Re: Error when using TemporalApplicableEquation

[Lia Domide]

Hi,

If I copy your code in a test.py module, and execute it, it does not generate errors.

Could you please be more explicit at what point you get the error ?

Best,

Lia.

[Silvana Huertas]

Hi, 

I get that error when I run this line:  stimulusFin = MultiStimuliRegion(trainLeft, trainRight)

Thanks, 

Silvana

[Lia Domide]

Hi Silvana,

For me that works, both in a python console and on Jupyter lab.

What is your configuration?

I have Python 3.10.4, numpy 1.21.5 and tvb-library 2.6.1 (dev from github master) - should be very similar with tvb-library 2.6 from Pypi.

Best,

Lia.

[Silvana Huertas]

Hi, 

I re-run it in another computer and it works. So no idea what happened. 

Thank you, 

Silvana

[WOODMAN Michael]

hi

Can you include the traceback as well please?

Thanks,

Marmaduke

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From: tvb-...@googlegroups.com <tvb-...@googlegroups.com> on behalf of Silvana Huertas <s.huert...@gmail.com>
Sent: Wednesday, August 3, 2022 2:24:10 PM
To: TVB Users
Subject: [RESEAUX SOCIAUX] [TVB] Error when using TemporalApplicableEquation

 

Hi all, 

I'm trying to generate a code for multi stimuli in region. However, I get an error saying "required attribute referenced before assignment. Use a default or assign a value before reading it attribute tvb.datatypes.patterns.SpatioTemporalPattern.temporal = Attr(field_type=<class 'tvb.datatypes.equations.TemporalApplicableEquation'>, default=None, required=True)" . I based my code in another post in this forum https://groups.google.com/g/tvb-users/c/a5FUVYsHB14/m/zvTG3pUeFwAJ. 

Could you please help me? Thanks :)

# Here we load the packages that we will need
from tvb.simulator.lab import *
import numpy as np

#We load the necessary variables
np.array
#I use the connectome of TVB that has 76 regions
conn = connectivity.Connectivity.from_file('connectivity_76.zip')

# configure stimulus spatial pattern.
#In this case I put stimulation only in the lM1 (51) and rM2(13).
#Next stimulation can have more regions of stimulation with different weight values
weightingLeft = np.zeros((76, ))
weightingRight = np.zeros((76, ))
#The weight of the stimulation in these regions is equal to 1. (Values between 0 and 1)
weightingLeft[[51]] = 1
weightingRight[[13]] =1

#Here we determine the values of the stimulus (form and parameters)
eqn_t = equations.Sinusoid

amp = 2; f = 0.002; w=2*np.pi*f; phase1=0; phase2=0;


# configure stimulus
class MultiStimuliRegion(patterns.StimuliRegion):
        def __init__(self, *stimuli):
            self.stimuli = stimuli
        def configure_space(self, *args, **kwds):
            [stim.configure_space(*args, **kwds) for stim in self.stimuli]
        def configure_time(self, *args, **kwds):
            [stim.configure_time(*args, **kwds) for stim in self.stimuli]
        def __call__(self, *args, **kwds):
            return np.array([stim(*args, **kwds) for stim in self.stimuli]).sum(axis=0)

#Here we generate a function with the sinusoidal stimulus
def make_train(conn,weighting , params):
        eqn_t = equations.TemporalApplicableEquation(equation='A*sin(w*var+phi_0)', parameters=params)
        stimulus = patterns.StimuliRegion(
            temporal=eqn_t,
            connectivity=conn,
            weight=weighting)
        return stimulus
   

#Here we create the stimuli using the previos functions    
paramsLeft={"A": amp, "w": w, "phi_0":phase1, "B": 1}
paramsRight={"A": amp, "w": 5.0, "phi_0":phase2, "B": 1}
trainLeft = make_train(conn,weightingLeft, paramsLeft)
trainRight = make_train(conn,weightingRight, paramsRight)

stimulusFin = MultiStimuliRegion(trainLeft, trainRight)
stimulusFin.configure_space()
time = np.arange(0,1)*30000
stimulusFin.configure_time(time)

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