Discover and read the best of Twitter Threads about #jupyter
Most recents (23)
[1/8] 📚 Introducing #Quarto: A Versatile, New and Exciting Publishing Tool! 🌟
Quarto is a powerful, open-source, and user-friendly publishing framework that streamlines the process of creating beautiful books, documents, and websites. Let’s explore it now!
#RStats #DataScience
Quarto is a powerful, open-source, and user-friendly publishing framework that streamlines the process of creating beautiful books, documents, and websites. Let’s explore it now!
#RStats #DataScience
[2/8] 🤓 Language Agnostic: Quarto works seamlessly with multiple languages, including #Markdown, #LaTeX, #RMarkdown, and #Jupyter notebooks. So, whether you're a researcher or a creative writer, Quarto has you covered! 🌍
#DataScience #RStats
#DataScience #RStats
[3/8] 🔁 Format Flexibility: With Quarto, you can convert your content into various formats, such as PDF, HTML, EPUB, and even slide presentations. It makes sharing your work with diverse audiences a breeze! 🌬️
#RStats #DataScience
#RStats #DataScience
Happy to announce my new course Mapping and Data Visualization with #Python. This has been in the making for over a year and excited to be able to share it with the world! The full course is free for self-study and shared under an open license. An overview thread below 👇 1/n
We start with the overview of the Python data visualization landscape and zero in on the core libraries for mapping. Day 1 covers vector data visualization with #Maptplotlib, #Pandas, #GeoPandas, and #Contextily (2/n)
Day 2 starts with a deep dive into XArray and raster data visualization. We use #Xarray, #rioxarray and #CartoPy to visualize elevation and gridded climate datasets and learn some advanced #Matplotlib tricks. (3/n)
As promised, a thread of amazing folks working in Open Source. Like most people in FOSS they probably don't get the recognition they deserve.
If you like #Python and programming, I suggest you following everyone in this tread.
Doubles as #FF
Please RT for visibility.
🧵
If you like #Python and programming, I suggest you following everyone in this tread.
Doubles as #FF
Please RT for visibility.
🧵
Follow João S. O. Bueno @gwidion
João is building terminedia and rows
github.com/jsbueno/termin…
github.com/turicas/rows
João is building terminedia and rows
github.com/jsbueno/termin…
github.com/turicas/rows
Patrick Loeber @python_engineer produces amazing Youtube content. Essential viewing for #Python engineers!
Displaying a #CesiumJS 3D map in a #streamlit web app or #Jupyter notebook with only one line of code. Try it out 👇
Web app: streamlit.gishub.org
GitHub: github.com/giswqs/streaml…
#leafmap #geospatial #mapping #dataviz
Web app: streamlit.gishub.org
GitHub: github.com/giswqs/streaml…
#leafmap #geospatial #mapping #dataviz
Displaying a #CesiumJS 3D map in a #Jupyter notebook, inspired by @banesullivan 's localtileserver package.
leafmap.display_html(src, width, height)
leafmap.org/common/#leafma…
leafmap.display_html(src, width, height)
leafmap.org/common/#leafma…
Displaying a #CesiumJS 3D map in a #streamlit web app.
leafmap.cesium_to_streamlit(html, width, height)
leafmap.org/common/#leafma…
leafmap.cesium_to_streamlit(html, width, height)
leafmap.org/common/#leafma…
(1/6)
I'm very happy that we (finally) got an acceptance for our submitted paper in the Journal of Open Source Education, JOSE🙂.
"An open source crash course on parameter estimation of computational models using a Bayesian optimization approach"
doi.org/10.21105/jose.…
I'm very happy that we (finally) got an acceptance for our submitted paper in the Journal of Open Source Education, JOSE🙂.
"An open source crash course on parameter estimation of computational models using a Bayesian optimization approach"
doi.org/10.21105/jose.…
(2/6)
Parameter estimation is a crucial aspect of model development in science and engineering. In the proposed educational module, we have a look at the #Bayesian optimization processes in general and model calibration (parameter estimation) in particular.
Parameter estimation is a crucial aspect of model development in science and engineering. In the proposed educational module, we have a look at the #Bayesian optimization processes in general and model calibration (parameter estimation) in particular.
(3/6)
For demonstration purposes, we implement a model parameter estimation process for a fitting problem step by step in Python such that the readers can adapt it to their own models and use-cases.
