Getting started with Python and QGIS 3 can be a bit overwhelming. In this post we give you a quick start to get you up and running and maybe make your PyQGIS life a little easier.

There are likely many ways to setup a working PyQGIS development environment---this one works pretty well.

Contents

• Requirements
• Installing
  • OSGeo4W
  • Setting the Environment
  • pb_tool
• Working on the Command Line
• IDE Example
• Workflow
  • Creating a New Plugin
  • Working with Existing Plugin Code
• Troubleshooting

Requirements

• OSGeo4W Advanced Install of QGIS
• pip (for installing/managing Python packages)
• pb_tool (cross-platform tool for compiling/deploying/distributing QGIS plugin)
• A customized startup script to set the environment (pyqgis.cmd)
• IDE (optional)
• Emacs (just kidding)
• Vim (just kidding)

We'll start with the installs.

Installing

Almost everything we need can be installed using the OSGeo4W installer available on the QGIS website.

OSGeo4W

From the QGIS website, download the appropriate network installer (32 or 64 bit) for QGIS 3.

• Run the installer and choose the Advanced Install option
• Install from Internet
• Choose a directory for the install---I prefer a path without spaces such as C:\OSGeo4W
• Accept default for local package directory and Start menu name
• Tweak network connection option if needed on the Select Your Internet Connection screen
• Accept default download site location
• From the Select packages screen, select: Desktop -> qgis: QGIS Desktop

When you click Next a bunch of additional packages will be suggested---just accept them and continue the install.

Once complete you will have a functioning QGIS install along with the other parts we need. If you want to work with the nightly build of QGIS, choose Desktop -> qgis-dev instead.

If you installed QGIS using the standalone installer, the easiest option is to remove it and install from OSGeo4W. You can run both the standalone and OSGeo4W versions on the same machine, but you need to be extra careful not to mix up the environment.

Setting the Environment

To continue with the setup, we need to set the environment by creating a .cmd script. The following is adapted from several sources, and trimmed down to the minimum. Copy and paste it into a file named pyqgis.cmd and save it to a convenient location (like your HOME directory).

@echo off
SET OSGEO4W_ROOT=C:\OSGeo4W3
call "%OSGEO4W_ROOT%"\bin\o4w_env.bat
call "%OSGEO4W_ROOT%"\apps\grass\grass-7.4.0\etc\env.bat
@echo off
path %PATH%;%OSGEO4W_ROOT%\apps\qgis-dev\bin
path %PATH%;%OSGEO4W_ROOT%\apps\grass\grass-7.4.0\lib
path %PATH%;C:\OSGeo4W3\apps\Qt5\bin
path %PATH%;C:\OSGeo4W3\apps\Python36\Scripts

set PYTHONPATH=%PYTHONPATH%;%OSGEO4W_ROOT%\apps\qgis-dev\python
set PYTHONHOME=%OSGEO4W_ROOT%\apps\Python36

set PATH=C:\Program Files\Git\bin;%PATH%

cmd.exe

You should customize the set PATH statement to add any paths you want available when working from the command line. I added paths to my git install.

The last line starts a cmd shell with the settings specified above it. We'll see an example of starting an IDE in a bit.

You can test to make sure all is well by double-clicking on our pyqgis.cmd script, then starting Python and attempting to import one of the QGIS modules:

C:\Users\gsherman>python3
Python 3.6.0 (v3.6.0:41df79263a11, Dec 23 2016, 07:18:10) [MSC v.1900 32 bit (In tel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import qgis.core
>>> import PyQt5.QtCore

If you don't get any complaints on import, things are looking good.

Installing pb_tool

Open your customized shell (double-click on pyqgis.cmd to start it) to install pb_tool:

python3 -m pip install pb_tool

Check to see if pb_tool is installed correctly:

C:\Users\gsherman>pb_tool
Usage: pb_tool [OPTIONS] COMMAND [ARGS]...

  Simple Python tool to compile and deploy a QGIS plugin. For help on a
  command use --help after the command: pb_tool deploy --help.

  pb_tool requires a configuration file (default: pb_tool.cfg) that declares
  the files and resources used in your plugin. Plugin Builder 2.6.0 creates
  a config file when you generate a new plugin template.

  See http://g-sherman.github.io/plugin_build_tool for for an example config
  file. You can also use the create command to generate a best-guess config
  file for an existing project, then tweak as needed.

  Bugs and enhancement requests, see:
  https://github.com/g-sherman/plugin_build_tool

Options:
  --help  Show this message and exit.

Commands:
  clean       Remove compiled resource and ui files
  clean_docs  Remove the built HTML help files from the...
  compile     Compile the resource and ui files
  config      Create a config file based on source files in...
  create      Create a new plugin in the current directory...
  dclean      Remove the deployed plugin from the...
  deploy      Deploy the plugin to QGIS plugin directory...
  doc         Build HTML version of the help files using...
  help        Open the pb_tools web page in your default...
  list        List the contents of the configuration file
  translate   Build translations using lrelease.
  update      Check for update to pb_tool
  validate    Check the pb_tool.cfg file for mandatory...
  version     Return the version of pb_tool and exit
  zip         Package the plugin into a zip file suitable...

If you get an error, make sure C:\OSGeo4W3\apps\Python36\Scripts is in your PATH.

More information on using pb_tool is available on the project website.

Working on the Command Line

Just double-click on your pyqgis.cmd script from the Explorer or a desktop shortcut to start a cmd shell. From here you can use Python interactively and also use pb_tool to compile and deploy your plugin for testing.

IDE Example

By adding one line to our pyqgis.cmd script, we can start our IDE with the proper settings to recognize the QGIS libraries:

start "PyCharm aware of Quantum GIS" /B "C:\Program Files (x86)\JetBrains\PyCharm 3.4.1\bin\pycharm.exe" %*

We added the start statement with the path to the IDE (in this case PyCharm). If you save this to something like pycharm.cmd, you can double-click on it to start PyCharm. The same method works for other IDEs, such as PyDev.

Within your IDE settings, point it to use the Python interpreter included with OSGeo4W---typically at: %OSGEO4W_ROOT%\bin\python3.exe. This will make it pick up all the QGIS goodies needed for development, completion, and debugging. In my case OSGEO4W_ROOT is C:\OSGeo4W3, so in the IDE, the path to the correct Python interpreter would be: C:\OSGeo4W3\bin\python3.exe.

Make sure you adjust the paths in your .cmd scripts to match your system and software locations.

Workflow

Here is an example of a workflow you can use once you're setup for development.

Creating a New Plugin

1. Use the Plugin Builder plugin to create a starting point [1]
2. Start your pyqgis.cmd shell
3. Use pb_tool to compile and deploy the plugin (pb_tool deploy will do it all in one pass)
4. Activate it in QGIS and test it out
5. Add code, deploy, test, repeat

Working with Existing Plugin Code

The steps are basically the same was creating a new plugin, except we start by using pb_tool to create a new config file:

1. Start your pyqgis.cmd shell
2. Change to the directory containing your plugin code
3. Use pb_tool create to create a config file
4. Edit pb_tool.cfg to adjust/add things create may have missed
5. Start at step 3 in Creating a New Plugin and press on

Troubleshooting

Assuming you have things properly installed, trouble usually stems from an incorrect environment.

• Make sure QGIS runs and the Python console is available and working
• Check all the paths in your pygis.cmd or your custom IDE cmd script
• Make sure your IDE is using the Python interpreter that comes with OSGeo4W

[1] Plugin Builder 3.x generates a pb_tool config file

Following up on my last post, Running Scripts in the Python Console, I created a plugin to simplify running scripts:

The Script Runner plugin allows you to add your scripts to a list so they are readily available. You can then run them to automate QGIS tasks and have full access to the PyQGIS API. In addition, you can view information about the classes, methods, and functions in your module as well as browse the source:

In order for your script to work with ScriptRunner it has to implement a single function as an entry point. Here is some additional information from the Help tab of the plugin:

Requirements

In order for Script Runner to execute your script you must define a run_script function that accepts a single argument. This is the standard entry point used by Script Runner. A reference to the qgis.utils.iface object will be passed to your run_script function. You don’t have to use the iface object in your script but your run_script function must accept it as an argument.

Here is an example of a simple run_script function:

    def run_script(iface):
        ldr = Loader(iface)
        ldr.load_shapefiles('/vmap0_shapefiles')

In this example, the run_script creates an instance (ldr) of a class named Loader that is defined in the same source file. It then calls a method in the Loader class named load_shapefiles to do something useful—in this case, load all the shapefiles in a specified directory.

Alternatively, you could choose not to use classes and just do everything within the run_script function, including having it call functions in the same script or others you might import. The important thing is to be sure you have defined a run_script function. If not, Script Runner won’t load your script.

Working with Scripts

To run a script, you must add it to Script Runner using the Add Script tool on the toolbar. This will add it to a list in the left panel. This list of scripts is persisted between uses of QGIS. You can remove a script using the Remove Script tool. This just removes it from the list; it does nothing to the script file on disk.

Once you have a script loaded, you can click the Script Info tool to populate the Info and Source tabs in the panel on the right. The Info tab contains the docstring from your module and then a list of the classes, methods, and functions found in the script. Having a proper docstring at the head of your script will help you determine the puprose of script.

You can view the source of the script on the Source tab. This allows you to quickly confirm that you are using the right script and it does what you think it will.

Installing the Plugin

To install the plugin:

1. Open the Python plugin installer: Plugins->Fetch Python Plugins
2. Check to see if you have the new Official repository in your list of plugins by clicking on Repositories tab. The URL is http://plugins.qgis.org/plugins/plugins.xml.
3. If you have it, skip to step 5. If the new repository isn’t in the list, add it by clicking the Add button. Give it a name and insert the URL http://plugins.qgis.org/plugins/plugins.xml
4. Click on the Plugins tab
5. Enter scriptrunner in the Filter box
6. Select the ScriptRunner plugin and click Install

ScriptRunner adds an entry to the Plugins menu as well as a tool on the Plugins toolbar: . Click it and you are off and running.

The QGIS Python console is great for doing one-off tasks or experimenting with the API. Sometimes you might want to automate a task using a script, and do it without writing a full blown plugin. Currently QGIS does not have a way to load an arbitrary Python script and run it¹. Until it does, this post illustrates a way you can create a script and run it from the console.

There are a couple of requirements to run a script in the console:

1. The script must be in your PYTHONPATH
2. Just like a QGIS plugin, the script needs a reference to qgis.utils.iface

Setting up the Environment

By default, the Python path includes the .qgis/python directory. The location depends on your platform:

• Windows: in your home directory under .qgis\python. For example, C:\Documents and Settings\gsherman\.qgis\python
• Linux and OS X: $HOME/.qgis/python

To see what is in your PYTHONPATH you can do the following in QGIS Python console:

import sys
sys.path

While you could use the .qgis\python directory for your custom scripts, a better way is to create a directory specifically for that purpose and add that directory to the PYTHONPATH environment variable. On Windows you can do this using the Environment Variables page in your system properties:

On Linux or OS X, you can add it to your .bash_profile, .profile, or other login script in your home directory:

export PYTHONPATH=$PYTHONPATH:/home/gsherman/qgis_scripts

Writing the Script

With the environment set, we can create scripts to automate QGIS tasks and run them from the console. For this example, we will use a simple script to load all shapefiles in a specified directory. There are a couple of ways to do this:

1. Write a simple script with a function that accepts qgis.utils.iface as an argument, along with a path to the shapefiles
2. Create a Python class that uses an __init__ method to store a reference to the iface object and then add methods to do the work

We will use the latter approach because it is more flexible and allows us to initialize once and then call methods without having to pass the iface object each time.

The script looks like this:

#!/usr/bin/env Python
"""Load all shapefiles in a given directory.
  This script (loader.py) runs from the QGIS Python console.
  From the console, use:
    from loader import Loader
    ldr = Loader(qgis.utils.iface)
    ldr.load_shapefiles('/my/path/to/shapefile/directory')
 
  """
from glob import glob
from os import path
 
class Loader:
    def __init__(self, iface):
        """Initialize using the qgis.utils.iface 
        object passed from the console.
 
        """
        self.iface = iface
 
    def load_shapefiles(self, shp_path):
        """Load all shapefiles found in shp_path"""
        print "Loading shapes from %s" % path.join(shp_path, "*.shp")
        shps = glob(path.join(shp_path, "*.shp"))
        for shp in shps:
            (shpdir, shpfile) = path.split(shp)
            self.iface.addVectorLayer(shp, shpfile, 'ogr' )

Running the Script

To open the console use the Plugins->Python Console menu item.

The comment at the head of the script explains how to use it.

First we import the Loader class from the script file (named loader.py). This script resides in the qgis_scripts directory that is our PYTHONPATH.

from loader import Loader

We then create an instance of Loader, passing it the reference to the iface object:

ldr = Loader(qgis.utils.iface)

This creates the Loader object and calls the __init__ method to initialize things.

