Fitting Hidden Markov Models Part II: Sample Python Script

This is the second part of a two-part blog series on fitting hidden Markov models (HMMs). In Part I, I explained what HMMs are, why we might want to use them to model hydro-climatological data, and the methods traditionally used to fit them. Here I will show how to apply these methods using the Python package hmmlearn using annual streamflows in the Colorado River basin at the Colorado/Utah state line (USGS gage 09163500). First, note that to use hmmlearn on a Windows machine, I had to install it on Cygwin as a Python 2.7 library.

For this example, we will assume the state each year is either wet or dry, and the distribution of annual streamflows under each state is modeled by a Gaussian distribution. More states can be considered, as well as other distributions, but we will use a two-state, Gaussian HMM here for simplicity. Since streamflow is strictly positive, it might make sense to first log-transform the annual flows at the state line so that the Gaussian models won’t generate negative streamflows, so that’s what we do here.

After installing hmmlearn, the first step is to load the Gaussian hidden Markov model class with from hmmlearn.hmm import GaussianHMM. The fit function of this class requires as inputs the number of states (n_components, here 2 for wet and dry), the number of iterations to run of the Baum-Welch algorithm described in Part I (n_iter; I chose 1000), and the time series to which the model is fit (here a column vector, Q, of the annual or log-transformed annual flows). You can also set initial parameter estimates before fitting the model and only state those which need to be initialized with the init_params argument. This is a string of characters where ‘s’ stands for startprob (the probability of being in each state at the start), ‘t’ for transmat (the probability transition matrix), ‘m’ for means (mean vector) and ‘c’ for covars (covariance matrix). As discussed in Part I it is good to test several different initial parameter estimates to prevent convergence to a local optimum. For simplicity, here I simply use default estimates, but this tutorial shows how to pass your own. I call the model I fit on line 5 model.

Among other attributes and methods, model will have associated with it the means (means_) and covariances (covars_) of the Gaussian distributions fit to each state, the state probability transition matrix (transmat_), the log-likelihood function of the model (score) and methods for simulating from the HMM (sample) and predicting the states of observed values with the Viterbi algorithm described in Part I (predict). The score attribute could be used to compare the performance of models fit with different initial parameter estimates.

It is important to note that which state (wet or dry) is assigned a 0 and which state is assigned a 1 is arbitrary and different assignments may be made with different runs of the algorithm. To avoid confusion, I choose to reorganize the vectors of means and variances and the transition probability matrix so that state 0 is always the dry state, and state 1 is always the wet state. This is done on lines 22-26 if the mean of state 0 is greater than the mean of state 1.

from hmmlearn.hmm import GaussianHMM

def fitHMM(Q, nSamples):
    # fit Gaussian HMM to Q
    model = GaussianHMM(n_components=2, n_iter=1000).fit(np.reshape(Q,[len(Q),1]))
    # classify each observation as state 0 or 1
    hidden_states = model.predict(np.reshape(Q,[len(Q),1]))

    # find parameters of Gaussian HMM
    mus = np.array(model.means_)
    sigmas = np.array(np.sqrt(np.array([np.diag(model.covars_[0]),np.diag(model.covars_[1])])))
    P = np.array(model.transmat_)

    # find log-likelihood of Gaussian HMM
    logProb = model.score(np.reshape(Q,[len(Q),1]))

    # generate nSamples from Gaussian HMM
    samples = model.sample(nSamples)

    # re-organize mus, sigmas and P so that first row is lower mean (if not already)
    if mus[0] > mus[1]:
        mus = np.flipud(mus)
        sigmas = np.flipud(sigmas)
        P = np.fliplr(np.flipud(P))
        hidden_states = 1 - hidden_states

    return hidden_states, mus, sigmas, P, logProb, samples

# load annual flow data for the Colorado River near the Colorado/Utah state line
AnnualQ = np.loadtxt('AnnualQ.txt')

# log transform the data and fit the HMM
logQ = np.log(AnnualQ)
hidden_states, mus, sigmas, P, logProb, samples = fitHMM(logQ, 100)

Okay great, we’ve fit an HMM! What does the model look like? Let’s plot the time series of hidden states. Since we made the lower mean always represented by state 0, we know that hidden_states == 0 corresponds to the dry state and hidden_states == 1 to the wet state.

from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np

def plotTimeSeries(Q, hidden_states, ylabel, filename):

    fig = plt.figure()
    ax = fig.add_subplot(111)

    xs = np.arange(len(Q))+1909
    masks = hidden_states == 0
    ax.scatter(xs[masks], Q[masks], c='r', label='Dry State')
    masks = hidden_states == 1
    ax.scatter(xs[masks], Q[masks], c='b', label='Wet State')
    ax.plot(xs, Q, c='k')
    handles, labels = plt.gca().get_legend_handles_labels()
    fig.legend(handles, labels, loc='lower center', ncol=2, frameon=True)

    return None

plt.switch_backend('agg') # turn off display when running with Cygwin
plotTimeSeries(logQ, hidden_states, 'log(Flow at State Line)', 'StateTseries_Log.png')

Wow, looks like there’s some persistence! What are the transition probabilities?


