minor changes to enable runs controlled by json files

models.py

@@ -2,13 +2,55 @@
 # -*- coding: utf-8 -*-
 import numpy as np
 from scipy.interpolate import CubicSpline
+from scipy.special import erfinv,erf
 
 verbose = True
 
+def transform_none(x,kind):
+    if kind=='dir':
+        return x
+    if kind=='inv':
+        return x
+
+def transform_log(x,kind):
+    if kind=='dir':
+        return np.log(x)
+    if kind=='inv':
+        return np.exp(x)
+
+def transform_pit(x,kind):
+    # mu and sigma could be guessed from the data, but...
+    a = 0.5
+    b = 4.5
+    mu = 0.5*(a+b)
+    sigma = (b-a)/12**0.5
+    # takes a uniform, returns a normal
+    if kind=='dir': 
+        p = (x-a)/(b-a)
+        xt = mu+sigma*2**0.5*erfinv(2*p-1)
+        return xt
+    # takes a normal), returns a uniform
+    if kind=='inv':
+        p = 0.5*(1+erf((x-mu)/sigma/2**0.5))
+        xt = a+p*(b-a)
+        return xt
+
 class CBL:
     def __init__(self,settings):
         for k, v in settings.items():
             setattr(self, k, v)
+        self.parameter_true = np.array([self.parameter_true])
+        self.parameter_ensemble_min = np.array([self.parameter_ensemble_min])
+        self.parameter_ensemble_max = np.array([self.parameter_ensemble_max])
+        self.parameter_inflation_rtps_alpha = np.array([self.parameter_inflation_rtps_alpha])
+        if self.parameter_transform == 'pit':
+            self.parameter_transform = [ transform_pit ]
+        elif self.parameter_transform == 'log':
+            self.parameter_transform = [ transform_log ]
+        elif self.parameter_transform == 'none':
+            self.parameter_transform = [ transform_none ]
+        if not hasattr(self,'do_parameter_estimation'):
+            self.do_parameter_estimation == False
 
     def initialize(self,argin,coordinates=None):