Source code for aspired.onedspec

# -*- coding: utf-8 -*-
import copy
import datetime
import logging
import os
import pkg_resources

import numpy as np
from plotly import graph_objects as go
from plotly import io as pio
from spectres import spectres
from scipy import optimize
from scipy.interpolate import interp1d

from .wavelength_calibration import WavelengthCalibration
from .flux_calibration import FluxCalibration
from .spectrum_oneD import SpectrumOneD
from .util import get_continuum

__all__ = ["OneDSpec"]


[docs]class OneDSpec: def __init__( self, verbose=True, logger_name="OneDSpec", log_level="INFO", log_file_folder="default", log_file_name=None, ): """ This class applies the wavelength calibrations and compute & apply the flux calibration to the extracted 1D spectra. The standard TwoDSpec object is not required for data reduction, but the flux calibrated standard observation will not be available for diagnostic. Parameters ---------- verbose: bool (Default: True) Set to False to suppress all verbose warnings, except for critical failure. logger_name: str (Default: 'OneDSpec') This will set the name of the logger, if the name is used already, it will reference to the existing logger. This will be the first part of the default log file name unless log_file_name is provided. log_level: str (Default: 'INFO') Four levels of logging are available, in decreasing order of information and increasing order of severity: (1) DEBUG, (2) INFO, (3) WARNING, (4) ERROR and (5) CRITICAL. WARNING means that there is suboptimal operations in some parts of that step. ERROR means that the requested operation cannot be performed, but the software can handle it by either using the default setting or skipping the operation. CRITICAL means that the requested operation cannot be resolved without human interaction, this is most usually coming from missing data. log_file_folder: None or str (Default: 'default') Folder in which the file is save, set to default to save to the current path. log_file_name: None or str (Default: None) File name of the log, set to None to print to screen only. """ # Set-up logger self.logger = logging.getLogger(logger_name) if (log_level == "CRITICAL") or (not verbose): self.logger.setLevel(logging.CRITICAL) elif log_level == "ERROR": self.logger.setLevel(logging.ERROR) elif log_level == "WARNING": self.logger.setLevel(logging.WARNING) elif log_level == "INFO": self.logger.setLevel(logging.INFO) elif log_level == "DEBUG": self.logger.setLevel(logging.DEBUG) else: raise ValueError("Unknonw logging level.") formatter = logging.Formatter( "[%(asctime)s] %(levelname)s [%(filename)s:%(lineno)d] " "%(message)s", datefmt="%a, %d %b %Y %H:%M:%S", ) if log_file_name is None: # Only print log to screen self.handler = logging.StreamHandler() else: if log_file_name == "default": log_file_name = "{}_{}.log".format( logger_name, datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S"), ) # Save log to file if log_file_folder == "default": log_file_folder = "" self.handler = logging.FileHandler( os.path.join(log_file_folder, log_file_name), "a+" ) self.handler.setFormatter(formatter) if self.logger.hasHandlers(): self.logger.handlers.clear() self.logger.addHandler(self.handler) self.verbose = verbose self.logger_name = logger_name self.log_level = log_level self.log_file_folder = log_file_folder self.log_file_name = log_file_name # Initialise empty calibration objects self.science_wavecal = {} self.standard_wavecal = WavelengthCalibration( verbose=self.verbose, logger_name=self.logger_name, log_level=self.log_level, log_file_folder=self.log_file_folder, log_file_name=self.log_file_name, ) self.fluxcal = FluxCalibration( verbose=self.verbose, logger_name=self.logger_name, log_level=self.log_level, log_file_folder=self.log_file_folder, log_file_name=self.log_file_name, ) # Create empty dictionary self.science_spectrum_list = {} self.standard_spectrum_list = { 0: SpectrumOneD( spec_id=0, verbose=self.verbose, logger_name=self.logger_name, log_level=self.log_level, log_file_folder=self.log_file_folder, log_file_name=self.log_file_name, ) } self.add_science_spectrum1D(0) # Link them up self.science_wavecal[0].from_spectrum1D(self.science_spectrum_list[0]) self.standard_wavecal.from_spectrum1D(self.standard_spectrum_list[0]) self.fluxcal.from_spectrum1D(self.standard_spectrum_list[0]) # Tracking data availability self.science_data_available = False self.standard_data_available = False self.science_arc_available = False self.standard_arc_available = False self.science_trace_available = False self.standard_trace_available = False self.science_arc_spec_available = False self.standard_arc_spec_available = False self.science_arc_lines_available = False self.standard_arc_lines_available = False self.science_atlas_available = False self.standard_atlas_available = False self.science_hough_pairs_available = False self.standard_hough_pairs_available = False self.science_wavecal_polynomial_available = False self.standard_wavecal_polynomial_available = False self.science_wavelength_calibrated = False self.standard_wavelength_calibrated = False self.science_wavelength_resampled = False self.standard_wavelength_resampled = False # This concerns the extinction itself self.atmospheric_extinction_correction_available = False self.telluric_profile_available = False self.telluric_strength_available = False # This concerns the spectrum being corrected or not self.atmospheric_extinction_corrected = False self.science_telluric_corrected = False self.standard_telluric_corrected = False self.extinction_fraction = 1.0 self.sensitivity_curve_available = False self.science_flux_calibrated = False self.standard_flux_calibrated = False self.science_flux_resampled = False self.standard_flux_calibrated = False
[docs] def add_science_spectrum1D(self, spec_id): """ Add a new SpectrumOneD with the ID spec_id. This overwrite the existing SpectrumOneD object if it already exists. Parameters ---------- spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object """ # Create the wavelength calibration object for the given spec_id self.science_wavecal.update( { spec_id: WavelengthCalibration( verbose=self.verbose, logger_name=self.logger_name, log_level=self.log_level, log_file_folder=self.log_file_folder, log_file_name=self.log_file_name, ) } ) # Create the SpectrumOneD object for the given spec_id self.science_spectrum_list.update( { spec_id: SpectrumOneD( spec_id=spec_id, verbose=self.verbose, logger_name=self.logger_name, log_level=self.log_level, log_file_folder=self.log_file_folder, log_file_name=self.log_file_name, ) } ) # Reference the wavecal to the SpectrumOneD object just created self.science_wavecal[spec_id].from_spectrum1D( self.science_spectrum_list[spec_id] ) self.logger.info( "spectrm1D object is added to spec_id: {}".format(spec_id) )
[docs] def add_fluxcalibration(self, fluxcal): """ Provide the pre-calibrated FluxCalibration object. Parameters ---------- fluxcal: FluxCalibration object The true mag/flux values. """ if type(fluxcal) == FluxCalibration: self.fluxcal = fluxcal self.logger.info("fluxcal object is added") else: err_msg = "Please provide a valid FluxCalibration object" self.logger.critical(err_msg) raise TypeError(err_msg)
[docs] def add_wavelengthcalibration( self, wavecal, spec_id=None, stype="science+standard" ): """ Provide the pre-calibrated WavelengthCalibration object. Parameters ---------- wavecal: list of WavelengthCalibration object The WavelengthPolyFit object for the science target, flux will not be calibrated if this is not provided. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if type(wavecal) == WavelengthCalibration: wavecal = [wavecal] elif type(wavecal) == list: pass else: err_msg = ( "Please provide a WavelengthCalibration object or " + "a list of them." ) self.logger.critical(err_msg) raise TypeError(err_msg) stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(wavecal) == len(spec_id): wavecal = {spec_id[i]: wavecal[i] for i in range(len(spec_id))} elif len(wavecal) == 1: wavecal = {spec_id[i]: wavecal[0] for i in range(len(spec_id))} else: error_msg = ( "wavecal must be the same length of shape " + "as spec_id." ) self.logger.critical(error_msg) raise ValueError(error_msg) for i in spec_id: if type(wavecal[i]) == WavelengthCalibration: self.science_wavecal[i] = wavecal[i] self.logger.info( "Added WavelengthCalibration to the " "science_spectrum_list for spec_id: {}.".format(i) ) else: err_msg = ( "Please provide a valid " + "WavelengthCalibration object." ) self.logger.critical(err_msg) raise TypeError(err_msg) if "standard" in stype_split: if type(wavecal[0]) == WavelengthCalibration: self.standard_wavecal = wavecal[0] self.logger.info( "Added WavelengthCalibration to " "the standard spectrum_list." ) else: err_msg = ( "Please provide a valid " + "WavelengthCalibration object" ) self.logger.critical(err_msg) raise TypeError(err_msg)
[docs] def add_wavelength(self, wave, spec_id=None, stype="science+standard"): """ Three combinations of wave and spec_id shapes are accepted. +-----------+-----------------+ | Parameter | Size | +-----------+-----+-----+-----+ | wave | 1 | 1 | N | +-----------+-----+-----+-----+ | spec_id | 1 | N | N | +-----------+-----+-----+-----+ Parameters ---------- wave : numeric value, list or numpy 1D array (N) The wavelength of each pixels of the spectrum. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if type(wave) == np.ndarray: wave = [wave] elif type(wave) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) stype_split = stype.split("+") if "science" in stype_split: if self.science_data_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(wave) == len(spec_id): wave = {spec_id[i]: wave[i] for i in range(len(spec_id))} elif len(wave) == 1: wave = {spec_id[i]: wave[0] for i in range(len(spec_id))} else: error_msg = ( "wave must be the same length of shape " + "as spec_id." ) self.logger.critical(error_msg) raise ValueError(error_msg) for i in spec_id: if len(wave[i]) == len( self.science_spectrum_list[i].count ): self.science_spectrum_list[i].add_wavelength( wave=wave[i] ) self.logger.info( "Added wavelength list to the " "science_spectrum_list for spec_id: {}.".format(i) ) else: err_msg = ( "The wavelength provided has a different " + "size to that of the extracted science spectrum." ) self.logger.critical(err_msg) raise RuntimeError(err_msg) self.science_wavelength_calibrated = True else: err_msg = ( "science data is not available, wavelength " + "cannot be added." ) self.logger.critical(err_msg) raise RuntimeError(err_msg) if "standard" in stype_split: if self.standard_data_available: if len(wave[0]) == len(self.standard_spectrum_list[0].count): self.standard_spectrum_list[0].add_wavelength(wave=wave[0]) else: err_msg = ( "The wavelength provided is of a different " + "size to that of the extracted standard spectrum." ) self.logger.critical(err_msg) raise RuntimeError(err_msg) self.standard_wavelength_calibrated = True else: err_msg = ( "standard data is not available, wavelength " + "cannot be added." ) self.logger.critical(err_msg) raise RuntimeError(err_msg)
[docs] def add_wavelength_resampled( self, wave_resampled, spec_id=None, stype="science+standard" ): """ Three combinations of wave and spec_id shapes are accepted. +-----------+-----------------+ | Parameter | Size | +-----------+-----+-----+-----+ | wave | 1 | 1 | N | +-----------+-----+-----+-----+ | spec_id | 1 | N | N | +-----------+-----+-----+-----+ Parameters ---------- wave_resampled: The wavelength of the resampled spectrum. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if type(wave_resampled) == np.ndarray: wave_resampled = [wave_resampled] elif type(wave_resampled) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) stype_split = stype.split("+") if "science" in stype_split: if self.science_data_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(wave_resampled) == len(spec_id): wave_resampled = { spec_id[i]: wave_resampled[i] for i in range(len(spec_id)) } elif len(wave_resampled) == 1: wave_resampled = { spec_id[i]: wave_resampled[0] for i in range(len(spec_id)) } else: error_msg = ( "wave must be the same length of shape " + "as spec_id." ) self.logger.critical(error_msg) raise ValueError(error_msg) for i in spec_id: if len(wave_resampled[i]) == len( self.science_spectrum_list[i].count ): self.science_spectrum_list[i].add_wavelength_resampled( wave_resampled=wave_resampled[i] ) self.logger.info( "Added wavelength_resampled list to " "the science_spectrum_list for spec_id: " "{}.".format(i) ) else: err_msg = ( "The wavelength provided has a different " + "size to that of the extracted science spectrum." ) self.logger.critical(err_msg) raise RuntimeError(err_msg) self.science_wavelength_resampled = True else: err_msg = ( "science data is not available, " + "wavelength_resampled cannot be added." ) self.logger.critical(err_msg) raise RuntimeError(err_msg) if "standard" in stype_split: if self.standard_data_available: if len(wave_resampled[0]) == len( self.standard_spectrum_list[0].count ): self.standard_spectrum_list[0].add_wavelength_resampled( wave_resampled=wave_resampled[0] ) self.logger.info( "Added wavelength list to the " "standard_spectrum_list." ) else: err_msg = ( "The wavelength provided is of a different " + "size to that of the extracted standard spectrum." ) self.logger.critical(err_msg) raise RuntimeError(err_msg) self.standard_wavelength_resampled_calibrated = True else: err_msg = ( "standard data is not available, " + "wavelength_resampled cannot be added." ) self.logger.critical(err_msg) raise RuntimeError(err_msg)
[docs] def add_spec( self, count, count_err=None, count_sky=None, spec_id=None, stype="science+standard", ): """ Parameters ---------- count: 1-d array The summed count at each column about the trace. count_err: 1-d array (Default: None) the uncertainties of the count values count_sky: 1-d array (Default: None) The integrated sky values along each column, suitable for subtracting from the output of ap_extract spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if type(count) == np.ndarray: count = [count] elif type(count) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) if count_err is not None: if type(count_err) == np.ndarray: count_err = [count_err] elif type(count_err) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) else: count_err = [None] if count_sky is not None: if type(count_sky) == np.ndarray: count_sky = [count_sky] elif type(count_sky) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) else: count_sky = [None] stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): for i in spec_id: if i not in list(self.science_spectrum_list.keys()): self.add_science_spectrum1D(i) self.logger.warning( "The given spec_id, {}, does not exist. A new " "spectrum1D is created. Please check you are " "providing the correct spec_id.".format( spec_id ) ) else: pass else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the count and spec_id and convert count # into a dictionary if len(count) == len(spec_id): count = {spec_id[i]: count[i] for i in range(len(spec_id))} elif len(count) == 1: count = {spec_id[i]: count[0] for i in range(len(spec_id))} else: error_msg = ( "count must be the same length of shape " + "as spec_id, or of size 1." ) self.logger.critical(error_msg) raise RuntimeError(error_msg) # Check the sizes of the count_sky and spec_id and convert # count_sky into a dictionary if count_sky is [None]: count_sky = {spec_id[i]: None for i in range(len(spec_id))} elif len(count_sky) == len(spec_id): count_sky = { spec_id[i]: count_sky[i] for i in range(len(spec_id)) } elif len(count_sky) == 1: count_sky = { spec_id[i]: count_sky[0] for i in range(len(spec_id)) } else: error_msg = ( "count_sky must be the same length of shape " + "as spec_id, or of size 1." ) self.logger.critical(error_msg) raise RuntimeError(error_msg) # Check the sizes of the count_err and spec_id and convert # count_err into a dictionary if count_err is [None]: count_err = {spec_id[i]: None for i in range(len(spec_id))} elif len(count_err) == len(spec_id): count_err = { spec_id[i]: count_err[i] for i in range(len(spec_id)) } elif len(count_err) == 1: count_err = { spec_id[i]: count_err[0] for i in range(len(spec_id)) } else: error_msg = ( "count_err must be the same length of shape " + "as spec_id, or of size 1." ) self.logger.critical(error_msg) raise RuntimeError(error_msg) for i in spec_id: self.science_spectrum_list[i].add_count( count=count[i], count_err=count_err[i], count_sky=count_sky[i], ) self.logger.info( "Added count, count_err, and count_sky to" "science_spectrum_list for spec_id: {}.".format(i) ) self.science_data_available = True if "standard" in stype_split: self.standard_spectrum_list[0].add_count( count=count[0], count_err=count_err[0], count_sky=count_sky[0] ) self.logger.info( "Added count, count_err, and count_sky to " "standard_spectrum_list." ) self.standard_data_available = True
