def save_ht_outs(h5_gp, tfp, shift):
""" Save processed Hilbert Transform outputs"""
# Error check
if isinstance(h5_gp, h5py.Dataset):
raise ValueError('Must pass an h5Py group')
# Filter bad pixels
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=2)
tfp_fixed = np.array(tfp_fixed)
# write data; note that this is all actually deprecated and should be fixed
# grp_name = h5_gp.name
# grp_tr = px.io.VirtualGroup(grp_name)
# tfp_px = px.io.VirtualDataset('tfp', tfp, parent = h5_gp)
# shift_px = px.io.VirtualDataset('shift', shift, parent = h5_gp)
# tfp_fixed_px = px.io.VirtualDataset('tfp_fixed', tfp_fixed, parent = h5_gp)
#
# grp_tr.attrs['timestamp'] = get_time_stamp()
# grp_tr.add_children([tfp_px])
# grp_tr.add_children([shift_px])
# grp_tr.add_children([tfp_fixed_px])
# write data using current pyUSID implementations
# grp_tr = h5_file.create_group(h5_gp.name)
tfp_px = h5_gp.create_dataset('tfp', data=tfp, dtype=np.float32)
shift_px = h5_gp.create_dataset('shift', data=shift, dtype=np.float32)
tfp_fixed_px = h5_gp.create_dataset('tfp_fixed',
data=tfp_fixed,
dtype=np.float32)
h5_gp.attrs['timestamp'] = get_time_stamp()
return tfp_px, shift_px, tfp_fixed_px
def save_CSV_from_file(h5_file, h5_path='/', append='', mirror=False):
"""
Saves the tfp, shift, and fixed_tfp as CSV files
Parameters
----------
h5_file : H5Py file of FFtrEFM class
Reminder you can always type: h5_svd.file or h5_avg.file for this
h5_path : str, optional
specific folder path to search for the tfp data. Usually not needed.
append : str, optional
text to append to file name
"""
h5_ff = h5_file
if isinstance(h5_file, ffta.hdf_utils.process.FFtrEFM):
print('Saving from FFtrEFM Class')
h5_ff = h5_file.h5_main.file
h5_path = h5_file.h5_results_grp.name
elif not isinstance(h5_file, h5py.File):
print('Saving from pyUSID object')
h5_ff = h5_file.file
tfp = usid.hdf_utils.find_dataset(h5_ff[h5_path], 'tfp')[0][()]
# tfp_fixed = usid.hdf_utils.find_dataset(h5_file[h5_path], 'tfp_fixed')[0][()]
shift = usid.hdf_utils.find_dataset(h5_ff[h5_path], 'shift')[0][()]
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=2)
tfp_fixed = np.array(tfp_fixed)
print(usid.hdf_utils.find_dataset(h5_ff[h5_path], 'shift')[0].parent.name)
path = h5_ff.file.filename.replace('\\', '/')
path = '/'.join(path.split('/')[:-1]) + '/'
os.chdir(path)
if mirror:
np.savetxt('tfp-' + append + '.csv', np.fliplr(tfp).T, delimiter=',')
np.savetxt('shift-' + append + '.csv',
np.fliplr(shift).T,
delimiter=',')
np.savetxt('tfp_fixed-' + append + '.csv',
np.fliplr(tfp_fixed).T,
delimiter=',')
else:
np.savetxt('tfp-' + append + '.csv', tfp.T, delimiter=',')
np.savetxt('shift-' + append + '.csv', shift.T, delimiter=',')
np.savetxt('tfp_fixed-' + append + '.csv', tfp_fixed.T, delimiter=',')
return
def plot_tfp(ffprocess, scale_tfp=1e6, scale_shift=1, threshold=2, **kwargs):
'''
Quickly plots the tfp and shift data. If there's a height image in the h5_file associated
with ffprocess, will plot that as well
Parameters
----------
ffprocess : FFtrEFM class object (inherits Process)
Returns
-------
fig, a : figure and axes objects
'''
fig, a = plt.subplots(nrows=2, ncols=2, figsize=(13, 6))
tfp_ax = a[0][1]
shift_ax = a[1][1]
img_length = ffprocess.parm_dict['FastScanSize']
img_height = ffprocess.parm_dict['SlowScanSize']
kwarg = {'origin': 'lower', 'x_vec': img_length * 1e6,
'y_vec': img_height * 1e6, 'num_ticks': 5, 'stdevs': 3, 'show_cbar': True}
for k, v in kwarg.items():
if k not in kwargs:
kwargs.update({k: v})
num_cols = ffprocess.parm_dict['num_cols']
num_rows = ffprocess.parm_dict['num_rows']
try:
ht = ffprocess.h5_main.file['/height/Raw_Data'][:, 0]
ht = np.reshape(ht, [num_cols, num_rows]).transpose()
ht_ax = a[0][0]
ht_image, cbar = usid.viz.plot_utils.plot_map(ht_ax, ht * 1e9, cmap='gray', **kwarg)
cbar.set_label('Height (nm)', rotation=270, labelpad=16)
except:
pass
tfp_ax.set_title('tFP Image')
shift_ax.set_title('Shift Image')
tfp_fixed, _ = badpixels.fix_array(ffprocess.h5_tfp[()], threshold=threshold)
tfp_image, cbar_tfp = usid.viz.plot_utils.plot_map(tfp_ax, tfp_fixed * scale_tfp,
cmap='inferno', **kwargs)
shift_image, cbar_sh = usid.viz.plot_utils.plot_map(shift_ax, ffprocess.h5_shift[()] * scale_shift,
cmap='inferno', **kwargs)
cbar_tfp.set_label('Time (us)', rotation=270, labelpad=16)
cbar_sh.set_label('Frequency Shift (Hz)', rotation=270, labelpad=16)
text = tfp_ax.text(num_cols / 2, num_rows + 3, '')
return fig, a
def save_CSV_from_file(h5_file, h5_path='/', append='', mirror=False):
"""
Saves the Q, Amp, as CSV files
:param h5_file: Reminder you can always type: h5_svd.file or h5_avg.file for this
:type h5_file: H5Py file
:param h5_path: specific folder path to search for the tfp data. Usually not needed.
:type h5_path: str, optional
:param append: text to append to file name (e.g. RD01 or something related to the file)
:type append: str, optional
:param mirror: Flips the ibw signal if acquired during a retrace, so data match the topography pixel-to-pixel
:type mirror: bool, optional
"""
Q = usid.hdf_utils.find_dataset(h5_file[h5_path], 'Q')[-1][()]
A = usid.hdf_utils.find_dataset(h5_file[h5_path], 'Amplitude')[-1][()]
Q_fixed, _ = badpixels.fix_array(Q, threshold=2)
print(usid.hdf_utils.find_dataset(h5_file[h5_path], 'Q')[-1].parent.name)
path = h5_file.file.filename.replace('\\', '/')
path = '/'.join(path.split('/')[:-1]) + '/'
os.chdir(path)
if mirror:
np.savetxt('Q-' + append + '.csv', np.fliplr(Q).T, delimiter=',')
np.savetxt('Qfixed-' + append + '.csv',
np.fliplr(Q_fixed).T,
delimiter=',')
np.savetxt('Amp-' + append + '.csv', np.fliplr(A).T, delimiter=',')
else:
np.savetxt('Q' + append + '.csv', Q.T, delimiter=',')
np.savetxt('Qfixed-' + append + '.csv', Q_fixed.T, delimiter=',')
np.savetxt('Amp-' + append + '.csv', A.T, delimiter=',')
return
def save_ht_outs(h5_gp, tfp, shift):
"""
Save processed Hilbert Transform outputs
:param h5_gp:
:type h5_gp:
:param tfp:
:type tfp:
:param shift:
:type shift:
:returns: tuple (tfp_px, shift_px, tfp_fixed_px)
WHERE
[type] tfp_px is...
[type] shift_px is...
[type] tfp_fixed_px is...
