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BridgeLib.py
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BridgeLib.py
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# -*- coding: utf-8 -*-
#
# Copyright (c) 2013 Egor Zindy <[email protected]>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
import numpy as np
import ImarisLib
import Ice
import sys
import time
import zlib
import datetime as dt
#make tType available
_M_Imaris = Ice.openModule('Imaris')
tType = _M_Imaris.tType
imaris_types = {'eTypeUInt8':np.uint8,'eTypeUInt16':np.uint16,'eTypeFloat':np.float32}
DEBUG = False
###########################################################################
## Helper functions
###########################################################################
def Reconnect(newId):
"""Given an ImarisId, Get the associated Application and DataSet instances
Useful inside an interactive sesssion.
"""
vImarisLib = ImarisLib.ImarisLib()
vServer = vImarisLib.GetServer()
vImaris,vDataSet = None,None
for vIndex in range(vServer.GetNumberOfObjects()):
vId = vServer.GetObjectID(vIndex)
if vId == newId:
vApp = vImarisLib.GetApplication(vId)
vImaris = vImarisLib.GetApplication(newId)
vDataSet = vImaris.GetDataSet()
if vDataSet is None:
print("Warning! No Dataset.")
break
return vImaris,vDataSet
def Cleanup(vImaris):
vScene = vImaris.GetSurpassScene()
nChildren = vScene.GetNumberOfChildren()
for i in range(nChildren):
child = vScene.GetChild(i)
if child is not None:
print(i,child)
vScene.RemoveChild(child)
vDataSet = vImaris.GetDataSet()
vDataSet.Dispose()
def GetType(vDataSet):
"""Get the numpy dtype of the dataset"""
return imaris_types[str(vDataSet.GetType())]
def GetIcon(old=False):
"""Returns a binary icon string (ICO file) for either old (v7) or newer (v8, v9) versions of Imaris"""
# Imaris icons used with permission from Bitplane
Icon8 = "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"
if old == True:
s = Icon7
else:
s = Icon8
return s.decode('base64').decode('zlib')
def GetRange(vDataSet,channel=None):
"""Get the pixel intensity range of the dataset"""
nc = vDataSet.GetSizeC()
maset = 0
if channel is None:
channels = range(nc)
else:
channels = [channel]
for i in channels:
ma = vDataSet.GetChannelRangeMax(i)
if ma > maset:
maset = ma
maset = np.power(2,np.ceil(np.log2(maset)))-1
return 0,maset
def GetTotalRange(vDataSet):
"""Get the maximum range that can be fit for the type of a given dataset"""
dtype = GetType(vDataSet)
if dtype == np.uint8 or dtype == np.uint16:
info = np.iinfo(dtype)
else:
info = np.finfo(dtype)
return info.min,info.max
def GetTimepoint(vDataSet, tpi):
dt = vDataSet.GetTimePoint(tpi)[:-4]
pattern = '%Y-%m-%d %H:%M:%S.%f'
return int(time.mktime(time.strptime(dt,pattern)))+float("0."+dt.split(".")[1])
def GetTimepoints(vDataSet,tpis=None,datetime=False):
"""Given a list of timepoint indexes, return the timepoints"""
t0 = GetTimepoint(vDataSet,0)
if tpis is None:
nt = vDataSet.GetSizeT()
tpis = range(nt)
else:
nt = len(tpis)
ret = []
for i in range(nt):
ts = GetTimepoint(vDataSet,tpis[i])-t0
if datetime:
ts = dt.datetime.fromtimestamp(ts)
ret.append(ts)
return ret
def GetExtent(vDataSet):
"""Get the X,Y,Z extents of a dataset"""
