# 15. Online-visualization¶

With the python interface, ESPResSo features two possibilities for online-visualization:

1. Using the mlab module to drive Mayavi, a “3D scientific data visualization and plotting in Python”. Mayavi has a user-friendly GUI to specify the appearance of the output. Additional requirements: python module mayavi, VTK (package python-vtk for Debian/Ubuntu). Note that only VTK from version 7.0.0 and higher has Python 3 support.

2. A direct rendering engine based on pyopengl. As it is developed for ESPResSo, it supports the visualization of several specific features like constraints, particle properties, the cell system, lattice-Boltzmann and more. It can be adjusted with a large number of parameters to set colors, materials, camera and interactive features like assigning callbacks to user input. Additional requirements: python module PyOpenGL.

Both are not meant to produce high quality renderings, but rather to debug your setup and equilibration process.

## 15.1. General usage¶

The recommended usage of both tools is similar: Create the visualizer of your choice and pass it the espressomd.System() object. Then write your integration loop in a separate function, which is started in a non-blocking thread. Whenever needed, call update() to synchronize the renderer with your system. Finally start the blocking visualization window with start(). See the following minimal code example:

import espressomd
from espressomd import visualization

system = espressomd.System()
system.cell_system.skin = 0.4
system.time_step = 0.01
system.box_l = [10, 10, 10]

#visualizer = visualization.mayaviLive(system)
visualizer = visualization.openGLLive(system)

while True:
system.integrator.run(1)
visualizer.update()

t.daemon = True
t.start()
visualizer.start()


## 15.2. Common methods for OpenGL and Mayavi¶

update() synchronizes system and visualizer, handles keyboard events for openGLLive.

start() starts the blocking visualizer window. Should be called after a separate thread containing update() has been started.

Registers the method callback(), which is called every interval milliseconds. Useful for live plotting (see sample script /samples/visualization_ljliquid.py).

## 15.3. Mayavi visualizer¶

The Mayavi visualizer is created with the following syntax:

espressomd.visualization.mayaviLive()

Required parameters:
Optional keywords:
• particle_sizes:
• "auto" (default): The Lennard-Jones sigma value of the self-interaction is used for the particle diameter.

• callable: A lambda function with one argument. Internally, the numerical particle type is passed to the lambda function to determine the particle radius.

• list: A list of particle radii, indexed by the particle type.

## 15.4. OpenGL visualizer¶

espressomd.visualization.openGLLive()

The optional keywords in **kwargs are used to adjust the appearance of the visualization. The parameters have suitable default values for most simulations.

Required parameters:
Optional keywords:

Note

The visualization of some constraints is either improved by (espressomd.shapes.SimplePore) or even relies on (espressomd.shapes.HollowConicalFrustum) the presence of an installed OpenGL Extrusion library on your system. Typically, the library will be available through the default package manager of your operating system. On Ubuntu the required package is called libgle3-dev, on Fedora libgle – just to name two examples.

### 15.4.1. Running the visualizer¶

To visually debug your simulation, run(n) can be used to conveniently start an integration loop with n integration steps in a separate thread once the visualizer is initialized:

import espressomd
from espressomd import visualization

system = espressomd.System(box_l=[10, 10, 10])
system.cell_system.skin = 0.4
system.time_step = 0.00001

system.part.add(pos=[1, 1, 1], v=[1, 0, 0])
system.part.add(pos=[9, 9, 9], v=[0, 1, 0])

visualizer = visualization.openGLLive(system, background_color=[1, 1, 1])
visualizer.run(1)


### 15.4.2. Screenshots¶

The OpenGL visualizer can also be used for offline rendering. After creating the visualizer object, call screenshot(path) to save an image of your simulation to path. Internally, the image is saved with matplotlib.pyplot.imsave, so the file format is specified by the extension of the filename. The image size is determined by the keyword argument window_size of the visualizer. This method can be used to create screenshots without blocking the simulation script:

import espressomd
from espressomd import visualization

system = espressomd.System(box_l=[10, 10, 10])
system.cell_system.skin = 1.0
system.time_step = 0.1

for i in range(1000):

system.thermostat.set_langevin(kT=1, gamma=1, seed=42)

visualizer = visualization.openGLLive(system, window_size=[500, 500])

for i in range(100):
system.integrator.run(1)
visualizer.screenshot('screenshot_{:0>5}.png'.format(i))

# You may consider creating a video with ffmpeg:
# ffmpeg -f image2 -framerate 30 -i 'screenshot_%05d.png' output.mp4


It is also possible to create a snapshot during online visualization. Simply press the enter key to create a snapshot of the current window, which saves it to <scriptname>_n.png (with incrementing n).

