• Open Source
• qgis tags:
• FOSSGIS
• gis
• Open Source
• osgeo
• qgis
• Quantum GIS

A quick update from the QGIS documentation team today on the mailing list. The QGIS 1.8 manual is now available in HTML and PDF form.

HTML manual can be found at: http://docs.qgis.org/html/en/docs/user_manual/index.html

PDF manual at: http://docs.qgis.org/pdf/QGIS-1.8-UserGuide-en.pdf

This has been part of an ongoing effort from the documentation team since before the 1.8 release to bring our all our documentation into reStructedText rather then LaTeX. Moving to reStructedText allows quicker updates and a larger range of final output formats.

I would like to thank everyone who has been involved in this process as I know what a grueling process updating documentation can be.

Community notice

Just remember you don't have to be a programmer to contribute to an open source project. If you think you could contribute to the updating of the QGIS documentation please contact the team on the mailing list.

A quick update from the QGIS documentation team today on the mailing list. The QGIS 1.8 manual is now available in HTML and PDF form.

HTML manual can be found at: http://docs.qgis.org/html/en/docs/user_manual/index.html

PDF manual at: http://docs.qgis.org/pdf/QGIS-1.8-UserGuide-en.pdf

This has been part of an ongoing effort from the documentation team since before the 1.8 release to bring our all our documentation into reStructedText rather then LaTeX. Moving to reStructedText allows quicker updates and a larger range of final output formats.

I would like to thank everyone who has been involved in this process as I know what a grueling process updating documentation can be.

Community notice

Just remember you don't have to be a programmer to contribute to an open source project. If you think you could contribute to the updating of the QGIS documentation please contact the team on the mailing list.

• Open Source
• qgis tags:
• FOSSGIS
• gis
• Open Source
• osgeo
• qgis
• Quantum GIS

A quick update from the QGIS documentation team today on the mailing list. The QGIS 1.8 manual is now available in HTML and PDF form.

HTML manual can be found at: http://docs.qgis.org/html/en/docs/user_manual/index.html

PDF manual at: http://docs.qgis.org/pdf/QGIS-1.8-UserGuide-en.pdf

This has been part of an ongoing effort from the documentation team since before the 1.8 release to bring our all our documentation into reStructedText rather then LaTeX. Moving to reStructedText allows quicker updates and a larger range of final output formats.

I would like to thank everyone who has been involved in this process as I know what a grueling process updating documentation can be.

Community notice

Just remember you don't have to be a programmer to contribute to an open source project. If you think you could contribute to the updating of the QGIS documentation please contact the team on the mailing list.

If you need to do any kind of spatial operations in QGIS using Python or C++ you really want them to be as fast a possible in order reduce the amount of time you make the user wait. Lets take the simple scenario of a recent question that was asked on gis.stackexchange; Summing up values of neighboring polygons?.

I went for the SQL approach as I like how quick SQL can express what you need to do, however SQL is not the only way to skin a cat as spatialthoughts has shown in his blog post. Here Ujaval has used Python to find the neighboring polygons of each feature. Running the script on a small dataset yields results in reasonable time however running it on a larger dataset can take a long time.

In order to check if a feature touches another you need to have two features to compare against each other. The simple way to do this is to create two loops where you check each feature against every other feature. Here is a quick code example of just that.

layer = qgis.utils.iface.activeLayer()
# Select all features along with their attributes
allAttrs = layer.pendingAllAttributesList()
layer.select(allAttrs)
# Get all the features to start
allfeatures = {feature.id(): feature for (feature) in layer}

def noindex():
    for feature in allfeatures.values():
        for f in allfeatures.values():
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

import timeit
print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

So the above code is pretty simple, just loop each feature and check against every other feature. No worries. No worries at least until you run this on a large dataset then I think you can see the issue here. Running the above code on a layer with around 28000 features takes 1912.41 seconds - that's 31 minutes. Holy crap!

Note: We put all the features of the layer into a dictionary as it will make lookup quicker in the later index example.

How can we speed up the above code? Lets take a gander at QgsSpatialIndex

QgsSpatialIndex to rule them all

QgsSpatialIndex is a wrapper around the open source SpatailIndex lib and uses a RTree for an index method. If you don't know what an index is you can think of it like the index in a book - a pointer to a location in the book rather then having to scan every page to find a word.

There isn't much to using QgsSpatialIndex just insert each QgsFeature and it handles the rest, when we need something out we just use the intersects method to return any features inside an area.

  layer = qgis.utils.iface.activeLayer()
  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def withindex():
      # Build the spatial index for faster lookup.
      index = QgsSpatialIndex()
      for f in allfeatures.values():
              index.insertFeature(f)

      # Loop each feature in the layer again and get only the features that are going to touch.
      for feature in allfeatures.values():
        # Get the ids of all the features in the index that are within
        # the bounding box of the current feature because these are the ones
        # that will be touching.
        ids = index.intersects(feature.geometry().boundingBox())
        for id in ids:
          f = allfeatures[id]
          if f == feature: continue
          touches = f.geometry().touches(feature.geometry())
          # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)

Running this code on our 28000 feature layer returns the results in 10 seconds. 31 minutes down to 10 seconds by just using a spatial index. Nice!

So the next time you need to do some spatial operations remember to use the handy QgsSpatialIndex in order to speed up your code. If you don't want to use QgsSpatialIndex, or need some more flexiblity, you could even use the Python RTree module.

Full code

  layer = qgis.utils.iface.activeLayer()

  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def noindex():
        for feature in allfeatures.values():
            for f in allfeatures.values():
                touches = f.geometry().touches(feature.geometry())
                # It doesn't matter if we don't return anything it's just an example

  def withindex():
        # Build the spatial index for faster lookup.
        index = QgsSpatialIndex()
        map(index.insertFeature, allfeatures.values())

        # Loop each feature in the layer again and get only the features that are going to touch.
        for feature in allfeatures.values():
          ids = index.intersects(feature.geometry().boundingBox())
          for id in ids:
            f = allfeatures[id]
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)
  print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

If you need to do any kind of spatial operations in QGIS using Python or C++ you really want them to be as fast a possible in order reduce the amount of time you make the user wait. Lets take the simple scenario of a recent question that was asked on gis.stackexchange; Summing up values of neighboring polygons?.

I went for the SQL approach as I like how quick SQL can express what you need to do, however SQL is not the only way to skin a cat as spatialthoughts has shown in his blog post. Here Ujaval has used Python to find the neighboring polygons of each feature. Running the script on a small dataset yields results in reasonable time however running it on a larger dataset can take a long time.

In order to check if a feature touches another you need to have two features to compare against each other. The simple way to do this is to create two loops where you check each feature against every other feature. Here is a quick code example of just that.

layer = qgis.utils.iface.activeLayer()
# Select all features along with their attributes
allAttrs = layer.pendingAllAttributesList()
layer.select(allAttrs)
# Get all the features to start
allfeatures = {feature.id(): feature for (feature) in layer}

def noindex():
        for feature in allfeatures.values():
                for f in allfeatures.values():
                        touches = f.geometry().touches(feature.geometry())
                        # It doesn't matter if we don't return anything it's just an example

import timeit
print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

So the above code is pretty simple, just loop each feature and check against every other feature. No worries. No worries at least until you run this on a large dataset then I think you can see the issue here. Running the above code on a layer with around 28000 features takes 1912.41 seconds – that’s 31 minutes. Holy crap!

