def testExtra(self):
tf = TrackFormat.createInstanceFromPrefixList(['start', 'a', 'b', 'c'], 'float64', 1, 'float64', 1)
self.assertTrue(tf.hasExtra(specificExtra='a'))
self.assertFalse(tf.hasExtra(specificExtra='d'))
self.assertEqual(['a','b','c'], tf.getExtraNames())
tfq = TrackFormatReq(interval=False, extra=['a','b'])
self.assertFalse(tfq.isCompatibleWith(tf))
def testCompatibilityWithExceptions(self):
tf = TrackFormat.createInstanceFromPrefixList(['start', 'val'], 'float64', 1, 'float64', 1)
self.assertFalse(TrackFormatReq(interval=True, strand=True, val='number')\
.isCompatibleWith(tf))
self.assertFalse(TrackFormatReq(interval=True, strand=True, val='number')\
.isCompatibleWith(tf, ['interval']))
self.assertTrue(TrackFormatReq(interval=True, strand=True, val='number')\
.isCompatibleWith(tf, ['interval', 'hasStrand']))
self.assertFalse(TrackFormatReq(interval=True, strand=True, val='tc')\
.isCompatibleWith(tf, ['interval', 'hasStrand']))
def testWeightTypes(self):
tf = TrackFormat.createInstanceFromPrefixList(['id', 'edges', 'weights'], 'float64', 1, 'S8', 3)
self.assertTrue(tf.isWeighted(specificWeightType='category_vector'))
self.assertFalse(tf.isWeighted(specificWeightType='number'))
self.assertEqual('Vector of categories', tf.getWeightTypeName())
self.assertEqual('Linked base pairs', tf.getFormatName())
tfq = TrackFormatReq(linked=True, weights='number')
self.assertFalse(tfq.isCompatibleWith(tf))
def testValTypes(self):
tf = TrackFormat.createInstanceFromPrefixList(['start', 'val'], 'float128', 2, 'float64', 1)
self.assertTrue(tf.isValued(specificValType='mean_sd'))
self.assertFalse(tf.isValued(specificValType='number'))
self.assertEqual('Mean and std.dev.', tf.getValTypeName())
self.assertEqual('Valued points', tf.getFormatName())
tfq = TrackFormatReq(interval=False, val='tc')
self.assertFalse(tfq.isCompatibleWith(tf))
def __iter__(self):
self = copy(self)
#does not support function, partitions and points:
if (False in [attrs in self._geSource.getPrefixList() for attrs in ['start', 'end']]):
raise NotSupportedError('Binning file must be segments. Current file format: ' + \
TrackFormat.createInstanceFromPrefixList(self._geSource.getPrefixList(), \
self._geSource.getValDataType(), \
self._geSource.getValDim(), \
self._geSource.getEdgeWeightDataType(), \
self._geSource.getEdgeWeightDim()).getFormatName() )
self._geIter = self._geSource.__iter__()
return self
def getTrackFormat(self):
return TrackFormat.createInstanceFromPrefixList(self._prefixList, \
self._valDataType, \
self._valDim, \
self._weightDataType, \
self._weightDim)
def _getRawTrackView(self, region, borderHandling, allowOverlaps):
assert len(region) == 1
from collections import OrderedDict
from gtrackcore_memmap.track.memmap.CommonMemmapFunctions import findEmptyVal
from gtrackcore_memmap.track.core.TrackView import TrackView
import numpy as np
geSource = ExternalTrackManager.getGESourceFromGalaxyOrVirtualTN(self.trackName, region.genome)
prefixList = geSource.getPrefixList()
valDataType = geSource.getValDataType()
valDim = geSource.getValDim()
weightDataType = geSource.getEdgeWeightDataType()
weightDim = geSource.getEdgeWeightDim()
startList, endList, valList, strandList, idList, edgesList, weightsList = [None] * 7
extraLists = OrderedDict()
tf = TrackFormat.createInstanceFromPrefixList(prefixList, valDataType, valDim, weightDataType, weightDim)
if allowOverlaps and (tf.isDense() or geSource.hasNoOverlappingElements()):
raise IncompatibleTracksError(
prettyPrintTrackName(self.trackName)
+ " with format: "
+ str(tf)
+ " does not satisfy "
+ str(self._trackFormatReq)
)
denseAndInterval = tf.isDense() and tf.isInterval()
numEls = 2 if denseAndInterval else 1
if valDataType == "S":
valDataType = "S2"
if weightDataType == "S":
weightDataType = "S2"
for prefix in prefixList:
if prefix == "start":
startList = np.array([-1], dtype="int32")
elif prefix == "end":
if denseAndInterval:
endList = np.array([0, 1], dtype="int32")
else:
endList = np.array([0], dtype="int32")
elif prefix == "val":
valList = np.array([findEmptyVal(valDataType)] * valDim * numEls, dtype=valDataType).reshape(
(numEls, valDim) if valDim > 1 else numEls
)
elif prefix == "strand":
strandList = np.array([1] * numEls, dtype="int8")
elif prefix == "id":
idList = np.array([""] * numEls, dtype="S1")
elif prefix == "edges":
edgesList = np.array([[""]] * numEls, dtype="S1")
elif prefix == "weights":
weightsList = np.array(
[[[findEmptyVal(weightDataType)]]] * weightDim * numEls, dtype=weightDataType
).reshape((numEls, 1, weightDim) if weightDim > 1 else (numEls, 1))
else:
extraLists[prefix] = np.array([""] * numEls, dtype="S1")
return TrackView(
region,
startList,
endList,
valList,
strandList,
idList,
edgesList,
weightsList,
borderHandling,
allowOverlaps,
extraLists,
)
