def __init__(self, env=None, viewer=None, result_file=None):
self.env = None
self.smallest = 99999.0
self.largest = 0.0
if env:
self.env = env
self.smallest, self.largest = self.getMinMaxProteinSize()
self.afviewer = viewer
self.helper = None
if viewer:
self.helper = self.afviewer.vi
self.resutl_file = result_file
self.center = [0, 0, 0]
self.bbox = [[0, 0, 0], [1, 1, 1]]
self.g = GeometriTools()
self.g.Resolution = 1.0 #or grid step?
def __init__(self, env=None, viewer=None, result_file=None):
self.env=None
self.smallest=99999.0
self.largest = 0.0
if env :
self.env = env
self.smallest,self.largest = self.getMinMaxProteinSize()
self.afviewer = viewer
self.helper = None
if viewer :
self.helper = self.afviewer.vi
self.resutl_file = result_file
self.center=[0,0,0]
self.bbox=[[0,0,0],[1,1,1]]
self.g=GeometriTools()
self.g.Resolution = 1.0#or grid step?
class AnalyseAP:
def __init__(self, env=None, viewer=None, result_file=None):
self.env = None
self.smallest = 99999.0
self.largest = 0.0
if env:
self.env = env
self.smallest, self.largest = self.getMinMaxProteinSize()
self.afviewer = viewer
self.helper = None
if viewer:
self.helper = self.afviewer.vi
self.resutl_file = result_file
self.center = [0, 0, 0]
self.bbox = [[0, 0, 0], [1, 1, 1]]
self.g = GeometriTools()
self.g.Resolution = 1.0 #or grid step?
def getMinMaxProteinSize(self):
smallest = 999999.0
largest = 0.0
for organelle in self.env.compartments:
mini, maxi = organelle.getMinMaxProteinSize()
if mini < smallest:
smallest = mini
if maxi > largest:
largest = maxi
if self.env.exteriorRecipe:
mini, maxi = self.env.exteriorRecipe.getMinMaxProteinSize()
if mini < smallest:
smallest = mini
if maxi > largest:
largest = maxi
return smallest, largest
def getPositionsFromResFile(self, ):
#could actually restore file using histoVol.
#or not
#need to parse apr file here anyway
return []
def getPositionsFromObject(self, parents):
positions = []
for parent in parents:
obparent = self.helper.getObject(parent)
childs = self.helper.getChilds(obparent)
for ch in childs:
ingr_name = self.helper.getName(ch)
meshp = self.helper.getObject("Meshs_" +
ingr_name.split("_")[0])
if meshp is None:
c = self.helper.getChilds(ch)
if not len(c):
continue
meshpchilds = self.helper.getChilds(
c[0]) #continue #should get sphere/cylnder parent ?
else:
meshpchilds = self.helper.getChilds(meshp)
for cc in meshpchilds:
pos = self.helper.ToVec(self.helper.getTranslation(cc))
positions.append(pos)
return positions
def getDistanceFrom(self, target, parents=None, **options):
"""
target : name or host object target or target position
parent : name of host parent object for the list of object to measre distance from
objects : list of object or list of points
"""
#get distance from object to the target.
#all object are in h.molecules and orga.molecules
#get options
targetPos = [0, 0, 0]
usePoint = False
threshold = 99999.
if "usePoint" in options:
usePoint = options["usePoint"]
if "threshold" in options:
threshold = options["threshold"]
if type(target) == list or type(target) == tuple:
targetPos = target
elif type(target) == unicode or type(target) == str:
o = self.helper.getObject(target)
if o is not None:
targetPos = self.helper.ToVec(
self.helper.getTranslation(o)) #hostForm
else:
o = self.helper.getObject(target)
if o is not None:
targetPos = self.helper.ToVec(
self.helper.getTranslation(o)) #hostForm
listeObjs = []
listeDistances = []
listeCenters = []
if self.resutl_file is None:
if parents is None and self.resutl_file is None:
listeParent = [self.env.name + "_cytoplasm"]
for o in self.env.compartments:
listeParent.append(o.name + "_Matrix")
listeParent.append(o.name + "_surface")
elif parents is not None and self.resutl_file is None:
listeParent = parents
listeCenters = self.getPositionsFromObject(listeParent)
else:
#use data from file
listeCenters = self.getPositionsFromResFile(listeParent)
delta = numpy.array(listeCenters) - numpy.array(targetPos)
delta *= delta
distA = numpy.sqrt(delta.sum(1))
return distA
def getClosestDistance(self, parents=None, **options):
if self.resutl_file is None:
if parents is None and self.resutl_file is None:
listeParent = [self.env.name + "_cytoplasm"]
for o in self.env.compartments:
listeParent.append(o.name + "_Matrix")
listeParent.append(o.name + "_surface")
elif parents is not None and self.resutl_file is None:
listeParent = parents
listeCenters = self.getPositionsFromObject(listeParent)
else:
#use data from file
listeCenters = self.getPositionsFromResFile(listeParent)
#is the distance in the result array ?
listeDistance = numpy.zeros(len(listeCenters)) + 99999
for i in range(len(listeCenters)):
for j in range(i + 1, len(listeCenters)):
#should use point
d = self.helper.measure_distance(listeCenters[i],
listeCenters[j])
if d < listeDistance[i]:
listeDistance[i] = d
return listeDistance
def displayDistance(self,
ramp_color1=[1, 0, 0],
ramp_color2=[0, 0, 1],
ramp_color3=None,
cutoff=60.0):
distances = self.env.grid.distToClosestSurf[:]
positions = self.env.grid.masterGridPositions[:]
#map the color as well ?
from upy import colors as col
from DejaVu.colorTool import Map
ramp = col.getRamp([[1, 0, 0], [0, 0, 1]], size=255) #color
mask = distances > cutoff
ind = numpy.nonzero(mask)[0]
distances[ind] = cutoff
mask = distances < -cutoff
ind = numpy.nonzero(mask)[0]
distances[ind] = cutoff
colors = Map(distances, ramp)
# sphs = self.helper.Spheres("distances",vertices=numpy.array(positions),radii=distances,colors=colors)
base = self.helper.getObject(self.env.name + "distances_base")
if base is None:
base = self.helper.Sphere(self.env.name + "distances_base")[0]
p = self.helper.getObject(self.env.name + "distances")
if p is not None:
self.helper.deleteObject(p) #recursif?
p = self.helper.newEmpty(self.env.name + "distances")
sphs = self.helper.instancesSphere(self.env.name + "distances",
positions,
distances,
base,
colors,
None,
parent=self.env.name + "distances")
def loadJSON(self, filename):
import json
with open(filename) as data_file:
data = json.load(data_file)
#should take any type of list...
def save_csv(self, data, filename=None):
if filename is None:
filename = "output.csv"
resultFile = open(filename, 'wb')
wr = csv.writer(resultFile, dialect='excel')
#wr.writerows(data) list of list ?
