def runtask(self,
base_class=experiment.Experiment,
feats=[],
cli=False,
**kwargs):
'''
Begin running of task
'''
now = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
log_str("\n{}\n-------------------\nRunning new task: {}\n".format(
now, base_class))
if not cli:
self.start_websock()
if None in feats:
raise ValueError("Features not found properly in database!")
# initialize task status
self.status.value = b"running" if 'saveid' in kwargs else b"testing"
if 'seq' in kwargs:
kwargs['seq_params'] = kwargs['seq'].params
kwargs['seq'] = kwargs['seq'].get(
) ## retreive the database data on this end of the pipe
use_websock = not (self.websock is None)
if use_websock:
feats.insert(0, websocket.NotifyFeat)
kwargs['params']['websock'] = self.websock
kwargs['params']['tracker_status'] = self.status
task_class = experiment.make(base_class, feats=feats)
# process parameters
params = kwargs['params']
if isinstance(params, str):
params = Parameters(params)
elif isinstance(params, dict):
params = Parameters.from_dict(params)
params.trait_norm(task_class.class_traits())
params = params.params # dict
kwargs.pop('params', None)
log_str("Spawning process...")
log_str(str(kwargs))
# Spawn the process
self.proc = experiment.task_wrapper.TaskWrapper(
log_filename=log_filename,
params=params,
target_class=task_class,
websock=self.websock,
status=self.status,
**kwargs)
# create a proxy for interacting with attributes/functions of the task.
# The task runs in a separate process and we cannot directly access python
# attributes of objects in other processes
self.task_proxy, _ = self.proc.start()
def test_save_hdf(self):
TestFeat = experiment.make(TestExp, feats=[SaveHDF])
feat = TestFeat()
feat.add_dtype("dummy_feat_for_test", "f8", (1, ))
# start the feature
feat.start()
feat.join()
mock_db = mocks.MockDatabase()
feat.cleanup(mock_db, "saveHDF_test_output")
hdf = tables.open_file("saveHDF_test_output.hdf")
saved_msgs = [x.decode('utf-8') for x in hdf.root.task_msgs[:]["msg"]]
self.assertEqual(saved_msgs,
['wait', 'trial', 'reward', 'wait', 'None'])
self.assertEqual(hdf.root.task_msgs[:]["time"].tolist(),
[0, 100, 101, 201, 601])
self.assertTrue(
np.all(hdf.root.task[:]["dummy_feat_for_test"][251:300] == -1))
self.assertTrue(
np.all(hdf.root.task[:]["dummy_feat_for_test"][:251] == 0))
self.assertTrue(
np.all(hdf.root.task[:]["dummy_feat_for_test"][300:] == 0))
def consolerun(base_class='',
feats=[],
exp_params=dict(),
gen_fn=None,
gen_params=dict()):
if isinstance(base_class, str):
# assume that it's the name of a task as stored in the database
base_class = models.Task.objects.get(name=base_class).get()
for k, feat in enumerate(feats):
# assume that the feature is input as the name of a feature already known to the database
if isinstance(feat, str):
feats[k] = models.Feature.objects.get(name=feat).get()
# Run the pseudo-metaclass constructor
Exp = experiment.make(base_class, feats=feats)
# create the sequence of targets
if gen_fn is None: gen_fn = Exp.get_default_seq_generator()
targ_seq = gen_fn(**gen_params)
# instantiate the experiment FSM
exp = Exp(targ_seq, **exp_params)
# run!
exp.run_sync()
def ismore_sim_bmi(baseline_data,
decoder,
targets_matrix=None,
session_length=0.):
import ismore.invasive.bmi_ismoretasks as bmi_ismoretasks
from riglib import experiment
from features.hdf_features import SaveHDF
from ismore.brainamp_features import SimBrainAmpData
import datetime
import numpy as np
import matplotlib.pyplot as plt
import multiprocessing as mp
from features.blackrock_features import BlackrockBMI
from ismore.exo_3D_visualization import Exo3DVisualizationInvasive
targets = bmi_ismoretasks.SimBMIControlReplayFile.sleep_gen(length=100)
plant_type = 'IsMore'
kwargs = dict(session_length=session_length,
replay_neural_features=baseline_data,
decoder=decoder)
if targets_matrix is not None:
kwargs['targets_matrix'] = targets_matrix
Task = experiment.make(bmi_ismoretasks.SimBMIControlReplayFile,
[SaveHDF]) #, Exo3DVisualizationInvasive])
task = Task(targets, plant_type=plant_type, **kwargs)
task.run_sync()
pnm = save_dec_enc(task)
return pnm
def test_mock_seq_with_features(self):
from riglib.experiment.mocks import MockSequenceWithGenerators
from features.hdf_features import SaveHDF
import h5py
task_cls = experiment.make(MockSequenceWithGenerators,
feats=(SaveHDF, ))
exp = task_cls(MockSequenceWithGenerators.gen_fn1())
exp.run_sync()
# optional delay if the OS is not ready to let you open the HDF file
# created just now
open_count = 0
while open_count < 3:
try:
hdf = h5py.File(exp.h5file.name)
break
except OSError:
open_count += 1
if open_count >= 3:
raise Exception("Unable to open HDF file!")
time.sleep(1)
ref_current_state = np.array([
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0
])
self.assertTrue(
np.array_equal(hdf["/task"]["current_state"].ravel(),
ref_current_state))
def run_task(session_length):
Task = experiment.make(SimObsCLDA, [SaveHDF])
Task.pre_init()
targets = bmimultitasks.BMIResettingObstacles.centerout_2D_discrete_w_obstacle()
kwargs=dict(assist_level_time=session_length, assist_level=(0.2, 0.),session_length=session_length,
half_life=(300., 300.), half_life_time = session_length, timeout_time=60.)
task = Task(targets, plant_type='cursor_14x14', **kwargs)
task.init()
task.run()
task.decoder.save()
ct = datetime.datetime.now()
pnm = os.path.expandvars('$BMI3D/tests/sim_clda/'+ct.strftime("%m%d%y_%H%M") + '.hdf')
f = open(task.h5file.name)
f.close()
time.sleep(1.)
#Wait after HDF cleaned up
task.cleanup_hdf()
time.sleep(1.)
#Copy temp file to actual desired location
shutil.copy(task.h5file.name, pnm)
f = open(pnm)
f.close()
return pnm
def run_task(session_length):
Task = experiment.make(SimObsCLDA, [SaveHDF])
Task.pre_init()
targets = bmimultitasks.BMIResettingObstacles.centerout_2D_discrete_w_obstacle()
kwargs=dict(assist_level_time=session_length, assist_level=(0.2, 0.),session_length=session_length,
half_life=(300., 300.), half_life_time = session_length, timeout_time=60.)
task = Task(targets, plant_type='cursor_14x14', **kwargs)
task.init()
task.run()
task.decoder.save()
ct = datetime.datetime.now()
pnm = os.path.expandvars('$BMI3D/tests/sim_clda/'+ct.strftime("%m%d%y_%H%M") + '.hdf')
f = open(task.h5file.name)
f.close()
time.sleep(1.)
#Wait after HDF cleaned up
task.cleanup_hdf()
time.sleep(1.)
#Copy temp file to actual desired location
shutil.copy(task.h5file.name, pnm)
f = open(pnm)
f.close()
return pnm
def init_exp(base_class, feats):
blocks = 1
targets = 3
seq = ManualControl.centerout_2D_discrete(blocks, targets)
Exp = experiment.make(base_class, feats=feats)
exp = Exp(seq)
exp.init()
return exp
def main_Y(session_length):
ssm_y = StateSpaceEndptVelY()
Task = experiment.make(SimVFB, [SaveHDF])
targets = SimVFB.centerout_Y_discrete()
#targets = manualcontrolmultitasks.ManualControlMulti.centerout_2D_discrete()
task = Task(ssm_y, SimVFB.y_sim, targets, plant_type='cursor_14x14', session_length=session_length)
task.run_sync()
return task
def test_start_experiment_python(self):
import json
from built_in_tasks.passivetasks import TargetCaptureVFB2DWindow
from riglib import experiment
from features import Autostart
from db.tracker import json_param
try:
import pygame
except ImportError:
print("Skipping test due to pygame missing")
return
# Create all the needed database entries
subj = models.Subject(name="test_subject")
subj.save()
task = models.Task(
name="test_vfb",
import_path="built_in_tasks.passivetasks.TargetCaptureVFB2DWindow")
task.save()
models.Generator.populate()
gen = models.Generator.objects.get(name='centerout_2D_discrete')
seq_params = dict(nblocks=1, ntargets=1)
seq_rec = models.Sequence(generator=gen,
params=json.dumps(seq_params),
task=task)
seq_rec.save()
print(seq_rec)
task_rec = models.Task.objects.get(name='test_vfb')
te = models.TaskEntry(task=task_rec, subject=subj)
te.save()
seq, seq_params = seq_rec.get()
# Start the task
base_class = task.get_base_class()
Task = experiment.make(base_class, feats=[])
params = json_param.Parameters.from_dict(dict(window_size=(480, 240)))
params.trait_norm(Task.class_traits())
saveid = te.id
task_start_data = dict(subj=subj.id,
base_class=base_class,
feats=[Autostart],
params=dict(window_size=(480, 240)),
seq=seq_rec,
seq_params=seq_params,
saveid=saveid)
tracker = tasktrack.Track.get_instance()
tracker.runtask(cli=True, **task_start_data)
def main_xz_CL_obstacles(session_length, task_kwargs=None):
ssm_xz = StateSpaceEndptVel2D()
Task = experiment.make(Sim_FA_Obs_BMI, [SaveHDF])
targets = BMIResettingObstacles.centerout_2D_discrete_w_obstacle()
task = Task(ssm_xz,
targets,
plant_type='cursor_14x14',
session_length=session_length,
**task_kwargs)
task.run_sync()
return task
def main_xz_CL(session_length, task_kwargs=None):
ssm_xz = StateSpaceEndptVel2D()
Task = experiment.make(Sim_FA_BMI, [SaveHDF])
targets = manualcontrolmultitasks.ManualControlMulti.centerout_2D_discrete(
)
task = Task(ssm_xz,
targets,
plant_type='cursor_14x14',
session_length=session_length,
**task_kwargs)
task.run_sync()
return task
def main_Y(session_length):
ssm_y = StateSpaceEndptPos1D()
Task = experiment.make(SimVFB, [SaveHDF])
targets = SimVFB.centerout_Y_discrete()
#targets = manualcontrolmultitasks.ManualControlMulti.centerout_2D_discrete()
task = Task(ssm_y,
SimVFB.y_sim,
targets,
plant_type='cursor_14x14',
session_length=session_length)
task.run_sync()
return task
def main_xz_obs(session_length, task_kwargs=None):
ssm_xz = StateSpaceEndptVel2D()
Task = experiment.make(SimVFB_obs, [SaveHDF])
targets = BMIResettingObstacles.centerout_2D_discrete_w_obstacle()
C = np.random.normal(0, 2, (20, 7))
task = Task(ssm_xz,
C,
targets,
plant_type='cursor_14x14',
session_length=session_length,
**task_kwargs)
task.run_sync()
return task
def get(self, feats=()):
from json_param import Parameters
from riglib import experiment
Exp = experiment.make(self.task.get(), tuple(f.get() for f in self.feats.all())+feats)
params = Parameters(self.params)
params.trait_norm(Exp.class_traits())
if issubclass(Exp, experiment.Sequence):
gen, gp = self.sequence.get()
seq = gen(Exp, **gp)
exp = Exp(seq, **params.params)
else:
exp = Exp(**params.params)
exp.event_log = json.loads(self.report)
return exp
def test_n_successful_targets(self):
TestFeat = experiment.make(TargetCaptureVFB2DWindow, feats=[SaveHDF])
n_targets = 8
seq = TargetCaptureVFB2DWindow.centerout_2D_discrete(
nblocks=1, ntargets=n_targets)
feat = TestFeat(seq, window_size=(480, 240))
feat.run_sync()
time.sleep(1) # small delay to allow HDF5 file to be written
hdf = tables.open_file(feat.h5file.name)
self.assertEqual(n_targets,
np.sum(hdf.root.task_msgs[:]['msg'] == b'reward'))
def main_xz(session_length, task_kwargs=None):
ssm_xz = StateSpaceEndptVel2D()
Task = experiment.make(SimVFB, [SaveHDF])
targets = manualcontrolmultitasks.ManualControlMulti.centerout_2D_discrete(
)
C = np.random.normal(0, 2, (20, 7))
task = Task(ssm_xz,
C,
targets,
plant_type='cursor_14x14',
session_length=session_length,
**task_kwargs)
task.run_sync()
return task
def test_experiment_unfixed(self):
for cls in [SimBMICosEncLinDec]:
N_TARGETS = 8
N_TRIALS = 16
seq = cls.sim_target_no_center(N_TARGETS, N_TRIALS)
base_class = cls
feats = []
Exp = experiment.make(base_class, feats=feats)
exp = Exp(seq)
exp.init()
exp.run()
rewards, time_penalties, hold_penalties = calculate_rewards(exp)
self.assertTrue(
rewards <= rewards + time_penalties + hold_penalties)
self.assertTrue(rewards > 0)
def test_experiment(self):
for cls in [SimBMICosEncLinDec, SimBMIVelocityLinDec]:
N_TARGETS = 8
N_TRIALS = 16
seq = cls.sim_target_seq_generator_multi(N_TARGETS, N_TRIALS)
base_class = cls
feats = []
Exp = experiment.make(base_class, feats=feats)
exp = Exp(seq)
exp.init()
exp.decoder.filt.fix_norm_attr()
exp.run()
rewards, time_penalties, hold_penalties = calculate_rewards(exp)
self.assertTrue(
rewards <= rewards + time_penalties + hold_penalties)
self.assertTrue(rewards > 0)
def test_mock_seq_with_features(self):
task_cls = experiment.make(MockSequenceWithGenerators, feats=(SaveHDF,))
exp = task_cls(MockSequenceWithGenerators.gen_fn1())
exp.run_sync()
time.sleep(2)
hdf = h5py.File(exp.h5file_name)
# test that the annotation appears in the messages
self.assertTrue(b'annotation: test annotation' in hdf["/task_msgs"]["msg"])
ref_current_state = np.array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0])
saved_current_state = hdf["/task"]["current_state"].ravel()
# TODO this length chopping should be needed, but the vector appears to be short sometimes
L = min(len(ref_current_state), len(saved_current_state))
self.assertTrue(np.array_equal(ref_current_state[:L], saved_current_state[:L]))
def test_make_params():
from riglib import experiment
from features.generator_features import Autostart
from features.phasespace_features import MotionSimulate
from features.hdf_features import SaveHDF
from tasks import manualcontrol
jsdesc = dict()
Exp = experiment.make(manualcontrol.TargetDirection, (features.Autostart, features.MotionSimulate, features.SaveHDF))
ctraits = Exp.class_traits()
for trait in Exp.class_editable_traits():
varname = dict()
varname['type'] = ctraits[trait].trait_type.__class__.__name__
varname['default'] = ctraits[trait].default
varname['desc'] = ctraits[trait].desc
jsdesc[trait] = varname
return json.dumps(jsdesc)
def get(self, feats=()):
print "models.Task.get()"
from namelist import tasks
if len(tasks) == 0:
print 'Import error in tracker.models.Task.get: from namelist import task returning empty -- likely error in task'
feature_classes = Feature.getall(feats)
if self.name in tasks and not None in feature_classes:
try:
# reload the module which contains the base task class
task_cls = tasks[self.name]
module_name = task_cls.__module__
if '.' in module_name:
module_names = module_name.split('.')
mod = __import__(module_names[0])
for submod in module_names[1:]:
mod = getattr(mod, submod)
else:
mod = __import__(module_name)
task_cls_module = mod
task_cls_module = imp.reload(task_cls_module)
task_cls = getattr(task_cls_module, task_cls.__name__)
# run the metaclass constructor
Exp = experiment.make(task_cls, feature_classes)
return Exp
except:
print "Problem making the task class!"
traceback.print_exc()
print self.name
print feats
print Feature.getall(feats)
print "*******"
return experiment.Experiment
elif self.name in tasks:
return tasks[self.name]
else:
return experiment.Experiment
def consolerun(base_class='', feats=[], exp_params=dict(), gen_fn=None, gen_params=dict()):
if isinstance(base_class, (str, unicode)):
# assume that it's the name of a task as stored in the database
base_class = models.Task.objects.get(name=base_class).get()
for k, feat in enumerate(feats):
# assume that the feature is input as the name of a feature already known to the database
if isinstance(feat, (str, unicode)):
feats[k] = models.Feature.objects.get(name=feat).get()
# Run the pseudo-metaclass constructor
Exp = experiment.make(base_class, feats=feats)
# create the sequence of targets
if gen_fn is None: gen_fn = Exp.get_default_seq_generator()
targ_seq = gen_fn(**gen_params)
# instantiate the experiment FSM
exp = Exp(targ_seq, **exp_params)
# run!
exp.run_sync()
def test_save_hdf(self):
TestFeat = experiment.make(TestExp, feats=[SaveHDF])
feat = TestFeat()
feat.add_dtype("dummy_feat_for_test", "f8", (1,))
# start the feature
feat.start()
feat.join()
mock_db = mocks.MockDatabase()
feat.cleanup(mock_db, "saveHDF_test_output")
hdf = tables.open_file("saveHDF_test_output.hdf")
self.assertEqual(hdf.root.task_msgs[:]["msg"].tolist(),
['wait', 'trial', 'reward', 'wait', 'None'])
self.assertEqual(hdf.root.task_msgs[:]["time"].tolist(), [0, 100, 101, 201, 601])
self.assertTrue(np.all(hdf.root.task[:]["dummy_feat_for_test"][251:300] == -1))
self.assertTrue(np.all(hdf.root.task[:]["dummy_feat_for_test"][:251] == 0))
self.assertTrue(np.all(hdf.root.task[:]["dummy_feat_for_test"][300:] == 0))
def ismore_sim_bmi(baseline_data, decoder, targets_matrix=None, session_length=0.):
import ismore.invasive.bmi_ismoretasks as bmi_ismoretasks
from riglib import experiment
from features.hdf_features import SaveHDF
from ismore.brainamp_features import SimBrainAmpData
import datetime
import numpy as np
import matplotlib.pyplot as plt
import multiprocessing as mp
from features.blackrock_features import BlackrockBMI
from ismore.exo_3D_visualization import Exo3DVisualizationInvasive
targets = bmi_ismoretasks.SimBMIControlReplayFile.sleep_gen(length=100)
plant_type = 'IsMore'
kwargs=dict(session_length=session_length, replay_neural_features=baseline_data, decoder=decoder)
if targets_matrix is not None:
kwargs['targets_matrix']=targets_matrix
Task = experiment.make(bmi_ismoretasks.SimBMIControlReplayFile, [SaveHDF])#, Exo3DVisualizationInvasive])
task = Task(targets, plant_type=plant_type, **kwargs)
task.run_sync()
pnm = save_dec_enc(task)
return pnm
def setUp(self):
sink_manager = sink.SinkManager.get_instance()
sink_manager.reset()
n_targets = 1
nblocks = 1
seq = TargetCaptureVFB2DWindow.centerout_2D_discrete(
nblocks=nblocks, ntargets=n_targets)
params = dict(window_size=(480, 240))
task_class = experiment.make(TargetCaptureVFB2DWindow, feats=[SaveHDF])
task_wrapper = experiment.task_wrapper.TaskWrapper(
subj=None,
params=params,
target_class=task_class,
seq=seq,
log_filename='tasktrack_log')
task_proxy, data_proxy = task_wrapper.start()
self.task_proxy = task_proxy
self.task_wrapper = task_wrapper
self.n_trials = n_targets * nblocks
def test_metaclass_constructor(self):
from features.hdf_features import SaveHDF
exp = experiment.make(experiment.LogExperiment, feats=(SaveHDF, ))
exp()
samples = samples.astype(np.float32)
# play the audio
self.audio_stream.write(samples)
# self.audio_t += self.audio_duration
super()._cycle()
def run(self):
super().run()
self.audio_stream.stop_stream()
self.audio_stream.close()
self.audio_p.terminate()
TestFeat = experiment.make(TargetCaptureVFB2DWindow, feats=[SaveHDF, SimLFPSensorFeature, SimLFPOutput])
# TestFeat.fps = 5
seq = TargetCaptureVFB2DWindow.centerout_2D_discrete(nblocks=2, ntargets=8)
feat = TestFeat(seq, window_size=(480, 240))
feat.run_sync()
time.sleep(1)
hdf = tables.open_file(feat.h5file.name)
os.rename(feat.h5file.name, "test_vfb_real_time_audio_feedback.hdf")
saved_msgs = [x.decode('utf-8') for x in hdf.root.task_msgs[:]["msg"]]
lfp = hdf.root.sim_lfp[:]['lfp'][:]
ts = hdf.root.sim_lfp[:]['ts']
from riglib import experiment
import pickle
from features.plexon_features import PlexonBMI
from features.hdf_features import SaveHDF
from riglib.bmi import train
# decoder = pickle.load(open('/Users/preeyakhanna/Dropbox/Carmena_Lab/FA_exp/grom_data/grom20151201_01_RMLC12011916.pkl'))
# Task = experiment.make(factor_analysis_tasks.FactorBMIBase, [CorticalBMI, SaveHDF])
# targets = factor_analysis_tasks.FactorBMIBase.generate_catch_trials()
# kwargs=dict(session_length=20.)
# task = Task(targets, plant_type="cursor_25x14", **kwargs)
# task.decoder = decoder
# import riglib.plexon
# task.sys_module = riglib.plexon
# task.init()
# task.run()
from tasks import choice_fa_tasks
decoder = pickle.load(
open(
'/storage/decoders/grom20160201_01_RMLC02011515_w_fa_dict_from_4048.pkl'
))
Task = experiment.make(choice_fa_tasks.FreeChoiceFA, [PlexonBMI, SaveHDF])
targets = choice_fa_tasks.FreeChoiceFA.centerout_2D_discrete_w_free_choice()
task = Task(targets, plant_type="cursor_25x14")
task.decoder = decoder
def run_iter_feat_addition(
total_exp_time=60,
n_neurons=128,
fraction_snr=0.25,
percent_high_SNR_noises=np.arange(0.7, 0.6, -0.2),
data_dump_folder='/home/sijia-aw/BMi3D_my/operation_funny_chicken/sim_data/neurons_128/run_3/',
random_seed=0):
#percent_high_SNR_noises[-1] = 0
num_noises = len(percent_high_SNR_noises)
percent_high_SNR_noises_labels = [
f'{s:.2f}' for s in percent_high_SNR_noises
]
import numpy as np
np.set_printoptions(precision=2, suppress=True)
mean_firing_rate_low = 50
mean_firing_rate_high = 100
noise_mode = 'fixed_gaussian'
fixed_noise_level = 5 #Hz
neuron_types = ['noisy', 'non_noisy']
n_neurons_no_noise_group = int(n_neurons * fraction_snr)
n_neurons_noisy_group = n_neurons - n_neurons_no_noise_group
no_noise_neuron_ind = np.arange(n_neurons_no_noise_group)
noise_neuron_ind = np.arange(
n_neurons_no_noise_group,
n_neurons_noisy_group + n_neurons_no_noise_group)
neuron_type_indices_in_a_list = [noise_neuron_ind, no_noise_neuron_ind]
noise_neuron_list = np.full(n_neurons, False, dtype=bool)
no_noise_neuron_list = np.full(n_neurons, False, dtype=bool)
noise_neuron_list[noise_neuron_ind] = True
no_noise_neuron_list[no_noise_neuron_ind] = True
N_TYPES_OF_NEURONS = 2
print('We have two types of indices: ')
for t, l in enumerate(neuron_type_indices_in_a_list):
print(f'{neuron_types[t]}:{l}')
# make percent of count into a list
percent_of_count_in_a_list = list()
for i in range(num_noises):
percent_of_count = np.ones(n_neurons)[:, np.newaxis]
percent_of_count[noise_neuron_ind] = 1
percent_of_count[no_noise_neuron_ind] = percent_high_SNR_noises[i]
percent_of_count_in_a_list.append(percent_of_count)
#for comparision
#for comparision
exp_conds_add = [
f'iter_{s}_{random_seed}_{n_neurons}' for s in percent_high_SNR_noises
]
exp_conds_keep = [
f'same_{s}_{random_seed}_{n_neurons}' for s in percent_high_SNR_noises
]
exp_conds = [
f'wo_FS_{s}_{random_seed}_{n_neurons}' for s in percent_high_SNR_noises
]
exp_conds.extend(exp_conds_add)
exp_conds.extend(exp_conds_keep)
print(f'we have experimental conditions {exp_conds}')
# In[7]:
# CHANGE: game mechanics: generate task params
N_TARGETS = 8
N_TRIALS = 2000
NUM_EXP = len(exp_conds) # how many experiments we are running.
# # Config the experiments
#
# this section largely copyied and pasted from
# bmi3d-sijia(branch)-bulti_in_experiemnts
# https://github.com/sijia66/brain-python-interface/blob/master/built_in_tasks/sim_task_KF.py
# import libraries
# make sure these directories are in the python path.,
from bmimultitasks import SimBMIControlMulti, SimBMICosEncKFDec, BMIControlMultiNoWindow, SimpleTargetCapture, SimpleTargetCaptureWithHold
from features import SaveHDF
from features.simulation_features import get_enc_setup, SimKFDecoderRandom, SimIntentionLQRController, SimClockTick
from features.simulation_features import SimHDF, SimTime
from riglib import experiment
from riglib.stereo_opengl.window import FakeWindow
from riglib.bmi import train
import weights
import time
import copy
import numpy as np
import matplotlib.pyplot as plt
import sympy as sp
import itertools #for identical sequences
np.set_printoptions(precision=2, suppress=True)
# ## behaviour and task setup
# In[10]:
#seq = SimBMIControlMulti.sim_target_seq_generator_multi(
#N_TARGETS, N_TRIALS)
from target_capture_task import ConcreteTargetCapture
seq = ConcreteTargetCapture.out_2D()
#create a second version of the tasks
seqs = itertools.tee(seq, NUM_EXP + 1)
target_seq = list(seqs[NUM_EXP])
seqs = seqs[:NUM_EXP]
SAVE_HDF = True
SAVE_SIM_HDF = True #this makes the task data available as exp.task_data_hist
DEBUG_FEATURE = False
#base_class = SimBMIControlMulti
base_class = SimpleTargetCaptureWithHold
#for adding experimental features such as encoder, decoder
feats = []
feats_2 = []
feats_set = [] # this is a going to be a list of lists
# ## Additional task setup
# In[11]:
from simulation_features import TimeCountDown
from features.sync_features import HDFSync
feats.append(HDFSync)
feats_2.append(HDFSync)
feats.append(TimeCountDown)
feats_2.append(TimeCountDown)
# ## encoder
#
# the cosine tuned encoder uses a poisson process, right
# https://en.wikipedia.org/wiki/Poisson_distribution
# so if the lambda is 1, then it's very likely
# In[12]:
from features.simulation_features import get_enc_setup
ENCODER_TYPE = 'cosine_tuned_encoder_with_poisson_noise'
#neuron set up : 'std (20 neurons)' or 'toy (4 neurons)'
N_NEURONS, N_STATES, sim_C = get_enc_setup(sim_mode='rot_90',
n_neurons=n_neurons)
#multiply our the neurons
sim_C[noise_neuron_list] = sim_C[noise_neuron_list] * mean_firing_rate_low
sim_C[no_noise_neuron_list] = sim_C[
no_noise_neuron_list] * mean_firing_rate_high
#set up the encoder
from features.simulation_features import SimCosineTunedEncWithNoise
#set up intention feedbackcontroller
#this ideally set before the encoder
feats.append(SimIntentionLQRController)
feats.append(SimCosineTunedEncWithNoise)
feats_2.append(SimIntentionLQRController)
feats_2.append(SimCosineTunedEncWithNoise)
# ## decoder setup
# In[13]:
#clda on random
DECODER_MODE = 'random' # random
#take care the decoder setup
if DECODER_MODE == 'random':
feats.append(SimKFDecoderRandom)
feats_2.append(SimKFDecoderRandom)
print(f'{__name__}: set base class ')
print(f'{__name__}: selected SimKFDecoderRandom \n')
else: #defaul to a cosEnc and a pre-traind KF DEC
from features.simulation_features import SimKFDecoderSup
feats.append(SimKFDecoderSup)
feats_2.append(SimKFDecoderSup)
print(f'{__name__}: set decoder to SimKFDecoderSup\n')
# ## clda: learner and updater
# In[14]:
#setting clda parameters
##learner: collects paired data at batch_sizes
RHO = 0.5
batch_size = 100
#learner and updater: actualy set up rho
UPDATER_BATCH_TIME = 1
UPDATER_HALF_LIFE = np.log(RHO) * UPDATER_BATCH_TIME / np.log(0.5)
LEARNER_TYPE = 'feedback' # to dumb or not dumb it is a question 'feedback'
UPDATER_TYPE = 'smooth_batch' #none or "smooth_batch"
#you know what?
