astrodata package
This package add another abstraction layer to astronomical data by parsing
the information contained in the headers as attributes. To do so,
one must subclass astrodata.AstroData and add parse methods
accordingly to the TagSet received.
- class astrodata.AstroData(provider)[source]
Bases:
objectBase class for the AstroData software package. It provides an interface to manipulate astronomical data sets.
- Parameters
provider (DataProvider) – The data that will be manipulated through the AstroData instance.
- add(oper)
Implements the augmented arithmetic assignment +=.
- Parameters
oper (number or object) – The operand to be added to this instance. The accepted types depend on the DataProvider.
- Return type
self
- append(extension, name=None, *args, **kw)[source]
Adds a new top-level extension to the provider. Please, read the the concrete DataProvider documentation that is being used to know the exact behavior and additional accepted arguments.
- Parameters
extension (array, Table, or other) – The contents for the new extension. Usually the underlying DataProvider will understand how to deal with regular NumPy arrays and with AstroData Table instances, but it may also accept other types.
name (string, optional) – A DataProvider will usually require a name for extensions. If the name cannot be derived from the metadata associated to extension, you will have to provider one.
args (optional) – The DataProvider may accept additional arguments. Please, refer to its documentation.
kw (optional) – The DataProvider may accept additional arguments. Please, refer to its documentation.
- Returns
The instance that has been added internally (potentially *not the same that*
was passed as *extension)*
- Raises
TypeError – Will be raised if the DataProvider doesn’t know how to deal with the data that has been passed.
ValueError – Raised if the extension is of a proper type, but its value is illegal somehow.
- property descriptors
Returns a sequence of names for the methods that have been decorated as descriptors.
- Return type
A tuple of str
- divide(oper)
Implements the augmented arithmetic assignment /=.
- Parameters
oper (number or other) – The operand to be divided to this instance. The accepted types depend on the DataProvider.
- Return type
self
- abstract info()[source]
Prints out information about the contents of this instance. Implemented by the derived classes.
- abstract load(source)[source]
Class method that returns an instance of this same class, properly initialized with a DataProvider that can deal with the object passed as source
This method is abstract and has to be implemented by derived classes.
- multiply(oper)
Implements the augmented arithmetic assignment *=.
- Parameters
oper (number or object) – The operand to be multiplied to this instance. The accepted types depend on the DataProvider.
- Return type
self
- operate(operator, *args, **kwargs)[source]
Applies a function to the main data array on each extension, replacing the data with the result. The data will be passed as the first argument to the function.
It will be applied to the mask and variance of each extension, too, if they exist.
This is a convenience method, which is equivalent to:
for ext in ad: ad.ext.data = operator(ad.ext.data, *args, **kwargs) ad.ext.mask = operator(ad.ext.mask, *args, **kwargs) if ad.ext.mask is not None else None ad.ext.variance = operator(ad.ext.variance, *args, **kwargs) if ad.ext.variance is not None else None
with the additional advantage that it will work on single slices, too.
- Parameters
operator (function, or bound method) – A function that takes an array (and, maybe, other arguments) and returns an array
args (optional) – Additional arguments to be passed positionally to the operator
kwargs (optional) – Additional arguments to be passed by name to the operator
Examples
>>> import numpy as np >>> ad.operate(np.squeeze)
- reset(data, mask=-23, variance=-23, check=True)[source]
Sets the .data, and optionally .mask and .variance attributes of a single-extension AstroData slice. This function will optionally check whether these attributes have the same shape.
- Parameters
data (ndarray) – The array to assign to the .data attribute (“SCI”)
mask (ndarray, optional) – The array to assign to the .mask attribute (“DQ”)
variance (ndarray, optional) – The array to assign to the .variance attribute (“VAR”)
check (bool) – If set, then the function will check that the mask and variance arrays have the same shape as the data array
- Raises
TypeError – if an attempt is made to set the .mask or .variance attributes with something other than an array
ValueError – if the .mask or .variance attributes don’t have the same shape as .data, OR if this is called on an AD instance that isn’t a single extension slice
- subtract(oper)
Implements the augmented arithmetic assignment -=.
- Parameters
oper (number or object) – The operand to be subtracted to this instance. The accepted types depend on the DataProvider.
- Return type
self
- property tags
A set of strings that represent the tags defining this instance
- class astrodata.NDAstroData(data, uncertainty=None, mask=None, wcs=None, meta=None, unit=None, copy=False, window=None)[source]
Bases:
NDArithmeticMixin,NDSlicingMixin,NDDataImplements
NDDatawith all Mixins, plus someAstroDataspecifics.This class implements an
NDData-like container that supports reading and writing as implemented in theastropy.io.registryand also slicing (indexing) and simple arithmetics (add, subtract, divide and multiply).A very important difference between
NDAstroDataandNDDatais that the former attempts to load all its data lazily. There are also some important differences in the interface (eg..datalets you reset its contents after initialization).Documentation is provided where our class differs.
