Author: Andreas Tille Last-Update: Mon, 29 Oct 2018 14:47:53 +0100 Description: Run 2to3 on examples Forwarded: Jaime Huerta-Cepas --- a/examples/clustering/bubbles_validation.py +++ b/examples/clustering/bubbles_validation.py @@ -13,7 +13,7 @@ array = t.arraytable # Calculates some stats on the matrix. Needed to establish the color # gradients. -matrix_dist = [i for r in xrange(len(array.matrix))\ +matrix_dist = [i for r in range(len(array.matrix))\ for i in array.matrix[r] if numpy.isfinite(i)] matrix_max = numpy.max(matrix_dist) matrix_min = numpy.min(matrix_dist) --- a/examples/clustering/cluster_visualization.py +++ b/examples/clustering/cluster_visualization.py @@ -13,8 +13,8 @@ F\t-1.04\t-1.11\t0.87\t-0.14\t-0.80\t1.7 G\t-1.57\t-1.17\t1.29\t0.23\t-0.20\t1.17\t0.26 H\t-1.53\t-1.25\t0.59\t-0.30\t0.32\t1.41\t0.77 """ -print "Example numerical matrix" -print matrix +print("Example numerical matrix") +print(matrix) # #Names col1 col2 col3 col4 col5 col6 col7 # A -1.23 -0.81 1.79 0.78 -0.42 -0.69 0.58 # B -1.76 -0.94 1.16 0.36 0.41 -0.35 1.12 --- a/examples/clustering/clustering_tree.py +++ b/examples/clustering/clustering_tree.py @@ -13,8 +13,8 @@ F\t-1.04\t-1.11\t0.87\t-0.14\t-0.80\t1.7 G\t-1.57\t-1.17\t1.29\t0.23\t-0.20\t1.17\t0.26 H\t-1.53\t-1.25\t0.59\t-0.30\t0.32\t1.41\t0.77 """ -print "Example numerical matrix" -print matrix +print("Example numerical matrix") +print(matrix) # #Names col1 col2 col3 col4 col5 col6 col7 # A -1.23 -0.81 1.79 0.78 -0.42 -0.69 0.58 # B -1.76 -0.94 1.16 0.36 0.41 -0.35 1.12 @@ -30,7 +30,7 @@ print matrix # numerical matrix. We use the text_array argument to link the tree # with numerical matrix. t = ClusterTree("(((A,B),(C,(D,E))),(F,(G,H)));", text_array=matrix) -print "Example tree", t +print("Example tree", t) # /-A # /--------| # | \-B @@ -49,18 +49,18 @@ print "Example tree", t # Now we can ask the numerical profile associated to each node A = t.search_nodes(name='A')[0] -print "A associated profile:\n", A.profile +print("A associated profile:\n", A.profile) # [-1.23 -0.81 1.79 0.78 -0.42 -0.69 0.58] # # Or we can ask for the mean numerical profile of an internal # partition, which is computed as the average of all vectors under the # the given node. cluster = t.get_common_ancestor("E", "A") -print "Internal cluster mean profile:\n", cluster.profile +print("Internal cluster mean profile:\n", cluster.profile) #[-1.574 -0.686 1.048 -0.012 -0.118 0.614 0.728] # # We can also obtain the std. deviation vector of the mean profile -print "Internal cluster std deviation profile:\n", cluster.deviation +print("Internal cluster std deviation profile:\n", cluster.deviation) #[ 0.36565558 0.41301816 0.40676283 0.56211743 0.50704635 0.94949671 # 0.26753691] # If would need to re-link the tree to a different matrix or use @@ -96,7 +96,7 @@ H\t0\t0\t0\t0\t0\t0\t0 # obviated from association. t.children[0].link_to_arraytable(matrix_ones) t.children[1].link_to_arraytable(matrix_zeros) -print "A profile (using matrix with 1s", (t&"A").profile -print "H profile (using matrix with 0s)", (t&"H").profile +print("A profile (using matrix with 1s", (t&"A").profile) +print("H profile (using matrix with 0s)", (t&"H").profile) #A profile (using matrix with 1s [ 1. 1. 1. 1. 1. 1. 1.] #H profile (using matrix with 0s) [ 0. 0. 0. 0. 0. 0. 0.] --- a/examples/evol/1_freeratio.py +++ b/examples/evol/1_freeratio.py @@ -23,13 +23,13 @@ tree = EvolTree ("data/S_example/measuri print (tree) -input ('\n tree loaded, hit some key.\n') +eval(input ('\n tree loaded, hit some key.\n')) print ('Now, it is necessary to link this tree to an alignment:') tree.link_to_alignment ('data/S_example/alignment_S_measuring_evol.fasta') -input ('\n alignment loaded, hit some key to see.\n') +eval(input ('\n alignment loaded, hit some key to see.\n')) tree.show() @@ -38,28 +38,28 @@ we will run free-ratio model that is one function run_model: +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ''') -print (tree.run_model.__doc__ +'\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++') +print(tree.run_model.__doc__ +'\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++') tree.run_model ('fb.example') -input ('free-ratio model runned, all results are store in a Model object.') +eval(input ('free-ratio model runned, all results are store in a Model object.')) fb = tree.get_evol_model('fb.example') print ('Have a look to the parameters used to run this model on codeml: ') -print (fb.get_ctrl_string()) -input ('hit some key...') +print(fb.get_ctrl_string()) +eval(input ('hit some key...')) print ('Have a look to run message of codeml: ') -print (fb.run) -input ('hit some key...') +print(fb.run) +eval(input ('hit some key...')) print ('Have a look to log likelihood value of this model, and number of parameters:') -print ('lnL: %s and np: %s' % (fb.lnL, fb.np)) -input ('hit some key...') +print('lnL: %s and np: %s' % (fb.lnL, fb.np)) +eval(input ('hit some key...')) -input ('finally have a look to two layouts available to display free-ratio:') +eval(input ('finally have a look to two layouts available to display free-ratio:')) tree.show() # have to import layou --- a/examples/evol/2_sites_model.py +++ b/examples/evol/2_sites_model.py @@ -24,14 +24,14 @@ try: except NameError: pass -input ('\n tree and alignment loaded\n Hit some key, to start computation of site models M1 and M2.\n') +eval(input ('\n tree and alignment loaded\n Hit some key, to start computation of site models M1 and M2.\n')) print ('running model M1') tree.run_model ('M1') print ('running model M2') tree.run_model ('M2') -print ('\n\n comparison of models M1 and M2, p-value: ' + str(tree.get_most_likely ('M2','M1'))) +print('\n\n comparison of models M1 and M2, p-value: ' + str(tree.get_most_likely ('M2','M1'))) #tree.show() @@ -73,8 +73,8 @@ tree.run_model ('M8a') print ('running model M3') tree.run_model ('M3') -print ('\n\n comparison of models M7 and M8, p-value: ' + str(tree.get_most_likely ('M8','M7'))) -print ('\n\n comparison of models M8a and M8, p-value: ' + str(tree.get_most_likely ('M8','M8a'))) +print('\n\n comparison of models M7 and M8, p-value: ' + str(tree.get_most_likely ('M8','M7'))) +print('\n\n comparison of models M8a and M8, p-value: ' + str(tree.get_most_likely ('M8','M8a'))) print ('The End.') --- a/examples/evol/3_branchsite_test.py +++ b/examples/evol/3_branchsite_test.py @@ -34,9 +34,9 @@ tree.run_model('M0') for leaf in tree: leaf.node_id - print ('\n---------\nNow working with leaf ' + leaf.name) + print('\n---------\nNow working with leaf ' + leaf.name) tree.mark_tree([leaf.node_id], marks=['#1']) - print (tree.write()) + print(tree.write()) # to organize a bit, we name model with the name of the marked node # any character after the dot, in model name, is not taken into account # for computation. (have a look in /tmp/ete3.../bsA.. directory) @@ -46,9 +46,9 @@ for leaf in tree: print ('p-value of positive selection for sites on this branch is: ') ps = tree.get_most_likely('bsA.' + leaf.name, 'bsA1.'+ leaf.name) rx = tree.get_most_likely('bsA1.'+ leaf.name, 'M0') - print (str(ps)) + print(str(ps)) print ('p-value of relaxation for sites on this branch is: ') - print (str(rx)) + print(str(rx)) model = tree.get_evol_model("bsA." + leaf.name) if ps < 0.05 and float(model.classes['foreground w'][2]) > 1: print ('we have positive selection on sites on this branch') @@ -58,7 +58,7 @@ for leaf in tree: else: print ('no signal detected on this branch, best fit for M0') print ('\nclean tree, remove marks') - tree.mark_tree(map(lambda x: x.node_id, tree.get_descendants()), + tree.mark_tree([x.node_id for x in tree.get_descendants()], marks=[''] * len(tree.get_descendants()), verbose=True) # nothing working yet to get which sites are under positive selection/relaxation, --- a/examples/evol/4_branch_models.py +++ b/examples/evol/4_branch_models.py @@ -28,12 +28,12 @@ tree.link_to_alignment ('data/S_example/ print (tree) print ('Tree and alignment loaded.') -input ('Tree will be mark in order to contrast Gorilla and Chimpanzee as foreground \nspecies.') +eval(input ('Tree will be mark in order to contrast Gorilla and Chimpanzee as foreground \nspecies.')) marks = ['1', '3', '7'] tree.mark_tree (marks, ['#1'] * 3) -print (tree.write ()) +print(tree.write ()) print ('we can easily colorize marked branches') # display marked branches in orange @@ -57,13 +57,13 @@ tree.run_model ('b_neut.137') print ('running M0 (all branches have the save value of omega)...') tree.run_model ('M0') -input ('''Now we can do comparisons... +eval(input ('''Now we can do comparisons... Compare first if we have one or 2 rates of evolution among phylogeny. LRT between b_free and M0 (that is one or two rates of omega value) -p-value ofthis comparison is:''') -print (tree.get_most_likely ('b_free.137', 'M0')) +p-value ofthis comparison is:''')) +print(tree.get_most_likely ('b_free.137', 'M0')) -input (''' +eval(input (''' Now test if foreground rate is significantly different of 1. (b_free with significantly better likelihood than b_neut) if significantly different, and higher than one, we will be under @@ -71,17 +71,17 @@ positive selection, if different and low negative selection. And finally if models are not significantly different we should accept null hypothesis that omega value on marked branches is equal to 1, what would be a signal of relaxation. -p-value for difference in rates between marked branches and the rest:''') -print (tree.get_most_likely ('b_free.137', 'M0')) +p-value for difference in rates between marked branches and the rest:''')) +print(tree.get_most_likely ('b_free.137', 'M0')) print ('p-value representing significance that omega is different of 1:') -print (tree.get_most_likely ('b_free.137', 'b_neut.137')) +print(tree.get_most_likely ('b_free.137', 'b_neut.137')) print ('value of omega in marked branch (frg branch):') b_free = tree.get_evol_model ('b_free.137') -print (b_free.branches[1]['w']) +print(b_free.branches[1]['w']) print ('and value of omega for background: ') -print (b_free.branches[2]['w']) +print(b_free.branches[2]['w']) print ('we will now run 2 branch models over this tree, one letting the omega \nvalue of foreground species to be free, and the other fixing it at one.\n') --- a/examples/evol/5_branchsite_cladetest.py +++ b/examples/evol/5_branchsite_cladetest.py @@ -26,12 +26,12 @@ tree.link_to_alignment ('data/S_example/ print (tree) print ('Tree and alignment loaded.') -input ('Tree will be mark in order to contrast Gorilla and Chimpanzee as foreground \nspecies.') +eval(input ('Tree will be mark in order to contrast Gorilla and Chimpanzee as foreground \nspecies.')) marks = ['1', 3, '7'] tree.mark_tree (marks, ['#1'] * 3) -print (tree.write ()) +print(tree.write ()) # display marked branches in orange for node in tree.traverse (): @@ -65,7 +65,7 @@ tree.run_model ('M1') print ('''p-value that, in marked clade, we have one class of site specifically evolving at a different rate:''') -print (tree.get_most_likely ('bsC.137', 'M1')) +print(tree.get_most_likely ('bsC.137', 'M1')) #print ('p-value representing significance that omega is different of 1:') #print (tree.get_most_likely ('bsD.137', 'M3')) --- a/examples/evol/measuring_evolution_trees.py +++ b/examples/evol/measuring_evolution_trees.py @@ -7,7 +7,7 @@ import sys, re typ = None while typ != 'L' and typ != 'S': - typ = raw_input(\ + typ = input(\ "choose kind of example [L]ong or [S]hort, hit [L] or [S]:\n").upper() TREE_PATH = "data/%s_example/measuring_%s_tree.nw" % (typ, typ) @@ -24,8 +24,8 @@ ALG_PATH = MY_PATH + re.sub('\./', '', # load tree -print '\n ----> we create a EvolTree object, and give to him a topology, from', -print TREE_PATH +print('\n ----> we create a EvolTree object, and give to him a topology, from\n') +print(TREE_PATH) out = True while out == True: try: @@ -33,7 +33,7 @@ while out == True: out = False except: sys.stderr.write('Bad path for working directory. Enter new path or quit("Q"):\n') - PATH = raw_input('') + PATH = input('') if PATH.startswith('q') or PATH.startswith('Q'): sys.exit() TREE_PATH = "./measuring_%s_tree.nw" % (typ) @@ -42,62 +42,62 @@ while out == True: ALG_PATH = PATH + re.sub('\./', '', ALG_PATH ) -print T -print '\n ----> and an alignment from: \n'+ALG_PATH+'\n\n' +print(T) +print('\n ----> and an alignment from: \n'+ALG_PATH+'\n\n') T.link_to_alignment(ALG_PATH) -raw_input(" ====> hit some key to see the Tree with alignment") +input(" ====> hit some key to see the Tree with alignment") T.show() ### # run free-branch model, and display result -print '\n\n\n ----> We define now our working directory, that will be created:', \ - WORKING_PATH +print('\n\n\n ----> We define now our working directory, that will be created:', \ + WORKING_PATH) T.workdir = (WORKING_PATH) -print '\n ----> and run the free-branch model with run_model function:\n\n%s\n%s\n%s\n'\ - % ('*'*10 + ' doc ' + '*'*10, T.run_model.func_doc, '*'*30) +print('\n ----> and run the free-branch model with run_model function:\n\n%s\n%s\n%s\n'\ + % ('*'*10 + ' doc ' + '*'*10, T.run_model.__doc__, '*'*30)) -raw_input(" ====> Hit some key to start free-branch computation with codeml...\n") +input(" ====> Hit some key to start free-branch computation with codeml...