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Tue Apr 24 05:25:16 2018

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QGIS Planet

Drill-down (cascading) forms in QGIS crowdfund launched!

We’ve just launched a new crowd funding campaign to implement a drill-down (cascading) field support within QGIS forms. Full details are available on the campaign page.

This is a really exciting new feature which would help add greater flexibility and power to QGIS feature forms! To make it possible we need 3500€ pledged before 11 May 2018. You can help make this a reality by supporting the campaign or by sharing the page and increasing exposure to the campaign. Updates to follow!

Implementing an in-house “New Project Wizard” for QGIS

Recently, we were required to implement a custom “New Project Wizard” for use in a client’s internal QGIS installation. The goal here was that users would be required to fill out certain metadata fields whenever they created a new QGIS project.

Fortunately, the PyQGIS (and underlying Qt) libraries makes this possibly, and relatively straightforward to do. Qt has a powerful API for creating multi-page “wizard” type dialogs, via the QWizard and QWizardPage classes. Let’s have a quick look at writing a custom wizard using these classes, and finally we’ll hook it into the QGIS interface using some PyQGIS magic.

We’ll start super simple, creating a single page wizard with no settings. To do this we first create a Page1 subclass of QWizardPage, a ProjectWizard subclass of QWizard, and a simple runNewProjectWizard function which launches the wizard. (The code below is designed for QGIS 3.0, but will run with only small modifications on QGIS 2.x):


class Page1(QWizardPage):

    def __init__(self, parent=None):
        super().__init__(parent)
        self.setTitle('General Properties')
        self.setSubTitle('Enter general properties for this project.')


class ProjectWizard(QWizard):
    
    def __init__(self, parent=None):
        super().__init__(parent)
        
        self.addPage(Page1(self))
        self.setWindowTitle("New Project")


def runNewProjectWizard():
    d=ProjectWizard()
    d.exec()

If this code is executed in the QGIS Python console, you’ll see something like this:

Not too fancy (or functional) yet, but still not bad for 20 lines of code! We can instantly make this a bit nicer by inserting a custom logo into the widget. This is done by calling setPixmap inside the ProjectWizard constructor.


class ProjectWizard(QWizard):
    
    def __init__(self, parent=None):
        super().__init__(parent)
        
        self.addPage(Page1(self))
        self.setWindowTitle("New Project")

        logo_image = QImage('path_to_logo.png')
        self.setPixmap(QWizard.LogoPixmap, QPixmap.fromImage(logo_image))

That’s a bit nicer. QWizard has HEAPS of options for tweaking the wizards — best to read about those over at the Qt documentation. Our next step is to start adding some settings to this wizard. We’ll keep things easy for now and just insert a number of text input boxes (QLineEdits) into Page1:


class Page1(QWizardPage):

    def __init__(self, parent=None):
        super().__init__(parent)
        self.setTitle('General Properties')
        self.setSubTitle('Enter general properties for this project.')

        # create some widgets
        self.project_number_line_edit = QLineEdit()
        self.project_title_line_edit = QLineEdit()
        self.author_line_edit = QLineEdit()        
        
        # set the page layout
        layout = QGridLayout()
        layout.addWidget(QLabel('Project Number'),0,0)
        layout.addWidget(self.project_number_line_edit,0,1)
        layout.addWidget(QLabel('Title'),1,0)
        layout.addWidget(self.project_title_line_edit,1,1)
        layout.addWidget(QLabel('Author'),2,0)
        layout.addWidget(self.author_line_edit,2,1)
        self.setLayout(layout)

There’s nothing particularly new here, especially if you’ve used Qt widgets before. We make a number of QLineEdit widgets, and then create a grid layout containing these widgets and accompanying labels (QLabels). Here’s the result if we run our wizard now:

So now there’s the option to enter a project number, title and author. The next step is to force users to populate these fields before they can complete the wizard. Fortunately, QWizardPage has us covered here and we can use the registerField() function to do this. By calling registerField, we make the wizard aware of the settings we’ve added on this page, allowing us to retrieve their values when the wizard completes. We can also use registerField to automatically force their population by appending a * to the end of the field names. Just like this…

class Page1(QWizardPage):
    def __init__(self, parent=None):
        super().__init__(parent)
        ...
        self.registerField('number*',self.project_number_line_edit)
        self.registerField('title*',self.project_title_line_edit)
        self.registerField('author*',self.author_line_edit)

If we ran the wizard now, we’d be forced to enter something for project number, title and author before the Finish button becomes enabled. Neat! By registering the fields, we’ve also allowed their values to be retrieved after the wizard completes. Let’s alter runNewProjectWizard to retrieve these values and do something with them:

def runNewProjectWizard():
   d=ProjectWizard()
   d.exec()

   # Set the project title
   title=d.field('title')
   QgsProject.instance().setTitle(d.field('title'))

   # Create expression variables for the author and project number
   number=d.field('number')
   QgsExpressionContextUtils.setProjectVariable(QgsProject.instance(),'project_number', number)
   author=d.field('author')
   QgsExpressionContextUtils.setProjectVariable(QgsProject.instance(),'project_author', author)
 

Here, we set the project title directly and create expression variables for the project number and author. This allows their use within QGIS expressions via the @project_number and @project_author variables. Accordingly, they can be embedded into print layout templates so that layout elements are automatically populated with the corresponding author and project number. Nifty!

