Merge branch '11-add-python-packaging' into 'dev'

Resolve "camel.py"

See merge request !7

work/tools/python/camel/__init__.py

+from .camel import *

work/tools/python/camel.py → work/tools/python/camel/camel.py

@@ -46,7 +46,7 @@ def mergeMC( filename, burnin=0.9, num=None, ext="txt", par=None, nelts=None):
 
         line_size=np.mean([len(f.readline()) for i in range(100)])
 #        print( line_size)
-        
+
         nsamp = os.stat(name).st_size / line_size
 #        print(nsamp)
         if 0 < burnin < 1:
@@ -55,15 +55,15 @@ def mergeMC( filename, burnin=0.9, num=None, ext="txt", par=None, nelts=None):
             ist = int(-burnin)
         else:
             ist = int(nsamp-burnin)
-        
+
         if int(line_size*ist*1.2) < os.path.getsize(name):
             f.seek(-int(line_size*ist*1.2),os.SEEK_END)
         lines_found=f.readlines()
         f.close()
-        
+
 #        data=[np.array(x.split(),float) for x in lines_found[-ist:]]
         data=[[float(c) for c in x.split()] for x in lines_found[-ist:]]
-        
+
         if len(chain)==0:
             chain = data
         else:
@@ -72,8 +72,8 @@ def mergeMC( filename, burnin=0.9, num=None, ext="txt", par=None, nelts=None):
     if nelts:
         if nelts < len(chain):
             chain = chain[np.array(np.random.random(nelts)*len(chain),int),:]
-    
-    dchain = dict( zip(par,np.transpose(chain)))
+
+    dchain = dict( list(zip(par,np.transpose(chain))))
 
     return(dchain)
 
@@ -89,10 +89,10 @@ def MCparnames( filename, num=1, ext="txt"):
 
 def Parnames( parfile, par_type=None):
     param_list = []
-    
+
     if not os.path.isfile(parfile):
         raise ValueError( "Filename do not exists: %s" % parfile)
-        
+
     sf = open(parfile)
 
     par_type=[par_type] if par_type else ["par","fix"]
@@ -124,7 +124,7 @@ def scan(filename):
         index = index+1
 
     print(l[index].replace("\n",""))
-    
+
     while not l[index].startswith("it "):
         index = index+1
 
@@ -141,38 +141,38 @@ def scan(filename):
 
 #expand given mat with the given parameters on the diag
 def expand_cov( mat, par, newpar, newvar):
-    
+
     match    = [newpar.index(i) for i in newpar if i in par]
     notmatch = [newpar.index(i) for i in newpar if i not in par]
-    
+
     sz = len(newpar)
-    newmat = np.zeros( (sz, sz))    
-    
+    newmat = np.zeros( (sz, sz))
+
     for i in match:
         for j in match:
             newmat[i,j] = mat[par.index(newpar[i]),par.index(newpar[j])]
-    
+
     for i in notmatch:
         newmat[i,i] = newvar[i]
-    
+
     return( newmat)
 
 
 
 def extract_chains( chainame, nchain):
-    
+
     chain = []
     for c in range(0,nchain):
         print( "%s%d.txt" % (chainame,c+1))
         data = []
-        
+
         with open("%s%d.txt" % (chainame,c+1)) as f:
             f.readline()
             for line in f:
                 data.append( [float(a) for a in line.split()])
 
         chain.append( np.array(np.transpose(data)))
-        
+
     return(chain)
 
 
@@ -197,12 +197,12 @@ def sig2ci( val, sig):
 
 
 def cov2cor( mat):
-    
+
     cor = mat.copy()
     for i in range( len(mat)):
         for j in range( len(mat)):
             cor[i,j] = mat[i,j]/np.sqrt(mat[i,i]*mat[j,j])
-    
+
     return(cor)
 