See also
NDData,NDArithmeticMixin,NDSlicingMixinExamples
The mixins allow operation that are not possible with
NDDataorNDDataBase, i.e. simple arithmetics:>>> from astropy.nddata import StdDevUncertainty >>> import numpy as np >>> data = np.ones((3,3), dtype=np.float) >>> ndd1 = NDAstroData(data, uncertainty=StdDevUncertainty(data)) >>> ndd2 = NDAstroData(data, uncertainty=StdDevUncertainty(data)) >>> ndd3 = ndd1.add(ndd2) >>> ndd3.data array([[2., 2., 2.], [2., 2., 2.], [2., 2., 2.]]) >>> ndd3.uncertainty.array array([[1.41421356, 1.41421356, 1.41421356], [1.41421356, 1.41421356, 1.41421356], [1.41421356, 1.41421356, 1.41421356]])
see
NDArithmeticMixinfor a complete list of all supported arithmetic operations.But also slicing (indexing) is possible:
>>> ndd4 = ndd3[1,:] >>> ndd4.data array([2., 2., 2.]) >>> ndd4.uncertainty.array array([1.41421356, 1.41421356, 1.41421356])
See
NDSlicingMixinfor a description how slicing works (which attributes) are sliced.- property T
- property data
An array representing the raw data stored in this instance. It implements a setter.
- property mask
Mask for the dataset, if any.
Masks should follow the
numpyconvention that valid data points are marked byFalseand invalid ones withTrue.- Type
any type
- set_section(section, input)[source]
Sets only a section of the data. This method is meant to prevent fragmentation in the Python heap, by reusing the internal structures instead of replacing them with new ones.
- Parameters
section (
slice) – The area that will be replacedinput (
NDData-like instance) – This object needs to implement at leastdata,uncertainty, andmask. Their entire contents will replace the data in the area defined bysection.
Examples
>>> sec = NDData(np.zeros((100,100))) >>> ad[0].nddata.set_section((slice(None,100),slice(None,100)), sec)
- property shape
- property uncertainty
Uncertainty in the dataset, if any.
Should have an attribute
uncertainty_typethat defines what kind of uncertainty is stored, such as'std'for standard deviation or'var'for variance. A metaclass defining such an interface is ~astropy.nddata.NDUncertainty but isn’t mandatory.- Type
any type
- property variance
A convenience property to access the contents of
uncertainty, squared (as the uncertainty data is stored as standard deviation).
- property wcs
A world coordinate system (WCS) for the dataset, if any.
- Type
any type
- property window
Interface to access a section of the data, using lazy access whenever possible.
- Returns
An instance of
NDWindowing, which provides__getitem__, to allow the useof square brackets when specifying the window. Ultimately, an
NDWindowingAstrodatainstance is returned
Examples
>>> ad[0].nddata.window[100:200, 100:200] <NDWindowingAstrodata .....>
- class astrodata.TagSet(add=None, remove=None, blocked_by=None, blocks=None, if_present=None)[source]
Bases:
TagSetNamed tuple that is used by tag methods to return which actions should be performed on a tag set. All the attributes are optional, and any combination of them can be used, allowing to create complex tag structures. Read the documentation on the tag-generating algorithm if you want to better understand the interactions.
The simplest TagSet, though, tends to just add tags to the global set.
It can be initialized by position, like any other tuple (the order of the arguments is the one in which the attributes are listed below). It can also be initialized by name.
Examples
>>> TagSet() TagSet(add=set(), remove=set(), blocked_by=set(), blocks=set(), if_present=set()) >>> TagSet({'BIAS', 'CAL'}) TagSet(add={'BIAS', 'CAL'}, remove=set(), blocked_by=set(), blocks=set(), if_present=set()) >>> TagSet(remove={'BIAS', 'CAL'}) TagSet(add=set(), remove={'BIAS', 'CAL'}, blocked_by=set(), blocks=set(), if_present=set())
- astrodata.astro_data_descriptor(fn)[source]
Decorator that will mark a class method as an AstroData descriptor. Useful to produce list of descriptors, for example.
If used in combination with other decorators, this one must be the one on the top (ie. the last one applying). It doesn’t modify the method in any other way.
- Parameters
fn (method) – The method to be decorated
- Return type
The tagged method (not a wrapper)
- astrodata.astro_data_tag(fn)[source]
Decorator that marks methods of an AstroData derived class as part of the tag-producing system.
It wraps the method around a function that will ensure a consistent return value: the wrapped method can return any sequence of sequences of strings, and they will be converted to a TagSet. If the wrapped method returns None, it will be turned into an empty TagSet.
- Parameters
fn (method) – The method to be decorated
- Return type
A wrapper function
- astrodata.create(phu, extensions=None)
Creates an AstroData object from a collection of objects.
- astrodata.keyword
alias of
KeywordCallableWrapper
- astrodata.open(source)
Takes either a string (with the path to a file) or an HDUList as input, and tries to return an AstroData instance.
It will raise exceptions if the file is not found, or if there is no match for the HDUList, among the registered AstroData classes.
Returns an instantiated object, or raises AstroDataError if it was not possible to find a match
- astrodata.version(short=False, tag='dev')[source]
Returns DRAGONS’s version based on the api, feature and bug numbers.
- Returns
str
- Return type
formatted version
Submodules
astrodata.core module
- class astrodata.core.AstroData(provider)[source]
Bases:
objectBase class for the AstroData software package. It provides an interface to manipulate astronomical data sets.
- Parameters
provider (DataProvider) – The data that will be manipulated through the AstroData instance.
- add(oper)
Implements the augmented arithmetic assignment +=.
- Parameters
oper (number or object) – The operand to be added to this instance. The accepted types depend on the DataProvider.
