From c9047edb5c868e1f60950d5e20a387722fd4eb40 Mon Sep 17 00:00:00 2001 From: alserene Date: Sat, 13 Jul 2024 08:24:53 +1000 Subject: [PATCH 01/24] Add work-in-progress file of figure 7 solve dependencies. --- python/jaxodi/figure_7.py | 484 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 484 insertions(+) create mode 100644 python/jaxodi/figure_7.py diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py new file mode 100644 index 0000000..aadf830 --- /dev/null +++ b/python/jaxodi/figure_7.py @@ -0,0 +1,484 @@ +""" + + +Figure 7 +-------- + + +Infer the stellar map assuming perfect knowledge of: + + - rest frame spectrum s + - baseline + - stellar inclination, rotation period & limb-darkening coefficients + + +""" + + + + +# when to use np.dot() vs DopplerMap.dot()? +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1357 + + + + +# Stuff I might need +# ------------------ + +from typing import Tuple +from collections.abc import Callable +from functools import partial + +import jax +import jax.numpy as jnp +import numpy as np + +from jaxoplanet.types import Array + +# To generate variables --- +import paparazzi +import starry + +from paparazzi.src.scripts.utils.generate import generate_data +# --- + + + + +# VARIABLES ----------------------------------------------------------------------------------------------- + +lazy = False # when reading starry code + + +# Generate the synthetic dataset --- +data = generate_data() +# --- + + +y_true = data["truths"]["y"] # array (256,) +spectrum_true = data["truths"]["spectrum"] # array (1,300) +theta = data["data"]["theta"] # array (16,) +flux = data["data"]["flux0"] # array (16,70) +flux_err = data["data"]["flux0_err"] # 0.00013241138706868687 + + +# Instantiate the map --- +map = starry.DopplerMap(lazy=False, **data["kwargs"]) +map.spectrum = data["truths"]["spectrum"] +for n in range(map.udeg): + map[1 + n] = data["props"]["u"][n] +# --- + + +ydeg = data["kwargs"]['ydeg'] # 15 +udeg = data["kwargs"]['udeg'] # 2 +nc = data["kwargs"]['nc'] # 1 +veq = data["kwargs"]['veq'] # 60000 +inc = data["kwargs"]['inc'] # 40 +vsini_max = data["kwargs"]['vsini_max'] # 50000 +nt = data["kwargs"]['nt'] # 16 +wav = data["kwargs"]['wav'] # array (70,) + +baseline = map.baseline # array (16,) + +normalised = False +fix_spectrum = True + +T = 1 + +baseline_var = 0 + +nw = 70 +Ny = 256 + +angle_factor = np.pi/180 # converts between degrees and radians + +interp = True + +Si2eBlk = map._Si2eBlk # array (1120, 5616) + +u = data["props"]["u"] # the vector of limb darkening coefficients + +spectrum = spectrum_true + + +spatial_mean +spatial_inv_cov # (array) +n +S +cho_C +mu +invL +meta["y_lin"] +y +cho_ycov + + +# FUNCTIONS ---------------------------------------------------------------------------------------------------------------------- + +# Functions from 2023B +# -------------------- + +# cho_solve +# +# https://github.com/ADACS-Australia/bpope_2023b/blob/df765188cd1182694f28da7f4dab28cfdcd76265/src/jaxoplanet/experimental/starry/light_curve/inference.py#L33 +# +@jax.jit +def cho_solve(A: Array, b: Array) -> Array: + b_ = jax.scipy.linalg.solve_triangular(A, b, lower=True) + return jax.scipy.linalg.solve_triangular(jnp.transpose(A), b_, lower=False) + + +# map_solve +# +# https://github.com/ADACS-Australia/bpope_2023b/blob/df765188cd1182694f28da7f4dab28cfdcd76265/src/jaxoplanet/experimental/starry/light_curve/inference.py#L192 +# +@jax.jit +def map_solve( + X: Array, flux: Array, cho_C: float | Array, mu: Array, LInv: float | Array +) -> Tuple[Array, Array]: + """ + Compute the maximum a posteriori (MAP) prediction for the + spherical harmonic coefficients of a map given a flux timeseries. + + Args: + X (matrix): The flux design matrix. + flux (array): The flux timeseries. + cho_C (scalar/vector/matrix): The lower cholesky factorization + of the data covariance. + mu (array): The prior mean of the spherical harmonic coefficients. + LInv (scalar/vector/matrix): The inverse prior covariance of the + spherical harmonic coefficients. + + Returns: + The vector of spherical harmonic coefficients corresponding to the + MAP solution and the Cholesky factorization of the corresponding + covariance matrix. + + """ + # Compute C^-1 . X + if cho_C.ndim == 0: + CInvX = X / cho_C**2 + elif cho_C.ndim == 1: + CInvX = jnp.dot(jnp.diag(1 / cho_C**2), X) + else: + CInvX = cho_solve(cho_C, X) + + # Compute W = X^T . C^-1 . X + L^-1 + W = jnp.dot(jnp.transpose(X), CInvX) + # If LInv is a scalar or a 1-dimensional array, increment the + # diagonal elements of W with the values from LInv. + if LInv.ndim == 0 or LInv.ndim == 1: + W = W.at[jnp.diag_indices_from(W)].set(W[jnp.diag_indices_from(W)] + LInv) + LInvmu = mu * LInv + # If LInv is a matrix, directly add LInv to W. + else: + W += LInv + LInvmu = jnp.dot(LInv, mu) + + # Compute the max like y and its covariance matrix + cho_W = jax.scipy.linalg.cholesky(W, lower=True) + M = cho_solve(cho_W, jnp.transpose(CInvX)) + yhat = jnp.dot(M, flux) + cho_solve(cho_W, LInvmu) + ycov = cho_solve(cho_W, jnp.eye(cho_W.shape[0])) + cho_ycov = jax.scipy.linalg.cholesky(ycov, lower=True) + + return yhat, cho_ycov + +# --- + + + + + +# Tiger's Functions +# ----------------- + +# get_default_theta +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1059 +# +def get_default_theta(theta): + + # if theta is None: + # theta = math.cast( + # np.linspace(0, 2 * np.pi, nt, endpoint=False) + # ) + # else: + # theta = ( + # ops.enforce_shape( + # math.cast(theta), np.array([nt]) + # ) + # * angle_factor + # ) + + return theta + + +# get_kT +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1894 +# +def get_kT(inc, theta, veq, u): + """ + Get the kernels at an array of angular phases `theta`. + """ + # # Compute the convolution kernels + # vsini = self.enforce_bounds(veq * tt.sin(inc), 0.0, self.vsini_max) + # x = self.get_x(vsini) + # rT = self.get_rT(x) + # kT0 = self.get_kT0(rT) + + # # Compute the limb darkening operator + # if self.udeg > 0: + # F = self.F( + # tt.as_tensor_variable(u), tt.as_tensor_variable([np.pi]) + # ) + # L = ts.dot(ts.dot(self.A1Inv, F), self.A1) + # kT0 = tt.dot(tt.transpose(L), kT0) + + # # Compute the kernels at each epoch + # kT = tt.zeros((self.nt, self.Ny, self.nk)) + # for m in range(self.nt): + # kT = tt.set_subtensor( + # kT[m], + # tt.transpose( + # self.right_project( + # tt.transpose(kT0), + # inc, + # tt.as_tensor_variable(0.0), + # theta[m], + # ) + # ), + # ) + # return kT + + pass + +# --- + + + + + + + + + + +# Functions called by Figure 7's map.solve() +# ------------------------------------------ + +# spatial_cov +S0e2i = map._S0e2i # array (603, 300) +# spectral_mean + +# process_inputs +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L222 +# +def process_inputs(spatial_cov, S0e2i, spectral_mean): + """ + Checks shapes and sets defaults. + """ + + # potentially calls to cho_factor and cho_solve + # if not spatial_cov[n].ndim < 2: then ^^ + + # spectral_mean[n] = S0e2i.dot(spectral_mean[n]).T + + pass + + +# get_D_fixed_spectrum +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1986 +# +def get_D_fixed_spectrum(inc, theta, veq, u, spectrum): + """ + Return the Doppler matrix for a fixed spectrum. + """ + + kT = get_kT(inc, theta, veq, u) # Tiger task + + # A bunch of theano action here. + product = 0 + # The dot product is just a 2d convolution! + # product = tt.nnet.conv2d( + # tt.reshape(spectrum, (self.nc, 1, 1, self.nwp)), + # tt.reshape(kT, (self.nt * self.Ny, 1, 1, self.nk)), + # border_mode="valid", + # filter_flip=False, + # input_shape=(self.nc, 1, 1, self.nwp), + # filter_shape=(self.nt * self.Ny, 1, 1, self.nk), + # ) + # product = tt.reshape(product, (self.nc, self.nt, self.Ny, self.nw)) + # product = tt.swapaxes(product, 1, 2) + # product = tt.reshape(product, (self.Ny * self.nc, self.nt * self.nw)) + # product = tt.transpose(product) + + return product + + +# sparse_dot +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/math.py#L218 +# +def sparse_dot(A, B): + """ + Performs matrix multiplication, optimising computation time by utilising sparse matrices. + """ + + # if scipy.sparse.issparse(A): + # return A.dot(B) + # elif scipy.sparse.issparse(B): + # return (B.T.dot(A.T)).T + # else: + # raise ValueError("At least one input must be sparse.") + + # if jax.issparse(A): + # return A.dot(B) + # elif jax.issparse(B): + # return (B.T.dot(A.T)).T + # else: + # raise ValueError("At least one input must be sparse.") + + pass + + +# design_matrix +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1170 +# +fix_spectrum +inc +veq +u +spectrum +interp +Si2eBlk +# +def design_matrix(): + """ + Return the Doppler imaging design matrix. + + This matrix dots into the spectral map to yield the model for the + observed spectral timeseries (the ``flux``). + + Note that if this method is used to compute the spectral timeseries, + the result should be reshaped into a matrix of shape + (:py:attr:`nt`, :py:attr:`nw`) and optionally divided by the + :py:meth:`baseline()` to match the return value of :py:meth:`flux()`. + """ + + """ + there is much more info in the docstring + it is overwhelming + havent read yet + """ + + theta = get_default_theta(theta) # Tiger task + + # if fix_spectrum: + D = get_D_fixed_spectrum(inc, theta, veq, u, spectrum) + + # if interp: + D = sparse_dot(Si2eBlk, D) + + return D + + + +# get_S +# +# S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L201 +# get_S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L124 +# +theta +angle_factor +fix_spectrum +# +def get_S(): + """ + Get design matrix conditioned on the current spectrum. + """ + return design_matrix(theta/angle_factor, fix_spectrum=True) + + + +# solve_for_map_linear +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L467 +# +flux +cho_C +mu_ +invL +# +def solve_for_map_linear(): + """ + Solve for `y` linearly, given a baseline or unnormalised data. + """ + + # ~100 lines of code that don't use theano + + # scipy.linalg.block_diag() + # cho_factor == scipy.linalg.cholesky(*args, **kwargs, lower=True) + # == jax.scipy.linalg.cholesky(_input_, lower=True) + + S = get_S() + # finishes by calling map_solve (already written) + mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) + # a few more lines + + return mean, cho_ycov + + +# solve_bilinear +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L737 +# +def solve_bilinear(flux, theta, y, spectrum, veq, inc, u, **kwargs): + """ + Solve the linear problem for the spatial and/or spectral map + given a spectral timeseries. + """ + + process_inputs(flux, **kwargs) + + # if fix_spectrum: + # Solve for the map conditioned on the spectrum + solve_for_map_linear() # The problem is exactly linear! + + metadata = True # {y, cho_ycov, spectrum, cho_scov} + + return metadata + + +# solve +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1773 +# +def solve(flux, theta, y, spectrum, veq, inc, u, **kwargs): + """ + Iteratively solves the bilinear or nonlinear problem for the spatial + and/or spectral map given a spectral timeseries. + """ + + if theta is None: + theta = get_default_theta(None) + else: + theta = get_default_theta(theta) + + # if bilinear + solution = solve_bilinear( + flux, theta, y, spectrum, veq, inc, u, **kwargs + ) + + # else linear + # solution = solve_nonlinear( + # flux, theta, y, spectrum, veq, inc, u, **kwargs + # ) + + return solution From ea13b0081aa0bd462cb23ee9b187f05d859c5409 Mon Sep 17 00:00:00 2001 From: alserene Date: Sat, 13 Jul 2024 13:47:06 +1000 Subject: [PATCH 02/24] Add class attributes and example calls to functions. --- python/jaxodi/figure_7.py | 287 +++++++++++++++++++++++++++----------- 1 file changed, 205 insertions(+), 82 deletions(-) diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py index aadf830..bff614b 100644 --- a/python/jaxodi/figure_7.py +++ b/python/jaxodi/figure_7.py @@ -72,7 +72,7 @@ ydeg = data["kwargs"]['ydeg'] # 15 -udeg = data["kwargs"]['udeg'] # 2 +udeg = data["kwargs"]['udeg'] # 2 (leaves out the 0th deg = -1) nc = data["kwargs"]['nc'] # 1 veq = data["kwargs"]['veq'] # 60000 inc = data["kwargs"]['inc'] # 40 @@ -97,10 +97,11 @@ interp = True Si2eBlk = map._Si2eBlk # array (1120, 5616) +S0e2i = map._S0e2i # array (603, 300) -u = data["props"]["u"] # the vector of limb darkening coefficients +u = map.u # array (3,) vector of limb darkening coefficients -spectrum = spectrum_true +spectrum = spectrum_true # array (1,300) spatial_mean @@ -111,7 +112,7 @@ mu invL meta["y_lin"] -y +y = map.y # array (256,) cho_ycov @@ -198,9 +199,19 @@ def map_solve( # get_default_theta # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1059 -# + +# how function is called in solve() +theta = get_default_theta(kwargs.pop("theta", None)) + +# class attributes +_nt +_angle_factor + def get_default_theta(theta): + math.cast() + ops.enforce_shape() + # if theta is None: # theta = math.cast( # np.linspace(0, 2 * np.pi, nt, endpoint=False) @@ -219,40 +230,33 @@ def get_default_theta(theta): # get_kT # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1894 -# + +# how function is called in get_D_fixed_spectrum() +get_kT(inc, theta, veq, u) + +# class attributes +vsini_max +F # not sure if function or attribute? +A1Inv +A1 +nt +Ny +nk + def get_kT(inc, theta, veq, u): """ Get the kernels at an array of angular phases `theta`. """ - # # Compute the convolution kernels - # vsini = self.enforce_bounds(veq * tt.sin(inc), 0.0, self.vsini_max) - # x = self.get_x(vsini) - # rT = self.get_rT(x) - # kT0 = self.get_kT0(rT) - - # # Compute the limb darkening operator - # if self.udeg > 0: - # F = self.F( - # tt.as_tensor_variable(u), tt.as_tensor_variable([np.pi]) - # ) - # L = ts.dot(ts.dot(self.A1Inv, F), self.A1) - # kT0 = tt.dot(tt.transpose(L), kT0) - - # # Compute the kernels at each epoch - # kT = tt.zeros((self.nt, self.Ny, self.nk)) - # for m in range(self.nt): - # kT = tt.set_subtensor( - # kT[m], - # tt.transpose( - # self.right_project( - # tt.transpose(kT0), - # inc, - # tt.as_tensor_variable(0.0), - # theta[m], - # ) - # ), - # ) - # return kT + + # many function calls + enforce_bounds() + get_x() + get_rT() + get_kT0() + F() #? + right_project() + + # many theano operations pass @@ -270,22 +274,56 @@ def get_kT(inc, theta, veq, u): # Functions called by Figure 7's map.solve() # ------------------------------------------ -# spatial_cov -S0e2i = map._S0e2i # array (603, 300) -# spectral_mean # process_inputs # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L222 -# -def process_inputs(spatial_cov, S0e2i, spectral_mean): + +# how function is called in solve_bilinear() +process_inputs(flux, **kwargs) + +# class attributes +Ny +nt +nw +nc +nw0 # maybe the shape of spectral_mean[n] arrays? +S0e2i +nw0_ # maybe a transformed version of nw0 for spectral covariance matrices? + +# def process_inputs(spatial_cov, S0e2i, spectral_mean): +def process_inputs( + flux, + flux_err=None, + spatial_mean=None, + spatial_cov=None, + spectral_mean=None, + spectral_cov=None, + spectral_guess=None, + spectral_lambda=None, + spectral_maxiter=None, + spectral_eps=None, + spectral_tol=None, + spectral_method=None, + normalized=True, + baseline=None, + baseline_var=None, + fix_spectrum=False, + fix_map=False, + logT0=None, + logTf=None, + nlogT=None, + quiet=False, + ): """ Checks shapes and sets defaults. """ - # potentially calls to cho_factor and cho_solve - # if not spatial_cov[n].ndim < 2: then ^^ + # if not spatial_cov[n].ndim < 2: + # cho_factor() + # cho_solve() + # is this dot function Numpy's or starry's? # spectral_mean[n] = S0e2i.dot(spectral_mean[n]).T pass @@ -294,7 +332,20 @@ def process_inputs(spatial_cov, S0e2i, spectral_mean): # get_D_fixed_spectrum # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1986 -# + +# how function is called in design_matrix() +D = get_D_fixed_spectrum( + inc, theta, veq, u, spectrum +) + +# class attributes +nc +nwp +nt +Ny +nk +nw + def get_D_fixed_spectrum(inc, theta, veq, u, spectrum): """ Return the Doppler matrix for a fixed spectrum. @@ -324,7 +375,10 @@ def get_D_fixed_spectrum(inc, theta, veq, u, spectrum): # sparse_dot # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/math.py#L218 -# + +# how function is called in design_matrix() +D = math.sparse_dot(Si2eBlk, D) + def sparse_dot(A, B): """ Performs matrix multiplication, optimising computation time by utilising sparse matrices. @@ -337,29 +391,33 @@ def sparse_dot(A, B): # else: # raise ValueError("At least one input must be sparse.") - # if jax.issparse(A): - # return A.dot(B) - # elif jax.issparse(B): - # return (B.T.dot(A.T)).T - # else: - # raise ValueError("At least one input must be sparse.") - - pass + # Guess at jax versions: + if jax.issparse(A): + return A.dot(B) + elif jax.issparse(B): + return (B.T.dot(A.T)).T + else: + raise ValueError("At least one input must be sparse.") # design_matrix # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1170 -# -fix_spectrum + +# how function is called in get_S() +map.design_matrix( + theta=theta/angle_factor, fix_spectrum=True +) + +# class attributes inc veq u spectrum -interp +interp = True Si2eBlk -# -def design_matrix(): + +def design_matrix(theta=None, fix_spectrum=None, fix_map=False): """ Return the Doppler imaging design matrix. @@ -374,7 +432,6 @@ def design_matrix(): """ there is much more info in the docstring - it is overwhelming havent read yet """ @@ -394,11 +451,12 @@ def design_matrix(): # # S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L201 # get_S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L124 -# + +# class attributes theta angle_factor fix_spectrum -# + def get_S(): """ Get design matrix conditioned on the current spectrum. @@ -406,76 +464,141 @@ def get_S(): return design_matrix(theta/angle_factor, fix_spectrum=True) - # solve_for_map_linear # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L467 -# + +# how function is called in solve_bilinear() +solve_for_map_linear() + +# class attributes +spatial_mean +spatial_inv_cov +nc +flux_err +nt +nw +baseline flux -cho_C -mu_ -invL -# -def solve_for_map_linear(): +S +Ny + +def solve_for_map_linear(T=1, baseline_var=0): """ Solve for `y` linearly, given a baseline or unnormalised data. """ - # ~100 lines of code that don't use theano + # ~60 lines of code that don't use theano + block_diag() # scipy.linalg.block_diag() + + cho_factor() # cho_factor == scipy.linalg.cholesky(*args, **kwargs, lower=True) # == jax.scipy.linalg.cholesky(_input_, lower=True) + # can get S via call S = get_S() - # finishes by calling map_solve (already written) + + # mean, cho_ycov = greedy_linalg.solve(S, flux, cho_C, mu, invL) mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) - # a few more lines return mean, cho_ycov +# reset +# +# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L177 + +# how function is called in solve_bilinear() +reset() + +# class attributes +spectrum +y +S +C +KT0 +meta + +def reset(): + + spectrum = None + y = None + S = None + C = None + KT0 = None + meta = {} + + # solve_bilinear # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L737 -# + +# how function is called in solve() +solution = solve_bilinear( + flux, theta, y, spectrum, veq, inc, u, **kwargs +) + +# class attributes +fix_spectrum +linear +meta["y"] +meta["cho_ycov"] +meta["spectrum"] +meta["cho_scov"] + def solve_bilinear(flux, theta, y, spectrum, veq, inc, u, **kwargs): """ Solve the linear problem for the spatial and/or spectral map given a spectral timeseries. """ + reset() process_inputs(flux, **kwargs) - # if fix_spectrum: + # if fix_spectrum and linear: # The problem is exactly linear! # Solve for the map conditioned on the spectrum - solve_for_map_linear() # The problem is exactly linear! + solve_for_map_linear() - metadata = True # {y, cho_ycov, spectrum, cho_scov} + meta = True # {y, cho_ycov, spectrum, cho_scov} - return metadata + return meta # solve # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1773 -# -def solve(flux, theta, y, spectrum, veq, inc, u, **kwargs): + +# how function is called in paper +soln = map.solve( + flux, + theta=theta, + normalized=False, + fix_spectrum=True, + flux_err=flux_err, +) + +# class attributes +y = map.y # array (256,) +spectrum = data["truths"]["spectrum"] # array (1,300) +veq = data["kwargs"]['veq'] # 60000 +inc = data["kwargs"]['inc'] # 40 +u = data["props"]["u"] # the vector of limb darkening coefficients + +def solve(flux, solver="bilinear", **kwargs): """ Iteratively solves the bilinear or nonlinear problem for the spatial and/or spectral map given a spectral timeseries. """ - if theta is None: - theta = get_default_theta(None) - else: - theta = get_default_theta(theta) + theta = get_default_theta(kwargs.pop("theta", None)) # if bilinear + # if solver.lower().startswith("bi"): solution = solve_bilinear( flux, theta, y, spectrum, veq, inc, u, **kwargs ) - # else linear # solution = solve_nonlinear( # flux, theta, y, spectrum, veq, inc, u, **kwargs From 05cda5d6ab6ebbcbdd193699fe2bcb6d1164618e Mon Sep 17 00:00:00 2001 From: alserene Date: Sat, 13 Jul 2024 14:06:50 +1000 Subject: [PATCH 03/24] Aligned variable preceeding and proceeding underscores with starry. --- python/jaxodi/figure_7.py | 71 ++++++++++++++++++++------------------- 1 file changed, 36 insertions(+), 35 deletions(-) diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py index bff614b..7d27a1d 100644 --- a/python/jaxodi/figure_7.py +++ b/python/jaxodi/figure_7.py @@ -92,17 +92,17 @@ nw = 70 Ny = 256 -angle_factor = np.pi/180 # converts between degrees and radians +_angle_factor = np.pi/180 # converts between degrees and radians interp = True -Si2eBlk = map._Si2eBlk # array (1120, 5616) +_Si2eBlk = map._Si2eBlk # array (1120, 5616) S0e2i = map._S0e2i # array (603, 300) u = map.u # array (3,) vector of limb darkening coefficients spectrum = spectrum_true # array (1,300) - +spectrum_ spatial_mean spatial_inv_cov # (array) @@ -201,13 +201,13 @@ def map_solve( # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1059 # how function is called in solve() -theta = get_default_theta(kwargs.pop("theta", None)) +theta = _get_default_theta(kwargs.pop("theta", None)) # class attributes _nt _angle_factor -def get_default_theta(theta): +def _get_default_theta(theta): math.cast() ops.enforce_shape() @@ -335,7 +335,7 @@ def process_inputs( # how function is called in design_matrix() D = get_D_fixed_spectrum( - inc, theta, veq, u, spectrum + _inc, theta, _veq, _u, _spectrum ) # class attributes @@ -377,7 +377,7 @@ def get_D_fixed_spectrum(inc, theta, veq, u, spectrum): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/math.py#L218 # how function is called in design_matrix() -D = math.sparse_dot(Si2eBlk, D) +D = math.sparse_dot(_Si2eBlk, D) def sparse_dot(A, B): """ @@ -406,16 +406,16 @@ def sparse_dot(A, B): # how function is called in get_S() map.design_matrix( - theta=theta/angle_factor, fix_spectrum=True + theta=theta/_angle_factor, fix_spectrum=True ) # class attributes -inc -veq -u -spectrum -interp = True -Si2eBlk +_inc +_veq +_u +_spectrum +_interp = True +_Si2eBlk def design_matrix(theta=None, fix_spectrum=None, fix_map=False): """ @@ -435,13 +435,13 @@ def design_matrix(theta=None, fix_spectrum=None, fix_map=False): havent read yet """ - theta = get_default_theta(theta) # Tiger task + theta = _get_default_theta(theta) # Tiger task # if fix_spectrum: - D = get_D_fixed_spectrum(inc, theta, veq, u, spectrum) + D = get_D_fixed_spectrum(_inc, theta, _veq, _u, _spectrum) - # if interp: - D = sparse_dot(Si2eBlk, D) + # if _interp: + D = sparse_dot(_Si2eBlk, D) return D @@ -453,15 +453,16 @@ def design_matrix(theta=None, fix_spectrum=None, fix_map=False): # get_S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L124 # class attributes +spectrum_ # map._spectrum = self.spectrum_ theta -angle_factor +_angle_factor fix_spectrum def get_S(): """ Get design matrix conditioned on the current spectrum. """ - return design_matrix(theta/angle_factor, fix_spectrum=True) + return design_matrix(theta/_angle_factor, fix_spectrum=True) # solve_for_map_linear @@ -514,20 +515,20 @@ def solve_for_map_linear(T=1, baseline_var=0): reset() # class attributes -spectrum +spectrum_ y -S -C -KT0 +_S +_C +_KT0 meta def reset(): - spectrum = None + spectrum_ = None y = None - S = None - C = None - KT0 = None + _S = None + _C = None + _KT0 = None meta = {} @@ -537,7 +538,7 @@ def reset(): # how function is called in solve() solution = solve_bilinear( - flux, theta, y, spectrum, veq, inc, u, **kwargs + flux, theta, y, spectrum_, veq, inc, u, **kwargs ) # class attributes @@ -580,11 +581,11 @@ def solve_bilinear(flux, theta, y, spectrum, veq, inc, u, **kwargs): ) # class attributes -y = map.y # array (256,) -spectrum = data["truths"]["spectrum"] # array (1,300) -veq = data["kwargs"]['veq'] # 60000 -inc = data["kwargs"]['inc'] # 40 -u = data["props"]["u"] # the vector of limb darkening coefficients +_y = map.y # array (256,) +spectrum_ = data["truths"]["spectrum"] # array (1,300) +_veq = data["kwargs"]['veq'] # 60000 +_inc = data["kwargs"]['inc'] # 40 +_u = data["props"]["u"] # the vector of limb darkening coefficients def solve(flux, solver="bilinear", **kwargs): """ @@ -592,7 +593,7 @@ def solve(flux, solver="bilinear", **kwargs): and/or spectral map given a spectral timeseries. """ - theta = get_default_theta(kwargs.pop("theta", None)) + theta = _get_default_theta(kwargs.pop("theta", None)) # if bilinear # if solver.lower().startswith("bi"): From 9be9d8f05de565f9ba1d86310cfc3e34c1bc17db Mon Sep 17 00:00:00 2001 From: alserene Date: Sat, 13 Jul 2024 16:36:13 +1000 Subject: [PATCH 04/24] Get values of class attributes. --- python/jaxodi/figure_7.py | 176 ++++++++++++++++++++++---------------- 1 file changed, 104 insertions(+), 72 deletions(-) diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py index 7d27a1d..cb931e5 100644 --- a/python/jaxodi/figure_7.py +++ b/python/jaxodi/figure_7.py @@ -201,7 +201,7 @@ def map_solve( # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1059 # how function is called in solve() -theta = _get_default_theta(kwargs.pop("theta", None)) +# theta = _get_default_theta(kwargs.pop("theta", None)) # class attributes _nt @@ -232,7 +232,7 @@ def _get_default_theta(theta): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1894 # how function is called in get_D_fixed_spectrum() -get_kT(inc, theta, veq, u) +# get_kT(inc, theta, veq, u) # class attributes vsini_max @@ -280,16 +280,16 @@ def get_kT(inc, theta, veq, u): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L222 # how function is called in solve_bilinear() -process_inputs(flux, **kwargs) +# process_inputs(flux, **kwargs) # class attributes -Ny -nt -nw -nc -nw0 # maybe the shape of spectral_mean[n] arrays? -S0e2i -nw0_ # maybe a transformed version of nw0 for spectral covariance matrices? +Ny = 256 +nt = 16 +nw = 70 +nc = 1 +nw0 = 300 # maybe the shape of spectral_mean[n] arrays? +S0e2i = map._S0e2i.eval() # the evaluated symbolic expression +nw0_ = 603 # maybe a transformed version of nw0 for spectral covariance matrices? # def process_inputs(spatial_cov, S0e2i, spectral_mean): def process_inputs( @@ -329,22 +329,56 @@ def process_inputs( pass +# ----------------------------------------- +# get log_wav0_int from DopplerMap.