I just realised that the tagline of this site still stated “MapBasic”, amongst other things. It’s been a good 4+ years since I last touched that abomination of a language, so it’s well and truly time for that tag to go!
To all those still stuck using MapInfo/MapBasic… you have my condolences.
Last week the first official “FOSS4G/SOTM Oceania” conference was held at Melbourne University. This was a fantastic event, and there’s simply no way I can extend sufficient thanks to all the organisers and volunteers who put this event together. They did a brilliant job, and their efforts are even more impressive considering it was the inaugural event!
Upfront — this is not a recap of the conference (I’m sure someone else is working on a much more detailed write up of the event!), just some musings I’ve had following my experiences assisting Nathan Woodrow deliver an introductory Python for QGIS workshop he put together for the conference. In short, we both found that delivering this workshop to a group of PyQGIS newcomers was a great way for us to identify “pain points” in the PyQGIS API and areas where we need to improve. The good news is that as a direct result of the experiences during this workshop the API has been improved and streamlined! Let’s explore how:
Part of Nathan’s workshop (notes are available here) focused on a hands-on example of creating a custom QGIS “Processing” script. I’ve found that preparing workshops is guaranteed to expose a bunch of rare and tricky software bugs, and this was no exception! Unfortunately the workshop was scheduled just before the QGIS 3.4.2 patch release which fixed these bugs, but at least they’re fixed now and we can move on…
The bulk of Nathan’s example algorithm is contained within the following block (where “distance” is the length of line segments we want to chop our features up into):
for input_feature in enumerate(features):
geom = feature.geometry().constGet()
if isinstance(geom, QgsLineString):
continue
first_part = geom.geometryN(0)
start = 0
end = distance
length = first_part.length()
while start < length:
new_geom = first_part.curveSubstring(start,end)
output_feature = input_feature
output_feature.setGeometry(QgsGeometry(new_geom))
sink.addFeature(output_feature)
start += distance
end += distance
There’s a lot here, but really the guts of this algorithm breaks down to one line:
new_geom = first_part.curveSubstring(start,end)
Basically, a new geometry is created for each trimmed section in the output layer by calling the “curveSubstring” method on the input geometry and passing it a start and end distance along the input line. This returns the portion of that input LineString (or CircularString, or CompoundCurve) between those distances. The PyQGIS API nicely hides the details here – you can safely call this one method and be confident that regardless of the input geometry type the result will be correct.
Unfortunately, while calling the “curveSubstring” method is elegant, all the code surrounding this call is not so elegant. As a (mostly) full-time QGIS developer myself, I tend to look over oddities in the API. It’s easy to justify ugly API as just “how it’s always been”, and over time it’s natural to develop a type of blind spot to these issues.
Let’s start with the first ugly part of this code:
geom = input_feature.geometry().constGet()
if isinstance(geom, QgsLineString):
continue
first_part = geom.geometryN(0)
# chop first_part into sections of desired length
...
This is rather… confusing… logic to follow. Here the script is fetching the geometry of the input feature, checking if it’s a LineString, and if it IS, then it skips that feature and continues to the next. Wait… what? It’s skipping features with LineString geometries?
Well, yes. The algorithm was written specifically for one workshop, which was using a MultiLineString layer as the demo layer. The script takes a huge shortcut here and says “if the input feature isn’t a MultiLineString, ignore it — we only know how to deal with multi-part geometries”. Immediately following this logic there’s a call to geometryN( 0 ), which returns just the first part of the MultiLineString geometry.
There’s two issues here — one is that the script just plain won’t work for LineString inputs, and the second is that it ignores everything BUT the first part in the geometry. While it would be possible to fix the script and add a check for the input geometry type, put in logic to loop over all the parts of a multi-part input, etc, that’s instantly going to add a LOT of complexity or duplicate code here.
Fortunately, this was the perfect excuse to improve the PyQGIS API itself so that this kind of operation is simpler in future! Nathan and I had a debrief/brainstorm after the workshop, and as a result a new “parts iterator” has been implemented and merged to QGIS master. It’ll be available from version 3.6 on. Using the new iterator, we can simplify the script:
geom = input_feature.geometry()
for part in geom.parts():
# chop part into sections of desired length
...
Win! This is simultaneously more readable, more Pythonic, and automatically works for both LineString and MultiLineString inputs (and in the case of MultiLineStrings, we now correctly handle all parts).
