#ipython — Public Fediverse posts

Taking a python path on plural sight now, and I find it puzzling that none of these python suggest installing `ipython` ... Why is it so?

#python #ipython #question #fediverse #askfedi #computerscience #datascience #devops #linux

One Open-source Project Daily

Beautiful spinners for terminal, IPython and Jupyter

https://github.com/manrajgrover/halo

#1ospd #opensource #async #halo #ipython #jupyter #ora #python #spinner

#sagemath #ipython #kitty #kitten #kissat
Много лет пользуюсь x11-terms/kitty поэтому радуюсь появлению https://github.com/sagemath/sage/issues/42054
(а sci-mathematics/kissat я собираю с --no-kitten)

Did not expect to get a reminder about International Transgender Day of Visibility in #ipython, but glad I did :)

Nice tip from IPython today.

> Tip: Happy International Transgender Day of Visibility! You are valid. You matter. https://en.wikipedia.org/wiki/International_Transgender_Day_of_Visibility

#Python #IPython

Windows at work, always a fresh inconvenience:

C:\>python -m pip install ipython
Requirement already satisfied: ipython in c:\users\[...]

C:\>ipython
'ipython' is not recognized [...]

#ipython #windows #python

I'm still learning how to submit a patch or pull request to Guix to propose updated definitions of some packages... but---while I figure things out---here's a general guide on how to get new packages installed by defining package variants and rewriting package inputs. In short, the steps to do so are:

1. Get the sha256sum hash in the nix-base-32 format (either via `guix download` or `guix hash` of a repository)
2. Prepare the package variant Scheme file (see images; e.g. "package-variant.scm")
3. Run `guix build -f ./package-variant.scm` to build the new package in the store
4. (Optional, but recommended) Test that things work by running `git shell -f ./package-variant.scm`
5. Install the package by running `guix package -f ./package-variant.scm`

#guix #python #emacs #JupyterNotebook #ipython #sql #package #variants

The site has a handful of "synthetic users" that serve to hold items from external sources like #Django debug pages and the #iPython "%pastebin" magic. Their profile pages recently got a little revamp, with a bot icon and more account info.

If you'd like a similar setup for your public dpaste.com integration, drop a line!

* https://dpaste.com/profile/2
* https://dpaste.com/profile/1003

How to best create, maintain and archive custom environments from within Jupyter? .. just updated the documentation for Carto-Lab Docker with examples for Python [1] and R [2].

The tricky part is linking Kernels from custom envs with a Jupyter kernelspec (specifically if the Jupyter server and the Kernel are in two different environments). However, most of this can be stored in Jupyter notebook cells, for reproducibility.

There's also a section on archival of package versions with Conda's `env export` (yml approach) and `conda list --explicit` (full archival).

[1]: https://cartolab.theplink.org/use-cases/#create-your-own-environment-in-a-bind-mount-and-install-the-ipkernel
[2]: https://cartolab.theplink.org/use-cases/#example-create-an-environment-with-a-specific-r-version

#jupyter #R #ipython # #reproducibility #Notebooks #conda

Do you use pg.ConsoleWidget and have some preference for it over #IPython ‘s QtConsole? If so I’d like to hear from you. We’re considering deprecating ConsoleWidget and directing users to use the Jupyter/IPython QtConsole instead.

That's a nice little surprise!
Happy #TransDayOfVisibility to you too #IPython! :heart_trans:

Apparently ipython has a special tip if launched today!

#Python #IPython

Jeden błąd w #PyPy naprawiony, i #IPython w #Gentoo jest na #PyPy 3.11.

Jeden błąd w bibliotece standardowej #Pythona naprawiony, #Django w Gentoo jest na PyPy 3.11.

Powiedziałbym, że całkiem udany dzień.

https://github.com/pypy/pypy/pull/5239
https://github.com/python/cpython/pull/130962

#Python

Jeden błąd w #PyPy naprawiony, i #IPython w #Gentoo jest na #PyPy 3.11.

Jeden błąd w bibliotece standardowej #Pythona naprawiony, #Django w Gentoo jest na PyPy 3.11.