Running that we get the following:

[[ 0.6794469   0.3205531 ]
[ 0.34904974  0.65095026]]

When in a dry state, there is a 68% chance of transitioning to a dry state again in the next year, while in a wet state there is a 65% chance of transitioning to a wet state again in the next year.

What does the distribution of flows look like in the wet and dry states, and how do these compare with the overall distribution? Since the probability distribution of the wet and dry states are Gaussian in log-space, and each state has some probability of being observed, the overall probability distribution is a mixed, or weighted, Gaussian distribution in which the weight of each of the two Gaussian models is the unconditional probability of being in their respective state. These probabilities make up the stationary distribution, π, which is the vector solving the equation π = πP, where P is the probability transition matrix. As briefly mentioned in Part I, this can be found using the method described here: π = (1/ Σi[ei])e in which e is the eigenvector of P corresponding to an eigenvalue of 1, and ei is the ith element of e. The overall distribution for our observations is then Y ~ π0N(μ0,σ02) + π1*N(μ1,σ12). We plot this distribution and the component distributions on top of a histogram of the log-space annual flows below.

from scipy import stats as ss

def plotDistribution(Q, mus, sigmas, P, filename):

    # calculate stationary distribution
    eigenvals, eigenvecs = np.linalg.eig(np.transpose(P))
    one_eigval = np.argmin(np.abs(eigenvals-1))
    pi = eigenvecs[:,one_eigval] / np.sum(eigenvecs[:,one_eigval])

    x_0 = np.linspace(mus[0]-4*sigmas[0], mus[0]+4*sigmas[0], 10000)
    fx_0 = pi[0]*ss.norm.pdf(x_0,mus[0],sigmas[0])

    x_1 = np.linspace(mus[1]-4*sigmas[1], mus[1]+4*sigmas[1], 10000)
    fx_1 = pi[1]*ss.norm.pdf(x_1,mus[1],sigmas[1])

    x = np.linspace(mus[0]-4*sigmas[0], mus[1]+4*sigmas[1], 10000)
    fx = pi[0]*ss.norm.pdf(x,mus[0],sigmas[0]) + \

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist(Q, color='k', alpha=0.5, density=True)
    l1, = ax.plot(x_0, fx_0, c='r', linewidth=2, label='Dry State Distn')
    l2, = ax.plot(x_1, fx_1, c='b', linewidth=2, label='Wet State Distn')
    l3, = ax.plot(x, fx, c='k', linewidth=2, label='Combined State Distn')

    handles, labels = plt.gca().get_legend_handles_labels()
    fig.legend(handles, labels, loc='lower center', ncol=3, frameon=True)

    return None

plotDistribution(logQ, mus, sigmas, P, 'MixedGaussianFit_Log.png')

Looks like a pretty good fit – seems like a Gaussian HMM is a decent model of log-transformed annual flows in the Colorado River at the Colorado/Utah state line. Hopefully you can find relevant applications for your work too. If so, I’d recommend reading through this hmmlearn tutorial, from which I learned how to do everything I’ve shown here.


Setting Up and Customizing Python Environments using Conda

Typing ‘python’ into your command line launches the default global Python environment (which you can change by changing your path) that includes every package you’ve likely installed since the dawn of man (or since you adopted your machine).

But what happens when you are working between Python 2.7 and Python 3.x due to collaboration, using Python 3.4 because the last time you updated your script was four years ago, collaborating with others and want to ensure reproducibility and compatible environments, or banging your head against the wall because that one Python library installation is throwing up errors (shakes fist at PIL/Pillow)?

Creating Python environments is a straightforward solution to save you headaches down the road.

Python environments are a topic that many of us have feared through the years due to ambiguous definitions filled with waving hands. An environment is simply the domain in which users run software or scripts. With this same train of thought, a python environment is the domain with all of the Python packages are installed where a user (you!) is executing a script (usually interfacing through an IDE or Terminal/Command Prompt).

