I have a data set with huge number of features, so analysing the correlation matrix has become very difficult. I want to plot a correlation matrix which we get using `dataframe.corr()` function from pandas library. Is there any built-in function provided by the pandas library to plot this matrix?

Plot correlation matrix using pandas

I have some data similar to the `data.frame` `d` as follows.

    d &lt;- structure(list(ID = c(&quot;KP1009&quot;, &quot;GP3040&quot;, &quot;KP1757&quot;, &quot;GP2243&quot;, 
                               &quot;KP682&quot;, &quot;KP1789&quot;, &quot;KP1933&quot;, &quot;KP1662&quot;, &quot;KP1718&quot;, &quot;GP3339&quot;, &quot;GP4007&quot;, 
                               &quot;GP3398&quot;, &quot;GP6720&quot;, &quot;KP808&quot;, &quot;KP1154&quot;, &quot;KP748&quot;, &quot;GP4263&quot;, &quot;GP1132&quot;, 
                               &quot;GP5881&quot;, &quot;GP6291&quot;, &quot;KP1004&quot;, &quot;KP1998&quot;, &quot;GP4123&quot;, &quot;GP5930&quot;, &quot;KP1070&quot;, 
                               &quot;KP905&quot;, &quot;KP579&quot;, &quot;KP1100&quot;, &quot;KP587&quot;, &quot;GP913&quot;, &quot;GP4864&quot;, &quot;KP1513&quot;, 
                               &quot;GP5979&quot;, &quot;KP730&quot;, &quot;KP1412&quot;, &quot;KP615&quot;, &quot;KP1315&quot;, &quot;KP993&quot;, &quot;GP1521&quot;, 
                               &quot;KP1034&quot;, &quot;KP651&quot;, &quot;GP2876&quot;, &quot;GP4715&quot;, &quot;GP5056&quot;, &quot;GP555&quot;, &quot;GP408&quot;, 
                               &quot;GP4217&quot;, &quot;GP641&quot;),
                        Type = c(&quot;B&quot;, &quot;A&quot;, &quot;B&quot;, &quot;A&quot;, &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, 
                                 &quot;B&quot;, &quot;B&quot;, &quot;A&quot;, &quot;A&quot;, &quot;A&quot;, &quot;A&quot;, &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, &quot;A&quot;, &quot;A&quot;, &quot;A&quot;, &quot;A&quot;, 
                                 &quot;B&quot;, &quot;B&quot;, &quot;A&quot;, &quot;A&quot;, &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, &quot;A&quot;, &quot;A&quot;, &quot;B&quot;, &quot;A&quot;, 
                                 &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, &quot;B&quot;, &quot;A&quot;, &quot;B&quot;, &quot;B&quot;, &quot;A&quot;, &quot;A&quot;, &quot;A&quot;, &quot;A&quot;, &quot;A&quot;, 
                                 &quot;A&quot;, &quot;A&quot;),
                        Set = c(15L, 1L, 10L, 21L, 5L, 9L, 12L, 15L, 16L, 
                                19L, 22L, 3L, 12L, 22L, 15L, 25L, 10L, 25L, 12L, 3L, 10L, 8L, 
                                8L, 20L, 20L, 19L, 25L, 15L, 6L, 21L, 9L, 5L, 24L, 9L, 20L, 5L, 
                                2L, 2L, 11L, 9L, 16L, 10L, 21L, 4L, 1L, 8L, 5L, 11L), Loc = c(3L, 
                                                                                              2L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 3L, 
                                                                                              2L, 2L, 2L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 
                                                                                              1L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L)),
                   .Names = c(&quot;ID&quot;, &quot;Type&quot;, &quot;Set&quot;, &quot;Loc&quot;), class = &quot;data.frame&quot;,
                   row.names = c(NA, -48L))

I want to explore the relationships between members of `d$ID` using a chord diagram similar to the one below.

![enter image description here][1]

It seesms there ar several options to do so in `R`. (https://stackoverflow.com/questions/14599150/chord-diagram-in-r). 

In my data the relationships are according to `d$Set` (not directional) and the grouping is according to `d$Loc`. The following are my attempts to map theser relationships as a chord diagram.