__init__ +wav = data["kwargs"]["wav"] # array (70,) +nw = 70 +vsini_max = data["kwargs"]['vsini_max'] # 50000 + +# Compute the padded internal wavelength grid (wav0_int). +# We add bins corresponding to the maximum kernel width to each +# end of wav_int to prevent edge effects + +_clight = 299792458.0 # m/s + +wav1 = np.min(wav) +wav2 = np.max(wav) +wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) +log_wav = np.linspace(np.log(wav1 / wavr), np.log(wav2 / wavr), nw) + +dlam = log_wav[1] - log_wav[0] +betasini_max = vsini_max / _clight +hw = np.array( + np.ceil( + 0.5 + * np.abs(np.log((1 + betasini_max) / (1 - betasini_max))) + / dlam + ), + dtype="int32", +) +x = np.arange(0, hw + 1) * dlam +pad_l = log_wav[0] - hw * dlam + x[:-1] +pad_r = log_wav[-1] + x[1:] +log_wav0_int = np.concatenate([pad_l, log_wav, pad_r]) +nwp = len(log_wav0_int) +# ----------------------------------------- + # get_D_fixed_spectrum # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1986 # how function is called in design_matrix() -D = get_D_fixed_spectrum( - _inc, theta, _veq, _u, _spectrum -) +# D = get_D_fixed_spectrum( +# _inc, theta, _veq, _u, _spectrum +# ) # class attributes -nc -nwp -nt -Ny -nk -nw +nc = 1 +nwp = len(log_wav0_int) # 120 # from DopplerMap.__init__ (see directly above for code) +nt = 16 +Ny = 256 +nk = int(2 * hw + 1) # 51 # hw also from DopplerMap.__init__ +nw = 70 def get_D_fixed_spectrum(inc, theta, veq, u, spectrum): """ @@ -377,7 +411,7 @@ def get_D_fixed_spectrum(inc, theta, veq, u, spectrum): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/math.py#L218 # how function is called in design_matrix() -D = math.sparse_dot(_Si2eBlk, D) +# D = math.sparse_dot(_Si2eBlk, D) def sparse_dot(A, B): """ @@ -405,17 +439,17 @@ def sparse_dot(A, B): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1170 # how function is called in get_S() -map.design_matrix( - theta=theta/_angle_factor, fix_spectrum=True -) +# map.design_matrix( +# theta=theta/_angle_factor, fix_spectrum=True +# ) # class attributes -_inc -_veq -_u -_spectrum +_inc = data["kwargs"]["inc"] # 40 +_veq = data["kwargs"]["veq"] # 60000 +_u = map._u # = array([-1. , 0.5 , 0.25]) +spectrum_ = map.spectrum_ # = map._spectrum # array (1,603) _interp = True -_Si2eBlk +_Si2eBlk = map._Si2eBlk # array (1120, 5616) def design_matrix(theta=None, fix_spectrum=None, fix_map=False): """ @@ -453,10 +487,9 @@ def design_matrix(theta=None, fix_spectrum=None, fix_map=False): # get_S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L124 # class attributes -spectrum_ # map._spectrum = self.spectrum_ -theta -_angle_factor -fix_spectrum +theta = map._get_default_theta(data["kwargs"].pop("theta", None)) # array (16,) +_angle_factor = np.pi/180 # converts between degrees and radians +fix_spectrum = True def get_S(): """ @@ -470,19 +503,20 @@ def get_S(): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L467 # how function is called in solve_bilinear() -solve_for_map_linear() +# solve_for_map_linear() # class attributes -spatial_mean -spatial_inv_cov -nc -flux_err -nt -nw -baseline -flux -S -Ny +Ny = 256 +spatial_mean = np.zeros(Ny) +spatial_mean[0] = 1.0 +spatial_inv_cov # set in process_inputs() +nc = 1 +flux_err = data["data"]["flux0_err"] +nt = 16 +nw = 70 +baseline = None +flux = data["data"]["flux0"] +S = get_S() def solve_for_map_linear(T=1, baseline_var=0): """ @@ -512,15 +546,15 @@ def solve_for_map_linear(T=1, baseline_var=0): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L177 # how function is called in solve_bilinear() -reset() +# reset() # class attributes -spectrum_ -y -_S -_C -_KT0 -meta +spectrum_ = None +y = None +_S = None +_C = None +_KT0 = None +meta = {} def reset(): @@ -537,19 +571,17 @@ def reset(): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L737 # how function is called in solve() -solution = solve_bilinear( - flux, theta, y, spectrum_, veq, inc, u, **kwargs -) +# solution = solve_bilinear( +# flux, theta, _y, spectrum_, _veq, _inc, _u, **kwargs +# ) # class attributes -fix_spectrum -linear -meta["y"] -meta["cho_ycov"] -meta["spectrum"] -meta["cho_scov"] - -def solve_bilinear(flux, theta, y, spectrum, veq, inc, u, **kwargs): +fix_spectrum = True +normalized = False +baseline = None +linear = (not normalized) or (baseline is not None) + +def solve_bilinear(flux, theta, y, spectrum_, veq, inc, u, **kwargs): """ Solve the linear problem for the spatial and/or spectral map given a spectral timeseries. @@ -562,7 +594,7 @@ def solve_bilinear(flux, theta, y, spectrum, veq, inc, u, **kwargs): # Solve for the map conditioned on the spectrum solve_for_map_linear() - meta = True # {y, cho_ycov, spectrum, cho_scov} + meta = True # {y, cho_ycov, spectrum_, cho_scov} return meta @@ -572,16 +604,16 @@ def solve_bilinear(flux, theta, y, spectrum, veq, inc, u, **kwargs): # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1773 # how function is called in paper -soln = map.solve( - flux, - theta=theta, - normalized=False, - fix_spectrum=True, - flux_err=flux_err, -) +# soln = map.solve( +# flux, +# theta=theta, +# normalized=False, +# fix_spectrum=True, +# flux_err=flux_err, +# ) # class attributes -_y = map.y # array (256,) +_y = map._y # array (256,) spectrum_ = data["truths"]["spectrum"] # array (1,300) _veq = data["kwargs"]['veq'] # 60000 _inc = data["kwargs"]['inc'] # 40 @@ -598,11 +630,11 @@ def solve(flux, solver="bilinear", **kwargs): # if bilinear # if solver.lower().startswith("bi"): solution = solve_bilinear( - flux, theta, y, spectrum, veq, inc, u, **kwargs + flux, theta, _y, spectrum_, _veq, _inc, _u, **kwargs ) # else linear # solution = solve_nonlinear( - # flux, theta, y, spectrum, veq, inc, u, **kwargs + # flux, theta, _y, spectrum_, _veq, _inc, _u, **kwargs # ) return solution From 30e769ae53f861ab19b9aaf31ad6695011c10737 Mon Sep 17 00:00:00 2001 From: alserene Date: Sat, 13 Jul 2024 17:41:58 +1000 Subject: [PATCH 05/24] Get values of class attributes. --- python/jaxodi/figure_7.py | 157 ++++++++++++++++++++++++++------------ 1 file changed, 108 insertions(+), 49 deletions(-) diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py index cb931e5..9d89bf8 100644 --- a/python/jaxodi/figure_7.py +++ b/python/jaxodi/figure_7.py @@ -71,53 +71,67 @@ # --- -ydeg = data["kwargs"]['ydeg'] # 15 -udeg = data["kwargs"]['udeg'] # 2 (leaves out the 0th deg = -1) -nc = data["kwargs"]['nc'] # 1 -veq = data["kwargs"]['veq'] # 60000 -inc = data["kwargs"]['inc'] # 40 -vsini_max = data["kwargs"]['vsini_max'] # 50000 -nt = data["kwargs"]['nt'] # 16 -wav = data["kwargs"]['wav'] # array (70,) +# Class attributes used in map.solve() & dependencies (except Tiger's functions) +_y = map._y # array (256,) +spectrum_ = data["truths"]["spectrum"] # array (1,300) +_veq = data["kwargs"]['veq'] # 60000 +_inc = data["kwargs"]['inc'] # 40 +_u = data["props"]["u"] # the vector of limb darkening coefficients +fix_spectrum = True +normalized = False +baseline = None +linear = (not normalized) or (baseline is not None) +Ny = 256 +spatial_mean = np.zeros(Ny) +spatial_mean[0] = 1.0 +spatial_inv_cov # array (256, 1) # set in process_inputs() +nc = 1 +flux_err = data["data"]["flux0_err"] +nt = 16 +nw = 70 +baseline = None +flux = data["data"]["flux0"] +S = get_S() # get_S() calls design_matrix() +theta = map._get_default_theta(data["kwargs"].pop("theta", None)) # array (16,) +_angle_factor = np.pi/180 # converts between degrees and radians +fix_spectrum = True +_inc = data["kwargs"]["inc"] # 40 +_veq = data["kwargs"]["veq"] # 60000 +_u = map._u # = array([-1. , 0.5 , 0.25]) +_spectrum = map.spectrum_ # = map._spectrum # array (1,603) +_interp = True +_Si2eBlk = map._Si2eBlk # array (1120, 5616) +nc = 1 +nwp = 120 # from DopplerMap.__init__ +nt = 16 +Ny = 256 +hw = 25 +nk = int(2 * hw + 1) # 51 +nw = 70 +Ny = 256 +nt = 16 +nw = 70 +nc = 1 +nw0 = 300 # maybe the shape of spectral_mean[n] arrays? +S0e2i = map._S0e2i.eval() # the evaluated symbolic expression +nw0_ = 603 # maybe a transformed version of nw0 for spectral covariance matrices? -baseline = map.baseline # array (16,) -normalised = False -fix_spectrum = True -T = 1 -baseline_var = 0 -nw = 70 -Ny = 256 -_angle_factor = np.pi/180 # converts between degrees and radians -interp = True -_Si2eBlk = map._Si2eBlk # array (1120, 5616) -S0e2i = map._S0e2i # array (603, 300) -u = map.u # array (3,) vector of limb darkening coefficients -spectrum = spectrum_true # array (1,300) -spectrum_ -spatial_mean -spatial_inv_cov # (array) -n -S -cho_C -mu -invL -meta["y_lin"] -y = map.y # array (256,) -cho_ycov # FUNCTIONS ---------------------------------------------------------------------------------------------------------------------- + + # Functions from 2023B # -------------------- @@ -193,6 +207,9 @@ def map_solve( + + + # Tiger's Functions # ----------------- @@ -204,7 +221,7 @@ def map_solve( # theta = _get_default_theta(kwargs.pop("theta", None)) # class attributes -_nt +_nt # = nt? _angle_factor def _get_default_theta(theta): @@ -235,13 +252,13 @@ def _get_default_theta(theta): # get_kT(inc, theta, veq, u) # class attributes -vsini_max +vsini_max = 50000 F # not sure if function or attribute? A1Inv A1 -nt -Ny -nk +nt = 16 +Ny = 256 +nk = 51 def get_kT(inc, theta, veq, u): """ @@ -291,7 +308,6 @@ def get_kT(inc, theta, veq, u): S0e2i = map._S0e2i.eval() # the evaluated symbolic expression nw0_ = 603 # maybe a transformed version of nw0 for spectral covariance matrices? -# def process_inputs(spatial_cov, S0e2i, spectral_mean): def process_inputs( flux, flux_err=None, @@ -363,6 +379,7 @@ def process_inputs( nwp = len(log_wav0_int) # ----------------------------------------- + # get_D_fixed_spectrum # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1986 @@ -447,7 +464,7 @@ def sparse_dot(A, B): _inc = data["kwargs"]["inc"] # 40 _veq = data["kwargs"]["veq"] # 60000 _u = map._u # = array([-1. , 0.5 , 0.25]) -spectrum_ = map.spectrum_ # = map._spectrum # array (1,603) +_spectrum = map.spectrum_ # = map._spectrum # array (1,603) _interp = True _Si2eBlk = map._Si2eBlk # array (1120, 5616) @@ -480,7 +497,6 @@ def design_matrix(theta=None, fix_spectrum=None, fix_map=False): return D - # get_S # # S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L201 @@ -498,6 +514,47 @@ def get_S(): return design_matrix(theta/_angle_factor, fix_spectrum=True) +# ----------------------------------------- +# code for spatial_inv_cov from process_inputs() +nc = data["kwargs"]['nc'] # 1 +Ny = 256 +spatial_cov = None # as far as I can tell + +if spatial_cov is None: + spatial_cov = 1e-4 + +if type(spatial_cov) not in (list, tuple): + # Use the same covariance for all components + spatial_cov = [spatial_cov for n in range(nc)] +else: + # Check that we have one covariance per component + assert len(spatial_cov) == nc + +spatial_inv_cov = [None for n in range(nc)] +ndim = np.array(spatial_cov[0]).ndim +for n in range(nc): + spatial_cov[n] = np.array(spatial_cov[n]) + assert spatial_cov[n].ndim == ndim + if spatial_cov[n].ndim < 2: + spatial_inv_cov[n] = np.reshape( + np.ones(Ny) / spatial_cov[n], (-1, 1) + ) + spatial_cov[n] = np.reshape( + np.ones(Ny) * spatial_cov[n], (-1, 1) + ) + # else: + # cho = cho_factor(spatial_cov[n]) + # inv = cho_solve(cho, np.eye(Ny)) + # spatial_inv_cov[n] = np.reshape(inv, (Ny, Ny, 1)) + # spatial_cov[n] = np.reshape( + # spatial_cov[n], (Ny, Ny, 1) + # ) + +# Tensor of nc (inverse) variance vectors or covariance matrices +spatial_cov = np.concatenate(spatial_cov, axis=-1) +spatial_inv_cov = np.concatenate(spatial_inv_cov, axis=-1) +# ----------------------------------------- + # solve_for_map_linear # # https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L467 @@ -509,7 +566,7 @@ def get_S(): Ny = 256 spatial_mean = np.zeros(Ny) spatial_mean[0] = 1.0 -spatial_inv_cov # set in process_inputs() +spatial_inv_cov # array (256, 1) # set in process_inputs() (relevant code directly above) nc = 1 flux_err = data["data"]["flux0_err"] nt = 16 @@ -525,16 +582,19 @@ def solve_for_map_linear(T=1, baseline_var=0): # ~60 lines of code that don't use theano - block_diag() - # scipy.linalg.block_diag() + # block_diag() # == scipy.linalg.block_diag() - cho_factor() - # cho_factor == scipy.linalg.cholesky(*args, **kwargs, lower=True) + # cho_factor() # == scipy.linalg.cholesky(*args, **kwargs, lower=True) # == jax.scipy.linalg.cholesky(_input_, lower=True) # can get S via call S = get_S() + # set within function + cho_C = None + mu = None + invL = None + # mean, cho_ycov = greedy_linalg.solve(S, flux, cho_C, mu, invL) mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) @@ -548,7 +608,7 @@ def solve_for_map_linear(T=1, baseline_var=0): # how function is called in solve_bilinear() # reset() -# class attributes +# class attributes (values are irrelevant) spectrum_ = None y = None _S = None @@ -590,9 +650,8 @@ def solve_bilinear(flux, theta, y, spectrum_, veq, inc, u, **kwargs): reset() process_inputs(flux, **kwargs) - # if fix_spectrum and linear: # The problem is exactly linear! - # Solve for the map conditioned on the spectrum - solve_for_map_linear() + if fix_spectrum and linear: # The problem is exactly linear! + solve_for_map_linear() # Solve for the map conditioned on the spectrum meta = True # {y, cho_ycov, spectrum_, cho_scov} From 2308cba60078a7559ad1d53696a1978926f28d71 Mon Sep 17 00:00:00 2001 From: alserene Date: Sat, 13 Jul 2024 17:43:50 +1000 Subject: [PATCH 06/24] Comment out unused imports --- python/jaxodi/figure_7.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py index 9d89bf8..b88c38f 100644 --- a/python/jaxodi/figure_7.py +++ b/python/jaxodi/figure_7.py @@ -27,8 +27,8 @@ # ------------------ from typing import Tuple -from collections.abc import Callable -from functools import partial +# from collections.abc import Callable +# from functools import partial import jax import jax.numpy as jnp @@ -37,7 +37,7 @@ from jaxoplanet.types import Array # To generate variables --- -import paparazzi +# import paparazzi import starry from paparazzi.src.scripts.utils.generate import generate_data From 95455477005880afbdf5df9b9f72078721d64c13 Mon Sep 17 00:00:00 2001 From: alserene Date: Sun, 14 Jul 2024 19:21:55 +1000 Subject: [PATCH 07/24] Fix -U variable definition. --- python/jaxodi/figure_7.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py index b88c38f..66a6ad8 100644 --- a/python/jaxodi/figure_7.py +++ b/python/jaxodi/figure_7.py @@ -72,11 +72,12 @@ # Class attributes used in map.solve() & dependencies (except Tiger's functions) +# in call order (i.e. map.solve() first) _y = map._y # array (256,) spectrum_ = data["truths"]["spectrum"] # array (1,300) _veq = data["kwargs"]['veq'] # 60000 _inc = data["kwargs"]['inc'] # 40 -_u = data["props"]["u"] # the vector of limb darkening coefficients +_u = map._u # = array([-1. , 0.5 , 0.25]) # the vector of limb darkening coefficients fix_spectrum = True normalized = False baseline = None @@ -676,7 +677,7 @@ def solve_bilinear(flux, theta, y, spectrum_, veq, inc, u, **kwargs): spectrum_ = data["truths"]["spectrum"] # array (1,300) _veq = data["kwargs"]['veq'] # 60000 _inc = data["kwargs"]['inc'] # 40 -_u = data["props"]["u"] # the vector of limb darkening coefficients +_u = map._u # = array([-1., 0.5, 0.25]) # the vector of limb darkening coefficients def solve(flux, solver="bilinear", **kwargs): """ From 91c7729d4dee6b9f02aeb8605c413c93a13bd34c Mon Sep 17 00:00:00 2001 From: alserene Date: Sat, 20 Jul 2024 17:00:08 +1000 Subject: [PATCH 08/24] Add files for figure 7 functions and tests. --- python/jaxodi/fig7_functions.py | 482 +++++++++++++++++++++++++++ python/jaxodi/test_fig7_functions.py | 349 +++++++++++++++++++ 2 files changed, 831 insertions(+) create mode 100644 python/jaxodi/fig7_functions.py create mode 100644 python/jaxodi/test_fig7_functions.py diff --git a/python/jaxodi/fig7_functions.py b/python/jaxodi/fig7_functions.py new file mode 100644 index 0000000..959cca1 --- /dev/null +++ b/python/jaxodi/fig7_functions.py @@ -0,0 +1,482 @@ +""" +Functions for Figure 7's map.solve() +""" + +from typing import Tuple +# from collections.abc import Callable +# from functools import partial + +import jax +import jax.numpy as jnp +import numpy as np +import scipy + +from jaxoplanet.types import Array + +# To generate variables --- +# import paparazzi +import starry + +from jaxodi.paparazzi.src.scripts.utils.generate import generate_data + + +# Functions +# +# - cho_solve +# - map_solve +# - process_inputs +# - solve_for_map_linear +# - get_default_theta +# - solve_bilinear +# - solve + + +# @jax.jit +def cho_solve(A: Array, b: Array) -> Array: + b_ = jax.scipy.linalg.solve_triangular(A, b, lower=True) + return jax.scipy.linalg.solve_triangular(jnp.transpose(A), b_, lower=False) + + +# @jax.jit +def map_solve( + X: Array, + flux: Array, + cho_C: float | Array, + mu: Array, + LInv: float | Array +) -> Tuple[Array, Array]: + """ + Compute the maximum a posteriori (MAP) prediction for the + spherical harmonic coefficients of a map given a flux timeseries. + + Args: + X (matrix): The flux design matrix. + flux (array): The flux timeseries. + cho_C (scalar/vector/matrix): The lower cholesky factorization + of the data covariance. + mu (array): The prior mean of the spherical harmonic coefficients. + LInv (scalar/vector/matrix): The inverse prior covariance of the + spherical harmonic coefficients. + + Returns: + The vector of spherical harmonic coefficients corresponding to the + MAP solution and the Cholesky factorization of the corresponding + covariance matrix. + + """ + # Compute C^-1 . X + if cho_C.ndim == 0: + CInvX = X / cho_C**2 + elif cho_C.ndim == 1: + CInvX = jnp.dot(jnp.diag(1 / cho_C**2), X) + else: + CInvX = cho_solve(cho_C, X) + print(f"CInvX: {CInvX}") + # breakpoint() + # Compute W = X^T . C^-1 . X + L^-1 + W = jnp.dot(jnp.transpose(X), CInvX) + print(f"W: {W}") + # If LInv is a scalar or a 1-dimensional array, increment the + # diagonal elements of W with the values from LInv. + if LInv.ndim == 0 or LInv.ndim == 1: + W = W.at[jnp.diag_indices_from(W)].set(W[jnp.diag_indices_from(W)] + LInv) + LInvmu = mu * LInv + # If LInv is a matrix, directly add LInv to W. + else: + W += LInv + LInvmu = jnp.dot(LInv, mu) + print(f"W: {W}") + print(f"LInvmu: {LInvmu}") + + # Compute the max like y and its covariance matrix + cho_W = jax.scipy.linalg.cholesky(W, lower=True) + print(f"cho_W: {cho_W}") + M = cho_solve(cho_W, jnp.transpose(CInvX)) + # print(f"M: {M}") + yhat = jnp.dot(M, flux) + cho_solve(cho_W, LInvmu) + # print(f"yhat: {yhat}") + ycov = cho_solve(cho_W, jnp.eye(cho_W.shape[0])) + # print(f"ycov: {ycov}") + cho_ycov = jax.scipy.linalg.cholesky(ycov, lower=True) + # print(f"cho_ycov: {cho_ycov}") + + return yhat, cho_ycov + + +def process_inputs( + flux, + nt, + nw, + nc, + Ny, + nw0, + nw0_, + S0e2i, + flux_err=None, + normalized=True, + baseline=None, + spatial_mean=None, + spatial_cov=None, + spectral_mean=None, + spectral_cov=None, + logT0=None, + logTf=None, + nlogT=None, + ): + + # Process defaults + if flux_err is None: + flux_err = 1e-4 + if spatial_mean is None: + spatial_mean = np.zeros(Ny) + spatial_mean[0] = 1.0 + if spatial_cov is None: + spatial_cov = 1e-4 + if spectral_mean is None: + spectral_mean = 1.0 + if spectral_cov is None: + spectral_cov = 1e-3 + if logT0 is None: + logT0 = 2 + if logTf is None: + logTf = 0 + if nlogT is None: + nlogT = 50 + else: + nlogT = max(1, nlogT) + + # Flux must be a matrix (nt, nw) + # if nt == 1: + # else: + assert np.array_equal( + np.shape(flux), np.array([nt, nw]) + ), "Invalid shape for `flux`." + + # Flux error may be a scalar, a vector, or a matrix (nt, nw) + flux_err = np.array(flux_err) + # if flux_err.ndim == 0: + # flux_err = flux_err + # else: + + # Spatial mean may be a scalar, a vector (Ny), or a list of those + # Reshape it to a matrix of shape (Ny, nc) + if type(spatial_mean) not in (list, tuple): + # Use the same mean for all components + spatial_mean = [spatial_mean for n in range(nc)] + # else: + for n in range(nc): + spatial_mean[n] = np.array(spatial_mean[n]) + assert spatial_mean[n].ndim < 2 + spatial_mean[n] = np.reshape( + spatial_mean[n] * np.ones(Ny), (-1, 1) + ) + spatial_mean = np.concatenate(spatial_mean, axis=-1) + + # Spatial cov may be a scalar, a vector, a matrix (Ny, Ny), + # or a list of those. Invert it and reshape to a matrix of + # shape (Ny, nc) (inverse variances) or a tensor of shape + # (Ny, Ny, nc) (nc separate inverse covariance matrices) + if type(spatial_cov) not in (list, tuple): + # Use the same covariance for all components + spatial_cov = [spatial_cov for n in range(nc)] + # else: + spatial_inv_cov = [None for n in range(nc)] + ndim = np.array(spatial_cov[0]).ndim + for n in range(nc): + spatial_cov[n] = np.array(spatial_cov[n]) + assert spatial_cov[n].ndim == ndim + if spatial_cov[n].ndim < 2: + spatial_inv_cov[n] = np.reshape( + np.ones(Ny) / spatial_cov[n], (-1, 1) + ) + spatial_cov[n] = np.reshape( + np.ones(Ny) * spatial_cov[n], (-1, 1) + ) + # else: + + # Tensor of nc (inverse) variance vectors or covariance matrices + spatial_cov = np.concatenate(spatial_cov, axis=-1) + spatial_inv_cov = np.concatenate(spatial_inv_cov, axis=-1) + + # Baseline must be a vector (nt,) + # if baseline is not None: + # else: + + # Spectral mean must be a scalar, a vector (nw0), or a list of those + # Interpolate it to the internal grid (nw0_) and reshape to (nc, nw0_) + if type(spectral_mean) not in (list, tuple): + # Use the same mean for all components + spectral_mean = [spectral_mean for n in range(nc)] + # else: + for n in range(nc): + spectral_mean[n] = np.array(spectral_mean[n]) + assert spectral_mean[n].ndim < 2 + spectral_mean[n] = np.reshape( + spectral_mean[n] * np.ones(nw0), (-1, 1) + ) + spectral_mean[n] = S0e2i.dot(spectral_mean[n]).T + spectral_mean = np.concatenate(spectral_mean, axis=0) + + # Spectral cov may be a scalar, a vector, a matrix (nw0, nw0), + # or a list of those. Interpolate it to the internal grid, + # then invert it and reshape to a matrix of + # shape (nc, nw0_) (inverse variances) or a tensor of shape + # (nc, nw0_, nw0_) (nc separate inverse covariance matrices) + if type(spectral_cov) not in (list, tuple): + # Use the same covariance for all components + spectral_cov = [spectral_cov for n in range(nc)] + # else: + spectral_inv_cov = [None for n in range(nc)] + ndim = np.array(spectral_cov[0]).ndim + for n in range(nc): + spectral_cov[n] = np.array(spectral_cov[n]) + assert spectral_cov[n].ndim == ndim + if spectral_cov[n].ndim < 2: + if spectral_cov[n].ndim == 0: + cov = np.ones(nw0_) * spectral_cov[n] + else: + cov = S0e2i.dot(spectral_cov[n]) + inv = 1.0 / cov + spectral_inv_cov[n] = np.reshape(inv, (1, -1)) + spectral_cov[n] = np.reshape(cov, (1, -1)) + # else: + + # Tensor of nc (inverse) variance vectors or covariance matrices + spectral_cov = np.concatenate(spectral_cov, axis=0) + spectral_inv_cov = np.concatenate(spectral_inv_cov, axis=0) + + # Spectral guess must be a scalar, a vector (nw0), or a list of those + # Interpolate it to the internal grid (nw0_) and reshape to (nc, nw0_) + # if spectral_guess is not None: + # else: + + # Tempering schedule + if nlogT == 1: + T = np.array([10 ** logTf]) + elif logT0 == logTf: + T = logTf * np.ones(nlogT) + else: + T = np.logspace(logT0, logTf, nlogT) + + # Are we lucky enough to do a purely linear solve for the map? + linear = (not normalized) or (baseline is not None) + + processed_inputs = {} + + processed_inputs["flux"] = flux + processed_inputs["flux_err"] = flux_err + processed_inputs["spatial_mean"] = spatial_mean + processed_inputs["spatial_cov"] = spatial_cov + processed_inputs["spatial_inv_cov"] = spatial_inv_cov + processed_inputs["spectral_mean"] = spectral_mean + processed_inputs["spectral_cov"] = spectral_cov + processed_inputs["spectral_inv_cov"] = spectral_inv_cov + processed_inputs["T"] = T + processed_inputs["linear"] = linear + + return processed_inputs + + +def get_kT(inc, theta, veq, u): + """ + Get the kernels at an array of angular phases `theta`. + """ + + # enforce_bounds() + # get_x() + # get_rT() + # get_kT0() + # F() #? + # right_project() + + # many theano operations + + pass + + +def get_D_fixed_spectrum(self, inc, theta, veq, u, spectrum): + """ + Return the Doppler matrix for a fixed spectrum. + + This routine is heavily optimized, and can often be the fastest + way to compute the flux! + + """ + # # Get the convolution kernels + # kT = self.get_kT(inc, theta, veq, u) + + # # The dot product is just a 2d convolution! + # product = tt.nnet.conv2d( + # tt.reshape(spectrum, (self.nc, 1, 1, self.nwp)), + # tt.reshape(kT, (self.nt * self.Ny, 1, 1, self.nk)), + # border_mode="valid", + # filter_flip=False, + # input_shape=(self.nc, 1, 1, self.nwp), + # filter_shape=(self.nt * self.Ny, 1, 1, self.nk), + # ) + # product = tt.reshape(product, (self.nc, self.nt, self.Ny, self.nw)) + # product = tt.swapaxes(product, 1, 2) + # product = tt.reshape(product, (self.Ny * self.nc, self.nt * self.nw)) + # product = tt.transpose(product) + # return product + + pass + + +def sparse_dot(A, B): + """ + Performs matrix multiplication, optimising computation time by utilising sparse matrices. + """ + + # if scipy.sparse.issparse(A): + # return A.dot(B) + # elif scipy.sparse.issparse(B): + # return (B.T.dot(A.T)).T + # else: + # raise ValueError("At least one input must be sparse.") + + # Guess at jax versions: + if jax.issparse(A): + return A.dot(B) + elif jax.issparse(B): + return (B.T.dot(A.T)).T + else: + raise ValueError("At least one input must be sparse.") + + +def design_matrix(theta, spectrum_, fix_spectrum=True, fix_map=False): + """ + Return the Doppler imaging design matrix. + + This matrix dots into the spectral map to yield the model for the + observed spectral timeseries (the ``flux``). + """ + # theta = _get_default_theta(theta) # this is just undoing what get_S() did! + + # # Compute the Doppler operator + # if fix_spectrum: + + # # Fixed spectrum (dense) + # D = get_D_fixed_spectrum(_inc, theta, _veq, _u, _spectrum) + # # Interpolate to the output grid + # if _interp: + # D = map._math.sparse_dot(_Si2eBlk, D) + + # return D + + pass + + +# S = <603x300 sparse matrix with 1206 stored elements> +def _get_S(theta, _angle_factor, fix_spectrum, spectrum_): + + theta = theta / _angle_factor + dm = design_matrix(theta=theta, fix_spectrum=True) + + return dm + + +def solve_for_map_linear( + spatial_mean, spatial_inv_cov, flux_err, nt, nw, baseline_var, T, flux, S, nc, Ny, + ): + """ + Solve for `y` linearly, given a baseline or unnormalized data. + """ + # Reshape the priors + mu = np.reshape(np.transpose(spatial_mean), (-1)) + if spatial_inv_cov.ndim == 2: + invL = np.reshape(np.transpose(spatial_inv_cov), (-1)) + + # Ensure the flux error is a vector + if flux_err.ndim == 0: + flux_err = flux_err * np.ones((nt, nw)) + + # Factorised data covariance + if baseline_var == 0: + cho_C = np.reshape(np.sqrt(T) * flux_err, (-1,)) + + # Unroll the data into a vector + flux = np.reshape(flux, (-1,)) + + # Get S + # S = _get_S() # TODO: _get_S() + + # load from data instead of _get_S() + with open("map_solve_S_input.npy", "rb") as f: + S = np.load(f) + + # Solve the L2 problem + # mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) # TODO: map_solve() + + # load from data instead of map_solve() + with open("map_solve_mean_output.npy", "rb") as f: + mean = np.load(f) + with open("map_solve_cho_ycov_output.npy", "rb") as f: + cho_ycov = np.load(f) + + y = np.transpose(np.reshape(mean, (nc, Ny))) + + return y, cho_ycov + + +def get_default_theta(theta, _angle_factor): + + return theta * _angle_factor + + +def solve_bilinear( + flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, # pass to process_inputs() + fix_spectrum, baseline_var, S, # pass to solve_for_map_linear() + # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? + ): + + # reset() - if have a class with self attributes. + + processed_inputs = process_inputs( + flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, + normalized=False, flux_err=flux_err, + ) + + linear = processed_inputs["linear"] + flux = processed_inputs["flux"] # doesn't change in this case + spatial_mean = processed_inputs["spatial_mean"] + spatial_inv_cov = processed_inputs["spatial_inv_cov"] + flux_err = processed_inputs["flux_err"] + # T = processed_inputs["T"] + + # print(f"------ linear: {linear}") # TODO: Remove after debugging. + + if fix_spectrum: + if linear: + solution = solve_for_map_linear( + spatial_mean, spatial_inv_cov, flux_err, nt, nw, baseline_var, 1, flux, S, nc, Ny, + ) + # else: + # else: + + return solution + + +def solve( + flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, fix_spectrum, baseline_var, S, + theta, _angle_factor, + solver="bilinear", + # y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? + ): + """ + Iteratively solves the bilinear or nonlinear problem for the spatial + and/or spectral map given a spectral timeseries. + """ + + # Used to calculate S. + theta = get_default_theta(theta, _angle_factor) + + if solver.lower().startswith("bi"): + solution = solve_bilinear( + flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, # pass to process_inputs() + fix_spectrum, baseline_var, S, # pass to solve_for_map_linear() + # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? + ) + # else: + + return solution diff --git a/python/jaxodi/test_fig7_functions.py b/python/jaxodi/test_fig7_functions.py new file mode 100644 index 0000000..d994b33 --- /dev/null +++ b/python/jaxodi/test_fig7_functions.py @@ -0,0 +1,349 @@ +""" +Tests for Figure 7's map.solve() and dependencies. + +19/7 Update +----------- + +test_get_default_theta | pass by call | +test_process_inputs | pass by data | +test_map_solve | fail by call | +test_solve_for_map_linear | pass by data fail by call | +test_solve_bilinear | pass by data | +test_solve | pass by call | + + +TODO: + + - load saved data in data() + - create separate tests for data loads and function calls + + +""" + +import pytest + +# import jax +import jax.numpy as jnp +import numpy as np +import scipy + +# from jaxoplanet.types import Array +from jaxoplanet.test_utils import assert_allclose + +# To generate variables --- +# import paparazzi +import starry +from starry._core.math import greedy_linalg + +from jaxodi.paparazzi.src.scripts.utils.generate import generate_data + +from jaxodi.fig7_functions import ( + cho_solve, + map_solve, + process_inputs, + solve_for_map_linear, + get_default_theta, + solve_bilinear, + solve +) + + +# TODO: load saved data. +@pytest.fixture(autouse=True) +def data(): + + data = generate_data() + + # truth + # y_true = data["truths"]["y"] + # spectrum_true = data["truths"]["spectrum"] + + # data + theta = data["data"]["theta"] # np.linspace(-180, 180, nt=16, endpoint=False) + flux = data["data"]["flux0"] + flux_err = data["data"]["flux0_err"] + + # kwargs + wav = data["kwargs"]["wav"] # array (70,1) + wav0 = data["kwargs"]["wav0"] + + # Instantiate the map + map = starry.DopplerMap(lazy=False, **data["kwargs"]) + map.spectrum = data["truths"]["spectrum"] + for n in range(map.udeg): + map[1 + n] = data["props"]["u"][n] + + # Provided values + # ydeg = 15 + # udeg = 2 + + # veq = 60000 + # inc = 40 + + vsini_max = 50000 + + # nc = 1 + # nt = 16 + + # Ny = 256 + # Nu = 3 + N = 324 + nw = 70 + # nw0 = 300 + # nw0_ = 603 + + _angle_factor = np.pi / 180 + + # ------------------------ + # Much code for setting S. + nw = 351 + vsini_max = 50000 + nt = 16 + wav1 = np.min(wav) + wav2 = np.max(wav) + wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) + log_wav = np.linspace(np.log(wav1 / wavr), np.log(wav2 / wavr), nw) + wav_int = wavr * np.exp(log_wav) + interp_tol = 1e-12 + _clight = 299792458.0 # m/s + dlam = log_wav[1] - log_wav[0] + betasini_max = vsini_max / _clight + hw = np.array( + np.ceil( + 0.5 + * np.abs(np.log((1 + betasini_max) / (1 - betasini_max))) + / dlam + ), + dtype="int32", + ) + x = np.arange(0, hw + 1) * dlam + pad_l = log_wav[0] - hw * dlam + x[:-1] + pad_r = log_wav[-1] + x[1:] + log_wav0_int = np.concatenate([pad_l, log_wav, pad_r]) + wav0_int = wavr * np.exp(log_wav0_int) + nwp = len(log_wav0_int) + wav0 = np.array(wav0) + nw0 = len(wav0) + S = map._get_spline_operator(wav_int, wav) + S[np.abs(S) < interp_tol] = 0 + S = scipy.sparse.csr_matrix(S) + S = map._get_spline_operator(wav0_int, wav0) + S[np.abs(S) < interp_tol] = 0 + S = scipy.sparse.csr_matrix(S) + S = map._get_spline_operator(wav, wav_int) + S[np.abs(S) < interp_tol] = 0 + S = scipy.sparse.csr_matrix(S) + S = map._get_spline_operator(wav0, wav0_int) + S[np.abs(S) < interp_tol] = 0 + S = scipy.sparse.csr_matrix(S) + # ---------------------------- + + return (map, theta, _angle_factor, flux, flux_err, wav, wav0, S) + + +# 19/7 -> load data -> +# -> call starry -> pass +def test_get_default_theta(data): + + # Load the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + + # Get calculated outputs. + calc_theta = get_default_theta(theta, _angle_factor) + + # NOTE: To get expected outputs, can either call starry function OR load saved data. + # Get expected outputs. + exp_theta = map._get_default_theta(theta) + # Compare calculated and expected results. + assert_allclose(calc_theta, exp_theta) + + +# 19/7 -> load data -> pass +# -> call starry -> +def test_process_inputs(data): + + # Load the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + + # Get calculated outputs. + calc_processed = process_inputs( + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, normalized=False, flux_err=flux_err, + ) + + # NOTE: To get expected outputs, can either call starry function OR load saved data. + # Get expected outputs. + map._solver.reset() + map._solver.process_inputs( + flux, normalized=False, flux_err=flux_err, + ) + + # TODO: What is this for? Do I still need it? + # print(f"test_process_inputs::exp_linear: {map._solver.linear}") + # print(f"test_process_inputs::calc_linear: {calc_processed['linear']}") + + # Compare calculated and expected results. + assert_allclose(map._solver.flux, calc_processed['flux']) + assert_allclose(map._solver.flux_err, calc_processed['flux_err']) + assert_allclose(map._solver.spatial_mean, calc_processed['spatial_mean']) + assert_allclose(map._solver.spatial_cov, calc_processed['spatial_cov']) + assert_allclose(map._solver.spatial_inv_cov, calc_processed['spatial_inv_cov']) + assert_allclose(map._solver.spectral_mean, calc_processed['spectral_mean']) + assert_allclose(map._solver.spectral_cov, calc_processed['spectral_cov']) + assert_allclose(map._solver.spectral_inv_cov, calc_processed['spectral_inv_cov']) + assert_allclose(map._solver.T, calc_processed['T']) + assert_allclose(map._solver.linear, calc_processed['linear']) + + +# TODO: write test +def test_cho_solve(data): + + # cho_solve() + pass + + +# 19/7 -> load data -> +# -> call starry -> fail +def test_map_solve(data): + + # Load the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + with open("map_solve_S_input.npy", "rb") as f: + S = np.load(f) + with open("map_solve_flux_input.npy", "rb") as f: + flux = np.load(f) + with open("map_solve_cho_C_input.npy", "rb") as f: + cho_C = np.load(f) + with open("map_solve_mu_input.npy", "rb") as f: + mu = np.load(f) + with open("map_solve_invL_input.npy", "rb") as f: + invL = np.load(f) + + # Convert them to jax arrays? + S = jnp.array(S) + flux = jnp.array(flux) + cho_C = jnp.array(cho_C) + mu = jnp.array(mu) + invL = jnp.array(invL) + + # Get calculated outputs. + calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) + + # NOTE: To get expected outputs, can either call starry function OR load saved data. + + # Get expected outputs. + exp_mean, exp_cho_ycov = greedy_linalg.solve(S, flux, cho_C, mu, invL) + + # TODO: Try loading data. Not sure if this code is correct? + # # Load saved expected outputs. + # with open("map_solve_mean_output.npy", "rb") as f: + # exp_mean = np.load(f) + # with open("map_solve_cho_ycov_output.npy", "rb") as f: + # exp_cho_ycov = np.load(f) + + # Compare calculated and expected results. + assert_allclose(calc_mean, exp_mean) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 19/7 -> load data -> pass +# -> call starry -> fail +def test_solve_for_map_linear(data): + + # Load the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + T = 1 + baseline_var = 0 + S = "dummy" + + # Process the inputs. + processed_inputs = process_inputs( + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, + normalized=False, flux_err=flux_err, + ) + flux = processed_inputs["flux"] # doesn't change in this case + spatial_mean = processed_inputs["spatial_mean"] + spatial_inv_cov = processed_inputs["spatial_inv_cov"] + flux_err = processed_inputs["flux_err"] + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve_for_map_linear( + spatial_mean, spatial_inv_cov, flux_err, map.nt, map.nw, baseline_var, T, flux, S, map.nc, map.Ny, + ) + + # NOTE: To get expected outputs, can either call starry function OR load saved data. + + # TODO: Fix spectrum is None error. + # Get expected outputs. + # map._solver.theta = theta * _angle_factor + # map._solver.process_inputs(flux) + # exp_y, exp_cho_ycov = map._solver.solve_for_map_linear() # --> ERROR: self.spectrum is None when design_matrix() is called + + # Load saved expected outputs. + with open("map_solve_mean_output.npy", "rb") as f: + exp_mean = np.load(f) + with open("map_solve_cho_ycov_output.npy", "rb") as f: + exp_cho_ycov = np.load(f) + exp_y = np.transpose(np.reshape(exp_mean, (map.nc, map.Ny))) + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_y) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 19/7 -> load data -> pass +# -> call starry -> ? +def test_solve_bilinear(data): + + # Process the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + fix_spectrum = True + baseline_var = 0 + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve_bilinear( + flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, + fix_spectrum, baseline_var, S, + ) + + # NOTE: To get expected outputs, can either call starry function OR load saved data. + # Get expected outputs. + # soln = map._solver.solve_bilinear( + # flux, theta, map.y, map.spectrum_, map._veq, map._inc, map._u, **kwargs # TODO: fix inputs + # ) + # Load saved expected outputs. + with open("map_solve_mean_output.npy", "rb") as f: + exp_mean = np.load(f) + with open("map_solve_cho_ycov_output.npy", "rb") as f: + exp_cho_ycov = np.load(f) + exp_y = np.transpose(np.reshape(exp_mean, (map.nc, map.Ny))) + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_y) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 19/7 -> load data -> +# -> call starry -> pass +def test_solve(data): + + # Process the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + fix_spectrum = True + baseline_var = 0 + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve( + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, + fix_spectrum, baseline_var, S, + theta, _angle_factor, + ) + + # NOTE: To get expected outputs, can either call starry function OR load saved data. + # Get expected outputs. + exp_solution = map.solve( + flux, theta=theta, normalized=False, fix_spectrum=True, flux_err=flux_err, quiet="false" + ) + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_solution["y"]) + assert_allclose(calc_cho_ycov, exp_solution["cho_ycov"]) From f9de4c619917871fc22508e1d08c0a7df1711172 Mon Sep 17 00:00:00 2001 From: alserene Date: Mon, 22 Jul 2024 12:20:28 +1000 Subject: [PATCH 09/24] Replace np with jnp in functions. --- python/jaxodi/fig7_functions.py | 179 +++++++++++++++++---------- python/jaxodi/test_fig7_functions.py | 2 +- 2 files changed, 113 insertions(+), 68 deletions(-) diff --git a/python/jaxodi/fig7_functions.py b/python/jaxodi/fig7_functions.py index 959cca1..f1a9ea8 100644 --- a/python/jaxodi/fig7_functions.py +++ b/python/jaxodi/fig7_functions.py @@ -86,11 +86,17 @@ def map_solve( W += LInv LInvmu = jnp.dot(LInv, mu) print(f"W: {W}") + # with open("solve_midpoint_W.npy", "rb") as f: + # exp_W = np.load(f) + # print(f"exp_W: {cho_W}") print(f"LInvmu: {LInvmu}") # Compute the max like y and its covariance matrix cho_W = jax.scipy.linalg.cholesky(W, lower=True) - print(f"cho_W: {cho_W}") + # print(f"cho_W: {cho_W}") + # with open("solve_midpoint_cho_W.npy", "rb") as f: + # exp_cho_W = np.load(f) + # print(f"exp_cho_W: {cho_W}") M = cho_solve(cho_W, jnp.transpose(CInvX)) # print(f"M: {M}") yhat = jnp.dot(M, flux) + cho_solve(cho_W, LInvmu) @@ -104,16 +110,16 @@ def map_solve( def process_inputs( - flux, - nt, - nw, - nc, - Ny, - nw0, - nw0_, - S0e2i, - flux_err=None, - normalized=True, + flux: Array, + nt: int, + nw: int, + nc: int, + Ny: int, + nw0: int, + nw0_: int, + S0e2i: Array, + flux_err: float=None, + normalized: bool=True, baseline=None, spatial_mean=None, spatial_cov=None, @@ -128,8 +134,8 @@ def process_inputs( if flux_err is None: flux_err = 1e-4 if spatial_mean is None: - spatial_mean = np.zeros(Ny) - spatial_mean[0] = 1.0 + spatial_mean = jnp.zeros(Ny) + spatial_mean = spatial_mean.at[0].set(1.0) if spatial_cov is None: spatial_cov = 1e-4 if spectral_mean is None: @@ -148,12 +154,12 @@ def process_inputs( # Flux must be a matrix (nt, nw) # if nt == 1: # else: - assert np.array_equal( - np.shape(flux), np.array([nt, nw]) + assert jnp.array_equal( + jnp.shape(flux), jnp.array([nt, nw]) ), "Invalid shape for `flux`." # Flux error may be a scalar, a vector, or a matrix (nt, nw) - flux_err = np.array(flux_err) + flux_err = jnp.array(flux_err) # if flux_err.ndim == 0: # flux_err = flux_err # else: @@ -165,12 +171,12 @@ def process_inputs( spatial_mean = [spatial_mean for n in range(nc)] # else: for n in range(nc): - spatial_mean[n] = np.array(spatial_mean[n]) + spatial_mean[n] = jnp.array(spatial_mean[n]) assert spatial_mean[n].ndim < 2 - spatial_mean[n] = np.reshape( - spatial_mean[n] * np.ones(Ny), (-1, 1) + spatial_mean[n] = jnp.reshape( + spatial_mean[n] * jnp.ones(Ny), (-1, 1) ) - spatial_mean = np.concatenate(spatial_mean, axis=-1) + spatial_mean = jnp.concatenate(spatial_mean, axis=-1) # Spatial cov may be a scalar, a vector, a matrix (Ny, Ny), # or a list of those. Invert it and reshape to a matrix of @@ -181,22 +187,22 @@ def process_inputs( spatial_cov = [spatial_cov for n in range(nc)] # else: spatial_inv_cov = [None for n in range(nc)] - ndim = np.array(spatial_cov[0]).ndim + ndim = jnp.array(spatial_cov[0]).ndim for n in range(nc): - spatial_cov[n] = np.array(spatial_cov[n]) + spatial_cov[n] = jnp.array(spatial_cov[n]) assert spatial_cov[n].ndim == ndim if spatial_cov[n].ndim < 2: - spatial_inv_cov[n] = np.reshape( - np.ones(Ny) / spatial_cov[n], (-1, 1) + spatial_inv_cov[n] = jnp.reshape( + jnp.ones(Ny) / spatial_cov[n], (-1, 1) ) - spatial_cov[n] = np.reshape( - np.ones(Ny) * spatial_cov[n], (-1, 1) + spatial_cov[n] = jnp.reshape( + jnp.ones(Ny) * spatial_cov[n], (-1, 1) ) # else: # Tensor of nc (inverse) variance vectors or covariance matrices - spatial_cov = np.concatenate(spatial_cov, axis=-1) - spatial_inv_cov = np.concatenate(spatial_inv_cov, axis=-1) + spatial_cov = jnp.concatenate(spatial_cov, axis=-1) + spatial_inv_cov = jnp.concatenate(spatial_inv_cov, axis=-1) # Baseline must be a vector (nt,) # if baseline is not None: @@ -209,13 +215,13 @@ def process_inputs( spectral_mean = [spectral_mean for n in range(nc)] # else: for n in range(nc): - spectral_mean[n] = np.array(spectral_mean[n]) + spectral_mean[n] = jnp.array(spectral_mean[n]) assert spectral_mean[n].ndim < 2 - spectral_mean[n] = np.reshape( - spectral_mean[n] * np.ones(nw0), (-1, 1) + spectral_mean[n] = jnp.reshape( + spectral_mean[n] * jnp.ones(nw0), (-1, 1) ) spectral_mean[n] = S0e2i.dot(spectral_mean[n]).T - spectral_mean = np.concatenate(spectral_mean, axis=0) + spectral_mean = jnp.concatenate(spectral_mean, axis=0) # Spectral cov may be a scalar, a vector, a matrix (nw0, nw0), # or a list of those. Interpolate it to the internal grid, @@ -227,23 +233,23 @@ def process_inputs( spectral_cov = [spectral_cov for n in range(nc)] # else: spectral_inv_cov = [None for n in range(nc)] - ndim = np.array(spectral_cov[0]).ndim + ndim = jnp.array(spectral_cov[0]).ndim for n in range(nc): - spectral_cov[n] = np.array(spectral_cov[n]) + spectral_cov[n] = jnp.array(spectral_cov[n]) assert spectral_cov[n].ndim == ndim if spectral_cov[n].ndim < 2: if spectral_cov[n].ndim == 0: - cov = np.ones(nw0_) * spectral_cov[n] + cov = jnp.ones(nw0_) * spectral_cov[n] else: cov = S0e2i.dot(spectral_cov[n]) inv = 1.0 / cov - spectral_inv_cov[n] = np.reshape(inv, (1, -1)) - spectral_cov[n] = np.reshape(cov, (1, -1)) + spectral_inv_cov[n] = jnp.reshape(inv, (1, -1)) + spectral_cov[n] = jnp.reshape(cov, (1, -1)) # else: # Tensor of nc (inverse) variance vectors or covariance matrices - spectral_cov = np.concatenate(spectral_cov, axis=0) - spectral_inv_cov = np.concatenate(spectral_inv_cov, axis=0) + spectral_cov = jnp.concatenate(spectral_cov, axis=0) + spectral_inv_cov = jnp.concatenate(spectral_inv_cov, axis=0) # Spectral guess must be a scalar, a vector (nw0), or a list of those # Interpolate it to the internal grid (nw0_) and reshape to (nc, nw0_) @@ -252,11 +258,11 @@ def process_inputs( # Tempering schedule if nlogT == 1: - T = np.array([10 ** logTf]) + T = jnp.array([10 ** logTf]) elif logT0 == logTf: - T = logTf * np.ones(nlogT) + T = logTf * jnp.ones(nlogT) else: - T = np.logspace(logT0, logTf, nlogT) + T = jnp.logspace(logT0, logTf, nlogT) # Are we lucky enough to do a purely linear solve for the map? linear = (not normalized) or (baseline is not None) @@ -377,58 +383,84 @@ def _get_S(theta, _angle_factor, fix_spectrum, spectrum_): def solve_for_map_linear( - spatial_mean, spatial_inv_cov, flux_err, nt, nw, baseline_var, T, flux, S, nc, Ny, - ): + spatial_mean: Array, + spatial_inv_cov: Array, + flux_err: float, + nt: int, + nw: int, + baseline_var: int, + T, + flux: Array, + S: Array, + nc: int, + Ny: int, + ) -> tuple[Array, Array]: """ Solve for `y` linearly, given a baseline or unnormalized data. """ # Reshape the priors - mu = np.reshape(np.transpose(spatial_mean), (-1)) + mu = jnp.reshape(jnp.transpose(spatial_mean), (-1)) if spatial_inv_cov.ndim == 2: - invL = np.reshape(np.transpose(spatial_inv_cov), (-1)) + invL = jnp.reshape(jnp.transpose(spatial_inv_cov), (-1)) # Ensure the flux error is a vector if flux_err.ndim == 0: - flux_err = flux_err * np.ones((nt, nw)) + flux_err = flux_err * jnp.ones((nt, nw)) # Factorised data covariance if baseline_var == 0: - cho_C = np.reshape(np.sqrt(T) * flux_err, (-1,)) + cho_C = jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)) # Unroll the data into a vector - flux = np.reshape(flux, (-1,)) + flux = jnp.reshape(flux, (-1,)) # Get S # S = _get_S() # TODO: _get_S() # load from data instead of _get_S() with open("map_solve_S_input.npy", "rb") as f: - S = np.load(f) + S = jnp.load(f) # Solve the L2 problem # mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) # TODO: map_solve() # load from data instead of map_solve() with open("map_solve_mean_output.npy", "rb") as f: - mean = np.load(f) + mean = jnp.load(f) with open("map_solve_cho_ycov_output.npy", "rb") as f: - cho_ycov = np.load(f) + cho_ycov = jnp.load(f) - y = np.transpose(np.reshape(mean, (nc, Ny))) + y = jnp.transpose(jnp.reshape(mean, (nc, Ny))) return y, cho_ycov -def get_default_theta(theta, _angle_factor): +def get_default_theta( + theta: Array, + _angle_factor: float, + ) -> Array: return theta * _angle_factor def solve_bilinear( - flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, # pass to process_inputs() - fix_spectrum, baseline_var, S, # pass to solve_for_map_linear() - # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? - ): + # pass to process_inputs() + flux: Array, + nt: int, + nw: int, + nc: int, + Ny: int, + nw0: int, + nw0_: int, + S0e2i: Array, + flux_err: float, + # pass to solve_for_map_linear() + fix_spectrum: bool, + baseline_var: int, + S: Array, + # not being passed in yet - all for S? + # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, + ) -> tuple[Array, Array]: # reset() - if have a class with self attributes. @@ -448,21 +480,34 @@ def solve_bilinear( if fix_spectrum: if linear: - solution = solve_for_map_linear( + y, cho_ycov = solve_for_map_linear( spatial_mean, spatial_inv_cov, flux_err, nt, nw, baseline_var, 1, flux, S, nc, Ny, ) # else: # else: - return solution + return y, cho_ycov def solve( - flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, fix_spectrum, baseline_var, S, - theta, _angle_factor, - solver="bilinear", - # y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? - ): + flux: Array, + nt: int, + nw: int, + nc: int, + Ny: int, + nw0: int, + nw0_: int, + S0e2i: Array, + flux_err: float, + fix_spectrum: bool, + baseline_var: int, + S: Array, + theta: Array, + _angle_factor: float, + solver: str="bilinear", + # not being passed in yet - all for S? + # y, spectrum_, veq, inc, u, normalized, + ) -> tuple[Array, Array]: """ Iteratively solves the bilinear or nonlinear problem for the spatial and/or spectral map given a spectral timeseries. @@ -472,11 +517,11 @@ def solve( theta = get_default_theta(theta, _angle_factor) if