rename program to make it more generic

svn path=/; revision=556

diff --git a/Personnel/preecej/python_singletons/incomparanoid.py b/Personnel/preecej/python_singletons/incomparanoid.py
new file mode 100755 (executable)
index 0000000..9a8a243
--- /dev/null
+++ b/Personnel/preecej/python_singletons/incomparanoid.py
@@ -0,0 +1,231 @@
+#!/bin/python
+"""
+InComparaNoid: Script with different analysis methods used to compare Ensembl and Inparanoid species projections.
+"""
+
+#----------------------------------------------------------------------------------------------------------------------
+# modules
+#----------------------------------------------------------------------------------------------------------------------
+import os
+import sys
+import argparse
+
+#----------------------------------------------------------------------------------------------------------------------
+# globals
+#----------------------------------------------------------------------------------------------------------------------
+ens_map = {}
+inp_map = {}
+
+#----------------------------------------------------------------------------------------------------------------------
+# classes
+#----------------------------------------------------------------------------------------------------------------------
+class switch(object):
+    def __init__(self, value):
+        self.value = value
+        self.fall = False
+
+    def __iter__(self):
+        """Return the match method once, then stop"""
+        yield self.match
+        raise StopIteration
+    
+    def match(self, *args):
+        """Indicate whether or not to enter a case suite"""
+        if self.fall or not args:
+            return True
+        elif self.value in args:
+            self.fall = True
+            return True
+        else:
+            return False
+
+#----------------------------------------------------------------------------------------------------------------------
+# functions
+#----------------------------------------------------------------------------------------------------------------------
+
+#----------------------------------------------------------------------------------------------------------------------
+def create_ens_map(ortho_path, rap_map_path, recip_id) :
+#----------------------------------------------------------------------------------------------------------------------
+    """
+    open the ensemble plants and rap::irgsp mapping files and generate a 2-col mapping of PRJ to LOC loci where 
+    reciprocal identity is >= param% and confidence is high
+    """
+    """
+    count_loc = 0
+    count_prj = 0
+    dict_ens_ids = {}
+    ENS = open(path + input_file)
+    ENS.readline();
+    for line in ENS :
+        cols = line.rstrip().split()
+        if len(cols) == 5 :
+            if int(cols[2]) >= recip_id and int(cols[3]) >= recip_id and int(cols[4]) == 1 :  # reciprocal identity is >= recip_id%, high confidence
+                if cols[0] in dict_ens_ids :
+                    dict_ens_ids[cols[0]].append(cols[1])
+                else :
+                    dict_ens_ids[cols[0]] = [cols[1]]
+    ENS.close()
+
+    #for k, v in dict_ens_ids.iteritems() :
+    #    print k, v 
+
+    #OS_2_PRJ_MAP = open(path + output_file,'w')
+     
+    RAP_IRGSP = open(path + "RAP-MSU.txt")
+    RAP_IRGSP.readline();
+    for line in RAP_IRGSP:
+        if line.strip() != "" :
+            cols = line.rstrip().split()
+            rap_id = cols[0].upper()
+            if rap_id in dict_ens_ids :
+                print cols[1].split(",")[0] + "\t" + ",".join(dict_ens_ids[rap_id])
+                #OS_2_PRJ_MAP.write(cols[1].upper() + "\t" + ",".join(set(dict_ens_ids[rap_id])) + "\n")
+                #count_prj += len(set(dict_ens_ids[rap_id]))
+                #count_loc += 1
+    RAP_IRGSP.close()
+    #OS_2_PRJ_MAP.close()
+    
+    return [{"Unique Os loci: ": str(count_loc)}, {"Total projected loci:": str(count_prj)}]
+    """ 
+#----------------------------------------------------------------------------------------------------------------------
+def create_inp_map(ortho_path) :
+#----------------------------------------------------------------------------------------------------------------------
+    """
+    open the inparanoid file and generate a 2-col mapping of PRJ to LOC loci
+    """ 
+    """    
+    count_loc = 0
+    count_prj = 0
+    dict_inp_ids = {}
+    INP = open(path + input_file)
+    INP.readline();
+    for line in INP :
+        cols = line.rstrip().split()
+        # ignore non-canonical orthologs
+        if int(cols[1].rsplit(".",1)[1]) > 1 :
+            continue
+        os_locus = cols[0].rstrip(".1")
