Networkx Node Size By Attribute

The following are code examples for showing how to use networkx. Posts about networkx written by sooonia. You received this message because you are subscribed to the Google Groups "networkx-discuss" group. Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. Making networkx graphs from source-target DataFrames Imports/setup. The Pandas DataFrame should contain at least two columns of node names and zero or more columns of node attributes. GraphSAGE is fed with graphs in the NetworkX 2 format, JSON files mapping node ids and node classes, and a numpy array containing node features (npy file). Each node will be randomly assigned a community with the condition that the community is large enough for the node’s intra-community degree, (1-mu) mathrm{deg}(u) as described in step 2. node has an associated sizeattribute, and each edge has a weightof the lambda value at which that edge forms. get_node_attributes (G, name) [source] NetworkX Developers. PyMetis is a Boost Python extension, while this library is pure python and will run under PyPy and interpreters with similarly compatible ctypes libraries. In [1]: import networkx as nx. Python: networkx: How to make node size auto-expand to fit the label Tag: networkx , deap I'm using this bit of code from a deap symbolic regression example problem and the graph displays fine but I want the nodes to expand as rounded rectangles to fit the text automatically. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. And it will give us a list of all the nodes. Node attributes are updated using the attribute dict. js to do graph analysis in the terminal or on a web server. NetworkX Viewer provides a basic interactive GUI to view networkx graphs. weight (string or None, optional (default=None)) – The edge attribute that holds the numerical value used as a weight. The concept is the same as table data or SQL. Python has an excellent library to map relationships called networkx. If None, then all edge weights are 1. All NetworkX graph classes allow (hashable) Python objects as nodes. coordinates (bool (optional, default False)) - If True, node labels are 4-tuples, equivalent to the chimera_index attribute as above. • node_size (dict_key (often str)) – The node attribute on which to specify the size of nodes. gene name) can be chosen from the Neo4j interface to be displayed as the label as well. Now you have all of your node data in a single object associated with the 'data' attribute. Tree TSDs generated by applying the cycle treatment algorithm to the non-tree TSD displayed in Fig. With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more. Node and edge data can be very useful if you want to customize the style of nodes and edges in a visualization. attr (keyword arguments, optional (default= no attributes)) Update attributes for all nodes in nodes. How do I do this? Using: nx. But, if you have some node-specific method (rare in graph problems, but possible) then you'd have method functions associated with a node. get_node_attributes(). Las claves GPG/PGP de los responsables de paquetes pueden conseguirse aquí. Now, I want to plot the graph such that the size of the node is the same as the degree of that node. If values is not a dictionary, then it is treated as a single attribute value that is then applied to every node in G. If I find the degree of the nodes, the only method I know how to use is: degree = nx. Parameters: data (input graph) – Data to initialize graph. ), then only one edge is created with an arbitrary choice of which edge data to use. draw_graphviz(G[, prog])Draw networkx graph with graphviz layout. get_node_attributes (G, name) [source] NetworkX Developers. To start, read in the modules and get the matplotlib graphics engine running properly (if you have a smaller screen, feel free to adjust the size of the plots). Advances in Bioinformatics is a peer-reviewed, Open Access journal that publishes original research articles and review articles focusing on computational and statistical methods to address biological problems. draw_networkx¶ draw_networkx(G, pos=None, with_labels=True, **kwds) [source] ¶ Draw the graph G using Matplotlib. Making networkx graphs from source-target DataFrames Imports/setup. See palette definitions for values. The choice of graph class depends on the structure of thegraph you want to represent. 0)) - Scale factor for positions. size = node. The chart #320 explain how to realise a basic network chart. This means that if you provide a mutable object, like a list, updates to that object will. We mainly discuss directed graphs. They are extracted from open source Python projects. This page explains how to draw a correlation network: a network build on a correlation matrix. The SIR model was introduced in 1927 by Kermack. What you probably intended was to set the 'method_name' attribute of the networkx. ) can be attached to graphs, nodes, and edges • Graph attributes are useful if you need to deal with (are lucky enough to have) several graphs >>> G1 = nx. I want to print the attribute on node ( instead of the label). The Pandas DataFrame should contain at least two columns of node names and zero or more columns of node attributes. MultiDiGraph, which enforces every node and edge to have a layers attribute (which maps to the set of layers (str) it belongs to). In the script, total export data is assigned as a node attribute and set aside to be used as the node size in the visualization. compression) and reduces the sytem call overhead when writing the resulting lazy bytestring to a file or sending it over the network. attribute is a string containing the value. 