Codes are available in this repo: github.com/mbarzegary/edu…
For demonstration purposes, we implement a model parameter estimation process for a fitting problem step by step in Python such that the readers can adapt it to their own models and use-cases.
Codes are available in this repo: github.com/mbarzegary/edu…
#GToolkit now features a new component: #Lepiter.
Knowledge Management + Multilanguage Notebooks + Moldable Development.
Let's take a tour.
1/
Knowledge Management + Multilanguage Notebooks + Moldable Development.
Let's take a tour.
1/
Lepiter is made of snippets assembled in pages. Each snippet has its own language and comes with its own editor.
In its simplest form, it's a Markdown note taker with live markup that appears on demand and expandable links.
2/
In its simplest form, it's a Markdown note taker with live markup that appears on demand and expandable links.
2/
When combined with a table of contents, pages can form a book.
3/
3/
Jupyter конечно говно, но очень удобное. Код, текст, графики, вывод: все в одном месте, как статья. Можно заставить на жирном сервере, не давать доступ по ssh, не мучаться с удаленным запуском, профилировать процессы
Поругайтесь на jupyter или выступите адвокатом дьявола. Можно выговориться как на кушетке у терапевта. После рабочих встреч и пары тасочек вернусь и пожалею вас. А еще расскажу свою историю с notebook-ами
#Julia言語 以下のリンク先で Julia カーネルの Jupyter notebook で音を鳴らす方法の例を示しておきました。
以下のリンク先の添付画像の部分では 880Hz と 1320Hz の正弦波の和が再生されるはずです。
nbviewer.jupyter.org/gist/genkuroki…
以下のリンク先の添付画像の部分では 880Hz と 1320Hz の正弦波の和が再生されるはずです。
nbviewer.jupyter.org/gist/genkuroki…
#Jupyter notebook で nbextensions の Gist-it を使えるようにすると、任意のカーネルで作った Jupyter notebook をワンボタンで GitHub Gist に投稿できるようにできます。
数式やコードを含む文章による解説
実際に動かすコード
計算結果
グラフ
動画
音
をまとめたものがワンボタンで公開可能！
数式やコードを含む文章による解説
実際に動かすコード
計算結果
グラフ
動画
音
をまとめたものがワンボタンで公開可能！
こういう目的には #Jupyter notebook が現時点において最適なツールになっていると思います。 #Jupyter notebook は多くのプログラミング言語に対応しており、ユーザー側の自由度が大きいです。
解説↓
nbviewer.jupyter.org/github/genkuro…
nbviewer.jupyter.org/github/genkuro…
解説↓
nbviewer.jupyter.org/github/genkuro…
nbviewer.jupyter.org/github/genkuro…
#統計
⓪確率変数Xの分布は期待値E[f(X)]達(fを動かす)から決まる。
①特性函数についてなら添付画像1の通り。単なるFourier解析。
②中心極限定理には、モーメント母函数や特性函数を使わずに、Taylorの定理のみを使う初等的な証明があります(概略は添付画像2)。この証明法はもっと普及するべき。
⓪確率変数Xの分布は期待値E[f(X)]達(fを動かす)から決まる。
①特性函数についてなら添付画像1の通り。単なるFourier解析。
②中心極限定理には、モーメント母函数や特性函数を使わずに、Taylorの定理のみを使う初等的な証明があります(概略は添付画像2)。この証明法はもっと普及するべき。
#統計 学部レベルの統計学入門の教科書の多くが、なぜか、(Fourier解析の知識を要求する)モーメント母函数や特性函数を経由する中心極限定理の証明またはその概略をコピー&ペーストのごとく載せているのは不思議なことです。
教科書の著者達が集団でコピペしまくっている疑いがあります。
教科書の著者達が集団でコピペしまくっている疑いがあります。
#数楽 Taylorの定理の証明も数十年以上コピペが蔓延している疑いが強い。
Cⁿ級を仮定すれば、n階の導函数f^{(n)}(x)をaからxまで積分することをn回繰り返すだけでTaylorの定理が得られる。
g(x) = g(a) + ∫_a^x g'(t)dt をn回使う。
Cⁿ級を仮定すれば、n階の導函数f^{(n)}(x)をaからxまで積分することをn回繰り返すだけでTaylorの定理が得られる。
g(x) = g(a) + ∫_a^x g'(t)dt をn回使う。
#geemap new feature: changing layer visualization interactively with a GUI. This is a very challenging implementation with many hours of work and >1000 lines of code.