Note: We put all the features of the layer into a dictionary as it will make lookup quicker in the later index example.

How can we speed up the above code? Lets take a gander at QgsSpatialIndex

layer = qgis.utils.iface.activeLayer()
# Select all features along with their attributes
allAttrs = layer.pendingAllAttributesList()
layer.select(allAttrs)
# Get all the features to start
allfeatures = {feature.id(): feature for (feature) in layer}

def withindex():
        # Build the spatial index for faster lookup.
        index = QgsSpatialIndex()
        for f in allfeatures.values():
                index.insertFeature(f)

        # Loop each feature in the layer again and get only the features that are going to touch.
        for feature in allfeatures.values():
          # Get the ids of all the features in the index that are within
          # the bounding box of the current feature because these are the ones
          # that will be touching.
          ids = index.intersects(feature.geometry().boundingBox())
          for id in ids:
            f = allfeatures[id]
            if f == feature: continue
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

import timeit
print "With Index: %s seconds " % timeit.timeit(withindex,number=1)

layer = qgis.utils.iface.activeLayer()

# Select all features along with their attributes
allAttrs = layer.pendingAllAttributesList()
layer.select(allAttrs)
# Get all the features to start
allfeatures = {feature.id(): feature for (feature) in layer}

def noindex():
	for feature in allfeatures.values():
		for f in allfeatures.values():
			touches = f.geometry().touches(feature.geometry())
			# It doesn't matter if we don't return anything it's just an example

def withindex():
	# Build the spatial index for faster lookup.
	index = QgsSpatialIndex()
	map(index.insertFeature, allfeatures.values())

	# Loop each feature in the layer again and get only the features that are going to touch.
	for feature in allfeatures.values():
	  ids = index.intersects(feature.geometry().boundingBox())
	  for id in ids:
	    f = allfeatures[id]
	    touches = f.geometry().touches(feature.geometry())
	    # It doesn't matter if we don't return anything it's just an example

import timeit
print "With Index: %s seconds " % timeit.timeit(withindex,number=1)
print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

• Open Source
• qgis tags:
• FOSSGIS
• gis
• qgis
• Quantum GIS
• spatial operations

If you need to do any kind of spatial operations in QGIS using Python or C++ you really want them to be as fast a possible in order reduce the amount of time you make the user wait. Lets take the simple scenario of a recent question that was asked on gis.stackexchange; Summing up values of neighboring polygons?.

I went for the SQL approach as I like how quick SQL can express what you need to do, however SQL is not the only way to skin a cat as spatialthoughts has shown in his blog post. Here Ujaval has used Python to find the neighboring polygons of each feature. Running the script on a small dataset yields results in reasonable time however running it on a larger dataset can take a long time.

In order to check if a feature touches another you need to have two features to compare against each other. The simple way to do this is to create two loops where you check each feature against every other feature. Here is a quick code example of just that.

layer = qgis.utils.iface.activeLayer()
# Select all features along with their attributes
allAttrs = layer.pendingAllAttributesList()
layer.select(allAttrs)
# Get all the features to start
allfeatures = {feature.id(): feature for (feature) in layer}

def noindex():
    for feature in allfeatures.values():
        for f in allfeatures.values():
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

import timeit
print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

So the above code is pretty simple, just loop each feature and check against every other feature. No worries. No worries at least until you run this on a large dataset then I think you can see the issue here. Running the above code on a layer with around 28000 features takes 1912.41 seconds - that's 31 minutes. Holy crap!

Note: We put all the features of the layer into a dictionary as it will make lookup quicker in the later index example.

How can we speed up the above code? Lets take a gander at QgsSpatialIndex

QgsSpatialIndex to rule them all

QgsSpatialIndex is a wrapper around the open source SpatailIndex lib and uses a RTree for an index method. If you don't know what an index is you can think of it like the index in a book - a pointer to a location in the book rather then having to scan every page to find a word.

There isn't much to using QgsSpatialIndex just insert each QgsFeature and it handles the rest, when we need something out we just use the intersects method to return any features inside an area.

  layer = qgis.utils.iface.activeLayer()
  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def withindex():
      # Build the spatial index for faster lookup.
      index = QgsSpatialIndex()
      for f in allfeatures.values():
              index.insertFeature(f)

      # Loop each feature in the layer again and get only the features that are going to touch.
      for feature in allfeatures.values():
        # Get the ids of all the features in the index that are within
        # the bounding box of the current feature because these are the ones
        # that will be touching.
        ids = index.intersects(feature.geometry().boundingBox())
        for id in ids:
          f = allfeatures[id]
          if f == feature: continue
          touches = f.geometry().touches(feature.geometry())
          # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)

Running this code on our 28000 feature layer returns the results in 10 seconds. 31 minutes down to 10 seconds by just using a spatial index. Nice!

So the next time you need to do some spatial operations remember to use the handy QgsSpatialIndex in order to speed up your code. If you don't want to use QgsSpatialIndex, or need some more flexiblity, you could even use the Python RTree module.

Full code

  layer = qgis.utils.iface.activeLayer()

  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def noindex():
        for feature in allfeatures.values():
            for f in allfeatures.values():
                touches = f.geometry().touches(feature.geometry())
                # It doesn't matter if we don't return anything it's just an example

  def withindex():
        # Build the spatial index for faster lookup.
        index = QgsSpatialIndex()
        map(index.insertFeature, allfeatures.values())

        # Loop each feature in the layer again and get only the features that are going to touch.
        for feature in allfeatures.values():
          ids = index.intersects(feature.geometry().boundingBox())
          for id in ids:
            f = allfeatures[id]
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)
  print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

If you need to do any kind of spatial operations in QGIS using Python or C++ you really want them to be as fast a possible in order reduce the amount of time you make the user wait. Lets take the simple scenario of a recent question that was asked on gis.stackexchange; Summing up values of neighboring polygons?.

I went for the SQL approach as I like how quick SQL can express what you need to do, however SQL is not the only way to skin a cat as spatialthoughts has shown in his blog post. Here Ujaval has used Python to find the neighboring polygons of each feature. Running the script on a small dataset yields results in reasonable time however running it on a larger dataset can take a long time.

In order to check if a feature touches another you need to have two features to compare against each other. The simple way to do this is to create two loops where you check each feature against every other feature. Here is a quick code example of just that.

layer = qgis.utils.iface.activeLayer()
# Select all features along with their attributes
allAttrs = layer.pendingAllAttributesList()
layer.select(allAttrs)
# Get all the features to start
allfeatures = {feature.id(): feature for (feature) in layer}

def noindex():
    for feature in allfeatures.values():
        for f in allfeatures.values():
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

import timeit
print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

So the above code is pretty simple, just loop each feature and check against every other feature. No worries. No worries at least until you run this on a large dataset then I think you can see the issue here. Running the above code on a layer with around 28000 features takes 1912.41 seconds - that's 31 minutes. Holy crap!