0)¶ Find communities in the graph and return the associated dendrogram. The chart #320 explain how to realise a basic network chart. Queue (maxsize=0) ¶ Constructor for a FIFO queue. Return type: float. have an additional list of values assigned to each node. There are 2 possibilitie. Again, we have found that this claim only applies to the value-based networks. A cut is a partition of the nodes of a graph into two sets. Note: This function iterates over DataFrame. ; values (dict) - Dictionary of attribute values keyed by node. In this case, the smaller individual among two will be selected with a probability size_tourn_size/2. display node size to isHead. A Fast and Dirty Intro to NetworkX (and D3) Calculate stats & save values as node attributes in the graph (Verify it's done with various inspections of the. Installation and Basic UsageConstructing GraphsAnalyzing GraphsPlotting (Matplotlib) NetworkX Tutorial Jacob Bank (adapted from slides by Evan Rosen). It means if I print G. A graph is a collection of nodes that are connected by links. You can vote up the examples you like or vote down the ones you don't like. And now we'll give it an attribute role, and we'll say that the role of node A is trader. But it is very useful to have a list of the nodes that belong to each node set Use of node attributes to identify membership in each node setConvention adopted at NetworkXUse of the node attribute. I have a added a length table in every line of my shapefile which contains lines that represent roads, and I want to create a weighted graph with this data using the length table as weights. Closeness centrality of a node u is the reciprocal of the average shortest path distance to u over all n-1 reachable nodes. How to use labels in excel file as the labels for the nodes in the graph. Python: networkx: How to make node size auto-expand to fit the label Tag: networkx , deap I'm using this bit of code from a deap symbolic regression example problem and the graph displays fine but I want the nodes to expand as rounded rectangles to fit the text automatically. get_node_attributes (G, name) [source] NetworkX Developers. The value(s) of the attr(s) must be hashable and comparable via the == operator since they are placed into a set([]) object. And that is I will try to play around with it and see if I can come up with something useful or at least nice and fun. mercator networkx sample. 5 will improve the situation (but you are using 1. Input will be provided as a graph in GEXF format, which can be read using the networkx. The first choice to be made when using NetworkX is what type of graph object to use. Now you have all of your node data in a single object associated with the 'data' attribute. I do realize that networkx isn't the drawing engine, but I was hoping. OSMnx is a Python package for downloading administrative boundary shapes and street networks from OpenStreetMap. Posts about networkx written by sooonia. The sets module provides classes for constructing and manipulating unordered collections of unique elements. The following are code examples for showing how to use networkx. The story is what is conveyed through these devices when the discourse has succeeded. For trivial name-value kinds of things, this is not obviously creating any real value. compression) and reduces the sytem call overhead when writing the resulting lazy bytestring to a file or sending it over the network. , drawing nodes with a very high value red and those with a low value blue (similar to a heatmap). First edge. Return type: NetworkX Graph. 56 seconds using a sequential implementation). by the number of connections i. NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and function of complex networks. multiNetX v2. NetworkX Viewer provides a basic interactive GUI to view networkx graphs. Check out the journal article about OSMnx. Detailed optioal argument documentation for nodes are in the network. Examine some of the plugins for Gephi. They are extracted from open source Python projects. 4) Use (node, attrdict) tuples to update attributes for specific nodes. Get node attributes from graph. node_size : scalar or array, optional (default=300) Size of nodes. For example, draw NetworkX uses the spring layout by default, which tries to position nodes with as few crossing edges as possible while keeping edge length similar. The value(s) of the attr(s) must be hashable and comparable via the == operator since they are placed into a set([]) object. weight : object Edge attribute key to use as weight. Community detection for NetworkX's documentation¶. Each node will be randomly assigned a community with the condition that the community is large enough for the node’s intra-community degree, (1-mu) mathrm{deg}(u) as described in step 2. Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. Let's set the node color based on the degree of the node, using this comprehension we can get a list of the degrees. , must be true before and after every function/method call): Among any connected set of Positions, there are no supervisory cycles, Among any connected set of Positions, there is one and only one Position whose `sups` attribute is []. Time: 27442 ms using a conventional Core i5 laptop (vs. You can vote up the examples you like or vote down the ones you don't like. The cliques are ordered according to size. OSMnx is a Python package for downloading administrative boundary shapes and street networks from OpenStreetMap. If my nodes are images, when I draw my graph, will the images be used to display the nodes? Or do I need to set an attribute? I've looked through the examples, and the ones I see, all the nodes seem to be circles. getAttribute(attributeName); where. Get node attributes from graph. I have a network of nodes created using python networkx. If None, then each edge has weight 1. The default node size is mapped to the node degree, but you can override that default by setting 'node_size_field' in the visjs_network function. js headlessly on Node. If desired, the elements of nodew may be tuples of the same size (>= 1) to provided multiple weights for each node. draw(g, with_labels = True, node_size = 5000, font_size = 20) plt. This page explains how to draw a correlation network: a network build on a correlation matrix. However, having no attribute that looks well before the word 'scientist' I will tackle Networkx from the developer's point of view. In order to minimize power peaking in ADS core, the core is divided into several zones with different fuel/matrix ratio. Nodes and G. Return type: number. Python: networkx: How to make node size auto-expand to fit the label Tag: networkx , deap I'm using this bit of code from a deap symbolic regression example problem and the graph displays fine but I want the nodes to expand as rounded rectangles to fit the text automatically. These nodes are often referred to as hubs, and calculating degree is the quickest way of identifying hubs. OSM nodes correspond directly to the nodes in the directed graph. handling large million node graphs for example. What version of WNTR are you using? And when do you get the error?. Si quiere ver un mapa del mundo que muestra la ubicación de muchos de los desarrolladores, visite el Mapa Mundial de Desarrolladores de Debian. Key/value pairs will update existing data associated with the node. Graph Optimization with NetworkX in Python This NetworkX tutorial will show you how to do graph optimization in Python by solving the Chinese Postman Problem in Python. Getting started with NetworkX. NetworkX is powerful but I was trying to plot a graph which shows node labels by default and I was surprised how tedious this seemingly simple task could be for someone new to Networkx. The club's president and the instructor were involved in a dispute, resulting in a split of this group. GitHub Gist: instantly share code, notes, and snippets. In order to use it with python import it, import networkx as nx. Drawing NetworkX plots with ellipses for nodes. Let's set the node color based on the degree of the node, using this comprehension we can get a list of the degrees. Documentation; Installing. for v in G. Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). The main idea is to use a layout to get the positions of the nodes and then use draw_networkx_nodes repeatedly for the n different classes of nodes. coordinates (bool (optional, default False)) - If True, node labels are 4-tuples, equivalent to the chimera_index attribute as above. (So, for Felluga, telling "non-linear" stories is an attribute of the discourse, not the story itself. T : sequence A sequence of nodes in `G`. The sample data file I have is in a file called 'file2. As NetworkX library is used to manage relationships using the Graph structure, we can get started by creating a graph with no nodes and edges: import networkx graph = networkx. class Queue. info(small_graph) #save as json for use in javascript - small graph, and full graph if you want. Lab 04: Graphs and networkx Network analysis. To label graph nodes, you can use draw_networkx_labels function as follows: [code]import networkx as nx from networkx. attr (keyword arguments, optional (default= no attributes)) – Update attributes for all nodes in nodes. Hemingway's short story's discourse structure is very different from a two-year old's discourse structure. ), then only one edge is created with an arbitrary choice of which edge data to use. If values is not a dictionary, then it is treated as a single attribute value that is then applied to every node in G. junction or pipe), or by specifying a threshold (i. Please report any bugs that you find here. Get node attributes from graph. Returns: size - The number of edges or (if weight keyword is provided) the total weight sum. Get node attributes from graph. Many of those characteristics are genuin network relations between countries (like trade flows), thus, in the sense of Social Network Analysis (SNA) edges. 