Improve masking

CHANGELOG.md

@@ -13,6 +13,8 @@
 - Use example_notebook to generate a 'How To Use' page in the documentation
 
 ### Changed
+- All functions and arguments are now in snake_case instead of CamelCase
+- Cropping and masking are now performed upon access to data
 - Overhaul of data storage in the Map classes
 - RDR calculation `calcRDR` in grain inspector is faster and more robust
 - Improve formatting of grain inspector and RDR plot window
@@ -25,6 +27,18 @@
 - Remove `IPython` and `jupyter` as requirements
 
 
+## 0.93.5 (20-11-2023)
+
+### Added
+- Add more options for colouring lines
+
+### Fixed
+- Fix bug with accessing slip systems in grain inspector
+- Replace np.float with python float
+- Remove in_place argument to skimage.morphology.remove_small_objects
+- set_window_title has been moved from figure.canvas to figure.canvas.manager
+
+
 ## 0.93.5 (07-03-2022)
 
 ### Added

defdap/base.py

@@ -56,7 +56,7 @@ def __init__(self, file_name, data_type=None, experiment=None,
 
         """
 
-        self.data = Datastore(crop_func=self.crop)
+        self.data = Datastore(crop_func=self.crop, mask_func=self.mask)
         self.frame = frame if frame is not None else Frame()
         if increment is not None:
             self.increment = increment
@@ -118,6 +118,9 @@ def y_dim(self):
 
     def crop(self, map_data, **kwargs):
         return map_data
+
+    def mask(self, map_data, **kwargs):
+        return map_data
 
     def set_homog_point(self, **kwargs):
         self.frame.set_homog_point(self, **kwargs)

defdap/ebsd.py

@@ -73,7 +73,8 @@ class Map(base.Map):
             Nye_tensor : numpy.ndarray
                 3x3 Nye tensor at each point.
         Derived data:
-            Grain list data to map data from all grains
+            grain_data_to_map : numpy.ndarray
+                Grain list data to map data from all grains
 
     """
     MAPNAME = 'ebsd'
@@ -1156,9 +1157,9 @@ class Grain(base.Grain):
                 (x, y)
         Generated data:
             GROD : numpy.ndarray
-
+                Grain reference orientation distribution magnitude
             GROD_axis : numpy.ndarray
-
+                Grain reference orientation distribution direction
         Derived data:
             Map data to list data from the map the grain is part of
 

defdap/hrdic.py

@@ -118,9 +118,10 @@ def __init__(self, *args, **kwargs):
         self.bse_scale = None                # size of pixels in pattern images
         self.crop_dists = np.array(((0, 0), (0, 0)), dtype=int)
 
-        ## TODO: cropping, have metadata to state if saved data is cropped, if
-        ## not cropped then crop on accesss. Maybe mark cropped data as invalid
-        ## if crop distances change
+        self.data.add_generator(
+            'mask', self.calc_mask, unit='', type='map', order=0,
+            cropped=True, apply_mask=False
+        )
 
         # Deformation gradient
         f = np.gradient(self.data.displacement, self.binning, axis=(1, 2))
@@ -132,9 +133,9 @@ def __init__(self, *args, **kwargs):
             plot_params={
                 'plot_colour_bar': True,
                 'clabel': 'Deformation gradient',
-            }
+            },
+            apply_mask=True
         )
-
         # Green strain
         e = 0.5 * (np.einsum('ki...,kj...->ij...', f, f))
         e[0, 0] -= 0.5
@@ -144,35 +145,34 @@ def __init__(self, *args, **kwargs):
             plot_params={
                 'plot_colour_bar': True,
                 'clabel': 'Green strain',
-            }
+            },
+            apply_mask=True
         )
-
         # max shear component
         max_shear = np.sqrt(((e[0, 0] - e[1, 1]) / 2.) ** 2 + e[0, 1] ** 2)
         self.data.add(
             'max_shear', max_shear, unit='', type='map', order=0,
             plot_params={
                 'plot_colour_bar': True,
                 'clabel': 'Effective shear strain',
-            }
+            },
+            apply_mask=True
         )
-
         # pattern image
         self.data.add_generator(
             'pattern', self.load_pattern, unit='', type='map', order=0,
-            save=False,
+            save=False, apply_mask=False,
             plot_params={
                 'cmap': 'gray'
             }
         )
-
         self.data.add_generator(
             'grains', self.find_grains, unit='', type='map', order=0,
-            cropped=True
+            cropped=True, apply_mask=False
         )
 