return [vDataSet.GetExtendMinX(),vDataSet.GetExtendMaxX(),
vDataSet.GetExtendMinY(),vDataSet.GetExtendMaxY(),
vDataSet.GetExtendMinZ(),vDataSet.GetExtendMaxZ()]
def SetExtent(vDataSet,extent):
"""Get the X,Y,Z extents of a dataset"""
vDataSet.SetExtendMinX(extent[0])
vDataSet.SetExtendMaxX(extent[1])
vDataSet.SetExtendMinY(extent[2])
vDataSet.SetExtendMaxY(extent[3])
vDataSet.SetExtendMinZ(extent[4])
vDataSet.SetExtendMaxZ(extent[5])
def GetResolution(vDataSet):
"""Get the X,Y,Z pixel resolution of a dataset"""
xmin,xmax,ymin,ymax,zmin,zmax = GetExtent(vDataSet)
nx,ny,nz = vDataSet.GetSizeX(),vDataSet.GetSizeY(),vDataSet.GetSizeZ()
return (xmax-xmin)/nx, (ymax-ymin)/ny, (zmax-zmin)/nz
def GetDataSlice(vDataSet,z,c,t):
"""Given z, channel, time indexes, return a numpy array"""
dtype = GetType(vDataSet)
if dtype == np.uint8 or dtype == np.uint16:
arr = np.array(vDataSet.GetDataSliceShorts(z,c,t),dtype)
else:
arr = np.array(vDataSet.GetDataSliceFloats(z,c,t),dtype)
return arr.swapaxes(0,1)
def SetDataSlice(vDataSet,arr,aIndexZ,aIndexC,aIndexT):
"""Given an array and z, channel, time indexes, replace a slice in an Imaris Dataset"""
nx = vDataSet.GetSizeX()
ny = vDataSet.GetSizeY()
nz = vDataSet.GetSizeZ()
dtype = GetType(vDataSet)
if DEBUG:
print("SetDataVolume")
print("vDataSet:",(nz,ny,nx),GetType(vDataSet))
print(arr.shape)
print(arr.dtype)
print(aIndexC)
print(aIndexT)
#Make sure the data is in range and convert the array
s = arr
if dtype != arr.dtype:
miset,maset = GetTotalRange(vDataSet)
arr[arr<miset]=miset
arr[arr>maset]=maset
s = arr.astype(dtype)
s = s.swapaxes(0,1)
if dtype == np.uint8:
SetData = vDataSet.SetDataSliceBytes
elif dtype == np.uint16:
SetData = vDataSet.SetDataSliceShorts
elif dtype == np.float32:
SetData = vDataSet.SetDataSliceFloat32
SetData(s,aIndexZ,aIndexC,aIndexT)
#vDataSet.SetChannelRange(aIndexC,miset,maset)
def GetDataVolume(vDataSet,aIndexC,aIndexT):
"""Given channel, time indexes, return a numpy array corresponding to the volume
progress is a Tk progress bar object"""
nx = vDataSet.GetSizeX()
ny = vDataSet.GetSizeY()
nz = vDataSet.GetSizeZ()
dtype = GetType(vDataSet)
if DEBUG:
print("GetDataVolume")
print("vDataSet:",(nz,ny,nx),GetType(vDataSet))
print(aIndexC)
print(aIndexT)
if dtype == np.uint8:
s = vDataSet.GetDataVolumeAs1DArrayBytes(aIndexC,aIndexT)
arr = np.frombuffer(s,dtype).reshape((nz,ny,nx)).copy()
else:
#We define an empty array of the final size
arr = np.empty(nz*ny*nx,dtype)
if dtype == np.uint16:
GetData = vDataSet.GetDataSubVolumeAs1DArrayShorts
elif dtype == np.float32:
GetData = vDataSet.GetDataSubVolumeAs1DArrayFloats
#Filling-up the array
for z in range(nz):
arr[z*ny*nx:(z+1)*ny*nx] = GetData(0,0,z,aIndexC,aIndexT,nx,ny,1)
arr = arr.reshape(nz,ny,nx)
return np.ascontiguousarray(arr)
def SetDataVolume(vDataSet,arr,aIndexC,aIndexT):
"""Given a numpy array, a channel and a time index, send the array back to Imaris"""
nx = vDataSet.GetSizeX()
ny = vDataSet.GetSizeY()
nz = vDataSet.GetSizeZ()
dtype = GetType(vDataSet)
if DEBUG:
print("SetDataVolume")
print("vDataSet:",(nz,ny,nx),GetType(vDataSet))
print(arr.shape)
print(arr.dtype)
print(aIndexC)
print(aIndexT)