However, different scripts will work or fail in different environments  avoid having to use all of these packages at once or having to completely reinstall Python, what we want to do is create new and independent Python environments. Applications of these environments include:

  • Have multiple versions of Python (e.g. 2.7 and 3.4 and 3.6) installed on your machine at once that you can easily switch between
  • Work with specific versions of packages and ensure they don’t update for the specific script you’re developing
  • Allow for individuals to install the same, reproducible environment between workstations
  • Create standardized environments for seamless collaboration
  • Use older versions of packages to utilize outdated code

Creating Your First Python Environment

One problem that recent arose in Ithaca was that someone was crunching towards deadlines and could only run PIL (Python Imaging Library) on their home machine and not their desktop on campus due to package installation issues. This individual had the following  packages they needed to install while using Python 2.7.5:

  • PIL
  • matplotlib
  • numpy
  • pandas
  • statsmodels
  • seaborn

To start, let’s first create an environment! To do this, we will be using Conda (install Anaconda for new users or MiniConda for anyone who doesn’t want their default Python environment to be jeopardized. If you want to avoid using Conda, feel free to explore Pipenv). As a quick note on syntax, I will be running everything in Windows 7 and every command I am using can be found on the Conda Cheatsheet. Only slight variations are required for MacOS/Linux.

First, with your Command Prompt open, type the following command to create the environment we will be working in:

conda create --name blog_pil_example python=2.7.5


At this point, a new environment titled blog_pil_example with Python 2.7.5 has been created. Congrats! Don’t forget to take screenshots to add to your new environment’s baby book (or just use the one above if it’s not your first environment).

From here, we need to activate the environment before interacting with it. To see which environments are available, use the following:

conda env list

Now, let’s go ahead and activate the environment that we want (blog_pil_example):

activate blog_pil_example

To leave the environment you’re in, simply use the following command:


(For Linux and MaxOS, put ‘source ‘ prior to these commands)


We can see in the screenshot above that multiple other environments exist, but the selected/activated environment is shown in parentheses. Note that you’re still navigating through the same directories as before, you’re just selecting and running a different version of Python and installed packages when you’re using this environment.

Building Your Python Environment

(Installing Packages)

Now onto the real meat and potatoes: installing the necessary packages. While you can use pip at this point, I’ve found Conda has run into fewer issues over the past year.  (Read into channel prioritization if you’re interested in where package files are being sourced from and how to change this.) As a quick back to basics, we’re going to install one of the desired packages, matplotlib, using Conda (or pip). Using these ensures that the proper versions of the packages for your environment (i.e. the Python version and operating system) are retrieved. At the same time, all dependent packages will also be installed (e.g. numpy). Use the following command when in the environment and confirm you want to install matplotlib:

conda install matplotlib

Note that you can specify a version much like how we specified the python version above for library compatibility issues:

conda install matplotlib=2.2.0

If you wish to remove matplotlib, use the following command:

conda remove matplotlib

If you wish to update a specific package, run:

conda update matplotlib

Or to update all packages:

conda update

Additionally, you can prevent specific packages from updating by creating a pinned file in the environment’s conda-meta directory. Be sure to do this prior to running the command to update all packages! 

After installing all of the packages that were required at the start of this tutorial, let’s look into which packages are actually installed in this environment:

conda list


By only installing the required packages, Conda was kind and installed all of the dependencies at the same time. Now you have a Python environment that you’ve created from scratch and developed into a hopefully productive part of your workflow.

Utilizing Your Python Environment

The simplest way to utilize your newly created python environment is simply run python directly in the Command Prompt above. You can run any script when this environment is activated (shown in the parentheses on the left of the command line) to utilize this setup!

If you want to use this environment in your IDE of choice, you can simply point the interpreter to this new environment. In PyCharm, you can easily create a new Conda Environment when creating a new project, or you can point the interpreter to a previously created environment (instructions here).

Additional Resources

For a good ground-up and more in depth tutorial with visualizations on how Conda works (including directory structure, channel prioritization) that has been a major source of inspiration and knowledge for me, please check out this blog post by Gergely Szerovay.

If you’re looking for a great (and nearly exhaustive) source of Python Packages (both current and previous versions), check out Gohlke’s webpage. To install these packages, download the associated file for your system (32/64 bit and then your operating system) then use pip to install the file (in Command Prompt, navigate to the folder the .whl file is located in, then type ‘pip install ,file_name>’). I’ve found that installing packages this way sometimes allows me to step around errors I’ve encountered while using

You can also create environments for R. Check it out here.