Note: We put all the features of the layer into a dictionary as it will make lookup quicker in the later index example.

How can we speed up the above code? Lets take a gander at QgsSpatialIndex

QgsSpatialIndex to rule them all

QgsSpatialIndex is a wrapper around the open source SpatailIndex lib and uses a RTree for an index method. If you don't know what an index is you can think of it like the index in a book - a pointer to a location in the book rather then having to scan every page to find a word.

There isn't much to using QgsSpatialIndex just insert each QgsFeature and it handles the rest, when we need something out we just use the intersects method to return any features inside an area.

layer = qgis.utils.iface.activeLayer()
  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def withindex():
      # Build the spatial index for faster lookup.
      index = QgsSpatialIndex()
      for f in allfeatures.values():
              index.insertFeature(f)

      # Loop each feature in the layer again and get only the features that are going to touch.
      for feature in allfeatures.values():
        # Get the ids of all the features in the index that are within
        # the bounding box of the current feature because these are the ones
        # that will be touching.
        ids = index.intersects(feature.geometry().boundingBox())
        for id in ids:
          f = allfeatures[id]
          if f == feature: continue
          touches = f.geometry().touches(feature.geometry())
          # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)

Running this code on our 28000 feature layer returns the results in 10 seconds. 31 minutes down to 10 seconds by just using a spatial index. Nice!

So the next time you need to do some spatial operations remember to use the handy QgsSpatialIndex in order to speed up your code. If you don't want to use QgsSpatialIndex, or need some more flexiblity, you could even use the Python RTree module.

Full code

layer = qgis.utils.iface.activeLayer()

  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def noindex():
        for feature in allfeatures.values():
            for f in allfeatures.values():
                touches = f.geometry().touches(feature.geometry())
                # It doesn't matter if we don't return anything it's just an example

  def withindex():
        # Build the spatial index for faster lookup.
        index = QgsSpatialIndex()
        map(index.insertFeature, allfeatures.values())

        # Loop each feature in the layer again and get only the features that are going to touch.
        for feature in allfeatures.values():
          ids = index.intersects(feature.geometry().boundingBox())
          for id in ids:
            f = allfeatures[id]
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)
  print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

• Open Source
• qgis tags:
• FOSSGIS
• gis
• qgis
• Quantum GIS
• spatial operations

If you need to do any kind of spatial operations in QGIS using Python or C++ you really want them to be as fast a possible in order reduce the amount of time you make the user wait. Lets take the simple scenario of a recent question that was asked on gis.stackexchange; Summing up values of neighboring polygons?.

I went for the SQL approach as I like how quick SQL can express what you need to do, however SQL is not the only way to skin a cat as spatialthoughts has shown in his blog post. Here Ujaval has used Python to find the neighboring polygons of each feature. Running the script on a small dataset yields results in reasonable time however running it on a larger dataset can take a long time.

In order to check if a feature touches another you need to have two features to compare against each other. The simple way to do this is to create two loops where you check each feature against every other feature. Here is a quick code example of just that.

layer = qgis.utils.iface.activeLayer()
# Select all features along with their attributes
allAttrs = layer.pendingAllAttributesList()
layer.select(allAttrs)
# Get all the features to start
allfeatures = {feature.id(): feature for (feature) in layer}

def noindex():
    for feature in allfeatures.values():
        for f in allfeatures.values():
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

import timeit
print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

So the above code is pretty simple, just loop each feature and check against every other feature. No worries. No worries at least until you run this on a large dataset then I think you can see the issue here. Running the above code on a layer with around 28000 features takes 1912.41 seconds - that's 31 minutes. Holy crap!

Note: We put all the features of the layer into a dictionary as it will make lookup quicker in the later index example.

How can we speed up the above code? Lets take a gander at QgsSpatialIndex

QgsSpatialIndex to rule them all

QgsSpatialIndex is a wrapper around the open source SpatailIndex lib and uses a RTree for an index method. If you don't know what an index is you can think of it like the index in a book - a pointer to a location in the book rather then having to scan every page to find a word.

There isn't much to using QgsSpatialIndex just insert each QgsFeature and it handles the rest, when we need something out we just use the intersects method to return any features inside an area.

  layer = qgis.utils.iface.activeLayer()
  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def withindex():
      # Build the spatial index for faster lookup.
      index = QgsSpatialIndex()
      for f in allfeatures.values():
              index.insertFeature(f)

      # Loop each feature in the layer again and get only the features that are going to touch.
      for feature in allfeatures.values():
        # Get the ids of all the features in the index that are within
        # the bounding box of the current feature because these are the ones
        # that will be touching.
        ids = index.intersects(feature.geometry().boundingBox())
        for id in ids:
          f = allfeatures[id]
          if f == feature: continue
          touches = f.geometry().touches(feature.geometry())
          # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)

Running this code on our 28000 feature layer returns the results in 10 seconds. 31 minutes down to 10 seconds by just using a spatial index. Nice!

So the next time you need to do some spatial operations remember to use the handy QgsSpatialIndex in order to speed up your code. If you don't want to use QgsSpatialIndex, or need some more flexiblity, you could even use the Python RTree module.

Full code

  layer = qgis.utils.iface.activeLayer()

  # Select all features along with their attributes
  allAttrs = layer.pendingAllAttributesList()
  layer.select(allAttrs)
  # Get all the features to start
  allfeatures = {feature.id(): feature for (feature) in layer}

  def noindex():
        for feature in allfeatures.values():
            for f in allfeatures.values():
                touches = f.geometry().touches(feature.geometry())
                # It doesn't matter if we don't return anything it's just an example

  def withindex():
        # Build the spatial index for faster lookup.
        index = QgsSpatialIndex()
        map(index.insertFeature, allfeatures.values())

        # Loop each feature in the layer again and get only the features that are going to touch.
        for feature in allfeatures.values():
          ids = index.intersects(feature.geometry().boundingBox())
          for id in ids:
            f = allfeatures[id]
            touches = f.geometry().touches(feature.geometry())
            # It doesn't matter if we don't return anything it's just an example

  import timeit
  print "With Index: %s seconds " % timeit.timeit(withindex,number=1)
  print "Without Index: %s seconds " % timeit.timeit(noindex,number=1)

The easiest way install Python libraries is to use easy_install and pip. easy_install and pip are package managers for Python. From the easy_install page:

Easy Install is a python module (easy_install) bundled with setuptools that lets you automatically download, build, install, and manage Python packages.

and from the pip page:

pip is a tool for installing and managing Python packages, such as those found in the Python Package Index. It's a replacement for easy_install.

To get easy_install you need to install Python setuptools and you are good to go. Sounds easy! However the setuptools installer assumes that you have the normal standalone Python installed which writes it's install location to the registry, and when you run the installer it will say that it can't find Python on the system. What the!?

