Merge pull request #29 from moritz-ritter/0_6_5_no_matplotlib

Made matplotlib+pylab dependencies optional

setup.py

@@ -37,7 +37,10 @@
     package_dir={ '' : 'src' },
 
     # Dependencies
-    install_requires = ['matplotlib', 'numpy', 'scipy', 'easydev'],
+    install_requires=['numpy', 'scipy', 'easydev'],
+    extras_require={
+        'plot': ['matplotlib']
+    },
     data_files = data_files,
     platforms=["Linux"],
     classifiers=["Development Status :: 1 - Planning",

src/spectrum/correlation.py

@@ -22,12 +22,20 @@
 """#from numpy.fft import fft, ifft
 import numpy
 from numpy import  arange, isrealobj
-from pylab import rms_flat
+# from pylab import rms_flat
 
 
 __all__ = ['CORRELATION', 'xcorr']
 
 
+def pylab_rms_flat(a):
+    """
+    Return the root mean square of all the elements of *a*, flattened out.
+    (Copied 1:1 from matplotlib.mlab.)
+    """
+    return numpy.sqrt(numpy.mean(numpy.absolute(a) ** 2))
+
+
 def CORRELATION(x, y=None, maxlags=None, norm='unbiased'):
     r"""Correlation function
 
@@ -104,8 +112,8 @@ def CORRELATION(x, y=None, maxlags=None, norm='unbiased'):
         r = numpy.zeros(maxlags, dtype=complex)
 
     if norm == 'coeff':
-        rmsx = rms_flat(x)
-        rmsy = rms_flat(y)
+        rmsx = pylab_rms_flat(x)
+        rmsy = pylab_rms_flat(y)
 
     for k in range(0, maxlags+1):
         nk = N - k - 1
@@ -208,7 +216,7 @@ def xcorr(x, y=None, maxlags=None, norm='biased'):
         res = res[lags] / (float(N)-abs(arange(-N+1, N)))[lags]
     elif norm == 'coeff':
         Nf = float(N)
-        rms = rms_flat(x) * rms_flat(y)
+        rms = pylab_rms_flat(x) * pylab_rms_flat(y)
         res = res[lags] / rms / Nf
     else:
         res = res[lags]

src/spectrum/datasets.py

@@ -12,7 +12,6 @@
  
     :Reference: [Marple]_
 """
-from  pylab import plot, linspace, xlabel, ylabel, grid
 import numpy
 from numpy import arange, pi, cos
 
@@ -150,14 +149,9 @@ def __init__(self, data, sampling=1):
     def plot(self, **kargs):
         """Plot the data set, using the sampling information to set the x-axis 
         correctly."""
+        from pylab import plot, linspace, xlabel, ylabel, grid
         time = linspace(1*self.dt, self.N*self.dt, self.N)
         plot(time, self.data, **kargs)
         xlabel('Time')
         ylabel('Amplitude')
         grid(True)
-
-
-
-
-
-

src/spectrum/mtm.py

@@ -20,7 +20,6 @@
 import os
 from os.path import join as pj
 from spectrum.tools import nextpow2
-from pylab import semilogy
 from numpy.ctypeslib import load_library
 
 """
@@ -53,7 +52,7 @@
     print("Library %s not found" % lib_name)
 
 
-def pmtm(x, NW=None, k=None, NFFT=None, e=None, v=None, method='adapt', show=True):
+def pmtm(x, NW=None, k=None, NFFT=None, e=None, v=None, method='adapt', show=False):
     """Multitapering spectral estimation
 
     :param array x: the data
@@ -173,6 +172,7 @@ def pmtm(x, NW=None, k=None, NFFT=None, e=None, v=None, method='adapt', show=Tru
         weights=wk
 
     if show is True:
+        from pylab import semilogy
         if method == "adapt":
             Sk = np.mean(Sk * weights, axis=1)
         else:

src/spectrum/periodogram.py

@@ -39,10 +39,9 @@
 """
 from .window import Window
 from .psd import Spectrum, FourierSpectrum
-from pylab import pi, fft, mean, rfft
-from numpy import array, ceil
+from numpy import array, ceil, pi, mean, linspace
+from numpy.fft import fft, rfft
 import numpy
-import pylab as plt
 
 
 
@@ -191,9 +190,10 @@ def WelchPeriodogram(data, NFFT=None,  sampling=1., **kargs):
 
 
     """
+    from pylab import psd
     spectrum = Spectrum(data, sampling=1.)
 
-    P = plt.psd(data, NFFT, Fs=sampling, **kargs)
+    P = psd(data, NFFT, Fs=sampling, **kargs)
     spectrum.psd = P[0]
     #spectrum.__Spectrum_sides = 'twosided'
 
@@ -305,10 +305,10 @@ def DaniellPeriodogram(data, P, NFFT=None, detrend='mean', sampling=1.,
 
     #todo: check this
     if datatype == 'complex':
-        freq = plt.linspace(0,sampling, len(newpsd))
+        freq = linspace(0,sampling, len(newpsd))
     else:
         df = 1./sampling
-        freq = plt.linspace(0,sampling/2., len(newpsd))
+        freq = linspace(0,sampling/2., len(newpsd))
     #psd.refreq(2*psd.size()/A.freq());
     #psd.retime(-1./psd.freq()+1./A.size());
 

src/spectrum/psd.py

@@ -1,6 +1,4 @@
 """This module provides the Base class for PSDs"""
-import pylab as plt
-import pylab
 import numpy
 
 from spectrum.tools import nextpow2
@@ -521,7 +519,7 @@ def _getdatatype(self):
 
     def scale(self):
         if self.scale_by_freq is True:
-            self.psd *= 2*plt.pi/self.df
+            self.psd *= 2*numpy.pi/self.df
 
     def frequencies(self, sides=None):
 
@@ -632,6 +630,7 @@ def plot(self, filename=None, norm=False, ylim=None,
             p.plot(norm=True, marker='o')
 
         """
+        import pylab
         #First, check that psd attribute is up-to-date
         if self.modified is True:
             raise errors.SpectrumModifiedError
@@ -667,8 +666,8 @@ def plot(self, filename=None, norm=False, ylim=None,
         from pylab import ylim as plt_ylim
 
         if 'ax' in list(kargs.keys()):
-            save_ax = plt.gca()
-            plt.sca(kargs['ax'])
+            save_ax = pylab.gca()
+            pylab.sca(kargs['ax'])
             rollback = True
             del kargs['ax']
         else:
@@ -679,8 +678,8 @@ def plot(self, filename=None, norm=False, ylim=None,
         else:
             pylab.plot(frequencies, 10*pylab.log10(psd),**kargs)
 
-        plt.xlabel('Frequency')
-        plt.ylabel('Power (dB)')
+        pylab.xlabel('Frequency')
+        pylab.ylabel('Power (dB)')
         pylab.grid(True)
         if ylim:
             plt_ylim(ylim)
@@ -693,7 +692,7 @@ def plot(self, filename=None, norm=False, ylim=None,
         if filename:
             pylab.savefig(filename)
         if rollback:
-            plt.sca(save_ax)
+            pylab.sca(save_ax)
         del psd, frequencies #is it needed?
 
     def power(self):
@@ -714,7 +713,7 @@ def power(self):
         if self.scale_by_freq == False:
             return sum(self.psd) * len(self.psd)
         else:
-            return sum(self.psd) * self.df/(2.*plt.pi)
+            return sum(self.psd) * self.df/(2.*numpy.pi)
 
     def _str_title(self):
         return "Spectrum summary\n"

src/spectrum/window.py

@@ -199,7 +199,8 @@ def compute_response(self, **kargs):
 
 
         """
-        from pylab import fft, fftshift, log10
+        from numpy import log10
+        from numpy.fft import fft, fftshift
 
         norm = kargs.get('norm', self.norm)