Use Tikhonov regularization to solve Fredholm integral equation

This article shows an algorithm to solve Fredholm integral equation of the first kind.


A Fredholm integral equation of the first kind is written as,


The problem is to find p(s), given that f(x) and K(x,s) are known. This equation occurs quite often in many different areas.

Discretization-based Method

Here we describe a discretization-based method to solve the Fredholm integral equation. The integral equation is approximately replaced by a Riemann summation over grids,

f(x_i)=\sum_j \Delta_s K(x_i, s_j) p(s_j)

where \Delta_s is the grid size along the dimension s and x_i, s_j are the grid points with i and j indicating their indices. When grid size \Delta_s\to0, the summation converges to the true integral. It is more convenient to write it in the matrix form,

\boldsymbol{f} = \boldsymbol{\Delta}_s \boldsymbol{K} \boldsymbol{p}


\boldsymbol{f}=(f(x_1), f(x_2),\cdots,f(x_n))^{\mathrm{T}},

\boldsymbol{K}= \begin{pmatrix} K(x_1,s_1) & K(x_1,s_2) & \cdots & K(x_1,s_m)\\ K(x_2,s_1) & K(x_2,s_2) & \cdots & K(x_2,s_m)\\ \vdots & \vdots & \ddots & \vdots \\ K(x_n,s_1) & K(x_n,s_2) & \cdots & K(x_n,s_m) \end{pmatrix},

\boldsymbol{p} = (p(s_1),p(s_2),\cdots,p(s_m))^{\mathrm{T}}

and \boldsymbol{\Delta}_s = \Delta_s \boldsymbol{I} with \boldsymbol{I} being the identity matrix of dimension n \times n.

Now solving the Fredholm integral equation is equivalent to solving a system of linear equations. The standard approach ordinary least squares linear regression, which is to find the vector \boldsymbol{p} minimizing the norm ||\boldsymbol{\Delta}_s \boldsymbol{K} \boldsymbol{p}-\boldsymbol{f}||_2^2. In principle, the Fredholm integral equation may have non-unique solutions, thus the corresponding linear equations are also ill-posed. The most commonly used method for ill-posed problem is Tikhonov regularization which is to minimize

||\boldsymbol{\Delta}_s \boldsymbol{K} \boldsymbol{p}-\boldsymbol{f}||_2^2+\alpha^2||\boldsymbol{p}||_2^2

Note that this is actually a subset of Tikhonov regularization (also called Ridge regularization) with \alpha being a constant.

When p(s) is a probability density function

In many cases, both f(x) and g(s) are probability density function (PDF), and K(x,s) is a conditional PDF, equivalent to K(x|s). Thus, there are two constraints on the solution p(s), that is p(s)\geq 0 and \int p(s)\mathrm{d}s = 1. These two constraints translate to p(s_i)\geq 0 for any s_i and \Delta_s\sum_i p(s_i)=1. Hence, we need to solve the Tikhonov regularization problem subject to these two constraints.

In the following, I will show how to solve the Tikhonov regularization problem with both equality and inequality constraints. First, I will show that the Tikhonov regularization problem with a non-negative constraint can be easily translated to a regular non-negative least square problem (NNLS) which can be solved using the active set algorithm.

Let us construct the matrix,

\boldsymbol{A}= \begin{pmatrix} \Delta_s \boldsymbol{K} \\ \alpha \boldsymbol{I} \end{pmatrix}

and the vector,

\boldsymbol{b}= \begin{pmatrix} \boldsymbol{f}\\ \boldsymbol{0} \end{pmatrix}

where \boldsymbol{I} is the m\times m identity matrix and \boldsymbol{0} is the zero vector of size m. It is easy to show that the Tikhonov regularization problem \mathrm{min}(||\boldsymbol{\Delta}_s \boldsymbol{K} \boldsymbol{p}-\boldsymbol{f}||_2^2+\alpha^2||\boldsymbol{p}||_2^2) subject to \boldsymbol{p}\geq 0 is equivalent to the regular NNLS problem,

\mathrm{min}(||\boldsymbol{A}\boldsymbol{p}-\boldsymbol{b}||_2^2),\mathrm{\ subject\ to\ }\boldsymbol{p}\geq 0

Now we add the equality constraint, \Delta_s\sum_i p(s_i)=1 or \boldsymbol{1}\boldsymbol{p}=1/\Delta_s written in matrix form. My implementation of solving such problem follows the algorithm described in Haskell and Hanson 1. According to their method, the problem becomes another NNLS problem,

\mathrm{min}(||\boldsymbol{1}\boldsymbol{p}-1/\Delta_s||_2^2+\epsilon^2||\boldsymbol{A}\boldsymbol{p}-\boldsymbol{b}||_2^2),\mathrm{\ subject\ to\ }\boldsymbol{p}\geq 0

The solution to the above equation converges to the true solution when \epsilon\to0^+. Now I have described the algorithm to solve the Fredholm equation of the first kind when p(s) is a probability density function. I call the algorithm described above as non-negative Tikhonov regularization with equality constraint (NNETR).


Here I show the core code of the algorithm described above.

# core algorithm of non-negative equality Tikhonov regularization (NNETR)
def NNETR(K, f, Delta, epsilon, alpha):
# the first step
A_nn = np.vstack((K, alpha * np.identity(K.shape[1])))
b_nn = np.hstack((f, np.zeros(K.shape[1])))

# the second step
A_nne = np.vstack((epsilon * A_nn, np.full(A_nn.shape[1], 1.0)))
b_nne = np.hstack((epsilon * b_nn, 1.0))

# Use NNLS solver provided by scipy
sol, residue = scipy.optimize.nnls(A_nne, b_nne)

# solution should be divided by Delta (grid size)
sol = sol/Delta
return sol, residue


  • Compounding an exponential distribution with its rate parameter distributed according to a gamma distribution yields a Lomax distribution f(x)=a(x+1)^{-(a+1)}, supported on (0,\infty), with a>0. k(x,\theta)=\theta e^{-\theta x} is an exponential density and p(\theta) = \Gamma(a)^{-1}\theta^{a-1}e^{-\theta} is a gamma density.


  • Compounding a Gaussian distribution with mean distributed according to another Gaussian distribution yields (again) a Gaussian distribution f(x)=\mathcal{N}(a,b^2+\sigma^2). k(x|\mu)=\mathcal{N}(\mu,\sigma^2) and p(\mu)=\mathcal{N}(a,b^2)


  • Compounding an exponential distribution with its rate parameter distributed according to a mixture distribution of two gamma distributions. Similar to the first example, we use k(x,\theta)=\theta e^{-\theta x}. But here we use p(\theta)=q p(\theta|a_1)+(1-q)p(\theta|a_2) where q, a_1 and a_2 are parameters. It is clear that p(\theta) is a mixture between two different gamma distributions such as it is a bimodal distribution. Following the first example, we have f(x)=qf(x|a_1)+(1-q)f(x|a_2) where f(x|a)=a(x+1)^{-(a+1)}


  • Compounding an exponential distribution with its rate parameter distributed as a discrete distribution.


[1]: Haskell, Karen H., and Richard J. Hanson. “An algorithm for linear least squares problems with equality and nonnegativity constraints.” Mathematical Programming 21.1 (1981): 98-118.


Use Multidimensional LSTM network to learn linear and non-linear mapping

This note is about the effectivenss of using multidimensional LSTM network to learn matrix operations, such as linear mapping as well as non-linear mapping. Recently I am trying to solve a research problem related to mapping between two matrices. And came up the idea of applying neural network to the problem. The Recurrent Neural Network (RNN) came to my sight not long after I started searching since it seems be able to capture the spatiotemporal dependence and correlation between different elements wheares the convolutional neural network is not able to (I am probably wrong, this is just my very simple understanding and I am not a expert on neural network). Perhaps the most famous article about RNN online is this blog where Andrej Karpathy demonstrated the effectiveness of using RNN to generate meaningful text content. For whoever interested in traditional chinese culture, here is a github repo on using RNN to generate 古诗 (Classical Chinese poetry).

However the above examples only focus taking the sequential input data and output sequential prediction. My problem is learning mapping between two matrices which is multidimensional. After researching a little bit, I found Multidimensional Recurrent Neural Network can be used here. If you google “Multidimensional Recurrent Neural Network”, the first entry would be this paper by Alex Graves, et al. However I want to point out that almost exact same idea is long proposed back in 2003 in the context of protein contact map prediction in this paper.

I have never had any experience using neural network before. Instead of learning from stratch, I decided that it is probably more efficient to just find a github repo available and study the code from there. Fortunately I did find a very good exemplary code here.