[docs] def add_arc_spec(self, arc_spec, spec_id=None, stype="science+standard"): """ Parameters ---------- arc_spec: 1-d array The count of the summed 1D arc spec spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if isinstance(arc_spec, np.ndarray): arc_spec = [arc_spec] elif isinstance(arc_spec, list): pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): for i in spec_id: if i not in list(self.science_spectrum_list.keys()): self.add_science_spectrum1D(i) self.logger.warning( "The given spec_id, {}, does not " "exist. A new spectrum1D is created. " "Please check you are providing the " "correct spec_id.".format(spec_id) ) else: pass else: pass else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(arc_spec) == len(spec_id): arc_spec = { spec_id[i]: arc_spec[i] for i in range(len(spec_id)) } elif len(arc_spec) == 1: arc_spec = { spec_id[i]: arc_spec[0] for i in range(len(spec_id)) } else: error_msg = ( "arc_spec must be the same length or shape as spec_id. " + "arc_spec has shape {} and ".format(np.shape(arc_spec)) + "spec_id has shape {}.".format(np.shape(spec_id)) ) self.logger.critical(error_msg) raise ValueError(error_msg) for i in spec_id: self.science_spectrum_list[i].add_arc_spec( arc_spec=arc_spec[i] ) self.logger.info( "Added arc_spec to" "science_spectrum_list for spec_id: {}.".format(i) ) self.science_arc_spec_available = True if "standard" in stype_split: self.standard_spectrum_list[0].add_arc_spec(arc_spec=arc_spec[0]) self.logger.info("Added arc_spec to" "standard_spectrum_list.") self.standard_arc_spec_available = True
[docs] def add_arc_lines(self, peaks, spec_id=None, stype="science+standard"): """ Parameters ---------- peaks: list of list or list of arrays The pixel locations of the arc lines. Multiple traces of the arc can be provided as list of list or list of arrays. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if type(peaks) == np.ndarray: peaks = [peaks] elif type(peaks) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): for i in spec_id: if i not in list(self.science_spectrum_list.keys()): self.add_science_spectrum1D(i) self.logger.warning( "The given spec_id, {}, does not " "exist. A new spectrum1D is created. " "Please check you are providing the " "correct spec_id.".format(spec_id) ) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(peaks) == len(spec_id): peaks = {spec_id[i]: peaks[i] for i in range(len(spec_id))} elif len(peaks) == 1: peaks = {i: peaks[0] for i in spec_id} else: error_msg = ( "peaks must be the same length of shape " + "as spec_id." ) self.logger.critical(error_msg) raise RuntimeError(error_msg) for i in spec_id: self.science_spectrum_list[i].add_peaks(peaks=peaks[i]) self.logger.info( "Added peaks to" "science_spectrum_list for spec_id: {}.".format(i) ) self.science_arc_lines_available = True if "standard" in stype_split: self.standard_spectrum_list[0].add_peaks(peaks=peaks[0]) self.logger.info("Added peaks to standard_spectrum_list.") self.standard_arc_lines_available = True
[docs] def add_trace( self, trace, trace_sigma, pixel_list=None, spec_id=None, stype="science+standard", ): """ Parameters ---------- trace: list or numpy.ndarray (N) The spatial pixel value (can be sub-pixel) of the trace at each spectral position. trace_sigma: list or numpy.ndarray (N) Standard deviation of the Gaussian profile of a trace pixel_list: list or numpy.narray (Default: None) The pixel position of the trace in the dispersion direction. This should be provided if you wish to override the default range(len(spec.trace[0])), for example, in the case of accounting for chip gaps (10 pixels) in a 3-CCD setting, you should provide [0,1,2,...90, 100,101,...190, 200,201,...290] spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if type(trace) == np.ndarray: trace = [trace] elif type(trace) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) if type(trace_sigma) == np.ndarray: trace_sigma = [trace_sigma] elif type(trace_sigma) == list: pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): for i in spec_id: if i not in list(self.science_spectrum_list.keys()): self.add_science_spectrum1D(i) self.logger.warning( "The given spec_id, {}, does not " "exist. A new spectrum1D is created. " "Please check you are providing the " "correct spec_id.".format(spec_id) ) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(trace) == len(spec_id): trace = {spec_id[i]: trace[i] for i in range(len(spec_id))} elif len(trace) == 1: trace = {spec_id[i]: trace[0] for i in range(len(spec_id))} else: error_msg = ( "trace must be the same length of shape " + "as spec_id." ) self.logger.critical(error_msg) raise RuntimeError(error_msg) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(trace_sigma) == len(spec_id): trace_sigma = { spec_id[i]: trace_sigma[i] for i in range(len(spec_id)) } elif len(trace_sigma) == 1: trace_sigma = { spec_id[i]: trace_sigma[0] for i in range(len(spec_id)) } else: error_msg = ( "wave must be the same length of shape " + "as spec_id." ) self.logger.critical(error_msg) raise ValueError(error_msg) for i in spec_id: self.science_spectrum_list[i].add_trace( trace=trace[i], trace_sigma=trace_sigma[i], pixel_list=pixel_list, ) self.logger.info( "Added trace, trace_sigma, and pixel_list to" "science_spectrum_list for spec_id: {}.".format(i) ) self.science_trace_available = True if "standard" in stype_split: self.standard_spectrum_list[0].add_trace( trace=trace[0], trace_sigma=trace_sigma[0], pixel_list=pixel_list, ) self.logger.info( "Added trace, trace_sigma, and pixel_list to" "standard_spectrum_list" ) self.standard_trace_available = True
[docs] def add_fit_coeff( self, fit_coeff, fit_type="poly", spec_id=None, stype="science+standard", ): """ Parameters ---------- fit_coeff: list or numpy array, or a list of them Polynomial fit coefficients. fit_type: str or list of str Strings starting with 'poly', 'leg' or 'cheb' for polynomial, legendre and chebyshev fits. Case insensitive. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if type(fit_coeff) == np.ndarray: fit_coeff = [fit_coeff] elif all(isinstance(i, list) for i in fit_coeff): pass elif isinstance(fit_coeff, list): if isinstance(fit_coeff[0], (list, np.ndarray)): pass elif isinstance(fit_coeff[0], (int, float)): fit_coeff = [fit_coeff] else: pass elif all(isinstance(i, np.ndarray) for i in fit_coeff): pass else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) if type(fit_type) == str: fit_type = [fit_type] elif all(isinstance(i, (str, list, np.ndarray)) for i in fit_type): for i, ft in enumerate(fit_type): if isinstance(ft, (list, np.ndarray)): fit_type[i] = ft[0] else: err_msg = "Please provide a numpy array or a list of them." self.logger.critical(err_msg) raise TypeError(err_msg) stype_split = stype.split("+") if "science" in stype_split: if self.science_data_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(fit_coeff) == len(spec_id): fit_coeff = { spec_id[i]: fit_coeff[i] for i in range(len(spec_id)) } elif len(fit_coeff) == 1: fit_coeff = { spec_id[i]: fit_coeff[0] for i in range(len(spec_id)) } else: error_msg = ( "fit_coeff must be the same length of " + "shape as spec_id." ) self.logger.critical(error_msg) raise RuntimeError(error_msg) # Check the sizes of the wave and spec_id and convert wave # into a dictionary if len(fit_type) == len(spec_id): fit_type = { spec_id[i]: fit_type[i] for i in range(len(spec_id)) } elif len(fit_type) == 1: fit_type = { spec_id[i]: fit_type[0] for i in range(len(spec_id)) } else: error_msg = ( "wave must be the same length of shape " + "as spec_id." ) self.logger.critical(error_msg) raise ValueError(error_msg) for i in spec_id: self.science_spectrum_list[i].add_fit_coeff( fit_coeff=fit_coeff[i] ) self.science_spectrum_list[i].add_fit_type( fit_type=fit_type[i] ) self.logger.info( "Added fit_coeff and fit_type to" "science_spectrum_list for spec_id: {}.".format(i) ) self.science_wavecal_polynomial_available = True if "standard" in stype_split: if self.standard_data_available: self.standard_spectrum_list[0].add_fit_coeff( fit_coeff=fit_coeff[0] ) self.standard_spectrum_list[0].add_fit_type( fit_type=fit_type[0] ) self.logger.info( "Added fit_coeff and fit_type to" "standard_spectrum_list." ) self.standard_wavecal_polynomial_available = True
[docs] def from_twodspec(self, twodspec, spec_id=None, stype="science+standard"): """ To add a TwoDSpec object or numpy array to provide the traces, line spread function of the traces, optionally the pixel values correcponding to the traces. If the arc is provided, the saxis and flip properties of the TwoDSpec will be applied to the arc, and then the spec_mask and the spatial_mask from the TwoDSpec object will be applied. Parameters ---------- twodspec: TwoDSpec object TwoDSpec of the science image containin the trace(s) and trace_sigma(s). spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(twodspec.spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(twodspec.spectrum_list.keys()) # reference the spectrum1D to the WavelengthCalibration for i in spec_id: self.add_science_spectrum1D(i) self.science_wavecal[i] = WavelengthCalibration( verbose=self.verbose, logger_name=self.logger_name, log_level=self.log_level, log_file_folder=self.log_file_folder, log_file_name=self.log_file_name, ) # By reference self.science_wavecal[i].from_spectrum1D( twodspec.spectrum_list[i] ) self.science_spectrum_list[i] = self.science_wavecal[ i ].spectrum1D self.logger.info( "Referenced SpectrumOneD of the" "science_spectrum_list for spec_id: {}.".format(i) + "to the corresponding science_wavecal." ) self.science_data_available = True self.science_arc_available = True self.science_arc_spec_available = True if "standard" in stype_split: # By reference self.standard_wavecal = WavelengthCalibration( verbose=self.verbose, logger_name=self.logger_name, log_level=self.log_level, log_file_folder=self.log_file_folder, log_file_name=self.log_file_name, ) self.standard_wavecal.from_spectrum1D(twodspec.spectrum_list[0]) self.fluxcal.from_spectrum1D(twodspec.spectrum_list[0]) self.standard_spectrum_list[0] = self.standard_wavecal.spectrum1D self.logger.info( "Referenced SpectrumOneD of the" "standard_spectrum_list to the standard_wavecal." ) self.standard_data_available = True self.standard_arc_available = True self.standard_arc_spec_available = True
[docs] def find_arc_lines( self, prominence=5.0, top_n_peaks=None, distance=5.0, refine=False, refine_window_width=5, display=False, width=1280, height=720, return_jsonstring=False, renderer="default", save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, spec_id=None, stype="science+standard", ): """ Parameters ---------- background: int or None (Default: None) User-supplied estimated background level percentile: float (Default: 2.) The percentile of the flux to be used as the estimate of the background sky level to the first order. Only used if background is None. [Count] prominence: float (Default: 5.) The minimum prominence to be considered as a peak (normalised) distance: float (Default: 5.) Minimum separation between peaks refine: bool (Default: True) Set to true to fit a gaussian to get the peak at sub-pixel precision refine_window_width: int or float (Default: 5) The number of pixels (on each side of the existing peaks) to be fitted with gaussian profiles over. display: bool (Default: False) Set to True to display disgnostic plot. renderer: str (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs return_jsonstring: bool (Default: False) set to True to return JSON-string that can be rendered by Plotly in any support language. renderer: str (Default: 'default') plotly renderer options. save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str or None (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter Returns ------- JSON strings if return_jsonstring is set to True """ stype_split = stype.split("+") if "science" in stype_split: if self.science_arc_spec_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: print(list(self.science_spectrum_list.keys())) if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].find_arc_lines( prominence=prominence, top_n_peaks=top_n_peaks, distance=distance, refine=refine, refine_window_width=refine_window_width, display=display, renderer=renderer, width=width, height=height, return_jsonstring=return_jsonstring, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) n_peaks = len(self.science_spectrum_list[i].peaks) self.logger.info( "{} arc lines are found in ".format(n_peaks) + "science_spectrum_list for spec_id: {}.".format(i) ) self.science_arc_lines_available = True else: self.logger.warning("Science arc spectrum/a are not imported.") if "standard" in stype_split: if self.standard_arc_spec_available: self.standard_wavecal.find_arc_lines( prominence=prominence, top_n_peaks=top_n_peaks, distance=distance, refine=refine, refine_window_width=refine_window_width, display=display, renderer=renderer, width=width, height=height, return_jsonstring=return_jsonstring, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) n_peaks = len(self.standard_spectrum_list[0].peaks) self.logger.info( "{} arc lines are found in ".format(n_peaks) + "standard_spectrum_list." ) self.standard_arc_lines_available = True else: self.logger.warning( "Standard arc spectrum/a are not imported." )
[docs] def inspect_arc_lines( self, display=False, width=1280, height=720, return_jsonstring=False, renderer="default", save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, spec_id=None, stype="science+standard", ): """ Parameters ---------- display: bool (Default: False) Set to True to display disgnostic plot. renderer: str (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs return_jsonstring: bool (Default: False) set to True to return JSON-string that can be rendered by Plotly in any support language. renderer: str (Default: 'default') plotly renderer options. save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str or None (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter Returns ------- JSON strings if return_jsonstring is set to True """ stype_split = stype.split("+") if "science" in stype_split: if self.science_arc_lines_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].inspect_arc_lines( display=display, width=width, height=height, return_jsonstring=return_jsonstring, renderer=renderer, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) else: self.logger.warning("Science arc spectrum/a are not imported.") if "standard" in stype_split: if self.standard_arc_lines_available: self.standard_wavecal.inspect_arc_lines( display=display, width=width, height=height, return_jsonstring=return_jsonstring, renderer=renderer, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) else: self.logger.warning( "Standard arc spectrum/a are not imported." )
[docs] def initialise_calibrator( self, peaks=None, arc_spec=None, spec_id=None, stype="science+standard" ): """ If the peaks were found with find_arc_lines(), peaks and spectrum can be None. Parameters ---------- peaks: list, numpy.ndarray or None (Default: None) The pixel values of the peaks (start from zero) spectrum: list, numpy.ndarray or None (Default: None) The spectral intensity as a function of pixel. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): for i in spec_id: if i not in list(self.science_spectrum_list.keys()): self.add_science_spectrum1D(i) self.logger.warning( "The given spec_id, {}, does not " "exist. A new spectrum1D is created. " "Please check you are providing the " "correct spec_id.".format(spec_id) ) else: pass else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].from_spectrum1D( self.science_spectrum_list[i] ) self.science_wavecal[i].initialise_calibrator( peaks=peaks, arc_spec=arc_spec ) self.science_wavecal[i].set_calibrator_properties() self.science_wavecal[i].set_hough_properties() self.science_wavecal[i].set_ransac_properties() self.logger.info( "Calibrator is initialised for " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_wavecal.from_spectrum1D( self.standard_spectrum_list[0] ) self.standard_wavecal.initialise_calibrator( peaks=peaks, arc_spec=arc_spec ) self.standard_wavecal.set_calibrator_properties() self.standard_wavecal.set_hough_properties() self.standard_wavecal.set_ransac_properties() self.logger.info( "Calibrator is initialised for the " "standard_spectrum_list." )
[docs] def set_calibrator_properties( self, num_pix=None, pixel_list=None, plotting_library="plotly", logger_name="Calibrator", log_level="info", spec_id=None, stype="science+standard", ): """ Parameters ---------- num_pix: int (Default: None) The number of pixels in the dispersion direction pixel_list: list or numpy array (Default: None) The pixel position of the trace in the dispersion direction. This should be provided if you wish to override the default range(num_pix), for example, in the case of accounting for chip gaps (10 pixels) in a 3-CCD setting, you should provide [0,1,2,...90, 100,101,...190, 200,201,...290] plotting_library : str (Default: 'plotly') Choose between matplotlib and plotly. logger_name: str (Default: 'Calibrator') This will set the name of the logger, if the name is used already, it will reference to the existing logger. This will be the first part of the default log file name unless log_file_name is provided. log_level : str (Default: 'info') Choose {critical, error, warning, info, debug, notset}. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].set_calibrator_properties( num_pix=num_pix, pixel_list=pixel_list, plotting_library=plotting_library, logger_name=logger_name, log_level=log_level, ) self.logger.info( "Calibrator properties are set for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_wavecal.set_calibrator_properties( num_pix=num_pix, pixel_list=pixel_list, plotting_library=plotting_library, logger_name=logger_name, log_level=log_level, ) self.logger.info( "Calibrator properties are set for the " "standard_spectrum_list." )
[docs] def set_hough_properties( self, num_slopes=5000, xbins=200, ybins=200, min_wavelength=3000.0, max_wavelength=10000.0, range_tolerance=500, linearity_tolerance=100, spec_id=None, stype="science+standard", ): """ Parameters ---------- num_slopes: int (Default: 5000) Number of slopes to consider during Hough transform xbins: int (Default: 200) Number of bins for Hough accumulation ybins: int (Default: 200) Number of bins for Hough accumulation min_wavelength: float (Default: 3000.) Minimum wavelength of the spectrum. max_wavelength: float (Default: 10000.) Maximum wavelength of the spectrum. range_tolerance: float (Default: 500) Estimation of the error on the provided spectral range e.g. 3000-5000 with tolerance 500 will search for solutions that may satisfy 2500-5500 linearity_tolerance: float (Default: 100) A toleranceold (Ansgtroms) which defines some padding around the range tolerance to allow for non-linearity. This should be the maximum expected excursion from linearity. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].set_hough_properties( num_slopes=num_slopes, xbins=xbins, ybins=ybins, min_wavelength=min_wavelength, max_wavelength=max_wavelength, range_tolerance=range_tolerance, linearity_tolerance=linearity_tolerance, ) self.logger.info( "Hough properties are set for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_wavecal.set_hough_properties( num_slopes=num_slopes, xbins=xbins, ybins=ybins, min_wavelength=min_wavelength, max_wavelength=max_wavelength, range_tolerance=range_tolerance, linearity_tolerance=linearity_tolerance, ) self.logger.info( "Hough properties are set for the " "standard_spectrum_list." )
[docs] def set_ransac_properties( self, sample_size=5, top_n_candidate=5, linear=True, filter_close=False, ransac_tolerance=5, candidate_weighted=True, hough_weight=1.0, minimum_matches=3, minimum_peak_utilisation=0.0, minimum_fit_error=1e-4, spec_id=None, stype="science+standard", ): """ Configure the Calibrator. This may require some manual twiddling before the calibrator can work efficiently. However, in theory, a large max_tries in fit() should provide a good solution in the expense of performance (minutes instead of seconds). Parameters ---------- sample_size: int (Default: 5) Number of pixel-wavelength hough pairs to be used for each arc line being picked. top_n_candidate: int (Default: 5) Top ranked lines to be fitted. linear: bool (Default: True) True to use the hough transformed gradient, otherwise, use the known polynomial. filter_close: bool (Default: False) Remove the pairs that are out of bounds in the hough space. ransac_tolerance: float (Default: 5) The distance criteria (Angstroms) to be considered an inlier to a fit. This should be close to the size of the expected residuals on the final fit (e.g. 1A is typical) candidate_weighted: bool (Default: True) Set to True to down-weight pairs that are far from the fit. hough_weight: float or None (Default: 1.0) Set to use the hough space to weigh the fit. The theoretical optimal weighting is unclear. The larger the value, the heavily it relies on the overdensity in the hough space for a good fit. minimum_matches: int (Default: 3) Minimum number of fitted peaks to accept as a solution. This has to be smaller than or equal to the sample size. minimum_peak_utilisation: float (Default: 0.) The minimum percentage of peaks used in order to accept as a valid solution. minimum_fit_error: float (Default 1e-4) Set to remove overfitted/unrealistic fits. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].set_ransac_properties( sample_size=sample_size, top_n_candidate=top_n_candidate, linear=linear, filter_close=filter_close, ransac_tolerance=ransac_tolerance, candidate_weighted=candidate_weighted, hough_weight=hough_weight, minimum_matches=minimum_matches, minimum_peak_utilisation=minimum_peak_utilisation, minimum_fit_error=minimum_fit_error, ) self.logger.info( "Ransac properties are set for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_wavecal.set_ransac_properties( sample_size=sample_size, top_n_candidate=top_n_candidate, linear=linear, filter_close=filter_close, ransac_tolerance=ransac_tolerance, candidate_weighted=candidate_weighted, hough_weight=hough_weight, minimum_matches=minimum_matches, minimum_peak_utilisation=minimum_peak_utilisation, minimum_fit_error=minimum_fit_error, ) self.logger.info( "Ransac properties are set for the " "standard_spectrum_list." )
[docs] def set_known_pairs( self, pix=None, wave=None, spec_id=None, stype="science+standard" ): """ Parameters ---------- pix : numeric value, list or numpy 1D array (N) (Default: None) Any pixel value, can be outside the detector chip and serve purely as anchor points. wave : numeric value, list or numpy 1D array (N) (Default: None) The matching wavelength for each of the pix. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].set_known_pairs(pix=pix, wave=wave) self.logger.info( "Known pixel-wavelength pairs are added to " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_wavecal.set_known_pairs(pix=pix, wave=wave) self.logger.info( "Known pixel-wavelength pairs are added to " "standard_spectrum_list." )
[docs] def add_user_atlas( self, elements, wavelengths, intensities=None, candidate_tolerance=10.0, constrain_poly=False, vacuum=False, pressure=101325.0, temperature=273.15, relative_humidity=0.0, spec_id=None, stype="science+standard", ): """ Append the user supplied arc lines to the calibrator. The vacuum to air wavelength conversion is deafult to False because observatories usually provide the line lists in the respective air wavelength, as the corrections from temperature and humidity are small. See https://emtoolbox.nist.gov/Wavelength/Documentation.asp Parameters ---------- elements : list Element (required). Preferably a standard (i.e. periodic table) name for convenience with built-in atlases wavelengths : list Wavelength to add (Angstrom) intensities : list or None (Default: None) Relative line intensities candidate_tolerance: float (Default: 10.) toleranceold (Angstroms) for considering a point to be an inlier during candidate peak/line selection. This should be reasonable small as we want to search for candidate points which are *locally* linear. constrain_poly: bool (Default: False) Apply a polygonal constraint on possible peak/atlas pairs vacuum: bool (Default: False) Set to true to convert the input wavelength to air-wavelengths based on the given pressure, temperature and humidity. pressure: float (Default: 101325.) Pressure when the observation took place, in Pascal. If it is not known, assume 10% decrement per 1000 meter altitude temperature: float (Default: 273.15) Temperature when the observation took place, in Kelvin. relative_humidity: float (Default: 0.) In percentage. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if pressure is None: pressure = 101325.0 self.logger.warning( "Pressure is not provided, set to 1 unit of " "standard atmosphere." ) if temperature is None: temperature = 273.15 self.logger.warning( "Temperature is not provided, set to 0 degrees " "Celsius." ) if relative_humidity is None: relative_humidity = 0.0 self.logger.warning( "Relative humidity is not provided, set to 0%." ) stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].add_user_atlas( elements=elements, wavelengths=wavelengths, intensities=intensities, candidate_tolerance=candidate_tolerance, constrain_poly=constrain_poly, vacuum=vacuum, pressure=pressure, temperature=temperature, relative_humidity=relative_humidity, ) self.logger.info( "Added user supplied atlas to " "science_spectrum_list for spec_id: {}.".format(i) ) self.science_atlas_available = True if "standard" in stype_split: self.standard_wavecal.add_user_atlas( elements=elements, wavelengths=wavelengths, intensities=intensities, candidate_tolerance=candidate_tolerance, constrain_poly=constrain_poly, vacuum=vacuum, pressure=pressure, temperature=temperature, relative_humidity=relative_humidity, ) self.logger.info( "Added user supplied atlas to " "standard_spectrum_list." ) self.standard_atlas_available = True
[docs] def add_atlas( self, elements, min_atlas_wavelength=3000.0, max_atlas_wavelength=10000.0, min_intensity=10.0, min_distance=10.0, candidate_tolerance=10.0, constrain_poly=False, vacuum=False, pressure=101325.0, temperature=273.15, relative_humidity=0, spec_id=None, stype="science+standard", ): """ Parameters ---------- elements: str or list of strings Chemical symbol, case insensitive min_atlas_wavelength: float (Default: 3000.) Minimum wavelength of the arc lines. max_atlas_wavelength: float (Default: 10000.) Maximum wavelength of the arc lines. min_intensity: float (Default: 10.) Minimum intensity of the arc lines. Refer to NIST for the intensity. min_distance: float (Default: 10.) Minimum separation between neighbouring arc lines. candidate_tolerance: float (Default: 10.) toleranceold (Angstroms) for considering a point to be an inlier during candidate peak/line selection. This should be reasonable small as we want to search for candidate points which are *locally* linear. constrain_poly: bool (Default: False) Apply a polygonal constraint on possible peak/atlas pairs vacuum: bool (Default: False) Set to true to convert the input wavelength to air-wavelengths based on the given pressure, temperature and humidity. pressure: float (Default: 101325.) Pressure when the observation took place, in Pascal. If it is not known, assume 10% decrement per 1000 meter altitude temperature: float (Default: 273.15) Temperature when the observation took place, in Kelvin. relative_humidity: float (Default: 0) In percentage. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].add_atlas( elements=elements, min_atlas_wavelength=min_atlas_wavelength, max_atlas_wavelength=max_atlas_wavelength, min_intensity=min_intensity, min_distance=min_distance, candidate_tolerance=candidate_tolerance, constrain_poly=constrain_poly, vacuum=vacuum, pressure=pressure, temperature=temperature, relative_humidity=relative_humidity, ) self.logger.info( "Added atlas to " "science_spectrum_list for spec_id: {}.".format(i) ) self.science_atlas_available = True if "standard" in stype_split: self.standard_wavecal.add_atlas( elements=elements, min_atlas_wavelength=min_atlas_wavelength, max_atlas_wavelength=max_atlas_wavelength, min_intensity=min_intensity, min_distance=min_distance, candidate_tolerance=candidate_tolerance, constrain_poly=constrain_poly, vacuum=vacuum, pressure=pressure, temperature=temperature, relative_humidity=relative_humidity, ) self.logger.info( "Added atlas to " "standard_spectrum_list for spec_id: {}.".format(i) ) self.standard_atlas_available = True
[docs] def remove_atlas_lines_range( self, wavelength, tolerance=10.0, spec_id=None, stype="science+standard", ): """ Remove arc lines within a certain wavelength range. Parameters ---------- wavelength: float Wavelength to remove (Angstrom) tolerance: float (Default: 10.) Tolerance around this wavelength where atlas lines will be removed spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if self.science_atlas_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].remove_atlas_lines_range( wavelength, tolerance ) self.logger.info( "Remove atlas in the range of " "{} +/- {}".format(wavelength, tolerance) + "science_spectrum_list for spec_id: {}.".format(i) ) else: self.logger.warning("Science atlas is not available.") if "standard" in stype_split: if self.standard_atlas_available: self.standard_wavecal.remove_atlas_lines_range( wavelength, tolerance ) self.logger.info( "Remove atlas in the range of " "{} +/- {}".format(wavelength, tolerance) + "standard_spectrum_list." ) else: self.logger.warning("Standard atlas is not available.")
[docs] def clear_atlas(self, spec_id=None, stype="science+standard"): """ Remove all the atlas lines from the calibrator. Parameters ---------- spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if self.science_atlas_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].clear_atlas() self.logger.info( "Atlas is removed from " "science_spectrum_list for spec_id: {}.".format(i) ) self.science_atlas_available = False else: self.logger.warning("Science atlas is not available.") if "standard" in stype_split: if self.standard_atlas_available: self.standard_wavecal.clear_atlas() self.logger.info( "Atlas is removed from standard_spectrum_list." ) self.standard_atlas_available = False else: self.logger.warning("Standard atlas is not available.")
[docs] def list_atlas(self, spec_id=None, stype="science+standard"): """ Remove all the atlas lines from the calibrator. Parameters ---------- spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if self.science_atlas_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].list_atlas() self.logger.info( "Listing the atlas of " "science_spectrum_list for spec_id: {}.".format(i) ) else: self.logger.warning("Science atlas is not available.") if "standard" in stype_split: if self.standard_atlas_available: self.standard_wavecal.list_atlas() self.logger.info( "Listing the atlas of " "standard_spectrum_list." ) else: self.logger.warning("Standard atlas is not available.")
[docs] def do_hough_transform( self, brute_force=False, spec_id=None, stype="science+standard" ): """ ** brute_force is EXPERIMENTAL as of 1 Oct 2021 ** The brute force method is supposed to provide all the possible solution, hence given a sufficiently large max_tries, the solution should always be the best possible outcome. However, it does not seem to work in a small fraction of our tests. Use with caution, and it is not the recommended way for now. Parameters ---------- brute_force: bool (Default: False) Set to true to compute the gradient and intercept between every two data points spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].do_hough_transform( brute_force=brute_force ) self.logger.info( "Hough Transform is performed on " "science_spectrum_list for spec_id: {}.".format(i) ) self.science_hough_pairs_available = True if "standard" in stype_split: self.standard_wavecal.do_hough_transform(brute_force=brute_force) self.logger.info( "Hough Transform is performed on " "standard_spectrum_list." ) self.standard_hough_pairs_available = True
[docs] def plot_search_space( self, fit_coeff=None, top_n_candidate=3, weighted=True, save_fig=False, fig_type="iframe+png", filename=None, return_jsonstring=False, renderer="default", display=False, spec_id=None, stype="science+standard", ): """ A wrapper function to plot the search space in the Hough space. If fit fit_coefficients are provided, the model solution will be overplotted. Parameters ---------- fit_coeff: list (default: None) List of best polynomial fit_coefficients top_n_candidate: int (default: 3) Top ranked lines to be fitted. weighted: (default: True) Draw sample based on the distance from the matched known wavelength of the atlas. save_fig: boolean (default: False) Save an image if set to True. matplotlib uses the pyplot.save_fig() while the plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: (default: None) The destination to save the image. return_jsonstring: (default: False) Set to True to save the plotly figure as json string. Ignored if matplotlib is used. renderer: (default: 'default') Set the rendered for the plotly display. Ignored if matplotlib is used. display: boolean (Default: False) Set to True to display disgnostic plot. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if self.science_hough_pairs_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].plot_search_space( fit_coeff=fit_coeff, top_n_candidate=top_n_candidate, weighted=weighted, save_fig=save_fig, fig_type=fig_type, filename=filename, return_jsonstring=return_jsonstring, renderer=renderer, display=display, ) self.logger.info( "Search area of the Hough space is plotted for the " "science_spectrum_list for spec_id: {}.".format(i) ) else: self.logger.warning("Science atlas is not available.") if "standard" in stype_split: if self.standard_hough_pairs_available: self.standard_wavecal.plot_search_space( fit_coeff=fit_coeff, top_n_candidate=top_n_candidate, weighted=weighted, save_fig=save_fig, fig_type=fig_type, filename=filename, return_jsonstring=return_jsonstring, renderer=renderer, display=display, ) self.logger.info( "Search area of the Hough space is plotted for the " "standard_spectrum_list." ) else: self.logger.warning("Standard atlas is not available.")