\n") T.run_model('fb') T.show() ### # run site model, and display result -print '\n\n\n ----> We are now goingn to run sites model M1 and M2 with run_model function:\n' -raw_input(" ====> hit some key to start") +print('\n\n\n ----> We are now goingn to run sites model M1 and M2 with run_model function:\n') +input(" ====> hit some key to start") for model in ['M1', 'M2']: - print 'running model ' + model + print('running model ' + model) T.run_model(model) -print '\n\n\n ----> and use the get_most_likely function to compute the LRT between those models:\n' -print 'get_most_likely function: \n\n'+ '*'*10 + ' doc ' + '*'*10 -print '\n' + T.get_most_likely.func_doc -print '*'*30 +print('\n\n\n ----> and use the get_most_likely function to compute the LRT between those models:\n') +print('get_most_likely function: \n\n'+ '*'*10 + ' doc ' + '*'*10) +print('\n' + T.get_most_likely.__doc__) +print('*'*30) -raw_input("\n ====> Hit some key to launch LRT") +input("\n ====> Hit some key to launch LRT") pv = T.get_most_likely('M2', 'M1') if pv <= 0.05: - print ' ----> -> most likely model is model M2, there is positive selection, pval: ',pv + print(' ----> -> most likely model is model M2, there is positive selection, pval: ',pv) else: - print ' ----> -> most likely model is model M1, pval: ',pv + print(' ----> -> most likely model is model M1, pval: ',pv) -raw_input(" ====> Hit some key...") +input(" ====> Hit some key...") ### # tengo que encontrar un ejemplo mas bonito pero bueno.... :P -print '\n\n\n ----> We now add histograms to our tree to repesent site models with add_histface function: \n\n%s\n%s\n%s\n'\ - % ('*'*10 + ' doc ' + '*'*10, T.get_evol_model('M2').set_histface.func_doc,'*'*30) -print 'Upper face is an histogram representing values of omega for each column in the alignment,' -print '\ +print('\n\n\n ----> We now add histograms to our tree to repesent site models with add_histface function: \n\n%s\n%s\n%s\n'\ + % ('*'*10 + ' doc ' + '*'*10, T.get_evol_model('M2').set_histface.__doc__,'*'*30)) +print('Upper face is an histogram representing values of omega for each column in the alignment,') +print('\ Colors represent significantly conserved sites(cyan to blue), neutral sites(greens), or under \n\ positive selection(orange to red). \n\ Lower face also represents values of omega(red line) and bars represent the error of the estimation.\n\ Also significance of belonging to one class of site can be painted in background(here lightgrey for\n\ evrething significant)\n\ Both representation are done according to BEB estimation of M2, M1 or M7 estimation can also be \n\ -drawn but should not be used.\n' -raw_input(" ====> Hit some key to display, histograms of omegas BEB from M2 model...") +drawn but should not be used.\n') +input(" ====> Hit some key to display, histograms of omegas BEB from M2 model...") col = {'NS' : 'grey', 'RX' : 'grey', @@ -118,12 +118,12 @@ T.show(histfaces = ['M1', 'M2']) ### # re-run without reeeeeeeeee-run -print '\n\n\n ----> Now we have runned once those 3 models, we can load again our tree from' -print ' ----> our tree file and alignment file, and this time load directly oufiles from previous' -print ' with the function link_to_evol_model \n\n%s\n%s\n%s\n' % ('*'*10 + ' doc ' + '*'*10, \ - T.link_to_evol_model.func_doc, \ - '*'*30) -raw_input('runs\n ====> hit some key to see...') +print('\n\n\n ----> Now we have runned once those 3 models, we can load again our tree from') +print(' ----> our tree file and alignment file, and this time load directly oufiles from previous') +print(' with the function link_to_evol_model \n\n%s\n%s\n%s\n' % ('*'*10 + ' doc ' + '*'*10, \ + T.link_to_evol_model.__doc__, \ + '*'*30)) +input('runs\n ====> hit some key to see...') T = EvolTree(TREE_PATH) T.link_to_alignment(ALG_PATH) T.workdir = (WORKING_PATH) @@ -141,41 +141,41 @@ T.show(histfaces = ['M1', 'M2']) ### # mark tree functionality -print T.write(format=10) +print(T.write(format=10)) name = None while name not in T.get_leaf_names(): - name = raw_input(' ====> As you need to mark some branches to run branch\n\ + name = input(' ====> As you need to mark some branches to run branch\n\ models, type the name of one leaf: ') idname = T.get_leaves_by_name(name)[0].node_id -print ' ----> you want to mark:',name,'that has this idname: ', idname +print(' ----> you want to mark:',name,'that has this idname: ', idname) T.mark_tree([idname]) # by default will mark with '#1' -print 'have a look to the mark: ' -print re.sub('#','|',re.sub('[0-9a-zA-Z_(),;]',' ',T.write(format=10))) -print re.sub('#','v',re.sub('[0-9a-zA-Z_(),;]',' ',T.write(format=10))) -print T.write(format=10) -print '\n You have marked the tree with a command like: T.mark_tree([%d])\n' % (idname) -print '\n%s\n%s\n%s\n' % ('*'*10 + ' doc ' + '*'*10, T.mark_tree.func_doc, \ - '*'*30) +print('have a look to the mark: ') +print(re.sub('#','|',re.sub('[0-9a-zA-Z_(),;]',' ',T.write(format=10)))) +print(re.sub('#','v',re.sub('[0-9a-zA-Z_(),;]',' ',T.write(format=10)))) +print(T.write(format=10)) +print('\n You have marked the tree with a command like: T.mark_tree([%d])\n' % (idname)) +print('\n%s\n%s\n%s\n' % ('*'*10 + ' doc ' + '*'*10, T.mark_tree.__doc__, \ + '*'*30)) -print '\n\n\n ----> We are now going to run branch-site models bsA and bsA1:\n\n' -raw_input(" ====> hit some key to start computation with our marked tree") +print('\n\n\n ----> We are now going to run branch-site models bsA and bsA1:\n\n') +input(" ====> hit some key to start computation with our marked tree") for model in ['bsA','bsA1']: - print 'running model ' + model + print('running model ' + model) T.run_model(model) -print '\n\n\n ----> again we use the get_most_likely function to compute the LRT between those models:\n' -raw_input(" ====> Hit some key to launch LRT") +print('\n\n\n ----> again we use the get_most_likely function to compute the LRT between those models:\n') +input(" ====> Hit some key to launch LRT") pv = T.get_most_likely('bsA', 'bsA1') if pv <= 0.05: - print ' ----> -> most likely model is model bsA, there is positive selection, pval: ',pv - print ' ' + name + ' is under positive selection.' + print(' ----> -> most likely model is model bsA, there is positive selection, pval: ',pv) + print(' ' + name + ' is under positive selection.') else: - print ' ----> -> most likely model is model bsA1, pval of LRT: ',pv - print ' ' + name + ' is not under positive selection.' + print(' ----> -> most likely model is model bsA1, pval of LRT: ',pv) + print(' ' + name + ' is not under positive selection.') sys.stderr.write('\n\nThe End.