#resultFile.close()
for item in data:
wr.writerow([
item,
])
resultFile.close()
def rectangle_circle_area(self, bbox, center, radius):
#http://www.eex-dev.net/index.php?id=100
#[[0.,0,0],[1000,1000,1]]
rect = Rectangle(bbox[0][0], bbox[1][0], bbox[0][1],
bbox[1][1]) #top,bottom, right, left
# rect=Rectangle(bbox[1][1],bbox[0][1],bbox[1][0],bbox[0][0])#top,bottom, right, left
m = [center[0], center[1]]
r = radius
area = 0.0
chs = self.g.check_sphere_inside(rect, m, r)
if chs:
# print "sphere go outside ",r
ch = self.g.check_rectangle_oustide(rect, m, r)
if ch:
leftBound, rightBound = self.g.getBoundary(rect, m, r)
area = self.g.get_rectangle_cercle_area(
rect, m, r, rightBound, leftBound)
# print area,leftBound,rightBound
return area
def getDistance(self, ingrname, center):
distA = []
ingrpositions = [
self.env.molecules[i][0] for i in xrange(len(self.env.molecules))
if self.env.molecules[i][2].name == ingrname
]
ingrpositions = numpy.array(ingrpositions)
if len(ingrpositions):
delta = numpy.array(ingrpositions) - numpy.array(center)
delta *= delta
distA = numpy.sqrt(delta.sum(1))
return ingrpositions, distA
def getAxeValue(self, ingrname, axe=0):
ingrpositions = [
self.env.molecules[i][0][axe]
for i in xrange(len(self.env.molecules))
if self.env.molecules[i][2].name == ingrname
]
return ingrpositions
def getAxesValues(self, positions):
pp = numpy.array(positions).transpose()
px = pp[0]
py = pp[1]
pz = pp[2]
return px, py, pz
def getVolumeShell(self, bbox, radii, center):
#rectangle_circle_area
volumes = []
box_size0 = bbox[1][0] - bbox[0][0]
for i in range(len(radii) - 1):
r1 = radii[i]
r2 = radii[i + 1]
v1 = self.g.calc_volume(r1, box_size0 / 2.)
v2 = self.g.calc_volume(r2, box_size0 / 2.)
# if v1 == 0 or v2 == 0 :
# volumes.append((4./3.)*numpy.pi*(numpy.power(r2,3)-numpy.power(r1, 3)))
# else :
volumes.append(v2 - v1)
return volumes
def rdf_3d(self, ingr):
#see for intersection volume here http://crowsandcats.blogspot.com/2013/04/cube-sphere-intersection-volume.html
#and here http://crowsandcats.blogspot.com/2013/05/extending-radial-distributions.html
#will require scipy...worth it ?
#should be pairewise distance ? or not ?
distances = numpy.array(self.env.distances[ingr.name])
basename = self.env.basename
numpy.savetxt(basename + ingr.name + "_pos.csv",
numpy.array(self.env.ingrpositions[ingr.name]),
delimiter=",")
self.histo(distances, basename + ingr.name + "_histo.png")
numpy.savetxt(basename + ingr.name + "_distances.csv",
numpy.array(distances),
delimiter=",")
# the bin should be not less than the biggest ingredient radius
#b=int(distances.max()/self.largest)
b = 100
#bin_edges = numpy.arange(0, min(box_size) / 2, bin_width)
new_rdf, edges = numpy.histogramdd(distances,
bins=b,
range=[(distances.min(),
distances.max())])
radii = edges[0]
#from http://isaacs.sourceforge.net/phys/rdfs.html
dnr = new_rdf
N = len(distances)
V = self.env.grid.nbGridPoints[0] * self.env.grid.nbGridPoints[
1] * self.env.grid.nbGridPoints[2] * self.env.grid.gridSpacing**3
density = 1 #len(x)/1000.0**2
# Vshell = (4./3.)*numpy.pi*(numpy.power(radii[1:],3)-numpy.power(radii[:-1], 3))
Vshell = numpy.array(self.getVolumeShell(self.bbox, radii,
self.center))
gr = (dnr * V) / (N * Vshell)
numpy.savetxt(basename + ingr.name + "_rdf.csv",
numpy.array(gr),
delimiter=",")
self.plot(gr, radii[:-1], basename + ingr.name + "_rdf.png")
def getAreaShell(self, bbox, radii, center):
#rectangle_circle_area
areas = []
for i in range(len(radii) - 1):
r1 = radii[i]
r2 = radii[i + 1]
area1 = self.rectangle_circle_area(bbox, center, r1)
area2 = self.rectangle_circle_area(bbox, center, r2)
if area1 == 0 or area2 == 0:
areas.append(numpy.pi *
(numpy.power(r2, 2) - numpy.power(r1, 2)))
else:
areas.append(area2 - area1)
return areas
def rdf_2d(self, ingr):
distances = numpy.array(self.env.distances[ingr.name])
basename = self.env.basename
numpy.savetxt(basename + ingr.name + "_pos.csv",
numpy.array(self.env.ingrpositions[ingr.name]),
delimiter=",")
self.histo(distances, basename + ingr.name + "_histo.png")
numpy.savetxt(basename + ingr.name + "_distances.csv",
numpy.array(distances),
delimiter=",")
# the bin should be not less than the biggest ingredient radius
# b=int(distances.max()/self.largest)
b = 100
new_rdf, edges = numpy.histogramdd(
distances
) #, bins=b, range=[(distances.min(), distances.max())],normed=0)
radii = edges[0]
# r=radii.tolist()
# r.insert(0,0.0)
# radii = numpy.array(r)
# rdf= new_rdf.tolist()
# rdf.insert(0,0)
# new_rdf = numpy.array(rdf)
#from http://isaacs.sourceforge.net/phys/rdfs.html
