add another example

.gitignore

@@ -1,3 +1,7 @@
 xMCA.egg
 .DS_Store
 xMCA.egg-info
+__pycache__
+.ipynb_checkpoints
+build
+dist

README.md

@@ -15,7 +15,7 @@ python setup.py install
 # Future Plan
 In next version, A Monte Carlo method will be added for statistical test.
 
-# Example
+# Example 1
 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
 
@@ -143,3 +143,90 @@ re[0].plot(ax=ax2[1])
 
 ![png](https://github.com/Yefee/xMCA/blob/master/xMCA/examples/example_files/example_9_1.png)
 
+
+
+# Example 2
+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
+
+
+```python
+from xMCA import xMCA
+import xarray as xr
+import matplotlib.pyplot as plt
+%matplotlib inline
+```
+
+
+```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, 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...
+
+
+
+```python
+fig, ax1 = plt.subplots(1, 2, figsize=(12, 5))
+lp[0].plot(ax=ax1[0])
+le[0].plot(ax=ax1[1])
+
+```
+
+
+
+![png](https://github.com/Yefee/xMCA/blob/master/xMCA/examples/example_files/example_eof_5_1.png)
+
+
+Regress PC1 to the original SST field
+
+
+```python
+lh, _ = sst_ts.homogeneousPatterns(n=1)
+```
+
+
+
+```python
+fig, ax1= plt.subplots()
+lh[0].plot(ax=ax1)
+
+```
+
+
+
+![png](https://github.com/Yefee/xMCA/blob/master/xMCA/examples/example_files/example_eof_8_1.png)
+

xMCA/examples/example.ipynb → xMCA/examples/example_svd.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -134,16 +134,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11e75aa58>]"
+       "[<matplotlib.lines.Line2D at 0x11d813c50>]"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -178,26 +178,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/he.1519/miniconda3/lib/python3.6/site-packages/xarray/core/nanops.py:161: RuntimeWarning: Mean of empty slice\n",
+      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
+     ]
+    }
+   ],
    "source": [
     "lh, rh = sst_ts.homogeneousPatterns(n=1)\n",
     "le, re = sst_ts.heterogeneousPatterns(n=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x11ecf3cc0>"
+       "<matplotlib.collections.QuadMesh at 0x11e68a668>"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },