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May and June of 2017

This is just the usual summary of recent events, outside of the more important ones that I have already covered in a few separate posts on the blog. One of these is that the blog turned 10 years old in June! I have been writing here for almost 1/3 of my life…

First of all, cycling: after coming home from La Palma I was not really in the mood for cycling for a while, and one of the main reasons was the bad weather. It is not unreasonable to ask for weather that is warm enough to put the winter outfits away at the end of April, right? Luckily while visiting the Netherlands with my parents, spring finally arrived for good, and the temperature in the past two months (at least when I was out riding, with an average of 19.5°C and 19.6°C) was even better than in the same period of the previous 3-4 years! Belgium is in the middle of a quite significant drought right now, so rain was not an issue either. As a result I had my third best month on the bike ever with 1399 kilometres in May (including nearly 11000 metres of elevation gain, which is a best when not counting months when I was riding outside of Belgium, and almost exactly 50 hours on the bike, which is good for a fourth place overall in my cycling career).

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I started by doing the Tour de Namur, where after 100 very strong kilometres I lost all my power and felt like dying on the climbs, therefore the remaining more than two hours were really difficult, maybe because I did not bike for almost two weeks before that, or simply because I was way too hot with arm warmers and a merino base layer in 22°C… That was stupid, but it was only 8°C at the start!

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Then a week later on the usual Saturday morning Squadra Tornado group ride I had very good legs, so I made some extra plans for the following week. Mainly thanks to taking a day off from work and going to the Ardennes for a 140 km loop over the highest point of Belgium (which by the way was really beautiful, but after 100 km I was dead again, thanks to the wind and the 30°C), this became a week of 475 km on the bike (with 4400 m+). Far from being on the podium, but a good one nevertheless (and likely my best for 2017). Then I was crazy enough to join Willem for a sprint training on one of the evenings, which was on one hand pretty cool (because I love sprints), but on the other hand I was so empty by the end, that when I arrived home Clio was worried that I would pass out on the couch. (I was just fine. Really.) Two days later I rode the Buurthuis Classic with Steven (Willem’s brother, who is probably going to kick my ass too after another year on the bike), but I could still feel the sprints in my legs. To keep things interesting I also did some off road biking here and there, and a few chill rides too, once even involving cake and coffee in Mechelen. Finally, last Saturday I did the first 200+ km ride of the year by doing the longest version of the Ti’Light Classic, extended with biking to the starting point in Tienen and then back home from the finish, making it a nice 219 km day. It was a great ride with good legs, until reaching the 150 km mark I was not tired at all, and only the last 25-30 kilometres were difficult, fighting alone against the headwind. This week it is basically too hot to bike (but I still went out on two evenings).

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In the good weather we also had a BBQ (thanks for inviting us Lies and Willem), cocktails in the city centre, ice creams on a boat, and I made my first non no-bake cheesecake (it was delicious). Moreover, we went to cheer on one of Willem’s many races, and I took some photos too, a few of which turned out quite OK ;)

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The end of another Premier League season arrived for Liverpool FC and I had a few nervous moments during the last weeks of it (because it would not be the “Liverpool Way” if there was nothing to worry about until the second half of the last game), but at the end everything turned out OK. They kept a clean sheet during the last 4 games while scoring 8 goals and collecting 10 out of the possible 12 points, and the team finished in the Top 4. This means that next season they will be in the  Champions League again (assuming they go through the play offs)! Also, the new jersey looks awesome (back to the classical Liverpool red).

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Concerning work, I took over the twitter account of the institute, I organised (delegated all the work to others) the open door days, and coordinated our outreach group during the past months. I think we are doing all right, but if the institute had an actual employee to do this as a full time (or 50%) job, things could be improved a lot.

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We are leaving on a road trip to Scandinavia soon, and I am planning on posting a few pictures along with a short text every day here, so the blog might get busier for a while again :) Thanks for reading!

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The Leuven Star Atlas – making a publication quality stellar atlas from scratch

1. Motivation

I am a professional astronomer who loves maps. But let me start with a small background story: I joined the Hungarian Astronomical Association in 1999 (the year of the great European total solar eclipse) at the age of 14, just after receiving a 76/700 Newton reflector from my parents for Christmas. Even though my love of astronomy dates back to long before that, joining the HAA was the start of my actual amateur astronomy career. I went on youth astronomy camps, where I tried to find as many Messier objects as I could under the dark rural skies of the Mátra mountains (besides regularly drawing sunspots and observing variable stars whenever I could), and for this I definitely needed a sky map.

Back then – just like 99% of the Hungarian amateurs – I had no money to order any of the nice star atlases from abroad (e.g., the actual edition of Uranometria 2000.0 was definitely high on my wish-list, but always way over my budget), so I used mostly the AAVSO charts for variable stars, and an “atlas” that I printed at home using a free version of the SkyMap Pro software. It took a few years until I managed to buy a proper atlas, thanks to the release of the “Égabrosz” in 2004, which was the first professional-quality sky atlas produced in Hungary. It was (and still is) a great black and white atlas positioned somewhere between Uranometria 2000.0 and Sky Atlas 2000.0 (based on its limited magnitude and resolution).

My practical participation in amateur astronomy ceased to continue soon after I started my university studies, and moving under the heavily light-polluted skies of Belgium did not help to reignite my interests later on, but I always kept my fascination towards mapping (the night sky), and I always wanted to make a proper (sky) atlas myself. Now that I know quite a lot about astronomical data, and I know how to make nice plots using the python programming language, I though the time was just about right to actually try and see if I can really make one. How difficult could it be, right?

2. Introduction

My goal was to produce a publication quality, both practical and visually pleasing star atlas aimed at amateur astronomers. To do this I had to first study the atlases currently available on the market, because I did not want to reinvent the wheel. Here is a non-complete list of the probably most widely-known atlases on the market:

While there are also a few free well-known atlases online:

Each atlas has their pros and cons. It is beyond the scope of this post to compare these atlases (but you find a nice comparison here, the text is in Polish, but the numbers and pictures are more important anyway), but there are a few things to consider when planning a new one. These are:

  • Format (size, number of pages)
  • Grayscale or colour
  • Legibility (colours, font types, label placement, print quality)
  • Resolution (scale in cm per degree of declination)
  • Limiting magnitude (typically 7.5 – 11.5)
  • Database and selection of non-stellar objects
  • Representation of objects (marker shapes, sizes, etc.)
  • Consistency (especially colour, line width, and font size)

I wanted to mix the best ingredients of these atlases, building around these main ideas:

  • A layout similar to the one of Uranometria 2000.0
  • Colours similar to Interstellarum Deep Sky Atlas
  • Milky way contours similar to Sky Atlas 2000.0

And then add the following refinements / improvements into the mix:

  • Slightly larger field of view (FOV) than in Uranometria 2000.0 with the full sky on 344 A4 – or alternatively B4 – pages (1.6 cm per degree or 1.9 cm per degree)
  • Non-stellar objects are printed to scale (down to a cutoff size), line width and fill colour relating to the actual brightness
  • Legible colour scheme: avoid using red since it is invisible under red light
  • Precise Milky Way representation (unlike in any printed atlas)
  • Clever navigation between pages
  • Hand-drawn bright and dark nebula outlines from scratch
  • Nice custom typography
  • Vector graphics
  • Automated label placement
  • Automated compilation of the full atlas by a press of the button
  • Flexible script (change FOV, magnitude limit, colours with ease)

I will go over all these aspects in detail in the following section and explain step-by-step how I built the atlas. As it is not 100% done yet, I will also note down the features that still need to be completed or refined in the (hopefully) near future. I started working on this during the first days of March, and by the middle of April most of the things that I am going to discuss here were done. Since then I spent less time on the project and took care of minor refinements only.

3. Practical execution – techniques and design aspects

3.1 Python setup

I am using a very basic setup installed via Canopy. My main environment is python 2.7, and I make use of the following packages extensively:

  • numpy for all kinds of data handling and numerical operations
  • pylab / matplotlib for all the main plotting operations
  • basemap for the mapping (takes care of the projection and the related transformations)
  • scipy for some specific interpolations and contours connected to the Milky Way
  • astropy and pyephem for celestial coordinate transformations

3.2 Source data

All databases that I am using are either publicly available from the internet (under various licences), or they are compiled by me from publicly available data (which is discussed in the following sections).

3.3 Custom, and compiled / processed databases

While some of the data can be used as is (for example the data of constellation boundaries and lines), most data needs to be first processed in a way or another before it is ready to be plotted. There are three large groups that need to be dealt with: stellar data, milky way contour data, and deep sky data (with a large stress on extended bright and dark nebulae).

3.3.1 Stellar database

Building the stellar database is taken care of inside the main plotting script (which will be discussed in detail in Section 3.4). Every time the script is executed, it checks if a precompiled magnitude limited stellar database is already available, and if not, it builds it from the databases discussed in Section 3.2. When the script is ran with a given magnitude limit for the first time, a new magnitude limited database must to be compiled, which happens in multiple stages (and at a magnitude limit of 10.0 it takes ~3 hours to complete):

  1. Tycho-2 data read in and magnitude limit applied (after computing V magnitudes from the available BT and VT magnitudes), magnitude limited Tycho-2 data saved.
  2. Bright Star Catalog (BSC) data read in, magnitude limit applied, names formatted to a pylab / LaTeX compatible format (so when annotating later on, proper Greek characters will appear automatically), magnitude limited BSC data saved.
  3. General Catalogue of Variable Stars (GCVS) data read in, stars in maximum dimmer than the magnitude limit thrown out, stars with an amplitude less than 0.1 mag (rounded to 1 decimal) are thrown away, and stars of some selected types such as Novae and Supernovae are thrown out, names formatted, then the remaining data is filtered and formatted, magnitude limited GCVS data is saved.
  4. The filtered GCVS data is used to update the filtered Tycho-2 data. First the GCVS coordinates are cross-matched one-by-one with the Tycho-2 coordinates (within 6″). There are three possible outcomes of this match:
    a) there are multiple Tycho-2 stars within 6″ from the coordinates of a given GCVS star (which happens sometimes in crowded areas), then we use the maximum magnitude information to match the Tycho-2 magnitudes, and update the best matching Tycho-2 entry with minimum and maximum magnitudes, the GCVS name, and a variability flag = 1
    b) there is only one match (which is the most common case), when life is easy, the GCVS entry is for that Tycho-2 star, so we add the magnitudes, the variable name, and the variability flag = 1 to the Tycho-2 entry
    c) there is no match (a rare case, but happens, e.g. when an irregular variable was much dimmer during the Hipparcos measurements then the magnitude limit, but on a longer period it can get brighter), then we need to add the GCVS star as it is to the compiled catalog, because it is not yet in the magnitude limited Tycho-2 sample.
    After this is done, the magnitude limited database with variability information is saved, and later on in the plotting stage stars with a variability flag = 1 will be plotted with a spacial symbol.
  5. In the next step, this database is updated with multiplicity information (in our case a multiplicity flag, and a separation value for the two brightest components). The reason for this is that for stars that appear closer to each other than a threshold (in our case this is set to be 60″), we do not want to plot each component, only the brightest one with a special symbol, which is common practice in stellar atlases. (Small not here: at this point, components that are closer to each other than 0.8″ are not included, but resolving stars that are so narrowly separated is practically impossible visually and/or without professional telescopes and exceptional weather conditions.) This is also done in multiple steps:
    a) stars are ordered from brightest to dimmest, each given a multiplicity flag = 0 to start with
    b) starting from the brightest we check each star one-by-one if there are other (dimmer) stars within 60″ from it
    c) if yes, then the brightest component (A) gets a multiplicity flag = 1 and the dimmer component(s) (B,…) get(s) a multiplicity flag = -1
    d) in the next steps only stars with multiplicity flag != -1 are tested as potential components, which means that, e.g., we will not identify a dimmer star later on as primary component when it was already found to be a secondary component of a brighter star earlier
    e) later on in the plotting stage only those stars will be plotted where the multiplicity flag is not -1, and from these the stars where the multiplicity flag = 1 will be plotted using a special symbol.
    When this is done, the data file is saved (now containing a magnitude limited Tycho-2 database that is updated with variability and multiplicity information). This step takes the longest time of all by a significant margin.
  6. In the last step this database is updated with names from the precompiled magnitude limited BSC file that was saved in step 2, similarly to how the GCVS entries were matched in step 4, but with a slightly higher tolerance in the coordinate match (to adjust for the possible apparent merging of multiple stars’ coordinates in step 5). Then this database is saved as the final stellar database of the atlas.

At this stage here is how a small section of the compiled stellar database looks like with a pair of coordinates, a pair of proper motions, a visual magnitude (which is a maximum brightness for variables), a variability flag, a possible minimum brightness (set to 99.000 for non variable stars), a multiplicity flag, a separation value of the brightest components for stars that have a multiplicity flag of 1 (otherwise set to 0.000), and a name (excluding the abbreviation of the constellation) in LaTeX format:

2017_LSA-01

3.3.2 Contours of the Milky Way

One of the main new features of my atlas (compared to other atlases on the market) is the inclusion of the (as) precise (as possible) contours of the Milky Way on its pages. For this I had to make my own data. I started from the APOD of the Milky Way by Nick Risinger (for which I still need to ask permission if I want to get this atlas distributed). I scaled this up slightly to 3600×1800 pixels (to match the 360×180 degree size of the full sky with a 0.1 degree pixel size), converted it to grayscale, applied a star-removal filter, a small blur, and enhanced the contrast slightly. Then using python I converted it to an array of brightness data on the galactic coordinate grid, and after a coordinate transformation from the galactic to the equatorial frame I saved it in a numpy specific file format using the numpy.savez() function.

This specific format is needed because reading in a 150 MB ASCII file is too slow with numpy, and it would create a bottleneck in speed when plotting the atlas pages. I use this brightness data later during the plotting and display it using contours set at some cleverly specified brightness levels to create a both visually pleasing and highly-realistic image of the Milky Way. The results are very nice, e.g., one can see multiple brightness levels even inside the Magellanic Clouds (as it can be seen below, but for the sake of clarity plotted using stronger colours than what is actually used in the atlas).

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I also tested if the coordinates of the original image were correct by repeating the same steps but without removing the stars, so one could actually see the stars on the contour plot too. In this experiment they lined up perfectly with the actual stellar data, confirming that the position data in the Milky Way image was correct.

3.3.3 Deep sky objects (contours of extended bright and dark nebulae)

The SAC database is a good starting point, since it has a great selection of objects that are accessible with a various range if amateur instruments. On the other hand, it has quite a few mistakes too, so I had to work on it a bit to make it more consistent and less messy. First of all I checked all objects that have an apparent size larger than 12′ (twice the cutoff size of my atlas) manually in DSS using the Aladin desktop client, and corrected sizes and/or position angles where I found it necessary (only a few cases). I also removed all duplicate entries from the database (which occurred with quite a few galaxies: many had multiple entries from different catalogues). I also decided to filter out those objects that had no useful/trustworthy brightness information (dropping mostly small open clusters that were barely identifiable on the DSS images anyway), and those open clusters that appeared too large on the scale of the atlas (typically the ones above 100′-120′, depending on the density of the field). Finally, I made sure that for Messier objects the Messier entry is used as name later on, not the NGC one. These were the easier parts.

The more difficult and time consuming part (taking around 40-50 hours) was drawing all bright and dark nebulae from the SAC database larger than 6′-12′ (meaning that 100% of the objects that are at 12′ or larger in diameter had to be drawn, and some of the smaller non-symmetric ones too), which I had to do manually, along with a few missing ones that I found worthy by looking through images in Aladin or by cross-checking with the selection on Sky Atlas 2000.0′s pages. (I am aware that there is a set of nebula outlines available online by Mark Smedley and Jan Kotek, but I did not find these good enough for my needs, and I did not want to copy other people’s data to begin with, because nebula outlines are one of those elements of an atlas that make it personal.) In practice I used Aladin to draw the contours of these objects with a mouse (I wish sometimes that I had a proper tablet and a digital pencil to do such things…), and export them as coordinate arrays.

For the brighter ones I tried to make the contours resemble the visual look based on drawings done by amateurs, while for the more difficult targets I went with the expected photographic outlines. It helped a lot that Aladin can create contour plots on the displayed images, leading my eyes while tracking the outlines. (For this I used the Mellinger Optical Survey and the DSS layers.) Some complex or large nebulae are drawn in multiple sections, for example the Vela Supernova Remnant in 22 pieces, IC 1318 in 14 pieces, and Sh2-240 in 42 pieces. Here is a rough example how this process looks like in Aladin:

2017_LSA-03

3.4 Plotting one page of the atlas

There is one single python script that takes care of the plotting of a single page of the atlas (plot_map.py). At the moment it is 1545 lines long, and contains – among others – 16 custom functions (some of which will be discussed later).

3.4.1 Arguments and control variables

To control what and how is plotted by the script, I am using a small set of arguments, and a larger set of variables (that I like to call control variables). These are separate for a good reason. Arguments can be given (values) simply from the terminal when running the script, and as such they can be also set by a higher level python script when compiling the whole atlas (see Section 3.5). These set the centre of the field of view, the output format (vector – pdf, or raster – png), and optionally the page number along with the page numbers of the pages that show the field above and below (thus up and down in declination) the current page in the atlas. These last three are optional, and when not given the script will simply produce a sky chart without the extra formatting of an atlas page.

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On the other hand control variables have to be set inside the main script, and they control the colours, size and magnitude limits, the different types of objects that need to be plotted, the field of view, etc. These stay the same for each page.

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Later on when closeup charts and chart index pages are created I will save a different version for each (e.g., plot_map_closeup.py and plot_map_index.py), where the limited magnitude, and a few other things will be set differently.

3.4.2 Colours and typography

When choosing the colours I experimented a bit with different colour combinations, but the main principles were always the same. I had to avoid red because it is invisible when the chart is browsed using a red torch (and this is common practice, since red light interferes the least with our dark-adapted eyes), and I wanted colours that go well together, but are easy to distinguish. At the end I choose my palette from Google’s material design guide.

When choosing the individual colours, the following ideas were factored into my decisions. First of all, I wanted the Milky Way to be plotted as shades of blue, not only because that looks great in Sky Atlas 2000.0, but also because the most prominent structures in a galaxy – the spiral arms – consist of young massive stars, and they are predominantly blueish in colour. As a consequence all galaxies had to be plotted in blue. Groups of stars are yellow as in most other colour atlases, but globular clusters are orange, since these are dominated by old(er), low-mass stars, and thus their light (spectrum) is more shifted towards this colour. Planetary nebulae are green, because the strongest emission line in their spectrum in the range where our eyes are the most sensitive is the [OIII] line at 500.7 nm, which appears to be green. Even though other types of bright nebulae might have different spectra (especially bright reflection nebulae), they are also plotted green (although a slightly different hue) to stay consistent. Stars are black and map-related lines are different shades of grey.

Outlines are always darker than the fill colours, and while the outline colours are constant, the line width and the opacity of the fill depend on the magnitude of a given object. Labels are always the same colour as the labelled objects’ outline.

I am using the Open Sans font family throughout the atlas, this is a nice, free, slightly narrower than default sans serif font with an extensive set of glyphs (including all necessary accented and Greek characters), that provides excellent legibility in many font sizes and weights (of which I am using the regular and bold variants).

3.4.3 Vertical layering

One important thing to handle when printing the chart is the vertical layering (or hierarchy) of the different object types, but also the vertical layering within a given object type. This way we can avoid larger objects overlapping – and blocking from view – smaller ones. Therefore, for example, the Milky Way needs to be plotted under everything else, dark nebulae need to be plotted over bright nebulae, small galaxies need to be plotted over large galaxies (otherwise, e.g, M 32 in the Andromeda Galaxy would not even be visible), stars need to be plotted over deep sky objects (so they are not covered by extended objects), and dim stars need to be plotted over bright stars (otherwise some dim stars close to bright stars could be covered by the larger disk of the bright star).

Placing different object types on different vertical layers is taken care using the zorder keyword inside any king of plotting command in python, while the required hierarchy within a given object type is ensured by sorting the objects according to size (from large to small, or from bright do dim in the case of stars), which means that when they are plotted by a plotting routine later on, they will be plotted according to the order they were sorted into. There are some special cases where the plotting order of open clusters and bright nebulae needs to be flipped (where a larger open cluster includes a smaller bright nebula), but this is also taken care of with a zorder-modifier value in the database of the hand-drawn nebulae, which effectively moves the given nebula to a layer above the open cluster.

3.4.4 Setting up the page

Before plotting any celestial data, we need to set up the plotting environment to be able to work with celestial coordinates. As already mentioned before, I use the basemap library to do this:

map = Basemap(projection=’stere’, width=fov_ra, height=fov_dec, lat_ts=central_dec, lat_0=central_dec, lon_0=360-(central_ra*15), celestial=True, rsphere=180/math.pi)

By using the celestial=True setting we make sure that we use the astronomical conventions for longitude (negative longitudes to the East of zero), and by setting the radius of the sphere to be 180/pi we can give the horizontal and vertical size of the area covered in the projection in degrees.

Like most celestial atlases I also use the stereographic projection. This is a conformal projection, meaning that it preserves the angles, but it does not preserve distances or areas. (There is no projection which would preserve all of these.) While indeed towards the edges equal distances seem to get larger, this effect is practically negligible at zoom levels that are used in a celestial atlas like this one.

After the map environment is set, we need to draw the grid of latitude and longitude lines (using the ICRS frame and a standard J2000.0 epoch), and I make sure that none of these lines get too dense as we get close to the poles, by tapering selected meridian lines before they reach the poles (see below). The density of the labelled meridian lines is also set by the distance from the pole, so labels never get too dense around the edges of the map area.

After the celestial grid is set up, I draw the ecliptic and the galactic equator along with the ecliptic and galactic poles, and small tick markers for each degree along the equator lines. Drawing the equators is very easy, one just needs to transform a constant (ecliptic or galactic) latitude = 0° line from ecliptic or galactic coordinates to equatorial coordinates, and plot the resulting array as a line. When drawing the poles, I draw their X shaped markers from two separate perpendicular lines (crossing at the poles) which point towards longitudes 0°, 90°, 180°, and 270°, this way, e.g., when looking at the galactic poles the reader can see how the galactic coordinate frame is rotated very differently compared to the equatorial grid.

As we are trying to produce not only a simple sky chart but a page in a stellar atlas, we also need to plot page numbers, navigation aids, and a legend box. Page numbers are plotted in the top and bottom outside corners (from a double page point of view) , which makes it easy to flip through the atlas when looking for a page number. There is also a special navigation bar plotted along the outer edge of each page. This navigation bar shows the central RA coordinate of the page, the minimum and maximum declination values of the page, and also the page number(s) of the closest neighbouring page(s) in declination (a.k.a. the pages above and below the current field). The location of this feature along the edge of the page depends on the central declination of the page. This means that just by looking at the edge of the closed atlas, or while flipping through it, the user can immediately see which region of the sky they are heading towards from the position of the navigation bar. (For example, on a page towards the North Celestial Pole this bar will be near the top of the edge of the page, while for pages around the Celestial Equator it will be at its centre.)

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Creating the legend box gave birth to probably the most interesting solution (in terms of coding) in the production of the atlas. In the following sections I will show that I set the size of all objects on the map using angles (angular diameters), and not by using marker sizes (in points or pixels) as done by default in python. This is very useful in the main map area of the page, but it meant that I had no idea how large an actual marker was when not working in a map instance. Therefore I could not simply create a simple subplot below the main map area and place the different symbols and their keys there without writing transformation functions to make sure that the size scales between the legend area and the main map area match. Instead of this, I created a 1 degree tall subplot with the same basemap settings (so also the same width in degrees, resulting in the same scale) as the main map area above, meaning that I could use the same functions and scales to plot objects here as I do in the main plot. Creating the legends themselves afterwards was only a question of placing a set of given objects next to each other, making sure that they are properly spaced, and that their labels are at the same horizontal levels. Odd and even pages got different legend boxes, so a double page always shows a full set of legends (stellar objects and lines on the left hand page, and deep sky objects on the right hand side – see examples later on).

I do not want to discuss all the remaining details here, but every piece of text (e.g., page numbers, the numbers in the navigation box, etc.) is aligned with other lines or texts in a very strict way to ensure symmetry at all times, no matter what area of the sky is being plotted. There was a very large effort put into this, even though the reader might not (consciously) notice these fine details.

