def GetData(self,Index, DataType="",Pos=None,Size=None):
""" Returns numpy array with image data
Index: The zero-based index of the image in the file
DataType: The edf type of the array to be returnd
If ommited, it is used the default one for the type
indicated in the image header
Attention to the absence of UnsignedShort,
UnsignedInteger and UnsignedLong types in
Numpy Python
Default relation between Edf types and NumPy's typecodes:
SignedByte int8 b
UnsignedByte uint8 B
SignedShort int16 h
UnsignedShort uint16 H
SignedInteger int32 i
UnsignedInteger uint32 I
SignedLong int32 i
UnsignedLong uint32 I
Signed64 int64 (l in 64bit, q in 32 bit)
Unsigned64 uint64 (L in 64bit, Q in 32 bit)
FloatValue float32 f
DoubleValue float64 d
Pos: Tuple (x) or (x,y) or (x,y,z) that indicates the begining
of data to be read. If ommited, set to the origin (0),
(0,0) or (0,0,0)
Size: Tuple, size of the data to be returned as x) or (x,y) or
(x,y,z) if ommited, is the distance from Pos to the end.
If Pos and Size not mentioned, returns the whole data.
"""
fastedf = self.fastedf
if Index < 0 or Index >= self.NumImages: raise ValueError, "EdfFile: Index out of limit"
if fastedf is None:fastedf = 0
if Pos is None and Size is None:
self.File.seek(self.Images[Index].DataPosition,0)
datatype = self.__GetDefaultNumpyType__(self.Images[Index].DataType, index= Index)
try:
datasize = self.__GetSizeNumpyType__(datatype)
except TypeError:
print "What is the meaning of this error?"
datasize = 8
if self.Images[Index].NumDim==3:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * \
self.Images[Index].Dim3 * datasize
Data = numpy.fromstring(self.File.read(sizeToRead),
datatype)
Data = numpy.reshape(Data, (self.Images[Index].Dim3,self.Images[Index].Dim2, self.Images[Index].Dim1))
elif self.Images[Index].NumDim==2:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * datasize
Data = numpy.fromstring(self.File.read(sizeToRead),
datatype)
#print "datatype = ",datatype
#print "Data.type = ", Data.dtype.char
#print "self.Images[Index].DataType ", self.Images[Index].DataType
#print "Data.shape",Data.shape
#print "datasize = ",datasize
#print "sizeToRead ",sizeToRead
#print "lenData = ", len(Data)
Data = numpy.reshape(Data, (self.Images[Index].Dim2, self.Images[Index].Dim1))
elif self.Images[Index].NumDim==1:
sizeToRead = self.Images[Index].Dim1 * datasize
Data = numpy.fromstring(self.File.read(sizeToRead),
datatype)
elif fastedf and CAN_USE_FASTEDF:
type= self.__GetDefaultNumpyType__(self.Images[Index].DataType, index= Index)
size_pixel=self.__GetSizeNumpyType__(type)
Data=Numpy.array([],type)
if self.Images[Index].NumDim==1:
if Pos==None: Pos=(0,)
if Size==None: Size=(0,)
sizex=self.Images[Index].Dim1
Size=list(Size)
if Size[0]==0:Size[0]=sizex-Pos[0]
self.File.seek((Pos[0]*size_pixel)+self.Images[Index].DataPosition,0)
Data = numpy.fromstring(self.File.read(Size[0]*size_pixel), type)
elif self.Images[Index].NumDim==2:
if Pos==None: Pos=(0,0)
if Size==None: Size=(0,0)
Size=list(Size)
sizex,sizey=self.Images[Index].Dim1,self.Images[Index].Dim2
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
Data=numpy.zeros([Size[1],Size[0]],type)
self.File.seek((((Pos[1]*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
extended_fread(Data, Size[0]*size_pixel , numpy.array([Size[1]]),
numpy.array([sizex*size_pixel]) ,self.File)
elif self.Images[Index].NumDim==3:
if Pos==None: Pos=(0,0,0)
if Size==None: Size=(0,0,0)
Size=list(Size)
sizex,sizey,sizez=self.Images[Index].Dim1,self.Images[Index].Dim2,self.Images[Index].Dim3
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
if Size[2]==0:Size[2]=sizez-Pos[2]
Data=numpy.zeros([Size[2],Size[1],Size[0]],type)
self.File.seek(((((Pos[2]*sizey+Pos[1])*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
extended_fread(Data, Size[0]*size_pixel , numpy.array([Size[2],Size[1]]),
numpy.array([ sizey*sizex*size_pixel , sizex*size_pixel]) ,self.File)
else:
if fastedf:print "I could not use fast routines"
type= self.__GetDefaultNumpyType__(self.Images[Index].DataType, index= Index)
size_pixel=self.__GetSizeNumpyType__(type)
Data=numpy.array([],type)
if self.Images[Index].NumDim==1:
if Pos==None: Pos=(0,)
if Size==None: Size=(0,)
sizex=self.Images[Index].Dim1
Size=list(Size)
if Size[0]==0:Size[0]=sizex-Pos[0]
self.File.seek((Pos[0]*size_pixel)+self.Images[Index].DataPosition,0)
Data = numpy.fromstring(self.File.read(Size[0]*size_pixel), type)
elif self.Images[Index].NumDim==2:
if Pos==None: Pos=(0,0)
if Size==None: Size=(0,0)
Size=list(Size)
sizex,sizey=self.Images[Index].Dim1,self.Images[Index].Dim2
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
#print len(range(Pos[1],Pos[1]+Size[1])), "LECTURES OF ", Size[0], "POINTS"
#print "sizex = ", sizex, "sizey = ", sizey
Data = numpy.zeros((Size[1],Size[0]), type)
dataindex =0
for y in range(Pos[1],Pos[1]+Size[1]):
self.File.seek((((y*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
line = numpy.fromstring(self.File.read(Size[0]*size_pixel), type)
Data[dataindex,:] = line[:]
#Data=numpy.concatenate((Data,line))
dataindex += 1
#print "DataSize = ",Data.shape
#print "Requested reshape = ",Size[1],'x',Size[0]
#Data = numpy.reshape(Data, (Size[1],Size[0]))
elif self.Images[Index].NumDim==3:
if Pos==None: Pos=(0,0,0)
if Size==None: Size=(0,0,0)
Size=list(Size)
sizex,sizey,sizez=self.Images[Index].Dim1,self.Images[Index].Dim2,self.Images[Index].Dim3
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
if Size[2]==0:Size[2]=sizez-Pos[2]
for z in range(Pos[2],Pos[2]+Size[2]):
for y in range(Pos[1],Pos[1]+Size[1]):
self.File.seek(((((z*sizey+y)*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
line = numpy.fromstring(self.File.read(Size[0]*size_pixel), type)
Data=numpy.concatenate((Data,line))
Data = numpy.reshape(Data, (Size[2],Size[1],Size[0]))
if string.upper(self.SysByteOrder)!=string.upper(self.Images[Index].ByteOrder):
Data=Data.byteswap()
if DataType != "":
Data=self.__SetDataType__ (Data,DataType)
return Data
def GetData(self, Index, DataType="", Pos=None, Size=None):
""" Returns numeric array with image data
Index: The zero-based index of the image in the file
DataType: The edf type of the array to be returnd
If ommited, it is used the default one for the type
indicated in the image header
Attention to the absence of UnsignedShort,
UnsignedInteger and UnsignedLong types in
Numeric Python
Default relation between Edf types and NumPy's typecodes:
SignedByte 1
UnsignedByte b
SignedShort s
UnsignedShort w
SignedInteger i
UnsignedInteger u
SignedLong l
UnsignedLong u
FloatValue f
DoubleValue d
Pos: Tuple (x) or (x,y) or (x,y,z) that indicates the begining
of data to be read. If ommited, set to the origin (0),
(0,0) or (0,0,0)
Size: Tuple, size of the data to be returned as x) or (x,y) or
(x,y,z) if ommited, is the distance from Pos to the end.
If Pos and Size not mentioned, returns the whole data.
