datasci/analysis-voidlinux-popcorn
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Add labels to popcorn qmd computations

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This commit is contained in:
Marty Oehme 2025-10-11 08:36:29 +02:00
parent 0e9064c49d
commit 4cd09fbd46
Signed by: Marty
GPG key ID: 4E535BC19C61886E
1 changed files with 13 additions and 2 deletions
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15
popcorn.qmd
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@ -25,7 +25,8 @@ enable its service to run in the background with `ln -s /etc/sv/popcorn
/var/service`.
```{python}
# | echo: false
#| label: setup
import os
from typing import Any, Awaitable, Mapping
@ -88,7 +89,7 @@ glance a more 'user-choice'-aligned view of how many aisles the buffet offers,
and how many of those the users are eating from.
```{python}
# | echo: true
#| label: fig-filesize
from notebooks.popcorn import plt_filesize
pplot(plt_filesize)
```
@ -136,6 +137,7 @@ and the number of unique installs (i.e. how many people provide their
statistics). We will look at both of these in turn.
```{python}
#| label: fig-weekly-packages
from notebooks.popcorn import plt_weekly_packages
pplot(plt_weekly_packages)
```
@ -155,6 +157,7 @@ unique uploads, in we can see a similar pattern, though even more strongly
pronounced.
```{python}
#| label: fig-unique-installs
from notebooks.popcorn import plt_unique_installs
pplot(plt_unique_installs)
```
@ -198,6 +201,7 @@ Next, let's verify that hunch by actually looking at the installed packages
_per user_ for each day.
```{python}
#| label: fig-packages-relative
from notebooks.popcorn import plt_pkg_relative
pplot(plt_pkg_relative)
```
@ -257,6 +261,7 @@ packages which take the top-installed spots on users' systems.
<!-- TODO: perhaps the pre-made ISOs play a role, especially Feb2024? no hang on feb 2025 -->
```{python}
#| label: fig-top-packages
from notebooks.popcorn import plt_top_packages
pplot(plt_top_packages)
```
@ -309,6 +314,7 @@ available.
Let's turn to the 'distribution' of package installations.
```{python}
#| label: fig-package-distribution
from notebooks.popcorn import plt_package_distribution
pplot(plt_package_distribution)
```
@ -383,6 +389,7 @@ themselves.
Let's start by looking at the prevalence of the different major versions.
```{python}
#| label: fig-kernel-versions
from notebooks.popcorn import plt_kernel_versions
pplot(plt_kernel_versions)
```
@ -436,6 +443,7 @@ major version as 99. The strange version starts appearing on
Let's turn to the actual adoption of kernels over time in the next visualization.
```{python}
#| label: fig-kernel-timeline
from notebooks.popcorn import plt_kernel_timeline
pplot(plt_kernel_timeline)
```
@ -565,6 +573,7 @@ instead: here we just add up all the files reported so far for each day, and
show the resulting growth line.
```{python}
#| label: fig-filesize-cumulative
from notebooks.popcorn import plt_filesize_cumulative
outp, _ = plt_filesize_cumulative.run() # pyright: ignore
outp
@ -581,6 +590,7 @@ Let's also look at the packages installed on systems for different time slices.
We'll start with a look at the packages per weekday.
```{python}
#| label: fig-weekday-packages
from notebooks.popcorn import plt_weekday_packages
pplot(plt_weekday_packages)
```
@ -600,6 +610,7 @@ Tuesdays and Wastebin Wednesdays if you will.
Alright, but let's also take a look at the package numbers per month instead.
```{python}
#| label: fig-month-packages
from notebooks.popcorn import plt_month_packages
pplot(plt_month_packages)
```