pysal / pysal

PySAL: Python Spatial Analysis Library Meta-Package

Python Spatial Analysis Library

PySAL, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. It supports the development of high level applications for spatial analysis, such as

• detection of spatial clusters, hot-spots, and outliers
• construction of graphs from spatial data
• spatial regression and statistical modeling on geographically embedded networks
• spatial econometrics
• exploratory spatio-temporal data analysis

PySAL Components

PySAL is a family of packages for spatial data science and is divided into four major components:

Lib

solve a wide variety of computational geometry problems including graph construction from polygonal lattices, lines, and points, construction and interactive editing of spatial weights matrices & graphs - computation of alpha shapes, spatial indices, and spatial-topological relationships, and reading and writing of sparse graph data, as well as pure python readers of spatial vector data. Unike other PySAL modules, these functions are exposed together as a single package.

• libpysal : libpysal provides foundational algorithms and data structures that support the rest of the library. This currently includes the following modules: input/output (io), which provides readers and writers for common geospatial file formats; weights (weights), which provides the main class to store spatial weights matrices, as well as several utilities to manipulate and operate on them; computational geometry (cg), with several algorithms, such as Voronoi tessellations or alpha shapes that efficiently process geometric shapes; and an additional module with example data sets (examples).

Explore

The explore layer includes modules to conduct exploratory analysis of spatial and spatio-temporal data. At a high level, packages in explore are focused on enabling the user to better understand patterns in the data and suggest new interesting questions rather than answer existing ones. They include methods to characterize the structure of spatial distributions (either on networks, in continuous space, or on polygonal lattices). In addition, this domain offers methods to examine the dynamics of these distributions, such as how their composition or spatial extent changes over time.

• esda : esda implements methods for the analysis of both global (map-wide) and local (focal) spatial autocorrelation, for both continuous and binary data. In addition, the package increasingly offers cutting-edge statistics about boundary strength and measures of aggregation error in statistical analyses

• giddy : giddy is an extension of esda to spatio-temporal data. The package hosts state-of-the-art methods that explicitly consider the role of space in the dynamics of distributions over time

• inequality : inequality provides indices for measuring inequality over space and time. These comprise classic measures such as the Theil T information index and the Gini index in mean deviation form; but also spatially-explicit measures that incorporate the location and spatial configuration of observations in the calculation of inequality measures.

• momepy : momepy is a library for quantitative analysis of urban form - urban morphometrics. It aims to provide a wide range of tools for a systematic and exhaustive analysis of urban form. It can work with a wide range of elements, while focused on building footprints and street networks. momepy stands for Morphological Measuring in Python.

• pointpats : pointpats supports the statistical analysis of point data, including methods to characterize the spatial structure of an observed point pattern: a collection of locations where some phenomena of interest have been recorded. This includes measures of centrography which provide overall geometric summaries of the point pattern, including central tendency, dispersion, intensity, and extent.

• segregation : segregation package calculates over 40 different segregation indices and provides a suite of additional features for measurement, visualization, and hypothesis testing that together represent the state-of-the-art in quantitative segregation analysis.

• spaghetti : spaghetti supports the the spatial analysis of graphs, networks, topology, and inference. It includes functionality for the statistical testing of clusters on networks, a robust all-to-all Dijkstra shortest path algorithm with multiprocessing functionality, and high-performance geometric and spatial computations using geopandas that are necessary for high-resolution interpolation along networks, and the ability to connect near-network observations onto the network

Model

In contrast to explore, the model layer focuses on confirmatory analysis. In particular, its packages focus on the estimation of spatial relationships in data with a variety of linear, generalized-linear, generalized-additive, nonlinear, multi-level, and local regression models.

• mgwr : mgwr provides scalable algorithms for estimation, inference, and prediction using single- and multi-scale geographically-weighted regression models in a variety of generalized linear model frameworks, as well model diagnostics tools

• spglm : spglm implements a set of generalized linear regression techniques, including Gaussian, Poisson, and Logistic regression, that allow for sparse matrix operations in their computation and estimation to lower memory overhead and decreased computation time.

• spint : spint provides a collection of tools to study spatial interaction processes and analyze spatial interaction data. It includes functionality to facilitate the calibration and interpretation of a family of gravity-type spatial interaction models, including those with production constraints, attraction constraints, or a combination of the two.

• spreg : spreg supports the estimation of classic and spatial econometric models. Currently it contains methods for estimating standard Ordinary Least Squares (OLS), Two Stage Least Squares (2SLS) and Seemingly Unrelated Regressions (SUR), in addition to various tests of homokestadicity, normality, spatial randomness, and different types of spatial autocorrelation. It also includes a suite of tests for spatial dependence in models with binary dependent variables.

• spvcm : spvcm provides a general framework for estimating spatially-correlated variance components models. This class of models allows for spatial dependence in the variance components, so that nearby groups may affect one another. It also also provides a general-purpose framework for estimating models using Gibbs sampling in Python, accelerated by the numba package.

• tobler : tobler provides functionality for for areal interpolation and dasymetric mapping. Its name is an homage to the legendary geographer Waldo Tobler a pioneer of dozens of spatial analytical methods. tobler includes functionality for interpolating data using area-weighted approaches, regression model-based approaches that leverage remotely-sensed raster data as auxiliary information, and hybrid approaches.

• access : access aims to make it easy for analysis to calculate measures of spatial accessibility. This work has traditionally had two challenges: [1] to calculate accurate travel time matrices at scale and [2] to derive measures of access using the travel times and supply and demand locations. access implements classic spatial access models, allowing easy comparison of methodologies and assumptions.

• spopt: spopt is an open-source Python library for solving optimization problems with spatial data. Originating from the original region module in PySAL, it is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions.

Viz

The viz layer provides functionality to support the creation of geovisualisations and visual representations of outputs from a variety of spatial analyses. Visualization plays a central role in modern spatial/geographic data science. Current packages provide classification methods for choropleth mapping and a common API for linking PySAL outputs to visualization tool-kits in the Python ecosystem.

• legendgram : legendgram is a small package that provides "legendgrams" legends that visualize the distribution of observations by color in a given map. These distributional visualizations for map classification schemes assist in analytical cartography and spatial data visualization

• mapclassify : mapclassify provides functionality for Choropleth map classification. Currently, fifteen different classification schemes are available, including a highly-optimized implementation of Fisher-Jenks optimal classification. Each scheme inherits a common structure that ensures computations are scalable and supports applications in streaming contexts.

• splot : splot provides statistical visualizations for spatial analysis. It methods for visualizing global and local spatial autocorrelation (through Moran scatterplots and cluster maps), temporal analysis of cluster dynamics (through heatmaps and rose diagrams), and multivariate choropleth mapping (through value-by-alpha maps. A high level API supports the creation of publication-ready visualizations

Installation

PySAL is available through Anaconda (in the defaults or conda-forge channel) We recommend installing PySAL from conda-forge:

conda config --add channels conda-forge
conda install pysal

PySAL can also be installed using pip:

pip install pysal

As of version 2.0.0 PySAL has shifted to Python 3 only.

Users who need an older stable version of PySAL that is Python 2 compatible can install version 1.14.3 through pip or conda:

conda install pysal==1.14.3

Documentation

For help on using PySAL, check out the following resources:

• Project Home
• Users
• Developers

Development

As of version 2.0.0, PySAL is now a collection of affiliated geographic data science packages. Changes to the code for any of the subpackages should be directed at the respective upstream repositories, and not made here. Infrastructural changes for the meta-package, like those for tooling, building the package, and code standards, will be considered.

Development is hosted on github.

Discussions of development as well as help for users occurs on the developer list as well as in PySAL's Discord channel.

Getting Involved

If you are interested in contributing to PySAL please see our development guidelines.

Bug reports

To search for or report bugs, please see PySAL's issues.

Build Instructions

To build the meta-package pysal see tools/README.md.

License information

See the file "LICENSE.txt" for information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.

pysal's Issues

clarifying *nix installation instructions

Original author: [email protected] (February 01, 2010 03:23:21)

I just installed PySAL on OS X using the directions from the wiki.

It went smoothly aside from one hitch at the end. When the user is editing the .bash_profile, the
instruction is to add the following text:

export PYTHONPATH=${PYTHONPATH}:"/path_to_desired/folder/pysal/"

However, I was wondering if for the sake of clarity it should say:

export PYTHONPATH=${PYTHONPATH}:"/path_to_desired/folder/pysal-read-only/"

Because in the installation instructions, when the user downloads the files from the SVN, they
create the folder it will be downloaded to, but there is no instruction (or need) to change the
name of the folder that is actually downloaded from the SVN. So if everything is left as default
the last part of that script in the .bash_profile should direct to the "pysal-read-only" folder that
the user just downloaded.

Might just be my inexperience, but I was a bit confused at first.

Original issue: http://code.google.com/p/pysal/issues/detail?id=44

versioning

Original author: [email protected] (August 24, 2009 18:57:57)

We need some kind of version module. It seems that some users may be running old versions and
we need an easy way to check.

Original issue: http://code.google.com/p/pysal/issues/detail?id=16

code error in Box_Plot in mapclassify

Original author: [email protected] (October 05, 2009 20:49:18)

Line 478 (iqr=pct[-1]-pct[0]) in esda.mapclassify should be fixed into the
following:

iqr=bins[-2]-bins[0]

Original issue: http://code.google.com/p/pysal/issues/detail?id=20

Populate data and esda directory

Original author: sjsrey (August 14, 2009 18:20:28)

Once http://code.google.com/p/pysal/issues/detail?id=1 and
http://code.google.com/p/pysal/issues/detail?id=2 are completed data and
esda can also be streamlined and populated

Original issue: http://code.google.com/p/pysal/issues/detail?id=3

cg.locators failing doctests

Original author: sjsrey (December 14, 2009 17:06:46)

---

File "locators.py", line 633, in main.PolygonLocator.nearest
Failed example:
str(min(n.vertices()))
Exception raised:
Traceback (most recent call last):
File
"/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/doctest.py",
line 1228, in run
compileflags, 1) in test.globs
File "<doctest __main.PolygonLocator.nearest[4]>", line 1, in <module>
str(min(n.vertices()))
AttributeError: 'NoneType' object has no attribute 'vertices'
Trying:
p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
Expecting nothing
ok
Trying:
p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
Expecting nothing
ok
Trying:
pl = PolygonLocator([p1, p2])
Expecting nothing
ok
Trying:
len(pl.proximity(Point((0, 0)), 2))
Expecting:
2

---

File "locators.py", line 671, in main.PolygonLocator.proximity
Failed example:
len(pl.proximity(Point((0, 0)), 2))
Exception raised:
Traceback (most recent call last):
File
"/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/doctest.py",
line 1228, in run
compileflags, 1) in test.globs
File "<doctest __main.PolygonLocator.proximity[3]>", line 1, in
<module>
len(pl.proximity(Point((0, 0)), 2))
TypeError: object of type 'NoneType' has no len()
Trying:
p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])
Expecting nothing
ok
Trying:
p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])
Expecting nothing
ok
Trying:
pl = PolygonLocator([p1, p2])
Expecting nothing
ok
Trying:
n = pl.region(Rectangle(0, 0, 4, 10))
Expecting nothing
ok
Trying:
len(n)
Expecting:
2

---

File "locators.py", line 652, in main.PolygonLocator.region
Failed example:
len(n)
Exception raised:
Traceback (most recent call last):
File
"/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/doctest.py",
line 1228, in run
compileflags, 1) in test.globs
File "<doctest __main.PolygonLocator.region[4]>", line 1, in <module>
len(n)
TypeError: object of type 'NoneType' has no len()

Original issue: http://code.google.com/p/pysal/issues/detail?id=38

Possible bug in dealing with islands

Original author: dreamessence (November 03, 2009 17:00:25)

When trying to write out a polygon with islands object, pysal fails.

Code:

In [1]: import pysal

In [2]: shp=pysal.open('Coastal_Provinces.shp','r')

In [3]: shp_i=shp.next()

In [4]: of=pysal.open('out1.shp','w')

In [5]: of.write(shp_i)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)

/Users/dreamessence/Desktop/euShp/<ipython console> in <module>()

/Applications/pysalSVN/trunk/pysal/core/_FileIO/pyShpIO.pyc in

__firstWrite(self, shape)
82 self.dataObj = shp_file(self.dataPath,'w',self.type)
83 self.write = self.__writer
---> 84 self.write(shape)
85 def __writer(self,shape):
86 if TYPE_TO_STRING[type(shape)] != self.type:

/Applications/pysalSVN/trunk/pysal/core/_FileIO/pyShpIO.pyc in

__writer(self, shape)
112 partsIndex.append(partsIndex[-1]+l)
113 rec['Parts Index'] = partsIndex
--> 114 verts = sum(all_parts,[])
115 verts = [(x,y) for x,y in verts]
116 rec['NumPoints'] = len(verts)

TypeError: can only concatenate list (not "tuple") to list

Attached example shapefile.

