"""
Tools for working with BRDF models and data
"""
import numpy as np
import scipy.optimize
import lumos.conversions
[docs]def read_brdf(brdf_file_path):
"""
Reads data from a BRDF file
:param brdf_file_path: Path to the BRDF file
:type brdf_file_path: str
:return: :math:`\\phi_i, \\theta_i, \\phi_o, \\theta_o, BRDF`
:rtype: tuple[:class:`np.ndarray`]
"""
data = np.loadtxt(brdf_file_path, skiprows = 1)
phi_in = data[:, 0]
theta_in = data[:, 1]
phi_out = data[:, 2]
theta_out = data[:, 3]
brdf = data[:, 4]
return phi_in, theta_in, phi_out, theta_out, brdf
[docs]def fit(
data_file,
model_func,
bounds = None,
p0 = None,
log_space = True,
clip = 0
):
"""
Fits a model to experimental data.
:param data_file: A path to a .csv file containing BRDF data. DESCRIBE FORMAT.
:type data_file: str
:param model_func: A BRDF model to fit
:type model_func: function
:param bounds: Bounds passed to :py:func:`scipy.optimize.curve_fit`
:type bounds: tuple
:param p0: Initial guess for parameters passed to :py:func:`scipy.optimize.curve_fit`
:type p0: tuple
:param log_space: Whether or not to fit BRDF in log_space
:type log_space: bool
:param clip: Removes BRDF data below this cutoff from fitting
:type clip: float
"""
phi_in, theta_in, phi_out, theta_out, brdf = read_brdf(data_file)
phi_in, theta_in = np.deg2rad(phi_in), np.deg2rad(theta_in)
phi_out, theta_out = np.deg2rad(phi_out), np.deg2rad(theta_out)
mask = brdf > clip
phi_in = phi_in[mask]
theta_in = theta_in[mask]
phi_out = phi_out[mask]
theta_out = theta_out[mask]
brdf = brdf[mask]
indexes = np.arange(brdf.size)
ix, iy, iz = lumos.conversions.spherical_to_unit(phi_in, theta_in)
ox, oy, oz = lumos.conversions.spherical_to_unit(phi_out, theta_out)
def fit_function(idx, *params):
model_brdf = model_func(*params)
idx = idx.astype(int)
f = model_brdf(
(ix[idx], iy[idx], iz[idx]),
(0, 0, 1),
(ox[idx], oy[idx], oz[idx])
)
return np.log10(f) if log_space else f
popt, _ = scipy.optimize.curve_fit(fit_function,
indexes,
np.log10(brdf) if log_space else brdf,
bounds = bounds,
p0 = p0)
return popt
[docs]def pack_binomial_parameters(n, m, l1, l2, *params):
"""
Convert list into B & C matrices, which can then be passed to the Binomial Model
:param n: n
:type n: int
:param m: m
:type m: int
:param l1: l1
:type l1: int
:param l2: l2, ensure l2 > l1
:type l2: int
:param params: list of values
:type params: list[float]
:return: B, C, d
:rtype: :class:`np.ndarray`, :class:`np.ndarray`, float
"""
params = np.array(params)
B = np.reshape( params[:n * m], (n, m) )
C = np.reshape( params[n * m:], (n, l2 - l1))
return B, C