Remove strenum optional dependancy

requirements-dev.txt

@@ -9,5 +9,4 @@ flake8
 flake8-bandit
 flake8-docstrings
 autopep8
-StrEnum
-ratelimit
+ratelimit

vip_hci/invprob/andromeda.py

@@ -24,7 +24,8 @@
 
 import numpy as np
 from dataclasses import dataclass
-from strenum import LowercaseStrEnum as LowEnum
+from enum import Enum
+
 from typing import Union
 
 from ..var.filters import frame_filter_highpass, cube_filter_highpass
@@ -155,7 +156,7 @@ class AndroParams:
     min_sep: float = 0.5
     annuli_width: float = 1.0
     roa: float = 2
-    opt_method: LowEnum = OptMethod.LSQ
+    opt_method: Enum = OptMethod.LSQ
     nsmooth_snr: int = 18
     iwa: float = None
     owa: float = None

vip_hci/invprob/fmmf.py

@@ -61,7 +61,7 @@
 from dataclasses import dataclass, field
 import numpy as np
 import numpy.linalg as la
-from strenum import LowercaseStrEnum as LowEnum
+from enum import Enum
 from skimage.draw import disk
 from ..var import get_annulus_segments, frame_center
 from ..preproc import frame_crop, cube_crop_frames, cube_derotate
@@ -106,7 +106,7 @@ class FMMFParams:
     model: string, optional
         Selected PSF-subtraction technique for the computation of the FMMF
         detection map. FMMF work either with KLIP or LOCI. Default is 'KLIP'.
-    var: LowerCaseStrEnum, see `vip_hci.var.paramenum.VarEstim`
+    var: Enum, see `vip_hci.var.paramenum.VarEstim`
         Model used for the residual noise variance estimation used in the
         matched filtering (maximum likelihood estimation of the flux and SNR).
     param: dict, optional
@@ -126,10 +126,10 @@ class FMMFParams:
     crop: int, optional
         Part of the PSF template considered in the estimation of the FMMF
         detection map. Default is 5.
-    imlib : LowerCaseStrEnum, see `vip_hci.var.paramenum.Imlib`
+    imlib : Enum, see `vip_hci.var.paramenum.Imlib`
         Parameter used for the derotation of the residual cube. See the
         documentation of the ``vip_hci.preproc.frame_rotate`` function.
-    interpolation : LowerCaseStrEnum, see `vip_hci.var.paramenum.Interpolation`
+    interpolation : Enum, see `vip_hci.var.paramenum.Interpolation`
         Parameter used for the derotation of the residual cube. See the
         documentation of the ``vip_hci.preproc.frame_rotate`` function.
     nproc : int or None, optional
@@ -148,13 +148,13 @@ class FMMFParams:
     min_r: int = None
     max_r: int = None
     model: str = "KLIP"
-    var: LowEnum = VarEstim.FR
+    var: Enum = VarEstim.FR
     param: dict = field(
         default_factory=lambda: {"ncomp": 20, "tolerance": 5e-3, "delta_rot": 0.5}
     )
     crop: int = 5
-    imlib: LowEnum = Imlib.VIPFFT
-    interpolation: LowEnum = Interpolation.LANCZOS4
+    imlib: Enum = Imlib.VIPFFT
+    interpolation: Enum = Interpolation.LANCZOS4
     nproc: int = 1
     verbose: bool = True
 

vip_hci/objects/pppca.py

@@ -160,7 +160,7 @@ def run(
 
         Parameters
         ----------
-        runmode : LowerCaseStrEnum, see ``vip_hci.var.paramenum.Runmode``
+        runmode : Enum, see ``vip_hci.var.paramenum.Runmode``
             Mode of execution for the PCA.
         dataset : Dataset, optional
             Dataset to process. If not provided, ``self.dataset`` is used (as

