def update_textlist_from_xmat(self):
"""given the X matrix and columns, fill screen data list"""
if not self.XM.matX: return
matx = self.XM.matX
ccols = self.XM.col_list
if self.XM.verb > 1: print '++ updating text for columns: %s' % ccols
ind = self.textlist_index(gSTR_XMAT_FILE)
if ind >= 0: self.tw_stlist[ind].SetLabel(os.path.basename(matx.fname))
# show columns
self.set_textlist_label(self.textlist_index(gSTR_COLS_CHOSEN),
str=UTIL.encode_1D_ints(ccols))
self.set_textlist_label(self.textlist_index(gSTR_COLS_ALL),
str=UTIL.encode_1D_ints(range(matx.ncols)))
self.set_textlist_label(self.textlist_index(gSTR_COLS_MAIN),
str=UTIL.encode_1D_ints(matx.cols_by_group_list([],allroi=1)))
self.set_textlist_label(self.textlist_index(gSTR_COLS_BASE),
str=UTIL.encode_1D_ints(matx.cols_by_group_list([-1])))
self.set_textlist_label(self.textlist_index(gSTR_COLS_MOTION),
str=UTIL.encode_1D_ints(matx.cols_by_group_list([0])))
# show select condition numbers
cond = matx.cond_num_by_cols(ccols)
self.set_textlist_label(self.textlist_index(gSTR_COND_CHOSEN), val=cond)
cond = matx.cond_num_by_cols(range(matx.ncols))
self.set_textlist_label(self.textlist_index(gSTR_COND_ALL), val=cond)
cond = matx.cond_num_by_cols(matx.cols_by_group_list([],allroi=1))
self.set_textlist_label(self.textlist_index(gSTR_COND_MAIN), val=cond)
cond = matx.cond_num_by_cols(matx.cols_by_group_list([-1],allroi=1))
self.set_textlist_label(self.textlist_index(gSTR_COND_M_BASE), val=cond)
cond = matx.cond_num_by_cols(matx.cols_by_group_list([0],allroi=1))
self.set_textlist_label(self.textlist_index(gSTR_COND_M_MOT), val=cond)
cond = matx.cond_num_by_cols(matx.cols_by_group_list([-1,0]))
self.set_textlist_label(self.textlist_index(gSTR_COND_BASE_MOT), val=cond)
def make_col_type_string(self):
"""return a string showing which columns are main, baseline or motion
(return error code (0=success) and cormat string)"""
mat = self.matX
if not mat.ready:
return 1, '** no X-matrix to get col types from'
mstr = 'Columns by regressor type:\n\n'
mstr += ' main regressor columns : %s\n' % \
UTIL.encode_1D_ints(range(self.matX.ncols))
mstr += ' chosen regressor columns : %s\n' % \
UTIL.encode_1D_ints(self.col_list)
mstr += ' baseline regressor columns : %s\n' % \
UTIL.encode_1D_ints(self.matX.cols_by_group_list([-1]))
mstr += ' motion regressor columns : %s\n' % \
UTIL.encode_1D_ints(self.matX.cols_by_group_list([0]))
return 0, mstr
def make_col_type_string(self):
"""return a string showing which columns are main, baseline or motion
(return error code (0=success) and cormat string)"""
mat = self.matX
if not mat.ready:
return 1, '** no X-matrix to get col types from'
mstr = 'Columns by regressor type:\n\n'
mstr += ' main regressor columns : %s\n' % \
UTIL.encode_1D_ints(range(self.matX.ncols))
mstr += ' chosen regressor columns : %s\n' % \
UTIL.encode_1D_ints(self.col_list)
mstr += ' baseline regressor columns : %s\n' % \
UTIL.encode_1D_ints(self.matX.cols_by_group_list([-1]))
mstr += ' motion regressor columns : %s\n' % \
UTIL.encode_1D_ints(self.matX.cols_by_group_list([0]))
return 0, mstr
# =================================================================
if __name__ == "__main__":
# --------------------- get input ------------------------
print("++ Command line:\n ", ' '.join(sys.argv))
(ifile, inumb, ofile) = get_arg(sys.argv[1:])
# --------------------- proc it ------------------------
bad_list_init = au.read_text_file(ifile)
bad_list_proc = Convert_StrList_to_NumArr(bad_list_init)
good_list = Invert_BadInd_List(bad_list_proc, inumb)
good_encoded = au.encode_1D_ints(good_list)
# ------------------- write out -------------------------
print("++ OK, the list of good indices in AFNI selector format is:")
print("\n %s\n" % good_encoded)
f = open(ofile, 'w')
f.write(good_encoded)
f.close()
print(" ... which has been written to file: %s" % ofile)
print("++ Done.")
sys.exit(0)
# --------------------- get input ------------------------
print "++ Command line:\n ", ' '.join(sys.argv)
(ofile, maxind, lstr) = get_arg(sys.argv[1:])
# put all selectors together
sss = set()
for ll in lstr:
sss = set.union(sss, set(au.decode_1D_ints(ll, imax=maxind)))
# listify and sort
list_final = list(sss)
list_final.sort()
good_encoded = au.encode_1D_ints(list_final)
# ------------------- write out -------------------------
print "++ OK, the list of good indices in AFNI selector format is:"
print "\n %s\n" % good_encoded
f = open(ofile, 'w')
f.write(good_encoded)
f.close()
print " ... which has been written to file: %s" % ofile
print "++ Done."
sys.exit(0)
def plot_mat_by_cols(self, amat, cols=[]):
"""plot the given columns from a single AfniMatrix"""
if not isinstance(amat, AM.AfniXmat):
print('** plot_MBC: instance is not AfniXmat')
return
if not cols:
if amat.verb > 1:
print('-- plotting default of %d cols' % amat.ncols)
cols = [i for i in range(amat.ncols)]
ncols = len(cols)
if ncols < 1: return
# create reduced matrix and column labels
mat = amat.mat[:, cols]
yformat = FormatStrFormatter('%5.1f')
if amat.labels:
labels = N.array(amat.labels)
labels = labels[cols]
labels = labels.tolist()
# create axis formatter string
striplabs = 1
for label in labels: # strip trailing '#' if indices are all 0
ind = regress_index(label)
if ind > 0: striplabs = 0
maxlen = 0
for l in range(len(labels)):
label = labels[l]
if striplabs:
labels[l] = labels[l][0:labels[l].rfind('#')]
if len(labels[l]) > maxlen: maxlen = len(labels[l])
for i in range(ncols):
if i == 0:
title = '%s [%s]' % (os.path.basename(
amat.fname), UTIL.encode_1D_ints(cols))
else:
title = ''
if self.as_one:
# then only create one axis
if i == 0: ax = self.figure.add_subplot(1, 1, 1, title=title)
else:
ax = self.figure.add_subplot(ncols, 1, i + 1, title=title)
data = mat[:, i]
ax.plot(data)
# apply y tickmarks
ymin, ymax = ax.get_ylim()
width = ymax - ymin
if ymin * ymax < 0.0: ymean = 0.0
else: ymean = round((ymin + ymax) / 2.0, 2)
ax.grid(True)
ax.yaxis.set_major_formatter(yformat)
amax = round(data.max(), 1)
if self.as_one: pass # use default yticks
elif amax == 1.0 and ymin == 0.0:
ax.set_yticks(N.array([ymin, amax]))
elif ncols > 10:
ax.set_yticks(N.array([ymin, ymax]))
else:
ax.set_yticks(N.array([ymin, ymean, ymax]))
# now that yticks are set, prevent tight limits
if not self.as_one:
if ymin == data.min():
ymin -= 0.15 * width
ax.set_ylim((ymin, ymax))
if ymax == data.max():
ymax += 0.15 * width
ax.set_ylim((ymin, ymax))
if amat.run_len > 10 and amat.nruns > 1:
ax.set_xticks(
N.array(
[r * amat.run_len for r in range(amat.nruns + 1)]))
if i < ncols - 1: ax.set_xticklabels([])
else: ax.set_xlabel('TRs')
if self.as_one: ax.set_ylabel('')
elif amat.labels:
ax.set_ylabel('%-*s ' % (maxlen, labels[i]),
rotation='horizontal')
rv, plist = self.axis_posn(ax)
# rcr - fix
#if rv == 0: ax.set_position((0.2, plist[1], plist[2], plist[3]))
