xMCA, inspired from EOFs https://github.com/ajdawson/eofs, is Maximum Covariance Analysis (sometimes also called SVD) in xarray.
API Documentation: http://xmca.readthedocs.io/
git clone https://github.com/Yefee/xMCA.git
cd xMCA
python setup.py install
In next version, A Monte Carlo method will be added for statistical test.
MCA analysis for US surface air temperature and SST over the Pacific. This example is taken from https://atmos.washington.edu/~breth/classes/AS552/matlab/lect/html/MCA_PSSTA_USTA.html
from xMCA import xMCA
import xarray as xr
import matplotlib.pyplot as plt
%matplotlib inlineusta = xr.open_dataarray('xMCA/examples/data/USTA.nc').transpose(*['time', 'lat', 'lon'])
usta.name = 'USTA'
print(usta)<xarray.DataArray 'USTA' (time: 396, lat: 5, lon: 12)>
array([[[-0.450303, -0.734848, ..., -4.270303, -2.69697 ],
[ 1.066061, 2.691515, ..., -4.947273, -3.330303],
...,
[ nan, -0.342424, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ 1.524545, 1.370606, ..., -1.430303, 0.048485],
[ 1.366364, 2.497273, ..., -0.593939, -0.079697],
...,
[ nan, 0.695455, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
...,
[[ 1.077879, 0.630303, ..., -1.262727, -1.496364],
[ 1.020606, 0.114848, ..., -0.786667, -0.573939],
...,
[ nan, 1.65 , ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ 1.768182, 2.807879, ..., 0.885758, 0.618182],
[ 1.555152, 3.435152, ..., -0.416667, 0.185152],
...,
[ nan, 0.012121, ..., nan, nan],
[ nan, nan, ..., nan, nan]]])
Coordinates:
* lat (lat) float64 47.5 42.5 37.5 32.5 27.5
* lon (lon) float64 -122.5 -117.5 -112.5 -107.5 ... -77.5 -72.5 -67.5
* time (time) int64 0 1 2 3 4 5 6 7 8 ... 388 389 390 391 392 393 394 395
sstpc = xr.open_dataarray('xMCA/examples/data/SSTPac.nc').transpose(*['time', 'lat', 'lon'])
sstpc.name = 'SSTPC'
print(sstpc)<xarray.DataArray 'SSTPC' (time: 396, lat: 30, lon: 84)>
[997920 values with dtype=float64]
Coordinates:
* lat (lat) int16 -29 -27 -25 -23 -21 -19 -17 ... 17 19 21 23 25 27 29
* lon (lon) uint16 124 126 128 130 132 134 ... 280 282 284 286 288 290
* time (time) int64 0 1 2 3 4 5 6 7 8 ... 388 389 390 391 392 393 394 395
'''
decomposition, time should be in the first axis
lp is for SSTPC
rp is for USTA
'''
sst_ts = xMCA(sstpc, usta)
sst_ts.solver()
lp, rp = sst_ts.patterns(n=2)
le, re = sst_ts.expansionCoefs(n=2)
frac = sst_ts.covFracs(n=2)
print(frac)<xarray.DataArray 'frac' (n: 2)>
array([0.407522, 0.391429])
Coordinates:
* n (n) int64 0 1
Attributes:
long_name: Fractions explained of the covariance matrix between SSTPC an...
fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(12, 5))
lp[0].plot(ax=ax1[0])
le[0].plot(ax=ax1[1])
rp[0].plot(ax=ax2[0])
re[0].plot(ax=ax2[1])
lh, rh = sst_ts.homogeneousPatterns(n=1)
le, re = sst_ts.heterogeneousPatterns(n=1)fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(12, 5))
lh[0].plot(ax=ax1[0])
rh[0].plot(ax=ax1[1])
le[0].plot(ax=ax2[0])
re[0].plot(ax=ax2[1])
le, re, lphet, rphet = sst_ts.heterogeneousPatterns(n=1, statistical_test=True)fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(12, 5))
le[0].plot(ax=ax1[0])
re[0].plot(ax=ax1[1])
# Only plot where p<0.01
lphet[0].where(lphet[0]<0.01).plot(ax=ax2[0])
rphet[0].where(rphet[0]<0.01).plot(ax=ax2[1])
EOF analysis for US surface air temperature and SST over the Pacific This example is taken from https://atmos.washington.edu/~breth/classes/AS552/matlab/lect/html/MCA_PSSTA_USTA.html
from xMCA import xMCA
import xarray as xr
import matplotlib.pyplot as plt
%matplotlib inlinesstpc = xr.open_dataarray('data/SSTPac.nc').transpose(*['time', 'lat', 'lon'])
sstpc.name = 'SSTPC'
print(sstpc)<xarray.DataArray 'SSTPC' (time: 396, lat: 30, lon: 84)>
[997920 values with dtype=float64]
Coordinates:
* lat (lat) int16 -29 -27 -25 -23 -21 -19 -17 ... 17 19 21 23 25 27 29
* lon (lon) uint16 124 126 128 130 132 134 ... 280 282 284 286 288 290
* time (time) int64 0 1 2 3 4 5 6 7 8 ... 388 389 390 391 392 393 394 395
Decompsition and retrieve the first and second loadings and expansion coefficeints
'''
decomposition, time should be in the first axis
lp is for SSTPC
rp is for USTA
'''
sst_ts = xMCA(sstpc, sstpc.rename('SSTPC_copy'))
sst_ts.solver()
lp, _ = sst_ts.patterns(n=2)
le, _ = sst_ts.expansionCoefs(n=2)
frac = sst_ts.covFracs(n=2)
print(frac)<xarray.DataArray 'frac' (n: 2)>
array([0.873075, 0.04946 ])
Coordinates:
* n (n) int64 0 1
Attributes:
long_name: Fractions explained of the covariance matrix between SSTPC an...
fig, ax1 = plt.subplots(1, 2, figsize=(12, 5))
lp[0].plot(ax=ax1[0])
le[0].plot(ax=ax1[1])
Regress PC1 to the original SST field
lh, _ = sst_ts.homogeneousPatterns(n=1)fig, ax1= plt.subplots()
lh[0].plot(ax=ax1)
xMCA, inspired from EOFs https://github.com/ajdawson/eofs, is Maximum Covariance Analysis (sometimes also called SVD) in xarray.
API Documentation: http://xmca.readthedocs.io/
git clone https://github.com/Yefee/xMCA.git
cd xMCA
python setup.py install
In next version, A Monte Carlo method will be added for statistical test.
MCA analysis for US surface air temperature and SST over the Pacific. This example is taken from https://atmos.washington.edu/~breth/classes/AS552/matlab/lect/html/MCA_PSSTA_USTA.html
from xMCA import xMCA
import xarray as xr
import matplotlib.pyplot as plt
%matplotlib inlineusta = xr.open_dataarray('xMCA/examples/data/USTA.nc').transpose(*['time', 'lat', 'lon'])
usta.name = 'USTA'
print(usta)<xarray.DataArray 'USTA' (time: 396, lat: 5, lon: 12)>
array([[[-0.450303, -0.734848, ..., -4.270303, -2.69697 ],
[ 1.066061, 2.691515, ..., -4.947273, -3.330303],
...,
[ nan, -0.342424, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ 1.524545, 1.370606, ..., -1.430303, 0.048485],
[ 1.366364, 2.497273, ..., -0.593939, -0.079697],
...,
[ nan, 0.695455, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
...,
[[ 1.077879, 0.630303, ..., -1.262727, -1.496364],
[ 1.020606, 0.114848, ..., -0.786667, -0.573939],
...,
[ nan, 1.65 , ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ 1.768182, 2.807879, ..., 0.885758, 0.618182],
[ 1.555152, 3.435152, ..., -0.416667, 0.185152],
...,
[ nan, 0.012121, ..., nan, nan],
[ nan, nan, ..., nan, nan]]])
Coordinates:
* lat (lat) float64 47.5 42.5 37.5 32.5 27.5
* lon (lon) float64 -122.5 -117.5 -112.5 -107.5 ... -77.5 -72.5 -67.5
* time (time) int64 0 1 2 3 4 5 6 7 8 ... 388 389 390 391 392 393 394 395
sstpc = xr.open_dataarray('xMCA/examples/data/SSTPac.nc').transpose(*['time', 'lat', 'lon'])
sstpc.name = 'SSTPC'
print(sstpc)<xarray.DataArray 'SSTPC' (time: 396, lat: 30, lon: 84)>
[997920 values with dtype=float64]
Coordinates:
* lat (lat) int16 -29 -27 -25 -23 -21 -19 -17 ... 17 19 21 23 25 27 29
* lon (lon) uint16 124 126 128 130 132 134 ... 280 282 284 286 288 290
* time (time) int64 0 1 2 3 4 5 6 7 8 ... 388 389 390 391 392 393 394 395
'''
decomposition, time should be in the first axis
lp is for SSTPC
rp is for USTA
'''
sst_ts = xMCA(sstpc, usta)
sst_ts.solver()
lp, rp = sst_ts.patterns(n=2)
le, re = sst_ts.expansionCoefs(n=2)
frac = sst_ts.covFracs(n=2)
print(frac)<xarray.DataArray 'frac' (n: 2)>
array([0.407522, 0.391429])
Coordinates:
* n (n) int64 0 1
Attributes:
long_name: Fractions explained of the covariance matrix between SSTPC an...
fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(12, 5))
lp[0].plot(ax=ax1[0])
le[0].plot(ax=ax1[1])
rp[0].plot(ax=ax2[0])
re[0].plot(ax=ax2[1])
lh, rh = sst_ts.homogeneousPatterns(n=1)
le, re = sst_ts.heterogeneousPatterns(n=1)fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(12, 5))
lh[0].plot(ax=ax1[0])
rh[0].plot(ax=ax1[1])
le[0].plot(ax=ax2[0])
re[0].plot(ax=ax2[1])
le, re, lphet, rphet = sst_ts.heterogeneousPatterns(n=1, statistical_test=True)fig, (ax1, ax2) = plt.subplots(2, 2, figsize=(12, 5))
le[0].plot(ax=ax1[0])
re[0].plot(ax=ax1[1])
# Only plot where p<0.01
lphet[0].where(lphet[0]<0.01).plot(ax=ax2[0])
rphet[0].where(rphet[0]<0.01).plot(ax=ax2[1])
EOF analysis for US surface air temperature and SST over the Pacific This example is taken from https://atmos.washington.edu/~breth/classes/AS552/matlab/lect/html/MCA_PSSTA_USTA.html
from xMCA import xMCA
import xarray as xr
import matplotlib.pyplot as plt
%matplotlib inlinesstpc = xr.open_dataarray('data/SSTPac.nc').transpose(*['time', 'lat', 'lon'])
sstpc.name = 'SSTPC'
print(sstpc)<xarray.DataArray 'SSTPC' (time: 396, lat: 30, lon: 84)>
[997920 values with dtype=float64]
Coordinates:
* lat (lat) int16 -29 -27 -25 -23 -21 -19 -17 ... 17 19 21 23 25 27 29
* lon (lon) uint16 124 126 128 130 132 134 ... 280 282 284 286 288 290
* time (time) int64 0 1 2 3 4 5 6 7 8 ... 388 389 390 391 392 393 394 395
Decompsition and retrieve the first and second loadings and expansion coefficeints
'''
decomposition, time should be in the first axis
lp is for SSTPC
rp is for USTA
'''
sst_ts = xMCA(sstpc, sstpc.rename('SSTPC_copy'))
sst_ts.solver()
lp, _ = sst_ts.patterns(n=2)
le, _ = sst_ts.expansionCoefs(n=2)
frac = sst_ts.covFracs(n=2)
print(frac)<xarray.DataArray 'frac' (n: 2)>
array([0.873075, 0.04946 ])
Coordinates:
* n (n) int64 0 1
Attributes:
long_name: Fractions explained of the covariance matrix between SSTPC an...
fig, ax1 = plt.subplots(1, 2, figsize=(12, 5))
lp[0].plot(ax=ax1[0])
le[0].plot(ax=ax1[1])
Regress PC1 to the original SST field
lh, _ = sst_ts.homogeneousPatterns(n=1)fig, ax1= plt.subplots()
lh[0].plot(ax=ax1)
He, C., Liu, Z., Otto-Bliesner, B. L., Brady, E. C., Zhu, C., Tomas, R., Clark, P. U., Zhu, J., Jahn, A., &Gu, S. (2021), Hydroclimate footprint of pan-Asian monsoon water isotope during the last deglaciation, Science Advances, 7(4), eabe2611.
He, C., Liu, Z., Otto-Bliesner, B. L., Brady, E. C., Zhu, C., Tomas, R., Buizert, C., &Severinghaus, J. P. (2021), Abrupt Heinrich Stadial 1 cooling missing in Greenland oxygen isotopes, Science Advances, 7(25), eabh1007.
He, C., Liu, Z., Otto-Bliesner, B. L., Brady, E. C., Zhu, C., Tomas, R., Gu, S., Han, J., &Jin, Y. (2021), Deglacial variability of South China hydroclimate heavily contributed by autumn rainfall, Nature communications, 12(1), 1-9.