def main():
with open("./data/shape-%s/%s%s%sbox-plot--%s-%s-%s-%s-%s-%s.dat"%(load.f_shape,load.debug_str,load.hires_str,load.slice_str,load.f_shape,load.f_param1,load.f_param2,load.f_param3,load.f_param4,load.f_param5),"r") as boxData:
fileLines = boxData.readlines()
#get number of boxes and protein types. little hokey but it works. in boxData.readlines(), there is exactly one '\n' newline string
#for each protein type block. therefor, the number of protein types is equal to the number of times "\n" appears by itself in the list.
numProteinTypes = len([line for line in fileLines if line=="\n"])
numNewLines = numProteinTypes
#it follows that the total number of lines in the data file, minus the number of blank lines in the data file, is equal to
#the number of protein types * the number of box types. divide by number of protein types to get number of box types.
numBoxes = (len(fileLines) - numNewLines)/numProteinTypes
#grab the names of the proteins used, and the names of the boxes
proteinTypeList = []
boxList = []
for line in fileLines:
if (line != "\n"):
proteinTypeList += [line.split("\t")[0]]
boxList += [line.split("\t")[1]]
#prune duplicates
proteinTypeList = list(set(proteinTypeList))
boxList = list(set(boxList))
#generate list of proteinType and box combinations to feed into stackData
plotNameList_D = []
plotNameList_E = []
numProteinTypes_D = 0
numProteinTypes_E = 0
for box in boxList:
for proteinType in proteinTypeList:
if "D_" in proteinType:
plotNameList_D += ["%s-%s"%(box,proteinType)]
if "E_" in proteinType:
plotNameList_E += ["%s-%s"%(box,proteinType)]
#print ""
#print "plotNameList before ", plotNameList_D, "\n"
new_plotNameList_D = [0]*len(plotNameList_D)
P_Ord = [3,0,2,1,7,4,6,5,11,8,10,9]
if load.f_param4 == '97.00':
P_Ord = [3,0,2,1,11,8,10,9,15,12,14,13,7,4,6,5]
if load.f_param4 == '96.00':
P_Ord = [15,12,14,13,3,0,2,1,7,4,6,5,11,8,10,9]
for i in range(len(P_Ord)):
new_plotNameList_D[i] = plotNameList_D[P_Ord[i]]
for i in range(len(plotNameList_D)):
plotNameList_D[i] = new_plotNameList_D[i]
#print "plotNameList after ",plotNameList_D,"\n"
plotProteinLabels = ['MinD:ATP (cyto)','MinD:ATP (mem)','MinE:MinD:ATP','MinD:ADP (cyto)']
#pass plotNameList through stackData to generate the list of line data to be plotted
plotCurveList_D = stackData(plotNameList_D)
plotCurveList_E = stackData(plotNameList_E)
#get a time axis for the plot from the length of one of the data sets we have
difD = 2.5 # (um)^2 s^- 1
time_step = .1*load.dx*load.dx/difD #sec
print_denominator = 1000 #This is from the c++ I wanted to format things the same here.
box_time_step = time_step*print_denominator
timeAxis = np.linspace(0,box_time_step*len(plotCurveList_D[0]),len(plotCurveList_D[0]))
#begin messy code (to deal with matplotlib) - don't judge me
start_time_as_frac_of_ten = float(load.f_param6)
end_time_as_frac_of_ten = float(load.f_param7)
tot_time = len(plotCurveList_D[0])*box_time_step
start = int(tot_time*start_time_as_frac_of_ten/10.0/box_time_step)
end = int(tot_time*end_time_as_frac_of_ten/10.0/box_time_step)
(start, end) = find_period(plotCurveList_D[len(plotCurveList_D)-2])
(start, end) = find_period(np.array(returnData(boxList[len(boxList)-1], 'D_ND')))
# print useful coordination data
period = timeAxis[end-1] - timeAxis[start]
print 'period is', period
firsttime = timeAxis[start]
while firsttime > 9*period:
firsttime -= period
print 'early start time is', firsttime
print 'and end time is ',firsttime+period
print 'and file numbers are', firsttime*2, 'and', (firsttime+period)*2
# now offset time so it starts at zero
timeAxis = timeAxis - timeAxis[start]
if load.f_param4 == '97.00' or (load.f_shape == 'triangle' and load.f_param3 == '6.01'):
if load.f_param4 == '97.00':
start_time_as_frac_of_ten = 0
end_time_as_frac_of_ten = 2.3
if load.f_shape == 'triangle' and load.f_param3 == '6.01':
start_time_as_frac_of_ten = 5.00
end_time_as_frac_of_ten = 9.00
start = int(tot_time*start_time_as_frac_of_ten/10.0/box_time_step)
end = int(tot_time*end_time_as_frac_of_ten/10.0/box_time_step)
#print set(plotCurveList_D[1]).union(set(plotCurveList_D[2]))
#get num on each plot
for proteinType in proteinTypeList:
if "D_" in proteinType:
numProteinTypes_D += 1
if "E_" in proteinType:
numProteinTypes_E +=1
#plot scales. colors limited for now.