Here’s another pain-point. Looking at the block:
new_geom = part.curveSubstring(start,end) output_feature = input_feature output_feature.setGeometry(QgsGeometry(new_geom))
At first glance this looks reasonable – we use curveSubstring to get the portion of the curve, then make a copy of the input_feature as output_feature (this ensures that the features output by the algorithm maintain all the attributes from the input features), and finally set the geometry of the output_feature to be the newly calculated curve portion. The ugliness here comes in this line:
output_feature.setGeometry(QgsGeometry(new_geom))
What’s that extra QgsGeometry(…) call doing here? Without getting too sidetracked into the QGIS geometry API internals, QgsFeature.setGeometry requires a QgsGeometry argument, not the QgsAbstractGeometry subclass which is returned by curveSubstring.
This is a prime example of a “paper-cut” style issue in the PyQGIS API. Experienced developers know and understand the reasons behind this, but for newcomers to PyQGIS, it’s an obscure complexity. Fortunately the solution here was simple — and after the workshop Nathan and I added a new overload to QgsFeature.setGeometry which accepts a QgsAbstractGeometry argument. So in QGIS 3.6 this line can be simplified to:
output_feature.setGeometry(new_geom)
Or, if you wanted to make things more concise, you could put the curveSubstring call directly in here:
output_feature = input_feature output_feature.setGeometry(part.curveSubstring(start,end))
Let’s have a look at the simplified script for QGIS 3.6:
for input_feature in enumerate(features):
geom = feature.geometry()
for part in geom.parts():
start = 0
end = distance
length = part.length()
while start < length:
output_feature = input_feature
output_feature.setGeometry(part.curveSubstring(start,end))
sink.addFeature(output_feature)
start += distance
end += distance
This is MUCH nicer, and will be much easier to explain in the next workshop! The good news is that Nathan has more niceness on the way which will further improve the process of writing QGIS Processing script algorithms. You can see some early prototypes of this work here:
I couldn’t be at the community day but managed to knock out some of the new API on the plane on the way home. **API subject to change 🙂#FOSS4G_SotM_Oceania
cc @nyalldawson @underdarkGIS this will create a alg under the hood and check for double inputs, etc pic.twitter.com/49VpyuukGU
— Nathan Woodrow (@madmanwoo) November 23, 2018
So there we go. The process of writing and delivering a workshop helps to look past “API blind spots” and identify the ugly points and traps for those new to the API. As a direct result of this FOSS4G/SOTM Oceania 2018 Workshop, the QGIS 3.6 PyQGIS API will be easier to use, more readable, and less buggy! That’s a win all round!
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:
for v in geom.vertices(): ... do something with the vertex!
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…
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!
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!
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:
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.
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.
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.
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!
Last week I posted regarding some thoughts I’ve had recently concerning what I perceive as a general confusion about how QGIS is developed and how users can successfully get things to change in the project. The post certainly started a lot of conversation! However, based on feedback received I realise some parts of the posts were being misinterpreted and some clarification is needed. So here we go…
I don’t think I was very clear about this – but my original post wasn’t meant to be a discouragement from filing bug reports or feature requests. The truth is that there is a LOT of value in these reports, and if you don’t file a report then the QGIS team will never be aware of the bug or your feature idea. Here’s some reasons why you SHOULD file a report:
Speaking for myself, I regularly check new incoming tickets (at least once a day), and I know I’m not the only one. So filing a report WILL bring your issue to developer’s attention. Which leads to…
I can honestly understand why people get frustrated and resort to an aggressive “why hasn’t this been fixed yet?!” style reply. I believe that these complaints are caused because people have the misunderstanding that filing a bug report is the ONLY thing they can do to get an issue fixed. If filing a report IS the only avenue you have to get something fixed/implemented, then it’s totally understandable to be annoyed when your ticket gets no results. This is a failing on behalf of the project though – we need to be clearly communicating that filing a report is the LEAST you can do. It’s a good first step, but on its own it’s just the beginning and needs to be followed up by one of the methods I described in the initial post.
When I wrote the original piece I focused on just the code aspect of the QGIS project. That’s only because I’m a developer and it’s the area I know best. But it applies equally across the whole project, including documentation, translations, infrastructure, websites, packaging QGIS releases, etc. In fact, some of these non-code areas are the best entry points into the project as they don’t require a development background, and eg the documentation and translation teams have done a great job making it easy to submit contributions. Find something missing in the QGIS documentation? Add it yourself! Missing a translation of the website which prevents QGIS adoption within your community? Why not sponsor a translator to tackle this task?!
