pyreduce.instruments package

Submodules

pyreduce.instruments.common module

Abstract parent module for all other instruments Contains some general functionality, which may be overridden by the children of course

class pyreduce.instruments.common.COMMON

Bases: Instrument

load_info()

Load the default/common instrument config from defaults/config.yaml

class pyreduce.instruments.common.Instrument

Bases: object

Abstract parent class for all instruments Handles the instrument specific information

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

Parameters:

• header (fits.header, dict) – header to read/write info from/to

• channel (str) – instrument channel

Returns:

header – header with added information

Return type:

fits.header, dict

apply_filters(files, expected, steps=None)

Determine the relevant files for a given set of expected values.

Parameters:

• files (list(files)) – list if fits files

• expected (dict) – dictionary with expected header values for each reduction step

• steps (list, optional) – list of steps that will be run. If provided, warnings about missing files are only shown for these steps.

Returns:

files – list of files. The first element of each tuple is the used setting, and the second are the files for each step.

Return type:

list((dict, dict))

assemble_amplifiers(hdu, amp_extensions, channel)

Assemble multi-amplifier readout into single frame.

Reads data from multiple FITS extensions, each representing one amplifier’s readout region, and assembles them into a single frame using DATASEC/DETSEC mappings.

Parameters:

• hdu (HDUList) – Open FITS file

• amp_extensions (list) – List of extension names to read

• channel (str) – Instrument channel

Returns:

• data (ndarray) – Assembled frame (float32)

• header (fits.Header) – Combined header with per-amplifier e_gain{n}, e_readn{n}

property channels: list[str] | None

Available instrument channels.

config

Validated configuration model dict: Information about the instrument (for backward compatibility)

Type:

InstrumentConfig

discover_channels(input_dir)

Discover available channels from files in the input directory.

Override in subclasses for instruments that require channel specification. The base implementation returns [None] which means no channel filtering.

Parameters:

input_dir (str) – Directory containing input files

Returns:

channels – List of channel identifiers found in the data

Return type:

list

property extension: int | str | list

FITS extension(s) to read.

find_files(input_dir)

Find fits files in the given folder

Parameters:

input_dir (string) – directory to look for fits and fits.gz files in, may include bash style wildcards

Returns:

files – absolute path filenames

Return type:

array(string)

get(key, header, channel, alt=None)

get_amplifier_extensions(header)

Get list of amplifier extensions if multi-amp mode is configured.

Parameters:

header (fits.Header) – Primary FITS header

Returns:

List of extension names/indices if multi-amp, None otherwise

Return type:

list or None

get_expected_values(target, night, channel=None, **kwargs)

get_extension(header, channel)

get_mask_filename(channel, **kwargs)

get_settings_fallbacks(channel)

Return channel names to try when looking up settings files.

Searched in order: most specific first, least specific last. The base settings.json is always the final fallback (handled by the caller).

Override in subclasses for instruments with composite channel names.

get_supported_channels()

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the pre-existing wavelength solution for the current setting

Parameters:

• header (fits.header, dict) – header of the wavelength calibration file

• channel (str) – instrument channel

Returns:

filename – name of the wavelength solution file

Return type:

str

get_wavelength_range(header, channel, **kwargs)

property id_instrument: str

Instrument identifier for header matching.

info

Validated configuration model dict: Information about the instrument (for backward compatibility)

Type:

InstrumentConfig

load_fits(fname, channel, extension=None, mask=None, header_only=False, dtype=None)

load fits file, REDUCE style

primary and extension header are combined channel-specific info is applied to header data is clipnflipped mask is applied

For multi-amplifier instruments, data from multiple extensions is assembled into a single frame before clipnflip.

Parameters:

• fname (str) – filename

• instrument (str) – name of the instrument

• channel (str) – instrument channel

• extension (int) – data extension of the FITS file to load

• mask (array, optional) – mask to add to the data

• header_only (bool, optional) – only load the header, not the data

• dtype (str, optional) – numpy datatype to convert the read data to

Returns:

• data (masked_array) – FITS data, clipped and flipped, and with mask

• header (fits.header) – FITS header (Primary and Extension + channel info)

• ONLY the header is returned if header_only is True

load_info()

Load static instrument information Either as fits header keywords or static values

Returns:

• config (InstrumentConfig) – Validated Pydantic model

• info (dict(str:object)) – dictionary of REDUCE names for properties to Header keywords/static values

name

Name of the instrument (lowercase)

Type:

str

property orientation: int | list[int]

Detector orientation code(s).

populate_filters(files)

Extract values from the fits headers and store them in self.filters

Parameters:

files (list(str)) – list of fits files

Returns:

filters – list of populated filters (identical to self.filters)

Return type:

list(Filter)

sort_files(input_dir, target, night, *args, steps=None, **kwargs)

Sort a set of fits files into different categories types are: bias, flat, wavecal, orderdef, spec

Parameters:

• input_dir (str) – input directory containing the files to sort

• target (str) – name of the target as in the fits headers

• night (str) – observation night, possibly with wildcards

• channel (str) – instrument channel

• steps (list, optional) – list of steps that will be run. If provided, warnings about missing files are only shown for these steps.

Returns:

• files_per_night (list[dict{str:dict{str:list[str]}}]) – a list of file sets, one entry per night, where each night consists of a dictionary with one entry per setting, each fileset has five lists of filenames: “bias”, “flat”, “order”, “wave”, “spec”, organised in another dict

• nights_out (list[datetime]) – a list of observation times, same order as files_per_night

pyreduce.instruments.common.create_custom_instrument(name, extension=0, info=None, mask_file=None, wavecal_file=None, **overrides)

Create a custom instrument for reducing data from unsupported spectrographs.

Parameters:

• name (str) – Instrument name (used for output file naming)

• extension (int or str) – FITS extension to read data from (default: 0)

• info (dict or str, optional) – Instrument properties as a dict, or path to a config file (JSON/YAML). If None, uses sensible defaults. Dict values can be literal numbers or FITS header keyword strings (looked up at runtime).

• mask_file (str, optional) – Path to bad pixel mask FITS file

• wavecal_file (str, optional) – Path to wavelength calibration file

• **overrides – Additional instrument properties, e.g. gain=1.1, readnoise=5, orientation=4. These override values from the info dict. See instruments/defaults/config.yaml for available keys.

Returns:

Configured instrument instance

Return type:

Instrument

pyreduce.instruments.common.find_first_index(arr, value)

find the first element equal to value in the array arr

class pyreduce.instruments.common.getter(header, info, channel)

Bases: object

Get data from a header/dict, based on the given channel, and applies replacements

get(key, alt=None)

Get data

Parameters:

• key (str) – key of the data in the header

• alt (obj, optional) – alternative value, if key does not exist (default: None)

Returns:

value – value found in header (or alternatively alt)

Return type:

obj

pyreduce.instruments.common.observation_date_to_night(observation_date)

Convert an observation timestamp into the date of the observation night Nights start at 12am and end at 12 am the next day

Parameters:

observation_date (datetime) – timestamp of the observation

Returns:

night – night of the observation

Return type:

datetime.date

pyreduce.instruments.common.parse_iraf_section(section_str)

Parse IRAF-style section string into pixel coordinates.

IRAF format: [x1:x2,y1:y2] where coordinates are 1-based inclusive.

Parameters:

section_str (str) – Section string like “[28:1179,1:4616]”

Returns:

x1, x2, y1, y2 – 1-based inclusive pixel coordinates

Return type:

int

pyreduce.instruments.models module

Pydantic models for instrument configuration validation.

These models provide type-safe validation for instrument configuration files. Currently validates the flat YAML structure; will evolve toward the nested structure described in REDESIGN.md.

class pyreduce.instruments.models.AmplifierConfig(*, id: str, gain: float | dict[str, str], readnoise: float | dict[str, str], region: dict[str, list[int]] | None = None, linearity: dict | None = None, bad_pixel_mask: str | None = None)

Bases: BaseModel

Configuration for a detector readout amplifier.

bad_pixel_mask: str | None

gain: float | dict[str, str]

id: str

linearity: dict | None

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

readnoise: float | dict[str, str]

region: dict[str, list[int]] | None

class pyreduce.instruments.models.AmplifiersConfig(*, extension_template: str, count: str | int, gain: str = 'GAIN', readnoise: str = 'RDNOISE', datasec: str = 'DATASEC', detsec: str = 'DETSEC')

Bases: BaseModel

Configuration for multi-amplifier readout assembly.