Once we have an instance of Loader we can load all the shapefiles in a directory by calling the load_shapefiles method, passing it the full path to the directory containing the shapefiles:

ldr.load_shapefiles('/home/gsherman/qgis_sample_data/vmap0_shapefiles')

The load_shapefiles method uses the path to get a list of all the shapefiles and then adds them to QGIS using addVectorLayer.

Here is the result, rendered in the random colors and order that the shapefiles were loaded:

Some Notes

• When testing a script in the console you may need to reload it as you make changes. This can be done using reload and the name of the module. In our example, reload(loader) does the trick.
• You can add more methods to your class to do additional tasks
• You can create a “driver” script that accepts the iface object and then initializes additional classes to do more complex tasks

1. I have plans on the drawing board to implement this feature.

This week we look at the OpenLayers plugin for QGIS. This plugin allows you to add a number of image services to your map canvas:

• Google
• Physical
• Streets
• Hybrid
• Satellite
• OpenStreetMap
• Yahoo
• Street
• Hybrid
• Satellite
• Bing
• Road
• Aerial
• Aerial with labels

Installing the Plugin

The OpenLayers plugin is installed like all other Python plugins. From the the Plugins menu in QGIS, choose Fetch Python Plugins. This brings up the plugin installer. To find the plugin, enter openlayers in the Filter box, then select OpenLayers Plugin from the list. Once it’s highlighted, click the Install plugin button. This will download the plugin from the repository, install it, and load it into QGIS.

Using the Plugin

The OpenLayers Plugin uses your view extent to fetch the data from the service you choose. For this reason you should load at least one of your own layers first. Since each of the services are expecting a request in latitude/longitude your layer either has to be geographic or you must enable on the fly projection.

To add one of the services you have two choices; you can pick the service from the Plugins->OpenLayers plugin menu or you can use the OpenLayers Overview. The Overview opens a new panel that allows you to choose a service from a drop-down list. Click the Enable map checkbox to enable the drop-down list and preview the service you want to add. If you are happy with what you see, you can add it to the map by clicking the Add map button.

In the screenshot below we have enabled the Overview panel, added the world boundaries layer¹, zoomed to an area of interest, and added the Google terrain (physical) data:

You can add as many services as you want, previewing them using the OpenLayers Overview panel.

¹ You can get the world boundaries layer from the Geospatial Desktop sample data set.

This week we take a look at a how to plot a terrain profile using the Profile plugin. The plugin can be used with any raster format supported by QGIS. You can can display profiles from up to three rasters at once, allowing you to compare the results. To illustrate, we’ll create a simple profile using a DEM of a 1:63,360 quadrangle in Alaska.

Installing the Plugin

The Profile plugin is installed like all other Python plugins. From the the Plugins menu in QGIS, choose Fetch Python Plugins. This brings up the plugin installer. To find the plugin, enter profile in the Filter box, then select Profile from the list. Once it’s highlighted, click the Install plugin button. This will download the plugin from the repository, install it, and load it into QGIS.¹

Using the Plugin

Here we have loaded the DEM as well as a raster (DRG) of the topography for the same quadrangle and zoomed in a bit to an area of interest:

The yellow line has been added to indicate where we will take the profile. While the Profile plugin allows you to interactively select the profile line it doesn’t display the line on the map.

To create a profile, first make sure the raster you want to profile is selected in the layer list, then activate the plugin from the Plugins toolbar by clicking on it. The cursor becomes a cross that you use to create the profile line: click at the start point, move to the end and click again. Once you have created the profile line, the plugin pops up the result:

The profile is taken from the northeast towards the southwest, based on where we clicked first. You can change the exaggeration of the profile by using the slider on the left. The X axis shows the distance along the profile line in map units and the Y axis shows the cell values from the raster—in this case, elevation in meters.

You can save the result as a PDF or SVG using the buttons at the bottom of the dialog.

The Statistics tab displays some information for the raster layer and the profile line:

If we had additional rasters loaded, we could use the Setup tab to add them to the profile analysis. This would display the results using the colors specified for each layer and we could compare them on the Profile tab.

This is just one of several QGIS plugins that deal with profiles. You can check out the rest of them using the Python Plugin installer.

1 The Profile plugin requires the Python module for QWT5. If you don’t have this installed, a warning will be displayed during the installation process.

This week we highlight the Points to Paths plugin, a handy way to convert a series of points into line features. This plugin lets you “connect the dots” based on an common attribute and a sequence field. The attribute field determines which points should be grouped together into a line. The sequence field determines the order in which the points will connected. The output from this plugin is a shapefile.

Let’s take a look at some example data. Here we have some fictional wildlife tracking data for two moose. The tracking data is in shapefile format, but you can use any vector format supported by QGIS. The tracking data is symbolized by our two animals: Moose1 and Moose2:

The moose_tracks layer has an animal tracking id field (animal_tid) and a sequence field (id). This is all we need to convert the individual observations into a line feature that represents the animals path.

Installing the Plugin

The Points to Paths plugin is installed like all other Python plugins. From the the Plugins menu in QGIS, choose Fetch Python Plugins. This brings up the plugin installer. To find the plugin, enter points to in the Filter box, then select Points to Paths from the list. Once it’s highlighted, click the Install plugin button. This will download the plugin from the repository, install it, and load it into QGIS.

Using the Plugin

Let’s convert the tracking data to paths. To get started, choose Plugins->Points to Paths from the menu or click on the Points to Paths tool on the Plugin toolbar. This brings up the PointsToPaths dialog box where we specify the paramaters needed to create the paths. Below is the completed dialog for our moose tracks:

The first drop-down box contains a list of the vector layers loaded in QGIS—in our case we have just one: moose_tracks. For the group field drop-down we chose the field that contains the tracking identifier for each animal. This determines which points will be selected and grouped to form an individual line. The point order field drop-down specifies the field that contains the ordering for the observations. In this case, the id field is incremented with each observation and can be used to construct the paths. We don’t have a date/time field in this data, but your observations may be sequenced in this way. The Python date format field allows you to specify a format string so the plugin can determine how to sequence your points based on date/time.

The last thing we need to specify is the output shapefile. You can do this by typing in the full path to a new shapefile or by using the Browse button.

With these options set, clicking the OK button will create the new shapefile containing the paths created from our point observations. Once the shapefile is created, the plugin gives you the option to add the new shapefile directly to QGIS.

The Result

The result of our simple example are shown below:

We symbolized the individual tracks using the Categorized renderer on the Style tab of the vector properties dialog. You can see we now have a track for each animal. The attribute table created by the plugin contains the following fields:

• group – the name of the animal taken from the field we chose as the group field
• begin – the value of the first point order field used to create the path
• end – the value of the last point order field used to create the path

In our data, group contains the animal name, begin the value of the lowest id field for animal, and end contains the greatest id value. In a more typical data set, begin and end would contain the start and end date/time values for the observation. Labeling the observation points with our sequence field or date/time values would allow us to determine the direction of movement.

If you have point data that represent a movement of an object, this plugin is a great way to convert it into a path that can be used for visualization, analysis, or map composition.

QGIS has a lot of plugins, including over 180 that have been contributed by users. If you aren’t using plugins, you are missing out on a lot that QGIS has to offer. I’m starting what I hope to be a regular feature: Plugin of the Week. This week we’ll take a look at Time Manager.

Time Manager lets you browse spatial data that has a temporal component. Essentially this includes anything that changes location through time. Examples include:

• Wildlife tracking
• Vehicles
• Storm centers
• QGIS users

Data Preparation

Expanding on our last post about QGIS Users Around the World, we’ll use Time Manager to watch access to the QGIS Python plugin repository through time. If you refer to the previous post, you’ll see that all the IP addresses contacting the repository were extracted from the web server log and geocoded to get the approximate geographic coordinates. To use Time Manager all we need is the time for each access to the repository.

A important part (for our purpose) of the web server log entry looks like this:

66.58.228.196 - - [23/Oct/2011:21:17:54 +0000] "GET /repo/contributed HTTP/1.1" 200 256

Time Manager supports date/time in the following formats:

• YYYY-MM-DD HH:MM:SS
• YYYY-MM-DD HH:MM
• YYYY-MM-DD

As you can see, this doesn’t work with the format in the web server log.