6910 As you can see this is a fairly connected network, and the number of edges in the network is more than 20x the number of nodes, so the network is densely clustered. attr_dict (dictionary, optional (default= no attributes)) - Dictionary of node attributes. # position is stored as node attribute data for random node_size = 80, node. Return type: NetworkX Graph. These include click stream data from websites, mobile phone call data, data from social networks (Twitter streams, Facebook updates), vehicular flow data from roadways, and power grid data, to name just a few. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. Clone via HTTPS Clone with Git or checkout with SVN using the repository's web address. The iteration is ordered by cardinality of the cliques: first all cliques of size one, then all cliques of size two, etc. Since I had used NetworkX a long time ago for drawing network graphs, I decided to use it again. values (dict) - Dictionary of attribute values keyed by node. Je suis en train de dessiner des DAGs à l'aide de networkx 1. node_label_off: is Boolean option which suppress all labels. Returns a comparison function for a categorical node attribute. Normally, there are 2-3 zones. Setting Sto Bmaximizes speed, and using smaller Scan improve quality, as the dependency between the rows in a block can be modeled by more than 1 steps. Weighted graphs using NetworkX I wanted to draw a network of nodes and use the thickness of the edges between the nodes to denote some information. The OSM tags become attributes of the node. edges() then the vertex IDs should appear as per attribute 'num'. Clustering¶. In [2]: G=nx. a customized node object, etc. And if we want to just for all, or a particular attribute for a particular node, then we would use. Click me to see the sample solution. Clone via HTTPS Clone with Git or checkout with SVN using the repository’s web address. Here’s how we can scale the node size and then set the text label size to be proportional to node size. Return a intersection graph with randomly chosen attribute sets for each node that are of equal size (k). If values is not a dictionary, then it is treated as a single attribute value that is then applied to every node in G. Then for each distance I plot the nodes with a given color. The default is all nodes. [1] Parameters-----G : NetworkX graph S : sequence A sequence of nodes in `G`. The full code for this project can be found in this github repo under the file Interactive. part_one_graphs is a list containing 5 networkx graphs. Non-trivial to plot in networkx, but if you load the labels in Python and then assign them to the nodes using set_node_attributes, when you save the graph as gexf you can turn on the node names in Gephi so that they display by the nodes. These nodes are often referred to as hubs, and calculating degree is the quickest way of identifying hubs. See palette definitions for values. The following figure visualizes the graph with the node sizeproportional to the page rank of the node. Revision 17b24d5f. OR A container of (node, attribute dict) tuples. However, I have a lot of trouble converting this into an actual networkx graph, which I will use for my simulation model. Now you have all of your node data in a single object associated with the 'data' attribute. The default is all nodes. attr (keyword arguments, optional) - Set or change attributes using key=value. T : sequence A sequence of nodes in `G`. normalized : bool, optional (default=True) Whether to normalize the edge weights by the total sum of edge weights. [英] Adding Color Attribute to Nodes on NetworkX to export to Gephi. Key/value pairs will update existing data associated with the node. Networkx allows us to create both directed and undirected Multigraphs. 1202547770700635 dev=9. The first choice to be made when using NetworkX is what type of graph object to use. NetworkX graph objects come in different flavors depending on two main properties of the network:. The sample data file I have is in a file called 'file2. ) which use the attribute and the type of the attribute (strings representing legal values of that type). NetworkX defines no custom node objects or edge objects • node-centric view of network • nodes can be any hashable object, while edges are tuples with optional edge data (stored in dictionary) • any Python object is allowed as edge data and it is assigned and stored in a Python dictionary (default empty) NetworkX is all based on Python. node_size : scalar or array, optional (default=300) Size of nodes. Clone via HTTPS Clone with Git or checkout with SVN using the repository’s web address. Returns-----dict Dictionary with nodes as keys and the constraint on the node as values. This includes strings, numbers, tuples of strings and numbers, etc. Node attributes are updated using the attribute dict. import networkx as nx import matplotlib. To ensure that the selection pressure is not too high, the size of the size tournament (the number of candidates evaluated) can be a real number between 1 and 2. Las claves GPG/PGP de los responsables de paquetes pueden conseguirse aquí. For example, in provenance graphs, node