 
@@ -213,16 +213,16 @@ def cov2cor( mat):
 def GelmanRubin( chains, gap=10000, length_min=1000, length_max=None, new=False):
     #plot GelmanRubin test skipping first parameter=chi2
     nchain = len(chains)
-    
+
     nsamples = min( [len(c[0]) for c in chains])
     if length_max==None:
         length_max = nsamples
-    
+
     if length_max > nsamples:
         print( "Not enough samples...")
-        exit()    
-    
-    it = range(length_min,length_max,gap)[1:-1]
+        exit()
+
+    it = list(range(length_min,length_max,gap))[1:-1]
     R = []
     for isamp in it:
         print( "%d%%" % int(it.index(isamp)*100./len(it)))
@@ -239,21 +239,21 @@ def GelmanRubin( chains, gap=10000, length_min=1000, length_max=None, new=False)
         #within Chain
         mchain = np.mean( tmp,2)
         schain =  np.var( tmp,2, ddof=1)
-        
+
         #between chains
         W = np.mean( schain,0)
         B =  np.var( mchain,0, ddof=1)*n
-        
+
         #sqrt ?
         R.append( ( (n-1.)/n*W + B/n )/W )
-    
+
     return(it,np.array(R))
 
 
 def Geweke( chains, gap=10000, length_max=None):
     length_min = 1000
     nchain = len(chains)
-    
+
     nsamples = min( [len(c[0]) for c in chains])
     if length_max==None:
         length_max = nsamples
@@ -261,22 +261,22 @@ def Geweke( chains, gap=10000, length_max=None):
     if length_max > nsamples:
         print( "Not enough samples...")
         exit()
-        
-    it = range(length_min,length_max,gap)[1:]
+
+    it = list(range(length_min,length_max,gap))[1:]
     T = []
     for isamp in it:
         print( "%d%%" % int(it.index(isamp)*100./len(it)))
         n = length_max-isamp
         tmp1 = [data[:,isamp:isamp+n*0.1] for data in chains]
         tmp2 = [data[:,isamp+n*0.5:length_max] for data in chains]
-        
+
         #Tscore
         Z1 = np.mean( tmp1,2)
         Z2 = np.mean( tmp2,2)
         s = np.sqrt( np.var(tmp1,2)/(n*0.1) + np.var(tmp2,2)/(n*0.5) )
-        
+
         T.append( (Z1-Z2)/s)
-        
+
     return(it,T)
 ########################################################################
 
@@ -402,7 +402,7 @@ to_cosmomcName = {
     'Aps143x217'           : 'Aps143x217',
     }
 
-from_cosmomcName = dict(zip(to_cosmomcName.values(),to_cosmomcName.keys()))
+from_cosmomcName = dict(list(zip(list(to_cosmomcName.values()),list(to_cosmomcName.keys()))))
 
 
 
@@ -427,7 +427,7 @@ def posterior1d( chains, params, nbin=50, smooth=1,
     if np.size(lw) == 1:
         lw = [lw]*nchain
     elif np.size(lw) < nchain:
-        lw = [1]*nchain    
+        lw = [1]*nchain
 
     if np.prod(subplot) < npar:
         subplot[1] = np.ceil(np.double(npar)/subplot[0])
@@ -442,16 +442,16 @@ def posterior1d( chains, params, nbin=50, smooth=1,
 
         for c in range(nchain):
             chain = chains[c]
-            if chain.has_key(par):
+            if par in chain:
                 hist1d( chain[par], smooth=smooth, color=colors[c], linestyle=linestyles[c], lw=lw[c], bins=nbin)
-        
+
         ax.tick_params(axis='both', which='major', labelsize=16)
         ax.locator_params(tight=True, nbins=6)
         plt.xlabel( parname.get(par,par))
 
         if extent:
             ax.set_xlim( extent[params.index(par)])
-    
+
     #legend
     if names != []:
 #        if len(params) >= 2:
@@ -477,7 +477,7 @@ def posterior2d( chains, par1, par2, *args, **kwargs):
 
     x = chain[par1]
     y = chain[par2]
-    
+
 #    ax = kwargs.pop("ax", plt.gca())
     extent     = kwargs.pop("extent", [[x.min(), x.max()], [y.min(), y.max()]])
     bins       = kwargs.pop("bins",          50)
@@ -486,12 +486,12 @@ def posterior2d( chains, par1, par2, *args, **kwargs):
     linewidths = kwargs.pop("linewidths",  None)
     fill       = kwargs.get("fill",        True)
     datapoints = kwargs.get("datapoints", False)
-    contours   = kwargs.get("contours",   False)    
+    contours   = kwargs.get("contours",   False)
     levels     = kwargs.get("levels",   [0.68,0.95])
     cmap       = kwargs.get("cmap",        None)
 
 #    cmap = cm.get_cmap(kwargs.get("cmap", None))
-    
+
     X = np.linspace(extent[0][0], extent[0][1], bins + 1)
     Y = np.linspace(extent[1][0], extent[1][1], bins + 1)
     try:
@@ -508,22 +508,22 @@ def posterior2d( chains, par1, par2, *args, **kwargs):
 
     gkde = scipy.stats.gaussian_kde([x, y])
     dx = (extent[0][1]-extent[0][0])/(bins+1.)
-    dy = (extent[1][1]-extent[1][0])/(bins+1.)    
-    
+    dy = (extent[1][1]-extent[1][0])/(bins+1.)
+
     x2,y2 = np.mgrid[extent[0][0]:extent[0][1]:dx, extent[1][0]:extent[1][1]:dy]
     z   = np.array(gkde.evaluate([x2.flatten(),y2.flatten()])).reshape(x2.shape)
-    
+
     W = np.append(ctr_level( z, levels),z.max())
-    
+
     if fill:
         plt.contourf(x2, y2, z, W, alpha=alpha, cmap=cm.get_cmap(cmap), antialiased=True, colors=colors)
 
     if contours:
         plt.contour(x2, y2, z, W,linewidths=linewidths, colors=colors, antialiased=False)
-    
+
     plt.xlim(extent[0])
     plt.ylim(extent[1])
-    
+
     plt.xlabel(parname.get(par1,par1))
     plt.ylabel(parname.get(par2,par2))
 
@@ -539,12 +539,12 @@ def hist2d( chain, par1, par2, *args, **kwargs):
 
     x = chain[par1]
     y = chain[par2]
-    
+
     extent     = kwargs.pop("extent", [[x.min(), x.max()], [y.min(), y.max()]])
     bins       = kwargs.pop("bins",          50)
     nsmooth    = kwargs.pop("nsmooth",       0.)
     cmap       = kwargs.get("cmap",        None)
-    
+
     X = np.linspace(extent[0][0], extent[0][1], bins + 1)
     Y = np.linspace(extent[1][0], extent[1][1], bins + 1)
     try:
@@ -557,13 +557,13 @@ def hist2d( chain, par1, par2, *args, **kwargs):
 
     if nsmooth != 0.:
         H = nd.gaussian_filter( H, nsmooth)
-    
+
     npts = 5.
 #    plt.imshow( H.T, origin='bottom', **kwargs)
 #    plt.xticks(np.arange(npts+1)/npts*bins,["%4.2f" % i for i in np.linspace(extent[0][0],extent[0][1],npts+1)])
 #    plt.yticks(np.arange(npts+1)/npts*bins,["%4.2f" % i for i in np.linspace(extent[1][0],extent[1][1],npts+1)])
     plt.pcolormesh( X, Y, H.T, **kwargs)
-    
+
     plt.xlabel(parname.get(par1,par1))
     plt.ylabel(parname.get(par2,par2))
 