If you have installed QGIS, or any other tool from the OSGeo4W install, you will see that OSGeo4W bundles its own version of Python in: C:\OSGeo4W\apps\python27. This is the Python that is used when calling python in the OSGeo4W shell. It seems someone on the OSGeo wiki has made a bootstrapped installer for setuptools that will install setuptools and easy_install into the C:\OSGeo4W\apps\python27 folder for you.

Steps to take

• Download ez_setup.py
  
• Run python ez_setup.py in your OSGeo4W shell
• Done!

To install a package with easy_install just use:

easy_install {packagename}

I wanted to have bottle and flask installed:

easy_install bottle

which gives you something like:

Searching for bottle
Reading http://pypi.python.org/simple/bottle/
Reading http://bottle.paws.de/
Reading http://github.com/defnull/bottle
Reading http://bottlepy.org/
Best match: bottle 0.11.4
Downloading http://pypi.python.org/packages/source/b/bottle/bottle-0.11.4.tar.gz#md5=f767c340de0b7c9581917c48e609479b
Processing bottle-0.11.4.tar.gz
Running bottle-0.11.4\setup.py -q bdist_egg --dist-dir c:\users\woo\appdata\local\temp\easy_install-5b4qq6\bottle-0.11.4\egg-dist-tmp-q2yd68
zip_safe flag not set; analyzing archive contents...
bottle: module references __file__
bottle: module references __path__
Adding bottle 0.11.4 to easy-install.pth file
Installing bottle.py script to C:\OSGeo4W\apps\Python27\Scripts
Installed c:\osgeo4w\apps\python27\lib\site-packages\bottle-0.11.4-py2.7.egg
Processing dependencies for bottle
Finished processing dependencies for bottle

Install pip

• You should also install pip as it is a better package manager then easy_install

• Just run easy_install pip

Note

Most of the time any Python packages that are needed by your OSGeo4W tools are bundled in the installer and can be downloaded using the OSGeo4W installer, however there have been cases when I wanted to install a non OSGeo4W package into my setup by using easy_install or pip. Like bottle and flask in the example above.

The easiest way install Python libraries is to use easy_install and pip.  easy_install and pip are package managers for Python. From the easy_install page:

Easy Install is a python module (easy_install) bundled with setuptools that lets you automatically download, build, install, and manage Python packages.

and from the pip page:

pip is a tool for installing and managing Python packages, such as those found in the Python Package Index. It’s a replacement for easy_install.

To get easy_install you need to install Python setuptools and you are good to go. Sounds easy!  However the setuptools installer assumes that you have the normal standalone Python installed which writes it’s install location to the registry, and when you run the installer it will say that it can’t find Python on the system.  What the!?

If you have installed QGIS, or any other tool from the OSGeo4W install, you will see that OSGeo4W bundles its own version of Python in: C:\OSGeo4W\apps\python27. This is the Python that is used when calling python in the OSGeo4W shell.  It seems someone on the OSGeo wiki has made a bootstrapped installer for setuptools that will install setuptools and easy_install into the  C:\OSGeo4W\apps\python27 folder for you.

Steps to take

• Download ez_setup.py
• Run python ez_setup.py in your OSGeo4W shell
• Done!

To install a package with easy_install just use:

easy_install {packagename}

I wanted to have bottle and flask installed:

easy_install bottle

which gives you something like:

Searching for bottle
Reading http://pypi.python.org/simple/bottle/
Reading http://bottle.paws.de/
Reading http://github.com/defnull/bottle
Reading http://bottlepy.org/
Best match: bottle 0.11.4
Downloading http://pypi.python.org/packages/source/b/bottle/bottle-0.11.4.tar.gz#md5=f767c340de0b7c9581917c48e609479b
Processing bottle-0.11.4.tar.gz
Running bottle-0.11.4\setup.py -q bdist_egg –dist-dir c:\users\woo\appdata\local\temp\easy_install-5b4qq6\bottle-0.11.4\egg-dist-tmp-q2yd68
zip_safe flag not set; analyzing archive contents…
bottle: module references __file__
bottle: module references __path__
Adding bottle 0.11.4 to easy-install.pth file
Installing bottle.py script to C:\OSGeo4W\apps\Python27\Scripts
Installed c:\osgeo4w\apps\python27\lib\site-packages\bottle-0.11.4-py2.7.egg
Processing dependencies for bottle
Finished processing dependencies for bottle

Install pip

• You should also install pip as it is a better package manager then easy_install
• Just run easy_install pip

Note

Most of the time any Python packages that are needed by your OSGeo4W tools are bundled in the installer and can be downloaded using the OSGeo4W installer, however there have been cases when I wanted to install a non OSGeo4W package into my setup by using easy_install or pip. Like bottle and flask in the example above.

• Open Source tags:
• FOSSGIS
• osgeo
• python
• qgis
• Quantum GIS

The easiest way install Python libraries is to use easy_install and pip. easy_install and pip are package managers for Python. From the easy_install page:

Easy Install is a python module (easy_install) bundled with setuptools that lets you automatically download, build, install, and manage Python packages.

and from the pip page:

pip is a tool for installing and managing Python packages, such as those found in the Python Package Index. It's a replacement for easy_install.

To get easy_install you need to install Python setuptools and you are good to go. Sounds easy! However the setuptools installer assumes that you have the normal standalone Python installed which writes it's install location to the registry, and when you run the installer it will say that it can't find Python on the system. What the!?

If you have installed QGIS, or any other tool from the OSGeo4W install, you will see that OSGeo4W bundles its own version of Python in: C:\OSGeo4W\apps\python27. This is the Python that is used when calling python in the OSGeo4W shell. It seems someone on the OSGeo wiki has made a bootstrapped installer for setuptools that will install setuptools and easy_install into the C:\OSGeo4W\apps\python27 folder for you.

Steps to take

• Download ez_setup.py
• Run python ez_setup.py in your OSGeo4W shell
• Done!

To install a package with easy_install just use:

easy_install {packagename}

I wanted to have bottle and flask installed:

easy_install bottle

which gives you something like:

Searching for bottle
Reading http://pypi.python.org/simple/bottle/
Reading http://bottle.paws.de/
Reading http://github.com/defnull/bottle
Reading http://bottlepy.org/
Best match: bottle 0.11.4
Downloading http://pypi.python.org/packages/source/b/bottle/bottle-0.11.4.tar.gz#md5=f767c340de0b7c9581917c48e609479b
Processing bottle-0.11.4.tar.gz
Running bottle-0.11.4\setup.py -q bdist_egg --dist-dir c:\users\woo\appdata\local\temp\easy_install-5b4qq6\bottle-0.11.4\egg-dist-tmp-q2yd68
zip_safe flag not set; analyzing archive contents...
bottle: module references __file__
bottle: module references __path__
Adding bottle 0.11.4 to easy-install.pth file
Installing bottle.py script to C:\OSGeo4W\apps\Python27\Scripts
Installed c:\osgeo4w\apps\python27\lib\site-packages\bottle-0.11.4-py2.7.egg
Processing dependencies for bottle
Finished processing dependencies for bottle

Install pip

• You should also install pip as it is a better package manager then easy_install

• Just run easy_install pip

Note

Most of the time any Python packages that are needed by your OSGeo4W tools are bundled in the installer and can be downloaded using the OSGeo4W installer, however there have been cases when I wanted to install a non OSGeo4W package into my setup by using easy_install or pip. Like bottle and flask in the example above.