App: gishub.org/geemap-vis
#EarthEngine #GIS #remotesensing #python #jupyter #mapping #ipyleaflet #dataviz
App: gishub.org/geemap-vis
#EarthEngine #GIS #remotesensing #python #jupyter #mapping #ipyleaflet #dataviz
Just added a new option for turning all layers on/off.
#Jupyter たまに Jupyter を嫌う頭の悪そうな人を見かけるがこの手の人ががんばっているせいでそうなっているのか。
この手のことを言う人達よりも、MITで Jupyter + Julia で講義をしている人達の方がわたしには賢い人達のように見える。
単に適材適所の問題。感情的になってバカを晒す必要はない。
この手のことを言う人達よりも、MITで Jupyter + Julia で講義をしている人達の方がわたしには賢い人達のように見える。
単に適材適所の問題。感情的になってバカを晒す必要はない。
#Jupyter しかし、多くの人が賛成してくれると思うが、配布する Jupyter notebook は
Kernerl -> Restart & Run All
で正常にすべてのセルが実行できるものにして欲しいと思います。
あと、パッケージ化した方が良さそうな場合には、野良パッケージでも良いので、そうしてもらえると助かる。
Kernerl -> Restart & Run All
で正常にすべてのセルが実行できるものにして欲しいと思います。
あと、パッケージ化した方が良さそうな場合には、野良パッケージでも良いので、そうしてもらえると助かる。
#Jupyter あと、コードそのものだけではなく、実行結果が重要な Jupyter notebook の場合には、事情が許すならば、実行した結果を含むものを配布して欲しいです。
Out[n]が全て空欄のままの配布だと内容の理解が困難な場合が多い。
Out[n]が全て空欄のままの配布だと内容の理解が困難な場合が多い。
Some thoughts on teaching online (a thread).
Background: I've done corporate #Python and data-science training for 20 years. Even before the pandemic, I taught live, online courses (via WebEx and Zoom) at least 1 week/month. I also offer many video (recorded) courses.
Background: I've done corporate #Python and data-science training for 20 years. Even before the pandemic, I taught live, online courses (via WebEx and Zoom) at least 1 week/month. I also offer many video (recorded) courses.
My work slowed down in April-May, when companies didn't know what was happening.
Training is now about where it was before. Except it's 100% online.
I teach everything from "Python for non-programmers" to "intro to data science." 5 days/week, 4-8 hours/day. All online.
Training is now about where it was before. Except it's 100% online.
I teach everything from "Python for non-programmers" to "intro to data science." 5 days/week, 4-8 hours/day. All online.
I've learned a lot in this time, and want to share these thoughts with others — learners (no pun intended), teachers, and training managers.
Also: I teach adults at companies. I have huge respect and sympathy for schoolteachers who have been thrust into this world.
Also: I teach adults at companies. I have huge respect and sympathy for schoolteachers who have been thrust into this world.
#Julia言語
Julia内部から、
文字列のCのコードをgccにstdinから食わせてコンパイルして、ライブラリを作って、そのライブラリ内部の函数をJuliaから呼び出して使う方法の例↓
gist.github.com/genkuroki/9d43…
さらに、文字列のCのコードをJupyter上で色付けして表示する方法。
Julia内部から、
文字列のCのコードをgccにstdinから食わせてコンパイルして、ライブラリを作って、そのライブラリ内部の函数をJuliaから呼び出して使う方法の例↓
gist.github.com/genkuroki/9d43…
さらに、文字列のCのコードをJupyter上で色付けして表示する方法。
#Julia言語 Julia内部からCで作ったライブラリを使ったり、Pythonのライブラリを使ったりすることによって、Juliaを学ぶついでにCやPythonも学べてしまいます。
Juliaは「高速なグルー言語」
Juliaは「高速なグルー言語」
#Julia言語 「気軽に使えるグルー言語で多くの高速なライブラリを貼り合わせて使う」というアイデアは秀逸だったのですが、「グルー言語は遅くてもよい」という考え方はJuliaの登場によって誤りであったことが明瞭になってしまったと思います。
Juliaは「高速なグルー言語」(高速糊言語)
Juliaは「高速なグルー言語」(高速糊言語)
まさに、それ！ #Julia言語 で感動的なのは、パッケージも含めて導入が容易なこと。
①公式バイナリのインストーラーをダウンロードして実行。
julialang.org/downloads/
