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Wed May 27 18:40:18 2015

A Django site.

QGIS Planet

Tak Nødebo

After a week with QGIS members from all over the world we arrived back home and can say that once again, the QGIS developer meeting #13 was a great event. It started with the QGIS User Conference where a lot

How to create illuminated contours, Tanaka-style

In the category “last night on Twitter”, a challenge I couldn’t resist: creating illuminated contours (aka Tanaka contours) in QGIS. Daniel P. Huffman started the thread by posting this great example:

CFnWnA5UkAAuFm9

This was quickly picked up by Hannes Kröger who blogged about his first attempt at reproducing the effect using QGIS and GIMP. Obviously, that left the challenge of finding a QGIS-only solution.

Everything that’s needed to create this effect is a DEM. As Hannes describes in his post, the DEM can then be used to compute the contour lines, e.g. with Raster | Extraction | Contour:

gdal_contour -a ELEV -i 100.0 C:\Users\anita\Geodata\misc\mt-st-helens\10.2.1.1043901.dem C:/Users/anita/Geodata/misc/mt-st-helens/countours

Screenshot 2015-05-24 11.17.49

contours

In order to be able to compute the brightness of the illuminated contours, we need to compute the orientation of every subsection of the contours. Therefore, we need to split the contour lines at each node. One way to do this is using v.split from the Processing toolbox:

Screenshot 2015-05-24 11.23.11

When we split the contours and visualize the result using arrows, we can see that they all wrap around the mountain in clockwise direction (light DEM cells equal higher elevation):

split_contours

After the split, we can compute the orientation of the contour subsections using, for example, a user-defined function:

Screenshot 2015-05-24 19.09.12

This function can then be used in a Field calculator expression:

Screenshot 2015-05-24 19.11.53

Based on the orientation, we can then write an expression to control the contour line color. For example, if we want the sun to appear in the north west (-45°) we can use:

color_hsl( 0,0, 
  scale_linear( abs(
    ( CASE WHEN "azimuth"-45 < 0
      THEN "azimuth"-45+360 
      ELSE "azimuth"-45
    END )
  -180), 0, 180, 0, 100)
  )

This will color the lines which are directly exposed to the sun white hsl(0,0,100) while the ones in the shadows will be black hsl(0,0,0).

Screenshot 2015-05-24 11.55.50

Use the Overlay layer blending mode to blend contours and DEM color:

illuminated_contours

The final step, to get as close to the original design as possible, is to create the effect of discrete elevation classes instead of a smooth color gradient. This can easily be achieved by changing the color interpolation mode of the DEM from Linear to Discrete:

Screenshot 2015-05-24 12.11.01

This leaves us with the following gorgeous effect:

tanaka_contours

As Hannes pointed out, another important aspect of Tanaka’s method is to also alter the contour line width. Lines in the sun or shadow should be wider (1 in this example) than those in orthogonal direction (0.2 in this example):

scale_linear( 
abs( abs(
  ( CASE WHEN "azimuth"-45 < 0
    THEN  "azimuth"-45+360
    ELSE  "azimuth"-45
  END )
-180) -90),
0, 90, 0.2, 1)

datadefined_line_width

Enjoy!


Time Manager workshop at #QGIS2015

Today was the final day of #QGIS2015 the first joint QGIS conference and developer meeting. I had the pleasure to meet Time Manager co-developer Karolina Alexiou aka carolinux in person and give a talk including a hands-on workshop on Time Manager together. Time Manager makes it possible to explore spatio-temporal data by creating animations directly in QGIS.

The talk presents QGIS visualization tools with a focus on efficient use of layer styling to both explore and present spatial data. Examples include the recently added heatmap style as well as sophisticated rule-based and data-defined styles. The focus of this presentation is exploring and presenting spatio-temporal data using the Time Manager plugin. A special treat are time-dependent styles using expression-based styling which access the current Time Manager timestamp.

To download the example data and QGIS projects download Time_Manager_Examples.zip.


Accessing composer item properties via custom expressions in QGIS

So here is a neat trick. Lets say you wanted to access the scale of a composer map to make it part of a label. The scale bar can already be set to numeric to show the number value but what if it needs to be part of an existing label with other text. Not to fear, expression functions are here.

  • Create a new composer. Add the map frame and a label.
  • Set the item ID of the map frame to something you can remember, lets just use themap
  • Select the label and add some text
  • Click Insert Expression

Now for the cool part

  • Select Function Editor
  • Click New File. Give the file a new name and hit save. I called it composer functions.

In the code editor paste this code:

from qgis.utils import iface
from qgis.core import *
from qgis.gui import *

@qgsfunction(args="auto", group='Composer')
def composeritemattr(composername, mapname, attrname, feature, parent):
    composers = iface.activeComposers()
    # Find the composer with the given name
    comp = [composer.composition() for composer in composers 
                if composer.composerWindow().windowTitle() == composername][0]
    # Find the item
    item = comp.getComposerItemById(mapname)
    # Get the attr by name and call 
    return getattr(item, attrname)()
  • Click Run Script

run

Now in your label use this text:

Scale: [% composeritemattr('Composer 1', 'themap', 'scale')%]

Update the Composer 1 to match your composer name, and the themap to match your item ID.

and like magic here is the scale from the map item in a label:

2015-05-21 22_00_09-Composer 1

Check the expression error section if the label doesn’t render

error


Filed under: Open Source, qgis Tagged: composer, python, qgis

QGIS goodies

Just a short post in case you missed it. Since some time QGIS is coorporating with spreadshirt.com, so we can open ‘virtual shops’ all over the world to sell QGIS t-shirst, caps and mugs. So: check your size, and go via this page to the nearest QGIS-shop and make your friends jealous with our great […]

ArcGIS REST API Connector Plugin for QGIS

ArcGIS REST Connector Plugin

Last year we described a command line method that adds ESRI REST layers in QGIS. Well, a team at the Geometa Lab in the University of Applied Sciences Rapperswil (HSR) Switzerland, have released a plugin for QGIS that adds ESRI REST layers via a GUI (Github page). The plugin is experimental so you will need to tick the box “Show also experimental plugins” in the settings panel of the “Plugins – Manage and Install Plugins” dialogue in order to add the plugin to QGIS. The following URLs lists numerous REST layers in the plugin’s GUI:

http://services.arcgisonline.com/arcgis/rest/services

http://basemap.nationalmap.gov/arcgis/rest/services

http://services.nationalmap.gov/arcgis/rest/services

Reference:

REST API Connector Plug-in Wiki Page


Trajectory animations with fadeout effect

Today’s post is a short tutorial for creating trajectory animations with a fadeout effect using QGIS Time Manager. This is the result we are aiming for:

The animation shows the current movement in pink which fades out and leaves behind green traces of the trajectories.

About the data

GeoLife GPS Trajectories were collected within the (Microsoft Research Asia) Geolife project by 182 users in a period of over three years (from April 2007 to August 2012). [1,2,3] The GeoLife GPS Trajectories download contains many text files organized in multiple directories. The data files are basically CSVs with 6 lines of header information. They contain the following fields:

Field 1: Latitude in decimal degrees.
Field 2: Longitude in decimal degrees.
Field 3: All set to 0 for this dataset.
Field 4: Altitude in feet (-777 if not valid).
Field 5: Date – number of days (with fractional part) that have passed since 12/30/1899.
Field 6: Date as a string.
Field 7: Time as a string.

Data prep: PostGIS

Since any kind of GIS operation on text files will be quite inefficient, I decided to load the data into a PostGIS database. This table of millions of GPS points can then be sliced into appropriate chunks for exploration, for example, a day in Beijing:

CREATE MATERIALIZED VIEW geolife.beijing 
AS SELECT trajectories.id,
    trajectories.t_datetime,
    trajectories.t_datetime + interval '1 day' as t_to_datetime,
    trajectories.geom,
    trajectories.oid
   FROM geolife.trajectories
   WHERE st_dwithin(trajectories.geom,
           st_setsrid(
             st_makepoint(116.3974589, 
                           39.9388838), 
             4326), 
           0.1) 
   AND trajectories.t_datetime >= '2008-11-11 00:00:00'
   AND trajectories.t_datetime < '2008-11-12 00:00:00'
WITH DATA

Trajectory viz: a fadeout effect for point markers

The idea behind this visualization is to show both the current movement as well as the history of the trajectories. This can be achieved with a fadeout effect which leaves behind traces of past movement while the most recent positions are highlighted to stand out.

Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

This effect can be created using a Single Symbol renderer with a marker symbol with two symbol layers: one layer serves as the highlights layer (pink) while the second layer represents the traces (green) which linger after the highlights disappear. Feature blending is used to achieve the desired effect for overlapping markers.

Screenshot 2015-05-06 23.52.40

The highlights layer has two expression-based properties: color and size. The color fades to white and the point size shrinks as the point ages. The age can be computed by comparing the point’s t_datetime timestamp to the Time Manager animation time $animation_datetime.

This expression creates the color fading effect:

color_hsv(  
  311,
  scale_exp( 
    minute(age($animation_datetime,"t_datetime")),
    0,60,
    100,0,
    0.2
  ),
  90
)

and this expression makes the point size shrink:

scale_exp( 
  minute(age($animation_datetime,"t_datetime")),
  0,60,
  24,0,
  0.2
)

Outlook

I’m currently preparing this and a couple of other examples for my Time Manager workshop at the upcoming 1st QGIS conference in Nødebo. The workshop materials will be made available online afterwards.

Literature

[1] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791-800.
[2] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312-321.
[3] Yu Zheng, Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp. 32-40.


Performance for mass updating features on layers

This post discusses how to improve the performance of pyqgis code which updates a lot of features by a factor of more than 10.

Ti piace avere QGIS ben tradotto in italiano? Ora puoi contribuire

Avere tutto QGIS, incluso il programma, i manuali e il sito web, tradotti in italiano è una bella comodità; questo richiede uno sforzo notevole, per cui il tuo aiuto è essenziale. Fai una donazione tramite: http://qgis.it/#translation

PSA: Please use new style Qt signals and slots not the old style

Don’t do this:

self.connect(self.widget, 
             SIGNAL("valueChanged(int)"), 
             self.valuechanged)

It’s the old way, the crappy way. It’s prone to error and typing mistakes. And who really wants to be typing strings as functions and arg names in it. Gross.