colorScale = ["b","g","r","c","m","y"]
alphaScale_D = [n/numProteinTypes for n in range(1,numProteinTypes_D+1)]
alphaScale_E = [n/numProteinTypes for n in range(1,numProteinTypes_E+1)]
#generate the plot
#f, (bax,sectionax) = plt.subplots(1, 2)
bax = plt.subplot2grid((2,5), (0,0), colspan=4, rowspan=2)
sectionax = plt.subplot2grid((2,5), (0,4), colspan=1,rowspan=2)
# first plot the section data...
sectiondata = np.loadtxt("data/shape-%s/membrane_files/%s%s%ssections-%s-%s-%s-%s-%s-%s.dat"
% (load.f_shape,load.debug_str,load.hires_str,load.slice_str,load.f_shape,
load.f_param1,load.f_param2,load.f_param3,load.f_param4,load.f_param5))
def plot_sections(sectionax, sectiondata):
dx = load.dx
x = np.arange(sectiondata.shape[1]*1.0)*dx
y = np.arange(sectiondata.shape[0]*1.0)*dx
X,Y = np.meshgrid(x,y)
inmembrane = np.zeros_like(sectiondata)
inmembrane[sectiondata>0] = 1.0
xmax = X[sectiondata>0].max()
xmin = X[sectiondata>0].min()
ymax = Y[sectiondata>0].max()
ymin = Y[sectiondata>0].min()
ymean = (Y*inmembrane).sum()/inmembrane.sum()
xmean = (X*inmembrane).sum()/inmembrane.sum()
yweighted = (Y*sectiondata).sum()/sectiondata.sum()
xweighted = (X*sectiondata).sum()/sectiondata.sum()
levels = [0.5, 1.5, 2.5, 3.5, 4.5]
mycolors = ["w","g","r","m","c","y"]
for i in xrange(min(4, len(boxList))):
if boxList[i] == 'Right':
mycolors[1] = colorScale[i]
if boxList[i] == 'Mid':
mycolors[2] = colorScale[i]
if boxList[i] == 'Left':
mycolors[3] = colorScale[i]
mycolors = colorScale[1:]
if load.f_param4 == '97.00':
mycolors = ['g','r','m','c']
if load.f_param4 == '96.00':
#rightup = 2, rightdown = 1, leftup = 4, leftdown = 3
mycolors = ['g','r','c','m']
#print mycolors
# here we rotate so that the order of sections will match the
# box plot.
xdir, ydir = xweighted - xmean, yweighted - ymean
xdir, ydir = xdir/np.sqrt(xdir**2+ydir**2), ydir/np.sqrt(xdir**2+ydir**2)
extraxspace = .5
extrayspace = 0
Yrotated = X*xdir + Y*ydir
Xrotated = X*ydir - Y*xdir
sectionax.contourf(Xrotated, Yrotated, sectiondata, levels=levels, colors=mycolors)
xmin = Xrotated[sectiondata>0].min()
xmax = Xrotated[sectiondata>0].max()
ymin = Yrotated[sectiondata>0].min()
ymax = Yrotated[sectiondata>0].max()
sectionax.set_xlim(xmin-extraxspace, xmax)
sectionax.set_ylim(ymin-extrayspace, ymax)
sectionax.set_aspect('equal')
sectionax.set_frame_on(False)
sectionax.axes.get_xaxis().set_visible(False)
sectionax.axes.get_yaxis().set_visible(False)
sectionax.add_artist(AnchoredSizeBar(
sectionax.transData,
1.00, # length of the bar in the data reference
"1$\mu$", # label of the bar
#bbox_to_anchor=(0.,0.,1.,1.),
loc=8, # 'best', # location (lower right)
pad=-(ymax-ymin)/2.0 + 0.4, borderpad=0.25, sep=3,
frameon=False
))
plot_sections(sectionax, sectiondata)
section_names = ['Bottom Section','Center Section','Top Section']
if load.f_param4 == '97.00':
section_names = ['Lower Section','Middle Left Section','Middle Right Section','Upper Section']
# section_names = ['rightup','mid','left','rightdown']
if load.f_param4 == '96.00':
section_names = ['Lower Left Section','Lower Right Section','Upper Left Section','Upper Right Section']
# section_names = ['rightdown','rightup','leftdown','leftup']
font=FontProperties()
font.set_family('serif')
text_adjust = -.2*box_time_step*(end-start)
j=0
k=0
for i in range(len(plotCurveList_D[:,0])):
if i%(numProteinTypes_D)==0:
j+=1
k=0
if i==0:
bax.plot(timeAxis[start:end],
plotCurveList_D[i, start:end],
color=colorScale[j],alpha=alphaScale_D[k])
y_text_label = i*.8/len(plotCurveList_D[:,0]) + .1*np.floor(i/numProteinTypes_D)
if load.f_param4 == '97.00' or load.f_param4 == '96.00':
y_text_label = i*.8/len(plotCurveList_D[:,0]) + .07*np.floor(i/numProteinTypes_D)
y_label = (plotCurveList_D[i, start+int(1/box_time_step)])/2.0
bax.annotate('%s'%plotProteinLabels[i],xy=(1,y_label),xytext=(text_adjust,y_text_label),
fontsize=7,
fontproperties=font,
arrowprops=dict(facecolor='black',shrink=0.05, width=.3, headwidth=5.))
bax.fill_between(timeAxis[start:end],
[0 for x in range(len(timeAxis))[start:end]],
plotCurveList_D[i, start:end],
alpha=alphaScale_D[k],facecolor=colorScale[j])
elif i!=0:
bax.plot(timeAxis[start:end],
plotCurveList_D[i,start:end],
color=colorScale[j],alpha=alphaScale_D[k])
y_text_label = i*.8/len(plotCurveList_D[:,0]) + .1*np.floor(i/numProteinTypes_D)
y_label = (plotCurveList_D[i, start+int(1/box_time_step)] + plotCurveList_D[i-1, start+int(1/box_time_step)])/2.0
if load.f_param4 == '97.00' or load.f_param4 == '96.00':
y_text_label = i*.8/len(plotCurveList_D[:,0]) + .07*np.floor(i/numProteinTypes_D)
bax.annotate('%s'%plotProteinLabels[i%numProteinTypes_D],xy=(1,y_label),xytext=(text_adjust,y_text_label),
fontsize=7,
fontproperties=font,
arrowprops=dict(facecolor='black',shrink=0.05, width=.3, headwidth=5.))
if (i+1)%(numProteinTypes_D)==0:
bax.text(-0.2,y_text_label+.04,section_names[int(np.floor(i/numProteinTypes_D))],transform=bax.transAxes,fontsize=9,fontproperties=font,)
bax.fill_between(timeAxis[start:end],
plotCurveList_D[i-1, start:end],
plotCurveList_D[i, start:end],
alpha=alphaScale_D[k],facecolor=colorScale[j])
k+=1
bax.set_xlim(timeAxis[start],timeAxis[end-1])
bax.get_yaxis().set_visible(False)
bax.set_ylim(0, 1)
bax.set_title("MinD protein counts over time")
bax.set_xlabel("Time (s)")
rax = bax.twinx()
rax.set_ylabel('Fraction of proteins in each stage and section',labelpad=-15)
rax.yaxis.set_ticklabels([0,"","","","",1.0])
#bax.set_ylabel("Fraction of proteins")
# 'A', xy=(Az, Ax), xytext=(1.2,-3.5),
# path_effects=texteff,
# arrowprops=dict(shrink=0.01, width=1,
# headwidth=hw, path_effects=arroweff))
#bax.legend(plotNameList_D,bbox_to_anchor=(0.3,-0.05,1.0,1.0),loc=4,prop={'size':8}).draw_frame(False)
plt.savefig(load.print_string("box-plot_D",""))
plt.figure()
#f, (bax,sectionax) = plt.subplots(1, 2)
bax = plt.subplot2grid((2,5), (0,0), colspan=4, rowspan=2)
sectionax = plt.subplot2grid((2,5), (0,4), colspan=1,rowspan=2)
# First plot the section data...
plot_sections(sectionax, sectiondata)
j=0
k=0
for i in range(len(plotCurveList_E)):
if i%(numProteinTypes_E)==0:
j+=1
k=0
if i==0:
bax.plot(timeAxis[start:end],plotCurveList_E[i][start:end],color=colorScale[j],alpha=alphaScale_E[k])
bax.fill_between(timeAxis[start:end],[0 for x in range(len(timeAxis))[start:end]],plotCurveList_E[i][start:end],alpha=alphaScale_E[k],facecolor=colorScale[j])
elif i!=0:
bax.plot(timeAxis[start:end],plotCurveList_E[i][start:end],color=colorScale[j],alpha=alphaScale_E[k])
bax.fill_between(timeAxis[start:end],plotCurveList_E[i-1][start:end],plotCurveList_E[i][start:end],alpha=alphaScale_E[k],facecolor=colorScale[j])
#print "i is ",i," || k is", k," || j is",j
k+=1
bax.set_xlim(timeAxis[start],timeAxis[end-1])
bax.set_ylim(0, 1)
bax.set_title("MinE protein counts over time")
bax.set_xlabel("Time (s)")
bax.set_ylabel("Fraction of proteins")
bax.legend(plotNameList_E,bbox_to_anchor=(0.3,-0.05,1.0,1.0),loc="lower right",prop={'size':8}).draw_frame(False)
plt.savefig(load.print_string("box-plot_E",""))
plt.show()
return 0
nlevels = 20
mylevels = np.linspace(0,(1+1.0/nlevels)*maxval,nlevels)
Z, Y = np.meshgrid(np.arange(0,proteins[i].dataset[0].shape[1],1),
np.arange(0,proteins[i].dataset[0].shape[0],1))
#generate a sequence of .png's for each file (printed time step). these will be used to create a gif.
for k in kvals:
page = proteins[i].dataset[k]
page[page>maxval] = maxval
plt.contourf(Y+k*dY, proteins[0].datashape[1]-Z+i*dZ, page, cmap=plt.cm.hot_r, levels=mylevels)
plt.axes().get_yaxis().set_ticks([(i+0.5)*dZ for i in range(len(proteinList))])
plt.axes().get_yaxis().set_ticklabels(proteinLabels)
plt.axes().get_xaxis().set_ticks([(0.5+k)*dY for k in kvals[::int(2.5*skip_times)]])
plt.axes().get_xaxis().set_ticklabels([int(k*dump_time_step) for k in kvals[::int(2.5*skip_times)]])
plt.axes().set_aspect('equal')
plt.axes().get_yaxis().set_ticks_position('none')
plt.axes().get_xaxis().set_ticks_position('none')
plt.xlabel('time (s)')
# I got the colorbar bit here from
# http://matplotlib.org/users/tight_layout_guide.html, which explains
# the difficulties with tight_layout() and colorbar positioning.
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "3%", pad="1%")
cbar = plt.colorbar(cax=cax,ticks=[0,maxval])
cbar.ax.set_yticklabels(['0', 'max'])
#plt.colorbar(ticks=[])
plt.tight_layout()
plt.savefig(load.print_string("image-plot",""))
def main():
with open("./data/shape-%s/%s%s%sbox-plot--%s-%s-%s-%s-%s-%s.dat"%(load.f_shape,load.debug_str,load.hires_str,load.slice_str,load.f_shape,load.f_param1,load.f_param2,load.f_param3,load.f_param4,load.f_param5),"r") as boxData:
fileLines = boxData.readlines()
#get number of boxes and protein types. little hokey but it works. in boxData.readlines(), there is exactly one '\n' newline string
#for each protein type block. therefor, the number of protein types is equal to the number of times "\n" appears by itself in the list.
numProteinTypes = len([line for line in fileLines if line=="\n"])
numNewLines = numProteinTypes
#it follows that the total number of lines in the data file, minus the number of blank lines in the data file, is equal to
#the number of protein types * the number of box types. divide by number of protein types to get number of box types.
