Merge pull request #37 from HiDiHlabs/dev

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HiDiHlabs · Sep 28, 2021 · 1d2af12 · 1d2af12
2 parents e882ec0 + 7140266
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diff --git a/ReleaseNotes.txt b/ReleaseNotes.txt
@@ -0,0 +1,6 @@
+0.2.0
+- Version and copyright added to interface
+- Drag and drop fixed
+
+0.1.0
+- Alpha release
diff --git a/docs/res/imgs/ControlPanel.png b/docs/res/imgs/ControlPanel.png
diff --git a/docs/res/imgs/DownloadPlot.png b/docs/res/imgs/DownloadPlot.png
diff --git a/docs/res/imgs/KDE.png b/docs/res/imgs/KDE.png
diff --git a/docs/res/imgs/KDE_Optimization.png b/docs/res/imgs/KDE_Optimization.png
diff --git a/docs/res/imgs/ParameterPreview.png b/docs/res/imgs/ParameterPreview.png
diff --git a/docs/res/imgs/Threshold_Optimization.png b/docs/res/imgs/Threshold_Optimization.png
diff --git a/docs/res/imgs/inferredCelltypes.png b/docs/res/imgs/inferredCelltypes.png
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
@@ -18,18 +18,16 @@ what the differences are follow the guide ":ref:`local-or-server`"
 Requirements
 ============
 
-..
-    TODO Check requirements
-
-SSAM-lite and SSAM-lite-server should run on all modern browsers supporting JavaScript ES5 
-and webGL.
+SSAM-lite and SSAM-lite-server should run on all modern browsers supporting JavaScript
+and webGL. It has been verified to run on Firefox, Chrome, Edge and Safari.
 
 SSAM-lite requires an internet connection to access the TensorFlow.js (v2.0.0) 
 library.
 
 SSAM-lite-server requires Python (v3.9.7) as well as the Python packages 
-Flask (v0.8), pandas (v1.3.2) and NumPy (v1.20.3). It also requires the 
-TensorFlow.js (v2.0.0) library, however, this will be downloaded upon first setup 
+Flask (v0.8), pandas (v1.3.2), NumPy (v1.20.3), and Numba (v0.53.1). It also requires the 
+TensorFlow.js (v2.0.0), Plotly (v2.5.1), and Bootstrap (v4.0.0) JavaScript libraries.
+However, these will be downloaded upon first setup 
 and after that, no internet connection is required anymore.
 
 
@@ -63,42 +61,49 @@ To install SSAM-lite-server you first need to clone the
     git clone https://github.com/HiDiHlabs/ssam-lite-server.git
 
 
-Next we create a ``conda`` environment and activate it.
+Next, we change into the cloned directory, create a ``conda`` environment including 
+all packages and dependencies, and activate it.
 
 .. code-block:: bash
 
-    conda create -n ssam-lite-server
+    cd ssam-lite-server
+    conda env create -f environment_ssam-lite-server.yml
     conda activate ssam-lite-server
 
 .. note::
 
-    You can set the environment name to your preferences.
+    You can adjust the YAML file to set the environment name to your preferences. Or 
+    alternatively, create your own environment and install the required Python version and
+    packages with the following command.
+
+    .. code-block:: bash
 
+        conda install python=3.9.7 flask=0.8 numpy=1.20.3 pandas=1.3.2 numba=0.53.1
 
-Now we need to install the required Python version and some 
-additional Python packages.
+
+We also retrieve the static files required by SSAM-lite-server including the Bootstrap, Plotly 
+and TensorFlow.js JavaScript libraries.
 
 .. code-block:: bash
 
-    conda install python=3.9.7 flask=0.8 numpy=1.20.3 pandas=1.3.2
+    python fetch_static_files.py
+
+
+..
+    Download the background signature matrix from Dropbox <https://www.dropbox.com/s/8qxkgg16zelg6ya/new_sheet.tar.xz?dl=0>
+    Decompress it and save it in the folder :file: `app/data/genetics/`
 
 
 Finally, start the Flask app (the default port will be 5000).
 
 .. code-block:: bash
 
-    export FLASK_APP=./ssam-lite-server/flask/run.py
+    export FLASK_APP=run.py
     flask run
 
 
 .. note::
 
    The port and further settings can be made in the *run.py* file or when starting the app with
    ``flask run``. For further information we would refer the reader to the 
-   `Flask documentation <https://flask.palletsprojects.com/>`__.
-
-..
-    TODO check with Shashwat
-
-Upon first setup the app will download some JavaScript dependencies such as
-the TensorFlow.js library
+   `Flask documentation <https://flask.palletsprojects.com/>`__.
diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst
@@ -33,11 +33,11 @@ Click on "Get going!"
 You are in the **Data Center** now. Click on "Coordinates" and select the *coordinates.csv* from the *Codeluppi_osmFISH* directory in the test data.
 Do the same for the "Signatures" (obviously use the *signature.csv* from the same directory this time, *duh!*)
 
-In the **Parameters** section leave the *Vector field wdith* and *KDE kernel bandwidth* at their default and set the 
+In the **Parameters** section leave the *Vector field wdith* at its default, set the *KDE kernel bandwidth* to 5 and the 
 *Cell assignment threshold* to 50.
 
 The time is flying by so we head straight for **Analysis** without any further explanation and click on 
-"Run Kernel Density Estimation". Time for a short break now, this step might take 1 or 2 minutes.
+"Run Kernel Density Estimation". Time for a short break now, this step might take a few seconds.
 
 When the KDE has been estimated scroll further down and hit "Infer Cell Types". Done!
 

diff --git a/docs/source/usage.rst b/docs/source/usage.rst
@@ -76,16 +76,22 @@ SSAM-lite lets you interactively explore and adjust the plots to your needs.
 Alternatively, when hovering over a plot a small control panel will be displayed in the top right corner
 which offers some additional functionality.
 
-Additional information on a data point can be displayed by hovering over it.
+.. image:: ../res/imgs/Control Panel.png
+        :width: 300
+        :align: center
+        :alt: Control panel for plots    
+
+|
 
-TODO screenshot
+Additional information on a data point can be displayed by hovering over it.
 
 
 Data
 ===========
 
-The data will be uploaded in the **Data Center** section of the tool. By clicking the "Coordinates" 
-or "Signatures" button and selecting the correct files.
+The data will be uploaded in the **Data Center** section of the tool. Just click the "Coordinates" 
+or "Signatures" button and selecting the correct files. Alternatively, drag and drop the file from your 
+file browser onto the corresponding button to upload it.
 To be able to use SSAM-lite you need to prepare your data in csv format.
 Two input files are required and must be structured as follows:
 
@@ -198,8 +204,8 @@ the browser to recalculate (this might take a few moments).
 
 Once you are happy with your choice you can proceed with the actual analysis.
 
-For our example analysis we are going to proceed with a *Vector field width* of 500, a *KDE kernel bandwidth*
-of 1, and a *Cell assignment threshold* of 50.
+For our example analysis we are going to proceed with a *Vector field width* of 1500, a *KDE kernel bandwidth*
+of 3, and a *Cell assignment threshold* of 12.
 
 
 Analysis
@@ -233,7 +239,7 @@ and refine those before rerunning the analysis.
 Save results
 ================
 
-All plots are produced with `Plotly <https://plotly.com/>`__ and can be downloaded
+All plots are produced with Plotly and can be downloaded
 by hovering over the plot which triggers a control panel to appear in the upper right corner,
 now click the camera icon which lets you download the current plot as png file.