Duna Vizallasa🌊 A DanubeAlert valós időben figyeli a Duna vízállását, és értesít, amikor az általad követett szakaszon emelkedik vagy csökken a vízszint.
🌊 Regisztrálj ingyen, és kapj személyre szabott riasztásokat a számodra fontos dunai helyekről.
🌊 https://danubealert.com/hu/functions/register
PyData HelsinkiTalks:
Olli Ruokojoki: #Data Driven #Batteries: Modeling State of Charge and Health from Operational Data
Tuomas Sivula: Stochastic Models in #EnergyStorage Operating Environments
Quentin Salomé: ⚡️ lightning talk (title TBA)
Osvaldo Martin: ⚡️ #ArviZ 1.0: Refactoring for flexibility, extensibility, and power
Jouni Seppänen: #Python Visualisation Tools
Paul H 
Mohács, 1956. Sadly, floods along the Danube are nothing new. The one in March 1956 was particular bad as it was caused by the sudden melting of ice from an especially cold winter. The ice dams that formed had to be bombed by the authorities. Mohács in southern Hungary was badly impacted, as we can see in this photo of some of those displaced by the flooding receiving a meal in a local hotel.
Source: Fortepan [129818] / Antal Kotnyék
GK
➴➴➴Æ🜔Ɲ.Ƈꭚ⍴𝔥єɼ👩🏻💻
-ok how do #arviz stylesheets work I'm malding
ArviZ
Seth Axen 🪓 I spent some time tonight working out a possible major redesign of the InferenceData conversion pipeline in InferenceObjects.jl.
The new design is more expressive and extensible. It also simplifies the API and enables developers to implement a converter for their container of #MCMC draws with much less code.
Feedback appreciated! https://github.com/arviz-devs/InferenceObjects.jl/issues/32
Finally, part of this effort involves moving this integration code out of #ArviZ.jl, which still has #Python dependencies, and into pure Julia packages, so users get all of this with the convenience of #JuliaLang's package management.
While Python interop in Julia usually works quite well, sometimes the Python environment gets messed up, which blocks users from using ArviZ.jl 😦 , so moving this code to pure Julia packages supports more users.
Things are coming together for #ArviZ's InferenceData (https://github.com/arviz-devs/InferenceObjects.jl) to be a supported output type for #Turing and #JuliaLang's 
#Stan interface, similarly to how it is for #PyMC.
For details, see https://github.com/TuringLang/MCMCChains.jl/issues/381 and https://github.com/StanJulia/StanSample.jl/issues/60