Original issue: http://code.google.com/p/pysal/issues/detail?id=28

support real/arbitrary ID values in weights

Original author: [email protected] (August 17, 2009 21:55:21)

Weights currently do not except non-integer/non-zero-based IDs. This creates a challenge in
maintaining links to the original IDS and can be lost when saving to files, etc.

Original issue: http://code.google.com/p/pysal/issues/detail?id=11

Polygons without holes writing not working

Original author: dreamessence (November 04, 2009 16:07:48)

When trying to write a polygon out to a new shapefile, if the polygon does
not have holes, it creates both the shapefile and the dbf but it's now empty.

Using same testing data as in Issue 28:

>>> import pysal
>>> shp=pysal.open('Coastal_Provinces.shp','r')
>>> poly0=shp.next()
>>> fo=pysal.open('test0.shp','w')
>>> fo.write(poly0)
>>> fo.close()

Writes the polygon fine (poly0 does have holes). Now keep going:

>>>
>>> poly1=shp.next()
>>> fo=pysal.open('test1.shp','w')
>>> fo.write(poly1)
>>> fo.close()
>>>

It writes the files but they are empty. 'poly1' does NOT have holes in it.

Original issue: http://code.google.com/p/pysal/issues/detail?id=29

Possible bug in mapclassify

Original author: [email protected] (October 09, 2009 05:03:11)

Running some variables from the 1990 census through Natural_Breaks results in an error.
Changing

cuts=[x[c0==c].max() for c in classes]
to
cuts=[x[c1==c].max() for c in classes]

allows the code to run, but I'm not sure this is correct. I believe the problem is cause when the while
loop breaks on the first pass. In all cases the last value of c1 is otherwise ignored?

Note: I put this in a code review, but it seems to have disappeared?

Original issue: http://code.google.com/p/pysal/issues/detail?id=22

mapclassify is slow

Original author: [email protected] (September 30, 2009 19:51:49)

Experimenting with mapclassify, only tested a few methods, but they seem to run unreasonably
slow. Natural_Breaks and Jenks_Caspall_Forced take minutes to compute for 65,000 observations.
Jenks_Caspall never seems to finish.

These are too slow to be used in a webservice, the request would timeout before the results are
ready. Is there anything that can be done to speed these methods?

Original issue: http://code.google.com/p/pysal/issues/detail?id=19

replace calempdensity

Original author: [email protected] (August 17, 2009 16:33:17)

existing file (removed) is a link. Please upload real file.

calempdensity.csv@ ->
/Users/serge/Research/e/Econometrics/spatialEconometrics/mapping/calempdensity.csv

Original issue: http://code.google.com/p/pysal/issues/detail?id=10

Percentile class missing k attributes

Original author: [email protected] (October 14, 2009 19:55:33)

In mapclassify Percentile class does not set k attribute
so that when summary function is called AttributeError is raised.
In Percentile class's _set_bins method,
we can add self.k = len(self.bins).

Original issue: http://code.google.com/p/pysal/issues/detail?id=24

streamline top level imports

Original author: sjsrey (January 21, 2010 19:06:28)

move public classes and functions up into top level init
so that instead of

pysal.esda.moran.Moran(w,y)

one can do

pysal.Moran(w,y)

for example

Original issue: http://code.google.com/p/pysal/issues/detail?id=40

Need Global Testing Module.

Original author: [email protected] (November 04, 2009 18:42:04)

Changes to shapes.py in the cg module yesterday caused unit tests in core/_FileIO/pyShpIO to fail.
These failures went undetected because we have not global testing module that calls the tests in all
sub modules.

Original issue: http://code.google.com/p/pysal/issues/detail?id=30

Require ids in weights creation

Original author: sjsrey (December 11, 2009 22:08:26)

Add ordered ids as required parameter to weights

Original issue: http://code.google.com/p/pysal/issues/detail?id=33

is tests.py in root still needed?

Original author: sjsrey (February 01, 2010 16:12:55)

do we still need
http://code.google.com/p/pysal/source/browse/trunk/test.py

given that we have pysal/tests/tests.py ?

Original issue: http://code.google.com/p/pysal/issues/detail?id=45

User-land documentation

Original author: sjsrey (August 27, 2009 03:54:58)

We can use sphinx to generate user documentation for PySAL, with an eye
towards tutorials illustrating the different modules.

