diff --git a/matlab_example/citation and license.txt b/matlab_example/citation and license.txt
new file mode 100755
index 0000000..a609d83
--- /dev/null
+++ b/matlab_example/citation and license.txt
@@ -0,0 +1,14 @@
+This dataset is posted online with the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
+
+It requires that you cite the source of this dataset for *any* reuse:
+
+A. Ghaffarizadeh, S.H. Friedman, S.M. Mumenthaler, and P. Macklin.
+PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems.
+PLoS Comput. Biol., 2017 (preprint, in review). DOI: 10.1101/088773.
+
+Please check http://PhysiCell.MathCancer.org for the most recent citation information as
+this article undergoes peer review.
+
+For the full text and legal conditions of the CC BY 4.0 license, see:
+
+https://creativecommons.org/licenses/by/4.0/
diff --git a/matlab_example/initial_mesh0.mat b/matlab_example/initial_mesh0.mat
new file mode 100644
index 0000000..917eac9
Binary files /dev/null and b/matlab_example/initial_mesh0.mat differ
diff --git a/matlab_example/output00003696.xml b/matlab_example/output00003696.xml
new file mode 100644
index 0000000..d7ada9b
--- /dev/null
+++ b/matlab_example/output00003696.xml
@@ -0,0 +1,90 @@
+
+
+
+
+ BioFVM
+ 1.1.6
+ http://BioFVM.MathCancer.org
+
+
+
+
+
+ 30239.999998
+ 191255.188684
+ 2017-08-27T04:28:34Z
+ 2017-08-27T04:28:34Z
+
+
+
+
+ -750.000000 -750.000000 -750.000000 750.000000 750.000000 750.000000
+ -740.000000 -720.000000 -700.000000 -680.000000 -660.000000 -640.000000 -620.000000 -600.000000 -580.000000 -560.000000 -540.000000 -520.000000 -500.000000 -480.000000 -460.000000 -440.000000 -420.000000 -400.000000 -380.000000 -360.000000 -340.000000 -320.000000 -300.000000 -280.000000 -260.000000 -240.000000 -220.000000 -200.000000 -180.000000 -160.000000 -140.000000 -120.000000 -100.000000 -80.000000 -60.000000 -40.000000 -20.000000 0.000000 20.000000 40.000000 60.000000 80.000000 100.000000 120.000000 140.000000 160.000000 180.000000 200.000000 220.000000 240.000000 260.000000 280.000000 300.000000 320.000000 340.000000 360.000000 380.000000 400.000000 420.000000 440.000000 460.000000 480.000000 500.000000 520.000000 540.000000 560.000000 580.000000 600.000000 620.000000 640.000000 660.000000 680.000000 700.000000 720.000000 740.000000
+ -740.000000 -720.000000 -700.000000 -680.000000 -660.000000 -640.000000 -620.000000 -600.000000 -580.000000 -560.000000 -540.000000 -520.000000 -500.000000 -480.000000 -460.000000 -440.000000 -420.000000 -400.000000 -380.000000 -360.000000 -340.000000 -320.000000 -300.000000 -280.000000 -260.000000 -240.000000 -220.000000 -200.000000 -180.000000 -160.000000 -140.000000 -120.000000 -100.000000 -80.000000 -60.000000 -40.000000 -20.000000 0.000000 20.000000 40.000000 60.000000 80.000000 100.000000 120.000000 140.000000 160.000000 180.000000 200.000000 220.000000 240.000000 260.000000 280.000000 300.000000 320.000000 340.000000 360.000000 380.000000 400.000000 420.000000 440.000000 460.000000 480.000000 500.000000 520.000000 540.000000 560.000000 580.000000 600.000000 620.000000 640.000000 660.000000 680.000000 700.000000 720.000000 740.000000
+ -740.000000 -720.000000 -700.000000 -680.000000 -660.000000 -640.000000 -620.000000 -600.000000 -580.000000 -560.000000 -540.000000 -520.000000 -500.000000 -480.000000 -460.000000 -440.000000 -420.000000 -400.000000 -380.000000 -360.000000 -340.000000 -320.000000 -300.000000 -280.000000 -260.000000 -240.000000 -220.000000 -200.000000 -180.000000 -160.000000 -140.000000 -120.000000 -100.000000 -80.000000 -60.000000 -40.000000 -20.000000 0.000000 20.000000 40.000000 60.000000 80.000000 100.000000 120.000000 140.000000 160.000000 180.000000 200.000000 220.000000 240.000000 260.000000 280.000000 300.000000 320.000000 340.000000 360.000000 380.000000 400.000000 420.000000 440.000000 460.000000 480.000000 500.000000 520.000000 540.000000 560.000000 580.000000 600.000000 620.000000 640.000000 660.000000 680.000000 700.000000 720.000000 740.000000
+
+ initial_mesh0.mat
+
+
+
+
+
+
+ 100000.000000
+ 0.100000
+
+
+
+
+
+ 1000.000000
+ 0.016000
+
+
+
+
+ output00003696_microenvironment0.mat
+
+
+
+
+
+
+
+
+ output00003696_cells.mat
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ output00003696_cells_physicell.mat
+
+
+
+
+
+
diff --git a/matlab_example/output00003696_cells.mat b/matlab_example/output00003696_cells.mat
new file mode 100644
index 0000000..8a184a3
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diff --git a/matlab_example/output00003696_cells_physicell.mat b/matlab_example/output00003696_cells_physicell.mat
new file mode 100644
index 0000000..a6df7f0
Binary files /dev/null and b/matlab_example/output00003696_cells_physicell.mat differ
diff --git a/matlab_example/output00003696_microenvironment0.mat b/matlab_example/output00003696_microenvironment0.mat
new file mode 100644
index 0000000..1387054
Binary files /dev/null and b/matlab_example/output00003696_microenvironment0.mat differ
diff --git a/matlab_example/snapshot00003696.svg b/matlab_example/snapshot00003696.svg
new file mode 100644
index 0000000..5a360c3
--- /dev/null
+++ b/matlab_example/snapshot00003696.svg
@@ -0,0 +1,3711 @@
+
+
+
diff --git a/pyMCDS.py b/pyMCDS.py
new file mode 100644
index 0000000..a34245b
--- /dev/null
+++ b/pyMCDS.py
@@ -0,0 +1,505 @@
+import xml.etree.ElementTree as ET
+import numpy as np
+import pandas as pd
+import scipy.io as sio
+import sys
+import warnings
+from pathlib import Path
+
+class pyMCDS:
+ """
+ This class contains a dictionary of dictionaries that contains all of the
+ output from a single time step of a PhysiCell Model. This class assumes that
+ all output files are stored in the same directory. Data is loaded by reading
+ the .xml file for a particular timestep.