\n\n') --- a/examples/evol/test_protamine.py +++ b/examples/evol/test_protamine.py @@ -30,15 +30,15 @@ def main(): tree.link_to_evol_model (WRKDIR + 'paml/M7/M7.out', 'M7') tree.link_to_evol_model (WRKDIR + 'paml/M8/M8.out', 'M8') tree.link_to_alignment (WRKDIR + 'alignments.fasta_ali') - print 'pv of LRT M2 vs M1: ', - print tree.get_most_likely ('M2','M1') - print 'pv of LRT M8 vs M7: ', - print tree.get_most_likely ('M8','M7') + print('pv of LRT M2 vs M1: \n') + print(tree.get_most_likely ('M2','M1')) + print('pv of LRT M8 vs M7: \n') + print(tree.get_most_likely ('M8','M7')) tree.show (histfaces=['M2']) - print 'The End.' + print('The End.') def random_swap(tree): @@ -49,9 +49,9 @@ def random_swap(tree): def check_annotation (tree): for node in tree.iter_descendants(): if not hasattr (node, 'paml_id'): - print 'Error, unable to label with paml ids' + print('Error, unable to label with paml ids') break - print 'Labelling ok!' + print('Labelling ok!') if __name__ == "__main__": --- a/examples/general/add_features.py +++ b/examples/general/add_features.py @@ -2,7 +2,7 @@ import random from ete3 import Tree # Creates a normal tree t = Tree( '((H:0.3,I:0.1):0.5, A:1, (B:0.4,(C:0.5,(J:1.3, (F:1.2, D:0.1):0.5):0.5):0.5):0.5);' ) -print t +print(t) # Let's locate some nodes using the get common ancestor method ancestor=t.get_common_ancestor("J", "F", "C") # the search_nodes method (I take only the first match ) @@ -26,8 +26,8 @@ for leaf in t.traverse(): else: leaf.add_features(vowel=False, confidence=random.random()) # Now we use these information to analyze the tree. -print "This tree has", len(t.search_nodes(vowel=True)), "vowel nodes" -print "Which are", [leaf.name for leaf in t.iter_leaves() if leaf.vowel==True] +print("This tree has", len(t.search_nodes(vowel=True)), "vowel nodes") +print("Which are", [leaf.name for leaf in t.iter_leaves() if leaf.vowel==True]) # But features may refer to any kind of data, not only simple # values. For example, we can calculate some values and store them # within nodes. @@ -38,10 +38,10 @@ matches = [leaf for leaf in ancestor.tra # And save this pre-computed information into the ancestor node ancestor.add_feature("long_branch_nodes", matches) # Prints the precomputed nodes -print "These are nodes under ancestor with long branches", \ - [n.name for n in ancestor.long_branch_nodes] +print("These are nodes under ancestor with long branches", \ + [n.name for n in ancestor.long_branch_nodes]) # We can also use the add_feature() method to dynamically add new features. -label = raw_input("custom label:") -value = raw_input("custom label value:") +label = input("custom label:") +value = input("custom label value:") ancestor.add_feature(label, value) -print "Ancestor has now the [", label, "] attribute with value [", value, "]" +print("Ancestor has now the [", label, "] attribute with value [", value, "]") --- a/examples/general/byoperand_search.py +++ b/examples/general/byoperand_search.py @@ -8,14 +8,14 @@ path = [] while node.up: path.append(node) node = node.up -print t +print(t) # I substract D node from the total number of visited nodes -print "There are", len(path)-1, "nodes between D and the root" +print("There are", len(path)-1, "nodes between D and the root") # Using parentheses you can use by-operand search syntax as a node # instance itself Dsparent= (t&"C").up Bsparent= (t&"B").up Jsparent= (t&"J").up # I check if nodes belong to certain partitions -print "It is", Dsparent in Bsparent, "that C's parent is under B's ancestor" -print "It is", Dsparent in Jsparent, "that C's parent is under J's ancestor" +print("It is", Dsparent in Bsparent, "that C's parent is under B's ancestor") +print("It is", Dsparent in Jsparent, "that C's parent is under J's ancestor") --- a/examples/general/copy_and_paste_trees.py +++ b/examples/general/copy_and_paste_trees.py @@ -3,13 +3,13 @@ from ete3 import Tree t1 = Tree('(A,(B,C));') t2 = Tree('((D,E), (F,G));') t3 = Tree('(H, ((I,J), (K,L)));') -print "Tree1:", t1 +print("Tree1:", t1) # /-A # ---------| # | /-B # \--------| # \-C -print "Tree2:", t2 +print("Tree2:", t2) # /-D # /--------| # | \-E @@ -17,7 +17,7 @@ print "Tree2:", t2 # | /-F # \--------| # \-G -print "Tree3:", t3 +print("Tree3:", t3) # /-H # | # ---------| /-I @@ -32,7 +32,7 @@ A = t1.search_nodes(name='A')[0] # and adds the two other trees as children. A.add_child(t2) A.add_child(t3) -print "Resulting concatenated tree:", t1 +print("Resulting concatenated tree:", t1) # /-D # /--------| # | \-E --- a/examples/general/create_trees_from_scratch.py +++ b/examples/general/create_trees_from_scratch.py @@ -15,7 +15,7 @@ R = A.add_child(name="R") # Adds a third # randomly. R.populate(6, names_library=["r1","r2","r3","r4","r5","r6"]) # Prints the tree topology -print t +print(t) # /-C # | # |--D --- a/examples/general/custom_search.py +++ b/examples/general/custom_search.py @@ -10,9 +10,9 @@ def conditional_function(node): # method in the filter function. This will iterate over all nodes to # assess if they meet our custom conditions and will return a list of # matches. -matches = filter(conditional_function, t.traverse()) -print len(matches), "nodes have distance >0.3" +matches = list(filter(conditional_function, t.traverse())) +print(len(matches), "nodes have distance >0.3") # depending on the complexity of your conditions you can do the same # in just one line with the help of lambda functions: -matches = filter(lambda n: n.dist>0.3 and n.is_leaf(), t.traverse() ) -print len(matches), "nodes have distance >0.3 and are leaves" +matches = [n for n in t.traverse() if n.dist>0.3 and n.is_leaf()] +print(len(matches), "nodes have distance >0.3 and are leaves") --- a/examples/general/custom_tree_traversing.py +++ b/examples/general/custom_tree_traversing.py @@ -3,7 +3,7 @@ t = Tree( '(A:1,(B:1,(C:1,D:1):0.5):0.5) # Browse the tree from a specific leaf to the root node = t.search_nodes(name="C")[0] while node: - print node + print(node) node = node.up # --C # /-C --- a/examples/general/get_common_ancestor.py +++ b/examples/general/get_common_ancestor.py @@ -1,8 +1,8 @@ from ete3 import Tree #Loads a tree tree = Tree( '((H:1,I:1):0.5, A:1, (B:1,(C:1,D:1):0.5):0.5);' ) -print "this is the original tree:" -print tree +print("this is the original tree:") +print(tree) # /-H # /--------| # | \-I @@ -16,15 +16,15 @@ print tree # \-D # Finds the first common ancestor between B and C. ancestor = tree.get_common_ancestor("D", "C") -print "The ancestor of C and D is:" -print ancestor +print("The ancestor of C and D is:") +print(ancestor) # /-C #---------| # \-D # You can use more than two nodes in the search ancestor = tree.get_common_ancestor("B", "C", "D") -print "The ancestor of B, C and D is:" -print ancestor +print("The ancestor of B, C and D is:") +print(ancestor) # /-B #---------| # | /-C @@ -33,9 +33,9 @@ print ancestor # Finds the first sister branch of the ancestor node. Because # multifurcations are allowed, many sister branches are possible. sisters = ancestor.get_sisters() -print "which has has", len(sisters), "sister nodes" -print "and the first of such sister nodes like this:" -print sisters[0] +print("which has has", len(sisters), "sister nodes") +print("and the first of such sister nodes like this:") +print(sisters[0]) # # /-H #---------| --- a/examples/general/get_distances_between_nodes.py +++ b/examples/general/get_distances_between_nodes.py @@ -7,7 +7,7 @@ nw = """(((A:0.1, B:0.01):0.001, C:0.000 (((((D:0.00001,I:0):0,F:0):0,G:0):0,H:0):0, E:0.000001):0.0000001):2.0;""" t = Tree(nw) -print t +print(t) # /-A # /--------| # /--------| \-B @@ -30,24 +30,24 @@ print t A = t&"A" C = t&"C" # Calculate distance from current node -print "The distance between A and C is", A.get_distance("C") +print("The distance between A and C is", A.get_distance("C")) # Calculate distance between two descendants of current node -print "The distance between A and C is", t.get_distance("A","C") +print("The distance between A and C is", t.get_distance("A","C")) # Calculate the toplogical distance (number of nodes in between) -print "The number of nodes between A and D is ", \ - t.get_distance("A","D", topology_only=True) +print("The number of nodes between A and D is ", \ + t.get_distance("A","D", topology_only=True)) # Calculate the farthest node from E within the whole structure farthest, dist = (t&"E").get_farthest_node() -print "The farthest node from E is", farthest.name, "with dist=", dist +print("The farthest node from E is", farthest.name, "with dist=", dist) # Calculate the farthest node from E within the whole structure, # regarding the number of nodes in between as distance value # Note that the result is differnt. farthest, dist = (t&"E").get_farthest_node(topology_only=True) -print "The farthest (topologically) node from E is", \ - farthest.name, "with", dist, "nodes in between" +print("The farthest (topologically) node from E is", \ + farthest.name, "with", dist, "nodes in between") # Calculate farthest node from an internal node farthest, dist = t.get_farthest_node() -print "The farthest node from root is", farthest.name, "with dist=", dist +print("The farthest node from root is", farthest.name, "with dist=", dist) # # The program results in the following information: # --- a/examples/general/get_midpoint_outgroup.py +++ b/examples/general/get_midpoint_outgroup.py @@ -2,7 +2,7 @@ from ete3 import Tree # generates a random tree t = Tree(); t.populate(15); -print t +print(t) # # # /-qogjl @@ -38,7 +38,7 @@ print t R = t.get_midpoint_outgroup() # and set it as tree outgroup t.set_outgroup(R) -print t +print(t) # /-opben # | # /--------| /-xoryn --- a/examples/general/getting_leaves.py +++ b/examples/general/getting_leaves.py @@ -1,7 +1,7 @@ from ete3 import Tree # Loads a basic tree t = Tree( '(A:0.2,(B:0.4,(C:1.1,D:0.45):0.5):0.1);' ) -print t +print(t) # /-A #---------| # | /-B @@ -13,16 +13,16 @@ print t nleaves = 0 for leaf in t.get_leaves(): nleaves += 1 -print "This tree has", nleaves, "terminal nodes" +print("This tree has", nleaves, "terminal nodes") # But, like this is much simpler :) nleaves = len(t) -print "This tree has", nleaves, "terminal nodes [proper way: len(tree) ]" +print("This tree has", nleaves, "terminal nodes [proper way: len(tree) ]") # Counts leaves within the tree ninternal = 0 for node in t.get_descendants(): if not node.is_leaf(): ninternal +=1 -print "This tree has", ninternal, "internal nodes" +print("This tree has", ninternal, "internal nodes") # Counts nodes with whose distance is higher than 0.3 nnodes = 0 for node in t.get_descendants(): @@ -30,4 +30,4 @@ for node in t.get_descendants(): nnodes +=1 # or, translated into a better pythonic nnodes = len([n for n in t.get_descendants() if n.dist>0.3]) -print "This tree has", nnodes, "nodes with a branch length > 0.3" +print("This tree has", nnodes, "nodes with a branch length > 0.3") --- a/examples/general/iterators.py +++ b/examples/general/iterators.py @@ -7,16 +7,16 @@ tree.populate(10000) t1 = time.time() for leaf in tree.iter_leaves(): if "aw" in leaf.name: - print "found a match:", leaf.name, + print("found a match:", leaf.name, '\n') break -print "Iterating: ellapsed time:", time.time()-t1 +print("Iterating: ellapsed time:", time.time()-t1) # This slower t1 = time.time() for leaf in tree.get_leaves(): if "aw" in leaf.name: - print "found a match:", leaf.name, + print("found a match:", leaf.name, '\n') break -print "Getting: ellapsed time:", time.time()-t1 +print("Getting: ellapsed time:", time.time()-t1) # Results in something like: # found a match: guoaw Iterating: ellapsed time: 0.00436091423035 secs # found a match: guoaw Getting: ellapsed time: 0.124316930771 secs --- a/examples/general/label_nodes.py +++ b/examples/general/label_nodes.py @@ -1,16 +1,16 @@ from ete3 import Tree tree = Tree( '(A:1,(B:1,(C:1,D:1):0.5):0.5);' ) # Prints the name of every leaf under the tree root -print "Leaf names:" +print("Leaf names:") for leaf in tree.get_leaves(): - print leaf.name + print(leaf.name) # Label nodes as terminal or internal. If internal, saves also the # number of leaves that it contains. -print "Labeled tree:" +print("Labeled tree:") for node in tree.get_descendants(): if node.is_leaf(): node.add_features(ntype="terminal") else: node.add_features(ntype="internal", size=len(node)) # Gets the extended newick of the tree including new node features -print tree.write(features=[]) +print(tree.write(features=[])) --- a/examples/general/nhx_format.py +++ b/examples/general/nhx_format.py @@ -2,7 +2,7 @@ import random from ete3 import Tree # Creates a normal tree t = Tree('((H:0.3,I:0.1):0.5, A:1,(B:0.4,(C:0.5,(J:1.3,(F:1.2, D:0.1):0.5):0.5):0.5):0.5);') -print t +print(t) # Let's locate some nodes using the get common ancestor method ancestor=t.get_common_ancestor("J", "F", "C") # Let's label leaf nodes @@ -16,21 +16,21 @@ for leaf in t.traverse(): matches = [leaf for leaf in ancestor.traverse() if leaf.dist>1.0] # And save this pre-computed information into the ancestor node ancestor.add_feature("long_branch_nodes", matches) -print -print "NHX notation including vowel and confidence attributes" -print -print t.write(features=["vowel", "confidence"]) -print -print "NHX notation including all node's data" -print +print() +print("NHX notation including vowel and confidence attributes") +print() +print(t.write(features=["vowel", "confidence"])) +print() +print("NHX notation including all node's data") +print() # Note that when all features are requested, only those with values # equal to text-strings or numbers are considered. "long_branch_nodes" # is not included into the newick string. -print t.write(features=[]) -print -print "basic newick formats are still available" -print -print t.write(format=9, features=["vowel"]) +print(t.write(features=[])) +print() +print("basic newick formats are still available") +print() +print(t.write(format=9, features=["vowel"])) # You don't need to do anything speciall to read NHX notation. Just # specify the newick format and the NHX tags will be automatically # detected. @@ -46,4 +46,4 @@ t = Tree(nw) # And access node's attributes. for n in t.traverse(): if hasattr(n,"S"): - print n.name, n.S + print(n.name, n.S) --- a/examples/general/prune_tree.py +++ b/examples/general/prune_tree.py @@ -1,8 +1,8 @@ from ete3 import Tree # Let's create simple tree t = Tree('((((H,K),(F,I)G),E),((L,(N,Q)O),(P,S)));', format=1) -print "Original tree looks like this:" -print t +print("Original tree looks like this:") +print(t) # # /-H # /--------| @@ -25,8 +25,8 @@ print t # \-S # Prune the tree in order to keep only some leaf nodes. t.prune(["H","F","E","Q", "P"]) -print "Pruned tree" -print t +print("Pruned tree") +print(t) # # /-F # /--------| --- a/examples/general/remove_and_delete_nodes.py +++ b/examples/general/remove_and_delete_nodes.py @@ -1,11 +1,11 @@ from ete3 import Tree # Loads a tree. Note that we use format 1 to read internal node names t = Tree('((((H,K)D,(F,I)G)B,E)A,((L,(N,Q)O)J,(P,S)M)C);', format=1) -print "original tree looks like this:" +print("original tree looks like this:") # This is an alternative way of using "print t". Thus we have a bit # more of control on how tree is printed. Here i print the tree # showing internal node names -print t.get_ascii(show_internal=True) +print(t.get_ascii(show_internal=True)) # # /-H # /D-------| @@ -37,8 +37,8 @@ C = t.search_nodes(name="C")[0] removed_node = J.detach() # = C.remove_child(J) # if we know print the original tree, we will see how J partition is # no longer there. -print "Tree after REMOVING the node J" -print t.get_ascii(show_internal=True) +print("Tree after REMOVING the node J") +print(t.get_ascii(show_internal=True)) # /-H # /D-------| # | \-K @@ -56,8 +56,8 @@ print t.get_ascii(show_internal=True) # tree, and all its descendants will then hang from the next upper # node. G.delete() -print "Tree after DELETING the node G" -print t.get_ascii(show_internal=True) +print("Tree after DELETING the node G") +print(t.get_ascii(show_internal=True)) # /-H # /D-------| # | \-K --- a/examples/general/rooting_subtrees.py +++ b/examples/general/rooting_subtrees.py @@ -1,7 +1,7 @@ from ete3 import Tree t = Tree('(((A,C),((H,F),(L,M))),((B,(J,K)),(E,D)));') -print "Original tree:" -print t +print("Original tree:") +print(t) # /-A # /--------| # | \-C @@ -29,8 +29,8 @@ node1 = t.get_common_ancestor("A","H") node2 = t.get_common_ancestor("B","D") node1.set_outgroup("H") node2.set_outgroup("E") -print "Tree after rooting each node independently:" -print t +print("Tree after rooting each node independently:") +print(t) # # /-F # | --- a/examples/general/rooting_trees.py +++ b/examples/general/rooting_trees.py @@ -3,8 +3,8 @@ from ete3 import Tree # node. This usually means that no information is available about # which of nodes is more basal. t = Tree('(A,(H,F),(B,(E,D)));') -print "Unrooted tree" -print t +print("Unrooted tree") +print(t) # /-A # | # | /-H @@ -21,8 +21,8 @@ print t # course, the definition of an outgroup will depend on user criteria. ancestor = t.get_common_ancestor("E","D") t.set_outgroup(ancestor) -print "Tree rooted at E and D's ancestor is more basal that the others." -print t +print("Tree rooted at E and D's ancestor is more basal that the others.") +print(t) # # /-B # /--------| @@ -39,8 +39,8 @@ print t # Note that setting a different outgroup, a different interpretation # of the tree is possible t.set_outgroup( t&"A" ) -print "Tree rooted at a terminal node" -print t +print("Tree rooted at a terminal node") +print(t) # /-H # /--------| # | \-F --- a/examples/general/search_nodes.py +++ b/examples/general/search_nodes.py @@ -1,7 +1,7 @@ from ete3 import Tree #Loads a tree t = Tree( '((H:1,I:1):0.5, A:1, (B:1,(C:1,D:1):0.5):0.5);' ) -print t +print(t) # /-H # /--------| # | \-I @@ -17,4 +17,4 @@ print t D = t.search_nodes(name="D") # I get all nodes with distance=0.5 nodes = t.search_nodes(dist=0.5) -print len(nodes), "nodes have distance=0.5" +print(len(nodes), "nodes have distance=0.5") --- a/examples/general/tree_basis.py +++ b/examples/general/tree_basis.py @@ -6,16 +6,16 @@ A = t.add_child(name="A") B = t.add_child(name="B") C = A.add_child(name="C") D = A.add_child(name="D") -print t +print(t) # /-C # /--------| #---------| \-D # | # \-B -print 'is "t" the root?', t.is_root() # True -print 'is "A" a terminal node?', A.is_leaf() # False -print 'is "B" a terminal node?', B.is_leaf() # True -print 'B.get_tree_root() is "t"?', B.get_tree_root() is t # True -print 'Number of leaves in tree:', len(t) # returns number of leaves under node (3) -print 'is C in tree?', C in t # Returns true -print "All leaf names in tree:", [node.name for node in t] +print('is "t" the root?', t.is_root()) # True +print('is "A" a terminal node?', A.is_leaf()) # False +print('is "B" a terminal node?', B.is_leaf()) # True +print('B.get_tree_root() is "t"?', B.get_tree_root() is t) # True +print('Number of leaves in tree:', len(t)) # returns number of leaves under node (3) +print('is C in tree?', C in t) # Returns true +print("All leaf names in tree:", [node.name for node in t]) --- a/examples/general/tree_traverse.py +++ b/examples/general/tree_traverse.py @@ -2,7 +2,7 @@ from ete3 import Tree t = Tree( '(A:1,(B:1,(C:1,D:1):0.5):0.5);' ) # Visit nodes in preorder (this is the default strategy) for n in t.traverse(): - print n + print(n) # It Will visit the nodes in the following order: # /-A # ---------| @@ -25,7 +25,7 @@ for n in t.traverse(): # --D # Visit nodes in postorder for n in t.traverse("postorder"): - print n + print(n) # It Will visit the nodes in the following order: # --A # --B --- a/examples/general/write_newick.py +++ b/examples/general/write_newick.py @@ -3,6 +3,6 @@ from ete3 import Tree t = Tree('(A:1,(B:1,(E:1,D:1)Internal_1:0.5)Internal_2:0.5)Root;', format=1) # And prints its newick representation omiting all the information but # the tree topology -print t.write(format=100) # (,(,(,))); +print(t.write(format=100)) # (,(,(,))); # We can also write into a file t.write(format=100, outfile="/tmp/tree.new") --- a/examples/nexml/nexml_annotated_trees.py +++ b/examples/nexml/nexml_annotated_trees.py @@ -13,12 +13,12 @@ trees = tree_collection.tree # For each loaded tree, prints its structure and some of its # meta-properties for t in trees: - print t - print - print "Leaf node meta information:\n" - print + print(t) + print() + print("Leaf node meta information:\n") + print() for meta in t.children[0].nexml_node.meta: - print meta.property, ":", (meta.content) + print(meta.property, ":", (meta.content)) # Output --- a/examples/nexml/nexml_parser.py +++ b/examples/nexml/nexml_parser.py @@ -7,10 +7,10 @@ nexml_project.build_from_file("trees.xml # All XML elements are within the project instance. # exist in each element to access their attributes. -print "Loaded Taxa:" +print("Loaded Taxa:") for taxa in nexml_project.get_otus(): for otu in taxa.get_otu(): - print "OTU:", otu.id + print("OTU:", otu.id) # Extracts all the collection of trees in the project tree_collections = nexml_project.get_trees() @@ -21,10 +21,10 @@ collection_1 = tree_collections[0] for tree in collection_1.get_tree(): # trees contain all the nexml information in their "nexml_node", # "nexml_tree", and "nexml_edge" attributes. - print "Tree id", tree.nexml_tree.id - print tree + print("Tree id", tree.nexml_tree.id) + print(tree) for node in tree.traverse(): - print "node", node.nexml_node.id, "is associated with", node.nexml_node.otu, "OTU" + print("node", node.nexml_node.id, "is associated with", node.nexml_node.otu, "OTU") # Output: --- a/examples/phylogenies/dating_evolutionary_events.py +++ b/examples/phylogenies/dating_evolutionary_events.py @@ -8,8 +8,8 @@ nw = """ ,Mmu_001),((Hsa_004,Ptr_004),Mmu_004))),(Ptr_002,(Hsa_002,Mmu_002)))); """ t = PhyloTree(nw) -print "Original tree:", -print t +print("Original tree:\n") +print(t) # # /-Dme_001 # /--------| @@ -59,11 +59,11 @@ age2name = { } event1= t.get_common_ancestor("Hsa_001", "Hsa_004") event2=t.get_common_ancestor("Hsa_001", "Hsa_002") -print -print "The duplication event leading to the human sequences Hsa_001 and "+\ - "Hsa_004 is dated at: ", age2name[event1.get_age(species2age)] -print "The duplication event leading to the human sequences Hsa_001 and "+\ - "Hsa_002 is dated at: ", age2name[event2.get_age(species2age)] +print() +print("The duplication event leading to the human sequences Hsa_001 and "+\ + "Hsa_004 is dated at: ", age2name[event1.get_age(species2age)]) +print("The duplication event leading to the human sequences Hsa_001 and "+\ + "Hsa_002 is dated at: ", age2name[event2.get_age(species2age)]) # The duplication event leading to the human sequences Hsa_001 and Hsa_004 # is dated at: primates # --- a/examples/phylogenies/link_sequences_to_phylogenies.py +++ b/examples/phylogenies/link_sequences_to_phylogenies.py @@ -25,9 +25,9 @@ iphylip_txt = """ t = PhyloTree("(((seqA,seqB),seqC),seqD);", alignment=fasta_txt, alg_format="fasta") #We can now access the sequence of every leaf node -print "These are the nodes and its sequences:" +print("These are the nodes and its sequences:") for leaf in t.iter_leaves(): - print leaf.name, leaf.sequence + print(leaf.name, leaf.sequence) #seqD MAEAPDETIQQFMALTNVSHNIAVQYLSEFGDLNEAL--------------REEAH #seqC MAEIPDATIQ---ALTNVSHNIAVQYLSEFGDLNEALNSYYASQTDDQPDRREEAH #seqA MAEIPDETIQQFMALT---HNIAVQYLSEFGDLNEALNSYYASQTDDIKDRREEAH @@ -36,9 +36,9 @@ for leaf in t.iter_leaves(): # The associated alignment can be changed at any time t.link_to_alignment(alignment=iphylip_txt, alg_format="iphylip") # Let's check that sequences have changed -print "These are the nodes and its re-linked sequences:" +print("These are the nodes and its re-linked sequences:") for leaf in t.iter_leaves(): - print leaf.name, leaf.sequence + print(leaf.name, leaf.sequence) #seqD MAEAPDETIQQFMALTNVSHNIAVQYLSEFGDLNEAL--------------REEAHQ----------FMALTNVSH #seqC MAEIPDATIQ---ALTNVSHNIAVQYLSEFGDLNEALNSYYASQTDDQPDRREEAHQFMALTNVSH---------- #seqA MAEIPDETIQQFMALT---HNIAVQYLSEFGDLNEALNSYYASQTDDIKDRREEAHQFMALTNVSHQFMALTNVSH @@ -46,7 +46,7 @@ for leaf in t.iter_leaves(): # # The sequence attribute is considered as node feature, so you can # even include sequences in your extended newick format! -print t.write(features=["sequence"], format=9) +print(t.write(features=["sequence"], format=9)) # # # (((seqA[&&NHX:sequence=MAEIPDETIQQFMALT---HNIAVQYLSEFGDLNEALNSYYASQTDDIKDRREEAHQF @@ -57,8 +57,8 @@ print t.write(features=["sequence"], for # # And yes, you can save this newick text and reload it into a PhyloTree instance. sametree = PhyloTree(t.write(features=["sequence"])) -print "Recovered tree with sequence features:" -print sametree +print("Recovered tree with sequence features:") +print(sametree) # # /-seqA # /--------| @@ -68,5 +68,5 @@ print sametree # | # \-seqD # -print "seqA sequence:", (t&"seqA").sequence +print("seqA sequence:", (t&"seqA").sequence) # MAEIPDETIQQFMALT---HNIAVQYLSEFGDLNEALNSYYASQTDDIKDRREEAHQFMALTNVSHQFMALTNVSH --- a/examples/phylogenies/orthology_and_paralogy_prediction.py +++ b/examples/phylogenies/orthology_and_paralogy_prediction.py @@ -5,7 +5,7 @@ nw = """ (Ptr_002,(Hsa_002,Mmu_002)))); """ t = PhyloTree(nw) -print t +print(t) # /-Dme_001 # /--------| # | \-Dme_002 @@ -33,22 +33,22 @@ human_seq = matches[0] # Obtains its evolutionary events events = human_seq.get_my_evol_events() # Print its orthology and paralogy relationships -print "Events detected that involve Hsa_001:" +print("Events detected that involve Hsa_001:") for ev in events: if ev.etype == "S": - print ' ORTHOLOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs) + print(' ORTHOLOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs)) elif ev.etype == "D": - print ' PARALOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs) + print(' PARALOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs)) # Alternatively, you can scan the whole tree topology events = t.get_descendant_evol_events() # Print its orthology and paralogy relationships -print "Events detected from the root of the tree" +print("Events detected from the root of the tree") for ev in events: if ev.etype == "S": - print ' ORTHOLOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs) + print(' ORTHOLOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs)) elif ev.etype == "D": - print ' PARALOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs) + print(' PARALOGY RELATIONSHIP:', ','.join(ev.in_seqs), "<====>", ','.join(ev.out_seqs)) # If we are only interested in the orthology and paralogy relationship # among a given set of species, we can filter the list of sequences @@ -56,16 +56,16 @@ for ev in events: # fseqs is a function that, given a list of sequences, returns only # those from human and mouse fseqs = lambda slist: [s for s in slist if s.startswith("Hsa") or s.startswith("Mms")] -print "Paralogy relationships among human and mouse" +print("Paralogy relationships among human and mouse") for ev in events: if ev.etype == "D": # Prints paralogy relationships considering only human and # mouse. Some duplication event may not involve such species, # so they will be empty - print ' PARALOGY RELATIONSHIP:', \ + print(' PARALOGY RELATIONSHIP:', \ ','.join(fseqs(ev.in_seqs)), \ "<====>",\ - ','.join(fseqs(ev.out_seqs)) + ','.join(fseqs(ev.out_seqs))) # Note that besides the list of events returned, the detection # algorithm has labeled the tree nodes according with the --- a/examples/phylogenies/species_aware_phylogenies.py +++ b/examples/phylogenies/species_aware_phylogenies.py @@ -14,9 +14,9 @@ t = PhyloTree("(((Hsa_001,Ptr_001),(Cfa_ # \-Dme_002 # # Prints current leaf names and species codes -print "Deafult mode:" +print("Deafult mode:") for n in t.get_leaves(): - print "node:", n.name, "Species name:", n.species + print("node:", n.name, "Species name:", n.species) # node: Dme_001 Species name: Dme # node: Dme_002 Species name: Dme # node: Hsa_001 Species name: Hsa @@ -42,9 +42,9 @@ def get_species_name(node_name_string): return code2name[spcode] # Now, let's ask the tree to use our custom species naming function t.set_species_naming_function(get_species_name) -print "Custom mode:" +print("Custom mode:") for n in t.get_leaves(): - print "node:", n.name, "Species name:", n.species + print("node:", n.name, "Species name:", n.species) # node: Dme_001 Species name: Drosophila melanogaster # node: Dme_002 Species name: Drosophila melanogaster # node: Hsa_001 Species name: Homo sapiens @@ -63,9 +63,9 @@ mynewick = """ (Dme_001[&&NHX:species=Fly],Dme_002[&&NHX:species=Fly])); """ t = PhyloTree(mynewick, sp_naming_function=None) -print "Disabled mode (manual set):" +print("Disabled mode (manual set):") for n in t.get_leaves(): - print "node:", n.name, "Species name:", n.species + print("node:", n.name, "Species name:", n.species) # node: Dme_001 Species name: Fly # node: Dme_002 Species name: Fly # node: Hsa_001 Species name: Human @@ -76,8 +76,8 @@ for n in t.get_leaves(): # Of course, once this info is available you can query any internal # node for species covered. human_mouse_ancestor = t.get_common_ancestor("Hsa_001", "Mms_001") -print "These are the species under the common ancestor of Human & Mouse" -print '\n'.join( human_mouse_ancestor.get_species() ) +print("These are the species under the common ancestor of Human & Mouse") +print('\n'.join( human_mouse_ancestor.get_species() )) # Mouse # Chimp # Dog --- a/examples/phylogenies/tree_reconciliation.py +++ b/examples/phylogenies/tree_reconciliation.py @@ -7,7 +7,7 @@ gene_tree_nw = '((Dme_001,Dme_002),(((Cf species_tree_nw = "((((Hsa, Ptr), Mmu), (Mms, Cfa)), Dme);" genetree = PhyloTree(gene_tree_nw) sptree = PhyloTree(species_tree_nw) -print genetree +print(genetree) # /-Dme_001 # /--------| # | \-Dme_002 @@ -32,14 +32,14 @@ print genetree recon_tree, events = genetree.reconcile(sptree) # a new "reconcilied tree" is returned. As well as the list of # inferred events. -print "Orthology and Paralogy relationships:" +print("Orthology and Paralogy relationships:") for ev in events: if ev.etype == "S": - print 'ORTHOLOGY RELATIONSHIP:', ','.join(ev.inparalogs), "<====>", ','.join(ev.orthologs) + print('ORTHOLOGY RELATIONSHIP:', ','.join(ev.inparalogs), "<====>", ','.join(ev.orthologs)) elif ev.etype == "D": - print 'PARALOGY RELATIONSHIP:', ','.join(ev.inparalogs), "<====>", ','.join(ev.outparalogs) + print('PARALOGY RELATIONSHIP:', ','.join(ev.inparalogs), "<====>", ','.join(ev.outparalogs)) # And we can explore the resulting reconciled tree -print recon_tree +print(recon_tree) # You will notice how the reconcilied tree is the same as the gene # tree with some added branches. They are inferred gene losses. # --- a/examples/phyloxml/phyloxml_from_scratch.py +++ b/examples/phyloxml/phyloxml_from_scratch.py @@ -9,7 +9,7 @@ phylo.phyloxml_phylogeny.set_name("test_ # Add the tree to the phyloxml project project.add_phylogeny(phylo) -print project.get_phylogeny()[0] +print(project.get_phylogeny()[0]) # /-iajom # /---| --- a/examples/phyloxml/phyloxml_parser.py +++ b/examples/phyloxml/phyloxml_parser.py @@ -4,13 +4,13 @@ project.build_from_file("apaf.xml") # Each tree contains the same methods as a PhyloTree object for tree in project.get_phylogeny(): - print tree + print(tree) # you can even use rendering options tree.show() # PhyloXML features are stored in the phyloxml_clade attribute for node in tree: - print "Node name:", node.name + print("Node name:", node.name) for seq in node.phyloxml_clade.get_sequence(): for domain in seq.domain_architecture.get_domain(): domain_data = [domain.valueOf_, domain.get_from(), domain.get_to()] - print " Domain:", '\t'.join(map(str, domain_data)) + print(" Domain:", '\t'.join(map(str, domain_data))) --- a/examples/treeview/barchart_and_piechart_faces.py +++ b/examples/treeview/barchart_and_piechart_faces.py @@ -2,7 +2,7 @@ import sys import random from ete3 import Tree, faces, TreeStyle, COLOR_SCHEMES -schema_names = COLOR_SCHEMES.keys() +schema_names = list(COLOR_SCHEMES.keys()) def layout(node): if node.is_leaf(): --- a/examples/treeview/floating_piecharts.py +++ b/examples/treeview/floating_piecharts.py @@ -2,7 +2,7 @@ import sys import random from ete3 import Tree, faces, TreeStyle, COLOR_SCHEMES -schema_names = COLOR_SCHEMES.keys() +schema_names = list(COLOR_SCHEMES.keys()) def layout(node): if not node.is_leaf(): --- a/examples/treeview/item_faces.py +++ b/examples/treeview/item_faces.py @@ -48,8 +48,7 @@ def random_color(h=None): return _hls2hex(h, l, s) def _hls2hex(h, l, s): - return '#%02x%02x%02x' %tuple(map(lambda x: int(x*255), - colorsys.hls_to_rgb(h, l, s))) + return '#%02x%02x%02x' %tuple([int(x*255) for x in colorsys.hls_to_rgb(h, l, s)]) def ugly_name_face(node, *args, **kargs): """ This is my item generator. It must receive a node object, and --- a/examples/treeview/new_seq_face.py +++ b/examples/treeview/new_seq_face.py @@ -297,9 +297,9 @@ if __name__ == "__main__": # Show very large algs tree = PhyloTree('(Orangutan,Human,Chimp);') - tree.link_to_alignment(">Human\n" + ''.join([_aabgcolors.keys()[int(random() * len (_aabgcolors))] for _ in xrange (5000)]) + \ - "\n>Chimp\n" + ''.join([_aabgcolors.keys()[int(random() * len (_aabgcolors))] for _ in xrange (5000)]) + \ - "\n>Orangutan\n" + ''.join([_aabgcolors.keys()[int(random() * len (_aabgcolors))] for _ in xrange (5000)])) + tree.link_to_alignment(">Human\n" + ''.join([list(_aabgcolors.keys())[int(random() * len (_aabgcolors))] for _ in range (5000)]) + \ + "\n>Chimp\n" + ''.join([list(_aabgcolors.keys())[int(random() * len (_aabgcolors))] for _ in range (5000)]) + \ + "\n>Orangutan\n" + ''.join([list(_aabgcolors.keys())[int(random() * len (_aabgcolors))] for _ in range (5000)])) tree.dist = 0 ts = TreeStyle() ts.title.add_face(TextFace("better not set interactivity if alg is very large", fsize=15), column=0) --- a/examples/treeview/random_draw.py +++ b/examples/treeview/random_draw.py @@ -28,7 +28,7 @@ def leaf_name(node): def aligned_faces(node): if node.is_leaf(): - for i in xrange(3): + for i in range(3): F = faces.TextFace("ABCDEFGHIJK"[0:random.randint(1,11)]) F.border.width = 1 F.border.line_style = 1