+        prj_locus = cols[1].rsplit("_",1)[0].rsplit(".",1)[0] # remove any isoform suffixes
+        if os_locus in dict_inp_ids :
+            dict_inp_ids[os_locus].append(prj_locus)
+        else :
+            dict_inp_ids[os_locus] = [prj_locus]
+    INP.close()
+
+    OS_2_PRJ_MAP = open(path + output_file,'w')
+    for k, v in sorted(dict_inp_ids.iteritems()) :
+        #print k, v 
+        OS_2_PRJ_MAP.write(k + "\t" + ",".join(set(v)) + "\n")
+        count_prj += len(set(v))
+        count_loc += 1
+    OS_2_PRJ_MAP.close()
+
+    return [{"Unique Os loci:": str(count_loc)}, {"Total projected loci:": str(count_prj)}]
+
+#----------------------------------------------------------------------------------------------------------------------
+def compare_maps(ensembl_map, inparanoid_map, output_path) :
+#----------------------------------------------------------------------------------------------------------------------
+    """compare Ensembl and Inparanoid projection results"""
+    
+    inc_os = 0              # Common Os loci
+    exc_ens_os = 0          # Exclusive Ensembl Os loci
+    exc_inp_os = 0          # Exclusive Inparanoid Os loci
+    total_common_prj = 0     # Total Common projected loci
+    inc_common_prj = 0       # Common projected loci where present in both Ens and Inp
+    avg_prj_overlap = 0.0    # Avg. common projected loci
+    avg_inc_prj_overlap = 0.0    # Avg. common projected loci present in both Ens and Inp
+
+    ref_dict = {}
+    set_ens_os_loci = set()
+    set_inp_os_loci = set()
+
+    """
+    ref_dict structure: {os locus : [[ens projected locus, ...], [inp projected locus, ...], # common loci]}
+    """
+    # iterate over both map files and build an overlap map, counting inclusions and exclusions at the reference and projection level
+
+    ENS = open(path + input_file.split(',')[0])
+    for line in ENS :
+        cols = line.rstrip().split()
+        os_locus = cols[0]
+        prj_loci = cols[1].split(',')
+
+        # build ref_dict
+        if os_locus in ref_dict :
+            ref_dict[os_locus][0].extend(prj_loci) # add ens projected loci in the first list slot
+        else :
+            ref_dict[os_locus] = [prj_loci, []]
+        
+        # build list of os ens loci for set operations
+        set_ens_os_loci.add(os_locus)
+    ENS.close()
+
+    INP = open(path + input_file.split(',')[1])
+    for line in INP :
+        cols = line.rstrip().split()
+        os_locus = cols[0]
+        prj_loci = cols[1].split(',')
+
+        # continue build of ref_dict
+        if os_locus in ref_dict :
+            ref_dict[os_locus][1].extend(prj_loci) # put the inp projected loci in the second list slot
+        else :
+            ref_dict[os_locus] = [[], prj_loci] # create an empty slot for the ens projected loci, put the inp projected loci in the second list slot
+
+        # build list of os inp loci for set operations
+        set_inp_os_loci.add(os_locus)
+    INP.close()
+
+    #print set_ens_os_loci & set_inp_os_loci
+
+    # calculate common projected loci overlap sets
+    for k, v in ref_dict.iteritems() :
+        curr_comm_prj_loci = len(set(v[0]) & set(v[1]))
+        v.append(curr_comm_prj_loci)
+        total_common_prj += curr_comm_prj_loci
+        if curr_comm_prj_loci > 0 :
+            inc_common_prj += curr_comm_prj_loci
+
+    inc_os = len(set_ens_os_loci & set_inp_os_loci)
+    exc_ens_os = len(set_ens_os_loci - set_inp_os_loci)
+    exc_inp_os = len(set_inp_os_loci - set_ens_os_loci)
+
+    # calculate avg. common projected loci overlaps    
+    avg_prj_overlap = float(total_common_prj) / float(len(ref_dict))
+    avg_inc_prj_overlap = float(inc_common_prj) / float(inc_os)
+    
+    #for k, v in ref_dict.iteritems() :
+    #    print k + "\n    Ens: " + str(v[0]) + "\n    Inp: " + str(v[1]) + "\n    Common: " + str(v[2]) 
+    
+    return [{"Common Os loci:": str(inc_os)}, 
+        {"Exclusive Ensembl Os loci:": str(exc_ens_os)}, 
+        {"Exclusive Inparanoid Os loci:": str(exc_inp_os)}, 
+        {"Total common projected loci:": str(total_common_prj)},
+        {"Avg. inclusive common projected loci:": str(avg_inc_prj_overlap)},
+        {"Avg. common projected loci:": str(avg_prj_overlap)}]
+    """    
+#----------------------------------------------------------------------------------------------------------------------
+# main
+#----------------------------------------------------------------------------------------------------------------------
+
+# process args
+parser = argparse.ArgumentParser(description='Script with different analysis methods used to compare Ensembl and Inparanoid species projections.')
+
+parser.add_argument('-e', '--ensembl_input_path', help='ensembl compara input file')
+parser.add_argument('-i', '--inparanoid_input_path', help='inparanoid supercluster input file')
+parser.add_argument('-m', '--rap_map_path', help='MSU-RAP mapping file')
+parser.add_argument('-r', '--reciprocal_id', type=int, help='reciprocal identity percentage')
+# TODO: add an "inparanoid super-cluster vs. conventional input" flag
+
+parser.add_argument('-c', '--comparison_file_path', help='output file containing statistical comparisons')
+
+args = parser.parse_args()
+print args.accumulate(args.integers)
+
+# create projection maps
+ens_map = create_ens_map(args.ensembl_input_path, args.rap_map_path, args.reciprocal_id)
+inp_map = create_inp_map(args.inparanoid_input_path)
+
+# generate stats and output them
+compare_maps(ens_map, inp_map, comparison_file_path)
+

diff --git a/Personnel/preecej/python_singletons/map_os_2_at.py b/Personnel/preecej/python_singletons/map_os_2_at.py
deleted file mode 100755 (executable)
index be86c39..0000000
--- a/Personnel/preecej/python_singletons/map_os_2_at.py
+++ /dev/null
@@ -1,240 +0,0 @@
-#!/bin/python
-"""
-Script with different analysis methods used to compare Ensembl and Inparanoid species projections.
-"""
-
-#----------------------------------------------------------------------------------------------------------------------
-# modules
-#----------------------------------------------------------------------------------------------------------------------
-import os
-import sys
-import argparse
-
-#----------------------------------------------------------------------------------------------------------------------
-# classes
-#----------------------------------------------------------------------------------------------------------------------
-class switch(object):
-    def __init__(self, value):
-        self.value = value
-        self.fall = False
-
-    def __iter__(self):
-        """Return the match method once, then stop"""
-        yield self.match
-        raise StopIteration
-    
-    def match(self, *args):
-        """Indicate whether or not to enter a case suite"""
-        if self.fall or not args:
-            return True
-        elif self.value in args:
-            self.fall = True
-            return True
-        else:
-            return False
-
-#----------------------------------------------------------------------------------------------------------------------
-# functions
-#----------------------------------------------------------------------------------------------------------------------
-
-#----------------------------------------------------------------------------------------------------------------------
-def ens_map(input_file, output_file, recip_id) :
-#----------------------------------------------------------------------------------------------------------------------
-    """
-    open the ensemble plants and rap::irgsp mapping files and generate a 2-col mapping of PRJ to LOC loci where 
-    reciprocal identity is >= param% and confidence is high
-    """
-    
-    count_loc = 0
-    count_prj = 0
-    dict_ens_ids = {}
-    ENS = open(path + input_file)
-    ENS.readline();
-    for line in ENS :
-        cols = line.rstrip().split()
-        if len(cols) == 5 :
-            if int(cols[2]) >= recip_id and int(cols[3]) >= recip_id and int(cols[4]) == 1 :  # reciprocal identity is >= recip_id%, high confidence
-                if cols[0] in dict_ens_ids :
-                    dict_ens_ids[cols[0]].append(cols[1])
-                else :
-                    dict_ens_ids[cols[0]] = [cols[1]]
-    ENS.close()
-
-    #for k, v in dict_ens_ids.iteritems() :
-    #    print k, v 
-
-    #OS_2_PRJ_MAP = open(path + output_file,'w')
-     
-    RAP_IRGSP = open(path + "RAP-MSU.txt")
-    RAP_IRGSP.readline();
-    for line in RAP_IRGSP:
-        if line.strip() != "" :
-            cols = line.rstrip().split()
-            rap_id = cols[0].upper()
-            if rap_id in dict_ens_ids :
-                print cols[1].split(",")[0] + "\t" + ",".join(dict_ens_ids[rap_id])
-                #OS_2_PRJ_MAP.write(cols[1].upper() + "\t" + ",".join(set(dict_ens_ids[rap_id])) + "\n")
-                #count_prj += len(set(dict_ens_ids[rap_id]))
-                #count_loc += 1
-    RAP_IRGSP.close()
-    #OS_2_PRJ_MAP.close()
-    
-    return [{"Unique Os loci: ": str(count_loc)}, {"Total projected loci:": str(count_prj)}]
-        
-#----------------------------------------------------------------------------------------------------------------------
-def inp_map(input_file, output_file) :
-#----------------------------------------------------------------------------------------------------------------------
-    """
-    open the inparanoid file and generate a 2-col mapping of PRJ to LOC loci
-    """ 
-    
-    count_loc = 0
-    count_prj = 0
-    dict_inp_ids = {}
-    INP = open(path + input_file)
-    INP.readline();
-    for line in INP :
-        cols = line.rstrip().split()
-        # ignore non-canonical orthologs
-        if int(cols[1].rsplit(".",1)[1]) > 1 :
-            continue
-        os_locus = cols[0].rstrip(".1")
-        prj_locus = cols[1].rsplit("_",1)[0].rsplit(".",1)[0] # remove any isoform suffixes
-        if os_locus in dict_inp_ids :
-            dict_inp_ids[os_locus].append(prj_locus)
-        else :
-            dict_inp_ids[os_locus] = [prj_locus]
-    INP.close()
-
-    OS_2_PRJ_MAP = open(path + output_file,'w')
-    for k, v in sorted(dict_inp_ids.iteritems()) :
-        #print k, v 
-        OS_2_PRJ_MAP.write(k + "\t" + ",".join(set(v)) + "\n")
-        count_prj += len(set(v))
-        count_loc += 1
-    OS_2_PRJ_MAP.close()
-
-    return [{"Unique Os loci:": str(count_loc)}, {"Total projected loci:": str(count_prj)}]
-
-#----------------------------------------------------------------------------------------------------------------------
-def compare_maps(input_file, output_file) :
-#----------------------------------------------------------------------------------------------------------------------
-    """compare Ensembl and Inparanoid projection results"""
-    
-    inc_os = 0              # Common Os loci
-    exc_ens_os = 0          # Exclusive Ensembl Os loci
-    exc_inp_os = 0          # Exclusive Inparanoid Os loci
-    total_common_prj = 0     # Total Common projected loci
-    inc_common_prj = 0       # Common projected loci where present in both Ens and Inp
-    avg_prj_overlap = 0.0    # Avg. common projected loci
-    avg_inc_prj_overlap = 0.0    # Avg. common projected loci present in both Ens and Inp
-
-    ref_dict = {}
-    set_ens_os_loci = set()
-    set_inp_os_loci = set()
-
-    """
-    ref_dict structure: {os locus : [[ens projected locus, ...], [inp projected locus, ...], # common loci]}
-    """
-    # iterate over both map files and build an overlap map, counting inclusions and exclusions at the reference and projection level
-
-    ENS = open(path + input_file.split(',')[0])
-    for line in ENS :
-        cols = line.rstrip().split()
-        os_locus = cols[0]
-        prj_loci = cols[1].split(',')
-
-        # build ref_dict
-        if os_locus in ref_dict :
-            ref_dict[os_locus][0].extend(prj_loci) # add ens projected loci in the first list slot