"""
# Error check
if isinstance(h5_gp, h5py.Dataset):
raise ValueError('Must pass an h5Py group')
# Filter bad pixels
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=2)
tfp_fixed = np.array(tfp_fixed)
# grp_tr = h5_file.create_group(h5_gp.name)
tfp_px = h5_gp.create_dataset('tfp', data=tfp, dtype=np.float32)
shift_px = h5_gp.create_dataset('shift', data=shift, dtype=np.float32)
tfp_fixed_px = h5_gp.create_dataset('tfp_fixed',
data=tfp_fixed,
dtype=np.float32)
# h5_gp.attrs['timestamp'] = write_book_keeping_attrs()
return tfp_px, shift_px, tfp_fixed_px
def plot_ringdown(h5_file, h5_path='/', append='', savefig=True, stdevs=2):
"""
Plots the relevant tfp, inst_freq, and shift values as separate image files
Parameters
----------
h5_file : h5Py File
h5_path : str, optional
Location of the relevant datasets to be saved/plotted. e.g. h5_rb.name
append : str, optional
A string to include in the saved figure filename
savefig : bool, optional
Whether or not to save the image
stdevs : int, optional
Number of standard deviations to display
"""
# h5_rd = usid.hdf_utils.find_dataset(h5_file[h5_path], 'Ringdown')[0]
if 'Dataset' in str(type(h5_file[h5_path])):
h5_path = h5_file[h5_path].parent.name
Q = usid.hdf_utils.find_dataset(h5_file[h5_path], 'Q')[0][()]
A = usid.hdf_utils.find_dataset(h5_file[h5_path], 'Amplitude')[0][()]
Q_fixed, _ = badpixels.fix_array(Q, threshold=2)
A_fixed, _ = badpixels.fix_array(A, threshold=2)
parm_dict = usid.hdf_utils.get_attributes(h5_file[h5_path])
if 'FastScanSize' not in parm_dict:
parm_dict = usid.hdf_utils.get_attributes(h5_file[h5_path].parent)
xs = parm_dict['FastScanSize']
ys = parm_dict['SlowScanSize']
asp = ys / xs
if asp != 1:
asp = asp * 2
fig, a = plt.subplots(nrows=2, figsize=(8, 9))
_, cbar_t = usid.viz.plot_utils.plot_map(a[0], Q_fixed, x_vec=xs * 1e6, y_vec=ys * 1e6,
aspect=asp, cmap='inferno', stdevs=stdevs)
_, cbar_s = usid.viz.plot_utils.plot_map(a[1], A_fixed * 1e9, x_vec=xs * 1e6, y_vec=ys * 1e6,
aspect=asp, cmap='inferno', stdevs=stdevs)
cbar_t.set_label('Q (a.u.)', rotation=270, labelpad=16)
a[0].set_title('Q', fontsize=12)
cbar_s.set_label('Amplitude (nm)', rotation=270, labelpad=16)
a[1].set_title('Amplitude', fontsize=12)
fig.tight_layout()
if savefig:
path = h5_file.file.filename.replace('\\', '/')
path = '/'.join(path.split('/')[:-1]) + '/'
os.chdir(path)
fig.savefig('Q_Amp_' + append + '_.tif', format='tiff')
return fig, a
def plot_tfps(h5_file, h5_path='/', append='', savefig=True, stdevs=2):
"""
Plots the relevant tfp, inst_freq, and shift values as separate image files
Parameters
----------
h5_file : h5Py File
h5_path : str, optional
Location of the relevant datasets to be saved/plotted. e.g. h5_rb.name
append : str, optional
A string to include in the saved figure filename
savefig : bool, optional
Whether or not to save the image
stdevs : int, optional
Number of standard deviations to display
"""
h5_file = px.io.HDFwriter(h5_file).file
try:
h5_if = usid.hdf_utils.find_dataset(h5_file[h5_path], 'Inst_Freq')[0]
except:
h5_if = usid.hdf_utils.find_dataset(h5_file[h5_path], 'inst_freq')[0]
parm_dict = get_utils.get_params(h5_if)
# parm_dict = usid.hdf_utils.get_attributes(h5_file[h5_path])
if 'Dataset' in str(type(h5_file[h5_path])):
h5_path = h5_file[h5_path].parent.name
tfp = usid.hdf_utils.find_dataset(h5_file[h5_path], 'tfp')[0][()]
shift = usid.hdf_utils.find_dataset(h5_file[h5_path], 'shift')[0][()]
if tfp.shape[1] == 1:
