def motion(self,event):
x_move = event.xdata
y_move = event.ydata
if opt !=2:
if self.a0 == event.inaxes:
# print("Moving through X-Y plot")
# x, y = event.inaxes.transData.inverted().transform((event.x, event.y)) #transforma de coordenadas del Tkinker Canvas a coordenadas del matplotlib plot
# print("Mouse position: (%s %s)" % (x, y))
x_lst = find_nearest(xy_pos_df.iloc[:,0],x_move)
y_lst = find_nearest(xy_pos_df.iloc[:,1], y_move)
# print(x_lst,y_lst)
mouse_pos = xy_pos_df[(xy_pos_df.iloc[:, 0] == x_lst) & (xy_pos_df.iloc[:, 1] == y_lst)].index.tolist()
# print(mouse_pos)
self.a1.clear()
self.a1.set_ylabel('Intensity [a.u.]')
self.a1.set_xlabel('Wavelength [nm]')
if len(mouse_pos) == 0:
pass
else:
if opt == 0:
self.a1.plot(wavel_df.iloc[:, 0], inten_df.iloc[mouse_pos, :].transpose(), '*')
if opt == 1:
# spectrum = np.column_stack((wavel_array,np.transpose(inten_array[mouse_pos,:])))
# np.asarray(wavel_array, inten_file.iloc[mouse_pos,:].transpose())
# w,I = spectrum_subplot(spectrum)
clim = (350, 780)
norm = plt.Normalize(*clim)
wl = np.arange(clim[0], clim[1] + 1, 2)
colorlist = list(zip(norm(wl), [wavelength_to_rgb(w) for w in wl]))
spectralmap = matplotlib.colors.LinearSegmentedColormap.from_list("spectrum", colorlist)
mouse_pos = mouse_pos[0]
wavelengths = wavel_array #wavel_array[:, 0]
spectrum = np.transpose(inten_array[mouse_pos, :])
plt.plot(wavel_array, spectrum, color='darkred')
y = np.linspace(0, np.max(spectrum), 100)
X, Y = np.meshgrid(wavelengths, y)
extent = (np.min(wavelengths), np.max(wavelengths), np.min(y), np.max(y))
plt.imshow(X, clim=clim, extent=extent, cmap=spectralmap, aspect='auto')
plt.xlabel('Wavelength [nm]')
plt.ylabel('Intensity [a.u.]')
plt.fill_between(wavelengths, spectrum, np.max(spectrum), color='w')
# if opt == 3:
# clim = (350, 780)
# norm = plt.Normalize(*clim)
# wl = np.arange(clim[0], clim[1] + 1, 2)
# colorlist = list(zip(norm(wl), [wavelength_to_rgb(w) for w in wl]))
# spectralmap = matplotlib.colors.LinearSegmentedColormap.from_list("spectrum", colorlist)
# mouse_pos = mouse_pos[0]
# wavelengths = wavel_array # wavel_array[:, 0]
# spectrum = np.transpose(inten_array[mouse_pos, :])
# plt.plot(wavel_array, spectrum, color='darkred')
#
# y = np.linspace(0, np.max(spectrum), 100)
# X, Y = np.meshgrid(wavelengths, y)
#
# extent = (np.min(wavelengths), np.max(wavelengths), np.min(y), np.max(y))
#
# plt.imshow(X, clim=clim, extent=extent, cmap=spectralmap, aspect='auto')
# plt.xlabel('Wavelength [nm]')
# plt.ylabel('Intensity [a.u.]')
#
# plt.fill_between(wavelengths, spectrum, np.max(spectrum), color='w')
else:
self.a1.clear()
self.a1.set_ylabel('Intensity [a.u.]')
self.a1.set_xlabel('Wavelength [nm]')
return
def motion(self, event):
x_move = event.xdata
y_move = event.ydata
if opt == 0 or opt == 1:
if self.a0 == event.inaxes:
x_lst = find_nearest(xy_pos_df.iloc[:, 0], x_move)
y_lst = find_nearest(xy_pos_df.iloc[:, 1], y_move)
mouse_pos = xy_pos_df[(xy_pos_df.iloc[:, 0] == x_lst) & (
xy_pos_df.iloc[:, 1] == y_lst)].index.tolist()
self.a1.clear()
self.a1.set_ylabel('Intensity [a.u.]')
self.a1.set_xlabel('Wavelength [nm]')
if len(mouse_pos) == 0:
pass
else:
if normalization:
tmp = 1 / light_df.iloc[:]
tmp[light_df.iloc[:] < 0.1] = 0
inten_norm = (inten_df.iloc[mouse_pos, :] * tmp).abs()
# print(inten_norm)
if opt == 0:
self.a1.plot(wavel_df.iloc[:, 0],
inten_norm.transpose(), '*')
elif opt == 1:
clim = (350, 780)
norm = plt.Normalize(*clim)
wl = np.arange(clim[0], clim[1] + 1, 2)
colorlist = list(
zip(norm(wl),
[wavelength_to_rgb(w) for w in wl]))
spectralmap = matplotlib.colors.LinearSegmentedColormap.from_list(
"spectrum", colorlist)
mouse_pos = mouse_pos[0]
wavelengths = wavel_array
spectrum = np.transpose(inten_norm)
# print(spectrum)
self.a1.plot(wavel_array,
spectrum,
color='darkred')
# print(np.max(spectrum))
y = np.linspace(0, np.max(spectrum), 100)
X, Y = np.meshgrid(wavelengths, y)
extent = (np.min(wavelengths), np.max(wavelengths),
np.min(y), np.max(y))
plt.imshow(X,
clim=clim,
extent=extent,
cmap=spectralmap,
aspect='auto')
plt.xlabel('Wavelength [nm]')
plt.ylabel('Intensity [a.u.]')
plt.fill_between(wavelengths,
spectrum,
np.max(spectrum),
color='w')
if not normalization:
if opt == 0:
self.a1.plot(
wavel_df.iloc[:, 0],
inten_df.iloc[mouse_pos, :].transpose(), '*')
elif opt == 1:
clim = (350, 780)
norm = plt.Normalize(*clim)
wl = np.arange(clim[0], clim[1] + 1, 2)
colorlist = list(
zip(norm(wl),
[wavelength_to_rgb(w) for w in wl]))
spectralmap = matplotlib.colors.LinearSegmentedColormap.from_list(
"spectrum", colorlist)
mouse_pos = mouse_pos[0]
wavelengths = wavel_array
spectrum = np.transpose(
inten_df.iloc[mouse_pos, :])
self.a1.plot(wavel_array,
spectrum,
color='darkred')
y = np.linspace(0, np.max(spectrum), 100)
X, Y = np.meshgrid(wavelengths, y)
extent = (np.min(wavelengths), np.max(wavelengths),
np.min(y), np.max(y))
plt.imshow(X,
clim=clim,
extent=extent,
cmap=spectralmap,
aspect='auto')
plt.xlabel('Wavelength [nm]')
plt.ylabel('Intensity [a.u.]')
plt.fill_between(wavelengths,
spectrum,
np.max(spectrum),
color='w')
else:
self.a1.clear()
self.a1.set_ylabel('Intensity [a.u.]')
self.a1.set_xlabel('Wavelength [nm]')
return
# inten_dff = banda_panc_df.iloc[0].values - abs(back_df.iloc[689].values*0.1)
inten_dff = inten_df.iloc[200].values # - abs(inten_df.iloc[500].values)
lighta = light_dff[940:3500]
intena = inten_dff[940:3500]
# intend = intena/abs(lighta)
inten = intena.transpose()
from scipy.signal import savgol_filter
yhat = savgol_filter(inten, 55, 3)
plt.grid()
clim = (350, 780)
norm = plt.Normalize(*clim)
wl = np.arange(clim[0], clim[1] + 1, 2)
colorlist = list(zip(norm(wl), [wavelength_to_rgb(w) for w in wl]))
spectralmap = matplotlib.colors.LinearSegmentedColormap.from_list(
"spectrum", colorlist)
wavel_array = wavel_dff.iloc[:, 0].values
spectrum = yhat
plt.plot(wavel_dff, spectrum, color='darkred')
y = np.linspace(0, np.max(spectrum), 100)
X, Y = np.meshgrid(wavel_array, y)
extent = (np.min(wavel_array), np.max(wavel_array), np.min(y), np.max(y))
plt.imshow(X, clim=clim, extent=extent, cmap=spectralmap, aspect='auto')
plt.xlabel('Longitud de onda [nm]', fontsize=20)
plt.ylabel('Transmisión [u.a.]', fontsize=20)
plt.ylim([0, 1])
plt.fill_between(wavel_array, spectrum, np.max(spectrum), color='w')