changed png to svg

docs/src/benchmarks.rst

@@ -18,7 +18,7 @@ See :ref:`PANACEA <details-panacea>`.
 **Assumption**: In this setting, we assume that, in a perturbational context, the effects that are measured via omics data is not only a product of the perturbation origin 
 (e.g KO, KD, drug perturbation), but also of other origins of perturbation that are not directly targeted by the perturbation agent (e.g a drug offtarget).
 
-.. image:: ./_static/nc_offtarget.png
+.. image:: ./_static/nc_offtarget.svg
    :alt: Evaluation based on offtarget recovery
    :width: 1000px
 
@@ -44,7 +44,7 @@ important in the contextualisation of said perturbation
 
 **Performance metric:** EC50 values for nodes included and excluded of the solution network.
 
-.. image:: ./_static/nc_ec50.png
+.. image:: ./_static/nc_ec50.svg
    :alt: Evaluation based on ensitivity to drug perturbation
    :width: 1000px
 
@@ -68,7 +68,7 @@ See :ref:`PANACEA <details-decryptm>`.
 
 **Performance metric:** rank of the selected gene set among all gene sets, according to ORA score
 
-.. image:: ./_static/nc_pathway.png
+.. image:: ./_static/nc_pathway.svg
    :alt: Evaluation based on pathway enrichment
    :width: 1000px
 
@@ -94,7 +94,7 @@ See :ref:`CPTAC <details-cptac>`.
 
 **Performance metric:** difference between kinase activity score in the solution network and the overall PKN.
 
-.. image:: ./_static/nc_multiomics.png
+.. image:: ./_static/nc_multiomics.svg
    :alt: Evaluation based on ensitivity to drug perturbation
    :width: 1000px
 

docs/src/conf.py

@@ -67,8 +67,8 @@
     conf_py_path='/docs/src/',  # Path in the checkout to the docs root
 )
 html_show_sphinx = False
-html_logo = '_static/nc_logo.png'
-html_favicon = '_static/nc_logo.png'
+html_logo = '_static/nc_logo.svg'
+html_favicon = '_static/nc_logo.svg'
 html_css_files = [
     'css/custom.css',
 ]

docs/src/index.rst

@@ -1,7 +1,9 @@
-.. image:: _static/nc_banner.png
+.. image:: _static/nc_banner.svg
+   :width: 1200px
+   :align: center
 
 ##############
-Bridging the gap between data, methods and knowledge in network inference
+Bridging the gap between data, methods and knowledge in the network inference field
 ##############
 
 |MainBuild| |Codecov| |Docs|
@@ -27,7 +29,7 @@ inferring context-specific protein interaction networks by integrating context-a
 These networks have a wide range of applications, from omics data interpretation to identifying drug targets and key driver 
 mutations.
 
-.. image:: _static/nc_graphabs.png
+.. image:: _static/nc_graphabs.svg
    :width: 1000px
    :alt: Networkcommons API
    :align: center

docs/src/methods.rst

@@ -16,7 +16,7 @@ Shortest path
 
 The shortest path is an algorithm for finding one or multiple paths that minimize the distance from a set of starting nodes to a set of destination nodes in a weighted graph (https://doi.org/10.1007/BF01386390).
 
-.. image:: ./_static/sp.png
+.. image:: ./_static/nc_sp.svg
    :alt: All paths
    :width: 1000px
 
@@ -34,7 +34,7 @@ Sign consistency
 
 The sign consistency method checks for sign consistency between the nodes in a given graph. Hereby, source and target nodes, as well as the edges in the graph have an assigned sign. 
 
-.. image:: ./_static/sign.png
+.. image:: ./_static/nc_sign.svg
    :alt: All paths
    :width: 1000px
 
@@ -52,7 +52,7 @@ Reachability filter
 
 The reachability filter generates a network consisting of all reachable nodes from a set of starting nodes.
 
-.. image:: ./_static/reach.png
+.. image:: ./_static/nc_reach.svg
    :alt: All paths
    :width: 1000px
 
@@ -70,7 +70,7 @@ All paths
 
 All paths find all possible connections between a set of source nodes and a set of target nodes. In contrast to the shortest path method or the sign consistency method it doesn’t take the distance or any sign information into account, respectively.
 
-.. image:: ./_static/ap.png
+.. image:: ./_static/nc_ap.svg
    :alt: All paths
    :width: 1000px
 
@@ -91,7 +91,7 @@ Page rank
 
 The Page rank algorithm initially calculates a weight for each node in a graph based on a random walk with restart method. It starts at a set of source or target nodes and determines the importance of the other nodes in the graph based on the structure of the incoming or outgoing edges. It then builds a network considering the highest-ranking nodes starting from each of the source and the target nodes.
 
-.. image:: ./_static/ppr.png
+.. image:: ./_static/nc_ppr.svg
    :alt: PPR
    :width: 1000px
 
@@ -132,20 +132,20 @@ Network compression
 
 This is one of the most important parts of this vignette. Here, we aim to remove redundant information from the network, in order to reduce its size without compromising the information contained in it. A common example would be the following:
 
-.. image:: ./_static/nc_moon_comp_normal.png
+.. image:: ./_static/nc_moon_comp_normal.svg
    :alt: MOON
    :width: 1000px
 
 However, in other cases, we would lose information:
 
-.. image:: ./_static/nc_moon_comp_edgecases.png
+.. image:: ./_static/nc_moon_comp_edgecases.svg
    :alt: MOON
    :width: 1000px
 
 MOON scoring
 ~~~~~~~~~~~~
 
-.. image:: ./_static/nc_moon_core.png
+.. image:: ./_static/nc_moon_core.svg
    :alt: MOON
    :width: 1000px