- Return type
self
- append(extension, name=None, *args, **kw)[source]
Adds a new top-level extension to the provider. Please, read the the concrete DataProvider documentation that is being used to know the exact behavior and additional accepted arguments.
- Parameters
extension (array, Table, or other) – The contents for the new extension. Usually the underlying DataProvider will understand how to deal with regular NumPy arrays and with AstroData Table instances, but it may also accept other types.
name (string, optional) – A DataProvider will usually require a name for extensions. If the name cannot be derived from the metadata associated to extension, you will have to provider one.
args (optional) – The DataProvider may accept additional arguments. Please, refer to its documentation.
kw (optional) – The DataProvider may accept additional arguments. Please, refer to its documentation.
- Returns
The instance that has been added internally (potentially *not the same that*
was passed as *extension)*
- Raises
TypeError – Will be raised if the DataProvider doesn’t know how to deal with the data that has been passed.
ValueError – Raised if the extension is of a proper type, but its value is illegal somehow.
- property descriptors
Returns a sequence of names for the methods that have been decorated as descriptors.
- Return type
A tuple of str
- divide(oper)
Implements the augmented arithmetic assignment /=.
- Parameters
oper (number or other) – The operand to be divided to this instance. The accepted types depend on the DataProvider.
- Return type
self
- abstract info()[source]
Prints out information about the contents of this instance. Implemented by the derived classes.
- abstract load(source)[source]
Class method that returns an instance of this same class, properly initialized with a DataProvider that can deal with the object passed as source
This method is abstract and has to be implemented by derived classes.
- multiply(oper)
Implements the augmented arithmetic assignment *=.
- Parameters
oper (number or object) – The operand to be multiplied to this instance. The accepted types depend on the DataProvider.
- Return type
self
- operate(operator, *args, **kwargs)[source]
Applies a function to the main data array on each extension, replacing the data with the result. The data will be passed as the first argument to the function.
It will be applied to the mask and variance of each extension, too, if they exist.
This is a convenience method, which is equivalent to:
for ext in ad: ad.ext.data = operator(ad.ext.data, *args, **kwargs) ad.ext.mask = operator(ad.ext.mask, *args, **kwargs) if ad.ext.mask is not None else None ad.ext.variance = operator(ad.ext.variance, *args, **kwargs) if ad.ext.variance is not None else None
with the additional advantage that it will work on single slices, too.
- Parameters
operator (function, or bound method) – A function that takes an array (and, maybe, other arguments) and returns an array
args (optional) – Additional arguments to be passed positionally to the operator
kwargs (optional) – Additional arguments to be passed by name to the operator
Examples
>>> import numpy as np >>> ad.operate(np.squeeze)
- reset(data, mask=-23, variance=-23, check=True)[source]
Sets the .data, and optionally .mask and .variance attributes of a single-extension AstroData slice. This function will optionally check whether these attributes have the same shape.
- Parameters
data (ndarray) – The array to assign to the .data attribute (“SCI”)
mask (ndarray, optional) – The array to assign to the .mask attribute (“DQ”)
variance (ndarray, optional) – The array to assign to the .variance attribute (“VAR”)
check (bool) – If set, then the function will check that the mask and variance arrays have the same shape as the data array
- Raises
TypeError – if an attempt is made to set the .mask or .variance attributes with something other than an array
ValueError – if the .mask or .variance attributes don’t have the same shape as .data, OR if this is called on an AD instance that isn’t a single extension slice
- subtract(oper)
Implements the augmented arithmetic assignment -=.
- Parameters
oper (number or object) – The operand to be subtracted to this instance. The accepted types depend on the DataProvider.
- Return type
self
- property tags
A set of strings that represent the tags defining this instance
- class astrodata.core.DataProvider[source]
Bases:
objectAbstract class describing the minimal interface that DataProvider derivative classes need to implement.
- abstract append(ext, name=None)[source]
Adds a new component to the provider. Objects appended to a single slice will actually be made hierarchically dependent of the science object represented by that slice. If appended to the provider as a whole, the new member will be independent (eg. global table, new science object).
- Parameters
ext (array, NDData, Table, etc) – The component to be added. The exact accepted types depend on the class implementing this interface. Implementations specific to certain data formats may accept specialized types (eg. a FITS provider will accept an ImageHDU and extract the array out of it)
name (str, optional) –
A name that may be used to access the new object, as an attribute of the provider. The name is typically ignored for top-level (global) objects, and required for the others.
It can consist in a combination of numbers and letters, with the restriction that the letters have to be all capital, and the first character cannot be a number (“[A-Z][A-Z0-9]*”).
- Returns
The same object, or a new one, if it was necessary to convert it to a more
suitable format for internal use.
- Raises
TypeError – If adding the object in an invalid situation (eg. name is None when adding to a single slice)
ValueError – If adding an object that is not acceptable
- abstract property data
A list of the the arrays (or single array, if this is a single slice) corresponding to the science data attached to each extension, in loading/appending order.
- property exposed
A collection of strings with the names of objects that can be accessed directly by name as attributes of this instance, and that are not part of its standard interface (ie. data objects that have been added dynamically).
Examples
>>> ad[0].exposed set(['OBJMASK', 'OBJCAT'])
- abstract is_settable(attribute)[source]
Predicate that can be used to figure out if certain attribute of the DataProvider is meant to be modified by an external object.
This is used mostly by AstroData, which acts as a proxy exposing attributes of its assigned provider, to decide if it should set a value on the provider or on itself.
- Parameters
attribute (str) –
- Return type
A boolean
- property is_single
If this data provider represents a single slice out of a whole dataset, return True. Otherwise, return False.
- Return type
A boolean
- property is_sliced
If this data provider instance represents the whole dataset, return False. If it represents a slice out of the whole, return True.
- Return type
A boolean
- abstract property mask
A list of the mask arrays (or a single array, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
For objects that miss a mask, None will be provided instead.
- abstract property uncertainty
A list of the uncertainty objects (or a single object, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
The objects are instances of AstroPy’s NDUncertainty, or None where no information is available.
See also
varianceThe actual array supporting the uncertainty object
- abstract property variance
A list of the variance arrays (or a single array, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
For objects that miss uncertainty information, None will be provided instead.
See also
uncertaintyThe NDUncertainty object used under the hood to propagate uncertainty when
operating
- class astrodata.core.TagSet(add=None, remove=None, blocked_by=None, blocks=None, if_present=None)[source]
Bases:
TagSetNamed tuple that is used by tag methods to return which actions should be performed on a tag set. All the attributes are optional, and any combination of them can be used, allowing to create complex tag structures. Read the documentation on the tag-generating algorithm if you want to better understand the interactions.
The simplest TagSet, though, tends to just add tags to the global set.
It can be initialized by position, like any other tuple (the order of the arguments is the one in which the attributes are listed below). It can also be initialized by name.
Examples
>>> TagSet() TagSet(add=set(), remove=set(), blocked_by=set(), blocks=set(), if_present=set()) >>> TagSet({'BIAS', 'CAL'}) TagSet(add={'BIAS', 'CAL'}, remove=set(), blocked_by=set(), blocks=set(), if_present=set()) >>> TagSet(remove={'BIAS', 'CAL'}) TagSet(add=set(), remove={'BIAS', 'CAL'}, blocked_by=set(), blocks=set(), if_present=set())
- astrodata.core.astro_data_descriptor(fn)[source]
Decorator that will mark a class method as an AstroData descriptor. Useful to produce list of descriptors, for example.
If used in combination with other decorators, this one must be the one on the top (ie. the last one applying). It doesn’t modify the method in any other way.
- Parameters
fn (method) – The method to be decorated
- Return type
The tagged method (not a wrapper)
- astrodata.core.astro_data_tag(fn)[source]
Decorator that marks methods of an AstroData derived class as part of the tag-producing system.
It wraps the method around a function that will ensure a consistent return value: the wrapped method can return any sequence of sequences of strings, and they will be converted to a TagSet. If the wrapped method returns None, it will be turned into an empty TagSet.
- Parameters
fn (method) – The method to be decorated
- Return type
A wrapper function
- astrodata.core.returns_list(fn)[source]
Decorator to ensure that descriptors that should return a list (of one value per extension) only returns single values when operating on single slices; and vice versa.
This is a common case, and you can use the decorator to simplify the logic of your descriptors.
- Parameters
fn (method) – The method to be decorated
- Return type
A function
astrodata.factory module
- class astrodata.factory.AstroDataFactory[source]
Bases:
object- addClass(cls)[source]
Add a new class to the AstroDataFactory registry. It will be used when instantiating an AstroData class for a FITS file.
- createFromScratch(phu, extensions=None)[source]
Creates an AstroData object from a collection of objects.
- getAstroData(source)[source]
Takes either a string (with the path to a file) or an HDUList as input, and tries to return an AstroData instance.
It will raise exceptions if the file is not found, or if there is no match for the HDUList, among the registered AstroData classes.
Returns an instantiated object, or raises AstroDataError if it was not possible to find a match
astrodata.fits module
- class astrodata.fits.AstroDataFits(provider)[source]
Bases:
AstroData- extver(ver)[source]
Get an extension using its EXTVER instead of the positional index in this object.
- Parameters
ver (int) – The EXTVER for the desired extension
- Return type
A sliced object containing the desired extension
- Raises
IndexError – If the provided EXTVER doesn’t exist
- property hdr
- info()[source]
Prints out information about the contents of this instance. Implemented by the derived classes.
- instrument()[source]
Returns the name of the instrument making the observation
- Returns
instrument name
- Return type
- classmethod load(source)[source]
Implementation of the abstract method load.
It takes an HDUList and returns a fully instantiated AstroData instance.
- property phu
- update_filename(prefix=None, suffix=None, strip=False)[source]
This method updates the “filename” attribute of the AstroData object. A prefix and/or suffix can be specified. If strip=True, these will replace the existing prefix/suffix; if strip=False, they will simply be prepended/appended.
The current filename is broken down into its existing prefix, root, and suffix using the ORIGNAME phu keyword, if it exists and is contained within the current filename. Otherwise, the filename is split at the last underscore and the part before is assigned as the root and the underscore and part after the suffix. No prefix is assigned.
Note that, if strip=True, a prefix or suffix will only be stripped if ‘’ is specified.
- Parameters
prefix (str/None) – new prefix (None => leave alone)
suffix (str/None) – new suffix (None => leave alone)
strip (bool) – Strip existing prefixes and suffixes if new ones are given?
- exception astrodata.fits.AstroDataFitsDeprecationWarning[source]
Bases:
DeprecationWarning
- class astrodata.fits.FitsHeaderCollection(headers)[source]
Bases:
objectThis class provides group access to a list of PyFITS Header-like objects. It exposes a number of methods (set, get, etc.) that operate over all the headers at the same time.
