Tag: PhysiCell_Friday
PhysiCell Tools : python-loader
The newest tool for PhysiCell provides an easy way to load your PhysiCell output data into python for analysis. This builds upon previous work on loading data into MATLAB. A post on that tool can be found at:
http://www.mathcancer.org/blog/working-with-physicell-snapshots-in-matlab/.
PhysiCell stores output data as a MultiCell Digital Snapshot (MultiCellDS) that consists of several files for each time step and is probably stored in your ./output directory. pyMCDS is a python object that is initialized with the .xml file
What you’ll need
- python-loader, available on GitHub at
- Python 3.x, recommended distribution available at
- A number of Python packages, included in anaconda or available through pip
- NumPy
- pandas
- scipy
- Some PhysiCell data, probably in your ./output directory
Anatomy of a MultiCell Digital Snapshot
Each time PhysiCell’s internal time tracker passes a time step where data is to be saved, it generates a number of files of various types. Each of these files will have a number at the end that indicates where it belongs in the sequence of outputs. All of the files from the first round of output will end in 00000000.* and the second round will be 00000001.* and so on. Let’s say we’re interested in a set of output from partway through the run, the 88th set of output files. The files we care about most from this set consists of:
- output00000087.xml: This file is the main organizer of the data. It contains an overview of the data stored in the MultiCellDS as well as some actual data including:
- Metadata about the time and runtime for the current time step
- Coordinates for the computational domain
- Parameters for diffusing substrates in the microenvironment
- Column labels for the cell data
- File names for the files that contain microenvironment and cell data at this time step
- output00000087_microenvironment0.mat: This is a MATLAB matrix file that contains all of the data about the microenvironment at this time step
- output00000087_cells_physicell.mat: This is a MATLAB matrix file that contains all of the tracked information about the individual cells in the model. It tells us things like the cells’ position, volume, secretion, cell cycle status, and user-defined cell parameters.
Setup
Using pyMCDS
From the appropriate file in your PhysiCell directory, wherever pyMCDS.py lives, you can use the data loader in your own scripts or in an interactive session. To start you have to import the pyMCDS class
from pyMCDS import pyMCDS
Loading the data
Data is loaded into python from the MultiCellDS by initializing the pyMCDS object. The initialization function for pyMCDS takes one required and one optional argument.
__init__(xml_file, [output_path = '.'])
'''
xml_file : string
String containing the name of the output xml file
output_path :
String containing the path (relative or absolute) to the directory
where PhysiCell output files are stored
'''
We are interested in reading output00000087.xml that lives in ~/path/to/PhysiCell/output (don’t worry Windows paths work too). We would initialize our pyMCDS object using those names and the actual data would be stored in a member dictionary called data.
mcds = pyMCDS('output00000087.xml', '~/path/to/PhysiCell/output')
# Now our data lives in:
mcds.data
We’ve tried to keep everything organized inside of this dictionary but let’s take a look at what we actually have in here. Of course in real output, there will probably not be a chemical named my_chemical, this is simply there to illustrate how multiple chemicals are handled.
The data member dictionary is a dictionary of dictionaries whose child dictionaries can be accessed through normal python dictionary syntax.
mcds.data['metadata'] mcds.data['continuum_variables']['my_chemical']
Each of these subdictionaries contains data, we will take a look at exactly what that data is and how it can be accessed in the following sections.
Metadata
The metadata dictionary contains information about the time of the simulation as well as units for both times and space. Here and in later sections blue boxes indicate scalars and green boxes indicate strings. We can access each of these things using normal dictionary syntax. We’ve also got access to a helper function get_time() for the common operation of retrieving the simulation time.
>>> mcds.data['metadata']['time_units'] 'min' >>> mcds.get_time() 5220.0
Mesh
The mesh dictionary has a lot more going on than the metadata dictionary. It contains three numpy arrays, indicated by orange boxes, as well as another dictionary. The three arrays contain \(x\), \(y\) and \(z\) coordinates for the centers of the voxels that constiture the computational domain in a meshgrid format. This means that each of those arrays is tensors of rank three. Together they identify the coordinates of each possible point in the space.
In contrast, the arrays in the voxel dictionary are stored linearly. If we know that we care about voxel number 42, we want to use the stuff in the voxels dictionary. If we want to make a contour plot, we want to use the x_coordinates, y_coordinates, and z_coordinates arrays.
# We can extract one of the meshgrid arrays as a numpy array
>>> y_coords = mcds.data['mesh']['y_coordinates']
>>> y_coords.shape
(75, 75, 75)
>>> y_coords[0, 0, :4]
array([-740., -740., -740., -740.])