The question is that can MDLSTM learn the mapping between two matrices? From basic linear algrebra, we know there are two types of mapping: linear map and non-linear map. So it is natural to study the problem in two cases. Any linear mapping can be represtened by a matrix. For simplicity, I use a random matrix to represent the linear mapping we want to learn, M. And apply it to a gaussian field matrix I to produce a new transformed matrix O, i.e. O = M\cdot I. We feed I and O into our MDLSTM network as our inputs and targets. Since our goal is to predict O given the input I where values of elements in O are continous rather than categorical. So we use linear activation function and mean square error as our loss function.

def fft_ind_gen(n):
a = list(range(0, int(n / 2 + 1)))
b = list(range(1, int(n / 2)))
b = [-i for i in b]
return a + b

def gaussian_random_field(pk=lambda k: k ** -3.0, size1=100, size2=100, anisotropy=True):
def pk2(kx_, ky_):
if kx_ == 0 and ky_ == 0:
return 0.0
if anisotropy:
if kx_ != 0 and ky_ != 0:
return 0.0
return np.sqrt(pk(np.sqrt(kx_ ** 2 + ky_ ** 2)))

noise = np.fft.fft2(np.random.normal(size=(size1, size2)))
amplitude = np.zeros((size1, size2))
for i, kx in enumerate(fft_ind_gen(size1)):
for j, ky in enumerate(fft_ind_gen(size2)):
amplitude[i, j] = pk2(kx, ky)
return np.fft.ifft2(noise * amplitude)

def next_batch_linear_map(bs, h, w, mapping, anisotropy=True):
x = []
for i in range(bs):
o = gaussian_random_field(pk=lambda k: k ** -4.0, size1=h, size2=w, anisotropy=anisotropy).real
x = np.array(x)

y = []
for idx, item in enumerate(x):
y.append(, item))
y = np.array(y)

# data normalization
for idx, item in enumerate(x):
x[idx] = (item - item.mean())/item.std()
for idx, item in enumerate(y):
y[idx] = (item - item.mean())/item.std()

return x, y

Note that we normalize the matrix elements by making their mean equals zero and variance equal 1. We can visualize the mapping by plotting the matrix

h, w = 10, 10
batch_size = 10
linear_map = np.random.rand(h, w)
batch_x, batch_y = next_batch(batch_size, h, w, linear_map)

fig, ax = plt.subplots(1,3)
ax[0].imshow(batch_x[0], cmap='jet', interpolation='none')
ax[1].imshow(my_multiply, cmap='jet', interpolation='none')
ax[2].imshow(batch_y[0], cmap='jet', interpolation='none')

ax[0].set_title(r'$\mathrm{Input\ Matrix\ }I$')
ax[1].set_title(r'$\mathrm{Linear\ Mapping\ Matrix\ }M$')
ax[2].set_title(r'$\mathrm{Output\ Matrix\ }O$')



As shown, the matrix M maps I to O. Such transformation is called linear mapping. I will show that MDLSTM can indeed learn this mapping up to reasonable accuracy. I use the codes here. The following code is the traning part

anisotropy = False
learning_rate = 0.005
batch_size = 200
h = 10
w = 10
channels = 1
x = tf.placeholder(tf.float32, [batch_size, h, w, channels])
y = tf.placeholder(tf.float32, [batch_size, h, w, channels])

linear_map = np.random.rand(h,w)

hidden_size = 100
rnn_out, _ = multi_dimensional_rnn_while_loop(rnn_size=hidden_size, input_data=x, sh=[1, 1])
# use linear activation function
model_out = slim.fully_connected(inputs=rnn_out,

# use a little different loss function from the original code
loss = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(y, model_out))))
grad_update = tf.train.AdamOptimizer(learning_rate).minimize(loss)

sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))

# Add tensorboard (Really usefull)
train_writer = tf.summary.FileWriter('Tensorboard_out' + '/MDLSTM',sess.graph)

steps = 1000
mypredict_result = []
loss_series = []
for i in range(steps):
batch = next_batch_linear_map(batch_size, h, w, linear_map, anisotropy)
st = time()
batch_x = np.expand_dims(batch[0], axis=3)
batch_y = np.expand_dims(batch[1], axis=3)

mypredict, loss_val, _ =[model_out, loss, grad_update], feed_dict={x: batch_x, y: batch_y})
mypredict_result.append([batch_x, batch_y, mypredict])
print('steps = {0} | loss = {1:.3f} | time {2:.3f}'.format(str(i).zfill(3),
time() - st))
loss_series.append([i+1, loss_val])

The loss as a function of steps is shown in the figure below. It seems the loss saturate around 70-75. Now let’s see how well our neural network learns? The following figures show five predictions on newly randomly generated input matrix. The results are pretty good for the purpose of illustration. I am sure there must be some room for improvements.


I choose the square of the matrix as the test for nonlinear mapping, I^{2}.

def next_batch_nonlinear_map(bs, h, w, anisotropy=True):
x = []
for i in range(bs):
o = gaussian_random_field(pk=lambda k: k ** -4.0, size1=h, size2=w, anisotropy=anisotropy).real
x = np.array(x)

y = []
for idx, item in enumerate(x):
y.append(, item)) # only changes here
y = np.array(y)

# data normalization
for idx, item in enumerate(x):
x[idx] = (item - item.mean())/item.std()
for idx, item in enumerate(y):
y[idx] = (item - item.mean())/item.std()

return x, y

The following image are the loss function and results.


As you can see, the results are not great but very promising. Any suggestions to improve the results would be welcome.

Generate gaussian chain/random walk using normal modes

Long time ago, I wrote about how to use Pivot algorithm to generate equilibrium conformations of a random walk, either self-avoiding or not. The volume exclusion of a self-avoiding chain make it non-trivial to generate conformations. Gaussian chain, on the other hand, is very easy and trivial to generate. In addition to the efficient pivot algorithm, in this article, I will show another interesting but non-straightforward method to generate gaussian chain conformations.

To illustrate this method which is used to generate static conformations of a gaussian chain, we need first consider the dynamics of a such system. It is well known the dynamics of a gaussian/ideal chain can be modeled by the Brownian motion of beads connected along a chain, which is ensured to give correct equilibrium ensemble. The model is called “Rouse model”, and is very well studied. I strongly suggest the book The Theory of Polymer Dynamics by Doi and Edwards to understand the method used here. I also found a useful material here. I will not go through the details of derivation of solution of Rouse model. To make it short, the motion of a gaussian chain is just linear combinations of a series of inifinite number of independent normal modes. Mathematically, that is,

\mathbf{R}_{n}=\mathbf{X}_{0}+2\sum_{p=1}^{\infty}\mathbf{X}_{p}\cos\big(\frac{p\pi n}{N}\big)

where \mathbf{R}_{n} is the position of n^{th} bead and \mathbf{X}_{p} are the normal modes. \mathbf{X}_{p} is the solution of langevin equation \xi_{p}\frac{\partial}{\partial t}\mathbf{X}_{p}=-k_{p}\mathbf{X}_{p}+\mathbf{f}_{p}. This is a special case of Orstein-Uhlenbeck process and the equilibrium solution of this equation is just a normal distribution with mean 0 and variance k_{\mathrm{B}}T/k_{p}.

X_{p,\alpha}\sim \mathcal{N}(0,k_{\mathrm{B}}T/k_{p})\quad, \quad\alpha=x,y,z

where k_{p}=\frac{6\pi^{2}k_{\mathrm{B}}T}{N b^{2}}p^{2}, N is the number of beads or number of steps. b is the kuhn length.

