def read_xml_data(folder=None, filename='output00000246.xml'):
output_path = folder or os.path.abspath(os.path.dirname(__file__))
xml_file = os.path.join(output_path, filename)
mcds = pyMCDS_cells(xml_file, output_path=output_path)
ncells = len(mcds.data['discrete_cells']['ID'])
centers = np.zeros((ncells, 3))
centers[:, 0] = mcds.data['discrete_cells']['position_x']
centers[:, 1] = mcds.data['discrete_cells']['position_y']
centers[:, 2] = mcds.data['discrete_cells']['position_z']
colors = np.zeros((ncells, 3))
colors[:, 0] = 1
colors[:, 1] = 1
colors[:, 2] = 0
cycle_model = mcds.data['discrete_cells']['cycle_model']
cell_type = mcds.data['discrete_cells']['cell_type']
onco = mcds.data['discrete_cells']['oncoprotein']
onco_min = onco.min()
onco_range = onco.max() - onco.min()
# This coloring is only approximately correct, but at least it shows
# variation in cell colors
for idx in range(ncells):
if cell_type[idx] == 1:
colors[idx, 0] = 1
colors[idx, 1] = 1
colors[idx, 2] = 0
if cycle_model[idx] < 100:
colors[idx, 0] = 1.0 - (onco[idx] - onco_min) / onco_range
colors[idx, 1] = colors[idx, 0]
colors[idx, 2] = 0
elif cycle_model[idx] == 100:
colors[idx, 0] = 1
colors[idx, 1] = 0
colors[idx, 2] = 0
elif cycle_model[idx] > 100:
colors[idx, 0] = 0.54 # 139./255
colors[idx, 1] = 0.27 # 69./255
colors[idx, 2] = 0.075 # 19./255
radius = mcds.data['discrete_cells']['total_volume'] * .75 / np.pi
radius = np.cbrt(radius)
return centers, colors, radius
def read_data():
global idx_xml, xml_files
mcds = pyMCDS_cells(xml_files[idx_xml], _DATA_DIR)
ncells = len(mcds.data['discrete_cells']['ID'])
centers = np.zeros((ncells, 3))
centers[:, 0] = mcds.data['discrete_cells']['position_x']
centers[:, 1] = mcds.data['discrete_cells']['position_y']
centers[:, 2] = mcds.data['discrete_cells']['position_z']
colors = np.zeros((ncells, 3))
colors[:, 0] = 1
colors[:, 1] = 1
colors[:, 2] = 0
cycle_model = mcds.data['discrete_cells']['cycle_model']
cell_type = mcds.data['discrete_cells']['cell_type']
onco = mcds.data['discrete_cells']['oncoprotein']
onco_min = onco.min()
onco_range = onco.max() - onco.min()
# This coloring is only approximately correct, but at least it shows
# variation in cell colors
for idx in range(ncells):
if cell_type[idx] == 1:
colors[idx, 0] = 1
colors[idx, 1] = 1
colors[idx, 2] = 0
if cycle_model[idx] < 100:
colors[idx, 0] = 1.0 - (onco[idx] - onco_min) / onco_range
colors[idx, 1] = colors[idx, 0]
colors[idx, 2] = 0
elif cycle_model[idx] == 100:
colors[idx, 0] = 1
colors[idx, 1] = 0
colors[idx, 2] = 0
elif cycle_model[idx] > 100:
colors[idx, 0] = 0.54 # 139./255
colors[idx, 1] = 0.27 # 69./255
colors[idx, 2] = 0.075 # 19./255
radius = mcds.data['discrete_cells']['total_volume'] * .75 / np.pi
radius = np.cbrt(radius)
return centers, colors, radius
print('# num_days = ', num_days)
kdx += 1
ymax = float(sys.argv[kdx])
print('ymax = ', ymax)
print('data_dir = ', data_dir)
os.chdir(data_dir)
#xml_files = glob.glob('output/output*.xml')
xml_files = glob.glob('output*.xml')
os.chdir('..')
