Update docs (#51)

README.md

@@ -58,23 +58,23 @@ two channels:
  * Background channel (i.e. autofluorescence)
  * Signal channel, the one with the cells to be detected:
 
-![raw](resources/raw.png)
+![raw](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/raw.png)
 **Raw coronal serial two-photon mouse brain image showing labelled cells**
 
 
 ### Cell candidate detection
 Classical image analysis (e.g. filters, thresholding) is used to find 
 cell-like objects (with false positives):
 
-![raw](resources/detect.png)
+![raw](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/detect.png)
 **Candidate cells (including many artefacts)**
 
 
 ### Cell candidate classification
 A deep-learning network (ResNet) is used to classify cell candidates as true 
 cells or artefacts:
 
-![raw](resources/classify.png)
+![raw](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/classify.png)
 **Cassified cell candidates. Yellow - cells, Blue - artefacts**
 
 ### Registration and segmentation (amap)
@@ -86,7 +86,7 @@ a brain region.
 This transformation can be inverted, allowing detected cells to be
 transformed to a standard anatomical space.
 
-![raw](resources/register.png)
+![raw](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/register.png)
 **ARA overlaid on sample image**
 
 ### Analysis of cell positions in a common anatomical space
@@ -101,7 +101,7 @@ Input cell somas detected by cellfinder, aligned to the Allen Reference Atlas,
 and visualised in [brainrender](https://github.com/brancolab/brainrender) along 
 with RSP.
 
-![brainrender](resources/brainrender.png)
+![brainrender](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/brainrender.png)
 
 Data courtesy of Sepiedeh Keshavarzi and Chryssanthi Tsitoura. [Details here](https://www.youtube.com/watch?v=pMHP0o-KsoQ)
 
@@ -112,10 +112,10 @@ cellfinder is packaged with
 additional tools for the analysis of visualisation of whole-brain imaging data.
 
 #### Heatmaps of detected cells:
-![heatmap](resources/heatmap.png)
+![heatmap](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/heatmap.png)
 
 #### Mapping non-cellular volumes in standard space:
-![injection](resources/injection.png)
+![injection](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/injection.png)
 **Virus injection site within the superior colliculus.**
 *(Data courtesy of [@FedeClaudi](https://github.com/fedeclaudi) and 
 [brainrender](https://github.com/brancolab/brainrender))*
@@ -124,6 +124,6 @@ additional tools for the analysis of visualisation of whole-brain imaging data.
 
 If you find cellfinder useful, and use it in your research, please cite this repository:
 
-> Adam L. Tyson, Charly V. Rousseau, Christian J. Niedworok and Troy W. Margrie (2020). cellfinder: automated 3D cell detection and registration of whole-brain images. [doi:10.5281/zenodo.3665329](hhttp://doi.org/10.5281/zenodo.3665329)
+> Adam L. Tyson, Charly V. Rousseau, Christian J. Niedworok and Troy W. Margrie (2020). cellfinder: automated 3D cell detection and registration of whole-brain images. [doi:10.5281/zenodo.3665329](http://doi.org/10.5281/zenodo.3665329)
 
 If you use any of the image registration functions in cellfinder, please also cite [amap](https://github.com/SainsburyWellcomeCentre/amap-python#citing-amap).

doc_build/main/about/release_notes.md

@@ -1,5 +1,13 @@
 # Releases 
 
+## Version 0.3.8 (2020-02-18)
+### Fixed
+Fix memory leak in training
+
+## Version 0.3.7 (2020-02-02)
+### Changed
+Update curation interface
+
 ## Version 0.3.5 (2020-02-03)
 ### Main updates
 * New all python training data generation interface, `cellfinder_curate`. 

docs/_sources/main/about/release_notes.md.txt

@@ -1,5 +1,13 @@
 # Releases 
 
+## Version 0.3.8 (2020-02-18)
+### Fixed
+Fix memory leak in training
+
+## Version 0.3.7 (2020-02-02)
+### Changed
+Update curation interface
+
 ## Version 0.3.5 (2020-02-03)
 ### Main updates
 * New all python training data generation interface, `cellfinder_curate`. 

docs/main/dev/CONTRIBUTING.html

@@ -230,7 +230,7 @@ <h2>Testing<a class="headerlink" href="#testing" title="Permalink to this headli
 take a long time (e.g. those requiring tensorflow if you don’t have a GPU).
 These tests should be marked with <code class="docutils literal notranslate"><span class="pre">&#64;pytest.mark.slow</span></code>, e.g.:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pytest</span>
-<span class="nd">@pytest.mark.slow</span>
+<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">slow</span>
 <span class="k">def</span> <span class="nf">test_something_slow</span><span class="p">():</span>
     <span class="n">slow_result</span> <span class="o">=</span> <span class="n">run_slow_processes</span><span class="p">()</span>
     <span class="k">assert</span> <span class="n">slow_result</span> <span class="o">==</span> <span class="n">expected_slow_thing</span>

docs/main/user_guide/misc/hpc.html

@@ -232,7 +232,7 @@ <h3>Set up conda environment and install cellfinder<a class="headerlink" href="#
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python
 </pre></div>
 </div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>   <span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>   <span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
    <span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">is_gpu_available</span><span class="p">()</span>
 </pre></div>
 </div>

setup.py

@@ -1,5 +1,10 @@
 import platform
 from setuptools import setup, find_namespace_packages, Extension
+from os import path
+
+this_directory = path.abspath(path.dirname(__file__))
+with open(path.join(this_directory, "README.md"), encoding="utf-8") as f:
+    long_description = f.read()
 
 requirements = [
     "numpy",
@@ -88,8 +93,10 @@
 
 setup(
     name="cellfinder",
-    version="0.3.8rc0",
-    description="Cell detection for whole-brain microscopy",
+    version="0.3.8",
+    description="Automated 3D cell detection and registration of whole-brain images",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
     install_requires=requirements,
     extras_require={
         "dev": [
@@ -136,7 +143,11 @@
             "cellfinder_curate = cellfinder.train.curation:main",
         ]
     },
-    url="https://github.com/SainsburyWellcomeCentre/cellfinder",
+    project_urls={
+        "Source Code": "https://github.com/SainsburyWellcomeCentre/cellfinder",
+        "Bug Tracker": "https://github.com/SainsburyWellcomeCentre/cellfinder/issues",
+        "Documentation": "https://sainsburywellcomecentre.github.io/cellfinder",
+    },
     author="Adam Tyson, Christian Niedworok, Charly Rousseau",
     author_email="[email protected]",
     classifiers=[