3.4.5 Plotting stellar data

I have written one single function to handle the plotting of stars, from the simplest single stars to multiple variable systems with high proper motions:

draw_screen_star(ra, dec, pm_ra, pm_dec, pmlimit, mag, maglimit, map, double=0, sep=0, variable=0, vmin=0, facecolor=’k', edgecolor=’w', zorder=90)

Stars are plotted from bright to dim as mentioned earlier. All plotting is done in map (sky) coordinates, meaning that the symbol sizes are given in degrees, and there is an inverse linear scale between symbol radius and magnitude value. All stars are plotted with a white edge, so if symbols of nearby stars overlap, they remain nicely visible.

The width of this wide edge is set to be the narrowest well visible line width in print (matching the width of the lines of the celestial grid), while the smallest stars’ size is set by the symbol of the dimmest variable star (where the black central region needs another white filling, which must be still well visible). The size of the largest star is set to be aesthetically pleasing (not too small so bright stars can be well differentiated from dim ones, but not too large so they do not became overly extended). This also sets the other fix point of the linear magnitude scale. On the figure below you can see various tests of the star plotting function, with one notable example of a set of stars plotted on top of each other where the magnitudes range from -2 to 10 with a step size of 1 (resulting in a nice well separated pattern).

2017_LSA-06

In practice, taking the example of a variable double star (where the star is also visible in minimum, so, e.g., the 3rd star from the left in the row of multiple variable stars), this is how the star symbol is created: first a white circle is plotted and a white bar across (these will be the white outlines), then a narrower black line (corresponding to the separation of the brightest components of the multiple system) and a black circle with a smaller radius (corresponding to the brightness of the variable star in maximum), followed by another white circle with a smaller radius (the inner fill of the variable), and finally a smaller black circle (with a radius corresponding to the minimum brightness of the star). If the star has a high proper motion, it is also plotted with an arrow in the background. In summary, the symbol of a star is built from a set of pylab.patches (circles, polygons, and fancyarrows).

2017_LSA-08

Not strictly speaking stellar data, but I note here that constellation boundaries and stick figures are also plotted using simple lines. It is worth noting that to avoid plotting the overlapping sections of neighbouring constellations’ borders twice (which could introduce artefacts in the representation of dashed lines), I am using a version of this data where the duplicate line sections were removed.

3.4.6 Plotting non-stellar data

Similar to the stars, all deep sky objects are plotted in sky coordinates using specific functions that I have written for each different object type so I can feed sizes in arc minutes into them. The symbols are again made from various pylab.patches (ellipses, wedges, and rectangles) inside these functions. As an example, the symbol of global clusters is made out of four wedges that have a 90° opening angle (instead of trying to draw a nice cross inside a circle, which believe me is not as easy as it sounds). But outside of the function drawing a GC looks simply like this:

draw_screen_gcsymbol(ra, dec, size ,map, **kwargs)

Objects are plotted to scale when larger than a cutoff size (6′ in the LSA), otherwise they are plotted at given (smaller) fix sizes depending on which size-bin they fall inside the category. As mentioned earlier, the edge line width and the fill colour represents the visual brightness of a given object. The orientation (position angle, or PA) of galaxies is plotted true to reality, and to do this the local North had to be calculated at each position in the map (with a small function), which was then used to correct the PA coming from the SAC catalog (since on our map North is only ‘up’ along the central longitude of each page – except for right above the North and right below the South Celestial Poles, where the apparent direction of North along the central meridian is inverted).

I have kept most of the classical symbols, with the exception that planetary nebulae that are larger than the cutoff size (so planetary nebula that are plotted to scale) are plotted with a doughnut symbol (symbolising the classical PN look). Size bins were chosen so they are more or less equally populated by the smaller objects.

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3.4.7 Labelling objects

The last large task in creating a page in the atlas is labelling all plotted objects. My original goal was a fully automated setup based on the adjustText package, but this seems to be a much larger challenge to implement than first thought. The package works relatively well when labelling a set of small points or a set of objects that have the same size, but things get complicated when one wants to label a mix of patch objects (that have various sizes) and lines… Therefore after some experimenting I decided to put the automatisation attempts aside, and write a labelling graphical user interface (GUI). In any case even if the automated labelling would work, I would need this GUI to be able to make refinements in the label placements.

The main idea is that I run a slightly modified version of the original plot_map.py script (label_map.py, updated with the functions that make up the GUI), which produces an interactive version of the given page where I can just click on the labels (that are placed at the bottom right corner of each object by default as a starting point) and manipulate them. Using the GUI I can move labels (this way I can place them in a way that there is no overlap between objects and labels), I can make labels disappear (should a region be too busy for all labels to be visible), and I can even add in additional labels (to label the constellations, the poles, and the Magellanic Clouds – since they are plotted using the Milky Way contours and not as separate objects with their own label entries). When the GUI is run for the first time, all plotted objects are also fed into the

appendtolabellist(ra, dec, name, colour, size, rotation=0, visibility=1)

function, which builds a label table (from the names that were already available both in the stellar database and in the deep sky database) for the given page (placement positions, label texts, sizes, colours, rotation values – that are used for the labels of the ecliptic and galactic equator -, and a visibility flag), that I can modify with the GUI, and export it to be used by the main plot_map.py script. See part of such a label table below for a dense Southern part of Scorpion.

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If the label table is available for a given page when the plot_map.py script is used, labels will be also plotted. The advantage of having an actual label table (so a file like labels_P_004.txt for Page 4) for each page is that I can also modify label texts and sizes manually to fine-tune details for the final version, without having to edit the original object databases.

Of course since now I have to move almost all labels around manually, it takes a while to make the label table for a page (even with a good default offset from the corresponding symbol, there are so many stars that the chance that the default placement will overlap with something is pretty big). In practice this can be anything from ~20 minutes to 2 hours depending on how dense the filed is… My estimate is that labelling all pages would easily require 300 hours of work. Or I need to come up with a clever solution and write an advanced function to move the labels around automatically, but this seems equally difficult to me right now. So for the time being I labelled 5 sample pages that can be seen in Section 4, and depending on the future distribution of the atlas (proper printed version or only on-line for free), I will see how much effort and what kind of effort I need/want to put into labelling the rest.