Used when detector data is split across multiple FITS extensions, each with its own gain/readnoise and region mapping.

count: str | int

datasec: str

detsec: str

extension_template: str

gain: str

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

readnoise: str

class pyreduce.instruments.models.BeamArmConfig(*, name: str, polarization: str | None = None, wavelength_shift: float = 0.0, trace_offset: float = 0.0)

Bases: BaseModel

Configuration for a beam-splitter arm.

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

polarization: str | None

trace_offset: float

wavelength_shift: float

class pyreduce.instruments.models.DetectorConfig(*, name: str, naxis: tuple[int, int] | list[int], orientation: int = 0, prescan: dict[str, list[int] | None] | None = None, overscan: dict[str, list[int] | None] | None = None, amplifiers: list[AmplifierConfig] = [], bad_pixel_mask: str | None = None)

Bases: BaseModel

Configuration for a physical detector.

amplifiers: list[AmplifierConfig]

bad_pixel_mask: str | None

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

naxis: tuple[int, int] | list[int]

orientation: int

overscan: dict[str, list[int] | None] | None

prescan: dict[str, list[int] | None] | None

class pyreduce.instruments.models.DimensionConfig(*, values: list[str], header_key: str | None = None, optional: bool = False)

Bases: BaseModel

Configuration for a varying dimension (mode, fiber, etc.).

header_key: str | None

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

optional: bool

values: list[str]

class pyreduce.instruments.models.FiberBundleConfig(*, size: int, count: int | None = None, merge: str | list[int] = 'center', height: str | float | None = 'derived', bundle_centers: dict[int, float] | None = None, bundle_centers_file: str | list[str] | None = None)

Bases: BaseModel

Configuration for repeating fiber bundles.

For instruments where fibers are organized in repeating bundles (e.g., 7 fibers per IFU target), this defines the bundle structure.

Bundle assignment can use either: - Fixed size division (n_traces must be divisible by size) - bundle_centers: assign each trace to nearest bundle center (handles missing fibers)

bundle_centers: dict[int, float] | None

bundle_centers_file: str | list[str] | None

classmethod convert_bundle_centers_keys(v)

Convert string keys to int (YAML loads int keys as strings).

count: int | None

height: str | float | None

merge: str | list[int]

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

size: int

class pyreduce.instruments.models.FiberGroupConfig(*, range: tuple[int, int], merge: str | list[int] = 'center', height: float | Literal['derived'] | None = None)

Bases: BaseModel

Configuration for a named fiber group.

Defines a range of physical fibers that form a logical group (e.g., “A”, “cal”, “B”). The merged trace can be the average, center, or specific fiber(s) from the group.

The height parameter specifies extraction height for this group: - float: explicit pixel value (or fraction if < 3) - “derived”: compute from trace positions as span + fiber_diameter - None: use settings.json extraction_height (default)

height: float | Literal['derived'] | None

merge: str | list[int]

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

range: tuple[int, int]

class pyreduce.instruments.models.FibersConfig(*, groups: dict[str, FiberGroupConfig] | None = None, bundles: FiberBundleConfig | None = None, use: dict[str, Literal['groups', 'per_fiber'] | list[str]] | None = None, trace_by: str | None = None, fibers_per_order: int | list[int] | None = None, order_centers: dict[int, float] | None = None, order_centers_file: str | list[str] | None = None)

Bases: BaseModel

Configuration for multi-fiber instruments.

Defines how physical fiber traces are organized into groups and which traces to use for each reduction step.

For multi-order instruments (echelle), set fibers_per_order and provide order_centers to apply grouping within each spectral order.

For multi-channel instruments, per-order fields can be lists indexed by channel (same pattern as orientation, extension, etc.).

bundles: FiberBundleConfig | None

classmethod convert_order_centers_keys(v)

Convert string keys to int (YAML loads int keys as strings).

fibers_per_order: int | list[int] | None

groups: dict[str, FiberGroupConfig] | None

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

order_centers: dict[int, float] | None

order_centers_file: str | list[str] | None

property per_order: bool

Whether per-order grouping is enabled (derived from fibers_per_order).

trace_by: str | None

use: dict[str, TraceSelection] | None

validate_no_bundle_named_groups()

Reject named groups whose names collide with bundle merge names.

Bundle merges produce group names of the form bundle_<int>; a named group with the same shape would create silent name collisions in selection.

validate_per_order_config()

Validate per-order config when fibers_per_order is set.

If order_centers or order_centers_file is provided, traces are matched by y-position. Otherwise, auto-pairing mode groups consecutive traces.

class pyreduce.instruments.models.FileClassification(*, kw_bias: str | None = None, kw_flat: str | None = None, kw_curvature: str | None = None, kw_scatter: str | None = None, kw_orders: str | None = None, kw_wave: str | None = None, kw_comb: str | None = None, kw_spec: str | None = None, id_bias: str | None = None, id_flat: str | None = None, id_curvature: str | None = None, id_scatter: str | None = None, id_orders: str | None = None, id_wave: str | None = None, id_comb: str | None = None, id_spec: str | None = None, **extra_data: Any)

Bases: BaseModel

Keywords and patterns for file type classification.

id_bias: str | None

id_comb: str | None

id_curvature: str | None

id_flat: str | None

id_orders: str | None

id_scatter: str | None

id_spec: str | None

id_wave: str | None

kw_bias: str | None

kw_comb: str | None

kw_curvature: str | None

kw_flat: str | None

kw_orders: str | None

kw_scatter: str | None

kw_spec: str | None

kw_wave: str | None

model_config = {'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class pyreduce.instruments.models.HeaderRef(*, key: str)

Bases: BaseModel

Reference to a FITS header keyword.

key: str

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class pyreduce.instruments.models.InstrumentConfig(*, instrument: str, id_instrument: str, telescope: str | None = None, date: str = 'DATE-OBS', date_format: str = 'fits', channels: list[str] | None = None, channels_id: list[str] | None = None, kw_channel: str | None = None, id_channel: list[str] | None = None, extension: int | str | list[int | str] = 0, orientation: int | list[int] = 0, transpose: bool = False, amplifiers: AmplifiersConfig | None = None, fibers: FibersConfig | None = None, naxis_x: str | int = 'NAXIS1', naxis_y: str | int = 'NAXIS2', prescan_x: int | str = 0, overscan_x: int | str = 0, prescan_y: int | str = 0, overscan_y: int | str = 0, gain: float | int | str = 1, readnoise: float | int | str = 0, dark: float | int | str = 0, sky: float | int | str = 0, exposure_time: str = 'EXPTIME', ra: str | None = 'RA', dec: str | None = 'DEC', longitude: float | str | None = None, latitude: float | str | None = None, altitude: float | str | None = None, target: str = 'OBJECT', observation_type: str | None = None, category: str | None = None, image_type: str | None = None, instrument_mode: str | None = None, kw_bias: str | None = None, kw_flat: str | None = None, kw_curvature: str | None = None, kw_scatter: str | None = None, kw_orders: str | None = None, kw_wave: str | None = None, kw_comb: str | None = None, kw_spec: str | None = None, id_bias: str | None = None, id_flat: str | None = None, id_curvature: str | None = None, id_scatter: str | None = None, id_orders: str | None = None, id_wave: str | None = None, id_comb: str | None = None, id_spec: str | None = None, wavelength_range: list | None = None, wavecal_specifier: str | None = None, **extra_data: Any)

Bases: BaseModel

Configuration for an astronomical instrument.

This model validates the flat YAML structure used by instrument configs. It allows extra fields for instrument-specific parameters.

altitude: float | str | None

amplifiers: AmplifiersConfig | None

category: str | None

channels: list[str] | None

channels_id: list[str] | None

dark: float | int | str

date: str

date_format: str

dec: str | None

classmethod ensure_list(v)

Convert single values to lists.

exposure_time: str

extension: int | str | list[int | str]

fibers: FibersConfig | None

gain: float | int | str

id_bias: str | None

id_channel: list[str] | None

id_comb: str | None

id_curvature: str | None

id_flat: str | None

id_instrument: str

id_orders: str | None

id_scatter: str | None

id_spec: str | None

id_wave: str | None

image_type: str | None

instrument: str

instrument_mode: str | None

kw_bias: str | None

kw_channel: str | None

kw_comb: str | None

kw_curvature: str | None

kw_flat: str | None

kw_orders: str | None

kw_scatter: str | None

kw_spec: str | None

kw_wave: str | None

latitude: float | str | None

longitude: float | str | None

model_config = {'extra': 'allow', 'populate_by_name': True, 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

naxis_x: str | int

naxis_y: str | int

classmethod normalize_list_or_scalar(v)

Keep as-is, validation handles both forms.

observation_type: str | None

orientation: int | list[int]

overscan_x: int | str

overscan_y: int | str

prescan_x: int | str

prescan_y: int | str

ra: str | None

readnoise: float | int | str

sky: float | int | str

target: str

telescope: str | None

transpose: bool

wavecal_specifier: str | None

wavelength_range: list | None

class pyreduce.instruments.models.InstrumentConfigV2(*, instrument: str, telescope: str | None = None, id_instrument: str, detectors: list[DetectorConfig] = [], optical_paths: list[OpticalPathConfig] = [], dimensions: dict[str, DimensionConfig] = {}, headers: dict[str, str] = {}, file_types: dict[str, dict[str, str]] = {}, **extra_data: Any)

Bases: BaseModel

Future nested instrument configuration structure.

This model represents the target architecture from REDESIGN.md. Not yet used - will be activated when migrating instruments.

detectors: list[DetectorConfig]

dimensions: dict[str, DimensionConfig]

file_types: dict[str, dict[str, str]]

headers: dict[str, str]

id_instrument: str

instrument: str

model_config = {'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

optical_paths: list[OpticalPathConfig]

telescope: str | None

class pyreduce.instruments.models.OpticalPathConfig(*, name: str, beam_arms: list[BeamArmConfig] | None = None)

Bases: BaseModel

Configuration for an optical path (fiber, slit position).

beam_arms: list[BeamArmConfig] | None

model_config = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

pyreduce.instruments.models.validate_instrument_config(data: dict[str, Any]) → InstrumentConfig

Validate instrument configuration data.

Parameters:

data (dict) – Raw configuration data (from YAML or JSON)

Returns:

Validated configuration

Return type:

InstrumentConfig

Raises:

pydantic.ValidationError – If validation fails

pyreduce.instruments.filters module

class pyreduce.instruments.filters.ChannelFilter(keyword, dtype='U20', wildcards=False, regex=False, flags=0, unique=True, ignorecase=True, replacement=None)

Bases: Filter

classify(value)

class pyreduce.instruments.filters.Filter(keyword, dtype='U20', wildcards=False, regex=False, flags=0, unique=True, ignorecase=True)

Bases: object

classify(value)

clear()

collect(header)

match(value)

class pyreduce.instruments.filters.InstrumentFilter(keyword='INSTRUME', **kwargs)

Bases: Filter

class pyreduce.instruments.filters.NightFilter(keyword='DATE-OBS', timeformat='fits', timezone='utc', timezone_local=None, **kwargs)

Bases: Filter

collect(header)

match(value)

static observation_date_to_night(observation_date)

Convert an observation timestamp into the date of the observation night Nights start at 12am and end at 12 am the next day

class pyreduce.instruments.filters.ObjectFilter(keyword='OBJECT', **kwargs)

Bases: Filter

pyreduce.instruments.instrument_info module

Interface for all instrument specific information The actual info is contained in the instruments/{name}.py modules/classes, which are all subclasses of “common”

pyreduce.instruments.instrument_info.channelinfo(header, instrument, channel, **kwargs)

Add instrument specific information to a header/dict

Parameters:

• header (fits.header, dict) – header to add information to

• instrument (str) – instrument name

• channel (str) – instrument channel (e.g. BLUE/RED for HARPS)

Returns:

header with added information

Return type:

header

pyreduce.instruments.instrument_info.get_instrument_info(instrument)

Load instrument specific information

Parameters:

instrument (str) – Name of the instrument

Returns:

dict{str – Dictionary with information

Return type:

obj}

pyreduce.instruments.instrument_info.get_supported_channels(instrument)

pyreduce.instruments.instrument_info.get_wavecal_filename(header, instrument, channel, **kwargs)

Get the filename of the pre-existing wavelength solution for the current settings

Parameters:

• header (fits.header, dict) – header of the wavelength calibration file

• instrument (str) – instrument name

• channel (str) – instrument channel (e.g. BLUE/RED for HARPS)