Ok, let’s beef up our wizard by adding a second page, asking the user to select a sensible projection (coordinate reference system) for their project. Thanks to improvements in QGIS 3.0, it’s super-easy to embed a powerful pre-made projection selector widget into your scripts, which even includes a handy preview of the area of the world that the projection is valid for.


class Page2(QWizardPage):

    def __init__(self, parent=None):
        super().__init__(parent)
        self.setTitle('Project Coordinate System')
        self.setSubTitle('Choosing an appropriate projection is important to ensure accurate distance and area measurements.')
        
        self.proj_selector = QgsProjectionSelectionTreeWidget()
        layout = QVBoxLayout()
        layout.addWidget(self.proj_selector)
        self.setLayout(layout)
        
        self.registerField('crs',self.proj_selector)
        self.proj_selector.crsSelected.connect(self.crs_selected)
        
    def crs_selected(self):
        self.setField('crs',self.proj_selector.crs())
        self.completeChanged.emit()
        
    def isComplete(self):
        return self.proj_selector.crs().isValid()

There’s a lot happening here. First, we subclass QWizardPage to create a second page in our widget. Then, just like before, we add some widgets to this page and set the page’s layout. In this case we are using the standard QgsProjectionSelectionTreeWidget to give users a projection choice. Again, we let the wizard know about our new setting by a call to registerField. However, since QWizard has no knowledge about how to handle a QgsProjectionSelectionTreeWidget, there’s a bit more to do here. So we make a connection to the projection selector’s crsSelected signal, hooking it up to a function which sets the wizard’s “crs” field value to the widget’s selected CRS. Here, we also emit the completeChanged signal, which indicates that the wizard page should re-validate the current settings. Lastly, we override QWizardPage’s isComplete method, checking that there’s a valid CRS selection in the selector widget. If we run the wizard now we’ll be forced to choose a valid CRS from the widget before the wizard allows us to proceed:

Lastly, we need to adapt runNewProjectWizard to also handle the projection setting:


def runNewProjectWizard():
    d=ProjectWizard()
    d.exec()

    # Set the project crs
    crs=d.field('crs')
    QgsProject.instance().setCrs(crs)

    # Set the project title
    title=d.field('title')
    ...

Great! A fully functional New Project wizard. The final piece of the puzzle is triggering this wizard when a user creates a new project within QGIS. To do this, we hook into the iface.newProjectCreated signal. By connecting to this signal, our code will be called whenever the user creates a new project (after all the logic for saving and closing the current project has been performed). It’s as simple as this:


iface.newProjectCreated.connect(runNewProjectWizard)

Now, whenever a new project is made, our wizard is triggered – forcing users to populate the required fields and setting up the project accordingly!

There’s one last little bit to do – we also need to prevent users cancelling or closing the wizard before completing it. That’s done by changing a couple of settings in the ProjectWizard constructor, and by overriding the default reject method (which prevents closing the dialog by pressing escape).


class ProjectWizard(QWizard):
    
    def __init__(self, parent=None):
        super().__init__(parent)
        ...
        self.setOption(QWizard.NoCancelButton, True)
        self.setWindowFlags(self.windowFlags() | QtCore.Qt.CustomizeWindowHint)
        self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowCloseButtonHint)

    def reject(self):
        pass

Here’s the full version of our code, ready for copying and pasting into the QGIS Python console:


icon_path = '/home/nyall/nr_logo.png'

class ProjectWizard(QWizard):
    
    def __init__(self, parent=None):
        super().__init__(parent)
        
        self.addPage(Page1(self))
        self.addPage(Page2(self))
        self.setWindowTitle("New Project")
        
        logo_image=QImage('path_to_logo.png')
        self.setPixmap(QWizard.LogoPixmap, QPixmap.fromImage(logo_image))
        
        self.setOption(QWizard.NoCancelButton, True)
        self.setWindowFlags(self.windowFlags() | QtCore.Qt.CustomizeWindowHint)
        self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowCloseButtonHint)
    def reject(self):
        pass
class Page1(QWizardPage):
    
    def __init__(self, parent=None):
        super().__init__(parent)
        self.setTitle('General Properties')
        self.setSubTitle('Enter general properties for this project.')

        # create some widgets
        self.project_number_line_edit = QLineEdit()
        self.project_title_line_edit = QLineEdit()
        self.author_line_edit = QLineEdit()        
        
        # set the page layout
        layout = QGridLayout()
        layout.addWidget(QLabel('Project Number'),0,0)
        layout.addWidget(self.project_number_line_edit,0,1)
        layout.addWidget(QLabel('Title'),1,0)
        layout.addWidget(self.project_title_line_edit,1,1)
        layout.addWidget(QLabel('Author'),2,0)
        layout.addWidget(self.author_line_edit,2,1)
        self.setLayout(layout)
        
        self.registerField('number*',self.project_number_line_edit)
        self.registerField('title*',self.project_title_line_edit)
        self.registerField('author*',self.author_line_edit)
 
 
class Page2(QWizardPage):
    
    def __init__(self, parent=None):
        super().__init__(parent)
        self.setTitle('Project Coordinate System')
        self.setSubTitle('Choosing an appropriate projection is important to ensure accurate distance and area measurements.')
        
        self.proj_selector = QgsProjectionSelectionTreeWidget()
        layout = QVBoxLayout()
        layout.addWidget(self.proj_selector)
        self.setLayout(layout)
        
        self.registerField('crs',self.proj_selector)
        self.proj_selector.crsSelected.connect(self.crs_selected)
        
    def crs_selected(self):
        self.setField('crs',self.proj_selector.crs())
        self.completeChanged.emit()
        
    def isComplete(self):
        return self.proj_selector.crs().isValid()
 
        
def runNewProjectWizard():
    d=ProjectWizard()
    d.exec()
    
    # Set the project crs
    crs=d.field('crs')
    QgsProject.instance().setCrs(crs)
    
    # Set the project title
    title=d.field('title')
    QgsProject.instance().setTitle(d.field('title'))

    # Create expression variables for the author and project number
    number=d.field('number')
    QgsExpressionContextUtils.setProjectVariable(QgsProject.instance(),'project_number', number)
    author=d.field('author')
    QgsExpressionContextUtils.setProjectVariable(QgsProject.instance(),'project_author', author)
    
    
iface.newProjectCreated.connect(runNewProjectWizard)

GRASS GIS 7.4.0 released

We are pleased to announce the GRASS GIS 7.4.0 release

GRASS GIS 7.4.0: Wildfire in Australia, seen by Sentinel-2B

What’s new in a nutshell

After a bit more than one year of development the new update release GRASS GIS 7.4.0 is available. It provides more than 480 stability fixes and improvements compared to the previous stable version 7.2. An overview of the new features in the 7.4 release series is available at New Features in GRASS GIS 7.4.

