def getCurvVectors(X, MaxOrder, sigma, loop = False, m = 'nearest'):
"""
Get smoothed curvature vectors up to a particular order
:param X: An N x d matrix of points in R^d
:param MaxOrder: The maximum order of torsion to compute (e.g. 3 for position, velocity, and curvature, and torsion)
:param sigma: The smoothing amount
:param loop: Treat this trajectory as a topological loop (i.e. add an edge between first and last point?)
"""
if loop:
m = 'wrap'
XSmooth = gf1d(X, sigma, axis=0, order = 0, mode = m)
Vel = gf1d(X, sigma, axis=0, order = 1, mode = m)
VelNorm = np.sqrt(np.sum(Vel**2, 1))
VelNorm[VelNorm == 0] = 1
Curvs = [XSmooth, Vel]
for order in range(2, MaxOrder+1):
Tors = gf1d(X, sigma, axis=0, order=order, mode = m)
for j in range(1, order):
#Project away other components
NormsDenom = np.sum(Curvs[j]**2, 1)
NormsDenom[NormsDenom == 0] = 1
Norms = np.sum(Tors*Curvs[j], 1)/NormsDenom
Tors = Tors - Curvs[j]*Norms[:, None]
Tors = Tors/(VelNorm[:, None]**order)
Curvs.append(Tors)
return Curvs
def get_derivative_shells(patches,
pd,
n_shells,
shells_fn=get_shells,
orders=[0, 1, 2],
r_max=None):
"""
Compute shell descriptors on the raw patch and after derivative
filters applied to the patch.
"""
patchesim = np.reshape(patches, (patches.shape[0], pd[0], pd[1]))
all_shells = []
for order in orders:
if order == 0:
shells = shells_fn(patches, pd, n_shells, r_max)
else:
imx = gf1d(patchesim, sigma=1, order=order, axis=2)
imy = gf1d(patchesim, sigma=1, order=order, axis=1)
patchesorder = np.sqrt(imx**2 + imy**2)
shells = shells_fn(np.reshape(patchesorder, patches.shape), pd,
n_shells, r_max)
# Normalize each by the standard deviation so they are comparable
shells /= np.std(shells)
all_shells.append(shells)
return np.concatenate(tuple(all_shells), 1)
def gau_smooth(arr0,typ='single',krn=75.0/4.0,verbose=False):
import numpy as np
from scipy.ndimage.filters import gaussian_filter1d as gf1d
from scipy.ndimage.filters import gaussian_filter as gf
N = arr0.ndim
ktyp = type(krn)
modes = ['reflect','wrap','wrap']
if ktyp==float:
krn = [krn,krn,krn]
if typ=='single':
ret = np.zeros(arr0.shape)
for i in range(3):
if verbose:
print('Smoothing type: wrap, component: {}'.format(i))
ret[i] = gf(arr0[i],sigma=krn,mode='wrap')
return ret
elif typ=='piecewise':
for i in range(3):
if N==3:
cax = i
if N==4:
cax = i+1
if verbose:
print('Axis = {}; k = {}, mode = {}'.format(cax,krn[i],modes[i]))
clab = 'arr{}'.format(i)
nlab = 'arr{}'.format(i+1)
if verbose:
print(clab,nlab)
locals()[nlab] = gf1d(locals()[clab],axis=cax,sigma=krn[i],mode=modes[i])
if verbose:
print('Returning {}'.format(nlab))
return locals()[nlab]
def plot_spectrum(wavelength,
spectrum,
ax=None,
plot_raw=True,
smooth=10,
raw_kwargs=dict(markersize=1.,
color='k',
linestyle='',
marker='.'),
**kwargs):
"""
Standardised method for plotting a spectrum on a matplotlib axes. The raw spectrum is plotted
as dots and a smoothed line is drawn through.