# We can also extract the array of voxel centers
>>> centers = mcds.data['mesh']['voxels']['centers']
>>> centers.shape
(3, 421875)
>>> centers[:, :4]
array([[-740., -720., -700., -680.],
[-740., -740., -740., -740.],
[-740., -740., -740., -740.]])
# We have a handy function to quickly extract the components of the full meshgrid
>>> xx, yy, zz = mcds.get_mesh()
>>> yy.shape
(75, 75, 75)
>>> yy[0, 0, :4]
array([-740., -740., -740., -740.])
# We can also use this to return the meshgrid describing an x, y plane
>>> xx, yy = mcds.get_2D_mesh()
>>> yy.shape
(75, 75)
Continuum variables
The continuum_variables dictionary is the most complicated of the four. It contains subdictionaries that we access using the names of each of the chemicals in the microenvironment. In our toy example above, these are oxygen and my_chemical. If our model tracked diffusing oxygen, VEGF, and glucose, then the continuum_variables dictionary would contain a subdirectory for each of them.
For a particular chemical species in the microenvironment we have two more dictionaries called decay_rate and diffusion_coefficient, and a numpy array called data. The diffusion and decay dictionaries each complete the value stored as a scalar and the unit stored as a string. The numpy array contains the concentrations of the chemical in each voxel at this time and is the same shape as the meshgrids of the computational domain stored in the .data[‘mesh’] arrays.
# we need to know the names of the substrates to work with
# this data. We have a function to help us find them.
>>> mcds.get_substrate_names()
['oxygen', 'my_chemical']
# The diffusable chemical dictionaries are messy
# if we need to do a lot with them it might be easier
# to put them into their own instance
>>> oxy_dict = mcds.data['continuum_variables']['oxygen']
>>> oxy_dict['decay_rate']
{'value': 0.1, 'units': '1/min'}
# What we care about most is probably the numpy
# array of concentrations
>>> oxy_conc = oxy_dict['data']
>>> oxy_conc.shape
(75, 75, 75)
# Alternatively, we can get the same array with a function
>>> oxy_conc2 = mcds.get_concentrations('oxygen')
>>> oxy_conc2.shape
(75, 75, 75)
# We can also get the concentrations on a plane using the
# same function and supplying a z value to "slice through"
# note that right now the z_value must be an exact match
# for a plane of voxel centers, in the future we may add
# interpolation.
>>> oxy_plane = mcds.get_concentrations('oxygen', z_value=100.0)
>>> oxy_plane.shape
(75, 75)
# we can also find the concentration in a single voxel using the
# position of a point within that voxel. This will give us an
# array of all concentrations at that point.
>>> mcds.get_concentrations_at(x=0., y=550., z=0.)
array([17.94514446, 0.99113448])
Discrete Cells
The discrete cells dictionary is relatively straightforward. It contains a number of numpy arrays that contain information regarding individual cells. These are all 1-dimensional arrays and each corresponds to one of the variables specified in the output*.xml file. With the default settings, these are:
- ID: unique integer that will identify the cell throughout its lifetime in the simulation
- position(_x, _y, _z): floating point positions for the cell in \(x\), \(y\), and \(z\) directions
- total_volume: total volume of the cell
- cell_type: integer label for the cell as used in PhysiCell
- cycle_model: integer label for the cell cycle model as used in PhysiCell
- current_phase: integer specification for which phase of the cycle model the cell is currently in
- elapsed_time_in_phase: time that cell has been in current phase of cell cycle model
- nuclear_volume: volume of cell nucleus
- cytoplasmic_volume: volume of cell cytoplasm
- fluid_fraction: proportion of the volume due to fliud
- calcified_fraction: proportion of volume consisting of calcified material
- orientation(_x, _y, _z): direction in which cell is pointing
- polarity:
- migration_speed: current speed of cell
- motility_vector(_x, _y, _z): current direction of movement of cell
- migration_bias: coefficient for stochastic movement (higher is “more deterministic”)
- motility_bias_direction(_x, _y, _z): direction of movement bias
- persistence_time: time in-between direction changes for cell
- motility_reserved:
# Extracting single variables is just like before
>>> cell_ids = mcds.data['discrete_cells']['ID']
>>> cell_ids.shape
(18595,)
>>> cell_ids[:4]
array([0., 1., 2., 3.])