Efforts have concentrated on making the user experience even better, providing many small, but useful additional functionalities to modules and further improving the graphical user interface. Users can now directly download pre-packaged demo data locations in the GUI startup window. Several modules were migrated from addons to the core GRASS GIS package and the suite of tools for ortho-rectification was re-implemented in the new GRASS 7 GUI style. In order to support the treatment of massive datasets, new compression algorithms were introduced and NULL (no-data) raster files are now also compressed by default. For a detailed overview, see the list of new features. As a stable release series, 7.4.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, Feb 2018

The post GRASS GIS 7.4.0 released appeared first on GFOSS Blog | GRASS GIS Courses.

Exploring Reports in QGIS 3.0 – the Ultimate Guide!

In 2017 North Road ran a crowd funding campaign for extending QGIS’ Print Composer and adding a brand new reporting framework to QGIS. Thanks to numerous generous backers, this campaign was a success. With the final QGIS 3.0 release just around the corner, we thought this was a great time to explore the new reporting engine and what it offers.

We’ll start with a relatively simple project, containing some administrative boundaries, populated places, ports and airports.

Using the “Project” – “New Report” command, we then create a new blank report. Initially, there’s not much to look at – the dialog which is displayed looks much like the QGIS 3.0 Layout Designer, except for the new “Report Organizer” panel shown on the left:

QGIS reports can consist of multiple, nested sections. In our new blank report we initially have only the main report section. The only options present for this report section is to include an optional header and footer for the report. If we enable these, the header will be included as the very first page (or pages… individual parts of reports can be multi-page if desired) in the report, and the footer would be the last page. Let’s go ahead and enable the header, and hit the “Edit” button next to it:

A few things happen as a result. Firstly, an edit pencil is now shown next to the “Report” section in the Report Organizer, indicating that the report section is currently being edited in the designer. We also see a new blank page shown in the designer itself, with the small “Report Header” title. In QGIS reports, every component of the report is made up of individual layouts. They can be created and modified using the exact same tools as are available for standard print layouts – so you can use any desired combination of labels, pictures, maps, tables, etc. Let’s add some items to our report header to demonstrate:

We’ll also create a simple footer for the report, by checking the “Include report footer” option and hitting “Edit“.

Before proceeding further, let’s export this report and see what we get. Exporting is done from the Report menu – in this case we select “Export Report as PDF” to render the whole report to a PDF file. Here’s the not-very-impressive result – a two page PDF consisting of our header and footer:

Let’s make things more interesting. By hitting the green “+” button in the Report Organizer, we’re given a choice of new sections to add to our report.

Currently there’s two options – a “Single section” and a “Field group“. Expect this list to grow in future QGIS releases, but for now we’ll add a Field Group to our report. At its most basic level, you can think of a Field Group as the equivalent of a print atlas. You select a layer to iterate over, and the report will insert a section for each feature found. Selecting the new Field Group section reveals a number of new related settings:

In this case we’ve setup our Field Group so that we iterate over all the states from the “Admin Level 1” layer, using the values from the “adm1name” field. The same options for header and footer are present, together with a new option to include a “body” for this section. We’ll do that, and edit the body:

We’ve setup this body with a map (set to follow the current report feature – just like how a map item in an atlas can follow the current atlas feature), and a label showing the state’s name. If we went ahead and exported our report now, we’d get something like this:

First, the report header, than a page for each state, and finally the report footer. So more or less an atlas, but with a header and footer page. Let’s make things more interesting by adding a subsection to our state group. We do this by first selecting the state field group in the organizer, then hitting the + button and adding a new Field Group:

When a field group is iterating over its features, it will automatically filter these features to match the feature attributes from its parent groups. In this case, the subsection we added will iterate over a “Populated Places” layer, including a body section for each place encountered. The magic here is that the Populated Places layer has an attribute named “adm1name“, tagging each place with the state it’s contained within (if you’re lucky your data will already be structured like this – if not, run the Processing “Join by Location” algorithm and create your own field). When we export this report, QGIS will grab the first state from the Admin Level 1 layer, and then iterate over all the Populated Places with a matching “adm1name” value. Here’s what we get:

(Here we created a basic body for the Populated Places group, including a map of the place and a table of some place attributes). So our report is now a report header, a page for each state followed by a page for every populated place within that state, and finally the report footer. If we were to add a header for the Populated Places group, it would be included just before listing the populated places for each state:

Similarly, a footer for the Populated Places group would be inserted after the final place for each state is included.

In addition to nested subsections, subsections in a report can also be included consecutively. If we add a second subsection to the Admin Level 1 group for Airports, then our report will first list ALL the populated places for each state, followed by all the airports within that state, before proceeding to the next state. In this case our report would be structured like this:

(The key point here is that our Airports group is a subsection of the Admin Level 1 group – not the Populated Places group). Here’s what our report could look like now:

Combining nested and consecutive sections, together with section headers and footers allows for tons of flexibility. For instance, in the below report we add another field group as a child of the main report for the Ports layer. Now, after listing the states together with their populated places and airports, we’ll get a summary list of all the ports in the region:

This results in the last part of our report exporting as:

As you can start to imagine, reports in QGIS are extremely powerful and flexible! We’re extremely thankful for all the backers of our crowd funding campaign, without whom this work would not have been possible.

Stay tuned for more reporting and layouts work we have planned for QGIS 3.2!

 

GRASS GIS 7.2.2 released

GRASS GIS 7.2.2 in action

What’s new in a nutshell

After three months of development the new update release GRASS GIS 7.2.2 is available. It provides more than 120 stability fixes and manual improvements compared to release version 7.2.1. An overview of new features in the 7.2 release series is available at New Features in GRASS GIS 7.2.

About GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, Sep 2017

The post GRASS GIS 7.2.2 released appeared first on GFOSS Blog | GRASS GIS Courses.

QGIS layouts rewrite – progress report #1

Following our recent successful QGIS Layout and Reporting Engine crowdfunding campaign, we’ve been hard at working ripping up the internals of the QGIS 2.x print composer and rebuilding a brand new, shiny QGIS layouts engine. This is exciting work – it’s very satisfying to be able to cleanup a lot of the old composer code in QGIS and take opportunities along the way to fix long standing bugs and add new features.

