first commit for xMCA

README.md

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+
+# About xMCA
+xMCA is Maximum Covariance Analysis (sometimes also called SVD)in xarray. 
+
+
+# How to install
+### via git
+```
+git clone https://github.com/Yefee/xMCA.git
+cd xcesm
+python setup.py install
+```
+
+
+# Example
+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
+
+
+```python
+from xMCA import xMCA
+import xarray as xr
+import matplotlib.pyplot as plt
+%matplotlib inline
+```
+
+
+```python
+usta = xr.open_dataarray('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
+
+
+
+```python
+sstpc = 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 
+
+
+```python
+'''
+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...
+
+
+
+```python
+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])
+```
+
+
+
+
+    [<matplotlib.lines.Line2D at 0x11e75aa58>]
+
+
+
+
+![png](example_files/example_6_1.png)
+
+
+Homogeneous and heterogeneous regression
+
+
+```python
+lh, rh = sst_ts.homogeneousPatterns(n=1)
+le, re = sst_ts.heterogeneousPatterns(n=1)
+```
+
+
+```python
+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])
+```
+
+
+
+
+    <matplotlib.collections.QuadMesh at 0x11ecf3cc0>
+
+
+
+
+![png](example_files/example_9_1.png)
+

setup.py

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+from setuptools import setup, find_packages
+
+setup(name='xMCA',
+      version='0.1',
+      description='Maximum Covariance Analysis in xarray.',
+      url='https://github.com/Yefee/xMCA',
+      author='Chengfei He',
+      author_email='che43@wisc.edu',
+      include_package_data=True,
+      classifiers=[
+        'Development Status :: 3 - Alpha',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
+        ],
+      keywords='statistical analysis MCA SVD xarray',
+      license='MIT',
+      # packages=['xcesm','config'],
+      packages=find_packages(),
+      package_data={'xMCA': ['examples/data/*.nc']},
+      install_requires=['xarray', 'numpy'],
+      zip_safe=False)
+print(find_packages())

xMCA/__init__.py

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+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from .core.xMCA import xMCA