#learner only collects firing rates labeled with estimated estimates
#we would also need to use the labeled data
#now, we can set up a dumb/or not-dumb learner
if LEARNER_TYPE == 'feedback':
from features.simulation_features import SimFeedbackLearner
feats.append(SimFeedbackLearner)
feats_2.append(SimFeedbackLearner)
else:
from features.simulation_features import SimDumbLearner
feats.append(SimDumbLearner)
feats_2.append(SimDumbLearner)
#to update the decoder.
if UPDATER_TYPE == 'smooth_batch':
from features.simulation_features import SimSmoothBatch
feats.append(SimSmoothBatch)
feats_2.append(SimSmoothBatch)
else: #defaut to none
print(f'{__name__}: need to specify an updater')
# ## feature selector setup
# In[15]:
from feature_selection_feature import FeatureTransformer, TransformerBatchToFit
from feature_selection_feature import FeatureSelector, LassoFeatureSelector, SNRFeatureSelector, IterativeFeatureSelector
from feature_selection_feature import ReliabilityFeatureSelector
#pass the real time limit on clock
feats.append(FeatureSelector)
feats_2.append(IterativeFeatureSelector)
feature_x_meth_arg = [
('transpose', None),
]
kwargs_feature = dict()
kwargs_feature = {
'transform_x_flag': False,
'transform_y_flag': False,
'n_starting_feats': n_neurons,
'n_states': 7,
"train_high_SNR_time": 60
}
print('kwargs will be updated in a later time')
print(
f'the feature adaptation project is tracking {kwargs_feature.keys()} ')
#assistor set up assist level
assist_level = (0.0, 0.0)
exp_feats = [feats] * num_noises
e_f_2 = [feats_2] * num_noises
e_f_3 = [feats] * num_noises
exp_feats.extend(e_f_2)
exp_feats.extend(e_f_3)
if DEBUG_FEATURE:
from features.simulation_features import DebugFeature
feats.append(DebugFeature)
if SAVE_HDF:
feats.append(SaveHDF)
feats_2.append(SaveHDF)
if SAVE_SIM_HDF:
feats.append(SimHDF)
feats_2.append(SimHDF)
#pass the real time limit on clock
feats.append(SimClockTick)
feats.append(SimTime)
feats_2.append(SimClockTick)
feats_2.append(SimTime)
# In[19]:
kwargs_exps = list()
for i in range(num_noises):
d = dict()
d['total_exp_time'] = total_exp_time
d['assist_level'] = assist_level
d['sim_C'] = sim_C
d['noise_mode'] = noise_mode
d['percent_noise'] = percent_of_count_in_a_list[i]
d['fixed_noise_level'] = fixed_noise_level
d['batch_size'] = batch_size
d['batch_time'] = UPDATER_BATCH_TIME
d['half_life'] = UPDATER_HALF_LIFE
d['no_noise_neuron_ind'] = no_noise_neuron_ind
d['noise_neuron_ind'] = noise_neuron_ind
d.update(kwargs_feature)
kwargs_exps.append(d)
kwargs_exps_add = copy.deepcopy(kwargs_exps)
kwargs_exps_start = copy.deepcopy(kwargs_exps)
for k in kwargs_exps_add:
k['init_feat_set'] = np.full(N_NEURONS, False, dtype=bool)
k['init_feat_set'][no_noise_neuron_list] = True
for k in kwargs_exps_start:
k['init_feat_set'] = np.full(N_NEURONS, False, dtype=bool)
k['init_feat_set'][no_noise_neuron_list] = True
kwargs_exps.extend(kwargs_exps_add)
kwargs_exps.extend(kwargs_exps_start)
print(f'we have got {len(kwargs_exps)} exps')
kwargs_exps[1]['init_feat_set']
# ## make and initalize experiment instances
# In[20]:
#seed the experiment
np.random.seed(0)
exps = list() #create a list of experiment
for i, s in enumerate(seqs):
#spawn the task
f = exp_feats[i]
Exp = experiment.make(base_class, feats=f)
e = Exp(s, **kwargs_exps[i])
exps.append(e)
exps_np = np.array(exps, dtype='object')
def get_KF_C_Q_from_decoder(first_decoder):
"""
get the decoder matrices C, Q from the decoder instance
Args:
first_decoder: riglib.bmi.decoder.
Returns:
target_C, target_Q: np.ndarray instances
"""
target_C = first_decoder.filt.C
target_Q = np.copy(first_decoder.filt.Q)
diag_val = 10000
np.fill_diagonal(target_Q, diag_val)
return (target_C, target_Q)
from feature_selection_feature import run_exp_loop
WAIT_FOR_HDF_FILE_TO_STOP = 10
for i, e in enumerate(exps):
np.random.seed(random_seed)
e.init()
# save the decoder if it is the first one.
if i == 0:
(target_C, target_Q) = get_KF_C_Q_from_decoder(e.decoder)
weights.change_target_kalman_filter_with_a_C_mat(e.decoder.filt,
target_C,
Q=target_Q,
debug=False)
else: # otherwise, just replace it.
weights.change_target_kalman_filter_with_a_C_mat(e.decoder.filt,
target_C,
Q=target_Q,
debug=False)
e.select_decoder_features(e.decoder)
e.record_feature_active_set(e.decoder)
#################################################################
# actual experiment begins
run_exp_loop(e, **kwargs_exps[i])
e.hdf.stop()
print(f'wait for {WAIT_FOR_HDF_FILE_TO_STOP}s for hdf file to save')
time.sleep(WAIT_FOR_HDF_FILE_TO_STOP)
e.save_feature_params()
time.sleep(WAIT_FOR_HDF_FILE_TO_STOP)
e.cleanup_hdf()
e.sinks.reset()
print(f'Finished running {exp_conds[i]}')
print()
import shutil
import os
import subprocess
for i, e in enumerate(exps):
import subprocess
old = e.h5file.name
new = data_dump_folder + exp_conds[i] + '.h5'
process = "cp {} {}".format(old, new)
print(process)
subprocess.run(
process,
shell=True) # do not remember, assign shell value to True.
