def norm_set(self, text):
stretch_dict = {
'No Stretch': None,
'Sqrt Stretch': SqrtStretch(),
'Linear Stretch': LinearStretch(),
'Squared Stretch': SquaredStretch(),
'Power Stretch': PowerStretch(self.a),
'Log Stretch': LogStretch(self.a)
}
self.stretch_val = stretch_dict[text]
if text == 'Log Stretch':
self.var_max = 10000
self.var_int = 10
self.cube = self.stretch_val(self.cube_base)
#self.norm = ImageNormalize(interval=self.interval, stretch=self.stretch_val, clip=True)
elif text == 'Sqrt Stretch':
self.cube = np.sqrt(self.cube)
elif text == 'No Stretch':
self.norm = None
self.cube = self.cube_base
else:
self.var_max = 10
self.var_int = 1
self.cube = self.stretch_val(self.cube_base, clip=False)
#self.norm = ImageNormalize(interval=self.interval, stretch=self.stretch_val, clip=True)
main.create_image(self.cube)
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
self.meta['CUNIT1'] = self.meta['CUNIT1'].lower()
self.meta['CUNIT2'] = self.meta['CUNIT2'].lower()
# Fill in some missing or broken info
# Test if change has already been applied
if 'T' not in self.meta['date-obs']:
datestr = "{date}T{time}".format(date=self.meta.get('date-obs',
self.meta.get('date_obs')
),
time=self.meta.get('time-obs',
self.meta.get('time_obs')
)
)
self.meta['date-obs'] = datestr
# If non-standard Keyword is present, correct it too, for compatibility.
if 'date_obs' in self.meta:
self.meta['date_obs'] = self.meta['date-obs']
self._nickname = self.instrument + "-" + self.detector
self.plot_settings['cmap'] = plt.get_cmap('soholasco{det!s}'.format(det=self.detector[1]))
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.5)))
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
# change 'figure_width': 1920, 'figure_height': 1080 =(19.2,10.8)
f = Figure(figsize=(8, 6))
a = f.add_subplot(111)
f.subplots_adjust(left=0, bottom=0.005, right=1, top=1)
a.get_xaxis().set_visible(False)
a.get_yaxis().set_visible(False)
# add axes for sliders
ax_norma = f.add_axes([0.81, 0.1, 0.15, 0.025])
ax_contr = f.add_axes([0.81, 0.05, 0.15, 0.025])
hdu_list = fits.open(sky_image)
hdu_list.info()
img = hdu_list[0].data
# interval = MinMaxInterval()
contrast=0.15
interval = ZScaleInterval(contrast=contrast)
vmin, vmax = interval.get_limits(img)
norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=PowerStretch(power_normalise))
a.imshow(img, origin='lower', norm=norm, cmap='cividis')#, vmax=max_saturation)
# Embedding In Tk
canvas = FigureCanvasTkAgg(f, self)
# import code; code.interact(local=dict(globals(), **locals()))
canvas.draw()
canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)
# Show ToolBar
toolbar = NavigationToolbar(canvas, self)
toolbar.update()
# Activate Zoom
toolbar.zoom(self)
canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)
delta_f = 0.1
# add sliders
s_norma = Slider(ax_norma, 'Normalise', 0.1, 5.0, valinit=power_normalise, valstep=delta_f)
s_contr = Slider(ax_contr, 'Contrast', 0.1, 1.0, valinit=contrast)
def update(val):
n_norma = s_norma.val
n_contr = s_contr.val
# assign new values to contrast and normalise
interval = ZScaleInterval(contrast=n_contr)
vmin, vmax = interval.get_limits(img)
norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=PowerStretch(n_norma))
a.imshow(img, origin='lower', norm=norm, cmap='cividis')
canvas.draw_idle()
s_norma.on_changed(update)
s_contr.on_changed(update)
hdu_list.close()
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
self._nickname = self.detector
self.plot_settings['cmap'] = self._get_cmap_name()
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.5)), clip=False)
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
self.meta['CUNIT1'] = self.meta['CUNIT1'].lower()
self.meta['CUNIT2'] = self.meta['CUNIT2'].lower()
# Fill in some missing or broken info
datestr = "{date}T{time}".format(date=self.meta.get('date-obs',
self.meta.get('date_obs')
),
time=self.meta.get('time-obs',
self.meta.get('time_obs')
)
)
