def save_text_file(text, filename=None):
"""save_text_file(text, filename=None)
Save a string to a text file
Parameters:
filename (str): Text will be saved here
text (str): Text to be saved
Returns:
Tuple with directory filename is stored in and filename's location
"""
if filename is None or filename is False:
filetypes = '*.txt'
prompt = 'Save File As Txt'
filename = save_file_gui(prompt, filetypes=filetypes)
if filename is None:
return None
g.m.statusBar().showMessage('Saving Points in {}'.format(
os.path.basename(filename)))
file = open(filename, 'w')
file.write(text)
file.close()
g.m.statusBar().showMessage('Successfully saved {}'.format(
os.path.basename(filename)))
directory = os.path.dirname(filename)
return directory, filename
def savetracksjson(self):
tracks = self.points.tracks
if isinstance(tracks[0][0], np.int64):
tracks = [[np.asscalar(a) for a in b] for b in tracks]
txy_pts = self.points.txy_pts.tolist()
filename = save_file_gui("Save tracks as json", filetypes='*.json')
pts = {'tracks': tracks, 'txy_pts': txy_pts}
json.dump(pts,
codecs.open(filename, 'w', encoding='utf-8'),
separators=(',', ':'),
sort_keys=True,
indent=4) ### this saves the array in .json format
def savetracks(self):
tracks = self.points.tracks
if isinstance(tracks[0][0], np.int64):
tracks = [[np.asscalar(a) for a in b] for b in tracks]
txy_pts = self.points.txy_pts.tolist()
txy_intensities = self.points.intensities
filename = save_file_gui("Save all tracks as json (also saves linked tracks as csv)", filetypes='*.json')
base = os.path.splitext(filename)[0]
filename_csv = base + '.csv'
pts = {'tracks': tracks, 'txy_pts': txy_pts, 'txy_intensities': txy_intensities}
json.dump(pts, codecs.open(filename, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4) ### this saves the array in .json format
self.savetrackscsv(filename_csv)
def saveInsight(self):
tracks = self.points.tracks
kwargs = {'pts': self.pts_refined, 'tracks': tracks}
save_file_gui(write_insight_bin, '.bin', 'Save File', kwargs)
def savetracksCSV(self):
self.exportIntensity = True
#Intensity value export option
if self.blurred_window_selector.window is None:
g.alert(
'If you wish to export intensity values (mean for 3x3 pixel around x,y position) first set a Blurred Window. Otherwise just x,y coordinates exported'
)
self.exportIntensity = False
if self.exportIntensity:
self.dataArray = self.blurred_window_selector.window.imageArray()
#get intensities
self.getIntensities()
tracks = self.points.tracks
if isinstance(tracks[0][0], np.int64):
tracks = [[np.asscalar(a) for a in b] for b in tracks]
txy_pts = self.points.txy_pts.tolist()
if self.exportIntensity:
txy_intensities = self.points.intensities
filename = save_file_gui("Save tracks as CSV", filetypes='*.csv')
#filter tracks list to only include linked tracks
linkedTracks = [item for item in tracks if len(item) > 1]
#get xy coordinates and intensities for linked tracks
trackNumber = []
txy_intensitiesByTrack = []
# for i, indices in enumerate(linkedTracks):
# trackNumber.append( i )
# txy_ptsByTrack.append(list(txy_pts[j] for j in indices))
# #txy_intensitiesByTrack.append(list(txy_intensities[k] for k in indices))
frameList = []
xList = []
yList = []
for i, indices in enumerate(linkedTracks):
ptsList = list(txy_pts[j] for j in indices)
if self.exportIntensity:
intensitiesList = list(txy_intensities[k] for k in indices)
for pts in ptsList:
trackNumber.append(i)
frameList.append(pts[0])
xList.append(pts[1])
yList.append(pts[2])
if self.exportIntensity:
for intensity in intensitiesList:
txy_intensitiesByTrack.append(intensity)
#make dataframe of tracks, xy coordianates and intensities for linked tracks
if self.exportIntensity:
dict = {
'track_number': trackNumber,
'frame': frameList,
'x': xList,
'y': yList,
'intensities': txy_intensitiesByTrack
}
else:
dict = {
'track_number': trackNumber,
'frame': frameList,
'x': xList,
'y': yList
}
self.linkedtrack_DF = pd.DataFrame(dict)
#save df as csv
self.linkedtrack_DF.to_csv(filename, index=False)
print('CSV file {} saved'.format(filename))
def saveLogData():
f = save_file_gui("Save Logged Ploynomial Fit Data",
filetypes="*.txt")
if f:
self.saveLoggedData(f)