In [11]:

    

import matplotlib.pyplot as plt
%matplotlib inline


    

In [12]:

    

fig = plt.figure()
ax = fig.add_subplot(2,1,1) # two rows, one column, first plot
import numpy as np
t = np.arange(0.0, 1.0, 0.01)
s = np.sin(2*np.pi*t)
line, = ax.plot(t, s, color='blue', lw=2)


    

In [13]:

    

ax.lines[0]


    

    
Out[13]:





<matplotlib.lines.Line2D at 0x148a00459e8>

In [14]:

    

fig


    

    
Out[14]:

In [15]:

    

line


    

    
Out[15]:





<matplotlib.lines.Line2D at 0x148a00459e8>

In [16]:

    

ax.lines.remove(line)


    

In [17]:

    

fig


    

    
Out[17]:

In [20]:

    

ax.lines.append(line)


    

In [21]:

    

fig


    

    
Out[21]:

In [22]:

    

xtext = ax.set_xlabel('my xdata') # returns a Text instance
ytext = ax.set_ylabel('my ydata')


    

In [23]:

    

fig


    

    
Out[23]:

In [24]:

    

plt.plot([1,2,3])


    

    
Out[24]:





[<matplotlib.lines.Line2D at 0x148a00dca58>]






    

In [25]:

    

plt.subplot(211)


    

    
Out[25]:





<matplotlib.axes._subplots.AxesSubplot at 0x148a00a3390>






    

In [26]:

    

plt.plot(range(12))


    

    
Out[26]:





[<matplotlib.lines.Line2D at 0x148a0186b00>]






    

In [27]:

    

plt.subplot(212, axisbg='y') # creates 2nd subplot with yellow background


    

    
Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0x148a0151cf8>






    

In [28]:

    

# plot a line, implicitly creating a subplot(111)
plt.plot([1,2,3])
# now create a subplot which represents the top plot of a grid
# with 2 rows and 1 column. Since this subplot will overlap the
# first, the plot (and its axes) previously created, will be removed
plt.subplot(211)
plt.plot(range(12))
plt.subplot(212, axisbg='y') # creates 2nd subplot with yellow background


    

    
Out[28]:





<matplotlib.axes._subplots.AxesSubplot at 0x148a026e710>






    

In [29]:

    

import matplotlib.pyplot as plt
import numpy as np

dt = 0.01
Fs = 1/dt
t = np.arange(0, 10, dt)
nse = np.random.randn(len(t))
r = np.exp(-t/0.05)

cnse = np.convolve(nse, r)*dt
cnse = cnse[:len(t)]
s = 0.1*np.sin(2*np.pi*t) + cnse

plt.subplot(3, 2, 1)
plt.plot(t, s)

plt.subplot(3, 2, 3)
plt.magnitude_spectrum(s, Fs=Fs)

plt.subplot(3, 2, 4)
plt.magnitude_spectrum(s, Fs=Fs, scale='dB')

plt.subplot(3, 2, 5)
plt.angle_spectrum(s, Fs=Fs)

plt.subplot(3, 2, 6)
plt.phase_spectrum(s, Fs=Fs)

plt.show()


    

In [ ]: