User @codyschank had noticed that for small datasets, stumpy.stomp._stomp is faster than stumpy.stump. Here is some very rough timing calculations from my 2-core laptop:

    length       stomp       stump  stomp/stump   stump/stomp
0      128    0.006628    0.018066     0.366867      2.725782
1      256    0.

I'm attempting to run the example posted on the GitHub page for diffeqpy. I've installed diffeqpy successfully on a Mac Pro (2019) running Mac OS X 10.15.4 (Catalina). I'm running the Anaconda distribution for Python ver. 3.7.6.

The code I'm running is the following:

#!/Users/user/opt/anaconda3/bin/python
from diffeqpy import de

def f(u,p,t):
    return -u

u0 = 0.5
tspan = (0.,

I'm in need of a gufunc that does something like scikit-image's block_reduce function, but in n-dimensions.

As a simple example, I want something like this

>>> x = np.ones(8)
>>> block_reduce(x, 2, how='sum')
[2, 2, 2, 2]

I would like to generalize this to ndims, have various options for reduction, and also possi

• NDVI
• Move NDVI to new multispectral.py module
• List highest priorty multispectral tools

And it should probably become a C++ method. It was only implemented in Python because we needed it right away to support Pandas. (That's also why it doesn't have an axis parameter—it's a bare minimum.)

Also, none of the "na" functions recognize floating-point nan as the same thing as None. Pandas seems to be trying to get away from that misidentification, but maybe there are specialized n

See https://numba.pydata.org/numba-doc/dev/user/performance-tips.html#fastmath

Simple to implement:

• add the argument in

  • https://github.com/fluiddyn/transonic/blob/master/transonic/aheadoftime.py#L101
  • https://github.com/fluiddyn/transonic/blob/master/transonic/justintime.py#L188

• improve the Numba backend https://github.com/fluiddyn/transonic/blob/master/transonic/backends/

With the last commit kratzert/RRMPG@27e420d all simulation functions were parallelized. We now have the output of the simulation function qsim being optionally a 2D-array, were the second dimension (first for Pythonista) holds the results for different parameter sets. It would now be good, if we could pass this array directly to all the