initial upload
This commit is contained in:
520
Samples/RRMS/TRACMT.cvg
Normal file
520
Samples/RRMS/TRACMT.cvg
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@@ -0,0 +1,520 @@
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================================================================================
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Now Frequency(Hz): 0.00146484, Period(s): 682.667
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.13035, Parameter c: 6.44795
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.00146484(Hz)
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Response functions:
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( 3.7058e-01, 4.1305e-01), ( 3.1084e-01, 3.6824e-01)
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( -6.6764e-01, -8.5137e-01), ( -1.4337e-01, -1.1723e-01)
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( 1.0083e+00, 1.1389e-02), ( -1.3077e-02, -7.4257e-03)
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( -1.0731e-02, -9.1421e-03), ( 8.8798e-01, -4.7033e-02)
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Scale: 0.00822998
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Covariance matrix without scale:
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7.0868e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 6.5392e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.1930e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.8088e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.00195312, Period(s): 512
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.13035, Parameter c: 6.44795
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.00195312(Hz)
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Response functions:
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( 4.4820e-01, 4.7670e-01), ( 3.6621e-01, 4.4235e-01)
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( -8.0252e-01, -1.0317e+00), ( -1.6708e-01, -1.5485e-01)
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( 1.0140e+00, 1.5732e-02), ( -1.0886e-02, -9.5520e-03)
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( -1.1840e-02, -7.4501e-03), ( 8.7759e-01, -5.4934e-02)
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Scale: 0.0075017
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Covariance matrix without scale:
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8.2958e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 5.8926e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.4899e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.3730e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.00292969, Period(s): 341.333
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.0742, Parameter c: 6.22496
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.00292969(Hz)
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Response functions:
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( 4.9932e-01, 6.0005e-01), ( 4.4578e-01, 5.8760e-01)
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( -9.1835e-01, -1.3259e+00), ( -1.6362e-01, -1.6386e-01)
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( 1.0029e+00, 1.7745e-02), ( -2.2270e-02, -1.7374e-02)
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( -1.6547e-02, -1.5790e-02), ( 8.5921e-01, -4.4379e-02)
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Scale: 0.0106855
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Covariance matrix without scale:
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6.3646e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 7.0234e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.5520e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.4414e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.00390625, Period(s): 256
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.0742, Parameter c: 6.22496
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.00390625(Hz)
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Response functions:
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( 6.2870e-01, 7.0256e-01), ( 4.4481e-01, 7.1476e-01)
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( -1.1810e+00, -1.5725e+00), ( -1.6914e-01, -2.1374e-01)
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( 1.0088e+00, 2.5192e-02), ( -1.5339e-02, -1.6230e-02)
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( -1.7235e-02, -1.7612e-02), ( 8.4992e-01, -4.3911e-02)
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Scale: 0.0105011
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Covariance matrix without scale:
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7.1573e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 7.0459e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.7118e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.1584e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.00585938, Period(s): 170.667
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.04576, Parameter c: 6.11911
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.00585938(Hz)
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Response functions:
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( 8.2966e-01, 9.2566e-01), ( 5.7677e-01, 8.9138e-01)
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( -1.5316e+00, -1.9854e+00), ( -2.5108e-01, -2.2433e-01)
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( 1.0146e+00, 2.6705e-02), ( -1.4792e-02, -1.3108e-02)
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( -2.1491e-02, -2.8939e-02), ( 8.2930e-01, -3.5456e-02)
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Scale: 0.0154722
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Covariance matrix without scale:
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6.7019e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 6.5431e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.6974e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.3434e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.0078125, Period(s): 128
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.04576, Parameter c: 6.11911
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.0078125(Hz)
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Response functions:
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( 9.7762e-01, 1.0646e+00), ( 6.9014e-01, 1.0247e+00)
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( -1.7969e+00, -2.3468e+00), ( -2.9436e-01, -2.3041e-01)
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( 1.0194e+00, 3.1520e-02), ( -2.5522e-02, -1.2464e-02)
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( -2.9621e-02, -3.5519e-02), ( 8.1093e-01, -2.6489e-02)
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Scale: 0.0149237
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Covariance matrix without scale:
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6.9002e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 6.3757e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.6616e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.3680e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.0117188, Period(s): 85.3333
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.03151, Parameter c: 6.06788
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.0117188(Hz)
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Response functions:
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( 1.3680e+00, 1.2284e+00), ( 8.8052e-01, 1.3322e+00)
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( -2.3571e+00, -2.9889e+00), ( -3.3867e-01, -3.1967e-01)
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( 1.0184e+00, 3.6961e-02), ( -3.6054e-02, -1.9506e-02)