## Attempt 1: Using `igraph`
I have tried `igraph` as follows with node size according to degree.

    # Get vertex relationships
    sets &lt;- unique(d$Set[duplicated(d$Set)])
    rel &lt;-  vector(&quot;list&quot;, length(sets))
    for (i in 1:length(sets)) {
      rel[[i]] &lt;- as.data.frame(t(combn(subset(d, d$Set ==sets[i])$ID, 2)))
    }
    library(data.table)
    rel &lt;- rbindlist(rel)
    
    # Get the graph
    g &lt;- graph.data.frame(rel, directed=F, vertices=d)
    clr &lt;- as.factor(V(g)$Loc)
    levels(clr) &lt;- c(&quot;salmon&quot;, &quot;wheat&quot;, &quot;lightskyblue&quot;)
    V(g)$color &lt;- as.character(clr)
    
    # Plot
    plot(g, layout = layout.circle, vertex.size=degree(g)*5, vertex.label=NA)
![enter image description here][2]

How to modify the plot to look like the first figure? It seems that there are no options to modify `igraph` `layout.circle`.

## Attempt 2: Using `Circlize`
It seems smoother bezier curves and grouping are possible in the `R` package `circlize`. But here I am not able to group the nodes as well as adjust their size according to degree as they are plotted as sectors.

    par(mar = c(1, 1, 1, 1), lwd = 0.1, cex = 0.7)
    circos.initialize(factors = as.factor(d$ID), xlim = c(0, 10))
    circos.trackPlotRegion(factors = as.factor(d$ID), ylim = c(0, 0.5), bg.col = V(g)$color,
                           bg.border = NA, track.height = 0.05)
    for(i in 1:nrow(rel)) {
      circos.link(rel[i,1], 0, rel[i,2],0, h = 0.4)
      
    }

![enter image description here][3]

Here however there are no options to modify the nodes. In fact they can be only plotted as sectors? In this case is there any way to modify the sectors into circular nodes of size according to the degree?

## Attempt 3: Using `edgebundleR`(https://github.com/garthtarr/edgebundleR)

    require(edgebundleR)
    edgebundle(g,tension = 0.1,cutoff = 0.5, fontsize = 18,padding=40)

![enter image description here][4]
It seems here there are limited options to modify the aesthetics.


  [1]: http://i.stack.imgur.com/Z5A5v.png
  [2]: http://i.stack.imgur.com/PdtWF.png
  [3]: http://i.stack.imgur.com/oawoW.png
  [4]: http://i.stack.imgur.com/kwKIU.jpg

Network chord diagram woes in R

I am trying to use IPython notebook on MacOS X with Python 2.7.2 and IPython 1.1.0.

I cannot get matplotlib graphics to show up inline.

    import matplotlib
    import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline  

I have also tried `%pylab inline` and the ipython command line arguments `--pylab=inline` but this makes no difference.

    x = np.linspace(0, 3*np.pi, 500)
    plt.plot(x, np.sin(x**2))
    plt.title(&#39;A simple chirp&#39;)
    plt.show()


Instead of inline graphics, I get this:

    &lt;matplotlib.figure.Figure at 0x110b9c450&gt;

And `matplotlib.get_backend()` shows that I have the `&#39;module://IPython.kernel.zmq.pylab.backend_inline&#39;` backend.

How to make IPython notebook matplotlib plot inline

Does anyone have any wisdom on workflows for data analysis related to custom report writing?  The use-case is basically this:

1.  Client commissions a report that uses data analysis, e.g. a population estimate and related maps for a water district.

2.  The analyst downloads some data, munges the data and saves the result (e.g. adding a column for population per unit, or subsetting the data based on district boundaries).

3.  The analyst analyzes the data created in (2), gets close to her goal, but sees that needs more data and so goes back to (1).

4.  Rinse repeat until the tables and graphics meet QA/QC and satisfy the client.

5.  Write report incorporating tables and graphics.

6.  Next year, the happy client comes back and wants an update.  This should be as simple as updating the upstream data by a new download (e.g. get the building permits from the last year), and pressing a &quot;RECALCULATE&quot; button, unless specifications change.