Powiedziałbym, że całkiem udany dzień.

https://github.com/pypy/pypy/pull/5239
https://github.com/python/cpython/pull/130962

#Python

Jeden błąd w #PyPy naprawiony, i #IPython w #Gentoo jest na #PyPy 3.11.

Jeden błąd w bibliotece standardowej #Pythona naprawiony, #Django w Gentoo jest na PyPy 3.11.

Powiedziałbym, że całkiem udany dzień.

https://github.com/pypy/pypy/pull/5239
https://github.com/python/cpython/pull/130962

#Python

Jeden błąd w #PyPy naprawiony, i #IPython w #Gentoo jest na #PyPy 3.11.

Jeden błąd w bibliotece standardowej #Pythona naprawiony, #Django w Gentoo jest na PyPy 3.11.

Powiedziałbym, że całkiem udany dzień.

https://github.com/pypy/pypy/pull/5239
https://github.com/python/cpython/pull/130962

#Python

Jeden błąd w #PyPy naprawiony, i #IPython w #Gentoo jest na #PyPy 3.11.

Jeden błąd w bibliotece standardowej #Pythona naprawiony, #Django w Gentoo jest na PyPy 3.11.

Powiedziałbym, że całkiem udany dzień.

https://github.com/pypy/pypy/pull/5239
https://github.com/python/cpython/pull/130962

#Python

@data @datadon

#DataViz on two requirements:
* zooming, panning and rescaling
* shareable dashboards

"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html

#dataDev #retrieval #dataMining #plotly #Dash #Bokeh #python #dataInteraction #data #dataDon #widgets #ipython #jupyter #dashboards #businessIntelligence

A guide on integrating #emacs + #python + #hatch using [mostly] builtin tooling and project local .dir-locals.el variables.

Specifically, this setup uses #Eglot + #pyright for live syntax checking, #pytest for code running, #MyPy for type-checking, and #IPython as the shell

Feedback would be welcome -- trying to get it robust + idiomatic.

https://jtmoulia.srht.site/guides/emacs-python-hatch/

#Checkliste für #Vim als #IDE für #Python

#Checkliste für die #Vervollständigung der #Datei #Vimrc

Bezüglich #Debugging sagt der Vortragende er benutze dafür nicht Vim, sondern #iPython und #ipdb (#IPython #pdb).

I will insert breakpoints with a shortkey, open another terminal window and start my program, waiting until the breakpoint is hit and then inspect the scope.

http://ipython.org/
https://pypi.org/project/ipdb/

https://youtu.be/YhqsjUUHj6g

What can you do with a #CATMAID server? Say, let's look at the #Drosophila (vinegar fly, often referred to as fruit fly) larval central nervous system, generously hosted by the #VirtualFlyBrain https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=108250&yp=82961.59999999999&xp=54210.799999999996&tool=tracingtool&sid0=1&s0=2.4999999999999996&help=true&layout=h(XY,%20%7B%20type:%20%22neuron-search%22,%20id:%20%22neuron-search-1%22,%20options:%20%7B%22annotation-name%22:%20%22papers%22%7D%7D,%200.6) or the #Platynereis (a marine annelid) server from the Jekely lab https://catmaid.jekelylab.ex.ac.uk/

First, directly interact by point-and-click: open widgets, find neurons by name or annotations, fire up a graph widget and rearrange neurons to make a neat synaptic connectivity diagram, or an adjacency matrix, or look at neuron anatomy in 3D. Most text–names, numbers–are clickable and filterable in some way, such as regular expressions.

Second, interact from other software. Head to r-catmaid https://natverse.org/rcatmaid/ (part of the #natverse suite by Philipp Schlegel @uni_matrix, Alex Bates and others) for an R-based solution from the Jefferis lab at the #MRCLMB. Includes tools such as #NBLAST for anatomical comparisons of neurons (see paper by Marta Costa et al. 2016 https://www.sciencedirect.com/science/article/pii/S0896627316302653 ).

If R is not your favourite, then how about #python: the #navis package, again by the prolific @uni_matrix, makes it trivial, and works also within #Blender too for fancy 3D renderings and animations. An earlier, simpler version was #catpy by @csdashm https://github.com/ceesem/catpy , who also has examples on access from #matlab.

Third, directly from a #psql prompt. As in, why not? #SQL is quite a straightforward language. Of course, you'll need privileged access to the server, so this one is only for insiders. Similarly privileged is from an #ipython prompt initialized via #django from the command line, with the entire server-side API at your disposal for queries.

Fourth, and one of my favourites: from the #javascript console in the browser itself. There are a handful of examples here https://github.com/catmaid/CATMAID/wiki/Scripting but the possibilities are huge. Key utilities are the "fetchSkeletons" macro-like javascript function https://github.com/catmaid/CATMAID/wiki/Scripting#count-the-number-of-presynaptic-sites-and-the-number-of-presynaptic-connectors-on-an-axon and the NeuronNameService.getInstance().getName(<skeleton_id>) function.

Notice every #CATMAID server has its /apis/, e.g., at https://l1em.catmaid.virtualflybrain.org/apis/ will list all GET or REST server access points. Reach to them as you please. See the documentation: https://catmaid.readthedocs.io/en/stable/api.html

In short: the data is there for you to reach out to, interactively or programmatically, and any fine mixture of the two as you see fit.

One #PyPy bug fixed, and #Gentoo gets #IPython on #PyPy 3.11.

One #Python stdlib bug fixed, and Gentoo gets #Django on PyPy 3.11.

I dare say that's a pretty good day.

https://github.com/pypy/pypy/pull/5239
https://github.com/python/cpython/pull/130962

Some fun numbers: out of 823 packages featuring #PyPy 3.x support in #Gentoo, 712 have already been ported to PyPy 3.11. There are a few significant blockers left (notably #IPython, with hanging IPyParallel), and a fair number of packages that simply don't have tests (so I haven't looked at them yet).

Interesting enough, the most common test failures seen while porting to PyPy 3.11 are:

• flaky tests (i.e. just need to rerun, especially without parallelization of testing 70 packages simultaneously)
• existing, irrelevant test regressions (i.e. confirmed by testing with CPython 3.11)
• existing test problems with PyPy3.10 (i.e. need to copy deselects)
• some minor differences, such as slightly different exception messages

Serious issues are really rare, and they are often fixed (or worked around by me) promptly. Really great release! Thanks to everyone involved!

It's been a while without news, but #IPython 9.0 beta 1 is out. "biggest" features are complete rewrite of the theme engine which now supports arbitrary colors, and unicode (I'd love new themes please !) also optional LLM integrations. PLease help me write the changelog as well : https://ipython.readthedocs.io/en/latest/whatsnew/version9.html

I have no idea what magic makes this possible, but I love it: https://github.com/evcxr/evcxr/blob/main/evcxr_jupyter/README.md #Evcxr, a #Rust #Jupyter kernel.

I've been planning to actually sit down and start learning the language finally but have been putting it off.

But... a Rust REPL in Jupyter? Yes, Finally. #IPython/Jupyter have become my natural habitat over the past decade. This will make playing around with and learning to think in Rust *much* easier.

My svg-turtle library now has #IPython support, so you can include #Python #TurtleGraphics in your #Jupyter notebooks.
https://donkirkby.github.io/svg-turtle/

Wow! New #Python discovery, I knew you could invoke a python or #ipython shell inside a script to examine variables and such, but I somehow always thought it was impossible to actually change them. Turns out there is no such protection in place, you can jump into an ipython shell in a running program with lots of threads doing stuff and manually actually... do stuff. Added a shell drop command to debug - now it's the most powerful UI element and my whole namespace is just.. there.

#Jupyter #Python folks:

Anyone else find that you just do *everything* in Jupyter and #ipython?

Every time I boot up my computer, I start a #jupyterlab server running and launch a browser to connect to it. That browser window will then run in full-screen on it's own desktop for days, weeks, possibly months to come, various notebook tabs being opened and closed and a scratch notebook always open for just doing "stuff" on my computer (whatever I wanna use my computer for at the moment).

