Merge pull request vortex-exoplanet#654 from IainHammond/master

more options to get_values_optimize, get_mu_and_sigma, speckle_noise_uncertainty. typo & docstring fixes

vip_hci/fm/negfc_fmerit.py

@@ -5,6 +5,7 @@
 __all__ = ["get_mu_and_sigma"]
 
 import numpy as np
+
 from hciplot import plot_frames
 from skimage.draw import disk
 from ..fm import cube_inject_companions, cube_planet_free
@@ -490,8 +491,10 @@ def get_values_optimize(
         max_frames_lib = algo_opt_copy.pop("max_frames_lib", 200)
         radius_int = max(1, int(np.floor(r_guess - annulus_width / 2)))
         radius_int = algo_opt_copy.pop("radius_int", radius_int)
+        asize = algo_opt_copy.pop("asize", annulus_width)
+        delta_rot = algo_opt_copy.pop("delta_rot", delta_rot)
         # crop cube to just be larger than annulus => FASTER PCA
-        crop_sz = int(2 * np.ceil(radius_int + annulus_width + 1))
+        crop_sz = int(2 * np.ceil(radius_int + asize + 1))
         if not crop_sz % 2:
             crop_sz += 1
         if crop_sz < cube.shape[-2] and crop_sz < cube.shape[-1]:
@@ -507,7 +510,7 @@ def get_values_optimize(
                 cube_ref=cube_ref,
                 radius_int=radius_int,
                 fwhm=fwhm,
-                asize=annulus_width,
+                asize=asize,
                 delta_rot=delta_rot,
                 ncomp=ncomp,
                 svd_mode=svd_mode,
@@ -553,6 +556,9 @@ def get_values_optimize(
     elif algo == pca:
         scale_list = algo_opt_copy.pop("scale_list", None)
         ifs_collapse_range = algo_opt_copy.pop("ifs_collapse_range", "all")
+        mask_rdi = algo_options.pop("mask_rdi", None)
+        delta_rot = algo_options.pop("delta_rot", delta_rot)
+        source_xy = algo_options.pop("source_xy", None)
         res = pca(
             cube=cube,
             angle_list=angs,
@@ -561,6 +567,8 @@ def get_values_optimize(
             ncomp=ncomp,
             svd_mode=svd_mode,
             scaling=scaling,
+            delta_rot=delta_rot,
+            source_xy=source_xy,
             imlib=imlib,
             interpolation=interpolation,
             collapse=collapse,
@@ -748,6 +756,7 @@ def get_mu_and_sigma(cube, angs, ncomp, annulus_width, aperture_radius, fwhm,
     collapse = algo_options.get("collapse", collapse)
 
     radius_int = max(int(np.floor(r_guess - annulus_width / 2)), 0)
+    radius_int = algo_options.get("radius_int", radius_int)
 
     # not recommended, except if large-scale residual sky present (NIRC2-L')
     hp_filter = algo_options.get("hp_filter", None)
@@ -864,6 +873,7 @@ def get_mu_and_sigma(cube, angs, ncomp, annulus_width, aperture_radius, fwhm,
         scale_list = algo_options.get("scale_list", None)
         ifs_collapse_range = algo_options.get("ifs_collapse_range", "all")
         nproc = algo_options.get("nproc", 1)
+        source_xy = algo_options.get("source_xy", None)
 
         pca_res = pca(
             cube=array,
@@ -873,6 +883,8 @@ def get_mu_and_sigma(cube, angs, ncomp, annulus_width, aperture_radius, fwhm,
             ncomp=ncomp,
             svd_mode=svd_mode,
             scaling=scaling,
+            delta_rot=delta_rot,
+            source_xy=source_xy,
             imlib=imlib,
             interpolation=interpolation,
             collapse=collapse,
@@ -890,6 +902,8 @@ def get_mu_and_sigma(cube, angs, ncomp, annulus_width, aperture_radius, fwhm,
                 ncomp=ncomp,
                 svd_mode=svd_mode,
                 scaling=scaling,
+                delta_rot=delta_rot,
+                source_xy=source_xy,
                 imlib=imlib,
                 interpolation=interpolation,
                 collapse=collapse,

vip_hci/fm/negfc_mcmc.py

@@ -516,7 +516,7 @@ def mcmc_negfc_sampling(cube, angs, psfn, initial_state, algo=pca_annulus,
           'sum' when residual speckle noise is still significant):\
           :math:`\chi^2 = N \sigma_{I_j}(values,ddof=1)*values.size`
         - ``mu_sigma=True`` or a tuple (as in [CHR21]_, new default):\
-          :math:`\chi^2 = \sum\frac{(I_j- mu)^2}{\sigma^2}`
+          :math:`\chi^2 = \sum\frac{I_j- \mu)^2}{\sigma^2}`
 
