Merge pull request #7 from pgervais/pep8

PEP8 fixes.

myCorr.py

@@ -2,73 +2,75 @@
 import matplotlib.pyplot as plt
 import scipy.fftpack
 
+
 def nextpow2(x):
     return np.ceil(np.log2(np.abs(x)))
 
+
 def myCorr(data, maxlag, plot=False):
     normalized = True
     allCpl = False
-    
+
     maxlag = np.round(maxlag)
-    #~ print "np.shape(data)",np.shape(data)
+    #~ print "np.shape(data)", np.shape(data)
     if np.shape(data)[0] == 2:
-        #~ print "2 matrix to correlate"
+        #~ print "2 matrices to correlate"
         if allCpl:
-            #Skipped this unused part
+            # Skipped this unused part
             pass
         else:
             K = np.shape(data)[0]
-            couples = np.concatenate( (np.arange(0,K), K+ np.arange(0,K) ) ) #couples de stations
-
+            # couples de stations
+            couples = np.concatenate((np.arange(0, K), K + np.arange(0, K)))
+
     Nt = np.shape(data)[1]
-    Nc = 2* Nt - 1
-    Nfft = 2**nextpow2(Nc)
-   
-    corr = scipy.fftpack.fft(data,int(Nfft),axis=1)
-    
+    Nc = 2 * Nt - 1
+    Nfft = 2 ** nextpow2(Nc)
+
+    corr = scipy.fftpack.fft(data, int(Nfft), axis=1)
+
     if plot:
-            plt.subplot(211)
-            plt.plot(np.arange(len(corr[0]))*0.05,np.abs(corr[0]))
-            plt.subplot(212)
-            plt.plot(np.arange(len(corr[1]))*0.05,np.abs(corr[1]))
-    
+        plt.subplot(211)
+        plt.plot(np.arange(len(corr[0])) * 0.05, np.abs(corr[0]))
+        plt.subplot(212)
+        plt.plot(np.arange(len(corr[1])) * 0.05, np.abs(corr[1]))
+
     corr = np.conj(corr[couples[0]]) * corr[couples[1]]
     corr = np.real(scipy.fftpack.ifft(corr)) / Nt
-    corr = np.concatenate( (corr[-Nt+1:],corr[:Nt+1]) )
-   
+    corr = np.concatenate((corr[-Nt + 1:], corr[:Nt + 1]))
+
     if plot:
         plt.figure()
         plt.plot(corr)
-    
-    E = np.sqrt(np.mean(data**2,axis=1))
+
+    E = np.sqrt(np.mean(data ** 2, axis=1))
     normFact = E[0] * E[1]
-    
+
     if normalized:
-        corr  /= normFact
-        
+        corr /= normFact
+
     if maxlag != Nt:
-        tcorr = np.arange(-Nt+1,Nt)
+        tcorr = np.arange(-Nt + 1, Nt)
         dN = np.where(np.abs(tcorr) <= maxlag)[0]
         corr = corr[dN]
-    
+
     del data
     return corr
-    
+
 if __name__ == "__main__":
-    
+
     import time
-    
-    data = np.random.random((2,86400*20))
+
+    data = np.random.random((2, 86400 * 20))
     print data.shape
     t = time.time()
     corr = myCorr(data, maxlag=25, plot=False)
-    print "Time:",time.time()-t
-    print np.mean(corr)
+    print("Time: %.3f" % (time.time() - t))
+    print(np.mean(corr))
 
     plt.figure()
     plt.plot(corr)
+    plt.xlabel("time lag")
+    plt.ylabel("covariance")
     plt.axhline(1)
     plt.show()
-
-
-