@data @datadon

#DataViz on two requirements:
* zooming, panning and rescaling
* shareable dashboards

"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html

#dataDev #retrieval #dataMining #plotly #Dash #Bokeh #python #dataInteraction #data #dataDon #widgets #ipython #jupyter #dashboards #businessIntelligence

@[email protected] @[email protected]

#DataViz on two requirements:
* zooming, panning and rescaling
* shareable dashboards

"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html

#dataDev #retrieval #dataMining #plotly #Dash #Bokeh #python #dataInteraction #data #dataDon #widgets #ipython #jupyter #dashboards #businessIntelligence

@data @datadon

#DataViz on two requirements:
* zooming, panning and rescaling
* shareable dashboards

"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html

#dataDev #retrieval #dataMining #plotly #Dash #Bokeh #python #dataInteraction #data #dataDon #widgets #ipython #jupyter #dashboards #businessIntelligence

@data @datadon

#DataViz on two requirements:
* zooming, panning and rescaling
* shareable dashboards

"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html

#dataDev #retrieval #dataMining #plotly #Dash #Bokeh #python #dataInteraction #data #dataDon #widgets #ipython #jupyter #dashboards #businessIntelligence

Noticias sobre Python científico de la semana, episodio 60 🐍⚙️🍇 ¡Hola 2023!

En resumen: Nuevas versiones de SciPy, NumPy, IPython y Polars, cálculo de energía renovable con atlite, un cron para notebooks de Jupyter, arrays infinitos, y un anuncio personal https://astrojuanlu.substack.com/p/episodio-60 Apoya el noticiero suscribiéndote por correo 📫

#noticieropythoncientifico #scipy #numpy #ipython #polars #conda #jupyter #pydata #python

What can you do with a #CATMAID server? Say, let's look at the #Drosophila (vinegar fly, often referred to as fruit fly) larval central nervous system, generously hosted by the #VirtualFlyBrain https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=108250&yp=82961.59999999999&xp=54210.799999999996&tool=tracingtool&sid0=1&s0=2.4999999999999996&help=true&layout=h(XY,%20%7B%20type:%20%22neuron-search%22,%20id:%20%22neuron-search-1%22,%20options:%20%7B%22annotation-name%22:%20%22papers%22%7D%7D,%200.6) or the #Platynereis (a marine annelid) server from the Jekely lab https://catmaid.jekelylab.ex.ac.uk/

First, directly interact by point-and-click: open widgets, find neurons by name or annotations, fire up a graph widget and rearrange neurons to make a neat synaptic connectivity diagram, or an adjacency matrix, or look at neuron anatomy in 3D. Most text–names, numbers–are clickable and filterable in some way, such as regular expressions.

Second, interact from other software. Head to r-catmaid https://natverse.org/rcatmaid/ (part of the #natverse suite by Philipp Schlegel @uni_matrix, Alex Bates and others) for an R-based solution from the Jefferis lab at the #MRCLMB. Includes tools such as #NBLAST for anatomical comparisons of neurons (see paper by Marta Costa et al. 2016 https://www.sciencedirect.com/science/article/pii/S0896627316302653 ).

If R is not your favourite, then how about #python: the #navis package, again by the prolific @uni_matrix, makes it trivial, and works also within #Blender too for fancy 3D renderings and animations. An earlier, simpler version was #catpy by @csdashm https://github.com/ceesem/catpy , who also has examples on access from #matlab.

Third, directly from a #psql prompt. As in, why not? #SQL is quite a straightforward language. Of course, you'll need privileged access to the server, so this one is only for insiders. Similarly privileged is from an #ipython prompt initialized via #django from the command line, with the entire server-side API at your disposal for queries.

Fourth, and one of my favourites: from the #javascript console in the browser itself. There are a handful of examples here https://github.com/catmaid/CATMAID/wiki/Scripting but the possibilities are huge. Key utilities are the "fetchSkeletons" macro-like javascript function https://github.com/catmaid/CATMAID/wiki/Scripting#count-the-number-of-presynaptic-sites-and-the-number-of-presynaptic-connectors-on-an-axon and the NeuronNameService.getInstance().getName(<skeleton_id>) function.

Notice every #CATMAID server has its /apis/, e.g., at https://l1em.catmaid.virtualflybrain.org/apis/ will list all GET or REST server access points. Reach to them as you please. See the documentation: https://catmaid.readthedocs.io/en/stable/api.html

In short: the data is there for you to reach out to, interactively or programmatically, and any fine mixture of the two as you see fit.

What can you do with a #CATMAID server? Say, let's look at the #Drosophila (vinegar fly, often referred to as fruit fly) larval central nervous system, generously hosted by the #VirtualFlyBrain https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=108250&yp=82961.59999999999&xp=54210.799999999996&tool=tracingtool&sid0=1&s0=2.4999999999999996&help=true&layout=h(XY,%20%7B%20type:%20%22neuron-search%22,%20id:%20%22neuron-search-1%22,%20options:%20%7B%22annotation-name%22:%20%22papers%22%7D%7D,%200.6) or the #Platynereis (a marine annelid) server from the Jekely lab https://catmaid.jekelylab.ex.ac.uk/

First, directly interact by point-and-click: open widgets, find neurons by name or annotations, fire up a graph widget and rearrange neurons to make a neat synaptic connectivity diagram, or an adjacency matrix, or look at neuron anatomy in 3D. Most text–names, numbers–are clickable and filterable in some way, such as regular expressions.

Second, interact from other software. Head to r-catmaid https://natverse.org/rcatmaid/ (part of the #natverse suite by Philipp Schlegel @uni_matrix, Alex Bates and others) for an R-based solution from the Jefferis lab at the #MRCLMB. Includes tools such as #NBLAST for anatomical comparisons of neurons (see paper by Marta Costa et al. 2016 https://www.sciencedirect.com/science/article/pii/S0896627316302653 ).

If R is not your favourite, then how about #python: the #navis package, again by the prolific @uni_matrix, makes it trivial, and works also within #Blender too for fancy 3D renderings and animations. An earlier, simpler version was #catpy by @csdashm https://github.com/ceesem/catpy , who also has examples on access from #matlab.

Third, directly from a #psql prompt. As in, why not? #SQL is quite a straightforward language. Of course, you'll need privileged access to the server, so this one is only for insiders. Similarly privileged is from an #ipython prompt initialized via #django from the command line, with the entire server-side API at your disposal for queries.

Fourth, and one of my favourites: from the #javascript console in the browser itself. There are a handful of examples here https://github.com/catmaid/CATMAID/wiki/Scripting but the possibilities are huge. Key utilities are the "fetchSkeletons" macro-like javascript function https://github.com/catmaid/CATMAID/wiki/Scripting#count-the-number-of-presynaptic-sites-and-the-number-of-presynaptic-connectors-on-an-axon and the NeuronNameService.getInstance().getName(<skeleton_id>) function.

Notice every #CATMAID server has its /apis/, e.g., at https://l1em.catmaid.virtualflybrain.org/apis/ will list all GET or REST server access points. Reach to them as you please. See the documentation: https://catmaid.readthedocs.io/en/stable/api.html

In short: the data is there for you to reach out to, interactively or programmatically, and any fine mixture of the two as you see fit.

What can you do with a #CATMAID server? Say, let's look at the #Drosophila (vinegar fly, often referred to as fruit fly) larval central nervous system, generously hosted by the #VirtualFlyBrain https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=108250&yp=82961.59999999999&xp=54210.799999999996&tool=tracingtool&sid0=1&s0=2.4999999999999996&help=true&layout=h(XY,%20%7B%20type:%20%22neuron-search%22,%20id:%20%22neuron-search-1%22,%20options:%20%7B%22annotation-name%22:%20%22papers%22%7D%7D,%200.6) or the #Platynereis (a marine annelid) server from the Jekely lab https://catmaid.jekelylab.ex.ac.uk/

First, directly interact by point-and-click: open widgets, find neurons by name or annotations, fire up a graph widget and rearrange neurons to make a neat synaptic connectivity diagram, or an adjacency matrix, or look at neuron anatomy in 3D. Most text–names, numbers–are clickable and filterable in some way, such as regular expressions.

Second, interact from other software. Head to r-catmaid https://natverse.org/rcatmaid/ (part of the #natverse suite by Philipp Schlegel @uni_matrix, Alex Bates and others) for an R-based solution from the Jefferis lab at the #MRCLMB. Includes tools such as #NBLAST for anatomical comparisons of neurons (see paper by Marta Costa et al. 2016 https://www.sciencedirect.com/science/article/pii/S0896627316302653 ).

If R is not your favourite, then how about #python: the #navis package, again by the prolific @uni_matrix, makes it trivial, and works also within #Blender too for fancy 3D renderings and animations. An earlier, simpler version was #catpy by @csdashm https://github.com/ceesem/catpy , who also has examples on access from #matlab.

Third, directly from a #psql prompt. As in, why not? #SQL is quite a straightforward language. Of course, you'll need privileged access to the server, so this one is only for insiders. Similarly privileged is from an #ipython prompt initialized via #django from the command line, with the entire server-side API at your disposal for queries.

Fourth, and one of my favourites: from the #javascript console in the browser itself. There are a handful of examples here https://github.com/catmaid/CATMAID/wiki/Scripting but the possibilities are huge. Key utilities are the "fetchSkeletons" macro-like javascript function https://github.com/catmaid/CATMAID/wiki/Scripting#count-the-number-of-presynaptic-sites-and-the-number-of-presynaptic-connectors-on-an-axon and the NeuronNameService.getInstance().getName(<skeleton_id>) function.

Notice every #CATMAID server has its /apis/, e.g., at https://l1em.catmaid.virtualflybrain.org/apis/ will list all GET or REST server access points. Reach to them as you please. See the documentation: https://catmaid.readthedocs.io/en/stable/api.html

In short: the data is there for you to reach out to, interactively or programmatically, and any fine mixture of the two as you see fit.

Noticias sobre Python científico de la semana, episodio 60 🐍⚙️🍇 ¡Hola 2023!

En resumen: Nuevas versiones de SciPy, NumPy, IPython y Polars, cálculo de energía renovable con atlite, un cron para notebooks de Jupyter, arrays infinitos, y un anuncio personal https://astrojuanlu.substack.com/p/episodio-60 Apoya el noticiero suscribiéndote por correo 📫

#noticieropythoncientifico #scipy #numpy #ipython #polars #conda #jupyter #pydata #python

Noticias sobre Python científico de la semana, episodio 60 🐍⚙️🍇 ¡Hola 2023!

En resumen: Nuevas versiones de SciPy, NumPy, IPython y Polars, cálculo de energía renovable con atlite, un cron para notebooks de Jupyter, arrays infinitos, y un anuncio personal https://astrojuanlu.substack.com/p/episodio-60 Apoya el noticiero suscribiéndote por correo 📫

#noticieropythoncientifico #scipy #numpy #ipython #polars #conda #jupyter #pydata #python

Noticias sobre Python científico de la semana, episodio 60 🐍⚙️🍇 ¡Hola 2023!

En resumen: Nuevas versiones de SciPy, NumPy, IPython y Polars, cálculo de energía renovable con atlite, un cron para notebooks de Jupyter, arrays infinitos, y un anuncio personal https://astrojuanlu.substack.com/p/episodio-60 Apoya el noticiero suscribiéndote por correo 📫

#noticieropythoncientifico #scipy #numpy #ipython #polars #conda #jupyter #pydata #python

Noticias sobre Python científico de la semana, episodio 60 🐍⚙️🍇 ¡Hola 2023!

En resumen: Nuevas versiones de SciPy, NumPy, IPython y Polars, cálculo de energía renovable con atlite, un cron para notebooks de Jupyter, arrays infinitos, y un anuncio personal https://astrojuanlu.substack.com/p/episodio-60 Apoya el noticiero suscribiéndote por correo 📫

#noticieropythoncientifico #scipy #numpy #ipython #polars #conda #jupyter #pydata #python