"""
fastedf = self.fastedf
if Index < 0 or Index >= self.NumImages:
raise "EdfFile: Index out of limit"
if fastedf is None: fastedf = 0
if Pos is None and Size is None:
self.File.seek(self.Images[Index].DataPosition, 0)
datatype = self.__GetDefaultNumericType__(
self.Images[Index].DataType)
if datatype in [1, "1", "b"]: datasize = 1
elif datatype in ["s", "w"]: datasize = 2
elif datatype in ["i", "u", "l", "f"]:
#I assume 32 bit because longs are 8 bit in 64 bit machines
datasize = 4
else:
datasize = 8
if self.Images[Index].NumDim == 3:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * \
self.Images[Index].Dim3 * datasize
Data = Numeric.fromstring(self.File.read(sizeToRead), datatype)
Data = Numeric.reshape(
Data, (self.Images[Index].Dim3, self.Images[Index].Dim2,
self.Images[Index].Dim1))
elif self.Images[Index].NumDim == 2:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * datasize
Data = Numeric.fromstring(self.File.read(sizeToRead), datatype)
#print "Data.type = ", Data.typecode()
#print "self.Images[Index].DataType ", self.Images[Index].DataType
#print Data.shape
#print sizeToRead
#print len(Data)
Data = Numeric.reshape(
Data, (self.Images[Index].Dim2, self.Images[Index].Dim1))
elif fastedf and CAN_USE_FASTEDF:
type = self.__GetDefaultNumericType__(self.Images[Index].DataType)
size_pixel = self.__GetSizeNumericType__(type)
Data = Numeric.array([], type)
if self.Images[Index].NumDim == 1:
if Pos == None: Pos = (0, )
if Size == None: Size = (0, )
sizex = self.Images[Index].Dim1
Size = list(Size)
if Size[0] == 0: Size[0] = sizex - Pos[0]
self.File.seek(
(Pos[0] * size_pixel) + self.Images[Index].DataPosition, 0)
Data = Numeric.fromstring(self.File.read(Size[0] * size_pixel),
type)
elif self.Images[Index].NumDim == 2:
if Pos == None: Pos = (0, 0)
if Size == None: Size = (0, 0)
Size = list(Size)
sizex, sizey = self.Images[Index].Dim1, self.Images[Index].Dim2
if Size[0] == 0: Size[0] = sizex - Pos[0]
if Size[1] == 0: Size[1] = sizey - Pos[1]
Data = Numeric.zeros([Size[1], Size[0]], type)
self.File.seek((((Pos[1] * sizex) + Pos[0]) * size_pixel) +
self.Images[Index].DataPosition, 0)
extended_fread(Data, Size[0] * size_pixel,
Numeric.array([Size[1]]),
Numeric.array([sizex * size_pixel]), self.File)
elif self.Images[Index].NumDim == 3:
if Pos == None: Pos = (0, 0, 0)
if Size == None: Size = (0, 0, 0)
Size = list(Size)
sizex, sizey, sizez = self.Images[Index].Dim1, self.Images[
Index].Dim2, self.Images[Index].Dim3
if Size[0] == 0: Size[0] = sizex - Pos[0]
if Size[1] == 0: Size[1] = sizey - Pos[1]
if Size[2] == 0: Size[2] = sizez - Pos[2]
Data = Numeric.zeros([Size[2], Size[1], Size[0]], type)
self.File.seek(
((((Pos[2] * sizey + Pos[1]) * sizex) + Pos[0]) *
size_pixel) + self.Images[Index].DataPosition, 0)
extended_fread(
Data, Size[0] * size_pixel,
Numeric.array([Size[2], Size[1]]),
Numeric.array(
[sizey * sizex * size_pixel, sizex * size_pixel]),
self.File)
else:
if fastedf: print "I could not use fast routines"
type = self.__GetDefaultNumericType__(self.Images[Index].DataType)
size_pixel = self.__GetSizeNumericType__(type)
Data = Numeric.array([], type)
if self.Images[Index].NumDim == 1:
if Pos == None: Pos = (0, )
if Size == None: Size = (0, )
sizex = self.Images[Index].Dim1
Size = list(Size)
if Size[0] == 0: Size[0] = sizex - Pos[0]
self.File.seek(
(Pos[0] * size_pixel) + self.Images[Index].DataPosition, 0)