Args:
wavelength (np.ndarray): array of wavelengths
spectra (np.ndarray): array of spectrum values
ax (matplotlib.axis): An matplotlib axis instance. If none exist then one will be created
plot_raw (bool): Plot the raw data as black dots
smooth (float): sigma for Gaussian smoothing
raw_kwargs (dict): Dictionary of keyword arguments passed to the axes plot method for the
raw spectrum plot
**kwargs: Arbitrary keyword arguments passed to the axes plot method for the smoothed
spectrum plot
"""
if ax is None:
ax = plt.gca()
if ax is None:
ax = plt.subplot(111)
if plot_raw:
print 'plotting'
ax.plot(wavelength, spectrum, **raw_kwargs)
if 'linestyle' not in kwargs:
kwargs['linestyle'] = '-'
ax.plot(wavelength, gf1d(spectrum, smooth), **kwargs)
def plot_spectrum(wavelength, spectrum, ax=None, plot_raw=True, smooth=10,
raw_kwargs=dict(markersize=1., color='k', linestyle='', marker='.'),
**kwargs):
"""
Standardised method for plotting a spectrum on a matplotlib axes. The raw spectrum is plotted
as dots and a smoothed line is drawn through.
Args:
wavelength (np.ndarray): array of wavelengths
spectra (np.ndarray): array of spectrum values
ax (matplotlib.axis): An matplotlib axis instance. If none exist then one will be created
plot_raw (bool): Plot the raw data as black dots
smooth (float): sigma for Gaussian smoothing
raw_kwargs (dict): Dictionary of keyword arguments passed to the axes plot method for the
raw spectrum plot
**kwargs: Arbitrary keyword arguments passed to the axes plot method for the smoothed
spectrum plot
"""
if ax is None:
ax = plt.gca()
if ax is None:
ax = plt.subplot(111)
if plot_raw:
print 'plotting'
ax.plot(wavelength, spectrum, **raw_kwargs)
if 'linestyle' not in kwargs:
kwargs['linestyle'] = '-'
ax.plot(wavelength, gf1d(spectrum, smooth), **kwargs)
def plot_rewards(rewards, args):
plt.cla()
p = plt.plot(rewards, alpha=0.3)
plt.plot(gf1d(rewards, sigma=15), c=p[0].get_color())
plt.title('BipedalWalker-v2: Test MP')
plt.savefig('./{}_rewards.png'.format(args.env_name))
def plot_fps():
data = pd.read_csv('fps_data.csv')
for col in data.columns:
p = plt.plot(gf1d(data[col], sigma=5), label=col)
plt.plot(data[col], alpha=0.2, color=p[0].get_color())
plt.legend()
plt.title('FPS per Tracking method', weight='bold')
plt.show()
def getCurvVectors(X, MaxOrder, sigma, loop = False):
m = 'nearest'
if loop:
m = 'wrap'
XSmooth = gf1d(X, sigma, axis=0, order = 0, mode = m)
Vel = gf1d(X, sigma, axis=0, order = 1, mode = m)
VelNorm = np.sqrt(np.sum(Vel**2, 1))
VelNorm[VelNorm == 0] = 1
Curvs = [XSmooth, Vel]
for order in range(2, MaxOrder+1):
Tors = gf1d(X, sigma, axis=0, order=order, mode = m)
for j in range(1, order):
#Project away other components
NormsDenom = np.sum(Curvs[j]**2, 1)
NormsDenom[NormsDenom == 0] = 1
Norms = np.sum(Tors*Curvs[j], 1)/NormsDenom
Tors = Tors - Curvs[j]*Norms[:, None]
Tors = Tors/(VelNorm[:, None]**order)
Curvs.append(Tors)
return Curvs
def getCurvVectors(X, MaxOrder, sigma, loop=False):
m = 'nearest'
if loop:
m = 'wrap'
XSmooth = gf1d(X, sigma, axis=0, order=0, mode=m)
Vel = gf1d(X, sigma, axis=0, order=1, mode=m)
VelNorm = np.sqrt(np.sum(Vel**2, 1))
VelNorm[VelNorm == 0] = 1
Curvs = [XSmooth, Vel]
for order in range(2, MaxOrder + 1):
Tors = gf1d(X, sigma, axis=0, order=order, mode=m)
for j in range(1, order):
#Project away other components
NormsDenom = np.sum(Curvs[j]**2, 1)
NormsDenom[NormsDenom == 0] = 1
Norms = np.sum(Tors * Curvs[j], 1) / NormsDenom
Tors = Tors - Curvs[j] * Norms[:, None]
Tors = Tors / (VelNorm[:, None]**order)
Curvs.append(Tors)
return Curvs
def getAccelerationFeatureStack(X, sigma):
"""
Compute an extimate of acceleration of each component
of a feature stack using convolution with a Gaussian derivative
Parameters
----------
X: ndarray(N, K)
An array of features
sigma: int
Width of the gaussian by which to convolve
"""
return gf1d(X, sigma, axis=0, order=2, mode='nearest')
def create_hill_x_input():
""" Create Input for TenStream caluclations on a gaussian hill"""
from scipy.ndimage.filters import gaussian_filter as gf
from scipy.ndimage.filters import gaussian_filter1d as gf1d
from scipy.interpolate import interp1d
# Create elevation map for gaussian hill
Nx, Ny = 3, 31
DX = .1 # 100m horizontal resolution
HILL_HEIGHT = 1.5 # 500m hill
elev = np.zeros((Nx, Ny))
elev[:, (Ny - 1) / 2] = 1.
elev = gf1d(elev, HILL_HEIGHT / DX, axis=1)
elev /= np.max(elev) / HILL_HEIGHT
# Interpolate atmosphere file on new grid
afglus = np.loadtxt('afglus_100m.dat')
z = afglus[:, 0]
p = afglus[:, 1]
Nz = np.shape(z)[-1]
pressure_by_height = interp1d(z, p, kind='linear')
hhl = np.tile(z, Nx * Ny).reshape(
(Nx, Ny, Nz))[:, :, ::-1] # hhl now begins at surface
hill = hhl.copy()
# Create surface following sigma coordinates with coordinate stretching in the vertical
for k in range(Nz):
hill[:, :,
k] = hhl[:, :, k] + elev * np.exp(-hhl[:, :, k] / HILL_HEIGHT)
hill = np.minimum(hill, np.max(z))
hill = hill[:, :, ::-1] # hhl now begins at TOA
p_hill = pressure_by_height(hill)
lev_coord = p_hill
create_var('plev', interp_var(p, afglus[:, 1], lev_coord), 'lev')
lay_coord = (lev_coord[:, :, 1:] + lev_coord[:, :, :-1]) / 2
create_var('tlay', interp_var(p, afglus[:, 2], lay_coord), 'lay')
create_var('air', interp_var(p, afglus[:, 3], lay_coord), 'lay')
create_var('o3vmr', interp_var(p, afglus[:, 4], lay_coord), 'lay')
create_var('o2vmr', interp_var(p, afglus[:, 5], lay_coord), 'lay')
create_var('h2ovmr', interp_var(p, afglus[:, 6], lay_coord), 'lay')
create_var('co2vmr', interp_var(p, afglus[:, 7], lay_coord), 'lay')
create_var('n2ovmr', interp_var(p, afglus[:, 8], lay_coord), 'lay')
def data_augmentation(inputs,
outputs,
input_channels=[],
kernels=(3, 5, 7, 11),
noises=None):
if not isinstance(input_channels, list):
input_channels = [input_channels]
input_channels = [ip for ip in input_channels if ip in list(inputs)]
# apply
repeat_channels = [ip for ip in list(inputs) if ip not in input_channels]
inputs_ = {ic: np.vstack((inputs[ic],) + \
tuple([gf1d(inputs[ic], k, axis = 1) \
for k in kernels])) \
for ic in input_channels}
inputs_ = dict(inputs_, **{ic: np.vstack((inputs[ic],) + \
tuple([inputs[ic] \
for k in kernels])) \
for ic in repeat_channels})
if noises is not None:
inputs__ = {ic: np.vstack(tuple([inputs[ic] + \
np.random.normal(loc=0.0, scale=n, size = inputs[ic].shape) \
for n in noises])) \
for ic in input_channels}
inputs__ = dict(inputs__, **{ic: np.vstack(tuple([inputs[ic] \
for n in noises])) \
for ic in repeat_channels})
inputs_ = {
ic: np.vstack((inputs_[ic], inputs__[ic]))
for ic in inputs_
}
# Repeat outputs
if outputs is not None:
outputs_ = {oc: np.vstack((outputs[oc],) + \
tuple([outputs[oc] \
for k in kernels])) \
for oc in list(outputs)}
if noises is not None:
outputs_ = {oc: np.vstack((outputs_[oc],) + \
tuple([outputs[oc] \
for n in noises])) \
for oc in list(outputs)}
return inputs_, outputs_
def GaussBlurG1(Chro,sigma,sigrange=0,returnPDF=False,normalize=False):
''' Outputs a new Chromosome which has converted 0s to 1s in Chro ...
... with odds based on a gaussian blur of Chro'''
blurred = gf1d( [float(i) for i in Chro] , sigma, mode='constant') # list comprehension needed to convert ints to floats
if normalize:
NormalizePDF(blurred,sum(Chro))
if returnPDF:
blurred = [max(0,i) for i in (blurred - Chro)] # converts 1s in Chro to 0s in blurred #### because Chro is binary, it either doesn't affect (if 0) or pushes it to 0 (if 1 which is always bigger than blurred)
if sigrange:
# raise NameError('CHECK FIRST WHETHER CUTOFF METHOD IS CORRECT, IT IS CURRENTLY UNTESTED')
cutoff = stats.norm.pdf(sigma*sigrange,scale=sigma) ## I THINK THIS IS CORRECT, UNTESTED THOUGH
blurred = [0 if i < cutoff else i for i in blurred] # turn low values to 0 to decrease memory used in numpy array
return blurred
return [1 if nprnd.rand() < nuc else 0 for nuc in Chro+blurred]
def getCurvVectors(X, MaxOrder, sigma, loop=False, m='nearest'):
"""
Get smoothed curvature vectors up to a particular order
Parameters
----------
X: ndarray(N, d)
An N x d matrix of points in R^d
MaxOrder: int
The maximum order of torsion to compute (e.g. 3 for position, velocity, and curvature, and torsion)
sigma: float
The smoothing amount
loop: boolean
Whether to treat this trajectory as a topological loop (i.e. add an edge between first and last point)
Returns
-------
Curvs: A list of ndarray(N, d), starting with the smoothed curve, then followed
by the smoothed velocity, curvature, torsion, etc. up to the MaxOrder
"""
if loop:
m = 'wrap'
XSmooth = gf1d(X, sigma, axis=0, order=0, mode=m)
Vel = gf1d(X, sigma, axis=0, order=1, mode=m)
VelNorm = np.sqrt(np.sum(Vel**2, 1))
VelNorm[VelNorm == 0] = 1
Curvs = [XSmooth, Vel]
for order in range(2, MaxOrder + 1):
Tors = gf1d(X, sigma, axis=0, order=order, mode=m)
for j in range(1, order):
#Project away other components
NormsDenom = np.sum(Curvs[j]**2, 1)
NormsDenom[NormsDenom == 0] = 1
Norms = np.sum(Tors * Curvs[j], 1) / NormsDenom
Tors = Tors - Curvs[j] * Norms[:, None]
Tors = Tors / (VelNorm[:, None]**order)
Curvs.append(Tors)
return Curvs
def getOnsetMeans(px, win=20, sigma=1, truncate=4, edge=10, do_plot=False):
"""
Do a mollified Gaussian derivative followed by
a moving average to get smoothed local tempo estimates
"""
x = px[edge:-edge] # Truncate edges since they seem to be unreliable
x = gf1d(x, sigma, truncate=truncate, order=1, mode='reflect')
x = x[truncate * sigma:-truncate * sigma]
if do_plot:
plt.figure()
plt.subplot(211)
plt.plot(px)
plt.subplot(212)
plt.plot(x)
plt.show()
M = x.size - win + 1
X = np.zeros((M, win))
for k in range(win):
X[:, k] = x[k:k + M]
ret = np.mean(X, 1)
return ret / np.median(ret)
def G1phase(Chro,G1method=GaussBlurG1,power=gausspow):
''' Gaussian blur (or other method) of Chro is made which functions as the blueprint for the chance of nascent
CENP-A incorporation.
The Distribution is only made once at the beginning of G1 phase, meaning that nascent CENP-A does not
guide more nascent CENP-A incorporation. This makes the code a lot faster.
