pysme package

Subpackages

Submodules

pysme.abund module

Elemental abundance data handling module

class pysme.abund.Abund(monh=0, pattern='solar', type='sme', citation_info='\n@article{loss2003atomic,\n  title={Atomic weights of the elements 2001 (IUPAC Technical Report)},\n  author={Loss, RD},\n  journal={Pure and Applied Chemistry},\n  volume={75},\n  number={8},\n  pages={1107--1122},\n  year={2003},\n  publisher={De Gruyter}\n}\n')[source]

Bases: IPersist

Elemental abundance data and methods. Valid abundance pattern types are:

‘sme’ - For hydrogen, the abundance value is the fraction of all

nuclei that are hydrogen, including all ionization states and treating molecules as constituent atoms. For the other elements, the abundance values are log10 of the fraction of nuclei of each element in any form relative to the total for all elements in any form. For the Sun, the abundance values of H, He, and Li are approximately 0.92, -1.11, and -11.0.

‘n/nTot’ - Abundance values are the fraction of nuclei

of each element in any form relative to the total for all elements in any form. For the Sun, the abundance values of H, He, and Li are approximately 0.92, 0.078, and 1.03e-11.

‘n/nH’ - Abundance values are the fraction of nuclei

of each element in any form relative to the number of hydrogen nuclei in any form. For the Sun, the abundance values of H, He, and Li are approximately 1, 0.085, and 1.12e-11.

‘H=12’ - Abundance values are log10 of the fraction of nuclei of

each element in any form relative to the number of hydrogen in any form plus an offset of 12. For the Sun, the nuclei abundance values of H, He, and Li are approximately 12, 10.9, and 1.05.

empty_pattern()[source]

Return an abundance pattern with value None for all elements.

classmethod from_dict(data)[source]
static fromtype(pattern, fromtype, raw=False)[source]

Return a copy of the input abundance pattern, transformed from the input type to the ‘H=12’ type. Valid abundance pattern types are ‘sme’, ‘n/nTot’, ‘n/nH’, ‘n/nFe’, and ‘H=12’.

get_element(elem, type=None)[source]
get_pattern(type=None, raw=False)[source]

Transform the specified abundance pattern from type used internally by SME to the requested type. Valid abundance pattern types are: ‘sme’, ‘n/nTot’, ‘n/nH’, ‘H=12’

Parameters:

  • type (str) – The pattern format to retrieve the pattern as. Defaults to the original input format.

  • raw (bool) – If True will return the pattern as a numpy array, with indices as defined in elem dict. Otherwise the return value is a dictionary, with the elements as keys.

set_pattern_by_name(pattern_name)[source]

Set the abundance pattern to one of the predefined options

Parameters:

pattern_name (str) – Name of the predefined option to use. One of ‘asplund2009’, ‘grevesse2007’, ‘lodders2003’, ‘empty’

Raises:

ValueError – If an undefined pattern_name was given

set_pattern_by_value(pattern, type)[source]
static solar()[source]

Return solar abundances of asplund 2009

to_dict()[source]
static totype(pattern, totype, raw=False, copy=True)[source]

Return a copy of the input abundance pattern, transformed from the ‘H=12’ type to the output type. Valid abundance pattern types are ‘sme’, ‘n/nTot’, ‘n/nH’, and ‘H=12’.

update_pattern(updates)[source]

Update the abundance pattern for several elements at once

The abundance is first converted into the initially specified format, before being converted back to the internal format

Parameters:

updates (dict{str:float}) – the elements to update

Raises:

KeyError – If any of the element keys is not valid

property elem

Return the standard abbreviation for each element. Use property so user will not redefine elements.

property elem_dict

Return the position of each element in the raw array

property monh

Metallicity, the logarithmic offset added to the abundance pattern for all elements except hydrogen and helium.

Type:

float

property pattern

Abundance pattern in the initial format

Type:

array

pysme.atmosphere module

pysme.broadening module

pysme.broadening.apply_broadening(ipres, x_seg, y_seg, type='gauss', sme=None)[source]

Broaden a spectrum by instrument resolution, with a given broadening type

Parameters:

  • ipres (float) – instrument resolution

  • x_seg (array (npoints,)) – x values (wavelength) of the spectrum to broaden

  • y_seg (array (npoints,)) – y values (intensities) of the spectrum to broaden

  • type ({str, None}, optional) – broadening type to apply. Options are “gauss”, “sinc”, “table”, None. If None, will try to use sme.iptype to determine type. “table” requires keyword sme to be passed as well. See functions the respective for details. (default: “gauss”)

  • sme (SME_Struct, optional) – sme structure with instrument profile data, required only for type=”table” or type=None (default: None)

Raises:

AttributeError – if type requires SME_Struct, but its missing passed type not recognized

Returns:

y_seg – broadened intensity spectrum

Return type:

array (npoints,)

pysme.broadening.gaussbroad(w, s, hwhm)[source]

Smooths a spectrum by convolution with a gaussian of specified hwhm.