+
+ Parameters
+ ----------
+ xml_name: str
+ String containing the name of the xml file without the path
+ output_path: str, optional
+ String containing the path (relative or absolute) to the directory
+ where PhysiCell output files are stored (default= ".")
+
+ Attributes
+ ----------
+ data : dict
+ Hierarchical container for all of the data retrieved by parsing the xml
+ file and the files referenced therein.
+ """
+ def __init__(self, xml_file, output_path='.'):
+ self.data = self._read_xml(xml_file, output_path)
+
+ ## METADATA RELATED FUNCTIONS
+
+ def get_time(self):
+ return self.data['metadata']['current_time']
+
+ ## MESH RELATED FUNCTIONS
+
+ def get_mesh(self, flat=False):
+ """
+ Return a meshgrid of the computational domain. Can return either full
+ 3D or a 2D plane for contour plots.
+
+ Parameters
+ ----------
+ flat : bool
+ If flat is set to true, we return only the x and y meshgrid.
+ Otherwise we return x, y, and z
+
+ Returns
+ -------
+ splitting : list length=2 if flat=True, else length=3
+ Contains arrays of voxel center coordinates as meshgrid with shape
+ [nx_voxel, ny_voxel, nz_voxel] or [nx_voxel, ny_voxel] if flat=True.
+ """
+ if flat == True:
+ xx = self.data['mesh']['x_coordinates'][:, :, 0]
+ yy = self.data['mesh']['y_coordinates'][:, :, 0]
+
+ return [xx, yy]
+
+ # if we dont want a plane just return appropriate values
+ else:
+ xx = self.data['mesh']['x_coordinates']
+ yy = self.data['mesh']['y_coordinates']
+ zz = self.data['mesh']['z_coordinates']
+
+ return [xx, yy, zz]
+
+ def get_2D_mesh(self):
+ """
+ This function returns the x, y meshgrid as two numpy arrays. It is
+ identical to get_mesh with the option flat=True
+
+ Returns
+ -------
+ splitting : list length=2
+ Contains arrays of voxel center coordinates in x and y dimensions
+ as meshgrid with shape [nx_voxel, ny_voxel]
+ """
+ xx = self.data['mesh']['x_coordinates'][:, :, 0]
+ yy = self.data['mesh']['y_coordinates'][:, :, 0]
+
+ return [xx, yy]
+
+ def get_linear_voxels(self):
+ """
+ Helper function to quickly grab voxel centers array stored linearly as
+ opposed to meshgrid-style.
+ """
+ return self.data['mesh']['voxels']['centers']
+
+ def get_mesh_spacing(self):
+ """
+ Returns the space in between voxel centers for the mesh in terms of the
+ mesh's spatial units. Assumes that voxel centers fall on integer values.
+
+ Returns
+ -------
+ dx : float
+ Distance between voxel centers in the same units as the other
+ spatial measurements
+ """
+ centers = self.get_linear_voxels()
+ X = np.unique(centers[0, :])
+ Y = np.unique(centers[1, :])
+ Z = np.unique(centers[2, :])
+
+ dx = (X.max() - X.min()) / X.shape[0]
+ dy = (Y.max() - Y.min()) / Y.shape[0]
+ dz = (Z.max() - Z.min()) / Z.shape[0]
+
+ if np.abs(dx - dy) > 1e-10 or np.abs(dy - dz) > 1e-10 \
+ or np.abs(dx - dz) > 1e-10:
+ print('Warning: grid spacing may be axis dependent.')
+
+ return round(dx)
+
+ def get_containing_voxel_ijk(self, x, y, z):
+ """
+ Internal function to get the meshgrid indices for the center of a voxel
+ that contains the given position.
+
+ Note that pyMCDS stores meshgrids as 'cartesian'
+ (indexing='xy' in np.meshgrid) which means that we will have
+ to use these indices as [j, i, k] on the actual meshgrid objects
+
+ Parameters
+ ----------
+ x : float
+ x-coordinate for the position
+ y : float
+ y-coordinate for the position
+ z : float
+ z-coordinate for the position
+
+ Returns
+ -------
+ ijk : list length=3
+ contains the i, j, and k indices for the containing voxel's center
+ """
+ xx, yy, zz = self.get_mesh()
+ ds = self.get_mesh_spacing()
+
+ if x > xx.max():
+ warnings.warn('Position out of bounds: x out of bounds in pyMCDS._get_voxel_idx({0}, {1}, {2}). Setting x = x_max!'.format(x, y, z))
+ x = xx.max()
+ elif x < xx.min():
+ warnings.warn('Position out of bounds: x out of bounds in pyMCDS._get_voxel_idx({0}, {1}, {2}). Setting x = x_min!'.format(x, y, z))
+ x = xx.min()
+ elif y > yy.max():
+ warnings.warn('Position out of bounds: y out of bounds in pyMCDS._get_voxel_idx({0}, {1}, {2}). Setting y = y_max!'.format(x, y, z))
+ y = yy.max()
+ elif y < yy.min():
+ warnings.warn('Position out of bounds: y out of bounds in pyMCDS._get_voxel_idx({0}, {1}, {2}). Setting y = y_min!'.format(x, y, z))
+ y = yy.min()
+ elif z > zz.max():
+ warnings.warn('Position out of bounds: z out of bounds in pyMCDS._get_voxel_idx({0}, {1}, {2}). Setting z = z_max!'.format(x, y, z))
+ z = zz.max()
+ elif z < zz.min():
+ warnings.warn('Position out of bounds: z out of bounds in pyMCDS._get_voxel_idx({0}, {1}, {2}). Setting z = z_min!'.format(x, y, z))
+ z = zz.min()
+
+ i = np.round((x - xx.min()) / ds)
+ j = np.round((y - yy.min()) / ds)
+ k = np.round((z - zz.min()) / ds)
+
+ ii, jj, kk = int(i), int(j), int(k)
+
+ return [ii, jj, kk]
+
+ ## MICROENVIRONMENT RELATED FUNCTIONS
+
+ def get_substrate_names(self):
+ """
+ Returns list of chemical species in microenvironment
+
+ Returns
+ -------
+ species_list : array (str), shape=[n_species,]
+ Contains names of chemical species in microenvironment
+ """
+ species_list = []
+ for name in self.data['continuum_variables']:
+ species_list.append(name)
+
+ return species_list
+
+ def get_concentrations(self, species_name, z_slice=None):
+ """
+ Returns the concentration array for the specified chemical species
+ in the microenvironment. Can return either the whole 3D picture, or
+ a 2D plane of concentrations.