At the moment, I just start a directory and ad-hoc it the best I can.  I would like a more systematic approach, so I am hoping someone has figured this out...  I use a mix of spreadsheets, SQL, ARCGIS, R, and Unix tools.

Thanks!

PS:

Below is a basic Makefile that checks for dependencies on various intermediate datasets (w/ `.RData` suffix) and scripts (`.R` suffix).  Make uses timestamps to check dependencies, so if you `touch ss07por.csv`, it will see that this file is newer than all the files / targets that depend on it, and execute the given scripts in order to update them accordingly.  This is still a work in progress, including a step for putting into SQL database, and a step for a templating language like sweave. Note that Make relies on tabs in its syntax, so read the manual before cutting and pasting. Enjoy and give feedback!

http://www.gnu.org/software/make/manual/html_node/index.html#Top

&lt;pre&gt;
R=/home/wsprague/R-2.9.2/bin/R

persondata.RData : ImportData.R ../../DATA/ss07por.csv Functions.R
   $R --slave -f ImportData.R

persondata.Munged.RData : MungeData.R persondata.RData Functions.R
      $R --slave -f MungeData.R

report.txt:  TabulateAndGraph.R persondata.Munged.RData Functions.R
      $R --slave -f TabulateAndGraph.R &gt; report.txt

&lt;/pre&gt;

Workflow for statistical analysis and report writing

I&#39;m new to Tensorflow and would greatly benefit from some visualizations of what I&#39;m doing. I understand that Tensorboard is a useful visualization tool, but how do I run it on my remote Ubuntu machine?

How can I run Tensorboard on a remote server?

How do I plot a histogram using [`matplotlib.pyplot.hist`][1]?

I have a list of y-values that correspond to bar height, and a list of x-value strings.

Related: [`matplotlib.pyplot.bar`][2].

[1]: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html
[2]: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html

How to plot a histogram using Matplotlib in Python with a list of data?

I got stuck on something that feels like should be relatively easy. The code I bring below is a sample based on a larger project I&#39;m working on. I saw no reason to post all the details, so please accept the data structures I bring as is.

Basically, I&#39;m creating a bar chart, and I just can figure out how to add value labels on the bars (in the center of the bar, or just above it). Been looking at samples around the web but with no success implementing on my own code. I believe the solution is either with &#39;text&#39; or &#39;annotate&#39;, but I:
a) don&#39;t know which one to use (and generally speaking, haven&#39;t figured out when to use which).
b) can&#39;t see to get either to present the value labels.
Would appreciate your help, my code below.
Thanks in advance!

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    pd.set_option(&#39;display.mpl_style&#39;, &#39;default&#39;) 
    %matplotlib inline
    
    # Bring some raw data.
    frequencies = [6, 16, 75, 160, 244, 260, 145, 73, 16, 4, 1]
    
    # In my original code I create a series and run on that, 
    # so for consistency I create a series from the list.
    freq_series = pd.Series.from_array(frequencies)
    
    x_labels = [108300.0, 110540.0, 112780.0, 115020.0, 117260.0, 119500.0, 
                121740.0, 123980.0, 126220.0, 128460.0, 130700.0]
    
    # Plot the figure.
    plt.figure(figsize=(12, 8))
    fig = freq_series.plot(kind=&#39;bar&#39;)
    fig.set_title(&#39;Amount Frequency&#39;)
    fig.set_xlabel(&#39;Amount ($)&#39;)
    fig.set_ylabel(&#39;Frequency&#39;)
    fig.set_xticklabels(x_labels)

Adding value labels on a matplotlib bar chart

Based on [this question about heatmaps in matplotlib][1], I wanted to move the x-axis titles to the top of the plot.

    import matplotlib.pyplot as plt
    import numpy as np
    column_labels = list(&#39;ABCD&#39;)
    row_labels = list(&#39;WXYZ&#39;)
    data = np.random.rand(4,4)
    fig, ax = plt.subplots()
    heatmap = ax.pcolor(data, cmap=plt.cm.Blues)
    
    # put the major ticks at the middle of each cell
    ax.set_xticks(np.arange(data.shape[0])+0.5, minor=False)
    ax.set_yticks(np.arange(data.shape[1])+0.5, minor=False)

    # want a more natural, table-like display
    ax.invert_yaxis()
    ax.xaxis.set_label_position(&#39;top&#39;) # &lt;-- This doesn&#39;t work!
    
    ax.set_xticklabels(row_labels, minor=False)
    ax.set_yticklabels(column_labels, minor=False)
    plt.show()

However, calling [matplotlib&#39;s set_label_position][2] (as notated above) doesn&#39;t seem to have the desired effect. Here&#39;s my output:

![enter image description here][3]

What am I doing wrong?