The easiest way install Python libraries is to use easy_install and pip. easy_install and pip are package managers for Python. From the easy_install page:

Easy Install is a python module (easy_install) bundled with setuptools that lets you automatically download, build, install, and manage Python packages.

and from the pip page:

pip is a tool for installing and managing Python packages, such as those found in the Python Package Index. It's a replacement for easy_install.

To get easy_install you need to install Python setuptools and you are good to go. Sounds easy! However the setuptools installer assumes that you have the normal standalone Python installed which writes it's install location to the registry, and when you run the installer it will say that it can't find Python on the system. What the!?

If you have installed QGIS, or any other tool from the OSGeo4W install, you will see that OSGeo4W bundles its own version of Python in: C:\OSGeo4W\apps\python27. This is the Python that is used when calling python in the OSGeo4W shell. It seems someone on the OSGeo wiki has made a bootstrapped installer for setuptools that will install setuptools and easy_install into the C:\OSGeo4W\apps\python27 folder for you.

Steps to take

• Download ez_setup.py
  
• Run python ez_setup.py in your OSGeo4W shell
• Done!

To install a package with easy_install just use:

easy_install {packagename}

I wanted to have bottle and flask installed:

easy_install bottle

which gives you something like:

Searching for bottle
Reading http://pypi.python.org/simple/bottle/
Reading http://bottle.paws.de/
Reading http://github.com/defnull/bottle
Reading http://bottlepy.org/
Best match: bottle 0.11.4
Downloading http://pypi.python.org/packages/source/b/bottle/bottle-0.11.4.tar.gz#md5=f767c340de0b7c9581917c48e609479b
Processing bottle-0.11.4.tar.gz
Running bottle-0.11.4\setup.py -q bdist_egg --dist-dir c:\users\woo\appdata\local\temp\easy_install-5b4qq6\bottle-0.11.4\egg-dist-tmp-q2yd68
zip_safe flag not set; analyzing archive contents...
bottle: module references __file__
bottle: module references __path__
Adding bottle 0.11.4 to easy-install.pth file
Installing bottle.py script to C:\OSGeo4W\apps\Python27\Scripts
Installed c:\osgeo4w\apps\python27\lib\site-packages\bottle-0.11.4-py2.7.egg
Processing dependencies for bottle
Finished processing dependencies for bottle

Install pip

• You should also install pip as it is a better package manager then easy_install

• Just run easy_install pip

Note

Most of the time any Python packages that are needed by your OSGeo4W tools are bundled in the installer and can be downloaded using the OSGeo4W installer, however there have been cases when I wanted to install a non OSGeo4W package into my setup by using easy_install or pip. Like bottle and flask in the example above.

• Open Source tags:
• FOSSGIS
• osgeo
• python
• qgis
• Quantum GIS

The easiest way install Python libraries is to use easy_install and pip. easy_install and pip are package managers for Python. From the easy_install page:

Easy Install is a python module (easy_install) bundled with setuptools that lets you automatically download, build, install, and manage Python packages.

and from the pip page:

pip is a tool for installing and managing Python packages, such as those found in the Python Package Index. It's a replacement for easy_install.

To get easy_install you need to install Python setuptools and you are good to go. Sounds easy! However the setuptools installer assumes that you have the normal standalone Python installed which writes it's install location to the registry, and when you run the installer it will say that it can't find Python on the system. What the!?

If you have installed QGIS, or any other tool from the OSGeo4W install, you will see that OSGeo4W bundles its own version of Python in: C:\OSGeo4W\apps\python27. This is the Python that is used when calling python in the OSGeo4W shell. It seems someone on the OSGeo wiki has made a bootstrapped installer for setuptools that will install setuptools and easy_install into the C:\OSGeo4W\apps\python27 folder for you.

Steps to take

• Download ez_setup.py
• Run python ez_setup.py in your OSGeo4W shell
• Done!

To install a package with easy_install just use:

easy_install {packagename}

I wanted to have bottle and flask installed:

easy_install bottle

which gives you something like:

Searching for bottle
Reading http://pypi.python.org/simple/bottle/
Reading http://bottle.paws.de/
Reading http://github.com/defnull/bottle
Reading http://bottlepy.org/
Best match: bottle 0.11.4
Downloading http://pypi.python.org/packages/source/b/bottle/bottle-0.11.4.tar.gz#md5=f767c340de0b7c9581917c48e609479b
Processing bottle-0.11.4.tar.gz
Running bottle-0.11.4\setup.py -q bdist_egg --dist-dir c:\users\woo\appdata\local\temp\easy_install-5b4qq6\bottle-0.11.4\egg-dist-tmp-q2yd68
zip_safe flag not set; analyzing archive contents...
bottle: module references __file__
bottle: module references __path__
Adding bottle 0.11.4 to easy-install.pth file
Installing bottle.py script to C:\OSGeo4W\apps\Python27\Scripts
Installed c:\osgeo4w\apps\python27\lib\site-packages\bottle-0.11.4-py2.7.egg
Processing dependencies for bottle
Finished processing dependencies for bottle

Install pip

• You should also install pip as it is a better package manager then easy_install

• Just run easy_install pip

Note

Most of the time any Python packages that are needed by your OSGeo4W tools are bundled in the installer and can be downloaded using the OSGeo4W installer, however there have been cases when I wanted to install a non OSGeo4W package into my setup by using easy_install or pip. Like bottle and flask in the example above.

As QGIS is such a fast moving project I have decided to make this a regular blog post in order to highlight some new features added to QGIS. If you haven’t already, don’t forget to check out round one.

Remember that some of these features may still only be new which might change between now and the next official released version. With that out of the way lets get listing.

Atlas integration

If you are a regular user of QGIS Python plugins, and who isn’t, then you would have used the awesome Atlas plugin developed by Vincent Picavet. This great tool can be used to generate mapbooks, or an atlas as some people like to say, using a coverage layer and a print composer. What makes this even more awesome is that it is now built into the print composer.

Atlas composer intergration

The builtin atlas function also gives you the ability to use an expression to do runtime text replacement, including access to all the fields on the coverage layer.  The coverage layer doesn’t even have to be a region layer, it can be a simple point layer, or even a line layer.   You can see the result of me running the atlas generation from the above example here

Big thanks to Oslandia for integrating this great feature, and the companies sponsoring the work.

New Python console

This new addition comes from the great work that Salvatore Larosa has been doing to add a better Python console to QGIS.

The new Python console includes attribute auto complete, syntax highlighting, better copy and paste, uploading to codepad, the ability to run code from a file, etc.  You don’t realise how much difference there is until you go back to using the old one in version 1.8.