+
+ Parameters
+ ----------
+ species_name : str
+ Name of the chemical species for which to get concentrations
+
+ z_slice : float
+ z-axis position to use as plane for 2D output. This value must match
+ a plane of voxel centers in the z-axis.
+ Returns
+ -------
+ conc_arr : array (np.float) shape=[nx_voxels, ny_voxels, nz_voxels]
+ Contains the concentration of the specified chemical in each voxel.
+ The array spatially maps to a meshgrid of the voxel centers.
+ """
+ if z_slice is not None:
+ # check to see that z_slice is a valid plane
+ zz = self.data['mesh']['z_coordinates']
+ assert z_slice in zz, 'Specified z_slice {} not in z_coordinates'.format(z_slice)
+
+ # do the processing if its ok
+ mask = zz == z_slice
+ full_conc = self.data['continuum_variables'][species_name]['data']
+ conc_arr = full_conc[mask].reshape((zz.shape[0], zz.shape[1]))
+ else:
+ conc_arr = self.data['continuum_variables'][species_name]['data']
+
+ return conc_arr
+
+ def get_concentrations_at(self, x, y, z):
+ """
+ Return concentrations of each chemical species inside a particular voxel
+ that contains the point described in the arguments.
+
+ Parameters
+ ----------
+ x : float
+ x-position for the point of interest
+ y : float
+ y_position for the point of interest
+ z : float
+ z_position for the point of interest
+
+ Returns
+ -------
+ concs : array, shape=[n_substrates,]
+ array of concentrations in the order given by get_substrate_names()
+ """
+ i, j, k = self.get_containing_voxel_ijk(x, y, z)
+ sub_name_list = self.get_substrate_names()
+ concs = np.zeros(len(sub_name_list))
+
+ for ix in range(len(sub_name_list)):
+ concs[ix] = self.get_concentrations(sub_name_list[ix])[j, i, k]
+
+ return concs
+
+
+ ## CELL RELATED FUNCTIONS
+
+ def get_cell_df(self):
+ """
+ Builds DataFrame from data['discrete_cells']
+
+ Returns
+ -------
+ cells_df : pd.Dataframe, shape=[n_cells, n_variables]
+ Dataframe containing the cell data for all cells at this time step
+ """
+ cells_df = pd.DataFrame(self.data['discrete_cells'])
+ return cells_df
+
+ def get_cell_variables(self):
+ """
+ Returns the names of all of the cell variables tracked in ['discrete cells']
+ dictionary
+
+ Returns
+ -------
+ var_list : list, shape=[n_variables]
+ Contains the names of the cell variables
+ """
+ var_list = []
+ for name in self.data['discrete_cells']:
+ var_list.append(name)
+ return var_list
+
+ def get_cell_df_at(self, x, y, z):
+ """
+ Returns a dataframe for cells in the same voxel as the position given by
+ x, y, and z.
+
+ Parameters
+ ----------
+ x : float
+ x-position for the point of interest
+ y : float
+ y_position for the point of interest
+ z : float
+ z_position for the point of interest
+
+ Returns
+ -------
+ vox_df : pd.DataFrame, shape=[n_cell_in_voxel, n_variables]
+ cell dataframe containing only cells in the same voxel as the point
+ specified by x, y, and z.
+ """
+ ds = self.get_mesh_spacing()
+ xx, yy, zz = self.get_mesh()
+ i, j, k = self.get_containing_voxel_ijk(x, y, z)
+ x_vox = xx[j, i, k]
+ y_vox = yy[j, i, k]
+ z_vox = zz[j, i, k]
+
+ cell_df = self.get_cell_df()
+ inside_voxel = ( (cell_df['position_x'] < x_vox + ds/2.) &
+ (cell_df['position_x'] > x_vox - ds/2.) &
+ (cell_df['position_y'] < y_vox + ds/2.) &
+ (cell_df['position_y'] > y_vox - ds/2.) &
+ (cell_df['position_z'] < z_vox + ds/2.) &
+ (cell_df['position_z'] > z_vox - ds/2.) )
+ vox_df = cell_df[inside_voxel]
+ return vox_df
+
+ def _read_xml(self, xml_file, output_path='.'):
+ """
+ Does the actual work of initializing MultiCellDS by parsing the xml
+ """
+
+ output_path = Path(output_path)
+ xml_file = output_path / xml_file
+ tree = ET.parse(xml_file)
+
+ print('Reading {}'.format(xml_file))
+
+ root = tree.getroot()
+ MCDS = {}
+
+ # Get current simulated time
+ metadata_node = root.find('metadata')
+ time_node = metadata_node.find('current_time')
+ MCDS['metadata'] = {}
+ MCDS['metadata']['current_time'] = float(time_node.text)
+ MCDS['metadata']['time_units'] = time_node.get('units')
+
+ # Get current runtime
+ time_node = metadata_node.find('current_runtime')
+ MCDS['metadata']['current_runtime'] = float(time_node.text)
+ MCDS['metadata']['runtime_units'] = time_node.get('units')
+
+ # find the microenvironment node
+ me_node = root.find('microenvironment')
+ me_node = me_node.find('domain')
+
+ # find the mesh node
+ mesh_node = me_node.find('mesh')
+ MCDS['metadata']['spatial_units'] = mesh_node.get('units')
+ MCDS['mesh'] = {}
+
+ # while we're at it, find the mesh
+ coord_str = mesh_node.find('x_coordinates').text
+ delimiter = mesh_node.find('x_coordinates').get('delimiter')
+ x_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ coord_str = mesh_node.find('y_coordinates').text
+ delimiter = mesh_node.find('y_coordinates').get('delimiter')
+ y_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ coord_str = mesh_node.find('z_coordinates').text
+ delimiter = mesh_node.find('z_coordinates').get('delimiter')
+ z_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ # reshape into a mesh grid
+ xx, yy, zz = np.meshgrid(x_coords, y_coords, z_coords)
+
+ MCDS['mesh']['x_coordinates'] = xx
+ MCDS['mesh']['y_coordinates'] = yy
+ MCDS['mesh']['z_coordinates'] = zz
+
+ # Voxel data must be loaded from .mat file
+ voxel_file = mesh_node.find('voxels').find('filename').text
+ voxel_path = output_path / voxel_file
+ try:
+ initial_mesh = sio.loadmat(voxel_path)['mesh']
+ except:
+ raise FileNotFoundError(
+ "No such file or directory:\n'{}' referenced in '{}'".format(voxel_path, xml_file))
+ sys.exit(1)
+
+ print('Reading {}'.format(voxel_path))
+
+ # center of voxel specified by first three rows [ x, y, z ]
+ # volume specified by fourth row
+ MCDS['mesh']['voxels'] = {}
+ MCDS['mesh']['voxels']['centers'] = initial_mesh[:3, :]
+ MCDS['mesh']['voxels']['volumes'] = initial_mesh[3, :]
+
+ # Continuum_variables, unlike in the matlab version the individual chemical
+ # species will be primarily accessed through their names e.g.