  [1]: https://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor
  [2]: http://matplotlib.org/api/axis_api.html#matplotlib.axis.XAxis.set_label_position
  [3]: http://i.stack.imgur.com/rdgE5.png

Moving x-axis to the top of a plot in matplotlib

Is there an easy way to increase the space between the plot title and plot area below it (i.e., the box with the data). Similarly, I&#39;d prefer to have some space between the axis title and axis labels.

In other words, is there a way to &quot;move the title a bit up, the y axis title a bit left, and the x axis title a bit down&quot;?

ggplot2 plot area margins?

I&#39;d like to make a heatmap like this (shown on [FlowingData][1]):
![heatmap][2]

The source data is [here][3], but random data and labels would be fine to use, i.e.

    import numpy
    column_labels = list(&#39;ABCD&#39;)
    row_labels = list(&#39;WXYZ&#39;)
    data = numpy.random.rand(4,4)

Making the heatmap is easy enough in matplotlib:

    from matplotlib import pyplot as plt
    heatmap = plt.pcolor(data)

And I even found a [colormap][4] arguments that look about right: `heatmap = plt.pcolor(data, cmap=matplotlib.cm.Blues)`

But beyond that, I can&#39;t figure out how to display labels for the columns and rows and display the data in the proper orientation (origin at the top left instead of bottom left).

Attempts to manipulate `heatmap.axes` (e.g. `heatmap.axes.set_xticklabels = column_labels`) have all failed. What am I missing here?


  [1]: http://flowingdata.com/2010/01/21/how-to-make-a-heatmap-a-quick-and-easy-solution/
  [2]: http://i.stack.imgur.com/pjnUL.png
  [3]: http://datasets.flowingdata.com/ppg2008.csv
  [4]: http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps
  [5]: http://stackoverflow.com/users/1552748/paul-h
  [6]: http://stackoverflow.com/users/190597/unutbu
  [7]: http://stackoverflow.com/questions/14406214/moving-x-axis-to-the-top-of-a-plot-in-matplotlib

Heatmap in matplotlib with pcolor?

I saw `D3.js` and I was interested so I bought this [book](http://shop.oreilly.com/product/0636920025429.do)! I may be wrong,  however I found it un-fulfilling.

Does anyone have prime material for mastering `D3.js`for data visualization?

I can see a lot of potential for `D3.js`and I am very keen and interested. 

thanks in advance!


**Update:**

I just completed reading [Interactive Data Visualization][1] for the Web by Scott Murray, great book! Try it, it&#39;s free.


  [1]: http://web.archive.org/web/20160424170530/http://chimera.labs.oreilly.com/books/1230000000345/

A good book for learning D3.js

I just installed graphite/statsd for production use. I&#39;m really happy with it, but one of my co-workers asked me if there was a way to make it look prettier. Honestly, I can&#39;t say that I haven&#39;t wonder the same.

Are there alternatives to the Graphite UI that do a better job rendering data, perhaps using one of the awesome frontend graphing libraries and http push?

GUI for statsd data other than Graphite?

I cannot figure out how to manually set up a legend for this plot. All I really want is a simple legend to the right that uses the three colors and has a name next to each.

![enter image description here][1]

The current code looks like this: 

    a &lt;-c(&quot;S1&quot;,&quot;S2&quot;,&quot;S3&quot;,&quot;S4&quot;,&quot;S5&quot;,&quot;S6&quot;,&quot;S7&quot;,&quot;S8&quot;,&quot;S9&quot;) #names
    b &lt;-c(0.23,0.26,0.55,0.56,0.36,0.23,0.18,0.06,0.04) #mean t0
    c &lt;-c(0.64,0.6,0.81,1.4,0.89,0.55,0.48,0.22,0.09) #mean t1
    d &lt;-c(0.20,0.23,0.52,0.53,0.33,0.20,0.15,0.04,0.03) #SD low t0
    e &lt;-c(0.26,0.29,0.58,.59,0.39,0.26,0.21,0.08,0.05) #SD high t0
    f &lt;-c(0.67,0.63,0.86,1.44,0.93,0.59,0.51,0.25,0.10) #SD high t1
    g &lt;-c(0.61,0.57,0.78,1.36,0.85,0.53,0.45,0.19,0.08) #SD low t1
    h &lt;-c(0.41,0.34,0.26,0.84,0.53,0.32,0.30,0.16,0.05) #absolute change
    
    data &lt;- data.frame(a,b,c,d,e,f,g,h)
    
    ggplot(data=data,aes(a)) + 
      geom_bar(stat=&quot;identity&quot;, aes(y=h),fill=&quot;#62c76b&quot;,colour=&quot;#333333&quot;)+ #green
      geom_line(aes(y=b,group=1),size=1.0,colour=&quot;#f04546&quot;) +   #red
      geom_point(aes(y=b),size=3, colour=&quot;#f04546&quot;) +           #red
      geom_errorbar(aes(ymin=d, ymax=e), colour=&quot;#f04546&quot;, width=0.1, size=.8) + 
      geom_line(aes(y=c,group=1),size=1.0,colour=&quot;#3591d1&quot;) +   #blue 
      geom_point(aes(y=c),size=3, colour=&quot;#3591d1&quot;) +           #blue
      geom_errorbar(aes(ymin=f, ymax=g), colour=&quot;#3591d1&quot;, width=0.1, size=.8) + 
      ylab(&quot;Symptom severity&quot;) + xlab(&quot;PHQ-9 symptoms&quot;) +
      ylim(0,1.6) +
      theme_bw() +
      theme(axis.title.x = element_text(size = 15, vjust=-.2)) +
      theme(axis.title.y = element_text(size = 15, vjust=0.3))




  [1]: http://i.stack.imgur.com/xYukB.png

Construct a manual legend for a complicated plot

I want some of the nodes in my force-directed layout to ignore all forces and stay in fixed positions based on an attribute of the node, while still being able to be dragged and exert repulsion on other nodes and maintain their link lines. 

I thought it would be as simple as this:

    force.on(&quot;tick&quot;, function() {
        vis.selectAll(&quot;g.node&quot;)
            .attr(&quot;transform&quot;, function(d) {
                return (d.someAttribute == true) ?
                   &quot;translate(&quot; + d.xcoordFromAttribute + &quot;,&quot; + d.ycoordFromAttribute +&quot;)&quot; :
                   &quot;translate(&quot; + d.x + &quot;,&quot; + d.y + &quot;)&quot;
            });
      });

I have also tried to manually set the node&#39;s x and y attributes each tick, but then the links continue to float out to where the node would be if it was affected by the force.

Obviously I have a basic misunderstanding of how this is supposed to work. How can I fix nodes in a position, while keeping links and still allowing for them to be draggable?


Fix Node Position in D3 Force Directed Layout

I&#39;m trying to learn D3 by experimenting with one of their [basic bubblecharts][1]. First task: figure out how to drag an bubble and have it become the topmost object while it&#39;s being dragged. (The problem is getting D3&#39;s object model to map onto the DOM, but I&#39;ll get there...)

To drag it, we can simply invoke d3&#39;s drag behavior using the code they provide:

    var drag = d3.behavior.drag()
        .on(&quot;dragstart&quot;, dragstart)
        .on(&quot;drag&quot;, dragmove)
        .on(&quot;dragend&quot;, dragend);

Works great. Drags well. Now, how do we get it to be the topmost item? Search for &quot;svg z-index&quot; here and it becomes quickly apparent that the only way to change the index is to move an object further down in the DOM. OK. They don&#39;t make it easy because the individual bubbles don&#39;t have ID&#39;s, but messing around with the console, we can locate one of those bubble objects with:

    $(&quot;text:contains(&#39;TimeScale&#39;)&quot;).parent()

and we can move it to the end of the containing svg element with:

    .appendTo(&#39;svg&#39;)

Drag it after you do that, and it&#39;s the top-most item. So far, so good, *if* you&#39;re working entirely within the DOM. 

BUT: what I really want to do is have that happen automatically whenever a given object/bubble is being dragged. D3 provides a model for `dragstart()` and `dragend()` functions that will allow us to embed a statement to do what we want during the dragging process. And D3 provides the `d3.select(this)` syntax  that allows us to access *d3&#39;s* object representation of the object/bubble you&#39;re currently dragging. But how do I cleanly turn that massive array they return into a reference to a DOM element that I can interact with and - for instance - move it to the end of the svg container, or perform other references in the DOM, such as form submission?

  [1]: http://mbostock.github.com/d3/ex/bubble.html

How to access the DOM element that correlates to a D3 SVG object?

I would like to have my main title and axis title have the same font size as the annotated text in my plot.

i used theme_get() and found that text size is 12, so I did that in my theme statement - this did not work. I also tried to send the relative size to 1, and this did not work

![enter image description here][1]
I was hoping someone could please help me. 

Code is below

    library(ggplot2)
    library(gridExtra)  #to set up plot grid
    library(stringr)  #string formatting functions
    library(plyr)  #rbind.fill function
    library(reshape2)  #transformation of tables
    
    dat&lt;-data.frame(
    est=c(2.75,2.95,2.86,2.99),
    ucl=c(2.92,3.23,3.38,4.91),
    lcl=c(2.24,1.67,2.44,1.82),
    ord=c(1,2,1,2)
    )
    dat$varname&lt;-c(&#39;Estimate&#39;,&#39;Predictive&#39;,&#39;Estimate&#39;,&#39;Predictive&#39;)
    dat$grp&lt;-c(&#39;Cobalt&#39;,&#39;Cobalt&#39;,&#39;Chromium&#39;,&#39;Chromium&#39;)
    
    for (i in unique(dat$grp)) {
      dat &lt;- rbind.fill(dat, data.frame(grp = i, ord=0,
                                        stringsAsFactors = F))
    }
    
    dat$grp_combo &lt;- factor(paste(dat$grp, dat$ord, sep = &quot;, &quot;))
    dat$grpN &lt;- as.numeric(dat$grp_combo)
    
    
    
    rng &lt;- c(0,6)
    scale.rng &lt;-1
    
    
    xstart=-(max(dat$grpN)+2)
    xend=4
    
    
    ThemeMain&lt;-theme(legend.position = &quot;none&quot;, plot.margin = unit(c(0,0,0, 0), &quot;npc&quot;), 
                     panel.margin = unit(c(0,0, 0, 0), &quot;npc&quot;), 
                     title =element_text(size=12, face=&#39;bold&#39;),
                     axis.text.y = element_blank(), 
                     axis.text.x = element_text(color=&#39;black&#39;),
                     axis.ticks.y = element_blank(),
                     axis.title.x = element_text(size=12,color=&#39;black&#39;,face=&#39;bold&#39;)
                     )
    BlankSettings &lt;- theme(legend.position = &quot;none&quot;, 
                           title =element_text(size=12, face=&#39;bold&#39;),
                           plot.margin = unit(c(0,0, 0, 0), &quot;npc&quot;), 
                           panel.margin = unit(c(0,0, 0, 0), &quot;npc&quot;),              
                           axis.text.x = element_text(color=&#39;white&#39;),                             
                           axis.text.y = element_blank(), 
                           axis.ticks.x = element_line(color = &quot;white&quot;),
                           axis.ticks.y=element_blank(),
                           axis.title.x = element_text(size=12,color=&#39;white&#39;,face=&#39;bold&#39;),
                           panel.grid = element_blank(),panel.grid.major = element_blank(),panel.background = element_blank()
                           )
      
    pd &lt;- position_dodge(width = 0.7)
    