Since the normal modes are independent with each other and they are just gaussian random number. It is very easy and straightforward to generate normal modes. And then we can just transform them to the actual position of beads using the first equation and we get the position of each beads, giving us the conformations. The idea is that simple! This may seems untrivial at first glance but should give us the correct result. To test this, let’s implement the algorithm in python.

def generate_gaussian_chain(N, b, pmax):
    # N = number of beads
    # b = kuhn length
    # pmax = maximum p modes used in the summation

    # compute normal modes xpx, xpy and xpz
    xpx = np.asarray(map(lambda p: np.random.normal(scale = np.sqrt(N * b**2.0/(6 * np.pi**2.0 * p**2.0))), xrange(1, pmax+1)))
    xpy = np.asarray(map(lambda p: np.random.normal(scale = np.sqrt(N * b**2.0/(6 * np.pi**2.0 * p**2.0))), xrange(1, pmax+1)))
    xpz = np.asarray(map(lambda p: np.random.normal(scale = np.sqrt(N * b**2.0/(6 * np.pi**2.0 * p**2.0))), xrange(1, pmax+1)))

    # compute cosin terms
    cosarray = np.asarray(map(lambda p: np.cos(p * np.pi * np.arange(1, N+1)/N), xrange(1, pmax+1)))

    # transform normal modes to actual position of beads
    x = 2.0 * np.sum(np.resize(xpx, (len(xpx),1)) * cosarray, axis=0)
    y = 2.0 * np.sum(np.resize(xpy, (len(xpy),1)) * cosarray, axis=0)
    z = 2.0 * np.sum(np.resize(xpz, (len(xpz),1)) * cosarray, axis=0)
    return np.dstack((x,y,z))[0]

Note that there is a parameter called pmax. Although actual position is the linear combination of inifinite number of normal modes, numerically we must truncate this summation. pmax set the number of normal modes computed. Also in the above code, we use numpy broadcasting to make the code very consie and efficient. Let’s use this code to generate three conformations with different values of pmax and plot them

# N = 300
# b = 1.0
conf1 = generate_gaussian_chain(300, 1.0, 10) # pmax = 10
conf2 = generate_gaussian_chain(300, 1.0, 100) # pmax = 100
conf3 = generate_gaussian_chain(300, 1.0, 1000) # pmax = 1000

fig = plt.figure(figsize=(15,5))

# matplotlib codes here


The three plots show the conformations with p_{\mathrm{max}}=10, p_{\mathrm{max}}=100 and p_{\mathrm{max}}=1000. N=300 and b=1.0. As clearly shown here, larger number of modes gives more correct result. The normal modes of small p corresponds the low frequency motion of the chain, thus with small pmax, we are only considering the low frequency modes. The conformation generated can be considered as some what coarse-grained representation of a gaussian chain. Larger the pmax is, more normal modes of higher frequency are included, leading to more detailed structure. The coarsing process can be vividly observed in the above figure from right to left (large pmax to small pmax). To test our conformations indeed are gaussian chain, we compute the mean end-to-end distance to test whether we get correct Flory scaling (\langle R_{ee}^{2}\rangle = b^{2}N).


As shown in the above plot, we indeed get the correct scaling result, \langle R_{ee}^{2}\rangle = b^{2}N. When using this method, care should be taken setting the parameter pmax, which is the number of normal modes computed. This number should be large enough to ensure the correct result. Longer the chain is, the larger pmax should be set.

Simulating Brownian Dynamics (overdamped Langevin Dynamics) using LAMMPS

I have migrated to my new domain and website here ⇒

LAMMPS is a very powerful Molecular Dynamics simulation software I use in my daily research. In our research group, we mainly run Langevin Dynamics (LD) or Brownian Dynamics (BD) simualtion. However, for some reason, LAMMPS doesn’t provide a way to do Brownian Dynamics (BD) simulation. Both the LD and BD can be used to sample correct canonical ensemble, which sometimes also be called NVT ensemble.

The BD is the large friction limit of LD, where the inertia is neglected. Thus BD is also called overdamped Langevin Dynamics. It is very important to know the difference between LD and BD since these two terms seems be used indifferently by many people which is simply not correct.

The equation of motion of LD is,

m \ddot{\mathbf{x}} = -\nabla U(\mathbf{x}) - m\gamma\dot{\mathbf{x}}+\mathbf{R}(t)

where m is the mass of the particle, \mathbf{x} is its position and \gamma is the damping constant. \mathbf{R}(t) is random force. The random force is subjected to fluctuation-dissipation theorem. \langle \mathbf{R}(0)\cdot\mathbf{R}(t) \rangle = 2m\gamma\delta(t)/\beta. \gamma=\xi/m where \xi is the drag coefficient. \mathbf{R}(t) is nowhere differentiable, its integral is called Wiener process. Denote the wiener process as \omega(t). It has the property \omega(t+\Delta t)-\omega(t)=\sqrt{\Delta t}\theta, \theta is the Gaussian random variable of zero mean, variance of 2m\gamma/\beta.

\langle \theta \rangle = 0\quad\quad\langle \theta^{2}\rangle=2m\gamma/\beta

The fix fix langevin provided in LAMMPS is the numerical simulation of the above equation. LAMMPS uses a very simple integration scheme. It is the Velocity-Verlet algorithm where the force on a particle includes the friction drag term and the noise term. Since it is just a first order algorithm in terms of the random noise, it can not be used for large friction case. Thus the langevin fix in LAMMPS is mainly just used as a way to conserve the temperature (thermostat) in the simulation to sample the conformation space. However in many cases, we want to study the dynamics of our interested system realistically where friction is much larger than the inertia. We need to do BD simulation.

For a overdamped system, \gamma=\xi/m is very large, let’s take the limit \gamma=\xi/m\to\infty, the bath becomes infinitely dissipative (overdamped). Then we can neglect the left side of the equation of LD. Thus for BD, the equation of motion becomes

\dot{\mathbf{x}}=-\frac{1}{\gamma m}\nabla U(\mathbf{x})+\frac{1}{\gamma m}\mathbf{R}(t)

The first order integration scheme of the above equation is called Euler-Maruyama algorithm, given as

\mathbf{x}(t+\Delta t)-\mathbf{x}(t)=-\frac{\Delta t}{m\gamma}\nabla U(\mathbf{x})+\sqrt{\frac{2\Delta t}{m\gamma\beta}}\omega(t)

where \omega(t) is the normal random variable with zero mean and unit variance. Since for BD, the velocities are not well defined anymore, only the positions are updated. The implementation of this scheme in LAMMPS is straightforward. Based on source codes fix_langevin.cpp and fix_langevin.h in the LAMMPS, I wrote a custom fix of BD myself. The core part of the code is the following. The whole code is here.

void FixBD::initial_integrate(int vflag)
double dtfm;
double randf;

// update x of atoms in group

double **x = atom->x;
double **f = atom->f;
double *rmass = atom->rmass;
double *mass = atom->mass;
int *type = atom->type;
int *mask = atom->mask;
int nlocal = atom->nlocal;
if (igroup == atom->firstgroup) nlocal = atom->nfirst;

if (rmass) {
for (int i = 0; i < nlocal; i++) if (mask[i] & groupbit) { dtfm = dtf / rmass[i]; randf = sqrt(rmass[i]) * gfactor; x[i][0] += dtv * dtfm * (f[i][0]+randf*random->gaussian());
x[i][1] += dtv * dtfm * (f[i][1]+randf*random->gaussian());
x[i][2] += dtv * dtfm * (f[i][2]+randf*random->gaussian());

} else {
for (int i = 0; i < nlocal; i++) if (mask[i] & groupbit) { dtfm = dtf / mass[type[i]]; randf = sqrt(mass[type[i]]) * gfactor; x[i][0] += dtv * dtfm * (f[i][0]+randf*random->gaussian());
x[i][1] += dtv * dtfm * (f[i][1]+randf*random->gaussian());
x[i][2] += dtv * dtfm * (f[i][2]+randf*random->gaussian());

As one can see, the implementation of the integration scheme is easy, shown above. dtv is the time step \Delta t used. dtfm is 1/(\gamma m) and randf is \sqrt{2m\gamma/(\Delta t\beta)}.

The Euler-Maruyama scheme is a simple first order algorithm. Many studies has been done on higher order integration scheme allowing large time step being used. I also implemented a method shown in this paper. The integration scheme called BAOAB is very simple, given as

\mathbf{x}(t+\Delta t)-\mathbf{x}(t)=-\frac{\Delta t}{m\gamma}\nabla U(\mathbf{x})+\sqrt{\frac{\Delta t}{2m\gamma\beta}}(\omega(t+\Delta t)+\omega(t))

The source code of this method can be downloaded here. In addition, feel free to fork my Github repository for fix bd and fix bd/baoab. I have done some tests and have been using this code in my research for a while and haven’t found problems. But please test the code yourself if you intend to use it and welcome any feedback if you find any problems.

Big Bend徒步露营攻略

5月不是最适合去Big Bend露营的季节, 沙漠在每天最热的时候温度会高达40度, 再加上缺水, 所以很少人会选择夏季来Big Bend. 来Austin之后, 偶然看到一张South rim落日后的照片, 紫色的天空, 脚下是一眼望不到头的Chiwawa沙漠, 看到这张照片后就下定决心一定要去一趟. 无奈一直没有合适的时间, 再加上去Big Bend最适宜的季节 (冬季) 很难订到campsite, 一年多了也没有去成. 5月份学期结束, 我们决定干脆说走就走, 现在来看我俩很庆幸有了这次稍显准备仓促的旅行, 要不然真不知道什么时候能去成了.

Chiwawa沙漠,远方背景处被云雾笼罩的山就是我们徒步露营的High Chisos。左侧是Elephant Tusk

big bend NP位于德州与墨西哥交界处, 由一条叫Rio Grande的河分界, 整个公园属于Chiwawa沙漠的一部分. 由于位置偏远, (我们从austin开过去要8个小时, 圣安东尼奥算是离公园最近的大城市也得6个小时), 所以是美国到访人数最少的几个国家公园之一 (全美一共59个国家公园big bend到访人数排名42). 这里最受欢迎的活动是徒步和露营, 整个公园的trail一共长180英里(290公里), 由于游客稀少, 所以在这里的露营和徒步会给你非常难忘的体验. Big Bend另外一个出名的是这里的夜空, 这里有美国最黑的夜晚 (因为附近实在太荒了, 几乎没有任何光污染), 遗憾的是我们没能好好欣赏这里的银河. 推荐如果有机会一定要熬到后半夜看看银河. 这里是公园官方关于观星的页面

从五月到八月沙漠地区白天会有极端干燥高温天气, 对于没有什么野外生存经验的人, 由于暴晒和缺水, 夏天在Big Bend的沙漠平原两天以上的徒步和露营是存在生命危险的. 所以我们决定只在High Chisos活动. High chisos是位于公园中央地区的一片拔地而起的山脉, 最高点比沙漠平原处提升了大约3000 feet, 即使在夏天最热的时候, 这里的气温也是可以承受的. 所以大多数夏天来Big Bend徒步露营的人都只会在High Chisos活动.

Big Bend的夏天非常的炎热,但属于全年降水最多的几个月,会有雷阵雨


Big bend公园内的住宿分为两种: camping和lodging.

顾名思义, lodging就是住在公园内的建好的屋子里,有空调,可以洗澡.位置在Chisos Basin,网上的资料基本上都说这个需要提前半年多才能订到,不过我们这次比较幸运, 由于第二天临时改变行程, 当天从山上下来后直接去柜台很幸运的竟然订到了唯一剩下的一间房. 如果你需要洗澡,空调,睡得舒服,那这个lodge就是最好的选择,位置是公园中心,开车去公园其他地方都是最方便的.再加上在海拔比较高的地方,即使是夏季,晚上也会比较凉快.

High Chisos的入口处提示游客这里有黑熊和山狮

不过来Big Bend不体验一次露营那真的太可惜了. camping大致分为两种, campgrounds和backcountry camping. campgrounds应该是公园内最常见的露营方式. campgrounds实际上就是公园官方修的专门让游客露营的地方, 每个site之间都离的很近, 会配备公共厕所. Big Bend共有三个campgrounds: chisos basin campground, cottonwood campground和rio grande village campground. chisos basin campground和上面介绍的lodging是在同一个地方, 剩下两个分别在公园的西边和东边. rio grande village是唯一可以停RV的campgrounds. campground的好处是提供厕所,如果只是想体验一下住帐篷,那么campground是最佳选择,因为提供厕所而且因为周围都是其他camping的人,所以很安全(不用担心野生动物).各个campground都有一部分sites是可以在网上提前预订的(预订网站), 剩余的是属于walk up类型,就是直接自己去营地找到还没有被占的campsite, 然后在campsite的入口处self check-in就行. 如果你没有提前在网上预订到,我建议在早上甚至凌晨到达,一般会有人很早就走了. 如果下午过去, 旺季基本是不可能找不到地方的.

厚重的雾气笼罩着High Chisos

但是如果你想真正的体验big bend,那我推荐一定要选择backcountry camping. Backcountry camping的好处就是你周围是没有其他人的,是真正的在野外露营. Backcountry又大致分为两种, 一种是primitive roadside campsite另外就是backpacking camping. roadside campsite是可以开车直接到达的, 很多开房车来big bend的人会选择这种方式. 需要注意的是roadside campsites都在沙漠平原, 而在公园里是不允许长时间发动着车吹空调的, 所以我认为这种方式只适合秋冬季 (除非你不怕热). 秋冬季, 人多, 又想比较悠闲的玩的话, 租辆RV来roadside camping是我最推荐的方式.

而对于想真正体验一把融入大自然的话, 那backpacking无疑是最好的方式. backpacking又分为两种: 在trail旁的公园指定好的地点camping和wilderness camping. widerness camping顾名思义, 就是你可以在(公园沙漠平原处的)任何地方露营和徒步, 需要提前把每天的行程 (大致的徒步路径, 每天的露营地点)告诉公园的工作人员, 这种方式需要足够多的沙漠生存的经验, 很可能徒步一星期过程中一个活人都看不到, 遇到危险是基本每人能帮忙, 新手还是不要想了, 万一挂了得不偿失. 所以我们选择了第一种方式. 这些campsites都在公园最受欢迎的high chisos trails旁, 每一个露营点都离徒步的路线不远,所以不会有什么危险. 所有的这些backpacking campsite是不接受预订的, 只能在计划的第一晚的当天去游客中心订露营点. 这个网站可以查询每个露营点的状态. 另外这个PDF提供了很详尽的每个campsite的情况. 我们的计划是三天两夜的行程, 从chisos basin出发, 在上山和下山的路径上选择两个campsite, 这样可以很充分的享受整个过程.

不像其他一些受欢迎的国家公园,Big Bend并没有paved trail, 有个登山杖能让徒步轻松很多

这里首先想说的是我俩在这之前是完全没有任何露营经验的, 所以是真正意义上的新手. 而Big Bend的徒步是有一定难度的, 我们算是完成了一半, 本来是三天两夜的行程中途改成了两天一夜, 放弃第二夜的主要原因是老婆晚上害怕睡不着… 所以如果你有一定经验, 那不要犹豫, 如果没有经验, 也不要退缩. 我们一开始是有一定顾虑的, 想过住公园的hotel或者campground, 大概在出发前的一个多星期偶然地在网上搜攻略的时候发现了这个论坛 bigbendchats. 网上Big Bend徒步的信息实在太少 (中文的游记/经验贴更是压根就没有, 这也是我写这篇游记的目的之一), 这个论坛的帮助非常大, 上面有详尽的资料, 经验贴和讨论帖, 可以说绝大多数有用的信息我是在这上面发现的.

我们的营地LW3,位于Laguna Meadow Trail上,需要从trail叉开继续走1 mile左右,绝对的isolated,周围很多动物足迹和分辨,旁边还有个小溪,叫做Upper Cattail,不过很多时候是干涸的,如果近期下过雨会有水


  1. shelter
    • 睡袋: 夏天晚上最冷的时候大概17,18度, 不需要低温的睡袋. 我们买的REI最便宜的一款
    • 帐篷: 双人帐篷
    • sleeping pad: 充气垫子
    • 防水布: 垫在垫子下面, 万一下雨多一层保护
  2. 食物
    • energy bar, 坚果类的, 牛肉干: 这些都属于边走边吃的, 补充糖分和能量
    • Frozen-dry food: 强烈推荐. 这玩意一开始实际上是设计给宇航员吃的, 后来进入户外领域, 我们买的是mountain house的. 一是比想象的好吃很多. 而是非常方便, 只需要用开水泡15分钟就行. 价格也不贵, 大包的大概8刀,够两个人吃一顿.
    • 一共16L水: 这一项最重要, Big Bend是个缺水的地方, 公园官方的建议是每人每天至少需要一加仑水. 我们的计划是3天两夜, 所以按最低标准准备了4加仑的水. 实际上还是准备少了, 后面会讲到. 如果在近期下过雨的话, 山里面是有可能有溪水的. 不过这些水很宝贵, 野生动物依靠这些水生存. 而且有可能会很脏, 没法直接喝的, 需要过滤.
    • 滤水装备: 我们准备了消毒药片和物理过滤工具 (户外用品店都有卖的). 我们在第一天的campsite旁发现了几个小水塘, 不过由于是死水,已经变成黄色的了, 我们过滤了颜色也没消掉, 所以最终也没下决心喝下去.
    • 水瓶和水袋: 水瓶少带, 减少重量和体积, 多用那种2L,3L的水袋
  3. 其他装备
    • 手电筒: 也可以用头灯
    • 折叠刀: 一般来说用不上
    • 驱虫剂: 强烈建议带上. 山里面非常多的苍蝇, 是会咬人的而且会痒, 当然这个看脸, 我老婆完全没事, 我被咬的死去活来…
    • 便携的野外用炉子: 烧水煮饭用
    • 急救包: 感冒药, 发烧药, 防中暑, 抗生素, 创可贴, 碘水, 棉球等等…
    • 帽子: 强烈推荐. 自我感觉可以非常明显的减低水分流失速度
    • 登山杖: 膝盖不好的最好还是带着吧
我们烧水用的炉子。烧开水,把水倒到freeze-dry food的包装袋里,15分钟后就能吃了


  1. 水一定要带够! 按照每人每天最低一加仑的量来计算. 可以在出发前一两天开始多喝水, 让身体多储存点水.
  2. 因为上一条, 所以背包会非常重, 这也是在Big Bend徒步露营和在其他地方的最大区别. 我和老婆一共带了16L水,这就已经16公斤了, 加上其余的东西, 我俩一共背了大概40公斤. 你需要精简东西,能不带的就不带,能少带的就少带, 另外就是从装备入手, 像睡袋, 帐篷, sleeping pad这些东西是可以很轻的, 当然价格嘛….. 所以最终还是要综合自己的体力以及预算来决定买哪些装备带什么东西.



