def _create_nod(metapath, fidlpath, scode):
triallen = 5
meta = pd.read_csv(metapath)
faceshouses = np.array(meta["exp"].tolist())
trs = np.array(meta["TR"].tolist())
trial_index = np.array(meta["trialcount"].tolist())
targets = construct_targets(
trs=trs,
faceshouses=faceshouses,
trial_index=trial_index)
keepers = ["face", "house"]
keep_fhs = construct_filter(targets["faceshouses"], keepers, True)
targets = filter_targets(keep_fhs, targets)
names = targets["faceshouses"]
onsets = targets["trs"]
durations = np.array([triallen, ] * len(targets["trial_index"]))
nod_mat(names, onsets, durations, os.path.join(fidlpath,
"nod_" + scode + "_stim_facehouse.mat"))
def filterX(filtname, X, targets):
""" Use a config file to filter both X and targets.
Parameters
----------
filtname - str, a file path
The name of valid json file (see Info)
X - 2D array-like (n_samples x n_features)
The data to filter
targets - dict-like
A dictionary of labels/targets for X. Keys
are names and values are sklearn compatible
lebels
Return
------
The filtered X, targets
Info
----
The named json file has the must can only have 3
top level nodes ["keep", "merge", "join"], one of
which must be present.
Below each top-level key is a label name which must be
present in targets.
From there it depends on which top-level branch you are in
TODO
"""
# load the json at name,
filterconf = load(open(filtname, "r"))
# Validate top level nodes
validnodes = ["keep", "merge", "join"]
for k in filterconf.keys():
if k not in validnodes:
raise ValueError("Unknown filter command {0}".format(k))
# Validate that X and targets match
for k, v in targets.items():
if v.shape[0] != X.shape[0]:
raise ValueError("Before: X/target shape mismatch for '{0}'".format(k))
# test for keep and do that
if "keep" in filterconf:
for k, keepers in filterconf["keep"].items():
labels = targets[k]
mask = construct_filter(labels, keepers, True)
targets = filter_targets(mask, targets)
X = X[mask, :]
# Test for merge and do that
if "merge" in filterconf:
for k, mmap in filterconf["merge"].items():
labels = targets[k]
targets[k] = merge_labels(labels, mmap)
# Test for join and do that
if "join" in filterconf:
raise NotImplementedError("join not yet implemented. Sorry.")
# revalidate that X and targets match
for k, v in targets.items():
if v.shape[0] != X.shape[0]:
raise ValueError("After: X/targets shape mismatch for '{0}'".format(k))
assert checkX(X)
return X, targets
def filterX(filtname, X, targets):
""" Use a config file to filter both X and targets.
Parameters
----------
filtname - str, a file path
The name of valid json file (see Info)
X - 2D array-like (n_samples x n_features)
The data to filter
targets - dict-like
A dictionary of labels/targets for X. Keys
are names and values are sklearn compatible
lebels
Return
------
The filtered X, targets
Info
----
The named json file has the must can only have 3
top level nodes ["keep", "merge", "join"], one of
which must be present.
Below each top-level key is a label name which must be
present in targets.
From there it depends on which top-level branch you are in
TODO
"""
# load the json at name,
filterconf = load(open(filtname, "r"))
# Validate top level nodes
validnodes = ["keep", "merge", "join"]
for k in filterconf.keys():
if k not in validnodes:
raise ValueError("Unknown filter command {0}".format(k))
# Validate that X and targets match
for k, v in targets.items():
if v.shape[0] != X.shape[0]:
raise ValueError(
"Before: X/target shape mismatch for '{0}'".format(k))
# test for keep and do that
if "keep" in filterconf:
for k, keepers in filterconf["keep"].items():
labels = targets[k]
mask = construct_filter(labels, keepers, True)
targets = filter_targets(mask, targets)
X = X[mask, :]
# Test for merge and do that
if "merge" in filterconf:
for k, mmap in filterconf["merge"].items():
labels = targets[k]
targets[k] = merge_labels(labels, mmap)
# Test for join and do that
if "join" in filterconf:
raise NotImplementedError("join not yet implemented. Sorry.")
# revalidate that X and targets match
for k, v in targets.items():
if v.shape[0] != X.shape[0]:
raise ValueError(
"After: X/targets shape mismatch for '{0}'".format(k))
assert checkX(X)
return X, targets
def run(self, basename, smooth=False, filtfile=None,
n=None, tr=None, n_rt=None, n_trials_per_cond=None,
durations=None ,noise=None, n_features=None, n_univariate=None,
n_accumulator=None, n_decision=None, n_noise=None,
n_repeated=None, drift_noise=False, step_noise=False):
# Write init
mode = 'w'
header = True
for scode in range(n):
# If were past the first Ss data, append.
if scode > 0:
mode = 'a'
header = False
# Create the data
X, y, y_trialcount = make_bold(
n_rt,
n_trials_per_cond,
tr,
durations=durations,
noise=noise,
n_features=n_features,
n_univariate=n_univariate,
n_accumulator=n_accumulator,
n_decision=n_decision,
n_noise=n_noise,
n_repeated=n_repeated,
drift_noise=drift_noise,
step_noise=step_noise)
targets = construct_targets(trial_index=y_trialcount, y=y)
# Drop baseline trials created by make_bold
baselinemask = np.arange(y.shape[0])[y != 0]
X = X[baselinemask, ]
targets = filter_targets(baselinemask, targets)
# Filter and
if filtfile is not None:
X, targets = filterX(filtfile, X, targets)
if smooth:
X = smoothfn(X, tr=1.5, ub=0.10, lb=0.001)
# Normalize
norm = MinMaxScaler((0,1))
X = norm.fit_transform(X.astype(np.float))
# finally decompose.
Xcs, csnames, ti_cs = self.spacetime.fit_transform(
X, targets["y"], targets["trial_index"],
self.window)
# Name them,
csnames = unique_nan(y)
csnames = sort_nanfirst(csnames)
# and write.
for Xc, csname, ti in zip(Xcs, csnames, ti_cs):
save_tcdf(
name=join_by_underscore(True, basename, csname),
X=Xc,
cond=csname,
dataname=join_by_underscore(False,
os.path.split(basename)[-1], scode),
index=ti.astype(np.int),
header=header,
mode=mode,
float_format="%.{0}f".format(self.nsig))