While it’s not ready for daily use yet, there’s already been a lot of interesting changes which have landed in the layouts work as a result of this campaign. Let’s take a look at what’s been implemented so far…

  • We’ve added support for different measurements units all throughout layouts. While this means it’s now possible to set page sizes using centimeters, inches, pixels, points, etc, it goes much deeper than just that. In layouts, everything which has a size or position can take advantage of this unit support. So you can have page sizes in centimeters, but a map item with a size set in points, and positioned in millimeters! Having pixels as a unit type makes creation of screen-based layouts much easier – even right down to pixel perfect positioning and sizing of items…
  • Page handling has been totally reworked. Instead of the single “number of pages” control available in QGIS 2.x, layouts have complete flexibility in page setup. It’s now possible to have a layout with mixed page sizes and orientations (including data defined page size for different pages in the layout!). 
  • A revised status bar, with improved layout interaction widgets. We’ve also taken the opportunity to add some new features like a zoom level slider and option to zoom to layout width:
  • Layout interaction tools (such as pan/zoom/insert item/etc) have been reworked. There’s now a much more flexible framework for creation of layout tools (based off the main QGIS map canvas approach), which even allows for plugins to implement their own layout interaction tools! As part of this we’ve addressed a long standing annoyance which meant that creating new items always drew the “preview” shape of the new item as a rectangle – even for non-rectangular items. Now you get a real shape showing exactly how the created item will be sized and positioned:
  • On the topic of plugins – the layout branch has full support for plugin-provided item types. This means that QGIS plugins can create new classes of items which can be added to a layout. This opens the door for plugins allowing charts and visualisations which take advantage of all the mature Python and JS charting libraries! This is a really exciting change – in 2.x there was no way for plugins to extend or interact with composer, so we’re really keen to see where the community takes this when 3.0 is released.
  • We’ve ported another feature commonly found in illustration/DTP applications. Now, when you’re creating a new item and just click in your layout (instead of click-and-drag), you get a handy dialog allowing you to specify the exact position and dimensions for the created item. You can again see in this dialog how layouts have full support for units for both the position and size:
  • Another oft-requested feature which we’ve finally been able to add (thanks to the refactored and cleaned code) is a context menu for layouts! It’s currently quite empty, but will be expanded as this work progresses…
  • Snapping to guides and grids has been reworked. We’ve added a new snapping marker to show exactly were items will be snapped to:
  • Snapping to guides now occurs when creating new layout items (this didn’t happen in Composer in 2.x – only snapping to grids occurred when drawing new items).
  • The snapped cursor position is shown in status bar whenever a snapped point will be used, instead of the unsnapped position.
  • Unlike in Composers in QGIS 2.x, Layouts in 3.0 adopt the standard UX of dragging out rulers to create guide lines (instead of clicking on a ruler position to create a new guide). Creation of a horizontal guide is now done by grabbing the top ruler and dragging it down, and a vertical guide is created by grabbing the left ruler and dragging it out to the layout.
  • Better feedback is given in the ruler when a guide can be dragged. We now show guide positions in the rulers, and give an indication (via mouse cursor change) when these guides can be repositioned by click-and-drag.
  • Another very exciting change is the addition of a new “Guide Manager”. The guide manager allows numeric modification of existing guides and creation of new guides. Finally it’s possible to position guides at exact locations! Again, you can see the full support for layout units in place here – guides can be positioned using any available unit.
  • There’s also a handy new shortcut in the Guide Manager to allow applying the guides from the current page to all other pages in your layout.
  • We’ve refined the snapping logic. In Composer in QGIS 2.x,  grids would always take precedence whenever both a grid and guide were within tolerance of a point. Now, guides will always take precedence – since they have been manually set by users we make the assumption that they have been explicitly placed at highly desirable snapping locations, and should be selected over the general background grid. Additionally, grid snapping was previously only done if BOTH the x and y of the point could be snapped to the grid. We now snap to the nearest grid line for x/y separately. This means if a point is close to a vertical grid line but not a horizontal one it will still snap to that nearby vertical grid line.
  • Lastly, we’ve added a handy context menu to the rulers:

This is just a taster of the great new functionality coming in QGIS 3.0. This is all a direct result of the forward-thinking investments and generosity of the backers in our QGIS Layout and Reporting Engine crowdfunding campaign. Without their contributions, none of this would be possible – so our thanks go out to those organisations and individuals once again!

Stay tuned for more updates as the work continues…

 

 

QGIS Layout and Reporting Engine Campaign – a success!

Thanks to the tireless efforts and incredible generosity of the QGIS user community, our crowdfunded QGIS Layout and Reporting Engine campaign was a tremendous success! We’ve reached the funding goal for this project, and as a result QGIS 3.0 will include a more powerful print composer with a reworked code base. You can read more about what we have planned at the campaign page.

We’d like to take this opportunity to extend our heartfelt thanks to all the backers who have pledged to support this project:

We’ve also received numerous anonymous contributions in addition to these – please know that the QGIS community extends their gratitude for your contributions too! This campaign was also successful thanks to The Agency for Data Supply and Efficiency, Denmark, who stepped up and have funded an initial component of this project directly.

We’d also like to thank every member of the QGIS community who assisted with promoting this campaign and bringing it to the attention of these backers. Without your efforts we would not have been able to reach these backers and the campaign would not have been successful.

We’ll be posting more updates as this work progresses. Stay tuned…

 

QGIS Composer Rewrite and Layout Engine crowdfund – half way there!

If you’ve been following our recent blog posts, you’ll be aware that we are currently running a crowd funding campaign to extend the capabilities of QGIS’ print composer. You can read full details about this over at the campaign page.

The good news is that we’ve just hit the mid way point of the funds! Many generous backers have stepped up with contributions and we’re well on the way to reaching the funding goal. However, we still need your help make this work a reality.

Right now, what we need most is interested users and community members who will reach out to their local QGIS users and seek more backing for the campaign. We need to publicise the campaign beyond the regular online QGIS community, to the thousands of enterprises and organisations which rely on QGIS for their daily mapping operations. We need community members who can get in contact with these organisations and help convince them that investing back into the open source software they utilise is beneficial (and often will even SAVE them money in the long run, due to the increased productivity that changes like our composer improvements will bring!).

So, while social media reshares have been vital to reaching the current stage, we now need more “hands on” helpers who will take this on. If you know of any organisations which depend on QGIS for their mapping outputs, now’s the time to get in contact with them directly and advise them of this campaign!