dnr = new_rdf[:]
N = len(distances)
V = self.env.grid.nbGridPoints[0] * self.env.grid.nbGridPoints[
1] * self.env.grid.gridSpacing**2
density = 1 #len(x)/1000.0**2
Vshell = numpy.array(self.getAreaShell(self.bbox, radii, self.center))
# print Vshell
# Vshell1 = numpy.pi*density*(numpy.power(radii[1:],2)-numpy.power(radii[:-1], 2))
# print Vshell1
# print radii
gr = (dnr * V) / (N * Vshell)
numpy.savetxt(basename + ingr.name + "_rdf.csv",
numpy.array(gr),
delimiter=",")
self.plot(gr, radii[:-1], basename + ingr.name + "_rdf.png")
#simpl approach Ni/Areai
G = dnr / Vshell
numpy.savetxt(basename + ingr.name + "_rdf_simple.csv",
numpy.array(G),
delimiter=",")
self.plot(numpy.array(G), radii[:-1],
basename + ingr.name + "_rdf_simple.png")
print G
def axis_distribution(self, ingr):
basename = self.env.basename
px, py, pz = self.getAxesValues(self.env.ingrpositions[ingr.name])
self.histo(px, basename + ingr.name + "_histo_X.png", bins=20)
self.histo(py, basename + ingr.name + "_histo_Y.png", bins=20)
self.histo(pz, basename + ingr.name + "_histo_Z.png", bins=20)
def correlation(self, ingr):
basename = self.env.basename
posxyz = numpy.array(self.env.ingrpositions[ingr.name]).transpose()
g_average, radii, x, y, z = self.PairCorrelationFunction_3D(
posxyz, 1000, 900, 100)
self.plot(g_average, radii, basename + ingr.name + "_corr.png")
def PairCorrelationFunction_3D(self, data, S, rMax, dr):
"""Compute the three-dimensional pair correlation function for a set of
spherical particles contained in a cube with side length S. This simple
function finds reference particles such that a sphere of radius rMax drawn
around the particle will fit entirely within the cube, eliminating the need
to compensate for edge effects. If no such particles exist, an error is
returned. Try a smaller rMax...or write some code to handle edge effects! ;)
Arguments:
x an array of x positions of centers of particles
y an array of y positions of centers of particles
z an array of z positions of centers of particles
S length of each side of the cube in space
rMax outer diameter of largest spherical shell
dr increment for increasing radius of spherical shell
Returns a tuple: (g, radii, interior_x, interior_y, interior_z)
g(r) a numpy array containing the correlation function g(r)
radii a numpy array containing the radii of the
spherical shells used to compute g(r)
interior_x x coordinates of reference particles
interior_y y coordinates of reference particles
interior_z z coordinates of reference particles
"""
from numpy import zeros, sqrt, where, pi, average, arange, histogram
x = data[0]
y = data[1]
z = data[2]
# Find particles which are close enough to the cube center that a sphere of radius
# rMax will not cross any face of the cube
bools1 = x > rMax
bools2 = x < (S - rMax)
bools3 = y > rMax
bools4 = y < (S - rMax)
bools5 = z > rMax
bools6 = z < (S - rMax)
interior_indices, = where(bools1 * bools2 * bools3 * bools4 * bools5 *
bools6)
num_interior_particles = len(interior_indices)
if num_interior_particles < 1:
raise RuntimeError(
"No particles found for which a sphere of radius rMax\
will lie entirely within a cube of side length S. Decrease rMax\
or increase the size of the cube.")
edges = arange(0., rMax + 1.1 * dr, dr)
num_increments = len(edges) - 1
g = zeros([num_interior_particles, num_increments])
radii = zeros(num_increments)
numberDensity = len(x) / S**3
# Compute pairwise correlation for each interior particle
for p in range(num_interior_particles):
index = interior_indices[p]
d = sqrt((x[index] - x)**2 + (y[index] - y)**2 + (z[index] - z)**2)
d[index] = 2 * rMax
(result, bins) = histogram(d, bins=edges, normed=False)
g[p, :] = result / numberDensity
# Average g(r) for all interior particles and compute radii
g_average = zeros(num_increments)
for i in range(num_increments):
radii[i] = (edges[i] + edges[i + 1]) / 2.
rOuter = edges[i + 1]
rInner = edges[i]
g_average[i] = average(g[:, i]) / (4. / 3. * pi *
(rOuter**3 - rInner**3))
return (g_average, radii, x[interior_indices], y[interior_indices],
z[interior_indices])
# Number of particles in shell/total number of particles/volume of shell/number density
# shell volume = 4/3*pi(r_outer**3-r_inner**3)
def histo(self, distances, filename, bins=100):
pylab.clf()
mu, sigma = numpy.mean(distances), numpy.std(distances)
## the histogram of the data
b = numpy.arange(distances.min(), distances.max(), 2)
n, bins, patches = pyplot.hist(distances,
bins=bins,
normed=0,
facecolor='green') #, alpha=0.75)