+ # MCDS['continuum_variables']['oxygen']['units']
+ # MCDS['continuum_variables']['glucose']['data']
+ MCDS['continuum_variables'] = {}
+ variables_node = me_node.find('variables')
+ file_node = me_node.find('data').find('filename')
+
+ # micro environment data is shape [4+n, len(voxels)] where n is the number
+ # of species being tracked. the first 3 rows represent (x, y, z) of voxel
+ # centers. The fourth row contains the voxel volume. The 5th row and up will
+ # contain values for that species in that voxel.
+ me_file = file_node.text
+ me_path = output_path / me_file
+ # Changes here
+ try:
+ me_data = sio.loadmat(me_path)['multiscale_microenvironment']
+ except:
+ raise FileNotFoundError(
+ "No such file or directory:\n'{}' referenced in '{}'".format(me_path, xml_file))
+ sys.exit(1)
+
+ print('Reading {}'.format(me_path))
+
+ var_children = variables_node.findall('variable')
+
+ # we're going to need the linear x, y, and z coordinates later
+ # but we dont need to get them in the loop
+ X, Y, Z = np.unique(xx), np.unique(yy), np.unique(zz)
+
+ for si, species in enumerate(var_children):
+ species_name = species.get('name')
+ MCDS['continuum_variables'][species_name] = {}
+ MCDS['continuum_variables'][species_name]['units'] = species.get(
+ 'units')
+
+ print('Parsing {:s} data'.format(species_name))
+
+ # initialize array for concentration data
+ MCDS['continuum_variables'][species_name]['data'] = np.zeros(xx.shape)
+
+ # travel down one level on tree
+ species = species.find('physical_parameter_set')
+
+ # diffusion data for each species
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient'] = {}
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient']['value'] \
+ = float(species.find('diffusion_coefficient').text)
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient']['units'] \
+ = species.find('diffusion_coefficient').get('units')
+
+ # decay data for each species
+ MCDS['continuum_variables'][species_name]['decay_rate'] = {}
+ MCDS['continuum_variables'][species_name]['decay_rate']['value'] \
+ = float(species.find('decay_rate').text)
+ MCDS['continuum_variables'][species_name]['decay_rate']['units'] \
+ = species.find('decay_rate').get('units')
+
+ # store data from microenvironment file as numpy array
+ # iterate over each voxel
+ for vox_idx in range(MCDS['mesh']['voxels']['centers'].shape[1]):
+ # find the center
+ center = MCDS['mesh']['voxels']['centers'][:, vox_idx]
+
+ i = np.where(np.abs(center[0] - X) < 1e-10)[0][0]
+ j = np.where(np.abs(center[1] - Y) < 1e-10)[0][0]
+ k = np.where(np.abs(center[2] - Z) < 1e-10)[0][0]
+
+ MCDS['continuum_variables'][species_name]['data'][j, i, k] \
+ = me_data[4+si, vox_idx]
+
+ # in order to get to the good stuff we have to pass through a few different
+ # hierarchal levels
+ cell_node = root.find('cellular_information')
+ cell_node = cell_node.find('cell_populations')
+ cell_node = cell_node.find('cell_population')
+ cell_node = cell_node.find('custom')
+ # we want the PhysiCell data, there is more of it
+ for child in cell_node.findall('simplified_data'):
+ if child.get('source') == 'PhysiCell':
+ cell_node = child
+ break
+
+ MCDS['discrete_cells'] = {}
+ data_labels = []
+ # iterate over 'label's which are children of 'labels' these will be used to
+ # label data arrays
+ for label in cell_node.find('labels').findall('label'):
+ # I don't like spaces in my dictionary keys
+ fixed_label = label.text.replace(' ', '_')
+ if int(label.get('size')) > 1:
+ # tags to differentiate repeated labels (usually space related)
+ dir_label = ['_x', '_y', '_z']
+ for i in range(int(label.get('size'))):
+ data_labels.append(fixed_label + dir_label[i])
+ else:
+ data_labels.append(fixed_label)
+
+ # load the file
+ cell_file = cell_node.find('filename').text
+ cell_path = output_path / cell_file
+ try:
+ cell_data = sio.loadmat(cell_path)['cells']
+ except:
+ raise FileNotFoundError(
+ "No such file or directory:\n'{}' referenced in '{}'".format(cell_path, xml_file))
+ sys.exit(1)
+
+ print('Reading {}'.format(cell_path))
+
+ for col in range(len(data_labels)):
+ MCDS['discrete_cells'][data_labels[col]] = cell_data[col, :]
+
+ return MCDS
diff --git a/pyMCDS_timeseries.py b/pyMCDS_timeseries.py
new file mode 100644
index 0000000..9b22256
--- /dev/null
+++ b/pyMCDS_timeseries.py
@@ -0,0 +1,97 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+from pyMCDS import pyMCDS
+
+class pyMCDS_timeseries:
+ '''
+ This class contains a np.array of pyMCDS objects as well as functions for
+ extracting information from that list.
+
+ Parameters
+ ----------
+ output_path : string
+ String containing the path (relative or absolute) to the directory
+ containing the PhysiCell output files
+
+ Attributes
+ ----------
+ timeseries : array-like (pyMCDS) [n_timesteps,]
+ Numpy array of pyMCDS objects sorted by time.
