def is_reference(self, spectrum):
refls = spectrum.reflectances
#if a reflectance is greater than 1.0 then it is a reference
if np.max(refls) > 1.0:
return True
if self._context == 'gveg':
#check the total range of reflectances
if np.ptp(refls) < self._req_reflectance_range:
return True
#check presence of minima in specified wavelength ranges
waves = spectrum.wavelengths
for (win, rad) in zip(self._minima_wins, self._minima_radii):
lt_idx = closest_array_index(win[0], waves)
rt_idx = closest_array_index(win[1], waves)
minima_found = False
for i in range(lt_idx, rt_idx):
cval = refls[i]
li = max(i - rad, 0)
ri = min(i + rad, len(refls))
if np.all(np.greater_equal(refls[li:ri], cval)):
minima_found = True
if not minima_found:
return True
return False
def wavelength_subset(self, wavestart, wavestop):
idx1 = closest_array_index(wavestart, self.wavelengths)
idx2 = closest_array_index(wavestop, self.wavelengths) + 1
return Spectrum(data = self._data[idx1:idx2, :],
idstr = self._idstr,
company = self._company,
instrument = self._instrument)
def is_green_vegetation(self, spectrum):
self._ndvi, self._reflectance_range = 0.0, 0.0
waves = spectrum.wavelengths
refls = spectrum.reflectances
#perform the ndvi based test
nir_start = closest_array_index(self._ndvi_nir_range[0], waves)
nir_end = closest_array_index(self._ndvi_nir_range[1], waves)
red_start = closest_array_index(self._ndvi_red_range[0], waves)
red_end = closest_array_index(self._ndvi_red_range[1], waves)
nir_mean = np.mean(refls[nir_start:nir_end])
red_mean = np.mean(refls[red_start:red_end])
self._ndvi = (nir_mean - red_mean)/(nir_mean + red_mean)
if self._ndvi < self._ndvi_thresh:
return (False, self._ndvi, self._reflectance_range)
#perform reflectance range based test
#consider the middle 90% of wavelengths
chop_size = np.size(refls)*5/100 + 1
start = chop_size
stop = np.size(refls) - chop_size
self._reflectance_range = np.ptp(refls[start:stop])
if self._reflectance_range < self._reflectance_range_thresh:
return (False, self._ndvi, self._reflectance_range)
#it is green vegetation if this point is reached
return (True, self._ndvi, self._reflectance_range)
def wavelength_subset(self, wavestart, wavestop):
idx1 = closest_array_index(wavestart, self.wavelengths)
idx2 = closest_array_index(wavestop, self.wavelengths) + 1
return Spectrum(data=self._data[idx1:idx2, :],
idstr=self._idstr,
company=self._company,
instrument=self._instrument)
def is_green_vegetation(self, spectrum):
self._ndvi, self._reflectance_range = 0.0, 0.0
waves = spectrum.wavelengths
refls = spectrum.reflectances
#perform the ndvi based test
nir_start = closest_array_index(self._ndvi_nir_range[0], waves)
nir_end = closest_array_index(self._ndvi_nir_range[1], waves)
red_start = closest_array_index(self._ndvi_red_range[0], waves)
red_end = closest_array_index(self._ndvi_red_range[1], waves)
nir_mean = np.mean(refls[nir_start:nir_end])
red_mean = np.mean(refls[red_start:red_end])
self._ndvi = (nir_mean - red_mean) / (nir_mean + red_mean)
if self._ndvi < self._ndvi_thresh:
return (False, self._ndvi, self._reflectance_range)
#perform reflectance range based test
#consider the middle 90% of wavelengths
chop_size = np.size(refls) * 5 / 100 + 1
start = chop_size
stop = np.size(refls) - chop_size
self._reflectance_range = np.ptp(refls[start:stop])
if self._reflectance_range < self._reflectance_range_thresh:
return (False, self._ndvi, self._reflectance_range)
#it is green vegetation if this point is reached
return (True, self._ndvi, self._reflectance_range)
def detect(self, spectrums):
#check the input is in the right format
if not isinstance(spectrums, list) or (len(spectrums) < 3):
print("spectrums must be a list of Spectrum objects")
sys.exit(0)
#check wavelengths are same for all spectrums
w0 = spectrums[0].wavelengths
if not np.all([np.allclose(w0, s.wavelengths) for s in spectrums]):
print("spectrums must have same wavelengths")
sys.exit(0)
#assemble the reflectances in a matrix
#one row = one spectrum's reflectances
refls = np.empty((len(spectrums), np.size(w0)), dtype = np.double)
for (r, s) in enumerate(spectrums):
refls[r, :] = np.copy(s.reflectances.transpose())
#find the median spectrum
median = np.median(refls, axis = 0)
#compute median of absolute deviations from median
mad = np.median(np.abs(refls - median), axis = 0)
#compute robust estimate of standard deviation
sigma = mad/0.67
#compute the zscores
