kinetics | Python script to mine the Kinetics dataset | Machine Learning library

Is there a way to extract just the estimates from nlsList()? Sample data:

I am doing training and put the dataset inside the data folder. The Structure looks like this.

help, please - I can't understand my own code! lol I'm fairly new at python and after many trials and errors, I got my code to work, but there is one particular part of it I don't understand.

In the code below, I'm solving a fairly basic ODE through scipy's odeint-function. My goal is then to build on this blue-print for more complicated systems.

My question(s): How could I call the method .reaction_rate_simple without any arguments and without the closing parenthesis? What does this mean in python? Should I use a static method here somewhere?

If anyone has any feedback on this - maybe this is a crappy piece of code and there's a better way of solving it!

I am very thankful for any response and help!

I have been looking for a while, but could not find the answer to this specific question anywhere, sorry if it is a duplicate!

I have started to build a python package based on the xarray-simlab framework with the goal to provide a modular toolbox for building reproducible and flexible marine ecosystem models. Xarray-simlab at the moment only supports explicit step-sizes to solve the model functions. In order to solve complex models more safely & efficiently, I have instead started using GEKKO as a solver backend, as the model syntax seems well suited. (Note: At the moment I will only need functionality to solve the model equations over time, but I would like to make use of GEKKO's optimization functionality to fit model parameters to field or lab data at later stages.)

The current prototype of the package creates a xsimlab process class that passes the GEKKO model instance m to all sub-processes. Process classes that inherit the model instance initialize m.SV, m.Param or define m.Intermediates based on the processes added to the model & parameters (incl. SV dimensions) supplied at runtime. In the next step all initialized intermediates are accumulated to the affected state variables in m.Equations. Once successfully solved, GEKKO variables are repackaged into a xarray data structure, that includes relevant metadata and can be analysed further. The package prototype can solve basic models using IMODE=7, but I have come across one issue related to the time steps of that solver:

I was expecting functionality similar to scipy's odeint, with adaptive time step evaluation, but obviously this does not seem to be the case and instead it evaluates the model at the discrete time-steps supplied.

The package is still under heavy development, and there are plenty of features that I am still trying to improve, so below is a minimal code example of a simple chemostat model. The model describes a phytoplankton state variable growing on a nutrient in a simplified flow-through system. The nutrient flows in at a constant rate, and phytoplankton dies and is lost from the system at a constant rate:

Due to Covid-19, I don't have access to a physical NAO and need to work with simulations. The goal is to model dialogues of different complexity, also involving gestures. Speech recognition is the most important feature here, but simulation of other features that add more realism (like voice) would be appreciated too.

I am working from a Mac (with Catalina).

What I've tried:

• Choregraphe: The included simulation works fine, but is very restricted in its abilities. If I'm not missing something, dialogues are only simulated in a written chat - so I type the speech input, getting 'speech bubbles' as a response
• Webots for NAO: No longer supported?
• Webots (using Python controllers): The most promising approach so far, but there is basically no documentation on how to write NAO controllers. I could not figure out how to make the Speaker() class work. The robot and world simulation from naoqisim (which is also no longer sustained) seem to run fine.
• Webots using ROS controller: There is no official support for Mac, and the recommended installation for ROS Kinetics has not yet worked for me.

I'd appreciate any hint on whether Webots is even suitable for dialogues (seems to be mostly focussed on movement) or advice for other suitable simulations.

I am attempting to convert a MATLAB code to python but I am getting answers that are completely different. I've attempted using scipy.ode,solve_ivp, and odeint.When running the code I get values that range from 1 to 0.2 but in MATLAB they range from 30 to 70. MATLAB code:

I am writing a GSON (Java) parser for the CORD19 dataset https://pages.semanticscholar.org/coronavirus-research of about 40K scientific papers which have been made open for everyone. I want to iterate over the JSON tree using GSON and convert them to HTML. In particular I want to iterate over the entries of the JsonObject elements.

Q1: If anyone has already written an F/OSS CORD19 parser in GSON or other Java parser I'd be delighted.

My specific problem is to iterate over the fields (entries) of a JsonObject.

Data (heavily snipped, but hopefully parsable if snips removed):

I am working on a binary classification problem with Tensorflow BERT language model. Here is the link to google colab. After saving and loading the model is trained, I get error while doing the prediction.

Saving the Model

Given this XML data: