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-rw-r--r--src/dab_util.py69
1 files changed, 67 insertions, 2 deletions
diff --git a/src/dab_util.py b/src/dab_util.py
index 2ad2af6..aa2030f 100644
--- a/src/dab_util.py
+++ b/src/dab_util.py
@@ -25,7 +25,7 @@ def freq_to_fft_sample(freq, fft_size, sampling_rate):
freq_ratio = 1.0 * fft_size / sampling_rate
return int(freq * freq_ratio + fft_size / 2)
-def crop_signal(signal, n_window = 1000, n_zeros = 1000, debug = False):
+def crop_signal(signal, n_window = 1000, n_zeros = 120000, debug = False):
#signal = signal[-10:-1]
mag = abs(signal)
window = np.ones(n_window) / float(n_window)
@@ -38,5 +38,70 @@ def crop_signal(signal, n_window = 1000, n_zeros = 1000, debug = False):
plt.plot(mag < thr)
plt.plot((idx_start,idx_start), (0,0.1), color='g', linewidth=2)
plt.plot((idx_end,idx_end), (0,0.1), color='r', linewidth=2)
- signal = signal[idx_start - n_zeros: idx_end + n_zeros]
+ signal = signal[max(0,idx_start - n_zeros): min(idx_end + n_zeros, signal.shape[0] -1)]
return signal
+
+def lagcorr(x,y,lag=None,verbose=False):
+ '''Compute lead-lag correlations between 2 time series.
+
+ <x>,<y>: 1-D time series.
+ <lag>: lag option, could take different forms of <lag>:
+ if 0 or None, compute ordinary correlation and p-value;
+ if positive integer, compute lagged correlation with lag
+ upto <lag>;
+ if negative integer, compute lead correlation with lead
+ upto <-lag>;
+ if pass in an list or tuple or array of integers, compute
+ lead/lag correlations at different leads/lags.
+
+ Note: when talking about lead/lag, uses <y> as a reference.
+ Therefore positive lag means <x> lags <y> by <lag>, computation is
+ done by shifting <x> to the left hand side by <lag> with respect to
+ <y>.
+ Similarly negative lag means <x> leads <y> by <lag>, computation is
+ done by shifting <x> to the right hand side by <lag> with respect to
+ <y>.
+
+ Return <result>: a (n*2) array, with 1st column the correlation
+ coefficients, 2nd column correpsonding p values.
+
+ Currently only works for 1-D arrays.
+ '''
+
+ import numpy
+ from scipy.stats import pearsonr
+
+ if len(x)!=len(y):
+ raise Exception('Input variables of different lengths.')
+
+ #--------Unify types of <lag>-------------
+ if numpy.isscalar(lag):
+ if abs(lag)>=len(x):
+ raise Exception('Maximum lag equal or larger than array.')
+ if lag<0:
+ lag=-numpy.arange(abs(lag)+1)
+ elif lag==0:
+ lag=[0,]
+ else:
+ lag=numpy.arange(lag+1)
+ elif lag is None:
+ lag=[0,]
+ else:
+ lag=numpy.asarray(lag)
+
+ #-------Loop over lags---------------------
+ result=[]
+ if verbose:
+ print '\n#<lagcorr>: Computing lagged-correlations at lags:',lag
+
+ for ii in lag:
+ if ii<0:
+ result.append(pearsonr(x[:ii],y[-ii:]))
+ elif ii==0:
+ result.append(pearsonr(x,y))
+ elif ii>0:
+ result.append(pearsonr(x[ii:],y[:-ii]))
+
+ result=numpy.asarray(result)
+
+ return result