-        self.plot_default = lambda *args, **kwargs: self.plot_map(map_name='max_shear',
-            plot_gbs=True, *args, **kwargs
+        self.plot_default = lambda *args, **kwargs: self.plot_map(
+            map_name='max_shear', plot_gbs=True, *args, **kwargs
         )
         self.homog_map_name = 'max_shear'
 
@@ -444,26 +444,27 @@ def warp_to_dic_frame(self, map_data, **kwargs):
             **kwargs
         )
 
-    # TODO: fix component stuff
-    def generate_threshold_mask(self, mask, dilation=0, preview=True):
+    def calc_mask(self, mask="unset_mask", dilation=0):
         """
-        Generate a dilated mask, based on a boolean array and previews the appication of
-        this mask to the max shear map.
+        Generate a dilated mask, based on a boolean array.
 
         Parameters
         ----------
-        mask: numpy.array(bool)
-            A boolean array where points to be removed are True
+        mask: numpy.array(bool) or str('unset_mask')
+            A boolean array where points to be removed are True. Set to string 'unset_mask' to disable masking.
         dilation: int, optional
             Number of pixels to dilate the mask by. Useful to remove anomalous points
             around masked values. No dilation applied if not specified.
-        preview: bool
-            If true, show the mask and preview the masked effective shear strain map.
 
         Examples
         ----------
-        To remove data points in dic_map where `max_shear` is above 0.8, use:
+
+        To disable masking:
 
+        >>> mask = 'unset_mask'
+
+        To remove data points in dic_map where `max_shear` is above 0.8, use:
+
         >>> mask = dic_map.data.max_shear > 0.8
 
         To remove data points in dic_map where e11 is above 1 or less than -1, use:
@@ -478,46 +479,32 @@ def generate_threshold_mask(self, mask, dilation=0, preview=True):
         see :func:`defdap.hrdic.load_corr_val_data`
 
         """
-        self.mask = mask
+        if type(mask) == str and mask == "unset_mask":
+            return mask
+
+        if not isinstance(mask, np.ndarray) or mask.shape != self.shape:
+            raise ValueError('The mask must be a numpy array the same shape as '
+                             'the cropped map.')
 
         if dilation != 0:
-            self.mask = binary_dilation(self.mask, iterations=dilation)
-
-        num_removed = np.sum(self.mask)
-        num_total = self.xdim * self.ydim
-        num_removed_crop = np.sum(self.crop(self.mask))
-        num_total_crop = self.x_dim * self.y_dim
-
-        print('Filtering will remove {0} \ {1} ({2:.3f} %) datapoints in map'
-              .format(num_removed, num_total, (num_removed / num_total) * 100))
-        print(
-            'Filtering will remove {0} \ {1} ({2:.3f} %) datapoints in cropped map'
-            .format(num_removed_crop, num_total_crop,
-                    (num_removed_crop / num_total_crop * 100)))
-
-        if preview == True:
-            plot1 = MapPlot.create(self, self.crop(self.mask), cmap='binary')
-            plot1.set_title('Removed datapoints in black')
-            plot2 = MapPlot.create(self,
-                                   self.crop(
-                                       np.where(self.mask == True, np.nan,
-                                                self.data.max_shear)),
-                                   plot_colour_bar='True',
-                                   clabel="Effective shear strain")
-            plot2.set_title('Effective shear strain preview')
-        print(
-            'Use apply_threshold_mask function to apply this filtering to data')
-
-    def apply_threshold_mask(self):
-        """ Apply mask to all DIC map data by setting masked values to nan.
+            mask = binary_dilation(mask, iterations=dilation)
 
