diff --git a/Treemmer.py b/Treemmer.py
index 73375c7b6685e1ec7cba2952e2db32cb2184fae3..049e15654d8f37ae978c256f0c5604845275d123 100644
--- a/Treemmer.py
+++ b/Treemmer.py
@@ -10,8 +10,6 @@ import argparse
-
-
############################################################ define arg type float 0 < X > 1 ###############################################################
def restricted_float(x):
@@ -221,7 +219,7 @@ def write_stop(t,output1,output2):
-###### SOFTWARE START
+###### SOFTWARE STARTS
@@ -230,14 +228,15 @@ def write_stop(t,output1,output2):
parser = argparse.ArgumentParser()
parser.add_argument('INFILE',type=str,help='path to the newick tree')
-parser.add_argument('-r','--resolution', metavar='INT', default=1,help='number of leaves top prune at each iteration (default: 1)',type =int, nargs='?')
+parser.add_argument('-X','--stop_at_X_leaves', metavar='0-n_leaves', default='0', help='stop pruning when the number of leaves = X', type =int, nargs='?')
+parser.add_argument('-RTL','--stop_at_RTL', metavar='0-1', default='0', help='stop pruning when the relative tree length falls below RTL', type =restricted_float,nargs='?')
+parser.add_argument('-r','--resolution', metavar='INT', default=1,help='number of leaves to prune at each iteration (default: 1)',type =int, nargs='?')
+parser.add_argument('-p','--solve_polytomies',help='resolve polytomies at random (default: FALSE)',action='store_true',default =False)
+parser.add_argument('-lp','--leaves_pair', metavar='0,1,2', default=2,help='After the pair of leaves with the smallest distance is dentified Treemmer prunes: 0: the longest leaf\n1: the shortest leaf\n2: random choice (default)',type =int, nargs='?')
+parser.add_argument('-np','--no_plot',help='do not load matplotlib and plot (default: FALSE)',action='store_true',default =False)
+parser.add_argument('-fp','--fine_plot',help='when --resolution > 1, plot RTL vs n leaves every time a leaf is pruned (default: FALSE => plot every X leaves (X = -r))',action='store_true',default =False)
parser.add_argument('-c','--cpu', metavar='INT', default=1,help='number of cpu to use (default: 1)',type =int, nargs='?')
parser.add_argument('-v' ,'--verbose', metavar='0,1,2', default='0', help='0: silent, 1: show progress, 2: print tree at each iteration, 3: only for testing (findN), 4: only for testing (prune_t) (default: 1)', type =int, nargs='?',choices=[0,1,2,3,4])
-parser.add_argument('-p','--solve_polytomies',help='resolve polytmies at random (default: FALSE)',action='store_true',default =False)
-parser.add_argument('-np','--no_plot',help='do not load matplotlib and plot (default: FALSE)',action='store_true',default =False)
-parser.add_argument('-X','--stop_at_X_leaves', metavar='0-n_leaves', default='0', help='stop pruning when the number of leaves fall below X (integer)', type =int, nargs='?')
-parser.add_argument('-RTL','--stop_at_RTL', metavar='0-1', default='0', help='stop pruning when the relative tree length falls below RTL (decimal number between 0 and 1)', type =restricted_float,nargs='?')
-parser.add_argument('-lp','--leaves_pair', metavar='0,1,2', default=2,help='After the pair of leaves with the smallest distance is dentified Treemmer prunes: 0: the longest leaf\n1: the shortest leaf\n2: random choice (default)',type =int, nargs='?')
arguments = parser.parse_args()
@@ -294,7 +293,7 @@ while (len(t) > 3):
DLIST = Parallel(n_jobs=arguments.cpu)(delayed(parallel_loop)(i) for i in range(0,arguments.cpu))
result = {}
- for d in DLIST: #when running in parallel DLIST is updated in a weird way, it is a dict of dicts, this for loop merge them all in one
+ for d in DLIST: #when running in parallel DLIST is updated in a weird way, it is a dict of dicts, this for loop merge them all in one
result.update(d)
DLIST=result
@@ -314,8 +313,15 @@ while (len(t) > 3):
TL=TL-dist
DLIST=prune_dist_matrix(DLIST,leaf_to_p)
rel_TL=TL/TOT_TL
+
+
+ if (arguments.fine_plot): # plot point in rtld after every leaf independently of -r
+ output.append (str(rel_TL) + ' ' + str(len(t)))
+ length=len(t)
+ x.append(length)
+ y.append(rel_TL)
- if arguments.stop_at_X_leaves:
+ if arguments.stop_at_X_leaves: # if stop criterium is met (X) ==> output
if arguments.stop_at_X_leaves >= len(t):
output1=arguments.INFILE+"_trimmed_tree_X_" + str(arguments.stop_at_X_leaves)
output2=arguments.INFILE+"_trimmed_list_X_" + str(arguments.stop_at_X_leaves)
@@ -323,8 +329,8 @@ while (len(t) > 3):
stop=1
break
- if arguments.stop_at_RTL:
- if arguments.stop_at_RTL >= rel_TL:
+ if arguments.stop_at_RTL: # if stop criterium is met (RTL) ==> output
+ if arguments.stop_at_RTL >= rel_T:
output1=arguments.INFILE+"_trimmed_tree_RTL_" + str(arguments.stop_at_RTL)
output2=arguments.INFILE+"_trimmed_list_RTL_" + str(arguments.stop_at_RTL)
write_stop(t,output1,output2)
@@ -344,10 +350,11 @@ while (len(t) > 3):
if (stop ==1):
break
- output.append (str(rel_TL) + ' ' + str(len(t)))
- length=len(t)
- x.append(length)
- y.append(rel_TL)
+ if not (arguments.fine_plot):
+ output.append (str(rel_TL) + ' ' + str(len(t)))
+ length=len(t)
+ x.append(length)
+ y.append(rel_TL)
if arguments.verbose==1:
@@ -372,69 +379,9 @@ if not arguments.no_plot:
plt.ylim(0,1.1)
plt.xlabel('Number of leaves')
plt.ylabel('Relative tree length')
- plt.savefig(arguments.INFILE+'_TLD.png')
+ plt.savefig(arguments.INFILE+'_res_'+ str(arguments.resolution)+'_TLD.png')
-
-
-
-########################################### LOOP THRU ALL LEAVES (replaced by joblib parallel line #############################################
-
-
-# for leaf in leaves:
-# counter1 = counter1 + 1
-# print "iter find N : "+ str(counter1)
-# N_list=find_N(t,leaf)
-# print N_list
-# if N_list:
-# DLIST.update(N_list)
-
-
-
-
-########################################### CALCULATE MATRIX DISTANCE into simple DICTIONARY very slow!! dendropy is orders of magnitude faster #############################################
-
-#def make_dist_matrix(leaves):
-# count=0
-# dlist = {}
-# for x in range (0,len(leaves)-1):
-# count=count+1
-#
-# for y in range (x+1,len(leaves)):
-#
-# DIS = leaves[x].get_distance(leaves[y])
-# dlist.update({leaves[x].name + "," +leaves[y].name : DIS})
-#
-# return dlist
-
-#print dlist
-
-
-########################################### CALCULATE MATRIX DISTANCE into nested DICTIONARY ##############################################
-
-
-#m = {}
-#for x in range (0,len(leaves)-2):
-# d = {}
-# for y in range (x+1,len(leaves)-1):
-#
-# DIS = leaves[x].get_distance(leaves[y])
-# #print str(leaves[x]) + str(leaves[y]) + "\n" + str(DIS)
-# d.update({leaves[y].name : DIS})
- #print "D"#
- #print d
-# m.update({leaves[x].name : d})
- #print "M"
- #print m
-
-#print m
-
-
-
-
-
-
-