Release notes

BrainPy 1.0.2

This release continues to improve the user-friendliness.

Highlights of core changes:

Remove support for Numba-CUDA backend
Super initialization super(XXX, self).__init__() can be done at anywhere (not required to add at the bottom of the __init__() function).
Add the output message of the step function running error.
More powerful support for Monitoring
More powerful support for running order scheduling
Remove unsqueeze() and squeeze() operations in brainpy.ops
Add reshape() operation in brainpy.ops
Improve docs for numerical solvers
Improve tests for numerical solvers
Add keywords checking in ODE numerical solvers
Add more unified operations in brainpy.ops
Support “@every” in steps and monitor functions
Fix ODE solver bugs for class bounded function
Add build phase in Monitor

Add ‘timeout’ in sympy solver in neuron dynamics analysis
Reconstruct and generalize phase plane analysis
Generalize the repeat mode of Network to different running duration between two runs
Update benchmarks
Update detailed documentation

Add a more flexible way for NeuState/SynState initialization
Fix bugs of “is_multi_return”
Add “hand_overs”, “requires” and “satisfies”.
Update documentation
Auto-transform range to numba.prange
Support _obj_i, _pre_i, _post_i for more flexible operation in scalar-based models

For computation: numpy, numba
For model definition: NeuType, SynConn
For model running: Network, NeuGroup, SynConn, Runner
For numerical integration: integrate, Integrator, DiffEquation
For connectivity: One2One, All2All, GridFour, grid_four, GridEight, grid_eight, GridN, FixedPostNum, FixedPreNum, FixedProb, GaussianProb, GaussianWeight, DOG
For visualization: plot_value, plot_potential, plot_raster, animation_potential
For measurement: cross_correlation, voltage_fluctuation, raster_plot, firing_rate
For inputs: constant_current, spike_current, ramp_current.