def setUp(self):
ambiguity_chars = {
"G": "G",
"A": "A",
"T": "T",
"C": "C",
"R": "AG",
"*": "AGTC"
}
self.motif_coder = Schema.Schema(ambiguity_chars)
self.match_string = "GATAG"
self.match_info = [("GA", ["GA"]), ("GATAG", ["GATAG"]),
("GA*AG", ["GATAG"]), ("GATRG", ["GATAG"]),
("*A", ["GA", "TA"])]
def setUp(self):
self.factory = Schema.SchemaFactory()
self.test_file = os.path.join(os.getcwd(), "NeuralNetwork",
"enolase.fasta")
ambiguity_chars = {
"G": "G",
"A": "A",
"T": "T",
"C": "C",
"R": "AG",
"*": "AGTC"
}
self.schema = Schema.Schema(ambiguity_chars)
def setUp(self):
ambiguity_chars = {"G" : "G",
"A" : "A",
"T" : "T",
"C" : "C",
"R" : "AG",
"*" : "AGTC"}
motif_representation = Schema.Schema(ambiguity_chars)
motifs = ("GA", "GATAG", "GA*AG", "GATRG", "*A")
self.motif_coder = Schema.SchemaCoder(motifs,
motif_representation)
self.match_strings = [("GATAG", [.5, .5, .5, .5, 1.0]),
("GAGAGATA", [float(3) / float(4), 0,
float(1) / float(4), 0,
1])]
