def runNeuroMAnalysis(path):
fullState = files.loadState(usePathOrPick(path))
# NOTE: need to load one image, so set up volume size
_IMG_CACHE.handleNewUIState(fullState.uiStates[0])
print("Loaded %d trees" % len(fullState.trees))
result = neuroMAnalysisForNeurons(fullState)
print(result)
def runFiloTipCluster(path):
fullState = files.loadState(usePathOrPick(path))
# Process each tree in order:
for treeIdx, tree in enumerate(fullState.trees):
branchIDList = util.sortedBranchIDList([tree])
# Find the types of each branch:
filoTypes, _, _, _, _, _ = addedSubtractedTransitioned([tree])
# Keep only interstitial filo...
interstitialFiloIDs = []
for branchID, filoType in zip(branchIDList, filoTypes[0]):
if filoType == FiloType.INTERSTITIAL:
interstitialFiloIDs.append(branchID)
print("%d Interstitial Filos detected" % (len(interstitialFiloIDs)))
# and map to the points at their tip:
tipPoints = []
for branch in tree.branches:
if branch.id in interstitialFiloIDs:
if len(branch.points) > 0:
tipPoints.append(branch.points[-1])
fig = plt.figure()
fig.suptitle("Interstitial Filo Tip clustering for [%d]" %
(treeIdx + 1))
fig.subplots_adjust(left=0.02,
bottom=0.07,
right=0.98,
top=0.9,
wspace=0.05,
hspace=0.2)
# 3D plot showing where the filo tips are on the branches.
ax3D = fig.add_subplot(121, projection='3d')
ax3D.set_title("3D positions")
for branch in tree.branches:
if branch.parentPoint is not None:
points = [branch.parentPoint] + branch.points
ax3D.plot(*tree.worldCoordPoints(points),
c=(0.5, 0.5, 0.5, 0.1))
ax3D.scatter(*tree.worldCoordPoints(tipPoints))
# 2D plot comparing spatial distance to tree distance
sDs, tDs = [], []
ax2D = fig.add_subplot(122)
ax2D.set_title("Spatial distance vs Tree Distance")
ax2D.set_xlabel("Spatial Distance (uM)")
ax2D.set_ylabel("Tree Distance (uM)")
for i, p1 in enumerate(tipPoints):
for p2 in tipPoints[i + 1:]:
spatialDist, treeDist = tree.spatialAndTreeDist(p1, p2)
sDs.append(spatialDist)
tDs.append(treeDist)
if len(sDs) > 0 and len(tDs) > 0:
ax2D.scatter(sDs, tDs)
ax2D.plot([0, np.max(sDs)], [0, np.max(sDs)],
'--',
c=(0.5, 0.5, 0.5, 0.7))
plt.show()
def doCleanups(path):
origPath = usePathOrPick(path)
fullState = files.loadState(origPath)
cleanupEmptyBranches(fullState)
# TODO - add more cleanups here
cleanPath = os.path.join(os.path.dirname(origPath),
"clean." + os.path.basename(origPath))
files.saveState(fullState, cleanPath)
print("State saved to %s" % cleanPath)
def testImportNoChange(path='data/localFirst.dyn.gz'):
print("Testing no motility changes after nodes copied...")
fullState = files.loadState(path)
assert len(fullState.trees) == 1
treeA = fullState.trees[0]
# NOTE: old trees sometimes have empty branches:
emptyBranches = [b for b in treeA.branches if len(b.points) == 0]
for emptyBranch in emptyBranches:
treeA.removeBranch(emptyBranch)
treeB = Tree()
treeB.clearAndCopyFrom(treeA, fullState)
treeB._parentState = treeA._parentState
# Copy branch and point IDs:
for i in range(len(treeA.branches)):
treeB.branches[i].id = treeA.branches[i].id
for j in range(len(treeA.branches[i].points)):
treeB.branches[i].points[j].id = treeA.branches[i].points[j].id
trees = [treeA, treeB]
print("\nResults:\n---------")
# TDBL the same for identical trees
allTDBL = [
TDBL(tree,
excludeAxon=True,
excludeBasal=False,
includeFilo=False,
filoDist=5) for tree in trees
]
# print (allTDBL)
assert allTDBL[0] == allTDBL[1]
print("🙌 TDBL match!")
filoTypes, added, subtracted, transitioned, masterChanged, masterNodes = \
addedSubtractedTransitioned(trees, excludeAxon=True, excludeBasal=False, terminalDist=5, filoDist=5)
assert not np.any(added)
assert not np.any(subtracted)
assert not np.any(transitioned)
print("🙌 Nothing added, subtracted or transitioned!")