 

 

 

The Inaugural QGIS Australia Hackfest – Noosa 2017

Last week we kicked off the first (of hopefully many) Australian QGIS hackfests Developers Meetings. It was attended by 3 of the core QGIS development team: Nathan Woodrow, Martin Dobias and myself (Nyall Dawson), along with various family members. While there’s been QGIS hackfests in Europe for over 10 years, and others scattered throughout various countries (I think there was a Japanese one recently… but Twitter’s translate tool leaves me with little confidence about this!), there’s been no events like this in the Southern hemisphere yet. I’ve been to a couple in Europe and found them to be a great way to build involvement in the project, for both developers and non-developers alike.

In truth the Australian hackfest plans began mostly an excuse for Nathan and I to catch up with Martin Dobias before he heads back out of this hemisphere and returns to Europe. That said, Nathan and I have long spoken about ways we can build up the QGIS community in Australia, so in many ways this was a trial run for future events. It was based it in Noosa, QLD (and yes, we did manage to tear ourselves away from our screens long enough to visit the beach!).

Nathan Woodrow (@NathanW2), myself (@nyalldawson), and Martin Dobias (@wonder-sk)

Here’s a short summary of what we worked on during the hackfest:

  • Martin implemented a new iterator style accessor for vertices within geometries. The current approach to accessing vertices in QGIS is far from optimal. You either have the choice of an inefficient methods (eg QgsGeometry.asPolyline(), asPolygon(), etc) which requires translations of all vertices to a different data structure (losing any z/m dimensional values in the process), or an equally inefficient QgsAbstractGeometry.coordinateSequence() method, which at least keeps z/m values but still requires expensive copies of every vertex in the geometry. For QGIS 3.0 we’ve made a huge focus on optimising geometry operations and vertex access is one of the largest performance killers remaining in the QGIS code. Martin’s work adds a proper iterator for the vertices within a geometry object, both avoiding all these expensive copies and also simplifying the API for plugins. When this work lands traversing the vertices will become as simple as
for v in geom.vertices():
   ... do something with the vertex!
  • Martin is also planning on extending this work to allow simple iteration over the parts and rings within geometries too. When this lands in QGIS we can expect to see much faster geometry operations.
  • Nathan fixed a long standing hassle with running standalone PyQGIS scripts outside of the QGIS application on Windows. In earlier versions there’s a LOT of batch file mangling and environment variable juggling required before you can safely import the qgis libraries within Python. Thanks to Nathan’s work, in QGIS 3.0 this will be as simple as just making sure that the QGIS python libraries are included in your Python path, and then importing qgis.core/gui etc will work without any need to create environment variables for OSGEO/GDAL/PLUGINS/etc. Anyone who has fought with this in the past will definitely appreciate this change, and users of Python IDEs will also appreciate how simple it is now to make the PyQGIS libraries available in these environments.
  • Nathan also worked on “profiles” support for QGIS 3.0. This work will add isolated user profiles within QGIS, similar to how Chrome handles this. Each profile has it’s own separate set of settings, plugins, etc. This work is designed to benefit both plugin developers and QGIS users within enterprise environments. You can read more about what Nathan has planned for this here.
  • I continued the ongoing work of moving long running interface “blocking” operations to background tasks. In QGIS 3.0 many of these tasks churn away in the background, allowing you to continue work while the operation completes. It’s been implemented so far for vector and raster layer saving, map exports to images/PDF (not composers unfortunately), and obtaining feature counts within legends. During the hackfest I moved the layer import which occurs when you drag and drop a layer to a destination in the browser to a background task.
  • On the same topic, I took some inspiration from a commit in Sourcepole’s QGIS fork and reworked how composer maps are cached. One of my biggest gripes with QGIS’ composer is how slow it is to work with when you’ve got a complex map included. This change pushes the map redrawing into a background thread, so that these redraws no longer “lock up” the UI. It makes a HUGE difference in how usable composer is. This improvement also allowed me to remove those confusing map item “modes” (Cache/Render/Rectangle) – now everything is redrawn silently in the background whenever required.
  • Lastly, I spent a lot of time on a fun feature I’ve long wanted in QGIS – a unified search “locator” bar. This feature is heavily inspired by Qt Creator’s locator bar. It sits away down in the status bar, and entering any text here fires up a bunch of background search tasks. Inbuilt searches include searching the layers within the current project (am I the only one who loses layers in the tree in complex projects!?), print layouts in the project, processing algorithms, and menu/toolbar actions. The intention here is that plugins will “take over” and add additional search functionality, such as OSM place names searching, data catalog searches, etc. I’m sure when QGIS 3.0 is released this will quickly become indispensable!

The upcoming QGIS 3.0 locator bar

Big thanks go out to Nathan’s wife, Stacey, who organized most of the event and without whom it probably would never have happened, and to Lutra Consulting who sponsored an awesome dinner for the attendees.

We’d love this to be the first of many. The mature European hackfests are attended by a huge swath of the community, including translators, documentation writers, and plugin developers (amongst others). If you’ve ever been interested in finding out how you can get more involved in the project it’s a great way to dive in and start contributing. There’s many QGIS users in this part of the world and we really want to encourage a community of contributors who “give back” to the project. So let Nathan or myself know if you’d be interested in attending other events like this, or helping to organize them locally yourself…

GRASS GIS 7.2.1 released

We are pleased to announce the update release GRASS GIS 7.2.1

GRASS GIS 7.2.1 in actionWhat’s new in a nutshell

After four months of development the new update release GRASS GIS 7.2.1 is available. It provides more than 150 stability fixes and manual improvements compared to the first stable release version 7.2.0. An overview of new features in this release series is available at New Features in GRASS GIS 7.2.

About GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

https://trac.osgeo.org/grass/wiki/Grass7/NewFeatures72 (overview of new 7.2 stable release series)

https://grass.osgeo.org/grass72/manuals/addons/ (list of available addons)

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, May 2017

The post GRASS GIS 7.2.1 released appeared first on GFOSS Blog | GRASS GIS Courses.

About label halos

A lot of cartographers have a love/hate relationship with label halos. On one hand they can be an essential technique for improving label readability, especially against complex background layers. On the other hand they tend to dominate maps and draw unwanted attention to the map labels.