# If we're clever we can extract 2D arrays
>>> cell_vec = np.zeros((cell_ids.shape[0], 3))
>>> vec_list = ['position_x', 'position_y', 'position_z']
>>> for i, lab in enumerate(vec_list):
... cell_vec[:, i] = mcds.data['discrete_cells'][lab]
...
array([[ -69.72657128, -39.02046405, -233.63178904],
[ -69.84507464, -22.71693265, -233.59277388],
[ -69.84891462, -6.04070516, -233.61816711],
[ -69.845265 , 10.80035554, -233.61667313]])
# We can get the list of all of the variables stored in this dictionary
>>> mcds.get_cell_variables()
['ID',
'position_x',
'position_y',
'position_z',
'total_volume',
'cell_type',
'cycle_model',
'current_phase',
'elapsed_time_in_phase',
'nuclear_volume',
'cytoplasmic_volume',
'fluid_fraction',
'calcified_fraction',
'orientation_x',
'orientation_y',
'orientation_z',
'polarity',
'migration_speed',
'motility_vector_x',
'motility_vector_y',
'motility_vector_z',
'migration_bias',
'motility_bias_direction_x',
'motility_bias_direction_y',
'motility_bias_direction_z',
'persistence_time',
'motility_reserved',
'oncoprotein',
'elastic_coefficient',
'kill_rate',
'attachment_lifetime',
'attachment_rate']
# We can also get all of the cell data as a pandas DataFrame
>>> cell_df = mcds.get_cell_df()
>>> cell_df.head()
ID position_x position_y position_z total_volume cell_type cycle_model ...
0.0 - 69.726571 - 39.020464 - 233.631789 2494.0 0.0 5.0 ...
1.0 - 69.845075 - 22.716933 - 233.592774 2494.0 0.0 5.0 ...
2.0 - 69.848915 - 6.040705 - 233.618167 2494.0 0.0 5.0 ...
3.0 - 69.845265 10.800356 - 233.616673 2494.0 0.0 5.0 ...
4.0 - 69.828161 27.324530 - 233.631579 2494.0 0.0 5.0 ...
# if we want to we can also get just the subset of cells that
# are in a specific voxel
>>> vox_df = mcds.get_cell_df_at(x=0.0, y=550.0, z=0.0)
>>> vox_df.iloc[:, :5]
ID position_x position_y position_z total_volume
26718 228761.0 6.623617 536.709341 -1.282934 2454.814507
52736 270274.0 -7.990034 538.184921 9.648955 1523.386488
Examples
These examples will not be made using our toy dataset described above but will instead be made using a single timepoint dataset that can be found at:
Substrate contour plot
One of the big advantages of working with PhysiCell data in python is that we have access to its plotting tools. For the sake of example let’s plot the partial pressure of oxygen throughout the computational domain along the \(z = 0\) plane. Once we’ve loaded our data by initializing a pyMCDS object, we can work entirely within python to produce the plot.
from pyMCDS import pyMCDS
import numpy as np
import matplotlib.pyplot as plt
# load data
mcds = pyMCDS('output00003696.xml', '../output')
# Set our z plane and get our substrate values along it
z_val = 0.00
plane_oxy = mcds.get_concentrations('oxygen', z_slice=z_val)
# Get the 2D mesh for contour plotting
xx, yy = mcds.get_mesh()
# We want to be able to control the number of contour levels so we
# need to do a little set up
num_levels = 21
min_conc = plane_oxy.min()
max_conc = plane_oxy.max()
my_levels = np.linspace(min_conc, max_conc, num_levels)
# set up the figure area and add data layers
fig, ax = plt.subplot()
cs = ax.contourf(xx, yy, plane_oxy, levels=my_levels)
ax.contour(xx, yy, plane_oxy, color='black', levels = my_levels,
linewidths=0.5)
# Now we need to add our color bar
cbar1 = fig.colorbar(cs, shrink=0.75)
cbar1.set_label('mmHg')
# Let's put the time in to make these look nice
ax.set_aspect('equal')
ax.set_xlabel('x (micron)')
ax.set_ylabel('y (micron)')
ax.set_title('oxygen (mmHg) at t = {:.1f} {:s}, z = {:.2f} {:s}'.format(
mcds.get_time(),
mcds.data['metadata']['time_units'],
z_val,
mcds.data['metadata']['spatial_units'])
plt.show()
Adding a cells layer
We can also use pandas to do fairly complex selections of cells to add to our plots. Below we use pandas and the previous plot to add a cells layer.
from pyMCDS import pyMCDS
import numpy as np
import matplotlib.pyplot as plt
# load data
mcds = pyMCDS('output00003696.xml', '../output')
# Set our z plane and get our substrate values along it
z_val = 0.00
plane_oxy = mcds.get_concentrations('oxygen', z_slice=z_val)
# Get the 2D mesh for contour plotting
xx, yy = mcds.get_mesh()
# We want to be able to control the number of contour levels so we
# need to do a little set up
num_levels = 21
min_conc = plane_oxy.min()
max_conc = plane_oxy.max()
my_levels = np.linspace(min_conc, max_conc, num_levels)
# get our cells data and figure out which cells are in the plane
cell_df = mcds.get_cell_df()
ds = mcds.get_mesh_spacing()
inside_plane = (cell_df['position_z'] < z_val + ds) \ & (cell_df['position_z'] > z_val - ds)
plane_cells = cell_df[inside_plane]
# We're going to plot two types of cells and we want it to look nice
colors = ['black', 'grey']
sizes = [20, 8]
labels = ['Alive', 'Dead']
# set up the figure area and add microenvironment layer
fig, ax = plt.subplot()
cs = ax.contourf(xx, yy, plane_oxy, levels=my_levels)
# get our cells of interest
# alive_cells = plane_cells[plane_cells['cycle_model'] < 6]
# dead_cells = plane_cells[plane_cells['cycle_model'] > 6]
# -- for newer versions of PhysiCell
alive_cells = plane_cells[plane_cells['cycle_model'] < 100]
dead_cells = plane_cells[plane_cells['cycle_model'] >= 100]
# plot the cell layer
for i, plot_cells in enumerate((alive_cells, dead_cells)):
ax.scatter(plot_cells['position_x'].values,
plot_cells['position_y'].values,
facecolor='none',
edgecolors=colors[i],
alpha=0.6,
s=sizes[i],
label=labels[i])
# Now we need to add our color bar
cbar1 = fig.colorbar(cs, shrink=0.75)
cbar1.set_label('mmHg')
# Let's put the time in to make these look nice
ax.set_aspect('equal')
ax.set_xlabel('x (micron)')
ax.set_ylabel('y (micron)')
ax.set_title('oxygen (mmHg) at t = {:.1f} {:s}, z = {:.2f} {:s}'.format(
mcds.get_time(),
mcds.data['metadata']['time_units'],
z_val,
mcds.data['metadata']['spatial_units'])
ax.legend(loc='upper right')
plt.show()
Future Direction
The first extension of this project will be timeseries functionality. This will provide similar data loading functionality but for a time series of MultiCell Digital Snapshots instead of simply one point in time.
PhysiCell Tools : PhysiCell-povwriter
As PhysiCell matures, we are starting to turn our attention to better training materials and an ecosystem of open source PhysiCell tools. PhysiCell-povwriter is is designed to help transform your 3-D simulation results into 3-D visualizations like this one:
PhysiCell-povwriter transforms simulation snapshots into 3-D scenes that can be rendered into still images using POV-ray: an open source software package that uses raytracing to mimic the path of light from a source of illumination to a single viewpoint (a camera or an eye). The result is a beautifully rendered scene (at any resolution you choose) with very nice shading and lighting.
If you repeat this on many simulation snapshots, you can create an animation of your work.
What you’ll need
This workflow is entirely based on open source software:
- 3-D simulation data (likely stored in ./output from your project)
- PhysiCell-povwriter, available on GitHub at
- POV-ray, available at
- ImageMagick (optional, for image file conversions)
- mencoder (optional, for making compressed movies)
Setup
Building PhysiCell-povwriter
After you clone PhysiCell-povwriter or download its source from a release, you’ll need to compile it. In the project’s root directory, compile the project by:
make
(If you need to set up a C++ PhysiCell development environment, click here for OSX or here for Windows.)
Next, copy povwriter (povwriter.exe in Windows) to either the root directory of your PhysiCell project, or somewhere in your path. Copy ./config/povwriter-settings.xml to the ./config directory of your PhysiCell project.
Editing resolutions in POV-ray
PhysiCell-povwriter is intended for creating “square” images, but POV-ray does not have any pre-created square rendering resolutions out-of-the-box. However, this is straightforward to fix.
- Open POV-Ray
- Go to the “tools” menu and select “edit resolution INI file”
- At the top of the INI file (which opens for editing in POV-ray), make a new profile:
[1080x1080, AA] Width=480 Height=480 Antialias=On
- Make similar profiles (with unique names) to suit your preferences. I suggest one at 480×480 (as a fast preview), another at 2160×2160, and another at 5000×5000 (because they will be absurdly high resolution). For example:
[2160x2160 no AA] Width=2160 Height=2160 Antialias=Off
You can optionally make more profiles with antialiasing on (which provides some smoothing for areas of high detail), but you’re probably better off just rendering without antialiasing at higher resolutions and the scaling the image down as needed. Also, rendering without antialiasing will be faster.
- Once done making profiles, save and exit POV-Ray.