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( -4.0394e-02, -3.5835e-02), ( 7.9770e-01, -1.9685e-02)
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Scale: 0.0213775
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Covariance matrix without scale:
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6.7231e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 7.6178e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.5493e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.2603e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.015625, Period(s): 64
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.03151, Parameter c: 6.06788
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.015625(Hz)
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Response functions:
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( 1.5420e+00, 1.3539e+00), ( 1.0247e+00, 1.5430e+00)
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( -2.7998e+00, -3.3080e+00), ( -4.2295e-01, -2.5968e-01)
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( 9.9824e-01, 3.7950e-02), ( -4.1825e-02, -1.4152e-02)
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( -3.4571e-02, -3.3532e-02), ( 7.7444e-01, -5.8188e-03)
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Scale: 0.0211478
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Covariance matrix without scale:
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6.9401e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 7.9500e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.5330e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.1823e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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||||
================================================================================
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Now Frequency(Hz): 0.0234375, Period(s): 42.6667
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.02439, Parameter c: 6.04266
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.0234375(Hz)
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Response functions:
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( 1.7683e+00, 1.3758e+00), ( 1.1824e+00, 1.7909e+00)
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( -3.4362e+00, -3.7891e+00), ( -4.0657e-01, -4.5945e-01)
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( 9.6281e-01, 3.2739e-02), ( -3.7156e-02, -1.9639e-02)
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( -5.3358e-02, -3.8313e-02), ( 7.0137e-01, 8.0755e-03)
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Scale: 0.030262
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Covariance matrix without scale:
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6.7592e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 8.6270e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.3999e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.2251e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.03125, Period(s): 32
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.02439, Parameter c: 6.04266
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.03125(Hz)
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Response functions:
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( 2.1049e+00, 1.4229e+00), ( 1.2594e+00, 2.0384e+00)
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( -3.8258e+00, -4.0601e+00), ( -4.0857e-01, -3.9035e-01)
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( 8.9655e-01, 4.5184e-02), ( -4.5584e-02, -2.1391e-02)
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( -4.7594e-02, -3.7817e-02), ( 6.3689e-01, 2.2577e-02)
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Scale: 0.0300554
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Covariance matrix without scale:
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7.1503e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 8.5113e+00 0.0000e+00 0.0000e+00
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0.0000e+00 0.0000e+00 1.3940e-01 0.0000e+00
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0.0000e+00 0.0000e+00 0.0000e+00 1.1787e-01
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--------------------------------------------------------------------------------
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Estimate errors by fixed-weights bootstrap
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--------------------------------------------------------------------------------
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================================================================================
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Now Frequency(Hz): 0.046875, Period(s): 21.3333
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================================================================================
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Number of initial candidates: 100
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Number of candidates: 100
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--------------------------------------------------------------------------------
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Improve all candidates
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Parameter b: 3.02083, Parameter c: 6.03006
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--------------------------------------------------------------------------------
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Perform further improvements to the best 10 candidates
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--------------------------------------------------------------------------------
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Best estimator of frequency 0.046875(Hz)
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Response functions:
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( 2.1337e+00, 1.1790e+00), ( 1.2440e+00, 2.2960e+00)
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( -3.9003e+00, -3.6418e+00), ( -4.6242e-01, -4.4726e-01)
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( 7.2944e-01, 1.7028e-02), ( -1.2928e-02, -1.5650e-02)
|
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( -7.8386e-02, -8.8658e-03), ( 5.3995e-01, 2.4729e-02)
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Scale: 0.0417988
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Covariance matrix without scale:
|
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7.3536e+00 0.0000e+00 0.0000e+00 0.0000e+00
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0.0000e+00 9.1494e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 1.3253e-01 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 1.1214e-01
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||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.0625, Period(s): 16
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.02083, Parameter c: 6.03006
|
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--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.0625(Hz)
|
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Response functions:
|
||||
( 1.8966e+00, 8.7189e-01), ( 1.2398e+00, 2.1257e+00)
|
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( -3.3450e+00, -3.1552e+00), ( -3.5709e-01, -1.9636e-01)
|
||||
( 5.6775e-01, -6.0974e-03), ( -3.7855e-02, -2.0796e-02)
|
||||
( -7.1993e-02, -2.5304e-03), ( 4.2134e-01, 1.4262e-02)
|
||||
Scale: 0.0404616
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||||
Covariance matrix without scale:
|
||||
7.8033e+00 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 9.7290e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 1.2780e-01 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 1.0307e-01