New Python console

Tabbed and groups in builtin forms

One of the things I really loved about QGIS, coming from MapInfo, was the builtin forms.  Just having the ability to enter data using controls like combo boxes, calendar widgets, etc makes you one step closer to having better data.   This feature is the exact reason I setup a 67 year old for kerb data collection.

As good as they are the builtin forms have an issue of ending up with as a big scrolling list with lots of fields; also the lack of  the ability to group or put fields on tabs in the UI meant you had to create a custom form.  Well not any more.

There is now a combo box on the Fields tab that allows you to build a generated form but also add tabs and group boxes.  You can even have the same field shown more then once on the form, handy for something like an ID field that you would like to show on each tab.

With this new ability the builtin forms can get me 95% of the way for data entry jobs, the other 5% I just make a custom form – but that is very rare.

Sextante

Sextante is a great and powerful analytical framework that has been added to the core of QGIS thanks to Victor Olaya.  This is not a feature that I use a lot but this is only due to most of my work being in development and not analysis, however that doesn’t mean that it’s not a really cool feature.

One of the greatest things about the Sextante toolbox is that it allows you to integrate other great open source tools like GRASS, SAGA, R, OTB, etc, right into your QGIS workflow and view the results in the canvas. It even includes a modeller so that you can build a connected diagram of all the bits of your process, even if it crosses between programs.

The toolbox

For me what is even better is that you can use Sextante in your plugins or custom Python code.  Sextante has a Python interface – well the whole thing is written in Python – that you can use to run a Sextante supported algorithm.

import sextante
outputs_0=sextante.runalg("grass:v.crossbones", /file, 0, ,, 1, 2, 3, 4, 1=3.0,2=8.0,5=6.0,8=6.0,11=6.0,14=6.0, None)
outputs_1=sextante.runalg("grass:v.delaunay", outputs_0['output'], True, True, None)
outputs_2=sextante.runalg("grass:v.dissolve", outputs_0['output'], , None)

Victor has created a blog to cover some Sextante recipes at QGIS-SEXTANTE cookbook/. There are also some really cool example of Sextante in use at:

• http://www.qgis.org/en/community/qgis-case-studies/pinhel-portugal.html
• http://www.qgis.org/en/community/qgis-case-studies/lisbon-portugal.html

Massive amount of composer additions

This last feature, or rather feature set,  comes from the sponsorship and support of the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR.  Most of this work was done to aid in the development of a new QGIS plugin called inaSAFE, which has also received some great praise

“(InaSAFE) is very beneficial for all of us. It’s a good example of our partnership.”
Dr Susilo Bambang Yudhoyono – President of Indonesia

Some of the improvements include:

• A new loadFromTemplate method, in the API, that can do text replacement in the QGIS composer template.
• Better support for EPSG:4326 scale bars
• Multipage in one composer
• Automatic overview frame in map frame
• HTML frame
• Zebra style grid frame border
• More control of grid labels. Inside, outside, disabled
• etc, etc

These are great additions to the QGIS composer and I have already used the overview frame feature along with the new atlas integration  to make some quick nice looking map books. \

A huge thanks to the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR, and all the developers included.

You can see some of the output that InaSAFE generates using some of these new features at http://quake.linfiniti.com/

P.S The World Bank also sponsored the new raster Save As.. ability just like we have for vector layers.

• Open Source
• qgis tags:
• FOSSGIS
• gis
• Open Source
• osgeo
• qgis
• Quantum GIS

As QGIS is such a fast moving project I have decided to make this a regular blog post in order to highlight some new features added to QGIS. If you haven't already, don't forget to check out round one.

Remember that some of these features may still only be new which might change between now and the next official released version. With that out of the way lets get listing.

Atlas integration

If you are a regular user of QGIS Python plugins, and who isn't, then you would have used the awesome Atlas plugin developed by Vincent Picavet. This great tool can be used to generate mapbooks, or an atlas as some people like to say, using a coverage layer and a print composer. What makes this even more awesome is that it is now built into the print composer.

{% image http://woostuff.files.wordpress.com/2012/11/atlas.png %} title: Atlas composer intergration. alt: Something descriptive link: http://woostuff.files.wordpress.com/2012/11/atlas.png

The builtin atlas function also gives you the ability to use an expression to do runtime text replacement, including access to all the fields on the coverage layer.  The coverage layer doesn't even have to be a region layer, it can be a simple point layer, or even a line layer.   You can see the result of me running the atlas generation from the above example here

Big thanks to Oslandia for integrating this great feature, and the companies sponsoring the work.

New Python console

This new addition comes from the great work that Salvatore Larosa has been doing to add a better Python console to QGIS.

The new Python console includes attribute auto complete, syntax highlighting, better copy and paste, uploading to codepad, the ability to run code from a file, etc.  You don't realise how much difference there is until you go back to using the old one in version 1.8.

{% image http://woostuff.files.wordpress.com/2012/11/python.png %} title: New Python console alt: New Python console link: http://woostuff.files.wordpress.com/2012/11/python.png

Tabbed and groups in builtin forms

One of the things I really loved about QGIS, coming from MapInfo, was the builtin forms.  Just having the ability to enter data using controls like combo boxes, calendar widgets, etc makes you one step closer to having better data.   This feature is the exact reason I setup a 67 year old for kerb data collection.

As good as they are the builtin forms have an issue of ending up with as a big scrolling list with lots of fields; also the lack of  the ability to group or put fields on tabs in the UI meant you had to create a custom form.  Well not any more.

There is now a combo box on the Fields tab that allows you to build a generated form but also add tabs and group boxes.  You can even have the same field shown more then once on the form, handy for something like an ID field that you would like to show on each tab.

With this new ability the builtin forms can get me 95% of the way for data entry jobs, the other 5% I just make a custom form - but that is very rare.

Sextante

Sextante is a great and powerful analytical framework that has been added to the core of QGIS thanks to Victor Olaya.  This is not a feature that I use a lot but this is only due to most of my work being in development and not analysis, however that doesn't mean that it's not a really cool feature.

One of the greatest things about the Sextante toolbox is that it allows you to integrate other great open source tools like GRASS, SAGA, R, OTB, etc, right into your QGIS workflow and view the results in the canvas. It even includes a modeller so that you can build a connected diagram of all the bits of your process, even if it crosses between programs.

For me what is even better is that you can use Sextante in your plugins or custom Python code.  Sextante has a Python interface - well the whole thing is written in Python - that you can use to run a Sextante supported algorithm.

import sextante
outputs_0=sextante.runalg("grass:v.crossbones", /file, 0, ,, 1, 2, 3, 4, 1=3.0,2=8.0,5=6.0,8=6.0,11=6.0,14=6.0, None)
outputs_1=sextante.runalg("grass:v.delaunay", outputs_0['output'], True, True, None)
outputs_2=sextante.runalg("grass:v.dissolve", outputs_0['output'], , None)

Victor has created a blog to cover some Sextante recipes at QGIS-SEXTANTE cookbook/. There are also some really cool example of Sextante in use at:

• http://www.qgis.org/en/community/qgis-case-studies/pinhel-portugal.html

• http://www.qgis.org/en/community/qgis-case-studies/lisbon-portugal.html

Massive amount of composer additions

This last feature, or rather feature set,  comes from the sponsorship and support of the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR.  Most of this work was done to aid in the development of a new QGIS plugin called inaSAFE, which has also received some great praise

“(InaSAFE) is very beneficial for all of us. It’s a good example of our partnership.” Dr Susilo Bambang Yudhoyono - President of Indonesia

Some of the improvements include:

• A new loadFromTemplate method, in the API, that can do text replacement in the QGIS composer template.

