Updated requirements & removed deprecations

requirements.txt

@@ -2,8 +2,10 @@
 # pip3 install -r requirements.txt
 numpy
 scipy
-sklearn
+scikit-learn
 numdifftools
 matplotlib
 deap
 mpi4py
+pandas
+pyswarms

requirements_full.txt

@@ -4,10 +4,12 @@ numpy
 numdifftools
 matplotlib
 scipy
-sklearn
+scikit-learn
 deap
 mpi4py
 corner
 getdist
+pandas
+pyswarms
 fgivenx
 tqdm

simplemc/analyzers/GA_deap.py

@@ -52,7 +52,7 @@ def __init__(self, like, model, outputname='deap_output',
         self.outputname = outputname
         self.params = like.freeParameters()
         self.vpars = [p.value for p in self.params]
-        self.sigma = sp.array([p.error for p in self.params])
+        self.sigma = np.array([p.error for p in self.params])
         self.bounds = [p.bounds for p in self.params]
         print("Minimizing...", self.vpars, "with bounds", self.bounds)
         self.cov = None
@@ -147,7 +147,7 @@ def main(self):
 
             # set errors:
             #for i, pars in enumerate(self.params):
-            #    pars.setError(sp.sqrt(self.cov[i, i]))
+            #    pars.setError(np.sqrt(self.cov[i, i]))
         # update with the final result
         #self.result(self.negloglike(self.res.x))
 

simplemc/analyzers/MCEvidence.py

@@ -34,7 +34,8 @@
 import pandas as pd
 import sklearn as skl
 import statistics
-from sklearn.neighbors import NearestNeighbors, DistanceMetric
+from sklearn.neighbors import NearestNeighbors
+from sklearn.metrics import DistanceMetric
 import scipy.special as sp
 from numpy.linalg import inv
 from numpy.linalg import det

simplemc/analyzers/MCMCAnalyzer.py

@@ -9,6 +9,7 @@
 import copy
 import random
 import sys
+import numpy as np
 from numpy import loadtxt
 
 
@@ -74,8 +75,8 @@ def __init__(self, like, outfile, skip=5000, nsamp=100000, temp=1.0,
         for lb, hb in [p.bounds for p in self.cpars]:
             minvals.append(lb)
             maxvals.append(hb)
-        self.minvals = sp.array(minvals)
-        self.maxvals = sp.array(maxvals)
+        self.minvals = np.array(minvals)
+        self.maxvals = np.array(maxvals)
         print("Bounds:", self.minvals, self.maxvals)
 
         if (like.name() == "Composite"):
@@ -143,7 +144,7 @@ def RunChain(self):
             if (ploglike > self.cloglike):
                 accept = True
             else:
-                accept = (sp.exp((ploglike-self.cloglike)/self.temp)
+                accept = (np.exp((ploglike-self.cloglike)/self.temp)
                           > random.uniform(0., 1.))
 
             # print [p.value for p in ppars], accept, ploglike
@@ -229,7 +230,7 @@ def GRDRun(self, chains):
         B = lchain/(len(chains)- 1.)*B
         R = (1. - 1./lchain)*W +  B/lchain
 
-        result = sp.array(sp.absolute(1- sp.sqrt(R/W)))
+        result = np.array(sp.absolute(1- np.sqrt(R/W)))
         return result
 
 
@@ -305,8 +306,8 @@ def GetProposal(self):
 
 
     def draw_pcov(self):
-        a = sp.array([random.gauss(0., 1,) for _ in range(self.N)])
-        return sp.dot(a, self.chol)
+        a = np.array([random.gauss(0., 1,) for _ in range(self.N)])
+        return np.dot(a, self.chol)
 
 
 