### 15.4.3. Colors and Materials¶

Colors for particles, bonds and constraints are specified by RGB arrays. Materials by an array for the ambient, diffuse, specular and shininess and opacity (ADSSO) components. To distinguish particle groups, arrays of RGBA or ADSSO entries are used, which are indexed circularly by the numerical particle type:

# Particle type 0 is red, type 1 is blue (type 2 is red etc)..
visualizer = visualization.openGLLive(system,
particle_coloring='type',
particle_type_colors=[[1, 0, 0], [0, 0, 1]])


particle_type_materials lists the materials by type:

# Particle type 0 is gold, type 1 is blue (type 2 is gold again etc).
visualizer = visualization.openGLLive(system,
particle_coloring='type',
particle_type_colors=[[1, 1, 1], [0, 0, 1]],
particle_type_materials=["steel", "bright"])


Materials are stored in espressomd.visualization_opengl.openGLLive.materials.

### 15.4.4. Visualize vectorial properties¶

Most vectorial particle properties can be visualized by 3D-arrows on the particles:

• ext_force: An external force. Activate with the keyword ext_force_arrows = True.

• f: The force acting on the particle. Activate with the keyword force_arrows = True.

• v: The velocity of the particle. Activate with the keyword velocity_arrows = True.

• director: A vector associated with the orientation of the particle. Activate with the keyword director_arrows = True.

Arrow colors, scales and radii can be adjusted. Again, the lists specifying these quantities are indexed circularly by the numerical particle type. The following code snippet demonstrates the visualization of the director property and individual settings for two particle types (requires the ROTATION feature):

import numpy
from espressomd import *
from espressomd.visualization_opengl import *

box_l = 10
system = espressomd.System(box_l=[box_l, box_l, box_l])
system.cell_system.skin = 0.4

system.time_step = 0.00001

visualizer = openGLLive(system,
director_arrows=True,
director_arrows_type_scale=[1.5, 1.0],
director_arrows_type_colors=[[1.0, 0, 0], [0, 1.0, 0]])

for i in range(10):
rotation=[1, 1, 1],
ext_torque=[5, 0, 0],
v=[10, 0, 0],
type=0)

rotation=[1, 1, 1],
ext_torque=[0, 5, 0],
v=[-10, 0, 0],
type=1)

visualizer.run(1)


### 15.4.5. Controls¶

The camera can be controlled via mouse and keyboard:

• hold left button: rotate the system

• hold right button: translate the system

• hold middle button: zoom / roll

• mouse wheel / key pair TG: zoom

• WASD-Keyboard control (WS: move forwards/backwards, AD: move sidewards)

• Key pairs QE, RF, ZC: rotate the system

• Double click on a particle: Show particle information

• Double click in empty space: Toggle system information

• Left/Right arrows: Cycle through particles

• Space: If started with run(n), this pauses the simulation

• Enter: Creates a snapshot of the current window and saves it to <scriptname>_n.png (with incrementing n)

Additional input functionality for mouse and keyboard is possible by assigning callbacks to specified keyboard or mouse buttons. This may be useful for realtime adjustment of system parameters (temperature, interactions, particle properties, etc.) or for demonstration purposes. The callbacks can be triggered by a timer or keyboard input:

def foo():
print("foo")

# Registers timed calls of foo()
visualizer.register_callback(foo, interval=500)

# Callbacks to control temperature
temperature = 1.0
def increaseTemp():
global temperature
temperature += 0.1
system.thermostat.set_langevin(kT=temperature, gamma=1.0)
print("T =", system.thermostat.get_state()[0]['kT'])

def decreaseTemp():
global temperature
temperature -= 0.1

if temperature > 0:
system.thermostat.set_langevin(kT=temperature, gamma=1.0)
print("T =", system.thermostat.get_state()[0]['kT'])
else:
temperature = 0
system.thermostat.turn_off()
print("T = 0")

# Registers input-based calls
visualizer.keyboard_manager.register_button(KeyboardButtonEvent('t', KeyboardFireEvent.Hold, increaseTemp))
visualizer.keyboard_manager.register_button(KeyboardButtonEvent('g', KeyboardFireEvent.Hold, decreaseTemp))


Further examples can be found in samples/billiard.py or samples/visualization_interactive.py.

### 15.4.6. Dragging particles¶

With the keyword drag_enabled set to True, the mouse can be used to exert a force on particles in drag direction (scaled by drag_force and the distance of particle and mouse cursor).

## 15.5. Visualization example scripts¶

Various Sample scripts can be found in /samples/visualization_*.py or in the Tutorials “Visualization” and “Charged Systems”.