def test_smoke_get_spectrum():
"""
ensure that get_spectrum returns reasonable objects with random inputs
in the correct format
"""
data = np.random.random((100))
tr = random.random()
spectrum, freqs = utils.get_spectrum(data, tr)
assert spectrum is not None
assert freqs is not None
def comp_figures(ts, mask, comptable, mmix, io_generator, png_cmap):
"""
Creates static figures that highlight certain aspects of tedana processing
This includes a figure for each component showing the component time course,
the spatial weight map and a fast Fourier transform of the time course
Parameters
----------
ts : (S x T) array_like
Time series from which to derive ICA betas
mask : (S,) array_like
Boolean mask array
comptable : (C x X) :obj:`pandas.DataFrame`
Component metric table. One row for each component, with a column for
each metric. The index should be the component number.
mmix : (C x T) array_like
Mixing matrix for converting input data to component space, where `C`
is components and `T` is the same as in `data`
io_generator : :obj:`tedana.io.OutputGenerator`
Output Generator object to use for this workflow
"""
# Get the lenght of the timeseries
n_vols = len(mmix)
# Flip signs of mixing matrix as needed
mmix = mmix * comptable["optimal sign"].values
# regenerate the beta images
ts_B = stats.get_coeffs(ts, mmix, mask)
ts_B = ts_B.reshape(io_generator.reference_img.shape[:3] + ts_B.shape[1:])
# trim edges from ts_B array
ts_B = _trim_edge_zeros(ts_B)
# Mask out remaining zeros
ts_B = np.ma.masked_where(ts_B == 0, ts_B)
# Get repetition time from reference image
tr = io_generator.reference_img.header.get_zooms()[-1]
# Create indices for 6 cuts, based on dimensions
cuts = [ts_B.shape[dim] // 6 for dim in range(3)]
expl_text = ""
# Remove trailing ';' from rationale column
comptable["rationale"] = comptable["rationale"].str.rstrip(";")
for compnum in comptable.index.values:
if comptable.loc[compnum, "classification"] == "accepted":
line_color = "g"
expl_text = "accepted"
elif comptable.loc[compnum, "classification"] == "rejected":
line_color = "r"
expl_text = "rejection reason(s): " + comptable.loc[compnum,
"rationale"]
elif comptable.loc[compnum, "classification"] == "ignored":
line_color = "k"
expl_text = "ignored reason(s): " + comptable.loc[compnum,
"rationale"]
else:
# Classification not added
# If new, this will keep code running
line_color = "0.75"
expl_text = "other classification"
allplot = plt.figure(figsize=(10, 9))
ax_ts = plt.subplot2grid((5, 6), (0, 0),
rowspan=1,
colspan=6,
fig=allplot)
ax_ts.set_xlabel("TRs")
ax_ts.set_xlim(0, n_vols)
plt.yticks([])
# Make a second axis with units of time (s)
max_xticks = 10
xloc = plt.MaxNLocator(max_xticks)
ax_ts.xaxis.set_major_locator(xloc)
ax_ts2 = ax_ts.twiny()
ax1Xs = ax_ts.get_xticks()
ax2Xs = []
for X in ax1Xs:
# Limit to 2 decimal places
seconds_val = round(X * tr, 2)
ax2Xs.append(seconds_val)
ax_ts2.set_xticks(ax1Xs)
ax_ts2.set_xlim(ax_ts.get_xbound())
ax_ts2.set_xticklabels(ax2Xs)
ax_ts2.set_xlabel("seconds")
ax_ts.plot(mmix[:, compnum], color=line_color)
# Title will include variance from comptable
comp_var = "{0:.2f}".format(comptable.loc[compnum,
"variance explained"])
comp_kappa = "{0:.2f}".format(comptable.loc[compnum, "kappa"])
comp_rho = "{0:.2f}".format(comptable.loc[compnum, "rho"])
plt_title = "Comp. {}: variance: {}%, kappa: {}, rho: {}, {}".format(
compnum, comp_var, comp_kappa, comp_rho, expl_text)
title = ax_ts.set_title(plt_title)
title.set_y(1.5)
# Set range to ~1/10th of max positive or negative beta
imgmax = 0.1 * np.abs(ts_B[:, :, :, compnum]).max()
imgmin = imgmax * -1
for idx, _ in enumerate(cuts):
for imgslice in range(1, 6):
ax = plt.subplot2grid((5, 6), (idx + 1, imgslice - 1),
rowspan=1,
colspan=1)
ax.axis("off")
if idx == 0:
to_plot = np.rot90(ts_B[imgslice * cuts[idx], :, :,
compnum])
if idx == 1:
to_plot = np.rot90(ts_B[:, imgslice * cuts[idx], :,
compnum])
if idx == 2:
to_plot = ts_B[:, :, imgslice * cuts[idx], compnum]
ax_im = ax.imshow(to_plot,
vmin=imgmin,
vmax=imgmax,
aspect="equal",
cmap=png_cmap)
# Add a color bar to the plot.
ax_cbar = allplot.add_axes([0.8, 0.3, 0.03, 0.37])
cbar = allplot.colorbar(ax_im, ax_cbar)
cbar.set_label("Component Beta", rotation=90)
cbar.ax.yaxis.set_label_position("left")
# Get fft and freqs for this subject
# adapted from @dangom
spectrum, freqs = utils.get_spectrum(mmix[:, compnum], tr)
# Plot it
ax_fft = plt.subplot2grid((5, 6), (4, 0), rowspan=1, colspan=6)
ax_fft.plot(freqs, spectrum)
ax_fft.set_title("One Sided fft")
ax_fft.set_xlabel("Hz")
ax_fft.set_xlim(freqs[0], freqs[-1])
plt.yticks([])
# Fix spacing so TR label does overlap with other plots
allplot.subplots_adjust(hspace=0.4)
plot_name = "comp_{}.png".format(str(compnum).zfill(3))
compplot_name = os.path.join(io_generator.out_dir, "figures",
plot_name)
plt.savefig(compplot_name)
plt.close()
def write_comp_figs(ts, mask, comptable, mmix, ref_img, out_dir, png_cmap):
"""
Creates static figures that highlight certain aspects of tedana processing
This includes a figure for each component showing the component time course,
the spatial weight map and a fast Fourier transform of the time course
Parameters
----------
ts : (S x T) array_like
Time series from which to derive ICA betas
mask : (S,) array_like
Boolean mask array
comptable : (C x X) :obj:`pandas.DataFrame`
Component metric table. One row for each component, with a column for
each metric. The index should be the component number.
mmix : (C x T) array_like
Mixing matrix for converting input data to component space, where `C`
is components and `T` is the same as in `data`
ref_img : :obj:`str` or img_like
Reference image to dictate how outputs are saved to disk
out_dir : :obj:`str`
Figures folder within output directory
png_cmap : :obj:`str`
The name of a matplotlib colormap to use when making figures. Optional.
Default colormap is 'coolwarm'
"""
# Get the lenght of the timeseries
n_vols = len(mmix)
# Check that colormap provided exists
if png_cmap not in plt.colormaps():
LGR.warning(
'Provided colormap is not recognized, proceeding with default')
png_cmap = 'coolwarm'
# regenerate the beta images
ts_B = metrics.get_coeffs(ts, mmix, mask)
ts_B = ts_B.reshape(ref_img.shape[:3] + ts_B.shape[1:])
# trim edges from ts_B array
ts_B = trim_edge_zeros(ts_B)
# Mask out remaining zeros
ts_B = np.ma.masked_where(ts_B == 0, ts_B)
# Get repetition time from ref_img
tr = ref_img.header.get_zooms()[-1]
# Create indices for 6 cuts, based on dimensions
cuts = [ts_B.shape[dim] // 6 for dim in range(3)]
expl_text = ''
# Remove trailing ';' from rationale column
comptable['rationale'] = comptable['rationale'].str.rstrip(';')
for compnum in comptable.index.values:
if comptable.loc[compnum, "classification"] == 'accepted':
line_color = 'g'
expl_text = 'accepted'
elif comptable.loc[compnum, "classification"] == 'rejected':
line_color = 'r'
expl_text = 'rejection reason(s): ' + comptable.loc[compnum,
"rationale"]
elif comptable.loc[compnum, "classification"] == 'ignored':
line_color = 'k'
expl_text = 'ignored reason(s): ' + comptable.loc[compnum,
"rationale"]
else:
# Classification not added
# If new, this will keep code running
line_color = '0.75'
expl_text = 'other classification'
allplot = plt.figure(figsize=(10, 9))
ax_ts = plt.subplot2grid((5, 6), (0, 0),
rowspan=1,
colspan=6,
fig=allplot)
ax_ts.set_xlabel('TRs')
ax_ts.set_xlim(0, n_vols)
plt.yticks([])
# Make a second axis with units of time (s)
max_xticks = 10
xloc = plt.MaxNLocator(max_xticks)
ax_ts.xaxis.set_major_locator(xloc)
ax_ts2 = ax_ts.twiny()
ax1Xs = ax_ts.get_xticks()
ax2Xs = []
for X in ax1Xs:
# Limit to 2 decimal places
seconds_val = round(X * tr, 2)
ax2Xs.append(seconds_val)
ax_ts2.set_xticks(ax1Xs)
ax_ts2.set_xlim(ax_ts.get_xbound())
ax_ts2.set_xticklabels(ax2Xs)
ax_ts2.set_xlabel('seconds')
ax_ts.plot(mmix[:, compnum], color=line_color)
# Title will include variance from comptable
comp_var = "{0:.2f}".format(comptable.loc[compnum,
"variance explained"])
comp_kappa = "{0:.2f}".format(comptable.loc[compnum, "kappa"])
comp_rho = "{0:.2f}".format(comptable.loc[compnum, "rho"])
plt_title = ('Comp. {}: variance: {}%, kappa: {}, rho: {}, '
'{}'.format(compnum, comp_var, comp_kappa, comp_rho,
expl_text))
title = ax_ts.set_title(plt_title)
title.set_y(1.5)
# Set range to ~1/10th of max positive or negative beta
imgmax = 0.1 * np.abs(ts_B[:, :, :, compnum]).max()
imgmin = imgmax * -1
for idx, cut in enumerate(cuts):
for imgslice in range(1, 6):
ax = plt.subplot2grid((5, 6), (idx + 1, imgslice - 1),
rowspan=1,
colspan=1)
ax.axis('off')
if idx == 0:
to_plot = np.rot90(ts_B[imgslice * cuts[idx], :, :,
compnum])
if idx == 1:
to_plot = np.rot90(ts_B[:, imgslice * cuts[idx], :,
compnum])
if idx == 2:
to_plot = ts_B[:, :, imgslice * cuts[idx], compnum]
ax_im = ax.imshow(to_plot,
vmin=imgmin,
vmax=imgmax,
aspect='equal',
cmap=png_cmap)
# Add a color bar to the plot.
ax_cbar = allplot.add_axes([0.8, 0.3, 0.03, 0.37])
cbar = allplot.colorbar(ax_im, ax_cbar)
cbar.set_label('Component Beta', rotation=90)
cbar.ax.yaxis.set_label_position('left')
# Get fft and freqs for this subject
# adapted from @dangom
spectrum, freqs = get_spectrum(mmix[:, compnum], tr)
# Plot it
ax_fft = plt.subplot2grid((5, 6), (4, 0), rowspan=1, colspan=6)
ax_fft.plot(freqs, spectrum)
ax_fft.set_title('One Sided fft')
ax_fft.set_xlabel('Hz')
ax_fft.set_xlim(freqs[0], freqs[-1])
plt.yticks([])
# Fix spacing so TR label does overlap with other plots
allplot.subplots_adjust(hspace=0.4)
plot_name = 'comp_{}.png'.format(str(compnum).zfill(3))
compplot_name = os.path.join(out_dir, plot_name)
plt.savefig(compplot_name)
plt.close()