In this post I’m going to share my preferred techniques for using label halos. I personally find this technique is a good approach which minimises the negative effects of halos, while still providing a good boost to label readability. (I’m also going to share some related QGIS 3.0 news at the end of this post!)

Let’s start with some simple white labels over an aerial image:

These labels aren’t very effective. The complex background makes them hard to read, especially the “Winton Shire” label at the bottom of the image. A quick and nasty way to improve readability is to add a black halo around the labels:

Sure, it’s easy to read the labels now, but they stand out way too much and it’s difficult to see anything here except the labels!

We can improve this somewhat through a better choice of halo colour:

This is much better. We’ve got readable labels which aren’t too domineering. Unfortunately the halo effect is still very prominent, especially where the background image varies a lot. In this case it works well for the labels toward the middle of the map, but not so well for the labels at the top and bottom.

A good way to improve this is to take advantage of blending (or “composition”) modes (which QGIS has native support for). The white labels will be most readable when there’s a good contrast with the background map, i.e. when the background map is dark. That’s why we choose a halo colour which is darker than the text colour (or vice versa if you’ve got dark coloured labels). Unfortunately, by choosing the mid-toned brown colour to make the halos blend in more, we are actually lightening up parts of this background layer and both reducing the contrast with the label and also making the halo more visible. By using the “darken” blend mode, the brown halo will only be drawn for pixels were the brown is darker then the existing background. It will darken light areas of the image, but avoid lightening pixels which are already dark and providing good contrast. Here’s what this looks like:

The most noticeable differences are the labels shown above darker areas – the “Winton Shire” label at the bottom and the “Etheridge Shire” at the top. For both these labels the halo is almost imperceptible whilst still subtly doing it’s part to make the label readable. (If you had dark label text with a lighter halo color, you can use the “lighten” blend mode for the same result).

The only issue with this map is that the halo is still very obvious around “Shire” in “Richmond Shire” and “McKinlay” on the left of the map. This can be reduced by applying a light blur to the halo:

There’s almost no loss of readability by applying this blur, but it’s made those last prominent halos disappear into the map. At first glance you probably wouldn’t even notice that there’s any halos being used here. But if we compare back against the original map (which used no halos) we can see the huge difference in readability:

Compare especially the Winton Shire label at the bottom, and the Richmond Shire label in the middle. These are much clearer on our tweaked map versus the above image.

Now for the good news… when QGIS 3.0 is released you’ll no longer have to rely on an external illustration/editing application to get this effect with your maps. In fact, QGIS 3.0 is bringing native support for applying many types of live layer effects to label buffers and background shapes, including blur. This means it will be possible to reproduce this technique directly inside your GIS, no external editing or tweaking required!

QGIS Composer Rewrite and Layout Engine crowdfund launched!

At North Road we believe that crowdfunding is a sustainable way to maintain and enhance open source software, like the QGIS open source GIS package. We’ve run a number of successful crowdfunding campaigns in the past, including support in QGIS for live layer effects, a point cluster renderer, and a unique value renderer for raster layers.

Now, we’re proud to announce our latest crowd funding endeavour, and our biggest to date, the QGIS Layout and Reporting Engine Campaign.

This campaign covers stage 1 of a large, ongoing project to modernise and expand on QGIS’ print composer and layout facilities. Over time QGIS’ composer functionality has grown extensively and now is capable of creating flexible, high quality cartographic outputs. However, we’ve now hit a limit where the current code architecture is prohibiting further improvements and important fixes. In order to add a reporting framework to QGIS, it is necessary for us to refactor and improve large sections of the composer code.

If this campaign is successful, we’ll be adding flexible report generation features to QGIS and cleaning up all the existing composer code. As part of these clean up, we’ll be taking the opportunity to tackle a number of current limitations which cannot be addressed in the current composition code:

  • Layouts will become unit aware, allowing for item placement and properties using millimetres, inches, pixels, centimetres, points, etc.
  • Layouts will have the ability to include mixed page sizes and orientations.
  • Plugins will be able to create custom composer item types (eg allow utilisation of 3rd party graphing and visualisation libraries!).
  • Individual layout items can be rasterised without affecting the rest of the layout. For instance, a map which requires rasterisation due to its use of blend modes will not require all other layout items (such as headings, legends, etc) to be rasterised. This will greatly benefit PDF outputs for complex map layouts.
  • The code refresh will allow more extensive use of data defined layout item properties.
  • A render caching system will be implemented for items, speeding up use of the layout designer and also paving the way for use of live paint effects on layout items (eg dynamic drop shadows).

Full details on what we have planned are available here: QGIS Layout and Reporting Engine Campaign.

To make it possible we need 30,000€ pledged before 31 May 2017. You can help make this a reality by supporting the campaign or by sharing the campaign page and increasing exposure to the campaign. Updates to follow!

New map coloring algorithms in QGIS 3.0

It’s been a long time since I last blogged here. Let’s just blame that on the amount of changes going into QGIS 3.0 and move on…

One new feature which landed in QGIS 3.0 today is a processing algorithm for automatic coloring of a map in such a way that adjoining polygons are all assigned different color indexes. Astute readers may be aware that this was possible in earlier versions of QGIS through the use of either the (QGIS 1.x only!) Topocolor plugin, or the Coloring a map plugin (2.x).

What’s interesting about this new processing algorithm is that it introduces several refinements for cartographically optimising the coloring. The earlier plugins both operated by pure “graph” coloring techniques. What this means is that first a graph consisting of each set of adjoining features is generated. Then, based purely on this abstract graph, the coloring algorithms are applied to optimise the solution so that connected graph nodes are assigned different colors, whilst keeping the total number of colors required minimised.

The new QGIS algorithm works in a different way. Whilst the first step is still calculating the graph of adjoining features (now super-fast due to use of spatial indexes and prepared geometry intersection tests!), the colors for the graph are assigned while considering the spatial arrangement of all features. It’s gone from a purely abstract mathematical solution to a context-sensitive cartographic solution.