It can also be iterated.
- class astrodata.fits.FitsLazyLoadable(obj)[source]
Bases:
object- property data
- property dtype
Need to to some overriding of astropy.io.fits since it doesn’t know about BITPIX=8
- property header
- property shape
- class astrodata.fits.FitsLoader(cls=<class 'astrodata.fits.FitsProvider'>)[source]
Bases:
object- load(source, extname_parser=None)[source]
Takes either a string (with the path to a file) or an HDUList as input, and tries to return a populated FitsProvider (or descendant) instance.
It will raise exceptions if the file is not found, or if there is no match for the HDUList, among the registered AstroData classes.
- class astrodata.fits.FitsProvider[source]
Bases:
DataProvider- append(ext, name=None, header=None, reset_ver=True, add_to=None)[source]
Adds a new component to the provider. Objects appended to a single slice will actually be made hierarchically dependent of the science object represented by that slice. If appended to the provider as a whole, the new member will be independent (eg. global table, new science object).
- Parameters
ext (array, NDData, Table, etc) – The component to be added. The exact accepted types depend on the class implementing this interface. Implementations specific to certain data formats may accept specialized types (eg. a FITS provider will accept an ImageHDU and extract the array out of it)
name (str, optional) –
A name that may be used to access the new object, as an attribute of the provider. The name is typically ignored for top-level (global) objects, and required for the others.
It can consist in a combination of numbers and letters, with the restriction that the letters have to be all capital, and the first character cannot be a number (“[A-Z][A-Z0-9]*”).
- Returns
The same object, or a new one, if it was necessary to convert it to a more
suitable format for internal use.
- Raises
TypeError – If adding the object in an invalid situation (eg. name is None when adding to a single slice)
ValueError – If adding an object that is not acceptable
- property data
A list of the the arrays (or single array, if this is a single slice) corresponding to the science data attached to each extension, in loading/appending order.
- default_extension = 'SCI'
- property exposed
A collection of strings with the names of objects that can be accessed directly by name as attributes of this instance, and that are not part of its standard interface (ie. data objects that have been added dynamically).
Examples
>>> ad[0].exposed set(['OBJMASK', 'OBJCAT'])
- extver_map()[source]
Provide a mapping between the FITS EXTVER of an extension and the index that will be used to access it within this object.
- Returns
A dictionary `{EXTVER
- Return type
index, …}`
- Raises
ValueError – If used against a single slice. It is of no use in that situation.
- property filename
- property header
- is_settable(attr)[source]
Predicate that can be used to figure out if certain attribute of the DataProvider is meant to be modified by an external object.
This is used mostly by AstroData, which acts as a proxy exposing attributes of its assigned provider, to decide if it should set a value on the provider or on itself.
- Parameters
attribute (str) –
- Return type
A boolean
- property mask
A list of the mask arrays (or a single array, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
For objects that miss a mask, None will be provided instead.
- property nddata
- property orig_filename
- property path
- property shape
- property tables
- property uncertainty
A list of the uncertainty objects (or a single object, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
The objects are instances of AstroPy’s NDUncertainty, or None where no information is available.
See also
varianceThe actual array supporting the uncertainty object
- property variance
A list of the variance arrays (or a single array, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
For objects that miss uncertainty information, None will be provided instead.
See also
uncertaintyThe NDUncertainty object used under the hood to propagate uncertainty when
operating
- class astrodata.fits.FitsProviderProxy(provider, mapping, single)[source]
Bases:
DataProvider- append(ext, name)[source]
Adds a new component to the provider. Objects appended to a single slice will actually be made hierarchically dependent of the science object represented by that slice. If appended to the provider as a whole, the new member will be independent (eg. global table, new science object).
- Parameters
ext (array, NDData, Table, etc) – The component to be added. The exact accepted types depend on the class implementing this interface. Implementations specific to certain data formats may accept specialized types (eg. a FITS provider will accept an ImageHDU and extract the array out of it)
name (str, optional) –
A name that may be used to access the new object, as an attribute of the provider. The name is typically ignored for top-level (global) objects, and required for the others.
It can consist in a combination of numbers and letters, with the restriction that the letters have to be all capital, and the first character cannot be a number (“[A-Z][A-Z0-9]*”).
- Returns
The same object, or a new one, if it was necessary to convert it to a more
suitable format for internal use.
- Raises
TypeError – If adding the object in an invalid situation (eg. name is None when adding to a single slice)
ValueError – If adding an object that is not acceptable
- property data
A list of the the arrays (or single array, if this is a single slice) corresponding to the science data attached to each extension, in loading/appending order.
- property exposed
A collection of strings with the names of objects that can be accessed directly by name as attributes of this instance, and that are not part of its standard interface (ie. data objects that have been added dynamically).
Examples
>>> ad[0].exposed set(['OBJMASK', 'OBJCAT'])
- extver_map()[source]
Provide a mapping between the FITS EXTVER of an extension and the index that will be used to access it within this object.
- Returns
A dictionary `{EXTVER
- Return type
index, …}`
- Raises
ValueError – If used against a single slice. It is of no use in that situation.
- property header
- is_settable(attr)[source]
Predicate that can be used to figure out if certain attribute of the DataProvider is meant to be modified by an external object.
