DimensionMismatchError in run time mode but not in standalone mode

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1

I encountered a DimensionMismatchError and any help is appreciated. The following is the example:
The first code save the synapse in a file:

from brian2 import *
import numpy as np 
eqs = '''
dv/dt = (I-v)/tau : 1
I : 1
tau : second
'''
G = NeuronGroup(2, eqs, threshold='v>1', reset='v = 0', method='exact')
G.I = [2, 0]
G.tau = [10, 100]*ms

# Comment these two lines out to see what happens without Synapses
S = Synapses(G, G, on_pre='v_post += 0.2')
S.connect(i=0, j=1)
S.delay = 10*ms
np.savez('test', i=S.i[:], j=S.j[:], delay=S.delay[:])

The second code load the saved the synapse and build the connection:

from brian2 import *
import numpy as np 
#set_device('cpp_standalone', build_on_run=False)

eqs = '''
dv/dt = (I-v)/tau : 1
I : 1
tau : second
'''

G = NeuronGroup(2, eqs, threshold='v>1', reset='v = 0', method='exact')
G.I = [2, 0]
G.tau = [10, 100]*ms

# Comment these two lines out to see what happens without Synapses
S = Synapses(G, G, on_pre='v_post += 0.2')

trained_S = np.load('test.npz')
S.connect(i=trained_S['i'], j=trained_S['j'])
S.delay[:] = trained_S['delay']
                
M = StateMonitor(G, 'v', record=True)

run(100*ms)
#device.build(directory='test', compile=True, run=True, debug=False)

The running of the 2nd code is fine in standalone mode, but in runtime mode, I’ve got the following error.

   ---------------------------------------------------------------------------
    DimensionMismatchError                    Traceback (most recent call last)
    <ipython-input-2-0d678e5714b0> in <module>
         18 trained_S = np.load('test.npz')
         19 S.connect(i=trained_S['i'], j=trained_S['j'])
    ---> 20 S.delay[:] = trained_S['delay']
         21
         22 M = StateMonitor(G, 'v', record=True)

    ~/anaconda3/lib/python3.8/site-packages/brian2/core/variables.py in __setitem__(self, item, value)
        912
        913     def __setitem__(self, item, value):
    --> 914         self.set_item(item, value, level=1)
        915
        916     @device_override('variableview_set_with_expression')

    ~/anaconda3/lib/python3.8/site-packages/brian2/core/variables.py in set_item(self, item, value, level, namespace)
        896             if item == 'True':
        897                 # We do not want to go through code generation for runtime
    --> 898                     self.set_with_index_array(slice(None), value,
        899                                               check_units=check_units)
        900             else:

    ~/anaconda3/lib/python3.8/site-packages/brian2/core/base.py in device_override_decorated_function(*args, **kwds)
        276                 return getattr(curdev, name)(*args, **kwds)
        277             else:
    --> 278                 return func(*args, **kwds)
        279
        280         device_override_decorated_function.__doc__ = func.__doc__

    ~/anaconda3/lib/python3.8/site-packages/brian2/core/variables.py in set_with_index_array(self, item, value, check_units)
       1164         variable = self.variable
       1165         if check_units:
    -> 1166             fail_for_dimension_mismatch(variable.dim, value,
       1167                                         'Incorrect unit for setting variable %s' % self.name)
       1168         if variable.scalar:

    ~/anaconda3/lib/python3.8/site-packages/brian2/units/fundamentalunits.py in fail_for_dimension_mismatch(obj1, obj2, error_message, **error_quantities)
        185             raise DimensionMismatchError(error_message, dim1)
        186         else:
    --> 187             raise DimensionMismatchError(error_message, dim1, dim2)
        188     else:
        189         return dim1, dim2

    DimensionMismatchError: Incorrect unit for setting variable delay (units are s and 1).

To avoid this error, I need to manually as the unit as follows:

  S.delay[:] = trained_S['delay']*second
2

Hi. I’m surprised it works in standalone mode, this should actually be considered a bug :slight_smile:

np.savez does not know anything about Brian quantities, so it just stores a standard numpy array without units to disk. When you load it, you therefore get an array without units which you cannot assign to the delay variable which expects a value in seconds. Multiplying the value with seconds is one solution, another would be to use Quantity(trained_S['delay'], dim=second.dim, copy=False). Admittedly this is a bit complicated, but it has the advantage that it can give you a “view” on an existing array without copying it in memory. This can be useful for very large arrays – not sure that this is very relevant here, though.

Finally, the simplest option would be to ask Brian to directly use a delay variable without units by appending an underscore, i.e. use

S.delay_[:] = trained_S['delay']

You could also use S.delay_[:] in the np.savez call, but it will throw away the unit information in either case.

3

Thanks Marcel. Does that mean the code running in standalone mode could have the results wrong?

4

No, if you didn’t convert the units manually in any way, then everything will work correctly (since this is the way things are represented internally anyway).

The reason why I think it should be a bug is that when assigning values to a variable, we check whether the unit of the values match, and this should happen without regard to runtime or standalone mode. In your use case you could have stored the values in a different scale (e.g. by doing np.savez(...delay=S.delay[:]/ms)) and then S.delay[:] = trained_S['delay'] would have interpreted these values in seconds without raising an error which should not happen. Of course by using delay_ etc. you can still make the main mistake, but at least you’d be explicit about the fact that you are not using Brian’s unit checking mechanism.

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