+        num_removed = np.sum(mask)
+        num_total = self.shape[0] * self.shape[1]
+        frac_removed = num_removed / num_total * 100
+        print(f'Masking will mask {num_removed} out of {num_total} '
+              f'({frac_removed:.3f} %) datapoints in cropped map.')
+
+        return mask
+
+    def mask(self, map_data):
+        """ Values set to False in mask will be set to nan in map.
         """
-        for comp in ('max_shear',
-                     'e11', 'e12', 'e22',
-                     'f11', 'f12', 'f21', 'e22',
-                     'x_map', 'y_map'):
-            # self.data[comp] = np.where(self.mask == True, np.nan, self.data[comp])
-            self.data[comp][self.mask] = np.nan
+        if self.data.mask == 'unset_mask':
+            return map_data
+        else:
+            return np.ma.array(map_data, 
+                            mask=np.broadcast_to(self.data.mask, np.shape(map_data)))
 
     def set_pattern(self, img_path, window_size):
         """Set the path to the image of the pattern.

defdap/utils.py

@@ -99,19 +99,21 @@ class Datastore(object):
         '_derivatives',
         '_group_id',
         '_crop_func',
+        '_mask_func'
     ]
     _been_to = None
 
     @staticmethod
     def generate_id():
         return uuid4()
 
-    def __init__(self, group_id=None, crop_func=None):
+    def __init__(self, group_id=None, crop_func=None, mask_func=None):
         self._store = {}
         self._generators = {}
         self._derivatives = []
         self._group_id = self.generate_id() if group_id is None else group_id
         self._crop_func = (lambda x, **kwargs: x) if crop_func is None else crop_func
+        self._mask_func = (lambda x, **kwargs: x) if mask_func is None else mask_func
 
     def __len__(self):
         """Number of data in the store, including data not yet generated."""
@@ -171,10 +173,12 @@ def __getitem__(self, key):
                 # No generator found
                 pass
 
-        if (attr == 'data' and self.get_metadata(key, 'type') == 'map' and
-                not self.get_metadata(key, 'cropped', False)):
-            binning = self.get_metadata(key, 'binning', 1)
-            val = self._crop_func(val, binning=binning)
+        if attr == 'data' and self.get_metadata(key, 'type') == 'map':
+            if not self.get_metadata(key, 'cropped', False):
+                binning = self.get_metadata(key, 'binning', 1)
+                val = self._crop_func(val, binning=binning)
+            if self.get_metadata(key, 'apply_mask', True):
+                val = self._mask_func(val)
 
         return val
 

docs/source/papers.rst

@@ -6,6 +6,8 @@ Here is a list of papers which have used the DefDAP Python library.
 2024
 ------
 
+* `E. Nieto-Valeiras, A. Orozco-Caballero, M. Sarebanzadeh, J. Sun, J. LLorca. Analysis of slip transfer across grain boundaries in Ti via diffraction contrast tomography and high-resolution digital image correlation: When the geometrical criteria are not sufficient. Volume 175, April 2024, 103941. <https://doi.org/10.1016/j.ijplas.2024.103941>`_
+
 * `I.Alakiozidis, C.Hunt, R.Thomas, D.Lunt, A.D.Smith, M.Maric, Z.Shah, A.Ambard, P.Frankel. Quantifying cracking and strain localisation in a cold spray chromium coating on a zirconium alloy substrate under tensile loading at room temperature. Journal of Nuclear Materials. Apr 2024. 154899. <https://doi.org/10.1016/j.jnucmat.2024.154899>`_
 
 * `B.Poole, A.Marsh, D.Lunt, M.Gorley, C.Hamelin, C. Hardie, A.Harte. High-resolution strain mapping in a thermionic LaB6 scanning electron microscope. Strain. Feb 2024. <https://doi.org/10.1111/str.12472>`_

setup.py

@@ -64,7 +64,7 @@ def get_version():
         'numba',
     ],
     extras_require={
-        'testing': ['pytest', 'coverage', 'pytest-cov', 'pytest_cases'],
+        'testing': ['pytest<8', 'coverage', 'pytest-cov', 'pytest_cases'],
         'docs': [
             'sphinx==5.0.2', 'sphinx_rtd_theme==0.5.0', 
             'sphinx_autodoc_typehints==1.11.1', 'nbsphinx==0.9.3',