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.09375, Period(s): 10.6667
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.01906, Parameter c: 6.02377
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.09375(Hz)
|
||||
Response functions:
|
||||
( 1.1128e+00, 4.3977e-01), ( 1.0145e+00, 1.8175e+00)
|
||||
( -2.4990e+00, -2.3000e+00), ( -2.4585e-01, -3.0278e-01)
|
||||
( 3.6971e-01, -1.5093e-02), ( -3.0602e-02, -1.4935e-02)
|
||||
( -5.0005e-02, 9.7930e-03), ( 2.6507e-01, 1.2915e-02)
|
||||
Scale: 0.054374
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||||
Covariance matrix without scale:
|
||||
8.0507e+00 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.0058e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 1.2053e-01 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 1.0245e-01
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.125, Period(s): 8
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.01906, Parameter c: 6.02377
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.125(Hz)
|
||||
Response functions:
|
||||
( 9.4278e-01, 2.7535e-01), ( 8.8142e-01, 1.4381e+00)
|
||||
( -1.9821e+00, -1.6299e+00), ( -4.2897e-02, -2.6497e-01)
|
||||
( 2.3808e-01, -8.4785e-03), ( -1.3353e-02, 1.0955e-02)
|
||||
( -1.6764e-02, 7.4497e-03), ( 1.7746e-01, -1.8394e-03)
|
||||
Scale: 0.0525039
|
||||
Covariance matrix without scale:
|
||||
8.4938e+00 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.0004e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 1.1078e-01 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 1.0623e-01
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.1875, Period(s): 5.33333
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.01817, Parameter c: 6.02062
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.1875(Hz)
|
||||
Response functions:
|
||||
( 4.3333e-01, 6.8758e-02), ( 5.4982e-01, 9.6089e-01)
|
||||
( -1.1808e+00, -1.0910e+00), ( -3.1559e-02, -2.2284e-01)
|
||||
( 1.2338e-01, -8.7698e-03), ( 1.4252e-03, 1.2247e-02)
|
||||
( -1.1720e-02, -1.0667e-03), ( 9.3049e-02, 5.2977e-04)
|
||||
Scale: 0.0670486
|
||||
Covariance matrix without scale:
|
||||
9.0341e+00 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.0842e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 1.0415e-01 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 9.8028e-02
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.25, Period(s): 4
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.01817, Parameter c: 6.02062
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.25(Hz)
|
||||
Response functions:
|
||||
( 2.9440e-01, 5.2417e-02), ( 3.5283e-01, 8.1291e-01)
|
||||
( -8.9501e-01, -8.5269e-01), ( -4.3739e-02, -1.2720e-01)
|
||||
( 8.3164e-02, 2.4325e-03), ( -4.4410e-05, 3.7468e-03)
|
||||
( -2.1733e-02, -6.1390e-03), ( 5.5416e-02, 3.8864e-03)
|
||||
Scale: 0.0647303
|
||||
Covariance matrix without scale:
|
||||
9.3697e+00 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.0611e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 1.0222e-01 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 9.8399e-02
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.375, Period(s): 2.66667
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.01772, Parameter c: 6.01905
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.375(Hz)
|
||||
Response functions:
|
||||
( 1.6094e-01, 2.8796e-02), ( 3.0870e-01, 5.2132e-01)
|
||||
( -4.5150e-01, -5.4099e-01), ( -7.6817e-02, -6.0189e-02)
|
||||
( 4.0335e-02, 6.1953e-04), ( 2.4940e-03, -6.8454e-03)
|
||||
( -2.5296e-05, 4.9876e-03), ( 2.5261e-02, 9.7822e-03)
|
||||
Scale: 0.0834926
|
||||
Covariance matrix without scale:
|
||||
1.0375e+01 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.1508e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 9.2212e-02 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 9.0830e-02
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.5, Period(s): 2
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.01772, Parameter c: 6.01905
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.5(Hz)
|
||||
Response functions:
|
||||
( 1.6565e-01, 7.2379e-02), ( 2.1090e-01, 3.5859e-01)
|
||||
( -2.6607e-01, -3.7411e-01), ( -9.6821e-02, -7.4930e-02)
|
||||
( 2.4204e-02, -7.0460e-04), ( -2.4448e-03, -5.5413e-03)
|
||||
( -5.1778e-04, 3.3146e-03), ( 1.4180e-02, 1.0598e-02)
|
||||
Scale: 0.0813483
|
||||
Covariance matrix without scale:
|
||||
1.0669e+01 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.1458e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 9.1509e-02 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 8.9388e-02
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 0.75, Period(s): 1.33333
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.0175, Parameter c: 6.01826
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 0.75(Hz)
|
||||
Response functions:
|
||||
( 4.8550e-02, 3.2119e-02), ( 1.6104e-01, 2.4290e-01)
|
||||
( -1.9514e-01, -2.1037e-01), ( -1.1007e-02, -1.3957e-02)
|
||||
( 1.4124e-02, 1.4807e-03), ( -4.9226e-03, -2.0914e-03)
|
||||
( 7.1309e-03, -7.3635e-04), ( 8.8656e-03, 2.7065e-03)
|
||||
Scale: 0.107675
|
||||
Covariance matrix without scale:
|
||||
1.1363e+01 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.1904e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 8.6804e-02 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 8.5168e-02
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
================================================================================
|
||||
Now Frequency(Hz): 1, Period(s): 1
|
||||
================================================================================
|
||||
Number of initial candidates: 100
|
||||
Number of candidates: 100
|
||||
--------------------------------------------------------------------------------
|
||||
Improve all candidates
|
||||
Parameter b: 3.0175, Parameter c: 6.01826
|
||||
--------------------------------------------------------------------------------
|
||||
Perform further improvements to the best 10 candidates
|
||||
--------------------------------------------------------------------------------
|
||||
Best estimator of frequency 1(Hz)
|
||||
Response functions:
|
||||
( -4.0422e-04, 1.7861e-02), ( 9.1192e-02, 1.5144e-01)
|
||||
( -1.2143e-01, -1.2261e-01), ( 4.5062e-03, 3.0887e-02)
|
||||
( 9.4648e-03, 2.4087e-03), ( -6.8454e-03, 3.2356e-03)
|
||||
( 3.0071e-03, -2.6903e-03), ( -2.1803e-03, 1.3900e-03)
|
||||
Scale: 0.105384
|
||||
Covariance matrix without scale:
|
||||
1.1353e+01 0.0000e+00 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 1.1880e+01 0.0000e+00 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 8.6785e-02 0.0000e+00
|
||||
0.0000e+00 0.0000e+00 0.0000e+00 8.5431e-02
|
||||
--------------------------------------------------------------------------------
|
||||
Estimate errors by fixed-weights bootstrap
|
||||
--------------------------------------------------------------------------------
|
||||
465
Samples/RRMS/TRACMT.log
Normal file
465
Samples/RRMS/TRACMT.log
Normal file
@@ -0,0 +1,465 @@
|
||||
Start TRACMT Version v2.0
|
||||
Read parameters. ( 0 sec )
|
||||
================================================================================
|
||||
Summary of control parameters
|
||||
================================================================================
|
||||
Number of threads : 1
|
||||
Procedure type : multivariate regression (RRMS)
|
||||
At each frequency, initial candidates are selected by random sampling
|
||||
Maximum number of initial candidates: 100
|
||||
Maximum number of iteration of the first imporvements: 3
|
||||
Convegence criteria of the first imporvements: 0.05
|
||||
