DOC: Change mentions of repository name from histomics_stream to HistomicsStream

README.md

@@ -1,6 +1,6 @@
 # histomics_stream
 
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/InsightSoftwareConsortium/ITK/blob/master/LICENSE) [![PyPI Version](https://img.shields.io/pypi/v/histomics_stream.svg)](https://pypi.python.org/pypi/histomics_stream) [![GitHub repository](https://img.shields.io/badge/Powered%20by-histomics__stream-blue.svg)](https://github.com/DigitalSlideArchive/histomics_stream) [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DigitalSlideArchive/histomics_stream/blob/master/example/tensorflow_stream.ipynb)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/InsightSoftwareConsortium/ITK/blob/master/LICENSE) [![PyPI Version](https://img.shields.io/pypi/v/histomics_stream.svg)](https://pypi.python.org/pypi/histomics_stream) [![GitHub repository](https://img.shields.io/badge/Powered%20by-histomics__stream-blue.svg)](https://github.com/DigitalSlideArchive/HistomicsStream) [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DigitalSlideArchive/HistomicsStream/blob/master/example/tensorflow_stream.ipynb)
 
 ## Overview
 
@@ -63,7 +63,7 @@ The `histomics_stream` package significantly improves runtime performance.  In a
 
 ### Implementation
 
-The steps of `histomics_stream` are demonstrated in the Jupyter lab notebook [`example/tensorflow_stream.ipynb`](https://github.com/DigitalSlideArchive/histomics_stream/blob/master/example/tensorflow_stream.ipynb), which is also available in [Google Colab](https://colab.research.google.com/github/DigitalSlideArchive/histomics_stream/blob/master/example/tensorflow_stream.ipynb).  Construction of a Python dictionary that describes the study data set is straightforward and key steps are implemented by `histomics_stream`.  Complexities from TensorFlow are seamlessly handled.  For example, the syntax for parallelizable for loops in TensorFlow, which are often essential for runtime performance, is non-intuitive; `histomics_stream` provides the desired parallelism without exposing this complexity.  Similarly TensorFlow can be temperamental about conditional control flows, requiring that its graph execution construction routines can prove that alternative execution branches that should be producing objects of the same shape actually do so; the design of `histomics_stream` gives the user the power to, e.g., efficiently select tiles under several alternative strategies, without exposing this graph execution complexity to the user.
+The steps of `histomics_stream` are demonstrated in the Jupyter lab notebook [`example/tensorflow_stream.ipynb`](https://github.com/DigitalSlideArchive/HistomicsStream/blob/master/example/tensorflow_stream.ipynb), which is also available in [Google Colab](https://colab.research.google.com/github/DigitalSlideArchive/HistomicsStream/blob/master/example/tensorflow_stream.ipynb).  Construction of a Python dictionary that describes the study data set is straightforward and key steps are implemented by `histomics_stream`.  Complexities from TensorFlow are seamlessly handled.  For example, the syntax for parallelizable for loops in TensorFlow, which are often essential for runtime performance, is non-intuitive; `histomics_stream` provides the desired parallelism without exposing this complexity.  Similarly TensorFlow can be temperamental about conditional control flows, requiring that its graph execution construction routines can prove that alternative execution branches that should be producing objects of the same shape actually do so; the design of `histomics_stream` gives the user the power to, e.g., efficiently select tiles under several alternative strategies, without exposing this graph execution complexity to the user.
 
 ## Conclusions
 

example/tensorflow_stream.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Demonstration of histomics_stream\n",
     "\n",
-    "Click to open in [[GitHub](https://github.com/DigitalSlideArchive/histomics_stream/tree/master/example/tensorflow_stream.ipynb)] [[Google Colab](https://colab.research.google.com/github/DigitalSlideArchive/histomics_stream/blob/master/example/tensorflow_stream.ipynb)]\n",
+    "Click to open in [[GitHub](https://github.com/DigitalSlideArchive/HistomicsStream/tree/master/example/tensorflow_stream.ipynb)] [[Google Colab](https://colab.research.google.com/github/DigitalSlideArchive/HistomicsStream/blob/master/example/tensorflow_stream.ipynb)]\n",
     "\n",
     "The `histomics_stream` Python package sits at the start of any machine learning workflow that is built on the TensorFlow machine learning library.  The package is responsible for efficient access to the input image data that will be used to fit a new machine learning model or will be used to predict regions of interest in novel inputs using an already learned model."
    ]

pyproject.toml

@@ -24,7 +24,7 @@ dependencies = [
 dynamic = ["version", "description"]
 
 [project.urls]
-Source = "https://github.com/DigitalSlideArchive/histomics_stream"
+Source = "https://github.com/DigitalSlideArchive/HistomicsStream"
 
 [project.scripts]
 flit = "flit:main"