Started to add Read-the-docs, added a minor fix to the stats.py and s…

…ome additional text in the README

CITATION.cff

@@ -0,0 +1,10 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - family-names: Blundell
+    given-names: Benjamin
+    orcid: https://orcid.org/0000-0002-4634-4779
+title: "HOLLy - Hypothesised Object from Light Localisations"
+version: 1.2
+doi:
+date-released: 2021-08-31

README.markdown

@@ -6,18 +6,40 @@
 
 A [PyTorch](https://pytorch.org/) based neural network program designed to recontruct single molecules in 3D from a series of 2D storm images.
 
-The goal of this network is to take a set of 2D representations of a molecule (or other simulated object) and create a 3D pointcloud of the molecule. We predict the pose and structure using a Convolutional Neural Network.
+The goal of this network is to take a set of 2D representations of a molecule (or other simulated object) and create a 3D pointcloud of the molecule. We predict the pose and structure using a Convolutional Neural Network. 
 
 ## Overview
 
-HOLLy takes a number of images, or generates images from a ground-truth point-cloud, trains on these attempting to improve it's own internal representation of what it thinks the ground-truth object is. At the end of training, an obj or ply file will be produced, representing the object the network converged upon.
+HOLLy takes a number of images (it can also generate images from a ground-truth point-cloud) and trains on these attempting to improve it's own internal representation of what it thinks the ground-truth object is. At the end of training, an *obj* or **ply* file will be produced, representing the object the network converged upon.
 
 HOLLy is split into *train.py* and *run.py* with the actual net stored in in *net/net.py*. *eval.py* will evaluate a trained network, creating statistics and visualistions. 
 
 *net/renderer.py* contains the code for the differentiable renderer. *data/loader.py*, along with *data/buffer.py* and *data/batcher.py* create our simulated data for all the tests, including adding noise. *data/imageload.py* is similar, but for pre-rendered images.
 
+(The diagram below is animated and may take a little time to appear).
 ![Overview diagram](https://shutr.benjamin.computer/static/shutr_files/original/diagram.gif)
 
+## Publication
+
+[3D Structure from 2D Microscopy images using Deep Learning - Frontiers in Bioinformatics](https://www.frontiersin.org/articles/10.3389/fbinf.2021.740342/abstract)
+
+ @article{blundell3DStructure2D01,
+   title = {{{3D Structure}} from {{2D Microscopy}} Images Using {{Deep Learning}}},
+   author = {Blundell, Benjamin James and Rosten, Ed and Ch'ng, QueeLim and Cox, Susan and Manley, Suliana and Sieben, Christian},
+   year = {2021},
+   journal = {Frontiers in Bioinformatics},
+   volume = {0},
+   publisher = {{Frontiers}},
+   issn = {2673-7647},
+   doi = {10.3389/fbinf.2021.740342},
+   copyright = {All rights reserved},
+   langid = {english},
+ }
+
+### Citing with Bibtex
+
+See the CITATION.cff file.
+
 ## Installation
 
 ### Requirements
@@ -221,13 +243,10 @@ Individual tests can be run as follows:
 
     python -m unittest test.data.Data.test_wobble
 
-## Our published paper and data
-
-Our paper "3D Structure from SMLM images using Deep Learning" is under review at the moment and hopefully will be available soon.
+## Datasets in the Publication
 
 The CEP152 data we used in the paper with HOLLy can be found on Zenodo at [https://zenodo.org/record/4751057](https://zenodo.org/record/4751057) and the results we mention in the paper can also be downloaded from Zenodo at [https://zenodo.org/record/4836173](https://zenodo.org/record/4836173).
 
-
 ## Command line and configuration options
 
 When running train.py, there are a number of options one can choose.

docs/source/index.rst

@@ -0,0 +1,16 @@
+Welcome to HOLLy's documentation!
+===================================
+
+** HOLLy ** is a PyTorch based program that attempts to recreate 3D structure from a series of 2D 'Single Molecule Localisation Microscopy' images.
+
+.. note::
+
+   This project is under active development.
+
+Contents
+--------
+
+.. toctree::
+
+   usage
+

stats/stats.py

@@ -31,6 +31,7 @@ def __init__(self):
 
     def on(self, savedir: str):
         self.savedir = savedir
+        self._error_message = False
         path = os.path.normpath(savedir)
         parts = path.split(os.sep)
         self.exp_name = parts[-1]  # WARNING - overwrite potential in the REDIS
@@ -66,7 +67,7 @@ def watch(self, obj, name: str):
 
     def close(self):
         """ Make sure we write to the DB. """
-        pass
+        self._error_message = False
         # TODO - will probably get rid
         # self.db.close()
         # Zip now happens in the generate_stats.sh script
@@ -183,10 +184,9 @@ def write_immediate(self, obj, name, epoch, step, idx):
         try:
             self._conv(obj, name, epoch, step, idx)
         except Exception:
-            pass
-            # This is naughty but until I find a way for it to be less
-            # verbose I'm leaving it in for this version.
-            #print("No database to store statistic.")
+            if not self._error_message:
+                print("No database to store statistic. Stats will not be stored.")
+                self._error_message = True
 
     def save_jpg(
         self,
@@ -239,6 +239,9 @@ def save_points(
         if not os.path.exists(savedir + "/objs"):
             os.makedirs(savedir + "/objs/")
 
+        if not os.path.exists(savedir + "/plys"):
+            os.makedirs(savedir + "/plys/")
+
         path_obj = (
             savedir + "/objs/shape_e" + str(epoch).zfill(3) + "_s" + str(step).zfill(5)
         )
@@ -294,4 +297,4 @@ def update(epoch: int, set_size: int, batch_size: int, step: int):
 
 
 def close():
-    pass
+    stat.close()