def initialize_parallel_test_harness():
# Parse command line args
(options, args) = parse_cmd_line_args()
# Load data from file or create synthetic test dataset
data = load_data(options)
print "Creating master population object"
model = make_model(options.model, N=data['N'])
stabilize_sparsity(model)
popn = Population(model)
popn.set_data(data)
# Initialize the GLM with the data
popn_true = None
x_true = None
if 'vars' in data:
x_true = data['vars']
# Load the true model
data_dir = os.path.dirname(options.dataFile)
model_file = os.path.join(data_dir, 'model.pkl')
print "Loading true model from %s" % model_file
with open(model_file) as f:
model_true = cPickle.load(f)
# HACK FOR EXISTING DATA!
if 'N_dims' not in model_true['network']['graph']:
model_true['network']['graph']['N_dims'] = 1
if 'location_prior' not in model_true['network']['graph']:
model_true['network']['graph']['location_prior'] = \
{
'type' : 'gaussian',
'mu' : 0.0,
'sigma' : 1.0
}
if 'L' in x_true['net']['graph']:
x_true['net']['graph']['L'] = x_true['net']['graph'][
'L'].ravel()
# END HACK
popn_true = Population(model_true)
popn_true.set_data(data)
# Create a client with direct view to all engines
if options.json is not None:
client = Client(options.json)
else:
client = Client(profile=options.profile)
dview = client[:]
print "Found %d engines." % len(dview)
print "Initializing imports on each engine"
initialize_imports(dview)
print "Creating population objects on each engine"
create_population_on_engines(dview, data, options.model)
return options, popn, data, client, popn_true, x_true
def initialize_test_harness():
""" Initialize a model with N neurons. Use the data if specified on the
command line, otherwise sample new data from the model.
Return a population object, the data, and a set of true parameters
which is expected for synthetic tests
"""
# Parse command line args
(options, args) = parse_cmd_line_args()
# Load data from file or create synthetic test dataset
data = load_data(options)
print "Creating master population object"
model = make_model(options.model, N=data['N'], dt=0.001)
stabilize_sparsity(model)
popn = Population(model)
popn.add_data(data)
# Initialize the GLM with the data
popn_true = None
x_true = None
if 'vars' in data:
x_true = data['vars']
# Load the true model
model_true = None
data_dir = os.path.dirname(options.dataFile)
model_file = os.path.join(data_dir, 'model.pkl')
print "Loading true model from %s" % model_file
with open(model_file) as f:
model_true = cPickle.load(f)
# HACK FOR EXISTING DATA!
if 'N_dims' not in model_true['network']['graph']:
model_true['network']['graph']['N_dims'] = 1
if 'location_prior' not in model_true['network']['graph']:
model_true['network']['graph']['location_prior'] = \
{
'type' : 'gaussian',
'mu' : 0.0,
'sigma' : 1.0
}
if 'L' in x_true['net']['graph']:
x_true['net']['graph']['L'] = x_true['net']['graph'][
'L'].ravel()
# END HACK
popn_true = Population(model_true)
popn_true.add_data(data)
ll_true = popn_true.compute_log_p(x_true)
print "true LL: %f" % ll_true
return options, popn, data, popn_true, x_true
def initialize_parallel_test_harness():
# Parse command line args
(options, args) = parse_cmd_line_args()
# Load data from file or create synthetic test dataset
data = load_data(options)
print "Creating master population object"
model = make_model(options.model, N=data["N"])
stabilize_sparsity(model)
popn = Population(model)
popn.set_data(data)
# Initialize the GLM with the data
popn_true = None
x_true = None
if "vars" in data:
x_true = data["vars"]
# Load the true model
data_dir = os.path.dirname(options.dataFile)
model_file = os.path.join(data_dir, "model.pkl")
print "Loading true model from %s" % model_file
with open(model_file) as f:
model_true = cPickle.load(f)