    #######################################################################################################
    #MAIN PLOT
    #######################################################################################################
    mainPart&lt;-
      ggplot(dat, aes(x=-grpN,y=est, ymin=lcl, ymax=ucl, group=1)) +
      scale_y_continuous(name=NULL, breaks=seq(rng[1], rng[2], scale.rng), limits=c(rng[1], rng[2]), expand=c(0,0)) + 
      ylab(&#39;Ion Concentration&#39;) +
      ggtitle(&#39;Mean with 95% HDI&#39;)+
      #geom_segment(aes(x=xstart, xend=0, y=0, yend=0), linetype=3, alpha=0.01) +
      geom_linerange(aes(linetype=&quot;1&quot;),position=pd) +
      geom_point(aes(shape=&quot;1&quot;), fill=&quot;white&quot;,position=pd) +
      coord_flip() +
      scale_x_continuous(limits=c(xstart,xend), expand=c(0,0))+xlab(NULL)+
      ThemeMain
    
    #######################################################################################################
    #varnameS
    #######################################################################################################
    # ystart &amp; yend are arbitrary. [0, 1] is
    # convinient for setting relative coordinates of
    # columns
    ystart = 0
    yend = 1
    p1 &lt;- 
      ggplot(dat, aes(x = -varnameN, y = 0)) + 
      coord_flip() + 
      scale_y_continuous(limits = c(ystart, yend)) + 
      BlankSettings+
      scale_x_continuous(limits = c(xstart, xend), expand = c(0, 0)) + 
      xlab(NULL) + 
      ylab(&#39;&#39;) +
      ggtitle(&#39;&#39;)
    
    studyList&lt;-
      p1 + 
      with(unique(dat[is.na(dat$varname),c(&quot;grpN&quot;,&quot;grp&quot;)]), annotate(&quot;text&quot;,label=grp, x=-grpN,y=0, fontface=&#39;bold&#39;, hjust=0)) + #Variable Group varnames
      with(dat[!is.na(dat$var),],annotate(&quot;text&quot;,label=varname,x=-grpN,y=0.04, hjust=0))  #Variables
      
    #######################################################################################################
    #EFFECTS
    #######################################################################################################
    f&lt;-function(x) round(x,2)
    dat$msmt&lt;-paste(f(dat$est),&#39; [&#39;,f(dat$lcl),&#39;, &#39;,f(dat$ucl),&#39;]&#39;,sep=&#39;&#39;)
    
    
    effectSizes&lt;-p1+
      annotate(&quot;text&quot;,x=-dat$grpN, y=0.25,label=ifelse(is.na(dat$varname)==T,&#39;&#39;,dat$msmt))
      
    
    
    grid.arrange(ggplotGrob(studyList), ggplotGrob(mainPart), 
                 ggplotGrob(effectSizes), ncol = 3, widths = unit(c(0.19, 
                                                                     0.4, 0.41), &quot;npc&quot;))


  [1]: http://i.stack.imgur.com/ORwka.jpg

ggplot2, change title size

I want to plot trees using Python. Decision trees, Organizational charts, etc. Any library that helps me with that?

Tree plotting in Python

I wish to present a [distance matrix][1] in an article I am writing, and I am looking for good visualization for it.

So far I came across balloon plots (I used it [here][2], but I don&#39;t think it will work in this case), heatmaps (here is [a nice example][3], but they don&#39;t allow to present the numbers in the table, correct me if I am wrong.  Maybe half the table in colors and half with numbers would be cool) and lastly correlation ellipse plots (here is [some code and example][4] - which is cool to use a shape, but I am not sure how to use it here).

There are also various clustering methods but they will aggregate the data (which is **not** what I want)  while what I want is to present all of the data.

Example data:

    nba &lt;- read.csv(&quot;http://datasets.flowingdata.com/ppg2008.csv&quot;)
    dist(nba[1:20, -1], )


I am open for ideas.


  [1]: http://en.wikipedia.org/wiki/Distance_matrix
  [2]: http://www.r-statistics.com/2010/02/nutritional-supplements-efficacy-score-graphing-plots-of-current-studies-results-using-r/
  [3]: http://flowingdata.com/2010/01/21/how-to-make-a-heatmap-a-quick-and-easy-solution/
  [4]: http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=149

What techniques exists in R to visualize a &quot;distance matrix&quot;?

I have created an array thusly:

    import numpy as np
    data = np.zeros( (512,512,3), dtype=np.uint8)
    data[256,256] = [255,0,0]

What I want this to do is display a single red dot in the center of a 512x512 image. (At least to begin with... I think I can figure out the rest from there)




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