Original issue: http://code.google.com/p/pysal/issues/detail?id=17

Create a Joins Module

Original author: [email protected] (December 07, 2009 23:51:29)

Standalone functionality for joining data sets.

Original issue: http://code.google.com/p/pysal/issues/detail?id=32

_FileIO/gal.py missing global name pysal

Original author: sjsrey (August 14, 2009 22:17:01)

What steps will reproduce the problem?
>>> import pysal
>>> shp=pysal.open("examples/10740.shp",'r')

>>> gal=pysal.open('examples/desmith.gal','r')

Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "pysal/core/_FileIO/gal.py", line 10, in init
NameError: global name 'pysal' is not defined

Original issue: http://code.google.com/p/pysal/issues/detail?id=5

doctests and/or unittests for FileIO

Original author: sjsrey (August 15, 2009 04:47:01)

There is a lot of powerful functionality in there but it needs to be
documented for others to fully tap into

Original issue: http://code.google.com/p/pysal/issues/detail?id=9

Update installation instructions

Original author: sjsrey (January 25, 2010 01:54:10)

Wiki page http://code.google.com/p/pysal/wiki/Installation still shows
dependencies that we are out of date and lacks specificity on versions for
numpy and scipy

It should mirror what is on the pysal.org
http://pysal.org/users/installation.html
page so the two are in sync

Original issue: http://code.google.com/p/pysal/issues/detail?id=41

conditional imports for contiguityWeights (rtree versus pure python)

Original author: sjsrey (August 15, 2009 01:06:55)

If user has rtree installed we use that for building a contiguity W from a
shapefile, otherwise we use our own binning or other algorithm)

Original issue: http://code.google.com/p/pysal/issues/detail?id=8

Add shared perimeter weights

Original author: sjsrey (February 25, 2010 23:44:16)

wij = l_ij/P_i
wji = l_ij/P_j

l_ij = length of shared border i and j
P_j = perimeter of j

Note: this is also related to issue 31

Original issue: http://code.google.com/p/pysal/issues/detail?id=46

pysal had better use sample standard deviation

Original author: [email protected] (October 13, 2009 22:52:11)

I compared pysal's breakpoints with geoda's for standard deviation
classification.
Geoda uses sample standard deviation, while pysal uses simple standard
deviation.
To make the results the same, I think pysal had better use sample standard
deviation.
To do that, we can change the line 636 in mapclassify.py
from s=y.std() to s=y.std(ddof=1).

Original issue: http://code.google.com/p/pysal/issues/detail?id=23

Add Installer

Original author: [email protected] (October 22, 2009 18:28:06)

We need to add a setup.py installer for Windows. and document how to install.

Original issue: http://code.google.com/p/pysal/issues/detail?id=25

Finalize weights api

Original author: sjsrey (February 26, 2010 00:08:22)

set this for 1.0 so other modules can move on

Original issue: http://code.google.com/p/pysal/issues/detail?id=48

rtree failing on sids2 example

Original author: sjsrey (December 17, 2009 17:10:58)

>>> w=rook("../examples/sids2.shp")

Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "ContiguityWeights.py", line 38, in rook
return _make_weights(geo,ROOK)
File "ContiguityWeights.py", line 63, in _make_weights
w = ContiguityWeights(geoObj,wt_type)
File
"/Users/serge/Research/p/Pysal/src/google/trunk/pysal/weights/_contW_rtree.py",
line 38, in init
self.create()
File
"/Users/serge/Research/p/Pysal/src/google/trunk/pysal/weights/_contW_rtree.py",
line 41, in create
self.append(poly)
File
"/Users/serge/Research/p/Pysal/src/google/trunk/pysal/weights/_contW_rtree.py",
line 43, in append
self.Q.add(poly)
File
"/Users/serge/Research/p/Pysal/src/google/trunk/pysal/weights/_contW_rtree.py",
line 25, in add
if poly.id not in self:
AttributeError: 'Polygon' object has no attribute 'id'

Original issue: http://code.google.com/p/pysal/issues/detail?id=39

_init_ methods not showing up in sphinx documentation

Original author: sjsrey (December 11, 2009 22:22:54)

This makes it difficult to document the APIs for say weights and such.

Original issue: http://code.google.com/p/pysal/issues/detail?id=35

Add union and subsetting methods for weights

Original author: sjsrey (December 11, 2009 22:11:19)

Union would allow construction of hybrid binary weights matrices (regimes +
contiguity)

Subsetting would allow for the creation of say a county contiguity weights
object for a specific state obtained from the county contiguity weights
object for the nation.