# add a 'best fit' line?
y = mlab.normpdf(bins, mu, sigma) #should be the excepted distribution
l = pyplot.plot(bins, y, 'r--', linewidth=1)
pyplot.savefig(filename)
pylab.close() # closes the current figure
def plot(self, rdf, radii, filname):
pylab.clf()
matplotlib.rc('font', size=14)
matplotlib.rc('figure', figsize=(5, 4))
# pylab.clf()
pylab.plot(radii, rdf, linewidth=3)
pylab.xlabel(r"distance $r$ in $\AA$")
pylab.ylabel(r"radial distribution function $g(r)$")
pylab.savefig(filname)
pylab.close() # closes the current figure
def grid_pack(self,
bb,
wrkDir,
forceBuild=True,
fill=0,
seed=20,
vTestid=3,
vAnalysis=0):
t1 = time()
# if bbox is None :
# box=self.helper.getCurrentSelection()[0]
# else :
# box = bbox[0]
# bb=self.helper.getCornerPointCube(box)
gridFileIn = None
gridFileOut = None
# if forceBuild :
# gridFileOut=wrkDir+os.sep+"fill_grid"
# else :
# gridFileIn=wrkDir+os.sep+"fill_grid"
print(gridFileIn, gridFileOut)
self.env.buildGrid(boundingBox=bb,
gridFileIn=gridFileIn,
rebuild=forceBuild,
gridFileOut=gridFileOut,
previousFill=False)
# h.buildGrid(gridFileIn=gridFileIn,
# gridFileOut=gridFileOut)
t2 = time()
gridTime = t2 - t1
if fill:
self.pack(seed=seed, vTestid=vTestid, vAnalysis=vAnalysis)
print('time to Build Grid', gridTime)
# afviewer.displayOrganellesPoints()
#return
#actine.updateFromBB(h.grid)
def pack(self, seed=20, forceBuild=True, vTestid=3, vAnalysis=0):
t1 = time()
print "seed is ", seed
self.env.fill5(seedNum=seed,
verbose=4,
vTestid=vTestid,
vAnalysis=vAnalysis)
t2 = time()
print('time to run Fill5', t2 - t1)
def calcDistanceMatrixFastEuclidean2(self, nDimPoints):
nDimPoints = numpy.array(nDimPoints)
n, m = nDimPoints.shape
delta = numpy.zeros((n, n), 'd')
for d in xrange(m):
data = nDimPoints[:, d]
delta += (data - data[:, numpy.newaxis])**2
return numpy.sqrt(delta)
def doloop(self,
n,
bbox,
wrkDir,
output,
rdf=True,
render=False,
plot=True,
twod=True):
# doLoop automatically produces result files, images, and documents from the recipe while adjusting parameters
# To run doLoop, 1) in your host's python console type:
# execfile(pathothis recipe) # for example, on my computer:
# " execfile("/Users/grahamold/Dev/autoFillSVN/autofillSVNversions/trunk/AutoFillClean/autoFillRecipeScripts/2DsphereFill/2DSpheres_setup_recipe.py")
# 2) prepare your scene for the rendering->render output should be 640,480 but you can change the size in the script at then end. Set up textures lights, and effects as you wish
# Results will appear in the result folder of your recipe path
# where n is the number of loop, seed = i
#analyse.doloop(n)
rangeseed = range(n)
distances = {}
ingrpositions = {}
self.bbox = bbox
rebuild = True
for i in rangeseed:
if i > 0: rebuild = False
basename = output + os.sep + "results_seed_" + str(i)
# self.env.saveResult = False
resultfilename = self.env.resultfile = basename
#Clear
if self.afviewer:
self.afviewer.clearFill("Test_Spheres2D")
else:
self.env.reset()
#no need to rebuild the grid ?
self.env.saveResult = True
self.env.resultfile = basename
self.grid_pack(bbox,
output,
seed=i,
fill=1,
vTestid=i,
vAnalysis=1,
forceBuild=rebuild) #build grid and fill
#store the result
# self.env.store_asJson(resultfilename=basename)
self.center = self.env.grid.getCenter()
if render:
#render/save scene if hosted otherwise nothing
self.helper.render(basename + ".jpg", 640, 480)
self.helper.write(basename + ".c4d", [])
if rdf:
if plot and twod:
width = 1000.0
fig = pyplot.figure()
ax = fig.add_subplot(111)
center = self.env.grid.getCenter(
) #[500,500,0.5]#center of the grid
r = self.env.exteriorRecipe
d = {}
if r:
for ingr in r.ingredients:
if ingr.name not in distances:
distances[ingr.name] = []
ingrpositions[ingr.name] = []
if ingr.packingMode == 'gradient' and self.env.use_gradient:
self.center = center = self.env.gradients[
ingr.gradient].direction
ingrpos, d = self.getDistance(ingr.name, center)
distances[ingr.name].extend(d)
ingrpositions[ingr.name].extend(ingrpos)
#print plot,twod
if plot and twod:
for i, p in enumerate(ingrpos):
ax.add_patch(
Circle((p[0], p[1]),
ingr.minRadius,
edgecolor="black",
facecolor=ingr.color))
if i == 0: #len(ingrpos)-1:
continue
if ingr.Type == "Grow":
pyplot.plot(
[ingrpos[-i][0], ingrpos[-i - 1][0]],
[ingrpos[-i][1], ingrpos[-i - 1][1]],
'k-',
lw=2)
#plot the sphere
if ingr.use_rbsphere:
r, pts = ingr.getInterpolatedSphere(
ingrpos[-i - 1], ingrpos[-i])
for pt in pts:
ax.add_patch(
Circle((pt[0], pt[1]),
ingr.minRadius,
edgecolor="black",
facecolor=ingr.color))
for o in self.env.compartments:
rs = o.surfaceRecipe
if rs:
for ingr in rs.ingredients:
if ingr.name not in distances:
distances[ingr.name] = []
ingrpositions[ingr.name] = []
if ingr.packingMode == 'gradient' and self.env.use_gradient:
center = self.env.gradients[
ingr.gradient].direction
ingrpos, d = self.getDistance(ingr.name, center)
distances[ingr.name].extend(d)
ingrpositions[ingr.name].extend(ingrpos)
if plot and twod:
for p in ingrpos:
ax.add_patch(
Circle((p[0], p[1]),
ingr.minRadius,
edgecolor="black",
facecolor=ingr.color))
ri = o.innerRecipe
if ri:
for ingr in ri.ingredients:
if ingr.name not in distances:
distances[ingr.name] = []
ingrpositions[ingr.name] = []
if ingr.packingMode == 'gradient' and self.env.use_gradient:
center = self.env.gradients[
ingr.gradient].direction
ingrpos, d = self.getDistance(ingr.name, center)
distances[ingr.name].extend(d)
ingrpositions[ingr.name].extend(ingrpos)
if plot and twod:
for p in ingrpos:
ax.add_patch(
Circle((p[0], p[1]),
ingr.minRadius,
edgecolor="black",
facecolor=ingr.color))
if plot and twod:
ax.set_aspect(1.0)
pyplot.axhline(y=0, color='k')
pyplot.axhline(y=width, color='k')
pyplot.axvline(x=0, color='k')
pyplot.axvline(x=width, color='k')
pyplot.axis(
[-0.1 * width, width * 1.1, -0.1 * width, width * 1.1])
pyplot.savefig(basename + ".png")
pylab.close() # closes the current figure
#plot(x)
self.env.ingrpositions = ingrpositions
self.env.distances = distances
self.env.basename = basename
if rdf:
if twod: self.env.loopThroughIngr(self.rdf_2d)
else: self.env.loopThroughIngr(self.rdf_3d)