xml_files.sort()
#print('xml_files = ',xml_files)
ds_count = len(xml_files)
print("----- ds_count = ", ds_count)
mcds = [pyMCDS_cells(xml_files[i], data_dir) for i in range(ds_count)]
tval = np.linspace(0, mcds[-1].get_time(), ds_count)
print('tval= ', tval)
y_load = np.array([
np.floor(mcds[idx].data['discrete_cells']['assembled_virion']).sum()
for idx in range(ds_count)
]).astype(int)
print(y_load)
xvals = [*range(1, num_days + 1)]
xvals = [el * 1440 for el in xvals]
xlabels = tuple(str(val) for val in range(1, num_days + 1))
plt.xticks(xvals, xlabels)
import numpy as np
import matplotlib.pyplot as plt # if you want to plot results
# run this script from your output directory
xml_files = glob.glob('output*.xml')
xml_files.sort()
print(xml_files)
n = len(xml_files)
t = np.zeros(n)
uninfected = np.zeros(n)
infected = np.zeros(n)
dead = np.zeros(n)
idx = 0
for f in xml_files:
mcds = pyMCDS_cells(f, '.')
t[idx] = mcds.get_time()
cycle = mcds.data['discrete_cells']['cycle_model']
cycle = cycle.astype(int)
ID_uninfected = np.where(
(mcds.data['discrete_cells']['assembled_virion'] < 1) & (cycle < 100))
ID_infected = np.where(
(mcds.data['discrete_cells']['assembled_virion'] >= 1) & (cycle < 100))
uninfected[idx] = len(ID_uninfected[0])
infected[idx] = len(ID_infected[0])
dead_ID = np.where(cycle >= 100)
dead[idx] = len(dead_ID[0])
# print(infected)
colors_array.Modified()
def win_callback(obj, event):
global size
global panel
if size != obj.GetSize():
size_old = size
size = obj.GetSize()
size_change = [size[0] - size_old[0], 0]
panel.re_align(size_change)
if __name__ == '__main__':
# mcds = pyMCDS_cells('output00000001.xml', '.') # 23123 cells
mcds = pyMCDS_cells('output00000246.xml', '.')
print('time=', mcds.get_time())
print(mcds.data['discrete_cells'].keys())
ncells = len(mcds.data['discrete_cells']['ID'])
global xyz
xyz = np.zeros((ncells, 3))
xyz[:, 0] = mcds.data['discrete_cells']['position_x']
xyz[:, 1] = mcds.data['discrete_cells']['position_y']
xyz[:, 2] = mcds.data['discrete_cells']['position_z']
#xyz = xyz[:1000]
np.random.seed(42)
# rgb = np.random.rand(xyz.shape[0], 3)
#from pyMCDS import pyMCDS
from pyMCDS_cells import pyMCDS_cells
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("TkAgg")
argc = len(sys.argv)
print('argv=', sys.argv)
print('argv[0]=', sys.argv[0])
#p1=string.atof(sys.argv[1])
#mcds = pyMCDS_cells('output00000000.xml','output');
#mcds = pyMCDS_cells('output00000002.xml')
fname = "output%08d.xml" % int(sys.argv[1])
mcds = pyMCDS_cells(fname)
#In [7]: mcds.data['discrete_cells'].keys()
#Out[7]: dict_keys(['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', 'receptor', 'elastic_coefficient'])
tval = int(mcds.get_time())
print('time = ', tval)
cx = mcds.data['discrete_cells']['position_x']
cy = mcds.data['discrete_cells']['position_y']
print('x size =', cx.size)
print('y size =', cy.size)
bad_max = 5000
#idx_bad = np.where((cx > bad_max) ^ (cy > bad_max))
#print('idx_bad=',idx_bad)
#cycle = mcds.data['discrete_cells']['cycle_model']
from pyMCDS_cells import pyMCDS_cells
import numpy as np
from fury import window, actor, ui
#mcds = pyMCDS_cells('output00000001.xml','data')
mcds = pyMCDS_cells('output00000001.xml','.') # 23123 cells
print('time=',mcds.get_time())
print(mcds.data['discrete_cells'].keys())
#Out[7]: dict_keys(['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'])