If you understand most of the materials above, you can now claim to be environmentally conscious!

Job scheduling on HPC resources

Architecture of a HPC Cluster

Modern High Performance Computing (HPC) resources are usually composed of a cluster of computing nodes that provide the user the ability to parallelize tasks and greatly reduce the time it takes to perform complex operations. A node is usually defined as a discrete unit of a computer system that runs its own instance of an operating system. Modern nodes have multiple chips, often known as Central Processing Units or CPUs, which each contain multiple cores each capable of processing a separate stream of instructions (such as a single Monte Carlo run). An example cluster configuration is shown in Figure 1.


Figure 1. An example cluster configuration

To efficiently make use of a cluster’s computational resources, it is essential to allow multiple users to use the resource at one time and to have an efficient and equatable way of allocating and scheduling computing resources on a cluster. This role is done by job scheduling software. The scheduling software is accessed via a shell script called in the command line. A scheduling  script does not actually run any code, rather it provides a set of instructions for the cluster specifying what code to run and how the cluster should run it. Instructions called from a scheduling script may include but are not limited to:

  • What code would you like the cluster to run
  • How would you like to parallelize your code (ie MPI, openMP ect)
  • How many nodes would you like to run on
  • How many core per processor would you like to run (normally you would use the maximum allowable per processor)
  • Where would you like error and output files to be saved
  • Set up email notifications about the status of your job

This post will highlight two commonly used Job Scheduling Languages, PBS and SLURM and detail some simple example scripts for using them.


The Portable Batching System (PBS) was originally developed by NASA in the early 1990’s [1] to facilitate access to computing resources.  The intellectual property associated with the software is now owned by Altair Engineering. PBS is a fully open source system and the source code can be found here. PBS is the job scheduler we use for the Cube Cluster here at Cornell.

An annotated PBS submission script called “” that runs a C++ code called “triangleSimulation.cpp” on 128 cores can be found below:

#PBS -l nodes=8:ppn=16    # how many nodes, how many cores per node (ppn)
#PBS -l walltime=5:00:00  # what is the maximum walltime for this job
#PBS -N SimpleScript      # Give the job this name.
#PBS -M # email address for notifications
#PBS -j oe                # combine error and output file
#PBS -o outputfolder/output.out # name output file

cd $PBS_O_WORKDIR # change working directory to current folder

#module load openmpi/intel # load MPI (Intel implementation)
time mpirun ./triangleSimulation -m batch -r 1000 -s 1 -c 5 -b 3

To submit this PBS script via the command line one would type:


Other helpful PBS commands for UNIX can be found here. For more on PBS flags and options, see this detailed post from 2012 and for more example PBS submission scripts see Jon Herman’s Github repository here.


A second common job scheduler is know as SLURM. SLURM stands for “Simple Linux Utility Resource Management” and is the scheduler used on many XSEDE resources such as Stampede2 and Comet.

An example SLURM submission script named “” that runs “triangleSimulation.cpp” on 128 core can be found below:

#SBATCH --nodes=8             # specify number of nodes
#SBATCH --ntasks-per-node=16  # specify number of core per node
#SBATCH --export=ALL
#SBATCH -t 5:00:00            # set max wallclock time
#SBATCH --job-name="triangle" # name your job #SBATCH --output="outputfolder/output.out"

#ibrun is the command for MPI
ibrun -v ./triangleSimulation -m batch -r 1000 -s 1 -c 5 -b 3 -p 2841

To submit this SLURM script from the command line one would type:

sbatch SLURM

The Cornell Center  for Advanced Computing has an excellent SLURM training module within the introduction to Stampede2 workshop that goes into detail on how to most effectively make use of SLURM. More examples of SLURM submission scripts can be found on Jon Herman’s Github. Billy also wrote a blog post last year about debugging with SLURM.


Evaluating and visualizing sampling quality

Evaluating and visualizing sampling quality

State sampling is a necessary step for any computational experiment, and the way sampling is carried out will influence the experiment’s results. This is the case for instance, for sensitivity analysis (i.e., the analysis of model output sensitivity to values of the input variables). The popular method of Sobol’ (Sobol’, 2001) relies on tailor-made sampling techniques that have been perfected through time (e.g., Joe and Kuo, 2008; Saltelli et al., 2010). Likewise, the method of Morris (Morris, 1991), less computationally demanding than Sobol’s (Herman et al., 2013) and used for screening (i.e., understanding which are the inputs that most influence outputs), relies on specific sampling techniques (Morris, 1991; Campolongo et al., 2007).