Parameters:

  • w (array[n]) – wavelength scale of spectrum to be smoothed

  • s (array[n]) – spectrum to be smoothed

  • hwhm (float) – half width at half maximum of smoothing gaussian.

Returns:

sout – the gaussian-smoothed spectrum.

Return type:

array[n]

pysme.broadening.sincbroad(w, s, hwhm)[source]

Smooths a spectrum by convolution with a sinc function of specified hwhm.

Parameters:

  • w (array of size (n,)) – wavelength scale of spectrum to be smoothed

  • s (array of size (n,)) – spectrum to be smoothed

  • hwhm (float) – half width at half maximum of smoothing gaussian.

Returns:

sout – the sinc-smoothed spectrum.

Return type:

array of size (n,)

pysme.broadening.tablebroad(w, s, xip, yip)[source]

Convolves a spectrum with an arbitrary instrumental profile.

Parameters:

  • w (array of size (n,)) – wavelength scale of spectrum to be smoothed

  • s (array of size (n,)) – spectrum to be smoothed

  • xip (array of size (m,)) – x points of the instrument profile

  • yip (array of size (m,)) – y points of the instrument profile

Returns:

sout – the smoothed spectrum.

Return type:

array[n]

pysme.config module

Handle the Json configuration file At the moment it is only used for the LargeFileStorage

class pysme.config.Config(fname='~/.sme/config.json')[source]

Bases: object

load()[source]
save(*args, **kwargs)[source]
property filename

pysme.continuum_and_radial_velocity module

pysme.cwrapper module

Wrapper for IDL style C libary code {return_type} {func_name}(int argv, void *argc[]);

with argv - number of parameters and argc - list of pointers to those parameters

class pysme.cwrapper.GlobalState[source]

Bases: Structure

free_ionization()[source]
free_linelist()[source]
free_opacities()[source]
ABUND

Structure/Union member

ACOOL

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AH2P

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AHE1

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AHE2

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AHEMIN

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AHMIN

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AHOT

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AHYD

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ALMAX

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ALUKE

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ANSTEE

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ATOTAL

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AUTOION

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AVOIGT

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BNLTE_low

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BNLTE_upp

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BNU

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COPBLU

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COPRED

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COPSTD

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EHVKT

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ENL4

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ENU4

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EXCIT

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EXCUP

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FRACT

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FREQ

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FREQLG

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GAMQST

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GAMRAD

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GAMVW

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GF

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GRAV

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H1FRACT

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H2molFRACT

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HE1FRACT

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HKT

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IDLHEL

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IFOP

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INDX_C

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ION

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IXAL1

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IXC1

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IXCH

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IXFE1

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IXH2

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IXH2mol

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IXH2pl

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IXHE2

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IXHE3

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IXHMIN

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IXMG1

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IXMG2

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IXN1

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IXNH

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IXO1

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IXOH

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IXSI1

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IXSI2

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LINEOP

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LTE_b

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MARK

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MOLWEIGHT

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MOTYPE

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NLINES

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NRHOX

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NRHOX_allocated

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NWAVE_C

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N_SPLIST

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NumberSpectralSegments

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PARTITION_FUNCTIONS

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PATH

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PATHLEN

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POTION

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RADIUS

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RAD_ATMO

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RHO

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RHOX

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RHO_eos

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SIGEL

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SIGH

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SIGH2

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SIGHE

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SPINDEX

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SPLIST

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STIM

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T

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TEFF

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TK

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TKEV

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TLOG

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VTURB

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VVOIGT

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VW_SCALE

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WFIRST

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WLAST

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WLCENT

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WLSTD

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Wlim_left

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Wlim_right

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XMASS

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XNA

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XNE

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XNE_eos

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YABUND

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allocated_NLTE_lines

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change_byte_order

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debug_print

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flagABUND

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flagCONTIN

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flagH2broad

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flagIONIZ

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flagLINELIST

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flagMODEL

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flagNLTE

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flagWLRANGE

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initNLTE

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lineOPACITIES

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result

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spname

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class pysme.cwrapper.IDL_DLL(*args, **kwargs)[source]

Bases: object

call(name, *args, raise_error=True, raise_warning=False, **kwargs)[source]

run idl_call_external and check for errors in the output

Parameters:

  • name (str) – name of the external C function to call

  • args – parameters for the function

  • kwargs – keywords for the function

Raises:

ValueError – If the returned string is not empty, it means an error occured in the C library

copy_state(state)[source]
free_state(state)[source]
get_interfaces()[source]
get_name(funname)[source]
static init(self, libfile=None)[source]
new_state()[source]
class pysme.cwrapper.IDL_String[source]

Bases: Structure

IDL strings are actually structures like this one, for correct passing of values we need to define this structure NOTE: the definition might have changed with IDL version, so make sure to use the same one as in the C code

s

Structure/Union member

slen

Structure/Union member

stype

Structure/Union member

pysme.cwrapper.get_c_dtype(ptr)[source]
pysme.cwrapper.get_c_shape(field, ptr, state)[source]
pysme.cwrapper.get_dtype(type)[source]

Get the ctypes dtype appropiate for the passed type string

Parameters:

type (str) – One of ‘int’, ‘short’, ‘long’, ‘float’, ‘double’, ‘unicode’ or one of the first letters ‘islfdu’

Returns:

type – corresponding ctypes type

Return type:

class

pysme.cwrapper.get_lib_name()[source]

Get the name of the sme C library

pysme.cwrapper.get_typenames(arg)[source]

Return internal typename based on the type of the argument strings -> “unicode” floating points -> “double” integers -> “int”

pysme.cwrapper.idl_call_external(funcname, *args, restype='str', type=None, lib=None, state=None)[source]

Call an external C library (here the SME lib) function that uses the IDL type interface i.e. restype func(int n, void *args[]), where n is the number of arguments, and args is a list of pointers to the arguments

Input arrays will be converted to the required datatype for the C function (see type keyword), and any changes to input arrays will be written back if possible. Input arrays that are already in the correct datatype will not be copied (and the values can therefore change in the C call)

Note that all strings are converted into IDL_String objects, even those that are in arrays

Parameters:

  • funcname (str) – Name of the function to call in the library

  • restype (str, optional) – expected type of the return value (default: “str”)

  • type (str, list(str), optional) – type of the input parameters, will default to int/double for all integer/floating point values. Accepted values are (‘short’, ‘int’, ‘long’, ‘float’, ‘double’, ‘unicode’) or their respective first letters. This means one can use a string as shorthand, e.g. “iidds”

Returns:

value – return value of the function call

Return type:

restype

pysme.cwrapper.is_nullptr(ptr)[source]

pysme.echelle module

Contains functions to read and modify echelle structures, just as in reduce

Mostly for compatibility reasons

pysme.echelle.calc_1dpolynomials(ncol, poly)[source]

Expand a set of 1d polynomials (one per order) seperately

Parameters:

  • ncol (int) – number of columns

  • poly (array[nord, degree]) – polynomial coefficients

Returns:

poly – expanded polynomials

Return type:

array[nord, ncol]

pysme.echelle.calc_2dpolynomial(solution2d)[source]

Expand a 2d polynomial, where the data is given in a REDUCE make_wave format Note that the coefficients are for order/100 and column/1000 respectively, where the order is counted from order base up

Parameters:

solution2d (array) – data in a REDUCE make_wave format: 0: version 1: number of columns 2: number of orders 3: order base, i.e. 0th order number 4-6: empty 7: number of cross coefficients 8: number of column only coefficients 9: number of order only coefficients 10: coefficient - constant 11-x: column coefficients x-y : order coefficients z- : cross coefficients (xy, xy2, x2y, x2y2, xy3, x3y), with x = orders, y = columns

Returns:

poly – expanded polynomial

Return type:

array[nord, ncol]

pysme.echelle.expand_polynomial(ncol, poly)[source]

Checks if and how to expand data poly, then expands the data if necessary

Parameters:

  • ncol (int) – number of columns in the image

  • poly (array[nord, ...]) – polynomial coefficients to expand, or already expanded data

Returns:

poly – expanded data

Return type:

array[nord, ncol]

pysme.echelle.read(fname, extension=1, raw=False, continuum_normalization=True, barycentric_correction=True, radial_velociy_correction=True)[source]

Read data from an echelle file Expand wavelength and continuum polynomials Apply barycentric/radial velocity correction Apply continuum normalization

Will load any fields in the binary table, however special attention is given only to specific names: “SPEC” : Spectrum “SIG” : Sigma, i.e. (absolute) uncertainty “CONT” : Continuum “WAVE” : Wavelength solution “COLUMNS” : Column range

Parameters:

  • fname (str) – filename to load

  • extension (int, optional) – fits extension of the data within the file (default: 1)

  • raw (bool, optional) – if true apply no corrections to the data (default: False)

  • continuum_normalization (bool, optional) – apply continuum normalization (default: True)

  • barycentric_correction (bool, optional) – apply barycentric correction (default: True)

  • radial_velociy_correction (bool, optional) – apply radial velocity correction (default: True)

Returns:

ech – Echelle structure, with data contained in attributes

Return type:

obj

pysme.echelle.save(fname, header, **kwargs)[source]

Save data in an Echelle fits, i.e. a fits file with a Binary Table in Extension 1

The data is passed in kwargs, with the name of the binary table column as the key Floating point data is saved as float32 (E), Integer data as int16 (I)

Parameters:

  • fname (str) – filename

  • header (fits.header) – FITS header

  • **kwargs (array[]) – data to be saved in the file

pysme.iliffe_vector module

class pysme.iliffe_vector.Iliffe_vector(values, offsets=None, dtype=None)[source]

Bases: NDArrayOperatorsMixin, MultipleDataExtension

Illiffe vectors are multidimensional (here 2D) but not necessarily rectangular Instead the index is a pointer to segments of a 1D array with varying sizes

all(*args, axis=None, **kwargs)[source]
any(*args, axis=None, **kwargs)[source]
astype(dtype)[source]
copy(*args, **kwargs)[source]
flatten(*args, **kwargs)[source]
classmethod from_dict(data)[source]

Convert from a dictionary

classmethod from_indices(array, indices)[source]
classmethod from_offsets(array, offsets)[source]
max(*args, axis=None, **kwargs)[source]
mean(*args, axis=None, **kwargs)[source]
min(*args, axis=None, **kwargs)[source]
ravel(*args, **kwargs)[source]
to_dict()[source]

Convert into a dictionary

where(*args, **kwargs)[source]
property dtype
property ndim
property nseg
property segments
property shape
property size
property sizes
pysme.iliffe_vector.implements(np_function)[source]

Register an __array_function__ implementation for DiagonalArray objects.

pysme.large_file_storage module

pysme.linelist module

pysme.nlte module

pysme.persistence module

class pysme.persistence.IPersist[source]

Bases: object

pysme.persistence.clean_temps()[source]
pysme.persistence.from_flex(ff, sme)[source]
pysme.persistence.get_typecode(dtype)[source]

Get the IDL typecode for a given dtype

pysme.persistence.load(fname, sme)[source]

Load the SME Structure from disk

Parameters:

  • fname (str) – file to load

  • sme (SME_Structure) – empty sme structure with default values set

Returns:

sme – loaded sme structure

Return type:

SME_Structure

pysme.persistence.load_v1(filename, data)[source]
pysme.persistence.loads_v1(file, data, names=None, folder='')[source]
pysme.persistence.save(filename, sme, format='flex', _async=False)[source]

Create a folder structure inside a tarfile See flex-format for details

Parameters:

  • filename (str) – Filename of the final file

  • sme (SME_Structure) – sme structure to save

  • compressed (bool, optional) – whether to compress the output

pysme.persistence.save_as_binary(arr)[source]
pysme.persistence.save_as_idl(sme, fname)[source]

Save the SME structure to disk as an idl save file

This writes a IDL script to a temporary file, which is then run with idl as a seperate process. Therefore this reqires a working idl installation.

There are two steps to this. First all the fields from the sme, structure need to be transformed into simple idl readable structures. All large arrays are stored in seperate binary files, for performance. The script then reads those files back into idl.

pysme.persistence.save_v1(filename, data, folder='', compressed=False)[source]

Create a folder structure inside a zipfile Add .json and .npy and .npz files with the correct names And subfolders for more complicated objects with the same layout Each class should have a save and a load method which can be used for this purpose

Parameters:

  • filename (str) – Filename of the final zipfile

  • data (SME_struct) – data to save

  • folder (str, optional) – subfolder to save data to

  • compressed (bool, optional) – whether to compress the output

pysme.persistence.saves_v1(file, data, folder='')[source]
pysme.persistence.toBaseType(value)[source]
pysme.persistence.to_flex(sme)[source]
pysme.persistence.write_as_idl(sme)[source]

Write SME structure into and idl format data arrays are stored in seperate temp files, and only the filename is passed to idl

pysme.sme module

pysme.sme_synth module

pysme.solve module

pysme.uncertainties module

pysme.uncertainties.gaussfit(x, y, nterms='none')[source]

Fit a simple gaussian to data

gauss(x, a, mu, sigma) = a * exp(-z**2/2) with z = (x - mu) / sigma

Parameters:

  • x (array(float)) – x values

  • y (array(float)) – y values

Returns:

fitted values for x, fit paramters (a, mu, sigma)

Return type:

gauss(x), parameters

pysme.uncertainties.uncertainties(pder, resid, unc, freep_name, plot=False)[source]

pysme.util module

Module contents