[docs] def fit( self, max_tries=5000, fit_deg=4, fit_coeff=None, fit_tolerance=10.0, fit_type="poly", candidate_tolerance=2.0, brute_force=False, progress=True, return_solution=False, display=False, renderer="default", save_fig=False, fig_type="iframe+png", filename=None, spec_id=None, stype="science+standard", ): """ A wrapper function to perform wavelength calibration with RASCAL. As of 14 January 2020, it supports He, Ne, Ar, Cu, Kr, Cd, Xe, Hg and Th from `NIST <https://physics.nist.gov/PhysRefData/ASD/lines_form.html>`_. Parameters ---------- max_tries: int Number of trials of polynomial fitting. fit_deg: int (Default: 4) The degree of the polynomial to be fitted. fit_coeff: list (Default: None) *NOT CURRENTLY USED, as of 17 Jan 2021* Set the baseline of the least square fit. If no fits outform this set of polynomial coefficients, this will be used as the best fit. fit_tolerance: float (Default: 10) Sets a tolerance on whether a fit found by RANSAC is considered acceptable. fit_type: string (Default: 'poly') One of 'poly', 'legendre' or 'chebyshev'. candidate_tolerance: float (default: 2.0) toleranceold (Angstroms) for considering a point to be an inlier brute_force: bool (Default: False) Set to True to try all possible combination in the given parameter space. progress: bool (Default: True) Set to show the progress using tdqm (if imported). return_jsonstring: (default: False) Set to True to save the plotly figure as json string. display: bool (Default: False) Set to show diagnostic plot. renderer: str (Default: 'default') plotly renderer options. save_fig: string (Default: False) Set to save figure. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str or None (Default: None) Filename for the output, all of them will share the same name but will have different extension. spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") solution = {} if "science" in stype_split: if self.science_hough_pairs_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) solution_science = [] for i in spec_id: self.logger.info( "Attempting to fit wavelength solution for " "science_spectrum_list for spec_id: {}.".format(i) ) solution_science.append( self.science_wavecal[i].fit( max_tries=max_tries, fit_deg=fit_deg, fit_coeff=fit_coeff, fit_tolerance=fit_tolerance, fit_type=fit_type, candidate_tolerance=candidate_tolerance, brute_force=brute_force, progress=progress, display=display, renderer=renderer, save_fig=save_fig, fig_type=fig_type, filename=filename, return_solution=return_solution, ) ) self.logger.info( "Wavelength solution is fitted for the " "science_spectrum_list for spec_id: {}.".format(i) ) self.science_wavecal_polynomial_available = True solution["science"] = solution_science else: self.logger.warning("Science hough pairs are not available.") if "standard" in stype_split: if self.standard_hough_pairs_available: self.logger.info( "Attempting to fit wavelength solution for " "standard_spectrum_list[0]." ) solution["standard"] = self.standard_wavecal.fit( max_tries=max_tries, fit_deg=fit_deg, fit_coeff=fit_coeff, fit_tolerance=fit_tolerance, fit_type=fit_type, candidate_tolerance=candidate_tolerance, brute_force=brute_force, progress=progress, display=display, renderer=renderer, save_fig=save_fig, fig_type=fig_type, filename=filename, return_solution=return_solution, ) self.logger.info( "Wavelength solution is fitted for the " "standard_spectrum_list." ) self.standard_wavecal_polynomial_available = True else: self.logger.warning("Standard spectrum/a are not imported.") if return_solution: return solution
[docs] def robust_refit( self, fit_coeff=None, n_delta=None, refine=False, tolerance=10.0, method="Nelder-Mead", convergence=1e-6, robust_refit=True, fit_deg=None, return_solution=False, display=False, renderer="default", save_fig=False, filename=None, spec_id=None, stype="science+standard", ): """ ** EXPERIMENTAL, as of 1 October 2021 ** Refine the fitted solution with a minimisation method as provided by scipy.optimize.minimize(). Parameters ---------- fit_coeff: list or None (Default: None) List of polynomial fit coefficients. n_delta: int (Default: None) The number of the highest polynomial order to be adjusted refine: bool (Default: True) Set to True to refine solution. tolerance : float (Default: 10.) Absolute difference between fit and model in the unit of nm. method: str (Default: 'Nelder-Mead') scipy.optimize.minimize method. convergence: float (Default: 1e-6) scipy.optimize.minimize tol. robust_refit: bool (Default: True) Set to True to fit all the detected peaks with the given polynomial solution. fit_deg: int (Default: length of the input coefficients - 1) Order of polynomial fit with all the detected peaks. return_solution: bool (Default: True) Set to True to return the best fit polynomial coefficients. display: bool (Default: False) Set to show diagnostic plot. renderer: str (Default: 'default') plotly renderer options. save_fig: bool (Default: False) Set to save figure. filename: str or None (Default: None) Filename for the output, all of them will share the same name but will have different extension. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") solution = {} if "science" in stype_split: if self.science_wavecal_polynomial_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) solution_science = [] for i in spec_id: if fit_coeff is None: fit_coeff = self.science_wavecal[ i ].spectrum1D.calibrator.fit_coeff solution_science.append( self.science_wavecal[i].robust_refit( fit_coeff=fit_coeff, n_delta=n_delta, refine=refine, tolerance=tolerance, method=method, convergence=convergence, robust_refit=robust_refit, fit_deg=fit_deg, display=display, renderer=renderer, save_fig=save_fig, filename=filename, return_solution=return_solution, ) ) self.logger.info( "Wavelength solution is refined for the " "science_spectrum_list for spec_id: {}.".format(i) ) solution["science"] = solution_science else: self.logger.warning("Science spectrum/a are not imported.") if "standard" in stype_split: if self.standard_wavecal_polynomial_available: if fit_coeff is None: fit_coeff = self.standard_wavecal[ 0 ].spectrum1D.calibrator.fit_coeff solution["standard"] = self.standard_wavecal.robust_refit( fit_coeff=fit_coeff, n_delta=n_delta, refine=refine, tolerance=tolerance, method=method, convergence=convergence, robust_refit=robust_refit, fit_deg=fit_deg, display=display, renderer=renderer, save_fig=save_fig, filename=filename, return_solution=return_solution, ) self.logger.info( "Wavelength solution is refined for the " "standard_spectrum_list." ) else: self.logger.warning("Standard spectrum/a are not imported.") if return_solution: return solution
[docs] def get_pix_wave_pairs(self, spec_id=None, stype="science+standard"): """ Return the list of matched_peaks and matched_atlas with their position in the array. Parameters ---------- spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter Return ------ pw_pairs: dictionary Dictionary of 'science' and/or 'standard' where the values are lists of tuples each containing the array position, peak (pixel) and atlas (wavelength) in the order of the given spec_id. """ stype_split = stype.split("+") pw_pairs = {} if "science" in stype_split: if self.science_wavecal_polynomial_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) pw_pairs_science = [] for i in spec_id: pw_pairs_science.append( self.science_wavecal[i].get_pix_wave_pairs() ) pw_pairs["science"] = pw_pairs_science if "standard" in stype_split: if self.standard_wavecal_polynomial_available: pw_pairs_standard = self.standard_wavecal.get_pix_wave_pairs() pw_pairs["standard"] = pw_pairs_standard return pw_pairs
[docs] def add_pix_wave_pair( self, pix, wave, spec_id=None, stype="science+standard" ): """ Adding extra pixel-wavelength pair to the Calibrator for refitting. This DOES NOT work before the Calibrator having fit for a solution yet: use set_known_pairs() for that purpose. Parameters ---------- pix: float pixel position wave: float wavelength spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if self.science_wavecal_polynomial_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].add_pix_wave_pair(pix, wave) if "standard" in stype_split: if self.standard_wavecal_polynomial_available: self.standard_wavecal.add_pix_wave_pair(pix, wave)
[docs] def remove_pix_wave_pair( self, arg, spec_id=None, stype="science+standard" ): """ Remove fitted pixel-wavelength pair from the Calibrator for refitting. The positions can be found from get_pix_wave_pairs(). One at a time. Parameters ---------- arg: int The position of the pairs in the arrays. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if self.science_wavecal_polynomial_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_wavecal[i].remove_pix_wave_pair(arg) if "standard" in stype_split: if self.standard_wavecal_polynomial_available: self.standard_wavecal.remove_pix_wave_pair(arg)
[docs] def manual_refit( self, matched_peaks=None, matched_atlas=None, degree=None, x0=None, return_solution=False, spec_id=None, stype="science+standard", ): """ Perform a refinement of the matched peaks and atlas lines. This function takes lists of matched peaks and atlases, along with user-specified lists of lines to add/remove from the lists. Any given peaks or atlas lines to remove are selected within a user-specified tolerance, by default 1 pixel and 5 atlas Angstrom. The final set of matching peaks/lines is then matched using a robust polyfit of the desired degree. Optionally, an initial fit x0 can be provided to condition the optimiser. The parameters are identical in the format in the fit() and match_peaks() functions, however, with manual changes to the lists of peaks and atlas, peak_utilisation and atlas_utilisation are meaningless so this function does not return in the same format. Parameters ---------- matched_peaks: list (Default: None) List of matched peaks matched_atlas: list (Default: None) List of matched atlas lines degree: int (Default: None) Polynomial fit degree (Only used if x0 is None) x0: list (Default: None) Initial fit coefficients return_solution: bool (Default: False) Set to True to return the best fit polynomial coefficients. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") solution = {} if "science" in stype_split: if self.science_wavecal_polynomial_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) solution_science = [] for i in spec_id: solution_science.append( self.science_wavecal[i].manual_refit( matched_peaks=matched_peaks, matched_atlas=matched_atlas, degree=degree, x0=x0, return_solution=return_solution, ) ) solution["science"] = solution_science if "standard" in stype_split: if self.standard_wavecal_polynomial_available: solution["standard"] = self.standard_wavecal.manual_refit( matched_peaks=matched_peaks, matched_atlas=matched_atlas, degree=degree, x0=x0, return_solution=return_solution, ) if return_solution: return solution
def get_calibrator(self, spec_id=None, stype="science+standard"): stype_split = stype.split("+") calibrators = {} if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) calibrator_science = [] for i in spec_id: calibrator_science.append( getattr(self.science_wavecal[i].spectrum1D, "calibrator") ) calibrators["science"] = calibrator_science if "standard" in stype_split: calibrators["standard"] = getattr( self.standard_wavecal.spectrum1D, "calibrator" ) return calibrators
[docs] def apply_wavelength_calibration( self, spec_id=None, stype="science+standard", ): """ Apply the wavelength calibration. Parameters ---------- spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str or None (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if self.science_wavecal_polynomial_available: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: spec = self.science_spectrum_list[i] # Adjust for pixel shift due to chip gaps wave = ( self.science_wavecal[i] .polyval[spec.fit_type]( np.array(spec.pixel_list), spec.fit_coeff ) .reshape(-1) ) spec.add_wavelength(wave) self.logger.info( "Wavelength calibration is applied for the " "science_spectrum_list for spec_id: {}.".format(i) ) self.science_wavelength_calibrated = True else: self.logger.warning("Science spectrum/a are not imported.") if "standard" in stype_split: if self.standard_wavecal_polynomial_available: spec = self.standard_spectrum_list[0] # Adjust for pixel shift due to chip gaps wave = self.standard_wavecal.polyval[spec.fit_type]( np.array(spec.pixel_list), spec.fit_coeff ).reshape(-1) spec.add_wavelength(wave) self.logger.info( "Wavelength calibration is applied for the " "standard_spectrum_list." ) self.standard_wavelength_calibrated = True else: self.logger.warning("Standard spectrum is not imported.")
[docs] def lookup_standard_libraries(self, target, cutoff=0.4): """ Parameters ---------- target: str Name of the standard star cutoff: float (Default: 0.4) The similarity tolerance [0=completely different, 1=identical] """ self.fluxcal.lookup_standard_libraries(target=target, cutoff=cutoff)
[docs] def load_standard(self, target, library=None, ftype="flux", cutoff=0.4): """ Read the standard flux/magnitude file. And return the wavelength and flux/mag. The units of the data are always in | wavelength: A | flux: ergs / cm / cm / s / A | mag: mag (AB) Parameters ---------- target: string Name of the standard star library: string (Default: None) Name of the library of standard star ftype: string (Default: 'flux') 'flux' or 'mag' cutoff: float (Default: 0.4) The toleranceold for the word similarity in the range of [0, 1]. """ self.fluxcal.load_standard( target=target, library=library, ftype=ftype, cutoff=cutoff ) self.logger.info("Loaded standard: {} from {}".format(target, library))
[docs] def inspect_standard( self, display=True, renderer="default", width=1280, height=720, return_jsonstring=False, save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, ): """ Parameters ---------- display: bool (Default: True) Set to True to display disgnostic plot. renderer: str (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs return_jsonstring: bool (Default: False) set to True to return json str that can be rendered by Plotly in any support language. save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str or None (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. Returns ------- JSON strings if return_jsonstring is set to True. """ self.fluxcal.inspect_standard( renderer=renderer, return_jsonstring=return_jsonstring, display=display, height=height, width=width, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) self.logger.info("Inspect standard.") if return_jsonstring: return return_jsonstring
[docs] def get_sensitivity( self, k=3, method="interpolate", mask_range=[[6850, 6960], [7580, 7700]], mask_fit_order=1, mask_fit_size=5, smooth=False, slength=5, sorder=3, return_function=False, sens_deg=7, recompute_continuum=True, **kwargs ): """ Parameters ---------- k: integer [1,2,3,4,5 only] The order of the spline. method: str (Default: 'interpolate') This should be either 'interpolate' of 'polynomial'. Note that the polynomial is computed from the interpolated function. The default is interpolate because it is much more stable at the wavelength limits of a spectrum in an automated system. mask_range: None or list of list (Default: 6850-6960, 7575-7700, 8925-9050) Masking out regions not suitable for fitting the sensitivity curve. None for no mask. List of list has the pattern [[min1, max1], [min2, max2],...] mask_fit_order: int (Default: 1) Order of polynomial to be fitted over the masked regions mask_fit_size: int (Default: 5) Number of "pixels" to be fitted on each side of the masked regions. smooth: bool (Default: False) set to smooth the input spectrum with scipy.signal.savgol_filter slength: int (Default:5) SG-filter window size sorder: int (Default: 3) SG-filter polynomial order return_function: bool (Default: False) Set to True to return the callable function of the sensitivity curve. sens_deg: int (Default: 7) The degree of polynomial of the sensitivity curve, only used if the method is 'polynomial'. recompute_continuum: bool (Default: True) Recompute the continuum before computing the sensitivity function. **kwargs: keyword arguments for passing to the LOWESS function, see `statsmodels.nonparametric.smoothers_lowess.lowess()` """ if self.standard_wavelength_calibrated: self.fluxcal.get_sensitivity( k=k, method=method, mask_range=mask_range, mask_fit_order=mask_fit_order, mask_fit_size=mask_fit_size, smooth=smooth, slength=slength, sorder=sorder, return_function=return_function, sens_deg=sens_deg, recompute_continuum=recompute_continuum, **kwargs ) self.logger.info("Sensitivity curve computed.") self.sensitivity_curve_available = True else: error_msg = ( "Standard star is not wavelength calibrated, " + "sensitivity curve cannot be computed." ) self.logger.critical(error_msg) raise RuntimeError(error_msg)
[docs] def save_sensitivity_func(self, filename="sensitivity_func.npy"): """ Not-implemented wrapper. Parameters ---------- filename: str Filename for the output interpolated sensivity curve. """ self.fluxcal.save_sensitivity_func(filename=filename) self.logger.info("Sensitivity curve saved at {}.".format(filename))
[docs] def add_sensitivity_func(self, sensitivity_func): """ Provide a callable function of the detector sensitivity response. Parameters ---------- sensitivity_func: str Interpolated sensivity curve object. stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ self.fluxcal.add_sensitivity_func(sensitivity_func=sensitivity_func) self.logger.info("User supplied sensitivity curve added.") self.sensitivity_curve_available = True
[docs] def inspect_sensitivity( self, display=True, renderer="default", width=1280, height=720, return_jsonstring=False, save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, ): """ Parameters ---------- display: bool (Default: True) Set to True to display disgnostic plot. renderer: str (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs return_jsonstring: bool (Default: False) set to True to return json str that can be rendered by Plotly in any support language. save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str or None (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. """ if self.sensitivity_curve_available: self.fluxcal.inspect_sensitivity( renderer=renderer, width=width, height=height, return_jsonstring=return_jsonstring, display=display, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) self.logger.info("Inspect sensitivity function.") else: self.logger.warning( "Sensitivity function not available, it " "cannot be inspected." )
[docs] def apply_flux_calibration( self, inspect=False, wave_min=3500.0, wave_max=8500.0, display=False, renderer="default", width=1280, height=720, return_jsonstring=False, save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, spec_id=None, stype="science+standard", ): """ Apply the computed sensitivity curve. And resample the spectra to match the highest resolution (the smallest wavelength bin) part of the spectrum. Note: This function directly modify the *target_spectrum1D*. Parameters ---------- inspect: bool (Default: False) Set to True to create/display/save figure wave_min: float (Default: 3500) Minimum wavelength to display wave_max: float (Default: 8500) Maximum wavelength to display display: bool (Default: False) Set to True to display disgnostic plot. renderer: string (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs return_jsonstring: bool (Default: False) set to True to return json string that can be rendered by Plotly in any support language. save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if self.sensitivity_curve_available: if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.fluxcal.apply_flux_calibration( target_spectrum1D=self.science_spectrum_list[i], inspect=inspect, wave_min=wave_min, wave_max=wave_max, display=display, renderer=renderer, width=width, height=height, return_jsonstring=return_jsonstring, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) self.science_spectrum_list[i].add_standard_header( self.standard_spectrum_list[0].spectrum_header ) self.logger.info( "Flux calibration is applied for the " "science_spectrum_list for spec_id: {}.".format(i) ) self.science_flux_calibrated = True if "standard" in stype_split: self.fluxcal.apply_flux_calibration( target_spectrum1D=self.standard_spectrum_list[0], inspect=inspect, wave_min=wave_min, wave_max=wave_max, display=display, renderer=renderer, width=width, height=height, return_jsonstring=return_jsonstring, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) self.standard_spectrum_list[0].add_standard_header( self.standard_spectrum_list[0].spectrum_header ) self.logger.info( "Flux calibration is applied for the " "standard_spectrum_list." ) self.standard_flux_calibrated = True else: self.logger.warning( "Sensitivity function is not available, " "flux calibration is not possible." )
def _min_std(self, factor, flux, telluric_profile, continuum, sigma=4.5): """ ** EXPERIMENTAL, as of 1 October 2021 ** Minimisation function to get the best mutiplier for the strength of the Telluric profile. Parameters ---------- factor: float The multiplier for the strength of the Telluric profile. flux: 1-d array (N) Flux of the target. telluric_profile: 1-d array (N) Telluric Profile normalised to 1 being the most strongly absorbed, 0 being outside the Telluric regions. continuum: 1-d array (N) Continuum flux array. sigma: float (default: 4.5) Level of sigma clipping. """ mask = telluric_profile != 0 telluric_absorption = factor * telluric_profile diff = flux + telluric_absorption - continuum nansum = np.nansum(diff[mask] ** 2.0) * 1e20 return nansum
[docs] def add_telluric_function( self, telluric, spec_id=None, stype="science+standard" ): """ Provide a callable function that gives the Telluric profile. Parameters ---------- telluric: callable function A function that gives the absorption profile as a function of wavelength. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: science_spec = self.science_spectrum_list[i] if callable(telluric): science_spec.add_telluric_func(telluric) elif isinstance(telluric, (np.ndarray, list)): science_spec.add_telluric_func( interp1d(telluric[0], telluric[1]) ) else: self.logger.warning( "telluric provided has to be a callable function, " "a list or a np.ndarray. " "{} is given".format(type(telluric)) ) if science_spec.wave is not None: science_spec.add_telluric_profile( science_spec.telluric_func(science_spec.wave) ) self.telluric_profile_available = True else: self.logger.warning( "wave is not available. Telluric correction cannot" "be performed." ) if "standard" in stype_split: # Add to the standard spectrum standard_spec = self.standard_spectrum_list[0] if callable(telluric): standard_spec.add_telluric_func(telluric) elif isinstance(telluric, (np.ndarray, list)): standard_spec.add_telluric_func( interp1d(telluric[0], telluric[1]) ) else: self.logger.warning( "telluric provided has to be a callable function, " "a list or a np.ndarray. " "{} is given".format(type(telluric)) ) if standard_spec.wave is not None: standard_spec.add_telluric_profile( standard_spec.telluric_func(standard_spec.wave) ) self.telluric_profile_available = True else: self.logger.warning( "wave is not available. Telluric correction cannot" "be performed." )
