def mplot_ANU(ax,
fnames,
path='',
labels='',
colors='',
corr=False,
shiftup=0,
plot=True,
xvar='eV',
norm=False,
logy=True,
eps=False,
cutoffs=(0, -1)):
# prepare the ground
if path != 'current':
path_full = genSZ.device() + path
elif path == 'current':
path = Path().absolute()
path_ori = Path().absolute()
os.chdir(path_full)
num_files = len(fnames)
if labels == '': # make labels the filenames, if no labels
labels = []
for f in fnames:
labels.append(os.path.splitext(f)[0])
if colors == '' or colors == 1: # the colours feature
colorRev = False
colors = [
None
] * num_files # so that the plotting section doesn't throw an error
elif colors == -1:
colorRev = True
colors = [None] * num_files
elif type(colors) == list:
colorRev = False
else:
raise TypeError(
'The colors have to be in list format. The number of colors specificed have to equal the number of PL spectra'
)
# if colors are specified, they will override this
if colorRev == True:
mpl.rcParams['axes.prop_cycle'] = plt.cycler(
color=plt.cm.rainbow(np.linspace(1, 0.1, num_files)))
else:
mpl.rcParams['axes.prop_cycle'] = plt.cycler(
color=plt.cm.rainbow(np.linspace(0.1, 1, num_files)))
# Adjust for correction
if corr == True or corr == False:
corr = [corr] * num_files
elif type(corr) != list or len(corr) != num_files:
raise NameError(
'Please ensure variable corr is of type bool or list, and has the same number of elements as files to be processed. Lists use sq brackets []'
)
# Adjust for shiftup
if type(shiftup) == int or type(shiftup) == float:
shiftup = np.full(num_files, shiftup)
elif shiftup != list or len(shiftup) != num_files:
raise NameError(
'Please ensure variable corr is of type int, float, or list, and has the same number of elements as files to be processed. Lists use sq brackets []'
)
# load data
# data = np.empty((0,0))
# for f in fnames:
# i = fnames.index(f)
# data = np.concatenate((data,load_corr_ANU(f,path=path,corr=corr[i],shiftup=shiftup[i])))
# print(data)
data = []
for f in fnames:
i = fnames.index(f)
data.append(
load_corr_ANU(f,
path=path,
corr=corr[i],
shiftup=shiftup[i],
cutoffs=cutoffs))
data = list(
zip(*data)
) # transpose the list of lists, and make it a list of lists instead of a list of tuples so I can call elements
eV = data[0]
PL = data[1]
PLn = data[2]
wvl = data[3]
PL_wvl = data[4]
PLn_wvl = data[5]
# return to original directory
os.chdir(path_ori)
#x-axis wvl or eV?
if xvar == 'wvl':
xax = wvl
xlabel = r'$ \rm Wavelength~[nm] $'
PL_plot = PL_wvl
if norm == True:
PL_plot = PLn_wvl
elif xvar == 'eV':
xax = eV
xlabel = r'$ \rm Emission~Energy~[eV] $'
PL_plot = PL
if norm == True:
PL_plot = PLn
else:
raise Exception('The x-axis must be in wvl or eV')
# Do we plot?
if plot == False:
return (eV, PL, PLn, wvl, PL_wvl, PLn_wvl)
elif plot != True:
raise NameError('The variable plot must be a boolean')
# Why, I shall plot, of course
if logy == False:
for i in range(num_files):
ax.plot(xax[i], PL_plot[i], label=labels[i], color=colors[i], lw=1)
elif logy == True:
for i in range(num_files):
# print(np.shape(PL_plot[1]))
if type(eps) == float or type(eps) == int: # apply epsilon cutting
y = np.where(
PL_plot[i] > eps, PL_plot[i], eps
) # account for possible length differences between lines in PL_plot
ax.semilogy(xax[i], y, label=labels[i], color=colors[i], lw=1)
else:
ax.semilogy(xax[i],
PL_plot[i],
label=labels[i],
color=colors[i],
lw=1)
else:
raise Exception('The plot must be linear or semilogy')
ax.set_xlabel(xlabel)
ax.legend()
ax.set_ylabel(r'$ \rm PL~Intensity~[AU] $')
plt.tight_layout()
return (eV, PL, PLn, wvl, PL_wvl, PLn_wvl)
def load_corr_ANU(fname, path='', corr=False, shiftup=0, cutoffs=(0, -1)):
# Go to the target directory
if path != 'current':
path = genSZ.device() + path
else:
path = Path().absolute()
path_ori = Path().absolute()
os.chdir(path)
# load the correction coefficients
# np.seterr(invalid='ignore') # invalid (probably NaN) values exist in the below operation. use this to override the warning. Corresponding expression below sets it back
corr_InGaAs = np.loadtxt(
genSZ.device() +
'Modules/Correction base files/ANU QE/no laser corrected.txt'
)[:,
1] / np.loadtxt(genSZ.device() +
'Modules/Correction base files/ANU QE/no laser.txt')[:,
1]
corr_Si = np.loadtxt(
genSZ.device() +
'Modules/Correction base files/ANU QE/Si corrected.txt'
)[:, 1] / np.loadtxt(
genSZ.device() +
'Modules/Correction base files/ANU QE/Si uncorrected.txt')[:, 1]
# np.seterr(invalid='warn')
# load and return to the original path
wvl, PL = np.loadtxt(fname, unpack=True)
os.chdir(path_ori)
if np.any(wvl) > 1200:
detector = 'InGaAs'
else:
detector = 'Si'
PL = PL + shiftup
if corr == True:
if detector == 'InGaAs':
PL = PL * corr_InGaAs
elif detector == 'Si':
PL = PL * corr_Si
elif corr != False:
raise NameError('The correction variable must be True or False')
PL_wvl = PL
dw = np.gradient(wvl)
dw = dw / dw.max()
eV = conSZ.nm2eV / wvl
PL = PL_wvl / (eV**2 * dw)
PL = PL[cutoffs[0]:cutoffs[1]]
PL_wvl = PL_wvl[cutoffs[0]:cutoffs[1]]
eV = eV[cutoffs[0]:cutoffs[1]]
wvl = wvl[cutoffs[0]:cutoffs[1]]
PLn = PL / PL.max()
PLn_wvl = PL_wvl / PL_wvl.max()
return (eV, PL, PLn, wvl, PL_wvl, PLn_wvl)
def imap(filename,
path,
times,
Nx,
Ny,
dx,
dy,
dark='PL0.csv',
detector='InGaAs',
cutoff=0):
# go to required directory
if path != 'current':
path = genSZ.device() + path
else:
path = Path().absolute()
path_ori = Path().absolute()
os.chdir(path)
# set up map
x = np.linspace(0, Nx * dx, Nx)
y = np.linspace(0, Ny * dy, Ny)
extent = (x.min(), x.max(), y.min(), y.max())
# Import map and PL0
PL0, noise = np.loadtxt(dark,
skiprows=80,
usecols=(6, 7),
delimiter=',',
unpack=True)
PL = np.loadtxt(filename)
PL = PL[:, 2:]
# Spectrum corrections setup: QE, wvl to eV, averaging wvl
path = genSZ.device(
) + 'Modules/' # make sure that it's using the QE files in the Modules folder
if detector == "InGaAs":
wvl, QE = np.loadtxt(path + 'QE_InGaAs.csv',
unpack=True,
delimiter=',',
skiprows=1,
usecols=(0, 1))
elif detector == "Si":
# wvl, QE = np.loadtxt(path+'QE_Si.csv', unpack=True,
# delimiter = ',', skiprows=1, usecols = (0, 1))
wvl, QE = np.loadtxt(
path + 'QE_Si_Avantes.csv',
unpack=True, # the calibration using the lamp
delimiter=',',
skiprows=1,
usecols=(0, 3))
PL = PL[:, 138:2041]