• Better support for EPSG:4326 scale bars

• Multipage in one composer

• Automatic overview frame in map frame

• HTML frame

• Zebra style grid frame border

• More control of grid labels. Inside, outside, disabled

• etc, etc

These are great additions to the QGIS composer and I have already used the overview frame feature along with the new atlas integration  to make some quick nice looking map books. \

A huge thanks to the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR, and all the developers included.

You can see some of the output that InaSAFE generates using some of these new features at http://quake.linfiniti.com/

P.S The World Bank also sponsored the new raster Save As.. ability just like we have for vector layers.

• Open Source
• qgis tags:
• FOSSGIS
• gis
• Open Source
• osgeo
• qgis
• Quantum GIS

As QGIS is such a fast moving project I have decided to make this a regular blog post in order to highlight some new features added to QGIS. If you haven't already, don't forget to check out round one.

Remember that some of these features may still only be new which might change between now and the next official released version. With that out of the way lets get listing.

Atlas integration

If you are a regular user of QGIS Python plugins, and who isn't, then you would have used the awesome Atlas plugin developed by Vincent Picavet. This great tool can be used to generate mapbooks, or an atlas as some people like to say, using a coverage layer and a print composer. What makes this even more awesome is that it is now built into the print composer.

{% image http://woostuff.files.wordpress.com/2012/11/atlas.png %} title: Atlas composer intergration. alt: Something descriptive link: http://woostuff.files.wordpress.com/2012/11/atlas.png

The builtin atlas function also gives you the ability to use an expression to do runtime text replacement, including access to all the fields on the coverage layer.  The coverage layer doesn't even have to be a region layer, it can be a simple point layer, or even a line layer.   You can see the result of me running the atlas generation from the above example here

Big thanks to Oslandia for integrating this great feature, and the companies sponsoring the work.

New Python console

This new addition comes from the great work that Salvatore Larosa has been doing to add a better Python console to QGIS.

The new Python console includes attribute auto complete, syntax highlighting, better copy and paste, uploading to codepad, the ability to run code from a file, etc.  You don't realise how much difference there is until you go back to using the old one in version 1.8.

{% image http://woostuff.files.wordpress.com/2012/11/python.png %} title: New Python console alt: New Python console link: http://woostuff.files.wordpress.com/2012/11/python.png

Tabbed and groups in builtin forms

One of the things I really loved about QGIS, coming from MapInfo, was the builtin forms.  Just having the ability to enter data using controls like combo boxes, calendar widgets, etc makes you one step closer to having better data.   This feature is the exact reason I setup a 67 year old for kerb data collection.

As good as they are the builtin forms have an issue of ending up with as a big scrolling list with lots of fields; also the lack of  the ability to group or put fields on tabs in the UI meant you had to create a custom form.  Well not any more.

There is now a combo box on the Fields tab that allows you to build a generated form but also add tabs and group boxes.  You can even have the same field shown more then once on the form, handy for something like an ID field that you would like to show on each tab.

With this new ability the builtin forms can get me 95% of the way for data entry jobs, the other 5% I just make a custom form - but that is very rare.

Sextante

Sextante is a great and powerful analytical framework that has been added to the core of QGIS thanks to Victor Olaya.  This is not a feature that I use a lot but this is only due to most of my work being in development and not analysis, however that doesn't mean that it's not a really cool feature.

One of the greatest things about the Sextante toolbox is that it allows you to integrate other great open source tools like GRASS, SAGA, R, OTB, etc, right into your QGIS workflow and view the results in the canvas. It even includes a modeller so that you can build a connected diagram of all the bits of your process, even if it crosses between programs.

For me what is even better is that you can use Sextante in your plugins or custom Python code.  Sextante has a Python interface - well the whole thing is written in Python - that you can use to run a Sextante supported algorithm.

import sextante
outputs_0=sextante.runalg("grass:v.crossbones", /file, 0, ,, 1, 2, 3, 4, 1=3.0,2=8.0,5=6.0,8=6.0,11=6.0,14=6.0, None)
outputs_1=sextante.runalg("grass:v.delaunay", outputs_0['output'], True, True, None)
outputs_2=sextante.runalg("grass:v.dissolve", outputs_0['output'], , None)

Victor has created a blog to cover some Sextante recipes at QGIS-SEXTANTE cookbook/. There are also some really cool example of Sextante in use at:

• http://www.qgis.org/en/community/qgis-case-studies/pinhel-portugal.html

• http://www.qgis.org/en/community/qgis-case-studies/lisbon-portugal.html

Massive amount of composer additions

This last feature, or rather feature set,  comes from the sponsorship and support of the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR.  Most of this work was done to aid in the development of a new QGIS plugin called inaSAFE, which has also received some great praise

“(InaSAFE) is very beneficial for all of us. It’s a good example of our partnership.” Dr Susilo Bambang Yudhoyono - President of Indonesia

Some of the improvements include:

• A new loadFromTemplate method, in the API, that can do text replacement in the QGIS composer template.

• Better support for EPSG:4326 scale bars

• Multipage in one composer

• Automatic overview frame in map frame

• HTML frame

• Zebra style grid frame border

• More control of grid labels. Inside, outside, disabled

• etc, etc

These are great additions to the QGIS composer and I have already used the overview frame feature along with the new atlas integration  to make some quick nice looking map books. \

A huge thanks to the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR, and all the developers included.

You can see some of the output that InaSAFE generates using some of these new features at http://quake.linfiniti.com/

P.S The World Bank also sponsored the new raster Save As.. ability just like we have for vector layers.

As QGIS is such a fast moving project I have decided to make this a regular blog post in order to highlight some new features added to QGIS. If you haven't already, don't forget to check out round one.

Remember that some of these features may still only be new which might change between now and the next official released version. With that out of the way lets get listing.

Atlas integration

If you are a regular user of QGIS Python plugins, and who isn't, then you would have used the awesome Atlas plugin developed by Vincent Picavet. This great tool can be used to generate mapbooks, or an atlas as some people like to say, using a coverage layer and a print composer. What makes this even more awesome is that it is now built into the print composer.

The builtin atlas function also gives you the ability to use an expression to do runtime text replacement, including access to all the fields on the coverage layer.  The coverage layer doesn't even have to be a region layer, it can be a simple point layer, or even a line layer.   You can see the result of me running the atlas generation from the above example here

Big thanks to Oslandia for integrating this great feature, and the companies sponsoring the work.

New Python console

This new addition comes from the great work that Salvatore Larosa has been doing to add a better Python console to QGIS.