The animation below shows how the formatting and the different layers of objects come together to form one page of the atlas.

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3.5 Plotting the whole atlas in one go

Plotting the whole atlas is taken care by a separate script called plot_atlas.py: this is only 91 lines long, and has a very limited amount of tasks to do. First of all it has a list of the central coordinates of the atlas pages (given as an equidistant list of declinations, and for each declination a list of right ascensions).

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From this the script compiles the set of arguments that were discussed in Section 3.4.1 for each page by first building a list of page numbers and central coordinates, then calculating the closest (neighbouring) pages in declination (to North and South) for each page, which are stored as the pageup and pagedown values, respectively. Finally the script simply calls the plot_map.py script for each page – feeding the corresponding list of arguments to it – to initiate their production one-by-one (or four-by-four when using all available cores).

The whole process takes around 4 hours on my laptop (using 4 cores in parallel). This time could actually be still shortened quite significantly, because right now all data files (e.g., the magnitude limited stellar catalog, or the deep sky catalog) are read in for each page over and over again, instead of just keeping them in the memory after the first read in operation. This originates from the fact that the plot_map.py script is written as a standalone script that can produce a page of a stellar atlas or a simple star map from scratch (and even compile databases from existing databases as we have seen it earlier) if necessary, and not only as a plotter routine. I do not think that I will change this, because 4 hours for a full atlas is not a lot (and in practice you do not do this very often anyway).

At the end I have a tidy ordered set of pages in the output folder. The size difference between the different pages shows nicely which pages have a larger number of objects plotted on them.

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4. Sample pages and statistics

At the current stage, these are the numbers for the Leuven Star Atlas (for more details, see Section 3.3):

  • Total number of stars: 361980 (down to and including magnitude 10.0)
  • Variable stars: 6998
  • Multiple (visual or physical) systems: 6411
  • Total number of deep sky objects: 8690
  • Galaxies: 6768
  • Galaxy clusters: 29
  • Globular clusters: 156
  • Open clusters: 644
  • Planetary nebulae: 742
  • Bright nebulae: 154 (of which 134 have hand-drawn outlines – many of these actually cover multiple NGC objects, thus the total number of plotted catalog entries is higher)
  • Dark nebulae: 197 (of which 163 have hand-drawn outlines)

The full sky is displayed on 344 A4 pages (1.6 cm per degree of declination resolution), five of which can be downloaded in pdf by clicking on the example rasterised images below.

Page 4: Northern Ursa Minor

20170620_LSA_P_004_RA_15.00_DEC_+84.0

Page 55: Cygnus around NGC 7000 (North America Nebula)

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Page 113: The Pleiades and the border of Taurus and Perseus

20170620_LSA_P_113_RA_04.00_DEC_+28.0

Page 136: Galaxies in Virgo and Coma Berenices

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Page 272: Southern Scorpius

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5. Future plans

The atlas is at a stage now where most of the larger work-packages are already completed, but there is still some work to be done before it can be printed into a nice book. A lot depends on whether I can actually get this published. I am determined to finish it and have a few copies printed in any case for myself and a few friends, but without an actual publication deadline, this will probably not happen next week, or the week after…

These are the components that are still missing or need completion:

  • Labelling the remaining 339 pages. As mentioned earlier this could take me minimum 300 hours of work… (Or ~8 full time work weeks, or a year if I just do one page every day.)
  • Close-up maps of selected dense regions: this means that I need to compile a stellar database down to ~11.0-11.5 magnitudes (press of a button, I do not need to change anything in my scripts), and implement a zoom-in factor (a scale multiplier that could be 2, 3, or 4), which I can use to modify, e.g., the field of view, and the cutoff sizes of deep sky objects. I could do this most likely in a day.
  • Refinements in the deep sky object database: I still need to clean up a smaller mess in the Large and Small Magellanic Clouds, because I am not happy about the completeness of the database there (another day of work, but I have already drawn quite a few bright nebula there too), and I am also not satisfied with the number of galaxy clusters on the map right now, so I would like to extend that based on the original Abell+ data (I guess I could also do this in one or maximum two days). In general, a small extension might be necessary to the deep sky database in general, but this is up for some further considerations.
  • Nicer constellation stick figures (as mentioned already earlier). This will take a bit longer (1-3 days), because I have to build a data file for this from scratch, and it is 100% manual work.
  • Index charts: I need to create 6 pages (looking towards the poles, and the four cardinal directions) with a large field of view showing only the brightest stars, the constellations, and the borders of the fields covered by the individual atlas pages labelled with the page numbers, as these overview charts provide the basic navigation feature of any stellar atlas. To make this I need a stellar database down to ~5.5 magnitudes (press of a button), a function that draws the outline of the sky that is displayed on given atlas pages (maximum an hour of work), and some labelling, so in total this will not take more than a day again.
  • An introduction chapter describing the use of the atlas, object types, etc. Also an index to the most interesting objects (Messier, Herschel 400, etc.) pointing to the atlas pages that contain them. This will probably be work for a week or two, since it includes quite some writing and brainstorming about what to write in the introduction.
  • A nice cover page: I would like to have either something very classical, or a montage of a page of this atlas with a classical illustration of a constellation (from here). This design process will definitely take a few days too.

When all these points are crossed off the list, I will write another post to showcase the new features and the final atlas. Without the labelling work this could be done in less than a month, but I have holidays coming up, and soon I will also start looking for a new job, so I would not expect much before Christmas. Please if you have any remarks or suggestions, let me know in the comments section, I would highly appreciate it.

LEGO NASA Apollo Saturn V

I have been waiting for this set to come out since it appeared on the LEGO Ideas website a few years ago, and – oh boy – it was worth the wait! I built it in a bit less than six hours over the weekend; the assembly was a lot of fun, and it showcased a set of interesting techniques. The final model is fully functional (except for the part where it flies to the Moon): all the stages can be properly separated, the LM fits nicely inside the fairing, etc. It is simply gorgeous. Just look at it:

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Watching Hidden Figures was a perfect match for the evening after the assembly.

A weekend in the Netherlands with my parents

Last week my parents flew over for the long weekend, and we spent the first two days visiting the most touristic sights in and around Leiden. (On the last day we did a very long walk across previously unexplored and new parts of Leuven). Our first stop was at the UNESCO World Heritage Site of the windmills in Kinderdijk.