- The next time you open POV-Ray, your new resolution profiles will be available in the lefthand dropdown box.
Configuring PhysiCell-povwriter
Once you have copied povwriter-settings.xml to your project’s config file, open it in a text editor. Below, we’ll show the different settings.
Camera settings
<camera> <distance_from_origin units="micron">1500</distance_from_origin> <xy_angle>3.92699081699</xy_angle> <!-- 5*pi/4 --> <yz_angle>1.0471975512</yz_angle> <!-- pi/3 --> </camera>
For simplicity, PhysiCell-POVray (currently) always aims the camera towards the origin (0,0,0), with “up” towards the positive z-axis. distance_from_origin sets how far the camera is placed from the origin. xy_angle sets the angle \(\theta\) from the positive x-axis in the xy-plane. yz_angle sets the angle \(\phi\) from the positive z-axis in the yz-plane. Both angles are in radians.
Options
<options> <use_standard_colors>true</use_standard_colors> <nuclear_offset units="micron">0.1</nuclear_offset> <cell_bound units="micron">750</cell_bound> <threads>8</threads> </options>
use_standard_colors (if set to true) uses a built-in “paint-by-numbers” color scheme, where each cell type (identified with an integer) gets XML-defined colors for live, apoptotic, and dead cells. More on this below. If use_standard_colors is set to false, then PhysiCell-povwriter uses the my_pigment_and_finish_function in ./custom_modules/povwriter.cpp to color cells.
The nuclear_offset is a small additional height given to nuclei when cropping to avoid visual artifacts when rendering (which can cause some “tearing” or “bleeding” between the rendered nucleus and cytoplasm). cell_bound is used for leaving some cells out of bound: any cell with |x|, |y|, or |z| exceeding cell_bound will not be rendered. threads is used for parallelizing on multicore processors; note that it only speeds up povwriter if you are converting multiple PhysiCell outputs to povray files.
Save
<save> <!-- done --> <folder>output</folder> <!-- use . for root --> <filebase>output</filebase> <time_index>3696</time_index> </save>
Use folder to tell PhysiCell-povwriter where the data files are stored. Use filebase to tell how the outputs are named. Typically, they have the form output########_cells_physicell.mat; in this case, the filebase is output. Lastly, use time_index to set the output number. For example if your file is output00000182_cells_physicell.mat, then filebase = output and time_index = 182.
Below, we’ll see how to specify ranges of indices at the command line, which would supersede the time_index given here in the XML.
Clipping planes
PhysiCell-povwriter uses clipping planes to help create cutaway views of the simulations. By default, 3 clipping planes are used to cut out an octant of the viewing area.
Recall that a plane can be defined by its normal vector n and a point p on the plane. With these, the plane can be defined as all points x satisfying
\[ \left( \vec{x} -\vec{p} \right) \cdot \vec{n} = 0 \]
These are then written out as a plane equation
\[ a x + by + cz + d = 0, \]
where
\[ (a,b,c) = \vec{n} \hspace{.5in} \textrm{ and } \hspace{0.5in} d = \: – \vec{n} \cdot \vec{p}. \]
As of Version 1.0.0, we are having some difficulties with clipping planes that do not pass through the origin (0,0,0), for which \( d = 0 \).
In the config file, these planes are written as \( (a,b,c,d) \):
<clipping_planes> <!-- done --> <clipping_plane>0,-1,0,0</clipping_plane> <clipping_plane>-1,0,0,0</clipping_plane> <clipping_plane>0,0,1,0</clipping_plane> </clipping_planes>
Note that cells “behind” the plane (where \( ( \vec{x} – \vec{p} ) \cdot \vec{n} \le 0 \)) are rendered, and cells in “front” of the plane (where \( (\vec{x}-\vec{p}) \cdot \vec{n} > 0 \)) are not rendered. Cells that intersect the plane are partially rendered (using constructive geometry via union and intersection commands in POV-ray).
Cell color definitions
Within <cell_color_definitions>, you’ll find multiple <cell_colors> blocks, each of which defines the live, dead, and necrotic colors for a specific cell type (with the type ID indicated in the attribute). These colors are only applied if use_standard_colors is set to true in options. See above.
The live colors are given as two rgb (red,green,blue) colors for the cytoplasm and nucleus of live cells. Each element of this triple can range from 0 to 1, and not from 0 to 255 as in many raw image formats. Next, finish specifies ambient (how much highly-scattered background ambient light illuminates the cell), diffuse (how well light rays can illuminate the surface), and specular (how much of a shiny reflective splotch the cell gets).
See the POV-ray documentation for for information on the finish.
This is repeated to give the apoptotic and necrotic colors for the cell type.
