def test_rangetree(self):
            c_rangetree.create_tree( self.mytuple1 )

            #we get our peptide out again with a large window
            res = c_rangetree.query_tree( self.q1 - 1, self.ssrcalc -1, 
                                         self.q1 + 1,  self.ssrcalc + 1, 1, 0) 
            self.assertEqual( len(res), 1)
            self.assertEqual( res[0][0], 101)

            #same result when lower boundary equals the value
            res = c_rangetree.query_tree( self.q1 , self.ssrcalc ,
                                         self.q1 + 1,  self.ssrcalc + 1, 1, 0) 
            self.assertEqual( len(res), 1)
            self.assertEqual( res[0][0], 101)

            #no result when upper boundary equals the value
            res = c_rangetree.query_tree( self.q1 - 1, self.ssrcalc -1, 
                                         self.q1,  self.ssrcalc, 1, 0) 
            self.assertEqual( len(res), 0)
Example #2
0
        'peptide_key': r[1],
        'parent_id': r[2],
        'q1_charge': r[3],
        'q1': r[4],
        'ssrcalc': r[5],
    } for r in alltuples if r[3] == 2  #charge is 2
    and r[6] == 0  #isotope is 0
    and r[4] >= min_q1 and r[4] < max_q1
]

# we use a rangetree to select the precursors
if not use_db:
    parentid_lookup = [[r[2], (r[4], r[0], r[1])] for r in alltuples]
    parentid_lookup = dict(parentid_lookup)
    print "building tree with %s Nodes" % len(alltuples)
    c_rangetree.create_tree(tuple(alltuples))

# in SWATH mode, select all precursors that are relevant for the background at
# once
if swath_mode:
    values = "q1, modified_sequence, peptide_key, q1_charge, ssrcalc"
    q1_low = min_q1
    q1_high = max_q1
    query2 = """
    select %(values)s
    from %(pep)s
    where q1 >= %(q1_low)s and q1 <= %(q1_high)s
    %(query_add)s
    """ % {
        'q1_high': q1_high,
        'q1_low': q1_low,
Example #3
0
                'ssrcalc' :    r[5],
            }
            for r in alltuples
    if r[3] == 2 #charge is 2
    and r[6] == 0 #isotope is 0
    and r[4] >= min_q1
    and r[4] < max_q1
]

# we use a rangetree to select the precursors
if not use_db:
    parentid_lookup = [ [ r[2], (r[4], r[0], r[1]) ] 
            for r in alltuples ]
    parentid_lookup  = dict(parentid_lookup)
    print "building tree with %s Nodes" % len(alltuples)
    c_rangetree.create_tree(tuple(alltuples))



# in SWATH mode, select all precursors that are relevant for the background at
# once
if swath_mode: 
    values="q1, modified_sequence, peptide_key, q1_charge, ssrcalc"
    q1_low = min_q1; q1_high = max_q1
    query2 = """
    select %(values)s
    from %(pep)s
    where q1 >= %(q1_low)s and q1 <= %(q1_high)s
    %(query_add)s
    """ % { 'q1_high' : q1_high, 'q1_low'  : q1_low,
           'query_add' : par.query2_add,