Data = Numeric.fromstring(self.File.read(Size[0] * size_pixel),
type)
elif self.Images[Index].NumDim == 2:
if Pos == None: Pos = (0, 0)
if Size == None: Size = (0, 0)
Size = list(Size)
sizex, sizey = self.Images[Index].Dim1, self.Images[Index].Dim2
if Size[0] == 0: Size[0] = sizex - Pos[0]
if Size[1] == 0: Size[1] = sizey - Pos[1]
#print len(range(Pos[1],Pos[1]+Size[1])), "LECTURES OF ", Size[0], "POINTS"
#print "sizex = ", sizex, "sizey = ", sizey
Data = Numeric.zeros((Size[1], Size[0]), type)
dataindex = 0
for y in range(Pos[1], Pos[1] + Size[1]):
self.File.seek((((y * sizex) + Pos[0]) * size_pixel) +
self.Images[Index].DataPosition, 0)
line = Numeric.fromstring(
self.File.read(Size[0] * size_pixel), type)
Data[dataindex, :] = line[:]
#Data=Numeric.concatenate((Data,line))
dataindex += 1
#print "DataSize = ",Data.shape
#print "Requested reshape = ",Size[1],'x',Size[0]
#Data = Numeric.reshape(Data, (Size[1],Size[0]))
elif self.Images[Index].NumDim == 3:
if Pos == None: Pos = (0, 0, 0)
if Size == None: Size = (0, 0, 0)
Size = list(Size)
sizex, sizey, sizez = self.Images[Index].Dim1, self.Images[
Index].Dim2, self.Images[Index].Dim3
if Size[0] == 0: Size[0] = sizex - Pos[0]
if Size[1] == 0: Size[1] = sizey - Pos[1]
if Size[2] == 0: Size[2] = sizez - Pos[2]
for z in range(Pos[2], Pos[2] + Size[2]):
for y in range(Pos[1], Pos[1] + Size[1]):
self.File.seek((((
(z * sizey + y) * sizex) + Pos[0]) * size_pixel) +
self.Images[Index].DataPosition, 0)
line = Numeric.fromstring(
self.File.read(Size[0] * size_pixel), type)
Data = Numeric.concatenate((Data, line))
Data = Numeric.reshape(Data, (Size[2], Size[1], Size[0]))
if string.upper(self.SysByteOrder) != string.upper(
self.Images[Index].ByteOrder):
Data = Data.byteswapped()
if DataType != "":
Data = self.__SetDataType__(Data, DataType)
return Data
def GetData(self, Index, DataType="", Pos=None, Size=None):
""" Returns numpy array with image data
Index: The zero-based index of the image in the file
DataType: The edf type of the array to be returnd
If ommited, it is used the default one for the type
indicated in the image header
Attention to the absence of UnsignedShort,
UnsignedInteger and UnsignedLong types in
Numpy Python
Default relation between Edf types and NumPy's typecodes:
SignedByte int8 b
UnsignedByte uint8 B
SignedShort int16 h
UnsignedShort uint16 H
SignedInteger int32 i
UnsignedInteger uint32 I
SignedLong int32 i
UnsignedLong uint32 I
Signed64 int64 (l in 64bit, q in 32 bit)
Unsigned64 uint64 (L in 64bit, Q in 32 bit)
FloatValue float32 f
DoubleValue float64 d
Pos: Tuple (x) or (x,y) or (x,y,z) that indicates the begining
of data to be read. If ommited, set to the origin (0),
(0,0) or (0,0,0)
Size: Tuple, size of the data to be returned as x) or (x,y) or
(x,y,z) if ommited, is the distance from Pos to the end.
If Pos and Size not mentioned, returns the whole data.
"""
fastedf = self.fastedf
if Index < 0 or Index >= self.NumImages: raise ValueError, "EdfFile: Index out of limit"
if fastedf is None:fastedf = 0
if Pos is None and Size is None:
if self.ADSC or self.MARCCD or self.PILATUS_CBF:
return self.__data
else:
self.File.seek(self.Images[Index].DataPosition, 0)
datatype = self.__GetDefaultNumpyType__(self.Images[Index].DataType, index=Index)
try:
datasize = self.__GetSizeNumpyType__(datatype)
except TypeError:
print "What is the meaning of this error?"