numBoxes = (len(fileLines) - numNewLines)/numProteinTypes
#grab the names of the proteins used, and the names of the boxes
proteinTypeList = []
boxList = []
for line in fileLines:
if (line != "\n"):
proteinTypeList += [line.split("\t")[0]]
boxList += [line.split("\t")[1]]
#prune duplicates
proteinTypeList = list(set(proteinTypeList))
boxList = list(set(boxList))
#generate list of proteinType and box combinations to feed into ckData
plotNameList_D = []
plotNameList_E = []
numProteinTypes_D = 0
numProteinTypes_E = 0
numPlots_D = 0
numPlots_E = 0
for box in boxList:
for proteinType in proteinTypeList:
if "D_" in proteinType and "n" not in proteinType:
plotNameList_D += ["%s-%s"%(box,proteinType)]
numPlots_D += 1
for proteinType in proteinTypeList:
if "D_" in proteinType and "n" not in proteinType:
numProteinTypes_D += 1
plotCurveList_D = []
for proteinData in plotNameList_D:
splitString = proteinData.split('-')
(protein, boxName) = (splitString[0], splitString[1])
plotCurveList_D += [returnData(boxName,protein)]
#get a time axis for the plot from the length of one of the data sets we have
#timeAxis = range(len(plotCurveList_D[0]))
difD = 2.5 # (um)^2 s^- 1
time_step = .1*load.dx*load.dx/difD #sec
print_denominator = 1000 #This is from the c++ I wanted to format things the same here.
box_time_step = time_step*print_denominator
timeAxis = np.linspace(0,box_time_step*len(plotCurveList_D[0]),len(plotCurveList_D[0]))
#begin messy code (to deal with matplotlib) - don't judge me
#(start, end) = (int(5.3*int(len(timeAxis)/10)),int(5.7*int(len(timeAxis)/10)))
(start, end) = find_period(plotCurveList_D[3])
#integral of proteins over time
for i in range(numPlots_D):
integral = 0
for j in range(len(plotCurveList_D[0])):
integral += plotCurveList_D[i][j]
#plot scales. colors limited for now.
colorScale = ["b","g","r","c","m","y"]
alphaScale_D = [n/numProteinTypes_D for n in range(1,numProteinTypes_D+1)]
# yLimit = max(plotCurveList_D[0][start:end])
#generate the plot
plt.figure()
j=0
k=0
for i in range(numPlots_D):
if i%(numProteinTypes_D)==0:
j+=1
k=0
plt.plot(timeAxis[start:end],
plotCurveList_D[i][start:end],
color=colorScale[j],alpha=alphaScale_D[k])
k+=1
plt.xlim(timeAxis[start],timeAxis[end-1])
#plt.ylim(0,10000)
plt.title("Min D protein counts over time")
plt.xlabel("Time (s)")
plt.ylabel("Fraction of proteins")
plt.legend(plotNameList_D,loc="best",prop={'size':10})
plt.savefig(load.print_string("ave-plot_D",""))
return 0
def main():
with open(
"./data/shape-%s/%s%s%sbox-plot--%s-%s-%s-%s-%s-%s.dat" %
(load.f_shape, load.debug_str, load.hires_str, load.slice_str,
load.f_shape, load.f_param1, load.f_param2, load.f_param3,
load.f_param4, load.f_param5), "r") as boxData:
fileLines = boxData.readlines()
#get number of boxes and protein types. little hokey but it works. in boxData.readlines(), there is exactly one '\n' newline string
#for each protein type block. therefor, the number of protein types is equal to the number of times "\n" appears by itself in the list.
numProteinTypes = len([line for line in fileLines if line == "\n"])
numNewLines = numProteinTypes
#it follows that the total number of lines in the data file, minus the number of blank lines in the data file, is equal to
#the number of protein types * the number of box types. divide by number of protein types to get number of box types.
numBoxes = (len(fileLines) - numNewLines) / numProteinTypes
#grab the names of the proteins used, and the names of the boxes
proteinTypeList = []
boxList = []
for line in fileLines:
if (line != "\n"):
proteinTypeList += [line.split("\t")[0]]
boxList += [line.split("\t")[1]]
#prune duplicates
proteinTypeList = list(set(proteinTypeList))
boxList = list(set(boxList))
#generate list of proteinType and box combinations to feed into stackData
plotNameList_D = []
plotNameList_E = []
numProteinTypes_D = 0
numProteinTypes_E = 0
for box in boxList:
for proteinType in proteinTypeList:
if "D_" in proteinType:
plotNameList_D += ["%s-%s" % (box, proteinType)]
if "E_" in proteinType:
plotNameList_E += ["%s-%s" % (box, proteinType)]
#print ""
#print "plotNameList before ", plotNameList_D, "\n"
new_plotNameList_D = [0] * len(plotNameList_D)
P_Ord = [3, 0, 2, 1, 7, 4, 6, 5, 11, 8, 10, 9]
if load.f_param4 == '97.00':
P_Ord = [3, 0, 2, 1, 11, 8, 10, 9, 15, 12, 14, 13, 7, 4, 6, 5]
if load.f_param4 == '96.00':
P_Ord = [15, 12, 14, 13, 3, 0, 2, 1, 7, 4, 6, 5, 11, 8, 10, 9]
for i in range(len(P_Ord)):
new_plotNameList_D[i] = plotNameList_D[P_Ord[i]]
for i in range(len(plotNameList_D)):
plotNameList_D[i] = new_plotNameList_D[i]
#print "plotNameList after ",plotNameList_D,"\n"
plotProteinLabels = [
'MinD:ATP (cyto)', 'MinD:ATP (mem)', 'MinE:MinD:ATP', 'MinD:ADP (cyto)'
]
#pass plotNameList through stackData to generate the list of line data to be plotted
plotCurveList_D = stackData(plotNameList_D)
plotCurveList_E = stackData(plotNameList_E)
#get a time axis for the plot from the length of one of the data sets we have
difD = 2.5 # (um)^2 s^- 1
time_step = .1 * load.dx * load.dx / difD #sec
print_denominator = 1000 #This is from the c++ I wanted to format things the same here.
box_time_step = time_step * print_denominator
timeAxis = np.linspace(0, box_time_step * len(plotCurveList_D[0]),
len(plotCurveList_D[0]))
#begin messy code (to deal with matplotlib) - don't judge me
start_time_as_frac_of_ten = float(load.f_param6)
end_time_as_frac_of_ten = float(load.f_param7)
tot_time = len(plotCurveList_D[0]) * box_time_step
start = int(tot_time * start_time_as_frac_of_ten / 10.0 / box_time_step)
end = int(tot_time * end_time_as_frac_of_ten / 10.0 / box_time_step)
(start, end) = find_period(plotCurveList_D[len(plotCurveList_D) - 2])
(start,
end) = find_period(np.array(returnData(boxList[len(boxList) - 1],
'D_ND')))
# print useful coordination data
period = timeAxis[end - 1] - timeAxis[start]
print 'period is', period
firsttime = timeAxis[start]
while firsttime > 9 * period:
firsttime -= period
print 'early start time is', firsttime
print 'and end time is ', firsttime + period
print 'and file numbers are', firsttime * 2, 'and', (firsttime +
period) * 2
# now offset time so it starts at zero
timeAxis = timeAxis - timeAxis[start]
if load.f_param4 == '97.00' or (load.f_shape == 'triangle'
and load.f_param3 == '6.01'):
if load.f_param4 == '97.00':
start_time_as_frac_of_ten = 0
end_time_as_frac_of_ten = 2.3
if load.f_shape == 'triangle' and load.f_param3 == '6.01':
start_time_as_frac_of_ten = 5.00
end_time_as_frac_of_ten = 9.00
start = int(tot_time * start_time_as_frac_of_ten / 10.0 /
box_time_step)
end = int(tot_time * end_time_as_frac_of_ten / 10.0 / box_time_step)
#print set(plotCurveList_D[1]).union(set(plotCurveList_D[2]))
#get num on each plot
for proteinType in proteinTypeList:
if "D_" in proteinType:
numProteinTypes_D += 1
if "E_" in proteinType:
numProteinTypes_E += 1
#plot scales. colors limited for now.
colorScale = ["b", "g", "r", "c", "m", "y"]
alphaScale_D = [
n / numProteinTypes for n in range(1, numProteinTypes_D + 1)
]
alphaScale_E = [
n / numProteinTypes for n in range(1, numProteinTypes_E + 1)
]
#generate the plot
#f, (bax,sectionax) = plt.subplots(1, 2)
bax = plt.subplot2grid((2, 5), (0, 0), colspan=4, rowspan=2)
sectionax = plt.subplot2grid((2, 5), (0, 4), colspan=1, rowspan=2)
# first plot the section data...
sectiondata = np.loadtxt(
"data/shape-%s/membrane_files/%s%s%ssections-%s-%s-%s-%s-%s-%s.dat" %
(load.f_shape, load.debug_str, load.hires_str, load.slice_str,
load.f_shape, load.f_param1, load.f_param2, load.f_param3,
load.f_param4, load.f_param5))
def plot_sections(sectionax, sectiondata):
dx = load.dx
x = np.arange(sectiondata.shape[1] * 1.0) * dx
y = np.arange(sectiondata.shape[0] * 1.0) * dx
X, Y = np.meshgrid(x, y)
inmembrane = np.zeros_like(sectiondata)
inmembrane[sectiondata > 0] = 1.0
xmax = X[sectiondata > 0].max()
xmin = X[sectiondata > 0].min()
ymax = Y[sectiondata > 0].max()
ymin = Y[sectiondata > 0].min()
ymean = (Y * inmembrane).sum() / inmembrane.sum()
xmean = (X * inmembrane).sum() / inmembrane.sum()
yweighted = (Y * sectiondata).sum() / sectiondata.sum()
xweighted = (X * sectiondata).sum() / sectiondata.sum()
levels = [0.5, 1.5, 2.5, 3.5, 4.5]
mycolors = ["w", "g", "r", "m", "c", "y"]
for i in xrange(min(4, len(boxList))):
if boxList[i] == 'Right':
mycolors[1] = colorScale[i]
if boxList[i] == 'Mid':
mycolors[2] = colorScale[i]
if boxList[i] == 'Left':
mycolors[3] = colorScale[i]
mycolors = colorScale[1:]
if load.f_param4 == '97.00':
mycolors = ['g', 'r', 'm', 'c']
if load.f_param4 == '96.00':
#rightup = 2, rightdown = 1, leftup = 4, leftdown = 3
mycolors = ['g', 'r', 'c', 'm']
#print mycolors
# here we rotate so that the order of sections will match the
# box plot.
xdir, ydir = xweighted - xmean, yweighted - ymean
xdir, ydir = xdir / np.sqrt(xdir**2 +
ydir**2), ydir / np.sqrt(xdir**2 + ydir**2)
extraxspace = .5
extrayspace = 0
Yrotated = X * xdir + Y * ydir
Xrotated = X * ydir - Y * xdir
sectionax.contourf(Xrotated,
Yrotated,
sectiondata,
levels=levels,
colors=mycolors)
xmin = Xrotated[sectiondata > 0].min()
xmax = Xrotated[sectiondata > 0].max()
ymin = Yrotated[sectiondata > 0].min()
ymax = Yrotated[sectiondata > 0].max()
sectionax.set_xlim(xmin - extraxspace, xmax)
sectionax.set_ylim(ymin - extrayspace, ymax)
sectionax.set_aspect('equal')
sectionax.set_frame_on(False)
sectionax.axes.get_xaxis().set_visible(False)
sectionax.axes.get_yaxis().set_visible(False)
sectionax.add_artist(
AnchoredSizeBar(
sectionax.transData,
1.00, # length of the bar in the data reference
"1$\mu$", # label of the bar
#bbox_to_anchor=(0.,0.,1.,1.),
loc=8, # 'best', # location (lower right)
pad=-(ymax - ymin) / 2.0 + 0.4,
borderpad=0.25,
sep=3,
frameon=False))
plot_sections(sectionax, sectiondata)
section_names = ['Bottom Section', 'Center Section', 'Top Section']
if load.f_param4 == '97.00':
section_names = [
'Lower Section', 'Middle Left Section', 'Middle Right Section',
'Upper Section'
]
# section_names = ['rightup','mid','left','rightdown']
if load.f_param4 == '96.00':
section_names = [
'Lower Left Section', 'Lower Right Section', 'Upper Left Section',
'Upper Right Section'
]
# section_names = ['rightdown','rightup','leftdown','leftup']
font = FontProperties()
font.set_family('serif')
text_adjust = -.2 * box_time_step * (end - start)
j = 0
k = 0
for i in range(len(plotCurveList_D[:, 0])):
if i % (numProteinTypes_D) == 0:
j += 1
k = 0
if i == 0:
bax.plot(timeAxis[start:end],
plotCurveList_D[i, start:end],
color=colorScale[j],
alpha=alphaScale_D[k])
y_text_label = i * .8 / len(plotCurveList_D[:, 0]) + .1 * np.floor(
i / numProteinTypes_D)
if load.f_param4 == '97.00' or load.f_param4 == '96.00':
y_text_label = i * .8 / len(
plotCurveList_D[:, 0]) + .07 * np.floor(
i / numProteinTypes_D)
y_label = (plotCurveList_D[i,
start + int(1 / box_time_step)]) / 2.0
bax.annotate('%s' % plotProteinLabels[i],
xy=(1, y_label),
xytext=(text_adjust, y_text_label),
fontsize=7,
fontproperties=font,
arrowprops=dict(facecolor='black',
shrink=0.05,
width=.3,
headwidth=5.))
bax.fill_between(timeAxis[start:end],
[0 for x in range(len(timeAxis))[start:end]],
plotCurveList_D[i, start:end],
alpha=alphaScale_D[k],
facecolor=colorScale[j])
elif i != 0:
bax.plot(timeAxis[start:end],
plotCurveList_D[i, start:end],
color=colorScale[j],
alpha=alphaScale_D[k])
y_text_label = i * .8 / len(plotCurveList_D[:, 0]) + .1 * np.floor(
i / numProteinTypes_D)
y_label = (
plotCurveList_D[i, start + int(1 / box_time_step)] +
plotCurveList_D[i - 1, start + int(1 / box_time_step)]) / 2.0
if load.f_param4 == '97.00' or load.f_param4 == '96.00':
y_text_label = i * .8 / len(
plotCurveList_D[:, 0]) + .07 * np.floor(
i / numProteinTypes_D)
bax.annotate('%s' % plotProteinLabels[i % numProteinTypes_D],
xy=(1, y_label),
xytext=(text_adjust, y_text_label),
fontsize=7,
fontproperties=font,
arrowprops=dict(facecolor='black',
shrink=0.05,
width=.3,
headwidth=5.))
if (i + 1) % (numProteinTypes_D) == 0:
bax.text(
-0.2,
y_text_label + .04,
section_names[int(np.floor(i / numProteinTypes_D))],
transform=bax.transAxes,
fontsize=9,
fontproperties=font,
)
bax.fill_between(timeAxis[start:end],
plotCurveList_D[i - 1, start:end],
plotCurveList_D[i, start:end],
alpha=alphaScale_D[k],
facecolor=colorScale[j])
k += 1
bax.set_xlim(timeAxis[start], timeAxis[end - 1])
bax.get_yaxis().set_visible(False)
bax.set_ylim(0, 1)
bax.set_title("MinD protein counts over time")
bax.set_xlabel("Time (s)")
rax = bax.twinx()
rax.set_ylabel('Fraction of proteins in each stage and section',
labelpad=-15)
rax.yaxis.set_ticklabels([0, "", "", "", "", 1.0])
#bax.set_ylabel("Fraction of proteins")
# 'A', xy=(Az, Ax), xytext=(1.2,-3.5),
# path_effects=texteff,
# arrowprops=dict(shrink=0.01, width=1,
# headwidth=hw, path_effects=arroweff))
#bax.legend(plotNameList_D,bbox_to_anchor=(0.3,-0.05,1.0,1.0),loc=4,prop={'size':8}).draw_frame(False)
plt.savefig(load.print_string("box-plot_D", ""))
plt.figure()
#f, (bax,sectionax) = plt.subplots(1, 2)
bax = plt.subplot2grid((2, 5), (0, 0), colspan=4, rowspan=2)
sectionax = plt.subplot2grid((2, 5), (0, 4), colspan=1, rowspan=2)
# First plot the section data...
plot_sections(sectionax, sectiondata)
j = 0
k = 0
for i in range(len(plotCurveList_E)):
if i % (numProteinTypes_E) == 0:
j += 1
k = 0
if i == 0:
bax.plot(timeAxis[start:end],
plotCurveList_E[i][start:end],
color=colorScale[j],
alpha=alphaScale_E[k])
bax.fill_between(timeAxis[start:end],
[0 for x in range(len(timeAxis))[start:end]],
plotCurveList_E[i][start:end],
alpha=alphaScale_E[k],
facecolor=colorScale[j])
elif i != 0:
bax.plot(timeAxis[start:end],
plotCurveList_E[i][start:end],
color=colorScale[j],
alpha=alphaScale_E[k])
bax.fill_between(timeAxis[start:end],
plotCurveList_E[i - 1][start:end],
plotCurveList_E[i][start:end],
alpha=alphaScale_E[k],
facecolor=colorScale[j])
#print "i is ",i," || k is", k," || j is",j
k += 1
bax.set_xlim(timeAxis[start], timeAxis[end - 1])
bax.set_ylim(0, 1)
bax.set_title("MinE protein counts over time")
bax.set_xlabel("Time (s)")
bax.set_ylabel("Fraction of proteins")
bax.legend(plotNameList_E,
bbox_to_anchor=(0.3, -0.05, 1.0, 1.0),
loc="lower right",
prop={
'size': 8
}).draw_frame(False)
plt.savefig(load.print_string("box-plot_E", ""))
plt.show()
return 0
page[page > maxval] = maxval
plt.contourf(Y + k * dY,
proteins[0].datashape[1] - Z + i * dZ,
page,
cmap=plt.cm.hot_r,
levels=mylevels)
plt.axes().get_yaxis().set_ticks([(i + 0.5) * dZ
for i in range(len(proteinList))])
plt.axes().get_yaxis().set_ticklabels(proteinLabels)
plt.axes().get_xaxis().set_ticks([(0.5 + k) * dY
for k in kvals[::int(2.5 * skip_times)]])
plt.axes().get_xaxis().set_ticklabels(
[int(k * dump_time_step) for k in kvals[::int(2.5 * skip_times)]])
plt.axes().set_aspect('equal')
plt.axes().get_yaxis().set_ticks_position('none')
plt.axes().get_xaxis().set_ticks_position('none')
plt.xlabel('time (s)')
# I got the colorbar bit here from
# http://matplotlib.org/users/tight_layout_guide.html, which explains
# the difficulties with tight_layout() and colorbar positioning.
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "3%", pad="1%")
cbar = plt.colorbar(cax=cax, ticks=[0, maxval])
cbar.ax.set_yticklabels(['0', 'max'])
#plt.colorbar(ticks=[])
plt.tight_layout()
plt.savefig(load.print_string("image-plot", ""))
plt.axes().get_xaxis().set_ticklabels(range(len(smeared_data))[::int(2.5*skip_times)])
plt.axes().set_aspect('equal')
plt.axes().get_yaxis().set_ticks_position('none')
plt.axes().get_xaxis().set_ticks_position('none')
if sim_type == 'full_array':
plt.axes().set_title('Stochastic Model',x=.2)
elif sim_type == 'exact':
plt.axes().set_title('Deterministic Model',x=.2)
else:
print '\nSomething went wrong while writing the title of the plots\n\n'
exit(1)
plt.xlabel('time (s)')
# I got the colorbar bit here from
# http://matplotlib.org/users/tight_layout_guide.html, which explains
# the difficulties with tight_layout() and colorbar positioning.
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "3%", pad="1%")
# cbar = plt.colorbar(cax=cax,ticks=[0,maxval])
# cbar.ax.set_yticklabels(['0', 'max'])
cbar = plt.colorbar(cax=cax)
cbar.set_label('molecules/$\mu^2$')
#plt.colorbar(ticks=[])
plt.tight_layout()
print 'saving as', load.print_string("single-image-plot","")
plt.savefig(load.print_string("single-image-plot",""), transparent=True)
#plt.savefig(load.print_string("real-gauss-single-image-plot",""))
plt.figure()
plt.axes().set_aspect('equal', 'datalim')
plt.ax = plt.gca()
font=FontProperties()
font.set_family('serif')
for i in range(len(tails)-1):
radial_length = np.sqrt((tails[i][0]-load.dx*Ny/2.0)**2+(tails[i][1]-load.dx*Nz/2.0)**2)
dir_z = (tails[i][0]-load.dx*Nz/2.0)/radial_length
dir_y = (tails[i][1]-load.dx*Ny/2.0)/radial_length
number_zpos = tails[i][0]+.18*dir_z
number_ypos = tails[i][1]+.18*dir_y
plt.ax.annotate('',xy=(tails[i+1][0],tails[i+1][1]),xytext=(tails[i][0],tails[i][1]),
fontsize=7,
arrowprops=dict(color='red',shrink=0.01, width=.3, headwidth=5.))
#plt.ax.text(number_zpos,number_ypos,"%d"%(i+1),fontsize=30,fontproperties=font,color='red')
cell_membrane[cell_membrane>0] = 1
cell_x = np.linspace(0,load.dx*len(cell_membrane[0,:]),len(cell_membrane[0,:]))
cell_y = np.linspace(0,load.dx*len(cell_membrane[:,0]),len(cell_membrane[:,0]))
plt.contour(cell_x,cell_y,cell_membrane, linewidths=2,levels=[.99])
#plt.ax.quiver(X,Y,U,V,scale_units='xy',angles='xy',scale=1)
plt.ax.get_yaxis().set_visible(True)
plt.ax.get_xaxis().set_visible(True)
plt.xlim((0,load.dx*cell_membrane.shape[1]))
plt.ylim((0,load.dx*cell_membrane.shape[0]))
plt.xlabel("Z grid position")
plt.ylabel("Y grid position")
#plt.title("Local temporal maxima, global spatial maxima view of %s"%(protein))
plt.savefig(load.print_string("arrow-plot",protein))
plt.show()
plt.axes().get_yaxis().set_ticks_position('none')
plt.axes().get_xaxis().set_ticks_position('none')
if sim_type == 'full_array':
plt.axes().set_title('Stochastic Model',x=.2)
elif sim_type == 'exact':
plt.axes().set_title('Deterministic Model',x=.2)
else:
print '\nSomething went wrong while writing the title of the plots\n\n'
exit(1)
plt.xlabel('time (s)')
# I got the colorbar bit here from
# http://matplotlib.org/users/tight_layout_guide.html, which explains
# the difficulties with tight_layout() and colorbar positioning.
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "3%", pad="1%")
# cbar = plt.colorbar(cax=cax,ticks=[0,maxval])
# cbar.ax.set_yticklabels(['0', 'max'])
cbar = plt.colorbar(cax=cax)
cbar.set_label('molecules/$\mu m^2$')
#plt.colorbar(ticks=[])
plt.tight_layout()
print 'saving as', load.print_string("single-image-plot","")
plt.savefig(load.print_string("single-image-plot",""), transparent=True)
plt.savefig(load.print_string("single-image-plot","")[:-4]+'.eps', transparent=True)
plt.savefig(load.print_string("single-image-plot","")[:-4]+'.png', transparent=True, dpi=500)
#plt.savefig(load.print_string("real-gauss-single-image-plot",""))
xy=(tails[i + 1][0], tails[i + 1][1]),
xytext=(tails[i][0], tails[i][1]),
fontsize=7,
arrowprops=dict(color='red',
shrink=0.01,
width=.3,
headwidth=5.))
plt.ax.text(number_zpos,
number_ypos,
"%d" % (i + 1),
fontsize=30,
fontproperties=font,
color='red')
cell_membrane[cell_membrane > 0] = 1
cell_x = np.linspace(0, load.dx * len(cell_membrane[0, :]),
len(cell_membrane[0, :]))
cell_y = np.linspace(0, load.dx * len(cell_membrane[:, 0]),
len(cell_membrane[:, 0]))
plt.contour(cell_x, cell_y, cell_membrane, linewidths=2, levels=[.99])
#plt.ax.quiver(X,Y,U,V,scale_units='xy',angles='xy',scale=1)
plt.ax.get_yaxis().set_visible(False)
plt.ax.get_xaxis().set_visible(False)
plt.xlim((0, load.dx * cell_membrane.shape[1]))
plt.ylim((0, load.dx * cell_membrane.shape[0]))
plt.xlabel("Z grid position")
plt.ylabel("Y grid position")
#plt.title("Local temporal maxima, global spatial maxima view of %s"%(protein))
plt.savefig(load.print_string("arrow-plot", protein))
plt.show()
if (x+i >= 0 and x+i < new.shape[0]-1 and y+j >= 0 and y+j < new.shape[1]-1):
new[x+i,y+j] += data[x,y]*math.exp( -(i*i+j*j)*.05*.05/2.0/sigma/sigma )
return new
proteinList = ['NflD']
for protein in proteinList:
job_string = "data/shape-%s/%s-%s-%s-%s-%s-%s/" % (load.f_shape,load.f_param1,load.f_param2,
load.f_param3,load.f_param4,load.f_param5,load.sim_type)
p = re.compile('[.]')
job_string = p.sub('_',job_string)
data_filename = job_string + protein + '/' + 'time-map.dat'
data_file = np.loadtxt(data_filename)
print "starting time_map.p for ",data_filename
data = gaussian_smear(data_file,.509)
timemax = np.max(data)
plt.figure()
plt.pcolormesh(data,vmin=0)
plt.axes().set_aspect('equal')
plt.xlim(0,data.shape[1])
plt.ylim(0,data.shape[0])
plt.xlabel("Z grid position")
plt.ylabel("Y grid position")
plt.title("Time averaged view of %s"%(protein))
plt.colorbar()
plt.savefig(load.print_string("time-map",protein))
from __future__ import division
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import file_loader as load
import sys
# WIP
for protein in load.proteinList:
file = np.loadtxt("./data/shape-%s/%s%s%stime-map-%s-%s-%s-%s-%s-%s-%s.dat"%(load.f_shape,load.debug_str,load.hires_str,load.slice_str,protein,load.f_shape,load.f_param1,load.f_param2,load.f_param3,load.f_param4,load.f_param5))
plt.figure()
plt.pcolormesh(file)
plt.axes().set_aspect('equal')
plt.xlim(0,file.shape[1])
plt.ylim(0,file.shape[0])
plt.xlabel("Z grid position")
plt.ylabel("Y grid position")
plt.title("Time averaged view of %s"%(protein))
plt.colorbar()
plt.savefig(load.print_string("time-map",protein))
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import file_loader as load
import sys
#create protein data objects (see file_loader.py)
NflE = load.data(protein="NflE")
NflD = load.data(protein="NflD")
nATP = load.data(protein="nATP")
nE = load.data(protein="nE")
nADP = load.data(protein="nADP")
Nd = load.data(protein="Nd")
Nde = load.data(protein="Nde")
#compute time average (removing first 10% happens in file loading)
def average_location(dataset):
tsum = np.zeros_like(dataset[0])
for t in range(nATP.tsteps):
tsum += dataset[t]
return tsum/(nATP.tsteps)
#plot each of the data set time maps individually.
for p in [NflE, NflD, nATP, nE, nADP, Nd]:
plt.figure()
plt.contourf(p.axes[0],p.axes[1],average_location(p.dataset),500)
plt.axes().set_aspect('equal', 'datalim')
plt.colorbar()
plt.savefig(load.print_string("time-map",p))
from __future__ import division
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import sys
import pylab
import file_loader as load
data = np.loadtxt(
"./data/shape-%s/membrane_files/%s%s%ssections-%s-%s-%s-%s-%s-%s.dat"
% (
load.f_shape,
load.debug_str,
load.hires_str,
load.slice_str,
load.f_shape,
load.f_param1,
load.f_param2,
load.f_param3,
load.f_param4,
load.f_param5,
)
)
plt.figure()
plt.pcolormesh(data)
plt.savefig(load.print_string("sections_plot", ""))
from __future__ import division
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import sys
import pylab
import file_loader as load
data = np.loadtxt("./data/shape-%s/membrane_files/%s%s%ssections-%s-%s-%s-%s-%s-%s.dat"%(load.f_shape,load.debug_str,load.hires_str,load.slice_str,load.f_shape,load.f_param1,load.f_param2,load.f_param3,load.f_param4,load.f_param5))
plt.figure()
plt.pcolormesh(data)
plt.savefig(load.print_string("sections_plot",""))
