def import_ThunderSTORM(filename, pixels_2_nm=115.):
""" Load localisation data from ThunderSTORM. """
with open(filename, 'rb') as csvfile:
localisations = csv.reader(csvfile, delimiter=',', quotechar='"')
header = localisations.next()
to_use = ('frame', 'x [nm]', 'y [nm]', 'z [nm]')
cols = [i for i in xrange(len(header)) if header[i] in to_use]
logger.info('Found: {0:s}'.format(', '.join([header[c]
for c in cols])))
objects = []
#make a new object
for data in localisations:
obj = btypes.PyTrackObject()
obj.t = int(float(data[cols[0]]))
obj.x = float(data[cols[1]]) / pixels_2_nm
obj.y = float(data[cols[2]]) / pixels_2_nm
if 'z [nm]' in header:
obj.z = float(data[cols[3]])
else:
obj.z = 0.
obj.dummy = False
obj.label = 0
# obj.prob = np.zeros((5,),dtype='float')
objects.append(obj)
return objects
def new_PyTrackObject(self, ID, txyz, label=None, type=0):
""" Set up a new PyTrackObject quickly using data from a file """
if label is not None:
class_label = label[0]
else:
class_label = 0
new_object = btypes.PyTrackObject()
new_object.ID = ID
new_object.t = txyz[0]
new_object.x = txyz[1]
new_object.y = txyz[2]
new_object.z = txyz[3]
new_object.dummy = False
new_object.label = class_label # DONE(arl): from the classifier
new_object.probability = np.zeros((1, ))
new_object.type = int(type)
return new_object
def import_JSON_observations(filename, labeller=None):
""" import_JSON_observations
Open a JSON file containing PyTrackObject objects
Basic format is:
{
"Object_203622": {
"x": 554.29737483861709,
"y": 1199.362071438818,
"z": 0.0,
"t": 862,
"label": "interphase",
"states": 5,
"probability": [
0.996992826461792,
0.0021888131741434336,
0.0006106126820668578,
0.000165432647918351,
4.232166247675195e-05
],
"dummy": false
}
}
Args:
filename: the filename of the JSON file
labeller: an instance of the Labeller class
Notes:
None
"""
# set a labeller
if not labeller: labeller = Labeller()
trk_objects = []
with open(filename, 'r') as json_file:
objects = json.load(json_file)
itern = 0
object_IDs = objects.keys()
while objects:
ID = object_IDs.pop()
obj = objects.pop(ID)
trk_obj = btypes.PyTrackObject()
trk_obj.ID = ID_from_name(ID)
for param in obj.keys():
if param == 'probability':
# TODO(arl): clean this up. prob need to change JSON
# writer to prevent this outcome rather than fix here
if isinstance(obj['probability'], list):
trk_obj.probability = np.array(obj['probability'],
dtype='float')
else:
trk_obj.probability = np.array((), dtype='float')
# append the number of states
trk_obj.states = len(obj['probability'])
elif param == 'label':
trk_obj.label = labeller(obj['label'])
else:
setattr(trk_obj, param, obj[param])
trk_objects.append(trk_obj)
return trk_objects