def create(name, fidl, filterf, nod=True):
"""Use a fild file to create a metadata label table, using the json
filter file to convert, rename, and reorganize labels as needed.
Parameters
----------
name : str
The name of the csv file (inculde extension)
fidl : str
The name of the .fidl file
filterf : str
The name of the filer json file (see Note for format details)
nod : Boolean
Write SPM compatile NOD (Names, Onsets, Durations) .mat files
Notes
-----
Overall filterf has two goals. 1. Configure create() for the
current exp. 2. Remap/join labels/conditions from the fidl file
before using them in the csv. Fidl names are often rather
confusing and noisy.
The filterf should be valid json file, where the top level
is a list. The first element in that list must be the exp parameters
in a dict. The next and remaining elements are dicts whose keys
will become a colname in the csv. The values are the
fuzzy_label : label pairs.
Nothing is done to validate the filterf. Use with care and
double check the results.
For example (delete comments (#) before use/validation):
[
# The parameters
{
"triallen" : 5,
"condcol" : 2,
"trialcol" : 4,
"final_ncol" : 8
},
{
# The first filteration, col with be trial
# and each fidl match on the right gets
# coded as 'trial'
"trial": {
"1FaceCor1" : "trial",
"2FaceCor2" : "trial",
"3FaceCor3" : "trial",
"4FaceCor4" : "trial",
"5FaceCor5" : "trial",
"6HouseCor1" : "trial",
"7HouseCor2" : "trial",
"8HouseCor3" : "trial",
"9HouseCor4" : "trial",
"10HouseCor5" : "trial",
"11NoiseResp1" : "trial",
"12NoiseResp2" : "trial",
"13NoiseResp3" : "trial",
"14NoiseResp4" : "trial",
"15NoiseResp5" : "trial",
"16NoiseResp5" : "trial",
"16MultiResp" : "trial",
"17NoResp" : "trial"
}
},
}
# Col is rt, and both Cor1 and Resp1
# get remapped to rt1 (and repeat for 2, 3 ...)
"rt": {
"Cor1" : "rt1",
"Cor2" : "rt2",
"Cor3" : "rt3",
"Cor4" : "rt4",
"Resp1" : "rt1",
"Resp2" : "rt2",
"Resp3" : "rt3",
"Resp4" : "rt4"
}
},
{
# And so on....
"exp": {
"Face" : "face",
"House" : "house",
"Noise" : "noise"
}
}
]
"""
fidl_to_csv(fidlfile, name, 0)
## Write a csv to disk
filterdata = load(open(filterf, 'r'))
expdata = filterdata.pop(0)
condcol = expdata["condcol"]
trialcol = expdata["trialcol"]
triallen = expdata["triallen"]
for fd in filterdata:
fuzzy_label(name, condcol, _get_filt(fd), _get_name(fd), header=True)
# Add the name of the fidl file as a factor
dname = os.path.splitext(os.path.basename(fidl))[0]
if len(dname) < 3:
dname += "__"
sublab = {"trial": dname}
fuzzy_label(name, trialcol, sublab, "scode", header=True)
# Fill in TR time
tdur = {"trial": triallen}
tr_time(name, trialcol, tdur, drop=True, header=True)
ncol = expdata["final_ncol"]
fill_tr_gaps(os.path.join(os.path.dirname(name),
"trtime_" + os.path.basename(name)),
ncol,
fill='nan')
# Be SPM compatible?
if nod:
csvdata = pd.read_csv(name)
names = csvdata["rt"]
trials = csvdata["trial"]
onsets = csvdata["TR"]
durations = np.array([
triallen,
] * len(trials))
nod_mat(
names, onsets, durations,
os.path.join(
os.path.dirname(name),
"nod_" + os.path.splitext(os.path.basename(name))[0] + ".mat"))
resplab = {
"Right" : "right",
"Left" : "left"}
fuzzy_label(csv_f, condcol, resplab, "resp", header=True)
# ----
# 5. Add labels for subject/fidl
sublab = {"trial" : basename}
fuzzy_label(csv_f, trialcol, sublab, "scode", header=True)
# ----
# 6. Expand labels so they cover every TR
final_ncol = 8
tdur = {"trial" : triallen}
tr_time(csv_f, trialcol, tdur, drop=True, header=True)
fill_tr_gaps("trtime_" + csv_f, final_ncol)
## As trial labels were added first, following
## csv conversion, the trial label lives in col 3
# ----
# 8. Create the NOD mat file
#
# Get the csv file"s data
# then get onsets, names and
# create durations
csvdata = pd.read_csv(csv_f)
trials = csvdata["trial"]
names = csvdata["resp"]
onsets = csvdata["TR"]
durations = np.array([triallen, ] * len(trials))
# ----
# 5. Add labels for stim type
explab = {"Face": "face", "House": "house", "Noise": "noise"}
fuzzy_label(csv_f, condcol, explab, "exp", header=True)
# ----
# 6. Add labels for subject/fidl
sublab = {"trial": basename}
fuzzy_label(csv_f, trialcol, sublab, "scode", header=True)
# ----
# 7. Expand labels so they cover every TR
final_ncol = 8
tdur = {"trial": triallen}
tr_time(csv_f, trialcol, tdur, drop=True, header=True)
fill_tr_gaps("trtime_" + csv_f, final_ncol)
## As trial labels were added first, following
## csv conversion, the trial label lives in col 3
# ----
# 8. Create the NOD mat file
#
# Get the csv file"s data
# then get onsets, names and
# create durations
csvdata = pd.read_csv(csv_f)
names = csvdata["rt"]
trials = csvdata["trial"]
onsets = csvdata["TR"]
def create(name, fidl, filterf, nod=True):
"""Use a fild file to create a metadata label table, using the json
filter file to convert, rename, and reorganize labels as needed.