+ '''
+ def __init__(self, output_path='.'):
+ # get a generator of output xml files sorted alphanumerically
+ sorted_files = sorted(Path(output_path).glob('output*.xml'))
+
+ # make a big array to keep them all, create a pyMCDS object with each
+ # self.timeseries = np.empty(len(sorted_files), dtype=pyMCDS)
+ # first we'll do a list, if its slow we'll figure out the array
+ ts_list = []
+ for f in sorted_files:
+ ts_list.append(pyMCDS(f.name, output_path))
+
+ self.timeseries = np.array(ts_list)
+
+ def get_times(self):
+ '''
+ Helper function to easily return the cumulative time at each step.
+
+ Returns
+ -------
+ time_array : ndaray (np.float) [n_timesteps,]
+ Contains the 'current time' at each recorded point.
+ '''
+ time_array = np.zeros(self.timeseries.shape[0])
+ for i in range(len(self.timeseries)):
+ time_array[i] = self.timeseries[i].get_time()
+
+ return time_array
+
+ def plot_menv_total(self, species):
+ '''
+ Plot total concentration of chemicals in the system over time.
+
+ Parameters
+ ----------
+ species : array-like (string) or string
+ Name os names of chemical species to plot
+ '''
+ # set up arrays, quicker than lists
+ times = self.get_times()
+ total_conc = np.zeros((times.shape[0], len(species)))
+ # iterate over the times to fill array
+ for time in range(times.shape[0]):
+ # iterate over the chemical species to fill array
+ for i in range(len(species)):
+ conc_arr = self.timeseries[time].get_concentrations(species[i])
+ total_conc[time, i] = np.sum(conc_arr)
+ # establish plots
+ fig, ax = plt.subplots(figsize=(10, 8))
+ # add plot elements and show plot
+ for i, chem in enumerate(species):
+ ax.plot(times, total_conc[:, i], label=chem)
+ ax.set_xlabel('Time (min)')
+ ax.set_ylabel('Total concentration')
+ ax.legend()
+ plt.show()
+
+ def plot_cell_type_counts(self):
+ '''
+ Plot the absolute counts of different types of cells over time.
+ '''
+ times = self.get_times()
+ unique_types = np.unique(self.timeseries[0].data['discrete_cells']['cell_type'])
+ cell_counts = np.zeros((times.shape[0], unique_types.shape[0]))
+ for time_i in range(times.shape[0]):
+ df = self.timeseries[time_i].get_cells_df()
+ counts = df['cell_type'].value_counts()
+ cell_counts[time_i, :] = counts
+ fig, ax = plt.subplots(figsize=(10, 8))
+ for i in range(cell_counts.shape[1]):
+ ax.scatter(times, cell_counts[:, i], label=i, s=10)
+ ax.set_xlabel('Time (min)')
+ ax.set_ylabel('# of cells in system')
+ ax.legend()
+ plt.show()
+
+
diff --git a/read_MultiCellDS_xml.py b/read_MultiCellDS_xml.py
new file mode 100644
index 0000000..b1a1690
--- /dev/null
+++ b/read_MultiCellDS_xml.py
@@ -0,0 +1,184 @@
+import xml.etree.ElementTree as ET
+import numpy as np
+import pandas as pd
+import scipy.io as sio
+from pathlib import Path
+
+
+def read_MultiCellDS_xml(xml_file, output_path='.'):
+ """
+ Parses xml and returns a dictionary
+ """
+ output_path = Path(output_path)
+ xml_file = output_path / xml_file
+ tree = ET.parse(xml_file)
+
+ root = tree.getroot()
+ MCDS = {}
+
+ # Get current simulated time
+ metadata_node = root.find('metadata')
+ time_node = metadata_node.find('current_time')
+ MCDS['metadata'] = {}
+ MCDS['metadata']['current_time'] = float(time_node.text)
+ MCDS['metadata']['time_units'] = time_node.get('units')
+
+ # Get current runtime
+ time_node = metadata_node.find('current_runtime')
+ MCDS['metadata']['current_runtime'] = float(time_node.text)
+ MCDS['metadata']['runtime_units'] = time_node.get('units')
+
+ # find the microenvironment node
+ me_node = root.find('microenvironment')
+ me_node = me_node.find('domain')
+
+ # find the mesh node
+ mesh_node = me_node.find('mesh')
+ MCDS['metadata']['spatial_units'] = mesh_node.get('units')
+ MCDS['mesh'] = {}
+
+ # while we're at it, find the mesh
+ coord_str = mesh_node.find('x_coordinates').text
+ delimiter = mesh_node.find('x_coordinates').get('delimiter')
+ x_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ coord_str = mesh_node.find('y_coordinates').text
+ delimiter = mesh_node.find('y_coordinates').get('delimiter')
+ y_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ coord_str = mesh_node.find('z_coordinates').text
+ delimiter = mesh_node.find('z_coordinates').get('delimiter')
+ z_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ # reshape into a mesh grid
+ xx, yy, zz = np.meshgrid(x_coords, y_coords, z_coords)
+
+ MCDS['mesh']['x_coordinates'] = xx
+ MCDS['mesh']['y_coordinates'] = yy
+ MCDS['mesh']['z_coordinates'] = zz
+
+ # Voxel data must be loaded from .mat file
+ voxel_file = mesh_node.find('voxels').find('filename').text
+ voxel_path = output_path / voxel_file
+ try:
+ initial_mesh = sio.loadmat(voxel_path)['mesh']
+ except:
+ raise FileNotFoundError(
+ "No such file or directory:\n'{}' referenced in '{}'".format(voxel_path, xml_file))
+ sys.exit(1)
+
+ # center of voxel specified by first three rows [ x, y, z ]
+ # volume specified by fourth row
+ MCDS['mesh']['voxels'] = {}
+ MCDS['mesh']['voxels']['centers'] = initial_mesh[:3, :]
+ MCDS['mesh']['voxels']['volumes'] = initial_mesh[3, :]
+
+ # Continuum_variables, unlike in the matlab version the individual chemical
+ # species will be primarily accessed through their names e.g.
+ # MCDS['continuum_variables']['oxygen']['units']
+ # MCDS['continuum_variables']['glucose']['data']
+ MCDS['continuum_variables'] = {}
+ variables_node = me_node.find('variables')
+ file_node = me_node.find('data').find('filename')
+
+ # micro environment data is shape [4+n, len(voxels)] where n is the number
+ # of species being tracked. the first 3 rows represent (x, y, z) of voxel
+ # centers. The fourth row contains the voxel volume. The 5th row and up will
+ # contain values for that species in that voxel.