datasize = 8
if self.Images[Index].NumDim == 3:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * \
self.Images[Index].Dim3 * datasize
Data = numpy.fromstring(self.File.read(sizeToRead),
datatype)
Data = numpy.reshape(Data, (self.Images[Index].Dim3, self.Images[Index].Dim2, self.Images[Index].Dim1))
elif self.Images[Index].NumDim == 2:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * datasize
Data = numpy.fromstring(self.File.read(sizeToRead),
datatype)
#print "datatype = ",datatype
#print "Data.type = ", Data.dtype.char
#print "self.Images[Index].DataType ", self.Images[Index].DataType
#print "Data.shape",Data.shape
#print "datasize = ",datasize
#print "sizeToRead ",sizeToRead
#print "lenData = ", len(Data)
Data = numpy.reshape(Data, (self.Images[Index].Dim2, self.Images[Index].Dim1))
elif self.Images[Index].NumDim == 1:
sizeToRead = self.Images[Index].Dim1 * datasize
Data = numpy.fromstring(self.File.read(sizeToRead),
datatype)
elif self.ADSC or self.MARCCD or self.PILATUS_CBF:
return self.__data[Pos[1]:(Pos[1] + Size[1]),
Pos[0]:(Pos[0] + Size[0])]
elif fastedf and CAN_USE_FASTEDF:
type = self.__GetDefaultNumpyType__(self.Images[Index].DataType, index=Index)
size_pixel = self.__GetSizeNumpyType__(type)
Data = numpy.array([], type)
if self.Images[Index].NumDim == 1:
if Pos == None: Pos = (0,)
if Size == None: Size = (0,)
sizex = self.Images[Index].Dim1
Size = list(Size)
if Size[0] == 0:Size[0] = sizex - Pos[0]
self.File.seek((Pos[0] * size_pixel) + self.Images[Index].DataPosition, 0)
Data = numpy.fromstring(self.File.read(Size[0] * size_pixel), type)
elif self.Images[Index].NumDim == 2:
if Pos == None: Pos = (0, 0)
if Size == None: Size = (0, 0)
Size = list(Size)
sizex, sizey = self.Images[Index].Dim1, self.Images[Index].Dim2
if Size[0] == 0:Size[0] = sizex - Pos[0]
if Size[1] == 0:Size[1] = sizey - Pos[1]
Data = numpy.zeros([Size[1], Size[0]], type)
self.File.seek((((Pos[1] * sizex) + Pos[0]) * size_pixel) + self.Images[Index].DataPosition, 0)
extended_fread(Data, Size[0] * size_pixel , numpy.array([Size[1]]),
numpy.array([sizex * size_pixel]) , self.File)
elif self.Images[Index].NumDim == 3:
if Pos == None: Pos = (0, 0, 0)
if Size == None: Size = (0, 0, 0)
Size = list(Size)
sizex, sizey, sizez = self.Images[Index].Dim1, self.Images[Index].Dim2, self.Images[Index].Dim3
if Size[0] == 0:Size[0] = sizex - Pos[0]
if Size[1] == 0:Size[1] = sizey - Pos[1]
if Size[2] == 0:Size[2] = sizez - Pos[2]
Data = numpy.zeros([Size[2], Size[1], Size[0]], type)
self.File.seek(((((Pos[2] * sizey + Pos[1]) * sizex) + Pos[0]) * size_pixel) + self.Images[Index].DataPosition, 0)
extended_fread(Data, Size[0] * size_pixel , numpy.array([Size[2], Size[1]]),
numpy.array([ sizey * sizex * size_pixel , sizex * size_pixel]) , self.File)
else:
if fastedf:print "I could not use fast routines"
type = self.__GetDefaultNumpyType__(self.Images[Index].DataType, index=Index)
size_pixel = self.__GetSizeNumpyType__(type)
Data = numpy.array([], type)
if self.Images[Index].NumDim == 1:
if Pos == None: Pos = (0,)
if Size == None: Size = (0,)
sizex = self.Images[Index].Dim1
Size = list(Size)
if Size[0] == 0:Size[0] = sizex - Pos[0]
self.File.seek((Pos[0] * size_pixel) + self.Images[Index].DataPosition, 0)
Data = numpy.fromstring(self.File.read(Size[0] * size_pixel), type)
elif self.Images[Index].NumDim == 2:
if Pos == None: Pos = (0, 0)
if Size == None: Size = (0, 0)
Size = list(Size)
sizex, sizey = self.Images[Index].Dim1, self.Images[Index].Dim2
if Size[0] == 0:Size[0] = sizex - Pos[0]
if Size[1] == 0:Size[1] = sizey - Pos[1]