vip_hci/psfsub/framediff.py

@@ -9,7 +9,7 @@
 from hciplot import plot_frames
 from multiprocessing import cpu_count
 from dataclasses import dataclass
-from strenum import LowercaseStrEnum as LowEnum
+from enum import Enum
 from sklearn.metrics import pairwise_distances
 from ..var import get_annulus_segments
 from ..var.object_utils import setup_parameters, separate_kwargs_dict
@@ -77,16 +77,16 @@ class FrameDiffParams:
     cube: np.ndarray = None
     angle_list: np.ndarray = None
     fwhm: float = 4
-    metric: LowEnum = Metric.MANHATTAN
+    metric: Enum = Metric.MANHATTAN
     dist_threshold: int = 50
     n_similar: int = None
     delta_rot: int = 0.5
     radius_int: int = 2
     asize: int = 4
     ncomp: int = None
-    imlib: LowEnum = Imlib.VIPFFT
-    interpolation: LowEnum = Interpolation.LANCZOS4
-    collapse: LowEnum = Collapse.MEDIAN
+    imlib: Enum = Imlib.VIPFFT
+    interpolation: Enum = Interpolation.LANCZOS4
+    collapse: Enum = Collapse.MEDIAN
     nproc: int = 1
     verbose: bool = True
     debug: bool = False

vip_hci/psfsub/llsg.py

@@ -22,7 +22,7 @@
 from multiprocessing import cpu_count
 from astropy.stats import median_absolute_deviation
 from dataclasses import dataclass
-from strenum import LowercaseStrEnum as LowEnum
+from enum import Enum
 from ..config import time_ini, timing
 from ..preproc import cube_derotate, cube_collapse
 from ..var import get_annulus_segments, cube_filter_highpass
@@ -54,9 +54,9 @@ class LLSGParams:
     low_rank_ref :
         If True the first estimation of the L component is obtained from the
         remaining segments in the same annulus.
-    low_rank_mode : LowerCaseStrEnum, see `vip_hci.var.paramenum.LowRankMode`
+    low_rank_mode : Enum, see `vip_hci.var.paramenum.LowRankMode`
         Sets the method of solving the L update.
-    auto_rank_mode : LowerCaseStrEnum, see `vip_hci.var.paramenum.AutoRankMode`
+    auto_rank_mode : Enum, see `vip_hci.var.paramenum.AutoRankMode`
         If ``rank`` is None, then ``auto_rank_mode`` sets the way that the
         ``rank`` is determined: the noise minimization or the cumulative
         explained variance ratio (when 'svd' is used).
@@ -66,7 +66,7 @@ class LLSGParams:
     cevr : float, optional
         Float value in the range [0,1] for selecting the cumulative explained
         variance ratio to choose the rank automatically (if ``rank`` is None).
-    thresh_mode : LowerCaseStrEnum, see `vip_hci.var.paramenum.ThreshMode`
+    thresh_mode : Enum, see `vip_hci.var.paramenum.ThreshMode`
         Sets the type of thresholding.
     nproc : None or int, optional
         Number of processes for parallel computing. If None the number of
@@ -91,7 +91,7 @@ class LLSGParams:
         first with the mode ``median-subt`` and a large window, and then with
         ``laplacian-conv`` and a kernel size equal to ``high_pass``. 5 is an
         optimal value when ``fwhm`` is ~4.
-    collapse : LowerCaseStrEnum, see `vip_hci.var.paramenum.Collapse`
+    collapse : Enum, see `vip_hci.var.paramenum.Collapse`
         Sets the way of collapsing the frames for producing a final image.
     full_output: bool, optional
         Whether to return the final median combined image only or with other
@@ -109,19 +109,19 @@ class LLSGParams:
     thresh: float = 1
     max_iter: int = 10
     low_rank_ref: bool = False
-    low_rank_mode: LowEnum = LowRankMode.SVD
-    auto_rank_mode: LowEnum = AutoRankMode.NOISE
+    low_rank_mode: Enum = LowRankMode.SVD
+    auto_rank_mode: Enum = AutoRankMode.NOISE
     residuals_tol: float = 1e-1
     cevr: float = 0.9
-    thresh_mode: LowEnum = ThreshMode.SOFT
+    thresh_mode: Enum = ThreshMode.SOFT
     nproc: int = 1
     asize: int = None
     n_segments: int = 4
     azimuth_overlap: int = None
     radius_int: int = None
     random_seed: int = None
     high_pass: int = None
-    collapse: LowEnum = Collapse.MEDIAN
+    collapse: Enum = Collapse.MEDIAN
     full_output: bool = False
     verbose: bool = True
     debug: bool = False

