def brahms_process(persist, input):
output = copy.deepcopy(persist["output"])
if input["event"]["type"] == EVENT_INIT_CONNECT:
# create output on first call
if input["event"]["flags"] & F_FIRST_CALL:
# create scalar output (fox population)
#
# operation: addPort, default set, class numeric, dims and
# type, output port name, sample rate
persist["outputPortHandle"] = brahms.operation(
persist["self"], OPERATION_ADD_PORT, "", "std/2009/data/numeric", "DOUBLE/REAL/1", "out"
)
# set initial persist
persist["foxes"] = 0.5
# processed
output["event"]["response"] = C_OK
elif input["event"]["type"] == EVENT_INIT_POSTCONNECT:
# processed
output["event"]["response"] = C_OK
elif input["event"]["type"] == EVENT_RUN_SERVICE:
# read input
rabbits = input["iif"]["default"]["ports"][0]["data"]
# run dynamics
T = 1 / input["time"]["sampleRate"]["rate"]
d_foxes = -persist["foxes"] + 0.9 * rabbits * persist["foxes"]
persist["foxes"] = persist["foxes"] + T * d_foxes
# update output
brahms.operation(
persist["self"],
OPERATION_SET_CONTENT,
persist["outputPortHandle"],
numpy.array(persist["foxes"], numpy.float64),
)
# processed
output["event"]["response"] = C_OK
elif input["event"]["type"] == EVENT_MODULE_INIT:
# update output
output["info"]["component"] = (0, 1)
output["info"]["additional"] = "Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n"
# processed
output["event"]["response"] = C_OK
return (persist, output)
def brahms_process(persist, input):
output = copy.deepcopy(persist['output'])
if input['event']['type'] == EVENT_INIT_CONNECT:
# create output on first call
if input['event']['flags'] & F_FIRST_CALL:
# create scalar output (fox population)
#
# operation: addPort, default set, class numeric, dims and
# type, output port name, sample rate
persist['outputPortHandle'] = brahms.operation(
persist['self'], OPERATION_ADD_PORT, '',
'std/2009/data/numeric', 'DOUBLE/REAL/1', 'out')
# set initial persist
persist['foxes'] = 0.5
# processed
output['event']['response'] = C_OK
elif input['event']['type'] == EVENT_INIT_POSTCONNECT:
# processed
output['event']['response'] = C_OK
elif input['event']['type'] == EVENT_RUN_SERVICE:
# read input
rabbits = input['iif']['default']['ports'][0]['data']
# run dynamics
T = 1 / input['time']['sampleRate']['rate']
d_foxes = -persist['foxes'] + 0.9 * rabbits * persist['foxes']
persist['foxes'] = persist['foxes'] + T * d_foxes
# update output
brahms.operation(persist['self'], OPERATION_SET_CONTENT,
persist['outputPortHandle'],
numpy.array(persist['foxes'], numpy.float64))
# processed
output['event']['response'] = C_OK
elif input['event']['type'] == EVENT_MODULE_INIT:
# update output
output['info']['component'] = (0, 1)
output['info'][
'additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n'
# processed
output['event']['response'] = C_OK
return (persist, output)
def brahms_process(persist, input):
# Nominal output
output = {'event': {'response': S_NULL}}
# Switch on event type
if input['event']['type'] == EVENT_MODULE_INIT:
# Component information
output['info'] = {}
output['info']['component'] = (0, 1)
output['info']['additional'] = ('Author=David Buxton\n')
output['info']['flags'] = (F_NOT_RATE_CHANGER)
# OK
output['event']['response'] = C_OK
# Switch on event type
elif input['event']['type'] == EVENT_STATE_SET:
# OK
output['event']['response'] = C_OK
# Switch on event type
elif input['event']['type'] == EVENT_INIT_CONNECT:
# On first call
if input['event']['flags'] & F_FIRST_CALL:
# Create output port with label 'bg_input'
# The number here must match the number of BG channels
persist['bg_input'] = brahms.operation(
persist['self'],
OPERATION_ADD_PORT,
'',
'std/2009/data/numeric',
'DOUBLE/REAL/6',
'bg_input'
)
# Initialise ROS nodes
persist['ros_bg'] = rospy.Publisher("/basal_ganglia/mctx", String, queue_size=10)
rospy.init_node('spineml_bg', anonymous=True)
# Access MCtx output
index = input['iif']['default']['index']['mctx_out']
port = input['iif']['default']['ports'][index]
# Store data
persist['mctx_data'] = port['data']
# Do nothing
pass
# On last call
if input['event']['flags'] & F_LAST_CALL:
# Do nothing
pass
# OK
output['event']['response'] = C_OK
# Switch on event type
elif input['event']['type'] == EVENT_RUN_SERVICE:
# DEBUG data
print(persist['mctx_data'])
# TODO: Run motor program associated with output channels of activity level 0.95 or above
# TODO: Create standalone Python motor programs for each action
# Set input values
brahms.operation(
persist['self'],
OPERATION_SET_CONTENT,
persist['bg_input'],
np.array([0.7, 0.2, 0.5, 0.1, 0, 0], np.float64)
)
# Send output values to ROS
persist['ros_bg'].publish(str(persist['mctx_data']))
# OK
output['event']['response'] = C_OK
# Return
return persist, output
def brahms_process(persist, input): output = copy.deepcopy(persist['output']) if input['event']['type'] == EVENT_INIT_CONNECT: # create output on first call if input['event']['flags'] & F_FIRST_CALL: # create scalar output (rabbit population) # # operation: addPort, default set, class numeric, dims and # type, output port name, sample rate persist['outputPortHandle'] = brahms.operation( persist['self'], OPERATION_ADD_PORT, '', 'std/2009/data/numeric', 'DOUBLE/REAL/1', 'out' ) # set initial persist persist['rabbits'] = 0.5 # processed output['event']['response'] = C_OK elif input['event']['type'] == EVENT_INIT_POSTCONNECT: # processed output['event']['response'] = C_OK elif input['event']['type'] == EVENT_RUN_SERVICE: # read input foxes = input['iif']['default']['ports'][0]['data'] # run dynamics T = 1 / input['time']['sampleRate']['rate'] d_rabbits = 2 * persist['rabbits'] - 1.2 * persist['rabbits'] * foxes persist['rabbits'] = persist['rabbits'] + T * d_rabbits # update output brahms.operation( persist['self'], OPERATION_SET_CONTENT, persist['outputPortHandle'], numpy.array(persist['rabbits'], numpy.float64) ) # processed output['event']['response'] = C_OK elif input['event']['type'] == EVENT_MODULE_INIT: # update output output['info']['component'] = (0, 1) output['info']['additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n' # processed output['event']['response'] = C_OK return (persist, output)
def brahms_process(persist, input):
# nominal output
output = {'event': {'response': S_NULL}}
# switch on event type
if input['event']['type'] == EVENT_MODULE_INIT:
# provide component information
output['info'] = {}
output['info']['flags'] = F_NEEDS_ALL_INPUTS + F_NOT_RATE_CHANGER
output['info']['component'] = (0, 0)
output['info'][
'additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n'
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_STATE_SET:
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_INIT_CONNECT:
# create empty table
persist['inputs'] = {}
# access inputs
ports = input['iif']['default']['ports']
nports = len(ports)
# for each input
for p in range(0, nports):
# access input
port = ports[p]
name = port['name']
persist['inputs'][name] = port['data']
# import
for k in persist['inputs'].keys():
persist['state']['function'] = persist['state'][
'function'].replace("$" + k, "persist['" + k + "']")
exec("persist['" + k + "']=persist['inputs']['" + k + "']")
# compute function
result = eval(persist['state']['function'])
# get data type of result
struc = numpyType2BrahmsType(result.dtype)
# get dimensions of result
dims = shape(result)
ndims = size(dims)
sdims = ""
for i in range(0, ndims):
if i:
sdims = "," + sdims
sdims = str(dims[i]) + sdims
struc += sdims
# create output
persist['hOutputPort'] = brahms.operation(persist['self'],
OPERATION_ADD_PORT, '',
'std/2009/data/numeric',
struc, 'out')
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_RUN_SERVICE:
