba4a990>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAHqCAYAAACUZj3HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VMXXgN/ZbDabQhJCgAQIRUKHEAhFUQQUpDcBQUFA\niiIKUUFFEBEBaUpVBOVHB4OFKiChV0U6QXpvCT0JIXWz8/2xm/0SSNuSgDDv89xn786dOTP3psy5\nZ86cI6SUKBQKhUKhUGge9QAUCoVCoVA8HiilQKFQKBQKBaCUAoVCoVAoFGaUUqBQKBQKhQJQSoFC\noVAoFAozSilQKBQKhUIBKKVAoXiiEEKUEkIYhRCP9G9bCDFKCDHnUY5BoVBYj1IKFIpMEEJcEELE\nCyFihRCRQoi5Qgi3HNo0EEJcfqBshBAi2SznjhBipxDi2bwdPZkGHxFC3DOPI1YIkWq+v7Sy1/N4\nTAqF [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x12ae86e10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHqCAYAAABRFKdWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FEUbwH9zLZceeui9t9BBlKLSpVkQFEEUrB8IgoCI\nFAtNURSliCCCIr1LUxFBQJogXXoLoYYkhJRr8/2xm5BAyl3uAJX5Pc8+tzc7bfcg8+47bxFSShQK\nhUKhUChyguFeT0ChUCgUCsW/FyVIKBQKhUKhyDFKkFAoFAqFQpFjlCChUCgUCoUixyhBQqFQKBQK\nRY5RgoRCoVAoFIocowQJxX2LEOJVIcQFIUScECJXJnWuCyFK3N2Z/TcQQhTVn62413NRKBR3DiVI\nKP5RCCFOCSES9AUoSgjxjRAiIJs2jYUQZzO59rwQwiWEeOqWchMwHnhUShkipbyWUXspZbCU8lQO\nb8cj [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1233c4850>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAHqCAYAAACUZj3HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VEXXwH+zm2waSSCEEunSmwQCQaQjIARBVKQqvYhY\nQFAQROEVBF7poHSkI0WCgCBSRekoIBiqICUh1IQQUjabne+PLV8SUrYlIO/8nuc+e3fuzJm5N2XO\nPXPmHCGlRKFQKBQKhULzuAegUCgUCoXiyUApBQqFQqFQKAClFCgUCoVCoTCjlAKFQqFQKBSAUgoU\nCoVCoVCYUUqBQqFQKBQKQCkFCoXNCCEGCiGihRBxQogCWdR5IIQonbcj+99ACNFHCLHncY9DoXia\nUUqB4l+NEOIfIUSCeaK+IYT4VgjhnUObxkKIa1lc6ymEMAoh3shQ7gZMAZpLKf2klDGZtZdS+kop\n/3Hw [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1271a0750>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Map Parameter estimates and T-vals for each covariate\n",
+ "\n",
+ "\n",
+ "for param in range(results.params.shape[1]):\n",
+ " shp[str(param)] = results.params[:,param]\n",
+ " vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n",
+ " ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='YlOrRd')\n",
+ " ax.set_title(labels[param] + ' Estimates')\n",
+ " fig = ax.get_figure()\n",
+ " cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
+ " sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='YlOrRd')\n",
+ " sm._A = []\n",
+ " fig.colorbar(sm, cax=cax)\n",
+ " \n",
+ " shp[str(param)] = results.tvalues[:,param]\n",
+ " vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n",
+ " ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='Greys')\n",
+ " ax.set_title(labels[param] + ' T-vals')\n",
+ " fig = ax.get_figure()\n",
+ " cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
+ " sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='Greys')\n",
+ " sm._A = []\n",
+ " fig.colorbar(sm, cax=cax)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.colorbar.Colorbar at 0x12679a490>"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgoAAAHqCAYAAABlbWZKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd0VEUXwH+zPZWEHnrvHQTFT+lNpQgKCAIqiKI0ld5B\nVEAEBKkKUqUI0kE6IkW6jSodQocUQsruZuf7Y19CgJTd7AYQ53fOnuzOm7lz3+Ykc9+dO/cKKSUK\nhUKhUCgUyaF73AooFAqFQqF4clGGgkKhUCgUihRRhoJCoVAoFIoUUYaCQqFQKBSKFFGGgkKhUCgU\nihRRhoJCoVAoFIoUUYaCQvEYEEI4hBCFHrcejxshRA0hxMXHrYdCoUgZZSgoFIAQ4qwQovYjnDLF\nBCZCiO1CiBghRKQQ4roQYpkQIkdqwoQQHYUQx4QQEUKIK0KINUIIP++rnSGoZC4KxROMMhQUiseD\nSOWa [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x128944590>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Map local R-square values which is a weighted R-square at each observation location\n",
+ "\n",
+ "shp['localR2'] = results.localR2\n",
+ "vmin, vmax = np.min(shp['localR2']), np.max(shp['localR2']) \n",
+ "ax = shp.plot('localR2', vmin=vmin, vmax=vmax, figsize=(8,8), cmap='PuBuGn')\n",
+ "ax.set_title('Local R-Squared')\n",
+ "fig = ax.get_figure()\n",
+ "cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
+ "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='PuBuGn')\n",
+ "sm._A = []\n",
+ "fig.colorbar(sm, cax=cax)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [Root]",
+ "language": "python",
+ "name": "Python [Root]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/gwr/notebooks/GWR_Georgia_Prediction.ipynb b/pysal/contrib/gwr/notebooks/GWR_Georgia_Prediction.ipynb
new file mode 100644
index 0000000..4b7ed33
--- /dev/null
+++ b/pysal/contrib/gwr/notebooks/GWR_Georgia_Prediction.ipynb
@@ -0,0 +1,1049 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import pysal as ps\n",
+ "from pysal.contrib.gwr.sel_bw import Sel_BW\n",
+ "from pysal.contrib.gwr.gwr import GWR\n",
+ "from pysal.contrib.glm.family import Gaussian\n",
+ "from scipy.stats import pearsonr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#Data\n",
+ "\n",
+ "data = '/Users/toshan/dev/pysal/pysal/examples/georgia/GData_utm.csv'\n",
+ "data = pd.read_csv(data)\n",
+ "#shp = gp.read_file('/Users/toshan/dev/pysal/pysal/examples/georgia/G_utm.shp')\n",
+ "#shp.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
+ "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
+ "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:8: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
+ "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:9: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Prep data into design matrix and coordinates\n",
+ "\n",
+ "#Dependent variable\n",
+ "y = data.PctBach.reshape((-1,1))\n",
+ "\n",
+ "#Design matrix - covariates - intercept added automatically\n",
+ "pov = data.PctPov.reshape((-1,1))\n",
+ "rural = data.PctRural.reshape((-1,1))\n",
+ "blk = data.PctBlack.reshape((-1,1))\n",
+ "X = np.hstack([rural, pov, blk])\n",
+ "labels = ['Intercept', 'PctPov', 'PctRural', 'PctBlack']\n",
+ "\n",
+ "#Coordinates for calibration points\n",
+ "u = data.X\n",
+ "v = data.Y\n",
+ "coords = np.array(zip(u,v))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "93.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = np.arange(len(y))\n",
+ "train = index[0:-10]\n",
+ "test = index[-10:]\n",
+ "\n",
+ "y_train = y[train]\n",
+ "X_train = X[train]\n",
+ "coords_train = list(coords[train])\n",
+ "\n",
+ "y_test = y[test]\n",
+ "X_test = X[test]\n",
+ "coords_test = list(coords[test])\n",
+ "\n",
+ "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n",
+ "bw = bw.search(search='golden_section', criterion='AICc')\n",
+ "print bw\n",
+ "\n",
+ "model = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n",
+ "results = model.predict(coords_test, X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 10.51695514],\n",
+ " [ 9.93321992],\n",
+ " [ 8.92473026],\n",
+ " [ 5.47350219],\n",
+ " [ 8.61756585],\n",
+ " [ 12.8141851 ],\n",
+ " [ 5.55619405],\n",
+ " [ 12.63004172],\n",
+ " [ 8.70638418],\n",
+ " [ 8.17582599]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results.predictions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "93.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n",
+ "bw = bw.search(search='golden_section', criterion='AICc')\n",
+ "print bw\n",
+ "\n",
+ "t = GWR(coords, y, X, bw).fit()\n",
+ "\n",
+ "model = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n",
+ "results = model.predict(coords_test, X_test)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(array([ 0.45389284]), array([ 0.18761856]))\n",
+ "[[ 2.27719806e+01 -2.15093080e-01 -1.02539506e-01 -1.40496359e-02]\n",
+ " [ 1.91053102e+01 -2.32528744e-01 -9.41767859e-02 7.19130384e-02]\n",
+ " [ 1.97434217e+01 -3.08929815e-01 -8.04472131e-02 8.32061891e-02]\n",
+ " [ 1.75057600e+01 -1.87954544e-01 -7.89191503e-02 5.17192228e-02]\n",
+ " [ 2.77474018e+01 -2.08553416e-01 -1.65334529e-01 4.06743861e-03]\n",
+ " [ 2.62106267e+01 -3.60514422e-01 -1.38397963e-01 7.21984934e-02]\n",
+ " [ 1.80348332e+01 -2.60555691e-01 -7.70473064e-02 8.43194028e-02]\n",
+ " [ 2.84528022e+01 -1.40970116e-01 -1.63408415e-01 -6.30756266e-02]\n",
+ " [ 2.23530956e+01 -2.26653837e-01 -1.03045606e-01 2.99244694e-03]\n",
+ " [ 1.82205077e+01 -3.09812424e-01 -7.40336720e-02 1.08635554e-01]]\n",
+ "[[ 2.27719806e+01 -2.15093080e-01 -1.02539506e-01 -1.40496359e-02]\n",
+ " [ 1.91053102e+01 -2.32528744e-01 -9.41767859e-02 7.19130384e-02]\n",
+ " [ 1.97434217e+01 -3.08929815e-01 -8.04472131e-02 8.32061891e-02]\n",
+ " [ 1.75057600e+01 -1.87954544e-01 -7.89191503e-02 5.17192228e-02]\n",
+ " [ 2.77474018e+01 -2.08553416e-01 -1.65334529e-01 4.06743861e-03]\n",
+ " [ 2.62106267e+01 -3.60514422e-01 -1.38397963e-01 7.21984934e-02]\n",
+ " [ 1.80348332e+01 -2.60555691e-01 -7.70473064e-02 8.43194028e-02]\n",
+ " [ 2.84528022e+01 -1.40970116e-01 -1.63408415e-01 -6.30756266e-02]\n",
+ " [ 2.23530956e+01 -2.26653837e-01 -1.03045606e-01 2.99244694e-03]\n",
+ " [ 1.82205077e+01 -3.09812424e-01 -7.40336720e-02 1.08635554e-01]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print pearsonr(results.predictions, y_test)\n",
+ "\n",
+ "print results.params[0:]\n",
+ "print t.params[-10:]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "93.0\n",
+ "159\n",
+ "0.56813744327\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Find optimal bandwidth using golden section search to minimize AICc\n",
+ "\n",
+ "#Instantiate bandwidth selection class - bisquare NN (adaptive)\n",
+ "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n",
+ "\n",
+ "#Find optimal bandwidth by minimizing AICc using golden section search algorithm\n",
+ "bw = bw.search(search='golden_section', criterion='AICc')\n",
+ "print bw\n",
+ "\n",
+ "model_a = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n",
+ "results_a = model_a.fit()\n",
+ "\n",
+ "print len(results_a.localR2)\n",
+ "print np.mean(results_a.localR2)\n",
+ "exog_scale = results_a.scale\n",
+ "exog_resid = results_a.resid_response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'exog_scale' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-9-06cdae655127>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGWR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m93\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGaussian\u001b[0m\u001b[0;34m [...]
+ "\u001b[0;31mNameError\u001b[0m: name 'exog_scale' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "model_b = GWR(coords, y, X, 93, family=Gaussian(), fixed=False, kernel='bisquare', points=coords_b, exog_scale=exog_scale, exog_resid=exog_resid)\n",
+ "results_b = model_b.fit()\n",
+ "\n",
+ "print len(results_b.localR2)\n",
+ "print np.mean(results_b.localR2)\n",
+ "results_b.params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.10295359 0.12385708 0.10256179 0.11029685 0.12763962 0.13120828\n",
+ " 0.11315837 0.11694384 0.09973951 0.1078668 ]\n",
+ "[-2.08922363 -1.87739571 -3.01213362 -1.70407901 -1.63392388 -2.74764987\n",
+ " -2.3025755 -1.20545144 -2.27245792 -2.87217588]\n",
+ "[[ 0.53068693]\n",
+ " [ 0.59582647]\n",
+ " [ 0.59700925]\n",
+ " [ 0.45769954]\n",
+ " [ 0.54634509]\n",
+ " [ 0.5494828 ]\n",
+ " [ 0.55159604]\n",
+ " [ 0.55634237]\n",
+ " [ 0.53903842]\n",
+ " [ 0.55884954]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print results_b.bse[0:10, 1]\n",
+ "\n",
+ "print results_b.tvalues[0:10, 1]\n",
+ "\n",
+ "print results_b.localR2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'results' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-12-d127818b2d26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Results in a set of mappable results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'results' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "#Results in a set of mappable results \n",
+ "results.params.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "#Map Parameter estimates and T-vals for each covariate\n",
+ "\n",
+ "for param in range(results.params.shape[1]):\n",
+ " shp[str(param)] = results.params[:,param]\n",
+ " vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n",
+ " ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='YlOrRd')\n",
+ " ax.set_title(labels[param] + ' Estimates')\n",
+ " fig = ax.get_figure()\n",
+ " cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
+ " sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='YlOrRd')\n",
+ " sm._A = []\n",
+ " fig.colorbar(sm, cax=cax)\n",
+ " \n",
+ " shp[str(param)] = results.tvalues[:,param]\n",
+ " vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n",
+ " ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='Greys')\n",
+ " ax.set_title(labels[param] + ' T-vals')\n",
+ " fig = ax.get_figure()\n",
+ " cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
+ " sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='Greys')\n",
+ " sm._A = []\n",
+ " fig.colorbar(sm, cax=cax)\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.colorbar.Colorbar at 0x136d8b150>"
+ ]
+ },
+ "execution_count": 121,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgoAAAHqCAYAAABlbWZKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd0VEUXwH+zPZWEHnrvHQTFT+lNpQgKCAIqiKI0ld5B\nVEAEBKkKUqUI0kE6IkW6jSodQocUQsruZuf7Y19CgJTd7AYQ53fOnuzOm7lz3+Ykc9+dO/cKKSUK\nhUKhUCgUyaF73AooFAqFQqF4clGGgkKhUCgUihRRhoJCoVAoFIoUUYaCQqFQKBSKFFGGgkKhUCgU\nihRRhoJCoVAoFIoUUYaCQvEYEEI4hBCFHrcejxshRA0hxMXHrYdCoUgZZSgoFIAQ4qwQovYjnDLF\nBCZCiO1CiBghRKQQ4roQYpkQIkdqwoQQHYUQx4QQEUKIK0KINUIIP++rnSGoZC4KxROMMhQUiseD\nSOWa [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1358c6410>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Map local R-square values which is a weighted R-square at each observation location\n",
+ "\n",
+ "shp['localR2'] = results.localR2\n",
+ "vmin, vmax = np.min(shp['localR2']), np.max(shp['localR2']) \n",
+ "ax = shp.plot('localR2', vmin=vmin, vmax=vmax, figsize=(8,8), cmap='PuBuGn')\n",
+ "ax.set_title('Local R-Squared')\n",
+ "fig = ax.get_figure()\n",
+ "cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
+ "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='PuBuGn')\n",
+ "sm._A = []\n",
+ "fig.colorbar(sm, cax=cax)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "??GWR\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "dists = np.zeros((10,159))\n",
+ "for x, point1 in enumerate(coords_b):\n",
+ " for y, point2 in enumerate(coords):\n",
+ " \n",
+ " dists[x,y] = np.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 135141.51708594, 176669.03854578, 150233.03041745, ...,\n",
+ " 101245.68031101, 37465.45393773, 186494.71599871],\n",
+ " [ 53079.72071743, 93989.76293746, 56523.70300113, ...,\n",
+ " 272750.51071353, 192366.73910219, 185946.3168796 ],\n",
+ " [ 211071.91332835, 179410.69684634, 203731.38642588, ...,\n",
+ " 259876.04309332, 157691.66651843, 90086.32599479],\n",
+ " ..., \n",
+ " [ 233459.02913062, 277886.65118938, 250155.19163993, ...,\n",
+ " 0. , 116483.88542009, 277512.51277944],\n",
+ " [ 142506.80885168, 169996.21028779, 153162.14012036, ...,\n",
+ " 116483.88542009, 0. , 161268.74061156],\n",
+ " [ 144540.5180503 , 95782.96853831, 130844.73173151, ...,\n",
+ " 277512.51277944, 161268.74061156, 0. ]])"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dists"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from pysal.contrib.gwr.kernels import adapt_bisquare, _Kernel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "all_coords = np.vstack([coords, coords_b])\n",
+ "\n",
+ "W = adapt_bisquare(all_coords, 93)\n",
+ "\n",
+ "i = len(coords_b)\n",
+ "j = len(coords)\n",
+ "W = W[i:, :-j].T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "test = _Kernel(coords, fixed=False, function='bisquare', truncate=False, k=92, points=coords_b)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 2.14343799e-01, 6.75977473e-03, 1.13120557e-01, ...,\n",
+ " 4.88014350e-01, 9.19154786e-01, 5.15666694e-04],\n",
+ " [ 9.27168014e-01, 7.80855123e-01, 9.17619421e-01, ...,\n",
+ " 4.11566468e-04, 2.62825351e-01, 2.96646476e-01],\n",
+ " [ 2.55461442e-03, 9.86090262e-02, 1.33250285e-02, ...,\n",
+ " 1.92971340e-01, 2.20950550e-01, 6.84004125e-01],\n",
+ " ..., \n",
+ " [ 3.01668776e-02, 4.39435726e-01, 1.20801314e-01, ...,\n",
+ " 1.00000000e+00, 5.00997875e-01, 4.33523139e-01],\n",
+ " [ 1.02455255e-01, 1.05479492e-03, 4.60574281e-02, ...,\n",
+ " 2.97821020e-01, 1.00000000e+00, 1.67110298e-02],\n",
+ " [ 3.42081437e-01, 6.68642075e-01, 4.35361409e-01, ...,\n",
+ " 2.81165199e-01, 2.33510795e-01, 1.00000000e+00]])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.kernel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0. , 0.17538125, 0.87525431, 0.42934275, 0.80242504,\n",
+ " 0.70359714, 0.27695634, 0. , 0.03083999, 0.76946793,\n",
+ " 0.7311884 , 0. , 0. , 0.79126134, 0.70002409,\n",
+ " 0. , 0. , 0. , 0. , 0.00106685,\n",
+ " 0. , 0.7250386 , 0. , 0.85643026, 0.34265747,\n",
+ " 0.01878214, 0.50266628, 0. , 0. , 0.1835491 ,\n",
+ " 0. , 0. , 0.11051018, 0. , 0.43041104,\n",
+ " 0.05143613, 0.10487 , 0. , 0.02865854, 0.6171386 ,\n",
+ " 0.66370484, 0. , 0.58953303, 0.85720095, 0. ,\n",
+ " 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0.06040493, 0.80528346, 0. , 0.17767534,\n",
+ " 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0.04826184, 0.58892109, 0. , 0. , 0.96186502,\n",
+ " 0.18599788, 0.4800177 , 0.40543209, 0. , 0. ,\n",
+ " 0.63230904, 0.42509109, 0.07603503, 0.85398556, 0. ,\n",
+ " 0.88942771, 0.55254905, 0. , 0.00509282, 0.8178033 ,\n",
+ " 0. , 0. , 0. , 0. , 0.06647948,\n",
+ " 0.17309084, 0. , 0.85232385, 0. , 0. ,\n",
+ " 0. , 0. , 0. , 0. , 0. ,\n",
+ " 0. , 0. , 0.94253873, 0. , 0. ,\n",
+ " 0.24485917, 0. , 0. , 0. , 0. ,\n",
+ " 0.10398147, 0. , 0. , 0.4884651 , 0.06003501,\n",
+ " 0. , 0. , 0. , 0.00648795, 0. ,\n",
+ " 0. , 0.92289548, 0. , 0.78258809, 0.01501169,\n",
+ " 0.30025862, 0.46065143, 0.9013738 , 0. , 0.68516546,\n",
+ " 0. , 0.31872397, 0.00733061, 0. , 0. ,\n",
+ " 0. , 0. , 0.83474417, 0.00833362, 0.17211238,\n",
+ " 0.920988 , 0. , 0.77178798, 0. , 0. ,\n",
+ " 0.37447591, 0. , 0.05710858, 0.28441712, 0.17211238,\n",
+ " 0.920988 , 0. , 0.77178798, 0. , 0. ,\n",
+ " 0.37447591, 0. , 0.05710858, 0.28441712])"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "W[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from pysal.contrib.gwr.kernels import adapt_bisquare, _Kernel\n",
+ "#W = adapt_bisquare(coords, 93, coords_b)\n",
+ "w = _Kernel(coords, fixed=False, k=93-1, function='bisquare', points=coords_b, truncate=False)\n",
+ "#W = _Kernel(coords, fixed=False, function='bisquare', truncate=False, k=92, points=coords_b)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 184412.62167661],\n",
+ " [ 275559.98944177],\n",
+ " [ 216617.15163301],\n",
+ " [ 187859.24904361],\n",
+ " [ 267873.107272 ],\n",
+ " [ 313030.23895156],\n",
+ " [ 177791.79358056],\n",
+ " [ 215493.75350882],\n",
+ " [ 172825.86012155],\n",
+ " [ 224337.23222372]])"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w.bandwidth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "??adapt_bisquare()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "operands could not be broadcast together with shapes (4,1) (159,1) ",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-8-4ac9090f9d7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTSS\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mresults_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRSS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,1) (159,1) "
+ ]
+ }
+ ],
+ "source": [
+ "results_a.TSS / results_a.RSS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.55018249],\n",
+ " [ 0.55508304],\n",
+ " [ 0.55067801],\n",
+ " [ 0.56737294],\n",
+ " [ 0.56034496],\n",
+ " [ 0.55163749],\n",
+ " [ 0.55802282],\n",
+ " [ 0.56646836],\n",
+ " [ 0.50958839],\n",
+ " [ 0.5488834 ],\n",
+ " [ 0.57973455],\n",
+ " [ 0.53019011],\n",
+ " [ 0.60128026],\n",
+ " [ 0.56221182],\n",
+ " [ 0.60339059],\n",
+ " [ 0.5763165 ],\n",
+ " [ 0.54286951],\n",
+ " [ 0.5833684 ],\n",
+ " [ 0.57449946],\n",
+ " [ 0.61737272],\n",
+ " [ 0.55380111],\n",
+ " [ 0.61102546],\n",
+ " [ 0.54879739],\n",
+ " [ 0.60489614],\n",
+ " [ 0.61531061],\n",
+ " [ 0.63084783],\n",
+ " [ 0.56313164],\n",
+ " [ 0.56211435],\n",
+ " [ 0.55190234],\n",
+ " [ 0.58028618],\n",
+ " [ 0.59173197],\n",
+ " [ 0.58525895],\n",
+ " [ 0.57748692],\n",
+ " [ 0.5337402 ],\n",
+ " [ 0.55415337],\n",
+ " [ 0.54475232],\n",
+ " [ 0.55563599],\n",
+ " [ 0.61699849],\n",
+ " [ 0.60978055],\n",
+ " [ 0.57534427],\n",
+ " [ 0.55258532],\n",
+ " [ 0.55419367],\n",
+ " [ 0.55921201],\n",
+ " [ 0.57623454],\n",
+ " [ 0.50215786],\n",
+ " [ 0.58823547],\n",
+ " [ 0.56940733],\n",
+ " [ 0.59411556],\n",
+ " [ 0.5705367 ],\n",
+ " [ 0.58869272],\n",
+ " [ 0.59467316],\n",
+ " [ 0.55097965],\n",
+ " [ 0.53283037],\n",
+ " [ 0.57561545],\n",
+ " [ 0.54449461],\n",
+ " [ 0.60433883],\n",
+ " [ 0.57143884],\n",
+ " [ 0.55955218],\n",
+ " [ 0.5486125 ],\n",
+ " [ 0.58597217],\n",
+ " [ 0.54926699],\n",
+ " [ 0.54599256],\n",
+ " [ 0.61630895],\n",
+ " [ 0.55746869],\n",
+ " [ 0.55499711],\n",
+ " [ 0.55931011],\n",
+ " [ 0.56417 ],\n",
+ " [ 0.54673444],\n",
+ " [ 0.5540521 ],\n",
+ " [ 0.56008229],\n",
+ " [ 0.59327121],\n",
+ " [ 0.64547689],\n",
+ " [ 0.54818115],\n",
+ " [ 0.62552538],\n",
+ " [ 0.58738464],\n",
+ " [ 0.57551537],\n",
+ " [ 0.52859729],\n",
+ " [ 0.55451107],\n",
+ " [ 0.56953983],\n",
+ " [ 0.50665376],\n",
+ " [ 0.53630241],\n",
+ " [ 0.54924515],\n",
+ " [ 0.51356766],\n",
+ " [ 0.56402094],\n",
+ " [ 0.60593352],\n",
+ " [ 0.5664703 ],\n",
+ " [ 0.48498454],\n",
+ " [ 0.57497136],\n",
+ " [ 0.603174 ],\n",
+ " [ 0.55005894],\n",
+ " [ 0.60048813],\n",
+ " [ 0.57134237],\n",
+ " [ 0.54907691],\n",
+ " [ 0.54975445],\n",
+ " [ 0.61465079],\n",
+ " [ 0.6060417 ],\n",
+ " [ 0.55180747],\n",
+ " [ 0.62700231],\n",
+ " [ 0.63110394],\n",
+ " [ 0.56627448],\n",
+ " [ 0.55936342],\n",
+ " [ 0.59153842],\n",
+ " [ 0.49072277],\n",
+ " [ 0.56039346],\n",
+ " [ 0.54861849],\n",
+ " [ 0.64223183],\n",
+ " [ 0.56429376],\n",
+ " [ 0.55584156],\n",
+ " [ 0.55414867],\n",
+ " [ 0.58173359],\n",
+ " [ 0.59096694],\n",
+ " [ 0.55677681],\n",
+ " [ 0.5811985 ],\n",
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+ " [ 0.58192915],\n",
+ " [ 0.56583346],\n",
+ " [ 0.5632224 ],\n",
+ " [ 0.59850011],\n",
+ " [ 0.54152829],\n",
+ " [ 0.58753223],\n",
+ " [ 0.54196418],\n",
+ " [ 0.56961293],\n",
+ " [ 0.60674024],\n",
+ " [ 0.56542027],\n",
+ " [ 0.56345589],\n",
+ " [ 0.59930614],\n",
+ " [ 0.54614222],\n",
+ " [ 0.61625657],\n",
+ " [ 0.59130554],\n",
+ " [ 0.63750061],\n",
+ " [ 0.55951303],\n",
+ " [ 0.56151487],\n",
+ " [ 0.62565668],\n",
+ " [ 0.47217495],\n",
+ " [ 0.58182132],\n",
+ " [ 0.55963473],\n",
+ " [ 0.54540066],\n",
+ " [ 0.53159056],\n",
+ " [ 0.54104851],\n",
+ " [ 0.49172269],\n",
+ " [ 0.6391049 ],\n",
+ " [ 0.55938683],\n",
+ " [ 0.54270534],\n",
+ " [ 0.54468759],\n",
+ " [ 0.6266155 ],\n",
+ " [ 0.55445185],\n",
+ " [ 0.55985567],\n",
+ " [ 0.59130847],\n",
+ " [ 0.55217823],\n",
+ " [ 0.53068693],\n",
+ " [ 0.59582647],\n",
+ " [ 0.59700925],\n",
+ " [ 0.45769954],\n",
+ " [ 0.54634509],\n",
+ " [ 0.5494828 ],\n",
+ " [ 0.55159604],\n",
+ " [ 0.55634237],\n",
+ " [ 0.53903842],\n",
+ " [ 0.55884954]])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results_a.localR2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "385.87740857134793"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.sum(results_b.W[0].reshape((-1,1)) * (results_b.y.reshape((-1,1)) - results_a.predy.reshape((-1,1)))**2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.62264884, 0.79783573, -1.90581472, 0.58585524, -1.77602421])"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(results_a.y - results_a.mu)[0:5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[-0.62264884],\n",
+ " [ 0.79783573],\n",
+ " [-1.90581472],\n",
+ " [ 0.58585524],\n",
+ " [-1.77602421]])"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(results_a.y.reshape((-1,1)) - results_a.predy.reshape((-1,1)))[0:5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.62264884, 0.79783573, -1.90581472, 0.58585524, -1.77602421])"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results_a.resid_response[0:5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "object of type 'GWRResults' has no len()",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-40-c10354c94228>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m: object of type 'GWRResults' has no len()"
+ ]
+ }
+ ],
+ "source": [
+ "print len(test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n",
+ " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n",
+ " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n",
+ " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
+ " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n",
+ " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n",
+ " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
+ " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
+ " 144 145 146 147 148]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "159\n"
+ ]
+ }
+ ],
+ "source": [
+ "print len(coords)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [test_gwr]",
+ "language": "python",
+ "name": "Python [test_gwr]"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pysal/contrib/gwr/search.py b/pysal/contrib/gwr/search.py
new file mode 100644
index 0000000..80b996f
--- /dev/null
+++ b/pysal/contrib/gwr/search.py
@@ -0,0 +1,143 @@
+#Bandwidth optimization methods
+
+__author__ = "Taylor Oshan"
+
+import numpy as np
+
+def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
+ """
+ Golden section search routine
+ Method: p212, 9.6.4
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying relationships.
+
+ Parameters
+ ----------
+ a : float
+ initial max search section value
+ b : float
+ initial min search section value
+ delta : float
+ constant used to determine width of search sections
+ function : function
+ obejective function to be evaluated at different section
+ values
+ int_score : boolean
+ False for float score, True for integer score
+ tol : float
+ tolerance used to determine convergence
+ max_iter : integer
+ maximum iterations if no convergence to tolerance
+
+ Returns
+ -------
+ opt_val : float
+ optimal value
+ opt_score : kernel
+ optimal score
+ output : list of tuples
+ searching history
+ """
+ b = a + delta * np.abs(c-a)
+ d = c - delta * np.abs(c-a)
+ score = 0.0
+ diff = 1.0e9
+ iters = 0
+ output = []
+ while np.abs(diff) > tol and iters < max_iter:
+ iters += 1
+ if int_score:
+ b = np.round(b)
+ d = np.round(d)
+
+ score_a = function(a)
+ score_b = function(b)
+ score_c = function(c)
+ score_d = function(d)
+
+ if score_b <= score_d:
+ opt_val = b
+ opt_score = score_b
+ c = d
+ d = b
+ b = a + delta * np.abs(c-a)
+ #if int_score:
+ #b = np.round(b)
+ else:
+ opt_val = d
+ opt_score = score_d
+ a = b
+ b = d
+ d = c - delta * np.abs(c-a)
+ #if int_score:
+ #d = np.round(b)
+
+ #if int_score:
+ # opt_val = np.round(opt_val)
+ output.append((opt_val, opt_score))
+ diff = score_b - score_d
+ score = opt_score
+ return np.round(opt_val, 2), opt_score, output
+
+def equal_interval(l_bound, u_bound, interval, function, int_score=False):
+ """
+ Interval search, using interval as stepsize
+
+ Parameters
+ ----------
+ l_bound : float
+ initial min search section value
+ u_bound : float
+ initial max search section value
+ interval : float
+ constant used to determine width of search sections
+ function : function
+ obejective function to be evaluated at different section
+ values
+ int_score : boolean
+ False for float score, True for integer score
+
+ Returns
+ -------
+ opt_val : float
+ optimal value
+ opt_score : kernel
+ optimal score
+ output : list of tuples
+ searching history
+ """
+ a = l_bound
+ c = u_bound
+ b = a + interval
+ if int_score:
+ a = np.round(a,0)
+ c = np.round(c,0)
+ b = np.round(b,0)
+
+ output = []
+
+ score_a = function(a)
+ score_c = function(c)
+
+ output.append((a,score_a))
+ output.append((c,score_c))
+
+ if score_a < score_c:
+ opt_val = a
+ opt_score = score_a
+ else:
+ opt_val = c
+ opt_score = score_c
+
+ while b < c:
+ score_b = function(b)
+
+ output.append((b,score_b))
+
+ if score_b < opt_score:
+ opt_val = b
+ opt_score = score_b
+ b = b + interval
+
+ return opt_val, opt_score, output
+
diff --git a/pysal/contrib/gwr/sel_bw.py b/pysal/contrib/gwr/sel_bw.py
new file mode 100644
index 0000000..cd30f8e
--- /dev/null
+++ b/pysal/contrib/gwr/sel_bw.py
@@ -0,0 +1,256 @@
+# GWR Bandwidth selection class
+
+#x_glob parameter does not yet do anything; it is for semiparametric
+
+
+__author__ = "Taylor Oshan Tayoshan at gmail.com"
+
+from kernels import *
+from search import golden_section, equal_interval
+from gwr import GWR
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial
+import pysal.spreg.user_output as USER
+from diagnostics import get_AICc, get_AIC, get_BIC, get_CV
+from scipy.spatial.distance import pdist, squareform
+from pysal.common import KDTree
+import numpy as np
+
+kernels = {1: fix_gauss, 2: adapt_gauss, 3: fix_bisquare, 4:
+ adapt_bisquare, 5: fix_exp, 6:adapt_exp}
+getDiag = {'AICc': get_AICc,'AIC':get_AIC, 'BIC': get_BIC, 'CV': get_CV}
+
+class Sel_BW(object):
+ """
+ Select bandwidth for kernel
+
+ Methods: p211 - p213, bandwidth selection
+ Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+ Geographically weighted regression: the analysis of spatially varying relationships.
+
+ Parameters
+ ----------
+ y : array
+ n*1, dependent variable.
+ X_glob : array
+ n*k1, fixed independent variable.
+ X_loc : array
+ n*k2, local independent variable, including constant.
+ coords : list of tuples
+ (x,y) of points used in bandwidth selection
+ family : string
+ GWR model type: 'Gaussian', 'logistic, 'Poisson''
+ offset : array
+ n*1, offset variable for Poisson model
+ kernel : string
+ kernel function: 'gaussian', 'bisquare', 'exponetial'
+ fixed : boolean
+ True for fixed bandwidth and False for adaptive (NN)
+ constant : boolean
+ True to include intercept (default) in model and False to exclude
+ intercept.
+
+
+ Attributes
+ ----------
+ y : array
+ n*1, dependent variable.
+ X_glob : array
+ n*k1, fixed independent variable.
+ X_loc : array
+ n*k2, local independent variable, including constant.
+ coords : list of tuples
+ (x,y) of points used in bandwidth selection
+ family : string
+ GWR model type: 'Gaussian', 'logistic, 'Poisson''
+ kernel : string
+ type of kernel used and wether fixed or adaptive
+ criterion : string
+ bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV'
+ search : string
+ bw search method: 'golden', 'interval'
+ bw_min : float
+ min value used in bandwidth search
+ bw_max : float
+ max value used in bandwidth search
+ interval : float
+ interval increment used in interval search
+ tol : float
+ tolerance used to determine convergence
+ max_iter : integer
+ max interations if no convergence to tol
+ constant : boolean
+ True to include intercept (default) in model and False to exclude
+ intercept.
+ Examples
+ ________
+
+ >>> import pysal
+ >>> from pysal.contrib.gwr.sel_bw import Sel_BW
+ >>> data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+ >>> coords = zip(data.bycol('X'), data.by_col('Y'))
+ >>> y = np.array(data.by_col('PctBach')).reshape((-1,1))
+ >>> rural = np.array(data.by_col('PctRural')).reshape((-1,1))
+ >>> pov = np.array(data.by_col('PctPov')).reshape((-1,1))
+ >>> african_amer = np.array(data.by_col('PctBlack')).reshape((-1,1))
+ >>> X = np.hstack([rural, pov, african_amer])
+
+ #Golden section search AICc - adaptive bisquare
+ >>> bw = Sel_BW(coords, y, X).search(criterion='AICc')
+ >>> print bw
+ 93.0
+
+ #Golden section search AIC - adaptive Gaussian
+ >>> bw = Sel_BW(coords, y, X, kernel='gaussian').search(criterion='AIC')
+ >>> print bw
+ 50.0
+
+ #Golden section search BIC - adaptive Gaussian
+ >>> bw = Sel_BW(coords, y, X, kernel='gaussian').search(criterion='BIC')
+ >>> print bw
+ 62.0
+
+ #Golden section search CV - adaptive Gaussian
+ >>> bw = Sel_BW(coords, y, X, kernel='gaussian').search(criterion='CV')
+ >>> print bw
+ 68.0
+
+ #Interval AICc - fixed bisquare
+ >>> sel = Sel_BW(coords, y, X, fixed=True).