if rv == 0: ax.set_position((0.15, plist[1], 0.7, 0.7 / ncols))
self.Fit()
self.canvas.draw()
def plot_mat_by_cols(self, amat, cols=[]):
"""plot the given columns from a single AfniMatrix"""
if not isinstance(amat, AM.AfniXmat):
print "** plot_MBC: instance is not AfniXmat"
return
if not cols:
if amat.verb > 1:
print "-- plotting default of %d cols" % amat.ncols
cols = [i for i in range(amat.ncols)]
ncols = len(cols)
if ncols < 1:
return
# create reduced matrix and column labels
mat = amat.mat[:, cols]
yformat = FormatStrFormatter("%5.1f")
if amat.labels:
labels = N.array(amat.labels)
labels = labels[cols]
labels = labels.tolist()
# create axis formatter string
striplabs = 1
for label in labels: # strip trailing '#' if indices are all 0
ind = regress_index(label)
if ind > 0:
striplabs = 0
maxlen = 0
for l in range(len(labels)):
label = labels[l]
if striplabs:
labels[l] = labels[l][0 : labels[l].rfind("#")]
if len(labels[l]) > maxlen:
maxlen = len(labels[l])
for i in range(ncols):
if i == 0:
title = "%s [%s]" % (os.path.basename(amat.fname), UTIL.encode_1D_ints(cols))
else:
title = ""
if self.as_one:
# then only create one axis
if i == 0:
ax = self.figure.add_subplot(1, 1, 1, title=title)
else:
ax = self.figure.add_subplot(ncols, 1, i + 1, title=title)
data = mat[:, i]
ax.plot(data)
# apply y tickmarks
ymin, ymax = ax.get_ylim()
width = ymax - ymin
if ymin * ymax < 0.0:
ymean = 0.0
else:
ymean = round((ymin + ymax) / 2.0, 2)
ax.grid(True)
ax.yaxis.set_major_formatter(yformat)
amax = round(data.max(), 1)
if self.as_one:
pass # use default yticks
elif amax == 1.0 and ymin == 0.0:
ax.set_yticks(N.array([ymin, amax]))
elif ncols > 10:
ax.set_yticks(N.array([ymin, ymax]))
else:
ax.set_yticks(N.array([ymin, ymean, ymax]))
# now that yticks are set, prevent tight limits
if not self.as_one:
if ymin == data.min():
ymin -= 0.15 * width
ax.set_ylim((ymin, ymax))
if ymax == data.max():
ymax += 0.15 * width
ax.set_ylim((ymin, ymax))
if amat.run_len > 10 and amat.nruns > 1:
ax.set_xticks(N.array([r * amat.run_len for r in range(amat.nruns + 1)]))
if i < ncols - 1:
ax.set_xticklabels([])
else:
ax.set_xlabel("TRs")
if self.as_one:
ax.set_ylabel("")
elif amat.labels:
ax.set_ylabel("%-*s " % (maxlen, labels[i]), rotation="horizontal")
rv, plist = self.axis_posn(ax)
# rcr - fix
# if rv == 0: ax.set_position((0.2, plist[1], plist[2], plist[3]))
if rv == 0:
ax.set_position((0.15, plist[1], 0.7, 0.7 / ncols))
self.Fit()
self.canvas.draw()
#!/usr/bin/env python2.7
# put an encoded string of ints into a text file
import afni_util as au
import sys as sys
import numpy as np
if len(sys.argv) - 4:
print "\n\nBAD! Need 3 arguments!\n\n"
sys.exit(1)
ofile = sys.argv[1]
N = int(sys.argv[2])
f = float(sys.argv[3])
x = np.arange(N)
L = int(f * N)
np.random.shuffle(x)
y = list(x[:L])
y.sort()
enums = au.encode_1D_ints(y)
print enums
f = open(ofile, 'w')
f.write(enums + '\n')
f.close()
