Eyes that follow the cursor in QGIS

I did this all in some random EPSG:25832 location and scale, this uses several magic numbers that make it work for that. I did not make it work for any CRS or canvas size. If you do, please share. But this is just silly fun so …..

Have two polygons for the eyes.

Set their Symbol Layer Type to Geometry Generator and smooth them:

smooth($geometry, 3)

Add another Geometry Generator symbol layer to it and throw in the following magic expression to build the pupils. It calculates the distance from your cursor to the centroids of the polygons and it prepares a line from each centroid to the cursor. Then it places a point geometry onto that line at a fraction of the distance. Use the Geometry Type “Point / MultiPoint” for this Geometry Generator.

with_variable(
  'distance',
  distance(@canvas_cursor_point, centroid($geometry)),
  with_variable(
    'line',
    make_line(centroid($geometry), @canvas_cursor_point),
    line_interpolate_point(@line, @distance/5)
  )
)

Set the layer itself to automatically refresh its rendering in the layer’s properties:

Now the eyes will follow your cursor as it moves across the map canvas!

Task for you: The pupils are not clipped and can exit the eyes. Oops! Head over to Topi’s for a hint how to solve this: https://twitter.com/tjukanov/status/1278689814288760837

And then you paint the rest of the fucking owl:

Those are lines with 3 vertices each:

And the middle vertex is moved vertically using the expression below. Basically the line is reconstructed.

smooth(
  make_line(
    start_point($geometry),  -- first point is kept
    translate(
      point_n($geometry, 2),  -- second point is translated
      0,
      distance(
        @canvas_cursor_point, centroid($geometry)
      )/10  -- move according the cursor distance to centroid
    ),
    end_point($geometry)  -- last point is kept
  ),
  4
)

Here is my project file including the temporary scratch layers (use the awesome Memory Layer Saver plugin to have them loaded automatically):

Das eigene kleine Deutschlandradio Archiv

Mediatheken des Öffentlich-rechtlichen Rundfunks müssen wegen asozialen Arschlöchern ihre Inhalte depublizieren. Wegen anderer Arschlöcher sind die Inhalte nicht konsequent unter freien Lizenzen, aber das ist ein anderes Thema.

Ich hatte mir irgendwann mal angesehen, was es eigentlich für ein Aufwand wäre, die Inhalte verschiedener Mediatheken in ein privates Archiv zu spiegeln. Mit dem Deutschlandradio hatte ich angefangen und mit den üblichen Tools täglich die neuen Audiobeiträge in ein Google Drive geschoben. Dieses Setup läuft jetzt seit mehr als 2 Jahren ohne Probleme und vielleicht hat ja auch wer anders Spaß dran:

Also:

  • rclone einrichten oder mit eigener Infrastruktur arbeiten (dann die rclone-Zeile mit z.B. rsync ersetzen)
  • <20 GB Platz haben
  • Untenstehendes Skript als täglichen Cronjob einrichten (und sich den Output zu mailen lassen)
#!/bin/bash

# exit if anything fails
# not a good idea as downloads might 404 :D
set -e

cd /home/dradio/deutschlandradio

# get all available files
wget -nv -nc -x "http://srv.deutschlandradio.de/aodlistaudio.1706.de.rpc?drau:page="{0..100}"&drau:limit=1000"
grep -hEo 'http.*mp3' srv.deutschlandradio.de/* | sort | uniq > urls

# check which ones are new according to the list of done files
comm -13 urls_done urls > todo

numberofnewfiles=$(wc -l todo | awk '{print $1}')
echo "${numberofnewfiles} new files"

if (( numberofnewfiles < 1 )); then
        echo "exiting"
        exit
fi

# get the new ones
echo "getting new ones"
wget -i todo -nv -x -nc || echo "true so that set -e does not exit here :)"
echo "new ones downloaded"

# copy them to remote storage
rclone copy /home/dradio_scraper/deutschlandradio remote:deutschlandradio && echo "rclone done"

## clean up
# remove files
echo "cleaning up"
rm -r srv.deutschlandradio.de/
rm -rv ondemand-mp3.dradio.de/
rm urls

# update list of done files
cat urls_done todo | sort | uniq > /tmp/urls_done
mv /tmp/urls_done urls_done

# save todo of today
mv todo urls_$(date +%Y%m%d)

echo "done"

In zwei Jahren sind rund 2,5 Terabyte zusammengekommen und ~300.000 Dateien, aber da sind eventuell auch die Seiten des Feeds mitgezählt worden.

Wer mehr will nimmt am besten direkt die Mediathekview-Datenbank als Grundlage.

Nächster Schritt wäre das eigentlich auch täglich nach archive.org zu schieben.