This is used mostly by AstroData, which acts as a proxy exposing attributes of its assigned provider, to decide if it should set a value on the provider or on itself.
- Parameters
attribute (str) –
- Return type
A boolean
- property is_single
If this data provider represents a single slice out of a whole dataset, return True. Otherwise, return False.
- Return type
A boolean
- property is_sliced
If this data provider instance represents the whole dataset, return False. If it represents a slice out of the whole, return True.
- Return type
A boolean
- property mask
A list of the mask arrays (or a single array, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
For objects that miss a mask, None will be provided instead.
- property nddata
- property shape
- property uncertainty
A list of the uncertainty objects (or a single object, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
The objects are instances of AstroPy’s NDUncertainty, or None where no information is available.
See also
varianceThe actual array supporting the uncertainty object
- property variance
A list of the variance arrays (or a single array, if this is a single slice) attached to the science data, for each extension, in loading/appending order.
For objects that miss uncertainty information, None will be provided instead.
See also
uncertaintyThe NDUncertainty object used under the hood to propagate uncertainty when
operating
- property wcs
- class astrodata.fits.KeywordCallableWrapper(keyword, default=<object object>, on_ext=False, coerce_with=None)[source]
Bases:
object
- astrodata.fits.asdftablehdu_to_wcs(hdu)[source]
Recreate a gWCS object from its serialization in a FITS table extension.
Returns None (issuing a warning) if the extension cannot be parsed, so the rest of the file can still be read.
- astrodata.fits.fits_ext_comp_key(ext)[source]
Returns a pair (integer, string) that will be used to sort extensions
- astrodata.fits.table_to_bintablehdu(table, extname=None)[source]
Convert an astropy Table object to a BinTableHDU before writing to disk.
- Parameters
table (astropy.table.Table instance) – the table to be converted to a BinTableHDU
extname (str) – name to go in the EXTNAME field of the FITS header
- Return type
BinTableHDU
- astrodata.fits.wcs_to_asdftablehdu(wcs, extver=None)[source]
Serialize a gWCS object as a FITS TableHDU (ASCII) extension.
The ASCII table is actually a mini ASDF file. The constituent AstroPy models must have associated ASDF “tags” that specify how to serialize them.
In the event that serialization as pure ASCII fails (this should not happen), a binary table representation will be used as a fallback.
- astrodata.fits.windowedOp(func, sequence, kernel, shape=None, dtype=None, with_uncertainty=False, with_mask=False, **kwargs)[source]
Apply function on a NDData obbjects, splitting the data in chunks to limit memory usage.
- Parameters
func (callable) – The function to apply.
sequence (list of NDData) – List of NDData objects.
shape (tuple of int) – Shape of inputs. Defaults to
sequence[0].shape.dtype (str or dtype) – Type of the output array. Defaults to
sequence[0].dtype.with_uncertainty (bool) – Compute uncertainty?
with_mask (bool) – Compute mask?
**kwargs – Additional args are passed to
func.
astrodata.nddata module
This module implements a derivative class based on NDData with some Mixins, implementing windowing and on-the-fly data scaling.
- class astrodata.nddata.NDAstroData(data, uncertainty=None, mask=None, wcs=None, meta=None, unit=None, copy=False, window=None)[source]
Bases:
NDArithmeticMixin,NDSlicingMixin,NDDataImplements
NDDatawith all Mixins, plus someAstroDataspecifics.This class implements an
NDData-like container that supports reading and writing as implemented in theastropy.io.registryand also slicing (indexing) and simple arithmetics (add, subtract, divide and multiply).A very important difference between
NDAstroDataandNDDatais that the former attempts to load all its data lazily. There are also some important differences in the interface (eg..datalets you reset its contents after initialization).Documentation is provided where our class differs.
See also
NDData,NDArithmeticMixin,NDSlicingMixinExamples
The mixins allow operation that are not possible with
NDDataorNDDataBase, i.e. simple arithmetics:>>> from astropy.nddata import StdDevUncertainty >>> import numpy as np >>> data = np.ones((3,3), dtype=np.float) >>> ndd1 = NDAstroData(data, uncertainty=StdDevUncertainty(data)) >>> ndd2 = NDAstroData(data, uncertainty=StdDevUncertainty(data)) >>> ndd3 = ndd1.add(ndd2) >>> ndd3.data array([[2., 2., 2.], [2., 2., 2.], [2., 2., 2.]]) >>> ndd3.uncertainty.array array([[1.41421356, 1.41421356, 1.41421356], [1.41421356, 1.41421356, 1.41421356], [1.41421356, 1.41421356, 1.41421356]])
see
NDArithmeticMixinfor a complete list of all supported arithmetic operations.But also slicing (indexing) is possible:
>>> ndd4 = ndd3[1,:] >>> ndd4.data array([2., 2., 2.]) >>> ndd4.uncertainty.array array([1.41421356, 1.41421356, 1.41421356])
See
NDSlicingMixinfor a description how slicing works (which attributes) are sliced.- property T
- property data
An array representing the raw data stored in this instance. It implements a setter.
- property mask
Mask for the dataset, if any.
Masks should follow the
numpyconvention that valid data points are marked byFalseand invalid ones withTrue.- Type
any type
- set_section(section, input)[source]
Sets only a section of the data. This method is meant to prevent fragmentation in the Python heap, by reusing the internal structures instead of replacing them with new ones.