Maximum number of the candidates of the second imporvements: 10
|
||||
Maximum number of iteration of the second imporvements: 16
|
||||
Convegence criteria of the second imporvements: 0.01
|
||||
Time range for selecting intial candidates: 00:00:00 - 24:00:00
|
||||
Error estimation method : fixed-weights bootstrap
|
||||
Number or repetitions in bootstrap : 1000
|
||||
Number of output variables : 2
|
||||
Number of input variables : 2
|
||||
Number of remote reference variables : 2
|
||||
Sampling frequency (Hz) : 32
|
||||
Number of time-series sections : 1
|
||||
Ratio of overlapping part to whole segment length : 0.5
|
||||
Output level : 0
|
||||
Output apparent resistivity and phase to a seperate csv file
|
||||
Information about the segment lengths and frequencies :
|
||||
Segment# Length Index Frequency(Hz) Period(sec)
|
||||
0 65536 3 1.464843750e-03 6.826666667e+02
|
||||
0 65536 4 1.953125000e-03 5.120000000e+02
|
||||
1 32768 3 2.929687500e-03 3.413333333e+02
|
||||
1 32768 4 3.906250000e-03 2.560000000e+02
|
||||
2 16384 3 5.859375000e-03 1.706666667e+02
|
||||
2 16384 4 7.812500000e-03 1.280000000e+02
|
||||
3 8192 3 1.171875000e-02 8.533333333e+01
|
||||
3 8192 4 1.562500000e-02 6.400000000e+01
|
||||
4 4096 3 2.343750000e-02 4.266666667e+01
|
||||
4 4096 4 3.125000000e-02 3.200000000e+01
|
||||
5 2048 3 4.687500000e-02 2.133333333e+01
|
||||
5 2048 4 6.250000000e-02 1.600000000e+01
|
||||
6 1024 3 9.375000000e-02 1.066666667e+01
|
||||
6 1024 4 1.250000000e-01 8.000000000e+00
|
||||
7 512 3 1.875000000e-01 5.333333333e+00
|
||||
7 512 4 2.500000000e-01 4.000000000e+00
|
||||
8 256 3 3.750000000e-01 2.666666667e+00
|
||||
8 256 4 5.000000000e-01 2.000000000e+00
|
||||
9 128 3 7.500000000e-01 1.333333333e+00
|
||||
9 128 4 1.000000000e+00 1.000000000e+00
|
||||
Information about the time-series data :
|
||||
Section# Channel# Type NSkip NData File
|
||||
0 0 Out0 0 2764800 ../ex.txt
|
||||
0 1 Out1 0 2764800 ../ey.txt
|
||||
0 2 Inp0 0 2764800 ../hx.txt
|
||||
0 3 Inp1 0 2764800 ../hy.txt
|
||||
0 4 RR0 0 2764800 ../hrx.txt
|
||||
0 5 RR1 0 2764800 ../hry.txt
|
||||
Rotation angle (deg.) : 0
|
||||
Channel# Type Azimuth(deg.)
|
||||
0 Out0 0
|
||||
1 Out1 90
|
||||
2 Inp0 0
|
||||
3 Inp1 90
|
||||
4 RR0 0
|
||||
5 RR1 90
|
||||
Parameters for robust prewhitening :
|
||||
Least square estimator is used
|
||||
Maximum degree of AR model: 10
|
||||
================================================================================
|
||||
Read data from ../ex.txt ( 0 sec )
|
||||
Read data from ../ey.txt ( 1 sec )
|
||||
Read data from ../hx.txt ( 2 sec )
|
||||
Read data from ../hy.txt ( 3 sec )
|
||||
Read data from ../hrx.txt ( 3 sec )
|
||||
Read data from ../hry.txt ( 4 sec )
|
||||
Perform preprocessing ( 5 sec )
|
||||
Secton 0, Channel 0 ( 5 sec )
|
||||
Subtract mean (-0.889991) ( 5 sec )
|
||||
Secton 0, Channel 1 ( 5 sec )
|
||||
Subtract mean (1.69968) ( 5 sec )
|
||||
Secton 0, Channel 2 ( 5 sec )
|
||||
Subtract mean (0.0092652) ( 5 sec )
|
||||
Secton 0, Channel 3 ( 5 sec )
|
||||
Subtract mean (1.30261) ( 5 sec )
|
||||
Secton 0, Channel 4 ( 5 sec )
|
||||
Subtract mean (0.0346805) ( 5 sec )
|
||||
Secton 0, Channel 5 ( 5 sec )
|
||||
Subtract mean (1.2857) ( 5 sec )
|
||||
Perform prewhitening for channel 0 ( 5 sec )
|
||||
Degree of AR model: 1, Sigma: 7.6076, AIC: 1.90665e+07 ( 5 sec )
|
||||
Degree of AR model: 2, Sigma: 7.60293, AIC: 1.90631e+07 ( 5 sec )
|
||||
Degree of AR model: 3, Sigma: 7.59862, AIC: 1.906e+07 ( 5 sec )
|
||||
Degree of AR model: 4, Sigma: 7.5946, AIC: 1.90571e+07 ( 5 sec )
|
||||
Degree of AR model: 5, Sigma: 7.59088, AIC: 1.90544e+07 ( 5 sec )
|
||||
Degree of AR model: 6, Sigma: 7.58783, AIC: 1.90521e+07 ( 5 sec )
|
||||
Degree of AR model: 7, Sigma: 7.58446, AIC: 1.90497e+07 ( 5 sec )
|
||||
Degree of AR model: 8, Sigma: 7.58162, AIC: 1.90476e+07 ( 5 sec )
|
||||
Degree of AR model: 9, Sigma: 7.57884, AIC: 1.90456e+07 ( 5 sec )
|
||||
Degree of AR model: 10, Sigma: 7.57665, AIC: 1.9044e+07 ( 6 sec )
|
||||
The AR model of 10 degress gives the minimum AIC (1.9044e+07) ( 6 sec )
|
||||
AR coefficients: 0.0320459 0.0276236 0.0274305 0.0273417 0.0271195 0.0250291 0.0274279 0.0257729 0.0263083 0.0240252 ( 6 sec )
|
||||
Perform prewhitening for channel 1 ( 6 sec )
|
||||
Degree of AR model: 1, Sigma: 7.54273, AIC: 1.90192e+07 ( 6 sec )
|
||||
Degree of AR model: 2, Sigma: 7.52209, AIC: 1.9004e+07 ( 6 sec )
|
||||
Degree of AR model: 3, Sigma: 7.50561, AIC: 1.89919e+07 ( 6 sec )
|
||||
Degree of AR model: 4, Sigma: 7.49133, AIC: 1.89814e+07 ( 6 sec )
|
||||
Degree of AR model: 5, Sigma: 7.47891, AIC: 1.89722e+07 ( 6 sec )
|
||||
Degree of AR model: 6, Sigma: 7.46782, AIC: 1.8964e+07 ( 6 sec )
|
||||
Degree of AR model: 7, Sigma: 7.45798, AIC: 1.89567e+07 ( 6 sec )
|
||||
Degree of AR model: 8, Sigma: 7.44899, AIC: 1.895e+07 ( 6 sec )
|
||||
Degree of AR model: 9, Sigma: 7.44151, AIC: 1.89444e+07 ( 6 sec )
|
||||
Degree of AR model: 10, Sigma: 7.43417, AIC: 1.8939e+07 ( 6 sec )
|
||||
The AR model of 10 degress gives the minimum AIC (1.8939e+07) ( 6 sec )
|
||||
AR coefficients: 0.0497931 0.0484711 0.0453945 0.0449254 0.0443603 0.0443792 0.0440958 0.0444201 0.0425092 0.0444076 ( 6 sec )
|
||||
Perform prewhitening for channel 2 ( 6 sec )
|
||||
Degree of AR model: 1, Sigma: 1.15041, AIC: 8.62097e+06 ( 6 sec )
|
||||
Degree of AR model: 2, Sigma: 0.999605, AIC: 7.84398e+06 ( 6 sec )
|
||||
Degree of AR model: 3, Sigma: 0.94325, AIC: 7.5231e+06 ( 6 sec )
|
||||
Degree of AR model: 4, Sigma: 0.913582, AIC: 7.34638e+06 ( 6 sec )
|
||||
Degree of AR model: 5, Sigma: 0.895124, AIC: 7.23352e+06 ( 6 sec )
|
||||
Degree of AR model: 6, Sigma: 0.882753, AIC: 7.15657e+06 ( 6 sec )
|
||||
Degree of AR model: 7, Sigma: 0.873879, AIC: 7.1007e+06 ( 6 sec )
|
||||
Degree of AR model: 8, Sigma: 0.867031, AIC: 7.0572e+06 ( 6 sec )
|
||||
Degree of AR model: 9, Sigma: 0.861586, AIC: 7.02236e+06 ( 6 sec )
|
||||
Degree of AR model: 10, Sigma: 0.857167, AIC: 6.99392e+06 ( 6 sec )
|
||||
The AR model of 10 degress gives the minimum AIC (6.99392e+06) ( 6 sec )
|
||||
AR coefficients: 0.100812 0.0994283 0.099345 0.0994338 0.100341 0.0991599 0.0983786 0.0996401 0.100555 0.101158 ( 6 sec )
|
||||
Perform prewhitening for channel 3 ( 6 sec )
|
||||
Degree of AR model: 1, Sigma: 1.15933, AIC: 8.66365e+06 ( 6 sec )
|
||||
Degree of AR model: 2, Sigma: 1.01002, AIC: 7.90131e+06 ( 6 sec )
|
||||
Degree of AR model: 3, Sigma: 0.95364, AIC: 7.58368e+06 ( 6 sec )
|
||||
Degree of AR model: 4, Sigma: 0.923737, AIC: 7.40751e+06 ( 6 sec )
|
||||
Degree of AR model: 5, Sigma: 0.905428, AIC: 7.29681e+06 ( 6 sec )
|
||||
Degree of AR model: 6, Sigma: 0.892914, AIC: 7.21985e+06 ( 6 sec )
|
||||
Degree of AR model: 7, Sigma: 0.884029, AIC: 7.16455e+06 ( 6 sec )
|
||||
Degree of AR model: 8, Sigma: 0.877216, AIC: 7.12177e+06 ( 6 sec )
|
||||
Degree of AR model: 9, Sigma: 0.871841, AIC: 7.08779e+06 ( 6 sec )
|
||||
Degree of AR model: 10, Sigma: 0.867445, AIC: 7.05983e+06 ( 7 sec )
|
||||
The AR model of 10 degress gives the minimum AIC (7.05983e+06) ( 7 sec )
|
||||
AR coefficients: 0.0992426 0.0997661 0.0999695 0.100685 0.0994892 0.0998831 0.0983119 0.0990847 0.0994668 0.100303 ( 7 sec )
|
||||
Perform prewhitening for channel 4 ( 7 sec )
|
||||
Degree of AR model: 1, Sigma: 1.16254, AIC: 8.67898e+06 ( 7 sec )
|
||||
Degree of AR model: 2, Sigma: 1.00903, AIC: 7.89586e+06 ( 7 sec )
|
||||
Degree of AR model: 3, Sigma: 0.952405, AIC: 7.57651e+06 ( 7 sec )
|
||||
Degree of AR model: 4, Sigma: 0.922235, AIC: 7.39851e+06 ( 7 sec )
|
||||
Degree of AR model: 5, Sigma: 0.90364, AIC: 7.28588e+06 ( 7 sec )
|
||||
Degree of AR model: 6, Sigma: 0.891244, AIC: 7.2095e+06 ( 7 sec )
|
||||
Degree of AR model: 7, Sigma: 0.882213, AIC: 7.15318e+06 ( 7 sec )
|
||||
Degree of AR model: 8, Sigma: 0.875262, AIC: 7.10944e+06 ( 7 sec )
|
||||
Degree of AR model: 9, Sigma: 0.869864, AIC: 7.07523e+06 ( 7 sec )
|
||||
Degree of AR model: 10, Sigma: 0.865547, AIC: 7.04772e+06 ( 7 sec )
|
||||
The AR model of 10 degress gives the minimum AIC (7.04772e+06) ( 7 sec )
|
||||
AR coefficients: 0.0989261 0.10121 0.0987917 0.100744 0.100552 0.0988028 0.0993482 0.100374 0.0999452 0.0995135 ( 7 sec )
|
||||
Perform prewhitening for channel 5 ( 7 sec )
|
||||
Degree of AR model: 1, Sigma: 1.16036, AIC: 8.66857e+06 ( 7 sec )
|
||||
Degree of AR model: 2, Sigma: 1.01086, AIC: 7.90589e+06 ( 7 sec )
|
||||
Degree of AR model: 3, Sigma: 0.953982, AIC: 7.58566e+06 ( 7 sec )
|
||||
Degree of AR model: 4, Sigma: 0.924434, AIC: 7.41168e+06 ( 7 sec )
|
||||
Degree of AR model: 5, Sigma: 0.906107, AIC: 7.30095e+06 ( 7 sec )
|
||||
Degree of AR model: 6, Sigma: 0.893431, AIC: 7.22305e+06 ( 7 sec )
|
||||
Degree of AR model: 7, Sigma: 0.884431, AIC: 7.16707e+06 ( 7 sec )
|
||||
Degree of AR model: 8, Sigma: 0.877486, AIC: 7.12347e+06 ( 7 sec )
|
||||
Degree of AR model: 9, Sigma: 0.872098, AIC: 7.08941e+06 ( 7 sec )
|
||||
Degree of AR model: 10, Sigma: 0.867816, AIC: 7.0622e+06 ( 7 sec )
|
||||
The AR model of 10 degress gives the minimum AIC (7.0622e+06) ( 7 sec )
|
||||
AR coefficients: 0.0995885 0.098903 0.101124 0.0988656 0.0991358 0.100713 0.0990429 0.100438 0.0997109 0.0989688 ( 7 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 65536 ( 7 sec )
|
||||
Convert time-series data to frequency-domain ( 7 sec )
|
||||
Total number of segments : 83 ( 7 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.00146484, Period(s): 682.667 ( 9 sec )
|
||||
Perform calibration correction ( 9 sec )
|
||||
Calculate rotated fields ( 9 sec )
|
||||
Calculate response functions by multivariate regression ( 9 sec )
|
||||
Determine candicates ( 9 sec )
|
||||
Number of initial candidates: 100 ( 9 sec )
|
||||
Number of candidates: 100 ( 9 sec )
|
||||
Improve all candidates ( 9 sec )
|
||||
Detetermine the best improved candidates ( 10 sec )
|
||||
Perform further improvements to the best 10 candidates ( 10 sec )
|
||||
Select the response with the smallest scale ( 10 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 10 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.00195312, Period(s): 512 ( 10 sec )
|
||||
Perform calibration correction ( 10 sec )
|
||||
Calculate rotated fields ( 10 sec )
|
||||
Calculate response functions by multivariate regression ( 10 sec )
|
||||
Determine candicates ( 10 sec )
|
||||
Number of initial candidates: 100 ( 10 sec )
|
||||
Number of candidates: 100 ( 10 sec )
|
||||
Improve all candidates ( 10 sec )
|
||||
Detetermine the best improved candidates ( 10 sec )
|
||||
Perform further improvements to the best 10 candidates ( 10 sec )
|
||||
Select the response with the smallest scale ( 10 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 10 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 32768 ( 10 sec )
|
||||
Convert time-series data to frequency-domain ( 10 sec )
|
||||
Total number of segments : 167 ( 10 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.00292969, Period(s): 341.333 ( 12 sec )
|
||||
Perform calibration correction ( 12 sec )
|
||||
Calculate rotated fields ( 12 sec )
|
||||
Calculate response functions by multivariate regression ( 12 sec )
|
||||
Determine candicates ( 12 sec )
|
||||
Number of initial candidates: 100 ( 12 sec )
|
||||
Number of candidates: 100 ( 12 sec )
|
||||
Improve all candidates ( 12 sec )
|
||||
Detetermine the best improved candidates ( 12 sec )
|
||||
Perform further improvements to the best 10 candidates ( 12 sec )
|
||||
Select the response with the smallest scale ( 12 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 12 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.00390625, Period(s): 256 ( 12 sec )
|
||||
Perform calibration correction ( 12 sec )
|
||||
Calculate rotated fields ( 12 sec )
|
||||
Calculate response functions by multivariate regression ( 12 sec )
|
||||
Determine candicates ( 12 sec )
|
||||
Number of initial candidates: 100 ( 12 sec )
|
||||
Number of candidates: 100 ( 12 sec )
|
||||
Improve all candidates ( 12 sec )
|
||||
Detetermine the best improved candidates ( 12 sec )
|
||||
Perform further improvements to the best 10 candidates ( 12 sec )
|
||||
Select the response with the smallest scale ( 12 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 12 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 16384 ( 12 sec )
|
||||
Convert time-series data to frequency-domain ( 12 sec )
|
||||
Total number of segments : 336 ( 12 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.00585938, Period(s): 170.667 ( 14 sec )
|
||||
Perform calibration correction ( 14 sec )
|
||||
Calculate rotated fields ( 14 sec )
|
||||
Calculate response functions by multivariate regression ( 14 sec )
|
||||
Determine candicates ( 14 sec )
|
||||
Number of initial candidates: 100 ( 14 sec )
|
||||
Number of candidates: 100 ( 14 sec )
|
||||
Improve all candidates ( 14 sec )
|
||||
Detetermine the best improved candidates ( 14 sec )
|
||||
Perform further improvements to the best 10 candidates ( 14 sec )
|
||||
Select the response with the smallest scale ( 14 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 14 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.0078125, Period(s): 128 ( 14 sec )
|
||||
Perform calibration correction ( 14 sec )
|
||||
Calculate rotated fields ( 14 sec )
|
||||
Calculate response functions by multivariate regression ( 14 sec )
|
||||
Determine candicates ( 14 sec )
|
||||
Number of initial candidates: 100 ( 14 sec )
|
||||
Number of candidates: 100 ( 14 sec )
|
||||
Improve all candidates ( 14 sec )
|
||||
Detetermine the best improved candidates ( 14 sec )
|
||||
Perform further improvements to the best 10 candidates ( 14 sec )
|
||||
Select the response with the smallest scale ( 14 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 14 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 8192 ( 14 sec )
|
||||
Convert time-series data to frequency-domain ( 14 sec )
|
||||
Total number of segments : 674 ( 14 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.0117188, Period(s): 85.3333 ( 16 sec )
|
||||
Perform calibration correction ( 16 sec )
|
||||
Calculate rotated fields ( 16 sec )
|
||||
Calculate response functions by multivariate regression ( 16 sec )
|
||||
Determine candicates ( 16 sec )
|
||||
Number of initial candidates: 100 ( 16 sec )
|
||||
Number of candidates: 100 ( 16 sec )
|
||||
Improve all candidates ( 16 sec )
|
||||
Detetermine the best improved candidates ( 16 sec )
|
||||
Perform further improvements to the best 10 candidates ( 16 sec )
|
||||
Select the response with the smallest scale ( 16 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 16 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.015625, Period(s): 64 ( 16 sec )
|
||||
Perform calibration correction ( 16 sec )
|
||||
Calculate rotated fields ( 16 sec )
|
||||
Calculate response functions by multivariate regression ( 16 sec )
|
||||
Determine candicates ( 16 sec )
|
||||
Number of initial candidates: 100 ( 16 sec )
|
||||
Number of candidates: 100 ( 16 sec )
|
||||
Improve all candidates ( 16 sec )
|
||||
Detetermine the best improved candidates ( 16 sec )
|
||||
Perform further improvements to the best 10 candidates ( 16 sec )
|
||||
Select the response with the smallest scale ( 16 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 16 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 4096 ( 16 sec )
|
||||
Convert time-series data to frequency-domain ( 16 sec )
|
||||
Total number of segments : 1349 ( 16 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.0234375, Period(s): 42.6667 ( 18 sec )