Do this:

self.widget.valueChanged.connect(self.valuechanged)
self.widget.valueChanged[str].connect(self.valuechanged)

Much nicer. Cleaner. Looks and feels like Python not some mash up between C++ and Python. The int argument is the default so it will use that. If you to pick the signal type you can use [type].

Don’t do this:

self.emit(SIGNAL("changed()", value1, value2))

Do this

class MyType(QObject):
   changed = pyqtSignal(str, int)

   def stuff(self):
       self.changed.emit(value1, value2)

pyqtSignal is a type you can use to define you signal. It will come with type checking, if you don’t want type checking just do pyqtSignal(object).

Please think of the poor kittens before using the old style in your code.


Filed under: pyqt, python, qgis Tagged: pyqt, qgis, qt

Review: Building Mapping Applications with QGIS

It seems like over the last year the amount of literature published regarding QGIS has really exploded. In the past few months alone there’s been at least three titles I can think of (Building Mapping Applications with QGISMastering QGIS, and the QGIS Python Programming Cookbook). I think this is a great sign of a healthy project. Judging by this there’s certainly a lot of demand for quality guides and documentation for QGIS.

I recently finished reading one of these titles – Building Mapping Applications with QGIS. (Erik Westra, Packt Publishing 2015) In short, I’m a huge fan of this work and think it may be my favourite QGIS book to date! I’ve read Erik’s previous work, Python Geospatial Development, and thought it was an entertaining and really well written book. He’s clearly got an in-depth knowledge about what he’s writing about and this confidence comes through in his writing. So when I first saw this title announced I knew it would be a must-read for me.

In Building Mapping Applications with QGIS, Erik has created a comprehensive guide through all the steps required to create QGIS plugins and standalone Python applications which utilise the QGIS libraries. It’s not a beginner’s guide to Python or to PyQGIS, but that’s what helps it stand out. There’s no introductory chapters on programming with Python or how to use QGIS and instead Erik dives straight into the meat of this topic. I found this approach really refreshing, as I’m often frustrated when the first few chapters of an advanced work just cover the basics. Instead, Building Mapping Applications with QGIS is packed with lessons about, well, actually building mapping applications!

So, why do I like this book so much? Personally, I think it fills a a really crucial void in the existing QGIS literature. There’s a lot of works covering using QGIS, and a few covering PyQGIS development (eg, the PyQGIS Programmer’s Guide, which I reviewed here). But to date, there hasn’t been any literature that covers developing QGIS based applications in such great depth. It’s just icing on the cake that Erik’s writing is also so interesting and easy to read.

Is there any criticisms I have with this book? Well, there’s one small omission which I would have liked to see addressed. While the chapter Learning the QGIS Python API goes into some detail about how QGIS is built using the Qt libraries and a great deal of depth about interpreting the QGIS c++ APIs, I think it could really benefit from some discussion about both the PyQt and Qt APIs themselves. Since a lot of the QGIS classes are either directly derived from Qt classes or heavily utilise them it’s really important that PyQGIS developers are also directed to the PyQt and Qt APIs. For instance, the Qt QColor class is used heavily throughout PyQGIS, but you won’t find any API documentation on QColor in QGIS’ API. Instead, you need to first consult the PyQt API docs and also the detailed Qt c++ docs. It’s often that you may think the PyQGIS API is missing a crucial method, but consulting the Qt docs reveals that the method is instead implemented in the base classes. It’s an important point to note for mastering PyQGIS development. To be fair, I’m yet to read a PyQGIS book which has nailed the interaction between the QGIS, PyQt and Qt APIs.

Honestly, that’s a really minor quibble with an otherwise outstanding work. I’m so glad Erik’s written this work and strongly recommend it to anyone wanting to take their PyQGIS development skills to the next level.

Routing in polygon layers? Yes we can!

A few weeks ago, the city of Vienna released a great dataset: the so-called “Flächen-Mehrzweckkarte” (FMZK) is a polygon vector layer with an amazing level of detail which contains roads, buildings, sidewalk, parking lots and much more detail:

preview of the Flächen-Mehrzweckkarte

preview of the Flächen-Mehrzweckkarte

Now, of course we can use this dataset to create gorgeous maps but wouldn’t it be great to use it for analysis? One thing that has been bugging me for a while is routing for pedestrians and how it’s still pretty bad in many situations. For example, if I’d be looking for a route from the northern to the southern side of the square in the previous screenshot, the suggestions would look something like this:

Pedestrian routing in Google Maps

Pedestrian routing in Google Maps

… Great! Google wants me to walk around it …

Pedestrian routing on openstreetmap.org

Pedestrian routing on openstreetmap.org

… Openstreetmap too – but on the other side :P

Wouldn’t it be nice if we could just cross the square? There’s no reason not to. The routing graphs of OSM and Google just don’t contain a connection. Polygon datasets like the FMZK could be a solution to the issue of routing pedestrians over squares. Here’s my first attempt using GRASS r.walk:

Routing with GRASS r.walk

Routing with GRASS r.walk (Green areas are walk-friendly, yellow/orange areas are harder to cross, and red buildings are basically impassable.)

