import time
import pickle
import zipfile
ubersttime = time.time()
# initializations
infile = 'NatfishFinalAllobs_20110513.CSV' # filename for the main input
inparfile = 'comps1.par'
# read parfile
compsrange = np.loadtxt(inparfile,dtype=int)
# set the range of all indices to knock out
indsrange = [92861]
# read and parse the input file
DL, MASTERKEY, SpC, Event = cu.read_and_parse_input(infile)
# convert to arrays the lists which need to be referenced as arrays
SpC = np.array(SpC,dtype='int')
Event = np.array(Event,dtype='int')
MASTERKEY = np.array(MASTERKEY,dtype='int')
DL = np.array(DL, dtype='int')
total_length = len(MASTERKEY)
all_inds = np.arange(0,total_length)
currresults = []
#------------------------------------------------------------------------------------
# Now, run through the indices to knock out and save down the comparable MASTERKEYS
#------------------------------------------------------------------------------------
for cind in indsrange:
currinds = np.nonzero(all_inds!=cind)[0]
# initializations infile = 'NatfishFinalAllobs_20111110_MNF.CSV' # filename for the main input #infile = 'NatfishFinalAllobs_20110617_MNF.CSV' cind = 2653 # initializations sttime = time.time() max_iter = 15 # maximum allowable iterations c_iter = 0 # iteration counter starting at zero converged = False # flag to indicate when convergence has been achieved compcount = [np.inf] # a "comparable_count" variable compcount that keeps track of how many samples are comparable # read and parse the input file DL, ID, SpC, Event = cu.read_and_parse_input(infile) # convert to arrays the lists which need to be referenced as arrays SpC = np.array(SpC,dtype='int') Event = np.array(Event,dtype='int') ID = np.array(ID,dtype='int') DL = np.array(DL, dtype='int') total_length = len(ID) all_inds = np.copy(ID) lenallinds = len(all_inds) currinds = np.nonzero(all_inds!=cind)[0] SpC = SpC[currinds] Event = Event[currinds] ID = ID[currinds] cc = ID[ID==currID]