#print len(range(Pos[1],Pos[1]+Size[1])), "LECTURES OF ", Size[0], "POINTS"
#print "sizex = ", sizex, "sizey = ", sizey
Data = numpy.zeros((Size[1], Size[0]), type)
dataindex = 0
for y in range(Pos[1], Pos[1] + Size[1]):
self.File.seek((((y * sizex) + Pos[0]) * size_pixel) + self.Images[Index].DataPosition, 0)
line = numpy.fromstring(self.File.read(Size[0] * size_pixel), type)
Data[dataindex, :] = line[:]
#Data=numpy.concatenate((Data,line))
dataindex += 1
#print "DataSize = ",Data.shape
#print "Requested reshape = ",Size[1],'x',Size[0]
#Data = numpy.reshape(Data, (Size[1],Size[0]))
elif self.Images[Index].NumDim == 3:
if Pos == None: Pos = (0, 0, 0)
if Size == None: Size = (0, 0, 0)
Size = list(Size)
sizex, sizey, sizez = self.Images[Index].Dim1, self.Images[Index].Dim2, self.Images[Index].Dim3
if Size[0] == 0:Size[0] = sizex - Pos[0]
if Size[1] == 0:Size[1] = sizey - Pos[1]
if Size[2] == 0:Size[2] = sizez - Pos[2]
for z in range(Pos[2], Pos[2] + Size[2]):
for y in range(Pos[1], Pos[1] + Size[1]):
self.File.seek(((((z * sizey + y) * sizex) + Pos[0]) * size_pixel) + self.Images[Index].DataPosition, 0)
line = numpy.fromstring(self.File.read(Size[0] * size_pixel), type)
Data = numpy.concatenate((Data, line))
Data = numpy.reshape(Data, (Size[2], Size[1], Size[0]))
if string.upper(self.SysByteOrder) != string.upper(self.Images[Index].ByteOrder):
Data = Data.byteswap()
if DataType != "":
Data = self.__SetDataType__ (Data, DataType)
return Data
def GetData(self,Index, DataType="",Pos=None,Size=None):
""" Returns numeric array with image data
Index: The zero-based index of the image in the file
DataType: The edf type of the array to be returnd
If ommited, it is used the default one for the type
indicated in the image header
Attention to the absence of UnsignedShort,
UnsignedInteger and UnsignedLong types in
Numeric Python
Default relation between Edf types and NumPy's typecodes:
SignedByte 1
UnsignedByte b
SignedShort s
UnsignedShort w
SignedInteger i
UnsignedInteger u
SignedLong l
UnsignedLong u
FloatValue f
DoubleValue d
Pos: Tuple (x) or (x,y) or (x,y,z) that indicates the begining
of data to be read. If ommited, set to the origin (0),
(0,0) or (0,0,0)
Size: Tuple, size of the data to be returned as x) or (x,y) or
(x,y,z) if ommited, is the distance from Pos to the end.
If Pos and Size not mentioned, returns the whole data.
"""
fastedf = self.fastedf
if Index < 0 or Index >= self.NumImages: raise "EdfFile: Index out of limit"
if fastedf is None:fastedf = 0
if Pos is None and Size is None:
self.File.seek(self.Images[Index].DataPosition,0)
datatype = self.__GetDefaultNumericType__(self.Images[Index].DataType)
if datatype in [1, "1", "b"]: datasize = 1
elif datatype in ["s", "w"]: datasize = 2
elif datatype in ["i", "u", "l", "f"]:
#I assume 32 bit because longs are 8 bit in 64 bit machines
datasize = 4
else:datasize = 8
if self.Images[Index].NumDim==3:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * \
self.Images[Index].Dim3 * datasize
Data = Numeric.fromstring(self.File.read(sizeToRead),
datatype)
Data = Numeric.reshape(Data, (self.Images[Index].Dim3,self.Images[Index].Dim2, self.Images[Index].Dim1))
elif self.Images[Index].NumDim==2:
sizeToRead = self.Images[Index].Dim1 * \
self.Images[Index].Dim2 * datasize
Data = Numeric.fromstring(self.File.read(sizeToRead),
datatype)
#print "Data.type = ", Data.typecode()
#print "self.Images[Index].DataType ", self.Images[Index].DataType
#print Data.shape
#print sizeToRead
#print len(Data)
Data = Numeric.reshape(Data, (self.Images[Index].Dim2, self.Images[Index].Dim1))