id is a string. nodes(); for v in V: for i in range (num_attributes):. weight : object Edge attribute key to use as weight. v : node A node in the graph ``G``. T : sequence A sequence of nodes in `G`. general_random_intersection_graph (n, m, p) Return a random intersection graph with independent probabilities for connections between node and attribute sets. In the script, total export data is assigned as a node attribute and set aside to be used as the node size in the visualization. The Pandas DataFrame should contain at least two columns of node names and zero or more columns of node attributes. Parameters-----G : NetworkX graph An undirected graph. However, Networkx seems to have a default size when it comes to actually drawing the node. One examples of a network graph with NetworkX. OR A container of (node, attribute dict) tuples. This allows for much more interesting analyses. in the case of a direct API for (nearly) all the world bank data. colorBy = 'isHead' # we'll compute node size by the `isHead` attribute web. MultiDiGraph, which enforces every node and edge to have a layers attribute (which maps to the set of layers (str) it belongs to). graphviz_layout (G[, prog, root, args]) Create node positions for G using Graphviz. I was able to give the attribute spell to the edges and Gephi understands that, but I'm not able to figure out how to pass from python networkx to Gephi through gexf the time varying weights. A simple example is shown in Figure 5. If True return a two-tuple of node and node data dictionary: Returns: nlist - A list of nodes. They are extracted from open source Python projects. I wanted to create a network using the geolocations of the nodes, and connect them with edges with source-target pair and add attributes such as distance, cost, width. NetworkX is built on top of Matplotlib, so just like that library, this one requires you to show or render the graph explicitly after you have created it. These are relevant when doing volume-based partitioning. The Pandas DataFrame should contain at least two columns of node names and zero or more columns of node attributes. weight (string or None, optional (default=None)) - The edge attribute that holds the numerical value used as a weight. With this tutorial, you'll tackle an established problem in graph theory called the Chinese Postman Problem. A cut is a partition of the nodes of a graph into two sets. NETWORK STATISTICS - Nodes: 27475 - Links: 85729 Degree distributions - Out-degrees: [n=27475 min=0. import networkx as nx import matplotlib. node_color (string) - the color of the nodes; node_size (int) - the size of the nodes; node_alpha (float) - the opacity of the nodes; node_edgecolor (string) - the color of the node's marker's border; node_zorder (int) - zorder to plot nodes, edges are always 2, so make node_zorder 1 to plot nodes beneath them or 3 to plot nodes atop them. I find it more convenient to set attributes before calling to_pydot. edges( data = True ): # if value/width not specified directly, and weight is specified, set 'value' to 'weight'. To start, read in the modules and get the matplotlib graphics engine running properly (if you have a smaller screen, feel free to adjust the size of the plots). For this, have a look to the networkx package documentation and apply the following community detection algorithms: ", " ", " 1. I have a shapefile which has attribute 'num' and I want it to use it as vertex ID. shp' The original LineStrings and the resulting nodes of the graph. Then I get the distances of each node from that graph. Adding Attributes. OR A container of (node, attribute dict) tuples. Returns-----dict Dictionary with nodes as keys and the constraint on the node as values. WNTR uses NetworkX data objects to store network connectivity as a graph. (D) Colors show different fields to which the papers apply. Dictionary of attribute values keyed by node. I'm looking for something to create a new graph with only nodes and edges of type 'X'. A call to add_node() supports various node properties that can be set individually. NetworkX provides data structures and methods for storing graphs. Parameters: node_for_adding (node) - A node can be any hashable Python object except None. #!/usr/bin/python # -*- coding: utf-8 -*- # Volker Fröhlich, 2013 # [email protected] Now you have all of your node data in a single object associated with the 'data' attribute. set_node_attributes(). weight : object Edge attribute key to use as weight. What you probably intended was to set the 'method_name' attribute of the networkx. I am afraid that this would have to be done using multiple passes. Lab 04: Graphs and networkx Network analysis. The data can be any format that is supported by the to_networkx_graph() function, currently including edge list, dict of dicts, dict of lists, NetworkX graph, NumPy matrix or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph. Return type: list. # for each edge and its attributes in the networkx graph for source,target,edge_attrs in networkx_graph. weight (string or None, optional (default=None)) - The edge attribute that holds the numerical value used as a weight. googlesource. How can I add this node list