# do the X Y histo averge !
self.env.loopThroughIngr(self.axis_distribution)
# self.env.loopThroughIngr(self.correlation)
return distances
class AnalyseAP:
def __init__(self, env=None, viewer=None, result_file=None):
self.env=None
self.smallest=99999.0
self.largest = 0.0
if env :
self.env = env
self.smallest,self.largest = self.getMinMaxProteinSize()
self.afviewer = viewer
self.helper = None
if viewer :
self.helper = self.afviewer.vi
self.resutl_file = result_file
self.center=[0,0,0]
self.bbox=[[0,0,0],[1,1,1]]
self.g=GeometriTools()
self.g.Resolution = 1.0#or grid step?
def getMinMaxProteinSize(self):
smallest=999999.0
largest= 0.0
for organelle in self.env.compartments:
mini, maxi = organelle.getMinMaxProteinSize()
if mini < smallest:
smallest = mini
if maxi > largest:
largest = maxi
if self.env.exteriorRecipe:
mini, maxi = self.env.exteriorRecipe.getMinMaxProteinSize()
if mini < smallest:
smallest = mini
if maxi > largest:
largest = maxi
return smallest,largest
def getPositionsFromResFile(self,):
#could actually restore file using histoVol.
#or not
#need to parse apr file here anyway
return []
def getPositionsFromObject(self,parents):
positions=[]
for parent in parents:
obparent = self.helper.getObject(parent)
childs = self.helper.getChilds(obparent)
for ch in childs:
ingr_name = self.helper.getName(ch)
meshp = self.helper.getObject("Meshs_"+ingr_name.split("_")[0])
if meshp is None :
c = self.helper.getChilds(ch)
if not len(c) :
continue
meshpchilds = self.helper.getChilds(c[0])#continue #should get sphere/cylnder parent ?
else :
meshpchilds = self.helper.getChilds(meshp)
for cc in meshpchilds:
pos = self.helper.ToVec(self.helper.getTranslation(cc))
positions.append(pos)
return positions
def getDistanceFrom(self,target,parents=None,**options):
"""
target : name or host object target or target position
parent : name of host parent object for the list of object to measre distance from
objects : list of object or list of points
"""
#get distance from object to the target.
#all object are in h.molecules and orga.molecules
#get options
targetPos = [0,0,0]
usePoint = False
threshold = 99999.
if "usePoint" in options:
usePoint = options["usePoint"]
if "threshold" in options:
threshold = options["threshold"]
if type(target) == list or type(target) == tuple:
targetPos = target
elif type(target) == unicode or type(target) == str :
o = self.helper.getObject(target)
if o is not None :
targetPos = self.helper.ToVec(self.helper.getTranslation(o)) #hostForm
else :
o = self.helper.getObject(target)
if o is not None :
targetPos = self.helper.ToVec(self.helper.getTranslation(o)) #hostForm
listeObjs=[]
listeDistances = []
listeCenters =[]
if self.resutl_file is None :
if parents is None and self.resutl_file is None:
listeParent = [self.env.name+"_cytoplasm"]
for o in self.env.compartments :
listeParent.append(o.name+"_Matrix")
listeParent.append(o.name+"_surface")
elif parents is not None and self.resutl_file is None:
listeParent = parents
listeCenters =self.getPositionsFromObject(listeParent)
else :
#use data from file
listeCenters =self.getPositionsFromResFile(listeParent)
delta = numpy.array(listeCenters)-numpy.array(targetPos)
delta *= delta
distA = numpy.sqrt( delta.sum(1) )
return distA
def getClosestDistance(self,parents=None,**options):
if self.resutl_file is None :
if parents is None and self.resutl_file is None:
listeParent = [self.env.name+"_cytoplasm"]
for o in self.env.compartments :
listeParent.append(o.name+"_Matrix")
listeParent.append(o.name+"_surface")
elif parents is not None and self.resutl_file is None:
listeParent = parents
listeCenters =self.getPositionsFromObject(listeParent)
else :
#use data from file
listeCenters =self.getPositionsFromResFile(listeParent)
#is the distance in the result array ?
listeDistance=numpy.zeros(len(listeCenters))+99999
for i in range(len(listeCenters) ):
for j in range(i+1,len(listeCenters)):
#should use point
d = self.helper.measure_distance(listeCenters[i],listeCenters[j])
if d < listeDistance[i] :
listeDistance[i] = d
return listeDistance
def displayDistance(self,ramp_color1=[1,0,0],ramp_color2=[0,0,1],
ramp_color3=None,cutoff=60.0):
distances = self.env.grid.distToClosestSurf[:]
positions = self.env.grid.masterGridPositions[:]
#map the color as well ?
from upy import colors as col
from DejaVu.colorTool import Map
ramp = col.getRamp([[1,0,0],[0,0,1]],size=255) #color
mask = distances > cutoff
ind=numpy.nonzero(mask)[0]
distances[ind]=cutoff
mask = distances < -cutoff
ind=numpy.nonzero(mask)[0]
distances[ind]=cutoff
colors = Map(distances, ramp)
# sphs = self.helper.Spheres("distances",vertices=numpy.array(positions),radii=distances,colors=colors)
base=self.helper.getObject(self.env.name+"distances_base")
if base is None :
base=self.helper.Sphere(self.env.name+"distances_base")[0]
p=self.helper.getObject(self.env.name+"distances")
if p is not None :
self.helper.deleteObject(p)#recursif?
p = self.helper.newEmpty(self.env.name+"distances")
sphs=self.helper.instancesSphere(self.env.name+"distances",positions,distances,base,colors,None,parent=self.env.name+"distances")
def loadJSON(self,filename):
import json
with open(filename) as data_file:
data = json.load(data_file)
#should take any type of list...
def save_csv(self,data,filename=None):
if filename is None :
filename = "output.csv"
resultFile = open(filename,'wb')
wr = csv.writer(resultFile, dialect='excel')
#wr.writerows(data) list of list ?
#resultFile.close()
for item in data: wr.writerow([item,])
resultFile.close()
def rectangle_circle_area(self,bbox,center,radius):
#http://www.eex-dev.net/index.php?id=100
#[[0.,0,0],[1000,1000,1]]
rect=Rectangle(bbox[0][0],bbox[1][0],bbox[0][1],bbox[1][1])#top,bottom, right, left
# rect=Rectangle(bbox[1][1],bbox[0][1],bbox[1][0],bbox[0][0])#top,bottom, right, left
m=[center[0],center[1]]
r=radius
area = 0.0
chs = self.g.check_sphere_inside(rect,m,r)
if chs :
# print "sphere go outside ",r
ch=self.g.check_rectangle_oustide(rect,m,r)
if ch :
leftBound,rightBound = self.g.getBoundary(rect,m,r)
area = self.g.get_rectangle_cercle_area(rect,m,r,rightBound,leftBound)
# print area,leftBound,rightBound
return area
def getDistance(self,ingrname, center):
distA=[]
ingrpositions=[self.env.molecules[i][0] for i in xrange(len(self.env.molecules)) if self.env.molecules[i][2].name == ingrname]
ingrpositions=numpy.array(ingrpositions)
if len(ingrpositions) :
delta = numpy.array(ingrpositions)-numpy.array(center)
delta *= delta
distA = numpy.sqrt( delta.sum(1) )
return ingrpositions,distA
def getAxeValue(self,ingrname,axe=0):
ingrpositions=[self.env.molecules[i][0][axe] for i in xrange(len(self.env.molecules)) if self.env.molecules[i][2].name == ingrname]
return ingrpositions
def getAxesValues(self,positions):
pp = numpy.array(positions).transpose()
px=pp[0]
py=pp[1]
pz=pp[2]
return px,py,pz
def getVolumeShell(self,bbox,radii,center):
#rectangle_circle_area
volumes=[]
box_size0 = bbox[1][0] - bbox[0][0]
for i in range(len(radii)-1):
r1=radii[i]
r2=radii[i+1]
v1 = self.g.calc_volume(r1, box_size0 / 2.)
v2 = self.g.calc_volume(r2, box_size0 / 2.)
# if v1 == 0 or v2 == 0 :
# volumes.append((4./3.)*numpy.pi*(numpy.power(r2,3)-numpy.power(r1, 3)))
# else :
volumes.append(v2-v1)
return volumes
def rdf_3d(self,ingr):
#see for intersection volume here http://crowsandcats.blogspot.com/2013/04/cube-sphere-intersection-volume.html
#and here http://crowsandcats.blogspot.com/2013/05/extending-radial-distributions.html
#will require scipy...worth it ?
#should be pairewise distance ? or not ?
distances = numpy.array(self.env.distances[ingr.name])
basename = self.env.basename
numpy.savetxt(basename+ingr.name+"_pos.csv", numpy.array(self.env.ingrpositions[ingr.name]), delimiter=",")
self.histo(distances,basename+ingr.name+"_histo.png")
numpy.savetxt(basename+ingr.name+"_distances.csv", numpy.array(distances), delimiter=",")
# the bin should be not less than the biggest ingredient radius
#b=int(distances.max()/self.largest)
b=100
#bin_edges = numpy.arange(0, min(box_size) / 2, bin_width)
new_rdf, edges = numpy.histogramdd(distances, bins=b, range=[(distances.min(), distances.max())])
radii = edges[0]
#from http://isaacs.sourceforge.net/phys/rdfs.html
dnr=new_rdf
N=len(distances)
V=self.env.grid.nbGridPoints[0]*self.env.grid.nbGridPoints[1]*self.env.grid.nbGridPoints[2]*self.env.grid.gridSpacing**3
density = 1#len(x)/1000.0**2
# Vshell = (4./3.)*numpy.pi*(numpy.power(radii[1:],3)-numpy.power(radii[:-1], 3))
Vshell = numpy.array(self.getVolumeShell(self.bbox,radii,self.center))
gr = (dnr*V)/(N*Vshell)
numpy.savetxt(basename+ingr.name+"_rdf.csv", numpy.array(gr), delimiter=",")
self.plot(gr,radii[:-1],basename+ingr.name+"_rdf.png")
def getAreaShell(self,bbox,radii,center):
#rectangle_circle_area
areas=[]
for i in range(len(radii)-1):
r1=radii[i]
r2=radii[i+1]
area1=self.rectangle_circle_area(bbox,center,r1)
area2=self.rectangle_circle_area(bbox,center,r2)
if area1 == 0 or area2 == 0 :
areas.append(numpy.pi*(numpy.power(r2,2)-numpy.power(r1, 2)))
else :
areas.append(area2-area1)
return areas
def rdf_2d(self,ingr):
distances = numpy.array(self.env.distances[ingr.name])
basename = self.env.basename
numpy.savetxt(basename+ingr.name+"_pos.csv", numpy.array(self.env.ingrpositions[ingr.name]), delimiter=",")
self.histo(distances,basename+ingr.name+"_histo.png")
numpy.savetxt(basename+ingr.name+"_distances.csv", numpy.array(distances), delimiter=",")
# the bin should be not less than the biggest ingredient radius
# b=int(distances.max()/self.largest)
b=100
new_rdf, edges = numpy.histogramdd(distances)#, bins=b, range=[(distances.min(), distances.max())],normed=0)
radii = edges[0]
# r=radii.tolist()
# r.insert(0,0.0)
# radii = numpy.array(r)
# rdf= new_rdf.tolist()
# rdf.insert(0,0)
# new_rdf = numpy.array(rdf)
#from http://isaacs.sourceforge.net/phys/rdfs.html
