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A self-answer: I downloaded an mlpy (Machine Learning PYthon) package and installed it (I use Gentoo, so the system-widely installed python modules are visible in Sage). After that I applied its functions to my time series in the following way:

signal01_02_array = numpy.array(signal01_02_AC) # 'signal01_02_AC' is a regular 1D python list containing my time series

import matplotlib.pyplot as plt
import mlpy

omega0 = 8
spec, scale = mlpy.cwt(signal01_02_array, dt=0.001, dj=0.05, wf='morlet', p=omega0,
                       extmethod='zeros', extlength='powerof2') # 'dt' is a time step in the time series

freq = scale[1:] # building a 'Frequency' scale (this is absolutely not correct, because the scale <-> frequency correspondence strongly depends on the certain wavelet form

t = numpy.array(signal01_02_array) #
                                   # building a 'Time' scale
for i in range(0,len(t)):          #
    t[i] = float(i)/1000           #

fig = plt.figure() # creating an ampty Matplotlib figure
ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2], xticks=[0,1,2,3,4,5,6,7,8,9]) # axis for the initial signal
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], xticks=[0,1,2,3,4,5,6,7,8,9], xlabel="Time, s", ylabel="Scales") # axis for the resulting wavelet transform data
ax3 = fig.add_axes([0.83,0.1,0.03,0.6], xlabel="Magnitude") # axis for a colour bar
ax1.plot(t, signal01_02_array, 'k') # plotting the initial time series

img = ax2.imshow(numpy.abs(spec), extent=[t[0], t[-1], freq[-1], freq[0]], aspect='auto') # plotting a CWT data
fig.colorbar(img, cax=ax3) # building a colour bar basing on the colours present in CWT data image
plt.show()

This works perfectly fine in a pure Python-2.7 shell, but fails in Sage with the following error:

Traceback (most recent call last):                           extmethod='none', extlength='powerof2')
  File "", line 1, in <module>

  File "/tmp/tmpVOEsSJ/___code___.py", line 9, in <module>
    extmethod='none', extlength='powerof2')
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 160, in cwt
    s = scales(x.shape[0], dj, dt, s0)
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 76, in scales
    s[i] = s0 * 2**(i * dj)
  TypeError: unsupported operand type(s) for ** or pow(): 'int' and 'sage.rings.real_mpfr.RealNumber'

When 'dj' is a whole number (like 1, 2, ...), everything works fine in Sage too.

A self-answer: I downloaded an mlpy mlpy ( (Machine Learning PYthon) PYthon)) package and installed it (I use Gentoo, so the system-widely installed python modules are visible in Sage). After that I applied its functions to my time series in the following way:

signal01_02_array = numpy.array(signal01_02_AC) # 'signal01_02_AC' is a regular 1D python list containing my time series

import matplotlib.pyplot as plt
import mlpy

omega0 = 8
spec, scale = mlpy.cwt(signal01_02_array, dt=0.001, dj=0.05, wf='morlet', p=omega0,
                       extmethod='zeros', extlength='powerof2') # 'dt' is a time step in the time series

freq = scale[1:] # building a 'Frequency' scale (this is absolutely not correct, because the scale <-> frequency correspondence strongly depends on the certain wavelet form

t = numpy.array(signal01_02_array) #
                                   # building a 'Time' scale
for i in range(0,len(t)):          #
    t[i] = float(i)/1000           #

fig = plt.figure() # creating an ampty Matplotlib figure
ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2], xticks=[0,1,2,3,4,5,6,7,8,9]) # axis for the initial signal
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], xticks=[0,1,2,3,4,5,6,7,8,9], xlabel="Time, s", ylabel="Scales") # axis for the resulting wavelet transform data
ax3 = fig.add_axes([0.83,0.1,0.03,0.6], xlabel="Magnitude") # axis for a colour bar
ax1.plot(t, signal01_02_array, 'k') # plotting the initial time series

img = ax2.imshow(numpy.abs(spec), extent=[t[0], t[-1], freq[-1], freq[0]], aspect='auto') # plotting a CWT data
fig.colorbar(img, cax=ax3) # building a colour bar basing on the colours present in CWT data image
plt.show()

This works perfectly fine in a pure Python-2.7 shell, but fails in Sage with the following error:

Traceback (most recent call last):                           extmethod='none', extlength='powerof2')
  File "", line 1, in <module>

  File "/tmp/tmpVOEsSJ/___code___.py", line 9, in <module>
    extmethod='none', extlength='powerof2')
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 160, in cwt
    s = scales(x.shape[0], dj, dt, s0)
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 76, in scales
    s[i] = s0 * 2**(i * dj)
  TypeError: unsupported operand type(s) for ** or pow(): 'int' and 'sage.rings.real_mpfr.RealNumber'

When 'dj' is a whole number (like 1, 2, ...), everything works fine in Sage too.