Original issue: http://code.google.com/p/pysal/issues/detail?id=34

add id type flag to lat2gal

Original author: sjsrey (February 26, 2010 00:11:30)

to allow for strings, floats as well as ints (ints default).

Original issue: http://code.google.com/p/pysal/issues/detail?id=49

Add a "joinable" tag to FileIO

Original author: [email protected] (August 14, 2009 23:12:16)

Not all types are "joinable", define this tag so that FileIO understands which types can be joined.
Maybe a mapping makes sense to? (TypeA--TypeB, etc?

Original issue: http://code.google.com/p/pysal/issues/detail?id=7

Add XLS support to FileIO

Original author: [email protected] (October 27, 2009 01:13:07)

= Add support to read (and write?) XLS in FileIO. =

Many data providers still distribute large quantities of data as Microsoft Excel SpreadSheets (eg.
HUD, http://www.huduser.org/datasets/nsp_target.html). Multiple open source libraries exist for
parsing XLS files.

• xlrd: http://www.lexicon.net/sjmachin/xlrd.htm
• pyExcelerator: http://sourceforge.net/projects/pyexcelerator/

examine drawbacks of adding another dependancy to pysal.

== xlrd ==

• Reader Only.
• light weight
• BSD-Style license
• easy to install (easy_install, setup.py, or windows installer)
• works with the HUD files

== pyExcelerator ==

• Reader and Writer
• did not work with the HUD files on first pass

Original issue: http://code.google.com/p/pysal/issues/detail?id=27

populate core directory

Original author: sjsrey (August 14, 2009 18:15:19)

Move files from geodacenter into clean core directory

Original issue: http://code.google.com/p/pysal/issues/detail?id=2

experimental external dependancies

Original author: [email protected] (October 09, 2009 04:02:56)

Scipy.spatial has not yet made it to a stable release of SciPy. This can create unexpected problems.
On my macbook installing the scipy 0.8dev with Python 2.5 results in a Bus Errors when using
scipy.stats which is not experimental and required by other parts of pysal. I can't revert back to
0.7.1 because pysal fails to import on scipy.spatial.

Can we put "experimental" external dependancies within conditional imports?
try:
import scipy.spatial
except:
disable this functionality

Original issue: http://code.google.com/p/pysal/issues/detail?id=21

check if weights iterator is bug free

Original author: sjsrey (February 26, 2010 23:12:00)

should it return

self
or
generator

Original issue: http://code.google.com/p/pysal/issues/detail?id=50

fix cronjob so docs at pysal.org are updated automatically

Original author: sjsrey (January 30, 2010 17:14:36)

script will checkout doc
build doc
rsync docs to pysal.org

run hourly

Original issue: http://code.google.com/p/pysal/issues/detail?id=43

endless loop in FileIO

Original author: [email protected] (October 23, 2009 00:26:19)

>>> db = pysal.open("example_file.csv")
>>> db[:]
[[1, 2001, 10, 1000], [2, 2001, 20, 952], [3, 2001, 30, 904], [4, 2001, 40, 856], [5, 2001, 50, 808], [1, 2002, 60, 760], [2,
2002, 70, 712], [3, 2002, 80, 664], [4, 2002, 90, 616], [5, 2002, 100, 568], [1, 2003, 110, 520], [2, 2003, 120, 472], [3, 2003,
130, 424], [4, 2003, 140, 376], [5, 2003, 150, 328]]
>>> db.read()
BEGIN ENDLESS LOOP.

This is only one usage of FileIO, but a pretty critical one, not sure if it effects all file types for just CSV.

Original issue: http://code.google.com/p/pysal/issues/detail?id=26

Populate cg directory

Original author: sjsrey (August 14, 2009 18:10:49)

Move modules from geodacenter svn into cg directory.

Original issue: http://code.google.com/p/pysal/issues/detail?id=1

Evaluate clockwise test methods

Original author: [email protected] (August 19, 2009 01:08:20)

Test various methods for testing if a polygon's are stored in clockwise order. There appear to be
pros and cons and various methods ranging from speed to robustness.Evaluate these and pick the
best or best combination of methods to use in PySAL

clockwise tests are used in pyShpIO, cg.shapes.Polygon and cg.voronoi

Original issue: http://code.google.com/p/pysal/issues/detail?id=14

clean up pyDbfIO

Original author: [email protected] (August 14, 2009 18:59:50)

pyDbfIO.py is a a pure python DBF reader that supports the pysal DataTable functionality, including
slicing. This code is not yet writing clean dbf files. Header attributes are lost.