elif fastedf and CAN_USE_FASTEDF:
type= self.__GetDefaultNumericType__(self.Images[Index].DataType)
size_pixel=self.__GetSizeNumericType__(type)
Data=Numeric.array([],type)
if self.Images[Index].NumDim==1:
if Pos==None: Pos=(0,)
if Size==None: Size=(0,)
sizex=self.Images[Index].Dim1
Size=list(Size)
if Size[0]==0:Size[0]=sizex-Pos[0]
self.File.seek((Pos[0]*size_pixel)+self.Images[Index].DataPosition,0)
Data = Numeric.fromstring(self.File.read(Size[0]*size_pixel), type)
elif self.Images[Index].NumDim==2:
if Pos==None: Pos=(0,0)
if Size==None: Size=(0,0)
Size=list(Size)
sizex,sizey=self.Images[Index].Dim1,self.Images[Index].Dim2
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
Data=Numeric.zeros([Size[1],Size[0]],type)
self.File.seek((((Pos[1]*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
extended_fread(Data, Size[0]*size_pixel , Numeric.array([Size[1]]), Numeric.array([sizex*size_pixel]) ,self.File)
elif self.Images[Index].NumDim==3:
if Pos==None: Pos=(0,0,0)
if Size==None: Size=(0,0,0)
Size=list(Size)
sizex,sizey,sizez=self.Images[Index].Dim1,self.Images[Index].Dim2,self.Images[Index].Dim3
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
if Size[2]==0:Size[2]=sizez-Pos[2]
Data=Numeric.zeros([Size[2],Size[1],Size[0]],type)
self.File.seek(((((Pos[2]*sizey+Pos[1])*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
extended_fread(Data, Size[0]*size_pixel , Numeric.array([Size[2],Size[1]]),
Numeric.array([ sizey*sizex*size_pixel , sizex*size_pixel]) ,self.File)
else:
if fastedf:print "I could not use fast routines"
type= self.__GetDefaultNumericType__(self.Images[Index].DataType)
size_pixel=self.__GetSizeNumericType__(type)
Data=Numeric.array([],type)
if self.Images[Index].NumDim==1:
if Pos==None: Pos=(0,)
if Size==None: Size=(0,)
sizex=self.Images[Index].Dim1
Size=list(Size)
if Size[0]==0:Size[0]=sizex-Pos[0]
self.File.seek((Pos[0]*size_pixel)+self.Images[Index].DataPosition,0)
Data = Numeric.fromstring(self.File.read(Size[0]*size_pixel), type)
elif self.Images[Index].NumDim==2:
if Pos==None: Pos=(0,0)
if Size==None: Size=(0,0)
Size=list(Size)
sizex,sizey=self.Images[Index].Dim1,self.Images[Index].Dim2
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
#print len(range(Pos[1],Pos[1]+Size[1])), "LECTURES OF ", Size[0], "POINTS"
#print "sizex = ", sizex, "sizey = ", sizey
Data = Numeric.zeros((Size[1],Size[0]), type)
dataindex =0
for y in range(Pos[1],Pos[1]+Size[1]):
self.File.seek((((y*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
line = Numeric.fromstring(self.File.read(Size[0]*size_pixel), type)
Data[dataindex,:] = line[:]
#Data=Numeric.concatenate((Data,line))
dataindex += 1
#print "DataSize = ",Data.shape
#print "Requested reshape = ",Size[1],'x',Size[0]
#Data = Numeric.reshape(Data, (Size[1],Size[0]))
elif self.Images[Index].NumDim==3:
if Pos==None: Pos=(0,0,0)
if Size==None: Size=(0,0,0)
Size=list(Size)
sizex,sizey,sizez=self.Images[Index].Dim1,self.Images[Index].Dim2,self.Images[Index].Dim3
if Size[0]==0:Size[0]=sizex-Pos[0]
if Size[1]==0:Size[1]=sizey-Pos[1]
if Size[2]==0:Size[2]=sizez-Pos[2]
for z in range(Pos[2],Pos[2]+Size[2]):
for y in range(Pos[1],Pos[1]+Size[1]):
self.File.seek(((((z*sizey+y)*sizex)+Pos[0])*size_pixel)+self.Images[Index].DataPosition,0)
line = Numeric.fromstring(self.File.read(Size[0]*size_pixel), type)
Data=Numeric.concatenate((Data,line))
Data = Numeric.reshape(Data, (Size[2],Size[1],Size[0]))
if string.upper(self.SysByteOrder)!=string.upper(self.Images[Index].ByteOrder):
Data=Data.byteswapped()
if DataType != "":
Data=self.__SetDataType__ (Data,DataType)
return Data