# changing from storing as persist['inputs']['varname'] to
# persist['varname'] saved 14us a call (a call costs about
# 175us now). that is to say, that indexing into these
# arrays is a significant performance hit. if there was a
# way of setting up the execution so it was "ready to go"
# without any indexing, performance might improve considerably
# in addition, we currently have to call ascontiguousarray() on
# result before we pass it back to the bindings (if it was complex,
# and we reduce it to real, for instance, it forms a non-contiguous
# array). if the bindings could handle these sparse arrays, it would
# be faster than converting it first.
# compute function
result = eval(persist['state']['function'])
# set output
brahms.operation(persist['self'], OPERATION_SET_CONTENT,
persist['hOutputPort'], ascontiguousarray(result))
# ok
output['event']['response'] = C_OK
# return
return (persist, output)
def brahms_process(persist, input): output = copy.deepcopy(persist['output']) if input['event']['type'] == EVENT_STATE_SET: # set initial pars persist['drive'] = 0 persist['num_channels'] = int(persist['state']['numchannels']) # use parameter! persist['refractory_time'] = 0 if input['event']['type'] == EVENT_INIT_CONNECT: # create output on first call if input['event']['flags'] & F_FIRST_CALL: # create spikes output (muscle drive) # # operation: addPort, default set, class numeric, dims and # type, output port name, sample rate persist['outputPortHandle'] = brahms.operation( persist['self'], OPERATION_ADD_PORT, '', 'std/2009/data/spikes', str(persist['num_channels']), 'out' ) # processed output['event']['response'] = C_OK if input['event']['type'] == EVENT_RUN_SERVICE: # read input t_now = float(input['time']['now']) * input['time']['baseSampleRate']['period'] head_tilt = input['iif']['default']['ports'][0]['data'] if head_tilt > 1: persist['refractory_time'] = t_now + 0.1 # run dynamics tau = 0.1 target_drive = 1 fS = input['time']['sampleRate']['rate'] Lambda = math.exp(-1/(tau * fS)) persist['drive'] = Lambda * persist['drive'] + (1-Lambda) * target_drive # refractory period (!!????!) if persist['refractory_time'] > t_now: persist['drive'] = 0 # generate spiking output max_drive = 0.5 prob_channel_on = max_drive * persist['drive'] out = [chs for chs in xrange(persist['num_channels']) if random.random() < prob_channel_on] # update output brahms.operation( persist['self'], OPERATION_SET_CONTENT, persist['outputPortHandle'], numpy.array(out, numpy.int32) ) # processed output['event']['response'] = C_OK if input['event']['type'] == EVENT_MODULE_INIT: # update output output['info']['component'] = (0, 1) output['info']['additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n' # processed output['event']['response'] = C_OK return (persist, output)
def brahms_process(persist, input): # nominal output (must use deepcopy) output = copy.deepcopy(persist['output']) # switch on event type if input['event']['type'] == EVENT_MODULE_INIT: # provide component information output['info']['flags'] = F_NOT_RATE_CHANGER output['info']['component'] = (__REL__, __REV__) output['info']['additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n' # ok output['event']['response'] = C_OK # switch on event type elif input['event']['type'] == EVENT_STATE_SET: # ok output['event']['response'] = C_OK # switch on event type elif input['event']['type'] == EVENT_INIT_CONNECT: # on first call if input['event']['flags'] & F_FIRST_CALL: # create output persist['hOutputPort'] = brahms.operation( persist['self'], OPERATION_ADD_PORT, '', 'std/2009/data/numeric', 'DOUBLE/COMPLEX/2,3', 'out' ) # on first call if input['event']['flags'] & F_LAST_CALL: # validate input p = input['iif']['default']['ports'] if len(p) != 1: output['error'] = 'expects one input' return (persist, output) if p[0]['class'] != 'std/2009/data/numeric': output['error'] = 'expects input to be data/numeric class' return (persist, output) if p[0]['structure'] != 'DOUBLE/COMPLEX/2,3': output['error'] = 'expects 2x3 complex input' return (persist, output) # ok output['event']['response'] = C_OK # switch on event type elif input['event']['type'] == EVENT_RUN_SERVICE: # get input inp = input['iif']['default']['ports'][0]['data'] inp[2][0] = 40+50j #print inp.shape # set output brahms.operation(persist['self'], OPERATION_SET_CONTENT, persist['hOutputPort'], inp) # ok output['event']['response'] = C_OK # return return (persist, output)