- Parameters
section (
slice) – The area that will be replacedinput (
NDData-like instance) – This object needs to implement at leastdata,uncertainty, andmask. Their entire contents will replace the data in the area defined bysection.
Examples
>>> sec = NDData(np.zeros((100,100))) >>> ad[0].nddata.set_section((slice(None,100),slice(None,100)), sec)
- property shape
- property uncertainty
Uncertainty in the dataset, if any.
Should have an attribute
uncertainty_typethat defines what kind of uncertainty is stored, such as'std'for standard deviation or'var'for variance. A metaclass defining such an interface is ~astropy.nddata.NDUncertainty but isn’t mandatory.- Type
any type
- property variance
A convenience property to access the contents of
uncertainty, squared (as the uncertainty data is stored as standard deviation).
- property wcs
A world coordinate system (WCS) for the dataset, if any.
- Type
any type
- property window
Interface to access a section of the data, using lazy access whenever possible.
- Returns
An instance of
NDWindowing, which provides__getitem__, to allow the useof square brackets when specifying the window. Ultimately, an
NDWindowingAstrodatainstance is returned
Examples
>>> ad[0].nddata.window[100:200, 100:200] <NDWindowingAstrodata .....>
astrodata.provenance module
- astrodata.provenance.add_provenance(ad, filename, md5, primitive, timestamp=None)[source]
Add the given Provenance entry to the full set of provenance records on this object.
Provenance is not added if the incoming md5 is None or ‘’. These indicate source data for the provenance that are not on disk, so not useful as provenance.
- Parameters
value (Provenance to add) –
- Return type
none
- astrodata.provenance.add_provenance_history(ad, timestamp_start, timestamp_stop, primitive, args)[source]
Add the given ProvenanceHistory entry to the full set of history records on this object.
- Parameters
ad (AstroData to add history record to) –
timestamp_start (datetime of the start of this operation) –
timestamp_stop (datetime of the end of this operation) –
primitive (str name of the primitive performed) –
args (str arguments used for the primitive call) –
- Return type
none
- astrodata.provenance.clone_provenance(provenance_data, ad)[source]
For a single input’s provenance, copy it into the output AstroData object as appropriate.
This takes a dictionary with a source filename, md5 and both it’s original provenance and provenance_history information. It duplicates the provenance data into the outgoing AstroData ad object.
- Parameters
provenance_data (pointer to the AstroData table with the provenance) – information. Note this may be the output AstroData as well, so we need to handle that.
ad (outgoing AstroData object to add provenance data to) –
- Return type
none
- astrodata.provenance.clone_provenance_history(provenance_history_data, ad)[source]
For a single input’s provenance history, copy it into the output AstroData object as appropriate.
This takes a dictionary with a source filename, md5 and both it’s original provenance and provenance_history information. It duplicates the provenance data into the outgoing AstroData ad object.
- Parameters
provenance_history_data (pointer to the AstroData table with the history information.) – Note this may be the output AstroData as well, so we need to handle that.
ad (outgoing AstroData object to add provenance history data to) –
- Return type
none
- astrodata.provenance.provenance_summary(ad, provenance=True, provenance_history=True)[source]
Generate a pretty text display of the provenance information for an ~astrodata.core.AstroData.
This pulls the provenance and history information from a ~astrodata.core.AstroData object and formats it for readability. The primitive arguments in the history are wrapped across multiple lines to keep the overall width manageable.
astrodata.testing module
Fixtures to be used in tests in DRAGONS
- astrodata.testing.assert_most_close(actual, desired, max_miss, rtol=1e-07, atol=0, equal_nan=True, verbose=True)[source]
Raises an AssertionError if the number of elements in two objects that are not equal up to desired tolerance is greater than expected.
See also
- Parameters
actual (array_like) – Array obtained.
desired (array_like) – Array desired.
max_miss (iny) – Maximum number of mismatched elements.
rtol (float, optional) – Relative tolerance.
atol (float, optional) – Absolute tolerance.
equal_nan (bool, optional.) – If True, NaNs will compare equal.
verbose (bool, optional) – If True, the conflicting values are appended to the error message.
- Raises
AssertionError – If actual and desired are not equal up to specified precision.
- astrodata.testing.assert_most_equal(actual, desired, max_miss, verbose=True)[source]
Raises an AssertionError if more than n elements in two objects are not equal. For more information, check
numpy.testing.assert_equal().- Parameters
- Raises
AssertionError – If actual and desired are not equal.
- astrodata.testing.assert_same_class(ad, ad_ref)[source]
Compare if two
AstroData(or any subclass) have the same class.- Parameters
ad (
astrodata.AstroDataor any subclass) – AstroData object to be checked.ad_ref (
astrodata.AstroDataor any subclass) – AstroData object used as reference
- astrodata.testing.astrofaker()[source]
Wrapper fixture that prevents undesired behaviour when using astrofaker.
- astrodata.testing.change_working_dir(path_to_outputs)[source]
Factory that returns the output path as a context manager object, allowing easy access to the path to where the processed data should be stored.
- Parameters
path_to_outputs (pytest.fixture) – Fixture containing the root path to the output files.
- Returns
Enable easy change to temporary folder when reducing data.
- Return type
contextmanager
- astrodata.testing.compare_models(model1, model2, rtol=1e-07, atol=0.0, check_inverse=True)[source]
Check that any two models are the same, within some tolerance on parameters (using the same defaults as numpy.assert_allclose()).