Returns:

filename – wavelength solution file

Return type:

str

pyreduce.instruments.instrument_info.load_instrument(instrument) → Instrument

Load an python instrument module

Parameters:

instrument (str) – name of the instrument

Returns:

instrument – Instance of the {instrument} class

Return type:

Instrument

pyreduce.instruments.instrument_info.sort_files(input_dir, target, night, instrument, channel, **kwargs)

Sort a list of files into different categories and discard files that are not used

Parameters:

• input_dir (str) – directory containing all files (with tags for target, night, and instrument)

• target (str) – observation target name, as found in the files

• night (str) – observation night of interest, as found in the files

• instrument (str) – instrument name

• channel (str) – instrument channel (e.g. BLUE/RED for HARPS)

Returns:

• biaslist (list(str)) – list of bias files

• flatlist (list(str)) – list of flat field files

• wavelist (list(str)) – list of wavelength calibration files

• orderlist (list(str)) – list of order definition files (for order tracing)

• speclist (list(str)) – list of science files, i.e. observations

Instrument Subpackages

ANDES R-band (RIZ) instrument.

class pyreduce.instruments.ANDES_RIZ.ANDES_RIZ

Bases: Instrument

ANDES R-band spectrograph (E2E simulation data).

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file.

get_wavelength_range(header, channel, **kwargs)

Get wavelength range from header WL_MIN/WL_MAX (in Angstrom).

ANDES blue arm (U/B/V bands, 66 fibers).

class pyreduce.instruments.ANDES_UBV.ANDES_UBV

Bases: Instrument

ANDES_YJH instrument - ANDES near-infrared echelle spectrograph with 75 fibers per order.

Three channels (selected by BAND header): - Y: ~0.95-1.12 µm - J: ~1.12-1.35 µm - H: ~1.45-1.8 µm

class pyreduce.instruments.ANDES_YJH.ANDES_YJH

Bases: Instrument

CRIRES+ instrument - upgraded CRIRES at ESO VLT.

Cross-dispersed IR spectrograph for YJHKLM bands with 3 detectors. Channels are {setting}_{detector}, e.g. J1228_det1.

class pyreduce.instruments.CRIRES_PLUS.CRIRES_PLUS

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

discover_channels(input_dir)

Discover available channels from CRIRES+ files.

Extracts wavelength setting from headers and combines with detector numbers from extension names to form channel identifiers.

get_expected_values(target, night, channel)

get_extension(header, channel)

get_mask_filename(channel, **kwargs)

get_settings_fallbacks(channel)

Return channel names to try when looking up settings files.

Searched in order: most specific first, least specific last. The base settings.json is always the final fallback (handled by the caller).

Override in subclasses for instruments with composite channel names.

get_supported_channels()

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

get_wavelength_range(header, channel, **kwargs)

parse_channel(channel)

Handles instrument specific info for the HARPS spectrograph

Mostly reading data from the header

class pyreduce.instruments.HARPS.FiberFilter(keyword='ESO DPR TYPE')

Bases: Filter

collect(header)

class pyreduce.instruments.HARPS.HARPS

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_expected_values(target, night, channel=None, mode=None, fiber=None, polarimetry=None, **kwargs)

Determine the default expected values in the headers for a given observation configuration

Any parameter may be None, to indicate that all values are allowed

Parameters:

• target (str) – Name of the star / observation target

• night (str) – Observation night/nights

• fiber ("A", "B", "AB") – Which of the fibers should carry observation signal

• polarimetry ("none", "linear", "circular", bool) – Whether the instrument is used in HARPS or HARPSpol mode and which polarization is observed. Set to true for both kinds of polarisation.