<cell_colors type="0"> <live> <cytoplasm>.25,1,.25</cytoplasm> <!-- red,green,blue --> <nuclear>0.03,0.125</nuclear> <finish>0.05,1,0.1</finish> <!-- ambient,diffuse,specular --> </live> <apoptotic> <cytoplasm>1,0,0</cytoplasm> <!-- red,green,blue --> <nuclear>0.125,0,0</nuclear> <finish>0.05,1,0.1</finish> <!-- ambient,diffuse,specular --> </apoptotic> <necrotic> <cytoplasm>1,0.5412,0.1490</cytoplasm> <!-- red,green,blue --> <nuclear>0.125,0.06765,0.018625</nuclear> <finish>0.01,0.5,0.1</finish> <!-- ambient,diffuse,specular --> </necrotic> </cell_colors>
Use multiple cell_colors blocks (each with type corresponding to the integer cell type) to define the colors of multiple cell types.
Using PhysiCell-povwriter
Use by the XML configuration file alone
The simplest syntax:
physicell$ ./povwriter
(Windows users: povwriter or povwriter.exe) will process ./config/povwriter-settings.xml and convert the single indicated PhysiCell snapshot to a .pov file.
If you run POV-writer with the default configuration file in the povwriter structure (with the supplied sample data), it will render time index 3696 from the immunotherapy example in our 2018 PhysiCell Method Paper:
physicell$ ./povwriter povwriter version 1.0.0 ================================================================================ Copyright (c) Paul Macklin 2019, on behalf of the PhysiCell project OSI License: BSD-3-Clause (see LICENSE.txt) Usage: ================================================================================ povwriter : run povwriter with config file ./config/settings.xml povwriter FILENAME.xml : run povwriter with config file FILENAME.xml povwriter x:y:z : run povwriter on data in FOLDER with indices from x to y in incremenets of z Example: ./povwriter 0:2:10 processes files: ./FOLDER/FILEBASE00000000_physicell_cells.mat ./FOLDER/FILEBASE00000002_physicell_cells.mat ... ./FOLDER/FILEBASE00000010_physicell_cells.mat (See the config file to set FOLDER and FILEBASE) povwriter x1,...,xn : run povwriter on data in FOLDER with indices x1,...,xn Example: ./povwriter 1,3,17 processes files: ./FOLDER/FILEBASE00000001_physicell_cells.mat ./FOLDER/FILEBASE00000003_physicell_cells.mat ./FOLDER/FILEBASE00000017_physicell_cells.mat (Note that there are no spaces.) (See the config file to set FOLDER and FILEBASE) Code updates at https://github.com/PhysiCell-Tools/PhysiCell-povwriter Tutorial & documentation at http://MathCancer.org/blog/povwriter ================================================================================ Using config file ./config/povwriter-settings.xml ... Using standard coloring function ... Found 3 clipping planes ... Found 2 cell color definitions ... Processing file ./output/output00003696_cells_physicell.mat... Matrix size: 32 x 66978 Creating file pov00003696.pov for output ... Writing 66978 cells ... done! Done processing all 1 files!
The result is a single POV-ray file (pov00003696.pov) in the root directory.
Now, open that file in POV-ray (double-click the file if you are in Windows), choose one of your resolutions in your lefthand dropdown (I’ll choose 2160×2160 no antialiasing), and click the green “run” button.
