# 15. Analysis¶

ESPResSo provides two concepts of system analysis:

## 15.1. Direct analysis routines¶

The direct analysis commands only take into account the current configuration of the system. Available commands are:

### 15.1.1. Energies¶

espressomd.analyze.Analysis.energy()

Returns the energies of the system. The different energetic contributions to the total energy can also be obtained (kinetic, bonded, non-bonded, Coulomb).

For example,

>>> energy = system.analysis.energy()
>>> print(energy["total"])
>>> print(energy["kinetic"])
>>> print(energy["bonded"])
>>> print(energy["non_bonded"])


### 15.1.2. Momentum of the System¶

espressomd.analyze.Analysis.linear_momentum()

This command returns the total linear momentum of the particles and the lattice-Boltzmann (LB) fluid, if one exists. Giving the optional parameters either causes the command to ignore the contribution of LB or of the particles.

### 15.1.3. Minimal distances between particles¶

espressomd.analyze.Analysis.min_dist() Returns the minimal distance between all particles in the system.

When used with type-lists as arguments, then the minimal distance between particles of only those types is determined.

For example,

>>> import espressomd
>>> system = espressomd.System(box_l=[100, 100, 100])
>>> for i in range(10):
>>> system.analysis.min_dist()
1.0


### 15.1.4. Particles in the neighborhood¶

espressomd.analyze.Analysis.nbhood()

Returns a list of the ids of particles that fall within a given radius of a target position. For example,

ids = system.analysis.nbhood(pos=system.box_l * 0.5, r_catch=5.0)


### 15.1.5. Particle distribution¶

espressomd.analyze.Analysis.distribution()

Returns the distance distribution of particles (probability of finding a particle of a certain type at a specified distance around a particle of another specified type, disregarding the fact that a spherical shell of a larger radius covers a larger volume). The distance is defined as the minimal distance between a particle of one group to any of the other group.

Two arrays are returned corresponding to the normalized distribution and the bins midpoints, for example

>>> system = espressomd.System(box_l=[10, 10, 10])
>>> for i in range(5):
>>> bins, count = system.analysis.distribution(type_list_a=[0], type_list_b=[0],
...                                            r_min=0.0, r_max=10.0, r_bins=10)
>>> print(bins)
[ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.5  8.5  9.5]
>>> print(count)
[ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]


### 15.1.6. Structure factor¶

espressomd.analyze.Analysis.structure_factor()

Calculate the structure factor for given types.

Returns the spherically averaged structure factor $$S(q)$$ of particles specified in sf_types. $$S(q)$$ is calculated for all possible wave vectors $$\frac{2\pi}{L} \leq q \leq \frac{2\pi}{L}$$ up to sf_order.

### 15.1.7. Center of mass¶

espressomd.analyze.Analysis.center_of_mass()

Returns the center of mass of particles of the given type given by part_type.

### 15.1.8. Moment of inertia matrix¶

espressomd.analyze.Analysis.moment_of_inertia_matrix()

Returns the 3x3 moment of inertia matrix for particles of a given type.

### 15.1.9. Gyration tensor¶

espressomd.analyze.Analysis.gyration_tensor()

Analyze the gyration tensor of particles of a given type, or of all particles in the system if no type is given. Returns a dictionary containing the squared radius of gyration, three shape descriptors (asphericity, acylindricity, and relative shape anisotropy), eigenvalues of the gyration tensor and their corresponding eigenvectors. The eigenvalues are sorted in descending order.

### 15.1.10. Pressure¶

espressomd.analyze.Analysis.pressure()

Computes the instantaneous virial pressure for an isotropic and homogeneous system. It returns all the contributions to the total pressure as well as the total pressure (see espressomd.analyze.Analysis.pressure()).

The instantaneous pressure is calculated (if there are no electrostatic interactions) by the volume averaged, direction averaged instantaneous virial pressure

(1)$p = \frac{2E_{\text{kinetic}}}{Vf} + \frac{\sum_{j>i} {F_{ij}r_{ij}}}{3V}$

where $$f=3$$ is the number of translational degrees of freedom of each particle, $$V$$ is the volume of the system, $$E_{\text{kinetic}}$$ is the kinetic energy, $$F_{ij}$$ the force between particles i and j, and $$r_{ij}$$ is the distance between them. The kinetic energy divided by the degrees of freedom is

$\frac{2E_{\text{kinetic}}}{f} = \frac{1}{3}\sum_{i} {m_{i}v_{i}^{2}}.$

Note that Equation (1) can only be applied to pair potentials and central forces. Description of how contributions from other interactions are calculated is beyond the scope of this manual. Three body potentials are implemented following the procedure in Ref. . A different formula is used to calculate contribution from electrostatic interactions. For electrostatic interactions in P3M, the $$k$$-space contribution is implemented according to . The implementation of the Coulomb P3M pressure is tested against LAMMPS.

Four-body dihedral potentials are not included. Except of VIRTUAL_SITES_RELATIVE constraints all other constraints of any kind are not currently accounted for in the pressure calculations. The pressure is no longer correct, e.g., when particles are confined to a plane.

Note: The different contributions which are returned are the summands that arise from force splitting $$\vec{F}_{i,j}={\vec{F}_{i,j}}_\text{bonded}+{\vec{F}_{i,j}}_\text{nonbonded}+...$$ in the virial pressure formula. Later when the user calculates the ensemble average via e.g. $$\langle p \rangle \approx 1/N \sum_{i=1}^N p_i$$ however the ensemble average with all interactions present is performed. That means the contributions are not easy to interpret! Those are the contributions to the pressure in a system where all interactions are present and therefore in a coupled system.

### 15.1.11. Pressure Tensor¶

espressomd.analyze.Analysis.pressure_tensor()

Computes the volume averaged instantaneous pressure tensor of the system with options which are described by in espressomd.analyze.Analysis.pressure_tensor(). In general do only use it for (on average) homogeneous systems. For inhomogeneous systems you need to use the local pressure tensor.

The instantaneous virial pressure tensor is calculated by

$p_{(k,l)} = \frac{\sum_{i} {m_{i}v_{i}^{(k)}v_{i}^{(l)}}}{V} + \frac{\sum_{j>i}{F_{ij}^{(k)}r_{ij}^{(l)}}}{V}$

where the notation is the same as for the pressure. The superscripts $$k$$ and $$l$$ correspond to the components in the tensors and vectors.

If electrostatic interactions are present then also the coulombic parts of the pressure tensor need to be calculated. If P3M is present, then the instantaneous pressure tensor is added to the above equation in accordance with :

$p^\text{Coulomb, P3M}_{(k,l)} =p^\text{Coulomb, P3M, dir}_{(k,l)} + p^\text{Coulomb, P3M, rec}_{(k,l)},$

where the first summand is the short ranged part and the second summand is the long ranged part.

The short ranged part is given by:

$\begin{split}p^\text{Coulomb, P3M, dir}_{(k,l)}= \frac{1}{4\pi \varepsilon_0 \varepsilon_r} \frac{1}{2V} \sum_{\vec{n}}^* \sum_{i,j=1}^N q_i q_j \left( \frac{ \mathrm{erfc}(\beta |\vec{r}_j-\vec{r}_i+\vec{n}|)}{|\vec{r}_j-\vec{r}_i+\vec{n}|^3} + \\ \frac{2\beta \pi^{-1/2} \exp(-(\beta |\vec{r}_j-\vec{r}_i+\vec{n}|)^2)}{|\vec{r}_j-\vec{r}_i+\vec{n}|^2} \right) (\vec{r}_j-\vec{r}_i+\vec{n})_k (\vec{r}_j-\vec{r}_i+\vec{n})_l,\end{split}$

where $$\beta$$ is the P3M splitting parameter, $$\vec{n}$$ identifies the periodic images, the asterisk denotes that terms with $$\vec{n}=\vec{0}$$ and i=j are omitted. The long ranged (k-space) part is given by:

$p^\text{Coulomb, P3M, rec}_{(k,l)}= \frac{1}{4\pi \varepsilon_0 \varepsilon_r} \frac{1}{2 \pi V^2} \sum_{\vec{k} \neq \vec{0}} \frac{\exp(-\pi^2 \vec{k}^2/\beta^2)}{\vec{k}^2} |S(\vec{k})|^2 \cdot (\delta_{k,l}-2\frac{1+\pi^2\vec{k}^2/\beta^2}{\vec{k}^2} \vec{k}_k \vec{k}_l),$

where $$S(\vec{k})$$ is the Fourier transformed charge density. Compared to Essmann we do not have the contribution $$p^\text{corr}_{k,l}$$ since we want to calculate the pressure that arises from all particles in the system.

Note: The different contributions which are returned are the summands that arise from force splitting $$\vec{F}_{i,j}={\vec{F}_{i,j}}_\text{bonded}+{\vec{F}_{i,j}}_\text{nonbonded}+...$$ in the virial pressure tensor formula. Later when the user calculates the pressure tensor via $$\langle p_{(k,l)}\rangle \approx 1/N \sum_{i=1}^N p_{k,l}$$ however the ensemble average with all interactions present is performed. That means the contributions are not easy to interpret! Those are the contributions to the pressure in a system where all interactions are present and therefore in a coupled system.

Note that the angular velocities of the particles are not included in the calculation of the pressure tensor.

### 15.1.12. Chains¶

All analysis functions in this section require the topology of the chains to be set correctly. A chain needs to be set up with a contiguous range of particle IDs, and the head resp. tail particle must be have the first resp. last id in the range. For chain observables that rely on connectivity information, the chain topology must be linear, i.e. particle $$n$$ is connected to $$n+1$$ and so on. Each chain is a set of consecutively numbered particles and all chains are supposed to consist of the same number of particles.

The particles image_box values must also be consistent, since these observables are calculated using unfolded coordinates. For example, if all particles were to be inserted in the central box except for the tail particle, which would be e.g. inserted in the fourth periodic image, the end-to-end distance and radius of gyration would be quite large, even if the tail particle is in close proximity to the penultimate particle in folded coordinates, because the last inter-particle distance would be evaluated as being 4 times the box length plus the bond length. Particles can have different image_box values, for example in a chain with equilibrium bond length 2, if the penultimate particle is at [box_l - 1, 0, 0] and the tail particle at [box_l + 1, 0, 0], their inter-particle distance would be evaluated as 2 and the observables would be correctly calculated. This can lead to counter-intuitive behavior when chains are longer than half the box size in a fully periodic system. As an example, consider the end-to-end distance of a linear polymer growing on the x-axis. While espressomd.system.System.distance() would feature a triangle wave with a period equal to the box length in the x-direction, espressomd.analyze.Analysis.calc_re() would be monotonically increasing, assuming the image_box values were properly set up. This is important to consider when setting up systems where the polymers are much longer than the box length, which is common when simulating polymer melts.

## 15.2. Observables framework¶

Observables extract properties of the particles and the LB fluid and return either the raw data or a statistic derived from them. Correlators and accumulators provide functionality to collect and process the output of observables automatically throughout the course of the simulation.

The Observables framework is progressively replacing the Analysis framework. This is motivated by the fact, that sometimes it is desirable that the analysis functions do more than just return a value to the scripting interface. For some observables it is desirable to be sampled every few integration steps. In addition, it should be possible to pass the observable values to other functions which compute history-dependent quantities, such as correlation functions. All this should be done without the need to interrupt the integration by passing the control to the script level and back, which produces a significant overhead when performed too often.

Some observables in the core have their corresponding counterparts in the espressomd.analyze module. However, only the core-observables can be used on the fly with the toolbox of accumulators and correlators.

The first step of the core analysis is to create an observable. An observable in the sense of the core analysis can be considered as a rule how to compute a certain set of numbers from a given state of the system or a rule how to collect data from other observables. Any observable is represented as a single array of double values in the core. Any more complex shape (tensor, complex number, …) must be compatible to this prerequisite. Every observable however documents the storage order and returns a reshaped numpy array.

The observables can be used in parallel simulations. However, not all observables carry out their calculations in parallel. Instead, the entire particle configuration is collected on the head node, and the calculations are carried out there. This is only performance-relevant if the number of processor cores is large and/or interactions are calculated very frequently.

### 15.2.1. Using observables¶

The observables are represented as Python classes derived from espressomd.observables.Observable. They are contained in the espressomd.observables module. An observable is instantiated as follows

import espressomd.observables
part_pos = espressomd.observables.ParticlePositions(ids=(1, 2, 3, 4, 5))


Here, the keyword argument ids specifies the ids of the particles, which the observable should take into account.

The current value of an observable can be obtained using its calculate() method:

print(part_pos.calculate())


Profile observables have additional methods bin_centers() and bin_edges() to facilitate plotting of histogram slices with functions that require either bin centers or bin edges for the axes. Example:

import matplotlib.pyplot as plt
import numpy as np
import espressomd
import espressomd.observables

system = espressomd.System(box_l=[10.0, 10.0, 10.0])

# histogram in Cartesian coordinates
density_profile = espressomd.observables.DensityProfile(
ids=[p1.id, p2.id],
n_x_bins=8, min_x=1.0, max_x=9.0,
n_y_bins=8, min_y=1.0, max_y=9.0,
n_z_bins=4, min_z=4.0, max_z=8.0)
obs_data = density_profile.calculate()
obs_bins = density_profile.bin_centers()

# 1D slice: requires bin centers
plt.plot(obs_bins[:, 2, 2, 0], obs_data[:, 2, 2])
plt.show()

# 2D slice: requires extent
plt.imshow(obs_data[:, :, 2].T, origin='lower',
extent=[density_profile.min_x, density_profile.max_x,
density_profile.min_y, density_profile.max_y])
plt.show()


Observables based on cylindrical coordinates are also available. They require special parameters if the cylindrical coordinate system is non-standard, e.g. if you want the origin of the cylindrical coordinates to be at a special location of the box or if you want to make use of symmetries along an axis that is not parallel to the z-axis. For this purpose, use espressomd.math.CylindricalTransformationParameters to create a consistent set of the parameters needed. Example:

import espressomd.math

# shifted and rotated cylindrical coordinates
cyl_transform_params = espressomd.math.CylindricalTransformationParameters(
center=[5.0, 0.0, 5.0], axis=[0, 1, 0], orientation=[0, 0, 1])

# histogram in cylindrical coordinates
density_profile = espressomd.observables.CylindricalDensityProfile(
ids=[p1.id, p2.id],
transform_params = cyl_transform_params,
n_r_bins=8, min_r=1.0, max_r=4.0,
n_phi_bins=16, min_phi=-np.pi, max_phi=np.pi,
n_z_bins=4, min_z=0.0, max_z=8.0)
obs_data = density_profile.calculate()
obs_bins = density_profile.bin_edges()

# 2D slice: requires bin edges
fig = plt.figure()
r = obs_bins[:, 0, 0, 0]
phi = obs_bins[0, :, 0, 1]
ax.pcolormesh(phi, r, obs_data[:, :, 1])
plt.show()


### 15.2.2. Available observables¶

The following list contains some of the available observables. You can find documentation for all available observables in espressomd.observables.

### 15.2.3. Accumulators¶

#### 15.2.3.1. Time series¶

In order to take snapshots of an observable, espressomd.accumulators.TimeSeries can be used:

import espressomd
import espressomd.observables
import espressomd.accumulators

system = espressomd.System(box_l=[10.0, 10.0, 10.0])
system.cell_system.skin = 0.4
system.time_step = 0.01
p1 = system.part.add(pos=[5.0, 5.0, 5.0], v=[0, 2, 0])
position_observable = espressomd.observables.ParticlePositions(ids=[p1.id])
accumulator = espressomd.accumulators.TimeSeries(
obs=position_observable, delta_N=2)
system.integrator.run(10)
print(accumulator.time_series())


In the example above the automatic update of the accumulator is used. However, it’s also possible to manually update the accumulator by calling espressomd.accumulators.TimeSeries.update().

#### 15.2.3.2. Mean-variance calculator¶

In order to calculate the running mean and variance of an observable, espressomd.accumulators.MeanVarianceCalculator can be used:

import espressomd
import espressomd.observables
import espressomd.accumulators

system = espressomd.System(box_l=[10.0, 10.0, 10.0])
system.cell_system.skin = 0.4
system.time_step = 0.01
p1 = system.part.add(pos=[5.0, 5.0, 5.0], v=[0, 2, 0])
position_observable = espressomd.observables.ParticlePositions(ids=[p1.id])
accumulator = espressomd.accumulators.MeanVarianceCalculator(
obs=position_observable, delta_N=2)
system.integrator.run(10)
print(accumulator.mean())
print(accumulator.variance())


In the example above the automatic update of the accumulator is used. However, it’s also possible to manually update the accumulator by calling espressomd.accumulators.MeanVarianceCalculator.update().

### 15.2.4. Correlations¶

Time correlation functions are ubiquitous in statistical mechanics and molecular simulations when dynamical properties of many-body systems are concerned. A prominent example is the velocity autocorrelation function, $$\left< \mathbf{v}(t) \cdot \mathbf{v}(t+\tau) \right>$$ which is used in the Green-Kubo relations. In general, time correlation functions are of the form

$C(\tau) = \left<A\left(t\right) \otimes B\left(t+\tau\right)\right>$

where $$t$$ is time, $$\tau$$ is the lag time (time difference) between the measurements of (vector) observables $$A$$ and $$B$$, and $$\otimes$$ is an operator which produces the vector quantity $$C$$ from $$A$$ and $$B$$. The ensemble average $$\left< \cdot \right>$$ is taken over all time origins $$t$$. Correlation functions describing dynamics of large and complex molecules such as polymers span many orders of magnitude, ranging from MD time step up to the total simulation time.

A correlator takes one or two observables, obtains values from them during the simulation and finally uses a fast correlation algorithm which enables efficient computation of correlation functions spanning many orders of magnitude in the lag time.

The implementation for computing averages and error estimates of a time series of observables relies on estimates of autocorrelation functions and the respective autocorrelation times. The correlator provides the same functionality as a by-product of computing the correlation function.

An example of the usage of observables and correlations is provided in the script samples/observables_correlators.py.

#### 15.2.4.1. Creating a correlation¶

Each correlator is represented by an instance of the espressomd.accumulators.Correlator. Please see its documentation for an explanation of the arguments that have to be passed to the constructor.

Correlators can be registered for automatic updating during the integration by adding them to espressomd.system.System.auto_update_accumulators.

system.auto_update_accumulators.add(corr)


Alternatively, an update can triggered by calling the update() method of the correlator instance. In that case, one has to make sure to call the update in the correct time intervals.

The current on-the-fly correlation result can of a correlator can be obtained using its result() method. The final result (including the latest data in the buffers) is obtained using the finalize() method. After this, no further update of the correlator is possible.

#### 15.2.4.2. Example: Calculating a particle’s diffusion coefficient¶

For setting up an observable and correlator to obtain the mean square displacement of particle 0, use:

pos_obs = ParticlePositions(ids=[p1.id])
c_pos = Correlator(obs1=pos_obs, tau_lin=16, tau_max=100., delta_N=10,


To obtain the velocity auto-correlation function of particle 0, use:

obs = ParticleVelocities(ids=[p1.id])
c_vel = Correlator(obs1=vel_obs, tau_lin=16, tau_max=20., delta_N=1,


The full example can be found in samples/diffusion_coefficient.py. Note that in this example, the operation square_distance_componentwise is used, which is not a correlation function in the mathematical sense. Other available operations include actual correlation functions, as described in the source documentation of espressomd.accumulators.Correlator.

### 15.2.5. Details of the multiple tau correlation algorithm¶

Here we briefly describe the multiple tau correlator which is implemented in ESPResSo. For a more detailed description and discussion of its behavior with respect to statistical and systematic errors, please read the cited literature. This type of correlator has been in use for years in the analysis of dynamic light scattering . About a decade later it found its way to the Fluorescence Correlation Spectroscopy (FCS) . The book of Frenkel and Smit  describes its application for the special case of the velocity autocorrelation function.

Let us consider a set of $$N$$ observable values as schematically shown in the figure above, where a value of index $$i$$ was measured at times $$i\delta t$$. We are interested in computing the correlation function for a range of lag times $$\tau = (i-j)\delta t$$ between the measurements $$i$$ and $$j$$. To simplify the notation, we drop $$\delta t$$ when referring to observables and lag times.

The trivial implementation takes all possible pairs of values corresponding to lag times $$\tau \in [{\tau_{\mathrm{min}}}:{\tau_{\mathrm{max}}}]$$. Without loss of generality, we consider $${\tau_{\mathrm{min}}}=0$$. The computational effort for such an algorithm scales as $${\cal O} \bigl({\tau_{\mathrm{max}}}^2\bigr)$$. As a rule of thumb, this is feasible if $${\tau_{\mathrm{max}}}< 10^3$$. The multiple tau correlator provides a solution to compute the correlation functions for arbitrary range of the lag times by coarse-graining the high $$\tau$$ values. It applies the naive algorithm to a relatively small range of lag times $$\tau \in [0:p-1]$$ ($$p$$ corresponds to parameter tau_lin). This we refer to as compression level 0. To compute the correlations for lag times $$\tau \in [p:2(p-1)]$$, the original data are first coarse-grained, so that $$m$$ values of the original data are compressed to produce a single data point in the higher compression level. Thus the lag time between the neighboring values in the higher compression level increases by a factor of $$m$$, while the number of stored values decreases by the same factor and the number of correlation operations at this level reduces by a factor of $$m^2$$. Correlations for lag times $$\tau \in [2p:4(p-1)]$$ are computed at compression level 2, which is created in an analogous manner from level 1. This can continue hierarchically up to an arbitrary level for which enough data is available. Due to the hierarchical reduction of the data, the algorithm scales as $${\cal O} \bigl( p^2 \log({\tau_{\mathrm{max}}}) \bigr)$$. Thus an additional order of magnitude in $${\tau_{\mathrm{max}}}$$ costs just a constant extra effort.

The speedup is gained at the expense of statistical accuracy. The loss of accuracy occurs at the compression step. In principle one can use any value of $$m$$ and $$p$$ to tune the algorithm performance. However, it turns out that using a high $$m$$ dilutes the data at high $$\tau$$. Therefore $$m=2$$ is hard-coded in the correlator and cannot be modified by user. The value of $$p$$ remains an adjustable parameter which can be modified by user by setting when defining a correlation. In general, one should choose $$p \gg m$$ to avoid loss of statistical accuracy. Choosing $$p=16$$ seems to be safe but it may depend on the properties of the analyzed correlation functions. A detailed analysis has been performed in Ref. .

The choice of the compression function also influences the statistical accuracy and can even lead to systematic errors. The default compression function discards the second value and pushes the first one to the higher level. This is robust and can be applied universally to any combination of observables and correlation operation. On the other hand, it reduces the statistical accuracy as the compression level increases. In many cases, the compression operation can be applied, which averages the two neighboring values and the average then enters the higher level, preserving almost the full statistical accuracy of the original data. In general, if averaging can be safely used or not, depends on the properties of the difference

$\frac{1}{2} (A_i \otimes B_{i+p} + A_{i+1} \otimes B_{i+p+1} ) - \frac{1}{2} (A_i + A_{i+1} ) \otimes \frac{1}{2} (B_{i+p} + B_{i+p+1}) \label{eq:difference}$

For example in the case of velocity autocorrelation function, the above-mentioned difference has a small value and a random sign, different contributions cancel each other. On the other hand, in the of the case of mean square displacement the difference is always positive, resulting in a non-negligible systematic error. A more general discussion is presented in Ref. .

## 15.3. Cluster analysis¶

ESPResSo provides support for online cluster analysis. Here, a cluster is a group of particles, such that you can get from any particle to any second particle by at least one path of neighboring particles. I.e., if particle B is a neighbor of particle A, particle C is a neighbor of A and particle D is a neighbor of particle B, all four particles are part of the same cluster. The cluster analysis is available in parallel simulations, but the analysis is carried out on the head node, only.

Whether or not two particles are neighbors is defined by a pair criterion. The available criteria can be found in espressomd.pair_criteria. For example, a distance criterion which will consider particles as neighbors if they are closer than 0.11 is created as follows:

import espressomd.pair_criteria
dc = espressomd.pair_criteria.DistanceCriterion(cut_off=0.11)


To obtain the cluster structure of a system, an instance of espressomd.cluster_analysis.ClusterStructure has to be created. To to create a cluster structure with above criterion:

import espressomd.cluster_analysis
cs = espressomd.cluster_analysis.ClusterStructure(distance_criterion=dc)


In most cases, the cluster analysis is carried out by calling the espressomd.cluster_analysis.ClusterStructure.run_for_all_pairs method. When the pair criterion is purely based on bonds, espressomd.cluster_analysis.ClusterStructure.run_for_bonded_particles can be used.

The results can be accessed via ClusterStructure.clusters, which is an instance of espressomd.cluster_analysis.Clusters.

Individual clusters are represented by instances of espressomd.cluster_analysis.Cluster, which provides access to the particles contained in a cluster as well as per-cluster analysis routines such as radius of gyration, center of mass and longest distance. Note that the cluster objects do not contain copies of the particles, but refer to the particles in the simulation. Hence, the objects become outdated if the simulation system changes. On the other hand, it is possible to directly manipulate the particles contained in a cluster.