Merge pull request #139 from jfennick/spelling_fixes

apply spelling fixes

.pre-commit-config.yaml

@@ -22,7 +22,7 @@ repos:
     # -   id: check-yaml  # checks yaml files for parseable syntax.
     # -   id: debug-statements  # checks for debugger imports and py37+ `breakpoint()` calls in python source.
     -   id: destroyed-symlinks  # detects symlinks which are changed to regular files with a content of a path which that symlink was pointing to.
-    -   id: detect-aws-credentials  # detects *your* aws credentials from the aws cli credentials file.
+    # -   id: detect-aws-credentials  # detects *your* aws credentials from the aws cli credentials file.
     -   id: detect-private-key  # detects the presence of private keys.
     # -   id: double-quote-string-fixer  # replaces double quoted strings with single quoted strings.
     # -   id: end-of-file-fixer  # ensures that a file is either empty, or ends with one newline.
@@ -66,7 +66,7 @@ repos:
 #    rev: v0.9.0.2
 #    hooks:
 #    -   id: shellcheck
-#- repo: https://github.com/codespell-project/codespell
-#  rev: v2.2.4
-#  hooks:
-#  - id: codespell
+- repo: https://github.com/codespell-project/codespell
+  rev: v2.2.4
+  hooks:
+  - id: codespell

README.md

@@ -32,7 +32,7 @@ or
 conda install nyxus -c conda-forge
 ```
 
-Usage is very straightforward. Given `intensities` and `labels` folders, Nyxus pairs up intensity-label images and extracts features from all of them. A summary of the avaialble feature are [listed below](#available-features).
+Usage is very straightforward. Given `intensities` and `labels` folders, Nyxus pairs up intensity-label images and extracts features from all of them. A summary of the available feature are [listed below](#available-features).
 
 ```python 
 from nyxus import Nyxus
@@ -138,7 +138,7 @@ Nyxus can also be [built from source](#building-from-source) and used from the c
 ## Getting and setting parameters of Nyxus
 
 All parameters to configure Nyxus are available to set within the constructor. These parameters can also be updated after the object is created using the `set_params`
-method. This method takes in keyword arguments where the key is a valid parameter in Nyxus and the value is the updated value for the paramter. For example, 
+method. This method takes in keyword arguments where the key is a valid parameter in Nyxus and the value is the updated value for the parameter. For example, 
 to update the `coarse_gray_depth` to 256 and the `gabor_f0` parameter to 0.1, the following can be done:
 
 ```python 
@@ -301,7 +301,7 @@ Nyxus provides a set of pixel intensity, morphology, texture, intensity distribu
 | INTEGRATED_INTENSITY | Integrated intensity of the region of interest (ROI) |
 | MEAN, MAX, MEDIAN, STANDARD_DEVIATION, MODE | Mean/max/median/stddev/mode intensity value of the ROI | 
 | SKEWNESS, KURTOSIS, HYPERSKEWNESS, HYPERFLATNESS  | higher standardized moments | 
-| MEAN_ABSOLUTE_DEVIATION  | Mean absolute devation | 
+| MEAN_ABSOLUTE_DEVIATION  | Mean absolute deviation | 
 | ENERGY  | ROI energy | 
 | ROOT_MEAN_SQUARED  | Root of mean squared deviation | 
 | ENTROPY  | ROI entropy - a measure of the amount of information in the ROI | 

ci-utils/build_conda.sh

@@ -13,7 +13,7 @@ then
       exit
 fi
 
-MINICONDA=$PWD/miniconda-for-nyxus # Modify this to your preferred location for persistance
+MINICONDA=$PWD/miniconda-for-nyxus # Modify this to your preferred location for persistence
 CPP_BUILD_DIR=$PWD
 SRC_ROOT=$1 #source dir location
 NYXUS_ROOT=$SRC_ROOT

codespellignore.txt

@@ -2,3 +2,7 @@ nd
 Nd
 ans
 ser
+jave
+Numer.
+Wee
+Fertil

docs/source/Math/f_2dmoments.rst

@@ -40,7 +40,7 @@ and
 represent the center of mass of the image :math:`f(x,y)`. Hu's Uniqueness Theorem states that if :math:`f(x,y)` is piecewise continuous and has nonzero values only in the finite part 
 of the :math:`(x,y)` plane, then geometric moments of all orders exist. It can then be shown that the moment set :math:`{\mu_{pq}}` is 
 uniquely determined by :math:`f(x,y)` and conversely, :math:`f(x,y)` is uniquely determined by :math:`{\mu_{pq}}`. Since an image has 
-finite area, a moment set can be evaluted computationally and used to uniquely describe the information contained in the image. 
+finite area, a moment set can be evaluated computationally and used to uniquely describe the information contained in the image. 
 
 Raw moments
 -----------

docs/source/Math/f_gldzm.rst

@@ -20,7 +20,7 @@ The continuity is meant as a 4-connected neighbourhood. For example, the followi
     \fbox{3} & 1 & 0 & 0 & 0 & 1 & 0
 	\end{bmatrix}
 
-The zone's distance is the minimum of its each pixel's distance to the ROI or image border measured as the number of pixel boudaries to the first off-ROI or off-image pixel.
+The zone's distance is the minimum of its each pixel's distance to the ROI or image border measured as the number of pixel boundaries to the first off-ROI or off-image pixel.
 
 Considering the following ROI image 
 
@@ -93,7 +93,7 @@ Applied to the example, the GLDZM :math:`M(I_2)` of image :math:`I_2` having dis
     1 & 1 & 0\end{bmatrix}
 
 Let
-:math:`m(x,d)` be an element ofthe distance zone matrix corresponding to grey level :math:`x` and zone distance :math:`d` ,
+:math:`m(x,d)` be an element of the distance zone matrix corresponding to grey level :math:`x` and zone distance :math:`d` ,
 
 :math:`N_g` -- the number of grey levels ,
 

docs/source/Math/f_ngtdm.rst

@@ -4,8 +4,8 @@ Texture features / NGTDM
 
 A Neighbouring Gray Tone Difference Matrix quantifies the difference between a gray value and the average gray value
 of its neighbours within distance :math:`\delta`. The sum of absolute differences for gray level :math:`i` is stored in the matrix.
-Let :math:`\textbf{X}_{gl}` be a set of segmented voxels and :math:`x_{gl}(j_x,j_y,j_z) \in \textbf{X}_{gl}` be the gray level of a voxel at postion
-:math:`(j_x,j_y,j_z)`, then the average gray level of the neigbourhood is:
+Let :math:`\textbf{X}_{gl}` be a set of segmented voxels and :math:`x_{gl}(j_x,j_y,j_z) \in \textbf{X}_{gl}` be the gray level of a voxel at position
+:math:`(j_x,j_y,j_z)`, then the average gray level of the neighborhood is:
 
 .. math::
 

docs/source/cmdline_and_examples.rst

@@ -4,7 +4,7 @@ Command line
 
 Assuming you built the Nyxus executable from source code, 
 the following parameters are available for the command 
-line usage. Regular comand line users should adhere 
+line usage. Regular command line users should adhere 
 parameter value to the "Type" column. WIPP developers 
 should adhere to columns "WIPP I/O role" and "WIPP type".
 
@@ -345,7 +345,7 @@ The `features` variable will now use the custom names, as shown below
    14  custom_seg_name2   Intensity2         6    54573.3    54573.3  ...  0.980769
 
 All parameters to configure Nyxus are available to set within the constructor. These parameters can also be updated after the object is created using the `set_params`
-method. This method takes in keyword arguments where the key is a valid parameter in Nyxus and the value is the updated value for the paramter. For example, 
+method. This method takes in keyword arguments where the key is a valid parameter in Nyxus and the value is the updated value for the parameter. For example, 
 to update the `coarse_gray_depth` to 256 and the `gabor_f0` parameter to 0.1, the following can be done:
 
 .. code-block:: python

docs/source/devguide.rst

@@ -45,7 +45,7 @@ edit the integer to string feature identifier mapping in mapping UserFacingFeatu
 Step 3 - create a feature method class
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Any Nyxus feature needs to be derived from class FeatureMethod defining a particular calculator of one or multiple features. FeatureMethod is a skeleton of the custom feature calculator responding to image data streamed to it in various ways - pixel by pixel (so called online mode), as a cached pixel cloud in the form of std::vector\ :raw-html-m2r:`<Pixel2>` object for images whose size permits caching a single ROI's data in user computer's RAM (so called trivial ROIs), and as a browser of a mask-intensity image pair for ROIs whose cache exceeds the RAM limit (so called non-trivial or oversized ROIs). All the particular feature calculation logic neds to be placed in your implementation of FeatureMethod's pure virtual methods. The class's header and source files are suggested to be placed in directory "features". For example, if we want to implement a class calculating a 3-segmental ROI intensity statistics (means) weighted by unit perimeter length delivered to user as features MYFEATURE1, MYFEATURE2, and MYFEATURE3: 
