Tag Archives: plotting

Color Points by Factor with Bokeh

Bokeh (https://bokeh.pydata.org/en/latest/) has been on my radar for some time as I move my data processing primarily to Jupyter notebooks.  The look and feel of the plots have sensible defaults and generally are visually pleasing without too much customization.  Compared to matplotlib, I find that I need to do much less customization to get my final product.

Unfortunately, sometimes the process of generating a plot isn’t a one-to-one mapping with my prior experiences.  One such area of difficulty recently was generating a plot with four treatments, coloring each group of circles independently.  After much trial and error, the following code generated a rough plot I was happy with.

from bokeh.io import output_notebook
from bokeh.palettes import brewer
from bokeh.plotting import figure, show
import pandas

# Assumes df => data frame with columns: X_Data, Y_Data, Factor

# Create colors for each treatment 
# Rough Source: http://bokeh.pydata.org/en/latest/docs/gallery/brewer.html#gallery-brewer
# Fine Tune Source: http://bokeh.pydata.org/en/latest/docs/gallery/iris.html

# Get the number of colors we'll need for the plot.
colors = brewer["Spectral"][len(df.Factor.unique())]

# Create a map between factor and color.
colormap = {i: colors[i] for i in df.Factor.unique()}

# Create a list of colors for each value that we will be looking at.
colors = [colormap[x] for x in df.Factor]

# Generate the figure.
output_notebook()
p = figure(plot_width=800, plot_height=400)

# add a circle renderer with a size, color, and alpha
p.circle(df['X_Data'], df['Y_Data'], size=5, color=colors)

# show the results
show(p)

The general process is to first get a color palette from bokeh.palettes.brewer.  I selected the number of colors based on how many unique values existed in the Factor column.  Then I created a map from the values in the column and the colors.  Next, create a new list that maps each data point to a color, and use this when plotting using the circle call.

You should get something similar to the following figure based on what data you have to import.  Enjoy!

Add color to your plots by factor!

Add color to your plots by factor!

(Bokeh 0.12.7)

Getting iPython Notebook to Run “Correctly” in Mac OS X 10.8

I’m going to keep this post brief so that the steps are clear and concise.  The reason for writing this post is that I wanted to get iPython Notebook, a powerful tool for data analysis, to run with plotting and pandas in Mac OS X 10.8.  When I initially tried to get this running, I would encounter errors where there were conflicts between 32-bit and 64-bit installations of different packages.  After a good deal of trial and error, I found the following steps resulted in a full iPython Notebook environment with Pandas and Matplotlib functioning flawlessly.

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Escaping the Spreadsheet Mentality: Start with the Right Data Format

Growing up on a healthy diet of Microsoft Office products, I am well versed in Word, Excel and Powerpoint.  As I have transitioned into the research world, these products still have their place, however, I sometimes find that the habits I developed for organizing data doesn’t necessarily transfer to statistical analysis.  Recently, I ran into a situation where I was evaluating the performance of solutions in multiple different environments.  Organizing this data appeared straightforward to me at first, I would simply group the different environments into one row grouped by the id of the individual.  My data then looked something like this:

GenerationEnvironment 1Environment 2Environment 3
110.312.18.2
214.110.27.4
38.613.410.2
49.811.29.3

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Artificial Neural Network bowl of spaghetti representation.

Adventures in Visualization: Understanding Artificial Neural Networks Pt. 1

In the field of evolutionary robotics, artificial neural networks (ANNs) are an intriguing control strategy attempting to replicate the functionality of natural brains.  These networks, essentially directed graphs, with the possibility for cycles, are comprised of nodes containing a mathematical function, connected by weighted edges.  Inputs are correlated with information that may be useful for a robot such as: orientation, speed, goal conditions, etc., which is then propagated through the edges and weights to arrive at a set of outputs to direct motor movements or sensor readings.  Unfortunately, the size and complexity of these networks can grow rapidly when anything but the most simple tasks are attempted, making these graphs very challenging to interpret what processes and information are being used by the ANN for controlling the robot. I’ll save the long description of ANNs, but for an idea of what they can do, the following video features an ANN to control a swimming robot in a simulated flow.

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