self.meta['date-obs'] = datestr
# If non-standard Keyword is present, correct it too, for compatibility.
if 'date_obs' in self.meta:
self.meta['date_obs'] = self.meta['date-obs']
self.meta['wavelnth'] = np.nan
self.meta['waveunit'] = 'nm'
self._nickname = self.instrument + "-" + self.detector
self.plot_settings['cmap'] = cm.get_cmap('soholasco{det!s}'.format(det=self.detector[1]))
self.plot_settings['norm'] = ImageNormalize(stretch=PowerStretch(0.5))
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
self.plot_settings['cmap'] = 'yohkohsxt' + self.measurement[0:2].lower(
)
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.5)),
clip=False)
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
self.plot_settings['cmap'] = 'soholasco{det!s}'.format(
det=self.detector[1])
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.5)),
clip=False)
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
self._nickname = "{}-{}".format(self.detector, self.observatory[-1])
self.plot_settings['cmap'] = 'stereocor{det!s}'.format(
det=self.detector[-1])
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.5)),
clip=False)
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
self._nickname = "{}-{}".format(self.detector, self.observatory[-1])
self.plot_settings['cmap'] = 'euvi{wl:d}'.format(
wl=int(self.wavelength.value))
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.25)),
clip=False)
def update(val):
n_norma = s_norma.val
n_contr = s_contr.val
# assign new values to contrast and normalise
interval = ZScaleInterval(contrast=n_contr)
vmin, vmax = interval.get_limits(img)
norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=PowerStretch(n_norma))
a.imshow(img, origin='lower', norm=norm, cmap='cividis')
canvas.draw_idle()
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
# Fill in some missing info
self.meta['detector'] = "EIT"
self.meta['waveunit'] = "Angstrom"
self._fix_dsun()
self._nickname = self.detector
self.plot_settings['cmap'] = cm.get_cmap(self._get_cmap_name())
self.plot_settings['norm'] = ImageNormalize(stretch=PowerStretch(0.5))
def __init__(self, data, header, **kwargs):
# Assume pixel units are arcesc if not given
header['cunit1'] = header.get('cunit1', 'arcsec')
header['cunit2'] = header.get('cunit2', 'arcsec')
super().__init__(data, header, **kwargs)
self._nickname = self.detector
self.plot_settings['cmap'] = plt.get_cmap(self._get_cmap_name())
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.5)))
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
self._nickname = self.detector
self.plot_settings['cmap'] = self._get_cmap_name()
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.25)), clip=False)
# Negative value pixels can appear that lead to ugly looking images.
# This can be fixed by setting the lower limit of the normalization.
self.plot_settings['norm'].vmin = 0.0
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
self._nickname = "{0}-{1}".format(self.detector, self.observatory[-1])
self.plot_settings['cmap'] = cm.get_cmap('sohoeit{wl:d}'.format(wl=int(self.wavelength.value)))
self.plot_settings['norm'] = ImageNormalize(stretch=PowerStretch(0.25))
self.meta['waveunit'] = 'Angstrom'
# Try to identify when the FITS meta data does not have the correct
# date FITS keyword
if ('date_obs' in self.meta) and not('date-obs' in self.meta):
self.meta['date-obs'] = self.meta['date_obs']
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
self._nickname = "{0}-{1}".format(self.detector, self.observatory[-1])
self.plot_settings['cmap'] = plt.get_cmap(
'stereohi{det!s}'.format(det=self.detector[-1]))
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.25)))
# Try to identify when the FITS meta data does not have the correct
# date FITS keyword
if ('date_obs' in self.meta) and not ('date-obs' in self.meta):
self.meta['date-obs'] = self.meta['date_obs']
def __init__(self, data, header, **kwargs):
# Assume pixel units are arcesc if not given
header['cunit1'] = header.get('cunit1', 'arcsec')
header['cunit2'] = header.get('cunit2', 'arcsec')
if 'waveunit' not in header or not header['waveunit']:
header['waveunit'] = "Angstrom"
super().__init__(data, header, **kwargs)
self._nickname = self.detector
self.plot_settings['cmap'] = self._get_cmap_name()