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Here we had a nice walk discovering the area, except for the first few hundred metres where the path along the small channels was packed with tourists from all over the world. Afterwards we drove to our hotel in Leiden, checked in, and almost immediately went for a walk (leaving the car in a nice new underground parking garage that was opened only two days earlier). Of course first we had to refill our energy reserves, so we went for pancakes in a “pancake house” (Pannenkoekenhuys Oudt Leyden). They were excellent (and huge)! Then we walked around the centrum, and among others we also made it to the top of the Burcht van Leiden (an old fortified point overlooking the city, providing great panoramas).

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The next morning we drove to the tulip gardens of Keukenhof right after a not too early breakfast. We arrived there around 11:15, and luckily we still got into the (otherwise amazingly well organised and coordinated) parking without a problem. People coming just a half hour later were not that lucky anymore, because by that time everything was full. It turns out that we managed to visit on one of the busiest days the park has ever had. This was pretty evident inside, since there were people everywhere, but there were also so many flowers and it was really so extremely beautiful, that it made up for the otherwise alarmingly high tourist-density to the full extent. Of course it was basically impossible to take photos without including a Chinese family or a Russian couple in the background, but I did my best to still get some nice pictures. It was definitely worth a visit, but I would recommend going during the week and maybe when the weather is a bit less perfect :D

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The past months

It is again the usual situation: I have not written anything here during the past months, but as always, this does not mean that I was not busy with various things basically all the time. So let’s have a short summary – in chronological order.

During November and December I played Uncharted 4 on the PS4, which looked very pretty, but progressed a bit slow for my liking, and the puzzles got quite repetitive after some time. For Christmas I got myself a new helmet (Giro Synthe MIPS) as a present, since my old one was getting way too old. Looks great, feels good. Then we spent two days between the holidays in Holland, visiting ‘s-Hertogenbosch and Nijmegen.

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The first day we had sunny weather and Den Bosch was a nice city to walk around, while the next day Nijmegen was grey and cold, but at least we met up with Steven for dinner (and for lunch we also found a great place with delicious healthy sandwiches and drinks). On the next morning I also got the scrape-all-the-ice-off-your-windshield experience for the first time… Although we celebrated New Year’s Eve at home, the night before we went to the Spaans Dak for a fancy dinner, which was nice.

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January was even colder than December. After cycling through the freezing fog in -5°C on the last day of the year (my coldest ride ever), I also biked through snow and freezing rain with my MTB during the first days of 2017 (this ride covered me in a layer of ice, and cost me a bit of skin around my left knee), and this kind of weather stayed for the following weeks too. This made January my coldest month on the bike (so far?) with an average ride temperature of 0.6°C (over 875 km in 34.5 hours).

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I also got new glasses (my eyes did not get much worse, but my old lenses were so scratched from the years of cleaning that I really needed new ones), so now I have a slightly different look. Being a good citizen, I even went for a Belgian brand (Louis) for the frame. After all these expenses, I decided it was time to save some money, so instead of buying energy bars, I decided to make them myself. I used more or less the recipe from the Global Cycling Network, with some extra ingredients (and twice the amount to fill my baking tin), and the result was delicious. Since then I made a batch (almost 1.5 kg) every month. I also got a brighter front light (Lezyne Power Drive 1100XL) to be able to bike in the dark, therefore I had a few rides well past sunset for the first time ever. At the end of January I sold my MTB because I barely used it, especially since I got the cross bike. So now I have only two bikes again…

I started February with a small meeting in Bern at the International Space Science Institute, discussing the challenges in modelling massive stars. Unluckily around the same time I caught a quite bad cold, and I only recovered a week after coming home from Switzerland. (I felt so weak that I had to turn back from a group ride after 5 kilometres, which was pretty depressing after already not being on the bike for quite some time…). A week later (almost fully recovered by then) I went to the velodrome in Ghent with Willem (and the Belgian Rapha Cycling Club), which was a really fun two hours on the track with 40+ km/h. (Photo: Bert Van Lent – and I am on the far right.)

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I wish there was a velodrome in Leuven, it would make my winter cycling training so much easier (and more fun). I had to buy a pair of new wheels for my road bike, because the originals were getting dangerously worn (partly due to the rainy and foggy descents in the Pyrenees last summer where we probably eroded a half millimetre from our rims in two days), and this time I did not go for the usual Mavic choice (although I am mostly happy with those wheels too), but I ordered a wheel-set from a smaller British company: HUNT. I choose their Race Aero wheels (1420g, 28 mm deep, and 22 mm wide rims, £379), and I am fully satisfied with them so far. They are hand built, light, aero, and HUNT offers a 60 day ride and return period, so I can only recommend giving them a try. Should I need a new pair of wheels, I find it likely that I would buy from HUNT again. Thanks to the wider rim, I also switched to a slightly wider tire (moving from 23 mm to 25 mm), and I can definitely feel the difference in ride quality (smoothness) and while cornering. The first ride with the new wheels took me to the French-Belgian border to do a reconnaissance of one of the routes that I had previously prepared for the Squadra Tornado training weekend. That week was great in terms of cycling overall (425 km in 15.5 hours), I even managed one ride in shorts (with knee and arm warmers, but still). Just before the end of the month I also completed the first century of the year too.

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In March I started a big project that I always wanted to do; making my own sky atlas. The final push came when I was redoing a few figures about all the observations that had been made with the Mercator telescope since its inauguration (some of which I also compiled into a video), because I realised that I was already using a lot of the tools that would be needed to create a star atlas. Since this is a huge topic I want to write a separate blog entry about it (in the near future), but let me just say that I lost plenty of sleep time since the beginning of March to this project (but it has been always fun and I learned a lot while doing so). I was also a member of a PhD jury for the first time in my life, and I got to wear a fancy gown on the public defence of the candidate. I really liked that :)

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For the second half of the month we finally got a spell of nice spring weather, so I returned to the South for the second reconnaissance ride, which was the nicest ride of the season so far (in Belgium). Unluckily the weather for the Tornado outing itself turned out to be pretty rainy, and I also had to drive to Antwerp in the middle of the weekend for an evening, therefore I only made it to the ride on Friday, so at the end I was really happy that I did the recon rides of the two other days earlier. On the PS4 I started playing Horizon Zero Dawn, which seems fun for now (although slightly repetitive). Towards the end of the month I bought a new cycling GPS, the Wahoo ELEMNT Bolt, which is much better than all the Garmins I had before, so I am very happy with the switch.