Returns:

expectations – Dictionary of expected header values, with one entry per step. The entries for each step refer to the filters defined in self.filters

Return type:

dict

Raises:

ValueError – Invalid combination of parameters

get_extension(header, channel)

get_wavecal_filename(header, channel, polarimetry, **kwargs)

Get the filename of the wavelength calibration config file

get_wavelength_range(header, channel, **kwargs)

class pyreduce.instruments.HARPS.PolarizationFilter(keyword='ESO INS RET?? POS')

Bases: Filter

collect(header)

class pyreduce.instruments.HARPS.TypeFilter(keyword='ESO DPR TYPE')

Bases: Filter

classify(value)

Handles instrument specific info for the HARPN spectrograph

Mostly reading data from the header

class pyreduce.instruments.HARPN.HARPN

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_expected_values(target, night, channel=None, mode=None, fiber=None, **kwargs)

Determine the default expected values in the headers for a given observation configuration

Any parameter may be None, to indicate that all values are allowed

Parameters:

• target (str) – Name of the star / observation target

• night (str) – Observation night/nights

Returns:

expectations – Dictionary of expected header values, with one entry per step. The entries for each step refer to the filters defined in self.filters

Return type:

dict

Raises:

ValueError – Invalid combination of parameters

get_extension(header, channel)

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

get_wavelength_range(header, channel, **kwargs)

class pyreduce.instruments.HARPN.TypeFilter(keyword='TNG DPR TYPE')

Bases: Filter

classify(value)

Handles instrument specific info for the HARPSpol (HARPS polarimeter) spectrograph.

HARPSpol uses a Wollaston prism that splits each echelle order into two beams on the detector. The two beams are treated as fiber groups “upper” and “lower” using PyReduce’s fibers_per_order auto-pairing mode.

class pyreduce.instruments.HARPSPOL.HARPSPOL

Bases: Instrument

add_header_info(header, channel, **kwargs)

Read data from header and add it as REDUCE keyword back to the header.

get_expected_values(target, night, channel=None, **kwargs)

get_extension(header, channel)

get_mask_filename(channel, **kwargs)

Delegate to HARPS mask files.

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file.

Uses the HARPS non-pol NPZ files, since both beams see the same spectral orders and the non-pol linelist has N orders matching per-group trace counts.

get_wavelength_range(header, channel, **kwargs)

Handles instrument specific info for the HARPS spectrograph

Mostly reading data from the header

class pyreduce.instruments.JWST_MIRI.JWST_MIRI

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

Handles instrument specific info for the HARPS spectrograph

Mostly reading data from the header

class pyreduce.instruments.JWST_NIRISS.JWST_NIRISS

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

sort_files(input_dir, target, night, channel, **kwargs)

Sort a set of fits files into different categories types are: bias, flat, wavecal, orderdef, spec

Parameters:

• input_dir (str) – input directory containing the files to sort

• target (str) – name of the target as in the fits headers

• night (str) – observation night, possibly with wildcards

• channel (str) – instrument channel

• steps (list, optional) – list of steps that will be run. If provided, warnings about missing files are only shown for these steps.

Returns:

• files_per_night (list[dict{str:dict{str:list[str]}}]) – a list of file sets, one entry per night, where each night consists of a dictionary with one entry per setting, each fileset has five lists of filenames: “bias”, “flat”, “order”, “wave”, “spec”, organised in another dict

• nights_out (list[datetime]) – a list of observation times, same order as files_per_night

split_observation(fname, channel)

Handles instrument specific info for the HARPS spectrograph

Mostly reading data from the header

class pyreduce.instruments.LICK_APF.LICK_APF

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

Handles instrument specific info for the HARPS spectrograph

Mostly reading data from the header

class pyreduce.instruments.MCDONALD.MCDONALD

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_mask_filename(channel, **kwargs)

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

METIS IFU instrument - ELT/METIS integral field unit spectrograph.

L/M band IFU with ~100 wavelength settings and 4 detectors. Channels are {wavelength}_{detector}, e.g. 4.555_det1.

class pyreduce.instruments.METIS_IFU.METIS_IFU

Bases: Instrument

add_header_info(header, channel, **kwargs)

Read data from header and add it as REDUCE keyword back to the header.

discover_channels(input_dir)

Discover available channels from METIS IFU files.

Extracts wavelength setting from headers and combines with detector numbers from extension names to form channel identifiers.

get_expected_values(target, night, channel)

Get expected header values for file classification.

get_extension(header, channel)

Get FITS extension for the given channel.

get_mask_filename(channel, **kwargs)

Get bad pixel mask filename (per detector).

get_supported_channels()

Return sample channels for testing.

Can’t enumerate all ~400 channels; actual channels are discovered dynamically via discover_channels().

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file.

parse_channel(channel)

Parse channel string into wavelength and detector.