#Make sure the data is in range and convert the array
s = arr
if dtype != arr.dtype:
miset,maset = GetTotalRange(vDataSet)
arr[arr<miset]=miset
arr[arr>maset]=maset
s = arr.astype(dtype)
if dtype == np.uint8:
SetData = vDataSet.SetDataVolumeAs1DArrayBytes
s = s.tostring()
elif dtype == np.uint16:
SetData = vDataSet.SetDataVolumeAs1DArrayShorts
s = np.ravel(s)
elif dtype == np.float32:
SetData = vDataSet.SetDataVolumeAs1DArrayFloats
s = np.ravel(s)
SetData(s,aIndexC,aIndexT)
if 0:
#Old method slice by slice
if dtype == np.uint8:
SetData = vDataSet.SetDataSubVolumeAs1DArrayBytes
elif dtype == np.uint16:
s = np.ravel(s)
SetData = vDataSet.SetDataSubVolumeAs1DArrayShorts
elif dtype == np.float32:
s = np.ravel(s)
SetData = vDataSet.SetDataSubVolumeAs1DArrayFloats
for z in range(nz):
t = time.time()
l = arr[z,...].swapaxes(0,1).tostring()
SetData(l,0,0,z,aIndexC,aIndexT,nx,ny,1)
print z,time.time()-t
#vDataSet.SetChannelRange(aIndexC,miset,maset)
def GetVoxelSize(vDataSet):
"""Returns the X,Y,X, voxel dimensions"""
nx = vDataSet.GetSizeX()
ny = vDataSet.GetSizeY()
nz = vDataSet.GetSizeZ()
if nx > 0: nx = abs(vDataSet.GetExtendMaxX()-vDataSet.GetExtendMinX())/nx;
if ny > 0: ny = abs(vDataSet.GetExtendMaxY()-vDataSet.GetExtendMinY())/ny;
if nz > 0: nz = abs(vDataSet.GetExtendMaxZ()-vDataSet.GetExtendMinZ())/nz;
return nx,ny,nz
def GetChannelDescription(vDataSet,aIndexC):
"""Get the Description string for a channel"""
s = ""
if aIndexC >= 0 and aIndexC < vDataSet.GetSizeC():
s = vDataSet.GetChannelDescription(aIndexC)
return s
def SetChannelDescription(vDataSet,aIndexC,s):
"""Set the Description string for a channel"""
vDataSet.SetChannelDescription(aIndexC,s)
def GetChannelColorRGBA(vDataSet,aIndexC):
"""Returns the R,G,B,alpha values (0-255) given a channel index"""
rgba = vDataSet.GetChannelColorRGBA(aIndexC)
r = rgba & 255
g = (rgba >> 8) & 255
b = (rgba >> 16) & 255
a = (rgba >> 24) & 255
return r,g,b,a
def SetChannelColorRGBA(vDataSet,aIndexC,color):
"""Sets the colour of a channel given its index and the R,G,B,alpha values (0-255) values"""
a = 0
r,g,b = color[0], color[1], color[2]
if len(color) > 3: a = color[3]
rgba = (int(a) << 24) + (int(b) << 16) + (int(g) << 8) + (int(r))
#print(r,g,b,a,rgba)
vDataSet.SetChannelColorRGBA(aIndexC,rgba)
def RemoveSurpassObject(vImaris,vChild):
vScene = vImaris.GetSurpassScene()
if vScene is None:
return False
vScene.RemoveChild(vChild)
return True
#Factory objects
#Based on XTGETSPOTFACES Get the Spots and Surfaces objects from Imaris
#spots, surfaces
def GetSurpassObjects(vImaris,search="spots"):
ret = {}
vFactory = vImaris.GetFactory()
vScene = vImaris.GetSurpassScene()
if vScene is None:
return ret
nChildren = vScene.GetNumberOfChildren()
for i in range(nChildren):
vChild = vScene.GetChild(i)
if search.lower() == "frame":
if vFactory.IsFrame(vChild):
vFrame = vFactory.ToFrame(vChild)
vName = vChild.GetName()
ret[vName] = vFrame
elif search.lower() == "spots":
if vFactory.IsSpots(vChild):
vSpots = vFactory.ToSpots(vChild)
vName = vChild.GetName()
ret[vName] = vSpots
elif search.lower() == "surfaces":
if vFactory.IsSurfaces(vChild):
vSurfaces = vFactory.ToSurfaces(vChild)
vName = vChild.GetName()
ret[vName] = vSurfaces
