del input
d[fl] = [i[0] for i in sorted(enumerate(dlist), key=lambda x: x[1])
] #, reverse = True)]
PIK = 'd.dat'
with open(PIK, "wb") as f:
pickle.dump(d, f, pickle.HIGHEST_PROTOCOL)
f.close()
return (d)
#------------------------------------------------------------------------------------------------------------------
datafiles = get_files_by_file_size(datalocation)
# The first file will contain all the structure data, the rest will contain pointodoses.
dpdata = dose_to_points_data_pb2.DoseToPointsData()
# Start with reading structures, numvoxels and all that.
f = open(datafiles.pop(0), "rb")
dpdata.ParseFromString(f.read())
f.close()
datafiles.sort()
#----------------------------------------------------------------------------------------
# Get the data about the structures
numstructs = len(dpdata.Structures)
structureList = []
structureDict = {
} # keys are the names of the structure and the value is the corresponding index location (integer)
print("Reading in structures")
for s in range(numstructs):
print('Reading:', dpdata.Structures[s].Id)
def individualAnalizer(caseis):
# What computer am I reading from?
datalocation = '~'
if 'radiation-math' == socket.gethostname(): # LAB
datalocation = "/mnt/fastdata/Data/" + caseis + "/by-Beam/"
dropbox = "/mnt/datadrive/Dropbox"
cutter = 44
if "lung360" == caseis:
cutter = 43
elif 'sharkpool' == socket.gethostname(): # MY HOUSE
datalocation = "/home/wilmer/Dropbox/Data/spine360/by-Beam/"
dropbox = "/home/wilmer/Dropbox"
cutter = 51
elif 'DESKTOP-EA1PG8V' == socket.gethostname(): # MY HOUSE
datalocation = "C:/Users/wihen/Data/"+ caseis + "/by-Beam/"
dropbox = "D:/Dropbox"
cutter = 45
numcores = 11
if "lung360" == caseis:
cutter = 44
elif ('arc-ts.umich.edu' == socket.gethostname().split('.', 1)[-1]): # FLUX
datalocation = "/scratch/engin_flux/wilmer/" + caseis + "/by-Beam/"
dropbox = "/home/wilmer/Dropbox"
cutter = 52
if "lung360" == caseis:
cutter = 51
else:
datalocation = "/home/wilmer/Dropbox/Data/spine360/by-Beam/" # MY LAPTOP
dropbox = "/home/wilmer/Dropbox"
datafiles = get_files_by_file_size(datalocation)
# The first file will contain all the structure data, the rest will contain pointodoses.
dpdata = dose_to_points_data_pb2.DoseToPointsData()
print('datafiles:', datafiles)
f = open(datafiles.pop(0), "rb")
dpdata.ParseFromString(f.read())
f.close()
datafiles.sort()
XSize = dpdata.Beamlets[0].XSize
YSize = dpdata.Beamlets[0].YSize
XSize = 1.0
YSize = 1.0
# Read all relevant files for a particular case
datafiles = get_files_by_file_size('./outputGraphics/')
matching = [s for s in datafiles if 'outputGraphics/allbeamshapesbefore-save-' + caseis + '-' in s]
print('Areas will be calculated in squared milimeters. Same as perimeter in milimeters')
KellyMeasures = dict()
MyMeasures = dict()
if caseis == 'lung360':
cutter = 50
else:
cutter = 51
mylegends = list()
for PIK in matching:
mylegends.append(float(PIK[cutter:-7]))
print(matching)
matching = [x for _,x in sorted(zip(mylegends, matching))]
print(matching)
mylegends = list()
pendients = list()
for myColor, PIK in enumerate(matching):
print(PIK)
Cval = float(PIK[cutter:-7])
mylegends.append(Cval)
MLCtrip = pickle.load(open(PIK, "rb"))
KellyMeasures[PIK] = np.zeros(len(MLCtrip))
MyMeasures[PIK] = np.zeros(len(MLCtrip))
Perimeters = np.zeros(len(MLCtrip))
Areas = np.zeros(len(MLCtrip))
for i, thisbeam in enumerate(MLCtrip):
l = thisbeam[0]
r = thisbeam[1]
Perimeter = (r[0] - l[0])
Area = 0.0
for n in range(1, len(l)):
Area += (r[n] - l[n]) * YSize
Perimeter += (np.abs(l[n] - l[n-1]) + np.abs(r[n] - r[n-1]) - 2 * np.maximum(0, l[n-1] - r[n]) - 2 * np.maximum(0, l[n] - r[n - 1])) * XSize
Perimeter += (r[len(r)-1] - l[len(l)-1]) * XSize + np.sign(r[len(r)-1] - l[len(l)-1])
Kellymeasure = Perimeter / Area
Mymeasure = 1.0 * Perimeter - 0.55 * Area
KellyMeasures[PIK][i] = Kellymeasure
MyMeasures[PIK][i] = Mymeasure
Perimeters[i] = Perimeter
Areas[i] = Area
lm = LinearRegression(fit_intercept=False)
y = KellyMeasures[PIK] - Perimeters
y = np.reshape(y, (len(y), 1))
Areas = np.reshape(Areas, (len(y), 1))
reg = lm.fit(-Areas, y)
print(reg, reg.coef_)
pendients.append((Cval, reg.coef_[0]))
#print(reg.coef_)
if mylegends[-1] == 1.0 or mylegends[-1] == 0.001:
plt.scatter(MyMeasures[PIK], KellyMeasures[PIK], s = 13.0, marker = 'v')
elif mylegends[-1] == 0.0:
plt.scatter(MyMeasures[PIK], KellyMeasures[PIK], s=10.0, marker='o')
else:
plt.scatter(MyMeasures[PIK], KellyMeasures[PIK], s=1.0)
plt.title('Comparison of measures for case: ' + caseis)
plt.xlabel('Our Measure')
plt.ylabel("Younge's Measure")
plt.legend(mylegends)
plt.savefig('outputGraphics/ComparisonOfMeasuresForCase' + caseis + '.png')
#plt.show()
return(pendients)