But what makes a good sample, and how can we understand the strengths and weaknesses of the sampling techniques (and also of the associated sensitivity techniques we are using) through quick visualization of some associated metrics?

This post aims to answer this question. It will first look at what makes a good sample using some examples from a sampling technique called latin hypercube sampling. Then it will show some handy visualization tools for quickly testing and visualizing a sample.

What makes a good sample?

Intuitively, the first criterion for a good sample is how well it covers the space from which to sample. The difficulty though, is how we define “how well” it practice, and the implications that has.

Let us take an example. A quick and popular way to generate a sample that covers the space fairly well is latin hypercube sampling (LHS; McKay et al., 1979). This algorithm relies on the following steps for drawing N samples from a hypercube-shaped of dimension p.:

1) Divide each dimension of the space in N equiprobabilistic bins. If we want uniform sampling, each bin will have the same length. Number bins from 1 to N each dimension.

2) Randomly draw points such that you have exactly one in each bin in each dimension.

For instance, for 6 points in 2 dimensions, this is a possible sample (points are selected randomly in each square labelled A to F):



It is easy to see that by definition, LHS has a good space coverage when projected on each individual axis. But space coverage in multiple dimensions all depends on the luck of the draw. Indeed, this is also a perfectly valid LHS configuration:


In the above configuration, it is easy to see that on top of poor space coverage, correlation between the sampled values along both axes is also a huge issue. For instance, if output values are hugely dependent on values of input 1, there will be large variations of the output values as values of input 2 change, regardless of the real impact of input 2 on the output.

Therefore, there are two kinds of issues to look at. One is correlation between sampled values of the input variables. We’ll look at it first because it is pretty straightforward. Then we’ll look at space coverage metrics, which are more numerous, do not look exactly at the same things, and can be sometimes conflicting. In fact, it is illuminating to see that sample quality metrics sometimes trade-off with one another, and several authors have turned to multi-objective optimization to come up with Pareto-optimal sample designs (e.g., Cioppa and Lucas, 2007; De Rainville et al., 2012).

One can look at authors such as Sheikholeslami and Razavi (2017) who summarize similar sets of variables. The goal there is not to write a summary of summaries but rather to give a sense that there is a relationship between which indicators of sampling quality matter, which sampling strategy to use, and what we want to do.

In what follows we note x_{k,i} the kth  sampled value of input variable i, with 1\leq k \leq N and 1\leq i \leq p.


Sample correlation is usually measured through the Pearson statistic. For inputs variables i and j among the p input variables, we note x_{k,i} and x_{k,j} the values of these variables i and j in sample k (1\leq k \leq N) have:

\rho_{ij} = \frac{\sum_{k=1}^N (x_{k,i}-\bar{x}_i)(x_{k,j}-\bar{x}_j)}{\sqrt{\sum_{k=1}^N (x_{k,i}-\bar{x}_i)^2 \sum_{k=1}^N (x_{k,j}-\bar{x}_j)^2}}

In the above equation, {\bar{x}_i}   and {\bar{x}_j} are the average sampled values of inputs i and j . 

Then, the indicator of sample quality looks at the maximal level of correlation across all variables:

\rho_{\max} = \max_{1\leq i \leq j \leq N} |\rho_{ij}|

This definition relies on the remark that \rho_{ij} = \rho_{ji}.

Space Coverage

There are different measures of space coverage.

We are best equipped to visualize space coverage via 1D or 2D projections of a sample. In 1D a measure of space coverage is by dividing each dimension in N equiprobable bins, and count the fraction of bins that have at least a point. Since N is the sample size, this measure is maximized when there is exactly one point in each bin — it is a measure that LHS maximizes.

Other measures of space coverage consider all dimension at once. A straightforward measure of space filling is the minimum Euclidean distance between two sampled points X in the generated ensemble:

D = \min_{1\leq k \leq m \leq N} \left\{ d(\textbf{X}^k, \textbf{X}^m) \right\}

Other indicators measure discrepancy which is a concept closely related to space coverage. In simple terms, a low discrepancy means that when we look at a subset of a sampled input space, its volume is roughly proportional to the number of points that are in it. In other words, there is no large subset with relatively few sampled points, and there is no small subset with a relatively large density of sampled points. A low discrepancy is desirable and in fact, Sobol’ sequences that form the basis of the Sobol’ sensitivity analysis method, are meant to minimize discrepancy.


Sample visualization

The figures that follow can be easily reproduced by cloning a little repository SampleVis I put together, and by entering on the command line python &> output.txt. That Python routine can be used with both latin hypercube and Sobol’ sampling (using the SAlib sampling tool; SAlib is a Python library developed primarily by Jon Herman and Will Usher, and which is extensively discussed in this blog.)

In what follows I give examples using a random draw of latin hypercube sampling with 100 members and 7 sampled variables.


No luck, there is statistically significant pairwise correlation between in three pairs of variables: x1 and x4, x4 and x6, and x5 and x6. Using LHS, it can take some time to be lucky enough until the drawn sample is correlation-free (alternatively, methods to minimize correlations have been extensively researched over the years, though no “silver bullet” really emerges).


This means any inference that works for both variables in any of these pairs may be suspect. The SampleVis toolbox contains also tools to plot whether these correlations are positive or negative.

Space coverage

The toolbox enables to plot several indicators of space coverage, assuming that the sampled space is the unit hypercube of dimension p (p=7 in this example). It computes discrepancy and minimal distance indicators. Ironically, my random LHS with 7 variables and 100 members has a better discrepancy (here I use an indicator called L2-star discrepancy) than a Sobol’ sequence with as many variables and members. The minimal Euclidean distance as well is better than for Sobol’ (0.330 vs. 0.348). This means that if for our experiment, space coverage is more important than correlation, the drawn LHS is pretty good.

To better grasp how well points cover the whole space, it is interesting to plot the distance of the point that is closest to each point, and to represent that in growing order:Distances

This means that some points are not evenly spaced, and some are more isolated than others. When dealing with a limited number of variables, it can also be interesting to visualize 2D projections of the sample, like this one:


This again goes to show that the sample is pretty-well distributed in space. We can compare with the same diagram for a Sobol’ sampling with 100 members and 7 variables:


It is pretty clear that the deterministic nature of Sobol’ sampling, for so few points, leaves more systematic holes in the sampled space. Of course, this sample is too small for any serious Sobol’ sensitivity analysis, and holes are plugged by a larger sample. But again, this comparison is a visual heuristic that tells a similar story as the global coverage indicator: this LHS draw is pretty good when it comes to coverage.



Campolongo, F., Cariboni, J. & Saltelli, A. (2007). An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, 22, 1509 – 1518.

Cioppa, T. M. & Lucas, T. W. (2007). Efficient Nearly Orthogonal and Space-Filling Latin Hypercubes. Technometrics, 49, 45-55.

De Rainville, F.-M., Gagné, C., Teytaud, O. & Laurendeau, D. (2012). Evolutionary Optimization of Low-discrepancy Sequences. ACM Trans. Model. Comput. Simul., ACM, 22, 9:1-9:25.

Herman, J. D., Kollat, J. B., Reed, P. M. & Wagener, T. (2013). Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models. Hydrology and Earth System Sciences, 17, 2893-2903.

Joe, S. & Kuo, F. (2008). Constructing Sobol Sequences with Better Two-Dimensional Projections. SIAM Journal on Scientific Computing, 30, 2635-2654.

McKay, M.D., Beckman R.J. & Conover, W.J. (1979).A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245.

Morris, M. D. (1991). Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics, 33, 161-174.

Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M. & Tarantola, S. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181, 259 – 270.

Sheikholeslami, R. & Razavi, S. (2017). Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models. Environmental Modelling & Software, 93, 109 – 126.

Sobol’, I. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55, 271 – 280.


Creating shaded dial plots in python

I recently created a code for plotting shaded dials (figures that look like gauges or speedometers) in python and I thought I’d share my code here. The dials are well suited to plot things such as risk or maybe the probability of meeting a set of robustness criteria across a range of decision variables (shameless plug: if you’re at EWRI this week, come check out my talk: Conflicts in Coalitions, Wednesday morning at 8:30 in Northstar B for which I created these figures).

As hinted at above, I originally created the plot to show bivariate data, with one variable plotted as the location on the dial and the other as the color. You could also plot the same variable as both color and location if you wanted to emphasize the meaning of increasing value on the dial. An example dial created with the code is shown below.



Example custom dial. The above figure consists of two images, a dial plot (originally constructed from a pie plot) and a color bar, made as a separate image but using the same data.

The color distribution, location of arrow and labeling of the gauge and colorbar are all fully customizable. I created the figure by first making a pie chart using marplotlib, inscribing a small white circle in the middle and then cropping the image in half using the Python image processing library (PIL also known as Pillow). The arrow is created using the matplotlib “arrow” function and will point to a specified location on the dial. The code is created such that you can add an array of any length to specify your colors, the array does not have to be monotonic like the one shown above, but will accept any values between zero and one (if your data is not in this range I’d suggest normalizing).

Annotated code is below:

import matplotlib.pyplot as plt
from matplotlib import cm, gridspec
import numpy as np
import math
from PIL import Image
from mpl_toolkits.axes_grid1 import make_axes_locatable

# set your color array and name of figure here:
dial_colors = np.linspace(0,1,1000) # using linspace here as an example
figname = 'myDial'

# specify which index you want your arrow to point to
arrow_index = 750

# create labels at desired locations
# note that the pie plot ploots from right to left
labels = [' ']*len(dial_colors)*2
labels[25] = '100'
labels[250] = '75'
labels[500] = '50'
labels[750] = '25'
labels[975] = '0'

# function plotting a colored dial
def dial(color_array, arrow_index, labels, ax):
    # Create bins to plot (equally sized)

    # Create a pieplot, half white, half colored by your color array
    white_half = np.ones(len(color_array))*.5
    color_half = color_array
    color_pallet = np.concatenate([color_half, white_half])

    pie_wedge_collection = ax.pie(size_of_groups, colors=cs, labels=labels)

    for pie_wedge in pie_wedge_collection[0]:

    # create a white circle to make the pie chart a dial
    my_circle=plt.Circle( (0,0), 0.3, color='white')

    # create the arrow, pointing at specified index
    arrow_angle = (arrow_index/float(len(color_array)))*3.14159
    arrow_x = 0.2*math.cos(arrow_angle)
    arrow_y = 0.2*math.sin(arrow_angle)
    ax.arrow(0,0,-arrow_x,arrow_y, width=.02, head_width=.05, \
        head_length=.1, fc='k', ec='k')

# create figure and specify figure name
fig, ax = plt.subplots()

# make dial plot and save figure
dial(dial_colors, arrow_index, labels, ax)
plt.savefig(figname + '.png', bbox_inches='tight') 

# create a figure for the colorbar (crop so only colorbar is saved)
fig, ax2 = plt.subplots()
cmap = cm.ScalarMappable(cmap='RdYlBu')
cmap.set_array([min(dial_colors), max(dial_colors)])
cbar = plt.colorbar(cmap, orientation='horizontal')"Risk")
plt.savefig('cbar.png', bbox_inches='tight')
cbar ='cbar.png')
c_width, c_height = cbar.size
cbar = cbar.crop((0, .8*c_height, c_width, c_height)).save('cbar.png')

# open figure and crop bottom half
im = + '.png')
width, height = im.size

# crop bottom half of figure
# function takes top corner <span 				data-mce-type="bookmark" 				id="mce_SELREST_start" 				data-mce-style="overflow:hidden;line-height:0" 				style="overflow:hidden;line-height:0" 			></span>and bottom corner coordinates
# of image to keep, (0,0) in python images is the top left corner
im = im.crop((0, 0, width+c_width, int(height/2.0))).save(figname + '.png')

Other ways of doing this from around the web

This code was my way of making a dial plot, and I think it works well for plotting gradients on the dial. In the course of writing this I came across a couple similar codes, I’m listing them below. They both have advantages if you want to plot a small number of colors on your dial but I had trouble getting them to scale.

Here’s an example that creates dials using matplotlib patches, this method looks useful for plotting a small number of categorical data, I like the customization of the labels:

Here’s another alternative using the plotly library, I like the aesthetics but if you’re unfamiliar with plotly there’s a lot to learn before you can nicely customize the final product:

Creating Dendrograms in R

A dendrogram is an effective way of visualizing results from hierarchical clustering. The purpose of this post is to show how to make a basic dendrogram in R and illustrate the ways in which one can add colors to dendrogram labels and branches to help identify key clustering drivers. Making dendrograms in R is quite straightforward. However, customizing a dendrogram is not so straightforward, so this post shows some tricks that I learned and should help expedite the process!

First and foremost, your data must be in an appropriate from for hierarchical clustering to be conducted. Table 1 shows an example of how your data can be set up. Four different spatial temperatures projected by CMIP5 models are shown along with various attributes that could be potential driving forces behind clustering: the institution at which the model comes from, the RCP (radiative forcing scenario) used in the model, and the initial conditions with which the model was run.

Table 1: Model Attributes

At this point, it is helpful to add the model names as the row names (shown in the leftmost column) of your data frame, otherwise the dendrogram function will use the row number as a label on the dendrogram which can make it hard to interpret the clustering results.

Next, create a distance matrix, which will be composed of Euclidean distances between pairs of model projections. This is what clustering will be based on. We first create a new data frame composed of just the temperature values (shown below) by removing columns from the Model Attributes table.


Table 2: Temperature Projections

The following code can be used to create Table 2 from the original table and then the distance matrix.

#Create a new data frame with just temperature values

just_temperature=Model_Attributes[ -c(1:4) ]

#Create a distance matrix


Now, one can make the clustering diagram. Here I chose to use complete linkage clustering as the agglomeration method and wanted my dendrogram to be horizontal.

#Perform clustering


#Adjust dimensions of dendrogram so that it fits in plotting window


plot(complete_linkage_cluster,horiz =TRUE)

And that’s it! Here is the most basic dendrogram.


Figure 1: Dendrogram

Now for customization. You will first need to install the “dendextend” library in R.

We have 11 institutions that the models can come from and we want to visualize if institution has some impact on clustering, by assigning a color to the label. Here we use the rainbow color palette to assign each model a color and then replot the dendrogram.


#Create a vector of colors with one color for each institution


#Add colors to the ordered dendrogram
labels_colors(complete_linkage_cluster)= col[Model_Attributes$Institution][order.dendrogram(complete_linkage_cluster)]

#Replot the dendrogram

par(mar=c(3,4,1,15)) #Dendrogram parameters
plot(complete_linkage_cluster,horiz =TRUE)


Figure 2: Dendrogram with Colored Labels

Now suppose we wanted to change the branch colors to show what RCP each model was run with. Here, we assign a color from the rainbow palette to each of the four RCPs and add it to the dendrogram.


col_branches= col[Model_Attributes$RCP][order.dendrogram(complete_linkage_cluster)]

plot(colored_dendrogram,horiz =TRUE)


Figure 3: Dendrogram with Colored Labels and Colored Branches

Now finally, we can change the node shapes to reflect the initial condition. There are 10 total initial conditions, so we’re going to use the first 10 standard pch (plot character) elements to represent the individual nodes.

nodePar = list(lab.cex = 0.6, pch = c(NA,19),cex = 0.7, col = "black") #node parameters

dend1 = colored_dendrogram %>% set("leaves_pch", c(nodes))

plot(dend1,horiz =TRUE)


Figure 4: Dendrogram with Colored Labels, Colored Branches, and Node Shapes

And that’s how you customize a dendrogram in R!

Creating parallel axis plots with multiple datasets, color gradients, and brushing in Python

Parallel axis plots (here is a good description of what they are) are a relatively recent development in the plotting world, so it is no surprise that there is no implementations of it with more than basic functionalities in the major plotting packages available online. Over the past couple of days I then created my own implementation of parallel axis plots in Python using Matplotlib Pandas’ and’s implementation get cumbersome when the user tries to apply brushing and multiple color gradients  to create versatile, high-resolution and story-telling plots for my next papers and presentations. This implementation allows for:

  • Plotting multiple datasets,
  • Displaying dataset names,
  • Choosing columns to be plot,
  • Coloring each dataset based on a column and a different Matplotlib color map,
  • Specifying ranges to be plotted,
  • Inverting multiple axis,
  • Brushing by intervales in multiple axis,
  • Choosing different fonts for title and rest of the plot, and
  • Export result as a figure file or viewing plot in Matplotlib’s interactive window.

The source code can be found here, and below is an example of how to use it:

import numpy as np
from plotting.parallel_axis import paxis_plot
from matplotlib.colors import LinearSegmentedColormap
from matplotlib import cm

bu_cy = LinearSegmentedColormap.from_list('BuCy', [(0, 0, 1), (0, 1, 1)])
bu_cy_r = bu_cy.reversed()

data1 = np.random.normal(size=(100, 8))
data2 = np.random.normal(size=(100, 8))
columns_to_plot = [0, 1, 3, 5, 7]
color_column = 0
axis_labels = ['axes ' + str(i) for i in range(8)]
dataset_names = ['Data set 1', 'Data set 2']
plot_ranges = [[-3.5, 3.5]] * 3 + [[-2.9, 3.1]] + [[-3.5, 3.5]] * 4
axis_to_invert = [1, 5]
brush_criteria = {1: [-10., 0.], 7: [10., 0.]}

paxis_plot((data1, data2),
           [bu_cy_r, cm.get_cmap('autumn_r')],
           'Title Here',
           fontname_title='Gill Sans MT',
           fontname_body='CMU Bright',

The output of this script should be a file named “test.png” that looks similar to the plot below: