print("WARNING: no data in {}.img.txt, please input:".format(
basename))
nscanlines = int(input("\tnscanlines (usually 127) "))
npoints = int(input("\tnpoints (usually 1020) "))
junk = int(input("\tjunk (usually 36, or 1020 - 984) "))
# TODO use metadata instead of hard-coded values
header = Header()
header.w = nscanlines # input image width
header.h = npoints - junk # input image height, trimmed
header.sf = 4000000 # magic number, sorry!
probe = Probe()
probe.radius = 10000 # based on '10' in transducer model number
probe.numElements = 128 # based on '128' in transducer model number
probe.pitch = 185 # based on Ultrasonix C9-5/10 transducer
conv = Converter(header, probe)
rdr = RawReader(rf, nscanlines=nscanlines, npoints=npoints)
# define "support" file names based on .raw
wav = os.path.join(parent, str(basename + ".ch1.wav"))
sync = os.path.join(parent, str(basename + '.sync.txt'))
idx_txt = os.path.join(parent, str(basename + ".idx.txt"))
# make destination and copy "support" files for parent file
copy_dir = os.path.join(output_dir, basename)
os.mkdir(copy_dir)
shutil.copy(wav, copy_dir)
shutil.copy(idx_txt, copy_dir)
shutil.copy(stimfile, copy_dir)
pca_data = data[mask]
pca_md = md[mask]
pca_md = pca_md.reset_index(drop=True)
# define Converter parameters from first acq for first subj
if conv is None:
print("Defining Converter ...")
header = Header()
header.w = pca_data[0].shape[1] # input image width
header.h = pca_data[0].shape[0] # input image height, trimmed
header.sf = 4000000 # magic number, sorry!
probe = Probe()
probe.radius = 10000 # based on '10' in transducer model number
probe.numElements = 128 # based on '128' in transducer model number
probe.pitch = 185 # based on Ultrasonix C9-5/10 transducer
conv = Converter(header, probe)
print("Defining region of interest ...")
# get mean frame and apply mask
mean_frame = pca_data.mean(axis=0)
conv_mean = np.flipud(conv.convert(np.flipud(mean_frame)))
plt.title("Mean frame, Spkr {:}".format(subject))
plt.imshow(conv_mean, cmap="Greys_r")
file_ending_mean = "subj{:}_mean.pdf".format(subject)
savepath_mean = os.path.join(root, d, file_ending_mean)
plt.savefig(savepath_mean)
roi_upper = 600
roi_lower = 300
roi_left = 20
roi_right = 100
class Probe(object):
def __init__(self):
pass
header = Header()
print(header)
# visualize components on (approximately) converted fan, if desired
header.w = 127 # input image width
header.h = 1020 #255 # input image height
header.sf = 4000000 # for ultrasonix this is the vec-freq value
probe = Probe()
probe.radius = 10000 # based on '10' in transducer model number
probe.numElements = 128 # based on '128' in transducer model number
probe.pitch = 185 #205 # guess based on Ultrasonix C9-5/10 transducer
c = Converter(header, probe)
image_shape = (1020,127)#(255,127)
rdr = RawReader(rf, nscanlines=nscanlines, npoints = npoints)
print(rf)
for f in np.arange(0,(last_idx+1)):
d = rdr.get_frame(f).reshape(image_shape)
mag = np.max(d) - np.min(d)
d = (d-np.min(d))/mag*255
pcn = np.flipud(c.as_bmp(np.flipud(d)))
#pcn = c.as_bmp(d)
plt.title("Frame{:}, {:}".format((f+1),stim))
plt.imshow(pcn, cmap="Greys_r")
file_ending = "subj{:}-{:}-{:}.png".format(subject, stim,(f))
ultradir = os.path.join(os.path.basename(barename),file_ending)
savepath = os.path.join(expdir,ultradir)