+Any Nyxus feature needs to be derived from class FeatureMethod defining a particular calculator of one or multiple features. FeatureMethod is a skeleton of the custom feature calculator responding to image data streamed to it in various ways - pixel by pixel (so called online mode), as a cached pixel cloud in the form of std::vector\ :raw-html-m2r:`<Pixel2>` object for images whose size permits caching a single ROI's data in user computer's RAM (so called trivial ROIs), and as a browser of a mask-intensity image pair for ROIs whose cache exceeds the RAM limit (so called non-trivial or oversized ROIs). All the particular feature calculation logic needs to be placed in your implementation of FeatureMethod's pure virtual methods. The class's header and source files are suggested to be placed in directory "features". For example, if we want to implement a class calculating a 3-segmental ROI intensity statistics (means) weighted by unit perimeter length delivered to user as features MYFEATURE1, MYFEATURE2, and MYFEATURE3: 
 
 .. code-block:: c++
 
@@ -135,7 +135,7 @@ For example
 
 Step 8 - implement feature calculation of oversized ROIs
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-An oversized ROI's cached data cannot fit in computer meory so in the oversized ROI scenarios we cannot rely on its pixel cloud or image matrix. Instead, all the calculations should be performed "in place" - using the image browser class ImageLoader (header image_loader.h) similarly to class ImageMatrix (image_matrix.h) and creating out of memory cache using classes OutOfRamPixelCloud, OOR_ReadMatrix, ReadImageMatrix_nontriv, and WriteImageMatrix_nontriv (header image_matrix.nontriv). You are guaranteed to have initialized object LR::osized_pixel_cloud prior to the call of method osized_calculate(). For example:
+An oversized ROI's cached data cannot fit in computer memory so in the oversized ROI scenarios we cannot rely on its pixel cloud or image matrix. Instead, all the calculations should be performed "in place" - using the image browser class ImageLoader (header image_loader.h) similarly to class ImageMatrix (image_matrix.h) and creating out of memory cache using classes OutOfRamPixelCloud, OOR_ReadMatrix, ReadImageMatrix_nontriv, and WriteImageMatrix_nontriv (header image_matrix.nontriv). You are guaranteed to have initialized object LR::osized_pixel_cloud prior to the call of method osized_calculate(). For example:
 
 .. code-block:: c++
 
@@ -225,7 +225,7 @@ Often multiple features need to be calculated together and the user faces the ne
    nyxus --features=AREA_PIXELS_COUNT,AREA_UM2,CENTROID_X,CENTROID_Y,BBOX_YMIN,BBOX_XMIN,BBOX_HEIGHT,BBOX_WIDTH --intDir=/home/ec2-user/work/datasetXYZ/int --segDir=/home/ec2-user/work/dataXYZ/seg --outDir=/home/ec2-user/work/datasetXYZ --filePattern=.* --outputType=separatecsv
 
 
-Features can be grouped toegther and gived convenient aliases, for example the above features AREA_PIXELS_COUNT, AREA_UM2, CENTROID_X, CENTROID_Y, BBOX_YMIN, BBOX_XMIN, BBOX_HEIGHT, and BBOX_WIDTH can be refered to as \*BASIC_MORPHOLOGY\* . (Asterisks are a part of the alias and aren't special symbols.) The command line then becomes simpler
+Features can be grouped toegther and given convenient aliases, for example the above features AREA_PIXELS_COUNT, AREA_UM2, CENTROID_X, CENTROID_Y, BBOX_YMIN, BBOX_XMIN, BBOX_HEIGHT, and BBOX_WIDTH can be referred to as \*BASIC_MORPHOLOGY\* . (Asterisks are a part of the alias and aren't special symbols.) The command line then becomes simpler
 
 .. code-block:: bash
 

docs/source/featurelist.rst

@@ -44,9 +44,9 @@ Nyxus provided features
    * - HYPERFLATNESS
      - Hyperflatness - the 6th standardized moment
    * - MEAN_ABSOLUTE_DEVIATION
-     - Mean absolute devation
+     - Mean absolute deviation
    * - MEDIAN_ABSOLUTE_DEVIATION
-     - Median absolute devation
+     - Median absolute deviation
    * - ENERGY
      - ROI energy
    * - ROOT_MEAN_SQUARED

docs/source/supported_formats.rst

@@ -5,7 +5,7 @@ Supported Image Formats
 Currently, Nyxus supports OME-TIFF, OME-Zarr and DICOM 2D Grayscale images.
 
 OME-TIFF images uses the standard TIFF specification to store one or multiple image planes. OME-TIFF images are always structured as 
-5D data((T)ime, (C)hannel, Z, Y and X). For 2D single channel image, Z, C and T dimentions are constrained to be one. OME-TIFF images also contain an XML 
+5D data((T)ime, (C)hannel, Z, Y and X). For 2D single channel image, Z, C and T dimensions are constrained to be one. OME-TIFF images also contain an XML 
 document stored under the ``IMAGE_DESCRIPTION`` tag. This XML document contains the metadata to extract Image File Directory (IFD) for a 
 given T, C and Z position. Since, Nyxus only processes grayscale 2D images at a fixed time point, the images are 
 assumed to have only one IFD, and the internal loader reads the first IFD even if the image file contains multiple IFDs. 

plugin.json

@@ -1191,7 +1191,7 @@
     {
       "key": "inputs.outputType",
       "title": "Output file type",
-      "description": "Save csv file separately for each image or one csv file for all images. Alternatively, outuput can be written as arrow or parquet file"
+      "description": "Save csv file separately for each image or one csv file for all images. Alternatively, output can be written as arrow or parquet file"
     },
     {
       "key": "inputs.embeddedpixelsize",

src/nyx/cli_nested_roi_options.cpp

@@ -441,7 +441,7 @@ namespace Nyxus
 			int lPar = p.first;
 			auto nCh = r.children.size();
 
-			// Search this parent's feature extraction result recod 
+			// Search this parent's feature extraction result record 
 			std::string csvFP = get_feature_output_fname(r.intFname, r.segFname);
 			std::string csvWholeline;
 			std::vector<std::string> csvHeader, csvFields;

src/nyx/environment.cpp

@@ -42,7 +42,7 @@ namespace Nyxus
 		// Check individual features
 		for (const auto &s : strings)
 		{
-			// Forgive user's typos of consequtive commas e.g. MIN,MAX,,MEDIAN
+			// Forgive user's typos of consecutive commas e.g. MIN,MAX,,MEDIAN
 			if (s.empty())
 				continue;
 

src/nyx/feature_method.h

@@ -29,7 +29,7 @@ class FeatureMethod
 	virtual void osized_reduce() final {};	// Get rid of this method in all derived
 	virtual void osized_calculate (LR& r, ImageLoader& imloader) = 0;	// Called once right after having scanned the ROI in the raster order. Put your reduction or summarization of data gathered in osized_add_online_pixel()
 
-	// Put method-dependent set of calculation results in the standard feature results list further savable as CSV-file
+	// Put method-dependent set of calculation results in the standard feature results list further saveable as CSV-file
 	virtual void save_value(std::vector<std::vector<double>>& feature_vals) = 0;
 
 	// Feature-specific cache clean-up 

src/nyx/features/basic_morphology.cpp

@@ -80,7 +80,7 @@ void BasicMorphologyFeatures::calculate(LR& r)
 
 	for (auto& px : r.raw_pixels)
 	{
-		// the "+1" is only for compatability with matlab code (where index starts from 1) 
+		// the "+1" is only for compatibility with matlab code (where index starts from 1) 
 		x_mass = x_mass + (px.x + 1) * px.inten;
 		y_mass = y_mass + (px.y + 1) * px.inten;
 		mass += px.inten;
@@ -171,7 +171,7 @@ void BasicMorphologyFeatures::osized_calculate(LR& r, ImageLoader& imloader)
 	for (size_t i = 0; i < r.raw_pixels_NT.size(); i++)	// for (auto& px : r.raw_pixels)
 	{
 		auto px = r.raw_pixels_NT.get_at(i);
-		// the "+1" is only for compatability with matlab code (where index starts from 1) 
+		// the "+1" is only for compatibility with matlab code (where index starts from 1) 
 		x_mass = x_mass + (px.x + 1) * px.inten;
 		y_mass = y_mass + (px.y + 1) * px.inten;
 		mass += px.inten;

src/nyx/features/chords.h

@@ -41,11 +41,11 @@ class Chords_feature
 	Chords_feature (const std::vector<Pixel2> & raw_pixels, const AABB & bb, const double cenx, const double ceny);
 
 	/// @brief Calculated maxchords statistics
-	/// @return Tupple of [0] max, [1] min, [2] median, [3] mean, [4] mode, [5] std, [6] min_angle, [7] max_angle
+	/// @return Tuple of [0] max, [1] min, [2] median, [3] mean, [4] mode, [5] std, [6] min_angle, [7] max_angle
 	std::tuple<double, double, double, double, double, double, double, double> get_maxchords_stats();
 
 	/// @brief Calculated allchords statistics
-	/// @return Tupple of [0] max, [1] min, [2] median, [3] mean, [4] mode, [5] std, [6] min_angle, [7] max_angle
+	/// @return Tuple of [0] max, [1] min, [2] median, [3] mean, [4] mode, [5] std, [6] min_angle, [7] max_angle
 	std::tuple<double, double, double, double, double, double, double, double> get_allchords_stats();
 
 	/// @brief Calculates "maxchords" and "allchords" features for a range of ROI labels

src/nyx/features/contour.cpp

@@ -159,7 +159,7 @@ void ContourFeature::buildRegularContour(LR& r)
 						if (counter2 > 8)
 						{
 							// If counter2 is above 8, we have sought around the neighborhood and
-							// therefor the border is a single non-blank pixel, and we can exit
+							// therefore the border is a single non-blank pixel, and we can exit
 							counter2 = 0;
 							break;
 						}
@@ -200,7 +200,7 @@ void ContourFeature::buildRegularContour(LR& r)
 		std::cout << "\n\n\n";
 	);
 
-	//==== Remove padding and save the countour image as a vector of non-blank pixels
+	//==== Remove padding and save the contour image as a vector of non-blank pixels
 	AABB & bb = r.aabb; // r.aux_image_matrix.original_aabb;
 	int base_x = bb.get_xmin(),
 		base_y = bb.get_ymin();
@@ -213,7 +213,7 @@ void ContourFeature::buildRegularContour(LR& r)
 			auto inte = borderImage[idx];
 			if (inte)
 			{
-				Pixel2 p(x, y, inte - 1);		// Undecorate the intesity		
+				Pixel2 p(x, y, inte - 1);		// Undecorate the intensity		
 				r.contour.push_back(p);
 			}
 		}
@@ -251,7 +251,7 @@ void ContourFeature::buildRegularContour(LR& r)
 			cands.push_back(px);
 		}
 
-		//	--are there any tip's neighbr candidate?
+		//	--are there any tip's neighbor candidate?
 		if (!cands.empty())
 		{
 			int distMin = pxTip.sqdist(cands[0]);
@@ -483,7 +483,7 @@ void ContourFeature::buildRegularContour_nontriv(LR& r)
 						if (counter2 > 8)
 						{
 							// If counter2 is above 8 we have traced around the neighborhood and
-							// therefor the border is a single black pixel and we can exit
+							// therefore the border is a single black pixel and we can exit
 							counter2 = 0;
 							break;
 						}
@@ -496,7 +496,7 @@ void ContourFeature::buildRegularContour_nontriv(LR& r)
 			}
 		}
 
-	//==== Remove padding and save the countour image as a vector of contour-onlu pixels
+	//==== Remove padding and save the contour image as a vector of contour-onlu pixels
 	AABB& bb = r.aabb; // r.aux_image_matrix.original_aabb;
 	int base_x = bb.get_xmin(),
 		base_y = bb.get_ymin();

src/nyx/features/ellipse_fitting.h

@@ -40,7 +40,7 @@ class EllipseFittingFeature: public FeatureMethod
 	/// @return Value of the ellipse elongation feature
 	double get_elongation();
 
-	/// @brief Orientation describes whather the ellipse is horizontal or vertical
+	/// @brief Orientation describes whether the ellipse is horizontal or vertical
 	/// @return 
 	double get_orientation();