[docs] def get_continuum(self, spec_id=None, **kwargs): """ ** EXPERIMENTAL, as of 1 October 2021 ** Get the continnum from the wave, count and flux. Parameters ---------- spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object **kwargs: dictionary The keyword arguments to be passed to the lowess function for generating the continuum. """ if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: spec_id = list(self.science_spectrum_list.keys()) # Get the continuum here for i in spec_id: science_spec = self.science_spectrum_list[i] wave = science_spec.wave count = science_spec.count flux = science_spec.flux science_spec.add_count_continuum( get_continuum(wave, count, **kwargs) ) if flux is not None: science_spec.add_flux_continuum( get_continuum(wave, flux, **kwargs) ) else: self.logger.warning( "flux is None, only count_continuum is found." )
[docs] def get_telluric_profile( self, spec_id=None, mask_range=[[6850, 6960], [7580, 7700]], return_function=False, ): """ Getting the Telluric absorption profile from the continuum of the standard star spectrum. Parameters ---------- spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object mask_range: list of list list of lists with 2 values indicating the range marked by each of the Telluric regions. return_function: bool (Default: False) Set to True to explicitly return the interpolated function of the Telluric profile. """ ( telluric_func, telluric_profile, telluric_factor, ) = self.fluxcal.get_telluric_profile( wave=self.standard_spectrum_list[0].wave, flux=self.standard_spectrum_list[0].flux, continuum=self.standard_spectrum_list[0].flux_continuum, mask_range=mask_range, return_function=True, ) self.logger.info( "Copying the telluric absorption profile to " "the science spectrum1D(s)." ) if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: spec_id = list(self.science_spectrum_list.keys()) # Add the telluric profile from fluxcal to science onedspec for i in spec_id: self.science_spectrum_list[i].add_telluric_func(telluric_func) self.science_spectrum_list[i].add_telluric_profile( telluric_profile ) self.science_spectrum_list[i].add_telluric_factor(telluric_factor) # Add the telluric profile from fluxcal to standard onedspec self.standard_spectrum_list[0].add_telluric_func(telluric_func) self.standard_spectrum_list[0].add_telluric_profile(telluric_profile) self.standard_spectrum_list[0].add_telluric_factor(telluric_factor) self.telluric_profile_available = True if return_function: return telluric_func
def get_telluric_correction( self, factor=1.0, auto_apply=False, spec_id=None, **kwargs ): self.logger.warning( DeprecationWarning( "get_telluric_correction() will be removed in version >=0.6." "Please use get_telluric_strength()." ) ) self.get_telluric_strength( factor=factor, auto_apply=auto_apply, spec_id=spec_id, **kwargs )
[docs] def get_telluric_strength( self, factor=1.0, auto_apply=False, spec_id=None, **kwargs ): """ Get the telluric absorption profile from the standard star based on the masked regions given in generating the sensitivity curve. Note that the profile has a "positive" flux so that in the step of applying a correction, a POSITIVE constant is found to multiply with the normalised telluric profile before ADDING to the spectrum for telluric absorption correction (counter-intuitive to the term telluric absorption subtraction). Parameters ---------- factor: float (Default: 1.0) The extra fudge factor multiplied to the telluric profile to manally adjust the strength. auto_apply: bool (Default: False) Set to True to accept the computed telluric absorption correction automatically, which is currently an irresversible process through the public API. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object """ if not self.telluric_profile_available: error_msg = ( "Telluric profile is not available. Please provide " "one or get one with get_telluric_profile(). Fine tuning can " "be done using also get_continuum() on the standard spectrum." ) raise ValueError(error_msg) if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: spec_id = list(self.science_spectrum_list.keys()) # Get the telluric profile for i in spec_id: science_spec = self.science_spectrum_list[i] # If there isn't a telluric profile, try to get it from the # standard star if science_spec.telluric_func is None: if self.standard_spectrum_list[0].telluric_func is None: err_msg = ( "Telluric profile is not available, please " + "compute from the standard star, or manually " + "supply one." ) self.logger.error(err_msg) else: science_spec.add_telluric_func( self.standard_spectrum_list[0].telluric_func ) wave = science_spec.wave flux = science_spec.flux if (science_spec.flux_continuum is None) or ( len(kwargs.keys()) > 0 ): self.get_continuum(i, **kwargs) flux_continuum = science_spec.flux_continuum if science_spec.telluric_profile is None: science_spec.add_telluric_profile( science_spec.telluric_func(wave) ) telluric_factor = optimize.minimize( self._min_std, np.nanmedian(np.abs(flux)), args=(flux, science_spec.telluric_profile, flux_continuum), tol=1e-20, method="Nelder-Mead", options={"maxiter": 10000}, ).x science_spec.add_telluric_factor(telluric_factor) self.logger.info("telluric_factor is {}.".format(telluric_factor)) self.telluric_strength_available = True if self.standard_wavelength_calibrated: standard_spec = self.standard_spectrum_list[0] wave_standard = standard_spec.wave if (standard_spec.telluric_profile is None) or ( len(kwargs.keys()) > 0 ): standard_spec.add_telluric_profile( standard_spec.telluric_func(wave_standard) ) telluric_factor = optimize.minimize( self._min_std, np.nanmedian(np.abs(flux)), args=( standard_spec.flux, standard_spec.telluric_profile, standard_spec.flux_continuum, ), tol=1e-20, method="Nelder-Mead", options={"maxiter": 10000}, ).x self.logger.info( "telluric_factor is {}.".format(telluric_factor) ) standard_spec.add_telluric_factor(telluric_factor) if auto_apply: self.apply_telluric_correction( factor=factor, spec_id=spec_id, stype="science+standard" )
[docs] def inspect_telluric_profile( self, display=True, renderer="default", width=1280, height=720, return_jsonstring=False, save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, ): """ Display the Telluric profile. Parameters ---------- display: bool (Default: True) Set to True to display disgnostic plot. renderer: string (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs return_jsonstring: bool (Default: False) set to True to return json string that can be rendered by Plotly in any support language. save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. Returns ------- JSON strings if return_jsonstring is set to True. """ self.fluxcal.inspect_telluric_profile( display=display, renderer=renderer, width=width, height=height, return_jsonstring=return_jsonstring, save_fig=save_fig, fig_type=fig_type, filename=filename, open_iframe=open_iframe, ) self.logger.info("Inspecting the telluric absorption profile.")
[docs] def inspect_telluric_correction( self, factor=1.0, display=True, renderer="default", width=1280, height=720, return_jsonstring=False, save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, spec_id=None, ): """ Inspect the Telluric absorption correction on top of the spectra. This does NOT apply the correction to the spectrum. This is for inspection and manually modifying an extrac multiplier (fnudge factor) to the absorption strength. Parameters ---------- factor: float (Default: 1.0) The extra fudge factor multiplied to the telluric profile to manally adjust the strength. display: bool (Default: True) Set to True to display disgnostic plot. renderer: string (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs return_jsonstring: bool (Default: False) set to True to return json string that can be rendered by Plotly in any support language. save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object Returns ------- JSON strings if return_jsonstring is set to True. """ if not self.telluric_profile_available: error_msg = ( "Telluric profile is not available. Please provide " "one or get one with get_telluric_profile(). Fine tuning can " "be done using also get_continuum() on the standard spectrum." ) raise ValueError(error_msg) if not self.telluric_strength_available: error_msg = ( "Telluric strength is not available. executing " "get_telluric_strength()." ) self.get_telluric_strength() if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) to_return = [] # Get the telluric profile for i in spec_id: spec = self.science_spectrum_list[i] wave = spec.wave fluxcount = spec.flux fluxcount_name = "Flux" fluxcount_continuum = spec.flux_continuum telluric_factor = spec.telluric_factor telluric_func = spec.telluric_func spec.add_telluric_nudge_factor(factor) flux_low = ( np.nanpercentile(np.array(fluxcount).reshape(-1), 5) / 1.5 ) flux_high = ( np.nanpercentile(np.array(fluxcount).reshape(-1), 95) * 1.5 ) flux_mask = (np.array(fluxcount).reshape(-1) > flux_low) & ( np.array(fluxcount).reshape(-1) < flux_high ) if np.sum(flux_mask) > 0: flux_min = np.nanmin( np.array(fluxcount).reshape(-1)[flux_mask] ) flux_max = np.nanmax( np.array(fluxcount).reshape(-1)[flux_mask] ) else: flux_min = np.nanmin(np.array(fluxcount).reshape(-1)) flux_max = np.nanmax(np.array(fluxcount).reshape(-1)) fig_sci = go.Figure( layout=dict( autosize=False, height=height, width=width, updatemenus=list( [ dict( active=0, buttons=list( [ dict( label="Log Scale", method="update", args=[ {"visible": [True, True]}, { "title": "Log scale", "yaxis": {"type": "log"}, }, ], ), dict( label="Linear Scale", method="update", args=[ {"visible": [True, False]}, { "title": "Linear scale", "yaxis": { "type": "linear" }, }, ], ), ] ), ) ] ), title="Log scale", ) ) # show the image on the top fig_sci.add_trace( go.Scatter( x=wave, y=fluxcount, line=dict(color="royalblue"), name=fluxcount_name, ) ) fig_sci.add_trace( go.Scatter( x=wave, y=fluxcount_continuum, line=dict(color="firebrick"), name="Continuum Flux", ) ) fig_sci.add_trace( go.Scatter( x=wave, y=( fluxcount + telluric_func(wave) * telluric_factor * factor ), line=dict(color="orange"), name="Telluric Corrected Spectrum", ) ) fig_sci.add_trace( go.Scatter( x=wave, y=(telluric_func(wave) * telluric_factor * factor), line=dict(color="grey"), name="Telluric Profile", ) ) fig_sci.update_layout( hovermode="closest", showlegend=True, xaxis=dict(title="Wavelength / A"), yaxis=dict( title="Flux", range=[flux_min, flux_max], type="linear" ), legend=go.layout.Legend( x=0, y=1, traceorder="normal", font=dict(family="sans-serif", size=12, color="black"), bgcolor="rgba(0,0,0,0)", ), ) if filename is None: filename = "telluric_inspection" if save_fig: fig_type_split = fig_type.split("+") for t in fig_type_split: save_path = filename + "_" + str(i) + "." + t if t == "iframe": pio.write_html( fig_sci, save_path, auto_open=open_iframe ) elif t in ["jpg", "png", "svg", "pdf"]: pio.write_image(fig_sci, save_path) self.logger.info( "Figure is saved to {} for the ".format(save_path) + "science_spectrum_list for spec_id: {}.".format(i) ) if display: if renderer == "default": fig_sci.show() else: fig_sci.show(renderer) if return_jsonstring: to_return.append(fig_sci.to_json()) if return_jsonstring: return to_return
[docs] def apply_telluric_correction( self, factor=None, spec_id=None, stype="science+standard" ): """ Apply the telluric correction with the extra multiplier 'factor'. The 'factor' provided in the profile() is propagated to this function, it has to be explicitly provided to this function. The telluric absorption profile is normalised to 1 at the most absorpted wavelegnth, the factor manually provided can be negative in case of over/under-subtraction. Parameters ---------- factor: float (Default: None) The extra fudge factor multiplied to the telluric profile to manally adjust the strength. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ if not self.telluric_profile_available: error_msg = ( "Telluric profile is not available. Please provide " "one or get one with get_telluric_profile(). Fine tuning can " "be done using also get_continuum() on the standard spectrum." ) raise ValueError(error_msg) stype_split = stype.split("+") if "science" in stype_split: if not self.telluric_strength_available: error_msg = ( "Telluric strength is not available. executing " "get_telluric_strength()." ) self.get_telluric_strength() if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) # Get the telluric profile for i in spec_id: science_spec = self.science_spectrum_list[i] if science_spec.telluric_profile is None: self.logger.warning( "A resampled telluric profile is not available, " "please construct a profile with " "get_telluric_profile()." ) else: case_a = ( self.science_telluric_corrected & self.atmospheric_extinction_corrected ) case_b = ( not self.science_telluric_corrected ) & self.atmospheric_extinction_corrected # case_c = self.science_telluric_corrected & ( # not self.atmospheric_extinction_corrected # ) # case_d = (not self.science_telluric_corrected) & ( # not self.atmospheric_extinction_corrected # ) if factor is None: factor = science_spec.telluric_nudge_factor else: science_spec.add_telluric_nudge_factor(factor) # in all cases flux_telluric_corrected = ( science_spec.flux + science_spec.telluric_func(science_spec.wave) * science_spec.telluric_factor * factor ) science_spec.add_flux_telluric_corrected( flux_telluric_corrected, science_spec.flux_err, science_spec.flux_sky, ) if case_a or case_b: flux_atm_ext_telluric_corrected = ( science_spec.flux_atm_ext_corrected + science_spec.telluric_func(science_spec.wave) * science_spec.telluric_factor * factor ) science_spec.add_flux_atm_ext_telluric_corrected( flux_atm_ext_telluric_corrected, science_spec.flux_err_atm_ext_corrected, science_spec.flux_sky_atm_ext_corrected, ) # Flag it as corrected self.science_telluric_corrected = True self.logger.info( "Telluric absorption in the science spectrum is corrected." ) if "standard" in stype_split: standard_spec = self.standard_spectrum_list[0] if standard_spec.telluric_profile is None: self.logger.warning( "A resampled telluric profile is not available, " "please construct a profile with " "get_telluric_profile()." ) else: if factor is None: factor = standard_spec.telluric_nudge_factor else: standard_spec.add_telluric_nudge_factor(factor) case_a = ( self.standard_telluric_corrected & self.atmospheric_extinction_corrected ) case_b = ( not self.standard_telluric_corrected ) & self.atmospheric_extinction_corrected # case_c = self.standard_telluric_corrected & ( # not self.atmospheric_extinction_corrected # ) # case_d = (not self.standard_telluric_corrected) & ( # not self.atmospheric_extinction_corrected # ) # in all cases flux_telluric_corrected = ( standard_spec.flux + standard_spec.telluric_func(standard_spec.wave) * standard_spec.telluric_factor * factor ) standard_spec.add_flux_telluric_corrected( flux_telluric_corrected, standard_spec.flux_err, standard_spec.flux_sky, ) if case_a or case_b: # standard doesn't require atmospheic extinction correction flux_atm_ext_telluric_corrected = ( standard_spec.flux + standard_spec.telluric_func(standard_spec.wave) * standard_spec.telluric_factor * factor ) standard_spec.add_flux_atm_ext_telluric_corrected( flux_atm_ext_telluric_corrected, standard_spec.flux_err_atm_ext_corrected, standard_spec.flux_sky_atm_ext_corrected, ) # Flag it as corrected self.standard_telluric_corrected = True self.logger.info( "Telluric absorption in the standard spectrum " "is corrected." )
[docs] def set_atmospheric_extinction( self, location="orm", extinction_func=None, kind="cubic", fill_value="extrapolate", **kwargs ): """ The ORM atmospheric extinction correction table is taken from http://www.ing.iac.es/astronomy/observing/manuals/ps/tech_notes/tn031.pdf The MK atmospheric extinction correction table is taken from Buton et al. (2013A&A...549A...8B) The CP atmospheric extinction correction table is taken from Patat et al. (2011A&A...527A..91P) The LS atmospheric extinction correction table is taken from THE ESO USERS MANUAL 1993 https://www.eso.org/public/archives/techdocs/pdf/report_0003.pdf Parameters ---------- location: str (Default: orm) Location of the observatory, currently contains: (1) orm - Roque de los Muchachos Observatory (2420 m) (2) mk - Mauna Kea (4205 m) (3) cp - Cerro Paranal (2635 m) (4) ls - La Silla (2400 m) [up to 9000A only] Only used if extinction_func is None. extinction_func: callable function (Default: None) Input wavelength in Angstrom, output magnitude of extinction per airmass. It will override the 'location'. """ if (extinction_func is not None) and (callable(extinction_func)): self.extinction_func = extinction_func self.logger.info( "Manual extinction correction function is loaded." ) else: filename = pkg_resources.resource_filename( "aspired", "extinction/{}_atm_extinct.txt".format(location.lower()), ) extinction_table = np.loadtxt(filename, delimiter=",") self.extinction_func = interp1d( extinction_table[:, 0], extinction_table[:, 1], kind=kind, fill_value=fill_value, **kwargs ) self.logger.info( "{} extinction correction function is loaded.".format( location.lower() ) ) self.atmospheric_extinction_correction_available = True
[docs] def apply_atmospheric_extinction_correction( self, science_airmass=None, standard_airmass=None, spec_id=None ): """ This is the first step in allowing atmospheric extinction correction of the spectra. Currently it only works if both the science and standard spectra are present and both airmass values are provided. Towards completion, this function should allow atmospheric extinction correction on any meaningful combination of (1) science and/or standard spectrum/a, and (2) airmass of either or both science and standard observations. Parameters ---------- science_airmass: float, str or None (Default: None) - If None, it will look for the airmass in the header, if the keyword AIRMASS is not found, correction will not be performed. - A string input will be used as the header keyword of the airmass, if the keyword or header is not found, correction will not be performed. - A floatpoint value will override the other two and directly be use as the airmass standard_airmass: float, str or None (Default: None) The same as science_airmass. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object """ if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) if not self.atmospheric_extinction_correction_available: self.logger.warning( "Atmospheric extinction correction is not configured, " "The default ORM extinction curve is used." ) self.set_atmospheric_extinction() standard_spec = self.standard_spectrum_list[0] if standard_airmass is not None: if isinstance(standard_airmass, (int, float)): standard_am = standard_airmass self.logger.info( "Airmass is set to be {}.".format(standard_am) ) if isinstance(standard_airmass, str): try: standard_am = standard_spec.spectrum_header[ standard_airmass ] except Exception as e: self.logger.warning(str(e)) standard_am = 1.0 self.logger.warning( "Keyword for airmass: {} cannot be found " "in header.".format(standard_airmass) ) self.logger.warning("Airmass is set to be 1.0") else: try: standard_am = standard_spec.spectrum_header["AIRMASS"] except Exception as e: self.logger.warning(str(e)) standard_am = 1.0 self.logger.warning( "Keyword for airmass: AIRMASS cannot be found " "in header." ) self.logger.warning("Airmass is set to be 1.0") if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) if isinstance(spec_id, int): spec_id = [spec_id] for i in spec_id: science_spec = self.science_spectrum_list[i] if science_airmass is not None: if isinstance(science_airmass, (int, float)): science_am = science_airmass if isinstance(science_airmass, str): try: science_am = science_spec.spectrum_header[ science_airmass ] except Exception as e: self.logger.warning(str(e)) science_am = 1.0 else: if science_airmass is None: try: science_am = science_spec.spectrum_header["AIRMASS"] except Exception as e: self.logger.warning(str(e)) science_am = 1.0 if science_am is None: science_am = 1.0 self.logger.info("Standard airmass is {}.".format(standard_am)) self.logger.info("Science airmass is {}.".format(science_am)) interpoalted_ext = self.extinction_func(science_spec.wave) # Get the atmospheric extinction correction factor science_flux_extinction_factor = 10.0 ** ( -(interpoalted_ext * science_am) / 2.5 ) # note that we are still using the science_spec.wave because we # want to "uncorrect" the atmospheric correction on the standard # star at the wavelength of of the science target standard_flux_extinction_factor = 10.0 ** ( -(interpoalted_ext * standard_am) / 2.5 ) # ratio of the +ve flux adjustment due to the airmass of the # science observation, and the -ve flux adjustment due to the # airmass of the standard observation self.extinction_fraction = ( science_flux_extinction_factor / standard_flux_extinction_factor ) self.science_spectrum_list[i].add_atm_ext(self.extinction_fraction) case_a = ( self.science_telluric_corrected & self.atmospheric_extinction_corrected ) # case_b = (not self.science_telluric_corrected) & # self.atmospheric_extinction_corrected case_c = self.science_telluric_corrected & ( not self.atmospheric_extinction_corrected ) # case_d = (not self.science_telluric_corrected) & # (not self.atmospheric_extinction_corrected) # Apply the correction science_flux_atm_ext_corrected = ( copy.deepcopy(science_spec.flux) / self.extinction_fraction ) science_flux_err_atm_ext_corrected = ( copy.deepcopy(science_spec.flux_err) / self.extinction_fraction ) science_flux_sky_atm_ext_corrected = ( copy.deepcopy(science_spec.flux_sky) / self.extinction_fraction ) # Add the corrected spectra to the spectrum1D science_spec.add_flux_atm_ext_corrected( science_flux_atm_ext_corrected, science_flux_err_atm_ext_corrected, science_flux_sky_atm_ext_corrected, ) # Add the corrected spectra to the spectrum1D standard_spec.add_flux_atm_ext_corrected( standard_spec.flux, standard_spec.flux_err, standard_spec.flux_sky, ) if case_a or case_c: # Apply the correction science_flux_atm_ext_telluric_corrected = ( copy.deepcopy(science_spec.flux_telluric_corrected) / self.extinction_fraction ) science_flux_err_atm_ext_telluric_corrected = ( copy.deepcopy(science_spec.flux_err_telluric_corrected) / self.extinction_fraction ) science_flux_sky_atm_ext_telluric_corrected = ( copy.deepcopy(science_spec.flux_sky_telluric_corrected) / self.extinction_fraction ) # Add the corrected spectra to the spectrum1D science_spec.add_flux_atm_ext_telluric_corrected( science_flux_atm_ext_telluric_corrected, science_flux_err_atm_ext_telluric_corrected, science_flux_sky_atm_ext_telluric_corrected, ) # Add the corrected spectra to the spectrum1D standard_spec.add_flux_atm_ext_telluric_corrected( standard_spec.flux_telluric_corrected, standard_spec.flux_err_telluric_corrected, standard_spec.flux_sky_telluric_corrected, ) # Flag it as corrected self.atmospheric_extinction_corrected = True self.logger.info("Atmospheric extinction is corrected.")
[docs] def inspect_reduced_spectrum( self, wave_min=3500.0, wave_max=8500.0, atm_ext_corrected=True, telluric_corrected=True, display=True, renderer="default", width=1280, height=720, save_fig=False, fig_type="iframe+png", filename=None, open_iframe=False, return_jsonstring=False, spec_id=None, stype="science+standard", ): """ Parameters ---------- wave_min: float (Default: 3500.) Minimum wavelength to display wave_max: float (Default: 8500.) Maximum wavelength to display atm_ext_corrected: bool (Default: True) Set to True to use the atmospheric extinction corrected spectrum (if available). telluric_corrected: bool (Default: True) Set to True to use the telluric corrected spectrum (if available). display: bool (Default: True) Set to True to display disgnostic plot. renderer: str (Default: 'default') plotly renderer options. width: int/float (Default: 1280) Number of pixels in the horizontal direction of the outputs height: int/float (Default: 720) Number of pixels in the vertical direction of the outputs save_fig: bool (default: False) Save an image if set to True. Plotly uses the pio.write_html() or pio.write_image(). The support format types should be provided in fig_type. fig_type: string (default: 'iframe+png') Image type to be saved, choose from: jpg, png, svg, pdf and iframe. Delimiter is '+'. filename: str or None (Default: None) Filename for the output, all of them will share the same name but will have different extension. open_iframe: bool (Default: False) Open the iframe in the default browser if set to True. return_jsonstring: bool (Default: False) set to True to return JSON-string that can be rendered by Plotly in any support language. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") to_return = [] if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all calibrators spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: spec = self.science_spectrum_list[i] telluric = None if self.science_wavelength_calibrated: wave = spec.wave if self.science_flux_calibrated: if ( atm_ext_corrected & self.atmospheric_extinction_corrected ): if ( telluric_corrected & self.science_telluric_corrected ): fluxcount = ( spec.flux_atm_ext_telluric_corrected ) fluxcount_sky = ( spec.flux_sky_atm_ext_telluric_corrected ) fluxcount_err = ( spec.flux_err_atm_ext_telluric_corrected ) fluxcount_name = "Flux" fluxcount_sky_name = "Sky Flux" fluxcount_err_name = "Flux Uncertainty" telluric = spec.telluric_profile telluric_factor = spec.telluric_factor telluric_nudge_factor = ( spec.telluric_nudge_factor ) fluxcount_continuum = spec.flux_continuum else: fluxcount = spec.flux_atm_ext_corrected fluxcount_sky = spec.flux_sky_atm_ext_corrected fluxcount_err = spec.flux_err_atm_ext_corrected fluxcount_name = "Flux" fluxcount_sky_name = "Sky Flux" fluxcount_err_name = "Flux Uncertainty" telluric = spec.telluric_profile telluric_factor = spec.telluric_factor telluric_nudge_factor = ( spec.telluric_nudge_factor ) fluxcount_continuum = spec.flux_continuum elif ( telluric_corrected & self.science_telluric_corrected ): fluxcount = spec.flux_telluric_corrected fluxcount_sky = spec.flux_sky_telluric_corrected fluxcount_err = spec.flux_err_telluric_corrected fluxcount_name = "Flux" fluxcount_sky_name = "Sky Flux" fluxcount_err_name = "Flux Uncertainty" telluric = spec.telluric_profile telluric_factor = spec.telluric_factor telluric_nudge_factor = spec.telluric_nudge_factor fluxcount_continuum = spec.flux_continuum else: fluxcount = spec.flux fluxcount_sky = spec.flux_sky fluxcount_err = spec.flux_err fluxcount_name = "Flux" fluxcount_sky_name = "Sky Flux" fluxcount_err_name = "Flux Uncertainty" telluric = spec.telluric_profile telluric_factor = spec.telluric_factor telluric_nudge_factor = spec.telluric_nudge_factor fluxcount_continuum = spec.flux_continuum else: fluxcount = spec.count fluxcount_sky = spec.count_sky fluxcount_err = spec.count_err fluxcount_name = "Count / (e- / s)" fluxcount_sky_name = "Sky Count / (e- / s)" fluxcount_err_name = "Count Uncertainty / (e- / s)" fluxcount_continuum = spec.count_continuum else: self.logger.warning( "Spectrum is not wavelength " "calibrated, it cannot be plotted." ) continue wave_mask = (np.array(wave).reshape(-1) > wave_min) & ( np.array(wave).reshape(-1) < wave_max ) flux_low = ( np.nanpercentile( np.array(fluxcount).reshape(-1)[wave_mask], 10 ) / 1.5 ) flux_high = ( np.nanpercentile( np.array(fluxcount).reshape(-1)[wave_mask], 90 ) * 1.5 ) flux_mask = (np.array(fluxcount).reshape(-1) > flux_low) & ( np.array(fluxcount).reshape(-1) < flux_high ) if np.sum(flux_mask) > 0: flux_min = np.nanmin( np.array(fluxcount).reshape(-1)[flux_mask] ) flux_max = np.nanmax( np.array(fluxcount).reshape(-1)[flux_mask] ) else: flux_min = np.nanmin(np.array(fluxcount).reshape(-1)) flux_max = np.nanmax(np.array(fluxcount).reshape(-1)) fig_sci = go.Figure( layout=dict( autosize=False, height=height, width=width, updatemenus=list( [ dict( active=0, buttons=list( [ dict( label="Log Scale", method="update", args=[ {"visible": [True, True]}, { "title": "Log", "yaxis": { "type": "log" }, }, ], ), dict( label="Linear Scale", method="update", args=[ {"visible": [True, False]}, { "title": "Linear", "yaxis": { "type": "linear" }, }, ], ), ] ), ) ] ), title="Science Spectrum", ) ) # show the image on the top fig_sci.add_trace( go.Scatter( x=wave, y=fluxcount, line=dict(color="royalblue"), name=fluxcount_name, ) ) if fluxcount_err is not None: fig_sci.add_trace( go.Scatter( x=wave, y=fluxcount_err, line=dict(color="firebrick"), name=fluxcount_err_name, ) ) if fluxcount_sky is not None: fig_sci.add_trace( go.Scatter( x=wave, y=fluxcount_sky, line=dict(color="orange"), name=fluxcount_sky_name, ) ) if telluric is not None: fig_sci.add_trace( go.Scatter( x=wave, y=telluric * telluric_factor * telluric_nudge_factor, line=dict(color="grey"), name="Telluric Correction", ) ) if fluxcount_continuum is not None: fig_sci.add_trace( go.Scatter( x=wave, y=fluxcount_continuum, line=dict(color="black"), name="Continuum", ) ) fig_sci.update_layout( hovermode="closest", showlegend=True, xaxis=dict( title="Wavelength / A", range=[wave_min, wave_max] ), yaxis=dict( title="Flux", range=[flux_min, flux_max], type="linear" ), legend=go.layout.Legend( x=0, y=1, traceorder="normal", font=dict(family="sans-serif", size=12, color="black"), bgcolor="rgba(0,0,0,0)", ), ) if filename is None: filename = "spectrum" if save_fig: fig_type_split = fig_type.split("+") for t in fig_type_split: save_path = filename + "_" + str(i) + "." + t if t == "iframe": pio.write_html( fig_sci, save_path, auto_open=open_iframe ) elif t in ["jpg", "png", "svg", "pdf"]: pio.write_image(fig_sci, save_path) self.logger.info( "Figure is saved to {} for the ".format(save_path) + "science_spectrum_list for spec_id: {}.".format( i ) ) if display: if renderer == "default": fig_sci.show() else: fig_sci.show(renderer) if return_jsonstring: to_return.append(fig_sci.to_json()) if "standard" in stype_split: spec = self.standard_spectrum_list[0] standard_telluric = None if self.standard_wavelength_calibrated: standard_wave = spec.wave if self.standard_flux_calibrated: if ( atm_ext_corrected & self.atmospheric_extinction_corrected ): if ( telluric_corrected & self.standard_telluric_corrected ): standard_fluxcount = ( spec.flux_atm_ext_telluric_corrected ) standard_fluxcount_sky = ( spec.flux_sky_atm_ext_telluric_corrected ) standard_fluxcount_err = ( spec.flux_err_atm_ext_telluric_corrected ) standard_fluxcount_name = "Flux" standard_fluxcount_sky_name = "Sky Flux" standard_fluxcount_err_name = "Flux Uncertainty" standard_telluric = spec.telluric_profile standard_telluric_factor = spec.telluric_factor standard_telluric_nudge_factor = ( spec.telluric_nudge_factor ) standard_fluxcount_continuum = spec.flux_continuum else: standard_fluxcount = spec.flux_atm_ext_corrected standard_fluxcount_sky = ( spec.flux_sky_atm_ext_corrected ) standard_fluxcount_err = ( spec.flux_err_atm_ext_corrected ) standard_fluxcount_name = "Flux" standard_fluxcount_sky_name = "Sky Flux" standard_fluxcount_err_name = "Flux Uncertainty" standard_telluric = spec.telluric_profile standard_telluric_factor = spec.telluric_factor standard_telluric_nudge_factor = ( spec.telluric_nudge_factor ) standard_fluxcount_continuum = spec.flux_continuum elif telluric_corrected & self.standard_telluric_corrected: standard_fluxcount = spec.flux_telluric_corrected standard_fluxcount_sky = ( spec.flux_sky_telluric_corrected ) standard_fluxcount_err = ( spec.flux_err_telluric_corrected ) standard_fluxcount_name = "Flux" standard_fluxcount_sky_name = "Sky Flux" standard_fluxcount_err_name = "Flux Uncertainty" standard_telluric = spec.telluric_profile standard_telluric_factor = spec.telluric_factor standard_telluric_nudge_factor = ( spec.telluric_nudge_factor ) standard_fluxcount_continuum = spec.flux_continuum else: standard_fluxcount = spec.flux standard_fluxcount_sky = spec.flux_sky standard_fluxcount_err = spec.flux_err standard_fluxcount_name = "Flux" standard_fluxcount_sky_name = "Sky Flux" standard_fluxcount_err_name = "Flux Uncertainty" standard_telluric = spec.telluric_profile standard_telluric_factor = spec.telluric_factor standard_telluric_nudge_factor = ( spec.telluric_nudge_factor ) standard_fluxcount_continuum = spec.flux_continuum else: standard_fluxcount = spec.count standard_fluxcount_sky = spec.count_sky standard_fluxcount_err = spec.count_err standard_fluxcount_name = "Count / (e- / s)" standard_fluxcount_sky_name = "Sky Count / (e- / s)" standard_fluxcount_err_name = ( "Count Uncertainty / (e- / s)" ) standard_fluxcount_continuum = spec.count_continuum else: self.logger.warning( "Spectrum is not wavelength " "calibrated, it cannot be plotted." ) standard_wave_mask = ( np.array(standard_wave).reshape(-1) > wave_min ) & (np.array(standard_wave).reshape(-1) < wave_max) standard_flux_mask = ( np.array(standard_fluxcount).reshape(-1) > np.nanpercentile( np.array(standard_fluxcount).reshape(-1)[ standard_wave_mask ], 10, ) / 1.5 ) & ( np.array(standard_fluxcount).reshape(-1) < np.nanpercentile( np.array(standard_fluxcount).reshape(-1)[ standard_wave_mask ], 90, ) * 1.5 ) if np.nansum(standard_flux_mask) > 0: standard_flux_min = np.nanmin( np.array(standard_fluxcount).reshape(-1)[ standard_flux_mask ] ) standard_flux_max = np.nanmax( np.array(standard_fluxcount).reshape(-1)[ standard_flux_mask ] ) else: standard_flux_min = np.nanmin( np.array(standard_fluxcount).reshape(-1) ) standard_flux_max = np.nanmax( np.array(standard_fluxcount).reshape(-1) ) fig_standard = go.Figure( layout=dict( updatemenus=list( [ dict( active=0, buttons=list( [ dict( label="Log Scale", method="update", args=[ {"visible": [True, True]}, { "title": "Log scale", "yaxis": {"type": "log"}, }, ], ), dict( label="Linear Scale", method="update", args=[ {"visible": [True, False]}, { "title": "Linear scale", "yaxis": { "type": "linear" }, }, ], ), ] ), ) ] ), autosize=False, height=height, width=width, title="Standard Spectrum", ) ) # show the image on the top fig_standard.add_trace( go.Scatter( x=standard_wave, y=standard_fluxcount, line=dict(color="royalblue"), name=standard_fluxcount_name, ) ) if standard_fluxcount_err is not None: fig_standard.add_trace( go.Scatter( x=standard_wave, y=standard_fluxcount_err, line=dict(color="firebrick"), name=standard_fluxcount_err_name, ) ) if standard_fluxcount_sky is not None: fig_standard.add_trace( go.Scatter( x=standard_wave, y=standard_fluxcount_sky, line=dict(color="orange"), name=standard_fluxcount_sky_name, ) ) if self.fluxcal.standard_fluxmag_true is not None: fig_standard.add_trace( go.Scatter( x=self.fluxcal.standard_wave_true, y=self.fluxcal.standard_fluxmag_true, line=dict(color="black"), name="Standard", ) ) if standard_telluric is not None: fig_standard.add_trace( go.Scatter( x=standard_wave, y=standard_telluric * standard_telluric_factor * standard_telluric_nudge_factor, line=dict(color="grey"), name="Telluric Correction", ) ) if standard_fluxcount_continuum is not None: fig_standard.add_trace( go.Scatter( x=standard_wave, y=standard_fluxcount_continuum, line=dict(color="grey"), name="Continuum", ) ) fig_standard.update_layout( hovermode="closest", showlegend=True, xaxis=dict(title="Wavelength / A", range=[wave_min, wave_max]), yaxis=dict( title="Flux", range=[standard_flux_min, standard_flux_max], type="linear", ), legend=go.layout.Legend( x=0, y=1, traceorder="normal", font=dict(family="sans-serif", size=12, color="black"), bgcolor="rgba(0,0,0,0)", ), ) if filename is None: filename = "spectrum_standard" if save_fig: fig_type_split = fig_type.split("+") for t in fig_type_split: save_path = filename + "." + t if t == "iframe": pio.write_html( fig_standard, save_path, auto_open=open_iframe ) elif t in ["jpg", "png", "svg", "pdf"]: pio.write_image(fig_standard, save_path) self.logger.info( "Figure is saved to {} for the ".format(save_path) + "standard_spectrum_list." ) if display: if renderer == "default": fig_standard.show(height=height, width=width) else: fig_standard.show(renderer, height=height, width=width) if return_jsonstring: to_return.append(fig_standard.to_json()) if return_jsonstring: return to_return if ("science" not in stype_split) and ("standard" not in stype_split): error_msg = ( "Unknown stype, please choose from (1) science; " + "and/or (2) standard. use + as delimiter." ) self.logger.critical(error_msg) raise TypeError(error_msg)
[docs] def resample( self, wave_start=None, wave_end=None, wave_bin=None, stype="science+standard", spec_id=None, ): """ Parameters ---------- wave_min: None (Default to the minimum fitted wavlength) Minimum wavelength to display wave_max: None (Default to the maximum fitted wavlength) Maximum wavelength to display wave_bin: None (Deafult to median of the wavelength bin size) Provide the resampling bin size spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str or None (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: spec = self.science_spectrum_list[i] if spec.wave is not None: # Adjust for pixel shift due to chip gaps wave = spec.wave # compute the new equally-spaced wavelength array if wave_bin is None: wave_bin = np.nanmedian(np.ediff1d(wave)) if wave_start is None: wave_start = wave[0] if wave_end is None: wave_end = wave[-1] wave_resampled = np.arange(wave_start, wave_end, wave_bin) spec.add_wavelength_resampled(wave_resampled) self.science_wavelength_resampled = True if spec.count is not None: count_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.count).reshape(-1), verbose=True, ) count_err_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.count_err).reshape(-1), verbose=True, ) count_sky_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.count_sky).reshape(-1), verbose=True, ) spec.add_count_resampled( count_resampled, count_err_resampled, count_sky_resampled, ) self.logger.info( "count is resampled for spec_id: {}.".format(i) ) if spec.sensitivity is not None: sensitivity_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.sensitivity).reshape(-1), verbose=True, ) spec.add_sensitivity_resampled( sensitivity_resampled, ) if spec.flux is not None: flux_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux).reshape(-1), verbose=True, ) flux_err_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_err).reshape(-1), verbose=True, ) flux_sky_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_sky).reshape(-1), verbose=True, ) spec.add_flux_resampled( flux_resampled, flux_err_resampled, flux_sky_resampled, ) self.logger.info( "flux is resampled for spec_id: {}.".format(i) ) if spec.flux_atm_ext_corrected is not None: flux_resampled_atm_ext_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_atm_ext_corrected).reshape(-1), verbose=True, ) flux_err_resampled_atm_ext_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_err_atm_ext_corrected).reshape( -1 ), verbose=True, ) flux_sky_resampled_atm_ext_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_sky_atm_ext_corrected).reshape( -1 ), verbose=True, ) spec.add_flux_resampled_atm_ext_corrected( flux_resampled_atm_ext_corrected, flux_err_resampled_atm_ext_corrected, flux_sky_resampled_atm_ext_corrected, ) self.logger.info( "flux_atm_ext_corrected