def _loadHTTP(self, filename):
"""Load PYMEs semi-custom HDF5 image data format. Offloads all the
hard work to the HDFDataSource class"""
import tables
from PYME.IO.DataSources import HTTPDataSource, BGSDataSource
#from PYME.LMVis import inpFilt
#open hdf5 file
self.dataSource = HTTPDataSource.DataSource(filename)
#chain on a background subtraction data source, so we can easily do
#background subtraction in the GUI the same way as in the analysis
self.data = BGSDataSource.DataSource(
self.dataSource) #this will get replaced with a wrapped version
#try: #should be true the whole time
self.mdh = MetaData.TIRFDefault
self.mdh.copyEntriesFrom(self.dataSource.getMetadata())
#except:
# self.mdh = MetaData.TIRFDefault
# wx.MessageBox("Carrying on with defaults - no gaurantees it'll work well", 'ERROR: No metadata found in file ...', wx.OK)
# print("ERROR: No metadata fond in file ... Carrying on with defaults - no gaurantees it'll work well")
#attempt to estimate any missing parameters from the data itself
MetaData.fillInBlanks(self.mdh, self.dataSource)
#calculate the name to use when we do batch analysis on this
#from PYME.ParallelTasks.relativeFiles import getRelFilename
self.seriesName = filename
self.events = self.dataSource.getEvents()
self.mode = 'LM'
def _loadClusterPZF(self, filename):
"""Load PYMEs semi-custom HDF5 image data format. Offloads all the
hard work to the HDFDataSource class"""
from PYME.IO.DataSources import ClusterPZFDataSource, BGSDataSource
self.dataSource = ClusterPZFDataSource.DataSource(filename)
#chain on a background subtraction data source, so we can easily do
#background subtraction in the GUI the same way as in the analysis
self.data = BGSDataSource.DataSource(
self.dataSource) #this will get replaced with a wrapped version
#try: #should be true the whole time
self.mdh = MetaData.TIRFDefault
self.mdh.copyEntriesFrom(self.dataSource.getMetadata())
#attempt to estimate any missing parameters from the data itself
MetaData.fillInBlanks(self.mdh, self.dataSource)
#calculate the name to use when we do batch analysis on this
#from PYME.ParallelTasks.relativeFiles import getRelFilename
self.seriesName = filename
self.events = self.dataSource.getEvents()
self.mode = 'LM'
def _loadh5(self, filename):
"""Load PYMEs semi-custom HDF5 image data format. Offloads all the
hard work to the HDFDataSource class"""
import tables
from PYME.IO.DataSources import HDFDataSource, BGSDataSource
from PYME.IO import tabular
self.dataSource = HDFDataSource.DataSource(filename, None)
#chain on a background subtraction data source, so we can easily do
#background subtraction in the GUI the same way as in the analysis
self.data = BGSDataSource.DataSource(
self.dataSource) #this will get replaced with a wrapped version
if 'MetaData' in self.dataSource.h5File.root: #should be true the whole time
self.mdh = MetaData.TIRFDefault
self.mdh.copyEntriesFrom(
MetaDataHandler.HDFMDHandler(self.dataSource.h5File))
else:
self.mdh = MetaData.TIRFDefault
import wx
wx.MessageBox(
"Carrying on with defaults - no gaurantees it'll work well",
'ERROR: No metadata found in file ...', wx.OK)
print(
"ERROR: No metadata fond in file ... Carrying on with defaults - no gaurantees it'll work well"
)
#attempt to estimate any missing parameters from the data itself
try:
MetaData.fillInBlanks(self.mdh, self.dataSource)
except:
logger.exception('Error attempting to populate missing metadata')
#calculate the name to use when we do batch analysis on this
#from PYME.IO.FileUtils.nameUtils import getRelFilename
self.seriesName = getRelFilename(filename)
#try and find a previously performed analysis
fns = filename.split(os.path.sep)
cand = os.path.sep.join(fns[:-2] + [
'analysis',
] + fns[-2:]) + 'r'
print(cand)
if False: #os.path.exists(cand):
h5Results = tables.open_file(cand)
if 'FitResults' in dir(h5Results.root):
self.fitResults = h5Results.root.FitResults[:]
self.resultsSource = tabular.H5RSource(h5Results)
self.resultsMdh = MetaData.TIRFDefault
self.resultsMdh.copyEntriesFrom(
MetaDataHandler.HDFMDHandler(h5Results))
self.events = self.dataSource.getEvents()
self.mode = 'LM'
def _loadTiff(self, filename):
#from PYME.IO.FileUtils import readTiff
from PYME.IO.DataSources import TiffDataSource, BGSDataSource
mdfn = self._findAndParseMetadata(filename)
self.dataSource = TiffDataSource.DataSource(filename, None)
print(self.dataSource.shape)
self.dataSource = BufferedDataSource.DataSource(
self.dataSource, min(self.dataSource.getNumSlices(), 50))
self.data = self.dataSource #this will get replaced with a wrapped version
if self.dataSource.getNumSlices(
) > 500: #this is likely to be a localization data set
#background subtraction in the GUI the same way as in the analysis
self.data = BGSDataSource.DataSource(
self.dataSource
) #this will get replaced with a wrapped version
print(self.data.shape)
#if we have a multi channel data set, try and pull in all the channels
if 'ChannelFiles' in self.mdh.getEntryNames() and not len(
self.mdh['ChannelFiles']) == self.data.shape[3]:
try:
from PYME.IO.dataWrap import ListWrap
#pull in all channels
chans = []
for cf in self.mdh.getEntry('ChannelFiles'):
cfn = os.path.join(os.path.split(filename)[0], cf)
ds = TiffDataSource.DataSource(cfn, None)
ds = BufferedDataSource.DataSource(
ds, min(ds.getNumSlices(), 50))
chans.append(ds)
self.data = ListWrap(
chans) #this will get replaced with a wrapped version
self.filename = mdfn
except:
pass
elif 'ChannelNames' in self.mdh.getEntryNames() and len(
self.mdh['ChannelNames']) == self.data.getNumSlices():
from PYME.IO.dataWrap import ListWrap
chans = [
numpy.atleast_3d(self.data.getSlice(i))
for i in range(len(self.mdh['ChannelNames']))
]
self.data = ListWrap(chans)
elif filename.endswith(
'.lsm') and 'LSM.images_number_channels' in self.mdh.keys(
) and self.mdh['LSM.images_number_channels'] > 1:
from PYME.IO.dataWrap import ListWrap
nChans = self.mdh['LSM.images_number_channels']
chans = []
for n in range(nChans):
ds = TiffDataSource.DataSource(filename, None, n)
ds = BufferedDataSource.DataSource(ds,
min(ds.getNumSlices(), 50))
chans.append(ds)
self.data = ListWrap(chans)
#from PYME.ParallelTasks.relativeFiles import getRelFilename
self.seriesName = getRelFilename(filename)
self.mode = 'default'
if self.mdh.getOrDefault('ImageType', '') == 'PSF':
self.mode = 'psf'
elif self.dataSource.getNumSlices() > 5000:
#likely to want to localize this
self.mode = 'LM'