+ me_file = file_node.text
+ me_path = output_path / me_file
+ # Changes here
+ try:
+ me_data = sio.loadmat(me_path)['multiscale_microenvironment']
+ except:
+ raise FileNotFoundError(
+ "No such file or directory:\n'{}' referenced in '{}'".format(me_path, xml_file))
+ sys.exit(1)
+
+ var_children = variables_node.findall('variable')
+
+ # we're going to need the linear x, y, and z coordinates later
+ # but we dont need to get them in the loop
+ X, Y, Z = np.unique(xx), np.unique(yy), np.unique(zz)
+
+ for si, species in enumerate(var_children):
+ species_name = species.get('name')
+ MCDS['continuum_variables'][species_name] = {}
+ MCDS['continuum_variables'][species_name]['units'] = species.get(
+ 'units')
+
+ # initialize array for concentration data
+ MCDS['continuum_variables'][species_name]['data'] = np.zeros(
+ xx.shape)
+
+ # travel down one level on tree
+ species = species.find('physical_parameter_set')
+
+ # diffusion data for each species
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient'] = {}
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient']['value'] \
+ = float(species.find('diffusion_coefficient').text)
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient']['units'] \
+ = species.find('diffusion_coefficient').get('units')
+
+ # decay data for each species
+ MCDS['continuum_variables'][species_name]['decay_rate'] = {}
+ MCDS['continuum_variables'][species_name]['decay_rate']['value'] \
+ = float(species.find('decay_rate').text)
+ MCDS['continuum_variables'][species_name]['decay_rate']['units'] \
+ = species.find('decay_rate').get('units')
+
+ # store data from microenvironment file as numpy array
+ # iterate over each voxel
+ for vox_idx in range(MCDS['mesh']['voxels']['centers'].shape[1]):
+ # find the center
+ center = MCDS['mesh']['voxels']['centers'][:, vox_idx]
+
+ i = np.where(np.abs(center[0] - X) < 1e-10)[0][0]
+ j = np.where(np.abs(center[1] - Y) < 1e-10)[0][0]
+ k = np.where(np.abs(center[2] - Z) < 1e-10)[0][0]
+
+ MCDS['continuum_variables'][species_name]['data'][j, i, k] \
+ = me_data[4+si, vox_idx]
+
+ # in order to get to the good stuff we have to pass through a few different
+ # hierarchal levels
+ cell_node = root.find('cellular_information')
+ cell_node = cell_node.find('cell_populations')
+ cell_node = cell_node.find('cell_population')
+ cell_node = cell_node.find('custom')
+ # we want the PhysiCell data, there is more of it
+ for child in cell_node.findall('simplified_data'):
+ if child.get('source') == 'PhysiCell':
+ cell_node = child
+ break
+
+ MCDS['discrete_cells'] = {}
+ data_labels = []
+ # iterate over 'label's which are children of 'labels' these will be used to
+ # label data arrays
+ for label in cell_node.find('labels').findall('label'):
+ # I don't like spaces in my dictionary keys
+ fixed_label = label.text.replace(' ', '_')
+ if int(label.get('size')) > 1:
+ # tags to differentiate repeated labels (usually space related)
+ dir_label = ['_x', '_y', '_z']
+ for i in range(int(label.get('size'))):
+ data_labels.append(fixed_label + dir_label[i])
+ else:
+ data_labels.append(fixed_label)
+
+ # load the file
+ cell_file = cell_node.find('filename').text
+ cell_path = output_path / cell_file
+ try:
+ cell_data = sio.loadmat(cell_path)['cells']
+ except:
+ raise FileNotFoundError(
+ "No such file or directory:\n'{}' referenced in '{}'".format(cell_path, xml_file))
+ sys.exit(1)
+
+ for col in range(len(data_labels)):
+ MCDS['discrete_cells'][data_labels[col]] = cell_data[col, :]
+
+ return MCDS
diff --git a/test_single.py b/test_single.py
new file mode 100644
index 0000000..ae2f09f
--- /dev/null
+++ b/test_single.py
@@ -0,0 +1,8 @@
+from pyMCDS import pyMCDS
+
+mcds1 = pyMCDS('output00000001.xml', 'timeseries_set')
+mcds2 = pyMCDS('output00000008.xml', 'timeseries_set')
+
+print(mcds1.get_time())
+print(mcds2.get_menv_species_list())
+print(mcds2.get_concentrations('quorum'))
\ No newline at end of file
diff --git a/test_timeseries.py b/test_timeseries.py
new file mode 100644
index 0000000..62e7724
--- /dev/null
+++ b/test_timeseries.py
@@ -0,0 +1,8 @@
+from pyMCDS import *
+from pyMCDS_timeseries import *
+
+mcds = pyMCDS_timeseries('timeseries_set')
+
+chem_list = mcds.timeseries[0].get_menv_species_list()
+mcds.plot_menv_total(chem_list)
+mcds.plot_cell_type_counts()
diff --git a/timeseries_set/config.xml b/timeseries_set/config.xml
new file mode 100644
index 0000000..caedea8
--- /dev/null
+++ b/timeseries_set/config.xml
@@ -0,0 +1,104 @@
+
+
+ -500.0
+ 500.0
+ -500.0
+ 500.0
+ -10.0
+ 10.0
+ 20.0
+ 20.0
+ 20.0
+ true
+
+
+
+ 7200.0
+ min
+ micron
+
+
+
+ 4
+
+
+
+ .