PL0 = PL0[138:2041]
noise = noise[138:2041] # end of Avantes calibration
else:
raise NameError('Detector must be string named InGaAs or Si')
Nw = len(wvl)
dw = np.gradient(
wvl
) # for elements in a 1D array it will output half the difference between the elements proceeding and following that element
dw = dw / dw.max()
eV = conSZ.nm2eV / wvl
# spectral corrections
PL_eV = (PL - times * PL0) / (QE * eV**2 * dw)
PL_wvl = (PL - times * PL0) / (QE * dw)
# reshape the data: 2D -> 3D
PL_eV = np.reshape(PL_eV, (Nx, Ny, Nw))
PL_eV[1::2, :, :] = PL_eV[1::2, ::-1, :]
PL_eV = np.flipud(PL_eV)
PL_eV = np.rot90(PL_eV, 3)
PL_wvl = np.reshape(PL_wvl, (Nx, Ny, Nw))
PL_wvl[1::2, :, :] = PL_wvl[1::2, ::-1, :]
PL_wvl = np.flipud(PL_wvl)
PL_wvl = np.rot90(PL_wvl, 3)
# Cut-off data where needed
if cutoff < 0:
PL_eV = PL_eV[:, :, :cutoff]
PL_wvl = PL_wvl[:, :, :cutoff]
PL0 = PL0[:cutoff]
eV = eV[:cutoff]
wvl = wvl[:cutoff]
elif cutoff >= 0:
PL_eV = PL_eV[:, :, cutoff:]
PL_wvl = PL_wvl[:, :, cutoff:]
PL0 = PL0[cutoff:]
eV = eV[cutoff:]
wvl = wvl[cutoff:]
# go back to previous directory
os.chdir(path_ori)
return PL_wvl, PL_eV, wvl, eV, PL0, noise, extent
def load_correct_0d(filename,
detector,
times,
xvar='eV',
delimiter=',',
cutoff=0):
#load data. unpack transposes the ndarray output
PL0, PL = np.loadtxt(filename,
skiprows=80,
usecols=(4, 6),
delimiter=delimiter,
unpack=True)
info = pd.read_csv(filename,
delimiter=delimiter,
usecols=(0, 1),
index_col='Name',
header=0,
names=['Name', 'Value'])
t_int = np.float(info.loc['intigration times(ms)'])
n_avg = np.float(info.loc['average number'])
# Spectrum corrections setup
path = genSZ.device(
) + 'Modules/' # make sure that it's using the QE files in the Modules folder
if detector == "InGaAs":
wvl, QE = np.loadtxt(path + 'QE_InGaAs.csv',
unpack=True,
delimiter=',',
skiprows=1,
usecols=(0, 1))
elif detector == "Si":
# wvl, QE = np.loadtxt(path+'QE_Si.csv', unpack=True,
# delimiter = ',', skiprows=1, usecols = (0, 1))
wvl, QE = np.loadtxt(
path + 'QE_Si_Avantes.csv',
unpack=True, # the calibration using the lamp
delimiter=',',
skiprows=1,
usecols=(0, 3))
PL = PL[138:2041]
PL0 = PL0[138:2041]
else:
raise NameError('Detector must be string named InGaAs or Si')
dw = np.gradient(
wvl
) # for elements in a 1D array it will output half the difference between the elements proceeding and following that element
dw = dw / dw.max()
eV = conSZ.nm2eV / wvl
if xvar == 'eV':
PL = (PL - times * PL0) / (QE * eV**2 * dw)
elif xvar == 'wvl':
PL = (PL - times * PL0) / (QE * dw)
else:
raise Exception('The x-axis must be in wvl or eV')
# Cut-off data where needed
if cutoff < 0:
PL = PL[:cutoff]
PL0 = PL0[:cutoff]
eV = eV[:cutoff]
wvl = wvl[:cutoff]
elif cutoff >= 0:
PL = PL[cutoff:]
PL0 = PL0[cutoff:]
eV = eV[cutoff:]
wvl = wvl[cutoff:]
return PL, wvl, eV, PL0, t_int, n_avg
def mplot(filenames,
times,
path='',
labels='',
detector='Si',
plot=True,
xvar='eV',
norm=False,
logy=True,
colors='',
delimiter=',',
cutoff=0,
ratio=0,
offset=False):
# prepare the ground
if path != 'current':
path = genSZ.device() + path
elif path == 'current':
path = Path().absolute()
path_ori = Path().absolute()
os.chdir(path)
if detector == 'Si': # which detector
# length=2048-np.abs(cutoff) # this is for the default Si calibration with blackbody at 400C, which uRob rarely uses and is therefore not optimal
length = 1903 - np.abs(
cutoff
) # this is measured with a lamp, the bottom turrent position #6 which I think is probably empty
elif detector == "InGaAs":
length = 512 - np.abs(cutoff)
else:
raise NameError('The detector must be Si or InGaAs')
num_files = len(
filenames) #the number of files this function will need to process
if labels == '': # make labels the filenames, if no labels
labels = []
for f in filenames:
labels.append(os.path.splitext(f)[0])
if colors == '' or colors == 1: # the colours feature
colorRev = False
colors = [None] * num_files
elif colors == -1:
colorRev = True
colors = [None] * num_files
elif type(colors) == list:
colorRev = False
else:
raise TypeError(
'The colors have to be in list format. The number of colors specificed have to equal the number of PL spectra'
)
# try:
if colorRev == True:
mpl.rcParams['axes.prop_cycle'] = plt.cycler(
color=plt.cm.rainbow(np.linspace(1, 0.1, num_files)))
else:
mpl.rcParams['axes.prop_cycle'] = plt.cycler(
color=plt.cm.rainbow(np.linspace(0.1, 1, num_files)))
# except:
# pass
if type(times) == float or type(
times) == int: # allowing customisation of the times variable
mul = np.empty((num_files, length))
for i in range(len(mul)):
mul[i] = times
elif len(times) != num_files:
raise Exception(
'The times variable must be a float, or an array with the same length as the filenames array'
)
#load the data
PL = np.empty((num_files, length))
wvl = np.empty((num_files, length))
eV = np.empty((num_files, length))
PL0 = np.empty((num_files, length))
t_int = np.empty((num_files, 1))
n_avg = np.empty((num_files, 1))
for f in filenames:
i = filenames.index(f)
PL[i], wvl[i], eV[i], PL0[i], t_int[i], n_avg[i] = load_correct_0d(
filename=f,
times=times,
detector=detector,
xvar=xvar,
delimiter=delimiter,
cutoff=cutoff)
# return to original directory
os.chdir(path_ori)
# account for possible different integration times or multipliers
if ratio == 0:
pass
elif type(ratio) == int or type(ratio) == float:
for i in range(num_files):
PL[i] = PL[i] / ratio
elif len(ratio) == num_files:
for i in range(num_files):
PL[i] = PL[i] / ratio[i]
else:
raise Exception(
'The ratio variable must be zero, or have length 1, or an array with the same length as the filenames array'
)
# Do we plot?
if plot == False:
return PL, wvl, eV, PL0, t_int, n_avg
elif plot != True:
raise NameError('The variable plot must be a boolean')
# prep the data for plotting
PL_plot = np.empty((num_files, length))
xax = np.empty((num_files, length))
if norm == True: #possible normalisation
for i in range(num_files):
PL_plot[i] = PL[i] / PL[i].max()
elif norm == False:
PL_plot = PL
else:
raise NameError(
'You must specify whether the graph should be normalised')
if xvar == 'wvl': #x-axis wavelength or energy?
xax = wvl
xlabel = r'$ \rm Wavelength~[nm] $'
elif xvar == 'eV':
xax = eV
xlabel = r'$ \rm Emission~Energy~[eV] $'
else:
raise Exception('The x-axis must be in wvl or eV')
# plot, both the semilogy and the linear plot
if logy == True:
eps = 0.3
PL_plot = np.flip(PL_plot, axis=0)
PL_plot = np.where(PL_plot > eps, PL_plot, eps)
if type(offset) == int or type(offset) == float:
for i in range(num_files):
PL_plot[i] = PL_plot[i] * offset**i
for i in range(num_files):
plt.semilogy(xax[i],
PL_plot[i],
label=labels[i],
color=colors[i],
lw=1)
elif logy == False:
if type(offset) == int or type(offset) == float:
for i in range(num_files):
PL_plot[i] = PL_plot[i] + offset * i
for i in range(num_files):
plt.plot(xax[i],
PL_plot[i],
label=labels[i],
color=colors[i],
lw=1)
else:
raise Exception(
'The plot must be linear or semilogy, the variable must be a Boolean'
)
plt.xlabel(xlabel)
plt.legend()
plt.ylabel(r'$ \rm PL~Intensity~[AU] $')
plt.tight_layout()
return PL, wvl, eV, PL0, t_int, n_avg