elif search.lower() == "filaments":
if vFactory.IsFilaments(vChild):
vSurfaces = vFactory.ToFilaments(vChild)
vName = vChild.GetName()
ret[vName] = vSurfaces
elif search.lower() == "cells":
if vFactory.IsCells(vChild):
vSurfaces = vFactory.ToCells(vChild)
vName = vChild.GetName()
ret[vName] = vSurfaces
return ret
def GetItemIds(vDataItem,tid):
"""Return the spot ids for a particular vDataItem object track id
"""
track_ids = np.array(vDataItem.GetTrackIds())
le = np.array(vDataItem.GetTrackEdges())
wh = (track_ids == tid)
idx = le[wh,:]
idx = np.append(idx[:,0],idx[-1,1])
return idx
def GetItemTrackIds(vDataItem,onlySelected=False):
if onlySelected:
sid = np.array(vDataItem.GetSelectedIds())
tid = sid[sid >= 1000000000]
else:
tid = np.array(vDataItem.GetTrackIds())
tid = np.unique(tid)
return tid
def GetItemXYZT(vImaris,vDataItem,physdim=True):
names = ['x','y','z','t']
formats = ['f','f','f','f']
dat_types = {
'names': names,
'formats': formats
}
if isSpot(vImaris,vDataItem):
p = np.array(vDataItem.GetPositionsXYZ())
pe = np.array(vDataItem.GetTrackEdges())
tid = np.array(vDataItem.GetTrackIds())
tpid = np.array(vDataItem.GetIndicesT())
else:
pe = np.array(vDataItem.GetTrackEdges())
tid = np.array(vDataItem.GetTrackIds())
n = vDataItem.GetNumberOfSurfaces()
p = np.zeros((n,3),float)
tpid = np.zeros(n,int)
for j in range(n):
p[j,:] = vDataItem.GetCenterOfMass(j)[0]
tpid[j] = vSpot.GetTimeIndex(j)
n = tpid.shape[0]
#Create the array
arr = np.zeros((n,),dtype=dat_types)
arr['x'] = p[:,0]
arr['y'] = p[:,1]
arr['z'] = p[:,2]
arr['t'] = tpid
vDataSet = vImaris.GetDataSet()
if physdim == True and vDataSet is not None:
xsize,ysize,zsize = GetVoxelSize(vDataSet)
tps_ = GetTimepoints(vDataSet)
arr['x']*=xsize
arr['y']*=ysize
arr['z']*=zsize
arr['t']=np.take(tps_,tpid)
return arr
def GetStatistics(vDataItem,mFactor=None,channel=None,vStatisticValues=None):
# Build the stats structured array for vDataItem
if type(vDataItem) == ImarisLib.Imaris.cStatisticValues:
vStatisticValues = vDataItem
else:
vStatisticValues = vDataItem.GetStatistics()
names = ['Factor', 'Id'] + vStatisticValues.mFactorNames + ['Value']
formats = ['a30','i']+['a30']*(len(names)-3)+['f']
formats[-2] = 'i' #Time
dat_types = {
'names': names,
'formats': formats
}
lNames = np.array(vStatisticValues.mNames)
lIds = np.array(vStatisticValues.mIds)
lValues = np.array(vStatisticValues.mValues)
if mFactor is not None:
wh = lNames == mFactor
lNames = lNames[wh]
lIds = lIds[wh]
lValues = lValues[wh]
stats = np.zeros((lNames.shape[0],),dtype=dat_types)
stats['Factor']=lNames
stats['Id']=lIds
stats['Value']=lValues
for i in range(len(vStatisticValues.mFactorNames)):
fac = np.array(vStatisticValues.mFactors[i])
if mFactor is not None:
fac = fac[wh]
#Time can normally be converted to an integer but for track data, time is ""
if vStatisticValues.mFactorNames[i] == "Time" and fac[0] == '':
continue
stats[vStatisticValues.mFactorNames[i]] = fac
if channel is not None:
wh = stats["Channel"] == str(channel+1)
stats = stats[wh]
#Time really needs to be zero-indexed
stats['Time'] -= 1
return stats
def GetStatisticsNames(vDataItem):
vStatisticValues = vDataItem.GetStatistics()
names = vs.mNames
#return unique names...