A self-answer: I downloaded an mlpy ( mlpy (Machine Learning PYthon)) PYthon) package and installed it (I use Gentoo, so the system-widely installed python modules are visible in Sage). After that I applied its functions to my time series in the following way:

signal01_02_array = numpy.array(signal01_02_AC) # 'signal01_02_AC' is a regular 1D python list containing my time series

import matplotlib.pyplot as plt
import mlpy

omega0 = 8
spec, scale = mlpy.cwt(signal01_02_array, dt=0.001, dj=0.05, wf='morlet', p=omega0,
                       extmethod='zeros', extlength='powerof2') # 'dt' is a time step in the time series

freq = scale[1:] # building a 'Frequency' scale (this is absolutely not correct, because the scale <-> frequency correspondence strongly depends on the certain wavelet form

t = numpy.array(signal01_02_array) #
                                   # building a 'Time' scale
for i in range(0,len(t)):          #
    t[i] = float(i)/1000           #

fig = plt.figure() # creating an ampty Matplotlib figure
ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2], xticks=[0,1,2,3,4,5,6,7,8,9]) # axis for the initial signal
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], xticks=[0,1,2,3,4,5,6,7,8,9], xlabel="Time, s", ylabel="Scales") # axis for the resulting wavelet transform data
ax3 = fig.add_axes([0.83,0.1,0.03,0.6], xlabel="Magnitude") # axis for a colour bar
ax1.plot(t, signal01_02_array, 'k') # plotting the initial time series

img = ax2.imshow(numpy.abs(spec), extent=[t[0], t[-1], freq[-1], freq[0]], aspect='auto') # plotting a CWT data
fig.colorbar(img, cax=ax3) # building a colour bar basing on the colours present in CWT data image
plt.show()

This works perfectly fine in a pure Python-2.7 shell, but fails in Sage with the following error:

Traceback (most recent call last):                           extmethod='none', extlength='powerof2')
  File "", line 1, in <module>

  File "/tmp/tmpVOEsSJ/___code___.py", line 9, in <module>
    extmethod='none', extlength='powerof2')
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 160, in cwt
    s = scales(x.shape[0], dj, dt, s0)
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 76, in scales
    s[i] = s0 * 2**(i * dj)
  TypeError: unsupported operand type(s) for ** or pow(): 'int' and 'sage.rings.real_mpfr.RealNumber'

When 'dj' is a whole number (like 1, 2, ...), everything works fine in Sage too.

A self-answer: I downloaded an mlpy (Machine Learning PYthon) package and installed it (I use Gentoo, so the system-widely installed python modules are visible in Sage). After that I applied its functions to my time series in the following way:

signal01_02_array = numpy.array(signal01_02_AC) # 'signal01_02_AC' is a regular 1D python list containing my time series

import matplotlib.pyplot as plt
import mlpy

omega0 = 8
spec, scale = mlpy.cwt(signal01_02_array, dt=0.001, dj=0.05, wf='morlet', p=omega0,
                       extmethod='zeros', extlength='powerof2') # 'dt' is a time step in the time series

freq = scale[1:] # building a 'Frequency' scale (this is absolutely not NOT correct, because the scale <-> frequency correspondence strongly depends on the certain wavelet form
form)

t = numpy.array(signal01_02_array) #
                                   # building a 'Time' scale
for i in range(0,len(t)):          #
    t[i] = float(i)/1000           #

fig = plt.figure() # creating an ampty empty Matplotlib figure
ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2], xticks=[0,1,2,3,4,5,6,7,8,9]) # axis for the initial signal
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], xticks=[0,1,2,3,4,5,6,7,8,9], xlabel="Time, s", ylabel="Scales") # axis for the resulting wavelet transform data
ax3 = fig.add_axes([0.83,0.1,0.03,0.6], xlabel="Magnitude") # axis for a colour bar
ax1.plot(t, signal01_02_array, 'k') # plotting the initial time series

img = ax2.imshow(numpy.abs(spec), extent=[t[0], t[-1], freq[-1], freq[0]], aspect='auto') # plotting a CWT data
fig.colorbar(img, cax=ax3) # building a colour bar basing on the colours present in CWT data image
plt.show()

This works perfectly fine in a pure Python-2.7 shell, but fails in Sage with the following error:

Traceback (most recent call last):                           extmethod='none', extlength='powerof2')
  File "", line 1, in <module>

  File "/tmp/tmpVOEsSJ/___code___.py", line 9, in <module>
    extmethod='none', extlength='powerof2')
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 160, in cwt
    s = scales(x.shape[0], dj, dt, s0)
  File "/usr/lib64/python2.7/site-packages/mlpy/_cwt.py", line 76, in scales
    s[i] = s0 * 2**(i * dj)
  TypeError: unsupported operand type(s) for ** or pow(): 'int' and 'sage.rings.real_mpfr.RealNumber'

When 'dj' is a whole number (like 1, 2, ...), everything works fine in Sage too.