周五早上准备好装备放在车上, 下午5点半开完组会我们就直接从学校出发了, 大概晚上11点半到达提前订的位于Fort Stockton一家motel, 入住好12点一到就上查询campsite的网站研究哪些露营点还没被占. 最终确定了两晚上的露营地点 (LW3和SW3). 虽然夏季不是高峰, 但是有些遗憾的是最受欢迎的那几个露营点还是被先到的人订了.

太阳落山前的营地,这里每一个营地都会配一个防熊箱。所有带气味的东西不要用的时候都需要放在防熊箱里,千万不要低估熊的嗅觉,他们几公里以外就能闻到特殊的气味。不过Big Bend里只有黑熊(Black Bear),相比棕熊(Grizzly)攻击性小很多。实际上,对于遇到熊之后的正确举措,公园给出官方建议是冲着熊吼,扔石子,吓走他们。

周六早上7点起床, 8点出发. Fort Stockton离公园的入口大概还有1个半小时的车程, 从公园入口开到Chisos Basin的游客中心又是半个多小时. 在高峰期, 如果想拿到露营的许可需要尽早去游客中心申请, 许可就是一张表, 需要随身携带. 我们10点在游客中心顺利拿到了许可就收拾好装备趁早出发了. 我们选择的是从西边的路 (Laguna Meadow Trail)上山到达第一晚的露营地LW3, 原本也就是3小时的行程由于我们背了太重的装备足足爬了6个小时. LW3离主路大概有一英里的距离, 四周有其余两个营地, 最近的离我们大概几百米, 所以这里是个非常隐蔽的地点. 把帐篷搭好, 我就去周围寻找野生动物的痕迹了, 营地四周发现了好几处鹿的粪便, 还有一条很明显的黑熊常走的路(bear trail), 路上每隔几十米就是一泡黑熊粪便. 粪便都非常干, 应该说明这里有段时间没有”游客”光临了. 另外还发现了一个小水哇, 不过水已经发黄. 用滤水装置加药片过滤了一瓶水, 但仍然是黄色的, 我俩纠结了半天也没喝, 最后用来洗手了. 下午5点多烧水吃了我们的freeze dry food. 出乎意料的好吃. 天黑前我们就睡下了, 由于这是我们第一次野外露营, 一晚上醒来很多次, 总觉得周围有东西在动. 因为要保存水量, 晚上非常渴但是舍不得喝水, 这一晚让我第一次真正意识到水的珍贵….当然对我来说最可怕的是虫子, 这边有一种像苍蝇的虫子会咬人, 被咬起鸡蛋大小的肿包, 非常痒. 我老婆说因为我的血太好吃, 所以虫子一口也没咬她. 不过由于晚上老婆害怕不敢出帐篷, 所以这次我俩很遗憾的没有看到Big Bend的星空.



第二天早上8点多起床, 吃了早饭, 水已经消耗了一半, 由于装备还是很重, 这一天又有很长的路要走, 我俩非常纠结不舍地决定放弃第二天晚上的露营, 改为直接当天从Colima trail转到东线下山. 从我们的营地出发, 继续爬升了1个多小时就到达Colima trail, 然后就开始了漫长的下山过程. 我们穿过Colima trail后到达Boot Canyon, 这里是High chisos最受欢迎的地方之一. 这里有条小溪是整个High Chisos唯一的”稳定”水源. 我们到达那里发现溪水很旺, 鸟类很多. 在Boot Canyon吃完午饭(freeze dry版的宫保鸡丁)后, 歇了一会我俩就继续下山, 东线有段路非常的陡, 我俩庆幸前一天是从西边上的山, 要不然估计第一天就半途放弃了. 又经过了漫长的似乎永远没有尽头的三个多小时的下山过程, 我俩终于到了Chisos Basin大本营. 直奔商店买了瓶可乐, 最想干的事情就是洗一个冷水澡. 这时候已经3点多了, 想到直接开回Austin会太累, 我们决定去Basin campground碰碰运气, 看看有没有空余的位置, 我们确实找到几个空的营地, 不过都没有遮阳的棚子, 当时正是太阳最毒的时候, 我们放弃了住在campground的想法. 我们又查了下公园外面的情况, 全都住满了, 这时候我俩只剩下一个选择了, 去lodge看看有没有剩房. 我俩本来是没抱什么希望的, 没想到还真让我们抢到了最后一个空房. 虽然贵了点, 不过对于已经快累趴下的我俩, 顾不了那么多了… 我俩洗完澡就美美的睡了一觉, 晚上6点起床去sotol vista看日落. 一开始因为太累了, 只想一觉睡到第二天, 庆幸的是我们没有这么干,要不然就会错过这个难忘的日落. 这里可以看我们在Sotol Vista拍的日落

从High Chisos到Sotol Vista的路上的黄昏美景,整片High Chisos呈现着迷人的红色


第三天我俩趁着早上还没有太热, 开车去Rio Grande Village看看. 营地基本是空的. 有个很短trail可以爬到一个山包顶上, 从上面可以看到远处的墨西哥小镇. 这边的Rio Grande河有个基本180度的弯, 所以在山顶你的左右两侧实际上都是墨西哥. 最有意思的是我俩看到一个墨西哥人骑着个毛驴悠然自得的从墨西哥一侧跨过Rio Grande河溜达到了美国境内, 然后有慢悠悠的跨过河回到墨西哥. 想到Trump要建墙的计划, 我看看四周, 这里太大太偏了, 觉得基本是痴人说梦.

背景中的河流就是分割美国和墨西哥的Rio Grande

大概10点钟我们决定返程Austin, 在离开公园的路上在Fossil Discovery Exhibit参观了一下. Big Bend在亿万年前实际上是片海洋, 后来成了一片湿地, 在之后变成了沙漠. 恐龙曾经在这里繁衍生息. 展览建在曾经的一个挖掘点旁, 有一些当地挖掘的化石(包括史前巨鳄的头骨, 翼展有10来米的翼龙化石, 2米高的阿拉莫龍腿骨等等). 另外这里提供一条trail, 游客可以在路上找化石, 所有找到的化石都可以自己带走. 不过我们到那里的时候已经中午了, 由于太热我俩看完展览就匆匆离开了.

恐龙化石展览, 房顶上实际上挂着一副完整的翼龙化石, 翼展长达10米 (光比太大照片找不出来)

Continue reading “Big Bend徒步露营攻略”

Automated python script to access ipython notebook on a remote server

My research work involves a lot of using of IPython Notebook. I usually do it on an office MAC. However I also very often need to access it from home. After a brief searching, I found these three wonderful articles on this topic.