Highlight current timeslice in a QGIS Atlas layout

Did this for an ex-colleague some months ago and forgot to share the how-to publically. We needed a visual representation of the current time in a layout that showed both a raster map (different layer per timeslice) and a timeseries plot of an aspect of the data (this was created outside QGIS).

Have lots of raster layers you want to iterate through. I have:

./ECMWF_ERA_40_subset/2019-01-01.tif
./ECMWF_ERA_40_subset/2019-01-02.tif
./ECMWF_ERA_40_subset/2019-01-03.tif
./ECMWF_ERA_40_subset/2019-01-04.tif
./ECMWF_ERA_40_subset/2019-01-05.tif
./ECMWF_ERA_40_subset/2019-01-06.tif
...

Create a new layer for your map extent. Draw your extent as geometry. Duplicate that geometry as many times as you have days. Alternatively you could of course have different geometries per day. Whatever you do, you need a layer with one feature per timeslice for the Atlas to iterate though. I have 30 days to visualise so I duplicated my extent 30 times.

Open the Field Calculator. Add a new field called date as string type (not as date type until some bug is fixed (sorry, did not make a note here, maybe sorting is/was broken?)) with an expression that represents time and orders chronologically if sorted by QGIS. For example: '2019-01-' || lpad(@row_number,2,0) (assuming your records are in the correct order if you have different geometries…)

Have your raster layers named the same way as the date attribute values.

Make a new layout.

For your Layout map check “Lock layers” and use date as expression for the “Lock layers” override. This will now select the appropriate raster layer, based on the attribute value <-> layer name, to display for each Atlas page.

Cool, if you preview the Atlas now you got a nice animation through your raster layers. Let’s do part 2:

In your layout add your timeseries graph. Give it a unique ID, e. g. “plot box”. Set its width and height via new variables (until you can get those via an expression this is needed for calculations below).

Create a box to visualise the timeslice. Set its width to map_get(item_variables('plot box'), 'plot_width') / @atlas_totalfeatures. For the height and y use/adjust this expression: map_get(item_variables('plot box'), 'plot_height'). For x comes the magic:

with_variable(
'days_total',
day(to_date(maximum("date"))-to_date(minimum("date")))+1,
-- number of days in timespan
-- +1 because we need the number of days in total
-- not the inbetween, day() to just get the number of days
with_variable(
'mm_per_day',
map_get(item_variables('plot box'), 'plot_width') / @days_total,
with_variable(
'days',
day(to_date(attribute(@atlas_feature, 'date'))-to_date(minimum("date"))),
-- number of days the current feature is from the first day
-- to_date because BUG attribute() returns datetime for date field
@mm_per_day * @days + map_get(item_variables('plot box'), 'plot_x')
)
)
)

This will move the box along the x axis accordingly.

Have fun!

How to get a dataset out of OpenStreetMap (OSM) and into QGIS easily

For small datasets just use the QuickOSM plugin, enter your key=value and it will load the data directly as QGIS layer(s).

For big datasets don’t run massive queries on the Overpass API but use prepared thematic or regional OpenStreetMap extracts.

For example, if you want all things tagged place=town globally, grab place_EPSG4326.zip from http://download.osmdata.xyz/ and run a filter on that locally.

Don’t be scared of processing a global OSM planet file either, Osmium makes filtering OSM data extremely easy:

osmium tags-filter in.osm.pbf n/place=town -o place=town.osm.pbf

Yes, a planet file is pretty big, but extracting specific features from that is not a big data problem and you must not be scared of it. Downloading the file will probably take you magnitudes longer than extracting something from it. For me it was 45 minutes for the download, then about 8 minutes for extracting on a seriously slow (~60MB/s) spinning metal hard disk drive (no SSD).

And yes, you can load OSM PBF directly into QGIS thanks to GDAL’s support for the format.

While we are at it: Don’t use GeoJSON for anything but data transfer and maybe storage. It is not an efficient format to power your layers and GIS analyses. (OSM PDF isn’t either.)

#30DayMapChallenge: Day 3 – Polygons OR Lego® style brick raster in QGIS using Geometry Generator expressions

I showed this at the annual meeting of the swiss user group in June 2019 and promptly forgot to post it for everyone to see. Let’s blame the scenery?

Have a raster.

Processing -> Create Grid, covering it with points in a spacing of your choice. Use the same CRS as your raster (unless you want to figure out expression-based geometry transformations on your own, like in my elevation lines code)

Change the Symbol layer type to Geometry Generator and enter

with_variable(
  'radius',
  3333,
  buffer($geometry, @radius, 16)
)

where radius should be a value about one third of your spacing.

You should see circles!

For the fill color use this expression and adjust the name of your raster layer:

with_variable(
    'raster_layer',
    'DHM200.xyz',
    ramp_color('Blues',  -- change to an other named ramp here if you like
        scale_linear(
            raster_value(
                @raster_layer, 1, 
                centroid($geometry)  -- back to our point
            ),
            raster_statistic(
                @raster_layer, 1, 
                'min'
            ),
            raster_statistic(
                @raster_layer, 1, 
                'max'
            ),
            0, 1  -- new scale as color ramps go 0 to 1
        )
    )
)

This will get the raster value below our grid point and fit it onto a color ramp between the min and max of all the raster values.

For the stroke use the same expression but wrap darker(..., 150) around it so you get a darker color.

Using Draw Effects add a small Drop Shadow to your circles. I used an offset of 0.5 mm and a blur radius of 1 mm.

Now add another Geometry Generator symbol layer below your existing one and use the following expression:

bounds(buffer($geometry, 5000))

with_variable(
  'radius',
  5000,
  bounds(
    buffer($geometry, @radius)
  )
)

with the radius being half your grid spacing.

Use the same expressions for the colors as above but set the darker value to 200.

For some more fanciness maybe add a “QGIS” text on top of the nupsies?

Exercise: Make it so that the result perfectly covers the raster, instead of being one grid cell off like mine.

Thanks Topi.

#30DayMapChallenge: Day 2 – Lines

Thanks Topi.

Addresses in Hamburg: http://suche.transparenz.hamburg.de/dataset/alkis-adressen-hamburg15

Convert the housenumber from string to integer: to_int("hausnr")

Processing -> Points to Path using above housenumber as order field and strname as group field.

Color those ziggyzaggety lines as you like.

Maybe explode the lines and style each segment by its housenumber?

And put them on an interactive slippy map.

Dynamic elevation profile lines as QGIS geometry generator

Note: You need the current master build of QGIS or wait for QGIS 3.10. I’m a cool kid.

Load a raster layer in QGIS.

Add a new Scratch Layer of Polygon type (in any CRS). Set its Symbology to Inverted Polygons. Use a Geometry Generator as symbol layer type. Set it to LineString/MultiLineString. Enter the expression below and adjust the layer ID (best enter the expression editor and find it in the “layers” tree). Then adjust the scaling factor at the very bottom.

-- UPPER CASE comments below are where you can change things

with_variable(
	'raster_layer',
	'long_and_complicated_layer_id',  -- RASTER LAYER to sample from
	-- this collects all the linestrings generated below into one multilinestring
	collect_geometries(
		-- a loop for each y value of the grid
		array_foreach(
			-- array_foreach loops over all elements of the series generated below
			-- which is a range of numbers from the bottom to the top of y values
			-- of the map canvas extent coordinates.
			-- the result will be an array of linestrings
			generate_series(
				y(@map_extent_center)-(@map_extent_height/2), -- bottom y
				y(@map_extent_center)+(@map_extent_height/2),  -- top y
				@map_extent_height/50  -- stepsize -> HOW MANY LINES
			),
			
			-- we want to enter another loop so we assign the name 'y' to
			-- the current element of the array_foreach loop
			with_variable(
				'y',
				@element,
				
				-- now we are ready to generate the line for this y value
				make_line(
					-- another loop, this time for the x values. same logic as before
					-- the result will be an array of points
					array_foreach(
						generate_series(
							x(@map_extent_center)-(@map_extent_width/2), -- left x
							x(@map_extent_center)+(@map_extent_width/2),  -- right x
							@map_extent_width/50  -- stepsize -> HOW MANY POINTS PER LINE
						),
						-- and here we create each point of the line
						make_point(
							@element,  -- the current value from the loop over the x value range
							@y  -- the y value from the outer loop
							+   -- will get an additional offset to generate the effect
							-- we look for values at _this point_ in the raster, and since
							-- the raster might not have any value here, we must use coalesce
							-- to use a replacement value in those cases
							coalesce(  -- coalesce to catch raster null values
								raster_value(
									@raster_layer,
									1,  -- band 1, *snore*
									-- to look up the raster value we need to look in the right position
									-- so we make a sampling point in the same CRS as the raster layer
									transform( 
										make_point(@element, @y),
										@map_crs,
										layer_property(@raster_layer,'crs')
									)
								),
								0  -- coalesce 0 if raster_value gave null
							-- here is where we set the scaling factor for the raster -> y values
							-- if things are weird, set it to 0 and try small multiplications or divisions
							-- to see what happens.
							-- for metric systems you will want to multiply
							-- for geographic coordinates you will want to divide
							)*10  -- user-defined factor for VERTICAL EXAGGERATION
						)
					)
				)
			)
		)
	)
)  -- wee

If you don’t have your raster data on a SSD this can be a bit slow.

Yes, this works if you change your CRS!

Anaglyph 3D contour lines

A slight misunderstanding about a weird pattern I posted to Twitter earlier made me wonder how easy it might be to present elevation data with real depth ™ as anaglyph 3D in QGIS. Anaglyph 3D, you know, those red and cyan glasses that rarely ever worked.