|
||||
Perform calibration correction ( 18 sec )
|
||||
Calculate rotated fields ( 18 sec )
|
||||
Calculate response functions by multivariate regression ( 18 sec )
|
||||
Determine candicates ( 18 sec )
|
||||
Number of initial candidates: 100 ( 18 sec )
|
||||
Number of candidates: 100 ( 18 sec )
|
||||
Improve all candidates ( 18 sec )
|
||||
Detetermine the best improved candidates ( 18 sec )
|
||||
Perform further improvements to the best 10 candidates ( 18 sec )
|
||||
Select the response with the smallest scale ( 19 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 19 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.03125, Period(s): 32 ( 19 sec )
|
||||
Perform calibration correction ( 19 sec )
|
||||
Calculate rotated fields ( 19 sec )
|
||||
Calculate response functions by multivariate regression ( 19 sec )
|
||||
Determine candicates ( 19 sec )
|
||||
Number of initial candidates: 100 ( 19 sec )
|
||||
Number of candidates: 100 ( 19 sec )
|
||||
Improve all candidates ( 19 sec )
|
||||
Detetermine the best improved candidates ( 19 sec )
|
||||
Perform further improvements to the best 10 candidates ( 19 sec )
|
||||
Select the response with the smallest scale ( 19 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 19 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 2048 ( 19 sec )
|
||||
Convert time-series data to frequency-domain ( 19 sec )
|
||||
Total number of segments : 2699 ( 19 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.046875, Period(s): 21.3333 ( 21 sec )
|
||||
Perform calibration correction ( 21 sec )
|
||||
Calculate rotated fields ( 21 sec )
|
||||
Calculate response functions by multivariate regression ( 21 sec )
|
||||
Determine candicates ( 21 sec )
|
||||
Number of initial candidates: 100 ( 21 sec )
|
||||
Number of candidates: 100 ( 21 sec )
|
||||
Improve all candidates ( 21 sec )
|
||||
Detetermine the best improved candidates ( 21 sec )
|
||||
Perform further improvements to the best 10 candidates ( 21 sec )
|
||||
Select the response with the smallest scale ( 21 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 21 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.0625, Period(s): 16 ( 22 sec )
|
||||
Perform calibration correction ( 22 sec )
|
||||
Calculate rotated fields ( 22 sec )
|
||||
Calculate response functions by multivariate regression ( 22 sec )
|
||||
Determine candicates ( 22 sec )
|
||||
Number of initial candidates: 100 ( 22 sec )
|
||||
Number of candidates: 100 ( 22 sec )
|
||||
Improve all candidates ( 22 sec )
|
||||
Detetermine the best improved candidates ( 22 sec )
|
||||
Perform further improvements to the best 10 candidates ( 22 sec )
|
||||
Select the response with the smallest scale ( 22 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 22 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 1024 ( 23 sec )
|
||||
Convert time-series data to frequency-domain ( 23 sec )
|
||||
Total number of segments : 5399 ( 23 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.09375, Period(s): 10.6667 ( 24 sec )
|
||||
Perform calibration correction ( 24 sec )
|
||||
Calculate rotated fields ( 24 sec )
|
||||
Calculate response functions by multivariate regression ( 24 sec )
|
||||
Determine candicates ( 24 sec )
|
||||
Number of initial candidates: 100 ( 24 sec )
|
||||
Number of candidates: 100 ( 24 sec )
|
||||
Improve all candidates ( 24 sec )
|
||||
Detetermine the best improved candidates ( 26 sec )
|
||||
Perform further improvements to the best 10 candidates ( 26 sec )
|
||||
Select the response with the smallest scale ( 26 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 26 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.125, Period(s): 8 ( 26 sec )
|
||||
Perform calibration correction ( 26 sec )
|
||||
Calculate rotated fields ( 26 sec )
|
||||
Calculate response functions by multivariate regression ( 26 sec )
|
||||
Determine candicates ( 26 sec )
|
||||
Number of initial candidates: 100 ( 26 sec )
|
||||
Number of candidates: 100 ( 26 sec )
|
||||
Improve all candidates ( 26 sec )
|
||||
Detetermine the best improved candidates ( 28 sec )
|
||||
Perform further improvements to the best 10 candidates ( 28 sec )
|
||||
Select the response with the smallest scale ( 28 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 28 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 512 ( 28 sec )
|
||||
Convert time-series data to frequency-domain ( 28 sec )
|
||||
Total number of segments : 10799 ( 28 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.1875, Period(s): 5.33333 ( 30 sec )
|
||||
Perform calibration correction ( 30 sec )
|
||||
Calculate rotated fields ( 30 sec )
|
||||
Calculate response functions by multivariate regression ( 30 sec )
|
||||
Determine candicates ( 30 sec )
|
||||
Number of initial candidates: 100 ( 30 sec )
|
||||
Number of candidates: 100 ( 30 sec )
|
||||
Improve all candidates ( 30 sec )
|
||||
Detetermine the best improved candidates ( 33 sec )
|
||||
Perform further improvements to the best 10 candidates ( 33 sec )
|
||||
Select the response with the smallest scale ( 34 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 34 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.25, Period(s): 4 ( 34 sec )
|
||||
Perform calibration correction ( 34 sec )
|
||||
Calculate rotated fields ( 34 sec )
|
||||
Calculate response functions by multivariate regression ( 34 sec )
|
||||
Determine candicates ( 34 sec )
|
||||
Number of initial candidates: 100 ( 34 sec )
|
||||
Number of candidates: 100 ( 34 sec )
|
||||
Improve all candidates ( 34 sec )
|
||||
Detetermine the best improved candidates ( 37 sec )
|
||||
Perform further improvements to the best 10 candidates ( 38 sec )
|
||||
Select the response with the smallest scale ( 38 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 38 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 256 ( 38 sec )
|
||||
Convert time-series data to frequency-domain ( 38 sec )
|
||||
Total number of segments : 21599 ( 38 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.375, Period(s): 2.66667 ( 40 sec )
|
||||
Perform calibration correction ( 40 sec )
|
||||
Calculate rotated fields ( 40 sec )
|
||||
Calculate response functions by multivariate regression ( 40 sec )
|
||||
Determine candicates ( 40 sec )
|
||||
Number of initial candidates: 100 ( 40 sec )
|
||||
Number of candidates: 100 ( 40 sec )
|
||||
Improve all candidates ( 40 sec )
|
||||
Detetermine the best improved candidates ( 47 sec )
|
||||
Perform further improvements to the best 10 candidates ( 47 sec )
|
||||
Select the response with the smallest scale ( 48 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 48 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.5, Period(s): 2 ( 49 sec )
|
||||
Perform calibration correction ( 49 sec )
|
||||
Calculate rotated fields ( 49 sec )
|
||||
Calculate response functions by multivariate regression ( 49 sec )
|
||||
Determine candicates ( 49 sec )
|
||||
Number of initial candidates: 100 ( 49 sec )
|
||||
Number of candidates: 100 ( 49 sec )
|
||||
Improve all candidates ( 49 sec )
|
||||
Detetermine the best improved candidates ( 56 sec )
|
||||
Perform further improvements to the best 10 candidates ( 56 sec )
|
||||
Select the response with the smallest scale ( 57 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 57 sec )
|
||||
===============================================================================
|
||||
Perform analysis for segment length : 128 ( 59 sec )
|
||||
Convert time-series data to frequency-domain ( 59 sec )
|
||||
Total number of segments : 43199 ( 59 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 0.75, Period(s): 1.33333 ( 60 sec )
|
||||
Perform calibration correction ( 60 sec )
|
||||
Calculate rotated fields ( 60 sec )
|
||||
Calculate response functions by multivariate regression ( 60 sec )
|
||||
Determine candicates ( 60 sec )
|
||||
Number of initial candidates: 100 ( 60 sec )
|
||||
Number of candidates: 100 ( 60 sec )
|
||||
Improve all candidates ( 60 sec )
|
||||
Detetermine the best improved candidates ( 74 sec )
|
||||
Perform further improvements to the best 10 candidates ( 75 sec )
|
||||
Select the response with the smallest scale ( 77 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 77 sec )
|
||||
-------------------------------------------------------------------------------
|
||||
Now Frequency(Hz): 1, Period(s): 1 ( 80 sec )
|
||||
Perform calibration correction ( 80 sec )
|
||||
Calculate rotated fields ( 80 sec )
|
||||
Calculate response functions by multivariate regression ( 80 sec )
|
||||
Determine candicates ( 80 sec )
|
||||
Number of initial candidates: 100 ( 80 sec )
|
||||
Number of candidates: 100 ( 80 sec )
|
||||
Improve all candidates ( 80 sec )
|
||||
Detetermine the best improved candidates ( 93 sec )
|
||||
Perform further improvements to the best 10 candidates ( 95 sec )
|
||||
Select the response with the smallest scale ( 97 sec )
|
||||
Estimate errors by fixed-weights bootstrap ( 97 sec )
|
||||
End TRACMT ( 99 sec )
|
||||
21
Samples/RRMS/apparent_resistivity_and_phase.csv
Normal file
21
Samples/RRMS/apparent_resistivity_and_phase.csv
Normal file
@@ -0,0 +1,21 @@
|
||||
frequency,period,app_res_0_2,phase_0_2,app_res_0_3,phase_0_3,coherence_0_2+3,app_res_1_2,phase_1_2,app_res_1_3,phase_1_3,coherence_1_2+3,dapp_res_0_2,dphase_0_2,dapp_res_0_3,dphase_0_3,dapp_res_1_2,dphase_1_2,dapp_res_1_3,dphase_1_3
|
||||
1.4648437500e-03,6.8266666667e+02,4.2168886420e+01,4.8022056661e+01,4.1340512817e+01,5.3319419010e+01,8.9973917276e-01,1.5794724150e+02,-1.2865818972e+02,6.7403069362e+00,-1.3457562207e+02,9.5233260437e-01,3.1073627735e+00,2.1114984689e+00,3.0281598128e+00,2.0989046392e+00,5.6655525723e+00,1.0276521864e+00,1.1674152903e+00,4.9680123869e+00
|
||||
1.9531250000e-03,5.1200000000e+02,4.3564597655e+01,4.6390553781e+01,4.4830923973e+01,5.4525018856e+01,9.0762085300e-01,1.7097600483e+02,-1.2868474783e+02,7.6490661368e+00,-1.3018648634e+02,9.6762206017e-01,2.9479943515e+00,1.9389583213e+00,3.3836954645e+00,2.1627651164e+00,4.5403323711e+00,7.6077788522e-01,1.2675251362e+00,4.7526841939e+00
|
||||
2.9296875000e-03,3.4133333333e+02,4.2733401792e+01,5.0335908713e+01,5.2692186912e+01,5.6754047095e+01,8.7725934040e-01,1.7760075640e+02,-1.2554997667e+02,6.4548704970e+00,-1.2472151967e+02,9.4176032684e-01,2.4448036538e+00,1.6391866265e+00,3.1308984849e+00,1.7024693740e+00,4.4291696053e+00,7.1446571271e-01,1.1663941392e+00,5.1837383710e+00
|
||||
3.9062500000e-03,2.5600000000e+02,4.5922557831e+01,4.8027962255e+01,5.2154354090e+01,6.1902804894e+01,8.5732632366e-01,1.9550851034e+02,-1.2813428751e+02,6.5365207374e+00,-1.1938995875e+02,9.3463939198e-01,2.9148856176e+00,1.8186997932e+00,3.5792471021e+00,1.9664323534e+00,5.3454839210e+00,7.8329891687e-01,1.1908118666e+00,5.2262665539e+00
|
||||
5.8593750000e-03,1.7066666667e+02,5.2880914162e+01,4.8563234411e+01,5.8043409512e+01,6.0252520232e+01,7.8685932594e-01,2.1004200837e+02,-1.2887160208e+02,6.7669962064e+00,-1.2986454583e+02,9.0661227178e-01,2.6455403173e+00,1.4333536856e+00,3.3869925812e+00,1.6719204213e+00,4.9908997363e+00,6.8073101359e-01,1.1762289054e+00,4.9858222570e+00
|
||||
7.8125000000e-03,1.2800000000e+02,5.4031076641e+01,4.8131918926e+01,6.3188640137e+01,5.8486935159e+01,7.6301287094e-01,2.1762526110e+02,-1.2887235377e+02,7.5692482270e+00,-1.3327791478e+02,8.9042963299e-01,2.9555564319e+00,1.5672649881e+00,4.0009495261e+00,1.8142171754e+00,6.0655335313e+00,7.9848432966e-01,1.2721803920e+00,4.8205998718e+00
|
||||
1.1718750000e-02,8.5333333333e+01,5.9358145327e+01,4.2743685642e+01,7.4840212945e+01,5.8611890978e+01,7.0568344865e-01,2.4183856944e+02,-1.2992422141e+02,9.6020683702e+00,-1.2670477300e+02,8.3234334691e-01,2.6382117200e+00,1.2733790843e+00,3.9528469430e+00,1.5132758942e+00,5.9927750718e+00,7.0991468737e-01,1.5025599390e+00,4.4874926656e+00
|
||||
1.5625000000e-02,6.4000000000e+01,5.7394006151e+01,4.1943056890e+01,8.0773573381e+01,5.7045642999e+01,6.3151710006e-01,2.4463749503e+02,-1.3210244199e+02,9.3997023175e+00,-1.3840676112e+02,7.7865956962e-01,3.2202482370e+00,1.6075793941e+00,4.8426116123e+00,1.7177819817e+00,7.2980438250e+00,8.5465765751e-01,1.7871987618e+00,5.4551631057e+00
|
||||
2.3437500000e-02,4.2666666667e+01,5.1633535806e+01,4.0069471518e+01,8.7611476461e+01,5.6425674438e+01,5.1676899773e-01,2.4596402093e+02,-1.3357402405e+02,1.1734941367e+01,-1.2531057719e+02,6.9813188573e-01,2.5965177182e+00,1.4407805346e+00,4.5851243259e+00,1.4994511738e+00,6.2573253002e+00,7.2882204079e-01,1.8441505613e+00,4.5066724729e+00
|
||||
3.1250000000e-02,3.2000000000e+01,5.6513262228e+01,3.5732461211e+01,1.0211148188e+02,5.6413030019e+01,4.4535065602e-01,2.5257613567e+02,-1.3561840051e+02,1.1679347670e+01,-1.2912532506e+02,6.2510658343e-01,3.5031029310e+00,1.7760889521e+00,6.3088942012e+00,1.7702736415e+00,8.0248801145e+00,9.1024257302e-01,2.3310671358e+00,5.7273316112e+00
|
||||
4.6875000000e-02,2.1333333333e+01,6.1065446320e+01,3.2488891062e+01,1.0512598799e+02,5.9524981611e+01,3.1049538562e-01,2.3744477188e+02,-1.3803063881e+02,8.2696066296e+00,-1.2992682674e+02,4.2061086650e-01,3.7918963404e+00,1.7791942094e+00,6.3681228233e+00,1.7356429997e+00,8.1443452845e+00,9.8266953975e-01,1.9557788832e+00,6.7911748646e+00
|
||||
6.2500000000e-02,1.6000000000e+01,6.0621323721e+01,3.1358260770e+01,1.2632810857e+02,5.7835967317e+01,2.1119255836e-01,2.2105819282e+02,-1.3590011340e+02,9.0950131491e+00,-1.3256296162e+02,2.6854473703e-01,5.3921814093e+00,2.5490302527e+00,1.0350422678e+01,2.3478604428e+00,1.1233845315e+01,1.4559994279e+00,2.9754839773e+00,9.4146243233e+00
|
||||
9.3750000000e-02,1.0666666667e+01,3.9603097417e+01,3.0438422556e+01,1.5034144676e+02,5.7752536033e+01,1.0806514363e-01,1.9429047294e+02,-1.3489892412e+02,1.5013595513e+01,-1.2269722451e+02,1.1000148190e-01,4.7489007736e+00,3.4372974651e+00,1.2649972609e+01,2.4111914410e+00,1.1416348883e+01,1.6835687701e+00,4.4177549858e+00,8.4603613564e+00
|
||||
1.2500000000e-01,8.0000000000e+00,3.7463475544e+01,2.0756375865e+01,1.4808960043e+02,5.6355234280e+01,4.7239197846e-02,1.9087428843e+02,-1.3871629002e+02,6.4597303390e+00,-1.2987559360e+02,4.7239197846e-02,7.2376619930e+00,5.5432004023e+00,1.9519439536e+01,3.7787686491e+00,1.6134676445e+01,2.4223386087e+00,4.1668647292e+00,1.8815768544e+01
|
||||
1.8750000000e-01,5.3333333333e+00,1.9584904684e+01,2.5028522642e+01,1.4172201875e+02,5.7792497193e+01,1.7617568989e-02,1.8499160388e+02,-1.3322297563e+02,3.4867331457e+00,-1.4440260121e+02,1.7617568989e-02,6.1507909811e+00,9.0344831802e+00,2.2587377678e+01,4.5706908467e+00,2.0524084328e+01,3.1800020527e+00,3.7257066954e+00,3.2294348747e+01
|
||||
2.5000000000e-01,4.0000000000e+00,3.4804617614e+01,4.5085997151e+01,1.9970647211e+02,6.1017427630e+01,7.5452420080e-03,1.9030275297e+02,-1.3754893906e+02,3.6704792101e+00,-1.3840778697e+02,7.5452420080e-03,1.5359698070e+01,1.2747568188e+01,5.1161851510e+01,7.3593861399e+00,3.2935235911e+01,4.9642294331e+00,6.2912088835e+00,5.8981507398e+01
|
||||
3.7500000000e-01,2.6666666667e+00,1.9811145761e+01,-1.5192713621e+01,3.0006978275e+02,4.0496483285e+01,2.0282928584e-03,1.6939822452e+02,-1.3203074227e+02,9.9913471627e+00,-8.9358360316e+01,2.0282928584e-03,1.6756584952e+01,2.5018319717e+01,9.6805854108e+01,9.2826842340e+00,3.8993666523e+01,6.6090856717e+00,1.4065510381e+01,4.4739501173e+01
|
||||