@@ -324,7 +325,7 @@ def ProcessAccepted(self, ppars, ploglike, ploglikes):
 
         if (self.co > self.skip):
             # weight rescaled
-            wers = self.cw*sp.exp((self.cloglike-self.logofs)
+            wers = self.cw*np.exp((self.cloglike-self.logofs)
                                * (self.temp-1.0)/self.temp)
 
             tmp = [wers, -self.cloglike] + vec
@@ -355,7 +356,7 @@ def ProcessAccepted(self, ppars, ploglike, ploglikes):
 
         elif (self.co < self.skip):
             self.swx += self.cw
-            v = sp.array(vec)
+            v = np.array(vec)
             self.meanx  += v*self.cw
             self.meanxx += sp.outer(v, v)*self.cw
             if (self.cw > 30):
@@ -370,7 +371,7 @@ def ProcessAccepted(self, ppars, ploglike, ploglikes):
             print(self.meanxx)
             print()
             # for i, p in enumerate(self.cpars):
-            #     print("{}: {} +/- {}".format(p.name, p.value, sp.sqrt(self.meanxx[i, i])))
+            #     print("{}: {} +/- {}".format(p.name, p.value, np.sqrt(self.meanxx[i, i])))
 
             self.init_pcov(self.meanxx)
 

simplemc/analyzers/MaxLikeAnalyzer.py

@@ -40,7 +40,7 @@ def __init__(self, like, model, compute_errors=False, compute_derived= False,
         self.outputname = outputname
         self.params = like.freeParameters()
         self.vpars = [p.value for p in self.params]
-        self.sigma = sp.array([p.error for p in self.params])
+        self.sigma = np.array([p.error for p in self.params])
         self.cov = None
         bounds = [p.bounds for p in self.params]
         print("Minimizing...", self.vpars, "with bounds", bounds)
@@ -74,7 +74,7 @@ def __init__(self, like, model, compute_errors=False, compute_derived= False,
             print('Covariance matrix \n', self.cov)
             # set errors:
             for i, pars in enumerate(self.params):
-                pars.setError(sp.sqrt(self.cov[i, i]))
+                pars.setError(np.sqrt(self.cov[i, i]))
 
         # update with the final result
         self.opt_loglike = self.negloglike(self.res.x)

simplemc/analyzers/PSO_optimizer.py

@@ -6,6 +6,7 @@
 #from pyswarms.utils.plotters import (plot_cost_history,plot_contour,plot_surface)
 
 import scipy as sp
+import numpy as np
 
 class PSO_optimizer():
     #explain some input params
@@ -21,7 +22,7 @@ def __init__(self, like, model, outputname='pso_output',
 
         self.params = like.freeParameters()
         self.vpars = [p.value for p in self.params]
-        self.sigma = sp.array([p.error for p in self.params])
+        self.sigma = np.array([p.error for p in self.params])
         self.bounds = [p.bounds for p in self.params]
         self.pso_bounds = list(zip(*self.bounds))
         print("Minimizing...", self.vpars, "with pso bounds", self.pso_bounds)

simplemc/analyzers/dynesty/dynamicsampler.py

@@ -1842,7 +1842,7 @@ def computeDerived(self, parname):
             sys.exit('Define derived parameter', parname)
 
     def compuAge(self, z):
-        return 1.0/((1+z)*100.0*self.like.theory_.h*sp.sqrt(self.like.theory_.RHSquared_a(1.0/(1+z))))
+        return 1.0/((1+z)*100.0*self.like.theory_.h*np.sqrt(self.like.theory_.RHSquared_a(1.0/(1+z))))
 
 class Derivedparam:
     def __init__(self, name, value, Ltxname=None):

simplemc/chains/LCDM_phy_HD+SN_nested_multi.covmat

@@ -0,0 +1,3 @@
+8.642855688074105791e-04,5.420777781013889510e-08,-4.952608240882511691e-04
+5.420777781013908039e-08,2.171156857794319018e-07,-6.366617780189736727e-07
+-4.952608240882510606e-04,-6.366617780189736727e-07,9.976278146934557719e-04

simplemc/chains/LCDM_phy_HD+SN_nested_multi.maxlike

@@ -0,0 +1,2 @@
+# -maxlogL
+23.910224060351062 0.30184806 0.02201481 0.69264943

simplemc/chains/LCDM_phy_HD+SN_nested_multi.paramnames

@@ -0,0 +1,6 @@
+Om			\Omega_m
+Obh2			\Omega_{b}h^2
+h			h
+HD_like 			HD
+BetouleSN_like 			BetouleSN
+theory_prior 			 None