@@ -572,7 +572,7 @@ def hist2d( chain, par1, par2, *args, **kwargs):
 def triangle( chains, params, nbin=50, parnames=None, names=[], colors=[], linestyles=[], smooth=1., fontsize=12,
               contour=True, fill=False, cmaps=[], bestfit=None, Norm=True, weights=None):
     import matplotlib.ticker as mtick
-    
+
     fig=plt.figure(figsize=(14,12))
     plt.subplots_adjust(hspace=0.001,wspace=0.001)
 
@@ -600,7 +600,7 @@ def triangle( chains, params, nbin=50, parnames=None, names=[], colors=[], lines
 
     if len(linestyles) < nchain:
         linestyles = ['-']*nchain
-    
+
     if weights == None:
         weights = [np.ones(len(c['chi2'])) for c in chains]
 
@@ -627,10 +627,10 @@ def triangle( chains, params, nbin=50, parnames=None, names=[], colors=[], lines
             Y = nd.gaussian_filter1d( h, smooth,mode='nearest')
             Y = Y/np.max(Y) if Norm else Y
             plt.plot( (X[1:]+X[:-1])/2., Y, color=colors[c], linestyle=linestyles[c])
-            
+
             if bestfit:
                 plt.axvline( (bestfit[c])[par], ls='--', color=colors[c])
-        
+
         ax.set_xlim( rge[par])
         ax.yaxis.set_visible(False)
         ax.xaxis.set_visible(xpar==(npar-1))
@@ -656,7 +656,7 @@ def triangle( chains, params, nbin=50, parnames=None, names=[], colors=[], lines
                 if contour[c]:
                     plt.contour( X,Y, hmap, levels=ctr_level( hmap, [0.68,0.95]),
                                  colors=colors[c], linestyles=linestyles[c], extent=tuple(rge[par]+rge[par2]))
-            
+
                 if bestfit:
                     plt.plot( (bestfit[c])[par2], (bestfit[c])[par], '+', color=colors[c])
 
@@ -680,7 +680,7 @@ def triangle( chains, params, nbin=50, parnames=None, names=[], colors=[], lines
         ax.set_xlabel(parname.get(parnames[xpar],parnames[xpar]))
         ax.tick_params(axis='both', which='major', labelsize=fontsize)
 #        plt.locator_params(tight=True, nbins=5)
-        
+
     #write legend
     if len(names) == nchain:
         ax=plt.subplot( npar, npar, int(0.25*npar)*npar+0.85*npar-1)
@@ -694,7 +694,7 @@ def triangle( chains, params, nbin=50, parnames=None, names=[], colors=[], lines
 
 def rectangle( chains, par0, params, nbin=50, parnames=None, names=[], colors=[], linestyles=[], smooth=1., fontsize=12,
                contour=True, fill=False, cmaps=[], Norm=True, weights=None, ylim=None):
-    import matplotlib.ticker as mtick    
+    import matplotlib.ticker as mtick
 
     #force tuple
     if not( isinstance(chains, list) or isinstance(chains, tuple)):
@@ -719,7 +719,7 @@ def rectangle( chains, par0, params, nbin=50, parnames=None, names=[], colors=[]
 
     if len(linestyles) < nchain:
         linestyles = ['-']*nchain
-    
+
     if weights == None:
         weights = [np.ones(len(c['chi2'])) for c in chains]
 
@@ -765,10 +765,10 @@ def rectangle( chains, par0, params, nbin=50, parnames=None, names=[], colors=[]
         ax.yaxis.set_visible(xpar==0)
         if xpar == 0:
             ax.set_ylabel(parname.get(par0,par0))
-        
+
 #        if 'PS' in parname.get(parnames[par],parnames[par]):
 #            ax.xaxis.set_major_formatter(mtick.FormatStrFormatter('%.0e'))
-        
+
     #write legend
     if len(names) == nchain:
         for c in range(nchain):
@@ -797,7 +797,7 @@ def MCcorrelation( chain, par, **kwargs):
     data = [chain[p] for p in par]