You might remember my post on fake chromatic aberration and dynamic label shadows in QGIS some months ago. Maybe with some small adjustments of the technique…?

Wikipedia has an detailed article on the topic if you are interested. It boils down to: One eye gets to see not the red but the cyan stuff. The other sees the red but not the cyan. By copying, coloring and shifting elements left-right increasingly the human vision gets tricked into seeing fake depth.

Got your anaglyph glasses? Oogled some images you found online to get in the mood? Excellent, let’s go!

Get some DEM data. I used a 10 meter DEM of Hamburg and a 200 meter DEM of Germany for testing. Make sure both your data and your project/canvas CRS match or you have a hard time with the math.

Extract contours at a reasonable interval. What’s reasonable depends on your data. I used an interval of 5 meters for the 10 meter DEM as Hamburg is rather flat (which you will NOT see here).

You may want to simplify or smooth this.

Now it’s time to zoom in, because the effect only works if the line density is appropriately light.

Now we need to duplicate those, color them and move them left and right.

Change the symbol layer of your contours to a Geometry Generator.

Let’s use that first layer for the left, red part. So change the color to red. Use a red that vanishes when you look through your left (red) eye but is clearly visible through the right (cyan) eye. The exact color depends on your glasses.

Set the Geometry Generator to LineString. I will now explain an intermediate step so if you just want the result, scroll a bit.

translate(
  $geometry,
  -  -- there is a minus here!
  (
    x_max(@map_extent) - x(centroid($geometry))
  )
  /100  -- magic scale value…
  ,
  0  -- no translation in y
)

This moves each contour line to the left for a value that increases with the geometry’s distance to the right side. Since we don’t want to move the geometries too far, a magic scale factor needs to be added and adjusted according to your coordinate values.

(Yes, that is a bug right here (centroid is a bad metric and some of the contours are huge geometries) but hey it works well enough. Segmentizing could fix this. Or just extract the vertices, that looks cool too TODO imagelink)

For the right, cyan side we need to add another Geometry Generator symbol layer, color it in cyan (so that you can only see it through your left eye) and do the geometry expression the other way around:

translate(
  $geometry,
  (
    x(centroid($geometry))-x_min(@map_extent)
  )
  /100  -- magic scale value…
  ,
  0  -- no translation in y
)

Cool! But this is lacking a certain depth, don’t you think? We need to scale the amount of horizontal shift by the elevation value of the contour lines. And then we also need to adjust the magic scale value again because reasons.

For the red symbol layer:

translate(
  $geometry,
  -- move to the LEFT
  -- scaled by the distance to the RIGHT side of the map
  -- scaled by the elevation

  -- minus so it goes to the left
  -
  "ELEV" -- the attribute field with the elevation values
  *
  (x_max(@map_extent)-x(centroid($geometry)))
  
  -- MAGIC scale value according to CRS and whatever
  /10000
  ,
  -- no change for y
  0
)

For the cyan symbol layer:

translate(
  $geometry,
  -- move to the RIGHT
  -- scaled by the distance to the LEFT side of the map
  -- scaled by the elevation
 
  "ELEV" -- the attribute field with the elevation values
  *
  (x(centroid($geometry))-x_min(@map_extent))
  
  -- MAGIC scale value according to CRS and whatever
  /10000
  ,
  -- no change for y
  0
)

That’s it!

Now, who is going to do Autostereograms? Also check out http://elasticterrain.xyz.

Make sure you actually do use spatial indexes


Ever ran some GIS analysis in QGIS and it took longer than a second? Chances are that your data did not have spatial indexes for QGIS to utilise and that it could have been magnitudes faster.

I realised just today, after years of using QGIS, that it did not automatically create a spatial index when saving a Shapefile. And because of that, lots of GIS stuff I did in the past, involving saving subsets of data to quick’n’dirty Shapefiles, was slower than necessary.

Sadly QGIS does not communicate lack of spatial indexing to the user in any way. I added a feature request to make Processing warn if no indexing is available.

An example: Running ‘Count points in polygon’ on 104 polygons with 223210 points:

  • Points in original GML file: 449 seconds
    • GML is not a format for processing but meant for data transfer, never ever think of using it for anything else
  • Points in ESRI Shapefile: 30 seconds
  • Points in GeoPackage: 3 seconds
  • Points in ESRI Shapefile with spatial index: 3 seconds
    • Same Shapefile as before but this time I had created a .qix index

So yeah, make sure you don’t only use a reasonable format for your data. And also make sure you do actually have an spatial index.

For Shapefiles, look for a .qix or .sbn side-car file somewhere in the heap of files. In QGIS you can create a spatial index for a vector layer either using the “Create spatial index” algorithm in Processing or the button of the same title in the layer properties’ source tab.

PS: GeoPackages always have a spatial index. That’s reason #143 why they are awesome.