The “Topological coloring” processing algorithm

Let’s explore the differences. First up, the algorithm has an option for the “minimum distance between features”. It’s often the case that features aren’t really touching, but are instead just very close to each other. Even though they aren’t touching, we still don’t want these features to be assigned the same color. This option allows you to control the minimum distance which two features can be to each other before they can be assigned the same color.

The biggest change comes in the “balancing” techniques available in the new algorithm. By default, the algorithm now tries to assign colors in such a way that the total number of features assigned each color is equalised. This avoids having a color which is only assigned to a couple of features in a large dataset, resulting in an odd looking map coloration.

Balancing color assignment by count – notice how each class has a (almost!) equal count

Another available balancing technique is to balance the color assignment by total area. This technique assigns colors so that the total area of the features assigned to each color is balanced. This mode can be useful to help avoid large features resulting in one of the colors appearing more dominant on a colored map.

Balancing assignment by area – note how only one large feature is assigned the red color

The final technique, and my personal preference, is to balance colors by distance between colors. This mode will assign colors in order to maximize the distance between features of the same color. Maximising the distance helps to create a more uniform distribution of colors across a map, and avoids certain colors clustering in a particular area of the map. It’s my preference as it creates a really nice balanced map – at a glance the colors look “randomly” assigned with no discernible pattern to the arrangement.

Balancing colors by distance

As these examples show, considering the geographic arrangement of features while coloring allows us to optimise the assigned colors for cartographic output.

The other nice thing about having this feature implemented as a processing algorithm is that unlike standalone plugins, processing algorithms can be incorporated as just one step of a larger model (and also reused by other plugins!).

QGIS 3.0 has tons of great new features, speed boosts and stability bumps. This is just a tiny taste of the handy new features which will be available when 3.0 is released!

New major release: GRASS GIS 7.2.0 available

We are pleased to announce the stable release of GRASS GIS 7.2.0

What’s new in a nutshell

After almost two years of development the new stable major release GRASS GIS 7.2.0 is available. It provides more than 1950 stability fixes and manual improvements compared to the former stable release version 7.0.5. The new version includes a series of new modules to analyse raster and vector data along with new temporal algebra functionality.More than 50 new addons are also available. A summary of the new features is available at New Features in GRASS GIS 7.2.

About GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, December 2016

The post New major release: GRASS GIS 7.2.0 available appeared first on GFOSS Blog | GRASS GIS Courses.

GDAL 2.1 packaged for Fedora 23 and 24

GDAL logoThe new GDAL 2.1 is now also packaged for Fedora 23 and 24 which is possible due to the tireless efforts of various Fedora packagers.

Repo: https://copr.fedorainfracloud.org/coprs/neteler/GDAL/

Installation Instructions:

su

# Fedora 23+24:
# install this extra repo
dnf copr enable neteler/GDAL

# A) in case of update, simply
dnf update

# B) in case of new installation (gdal-devel is optional)
dnf install gdal gdal-python gdal-devel

The post GDAL 2.1 packaged for Fedora 23 and 24 appeared first on GFOSS Blog | GRASS GIS Courses.

QGIS 2.18 packaged for Fedora 23 and 24

qgis-icon_smallThanks to the work of Volker Fröhlich and other Fedora packagers I was able to create RPM packages of QGIS 2.18 Las Palmas for Fedora 23 and 24 using Fedora’s COPR platform.

Repo: https://copr.fedorainfracloud.org/coprs/neteler/QGIS-2.18-Las-Palmas

The following packages can now be installed:

  • qgis 2.18.0
  • qgis-debuginfo 2.18.0
  • qgis-devel 2.18.0
  • qgis-grass 2.18.0
  • qgis-python 2.18.0
  • qgis-server 2.18.0

Installation instructions (run as “root” user or use “sudo”):

su

# Fedora 23, Fedora 24:
dnf copr enable neteler/QGIS-2.18-Las-Palmas
dnf update
# note: the "qca-ossl" package is the OpenSSL plugin for QCA
dnf install qgis qgis-grass qgis-python qca-ossl

Enjoy!

The post QGIS 2.18 packaged for Fedora 23 and 24 appeared first on GFOSS Blog | GRASS GIS Courses.

GRASS GIS 7.2.0RC1 released

We are pleased to announce the first release candidate of GRASS GIS 7.2.0

What’s new in a nutshell

This is the first release candidate of the upcoming major release GRASS GIS 7.2.0.

The new GRASS GIS 7.2.0RC1 release provides more than 1900 stability fixes and manual improvements compared to the stable releases 7.0.x.

hexagons_python_editorAbout GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures (overview of new 7 stable release series)

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (http://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, October 2016

The post GRASS GIS 7.2.0RC1 released appeared first on GFOSS Blog | GRASS GIS Courses.

Speeding up your PyQGIS scripts

I’ve recently spent some time optimising the performance of various QGIS plugins and algorithms, and I’ve noticed that there’s a few common performance traps which developers fall into when fetching features from a vector layer. In this post I’m going to explore these traps, what makes them slow, and how to avoid them.

As a bit of background, features are fetched from a vector layer in QGIS using a QgsFeatureRequest object. Common use is something like this:

request = QgsFeatureRequest()
for feature in vector_layer.getFeatures(request):
    # do something

This code would iterate over all the features in layer. Filtering the features is done by tweaking the QgsFeatureRequest, such as:

request = QgsFeatureRequest().setFilterFid(1001)
feature_1001 = next(vector_layer.getFeatures(request))

In this case calling getFeatures(request) just returns the single feature with an ID of 1001 (which is why we shortcut and use next(…) here instead of iterating over the results).

Now, here’s the trap: calling getFeatures is expensive. If you call it on a vector layer, QGIS will be required to setup an new connection to the data store (the layer provider), create some query to return data, and parse each result as it is returned from the provider. This can be slow, especially if you’re working with some type of remote layer, such as a PostGIS table over a VPN connection. This brings us to our first trap:

Trap #1: Minimise the calls to getFeatures()

A common task in PyQGIS code is to take a list of feature IDs and then request those features from the layer. A see a lot of older code which does this using something like:

for id in some_list_of_feature_ids:
    request = QgsFeatureRequest().setFilterFid(id)
    feature = next(vector_layer.getFeatures(request))
    # do something with the feature

Why is this a bad idea? Well, remember that every time you call getFeatures() QGIS needs to do a whole bunch of things before it can start giving you the matching features. In this case, the code is calling getFeatures() once for every feature ID in the list. So if the list had 100 features, that means QGIS is having to create a connection to the data source, set up and prepare a query to match a single feature, wait for the provider to process that, and then finally parse the single feature result. That’s a lot of wasted processing!