A method was built in to ensure that there is no 'reloading' of CENP-A at any given position'''
global sendChro
if gaussOffset:
sendChro = np.zeros(ChroL)
for nuc in xrange(ChroL):
if Chro[nuc] == 1:
if nuc >= gaussOffset: sendChro[nuc-gaussOffset] += 0.5
if nuc < ChroL-gaussOffset: sendChro[nuc+gaussOffset] += 0.5
else: sendChro = Chro
Distrib = gf1d( [float(i) for i in sendChro] , sigma, mode='constant')
Distrib = [max(0,i) for i in (Distrib - Chro)]
if power != 1: # >1 makes the distribution favor clusters (at least in theory)
Distrib = np.power(Distrib,[power]*len(Distrib))
print sum(Distrib)
Distrib /= sum(Distrib)/efficiency # normailizes the distribution to sum to efficiency (i.e. random numbers above efficiency will not lead to CA incorporation)
Cumulative = np.cumsum(Distrib) # converts the distribution to a cumulative distribution list
plt.plot(Distrib)
print sum(Distrib)
reloads = 0
for n in xrange(CApool): # for each molecule in the available pool of CA
X = nprnd.rand()
if X < efficiency: # test if it will be loaded or not
newPos = np.searchsorted(Cumulative,X)
while Chro[newPos]: # ensures that previously loaded sites are not 'reloaded'; this also alleviates the necessity of converting 1s to 0s in GaussBlur
X = nprnd.rand()/2
newPos = np.searchsorted(Cumulative,X)
reloads += 1 # counter to check how often a previously loaded site revisited
Chro[newPos] = 1 # convert a 0 from the cumulative distribution to 1
return Chro , reloads
def G1phase(Chro):
''' Gaussian blur (or other method) of Chro is made which functions as the blueprint for the chance of nascent
CENP-A incorporation.
The Distribution is only made once at the beginning of G1 phase, meaning that nascent CENP-A does not
guide more nascent CENP-A incorporation. This makes the code a lot faster.
A method was built in to ensure that there is no 'reloading' of CENP-A at any given position'''
# Create Offset array which will be used to calculate the frequency distribution of nascent CA loading
if gaussOffset:
OffsetChro = np.zeros(ChroL)
for nuc in xrange(ChroL):
if Chro[nuc] == 1:
if nuc >= gaussOffset: OffsetChro[nuc-gaussOffset] += 0.5
if nuc < ChroL-gaussOffset: OffsetChro[nuc+gaussOffset] += 0.5
else: OffsetChro = np.array(Chro)
# Create frequency distribution based on gaussian blurred
Distrib = gf1d( [float(i) for i in OffsetChro] , sigma, mode='constant') #this line is no longer outsourced to G1_phase_Methods file!
Distrib = [max(0,i) for i in (Distrib - Chro)] # convert 1s in Chro to 0s in Distrib so that there is not reloading of CA at old positions
if gausspow != 1: # >1 makes the distribution favor clusters (at least in theory)
Distrib = np.power(Distrib,[gausspow]*len(Distrib))
Distrib /= sum(Distrib)/efficiency # normailizes the distribution to sum to efficiency (i.e. random numbers above efficiency will not lead to CA incorporation)
Cumulative = np.cumsum(Distrib) # converts the distribution to a cumulative distribution list
reloads=0 # counter to check how often a previously loaded site revisited
for n in xrange(CApool): # for each molecule in the available pool of CA
X = nprnd.rand()
if X < efficiency: # test if it will be loaded or not
newPos = np.searchsorted(Cumulative,X)
while Chro[newPos]: # ensures that previously loaded sites are not 'reloaded'; this also alleviates the necessity of converting 1s to 0s in GaussBlur
X = nprnd.rand()/2
newPos = np.searchsorted(Cumulative,X)
reloads += 1 # counter to check how often a previously loaded site revisited
Chro[newPos] = 1 # convert a 0 from the cumulative distribution to 1
return Chro , reloads
batch_reward.extend([p_r, n_r])
l2_update, mean_r, std_r = train_step(net, batch_noise, batch_reward)
p_bar.update(1)