The geocoding process created a file containing IP address, country, city (where available), latitude and longitude. This file is used to create a Python dictionary to look up locations by IP address. Using this file and a bit of Python, the web server log entries were converted into a CSV file containing:

ip,date_time,country,latitude,longitude
66.58.228.196,2011-10-23 21:13:53,United States,61.2149,-149.258
66.58.228.196,2011-10-23 21:14:22,United States,61.2149,-149.258
66.58.228.196,2011-10-23 21:17:54,United States,61.2149,-149.258
66.58.228.196,2011-10-23 21:18:04,United States,61.2149,-149.258
...


The CSV file was first converted to a shapefile using the QGIS Delimited Text plugin. Performance with Time Manager was somewhat slow using a shapefile containing 134,171 locations. The shapefile was imported into a SpatiaLite database (you can do this using ogr2ogr or the SpatiaLite GUI).

Using Time Manager

To display the progression of access to the repository (and thus users of QGIS), we first have to have the Time Manager plugin installed.^[1] Once installed, we enable it using the Plugin Manger.

As you can see from the screenshot above, Time Manager installs a new panel in QGIS that sits below the map canvas. You can set a number of options by clicking Settings; the most important being the layer to use in the visualization. For the QGIS users, we use the time_manager_req layer that was created from the web server logs. With the location and date/time data in the proper format, you can click the “Play” button to start the display. For each time interval, the plugin selects the appropriate entries and displays a frame for the duration specified in the settings.

You can use the time slider to move around or move the time interval forward or backward using the buttons on each end of the slider.

A really nice feature is the ability to export to video. At present this saves a PNG file and world file for each time interval. You can then use another software package to combine these to create a video animation of the time sequence. Once solution is to use mencoder^[1]:

mencoder "mf://*.PNG" -mf fps=10 -o output.avi -ovc lavc -lavcopts vcodec=mpeg4

Putting all this together gives us the following visualization of QGIS user activity from October 23 through December 19, 2011:

QGIS User Activity

You can see the “wave” of activity progress from east to west as the daylight hours come and go.

Summary

If you have spatial data with a date or time component, the Time Manager plugin provides a convenient way to visualize the temporal relationships.

[1]http://www.geofrogger.net/trac/wiki

One of the difficult things to track in the open source world is the number of people who actually use your software. In the proprietary commercial world you have licenses, invoices, and so forth. In the case of QGIS, we can track the total number of downloads from qgis.org, but this doesn’t represent the total installed base. It is impossible to accurately determine the actual number of people using QGIS, but we can get an approximation of the number and where they are in the world.

The analysis was done using the log files from the QGIS contributed repository:

• The IP address of each entry that retrieved the plugin list from the server represents one or more users—these IPs were collected into a unique list
• Using a Python script, each IP address in the log was geocoded to get the approximate latitude and longitude of the user
• The IP address, country, latitude, and longitude were written to a CSV file
• The CSV file was converted to a Spatialite layer to create the map of users

The map represents 35,603 unique IP addresses of users that accessed the repository between October 23, 2011 and December 17, 2011.

The geocoding process varies in precision—some IPs are located to the city level while others only return a general location for the country.

Some assumptions and observations:

• Most (maybe all) users make use of Python plugins and therefore access the contributed repository at some point
• Country-level points (blue) on the map represent more than one user
• Some points represent organizations that use a single IP for all users accessing the Internet. These points will represent more than one user
• Some users may access the repository from more than one IP address

So how many people use QGIS? At the very minimum, 35,000. We know that the downloads of just the Windows version exceeded 100,000. Given that there are 7,183 IP addresses that are generalized to a country location, we can safely assume that the number of actual users is much higher than that.

Considering the number of points that represent an organization and those that represent a country location, I think we can safely assume that the number of QGIS users easily exceeds 100,000 worldwide.

The Plugin Builder allows you to quickly create a skeleton Python plugin by generating all that boring boilerplate that every plugin requires.

Here is a short video showing how to create, compile, and install a new plugin.

For more information, see QGIS Workshop Documentation and the PyQGIS Cookbook.

This is a project I have had lingering around for a while. It is a geospatial data browser written in Python using the PyQt and QGIS bindings. It allows you to navigate a tree structure and preview raster and vector datasets. Metadata extracted from the data can be viewed as well. It supports drag and drop for any target that accepts filenames (e.g. QGIS). For screenshots and more, see http://geoapt.com/geoapt-data-browser.

The code is now available on GitHub (see https://github.com/g-sherman/GeoApt) and ready for you to contribute. Take a look and if you want to get your hands dirty fork the project and start coding.

Lots of features are missing—consider this an alpha version…