Original issue: http://code.google.com/p/pysal/issues/detail?id=4

Rtreeweights rook check has a logic error

Original author: [email protected] (November 09, 2009 20:46:36)

Having more than 1 common vertex is a necessary but not sufficient condition for rook contiguity. There are cases where two polygons can share
two vertices but they are not queen neighbors. This can happen when the first polygon has the two vertices defining a segment, while for the second
polygon the two vertices do not define a segment belonging to the polygon.

Example:
{{{
Vertex X Y
1 2 6
2 4 6
3 4 4
4 2 4
5 4 3
6 2 3
7 6 4
8 3 0
9 0 4

Polygons (vertex sets):
A=[1,2,3,4,1]
B=[4,3,5,6,4]
C=[9,4,6,5,3,7,8,9]

A int B = [3,4]
B int C = [3,4,5,6]
A int C = [3,4]
}}}

A and B are rook neighbors because (3,4) is a segment for both A and B.

B and C are rook neighbors because of segments (5,6) (3,5), and (4,6) not because of (3,4) since that segment is only in B but not in C

Vertices 3 and 4 are common to both C and A but only define a segment in A and not C, so A and C are queen and not rook neighbors.

So we need an additional check for common segments to distinguish rook from queen in these cases.

-- serge.

Original issue: http://code.google.com/p/pysal/issues/detail?id=31

observation_ids as a required parameter in w.lag has some issues

Original author: sjsrey (December 13, 2009 01:00:01)

I think we need to revisit the observation_ids scheme as the new approach
of having it as a required argument in w.lag is introducing a lot of ugly
interfaces to some of the esda methods (and likely many of the future ones).

instead of having to do things like

w.lag(y,observation_ids)

Moran(y,w,observation_ids)

id rather have something like:

y=y[observation_ids]
w.lag(y)
Moran(y,w)

this was the old scheme which puts the responsibility on the user to align
y with the id_order in w. the latter can also be set as well as inspected .
by the user.

the new scheme doesn't really simplify the w.lag code, but definitely
complicates the interfaces for upstream functions.

Original issue: http://code.google.com/p/pysal/issues/detail?id=37

update GeoDa Center page upon official release

Original author: sjsrey (January 28, 2010 18:45:02)

hook them together

Original issue: http://code.google.com/p/pysal/issues/detail?id=42

Add a README to examples

Original author: sjsrey (August 14, 2009 22:22:11)

Document the files that are included along with sources and permissions

Original issue: http://code.google.com/p/pysal/issues/detail?id=6

_init_ methods not showing up in sphinx documentation

Original author: sjsrey (December 11, 2009 22:22:56)

This makes it difficult to document the APIs for say weights and such.

Original issue: http://code.google.com/p/pysal/issues/detail?id=36

Profile ShapeReader

Original author: [email protected] (February 25, 2010 23:57:22)

PySAL Shape reading does not read the entire file in at one shot, as a result it often re-reads the
same shapes over and over.

Look into short cuts, either caching the centroids or reading the entire file if file length is less than
pysal.common.SMALL_FILESIZE

Profile to identify other issues and speed savings.

Original issue: http://code.google.com/p/pysal/issues/detail?id=47

Mark and Julia testing

Original author: [email protected] (September 01, 2009 18:43:20)

What steps will reproduce the problem?

1. nothing
   2.
   3.

What is the expected output? What do you see instead?
stuff

What version of the product are you using? On what operating system?
11.0

Please provide any additional information below.
whatever

Original issue: http://code.google.com/p/pysal/issues/detail?id=18

Remove "Not to be used without permission of the authors." for any included modules

Original author: sjsrey (August 19, 2009 02:38:54)

Since we are using the BSD license, that language can't be included in any
modules we release as part of PySAL.

Original issue: http://code.google.com/p/pysal/issues/detail?id=15

doctests and/or unittests for Weights

Original author: [email protected] (August 17, 2009 22:51:17)

weights are still widely untested.

Original issue: http://code.google.com/p/pysal/issues/detail?id=13

doctests and/or unittests for Weights

Original author: [email protected] (August 17, 2009 22:51:07)

weights are still widely untested.

Original issue: http://code.google.com/p/pysal/issues/detail?id=12