solver.lower().startswith("bi"): - solution = solve_bilinear( + y, cho_ycov = solve_bilinear( flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, # pass to process_inputs() fix_spectrum, baseline_var, S, # pass to solve_for_map_linear() # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? ) # else: - return solution + return y, cho_ycov diff --git a/python/jaxodi/test_fig7_functions.py b/python/jaxodi/test_fig7_functions.py index d994b33..3f080f7 100644 --- a/python/jaxodi/test_fig7_functions.py +++ b/python/jaxodi/test_fig7_functions.py @@ -27,7 +27,7 @@ import numpy as np import scipy -# from jaxoplanet.types import Array +from jaxoplanet.types import Array from jaxoplanet.test_utils import assert_allclose # To generate variables --- From 97b374601afdcac7915280d054b8dfa3112a392c Mon Sep 17 00:00:00 2001 From: alserene Date: Mon, 22 Jul 2024 16:11:08 +1000 Subject: [PATCH 10/24] Fix tests and split into starry vs data for comparisons. --- python/jaxodi/fig7_functions.py | 34 +---- python/jaxodi/test_fig7_functions.py | 208 +++++++++++++++++++-------- 2 files changed, 153 insertions(+), 89 deletions(-) diff --git a/python/jaxodi/fig7_functions.py b/python/jaxodi/fig7_functions.py index f1a9ea8..c1baf20 100644 --- a/python/jaxodi/fig7_functions.py +++ b/python/jaxodi/fig7_functions.py @@ -71,11 +71,10 @@ def map_solve( CInvX = jnp.dot(jnp.diag(1 / cho_C**2), X) else: CInvX = cho_solve(cho_C, X) - print(f"CInvX: {CInvX}") - # breakpoint() + # Compute W = X^T . C^-1 . X + L^-1 W = jnp.dot(jnp.transpose(X), CInvX) - print(f"W: {W}") + # If LInv is a scalar or a 1-dimensional array, increment the # diagonal elements of W with the values from LInv. if LInv.ndim == 0 or LInv.ndim == 1: @@ -85,26 +84,13 @@ def map_solve( else: W += LInv LInvmu = jnp.dot(LInv, mu) - print(f"W: {W}") - # with open("solve_midpoint_W.npy", "rb") as f: - # exp_W = np.load(f) - # print(f"exp_W: {cho_W}") - print(f"LInvmu: {LInvmu}") # Compute the max like y and its covariance matrix cho_W = jax.scipy.linalg.cholesky(W, lower=True) - # print(f"cho_W: {cho_W}") - # with open("solve_midpoint_cho_W.npy", "rb") as f: - # exp_cho_W = np.load(f) - # print(f"exp_cho_W: {cho_W}") M = cho_solve(cho_W, jnp.transpose(CInvX)) - # print(f"M: {M}") yhat = jnp.dot(M, flux) + cho_solve(cho_W, LInvmu) - # print(f"yhat: {yhat}") ycov = cho_solve(cho_W, jnp.eye(cho_W.shape[0])) - # print(f"ycov: {ycov}") cho_ycov = jax.scipy.linalg.cholesky(ycov, lower=True) - # print(f"cho_ycov: {cho_ycov}") return yhat, cho_ycov @@ -422,13 +408,7 @@ def solve_for_map_linear( S = jnp.load(f) # Solve the L2 problem - # mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) # TODO: map_solve() - - # load from data instead of map_solve() - with open("map_solve_mean_output.npy", "rb") as f: - mean = jnp.load(f) - with open("map_solve_cho_ycov_output.npy", "rb") as f: - cho_ycov = jnp.load(f) + mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) y = jnp.transpose(jnp.reshape(mean, (nc, Ny))) @@ -454,6 +434,7 @@ def solve_bilinear( nw0_: int, S0e2i: Array, flux_err: float, + normalized: bool, # pass to solve_for_map_linear() fix_spectrum: bool, baseline_var: int, @@ -466,7 +447,7 @@ def solve_bilinear( processed_inputs = process_inputs( flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, - normalized=False, flux_err=flux_err, + normalized=normalized, flux_err=flux_err, ) linear = processed_inputs["linear"] @@ -476,8 +457,6 @@ def solve_bilinear( flux_err = processed_inputs["flux_err"] # T = processed_inputs["T"] - # print(f"------ linear: {linear}") # TODO: Remove after debugging. - if fix_spectrum: if linear: y, cho_ycov = solve_for_map_linear( @@ -499,6 +478,7 @@ def solve( nw0_: int, S0e2i: Array, flux_err: float, + normalized: bool, fix_spectrum: bool, baseline_var: int, S: Array, @@ -518,7 +498,7 @@ def solve( if solver.lower().startswith("bi"): y, cho_ycov = solve_bilinear( - flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, # pass to process_inputs() + flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, normalized, # pass to process_inputs() fix_spectrum, baseline_var, S, # pass to solve_for_map_linear() # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? ) diff --git a/python/jaxodi/test_fig7_functions.py b/python/jaxodi/test_fig7_functions.py index 3f080f7..0b71149 100644 --- a/python/jaxodi/test_fig7_functions.py +++ b/python/jaxodi/test_fig7_functions.py @@ -1,28 +1,15 @@ """ Tests for Figure 7's map.solve() and dependencies. -19/7 Update ------------ - -test_get_default_theta | pass by call | -test_process_inputs | pass by data | -test_map_solve | fail by call | -test_solve_for_map_linear | pass by data fail by call | -test_solve_bilinear | pass by data | -test_solve | pass by call | - - TODO: - load saved data in data() - - create separate tests for data loads and function calls - """ import pytest -# import jax +import jax import jax.numpy as jnp import numpy as np import scipy @@ -47,6 +34,7 @@ solve ) +jax.config.update("jax_enable_x64", True) # TODO: load saved data. @pytest.fixture(autouse=True) @@ -141,9 +129,14 @@ def data(): return (map, theta, _angle_factor, flux, flux_err, wav, wav0, S) -# 19/7 -> load data -> -# -> call starry -> pass -def test_get_default_theta(data): +# 19/7 -> +def test_get_default_theta_against_data(data): + + pass + + +# 22/7 -> pass +def test_get_default_theta_compare_stary(data): # Load the inputs. map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data @@ -151,16 +144,21 @@ def test_get_default_theta(data): # Get calculated outputs. calc_theta = get_default_theta(theta, _angle_factor) - # NOTE: To get expected outputs, can either call starry function OR load saved data. # Get expected outputs. exp_theta = map._get_default_theta(theta) + # Compare calculated and expected results. assert_allclose(calc_theta, exp_theta) -# 19/7 -> load data -> pass -# -> call starry -> -def test_process_inputs(data): +# 22/7 -> +def test_process_inputs_against_data(data): + + pass + + +# 22/7 -> pass +def test_process_inputs_compare_starry(data): # Load the inputs. map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data @@ -177,10 +175,6 @@ def test_process_inputs(data): flux, normalized=False, flux_err=flux_err, ) - # TODO: What is this for? Do I still need it? - # print(f"test_process_inputs::exp_linear: {map._solver.linear}") - # print(f"test_process_inputs::calc_linear: {calc_processed['linear']}") - # Compare calculated and expected results. assert_allclose(map._solver.flux, calc_processed['flux']) assert_allclose(map._solver.flux_err, calc_processed['flux_err']) @@ -194,16 +188,15 @@ def test_process_inputs(data): assert_allclose(map._solver.linear, calc_processed['linear']) -# TODO: write test +# TODO: how to get inputs/outputs to compare against? def test_cho_solve(data): # cho_solve() pass -# 19/7 -> load data -> -# -> call starry -> fail -def test_map_solve(data): +# 22/7 -> pass +def test_map_solve_against_data(data): # Load the inputs. map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data @@ -228,26 +221,53 @@ def test_map_solve(data): # Get calculated outputs. calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) - # NOTE: To get expected outputs, can either call starry function OR load saved data. + # Load saved expected outputs. + with open("map_solve_mean_output.npy", "rb") as f: + exp_mean = np.load(f) + with open("map_solve_cho_ycov_output.npy", "rb") as f: + exp_cho_ycov = np.load(f) + + # Compare calculated and expected results. + assert_allclose(calc_mean, exp_mean) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 22/7 -> pass +def test_map_solve_compare_starry(data): + + # Load the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + with open("map_solve_S_input.npy", "rb") as f: + S = np.load(f) + with open("map_solve_flux_input.npy", "rb") as f: + flux = np.load(f) + with open("map_solve_cho_C_input.npy", "rb") as f: + cho_C = np.load(f) + with open("map_solve_mu_input.npy", "rb") as f: + mu = np.load(f) + with open("map_solve_invL_input.npy", "rb") as f: + invL = np.load(f) + + # Convert them to jax arrays? + S = jnp.array(S) + flux = jnp.array(flux) + cho_C = jnp.array(cho_C) + mu = jnp.array(mu) + invL = jnp.array(invL) + + # Get calculated outputs. + calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) # Get expected outputs. exp_mean, exp_cho_ycov = greedy_linalg.solve(S, flux, cho_C, mu, invL) - # TODO: Try loading data. Not sure if this code is correct? - # # Load saved expected outputs. - # with open("map_solve_mean_output.npy", "rb") as f: - # exp_mean = np.load(f) - # with open("map_solve_cho_ycov_output.npy", "rb") as f: - # exp_cho_ycov = np.load(f) - # Compare calculated and expected results. assert_allclose(calc_mean, exp_mean) assert_allclose(calc_cho_ycov, exp_cho_ycov) -# 19/7 -> load data -> pass -# -> call starry -> fail -def test_solve_for_map_linear(data): +# 22/7 -> pass +def test_solve_for_map_linear_against_data(data): # Load the inputs. map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data @@ -270,14 +290,6 @@ def test_solve_for_map_linear(data): spatial_mean, spatial_inv_cov, flux_err, map.nt, map.nw, baseline_var, T, flux, S, map.nc, map.Ny, ) - # NOTE: To get expected outputs, can either call starry function OR load saved data. - - # TODO: Fix spectrum is None error. - # Get expected outputs. - # map._solver.theta = theta * _angle_factor - # map._solver.process_inputs(flux) - # exp_y, exp_cho_ycov = map._solver.solve_for_map_linear() # --> ERROR: self.spectrum is None when design_matrix() is called - # Load saved expected outputs. with open("map_solve_mean_output.npy", "rb") as f: exp_mean = np.load(f) @@ -290,26 +302,60 @@ def test_solve_for_map_linear(data): assert_allclose(calc_cho_ycov, exp_cho_ycov) -# 19/7 -> load data -> pass -# -> call starry -> ? -def test_solve_bilinear(data): +# 22/7 -> pass +def test_solve_for_map_linear_compare_starry(data): + + # Load the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + T = 1 + baseline_var = 0 + S = "dummy" + + # Process the inputs. + processed_inputs = process_inputs( + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, + normalized=False, flux_err=flux_err, + ) + flux = processed_inputs["flux"] # doesn't change in this case + spatial_mean = processed_inputs["spatial_mean"] + spatial_inv_cov = processed_inputs["spatial_inv_cov"] + flux_err = processed_inputs["flux_err"] + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve_for_map_linear( + spatial_mean, spatial_inv_cov, flux_err, map.nt, map.nw, baseline_var, T, flux, S, map.nc, map.Ny, + ) + + # TODO: Fix spectrum is None error. + # Get expected outputs. + map._solver.theta = theta * _angle_factor + map._solver.process_inputs(flux, normalized=False, fix_spectrum=True, flux_err=flux_err) + map._solver.spectrum_ = map.spectrum_ + map._solver.solve_for_map_linear() # --> ERROR: self.spectrum is None when design_matrix() is called + + exp_y = map._solver.y + exp_cho_ycov = map._solver.cho_ycov + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_y) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 22/7 -> pass +def test_solve_bilinear_against_data(data): # Process the inputs. map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data fix_spectrum = True + normalized = False baseline_var = 0 # Get calculated outputs. calc_y, calc_cho_ycov = solve_bilinear( - flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, + flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, normalized, fix_spectrum, baseline_var, S, ) - # NOTE: To get expected outputs, can either call starry function OR load saved data. - # Get expected outputs. - # soln = map._solver.solve_bilinear( - # flux, theta, map.y, map.spectrum_, map._veq, map._inc, map._u, **kwargs # TODO: fix inputs - # ) # Load saved expected outputs. with open("map_solve_mean_output.npy", "rb") as f: exp_mean = np.load(f) @@ -322,18 +368,56 @@ def test_solve_bilinear(data): assert_allclose(calc_cho_ycov, exp_cho_ycov) -# 19/7 -> load data -> -# -> call starry -> pass -def test_solve(data): +# 22/7 -> pass +def test_solve_bilinear_compare_starry(data): + + # Process the inputs. + map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + fix_spectrum = True + normalized = False + baseline_var = 0 + + theta = theta * _angle_factor + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve_bilinear( + flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, normalized, + fix_spectrum, baseline_var, S, + ) + + # Get expected outputs. + metadata = map._solver.solve_bilinear( + flux, theta, map.y, map.spectrum_, map._veq, map._inc, map._u, + fix_spectrum=True, + flux_err=flux_err, + normalized=False, + ) + + exp_y = metadata["y"] + exp_cho_ycov = metadata["cho_ycov"] + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_y) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +def test_solve_against_data(data): + + pass + + +# 22/7 -> pass +def test_solve_compare_starry(data): # Process the inputs. map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data fix_spectrum = True + normalized = False baseline_var = 0 # Get calculated outputs. calc_y, calc_cho_ycov = solve( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, normalized, fix_spectrum, baseline_var, S, theta, _angle_factor, ) @@ -346,4 +430,4 @@ def test_solve(data): # Compare calculated and expected results. assert_allclose(calc_y, exp_solution["y"]) - assert_allclose(calc_cho_ycov, exp_solution["cho_ycov"]) + assert_allclose(calc_cho_ycov, exp_solution["cho_ycov"]) \ No newline at end of file From e092423ead3328b1c9c7f34bd874f33f42fed39c Mon Sep 17 00:00:00 2001 From: alserene Date: Thu, 25 Jul 2024 16:56:15 +1000 Subject: [PATCH 11/24] Jitted the jaxified functions. --- python/jaxodi/fig7_functions.py | 44 ++++++++++++++++----------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/python/jaxodi/fig7_functions.py b/python/jaxodi/fig7_functions.py index c1baf20..a45bdfc 100644 --- a/python/jaxodi/fig7_functions.py +++ b/python/jaxodi/fig7_functions.py @@ -4,7 +4,7 @@ from typing import Tuple # from collections.abc import Callable -# from functools import partial +from functools import partial import jax import jax.numpy as jnp @@ -13,12 +13,6 @@ from jaxoplanet.types import Array -# To generate variables --- -# import paparazzi -import starry - -from jaxodi.paparazzi.src.scripts.utils.generate import generate_data - # Functions # @@ -31,13 +25,13 @@ # - solve -# @jax.jit +@jax.jit def cho_solve(A: Array, b: Array) -> Array: b_ = jax.scipy.linalg.solve_triangular(A, b, lower=True) return jax.scipy.linalg.solve_triangular(jnp.transpose(A), b_, lower=False) -# @jax.jit +@jax.jit def map_solve( X: Array, flux: Array, @@ -95,6 +89,7 @@ def map_solve( return yhat, cho_ycov +@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nw0", "nw0_")) def process_inputs( flux: Array, nt: int, @@ -140,9 +135,9 @@ def process_inputs( # Flux must be a matrix (nt, nw) # if nt == 1: # else: - assert jnp.array_equal( - jnp.shape(flux), jnp.array([nt, nw]) - ), "Invalid shape for `flux`." + # assert jnp.array_equal( + # jnp.shape(flux), jnp.array([nt, nw]) + # ), "Invalid shape for `flux`." # Flux error may be a scalar, a vector, or a matrix (nt, nw) flux_err = jnp.array(flux_err) @@ -251,7 +246,7 @@ def process_inputs( T = jnp.logspace(logT0, logTf, nlogT) # Are we lucky enough to do a purely linear solve for the map? - linear = (not normalized) or (baseline is not None) + linear = jnp.logical_or(jnp.logical_not(normalized), baseline is not None) processed_inputs = {} @@ -368,6 +363,7 @@ def _get_S(theta, _angle_factor, fix_spectrum, spectrum_): return dm +@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny")) def solve_for_map_linear( spatial_mean: Array, spatial_inv_cov: Array, @@ -394,8 +390,12 @@ def solve_for_map_linear( flux_err = flux_err * jnp.ones((nt, nw)) # Factorised data covariance - if baseline_var == 0: - cho_C = jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)) + # cho_C = jnp.where( + # jnp.equal(baseline_var, 0), + # jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)), + # None + # ) + cho_C = jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)) # Unroll the data into a vector flux = jnp.reshape(flux, (-1,)) @@ -415,6 +415,7 @@ def solve_for_map_linear( return y, cho_ycov +@jax.jit def get_default_theta( theta: Array, _angle_factor: float, @@ -423,6 +424,7 @@ def get_default_theta( return theta * _angle_factor +@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nw0", "nw0_", "fix_spectrum")) def solve_bilinear( # pass to process_inputs() flux: Array, @@ -457,17 +459,15 @@ def solve_bilinear( flux_err = processed_inputs["flux_err"] # T = processed_inputs["T"] - if fix_spectrum: - if linear: - y, cho_ycov = solve_for_map_linear( - spatial_mean, spatial_inv_cov, flux_err, nt, nw, baseline_var, 1, flux, S, nc, Ny, - ) - # else: - # else: + # if fix_spectrum and linear: + y, cho_ycov = solve_for_map_linear( + spatial_mean, spatial_inv_cov, flux_err, nt, nw, baseline_var, 1, flux, S, nc, Ny, + ) return y, cho_ycov +@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nw0", "nw0_", "fix_spectrum")) def solve( flux: Array, nt: int, From 2d3df023eda91b6e5bbed68783d76674700bb634 Mon Sep 17 00:00:00 2001 From: alserene Date: Thu, 25 Jul 2024 16:57:07 +1000 Subject: [PATCH 12/24] Cleaned up and jaxified test data. --- python/jaxodi/test_fig7_functions.py | 216 ++++++++++++--------------- 1 file changed, 94 insertions(+), 122 deletions(-) diff --git a/python/jaxodi/test_fig7_functions.py b/python/jaxodi/test_fig7_functions.py index 0b71149..05a41c6 100644 --- a/python/jaxodi/test_fig7_functions.py +++ b/python/jaxodi/test_fig7_functions.py @@ -1,10 +1,5 @@ """ Tests for Figure 7's map.solve() and dependencies. - -TODO: - - - load saved data in data() - """ import pytest @@ -36,9 +31,37 @@ jax.config.update("jax_enable_x64", True) -# TODO: load saved data. + +@pytest.fixture(autouse=True) +def saved_input_data(): + + with open("map_solve_S_input.npy", "rb") as f: + S = jnp.load(f) + with open("map_solve_flux_input.npy", "rb") as f: + flux = jnp.load(f) + with open("map_solve_cho_C_input.npy", "rb") as f: + cho_C = jnp.load(f) + with open("map_solve_mu_input.npy", "rb") as f: + mu = jnp.load(f) + with open("map_solve_invL_input.npy", "rb") as f: + invL = jnp.load(f) + + return (S, flux, cho_C, mu, invL) + + +@pytest.fixture(autouse=True) +def saved_output_data(): + + with open("map_solve_mean_output.npy", "rb") as f: + exp_mean = np.load(f) + with open("map_solve_cho_ycov_output.npy", "rb") as f: + exp_cho_ycov = np.load(f) + + return (exp_mean, exp_cho_ycov) + + @pytest.fixture(autouse=True) -def data(): +def generated_data(): data = generate_data() @@ -75,43 +98,48 @@ def data(): # Ny = 256 # Nu = 3 - N = 324 + # N = 324 nw = 70 # nw0 = 300 # nw0_ = 603 _angle_factor = np.pi / 180 + fix_spectrum = True + normalized = False + baseline_var = 0 + _S0e2i = jnp.array(map._S0e2i.toarray()) + # ------------------------ # Much code for setting S. nw = 351 vsini_max = 50000 - nt = 16 wav1 = np.min(wav) wav2 = np.max(wav) wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) - log_wav = np.linspace(np.log(wav1 / wavr), np.log(wav2 / wavr), nw) + log_wav = jnp.linspace(np.log(wav1 / wavr), jnp.log(wav2 / wavr), nw) wav_int = wavr * np.exp(log_wav) interp_tol = 1e-12 _clight = 299792458.0 # m/s dlam = log_wav[1] - log_wav[0] betasini_max = vsini_max / _clight - hw = np.array( + hw = jnp.array( np.ceil( 0.5 - * np.abs(np.log((1 + betasini_max) / (1 - betasini_max))) + * jnp.abs(jnp.log((1 + betasini_max) / (1 - betasini_max))) / dlam ), dtype="int32", ) - x = np.arange(0, hw + 1) * dlam + x = jnp.arange(0, hw + 1) * dlam pad_l = log_wav[0] - hw * dlam + x[:-1] pad_r = log_wav[-1] + x[1:] - log_wav0_int = np.concatenate([pad_l, log_wav, pad_r]) - wav0_int = wavr * np.exp(log_wav0_int) - nwp = len(log_wav0_int) - wav0 = np.array(wav0) - nw0 = len(wav0) + log_wav0_int = jnp.concatenate([pad_l, log_wav, pad_r]) + wav0_int = wavr * jnp.exp(log_wav0_int) + wav0 = jnp.array(wav0) + # nw0 = len(wav0) + # nwp = len(log_wav0_int) + S = map._get_spline_operator(wav_int, wav) S[np.abs(S) < interp_tol] = 0 S = scipy.sparse.csr_matrix(S) @@ -124,22 +152,24 @@ def data(): S = map._get_spline_operator(wav0, wav0_int) S[np.abs(S) < interp_tol] = 0 S = scipy.sparse.csr_matrix(S) + + S = jnp.array(S.toarray()) # ---------------------------- - - return (map, theta, _angle_factor, flux, flux_err, wav, wav0, S) + + return (map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i) # 19/7 -> -def test_get_default_theta_against_data(data): +def test_get_default_theta_against_data(): pass # 22/7 -> pass -def test_get_default_theta_compare_stary(data): +def test_get_default_theta_compare_stary(generated_data): # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data # Get calculated outputs. calc_theta = get_default_theta(theta, _angle_factor) @@ -152,23 +182,22 @@ def test_get_default_theta_compare_stary(data): # 22/7 -> -def test_process_inputs_against_data(data): +def test_process_inputs_against_data(): pass # 22/7 -> pass -def test_process_inputs_compare_starry(data): +def test_process_inputs_compare_starry(generated_data): # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data # Get calculated outputs. calc_processed = process_inputs( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, normalized=False, flux_err=flux_err, + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, normalized=False, flux_err=flux_err, ) - # NOTE: To get expected outputs, can either call starry function OR load saved data. # Get expected outputs. map._solver.reset() map._solver.process_inputs( @@ -189,71 +218,34 @@ def test_process_inputs_compare_starry(data): # TODO: how to get inputs/outputs to compare against? -def test_cho_solve(data): +def test_cho_solve(): # cho_solve() pass # 22/7 -> pass -def test_map_solve_against_data(data): +def test_map_solve_against_data(saved_input_data, saved_output_data): # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data - with open("map_solve_S_input.npy", "rb") as f: - S = np.load(f) - with open("map_solve_flux_input.npy", "rb") as f: - flux = np.load(f) - with open("map_solve_cho_C_input.npy", "rb") as f: - cho_C = np.load(f) - with open("map_solve_mu_input.npy", "rb") as f: - mu = np.load(f) - with open("map_solve_invL_input.npy", "rb") as f: - invL = np.load(f) - - # Convert them to jax arrays? - S = jnp.array(S) - flux = jnp.array(flux) - cho_C = jnp.array(cho_C) - mu = jnp.array(mu) - invL = jnp.array(invL) + S, flux, cho_C, mu, invL = saved_input_data + + # Load saved expected outputs. + exp_mean, exp_cho_ycov = saved_output_data # Get calculated outputs. calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) - # Load saved expected outputs. - with open("map_solve_mean_output.npy", "rb") as f: - exp_mean = np.load(f) - with open("map_solve_cho_ycov_output.npy", "rb") as f: - exp_cho_ycov = np.load(f) - # Compare calculated and expected results. assert_allclose(calc_mean, exp_mean) assert_allclose(calc_cho_ycov, exp_cho_ycov) # 22/7 -> pass -def test_map_solve_compare_starry(data): +def test_map_solve_compare_starry(saved_input_data): # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data - with open("map_solve_S_input.npy", "rb") as f: - S = np.load(f) - with open("map_solve_flux_input.npy", "rb") as f: - flux = np.load(f) - with open("map_solve_cho_C_input.npy", "rb") as f: - cho_C = np.load(f) - with open("map_solve_mu_input.npy", "rb") as f: - mu = np.load(f) - with open("map_solve_invL_input.npy", "rb") as f: - invL = np.load(f) - - # Convert them to jax arrays? - S = jnp.array(S) - flux = jnp.array(flux) - cho_C = jnp.array(cho_C) - mu = jnp.array(mu) - invL = jnp.array(invL) + S, flux, cho_C, mu, invL = saved_input_data # Get calculated outputs. calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) @@ -267,20 +259,19 @@ def test_map_solve_compare_starry(data): # 22/7 -> pass -def test_solve_for_map_linear_against_data(data): +def test_solve_for_map_linear_against_data(generated_data, saved_output_data): # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data T = 1 - baseline_var = 0 - S = "dummy" + # TODO: S is dummy variable, passed in but not used until my functions are combined with Tiger's. # Process the inputs. processed_inputs = process_inputs( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, normalized=False, flux_err=flux_err, ) - flux = processed_inputs["flux"] # doesn't change in this case + # flux = processed_inputs["flux"] # doesn't change in this case spatial_mean = processed_inputs["spatial_mean"] spatial_inv_cov = processed_inputs["spatial_inv_cov"] flux_err = processed_inputs["flux_err"] @@ -291,10 +282,7 @@ def test_solve_for_map_linear_against_data(data): ) # Load saved expected outputs. - with open("map_solve_mean_output.npy", "rb") as f: - exp_mean = np.load(f) - with open("map_solve_cho_ycov_output.npy", "rb") as f: - exp_cho_ycov = np.load(f) + exp_mean, exp_cho_ycov = saved_output_data exp_y = np.transpose(np.reshape(exp_mean, (map.nc, map.Ny))) # Compare calculated and expected results. @@ -303,20 +291,19 @@ def test_solve_for_map_linear_against_data(data): # 22/7 -> pass -def test_solve_for_map_linear_compare_starry(data): +def test_solve_for_map_linear_compare_starry(generated_data): # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data + map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data T = 1 - baseline_var = 0 - S = "dummy" + # TODO: S is dummy variable, passed in but not used until my functions are combined with Tiger's. # Process the inputs. processed_inputs = process_inputs( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, normalized=False, flux_err=flux_err, ) - flux = processed_inputs["flux"] # doesn't change in this case + # flux = processed_inputs["flux"] # doesn't change in this case spatial_mean = processed_inputs["spatial_mean"] spatial_inv_cov = processed_inputs["spatial_inv_cov"] flux_err = processed_inputs["flux_err"] @@ -326,12 +313,11 @@ def test_solve_for_map_linear_compare_starry(data): spatial_mean, spatial_inv_cov, flux_err, map.nt, map.nw, baseline_var, T, flux, S, map.nc, map.Ny, ) - # TODO: Fix spectrum is None error. # Get expected outputs. map._solver.theta = theta * _angle_factor map._solver.process_inputs(flux, normalized=False, fix_spectrum=True, flux_err=flux_err) map._solver.spectrum_ = map.spectrum_ - map._solver.solve_for_map_linear() # --> ERROR: self.spectrum is None when design_matrix() is called + map._solver.solve_for_map_linear() exp_y = map._solver.y exp_cho_ycov = map._solver.cho_ycov @@ -342,25 +328,19 @@ def test_solve_for_map_linear_compare_starry(data): # 22/7 -> pass -def test_solve_bilinear_against_data(data): +def test_solve_bilinear_against_data(generated_data, saved_output_data): # Process the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data - fix_spectrum = True - normalized = False - baseline_var = 0 + map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data # Get calculated outputs. calc_y, calc_cho_ycov = solve_bilinear( - flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, normalized, + flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, fix_spectrum, baseline_var, S, ) # Load saved expected outputs. - with open("map_solve_mean_output.npy", "rb") as f: - exp_mean = np.load(f) - with open("map_solve_cho_ycov_output.npy", "rb") as f: - exp_cho_ycov = np.load(f) + exp_mean, exp_cho_ycov = saved_output_data exp_y = np.transpose(np.reshape(exp_mean, (map.nc, map.Ny))) # Compare calculated and expected results. @@ -369,29 +349,25 @@ def test_solve_bilinear_against_data(data): # 22/7 -> pass -def test_solve_bilinear_compare_starry(data): +def test_solve_bilinear_compare_starry(generated_data): # Process the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data - fix_spectrum = True - normalized = False - baseline_var = 0 - + map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data theta = theta * _angle_factor # Get calculated outputs. calc_y, calc_cho_ycov = solve_bilinear( - flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, normalized, + flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, fix_spectrum, baseline_var, S, ) # Get expected outputs. metadata = map._solver.solve_bilinear( - flux, theta, map.y, map.spectrum_, map._veq, map._inc, map._u, - fix_spectrum=True, - flux_err=flux_err, - normalized=False, - ) + flux, theta, map.y, map.spectrum_, map._veq, map._inc, map._u, + fix_spectrum=True, + flux_err=flux_err, + normalized=False, + ) exp_y = metadata["y"] exp_cho_ycov = metadata["cho_ycov"] @@ -401,28 +377,24 @@ def test_solve_bilinear_compare_starry(data): assert_allclose(calc_cho_ycov, exp_cho_ycov) -def test_solve_against_data(data): +def test_solve_against_data(): pass # 22/7 -> pass -def test_solve_compare_starry(data): +def test_solve_compare_starry(generated_data): # Process the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, S = data - fix_spectrum = True - normalized = False - baseline_var = 0 + map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data # Get calculated outputs. calc_y, calc_cho_ycov = solve( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, map._S0e2i, flux_err, normalized, + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, fix_spectrum, baseline_var, S, theta, _angle_factor, ) - # NOTE: To get expected outputs, can either call starry function OR load saved data. # Get expected outputs. exp_solution = map.solve( flux, theta=theta, normalized=False, fix_spectrum=True, flux_err=flux_err, quiet="false" @@ -430,4 +402,4 @@ def test_solve_compare_starry(data): # Compare calculated and expected results. assert_allclose(calc_y, exp_solution["y"]) - assert_allclose(calc_cho_ycov, exp_solution["cho_ycov"]) \ No newline at end of file + assert_allclose(calc_cho_ycov, exp_solution["cho_ycov"]) From e0bff3d75169c000b56a2fda63404a2386b9abdb Mon Sep 17 00:00:00 2001 From: alserene Date: Fri, 26 Jul 2024 11:59:15 +1000 Subject: [PATCH 13/24] Create jaxodi version of spot_infer_y script with inferred map and timeseries plots (no uncertainty yet). --- python/jaxodi/jaxodi_spot_infer_y.py | 125 +++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 python/jaxodi/jaxodi_spot_infer_y.py diff --git a/python/jaxodi/jaxodi_spot_infer_y.py b/python/jaxodi/jaxodi_spot_infer_y.py new file mode 100644 index 0000000..7e06be2 --- /dev/null +++ b/python/jaxodi/jaxodi_spot_infer_y.py @@ -0,0 +1,125 @@ +# -*- coding: utf-8 -*- +""" +Setup for the SPOT problem. + +""" +import starry +import numpy as np +import os + +from jaxodi.paparazzi.src.scripts.utils.generate import generate_data +from jaxodi.paparazzi.src.scripts.utils.plot import plot_timeseries, plot_maps +from jaxodi.fig7_functions import solve + +import jax +import jax.numpy as jnp +import scipy + +jax.config.update("jax_enable_x64", True) + +# Generate the synthetic dataset +data = generate_data() +y_true = data["truths"]["y"] +spectrum_true = data["truths"]["spectrum"] +theta = data["data"]["theta"] +flux = data["data"]["flux0"] +flux_err = data["data"]["flux0_err"] + +# Instantiate the map +map = starry.DopplerMap(lazy=False, **data["kwargs"]) +map.spectrum = data["truths"]["spectrum"] +for n in range(map.udeg): + map[1 + n] = data["props"]["u"][n] + +# Solve for the Ylm coeffs +starry_solution = map.solve( + flux, + theta=theta, + normalized=False, + fix_spectrum=True, + flux_err=flux_err, + quiet=os.getenv("CI", "false") == "true", +) + +# # Get the inferred map +# starry_y_inferred = map.y + +# # Compute the Ylm expansion of the posterior standard deviation field +# P = map.sht_matrix(inverse=True) +# Q = map.sht_matrix() +# L = np.tril(soln["cho_ycov"]) +# W = P @ L +# y_uncert = Q @ np.sqrt(np.diag(W @ W.T)) + +# # Plot the maps +# starry_fig = plot_maps(y_true, starry_y_inferred) +# starry_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_maps_starry.pdf", bbox_inches="tight", dpi=150) + +# # Plot the timeseries +# s_fig = plot_timeseries(data, starry_y_inferred, spectrum_true, normalized=False) +# s_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_starry_timeseries.pdf", bbox_inches="tight", dpi=300) + + +# --------------- jaxodi --------------- +wav = data["kwargs"]["wav"] +wav0 = data["kwargs"]["wav0"] +vsini_max = 50000 +nw = 70 +_angle_factor = np.pi / 180 +fix_spectrum = True +normalized = False +baseline_var = 0 +_S0e2i = jnp.array(map._S0e2i.toarray()) +# Much code for setting S. +nw = 351 +vsini_max = 50000 +wav1 = np.min(wav) +wav2 = np.max(wav) +wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) +log_wav = jnp.linspace(np.log(wav1 / wavr), jnp.log(wav2 / wavr), nw) +wav_int = wavr * np.exp(log_wav) +interp_tol = 1e-12 +_clight = 299792458.0 # m/s +dlam = log_wav[1] - log_wav[0] +betasini_max = vsini_max / _clight +hw = jnp.array( + np.ceil( + 0.5 + * jnp.abs(jnp.log((1 + betasini_max) / (1 - betasini_max))) + / dlam + ), + dtype="int32", +) +x = jnp.arange(0, hw + 1) * dlam +pad_l = log_wav[0] - hw * dlam + x[:-1] +pad_r = log_wav[-1] + x[1:] +log_wav0_int = jnp.concatenate([pad_l, log_wav, pad_r]) +wav0_int = wavr * jnp.exp(log_wav0_int) +wav0 = jnp.array(wav0) + +S = map._get_spline_operator(wav_int, wav) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) +S = map._get_spline_operator(wav0_int, wav0) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) +S = map._get_spline_operator(wav, wav_int) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) +S = map._get_spline_operator(wav0, wav0_int) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) +S = jnp.array(S.toarray()) + +# Get calculated outputs. +jaxodi_y_inferred, jaxodi_cho_ycov = solve( + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, + fix_spectrum, baseline_var, S, + theta, _angle_factor, +) + +# Plot the maps +jaxodi_fig = plot_maps(y_true, jnp.squeeze(jaxodi_y_inferred)) +jaxodi_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_maps_jaxodi.pdf", bbox_inches="tight", dpi=150) +j_fig = plot_timeseries(data, jnp.squeeze(jaxodi_y_inferred), spectrum_true, normalized=False) +j_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_jaxodi_timeseries.pdf", bbox_inches="tight", dpi=300) From 2f4cdc796a0666bae62d4fc6bf00d85f852e6eda Mon Sep 17 00:00:00 2001 From: alserene Date: Fri, 26 Jul 2024 12:01:46 +1000 Subject: [PATCH 14/24] Remove commented code. --- python/jaxodi/jaxodi_spot_infer_y.py | 28 ++++++---------------------- 1 file changed, 6 insertions(+), 22 deletions(-) diff --git a/python/jaxodi/jaxodi_spot_infer_y.py b/python/jaxodi/jaxodi_spot_infer_y.py index 7e06be2..56b92e6 100644 --- a/python/jaxodi/jaxodi_spot_infer_y.py +++ b/python/jaxodi/jaxodi_spot_infer_y.py @@ -41,26 +41,7 @@ quiet=os.getenv("CI", "false") == "true", ) -# # Get the inferred map -# starry_y_inferred = map.y - -# # Compute the Ylm expansion of the posterior standard deviation field -# P = map.sht_matrix(inverse=True) -# Q = map.sht_matrix() -# L = np.tril(soln["cho_ycov"]) -# W = P @ L -# y_uncert = Q @ np.sqrt(np.diag(W @ W.T)) - -# # Plot the maps -# starry_fig = plot_maps(y_true, starry_y_inferred) -# starry_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_maps_starry.pdf", bbox_inches="tight", dpi=150) - -# # Plot the timeseries -# s_fig = plot_timeseries(data, starry_y_inferred, spectrum_true, normalized=False) -# s_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_starry_timeseries.pdf", bbox_inches="tight", dpi=300) - - -# --------------- jaxodi --------------- +# Compute needed data wav = data["kwargs"]["wav"] wav0 = data["kwargs"]["wav0"] vsini_max = 50000 @@ -118,8 +99,11 @@ theta, _angle_factor, ) +# Reshape for plotting function +jaxodi_y_inferred = jnp.squeeze(jaxodi_y_inferred) + # Plot the maps -jaxodi_fig = plot_maps(y_true, jnp.squeeze(jaxodi_y_inferred)) +jaxodi_fig = plot_maps(y_true, jaxodi_y_inferred) jaxodi_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_maps_jaxodi.pdf", bbox_inches="tight", dpi=150) -j_fig = plot_timeseries(data, jnp.squeeze(jaxodi_y_inferred), spectrum_true, normalized=False) +j_fig = plot_timeseries(data, jaxodi_y_inferred, spectrum_true, normalized=False) j_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_jaxodi_timeseries.pdf", bbox_inches="tight", dpi=300) From 6f4d682fee5b11ca411d74655d814e36920657e6 Mon Sep 17 00:00:00 2001 From: alserene Date: Tue, 30 Jul 2024 18:41:22 +1000 Subject: [PATCH 15/24] Inference functions, tests and test data. --- python/jaxodi/doppler_inference.py | 522 +++++++++++++++++++++++++++++ tests/map_solve_S_input.npy | Bin 0 -> 2293888 bytes tests/map_solve_cho_C_input.npy | Bin 0 -> 9088 bytes tests/map_solve_flux_input.npy | Bin 0 -> 9088 bytes tests/map_solve_invL_input.npy | Bin 0 -> 2176 bytes tests/map_solve_mu_input.npy | Bin 0 -> 2176 bytes tests/test_doppler_inference.py | 365 ++++++++++++++++++++ 7 files changed, 887 insertions(+) create mode 100644 python/jaxodi/doppler_inference.py create mode 100644 tests/map_solve_S_input.npy create mode 100644 tests/map_solve_cho_C_input.npy create mode 100644 tests/map_solve_flux_input.npy create mode 100644 tests/map_solve_invL_input.npy create mode 100644 tests/map_solve_mu_input.npy create mode 100644 tests/test_doppler_inference.py diff --git a/python/jaxodi/doppler_inference.py b/python/jaxodi/doppler_inference.py new file mode 100644 index 0000000..e6a05e4 --- /dev/null +++ b/python/jaxodi/doppler_inference.py @@ -0,0 +1,522 @@ +""" +Doppler inference functions. +""" + +import jax +import jax.numpy as jnp +import numpy as np +import scipy + +from typing import Tuple +from functools import partial +from jaxoplanet.types import Array +from .doppler_forward import get_kT + + +@jax.jit +def cho_solve(A: Array, b: Array) -> Array: + b_ = jax.scipy.linalg.solve_triangular(A, b, lower=True) + return jax.scipy.linalg.solve_triangular(jnp.transpose(A), b_, lower=False) + + +@jax.jit +def map_solve( + X: Array, + flux: Array, + cho_C: float | Array, + mu: Array, + LInv: float | Array +) -> Tuple[Array, Array]: + """ + Compute the maximum a posteriori (MAP) prediction for the + spherical harmonic coefficients of a map given a flux timeseries. + + Args: + X (matrix): The flux design matrix. + flux (array): The flux timeseries. + cho_C (scalar/vector/matrix): The lower cholesky factorization + of the data covariance. + mu (array): The prior mean of the spherical harmonic coefficients. + LInv (scalar/vector/matrix): The inverse prior covariance of the + spherical harmonic coefficients. + + Returns: + The vector of spherical harmonic coefficients corresponding to the + MAP solution and the Cholesky factorization of the corresponding + covariance matrix. + + """ + # Compute C^-1 . X + if cho_C.ndim == 0: + CInvX = X / cho_C**2 + elif cho_C.ndim == 1: + CInvX = jnp.dot(jnp.diag(1 / cho_C**2), X) + else: + CInvX = cho_solve(cho_C, X) + + # Compute W = X^T . C^-1 . X + L^-1 + W = jnp.dot(jnp.transpose(X), CInvX) + + # If LInv is a scalar or a 1-dimensional array, increment the + # diagonal elements of W with the values from LInv. + if LInv.ndim == 0 or LInv.ndim == 1: + W = W.at[jnp.diag_indices_from(W)].set(W[jnp.diag_indices_from(W)] + LInv) + LInvmu = mu * LInv + # If LInv is a matrix, directly add LInv to W. + else: + W += LInv + LInvmu = jnp.dot(LInv, mu) + + # Compute the max like y and its covariance matrix + cho_W = jax.scipy.linalg.cholesky(W, lower=True) + M = cho_solve(cho_W, jnp.transpose(CInvX)) + yhat = jnp.dot(M, flux) + cho_solve(cho_W, LInvmu) + ycov = cho_solve(cho_W, jnp.eye(cho_W.shape[0])) + cho_ycov = jax.scipy.linalg.cholesky(ycov, lower=True) + + return yhat, cho_ycov + + +@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nw0", "nw0_")) +def process_inputs( + flux: Array, + nt: int, + nw: int, + nc: int, + Ny: int, + nw0: int, + nw0_: int, + S0e2i: Array, + flux_err: float=None, + normalized: bool=True, + baseline=None, + spatial_mean=None, + spatial_cov=None, + spectral_mean=None, + spectral_cov=None, + logT0=None, + logTf=None, + nlogT=None, + ): + + # Process defaults + if flux_err is None: + flux_err = 1e-4 + if spatial_mean is None: + spatial_mean = jnp.zeros(Ny) + spatial_mean = spatial_mean.at[0].set(1.0) + if spatial_cov is None: + spatial_cov = 1e-4 + if spectral_mean is None: + spectral_mean = 1.0 + if spectral_cov is None: + spectral_cov = 1e-3 + if logT0 is None: + logT0 = 2 + if logTf is None: + logTf = 0 + if nlogT is None: + nlogT = 50 + else: + nlogT = max(1, nlogT) + + # Flux must be a matrix (nt, nw) + # if nt == 1: + # else: + # assert jnp.array_equal( + # jnp.shape(flux), jnp.array([nt, nw]) + # ), "Invalid shape for `flux`." + + # Flux error may be a scalar, a vector, or a matrix (nt, nw) + flux_err = jnp.array(flux_err) + # if flux_err.ndim == 0: + # flux_err = flux_err + # else: + + # Spatial mean may be a scalar, a vector (Ny), or a list of those + # Reshape it to a matrix of shape (Ny, nc) + if type(spatial_mean) not in (list, tuple): + # Use the same mean for all components + spatial_mean = [spatial_mean for n in range(nc)] + # else: + for n in range(nc): + spatial_mean[n] = jnp.array(spatial_mean[n]) + assert spatial_mean[n].ndim < 2 + spatial_mean[n] = jnp.reshape( + spatial_mean[n] * jnp.ones(Ny), (-1, 1) + ) + spatial_mean = jnp.concatenate(spatial_mean, axis=-1) + + # Spatial cov may be a scalar, a vector, a matrix (Ny, Ny), + # or a list of those. Invert it and reshape to a matrix of + # shape (Ny, nc) (inverse variances) or a tensor of shape + # (Ny, Ny, nc) (nc separate inverse covariance matrices) + if type(spatial_cov) not in (list, tuple): + # Use the same covariance for all components + spatial_cov = [spatial_cov for n in range(nc)] + # else: + spatial_inv_cov = [None for n in range(nc)] + ndim = jnp.array(spatial_cov[0]).ndim + for n in range(nc): + spatial_cov[n] = jnp.array(spatial_cov[n]) + assert spatial_cov[n].ndim == ndim + if spatial_cov[n].ndim < 2: + spatial_inv_cov[n] = jnp.reshape( + jnp.ones(Ny) / spatial_cov[n], (-1, 1) + ) + spatial_cov[n] = jnp.reshape( + jnp.ones(Ny) * spatial_cov[n], (-1, 1) + ) + # else: + + # Tensor of nc (inverse) variance vectors or covariance matrices + spatial_cov = jnp.concatenate(spatial_cov, axis=-1) + spatial_inv_cov = jnp.concatenate(spatial_inv_cov, axis=-1) + + # Baseline must be a vector (nt,) + # if baseline is not None: + # else: + + # Spectral mean must be a scalar, a vector (nw0), or a list of those + # Interpolate it to the internal grid (nw0_) and reshape to (nc, nw0_) + if type(spectral_mean) not in (list, tuple): + # Use the same mean for all components + spectral_mean = [spectral_mean for n in range(nc)] + # else: + for n in range(nc): + spectral_mean[n] = jnp.array(spectral_mean[n]) + assert spectral_mean[n].ndim < 2 + spectral_mean[n] = jnp.reshape( + spectral_mean[n] * jnp.ones(nw0), (-1, 1) + ) + spectral_mean[n] = S0e2i.dot(spectral_mean[n]).T + spectral_mean = jnp.concatenate(spectral_mean, axis=0) + + # Spectral cov may be a scalar, a vector, a matrix (nw0, nw0), + # or a list of those. Interpolate it to the internal grid, + # then invert it and reshape to a matrix of + # shape (nc, nw0_) (inverse variances) or a tensor of shape + # (nc, nw0_, nw0_) (nc separate inverse covariance matrices) + if type(spectral_cov) not in (list, tuple): + # Use the same covariance for all components + spectral_cov = [spectral_cov for n in range(nc)] + # else: + spectral_inv_cov = [None for n in range(nc)] + ndim = jnp.array(spectral_cov[0]).ndim + for n in range(nc): + spectral_cov[n] = jnp.array(spectral_cov[n]) + assert spectral_cov[n].ndim == ndim + if spectral_cov[n].ndim < 2: + if spectral_cov[n].ndim == 0: + cov = jnp.ones(nw0_) * spectral_cov[n] + else: + cov = S0e2i.dot(spectral_cov[n]) + inv = 1.0 / cov + spectral_inv_cov[n] = jnp.reshape(inv, (1, -1)) + spectral_cov[n] = jnp.reshape(cov, (1, -1)) + # else: + + # Tensor of nc (inverse) variance vectors or covariance matrices + spectral_cov = jnp.concatenate(spectral_cov, axis=0) + spectral_inv_cov = jnp.concatenate(spectral_inv_cov, axis=0) + + # Spectral guess must be a scalar, a vector (nw0), or a list of those + # Interpolate it to the internal grid (nw0_) and reshape to (nc, nw0_) + # if spectral_guess is not None: + # else: + + # Tempering schedule + if nlogT == 1: + T = jnp.array([10 ** logTf]) + elif logT0 == logTf: + T = logTf * jnp.ones(nlogT) + else: + T = jnp.logspace(logT0, logTf, nlogT) + + # Are we lucky enough to do a purely linear solve for the map? + linear = jnp.logical_or(jnp.logical_not(normalized), baseline is not None) + + processed_inputs = {} + + processed_inputs["flux"] = flux + processed_inputs["flux_err"] = flux_err + processed_inputs["spatial_mean"] = spatial_mean + processed_inputs["spatial_cov"] = spatial_cov + processed_inputs["spatial_inv_cov"] = spatial_inv_cov + processed_inputs["spectral_mean"] = spectral_mean + processed_inputs["spectral_cov"] = spectral_cov + processed_inputs["spectral_inv_cov"] = spectral_inv_cov + processed_inputs["T"] = T + processed_inputs["linear"] = linear + + return processed_inputs + + +@partial(jax.jit, static_argnames=("ydeg", "udeg", "nk", "nt", "nw", "nc", "Ny", "nwp")) +def get_D_fixed_spectrum( + xamp, vsini, ydeg, udeg, nk, inc, theta, spectrum, nc, nwp, nt, Ny, nw, + ): + """ + Return the Doppler matrix for a fixed spectrum. + """ + # Get the convolution kernels + kT = get_kT(xamp, vsini, ydeg, udeg, nk, inc, theta) + + # The dot product is just a 2d convolution! + product = jax.lax.conv_general_dilated( + jnp.reshape(spectrum, (nc, 1, 1, nwp)), + jnp.reshape(kT, (nt * Ny, 1, 1, nk)), + window_strides=(1,1), + padding="VALID", + lhs_dilation=None, + rhs_dilation=None, + ) + + product = jnp.reshape(product, (nc, nt, Ny, nw)) + product = jnp.swapaxes(product, 1, 2) + product = jnp.reshape(product, (Ny * nc, nt * nw)) + product = jnp.transpose(product) + + return product + + +# JAX does not store sparse matrices in sparse format +# and has no way to check for sparsity. +# Not currently jittable with is_sparse() returning a bool. +# @jax.jit +def sparse_dot(A, B): + """ + Performs matrix multiplication, optimising computation time by utilising sparse matrices. + """ + + def is_sparse(dense_matrix, threshold=0.9): + flattened_matrix = dense_matrix.flatten() + num_zeros = jnp.sum(flattened_matrix == 0) + total_elements = flattened_matrix.size + sparsity_ratio = num_zeros / total_elements + return sparsity_ratio > threshold + + # JAX has no check for sparse matrices. + if is_sparse(A): + return A.dot(B) + elif is_sparse(B): + return (B.T.dot(A.T)).T + else: + raise ValueError("At least one input must be sparse.") + + +@jax.jit +def get_default_theta( + theta: Array, + _angle_factor: float, + ) -> Array: + + return theta * _angle_factor + + +# Can't be jitted until sparse_dot is jitted +@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nwp", "_angle_factor", "ydeg", "udeg", "nk")) +def design_matrix( + theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, inc, spectrum, + nc, nwp, nt, Ny, nw, _interp, _Si2eBlk, + fix_spectrum=True, fix_map=False + ): + """ + Return the Doppler imaging design matrix. + + This matrix dots into the spectral map to yield the model for the + observed spectral timeseries (the ``flux``). + """ + theta = get_default_theta(theta, _angle_factor) # this is just undoing what get_S() did! + + # Compute the Doppler operator + # if fix_spectrum: + D = get_D_fixed_spectrum( + xamp, vsini, ydeg, udeg, nk, inc, theta, spectrum, nc, nwp, nt, Ny, nw, + ) + + # Interpolate to the output grid + # if _interp: + # D = sparse_dot(_Si2eBlk, D) + D = jnp.dot(_Si2eBlk, D) + + return D + + +@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nwp", "_angle_factor", "ydeg", "udeg", "nk", "fix_spectrum")) +def _get_S( + theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, inc, spectrum, + nc, nwp, nt, Ny, nw, _interp, _Si2eBlk, fix_spectrum, + ): + + theta = theta / _angle_factor + + dm = design_matrix( + theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, inc, spectrum, + nc, nwp, nt, Ny, nw, _interp, _Si2eBlk, fix_spectrum=fix_spectrum + ) + + return dm + + +@partial(jax.jit, static_argnames=("nt", "nw", "nw_", "nc", "Ny", "nwp", "_angle_factor", "ydeg", "udeg", "nk", "fix_spectrum")) +def solve_for_map_linear( + spatial_mean: Array, + spatial_inv_cov: Array, + flux_err: float, + nt: int, + nw: int, + nw_: int, + T, + flux: Array, + theta: Array, + _angle_factor: float, + xamp, + vsini: float, + ydeg: int, + udeg: int, + nk: int, + inc: float, + spectrum: Array, + nc: int, + nwp: int, + Ny: int, + _interp: bool, + _Si2eBlk: Array, + fix_spectrum: bool, + ) -> tuple[Array, Array]: + """ + Solve for `y` linearly, given a baseline or unnormalized data. + """ + # Reshape the priors + mu = jnp.reshape(jnp.transpose(spatial_mean), (-1)) + if spatial_inv_cov.ndim == 2: + invL = jnp.reshape(jnp.transpose(spatial_inv_cov), (-1)) + + # Ensure the flux error is a vector + if flux_err.ndim == 0: + flux_err = flux_err * jnp.ones((nt, nw_)) + + # Factorised data covariance + # cho_C = jnp.where( + # jnp.equal(baseline_var, 0), + # jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)), + # None + # ) + cho_C = jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)) + + # Unroll the data into a vector + flux = jnp.reshape(flux, (-1,)) + + # Get S + S = _get_S( + theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, inc, spectrum, + nc, nwp, nt, Ny, nw, _interp, _Si2eBlk, fix_spectrum, + ) + + # Solve the L2 problem + mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) + y = jnp.transpose(jnp.reshape(mean, (nc, Ny))) + + return y, cho_ycov + + +@partial(jax.jit, static_argnames=("nt", "nw", "nw_", "nc", "Ny", "nwp", "_angle_factor", "ydeg", "udeg", "nk", "nw0", "nw0_", "fix_spectrum")) +def solve_bilinear( + # pass to process_inputs() + flux: Array, + nt: int, + nw: int, + nw_: int, + nc: int, + Ny: int, + nw0: int, + nw0_: int, + S0e2i: Array, + flux_err: float, + normalized: bool, + # pass to solve_for_map_linear() + theta: Array, + _angle_factor: float, + xamp, + vsini: float, + ydeg: int, + udeg: int, + nk: int, + inc: float, + spectrum, + nwp: int, + _interp: bool, + _Si2eBlk: Array, + fix_spectrum: bool, + ) -> tuple[Array, Array]: + + # reset() - if have a class with self attributes. + + processed_inputs = process_inputs( + flux, nt, nw_, nc, Ny, nw0, nw0_, S0e2i, + normalized=normalized, flux_err=flux_err, + ) + + linear = processed_inputs["linear"] + flux = processed_inputs["flux"] # doesn't change in this case + spatial_mean = processed_inputs["spatial_mean"] + spatial_inv_cov = processed_inputs["spatial_inv_cov"] + flux_err = processed_inputs["flux_err"] + # T = processed_inputs["T"] + + # if fix_spectrum and linear: + y, cho_ycov = solve_for_map_linear( + spatial_mean, spatial_inv_cov, flux_err, nt, nw, nw_, 1, flux, + theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, inc, spectrum, + nc, nwp, Ny, _interp, _Si2eBlk, fix_spectrum, + ) + + return y, cho_ycov + + +@partial(jax.jit, static_argnames=("nt", "nw", "nw_", "nc", "Ny", "nwp", "_angle_factor", "ydeg", "udeg", "nk", "nw0", "nw0_", "fix_spectrum")) +def solve( + flux: Array, + nt: int, + nw: int, + nw_: int, + nc: int, + Ny: int, + nw0: int, + nw0_: int, + S0e2i: Array, + flux_err: float, + normalized: bool, + theta: Array, + _angle_factor: float, + xamp, + vsini: float, + ydeg: int, + udeg: int, + nk: int, + inc: float, + spectrum, + nwp: int, + _interp: bool, + _Si2eBlk: Array, + fix_spectrum: bool, + solver: str="bilinear", + ) -> tuple[Array, Array]: + """ + Iteratively solves the bilinear or nonlinear problem for the spatial + and/or spectral map given a spectral timeseries. + """ + + # Used to calculate S. + theta = get_default_theta(theta, _angle_factor) + + if solver.lower().startswith("bi"): + y, cho_ycov = solve_bilinear( + flux, nt, nw, nw_, nc, Ny, nw0, nw0_, S0e2i, flux_err, normalized, # pass to process_inputs() + theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, inc, spectrum, # pass to solve_for_map_linear() + nwp, _interp, _Si2eBlk, fix_spectrum, + ) + # else: + + return y, cho_ycov diff --git a/tests/map_solve_S_input.npy b/tests/map_solve_S_input.npy new file mode 100644 index 0000000000000000000000000000000000000000..2a4ee8ef26950262d1df76814a36d5644f370027 GIT binary patch literal 2293888 zcmbT-_dnJD`v-7juM|nLN4D%0&!@e~UfDa@WF;d)ltf07JxYkILa(Q-GO~A(%tGi* zcHi^)7ry7W*XxJ#dfsm5+|K>F9@ll!^iA~)Eb;Mz@b2(9y97A-^T@IB$h*q$2(t0G z`uGR>J9yjq_&dA&@4mW&XMhXtet^4!uM6&)S5#C?M37BPQi@NI?JnE@=S3bc8~ico z-*0%oBR0#@X%oufe={nLTtiXCC;HE}{V{6ww#5e_mY5(Y${KjIhzz5c4&Tu>!umI) zRStHh;E*r&ioxAUB-j}0byQr9GAGOky2NFGhIZwGpl2g=!ZTeZ<*0ykUmRKbm%RXi zpk>JG%Wd%5$m{jeul`u-vMT?umOo8wZ9F#;2YtdGoF+>i#X)x%8f zT=?#;+|Axk_82~uMSROz3G6srHoI}j2F$rFX_T)%LAJE-8;HW9Q0Um+6t#{Xm?D*U zqLr8f%~l?IewkFnj2T|K+k8)hD*-2b%w2IvaeB_=?y2@GF-vm zwLUB2jdU35`M&kmJ5S7X?CpQQJ$@do%4it9*QNjz zb+GG{`qT169)&?7EUgdb zFO#+bF3skaUuIpAeA)9Cauyb-{@CHmn`Ki#Tiri8!w?Na-LBkcJ7q zEtCJ*F#977GmW{Kd3%t`QfaArY>!f2WZYuqw#6n>J}b4?-iG)dbQfRzFbAUa$yIR` 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b/tests/map_solve_mu_input.npy new file mode 100644 index 0000000000000000000000000000000000000000..b357b8e1c2a6cfa7532ed09d42b9dd1fc79ac6da GIT binary patch literal 2176 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= xXCxM+0{I$7re->tItsN4WCJb+F!*2(p-0Kl5Eu=C(GVC7fzc2c4S|sm0stId9Jc@f literal 0 HcmV?d00001 diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py new file mode 100644 index 0000000..35839be --- /dev/null +++ b/tests/test_doppler_inference.py @@ -0,0 +1,365 @@ +""" +Doppler inference tests. +""" + +import pytest + +import jax +import jax.numpy as jnp +import numpy as np +import scipy + +from jaxoplanet.types import Array +from jaxoplanet.test_utils import assert_allclose + +# To generate variables --- +import starry +from starry._core.math import greedy_linalg + +from jaxodi.paparazzi.src.scripts.utils.generate import generate_data + +from jaxodi.doppler_inference import ( + get_kT, + get_D_fixed_spectrum, + sparse_dot, + design_matrix, + _get_S, + cho_solve, + map_solve, + process_inputs, + solve_for_map_linear, + get_default_theta, + solve_bilinear, + solve +) + +from jaxodi.doppler_forward import get_kT + +jax.config.update("jax_enable_x64", True) + + +@pytest.fixture(autouse=True) +def saved_input_data(): + + with open("map_solve_S_input.npy", "rb") as f: + S = jnp.load(f) + with open("map_solve_flux_input.npy", "rb") as f: + flux = jnp.load(f) + with open("map_solve_cho_C_input.npy", "rb") as f: + cho_C = jnp.load(f) + with open("map_solve_mu_input.npy", "rb") as f: + mu = jnp.load(f) + with open("map_solve_invL_input.npy", "rb") as f: + invL = jnp.load(f) + + return (S, flux, cho_C, mu, invL) + + +data = generate_data(u=[]) # get_kT doesn't yet have limb darkening implemented + +# data +theta = data["data"]["theta"] # np.linspace(-180, 180, nt=16, endpoint=False) +flux = data["data"]["flux0"] +flux_err = data["data"]["flux0_err"] + +# kwargs +wav = data["kwargs"]["wav"] # array (70,1) +wav0 = data["kwargs"]["wav0"] + +# Instantiate the map +map = starry.DopplerMap(lazy=False, **data["kwargs"]) +map.spectrum = data["truths"]["spectrum"] + +vsini_max = 50000 +nt = 16 +ydeg = 15 +udeg = 0 # Tiger has set at 0 -> no limb darkening. +xamp = map.ops.xamp +nwp = map.ops.nwp + +_angle_factor = np.pi / 180 +fix_spectrum = True +normalized = False +baseline_var = 0 +_S0e2i = jnp.array(map._S0e2i.toarray()) +_interp = True + +xamp = map.ops.xamp # (253,) +nwp = map.ops.nwp # 603 +vsini = map.vsini # 38567.256581192356 +ydeg = map.ydeg # 15 +udeg = map.udeg # 0 +nk = map.ops.nk # 253 +nw = 351 # != map.nw = 70 + +# veq = 60000 +# inc = 40 +# nc = 1 +# Ny = 256 +# Nu = 3 +# N = 324 +# nw = 70 # <- also 351 +# nw0 = 300 +# nw0_ = 603 + +# ------------------------ +# Much code for setting S. +nw = 351 +vsini_max = 50000 +wav1 = np.min(wav) +wav2 = np.max(wav) +wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) +log_wav = jnp.linspace(np.log(wav1 / wavr), jnp.log(wav2 / wavr), nw) +wav_int = wavr * np.exp(log_wav) +interp_tol = 1e-12 +_clight = 299792458.0 # m/s +dlam = log_wav[1] - log_wav[0] +betasini_max = vsini_max / _clight +hw = jnp.array( + np.ceil( + 0.5 + * jnp.abs(jnp.log((1 + betasini_max) / (1 - betasini_max))) + / dlam + ), + dtype="int32", +) +x = jnp.arange(0, hw + 1) * dlam +pad_l = log_wav[0] - hw * dlam + x[:-1] +pad_r = log_wav[-1] + x[1:] +log_wav0_int = jnp.concatenate([pad_l, log_wav, pad_r]) +wav0_int = wavr * jnp.exp(log_wav0_int) +wav0 = jnp.array(wav0) +# nw0 = len(wav0) +# nwp = len(log_wav0_int) + +S = map._get_spline_operator(wav_int, wav) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) +# _Si2eTr = map._math.sparse_cast(S.T) +_Si2eBlk = map._math.sparse_cast( + scipy.sparse.block_diag([S for n in range(nt)], format="csr") +) +S = map._get_spline_operator(wav0_int, wav0) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) +S = map._get_spline_operator(wav, wav_int) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) +S = map._get_spline_operator(wav0, wav0_int) +S[np.abs(S) < interp_tol] = 0 +S = scipy.sparse.csr_matrix(S) + +S = jnp.array(S.toarray()) +_Si2eBlk = jnp.array(_Si2eBlk.toarray()) + + + +def test_get_D_fixed_spectrum(): + + # Get calculated output. + calc_doppler_spectrum = get_D_fixed_spectrum( + xamp, vsini, ydeg, udeg, nk, map._inc, theta, map._spectrum, map.nc, nwp, map.nt, map.Ny, nw + ) + + # Get expected output. + exp_doppler_spectrum = map.ops.get_D_fixed_spectrum(map._inc, theta, map._veq, map._u, map._spectrum) + + # Compare calculated and expected results. + assert_allclose(calc_doppler_spectrum, exp_doppler_spectrum) + + +# 29/7 -> pass unjitted, fail jitted +def test_sparse_dot(): + + sparse_matrix = jnp.where(jax.random.uniform(jax.random.PRNGKey(0), (100, 100)) > 0.95, 1.0, 0.0) + sparse_csr_matrix = sparse_csr_matrix = scipy.sparse.csr_matrix(np.array(sparse_matrix)) + dense_matrix = jax.random.normal(jax.random.PRNGKey(1), (100, 50)) + + # Get calculated output. + calc_D = sparse_dot(sparse_matrix, dense_matrix) + + # Get expected output. + exp_D = map._math.sparse_dot(sparse_csr_matrix, dense_matrix) + + # Compare calculated and expected results. + assert_allclose(calc_D, exp_D) + + +def test_design_matrix(): + + _interp = True + theta = data["data"]["theta"] + theta_ = theta / _angle_factor + + # Get calculated output. + calc_dm = design_matrix( + theta_, _angle_factor, xamp, vsini, ydeg, udeg, nk, map._inc, + map._spectrum, map.nc, nwp, map.nt, map.Ny, nw, _interp, _Si2eBlk, fix_spectrum=True + ) + + # Get expected output. + exp_dm = map.design_matrix(theta_, fix_spectrum=True) + + # Compare calculated and expected results. + assert_allclose(calc_dm, exp_dm) + + +# # Not sure how to call starry's equivalent function +# def test_get_S(): + +# # Get calculated output. +# calc_S = _get_S( +# theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, map._inc, map._spectrum, +# map.nc, nwp, nt, map.Ny, nw, _interp, _Si2eBlk, fix_spectrum, +# ) + +# # Get expected output. +# map._solver.theta = theta * _angle_factor +# exp_S = map._solver._get_S() + +# # Compare calculated and expected results. +# assert_allclose(calc_S, exp_S) + + +# 22/7 -> pass +def test_get_default_theta(): + + # Get calculated outputs. + calc_theta = get_default_theta(theta, _angle_factor) + + # Get expected outputs. + exp_theta = map._get_default_theta(theta) + + # Compare calculated and expected results. + assert_allclose(calc_theta, exp_theta) + + +# 22/7 -> pass +def test_process_inputs(): + + # Get calculated outputs. + calc_processed = process_inputs( + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, normalized=False, flux_err=flux_err, + ) + + # Get expected outputs. + map._solver.reset() + map._solver.process_inputs( + flux, normalized=False, flux_err=flux_err, + ) + + # Compare calculated and expected results. + assert_allclose(map._solver.flux, calc_processed['flux']) + assert_allclose(map._solver.flux_err, calc_processed['flux_err']) + assert_allclose(map._solver.spatial_mean, calc_processed['spatial_mean']) + assert_allclose(map._solver.spatial_cov, calc_processed['spatial_cov']) + assert_allclose(map._solver.spatial_inv_cov, calc_processed['spatial_inv_cov']) + assert_allclose(map._solver.spectral_mean, calc_processed['spectral_mean']) + assert_allclose(map._solver.spectral_cov, calc_processed['spectral_cov']) + assert_allclose(map._solver.spectral_inv_cov, calc_processed['spectral_inv_cov']) + assert_allclose(map._solver.T, calc_processed['T']) + assert_allclose(map._solver.linear, calc_processed['linear']) + + +# 22/7 -> pass +def test_map_solve(saved_input_data): + + # Load the inputs. + S, flux, cho_C, mu, invL = saved_input_data + + # Get calculated outputs. + calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) + + # Get expected outputs. + exp_mean, exp_cho_ycov = greedy_linalg.solve(S, flux, cho_C, mu, invL) + + # Compare calculated and expected results. + assert_allclose(calc_mean, exp_mean) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 22/7 -> pass +def test_solve_for_map_linear(): + + T = 1 + flux_err = data["data"]["flux0_err"] + + theta = data["data"]["theta"] + theta_ = theta * _angle_factor + + # Process the inputs. + processed_inputs = process_inputs( + flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, + normalized=False, flux_err=flux_err, + ) + # flux = processed_inputs["flux"] # doesn't change in this case + spatial_mean = processed_inputs["spatial_mean"] + spatial_inv_cov = processed_inputs["spatial_inv_cov"] + flux_err = processed_inputs["flux_err"] + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve_for_map_linear( + spatial_mean, spatial_inv_cov, flux_err, map.nt, nw, map.nw, T, flux, + theta_, _angle_factor, xamp, vsini, ydeg, udeg, nk, map._inc, map._spectrum, + map.nc, nwp, map.Ny, _interp, _Si2eBlk, fix_spectrum, + ) + + # Get expected outputs. + map._solver.theta = theta * _angle_factor + map._solver.process_inputs(flux, normalized=False, fix_spectrum=True, flux_err=flux_err) + map._solver.spectrum_ = map.spectrum_ + map._solver.solve_for_map_linear() + + exp_y = map._solver.y + exp_cho_ycov = map._solver.cho_ycov + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_y) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 22/7 -> pass +def test_solve_bilinear(): + + theta = data["data"]["theta"] + theta_ = theta * _angle_factor + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve_bilinear( + flux, nt, nw, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, + theta_, _angle_factor, xamp, vsini, ydeg, udeg, nk, map._inc, map._spectrum, + nwp, _interp, _Si2eBlk, fix_spectrum, + ) + + # Get expected outputs. + metadata = map._solver.solve_bilinear( + flux, theta_, map.y, map.spectrum_, map._veq, map._inc, map._u, + fix_spectrum=True, + flux_err=flux_err, + normalized=False, + ) + exp_y = metadata["y"] + exp_cho_ycov = metadata["cho_ycov"] + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_y) + assert_allclose(calc_cho_ycov, exp_cho_ycov) + + +# 22/7 -> pass +def test_solve(): + + # Get calculated outputs. + calc_y, calc_cho_ycov = solve( + flux, map.nt, nw, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, + theta, _angle_factor, xamp, vsini, ydeg, udeg, nk, map._inc, map._spectrum, nwp, _interp, _Si2eBlk, fix_spectrum, + ) + + # Get expected outputs. + exp_solution = map.solve( + flux, theta=theta, normalized=False, fix_spectrum=True, flux_err=flux_err, quiet="false" + ) + + # Compare calculated and expected results. + assert_allclose(calc_y, exp_solution["y"]) + assert_allclose(calc_cho_ycov, exp_solution["cho_ycov"]) From 8d5d9154dcf682b968bf4ceed2fcd075604f2909 Mon Sep 17 00:00:00 2001 From: alserene Date: Tue, 30 Jul 2024 19:18:46 +1000 Subject: [PATCH 16/24] Remove unneeded variables. --- tests/test_doppler_inference.py | 80 +++------------------------------ 1 file changed, 7 insertions(+), 73 deletions(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index 35839be..0b98a9b 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -12,7 +12,6 @@ from jaxoplanet.types import Array from jaxoplanet.test_utils import assert_allclose -# To generate variables --- import starry from starry._core.math import greedy_linalg @@ -72,18 +71,6 @@ def saved_input_data(): vsini_max = 50000 nt = 16 -ydeg = 15 -udeg = 0 # Tiger has set at 0 -> no limb darkening. -xamp = map.ops.xamp -nwp = map.ops.nwp - -_angle_factor = np.pi / 180 -fix_spectrum = True -normalized = False -baseline_var = 0 -_S0e2i = jnp.array(map._S0e2i.toarray()) -_interp = True - xamp = map.ops.xamp # (253,) nwp = map.ops.nwp # 603 vsini = map.vsini # 38567.256581192356 @@ -92,66 +79,13 @@ def saved_input_data(): nk = map.ops.nk # 253 nw = 351 # != map.nw = 70 -# veq = 60000 -# inc = 40 -# nc = 1 -# Ny = 256 -# Nu = 3 -# N = 324 -# nw = 70 # <- also 351 -# nw0 = 300 -# nw0_ = 603 - -# ------------------------ -# Much code for setting S. -nw = 351 -vsini_max = 50000 -wav1 = np.min(wav) -wav2 = np.max(wav) -wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) -log_wav = jnp.linspace(np.log(wav1 / wavr), jnp.log(wav2 / wavr), nw) -wav_int = wavr * np.exp(log_wav) -interp_tol = 1e-12 -_clight = 299792458.0 # m/s -dlam = log_wav[1] - log_wav[0] -betasini_max = vsini_max / _clight -hw = jnp.array( - np.ceil( - 0.5 - * jnp.abs(jnp.log((1 + betasini_max) / (1 - betasini_max))) - / dlam - ), - dtype="int32", -) -x = jnp.arange(0, hw + 1) * dlam -pad_l = log_wav[0] - hw * dlam + x[:-1] -pad_r = log_wav[-1] + x[1:] -log_wav0_int = jnp.concatenate([pad_l, log_wav, pad_r]) -wav0_int = wavr * jnp.exp(log_wav0_int) -wav0 = jnp.array(wav0) -# nw0 = len(wav0) -# nwp = len(log_wav0_int) - -S = map._get_spline_operator(wav_int, wav) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) -# _Si2eTr = map._math.sparse_cast(S.T) -_Si2eBlk = map._math.sparse_cast( - scipy.sparse.block_diag([S for n in range(nt)], format="csr") -) -S = map._get_spline_operator(wav0_int, wav0) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) -S = map._get_spline_operator(wav, wav_int) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) -S = map._get_spline_operator(wav0, wav0_int) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) - -S = jnp.array(S.toarray()) -_Si2eBlk = jnp.array(_Si2eBlk.toarray()) - +_angle_factor = np.pi / 180 +fix_spectrum = True +normalized = False +_interp = True +baseline_var = 0 +_S0e2i = jnp.array(map._S0e2i.toarray()) +_Si2eBlk = jnp.array(map._Si2eBlk.toarray()) def test_get_D_fixed_spectrum(): From 7cfacd4175d6590d0f6124629e3d4bab6b95bd50 Mon Sep 17 00:00:00 2001 From: alserene Date: Tue, 30 Jul 2024 19:29:02 +1000 Subject: [PATCH 17/24] Remove old files. --- python/jaxodi/fig7_functions.py | 507 ------------------- python/jaxodi/figure_7.py | 700 --------------------------- python/jaxodi/jaxodi_spot_infer_y.py | 109 ----- python/jaxodi/test_fig7_functions.py | 405 ---------------- 4 files changed, 1721 deletions(-) delete mode 100644 python/jaxodi/fig7_functions.py delete mode 100644 python/jaxodi/figure_7.py delete mode 100644 python/jaxodi/jaxodi_spot_infer_y.py delete mode 100644 python/jaxodi/test_fig7_functions.py diff --git a/python/jaxodi/fig7_functions.py b/python/jaxodi/fig7_functions.py deleted file mode 100644 index a45bdfc..0000000 --- a/python/jaxodi/fig7_functions.py +++ /dev/null @@ -1,507 +0,0 @@ -""" -Functions for Figure 7's map.solve() -""" - -from typing import Tuple -# from collections.abc import Callable -from functools import partial - -import jax -import jax.numpy as jnp -import numpy as np -import scipy - -from jaxoplanet.types import Array - - -# Functions -# -# - cho_solve -# - map_solve -# - process_inputs -# - solve_for_map_linear -# - get_default_theta -# - solve_bilinear -# - solve - - -@jax.jit -def cho_solve(A: Array, b: Array) -> Array: - b_ = jax.scipy.linalg.solve_triangular(A, b, lower=True) - return jax.scipy.linalg.solve_triangular(jnp.transpose(A), b_, lower=False) - - -@jax.jit -def map_solve( - X: Array, - flux: Array, - cho_C: float | Array, - mu: Array, - LInv: float | Array -) -> Tuple[Array, Array]: - """ - Compute the maximum a posteriori (MAP) prediction for the - spherical harmonic coefficients of a map given a flux timeseries. - - Args: - X (matrix): The flux design matrix. - flux (array): The flux timeseries. - cho_C (scalar/vector/matrix): The lower cholesky factorization - of the data covariance. - mu (array): The prior mean of the spherical harmonic coefficients. - LInv (scalar/vector/matrix): The inverse prior covariance of the - spherical harmonic coefficients. - - Returns: - The vector of spherical harmonic coefficients corresponding to the - MAP solution and the Cholesky factorization of the corresponding - covariance matrix. - - """ - # Compute C^-1 . X - if cho_C.ndim == 0: - CInvX = X / cho_C**2 - elif cho_C.ndim == 1: - CInvX = jnp.dot(jnp.diag(1 / cho_C**2), X) - else: - CInvX = cho_solve(cho_C, X) - - # Compute W = X^T . C^-1 . X + L^-1 - W = jnp.dot(jnp.transpose(X), CInvX) - - # If LInv is a scalar or a 1-dimensional array, increment the - # diagonal elements of W with the values from LInv. - if LInv.ndim == 0 or LInv.ndim == 1: - W = W.at[jnp.diag_indices_from(W)].set(W[jnp.diag_indices_from(W)] + LInv) - LInvmu = mu * LInv - # If LInv is a matrix, directly add LInv to W. - else: - W += LInv - LInvmu = jnp.dot(LInv, mu) - - # Compute the max like y and its covariance matrix - cho_W = jax.scipy.linalg.cholesky(W, lower=True) - M = cho_solve(cho_W, jnp.transpose(CInvX)) - yhat = jnp.dot(M, flux) + cho_solve(cho_W, LInvmu) - ycov = cho_solve(cho_W, jnp.eye(cho_W.shape[0])) - cho_ycov = jax.scipy.linalg.cholesky(ycov, lower=True) - - return yhat, cho_ycov - - -@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nw0", "nw0_")) -def process_inputs( - flux: Array, - nt: int, - nw: int, - nc: int, - Ny: int, - nw0: int, - nw0_: int, - S0e2i: Array, - flux_err: float=None, - normalized: bool=True, - baseline=None, - spatial_mean=None, - spatial_cov=None, - spectral_mean=None, - spectral_cov=None, - logT0=None, - logTf=None, - nlogT=None, - ): - - # Process defaults - if flux_err is None: - flux_err = 1e-4 - if spatial_mean is None: - spatial_mean = jnp.zeros(Ny) - spatial_mean = spatial_mean.at[0].set(1.0) - if spatial_cov is None: - spatial_cov = 1e-4 - if spectral_mean is None: - spectral_mean = 1.0 - if spectral_cov is None: - spectral_cov = 1e-3 - if logT0 is None: - logT0 = 2 - if logTf is None: - logTf = 0 - if nlogT is None: - nlogT = 50 - else: - nlogT = max(1, nlogT) - - # Flux must be a matrix (nt, nw) - # if nt == 1: - # else: - # assert jnp.array_equal( - # jnp.shape(flux), jnp.array([nt, nw]) - # ), "Invalid shape for `flux`." - - # Flux error may be a scalar, a vector, or a matrix (nt, nw) - flux_err = jnp.array(flux_err) - # if flux_err.ndim == 0: - # flux_err = flux_err - # else: - - # Spatial mean may be a scalar, a vector (Ny), or a list of those - # Reshape it to a matrix of shape (Ny, nc) - if type(spatial_mean) not in (list, tuple): - # Use the same mean for all components - spatial_mean = [spatial_mean for n in range(nc)] - # else: - for n in range(nc): - spatial_mean[n] = jnp.array(spatial_mean[n]) - assert spatial_mean[n].ndim < 2 - spatial_mean[n] = jnp.reshape( - spatial_mean[n] * jnp.ones(Ny), (-1, 1) - ) - spatial_mean = jnp.concatenate(spatial_mean, axis=-1) - - # Spatial cov may be a scalar, a vector, a matrix (Ny, Ny), - # or a list of those. Invert it and reshape to a matrix of - # shape (Ny, nc) (inverse variances) or a tensor of shape - # (Ny, Ny, nc) (nc separate inverse covariance matrices) - if type(spatial_cov) not in (list, tuple): - # Use the same covariance for all components - spatial_cov = [spatial_cov for n in range(nc)] - # else: - spatial_inv_cov = [None for n in range(nc)] - ndim = jnp.array(spatial_cov[0]).ndim - for n in range(nc): - spatial_cov[n] = jnp.array(spatial_cov[n]) - assert spatial_cov[n].ndim == ndim - if spatial_cov[n].ndim < 2: - spatial_inv_cov[n] = jnp.reshape( - jnp.ones(Ny) / spatial_cov[n], (-1, 1) - ) - spatial_cov[n] = jnp.reshape( - jnp.ones(Ny) * spatial_cov[n], (-1, 1) - ) - # else: - - # Tensor of nc (inverse) variance vectors or covariance matrices - spatial_cov = jnp.concatenate(spatial_cov, axis=-1) - spatial_inv_cov = jnp.concatenate(spatial_inv_cov, axis=-1) - - # Baseline must be a vector (nt,) - # if baseline is not None: - # else: - - # Spectral mean must be a scalar, a vector (nw0), or a list of those - # Interpolate it to the internal grid (nw0_) and reshape to (nc, nw0_) - if type(spectral_mean) not in (list, tuple): - # Use the same mean for all components - spectral_mean = [spectral_mean for n in range(nc)] - # else: - for n in range(nc): - spectral_mean[n] = jnp.array(spectral_mean[n]) - assert spectral_mean[n].ndim < 2 - spectral_mean[n] = jnp.reshape( - spectral_mean[n] * jnp.ones(nw0), (-1, 1) - ) - spectral_mean[n] = S0e2i.dot(spectral_mean[n]).T - spectral_mean = jnp.concatenate(spectral_mean, axis=0) - - # Spectral cov may be a scalar, a vector, a matrix (nw0, nw0), - # or a list of those. Interpolate it to the internal grid, - # then invert it and reshape to a matrix of - # shape (nc, nw0_) (inverse variances) or a tensor of shape - # (nc, nw0_, nw0_) (nc separate inverse covariance matrices) - if type(spectral_cov) not in (list, tuple): - # Use the same covariance for all components - spectral_cov = [spectral_cov for n in range(nc)] - # else: - spectral_inv_cov = [None for n in range(nc)] - ndim = jnp.array(spectral_cov[0]).ndim - for n in range(nc): - spectral_cov[n] = jnp.array(spectral_cov[n]) - assert spectral_cov[n].ndim == ndim - if spectral_cov[n].ndim < 2: - if spectral_cov[n].ndim == 0: - cov = jnp.ones(nw0_) * spectral_cov[n] - else: - cov = S0e2i.dot(spectral_cov[n]) - inv = 1.0 / cov - spectral_inv_cov[n] = jnp.reshape(inv, (1, -1)) - spectral_cov[n] = jnp.reshape(cov, (1, -1)) - # else: - - # Tensor of nc (inverse) variance vectors or covariance matrices - spectral_cov = jnp.concatenate(spectral_cov, axis=0) - spectral_inv_cov = jnp.concatenate(spectral_inv_cov, axis=0) - - # Spectral guess must be a scalar, a vector (nw0), or a list of those - # Interpolate it to the internal grid (nw0_) and reshape to (nc, nw0_) - # if spectral_guess is not None: - # else: - - # Tempering schedule - if nlogT == 1: - T = jnp.array([10 ** logTf]) - elif logT0 == logTf: - T = logTf * jnp.ones(nlogT) - else: - T = jnp.logspace(logT0, logTf, nlogT) - - # Are we lucky enough to do a purely linear solve for the map? - linear = jnp.logical_or(jnp.logical_not(normalized), baseline is not None) - - processed_inputs = {} - - processed_inputs["flux"] = flux - processed_inputs["flux_err"] = flux_err - processed_inputs["spatial_mean"] = spatial_mean - processed_inputs["spatial_cov"] = spatial_cov - processed_inputs["spatial_inv_cov"] = spatial_inv_cov - processed_inputs["spectral_mean"] = spectral_mean - processed_inputs["spectral_cov"] = spectral_cov - processed_inputs["spectral_inv_cov"] = spectral_inv_cov - processed_inputs["T"] = T - processed_inputs["linear"] = linear - - return processed_inputs - - -def get_kT(inc, theta, veq, u): - """ - Get the kernels at an array of angular phases `theta`. - """ - - # enforce_bounds() - # get_x() - # get_rT() - # get_kT0() - # F() #? - # right_project() - - # many theano operations - - pass - - -def get_D_fixed_spectrum(self, inc, theta, veq, u, spectrum): - """ - Return the Doppler matrix for a fixed spectrum. - - This routine is heavily optimized, and can often be the fastest - way to compute the flux! - - """ - # # Get the convolution kernels - # kT = self.get_kT(inc, theta, veq, u) - - # # The dot product is just a 2d convolution! - # product = tt.nnet.conv2d( - # tt.reshape(spectrum, (self.nc, 1, 1, self.nwp)), - # tt.reshape(kT, (self.nt * self.Ny, 1, 1, self.nk)), - # border_mode="valid", - # filter_flip=False, - # input_shape=(self.nc, 1, 1, self.nwp), - # filter_shape=(self.nt * self.Ny, 1, 1, self.nk), - # ) - # product = tt.reshape(product, (self.nc, self.nt, self.Ny, self.nw)) - # product = tt.swapaxes(product, 1, 2) - # product = tt.reshape(product, (self.Ny * self.nc, self.nt * self.nw)) - # product = tt.transpose(product) - # return product - - pass - - -def sparse_dot(A, B): - """ - Performs matrix multiplication, optimising computation time by utilising sparse matrices. - """ - - # if scipy.sparse.issparse(A): - # return A.dot(B) - # elif scipy.sparse.issparse(B): - # return (B.T.dot(A.T)).T - # else: - # raise ValueError("At least one input must be sparse.") - - # Guess at jax versions: - if jax.issparse(A): - return A.dot(B) - elif jax.issparse(B): - return (B.T.dot(A.T)).T - else: - raise ValueError("At least one input must be sparse.") - - -def design_matrix(theta, spectrum_, fix_spectrum=True, fix_map=False): - """ - Return the Doppler imaging design matrix. - - This matrix dots into the spectral map to yield the model for the - observed spectral timeseries (the ``flux``). - """ - # theta = _get_default_theta(theta) # this is just undoing what get_S() did! - - # # Compute the Doppler operator - # if fix_spectrum: - - # # Fixed spectrum (dense) - # D = get_D_fixed_spectrum(_inc, theta, _veq, _u, _spectrum) - # # Interpolate to the output grid - # if _interp: - # D = map._math.sparse_dot(_Si2eBlk, D) - - # return D - - pass - - -# S = <603x300 sparse matrix with 1206 stored elements> -def _get_S(theta, _angle_factor, fix_spectrum, spectrum_): - - theta = theta / _angle_factor - dm = design_matrix(theta=theta, fix_spectrum=True) - - return dm - - -@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny")) -def solve_for_map_linear( - spatial_mean: Array, - spatial_inv_cov: Array, - flux_err: float, - nt: int, - nw: int, - baseline_var: int, - T, - flux: Array, - S: Array, - nc: int, - Ny: int, - ) -> tuple[Array, Array]: - """ - Solve for `y` linearly, given a baseline or unnormalized data. - """ - # Reshape the priors - mu = jnp.reshape(jnp.transpose(spatial_mean), (-1)) - if spatial_inv_cov.ndim == 2: - invL = jnp.reshape(jnp.transpose(spatial_inv_cov), (-1)) - - # Ensure the flux error is a vector - if flux_err.ndim == 0: - flux_err = flux_err * jnp.ones((nt, nw)) - - # Factorised data covariance - # cho_C = jnp.where( - # jnp.equal(baseline_var, 0), - # jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)), - # None - # ) - cho_C = jnp.reshape(jnp.sqrt(T) * flux_err, (-1,)) - - # Unroll the data into a vector - flux = jnp.reshape(flux, (-1,)) - - # Get S - # S = _get_S() # TODO: _get_S() - - # load from data instead of _get_S() - with open("map_solve_S_input.npy", "rb") as f: - S = jnp.load(f) - - # Solve the L2 problem - mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) - - y = jnp.transpose(jnp.reshape(mean, (nc, Ny))) - - return y, cho_ycov - - -@jax.jit -def get_default_theta( - theta: Array, - _angle_factor: float, - ) -> Array: - - return theta * _angle_factor - - -@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nw0", "nw0_", "fix_spectrum")) -def solve_bilinear( - # pass to process_inputs() - flux: Array, - nt: int, - nw: int, - nc: int, - Ny: int, - nw0: int, - nw0_: int, - S0e2i: Array, - flux_err: float, - normalized: bool, - # pass to solve_for_map_linear() - fix_spectrum: bool, - baseline_var: int, - S: Array, - # not being passed in yet - all for S? - # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, - ) -> tuple[Array, Array]: - - # reset() - if have a class with self attributes. - - processed_inputs = process_inputs( - flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, - normalized=normalized, flux_err=flux_err, - ) - - linear = processed_inputs["linear"] - flux = processed_inputs["flux"] # doesn't change in this case - spatial_mean = processed_inputs["spatial_mean"] - spatial_inv_cov = processed_inputs["spatial_inv_cov"] - flux_err = processed_inputs["flux_err"] - # T = processed_inputs["T"] - - # if fix_spectrum and linear: - y, cho_ycov = solve_for_map_linear( - spatial_mean, spatial_inv_cov, flux_err, nt, nw, baseline_var, 1, flux, S, nc, Ny, - ) - - return y, cho_ycov - - -@partial(jax.jit, static_argnames=("nt", "nw", "nc", "Ny", "nw0", "nw0_", "fix_spectrum")) -def solve( - flux: Array, - nt: int, - nw: int, - nc: int, - Ny: int, - nw0: int, - nw0_: int, - S0e2i: Array, - flux_err: float, - normalized: bool, - fix_spectrum: bool, - baseline_var: int, - S: Array, - theta: Array, - _angle_factor: float, - solver: str="bilinear", - # not being passed in yet - all for S? - # y, spectrum_, veq, inc, u, normalized, - ) -> tuple[Array, Array]: - """ - Iteratively solves the bilinear or nonlinear problem for the spatial - and/or spectral map given a spectral timeseries. - """ - - # Used to calculate S. - theta = get_default_theta(theta, _angle_factor) - - if solver.lower().startswith("bi"): - y, cho_ycov = solve_bilinear( - flux, nt, nw, nc, Ny, nw0, nw0_, S0e2i, flux_err, normalized, # pass to process_inputs() - fix_spectrum, baseline_var, S, # pass to solve_for_map_linear() - # theta, _angle_factor, y, spectrum_, veq, inc, u, normalized, # not being passed in yet - all for S? - ) - # else: - - return y, cho_ycov diff --git a/python/jaxodi/figure_7.py b/python/jaxodi/figure_7.py deleted file mode 100644 index 66a6ad8..0000000 --- a/python/jaxodi/figure_7.py +++ /dev/null @@ -1,700 +0,0 @@ -""" - - -Figure 7 --------- - - -Infer the stellar map assuming perfect knowledge of: - - - rest frame spectrum s - - baseline - - stellar inclination, rotation period & limb-darkening coefficients - - -""" - - - - -# when to use np.dot() vs DopplerMap.dot()? -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1357 - - - - -# Stuff I might need -# ------------------ - -from typing import Tuple -# from collections.abc import Callable -# from functools import partial - -import jax -import jax.numpy as jnp -import numpy as np - -from jaxoplanet.types import Array - -# To generate variables --- -# import paparazzi -import starry - -from paparazzi.src.scripts.utils.generate import generate_data -# --- - - - - -# VARIABLES ----------------------------------------------------------------------------------------------- - -lazy = False # when reading starry code - - -# Generate the synthetic dataset --- -data = generate_data() -# --- - - -y_true = data["truths"]["y"] # array (256,) -spectrum_true = data["truths"]["spectrum"] # array (1,300) -theta = data["data"]["theta"] # array (16,) -flux = data["data"]["flux0"] # array (16,70) -flux_err = data["data"]["flux0_err"] # 0.00013241138706868687 - - -# Instantiate the map --- -map = starry.DopplerMap(lazy=False, **data["kwargs"]) -map.spectrum = data["truths"]["spectrum"] -for n in range(map.udeg): - map[1 + n] = data["props"]["u"][n] -# --- - - -# Class attributes used in map.solve() & dependencies (except Tiger's functions) -# in call order (i.e. map.solve() first) -_y = map._y # array (256,) -spectrum_ = data["truths"]["spectrum"] # array (1,300) -_veq = data["kwargs"]['veq'] # 60000 -_inc = data["kwargs"]['inc'] # 40 -_u = map._u # = array([-1. , 0.5 , 0.25]) # the vector of limb darkening coefficients -fix_spectrum = True -normalized = False -baseline = None -linear = (not normalized) or (baseline is not None) -Ny = 256 -spatial_mean = np.zeros(Ny) -spatial_mean[0] = 1.0 -spatial_inv_cov # array (256, 1) # set in process_inputs() -nc = 1 -flux_err = data["data"]["flux0_err"] -nt = 16 -nw = 70 -baseline = None -flux = data["data"]["flux0"] -S = get_S() # get_S() calls design_matrix() -theta = map._get_default_theta(data["kwargs"].pop("theta", None)) # array (16,) -_angle_factor = np.pi/180 # converts between degrees and radians -fix_spectrum = True -_inc = data["kwargs"]["inc"] # 40 -_veq = data["kwargs"]["veq"] # 60000 -_u = map._u # = array([-1. , 0.5 , 0.25]) -_spectrum = map.spectrum_ # = map._spectrum # array (1,603) -_interp = True -_Si2eBlk = map._Si2eBlk # array (1120, 5616) -nc = 1 -nwp = 120 # from DopplerMap.__init__ -nt = 16 -Ny = 256 -hw = 25 -nk = int(2 * hw + 1) # 51 -nw = 70 -Ny = 256 -nt = 16 -nw = 70 -nc = 1 -nw0 = 300 # maybe the shape of spectral_mean[n] arrays? -S0e2i = map._S0e2i.eval() # the evaluated symbolic expression -nw0_ = 603 # maybe a transformed version of nw0 for spectral covariance matrices? - - - - - - - - - - - - - -# FUNCTIONS ---------------------------------------------------------------------------------------------------------------------- - - - -# Functions from 2023B -# -------------------- - -# cho_solve -# -# https://github.com/ADACS-Australia/bpope_2023b/blob/df765188cd1182694f28da7f4dab28cfdcd76265/src/jaxoplanet/experimental/starry/light_curve/inference.py#L33 -# -@jax.jit -def cho_solve(A: Array, b: Array) -> Array: - b_ = jax.scipy.linalg.solve_triangular(A, b, lower=True) - return jax.scipy.linalg.solve_triangular(jnp.transpose(A), b_, lower=False) - - -# map_solve -# -# https://github.com/ADACS-Australia/bpope_2023b/blob/df765188cd1182694f28da7f4dab28cfdcd76265/src/jaxoplanet/experimental/starry/light_curve/inference.py#L192 -# -@jax.jit -def map_solve( - X: Array, flux: Array, cho_C: float | Array, mu: Array, LInv: float | Array -) -> Tuple[Array, Array]: - """ - Compute the maximum a posteriori (MAP) prediction for the - spherical harmonic coefficients of a map given a flux timeseries. - - Args: - X (matrix): The flux design matrix. - flux (array): The flux timeseries. - cho_C (scalar/vector/matrix): The lower cholesky factorization - of the data covariance. - mu (array): The prior mean of the spherical harmonic coefficients. - LInv (scalar/vector/matrix): The inverse prior covariance of the - spherical harmonic coefficients. - - Returns: - The vector of spherical harmonic coefficients corresponding to the - MAP solution and the Cholesky factorization of the corresponding - covariance matrix. - - """ - # Compute C^-1 . X - if cho_C.ndim == 0: - CInvX = X / cho_C**2 - elif cho_C.ndim == 1: - CInvX = jnp.dot(jnp.diag(1 / cho_C**2), X) - else: - CInvX = cho_solve(cho_C, X) - - # Compute W = X^T . C^-1 . X + L^-1 - W = jnp.dot(jnp.transpose(X), CInvX) - # If LInv is a scalar or a 1-dimensional array, increment the - # diagonal elements of W with the values from LInv. - if LInv.ndim == 0 or LInv.ndim == 1: - W = W.at[jnp.diag_indices_from(W)].set(W[jnp.diag_indices_from(W)] + LInv) - LInvmu = mu * LInv - # If LInv is a matrix, directly add LInv to W. - else: - W += LInv - LInvmu = jnp.dot(LInv, mu) - - # Compute the max like y and its covariance matrix - cho_W = jax.scipy.linalg.cholesky(W, lower=True) - M = cho_solve(cho_W, jnp.transpose(CInvX)) - yhat = jnp.dot(M, flux) + cho_solve(cho_W, LInvmu) - ycov = cho_solve(cho_W, jnp.eye(cho_W.shape[0])) - cho_ycov = jax.scipy.linalg.cholesky(ycov, lower=True) - - return yhat, cho_ycov - -# --- - - - - - - - - -# Tiger's Functions -# ----------------- - -# get_default_theta -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1059 - -# how function is called in solve() -# theta = _get_default_theta(kwargs.pop("theta", None)) - -# class attributes -_nt # = nt? -_angle_factor - -def _get_default_theta(theta): - - math.cast() - ops.enforce_shape() - - # if theta is None: - # theta = math.cast( - # np.linspace(0, 2 * np.pi, nt, endpoint=False) - # ) - # else: - # theta = ( - # ops.enforce_shape( - # math.cast(theta), np.array([nt]) - # ) - # * angle_factor - # ) - - return theta - - -# get_kT -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1894 - -# how function is called in get_D_fixed_spectrum() -# get_kT(inc, theta, veq, u) - -# class attributes -vsini_max = 50000 -F # not sure if function or attribute? -A1Inv -A1 -nt = 16 -Ny = 256 -nk = 51 - -def get_kT(inc, theta, veq, u): - """ - Get the kernels at an array of angular phases `theta`. - """ - - # many function calls - enforce_bounds() - get_x() - get_rT() - get_kT0() - F() #? - right_project() - - # many theano operations - - pass - -# --- - - - - - - - - - - -# Functions called by Figure 7's map.solve() -# ------------------------------------------ - - -# process_inputs -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L222 - -# how function is called in solve_bilinear() -# process_inputs(flux, **kwargs) - -# class attributes -Ny = 256 -nt = 16 -nw = 70 -nc = 1 -nw0 = 300 # maybe the shape of spectral_mean[n] arrays? -S0e2i = map._S0e2i.eval() # the evaluated symbolic expression -nw0_ = 603 # maybe a transformed version of nw0 for spectral covariance matrices? - -def process_inputs( - flux, - flux_err=None, - spatial_mean=None, - spatial_cov=None, - spectral_mean=None, - spectral_cov=None, - spectral_guess=None, - spectral_lambda=None, - spectral_maxiter=None, - spectral_eps=None, - spectral_tol=None, - spectral_method=None, - normalized=True, - baseline=None, - baseline_var=None, - fix_spectrum=False, - fix_map=False, - logT0=None, - logTf=None, - nlogT=None, - quiet=False, - ): - """ - Checks shapes and sets defaults. - """ - - # if not spatial_cov[n].ndim < 2: - # cho_factor() - # cho_solve() - - # is this dot function Numpy's or starry's? - # spectral_mean[n] = S0e2i.dot(spectral_mean[n]).T - - pass - - -# ----------------------------------------- -# get log_wav0_int from DopplerMap.__init__ -wav = data["kwargs"]["wav"] # array (70,) -nw = 70 -vsini_max = data["kwargs"]['vsini_max'] # 50000 - -# Compute the padded internal wavelength grid (wav0_int). -# We add bins corresponding to the maximum kernel width to each -# end of wav_int to prevent edge effects - -_clight = 299792458.0 # m/s - -wav1 = np.min(wav) -wav2 = np.max(wav) -wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) -log_wav = np.linspace(np.log(wav1 / wavr), np.log(wav2 / wavr), nw) - -dlam = log_wav[1] - log_wav[0] -betasini_max = vsini_max / _clight -hw = np.array( - np.ceil( - 0.5 - * np.abs(np.log((1 + betasini_max) / (1 - betasini_max))) - / dlam - ), - dtype="int32", -) -x = np.arange(0, hw + 1) * dlam -pad_l = log_wav[0] - hw * dlam + x[:-1] -pad_r = log_wav[-1] + x[1:] -log_wav0_int = np.concatenate([pad_l, log_wav, pad_r]) -nwp = len(log_wav0_int) -# ----------------------------------------- - - -# get_D_fixed_spectrum -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/core.py#L1986 - -# how function is called in design_matrix() -# D = get_D_fixed_spectrum( -# _inc, theta, _veq, _u, _spectrum -# ) - -# class attributes -nc = 1 -nwp = len(log_wav0_int) # 120 # from DopplerMap.__init__ (see directly above for code) -nt = 16 -Ny = 256 -nk = int(2 * hw + 1) # 51 # hw also from DopplerMap.__init__ -nw = 70 - -def get_D_fixed_spectrum(inc, theta, veq, u, spectrum): - """ - Return the Doppler matrix for a fixed spectrum. - """ - - kT = get_kT(inc, theta, veq, u) # Tiger task - - # A bunch of theano action here. - product = 0 - # The dot product is just a 2d convolution! - # product = tt.nnet.conv2d( - # tt.reshape(spectrum, (self.nc, 1, 1, self.nwp)), - # tt.reshape(kT, (self.nt * self.Ny, 1, 1, self.nk)), - # border_mode="valid", - # filter_flip=False, - # input_shape=(self.nc, 1, 1, self.nwp), - # filter_shape=(self.nt * self.Ny, 1, 1, self.nk), - # ) - # product = tt.reshape(product, (self.nc, self.nt, self.Ny, self.nw)) - # product = tt.swapaxes(product, 1, 2) - # product = tt.reshape(product, (self.Ny * self.nc, self.nt * self.nw)) - # product = tt.transpose(product) - - return product - - -# sparse_dot -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/_core/math.py#L218 - -# how function is called in design_matrix() -# D = math.sparse_dot(_Si2eBlk, D) - -def sparse_dot(A, B): - """ - Performs matrix multiplication, optimising computation time by utilising sparse matrices. - """ - - # if scipy.sparse.issparse(A): - # return A.dot(B) - # elif scipy.sparse.issparse(B): - # return (B.T.dot(A.T)).T - # else: - # raise ValueError("At least one input must be sparse.") - - # Guess at jax versions: - if jax.issparse(A): - return A.dot(B) - elif jax.issparse(B): - return (B.T.dot(A.T)).T - else: - raise ValueError("At least one input must be sparse.") - - -# design_matrix -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1170 - -# how function is called in get_S() -# map.design_matrix( -# theta=theta/_angle_factor, fix_spectrum=True -# ) - -# class attributes -_inc = data["kwargs"]["inc"] # 40 -_veq = data["kwargs"]["veq"] # 60000 -_u = map._u # = array([-1. , 0.5 , 0.25]) -_spectrum = map.spectrum_ # = map._spectrum # array (1,603) -_interp = True -_Si2eBlk = map._Si2eBlk # array (1120, 5616) - -def design_matrix(theta=None, fix_spectrum=None, fix_map=False): - """ - Return the Doppler imaging design matrix. - - This matrix dots into the spectral map to yield the model for the - observed spectral timeseries (the ``flux``). - - Note that if this method is used to compute the spectral timeseries, - the result should be reshaped into a matrix of shape - (:py:attr:`nt`, :py:attr:`nw`) and optionally divided by the - :py:meth:`baseline()` to match the return value of :py:meth:`flux()`. - """ - - """ - there is much more info in the docstring - havent read yet - """ - - theta = _get_default_theta(theta) # Tiger task - - # if fix_spectrum: - D = get_D_fixed_spectrum(_inc, theta, _veq, _u, _spectrum) - - # if _interp: - D = sparse_dot(_Si2eBlk, D) - - return D - - -# get_S -# -# S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L201 -# get_S: https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L124 - -# class attributes -theta = map._get_default_theta(data["kwargs"].pop("theta", None)) # array (16,) -_angle_factor = np.pi/180 # converts between degrees and radians -fix_spectrum = True - -def get_S(): - """ - Get design matrix conditioned on the current spectrum. - """ - return design_matrix(theta/_angle_factor, fix_spectrum=True) - - -# ----------------------------------------- -# code for spatial_inv_cov from process_inputs() -nc = data["kwargs"]['nc'] # 1 -Ny = 256 -spatial_cov = None # as far as I can tell - -if spatial_cov is None: - spatial_cov = 1e-4 - -if type(spatial_cov) not in (list, tuple): - # Use the same covariance for all components - spatial_cov = [spatial_cov for n in range(nc)] -else: - # Check that we have one covariance per component - assert len(spatial_cov) == nc - -spatial_inv_cov = [None for n in range(nc)] -ndim = np.array(spatial_cov[0]).ndim -for n in range(nc): - spatial_cov[n] = np.array(spatial_cov[n]) - assert spatial_cov[n].ndim == ndim - if spatial_cov[n].ndim < 2: - spatial_inv_cov[n] = np.reshape( - np.ones(Ny) / spatial_cov[n], (-1, 1) - ) - spatial_cov[n] = np.reshape( - np.ones(Ny) * spatial_cov[n], (-1, 1) - ) - # else: - # cho = cho_factor(spatial_cov[n]) - # inv = cho_solve(cho, np.eye(Ny)) - # spatial_inv_cov[n] = np.reshape(inv, (Ny, Ny, 1)) - # spatial_cov[n] = np.reshape( - # spatial_cov[n], (Ny, Ny, 1) - # ) - -# Tensor of nc (inverse) variance vectors or covariance matrices -spatial_cov = np.concatenate(spatial_cov, axis=-1) -spatial_inv_cov = np.concatenate(spatial_inv_cov, axis=-1) -# ----------------------------------------- - -# solve_for_map_linear -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L467 - -# how function is called in solve_bilinear() -# solve_for_map_linear() - -# class attributes -Ny = 256 -spatial_mean = np.zeros(Ny) -spatial_mean[0] = 1.0 -spatial_inv_cov # array (256, 1) # set in process_inputs() (relevant code directly above) -nc = 1 -flux_err = data["data"]["flux0_err"] -nt = 16 -nw = 70 -baseline = None -flux = data["data"]["flux0"] -S = get_S() - -def solve_for_map_linear(T=1, baseline_var=0): - """ - Solve for `y` linearly, given a baseline or unnormalised data. - """ - - # ~60 lines of code that don't use theano - - # block_diag() # == scipy.linalg.block_diag() - - # cho_factor() # == scipy.linalg.cholesky(*args, **kwargs, lower=True) - # == jax.scipy.linalg.cholesky(_input_, lower=True) - - # can get S via call - S = get_S() - - # set within function - cho_C = None - mu = None - invL = None - - # mean, cho_ycov = greedy_linalg.solve(S, flux, cho_C, mu, invL) - mean, cho_ycov = map_solve(S, flux, cho_C, mu, invL) - - return mean, cho_ycov - - -# reset -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L177 - -# how function is called in solve_bilinear() -# reset() - -# class attributes (values are irrelevant) -spectrum_ = None -y = None -_S = None -_C = None -_KT0 = None -meta = {} - -def reset(): - - spectrum_ = None - y = None - _S = None - _C = None - _KT0 = None - meta = {} - - -# solve_bilinear -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler_solve.py#L737 - -# how function is called in solve() -# solution = solve_bilinear( -# flux, theta, _y, spectrum_, _veq, _inc, _u, **kwargs -# ) - -# class attributes -fix_spectrum = True -normalized = False -baseline = None -linear = (not normalized) or (baseline is not None) - -def solve_bilinear(flux, theta, y, spectrum_, veq, inc, u, **kwargs): - """ - Solve the linear problem for the spatial and/or spectral map - given a spectral timeseries. - """ - - reset() - process_inputs(flux, **kwargs) - - if fix_spectrum and linear: # The problem is exactly linear! - solve_for_map_linear() # Solve for the map conditioned on the spectrum - - meta = True # {y, cho_ycov, spectrum_, cho_scov} - - return meta - - -# solve -# -# https://github.com/rodluger/starry/blob/b72dff08588532f96bd072f2f1005e227d8e4ed8/starry/doppler.py#L1773 - -# how function is called in paper -# soln = map.solve( -# flux, -# theta=theta, -# normalized=False, -# fix_spectrum=True, -# flux_err=flux_err, -# ) - -# class attributes -_y = map._y # array (256,) -spectrum_ = data["truths"]["spectrum"] # array (1,300) -_veq = data["kwargs"]['veq'] # 60000 -_inc = data["kwargs"]['inc'] # 40 -_u = map._u # = array([-1., 0.5, 0.25]) # the vector of limb darkening coefficients - -def solve(flux, solver="bilinear", **kwargs): - """ - Iteratively solves the bilinear or nonlinear problem for the spatial - and/or spectral map given a spectral timeseries. - """ - - theta = _get_default_theta(kwargs.pop("theta", None)) - - # if bilinear - # if solver.lower().startswith("bi"): - solution = solve_bilinear( - flux, theta, _y, spectrum_, _veq, _inc, _u, **kwargs - ) - # else linear - # solution = solve_nonlinear( - # flux, theta, _y, spectrum_, _veq, _inc, _u, **kwargs - # ) - - return solution diff --git a/python/jaxodi/jaxodi_spot_infer_y.py b/python/jaxodi/jaxodi_spot_infer_y.py deleted file mode 100644 index 56b92e6..0000000 --- a/python/jaxodi/jaxodi_spot_infer_y.py +++ /dev/null @@ -1,109 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Setup for the SPOT problem. - -""" -import starry -import numpy as np -import os - -from jaxodi.paparazzi.src.scripts.utils.generate import generate_data -from jaxodi.paparazzi.src.scripts.utils.plot import plot_timeseries, plot_maps -from jaxodi.fig7_functions import solve - -import jax -import jax.numpy as jnp -import scipy - -jax.config.update("jax_enable_x64", True) - -# Generate the synthetic dataset -data = generate_data() -y_true = data["truths"]["y"] -spectrum_true = data["truths"]["spectrum"] -theta = data["data"]["theta"] -flux = data["data"]["flux0"] -flux_err = data["data"]["flux0_err"] - -# Instantiate the map -map = starry.DopplerMap(lazy=False, **data["kwargs"]) -map.spectrum = data["truths"]["spectrum"] -for n in range(map.udeg): - map[1 + n] = data["props"]["u"][n] - -# Solve for the Ylm coeffs -starry_solution = map.solve( - flux, - theta=theta, - normalized=False, - fix_spectrum=True, - flux_err=flux_err, - quiet=os.getenv("CI", "false") == "true", -) - -# Compute needed data -wav = data["kwargs"]["wav"] -wav0 = data["kwargs"]["wav0"] -vsini_max = 50000 -nw = 70 -_angle_factor = np.pi / 180 -fix_spectrum = True -normalized = False -baseline_var = 0 -_S0e2i = jnp.array(map._S0e2i.toarray()) -# Much code for setting S. -nw = 351 -vsini_max = 50000 -wav1 = np.min(wav) -wav2 = np.max(wav) -wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) -log_wav = jnp.linspace(np.log(wav1 / wavr), jnp.log(wav2 / wavr), nw) -wav_int = wavr * np.exp(log_wav) -interp_tol = 1e-12 -_clight = 299792458.0 # m/s -dlam = log_wav[1] - log_wav[0] -betasini_max = vsini_max / _clight -hw = jnp.array( - np.ceil( - 0.5 - * jnp.abs(jnp.log((1 + betasini_max) / (1 - betasini_max))) - / dlam - ), - dtype="int32", -) -x = jnp.arange(0, hw + 1) * dlam -pad_l = log_wav[0] - hw * dlam + x[:-1] -pad_r = log_wav[-1] + x[1:] -log_wav0_int = jnp.concatenate([pad_l, log_wav, pad_r]) -wav0_int = wavr * jnp.exp(log_wav0_int) -wav0 = jnp.array(wav0) - -S = map._get_spline_operator(wav_int, wav) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) -S = map._get_spline_operator(wav0_int, wav0) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) -S = map._get_spline_operator(wav, wav_int) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) -S = map._get_spline_operator(wav0, wav0_int) -S[np.abs(S) < interp_tol] = 0 -S = scipy.sparse.csr_matrix(S) -S = jnp.array(S.toarray()) - -# Get calculated outputs. -jaxodi_y_inferred, jaxodi_cho_ycov = solve( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, - fix_spectrum, baseline_var, S, - theta, _angle_factor, -) - -# Reshape for plotting function -jaxodi_y_inferred = jnp.squeeze(jaxodi_y_inferred) - -# Plot the maps -jaxodi_fig = plot_maps(y_true, jaxodi_y_inferred) -jaxodi_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_maps_jaxodi.pdf", bbox_inches="tight", dpi=150) -j_fig = plot_timeseries(data, jaxodi_y_inferred, spectrum_true, normalized=False) -j_fig.savefig("paparazzi/src/tex/figures/spot_infer_y_jaxodi_timeseries.pdf", bbox_inches="tight", dpi=300) diff --git a/python/jaxodi/test_fig7_functions.py b/python/jaxodi/test_fig7_functions.py deleted file mode 100644 index 05a41c6..0000000 --- a/python/jaxodi/test_fig7_functions.py +++ /dev/null @@ -1,405 +0,0 @@ -""" -Tests for Figure 7's map.solve() and dependencies. -""" - -import pytest - -import jax -import jax.numpy as jnp -import numpy as np -import scipy - -from jaxoplanet.types import Array -from jaxoplanet.test_utils import assert_allclose - -# To generate variables --- -# import paparazzi -import starry -from starry._core.math import greedy_linalg - -from jaxodi.paparazzi.src.scripts.utils.generate import generate_data - -from jaxodi.fig7_functions import ( - cho_solve, - map_solve, - process_inputs, - solve_for_map_linear, - get_default_theta, - solve_bilinear, - solve -) - -jax.config.update("jax_enable_x64", True) - - -@pytest.fixture(autouse=True) -def saved_input_data(): - - with open("map_solve_S_input.npy", "rb") as f: - S = jnp.load(f) - with open("map_solve_flux_input.npy", "rb") as f: - flux = jnp.load(f) - with open("map_solve_cho_C_input.npy", "rb") as f: - cho_C = jnp.load(f) - with open("map_solve_mu_input.npy", "rb") as f: - mu = jnp.load(f) - with open("map_solve_invL_input.npy", "rb") as f: - invL = jnp.load(f) - - return (S, flux, cho_C, mu, invL) - - -@pytest.fixture(autouse=True) -def saved_output_data(): - - with open("map_solve_mean_output.npy", "rb") as f: - exp_mean = np.load(f) - with open("map_solve_cho_ycov_output.npy", "rb") as f: - exp_cho_ycov = np.load(f) - - return (exp_mean, exp_cho_ycov) - - -@pytest.fixture(autouse=True) -def generated_data(): - - data = generate_data() - - # truth - # y_true = data["truths"]["y"] - # spectrum_true = data["truths"]["spectrum"] - - # data - theta = data["data"]["theta"] # np.linspace(-180, 180, nt=16, endpoint=False) - flux = data["data"]["flux0"] - flux_err = data["data"]["flux0_err"] - - # kwargs - wav = data["kwargs"]["wav"] # array (70,1) - wav0 = data["kwargs"]["wav0"] - - # Instantiate the map - map = starry.DopplerMap(lazy=False, **data["kwargs"]) - map.spectrum = data["truths"]["spectrum"] - for n in range(map.udeg): - map[1 + n] = data["props"]["u"][n] - - # Provided values - # ydeg = 15 - # udeg = 2 - - # veq = 60000 - # inc = 40 - - vsini_max = 50000 - - # nc = 1 - # nt = 16 - - # Ny = 256 - # Nu = 3 - # N = 324 - nw = 70 - # nw0 = 300 - # nw0_ = 603 - - _angle_factor = np.pi / 180 - - fix_spectrum = True - normalized = False - baseline_var = 0 - _S0e2i = jnp.array(map._S0e2i.toarray()) - - # ------------------------ - # Much code for setting S. - nw = 351 - vsini_max = 50000 - wav1 = np.min(wav) - wav2 = np.max(wav) - wavr = np.exp(0.5 * (np.log(wav1) + np.log(wav2))) - log_wav = jnp.linspace(np.log(wav1 / wavr), jnp.log(wav2 / wavr), nw) - wav_int = wavr * np.exp(log_wav) - interp_tol = 1e-12 - _clight = 299792458.0 # m/s - dlam = log_wav[1] - log_wav[0] - betasini_max = vsini_max / _clight - hw = jnp.array( - np.ceil( - 0.5 - * jnp.abs(jnp.log((1 + betasini_max) / (1 - betasini_max))) - / dlam - ), - dtype="int32", - ) - x = jnp.arange(0, hw + 1) * dlam - pad_l = log_wav[0] - hw * dlam + x[:-1] - pad_r = log_wav[-1] + x[1:] - log_wav0_int = jnp.concatenate([pad_l, log_wav, pad_r]) - wav0_int = wavr * jnp.exp(log_wav0_int) - wav0 = jnp.array(wav0) - # nw0 = len(wav0) - # nwp = len(log_wav0_int) - - S = map._get_spline_operator(wav_int, wav) - S[np.abs(S) < interp_tol] = 0 - S = scipy.sparse.csr_matrix(S) - S = map._get_spline_operator(wav0_int, wav0) - S[np.abs(S) < interp_tol] = 0 - S = scipy.sparse.csr_matrix(S) - S = map._get_spline_operator(wav, wav_int) - S[np.abs(S) < interp_tol] = 0 - S = scipy.sparse.csr_matrix(S) - S = map._get_spline_operator(wav0, wav0_int) - S[np.abs(S) < interp_tol] = 0 - S = scipy.sparse.csr_matrix(S) - - S = jnp.array(S.toarray()) - # ---------------------------- - - return (map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i) - - -# 19/7 -> -def test_get_default_theta_against_data(): - - pass - - -# 22/7 -> pass -def test_get_default_theta_compare_stary(generated_data): - - # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data - - # Get calculated outputs. - calc_theta = get_default_theta(theta, _angle_factor) - - # Get expected outputs. - exp_theta = map._get_default_theta(theta) - - # Compare calculated and expected results. - assert_allclose(calc_theta, exp_theta) - - -# 22/7 -> -def test_process_inputs_against_data(): - - pass - - -# 22/7 -> pass -def test_process_inputs_compare_starry(generated_data): - - # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data - - # Get calculated outputs. - calc_processed = process_inputs( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, normalized=False, flux_err=flux_err, - ) - - # Get expected outputs. - map._solver.reset() - map._solver.process_inputs( - flux, normalized=False, flux_err=flux_err, - ) - - # Compare calculated and expected results. - assert_allclose(map._solver.flux, calc_processed['flux']) - assert_allclose(map._solver.flux_err, calc_processed['flux_err']) - assert_allclose(map._solver.spatial_mean, calc_processed['spatial_mean']) - assert_allclose(map._solver.spatial_cov, calc_processed['spatial_cov']) - assert_allclose(map._solver.spatial_inv_cov, calc_processed['spatial_inv_cov']) - assert_allclose(map._solver.spectral_mean, calc_processed['spectral_mean']) - assert_allclose(map._solver.spectral_cov, calc_processed['spectral_cov']) - assert_allclose(map._solver.spectral_inv_cov, calc_processed['spectral_inv_cov']) - assert_allclose(map._solver.T, calc_processed['T']) - assert_allclose(map._solver.linear, calc_processed['linear']) - - -# TODO: how to get inputs/outputs to compare against? -def test_cho_solve(): - - # cho_solve() - pass - - -# 22/7 -> pass -def test_map_solve_against_data(saved_input_data, saved_output_data): - - # Load the inputs. - S, flux, cho_C, mu, invL = saved_input_data - - # Load saved expected outputs. - exp_mean, exp_cho_ycov = saved_output_data - - # Get calculated outputs. - calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) - - # Compare calculated and expected results. - assert_allclose(calc_mean, exp_mean) - assert_allclose(calc_cho_ycov, exp_cho_ycov) - - -# 22/7 -> pass -def test_map_solve_compare_starry(saved_input_data): - - # Load the inputs. - S, flux, cho_C, mu, invL = saved_input_data - - # Get calculated outputs. - calc_mean, calc_cho_ycov = map_solve(S, flux, cho_C, mu, invL) - - # Get expected outputs. - exp_mean, exp_cho_ycov = greedy_linalg.solve(S, flux, cho_C, mu, invL) - - # Compare calculated and expected results. - assert_allclose(calc_mean, exp_mean) - assert_allclose(calc_cho_ycov, exp_cho_ycov) - - -# 22/7 -> pass -def test_solve_for_map_linear_against_data(generated_data, saved_output_data): - - # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data - T = 1 - # TODO: S is dummy variable, passed in but not used until my functions are combined with Tiger's. - - # Process the inputs. - processed_inputs = process_inputs( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, - normalized=False, flux_err=flux_err, - ) - # flux = processed_inputs["flux"] # doesn't change in this case - spatial_mean = processed_inputs["spatial_mean"] - spatial_inv_cov = processed_inputs["spatial_inv_cov"] - flux_err = processed_inputs["flux_err"] - - # Get calculated outputs. - calc_y, calc_cho_ycov = solve_for_map_linear( - spatial_mean, spatial_inv_cov, flux_err, map.nt, map.nw, baseline_var, T, flux, S, map.nc, map.Ny, - ) - - # Load saved expected outputs. - exp_mean, exp_cho_ycov = saved_output_data - exp_y = np.transpose(np.reshape(exp_mean, (map.nc, map.Ny))) - - # Compare calculated and expected results. - assert_allclose(calc_y, exp_y) - assert_allclose(calc_cho_ycov, exp_cho_ycov) - - -# 22/7 -> pass -def test_solve_for_map_linear_compare_starry(generated_data): - - # Load the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data - T = 1 - # TODO: S is dummy variable, passed in but not used until my functions are combined with Tiger's. - - # Process the inputs. - processed_inputs = process_inputs( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, - normalized=False, flux_err=flux_err, - ) - # flux = processed_inputs["flux"] # doesn't change in this case - spatial_mean = processed_inputs["spatial_mean"] - spatial_inv_cov = processed_inputs["spatial_inv_cov"] - flux_err = processed_inputs["flux_err"] - - # Get calculated outputs. - calc_y, calc_cho_ycov = solve_for_map_linear( - spatial_mean, spatial_inv_cov, flux_err, map.nt, map.nw, baseline_var, T, flux, S, map.nc, map.Ny, - ) - - # Get expected outputs. - map._solver.theta = theta * _angle_factor - map._solver.process_inputs(flux, normalized=False, fix_spectrum=True, flux_err=flux_err) - map._solver.spectrum_ = map.spectrum_ - map._solver.solve_for_map_linear() - - exp_y = map._solver.y - exp_cho_ycov = map._solver.cho_ycov - - # Compare calculated and expected results. - assert_allclose(calc_y, exp_y) - assert_allclose(calc_cho_ycov, exp_cho_ycov) - - -# 22/7 -> pass -def test_solve_bilinear_against_data(generated_data, saved_output_data): - - # Process the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data - - # Get calculated outputs. - calc_y, calc_cho_ycov = solve_bilinear( - flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, - fix_spectrum, baseline_var, S, - ) - - # Load saved expected outputs. - exp_mean, exp_cho_ycov = saved_output_data - exp_y = np.transpose(np.reshape(exp_mean, (map.nc, map.Ny))) - - # Compare calculated and expected results. - assert_allclose(calc_y, exp_y) - assert_allclose(calc_cho_ycov, exp_cho_ycov) - - -# 22/7 -> pass -def test_solve_bilinear_compare_starry(generated_data): - - # Process the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data - theta = theta * _angle_factor - - # Get calculated outputs. - calc_y, calc_cho_ycov = solve_bilinear( - flux, map._nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, - fix_spectrum, baseline_var, S, - ) - - # Get expected outputs. - metadata = map._solver.solve_bilinear( - flux, theta, map.y, map.spectrum_, map._veq, map._inc, map._u, - fix_spectrum=True, - flux_err=flux_err, - normalized=False, - ) - - exp_y = metadata["y"] - exp_cho_ycov = metadata["cho_ycov"] - - # Compare calculated and expected results. - assert_allclose(calc_y, exp_y) - assert_allclose(calc_cho_ycov, exp_cho_ycov) - - -def test_solve_against_data(): - - pass - - -# 22/7 -> pass -def test_solve_compare_starry(generated_data): - - # Process the inputs. - map, theta, _angle_factor, flux, flux_err, wav, wav0, fix_spectrum, normalized, baseline_var, S, _S0e2i = generated_data - - # Get calculated outputs. - calc_y, calc_cho_ycov = solve( - flux, map.nt, map.nw, map.nc, map.Ny, map.nw0, map.nw0_, _S0e2i, flux_err, normalized, - fix_spectrum, baseline_var, S, - theta, _angle_factor, - ) - - # Get expected outputs. - exp_solution = map.solve( - flux, theta=theta, normalized=False, fix_spectrum=True, flux_err=flux_err, quiet="false" - ) - - # Compare calculated and expected results. - assert_allclose(calc_y, exp_solution["y"]) - assert_allclose(calc_cho_ycov, exp_solution["cho_ycov"]) From 87d5b3ad7374cbad38c873b6d015261d99354078 Mon Sep 17 00:00:00 2001 From: alserene Date: Wed, 4 Sep 2024 10:29:47 +1000 Subject: [PATCH 18/24] Remove paparazzi data dependency from tests. --- tests/test_doppler_inference.py | 68 ++++++++++++++++++++++----------- 1 file changed, 46 insertions(+), 22 deletions(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index 0b98a9b..d75ce09 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -15,8 +15,6 @@ import starry from starry._core.math import greedy_linalg -from jaxodi.paparazzi.src.scripts.utils.generate import generate_data - from jaxodi.doppler_inference import ( get_kT, get_D_fixed_spectrum, @@ -54,21 +52,52 @@ def saved_input_data(): return (S, flux, cho_C, mu, invL) -data = generate_data(u=[]) # get_kT doesn't yet have limb darkening implemented +# Set the seed. +np.random.seed(0) + +# Set arguments for instantiating a Doppler map. +kwargs = dict( + ydeg=15, + udeg=0, + nc=1, + veq=60000, + inc=40, + vsini_max=50000, + nt=16, + wav=np.linspace(642.85, 643.15, 70), + wav0=np.linspace(642.74, 643.26, 300), +) + +# Instantiate the Doppler map. +map = starry.DopplerMap(lazy=False, **kwargs) -# data -theta = data["data"]["theta"] # np.linspace(-180, 180, nt=16, endpoint=False) -flux = data["data"]["flux0"] -flux_err = data["data"]["flux0_err"] +# No limb darkening at this stage. +# get_kT doesn't yet have limb darkening implemented. -# kwargs -wav = data["kwargs"]["wav"] # array (70,1) -wav0 = data["kwargs"]["wav0"] +# Rest frame spectrum. +spectrum = ( + 1.0 + - 0.85 * np.exp(-0.5 * (map.wav0 - 643.0) ** 2 / 0.0085 ** 2) + - 0.40 * np.exp(-0.5 * (map.wav0 - 642.97) ** 2 / 0.0085 ** 2) + - 0.20 * np.exp(-0.5 * (map.wav0 - 643.1) ** 2 / 0.0085 ** 2) +) -# Instantiate the map -map = starry.DopplerMap(lazy=False, **data["kwargs"]) -map.spectrum = data["truths"]["spectrum"] +# Load the component maps. +map.load(spectra=spectrum, smoothing=0.075) +# Get rotational phases. +theta = np.linspace(-180, 180, map.nt, endpoint=False) + +# Generate unnormalized data. Scale the error so it's +# the same magnitude relative to the baseline as the +# error in the normalized dataset so we can more easily +# compare the inference results in both cases. +flux_err = 2e-4 +flux = map.flux(theta=theta, normalize=False) +flux_err = flux_err * np.median(flux) +flux += flux_err * np.random.randn(*flux.shape) + +# Set test data. vsini_max = 50000 nt = 16 xamp = map.ops.xamp # (253,) @@ -121,8 +150,7 @@ def test_sparse_dot(): def test_design_matrix(): - _interp = True - theta = data["data"]["theta"] + # _interp = True theta_ = theta / _angle_factor # Get calculated output. @@ -216,9 +244,6 @@ def test_map_solve(saved_input_data): def test_solve_for_map_linear(): T = 1 - flux_err = data["data"]["flux0_err"] - - theta = data["data"]["theta"] theta_ = theta * _angle_factor # Process the inputs. @@ -229,18 +254,18 @@ def test_solve_for_map_linear(): # flux = processed_inputs["flux"] # doesn't change in this case spatial_mean = processed_inputs["spatial_mean"] spatial_inv_cov = processed_inputs["spatial_inv_cov"] - flux_err = processed_inputs["flux_err"] + flux_err_ = processed_inputs["flux_err"] # Get calculated outputs. calc_y, calc_cho_ycov = solve_for_map_linear( - spatial_mean, spatial_inv_cov, flux_err, map.nt, nw, map.nw, T, flux, + spatial_mean, spatial_inv_cov, flux_err_, map.nt, nw, map.nw, T, flux, theta_, _angle_factor, xamp, vsini, ydeg, udeg, nk, map._inc, map._spectrum, map.nc, nwp, map.Ny, _interp, _Si2eBlk, fix_spectrum, ) # Get expected outputs. map._solver.theta = theta * _angle_factor - map._solver.process_inputs(flux, normalized=False, fix_spectrum=True, flux_err=flux_err) + map._solver.process_inputs(flux, normalized=False, fix_spectrum=True, flux_err=flux_err_) map._solver.spectrum_ = map.spectrum_ map._solver.solve_for_map_linear() @@ -255,7 +280,6 @@ def test_solve_for_map_linear(): # 22/7 -> pass def test_solve_bilinear(): - theta = data["data"]["theta"] theta_ = theta * _angle_factor # Get calculated outputs. From 5213c53a4f46a1443e03b3d1d3d54604039cf8bf Mon Sep 17 00:00:00 2001 From: alserene Date: Wed, 4 Sep 2024 11:38:02 +1000 Subject: [PATCH 19/24] Test data filepaths. --- tests/test_doppler_inference.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index d75ce09..4521832 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -38,13 +38,13 @@ @pytest.fixture(autouse=True) def saved_input_data(): - with open("map_solve_S_input.npy", "rb") as f: + with open("/sdholakia_2024a/tests/map_solve_S_input.npy", "rb") as f: S = jnp.load(f) - with open("map_solve_flux_input.npy", "rb") as f: + with open("sdholakia_2024a/tests/map_solve_flux_input.npy", "rb") as f: flux = jnp.load(f) - with open("map_solve_cho_C_input.npy", "rb") as f: + with open("/tests/map_solve_cho_C_input.npy", "rb") as f: cho_C = jnp.load(f) - with open("map_solve_mu_input.npy", "rb") as f: + with open("tests/map_solve_mu_input.npy", "rb") as f: mu = jnp.load(f) with open("map_solve_invL_input.npy", "rb") as f: invL = jnp.load(f) From 00142507a848bb69e380654f88a7ea7f51103899 Mon Sep 17 00:00:00 2001 From: alserene Date: Wed, 4 Sep 2024 11:51:49 +1000 Subject: [PATCH 20/24] Test data filepaths. --- tests/test_doppler_inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index 4521832..a2086e8 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -38,7 +38,7 @@ @pytest.fixture(autouse=True) def saved_input_data(): - with open("/sdholakia_2024a/tests/map_solve_S_input.npy", "rb") as f: + with open("/tests/map_solve_S_input.npy", "rb") as f: S = jnp.load(f) with open("sdholakia_2024a/tests/map_solve_flux_input.npy", "rb") as f: flux = jnp.load(f) From 4cd4a4bb240835950497cb9e80ab0be794d47016 Mon Sep 17 00:00:00 2001 From: alserene Date: Wed, 4 Sep 2024 12:21:56 +1000 Subject: [PATCH 21/24] Test data filepaths. --- tests/test_doppler_inference.py | 35 ++++++++++++++++++++++++++++----- 1 file changed, 30 insertions(+), 5 deletions(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index a2086e8..09aa08a 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -34,23 +34,48 @@ jax.config.update("jax_enable_x64", True) +import importlib.resources +import jaxodi + +TEST_DATA_S = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_S_input.npy")) +TEST_DATA_FLUX = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_flux_input.npy")) +TEST_DATA_CHO_C = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_cho_C_input.npy")) +TEST_DATA_MU = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_mu_input.npy")) +TEST_DATA_INVL = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_invL_input.npy")) + @pytest.fixture(autouse=True) def saved_input_data(): - with open("/tests/map_solve_S_input.npy", "rb") as f: + with open(TEST_DATA_S, "rb") as f: S = jnp.load(f) - with open("sdholakia_2024a/tests/map_solve_flux_input.npy", "rb") as f: + with open(TEST_DATA_FLUX, "rb") as f: flux = jnp.load(f) - with open("/tests/map_solve_cho_C_input.npy", "rb") as f: + with open(TEST_DATA_CHO_C, "rb") as f: cho_C = jnp.load(f) - with open("tests/map_solve_mu_input.npy", "rb") as f: + with open(TEST_DATA_MU, "rb") as f: mu = jnp.load(f) - with open("map_solve_invL_input.npy", "rb") as f: + with open(TEST_DATA_INVL, "rb") as f: invL = jnp.load(f) return (S, flux, cho_C, mu, invL) +# @pytest.fixture(autouse=True) +# def saved_input_data(): + +# with open("/tests/map_solve_S_input.npy", "rb") as f: +# S = jnp.load(f) +# with open("sdholakia_2024a/tests/map_solve_flux_input.npy", "rb") as f: +# flux = jnp.load(f) +# with open("/tests/map_solve_cho_C_input.npy", "rb") as f: +# cho_C = jnp.load(f) +# with open("tests/map_solve_mu_input.npy", "rb") as f: +# mu = jnp.load(f) +# with open("map_solve_invL_input.npy", "rb") as f: +# invL = jnp.load(f) + +# return (S, flux, cho_C, mu, invL) + # Set the seed. np.random.seed(0) From 3c0b3f316b6f5ac5520d93da3a168de8224f284b Mon Sep 17 00:00:00 2001 From: alserene Date: Wed, 4 Sep 2024 12:56:24 +1000 Subject: [PATCH 22/24] Test data filepaths. --- tests/test_doppler_inference.py | 58 +++++++++++++++++---------------- 1 file changed, 30 insertions(+), 28 deletions(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index 09aa08a..ddb5b7f 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -34,48 +34,50 @@ jax.config.update("jax_enable_x64", True) -import importlib.resources -import jaxodi +# import importlib.resources +# import jaxodi -TEST_DATA_S = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_S_input.npy")) -TEST_DATA_FLUX = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_flux_input.npy")) -TEST_DATA_CHO_C = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_cho_C_input.npy")) -TEST_DATA_MU = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_mu_input.npy")) -TEST_DATA_INVL = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_invL_input.npy")) +# TEST_DATA_S = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_S_input.npy")) +# TEST_DATA_FLUX = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_flux_input.npy")) +# TEST_DATA_CHO_C = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_cho_C_input.npy")) +# TEST_DATA_MU = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_mu_input.npy")) +# TEST_DATA_INVL = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_invL_input.npy")) +# @pytest.fixture(autouse=True) +# def saved_input_data(): + +# with open(TEST_DATA_S, "rb") as f: +# S = jnp.load(f) +# with open(TEST_DATA_FLUX, "rb") as f: +# flux = jnp.load(f) +# with open(TEST_DATA_CHO_C, "rb") as f: +# cho_C = jnp.load(f) +# with open(TEST_DATA_MU, "rb") as f: +# mu = jnp.load(f) +# with open(TEST_DATA_INVL, "rb") as f: +# invL = jnp.load(f) + +# return (S, flux, cho_C, mu, invL) + +import os +cwd = os.getcwd() @pytest.fixture(autouse=True) def saved_input_data(): - with open(TEST_DATA_S, "rb") as f: + with open("/home/runner/work/sdholakia_2024a/sdholakia_2024a/tests/map_solve_S_input.npy", "rb") as f: S = jnp.load(f) - with open(TEST_DATA_FLUX, "rb") as f: + with open(f"{cwd}/map_solve_flux_input.npy", "rb") as f: flux = jnp.load(f) - with open(TEST_DATA_CHO_C, "rb") as f: + with open(f"{cwd}/map_solve_cho_C_input.npy", "rb") as f: cho_C = jnp.load(f) - with open(TEST_DATA_MU, "rb") as f: + with open(f"{cwd}/map_solve_mu_input.npy", "rb") as f: mu = jnp.load(f) - with open(TEST_DATA_INVL, "rb") as f: + with open(f"{cwd}/map_solve_invL_input.npy", "rb") as f: invL = jnp.load(f) return (S, flux, cho_C, mu, invL) -# @pytest.fixture(autouse=True) -# def saved_input_data(): - -# with open("/tests/map_solve_S_input.npy", "rb") as f: -# S = jnp.load(f) -# with open("sdholakia_2024a/tests/map_solve_flux_input.npy", "rb") as f: -# flux = jnp.load(f) -# with open("/tests/map_solve_cho_C_input.npy", "rb") as f: -# cho_C = jnp.load(f) -# with open("tests/map_solve_mu_input.npy", "rb") as f: -# mu = jnp.load(f) -# with open("map_solve_invL_input.npy", "rb") as f: -# invL = jnp.load(f) - -# return (S, flux, cho_C, mu, invL) - # Set the seed. np.random.seed(0) From b340bd9a9a5a07e35a8e6ac3b954c2e09b4b1e00 Mon Sep 17 00:00:00 2001 From: alserene Date: Wed, 4 Sep 2024 13:08:28 +1000 Subject: [PATCH 23/24] Test data filepaths. --- tests/test_doppler_inference.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index ddb5b7f..c69eda3 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -65,15 +65,15 @@ @pytest.fixture(autouse=True) def saved_input_data(): - with open("/home/runner/work/sdholakia_2024a/sdholakia_2024a/tests/map_solve_S_input.npy", "rb") as f: + with open(f"{cwd}/tests/map_solve_S_input.npy", "rb") as f: S = jnp.load(f) - with open(f"{cwd}/map_solve_flux_input.npy", "rb") as f: + with open(f"{cwd}/tests/map_solve_flux_input.npy", "rb") as f: flux = jnp.load(f) - with open(f"{cwd}/map_solve_cho_C_input.npy", "rb") as f: + with open(f"{cwd}/tests/map_solve_cho_C_input.npy", "rb") as f: cho_C = jnp.load(f) - with open(f"{cwd}/map_solve_mu_input.npy", "rb") as f: + with open(f"{cwd}/tests/map_solve_mu_input.npy", "rb") as f: mu = jnp.load(f) - with open(f"{cwd}/map_solve_invL_input.npy", "rb") as f: + with open(f"{cwd}/tests/map_solve_invL_input.npy", "rb") as f: invL = jnp.load(f) return (S, flux, cho_C, mu, invL) From 34c20c626ea4f5763347e23b82d39ad9c756e3be Mon Sep 17 00:00:00 2001 From: alserene Date: Wed, 4 Sep 2024 13:42:24 +1000 Subject: [PATCH 24/24] Set correct data filepaths. --- tests/test_doppler_inference.py | 42 ++++++++++----------------------- 1 file changed, 12 insertions(+), 30 deletions(-) diff --git a/tests/test_doppler_inference.py b/tests/test_doppler_inference.py index c69eda3..ec8dd8b 100644 --- a/tests/test_doppler_inference.py +++ b/tests/test_doppler_inference.py @@ -2,6 +2,7 @@ Doppler inference tests. """ +import os import pytest import jax @@ -34,46 +35,27 @@ jax.config.update("jax_enable_x64", True) -# import importlib.resources -# import jaxodi - -# TEST_DATA_S = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_S_input.npy")) -# TEST_DATA_FLUX = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_flux_input.npy")) -# TEST_DATA_CHO_C = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_cho_C_input.npy")) -# TEST_DATA_MU = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_mu_input.npy")) -# TEST_DATA_INVL = str(importlib.resources.files(jaxodi).joinpath("tests", "map_solve_invL_input.npy")) - -# @pytest.fixture(autouse=True) -# def saved_input_data(): +# Get current working directory +CWD = os.getcwd() -# with open(TEST_DATA_S, "rb") as f: -# S = jnp.load(f) -# with open(TEST_DATA_FLUX, "rb") as f: -# flux = jnp.load(f) -# with open(TEST_DATA_CHO_C, "rb") as f: -# cho_C = jnp.load(f) -# with open(TEST_DATA_MU, "rb") as f: -# mu = jnp.load(f) -# with open(TEST_DATA_INVL, "rb") as f: -# invL = jnp.load(f) +# If running the tests from within the test folder +# (rather than the root). +if "/tests" in CWD: + CWD = CWD.replace("/tests", "") -# return (S, flux, cho_C, mu, invL) - -import os -cwd = os.getcwd() @pytest.fixture(autouse=True) def saved_input_data(): - with open(f"{cwd}/tests/map_solve_S_input.npy", "rb") as f: + with open(f"{CWD}/tests/map_solve_S_input.npy", "rb") as f: S = jnp.load(f) - with open(f"{cwd}/tests/map_solve_flux_input.npy", "rb") as f: + with open(f"{CWD}/tests/map_solve_flux_input.npy", "rb") as f: flux = jnp.load(f) - with open(f"{cwd}/tests/map_solve_cho_C_input.npy", "rb") as f: + with open(f"{CWD}/tests/map_solve_cho_C_input.npy", "rb") as f: cho_C = jnp.load(f) - with open(f"{cwd}/tests/map_solve_mu_input.npy", "rb") as f: + with open(f"{CWD}/tests/map_solve_mu_input.npy", "rb") as f: mu = jnp.load(f) - with open(f"{cwd}/tests/map_solve_invL_input.npy", "rb") as f: + with open(f"{CWD}/tests/map_solve_invL_input.npy", "rb") as f: invL = jnp.load(f) return (S, flux, cho_C, mu, invL)

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