Installing Snap.pySnap.py can be installed via the pip module. To install Snap.py, execute pip from the command line as follows: python -m pip install snap-stanfordIf you have more than one version of Python installed on the system, make sure that python refers to the executable that you want to install Snap.py for. You might also need to add --user after install, if pip complains about your adminsitrative rights. The most recent notes about installing Snap.py on various systems is available at this document: Snap.py Installation Matrix.Manual Install of Snap.pyIf you want to use Snap.py in a local directory without installing it, then download the corresponding Snap.py package for your system, unpack it, and copy files snap.py and _snap.so (or _snap.pyd) to your working directory. The working directory must be different than the install directory.Documentation and SupportSnap.py Tutorial and Manual are available.Snap.py is a Python interface for SNAP, which is written in C++. Most of the SNAP functionality is supported.For more details on SNAP C++, check out SNAP C++ documentation.A tutorial on Large Scale Network Analytics with SNAP with a significant Snap.py specific component was given at the WWW2015 conference in Florence.Use the SNAP and Snap.py users mailing list for any questions or a discussion about Snap.py installation, use, and development. To post to the group, send your message to snap-discuss at googlegroups dot com.Quick Introduction to Snap.pyThis document gives a quick introduction to a range of Snap.py operations.Several programs are available to demonstrate the use of Snap.py. The programs are also useful as tests to confirm that your installation of Snap.py is working correctly: quick_test.py: a quick test to confirm that Snap.py works on your computer; intro.py: combines the code that is shown below on this page; tutorial.py: contains the code from Snap.py tutorial; tneanet.py: demonstrates the use of the TNEANet network class; cncom.py: demonstrates the use of functions for connected components; attributes.py: demonstrates the use of attributes in TNEANet network class; test-gnuplot.py: a quick test to confirm that gnuplot works; test-graphviz.py: a quick test to confirm that Graphviz works.The code from intro.py is explained in more details below.All the code assumes that Snap.py has been imported by the Python program. Make sure that you execute this line in Python before running any of the code below:import snap Graph and Network TypesSnap.py supports graphs and networks. Graphs describe topologies. That is nodes with unique integer ids and directed/undirected/multiple edges between the nodes of the graph. Networks are graphs with data on nodes and/or edges of the network. Data types that reside on nodes and edges are simply passed as template parameters which provides a very fast and convenient way to implement various kinds of networks with rich data on nodes and edges.
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For lens-profile-based correction to be useful, it needs to support a lot of lenses. CyberLink's library of profiles is limited compared with Lightroom's but you can manually adjust the distortion, and CyberLink users can create their own profiles and make them downloadable from DirectorZone.com. PhotoDirector still didn't have my Sigma 150-600mm zoom lens in its database when I tested it, but it does have one for my Canon EF 70-300mm f/4-5.6 IS USM. 2ff7e9595c
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