5.0000000000e-01,2.0000000000e+00,3.1078209375e+01,9.7750421526e-01,2.8072073624e+02,2.3630845293e+01,9.4692383969e-04,1.4226306311e+02,-1.2803054508e+02,4.1720991574e+01,-1.4050409799e+02,9.4692383969e-04,3.5150092832e+01,3.4437732519e+01,1.5068632777e+02,1.5568596247e+01,5.9895366204e+01,1.2152194119e+01,4.4339110949e+01,3.2098508259e+01
|
||||
7.5000000000e-01,1.3333333333e+00,2.2202358119e+01,-1.5105060212e+02,1.8297081679e+02,4.0381278587e+01,4.8556897195e-04,5.9609382797e+01,-1.3661518416e+02,2.9360847370e+01,-1.3322492493e+02,4.8556897195e-04,2.6756826605e+01,3.7053983647e+01,1.0306264369e+02,1.6357920486e+01,2.9732060571e+01,1.4441464439e+01,2.6328428830e+01,2.6638478853e+01
|
||||
1.0000000000e+00,1.0000000000e+00,2.0141313837e+02,-3.9642468829e+01,1.2450119453e+03,-1.6642143834e+02,8.3444887217e-05,4.1321240276e+01,1.2388481787e+02,5.7793087565e+02,-5.2634102076e+01,8.3444887217e-05,4.2392885822e+02,3.6000000000e+02,2.2428379410e+03,6.4254121306e+01,1.5342552971e+02,3.6000000000e+02,1.1618730174e+03,3.6000000000e+02
|
||||
|
48
Samples/RRMS/param.dat
Normal file
48
Samples/RRMS/param.dat
Normal file
@@ -0,0 +1,48 @@
|
||||
NUM_OUT
|
||||
2
|
||||
SAMPLING_FREQ
|
||||
32
|
||||
NUM_SECTION
|
||||
1
|
||||
SEGMENT
|
||||
10
|
||||
65536 2 3 4
|
||||
32768 2 3 4
|
||||
16384 2 3 4
|
||||
8192 2 3 4
|
||||
4096 2 3 4
|
||||
2048 2 3 4
|
||||
1024 2 3 4
|
||||
512 2 3 4
|
||||
256 2 3 4
|
||||
128 2 3 4
|
||||
ROTATION
|
||||
0.0
|
||||
AZIMUTH
|
||||
0.0 90.0
|
||||
0.0 90.0
|
||||
0.0 90.0
|
||||
OUTPUT_RHOA_PHS
|
||||
PREWHITENING
|
||||
0
|
||||
10
|
||||
5
|
||||
PROCEDURE
|
||||
1
|
||||
ERROR_ESTIMATION
|
||||
1
|
||||
DATA_FILES
|
||||
2764800
|
||||
../ex.txt
|
||||
0
|
||||
../ey.txt
|
||||
0
|
||||
../hx.txt
|
||||
0
|
||||
../hy.txt
|
||||
0
|
||||
../hrx.txt
|
||||
0
|
||||
../hry.txt
|
||||
0
|
||||
END
|
||||
21
Samples/RRMS/response_functions.csv
Normal file
21
Samples/RRMS/response_functions.csv
Normal file
@@ -0,0 +1,21 @@
|
||||
frequency,period,resp_real_0_2,resp_imag_0_2,resp_real_0_3,resp_imag_0_3,coherence_0_2+3,resp_real_1_2,resp_imag_1_2,resp_real_1_3,resp_imag_1_3,coherence_1_2+3,dresp_0_2,dresp_0_3,dresp_1_2,dresp_1_3
|
||||
1.4648437500e-03,6.8266666667e+02,3.7170796163e-01,4.1314324373e-01,3.2870014945e-01,4.4129714577e-01,8.9973917276e-01,-6.7187642587e-01,-8.3989416958e-01,-1.5594261737e-01,-1.5826996724e-01,9.5233260437e-01,2.0476067380e-02,2.0153084204e-02,1.9290207924e-02,1.9241379181e-02
|
||||
1.9531250000e-03,5.1200000000e+02,4.4988510272e-01,4.7226996340e-01,3.8399619001e-01,5.3884034554e-01,9.0762085300e-01,-8.0764751289e-01,-1.0086590317e+00,-1.7636027481e-01,-2.0879407310e-01,9.6762206017e-01,2.2068855922e-02,2.4970224693e-02,1.7156950165e-02,2.2645003835e-02
|
||||
2.9296875000e-03,3.4133333333e+02,5.0500346283e-01,6.0905588236e-01,4.8165364720e-01,7.3475708289e-01,8.7725934040e-01,-9.3778371126e-01,-1.3123015244e+00,-1.7514609390e-01,-2.5274020819e-01,9.4176032684e-01,2.2632154980e-02,2.6101263448e-02,2.0112477022e-02,2.7782237369e-02
|
||||
3.9062500000e-03,2.5600000000e+02,6.3336409438e-01,7.0411283299e-01,4.7533779502e-01,8.9033348187e-01,8.5732632366e-01,-1.2066727908e+00,-1.5370316746e+00,-1.7534750487e-01,-3.1131924641e-01,9.3463939198e-01,3.0056852933e-02,3.4632306809e-02,2.6714018234e-02,3.2546542262e-02
|
||||
5.8593750000e-03,1.7066666667e+02,8.2372516745e-01,9.3312506160e-01,6.4703069797e-01,1.1321845207e+00,7.8685932594e-01,-1.5567931963e+00,-1.9313128716e+00,-2.8539710697e-01,-3.4176063746e-01,9.0661227178e-01,3.1134762749e-02,3.8046826777e-02,2.9471783455e-02,3.8696753830e-02
|
||||
7.8125000000e-03,1.2800000000e+02,9.6961564692e-01,1.0818661787e+00,8.2119478794e-01,1.3393824605e+00,7.6301287094e-01,-1.8298223717e+00,-2.2699640636e+00,-3.7276727742e-01,-3.9587664209e-01,8.9042963299e-01,3.9734473874e-02,4.9738603749e-02,4.0631642345e-02,4.5695385483e-02
|
||||
1.1718750000e-02,8.5333333333e+01,1.3696099030e+00,1.2657744828e+00,1.0906645861e+00,1.7876295724e+00,7.0568344865e-01,-2.4158528204e+00,-2.8868466929e+00,-4.4831724068e-01,-6.0135916495e-01,8.3234334691e-01,4.1444331150e-02,5.5301656768e-02,4.6640247476e-02,5.8687425702e-02
|
||||
1.5625000000e-02,6.4000000000e+01,1.5750342011e+00,1.4153352946e+00,1.3664861022e+00,2.1078783535e+00,6.3151710006e-01,-2.9310839653e+00,-3.2436169762e+00,-6.4088766373e-01,-5.6887146705e-01,7.7865956962e-01,5.9404740172e-02,7.5302606759e-02,6.5209365722e-02,8.1466831280e-02
|
||||
2.3437500000e-02,4.2666666667e+01,1.8824269968e+00,1.5834372672e+00,1.7719873403e+00,2.6696499328e+00,5.1676899773e-01,-3.7006596458e+00,-3.8896049785e+00,-6.7782122777e-01,-9.5694661536e-01,6.9813188573e-01,6.1849473428e-02,8.3845790860e-02,6.8291018558e-02,9.2143894119e-02
|
||||
3.1250000000e-02,3.2000000000e+01,2.4121759935e+00,1.7353974183e+00,2.2096888999e+00,3.3274906477e+00,4.4535065602e-01,-4.4898138524e+00,-4.3939268051e+00,-8.5243560071e-01,-1.0479750093e+00,6.2510658343e-01,9.2099596351e-02,1.2339454824e-01,9.9798132210e-02,1.3481105444e-01
|
||||
4.6875000000e-02,2.1333333333e+01,3.1910692923e+00,2.0320656370e+00,2.5174330933e+00,4.2780175380e+00,3.1049538562e-01,-5.5465049161e+00,-4.9887274554e+00,-8.9351882813e-01,-1.0676203246e+00,4.2061086650e-01,1.1745846893e-01,1.5034259177e-01,1.2793828320e-01,1.6462772392e-01
|
||||
6.2500000000e-02,1.6000000000e+01,3.7167217626e+00,2.2649818988e+00,3.3447846899e+00,5.3188296932e+00,2.1119255836e-01,-5.9687031811e+00,-5.7840528689e+00,-1.1403292528e+00,-1.2417088242e+00,2.6854473703e-01,1.9357390099e-01,2.5739677645e-01,2.1118844238e-01,2.7577250591e-01
|
||||
9.3750000000e-02,1.0666666667e+01,3.7147552433e+00,2.1827838639e+00,4.4792692664e+00,7.0999084505e+00,1.0806514363e-01,-6.7361833599e+00,-6.7599920806e+00,-1.4330693869e+00,-2.2324728506e+00,1.1000148190e-01,2.5832662080e-01,3.5317574278e-01,2.8037683201e-01,3.9030122539e-01
|
||||
1.2500000000e-01,8.0000000000e+00,4.5248078117e+00,1.7148721474e+00,5.3302195115e+00,8.0090424041e+00,4.7239197846e-02,-8.2075743408e+00,-7.2063967218e+00,-1.2882151817e+00,-1.5420224083e+00,4.7239197846e-02,4.6741675676e-01,6.3403769126e-01,4.6163269310e-01,6.4805552956e-01
|
||||
1.8750000000e-01,5.3333333333e+00,3.8825870402e+00,1.8128336980e+00,6.1435724504e+00,9.7529949310e+00,1.7617568989e-02,-9.0188334027e+00,-9.5963676820e+00,-1.4701223363e+00,-1.0524032689e+00,1.7617568989e-02,6.7286181123e-01,9.1854997818e-01,7.3053903070e-01,9.6595067357e-01
|
||||
2.5000000000e-01,4.0000000000e+00,4.6569933261e+00,4.6709940246e+00,7.6556823753e+00,1.3821129386e+01,7.5452420080e-03,-1.1380151653e+01,-1.0410119575e+01,-1.6019654290e+00,-1.4219021686e+00,7.5452420080e-03,1.4554230752e+00,2.0238354267e+00,1.3346372869e+00,1.8356831929e+00
|
||||
3.7500000000e-01,2.6666666667e+00,5.8817311740e+00,-1.5972278172e+00,1.8037653855e+01,1.5403697156e+01,2.0282928584e-03,-1.1932312433e+01,-1.3237884687e+01,4.8469904838e-02,-4.3279818159e+00,2.0282928584e-03,2.5775159198e+00,3.8261424007e+00,2.0512107625e+00,3.0465906946e+00
|
||||
5.0000000000e-01,2.0000000000e+00,8.8132236485e+00,1.5037406383e-01,2.4270149278e+01,1.0618930956e+01,9.4692383969e-04,-1.1618611763e+01,-1.4854814657e+01,-7.8809573017e+00,-6.4956132077e+00,9.4692383969e-04,4.9846938608e+00,7.1101147657e+00,3.9699693656e+00,5.4268732534e+00
|
||||
7.5000000000e-01,1.3333333333e+00,-7.9844835846e+00,-4.4166576540e+00,1.9953497599e+01,1.6970518452e+01,4.8556897195e-04,-1.0865807590e+01,-1.0269830131e+01,-7.1862838138e+00,-7.6459468076e+00,4.8556897195e-04,5.4982019907e+00,7.3772755176e+00,3.7286647286e+00,4.7046388276e+00
|
||||
1.0000000000e+00,1.0000000000e+00,2.4436696847e+01,-2.0246321618e+01,-7.6693733746e+01,-1.8523793629e+01,8.3444887217e-05,-8.0137565169e+00,1.1932556636e+01,3.2624372311e+01,-4.2723584934e+01,8.3444887217e-05,3.3396741085e+01,7.1066698734e+01,2.6684923591e+01,5.4035070700e+01
|
||||
|
Reference in New Issue
Block a user