-        else :
-            ref_dict[os_locus] = [prj_loci, []]
-        
-        # build list of os ens loci for set operations
-        set_ens_os_loci.add(os_locus)
-    ENS.close()
-
-    INP = open(path + input_file.split(',')[1])
-    for line in INP :
-        cols = line.rstrip().split()
-        os_locus = cols[0]
-        prj_loci = cols[1].split(',')
-
-        # continue build of ref_dict
-        if os_locus in ref_dict :
-            ref_dict[os_locus][1].extend(prj_loci) # put the inp projected loci in the second list slot
-        else :
-            ref_dict[os_locus] = [[], prj_loci] # create an empty slot for the ens projected loci, put the inp projected loci in the second list slot
-
-        # build list of os inp loci for set operations
-        set_inp_os_loci.add(os_locus)
-    INP.close()
-
-    #print set_ens_os_loci & set_inp_os_loci
-
-    # calculate common projected loci overlap sets
-    for k, v in ref_dict.iteritems() :
-        curr_comm_prj_loci = len(set(v[0]) & set(v[1]))
-        v.append(curr_comm_prj_loci)
-        total_common_prj += curr_comm_prj_loci
-        if curr_comm_prj_loci > 0 :
-            inc_common_prj += curr_comm_prj_loci
-
-    inc_os = len(set_ens_os_loci & set_inp_os_loci)
-    exc_ens_os = len(set_ens_os_loci - set_inp_os_loci)
-    exc_inp_os = len(set_inp_os_loci - set_ens_os_loci)
-
-    # calculate avg. common projected loci overlaps    
-    avg_prj_overlap = float(total_common_prj) / float(len(ref_dict))
-    avg_inc_prj_overlap = float(inc_common_prj) / float(inc_os)
-    
-    #for k, v in ref_dict.iteritems() :
-    #    print k + "\n    Ens: " + str(v[0]) + "\n    Inp: " + str(v[1]) + "\n    Common: " + str(v[2]) 
-    
-    return [{"Common Os loci:": str(inc_os)}, 
-        {"Exclusive Ensembl Os loci:": str(exc_ens_os)}, 
-        {"Exclusive Inparanoid Os loci:": str(exc_inp_os)}, 
-        {"Total common projected loci:": str(total_common_prj)},
-        {"Avg. inclusive common projected loci:": str(avg_inc_prj_overlap)},
-        {"Avg. common projected loci:": str(avg_prj_overlap)}]
-    
-#----------------------------------------------------------------------------------------------------------------------
-# main
-#----------------------------------------------------------------------------------------------------------------------
-
-# process args
-parser = argparse.ArgumentParser(description='Script with different analysis methods used to compare Ensembl and Inparanoid species projections.')
-
-parser.add_argument('-r', '--reciprocal_id', type=int, help='reciprocal identity percentage')
-parser.add_argument('-e', '--ensembl_input', help='ensmbl input file')
-parser.add_argument('-i', '--inparanoid_input', help='inparanoid input file')
-parser.add_argument('-o', '--output', help='destination output file')
-
-args = parser.parse_args()
-print args.accumulate(args.integers)
-
-input_file = 
-output_file = 
-map_source_type =  
-recip_id = sys.argv[4]
-
-
-list_counts = []
-
-for case in switch(map_source_type):
-    if case("ens"):
-        list_counts = ens_map(input_file, output_file, recip_id)
-        break
-    if case("inp"):
-        list_counts = inp_map(input_file, output_file)
-        break
-    if case("compare"):
-        list_counts = compare_maps(input_file, output_file) # input_file is actually two comma-del input files (ens 1st, inp 2nd)
-        break
-    if case(): # default, could also just omit condition or 'if True'
-        print "Must provide a valid map source type: 'ens' (Ensembl), 'inp' (Inparanoid), or 'compare'"
-
-for item in list_counts :
-    for k, v in item.iteritems():
-        print k, v
-