last_r = eval_indiv(env, net)
p_bar.set_description('Reward: {}'.format(last_r))
writer.add_scalar('L2 update', np.mean(l2_update), step)
writer.add_scalar('Mean reward', mean_r, step)
writer.add_scalar('Reward std', std_r, step)
recap['mean_r'].append(mean_r)
recap['l2_updates'].append(np.mean(l2_update))
recap['std_r'].append(std_r)
p = plt.plot(recap['mean_r'], alpha=0.3)
plt.plot(gf1d(recap['mean_r'], sigma=5), color=p[0].get_color())
plt.title('Mean reward')
plt.savefig('mean.png')
plt.cla()
p = plt.plot(recap['l2_updates'], alpha=0.3)
plt.plot(gf1d(recap['l2_updates'], sigma=5), color=p[0].get_color())
plt.title('L2 update')
plt.savefig('l2.png')
plt.cla()
p = plt.plot(recap['std_r'], alpha=0.3)
plt.plot(gf1d(recap['std_r'], sigma=5), color=p[0].get_color())
plt.title('Std reward')
plt.savefig('std.png')
plt.cla()
p_bar.close()
def get_DLNC0(x, sr, hop_length, lag=10, do_plot=False):
"""
Compute decaying locally adaptive normalize C0 (DLNC0) features
Parameters
----------
x: ndarray(N)
Audio samples
sr: int
Sample rate
hop_length: int
Hop size between windows
lag: int
Number of lags to include
Returns
-------
X: ndarray(n_win, 12)
The DLNC0 features
"""
from scipy.ndimage.filters import gaussian_filter1d as gf1d
from scipy.ndimage.filters import maximum_filter1d
import librosa
X = np.abs(librosa.cqt(x, sr=sr, hop_length=hop_length,
bins_per_octave=12))
# Half-wave rectify discrete derivative
#X = librosa.amplitude_to_db(X, ref=np.max)
#X[:, 0:-1] = X[:, 1::] - X[:, 0:-1]
X = gf1d(X, 5, axis=1, order=1)
X[X < 0] = 0
# Retain peaks
XLeft = X[:, 0:-2]
XRight = X[:, 2::]
mask = np.zeros_like(X)
mask[:, 1:-1] = (X[:, 1:-1] > XLeft) * (X[:, 1:-1] > XRight)
X[mask < 1] = 0
# Fold into octave
n_octaves = int(X.shape[0] / 12)
X2 = np.zeros((12, X.shape[1]), dtype=X.dtype)
for i in range(n_octaves):
X2 += X[i * 12:(i + 1) * 12, :]
X = X2
# Compute norms
if do_plot:
import librosa.display
plt.subplot(411)
librosa.display.specshow(X, sr=sr, x_axis='time', y_axis='chroma')
norms = np.sqrt(np.sum(X**2, 0))
if do_plot:
plt.subplot(412)
plt.plot(norms)
norms = maximum_filter1d(norms, size=int(2 * sr / hop_length))
if do_plot:
import librosa.display
plt.plot(norms)
plt.subplot(413)
X = X / norms[None, :]
librosa.display.specshow(X, sr=sr, x_axis='time', y_axis='chroma')
# Apply LNCO
decays = np.linspace(0, 1, lag + 1)[1::]
decays = np.sqrt(decays[::-1])
XRet = np.zeros_like(X)
M = X.shape[1] - lag + 1
for i in range(lag):
XRet[:, i:i + M] += X[:, 0:M] * decays[i]
if do_plot:
plt.subplot(414)
librosa.display.specshow(XRet, sr=sr, x_axis='time', y_axis='chroma')
plt.show()
return XRet
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import glob
import numpy as np
from scipy.ndimage.filters import gaussian_filter1d as gf1d
plt.style.use('ggplot')
if __name__ == "__main__":
smoother = lambda x: gf1d(x, sigma=15)
files = glob.glob('./runs/*csv')
ppo_files = [f for f in files if 'ppo' in f]
ppg_files = [f for f in files if 'ppg' in f]
ppo_df = pd.concat([pd.read_csv(f) for f in ppo_files], axis=1)
ppg_df = pd.concat([pd.read_csv(f) for f in ppg_files], axis=1)
ppo_df['min_val'] = ppo_df.min(axis=1)
ppo_df['max_val'] = ppo_df.max(axis=1)
ppo_df['mean_val'] = ppo_df.mean(axis=1)
ppg_df['min_val'] = ppg_df.min(axis=1)
ppg_df['max_val'] = ppg_df.max(axis=1)
ppg_df['mean_val'] = ppg_df.mean(axis=1)
f, ax = plt.subplots(figsize=(20, 14))
x_ticks = np.arange(0, ppo_df.shape[0])
plt.fill_between(x_ticks,
def interpolate_spectrum(wavelength, spectrum, smooth=10):
func = interp1d(wavelength, spectrum)
new_wavelength = np.arange(wavelength.min(), wavelength.max(), 1)
new_spectrum = func(new_wavelength)
new_spectrum = gf1d(new_spectrum, smooth)
return new_wavelength, new_spectrum