From 294b0fbf66041c4deb99541fe08db788a4ae846c Mon Sep 17 00:00:00 2001
From: Marco Cammarata <marcocamma@gmail.com>
Date: Mon, 22 May 2017 15:28:53 +0200
Subject: [PATCH] added svd based smoothing

---
 mcutils.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/mcutils.py b/mcutils.py
index 7972ca9..cf8198d 100644
--- a/mcutils.py
+++ b/mcutils.py
@@ -14,8 +14,11 @@ import collections
 import os
 import pylab as plt
 from itertools import chain
+
 sqrt2=np.sqrt(2)
 
+_svd_ret = collections.namedtuple("svd_return",["basis","s",\
+           "population","data"])
 
 ### COLORS, ETC ###
 colors = None
@@ -136,6 +139,27 @@ def poly_approximant(x,y,order=10,allowExtrapolation=False,fill_value=0):
   return f
 
 
+def svd(data,ncomp=None,truncate=True):
+  """ do SVD based cleaning 
+      
+      Parameters
+      ----------
+      ncomp : int
+        number of components to keep
+      truncate : bool
+        if True, returns only basis and populations of up to ncomp
+  """
+  # first index is time/T/P
+  u,s,v = np.linalg.svd(data,full_matrices=False)
+  if ncomp is not None:
+    s[ncomp:]=0
+    data = np.dot(u,np.dot(np.diag(s),v))
+    if truncate:
+      v = v[:ncomp]
+      u = u[:,:ncomp]
+  return _svd_ret(basis=v,s=s,population=u.T,data=data)
+  
+
 def smoothing(x,y,err=None,k=5,s=None,newx=None,derivative_order=0):
   idx = np.isnan(x)|np.isnan(y)
   idx = ~ idx