Replacement for deprecated tsplot

Question

I have a time-series with uniform samples save to a numpy array and I\'d like to plot their mean value with a bootstrapped confidence interval. Typically, I\'ve used t

Answer 1

The example tsplot from the question can easily be replicated using matplotlib.

Using standard deviation as error estimate

import numpy as np; np.random.seed(1)
import matplotlib.pyplot as plt
import seaborn as sns

x = np.linspace(0, 15, 31)
data = np.sin(x) + np.random.rand(10, 31) + np.random.randn(10, 1)


fig, (ax,ax2) = plt.subplots(ncols=2, sharey=True)
ax = sns.tsplot(data=data,ax=ax, ci="sd")

def tsplot(ax, data,**kw):
    x = np.arange(data.shape[1])
    est = np.mean(data, axis=0)
    sd = np.std(data, axis=0)
    cis = (est - sd, est + sd)
    ax.fill_between(x,cis[0],cis[1],alpha=0.2, **kw)
    ax.plot(x,est,**kw)
    ax.margins(x=0)

tsplot(ax2, data)

ax.set_title("sns.tsplot")
ax2.set_title("custom tsplot")

plt.show()

Using bootstrapping for error estimate

import numpy as np; np.random.seed(1)
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns

x = np.linspace(0, 15, 31)
data = np.sin(x) + np.random.rand(10, 31) + np.random.randn(10, 1)


fig, (ax,ax2) = plt.subplots(ncols=2, sharey=True)
ax = sns.tsplot(data=data,ax=ax)

def bootstrap(data, n_boot=10000, ci=68):
    boot_dist = []
    for i in range(int(n_boot)):
        resampler = np.random.randint(0, data.shape[0], data.shape[0])
        sample = data.take(resampler, axis=0)
        boot_dist.append(np.mean(sample, axis=0))
    b = np.array(boot_dist)
    s1 = np.apply_along_axis(stats.scoreatpercentile, 0, b, 50.-ci/2.)
    s2 = np.apply_along_axis(stats.scoreatpercentile, 0, b, 50.+ci/2.)
    return (s1,s2)
    
def tsplotboot(ax, data,**kw):
    x = np.arange(data.shape[1])
    est = np.mean(data, axis=0)
    cis = bootstrap(data)
    ax.fill_between(x,cis[0],cis[1],alpha=0.2, **kw)
    ax.plot(x,est,**kw)
    ax.margins(x=0)

tsplotboot(ax2, data)

ax.set_title("sns.tsplot")
ax2.set_title("custom tsplot")

plt.show()

---

I guess the reason this is deprecated is exactly that the use of this function is rather limited and in most cases you are better off just plotting the data you want to plot directly.