②Juliaを実行して
julia> ]
pkg> add 使用するパッケージのリスト
でパッケージ群も入れられる。
①公式バイナリのインストーラーをダウンロードして実行。
julialang.org/downloads/
②Juliaを実行して
julia> ]
pkg> add 使用するパッケージのリスト
でパッケージ群も入れられる。
#Julia言語 すでにJupyterを使っているなら
julia> ENV["JUPYTER"] = """使用しているjupyterバイナリのフルパス"""
julia> ]
pkg> add IJulia
pkg> build IJulia
JupyterもJuliaが入れたものを使うなら、
julia> ENV["JUPYTER"] = ""
として以下同様。
これでJupyter上でJuliaを使える。
julia> ENV["JUPYTER"] = """使用しているjupyterバイナリのフルパス"""
julia> ]
pkg> add IJulia
pkg> build IJulia
JupyterもJuliaが入れたものを使うなら、
julia> ENV["JUPYTER"] = ""
として以下同様。
これでJupyter上でJuliaを使える。
#Julia言語 Juliaのライブラリとして、Pythonをインストールしてしまうなら(~/.julia/cond以下にminicondaが入る)、
julia> ENV["PYTHON"] = ""
julia> ]
pkg> add PyCall
pkg> build PyCall
この他に
pkg> add PyPlot Seaborn SymPy SciPy
しておくと、Pythonに慣れた人にとっても便利なはず。
julia> ENV["PYTHON"] = ""
julia> ]
pkg> add PyCall
pkg> build PyCall
この他に
pkg> add PyPlot Seaborn SymPy SciPy
しておくと、Pythonに慣れた人にとっても便利なはず。
Direita? Esquerda? Centro? Onde se devem sentar os partidos? Qual a proximidade entre eles?
Com base na análise de todas as votações da passada legislatura pode-se ir para além dos discursos
nbviewer.jupyter.org/github/fsmunoz…
#DataScience #portugal
Com base na análise de todas as votações da passada legislatura pode-se ir para além dos discursos
nbviewer.jupyter.org/github/fsmunoz…
#DataScience #portugal
* CDU separada de BE/Livre(JKM)
* PAN como primeira separação mas "à esquerda"
* PS agrupado com PSD
* IL/CDS/CH próximos
* PAN como primeira separação mas "à esquerda"
* PS agrupado com PSD
* IL/CDS/CH próximos
- Partidos à esquerda mais próximos em termos absolutos que os à direita
- Ainda assim, partidos à direita com menores diferenças no grupo IL/CDS/CH
- PS quase equidistante mas com maior proximidade à direita.
- LIVRE/JKM mais próximos do BE
- Ainda assim, partidos à direita com menores diferenças no grupo IL/CDS/CH
- PS quase equidistante mas com maior proximidade à direita.
- LIVRE/JKM mais próximos do BE
How do you measure #SOC quality? 🤔
1. ISO 2859-1 (#AQL) to determine sample size
2. #Python #Jupyter notebook to perform random selection
3. Check sheet to spot defects
4. Process runs every 24 hrs
5. (Digestible) #Metrics to improve
How'd we get there? Story in /thread
1. ISO 2859-1 (#AQL) to determine sample size
2. #Python #Jupyter notebook to perform random selection
3. Check sheet to spot defects
4. Process runs every 24 hrs
5. (Digestible) #Metrics to improve
How'd we get there? Story in /thread
I'll break the thread down into four key points:
1. What we're solving for
2. Guiding principles
3. Our (current) solution
4. Quick recap
My goal is to share what's working for us and how we get there. But I'd love to hear from others. What's working for you?
1. What we're solving for
2. Guiding principles
3. Our (current) solution
4. Quick recap
My goal is to share what's working for us and how we get there. But I'd love to hear from others. What's working for you?
What we're solving for: All work is high quality, not just incidents.
On a typical day in our #SOC we'll:
- Process Ms of alerts w/ detection engine
- Send 100s to analysts for human judgement
Those 100s of alerts result in:
- Tens of investigations
- Handful of incidents
On a typical day in our #SOC we'll:
- Process Ms of alerts w/ detection engine
- Send 100s to analysts for human judgement
Those 100s of alerts result in:
- Tens of investigations
- Handful of incidents
A true gem among #multiomics preprints: Integrative Network Fusion by @MarcoChierici, @nicole_bussola, @viperale, et al:
✓ 3 TCGA cancers & simulated data
✓ cross-validation described in detail
✓ flow diagram
✓ source code & data shared
✓ packages w/ version, cited
/n
✓ 3 TCGA cancers & simulated data
✓ cross-validation described in detail
✓ flow diagram
✓ source code & data shared
✓ packages w/ version, cited
/n
- [the method description & comments follows]
- link: biorxiv.org/content/10.110…
- licence the above figures/tables: CC BY-NC-ND 4.0
- an earlier version of INF was previously presented in 2018: doi.org/10.1186/s13062…
- this is the first tweet in #SundayMultiOmics series
- link: biorxiv.org/content/10.110…
- licence the above figures/tables: CC BY-NC-ND 4.0
- an earlier version of INF was previously presented in 2018: doi.org/10.1186/s13062…
- this is the first tweet in #SundayMultiOmics series
[[Introduction]]: Similarity network fusion (SNF, doi.org/10.1038/nmeth.…) is a popular technique (600+ citations, a lot for multi-omics!) for getting a sort of consensus signal from multiple omics; it requires the same patients (less commonly - observations) in each omic.
I had a lot of favorite moments on Twitter this year. Here are some:
Favorite 'can't wait for this tool drop' moment:
Favorite 'this tool was meant for the job moment:
For 2020, here are 20 reasons to look into #AzureSentinel
👇👇
👇👇
Detect AWS attacks:
Detonate phishing URLs in a VM:
I was invited by @ram_ssk to speak at the Microsoft Security Data Science Colloquium. 🙏 COOL EVENT
His summary 👉
His summary 👉
I talked on accelerating learning in infosec, importance of organizing knowledge w/ @MITREattack (and extending to cloud), executable know-how with @cyb3rops #Sigma, cloud native #AzureSentinel, #Jupyter notebooks for sharing repeatable analysis.
SLIDES: github.com/ramshans/2019-…
SLIDES: github.com/ramshans/2019-…
I ♥️that it has attendees from the community including @Google, @salesforce, @netflix, @Facebook, @splunk (@meansec, @daveherrald, @davidveuve), @NicolasPapernot
Shout out to @Cyb3rWard0g and @ianhellen in my presentation!
Shout out to @Cyb3rWard0g and @ianhellen in my presentation!
(1/n) Excited that final version of work led by @D4N__ studying evolutionary repeatability and collateral sensitivity is out. #mathevo #clecliniccancer nature.com/articles/s4146… @CCLRI @NatureComms @CWRUSOM
(2/n) This collab between @CompSciOxford @CCLRI @CWRUSOM & @MoffittPSOC is an extension of @D4N__ ‘s initial theory paper that proposed ‘steering evolution’ as a method of extending the use of existing drugs to control infection. #AntibioticResistance journals.plos.org/ploscompbiol/a…
(3/n) using the fitness landscape metaphor, we reasoned that instances of collateral sensitivity need not be repeatable, as evolutionary branch points could lead to multiple peaks - which could have different sensitivity profiles.
At #ATTACKcon I talked about #Jupyter notebooks as a way to share repeatable analysis. I was asked to share mine. Promise kept!
Learn how a notebook can speed hunting and automation with the new WDATP APIs.
🆕techcommunity.microsoft.com/t5/Threat-Inte…
📔github.com/Microsoft/Wind…
h/t @killchain
Learn how a notebook can speed hunting and automation with the new WDATP APIs.
🆕techcommunity.microsoft.com/t5/Threat-Inte…
📔github.com/Microsoft/Wind…
h/t @killchain
#ATTACKCon keynote section on Jupyter:
And thanks to @killchain for introducing me to notebooks years ago 🙏
Tuesday morning, at #JupyterPopup DC, I'll give a preview of "Flipped Learning with Jupyter," my joint #JupyterCon talk with @RobertTalbert jupyter-pop-up-dc.eventbrite.com
Two weeks later, I'll give a somewhat related talk at #OpenedX2018: "Jupyter-based courses in Open edX: authoring and grading with notebooks" sched.co/EUAl #Jupyter
One of the biggest misconceptions about #flippedclass is "Assuming that flipped learning requires videos," as @RobertTalbert discusses here: rtalbert.org/how-to-define-…
