Running and Saving Batch Jobs
h.batch_run(tstop, tstep, 'filename') h.batch_run(tstop, tstep, 'filename', 'comment')
This command replaces the set of commands:
while h.t < tstop: for i in range(int(tstep / dt)): h.fadvance() # print results to filename
and produces the most efficient run on any given neuron model. This command was created specifically for Cray computers in order eliminate the interpreter overhead as the rate limiting simulation step.
This command will save selected variables, as they are changed in the run, into a file whose name is given as the third argument. The 4th comment argument is placed at the beginning of the file. The
batch_save()command specifies which variable are to be saved.
The variables are stored in plain text and separated by spaces (see the example output below).
The following code creates a single compartment neuron, adds Hodgkin-Huxley channels, applies a current clamp, runs, and stores a voltage trace.
from neuron import h # define a geometry soma = h.Section(name="soma") soma.L = 10 soma.diam = 10 # biophysics: Hodgkin-Huxley channels soma.insert('hh') # add a stimulus iclamp = h.IClamp(soma(0.5)) iclamp.dur = 0.2 iclamp.delay = 0.3 iclamp.amp = 0.5 # define variables to be stored (time and the soma's membrane potential) h.batch_save() h.batch_save(h._ref_t, soma(0.5)._ref_v) # initialize, run, and save h.finitialize(-65) h.batch_run(2, 0.1, 'hhsim.dat', 'My HH sim')
The output (the time series of an action potential) is stored in the
My HH sim batch_run from t = 0 to 2 in steps of 0.1 with dt = 0.025 0 -65 0.1 -64.9971 0.2 -64.9943 0.3 -64.9917 0.4 -49.6876 0.5 -34.9008 0.6 -33.426 0.7 -25.5015 0.8 -7.00019 0.9 20.989 1 38.2226 1.1 40.9284 1.2 39.1047 1.3 35.8921 1.4 31.8901 1.5 27.3462 1.6 22.4496 1.7 17.3559 1.8 12.1873 1.9 7.0331 2 1.9538
h.batch_save() h.batch_save(varref1, varref2, ...)
- starts a new list of variables to save in a
h.batch_save(varref1, varref2, ...)
- adds pointers to the list of variables to be saved in a
h.batch_save() # This clears whatever list existed and starts a new # list of variables to be saved. h.batch_save(soma(0.5)._ref_v, axon(1)._ref_v) for i in range(3): h.batch_save(dend[i](0.3)._ref_v)
specifies five quantities to be saved from each