"""
Spectrum class and function
"""
import numpy as _np
import copy as _copy
from crikit.data.frequency import Frequency as _Frequency
__all__ = ['Spectrum']
[docs]class Spectrum:
"""
Spectrum class
Attributes
----------
data : 1D ndarray [f_pix]
Spectrum
freq : crikit.data.Frequency instance
Frequency [wavelength, wavenumber] object (i.e., the independent \
variable)
label : str
Spectrum label (i.e., a string describing what the spectrum is)
units : str
Units of spectrum
meta : dict
Meta-data dictionary
f_pix : int, read-only
Size of data. Note: this matches the size of data and does NOT check \
the size of freq.freq_vec.
ndim : int, read-only
Number of data dimensions
shape : tuple, read-only
Shape of data
size : int, read-only
Size of data (i.e., total number of entries)
Methods
-------
mean : int
Mean value. If extent [a,b] is provided, calculate mean over that\
inclusive region.
std : int
Standard deviation. If extent [a,b] is provided, calculate standard\
deviation over that inclusive region.
subtract : 1D ndarray or None
Subtract spectrum or object
Notes
-----
* freq object contains some useful parameters such as op_range* and \
plot_range*, which define spectral regions-of-interest. (It's debatable \
as to whether those parameters should be in Frequency or Spectrum classes)
"""
# Configurations
config = {}
config['nd_axis'] = -1
config['nd_fcn'] = _np.mean
def __init__(self, data=None, freq=None, label=None, units=None, meta=None):
self._data = None
self._freq = _Frequency()
self._label = None
self._units = None
self._meta = {}
if data is not None:
self.data = _copy.deepcopy(data)
if freq is not None:
self.freq = _copy.deepcopy(freq)
else:
self.freq = _Frequency()
if label is not None:
self.label = _copy.deepcopy(label)
if units is not None:
self.units = _copy.deepcopy(units)
if meta is not None:
self._meta = _copy.deepcopy(meta)
@staticmethod
def _mean_axes(ndim, axis):
"""
Parameters
----------
ndim : int
Number of dimensions of input data (target is 1D spectrum)
axis : int
For ND data, axis is remaining axis
Returns
-------
Vector that describes what axes to operate (using a mean or similar method) with
axis parameter
"""
if axis < 0:
axis2 = ndim + axis
else:
axis2 = axis
return tuple([n for n in range(ndim) if n != axis2])
@property
def data(self):
return self._data
@data.setter
def data(self, value):
if not isinstance(value, _np.ndarray):
raise TypeError('data must be of type ndarray')
# If sub-range of operation is defined. Only perform action over op_range_pix
if self.freq is not None and self.freq.op_list_pix is not None:
if value.shape[self.config['nd_axis']] == self.freq.op_range_pix.size:
temp = _np.zeros((self.freq.size), dtype=value.dtype)
if value.ndim == 1:
temp[self.freq.op_range_pix] = value
else:
print('Input data is {}-dim. Performing {}'.format(value.ndim, self.config['nd_fcn'].__name__))
nd_ax = self._mean_axes(value.ndim, axis=self.config['nd_axis'])
temp[self.freq.op_range_pix] = self.config['nd_fcn'](value, axis=nd_ax)
elif value.shape[self.config['nd_axis']] == self.freq.size:
temp = _np.zeros((self.freq.size), dtype=value.dtype)
if value.ndim == 1:
temp[self.freq.op_range_pix] = value[self.freq.op_range_pix]
else:
print('Input data is {}-dim. Performing {}'.format(value.ndim, self.config['nd_fcn'].__name__))
nd_ax = self._mean_axes(value.ndim, axis=self.config['nd_axis'])
temp[self.freq.op_range_pix] = self.config['nd_fcn'](value, axis=nd_ax)[self.freq.op_range_pix]
else:
raise TypeError('data is of an unrecognized shape: {}'.format(value.shape))
self._data = 1*temp
del temp
else:
if value.ndim == 1:
self._data = value
else:
print('Input data is {}-dim. Performing {}'.format(value.ndim, self.config['nd_fcn'].__name__))
nd_ax = self._mean_axes(value.ndim, axis=self.config['nd_axis'])
self._data = self.config['nd_fcn'](value, axis=nd_ax)
@property
def freq(self):
return self._freq
@freq.setter
def freq(self, value):
if isinstance(value, _Frequency):
self._freq = value
elif isinstance(value, _np.ndarray):
self.freq = _Frequency(data=value)
else:
raise TypeError('freq must be of type crikit.data.Frequency')
@property
def f(self):
"""
Convenience attribute: return frequency vector within operating (op) \
range
"""
return self.freq.op_range_freq
@property
def f_full(self):
"""
Convenience attribute: return full frequency vector
"""
return self.freq.data
@property
def units(self):
return self._units
@units.setter
def units(self, value):
if isinstance(value, str):
self._units = value
else:
raise TypeError('units must be of type str')
@property
def label(self):
return self._label
@label.setter
def label(self, value):
if isinstance(value, str):
self._label = value
else:
raise TypeError('label must be of type str')
@property
def meta(self):
temp_dict = self._meta.copy()
if self.freq.calib is not None:
try:
calib_dict = {}
calib_prefix = 'Calib.'
calib_dict[calib_prefix + 'a_vec'] = self.freq.calib['a_vec']
calib_dict[calib_prefix + 'ctr_wl'] = self.freq.calib['ctr_wl']
calib_dict[calib_prefix + 'ctr_wl0'] = self.freq.calib['ctr_wl0']
calib_dict[calib_prefix + 'n_pix'] = self.freq.calib['n_pix']
calib_dict[calib_prefix + 'probe'] = self.freq.calib['probe']
try: # Doesn't really matter if we have the units
calib_dict[calib_prefix + 'units'] = self.freq.calib['units']
except:
pass
except:
print('Could not get calibration information')
else:
temp_dict.update(calib_dict)
if self.freq.calib_orig is not None:
try:
calib_dict = {}
calib_prefix = 'CalibOrig.'
calib_dict[calib_prefix + 'a_vec'] = self.freq.calib_orig['a_vec']
calib_dict[calib_prefix + 'ctr_wl'] = self.freq.calib_orig['ctr_wl']
calib_dict[calib_prefix + 'ctr_wl0'] = self.freq.calib_orig['ctr_wl0']
calib_dict[calib_prefix + 'n_pix'] = self.freq.calib_orig['n_pix']
calib_dict[calib_prefix + 'probe'] = self.freq.calib_orig['probe']
try: # Doesn't really matter if we have the units
calib_dict[calib_prefix + 'units'] = self.freq.calib_orig['units']
except:
pass
except:
print('Could not get calibration information')
else:
temp_dict.update(calib_dict)
# return self._meta
return temp_dict
@meta.setter
def meta(self, value):
if isinstance(value, dict):
self._meta = value
else:
raise TypeError('meta must be of type dict')
@property
def f_pix(self):
if self._data is not None:
return self._data.shape[-1]
@property
def ndim(self):
if self._data is None:
return None
elif isinstance(self._data, _np.ndarray):
return self._data.ndim
else:
return len(self._data.shape)
@property
def shape(self):
if self._data is None:
return None
else:
return self._data.shape
@property
def size(self):
if self._data is None:
return None
else:
return self._data.size
[docs] def mean(self, extent=None, over_space=True):
"""
Return mean spectrum (or mean over extent [list with 2 elements]). If\
over_space is False, returns reps-number of mean spectra
"""
if self._data is None:
return None
ndim = len(self._data.shape)
if ndim == 1:
if isinstance(self._data, _np.ndarray):
return self._data.mean()
else:
return _np.mean(self._data)
if ndim > 1:
if over_space == True:
axes = tuple(_np.arange(ndim-1))
else:
axes = -1
if isinstance(self._data, _np.ndarray):
if extent is None:
return self._data.mean(axis=axes)
else:
return self._data[:,extent[0]:extent[1]+1].mean(axis=axes)
else:
if extent is None:
return _np.mean(self._data, axis=axes)
else:
return _np.mean(self._data[:,extent[0]:extent[1]+1],
axis=axes)
[docs] def std(self, extent=None, over_space=True):
"""
Return standard deviation (std) spectrum (or std over extent
[list with 2 elements]). If over_space is False, reps (or reps x reps)
number of std's.
"""
if self._data is None:
return None
ndim = len(self._data.shape)
if ndim == 1:
if isinstance(self._data, _np.ndarray):
return self._data.std()
else:
return _np.std(self._data)
if ndim > 1:
if over_space == True:
axes = tuple(_np.arange(ndim-1))
else:
axes = -1
if isinstance(self._data, _np.ndarray):
if extent is None:
return self._data.std(axis=axes)
else:
return self._data[:,extent[0]:extent[1]+1].std(axis=axes)
else:
if extent is None:
return _np.std(self._data, axis=axes)
else:
return _np.std(self._data[:,extent[0]:extent[1]+1],
axis=axes)
[docs] def subtract(self, spectrum, overwrite=True):
"""
Subtract spectrum from data
"""
if isinstance(spectrum, Spectrum):
if overwrite:
self.data -= spectrum.data
return None
else:
return self.data - spectrum.data
elif isinstance(spectrum, _np.ndarray):
if overwrite:
self.data -= spectrum
return None
else:
return self.data - spectrum
def __sub__(self, spectrum):
return self.subtract(spectrum, overwrite=False)
if __name__ == '__main__': # pragma: no cover
import timeit as _timeit
N = 10001
wn = _np.linspace(500,3000,N)
sp = Spectrum(data=_np.random.rand(N) + 1j*_np.random.rand(N), freq=wn)
tmr = _timeit.default_timer()
sp.data[200:500]
tmr -= _timeit.default_timer()
print(-tmr)