vip_hci/psfsub/loci.py

@@ -22,7 +22,7 @@
 from multiprocessing import cpu_count
 from sklearn.metrics import pairwise_distances
 from dataclasses import dataclass
-from strenum import LowercaseStrEnum as LowEnum
+from enum import Enum
 from typing import Tuple, Union
 from ..var import get_annulus_segments
 from ..var.object_utils import setup_parameters, separate_kwargs_dict
@@ -55,7 +55,7 @@ class LOCIParams:
         e.g. with ``vip_hci.preproc.find_scal_vector``).
     fwhm : float, optional
         Size of the FHWM in pixels. Default is 4.
-    metric : LowerCaseStrEnum, see `vip_hci.var.paramenum.Metric`
+    metric : Enum, see `vip_hci.var.paramenum.Metric`
         Distance metric to be used ('cityblock', 'cosine', 'euclidean', 'l1',
         'l2', 'manhattan', 'correlation', etc). It uses the scikit-learn
         function ``sklearn.metrics.pairwise.pairwise_distances`` (check its
@@ -87,7 +87,7 @@ class LOCIParams:
         Number of processes for parallel computing. If None the number of
         processes will be set to cpu_count()/2. By default the algorithm works
         in single-process mode.
-    solver : LowerCaseStrEnum, see `vip_hci.var.paramenum.Solver`
+    solver : Enum, see `vip_hci.var.paramenum.Solver`
         Choosing the solver of the least squares problem. ``lstsq`` uses the
         standard scipy least squares solver. ``nnls`` uses the scipy
         non-negative least-squares solver.
@@ -102,7 +102,7 @@ class LOCIParams:
         similar to LOCI. The optimization segments share the same inner radius,
         mean angular position and angular width as their corresponding
         subtraction segments.
-    adimsdi : LowerCaseStrEnum, see `vip_hci.var.paramenum.Adimsdi`
+    adimsdi : Enum, see `vip_hci.var.paramenum.Adimsdi`
         Changes the way the 4d cubes (ADI+mSDI) are processed.
 
         ``skipadi``: the multi-spectral frames are rescaled wrt the largest
@@ -113,11 +113,11 @@ class LOCIParams:
         (as in the ``skipadi`` case). Then the residuals are processed again in
         an ADI fashion.
 
-    imlib : LowerCaseStrEnum, see `vip_hci.var.paramenum.Imlib`
+    imlib : Enum, see `vip_hci.var.paramenum.Imlib`
         See the documentation of the ``vip_hci.preproc.frame_rotate`` function.
-    interpolation : LowerCaseStrEnum, see `vip_hci.var.paramenum.Interpolation`
+    interpolation : Enum, see `vip_hci.var.paramenum.Interpolation`
         See the documentation of the ``vip_hci.preproc.frame_rotate`` function.
-    collapse : LowerCaseStrEnum, see `vip_hci.var.paramenum.Collapse`
+    collapse : Enum, see `vip_hci.var.paramenum.Collapse`
         Sets the way of collapsing the frames for producing a final image.
     verbose: bool, optional
         If True prints info to stdout.
@@ -130,21 +130,21 @@ class LOCIParams:
     angle_list: np.ndarray = None
     scale_list: np.ndarray = None
     fwhm: float = 4
-    metric: LowEnum = Metric.MANHATTAN
+    metric: Enum = Metric.MANHATTAN
     dist_threshold: int = 100
     delta_rot: Union[float, Tuple[float]] = (0.1, 1)
     delta_sep: Union[float, Tuple[float]] = (0.1, 1)
     radius_int: int = 0
     asize: int = 4
     n_segments: int = 4
     nproc: int = 1
-    solver: LowEnum = Solver.LSTSQ
+    solver: Enum = Solver.LSTSQ
     tol: float = 1e-2
     optim_scale_fact: float = 2
-    adimsdi: LowEnum = Adimsdi.SKIPADI
-    imlib: LowEnum = Imlib.VIPFFT
-    interpolation: LowEnum = Interpolation.LANCZOS4
-    collapse: LowEnum = Collapse.MEDIAN
+    adimsdi: Enum = Adimsdi.SKIPADI
+    imlib: Enum = Imlib.VIPFFT
+    interpolation: Enum = Interpolation.LANCZOS4
+    collapse: Enum = Collapse.MEDIAN
     verbose: bool = True
     full_output: bool = False
 

vip_hci/psfsub/medsub.py

@@ -36,8 +36,8 @@
 import numpy as np
 from multiprocessing import cpu_count
 from dataclasses import dataclass
+from enum import Enum
 from typing import Tuple, Union
-from strenum import LowercaseStrEnum as LowEnum
 from ..config import time_ini, timing
 from ..var import get_annulus_segments, mask_circle
 from ..var.paramenum import Imlib, Interpolation, Collapse
@@ -102,11 +102,11 @@ class MedsubParams:
     sdi_only: bool, optional
         In the case of IFS data (ADI+SDI), whether to perform median-SDI, or
         median-ASDI (default).
-    imlib : LowerCaseStrEnum, see `vip_hci.var.paramenum.Imlib`
+    imlib : Enum, see `vip_hci.var.paramenum.Imlib`
         See the documentation of ``vip_hci.preproc.frame_rotate``.
-    interpolation : LowerCaseStrEnum, see `vip_hci.var.paramenum.Interpolation`
+    interpolation : Enum, see `vip_hci.var.paramenum.Interpolation`
         See the documentation of the ``vip_hci.preproc.frame_rotate`` function.
-    collapse : LowerCaseStrEnum, see `vip_hci.var.paramenum.Collapse`
+    collapse : Enum, see `vip_hci.var.paramenum.Collapse`
         Sets how temporal residual frames should be combined to produce an
         ADI image.
     nproc : None or int, optional
@@ -132,9 +132,9 @@ class MedsubParams:
     mode: str = "fullfr"
     nframes: int = 4
     sdi_only: bool = False
-    imlib: LowEnum = Imlib.VIPFFT
-    interpolation: LowEnum = Interpolation.LANCZOS4
-    collapse: LowEnum = Collapse.MEDIAN
+    imlib: Enum = Imlib.VIPFFT
+    interpolation: Enum = Interpolation.LANCZOS4
+    collapse: Enum = Collapse.MEDIAN
     nproc: int = 1
     full_output: bool = False
     verbose: bool = True

