exo_k.util.interp

@author: jeremy leconte Library of useful functions for interpolation

Module Contents

exo_k.util.interp.bilinear_interpolation(z00, z10, z01, z11, x, y)

2D interpolation Applies linear interpolation across x and y between xmin,xmax and ymin,ymax

Parameters:

• z00 (array, np.ndarray) – Array corresponding to xmin,ymin

• z10 (array, np.ndarray) – Array corresponding to xmax,ymin

• z01 (array, np.ndarray) – Array corresponding to xmin,ymax

• z11 (array, np.ndarray) – Array corresponding to xmax,ymax

• x (float) – weight on x coord

• y (float) – weight on y coord

exo_k.util.interp.bilinear_interpolation_array(z00, z10, z01, z11, x, y)

2D interpolation Applies linear interpolation across x and y between xmin,xmax and ymin,ymax. For this variant of bilinear_interpolation (mainly for chemistry), x, y and z must be 1D-arrays of the same length.

Parameters:

• z00 (array, np.ndarray) – Array corresponding to xmin,ymin

• z10 (array, np.ndarray) – Array corresponding to xmax,ymin

• z01 (array, np.ndarray) – Array corresponding to xmin,ymax

• z11 (array, np.ndarray) – Array corresponding to xmax,ymax

• x (array, np.ndarray) – weights on x coord

• y (array, np.ndarray) – weights on y coord

exo_k.util.interp.linear_interpolation(z00, z10, x)

1D interpolation.

Applies linear interpolation in x between xmin, xmax.

Parameters:

• z00 (array, np.ndarray) – Array corresponding to xmin

• z10 (array, np.ndarray) – Array corresponding to xmax

• x (float) – weight on x coord

exo_k.util.interp.interp_ind_weights(x_to_interp, x_grid)

Finds the indices and weights to interpolate from a x_grid to a x_to_interp.

exo_k.util.interp.rebin_ind_weights(old_bin_grid, new_bin_grid)

Computes the indices and weights to be used when rebinning an array of values f of size N in the bins delimited by old_bin_grid (of size N+1) onto new bins delimited by new_bin_grid (of size Nnew+1) Returns indices, weights to be used as follows to proceed with the rebinning

>>> f_rebinned=[np.dot(f[indicestosum[ii]-1:indicestosum[ii+1]],final_weights[ii])
for ii in range(Nnew)]

exo_k.util.interp.kdata_conv_loop(kdata1, kdata2, kdataconv, shape)

Computes the convolution of two kdata tables.

Parameters:

• kdata1 (array, np.ndarrays) – The two ktable.kdata tables to convolve.

• kdata2 (array, np.ndarrays) – The two ktable.kdata tables to convolve.

• shape (array, np.ndarray) – shape of the ktabke.kdata tables to convolve (last size is Ng).

• kdataconv (array, np.ndarray) – Result table where the last dimension as a length equal to Ng^2.

exo_k.util.interp.kdata_conv_loop_profile(kdata1, kdata2, kdataconv, Nlay, Nw, Ng)

Computes the convolution of two atmospheric kdata profiles.

Nothing is returned. kdataconv is changed in place.

Parameters:

• kdata1 (array, np.ndarrays) – The two ktable.kdata tables to convolve.

• kdata2 (array, np.ndarrays) – The two ktable.kdata tables to convolve.

• kdataconv (array, np.ndarray) – Result table where the last dimension as a length equal to Ng^2.

• Nlay (int) – Number of atmospheric layers

• Nw (int) – Number of wavenumber points

• Ng (int) – Number of g-points

exo_k.util.interp.rebin(f_fine, fine_grid, coarse_grid)

Computes the binned version of a function on a coarser grid. The two grids do not need to have the same boundaries.

Parameters:

• f_fine (array, np.ndarray) – Function to be rebinned, given on the fine_grid.

• fine_grid (array, np.ndarray) – The high resolution grid edges we start with.

• coarse_grid (array, np.ndarray) – The coarser resolution grid edges inside which we want to bin the function f.

exo_k.util.interp.g_sample_4d(new_ggrid, new_kdata, old_ggrid, old_kdata)

Reinterpolte the g grid inplace.

exo_k.util.interp.g_sample_5d(new_ggrid, new_kdata, old_ggrid, old_kdata)

Reinterpolte the g grid inplace.

exo_k.util.interp.RandOverlap_2_kdata_prof(Nlay, Nw, Ng, kdata1, kdata2, weights, ggrid)

Function to randomely mix the opacities of 2 species in an atmospheric profile.

Parameters:

• Nlay (int) – Number of layers, spectral bins, and gauss points.

• Nw (int) – Number of layers, spectral bins, and gauss points.

• Ng (int) – Number of layers, spectral bins, and gauss points.

• kdata1 (array, np.ndarrays of size (Nlay, Nw, Ng)) – vmr weighted cross-sections for the two species.

• kdata2 (array, np.ndarrays of size (Nlay, Nw, Ng)) – vmr weighted cross-sections for the two species.

• weights (array, np.ndarray) – gauss weights.

• ggrid (array, np.ndarray) – g-points.

Returns:

array

k-coefficient array of the mix over the atmospheric column.

exo_k.util.interp.unit_convert(quantity, unit_file='unspecified', unit_in='unspecified', unit_out='unspecified')

Chooses the final unit to use and the conversion factor to apply.

Parameters:

• quantity (str) – The name of the pysical quantity handled for potential error messages

• unit_file/unit_in/unit_out (str) –

  Respectively:

  • String with the unit found in (or assumed from the format of) the initial data,

  • The unit we think the initial data are in if unit_file is ‘unspecified’ or (we believe) wrong,

  • The unit we want to convert to. If unspecified, we do not convert.

Returns:

unit_to_write: str

Resulting unit.

conversion_factor: float

A multiplicating factor for the data to proceed to the conversion.

exo_k.util.interp.rm_molec(unit_name)

Removes “/molecule” or “/molec” from a unit string.

Parameters:

unit_name (str) – String to be changed.

Returns:

str

The unit name without the ending “/molecule” or “/molec”

exo_k.util.interp.is_sorted(a)

Finds out if an array is sorted. Returns True if it is.

exo_k.util.interp.gauss_legendre(order)

Computes the weights and abscissa for a Gauss Legendre quadrature of order order

Parameters:

order (int) – Order of the quadrature wanted.

Returns:

weights: array, np.ndarray(order)

Weights to be used in the quadrature.

ggrid: array, np.ndarray(order)

Abscissa to be used for the quadrature.

gedges: array, np.ndarray(order+1)

Cumulative sum of the weights. Goes from 0 to 1.

exo_k.util.interp.split_gauss_legendre(order, g_split)

Computes the weights and abscissa for a split Gauss Legendre quadrature of order order: Half the points will be put between 0 and g_split, and half between `g_split`and 1.

(This is what is used in petitRADTRANS with g_split=0.9)

Parameters:

• order (int) – Order of the quadrature wanted. Needs to be even.

• g_split (float) – Splitting point, it needs to be between 0 and 1.

Returns:

weights: array, np.ndarray(order)

Weights to be used in the quadrature.

ggrid: array, np.ndarray(order)

Abscissa to be used for the quadrature.

gedges: array, np.ndarray(order+1)

Cumulative sum of the weights. Goes from 0 to 1.

exo_k.util.interp.spectrum_to_kdist(k_hr, wn_hr, dwn_hr, wnedges, ggrid)

Creates the k-distribution from a single high resolution spectrum

Parameters:

• k_hr (array, np.ndarray) – Spectrum

• wn_hr (array, np.ndarray) – Wavenumber grid

• dwn_hr (array, np.ndarray) – Width of the high resolution wavenumber bins.

• wnedges (array, np.ndarray) – Lower resolution wavenumber bin edges inside which the k-dist will be computed.

• ggrid (array, np.ndarray) – Grid of g-point abscissas

Returns:

kdataarray, np.ndarray

k coefficients for each (Wn, g) bin.

exo_k.util.interp.bin_down_corrk_numba(newshape, kdata, old_ggrid, new_ggrid, gedges, indicestosum, wngrid_filter, wn_weigths, num, use_rebin)

bins down a kcoefficient table (see bin_down() for details)

Parameters:

• newshape (array, np.ndarray) – Shape of kdata.

• kdata (array, np.ndarray) – table to bin down.

• old_ggrid (array, np.ndarray) – Grid of old g-point abscissas for kdata.

• new_ggrid (array, np.ndarray) – New g-points for the binned-down k-coefficients.

• gedges (array, np.ndarray) – Cumulative sum of the weights. Goes from 0 to 1. Used only if use_rebin=True

• indicestosum (list of lists) – Indices of wavenumber bins to be used for the averaging

• wngrid_filter (array, np.ndarray) – Indices of the new table where there will actually be data (zero elsewhere)

• wn_weigths (list of lists) – Weights to be used for the averaging (same size as indicestosum)

• num (int) – Number of points to fine sample the g function in log-k space

• use_rebin (boolean) – Whether to use rebin or interp method.

exo_k.util.interp.kdata_conv(kdata1, kdata2, kdataconv, Ng)

Deprecated.

Performs the convolution of the kdata over g-space.

exo_k.util.interp.kdata_conv_loop_bad(kdata1, kdata2, kdataconv, shape)

Deprecated.

Performs the convolution of the kdata over g-space. But the loop is in bad order. Keep for test.

exo_k.util.interp.g_interp(logk, Nw, Ng, Num, ggrid, wl_weights, indices)

Interpolates logk on a new g-grid.