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.5)),
clip=False)
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
self._nickname = "{}-{}".format(self.detector, self.observatory[-1])
self.plot_settings['cmap'] = 'stereocor{det!s}'.format(
det=self.detector[-1])
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.5)),
clip=False)
# Try to identify when the FITS meta data does not have the correct
# date FITS keyword
if ('date_obs' in self.meta) and not ('date-obs' in self.meta):
self.meta['date-obs'] = self.meta['date_obs']
def __init__(self, data, header, **kwargs):
# Assume pixel units are arcesc if not given
header['cunit1'] = header.get('cunit1', 'arcsec')
header['cunit2'] = header.get('cunit2', 'arcsec')
GenericMap.__init__(self, data, header, **kwargs)
# Fill in some missing info
self.meta['detector'] = "EIT"
self.meta['waveunit'] = "Angstrom"
self._fix_dsun()
self._nickname = self.detector
self.plot_settings['cmap'] = plt.get_cmap(self._get_cmap_name())
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.5)))
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
self.meta['detector'] = "SXT"
self.meta['telescop'] = "Yohkoh"
self.plot_settings['cmap'] = cm.get_cmap(name='yohkohsxt' + self.measurement[0:2].lower())
self.plot_settings['norm'] = ImageNormalize(stretch=PowerStretch(0.5))
# 2012/12/19 - the SXT headers do not have a value of the distance from
# the spacecraft to the center of the Sun. The FITS keyword 'DSUN_OBS'
# appears to refer to the observed diameter of the Sun. Until such
# time as that is calculated and properly included in the file, we will
# use simple trigonometry to calculate the distance of the center of
# the Sun from the spacecraft. Note that the small angle approximation
# is used, and the solar radius stored in SXT FITS files is in arcseconds.
self.meta['dsun_apparent'] = constants.au
if 'solar_r' in self.meta:
self.meta['dsun_apparent'] = constants.radius/(np.deg2rad(self.meta['solar_r']/3600.0))
def __init__(self, data, header, **kwargs):
super().__init__(data, header, **kwargs)
# Fill in some missing info
self.meta['observatory'] = 'MLSO'
self.meta['detector'] = 'KCor'
self.meta['waveunit'] = 'nanometer'
# Since KCor is on Earth, no need to raise the warning in mapbase
self.meta['dsun_obs'] = (sun.earth_distance(self.date)).to(u.m).value
self.meta['hgln_obs'] = 0.0
self._nickname = self.detector
self.plot_settings['cmap'] = plt.get_cmap(self._get_cmap_name())
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.25)))
# Negative value pixels can appear that lead to ugly looking images.
# This can be fixed by setting the lower limit of the normalization.
self.plot_settings['norm'].vmin = 0.0
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
self._nickname = "{}-{}".format(self.detector, self.observatory[-1])
self.plot_settings['cmap'] = 'sohoeit{wl:d}'.format(
wl=int(self.wavelength.value))
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.25)),
clip=False)
self.meta['waveunit'] = 'Angstrom'
# Try to identify when the FITS meta data does not have the correct
# date FITS keyword
if ('date_obs' in self.meta) and not ('date-obs' in self.meta):
self.meta['date-obs'] = self.meta['date_obs']
# fix CROTA to CROTAn
if "crota" in self.meta and "crota2" not in self.meta:
log.debug("EUVIMap: Changing the CROTA keyword to CROTA2")
self.meta["crota2"] = self.meta.pop("crota")
def __init__(self, data, header, **kwargs):
header['cunit1'] = header['cunit1'].lower()
header['cunit2'] = header['cunit2'].lower()
super().__init__(data, header, **kwargs)
# Fill in some missing or broken info
# Test if change has already been applied
if 'T' not in self.meta['date-obs']:
datestr = "{date}T{time}".format(
date=self.meta.get('date-obs', self.meta.get('date_obs')),
time=self.meta.get('time-obs', self.meta.get('time_obs')))
self.meta['date-obs'] = parse_time(datestr).isot
# If non-standard Keyword is present, correct it too, for compatibility.
if 'date_obs' in self.meta:
self.meta['date_obs'] = self.meta['date-obs']
self._nickname = self.instrument + "-" + self.detector
self.plot_settings['cmap'] = 'soholasco{det!s}'.format(
det=self.detector[1])
self.plot_settings['norm'] = ImageNormalize(stretch=source_stretch(
self.meta, PowerStretch(0.5)),
clip=False)
# For Helioviewer images, clear rotation metadata, as these have already been rotated.
# Also check that all CROTAn keywords exist to make sure that it's an untouched
# Helioviewer file.
if ('helioviewer' in self.meta and 'crota' in self.meta
and 'crota1' in self.meta and 'crota2' in self.meta):
log.debug(
"LASCOMap: Cleaning up CROTAn keywords "
"because the map has already been rotated by Helioviewer")
self.meta.pop('crota')
self.meta.pop('crota1')
self.meta['crota2'] = 0
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
# change 'figure_width': 1920, 'figure_height': 1080 =(19.2,10.8)
f = Figure(figsize=(8, 6))
a = f.add_subplot(111)
f.subplots_adjust(left=0, bottom=0.005, right=1, top=1)
a.get_xaxis().set_visible(False)
a.get_yaxis().set_visible(False)
hdu_list = fits.open(sky_image)
hdu_list.info()
img = hdu_list[0].data
# interval = MinMaxInterval()
interval = ZScaleInterval(contrast=0.15)
vmin, vmax = interval.get_limits(img)
norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=PowerStretch(0.7))
a.imshow(img, origin='lower', norm=norm, cmap='cividis')#, vmax=max_saturation)
# Embedding In Tk
canvas = FigureCanvasTkAgg(f, self)
# import code; code.interact(local=dict(globals(), **locals()))
canvas.draw()
canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)
# Show ToolBar
toolbar = NavigationToolbar(canvas, self)
toolbar.update()
# Activate Zoom
toolbar.zoom(self)
canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)
hdu_list.close()
xaxis.display_minor_ticks(True)
yaxis.display_minor_ticks(True)
# xaxis.set_minor_frequency(xmi_num)
# yaxis.set_minor_frequency(ymi_num)
""" scaling and imaging """
interp='none'
# interpolation of the datacube, e.g., 'none'/'nearest'/'bilinear'/'bicubic', defaut is 'none'
scaling='linear'
# scaling of the datacube, e.g., 'linear'/'log'/'power'/'sqrt'/'squared', defaut is 'linear'
index=1e3
# index of log and power scaling, default is 1e3
if scaling=='linear': stretch=LinearStretch()
elif scaling=='log': stretch=LogStretch(index)
elif scaling=='power': stretch=PowerStretch(index)
elif scaling=='sqrt': stretch=SqrtStretch()
elif scaling=='squared': stretch=SquaredStretch()
if scale is None: interval=ManualInterval(vmin=np.nanmin(dat), vmax=np.nanmax(dat))
else: interval=ManualInterval(vmin=scale[0], vmax=scale[1])
# norm=ImageNormalize(dat, interval=interval, stretch=stretch)
if scale is None: norm=colors.Normalize(vmin=np.nanmin(dat), vmax=np.nanmax(dat))
else: norm=colors.Normalize(vmin=scale[0], vmax=scale[1])
# ax.set_xlim(-0.5, dat.shape[1]-0.5)
# ax.set_ylim(-0.5, dat.shape[0]-0.5)
if colormap is not None: plt.set_cmap(colormap)
cmap=mpl.cm.get_cmap()
cmap.set_bad(color='white')
im=ax.imshow(dat, norm=norm, interpolation=interp, cmap=cmap, origin='lower')
image = fits.open('image/NGC5257_12CO21_pbcor_cube_masked.fits')
image_mas = fits.open(outfolder + 'mask.fits')
xmin, xmax = 345, 615
ymin, ymax = 347, 617
zmin, zmax = 0, 69
data = image[0].data[zmin:zmax + 1, ymin:ymax + 1, xmin:xmax + 1]
data_mas = image_mas[0].data[zmin:zmax + 1, ymin:ymax + 1, xmin:xmax + 1]
head = image[0].header
zsize = data[:, 0, 0].size
cdeltsp = 0.1
cont = 0.0182647
v = np.array([1, 2, 4, 8, 16, 32, 64]) * cont
v_neg = [-cont]
interval = PercentileInterval(99.5)
vmax = interval.get_limits(data)[1]
norm = ImageNormalize(vmin=cont, vmax=vmax, stretch=PowerStretch(0.5))
files_mod, typ = [], []
for thisFile in os.listdir(outfolder):
if 'mod_azim.fits' in thisFile: files_mod.append(thisFile)
if len(files_mod) == 1: typ.append('AZIM')
for thisFile in os.listdir(outfolder):
if 'mod_local.fits' in thisFile: files_mod.append(thisFile)
if len(files_mod) == 2: typ.append('LOCAL')
elif (len(files_mod) == 1 and len(typ) == 0): typ.append('LOCAL')
elif (len(files_mod) == len(typ) == 0): exit()
# Beginning channel map plot
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
label = tk.Label(self, text="u", font=LARGE_FONT)
label.pack(pady=10, padx=10)
button = ttk.Button(self,
text="optical g",
command=lambda: controller.show_frame(GPage))
button.pack()
button2 = ttk.Button(self,
text="optical r",
command=lambda: controller.show_frame(RPage))
button2.pack()
button3 = ttk.Button(self,
text="optical i",
command=lambda: controller.show_frame(IPage))
button3.pack()
f = Figure(figsize=(8, 6))
a = f.add_subplot(111)
f.subplots_adjust(left=0, bottom=0.005, right=1, top=1)
a.get_xaxis().set_visible(False)
a.get_yaxis().set_visible(False)
# add axes for sliders
ax_norma = f.add_axes([0.81, 0.1, 0.15, 0.025])
ax_contr = f.add_axes([0.81, 0.05, 0.15, 0.025])
hdu_list = fits.open(sky_image)
hdu_list.info()
img = hdu_list[0].data
contrast = image_contrast
interval = ZScaleInterval(contrast=contrast)
vmin, vmax = interval.get_limits(img)
stretch_options = {
'LogStretch': LogStretch(power_normalise),
'PowerStretch': PowerStretch(power_normalise),
}
norm = ImageNormalize(vmin=vmin,
vmax=vmax,
stretch=stretch_options[image_scaling])
a.imshow(img, origin='lower', norm=norm, cmap='cividis')
# Embedding In Tk
canvas = FigureCanvasTkAgg(f, self)
canvas.draw()
canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)
# Show ToolBar
toolbar = NavigationToolbar(canvas, self)
toolbar.update()
# Activate Zoom
toolbar.zoom(self)
canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)
delta_f = 0.1
# add sliders
s_norma = Slider(ax_norma,
'Normalise',
0.1,
5.0,
valinit=power_normalise,
valstep=delta_f)
s_contr = Slider(ax_contr, 'Contrast', 0.1, 1.0, valinit=contrast)
def update(val):
n_norma = s_norma.val
n_contr = s_contr.val
# assign new values to contrast and normalise
interval = ZScaleInterval(contrast=n_contr)
vmin, vmax = interval.get_limits(img)
norm = ImageNormalize(vmin=vmin,
vmax=vmax,
stretch=PowerStretch(n_norma))
a.imshow(img, origin='lower', norm=norm, cmap='cividis')
# saving data to json file
SaveJson(n_norma, n_contr)
canvas.draw_idle()
s_norma.on_changed(update)
s_contr.on_changed(update)
hdu_list.close()
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser(
description="Temporal drizzle animation utility")
parser.add_argument('cfg',
type=str,
help='YML config file for the drizzle animator.')
args = parser.parse_args()
pipe.configure_pipeline(args.cfg)
config = pipe.config.config
if config.animation.power:
VALID_STRETCHES['power'] = PowerStretch(config.animation.power)
if not config.animation.target:
log.warning(
'Target missing! Must be specified in the config for metadata.')
if not config.data.startt:
start = 0
else:
start = config.data.startt
if config.data.duration and config.data.stopt:
end = start + config.data.duration
elif config.data.duration and not config.data.stopt:
end = start + config.data.duration
elif not config.data.duration and config.data.stopt:
backgroundcolor="white",
transform=ax1[i].transAxes)
ax1[i].plot(null, np.zeros_like(null), "r:", lw=2)
ax1[i].errorbar(distance,
fluxring[i],
yerr=erb[i],
fmt="ko-",
lw=3,
elinewidth=1,
capsize=2,
label=wav)
# Plots SZ signal
# normalises data with 'zscale' and stretches it by 'PowerStretch' as in ds9
norm = ImageNormalize(SZ[i], interval=zscale(), stretch=PowerStretch(1.3))
P1 = ax2[1, i].imshow(MCsim[i], norm=norm, aspect="auto")
P2 = ax2[0, i].imshow(SZ[i], norm=norm, aspect="auto")
cb = fig2.colorbar(P1, cax=cax[i], orientation="horizontal")
cb.ax.xaxis.set_ticks_position("top")
cb.set_label(r"$\mathrm{mJy/px}$", fontsize=10)
cb.ax.xaxis.set_label_position("top")
cb.ax.tick_params(labelsize=8)
for j in range(len(ax2)):
ax2[j, i].axis("off")
t = ax2[j, i].text(0.69,
0.90,
wav,
color="w",