I have been doing this for a while. But it eventually comes to me that how good it would be if I can make it automatic. So I wrote this python script to do the procedures described in those three articles. I am sure there must be some more elegant way to do this. But this is what I got so far and it works.

import paramiko
import sys
import subprocess
import socket
import argparse
import pyperclip

# function to get available port
def get_free_port():
      s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
      port = s.getsockname()[1]
      return port

# print out the output from paramiko SSH connection
def print_output(output):
    for line in output:

parser = argparse.ArgumentParser(description='Locally open IPython Notebook on remote server\n')
parser.add_argument('-t', '--terminate', dest='terminate', action='store_true', \
                    help='terminate the IPython notebook on remote server')
args = parser.parse_args()

host=&quot;***&quot; # host name
user=&quot;***&quot; # username

# write a temporary python script to upload to server to execute
# this python script will get available port number

def temp():
    with open('', 'w') as f:
        f.write('import socket\nimport sys\n')
        f.write('def get_free_port():\n')
        f.write('    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n')
        f.write(&quot;    s.bind(('localhost', 0))\n&quot;)
        f.write('    s.listen(1)\n')
        f.write('    port = s.getsockname()[1]\n')
        f.write('    s.close()\n')
        f.write('    return port\n')

def connect():
    # create SSH client
    client = paramiko.SSHClient()
    client.connect(host, username=user)

    # generate the temp file and upload to server
    ftpClient = client.open_sftp()
    ftpClient.put('', &quot;/tmp/;)

    # execute python script on remote server to get available port id
    stdin, stdout, stderr = client.exec_command(&quot;python /tmp/;)
    stderr_lines = stderr.readlines()

    port_remote = int(stdout.readlines()[0])
    print('REMOTE IPYTHON NOTEBOOK FORWARDING PORT: {}\n'.format(port_remote))

    ipython_remote_command = &quot;source ~/.zshrc;tmux \
                              new-session -d -s remote_ipython_session 'ipython notebook \
                              --no-browser --port={}'&quot;.format(port_remote)

    stdin, stdout, stderr = client.exec_command(ipython_remote_command)
    stderr_lines = stderr.readlines()

    if len(stderr_lines) != 0:
        if 'duplicate session: remote_ipython_session' in stderr_lines[0]:
            print(&quot;ERROR: \&quot;duplicate session: remote_ipython_session already exists\&quot;\n&quot;)


    # delete the temp files on local machine and server'rm -rf', shell=True)
    client.exec_command('rm -rf /tmp/')


    port_local = int(get_free_port())
    print('LOCAL SSH TUNNELING PORT: {}\n'.format(port_local))

    ipython_local_command = &quot;ssh -N -f -L localhost:{}:localhost:{} \
                  ;.format(port_local, port_remote), shell=True)

def close():
    # create SSH client
    client = paramiko.SSHClient()
    client.connect(host, username=user)
    stdin, stdout, stderr = client.exec_command(&quot;source ~/.zshrc;tmux kill-session -t remote_ipython_session&quot;)
    stderr_lines = stderr.readlines()
    if len(stderr_lines) == 0:
        print('Successfully terminate the IPython notebook on {}\n'.format(host))

    # kill the ssh tunneling on the local machine
        pid = int(subprocess.check_output("ps aux | grep localhost | grep -v grep | awk '{print $2}'", shell=True))"kill {}".format(pid), shell=True)
    except ValueError:

if args.terminate:

Basically this script does the following:
1. Connect to the server using python package paramiko.
2. Upload a temporary python script. Use paramiko to execute the python script. This script gets an available port on localhost on remote machine.
3. Open Ipython Notebook on remote machine using the port we get from the last step. I used tmux to do this. The reason tmux is used here is that it allows us to exit the session after launching notebook otherwise we cannot proceed the following steps. And my shell is zsh. You can modify that part of code based on your situation
4. On the local machine, find an available port and create an SSH tunneling to port forwarding the port on the remote machine to local machine.

If the script runs successfully, you will see something like this.
alt text
If you want to check is IPython Notebook really running on the remote machine. SSH to remote machine, and use command tmux ls. A tmux session named remote_ipython_session should exist.
alt text
In browser, open http://localhost: 50979. You should be able to access your ipython notebook.

To terminate the ipython notebook on the remote machine, simply do
alt text

P.S. If you don’t even want to input link in browser, you can use python package pyperclip to send the link to clipboard.

Learn LAMMPS source code note #1

This is a note about learning LAMMPS source codes. This note focuses on compute style of Lammps which is used to compute certain quantity during the simulation run. Of course you can as well compute these quantities in post-process, however it’s usually faster to do it in the simulation since you can take advantage of the all the distance, forces, et al generated during the simulation instead of computing them again in post-process. I will go through the LAMMPS source code compute_gyration.h and compute_gyration.cpp. I am not very familiar with c++, so I will also explain some language related details which is what I learn when studying the code. Hope this article can be helpful when someone want to modify or make their own Lammps compute style.






#include "compute.h"

namespace LAMMPS_NS {

class ComputeGyration : public Compute {
  ComputeGyration(class LAMMPS *, int, char **);
  void init();
  double compute_scalar();
  void compute_vector();

  double masstotal;



First part of this code




is where this specific compute style is defined. If you want to write your own compute style, let's say intermediate scattering function. Then we write like this


ComputeStyle(isf,ComputeISF)  // ISF stands for intermediate scattering function


Move to the rest part. #include "compute.h" and namespace LAMMPS_NS is to include the base class and namespace. Nothing special is here, you need to have this in every specific compute style header file.

class ComputeGyration : public Compute {
  ComputeGyration(class LAMMPS *, int, char **);
  void init();
  double compute_scalar();
  void compute_vector();

  double masstotal;

You can see there is a overal structure in the above code class A : public B. This basically means that our derived class A will inherit all the public and protected member of class B. More details can be found here, here and here

Next, we declare two types of member of our derived class, public and private. public is the member we want the other code can access to and private is the member which is only used in the derived class scope. Now let’s look at the public class member. Note that there is no type declaration of class member ComputeGyration and ~ComputeGyration. They are called Class Constructor and Class Destructor. They are usually used to set up the initial values for certain member variables as we can see later in compute_gyration.cpp. Note that for some compute style such as compute_msd.h, the destructor is virtual, that is virtual ~ComputeMSD instead of just ~ComputeMSD. This is because class ComputeMSD is also inherited by derive class ComputeMSDNonGauss. So you need to decalre the base destructor as being virtual. Look at this page for more details. Now let’s move forward.

  void init();
  double compute_scalar();
  void compute_vector();

here all the function init, compute_scalar and compute_vector all are the base class member which are already defined in compute.h. However they are all virtual functions, which means that they can be overrided in the derived class, here it is the ComputeGyration. This and this pages provide some basic explanations for the use of virtual functions. Here is a list shown in LAMMPS documentation of some examples of the virtual functions you can use in your derived class.

virtual functions list of compute.h

In our case, gyration computation will return a scalor and a vector, then we need compute_scalar() and compute_vector(). Private member masstotal is the quantity calculated locally which is only used within the class and not needed for the rest of the codes.


Now let’s look at the compute_gyration.cpp.

#include <math.h>
#include "compute_gyration.h"
#include "update.h"
#include "atom.h"
#include "group.h"
#include "domain.h"
#include "error.h"

Here the necessary header files are include. The name of these header file is self-explanary. For instance, updata.h declare the functions to update the timestep, et al.

ComputeGyration::ComputeGyration(LAMMPS *lmp, int narg, char **arg) :
  Compute(lmp, narg, arg)
  if (narg != 3) error->all(FLERR,"Illegal compute gyration command");

  scalar_flag = vector_flag = 1;
  size_vector = 6;
  extscalar = 0;
  extvector = 0;

  vector = new double[6];

The above code define the what the constructor ComputeGyration actually does. :: is called scope operator, it is used to specify that the function being defined is a member (in our case which is the constructor which has the same name as the its class) of the class ComputeGyration and not a regular non-member function. The structure ComputeGyration : Compute() is called a Member Initializer List. It initializes the member Compute() with the arguments lmp, narg, arg. narg is the number of arguments provided. scalar_flag, vector_flag, size_vector, extscalar and extvector all are the flags parameter defined in Compute.h. For instance, scalar_flag = 1/0 indicates we will/won’t use function compute_scalar() in our derived class. The meaning of each parameter is explained in compute.h. This line vector = new double[6] is to dynamically allocate the memory for array of length 6. Normally the syntax of new operator is such

double *vector = NULL;
vector = new double[6];

Here the line double *vector = NULL is actually in compute.h and compute.cpp. Where pointer vector is defined in compute.h and its value is set to NULL in compute.cpp.

  delete [] vector;

The above code speficy destructor that is what will be excuted when class ComputeGyration goes out of scope or is deleted. In this case, it delete the gyration tensor vector defined above. The syntax of delete operator for array is delete [] vector. For details of new and delete can be found here.

void ComputeGyration::init()
  masstotal = group->mass(igroup);

This part perform one time setup like initialization. Operator -> is just a syntax sugar, class-&gt;member is equivalent with (*class).member. What group-&gt;mass(igroup) does is to call the member mass() function of class group, provided the group-ID, and return the total mass of this group. How value of igroup is set can be examined in compute.cpp. It’s the second argument of compute style.

double ComputeGyration::compute_scalar()
  invoked_scalar = update->ntimestep;

  double xcm[3];
  scalar = group->gyration(igroup,masstotal,xcm);
  return scalar;

invoked_scalar is defined in base class Compute. The value is the last timestep on which compute_scalar() was invoked. ntimestep is the member of class update which is the current timestep. xcm function of class group calculate the center-of-mass coords. The result will be stored in xcm. gyration function calculate the gyration of a group given the total mass and center of mass of the group. The total mass is calculated in init(). And in order for it to be accessed here, it is defined as private in compute_gyration.h. Notice that here there is no explicit code to calculte the gyration scalor because the member function which does this job is already defined in class group. So we just need to call it. However we also want to calculate the gyration tensor, we need to write a function to calculate it.

void ComputeGyration::compute_vector()
  invoked_vector = update->ntimestep;

  double xcm[3];

  double **x = atom->x;
  int *mask = atom->mask;
  int *type = atom->type;
  imageint *image = atom->image;
  double *mass = atom->mass;
  double *rmass = atom->rmass;
  int nlocal = atom->nlocal;

  double dx,dy,dz,massone;
  double unwrap[3];

  double rg[6];
  rg[0] = rg[1] = rg[2] = rg[3] = rg[4] = rg[5] = 0.0;

  for (int i = 0; i < nlocal; i++)
    if (mask[i] & groupbit) {
      if (rmass) massone = rmass[i];
      else massone = mass[type[i]];

      dx = unwrap[0] - xcm[0];
      dy = unwrap[1] - xcm[1];
      dz = unwrap[2] - xcm[2];

      rg[0] += dx*dx * massone;
      rg[1] += dy*dy * massone;
      rg[2] += dz*dz * massone;
      rg[3] += dx*dy * massone;
      rg[4] += dx*dz * massone;
      rg[5] += dy*dz * massone;

  if (masstotal == 0.0) return;
  for (int i = 0; i < 6; i++) vector[i] = vector[i]/masstotal;

The above code do the actual computation of gyration tensor.

Here is the list of meaning of each variable

  • x: 2D array of the position of atoms.
  • mask: array of group information of each atom. if (mask[i] &amp; groupbit) check whether the atom is in the group on which we want to perform calculation.
  • type: type of atom.
  • image: image flags of atoms. For instance a value of 2 means add 2 box lengths to get the unwrapped coordinate.
  • mass: mass of atoms.
  • rmass: mass of atoms with finite-size (meaning that it can have rotational motion). Notice that mass of such particle is set by density and diameter, not directly by the mass. That’s why they set two variables rmass and mass. To extract mass of atom i, use rmass[i] or mass[type[i]].
  • nlocal: number of atoms in one processor.

Look at this line domain-&gt;unmap(x[i],image[i],unwrap), domain.cpp tells that function unmap return the unwrapped coordination of atoms in unwrap. The following several lines calculate the gyration tensor. The MPI code MPI_Allreduce(rg,vector,6,MPI_DOUBLE,MPI_SUM,world) sums all the six components of rg calculated by each processor, store the value in vector and then distribute vector to all the processors. Refer to this article for details.

Here are two good articles about understanding and hacking LAMMPS code.

Build numpy with LAPACK/openblas on Deepthought

Installation of numpy through pip won’t link lapack correctly. However since Deepthought 2 has openblas software available,  we can build bumpy manually to link it to LAPACK/openblas library.

Download the newest version of numpy

pip download numpy


git clone

Load necessary libraries and packages on Deepthought 2

Load multithreads version of openblas.

module load openblas/0.2.15/gcc/4.9.3/pthreads

Load gcc

module load gcc/4.9.3

Load lapack

module load lapack/3.4.2

Edit site.cfg to link openblas

Find the openblas section, edit as the following

libraries = openblas
library_dirs = /cell_root/software/openblas/0.2.15/gcc/4.9.3/pthreads/sys/lib
include_dirs = /cell_root/software/openblas/0.2.15/gcc/4.9.3/pthreads/sys/include
runtime_library_dirs = /cell_root/software/openblas/0.2.15/gcc/4.9.3/pthreads/sys/lib

Build and install numpy

python config

the output should look something like this

libraries = ['openblas', 'openblas']
library_dirs = ['/opt/OpenBLAS/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]

libraries = ['openblas', 'openblas']
library_dirs = ['/opt/OpenBLAS/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]

Then build and install

python build && python install


Use these two scripts to test performance. To test it properly, use script like this

#SBATCH -n 1
#SBATCH -t 00:01:00

source ~/.virtualenv/py2env/bin/activate.csh


setenv OMP_NUM_THREADS 4 tell python to use four threads.

Several references:

Book Summary: Interpreting NAFTA by Frederick W.Mayer

I have a book list to finish reading before taking my Political Analysis Comprehensive Exam this May. Interpreting NAFTA is the first book I finished and is such a fascinating one. This book not only tells vivid stories behind the negotiation of NAFTA, it also provides a multilevel framework we could use to analyze the political decisions. For those we have read this book, I hope you enjoyed it as much as I do.

Frederick W. Mayer, Interpreting NAFTA: The Science and Art of Political Analysis, New York: Columbia University Press, 1998.


Ch1 Introduction

1.Goals of the book:

  • To understand the history of NAFTA, specifically:
    1. Why did the US/Canada/Mexico decide to negotiate?
    2. Why did the agreements take the form that they did?
    3. Why was ratification so fiercely contested in the US?
  • To evaluate existing political theory in light of the NAFTA case.
  1. Brief Introduction of NAFTA

In February of 1990, Mexico (President Salinas) asked the U.S. to consider a bilateral trade agreement, to which the U.S. (President George Bush) agreed. Canada (Prime Minister Mulroney) was not initially interested in participating (they had completed a Canada-U.S. bilateral agreement in 1987 with substantial political costs), but later changed its mind. Negotiations formally began in 1991, lasting until August of 1992 – which meant that the U.S. presidential election campaigns were under way. Candidate Clinton came out in favor of NAFTA, but conditionally on strong environmental and labor side agreements (Bush had already made some concessions to these groups, but they had more leverage with Clinton both because of the election and because he was a democrat). NAFTA was passed in the U.S. in November of 1993 following a major political battle.

Ch2 A Framework for Political Analysis (most important chapter)

Understanding the history of NAFTA (including why it took the specific form that it did) requires the use of multiple modes/theories and levels of analysis. Different theories and levels are more important at different stages of the process, but we cannot fully explain the observed outcomes without drawing from (usually competing) models of rational choice, institutional process, and symbolic politics – often nested at the international, domestic, and individual levels.

Based on his findings, Mayer argues against the idea that one or another theory or mode of analysis is dominant or most important, and that using them synergistically is the best approach for political analysis in many cases.

Level of Analysis Mode of Politics
Rational Choice


Institutional Process


Symbolic Response


International Realism Regime Theory Epistemic Communities
Domestic Political Economy Organizational Behavior Cultural Anthropology
Individual Public Choice & Institutional Economics Cognitive Psychology Constructivism, Symbolic Interactionism

Dimensions of the Analytic Framework

International Level

Focuses on processes in the international arena (politics among states). The dominant theories in International Relations concentrate on this level (states as unitary actors).

Domestic Level

Analyzes international relations as the consequence of domestic politics. “National behavior is determined by the action and interaction of bureaucracies and legislatures, political parties, business, and union lobbies, and other advocacy groups.” (p. 15)

Individual Actors

Focuses on elite actions and the particular worldviews of individuals. This may include theories that concentrate on elite decision-making or the individual interaction within groups.

Modes of Politics (assumptions about the fundamental nature of political behavior and political processes)

Rational Choice

Assumes the behavior of actors in the system is determined by rational choice. Two types of rational choice: substantively rational (seek to maximize the same set of core interests) and procedural rationality (strategically rational). This approach seeks to predict how individual choices will aggregate into collective action. Consider the example of NAFTA.

Institutional Process

The behavior of actors is determined by preexisting institutions. “In this concept of politics, rule, norms, routines, and other institutions limit options for action and at least partially predetermine their selection, thus channeling behavior along established paths. Political behavior is less a choice than an execution of programs” (p. 19).

Symbolic Response

Political behavior is a response to the way in which political circumstances are symbolically constructed. People respond to and interpret symbols. The emphasis is on ideas, symbolic representations, and their competing influences. “The essence of politics, therefore, is a contest among competing constructions.” (p. 20).

Section 1 Deciding to Negotiate

Ch3 Why a North American Free Trade Agreement?

Background: In early February 1990, Mexican President Carlos Salinas de Gortari decided to ask the United States to negotiate a free trade agreement with Mexico. US President George Bush agreed and Canadian Prime Minister Brian Mulroney decided it like a seat at the negotiating table. In January 1991, the three countries finally agree to negotiate.

Apply the framework with why NAFTA:

International Level:

Neo-realism (international level rational choice theory) assumes (1) states have clear interests, primarily in security; (2) states are rational actors; (3) the international system is anarchic, so that international outcomes are determined solely by the balance of power and the strategic interplay among states.

