mangadap.proc.util module

Provides a set of processing utility functions for the MaNGA DAP.

mangadap.proc.util.HDUList_mask_wavelengths(hdu, bitmask, bitmask_flag, wave_limits, wave_ext='WAVE', mask_ext='MASK', invert=False)[source]

Mask pixels in a specified wavelength range by turning on the bit value in the specified extention in a provided HDUList object.

Parameters

hdu (astropy.io.fits.HDUList) – HDUList to alter
(class (bitmask) – BitMask): Bit mask object used to turn on the named bit mask.
bitmask_flag (str) – Name of the bit to turn on.
wave_limits (list or numpy.ndarray) – Two-element array with the low and high wavelength limits.
wave_ext (str) – (Optional) Name of the wavelength extension in hdu.
mask_ext (str) – (Optional) Name of the mask extension in hdu.
invert (bool) – (Optional) Invert the sense of the masking. Instead of masking pixel in the wavelength interval, mask all pixels outside it.

Returns

The modified HDUList object.

Return type

astropy.io.fits.HDUList

Raises

Exception – Raised if wave_limits does not have a length of two.

mangadap.proc.util.flux_to_fnu(wave, flambda, unit_norm=1e-17)[source]

Convert a spectrum with flux per unit wavelength to flux per unit frequency; i.e., calculate

\[F_{\nu} = F_{\lambda} \frac{d\lambda}{d\nu} = F_{\lambda} \frac{\lambda^2}{c},\]

where the first two arguments of the function are \(\lambda\) and \(F_{\lambda}\). The input wavelength units are expected to be angstroms, and the input flux units are expected to be \(n\ {\rm erg\ s}^{-1}\ {\rm cm}^{-2}\ {\rm A}^{-1}\), where \(n\) is the value of unit_norm. The output flux units are microjanskys, \(10^{-29} {\rm erg\ s}^{-1}\ {\rm cm}^{-2}\ {\rm Hz}^{-1}\).

Parameters

wave (numpy.ndarray, list) – The vector with the wavelengths in angstroms.
flambda (numpy.ndarray, list) – The vector with the flux per unit wavelength (angstroms).
unit_norm (float) – (Optional) The unit normalization of the flux. For example, this is \(10^{-17}\) when the flux units are \(10^{-17} {\rm erg\ s}^{-1}\ {\rm cm}^{-2}\ {\rm A}^{-1}\).

Returns

The flux in units of microjanskys.

Return type

float,numpy.ndarray

Raises

ValueError – Raised if the arguments do not have the same shape.

mangadap.proc.util.get_database_key(f)[source]

Construct a key from the provided file or file path.

The key is a capitalized version of the file after removing any extension.

Parameters: f (str) – The file name or path.
Returns: The keyword.
Return type: str

Examples

>>> get_database_key('junk')
'JUNK'
>>> get_database_key('test.par')
'TEST'
>>> get_database_key('/path/to/test.par')
'TEST'

mangadap.proc.util.growth_lim(a, lim, fac=1.0, midpoint=None, default=[0.0, 1.0])[source]

Set the plots limits of an array based on two growth limits.

Parameters

a (array-like) – Array for which to determine limits.
lim (float) – Percentage of the array values to cover.
fac (float) – (Optional) Factor to increase the range based on the growth limits. Default is no increase.
midpoint (float) – (Optional) Force the midpoint of the range to be centered on this value. Default is to middle of growth range.
default (list) – (Optional) Default range to return if a has no data. Default is 0 to 1.

Returns

Lower and upper limits for the range of a plot of the data in a.

Return type

list

mangadap.proc.util.inverse(d)[source]

Return 1/d, where any division by 0 returns 0 instead of NaN or Inf.

Parameters: d (scalar-like, array-like) – Data values.
Returns: Returns 1/d where values with d == 0. are replaced by 0. Return type matches input type: float for scalar, numpy.ndarray for array-like.
Return type: float, numpy.ndarray

mangadap.proc.util.optimal_scale(dat, mod, wgt=None)[source]

Calculate the optimal scaling of an input model that minimizes the weighted root-mean-square difference between a set of data and a model. When defining the weighted RMS as:

\[{\rm RMS}^2 = \frac{1}{N} \sum_i w_i^2(d_i - f m_i)^2\]

The optimal renormalization factor that minimizes the RMS is:

\[f = \frac{\mathbf{d}^\prime \dot \mathbf{m}^\prime}{||\mathbf{m}^\prime||^2}\]

where \(d^\prime_i = w_i d_i\) and \(m^\prime_i = w_i m_i\).

Parameters

dat (array-like) – Array of data.
mod (array-like) – Model to renormalize.
wgt (array-like) – (Optional) Array of weights to apply to each residual.

Returns

The optimal scaling that minimizes the weighted root-mean-square difference betwen the data and the model.

Return type

float

Raises

ValueError – Raised if the array sizes do not match.

mangadap.proc.util.replace_with_data_from_nearest_coo(coo, data, replace)[source]

Replace data in array with the spatially nearest neighbor.

Parameters

coo (numpy.ndarray) – A 2D array with the x and y coordinates of all the data. Shape must be (NDATA, 2).
data (numpy.ndarray) – A 1D or 2D array with data to replace. The length of the first (or only) axis must be NDATA.
replace (numpy.ndarray) – Boolean array that is True for elements that should be replaced on output. Shape must be (NDATA,).

Returns

The data array with the selected rows replaced with the nearest data set.

Return type

numpy.ndarray

Raises

ValueError – Raised if the array sizes are inappropriate.

mangadap.proc.util.sample_growth(a, samples, default=- 9999.0, use_interpolate=True)[source]

mangadap.proc.util.select_database(key, directory_path)[source]

Select a database using a keyword and directory path.

Parameters

key (str) – Keyword used to select the method.
directory_path (str) – Full path with the valid database files. All files in the directory with a .par extension will be included.

Returns

Returns the file with the selected database.

Return type

str

Raises

NotADirectoryError – Raised if the provided directory path does not exist.j
KeyError – Raised if the selected keyword cannot be associated with a file in the provided directory.

mangadap.proc.util.select_proc_method(method_key, method_type, method_list=None, available_func=None)[source]

Select a method from a list. One of method_list or available_func must be provided.

Parameters

method_key (str) – Keyword used to select the method.
method_type (object) – Object type to check method_list against.
method_list (list, optional) – List of methods from which to find the selection keyword. If None, available_func must be provided.
available_func (callable, optional) – Callable function that returns a list of default methods in place of method_list. For example, see mangadap.proc.templatelibrary.available_template_libraries().

Returns

An object with base class mangadap.par.ParSet, containing a set of parameters used to define a method or database.

Return type

object

Raises

KeyError – Raised if the selected keyword is not among the provided list or if the provided list has more than one identical keyword.
TypeError – Raised if the input method_list object is not a list or method_type, or if available function is not a callable function.