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))