# Forgot to reconvert and/or needs to be converted
[ysz, xsz] = h5_if.pos_dim_sizes
tfp = np.reshape(tfp, [ysz, xsz])
shift = np.reshape(shift, [ysz, xsz])
try:
tfp_fixed = usid.hdf_utils.find_dataset(h5_file[h5_path],
'tfp_fixed')[0][()]
except:
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=2)
tfp_fixed = np.array(tfp_fixed)
xs = parm_dict['FastScanSize']
ys = parm_dict['SlowScanSize']
asp = ys / xs
if asp != 1:
asp = asp * 2
fig, a = plt.subplots(nrows=3, figsize=(8, 9))
[vmint,
vmaxt] = np.mean(tfp) - 2 * np.std(tfp), np.mean(tfp) - 2 * np.std(tfp)
[vmins, vmaxs
] = np.mean(shift) - 2 * np.std(shift), np.mean(shift) - 2 * np.std(shift)
_, cbar_t = usid.viz.plot_utils.plot_map(a[0],
tfp_fixed * 1e6,
x_vec=xs * 1e6,
y_vec=ys * 1e6,
aspect=asp,
cmap='inferno',
stdevs=stdevs)
_, cbar_r = usid.viz.plot_utils.plot_map(a[1],
1 / (1e3 * tfp_fixed),
x_vec=xs * 1e6,
y_vec=ys * 1e6,
aspect=asp,
cmap='inferno',
stdevs=stdevs)
_, cbar_s = usid.viz.plot_utils.plot_map(a[2],
shift,
x_vec=xs * 1e6,
y_vec=ys * 1e6,
aspect=asp,
cmap='inferno',
stdevs=stdevs)
cbar_t.set_label('tfp (us)', rotation=270, labelpad=16)
a[0].set_title('tfp', fontsize=12)
cbar_r.set_label('Rate (kHz)', rotation=270, labelpad=16)
a[1].set_title('1/tfp', fontsize=12)
cbar_s.set_label('shift (Hz)', rotation=270, labelpad=16)
a[2].set_title('shift', fontsize=12)
fig.tight_layout()
if savefig:
path = h5_file.file.filename.replace('\\', '/')
path = '/'.join(path.split('/')[:-1]) + '/'
os.chdir(path)
fig.savefig('tfp_shift_' + append + '_.tif', format='tiff')
return
def plot_tfps(h5_file,
h5_path='/',
append='',
savefig=True,
stdevs=2,
scale=None):
"""
Plots the relevant tfp, inst_freq, and shift values as separate image files
:param h5_file:
:type h5_file: h5Py File
:param h5_path: Location of the relevant datasets to be saved/plotted. e.g. h5_rb.name
:type h5_path: str, optional
:param append: A string to include in the saved figure filename
:type append: str, optional
:param savefig: Whether or not to save the image
:type savefig: bool, optional
:param stdevs: Number of standard deviations to display
:type stdevs: int, optional
:param scale: Scale bar size, in microns
:type scale: float, optional
:returns: tuple (fig, ax)
WHERE
[type] fig is...
[type] ax is...
"""
try:
h5_if = usid.hdf_utils.find_dataset(h5_file[h5_path], 'Inst_Freq')[0]
except:
h5_if = usid.hdf_utils.find_dataset(h5_file[h5_path], 'inst_freq')[0]
parm_dict = get_utils.get_params(h5_if)
# parm_dict = usid.hdf_utils.get_attributes(h5_file[h5_path])
if 'Dataset' in str(type(h5_file[h5_path])):
h5_path = h5_file[h5_path].parent.name
tfp = usid.hdf_utils.find_dataset(h5_file[h5_path], 'tfp')[0][()]
shift = usid.hdf_utils.find_dataset(h5_file[h5_path], 'shift')[0][()]
if tfp.shape[1] == 1:
# Forgot to reconvert and/or needs to be converted
[ysz, xsz] = h5_if.pos_dim_sizes
tfp = np.reshape(tfp, [ysz, xsz])
shift = np.reshape(shift, [ysz, xsz])
try:
tfp_fixed = usid.hdf_utils.find_dataset(h5_file[h5_path],
'tfp_fixed')[0][()]
except:
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=2)
tfp_fixed = np.array(tfp_fixed)
xs = parm_dict['FastScanSize']
ys = parm_dict['SlowScanSize']
asp = ys / xs
if asp != 1:
asp = asp * 2
fig, ax = plt.subplots(nrows=3, figsize=(8, 9), facecolor='white')
[vmint,
vmaxt] = np.mean(tfp) - 2 * np.std(tfp), np.mean(tfp) - 2 * np.std(tfp)
[vmins, vmaxs
] = np.mean(shift) - 2 * np.std(shift), np.mean(shift) - 2 * np.std(shift)
_, cbar_t = usid.viz.plot_utils.plot_map(ax[0],
tfp_fixed * 1e6,
x_vec=xs * 1e6,
y_vec=ys * 1e6,
aspect=asp,
cmap='inferno',
stdevs=stdevs)
_, cbar_r = usid.viz.plot_utils.plot_map(ax[1],
1 / (1e3 * tfp_fixed),
x_vec=xs * 1e6,
y_vec=ys * 1e6,
aspect=asp,
cmap='inferno',
stdevs=stdevs)
_, cbar_s = usid.viz.plot_utils.plot_map(ax[2],
shift,
x_vec=xs * 1e6,
y_vec=ys * 1e6,
aspect=asp,
cmap='inferno',
stdevs=stdevs)
cbar_t.set_label('tfp (us)', rotation=270, labelpad=16)
ax[0].set_title('tfp', fontsize=12)
if scale:
sz = np.floor(xs * 1e6 / scale)
ratio = int(tfp_fixed.shape[1] / sz)
if scale > 1:
sz = str(scale) + ' $\mu m$'
else:
sz = str(int(scale * 1000)) + ' nm'
fontprops = fm.FontProperties(size=14, weight='bold')
bar1 = AnchoredSizeBar(ax[0].transData,
ratio,
sz,
frameon=False,
loc='lower right',
size_vertical=2,
pad=0.2,
color='white',
fontproperties=fontprops)
ax[0].add_artist(bar1)
cbar_r.set_label('Rate (kHz)', rotation=270, labelpad=16)
ax[1].set_title('1/tfp', fontsize=12)
if scale:
bar1 = AnchoredSizeBar(ax[0].transData,
ratio,
sz,
frameon=False,
loc='lower right',
size_vertical=2,
pad=0.2,
color='white',
fontproperties=fontprops)
ax[1].add_artist(bar1)
cbar_s.set_label('shift (Hz)', rotation=270, labelpad=16)
ax[2].set_title('shift', fontsize=12)
if scale:
bar1 = AnchoredSizeBar(ax[0].transData,
ratio,
sz,
frameon=False,
loc='lower right',
size_vertical=2,
pad=0.2,
color='white',
fontproperties=fontprops)
ax[2].add_artist(bar1)
fig.tight_layout()
if savefig:
path = h5_file.file.filename.replace('\\', '/')
path = '/'.join(path.split('/')[:-1]) + '/'
os.chdir(path)
fig.savefig('tfp_shift_' + append + '_.tif', format='tiff')
return fig, ax
def plot_tfp(ffprocess, scale_tfp=1e6, scale_shift=1, threshold=2, **kwargs):
'''
Quickly plots the tfp and shift data. If there's a height image in the h5_file associated
with ffprocess, will plot that as well
:param ffprocess:
:type ffprocess: FFtrEFM class object (inherits Process) or the parent Group
:param scale_tfp:
:type scale_tfp:
:param scale_shift:
:type scale_shift:
:param threshold:
:type threshold:
:param kwargs:
:type kwargs:
:returns: tuple (fig, a)
WHERE
fig is figure object
ax is axes object
'''
fig, a = plt.subplots(nrows=2, ncols=2, figsize=(13, 6))
tfp_ax = a[0][1]
shift_ax = a[1][1]
tfp_cal_ax = a[1][0]
if isinstance(ffprocess, ffta.hdf_utils.process.FFtrEFM):
img_length = ffprocess.parm_dict['FastScanSize']
img_height = ffprocess.parm_dict['SlowScanSize']
num_cols = ffprocess.parm_dict['num_cols']
num_rows = ffprocess.parm_dict['num_rows']
tfp = ffprocess.h5_tfp[()]
shift = ffprocess.h5_shift[()]
if 'tfp_cal' in ffprocess.h5_tfp.parent:
tfp_cal = ffprocess.h5_tfp.parent['tfp_cal'][()]
tfp_cal_fixed, _ = badpixels.fix_array(tfp_cal,
threshold=threshold)
else:
tfp_cal_fixed = tfp_cal = np.ones(tfp.shape)
elif isinstance(ffprocess, h5py.Group):
attr = get_attributes(ffprocess['Inst_Freq'])
img_length = attr['FastScanSize']
img_height = attr['SlowScanSize']
num_cols = attr['num_cols']
num_rows = attr['num_rows']
tfp = ffprocess['tfp']
shift = ffprocess['shift']
if 'tfp_cal' in ffprocess:
tfp_cal = ffprocess['tfp_cal'][()]
tfp_cal_fixed, _ = badpixels.fix_array(tfp_cal,
threshold=threshold)
else:
tfp_cal_fixed = tfp_cal = np.ones(tfp.shape)
kwarg = {
'origin': 'lower',
'x_vec': img_length * 1e6,
'y_vec': img_height * 1e6,
'num_ticks': 5,
'stdevs': 3,
'show_cbar': True
}
for k, v in kwarg.items():
if k not in kwargs:
