def get_totaldos(lines, index, natoms, nedos, efermi):
write_content = [] # write doscar0
plt = [] #plot figures
for n in range(nedos):
part_content = ''
plt_content = []
line = lines[index].strip().split()
e = float(line[0])
e_f = e - efermi
plt_content.append(round(float(e_f), 8))
part_content += '%15.8f ' % (e_f)
for col in range(1, len(line)):
dos = float(lines[index].strip().split()[col])
if gogal == 'total':
pass
else:
if col == 1:
dos = dos / natoms
part_content += '%15.8f ' % (dos)
plt_content.append(round(float(dos), 8))
part_content += '\n'
write_content.append(part_content)
plt.append(plt_content)
index += 1
return write_content, plt
def swapProb(temps):
p = []
for t in xrange(len(temps)):
for i in xrange(len(temps)):
f = math.exp((temps[t] - temps[i]) / temps[t])
if t < len(temps) - 1 and temps[i] == temps[t + 1]:
p.append(f)
return p
def setInitPert(np, ks, w):
global tau, MPL, epsilon
p = []
for i in range(len(ks)):
""" The perturvations in the adiabatic universe. """
p.append(Perturbation([0.000], [0.000], [0.00, -(3./2.) * w[1] * np[i], -(3./2.) * w[2] * np[i], -(3./2.) * w[3] * np[i]], [0.00, (ks[i] ** 2) * tau * np[i]/2., (ks[i] ** 2) * tau * np[i]/2., (ks[i] ** 2) * tau * np[i]/2.], np[i], ks[i]))
""" The perturvations in the adiabatic universe. """
p.append(Perturbation([-3. * MPL * np[i] * sqrt(epsilon)], [0.000], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], 0.0, ks[i]))
return p
def get_pdos(lines, index, natoms, nedos, efermi):
'''
:param lines: 输入文件内容，按照行读取所有内容
:param index: 开始行号
:param natoms: 原子个数
:param nedos: dos中点的个数
:param efermi: 费米面位置
:return: 返回第一个为可以写入读取的分dos的列表，第二个为可以用来画图的列表
画图的部分数据分别为：横坐标值，spd三个轨道分别的值，spd加起来的total值，各个轨道加起来的值
x s p1 p2 p3 d1 d2 d3 d4 d5 total s p_total d_total(共14列，0,1-9,10,11-13)
'''
write_content = [] # write doscar0
plt = [] #plot figures
a = lines[index - 1].strip().split()
for n in range(nedos):
part_content = ''
plt_content = []
s, p, d, total = (0, 0, 0, 0)
line = lines[index].strip().split()
if len(line) == 10:
e = float(line[0])
e_f = e - efermi
plt_content.append(round(float(e_f), 8))
part_content += '%15.8f ' % (e_f)
for col in range(1, len(line)):
dos = float(lines[index].strip().split()[col])
if col == 1:
s = dos
elif col > 1 and col <= 4:
p += dos
elif col > 4:
d += dos
plt_content.append(round(float(dos), 8))
total = s + p + d
# each_atom =
part_content += '%15.8f' % (total)
part_content += '%15.8f' % (s)
part_content += '%15.8f' % (p)
part_content += '%15.8f' % (d)
part_content += '\n'
plt_content.append(total)
plt_content.append(s)
plt_content.append(p)
plt_content.append(d)
write_content.append(part_content)
plt.append(plt_content)
index += 1
return write_content, plt
def jsonInput(self, filename):
f = open(filename, 'r')
jsonData = json.load(f)
f.close()
#angle
datas = []
for user in jsonData:
#data is joint angle
data = []
#dts:data_size, dtd:data_dimension
self.dts = len(user["datas"])
self.dtd = len(user["datas"][0]["data"])
for j in range(self.dts):
data.append(user["datas"][j]["data"])
datas.append(data)
poses = []
for user in jsonData:
pos = []
psize = len(user["datas"][0]["jdata"])
for j in range(self.dts):
pls = []
for p in range(psize):
pl = []
for xyz in range(3):
pl.append(user["datas"][j]["jdata"][p][xyz])
pls.append(pl)
pos.append(pls)
poses.append(pos)
#time ただし1.14現在,値が入ってない
time = []
for t in jsonData[0]["datas"]:
time.append(t["time"])
#print "poses[0]:",poses[0]