dnr=new_rdf[:]
N=len(distances)
V=self.env.grid.nbGridPoints[0]*self.env.grid.nbGridPoints[1]*self.env.grid.gridSpacing**2
density = 1#len(x)/1000.0**2
Vshell = numpy.array(self.getAreaShell(self.bbox,radii,self.center))
# print Vshell
# Vshell1 = numpy.pi*density*(numpy.power(radii[1:],2)-numpy.power(radii[:-1], 2))
# print Vshell1
# print radii
gr = (dnr*V)/(N*Vshell)
numpy.savetxt(basename+ingr.name+"_rdf.csv", numpy.array(gr), delimiter=",")
self.plot(gr,radii[:-1],basename+ingr.name+"_rdf.png")
#simpl approach Ni/Areai
G=dnr/Vshell
numpy.savetxt(basename+ingr.name+"_rdf_simple.csv", numpy.array(G), delimiter=",")
self.plot(numpy.array(G),radii[:-1],basename+ingr.name+"_rdf_simple.png")
print G
def axis_distribution(self,ingr):
basename = self.env.basename
px,py,pz = self.getAxesValues(self.env.ingrpositions[ingr.name])
self.histo(px,basename+ingr.name+"_histo_X.png",bins=20)
self.histo(py,basename+ingr.name+"_histo_Y.png",bins=20)
self.histo(pz,basename+ingr.name+"_histo_Z.png",bins=20)
def correlation(self,ingr):
basename = self.env.basename
posxyz = numpy.array(self.env.ingrpositions[ingr.name]).transpose()
g_average, radii, x,y,z=self.PairCorrelationFunction_3D(posxyz,1000,900,100)
self.plot(g_average,radii,basename+ingr.name+"_corr.png")
def PairCorrelationFunction_3D(self,data,S,rMax,dr):
"""Compute the three-dimensional pair correlation function for a set of
spherical particles contained in a cube with side length S. This simple
function finds reference particles such that a sphere of radius rMax drawn
around the particle will fit entirely within the cube, eliminating the need
to compensate for edge effects. If no such particles exist, an error is
returned. Try a smaller rMax...or write some code to handle edge effects! ;)
Arguments:
x an array of x positions of centers of particles
y an array of y positions of centers of particles
z an array of z positions of centers of particles
S length of each side of the cube in space
rMax outer diameter of largest spherical shell
dr increment for increasing radius of spherical shell
Returns a tuple: (g, radii, interior_x, interior_y, interior_z)
g(r) a numpy array containing the correlation function g(r)
radii a numpy array containing the radii of the
spherical shells used to compute g(r)
interior_x x coordinates of reference particles
interior_y y coordinates of reference particles
interior_z z coordinates of reference particles
"""
from numpy import zeros, sqrt, where, pi, average, arange, histogram
x=data[0]
y=data[1]
z=data[2]
# Find particles which are close enough to the cube center that a sphere of radius
# rMax will not cross any face of the cube
bools1 = x>rMax
bools2 = x<(S-rMax)
bools3 = y>rMax
bools4 = y<(S-rMax)
bools5 = z>rMax
bools6 = z<(S-rMax)
interior_indices, = where(bools1*bools2*bools3*bools4*bools5*bools6)
num_interior_particles = len(interior_indices)
if num_interior_particles < 1:
raise RuntimeError ("No particles found for which a sphere of radius rMax\
will lie entirely within a cube of side length S. Decrease rMax\
or increase the size of the cube.")
edges = arange(0., rMax+1.1*dr, dr)
num_increments = len(edges)-1
g = zeros([num_interior_particles, num_increments])
radii = zeros(num_increments)
numberDensity = len(x)/S**3
# Compute pairwise correlation for each interior particle
for p in range(num_interior_particles):
index = interior_indices[p]
d = sqrt((x[index]-x)**2 + (y[index]-y)**2 + (z[index]-z)**2)
d[index] = 2*rMax
(result,bins) = histogram(d, bins=edges, normed=False)
g[p,:] = result/numberDensity
# Average g(r) for all interior particles and compute radii
g_average = zeros(num_increments)
for i in range(num_increments):
radii[i] = (edges[i] + edges[i+1])/2.
rOuter = edges[i+1]
rInner = edges[i]
g_average[i] = average(g[:,i])/(4./3.*pi*(rOuter**3 - rInner**3))
return (g_average, radii, x[interior_indices], y[interior_indices], z[interior_indices])
# Number of particles in shell/total number of particles/volume of shell/number density
# shell volume = 4/3*pi(r_outer**3-r_inner**3)
def histo(self,distances,filename,bins=100):
pylab.clf()
mu, sigma = numpy.mean(distances) , numpy.std(distances)
## the histogram of the data
b=numpy.arange(distances.min(), distances.max(), 2)
n, bins, patches = pyplot.hist(distances, bins=bins, normed=0, facecolor='green')#, alpha=0.75)