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Unluckily it turns out that my unit has some kind of a hardware issue that only appears around an altitude of 2000 metres (and manifests in huge spikes or a complete loss in elevation data), so when I did my first bike ride from the Observatory on La Palma I had to face some nasty surprises. Of course – being a data scientist nerd – I was not happy about that, but luckily the Wahoo support was very helpful (maybe partly because I provided a detailed, ~5000 character-long description of the issue), and a replacement unit is already on its way as we speak (and as long as I am not crossing the ~2000 metre line I have no issues, so I don’t have to go back to one of my Garmin units in the meantime). Speaking of La Palma: I had an observing run at the Mercator telescope starting on the 4th of April (and lasting 9 nights, bringing my totals there up to 128 nights), then I took a week of holidays on the island just to bike around. The last time I travelled with my bike was in 2013, and most likely this is the last year that I have the opportunity to go to the Canary Islands for work, so I though I had to do it once again. During the nights I spent most of my time working on the sky atlas (drawing the outlines of ~300 dark and bright nebulae by hand) in the control room, and I did a 1.5-2 hour ride from the Observatory before every night (except for a very cold Monday). I have never biked this much up on the mountain. The previous years during the observing runs I only rode the bike between the telescope and the place where we stay during the day, which only added up to 10.5 km (when I rode up to the telescope twice) a day. Now I have a licence so I always drove the car to the telescope which saved a lot of time and energy, therefore I could go for longer rides between starting the instrument calibrations (right after getting out of bed on the afternoon) and having dinner (right before the beginning of the worknight). The weather was also quite nice at 2000 metres. It was almost always sunny, and during the first days it was even so hot that I could just bike in shorts, but a week into my stay I made good use of all the knee/arm warmers and the wind jacket I had with me too…

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Normally I would take a taxi down to the hotel in Santa Cruz, but this time I made some arrangements so that I could just bike down, and a taxi would follow me with my bags :) That was quite cool (except for the part where a dog almost jumped at me while I was going down with 60 km/h), and I got an extremely helpful driver (via some local connections) who helped me with my bags a lot. I had six full days to bike, and I managed to bike on each day (which was a first again), although I had to cut a ride much shorter than planned, because I could not sleep anything the night before (thanks to my messed up internal clock). All in all I am happy with what I managed, especially the three longer rides. On the first day I biked around the Southern tip of the island before doing two 2nd category climbs, then after two days of fighting with insomnia I rode around the whole island (a really epic ride of 155 km with 3385 m of elevation gain), while on the last day of my holidays I biked up to the top of the island for the fifth time in my life, but for the first time without stopping.

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This climb – leading up to the highest paved point (the Roque de los Muchachos at 2426 metres ASL) of the Canary Islands – is a monster. It is 41.5 km long, it starts at a few metres above sea level and climbs at an average gradient of 6%, but since there is a short downhill/flat section after reaching 2300 metres for the first time, the actual average gradient for the uphill parts is 7% (both for the first ~33 km and the last 3.5 km). Long story short, simply going up and down involves 2750 metres of elevation gain… This specific ride will stay in my memory not only because I finally managed to get up there without stopping, but also because the temperature went from 23°C at sea level to 10°C in the cloud layer (staying between 800 and 1800 metres, but while climbing that was not too cold, even though I was wearing only in shorts), then back up to comfortable (quite warm) levels thanks to the strong sunshine above the clouds.

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This was all good until I had to go downhill though the cloud layer, because by that time the clouds became even thicker, so the visibility dropped to ~25 metres, and the temperature to 8°C. This means that I had to descend for a half hour through this layer (and due to the low visibility I had to go relatively slow, so it took me much longer than usual to get down), and I only had an extra gilet with me against the wind, but no jacket or arm warmers against the cold (since although I checked out what the weather situation was at the telescopes before leaving, I did not think about the possibility of having colder temperatures in the cloud layer at lower altitudes – quite stupid of me now that I think of it). With windchill values between 6°C and 2°C that was not a pleasant half hour, and I was shaking slightly by the time I emerged from the cloud layer, but I survived, so it is OK :D In total I biked 783 km in 34.5 hours on La Palma (with an elevation gain of 18140 metres, which explains the horrible average speed).

That afternoon (after warming up in the shower) I even managed to walk down to the new beach which finally opened after years of political games over permits and who knows what else, but now it is open, and it is a great addition to the city (providing not only access to the sea, but also a nice, new view over the colourful houses of the historical seafront with the mountains in the background). Speaking of things other than biking, just after cycling down from the observatory after my observing run, I got to watch two easter processions across the city, and they were both quite an interesting sight. My favourite italian place is luckily still open, so I ate there basically every night (except for one evening when I was so tired/lazy that I just stayed in my room and made spaghetti while watching Netflix). Although I am not a frequent coffee-drinker, I had to check out a place that I had read about in the Guardian a few months ago (El Cafe de Don Manuel). It turnes out that the article was not lying, it is a cosy, calm spot inside a beautiful renovated courtyard with only a few tables, and besides the good coffee, they serve delicious cakes too (one afternoon I simply could not resist taking a second piece, it was so good).

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Flying home I got to sit on Business Class for the first time in my life (since for some reason when I booked my tickets to La Palma, this was the cheapest combination for these dates). It was a nice change to have legspace (and incredible amounts of it, especially from Madrid to Brussels) and proper meals, so I did not have to live on sandwiches and chocolates all day long. Of course if you think about it the price difference between business and economy tickets is basically more than what a (three) Michelin starred dinner costs (for two), so I would rather go for that and sit 5 hours without food and legspace if it was about my own hard-earned money. Ridiculous… (And then we are still not talking about First Class tickets on overseas trips.)

It was a bit of a shock to come back to the 7°C and rain, but hopefully from May the weather will finally turn a bit warmer. Besides work, I am also trying to put together a nice non-scientific CV for the corporate world, since soon I will start looking for a new job outside of academia, which will definitely bring some changes into my life.