You can watch the image as it renders. The result should be a PNG file (named pov00003696.png) that looks like this:
Using command-line options to process multiple times (option #1)
Now, suppose we have more outputs to process. We still state most of the options in the XML file as above, but now we also supply a command-line argument in the form of start:interval:end. If you’re still in the povwriter project, note that we have some more sample data there. Let’s grab and process it:
physicell$ cd output physicell$ unzip more_samples.zip Archive: more_samples.zip inflating: output00000000_cells_physicell.mat inflating: output00000001_cells_physicell.mat inflating: output00000250_cells_physicell.mat inflating: output00000300_cells_physicell.mat inflating: output00000500_cells_physicell.mat inflating: output00000750_cells_physicell.mat inflating: output00001000_cells_physicell.mat inflating: output00001250_cells_physicell.mat inflating: output00001500_cells_physicell.mat inflating: output00001750_cells_physicell.mat inflating: output00002000_cells_physicell.mat inflating: output00002250_cells_physicell.mat inflating: output00002500_cells_physicell.mat inflating: output00002750_cells_physicell.mat inflating: output00003000_cells_physicell.mat inflating: output00003250_cells_physicell.mat inflating: output00003500_cells_physicell.mat inflating: output00003696_cells_physicell.mat physicell$ ls citation and license.txt more_samples.zip output00000000_cells_physicell.mat output00000001_cells_physicell.mat output00000250_cells_physicell.mat output00000300_cells_physicell.mat output00000500_cells_physicell.mat output00000750_cells_physicell.mat output00001000_cells_physicell.mat output00001250_cells_physicell.mat output00001500_cells_physicell.mat output00001750_cells_physicell.mat output00002000_cells_physicell.mat output00002250_cells_physicell.mat output00002500_cells_physicell.mat output00002750_cells_physicell.mat output00003000_cells_physicell.mat output00003250_cells_physicell.mat output00003500_cells_physicell.mat output00003696.xml output00003696_cells_physicell.mat
Let’s go back to the parent directory and run povwriter:
physicell$ ./povwriter 0:250:3500 povwriter version 1.0.0 ================================================================================ Copyright (c) Paul Macklin 2019, on behalf of the PhysiCell project OSI License: BSD-3-Clause (see LICENSE.txt) Usage: ================================================================================ povwriter : run povwriter with config file ./config/settings.xml povwriter FILENAME.xml : run povwriter with config file FILENAME.xml povwriter x:y:z : run povwriter on data in FOLDER with indices from x to y in incremenets of z Example: ./povwriter 0:2:10 processes files: ./FOLDER/FILEBASE00000000_physicell_cells.mat ./FOLDER/FILEBASE00000002_physicell_cells.mat ... ./FOLDER/FILEBASE00000010_physicell_cells.mat (See the config file to set FOLDER and FILEBASE) povwriter x1,...,xn : run povwriter on data in FOLDER with indices x1,...,xn Example: ./povwriter 1,3,17 processes files: ./FOLDER/FILEBASE00000001_physicell_cells.mat ./FOLDER/FILEBASE00000003_physicell_cells.mat ./FOLDER/FILEBASE00000017_physicell_cells.mat (Note that there are no spaces.) (See the config file to set FOLDER and FILEBASE) Code updates at https://github.com/PhysiCell-Tools/PhysiCell-povwriter Tutorial & documentation at http://MathCancer.org/blog/povwriter ================================================================================ Using config file ./config/povwriter-settings.xml ... Using standard coloring function ... Found 3 clipping planes ... Found 2 cell color definitions ... Matrix size: 32 x 18317 Processing file ./output/output00000000_cells_physicell.mat... Creating file pov00000000.pov for output ... Writing 18317 cells ... Processing file ./output/output00002000_cells_physicell.mat... Matrix size: 32 x 33551 Creating file pov00002000.pov for output ... Writing 33551 cells ... Processing file ./output/output00002500_cells_physicell.mat... Matrix size: 32 x 43440 Creating file pov00002500.pov for output ... Writing 43440 cells ... Processing file ./output/output00001500_cells_physicell.mat... Matrix size: 32 x 40267 Creating file pov00001500.pov for output ... Writing 40267 cells ... Processing file ./output/output00003000_cells_physicell.mat... Matrix size: 32 x 56659 Creating file pov00003000.pov for output ... Writing 56659 cells ... Processing file ./output/output00001000_cells_physicell.mat... Matrix size: 32 x 74057 Creating file pov00001000.pov for output ... Writing 74057 cells ... Processing file ./output/output00003500_cells_physicell.mat... Matrix size: 32 x 66791 Creating file pov00003500.pov for output ... Writing 66791 cells ... Processing file ./output/output00000500_cells_physicell.mat... Matrix size: 32 x 114316 Creating file pov00000500.pov for output ... Writing 114316 cells ... done! Processing file ./output/output00000250_cells_physicell.mat... Matrix size: 32 x 75352 Creating file pov00000250.pov for output ... Writing 75352 cells ... done! Processing file ./output/output00002250_cells_physicell.mat... Matrix size: 32 x 37959 Creating file pov00002250.pov for output ... Writing 37959 cells ... done! Processing file ./output/output00001750_cells_physicell.mat... Matrix size: 32 x 32358 Creating file pov00001750.pov for output ... Writing 32358 cells ... done! Processing file ./output/output00002750_cells_physicell.mat... Matrix size: 32 x 49658 Creating file pov00002750.pov for output ... Writing 49658 cells ... done! Processing file ./output/output00003250_cells_physicell.mat... Matrix size: 32 x 63546 Creating file pov00003250.pov for output ... Writing 63546 cells ... done! done! done! done! Processing file ./output/output00001250_cells_physicell.mat... Matrix size: 32 x 54771 Creating file pov00001250.pov for output ... Writing 54771 cells ... done! done! done! done! Processing file ./output/output00000750_cells_physicell.mat... Matrix size: 32 x 97642 Creating file pov00000750.pov for output ... Writing 97642 cells ... done! done! Done processing all 15 files!
Notice that the output appears a bit out of order. This is normal: povwriter is using 8 threads to process 8 files at the same time, and sending some output to the single screen. Since this is all happening simultaneously, it’s a bit jumbled (and non-sequential). Don’t panic. You should now have created pov00000000.pov, pov00000250.pov, … , pov00003500.pov.
Now, go into POV-ray, and choose “queue.” Click “Add File” and select all 15 .pov files you just created:
Hit “OK” to let it render all the povray files to create PNG files (pov00000000.png, … , pov00003500.png).
Using command-line options to process multiple times (option #2)
You can also give a list of indices. Here’s how we render time indices 250, 1000, and 2250:
physicell$ ./povwriter 250,1000,2250 povwriter version 1.0.0 ================================================================================ Copyright (c) Paul Macklin 2019, on behalf of the PhysiCell project OSI License: BSD-3-Clause (see LICENSE.txt) Usage: ================================================================================ povwriter : run povwriter with config file ./config/settings.xml povwriter FILENAME.xml : run povwriter with config file FILENAME.xml povwriter x:y:z : run povwriter on data in FOLDER with indices from x to y in incremenets of z Example: ./povwriter 0:2:10 processes files: ./FOLDER/FILEBASE00000000_physicell_cells.mat ./FOLDER/FILEBASE00000002_physicell_cells.mat ... ./FOLDER/FILEBASE00000010_physicell_cells.mat (See the config file to set FOLDER and FILEBASE) povwriter x1,...,xn : run povwriter on data in FOLDER with indices x1,...,xn Example: ./povwriter 1,3,17 processes files: ./FOLDER/FILEBASE00000001_physicell_cells.mat ./FOLDER/FILEBASE00000003_physicell_cells.mat ./FOLDER/FILEBASE00000017_physicell_cells.mat (Note that there are no spaces.) (See the config file to set FOLDER and FILEBASE) Code updates at https://github.com/PhysiCell-Tools/PhysiCell-povwriter Tutorial & documentation at http://MathCancer.org/blog/povwriter ================================================================================ Using config file ./config/povwriter-settings.xml ... Using standard coloring function ... Found 3 clipping planes ... Found 2 cell color definitions ... Processing file ./output/output00002250_cells_physicell.mat... Matrix size: 32 x 37959 Creating file pov00002250.pov for output ... Writing 37959 cells ... Processing file ./output/output00001000_cells_physicell.mat... Matrix size: 32 x 74057 Creating file pov00001000.pov for output ... Processing file ./output/output00000250_cells_physicell.mat... Matrix size: 32 x 75352 Writing 74057 cells ... Creating file pov00000250.pov for output ... Writing 75352 cells ... done! done! done! Done processing all 3 files!
This will create files pov00000250.pov, pov00001000.pov, and pov00002250.pov. Render them in POV-ray just as before.
Advanced options (at the source code level)
If you set use_standard_colors to false, povwriter uses the function my_pigment_and_finish_function (at the end of ./custom_modules/povwriter.cpp). Make sure that you set colors.cyto_pigment (RGB) and colors.nuclear_pigment (also RGB). The source file in povwriter has some hinting on how to write this. Note that the XML files saved by PhysiCell have a legend section that helps you do determine what is stored in each column of the matlab file.
Optional postprocessing
Image conversion / manipulation with ImageMagick
Suppose you want to convert the PNG files to JPEGs, and scale them down to 60% of original size. That’s very straightforward in ImageMagick:
physicell$ magick mogrify -format jpg -resize 60% pov*.png
Creating an animated GIF with ImageMagick
Suppose you want to create an animated GIF based on your images. I suggest first converting to JPG (see above) and then using ImageMagick again. Here, I’m adding a 20 ms delay between frames:
physicell$ magick convert -delay 20 *.jpg out.gif
Here’s the result:
Creating a compressed movie with Mencoder
Syntax coming later.
Closing thoughts and future work
In the future, we will probably allow more control over the clipping planes and a bit more debugging on how to handle planes that don’t pass through the origin. (First thoughts: we need to change how we use union and intersection commands in the POV-ray outputs.)
We should also look at adding some transparency for the cells. I’d prefer something like rgba (red-green-blue-alpha), but POV-ray uses filters and transmission, and we want to make sure to get it right.
Lastly, it would be nice to find a balance between the current very simple camera setup and better control.
Thanks for reading this PhysiCell Friday tutorial! Please do give PhysiCell at try (at http://PhysiCell.org) and read the method paper at PLoS Computational Biology.