motilities, filoLengths = motility(trees,
excludeAxon=True,
excludeBasal=False,
terminalDist=5,
filoDist=5)
mot = motilities['raw'][0]
assert np.all(np.logical_or(mot == 0, np.isnan(mot)))
assert np.all(
np.logical_or(filoLengths[0] == filoLengths[1],
np.isnan(filoLengths[0])))
assert np.array_equal(filoTypes[0], filoTypes[1])
print("🙌 Filotypes, filo lengths match!")
def openFromFile(self, filePath=""):
print("File: '%s'" % filePath)
if filePath == "":
filePath, _ = QtWidgets.QFileDialog.getOpenFileName(
self, "Open dynamo save file", "", "Dynamo files (*.dyn.gz)")
if filePath != "":
self.stackList.show()
self.fullState = loadState(filePath)
self.history = History(self.fullState)
self.fullActions = FullStateActions(self.fullState, self.history)
self.autoSaver = AutoSaver(self.fullState)
BranchToColorMap().initFromFullState(self.fullState)
self.initialMenu.hide()
self.makeNewWindows()
def testParents():
fullState = files.loadState(
"pydynamo_brain/pydynamo_brain/test/files/example2.dyn.gz")
fullID = id(fullState)
for uiState in fullState.uiStates:
assert id(uiState.parent()) == fullID
h = History(fullState)
h.pushState()
h.undo()
for uiState in fullState.uiStates:
assert id(uiState.parent()) == fullID
print("Full State history passed! 🙌")
def allTrees(stateOrPath: Union[FullState,
str], funcs: List[Callable[..., pd.DataFrame]],
**kwargs: Any) -> pd.DataFrame:
# Load state first if provided as a string.
fullState = None
if isinstance(stateOrPath, FullState):
fullState = stateOrPath
else:
assert isinstance(stateOrPath, str)
fullState = files.loadState(stateOrPath)
assert fullState is not None
# Merge all results into one dataframe.
result = pd.DataFrame(index=list(range(len(fullState.trees))))
for func in funcs:
result = result.join(func(fullState, **kwargs))
return result
def allPuncta(stateOrPath: Union[FullState, str],
funcs: List[Callable[..., pd.DataFrame]],
**kwargs: Any) -> pd.DataFrame:
# Load state first if provided as a string.
fullState = None
if isinstance(stateOrPath, FullState):
fullState = stateOrPath
else:
assert isinstance(stateOrPath, str)
fullState = files.loadState(stateOrPath)
assert fullState is not None
# Merge all results into one dataframe.
sortedPunctaIDs = util.sortedPunctaIDList(fullState.puncta)
result = pd.DataFrame(index=sortedPunctaIDs)
for func in funcs:
result = result.join(func(fullState, sortedPunctaIDs, **kwargs))
return result
def calculateResults(path='data/movie5local.mat'):
results = {}
if path.endswith('.mat'):
# Keep orphans around, to match against old matlab analysis
fullState = files.importFromMatlab(path, removeOrphanBranches=False)
else:
assert path.endswith('.dyn.gz')
fullState = files.loadState(path)
trees = fullState.trees
TERM_DIST = 5
FILO_DIST = 5
allTDBL = [
TDBL(tree,
excludeAxon=True,
excludeBasal=False,
includeFilo=False,
filoDist=FILO_DIST) for tree in trees
]
results['tdbl'] = np.array([allTDBL])
filoTypes, added, subtracted, transitioned, masterChanged, masterNodes = \
addedSubtractedTransitioned(trees, excludeAxon=True, excludeBasal=False, terminalDist=TERM_DIST, filoDist=FILO_DIST)
results['filotypes'] = filoTypes
results['added'] = added
results['subtracted'] = subtracted
results['transitioned'] = transitioned
results['masterChanged'] = masterChanged
results['masterNodes'] = masterNodes
motilities, filoLengths = motility(trees,
excludeAxon=True,
excludeBasal=False,
terminalDist=TERM_DIST,
filoDist=FILO_DIST)
results['filolengths'] = filoLengths
return fullState, results
def run(path='pydynamo_brain/pydynamo_brain/test/files/example2.dyn.gz'):
tree = files.loadState(path).trees[0]
pointX, pointY = calculatePositions(tree)
# TODO: Verify X and Y positions
return True