In this case, neo-realism could be used to explain why NAFTA was in the economic interest of all three countries (benefits of free trade), but it can’t explain the timing of the decision to negotiate, or why the road to an agreement mutually beneficial to all three states would be so long and uncertain.

International Regime Theory argues that international institutions matter and that internationally held rules, norms, and organizations affect the choices that nations make. Most International Regime Theory assumes a moderately weak system of international institutions in which countries are rational actors but occasionally bounded by international regimes (much like a repeated Prisoner’s Dilemma game). Regimes can facilitate mutually advantageous cooperation under the right circumstances. This theory helps explain (1) that the three countries were creating a new regime by negotiating, (2) the timing of NAFTA was influenced by Mexico’s entering into new international regimes by having to obtain IMF stabilization loans in the 1980s and, therefore, open its economy along mandated lines; (3) the importance of free trade agreements, which are the regimes of choice in the 1980s and 1990s for increasing cooperation. Neither neo-realism or regime theory can explain the exact timing of the decision to negotiate.

International Symbolic Politics maintains that ideas, ideologies, and worldviews affect the behavior of nations, independent of interests and institutions. This includes various notions including (1) that ideas are potential solutions to collective action problems, (2) symbolic systems establish beliefs about cause and effect (e.g. what we believe is the outcome of trade), (3) ideas frame issues to make some dimension more important than another (e.g. free trade vs. human rights), and (4) ideas and symbols help construct a sense of national interest. This helps to explain Mexico’s decision to seek a free-trade agreement – it was a symbolic step signaling a changed Mexico to an international audience, and a signal to international investors.


Domestic Level:

While much of the decision to negotiate can be explained at the international level, we must turn to the domestic level to understand the key question of why countries must negotiate something that it is in their economic interest to do unilaterally. In fact, the need to negotiate an international agreement can be explained by the need to overcome a domestic collective action problem in international trade. International agreements serve as vehicles for solving domestic political problems by tying the hands of domestic interests. Mexico in particular used NAFTA as a way of committing domestic interests to free market policies.

Individual Level:

There is decision-making by elites. This is a story about the relationship among the three heads of state. Bush was a Texan, comfortable with Mexico and a free trader; Salinas has a graduate degree from the US and was much bolder than his predecessor in liberalizing the economy; Brian Mulroney, a pro-trade Conservative prime minister who at the time of the decision-to-negotiate was in a politically secure position despite the unpopularity of CAFTA a few years before (he was no longer PM when the agreement was signed).

Ch2 Domestic Politics Matter: The Fast Track Fight

Background: For Mexico and Canada, once Salinas and Mulroney committed to NAFTA, the decision was made. But for the United States, Bush would need to obtain “fast track” negotiating authority from Congress. “Fast track” is the standard process by which Congress delegates authority to the president to negotiate on its behalf and commits itself to both to limited debate and to an up-down vote. The 1988 Trade Act had authorized the fast track for three years, Bush need to ask for the extension by March 1991. Organizations concerned about the workers rights, environment, food safety, family farmers and other issues raised oppositions. In May 1991, Congress granted the president tow years of negotiating authority while Bush had to address the labor and environment issues in the negotiation.

Apply the framework with why Fast Track:

Domestic Level:

Domestic Rational Choice: Interest groups understood the quite well on the effects of Fast Track and correctly anticipated the likely outcome of the extensions. So their fight over the institution of fast track can be viewed as a rational choice process among competing domestic interests. Three interest groups: supporters, especially business groups fought hard for it; opponents, like unions, some grassroots environmental organizations and highly protected sectors; opportunities, mainstream environmental organizations, gain leverage to advance issues of concern to them. The features of the politics of fast track extension can be regarded as a bargain between two players – business and mainstream environment. By linking the fast track extension issues with the environmental extension problems, environmental groups made Bush administration make a modest side payment in exchange for support as a result of such bargain. The payment is in the form of the Action Plan, a promise to address the border issues, to discuss greater cooperation with Mexico on environmental issues, and put environmentalists on the trade advisory committees.

Domestic Institutions: the choice of rules in one period must be made according to rules established at an earlier period. The congressional decision about Fast Track in 1991 was based on rules laid down in 1988. In the Omnibus Trade and Competitiveness Act, Congress granted the president three years of fast track negotiating authority, and allowed for an extension of two more years. The 1988 process was itself structured by institutions decided upon earlier, most notably in 1974 when the fast track process was invented for the Tokyo Round of the GATT negotiations.

Section II International Negotiation

Ch5 Two-Level Bargaining: The NAFTA Negotiation


President Salinas, President Bush and Prime Minister Mulroney hoped to conclude the negotiation by the end of 1991 or early 1992, before the US presidential campaign heated up. But the not until August 12, 1992, did the negotiators finally reach agreement and a new Congress and a new president would decide what to do with it. The NAFTA agreements all three countries to eliminate most barriers to trade goods and services; to open investment in most sectors of the economy and to adhere to new rules for intellectual property, government procurement, and dispute settlement (p110). However, a few politically privileged industries in all three countries were exempted, e.g. Mexican oil, Canadian culture industries, and US shipping. Only narrow class of professionals and businesspersons were free to work across national borders.

Two Stage Analytical Framework: the auto rule of origin

Much of the NAFTA negotiation focused on the rules of origin that established how much of a good needed to be made in North America for it to be considered “North American”, thus qualifying for preferential tariff treatment (P155). After long and tough bargaining, the negotiators finally settled on 62.5 percent. Such number is a result of both international-level bargain and domestic-level bargain among strong interests groups. For instance, in the US domestic level, the three automakers-GM, Ford, and Chrysler- interested in high rule of origin to make it more difficult for European and Japanese competitors to locate assembly plants in Canada or Mexico and therefore ship autoparts to US duty free. Based on their own patterns of production and competitive position, each of them sets a different threshold.




Ch6 Making Side Issues Central: The Labor and Environment Negotiations

Background: With Bill Clinton’s election in November, the side issues-environment and labor became central since Clinton tried to gain Democratic supporters during the campaign. In May of 1993, Clinton administration tabled its first position that satisfies enough Democrats sympathetic to labor and environmental interests to win Congressional approval, but it was rejected by Mexico and Canada. The negotiation also failed by summer’s end of 1993 when the vast majority of Democrats in Congress were ready to oppose the agreement. Negotiators for all three countries found ways to make compromises and finally reached an agreement on August 13,1993.

Three Stage Analytical Framework: the environment negotiation

The thoughness, “teeth”, of the enforcement mechanism for environmental issues is measured from “gum” to “fangs”. At international level, Mexico preferred no enforcement; Canada preferred some enforcement and the United States had a preference for sharp teeth. The US preference on international level reflects the domestic level bargain between business and mainstream environmental groups. The presidential election between Bush and Clinton changed the domestic game in the U.S. in a way which gave more power to unions and environmentalists, forcing the negotiation of labor and environmental side-agreements with “teeth.” Mainstream environment’s preferences at the domestic level reflect the individual-level bargain between members and staff. Mainstream environmentalist leadership did not originally insist on sanction-enforcement for the environmental side-agreement, but their position changed in part because of the way the issue was framed to the public – which influenced their membership and therefore constrained their actions.

Section 3 The Politics of Ratification

Ch7 Symbolic Politics: Growing Grassroots Opposition

Background: In the fall of 1993, NAFTA faced remarkable breadth of opposition in the United States.

Symbolic politics played against NAFTA on a domestic level in all three countries. Canadian and Mexican fears about losing their identity were particularly strong, although US opinion also soon became highly charged against NAFTA. In the US, the citizens’ participation in opposing NAFTA can be explained by that ideological consumption of individuals overcame the logic of collective action. Individuals received satisfaction from the act of joining, writing, or attending because some form of ideological consumption(p260). But why would they join the opposite acts? The symbolic politics is powerful when the costs of information acquisition and processing are high and the potential benefits of using symbols to reinforce identities or worldviews are also high(p272). So as apprehended by union members, grassroots environmentalists, and so on, NAFTA became the story of corrupt politicians and greedy corporation. Opposition to NAFTA was a matter of honor, a matter of moral imperative and an affirmation of identity(p272).

Ch8 Diagnosis and Strategy

Background: NAFTA supporters won the battle.

To diagnose NAFTA’s problem and revise strategy, what is needed is a framework that allows us to assess the nature of Congressional institutions, interest group politics, and symbolic constructions in the broader public; understand the complex interrelationships among them; the most importantly, identify the possible points of leverage in this system (p334).