+ >>> bw = sel.search(search='interval', bw_min=211001.0, bw_max=211035.0, interval=2)
+ >>> print bw
+ 211025.0
+
+ """
+ def __init__(self, coords, y, X_loc, X_glob=None, family=Gaussian(),
+ offset=None, kernel='bisquare', fixed=False, constant=True):
+ self.coords = coords
+ self.y = y
+ self.X_loc = X_loc
+ if X_glob is not None:
+ self.X_glob = X_glob
+ else:
+ self.X_glob = []
+ self.family=family
+ self.fixed = fixed
+ self.kernel = kernel
+ if offset is None:
+ self.offset = np.ones((len(y), 1))
+ else:
+ self.offset = offset * 1.0
+ self.constant = constant
+
+ def search(self, search='golden_section', criterion='AICc', bw_min=0.0,
+ bw_max=0.0, interval=0.0, tol=1.0e-6, max_iter=200):
+ """
+ Parameters
+ ----------
+ criterion : string
+ bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV'
+ search : string
+ bw search method: 'golden', 'interval'
+ bw_min : float
+ min value used in bandwidth search
+ bw_max : float
+ max value used in bandwidth search
+ interval : float
+ interval increment used in interval search
+ tol : float
+ tolerance used to determine convergence
+ max_iter : integer
+ max iterations if no convergence to tol
+
+ Returns
+ -------
+ bw : scalar or array
+ optimal bandwidth value
+ """
+ self.search = search
+ self.criterion = criterion
+ self.bw_min = bw_min
+ self.bw_max = bw_max
+ self.interval = interval
+ self.tol = tol
+ self.max_iter = max_iter
+
+ if self.fixed:
+ if self.kernel == 'gaussian':
+ ktype = 1
+ elif self.kernel == 'bisquare':
+ ktype = 3
+ elif self.kernel == 'exponential':
+ ktype = 5
+ else:
+ raise TypeError('Unsupported kernel function ', self.kernel)
+ else:
+ if self.kernel == 'gaussian':
+ ktype = 2
+ elif self.kernel == 'bisquare':
+ ktype = 4
+ elif self.kernel == 'exponential':
+ ktype = 6
+ else:
+ raise TypeError('Unsupported kernel function ', self.kernel)
+
+ function = lambda bw: getDiag[criterion](
+ GWR(self.coords, self.y, self.x_loc, bw, family=self.family,
+ kernel=self.kernel, fixed=self.fixed, offset=self.offset).fit())
+
+ if ktype % 2 == 0:
+ int_score = True
+ else:
+ int_score = False
+ self.int_score = int_score
+
+ self._bw()
+
+ return self.bw[0]
+
+ def _bw(self):
+ gwr_func = lambda bw: getDiag[self.criterion](
+ GWR(self.coords, self.y, self.X_loc, bw, family=self.family,
+ kernel=self.kernel, fixed=self.fixed, constant=self.constant).fit())
+ if self.search == 'golden_section':
+ a,c = self._init_section(self.X_glob, self.X_loc, self.coords,
+ self.constant)
+ delta = 0.38197 #1 - (np.sqrt(5.0)-1.0)/2.0
+ self.bw = golden_section(a, c, delta, gwr_func, self.tol,
+ self.max_iter, self.int_score)
+ elif self.search == 'interval':
+ self.bw = equal_interval(self.bw_min, self.bw_max, self.interval,
+ gwr_func, self.int_score)
+ else:
+ raise TypeError('Unsupported computational search method ', search)
+
+
+ def _init_section(self, X_glob, X_loc, coords, constant):
+ if len(X_glob) > 0:
+ n_glob = X_glob.shape[1]
+ else:
+ n_glob = 0
+ if len(X_loc) > 0:
+ n_loc = X_loc.shape[1]
+ else:
+ n_loc = 0
+ if constant:
+ n_vars = n_glob + n_loc + 1
+ else:
+ n_vars = n_glob + n_loc
+ n = np.array(coords).shape[0]
+
+ if self.int_score:
+ a = 40 + 2 * n_vars
+ c = n
+ else:
+ nn = 40 + 2 * n_vars
+ sq_dists = squareform(pdist(coords))
+ sort_dists = np.sort(sq_dists, axis=1)
+ min_dists = sort_dists[:,nn-1]
+ max_dists = sort_dists[:,-1]
+ a = np.min(min_dists)/2.0
+ c = np.max(max_dists)/2.0
+
+ if a < self.bw_min:
+ a = self.bw_min
+ if c > self.bw_max and self.bw_max > 0:
+ c = self.bw_max
+ return a, c
diff --git a/pysal/contrib/gwr/tests/test_gwr.py b/pysal/contrib/gwr/tests/test_gwr.py
new file mode 100644
index 0000000..ed64af3
--- /dev/null
+++ b/pysal/contrib/gwr/tests/test_gwr.py
@@ -0,0 +1,849 @@
+"""
+GWR is tested against results from GWR4
+"""
+
+import unittest
+import pickle as pk
+from pysal.contrib.gwr.gwr import GWR
+from pysal.contrib.gwr.sel_bw import Sel_BW
+from pysal.contrib.gwr.diagnostics import get_AICc, get_AIC, get_BIC, get_CV
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial
+import numpy as np
+import pysal
+
+class TestGWRGaussian(unittest.TestCase):
+ def setUp(self):
+ data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+ self.coords = zip(data.by_col('X'), data.by_col('Y'))
+ self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
+ rural = np.array(data.by_col('PctRural')).reshape((-1,1))
+ pov = np.array(data.by_col('PctPov')).reshape((-1,1))
+ black = np.array(data.by_col('PctBlack')).reshape((-1,1))
+ self.X = np.hstack([rural, pov, black])
+ self.BS_F = pysal.open(pysal.examples.get_path('georgia_BS_F_listwise.csv'))
+ self.BS_NN = pysal.open(pysal.examples.get_path('georgia_BS_NN_listwise.csv'))
+ self.GS_F = pysal.open(pysal.examples.get_path('georgia_GS_F_listwise.csv'))
+ self.GS_NN = pysal.open(pysal.examples.get_path('georgia_GS_NN_listwise.csv'))
+ self.MGWR = pk.load(open(pysal.examples.get_path('FB.p'), 'r'))
+ self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r'))
+ self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r'))
+
+ def test_BS_F(self):
+ est_Int = self.BS_F.by_col(' est_Intercept')
+ se_Int = self.BS_F.by_col(' se_Intercept')
+ t_Int = self.BS_F.by_col(' t_Intercept')
+ est_rural = self.BS_F.by_col(' est_PctRural')
+ se_rural = self.BS_F.by_col(' se_PctRural')
+ t_rural = self.BS_F.by_col(' t_PctRural')
+ est_pov = self.BS_F.by_col(' est_PctPov')
+ se_pov = self.BS_F.by_col(' se_PctPov')
+ t_pov = self.BS_F.by_col(' t_PctPov')
+ est_black = self.BS_F.by_col(' est_PctBlack')
+ se_black = self.BS_F.by_col(' se_PctBlack')
+ t_black = self.BS_F.by_col(' t_PctBlack')
+ yhat = self.BS_F.by_col(' yhat')
+ res = np.array(self.BS_F.by_col(' residual'))
+ std_res = np.array(self.BS_F.by_col(' std_residual')).reshape((-1,1))
+ localR2 = np.array(self.BS_F.by_col(' localR2')).reshape((-1,1))
+ inf = np.array(self.BS_F.by_col(' influence')).reshape((-1,1))
+ cooksD = np.array(self.BS_F.by_col(' CooksD')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=209267.689, fixed=True)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+ CV = get_CV(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 894.0)
+ self.assertAlmostEquals(np.floor(AIC), 890.0)
+ self.assertAlmostEquals(np.floor(BIC), 944.0)
+ self.assertAlmostEquals(np.round(CV,2), 18.25)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+ np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+ np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+ np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+ np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+ np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+ np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+ np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+ np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+ np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+ np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+ np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+ np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+ np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+ def test_BS_NN(self):
+ est_Int = self.BS_NN.by_col(' est_Intercept')
+ se_Int = self.BS_NN.by_col(' se_Intercept')
+ t_Int = self.BS_NN.by_col(' t_Intercept')
+ est_rural = self.BS_NN.by_col(' est_PctRural')
+ se_rural = self.BS_NN.by_col(' se_PctRural')
+ t_rural = self.BS_NN.by_col(' t_PctRural')
+ est_pov = self.BS_NN.by_col(' est_PctPov')
+ se_pov = self.BS_NN.by_col(' se_PctPov')
+ t_pov = self.BS_NN.by_col(' t_PctPov')
+ est_black = self.BS_NN.by_col(' est_PctBlack')
+ se_black = self.BS_NN.by_col(' se_PctBlack')
+ t_black = self.BS_NN.by_col(' t_PctBlack')
+ yhat = self.BS_NN.by_col(' yhat')
+ res = np.array(self.BS_NN.by_col(' residual'))
+ std_res = np.array(self.BS_NN.by_col(' std_residual')).reshape((-1,1))
+ localR2 = np.array(self.BS_NN.by_col(' localR2')).reshape((-1,1))
+ inf = np.array(self.BS_NN.by_col(' influence')).reshape((-1,1))
+ cooksD = np.array(self.BS_NN.by_col(' CooksD')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=90.000, fixed=False)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+ CV = get_CV(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 896.0)
+ self.assertAlmostEquals(np.floor(AIC), 892.0)
+ self.assertAlmostEquals(np.floor(BIC), 941.0)
+ self.assertAlmostEquals(np.around(CV, 2), 19.19)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+ np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+ np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+ np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+ np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+ np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+ np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+ np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+ np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+ np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+ np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+ np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+ np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+ np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+ def test_GS_F(self):
+ est_Int = self.GS_F.by_col(' est_Intercept')
+ se_Int = self.GS_F.by_col(' se_Intercept')
+ t_Int = self.GS_F.by_col(' t_Intercept')
+ est_rural = self.GS_F.by_col(' est_PctRural')
+ se_rural = self.GS_F.by_col(' se_PctRural')
+ t_rural = self.GS_F.by_col(' t_PctRural')
+ est_pov = self.GS_F.by_col(' est_PctPov')
+ se_pov = self.GS_F.by_col(' se_PctPov')
+ t_pov = self.GS_F.by_col(' t_PctPov')
+ est_black = self.GS_F.by_col(' est_PctBlack')
+ se_black = self.GS_F.by_col(' se_PctBlack')
+ t_black = self.GS_F.by_col(' t_PctBlack')
+ yhat = self.GS_F.by_col(' yhat')
+ res = np.array(self.GS_F.by_col(' residual'))
+ std_res = np.array(self.GS_F.by_col(' std_residual')).reshape((-1,1))
+ localR2 = np.array(self.GS_F.by_col(' localR2')).reshape((-1,1))
+ inf = np.array(self.GS_F.by_col(' influence')).reshape((-1,1))
+ cooksD = np.array(self.GS_F.by_col(' CooksD')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=87308.298,
+ kernel='gaussian', fixed=True)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+ CV = get_CV(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 895.0)
+ self.assertAlmostEquals(np.floor(AIC), 890.0)
+ self.assertAlmostEquals(np.floor(BIC), 943.0)
+ self.assertAlmostEquals(np.around(CV, 2), 18.21)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+ np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+ np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+ np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+ np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+ np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+ np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+ np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+ np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+ np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+ np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+ np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+ np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+ np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+ def test_GS_NN(self):
+ est_Int = self.GS_NN.by_col(' est_Intercept')
+ se_Int = self.GS_NN.by_col(' se_Intercept')
+ t_Int = self.GS_NN.by_col(' t_Intercept')
+ est_rural = self.GS_NN.by_col(' est_PctRural')
+ se_rural = self.GS_NN.by_col(' se_PctRural')
+ t_rural = self.GS_NN.by_col(' t_PctRural')
+ est_pov = self.GS_NN.by_col(' est_PctPov')
+ se_pov = self.GS_NN.by_col(' se_PctPov')
+ t_pov = self.GS_NN.by_col(' t_PctPov')
+ est_black = self.GS_NN.by_col(' est_PctBlack')
+ se_black = self.GS_NN.by_col(' se_PctBlack')
+ t_black = self.GS_NN.by_col(' t_PctBlack')
+ yhat = self.GS_NN.by_col(' yhat')
+ res = np.array(self.GS_NN.by_col(' residual'))
+ std_res = np.array(self.GS_NN.by_col(' std_residual')).reshape((-1,1))
+ localR2 = np.array(self.GS_NN.by_col(' localR2')).reshape((-1,1))
+ inf = np.array(self.GS_NN.by_col(' influence')).reshape((-1,1))
+ cooksD = np.array(self.GS_NN.by_col(' CooksD')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=49.000,
+ kernel='gaussian', fixed=False)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+ CV = get_CV(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 896)
+ self.assertAlmostEquals(np.floor(AIC), 894.0)
+ self.assertAlmostEquals(np.floor(BIC), 922.0)
+ self.assertAlmostEquals(np.around(CV, 2), 17.91)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+ np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+ np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+ np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+ np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+ np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+ np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+ np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+ np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+ np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+ np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+ np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+ np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+ np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+ def test_Prediction(self):
+ coords =np.array(self.coords)
+ index = np.arange(len(self.y))
+ test = index[-10:]
+
+ X_test = self.X[test]
+ coords_test = list(coords[test])
+
+
+ model = GWR(self.coords, self.y, self.X, 93, family=Gaussian(),
+ fixed=False, kernel='bisquare')
+ results = model.predict(coords_test, X_test)
+
+ params = np.array([22.77198, -0.10254, -0.215093, -0.01405,
+ 19.10531, -0.094177, -0.232529, 0.071913,
+ 19.743421, -0.080447, -0.30893, 0.083206,
+ 17.505759, -0.078919, -0.187955, 0.051719,
+ 27.747402, -0.165335, -0.208553, 0.004067,
+ 26.210627, -0.138398, -0.360514, 0.072199,
+ 18.034833, -0.077047, -0.260556, 0.084319,
+ 28.452802, -0.163408, -0.14097, -0.063076,
+ 22.353095, -0.103046, -0.226654, 0.002992,
+ 18.220508, -0.074034, -0.309812, 0.108636]).reshape((10,4))
+ np.testing.assert_allclose(params, results.params, rtol=1e-03)
+
+ bse = np.array([2.080166, 0.021462, 0.102954, 0.049627,
+ 2.536355, 0.022111, 0.123857, 0.051917,
+ 1.967813, 0.019716, 0.102562, 0.054918,
+ 2.463219, 0.021745, 0.110297, 0.044189,
+ 1.556056, 0.019513, 0.12764, 0.040315,
+ 1.664108, 0.020114, 0.131208, 0.041613,
+ 2.5835, 0.021481, 0.113158, 0.047243,
+ 1.709483, 0.019752, 0.116944, 0.043636,
+ 1.958233, 0.020947, 0.09974, 0.049821,
+ 2.276849, 0.020122, 0.107867, 0.047842]).reshape((10,4))
+ np.testing.assert_allclose(bse, results.bse, rtol=1e-03)
+
+ tvalues = np.array([10.947193, -4.777659, -2.089223, -0.283103,
+ 7.532584, -4.259179, -1.877395, 1.385161,
+ 10.033179, -4.080362, -3.012133, 1.515096,
+ 7.106862, -3.629311, -1.704079, 1.17042,
+ 17.831878, -8.473156, -1.633924, 0.100891,
+ 15.750552, -6.880725, -2.74765, 1.734978,
+ 6.980774, -3.586757, -2.302575, 1.784818,
+ 16.644095, -8.273001, -1.205451, -1.445501,
+ 11.414933, -4.919384, -2.272458, 0.060064,
+ 8.00251, -3.679274, -2.872176, 2.270738]).reshape((10,4))
+ np.testing.assert_allclose(tvalues, results.tvalues, rtol=1e-03)
+
+ localR2 = np.array([[ 0.53068693],
+ [ 0.59582647],
+ [ 0.59700925],
+ [ 0.45769954],
+ [ 0.54634509],
+ [ 0.5494828 ],
+ [ 0.55159604],
+ [ 0.55634237],
+ [ 0.53903842],
+ [ 0.55884954]])
+ np.testing.assert_allclose(localR2, results.localR2, rtol=1e-05)
+
+ predictions = np.array([[ 10.51695514],
+ [ 9.93321992],
+ [ 8.92473026],
+ [ 5.47350219],
+ [ 8.61756585],
+ [ 12.8141851 ],
+ [ 5.55619405],
+ [ 12.63004172],
+ [ 8.70638418],
+ [ 8.17582599]])
+ np.testing.assert_allclose(predictions, results.predictions, rtol=1e-05)
+
+class TestGWRPoisson(unittest.TestCase):
+ def setUp(self):
+ data = pysal.open(pysal.examples.get_path('Tokyomortality.csv'), mode='Ur')
+ self.coords = zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID'))
+ self.y = np.array(data.by_col('db2564')).reshape((-1,1))
+ self.off = np.array(data.by_col('eb2564')).reshape((-1,1))
+ OCC = np.array(data.by_col('OCC_TEC')).reshape((-1,1))
+ OWN = np.array(data.by_col('OWNH')).reshape((-1,1))
+ POP = np.array(data.by_col('POP65')).reshape((-1,1))
+ UNEMP = np.array(data.by_col('UNEMP')).reshape((-1,1))
+ self.X = np.hstack([OCC,OWN,POP,UNEMP])
+ self.BS_F = pysal.open(pysal.examples.get_path('tokyo_BS_F_listwise.csv'))
+ self.BS_NN = pysal.open(pysal.examples.get_path('tokyo_BS_NN_listwise.csv'))
+ self.GS_F = pysal.open(pysal.examples.get_path('tokyo_GS_F_listwise.csv'))
+ self.GS_NN = pysal.open(pysal.examples.get_path('tokyo_GS_NN_listwise.csv'))
+ self.BS_NN_OFF = pysal.open(pysal.examples.get_path('tokyo_BS_NN_OFF_listwise.csv'))
+
+ def test_BS_F(self):
+ est_Int = self.BS_F.by_col(' est_Intercept')
+ se_Int = self.BS_F.by_col(' se_Intercept')
+ t_Int = self.BS_F.by_col(' t_Intercept')
+ est_OCC = self.BS_F.by_col(' est_OCC_TEC')
+ se_OCC = self.BS_F.by_col(' se_OCC_TEC')
+ t_OCC = self.BS_F.by_col(' t_OCC_TEC')
+ est_OWN = self.BS_F.by_col(' est_OWNH')
+ se_OWN = self.BS_F.by_col(' se_OWNH')
+ t_OWN = self.BS_F.by_col(' t_OWNH')
+ est_POP = self.BS_F.by_col(' est_POP65')
+ se_POP = self.BS_F.by_col(' se_POP65')
+ t_POP = self.BS_F.by_col(' t_POP65')
+ est_UNEMP = self.BS_F.by_col(' est_UNEMP')
+ se_UNEMP = self.BS_F.by_col(' se_UNEMP')
+ t_UNEMP = self.BS_F.by_col(' t_UNEMP')
+ yhat = self.BS_F.by_col(' yhat')
+ pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=26029.625, family=Poisson(),
+ kernel='bisquare', fixed=True)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 13294.0)
+ self.assertAlmostEquals(np.floor(AIC), 13247.0)
+ self.assertAlmostEquals(np.floor(BIC), 13485.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-05)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-03)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-03)
+ np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-04)
+ np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+ np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+ np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+ np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-03)
+ np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-03)
+ np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-04)
+ np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04)
+ np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+ np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+ np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+
+ def test_BS_NN(self):
+ est_Int = self.BS_NN.by_col(' est_Intercept')
+ se_Int = self.BS_NN.by_col(' se_Intercept')
+ t_Int = self.BS_NN.by_col(' t_Intercept')
+ est_OCC = self.BS_NN.by_col(' est_OCC_TEC')
+ se_OCC = self.BS_NN.by_col(' se_OCC_TEC')
+ t_OCC = self.BS_NN.by_col(' t_OCC_TEC')
+ est_OWN = self.BS_NN.by_col(' est_OWNH')
+ se_OWN = self.BS_NN.by_col(' se_OWNH')
+ t_OWN = self.BS_NN.by_col(' t_OWNH')
+ est_POP = self.BS_NN.by_col(' est_POP65')
+ se_POP = self.BS_NN.by_col(' se_POP65')
+ t_POP = self.BS_NN.by_col(' t_POP65')
+ est_UNEMP = self.BS_NN.by_col(' est_UNEMP')
+ se_UNEMP = self.BS_NN.by_col(' se_UNEMP')
+ t_UNEMP = self.BS_NN.by_col(' t_UNEMP')
+ yhat = self.BS_NN.by_col(' yhat')
+ pdev = np.array(self.BS_NN.by_col(' localpdev')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(),
+ kernel='bisquare', fixed=False)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 13285)
+ self.assertAlmostEquals(np.floor(AIC), 13259.0)
+ self.assertAlmostEquals(np.floor(BIC), 13442.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+ np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+ np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+ np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+ np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+ np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+ np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+ np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03)
+ np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04)
+ np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+ np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+ np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+ def test_BS_NN_Offset(self):
+ est_Int = self.BS_NN_OFF.by_col(' est_Intercept')
+ se_Int = self.BS_NN_OFF.by_col(' se_Intercept')
+ t_Int = self.BS_NN_OFF.by_col(' t_Intercept')
+ est_OCC = self.BS_NN_OFF.by_col(' est_OCC_TEC')
+ se_OCC = self.BS_NN_OFF.by_col(' se_OCC_TEC')
+ t_OCC = self.BS_NN_OFF.by_col(' t_OCC_TEC')
+ est_OWN = self.BS_NN_OFF.by_col(' est_OWNH')
+ se_OWN = self.BS_NN_OFF.by_col(' se_OWNH')
+ t_OWN = self.BS_NN_OFF.by_col(' t_OWNH')
+ est_POP = self.BS_NN_OFF.by_col(' est_POP65')
+ se_POP = self.BS_NN_OFF.by_col(' se_POP65')
+ t_POP = self.BS_NN_OFF.by_col(' t_POP65')
+ est_UNEMP = self.BS_NN_OFF.by_col(' est_UNEMP')
+ se_UNEMP = self.BS_NN_OFF.by_col(' se_UNEMP')
+ t_UNEMP = self.BS_NN_OFF.by_col(' t_UNEMP')
+ yhat = self.BS_NN_OFF.by_col(' yhat')
+ pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(),
+ kernel='bisquare', fixed=False)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 367.0)
+ self.assertAlmostEquals(np.floor(AIC), 361.0)
+ self.assertAlmostEquals(np.floor(BIC), 451.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02,
+ atol=1e-02)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-01,
+ atol=1e-02)
+ np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03,
+ atol=1e-02)
+ np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02, atol=1e-02)
+ np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-01,
+ atol=1e-02)
+ np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04,
+ atol=1e-02)
+ np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02, atol=1e-02)
+ np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-01,
+ atol=1e-02)
+ np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03,
+ atol=1e-02)
+ np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02, atol=1e-02)
+ np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-01,
+ atol=1e-02)
+ np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04,
+ atol=1e-02)
+ np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02,
+ atol=1e-02)
+ np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-01,
+ atol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-03, atol=1e-02)
+ np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-04, atol=1e-02)
+
+ def test_GS_F(self):
+ est_Int = self.GS_F.by_col(' est_Intercept')
+ se_Int = self.GS_F.by_col(' se_Intercept')
+ t_Int = self.GS_F.by_col(' t_Intercept')
+ est_OCC = self.GS_F.by_col(' est_OCC_TEC')
+ se_OCC = self.GS_F.by_col(' se_OCC_TEC')
+ t_OCC = self.GS_F.by_col(' t_OCC_TEC')
+ est_OWN = self.GS_F.by_col(' est_OWNH')
+ se_OWN = self.GS_F.by_col(' se_OWNH')
+ t_OWN = self.GS_F.by_col(' t_OWNH')
+ est_POP = self.GS_F.by_col(' est_POP65')
+ se_POP = self.GS_F.by_col(' se_POP65')
+ t_POP = self.GS_F.by_col(' t_POP65')
+ est_UNEMP = self.GS_F.by_col(' est_UNEMP')
+ se_UNEMP = self.GS_F.by_col(' se_UNEMP')
+ t_UNEMP = self.GS_F.by_col(' t_UNEMP')
+ yhat = self.GS_F.by_col(' yhat')
+ pdev = np.array(self.GS_F.by_col(' localpdev')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=8764.474, family=Poisson(),
+ kernel='gaussian', fixed=True)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 11283.0)
+ self.assertAlmostEquals(np.floor(AIC), 11211.0)
+ self.assertAlmostEquals(np.floor(BIC), 11497.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-03)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+ np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+ np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+ np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+ np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-03)
+ np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+ np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+ np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02)
+ np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02)
+ np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+ np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+ np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+ def test_GS_NN(self):
+ est_Int = self.GS_NN.by_col(' est_Intercept')
+ se_Int = self.GS_NN.by_col(' se_Intercept')
+ t_Int = self.GS_NN.by_col(' t_Intercept')
+ est_OCC = self.GS_NN.by_col(' est_OCC_TEC')
+ se_OCC = self.GS_NN.by_col(' se_OCC_TEC')
+ t_OCC = self.GS_NN.by_col(' t_OCC_TEC')
+ est_OWN = self.GS_NN.by_col(' est_OWNH')
+ se_OWN = self.GS_NN.by_col(' se_OWNH')
+ t_OWN = self.GS_NN.by_col(' t_OWNH')
+ est_POP = self.GS_NN.by_col(' est_POP65')
+ se_POP = self.GS_NN.by_col(' se_POP65')
+ t_POP = self.GS_NN.by_col(' t_POP65')
+ est_UNEMP = self.GS_NN.by_col(' est_UNEMP')
+ se_UNEMP = self.GS_NN.by_col(' se_UNEMP')
+ t_UNEMP = self.GS_NN.by_col(' t_UNEMP')
+ yhat = self.GS_NN.by_col(' yhat')
+ pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(),
+ kernel='gaussian', fixed=False)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 21070.0)
+ self.assertAlmostEquals(np.floor(AIC), 21069.0)
+ self.assertAlmostEquals(np.floor(BIC), 21111.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+ np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+ np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+ np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+ np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+ np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+ np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+ np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02)
+ np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+ np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+ np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02)
+ np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+ np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+ np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+class TestGWRBinomial(unittest.TestCase):
+ def setUp(self):
+ data = pysal.open(pysal.examples.get_path('landslides.csv'))
+ self.coords = zip(data.by_col('X'), data.by_col('Y'))
+ self.y = np.array(data.by_col('Landslid')).reshape((-1,1))
+ ELEV = np.array(data.by_col('Elev')).reshape((-1,1))
+ SLOPE = np.array(data.by_col('Slope')).reshape((-1,1))
+ SIN = np.array(data.by_col('SinAspct')).reshape((-1,1))
+ COS = np.array(data.by_col('CosAspct')).reshape((-1,1))
+ SOUTH = np.array(data.by_col('AbsSouth')).reshape((-1,1))
+ DIST = np.array(data.by_col('DistStrm')).reshape((-1,1))
+ self.X = np.hstack([ELEV, SLOPE, SIN, COS, SOUTH, DIST])
+ self.BS_F = pysal.open(pysal.examples.get_path('clearwater_BS_F_listwise.csv'))
+ self.BS_NN = pysal.open(pysal.examples.get_path('clearwater_BS_NN_listwise.csv'))
+ self.GS_F = pysal.open(pysal.examples.get_path('clearwater_GS_F_listwise.csv'))
+ self.GS_NN = pysal.open(pysal.examples.get_path('clearwater_GS_NN_listwise.csv'))
+
+ def test_BS_F(self):
+ est_Int = self.BS_F.by_col(' est_Intercept')
+ se_Int = self.BS_F.by_col(' se_Intercept')
+ t_Int = self.BS_F.by_col(' t_Intercept')
+ est_elev = self.BS_F.by_col(' est_Elev')
+ se_elev = self.BS_F.by_col(' se_Elev')
+ t_elev = self.BS_F.by_col(' t_Elev')
+ est_slope = self.BS_F.by_col(' est_Slope')
+ se_slope = self.BS_F.by_col(' se_Slope')
+ t_slope = self.BS_F.by_col(' t_Slope')
+ est_sin = self.BS_F.by_col(' est_SinAspct')
+ se_sin = self.BS_F.by_col(' se_SinAspct')
+ t_sin = self.BS_F.by_col(' t_SinAspct')
+ est_cos = self.BS_F.by_col(' est_CosAspct')
+ se_cos = self.BS_F.by_col(' se_CosAspct')
+ t_cos = self.BS_F.by_col(' t_CosAspct')
+ est_south = self.BS_F.by_col(' est_AbsSouth')
+ se_south = self.BS_F.by_col(' se_AbsSouth')
+ t_south = self.BS_F.by_col(' t_AbsSouth')
+ est_strm = self.BS_F.by_col(' est_DistStrm')
+ se_strm = self.BS_F.by_col(' se_DistStrm')
+ t_strm = self.BS_F.by_col(' t_DistStrm')
+ yhat = self.BS_F.by_col(' yhat')
+ pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1))
+
+ model = GWR(self.coords, self.y, self.X, bw=19642.170, family=Binomial(),
+ kernel='bisquare', fixed=True)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 275.0)
+ self.assertAlmostEquals(np.floor(AIC), 271.0)
+ self.assertAlmostEquals(np.floor(BIC), 349.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+ np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+ np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+ np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+ np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+ np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+ np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+ np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+ np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+ np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+ np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+ np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+ np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+ np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+ np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+ np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+ np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+ np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+ np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+ #This test fails - likely due to compound rounding errors
+ #Has been tested using statsmodels.family calculations and
+ #code from Jing's python version, which both yield the same
+ #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+ def test_BS_NN(self):
+ est_Int = self.BS_NN.by_col(' est_Intercept')
+ se_Int = self.BS_NN.by_col(' se_Intercept')
+ t_Int = self.BS_NN.by_col(' t_Intercept')
+ est_elev = self.BS_NN.by_col(' est_Elev')
+ se_elev = self.BS_NN.by_col(' se_Elev')
+ t_elev = self.BS_NN.by_col(' t_Elev')
+ est_slope = self.BS_NN.by_col(' est_Slope')
+ se_slope = self.BS_NN.by_col(' se_Slope')
+ t_slope = self.BS_NN.by_col(' t_Slope')
+ est_sin = self.BS_NN.by_col(' est_SinAspct')
+ se_sin = self.BS_NN.by_col(' se_SinAspct')
+ t_sin = self.BS_NN.by_col(' t_SinAspct')
+ est_cos = self.BS_NN.by_col(' est_CosAspct')
+ se_cos = self.BS_NN.by_col(' se_CosAspct')
+ t_cos = self.BS_NN.by_col(' t_CosAspct')
+ est_south = self.BS_NN.by_col(' est_AbsSouth')
+ se_south = self.BS_NN.by_col(' se_AbsSouth')
+ t_south = self.BS_NN.by_col(' t_AbsSouth')
+ est_strm = self.BS_NN.by_col(' est_DistStrm')
+ se_strm = self.BS_NN.by_col(' se_DistStrm')
+ t_strm = self.BS_NN.by_col(' t_DistStrm')
+ yhat = self.BS_NN.by_col(' yhat')
+ pdev = self.BS_NN.by_col(' localpdev')
+
+ model = GWR(self.coords, self.y, self.X, bw=158, family=Binomial(),
+ kernel='bisquare', fixed=False)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 277.0)
+ self.assertAlmostEquals(np.floor(AIC), 271.0)
+ self.assertAlmostEquals(np.floor(BIC), 358.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+ np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+ np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+ np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+ np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+ np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+ np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+ np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+ np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+ np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+ np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+ np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+ np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+ np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+ np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+ np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+ np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e03)
+ np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+ np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e03)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+ #This test fails - likely due to compound rounding errors
+ #Has been tested using statsmodels.family calculations and
+ #code from Jing's python version, which both yield the same
+ #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+ def test_GS_F(self):
+ est_Int = self.GS_F.by_col(' est_Intercept')
+ se_Int = self.GS_F.by_col(' se_Intercept')
+ t_Int = self.GS_F.by_col(' t_Intercept')
+ est_elev = self.GS_F.by_col(' est_Elev')
+ se_elev = self.GS_F.by_col(' se_Elev')
+ t_elev = self.GS_F.by_col(' t_Elev')
+ est_slope = self.GS_F.by_col(' est_Slope')
+ se_slope = self.GS_F.by_col(' se_Slope')
+ t_slope = self.GS_F.by_col(' t_Slope')
+ est_sin = self.GS_F.by_col(' est_SinAspct')
+ se_sin = self.GS_F.by_col(' se_SinAspct')
+ t_sin = self.GS_F.by_col(' t_SinAspct')
+ est_cos = self.GS_F.by_col(' est_CosAspct')
+ se_cos = self.GS_F.by_col(' se_CosAspct')
+ t_cos = self.GS_F.by_col(' t_CosAspct')
+ est_south = self.GS_F.by_col(' est_AbsSouth')
+ se_south = self.GS_F.by_col(' se_AbsSouth')
+ t_south = self.GS_F.by_col(' t_AbsSouth')
+ est_strm = self.GS_F.by_col(' est_DistStrm')
+ se_strm = self.GS_F.by_col(' se_DistStrm')
+ t_strm = self.GS_F.by_col(' t_DistStrm')
+ yhat = self.GS_F.by_col(' yhat')
+ pdev = self.GS_F.by_col(' localpdev')
+
+ model = GWR(self.coords, self.y, self.X, bw=8929.061, family=Binomial(),
+ kernel='gaussian', fixed=True)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 276.0)
+ self.assertAlmostEquals(np.floor(AIC), 272.0)
+ self.assertAlmostEquals(np.floor(BIC), 341.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+ np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+ np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+ np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+ np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+ np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+ np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+ np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+ np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+ np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+ np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+ np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+ np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+ np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+ np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+ np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+ np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+ np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+ np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+ #This test fails - likely due to compound rounding errors
+ #Has been tested using statsmodels.family calculations and
+ #code from Jing's python version, which both yield the same
+ #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+ def test_GS_NN(self):
+ est_Int = self.GS_NN.by_col(' est_Intercept')
+ se_Int = self.GS_NN.by_col(' se_Intercept')
+ t_Int = self.GS_NN.by_col(' t_Intercept')
+ est_elev = self.GS_NN.by_col(' est_Elev')
+ se_elev = self.GS_NN.by_col(' se_Elev')
+ t_elev = self.GS_NN.by_col(' t_Elev')
+ est_slope = self.GS_NN.by_col(' est_Slope')
+ se_slope = self.GS_NN.by_col(' se_Slope')
+ t_slope = self.GS_NN.by_col(' t_Slope')
+ est_sin = self.GS_NN.by_col(' est_SinAspct')
+ se_sin = self.GS_NN.by_col(' se_SinAspct')
+ t_sin = self.GS_NN.by_col(' t_SinAspct')
+ est_cos = self.GS_NN.by_col(' est_CosAspct')
+ se_cos = self.GS_NN.by_col(' se_CosAspct')
+ t_cos = self.GS_NN.by_col(' t_CosAspct')
+ est_south = self.GS_NN.by_col(' est_AbsSouth')
+ se_south = self.GS_NN.by_col(' se_AbsSouth')
+ t_south = self.GS_NN.by_col(' t_AbsSouth')
+ est_strm = self.GS_NN.by_col(' est_DistStrm')
+ se_strm = self.GS_NN.by_col(' se_DistStrm')
+ t_strm = self.GS_NN.by_col(' t_DistStrm')
+ yhat = self.GS_NN.by_col(' yhat')
+ pdev = self.GS_NN.by_col(' localpdev')
+
+ model = GWR(self.coords, self.y, self.X, bw=64, family=Binomial(),
+ kernel='gaussian', fixed=False)
+ rslt = model.fit()
+
+ AICc = get_AICc(rslt)
+ AIC = get_AIC(rslt)
+ BIC = get_BIC(rslt)
+
+ self.assertAlmostEquals(np.floor(AICc), 276.0)
+ self.assertAlmostEquals(np.floor(AIC), 273.0)
+ self.assertAlmostEquals(np.floor(BIC), 331.0)
+ np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+ np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+ np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+ np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+ np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+ np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+ np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+ np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+ np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+ np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+ np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+ np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+ np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+ np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+ np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+ np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+ np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+ np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+ np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+ np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+ np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+ np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-00)
+ #This test fails - likely due to compound rounding errors
+ #Has been tested using statsmodels.family calculations and
+ #code from Jing's python version, which both yield the same
+ #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/contrib/gwr/tests/test_kernels.py b/pysal/contrib/gwr/tests/test_kernels.py
new file mode 100644
index 0000000..ea044b9
--- /dev/null
+++ b/pysal/contrib/gwr/tests/test_kernels.py
@@ -0,0 +1,84 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.contrib.gwr.kernels import *
+
+PEGP = pysal.examples.get_path
+
+class TestKernels(unittest.TestCase):
+ def setUp(self):
+ np.random.seed(1234)
+ x = np.arange(1,6)
+ y = np.arange(5,0, -1)
+ np.random.shuffle(x)
+ np.random.shuffle(y)
+ self.coords = np.array(zip(x, y))
+ self.fix_gauss_kern = np.array([
+ [ 1. , 0.38889556, 0.48567179, 0.48567179, 0.89483932],
+ [ 0.38889556, 1. , 0.89483932, 0.64118039, 0.48567179],
+ [ 0.48567179, 0.89483932, 1. , 0.89483932, 0.48567179],
+ [ 0.48567179, 0.64118039, 0.89483932, 1. , 0.38889556],
+ [ 0.89483932, 0.48567179, 0.48567179, 0.38889556, 1. ]])
+ self.adapt_gauss_kern = np.array([
+ [ 1. , 0.52004183, 0.60653072, 0.60653072, 0.92596109],
+ [ 0.34559083, 1. , 0.88249692, 0.60653072, 0.44374738],
+ [ 0.03877423, 0.60653072, 1. , 0.60653072, 0.03877423],
+ [ 0.44374738, 0.60653072, 0.88249692, 1. , 0.34559083],
+ [ 0.92596109, 0.60653072, 0.60653072, 0.52004183, 1. ]])
+ self.fix_bisquare_kern = np.array([
+ [ 1. , 0. , 0. , 0. , 0.60493827],
+ [ 0. , 1. , 0.60493827, 0.01234568, 0. ],
+ [ 0. , 0.60493827, 1. , 0.60493827, 0. ],
+ [ 0. , 0.01234568, 0.60493827, 1. , 0. ],
+ [ 0.60493827, 0. , 0. , 0. , 1. ]])
+ self.adapt_bisquare_kern = np.array([
+ [ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+ 3.99999881e-14, 7.15976383e-01],
+ [ 0.00000000e+00, 1.00000000e+00, 5.62500075e-01,
+ 3.99999881e-14, 0.00000000e+00],
+ [ 0.00000000e+00, 3.99999881e-14, 1.00000000e+00,
+ 3.99999881e-14, 0.00000000e+00],
+ [ 0.00000000e+00, 3.99999881e-14, 5.62500075e-01,
+ 1.00000000e+00, 0.00000000e+00],
+ [ 7.15976383e-01, 0.00000000e+00, 3.99999881e-14,
+ 0.00000000e+00, 1.00000000e+00]])
+ self.fix_exp_kern = np.array([
+ [ 1. , 0.2529993 , 0.30063739, 0.30063739, 0.62412506],
+ [ 0.2529993 , 1. , 0.62412506, 0.38953209, 0.30063739],
+ [ 0.30063739, 0.62412506, 1. , 0.62412506, 0.30063739],
+ [ 0.30063739, 0.38953209, 0.62412506, 1. , 0.2529993 ],
+ [ 0.62412506, 0.30063739, 0.30063739, 0.2529993 , 1. ]])
+ self.adapt_exp_kern = np.array([
+ [ 1. , 0.31868771, 0.36787948, 0.36787948, 0.67554721],
+ [ 0.23276223, 1. , 0.60653069, 0.36787948, 0.27949951],
+ [ 0.07811997, 0.36787948, 1. , 0.36787948, 0.07811997],
+ [ 0.27949951, 0.36787948, 0.60653069, 1. , 0.23276223],
+ [ 0.67554721, 0.36787948, 0.36787948, 0.31868771, 1. ]])
+
+ def test_fix_gauss(self):
+ kern = fix_gauss(self.coords, 3)
+ np.testing.assert_allclose(kern, self.fix_gauss_kern)
+
+ def test_adapt_gauss(self):
+ kern = adapt_gauss(self.coords, 3)
+ np.testing.assert_allclose(kern, self.adapt_gauss_kern)
+
+ def test_fix_biqsquare(self):
+ kern = fix_bisquare(self.coords, 3)
+ np.testing.assert_allclose(kern, self.fix_bisquare_kern,
+ atol=1e-01)
+
+ def test_adapt_bisqaure(self):
+ kern = adapt_bisquare(self.coords, 3)
+ np.testing.assert_allclose(kern, self.adapt_bisquare_kern, atol=1e-012)
+
+ def test_fix_exp(self):
+ kern = fix_exp(self.coords, 3)
+ np.testing.assert_allclose(kern, self.fix_exp_kern)
+
+ def test_adapt_exp(self):
+ kern = adapt_exp(self.coords, 3)
+ np.testing.assert_allclose(kern, self.adapt_exp_kern)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/contrib/gwr/tests/test_sel_bw.py b/pysal/contrib/gwr/tests/test_sel_bw.py
new file mode 100644
index 0000000..f34fb3d
--- /dev/null
+++ b/pysal/contrib/gwr/tests/test_sel_bw.py
@@ -0,0 +1,130 @@
+
+"""
+GWR is tested against results from GWR4
+"""
+
+import unittest
+import pickle as pk
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial
+from pysal.contrib.gwr.sel_bw import Sel_BW
+import numpy as np
+import pysal
+
+class TestSelBW(unittest.TestCase):
+ def setUp(self):
+ data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+ self.coords = zip(data.by_col('X'), data.by_col('Y'))
+ self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
+ rural = np.array(data.by_col('PctRural')).reshape((-1,1))
+ pov = np.array(data.by_col('PctPov')).reshape((-1,1))
+ black = np.array(data.by_col('PctBlack')).reshape((-1,1))
+ self.X = np.hstack([rural, pov, black])
+ self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r'))
+ self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r'))
+
+ def test_golden_fixed_AICc(self):
+ bw1 = 211027.34
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+ fixed=True).search(criterion='AICc')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_golden_adapt_AICc(self):
+ bw1 = 93.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+ fixed=False).search(criterion='AICc')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_golden_fixed_AIC(self):
+ bw1 = 76169.15
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=True).search(criterion='AIC')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_golden_adapt_AIC(self):
+ bw1 = 50.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=False).search(criterion='AIC')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_golden_fixed_BIC(self):
+ bw1 = 279451.43
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=True).search(criterion='BIC')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_golden_adapt_BIC(self):
+ bw1 = 62.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=False).search(criterion='BIC')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_golden_fixed_CV(self):
+ bw1 = 130406.67
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=True).search(criterion='CV')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_golden_adapt_CV(self):
+ bw1 = 68.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=False).search(criterion='CV')
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_fixed_AICc(self):
+ bw1 = 211025.0#211027.00
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+ fixed=True).search(criterion='AICc', search='interval',
+ bw_min=211001.0, bw_max=211035.0, interval=2)
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_adapt_AICc(self):
+ bw1 = 93.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+ fixed=False).search(criterion='AICc', search='interval',
+ bw_min=90.0, bw_max=95.0, interval=1)
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_fixed_AIC(self):
+ bw1 = 76175.0#76169.00
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=True).search(criterion='AIC', search='interval',
+ bw_min=76161.0, bw_max=76175.0, interval=1)
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_adapt_AIC(self):
+ bw1 = 40.0#50.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=False).search(criterion='AIC', search='interval', bw_min=40.0,
+ bw_max=60.0, interval=2)
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_fixed_BIC(self):
+ bw1 = 279461.0#279451.00
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=True).search(criterion='BIC', search='interval', bw_min=279441.0,
+ bw_max=279461.0, interval=2)
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_adapt_BIC(self):
+ bw1 = 62.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=False).search(criterion='BIC', search='interval',
+ bw_min=52.0, bw_max=72.0, interval=2)
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_fixed_CV(self):
+ bw1 = 130400.0#130406.00
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=True).search(criterion='CV', search='interval', bw_min=130400.0,
+ bw_max=130410.0, interval=1)
+ self.assertAlmostEqual(bw1, bw2)
+
+ def test_interval_adapt_CV(self):
+ bw1 = 62.0#68.0
+ bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+ fixed=False).search(criterion='CV', search='interval', bw_min=60.0,
+ bw_max=76.0 , interval=2)
+ self.assertAlmostEqual(bw1, bw2)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/pysal/contrib/network/klincs.py b/pysal/contrib/network/klincs.py
index 472e749..bd7d4ad 100644
--- a/pysal/contrib/network/klincs.py
+++ b/pysal/contrib/network/klincs.py
@@ -1,4 +1,4 @@
-#!/usr/env python
+#!/usr/bin/env python
"""
A library for computing local K function for network-constrained data
diff --git a/pysal/contrib/spint/gravity.py b/pysal/contrib/spint/gravity.py
index 111722a..2bef036 100644
--- a/pysal/contrib/spint/gravity.py
+++ b/pysal/contrib/spint/gravity.py
@@ -466,6 +466,7 @@ class Gravity(BaseGravity):
results['SRMSE'] = []
for cov in range(covs):
results['param' + str(cov)] = []
+ results['stde' + str(cov)] = []
results['pvalue' + str(cov)] = []
results['tvalue' + str(cov)] = []
for loc in locs:
@@ -486,6 +487,7 @@ class Gravity(BaseGravity):
results['SRMSE'].append(model.SRMSE)
for cov in range(covs):
results['param' + str(cov)].append(model.params[cov])
+ results['stde' + str(cov)].append(model.std_err[cov])
results['pvalue' + str(cov)].append(model.pvalues[cov])
results['tvalue' + str(cov)].append(model.tvalues[cov])
return results
@@ -651,6 +653,7 @@ class Production(BaseGravity):
and values are lists of location specific values
"""
results = {}
+ offset = 1
covs = self.dv.shape[1] + 1
results['AIC'] = []
results['deviance'] = []
@@ -662,6 +665,7 @@ class Production(BaseGravity):
results['SRMSE'] = []
for cov in range(covs):
results['param' + str(cov)] = []
+ results['stde' + str(cov)] = []
results['pvalue' + str(cov)] = []
results['tvalue' + str(cov)] = []
if locs is None:
@@ -682,9 +686,10 @@ class Production(BaseGravity):
results['SSI'].append(model.SSI)
results['SRMSE'].append(model.SRMSE)
for cov in range(covs):
- results['param' + str(cov)].append(model.params[cov])
- results['pvalue' + str(cov)].append(model.pvalues[cov])
- results['tvalue' + str(cov)].append(model.tvalues[cov])
+ results['param' + str(cov)].append(model.params[offset+cov])
+ results['stde' + str(cov)].append(model.std_err[offset+cov])
+ results['pvalue' + str(cov)].append(model.pvalues[offset+cov])
+ results['tvalue' + str(cov)].append(model.tvalues[offset+cov])
return results
class Attraction(BaseGravity):
@@ -846,6 +851,7 @@ class Attraction(BaseGravity):
and values are lists of location specific values
"""
results = {}
+ offset = 1
covs = self.ov.shape[1] + 1
results['AIC'] = []
results['deviance'] = []
@@ -857,6 +863,7 @@ class Attraction(BaseGravity):
results['SRMSE'] = []
for cov in range(covs):
results['param' + str(cov)] = []
+ results['stde' + str(cov)] = []
results['pvalue' + str(cov)] = []
results['tvalue' + str(cov)] = []
if locs is None:
@@ -877,9 +884,10 @@ class Attraction(BaseGravity):
results['SSI'].append(model.SSI)
results['SRMSE'].append(model.SRMSE)
for cov in range(covs):
- results['param' + str(cov)].append(model.params[cov])
- results['pvalue' + str(cov)].append(model.pvalues[cov])
- results['tvalue' + str(cov)].append(model.tvalues[cov])
+ results['param' + str(cov)].append(model.params[offset+cov])
+ results['stde' + str(cov)].append(model.std_err[offset+cov])
+ results['pvalue' + str(cov)].append(model.pvalues[offset+cov])
+ results['tvalue' + str(cov)].append(model.tvalues[offset+cov])
return results
class Doubly(BaseGravity):
diff --git a/pysal/contrib/spint/tests/test_gravity.py b/pysal/contrib/spint/tests/test_gravity.py
index c37fd42..773878c 100644
--- a/pysal/contrib/spint/tests/test_gravity.py
+++ b/pysal/contrib/spint/tests/test_gravity.py
@@ -249,7 +249,10 @@ class TestGravity(unittest.TestCase):
def test_local_Gravity(self):
model = grav.Gravity(self.f, self.o_var, self.d_var, self.dij, 'exp')
local = model.local(loc_index=self.o, locs=np.unique(self.o))
- self.assertEqual(local.keys().sort(), ['pvalue2',
+ self.assertEqual(local.keys().sort(), ['stde0',
+ 'stde1',
+ 'stde2',
+ 'pvalue2',
'SRMSE',
'pvalue0',
'deviance',
@@ -372,7 +375,10 @@ class TestGravity(unittest.TestCase):
def test_local_Production(self):
model = grav.Production(self.f, self.o, self.d_var, self.dij, 'exp')
local = model.local(locs=np.unique(self.o))
- self.assertEqual(local.keys().sort(), ['pvalue2',
+ self.assertEqual(local.keys().sort(), ['stde0',
+ 'stde1',
+ 'stde2',
+ 'pvalue2',
'SRMSE',
'pvalue0',
'deviance',
@@ -495,7 +501,10 @@ class TestGravity(unittest.TestCase):
def test_local_Attraction(self):
model = grav.Attraction(self.f, self.d, self.o_var, self.dij, 'exp')
local = model.local(locs=np.unique(self.d))
- self.assertEqual(local.keys().sort(), ['pvalue2',
+ self.assertEqual(local.keys().sort(), ['stde0',
+ 'stde1',
+ 'stde2',
+ 'pvalue2',
'SRMSE',
'pvalue0',
'deviance',
diff --git a/pysal/contrib/viz/mapping.py b/pysal/contrib/viz/mapping.py
index b030685..3f857d4 100644
--- a/pysal/contrib/viz/mapping.py
+++ b/pysal/contrib/viz/mapping.py
@@ -626,9 +626,13 @@ def geoplot(db, col=None, palette='BuGn', classi='Quantiles',
except KeyError:
pass
col = [(col, db[col])]
- if tips:
- for tip in tips:
- col.append((tip, db[tip]))
+
+ if tips:
+ col = col or []
+ for tip in tips:
+ col.append((tip, db[tip]))
+
+ if col or tips:
col.append(('index', db.index.values))
col = collections.OrderedDict(col) # put mapped variable at the top
diff --git a/pysal/core/IOHandlers/arcgis_swm.py b/pysal/core/IOHandlers/arcgis_swm.py
index 1fd96c9..60cac74 100644
--- a/pysal/core/IOHandlers/arcgis_swm.py
+++ b/pysal/core/IOHandlers/arcgis_swm.py
@@ -29,7 +29,6 @@ class ArcGISSwmIO(FileIO.FileIO):
The specifics of each part of the above structure is as follows.
.. table:: ArcGIS SWM Components
-
============ ============ ==================================== ================================
Part Data type Description Length
============ ============ ==================================== ================================
@@ -53,6 +52,7 @@ class ArcGISSwmIO(FileIO.FileIO):
def __init__(self, *args, **kwargs):
self._varName = 'Unknown'
+ self._srs = "Unknow"
FileIO.FileIO.__init__(self, *args, **kwargs)
self.file = open(self.dataPath, self.mode + 'b')
@@ -62,8 +62,18 @@ class ArcGISSwmIO(FileIO.FileIO):
def _get_varName(self):
return self._varName
+
varName = property(fget=_get_varName, fset=_set_varName)
+ def _set_srs(self, val):
+ if issubclass(type(val), basestring):
+ self._srs = val
+
+ def _get_srs(self):
+ return self._srs
+
+ srs = property(fget=_get_srs, fset=_set_srs)
+
def read(self, n=-1):
self._complain_ifclosed(self.closed)
return self._read()
@@ -102,17 +112,30 @@ class ArcGISSwmIO(FileIO.FileIO):
{2: 1.0, 11: 1.0, 6: 1.0, 7: 1.0}
"""
-
if self.pos > 0:
raise StopIteration
- header01 = self.file.readline()
- header01 = header01.decode()
- id_var, srs = header01[:-1].split(';')
+ header = self.file.readline()
+ header = header.decode()
+
+ if header.upper().strip().startswith("VERSION@"):
+ # deal with the new SWM version
+ return self.read_new_version(header)
+ else:
+ # deal with the old SWM version
+ return self.read_old_version(header)
+
+ def read_old_version(self, header):
+ """
+ Read the old version of ArcGIS(<10.1) swm file
+ :param header:
+ :return:
+ """
+ id_var, srs = header[:-1].split(';')
self.varName = id_var
- self.header_len = len(header01) + 8
+ self.srs = srs
+ self.header_len = len(header) + 8
no_obs, row_std = tuple(unpack('<2l', self.file.read(8)))
-
neighbors = {}
weights = {}
for i in xrange(no_obs):
@@ -129,6 +152,48 @@ class ArcGISSwmIO(FileIO.FileIO):
self.pos += 1
return W(neighbors, weights)
+ def read_new_version(self, header_line):
+ """
+ Read the new version of ArcGIS(<10.1) swm file, which contains more parameters
+ and records weights in two ways: fixed or variable
+ :param header_line: str, the firs line of the swm file, which contains a lot of parameters.
+ The parameters are divided by ";" and the key-value of each parameter is divided by "@"
+ :return:
+ """
+ headerDict = {}
+ for item in header_line.split(";"):
+ key, value = item.split("@")
+ headerDict[key] = value
+ # for the reader, in order to generate the PySAL Weight class, only a few of the parameters are needed.
+ self.varName = headerDict["UNIQUEID"]
+ self.srs = headerDict["SPATIALREFNAME"]
+
+ fixedWeights = False
+ if "FIXEDWEIGHTS" in headerDict:
+ fixedWeights = headerDict["FIXEDWEIGHTS"].upper().strip() == 'TRUE'
+
+ no_obs, row_std = tuple(unpack('<2l', self.file.read(8)))
+ is_row_standard = row_std == 1
+
+ neighbors = {}
+ weights = {}
+ for i in xrange(no_obs):
+ origin, no_nghs = tuple(unpack('<2l', self.file.read(8)))
+ neighbors[origin] = []
+ weights[origin] = []
+ if no_nghs > 0:
+ neighbors[origin] = list(unpack('<%il' %
+ no_nghs, self.file.read(4 * no_nghs)))
+ if fixedWeights:
+ weights[origin] = list(unpack('<d', self.file.read(8))) * no_nghs
+ else:
+ weights[origin] = list(unpack('<%id' %
+ no_nghs, self.file.read(8 * no_nghs)))
+ w_sum = list(unpack('<d', self.file.read(8)))[0]
+
+ self.pos += 1
+ return W(neighbors, weights)
+
def write(self, obj, useIdIndex=False):
"""
Writes a spatial weights matrix data file in swm format.
@@ -167,6 +232,11 @@ class ArcGISSwmIO(FileIO.FileIO):
>>> o = pysal.open(fname,'w')
+ Add properities to the file to write
+
+ >>> o.varName = testfile.varName
+ >>> o.srs = testfile.srs
+
Write the Weights object into the open file
>>> o.write(w)
@@ -186,28 +256,27 @@ class ArcGISSwmIO(FileIO.FileIO):
>>> os.remove(fname) """
self._complain_ifclosed(self.closed)
- if issubclass(type(obj), W):
- if not (type(obj.id_order[0]) in (np.int32, np.int64, int)) and not useIdIndex:
- raise TypeError("ArcGIS SWM files support only integer IDs")
- if useIdIndex:
- id2i = obj.id2i
- obj = remap_ids(obj, id2i)
- unk = str('%s;Unknown\n' % self.varName).encode()
- self.file.write(unk)
- self.file.write(pack('<l', obj.n))
- self.file.write(pack('<l', obj.transform.upper() == 'R'))
- for obs in obj.weights:
- self.file.write(pack('<l', obs))
- no_nghs = len(obj.weights[obs])
- self.file.write(pack('<l', no_nghs))
- self.file.write(pack('<%il' % no_nghs, *obj.neighbors[obs]))
- self.file.write(pack('<%id' % no_nghs, *obj.weights[obs]))
- self.file.write(pack('<d', sum(obj.weights[obs])))
- self.pos += 1
-
- else:
+ if not issubclass(type(obj), W):
raise TypeError("Expected a pysal weights object, got: %s" % (
type(obj)))
+ if not (type(obj.id_order[0]) in (np.int32, np.int64, int)) and not useIdIndex:
+ raise TypeError("ArcGIS SWM files support only integer IDs")
+ if useIdIndex:
+ id2i = obj.id2i
+ obj = remap_ids(obj, id2i)
+
+ unk = str('%s;%s\n' % (self.varName, self.srs)).encode()
+ self.file.write(unk)
+ self.file.write(pack('<l', obj.n))
+ self.file.write(pack('<l', obj.transform.upper() == 'R'))
+ for obs in obj.weights:
+ self.file.write(pack('<l', obs))
+ no_nghs = len(obj.weights[obs])
+ self.file.write(pack('<l', no_nghs))
+ self.file.write(pack('<%il' % no_nghs, *obj.neighbors[obs]))
+ self.file.write(pack('<%id' % no_nghs, *obj.weights[obs]))
+ self.file.write(pack('<d', sum(obj.weights[obs])))
+ self.pos += 1
def close(self):
self.file.close()
diff --git a/pysal/core/util/shapefile.py b/pysal/core/util/shapefile.py
index ef7d113..98a020a 100644
--- a/pysal/core/util/shapefile.py
+++ b/pysal/core/util/shapefile.py
@@ -733,6 +733,7 @@ class Polygon(PolyLine):
class MultiPoint:
+ String_Type = 'MULTIPOINT'
def __init__(self):
raise NotImplementedError("No MultiPoint Support at this time.")
@@ -742,31 +743,37 @@ class PolygonZ(PolyLineZ):
class MultiPointZ:
+ String_Type = 'MULTIPOINTZ'
def __init__(self):
raise NotImplementedError("No MultiPointZ Support at this time.")
class PointM:
+ String_Type = 'POINTM'
def __init__(self):
raise NotImplementedError("No PointM Support at this time.")
class PolyLineM:
+ String_Type = 'ARCM'
def __init__(self):
raise NotImplementedError("No PolyLineM Support at this time.")
class PolygonM:
+ String_Type = 'POLYGONM'
def __init__(self):
raise NotImplementedError("No PolygonM Support at this time.")
class MultiPointM:
+ String_Type = 'MULTIPOINTM'
def __init__(self):
raise NotImplementedError("No MultiPointM Support at this time.")
class MultiPatch:
+ String_Type = 'MULTIPATCH'
def __init__(self):
raise NotImplementedError("No MultiPatch Support at this time.")
diff --git a/pysal/esda/geary.py b/pysal/esda/geary.py
index 9be01ef..bf39fc7 100644
--- a/pysal/esda/geary.py
+++ b/pysal/esda/geary.py
@@ -89,7 +89,8 @@ class Geary(object):
0.3330108
>>> print round(c.p_norm,7)
9.2e-05
- >>>
+ >>> c.z_rand
+ -3.6489513837253944
"""
def __init__(self, y, w, transformation="r", permutations=999):
if not isinstance(w, weights.W):
@@ -149,14 +150,15 @@ class Geary(object):
s1 = w.s1
s2 = w.s2
s02 = s0 * s0
-
yd = y - y.mean()
- k = (1 / (sum(yd ** 4)) * ((sum(yd ** 2)) ** 2))
- vc_rand = (1 / (n * ((n - 2) ** 2) * s02)) * \
- ((((n - 1) * s1) * (n * n - 3 * n + 3 - (n - 1) * k))
- - ((.25 * (n - 1) * s2) * (n * n + 3 * n - 6 -
- (n * n - n + 2) * k))
- + (s02 * (n * n - 3 - ((n - 1) ** 2) * k)))
+ yd4 = yd**4
+ yd2 = yd**2
+ n2 = n * n
+ k = (yd4.sum() / n) / ((yd2.sum()/n)**2)
+ A = (n-1) * s1 * (n2 - 3*n + 3 - (n-1) * k)
+ B = (1./4) * ((n-1) * s2 * (n2 + 3*n - 6 - (n2 - n +2) * k ))
+ C = s02 * (n2 - 3 - (n-1)**2 * k)
+ vc_rand = (A-B+C) / (n * (n-2) * (n-3)*s02)
vc_norm = ((1 / (2 * (n + 1) * s02)) *
((2 * s1 + s2) * (n - 1) - 4 * s02))
diff --git a/pysal/esda/mapclassify.py b/pysal/esda/mapclassify.py
index aac2bcc..5e7c46c 100644
--- a/pysal/esda/mapclassify.py
+++ b/pysal/esda/mapclassify.py
@@ -392,7 +392,7 @@ class Map_Classifier(object):
.. math::
- C_j^l < y_i \le C_j^u \ \forall i \in C_j
+ C_j^l < y_i \le C_j^u \ \ \\forall i \in C_j
where :math:`C_j` denotes class :math:`j` which has lower bound
:math:`C_j^l` and upper bound :math:`C_j^u`.
diff --git a/pysal/esda/moran.py b/pysal/esda/moran.py
index cbc5f06..6f356a6 100644
--- a/pysal/esda/moran.py
+++ b/pysal/esda/moran.py
@@ -102,7 +102,7 @@ class Moran(object):
>>> w = pysal.open(pysal.examples.get_path("stl.gal")).read()
>>> f = pysal.open(pysal.examples.get_path("stl_hom.txt"))
>>> y = np.array(f.by_col['HR8893'])
- >>> mi = Moran(y, w)
+ >>> mi = pysal.Moran(y, w)
>>> "%7.5f" % mi.I
'0.24366'
>>> mi.EI
@@ -114,6 +114,7 @@ class Moran(object):
>>> w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
>>> f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+ >>> import numpy as np
>>> SIDR = np.array(f.by_col("SIDR74"))
>>> mi = pysal.Moran(SIDR, w)
>>> "%6.4f" % mi.I
@@ -129,6 +130,15 @@ class Moran(object):
>>> mi_1.p_norm
5.7916539074498452e-05
+ Example from http://www.lpc.uottawa.ca/publications/moransi/moran.htm
+
+ >>> w = pysal.lat2W(3, 3)
+ >>> y = np.arange(1, 10)
+ >>> mi = pysal.Moran(y, w, transformation='B')
+ >>> mi.VI_rand
+ 0.059687500000000004
+ >>> mi.VI_norm
+ 0.053125000000000006
"""
def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
two_tailed=True):
@@ -179,22 +189,28 @@ class Moran(object):
self.z2ss = (z * z).sum()
self.EI = -1. / (self.n - 1)
n = self.n
+ n2 = n * n
s1 = self.w.s1
s0 = self.w.s0
s2 = self.w.s2
s02 = s0 * s0
- v_num = n * n * s1 - n * s2 + 3 * s0 * s0
- v_den = (n - 1) * (n + 1) * s0 * s0
+ v_num = n2 * s1 - n * s2 + 3 * s02
+ v_den = (n - 1) * (n + 1) * s02
self.VI_norm = v_num / v_den - (1.0 / (n - 1)) ** 2
self.seI_norm = self.VI_norm ** (1 / 2.)
- k = (1 / ((z ** 4).sum()) * (((z ** 2).sum()) ** 2))
- vi = (1 / (((n - 1) ** 3) * s02)) * ((n * ((n * n - 3 * n + 3)
- * s1 - n * s2 + 3 * s02))
- - (k * ((n * n - n) * s1 - 2 * n *
- s2 + 6 * s02)))
- self.VI_rand = vi
- self.seI_rand = vi ** (1 / 2.)
+ # variance under randomization
+ xd4 = z**4
+ xd2 = z**2
+ k_num = xd4.sum() / n
+ k_den = (xd2.sum() / n)**2
+ k = k_num / k_den
+ EI = self.EI
+ A = n * ((n2 - 3 * n + 3) * s1 - n * s2 + 3 * s02)
+ B = k * ((n2 - n) * s1 - 2 * n * s2 + 6 * s02 )
+ VIR = (A - B) / ((n - 1) * (n - 2) * (n - 3 ) * s02) - EI*EI
+ self.VI_rand = VIR
+ self.seI_rand = VIR ** (1 / 2.)
def __calc(self, z):
zl = slag(self.w, z)
diff --git a/pysal/esda/smoothing.py b/pysal/esda/smoothing.py
index 1334e24..7a27966 100644
--- a/pysal/esda/smoothing.py
+++ b/pysal/esda/smoothing.py
@@ -549,17 +549,18 @@ def assuncao_rate(e, b):
ebi_b = e_sum * 1.0 / b_sum
s2 = sum(b * ((y - ebi_b) ** 2)) / b_sum
ebi_a = s2 - ebi_b / (float(b_sum) / len(e))
- ebi_v = ebi_a + ebi_b / b
+ ebi_v_raw = ebi_a + ebi_b / b
+ ebi_v = np.where(ebi_v_raw < 0, ebi_b / b, ebi_v_raw)
return (y - ebi_b) / np.sqrt(ebi_v)
class _Smoother(object):
"""
This is a helper class that implements things that all smoothers should do.
- Right now, the only thing that we need to propagate is the by_col function.
+ Right now, the only thing that we need to propagate is the by_col function.
TBQH, most of these smoothers should be functions, not classes (aside from
maybe headbanging triples), since they're literally only inits + one
- attribute.
+ attribute.
"""
def __init__(self):
pass
@@ -567,7 +568,7 @@ class _Smoother(object):
@classmethod
def by_col(cls, df, e,b, inplace=False, **kwargs):
"""
- Compute smoothing by columns in a dataframe.
+ Compute smoothing by columns in a dataframe.
Parameters
-----------
@@ -582,15 +583,15 @@ class _Smoother(object):
inplace : bool
a flag denoting whether to output a copy of `df` with the
relevant smoothed columns appended, or to append the columns
- directly to `df` itself.
+ directly to `df` itself.
**kwargs: optional keyword arguments
optional keyword options that are passed directly to the
- smoother.
+ smoother.
Returns
---------
a copy of `df` containing the columns. Or, if `inplace`, this returns
- None, but implicitly adds columns to `df`.
+ None, but implicitly adds columns to `df`.
"""
if not inplace:
new = df.copy()
@@ -718,13 +719,13 @@ class Empirical_Bayes(_Smoother):
class _Spatial_Smoother(_Smoother):
"""
This is a helper class that implements things that all the things that
- spatial smoothers should do.
+ spatial smoothers should do.
.
- Right now, the only thing that we need to propagate is the by_col function.
+ Right now, the only thing that we need to propagate is the by_col function.
TBQH, most of these smoothers should be functions, not classes (aside from
maybe headbanging triples), since they're literally only inits + one
- attribute.
+ attribute.
"""
def __init__(self):
pass
@@ -732,7 +733,7 @@ class _Spatial_Smoother(_Smoother):
@classmethod
def by_col(cls, df, e,b, w=None, inplace=False, **kwargs):
"""
- Compute smoothing by columns in a dataframe.
+ Compute smoothing by columns in a dataframe.
Parameters
-----------
@@ -748,19 +749,19 @@ class _Spatial_Smoother(_Smoother):
the spatial weights object or objects to use with the
event-population pairs. If not provided and a weights object
is in the dataframe's metadata, that weights object will be
- used.
+ used.
inplace : bool
a flag denoting whether to output a copy of `df` with the
relevant smoothed columns appended, or to append the columns
- directly to `df` itself.
+ directly to `df` itself.
**kwargs: optional keyword arguments
optional keyword options that are passed directly to the
- smoother.
+ smoother.
Returns
---------
a copy of `df` containing the columns. Or, if `inplace`, this returns
- None, but implicitly adds columns to `df`.
+ None, but implicitly adds columns to `df`.
"""
if not inplace:
new = df.copy()
@@ -1072,12 +1073,12 @@ class Age_Adjusted_Smoother(_Spatial_Smoother):
r = direct_age_standardization(e_n, b_n, s, w.n, alpha=alpha)
self.r = np.array([i[0] for i in r])
w.transform = 'o'
-
+
@_requires('pandas')
@classmethod
def by_col(cls, df, e,b, w=None, s=None, **kwargs):
"""
- Compute smoothing by columns in a dataframe.
+ Compute smoothing by columns in a dataframe.
Parameters
-----------
@@ -1093,22 +1094,22 @@ class Age_Adjusted_Smoother(_Spatial_Smoother):
the spatial weights object or objects to use with the
event-population pairs. If not provided and a weights object
is in the dataframe's metadata, that weights object will be
- used.
+ used.
s : string or list of strings
the name or names of columns to use as a standard population
- variable for the events `e` and at-risk populations `b`.
+ variable for the events `e` and at-risk populations `b`.
inplace : bool
a flag denoting whether to output a copy of `df` with the
relevant smoothed columns appended, or to append the columns
- directly to `df` itself.
+ directly to `df` itself.
**kwargs: optional keyword arguments
optional keyword options that are passed directly to the
- smoother.
+ smoother.
Returns
---------
a copy of `df` containing the columns. Or, if `inplace`, this returns
- None, but implicitly adds columns to `df`.
+ None, but implicitly adds columns to `df`.
"""
if s is None:
raise Exception('Standard population variable "s" must be supplied.')
@@ -1326,7 +1327,7 @@ class Spatial_Median_Rate(_Spatial_Smoother):
if not w.id_order_set:
raise ValueError("w id_order must be set to align with the order of e and b")
e = np.asarray(e).flatten()
- b = np.asarray(b).flatten()
+ b = np.asarray(b).flatten()
self.r = e * 1.0 / b
self.aw, self.w = aw, w
while iteration:
@@ -1469,13 +1470,13 @@ class Spatial_Filtering(_Smoother):
b_n_f = b_n[[0]]
self.r.append(e_n_f[-1] * 1.0 / b_n_f[-1])
self.r = np.array(self.r)
-
+
@_requires('pandas')
@classmethod
def by_col(cls, df, e, b, x_grid, y_grid, geom_col='geometry', **kwargs):
"""
Compute smoothing by columns in a dataframe. The bounding box and point
- information is computed from the geometry column.
+ information is computed from the geometry column.
Parameters
-----------
@@ -1493,13 +1494,13 @@ class Spatial_Filtering(_Smoother):
number of grid cells to use along the y-axis
geom_col: string
the name of the column in the dataframe containing the
- geometry information.
+ geometry information.
**kwargs: optional keyword arguments
optional keyword options that are passed directly to the
- smoother.
+ smoother.
Returns
---------
- a new dataframe of dimension (x_grid*y_grid, 3), containing the
+ a new dataframe of dimension (x_grid*y_grid, 3), containing the
coordinates of the grid cells and the rates associated with those grid
cells.
"""
@@ -1525,13 +1526,16 @@ class Spatial_Filtering(_Smoother):
grid = np.asarray(r.grid).reshape(-1,2)
name = '_'.join(('-'.join((ename, bname)), cls.__name__.lower()))
colnames = ('_'.join((name, suffix)) for suffix in ['X', 'Y', 'R'])
- items = [(name, col) for name,col in zip(colnames, [grid[:,0],
- grid[:,1],
+ items = [(name, col) for name,col in zip(colnames, [grid[:,0],
+ grid[:,1],
r.r])]
res.append(pd.DataFrame.from_items(items))
outdf = pd.concat(res)
return outdf
+
+
+import warnings
class Headbanging_Triples(object):
"""Generate a pseudo spatial weights instance that contains headbanging triples
@@ -1661,6 +1665,7 @@ class Headbanging_Triples(object):
(0.33753, 0.302707)
"""
def __init__(self, data, w, k=5, t=3, angle=135.0, edgecor=False):
+ raise DepricationWarning('Depricated')
if k < 3:
raise ValueError("w should be NeareastNeighbors instance & the number of neighbors should be more than 3.")
if not w.id_order_set:
@@ -1806,6 +1811,7 @@ class Headbanging_Median_Rate(object):
array([ 0.00091659, 0. , 0.00156838, 0.0018315 , 0.00498891])
"""
def __init__(self, e, b, t, aw=None, iteration=1):
+ warnings.warn("Deprecated", DepricationWarning)
self.r = e * 1.0 / b
self.tr, self.aw = t.triples, aw
if hasattr(t, 'extra'):
@@ -1821,9 +1827,9 @@ class Headbanging_Median_Rate(object):
if hasattr(self, 'extra') and id in self.extra:
extra = self.extra
trp_r = r[list(triples[0])]
- # observed rate
+ # observed rate
# plus difference in rate scaled by ratio of extrapolated distance
- # & observed distance.
+ # & observed distance.
trp_r[-1] = trp_r[0] + (trp_r[0] - trp_r[-1]) * (
extra[id][-1] * 1.0 / extra[id][1])
trp_r = sorted(trp_r)
@@ -1852,14 +1858,14 @@ class Headbanging_Median_Rate(object):
trp_r.sort(order='r')
lowest.append(trp_r['r'][0])
highest.append(trp_r['r'][-1])
- lowest_aw.append(self.aw[trp_r['w'][0]])
- highest_aw.append(self.aw[trp_r['w'][-1]])
+ lowest_aw.append(self.aw[int(trp_r['w'][0])])
+ highest_aw.append(self.aw[int(trp_r['w'][-1])])
wm_lowest = weighted_median(np.array(lowest), np.array(lowest_aw))
wm_highest = weighted_median(
np.array(highest), np.array(highest_aw))
triple_members = flatten(triples, unique=False)
return r[id], wm_lowest, wm_highest, self.aw[id] * len(triples), self.aw[triple_members].sum()
-
+
def __get_median_from_screens(self, screens):
if isinstance(screens, float):
return screens
@@ -1884,13 +1890,13 @@ class Headbanging_Median_Rate(object):
k, tr[k], weighted=(self.aw is not None))
new_r.append(self.__get_median_from_screens(screens))
self.r = np.array(new_r)
-
+
@_requires('pandas')
@classmethod
def by_col(cls, df, e, b, t=None, geom_col='geometry', inplace=False, **kwargs):
"""
Compute smoothing by columns in a dataframe. The bounding box and point
- information is computed from the geometry column.
+ information is computed from the geometry column.
Parameters
-----------
@@ -1904,22 +1910,22 @@ class Headbanging_Median_Rate(object):
variables to be smoothed
t : Headbanging_Triples instance or list of Headbanging_Triples
list of headbanging triples instances. If not provided, this
- is computed from the geometry column of the dataframe.
+ is computed from the geometry column of the dataframe.
geom_col: string
the name of the column in the dataframe containing the
- geometry information.
+ geometry information.
inplace : bool
a flag denoting whether to output a copy of `df` with the
relevant smoothed columns appended, or to append the columns
- directly to `df` itself.
+ directly to `df` itself.
**kwargs: optional keyword arguments
optional keyword options that are passed directly to the
- smoother.
+ smoother.
Returns
---------
a new dataframe containing the smoothed Headbanging Median Rates for the
event/population pairs. If done inplace, there is no return value and
- `df` is modified in place.
+ `df` is modified in place.
"""
import pandas as pd
if not inplace:
@@ -1939,7 +1945,7 @@ class Headbanging_Median_Rate(object):
#Headbanging_Triples doesn't take **kwargs, so filter its arguments
# (self, data, w, k=5, t=3, angle=135.0, edgecor=False):
-
+
w = kwargs.pop('w', None)
if w is None:
found = False
@@ -1951,7 +1957,7 @@ class Headbanging_Median_Rate(object):
raise Exception('Weights not provided and no weights attached to frame!'
' Please provide a weight or attach a weight to the'
' dataframe')
-
+
k = kwargs.pop('k', 5)
t = kwargs.pop('t', 3)
angle = kwargs.pop('angle', 135.0)
@@ -1959,7 +1965,7 @@ class Headbanging_Median_Rate(object):
hbt = Headbanging_Triples(data, w, k=k, t=t, angle=angle,
edgecor=edgecor)
-
+
res = []
for ename, bname in zip(e, b):
r = cls(df[ename], df[bname], hbt, **kwargs).r
diff --git a/pysal/esda/tests/test_geary.py b/pysal/esda/tests/test_geary.py
index 6cff335..922901d 100644
--- a/pysal/esda/tests/test_geary.py
+++ b/pysal/esda/tests/test_geary.py
@@ -26,10 +26,10 @@ class Geary_Tester(unittest.TestCase):
self.assertAlmostEquals(c.z_norm, -3.7399778367629564)
self.assertAlmostEquals(c.seC_norm, 0.17834040553138225)
- self.assertAlmostEquals(c.VC_rand, 0.018437747611029367)
- self.assertAlmostEquals(c.p_rand, 4.5059156794646782e-07)
- self.assertAlmostEquals(c.z_rand, -4.9120733751216008)
- self.assertAlmostEquals(c.seC_rand, 0.13578566791465646)
+ self.assertAlmostEquals(c.VC_rand,0.033411917666958356)
+ self.assertAlmostEquals(c.p_rand,0.00013165646189214729)
+ self.assertAlmostEquals(c.z_rand, -3.6489513837253944)
+ self.assertAlmostEquals(c.seC_rand, 0.18278927120309429)
np.random.seed(12345)
c = geary.Geary(self.y, self.w, permutations=999)
@@ -41,10 +41,10 @@ class Geary_Tester(unittest.TestCase):
self.assertAlmostEquals(c.z_norm, -3.7399778367629564)
self.assertAlmostEquals(c.seC_norm, 0.17834040553138225)
- self.assertAlmostEquals(c.VC_rand, 0.018437747611029367)
- self.assertAlmostEquals(c.p_rand, 4.5059156794646782e-07)
- self.assertAlmostEquals(c.z_rand, -4.9120733751216008)
- self.assertAlmostEquals(c.seC_rand, 0.13578566791465646)
+ self.assertAlmostEquals(c.VC_rand, 0.033411917666958356)
+ self.assertAlmostEquals(c.p_rand, 0.00013165646189214729)
+ self.assertAlmostEquals(c.z_rand, -3.6489513837253944)
+ self.assertAlmostEquals(c.seC_rand, 0.18278927120309429)
self.assertAlmostEquals(c.EC_sim, 0.9980676303238214)
self.assertAlmostEquals(c.VC_sim, 0.034430408799858946)
diff --git a/pysal/esda/tests/test_moran.py b/pysal/esda/tests/test_moran.py
index 2f0143a..9a94c1e 100644
--- a/pysal/esda/tests/test_moran.py
+++ b/pysal/esda/tests/test_moran.py
@@ -25,6 +25,13 @@ class Moran_Tester(unittest.TestCase):
np.testing.assert_allclose(mi.I, 0.24772519320480135, atol=ATOL, rtol=RTOL)
self.assertAlmostEquals(mi.p_norm, 5.7916539074498452e-05)
+ def test_variance(self):
+ y = np.arange(1, 10)
+ w = pysal.lat2W(3, 3)
+ mi = pysal.Moran(y, w, transformation='B')
+ np.testing.assert_allclose(mi.VI_rand, 0.059687500000000004, atol=ATOL, rtol=RTOL)
+ np.testing.assert_allclose(mi.VI_norm, 0.053125000000000006, atol=ATOL, rtol=RTOL)
+
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_by_col(self):
import pysal.contrib.pdio as pdio
@@ -37,6 +44,7 @@ class Moran_Tester(unittest.TestCase):
self.assertAlmostEquals(pval, 0.001)
+
class Moran_Rate_Tester(unittest.TestCase):
def setUp(self):
self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
diff --git a/pysal/esda/tests/test_smoothing.py b/pysal/esda/tests/test_smoothing.py
index 85b4965..0665270 100644
--- a/pysal/esda/tests/test_smoothing.py
+++ b/pysal/esda/tests/test_smoothing.py
@@ -77,13 +77,13 @@ class TestSRate(unittest.TestCase):
4.93034844e-05, 5.09387329e-05, 3.72735210e-05,
3.69333797e-05, 5.40245456e-05, 2.99806055e-05,
3.73034109e-05, 3.47270722e-05]).reshape(-1,1)
-
+
self.stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
self.stl_e, self.stl_b = np.array(self.stl[:, 10]), np.array(self.stl[:, 13])
self.stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
if not self.stl_w.id_order_set:
self.stl_w.id_order = range(1, len(self.stl) + 1)
-
+
if not PANDAS_EXTINCT:
self.df = pysal.open(pysal.examples.get_path('sids2.dbf')).to_df()
self.ename = 'SID74'
@@ -94,44 +94,44 @@ class TestSRate(unittest.TestCase):
def test_Excess_Risk(self):
out_er = sm.Excess_Risk(self.e, self.b).r
- np.testing.assert_allclose(out_er[:5].flatten(), self.er,
+ np.testing.assert_allclose(out_er[:5].flatten(), self.er,
rtol=RTOL, atol=ATOL)
-
+
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_Excess_Risk_tabular(self):
out_er = sm.Excess_Risk(self.df[self.ename], self.df[self.bname]).r
- np.testing.assert_allclose(out_er[:5].flatten(), self.er,
+ np.testing.assert_allclose(out_er[:5].flatten(), self.er,
rtol=RTOL, atol=ATOL)
self.assertIsInstance(out_er, np.ndarray)
out_er = sm.Excess_Risk.by_col(self.df, self.ename, self.bname)
outcol = '{}-{}_excess_risk'.format(self.ename, self.bname)
outcol = out_er[outcol]
- np.testing.assert_allclose(outcol[:5], self.er,
+ np.testing.assert_allclose(outcol[:5], self.er,
rtol=RTOL, atol=ATOL)
self.assertIsInstance(outcol.values, np.ndarray)
def test_Empirical_Bayes(self):
out_eb = sm.Empirical_Bayes(self.e, self.b).r
- np.testing.assert_allclose(out_eb[:5].flatten(), self.eb,
+ np.testing.assert_allclose(out_eb[:5].flatten(), self.eb,
rtol=RTOL, atol=ATOL)
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_Empirical_Bayes_tabular(self):
out_eb = sm.Empirical_Bayes(self.df[self.ename], self.df[self.bname]).r
- np.testing.assert_allclose(out_eb[:5].flatten(), self.eb,
+ np.testing.assert_allclose(out_eb[:5].flatten(), self.eb,
rtol=RTOL, atol=ATOL)
self.assertIsInstance(out_eb, np.ndarray)
out_eb = sm.Empirical_Bayes.by_col(self.df, self.ename, self.bname)
outcol = '{}-{}_empirical_bayes'.format(self.ename, self.bname)
outcol = out_eb[outcol]
- np.testing.assert_allclose(outcol[:5], self.eb,
+ np.testing.assert_allclose(outcol[:5], self.eb,
rtol=RTOL, atol=ATOL)
self.assertIsInstance(outcol.values, np.ndarray)
def test_Spatial_Empirical_Bayes(self):
s_eb = sm.Spatial_Empirical_Bayes(self.stl_e, self.stl_b, self.stl_w)
- np.testing.assert_allclose(self.s_ebr10, s_eb.r[:10],
+ np.testing.assert_allclose(self.s_ebr10, s_eb.r[:10],
rtol=RTOL, atol=ATOL)
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
@@ -142,7 +142,7 @@ class TestSRate(unittest.TestCase):
self.assertIsInstance(s_eb, np.ndarray)
np.testing.assert_allclose(self.s_ebr10, s_eb[:10])
- s_eb = sm.Spatial_Empirical_Bayes.by_col(self.stl_df,
+ s_eb = sm.Spatial_Empirical_Bayes.by_col(self.stl_df,
self.stl_ename,
self.stl_bname,
self.stl_w)
@@ -153,13 +153,13 @@ class TestSRate(unittest.TestCase):
def test_Spatial_Rate(self):
out_sr = sm.Spatial_Rate(self.e, self.b, self.w).r
- np.testing.assert_allclose(out_sr[:5].flatten(), self.sr,
+ np.testing.assert_allclose(out_sr[:5].flatten(), self.sr,
rtol=RTOL, atol=ATOL)
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_Spatial_Rate_tabular(self):
out_sr = sm.Spatial_Rate(self.df[self.ename], self.df[self.bname], self.w).r
- np.testing.assert_allclose(out_sr[:5].flatten(), self.sr,
+ np.testing.assert_allclose(out_sr[:5].flatten(), self.sr,
rtol=RTOL, atol=ATOL)
self.assertIsInstance(out_sr, np.ndarray)
@@ -173,7 +173,7 @@ class TestSRate(unittest.TestCase):
out_smr = sm.Spatial_Median_Rate(self.e, self.b, self.w).r
out_smr_w = sm.Spatial_Median_Rate(self.e, self.b, self.w, aw=self.b).r
out_smr2 = sm.Spatial_Median_Rate(self.e, self.b, self.w, iteration=2).r
- np.testing.assert_allclose(out_smr[:5].flatten(), self.smr,
+ np.testing.assert_allclose(out_smr[:5].flatten(), self.smr,
atol=ATOL, rtol=RTOL)
np.testing.assert_allclose(out_smr_w[:5].flatten(), self.smr_w,
atol=ATOL, rtol=RTOL)
@@ -187,35 +187,35 @@ class TestSRate(unittest.TestCase):
self.w).r
out_smr_w = sm.Spatial_Median_Rate(self.df[self.ename],
self.df[self.bname],
- self.w,
+ self.w,
aw = self.df[self.bname]).r
- out_smr2 = sm.Spatial_Median_Rate(self.df[self.ename],
+ out_smr2 = sm.Spatial_Median_Rate(self.df[self.ename],
self.df[self.bname],
self.w,
iteration=2).r
-
+
self.assertIsInstance(out_smr, np.ndarray)
self.assertIsInstance(out_smr_w, np.ndarray)
self.assertIsInstance(out_smr2, np.ndarray)
- np.testing.assert_allclose(out_smr[:5].flatten(), self.smr,
+ np.testing.assert_allclose(out_smr[:5].flatten(), self.smr,
atol=ATOL, rtol=RTOL)
np.testing.assert_allclose(out_smr_w[:5].flatten(), self.smr_w,
atol=ATOL, rtol=RTOL)
np.testing.assert_allclose(out_smr2[:5].flatten(), self.smr2,
atol=ATOL, rtol=RTOL)
-
+
out_smr = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
self.bname, self.w)
- out_smr_w = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
- self.bname, self.w,
+ out_smr_w = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
+ self.bname, self.w,
aw = self.df[self.bname])
- out_smr2 = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
+ out_smr2 = sm.Spatial_Median_Rate.by_col(self.df, self.ename,
self.bname, self.w,
iteration=2)
outcol = '{}-{}_spatial_median_rate'.format(self.ename, self.bname)
- np.testing.assert_allclose(out_smr[outcol].values[:5], self.smr,
+ np.testing.assert_allclose(out_smr[outcol].values[:5], self.smr,
rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(out_smr_w[outcol].values[:5], self.smr_w,
rtol=RTOL, atol=ATOL)
@@ -231,14 +231,14 @@ class TestSRate(unittest.TestCase):
enames = [self.ename, 'SID79']
bnames = [self.bname, 'BIR79']
out_df = sm.Spatial_Median_Rate.by_col(self.df, enames, bnames, self.w)
- outcols = ['{}-{}_spatial_median_rate'.format(e,b)
+ outcols = ['{}-{}_spatial_median_rate'.format(e,b)
for e,b in zip(enames, bnames)]
smr79 = np.array([0.00122129, 0.00176924, 0.00176924,
0.00240964, 0.00272035])
answers = [self.smr, smr79]
for col, answer in zip(outcols, answer):
self.assertIn(out_df.columns, col)
- np.testing.assert_allclose(out_df[col].values[:5], answer,
+ np.testing.assert_allclose(out_df[col].values[:5], answer,
rtol=RTOL, atol=ATOL)
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
@@ -249,14 +249,14 @@ class TestSRate(unittest.TestCase):
enames = [self.ename, 'SID79']
bnames = [self.bname, 'BIR79']
out_df = sm.Excess_Risk.by_col(self.df, enames, bnames)
- outcols = ['{}-{}_excess_risk'.format(e,b)
+ outcols = ['{}-{}_excess_risk'.format(e,b)
for e,b in zip(enames, bnames)]
er79 = np.array([0.000000, 2.796607, 0.8383863,
1.217479, 0.943811])
answers = [self.er, er79]
for col, answer in zip(outcols, answer):
self.assertIn(out_df.columns, col)
- np.testing.assert_allclose(out_df[col].values[:5], answer,
+ np.testing.assert_allclose(out_df[col].values[:5], answer,
rtol=RTOL, atol=ATOL)
class TestHB(unittest.TestCase):
@@ -281,9 +281,9 @@ class TestHB(unittest.TestCase):
self.bname = 'BIR74'
self.enames = [self.ename, 'SID79']
self.bnames = [self.bname, 'BIR79']
- self.sids79r = np.array([.000563, .001659, .001879,
+ self.sids79r = np.array([.000563, .001659, .001879,
.002410, .002720])
-
+ @unittest.skip("Depreication")
def test_Headbanging_Triples(self):
ht = sm.Headbanging_Triples(self.d, self.w)
self.assertEquals(len(ht.triples), len(self.d))
@@ -295,6 +295,7 @@ class TestHB(unittest.TestCase):
for i in htr.r:
self.assertTrue(i is not None)
+ @unittest.skip("Deprication")
def test_Headbanging_Median_Rate(self):
s_ht = sm.Headbanging_Triples(self.d, self.w, k=5)
sids_hb_r = sm.Headbanging_Median_Rate(self.e, self.b, s_ht)
@@ -303,29 +304,30 @@ class TestHB(unittest.TestCase):
np.testing.assert_array_almost_equal(self.sids_hb_r2r5, sids_hb_r2.r[:5])
sids_hb_r3 = sm.Headbanging_Median_Rate(self.e, self.b, s_ht, aw=self.b)
np.testing.assert_array_almost_equal(self.sids_hb_r3r5, sids_hb_r3.r[:5])
-
- @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+
+ #@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
+ @unittest.skip("Deprication")
def test_Headbanging_Median_Rate_tabular(self):
# test that dataframe columns are treated correctly
s_ht = sm.Headbanging_Triples(self.d, self.w, k=5)
- sids_hb_r = sm.Headbanging_Median_Rate(self.df[self.ename],
+ sids_hb_r = sm.Headbanging_Median_Rate(self.df[self.ename],
self.df[self.bname], s_ht)
self.assertIsInstance(sids_hb_r.r, np.ndarray)
np.testing.assert_array_almost_equal(self.sids_hb_rr5, sids_hb_r.r[:5])
-
- sids_hb_r2 = sm.Headbanging_Median_Rate(self.df[self.ename],
+
+ sids_hb_r2 = sm.Headbanging_Median_Rate(self.df[self.ename],
self.df[self.bname], s_ht,
iteration=5)
self.assertIsInstance(sids_hb_r2.r, np.ndarray)
np.testing.assert_array_almost_equal(self.sids_hb_r2r5, sids_hb_r2.r[:5])
- sids_hb_r3 = sm.Headbanging_Median_Rate(self.df[self.ename],
- self.df[self.bname], s_ht,
+ sids_hb_r3 = sm.Headbanging_Median_Rate(self.df[self.ename],
+ self.df[self.bname], s_ht,
aw=self.df[self.bname])
self.assertIsInstance(sids_hb_r3.r, np.ndarray)
np.testing.assert_array_almost_equal(self.sids_hb_r3r5, sids_hb_r3.r[:5])
-
+
#test that the by col on multiple names works correctly
sids_hr_df = sm.Headbanging_Median_Rate.by_col(self.df, self.enames,
self.bnames, w=self.w)
@@ -334,7 +336,7 @@ class TestHB(unittest.TestCase):
for col, answer in zip(outcols, [self.sids_hb_rr5, self.sids79r]):
this_col = sids_hr_df[col].values
self.assertIsInstance(this_col, np.ndarray)
- np.testing.assert_allclose(sids_hr_df[col][:5], answer,
+ np.testing.assert_allclose(sids_hr_df[col][:5], answer,
rtol=RTOL, atol=ATOL*10)
class TestKernel_AgeAdj_SM(unittest.TestCase):
@@ -367,11 +369,11 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
self.disk_exp = [0.12222222000000001, 0.10833333, 0.08055556,
0.08944444, 0.09944444, 0.09351852]
self.sf_exp = np.array([ 0.111111, 0.111111, 0.085106, 0.076923])
-
+
def test_Kernel_Smoother(self):
kr = sm.Kernel_Smoother(self.e, self.b, self.kw)
np.testing.assert_allclose(kr.r.flatten(), self.kernel_exp)
-
+
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_Kernel_Smoother_tabular(self):
dfa, dfb = self.dfa, self.dfb
@@ -381,10 +383,10 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
kr = sm.Kernel_Smoother.by_col(dfa, 'e', 'b', w=self.kw)
colname = 'e_b_kernel_smoother'
np.testing.assert_allclose(kr[colname].values, kernel_exp)
-
+
kr = sm.Kernel_Smoother.by_col(dfb, ['e', 's'], 'b', w=self.kw)
outcols = ['{}-b_kernel_smoother'.format(l) for l in ['e','s']]
-
+
exp_eb = np.array([ 0.08276363, 0.08096262, 0.03636364, 0.0704302 ,
0.07996067, 0.1287226 , 0.09831286, 0.0952105 ,
0.02857143, 0.06671039, 0.07129231, 0.08078792])
@@ -397,7 +399,7 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
def test_Age_Adjusted_Smoother(self):
ar = sm.Age_Adjusted_Smoother(self.e1, self.b1, self.kw, self.s)
np.testing.assert_allclose(ar.r, self.ageadj_exp)
-
+
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_Age_Adjusted_Smoother_tabular(self):
dfb = self.dfb
@@ -406,16 +408,16 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
np.testing.assert_allclose(kr.r, self.ageadj_exp)
kr = sm.Age_Adjusted_Smoother.by_col(dfb, 'e', 'b', w=self.kw, s='s')
- answer = np.array([ 0.10519625, 0.08494318, 0.06440072,
+ answer = np.array([ 0.10519625, 0.08494318, 0.06440072,
0.06898604, 0.06952076, 0.05020968])
- colname = 'e-b_age_adjusted_smoother'
+ colname = 'e-b_age_adjusted_smoother'
np.testing.assert_allclose(kr[colname].values, answer, rtol=RTOL, atol=ATOL)
def test_Disk_Smoother(self):
self.kw.transform = 'b'
disk = sm.Disk_Smoother(self.e, self.b, self.kw)
np.testing.assert_allclose(disk.r.flatten(), self.disk_exp)
-
+
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_Disk_Smoother_tabular(self):
self.kw.transform = 'b'
@@ -425,7 +427,7 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
disk = sm.Disk_Smoother.by_col(dfa, 'e', 'b', self.kw)
col = 'e-b_disk_smoother'
- np.testing.assert_allclose(disk[col].values, self.disk_exp,
+ np.testing.assert_allclose(disk[col].values, self.disk_exp,
rtol=RTOL, atol=ATOL)
def test_Spatial_Filtering(self):
@@ -433,7 +435,7 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
bbox = [[0, 0], [45, 45]]
sf = sm.Spatial_Filtering(bbox, points, self.e, self.b, 2, 2, r=30)
np.testing.assert_allclose(sf.r, self.sf_exp, rtol=RTOL, atol=ATOL)
-
+
@unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
def test_Kernel_Smoother_tabular(self):
point_array = np.array(self.points)
@@ -449,11 +451,11 @@ class TestKernel_AgeAdj_SM(unittest.TestCase):
0.07692308, 0.07692308, 0.07692308, 0.07692308,
0.07692308])
x_answer = np.array([10.0, 10.0, 10.0, 20.0, 20.0, 20.0, 30.0, 30.0, 30.0])
- y_answer = np.array([10.000000, 16.666667, 23.333333,
- 10.000000, 16.666667, 23.333333,
+ y_answer = np.array([10.000000, 16.666667, 23.333333,
+ 10.000000, 16.666667, 23.333333,
10.000000, 16.666667, 23.333333])
columns = ['e-b_spatial_filtering_{}'.format(name) for name in ['X', 'Y', 'R']]
-
+
for col, answer in zip(columns, [x_answer, y_answer, r_answer]):
np.testing.assert_allclose(sf[col].values, answer, rtol=RTOL, atol=ATOL)
diff --git a/pysal/examples/clearwater/clearwater_BS_F.ctl b/pysal/examples/clearwater/clearwater_BS_F.ctl
new file mode 100755
index 0000000..403f563
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_BS_F.ctl
@@ -0,0 +1,42 @@
+
+C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 10
+Number_of_areas: 239
+Fields
+AreaKey 001 UserID
+X 002 X
+Y 003 Y
+Gmetric 0
+Dependent 009 Landslid
+Offset
+Independent_geo 7
+000 Intercept
+004 Elev
+005 Slope
+006 SinAspct
+007 CosAspct
+008 AbsSouth
+010 DistStrm
+Independent_fix 0
+Unused_fields 0
+MODELTYPE: 2
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 1
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\clearwater_BS_F_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\clearwater_BS_F_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/clearwater/clearwater_BS_F_listwise.csv b/pysal/examples/clearwater/clearwater_BS_F_listwise.csv
new file mode 100755
index 0000000..9de3f3b
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_BS_F_listwise.csv
@@ -0,0 +1,240 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_Elev, se_Elev, t_Elev, est_Slope, se_Slope, t_Slope, est_SinAspct, se_SinAspct, t_SinAspct, est_CosAspct, se_CosAspct, t_CosAspct, est_AbsSouth, se_AbsSouth, t_AbsSouth, est_DistStrm, se_DistStrm, t_DistStrm, y, yhat, localpdev, Ginfluence
+ 0, 1, 616168.5625, 5201076.5, 2.626518, 3.897353, 0.673924, -0.001557, 0.000859, -1.812078, 0.015175, 0.032058, 0.473372, 0.424178, 0.355661, 1.192648, -0.281684, 2.932590, -0.096053, -0.001499, 0.040732, -0.036805, -0.003748, 0.002504, -1.496783, 1.000000, 0.758965, 0.122902, 0.068763
+ 1, 2, 624923.8125, 5201008.5, 0.861697, 4.342735, 0.198423, -0.001687, 0.001114, -1.514911, 0.012020, 0.038644, 0.311042, 0.356290, 0.392114, 0.908639, -2.255666, 3.264608, -0.690945, 0.024318, 0.044737, 0.543586, -0.004650, 0.003022, -1.538920, 1.000000, 0.644117, 0.111971, 0.177130
+ 2, 3, 615672, 5199187.5, 2.764286, 3.564231, 0.775563, -0.001745, 0.000786, -2.221099, 0.019070, 0.028902, 0.659815, 0.431469, 0.331581, 1.301247, -0.175836, 2.703311, -0.065045, -0.002272, 0.037404, -0.060747, -0.003114, 0.002328, -1.337256, 1.000000, 0.640211, 0.129543, 0.107963
+ 3, 4, 615209.3125, 5199112, 2.826452, 3.542290, 0.797917, -0.001805, 0.000780, -2.314163, 0.019453, 0.028709, 0.677583, 0.440125, 0.331218, 1.328808, -0.163761, 2.690498, -0.060866, -0.002499, 0.037231, -0.067109, -0.003038, 0.002321, -1.309168, 1.000000, 0.560902, 0.135133, 0.106065
+ 4, 5, 616354.6875, 5198945.5, 2.631977, 3.537136, 0.744098, -0.001683, 0.000785, -2.143032, 0.019376, 0.028770, 0.673490, 0.416427, 0.328344, 1.268265, -0.218670, 2.683965, -0.081473, -0.001477, 0.037085, -0.039823, -0.003122, 0.002316, -1.347957, 1.000000, 0.595910, 0.122280, 0.088599
+ 5, 6, 614559, 5198855, 2.892150, 3.489975, 0.828702, -0.001902, 0.000771, -2.468744, 0.020389, 0.028324, 0.719852, 0.450645, 0.329393, 1.368108, -0.163092, 2.658559, -0.061346, -0.002551, 0.036771, -0.069372, -0.002895, 0.002304, -1.256848, 1.000000, 0.605629, 0.143629, 0.052455
+ 6, 7, 618648.5, 5198468, 2.154245, 3.528016, 0.610611, -0.001561, 0.000806, -1.937598, 0.019620, 0.029293, 0.669799, 0.375195, 0.326160, 1.150342, -0.515683, 2.685897, -0.191997, 0.002901, 0.036946, 0.078526, -0.003228, 0.002346, -1.375988, 1.000000, 0.654140, 0.107497, 0.108132
+ 7, 8, 622724.0625, 5197661, 1.470775, 3.657582, 0.402117, -0.001760, 0.000881, -1.998835, 0.019197, 0.031553, 0.608399, 0.347344, 0.337843, 1.028125, -1.370673, 2.802693, -0.489056, 0.014011, 0.038332, 0.365520, -0.003213, 0.002558, -1.255948, 1.000000, 0.735343, 0.116198, 0.066507
+ 8, 9, 622363.4375, 5196686.5, 1.419063, 3.482373, 0.407499, -0.001804, 0.000837, -2.155759, 0.022214, 0.030083, 0.738428, 0.340162, 0.324424, 1.048510, -1.313986, 2.689775, -0.488512, 0.013611, 0.036738, 0.370493, -0.002868, 0.002469, -1.161923, 1.000000, 0.724739, 0.120784, 0.040043
+ 9, 10, 622308.125, 5196407.5, 1.391554, 3.437809, 0.404779, -0.001821, 0.000826, -2.204935, 0.023113, 0.029737, 0.777261, 0.336587, 0.320803, 1.049201, -1.316747, 2.661058, -0.494821, 0.013715, 0.036331, 0.377486, -0.002775, 0.002448, -1.133533, 1.000000, 0.686973, 0.122661, 0.084404
+ 10, 11, 616650.125, 5195894, 2.070992, 3.063895, 0.675934, -0.001783, 0.000711, -2.509296, 0.027827, 0.025544, 1.089354, 0.377668, 0.294924, 1.280560, -0.443763, 2.375396, -0.186816, 0.002551, 0.032534, 0.078409, -0.002285, 0.002122, -1.077140, 1.000000, 0.654278, 0.126002, 0.047109
+ 11, 12, 616583.3125, 5195804, 2.064851, 3.050304, 0.676933, -0.001790, 0.000708, -2.528213, 0.028154, 0.025451, 1.106221, 0.377459, 0.294023, 1.283773, -0.442788, 2.365839, -0.187159, 0.002552, 0.032397, 0.078765, -0.002255, 0.002116, -1.065930, 1.000000, 0.512859, 0.126796, 0.117256
+ 12, 13, 615594.5625, 5195718, 2.200545, 3.013874, 0.730138, -0.001866, 0.000700, -2.665813, 0.029088, 0.025160, 1.156127, 0.387380, 0.293282, 1.320846, -0.365397, 2.336899, -0.156360, 0.001406, 0.031994, 0.043956, -0.002150, 0.002101, -1.023395, 1.000000, 0.625295, 0.135084, 0.077822
+ 13, 14, 620206.75, 5194831, 1.378340, 3.088050, 0.446346, -0.001794, 0.000733, -2.447029, 0.030023, 0.026545, 1.131026, 0.325011, 0.291682, 1.114267, -1.003225, 2.414909, -0.415430, 0.010117, 0.032945, 0.307101, -0.002255, 0.002211, -1.020158, 1.000000, 0.697599, 0.127222, 0.049682
+ 14, 15, 630007.1875, 5194813.5, 1.985739, 4.183512, 0.474658, -0.003489, 0.001088, -3.207511, 0.014000, 0.037310, 0.375236, 0.305013, 0.390562, 0.780961, -3.098101, 3.230145, -0.959121, 0.032843, 0.044048, 0.745620, -0.002360, 0.003391, -0.696091, 1.000000, 0.525964, 0.228285, 0.140851
+ 15, 16, 627247, 5194165, 1.460203, 3.632775, 0.401952, -0.002890, 0.000922, -3.133461, 0.023382, 0.032707, 0.714876, 0.285955, 0.338301, 0.845270, -2.453714, 2.830299, -0.866945, 0.026622, 0.038520, 0.691131, -0.001969, 0.002881, -0.683283, 1.000000, 0.792811, 0.198838, 0.097696
+ 16, 17, 621390.75, 5193554, 1.084164, 3.027332, 0.358125, -0.001951, 0.000728, -2.679084, 0.034087, 0.026613, 1.280863, 0.291008, 0.282780, 1.029097, -1.288959, 2.381262, -0.541292, 0.013743, 0.032411, 0.424016, -0.001938, 0.002221, -0.872629, 1.000000, 0.674138, 0.145738, 0.038652
+ 17, 18, 618200.875, 5193368, 1.427426, 2.852404, 0.500429, -0.001821, 0.000674, -2.702319, 0.035822, 0.024472, 1.463799, 0.328662, 0.272494, 1.206126, -0.745542, 2.237579, -0.333191, 0.006940, 0.030503, 0.227509, -0.001761, 0.002042, -0.862324, 1.000000, 0.880683, 0.136191, 0.038318
+ 18, 19, 621822.375, 5193318.5, 1.028185, 3.034775, 0.338801, -0.002006, 0.000733, -2.735819, 0.034680, 0.026835, 1.292359, 0.281898, 0.282534, 0.997750, -1.380354, 2.388779, -0.577849, 0.014849, 0.032495, 0.456947, -0.001873, 0.002240, -0.836108, 1.000000, 0.541852, 0.151325, 0.056451
+ 19, 20, 606497.125, 5193203.5, 2.620405, 3.378747, 0.775555, -0.003147, 0.000825, -3.812732, 0.052219, 0.027914, 1.870732, 0.390609, 0.331598, 1.177958, -0.631675, 2.592470, -0.243658, 0.003026, 0.035427, 0.085426, -0.001655, 0.002226, -0.743537, 1.000000, 0.693754, 0.276576, 0.128555
+ 20, 21, 599814.4375, 5191332, 3.963853, 4.884882, 0.811453, -0.005130, 0.001323, -3.878809, 0.075181, 0.041254, 1.822381, 0.268597, 0.496851, 0.540599, -0.800352, 3.652613, -0.219118, 0.003800, 0.049830, 0.076265, -0.001162, 0.002942, -0.394962, 1.000000, 0.951269, 0.418596, 0.051586
+ 21, 22, 622664.625, 5191268, 0.803268, 2.942572, 0.272982, -0.002238, 0.000724, -3.090792, 0.041840, 0.026577, 1.574273, 0.223881, 0.269893, 0.829517, -1.587865, 2.322414, -0.683713, 0.017135, 0.031507, 0.543837, -0.001333, 0.002183, -0.610618, 1.000000, 0.723089, 0.184778, 0.141465
+ 22, 23, 604957.75, 5191129.5, 2.348043, 3.499647, 0.670937, -0.003370, 0.000863, -3.904755, 0.065156, 0.028663, 2.273166, 0.283120, 0.339800, 0.833196, -0.674377, 2.684998, -0.251165, 0.003955, 0.036710, 0.107729, -0.001383, 0.002214, -0.624439, 1.000000, 0.836394, 0.300918, 0.104890
+ 23, 24, 605312.5, 5190803.5, 2.277932, 3.400741, 0.669834, -0.003255, 0.000837, -3.887010, 0.065697, 0.027828, 2.360818, 0.267153, 0.330642, 0.807983, -0.586964, 2.613204, -0.224615, 0.002968, 0.035714, 0.083112, -0.001312, 0.002160, -0.607243, 1.000000, 0.738964, 0.292317, 0.104689
+ 24, 25, 619922.3125, 5190778.5, 0.865710, 2.752528, 0.314515, -0.001979, 0.000660, -2.997954, 0.045636, 0.024319, 1.876594, 0.251580, 0.254884, 0.987039, -1.091997, 2.175328, -0.501992, 0.011194, 0.029550, 0.378806, -0.001285, 0.001977, -0.649772, 1.000000, 0.716642, 0.169669, 0.063042
+ 25, 26, 620032.5, 5190592, 0.832964, 2.749249, 0.302979, -0.001993, 0.000660, -3.018803, 0.046342, 0.024334, 1.904416, 0.245267, 0.254084, 0.965299, -1.114484, 2.173136, -0.512846, 0.011446, 0.029514, 0.387805, -0.001249, 0.001974, -0.632375, 1.000000, 0.691202, 0.172630, 0.090373
+ 26, 27, 611927.3125, 5189356.5, 1.694670, 2.588826, 0.654609, -0.002098, 0.000634, -3.309390, 0.058619, 0.021572, 2.717391, 0.274663, 0.258694, 1.061726, -0.039155, 2.014248, -0.019439, -0.002835, 0.027355, -0.103644, -0.000893, 0.001771, -0.504437, 1.000000, 0.844318, 0.194123, 0.035307
+ 27, 28, 604447.4375, 5189277, 2.185619, 3.482147, 0.627664, -0.003374, 0.000865, -3.900355, 0.074598, 0.028663, 2.602598, 0.176674, 0.338866, 0.521370, -0.488839, 2.683405, -0.182171, 0.001903, 0.036646, 0.051918, -0.001051, 0.002184, -0.481363, 1.000000, 0.932642, 0.304494, 0.043373
+ 28, 29, 625240.6875, 5188786.5, 0.691286, 3.047455, 0.226841, -0.002859, 0.000794, -3.600522, 0.047470, 0.028440, 1.669128, 0.095807, 0.279608, 0.342648, -2.206357, 2.400899, -0.918971, 0.023606, 0.032431, 0.727880, -0.000479, 0.002274, -0.210700, 1.000000, 0.860181, 0.249842, 0.039058
+ 29, 30, 615202.125, 5187595.5, 1.108711, 2.556555, 0.433674, -0.001879, 0.000610, -3.082784, 0.062846, 0.021463, 2.928155, 0.222240, 0.243481, 0.912763, -0.204869, 1.995411, -0.102670, -0.000567, 0.027092, -0.020931, -0.000755, 0.001702, -0.443439, 1.000000, 0.765393, 0.190836, 0.029082
+ 30, 31, 625293, 5186286.5, 0.518742, 3.055664, 0.169764, -0.003010, 0.000815, -3.695051, 0.056305, 0.028640, 1.965953, -0.008144, 0.283446, -0.028731, -2.246464, 2.409186, -0.932458, 0.023263, 0.032429, 0.717363, 0.000069, 0.002273, 0.030517, 1.000000, 0.464339, 0.286441, 0.067189
+ 31, 32, 606734.9375, 5185860, 2.014537, 3.018451, 0.667407, -0.002671, 0.000755, -3.536594, 0.082635, 0.025373, 3.256847, 0.096067, 0.302726, 0.317339, 0.381709, 2.352474, 0.162258, -0.009141, 0.031901, -0.286531, -0.000519, 0.001988, -0.260855, 1.000000, 0.287409, 0.262344, 0.073309
+ 32, 33, 608376.25, 5185400.5, 1.888510, 2.851697, 0.662241, -0.002384, 0.000709, -3.365091, 0.081206, 0.023869, 3.402157, 0.124316, 0.286947, 0.433237, 0.523809, 2.221605, 0.235780, -0.010859, 0.030087, -0.360916, -0.000500, 0.001885, -0.265303, 1.000000, 0.756437, 0.244607, 0.044910
+ 33, 34, 609096.75, 5185224.5, 1.825928, 2.799902, 0.652140, -0.002283, 0.000693, -3.293790, 0.080670, 0.023385, 3.449565, 0.135057, 0.281511, 0.479759, 0.556735, 2.180615, 0.255311, -0.011234, 0.029522, -0.380516, -0.000497, 0.001847, -0.268912, 1.000000, 0.873309, 0.238621, 0.051347
+ 34, 35, 609181.5625, 5185170.5, 1.818467, 2.796176, 0.650341, -0.002271, 0.000692, -3.282043, 0.080754, 0.023352, 3.458174, 0.135440, 0.281090, 0.481838, 0.565088, 2.177771, 0.259480, -0.011341, 0.029481, -0.384672, -0.000493, 0.001843, -0.267526, 1.000000, 0.852878, 0.238113, 0.046048
+ 35, 36, 616571.25, 5184687.5, 0.707419, 2.643957, 0.267561, -0.001863, 0.000642, -2.899593, 0.075472, 0.022507, 3.353226, 0.121484, 0.246437, 0.492963, -0.195458, 2.063353, -0.094728, -0.001357, 0.027964, -0.048525, -0.000571, 0.001781, -0.320622, 1.000000, 0.603051, 0.223418, 0.091952
+ 36, 37, 621072.5625, 5184409.5, 0.229079, 2.807478, 0.081596, -0.002278, 0.000712, -3.199663, 0.071297, 0.025355, 2.811968, 0.032155, 0.258060, 0.124603, -1.201771, 2.208789, -0.544086, 0.010920, 0.029808, 0.366340, -0.000273, 0.002136, -0.127751, 1.000000, 0.924934, 0.261527, 0.034873
+ 37, 38, 619478.6875, 5184344.5, 0.336529, 2.732154, 0.123174, -0.002083, 0.000680, -3.061264, 0.073878, 0.024158, 3.058083, 0.064117, 0.251569, 0.254869, -0.815203, 2.141957, -0.380588, 0.006232, 0.028958, 0.215208, -0.000409, 0.002008, -0.203884, 1.000000, 0.924549, 0.247143, 0.025407
+ 38, 39, 620614.4375, 5184022.5, 0.235600, 2.798935, 0.084175, -0.002222, 0.000708, -3.137746, 0.073714, 0.025151, 2.930847, 0.028870, 0.257895, 0.111945, -1.064859, 2.199412, -0.484156, 0.009141, 0.029686, 0.307935, -0.000293, 0.002134, -0.137197, 1.000000, 0.907513, 0.261918, 0.051277
+ 39, 40, 621050.375, 5181352, 0.067807, 2.970155, 0.022829, -0.002270, 0.000771, -2.945553, 0.085151, 0.027139, 3.137574, -0.080209, 0.280580, -0.285870, -0.976891, 2.336286, -0.418139, 0.007000, 0.031440, 0.222638, -0.000285, 0.002500, -0.113979, 1.000000, 0.776808, 0.299996, 0.052469
+ 40, 41, 614195.125, 5180384, 0.737451, 2.942763, 0.250598, -0.001720, 0.000745, -2.309351, 0.099061, 0.025401, 3.899959, 0.041049, 0.284711, 0.144178, 0.626981, 2.304418, 0.272078, -0.013090, 0.031137, -0.420382, -0.000678, 0.002069, -0.327650, 1.000000, 0.621150, 0.257309, 0.054537
+ 41, 42, 609198.875, 5179613, 1.570829, 3.283302, 0.478430, -0.002002, 0.000844, -2.371237, 0.104677, 0.028452, 3.679073, 0.069863, 0.326707, 0.213839, 1.299283, 2.591873, 0.501291, -0.022181, 0.034915, -0.635269, -0.000285, 0.002239, -0.127435, 1.000000, 0.726304, 0.257257, 0.067816
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diff --git a/pysal/examples/clearwater/clearwater_BS_F_summary.txt b/pysal/examples/clearwater/clearwater_BS_F_summary.txt
new file mode 100755
index 0000000..4a38289
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_BS_F_summary.txt
@@ -0,0 +1,173 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:12:16 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\clearwater_BS_F.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+Number of areas/points: 239
+
+Model settings---------------------------------
+Model type: Logistic
+Geographic kernel: fixed bi-square
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 7
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: UserID
+Easting (x-coord): field2 : X
+Northing (y-coord): field3: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field9: Landslid
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field4: Elev
+Independent variable with varying (Local) coefficient: field5: Slope
+Independent variable with varying (Local) coefficient: field6: SinAspct
+Independent variable with varying (Local) coefficient: field7: CosAspct
+Independent variable with varying (Local) coefficient: field8: AbsSouth
+Independent variable with varying (Local) coefficient: field10: DistStrm
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 7
+Deviance: 264.959861
+Classic AIC: 278.959861
+AICc: 279.444709
+BIC/MDL: 303.295106
+Percent deviance explained 0.186174
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 0.491477 2.249648 0.218469 1.634730
+Elev -0.002408 0.000548 -4.394976 0.997595
+Slope 0.076097 0.019801 3.842994 1.079067
+SinAspct 0.083827 0.213948 0.391807 1.087440
+CosAspct -1.191339 1.747013 -0.681929 0.303814
+AbsSouth 0.008819 0.023743 0.371420 1.008858
+DistStrm -0.001732 0.001605 -1.078779 0.998270
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 7414.12203822248, 39427.5660570219
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 7414.122 Criterion: 286.000
+ p1 Bandwidth: 19642.170 Criterion: 275.989
+ p2 Bandwidth: 27199.519 Criterion: 277.538
+ pU Bandwidth: 39427.566 Criterion: 278.558
+ iter 1 (p1) Bandwidth: 19642.170 Criterion: 275.989 Diff: 7557.349
+ iter 2 (p2) Bandwidth: 19642.170 Criterion: 275.989 Diff: 4670.699
+ iter 3 (p1) Bandwidth: 19642.170 Criterion: 275.989 Diff: 2886.650
+ iter 4 (p2) Bandwidth: 19642.170 Criterion: 275.989 Diff: 1784.048
+Best bandwidth size 19642.170
+Minimum AICc 275.989
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 19642.169556
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 599444.687500 632987.750000 33543.062500
+Y-coord 5172366.500000 5201169.128000 28802.628000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 22.450210
+Effective number of parameters (variance: trace(S'WSW^-1)): 0.276740
+Degree of freedom (model: n - trace(S)): 216.549790
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 194.376319
+Deviance: 226.203496
+Classic AIC: 271.103916
+AICc: 275.988747
+BIC/MDL: 349.151253
+Percent deviance explained 0.305215
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\clearwater_BS_F_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 1.419798 1.209409
+Elev -0.002684 0.001269
+Slope 0.067124 0.033621
+SinAspct 0.126945 0.320483
+CosAspct -0.850236 1.475146
+AbsSouth 0.004802 0.017283
+DistStrm -0.000926 0.001670
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept -1.875538 5.601441 7.476978
+Elev -0.010165 -0.000721 0.009444
+Slope 0.003541 0.140436 0.136895
+SinAspct -1.401238 1.019611 2.420849
+CosAspct -5.802768 2.280865 8.083634
+AbsSouth -0.040024 0.053497 0.093521
+DistStrm -0.005188 0.008572 0.013760
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 0.518742 1.413040 2.277932
+Elev -0.003166 -0.002238 -0.001852
+Slope 0.035822 0.070416 0.087936
+SinAspct 0.019086 0.149218 0.343557
+CosAspct -1.602258 -0.745542 0.254795
+AbsSouth -0.007011 0.003997 0.015836
+DistStrm -0.001969 -0.000893 -0.000285
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 1.759190 1.304070
+Elev 0.001314 0.000974
+Slope 0.052114 0.038632
+SinAspct 0.324471 0.240527
+CosAspct 1.857054 1.376615
+AbsSouth 0.022847 0.016936
+DistStrm 0.001683 0.001248
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 264.960 232.000 1.142
+GWR model 226.203 194.376 1.164
+Difference 38.756 37.624 1.030
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:12:25 AM
diff --git a/pysal/examples/clearwater/clearwater_BS_NN.ctl b/pysal/examples/clearwater/clearwater_BS_NN.ctl
new file mode 100755
index 0000000..e0753d9
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_BS_NN.ctl
@@ -0,0 +1,42 @@
+
+C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 10
+Number_of_areas: 239
+Fields
+AreaKey 001 UserID
+X 002 X
+Y 003 Y
+Gmetric 0
+Dependent 009 Landslid
+Offset
+Independent_geo 7
+000 Intercept
+004 Elev
+005 Slope
+006 SinAspct
+007 CosAspct
+008 AbsSouth
+010 DistStrm
+Independent_fix 0
+Unused_fields 0
+MODELTYPE: 2
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 2
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\clearwater_BS_NN_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\clearwater_BS_NN_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/clearwater/clearwater_BS_NN_listwise.csv b/pysal/examples/clearwater/clearwater_BS_NN_listwise.csv
new file mode 100755
index 0000000..36ef251
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_BS_NN_listwise.csv
@@ -0,0 +1,240 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_Elev, se_Elev, t_Elev, est_Slope, se_Slope, t_Slope, est_SinAspct, se_SinAspct, t_SinAspct, est_CosAspct, se_CosAspct, t_CosAspct, est_AbsSouth, se_AbsSouth, t_AbsSouth, est_DistStrm, se_DistStrm, t_DistStrm, y, yhat, localpdev, Ginfluence
+ 0, 1, 616168.5625, 5201076.5, 2.635498, 3.923972, 0.671640, -0.001538, 0.000865, -1.777497, 0.014891, 0.032310, 0.460867, 0.423446, 0.357511, 1.184430, -0.265370, 2.951672, -0.089905, -0.001754, 0.041007, -0.042783, -0.003775, 0.002517, -1.499898, 1.000000, 0.760351, 0.143657, 0.069225
+ 1, 2, 624923.8125, 5201008.5, 1.427987, 3.838231, 0.372043, -0.001893, 0.000928, -2.040076, 0.017314, 0.033313, 0.519740, 0.347142, 0.350817, 0.989523, -1.740027, 2.920142, -0.595871, 0.017706, 0.040001, 0.442635, -0.003933, 0.002710, -1.451101, 1.000000, 0.581256, 0.159342, 0.145306
+ 2, 3, 615672, 5199187.5, 3.096499, 3.956576, 0.782621, -0.001474, 0.000855, -1.724830, 0.015048, 0.032031, 0.469806, 0.433967, 0.357399, 1.214236, 0.237002, 2.980343, 0.079522, -0.008147, 0.041426, -0.196658, -0.003430, 0.002485, -1.380130, 1.000000, 0.647884, 0.135514, 0.125463
+ 3, 4, 615209.3125, 5199112, 3.255989, 3.981938, 0.817690, -0.001508, 0.000853, -1.767421, 0.014990, 0.032047, 0.467744, 0.449009, 0.359899, 1.247595, 0.341254, 3.000505, 0.113732, -0.009723, 0.041755, -0.232861, -0.003376, 0.002489, -1.356498, 1.000000, 0.571610, 0.139164, 0.125551
+ 4, 5, 616354.6875, 5198945.5, 2.909698, 3.864668, 0.752897, -0.001462, 0.000844, -1.732088, 0.015818, 0.031420, 0.503446, 0.415406, 0.349491, 1.188603, 0.114237, 2.912056, 0.039229, -0.006144, 0.040380, -0.152148, -0.003392, 0.002449, -1.385211, 1.000000, 0.603209, 0.131697, 0.101817
+ 5, 6, 614559, 5198855, 3.437230, 3.937896, 0.872859, -0.001617, 0.000838, -1.928702, 0.015744, 0.031446, 0.500674, 0.470046, 0.358182, 1.312309, 0.421195, 2.971637, 0.141738, -0.010924, 0.041372, -0.264049, -0.003213, 0.002463, -1.304534, 1.000000, 0.613763, 0.147672, 0.060868
+ 6, 7, 618648.5, 5198468, 2.226028, 3.786668, 0.587859, -0.001377, 0.000865, -1.591868, 0.016848, 0.031653, 0.532280, 0.368009, 0.343421, 1.071595, -0.376417, 2.861462, -0.131547, 0.000951, 0.039432, 0.024116, -0.003471, 0.002459, -1.411552, 1.000000, 0.643295, 0.120973, 0.120280
+ 7, 8, 622724.0625, 5197661, 1.473118, 3.682271, 0.400057, -0.001751, 0.000888, -1.972659, 0.018824, 0.031765, 0.592601, 0.347886, 0.339700, 1.024099, -1.373343, 2.818974, -0.487178, 0.014034, 0.038560, 0.363958, -0.003231, 0.002570, -1.257061, 1.000000, 0.736228, 0.144558, 0.067211
+ 8, 9, 622363.4375, 5196686.5, 1.476410, 3.648487, 0.404664, -0.001737, 0.000880, -1.973596, 0.019396, 0.031451, 0.616706, 0.346924, 0.337373, 1.028308, -1.294633, 2.799834, -0.462397, 0.013349, 0.038277, 0.348759, -0.002949, 0.002546, -1.158229, 1.000000, 0.730146, 0.141270, 0.042555
+ 9, 10, 622308.125, 5196407.5, 1.473834, 3.637606, 0.405166, -0.001741, 0.000878, -1.984190, 0.019610, 0.031360, 0.625317, 0.346425, 0.336662, 1.029002, -1.282241, 2.793900, -0.458943, 0.013281, 0.038188, 0.347784, -0.002858, 0.002542, -1.124301, 1.000000, 0.700835, 0.140940, 0.091013
+ 10, 11, 616650.125, 5195894, 3.135481, 3.732608, 0.840024, -0.001303, 0.000826, -1.578105, 0.020213, 0.030165, 0.670083, 0.389603, 0.336907, 1.156414, 0.675176, 2.842177, 0.237556, -0.012377, 0.039207, -0.315692, -0.002668, 0.002361, -1.129889, 1.000000, 0.650630, 0.110968, 0.058183
+ 11, 12, 616583.3125, 5195804, 3.173053, 3.728522, 0.851022, -0.001303, 0.000824, -1.580186, 0.020429, 0.030102, 0.678667, 0.390338, 0.336574, 1.159739, 0.716758, 2.840002, 0.252380, -0.012915, 0.039175, -0.329664, -0.002636, 0.002358, -1.118259, 1.000000, 0.547284, 0.110790, 0.152110
+ 12, 13, 615594.5625, 5195718, 3.662616, 3.752704, 0.975994, -0.001374, 0.000814, -1.687082, 0.020554, 0.029883, 0.687826, 0.417554, 0.338687, 1.232862, 1.063791, 2.853182, 0.372844, -0.017929, 0.039430, -0.454704, -0.002544, 0.002343, -1.086170, 1.000000, 0.667367, 0.117397, 0.093650
+ 13, 14, 620206.75, 5194831, 1.721018, 3.576292, 0.481230, -0.001466, 0.000853, -1.719864, 0.021774, 0.030390, 0.716491, 0.338503, 0.329686, 1.026745, -0.614449, 2.753925, -0.223117, 0.005515, 0.037654, 0.146455, -0.002288, 0.002422, -0.944698, 1.000000, 0.700192, 0.116929, 0.061732
+ 14, 15, 630007.1875, 5194813.5, 1.483986, 3.690592, 0.402100, -0.003193, 0.000957, -3.337120, 0.025581, 0.033579, 0.761814, 0.239948, 0.341388, 0.702860, -2.805936, 2.874073, -0.976293, 0.029846, 0.039071, 0.763907, -0.002052, 0.002963, -0.692521, 1.000000, 0.555283, 0.252738, 0.111989
+ 15, 16, 627247, 5194165, 1.472082, 3.641635, 0.404237, -0.002894, 0.000925, -3.130668, 0.023126, 0.032771, 0.705697, 0.287822, 0.339244, 0.848423, -2.456646, 2.836733, -0.866013, 0.026652, 0.038610, 0.690292, -0.001973, 0.002889, -0.682715, 1.000000, 0.792276, 0.226169, 0.098239
+ 16, 17, 621390.75, 5193554, 1.514454, 3.536606, 0.428222, -0.001762, 0.000854, -2.063266, 0.022781, 0.030473, 0.747580, 0.328747, 0.329531, 0.997620, -1.000373, 2.741413, -0.364912, 0.010798, 0.037395, 0.288757, -0.001636, 0.002502, -0.653791, 1.000000, 0.686355, 0.133757, 0.049777
+ 17, 18, 618200.875, 5193368, 2.370649, 3.579893, 0.662212, -0.001218, 0.000844, -1.443295, 0.026320, 0.029690, 0.886467, 0.328006, 0.326662, 1.004115, 0.414803, 2.767572, 0.149880, -0.007304, 0.037859, -0.192917, -0.001608, 0.002363, -0.680312, 1.000000, 0.853838, 0.094682, 0.060190
+ 18, 19, 621822.375, 5193318.5, 1.518697, 3.562136, 0.426344, -0.001876, 0.000862, -2.175228, 0.022238, 0.030771, 0.722672, 0.331395, 0.332587, 0.996416, -1.122725, 2.762568, -0.406406, 0.012357, 0.037681, 0.327943, -0.001511, 0.002557, -0.591143, 1.000000, 0.569978, 0.140046, 0.080040
+ 19, 20, 606497.125, 5193203.5, 3.442886, 3.943143, 0.873132, -0.003603, 0.000960, -3.752308, 0.045482, 0.032671, 1.392140, 0.531484, 0.388099, 1.369453, -0.630700, 3.000510, -0.210198, 0.002130, 0.041116, 0.051804, -0.001767, 0.002625, -0.673065, 1.000000, 0.738316, 0.314315, 0.154655
+ 20, 21, 599814.4375, 5191332, 2.694172, 4.318630, 0.623849, -0.004467, 0.001128, -3.959894, 0.079121, 0.036245, 2.182920, 0.225077, 0.424790, 0.529855, -1.115682, 3.280145, -0.340132, 0.009201, 0.044836, 0.205207, -0.001406, 0.002631, -0.534292, 1.000000, 0.935377, 0.387939, 0.053007
+ 21, 22, 622664.625, 5191268, 1.528553, 3.507914, 0.435744, -0.002421, 0.000865, -2.799403, 0.024311, 0.031117, 0.781278, 0.291009, 0.330504, 0.880499, -1.490706, 2.759233, -0.540261, 0.017229, 0.037482, 0.459669, -0.000283, 0.002740, -0.103361, 1.000000, 0.793079, 0.175443, 0.173175
+ 22, 23, 604957.75, 5191129.5, 2.912672, 3.935232, 0.740153, -0.003819, 0.000986, -3.875038, 0.062423, 0.032995, 1.891894, 0.344156, 0.389855, 0.882779, -0.748419, 3.001621, -0.249338, 0.004476, 0.040993, 0.109191, -0.001359, 0.002498, -0.544056, 1.000000, 0.866901, 0.334276, 0.118871
+ 23, 24, 605312.5, 5190803.5, 2.958848, 3.921013, 0.754613, -0.003777, 0.000980, -3.853407, 0.062324, 0.032945, 1.891747, 0.331122, 0.389783, 0.849503, -0.654612, 2.993648, -0.218667, 0.003357, 0.040848, 0.082172, -0.001258, 0.002493, -0.504627, 1.000000, 0.788923, 0.329091, 0.120376
+ 24, 25, 619922.3125, 5190778.5, 1.685552, 3.551023, 0.474667, -0.001675, 0.000865, -1.936111, 0.030719, 0.030246, 1.015610, 0.251393, 0.331340, 0.758715, -0.299404, 2.782582, -0.107599, 0.003148, 0.037792, 0.083311, 0.000251, 0.002608, 0.096137, 1.000000, 0.760183, 0.119746, 0.088686
+ 25, 26, 620032.5, 5190592, 1.698664, 3.574827, 0.475174, -0.001726, 0.000871, -1.980149, 0.030628, 0.030439, 1.006190, 0.245033, 0.334207, 0.733177, -0.322690, 2.803311, -0.115110, 0.003515, 0.038056, 0.092359, 0.000433, 0.002659, 0.162957, 1.000000, 0.791771, 0.122024, 0.117923
+ 26, 27, 611927.3125, 5189356.5, 3.964166, 3.812293, 1.039838, -0.001794, 0.000861, -2.083671, 0.054849, 0.030233, 1.814209, 0.305511, 0.359316, 0.850256, 2.141525, 2.945589, 0.727028, -0.031055, 0.040186, -0.772772, 0.000063, 0.002407, 0.026016, 1.000000, 0.845009, 0.148942, 0.086533
+ 27, 28, 604447.4375, 5189277, 2.824505, 4.036573, 0.699728, -0.004003, 0.001035, -3.866647, 0.073806, 0.034290, 2.152427, 0.166859, 0.406494, 0.410483, -0.609712, 3.097569, -0.196836, 0.003204, 0.042122, 0.076073, -0.000936, 0.002498, -0.374843, 1.000000, 0.954216, 0.340767, 0.043379
+ 28, 29, 625240.6875, 5188786.5, 1.494862, 3.485399, 0.428893, -0.003644, 0.000956, -3.812404, 0.031481, 0.032780, 0.960367, 0.091125, 0.332127, 0.274367, -2.629508, 2.791594, -0.941938, 0.029229, 0.037442, 0.780635, 0.001019, 0.002699, 0.377379, 1.000000, 0.856897, 0.282085, 0.055583
+ 29, 30, 615202.125, 5187595.5, 1.703396, 3.396273, 0.501549, -0.001256, 0.000865, -1.452298, 0.067451, 0.028964, 2.328805, 0.114122, 0.334585, 0.341087, 1.203970, 2.681979, 0.448911, -0.016874, 0.036180, -0.466375, 0.000629, 0.002507, 0.250831, 1.000000, 0.728503, 0.138389, 0.056968
+ 30, 31, 625293, 5186286.5, 1.198493, 3.525579, 0.339942, -0.003905, 0.000994, -3.928474, 0.046023, 0.032802, 1.403059, -0.133774, 0.340760, -0.392574, -2.766627, 2.829861, -0.977655, 0.029496, 0.037747, 0.781408, 0.001666, 0.002397, 0.694952, 1.000000, 0.494774, 0.335302, 0.095101
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+ 228, 229, 607131.881, 5185808.074, 1.729745, 3.709275, 0.466330, -0.002902, 0.000933, -3.109991, 0.086107, 0.031377, 2.744304, -0.048450, 0.364441, -0.132944, 0.061765, 2.891781, 0.021359, -0.004257, 0.039204, -0.108588, -0.000123, 0.002221, -0.055495, 0.000000, 0.160229, 0.267735, 0.085425
+ 229, 230, 627346.0895, 5201105.558, 1.408409, 3.874493, 0.363508, -0.002168, 0.000943, -2.299229, 0.018265, 0.033813, 0.540171, 0.343674, 0.353410, 0.972451, -2.057944, 2.959835, -0.695290, 0.021337, 0.040502, 0.526812, -0.003847, 0.002820, -1.364003, 0.000000, 0.377001, 0.179271, 0.193815
+ 230, 231, 620349.895, 5173941.073, -1.493018, 3.568071, -0.418438, -0.001541, 0.000928, -1.661260, 0.124877, 0.034292, 3.641587, -0.088843, 0.349148, -0.254457, -0.767296, 2.823759, -0.271729, 0.001069, 0.037777, 0.028292, -0.001963, 0.003463, -0.566793, 0.000000, 0.041704, 0.383657, 0.077997
+ 231, 232, 604062.6384, 5179813.881, 2.407413, 3.655779, 0.658523, -0.002763, 0.000985, -2.804449, 0.108713, 0.033240, 3.270537, -0.117403, 0.368640, -0.318477, 1.363958, 2.914501, 0.467990, -0.023485, 0.039149, -0.599874, 0.000119, 0.002485, 0.048082, 0.000000, 0.467670, 0.307213, 0.133170
+ 232, 233, 620783.4227, 5200574.436, 1.779071, 3.792958, 0.469046, -0.001533, 0.000890, -1.721876, 0.016413, 0.032370, 0.507051, 0.354912, 0.345697, 1.026656, -1.018145, 2.862713, -0.355658, 0.008828, 0.039362, 0.224267, -0.003910, 0.002541, -1.538910, 0.000000, 0.440594, 0.134929, 0.195210
+ 233, 234, 599549.2386, 5193505.934, 2.701977, 4.375356, 0.617544, -0.004437, 0.001131, -3.923965, 0.071348, 0.036551, 1.952009, 0.367395, 0.428213, 0.857973, -1.366521, 3.304427, -0.413543, 0.012024, 0.045259, 0.265664, -0.001811, 0.002689, -0.673597, 0.000000, 0.358235, 0.388960, 0.338271
+ 234, 235, 612445.4671, 5194328.622, 4.694436, 3.751834, 1.251238, -0.001903, 0.000807, -2.357402, 0.024148, 0.029216, 0.826528, 0.521059, 0.342974, 1.519235, 1.541507, 2.862970, 0.538429, -0.025467, 0.039582, -0.643403, -0.001966, 0.002330, -0.843748, 0.000000, 0.294435, 0.160934, 0.169173
+ 235, 236, 610288.3161, 5176959.211, -0.081446, 3.804429, -0.021408, -0.001212, 0.001011, -1.199028, 0.120965, 0.033933, 3.564835, 0.254690, 0.375107, 0.678979, 1.000783, 3.002014, 0.333371, -0.019735, 0.040136, -0.491699, -0.001071, 0.002794, -0.383300, 0.000000, 0.631473, 0.283136, 0.090771
+ 236, 237, 602866.8612, 5198870.173, 2.682277, 4.143859, 0.647290, -0.003878, 0.001032, -3.755945, 0.044678, 0.034930, 1.279047, 0.612201, 0.412000, 1.485925, -1.668810, 3.155010, -0.528940, 0.015013, 0.043317, 0.346593, -0.002628, 0.002748, -0.956064, 0.000000, 0.151981, 0.343799, 0.091186
+ 237, 238, 623024.8193, 5194185.201, 1.503115, 3.653848, 0.411379, -0.002048, 0.000889, -2.304791, 0.019568, 0.031650, 0.618254, 0.348067, 0.340960, 1.020843, -1.484809, 2.822531, -0.526056, 0.016336, 0.038548, 0.423786, -0.001994, 0.002657, -0.750443, 0.000000, 0.817877, 0.153488, 0.052783
+ 238, 239, 620219.8678, 5189947.304, 1.740308, 3.647879, 0.477074, -0.001873, 0.000892, -2.100245, 0.030942, 0.030951, 0.999694, 0.215632, 0.343509, 0.627734, -0.360500, 2.868735, -0.125665, 0.004187, 0.038865, 0.107732, 0.001061, 0.002832, 0.374712, 0.000000, 0.826365, 0.129397, 0.119622
diff --git a/pysal/examples/clearwater/clearwater_BS_NN_summary.txt b/pysal/examples/clearwater/clearwater_BS_NN_summary.txt
new file mode 100755
index 0000000..2583565
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_BS_NN_summary.txt
@@ -0,0 +1,179 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:10:09 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\clearwater_BS_NN.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+Number of areas/points: 239
+
+Model settings---------------------------------
+Model type: Logistic
+Geographic kernel: adaptive bi-square
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 7
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: UserID
+Easting (x-coord): field2 : X
+Northing (y-coord): field3: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field9: Landslid
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field4: Elev
+Independent variable with varying (Local) coefficient: field5: Slope
+Independent variable with varying (Local) coefficient: field6: SinAspct
+Independent variable with varying (Local) coefficient: field7: CosAspct
+Independent variable with varying (Local) coefficient: field8: AbsSouth
+Independent variable with varying (Local) coefficient: field10: DistStrm
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 7
+Deviance: 264.959861
+Classic AIC: 278.959861
+AICc: 279.444709
+BIC/MDL: 303.295106
+Percent deviance explained 0.186174
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 0.491477 2.249648 0.218469 1.634730
+Elev -0.002408 0.000548 -4.394976 0.997595
+Slope 0.076097 0.019801 3.842994 1.079067
+SinAspct 0.083827 0.213948 0.391807 1.087440
+CosAspct -1.191339 1.747013 -0.681929 0.303814
+AbsSouth 0.008819 0.023743 0.371420 1.008858
+DistStrm -0.001732 0.001605 -1.078779 0.998270
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 64, 239
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 64.000 Criterion: 307.217
+ p1 Bandwidth: 130.844 Criterion: 278.396
+ p2 Bandwidth: 172.156 Criterion: 277.767
+ pU Bandwidth: 239.000 Criterion: 277.888
+ iter 1 (p2) Bandwidth: 172.156 Criterion: 277.767 Diff: 41.312
+ iter 2 (p1) Bandwidth: 172.156 Criterion: 277.767 Diff: 25.532
+ iter 3 (p1) Bandwidth: 156.376 Criterion: 277.695 Diff: 15.780
+ iter 4 (p2) Bandwidth: 156.376 Criterion: 277.695 Diff: 9.752
+ iter 5 (p1) Bandwidth: 156.376 Criterion: 277.695 Diff: 6.027
+ iter 6 (p2) Bandwidth: 156.376 Criterion: 277.695 Diff: 3.725
+ iter 7 (p2) Bandwidth: 158.678 Criterion: 277.627 Diff: 2.302
+ iter 8 (p1) Bandwidth: 158.678 Criterion: 277.627 Diff: 1.423
+ iter 9 (p2) Bandwidth: 158.678 Criterion: 277.627 Diff: 0.879
+ iter 10 (p1) Bandwidth: 158.678 Criterion: 277.627 Diff: 0.543
+Best bandwidth size 158.000
+Minimum AICc 277.627
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 158.678441
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 599444.687500 632987.750000 33543.062500
+Y-coord 5172366.500000 5201169.128000 28802.628000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 24.971219
+Effective number of parameters (variance: trace(S'WSW^-1)): 0.187579
+Degree of freedom (model: n - trace(S)): 214.028781
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 189.245141
+Deviance: 221.595520
+Classic AIC: 271.537957
+AICc: 277.626646
+BIC/MDL: 358.349489
+Percent deviance explained 0.319368
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\clearwater_BS_NN_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 1.466570 1.376473
+Elev -0.002510 0.001063
+Slope 0.066079 0.035704
+SinAspct 0.131517 0.281864
+CosAspct -0.593534 1.417622
+AbsSouth 0.002294 0.017082
+DistStrm -0.000804 0.001563
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept -1.493018 4.863537 6.356555
+Elev -0.004867 -0.001109 0.003758
+Slope 0.014891 0.130219 0.115328
+SinAspct -0.507034 0.619998 1.127033
+CosAspct -4.300951 2.327529 6.628479
+AbsSouth -0.034552 0.043573 0.078125
+DistStrm -0.003933 0.002501 0.006434
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 0.354199 1.472082 2.646517
+Elev -0.003376 -0.002206 -0.001471
+Slope 0.027758 0.068542 0.092665
+SinAspct -0.081267 0.197064 0.343674
+CosAspct -1.539245 -0.367067 0.421195
+AbsSouth -0.009257 0.001069 0.014802
+DistStrm -0.002113 -0.000712 0.000331
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 2.292318 1.699272
+Elev 0.001905 0.001412
+Slope 0.064907 0.048115
+SinAspct 0.424941 0.315004
+CosAspct 1.960439 1.453254
+AbsSouth 0.024059 0.017835
+DistStrm 0.002444 0.001812
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 264.960 232.000 1.142
+GWR model 221.596 189.245 1.171
+Difference 43.364 42.755 1.014
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:10:14 AM
diff --git a/pysal/examples/clearwater/clearwater_GS_F.ctl b/pysal/examples/clearwater/clearwater_GS_F.ctl
new file mode 100755
index 0000000..5dc9f9d
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_GS_F.ctl
@@ -0,0 +1,42 @@
+
+C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 10
+Number_of_areas: 239
+Fields
+AreaKey 001 UserID
+X 002 X
+Y 003 Y
+Gmetric 0
+Dependent 009 Landslid
+Offset
+Independent_geo 7
+000 Intercept
+004 Elev
+005 Slope
+006 SinAspct
+007 CosAspct
+008 AbsSouth
+010 DistStrm
+Independent_fix 0
+Unused_fields 0
+MODELTYPE: 2
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 0
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\clearwater_GS_F_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\clearwater_GS_F_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/clearwater/clearwater_GS_F_listwise.csv b/pysal/examples/clearwater/clearwater_GS_F_listwise.csv
new file mode 100755
index 0000000..9a616f2
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_GS_F_listwise.csv
@@ -0,0 +1,240 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_Elev, se_Elev, t_Elev, est_Slope, se_Slope, t_Slope, est_SinAspct, se_SinAspct, t_SinAspct, est_CosAspct, se_CosAspct, t_CosAspct, est_AbsSouth, se_AbsSouth, t_AbsSouth, est_DistStrm, se_DistStrm, t_DistStrm, y, yhat, localpdev, Ginfluence
+ 0, 1, 616168.5625, 5201076.5, 1.730405, 3.206169, 0.539711, -0.001833, 0.000727, -2.523155, 0.027145, 0.027089, 1.002060, 0.381700, 0.312785, 1.220327, -0.972648, 2.473667, -0.393201, 0.007595, 0.033929, 0.223837, -0.003264, 0.002261, -1.443144, 1.000000, 0.734095, 0.147645, 0.063248
+ 1, 2, 624923.8125, 5201008.5, 0.592114, 3.601702, 0.164398, -0.001866, 0.000860, -2.169288, 0.023690, 0.031347, 0.755747, 0.325893, 0.334522, 0.974205, -2.209162, 2.781337, -0.794280, 0.024194, 0.037974, 0.637120, -0.003988, 0.002597, -1.535739, 1.000000, 0.576794, 0.136887, 0.154913
+ 2, 3, 615672, 5199187.5, 1.773415, 3.019972, 0.587229, -0.001895, 0.000690, -2.744058, 0.031325, 0.025443, 1.231151, 0.377277, 0.296575, 1.272115, -0.823968, 2.340081, -0.352111, 0.006046, 0.032025, 0.188800, -0.002859, 0.002143, -1.334051, 1.000000, 0.666062, 0.153452, 0.092463
+ 3, 4, 615209.3125, 5199112, 1.824704, 3.007723, 0.606673, -0.001930, 0.000689, -2.800525, 0.031798, 0.025316, 1.256064, 0.382858, 0.296407, 1.291661, -0.797180, 2.331327, -0.341943, 0.005651, 0.031903, 0.177126, -0.002826, 0.002138, -1.321892, 1.000000, 0.562961, 0.157247, 0.093224
+ 4, 5, 616354.6875, 5198945.5, 1.688580, 3.009009, 0.561175, -0.001855, 0.000688, -2.697183, 0.031406, 0.025390, 1.236932, 0.366934, 0.294002, 1.248065, -0.851603, 2.332244, -0.365143, 0.006553, 0.031908, 0.205374, -0.002839, 0.002133, -1.331057, 1.000000, 0.596195, 0.148915, 0.077049
+ 5, 6, 614559, 5198855, 1.889837, 2.979985, 0.634177, -0.001985, 0.000686, -2.895348, 0.032809, 0.025054, 1.309536, 0.389662, 0.295072, 1.320566, -0.758450, 2.311502, -0.328120, 0.005116, 0.031620, 0.161806, -0.002751, 0.002123, -1.295886, 1.000000, 0.606380, 0.163166, 0.047793
+ 6, 7, 618648.5, 5198468, 1.393810, 3.032237, 0.459664, -0.001786, 0.000695, -2.571477, 0.030924, 0.025717, 1.202462, 0.340871, 0.290764, 1.172331, -1.051907, 2.351365, -0.447360, 0.009511, 0.032149, 0.295848, -0.002849, 0.002140, -1.331459, 1.000000, 0.654650, 0.138917, 0.096300
+ 7, 8, 622724.0625, 5197661, 0.935597, 3.159078, 0.296161, -0.001927, 0.000738, -2.609741, 0.029854, 0.027105, 1.101419, 0.316294, 0.295150, 1.071639, -1.656416, 2.453884, -0.675018, 0.017499, 0.033508, 0.522236, -0.002907, 0.002258, -1.287396, 1.000000, 0.730865, 0.143577, 0.060311
+ 8, 9, 622363.4375, 5196686.5, 0.982215, 3.057326, 0.321266, -0.001959, 0.000714, -2.742152, 0.032133, 0.026245, 1.224333, 0.308922, 0.285918, 1.080458, -1.552482, 2.378904, -0.652604, 0.016242, 0.032470, 0.500230, -0.002643, 0.002188, -1.208019, 1.000000, 0.718376, 0.147810, 0.037747
+ 9, 10, 622308.125, 5196407.5, 0.989106, 3.031799, 0.326244, -0.001971, 0.000709, -2.780566, 0.032790, 0.026041, 1.259151, 0.306291, 0.283530, 1.080277, -1.532079, 2.360147, -0.649145, 0.015997, 0.032208, 0.496672, -0.002571, 0.002170, -1.184622, 1.000000, 0.646096, 0.149279, 0.077506
+ 10, 11, 616650.125, 5195894, 1.569206, 2.756256, 0.569325, -0.001891, 0.000640, -2.953998, 0.038638, 0.023368, 1.653467, 0.332842, 0.268485, 1.239704, -0.707094, 2.147765, -0.329223, 0.005268, 0.029295, 0.179830, -0.002233, 0.001963, -1.137574, 1.000000, 0.642277, 0.154856, 0.044947
+ 11, 12, 616583.3125, 5195804, 1.572900, 2.747818, 0.572418, -0.001895, 0.000639, -2.966703, 0.038929, 0.023298, 1.670866, 0.332257, 0.267778, 1.240796, -0.696788, 2.141456, -0.325381, 0.005141, 0.029206, 0.176027, -0.002213, 0.001957, -1.130690, 1.000000, 0.486276, 0.155476, 0.107727
+ 12, 13, 615594.5625, 5195718, 1.677608, 2.721993, 0.616316, -0.001939, 0.000636, -3.049208, 0.039896, 0.023049, 1.730884, 0.340279, 0.267417, 1.272464, -0.616095, 2.121672, -0.290382, 0.004007, 0.028927, 0.138507, -0.002166, 0.001944, -1.114055, 1.000000, 0.610445, 0.161130, 0.075078
+ 13, 14, 620206.75, 5194831, 1.157395, 2.807796, 0.412208, -0.001933, 0.000654, -2.955301, 0.038465, 0.024072, 1.597894, 0.295024, 0.265014, 1.113238, -1.116919, 2.190500, -0.509892, 0.010825, 0.029867, 0.362433, -0.002126, 0.001994, -1.066190, 1.000000, 0.697828, 0.153781, 0.045879
+ 14, 15, 630007.1875, 5194813.5, 0.935750, 3.545484, 0.263927, -0.003063, 0.000895, -3.423930, 0.031292, 0.031814, 0.983589, 0.251961, 0.327080, 0.770334, -2.983310, 2.774428, -1.075288, 0.031992, 0.037764, 0.847158, -0.002260, 0.002795, -0.808544, 1.000000, 0.563343, 0.222323, 0.116563
+ 15, 16, 627247, 5194165, 0.855959, 3.206677, 0.266930, -0.002678, 0.000795, -3.369638, 0.033877, 0.028562, 1.186085, 0.257385, 0.296940, 0.866792, -2.434984, 2.509491, -0.970310, 0.026308, 0.034142, 0.770546, -0.002086, 0.002448, -0.852045, 1.000000, 0.803841, 0.200505, 0.085150
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+ 214, 215, 631608.2396, 5188665.756, 1.275321, 3.382795, 0.377002, -0.004034, 0.000998, -4.044278, 0.043299, 0.032443, 1.334611, -0.007877, 0.328904, -0.023948, -3.347134, 2.671628, -1.252844, 0.033710, 0.035922, 0.938398, 0.000109, 0.002693, 0.040525, 0.000000, 0.475524, 0.303081, 0.121521
+ 215, 216, 629692.1378, 5180036.782, 0.242544, 3.529737, 0.068714, -0.003844, 0.001018, -3.776067, 0.077497, 0.032935, 2.353059, -0.365020, 0.354386, -1.030007, -3.104030, 2.817604, -1.101656, 0.028443, 0.037567, 0.757130, 0.001270, 0.002845, 0.446512, 0.000000, 0.063660, 0.369033, 0.056769
+ 216, 217, 629298.0091, 5191396.511, 1.019597, 3.222267, 0.316422, -0.003286, 0.000859, -3.824060, 0.038435, 0.029814, 1.289143, 0.156265, 0.302071, 0.517312, -2.828182, 2.533186, -1.116452, 0.029715, 0.034300, 0.866325, -0.001092, 0.002537, -0.430331, 0.000000, 0.310593, 0.249115, 0.090106
+ 217, 218, 602584.5477, 5199364.143, 2.155733, 3.985328, 0.540917, -0.003982, 0.001030, -3.864727, 0.046706, 0.034023, 1.372763, 0.638137, 0.408690, 1.561421, -2.077615, 3.087539, -0.672903, 0.021531, 0.042062, 0.511883, -0.002850, 0.002736, -1.041800, 0.000000, 0.693346, 0.331178, 0.108075
+ 218, 219, 614412.9294, 5176900.203, 0.216416, 2.933988, 0.073762, -0.001643, 0.000727, -2.260148, 0.106141, 0.026204, 4.050518, 0.088471, 0.281260, 0.314552, 0.363215, 2.306895, 0.157448, -0.011024, 0.031203, -0.353293, -0.001161, 0.002173, -0.534452, 0.000000, 0.542297, 0.271384, 0.050300
+ 219, 220, 607340.04, 5198506.602, 2.243034, 3.276758, 0.684528, -0.002992, 0.000804, -3.721202, 0.040282, 0.027430, 1.468507, 0.511608, 0.329688, 1.551796, -1.206468, 2.543536, -0.474327, 0.010328, 0.034725, 0.297428, -0.002595, 0.002296, -1.129882, 0.000000, 0.103503, 0.254547, 0.122787
+ 220, 221, 615188.3811, 5181072.612, 0.426793, 2.650029, 0.161052, -0.001836, 0.000644, -2.852805, 0.090305, 0.023030, 3.921167, 0.080092, 0.252068, 0.317739, -0.040099, 2.066797, -0.019402, -0.004430, 0.028013, -0.158125, -0.000937, 0.001847, -0.507444, 0.000000, 0.828920, 0.247947, 0.057503
+ 221, 222, 622387.6441, 5178096.969, -0.360073, 2.992237, -0.120336, -0.002285, 0.000774, -2.953685, 0.094493, 0.027237, 3.469351, -0.140566, 0.284035, -0.494891, -1.260650, 2.339185, -0.538927, 0.008773, 0.031555, 0.278014, -0.000661, 0.002443, -0.270631, 0.000000, 0.097368, 0.319086, 0.052569
+ 222, 223, 613872.1485, 5175588.719, 0.222050, 3.062254, 0.072512, -0.001567, 0.000759, -2.063784, 0.111113, 0.027571, 4.030034, 0.106481, 0.293456, 0.362852, 0.566250, 2.415711, 0.234403, -0.014165, 0.032663, -0.433673, -0.001213, 0.002289, -0.529865, 0.000000, 0.743247, 0.276129, 0.063413
+ 223, 224, 619242.8056, 5195635.056, 1.274684, 2.823383, 0.451474, -0.001876, 0.000654, -2.867281, 0.037224, 0.024075, 1.546158, 0.310540, 0.268868, 1.154989, -1.000224, 2.200371, -0.454571, 0.009258, 0.030019, 0.308394, -0.002267, 0.002002, -1.132253, 0.000000, 0.746233, 0.149029, 0.103214
+ 224, 225, 619211.842, 5200420.328, 1.321538, 3.229561, 0.409201, -0.001721, 0.000739, -2.328470, 0.026614, 0.027517, 0.967177, 0.345607, 0.307965, 1.122228, -1.227940, 2.493280, -0.492500, 0.011485, 0.034135, 0.336456, -0.003306, 0.002267, -1.458252, 0.000000, 0.783811, 0.132189, 0.075012
+ 225, 226, 609101.2685, 5180122.778, 1.101582, 2.878006, 0.382759, -0.002018, 0.000691, -2.921926, 0.096673, 0.025057, 3.858133, 0.061016, 0.281949, 0.216409, 0.580038, 2.263359, 0.256273, -0.012887, 0.030619, -0.420899, -0.000646, 0.001978, -0.326392, 0.000000, 0.693325, 0.252884, 0.052802
+ 226, 227, 632109.6431, 5187482.36, 1.319753, 3.435083, 0.384198, -0.004256, 0.001046, -4.067722, 0.046250, 0.033223, 1.392128, -0.081704, 0.339713, -0.240509, -3.489077, 2.719343, -1.283059, 0.034593, 0.036446, 0.949136, 0.000567, 0.002711, 0.209088, 0.000000, 0.249778, 0.320744, 0.173685
+ 227, 228, 607882.219, 5195381.365, 2.237184, 2.971152, 0.752969, -0.002826, 0.000736, -3.841522, 0.047358, 0.024685, 1.918505, 0.419883, 0.296339, 1.416901, -0.776587, 2.304170, -0.337036, 0.005256, 0.031377, 0.167502, -0.001979, 0.002079, -0.951907, 0.000000, 0.834940, 0.244186, 0.069803
+ 228, 229, 607131.881, 5185808.074, 1.718200, 2.752119, 0.624319, -0.002566, 0.000679, -3.778985, 0.079857, 0.023330, 3.422901, 0.106030, 0.271567, 0.390438, 0.099716, 2.147690, 0.046430, -0.005832, 0.029134, -0.200187, -0.000671, 0.001839, -0.365047, 0.000000, 0.161641, 0.252771, 0.055723
+ 229, 230, 627346.0895, 5201105.558, 0.384396, 3.819139, 0.100650, -0.002058, 0.000923, -2.230101, 0.023884, 0.033514, 0.712649, 0.328255, 0.353280, 0.929165, -2.672366, 2.953959, -0.904673, 0.029734, 0.040298, 0.737851, -0.004315, 0.002830, -1.524804, 0.000000, 0.365282, 0.151615, 0.230600
+ 230, 231, 620349.895, 5173941.073, -0.735000, 3.187194, -0.230610, -0.001803, 0.000824, -2.187706, 0.113288, 0.029640, 3.822177, -0.085590, 0.304573, -0.281017, -0.630727, 2.507074, -0.251579, -0.000531, 0.033813, -0.015697, -0.001311, 0.002741, -0.478318, 0.000000, 0.047460, 0.330909, 0.074429
+ 231, 232, 604062.6384, 5179813.881, 1.943061, 3.235409, 0.600561, -0.002620, 0.000804, -3.260127, 0.102436, 0.029079, 3.522709, -0.068508, 0.327053, -0.209470, 0.863596, 2.580583, 0.334652, -0.017075, 0.034801, -0.490642, -0.000327, 0.002282, -0.143457, 0.000000, 0.458973, 0.279576, 0.114624
+ 232, 233, 620783.4227, 5200574.436, 1.104016, 3.311543, 0.333384, -0.001715, 0.000766, -2.239927, 0.025525, 0.028374, 0.899593, 0.335236, 0.312409, 1.073066, -1.460934, 2.555615, -0.571656, 0.014620, 0.034965, 0.418131, -0.003458, 0.002326, -1.486684, 0.000000, 0.366572, 0.128965, 0.165084
+ 233, 234, 599549.2386, 5193505.934, 2.683907, 4.094130, 0.655550, -0.004379, 0.001080, -4.053181, 0.069797, 0.033410, 2.089101, 0.352302, 0.397484, 0.886331, -1.269341, 3.098093, -0.409717, 0.011036, 0.042331, 0.260719, -0.001522, 0.002591, -0.587537, 0.000000, 0.343886, 0.371672, 0.329304
+ 234, 235, 612445.4671, 5194328.622, 1.906788, 2.634132, 0.723877, -0.002183, 0.000634, -3.442298, 0.046128, 0.022231, 2.075004, 0.347113, 0.262758, 1.321038, -0.439631, 2.053593, -0.214079, 0.001550, 0.027939, 0.055490, -0.001833, 0.001884, -0.973366, 0.000000, 0.211169, 0.189171, 0.088540
+ 235, 236, 610288.3161, 5176959.211, 0.829038, 3.081617, 0.269027, -0.001740, 0.000744, -2.339393, 0.106652, 0.027277, 3.909995, 0.090725, 0.298504, 0.303934, 0.859967, 2.435363, 0.353117, -0.017471, 0.032918, -0.530764, -0.000819, 0.002178, -0.375880, 0.000000, 0.679567, 0.262237, 0.071920
+ 236, 237, 602866.8612, 5198870.173, 2.191445, 3.900129, 0.561890, -0.003906, 0.001004, -3.889822, 0.047124, 0.033099, 1.423720, 0.613638, 0.397360, 1.544285, -1.952892, 3.017635, -0.647160, 0.019942, 0.041125, 0.484908, -0.002743, 0.002671, -1.026849, 0.000000, 0.150903, 0.326360, 0.097830
+ 237, 238, 623024.8193, 5194185.201, 0.937932, 2.909379, 0.322382, -0.002156, 0.000691, -3.120478, 0.037438, 0.025318, 1.478716, 0.274305, 0.270466, 1.014193, -1.597051, 2.272425, -0.702796, 0.016766, 0.030956, 0.541610, -0.002050, 0.002104, -0.974239, 0.000000, 0.816236, 0.167935, 0.036750
+ 238, 239, 620219.8678, 5189947.304, 0.902774, 2.583790, 0.349399, -0.002106, 0.000616, -3.419184, 0.053071, 0.022563, 2.352096, 0.200171, 0.239171, 0.836936, -1.012431, 2.019536, -0.501319, 0.009376, 0.027443, 0.341653, -0.001213, 0.001798, -0.674835, 0.000000, 0.870567, 0.190668, 0.057395
diff --git a/pysal/examples/clearwater/clearwater_GS_F_summary.txt b/pysal/examples/clearwater/clearwater_GS_F_summary.txt
new file mode 100755
index 0000000..602fb3b
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_GS_F_summary.txt
@@ -0,0 +1,175 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:13:55 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\clearwater_GS_F.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+Number of areas/points: 239
+
+Model settings---------------------------------
+Model type: Logistic
+Geographic kernel: fixed Gaussian
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 7
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: UserID
+Easting (x-coord): field2 : X
+Northing (y-coord): field3: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field9: Landslid
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field4: Elev
+Independent variable with varying (Local) coefficient: field5: Slope
+Independent variable with varying (Local) coefficient: field6: SinAspct
+Independent variable with varying (Local) coefficient: field7: CosAspct
+Independent variable with varying (Local) coefficient: field8: AbsSouth
+Independent variable with varying (Local) coefficient: field10: DistStrm
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 7
+Deviance: 264.959861
+Classic AIC: 278.959861
+AICc: 279.444709
+BIC/MDL: 303.295106
+Percent deviance explained 0.186174
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 0.491477 2.249648 0.218469 1.634730
+Elev -0.002408 0.000548 -4.394976 0.997595
+Slope 0.076097 0.019801 3.842994 1.079067
+SinAspct 0.083827 0.213948 0.391807 1.087440
+CosAspct -1.191339 1.747013 -0.681929 0.303814
+AbsSouth 0.008819 0.023743 0.371420 1.008858
+DistStrm -0.001732 0.001605 -1.078779 0.998270
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 3707.06101911124, 19713.7830285109
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 3707.061 Criterion: 291.565
+ p1 Bandwidth: 9821.085 Criterion: 276.073
+ p2 Bandwidth: 13599.759 Criterion: 277.212
+ pU Bandwidth: 19713.783 Criterion: 278.277
+ iter 1 (p1) Bandwidth: 9821.085 Criterion: 276.073 Diff: 3778.674
+ iter 2 (p2) Bandwidth: 9821.085 Criterion: 276.073 Diff: 2335.349
+ iter 3 (p1) Bandwidth: 9821.085 Criterion: 276.073 Diff: 1443.325
+ iter 4 (p1) Bandwidth: 8929.061 Criterion: 275.951 Diff: 892.024
+ iter 5 (p2) Bandwidth: 8929.061 Criterion: 275.951 Diff: 551.301
+ iter 6 (p1) Bandwidth: 8929.061 Criterion: 275.951 Diff: 340.723
+Best bandwidth size 8929.061
+Minimum AICc 275.951
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 8929.060733
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 599444.687500 632987.750000 33543.062500
+Y-coord 5172366.500000 5201169.128000 28802.628000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 20.030117
+Effective number of parameters (variance: trace(S'WSW^-1)): 0.241124
+Degree of freedom (model: n - trace(S)): 218.969883
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 199.180889
+Deviance: 232.025375
+Classic AIC: 272.085609
+AICc: 275.950691
+BIC/MDL: 341.719582
+Percent deviance explained 0.287333
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\clearwater_GS_F_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 1.105642 0.866015
+Elev -0.002520 0.000755
+Slope 0.068707 0.025901
+SinAspct 0.127146 0.224751
+CosAspct -0.888401 1.170416
+AbsSouth 0.005735 0.014309
+DistStrm -0.001137 0.001074
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept -1.043824 2.973450 4.017274
+Elev -0.004937 -0.001391 0.003546
+Slope 0.023690 0.122655 0.098965
+SinAspct -0.631698 0.638137 1.269835
+CosAspct -4.018434 2.081159 6.099593
+AbsSouth -0.035317 0.035965 0.071281
+DistStrm -0.004315 0.002921 0.007236
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 0.459690 0.989106 1.824027
+Elev -0.002936 -0.002250 -0.001951
+Slope 0.044754 0.071742 0.088308
+SinAspct 0.034669 0.118862 0.289318
+CosAspct -1.597051 -0.815072 -0.035431
+AbsSouth -0.004537 0.005944 0.015533
+DistStrm -0.001841 -0.000951 -0.000644
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 1.364337 1.011369
+Elev 0.000985 0.000731
+Slope 0.043553 0.032286
+SinAspct 0.254650 0.188769
+CosAspct 1.561620 1.157613
+AbsSouth 0.020070 0.014877
+DistStrm 0.001196 0.000887
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 264.960 232.000 1.142
+GWR model 232.025 199.181 1.165
+Difference 32.934 32.819 1.004
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:14:00 AM
diff --git a/pysal/examples/clearwater/clearwater_GS_NN.ctl b/pysal/examples/clearwater/clearwater_GS_NN.ctl
new file mode 100755
index 0000000..8ca560a
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_GS_NN.ctl
@@ -0,0 +1,42 @@
+
+C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 10
+Number_of_areas: 239
+Fields
+AreaKey 001 UserID
+X 002 X
+Y 003 Y
+Gmetric 0
+Dependent 009 Landslid
+Offset
+Independent_geo 7
+000 Intercept
+004 Elev
+005 Slope
+006 SinAspct
+007 CosAspct
+008 AbsSouth
+010 DistStrm
+Independent_fix 0
+Unused_fields 0
+MODELTYPE: 2
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 3
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\clearwater_GS_NN_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\clearwater_GS_NN_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/clearwater/clearwater_GS_NN_listwise.csv b/pysal/examples/clearwater/clearwater_GS_NN_listwise.csv
new file mode 100755
index 0000000..cfa020a
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_GS_NN_listwise.csv
@@ -0,0 +1,240 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_Elev, se_Elev, t_Elev, est_Slope, se_Slope, t_Slope, est_SinAspct, se_SinAspct, t_SinAspct, est_CosAspct, se_CosAspct, t_CosAspct, est_AbsSouth, se_AbsSouth, t_AbsSouth, est_DistStrm, se_DistStrm, t_DistStrm, y, yhat, localpdev, Ginfluence
+ 0, 1, 616168.5625, 5201076.5, 1.130178, 2.618658, 0.431587, -0.002166, 0.000616, -3.515336, 0.043009, 0.022388, 1.921087, 0.317213, 0.259325, 1.223225, -1.303400, 2.042808, -0.638043, 0.012233, 0.027812, 0.439850, -0.002619, 0.001910, -1.370992, 1.000000, 0.681633, 0.182103, 0.046709
+ 1, 2, 624923.8125, 5201008.5, 0.678550, 2.748040, 0.246921, -0.002176, 0.000642, -3.391518, 0.042362, 0.023685, 1.788574, 0.262447, 0.260785, 1.006373, -1.815914, 2.146941, -0.845815, 0.018479, 0.029241, 0.631946, -0.002797, 0.002013, -1.389836, 1.000000, 0.465041, 0.184004, 0.089686
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diff --git a/pysal/examples/clearwater/clearwater_GS_NN_summary.txt b/pysal/examples/clearwater/clearwater_GS_NN_summary.txt
new file mode 100755
index 0000000..99b228c
--- /dev/null
+++ b/pysal/examples/clearwater/clearwater_GS_NN_summary.txt
@@ -0,0 +1,174 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:13:25 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\clearwater_GS_NN.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\clearwater\clearwater\landslides.csv
+Number of areas/points: 239
+
+Model settings---------------------------------
+Model type: Logistic
+Geographic kernel: adaptive Gaussian
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 7
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: UserID
+Easting (x-coord): field2 : X
+Northing (y-coord): field3: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field9: Landslid
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field4: Elev
+Independent variable with varying (Local) coefficient: field5: Slope
+Independent variable with varying (Local) coefficient: field6: SinAspct
+Independent variable with varying (Local) coefficient: field7: CosAspct
+Independent variable with varying (Local) coefficient: field8: AbsSouth
+Independent variable with varying (Local) coefficient: field10: DistStrm
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 7
+Deviance: 264.959861
+Classic AIC: 278.959861
+AICc: 279.444709
+BIC/MDL: 303.295106
+Percent deviance explained 0.186174
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 0.491477 2.249648 0.218469 1.634730
+Elev -0.002408 0.000548 -4.394976 0.997595
+Slope 0.076097 0.019801 3.842994 1.079067
+SinAspct 0.083827 0.213948 0.391807 1.087440
+CosAspct -1.191339 1.747013 -0.681929 0.303814
+AbsSouth 0.008819 0.023743 0.371420 1.008858
+DistStrm -0.001732 0.001605 -1.078779 0.998270
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 64, 239
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 64.000 Criterion: 276.386
+ p1 Bandwidth: 67.725 Criterion: 276.407
+ p2 Bandwidth: 70.027 Criterion: 276.568
+ pU Bandwidth: 73.752 Criterion: 276.533
+ iter 1 (p1) Bandwidth: 67.725 Criterion: 276.407 Diff: 2.302
+ iter 2 (p2) Bandwidth: 67.725 Criterion: 276.407 Diff: 1.423
+ iter 3 (p1) Bandwidth: 67.725 Criterion: 276.407 Diff: 0.879
+The lower limit in your search has been selected as the optimal bandwidth size.
+A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search.
+Best bandwidth size 64.000
+Minimum AICc 276.386
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 64.000000
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 599444.687500 632987.750000 33543.062500
+Y-coord 5172366.500000 5201169.128000 28802.628000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 16.605439
+Effective number of parameters (variance: trace(S'WSW^-1)): 0.186461
+Degree of freedom (model: n - trace(S)): 222.394561
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 205.975584
+Deviance: 240.533728
+Classic AIC: 273.744605
+AICc: 276.385556
+BIC/MDL: 331.472808
+Percent deviance explained 0.261199
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\clearwater_GS_NN_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 0.939810 0.667065
+Elev -0.002323 0.000389
+Slope 0.069215 0.021083
+SinAspct 0.130582 0.146202
+CosAspct -0.779778 0.852770
+AbsSouth 0.004892 0.010695
+DistStrm -0.001187 0.000715
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept -0.441478 1.976117 2.417595
+Elev -0.003157 -0.001675 0.001482
+Slope 0.035256 0.104522 0.069266
+SinAspct -0.147649 0.380231 0.527880
+CosAspct -2.541313 0.774141 3.315454
+AbsSouth -0.016221 0.024797 0.041018
+DistStrm -0.002797 0.000078 0.002875
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 0.415644 1.050445 1.556356
+Elev -0.002640 -0.002280 -0.002005
+Slope 0.048848 0.071095 0.085036
+SinAspct 0.016837 0.116498 0.261812
+CosAspct -1.453046 -0.747009 0.010831
+AbsSouth -0.004595 0.005464 0.013481
+DistStrm -0.001758 -0.000978 -0.000639
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 1.140712 0.845599
+Elev 0.000636 0.000471
+Slope 0.036188 0.026825
+SinAspct 0.244975 0.181597
+CosAspct 1.463877 1.085157
+AbsSouth 0.018077 0.013400
+DistStrm 0.001119 0.000830
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 264.960 232.000 1.142
+GWR model 240.534 205.976 1.168
+Difference 24.426 26.024 0.939
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:13:29 AM
diff --git a/pysal/examples/clearwater/landslides.csv b/pysal/examples/clearwater/landslides.csv
new file mode 100755
index 0000000..128a5ca
--- /dev/null
+++ b/pysal/examples/clearwater/landslides.csv
@@ -0,0 +1,240 @@
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diff --git a/pysal/examples/georgia/GData_utm.csv b/pysal/examples/georgia/GData_utm.csv
new file mode 100755
index 0000000..b6fb968
--- /dev/null
+++ b/pysal/examples/georgia/GData_utm.csv
@@ -0,0 +1,160 @@
+AreaKey,Latitude,Longitud,TotPop90,PctRural,PctBach,PctEld,PctFB,PctPov,PctBlack,ID,X,Y
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+13115,34.26330,-85.21503,81251,36.10,13.70,13.84,1.09,13.60,13.56,18,662257.40,3789664.00
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new file mode 100644
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diff --git a/pysal/examples/georgia/err.p b/pysal/examples/georgia/err.p
new file mode 100644
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diff --git a/pysal/examples/georgia/georgia_BS_F.ctl b/pysal/examples/georgia/georgia_BS_F.ctl
new file mode 100755
index 0000000..5dc124d
--- /dev/null
+++ b/pysal/examples/georgia/georgia_BS_F.ctl
@@ -0,0 +1,45 @@
+
+C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 13
+Number_of_areas: 159
+Fields
+AreaKey 001 AreaKey
+X 012 X
+Y 013 Y
+Gmetric 0
+Dependent 006 PctBach
+Offset
+Independent_geo 4
+000 Intercept
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+010 PctBlack
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+Unused_fields 6
+002 Latitude
+003 Longitud
+004 TotPop90
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+008 PctFB
+011 ID
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+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 1
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\georgia_BS_F_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\georgia_BS_F_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/georgia/georgia_BS_F_listwise.csv b/pysal/examples/georgia/georgia_BS_F_listwise.csv
new file mode 100755
index 0000000..ec525a4
--- /dev/null
+++ b/pysal/examples/georgia/georgia_BS_F_listwise.csv
@@ -0,0 +1,160 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD
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+ 149, 13303, 894704.3, 3648583, 23.561458, 1.780554, 13.232656, -0.110584, 0.018676, -5.921070, -0.228893, 0.090454, -2.530499, -0.009672, 0.042947, -0.225197, 9.800000, 10.695591, -0.895591, -0.243482, 0.537175, 0.077885, 0.000299
+ 150, 13305, 986832.8, 3494323, 18.297991, 3.326648, 5.500429, -0.091330, 0.027475, -3.324157, -0.227218, 0.156553, -1.451380, 0.093881, 0.077548, 1.210625, 9.600000, 9.836261, -0.236261, -0.064049, 0.621505, 0.072615, 0.000019
+ 151, 13307, 731576.3, 3544716, 19.661101, 2.010838, 9.777565, -0.079654, 0.019877, -4.007338, -0.303385, 0.104883, -2.892595, 0.080652, 0.056418, 1.429528, 5.500000, 8.918201, -3.418201, -0.950050, 0.599234, 0.117732, 0.007202
+ 152, 13309, 898776.3, 3563384, 18.501153, 2.113110, 8.755415, -0.085034, 0.019080, -4.456837, -0.212335, 0.097675, -2.173892, 0.052857, 0.040097, 1.318242, 8.600000, 5.152843, 3.447157, 0.958650, 0.511966, 0.118747, 0.007405
+ 153, 13311, 796905.6, 3841086, 28.733091, 1.787983, 16.070111, -0.179033, 0.022232, -8.052889, -0.160660, 0.144555, -1.111412, -0.003425, 0.046862, -0.073091, 13.600000, 8.812706, 4.787294, 1.289709, 0.547887, 0.060936, 0.006454
+ 154, 13313, 686891.4, 3855274, 23.394471, 2.554012, 9.159890, -0.083855, 0.032799, -2.556635, -0.572061, 0.189459, -3.019437, 0.255288, 0.086126, 2.964126, 12.000000, 12.211185, -0.211185, -0.056625, 0.650040, 0.052012, 0.000011
+ 155, 13315, 838551.5, 3538547, 18.192778, 2.093412, 8.690492, -0.079700, 0.018426, -4.325377, -0.252670, 0.094685, -2.668518, 0.078927, 0.040585, 1.944708, 7.600000, 5.503119, 2.096881, 0.573156, 0.532468, 0.087779, 0.001890
+ 156, 13317, 891228.5, 3749769, 28.509057, 1.735328, 16.428630, -0.164395, 0.019996, -8.221384, -0.132861, 0.119490, -1.111898, -0.066138, 0.044269, -1.494004, 10.400000, 12.670101, -2.270101, -0.611560, 0.541756, 0.060902, 0.001450
+ 157, 13319, 858796.9, 3637891, 24.021985, 1.546444, 15.533688, -0.114677, 0.016624, -6.898087, -0.258557, 0.084212, -3.070304, 0.004043, 0.040248, 0.100446, 8.800000, 8.768099, 0.031901, 0.008848, 0.564146, 0.113973, 0.000001
+ 158, 13321, 801018.1, 3487328, 18.349546, 2.358175, 7.781248, -0.072543, 0.020883, -3.473737, -0.323199, 0.114810, -2.815070, 0.112093, 0.049993, 2.242190, 6.300000, 8.166285, -1.866285, -0.499215, 0.558495, 0.047469, 0.000743
diff --git a/pysal/examples/georgia/georgia_BS_F_summary.txt b/pysal/examples/georgia/georgia_BS_F_summary.txt
new file mode 100755
index 0000000..4333fb2
--- /dev/null
+++ b/pysal/examples/georgia/georgia_BS_F_summary.txt
@@ -0,0 +1,170 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:04:40 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\georgia_BS_F.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+Number of areas/points: 159
+
+Model settings---------------------------------
+Model type: Gaussian
+Geographic kernel: fixed bi-square
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 4
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: AreaKey
+Easting (x-coord): field12 : X
+Northing (y-coord): field13: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field6: PctBach
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field5: PctRural
+Independent variable with varying (Local) coefficient: field9: PctPov
+Independent variable with varying (Local) coefficient: field10: PctBlack
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Residual sum of squares: 2639.559476
+Number of parameters: 4
+ (Note: this num does not include an error variance term for a Gaussian model)
+ML based global sigma estimate: 4.074433
+Unbiased global sigma estimate: 4.126671
+-2 log-likelihood: 897.927089
+Classic AIC: 907.927089
+AICc: 908.319245
+BIC/MDL: 923.271610
+CV: 18.100197
+R square: 0.485273
+Adjusted R square: 0.471903
+
+Variable Estimate Standard Error t(Est/SE)
+-------------------- --------------- --------------- ---------------
+Intercept 23.854615 1.173043 20.335661
+PctRural -0.111395 0.012878 -8.649661
+PctPov -0.345778 0.070863 -4.879540
+PctBlack 0.058331 0.029187 1.998499
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 108972.626308445, 558903.094487309
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 108972.626 Criterion: 948.618
+ p1 Bandwidth: 280830.773 Criterion: 898.912
+ p2 Bandwidth: 387044.948 Criterion: 903.831
+ pU Bandwidth: 558903.094 Criterion: 906.526
+ iter 1 (p1) Bandwidth: 280830.773 Criterion: 898.912 Diff: 106214.176
+ iter 2 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 65643.971
+ iter 3 (p2) Bandwidth: 215186.802 Criterion: 895.030 Diff: 40570.205
+ iter 4 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 25073.766
+ iter 5 (p2) Bandwidth: 215186.802 Criterion: 895.030 Diff: 15496.439
+ iter 6 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 9577.326
+ iter 7 (p1) Bandwidth: 209267.689 Criterion: 894.983 Diff: 5919.113
+ iter 8 (p2) Bandwidth: 209267.689 Criterion: 894.983 Diff: 3658.213
+Best bandwidth size 209267.689
+Minimum AICc 894.983
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 209267.688808
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 635964.300000 1059706.000000 423741.700000
+Y-coord 3401148.000000 3872640.000000 471492.000000
+
+ Diagnostic information
+Residual sum of squares: 2012.563924
+Effective number of parameters (model: trace(S)): 16.722876
+Effective number of parameters (variance: trace(S'S)): 11.612295
+Degree of freedom (model: n - trace(S)): 142.277124
+Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 137.166544
+ML based sigma estimate: 3.557757
+Unbiased sigma estimate: 3.830458
+-2 log-likelihood: 854.805884
+Classic AIC: 890.251635
+AICc: 894.982602
+BIC/MDL: 944.641443
+CV: 18.254062
+R square: 0.607540
+Adjusted R square: 0.544612
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\georgia_BS_F_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 23.100425 4.102437
+PctRural -0.115556 0.038728
+PctPov -0.284599 0.131956
+PctBlack 0.058275 0.075808
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept 17.723650 30.349333 12.625683
+PctRural -0.197305 -0.054520 0.142786
+PctPov -0.772356 -0.072209 0.700147
+PctBlack -0.075941 0.308952 0.384893
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 18.856279 23.103493 27.099926
+PctRural -0.150482 -0.106458 -0.082746
+PctPov -0.339158 -0.271117 -0.198485
+PctBlack 0.000226 0.057840 0.110329
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 8.243647 6.110932
+PctRural 0.067736 0.050212
+PctPov 0.140673 0.104280
+PctBlack 0.110103 0.081618
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR ANOVA Table
+*****************************************************************************
+Source SS DF MS F
+----------------- ------------------- ---------- --------------- ----------
+Global Residuals 2639.559 155.000
+GWR Improvement 626.996 17.833 35.158
+GWR Residuals 2012.564 137.167 14.672 2.396224
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:04:40 AM
diff --git a/pysal/examples/georgia/georgia_BS_NN.ctl b/pysal/examples/georgia/georgia_BS_NN.ctl
new file mode 100755
index 0000000..9aec16d
--- /dev/null
+++ b/pysal/examples/georgia/georgia_BS_NN.ctl
@@ -0,0 +1,45 @@
+
+C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 13
+Number_of_areas: 159
+Fields
+AreaKey 001 AreaKey
+X 012 X
+Y 013 Y
+Gmetric 0
+Dependent 006 PctBach
+Offset
+Independent_geo 4
+000 Intercept
+005 PctRural
+009 PctPov
+010 PctBlack
+Independent_fix 0
+Unused_fields 6
+002 Latitude
+003 Longitud
+004 TotPop90
+007 PctEld
+008 PctFB
+011 ID
+MODELTYPE: 0
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 2
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\georgia_BS_NN_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\georgia_BS_NN_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/georgia/georgia_BS_NN_listwise.csv b/pysal/examples/georgia/georgia_BS_NN_listwise.csv
new file mode 100755
index 0000000..f51d270
--- /dev/null
+++ b/pysal/examples/georgia/georgia_BS_NN_listwise.csv
@@ -0,0 +1,160 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD
+ 0, 13001, 941396.6, 3521764, 18.375924, 2.414905, 7.609379, -0.087919, 0.021113, -4.164093, -0.218522, 0.115485, -1.892203, 0.069101, 0.048422, 1.427054, 8.200000, 8.815245, -0.615245, -0.162278, 0.551117, 0.041718, 0.000077
+ 1, 13003, 895553, 3471916, 18.039692, 2.693495, 6.697503, -0.077996, 0.022571, -3.455642, -0.291285, 0.126975, -2.294038, 0.109652, 0.053382, 2.054089, 6.400000, 5.611921, 0.788079, 0.213714, 0.557455, 0.093454, 0.000315
+ 2, 13005, 930946.4, 3502787, 18.173904, 2.525672, 7.195672, -0.085464, 0.021449, -3.984466, -0.235007, 0.118227, -1.987757, 0.081621, 0.050307, 1.622470, 6.600000, 8.495724, -1.895724, -0.518796, 0.553851, 0.109830, 0.002225
+ 3, 13007, 745398.6, 3474765, 18.612431, 2.254469, 8.255795, -0.072676, 0.020748, -3.502894, -0.325567, 0.109910, -2.962119, 0.107978, 0.052233, 2.067223, 9.400000, 8.849965, 0.550035, 0.151238, 0.571077, 0.118198, 0.000205
+ 4, 13009, 849431.3, 3665553, 25.027931, 1.767947, 14.156497, -0.128431, 0.019962, -6.433839, -0.188146, 0.106755, -1.762416, -0.028756, 0.050361, -0.571001, 13.300000, 15.032420, -1.732420, -0.470868, 0.559486, 0.097548, 0.001606
+ 5, 13011, 819317.3, 3807616, 28.868732, 1.568279, 18.407904, -0.180965, 0.020098, -9.004150, -0.137907, 0.130001, -1.060813, -0.031319, 0.041694, -0.751165, 6.400000, 8.580509, -2.180509, -0.580528, 0.551175, 0.059443, 0.001427
+ 6, 13013, 803747.1, 3769623, 29.126594, 1.578247, 18.455024, -0.185670, 0.019899, -9.330583, -0.119547, 0.128153, -0.932843, -0.039784, 0.041354, -0.962027, 9.200000, 14.919817, -5.719817, -1.508125, 0.558752, 0.041031, 0.006520
+ 7, 13015, 699011.5, 3793408, 26.738740, 1.663068, 16.077957, -0.143921, 0.019787, -7.273569, -0.379195, 0.129687, -2.923921, 0.074043, 0.041423, 1.787476, 9.000000, 12.540446, -3.540446, -0.929355, 0.571809, 0.032462, 0.001942
+ 8, 13017, 863020.8, 3520432, 17.332852, 2.862878, 6.054346, -0.072048, 0.022398, -3.216792, -0.259626, 0.122903, -2.112442, 0.093798, 0.048378, 1.938852, 7.600000, 11.173490, -3.573490, -0.950910, 0.513439, 0.058498, 0.003764
+ 9, 13019, 859915.8, 3466377, 18.009999, 2.499664, 7.204967, -0.074505, 0.021286, -3.500257, -0.310736, 0.120344, -2.582057, 0.116161, 0.049213, 2.360389, 7.500000, 8.430319, -0.930319, -0.253303, 0.550571, 0.100714, 0.000481
+ 10, 13021, 809736.9, 3636468, 23.331917, 1.754697, 13.296838, -0.117008, 0.019905, -5.878384, -0.244202, 0.105854, -2.306955, 0.017538, 0.051088, 0.343293, 17.000000, 17.490415, -0.490415, -0.139052, 0.578390, 0.170747, 0.000267
+ 11, 13023, 844270.1, 3595691, 18.575691, 2.534320, 7.329656, -0.087278, 0.023528, -3.709493, -0.206076, 0.108188, -1.904795, 0.051458, 0.050376, 1.021487, 10.300000, 10.901700, -0.601700, -0.162155, 0.545373, 0.082058, 0.000157
+ 12, 13025, 979288.9, 3463849, 18.853338, 2.745800, 6.866246, -0.091904, 0.023206, -3.960341, -0.248679, 0.131652, -1.888905, 0.086563, 0.057140, 1.514925, 5.800000, 5.533408, 0.266592, 0.076204, 0.604611, 0.184081, 0.000088
+ 13, 13027, 827822, 3421638, 18.212539, 2.372760, 7.675677, -0.073817, 0.021056, -3.505663, -0.334842, 0.119039, -2.812877, 0.126093, 0.049579, 2.543305, 9.100000, 9.926865, -0.826865, -0.217609, 0.563673, 0.037431, 0.000123
+ 14, 13029, 1023145, 3554982, 20.021869, 2.358372, 8.489700, -0.099557, 0.022023, -4.520597, -0.215786, 0.120983, -1.783596, 0.045851, 0.047733, 0.960576, 11.800000, 9.830106, 1.969894, 0.545752, 0.606627, 0.131419, 0.003019
+ 15, 13031, 994903.4, 3600493, 20.563701, 2.266851, 9.071485, -0.098698, 0.021867, -4.513625, -0.210540, 0.118868, -1.771213, 0.026472, 0.045694, 0.579333, 19.900000, 9.223105, 10.676895, 2.926313, 0.579241, 0.112510, 0.072736
+ 16, 13033, 971593.8, 3671394, 23.303807, 2.330530, 9.999358, -0.108670, 0.022178, -4.899843, -0.199793, 0.116824, -1.710202, -0.024031, 0.048206, -0.498492, 9.600000, 8.139072, 1.460928, 0.397864, 0.547193, 0.101114, 0.001193
+ 17, 13035, 782448.2, 3684504, 27.304692, 1.530430, 17.841186, -0.160167, 0.018818, -8.511440, -0.192097, 0.124573, -1.542034, -0.017182, 0.048881, -0.351502, 7.200000, 11.942142, -4.742142, -1.254875, 0.584010, 0.047942, 0.005313
+ 18, 13037, 724741.2, 3492653, 18.937685, 2.200939, 8.604366, -0.073158, 0.020785, -3.519730, -0.322443, 0.109529, -2.943902, 0.099296, 0.054756, 1.813430, 10.100000, 7.215765, 2.884235, 0.790774, 0.578040, 0.113107, 0.005343
+ 19, 13039, 1008480, 3437933, 19.091389, 2.850729, 6.697020, -0.094154, 0.024029, -3.918325, -0.253757, 0.137167, -1.849990, 0.088445, 0.060000, 1.474091, 13.500000, 13.524229, -0.024229, -0.006914, 0.622744, 0.181309, 0.000001
+ 20, 13043, 964264.9, 3598842, 20.107213, 2.214298, 9.080628, -0.094601, 0.021563, -4.387163, -0.206276, 0.114816, -1.796571, 0.026868, 0.044953, 0.597696, 9.900000, 11.038573, -1.138573, -0.302560, 0.554506, 0.055910, 0.000363
+ 21, 13045, 678778.6, 3713250, 26.584070, 1.581426, 16.810189, -0.135079, 0.019980, -6.760826, -0.534973, 0.135058, -3.961050, 0.126713, 0.046902, 2.701643, 12.000000, 11.586549, 0.413451, 0.108831, 0.616314, 0.037814, 0.000031
+ 22, 13047, 670055.9, 3862318, 25.993181, 1.713271, 15.171670, -0.133974, 0.020694, -6.474081, -0.384352, 0.134571, -2.856124, 0.083942, 0.043006, 1.951858, 8.100000, 15.616062, -7.516062, -2.056611, 0.553322, 0.109586, 0.034878
+ 23, 13049, 962612.3, 3432769, 18.861404, 2.759415, 6.835291, -0.089420, 0.023149, -3.862817, -0.274742, 0.132733, -2.069892, 0.099041, 0.057079, 1.735149, 6.400000, 7.570655, -1.170655, -0.324220, 0.610492, 0.130853, 0.001060
+ 24, 13051, 1059706, 3556747, 20.315761, 2.423043, 8.384399, -0.101838, 0.022656, -4.494979, -0.216591, 0.125196, -1.730021, 0.043496, 0.048872, 0.890002, 18.600000, 17.724733, 0.875267, 0.258772, 0.618849, 0.237285, 0.001396
+ 25, 13053, 704959.2, 3577608, 21.024737, 1.758621, 11.955242, -0.091672, 0.019163, -4.783707, -0.341741, 0.107398, -3.181992, 0.091923, 0.056886, 1.615912, 20.200000, 19.058832, 1.141168, 0.323870, 0.631907, 0.172302, 0.001463
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+ 152, 13309, 898776.3, 3563384, 16.844017, 2.682408, 6.279439, -0.074241, 0.023436, -3.167862, -0.172899, 0.117933, -1.466082, 0.050370, 0.046776, 1.076834, 8.600000, 5.695227, 2.904773, 0.818087, 0.423182, 0.159493, 0.008509
+ 153, 13311, 796905.6, 3841086, 27.838898, 1.574679, 17.679091, -0.166676, 0.019761, -8.434538, -0.203986, 0.129239, -1.578360, 0.003179, 0.040768, 0.077966, 13.600000, 8.629694, 4.970306, 1.317270, 0.548624, 0.050856, 0.006229
+ 154, 13313, 686891.4, 3855274, 26.164642, 1.710298, 15.298295, -0.137364, 0.020596, -6.669445, -0.367377, 0.134044, -2.740722, 0.077726, 0.042667, 1.821677, 12.000000, 12.786831, -0.786831, -0.206834, 0.553793, 0.035205, 0.000105
+ 155, 13315, 838551.5, 3538547, 18.025600, 2.660378, 6.775579, -0.076497, 0.021987, -3.479159, -0.261962, 0.115963, -2.259014, 0.084694, 0.048373, 1.750865, 7.600000, 5.573625, 2.026375, 0.558896, 0.557453, 0.123618, 0.002952
+ 156, 13317, 891228.5, 3749769, 28.516555, 1.762358, 16.180906, -0.164552, 0.020298, -8.106824, -0.131495, 0.121461, -1.082611, -0.066626, 0.044950, -1.482242, 10.400000, 12.676648, -2.276648, -0.606735, 0.555125, 0.061337, 0.001612
+ 157, 13319, 858796.9, 3637891, 22.169471, 1.996974, 11.101535, -0.101861, 0.021349, -4.771271, -0.224866, 0.101262, -2.220636, 0.003954, 0.050613, 0.078119, 8.800000, 8.708923, 0.091077, 0.025605, 0.538754, 0.156479, 0.000008
+ 158, 13321, 801018.1, 3487328, 18.263625, 2.314566, 7.890733, -0.073520, 0.020449, -3.595321, -0.314540, 0.110659, -2.842413, 0.109955, 0.048770, 2.254575, 6.300000, 8.172088, -1.872088, -0.494558, 0.561673, 0.044714, 0.000767
diff --git a/pysal/examples/georgia/georgia_BS_NN_summary.txt b/pysal/examples/georgia/georgia_BS_NN_summary.txt
new file mode 100755
index 0000000..52cbf74
--- /dev/null
+++ b/pysal/examples/georgia/georgia_BS_NN_summary.txt
@@ -0,0 +1,170 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:04:17 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\georgia_BS_NN.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+Number of areas/points: 159
+
+Model settings---------------------------------
+Model type: Gaussian
+Geographic kernel: adaptive bi-square
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 4
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: AreaKey
+Easting (x-coord): field12 : X
+Northing (y-coord): field13: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field6: PctBach
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field5: PctRural
+Independent variable with varying (Local) coefficient: field9: PctPov
+Independent variable with varying (Local) coefficient: field10: PctBlack
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Residual sum of squares: 2639.559476
+Number of parameters: 4
+ (Note: this num does not include an error variance term for a Gaussian model)
+ML based global sigma estimate: 4.074433
+Unbiased global sigma estimate: 4.126671
+-2 log-likelihood: 897.927089
+Classic AIC: 907.927089
+AICc: 908.319245
+BIC/MDL: 923.271610
+CV: 18.100197
+R square: 0.485273
+Adjusted R square: 0.471903
+
+Variable Estimate Standard Error t(Est/SE)
+-------------------- --------------- --------------- ---------------
+Intercept 23.854615 1.173043 20.335661
+PctRural -0.111395 0.012878 -8.649661
+PctPov -0.345778 0.070863 -4.879540
+PctBlack 0.058331 0.029187 1.998499
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 48, 159
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 48.000 Criterion: 909.256
+ p1 Bandwidth: 90.398 Criterion: 896.463
+ p2 Bandwidth: 116.602 Criterion: 898.615
+ pU Bandwidth: 159.000 Criterion: 903.072
+ iter 1 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 26.204
+ iter 2 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 16.195
+ iter 3 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 10.009
+ iter 4 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 6.186
+ iter 5 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 3.823
+ iter 6 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 2.363
+ iter 7 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 1.460
+ iter 8 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 0.902
+Best bandwidth size 90.000
+Minimum AICc 896.463
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 90.398227
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 635964.300000 1059706.000000 423741.700000
+Y-coord 3401148.000000 3872640.000000 471492.000000
+
+ Diagnostic information
+Residual sum of squares: 2090.125305
+Effective number of parameters (model: trace(S)): 14.925095
+Effective number of parameters (variance: trace(S'S)): 10.193958
+Degree of freedom (model: n - trace(S)): 144.074905
+Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 139.343769
+ML based sigma estimate: 3.625664
+Unbiased sigma estimate: 3.872954
+-2 log-likelihood: 860.818394
+Classic AIC: 892.668583
+AICc: 896.462831
+BIC/MDL: 941.541173
+CV: 19.186726
+R square: 0.592415
+Adjusted R square: 0.534505
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\georgia_BS_NN_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 23.067890 4.184766
+PctRural -0.118169 0.038043
+PctPov -0.261744 0.097335
+PctBlack 0.044847 0.059488
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept 16.844017 29.625733 12.781716
+PctRural -0.190065 -0.070745 0.119320
+PctPov -0.534973 -0.064893 0.470080
+PctBlack -0.073499 0.133808 0.207307
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 18.853338 22.653152 27.237578
+PctRural -0.153281 -0.103888 -0.082675
+PctPov -0.326117 -0.248679 -0.199793
+PctBlack 0.002808 0.057636 0.093798
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 8.384240 6.215152
+PctRural 0.070606 0.052340
+PctPov 0.126324 0.093643
+PctBlack 0.090990 0.067450
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR ANOVA Table
+*****************************************************************************
+Source SS DF MS F
+----------------- ------------------- ---------- --------------- ----------
+Global Residuals 2639.559 155.000
+GWR Improvement 549.434 15.656 35.094
+GWR Residuals 2090.125 139.344 15.000 2.339611
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:04:17 AM
diff --git a/pysal/examples/georgia/georgia_GS_F.ctl b/pysal/examples/georgia/georgia_GS_F.ctl
new file mode 100755
index 0000000..b21c0ff
--- /dev/null
+++ b/pysal/examples/georgia/georgia_GS_F.ctl
@@ -0,0 +1,45 @@
+
+C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 13
+Number_of_areas: 159
+Fields
+AreaKey 001 AreaKey
+X 012 X
+Y 013 Y
+Gmetric 0
+Dependent 006 PctBach
+Offset
+Independent_geo 4
+000 Intercept
+005 PctRural
+009 PctPov
+010 PctBlack
+Independent_fix 0
+Unused_fields 6
+002 Latitude
+003 Longitud
+004 TotPop90
+007 PctEld
+008 PctFB
+011 ID
+MODELTYPE: 0
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 0
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\goergia_GS_F_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\goergia_GS_F_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/georgia/georgia_GS_F_listwise.csv b/pysal/examples/georgia/georgia_GS_F_listwise.csv
new file mode 100755
index 0000000..c0d326b
--- /dev/null
+++ b/pysal/examples/georgia/georgia_GS_F_listwise.csv
@@ -0,0 +1,160 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD
+ 0, 13001, 941396.6, 3521764, 18.497787, 2.275693, 8.128420, -0.085666, 0.020579, -4.162817, -0.232021, 0.108742, -2.133681, 0.070628, 0.046608, 1.515356, 8.200000, 8.870416, -0.670416, -0.178093, 0.544113, 0.046918, 0.000096
+ 1, 13003, 895553, 3471916, 18.243737, 2.412516, 7.562122, -0.080182, 0.021233, -3.776309, -0.288793, 0.114221, -2.528381, 0.104956, 0.048012, 2.186025, 6.400000, 5.536022, 0.863978, 0.236274, 0.560546, 0.100691, 0.000383
+ 2, 13005, 930946.4, 3502787, 18.330304, 2.348055, 7.806591, -0.083870, 0.020823, -4.027677, -0.249953, 0.111167, -2.248448, 0.083855, 0.048057, 1.744900, 6.600000, 8.424698, -1.824698, -0.504834, 0.556554, 0.121336, 0.002159
+ 3, 13007, 745398.6, 3474765, 18.917715, 2.311541, 8.184027, -0.069859, 0.022128, -3.157073, -0.349929, 0.116454, -3.004877, 0.112187, 0.055260, 2.030171, 9.400000, 9.050272, 0.349728, 0.098353, 0.564771, 0.149601, 0.000104
+ 4, 13009, 849431.3, 3665553, 25.415820, 1.399180, 18.164795, -0.128816, 0.015566, -8.275723, -0.253965, 0.083198, -3.052547, -0.002954, 0.036691, -0.080518, 13.300000, 15.345744, -2.045744, -0.549028, 0.575322, 0.066205, 0.001311
+ 5, 13011, 819317.3, 3807616, 29.172419, 1.564461, 18.646951, -0.182481, 0.020052, -9.100191, -0.136824, 0.126886, -1.078321, -0.037705, 0.041646, -0.905356, 6.400000, 8.726694, -2.326694, -0.624508, 0.537646, 0.066445, 0.001703
+ 6, 13013, 803747.1, 3769623, 28.726583, 1.482489, 19.377264, -0.176199, 0.018371, -9.590984, -0.167093, 0.114390, -1.460727, -0.026643, 0.038925, -0.684461, 9.200000, 14.583064, -5.383064, -1.423997, 0.556481, 0.038878, 0.005031
+ 7, 13015, 699011.5, 3793408, 26.843339, 1.697240, 15.815879, -0.139364, 0.021008, -6.633810, -0.453352, 0.134668, -3.366454, 0.105048, 0.043602, 2.409215, 9.000000, 12.479809, -3.479809, -0.922019, 0.621765, 0.041994, 0.002286
+ 8, 13017, 863020.8, 3520432, 18.813192, 1.947256, 9.661387, -0.080531, 0.017818, -4.519606, -0.278134, 0.091946, -3.024985, 0.081732, 0.039561, 2.066004, 7.600000, 11.469966, -3.869966, -1.027285, 0.538207, 0.045513, 0.003086
+ 9, 13019, 859915.8, 3466377, 18.377663, 2.336748, 7.864631, -0.077065, 0.020629, -3.735820, -0.312784, 0.110420, -2.832689, 0.111258, 0.046568, 2.389130, 7.500000, 8.532026, -1.032026, -0.284282, 0.549111, 0.113621, 0.000635
+ 10, 13021, 809736.9, 3636468, 24.324926, 1.351949, 17.992494, -0.120392, 0.015293, -7.872225, -0.283487, 0.080945, -3.502229, 0.023673, 0.035491, 0.667014, 17.000000, 17.930349, -0.930349, -0.255398, 0.595805, 0.107532, 0.000482
+ 11, 13023, 844270.1, 3595691, 21.908296, 1.530577, 14.313753, -0.099822, 0.016021, -6.230772, -0.283128, 0.081375, -3.479289, 0.041593, 0.036618, 1.135857, 10.300000, 11.877407, -1.577407, -0.417684, 0.578132, 0.040754, 0.000455
+ 12, 13025, 979288.9, 3463849, 19.204402, 3.142145, 6.111876, -0.093571, 0.026682, -3.506869, -0.279525, 0.151492, -1.845151, 0.103327, 0.069309, 1.490811, 5.800000, 5.233168, 0.566832, 0.170879, 0.676554, 0.259938, 0.000629
+ 13, 13027, 827822, 3421638, 18.695340, 2.764511, 6.762622, -0.075022, 0.023684, -3.167601, -0.365136, 0.133306, -2.739074, 0.134828, 0.055677, 2.421609, 9.100000, 9.908206, -0.808206, -0.216174, 0.554388, 0.059899, 0.000183
+ 14, 13029, 1023145, 3554982, 19.456316, 2.881836, 6.751361, -0.101365, 0.025916, -3.911217, -0.162463, 0.147809, -1.099146, 0.036312, 0.058356, 0.622260, 11.800000, 9.681023, 2.118977, 0.615241, 0.581168, 0.202190, 0.005884
+ 15, 13031, 994903.4, 3600493, 20.392912, 2.315553, 8.806931, -0.100602, 0.022436, -4.483909, -0.169488, 0.123467, -1.372734, 0.010719, 0.047725, 0.224606, 19.900000, 9.652136, 10.247864, 2.869181, 0.532630, 0.141998, 0.083560
+ 16, 13033, 971593.8, 3671394, 23.793175, 2.175345, 10.937657, -0.112552, 0.021486, -5.238425, -0.224380, 0.110952, -2.022324, -0.014762, 0.045841, -0.322025, 9.600000, 8.086492, 1.513508, 0.416114, 0.576048, 0.110220, 0.001316
+ 17, 13035, 782448.2, 3684504, 26.510713, 1.334747, 19.861972, -0.145234, 0.015639, -9.286381, -0.268294, 0.088566, -3.029294, 0.011992, 0.035456, 0.338239, 7.200000, 12.090632, -4.890632, -1.294273, 0.592581, 0.039681, 0.004245
+ 18, 13037, 724741.2, 3492653, 19.220671, 2.224523, 8.640357, -0.071804, 0.021675, -3.312706, -0.342620, 0.114613, -2.989362, 0.104370, 0.056241, 1.855755, 10.100000, 7.291232, 2.808768, 0.780655, 0.585776, 0.129334, 0.005552
+ 19, 13039, 1008480, 3437933, 19.942606, 3.729350, 5.347475, -0.100203, 0.030602, -3.274342, -0.298317, 0.176298, -1.692119, 0.110085, 0.082565, 1.333317, 13.500000, 14.015029, -0.515029, -0.159990, 0.747834, 0.303028, 0.000683
+ 20, 13043, 964264.9, 3598842, 20.511683, 2.032624, 10.091234, -0.096423, 0.020386, -4.729889, -0.202809, 0.106214, -1.909443, 0.018980, 0.043427, 0.437050, 9.900000, 11.187587, -1.287587, -0.344390, 0.527058, 0.059865, 0.000463
+ 21, 13045, 678778.6, 3713250, 26.239766, 1.504491, 17.440955, -0.134549, 0.018900, -7.119080, -0.496961, 0.119399, -4.162187, 0.119626, 0.043809, 2.730609, 12.000000, 11.716358, 0.283642, 0.075030, 0.630415, 0.038813, 0.000014
+ 22, 13047, 670055.9, 3862318, 23.647585, 2.263072, 10.449332, -0.089870, 0.030008, -2.994923, -0.560872, 0.182879, -3.066908, 0.189068, 0.064193, 2.945311, 8.100000, 13.170829, -5.070829, -1.489836, 0.601489, 0.220855, 0.038588
+ 23, 13049, 962612.3, 3432769, 19.336749, 3.310029, 5.841868, -0.093840, 0.028496, -3.293056, -0.308039, 0.164476, -1.872853, 0.118219, 0.072447, 1.631803, 6.400000, 7.513486, -1.113486, -0.326995, 0.693452, 0.220126, 0.001851
+ 24, 13051, 1059706, 3556747, 19.698051, 3.278939, 6.007447, -0.107620, 0.029300, -3.673092, -0.132112, 0.168471, -0.784182, 0.026855, 0.067535, 0.397651, 18.600000, 17.897897, 0.702103, 0.233366, 0.612520, 0.391215, 0.002146
+ 25, 13053, 704959.2, 3577608, 21.624188, 1.660805, 13.020305, -0.094448, 0.018655, -5.063010, -0.335364, 0.100033, -3.352521, 0.079174, 0.048997, 1.615889, 20.200000, 19.292100, 0.907900, 0.260443, 0.642381, 0.182687, 0.000930
+ 26, 13055, 653026.6, 3813760, 25.401577, 1.938885, 13.101126, -0.108359, 0.026201, -4.135739, -0.618126, 0.166692, -3.708196, 0.177630, 0.054586, 3.254110, 5.900000, 9.519327, -3.619327, -0.966887, 0.626307, 0.057587, 0.003504
+ 27, 13057, 734240.9, 3794110, 27.599499, 1.625034, 16.983947, -0.156416, 0.019579, -7.988854, -0.329309, 0.125216, -2.629923, 0.053979, 0.040478, 1.333543, 18.400000, 16.645390, 1.754610, 0.473082, 0.602584, 0.074822, 0.001110
+ 28, 13059, 832508.6, 3762905, 28.753571, 1.467127, 19.598557, -0.175090, 0.018313, -9.561152, -0.142205, 0.112222, -1.267174, -0.043887, 0.039524, -1.110395, 37.500000, 20.681305, 16.818695, 5.472825, 0.542848, 0.364817, 1.055090
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+ 155, 13315, 838551.5, 3538547, 19.592759, 1.793470, 10.924498, -0.084198, 0.017164, -4.905592, -0.288637, 0.087414, -3.301955, 0.075524, 0.038778, 1.947599, 7.600000, 5.316609, 2.283391, 0.620364, 0.560011, 0.088819, 0.002301
+ 156, 13317, 891228.5, 3749769, 27.962051, 1.636165, 17.089996, -0.159172, 0.018915, -8.415137, -0.150957, 0.107657, -1.402208, -0.054321, 0.040961, -1.326172, 10.400000, 12.568266, -2.168266, -0.580414, 0.538262, 0.061387, 0.001351
+ 157, 13319, 858796.9, 3637891, 23.916868, 1.456211, 16.424038, -0.114371, 0.015790, -7.243026, -0.271957, 0.081857, -3.322330, 0.013616, 0.037177, 0.366235, 8.800000, 8.890582, -0.090582, -0.025045, 0.576505, 0.120216, 0.000005
+ 158, 13321, 801018.1, 3487328, 18.929377, 2.092550, 9.046081, -0.075227, 0.019763, -3.806492, -0.330297, 0.102164, -3.233022, 0.105827, 0.046739, 2.264206, 6.300000, 8.176916, -1.876916, -0.499318, 0.559498, 0.049672, 0.000799
diff --git a/pysal/examples/georgia/georgia_GS_F_summary.txt b/pysal/examples/georgia/georgia_GS_F_summary.txt
new file mode 100755
index 0000000..478708f
--- /dev/null
+++ b/pysal/examples/georgia/georgia_GS_F_summary.txt
@@ -0,0 +1,168 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:05:10 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\goergia_GS_F.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+Number of areas/points: 159
+
+Model settings---------------------------------
+Model type: Gaussian
+Geographic kernel: fixed Gaussian
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 4
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: AreaKey
+Easting (x-coord): field12 : X
+Northing (y-coord): field13: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field6: PctBach
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field5: PctRural
+Independent variable with varying (Local) coefficient: field9: PctPov
+Independent variable with varying (Local) coefficient: field10: PctBlack
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Residual sum of squares: 2639.559476
+Number of parameters: 4
+ (Note: this num does not include an error variance term for a Gaussian model)
+ML based global sigma estimate: 4.074433
+Unbiased global sigma estimate: 4.126671
+-2 log-likelihood: 897.927089
+Classic AIC: 907.927089
+AICc: 908.319245
+BIC/MDL: 923.271610
+CV: 18.100197
+R square: 0.485273
+Adjusted R square: 0.471903
+
+Variable Estimate Standard Error t(Est/SE)
+-------------------- --------------- --------------- ---------------
+Intercept 23.854615 1.173043 20.335661
+PctRural -0.111395 0.012878 -8.649661
+PctPov -0.345778 0.070863 -4.879540
+PctBlack 0.058331 0.029187 1.998499
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 54486.3131542225, 279451.547243655
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 54486.313 Criterion: 914.115
+ p1 Bandwidth: 140415.386 Criterion: 900.141
+ p2 Bandwidth: 193522.474 Criterion: 903.842
+ pU Bandwidth: 279451.547 Criterion: 906.294
+ iter 1 (p1) Bandwidth: 140415.386 Criterion: 900.141 Diff: 53107.088
+ iter 2 (p1) Bandwidth: 107593.401 Criterion: 896.616 Diff: 32821.985
+ iter 3 (p1) Bandwidth: 87308.298 Criterion: 895.290 Diff: 20285.103
+ iter 4 (p2) Bandwidth: 87308.298 Criterion: 895.290 Diff: 12536.883
+ iter 5 (p1) Bandwidth: 87308.298 Criterion: 895.290 Diff: 7748.220
+ iter 6 (p2) Bandwidth: 87308.298 Criterion: 895.290 Diff: 4788.663
+Best bandwidth size 87308.298
+Minimum AICc 895.290
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 87308.298470
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 635964.300000 1059706.000000 423741.700000
+Y-coord 3401148.000000 3872640.000000 471492.000000
+
+ Diagnostic information
+Residual sum of squares: 2030.010213
+Effective number of parameters (model: trace(S)): 16.304601
+Effective number of parameters (variance: trace(S'S)): 10.141574
+Degree of freedom (model: n - trace(S)): 142.695399
+Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 136.532371
+ML based sigma estimate: 3.573144
+Unbiased sigma estimate: 3.855949
+-2 log-likelihood: 856.178266
+Classic AIC: 890.787468
+AICc: 895.290158
+BIC/MDL: 943.893632
+CV: 18.212841
+R square: 0.604138
+Adjusted R square: 0.538515
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\goergia_GS_F_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 23.315956 3.742747
+PctRural -0.116469 0.034541
+PctPov -0.290012 0.105098
+PctBlack 0.053228 0.060773
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept 18.016084 29.440723 11.424639
+PctRural -0.185429 -0.058428 0.127001
+PctPov -0.661246 -0.100954 0.560292
+PctBlack -0.064110 0.222182 0.286293
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 19.466237 23.494623 26.843339
+PctRural -0.145428 -0.108359 -0.087985
+PctPov -0.337452 -0.285118 -0.224337
+PctBlack 0.003985 0.055632 0.102447
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 7.377102 5.468571
+PctRural 0.057443 0.042582
+PctPov 0.113114 0.083851
+PctBlack 0.098462 0.072989
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR ANOVA Table
+*****************************************************************************
+Source SS DF MS F
+----------------- ------------------- ---------- --------------- ----------
+Global Residuals 2639.559 155.000
+GWR Improvement 609.549 18.468 33.006
+GWR Residuals 2030.010 136.532 14.868 2.219909
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:05:11 AM
diff --git a/pysal/examples/georgia/georgia_GS_NN.ctl b/pysal/examples/georgia/georgia_GS_NN.ctl
new file mode 100755
index 0000000..dfd46ed
--- /dev/null
+++ b/pysal/examples/georgia/georgia_GS_NN.ctl
@@ -0,0 +1,45 @@
+
+C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+FORMAT/DELIMITER: 1
+Number_of_fields: 13
+Number_of_areas: 159
+Fields
+AreaKey 001 AreaKey
+X 012 X
+Y 013 Y
+Gmetric 0
+Dependent 006 PctBach
+Offset
+Independent_geo 4
+000 Intercept
+005 PctRural
+009 PctPov
+010 PctBlack
+Independent_fix 0
+Unused_fields 6
+002 Latitude
+003 Longitud
+004 TotPop90
+007 PctEld
+008 PctFB
+011 ID
+MODELTYPE: 0
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 3
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\georgia_GS_NN_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\georgia_GS_NN_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/georgia/georgia_GS_NN_listwise.csv b/pysal/examples/georgia/georgia_GS_NN_listwise.csv
new file mode 100755
index 0000000..3f4e06e
--- /dev/null
+++ b/pysal/examples/georgia/georgia_GS_NN_listwise.csv
@@ -0,0 +1,160 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD
+ 0, 13001, 941396.6, 3521764, 21.626865, 1.457152, 14.841875, -0.099036, 0.015041, -6.584203, -0.301756, 0.078805, -3.829137, 0.058822, 0.035097, 1.675968, 8.200000, 9.355951, -1.155951, -0.296583, 0.548471, 0.025265, 0.000284
+ 1, 13003, 895553, 3471916, 20.960601, 1.471999, 14.239551, -0.093944, 0.015137, -6.206105, -0.317883, 0.078902, -4.028843, 0.077650, 0.035724, 2.173583, 6.400000, 5.386899, 1.013101, 0.263500, 0.547804, 0.051486, 0.000469
+ 2, 13005, 930946.4, 3502787, 21.340670, 1.474600, 14.472180, -0.097237, 0.015138, -6.423569, -0.305775, 0.079193, -3.861155, 0.065534, 0.035436, 1.849372, 6.600000, 8.982485, -2.382485, -0.626296, 0.548923, 0.071458, 0.003758
+ 3, 13007, 745398.6, 3474765, 21.652602, 1.362160, 15.895783, -0.093473, 0.014904, -6.271598, -0.354833, 0.076919, -4.613079, 0.086734, 0.034545, 2.510765, 9.400000, 7.986999, 1.413001, 0.370642, 0.559055, 0.067443, 0.001237
+ 4, 13009, 849431.3, 3665553, 25.292516, 1.277116, 19.804392, -0.126365, 0.014113, -8.953548, -0.286840, 0.076082, -3.770127, 0.014001, 0.032322, 0.433170, 13.300000, 15.470543, -2.170543, -0.563221, 0.550567, 0.047028, 0.001949
+ 5, 13011, 819317.3, 3807616, 26.978495, 1.333220, 20.235594, -0.148715, 0.015739, -9.449124, -0.260333, 0.091248, -2.853020, 0.007208, 0.034424, 0.209374, 6.400000, 8.201092, -1.801092, -0.467232, 0.524135, 0.046529, 0.001326
+ 6, 13013, 803747.1, 3769623, 27.413438, 1.366252, 20.064702, -0.155327, 0.016220, -9.576329, -0.244799, 0.095199, -2.571454, 0.001305, 0.035223, 0.037042, 9.200000, 13.795686, -4.595686, -1.181006, 0.529843, 0.028379, 0.005071
+ 7, 13015, 699011.5, 3793408, 25.928870, 1.348245, 19.231566, -0.135314, 0.015806, -8.560655, -0.351185, 0.091376, -3.843275, 0.058437, 0.034661, 1.685933, 9.000000, 12.533795, -3.533795, -0.907169, 0.539704, 0.026342, 0.002772
+ 8, 13017, 863020.8, 3520432, 21.263945, 1.402561, 15.160798, -0.094378, 0.014629, -6.451384, -0.317260, 0.076506, -4.146880, 0.070311, 0.034527, 2.036377, 7.600000, 12.051309, -4.451309, -1.143223, 0.541964, 0.027221, 0.004553
+ 9, 13019, 859915.8, 3466377, 20.946762, 1.437381, 14.572870, -0.092411, 0.014981, -6.168355, -0.328129, 0.077704, -4.222829, 0.082514, 0.035502, 2.324250, 7.500000, 9.455091, -1.955091, -0.509968, 0.546717, 0.056922, 0.001954
+ 10, 13021, 809736.9, 3636468, 24.532429, 1.242482, 19.744688, -0.119568, 0.013828, -8.646600, -0.311192, 0.074254, -4.190933, 0.034437, 0.031432, 1.095605, 17.000000, 18.067824, -1.067824, -0.280841, 0.564208, 0.072364, 0.000766
+ 11, 13023, 844270.1, 3595691, 23.262635, 1.262917, 18.419766, -0.107683, 0.013697, -7.861986, -0.315491, 0.073405, -4.297938, 0.044395, 0.031850, 1.393871, 10.300000, 12.246973, -1.946973, -0.498717, 0.556205, 0.022063, 0.000698
+ 12, 13025, 979288.9, 3463849, 22.023185, 1.396647, 15.768609, -0.101057, 0.014628, -6.908428, -0.317358, 0.076963, -4.123500, 0.063226, 0.034002, 1.859490, 5.800000, 6.431159, -0.631159, -0.167422, 0.563934, 0.088082, 0.000337
+ 13, 13027, 827822, 3421638, 21.329854, 1.366845, 15.605175, -0.093531, 0.014621, -6.396952, -0.343350, 0.076056, -4.514434, 0.085757, 0.034564, 2.481142, 9.100000, 9.857815, -0.757815, -0.194052, 0.555169, 0.021434, 0.000103
+ 14, 13029, 1023145, 3554982, 22.981765, 1.347595, 17.053916, -0.107186, 0.014312, -7.489271, -0.305664, 0.075797, -4.032678, 0.042913, 0.032692, 1.312639, 11.800000, 10.945100, 0.854900, 0.221137, 0.559079, 0.041022, 0.000260
+ 15, 13031, 994903.4, 3600493, 23.375461, 1.335470, 17.503547, -0.109687, 0.014217, -7.714992, -0.298428, 0.075780, -3.938083, 0.033330, 0.032506, 1.025339, 19.900000, 9.101424, 10.798576, 2.820742, 0.552292, 0.059614, 0.062787
+ 16, 13033, 971593.8, 3671394, 24.524585, 1.271622, 19.286061, -0.118149, 0.013720, -8.611196, -0.293751, 0.074107, -3.963890, 0.019467, 0.031047, 0.627025, 9.600000, 8.097758, 1.502242, 0.389514, 0.546350, 0.045594, 0.000902
+ 17, 13035, 782448.2, 3684504, 25.845630, 1.264212, 20.444069, -0.134835, 0.014378, -9.377889, -0.301626, 0.078752, -3.830055, 0.027407, 0.032060, 0.854867, 7.200000, 12.215750, -5.015750, -1.291497, 0.560975, 0.032201, 0.006908
+ 18, 13037, 724741.2, 3492653, 22.163047, 1.303120, 17.007678, -0.097085, 0.014424, -6.730702, -0.357953, 0.075290, -4.754320, 0.082866, 0.032980, 2.512604, 10.100000, 5.951669, 4.148331, 1.087798, 0.564186, 0.066852, 0.010553
+ 19, 13039, 1008480, 3437933, 22.298215, 1.354182, 16.466182, -0.102417, 0.014323, -7.150385, -0.322044, 0.075664, -4.256225, 0.062073, 0.033138, 1.873175, 13.500000, 15.024108, -1.524108, -0.396779, 0.566825, 0.053252, 0.001102
+ 20, 13043, 964264.9, 3598842, 23.220616, 1.345121, 17.262846, -0.108483, 0.014277, -7.598441, -0.296664, 0.076121, -3.897282, 0.033632, 0.032948, 1.020737, 9.900000, 11.459604, -1.559604, -0.401240, 0.548581, 0.030562, 0.000632
+ 21, 13045, 678778.6, 3713250, 25.157218, 1.269114, 19.822667, -0.126873, 0.014523, -8.736229, -0.363915, 0.080214, -4.536773, 0.067373, 0.032358, 2.082123, 12.000000, 12.267665, -0.267665, -0.068432, 0.559438, 0.018319, 0.000011
+ 22, 13047, 670055.9, 3862318, 25.331887, 1.282320, 19.754728, -0.127703, 0.014738, -8.664685, -0.349253, 0.082617, -4.227359, 0.057703, 0.032827, 1.757798, 8.100000, 15.625499, -7.525499, -1.969222, 0.530409, 0.062910, 0.032406
+ 23, 13049, 962612.3, 3432769, 21.933757, 1.374678, 15.955558, -0.100015, 0.014470, -6.911946, -0.324232, 0.076267, -4.251280, 0.068344, 0.033790, 2.022611, 6.400000, 7.847483, -1.447483, -0.375138, 0.564887, 0.044685, 0.000819
+ 24, 13051, 1059706, 3556747, 23.289156, 1.293318, 18.007290, -0.108878, 0.013869, -7.850502, -0.311044, 0.073959, -4.205622, 0.042239, 0.031514, 1.340328, 18.600000, 18.989849, -0.389849, -0.104014, 0.560384, 0.098616, 0.000147
+ 25, 13053, 704959.2, 3577608, 23.135000, 1.280280, 18.070272, -0.105939, 0.014463, -7.324642, -0.357265, 0.075946, -4.704196, 0.075003, 0.032382, 2.316225, 20.200000, 20.288682, -0.088682, -0.023620, 0.575797, 0.095466, 0.000007
+ 26, 13055, 653026.6, 3813760, 25.238584, 1.281379, 19.696426, -0.126760, 0.014709, -8.617714, -0.360001, 0.082164, -4.381511, 0.064186, 0.032783, 1.957891, 5.900000, 10.724024, -4.824024, -1.237205, 0.537294, 0.024480, 0.004781
+ 27, 13057, 734240.9, 3794110, 26.738871, 1.424971, 18.764502, -0.146237, 0.016977, -8.613888, -0.320312, 0.102328, -3.130254, 0.043669, 0.036422, 1.198981, 18.400000, 16.409754, 1.990246, 0.518110, 0.537072, 0.053174, 0.001877
+ 28, 13059, 832508.6, 3762905, 27.241892, 1.337499, 20.367791, -0.151566, 0.015698, -9.655305, -0.241590, 0.089623, -2.695614, -0.005162, 0.034409, -0.150026, 37.500000, 17.916007, 19.583993, 5.637950, 0.528664, 0.225787, 1.153944
+ 29, 13061, 695793.9, 3495219, 22.465026, 1.270648, 17.679979, -0.099292, 0.014146, -7.018840, -0.361903, 0.074478, -4.859210, 0.082242, 0.032021, 2.568401, 11.200000, 4.612950, 6.587050, 1.736724, 0.565559, 0.076960, 0.031304
+ 30, 13063, 745538.8, 3711726, 26.037250, 1.288900, 20.201146, -0.137682, 0.014805, -9.299565, -0.322383, 0.082647, -3.900711, 0.041183, 0.032699, 1.259476, 14.700000, 23.639943, -8.939943, -2.380434, 0.559406, 0.094981, 0.074028
+ 31, 13065, 908046.1, 3428340, 21.390248, 1.409248, 15.178488, -0.096206, 0.014752, -6.521417, -0.328048, 0.077266, -4.245695, 0.077908, 0.034791, 2.239323, 6.700000, 9.218220, -2.518220, -0.653012, 0.559225, 0.045789, 0.002547
+ 32, 13067, 724646.8, 3757187, 26.497784, 1.374988, 19.271281, -0.143156, 0.016165, -8.855953, -0.335996, 0.094592, -3.552069, 0.049705, 0.035141, 1.414433, 33.000000, 24.274999, 8.725001, 2.347843, 0.549968, 0.113879, 0.088184
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diff --git a/pysal/examples/georgia/georgia_GS_NN_summary.txt b/pysal/examples/georgia/georgia_GS_NN_summary.txt
new file mode 100755
index 0000000..97f0764
--- /dev/null
+++ b/pysal/examples/georgia/georgia_GS_NN_summary.txt
@@ -0,0 +1,165 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 2:04:57 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\georgia_GS_NN.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv
+Number of areas/points: 159
+
+Model settings---------------------------------
+Model type: Gaussian
+Geographic kernel: adaptive Gaussian
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 4
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: AreaKey
+Easting (x-coord): field12 : X
+Northing (y-coord): field13: Y
+Cartesian coordinates: Euclidean distance
+Dependent variable: field6: PctBach
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field5: PctRural
+Independent variable with varying (Local) coefficient: field9: PctPov
+Independent variable with varying (Local) coefficient: field10: PctBlack
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Residual sum of squares: 2639.559476
+Number of parameters: 4
+ (Note: this num does not include an error variance term for a Gaussian model)
+ML based global sigma estimate: 4.074433
+Unbiased global sigma estimate: 4.126671
+-2 log-likelihood: 897.927089
+Classic AIC: 907.927089
+AICc: 908.319245
+BIC/MDL: 923.271610
+CV: 18.100197
+R square: 0.485273
+Adjusted R square: 0.471903
+
+Variable Estimate Standard Error t(Est/SE)
+-------------------- --------------- --------------- ---------------
+Intercept 23.854615 1.173043 20.335661
+PctRural -0.111395 0.012878 -8.649661
+PctPov -0.345778 0.070863 -4.879540
+PctBlack 0.058331 0.029187 1.998499
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 48, 159
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 48.000 Criterion: 896.274
+ p1 Bandwidth: 50.363 Criterion: 896.244
+ p2 Bandwidth: 51.823 Criterion: 897.469
+ pU Bandwidth: 54.186 Criterion: 897.673
+ iter 1 (p1) Bandwidth: 50.363 Criterion: 896.244 Diff: 1.460
+ iter 2 (p1) Bandwidth: 49.460 Criterion: 896.184 Diff: 0.902
+ iter 3 (p2) Bandwidth: 49.460 Criterion: 896.184 Diff: 0.558
+Best bandwidth size 49.000
+Minimum AICc 896.184
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 49.460274
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 635964.300000 1059706.000000 423741.700000
+Y-coord 3401148.000000 3872640.000000 471492.000000
+
+ Diagnostic information
+Residual sum of squares: 2312.592458
+Effective number of parameters (model: trace(S)): 8.033359
+Effective number of parameters (variance: trace(S'S)): 5.454906
+Degree of freedom (model: n - trace(S)): 150.966641
+Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 148.388187
+ML based sigma estimate: 3.813739
+Unbiased sigma estimate: 3.947752
+-2 log-likelihood: 876.900473
+Classic AIC: 894.967192
+AICc: 896.184041
+BIC/MDL: 922.689706
+CV: 17.914091
+R square: 0.549033
+Adjusted R square: 0.516564
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\georgia_GS_NN_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 24.095617 1.993847
+PctRural -0.118018 0.019034
+PctPov -0.315782 0.031402
+PctBlack 0.047231 0.026783
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept 20.871637 27.413438 6.541801
+PctRural -0.155327 -0.089579 0.065748
+PctPov -0.366066 -0.241590 0.124476
+PctBlack -0.005162 0.088745 0.093907
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 22.299833 24.112693 25.928870
+PctRural -0.134835 -0.115582 -0.100015
+PctPov -0.343436 -0.317883 -0.293751
+PctBlack 0.024811 0.050079 0.068774
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 3.629037 2.690168
+PctRural 0.034820 0.025812
+PctPov 0.049685 0.036831
+PctBlack 0.043963 0.032589
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR ANOVA Table
+*****************************************************************************
+Source SS DF MS F
+----------------- ------------------- ---------- --------------- ----------
+Global Residuals 2639.559 155.000
+GWR Improvement 326.967 6.612 49.452
+GWR Residuals 2312.592 148.388 15.585 3.173099
+
+*****************************************************************************
+Program terminated at 7/25/2016 2:04:57 AM
diff --git a/pysal/examples/londonhp/londonhp.dbf b/pysal/examples/londonhp/londonhp.dbf
new file mode 100644
index 0000000..d2897f8
Binary files /dev/null and b/pysal/examples/londonhp/londonhp.dbf differ
diff --git a/pysal/examples/londonhp/londonhp.shp b/pysal/examples/londonhp/londonhp.shp
new file mode 100644
index 0000000..0cc24ac
Binary files /dev/null and b/pysal/examples/londonhp/londonhp.shp differ
diff --git a/pysal/examples/londonhp/londonhp.shx b/pysal/examples/londonhp/londonhp.shx
new file mode 100644
index 0000000..ddfffaa
Binary files /dev/null and b/pysal/examples/londonhp/londonhp.shx differ
diff --git a/pysal/examples/tokyo/Readme_tokyomortality.txt b/pysal/examples/tokyo/Readme_tokyomortality.txt
new file mode 100755
index 0000000..382247c
--- /dev/null
+++ b/pysal/examples/tokyo/Readme_tokyomortality.txt
@@ -0,0 +1,55 @@
+Dataset name:
+ Tokyo Mortality Data
+
+File name
+ tokyomortality.txt
+
+File type
+ text file (space delimited data matrix)
+
+Fields
+ IDnum0: sequential areal id
+ X_CENTROID: x coordinate of areal centroid
+ Y_CENTROID: y coordinate of areal centroid
+ db2564: observed number of working age (25-64 yrs) deaths
+ eb2564: expected number of working age (25-64 yrs) deaths
+ OCC_TEC: proportion of professional workers
+ OWNH: proportion of owned houses
+ POP65: proportion of elderly people (equal to or older than 65)
+ UNEMP: unemployment rate
+
+Areal unit
+ 262 municipality
+
+Areal extent
+ The Tokyo metropolitan area is enclosed by an approximate 70 km radius
+ from the centroid of the Chiyoda ward of Tokyo where the Imperial Palace is located.
+
+Year
+ 1990
+
+Source
+ Vital Statistics and Population Census of Japan
+
+Sample session control file
+ SampleTokyoMortalityGWPR.ctl
+
+ Note 1: This sample fit a semiparametric geographically weighted Poisson regression model
+ used in Nakaya et al. (2005).
+ Note 2: Since all of the explanatory variables are standardised in the paper, the standardisation
+ option is turned on in this sample.
+
+Additional file:
+ tokyomet262.shp, shx, dbf, prj: ESRI shape file of the Tokyo metropolitan area
+
+ Note 1: IDnum0 in tokyomortality.txt can be matched with AreaID in this shapefile dbf.
+ Note 2: Coordinates are projected using UTM54 (Tokyo datum).
+ Note 3: distance unit is metre.
+
+Reference
+ Nakaya, T., Fotheringham, S., Brunsdon, C. and Charlton, M. (2005):
+ Geographically weighted Poisson regression for disease associative mapping,
+ Statistics in Medicine 24, 2695-2717.
+
+History
+ 15 May 2012 The dataset is prepared for GWR4 sample dataset by TN.
diff --git a/pysal/examples/tokyo/SampleTokyoMortalityGWPR.ctl b/pysal/examples/tokyo/SampleTokyoMortalityGWPR.ctl
new file mode 100755
index 0000000..6da5f89
--- /dev/null
+++ b/pysal/examples/tokyo/SampleTokyoMortalityGWPR.ctl
@@ -0,0 +1,40 @@
+Semiparametric GWPR: Tokyo mortality data (see Nakaya et al. 2005, Stat in Med.)
+Tokyomortality.txt
+FORMAT/DELIMITER: 0
+Number_of_fields: 9
+Number_of_areas: 262
+Fields
+AreaKey 001 IDnum0
+X 002 X_CENTROID
+Y 003 Y_CENTROID
+Gmetric 0
+Dependent 004 db2564
+Offset 005 eb2564
+Independent_geo 3
+000 Intercept
+006 OCC_TEC
+009 UNEMP
+Independent_fix 2
+007 OWNH
+008 POP65
+Unused_fields 0
+MODELTYPE: 1
+STANDARDISATION: 1
+GTEST: 0
+VSG2F: 0
+VSF2G: 0
+KERNELTYPE: 0
+BANDSELECTIONMETHOD: 2
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax: 20000
+IntervalMin: 10000
+IntervalStep: 1000
+Criteria: 1000
+summary_output: defaultGWRsummary.txt
+listwise_output: defaultGWRlistwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/tokyo/Tokyomortality.csv b/pysal/examples/tokyo/Tokyomortality.csv
new file mode 100644
index 0000000..4e69146
--- /dev/null
+++ b/pysal/examples/tokyo/Tokyomortality.csv
@@ -0,0 +1 @@
+IDnum0,X_CENTROID,Y_CENTROID,db2564,eb2564,OCC_TEC,OWNH,POP65,UNEMP
0,378906.83,17310.41,189,194.572,0.126,0.606,0.104,2.865
1,334095.21,25283.2,95,97.526,0.107,0.671,0.111,3.401
2,378200.19,-877.05,70,83.235,0.106,0.733,0.11,1.724
3,357191.03,29064.39,48,52.392,0.075,0.767,0.14,1.829
4,358056.34,10824.73,65,67.664,0.075,0.812,0.146,1.961
5,366747.61,-3073.12,107,120.745,0.14,0.623,0.078,2.636
6,351099.27,11800.35,65,67.196,0.066,0.824,0.121,1.603
7,377929.98,4635.1,76,87.746,0.13,0.778,0.083,2.438
8,367529.91,20192.51,192,190.255,0.227,0.449,0.101,1.783
9,389231.47,3489.35,27,23.939,0.075,0.821,0.146,2.081
10,389427.64,9290.1,28,23.832,0.088,0.61,0.119,2.589
11,381089.82,9125.81,63,62.498,0.124,0.674,0.099,2.661
12,371082.66,6843.9,34,36.137,0.113,0.914,0.078,2.08
13,388281.84,-1760.78,17,16.77,0.062,0.92,0.161,2.509
14,386771.66,-4857.11,25,20.209,0.055,0.959,0.166,1.824
15,397029.93,4912.15,17,14.952,0.07,0.961,0.178,1.725
16,399583.28,1217.51,31,24.675,0.062,0.931,0.165,2.261
17,389413.79,18915.59,27,32.009,0.069,0.938,0.168,1.45
18,374811.31,23395.2,10,16.171,0.098,0.892,0.159,1.997
19,366291.01,3851.09,42,40.773,0.076,0.929,0.102,2.704
20,362053.67,7027.25,20,19.021,0.075,0.935,0.148,2.479
21,350567.45,26456.28,40,38.2,0.048,0.945,0.146,1.784
22,356783.59,23682.89,15,14.404,0.066,0.891,0.149,1.983
23,356225.47,19763.98,47,34.798,0.063,0.828,0.135,1.677
24,338360.54,25697.56,68,63.104,0.066,0.672,0.082,1.782
25,337846.31,18213.38,17,14.095,0.064,0.874,0.134,2.416
26,344074.13,27136.92,57,48.675,0.05,0.85,0.094,2.25
27,349087.82,19336.47,40,24.57,0.057,0.932,0.138,1.458
28,343402.57,18620.67,47,40.968,0.064,0.77,0.124,2.265
29,359036.48,1198.74,49,47.597,0.129,0.817,0.08,2.01
30,370771.99,-1522.12,49,48.444,0.107,0.856,0.095,2.752
31,376842.13,-7139.16,27,29.49,0.106,0.949,0.097,2.667
32,318049.53,32744.59,120,120.934,0.088,0.687,0.121,2.725
33,325761.21,31092.21,28,25.762,0.054,0.937,0.163,1.68
34,318112.24,28405.62,16,17.138,0.062,0.914,0.143,1.617
35,310480.1,28809.03,14,17.677,0.062,0.881,0.138,2.226
36,306513.97,32751.48,43,49.712,0.096,0.446,0.082,2.421
37,311395.51,33538.42,29,38.093,0.07,0.843,0.109,2.222
38,314408.34,-4572.95,401,451.407,0.126,0.657,0.083,2.507
39,303850.22,22478,210,230.061,0.11,0.638,0.103,2.74
40,337540.25,-12310.61,711,665.501,0.098,0.519,0.073,2.718
41,330948.96,-8687.59,544,620.327,0.149,0.514,0.084,2.674
42,327143.99,-3103.01,557,622.818,0.129,0.587,0.09,2.645
43,312830.72,21412.1,132,123.957,0.099,0.734,0.114,2.43
44,312874.14,-17053.63,395,440.38,0.157,0.578,0.073,2.8
45,293680.38,-8010.1,97,109.945,0.12,0.722,0.116,2.828
46,325185.11,20460.45,91,86.221,0.095,0.766,0.118,2.115
47,305971.31,8472,97,122.976,0.117,0.661,0.087,2.394
48,335330.5,-108.19,148,162.923,0.099,0.711,0.081,3.026
49,339115.66,3202.05,269,271.36,0.107,0.624,0.064,2.994
50,309271.13,-10589.17,183,224.968,0.133,0.645,0.072,2.589
51,319972.62,24634.35,86,84.885,0.089,0.792,0.126,2.33
52,317013.43,12374.14,86,105.836,0.113,0.715,0.084,2.577
53,323345.93,2314.46,244,294.09,0.113,0.562,0.066,2.759
54,327610.55,-7504.02,120,116.831,0.152,0.5,0.091,2.936
55,343813.29,-11626.17,297,306.205,0.091,0.514,0.062,2.712
56,342508.85,-4698.16,393,414.887,0.103,0.637,0.062,2.758
57,333426.78,-13559.3,103,114.209,0.132,0.452,0.094,3.357
58,330824.95,-14794.45,136,129.291,0.098,0.351,0.065,3.456
59,304617.97,-15261.45,160,197.739,0.137,0.674,0.07,2.886
60,338062.78,-13156.47,102,96.519,0.076,0.621,0.085,3.045
61,325419.58,-15527.5,142,150.75,0.126,0.434,0.063,3.041
62,324052.62,-12510.9,83,94.684,0.135,0.599,0.062,3.13
63,327521.88,-17674.68,78,83.535,0.144,0.463,0.069,2.77
64,322114.34,-17894.35,201,208.889,0.122,0.606,0.066,2.516
65,320355.21,5840.48,87,107.467,0.11,0.713,0.079,2.539
66,330341.07,12925.79,89,97.163,0.123,0.649,0.077,2.809
67,318527.16,8318.24,80,93.243,0.115,0.665,0.072,2.399
68,347297.26,-13547.71,105,106.606,0.06,0.605,0.061,3.229
69,321375.58,-10594.12,114,140.264,0.109,0.575,0.066,2.479
70,318675.19,-8454.47,101,94.489,0.126,0.439,0.078,3.459
71,350174.04,-12060.87,181,172.497,0.091,0.579,0.052,2.388
72,329442.54,5939.52,82,92.061,0.126,0.782,0.085,2.424
73,307098.94,992.68,93,126.491,0.121,0.539,0.067,2.431
74,337247.61,14030.91,67,78.427,0.094,0.658,0.085,3.416
75,306612.95,-2173.79,55,79.955,0.132,0.607,0.054,2.304
76,301727,-6640.87,68,74.512,0.113,0.758,0.09,2.324
77,326724.18,5478.1,47,38.462,0.103,0.809,0.069,2.164
78,311503.39,15708.66,33,40.921,0.117,0.77,0.081,2.083
79,316934.08,-10632.09,43,55.32,0.108,0.643,0.057,2.695
80,317980.71,-13171.59,52,51.875,0.104,0.73,0.065,2.308
81,298790.59,-2464.08,75,55.068,0.181,0.651,0.088,2.833
82,294903.64,214.57,33,19.923,0.1,0.866,0.14,2.113
83,284950.61,-7897.72,11,4.858,0.084,0.948,0.224,2.895
84,302616.14,12642.65,23,17.416,0.087,0.819,0.117,2.735
85,298937.62,11074.43,43,27.544,0.108,0.831,0.109,2.48
86,292980.66,10621.27,46,52.123,0.107,0.836,0.124,2.887
87,291341.64,3602.46,19,13.33,0.081,0.934,0.143,2.289
88,296052.78,6812.78,11,8.054,0.08,0.89,0.128,1.858
89,314476.95,3490.04,30,32.284,0.085,0.944,0.124,1.73
90,311673.48,10101.08,27,28.431,0.078,0.931,0.124,2.36
91,300937.58,3470.02,18,24.098,0.156,0.938,0.102,2.262
92,286991.93,9571.27,7,7.428,0.071,0.946,0.189,2.323
93,307386.12,16090.18,7,11.046,0.08,0.928,0.146,1.973
94,300604.96,17843.82,15,17.145,0.086,0.854,0.119,2.527
95,303917.55,29223.91,44,42.828,0.081,0.877,0.121,1.844
96,296097.2,19299.56,25,18.829,0.073,0.883,0.131,1.749
97,291327.94,19385.45,15,17.662,0.074,0.889,0.114,2.012
98,288651.19,16782.21,53,53.478,0.091,0.821,0.13,3.125
99,321850.11,16542.18,24,28.667,0.066,0.921,0.124,1.794
100,309623.17,24691.81,7,6.125,0.062,0.881,0.128,1.775
101,317273.81,16350.36,13,12.349,0.066,0.962,0.154,1.957
102,330127.65,26472.36,18,16.755,0.07,0.896,0.112,2.779
103,330024.3,22050.13,24,22.623,0.086,0.917,0.128,2.115
104,335366.5,8522.69,45,50.9,0.105,0.711,0.076,2.512
105,330795.7,8625.57,56,59.574,0.096,0.838,0.09,2.449
106,324461.1,12021.3,33,33.073,0.071,0.874,0.116,1.967
107,333249.02,19193.73,35,33.069,0.096,0.816,0.107,2.911
108,330905.38,16199.04,36,41.484,0.12,0.663,0.065,3.294
109,338740.71,9995.65,58,59.655,0.098,0.787,0.084,2.434
110,345541.9,-607.56,37,34.33,0.074,0.871,0.082,2.536
111,348908.69,-5077.51,53,65.655,0.088,0.678,0.069,2.275
112,343120.93,5902.89,49,55.521,0.101,0.845,0.091,2.039
113,377836.69,-36378.58,1070,1243.759,0.136,0.523,0.074,2.519
114,356153.1,-24448.15,547,607.845,0.137,0.422,0.074,2.635
115,363934.49,-23252.2,660,811.312,0.13,0.518,0.073,2.921
116,362715.03,-62961.03,175,193.735,0.101,0.62,0.1,3.467
117,355515.39,-15862.17,594,664.519,0.137,0.48,0.067,2.571
118,350331.74,2259.59,189,175.679,0.115,0.743,0.094,2.796
119,390869.17,-52824.71,121,132.775,0.123,0.718,0.117,2.875
120,391663.46,-13955.69,125,116.253,0.102,0.499,0.091,2.183
121,381676.42,-24737.89,175,218.258,0.124,0.772,0.086,2.416
122,396227.77,-39792.02,85,70.583,0.099,0.772,0.132,2.541
123,365535.4,-28214.23,200,220.195,0.149,0.461,0.072,2.81
124,358761.78,-6929.68,389,454.815,0.145,0.584,0.071,2.711
125,376070.01,-56303.59,381,386.197,0.11,0.604,0.084,2.772
126,353788.69,-7340.62,173,208.943,0.136,0.672,0.079,2.628
127,371670.71,-21543.62,206,230.127,0.117,0.579,0.072,2.925
128,367522.64,-7189.91,142,185.604,0.159,0.645,0.081,2.372
129,361892.77,-18166,148,146.777,0.109,0.723,0.068,2.281
130,366450.66,-74634.65,141,142.727,0.097,0.602,0.108,3.16
131,355381.88,-79216.89,106,95.232,0.071,0.808,0.15,2.477
132,353694.67,-32885.23,116,143.243,0.146,0.424,0.043,2.633
133,378726.25,-28678.9,84,113.452,0.135,0.759,0.078,2.445
134,365246.77,-57670.24,78,76.222,0.113,0.709,0.096,2.529
135,389517.13,-31493.43,88,74.094,0.077,0.823,0.102,2.707
136,344415.42,11511.89,31,39.895,0.054,0.928,0.082,2.317
137,365053.58,-11168.12,60,59.313,0.099,0.762,0.069,2.316
138,387334.51,-21934.03,25,27.294,0.116,0.765,0.082,3.312
139,393097.28,-22717.2,57,56.813,0.075,0.766,0.079,2.46
140,380945.88,-17224.24,17,13.907,0.065,0.985,0.166,2.451
141,367373.14,-14712.42,47,51.913,0.121,0.826,0.063,2.205
142,374567.12,-13256.38,51,53.296,0.135,0.738,0.086,2.093
143,380862.18,-12688.75,11,7.634,0.072,0.989,0.177,2.358
144,383629.18,-9335.24,23,31.803,0.114,0.947,0.099,2.559
145,394378.41,-45752.97,64,55.296,0.102,0.849,0.139,3.293
146,402514.52,-43075.49,61,32.211,0.066,0.91,0.168,3.676
147,402518.42,-36236.31,37,36.163,0.08,0.854,0.16,2.236
148,396061.3,-30927.23,22,21.578,0.061,0.935,0.142,2.462
149,408226.18,-35513.98,18,7.467,0.063,0.969,0.172,3.462
150,403471.42,-31311.84,20,18.775,0.073,0.852,0.163,2.315
151,406033.77,-29345.02,20,24.596,0.064,0.82,0.154,2.725
152,399386.5,-22290.57,18,14.326,0.052,0.847,0.166,1.836
153,397587.4,-62378.67,25,19.169,0.089,0.808,0.171,2.747
154,391305.58,-63700.85,11,13.486,0.084,0.943,0.181,2.306
155,396203.55,-57412.21,23,19.247,0.085,0.92,0.166,2.395
156,397924.17,-52596.47,28,20.984,0.064,0.933,0.173,2.727
157,382876.06,-53653.14,19,13.687,0.085,0.868,0.176,2.881
158,385919.86,-61374.5,26,19.642,0.092,0.942,0.196,2.329
159,340110.86,-28521.01,70,64.958,0.135,0.499,0.166,1.36
160,342259.81,-30180.57,117,110.919,0.1,0.441,0.153,2.131
161,338829.19,-32435.83,256,255.792,0.16,0.392,0.135,2.711
162,335964.11,-27144.87,429,457.126,0.171,0.335,0.124,3.599
163,339454.02,-25208.35,285,280.924,0.193,0.439,0.138,2.557
164,342858.93,-25291.03,362,301.482,0.087,0.509,0.158,3.346
165,345601.8,-25527.73,452,371.254,0.086,0.469,0.13,3.008
166,345909.97,-30691.34,728,629.225,0.098,0.398,0.094,3.72
167,338436.09,-37186.98,562,553.703,0.133,0.379,0.116,2.33
168,334457.21,-35114.84,354,375.609,0.181,0.382,0.123,2.985
169,338261.22,-41722.33,1072,1038.344,0.127,0.403,0.116,2.91
170,329570.75,-34216.76,1037,1166.108,0.192,0.365,0.11,2.554
171,334989.23,-30867,309,312.454,0.189,0.363,0.124,3.036
172,331704.61,-26241.59,472,469.179,0.174,0.329,0.118,4.446
173,328431.53,-27940.2,699,775.103,0.196,0.345,0.117,3.251
174,336081.28,-23805.64,449,403.901,0.148,0.335,0.125,3.286
175,337470.12,-19925.9,615,587.955,0.117,0.35,0.126,3.652
176,342348.74,-22559.69,369,326.979,0.087,0.483,0.137,3.67
177,332725.59,-19390.99,812,800.918,0.131,0.363,0.099,3.208
178,327487.37,-22298.2,904,981.601,0.164,0.416,0.094,2.68
179,343411.59,-18219.15,1215,1037.79,0.085,0.417,0.093,3.634
180,349055.69,-20887.1,802,709.067,0.098,0.452,0.106,3.633
181,351097.21,-27497.53,983,874.668,0.095,0.427,0.08,3.223
182,297942.15,-33105.95,639,694.592,0.161,0.502,0.092,2.886
183,308339.92,-26657.04,251,230.764,0.142,0.395,0.093,3.353
184,322572.21,-26917.93,160,196.476,0.205,0.334,0.113,2.827
185,322565.6,-29522.07,233,246.203,0.189,0.338,0.104,2.841
186,293318.13,-17312.06,166,182.621,0.143,0.613,0.108,2.405
187,315646.55,-31303.34,259,315.043,0.189,0.397,0.088,2.625
188,304719.13,-27487.57,151,157.977,0.146,0.422,0.09,3.497
189,321954.36,-32546,270,299.219,0.182,0.356,0.086,3.286
190,311343.05,-41967,474,532.694,0.169,0.472,0.082,2.77
191,318030.71,-27812.93,139,156.098,0.206,0.368,0.098,2.888
192,315373.57,-24947.11,213,249.881,0.19,0.4,0.089,2.639
193,308034.74,-32419.4,195,245.676,0.198,0.405,0.079,2.613
194,314330.79,-21499.59,195,215.951,0.165,0.484,0.102,3.143
195,313503.88,-27427.64,114,152.567,0.219,0.425,0.092,2.867
196,311593.95,-29691.66,89,96.433,0.204,0.347,0.092,3.061
197,320162.13,-24446.09,95,115.318,0.166,0.403,0.09,3.039
198,321749.08,-23511,116,154.73,0.174,0.436,0.1,3.331
199,302066.12,-24471.58,82,85.46,0.129,0.372,0.079,3.503
200,324399.73,-35058.09,109,117.435,0.177,0.409,0.091,3.068
201,310172.99,-22574.96,96,119.523,0.137,0.493,0.075,3.306
202,318769.5,-18902.57,140,111.022,0.169,0.395,0.1,3.607
203,318576.81,-21935.03,156,180.434,0.153,0.432,0.072,2.77
204,306334.75,-22731.84,125,98.953,0.096,0.432,0.072,3.789
205,311907.21,-35905.87,176,191.709,0.202,0.411,0.057,2.412
206,316724.95,-35492.33,89,83.526,0.165,0.415,0.066,2.842
207,298239.3,-24996.76,63,78.267,0.127,0.716,0.089,3.141
208,300046.48,-21453.27,72,73.702,0.138,0.465,0.063,2.296
209,303145.8,-20159.37,40,44.426,0.08,0.64,0.083,3.084
210,292145.09,-22376.41,22,24.446,0.143,0.873,0.128,1.943
211,289344.08,-25302.15,37,33.367,0.133,0.795,0.149,2.325
212,281144.54,-26368.4,4,6.991,0.099,0.936,0.252,3.36
213,276385.4,-15692.77,18,16.121,0.092,0.854,0.209,2.99
214,333949.36,-49547.84,449,385.205,0.127,0.446,0.1,2.931
215,328639.25,-51354.28,350,322.831,0.148,0.437,0.108,2.875
216,328766.37,-54568.85,145,128.505,0.128,0.485,0.138,3.508
217,332223.73,-57396.73,329,215.339,0.12,0.435,0.123,4.385
218,327365.26,-57784.04,448,334.183,0.129,0.51,0.116,3.907
219,325148.15,-54327.48,295,315.196,0.151,0.494,0.092,3.083
220,328631.89,-61735.25,263,266.041,0.163,0.474,0.092,2.883
221,329641.62,-66529.75,204,301.101,0.174,0.57,0.092,2.655
222,327916.01,-45847.58,376,448.358,0.179,0.444,0.082,2.325
223,321117.24,-60823.26,290,357.977,0.173,0.551,0.071,2.51
224,325433.51,-61656.41,294,348.879,0.167,0.552,0.067,2.528
225,320495.28,-52701.22,361,401.562,0.166,0.554,0.081,2.899
226,320831.13,-45866.47,432,578.273,0.194,0.455,0.061,2.207
227,316915.15,-53631.62,156,188.688,0.135,0.49,0.081,3.125
228,323595.31,-65306.25,153,191.558,0.174,0.552,0.07,2.389
229,318116.79,-58966.35,186,201.211,0.137,0.634,0.078,3.118
230,339293.11,-47659.81,446,346.615,0.09,0.407,0.107,4.943
231,334055.07,-44545.18,248,227.145,0.118,0.373,0.097,4.169
232,331453.95,-41443.1,260,260.364,0.194,0.286,0.088,3.265
233,327954.56,-39544.67,236,229.34,0.165,0.322,0.07,3.575
234,321981.74,-36838.07,175,238.622,0.206,0.343,0.067,3.078
235,324590.43,-40337.23,192,242.805,0.173,0.405,0.052,2.771
236,317357.31,-39249.39,118,184.698,0.214,0.499,0.076,2.883
237,333435.39,-77430.79,735,704.883,0.127,0.622,0.113,3.698
238,302126.02,-67286.13,346,364.36,0.132,0.51,0.091,3.115
239,321943.14,-68916.22,247,299.298,0.206,0.606,0.139,2.508
240,314598.5,-64965.34,463,523.326,0.161,0.483,0.091,2.599
241,310206.19,-67759.02,279,314.882,0.159,0.574,0.098,3.118
242,326961.41,-72189.14,93,100.995,0.181,0.687,0.153,3.157
243,306522.23,-44854.32,686,763.182,0.148,0.508,0.069,3.152
244,332009.57,-86587.48,105,89.021,0.08,0.699,0.122,3.004
245,291250.63,-62212.96,200,212.373,0.161,0.517,0.081,2.481
246,302274.84,-54583.9,209,264.407,0.152,0.506,0.067,2.443
247,313999.38,-53197.4,265,288.902,0.139,0.454,0.071,2.977
248,299312.82,-60819.66,88,124.432,0.174,0.525,0.082,2.759
249,308373.06,-57401.82,121,152.899,0.146,0.596,0.065,2.645
250,309678.63,-51875.49,140,160.488,0.152,0.515,0.066,2.678
251,311833.72,-57007.52,104,114.083,0.1,0.63,0.059,2.315
252,327926.88,-75230.85,41,50.776,0.173,0.756,0.145,2.713
253,307926.73,-64266.21,64,64.614,0.104,0.554,0.07,2.539
254,299390.82,-71196.86,49,51.988,0.165,0.737,0.138,2.696
255,295866.34,-72353.52,44,48.284,0.173,0.634,0.116,2.788
256,299578.37,-47629.99,35,57.237,0.08,0.622,0.074,2.322
257,293314.45,-52457.68,6,6.325,0.11,0.664,0.118,1.999
258,299215.09,-41823.07,32,30.727,0.14,0.745,0.071,2.602
259,288944.87,-47144.31,28,41.043,0.103,0.851,0.098,1.87
260,290541.99,-38708.26,10,16.491,0.13,0.673,0.118,2.608
261,285964.14,-39392.46,12,16.168,0.117,0.889,0.14,2.132
\ No newline at end of file
diff --git a/pysal/examples/tokyo/Tokyomortality.txt b/pysal/examples/tokyo/Tokyomortality.txt
new file mode 100755
index 0000000..f10bd73
--- /dev/null
+++ b/pysal/examples/tokyo/Tokyomortality.txt
@@ -0,0 +1,263 @@
+ IDnum0 X_CENTROID Y_CENTROID db2564 eb2564 OCC_TEC OWNH POP65 UNEMP
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+ 1 334095.21 25283.20 95 97.526 0.107 0.671 0.111 3.401
+ 2 378200.19 -877.05 70 83.235 0.106 0.733 0.110 1.724
+ 3 357191.03 29064.39 48 52.392 0.075 0.767 0.140 1.829
+ 4 358056.34 10824.73 65 67.664 0.075 0.812 0.146 1.961
+ 5 366747.61 -3073.12 107 120.745 0.140 0.623 0.078 2.636
+ 6 351099.27 11800.35 65 67.196 0.066 0.824 0.121 1.603
+ 7 377929.98 4635.10 76 87.746 0.130 0.778 0.083 2.438
+ 8 367529.91 20192.51 192 190.255 0.227 0.449 0.101 1.783
+ 9 389231.47 3489.35 27 23.939 0.075 0.821 0.146 2.081
+ 10 389427.64 9290.10 28 23.832 0.088 0.610 0.119 2.589
+ 11 381089.82 9125.81 63 62.498 0.124 0.674 0.099 2.661
+ 12 371082.66 6843.90 34 36.137 0.113 0.914 0.078 2.080
+ 13 388281.84 -1760.78 17 16.770 0.062 0.920 0.161 2.509
+ 14 386771.66 -4857.11 25 20.209 0.055 0.959 0.166 1.824
+ 15 397029.93 4912.15 17 14.952 0.070 0.961 0.178 1.725
+ 16 399583.28 1217.51 31 24.675 0.062 0.931 0.165 2.261
+ 17 389413.79 18915.59 27 32.009 0.069 0.938 0.168 1.450
+ 18 374811.31 23395.20 10 16.171 0.098 0.892 0.159 1.997
+ 19 366291.01 3851.09 42 40.773 0.076 0.929 0.102 2.704
+ 20 362053.67 7027.25 20 19.021 0.075 0.935 0.148 2.479
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+ 23 356225.47 19763.98 47 34.798 0.063 0.828 0.135 1.677
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diff --git a/pysal/examples/tokyo/tokyo_BS_F.ctl b/pysal/examples/tokyo/tokyo_BS_F.ctl
new file mode 100755
index 0000000..bbc4b56
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_F.ctl
@@ -0,0 +1,41 @@
+
+C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+FORMAT/DELIMITER: 0
+Number_of_fields: 9
+Number_of_areas: 262
+Fields
+AreaKey 001 IDnum0
+X 002 X_CENTROID
+Y 003 Y_CENTROID
+Gmetric 0
+Dependent 004 db2564
+Offset
+Independent_geo 5
+000 Intercept
+006 OCC_TEC
+007 OWNH
+008 POP65
+009 UNEMP
+Independent_fix 0
+Unused_fields 1
+005 eb2564
+MODELTYPE: 1
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 1
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/tokyo/tokyo_BS_F_listwise.csv b/pysal/examples/tokyo/tokyo_BS_F_listwise.csv
new file mode 100755
index 0000000..1115378
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_F_listwise.csv
@@ -0,0 +1,263 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence
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diff --git a/pysal/examples/tokyo/tokyo_BS_F_summary.txt b/pysal/examples/tokyo/tokyo_BS_F_summary.txt
new file mode 100755
index 0000000..346d61e
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_F_summary.txt
@@ -0,0 +1,183 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 8:23:34 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+Number of areas/points: 262
+
+Model settings---------------------------------
+Model type: Poisson
+Geographic kernel: fixed bi-square
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 5
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: IDnum0
+Easting (x-coord): field2 : X_CENTROID
+Northing (y-coord): field3: Y_CENTROID
+Cartesian coordinates: Euclidean distance
+Dependent variable: field4: db2564
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field6: OCC_TEC
+Independent variable with varying (Local) coefficient: field7: OWNH
+Independent variable with varying (Local) coefficient: field8: POP65
+Independent variable with varying (Local) coefficient: field9: UNEMP
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 5
+Deviance: 24597.455544
+Classic AIC: 24607.455544
+AICc: 24607.689919
+BIC/MDL: 24625.297266
+Percent deviance explained 0.526746
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 8.432403 0.061613 136.859875 4593.526955
+OCC_TEC -4.270431 0.156467 -27.292831 0.013976
+OWNH -4.789311 0.046070 -103.957933 0.008318
+POP65 -1.252659 0.178384 -7.022265 0.285744
+UNEMP 0.061305 0.010099 6.070542 1.063223
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 17528.9489150947, 134660.843497798
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 26029.625 Criterion: 13294.025
+ p1 Bandwidth: 28341.975 Criterion: 14337.915
+ p2 Bandwidth: 29771.086 Criterion: 14934.143
+ pU Bandwidth: 32083.436 Criterion: 15739.259
+ iter 1 (p1) Bandwidth: 28341.975 Criterion: 14337.915 Diff: 1429.111
+ iter 2 (p1) Bandwidth: 27458.736 Criterion: 13940.360 Diff: 883.239
+ iter 3 (p1) Bandwidth: 26912.864 Criterion: 13690.874 Diff: 545.872
+ iter 4 (p1) Bandwidth: 26575.497 Criterion: 13538.151 Diff: 337.367
+ iter 5 (p1) Bandwidth: 26366.993 Criterion: 13445.104 Diff: 208.504
+ iter 6 (p1) Bandwidth: 26238.130 Criterion: 13387.798 Diff: 128.863
+ iter 7 (p1) Bandwidth: 26158.488 Criterion: 13352.102 Diff: 79.642
+ iter 8 (p1) Bandwidth: 26109.267 Criterion: 13329.966 Diff: 49.221
+ iter 9 (p1) Bandwidth: 26078.847 Criterion: 13316.257 Diff: 30.420
+ iter 10 (p1) Bandwidth: 26060.046 Criterion: 13307.773 Diff: 18.801
+ iter 11 (p1) Bandwidth: 26048.426 Criterion: 13302.525 Diff: 11.620
+ iter 12 (p1) Bandwidth: 26041.245 Criterion: 13299.279 Diff: 7.181
+ iter 13 (p1) Bandwidth: 26036.807 Criterion: 13297.273 Diff: 4.438
+ iter 14 (p1) Bandwidth: 26034.064 Criterion: 13296.032 Diff: 2.743
+ iter 15 (p1) Bandwidth: 26032.368 Criterion: 13295.266 Diff: 1.695
+ iter 16 (p1) Bandwidth: 26031.321 Criterion: 13294.792 Diff: 1.048
+ iter 17 (p1) Bandwidth: 26030.673 Criterion: 13294.499 Diff: 0.648
+ iter 18 (p1) Bandwidth: 26030.273 Criterion: 13294.318 Diff: 0.400
+ iter 19 (p1) Bandwidth: 26030.026 Criterion: 13294.206 Diff: 0.247
+ iter 20 (p1) Bandwidth: 26029.873 Criterion: 13294.137 Diff: 0.153
+ iter 21 (p1) Bandwidth: 26029.778 Criterion: 13294.094 Diff: 0.094
+ iter 22 (p1) Bandwidth: 26029.720 Criterion: 13294.067 Diff: 0.058
+ iter 23 (p1) Bandwidth: 26029.684 Criterion: 13294.051 Diff: 0.036
+ iter 24 (p1) Bandwidth: 26029.661 Criterion: 13294.041 Diff: 0.022
+The lower limit in your search has been selected as the optimal bandwidth size.
+A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search.
+Best bandwidth size 26029.625
+Minimum AICc 13294.025
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 26029.625402
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 276385.400000 408226.180000 131840.780000
+Y-coord -86587.480000 33538.420000 120125.900000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 66.434760
+Effective number of parameters (variance: trace(S'WSW^-1)): 51.742762
+Degree of freedom (model: n - trace(S)): 195.565240
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 180.873243
+Deviance: 13115.103705
+Classic AIC: 13247.973224
+AICc: 13294.024739
+BIC/MDL: 13485.035334
+Percent deviance explained 0.747666
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 6.956437 3.585435
+OCC_TEC 2.808094 16.199511
+OWNH -4.025297 2.176207
+POP65 0.709104 13.441155
+UNEMP 0.083338 0.534893
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept -4.698846 29.167648 33.866494
+OCC_TEC -127.606495 60.091449 187.697944
+OWNH -12.474311 3.217910 15.692220
+POP65 -98.487455 39.324447 137.811902
+UNEMP -2.869207 1.771710 4.640917
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 5.487963 7.375409 8.489884
+OCC_TEC -4.826044 1.873720 7.407511
+OWNH -5.189891 -4.171458 -2.506044
+POP65 -5.189369 -0.656530 6.532191
+UNEMP -0.277246 0.052468 0.483072
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 3.001921 2.225294
+OCC_TEC 12.233554 9.068610
+OWNH 2.683847 1.989508
+POP65 11.721560 8.689074
+UNEMP 0.760318 0.563616
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 24597.456 257.000 95.710
+GWR model 13115.104 180.873 72.510
+Difference 11482.352 76.127 150.832
+
+*****************************************************************************
+Program terminated at 7/25/2016 8:23:42 AM
diff --git a/pysal/examples/tokyo/tokyo_BS_NN.ctl b/pysal/examples/tokyo/tokyo_BS_NN.ctl
new file mode 100755
index 0000000..943c959
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_NN.ctl
@@ -0,0 +1,41 @@
+
+C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+FORMAT/DELIMITER: 0
+Number_of_fields: 9
+Number_of_areas: 262
+Fields
+AreaKey 001 IDnum0
+X 002 X_CENTROID
+Y 003 Y_CENTROID
+Gmetric 0
+Dependent 004 db2564
+Offset
+Independent_geo 5
+000 Intercept
+006 OCC_TEC
+007 OWNH
+008 POP65
+009 UNEMP
+Independent_fix 0
+Unused_fields 1
+005 eb2564
+MODELTYPE: 1
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 2
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/tokyo/tokyo_BS_NN_OFF.ctl b/pysal/examples/tokyo/tokyo_BS_NN_OFF.ctl
new file mode 100644
index 0000000..b8a92b8
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_NN_OFF.ctl
@@ -0,0 +1,40 @@
+
+C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+FORMAT/DELIMITER: 0
+Number_of_fields: 9
+Number_of_areas: 262
+Fields
+AreaKey 001 IDnum0
+X 002 X_CENTROID
+Y 003 Y_CENTROID
+Gmetric 0
+Dependent 004 db2564
+Offset 005 eb2564
+Independent_geo 5
+000 Intercept
+006 OCC_TEC
+007 OWNH
+008 POP65
+009 UNEMP
+Independent_fix 0
+Unused_fields 0
+MODELTYPE: 1
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 2
+BANDSELECTIONMETHOD: 0
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize: 100
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/tokyo/tokyo_BS_NN_OFF_listwise.csv b/pysal/examples/tokyo/tokyo_BS_NN_OFF_listwise.csv
new file mode 100644
index 0000000..ef5a7d4
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_NN_OFF_listwise.csv
@@ -0,0 +1,263 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence
+ 0, 0, 378906.83, 17310.41, 0.190926, 0.189581, 1.007098, -1.544184, 0.493528, -3.128868, -0.340089, 0.120284, -2.827371, 2.106230, 0.601909, 3.499251, -0.011423, 0.033762, -0.338340, 189.000000, 190.069178, 0.430166, 0.169991
+ 1, 1, 334095.21, 25283.2, 0.109053, 0.243659, 0.447562, -1.397581, 0.633099, -2.207523, -0.142401, 0.177875, -0.800565, 1.595709, 0.804852, 1.982612, -0.024374, 0.044034, -0.553529, 95.000000, 93.532367, 0.330260, 0.290894
+ 2, 2, 378200.19, -877.05, 0.196020, 0.178653, 1.097206, -2.274916, 0.465536, -4.886664, -0.339069, 0.109996, -3.082558, 2.327111, 0.466099, 4.992736, 0.007552, 0.031193, 0.242104, 70.000000, 81.207744, 0.625957, 0.119069
+ 3, 3, 357191.03, 29064.39, 0.281538, 0.215131, 1.308678, -1.259806, 0.530923, -2.372861, -0.268462, 0.149378, -1.797199, 1.351389, 0.714549, 1.891246, -0.046636, 0.038902, -1.198810, 48.000000, 57.042355, 0.257838, 0.130946
+ 4, 4, 358056.34, 10824.73, 0.236405, 0.176395, 1.340200, -1.821119, 0.446716, -4.076678, -0.328990, 0.113764, -2.891849, 2.004754, 0.504128, 3.976677, -0.011757, 0.031054, -0.378593, 65.000000, 74.954011, 0.466956, 0.160332
+ 5, 5, 366747.61, -3073.12, 0.251438, 0.163019, 1.542383, -2.596579, 0.420675, -6.172406, -0.323817, 0.102634, -3.155062, 2.101766, 0.378404, 5.554286, 0.006795, 0.028565, 0.237874, 107.000000, 105.816789, 0.684930, 0.041579
+ 6, 6, 351099.27, 11800.35, 0.268743, 0.183927, 1.461145, -1.861662, 0.445990, -4.174221, -0.333762, 0.118738, -2.810904, 1.967001, 0.549317, 3.580811, -0.019181, 0.032396, -0.592067, 65.000000, 72.655433, 0.427429, 0.164914
+ 7, 7, 377929.98, 4635.1, 0.176225, 0.182966, 0.963153, -1.948520, 0.475524, -4.097626, -0.342963, 0.113375, -3.025030, 2.335310, 0.512055, 4.560665, 0.002540, 0.031878, 0.079682, 76.000000, 75.986967, 0.563366, 0.066055
+ 8, 8, 367529.91, 20192.51, 0.228473, 0.191417, 1.193589, -1.430526, 0.493219, -2.900385, -0.315294, 0.127603, -2.470892, 1.781975, 0.624174, 2.854934, -0.022654, 0.034459, -0.657423, 192.000000, 172.453844, 0.343022, 0.716351
+ 9, 9, 389231.47, 3489.35, 0.063528, 0.188614, 0.336816, -1.872697, 0.503667, -3.718127, -0.308254, 0.114714, -2.687160, 2.611813, 0.536720, 4.866250, 0.022630, 0.032879, 0.688293, 27.000000, 26.414842, 0.572204, 0.045739
+ 10, 10, 389427.64, 9290.1, 0.089160, 0.191613, 0.465312, -1.707219, 0.506532, -3.370407, -0.321403, 0.117112, -2.744408, 2.519319, 0.564448, 4.463333, 0.012610, 0.033458, 0.376904, 28.000000, 25.696257, 0.527451, 0.027351
+ 11, 11, 381089.82, 9125.81, 0.166929, 0.186784, 0.893701, -1.776889, 0.486654, -3.651235, -0.344356, 0.115507, -2.981264, 2.320602, 0.546250, 4.248244, -0.000422, 0.032607, -0.012953, 63.000000, 59.041988, 0.519995, 0.028861
+ 12, 12, 371082.66, 6843.9, 0.188988, 0.179412, 1.053373, -1.840255, 0.465143, -3.956321, -0.338069, 0.113890, -2.968381, 2.217243, 0.506557, 4.377090, -0.001872, 0.031396, -0.059637, 34.000000, 30.827624, 0.526231, 0.052544
+ 13, 13, 388281.84, -1760.78, 0.068741, 0.179817, 0.382283, -2.138195, 0.485831, -4.401112, -0.295700, 0.109578, -2.698542, 2.587456, 0.485656, 5.327757, 0.029850, 0.031558, 0.945878, 17.000000, 19.593423, 0.620771, 0.044383
+ 14, 14, 386771.66, -4857.11, 0.090847, 0.178128, 0.510011, -2.329981, 0.482650, -4.827475, -0.292229, 0.108391, -2.696066, 2.531569, 0.470629, 5.379125, 0.030665, 0.031445, 0.975217, 25.000000, 23.682895, 0.642818, 0.073527
+ 15, 15, 397029.93, 4912.15, -0.030719, 0.188072, -0.163334, -1.733124, 0.514432, -3.369007, -0.280571, 0.114848, -2.442986, 2.810490, 0.548749, 5.121635, 0.038824, 0.032917, 1.179450, 17.000000, 17.295009, 0.571247, 0.063174
+ 16, 16, 399583.28, 1217.51, -0.071357, 0.187171, -0.381240, -1.813545, 0.518312, -3.498946, -0.262665, 0.113747, -2.309207, 2.896545, 0.541433, 5.349777, 0.050229, 0.032926, 1.525512, 31.000000, 29.048022, 0.594572, 0.070873
+ 17, 17, 389413.79, 18915.59, 0.144879, 0.203719, 0.711174, -1.459867, 0.525407, -2.778547, -0.326070, 0.127002, -2.567443, 2.228504, 0.656530, 3.394369, -0.007508, 0.036806, -0.203981, 27.000000, 35.439057, 0.430129, 0.140087
+ 18, 18, 374811.31, 23395.2, 0.220137, 0.193547, 1.137384, -1.400246, 0.503342, -2.781896, -0.321527, 0.128572, -2.500755, 1.827231, 0.648266, 2.818642, -0.022141, 0.035380, -0.625786, 10.000000, 16.871828, 0.349867, 0.050613
+ 19, 19, 366291.01, 3851.09, 0.206948, 0.174561, 1.185535, -2.047118, 0.448117, -4.568263, -0.329007, 0.110971, -2.964809, 2.165268, 0.456260, 4.745688, 0.000730, 0.030612, 0.023844, 42.000000, 39.510632, 0.575066, 0.074604
+ 20, 20, 362053.67, 7027.25, 0.215609, 0.173929, 1.239639, -1.939989, 0.443894, -4.370388, -0.324545, 0.111511, -2.910438, 2.082471, 0.467026, 4.459001, -0.003750, 0.030596, -0.122553, 20.000000, 20.310019, 0.531829, 0.052977
+ 21, 21, 350567.45, 26456.28, 0.265518, 0.220301, 1.205255, -1.248533, 0.533594, -2.339857, -0.242465, 0.151709, -1.598228, 1.399710, 0.735324, 1.903529, -0.049906, 0.039964, -1.248767, 40.000000, 41.872140, 0.273696, 0.105269
+ 22, 22, 356783.59, 23682.89, 0.282733, 0.203357, 1.390329, -1.403866, 0.500383, -2.805584, -0.303040, 0.135722, -2.232796, 1.557034, 0.666589, 2.335822, -0.038558, 0.036593, -1.053714, 15.000000, 15.535262, 0.294855, 0.035412
+ 23, 23, 356225.47, 19763.98, 0.275582, 0.193155, 1.426741, -1.537934, 0.477453, -3.221118, -0.325532, 0.125475, -2.594404, 1.739699, 0.609083, 2.856258, -0.030382, 0.034279, -0.886307, 47.000000, 38.191824, 0.338485, 0.084599
+ 24, 24, 338360.54, 25697.56, 0.172067, 0.237414, 0.724755, -1.277960, 0.581157, -2.198992, -0.177992, 0.168263, -1.057818, 1.517759, 0.779903, 1.946088, -0.038665, 0.042792, -0.903556, 68.000000, 64.615270, 0.313980, 0.251748
+ 25, 25, 337846.31, 18213.38, 0.245898, 0.218577, 1.124994, -1.889542, 0.518532, -3.644019, -0.276589, 0.143284, -1.930363, 1.730226, 0.670713, 2.579681, -0.022572, 0.039974, -0.564675, 17.000000, 14.974733, 0.359468, 0.027090
+ 26, 26, 344074.13, 27136.92, 0.220075, 0.234138, 0.939938, -1.156317, 0.565796, -2.043700, -0.191899, 0.165125, -1.162145, 1.385927, 0.774796, 1.788764, -0.051157, 0.042200, -1.212255, 57.000000, 49.376989, 0.284038, 0.082826
+ 27, 27, 349087.82, 19336.47, 0.288771, 0.199491, 1.447537, -1.623913, 0.473674, -3.428336, -0.323834, 0.126882, -2.552244, 1.779369, 0.609293, 2.920385, -0.034279, 0.035454, -0.966875, 40.000000, 26.883008, 0.350070, 0.069504
+ 28, 28, 343402.57, 18620.67, 0.282340, 0.210489, 1.341350, -1.697631, 0.485213, -3.498733, -0.304868, 0.134708, -2.263182, 1.751649, 0.642453, 2.726503, -0.034510, 0.037786, -0.913314, 47.000000, 44.290631, 0.349761, 0.058795
+ 29, 29, 359036.48, 1198.74, 0.233622, 0.170665, 1.368895, -2.374106, 0.426124, -5.571396, -0.304242, 0.107585, -2.827912, 2.022770, 0.406158, 4.980255, 0.004877, 0.030054, 0.162286, 49.000000, 40.984421, 0.637914, 0.036201
+ 30, 30, 370771.99, -1522.12, 0.229405, 0.167280, 1.371385, -2.418988, 0.433956, -5.574266, -0.333938, 0.104979, -3.180986, 2.199236, 0.409366, 5.372301, 0.005879, 0.029282, 0.200787, 49.000000, 44.266967, 0.656847, 0.061871
+ 31, 31, 376842.13, -7139.16, 0.240204, 0.165541, 1.451020, -2.755715, 0.433437, -6.357814, -0.327805, 0.102802, -3.188706, 2.200147, 0.386718, 5.689277, 0.012772, 0.029182, 0.437658, 27.000000, 26.272906, 0.697026, 0.053511
+ 32, 32, 318049.53, 32744.59, -0.247365, 0.243840, -1.014455, -0.926965, 0.786639, -1.178388, 0.028041, 0.196415, 0.142766, 2.149458, 0.843408, 2.548541, 0.021761, 0.045220, 0.481220, 120.000000, 122.113836, 0.304744, 0.194182
+ 33, 33, 325761.21, 31092.21, -0.090731, 0.254307, -0.356778, -1.220510, 0.754304, -1.618061, -0.030182, 0.195782, -0.154161, 1.838380, 0.843750, 2.178822, 0.002077, 0.044988, 0.046171, 28.000000, 28.995105, 0.322726, 0.105439
+ 34, 34, 318112.24, 28405.62, -0.265526, 0.240853, -1.102437, -0.970020, 0.792088, -1.224636, 0.023722, 0.192996, 0.122917, 2.212323, 0.844409, 2.619967, 0.029304, 0.045713, 0.641050, 16.000000, 18.193435, 0.308164, 0.050930
+ 35, 35, 310480.1, 28809.03, -0.480198, 0.254603, -1.886067, -0.042384, 0.834890, -0.050766, 0.092387, 0.210808, 0.438253, 2.817320, 0.888884, 3.169503, 0.030485, 0.047949, 0.635781, 14.000000, 18.680516, 0.282427, 0.038288
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diff --git a/pysal/examples/tokyo/tokyo_BS_NN_OFF_summary.txt b/pysal/examples/tokyo/tokyo_BS_NN_OFF_summary.txt
new file mode 100644
index 0000000..9447f51
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_NN_OFF_summary.txt
@@ -0,0 +1,148 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 9/7/2016 12:36:11 PM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+Number of areas/points: 262
+
+Model settings---------------------------------
+Model type: Poisson
+Geographic kernel: adaptive bi-square
+Method for optimal bandwidth search: fixed value
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 5
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: IDnum0
+Easting (x-coord): field2 : X_CENTROID
+Northing (y-coord): field3: Y_CENTROID
+Cartesian coordinates: Euclidean distance
+Dependent variable: field4: db2564
+Offset variable: field5: eb2564
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field6: OCC_TEC
+Independent variable with varying (Local) coefficient: field7: OWNH
+Independent variable with varying (Local) coefficient: field8: POP65
+Independent variable with varying (Local) coefficient: field9: UNEMP
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 5
+Deviance: 389.281580
+Classic AIC: 399.281580
+AICc: 399.515955
+BIC/MDL: 417.123303
+Percent deviance explained 0.594601
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 0.007470 0.065139 0.114679 1.007498
+OCC_TEC -2.287906 0.162000 -14.122903 0.101479
+OWNH -0.259692 0.047050 -5.519470 0.771289
+POP65 2.199387 0.198270 11.092878 9.019480
+UNEMP 0.064025 0.010997 5.822059 1.066119
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <fixed value>: 100
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 100.000000
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 276385.400000 408226.180000 131840.780000
+Y-coord -86587.480000 33538.420000 120125.900000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 25.145091
+Effective number of parameters (variance: trace(S'WSW^-1)): 17.142370
+Degree of freedom (model: n - trace(S)): 236.854909
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 228.852188
+Deviance: 311.245301
+Classic AIC: 361.535483
+AICc: 367.110273
+BIC/MDL: 451.261832
+Percent deviance explained 0.675868
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 0.038565 0.295395
+OCC_TEC -2.132644 1.004125
+OWNH -0.275802 0.157200
+POP65 2.169549 0.626589
+UNEMP 0.047531 0.038121
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept -0.879764 0.408928 1.288692
+OCC_TEC -3.607038 1.218879 4.825918
+OWNH -0.547011 0.111386 0.658397
+POP65 1.319626 4.095840 2.776214
+UNEMP -0.051157 0.159427 0.210584
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 0.001123 0.090003 0.254783
+OCC_TEC -2.661121 -2.503268 -1.837056
+OWNH -0.375947 -0.321084 -0.208341
+POP65 1.676893 2.083871 2.420870
+UNEMP 0.022264 0.044555 0.075398
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 0.253660 0.188036
+OCC_TEC 0.824066 0.610872
+OWNH 0.167607 0.124245
+POP65 0.743976 0.551502
+UNEMP 0.053134 0.039388
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 389.282 257.000 1.515
+GWR model 311.245 228.852 1.360
+Difference 78.036 28.148 2.772
+
+*****************************************************************************
+Program terminated at 9/7/2016 12:36:11 PM
diff --git a/pysal/examples/tokyo/tokyo_BS_NN_listwise.csv b/pysal/examples/tokyo/tokyo_BS_NN_listwise.csv
new file mode 100755
index 0000000..1da4e06
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_NN_listwise.csv
@@ -0,0 +1,263 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence
+ 0, 0, 378906.83, 17310.41, 6.981148, 0.382489, 18.251914, 1.278293, 0.864533, 1.478595, -3.489627, 0.236971, -14.725942, -7.767023, 1.124270, -6.908502, 0.219992, 0.069022, 3.187270, 189.000000, 127.740980, 0.799454, 0.410547
+ 1, 1, 334095.21, 25283.2, 7.895318, 0.421526, 18.730305, 5.671716, 1.783639, 3.179858, -5.555414, 0.402457, -13.803755, 5.471330, 1.448255, 3.777876, -0.265160, 0.066358, -3.995882, 95.000000, 88.236162, 0.647998, 0.460995
+ 2, 2, 378200.19, -877.05, 7.173261, 0.301423, 23.797978, 2.271269, 0.714557, 3.178570, -4.202776, 0.175817, -23.904278, -9.342388, 0.991280, -9.424572, 0.365941, 0.061546, 5.945852, 70.000000, 51.244096, 0.810739, 0.227173
+ 3, 3, 357191.03, 29064.39, 7.588873, 0.378109, 20.070619, -0.688604, 0.901905, -0.763499, -3.986198, 0.322474, -12.361305, -3.779300, 1.189256, -3.177870, 0.022004, 0.046961, 0.468565, 48.000000, 54.106687, 0.804137, 0.226219
+ 4, 4, 358056.34, 10824.73, 7.752999, 0.294689, 26.309115, -1.632266, 0.678929, -2.404178, -4.767460, 0.241295, -19.757788, -4.700026, 1.024001, -4.589864, 0.380323, 0.050590, 7.517689, 65.000000, 45.559119, 0.822351, 0.202609
+ 5, 5, 366747.61, -3073.12, 7.257934, 0.265644, 27.322062, 1.772214, 0.661584, 2.678743, -5.620995, 0.168210, -33.416569, -2.740331, 0.859815, -3.187116, 0.582308, 0.053016, 10.983630, 107.000000, 205.487136, 0.854208, 0.147547
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+ 256, 256, 299578.37, -47629.99, 3.183204, 0.273979, 11.618424, 5.451985, 0.582634, 9.357473, -2.087656, 0.213173, -9.793232, -13.204366, 1.140220, -11.580541, 1.238068, 0.051951, 23.831453, 35.000000, 67.932402, 0.555682, 0.294512
+ 257, 257, 293314.45, -52457.68, 3.743547, 0.272761, 13.724658, 5.671835, 0.631539, 8.980970, -3.307270, 0.218146, -15.160835, -11.917304, 1.090726, -10.926031, 1.231454, 0.053652, 22.952700, 6.000000, 25.200319, 0.600419, 0.207435
+ 258, 258, 299215.09, -41823.07, 3.290740, 0.265001, 12.417839, 7.425451, 0.549157, 13.521553, -1.009346, 0.202995, -4.972264, -13.948162, 1.025584, -13.600217, 0.871965, 0.047625, 18.308983, 32.000000, 128.621564, 0.521383, 0.415241
+ 259, 259, 288944.87, -47144.31, 3.128914, 0.282635, 11.070494, 9.357168, 0.623523, 15.006943, -1.959170, 0.218843, -8.952387, -13.981412, 1.046928, -13.354704, 1.028433, 0.052382, 19.633356, 28.000000, 19.657121, 0.595229, 0.125451
+ 260, 260, 290541.99, -38708.26, 2.894726, 0.277214, 10.442221, 11.267686, 0.596214, 18.898743, -1.021710, 0.207539, -4.922984, -12.640997, 0.954033, -13.250058, 0.753552, 0.049071, 15.356327, 10.000000, 63.151762, 0.567780, 0.142821
+ 261, 261, 285964.14, -39392.46, 2.745031, 0.289122, 9.494353, 12.451695, 0.625127, 19.918660, -1.151215, 0.214098, -5.377059, -12.372224, 0.961801, -12.863601, 0.766503, 0.050801, 15.088282, 12.000000, 21.769771, 0.588884, 0.138953
diff --git a/pysal/examples/tokyo/tokyo_BS_NN_summary.txt b/pysal/examples/tokyo/tokyo_BS_NN_summary.txt
new file mode 100755
index 0000000..69a91d7
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_BS_NN_summary.txt
@@ -0,0 +1,162 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 8:23:10 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+Number of areas/points: 262
+
+Model settings---------------------------------
+Model type: Poisson
+Geographic kernel: adaptive bi-square
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 5
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: IDnum0
+Easting (x-coord): field2 : X_CENTROID
+Northing (y-coord): field3: Y_CENTROID
+Cartesian coordinates: Euclidean distance
+Dependent variable: field4: db2564
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field6: OCC_TEC
+Independent variable with varying (Local) coefficient: field7: OWNH
+Independent variable with varying (Local) coefficient: field8: POP65
+Independent variable with varying (Local) coefficient: field9: UNEMP
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 5
+Deviance: 24597.455544
+Classic AIC: 24607.455544
+AICc: 24607.689919
+BIC/MDL: 24625.297266
+Percent deviance explained 0.526746
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 8.432403 0.061613 136.859875 4593.526955
+OCC_TEC -4.270431 0.156467 -27.292831 0.013976
+OWNH -4.789311 0.046070 -103.957933 0.008318
+POP65 -1.252659 0.178384 -7.022265 0.285744
+UNEMP 0.061305 0.010099 6.070542 1.063223
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 50, 262
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 50.000 Criterion: 13285.297
+ p1 Bandwidth: 54.513 Criterion: 13883.832
+ p2 Bandwidth: 57.302 Criterion: 14277.220
+ pU Bandwidth: 61.814 Criterion: 14823.882
+ iter 1 (p1) Bandwidth: 54.513 Criterion: 13883.832 Diff: 2.789
+ iter 2 (p1) Bandwidth: 52.789 Criterion: 13595.764 Diff: 1.724
+ iter 3 (p1) Bandwidth: 51.724 Criterion: 13457.435 Diff: 1.065
+The lower limit in your search has been selected as the optimal bandwidth size.
+A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search.
+Best bandwidth size 50.000
+Minimum AICc 13285.297
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 50.000000
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 276385.400000 408226.180000 131840.780000
+Y-coord -86587.480000 33538.420000 120125.900000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 51.200710
+Effective number of parameters (variance: trace(S'WSW^-1)): 37.243822
+Degree of freedom (model: n - trace(S)): 210.799290
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 196.842402
+Deviance: 13157.416861
+Classic AIC: 13259.818280
+AICc: 13285.297045
+BIC/MDL: 13442.520052
+Percent deviance explained 0.746852
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 6.999979 2.285454
+OCC_TEC 2.878608 8.847676
+OWNH -3.894854 1.746973
+POP65 1.787710 10.486526
+UNEMP 0.025665 0.509711
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept 0.092823 13.643524 13.550701
+OCC_TEC -15.109710 37.386385 52.496096
+OWNH -7.776524 0.128994 7.905518
+POP65 -13.981412 34.398356 48.379768
+UNEMP -1.889846 1.238068 3.127914
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 5.389016 7.348540 8.493162
+OCC_TEC -3.950324 2.752242 7.473013
+OWNH -5.048143 -4.141796 -2.717312
+POP65 -5.428713 -1.972543 6.458122
+UNEMP -0.283051 0.096767 0.340126
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 3.104146 2.301072
+OCC_TEC 11.423338 8.468004
+OWNH 2.330831 1.727821
+POP65 11.886835 8.811590
+UNEMP 0.623177 0.461955
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 24597.456 257.000 95.710
+GWR model 13157.417 196.842 66.842
+Difference 11440.039 60.158 190.168
+
+*****************************************************************************
+Program terminated at 7/25/2016 8:23:12 AM
diff --git a/pysal/examples/tokyo/tokyo_GS_F.ctl b/pysal/examples/tokyo/tokyo_GS_F.ctl
new file mode 100755
index 0000000..1056847
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_GS_F.ctl
@@ -0,0 +1,41 @@
+
+C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+FORMAT/DELIMITER: 0
+Number_of_fields: 9
+Number_of_areas: 262
+Fields
+AreaKey 001 IDnum0
+X 002 X_CENTROID
+Y 003 Y_CENTROID
+Gmetric 0
+Dependent 004 db2564
+Offset
+Independent_geo 5
+000 Intercept
+006 OCC_TEC
+007 OWNH
+008 POP65
+009 UNEMP
+Independent_fix 0
+Unused_fields 1
+005 eb2564
+MODELTYPE: 1
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 0
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/tokyo/tokyo_GS_F_listwise.csv b/pysal/examples/tokyo/tokyo_GS_F_listwise.csv
new file mode 100755
index 0000000..00da8fe
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_GS_F_listwise.csv
@@ -0,0 +1,263 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence
+ 0, 0, 378906.83, 17310.41, 3.583271, 1.027423, 3.487630, 7.503378, 1.913482, 3.921323, -2.707419, 0.461286, -5.869289, -2.758479, 2.654843, -1.039036, 0.839897, 0.147079, 5.710524, 189.000000, 149.519270, 0.912745, 0.762091
+ 1, 1, 334095.21, 25283.2, 7.605694, 0.473666, 16.057094, 6.168525, 1.937685, 3.183451, -5.661664, 0.494936, -11.439174, 8.024303, 1.771118, 4.530642, -0.262738, 0.072732, -3.612410, 95.000000, 86.823391, 0.751551, 0.638654
+ 2, 2, 378200.19, -877.05, 7.332111, 0.446111, 16.435606, 2.886554, 1.028578, 2.806353, -3.823065, 0.240810, -15.875840, -7.680216, 1.303281, -5.892986, 0.059754, 0.099284, 0.601850, 70.000000, 59.977644, 0.855759, 0.544328
+ 3, 3, 357191.03, 29064.39, 7.769326, 0.714282, 10.877117, 0.665213, 1.832467, 0.363015, -2.765460, 0.826580, -3.345664, -9.576680, 2.462993, -3.888229, -0.258572, 0.095482, -2.708079, 48.000000, 48.640000, 0.870092, 0.562028
+ 4, 4, 358056.34, 10824.73, 8.243570, 0.399626, 20.628200, -3.395993, 0.821400, -4.134397, -5.186489, 0.275598, -18.819043, -3.607446, 1.292753, -2.790514, 0.358330, 0.065784, 5.447071, 65.000000, 52.114302, 0.855175, 0.329222
+ 5, 5, 366747.61, -3073.12, 7.564261, 0.317851, 23.798150, 0.577583, 0.828971, 0.696747, -5.719636, 0.190156, -30.078716, -3.545991, 0.985423, -3.598447, 0.561150, 0.065277, 8.596466, 107.000000, 197.234731, 0.873885, 0.229012
+ 6, 6, 351099.27, 11800.35, 8.106939, 0.318757, 25.432986, -0.155342, 0.736326, -0.210969, -4.730732, 0.266637, -17.742225, -4.033351, 1.124534, -3.586687, 0.130943, 0.051685, 2.533501, 65.000000, 50.422125, 0.815303, 0.266846
+ 7, 7, 377929.98, 4635.1, 6.714674, 0.565174, 11.880721, 3.374096, 1.169632, 2.884751, -3.352372, 0.293283, -11.430483, -7.515642, 1.429521, -5.257453, 0.135820, 0.103493, 1.312356, 76.000000, 70.280322, 0.849061, 0.258663
+ 8, 8, 367529.91, 20192.51, 11.146358, 0.796408, 13.995790, -8.451825, 1.542881, -5.477951, -6.349870, 0.474024, -13.395660, -11.903423, 2.091466, -5.691425, 0.044412, 0.093230, 0.476377, 192.000000, 191.261883, 0.944671, 0.988064
+ 9, 9, 389231.47, 3489.35, 5.165397, 0.581240, 8.886859, 11.651789, 1.594139, 7.309142, -2.003645, 0.319804, -6.265234, -3.417730, 1.783715, -1.916074, -0.243895, 0.114620, -2.127859, 27.000000, 29.599423, 0.822465, 0.206989
+ 10, 10, 389427.64, 9290.1, 2.085473, 0.894051, 2.332611, 16.162142, 2.041381, 7.917261, -0.987040, 0.451632, -2.185499, 0.871715, 2.039607, 0.427394, 0.335267, 0.125729, 2.666580, 28.000000, 48.297877, 0.824849, 0.685177
+ 11, 11, 381089.82, 9125.81, 4.345788, 0.715714, 6.071957, 8.001751, 1.504088, 5.320002, -2.350789, 0.348904, -6.737629, -3.783707, 1.780801, -2.124721, 0.421303, 0.112712, 3.737873, 63.000000, 90.023658, 0.867041, 0.277608
+ 12, 12, 371082.66, 6843.9, 8.305331, 0.509114, 16.313305, -2.237278, 1.059384, -2.111867, -4.685393, 0.313517, -14.944626, -7.925982, 1.406658, -5.634620, 0.220273, 0.085624, 2.572573, 34.000000, 36.966460, 0.862134, 0.379282
+ 13, 13, 388281.84, -1760.78, 6.778143, 0.475673, 14.249590, 7.005023, 1.711122, 4.093818, -2.591444, 0.271794, -9.534578, -6.004250, 1.807191, -3.322422, -0.409358, 0.113716, -3.599836, 17.000000, 17.022965, 0.851631, 0.266099
+ 14, 14, 386771.66, -4857.11, 7.081289, 0.445029, 15.911986, 5.469208, 1.638971, 3.336977, -2.923965, 0.267361, -10.936390, -6.487191, 1.768168, -3.668877, -0.329698, 0.104184, -3.164577, 25.000000, 18.169965, 0.861207, 0.305453
+ 15, 15, 397029.93, 4912.15, 4.753586, 0.842220, 5.644113, 11.000473, 4.019815, 2.736562, -1.156187, 0.427339, -2.705546, -5.191879, 3.642218, -1.425472, -0.229300, 0.158028, -1.451006, 17.000000, 22.039289, 0.703781, 0.613688
+ 16, 16, 399583.28, 1217.51, 7.586034, 0.780414, 9.720521, -1.517378, 4.795840, -0.316395, -1.390538, 0.380046, -3.658871, -13.141668, 3.903645, -3.366512, -0.439975, 0.180023, -2.443998, 31.000000, 20.784505, 0.740272, 0.397756
+ 17, 17, 389413.79, 18915.59, -1.730128, 1.299855, -1.331016, 19.145329, 2.586813, 7.401127, 0.215032, 0.584278, 0.368031, 7.572270, 2.610111, 2.901129, 1.147673, 0.194376, 5.904386, 27.000000, 15.315532, 0.862948, 0.562543
+ 18, 18, 374811.31, 23395.2, 9.785750, 1.247184, 7.846277, -5.133577, 2.130728, -2.409307, -5.690811, 0.594372, -9.574486, -12.194400, 3.509347, -3.474835, 0.239084, 0.139760, 1.710677, 10.000000, 15.564568, 0.945480, 0.766519
+ 19, 19, 366291.01, 3851.09, 8.260723, 0.353326, 23.379893, -2.978271, 0.715336, -4.163459, -5.436460, 0.249291, -21.807706, -4.907749, 1.239158, -3.960551, 0.424655, 0.071007, 5.980481, 42.000000, 37.772440, 0.863505, 0.234206
+ 20, 20, 362053.67, 7027.25, 8.182824, 0.361668, 22.625251, -3.537416, 0.728688, -4.854497, -5.471257, 0.259767, -21.062134, -3.195925, 1.302194, -2.454262, 0.455804, 0.066410, 6.863486, 20.000000, 31.779904, 0.862923, 0.354732
+ 21, 21, 350567.45, 26456.28, 6.567726, 0.516736, 12.710027, 2.343458, 1.265270, 1.852141, -2.316452, 0.560271, -4.134523, -5.673143, 1.870016, -3.033741, -0.139021, 0.062073, -2.239624, 40.000000, 30.413083, 0.745055, 0.253060
+ 22, 22, 356783.59, 23682.89, 7.972697, 0.572028, 13.937600, -0.458254, 1.322276, -0.346565, -3.355186, 0.539526, -6.218771, -7.940967, 1.745047, -4.550575, -0.164140, 0.079425, -2.066605, 15.000000, 31.320945, 0.873779, 0.284045
+ 23, 23, 356225.47, 19763.98, 8.056675, 0.488213, 16.502360, -1.273385, 1.068521, -1.191727, -3.794711, 0.399926, -9.488528, -6.777468, 1.445978, -4.687116, -0.046108, 0.069638, -0.662113, 47.000000, 46.625584, 0.864269, 0.241495
+ 24, 24, 338360.54, 25697.56, 7.238068, 0.488495, 14.817068, 2.218634, 1.786882, 1.241623, -4.505027, 0.504016, -8.938259, 3.221451, 1.721941, 1.870826, -0.127814, 0.068806, -1.857612, 68.000000, 80.924279, 0.722950, 0.801990
+ 25, 25, 337846.31, 18213.38, 8.157668, 0.403901, 20.197192, 5.181904, 1.411085, 3.672283, -5.135945, 0.377811, -13.593940, 1.050249, 1.439262, 0.729714, -0.294510, 0.059855, -4.920430, 17.000000, 30.867093, 0.719785, 0.177535
+ 26, 26, 344074.13, 27136.92, 6.677757, 0.497390, 13.425604, 0.557351, 1.424316, 0.391311, -3.167179, 0.509139, -6.220661, -1.423756, 1.746432, -0.815237, -0.052758, 0.062907, -0.838666, 57.000000, 42.990723, 0.694171, 0.385570
+ 27, 27, 349087.82, 19336.47, 7.267366, 0.417034, 17.426312, 1.453143, 1.004612, 1.446473, -3.239515, 0.405461, -7.989704, -5.000441, 1.365174, -3.662858, -0.104307, 0.054018, -1.930976, 40.000000, 32.747531, 0.772776, 0.234407
+ 28, 28, 343402.57, 18620.67, 7.649818, 0.412220, 18.557621, 2.703995, 1.140916, 2.370020, -3.953814, 0.397841, -9.938172, -3.180580, 1.407996, -2.258941, -0.177137, 0.054491, -3.250781, 47.000000, 53.668724, 0.737241, 0.254496
+ 29, 29, 359036.48, 1198.74, 6.600899, 0.266005, 24.814945, 2.526918, 0.613045, 4.121911, -5.139025, 0.168999, -30.408696, -0.478942, 0.878720, -0.545045, 0.634242, 0.051182, 12.391885, 49.000000, 52.709712, 0.843163, 0.192972
+ 30, 30, 370771.99, -1522.12, 8.137645, 0.397337, 20.480465, -1.166856, 0.964096, -1.210311, -5.412199, 0.229516, -23.580963, -6.392830, 1.101124, -5.805733, 0.406421, 0.083276, 4.880393, 49.000000, 48.964753, 0.867845, 0.252182
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+ 232, 232, 331453.95, -41443.1, 8.250501, 0.159082, 51.863124, -5.595284, 0.345996, -16.171504, -2.893323, 0.176477, -16.394907, 4.571839, 0.385974, 11.844946, -0.243471, 0.015557, -15.650636, 260.000000, 381.806030, 0.374884, 0.292845
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+ 242, 242, 326961.41, -72189.14, 3.673470, 0.261511, 14.047098, 4.209398, 0.767571, 5.484054, -0.526382, 0.233335, -2.255905, -12.708785, 0.876334, -14.502223, 0.945476, 0.044034, 21.471285, 93.000000, 166.362700, 0.779023, 0.457223
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+ 246, 246, 302274.84, -54583.9, 3.732571, 0.300178, 12.434508, 5.838769, 0.736308, 7.929789, -3.846621, 0.257536, -14.936256, -11.169322, 1.203124, -9.283602, 1.306295, 0.058664, 22.267253, 209.000000, 166.768808, 0.672263, 0.272049
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+ 248, 248, 299312.82, -60819.66, 5.645681, 0.354478, 15.926738, 1.909245, 1.022615, 1.867023, -6.584668, 0.372388, -17.682261, -3.827456, 1.488754, -2.570912, 1.133583, 0.071961, 15.752745, 88.000000, 207.399083, 0.710547, 0.390822
+ 249, 249, 308373.06, -57401.82, 4.250054, 0.252272, 16.847100, 8.488547, 0.623475, 13.614886, -3.419078, 0.233612, -14.635721, -1.492453, 0.806546, -1.850426, 0.645091, 0.044803, 14.398342, 121.000000, 157.727388, 0.635882, 0.223037
+ 250, 250, 309678.63, -51875.49, 3.731959, 0.220817, 16.900696, 6.562327, 0.527231, 12.446783, -1.738874, 0.182319, -9.537547, -3.080539, 0.701412, -4.391914, 0.664280, 0.038326, 17.332387, 140.000000, 223.528422, 0.566952, 0.121819
+ 251, 251, 311833.72, -57007.52, 4.365929, 0.231744, 18.839415, 8.472164, 0.573243, 14.779359, -2.694249, 0.207475, -12.985926, 0.477474, 0.668478, 0.714270, 0.421189, 0.040285, 10.455128, 104.000000, 91.743026, 0.591740, 0.306446
+ 252, 252, 327926.88, -75230.85, 2.332484, 0.329647, 7.075702, 5.921599, 0.924641, 6.404217, 0.497070, 0.262499, 1.893603, -18.520336, 1.029104, -17.996558, 1.320597, 0.056201, 23.497723, 41.000000, 102.517783, 0.834305, 0.371436
+ 253, 253, 307926.73, -64266.21, 5.705812, 0.311743, 18.302926, 8.089295, 0.869786, 9.300326, -5.883929, 0.294705, -19.965505, 1.844269, 0.972730, 1.895972, 0.526924, 0.058560, 8.998026, 64.000000, 116.089500, 0.700326, 0.425985
+ 254, 254, 299390.82, -71196.86, 7.609224, 0.422816, 17.996547, -0.609131, 1.331563, -0.457456, -10.105874, 0.514512, -19.641680, 9.374486, 1.994834, 4.699381, 0.838336, 0.092674, 9.046110, 49.000000, 37.133509, 0.798072, 0.536499
+ 255, 255, 295866.34, -72353.52, 8.490293, 0.492702, 17.232105, -3.925160, 1.403947, -2.795804, -10.876421, 0.626536, -17.359606, 11.822271, 2.378936, 4.969563, 0.753921, 0.105553, 7.142611, 44.000000, 80.552073, 0.802217, 0.464202
+ 256, 256, 299578.37, -47629.99, 2.751404, 0.351591, 7.825577, 6.381897, 0.760867, 8.387660, -2.505954, 0.267870, -9.355126, -15.862070, 1.224738, -12.951402, 1.505534, 0.061466, 24.493769, 35.000000, 55.997637, 0.645352, 0.422272
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+ 258, 258, 299215.09, -41823.07, 2.662286, 0.315556, 8.436818, 8.188125, 0.664188, 12.328028, -1.401700, 0.239266, -5.858326, -12.647961, 1.088299, -11.621768, 1.125050, 0.053519, 21.021324, 32.000000, 120.751255, 0.563070, 0.540024
+ 259, 259, 288944.87, -47144.31, 3.440681, 0.499681, 6.885751, 10.079538, 1.172426, 8.597161, -3.700774, 0.388682, -9.521337, -14.828896, 1.853637, -7.999892, 1.284640, 0.078486, 16.367786, 28.000000, 9.762612, 0.676371, 0.208265
+ 260, 260, 290541.99, -38708.26, 2.270856, 0.361718, 6.277982, 16.688539, 0.807689, 20.662092, -2.287035, 0.271928, -8.410437, -9.551355, 1.319205, -7.240235, 0.896833, 0.061683, 14.539328, 10.000000, 61.134170, 0.598134, 0.296692
+ 261, 261, 285964.14, -39392.46, 2.354988, 0.451343, 5.217736, 20.741199, 1.011741, 20.500508, -2.927544, 0.314018, -9.322858, -10.527290, 1.560097, -6.747844, 0.831214, 0.075129, 11.063839, 12.000000, 11.910844, 0.627052, 0.245258
diff --git a/pysal/examples/tokyo/tokyo_GS_F_summary.txt b/pysal/examples/tokyo/tokyo_GS_F_summary.txt
new file mode 100755
index 0000000..b929618
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_GS_F_summary.txt
@@ -0,0 +1,183 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 8:24:34 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+Number of areas/points: 262
+
+Model settings---------------------------------
+Model type: Poisson
+Geographic kernel: fixed Gaussian
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 5
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: IDnum0
+Easting (x-coord): field2 : X_CENTROID
+Northing (y-coord): field3: Y_CENTROID
+Cartesian coordinates: Euclidean distance
+Dependent variable: field4: db2564
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field6: OCC_TEC
+Independent variable with varying (Local) coefficient: field7: OWNH
+Independent variable with varying (Local) coefficient: field8: POP65
+Independent variable with varying (Local) coefficient: field9: UNEMP
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 5
+Deviance: 24597.455544
+Classic AIC: 24607.455544
+AICc: 24607.689919
+BIC/MDL: 24625.297266
+Percent deviance explained 0.526746
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 8.432403 0.061613 136.859875 4593.526955
+OCC_TEC -4.270431 0.156467 -27.292831 0.013976
+OWNH -4.789311 0.046070 -103.957933 0.008318
+POP65 -1.252659 0.178384 -7.022265 0.285744
+UNEMP 0.061305 0.010099 6.070542 1.063223
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 8764.47445754735, 67330.4217488991
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 8764.474 Criterion: 11283.153
+ p1 Bandwidth: 10011.123 Criterion: 12806.152
+ p2 Bandwidth: 10781.594 Criterion: 13629.486
+ pU Bandwidth: 12028.243 Criterion: 14799.293
+ iter 1 (p1) Bandwidth: 10011.123 Criterion: 12806.152 Diff: 770.471
+ iter 2 (p1) Bandwidth: 9534.946 Criterion: 12254.404 Diff: 476.177
+ iter 3 (p1) Bandwidth: 9240.652 Criterion: 11895.443 Diff: 294.294
+ iter 4 (p1) Bandwidth: 9058.768 Criterion: 11666.303 Diff: 181.884
+ iter 5 (p1) Bandwidth: 8946.358 Criterion: 11521.794 Diff: 112.410
+ iter 6 (p1) Bandwidth: 8876.885 Criterion: 11431.353 Diff: 69.473
+ iter 7 (p1) Bandwidth: 8833.948 Criterion: 11375.019 Diff: 42.937
+ iter 8 (p1) Bandwidth: 8807.411 Criterion: 11340.034 Diff: 26.536
+ iter 9 (p1) Bandwidth: 8791.011 Criterion: 11318.348 Diff: 16.400
+ iter 10 (p1) Bandwidth: 8780.875 Criterion: 11304.920 Diff: 10.136
+ iter 11 (p1) Bandwidth: 8774.610 Criterion: 11296.612 Diff: 6.264
+ iter 12 (p1) Bandwidth: 8770.739 Criterion: 11291.473 Diff: 3.872
+ iter 13 (p1) Bandwidth: 8768.346 Criterion: 11288.296 Diff: 2.393
+ iter 14 (p1) Bandwidth: 8766.867 Criterion: 11286.332 Diff: 1.479
+ iter 15 (p1) Bandwidth: 8765.953 Criterion: 11285.118 Diff: 0.914
+ iter 16 (p1) Bandwidth: 8765.388 Criterion: 11284.367 Diff: 0.565
+ iter 17 (p1) Bandwidth: 8765.039 Criterion: 11283.903 Diff: 0.349
+ iter 18 (p1) Bandwidth: 8764.824 Criterion: 11283.617 Diff: 0.216
+ iter 19 (p1) Bandwidth: 8764.690 Criterion: 11283.440 Diff: 0.133
+ iter 20 (p1) Bandwidth: 8764.608 Criterion: 11283.330 Diff: 0.082
+ iter 21 (p1) Bandwidth: 8764.557 Criterion: 11283.262 Diff: 0.051
+ iter 22 (p1) Bandwidth: 8764.525 Criterion: 11283.221 Diff: 0.031
+ iter 23 (p1) Bandwidth: 8764.506 Criterion: 11283.195 Diff: 0.019
+ iter 24 (p1) Bandwidth: 8764.494 Criterion: 11283.179 Diff: 0.012
+The lower limit in your search has been selected as the optimal bandwidth size.
+A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search.
+Best bandwidth size 8764.474
+Minimum AICc 11283.153
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 8764.474458
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 276385.400000 408226.180000 131840.780000
+Y-coord -86587.480000 33538.420000 120125.900000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 80.249343
+Effective number of parameters (variance: trace(S'WSW^-1)): 56.479620
+Degree of freedom (model: n - trace(S)): 181.750657
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 157.980934
+Deviance: 11050.508287
+Classic AIC: 11211.006974
+AICc: 11283.152841
+BIC/MDL: 11497.364277
+Percent deviance explained 0.787389
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 6.906204 3.107262
+OCC_TEC 2.860801 11.608416
+OWNH -3.926832 2.110848
+POP65 0.391512 10.603676
+UNEMP 0.089393 0.519691
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept -4.544321 18.812885 23.357206
+OCC_TEC -71.639003 46.740425 118.379428
+OWNH -11.685117 4.070490 15.755607
+POP65 -59.137061 32.133177 91.270237
+UNEMP -2.151689 1.903855 4.055543
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 5.562201 7.334816 8.453658
+OCC_TEC -5.092611 1.811224 7.880221
+OWNH -5.242645 -4.012946 -2.522777
+POP65 -4.995400 -1.404697 6.833327
+UNEMP -0.297128 0.075154 0.423059
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 2.891457 2.143408
+OCC_TEC 12.972832 9.616629
+OWNH 2.719868 2.016210
+POP65 11.828727 8.768515
+UNEMP 0.720188 0.533868
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 24597.456 257.000 95.710
+GWR model 11050.508 157.981 69.948
+Difference 13546.947 99.019 136.812
+
+*****************************************************************************
+Program terminated at 7/25/2016 8:24:42 AM
diff --git a/pysal/examples/tokyo/tokyo_GS_NN.ctl b/pysal/examples/tokyo/tokyo_GS_NN.ctl
new file mode 100755
index 0000000..5d1cae6
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_GS_NN.ctl
@@ -0,0 +1,41 @@
+
+C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+FORMAT/DELIMITER: 0
+Number_of_fields: 9
+Number_of_areas: 262
+Fields
+AreaKey 001 IDnum0
+X 002 X_CENTROID
+Y 003 Y_CENTROID
+Gmetric 0
+Dependent 004 db2564
+Offset
+Independent_geo 5
+000 Intercept
+006 OCC_TEC
+007 OWNH
+008 POP65
+009 UNEMP
+Independent_fix 0
+Unused_fields 1
+005 eb2564
+MODELTYPE: 1
+STANDARDISATION: 0
+GTEST: 0
+VSL2G: 0
+VSG2L: 0
+KERNELTYPE: 3
+BANDSELECTIONMETHOD: 1
+Goldrangeflag: 0
+Goldenmax:
+Goldenmin:
+Fixedbandsize:
+IntervalMax:
+IntervalMin:
+IntervalStep:
+Criteria:
+summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN_summary.txt
+listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN_listwise.csv
+predictflag: 0
+prediction_def:
+prediction_output:
diff --git a/pysal/examples/tokyo/tokyo_GS_NN_listwise.csv b/pysal/examples/tokyo/tokyo_GS_NN_listwise.csv
new file mode 100755
index 0000000..8513136
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_GS_NN_listwise.csv
@@ -0,0 +1,263 @@
+Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence
+ 0, 0, 378906.83, 17310.41, 9.074036, 0.073984, 122.648331, -5.142149, 0.187186, -27.470845, -5.248993, 0.055641, -94.336143, -3.748701, 0.211766, -17.702104, 0.063281, 0.012091, 5.233592, 189.000000, 153.956623, 0.669769, 0.017050
+ 1, 1, 334095.21, 25283.2, 8.410777, 0.093472, 89.981413, -3.826044, 0.210624, -18.165278, -4.991247, 0.066799, -74.720395, -1.291807, 0.253416, -5.097581, 0.071839, 0.015879, 4.524037, 95.000000, 115.954351, 0.637569, 0.060948
+ 2, 2, 378200.19, -877.05, 9.266997, 0.078175, 118.541648, -5.418820, 0.194849, -27.810354, -5.317602, 0.059268, -89.720968, -4.646020, 0.217230, -21.387574, 0.068904, 0.012438, 5.539975, 70.000000, 81.656996, 0.675216, 0.033105
+ 3, 3, 357191.03, 29064.39, 8.819506, 0.078174, 112.818937, -4.725862, 0.193624, -24.407415, -5.178919, 0.057417, -90.199013, -2.674651, 0.221580, -12.070818, 0.069701, 0.013202, 5.279692, 48.000000, 69.816366, 0.662878, 0.042822
+ 4, 4, 358056.34, 10824.73, 9.134424, 0.079429, 115.001420, -5.429366, 0.195983, -27.703250, -5.331736, 0.058886, -90.544115, -3.155001, 0.222122, -14.203894, 0.053647, 0.013087, 4.099294, 65.000000, 56.964223, 0.666054, 0.035981
+ 5, 5, 366747.61, -3073.12, 9.343691, 0.076804, 121.655760, -5.931095, 0.190135, -31.194118, -5.359711, 0.058112, -92.231188, -3.932469, 0.213200, -18.444977, 0.050443, 0.012093, 4.171209, 107.000000, 148.483203, 0.660986, 0.025549
+ 6, 6, 351099.27, 11800.35, 9.083190, 0.080970, 112.180119, -5.437597, 0.195029, -27.880971, -5.316756, 0.059557, -89.271287, -2.504094, 0.224224, -11.167820, 0.041363, 0.013385, 3.090139, 65.000000, 60.740515, 0.656304, 0.038487
+ 7, 7, 377929.98, 4635.1, 9.210313, 0.077351, 119.071609, -5.322749, 0.194119, -27.420016, -5.305609, 0.058473, -90.736491, -4.409552, 0.217023, -20.318348, 0.068710, 0.012430, 5.527824, 76.000000, 66.162583, 0.675688, 0.025443
+ 8, 8, 367529.91, 20192.51, 9.013437, 0.077806, 115.845185, -4.988202, 0.197298, -25.282531, -5.263056, 0.057886, -90.921786, -3.555127, 0.222052, -16.010346, 0.071542, 0.013021, 5.494533, 192.000000, 197.649432, 0.673927, 0.204641
+ 9, 9, 389231.47, 3489.35, 9.194835, 0.075021, 122.563621, -5.293082, 0.186581, -28.368882, -5.258926, 0.056708, -92.737218, -4.371140, 0.211092, -20.707284, 0.060387, 0.011979, 5.041098, 27.000000, 52.865691, 0.671303, 0.027392
+ 10, 10, 389427.64, 9290.1, 9.141264, 0.074693, 122.383814, -5.183981, 0.186998, -27.722182, -5.246591, 0.056377, -93.062607, -4.237223, 0.211628, -20.021991, 0.063926, 0.012019, 5.318651, 28.000000, 171.730664, 0.671966, 0.036983
+ 11, 11, 381089.82, 9125.81, 9.157106, 0.076461, 119.762361, -5.203088, 0.192631, -27.010683, -5.279801, 0.057695, -91.512183, -4.295181, 0.216166, -19.869835, 0.069048, 0.012367, 5.583183, 63.000000, 111.258088, 0.675491, 0.017910
+ 12, 12, 371082.66, 6843.9, 9.186409, 0.078873, 116.471335, -5.298157, 0.198732, -26.659802, -5.324574, 0.059392, -89.651995, -4.230761, 0.221438, -19.105863, 0.073229, 0.012807, 5.717804, 34.000000, 34.583360, 0.677431, 0.022704
+ 13, 13, 388281.84, -1760.78, 9.247242, 0.075910, 121.818623, -5.377844, 0.187730, -28.646717, -5.272459, 0.057462, -91.754793, -4.564542, 0.211969, -21.533983, 0.059052, 0.012044, 4.902815, 17.000000, 32.343811, 0.671604, 0.025496
+ 14, 14, 386771.66, -4857.11, 9.283836, 0.076489, 121.374318, -5.443946, 0.188653, -28.856895, -5.283609, 0.057975, -91.135542, -4.678866, 0.212575, -22.010445, 0.058079, 0.012092, 4.803201, 25.000000, 25.704914, 0.671464, 0.025031
+ 15, 15, 397029.93, 4912.15, 9.154871, 0.073950, 123.797997, -5.212980, 0.183267, -28.444755, -5.225559, 0.055830, -93.597224, -4.279668, 0.208880, -20.488641, 0.059318, 0.011812, 5.021672, 17.000000, 22.392866, 0.669267, 0.024045
+ 16, 16, 399583.28, 1217.51, 9.168450, 0.073158, 125.323834, -5.271901, 0.180344, -29.232492, -5.216248, 0.055256, -94.401444, -4.223207, 0.206449, -20.456457, 0.054280, 0.011640, 4.663153, 31.000000, 30.300505, 0.666263, 0.023312
+ 17, 17, 389413.79, 18915.59, 9.047782, 0.071651, 126.275723, -5.110305, 0.180358, -28.334227, -5.207427, 0.053977, -96.474067, -3.665047, 0.205657, -17.821124, 0.059159, 0.011628, 5.087487, 27.000000, 26.593260, 0.664824, 0.025958
+ 18, 18, 374811.31, 23395.2, 8.999106, 0.074599, 120.633246, -4.990847, 0.189421, -26.347875, -5.230304, 0.055832, -93.678941, -3.533458, 0.214398, -16.480873, 0.067797, 0.012353, 5.488264, 10.000000, 30.512712, 0.669952, 0.023886
+ 19, 19, 366291.01, 3851.09, 9.223767, 0.079786, 115.606018, -5.464277, 0.199318, -27.414922, -5.348168, 0.059998, -89.139151, -4.082177, 0.222215, -18.370382, 0.070852, 0.012875, 5.502888, 42.000000, 37.160080, 0.674355, 0.026699
+ 20, 20, 362053.67, 7027.25, 9.183685, 0.080617, 113.916836, -5.417327, 0.200624, -27.002372, -5.348398, 0.060130, -88.947354, -3.717818, 0.224682, -16.546986, 0.066457, 0.013178, 5.042947, 20.000000, 29.699854, 0.672858, 0.027742
+ 21, 21, 350567.45, 26456.28, 8.784883, 0.080222, 109.507285, -4.716307, 0.194644, -24.230420, -5.171281, 0.058585, -88.269742, -2.311228, 0.224823, -10.280218, 0.064987, 0.013539, 4.800032, 40.000000, 31.504735, 0.657831, 0.025902
+ 22, 22, 356783.59, 23682.89, 8.902883, 0.078129, 113.950515, -4.926818, 0.193251, -25.494427, -5.222062, 0.057521, -90.784721, -2.728844, 0.221115, -12.341289, 0.062867, 0.013135, 4.786385, 15.000000, 38.203355, 0.663177, 0.026242
+ 23, 23, 356225.47, 19763.98, 8.964701, 0.079165, 113.241165, -5.049515, 0.195340, -25.849823, -5.257602, 0.058277, -90.217964, -2.813649, 0.223123, -12.610303, 0.059704, 0.013291, 4.491980, 47.000000, 55.344059, 0.664599, 0.037684
+ 24, 24, 338360.54, 25697.56, 8.549179, 0.088387, 96.724930, -4.204848, 0.203537, -20.658924, -5.063401, 0.063573, -79.646686, -1.549956, 0.241424, -6.420043, 0.067885, 0.014966, 4.535871, 68.000000, 129.402746, 0.644564, 0.099890
+ 25, 25, 337846.31, 18213.38, 8.723631, 0.087477, 99.725252, -4.652824, 0.200282, -23.231309, -5.151293, 0.063417, -81.229477, -1.377811, 0.239016, -5.764515, 0.042412, 0.014721, 2.881016, 17.000000, 46.593449, 0.638758, 0.027529
+ 26, 26, 344074.13, 27136.92, 8.661581, 0.083777, 103.388539, -4.468582, 0.198221, -22.543386, -5.116281, 0.060674, -84.323712, -1.912029, 0.231625, -8.254833, 0.067503, 0.014164, 4.765830, 57.000000, 58.059217, 0.651764, 0.025659
+ 27, 27, 349087.82, 19336.47, 8.904696, 0.080340, 110.838172, -5.041168, 0.193427, -26.062404, -5.233113, 0.058843, -88.932740, -2.237014, 0.224131, -9.980849, 0.050943, 0.013450, 3.787539, 40.000000, 33.303240, 0.655176, 0.027516
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+ 29, 29, 359036.48, 1198.74, 9.290659, 0.077763, 119.473605, -5.981911, 0.190454, -31.408672, -5.362232, 0.058148, -92.216336, -3.224461, 0.215445, -14.966516, 0.042072, 0.012360, 3.403937, 49.000000, 52.702201, 0.654747, 0.022895
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+ 31, 31, 376842.13, -7139.16, 9.353759, 0.077575, 120.576468, -5.673078, 0.192070, -29.536528, -5.332972, 0.059049, -90.314517, -4.677717, 0.214235, -21.834530, 0.059114, 0.012198, 4.846099, 27.000000, 29.824825, 0.669125, 0.025759
+ 32, 32, 318049.53, 32744.59, 7.942592, 0.097688, 81.305675, -2.859484, 0.218220, -13.103690, -4.704135, 0.069642, -67.547769, -0.678335, 0.256249, -2.647174, 0.095991, 0.016066, 5.974700, 120.000000, 103.411338, 0.615086, 0.030253
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+ 41, 41, 330948.96, -8687.59, 9.052416, 0.082188, 110.143109, -7.034787, 0.191726, -36.691890, -5.008471, 0.059833, -83.706880, 1.606830, 0.215339, 7.461858, -0.075423, 0.012660, -5.957587, 544.000000, 213.400699, 0.498491, 0.020420
+ 42, 42, 327143.99, -3103.01, 9.001686, 0.081347, 110.657272, -6.378519, 0.191231, -33.354995, -5.099398, 0.059529, -85.662011, 1.397353, 0.216359, 6.458502, -0.078274, 0.012806, -6.112505, 557.000000, 164.718437, 0.533774, 0.014958
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+ 44, 44, 312874.14, -17053.63, 8.560752, 0.083886, 102.052173, -5.390806, 0.204476, -26.363982, -4.624174, 0.061143, -75.628395, 3.698231, 0.214073, 17.275582, -0.153164, 0.013544, -11.308570, 395.000000, 131.991244, 0.405937, 0.027164
+ 45, 45, 293680.38, -8010.1, 8.232986, 0.089730, 91.752534, -3.216455, 0.214507, -14.994646, -4.663579, 0.066109, -70.544076, 0.985854, 0.247824, 3.978045, -0.074799, 0.014892, -5.022877, 97.000000, 80.056623, 0.507185, 0.025693
+ 46, 46, 325185.11, 20460.45, 8.232413, 0.097408, 84.514562, -3.394082, 0.215912, -15.719721, -4.867942, 0.069882, -69.659253, -0.602126, 0.265750, -2.265758, 0.056726, 0.016502, 3.437486, 91.000000, 68.721077, 0.613337, 0.024838
+ 47, 47, 305971.31, 8472, 8.000630, 0.100482, 79.622771, -2.564990, 0.233538, -10.983174, -4.593543, 0.072550, -63.315872, 0.352386, 0.277122, 1.271591, -0.015931, 0.016963, -0.939191, 97.000000, 105.289303, 0.550473, 0.021199
+ 48, 48, 335330.5, -108.19, 9.135363, 0.081822, 111.649110, -6.448630, 0.190371, -33.873940, -5.228087, 0.060267, -86.748894, 0.023699, 0.218219, 0.108603, -0.039466, 0.012874, -3.065416, 148.000000, 105.876734, 0.576136, 0.029859
+ 49, 49, 339115.66, 3202.05, 9.142456, 0.081871, 111.669146, -6.164959, 0.190339, -32.389384, -5.283206, 0.060376, -87.505604, -0.785409, 0.220256, -3.565902, -0.015011, 0.013041, -1.151060, 269.000000, 162.531835, 0.605252, 0.033527
+ 50, 50, 309271.13, -10589.17, 8.564056, 0.088114, 97.192585, -4.713345, 0.212330, -22.198241, -4.741562, 0.063918, -74.181586, 2.784304, 0.227496, 12.238901, -0.149561, 0.014551, -10.278076, 183.000000, 109.085262, 0.454006, 0.024999
+ 51, 51, 319972.62, 24634.35, 8.006377, 0.099299, 80.628907, -2.922593, 0.220714, -13.241555, -4.733640, 0.070951, -66.716574, -0.527917, 0.266357, -1.981987, 0.077899, 0.016591, 4.695232, 86.000000, 61.081627, 0.608800, 0.024045
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+ 238, 238, 302126.02, -67286.13, 7.695633, 0.074486, 103.316032, -3.523968, 0.185387, -19.008691, -4.021627, 0.056838, -70.756248, 1.709282, 0.205954, 8.299352, 0.052541, 0.011781, 4.459990, 346.000000, 244.361854, 0.411450, 0.016921
+ 239, 239, 321943.14, -68916.22, 7.830279, 0.072427, 108.112287, -4.159715, 0.179212, -23.211197, -3.904796, 0.055448, -70.422298, 1.669827, 0.200466, 8.329723, 0.037554, 0.011479, 3.271509, 247.000000, 138.845552, 0.393170, 0.074678
+ 240, 240, 314598.5, -64965.34, 7.597993, 0.076070, 99.881181, -3.646931, 0.187905, -19.408396, -3.767269, 0.059223, -63.611772, 2.236142, 0.211967, 10.549502, 0.043827, 0.011883, 3.688099, 463.000000, 246.815793, 0.366101, 0.021169
+ 241, 241, 310206.19, -67759.02, 7.670692, 0.074621, 102.795898, -3.659950, 0.185125, -19.770178, -3.908316, 0.057301, -68.206252, 1.898972, 0.206540, 9.194217, 0.049935, 0.011749, 4.250237, 279.000000, 178.963443, 0.392639, 0.028696
+ 242, 242, 326961.41, -72189.14, 7.982312, 0.070429, 113.337673, -4.394465, 0.175167, -25.087288, -4.023536, 0.053319, -75.461661, 1.175622, 0.194378, 6.048130, 0.035624, 0.011273, 3.160010, 93.000000, 111.617692, 0.417214, 0.078768
+ 243, 243, 306522.23, -44854.32, 7.668422, 0.079345, 96.646451, -3.304340, 0.200693, -16.464665, -3.927448, 0.062388, -62.951496, 2.987316, 0.220177, 13.567803, -0.023813, 0.012621, -1.886699, 686.000000, 203.426131, 0.344668, 0.033019
+ 244, 244, 332009.57, -86587.48, 8.209034, 0.066568, 123.317323, -4.515122, 0.167641, -26.933237, -4.315836, 0.049529, -87.137304, 0.167619, 0.184537, 0.908319, 0.042912, 0.010812, 3.968957, 105.000000, 145.534304, 0.482077, 0.031988
+ 245, 245, 291250.63, -62212.96, 7.735417, 0.075190, 102.877745, -3.328645, 0.187252, -17.776242, -4.155833, 0.057179, -72.681644, 1.490192, 0.209003, 7.130020, 0.048308, 0.011953, 4.041537, 200.000000, 198.640370, 0.431034, 0.022714
+ 246, 246, 302274.84, -54583.9, 7.590615, 0.078355, 96.874541, -3.141350, 0.196214, -16.009780, -3.950704, 0.060846, -64.929700, 2.243142, 0.217890, 10.294855, 0.030018, 0.012401, 2.420535, 209.000000, 208.033136, 0.379068, 0.034222
+ 247, 247, 313999.38, -53197.4, 7.585872, 0.076735, 98.858187, -3.645689, 0.190777, -19.109683, -3.697454, 0.061067, -60.547672, 3.089781, 0.217276, 14.220534, 0.002201, 0.012053, 0.182603, 265.000000, 277.661955, 0.331060, 0.032885
+ 248, 248, 299312.82, -60819.66, 7.680816, 0.075713, 101.445923, -3.375155, 0.188734, -17.883164, -4.045412, 0.058036, -69.704725, 1.836593, 0.210015, 8.745075, 0.043719, 0.012001, 3.643015, 88.000000, 188.843606, 0.407226, 0.024695
+ 249, 249, 308373.06, -57401.82, 7.488749, 0.078856, 94.967521, -3.178750, 0.196830, -16.149741, -3.765589, 0.061910, -60.823226, 2.580713, 0.220777, 11.689216, 0.034504, 0.012339, 2.796291, 121.000000, 154.372815, 0.353274, 0.034115
+ 250, 250, 309678.63, -51875.49, 7.503222, 0.079167, 94.776563, -3.200445, 0.198803, -16.098605, -3.732602, 0.062739, -59.493985, 3.005632, 0.222908, 13.483731, 0.008023, 0.012437, 0.645066, 140.000000, 203.226629, 0.333986, 0.030122
+ 251, 251, 311833.72, -57007.52, 7.477564, 0.078735, 94.971657, -3.297160, 0.195895, -16.831262, -3.678675, 0.062325, -59.024093, 2.820530, 0.221943, 12.708332, 0.026768, 0.012270, 2.181544, 104.000000, 157.379712, 0.338244, 0.069971
+ 252, 252, 327926.88, -75230.85, 8.034497, 0.069555, 115.513649, -4.421714, 0.173579, -25.473800, -4.091965, 0.052362, -78.147372, 0.927252, 0.191815, 4.834096, 0.038200, 0.011175, 3.418247, 41.000000, 82.607700, 0.432509, 0.053779
+ 253, 253, 307926.73, -64266.21, 7.578823, 0.076575, 98.972470, -3.418587, 0.190163, -17.977179, -3.855189, 0.059275, -65.039018, 2.123018, 0.212588, 9.986515, 0.049632, 0.012004, 4.134722, 64.000000, 213.181115, 0.379539, 0.057902
+ 254, 254, 299390.82, -71196.86, 7.793609, 0.072467, 107.546718, -3.672553, 0.180690, -20.325203, -4.124214, 0.054779, -75.287825, 1.409758, 0.200403, 7.034605, 0.053292, 0.011529, 4.622333, 49.000000, 88.793492, 0.433644, 0.047500
+ 255, 255, 295866.34, -72353.52, 7.842154, 0.071570, 109.573739, -3.710173, 0.178682, -20.764143, -4.188660, 0.053888, -77.728850, 1.254719, 0.198343, 6.325994, 0.052881, 0.011423, 4.629318, 44.000000, 126.174356, 0.446523, 0.034969
+ 256, 256, 299578.37, -47629.99, 7.640659, 0.080691, 94.690096, -2.954323, 0.202739, -14.572015, -4.039985, 0.062604, -64.532079, 2.198968, 0.223411, 9.842714, 0.008024, 0.012866, 0.623680, 35.000000, 159.620279, 0.380325, 0.084547
+ 257, 257, 293314.45, -52457.68, 7.724168, 0.077730, 99.371708, -3.149705, 0.193810, -16.251502, -4.158636, 0.059548, -69.836625, 1.708013, 0.216389, 7.893265, 0.028558, 0.012396, 2.303708, 6.000000, 130.973974, 0.416750, 0.058187
+ 258, 258, 299215.09, -41823.07, 7.801636, 0.080578, 96.821153, -3.188515, 0.202027, -15.782601, -4.169991, 0.062229, -67.010845, 2.225587, 0.221869, 10.031064, -0.021187, 0.012923, -1.639555, 32.000000, 77.593803, 0.389159, 0.041615
+ 259, 259, 288944.87, -47144.31, 7.838920, 0.077525, 101.115181, -3.216458, 0.192464, -16.712036, -4.290320, 0.059062, -72.640555, 1.490054, 0.217137, 6.862286, 0.015489, 0.012423, 1.246778, 28.000000, 56.348297, 0.436942, 0.033521
+ 260, 260, 290541.99, -38708.26, 7.968411, 0.078516, 101.487407, -3.352787, 0.194235, -17.261530, -4.393288, 0.059683, -73.610546, 1.542132, 0.219524, 7.024887, -0.013788, 0.012635, -1.091273, 10.000000, 112.379240, 0.442772, 0.025182
+ 261, 261, 285964.14, -39392.46, 7.990553, 0.076895, 103.914455, -3.396701, 0.189788, -17.897312, -4.438375, 0.058250, -76.195308, 1.307813, 0.217025, 6.026087, -0.002277, 0.012369, -0.184108, 12.000000, 45.863155, 0.460624, 0.033337
diff --git a/pysal/examples/tokyo/tokyo_GS_NN_summary.txt b/pysal/examples/tokyo/tokyo_GS_NN_summary.txt
new file mode 100755
index 0000000..86e9c8e
--- /dev/null
+++ b/pysal/examples/tokyo/tokyo_GS_NN_summary.txt
@@ -0,0 +1,162 @@
+*****************************************************************************
+* Semiparametric Geographically Weighted Regression *
+* Release 1.0.90 (GWR 4.0.90) *
+* 12 May 2015 *
+* (Originally coded by T. Nakaya: 1 Nov 2009) *
+* *
+* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) *
+* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) *
+* (c) GWR4 development team *
+* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, *
+* (3) University of Glasgow, (4) Arizona State University *
+*****************************************************************************
+
+Program began at 7/25/2016 8:24:18 AM
+
+*****************************************************************************
+Session:
+Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN.ctl
+*****************************************************************************
+Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt
+Number of areas/points: 262
+
+Model settings---------------------------------
+Model type: Poisson
+Geographic kernel: adaptive Gaussian
+Method for optimal bandwidth search: Golden section search
+Criterion for optimal bandwidth: AICc
+Number of varying coefficients: 5
+Number of fixed coefficients: 0
+
+Modelling options---------------------------------
+Standardisation of independent variables: OFF
+Testing geographical variability of local coefficients: OFF
+Local to Global Variable selection: OFF
+Global to Local Variable selection: OFF
+Prediction at non-regression points: OFF
+
+Variable settings---------------------------------
+Area key: field1: IDnum0
+Easting (x-coord): field2 : X_CENTROID
+Northing (y-coord): field3: Y_CENTROID
+Cartesian coordinates: Euclidean distance
+Dependent variable: field4: db2564
+Offset variable is not specified
+Intercept: varying (Local) intercept
+Independent variable with varying (Local) coefficient: field6: OCC_TEC
+Independent variable with varying (Local) coefficient: field7: OWNH
+Independent variable with varying (Local) coefficient: field8: POP65
+Independent variable with varying (Local) coefficient: field9: UNEMP
+*****************************************************************************
+
+*****************************************************************************
+ Global regression result
+*****************************************************************************
+ < Diagnostic information >
+Number of parameters: 5
+Deviance: 24597.455544
+Classic AIC: 24607.455544
+AICc: 24607.689919
+BIC/MDL: 24625.297266
+Percent deviance explained 0.526746
+
+Variable Estimate Standard Error z(Est/SE) Exp(Est)
+-------------------- --------------- --------------- --------------- ---------------
+Intercept 8.432403 0.061613 136.859875 4593.526955
+OCC_TEC -4.270431 0.156467 -27.292831 0.013976
+OWNH -4.789311 0.046070 -103.957933 0.008318
+POP65 -1.252659 0.178384 -7.022265 0.285744
+UNEMP 0.061305 0.010099 6.070542 1.063223
+
+*****************************************************************************
+ GWR (Geographically weighted regression) bandwidth selection
+*****************************************************************************
+
+Bandwidth search <golden section search>
+ Limits: 50, 262
+ Golden section search begins...
+ Initial values
+ pL Bandwidth: 50.000 Criterion: 21070.385
+ p1 Bandwidth: 54.513 Criterion: 21300.064
+ p2 Bandwidth: 57.302 Criterion: 21467.046
+ pU Bandwidth: 61.814 Criterion: 21628.107
+ iter 1 (p1) Bandwidth: 54.513 Criterion: 21300.064 Diff: 2.789
+ iter 2 (p1) Bandwidth: 52.789 Criterion: 21186.366 Diff: 1.724
+ iter 3 (p1) Bandwidth: 51.724 Criterion: 21119.354 Diff: 1.065
+The lower limit in your search has been selected as the optimal bandwidth size.
+A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search.
+Best bandwidth size 50.000
+Minimum AICc 21070.385
+
+*****************************************************************************
+ GWR (Geographically weighted regression) result
+*****************************************************************************
+ Bandwidth and geographic ranges
+Bandwidth size: 50.000000
+Coordinate Min Max Range
+--------------- --------------- --------------- ---------------
+X-coord 276385.400000 408226.180000 131840.780000
+Y-coord -86587.480000 33538.420000 120125.900000
+
+ Diagnostic information
+Effective number of parameters (model: trace(S)): 11.723460
+Effective number of parameters (variance: trace(S'WSW^-1)): 7.749046
+Degree of freedom (model: n - trace(S)): 250.276540
+Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 246.302127
+Deviance: 21045.741163
+Classic AIC: 21069.188082
+AICc: 21070.384849
+BIC/MDL: 21111.021425
+Percent deviance explained 0.595081
+
+***********************************************************
+ << Geographically varying (Local) coefficients >>
+***********************************************************
+Estimates of varying coefficients have been saved in the following file.
+ Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN_listwise.csv
+
+Summary statistics for varying (Local) coefficients
+Variable Mean STD
+-------------------- --------------- ---------------
+Intercept 8.599793 0.585380
+OCC_TEC -5.080341 1.478737
+OWNH -4.701071 0.496447
+POP65 0.046837 2.647462
+UNEMP -0.013079 0.063278
+
+Variable Min Max Range
+-------------------- --------------- --------------- ---------------
+Intercept 7.477564 9.572760 2.095196
+OCC_TEC -7.742931 -1.917576 5.825356
+OWNH -5.362232 -3.589268 1.772965
+POP65 -4.885369 4.836340 9.721710
+UNEMP -0.153164 0.095991 0.249155
+
+Variable Lwr Quartile Median Upr Quartile
+-------------------- --------------- --------------- ---------------
+Intercept 8.093103 8.594024 9.143286
+OCC_TEC -6.116852 -5.256411 -3.711580
+OWNH -5.168985 -4.721204 -4.342757
+POP65 -2.208547 0.299896 2.173021
+UNEMP -0.061169 -0.002452 0.038315
+
+Variable Interquartile R Robust STD
+-------------------- --------------- ---------------
+Intercept 1.050183 0.778490
+OCC_TEC 2.405272 1.783003
+OWNH 0.826228 0.612474
+POP65 4.381568 3.248012
+UNEMP 0.099484 0.073747
+ (Note: Robust STD is given by (interquartile range / 1.349) )
+
+*****************************************************************************
+ GWR Analysis of Deviance Table
+*****************************************************************************
+Source Deviance DOF Deviance/DOF
+------------ ------------------- ---------- ----------------
+Global model 24597.456 257.000 95.710
+GWR model 21045.741 246.302 85.447
+Difference 3551.714 10.698 332.002
+
+*****************************************************************************
+Program terminated at 7/25/2016 8:24:21 AM
diff --git a/pysal/examples/tokyo/tokyomet262.dbf b/pysal/examples/tokyo/tokyomet262.dbf
new file mode 100755
index 0000000..bf50ed0
Binary files /dev/null and b/pysal/examples/tokyo/tokyomet262.dbf differ
diff --git a/pysal/examples/tokyo/tokyomet262.prj b/pysal/examples/tokyo/tokyomet262.prj
new file mode 100755
index 0000000..4b5ef4e
--- /dev/null
+++ b/pysal/examples/tokyo/tokyomet262.prj
@@ -0,0 +1 @@
+PROJCS["Japan_Zone_6",GEOGCS["GCS_Tokyo",DATUM["D_Tokyo",SPHEROID["Bessel_1841",6377397.155,299.1528128]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",136.0],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",36.0],UNIT["Meter",1.0]]
\ No newline at end of file
diff --git a/pysal/examples/tokyo/tokyomet262.shp b/pysal/examples/tokyo/tokyomet262.shp
new file mode 100755
index 0000000..e9ebcfb
Binary files /dev/null and b/pysal/examples/tokyo/tokyomet262.shp differ
diff --git a/pysal/examples/tokyo/tokyomet262.shp.xml b/pysal/examples/tokyo/tokyomet262.shp.xml
new file mode 100755
index 0000000..9b6811f
--- /dev/null
+++ b/pysal/examples/tokyo/tokyomet262.shp.xml
@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<metadata xml:lang="ja"><Esri><CreaDate>20120518</CreaDate><CreaTime>14182000</CreaTime><ArcGISFormat>1.0</ArcGISFormat><SyncOnce>TRUE</SyncOnce><DataProperties><lineage><Process ToolSource="C:\Program Files (x86)\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes\Data Management Tools.tbx\CalculateField" Date="20120518" Time="141820">CalculateField tokyomet262 AreaID [FID] VB #</Process><Process ToolSource="C:\Program Files (x86)\ArcGIS\Desktop10.0\ArcToolbox\Toolboxes\Data Management Tools.tbx\De [...]
diff --git a/pysal/examples/tokyo/tokyomet262.shx b/pysal/examples/tokyo/tokyomet262.shx
new file mode 100755
index 0000000..3b81c28
Binary files /dev/null and b/pysal/examples/tokyo/tokyomet262.shx differ
diff --git a/pysal/examples/wmat/us48_CONTIGUITY_EDGES_ONLY.swm b/pysal/examples/wmat/us48_CONTIGUITY_EDGES_ONLY.swm
new file mode 100644
index 0000000..b194d9c
Binary files /dev/null and b/pysal/examples/wmat/us48_CONTIGUITY_EDGES_ONLY.swm differ
diff --git a/pysal/examples/wmat/us48_INVERSE_DISTANCE.swm b/pysal/examples/wmat/us48_INVERSE_DISTANCE.swm
new file mode 100644
index 0000000..25286d2
Binary files /dev/null and b/pysal/examples/wmat/us48_INVERSE_DISTANCE.swm differ
diff --git a/pysal/network/network.py b/pysal/network/network.py
index 0603ba9..0ec3171 100644
--- a/pysal/network/network.py
+++ b/pysal/network/network.py
@@ -331,19 +331,24 @@ class Network:
return ps.weights.W(neighbors, weights=weights)
- def distancebandweights(self, threshold):
+ def distancebandweights(self, threshold, n_proccess=None):
"""
Create distance based weights
Parameters
----------
- threshold: float
- Distance threshold value.
+ threshold: float
+ Distance threshold value.
+
+ n_processes: int, str
+ (Optional) Specify the number of cores to utilize.
+ Default is 1 core. Use (int) to specify an exact number or cores.
+ Use ("all") to request all available cores.
"""
try:
hasattr(self.alldistances)
except:
- self.node_distance_matrix()
+ self.node_distance_matrix(n_proccess)
neighbor_query = np.where(self.distancematrix < threshold)
neighbors = defaultdict(list)
@@ -627,19 +632,46 @@ class Network:
links.append(tuple(sorted([n, v0])))
return links
- def node_distance_matrix(self):
+ def node_distance_matrix(self, n_processes):
+ """
+ Called from: allneighbordistances()
+ nearestneighbordistances()
+ distancebandweights()
+ """
self.alldistances = {}
nnodes = len(self.node_list)
self.distancematrix = np.empty((nnodes, nnodes))
- for node in self.node_list:
- distance, pred = util.dijkstra(self, self.edge_lengths, node, n=float('inf'))
+ # Single-core processing
+ if not n_processes:
+ for node in self.node_list:
+ distance, pred = util.dijkstra(self, self.edge_lengths, node, n=float('inf'))
+ pred = np.array(pred)
+ #tree = util.generatetree(pred) <---- something to look at in the future
+ tree = None
+ self.alldistances[node] = (distance, tree)
+ self.distancematrix[node] = distance
+ # Multiprocessing
+ if n_processes:
+ import multiprocessing as mp
+ from itertools import repeat
+ if n_processes == "all":
+ cores = mp.cpu_count()
+ else:
+ cores = n_processes
+ p = mp.Pool(processes=cores)
+ distance_pred = p.map(util.dijkstra_multi, zip(repeat(self),
+ repeat(self.edge_time),
+ self.node_list))
+ distance = [distance_pred[iteration][0] for iteration in range(len(distance_pred))]
+ pred = [distance_pred[iteration][1] for iteration in range(len(distance_pred))]
pred = np.array(pred)
- #tree = util.generatetree(pred) <---- something to look at in the future
- tree = None
- self.alldistances[node] = (distance, tree)
- self.distancematrix[node] = distance
-
- def allneighbordistances(self, sourcepattern, destpattern=None, fill_diagonal=None):
+ #tree = util.generatetree(pred[node])
+ for node in self.node_list:
+ self.distancematrix[node] = distance[node]
+
+
+ def allneighbordistances(self, sourcepattern, destpattern=None, fill_diagonal=None,
+ n_processes=None):
"""
Compute either all distances between i and j in a single point pattern or all
distances between each i from a source pattern and all j from a destination pattern.
@@ -657,6 +689,10 @@ class Network:
Default in None and will populate the diagonal with numpy.nan
Do not declare a destpattern for a custom fill_diagonal.
+ n_processes: int, str
+ (Optional) Specify the number of cores to utilize.
+ Default is 1 core. Use (int) to specify an exact number or cores.
+ Use ("all") to request all available cores.
Returns
-------
nearest: array (n,n)
@@ -664,7 +700,7 @@ class Network:
"""
if not hasattr(self,'alldistances'):
- self.node_distance_matrix()
+ self.node_distance_matrix(n_processes)
# Source setup
src_indices = sourcepattern.points.keys()
@@ -709,9 +745,8 @@ class Network:
if set1 == set2: # same edge
x1,y1 = sourcepattern.snapped_coordinates[p1]
x2,y2 = destpattern.snapped_coordinates[p2]
- xd = x1-x2
- yd = y1-y2
- nearest[p1,p2] = np.sqrt(xd*xd + yd*yd)
+ computed_length = util.compute_length((x1,y1),(x2,y2))
+ nearest[p1,p2] = computed_length
else:
ddist1, ddist2 = dest_dist_to_node[p2].values()
@@ -757,7 +792,7 @@ class Network:
return nearest
- def nearestneighbordistances(self, sourcepattern, destpattern=None):
+ def nearestneighbordistances(self, sourcepattern, destpattern=None, n_processes=None):
"""
Compute the interpattern nearest neighbor distances or the intrapattern
nearest neighbor distances between a source pattern and a destination pattern.
@@ -770,6 +805,11 @@ class Network:
destpattern: str
(Optional) The key of a point pattern snapped to the network.
+ n_processes: int, str
+ (Optional) Specify the number of cores to utilize.
+ Default is 1 core. Use (int) to specify an exact number or cores.
+ Use ("all") to request all available cores.
+
Returns
-------
nearest: ndarray (n,2)
@@ -781,7 +821,7 @@ class Network:
raise KeyError("Available point patterns are {}".format(self.pointpatterns.keys()))
if not hasattr(self,'alldistances'):
- self.node_distance_matrix()
+ self.node_distance_matrix(n_processes)
pt_indices = self.pointpatterns[sourcepattern].points.keys()
dist_to_node = self.pointpatterns[sourcepattern].dist_to_node
diff --git a/pysal/network/tests/test_network.py b/pysal/network/tests/test_network.py
index dbacff2..d2b5a55 100644
--- a/pysal/network/tests/test_network.py
+++ b/pysal/network/tests/test_network.py
@@ -135,6 +135,11 @@ class TestNetworkUtils(unittest.TestCase):
self.distance, self.pred = util.dijkstra(self.ntw, self.ntw.edge_lengths, 0)
self.assertAlmostEqual(self.distance[196], 5505.668247, places=4)
self.assertEqual(self.pred[196], 133)
+
+ def test_dijkstra_mp(self):
+ self.distance, self.pred = util.dijkstra_mp((self.ntw, self.ntw.edge_lengths, 0))
+ self.assertAlmostEqual(self.distance[196], 5505.668247, places=4)
+ self.assertEqual(self.pred[196], 133)
if __name__ == '__main__':
unittest.main()
diff --git a/pysal/network/util.py b/pysal/network/util.py
index d4c78e8..adcec48 100644
--- a/pysal/network/util.py
+++ b/pysal/network/util.py
@@ -1,6 +1,6 @@
-from collections import OrderedDict
-import math
-import operator
+#from collections import OrderedDict
+#import math
+#import operator
import pysal as ps
import numpy as np
@@ -19,9 +19,13 @@ def compute_length(v0, v1):
--------
Euclidean distance
"""
-
- return math.sqrt((v0[0] - v1[0])**2 + (v0[1] - v1[1])**2)
-
+ # Is there a reason we are using `math` as opposed to `numpy` is this function?
+ # Moreover, we are not using `OrderedDict` or `operator` for anything currently in
+ # any of the defined functions in `utils.py`.
+
+ #return math.sqrt((v0[0] - v1[0])**2 + (v0[1] - v1[1])**2)
+ return np.sqrt((v0[0] - v1[0])**2 + (v0[1] - v1[1])**2)
+
def get_neighbor_distances(ntw, v0, l):
edges = ntw.enum_links_node(v0)
@@ -109,6 +113,39 @@ def dijkstra(ntw, cost, node, n=float('inf')):
return distance, np.array(pred, dtype=np.int)
+def dijkstra_mp((ntw, cost, node)):
+ """
+ Compute the shortest path between a start node and all other nodes in the web
+ utilizing multiple cores upon request.
+
+ Parameters
+ ----------
+ ntw: object
+ PySAL network object
+
+ cost: dict
+ key: tuple
+ (start node, end node)
+ value: float
+ Cost per edge to travel, e.g. distance
+
+ node: int
+ Start node ID
+
+ n: float('inf')
+ integer break point to stop iteration and return n neighbors
+
+ Returns
+ -------
+ distance: list
+ List of distances from node to all other nodes.
+
+ pred: list
+ List of preceeding nodes for traversal route.
+ """
+ return dijkstra(ntw, cost, node)
+
+
def squaredDistancePointSegment(point, segment):
"""Find the squared distance between a point and a segment
diff --git a/pysal/spatial_dynamics/util.py b/pysal/spatial_dynamics/util.py
index 7181a9c..dfda191 100644
--- a/pysal/spatial_dynamics/util.py
+++ b/pysal/spatial_dynamics/util.py
@@ -12,14 +12,14 @@ def shuffle_matrix(X, ids):
Parameters
----------
- X : array
+ X : array
(k, k), array to be permutated.
ids : array
range (k, ).
Returns
-------
- X : array
+ X : array
(k, k) with rows and columns randomly shuffled.
Examples
@@ -50,7 +50,7 @@ def get_lower(matrix):
Returns
-------
lowvec : array
- numpy array, the lower half of the distance matrix flattened into
+ numpy array, the lower half of the distance matrix flattened into
a vector of length n*(n-1)/2.
Examples
@@ -75,6 +75,6 @@ def get_lower(matrix):
if i > j:
lowerlist.append(matrix[i, j])
veclen = n * (n - 1) / 2
- lowvec = np.reshape(lowerlist, (veclen, 1))
+ lowvec = np.reshape(np.array(lowerlist), (int(veclen), 1))
return lowvec
diff --git a/pysal/spreg/error_sp_hom.py b/pysal/spreg/error_sp_hom.py
index 7cf96ee..39772d7 100644
--- a/pysal/spreg/error_sp_hom.py
+++ b/pysal/spreg/error_sp_hom.py
@@ -1363,7 +1363,7 @@ def get_omega_hom(w, wA1, wA2, reg, lamb, G):
j = np.dot(G, np.array([[1.], [2 * lamb]]))
psii = la.inv(psi)
t2 = spdot(reg.h.T, np.hstack((a1, a2)))
- psiDL = (mu3 * spdot(reg.h.T, np.hstack((vecdA1, np.zeros((n, 1))))) +
+ psiDL = (mu3 * spdot(reg.h.T, np.hstack((vecdA1, np.zeros((int(n), 1))))) +
sig2 * spdot(reg.h.T, np.hstack((a1, a2)))) / n
oDD = spdot(la.inv(spdot(reg.h.T, reg.h)), spdot(reg.h.T, z_s))
@@ -1413,7 +1413,7 @@ def get_omega_hom_ols(w, wA1, wA2, reg, lamb, G):
oLL = la.inv(spdot(j.T, spdot(psii, j))) / n
#oDL = np.zeros((oDD.shape[0], oLL.shape[1]))
mu3 = np.sum(u_s ** 3) / n
- psiDL = (mu3 * spdot(reg.x.T, np.hstack((vecdA1, np.zeros((n, 1)))))) / n
+ psiDL = (mu3 * spdot(reg.x.T, np.hstack((vecdA1, np.zeros((int(n), 1)))))) / n
oDL = spdot(spdot(spdot(p.T, psiDL), spdot(psii, j)), oLL)
o_upper = np.hstack((oDD, oDL))
diff --git a/pysal/spreg/summary_output.py b/pysal/spreg/summary_output.py
index 70b160e..788e16b 100644
--- a/pysal/spreg/summary_output.py
+++ b/pysal/spreg/summary_output.py
@@ -1370,7 +1370,7 @@ def summary_diag_sur(reg, nonspat_diag, spat_diag, tsls, lambd, rho=False):
if lambd and spat_diag:
strSummary += "%41s %2d %10.3f %6.4f\n" % (
"LR test on lambda", reg.likrlambda[1], reg.likrlambda[0], reg.likrlambda[2])
- if reg.vm != None:
+ if reg.vm is not None:
strSummary += "%41s %2d %10.3f %6.4f\n" % (
"Joint significance (lambda)", reg.joinlam[1], reg.joinlam[0], reg.joinlam[2])
summary_add_other_end(reg, strSummary)
diff --git a/pysal/spreg/tests/test_error_sp_het_regimes.py b/pysal/spreg/tests/test_error_sp_het_regimes.py
index 9a6d80b..dee2013 100644
--- a/pysal/spreg/tests/test_error_sp_het_regimes.py
+++ b/pysal/spreg/tests/test_error_sp_het_regimes.py
@@ -30,7 +30,7 @@ class TestGM_Error_Het_Regimes(unittest.TestCase):
self.regimes = db.by_col(self.r_var)
#Artficial:
n = 256
- self.n2 = n/2
+ self.n2 = n//2
self.x_a1 = np.random.uniform(-10,10,(n,1))
self.x_a2 = np.random.uniform(1,5,(n,1))
self.q_a = self.x_a2 + np.random.normal(0,1,(n,1))
diff --git a/pysal/spreg/tests/test_error_sp_hom_regimes.py b/pysal/spreg/tests/test_error_sp_hom_regimes.py
index f9de3cc..42138e7 100644
--- a/pysal/spreg/tests/test_error_sp_hom_regimes.py
+++ b/pysal/spreg/tests/test_error_sp_hom_regimes.py
@@ -29,7 +29,7 @@ class TestGM_Error_Hom_Regimes(unittest.TestCase):
self.regimes = db.by_col(self.r_var)
#Artficial:
n = 256
- self.n2 = n/2
+ self.n2 = n//2
self.x_a1 = np.random.uniform(-10,10,(n,1))
self.x_a2 = np.random.uniform(1,5,(n,1))
self.q_a = self.x_a2 + np.random.normal(0,1,(n,1))
diff --git a/pysal/spreg/tests/test_error_sp_regimes.py b/pysal/spreg/tests/test_error_sp_regimes.py
index d4f89eb..5065758 100644
--- a/pysal/spreg/tests/test_error_sp_regimes.py
+++ b/pysal/spreg/tests/test_error_sp_regimes.py
@@ -32,7 +32,7 @@ class TestGM_Error_Regimes(unittest.TestCase):
self.q = np.array(q).T
#Artficial:
n = 256
- self.n2 = n/2
+ self.n2 = n//2
self.x_a1 = np.random.uniform(-10,10,(n,1))
self.x_a2 = np.random.uniform(1,5,(n,1))
self.q_a = self.x_a2 + np.random.normal(0,1,(n,1))
diff --git a/pysal/spreg/utils.py b/pysal/spreg/utils.py
old mode 100755
new mode 100644
index 7c69aaf..d7b4110
--- a/pysal/spreg/utils.py
+++ b/pysal/spreg/utils.py
@@ -248,7 +248,7 @@ def get_A1_hom(s, scalarKP=False):
wpw = s.T * s
twpw = np.sum(wpw.diagonal())
e = SP.eye(n, n, format='csr')
- e.data = np.ones(n) * (twpw / n)
+ e.data = np.ones(int(n)) * (twpw / n)
num = wpw - e
if not scalarKP:
return num
diff --git a/pysal/version.py b/pysal/version.py
index f08e764..a2665ea 100644
--- a/pysal/version.py
+++ b/pysal/version.py
@@ -1,3 +1,3 @@
import datetime
-version = "1.13.0"
-stable_release_date = datetime.date(2016, 12, 9)
+version = "1.14.2"
+stable_release_date = datetime.date(2017, 9, 23)
diff --git a/pysal/weights/Contiguity.py b/pysal/weights/Contiguity.py
index 9a2b7fc..346aa9a 100644
--- a/pysal/weights/Contiguity.py
+++ b/pysal/weights/Contiguity.py
@@ -31,9 +31,9 @@ class Rook(W):
:class:`pysal.weights.W`
"""
criterion = 'rook'
- ids = kw.pop('ids', None)
- neighbors, ids = _build(polygons, criterion=criterion,
- ids=ids, method=method)
+ ids = kw.pop('ids', None)
+ neighbors, ids = _build(polygons, ids=ids,
+ criterion=criterion, method=method)
W.__init__(self, neighbors, ids=ids, **kw)
@classmethod
@@ -82,6 +82,7 @@ class Rook(W):
ids = get_ids(filepath, idVariable)
else:
ids = None
+ iterable = FileIO(filepath)
w = cls(FileIO(filepath), ids=ids, **kwargs)
w.set_shapefile(filepath, idVariable=idVariable, full=full)
if sparse:
@@ -89,7 +90,7 @@ class Rook(W):
return w
@classmethod
- def from_iterable(cls, iterable, **kwargs):
+ def from_iterable(cls, iterable, sparse=False, **kwargs):
"""
Construct a weights object from a collection of arbitrary polygons. This
will cast the polygons to PySAL polygons, then build the W.
@@ -109,11 +110,15 @@ class Rook(W):
:class:`pysal.weights.Rook`
"""
new_iterable = [asShape(shape) for shape in iterable]
- return cls(new_iterable, **kwargs)
+
+ w = cls(new_iterable, **kwargs)
+ if sparse:
+ w = WSP.from_W(w)
+
+ return w
@classmethod
- def from_dataframe(cls, df, geom_col='geometry',
- idVariable=None, ids=None, id_order=None, **kwargs):
+ def from_dataframe(cls, df, geom_col='geometry', **kwargs):
"""
Construct a weights object from a pandas dataframe with a geometry
column. This will cast the polygons to PySAL polygons, then build the W
@@ -144,20 +149,27 @@ class Rook(W):
:class:`pysal.weights.W`
:class:`pysal.weights.Rook`
"""
- if id_order is not None:
- if id_order is True and ((idVariable is not None)
- or (ids is not None)):
+ idVariable = kwargs.pop('idVariable', None)
+ ids = kwargs.pop('ids', None)
+ id_order = kwargs.pop('id_order', True)
+ if id_order is True and ((idVariable is not None)
+ or (ids is not None)):
# if idVariable is None, we want ids. Otherwise, we want the
# idVariable column
- id_order = list(df.get(idVariable, ids))
- else:
- id_order = df.get(id_order, ids)
+ ids = list(df.get(idVariable, ids))
+ id_order = ids
+ elif isinstance(id_order, str):
+ ids = df.get(id_order, ids)
+ id_order = ids
elif idVariable is not None:
ids = df.get(idVariable).tolist()
elif isinstance(ids, str):
ids = df.get(ids).tolist()
- return cls.from_iterable(df[geom_col].tolist(), ids=ids,
- id_order=id_order, **kwargs)
+ else:
+ id_order = list(df.index)
+ ids = list(df.index)
+ w = cls.from_iterable(df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs)
+ return w
class Queen(W):
def __init__(self, polygons,method='binning', **kw):
@@ -303,21 +315,23 @@ class Queen(W):
"""
idVariable = kwargs.pop('idVariable', None)
ids = kwargs.pop('ids', None)
- id_order = kwargs.pop('id_order', None)
- if id_order is not None:
- if id_order is True and ((idVariable is not None)
- or (ids is not None)):
+ id_order = kwargs.pop('id_order', True)
+ if id_order is True and ((idVariable is not None)
+ or (ids is not None)):
# if idVariable is None, we want ids. Otherwise, we want the
# idVariable column
- ids = list(df.get(idVariable, ids))
- id_order = ids
- elif isinstance(id_order, str):
- ids = df.get(id_order, ids)
- id_order = ids
+ ids = list(df.get(idVariable, ids))
+ id_order = ids
+ elif isinstance(id_order, str):
+ ids = df.get(id_order, ids)
+ id_order = ids
elif idVariable is not None:
ids = df.get(idVariable).tolist()
elif isinstance(ids, str):
ids = df.get(ids).tolist()
+ else:
+ id_order = list(df.index)
+ ids = list(df.index)
w = cls.from_iterable(df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs)
return w
diff --git a/pysal/weights/Distance.py b/pysal/weights/Distance.py
index 3a55d2b..2ac60f1 100644
--- a/pysal/weights/Distance.py
+++ b/pysal/weights/Distance.py
@@ -762,6 +762,8 @@ class DistanceBand(W):
See detail in pysal issue #126.
"""
+ if ids is not None:
+ ids=list(ids)
self.p = p
self.threshold = threshold
self.binary = binary
@@ -857,6 +859,8 @@ class DistanceBand(W):
ids = df.index.tolist()
elif isinstance(ids, str):
ids = df[ids].tolist()
+ else:
+ ids = df.index.tolist()
return cls(pts, threshold, ids=ids, **kwargs)
def _band(self):
@@ -878,7 +882,7 @@ class DistanceBand(W):
if self.binary:
self.dmat[self.dmat>0] = 1
self.dmat.eliminate_zeros()
- tempW = WSP2W(WSP(self.dmat), silent_island_warning=self.silent)
+ tempW = WSP2W(WSP(self.dmat, id_order=ids), silent_island_warning=self.silent)
neighbors = tempW.neighbors
weight_keys = tempW.weights.keys()
weight_vals = tempW.weights.values()
@@ -888,7 +892,7 @@ class DistanceBand(W):
weighted = self.dmat.power(self.alpha)
weighted[weighted==np.inf] = 0
weighted.eliminate_zeros()
- tempW = WSP2W(WSP(weighted), silent_island_warning=self.silent)
+ tempW = WSP2W(WSP(weighted, id_order=ids), silent_island_warning=self.silent)
neighbors = tempW.neighbors
weight_keys = tempW.weights.keys()
weight_vals = tempW.weights.values()
diff --git a/pysal/weights/spatial_lag.py b/pysal/weights/spatial_lag.py
index 24c88fa..760a47b 100644
--- a/pysal/weights/spatial_lag.py
+++ b/pysal/weights/spatial_lag.py
@@ -109,6 +109,8 @@ def lag_categorical(w, y, ties='tryself'):
and break a tie. If this does not resolve the tie,
a winner is chosen randomly. To just use random choice to
break ties, pass "random" instead.
+ Also supported are selecting the ``lowest'' class in the
+ sorted list of ties or the ``highest'' class.
Returns
-------
an (n x k) column vector containing the most common neighboring observation
@@ -170,7 +172,7 @@ def lag_categorical(w, y, ties='tryself'):
for neighb, weight in diter(neighbors):
vals[inty[w.id2i[neighb]]] += weight
outidx = _resolve_ties(idx,inty,vals,neighbors,ties, w)
- output[w.id2i[idx]] = keys[outidx]
+ output[w.id2i[int(idx)]] = keys[int(outidx)]
return output.reshape(orig_shape)
def _resolve_ties(i,inty,vals,neighbors,method,w):
@@ -190,8 +192,15 @@ def _resolve_ties(i,inty,vals,neighbors,method,w):
return np.argmax(vals)
elif method.lower() == 'random':
ties = np.where(vals == vals.max())
- return np.random.choice(vals[ties])
+ return np.random.choice(np.squeeze(ties))
elif method.lower() == 'tryself':
vals[inty[w.id2i[i]]] += np.mean(neighbors.values())
return _resolve_ties(i,inty,vals,neighbors,'random', w)
-
+ elif method.lower() == 'lowest':
+ ties = np.where(vals == vals.max())
+ return ties[0]
+ elif method.lower() == 'highest':
+ ties = np.where(vals.max())
+ return ties[-1]
+ else:
+ raise KeyError('Tie-Breaking method for categorical lag not recognized')
diff --git a/pysal/weights/tests/test_Contiguity.py b/pysal/weights/tests/test_Contiguity.py
index 26778fc..03e187b 100644
--- a/pysal/weights/tests/test_Contiguity.py
+++ b/pysal/weights/tests/test_Contiguity.py
@@ -92,6 +92,13 @@ class Contiguity_Mixin(object):
w = self.cls.from_dataframe(df, geom_col='the_geom', idVariable=self.idVariable)
self.assertEqual(w[self.known_name], self.known_namedw)
+ # order preserving
+ permute = df.sample(frac=1)
+ w = self.cls.from_dataframe(permute, geom_col='the_geom')
+ with self.assertRaises(AssertionError):
+ assert w.id_order == df.index.tolist()
+ self.assertEqual(w.id_order, permute.index.tolist())
+
class Test_Queen(ut.TestCase, Contiguity_Mixin):
def setUp(self):
Contiguity_Mixin.setUp(self)
diff --git a/pysal/weights/tests/test_Distance.py b/pysal/weights/tests/test_Distance.py
index d8eec70..b3ce48e 100644
--- a/pysal/weights/tests/test_Distance.py
+++ b/pysal/weights/tests/test_Distance.py
@@ -86,6 +86,12 @@ class Test_KNN(ut.TestCase, Distance_Mixin):
w = d.KNN.from_dataframe(df, k=4)
self.assertEqual(w.neighbors[self.known_wi0], self.known_w0)
self.assertEqual(w.neighbors[self.known_wi1], self.known_w1)
+ perm = df.sample(frac=1)
+ w = d.KNN.from_dataframe(perm, k=4)
+ with self.assertRaises(AssertionError):
+ assert w.id_order == df.index.tolist()
+ self.assertEqual(perm.index.tolist(), w.id_order)
+
def test_from_array(self):
w = d.KNN.from_array(self.poly_centroids, k=4)
@@ -146,6 +152,11 @@ class Test_DistanceBand(ut.TestCase, Distance_Mixin):
w = d.DistanceBand.from_dataframe(df, 1)
for k,v in w:
self.assertEquals(v, self.grid_rook_w[k])
+ perm = df.sample(frac=1)
+ w = d.DistanceBand.from_dataframe(perm, 1)
+ with self.assertRaises(AssertionError):
+ assert w.id_order == df.index.tolist()
+ self.assertEqual(w.id_order, perm.index.tolist())
##########################
# Function/User tests #
@@ -252,7 +263,12 @@ class Test_Kernel(ut.TestCase, Distance_Mixin):
w = d.Kernel.from_dataframe(df)
for k,v in w[self.known_wi5-1].items():
np.testing.assert_allclose(v, self.known_w5[k+1], rtol=RTOL)
-
+ perm = df.sample(frac=1)
+ w = d.Kernel.from_dataframe(perm)
+ with self.assertRaises(AssertionError):
+ assert w.id_order == df.index.tolist()
+ self.assertEqual(w.id_order, perm.index.tolist())
+
##########################
# Function/User tests #
##########################
diff --git a/pysal/weights/tests/test_spatial_lag.py b/pysal/weights/tests/test_spatial_lag.py
index e841903..6003581 100644
--- a/pysal/weights/tests/test_spatial_lag.py
+++ b/pysal/weights/tests/test_spatial_lag.py
@@ -43,7 +43,7 @@ class Test_spatial_lag(unittest.TestCase):
known = np.array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'])
np.testing.assert_array_equal(yl, known)
ym_lag = pysal.weights.spatial_lag.lag_categorical(self.wlat,self.ym)
- known = np.array([['b', 'b'],
+ known = np.array([['b', 'c'],
['a', 'c'],
['b', 'c'],
['c', 'd'],
@@ -51,7 +51,7 @@ class Test_spatial_lag(unittest.TestCase):
['c', 'c'],
['b', 'd'],
['c', 'd'],
- ['b', 'b']])
+ ['b', 'd']])
np.testing.assert_array_equal(ym_lag, np.asarray(known))
suite = unittest.TestLoader().loadTestsFromTestCase(Test_spatial_lag)
diff --git a/pysal/weights/tests/test_swm_IO.py b/pysal/weights/tests/test_swm_IO.py
new file mode 100644
index 0000000..e1ae0cb
--- /dev/null
+++ b/pysal/weights/tests/test_swm_IO.py
@@ -0,0 +1,42 @@
+import unittest
+import pysal
+import tempfile, os
+
+class TestWIO(unittest.TestCase):
+ def setUp(self):
+ self.swmFile1 = pysal.examples.get_path('ohio.swm')
+ self.swmFile2 = pysal.examples.get_path('us48_CONTIGUITY_EDGES_ONLY.swm')
+ self.swmFile3 = pysal.examples.get_path('us48_INVERSE_DISTANCE.swm')
+
+ def SWMIO(self, file_path):
+ f1 = pysal.open(file_path)
+ w1 = f1.read()
+ # print f1.varName
+ # print f1.srs
+
+ f = tempfile.NamedTemporaryFile(suffix='.swm')
+ fname = f.name
+ f.close()
+
+ f2 = pysal.open(fname, 'w')
+ f2.varName = f1.varName
+ f2.srs = f1.srs
+
+ f2.write(w1)
+ f2.close()
+ w2 = pysal.open(fname, 'r').read()
+ assert w1.pct_nonzero == w2.pct_nonzero
+ os.remove(fname)
+ # print w2.neighbors
+ # print w2.weights
+
+ def test_RWFunctions(self):
+ self.SWMIO(self.swmFile1)
+ self.SWMIO(self.swmFile2)
+ self.SWMIO(self.swmFile3)
+
+suite = unittest.TestLoader().loadTestsFromTestCase(TestWIO)
+
+if __name__ == '__main__':
+ runner = unittest.TextTestRunner()
+ runner.run(suite)
diff --git a/pysal/weights/util.py b/pysal/weights/util.py
index d76f8bd..15a16a9 100644
--- a/pysal/weights/util.py
+++ b/pysal/weights/util.py
@@ -521,14 +521,17 @@ def higher_order_sp(w, k=2, shortest_path=True, diagonal=False):
"""
- tw = type(w)
id_order = None
- if tw == pysal.weights.weights.W:
- id_order = w.id_order
- w = w.sparse
- elif tw != scipy.sparse.csr.csr_matrix:
- print "Unsupported sparse argument."
- return None
+ if issubclass(type(w), pysal.weights.W):
+ if np.unique(np.hstack(w.weights.values())) == np.array([1.0]):
+ id_order = w.id_order
+ w = w.sparse
+ elif scipy.sparse.isspmatrix_csr(w):
+ if not np.unique(w.data) == np.array([1.0]):
+ raise ValueError('Sparse weights matrix is not binary (0,1) weights matrix.')
+ else:
+ raise TypeError("Weights provided are neither a binary W object nor "
+ "a scipy.sparse.csr_matrix")
wk = w**k
rk, ck = wk.nonzero()
diff --git a/requirements_dev.txt b/requirements_dev.txt
index 71110ce..2c950c0 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -2,6 +2,6 @@ nose
nose-progressive
nose-exclude
coverage
-sphinx
+sphinx>=1.4.3
sphinxcontrib-napoleon
coveralls
diff --git a/rtd.txt b/rtd.txt
index b9d419a..12dd8f7 100644
--- a/rtd.txt
+++ b/rtd.txt
@@ -1 +1,2 @@
+sphinx>=1.4.3
sphinxcontrib-napoleon
diff --git a/setup.py b/setup.py
index 12f8136..343be39 100644
--- a/setup.py
+++ b/setup.py
@@ -13,8 +13,8 @@ with open('README.rst') as file:
long_description = file.read()
MAJOR = 1
-MINOR = 13
-MICRO = 0
+MINOR = 14
+MICRO = 2
ISRELEASED = False
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
--
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