sample_time=0.1 * doubling_time,
nameCRM="./data/dataCRMnoisy.csv")
print('It took', np.int(time.time() - start), 'seconds.')
data1 = pd.read_csv("./data/dataCRMnoisy.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)
mnszarray.append(np.mean(szs))
errmnsz.append(mean_cntr[1][1] - mean_cntr[0])
cvszarray.append(np.var(szs) / np.mean(szs)**2)
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
errcv2sz.append(errv)
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
ax[0].plot(np.array(timearray) / doubling_time, np.array(mnszarray))
ax[0].fill_between(np.array(timearray) / doubling_time,
np.array(mnszarray) - np.array(errmnsz),
np.array(mnszarray) + np.array(errmnsz),
alpha=1,
edgecolor='#4db8ff',
facecolor='#4db8ff',
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)
mnszarray.append(np.mean(szs))
errmnsz.append(mean_cntr[1][1] - mean_cntr[0])
cvszarray.append(np.var(szs) / np.mean(szs)**2)
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
errcv2sz.append(errv)
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
ax[0].plot(np.array(timearray) / doubling_time, mnszarray)
ax[0].fill_between(np.array(timearray) / doubling_time,
np.array(mnszarray) - np.array(errmnsz),
np.array(mnszarray) + np.array(errmnsz),
alpha=1,
edgecolor='#4db8ff',
facecolor='#4db8ff',