+
+
+ 60
+ True
+
+
+
+
+
+ false
+
+
+
+
+ 0
+
+
+ 100000.0
+ 0.1
+
+ 100000.0
+ 10.0
+
+ 40000.0
+ 1.0
+
+ 25000.0
+ 0.1
+
+ 50000.0
+ 20.0
+
+
+ 15
+ 50
+ 10
+ 50
+
+
+ 0.0028
+ 0.001
+
+ 15.0
+ 0.25
+ 0.5
+
+ 100.0
+ 0.1
+
+
+ 15.0
+ 0.5
+ 0.125
+
+ 100.0
+ 0.1
+
+
+ 0.0005
+ 0.0001
+
+ 15.0
+ 1.0
+ 0.25
+
+ 100.0
+ 0.1
+
+
+ 100.0
+
+
+
+
+
\ No newline at end of file
diff --git a/timeseries_set/final.xml b/timeseries_set/final.xml
new file mode 100644
index 0000000..022623e
--- /dev/null
+++ b/timeseries_set/final.xml
@@ -0,0 +1,106 @@
+
+
+
+
+ BioFVM
+ 1.1.7
+ http://BioFVM.MathCancer.org
+
+
+
+
+
+ 7200.010000
+ 121.639080
+ 2019-08-30T19:50:09Z
+ 2019-08-30T19:50:09Z
+
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+ 0.000000
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+
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+
+
+
+
+ final_microenvironment0.mat
+
+
+
+
+
+
+
+
+ final_cells.mat
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ final_cells_physicell.mat
+
+
+
+
+
+
diff --git a/timeseries_set/final_cells.mat b/timeseries_set/final_cells.mat
new file mode 100644
index 0000000..2f83f1a
Binary files /dev/null and b/timeseries_set/final_cells.mat differ
diff --git a/timeseries_set/final_cells_physicell.mat b/timeseries_set/final_cells_physicell.mat
new file mode 100644
index 0000000..08e88fa
Binary files /dev/null and b/timeseries_set/final_cells_physicell.mat differ
diff --git a/timeseries_set/final_microenvironment0.mat b/timeseries_set/final_microenvironment0.mat
new file mode 100644
index 0000000..110f2aa
Binary files /dev/null and b/timeseries_set/final_microenvironment0.mat differ
diff --git a/timeseries_set/initial.xml b/timeseries_set/initial.xml
new file mode 100644
index 0000000..c683da6
--- /dev/null
+++ b/timeseries_set/initial.xml
@@ -0,0 +1,106 @@
+
+
+
+
+ BioFVM
+ 1.1.7
+ http://BioFVM.MathCancer.org
+
+
+
+
+
+ 0.000000
+ 0.008113
+ 2019-08-30T19:48:08Z
+ 2019-08-30T19:48:08Z
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+
+
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+
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+ initial_cells.mat
+
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+
+
+
+
+
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+
+
+
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+
+
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+
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+
+
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diff --git a/timeseries_set/initial_cells.mat b/timeseries_set/initial_cells.mat
new file mode 100644
index 0000000..00c174a
Binary files /dev/null and b/timeseries_set/initial_cells.mat differ
diff --git a/timeseries_set/initial_cells_physicell.mat b/timeseries_set/initial_cells_physicell.mat
new file mode 100644
index 0000000..9507b33
Binary files /dev/null and b/timeseries_set/initial_cells_physicell.mat differ
diff --git a/timeseries_set/initial_mesh0.mat b/timeseries_set/initial_mesh0.mat
new file mode 100644
index 0000000..a6ebd75
Binary files /dev/null and b/timeseries_set/initial_mesh0.mat differ
diff --git a/timeseries_set/initial_microenvironment0.mat b/timeseries_set/initial_microenvironment0.mat
new file mode 100644
index 0000000..195d58b
Binary files /dev/null and b/timeseries_set/initial_microenvironment0.mat differ
diff --git a/timeseries_set/output00000000.xml b/timeseries_set/output00000000.xml
new file mode 100644
index 0000000..57dd82a
--- /dev/null
+++ b/timeseries_set/output00000000.xml
@@ -0,0 +1,106 @@
+
+
+
+
+ BioFVM
+ 1.1.7
+ http://BioFVM.MathCancer.org
+
+
+
+
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+
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+
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+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+ output00000000_cells_physicell.mat
+
+
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diff --git a/timeseries_set/output00000000_cells.mat b/timeseries_set/output00000000_cells.mat
new file mode 100644
index 0000000..00c174a
Binary files /dev/null and b/timeseries_set/output00000000_cells.mat differ
diff --git a/timeseries_set/output00000000_cells_physicell.mat b/timeseries_set/output00000000_cells_physicell.mat
new file mode 100644
index 0000000..9507b33
Binary files /dev/null and b/timeseries_set/output00000000_cells_physicell.mat differ
diff --git a/timeseries_set/output00000000_microenvironment0.mat b/timeseries_set/output00000000_microenvironment0.mat
new file mode 100644
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+ -490.000000 -470.000000 -450.000000 -430.000000 -410.000000 -390.000000 -370.000000 -350.000000 -330.000000 -310.000000 -290.000000 -270.000000 -250.000000 -230.000000 -210.000000 -190.000000 -170.000000 -150.000000 -130.000000 -110.000000 -90.000000 -70.000000 -50.000000 -30.000000 -10.000000 10.000000 30.000000 50.000000 70.000000 90.000000 110.000000 130.000000 150.000000 170.000000 190.000000 210.000000 230.000000 250.000000 270.000000 290.000000 310.000000 330.000000 350.000000 370.000000 390.000000 410.000000 430.000000 450.000000 470.000000 490.000000
+ 0.000000
+
+ initial_mesh0.mat
+
+
+
+
+
+
+ 100000.000000
+ 0.100000
+
+
+
+
+
+ 100000.000000
+ 10.000000
+
+
+
+
+
+ 40000.000000
+ 1.000000
+
+
+
+
+
+ 25000.000000
+ 0.100000
+
+
+
+
+
+ 50000.000000
+ 20.000000
+
+
+
+
+ output00000120_microenvironment0.mat
+
+
+
+
+
+
+
+
+ output00000120_cells.mat
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ output00000120_cells_physicell.mat
+
+
+
+
+
+
diff --git a/timeseries_set/output00000120_cells.mat b/timeseries_set/output00000120_cells.mat
new file mode 100644
index 0000000..3585884
Binary files /dev/null and b/timeseries_set/output00000120_cells.mat differ
diff --git a/timeseries_set/output00000120_cells_physicell.mat b/timeseries_set/output00000120_cells_physicell.mat
new file mode 100644
index 0000000..439baf7
Binary files /dev/null and b/timeseries_set/output00000120_cells_physicell.mat differ
diff --git a/timeseries_set/output00000120_microenvironment0.mat b/timeseries_set/output00000120_microenvironment0.mat
new file mode 100644
index 0000000..8de6f95
Binary files /dev/null and b/timeseries_set/output00000120_microenvironment0.mat differ
diff --git a/timeseries_set/read_MultiCellDS_xml.py b/timeseries_set/read_MultiCellDS_xml.py
new file mode 100644
index 0000000..87ea2bb
--- /dev/null
+++ b/timeseries_set/read_MultiCellDS_xml.py
@@ -0,0 +1,171 @@
+# %%
+import xml.etree.ElementTree as TE
+import numpy as np
+import pandas as pd
+import scipy.io as sio
+from pathlib import Path
+
+def read_MultiCellDS_xml(file_name, output_dir = '.'):
+
+ output_path = Path(output_dir)
+ xml_file = output_path / 'output00000001.xml'
+ try:
+ tree = TE.parse(xml_file)
+ except:
+ print('Data File:', xml_file, 'not found!')
