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Related to #1055 and some inspiration from @ekmett - Ed if you are reading this, would like to hear your latest thinking / links to more reading for this sort of thing.
This is an idea I'm interest…
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msg from Mathias Möller, TU Delft
Dear Sylvain,
while experimenting with CoolProp I came across another issue.
The vectorised implementation is very helpful when using CoolProp inside a CFD code.
…
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Improve performance of the simulation by processing data rather than objects.
The central idea is to collocate memory so it can be operated on in batch to reduce the number of jumps and improve the…
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When reducing the time series forecasting problem to a tabular regression and when the model is considered fixed once trained it would be possible in theory to pass an array of samples to `update_pred…
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### What happened?
Code that worked with xarray 2024.9.0 has begun to fail with xarray 2024.10.0, even though [no breaking changes](https://docs.xarray.dev/en/stable/whats-new.html#breaking-changes…
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Start of `core.algorithms.simplfy.loop()`
```python
dropped = edges[es_mask].geometry.item()
segments = list(
map(
shapely.LineString,
zip(dropped.coord…
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
v2.17.0
### Custom code
Yes
### OS platform and distribution
Colab
…
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Velox framework for vectorized processing - https://github.com/facebookincubator/velox
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**Describe the bug**
I want to write a generic (over numpy array dtypes) function that is able to handle arbitrary shaped numpy arrays to perform simple, element-wise operations. For this, I fo…
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I have previously used scipy's solve_ivp, which lets me pass multiple arguments like this.
x = solve_ivp(Model, t_span, y0s, args=(
ATP_demand, VO2max, vlamax, Ks1, Ks2, Ks3, Kelox, KpyrO2…