import os
import aopy
import tables
exp_data_all = list()
exp_data_metadata_all = list()
for i, e in enumerate(exp_conds):
files = {'hdf': e + '.h5'}
file_name = os.path.join(data_dump_folder, files['hdf'])
# write in the exp processing files
aopy.data.save_hdf(data_dump_folder,
file_name,
kwargs_exps[i],
data_group="/feature_selection",
append=True)
with tables.open_file(file_name, mode='r') as f:
print(f)
try:
d, m = aopy.preproc.parse_bmi3d(data_dump_folder, files)
except:
print(f'cannot parse {e}')
import os
os.environ['DISPLAY'] = ':0'
save = True
#task = models.Task.objects.get(name='lfp_mod')
task = models.Task.objects.get(name='lfp_mod_mc_reach_out')
#task = models.Task.objects.get(name='manual_control_multi')
base_class = task.get()
feats = [SaveHDF, Autostart, PlexonBMI, MotionData]
Exp = experiment.make(base_class, feats=feats)
#params.trait_norm(Exp.class_traits())
params = dict(session_length=10, plant_visible=True, lfp_plant_type='cursor_onedimLFP', mc_plant_type='cursor_14x14'
rand_start=(0.,0.), max_tries=1)
gen = SimBMIControlMulti.sim_target_seq_generator_multi(8, 1000)
exp = Exp(gen, **params)
import pickle
#decoder = pickle.load(open('/storage/decoders/cart20141206_06_test_lfp1d2.pkl'))
d#ecoder = pickle.load(open('/storage/decoders/cart20141208_12_test_PK.pkl'))
decoder = pickle.load(open('/storage/decoders/cart20141209_08_cart_2015_pilot_2.pkl'))
exp.decoder = decoder
exp.init()
def test_metaclass_constructor(self):
exp = experiment.make(experiment.LogExperiment, feats=(SaveHDF, ))
exp()
from ismore.invasive import bmi_ismoretasks
from riglib import experiment
from features.hdf_features import SaveHDF
from features.arduino_features import BlackrockSerialDIORowByte
import numpy as np
targets = bmi_ismoretasks.SimBMIControl.rehand_simple(length=100)
Task = experiment.make(bmi_ismoretasks.VisualFeedback, [SaveHDF, BlackrockSerialDIORowByte])
kwargs=dict(session_length=15., assist_level = (1., 1.), assist_level_time=60.,
timeout_time=60.,)
task = Task(targets, plant_type='ReHand', **kwargs)
task.init()
from tasks import manualcontrolmultitasks
from riglib.bmi.state_space_models import StateSpaceEndptVel2D
from features.hdf_features import SaveHDF
from tasks import sim_fa_decoding
from riglib import experiment
ssm = StateSpaceEndptVel2D()
targ = manualcontrolmultitasks.ManualControlMulti.centerout_2D_discrete()
import os
kw = dict(encoder_name = os.path.expandvars('$FA_GROM_DATA/sims/enc030816_2325vfb_red_spk.pkl'))
from sim_neurons import parse_hdf
kf_decoder, dim_red_dict, hdf = parse_hdf.train_xz_KF(kw['encoder_name'])
kw['fa_dict'] = dim_red_dict
Task = experiment.make(sim_fa_decoding.Sim_FA_BMI, [SaveHDF])
task = Task(ssm, targ,**kw)
def __init__(self, subj, task_rec, feats, params, seq=None, seq_params=None, saveid=None):
'''
Parameters
----------
subj : tracker.models.Subject instance
Database record for subject performing the task
task_rec : tracker.models.Task instance
Database record for base task being run (without features)
feats : list
List of features to enable for the task
params : json_param.Parameters, or string representation of JSON object
user input on configurable task parameters
seq : models.Sequence instance, or tuple
Database record of Sequence parameters/static target sequence
If passed in as a tuple, then it's the result of calling 'seq.get' on the models.Sequence instance
seq_params: params from seq (see above)
saveid : int, optional
ID number of db.tracker.models.TaskEntry associated with this task
if None specified, then the data saved will not be linked to the
database entry and will be lost after the program exits
'''
self.saveid = saveid
self.taskname = task_rec.name
self.subj = subj
if isinstance(params, Parameters):
self.params = params
elif isinstance(params, (string, unicode)):
self.params = Parameters(params)
base_class = task_rec.get()
if None in feats:
raise Exception("Features not found properly in database!")
else:
Task = experiment.make(base_class, feats=feats)
# Run commands which must be executed before the experiment class can be instantiated (e.g., starting neural recording)
Task.pre_init(saveid=saveid)
self.params.trait_norm(Task.class_traits())
if issubclass(Task, experiment.Sequence):
# retreive the sequence data from the db, or from the input argument if the input arg was a tuple
if isinstance(seq, tuple):
gen_constructor, gen_params = seq
elif isinstance(seq, models.Sequence):
gen_constructor, gen_params = seq.get()
# Typically, 'gen_constructor' is the experiment.generate.runseq function (not an element of namelist.generators)
else:
raise ValueError("Unrecognized type for seq")
gen = gen_constructor(Task, **gen_params)
self.params.params['seq_params'] = seq_params
# 'gen' is now a true python generator usable by experiment.Sequence
self.task = Task(gen, **self.params.params)
with open(log_filename, 'a') as f:
f.write("instantiating task with a generator\n")
else:
self.task = Task(**self.params.params)
self.task.start()
#print tmp-bound
elif tmp[i] < 0:
tmp[i] = 1
#print tmp-bound
self.current_pt = tmp - bound
#Update screen
self._update(np.array(self.current_pt))
#Update 'last_pt' for next iteration
self.last_pt = self.current_pt.copy()
#write to screen
self.draw_world()
#Save Cursor position to HDF
self.task_data['cursor'] = np.array(self.current_pt)
#Save Target position to HDF (super form )
self.update_target_location()
if __name__ == "__main__":
from riglib.experiment import make
from riglib.experiment.generate import runseq
from riglib.experiment.features import SimulatedEyeData, MotionData, RewardSystem
seq = rand_target_sequence()
Exp = make(ManualControl, (SimulatedEyeData, MotionData, RewardSystem))
exp = Exp(runseq(Exp, seq), fixations=[(0, 0)])
exp.run()
from ismore.invasive import bmi_ismoretasks
from riglib import experiment
from features.hdf_features import SaveHDF
from features.arduino_features import BlackrockSerialDIORowByte
import numpy as np
targets = bmi_ismoretasks.SimBMIControl.rehand_simple(length=100)
Task = experiment.make(bmi_ismoretasks.VisualFeedback,
[SaveHDF, BlackrockSerialDIORowByte])
kwargs = dict(
session_length=15.,
assist_level=(1., 1.),
assist_level_time=60.,
timeout_time=60.,
)
task = Task(targets, plant_type='ReHand', **kwargs)
task.init()
def __init__(self, subj, task_rec, feats, params, seq=None, seq_params=None, saveid=None):
'''
Parameters
----------
subj : tracker.models.Subject instance
Database record for subject performing the task
task_rec : tracker.models.Task instance
Database record for base task being run (without features)
feats : list
List of features to enable for the task
params : json_param.Parameters, or string representation of JSON object
user input on configurable task parameters
seq : models.Sequence instance, or tuple
Database record of Sequence parameters/static target sequence
If passed in as a tuple, then it's the result of calling 'seq.get' on the models.Sequence instance
seq_params: params from seq (see above)
saveid : int, optional
ID number of db.tracker.models.TaskEntry associated with this task
if None specified, then the data saved will not be linked to the
database entry and will be lost after the program exits
'''
self.saveid = saveid
self.taskname = task_rec.name
self.subj = subj
if isinstance(params, Parameters):
self.params = params
elif isinstance(params, (string, str)):
self.params = Parameters(params)
base_class = task_rec.get()
if None in feats:
raise Exception("Features not found properly in database!")
else:
Task = experiment.make(base_class, feats=feats)
# Run commands which must be executed before the experiment class can be instantiated (e.g., starting neural recording)
Task.pre_init(saveid=saveid)
self.params.trait_norm(Task.class_traits())
if issubclass(Task, experiment.Sequence):
# retreive the sequence data from the db, or from the input argument if the input arg was a tuple
if isinstance(seq, tuple):
gen_constructor, gen_params = seq
elif isinstance(seq, models.Sequence):
gen_constructor, gen_params = seq.get()
# Typically, 'gen_constructor' is the experiment.generate.runseq function (not an element of namelist.generators)
else:
raise ValueError("Unrecognized type for seq")
gen = gen_constructor(Task, **gen_params)
self.params.params['seq_params'] = seq_params
# 'gen' is now a true python generator usable by experiment.Sequence
self.task = Task(gen, **self.params.params)
with open(log_filename, 'a') as f:
f.write("instantiating task with a generator\n")
else:
self.task = Task(**self.params.params)
self.task.start()
decoder_list = models.Decoder.objects.filter(entry=decoder_trained_id)
Decoder = decoder_list[decoder_list_ix]
decoder = pickle.load(open('/storage/decoders/'+Decoder.path))
kw=dict(decoder=decoder)
s = models.Subject.objects.filter(name='Gromit')
t = models.Task.objects.filter(name='rat_bmi')
entry = models.TaskEntry(subject_id=s[0].id, task=t[0])
entry.sequence_id = -1
params = Parameters.from_dict(dict(decoder=Decoder.pk, decoder_path=Decoder.path))
entry.params = params.to_json()
entry.save()
saveid = entry.id
Task = experiment.make(rat_bmi_tasks.RatBMI, [Autostart, PlexonBMI, PlexonSerialDIORowByte, RewardSystem, SaveHDF])
Task.pre_init(saveid=saveid)
task = Task(plant_type='aud_cursor', session_length=session_length, reward_time=reward_time, **kw)
task.subj=s[0]
task.init()
task.run()
#Cleanup
from db.tracker import dbq
cleanup_successful = task.cleanup(dbq, saveid, subject=task.subj)
task.decoder.save()
task.cleanup_hdf()
task.terminate()
kwargs_exps
# ## seed the exp
# In[7]:
np.random.seed(0)
#
# ## make our experiment class
# In[8]:
#spawn the task
Exp = experiment.make(base_class, feats=feats)
# # creat experiments with different C batch lengths
# ## create exps
# In[9]:
exps = list() #create a list of experiment
for i, s in enumerate(seqs):
e = Exp(s, **kwargs_exps[i])
exps.append(e)
# In[10]:
from tasks import factor_analysis_tasks
from riglib import experiment
import pickle
from features.plexon_features import PlexonBMI
from features.hdf_features import SaveHDF
from riglib.bmi import train
# decoder = pickle.load(open('/Users/preeyakhanna/Dropbox/Carmena_Lab/FA_exp/grom_data/grom20151201_01_RMLC12011916.pkl'))
# Task = experiment.make(factor_analysis_tasks.FactorBMIBase, [CorticalBMI, SaveHDF])
# targets = factor_analysis_tasks.FactorBMIBase.generate_catch_trials()
# kwargs=dict(session_length=20.)
# task = Task(targets, plant_type="cursor_25x14", **kwargs)
# task.decoder = decoder
# import riglib.plexon
# task.sys_module = riglib.plexon
# task.init()
# task.run()
from tasks import choice_fa_tasks
decoder = pickle.load(open('/storage/decoders/grom20160201_01_RMLC02011515_w_fa_dict_from_4048.pkl'))
Task = experiment.make(choice_fa_tasks.FreeChoiceFA, [PlexonBMI, SaveHDF])
targets = choice_fa_tasks.FreeChoiceFA.centerout_2D_discrete_w_free_choice()
task = Task(targets, plant_type="cursor_25x14")
task.decoder =decoder