+ exit(0)
+
+ root = tree.getroot()
+ MCDS = {}
+
+ # Get current simulated time
+ metadata_node = root.find('metadata')
+ time_node = metadata_node.find('current_time')
+ MCDS['metadata'] = {}
+ MCDS['metadata']['current_time'] = float(time_node.text)
+ MCDS['metadata']['time_units'] = time_node.get('units')
+
+ # Get current runtime
+ time_node = metadata_node.find('current_runtime')
+ MCDS['metadata']['current_runtime'] = float(time_node.text)
+ MCDS['metadata']['runtime_units'] = time_node.get('units')
+
+ # find the microenvironment node
+ me_node = root.find('microenvironment')
+ me_node = me_node.find('domain')
+
+ # find the mesh node
+ mesh_node = me_node.find('mesh')
+ MCDS['metadata']['spatial_units'] = mesh_node.get('units')
+ MCDS['mesh'] = {}
+
+ # check for cartesian mesh
+ cartesian = False
+ mesh_type = mesh_node.get('type')
+ if mesh_type == 'Cartesian':
+ cartesian = True
+
+ # while we're at it, find the mesh
+ coord_str = mesh_node.find('x_coordinates').text
+ delimiter = mesh_node.find('x_coordinates').get('delimiter')
+ x_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ coord_str = mesh_node.find('y_coordinates').text
+ delimiter = mesh_node.find('y_coordinates').get('delimiter')
+ y_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ coord_str = mesh_node.find('z_coordinates').text
+ delimiter = mesh_node.find('z_coordinates').get('delimiter')
+ z_coords = np.array(coord_str.split(delimiter), dtype=np.float)
+
+ # reshape into a mesh grid
+ xx, yy, zz = np.meshgrid(x_coords, y_coords, z_coords)
+
+ MCDS['mesh']['x_coordinates'] = xx
+ MCDS['mesh']['y_coordinates'] = yy
+ MCDS['mesh']['z_coordinates'] = zz
+
+ # Voxel data must be loaded from .mat file
+ voxel_file = mesh_node.find('voxels').find('filename').text
+ voxel_path = output_path / voxel_file
+ try:
+ initial_mesh = sio.loadmat(voxel_path)['mesh']
+ except:
+ print('Data file', voxel_path, 'missing!')
+ print('Referenced in', xml_file)
+ exit(0)
+
+ # center of voxel specified by first three rows [ x, y, z ]
+ # volume specified by fourth row
+ MCDS['mesh']['voxels'] = {}
+ MCDS['mesh']['voxels']['centers'] = initial_mesh[:3, :]
+ MCDS['mesh']['voxels']['volumes'] = initial_mesh[3, :]
+
+ # Continuum_variables, unlike in the matlab version the individual chemical
+ # species will be primarily accessed through their names e.g.
+ # MCDS['continuum_variables']['oxygen']['units']
+ # MCDS['continuum_variables']['glucose']['data']
+ MCDS['continuum_variables'] = {}
+ variables_node = me_node.find('variables')
+ file_node = me_node.find('data').find('filename')
+
+ # micro environment data is shape [4+n, len(voxels)] where n is the number
+ # of species being tracked. the first 3 rows represent (x, y, z) of voxel centers.
+ # The fourth row contains the voxel volume. The 5th row and up will contain values
+ # for that species in that voxel.
+ me_file = file_node.text
+ me_path = output_path / me_file
+ try:
+ me_data = sio.loadmat(me_path)['multiscale_microenvironment']
+ except:
+ print('Data file', me_path, 'missing!')
+ print('Referenced in', xml_file)
+ exit(0)
+
+ var_children = variables_node.findall('variable')
+
+ for i, species in enumerate(var_children):
+ species_name = species.get('name')
+ MCDS['continuum_variables'][species_name] = {}
+ MCDS['continuum_variables'][species_name]['units'] = species.get('units')
+
+ # travel down one level on tree
+ species = species.find('physical_parameter_set')
+
+ # diffusion data for each species
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient'] = {}
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient']['value'] \
+ = float(species.find('diffusion_coefficient').text)
+ MCDS['continuum_variables'][species_name]['diffusion_coefficient']['units'] \
+ = species.find('diffusion_coefficient').get('units')
+
+ # decay data for each species
+ MCDS['continuum_variables'][species_name]['decay_rate'] = {}
+ MCDS['continuum_variables'][species_name]['decay_rate']['value'] \
+ = float(species.find('decay_rate').text)
+ MCDS['continuum_variables'][species_name]['decay_rate']['units'] \
+ = species.find('decay_rate').get('units')
+
+ # store data from microenvironment file as numpy array
+ MCDS['continuum_variables'][species_name]['data'] \
+ = me_data[4+i, :].reshape(xx.shape)
+
+ # in order to get to the good stuff we have to pass through a few different
+ # hierarchal levels
+ cell_node = root.find('cellular_information')
+ cell_node = cell_node.find('cell_populations')
+ cell_node = cell_node.find('cell_population')
+ cell_node = cell_node.find('custom')
+ # we want the PhysiCell data, there is more of it
+ for child in cell_node.findall('simplified_data'):
+ if child.get('source') == 'PhysiCell':
+ cell_node = child
+ break
+
+ MCDS['discrete_cells'] = {}
+ df_labels = []
+ # iterate over 'label's which are children of 'labels' these will be used to
+ # label dataframe columns
+ for label in cell_node.find('labels').findall('label'):
+ if int(label.get('size')) > 1:
+ dir_label = ['_x', '_y', '_z']
+ for i in range(int(label.get('size'))):
+ df_labels.append(label.text + dir_label[i])
+ else:
+ df_labels.append(label.text)
+
+ # load the file, we want it transposed from the matlab format
+ cell_file = cell_node.find('filename').text
+ cell_path = output_path / cell_file
+ try:
+ cell_data = sio.loadmat(cell_path)['cells'].T
+ except:
+ print('Data file', cell_path, 'missing!')
+ print('Referenced in', xml_file)
+ exit(0)
+
+ # we will save the cell information as a dataframe in the MCDS object
+ cell_df = pd.DataFrame(cell_data, columns=df_labels)
+ MCDS['discrete_cells']['data_df'] = cell_df
+
+ return MCDS