zs = (refls - median)/sigma
#range indices to look for abnormal deviations
rngidxs = []
for rng in self._ranges:
start = closest_array_index(rng[0], w0)
end = closest_array_index(rng[1], w0) + 1
rngidxs.append(start, end)
#do the outlier detection
inliers = []
outliers = []
for (specidx, spec) in enumerate(spectrums):
outlier = False
for (start, end) in rngidxs:
if np.any(np.abs(zs[specidx, start:end]) > self._zthresh):
outlier = True
if outlier:
outliers.append(spec)
else:
inliers.append(spec)
#store some stuff in class - useful for plotting
self._median = median
self._std = sigma
return (inliers, outliers)
def _correct_postzones(self):
zones = range(self._stablezone + 1, len(self._jumpwaves) + 1)
for z in zones:
ix = closest_array_index(self._jumpwaves[z - 1], self._dm[:, 0])
sjump = self._dm[ix, 1] - self._dm[(ix - 1), 1]
ujump = self._dm[(ix + 2), 1] - self._dm[(ix + 1), 1]
avjump = (sjump + ujump) / 2.0
scale = (self._dm[ix, 1] + avjump) / self._dm[(ix + 1), 1]
self._dm[(ix + 1):, 1] = self._dm[(ix + 1):, 1] * scale
def _correct_prezones(self):
zones = range(self._stablezone - 1, -1, -1)
for z in zones:
ix = closest_array_index(self._jumpwaves[z], self._dm[:, 0])
sjump = self._dm[ix, 1] - self._dm[(ix + 1), 1]
ujump = self._dm[(ix - 2), 1] - self._dm[(ix - 1), 1]
avjump = (sjump + ujump) / 2.0
scale = (self._dm[ix, 1] + avjump) / self._dm[(ix - 1), 1]
self._dm[0:ix, 1] = self._dm[0:ix, 1] * scale
def _correct_postzones(self):
zones = range(self._stablezone + 1, len(self._jumpwaves) + 1)
for z in zones:
ix = closest_array_index(self._jumpwaves[z - 1], self._dm[:, 0])
sjump = self._dm[ix, 1] - self._dm[(ix - 1), 1]
ujump = self._dm[(ix + 2), 1] - self._dm[(ix + 1), 1]
avjump = (sjump + ujump)/2.0
scale = (self._dm[ix, 1] + avjump)/self._dm[(ix + 1), 1]
self._dm[(ix + 1):, 1] = self._dm[(ix + 1):, 1]*scale
def _correct_prezones(self):
zones = range(self._stablezone - 1, -1, -1)
for z in zones:
ix = closest_array_index(self._jumpwaves[z], self._dm[:, 0])
sjump = self._dm[ix, 1] - self._dm[(ix + 1), 1]
ujump = self._dm[(ix - 2), 1] - self._dm[(ix - 1), 1]
avjump = (sjump + ujump)/2.0
scale = (self._dm[ix, 1] + avjump)/self._dm[(ix - 1), 1]
self._dm[0:ix, 1] = self._dm[0:ix, 1]*scale
def process_overlap(self, spec):
#find the forward difference for the wavelengths
diffs = np.diff(spec.wavelengths)
#find where the wavelength differences are negative
idxs = np.nonzero(diffs <= -0.05)[0]
idxs = idxs + 1
idxs = np.hstack((np.array([0]), idxs, np.size(spec.wavelengths)))
#create pieces os spectrums with increasing wavelengths
pcs = []
data = spec.data
uniquifier = WaveUniquifier()
for k in range(1, len(idxs)):
i1 = idxs[k - 1]
i2 = idxs[k]
s = Spectrum(data = data[i1:i2, :],
idstr = spec.idstr,
company = spec.company,
instrument = spec.instrument)
pcs.append(uniquifier.uniquify(s))
#resample the pieces into 1 nm wavelengths
rspcs = []
resampler = WaveResampler()
for s in pcs:
wr = s.wavelength_range()
start = math.ceil(wr[0])
stop = math.floor(wr[1])
resampler = WaveResampler(rstype = self._rstype,
wavestart = start,
wavestop = stop,
spacing = 1.0)
rspcs.append(resampler.resample(s))
# print(rspcs[-1].wavelengths[0], rspcs[-1].wavelengths[-1])
# print("------------------------")
#chop and stitch
if len(rspcs) > 1:
#find the wavelengths to chop at
critwaves = [rspcs[0].wavelengths[0]]
for i in range(1, len(rspcs)):
#find the overlapping indices
lstart, lstop, rstart, rstop = -1, -1, -1, -1
rstart = 0
lstart = closest_array_index(rspcs[i].wavelengths[0],
rspcs[i - 1].wavelengths)
lstop = len(rspcs[i - 1].wavelengths)
rstop = closest_array_index(rspcs[i - 1].wavelengths[-1],
rspcs[i].wavelengths) + 1
lrefls = rspcs[i - 1].reflectances[lstart:lstop]
rrefls = rspcs[i].reflectances[rstart:rstop]
lwaves = rspcs[i - 1].wavelengths[lstart:lstop]
critwaves.append(lwaves[np.argmin(np.abs(lrefls - rrefls))])
critwaves.append(rspcs[-1].wavelengths[-1])
# print("critwaves = {}".format(critwaves))
subdms = []
for i in range(len(rspcs)):
start = closest_array_index(critwaves[i],
rspcs[i].wavelengths)
stop = closest_array_index(critwaves[i + 1],
rspcs[i].wavelengths) + 1
subdms.append(rspcs[i].data[start:stop, :])
# print(rspcs[i].data[start:stop, :])
# print("========================")
return uniquifier.uniquify(Spectrum(data = np.vstack(tuple(subdms)),
idstr = spec.idstr,
company = spec.company,
instrument = spec.instrument))
else:
return rspcs[0]