This is constructed like a test, rather than returning True/False, in order to provide more useful information as to how the models differ when a test fails (and with more concise syntax).
If check_inverse is True (the default), only first-level inverses are compared, to avoid unending recursion, since the inverse of an inverse should be the supplied input model, if defined. The types of any inverses (and their inverses in turn) are required to match whether or not their parameters etc. are compared.
This function might not completely guarantee that model1 & model2 are identical for some models whose evaluation depends on class-specific parameters controlling how the array of model parameters is interpreted (eg. the orders in SIP?), but it does cover our common use of compound models involving orthonormal polynomials etc.
- astrodata.testing.download_from_archive(filename, sub_path='raw_files', env_var='DRAGONS_TEST')[source]
Download file from the archive and store it in the local cache.
- Parameters
- Returns
Name of the cached file with the path added to it.
- Return type
- astrodata.testing.get_active_git_branch()[source]
Returns the name of the active GIT branch to be used in Continuous Integration tasks and organize input/reference files.
Note: This works currently only if the remote name is “origin”, though it would be easy to adapt for other cases if needed.
- Returns
str or None – the branch name could not be retrieved.
- Return type
Name of the input active git branch. It returns None if
- astrodata.testing.get_associated_calibrations(filename, nbias=5)[source]
Queries Gemini Observatory Archive for associated calibrations to reduce the data that will be used for testing. :param filename: Input file name :type filename: str
- astrodata.testing.path_to_inputs(request, env_var='DRAGONS_TEST')[source]
PyTest fixture that returns the path to where the input files for a given test module live.
- astrodata.testing.path_to_outputs(request, tmp_path_factory)[source]
PyTest fixture that creates a temporary folder to save tests outputs. You can set the base directory by passing the
--basetemp=mydir/argument to the PyTest call (See [Pytest - Temporary Directories and Files][1]).[1]: https://docs.pytest.org/en/stable/tmpdir.html#temporary-directories-and-files
astrodata.wcs module
- class astrodata.wcs.AffineMatrices(matrix, offset)
Bases:
tuple- property matrix
Alias for field number 0
- property offset
Alias for field number 1
- class astrodata.wcs.FrameMapping(cls, description)
Bases:
tuple- property cls
Alias for field number 0
- property description
Alias for field number 1
- astrodata.wcs.calculate_affine_matrices(func, shape)[source]
Compute the matrix and offset necessary of an affine transform that represents the supplied function. This is done by computing the linear matrix along all axes extending from the centre of the region, and then calculating the offset such that the transformation is accurate at the centre of the region. The matrix and offset are returned in the standard python order (i.e., y-first for 2D).
- Parameters
func (callable) – function that maps input->output coordinates
shape (sequence) – shape to use for fiducial points
- Returns
AffineMatrices(array, array)
- Return type
affine matrix and offset
- astrodata.wcs.fitswcs_image(header)[source]
Make a complete transform from CRPIX-shifted pixels to sky coordinates from FITS WCS keywords. A Mapping is inserted at the beginning, which may be removed later
- Parameters
header (astropy.io.fits.Header or dict) – FITS Header or dict with basic FITS WCS keywords.
- astrodata.wcs.fitswcs_linear(header)[source]
Create WCS linear transforms for any axes not associated with celestial coordinates. We require that each world axis aligns precisely with only a single pixel axis.
- Parameters
header (astropy.io.fits.Header or dict) – FITS Header or dict with basic FITS WCS keywords.
- astrodata.wcs.fitswcs_to_gwcs(hdr)[source]
Create and return a gWCS object from a FITS header. If it can’t construct one, it should quietly return None.
- astrodata.wcs.get_axes(header)[source]
Matches input with spectral and sky coordinate axes.
- Parameters
header (astropy.io.fits.Header or dict) – FITS Header (or dict) with basic WCS information.
- Returns
sky_inmap, spectral_inmap, unknown – indices in the output representing sky and spectral coordinates.
- Return type
lists
- astrodata.wcs.gwcs_to_fits(ndd, hdr=None)[source]
Convert a gWCS object to a collection of FITS WCS keyword/value pairs, if possible. If the FITS WCS is only approximate, this should be indicated with a dict entry {‘FITS-WCS’: ‘APPROXIMATE’}. If there is no suitable FITS representation, then a ValueError or NotImplementedError can be raised.
- Parameters
ndd (NDData) – The NDData whose wcs attribute we want converted
hdr (fits.Header) – A Header object that may contain some useful keywords
- Returns
dict
- Return type
values to insert into the FITS header to express this WCS
- astrodata.wcs.make_fitswcs_transform(header)[source]
Create a basic FITS WCS transform. It does not include distortions.
- Parameters
header (astropy.io.fits.Header or dict) – FITS Header (or dict) with basic WCS information
- astrodata.wcs.model_is_affine(model)[source]
” Test a Model for affinity. This is currently done by checking the name of its class (or the class names of all its submodels)
TODO: Is this the right thing to do? We could compute the affine matrices assuming affinity, and then check that a number of random points behave as expected. Is that better?
- astrodata.wcs.read_wcs_from_header(header)[source]
Extract basic FITS WCS keywords from a FITS Header.
- Parameters
header (astropy.io.fits.Header) – FITS Header with WCS information.
- Returns
wcs_info – A dictionary with WCS keywords.
- Return type