… The route crosses the square – like any sane pedestrian would.

The key steps are:

  1. Assigning pedestrian costs to different polygon classes
  2. Rasterizing the polygons
  3. Computing a cost raster for moving using r.walk
  4. Computing the route using r.drain

I’ve been using GRASS 7 for this example. GRASS 7 is not yet compatible with QGIS but it would certainly be great to have access to this functionality from within QGIS. You can help make this happen by supporting the crowdfunding initiative for the GRASS plugin update.


How to run a Linux GUI application on OSX using Docker

Ok so here is the scenario:

You just got a nice new MacBook 15″ Retina computer thinking it would work as nicely for Linux as your 13″ MacBook did and then you discover that the hybrid Intel/Nvidia card support in Linux is a show stopper and the WebCam does not work under Linux.

Well that is what happened to me, so I decided to give working with OSX a try on this laptop with the help of docker for running all those essential apps that I use for development. One thing I was curious about was whether it would be possible to run native GUI (X11) applications from inside docker and have them show up on my OSX desktop. I turns out that it is fairly easy to do this – here is what I did:

Overview

  • Install brew
  • Install socat
  • Install XQuartz
  • Install Docker (I used Kitematic beta)
  • Grab a docker image that has a gui app you want to run (I used my the QGIS Desktop image published by Kartoza on the docker hub)
  • Run it forwarding the display to your OSX host

 

Digging In

Ok first install brew (an apt-like package manager for OSX).

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Now install socat – a command line tool that lets you redirect sockets in unix like OS’s – thankfully it runs in OSX too as it is a really neat tool!

brew install socat

Next we are going to install XQuartz – which basically gives you an X11 display client on your OSX desktop. Just grab the package at http://xquartz.macosforge.org/landing/ and do the usual OSX procedure for installing it.

Unfortunately docker does not run natively on OSX, and the whole boot2docker setup is probably quite difficult to explain to people. However there is a very nice (currently beta) docker client being developed for OSX called kinematic. I installed kinematic and then simply hit shift-command-t in order to get a bash shell with docker available in it.

Now grab my QGIS desktop image for docker:

docker pull kartoza/qgis-desktop

Once the image is downloaded we are done with the basic setup and can kick over to running our Linux GUI application (obviously QGIS in this example).

Running QGIS

Ok so there are four steps we need to do to run our Linux app:

  1. Start socat (in my testing it had to be done first)
  2. Start XQuartz
  3. Start Kinematic
  4. Start QGIS

I started socat like this:

socat TCP-LISTEN:6000,reuseaddr,fork UNIX-CLIENT:\"$DISPLAY\"

It will run in the foreground waiting for connections and then pass them over to XQuartz.

Next I started XQuartz (you can close the XTerm window that opens by default).  In X11 preferences in XQuartz, in the security tab, check both boxes:

Screen Shot 2015-04-13 at 23.40.21

Next I started kinematic, and pressed SHIFT-COMMAND-T to open a docker terminal.

Screen Shot 2015-04-13 at 23.16.21

Lastly I ran the QGIS docker container like this:

docker run --rm -e DISPLAY=192.168.0.3:0 \
    -i -t -v /Users/timlinux:/home/timlinux \
    kartoza/qgis-desktop qgis

You can mix in any standard docker options there – in this case I created  shared volume between my OSX home directory and a /home/timlinux directory in the container. You need to determine the IP address of your OSX machine and use it instead of the IP address listed after DISPLAY in the above command. Here is a nice picture of QGIS (from a Linux container) running on my OSX desktop:

Screen Shot 2015-04-13 at 23.52.21

 

 

This same technique should work nicely with any other GUI application under Linux – I will mostly use if for running tests of QGIS based plugins and for using QGIS in my docker orchestrated environments.

Fun with docker and GRASS GIS software

GRASS GIS and dockerSometimes, we developers get reports via mailing list that this & that would not work on whatever operating system. Now what? Should we be so kind and install the operating system in question in order to reproduce the problem? Too much work… but nowadays it has become much easier to perform such tests without having the need to install a full virtual machine – thanks to docker.

Disclaimer: I don’t know much about docker yet, so take the code below with a grain of salt!

In my case I usually work on Fedora or Scientific Linux based systems. In order to quickly (i.e. roughly 10 min of automated installation on my slow laptop) try out issues of GRASS GIS 7 on e.g., Ubuntu, I can run all my tests in docker installed on my Fedora box:

# we need to run stuff as root user
su
# install docker on Fedora
yum -y docker-io
systemctl start docker
systemctl enable docker

Now we have a running docker environment. Since we want to exchange data (e.g. GIS data) with the docker container later, we prepare a shared directory beforehand:

# we'll later map /home/neteler/data/docker_tmp to /tmp within the docker container
mkdir /home/neteler/data/docker_tmp

Now we can start to install a Ubuntu docker image (may be “any” image, here we use “Ubuntu trusty” in our example). We will share the X11 display in order to be able to use the GUI as well:

# enable X11 forwarding
xhost +local:docker

# search for available docker images
docker search trusty

# fetch docker image from internet, establish shared directory and display redirect
# and launch the container along with a shell
docker run -v /data/docker_tmp:/tmp:rw -v /tmp/.X11-unix:/tmp/.X11-unix \
       -e uid=$(id -u) -e gid=$(id -g) -e DISPLAY=unix$DISPLAY \
       --name grass70trusty -i -t corbinu/docker-trusty /bin/bash

In almost no time we reach the command line of this minimalistic Ubuntu container which will carry the name “grass70trusty” in our case (btw: read more about Working with Docker Images):

root@8e0f233c3d68:/# 
# now we register the Ubuntu-GIS repos and get GRASS GIS 7.0
add-apt-repository ppa:ubuntugis/ubuntugis-unstable
add-apt-repository ppa:grass/grass-stable
apt-get update
apt-get install grass7

This will take a while (the remaining 9 minutes or so of the overall 10 minutes).

Since I like cursor support on the command line, I launch (again?) the bash in the container session:

root@8e0f233c3d68:/# bash
# yes, we are in Ubuntu here
root@8e0f233c3d68:/# cat /etc/issue

Now we can start to use GRASS GIS 7, even with its graphical user interface from inside the docker container:

# create a directory for our data, it is mapped to /home/neteler/data/docker_tmp/
# on the host machine 
root@8e0f233c3d68:/# mkdir /tmp/grassdata
# create a new LatLong location from EPSG code
# (or copy a location into /home/neteler/data/docker_tmp/)
root@8e0f233c3d68:/# grass70 -c epsg:4326 ~/grassdata/latlong_wgs84
# generate some data to play with
root@8e0f233c3d68:/# v.random n=30 output=random30
# start the GUI manually (since we didn't start GRASS GIS right away with it before)
root@8e0f233c3d68:/# g.gui

Indeed, the GUI comes up as expected!

GRASS GIS 7 GUI in docker container

GRASS GIS 7 GUI in docker container

You may now perform all tests, bugfixes, whatever you like and leave the GRASS GIS session as usual.
To get out of the docker session:

root@8e0f233c3d68:/# exit    # leave the extra bash shell
root@8e0f233c3d68:/# exit    # leave docker session

# disable docker connections to the X server
[root@oboe neteler]# xhost -local:docker

To restart this session later again, you will call it with the name which we have earlier assigned:

[root@oboe neteler]# docker ps -a
# ... you should see "grass70trusty" in the output in the right column

# we are lazy and automate the start a bit
[root@oboe neteler]# GRASSDOCKER_ID=`docker ps -a | grep grass70trusty | cut -d' ' -f1`
[root@oboe neteler]# echo $GRASSDOCKER_ID 
[root@oboe neteler]# xhost +local:docker
[root@oboe neteler]# docker start -a -i $GRASSDOCKER_ID

### ... and so on as described above.

Enjoy.

The post Fun with docker and GRASS GIS software appeared first on GFOSS Blog | GRASS GIS Courses.

A interactive command bar for QGIS

Something that has been on my mind for a long time is a interactive command interface for QGIS.  Something that you can easily open, run simple commands, and is interactive to ask for arguments when they are needed.

After using the command interface in Emacs for a little bit over the weekend – you can almost hear the Boos! from heavy Vim users :) – I thought this is something I must have in QGIS as well.  I’m sure it can’t be that hard to add.

So here it is.  A interactive command interface for QGIS.

commandbar

commandbar2

The command bar plugin (find it in the plugin installer) adds a simple interactive command bar to QGIS. Commands are defined as Python code and may take arguments.

Here is an example function:

@command.command("Name")
def load_project(name):
    """
    Load a project from the set project paths
    """
    _name = name
    name += ".qgs"
    for path in project_paths:
        for root, dirs, files in os.walk(path):
            if name in files:
                path = os.path.join(root, name)
                iface.addProject(path)
                return
    iface.addProject(_name)

All functions are interactive and if not all arguments are given when called it will prompt for each one.

Here is an example of calling the point-at function with no args. It will ask for the x and then the y

pointat

Here is calling point-at with all the args

pointatfunc

Functions can be called in the command bar like so:

my-function arg1 arg2 arg2

The command bar will split the line based on space and the first argument is always the function name, the rest are arguments passed to the function. You will also note that it will convert _ to - which is easier to type and looks nicer.

The command bar also has auto complete for defined functions – and tooltips once I get that to work correctly.

You can use CTRL + ; (CTRL + Semicolon), or CTRL + ,, to open and close the command bar.

What is a command interface without auto complete

autocomplete

Use Enter to select the item in the list.

How about a function to hide all the dock panels. Sure why not.

@command.command()
def hide_docks():
    docks = iface.mainWindow().findChildren(QDockWidget)
    for dock in docks:
        dock.setVisible(False)

alias command

You can also alias a function by calling the alias function in the command bar.

The alias command format is alias {name} {function} {args}

Here is an example of predefining the x for point-at as mypoint

-> alias mypoint point-at 100

point-at is a built in function that creates a point at x y however we can alias it so that it will be pre-called with the x argument set. Now when we call mypoint we only have to pass the y each time.

-> mypoint
(point-at) What is the Y?: 200

You can even alias the alias command – because why the heck not :)

-> alias a alias
a mypoint 100

a is now the shortcut hand for alias

WHY U NO USE PYTHON CONSOLE

The Python console is fine and dandy but we are not going for a full programming language here, that isn’t the point. The point is easy to use commands.

You could have a function called point_at in Python that would be

point_at(123,1331)

Handling incomplete functions is a lot harder because of the Python parser. In the end it’s easier and better IMO to just make a simple DSL for this and get all the power of a DSL then try and fit into Python.

It should also be noted that the commands defined in the plugin can still be called like normal Python functions because there is no magic there. The command bar is just a DSL wrapper around them.

Notes

This is still a bit of an experiment for me so things might change or things might not work as full expected just yet.

Check out the projects readme for more info on things that need to be done, open to suggestions and pull requests.

Also see the docs page for more in depth information


Filed under: Open Source, python, qgis Tagged: plugin, pyqgis, qgis

Crayfish 2.0: What's New!

Crayfish 2.0

After listening to user feedback we decided to do some major work on Crayfish. The changes include code refactoring, changes to the user interface, support for an additional file format, adding a vector and contour overlay, and a shiny new logo!

Read on for a look at some of the new features in more detail...

Time control

In the new version of Crayfish a time slider allows users to quickly browse through time. A drop-down menu allows the selection of an exact time.

Vector and contour overlay

In previous versions of Crayfish it was only possible to load contours and vectors from the same dataset. For example, it was not possible to show velocity vectors on top of depth contours. With the new Crayfish you can "unlock" the legend and choose different vectors or contours to be displayed. The video below demonstrates this in action.

Special times

Some datasets contain a special time-step outside the outputted time range. For example, Maximums and Minimums are stored at time 99999 in TUFLOW modelling package. Within the layer tree, additional time-steps items will now be shown if they exist within the dataset.

Additional file formats

We have added support for the XMDF file format. In addition, Hydro_AS 2D users should be able to open their files in the latest Crayfish.

New Python Module

We’ve refactored lots of code in the Crayfish library which makes it much easier to add support for further file formats and additional functionality. The Crayfish library now comes with a new Python module that allows easy manipulation with the mesh and results data – either in your custom scripts or within the QGIS Python console. For example, printing the coordinates of the nodes of a mesh together with their elevation takes just few lines of code:

import crayfish

m = crayfish.Mesh("/data/my_mesh.2dm")
o = m.dataset(0).output(0) # bed elevation data

for index, node in enumerate(m.nodes()):
print "Node XYZ: ", node.x, node.y, o.value(index)

New Profile tool plugin

If you use Profile tool plugin in QGIS, you can create a profile from the Crayfish layer and browse through the time. The profile gets updated as you change the output time.

Problems

If you have some feedback on our changes, suggestions for new functionality, or come across a bug, feel free to file a ticket on the issues page of the Crayfish github repository.

Sponsors

We’d like to thank Maroondah City Council for sponsoring some of the great features in this release.

Introducing QGIS live layer effects!

I’m pleased to announce that the crowdfunded work on layer effects for QGIS is now complete and available in the current development snapshots! Let’s dive in and explore how these effects work, and check out some of the results possible using them.

I’ll start with a simple polygon layer, with some nice plain styling:

Nice and boring polygon layer

A nice and boring polygon layer

If I open the properties for this layer and switch to the Style tab, there’s a new checkbox for “Draw effects“. Let’s enable that, and then click the little customise effects button to its right:

Enabling effects for the layer

Enabling effects for the layer

A new “Effects Properties” dialog opens:

Effects Properties dialog

Effects Properties dialog

You can see that currently the only effect listed is a “Source” effect. Source effects aren’t particularly exciting – all they do is draw the original layer unchanged. I’m going to change this to a “Blur” effect by clicking the “Effect type” combo box and selecting “Blur“:

Changing to a blur effect

Changing to a blur effect

If I apply the settings now, you’ll see that the polygon layer is now blurry. Now we’re getting somewhere!

Blurry polygons!

Blurry polygons!

Ok, so back to the Effects Properties dialog. Let’s try something a bit more advanced. Instead of just a single effect, it’s possible to chain multiple effects together to create different results. Let’s make a traditional drop shadow by adding a “Drop shadow” effect under the “Source” effect:

Setting up a drop shadow

Setting up a drop shadow

Effects are drawn top-down, so the drop shadow will appear below the source polygons:

Live drop shadows!

Live drop shadows!

Of course, if you really wanted, you could rearrange the effects so that the drop shadow effect is drawn above the source!..

Hmmmm

Hmmmm…

You can stack as many effects as you like. Here’s a purple inner glow over a source effect, with a drop shadow below everything:

Inner glow, source, drop shadow...

Inner glow, source, drop shadow…

Now it’s time to get a bit more creative… Let’s explore the “transform” effect. This effect allows you to apply all kinds of transformations to your layer, including scaling, shearing, rotation and translation:

The transform effect

The transform effect

Here’s what the layer looks like if I add a horizontally shearing transform effect above an outer glow effect:

Getting freaky...

Getting tricky…

Transforms can get really freaky. Here’s what happens if we apply a 180° rotation to a continents layer (with a subtle nod to xkcd):

Change your perspective on the world!

Change your perspective on the world!

Remember that all these effects are applied when the layers are rendered, so no modifications are made to the underlying data.

Now, there’s one last concept regarding effects which really blasts open what’s possible with them, and that’s “Draw modes“. You’ll notice that this combo box contains a number of choices, including “Render“, “Modify” and “Render and Modify“:

"Draw mode" options

“Draw mode” options

These draw modes control how effects are chained together. It’s easiest to demonstrate how draw modes work with an example, so this time I’ll start with a Transform effect over a Colorise effect. The transform effect is set to a 45° rotation, and the colorise effect set to convert to grayscale. To begin, I’ll set the transform effect to a draw mode of Render only:

The "Render only" draw mode

The “Render only” draw mode

In this mode, the results of the effect will be drawn but won’t be used to modify the underlying effects:

Rotation effect over the grayscale effect

Rotation effect over the grayscale effect

So what we have here is that the polygon is drawn rotated by 45° by the transform effect, and then underneath that there’s a grayscale copy of the original polygon drawn by the colorise effect. The results of the transform effect have been rendered, but they haven’t affected the underlying colorise effect.

If I instead set the Transform effect’s draw mode to “Modifier only” the results are quite different:

Rotation modifier for grayscale effect

Rotation modifier for grayscale effect

Now, the transform effect is rotating the polygon by 45° but the result is not rendered. Instead, it is passed on to the subsequent colorise effect, so that now the colorise effect draws a grayscale copy of the rotated polygon. Make sense? We could potentially chain a whole stack of modifier effects together to get some great results. Here’s a transform, blur, colorise, and drop shadow effect all chained together using modifier only draw modes:

A stack of modifier effects

A stack of modifier effects

The final draw mode, “Render and modify” both renders the effect and applies its result to underlying effects. It’s a combination of the two other modes. Using draw modes to customise the way effects chain is really powerful. Here’s a combination of effects which turn an otherwise flat star marker into something quite different:

Lots of effects!

Lots of effects!

The last thing I’d like to point out is that effects can be either applied to an entire layer, or to the individual symbol layers for features within a layer. Basically, the possibilities are almost endless! Python plugins can also extend this further by implementing additional effects.

All this work was funded through the 71 generous contributors who donated to the crowdfunding campaign. A big thank you goes out to you all whole made this work possible! I honestly believe that this feature takes QGIS’ cartographic possibilities to whole new levels, and I’m really excited to see the maps which come from it.

Lastly, there’s two other crowdfunding campaigns which are currently in progress. Lutra consulting is crowdfunding for a built in auto trace feature, and Radim’s campaign to extend the functionality of the QGIS GRASS plugin. Please check these out and contribute if you’re interested in their work and would like to see these changes land in QGIS.

GRASS GIS 6.4.5RC1 released

GRASS GIS logoAfter months of development a first release candidate of GRASS GIS 6.4.5 is now available. This is a stability release of the GRASS GIS 6 line.

Source code download:
http://grass.osgeo.org/grass64/source/
http://grass.osgeo.org/grass64/source/grass-6.4.5RC1.tar.gz

Binaries download:
http://grass.osgeo.org/download/software/#g64x

To get the GRASS GIS 6.4.5RC1 source code directly from SVN:
svn checkout http://svn.osgeo.org/grass/grass/tags/release_20150406_grass_6_4_5RC1

Key improvements:
Key improvements of the GRASS GIS 6.4.5RC1 release include stability fixes (esp. vector library), some fixes for wxPython3 support, some module fixes, and more message translations.

See also our detailed announcement:
http://trac.osgeo.org/grass/wiki/Release/6.4.5RC1-News

First time users should explore the first steps tutorial after installation:
http://grasswiki.osgeo.org/wiki/Quick_wxGUI_tutorial

Release candidate management at
http://trac.osgeo.org/grass/wiki/Grass6Planning

Please join us in testing this release candidate for the final release.

Consider to donate pizza or beer for the next GRASS GIS Community Sprint (following the FOSS4G Europe 2015 in Como):
http://grass.osgeo.org/donations/

Thanks to all contributors!

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, April 2015

The post GRASS GIS 6.4.5RC1 released appeared first on GFOSS Blog | GRASS GIS Courses.

Create great looking topographic maps in QGIS

Wicklow-Topo-original

In this tutorial I will show you how to create a Hillshaded topographic map in QGIS. We will be using Shuttle Radar Topography Mission (SRTM) data, a near global Digital Elevation Model (DEM) collected in February 2000 aboard NASA’s Space Shuttle Endeavour (mission STS-99). The mission used a X-Band mapping radar to measure the Earth’s topography, built in collaboration with the U.S. Jet Propulsion Laboratory, the U.S. National Imagery and Mapping Agency (now the National Geospatial-Intelligence Agency), and the German and Italian space agencies.

The raw radar data has been continuously processed and improved since it was first collected. Countless artefacts have been painstakingly removed and areas of missing data have been filled using alternate data sources. The version we will be using is the 1 Arc-Second Global SRTM dataset, an enhanced 30 meter resolution DEM that was released last year. It is a substantial improvement over the 3 Arc-Second / 90 meter SRTM data previously available for Ireland. SRTM elevation data can be downloaded from the United States Geological Survey’s EarthExplorer website.

When first loaded into QGIS (via Add Raster Layer), the DEM is displayed as a rather uninformative black and white image.

Wicklow-Topo-blackwhite

It is therefore necessary to apply a suitable colour ramp that accentuates topography. While it is possible to create your own colour ramp, or use one of the colour ramps provided by QGIS, superior colour ramps can be downloaded using Etienne Tourigny’s Color Ramp Manager (Plugins – Manage and Install Plugins). After the plugin is added to QGIS, go to the Plugins menu again and choose the Colour Ramp Manager.

In the window that pops up, choose the full opt-city package and click check for update. The plugin will then download the cpt-city library, a collection of over a hundred cartographic gradients (version 2.15). After the package downloads, quit the dialogue.

Back in QGIS, right click the DEM layer to bring up the Layer Properties dialogue. In the Style tab, change the render type from single band grey to single band pseudocolor. Then click new color ramp and new color ramp again, choose the cpt-city color ramp to bring up the cpt-city dialogue. Click topography and choose the sd-a colour ramp. While this is an excellent colour ramp, I find its colours are a bit too strong for my liking.

Still in the Layer Properties dialogue, change the min and max values to match your DEM’s lowest and highest elevations values and click classify, this applies the new colour ramp. Next, change the brightness to 30 and lower the contrast and saturation to -20. Click OK to apply the new style and quit the Layer Properties dialogue.

Wicklow-Topo-noShade

Next we need to create a Hillshade layer from the DEM, a 3D like visual representation of topographic relief. This is achieved via the menu Raster – Analysis – DEM (Terrain models). There is one small catch, the hillshading algorithm assumes the DEM’s horizontal units are in meters (they are decimal degrees). We need to enter a scale correction factor of 111120 (in the Scale ratio vert. units to horiz. box). Once that is all done, select an output path to save the generated hillshade and click OK. Generating a hillshade may take up to a minute depending on the size of your DEM.

Wicklow-Topo-hillshade

After the hillshade is created, open its Layer properties dialogue. Change the min and max values to 125 and 255, increase its brightness to 45 and contrast to 20. Finally, switch the blending mode from normal to multiply. This allows the DEM beneath the hillshade to show though. Click OK to apply the new style.

If you followed these steps correctly you will have created a fine looking topographic map similar to the one below. It’s also possible to create contours but that’s a tutorial for another day.

Wicklow-Topo

Technical note:

There are two hillshading algorithms available in QGIS, one by Horne (1981) and another by Zevenbergen and Thorne (1987). Jones (1998) examined the quality of hillshading algorithms, he found the algorithm of Fleming and Ho€er (1979) is slightly superior to Horne’s (1981) algorithm. Zevenbergen and Thorne’s (1987) algorithm is a derivation of Fleming and Ho€er’s (1979) formula. QGIS uses Horne’s (1981) algorithm by default.

References:

Horn, B.K., 1981. Hill shading and the reflectance map. Proceedings of the IEEE, 69, 14–47.

Jones, K.H., 1998. A comparison of algorithms used to compute hill slope as a property of the DEM [PDF]. Computers & Geosciences, 24, 315–323.

Zevenbergen, L.W. & Thorne, C.R., 1987. Quantitative analysis of land surface topography. Earth surface processes and landforms, 12, 47–56.

Time Manager 1.6 – now with feature interpolation

Over the last couple of weeks, Karolina has been very busy improving and expanding Time Manager. This post is to announce the 1.6 release of Time Manager which brings you many fixes and exciting new features.

Screenshot 2015-03-25 17.58.38

What’s this feature interpolation you’re talking about?

Interpolation is really helpful if you have multiple observations of the same (moving) real-world object at different points in time and you want to visualize the movement between the observations. This can be used to visualize animal paths, vehicle tracks, or any other movement in space.

The following example shows a simple layer which contains 12 point features (3 for each id value).

Screenshot 2015-03-25 17.50.55

Using Time Manager interpolation, it is easy to create animations with interpolated positions between observations:

animation

How is it done?

When you open the Time Manager 1.6 Settings | Add layer dialog, you will find a new option for interpolation settings. This first version supports linear interpolation of point features but more options might be added in the future. Note how the id attribute is specified to let Time Manager know which features belong to the same real-world object.

Screenshot 2015-03-25 17.43.08

For the interpolation, Time Manager creates a new layer which contains the interpolated features. You can see this layer in the layer list.

Screenshot 2015-03-25 17.46.13

I’m really looking forward to seeing all the great animations this feature will enable. Thanks Karolina for making this possible!


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