The new Python console includes attribute auto complete, syntax highlighting, better copy and paste, uploading to codepad, the ability to run code from a file, etc.  You don't realise how much difference there is until you go back to using the old one in version 1.8.

Tabbed and groups in builtin forms

One of the things I really loved about QGIS, coming from MapInfo, was the builtin forms.  Just having the ability to enter data using controls like combo boxes, calendar widgets, etc makes you one step closer to having better data.   This feature is the exact reason I setup a 67 year old for kerb data collection.

As good as they are the builtin forms have an issue of ending up with as a big scrolling list with lots of fields; also the lack of  the ability to group or put fields on tabs in the UI meant you had to create a custom form.  Well not any more.

There is now a combo box on the Fields tab that allows you to build a generated form but also add tabs and group boxes.  You can even have the same field shown more then once on the form, handy for something like an ID field that you would like to show on each tab.

With this new ability the builtin forms can get me 95% of the way for data entry jobs, the other 5% I just make a custom form - but that is very rare.

Sextante

Sextante is a great and powerful analytical framework that has been added to the core of QGIS thanks to Victor Olaya.  This is not a feature that I use a lot but this is only due to most of my work being in development and not analysis, however that doesn't mean that it's not a really cool feature.

One of the greatest things about the Sextante toolbox is that it allows you to integrate other great open source tools like GRASS, SAGA, R, OTB, etc, right into your QGIS workflow and view the results in the canvas. It even includes a modeller so that you can build a connected diagram of all the bits of your process, even if it crosses between programs.

For me what is even better is that you can use Sextante in your plugins or custom Python code.  Sextante has a Python interface - well the whole thing is written in Python - that you can use to run a Sextante supported algorithm.

import sextante
outputs_0=sextante.runalg("grass:v.crossbones", /file, 0, ,, 1, 2, 3, 4, 1=3.0,2=8.0,5=6.0,8=6.0,11=6.0,14=6.0, None)
outputs_1=sextante.runalg("grass:v.delaunay", outputs_0['output'], True, True, None)
outputs_2=sextante.runalg("grass:v.dissolve", outputs_0['output'], , None)

Victor has created a blog to cover some Sextante recipes at QGIS-SEXTANTE cookbook/. There are also some really cool example of Sextante in use at:

• http://www.qgis.org/en/community/qgis-case-studies/pinhel-portugal.html

• http://www.qgis.org/en/community/qgis-case-studies/lisbon-portugal.html

Massive amount of composer additions

This last feature, or rather feature set,  comes from the sponsorship and support of the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR.  Most of this work was done to aid in the development of a new QGIS plugin called inaSAFE, which has also received some great praise

“(InaSAFE) is very beneficial for all of us. It’s a good example of our partnership.” Dr Susilo Bambang Yudhoyono - President of Indonesia

Some of the improvements include:

• A new loadFromTemplate method, in the API, that can do text replacement in the QGIS composer template.

• Better support for EPSG:4326 scale bars

• Multipage in one composer

• Automatic overview frame in map frame

• HTML frame

• Zebra style grid frame border

• More control of grid labels. Inside, outside, disabled

• etc, etc

These are great additions to the QGIS composer and I have already used the overview frame feature along with the new atlas integration  to make some quick nice looking map books. \

A huge thanks to the World Bank, Australia-Indonesia Facility for Disaster Reduction, Geoscience Australia and the GFDRR, and all the developers included.

You can see some of the output that InaSAFE generates using some of these new features at http://quake.linfiniti.com/

P.S The World Bank also sponsored the new raster Save As.. ability just like we have for vector layers.

Last Thursday and Friday was our first local Australian FOSS4G event which was hosted at the CSRIO building in Brisbane.  Very big thanks to CSRIO for hosting the event.  The venue was setup perfectly for  hosting an event like this, including dual projectors for presenting, video calls over to Perth, etc.

The first day was done using a un-conference style of event. This is the first time I've been to a un-conference and I liked the format a lot.   Once everyone was there on the first morning we collected ideas from people and everyone voted on which ones they would like to see.  After we had picked enough topics Shaun and I made a program for the day and we started.

Topics included:

• State of GeoServer
• QMap
• CouchDB
  
• Moblie mapping
• Full open source stacks
• Open Source GIS in Education

The second day was a code sprint.  I worked on converting a MapBasic scripts from one of the guys to QGIS, and Jody enlisted the others to help check the headers of the GeoServer project so that it can finally pass OSGeo incubation.

Overall I think it was a very successful event.  I would like to make these a yearly event if we can, provided that we have people to talk, or projects to work on.

More information about up coming OSGeo events in Australia and New Zealand can be found at http://www.meetup.com/osgeo-aust-nz/

Last Thursday and Friday was our first local Australian FOSS4G event which was hosted at the CSRIO building in Brisbane.  Very big thanks to CSRIO for hosting the event.  The venue was setup perfectly for  hosting an event like this, including dual projectors for presenting, video calls over to Perth, etc.

The first day was done using a un-conference style of event. This is the first time I’ve been to a un-conference and I liked the format a lot.   Once everyone was there on the first morning we collected ideas from people and everyone voted on which ones they would like to see.  After we had picked enough topics Shaun and I made a program for the day and we started.

Topics included:

• State of GeoServer
• QMap
• CouchDB
• Moblie mapping
• Full open source stacks
• Open Source GIS in Education

Me presenting QMap

The second day was a code sprint.  I worked on converting a MapBasic scripts from one of the guys to QGIS, and Jody enlisted the others to help check the headers of the GeoServer project so that it can finally pass OSGeo incubation.

Overall I think it was a very successful event.  I would like to make these a yearly event if we can, provided that we have people to talk, or projects to work on.

More information about up coming OSGeo events in Australia and New Zealand can be found at http://www.meetup.com/osgeo-aust-nz/

• Open Source
• qgis tags:
• FOSSGIS
• gis
• Open Source
• open source gis
• osgeo
• qgis
• Quantum GIS

Last Thursday and Friday was our first local Australian FOSS4G event which was hosted at the CSRIO building in Brisbane.  Very big thanks to CSRIO for hosting the event.  The venue was setup perfectly for  hosting an event like this, including dual projectors for presenting, video calls over to Perth, etc.

The first day was done using a un-conference style of event. This is the first time I've been to a un-conference and I liked the format a lot.   Once everyone was there on the first morning we collected ideas from people and everyone voted on which ones they would like to see.  After we had picked enough topics Shaun and I made a program for the day and we started.

Topics included:

• State of GeoServer
• QMap
• CouchDB
• Moblie mapping
• Full open source stacks
• Open Source GIS in Education

The second day was a code sprint.  I worked on converting a MapBasic scripts from one of the guys to QGIS, and Jody enlisted the others to help check the headers of the GeoServer project so that it can finally pass OSGeo incubation.

Overall I think it was a very successful event.  I would like to make these a yearly event if we can, provided that we have people to talk, or projects to work on.

More information about up coming OSGeo events in Australia and New Zealand can be found at http://www.meetup.com/osgeo-aust-nz/