start = time.time() sim.szdyn(tmax=tmax, sample_time=0.1 * doubling_time, nameCRM="./data/dataCRM.csv") #Simulating the size for all the cells print('It took', np.int(time.time() - start), 'seconds.') start = time.time() sim.szdynFSP(tmax=tmax, nameFSP="./data/dataFSP.csv" ) #Obtaining trends using numerical FSP algorithm print('It took', np.int(time.time() - start), 'seconds.') sbar = np.linspace(0.5, 1.5, 100) * mean_size cv2sz = [] deltsz = [] for i in sbar: sd, cv2 = sim.SdStat( i) #Obtaining trends in sd vs Sb using master equation formulation cv2sz.append(cv2) deltsz.append(sd - i) data1 = pd.read_csv("./data/dataCRM.csv") timearray = data1.time.unique() mnszarray = [] cvszarray = [] errcv2sz = [] errmnsz = [] df = data1 del df['time'] for m in range(len(df)): szs = df.loc[m, :].values.tolist() mean_cntr, var_cntr, std_cntr = bayesest(szs, alpha=0.95)
alpha=0.95) CV2d.append(var_cntr[0] / mean_cntr[0]**2) delt.append(mean_cntr[0]) sb.append(meanv0_cntr[0]) errv = (var_cntr[1][1] - var_cntr[0]) / mean_cntr[0]**2 + 2 * ( mean_cntr[1][1] - mean_cntr[0]) * var_cntr[0] / mean_cntr[0]**3 errcv2d.append(errv) errdelt.append(mean_cntr[1][1] - mean_cntr[0]) errsb.append(meanv0_cntr[1][1] - meanv0_cntr[0]) start = time.time() sbar = np.linspace(0.5, 1.5, 100) * mean_size cv2sz = [] deltsz = [] for i in sbar: sd, cv2 = sim.SdStat(i) cv2sz.append(cv2) deltsz.append(sd - i) print('It took', np.int(time.time() - start), 'seconds.') data2 = pd.read_csv("./data/dataDSM.csv") mn = mean_size data2 = data2[data2.time > 3 * doubling_time] fig, ax = plt.subplots(1, 2, figsize=(12, 4)) ax[0].scatter(data2.S_b / mn, (data2.S_d - data2.S_b) / mn, s=2) ax[0].errorbar(np.array(sb), np.array(delt), xerr=errsb, yerr=errdelt, fmt='o',