kwargs.update({k: v})
try:
ht = ffprocess.h5_main.file['/height_000/Raw_Data'][:, 0]
ht = np.reshape(ht, [num_cols, num_rows]).transpose()
ht_ax = a[0][0]
ht_image, cbar = plot_utils.plot_map(ht_ax,
ht * 1e9,
cmap='gray',
**kwarg)
cbar.set_label('Height (nm)', rotation=270, labelpad=16)
except:
pass
tfp_ax.set_title('tFP Image')
shift_ax.set_title('Shift Image')
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=threshold)
tfp_image, cbar_tfp = plot_utils.plot_map(tfp_ax,
tfp_fixed * scale_tfp,
cmap='inferno',
**kwargs)
shift_image, cbar_sh = plot_utils.plot_map(shift_ax,
shift * scale_shift,
cmap='inferno',
**kwargs)
try:
tfp_cal_image, cbar_tfp_cal = plot_utils.plot_map(tfp_cal_ax,
tfp_cal_fixed *
scale_tfp,
cmap='inferno',
**kwargs)
cbar_tfp_cal.set_label('Time Calib. (us)', rotation=90, labelpad=16)
except:
tfp_cal_image = tfp_cal_ax.imshow(tfp_cal_fixed * scale_tfp,
cmap='inferno',
origin='lower')
cbar_tfp.set_label('Time (us)', rotation=270, labelpad=16)
cbar_sh.set_label('Frequency Shift (Hz)', rotation=270, labelpad=16)
text = tfp_ax.text(num_cols / 2, num_rows + 3, '')
return fig, a
def save_CSV_from_file(h5_file,
h5_path='/',
append='',
mirror=False,
offset=0):
"""
Saves the tfp, shift, and fixed_tfp as CSV files
:param h5_file: Reminder you can always type: h5_svd.file or h5_avg.file for this
:type h5_file: H5Py file of FFtrEFM class
:param h5_path: specific folder path to search for the tfp data. Usually not needed.
:type h5_path: str, optional
:param append: text to append to file name
:type append: str, optional
:param mirror:
:type mirror: bool, optional
:param offset: if calculating tFP with a fixed offset for fitting, this subtracts it out
:type offset: float
"""
h5_ff = h5_file
if isinstance(h5_file, ffta.hdf_utils.process.FFtrEFM):
print('Saving from FFtrEFM Class')
h5_ff = h5_file.h5_main.file
h5_path = h5_file.h5_results_grp.name
elif not isinstance(h5_file, h5py.File):
print('Saving from pyUSID object')
h5_ff = h5_file.file
tfp = find_dataset(h5_ff[h5_path], 'tfp')[0][()]
shift = find_dataset(h5_ff[h5_path], 'shift')[0][()]
try:
tfp_cal = find_dataset(h5_ff[h5_path], 'tfp_cal')[0][()]
except:
tfp_cal = None
tfp_fixed, _ = badpixels.fix_array(tfp, threshold=2)
tfp_fixed = np.array(tfp_fixed)
if isinstance(tfp_cal, np.ndarray):
tfp_cal_fixed, _ = badpixels.fix_array(tfp_cal, threshold=2)
tfp_cal_fixed = np.array(tfp_cal_fixed)
print(find_dataset(h5_ff[h5_path], 'shift')[0].parent.name)
path = h5_ff.file.filename.replace('\\', '/')
path = '/'.join(path.split('/')[:-1]) + '/'
os.chdir(path)
if mirror:
np.savetxt('tfp-' + append + '.csv',
np.fliplr(tfp - offset).T,
delimiter=',')
np.savetxt('shift-' + append + '.csv',
np.fliplr(shift).T,
delimiter=',')
np.savetxt('tfp_fixed-' + append + '.csv',
np.fliplr(tfp_fixed - offset).T,
delimiter=',')
else:
np.savetxt('tfp-' + append + '.csv', (tfp - offset).T, delimiter=',')
np.savetxt('shift-' + append + '.csv', shift.T, delimiter=',')
np.savetxt('tfp_fixed-' + append + '.csv', (tfp_fixed - offset).T,
delimiter=',')
if isinstance(tfp_cal, np.ndarray):
if mirror:
np.savetxt('tfp_cal-' + append + '.csv',
np.fliplr(tfp_cal - offset).T,
delimiter=',')
np.savetxt('tfp_cal_fixed-' + append + '.csv',
np.fliplr(tfp_cal_fixed - offset).T,
delimiter=',')
else:
np.savetxt('tfp_cal-' + append + '.csv', (tfp_cal - offset).T,
delimiter=',')
np.savetxt('tfp_cal_fixed-' + append + '.csv',
(tfp_cal_fixed - offset).T,
delimiter=',')
return