# HACK FOR EXISTING DATA!
if "N_dims" not in model_true["network"]["graph"]:
model_true["network"]["graph"]["N_dims"] = 1
if "location_prior" not in model_true["network"]["graph"]:
model_true["network"]["graph"]["location_prior"] = {"type": "gaussian", "mu": 0.0, "sigma": 1.0}
if "L" in x_true["net"]["graph"]:
x_true["net"]["graph"]["L"] = x_true["net"]["graph"]["L"].ravel()
# END HACK
popn_true = Population(model_true)
popn_true.set_data(data)
# Create a client with direct view to all engines
if options.json is not None:
client = Client(options.json)
else:
client = Client(profile=options.profile)
dview = client[:]
print "Found %d engines." % len(dview)
print "Initializing imports on each engine"
initialize_imports(dview)
print "Creating population objects on each engine"
create_population_on_engines(dview, data, options.model)
return options, popn, data, client, popn_true, x_true
def initialize_test_harness():
""" Initialize a model with N neurons. Use the data if specified on the
command line, otherwise sample new data from the model.
Return a population object, the data, and a set of true parameters
which is expected for synthetic tests
"""
# Parse command line args
(options, args) = parse_cmd_line_args()
# Load data from file or create synthetic test dataset
data = load_data(options)
print "Creating master population object"
model = make_model(options.model, N=data['N'], dt=0.001)
stabilize_sparsity(model)
popn = Population(model)
popn.add_data(data)
# Initialize the GLM with the data
popn_true = None
x_true = None
if 'vars' in data:
x_true = data['vars']
# Load the true model
model_true = None
data_dir = os.path.dirname(options.dataFile)
model_file = os.path.join(data_dir, 'model.pkl')
print "Loading true model from %s" % model_file
with open(model_file) as f:
model_true = cPickle.load(f)
# HACK FOR EXISTING DATA!
if 'N_dims' not in model_true['network']['graph']:
model_true['network']['graph']['N_dims'] = 1
if 'location_prior' not in model_true['network']['graph']:
model_true['network']['graph']['location_prior'] = \
{
'type' : 'gaussian',
'mu' : 0.0,
'sigma' : 1.0
}
if 'L' in x_true['net']['graph']:
x_true['net']['graph']['L'] = x_true['net']['graph']['L'].ravel()
# END HACK
popn_true = Population(model_true)
popn_true.add_data(data)
ll_true = popn_true.compute_log_p(x_true)
print "true LL: %f" % ll_true
return options, popn, data, popn_true, x_true
import os
import numpy as np
import matplotlib
if 'DISPLAY' not in os.environ:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from pyglm.utils.io import parse_cmd_line_args, load_data
from pyglm.utils.sta import sta
# Parse command line args
(options, args) = parse_cmd_line_args()
# Load data and model
data = load_data(options)
stim = data['stim']
spks = data['S']
# Plot the STA at various lags
maxlag = 100
lags_to_plot = np.arange(maxlag, step=20)
# Downsample the spikes to the resolution of the stimulus
Tspks, N = spks.shape
Tstim = stim.shape[0]
# Flatten higher dimensional stimuli
if stim.ndim == 3:
stimf = stim.reshape((Tstim, -1))
else:
stimf = stim
import os
import numpy as np
import matplotlib
if 'DISPLAY' not in os.environ:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from pyglm.utils.io import parse_cmd_line_args, load_data
from pyglm.utils.sta import sta
# Parse command line args
(options, args) = parse_cmd_line_args()
# Load data and model
data = load_data(options)
stim = data['stim']
spks = data['S']
# Plot the STA at various lags
maxlag = 100
lags_to_plot = np.arange(maxlag, step=20)
# Downsample the spikes to the resolution of the stimulus
Tspks, N = spks.shape
Tstim = stim.shape[0]
# Flatten higher dimensional stimuli
if stim.ndim == 3:
stimf = stim.reshape((Tstim, -1))
else:
stimf = stim