def __call__(self, mismatches):
"""Consolidates ModuleInstances in KFinder.ModuleInstances into Modules.
- mismatches accounts for the difference between two ModuleInstances
in terms of purine vs pyrimidine or strong vs weak bonding.
"""
# New list to hold consolidated modules
consolidated_list = []
# Bind module_dict and module_order locally
module_dict = self.KFinder.ModuleDict
module_order = self.KFinder.ModuleOrder
# Check that dict is not empty
if module_dict:
# Iterate through modules in order
for pattern in module_order:
# Create a Bitvector representation of pattern
pat_array = fromstring(pattern, "c")
added = False
# Iterate through consolidated_list
for curr_module, curr_array in consolidated_list:
# If pat_vec and curr_vec are in the allowed mismatch cutoff
if sum(pat_array != curr_array) <= mismatches:
# Add module information to curr_module
curr_module.update(module_dict[pattern])
added = True
break
if not added:
# Add new module to consolidated list
consolidated_list.append([module_dict[pattern], pat_array])
# self.Modules should have only the list of modules
self.Modules = [i[0] for i in consolidated_list]
def get_two_stock_data():
"""
load stock time and price data for two stocks The return values
(d1,p1,d2,p2) are the trade time (in days) and prices for stocks 1
and 2 (intc and aapl)
"""
ticker1, ticker2 = 'INTC', 'AAPL'
M1 = fromstring( file('data/%s.dat' % ticker1, 'rb').read(), 'd')
M1 = M1.resize( (M1.shape[0]/2,2) )
M2 = fromstring( file('data/%s.dat' % ticker2, 'rb').read(), 'd')
M2 = M2.resize( (M2.shape[0]/2,2) )
d1, p1 = M1[:,0], M1[:,1]
d2, p2 = M2[:,0], M2[:,1]
return (d1,p1,d2,p2)
def read_nicolet(tmin, tmax):
"""Load Nicolet BMSI data."""
if tmin < 0: tmin = 0
fh = file(filename, 'rb')
indmin = Fs * tmin
numsamples = os.path.getsize(filename) / (channels * 2)
indmax = min(numsamples, Fs * tmax)
byte0 = int(indmin * channels * 2)
numbytes = int((indmax - indmin) * channels * 2)
fh.seek(byte0)
data = fromstring(fh.read(numbytes), Int16).astype(Float)
data.shape = -1, channels
t = (1 / Fs) * arange(indmin, indmax)
return t, data
def read_nicolet(tmin, tmax):
"""Load Nicolet BMSI data."""
if tmin<0: tmin=0
fh = file(filename, 'rb')
indmin = Fs*tmin
numsamples = os.path.getsize(filename)/(channels*2)
indmax = min(numsamples, Fs*tmax)
byte0 = int(indmin*channels*2)
numbytes = int( (indmax-indmin)*channels*2 )
fh.seek(byte0)
data = fromstring(fh.read(numbytes), Int16).astype(Float)
data.shape = -1, channels
t = (1/Fs)*arange(indmin, indmax)
return t, data