Again, I wrote the original piece focusing on QGIS because that’s the project I’m most familiar with. You could just as easily substitute GDAL, GEOS, OpenLayers, PostGIS, Geoserver, R, D3, etc… in and it would be equally valid!
Hopefully that helps clarify some of the points raised by the earlier article. Let’s keep the discussion flowing – I’d love to hear if you have any other suggestions or questions raised by this topic.
I’ve been heavily involved in the open source QGIS mapping project for a number of years now. During this time I’ve kept a close watch on the various mailing lists, issue trackers, stackexchange, tweets and other various means users have to provide feedback to the project. Recently, I’ve started to come to the conclusion that there’s a lot of fundamental confusion about how the project works and how users can get changes made to the project. Read on for these insights, but keep in mind that these are just my thoughts and not reflective of the whole community’s views!..
Firstly – QGIS is a community driven project. Unlike some open source projects (and unlike the proprietary GIS offerings) there is no corporate backer or singular organisation directing the project. This means two things:
So how exactly can users get changes implemented in QGIS? Well, let’s take a look at all the possible different ways that changes get made and how effective each one is:
Finally, there’s two more very ineffective approaches:
That’s it. Those are the ONLY ways changes get made in QGIS. There’s no other magical short-cuts you can take. Some of these approaches are much more effective than others, and some require skills or resources which may not be available. If you want to see something change in QGIS, you need to take a look at these options and decide for yourself which best meets your needs. But please, just don’t choose option 7!
Update: a follow up to this article was published
In part 3 of my exploration of variables in QGIS 2.12, I’m going to dig into how variables are scoped in QGIS and what layer level variables are available (you can read parts 1 and 2 for a general introduction to variables).
Before we get to the good stuff, a bit of background in how variables work behind-the-scenes is important. Whenever an expression is evaluated in QGIS the context of the expression is considered. The context is built up from a set of scopes, which are all stacked on top of each other in order from least-specific to most-specific. It’s easier to explain with an example. Let’s take an expression used to set the source of a picture in a map composer. When this expression is evaluated, the context will consist of:
The more specific scopes will override any existing clashing variables from less specific scopes. So a global @my_var variable will be overridden by an @my_var variable set for the composer:
Another example. Let’s consider now an expression set for a data-defined label size. When this expression is evaluated the context will depend on where the map is being rendered. If it’s in the main map canvas then the context will be:
If instead the map is being rendered inside a map item in a map composer, the context will be:
Ok, enough with the details. The reason I’ve explained all this is to help explain when layer level variables come into play. Basically, they’ll be available whenever an expression is evaluated inside of a particular layer. This includes data defined symbology and labeling, field calculator, and diagrams. You can’t use a layer-level variable inside a composer label, because there’s no layer scope used when evaluating this. Make sense? Great! To set a layer level variable, you use the Variables section in the Layer Properties dialog:
Setting a layer variable
Any layer level variables you set will be saved inside your current project, i.e. layer variables are per-layer and per-project. You can also see in the above screenshot that as well as the layer level variables QGIS also lists the existing variables from the Project and Global scopes. This helps show exactly what variables are accessible by the layer and whether they’ve been overridden by any scopes. You can also see that there’s two automatic variables, @layer_id and @layer_name, which contain the unique layer ID and user-set layer name too.
In the screenshot above I’ve set two variables, @class1_threshold and @class2_threshold. I’m going to use these to sync up some manual class breaks between rule based symbology and rule based labeling. Here’s how I’ve set up the rule-based symbols for the layer:
Rule based symbology using layer level variables
In a similar way, I’ve also created matching rule-based labeling (another new feature in QGIS 2.12):
Matching rule-based labels
Here’s what my map looks like now, with label and symbol colors matched:
*Map for illustrative purposes only… not for cartographic/visual design excellence!
If I’d hard-coded the manual class breaks, it would be a pain to keep the labeling and symbology in sync. I’d have to make sure that the breaks are updated everywhere I’ve used them in both the symbology and labeling settings. Aside from being boring, tedious work, this would also prevent immediate before/after comparisons. Using variables instead means that I can update the break value in a single place (the variables panel) and have all my labeling and symbols immediately reflect this change when I hit apply!
Another recent use case I had was teaming layer-level variables along with Time Manager. I wanted my points to falloff in both transparency and size with age, and this involved data defined symbol settings scattered all throughout my layer symbology. By storing the decay fall-off rate in a variable, I could again tweak this falloff by changing the value in a single place and immediately see the result. It also helps with readability of the data defined expressions. Instead of trying to decipher a random, hard-coded value, it’s instead immediately obvious that this value relates to a decay fall-off rate. Much nicer!