def brahms_process(persist, input):
# nominal output
output = {'event':{'response':S_NULL}}
# switch on event type
if input['event']['type'] == EVENT_MODULE_INIT:
# provide component information
output['info'] = {}
output['info']['flags'] = F_NEEDS_ALL_INPUTS + F_NOT_RATE_CHANGER
output['info']['component'] = (0, 0)
output['info']['additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n'
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_STATE_SET:
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_INIT_CONNECT:
# create empty table
persist['inputs'] = {}
# access inputs
ports = input['iif']['default']['ports']
nports = len(ports)
# for each input
for p in range(0, nports):
# access input
port = ports[p]
name = port['name']
persist['inputs'][name] = port['data']
# import
for k in persist['inputs'].keys():
persist['state']['function'] = persist['state']['function'].replace("$" + k, "persist['" + k + "']")
exec("persist['" + k + "']=persist['inputs']['" + k + "']")
# compute function
result = eval(persist['state']['function'])
# get data type of result
struc = numpyType2BrahmsType(result.dtype)
# get dimensions of result
dims = shape(result)
ndims = size(dims)
sdims = ""
for i in range(0,ndims):
if i:
sdims = "," + sdims
sdims = str(dims[i]) + sdims
struc += sdims
# create output
persist['hOutputPort'] = brahms.operation(
persist['self'],
OPERATION_ADD_PORT,
'',
'std/2009/data/numeric',
struc,
'out'
)
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_RUN_SERVICE:
# changing from storing as persist['inputs']['varname'] to
# persist['varname'] saved 14us a call (a call costs about
# 175us now). that is to say, that indexing into these
# arrays is a significant performance hit. if there was a
# way of setting up the execution so it was "ready to go"
# without any indexing, performance might improve considerably
# in addition, we currently have to call ascontiguousarray() on
# result before we pass it back to the bindings (if it was complex,
# and we reduce it to real, for instance, it forms a non-contiguous
# array). if the bindings could handle these sparse arrays, it would
# be faster than converting it first.
# compute function
result = eval(persist['state']['function'])
# set output
brahms.operation(persist['self'], OPERATION_SET_CONTENT, persist['hOutputPort'], ascontiguousarray(result))
# ok
output['event']['response'] = C_OK
# return
return (persist, output)
def brahms_process(persist, input):
# nominal output (must use deepcopy)
output = copy.deepcopy(persist['output'])
# switch on event type
if input['event']['type'] == EVENT_MODULE_INIT:
# provide component information
output['info']['flags'] = F_NEEDS_ALL_INPUTS + F_NOT_RATE_CHANGER
output['info']['component'] = (__REL__, __REV__)
output['info']['additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n'
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_STATE_SET:
# say hello
brahms.operation(
persist['self'],
OPERATION_BOUT,
'Hello from Python',
D_INFO
)
# set state
persist['accum'] = 0
# get random seed from framework
seed = brahms.operation(
persist['self'],
OPERATION_GET_RANDOM_SEED
)
# create an RNG utility
rng = brahms.operation(
persist['self'],
OPERATION_GET_UTILITY_OBJECT,
'std/2009/util/rng',
0,
)
# get select function from RNG utility
fselect = brahms.operation(
persist['self'],
OPERATION_GET_UTILITY_FUNCTION,
rng,
'select'
)
# get seed function from RNG utility
fseed = brahms.operation(
persist['self'],
OPERATION_GET_UTILITY_FUNCTION,
rng,
'seed')
# get fill function from RNG utility
persist['ffill'] = brahms.operation(
persist['self'],
OPERATION_GET_UTILITY_FUNCTION,
rng,
'fill'
)
# select the MT2000.normal generator
brahms.operation(
persist['self'],
OPERATION_CALL_UTILITY_FUNCTION,
fselect,
('MT2000.normal', )
)
# seed the generator
brahms.operation(
persist['self'],
OPERATION_CALL_UTILITY_FUNCTION,
fseed,
(seed, )
)
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_INIT_CONNECT:
# validate input
p = input['iif']['default']['ports']
if len(p) != 1:
output['error'] = 'expects one input'
return (persist, output)