vip_hci/psfsub/nmf_fullfr.py

@@ -18,7 +18,7 @@
 import numpy as np
 from sklearn.decomposition import NMF
 from dataclasses import dataclass, field
-from strenum import LowercaseStrEnum as LowEnum
+from enum import Enum
 from typing import Tuple
 from ..preproc import cube_derotate, cube_collapse
 from ..preproc.derotation import _compute_pa_thresh, _find_indices_adi
@@ -51,7 +51,7 @@ class NMFParams:
     ncomp : int, optional
         How many components are used as for low-rank approximation of the
         datacube.
-    scaling : LowerCaseStrEnum, see `vip_hci.var.paramenum.Scaling`
+    scaling : Enum, see `vip_hci.var.paramenum.Scaling`
         With None, no scaling is performed on the input data before SVD. With
         "temp-mean" then temporal px-wise mean subtraction is done, with
         "spat-mean" then the spatial mean is subtracted, with "temp-standard"
@@ -76,20 +76,20 @@ class NMFParams:
         Default is 1 (excludes 1xFHWM on each side of the considered frame).
     fwhm : float, optional
         Known size of the FHWM in pixels to be used. Default value is 4.
-    init_svd: LowerCaseStrEnum, see `vip_hci.var.paramenum.Initsvd`
+    init_svd: Enum, see `vip_hci.var.paramenum.Initsvd`
         Method used to initialize the iterative procedure to find H and W.
         'nndsvd': non-negative double SVD recommended for sparseness
         'nndsvda': NNDSVD where zeros are filled with the average of cube;
         recommended when sparsity is not desired
         'random': random initial non-negative matrix
-    collapse : LowerCaseStrEnum, see `vip_hci.var.paramenum.Collapse`
+    collapse : Enum, see `vip_hci.var.paramenum.Collapse`
         Sets the way of collapsing the frames for producing a final image.
     full_output: boolean, optional
         Whether to return the final median combined image only or with other
         intermediate arrays.
     verbose : {True, False}, bool optional
         If True prints intermediate info and timing.
-    handle_neg: LowerCaseStrEnum, see `vip_hci.var.paramenum.HandleNeg`
+    handle_neg: Enum, see `vip_hci.var.paramenum.HandleNeg`
         Determines how to handle negative values: mask them, set them to zero,
         or subtract the minimum value in the arrays. Note: 'mask' or 'null'
         may leave significant artefacts after derotation of residual cube
@@ -104,19 +104,19 @@ class NMFParams:
     angle_list: np.ndarray = None
     cube_ref: np.ndarray = None
     ncomp: int = 1
-    scaling: LowEnum = None
+    scaling: Enum = None
     max_iter: int = 10000
     random_state: int = None
     mask_center_px: int = None
     source_xy: Tuple[int] = None
     delta_rot: float = 1
     fwhm: float = 4
-    init_svd: LowEnum = Initsvd.NNDSVD
-    collapse: LowEnum = Collapse.MEDIAN
+    init_svd: Enum = Initsvd.NNDSVD
+    collapse: Enum = Collapse.MEDIAN
     full_output: bool = False
     verbose: bool = True
     cube_sig: np.ndarray = None
-    handle_neg: LowEnum = HandleNeg.MASK
+    handle_neg: Enum = HandleNeg.MASK
     nmf_args: dict = field(default_factory=lambda: {})
 
 

vip_hci/psfsub/nmf_local.py

@@ -12,7 +12,7 @@
 from sklearn.decomposition import NMF
 from dataclasses import dataclass, field
 from typing import Tuple, List, Union
-from strenum import LowercaseStrEnum as LowEnum
+from enum import Enum
 from ..preproc import cube_derotate, cube_collapse, check_pa_vector
 from ..preproc.derotation import _find_indices_adi, _define_annuli
 from ..var import get_annulus_segments, matrix_scaling
@@ -123,20 +123,20 @@ class NMFAnnParams:
     n_segments: int = 1
     delta_rot: Union[float, Tuple[float]] = (0.1, 1)
     ncomp: int = 1
-    init_svd: LowEnum = Initsvd.NNDSVD
+    init_svd: Enum = Initsvd.NNDSVD
     nproc: int = 1
     min_frames_lib: int = 2
     max_frames_lib: int = 200
-    scaling: LowEnum = None
-    imlib: LowEnum = Imlib.VIPFFT
-    interpolation: LowEnum = Interpolation.LANCZOS4
-    collapse: LowEnum = Collapse.MEDIAN
+    scaling: Enum = None
+    imlib: Enum = Imlib.VIPFFT
+    interpolation: Enum = Interpolation.LANCZOS4
+    collapse: Enum = Collapse.MEDIAN
     full_output: bool = False
     verbose: bool = True
     theta_init: float = 0
     weights: List = None
     cube_sig: np.ndarray = None
-    handle_neg: LowEnum = HandleNeg.MASK
+    handle_neg: Enum = HandleNeg.MASK
     max_iter: int = 1000
     random_state: int = None
     nmf_args: dict = field(default_factory=lambda: {})