I’m sure there’s going to be hundreds of novel uses of layer-level variables which I never planned for when adding this feature. I’d love to hear about them though – leave a comment if you’d like to share your ideas!
This isn’t strictly related to variables, but another new feature which was introduced in QGIS 2.12 was a new “layer_property” expression function. This function allows you to retrieve any one of a bunch of properties relating to a specific map layer, including the layer CRS, metadata, source path, etc.
This function can be used anywhere in QGIS. For instance, it allows you to insert dynamic metadata about layers into a print composer layout. In the screenshot below I’ve used expressions like layer_property(‘patron’,’crs’) and layer_property(‘patron’,’source’) to insert the CRS and source path of the “patron” layer into the label. If either the CRS or the file path ever changes, this label will be automatically updated to reflect the new values.
Inserting dynamic layer properties into a composer label
So there you go – layer level variables and the layer_property function – here in QGIS 2.12 and making your workflow in QGIS easier. In the final part of this series, we’ll explore the magical @value variable. Trust me, I’ve saved the best for last!
Following on from part 1 in which I introduced how variables can be used in map composers, I’d like to now explore how using variables can make it easier to manage your QGIS projects. As a quick refresher, variables are a new feature in QGIS 2.12 which allow you to create preset values for use anywhere you can use an expression in QGIS.
Let’s imagine a typical map project. You load up QGIS, throw a bunch of layers on your map, and then get stuck into styling and labelling them ‘just right’. Over time the project gets more and more complex, with a stack of layers all styled using different rendering and labelling rules. You keep tweaking settings until you’re almost happy with the result, but eventually realise that you made the wrong choice of font for the labelling and now need to go through all your layers and labelling rules and update each in turn to the new typeface. Ouch.
Variables to the rescue! As you may recall from part 1, you can reuse variables anywhere in QGIS where you can enter an expression. This includes using them for data defined overrides in symbology and labelling. So, lets imagine that way back at the beginning of our project we created a project level variable called @main_label_font:
Creating a variable for label font
Now, we can re-use that variable in a data defined override for the label font setting. In fact, QGIS makes this even easier for you by showing a “variables” sub-menu allowing easy access to all the currently defined variables accessible to the layer:
Binding the label font to the @main_label_font variable
When we hit Apply all our labels will be updated to use the font face defined by the @main_label_font variable, so in this case ‘Courier New’:
In a similar way we can bind all the other layer’s label fonts to the same variable, so @main_label_font will be reused by all the layers in the project. Then, when we later realise that Courier New was a horrible choice for labelling the map, it’s just a matter of opening up the Project Properties dialog and updating the value of the @main_label_font variable:
And now when we hit Apply the font for all our labelled layers will be updated all at once:
It’s not only a huge time saver, it also makes changes like this easier because you can try out different font faces by updating the variable and hitting apply and seeing the effect that the changes have all at once. Updating multiple layers manually tends to have the consequence that you forget what the map looked like before you started making the change, making direct comparisons harder.
Of course, you could have multiple variables for other fonts used by your project too, eg @secondary_label_font and @highlighted_feature_font. Plus, this approach isn’t limited to just setting the label font. You could utilise project level variables for consolidating font sizes, symbol line thickness, marker rotation, in fact, ANYTHING that has one of those handy little data defined override buttons next to it:
See all those nice little yellow buttons? All those controls can be bound to variables…
One last thing before I wrap up part 2 of this series. The same underlying changes which introduced variables to QGIS also allows us to begin introducing a whole stack of new, useful functions to the expression engine. One of these which also helps with project management is the new project_color function. Just like how we can use project level variables throughout a project, project_color lets you reuse a color throughout your project. First, you need to create a named colour in the Default Styles group under the Project Properties dialog:
Define a colour in the project’s colour scheme…
Then, you can set a data defined override for a symbol or label colour to the expression “project_color(‘red alert!’)“:
When you go back and change the corresponding colour in the Project Properties dialog, every symbol bound to this colour will also be updated!
So, there you have it. With a little bit of forward planning and by taking advantage of the power of expression variables in QGIS 2.12 you can help make your mapping projects much easier to manage and update!
That’s all for now, but we’re still only just getting started with variables. Part 3, coming soon!.. (Update: Part 3 is available now)