excel file into Networkx python and when I map them use their attributes. With this tutorial, you'll tackle an established problem in graph theory called the Chinese Postman Problem. It allows to display more information in your chart. node_label_off: is Boolean option which suppress all labels. Node id files map any domain-dependent node id attribute to an integer id value. isomorphism. How do I do this? Using: nx. See draw() for simple drawing without labels or axes. in_degree (nbunch=None, weight=None) [source] ¶ Return an iterator for (node, in-degree) or in-degree for single node. If data=True a list of two-tuples containing (node, node data dictionary). Join GitHub today. Im using networkx for visualization. The choice of graph class depends on the structure of thegraph you want to represent. By default these are empty, but can be added or changed using add_edge, add_node or direct manipulation of the attribute dictionaries named graph, node and edge respectively. Now you have all of your node data in a single object associated with the 'data' attribute. 11 mais je suis confronté à quelques erreurs, voici le test: import networkx as nx print. Additional network models can be downloaded from the University of Kentucky Water Distribution System Research Database at https://uknowledge. Returns: size - The number of edges or (if weight keyword is provided) the total weight sum. Each of these graphs were generated by one of three possible algorithms: Preferential Attachment (`'PA'`) Small World with low probability of rewiring (`'SW L '`) Small World with high probability of rewiring (`'SW H '`). Node attributes are updated using the attribute dict. The first choice to be made when using NetworkX is what type of graph object to use. With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more. weight : object Edge attribute key to use as weight. Edges that defines an attribute of the graph nodes or edges. NetworkX graph¶. However, having no attribute that looks well before the word 'scientist' I will tackle Networkx from the developer's point of view. • node_size (dict_key (often str)) – The node attribute on which to specify the size of nodes. get_node_attributes¶ get_node_attributes (G, name) [source] ¶. Node and edge data can be very useful if you want to customize the style of nodes and edges in a visualization. Documentation; Installing. I am working on a small example node set belonging to two types {'human', 'machine'} and I want to label node attribute in dictionary format outside of each node in networkx graph, such as those shown in node c, e, j in the graph below (I used MS Word to add dictionary-type attribute on the graph):. If desired, the elements of nodew may be tuples of the same size (>= 1) to provided multiple weights for each node. The available data on country attributes is permanently growing and their access is getting more and more comfortable, e. from webweb import Web import networkx as nx # define the node = 'hunger' # we'll compute node size by the `isHead` attribute web. draw(g, with_labels = True, node_size = 5000, font_size = 20) plt. The nodes with the highest degree in a social network are the people who know the most people. attributes node NetworkXノード属性描画 networkx node size draw (1) labels =キーワードを指定することによってそれを行うことができます - それは少し不器用です。. METIS for Python Documentation, Release 0. Return type: int. Example : nx. In the code below I create a graph. And it will give us a list of all the nodes. Calculate each country’s total exports. (Note: Python’s None object should not be used as a node as it determines whether optional function arguments have been assigned in many functions. These include click stream data from websites, mobile phone call data, data from social networks (Twitter streams, Facebook updates), vehicular flow data from roadways, and power grid data, to name just a few. v : node A node in the graph ``G``. Sonnet J(SON + Net)workX ===== Sonnet wraps a NetworkX graph and produces detailed JSON output for use with JavaScript to produce detailed graph visualizations in the browser. import networkx as nx import matplotlib. Return type: float. Plot the bipartite graph using networkx in Python This question already has an answer here: Bipartite graph in NetworkX 1 answer I have an n1-by-n2 bi-adjacency matrix A of a bipartite graph. 11 I'm trying to draw some DAGs using networkx 1. Edges to indicate whether each edge is 'on' or 'off'. Generator functions allow you to declare a function that behaves like an iterator, i. My boss came to me the other day with a new type of project. maxsize is an integer that sets the upperbound limit on the number of items that can be placed in the queue. This is difficult to impossible though with node lists of any substantial size. Python: networkx: How to make node size auto-expand to fit the label Tag: networkx , deap I'm using this bit of code from a deap symbolic regression example problem and the graph displays fine but I want the nodes to expand as rounded rectangles to fit the text automatically. small_graph = trim_nodes_by_attribute_for_remaining_number(undir_g, eigen_sorted, 100) print nx. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: