Tinkering with sphinx

doc/conf.py

@@ -10,9 +10,9 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
+import os
+import sys
+sys.path.insert(0, os.path.abspath('../'))
 
 
 # -- Project information -----------------------------------------------------
@@ -32,13 +32,19 @@ release = '2019.1.0'
 # ones.
 extensions = [
     'recommonmark',
-    'numpydoc',
+#    'numpydoc',
+    'sphinx.ext.napoleon',
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
-    'nbsphinx'
+    'nbsphinx',
+#    'sphinx.ext.autosummary',
+#    'sphinx.ext.doctest',
+#    'sphinx.ext.inheritance_diagram',
 ]
 
+#autosummary_generate = True
+autodoc_member_order = 'bysource'
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -73,3 +79,5 @@ def setup(app):
             'auto_toc_tree_section': 'Contents',
             }, True)
     app.add_transform(AutoStructify)
+
+

doc/definitions.md

@@ -11,7 +11,7 @@ A **radiograph** (or **radio**) is the raw data at the output of a X-ray detecto
 
 In this documentation, we distinguish between radios and sinograms.  
 
-As stated before, a radio is a raw projection data, i.e what comes out of the detector. "Corrected radios" refer to radios after some CCD corrections and flat-field normalization.
+As stated before, a radio is a raw projection data, i.e what comes out of the detector. "Corrected radios" refer to radios after some CCD corrections.
 
 Each radio has *Nx* pixels horizontally, and *Nz* pixels vertically. During a scan, a collections of *Na* radios are acquired, where *Na* is the number of projection angles.
 

doc/index.rst

@@ -7,13 +7,20 @@ Welcome to Nabu's documentation!
 ================================
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :caption: Contents:
  
    definitions.md
+   ccdprocessing.md
    nabu_config_file.md
    validators.md
 
+   modules/index.rst
+
+  ..
+    apidoc/nabu.rst
+  ..
+
 
 Indices and tables
 ==================

nabu/cuda/medfilt.py

@@ -145,8 +145,8 @@ class MedianFilter(CudaProcessing):
         else:
             self._allocate_array("d_output", self.shape)
         self.cuda_kernel_2d(
-            self.d_input.gpudata,
-            self.d_output.gpudata,
+            self.d_input,
+            self.d_output,
             self.Nx,
             self.Ny,
             self.Nz,

nabu/cuda/src/medfilt.cu

@@ -71,12 +71,3 @@ __global__ void medfilt2d(
     #endif
 }
 
-
-
-
-
-
-
-
-
-

nabu/preproc/ccd.py

@@ -286,34 +286,6 @@ class FlatField(CCDProcessing):
         return radios_corrected
 
 
-"""
-# It should generate a mask of valid/invalid pixels.
-# This does not work yet !
-def sigma_clip_mask(data, nsigma_low=4, nsigma_up=None, axis=None, maxiter=5):
-    NAN = np.float32(np.nan)
-    nsigma_up = nsigma_up or nsigma_low
-    res = np.zeros(data.shape, np.float32)
-    res[:] = data[:]
-    iteration = 0
-    outliers = 1
-    invalids = np.zeros(data.shape, np.bool)
-    valid_mask = np.ones_like(invalids)
-    while outliers > 0 and iteration < maxiter:
-        m = np.nanmean(res, axis=axis)
-        std = np.nanstd(res, axis=axis)
-        lower_bound = m - std * nsigma_low
-        upper_bound = m + std * nsigma_up
-
-
-        invalid_mask = (res[valid_mask] < lower_bound) + (res[valid_mask] > upper_bound)
-        valid_mask = np.logical_not(invalid_mask)
-        invalids = invalids + invalid_mask
-        res[invalid_mask] = NAN
-        outliers = invalid_mask.sum()
-        iteration += 1
-    return invalids
-"""
-
 
 class CCDCorrection(CCDProcessing):
     """

nabu/preproc/phase_cuda.py

@@ -6,7 +6,6 @@ import pycuda.driver as cuda
 from pycuda import gpuarray as garray
 
 from ..utils import updiv, get_cuda_srcfile, _sizeof
-#~ from ..cuda.utils import copy_array, get_cuda_context
 from ..cuda.processing import CudaProcessing
 from ..cuda.kernel import CudaKernel