Parameters
----------
name : str
The name of the csv file (inculde extension)
fidl : str
The name of the .fidl file
filterf : str
The name of the filer json file (see Note for format details)
nod : Boolean
Write SPM compatile NOD (Names, Onsets, Durations) .mat files
Notes
-----
Overall filterf has two goals. 1. Configure create() for the
current exp. 2. Remap/join labels/conditions from the fidl file
before using them in the csv. Fidl names are often rather
confusing and noisy.
The filterf should be valid json file, where the top level
is a list. The first element in that list must be the exp parameters
in a dict. The next and remaining elements are dicts whose keys
will become a colname in the csv. The values are the
fuzzy_label : label pairs.
Nothing is done to validate the filterf. Use with care and
double check the results.
For example (delete comments (#) before use/validation):
[
# The parameters
{
"triallen" : 5,
"condcol" : 2,
"trialcol" : 4,
"final_ncol" : 8
},
{
# The first filteration, col with be trial
# and each fidl match on the right gets
# coded as 'trial'
"trial": {
"1FaceCor1" : "trial",
"2FaceCor2" : "trial",
"3FaceCor3" : "trial",
"4FaceCor4" : "trial",
"5FaceCor5" : "trial",
"6HouseCor1" : "trial",
"7HouseCor2" : "trial",
"8HouseCor3" : "trial",
"9HouseCor4" : "trial",
"10HouseCor5" : "trial",
"11NoiseResp1" : "trial",
"12NoiseResp2" : "trial",
"13NoiseResp3" : "trial",
"14NoiseResp4" : "trial",
"15NoiseResp5" : "trial",
"16NoiseResp5" : "trial",
"16MultiResp" : "trial",
"17NoResp" : "trial"
}
},
}
# Col is rt, and both Cor1 and Resp1
# get remapped to rt1 (and repeat for 2, 3 ...)
"rt": {
"Cor1" : "rt1",
"Cor2" : "rt2",
"Cor3" : "rt3",
"Cor4" : "rt4",
"Resp1" : "rt1",
"Resp2" : "rt2",
"Resp3" : "rt3",
"Resp4" : "rt4"
}
},
{
# And so on....
"exp": {
"Face" : "face",
"House" : "house",
"Noise" : "noise"
}
}
]
"""
fidl_to_csv(fidlfile, name, 0)
## Write a csv to disk
filterdata = load(open(filterf, "r"))
expdata = filterdata.pop(0)
condcol = expdata["condcol"]
trialcol = expdata["trialcol"]
triallen = expdata["triallen"]
for fd in filterdata:
fuzzy_label(name, condcol, _get_filt(fd), _get_name(fd), header=True)
# Add the name of the fidl file as a factor
dname = os.path.splitext(os.path.basename(fidl))[0]
if len(dname) < 3:
dname += "__"
sublab = {"trial": dname}
fuzzy_label(name, trialcol, sublab, "scode", header=True)
# Fill in TR time
tdur = {"trial": triallen}
tr_time(name, trialcol, tdur, drop=True, header=True)
ncol = expdata["final_ncol"]
fill_tr_gaps(os.path.join(os.path.dirname(name), "trtime_" + os.path.basename(name)), ncol, fill="nan")
# Be SPM compatible?
if nod:
csvdata = pd.read_csv(name)
names = csvdata["rt"]
trials = csvdata["trial"]
onsets = csvdata["TR"]
durations = np.array([triallen] * len(trials))
nod_mat(
names,
onsets,
durations,
os.path.join(os.path.dirname(name), "nod_" + os.path.splitext(os.path.basename(name))[0] + ".mat"),
)