From da77a606f5d48ff31d72816d858736954f4585aa Mon Sep 17 00:00:00 2001
From: "Suren A. Chilingaryan" <csa@suren.me>
Date: Fri, 27 Mar 2020 20:25:10 +0100
Subject: Cache computed lipschitz constant, seed random number generator to
 ensure that different runs give exactly the same result

---
 .../Python/ccpi/optimisation/operators/Operator.py | 31 +++++++++++++++++++---
 1 file changed, 27 insertions(+), 4 deletions(-)

(limited to 'Wrappers/Python/ccpi')

diff --git a/Wrappers/Python/ccpi/optimisation/operators/Operator.py b/Wrappers/Python/ccpi/optimisation/operators/Operator.py
index d49bc1a..4e2d04d 100644
--- a/Wrappers/Python/ccpi/optimisation/operators/Operator.py
+++ b/Wrappers/Python/ccpi/optimisation/operators/Operator.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 from numbers import Number
 import numpy
 import functools
+from hashlib import md5
 
 class Operator(object):
     '''Operator that maps from a space X -> Y'''
@@ -126,11 +127,13 @@ class LinearOperator(Operator):
         :returns: tuple with: L, list of L at each iteration, the data the iteration worked on.
         '''
         
+
         # Initialise random
-        if x_init is None:
-            x0 = operator.domain_geometry().allocate('random')
-        else:
-            x0 = x_init.copy()
+        x0 = operator.domain_geometry().allocate('random', seed=1)
+        #if x_init is None:
+        #    x0 = operator.domain_geometry().allocate('random', seed=1)
+        #else:
+        #    x0 = x_init.copy()
             
         x1 = operator.domain_geometry().allocate()
         y_tmp = operator.range_geometry().allocate()
@@ -158,9 +161,29 @@ class LinearOperator(Operator):
         :parameter force: forces the recalculation of the norm
         :type force: boolean, default :code:`False`
         '''
+
+        fname = md5("{} {}".format(self.domain_geometry(), self.range_geometry()).encode('utf-8')).hexdigest()
+        fname = "/tmp/ccpi_cache_opnorm_{}".format(fname)
+        try:
+            f = open(fname)
+            s1 = float(f.read())
+            print ("Returning norm {} from cache".format(s1))
+            return s1
+        except:
+            pass
+
         x0 = kwargs.get('x_init', None)
         iterations = kwargs.get('iterations', 25)
         s1, sall, svec = LinearOperator.PowerMethod(self, iterations, x_init=x0)
+
+        try:
+            print(fname)
+            f = open(fname, "w")
+            f.write("{}".format(s1))
+        except:
+            pass
+
+
         return s1
 
     @staticmethod
-- 
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