return list(set(names))
def isSpot(vImaris,vChild):
ret = False
vFactory = vImaris.GetFactory()
if vFactory.IsSpots(vChild):
ret = True
return ret
def SetSurpassObject(vImaris,search="spots",name=None,pos=-1):
vFactory = vImaris.GetFactory()
vScene = vImaris.GetSurpassScene()
if vScene is None:
return None
nChildren = vScene.GetNumberOfChildren()
if search.lower() == "spots":
vChild = vFactory.CreateSpots()
elif search.lower() == "surfaces":
vChild = vFactory.CreateSurfaces()
elif search.lower() == "frame":
vChild = vFactory.CreateFrame()
else:
return None
if name is not None:
vChild.SetName(name)
vScene.AddChild(vChild,pos)
return vChild
def GetChannelNames(vDataSet):
"""Return the names of all the channels in vDataSet
"""
nc = vDataSet.GetSizeC()
ret = []
for i in range(nc):
name = vDataSet.GetChannelName(i)
ret.append(name)
return ret
def FindChannel(vDataSet,match="(output)",create=True, color=None):
"""Finds a channel from a substring. If not found then create a new one (if create true)
If none found and create is True, this method will create a new channel and return
its new index. If not found and create is False, returns -1
"""
ret = -1
nc = vDataSet.GetSizeC()
if match is not None:
for i in range(nc):
name = vDataSet.GetChannelName(i)
if match in name:
ret = i
break
miset,maset = GetRange(vDataSet)
if ret == -1 and create == True:
vDataSet.SetSizeC(nc+1)
ret = nc
vDataSet.SetChannelName(ret,match)
vDataSet.SetChannelRange(ret,miset,maset)
#allows to change the channel colour once the channel was found
#color specified as
if ret >= 0 and color is not None:
#r = 255.*color[0]/maset
#g = 255.*color[1]/maset
#b = 255.*color[2]/maset
#a = 0.
#if len(color) > 3:
# a = 255*color[2]/maset
SetChannelColorRGBA(vDataSet,ret,color) #r,g,b,a)
return ret
def query_yes_no(question, default="yes"):
"""Ask a yes/no question via raw_input() and return their answer.
"question" is a string that is presented to the user.
"default" is the presumed answer if the user just hits <Enter>.
It must be "yes" (the default), "no" or None (meaning
an answer is required of the user).
The "answer" return value is one of "yes" or "no".
"""
valid = {"yes":True, "y":True, "ye":True,
"no":False, "n":False}
if default == None:
prompt = " [y/n] "
elif default == "yes":
prompt = " [Y/n] "
elif default == "no":
prompt = " [y/N] "
else:
raise ValueError("invalid default answer: '%s'" % default)
while True:
sys.stdout.write(question + prompt)
choice = raw_input().lower()
if default is not None and choice == '':
return valid[default]
elif choice in valid:
return valid[choice]
else:
sys.stdout.write("Please respond with 'yes' or 'no' "\
"(or 'y' or 'n').\n")
def query_num(prompt="Type a number between 1 and 10", default=None, lims = [1,10]):
if default is not None:
if type(default) == int or type(default) == long:
prompt = prompt+" (%d)" % default
else:
prompt = prompt+" (%.2f)" % default
prompt += ": "
while True:
s = raw_input(prompt)
if s == "":
if default is None:
continue
else:
val = default
break
try:
if type(default) == int or type(default) == long:
val = int(s)
else:
val = float(s)
except ValueError:
continue
if lims is not None and (val < lims[0] or val > lims[1]):
print(" Type a number between %d and %d" % (lims[0],lims[1]))
continue
break
return val