a ߙfb%0@sdZddlZddlZddlmZddlmZzddl m Z dZ Wne yVdZ Yn0gd Z d d giZGd d d eZddd ZedddZedddZdZdee_dee_dS)a Tabular models. Tabular models of any dimension can be created using `tabular_model`. For convenience `Tabular1D` and `Tabular2D` are provided. Examples -------- >>> table = np.array([[ 3., 0., 0.], ... [ 0., 2., 0.], ... [ 0., 0., 0.]]) >>> points = ([1, 2, 3], [1, 2, 3]) >>> t2 = Tabular2D(points, lookup_table=table, bounds_error=False, ... fill_value=None, method='nearest') N)units)Model)interpnTF) tabular_model Tabular1D Tabular2DrZscipycseZdZdZdZdZdZdZdZdddde j ffdd Z d d Z d d Z ed dZeddZeddZddZeddZZS)_Tabulara Returns an interpolated lookup table value. Parameters ---------- points : tuple of ndarray of float, optional The points defining the regular grid in n dimensions. ndarray must have shapes (m1, ), ..., (mn, ), lookup_table : array-like The data on a regular grid in n dimensions. Must have shapes (m1, ..., mn, ...) method : str, optional The method of interpolation to perform. Supported are "linear" and "nearest", and "splinef2d". "splinef2d" is only supported for 2-dimensional data. Default is "linear". bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then ``fill_value`` is used. fill_value : float or `~astropy.units.Quantity`, optional If provided, the value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Extrapolation is not supported by method "splinef2d". If Quantity is given, it will be converted to the unit of ``lookup_table``, if applicable. Returns ------- value : ndarray Interpolated values at input coordinates. Raises ------ ImportError Scipy is not installed. Notes ----- Uses `scipy.interpolate.interpn`. FTrNlinearc  sr|dd}|dkrtdtjfi|d|_|durDtdt|tjsZt |}|j j |j krztd |j j |durtdd|jD}n~|j dkrt|ts|f}t|}||j krtd |j ||dkrt|d tjrttd d |Ddkrtd t|tjrPt|tjsBtd |j||jj}||_||_ ||_||_||_dS)Nn_modelsrzOnly n_models=1 is supported.)yzMust provide a lookup table.z3lookup_table should be an array with {} dimensions.css|]}tj|tdVqdS))ZdtypeN)npZarangefloat.0xr7lib/python3.9/site-packages/astropy/modeling/tabular.py msz$_Tabular.__init__..z.Expected grid points in {} directions, got {}.rcSsg|]}t|ddqS)unitN)getattrrprrr xz%_Tabular.__init__..z#points must all have the same unit.z0fill value is in {} but expected to be unitless.)getNotImplementedErrorsuper__init__outputs ValueError isinstanceuQuantityr Zasarray lookup_tablendimformattupleshapelensetrtovaluepoints bounds_errormethod fill_value) selfr-r$r/r.r0kwargsr Znpts __class__rrrZsR     z_Tabular.__init__cCsd|jj|j|jS)Nz <{}(points={}, lookup_table={})>)r&r4__name__r-r$r1rrr__repr__sz_Tabular.__repr__c Cshd|jjfd|jfd|jfd|jfdd|jfd|jfd|jfd |jfd |j fg }d d |D}d |S)NrNameZN_inputsZ N_outputs)Z Parametersz pointsz lookup_tablez methodz fill_valuez bounds_errorcSs&g|]\}}|dur|d|qS)Nz: r)rkeywordr,rrrrsz$_Tabular.__str__.. ) r4r5namen_inputs n_outputsr-r$r/r0r.join)r1Zdefault_keywordspartsrrr__str__s  z_Tabular.__str__cs2|jdttjsdStfdd|jDS)Nrcsg|]}|jfqSr)rrZptsrrrrz(_Tabular.input_units..)r-r!r"r#dictinputsr6rrBr input_unitss  z_Tabular.input_unitscCs$t|jtjsdS|jd|jjiS)Nr)r!r$r"r#rrr6rrr return_unitssz_Tabular.return_unitscCs2dd|jDddd}t|dkr.|d}|S)ay Tuple defining the default ``bounding_box`` limits, ``(points_low, points_high)``. Examples -------- >>> from astropy.modeling.models import Tabular1D, Tabular2D >>> t1 = Tabular1D(points=[1, 2, 3], lookup_table=[10, 20, 30]) >>> t1.bounding_box ModelBoundingBox( intervals={ x: Interval(lower=1, upper=3) } model=Tabular1D(inputs=('x',)) order='C' ) >>> t2 = Tabular2D(points=[[1, 2, 3], [2, 3, 4]], ... lookup_table=[[10, 20, 30], [20, 30, 40]]) >>> t2.bounding_box ModelBoundingBox( intervals={ x: Interval(lower=1, upper=3) y: Interval(lower=2, upper=4) } model=Tabular2D(inputs=('x', 'y')) order='C' ) cSsg|]}t|t|fqSr)minmaxrrrrrrz)_Tabular.bounding_box..Nrr)r-r))r1Zbboxrrr bounding_boxs z_Tabular.bounding_boxcstj|}|djdd|d|jD}t|j}tsDtdt|j |j ||j |j |j d}t|j tjrt|j dtjs||j j}|jdkr|}nfdd|D}|S) a Return the interpolated values at the input coordinates. Parameters ---------- inputs : list of scalar or list of ndarray Input coordinates. The number of inputs must be equal to the dimensions of the lookup table. rcSsg|] }|qSr)Zflatten)rZinprrrrrz%_Tabular.evaluate..NzTabular model requires scipy.)r/r.r0rcsg|]}|qSr)reshape)rrr(rrrr)r Zbroadcast_arraysr(r=ZarrayT has_scipy ImportErrorrr-r$r/r.r0r!r"r#rr>rK)r1rDresultrrMrevaluates$       z_Tabular.evaluatecCs|jdkrtt|jdkr2|j}|jd}n@tt|jdkrn|jddd}|jdddd}ntt|||j|j |j dStddS)NrrrI)r-r$r/r.r0zHAn analytical inverse transform has not been implemented for this model.) r=r allZdiffr$r-rrr/r.r0)r1r-r$rrrinverses   z_Tabular.inverse)r5 __module__ __qualname____doc__r ZfittableZstandard_broadcastingZ _is_dynamic_idr nanrr7rApropertyrErFrJrRrT __classcell__rrr3rr &s(*-   # r cCs|dkrtdtdg|}||dd}|dkr>d|d<nd|d<|durltj}tjd7_d |}tt|tf|}d |_|S) a Make a ``Tabular`` model where ``n_inputs`` is based on the dimension of the lookup_table. This model has to be further initialized and when evaluated returns the interpolated values. Parameters ---------- dim : int Dimensions of the lookup table. name : str Name for the class. Examples -------- >>> table = np.array([[3., 0., 0.], ... [0., 2., 0.], ... [0., 0., 0.]]) >>> tab = tabular_model(2, name='Tabular2D') >>> print(tab) Name: Tabular2D N_inputs: 2 N_outputs: 1 >>> points = ([1, 2, 3], [1, 2, 3]) Setting fill_value to None, allows extrapolation. >>> m = tab(points, lookup_table=table, name='my_table', ... bounds_error=False, fill_value=None, method='nearest') >>> xinterp = [0, 1, 1.5, 2.72, 3.14] >>> m(xinterp, xinterp) # doctest: +FLOAT_CMP array([3., 3., 3., 0., 0.]) rz.Lookup table must have at least one dimension.)r$r=r>TZ _separableFNZTabularzastropy.modeling.tabular)r r Zzerosr rXtypestrrU)Zdimr<tablemembersZmodel_idZ model_classrrrrs'   r)r<r\ra method : str, optional The method of interpolation to perform. Supported are "linear" and "nearest", and "splinef2d". "splinef2d" is only supported for 2-dimensional data. Default is "linear". bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then ``fill_value`` is used. fill_value : float, optional If provided, the value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Extrapolation is not supported by method "splinef2d". Returns ------- value : ndarray Interpolated values at input coordinates. Raises ------ ImportError Scipy is not installed. Notes ----- Uses `scipy.interpolate.interpn`. a! Tabular model in 1D. Returns an interpolated lookup table value. Parameters ---------- points : array-like of float of ndim=1. The points defining the regular grid in n dimensions. lookup_table : array-like, of ndim=1. The data in one dimensions. ai Tabular model in 2D. Returns an interpolated lookup table value. Parameters ---------- points : tuple of ndarray of float, optional The points defining the regular grid in n dimensions. ndarray with shapes (m1, m2). lookup_table : array-like The data on a regular grid in 2 dimensions. Shape (m1, m2). )N)rWabcZnumpyr Zastropyrr"corerZscipy.interpolaterrOrP__all__Z__doctest_requires__r rrrZ _tab_docsrrrrs.      b <