def collect_data(self, filter=None):
''' collect data for some or all of the samples based on type '''
# create directory
if not os.path.isdir(self.dir):
os.mkdir(self.dir)
# create recipe, run it
# TODO: filename based on type
filenames = [sn + '_PerpHys.dat' for sn in self.snames]
for i,fn in enumerate(filenames):
dpath = os.path.join(self.dir, fn)
seq.hys(5000, -5000, fn=dpath, rate=100, pts=1000)
# TODO: run
# TODO: detect when done, motor friction scan
c = raw_input('(n)ext / (q)uit?')
# link data to sname
try:
self.data[i] = pyppms.ppms(dpath)
except:
# could not find data
pass
if c == 'q':
break
print('Done')
def fromfile(self, filter):
''' import ppms data from files. Could make it a classmethod'''
# could be intelligent about which files are used, so they don't have
# to be specified one by one
file = pyppms.latest(filter)
# name sample relative to others in its directory
path, fn = os.path.split(file)
files = fnmatch.filter(os.listdir(path), '*.dat')
sname = fn.replace(_long_substr(files),'')
self.snames.append(sname)
data = pyppms.ppms(file)
self.data.append(data)
def fromdir(self, dirfilter='', ffilter=''):
dir = pyppms.latestdir(dirfilter)
print('importing data from ' + dir)
files = fnmatch.filter(os.listdir(dir), '*'+ffilter+'*.dat')
# find something different about the file names and call that the
# sample name
# There's an issue here if you want case insensitivity
snames = [s.replace(_long_substr(files),'') for s in files]
#os.mkdir(dirpath)
self.snames.extend(snames)
data = [pyppms.ppms(fn) for fn in files]
self.data.extend(data)