     where :math:`j \in {1,...,N}` with N the total number of pixels
     contained in the circular aperture, :math:`\sigma_{I_j}` is the standard
@@ -999,7 +999,7 @@ def mcmc_negfc_sampling(cube, angs, psfn, initial_state, algo=pca_annulus,
                     if verbosity > 0:
                         print('   r_hat = {}'.format(rhat))
                         cond = rhat <= rhat_threshold
-                        print('   r_hat <= threshold = {} \n'.format(cond))
+                        print('   r_hat <= threshold = {} \n'.format(cond), flush=True)
                     # We test the rhat.
                     if (rhat <= rhat_threshold).all():
                         rhat_count += 1
@@ -1025,7 +1025,7 @@ def mcmc_negfc_sampling(cube, angs, psfn, initial_state, algo=pca_annulus,
                     thr = 1./ac_c
                     if verbosity > 0:
                         print('Auto-corr tau/N = {}'.format(rhat))
-                        print('tau/N <= {} = {} \n'.format(thr, rhat < thr))
+                        print('tau/N <= {} = {} \n'.format(thr, rhat < thr), flush=True)
                     if (rhat <= thr).all():
                         ac_count += 1
                         if verbosity > 0:

vip_hci/fm/negfc_simplex.py

@@ -619,7 +619,7 @@ def firstguess(cube, angs, psfn, planets_xy_coord, ncomp=1, fwhm=4,
         If set to None: not used, and falls back to original version of the
         algorithm, using fmerit.
         If a tuple of 2 elements: should be the mean and standard deviation of
-        pixel intensities in an annulus centered on the lcoation of the
+        pixel intensities in an annulus centered on the location of the
         companion candidate, excluding the area directly adjacent to the CC.
         If set to anything else, but None/False/tuple: will compute said mean
         and standard deviation automatically.
@@ -762,7 +762,7 @@ def firstguess(cube, angs, psfn, planets_xy_coord, ncomp=1, fwhm=4,
             print(msg3a.format(i_planet, r_pre, theta_pre))
             msg3b = 'Planet {}: preliminary flux guess: '.format(i_planet)
             for z in range(len(f_pre)):
-                msg3b += '{:.1f}'.format(f_pre[z])
+                msg3b += '{:.2f}'.format(f_pre[z])
                 if z < len(f_pre)-1:
                     msg3b += ', '
             print(msg3b)

vip_hci/fm/negfc_speckle_noise.py

@@ -60,7 +60,7 @@ def speckle_noise_uncertainty(cube, p_true, angle_range, derot_angles, algo,
         of spectral channels (flux at each wavelength).
     angle_range: 1d numpy array
         Range of angles (counted from x=0 axis, counter-clockwise) at which the
-        fake companions will be injected, in [0,360[.
+        fake companions will be injected, in [0,360].
     derot_angles: 1d numpy array
         Derotation angles for ADI. Length should match input cube.
     algo: python routine
@@ -96,7 +96,7 @@ def speckle_noise_uncertainty(cube, p_true, angle_range, derot_angles, algo,
         If set to None: not used, and falls back to original version of the
         algorithm, using fmerit.
         If a tuple of 2 elements: should be the mean and standard deviation of
-        pixel intensities in an annulus centered on the lcoation of the
+        pixel intensities in an annulus centered on the location of the
         companion candidate, excluding the area directly adjacent to the CC.
         If set to anything else, but None/False/tuple: will compute said mean
         and standard deviation automatically.
@@ -157,7 +157,7 @@ def speckle_noise_uncertainty(cube, p_true, angle_range, derot_angles, algo,
     save: bool, optional
         If True, the result are pickled.
     verbose: bool, optional
-        If True, informations are displayed in the shell.
+        If True, information is displayed in the shell.
     plot: bool, optional
         Whether to plot the gaussian fit to the distributions of parameter
         deviations (between retrieved and injected).
@@ -366,9 +366,6 @@ def _estimate_speckle_one_angle(angle, cube_pf, psfn, angs, r_true, f_true,
                                      imlib=imlib, interpolation=interpolation,
                                      verbose=False)
 
-    ncomp = algo_options.get('ncomp', 1)
-    annulus_width = algo_options.get('annulus_width', int(fwhm))
-
     if cube_pf.ndim == 4:
         p_ini = [r_true, angle]
         for f in f_true:
@@ -377,10 +374,14 @@ def _estimate_speckle_one_angle(angle, cube_pf, psfn, angs, r_true, f_true,
     else:
         p_ini = (r_true, angle, f_true)
 
+    ncomp = algo_options.get('ncomp', 1)
+    annulus_width = algo_options.get('annulus_width', int(fwhm))
+    delta_rot = algo_options.get('delta_rot', 1)
+
     res_simplex = firstguess_simplex(p_ini, cube_fc, angs,
                                      psfn, ncomp, fwhm, annulus_width,
                                      aperture_radius, cube_ref=cube_ref,
-                                     fmerit=fmerit, algo=algo,
+                                     fmerit=fmerit, algo=algo, delta_rot=delta_rot,
                                      algo_options=algo_options, imlib=imlib,
                                      interpolation=interpolation,
                                      transmission=transmission,

vip_hci/greedy/ipca_fullfr.py

@@ -49,7 +49,6 @@
 from dataclasses import dataclass
 import numpy as np
 from typing import Union, List
-import warnings
 from ..config.paramenum import ALGO_KEY
 from ..config.utils_param import separate_kwargs_dict
 from ..config import Progressbar
@@ -62,8 +61,9 @@
     from GreeDS import GreeDS
     no_greeds = False
 except ImportError:
+    from warnings import warn
     msg = "GreeDS python bindings are missing."
-    warnings.warn(msg, ImportWarning)
+    warn(msg, ImportWarning)
     no_greeds = True
 
 

vip_hci/preproc/derotation.py

@@ -291,7 +291,7 @@ def frame_rotate(array, angle, imlib='vip-fft', interpolation='lanczos4',
         elif interpolation == 'lanczos4':
             intp = cv2.INTER_LANCZOS4
         else:
-            raise ValueError('Opencv interpolation method not recognized')
+            raise ValueError(f'Opencv interpolation method `{interpolation}` is not recognized')
 
         if border_mode == 'constant':
             bormo = cv2.BORDER_CONSTANT  # iiiiii|abcdefgh|iiiiiii

vip_hci/psfsub/pca_fullfr.py

@@ -132,7 +132,7 @@ class PCA_Params:
 
 
 def pca(*all_args: List, **all_kwargs: dict):
-    """Full-frame PCA algorithm applied to PSF substraction.
+    """Full-frame PCA algorithm applied to PSF subtraction.
 
     The reference PSF and the quasi-static speckle pattern are modeled using
     Principal Component Analysis. Depending on the input parameters this PCA
@@ -156,11 +156,11 @@ def pca(*all_args: List, **all_kwargs: dict):
     Parameters
     ----------
     all_args: list, optional
-        Positionnal arguments for the PCA algorithm. Full list of parameters
+        Positional arguments for the PCA algorithm. Full list of parameters
         below.
     all_kwargs: dictionary, optional
         Mix of keyword arguments that can initialize a PCA_Params and the
-        optional 'rot_options' dictionnary (with keyword values ``border_mode``,
+        optional 'rot_options' dictionary (with keyword values ``border_mode``,
         ``mask_val``, ``edge_blend``, ``interp_zeros``, ``ker``; see docstring
         of ``vip_hci.preproc.frame_rotate``). Can also contain a PCA_Params
         dictionary named `algo_params`.

vip_hci/psfsub/pca_local.py

@@ -94,7 +94,7 @@ def pca_annular(*all_args: List, **all_kwargs: dict):
         parameters below.
     all_kwargs: dictionary, optional
         Mix of keyword arguments that can initialize a PCA_ANNULAR_Params and
-        the optional 'rot_options' dictionnary, with keyword values for
+        the optional 'rot_options' dictionary, with keyword values for
         "border_mode", "mask_val", "edge_blend", "interp_zeros", "ker" (see
         documentation of ``vip_hci.preproc.frame_rotate``). Can also contain a
         PCA_ANNULAR_Params named as `algo_params`.

vip_hci/psfsub/utils_pca.py

@@ -309,6 +309,9 @@ def get_snr(frame, y, x, fwhm, fmerit):
                             'to (PC_INI, PC_MAX) or (PC_INI, PC_MAX, STEP)')
         pclist = list(range(pcmin, pcmax+1, step))
 
+    if fmerit not in ['px', 'max', 'mean']:
+        raise ValueError(f"Invalid value for fmerit: {fmerit}.")
+
     # Getting `pcmax` principal components once
     if mode == 'fullfr':
         matrix = prepare_matrix(cube, scaling, mask_center_px, verbose=False)

vip_hci/var/filters.py

@@ -574,7 +574,7 @@ def cube_filter_lowpass(array, mode='gauss', median_size=5, fwhm_size=5,
         mode is set to 'psf'.
     mask: numpy ndarray, optional
         Binary mask indicating where the low-pass filtered image should be
-        interpolated with astropy.convolution. This otion can be useful if the
+        interpolated with astropy.convolution. This option can be useful if the
         low-pass filtered image is aimed to capture low-spatial frequency sky
         signal, while avoiding a stellar halo (set to one in the binary mask).
         Note: only works with Gaussian kernel or PSF convolution.

vip_hci/var/fit_2d.py

@@ -75,9 +75,9 @@ def create_synth_psf(model='gauss', shape=(9, 9), amplitude=1, x_mean=None,
     Note
     ----
     More information can be found at the following links:
-        http://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Gaussian2D.html
-        http://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Moffat2D.html
-        http://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.AiryDisk2D.html
+        https://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Gaussian2D.html
+        https://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Moffat2D.html
+        https://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.AiryDisk2D.html
 
     https://www.gnu.org/software/gnuastro/manual/html_node/PSF.html
     web.ipac.caltech.edu/staff/fmasci/home/astro_refs/PSFtheory.pdf