removing nvtx instances to allow docs to build, removing function doubleundescore duplications

Author: dkazanc

httomolibgpu/cupywrapper.py

@@ -14,6 +14,7 @@
     )
     from unittest.mock import Mock
     import numpy as cp
+
     cupy_run = False
 
     nvtx = Mock()

httomolibgpu/misc/morph.py

@@ -24,7 +24,6 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-nvtx = cupywrapper.nvtx
 cupy_run = cupywrapper.cupy_run
 
 from unittest.mock import Mock
@@ -64,17 +63,6 @@ def sino_360_to_180(
     cp.ndarray
         Output 3D data.
     """
-    if cupywrapper.cupy_run:
-        return __sino_360_to_180(data, overlap, rotation)
-    else:
-        print("sino_360_to_180 won't be executed because CuPy is not installed")
-        return data
-
-
-@nvtx.annotate()
-def __sino_360_to_180(
-    data: cp.ndarray, overlap: int = 0, rotation: Literal["left", "right"] = "left"
-) -> cp.ndarray:
     if data.ndim != 3:
         raise ValueError("only 3D data is supported")
 
@@ -85,8 +73,8 @@ def __sino_360_to_180(
         raise ValueError("overlap must be less than data.shape[2]")
     if overlap < 0:
         raise ValueError("only positive overlaps are allowed.")
-    
-    if rotation not in ['left', 'right']:
+
+    if rotation not in ["left", "right"]:
         raise ValueError('rotation parameter must be either "left" or "right"')
 
     n = dx // 2
@@ -133,18 +121,6 @@ def data_resampler(
     Returns:
         cp.ndarray: Up/Down-scaled 3D cupy array
     """
-    if cupywrapper.cupy_run:
-        return __data_resampler(data, newshape, axis, interpolation)
-    else:
-        print("data_resampler won't be executed because CuPy is not installed")
-        return data
-
-
-@nvtx.annotate()
-def __data_resampler(
-    data: cp.ndarray, newshape: list, axis: int = 1, interpolation: str = "linear"
-) -> cp.ndarray:    
-
     if data.ndim != 3:
         raise ValueError("only 3D data is supported")
 

httomolibgpu/misc/rescale.py

@@ -23,7 +23,6 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-nvtx = cupywrapper.nvtx
 
 from typing import Literal, Optional, Tuple, Union
 
@@ -70,53 +69,6 @@ def rescale_to_int(
         The original data, clipped to the range specified with the perc_range_min and
         perc_range_max, and scaled to the full range of the output integer type
     """
-    if cupywrapper.cupy_run:
-        return __rescale_to_int(data, perc_range_min, perc_range_max, bits, glob_stats)
-    else:
-        print("rescale_to_int won't be executed because CuPy is not installed")
-        return data
-
-
-@nvtx.annotate()
-def __rescale_to_int(
-    data: cp.ndarray,
-    perc_range_min: float = 0.0,
-    perc_range_max: float = 100.0,
-    bits: Literal[8, 16, 32] = 8,
-    glob_stats: Optional[Tuple[float, float, float, int]] = None,
-):
-    """
-    Rescales the data and converts it fit into the range of an unsigned integer type
-    with the given number of bits.
-
-    Parameters
-    ----------
-    data : cp.ndarray
-        Required input data array, on GPU
-    perc_range_min: float, optional
-        The lower cutoff point in the input data, in percent of the data range (defaults to 0).
-        The lower bound is computed as min + perc_range_min/100*(max-min)
-    perc_range_max: float, optional
-        The upper cutoff point in the input data, in percent of the data range (defaults to 100).
-        The upper bound is computed as min + perc_range_max/100*(max-min)
-    bits: Literal[8, 16, 32], optional
-        The number of bits in the output integer range (defaults to 8).
-        Allowed values are:
-        - 8 -> uint8
-        - 16 -> uint16
-        - 32 -> uint32
-    glob_stats: tuple, optional
-        Global statistics of the full dataset (beyond the data passed into this call).
-        It's a tuple with (min, max, sum, num_items). If not given, the min/max is
-        computed from the given data.
-
-    Returns
-    -------
-    cp.ndarray
-        The original data, clipped to the range specified with the perc_range_min and
-        perc_range_max, and scaled to the full range of the output integer type
-    """
-
     if bits == 8:
         output_dtype: Union[type[np.uint8], type[np.uint16], type[np.uint32]] = np.uint8
     elif bits == 16:

httomolibgpu/prep/alignment.py

@@ -24,7 +24,6 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-nvtx = cupywrapper.nvtx
 cupy_run = cupywrapper.cupy_run
 
 from unittest.mock import Mock
@@ -82,55 +81,6 @@ def distortion_correction_proj_discorpy(
     cp.ndarray
         3D array. Distortion-corrected array.
     """
-    if cupywrapper.cupy_run:
-        return __distortion_correction_proj_discorpy(
-            data, metadata_path, preview, order, mode
-        )
-    else:
-        print(
-            "distortion_correction_proj_discorpy won't be executed because CuPy is not installed"
-        )
-        return data
-
-
-@nvtx.annotate()
-def __distortion_correction_proj_discorpy(
-    data: cp.ndarray,
-    metadata_path: str,
-    preview: Dict[str, List[int]],
-    order: int = 1,
-    mode: str = "reflect",
-):
-    """Unwarp a stack of images using a backward model.
-
-    Parameters
-    ----------
-    data : cp.ndarray
-        3D array.
-
-    metadata_path : str
-        The path to the file containing the distortion coefficients for the
-        data.
-
-    preview : Dict[str, List[int]]
-        A dict containing three key-value pairs:
-        - a list containing the `start` value of each dimension
-        - a list containing the `stop` value of each dimension
-        - a list containing the `step` value of each dimension
-
-    order : int, optional.
-        The order of the spline interpolation.
-
-    mode : {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest',
-           'mirror', 'grid-wrap', 'wrap'}, optional
-        To determine how to handle image boundaries.
-
-    Returns
-    -------
-    cp.ndarray
-        3D array. Distortion-corrected image(s).
-    """   
-
     # Check if it's a stack of 2D images, or only a single 2D image
     if len(data.shape) == 2:
         data = cp.expand_dims(data, axis=0)

httomolibgpu/prep/normalize.py

@@ -24,7 +24,6 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-nvtx = cupywrapper.nvtx
 cupy_run = cupywrapper.cupy_run
 
 from unittest.mock import Mock
@@ -75,26 +74,6 @@ def normalize(
     cp.ndarray
         Normalised 3D tomographic data as a CuPy array.
     """
-    if cupywrapper.cupy_run:
-        return __normalize(
-            data, flats, darks, cutoff, minus_log, nonnegativity, remove_nans
-        )
-    else:
-        print("normalize won't be executed because CuPy is not installed")
-        return data
-
-
-@nvtx.annotate()
-def __normalize(
-    data: cp.ndarray,
-    flats: cp.ndarray,
-    darks: cp.ndarray,
-    cutoff: float = 10.0,
-    minus_log: bool = True,
-    nonnegativity: bool = False,
-    remove_nans: bool = True,
-) -> cp.ndarray:    
-
     _check_valid_input(data, flats, darks)
 
     dark0 = cp.empty(darks.shape[1:], dtype=float32)

httomolibgpu/prep/phase.py

@@ -24,10 +24,10 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-nvtx = cupywrapper.nvtx
 cupy_run = cupywrapper.cupy_run
 
 from unittest.mock import Mock
+
 if cupy_run:
     from httomolibgpu.cuda_kernels import load_cuda_module
     from cupyx.scipy.fft import fft2, ifft2, fftshift
@@ -98,36 +98,6 @@ def paganin_filter_savu(
     cp.ndarray
         The stack of filtered projections.
     """
-    if cupywrapper.cupy_run:
-        return __paganin_filter_savu(
-            data,
-            ratio,
-            energy,
-            distance,
-            resolution,
-            pad_y,
-            pad_x,
-            pad_method,
-            increment,
-        )
-    else:
-        print("__paganin_filter_savu won't be executed because CuPy is not installed")
-        return data
-
-
-@nvtx.annotate()
-def __paganin_filter_savu(
-    data: cp.ndarray,
-    ratio: float = 250.0,
-    energy: float = 53.0,
-    distance: float = 1.0,
-    resolution: float = 1.28,
-    pad_y: int = 100,
-    pad_x: int = 100,
-    pad_method: str = "edge",
-    increment: float = 0.0,
-) -> cp.ndarray:
-
     # Check the input data is valid
     if data.ndim != 3:
         raise ValueError(
@@ -321,22 +291,6 @@ def paganin_filter_tomopy(
     cp.ndarray
         The 3D array of Paganin phase-filtered projection images.
     """
-    if cupywrapper.cupy_run:
-        return __paganin_filter_tomopy(tomo, pixel_size, dist, energy, alpha)
-    else:
-        print("paganin_filter_tomopy won't be executed because CuPy is not installed")
-        return tomo
-
-
-@nvtx.annotate()
-def __paganin_filter_tomopy(
-    tomo: cp.ndarray,
-    pixel_size: float = 1e-4,
-    dist: float = 50.0,
-    energy: float = 53.0,
-    alpha: float = 1e-3,
-) -> cp.ndarray:   
-
     # Check the input data is valid
     if tomo.ndim != 3:
         raise ValueError(