if p[0]['class'] != 'std/2009/data/numeric':
output['error'] = 'expects input to be data/numeric class'
return (persist, output)
if p[0]['structure'] != 'DOUBLE/REAL/1':
output['error'] = 'expects scalar real double input'
return (persist, output)
# create output
persist['hOutputPort'] = brahms.operation(
persist['self'],
OPERATION_ADD_PORT,
'',
'std/2009/data/numeric',
'DOUBLE/REAL/' + str(int(persist['state']['count'][0])),
'out'
)
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_RUN_SERVICE:
# get input
inp = input['iif']['default']['ports'][0]['data']
# process
persist['accum'] = 0.9 * persist['accum'] + inp
# get values from the generator
rnd = brahms.operation(
persist['self'],
OPERATION_CALL_UTILITY_FUNCTION,
persist['ffill'],
(
numpy.array([]),
numpy.array(persist['state']['count'], numpy.uint32),
numpy.array(persist['state']['noise']),
numpy.array(0.0)
)
)
# spread to multiple channels and add some noise
myOutput = persist['accum'] * numpy.ones(persist['state']['count'], numpy.float64) + rnd
# set output
brahms.operation(persist['self'], OPERATION_SET_CONTENT, persist['hOutputPort'], myOutput)
# ok
output['event']['response'] = C_OK
# return
return (persist, output)
def brahms_process(persist, input):
# nominal output
output = {'event':{'response':S_NULL}}
# switch on event type
if input['event']['type'] == EVENT_MODULE_INIT:
# provide component information
output['info'] = {}
output['info']['component'] = (0, 1)
output['info']['additional'] = ('Author=David Buxton\n')
output['info']['flags'] = (F_NOT_RATE_CHANGER)
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_STATE_SET:
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_INIT_CONNECT:
# on first call
if input['event']['flags'] & F_FIRST_CALL:
# Access input port 'python_in'
index = input['iif']['default']['index']['python_in']
port = input['iif']['default']['ports'][index]
# Store data
persist['python_in'] = port['data']
# Create output port with label 'python_out'
persist['hOutputPort'] = brahms.operation(persist['self'], OPERATION_ADD_PORT, '', 'std/2009/data/numeric', 'DOUBLE/REAL/1', 'python_out')
# do nothing
pass
# on last call
if input['event']['flags'] & F_LAST_CALL:
# do nothing
pass
# ok
output['event']['response'] = C_OK
# switch on event type
elif input['event']['type'] == EVENT_RUN_SERVICE:
# Move input data to local variable
input_value = persist['python_in']
# Set output value to input * 2
brahms.operation(
persist['self'],
OPERATION_SET_CONTENT,
persist['hOutputPort'],
numpy.array([input_value * 2], numpy.float64)
)
# ok
output['event']['response'] = C_OK
# return
return (persist, output)
def brahms_process(persist, input):
output = copy.deepcopy(persist['output'])
if input['event']['type'] == EVENT_INIT_CONNECT:
# create output on first call
if input['event']['flags'] & F_FIRST_CALL:
# create scalar output (muscle position)
#
# operation: addPort, default set, class numeric, dims and
# type, output port name, sample rate
persist['outputPortHandle'] = brahms.operation(
persist['self'], OPERATION_ADD_PORT, '',
'std/2009/data/numeric', 'DOUBLE/REAL/1', 'tilt')
# set initial persist
persist['position'] = 0
# processed
output['event']['response'] = C_OK
if input['event']['type'] == EVENT_INIT_POSTCONNECT:
# can get structure of input now
persist['num_channels'] = int(
input['iif']['default']['ports'][0]['structure'])
# processed
output['event']['response'] = C_OK
if input['event']['type'] == EVENT_RUN_SERVICE:
# read input (spikes)
drive = copy.copy(input['iif']['default']['ports'][0]['data'])
# convert to activity
drive = sum(drive) / persist['num_channels']
# run dynamics
tau = 0.2
fS = input['time']['sampleRate']['rate']
Lambda = math.exp(-1 / (tau * fS))
T = 1 / fS
persist['position'] = Lambda * persist['position'] + T * drive
# update output
brahms.operation(persist['self'], OPERATION_SET_CONTENT,
persist['outputPortHandle'],
numpy.array(persist['position'], numpy.float64))
# processed
output['event']['response'] = C_OK
if input['event']['type'] == EVENT_MODULE_INIT:
# update output
output['info']['component'] = (0, 1)
output['info'][
'additional'] = 'Author=Ben Mitch\nURL=http://brahms.sourceforge.net\n'
# processed
output['event']['response'] = C_OK
return (persist, output)