If the code is rewritten to take the call to getFeatures() outside of the loop, then the result is:

request = QgsFeatureRequest().setFilterFids(some_list_of_feature_ids)
for feature in vector_layer.getFeatures(request):
    # do something with the feature

Now there’s just a single call to getFeatures() here. QGIS optimises this request by using a single connection to the data source, preparing the query just once, and fetching the results in appropriately sized batches. The difference is huge, especially if you’re dealing with a large number of features.

Trap #2: Use QgsFeatureRequest filters appropriately

Here’s another common mistake I see in PyQGIS code. I often see this one when an author is trying to do something with all the selected features in a layer:

for feature in vector_layer.getFeatures():
    if not feature.id() in vector_layer.selectedFeaturesIds():
        continue

    # do something with the feature

What’s happening here is that the code is iterating over all the features in the layer, and then skipping over any which aren’t in the list of selected features. See the problem here? This code iterates over EVERY feature in the layer. If you’re layer has 10 million features, we are fetching every one of these from the data source, going through all the work of parsing it into a QGIS feature, and then promptly discarding it if it’s not in our list of selected features. It’s very inefficient, especially if fetching features is slow (such as when connecting to a remote database source).

Instead, this code should use the setFilterFids() method for QgsFeatureRequest:

request = QgsFeatureRequest().setFilterFids(vector_layer.selectedFeaturesIds())
for feature in vector_layer.getFeatures(request):
    # do something with the feature

Now, QGIS will only fetch features from the provider with matching feature IDs from the list. Instead of fetching and processing every feature in the layer, only the actual selected features will be fetched. It’s not uncommon to see operations which previously took many minutes (or hours!) drop down to a few seconds after applying this fix.

Another variant of this trap uses expressions to test the returned features:

filter_expression = QgsExpression('my_field > 20')
for feature in vector_layer.getFeatures():
    if not filter_expression.evaluate(feature):
        continue

    # do something with the feature

Again, this code is fetching every single feature from the layer and then discarding it if it doesn’t match the “my_field > 20” filter expression. By rewriting this to:

request = QgsFeatureRequest().setFilterExpression('my_field > 20')
for feature in vector_layer.getFeatures(request):
    # do something with the feature

we hand over the bulk of the filtering to the data source itself. Recent QGIS versions intelligently translate the filter into a format which can be applied directly at the provider, meaning that any relevant indexes and other optimisations can be applied by the provider itself. In this case the rewritten code means that ONLY the features matching the ‘my_field > 20’ criteria are fetched from the provider – there’s no time wasted messing around with features we don’t need.

 

Trap #3: Only request values you need

The last trap I often see is that more values are requested from the layer then are actually required. Let’s take the code:

my_sum = 0
for feature in vector_layer.getFeatures(request):
    my_sum += feature['value']

In this case there’s no way we can optimise the filters applied, since we need to process every feature in the layer. But – this code is still inefficient. By default QGIS will fetch all the details for a feature from the provider. This includes all attribute values and the feature’s geometry. That’s a lot of processing – QGIS needs to transform the values from their original format into a format usable by QGIS, and the feature’s geometry needs to be parsed from it’s original type and rebuilt as a QgsGeometry object. In our sample code above we aren’t doing anything with the geometry, and we are only using a single attribute from the layer. By calling setFlags( QgsFeatureRequest.NoGeometry ) and setSubsetOfAttributes() we can tell QGIS that we don’t need the geometry, and we only require a single attribute’s value:

my_sum = 0
request = QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry).setSubsetOfAttributes(['value'], vector_layer.fields() )
for feature in vector_layer.getFeatures(request):
    my_sum += feature['value']

None of the unnecessary geometry parsing will occur, and only the ‘value’ attribute will be fetched and populated in the features. This cuts down both on the processing required AND the amount of data transfer between the layer’s provider and QGIS. It’s a significant improvement if you’re dealing with larger layers.

Conclusion

Optimising your feature requests is one of the easiest ways to speed up your PyQGIS script! It’s worth spending some time looking over all your uses of getFeatures() to see whether you can cut down on what you’re requesting – the results can often be mind blowing!

Point cluster renderer crowdfunding – successful!

Great news! Thanks in part to some generous last minute pledges, our QGIS Point Cluster Renderer campaign has successfully reached its target. This means that QGIS 3.0 will now include a full feature and flexible cluster renderer.

In the meantime, we’d like to extend our warmest thanks to the following generous contributors, whose pledges have made this work possible:

  • Andreas Neumann
  • Qtibia Engineering (Tudor Barascu)
  • Karl-Magnus Jönsson
  • Geonesia (Nicolas Ponzo)

Plus numerous additional anonymous backers whose generous contributions are also highly valued. If you run into any of these funders at a QGIS user group or conference, make sure you treat them like the GIS rock-stars they are!

Keep an eye out on our social media accounts as we’ll be posting more video demonstrations of this work as it lands in the QGIS codebase.

BOTH

GRASS GIS PSC election 2016 results

The new GRASS GIS Project Steering Committee (PSC) is composed of the following nine members (ranking, name, votes):

1 Markus Neteler 62
2 Helena Mitasova 53
3 Martin Landa 52
4 Anna Petrasova 45
5 Moritz Lennert 41
6 Margherita Di Leo 39
7 Michael Barton 35
8 Peter Löwe 33
9 Vaclav Petras 31

More details in earlier announcement sent to the “grass-psc” mailing list:
https://lists.osgeo.org/pipermail/grass-psc/2016-August/001571.html.

For completeness, all relevant candidacy communications, as well as details about the voting process, are published at
https://trac.osgeo.org/grass/wiki/PSC/Election2016

Cited from the original announcement email:
https://lists.osgeo.org/pipermail/grass-announce/2016-September/000119.html

The post GRASS GIS PSC election 2016 results appeared first on GFOSS Blog | GRASS GIS Courses.

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