# Comparing water filters

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Lets say, you want to reduce the water carbonate hardness because you got a shiny coffee machine and descaling that is a time-consuming mess.

If you dont happen to run a coffee-shop, using a water-jug is totally sufficient for this. Unfortunately, while the jug itself is quite cheap, the filters you need will cost you an arm and a leg – similar to how the printer-ink business works.

## The setup

Here, we want to look at the different filter options and compare their performance. The contenders are

NamePricingBrita Classic~15.19 €PearlCo Classic12.90 €PearlCo Protect+15.90 €

As said initially, the primary goal of using these filters is to reduce the water carbonate – any other changes, like pH mythology, will not be considered.

To measure the performance in this regard, I am using a digital total dissolved solid meter – just like the one used in the Wikipedia article. To make the measurement robust against environmental variations, I am not only measuring the PPM in the filtered water, but also in the tap water before filtering. The main indicator is then the reduction factor.

Also, you are not using the filter only once, so I repeat the measuring over the course of 37 days. Why 37? Well, most filters are specified for 30 days of usage – but I want to see how much cushion we got there.

So – without further ado – the results:

## Results

NameØ PPM reductionØ absolute PPMBrite Classic31%206PearlCo Classic24%218PearlCo Protect+32%191

As motivated above, the difference in absolute PPM can be explained by environmental variation – after all the measurements took place over the course of more than 3 months.

However, we see that the pricing difference is indeed reflected by filtering performance. By paying ~20% more, you get a ~30% higher PPM reduction.

The only thing missing, is the time-series to see beyond 30 days:

As you can see, the filtering performance is continuously declining after a peak at about 10-15 days of use.

And for completeness, the absolute PPM values:

Categories: Articles

# GStreamer WebKit debugging by instrumenting source code (2/3)

2021-05-04 06:00 UTC  by  Enrique Ocaña González
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In this post I show some more useful debugging tricks. Check also the other posts of the series:

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Categories: Gnome

# GStreamer WebKit debugging by instrumenting source code (1/3)

2021-04-27 06:00 UTC  by  Enrique Ocaña González
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This is the continuation of the GStreamer WebKit debugging tricks post series. In the next three posts, I’ll focus on what we can get by doing some little changes to the source code for debugging purposes (known as “instrumenting”), but before, you might want to check the previous posts of the series:

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Categories: Gnome

# GStreamer WebKit debugging tricks using GDB (2/2)

2021-04-20 06:00 UTC  by  Enrique Ocaña González
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This post is a continuation of a series of blog posts about the most interesting debugging tricks I’ve found while working on GStreamer WebKit on embedded devices. These are the other posts of the series published so far:

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Categories: Gnome

# GStreamer WebKit debugging tricks using GDB (1/2)

2021-04-13 10:49 UTC  by  Enrique Ocaña González
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I’ve been developing and debugging desktop and mobile applications on embedded devices over the last decade or so. The main part of this period I’ve been focused on the multimedia side of the WebKit ports using GStreamer, an area that is a mix of C (glib, GObject and GStreamer) and C++ (WebKit).

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Categories: Gnome

# reBounce - softfp-to-hardfp LD_PRELOAD hack for Bounce on N9

2021-02-11 07:41 UTC  by  Thomas Perl
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This depends on Bounce (the N900 .deb) and SDL 1.2 being installed. Google "bounce_1.0.0_armel.deb" for the former, and use n9repomirror for the latter.

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# Ubuntu Touch porting notes for the Redmi Note 7 Pro, part 2

2021-02-04 20:34 UTC  by  Alberto Mardegan
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This is the second part of my porting odyssey; for the first part, follow this link.

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Categories: energia

# Ubuntu Touch porting notes for the Redmi Note 7 Pro

2021-01-17 19:15 UTC  by  Alberto Mardegan
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In case you have a sense of deja-vu when reading this post, it's because indeed this is not the first time I try porting a device to Ubuntu Touch. The previous attempt, however, was with another phone model (and manufacturer), and did not have a happy ending. This time it went better, although the real ending is still far away; but at least I have something to celebrate.

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Categories: energia

# Avast, Qt6 announcing new QPromise and QFuture APIs

2020-12-17 21:29 UTC  by  Philip Van Hoof
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Qt published its New_Features in Qt 6.0.

Some noteworthy items in their list:

• QPromise allows setting values, progress and exceptions to QFuture
• QFuture supports attaching continuations

I like to think I had my pirate-hook in it at least a little bit with QTBUG-61928.

I need to print this out and put it above my bed:

Thiago Macieira added a comment – 13 Jul ’17 03:51 You’re right Philip Van Hoof added a comment – 13 Jul ’17 07:32 Damn, and I was worried the entire morning that I had been ranting again. Thiago Macieira added a comment – 13 Jul ’17 16:06 oh, you were ranting. Doesn’t mean you’re wrong.

Thanks for prioritizing this Thiago.

Categories: controversial

# How to generate random points on a sphere

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This question often pops up, when you need a random direction vector to place things in 3D or you want to do a particle simulation.

We recall that a 3D unit-sphere (and hence a direction) is parametrized only by two variables; elevation $\theta \in [0; \pi]$ and azimuth $\varphi \in [0; 2\,\pi]$ which can be converted to Cartesian coordinates as

\begin{aligned} x &= \sin\theta \, \cos\varphi \\ y &= \sin\theta \, \sin\varphi \\ z &= \cos\theta \end{aligned}

If one takes the easy way and uniformly samples this parametrization in numpy like

phi = np.random.rand() * 2 * np.pi
theta = np.random.rand() * np.pi

One (i.e. you as you are reading this) ends with something like this:

While the 2D surface of polar coordinates uniformly sampled (left), we observe a bias of sampling density towards the poles when projecting to the Cartesian coordinates (right).
The issue is that the cos mapping of the elevation has an uneven step size in Cartesian space, as you can easily verify: $cos^{'}(x) = sin(x)$.

The solution is to simply sample the elevation in the Cartesian space instead of the spherical space – i.e. sampling $z \in [-1; 1]$. From that we can get back to our elevation as $\theta = \arccos z$:

z = 1 - np.random.rand() * 2 # convert rand() range 0..1 to -1..1
theta = np.arccos(z)

As desired, this compensates the spherical coordinates such that we end up with uniform sampling in the Cartesian space:

## Custom opening angle

If you want to further restrict the opening angle instead of sampling the full sphere you can also easily extend the above. Here, you must re-map the cos values from $[1; -1]$ to $[0; 2]$ as

cart_range = -np.cos(angle) + 1 # maximal range in cartesian coords
z = 1 - np.random.rand() * cart_range
theta = np.arccos(z)

## Optimized computation

If you do not actually need the parameters $\theta, \varphi$, you can spare some trigonometric functions by using $\sin \theta = \sqrt { 1 - z^2}$ as

\begin{aligned} x &= \sqrt { 1 - z^2} \, \cos\varphi \\ y &= \sqrt { 1 - z^2} \, \sin\varphi \end{aligned}
Categories: Articles

# Self-built NAS for Nextcloud hosting

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With Google cutting its unlimited storage and ending the Play Music service, I decided to use my own Nextcloud more seriously.
In part because Google forced all its competitors out of the market, but mostly because I want to be independent of any cloudy services.

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Categories: Articles

# Imaginario 0.10

2020-11-12 16:12 UTC  by  Alberto Mardegan
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Today I released Imaginario 0.10. No bigger changes there, but two important bugfixes.

Categories: english