is resampled for spec_id: " "{}.".format(i) ) if spec.flux_telluric_corrected is not None: flux_resampled_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_telluric_corrected).reshape(-1), verbose=True, ) flux_err_resampled_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_err_telluric_corrected).reshape( -1 ), verbose=True, ) flux_sky_resampled_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_sky_telluric_corrected).reshape( -1 ), verbose=True, ) spec.add_flux_resampled_telluric_corrected( flux_resampled_telluric_corrected, flux_err_resampled_telluric_corrected, flux_sky_resampled_telluric_corrected, ) self.logger.info( "flux_telluric_corrected is resampled for " "spec_id: {}.".format(i) ) if spec.flux_atm_ext_telluric_corrected is not None: flux_resampled_atm_ext_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array( spec.flux_atm_ext_telluric_corrected ).reshape(-1), verbose=True, ) flux_err_resampled_atm_ext_telluric_corrected = ( spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array( spec.flux_err_atm_ext_telluric_corrected ).reshape(-1), verbose=True, ) ) flux_sky_resampled_atm_ext_telluric_corrected = ( spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array( spec.flux_sky_atm_ext_telluric_corrected ).reshape(-1), verbose=True, ) ) spec.add_flux_resampled_atm_ext_telluric_corrected( flux_resampled_atm_ext_telluric_corrected, flux_err_resampled_atm_ext_telluric_corrected, flux_sky_resampled_atm_ext_telluric_corrected, ) self.logger.info( "flux_resampled_atm_ext_telluric_corrected is " "resampled for spec_id: {}.".format(i) ) if "standard" in stype_split: spec = self.standard_spectrum_list[0] if spec.wave is not None: # Adjust for pixel shift due to chip gaps wave = spec.wave # compute the new equally-spaced wavelength array if wave_bin is None: wave_bin = np.nanmedian(np.ediff1d(wave)) if wave_start is None: wave_start = wave[0] if wave_end is None: wave_end = wave[-1] wave_resampled = np.arange(wave_start, wave_end, wave_bin) spec.add_wavelength_resampled(wave_resampled) self.standard_wavelength_resampled = True if spec.count is not None: count_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.count).reshape(-1), verbose=True, ) count_err_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.count_err).reshape(-1), verbose=True, ) count_sky_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.count_sky).reshape(-1), verbose=True, ) spec.add_count_resampled( count_resampled, count_err_resampled, count_sky_resampled, ) self.logger.info( "Wavelength calibration is applied for the " "standard_spectrum_list." ) if spec.sensitivity is not None: sensitivity_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.sensitivity).reshape(-1), verbose=True, ) spec.add_sensitivity_resampled( sensitivity_resampled, ) if spec.flux is not None: flux_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux).reshape(-1), verbose=True, ) flux_err_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_err).reshape(-1), verbose=True, ) flux_sky_resampled = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_sky).reshape(-1), verbose=True, ) spec.add_flux_resampled( flux_resampled, flux_err_resampled, flux_sky_resampled, ) if spec.flux_atm_ext_corrected is not None: flux_resampled_atm_ext_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_atm_ext_corrected).reshape(-1), verbose=True, ) flux_err_resampled_atm_ext_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_err_atm_ext_corrected).reshape(-1), verbose=True, ) flux_sky_resampled_atm_ext_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_sky_atm_ext_corrected).reshape(-1), verbose=True, ) spec.add_flux_resampled_atm_ext_corrected( flux_resampled_atm_ext_corrected, flux_err_resampled_atm_ext_corrected, flux_sky_resampled_atm_ext_corrected, ) if spec.flux_telluric_corrected is not None: flux_resampled_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_telluric_corrected).reshape(-1), verbose=True, ) flux_err_resampled_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_err_telluric_corrected).reshape(-1), verbose=True, ) flux_sky_resampled_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_sky_telluric_corrected).reshape(-1), verbose=True, ) spec.add_flux_resampled_telluric_corrected( flux_resampled_telluric_corrected, flux_err_resampled_telluric_corrected, flux_sky_resampled_telluric_corrected, ) if spec.flux_atm_ext_telluric_corrected is not None: flux_resampled_atm_ext_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array(spec.flux_atm_ext_telluric_corrected).reshape( -1 ), verbose=True, ) flux_err_resampled_atm_ext_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array( spec.flux_err_atm_ext_telluric_corrected ).reshape(-1), verbose=True, ) flux_sky_resampled_atm_ext_telluric_corrected = spectres( np.array(wave_resampled).reshape(-1), np.array(wave).reshape(-1), np.array( spec.flux_sky_atm_ext_telluric_corrected ).reshape(-1), verbose=True, ) spec.add_flux_resampled_atm_ext_telluric_corrected( flux_resampled_atm_ext_telluric_corrected, flux_err_resampled_atm_ext_telluric_corrected, flux_sky_resampled_atm_ext_telluric_corrected, )
[docs] def create_fits( self, output="*", recreate=True, empty_primary_hdu=True, spec_id=None, stype="science+standard", ): """ Create a HDU list, with a choice of any combination of the data, see below the 'output' parameters for details. Parameters ---------- output: String (Default: '*') Type of data to be saved, the order is fixed (in the order of the following description), but the options are flexible. The input strings are delimited by "+", trace: 2 HDUs Trace, and trace width (pixel) count: 3 HDUs Count, uncertainty, and sky (pixel) weight_map: 1 HDU Weight (pixel) arc_spec: 3 HDUs 1D arc spectrum, arc line position (pixel), and arc line effective position (pixel) wavecal: 1 HDU Polynomial coefficients for wavelength calibration wavelength: 1 HDU Wavelength of each pixel wavelength_resampled: 1 HDU Wavelength of each resampled position count_resampled: 3 HDUs Resampled Count, uncertainty, and sky (wavelength) sensitivity: 1 HDU Sensitivity (pixel) flux: 3 HDUs Flux, uncertainty, and sky (pixel) atm_ext: 1 HDU Atmospheric extinction correction factor flux_atm_ext_corrected: 3 HDUs Atmospheric extinction corrected flux, uncertainty, and sky (pixel) telluric_profile: 1 HDU Telluric absorption profile flux_telluric_corrected: 3 HDUs Telluric corrected flux, uncertainty, and sky (pixel) flux_atm_ext_telluric_corrected: 3 HDUs Atmospheric extinction and telluric corrected flux, uncertainty, and sky (pixel) sensitivity_resampled: 1 HDU Sensitivity (wavelength) flux_resampled: 4 HDUs Flux, uncertainty, and sky (wavelength) atm_ext_resampled: 1 HDU Atmospheric extinction correction factor flux_resampled_atm_ext_corrected: 3 HDUs Atmospheric extinction corrected flux, uncertainty, and sky (wavelength) telluric_profile_resampled: 1 HDU Telluric absorption profile flux_resampled_telluic_corrected: 3 HDUs Telluric corrected flux, uncertainty, and sky (wavelength) flux_resampled_atm_ext_telluric_corrected: 3 HDUs Atmospheric extinction and telluric corrected flux, uncertainty, and sky (wavelength) recreate: bool (Default: True) Set to True to overwrite the FITS data and header. empty_primary_hdu: bool (Default: True) Set to True to leave the Primary HDU blank spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # If output is *, chamge it to everything if output == "*": output = ( "trace+count+weight_map+arc_spec+wavecal+wavelength+" + "wavelength_resampled+count_resampled+sensitivity+flux+" + "atm_ext+flux_atm_ext_corrected+flux_telluric_corrected+" + "telluric_profile+flux_atm_ext_telluric_corrected+" + "sensitivity_resampled+" + "flux_resampled+atm_ext_resampled+" + "flux_resampled_atm_ext_corrected+" + "telluric_profile_resampled+" + "flux_resampled_telluric_corrected+" + "flux_resampled_atm_ext_telluric_corrected" ) # Split the string into strings stype_split = stype.split("+") output_split = output.split("+") for i in output_split: if i not in [ "trace", "count", "weight_map", "arc_spec", "wavecal", "wavelength", "wavelength_resampled", "count_resampled", "sensitivity", "flux", "atm_ext", "flux_atm_ext_corrected", "telluric_profile", "flux_telluric_corrected", "flux_atm_ext_telluric_corrected", "sensitivity_resampled", "flux_resampled", "atm_ext_resampled", "flux_resampled_atm_ext_corrected", "telluric_profile_resampled", "flux_resampled_telluric_corrected", "flux_resampled_atm_ext_telluric_corrected", ]: error_msg = "{} is not a valid output.".format(i) self.logger.critical(error_msg) raise ValueError(error_msg) if ("science" not in stype_split) and ("standard" not in stype_split): error_msg = ( "Unknown stype, please choose from (1) science; " + "and/or (2) standard. use + as delimiter." ) self.logger.critical(error_msg) raise ValueError(error_msg) if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: spec = self.science_spectrum_list[i] for j in output_split: if ("resampled" in j) and (not spec.hdu_content[j]): self.resample(stype="science") spec.create_fits( output=output, recreate=recreate, empty_primary_hdu=empty_primary_hdu, ) self.logger.info( "FITS is created for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: spec = self.standard_spectrum_list[0] for j in output_split: if ("resampled" in j) & (not spec.hdu_content[j]): self.resample(stype="standard") spec.create_fits( output=output, recreate=recreate, empty_primary_hdu=empty_primary_hdu, ) self.logger.info( "FITS is created for the " "standard_spectrum_list for spec_id: {}.".format(i) )
[docs] def modify_trace_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the trace header. Parameters ---------- idx: int The HDU number of the trace FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_trace_header( idx, method, *args ) self.logger.info( "trace header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_trace_header( idx, method, *args ) self.logger.info( "trace header is moldified for the " "standard_spectrum_list." )
[docs] def modify_count_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the count header. Parameters ---------- idx: int The HDU number of the trace FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_count_header( idx, method, *args ) self.logger.info( "count header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_count_header( idx, method, *args ) self.logger.info( "count header is moldified for the " "standard_spectrum_list." )
[docs] def modify_weight_map_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the weight map header. Parameters ---------- idx: int The HDU number of the weight map FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_weight_map_header( idx, method, *args ) self.logger.info( "weight_map header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_weight_map_header( idx, method, *args ) self.logger.info( "weight_map header is moldified for the " "standard_spectrum_list." )
[docs] def modify_arc_spec_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the arc spectrum header. Parameters ---------- idx: int The HDU number of the arc spectrum FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_arc_spec_header( idx, method, *args ) self.logger.info( "arc_spec header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_arc_spec_header( idx, method, *args ) self.logger.info( "arc_spec header is moldified for the " "standard_spectrum_list." )
[docs] def modify_wavecal_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the wavecal header. Parameters ---------- idx: int The HDU number of the wavecal FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_wavecal_header( idx, method, *args ) self.logger.info( "wavecal header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_wavecal_header( idx, method, *args ) self.logger.info( "wavecal header is moldified for the " "standard_spectrum_list." )
[docs] def modify_wavelength_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the wavelength header. Parameters ---------- idx: int The HDU number of the wavelength FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_wavelength_header( method, *args ) self.logger.info( "wavelength header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_wavelength_header( method, *args ) self.logger.info( "wavelength header is moldified for the " "standard_spectrum_list." )
[docs] def modify_wavelength_resampled_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the wavelength_resampled header. Parameters ---------- idx: int The HDU number of the wavelength_resampled FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_wavelength_resampled_header(method, *args) self.logger.info( "wavelength header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_wavelength_resampled_header( method, *args ) self.logger.info( "wavelength header is moldified for the " "standard_spectrum_list." )
[docs] def modify_count_resampled_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the count resampled header. Parameters ---------- idx: int The HDU number of the count resampled FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_count_resampled_header( idx, method, *args ) self.logger.info( "count_resampled header is moldified for " "the science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_count_resampled_header( idx, method, *args ) self.logger.info( "count_resampled header is moldified for the " "standard_spectrum_list." )
[docs] def modify_sensitivity_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the sensitivity header. Parameters ---------- idx: int The HDU number of the sensitivity FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_sensitivity_header( method, *args ) self.logger.info( "sensitivity header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_sensitivity_header( method, *args ) self.logger.info( "sensitivity header is moldified for the " "standard_spectrum_list." )
[docs] def modify_flux_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the flux header. Parameters ---------- idx: int The HDU number of the flux FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_flux_header( idx, method, *args ) self.logger.info( "flux header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_flux_header( idx, method, *args ) self.logger.info( "flux header is moldified for the " "standard_spectrum_list." )
[docs] def modify_atm_ext_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the atmospheric extinction factor header. Parameters ---------- idx: int The HDU number of the sensitivity FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_atm_ext_header( method, *args ) self.logger.info( "atm_ext header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_atm_ext_header(method, *args) self.logger.info( "atm_ext header is moldified for the " "standard_spectrum_list." )
[docs] def modify_flux_atm_ext_corrected_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the flux_atm_ext_corrected header. Parameters ---------- idx: int The HDU number of the flux FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_flux_atm_ext_corrected_header(idx, method, *args) self.logger.info( "flux_atm_ext_corrected header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[ 0 ].modify_flux_atm_ext_corrected_header(idx, method, *args) self.logger.info( "flux_atm_ext_corrected header is moldified for the " "standard_spectrum_list." )
[docs] def modify_telluric_profile_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the telluric_profile header. Parameters ---------- idx: int The HDU number of the telluric_profile FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_telluric_profile_header( method, *args ) self.logger.info( "telluric_profile header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_telluric_profile_header( method, *args ) self.logger.info( "telluric_profile header is moldified for the " "standard_spectrum_list." )
