added mean-eddy decomposition function to utils

OceanLab/__init.py__

@@ -1,6 +1,6 @@
 from .dyn import zeta, vmode_amp, psi2uv, eqmodes, vmodes
 from .eof  import my_eof_interp, eoft
 from .oa  import scaloa, vectoa
-from utils import argdistnear
+from utils import argdistnear, meaneddy
 
 __version__ = '0.0.7'

OceanLab/utils.py

@@ -1,4 +1,6 @@
 import numpy as np
+import scipy.signal as sg
+import xarray as xr
 
 ##### Function
 #=============================================================================
@@ -16,8 +18,110 @@ def argdistnear(x,y,xi,yi):
     INPUT:
     --> (x,y): points [list]
     --> (xi,yi): series to search nearest point [list]
+    
+    Iury T.Simões-Sousa
+    (IO-USP/ UMass-Dartmouth)
     '''
+
     idxs = [np.argmin(np.sqrt((xi-xx)**2 + (yi-yy)**2)) for xx,yy in zip(x,y)]
     idxs = np.array(idxs)
     return idxs
 #=============================================================================
+
+#=============================================================================
+# LOW PASS FILTER
+#=============================================================================
+def meaneddy(prop,days=60,ndim=1,DataArray=False,timedim=None):
+
+    """
+    Apply a low-pass filter (scipy.signal.butter) to 'prop' and obtain the mean and eddy components.
+    
+    usage [1]:
+    Velocity = np.random.randn(365,17,13) # one year, 17 lat x 13 lon domain
+    Filtered, Residual = meaneddy(Velocity, days=10, ndim=3, DataArray=False,timedim=None)
+    
+    usage [2]:
+    Velocity = xr.DataArray(data=np.random.randn(365,17,13), dims=["time","lat","lon"],
+coords=dict(time=(["time"],range(0,365)), lat=(["lat"],np.arange(-4,4.5,0.5)), lon=(["lon"],np.arange(1,7.5,0.5)))) # one year, 17 lat x 13 lon domain
+    Filtered, Residual = meaneddy(Velocity, days=10, DataArray=True,timedim=["time"])
+
+    
+    INPUT:
+       -> prop: 1, 2 or 3D array to filter
+       -> days: number of days to set up the filter
+       -> ndim: number of dimensions of the data [only used for DataArray=False, max:3]
+       -> DataArray: True if prop is in xr.DataArray format
+       -> dim: name of time dimension to filter (only used for DataArray=True)
+   
+    OUTPUT:
+       -> m_prop: mean (filtered) part of the property
+       -> p_prop: prime part of the property, essentially prop - m_prop
+    
+    v1 (February 2018)
+    Cesar B. Rocha
+    Dante C. Napolitano ([email protected])
+    
+    v2 (December 2020)
+    Dante C. Napolitano ([email protected])
+    Mariana M. Lage ([email protected])
+   """
+
+    # creating filter
+    def timefilter(prop,filtdays=60):
+        filt_b,filt_a = sg.butter(4,1./filtdays)
+        return sg.filtfilt(filt_b,filt_a,prop)
+
+    if DataArray:
+        m_prop = xr.apply_ufunc(
+                timefilter, # first the function
+                prop,# now arguments in the order expected by 'butter_filt'
+                input_core_dims=[timedim],  # list with one entry per arg
+                output_core_dims=[timedim],  # returned data
+                kwargs={'filtdays':days},
+                vectorize=True,  # loop over non-core dims
+                dask='vectorized')
+
+        p_prop = prop - m_prop
+
+    elif ndim ==3:
+        ti,lt,ln = prop.shape
+        prop = prop.reshape(ti,lt*ln)
+
+        m_prop = []
+
+        for tot in prop.T:
+        # filtered series (mean)
+            m_prop.append(timefilter(tot,filtdays=days))
+
+        m_prop = np.array(m_prop)
+        m_prop = m_prop.T
+
+        p_prop = prop - m_prop
+
+        m_prop = m_prop.reshape(ti,lt,ln)
+        p_prop = p_prop.reshape(ti,lt,ln)
+
+    elif ndim ==2:
+
+        ti,lt = prop.shape
+
+        m_prop = []
+
+        for tot in prop.T:
+            # filtered series (mean)
+            m_prop.append(timefilter(tot,filtdays=days))
+
+        m_prop = np.array(m_prop)
+        m_prop = m_prop.T
+
+        p_prop = prop - m_prop
+
+        m_prop = m_prop.reshape(ti,lt)
+        p_prop = p_prop.reshape(ti,lt)
+
+    elif ndim ==1:
+        m_prop = timefilter(prop,filtdays=days)
+        p_prop = prop - m_prop
+
+    return m_prop,p_prop
+#=============================================================================