Color palette tools

Wednesday 15 January 2020

Two useful sites for choosing color palettes, both from map-making backgrounds. They both consider qualitative, sequential, and diverging palettes as different needs, which I found insightful.

  • Paul Tol’s notes, which gives special consideration to color-blindness. He has some visual demonstrations that picked up my own slight color-blindness.
  • Cynthia Brewer’s ColorBrewer, with interactive elements so you can create your own palette for your particular needs.

Color Palette Ideas is different: palettes based on photographs, but can also be a good source for ideas.

As an update to my ancient blog post about this same topic, Adobe Color and paletton both have tools for generating palettes in lots of over-my-head ways. And Color Synth Axis is still very appealing to the geek in me, though it needs Flash, and so I fear is not long for this world...

Bug #915: solved!

Monday 13 January 2020

Yesterday I pleaded, Bug #915: please help! It got posted to Hacker News, where Robert Xiao (nneonneo) did some impressive debugging and found the answer.

The user’s code used mocks to simulate an OSError when trying to make temporary files (source):

with patch('tempfile._TemporaryFileWrapper') as mock_ntf:
    mock_ntf.side_effect = OSError()

Inside tempfile.NamedTemporaryFile, the error handling misses the possibility that _TemporaryFileWrapper will fail (source):

(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
    file =, mode, buffering=buffering,
                    newline=newline, encoding=encoding, errors=errors)

    return _TemporaryFileWrapper(file, name, delete)
except BaseException:

If _TemporaryFileWrapper fails, the file descriptor fd is closed, but the file object referencing it still exists. Eventually, it will be garbage collected, and the file descriptor it references will be closed again.

But file descriptors are just small integers which will be reused. The failure in bug 915 is that the file descriptor did get reused, by SQLite. When the garbage collector eventually reclaimed the file object leaked by NamedTemporaryFile, it closed a file descriptor that SQLite was using. Boom.

There are two improvements to be made here. First, the user code should be mocking public functions, not internal details of the Python stdlib. In fact, the variable is already named mock_ntf as if it had been a mock of NamedTemporaryFile at some point.

NamedTemporaryFile would be a better mock because that is the function being used by the user’s code. Mocking _TemporaryFileWrapper is relying on an internal detail of the standard library.

The other improvement is to close the leak in NamedTemporaryFile. That request is now bpo39318. As it happens, the leak had also been reported as bpo21058 and bpo26385.

Lessons learned:

  • Hacker News can be helpful, in spite of the tangents about shell redirection, authorship attribution, and GitHub monoculture.
  • There are always people more skilled at debugging. I had no idea you could script gdb.
  • Error handling is hard to get right. Edge cases can be really subtle. Bugs can linger for years.

I named Robert Xiao at the top, but lots of people chipped in effort to help get to the bottom of this. ikanobori posted it to Hacker News in the first place. Chris Caron reported the original #915 and stuck with the process as it dragged on. Thanks everybody.

Bug #915: please help!

Sunday 12 January 2020

Updated: this was solved on Hacker News. Details in Bug #915: solved!

I just released 5.0.3, with two bug fixes. There was another bug I really wanted to fix, but it has stumped me. I’m hoping someone can figure it out.

Bug #915 describes a disk I/O failure. Thanks to some help from Travis support, Chris Caron has provided instructions for reproducing it in Docker, and they work: I can generate disk I/O errors at will. What I can’t figure out is what is doing wrong that causes the errors.

To reproduce it, start a Travis-based docker image:

cid=$(docker run -dti --privileged=true --entrypoint=/sbin/init \
    -v /sys/fs/cgroup:/sys/fs/cgroup:ro \
docker exec -it $cid /bin/bash

Then in the container, run these commands:

su - travis
git clone --branch=nedbat/debug-915
cd apprise-api
source ~/virtualenv/python3.6/bin/activate
pip install tox
tox -e bad,good

This will run two tox environments, called good and bad. Bad will fail with a disk I/O error, good will succeed. The difference is that bad uses the pytest-cov plugin, good does not. Two detailed debug logs will be created: debug-good.txt and debug-bad.txt. They show what operations were executed in the SqliteDb class in

The Big Questions: Why does bad fail? What is it doing at the SQLite level that causes the failure? And most importantly, what can I change in to prevent the failure?

Some observations and questions:

  • If I change the last line of the steps to “tox -e good,bad” (that is, run the environments in the other order) then the error doesn’t happen. I don’t understand why that would make a difference.
  • I’ve tried adding time.sleep’s to try to slow the pace of database access, but maybe in not enough places? And if this fixes it, what’s the right way to productize that change?
  • I’ve tried using the detailed debug log to create a small Python program that in theory accesses the SQLite database in exactly the same way, but I haven’t managed to create the error that way. What aspect of access am I overlooking?

If you come up with answers to any of these questions, I will reward you somehow. I am also eager to chat if that would help you solve the mysteries. I can be reached on email, Twitter, as nedbat on IRC, or in Slack. Please get in touch if you have any ideas. Thanks.

Fancy console output in GitHub comments

Tuesday 17 December 2019

Providing detailed command output in GitHub issues is hard: I want to be complete, but I don’t want to paste unreadable walls of text. Some commands have long output that is usually uninteresting (pip install), but which every once in a while has a useful clue. I want to include that output without making it hard to find the important stuff.

While working on an issue with 5.0, I came up with a way to show commands and their output that I think works well.

I used GitHub’s <details> support to show the commands I ran with their output in collapsible sections. I like the way it came out: you can copy all the commands, or open a section to see what happened for the command you’re interested in.

The raw markdown looks like this:

<summary>cd meltano</summary>

<summary>pip install '.[dev]'</summary>

Processing /private/tmp/bug881a/meltano
Collecting aenum==2.1.2
  Using cached
Collecting idna==2.7
  Using cached
Collecting asn1crypto==0.24.0
  Using cached


(The GitHub renderer was very particular about the blank lines around the <details> and <summary> tags, so be sure to include them if you try this.)

Other people have done this: after I wrote this comment, one of the newer issues used the same technique, but with <tt> in the summaries to make them look like commands, nice. There are a few manual steps to get that result, but I’ll be refining how to produce that style more conveniently from a terminal console.

Pytest trick: subsetting unknown suites

Tuesday 17 December 2019

While trying to reproduce an issue with 5.0, I had a test suite that showed the problem, but it was inconvenient to run the whole suite repeatedly, because it took too long. I wanted to find just one test (or small handful of tests) that would demonstrate the problem.

But I knew nothing about these tests. I didn’t know what subset might be useful, or even what subsets there were, so I had to try random subsets and hope for the best.

I selected random subsets with a new trick: I used the -k option (select tests by a substring of their names) using single consonants. “pytest -k b” will run only the tests with a b in their name, for example. Then I tried “-k c”, “-k d”, “-k f”, and so on. Some will run the whole test suite (“-k t” is useless because t is in every test name), but some ran usefully small collections.

This is a mindless way to select tests, but I knew nothing about this test suite, so it was a quick way to run fewer than all of them. Running “-k q” was the best (only 16 tests). Then I looked at the test names, and selected yet smaller subsets with more thought. In the end, I could reduce it to just one test that demonstrated the problem.