| @@ -939,11 +939,20 class CoherenceMap(Figure): | |||||
| 939 | for i in range(self.nplots): |
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939 | for i in range(self.nplots): | |
| 940 |
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940 | |||
| 941 | pair = dataOut.pairsList[pairsIndexList[i]] |
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941 | pair = dataOut.pairsList[pairsIndexList[i]] | |
| 942 | coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:]/numpy.sqrt(dataOut.data_spc[pair[0],:,:]*dataOut.data_spc[pair[1],:,:]) |
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942 | # coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:]/numpy.sqrt(dataOut.data_spc[pair[0],:,:]*dataOut.data_spc[pair[1],:,:]) | |
| 943 | avgcoherenceComplex = numpy.average(coherenceComplex, axis=0) |
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943 | # avgcoherenceComplex = numpy.average(coherenceComplex, axis=0) | |
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944 | # coherence = numpy.abs(avgcoherenceComplex) | |||
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945 | ||||
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946 | ## coherence = numpy.abs(coherenceComplex) | |||
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947 | ## avg = numpy.average(coherence, axis=0) | |||
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948 | ||||
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949 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i],:,:],axis=0) | |||
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950 | powa = numpy.average(dataOut.data_spc[pair[0],:,:],axis=0) | |||
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951 | powb = numpy.average(dataOut.data_spc[pair[1],:,:],axis=0) | |||
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952 | ||||
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953 | ||||
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954 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) | |||
| 944 | coherence = numpy.abs(avgcoherenceComplex) |
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955 | coherence = numpy.abs(avgcoherenceComplex) | |
| 945 | # coherence = numpy.abs(coherenceComplex) |
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| 946 | # avg = numpy.average(coherence, axis=0) |
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| 947 |
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956 | |||
| 948 | z = coherence.reshape((1,-1)) |
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957 | z = coherence.reshape((1,-1)) | |
| 949 |
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958 | |||
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