def parse(datafilename, patterns):
# This loads the CTX file
#dir = os.path.dirname(os.path.realpath(__file__))
#datafilename = os.path.join(dir, '2014_05_13_tuesday_11h57m00s.ctx')
ctxfile=ctx(datafilename)
#print ctxfile
# This pulls out all the stuff to do with count rates
all_counts = list(ctxfile.stream('count_rates'))
# This filters for the count rates we care about
select_counts = lambda data: [parse_coincidence_pattern(pattern, data) for pattern in patterns]
interesting_counts_table = np.array(map(select_counts, all_counts))
print interesting_counts_table
# Now it's a numpy array, so we can integrate, average sum etc
#print interesting_counts_table
fit_data = np.sum(interesting_counts_table, axis=1)
#print np.sum(interesting_counts_table, axis=1)
print fit_data
raw_input()
return fit_data
def parse(datafilename, patterns):
# This loads the CTX file
#dir = os.path.dirname(os.path.realpath(__file__))
#datafilename = os.path.join(dir, '2014_05_13_tuesday_11h57m00s.ctx')
ctxfile = ctx(datafilename)
#print ctxfile
# This pulls out all the stuff to do with count rates
all_counts = list(ctxfile.stream('count_rates'))
# This filters for the count rates we care about
select_counts = lambda data: [
parse_coincidence_pattern(pattern, data) for pattern in patterns
]
interesting_counts_table = np.array(map(select_counts, all_counts))
print interesting_counts_table
# Now it's a numpy array, so we can integrate, average sum etc
#print interesting_counts_table
fit_data = np.sum(interesting_counts_table, axis=1)
#print np.sum(interesting_counts_table, axis=1)
print fit_data
raw_input()
return fit_data
filename=all_files[-1]
# Load up a file for reading
my_file=ctx(filename)
# What do we know about it already?
print my_file
###############################
# Simple reading of data
###############################
# Read out some countrates
for data in my_file.stream('count_rates'):
print 'Raw data:', data
twofolds = pattern_parser.parse_coincidence_pattern('**', data)
print 'Total twofolds (**):', twofolds
# Read out some positions
for data in my_file.stream('position'):
print 'Position:', data
###############################
# A different approach --- good for dips
###############################
# Define a lambda function "get_mn", which computes the "MN" count rate some raw data
# This could equally be a named ("deffed") function.
get_mn=lambda data: pattern_parser.parse_coincidence_pattern('MN', data)