# http://www.mathcancer.org/blog/paraview-for-physicell-part-1/
# The following lines assign an integer to represent
# a color, defined in a Color Map.
# sval = 0 # immune cells are yellow?
# if val[5,idx] == 1: # [5]=cell_type
# sval = 1 # lime green
# if (val[6,idx] == 6) or (val[6,idx] == 7):
# sval = 0
# if val[7,idx] == 100: # [7]=current_phase
# sval = 3 # apoptotic: red
# if val[7,idx] > 100 and val[7,idx] < 104:
# sval = 2 # necrotic: brownish
ncells = len(mcds.data['discrete_cells']['ID'])
print('num cells = ',ncells)
#xyz = np.empty((ncells,3))
xyz = np.zeros((ncells,3))
import glob
from pyMCDS_cells import pyMCDS_cells
import numpy as np
xml_files = glob.glob('output*.xml')
xml_files.sort()
print(xml_files)
num_xml = len(xml_files)
print(num_xml)
tcount = 0
count_macs = np.zeros(44)
#mcds1 = pyMCDS('output00000001.xml', 'timeseries_set')
for xml_f in xml_files:
# mcds = pyMCDS_cells('output00000001.xml','.')
mcds = pyMCDS_cells(xml_f,'.')
# Matlab (Adrianne)
# ind1(tcount) = length(find( MCDS.discrete_cells.metadata.type == 3)); %CD8
# ind2(tcount) = length(find( MCDS.discrete_cells.metadata.type == 4)); %macs
# ind3(tcount) = length(find( MCDS.discrete_cells.metadata.type == 5)); %neutrophils
# dead_cells(tcount) = length(MCDS.discrete_cells.dead_cells);
# live_target_cells(tcount) = length(find( MCDS.discrete_cells.metadata.type == 1))-dead_cells(tcount);
# infected_cells(tcount) = length(find(MCDS.discrete_cells.custom.virion>1));
print('time=',mcds.get_time())
cell_id = mcds.data['discrete_cells']['ID']
num_cells = cell_id.shape[0]
print('# cells = ',num_cells)
cell_type = mcds.data['discrete_cells']['cell_type']
macs = np.where(cell_type == 4.0)
count_macs[tcount] = len(macs[0])
from pyMCDS_cells import pyMCDS_cells
import numpy as np
from fury import window, actor, ui
#mcds = pyMCDS_cells('output00000001.xml','data')
#mcds = pyMCDS_cells('output00000001.xml','.') # 23123 cells
mcds = pyMCDS_cells('output00000001.xml', '../tmpdir') # 23123 cells
print('time=', mcds.get_time())
print(mcds.data['discrete_cells'].keys())
#Out[7]: dict_keys(['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'])
# http://www.mathcancer.org/blog/paraview-for-physicell-part-1/
# The following lines assign an integer to represent
# a color, defined in a Color Map.
# sval = 0 # immune cells are yellow?
# if val[5,idx] == 1: # [5]=cell_type
# sval = 1 # lime green
# if (val[6,idx] == 6) or (val[6,idx] == 7):
# sval = 0
# if val[7,idx] == 100: # [7]=current_phase
# sval = 3 # apoptotic: red
# if val[7,idx] > 100 and val[7,idx] < 104:
# sval = 2 # necrotic: brownish
ncells = len(mcds.data['discrete_cells']['ID'])
print('num cells = ', ncells)
#xyz = np.empty((ncells,3))
xyz = np.zeros((ncells, 3))