# add a 'best fit' line?
y = mlab.normpdf( bins, mu, sigma)#should be the excepted distribution
l = pyplot.plot(bins, y, 'r--', linewidth=1)
pyplot.savefig(filename)
pylab.close() # closes the current figure
def plot(self,rdf,radii,filname):
pylab.clf()
matplotlib.rc('font', size=14)
matplotlib.rc('figure', figsize=(5, 4))
# pylab.clf()
pylab.plot(radii, rdf, linewidth=3)
pylab.xlabel(r"distance $r$ in $\AA$")
pylab.ylabel(r"radial distribution function $g(r)$")
pylab.savefig(filname)
pylab.close() # closes the current figure
def grid_pack(self,bb,wrkDir,forceBuild=True, fill=0,seed=20,vTestid = 3,vAnalysis = 0):
t1 = time()
# if bbox is None :
# box=self.helper.getCurrentSelection()[0]
# else :
# box = bbox[0]
# bb=self.helper.getCornerPointCube(box)
gridFileIn=None
gridFileOut=None
# if forceBuild :
# gridFileOut=wrkDir+os.sep+"fill_grid"
# else :
# gridFileIn=wrkDir+os.sep+"fill_grid"
print (gridFileIn,gridFileOut)
self.env.buildGrid(boundingBox=bb,gridFileIn=gridFileIn,rebuild=forceBuild ,
gridFileOut=gridFileOut,previousFill=False)
# h.buildGrid(gridFileIn=gridFileIn,
# gridFileOut=gridFileOut)
t2 = time()
gridTime = t2-t1
if fill :
self.pack(seed=seed,vTestid = vTestid,vAnalysis = vAnalysis)
print ('time to Build Grid', gridTime)
# afviewer.displayOrganellesPoints()
#return
#actine.updateFromBB(h.grid)
def pack(self,seed=20,forceBuild=True,vTestid = 3,vAnalysis = 0):
t1 = time()
print "seed is ",seed
self.env.fill5(seedNum=seed,verbose=4, vTestid = vTestid,vAnalysis = vAnalysis)
t2 = time()
print('time to run Fill5', t2-t1)
def calcDistanceMatrixFastEuclidean2(self,nDimPoints):
nDimPoints = numpy.array(nDimPoints)
n,m = nDimPoints.shape
delta = numpy.zeros((n,n),'d')
for d in xrange(m):
data = nDimPoints[:,d]
delta += (data - data[:,numpy.newaxis])**2
return numpy.sqrt(delta)
def doloop(self,n,bbox,wrkDir,output,rdf=True, render=False, plot = True,twod=True):
# doLoop automatically produces result files, images, and documents from the recipe while adjusting parameters
# To run doLoop, 1) in your host's python console type:
# execfile(pathothis recipe) # for example, on my computer:
# " execfile("/Users/grahamold/Dev/autoFillSVN/autofillSVNversions/trunk/AutoFillClean/autoFillRecipeScripts/2DsphereFill/2DSpheres_setup_recipe.py")
# 2) prepare your scene for the rendering->render output should be 640,480 but you can change the size in the script at then end. Set up textures lights, and effects as you wish
# Results will appear in the result folder of your recipe path
# where n is the number of loop, seed = i
#analyse.doloop(n)
rangeseed=range(n)
distances={}
ingrpositions={}
self.bbox = bbox
rebuild = True
for i in rangeseed:
if i > 0 : rebuild = False
basename = output+os.sep+"results_seed_"+str(i)
# self.env.saveResult = False
resultfilename = self.env.resultfile = basename
#Clear
if self.afviewer :
self.afviewer.clearFill("Test_Spheres2D")
else :
self.env.reset()
#no need to rebuild the grid ?
self.env.saveResult = True
self.env.resultfile = basename
self.grid_pack(bbox,output,seed=i, fill=1,vTestid = i,vAnalysis = 1,forceBuild=rebuild)#build grid and fill
#store the result
# self.env.store_asJson(resultfilename=basename)
self.center = self.env.grid.getCenter()
if render :
#render/save scene if hosted otherwise nothing
self.helper.render(basename+".jpg",640,480)
self.helper.write(basename+".c4d",[])
if rdf :
if plot and twod:
width = 1000.0
fig = pyplot.figure()
ax = fig.add_subplot(111)
center = self.env.grid.getCenter()#[500,500,0.5]#center of the grid
r = self.env.exteriorRecipe
d={}
if r :
for ingr in r.ingredients:
if ingr.name not in distances :
distances[ingr.name]=[]
ingrpositions[ingr.name]=[]
if ingr.packingMode=='gradient' and self.env.use_gradient:
self.center = center = self.env.gradients[ingr.gradient].direction
ingrpos,d=self.getDistance(ingr.name, center)
distances[ingr.name].extend(d)
ingrpositions[ingr.name].extend(ingrpos)
#print plot,twod
if plot and twod:
for i,p in enumerate(ingrpos):
ax.add_patch(Circle((p[0], p[1]), ingr.minRadius,
edgecolor="black", facecolor=ingr.color))
if i == 0:#len(ingrpos)-1:
continue
if ingr.Type== "Grow":
pyplot.plot([ingrpos[-i][0], ingrpos[-i-1][0]],
[ingrpos[-i][1], ingrpos[-i-1][1]], 'k-', lw=2)
#plot the sphere
if ingr.use_rbsphere :
r,pts = ingr.getInterpolatedSphere(ingrpos[-i-1],ingrpos[-i])
for pt in pts :
ax.add_patch(Circle((pt[0], pt[1]), ingr.minRadius,
edgecolor="black", facecolor=ingr.color))
for o in self.env.compartments:
rs = o.surfaceRecipe
if rs :
for ingr in rs.ingredients:
if ingr.name not in distances :
distances[ingr.name]=[]
ingrpositions[ingr.name]=[]
if ingr.packingMode=='gradient' and self.env.use_gradient :
center = self.env.gradients[ingr.gradient].direction
ingrpos,d=self.getDistance(ingr.name, center)
distances[ingr.name].extend(d)
ingrpositions[ingr.name].extend(ingrpos)
if plot and twod:
for p in ingrpos:
ax.add_patch(Circle((p[0], p[1]), ingr.minRadius,
edgecolor="black", facecolor=ingr.color))
ri = o.innerRecipe
if ri :
for ingr in ri.ingredients:
if ingr.name not in distances :
distances[ingr.name]=[]
ingrpositions[ingr.name]=[]
if ingr.packingMode=='gradient' and self.env.use_gradient:
center = self.env.gradients[ingr.gradient].direction
ingrpos,d=self.getDistance(ingr.name, center)
distances[ingr.name].extend(d)
ingrpositions[ingr.name].extend(ingrpos)
if plot and twod:
for p in ingrpos:
ax.add_patch(Circle((p[0], p[1]), ingr.minRadius,
edgecolor="black", facecolor=ingr.color))
if plot and twod:
ax.set_aspect(1.0)
pyplot.axhline(y=0, color='k')
pyplot.axhline(y=width, color='k')
pyplot.axvline(x=0, color='k')
pyplot.axvline(x=width, color='k')
pyplot.axis([-0.1*width, width*1.1, -0.1*width, width*1.1])
pyplot.savefig(basename+".png")
pylab.close() # closes the current figure
#plot(x)
self.env.ingrpositions=ingrpositions
self.env.distances = distances
self.env.basename = basename
if rdf :
if twod : self.env.loopThroughIngr(self.rdf_2d)
else : self.env.loopThroughIngr(self.rdf_3d)
# do the X Y histo averge !
self.env.loopThroughIngr(self.axis_distribution)
# self.env.loopThroughIngr(self.correlation)
return distances