[docs] def modify_flux_telluric_corrected_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the flux_atm_ext_corrected header. Parameters ---------- idx: int The HDU number of the flux FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_flux_telluric_corrected_header(idx, method, *args) self.logger.info( "flux_telluric_corrected header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[ 0 ].modify_flux_telluric_corrected_header(idx, method, *args) self.logger.info( "flux_telluric_corrected header is moldified for the " "standard_spectrum_list." )
[docs] def modify_flux_atm_ext_telluric_corrected_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the flux_atm_ext_telluric_corrected header. Parameters ---------- idx: int The HDU number of the flux FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_flux_atm_ext_telluric_corrected_header( idx, method, *args ) self.logger.info( "flux_atm_ext_telluric_corrected header is moldified for " "the science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[ 0 ].modify_flux_atm_ext_telluric_corrected_header(idx, method, *args) self.logger.info( "flux_atm_ext_telluric_corrected header is moldified for the " "standard_spectrum_list." )
[docs] def modify_sensitivity_resampled_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the sensitivity resampled header. Parameters ---------- method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_sensitivity_resampled_header(method, *args) self.logger.info( "sensitivity_resampled header is moldified " "for the science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_sensitivity_resampled_header( method, *args ) self.logger.info( "sensitivity_resampled header is moldified for " "the standard_spectrum_list." )
[docs] def modify_flux_resampled_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the flux resampled header. Parameters ---------- idx: int The HDU number of the flux resampled FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_flux_resampled_header( idx, method, *args ) self.logger.info( "flux_resampled header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_flux_resampled_header( idx, method, *args ) self.logger.info( "flux_resampled header is moldified for the " "standard_spectrum_list." )
[docs] def modify_atm_ext_resampled_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the resampled atmospheric extinction factor header. Parameters ---------- idx: int The HDU number of the resampled atmospheric extinction FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[i].modify_atm_ext_resampled_header( method, *args ) self.logger.info( "atm_ext_resampled header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[0].modify_atm_ext_resampled_header( method, *args ) self.logger.info( "atm_ext_resampled header is moldified for the " "standard_spectrum_list." )
[docs] def modify_flux_resampled_atm_ext_corrected_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the atmospheric extinction corrected flux resampled header. Parameters ---------- idx: int The HDU number of the flux resampled FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_flux_resampled_atm_ext_corrected_header( idx, method, *args ) self.logger.info( "flux_resampled_atm_ext header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[ 0 ].modify_flux_resampled_atm_ext_corrected_header( idx, method, *args ) self.logger.info( "flux_resampled_atm_ext header is moldified for the " "standard_spectrum_list." )
[docs] def modify_telluric_profile_resampled_header( self, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the resampled telluric profile header. Parameters ---------- idx: int The HDU number of the telluric_profile FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_telluric_profile_resampled_header(method, *args) self.logger.info( "telluric_profile_resampled header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[ 0 ].modify_telluric_profile_resampled_header(method, *args) self.logger.info( "telluric_profile_resampled header is moldified for the " "standard_spectrum_list." )
[docs] def modify_flux_resampled_telluric_corrected_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the telluric absorption corrected flux resampled header. Parameters ---------- idx: int The HDU number of the flux resampled FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_flux_resampled_telluric_corrected_header( idx, method, *args ) self.logger.info( "flux_resampled_telluric header is moldified for the " "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[ 0 ].modify_flux_resampled_telluric_corrected_header( idx, method, *args ) self.logger.info( "flux_resampled_telluric header is moldified for the " "standard_spectrum_list." )
[docs] def modify_flux_resampled_atm_ext_telluric_corrected_header( self, idx, method, *args, spec_id=None, stype="science+standard" ): """ Wrapper function to modify the telluric absorption corrected flux resampled header. Parameters ---------- idx: int The HDU number of the flux resampled FITS method: str The operation to modify the header with *args: Extra arguments for the method spec_id: int (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Split the string into strings stype_split = stype.split("+") if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, calibrators are initialised to all spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: self.science_spectrum_list[ i ].modify_flux_resampled_atm_ext_telluric_corrected_header( idx, method, *args ) self.logger.info( "flux_resampled_atm_ext_telluric header is moldified for " "the science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: self.standard_spectrum_list[ 0 ].modify_flux_resampled_atm_ext_telluric_corrected_header( idx, method, *args ) self.logger.info( "flux_resampled_atm_ext_telluric header is moldified for the " "standard_spectrum_list." )
[docs] def save_fits( self, output="*", filename="reduced", recreate=False, empty_primary_hdu=True, overwrite=False, spec_id=None, stype="science+standard", ): """ Save the reduced data to disk, with a choice of any combination of the data, see below the 'output' parameters for details. Parameters ---------- output: String (Default: 'arc_spec+wavecal+wavelength+flux+flux_resampled') Type of data to be saved, the order is fixed (in the order of the following description), but the options are flexible. The input strings are delimited by "+", trace: 2 HDUs Trace, and trace width (pixel) count: 3 HDUs Count, uncertainty, and sky (pixel) weight_map: 1 HDU Weight (pixel) arc_spec: 3 HDUs 1D arc spectrum, arc line position (pixel), and arc line effective position (pixel) wavecal: 1 HDU Polynomial coefficients for wavelength calibration wavelength: 1 HDU Wavelength of each pixel wavelength_resampled: 1 HDU Wavelength of each resampled position count_resampled: 3 HDUs Resampled Count, uncertainty, and sky (wavelength) sensitivity: 1 HDU Sensitivity (pixel) flux: 3 HDUs Flux, uncertainty, and sky (pixel) atm_ext: 1 HDU Atmospheric extinction correction factor flux_atm_ext_corrected: 3 HDUs Atmospheric extinction corrected flux, uncertainty, and sky (pixel) telluric_profile: 1 HDU Telluric absorption profile flux_telluric_corrected: 3 HDUs Telluric corrected flux, uncertainty, and sky (pixel) flux_atm_ext_telluric_corrected: 3 HDUs Atmospheric extinction and telluric corrected flux, uncertainty, and sky (pixel) sensitivity_resampled: 1 HDU Sensitivity (wavelength) flux_resampled: 4 HDUs Flux, uncertainty, and sky (wavelength) atm_ext_resampled: 1 HDU Atmospheric extinction correction factor flux_resampled_atm_ext_corrected: 3 HDUs Atmospheric extinction corrected flux, uncertainty, and sky (wavelength) telluric_profile_resampled: 1 HDU Telluric absorption profile flux_resampled_telluic_corrected: 3 HDUs Telluric corrected flux, uncertainty, and sky (wavelength) flux_resampled_atm_ext_telluric_corrected: 3 HDUs Atmospheric extinction and telluric corrected flux, uncertainty, and sky (wavelength) filename: String (Default: 'reduced') Disk location to be written to. Default is at where the process/subprocess is execuated. recreate: bool (Default: False) Set to True to overwrite the FITS data and header. empty_primary_hdu: bool (Default: True) Set to True to leave the Primary HDU blank overwrite: bool (Default: False) Default is False. spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter """ # Fix the names and extensions filename = os.path.splitext(filename)[0] # If output is *, chamge it to everything if output == "*": output = ( "trace+count+weight_map+arc_spec+wavecal+wavelength+" + "wavelength_resampled+count_resampled+sensitivity+flux+" + "atm_ext+flux_atm_ext_corrected+flux_telluric_corrected+" + "telluric_profile+flux_atm_ext_telluric_corrected+" + "sensitivity_resampled+" + "flux_resampled+atm_ext_resampled+" + "flux_resampled_atm_ext_corrected+" + "telluric_profile_resampled+" + "flux_resampled_telluric_corrected+" + "flux_resampled_atm_ext_telluric_corrected" ) # Split the string into strings stype_split = stype.split("+") output_split = output.split("+") for i in output_split: if i not in [ "trace", "count", "weight_map", "arc_spec", "wavecal", "wavelength", "wavelength_resampled", "count_resampled", "sensitivity", "flux", "atm_ext", "flux_atm_ext_corrected", "telluric_profile", "flux_telluric_corrected", "flux_atm_ext_telluric_corrected", "sensitivity_resampled", "flux_resampled", "atm_ext_resampled", "flux_resampled_atm_ext_corrected", "telluric_profile_resampled", "flux_resampled_telluric_corrected", "flux_resampled_atm_ext_telluric_corrected", ]: error_msg = "{} is not a valid output.".format(i) self.logger.critical(error_msg) raise ValueError(error_msg) if ("science" not in stype_split) and ("standard" not in stype_split): error_msg = ( "Unknown stype, please choose from (1) science; " + "and/or (2) standard. use + as delimiter." ) self.logger.critical(error_msg) raise ValueError(error_msg) if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): error_msg = "The given spec_id does not exist." self.logger.critical(error_msg) raise ValueError(error_msg) else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: filename_i = filename + "_science_" + str(i) spec = self.science_spectrum_list[i] output_filtered = [] for j in output_split: if spec.hdu_content[j]: output_filtered.append(j) spec.save_fits( output="+".join(output_filtered), filename=filename_i, overwrite=overwrite, recreate=recreate, empty_primary_hdu=empty_primary_hdu, ) self.logger.info( "FITS file is saved to {} for the ".format(filename_i) + "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: spec = self.standard_spectrum_list[0] output_filtered = [] for j in output_split: if spec.hdu_content[j]: output_filtered.append(j) spec.save_fits( output="+".join(output_filtered), filename=filename + "_standard", overwrite=overwrite, recreate=recreate, empty_primary_hdu=empty_primary_hdu, ) self.logger.info( "FITS file is saved to {}_standard ".format(filename) + "for the standard_spectrum_list." )
[docs] def save_csv( self, output="*", filename="reduced", recreate=False, overwrite=False, spec_id=None, stype="science+standard", ): """ Save the reduced data to disk, with a choice of any combination of the 5 sets of data, see below the 'output' parameters for details. Parameters ---------- spec_id: int or None (Default: None) The ID corresponding to the spectrum1D object output: String (Default: 'arc_spec+wavecal+wavelength+flux+flux_resampled') Type of data to be saved, the order is fixed (in the order of the following description), but the options are flexible. The input strings are delimited by "+", trace: 2 HDUs Trace, and trace width (pixel) count: 3 HDUs Count, uncertainty, and sky (pixel) weight_map: 1 HDU Weight (pixel) arc_spec: 3 HDUs 1D arc spectrum, arc line position (pixel), and arc line effective position (pixel) wavecal: 1 HDU Polynomial coefficients for wavelength calibration wavelength: 1 HDU Wavelength of each pixel wavelength_resampled: 1 HDU Wavelength of each resampled position count_resampled: 3 HDUs Resampled Count, uncertainty, and sky (wavelength) sensitivity: 1 HDU Sensitivity (pixel) flux: 3 HDUs Flux, uncertainty, and sky (pixel) atm_ext: 1 HDU Atmospheric extinction correction factor flux_atm_ext_corrected: 3 HDUs Atmospheric extinction corrected flux, uncertainty, and sky (pixel) telluric_profile: 1 HDU Telluric absorption profile flux_telluric_corrected: 3 HDUs Telluric corrected flux, uncertainty, and sky (pixel) flux_atm_ext_telluric_corrected: 3 HDUs Atmospheric extinction and telluric corrected flux, uncertainty, and sky (pixel) sensitivity_resampled: 1 HDU Sensitivity (wavelength) flux_resampled: 4 HDUs Flux, uncertainty, and sky (wavelength) atm_ext_resampled: 1 HDU Atmospheric extinction correction factor flux_resampled_atm_ext_corrected: 3 HDUs Atmospheric extinction corrected flux, uncertainty, and sky (wavelength) telluric_profile_resampled: 1 HDU Telluric absorption profile flux_resampled_telluic_corrected: 3 HDUs Telluric corrected flux, uncertainty, and sky (wavelength) flux_resampled_atm_ext_telluric_corrected: 3 HDUs Atmospheric extinction and telluric corrected flux, uncertainty, and sky (wavelength) filename: String (Default: 'reduced') Disk location to be written to. Default is at where the process/subprocess is execuated. stype: str (Default: 'science+standard') 'science' and/or 'standard' to indicate type, use '+' as delimiter recreate: bool (Default: False) Set to True to overwrite the FITS data and header. overwrite: bool (Default: False) Default is False. """ # Fix the names and extensions filename = os.path.splitext(filename)[0] # If output is *, chamge it to everything if output == "*": output = ( "trace+count+weight_map+arc_spec+wavecal+wavelength+" + "wavelength_resampled+count_resampled+sensitivity+flux+" + "atm_ext+flux_atm_ext_corrected+telluric_profile+" + "flux_telluric_corrected+flux_atm_ext_telluric_corrected+" + "sensitivity_resampled+" + "flux_resampled+atm_ext_resampled+" + "flux_resampled_atm_ext_corrected+" + "telluric_profile_resampled+" + "flux_resampled_telluric_corrected+" + "flux_resampled_atm_ext_telluric_corrected" ) # Split the string into strings stype_split = stype.split("+") output_split = output.split("+") for i in output_split: if i not in [ "trace", "count", "weight_map", "arc_spec", "wavecal", "wavelength", "wavelength_resampled", "count_resampled", "sensitivity", "flux", "atm_ext", "flux_atm_ext_corrected", "telluric_profile", "flux_telluric_corrected", "flux_atm_ext_telluric_corrected", "sensitivity_resampled", "flux_resampled", "atm_ext_resampled", "flux_resampled_atm_ext_corrected", "telluric_profile_resampled", "flux_resampled_telluric_corrected", "flux_resampled_atm_ext_telluric_corrected", ]: error_msg = "{} is not a valid output.".format(i) self.logger.critical(error_msg) raise ValueError(error_msg) if ("science" not in stype_split) and ("standard" not in stype_split): error_msg = ( "Unknown stype, please choose from (1) science; " + "and/or (2) standard. use + as delimiter." ) self.logger.critical(error_msg) raise ValueError(error_msg) if "science" in stype_split: if isinstance(spec_id, int): spec_id = [spec_id] if spec_id is not None: if not set(spec_id).issubset( list(self.science_spectrum_list.keys()) ): raise ValueError("The given spec_id does not exist.") else: # if spec_id is None, contraints are applied to all # calibrators spec_id = list(self.science_spectrum_list.keys()) for i in spec_id: spec = self.science_spectrum_list[i] output_filtered = [] for j in output_split: if ("resampled" in j) and (not spec.hdu_content[j]): self.resample(stype="science") if spec.hdu_content[j]: output_filtered.append(j) filename_i = filename + "_science_" + str(i) spec.save_csv( output="+".join(output_filtered), filename=filename_i, recreate=recreate, overwrite=overwrite, ) self.logger.info( "CSV file is saved to {} for the ".format(filename_i) + "science_spectrum_list for spec_id: {}.".format(i) ) if "standard" in stype_split: spec = self.standard_spectrum_list[0] output_filtered = [] for j in output_split: if ("resampled" in j) and (not spec.hdu_content[j]): self.resample(stype="standard") if spec.hdu_content[j]: output_filtered.append(j) spec.save_csv( output="+".join(output_filtered), filename=filename + "_standard", recreate=recreate, overwrite=overwrite, ) self.logger.info( "FITS file is saved to {}_standard ".format(filename) + "for the standard_spectrum_list." )