Parameters:

channel (str) – Channel identifier, e.g. “4.555_det1”

Returns:

• wavelength (str) – Wavelength setting, e.g. “4.555”

• detector (str) – Detector number, e.g. “1”

Handles instrument specific info for the METIS_LSS LSS spectrograph

Mostly reading data from the header

class pyreduce.instruments.METIS_LSS.METIS_LSS

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_extension(header, channel)

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

Handles instrument specific info for the MICADO spectrograph

Mostly reading data from the header

class pyreduce.instruments.MICADO.MICADO

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_extension(header, channel)

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

MOSAIC instrument - ELT multi-object spectrograph with 630 fibers.

NIR channel: 90 fiber groups (7 fibers each), 4096x4096 H4RG detector. VIS channel: 4 quadrants (VIS1-VIS4) from a 12788x12394 mosaic detector. Fibers split into two halves with a gap in the middle.

class pyreduce.instruments.MOSAIC.MOSAIC

Bases: Instrument

VIS_QUADRANTS = {'VIS1': {'xhi': 6144, 'xlo': 0, 'yhi': 6000, 'ylo': 1780}, 'VIS2': {'xhi': 12393, 'xlo': 6249, 'yhi': 6005, 'ylo': 1810}, 'VIS3': {'xhi': 6144, 'xlo': 0, 'yhi': 10990, 'ylo': 6775}, 'VIS4': {'xhi': 12393, 'xlo': 6249, 'yhi': 10945, 'ylo': 6644}}

add_header_info(header, channel, **kwargs)

Override to handle VIS quadrant extraction.

Handles instrument specific info for the NEID spectrograph

NEID is a fiber-fed, high-resolution (R~110,000) spectrograph on the WIYN 3.5m telescope at Kitt Peak. It has three fibers: - Science fiber (SCI-OBJ): Target or calibration light - Calibration fiber (CAL-OBJ): Simultaneous calibration (Etalon, LFC, etc.) - Sky fiber (SKY-OBJ): Sky background or calibration

L0 data has 16 amplifiers stored in separate FITS extensions.

class pyreduce.instruments.NEID.NEID

Bases: Instrument

get_expected_values(target, night, channel=None, mode='HR', **kwargs)

Determine the expected header values for file classification.

Parameters:

• target (str) – Name of the observation target

• night (str) – Observation night(s)

• channel (str) – Instrument channel (HR mode only for now)

• mode (str) – Observation mode (HR or HE)

Returns:

expectations – Expected header values per reduction step

Return type:

dict

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file.

get_wavelength_range(header, channel, **kwargs)

NEID covers ~380-930 nm.

Handles instrument specific info for the UVES spectrograph

Mostly reading data from the header

class pyreduce.instruments.NIRSPEC.NIRSPEC

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

static get_arm(header)

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

sort_files(input_dir, target, night, channel, calibration_dir, **kwargs)

Sort a set of fits files into different categories types are: bias, flat, wavecal, orderdef, spec

Parameters:

• input_dir (str) – input directory containing the files to sort

• target (str) – name of the target as in the fits headers

• night (str) – observation night, possibly with wildcards

• channel (str) – instrument channel

Returns:

• files_per_night (list[dict{str:dict{str:list[str]}}]) – a list of file sets, one entry per night, where each night consists of a dictionary with one entry per setting, each fileset has five lists of filenames: “bias”, “flat”, “order”, “wave”, “spec”, organised in another dict

• nights_out (list[datetime]) – a list of observation times, same order as files_per_night

Handles instrument specific info for the NTE spectrograph

Mostly reading data from the header

class pyreduce.instruments.NTE.NTE

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

get_wavelength_range(header, channel)

Handles instrument specific info for the UVES spectrograph

Mostly reading data from the header

class pyreduce.instruments.UVES.UVES

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_mask_filename(channel, **kwargs)

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

Handles instrument specific info for the UVES spectrograph

Mostly reading data from the header

class pyreduce.instruments.XSHOOTER.XSHOOTER

Bases: Instrument

add_header_info(header, channel, **kwargs)

read data from header and add it as REDUCE keyword back to the header

get_wavecal_filename(header, channel, **kwargs)

Get the filename of the wavelength calibration config file

Module contents