#可視化用,ジョイント
f = open('/home/uema/catkin_ws/src/rqt_cca/joint_index.json', 'r')
jsonIdxDt = json.load(f)
f.close
self.jIdx = []
for idx in jsonIdxDt:
jl = []
for i in idx:
jl.append(i)
self.jIdx.append(jl)
return datas[0], datas[1], poses[0], poses[1], time
def setInitPert(np, ks):
global tau, MPL, epsilon
p = []
for i in range(len(ks)):
""" The perturvations in the adiabatic universe. """
p.append(
Perturbation([0.000], [0.000],
[-2 * np[i], -(3. / 2.) * np[i], -2 * np[i]],
[(ks[i]**2) * tau * np[i] / 2.,
(ks[i]**2) * tau * np[i] / 2.,
(ks[i]**2) * tau * np[i] / 2.], np[i], ks[i]))
""" The perturbations in the entropic universe. """
p.append(
Perturbation([-3. * MPL * np[i] * sqrt(epsilon)], [0.000],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], 0.0, ks[i]))
return p
def macro_avg_precision(clusters_merged_labelled):
precision = []
for label, items in clusters_merged_labelled.items():
p = []
for label, item in items.items():
p.append(item)
precision.append(p)
macro_precision = []
for i in precision:
pre = (max(i)/sum(i))*100
macro_precision.append(pre)
macro_precision = sum(macro_precision)/len(macro_precision)
return macro_precision
def poseInput(self, filename):
"""
f = open(filename, 'r')
js = json.load(f)
f.close()
#fposename = "/home/uema/catkin_ws/src/rqt_cca/data2/"+str(js["prop"]["fname"])
fposename = str(js["prop"]["fname"])
"""
with h5py.File(filename) as f:
fposename = f["/prop/fname"].value
print "open pose file:",fposename
fp = open(fposename, 'r')
jsp = json.load(fp)
f.close()
datas = []
for user in jsp:
#data is joint angle
data = []
for j in range(self.dts):
data.append(user["datas"][j]["data"])
datas.append(data)
poses = []
for user in jsp:
pos = []
psize = len(user["datas"][0]["jdata"])
for j in range(self.dts):
pls = []
for p in range(psize):
pl = []
for xyz in range(3):
pl.append(user["datas"][j]["jdata"][p][xyz])
pls.append(pl)
pos.append(pls)
poses.append(pos)
return datas[0], datas[1], poses[0], poses[1]
def run_test(datasetdir, label_col, data_begin, data_end, numclasses, model_array, graphing, printing, folds, ps, pe, outfile):
result_log = open(outfile, "w")
corrplt = resplt.subplot()
dataset_str_array = datasetdir.split("/")
dataset_str_nameP = dataset_str_array[len(dataset_str_array)-1].split(".")
dataset_str_name = dataset_str_nameP[0]
if(not printing):
blockPrint()
else:
enablePrint(dataset_str_name + "_" + str(model_array) + "_" + str(folds)+".txt")
dataset_str_array = datasetdir.split("/")
dataset_str_name = dataset_str_array[len(dataset_str_array)-1].strip(".csv")
codebook = MatrixGeneration.GenerateMatrix(numclasses, numclasses)
listOfCBs = [codebook]
accuracy_array = [[]]
correlation_array = [[]]
for model in model_array:
accuracy_array.append([])
correlation_array.append([])
print("starting run")
pdomain = []
p = ps
while p >= pe:
print("DATA PER CLASSIFIER " + str(p))
pdomain.append(round(p, 2))
counter = 0
for model in model_array:
temp = getECOCBaselineAccuracies_VC_VD(datasetdir, listOfCBs, label_col, data_begin, data_end, [model], p, folds, graphing)
accuracy_array[counter].append(temp[0])
correlation_array[counter].append(temp[1])
counter += 1
mixed = getECOCBaselineAccuracies_VC_VD(datasetdir, listOfCBs, label_col, data_begin, data_end, model_array, p, folds, graphing)
accuracy_array[len(accuracy_array)-1].append(mixed[0])
correlation_array[len(correlation_array)-1].append(mixed[1])
p = p - 0.1
labels = "P "
accuracy_results = ""
correlation_results = ""
for j in range(len(model_array)):
labels += models_String[model_array[j]-1] + " "
labels += "ALL\n"
for i in range(len(pdomain)):
accuracy_results += str(pdomain[i]) + " "
for j in range(len(model_array)+1):
accuracy_results += str(accuracy_array[j][i]) + " "
accuracy_results += "\n"
for i in range(len(pdomain)):
correlation_results += str(pdomain[i]) + " "
for j in range(len(model_array)+1):
correlation_results += str(correlation_array[j][i]) + " "
correlation_results += "\n"
result_log.write(labels + "Accuracies\n" + accuracy_results + "Correlation\n" + correlation_results)
print(accuracy_array)
print(correlation_array)
if (graphing):
accplt = [[]]
corrplt = [[]]
for model in model_array:
accplt.append([])
corrplt.append([])
counter = 0
for acc, corr in zip(accuracy_array, correlation_array):
for modelacc, modelcorr in zip(acc, corr):
accplt[counter].append(modelacc[0])
corrplt[counter].append(modelcorr[0])
counter+=1
line_references = ['-', ':', '-.', '--']
resplt.suptitle(dataset_str_name + " Varied Data Accuracies")
resplt.xlabel("Percent of Data Per Learner")
resplt.ylabel("Accuracy")
for i in range(len(model_array)):
resplt.plot(pdomain, accplt[i], line_references[i], label= models_String[model_array[i]-1])
resplt.plot(pdomain, accplt[len(accplt)-1], line_references[len(line_references)-1], label = "All")
resplt.legend(loc= "lower right")
resplt.savefig(dataset_str_name + " Varied Data Accuracies.png")
resplt.clf()
resplt.suptitle(dataset_str_name + " Varied Data Correlation")
resplt.xlabel("Percent of Data Per Learner")
resplt.ylabel("Correlation")
for i in range(len(model_array)):
resplt.plot(pdomain, corrplt[i], line_references[i], label= models_String[model_array[i]-1])
resplt.plot(pdomain, corrplt[len(corrplt)-1], line_references[len(line_references)-1], label = "All")
resplt.legend(loc= "lower right")
resplt.savefig(dataset_str_name + " Varied Data Correlation.png")
result_log.close()
return result_log
lines = a.readlines()
d = OrderedDict({})
p = []
coldefs = []
for j,line in enumerate(lines):
if 'ID' in line:
colnames = line.split()
tmp = colnames
for i,col in enumerate(colnames):
if '+' in col:
tmp[i] = tmp[i-1]+'err'
d[tmp[i]] = []
coldefs.append(tmp)
p.append(j+2)
if 'NGC 7492' in line:
p.append(j+1)
#datalines = lines[p[0]:p[1]] + lines[p[2]:p[3]] + lines[p[4]:p[5]]
datalines = lines[p[0]:p[1]]
# Part 1
for j, line in enumerate(datalines):
cols = coldefs[0]
d[cols[0]].append(line[0:9].strip())
d[cols[1]].append(line[10:21].strip())
d[cols[2]].append(line[23:37].strip())
d[cols[3]].append(line[38:51].strip())
d[cols[4]].append(line[52:60].strip())
d[cols[5]].append(line[61:68].strip())
Yc = []
for el in Diameters:
Ds.append(el[0])
Xc.append(el[1])
Yc.append(el[2])
lines, t, p = file_parser("0_700_out.txt")
if len(t)>len(Ds):
del t[-(len(t)-len(Ds)):len(t)]
del p[-(len(p)-len(Ds)):len(p)]
if len(t)<len(Ds):
last_t= t[-1]
last_p = p[-1]
for i in range(len(t),len(Ds)):
t.append(last_t+(i-len(t))*FPS)
for i in range (len(p),len(Ds)):
p.append(last_p)
p = array(p)
t = array(t)
pixTomm = 9.9/132.0
D = pixTomm*array(Ds)
R = array(Residus)
pixTomm = 9.9/132.0
X_center = pixTomm*(array(Xc)-Xc[0])
yc0 = Yc[0]
Y_center = pixTomm*(array(Yc)-yc0)
Y_lines = array(Y_line)
L0 = Y_lines[0]
spring_displacement = (Y_lines-L0)*pixTomm
# Dat_Xc_t = column_stack((t, X_center))
# Dat_Yc_t = column_stack((t, Y_center))
