def setMapyx(fn, doc):
"""
set doc.mapyx from the filename(fn)
"""
arr = Mrc.bindFile(fn)
nz = arr.Mrc.hdr.Num[-1] / (arr.Mrc.hdr.NumTimes * arr.Mrc.hdr.NumWaves *
2)
arr = arr.reshape((arr.Mrc.hdr.NumTimes, arr.Mrc.hdr.NumWaves, nz, 2,
arr.Mrc.hdr.Num[1], arr.Mrc.hdr.Num[0]))
doc.mapyx = arr
def setMapyx(fn, doc):
"""
set doc.mapyx from the filename(fn)
"""
arr = Mrc.bindFile(fn)
nz = arr.Mrc.hdr.Num[-1] / (arr.Mrc.hdr.NumTimes * arr.Mrc.hdr.NumWaves * 2)
arr = arr.reshape((arr.Mrc.hdr.NumTimes,
arr.Mrc.hdr.NumWaves,
nz,
2,
arr.Mrc.hdr.Num[1],
arr.Mrc.hdr.Num[0]))
doc.mapyx = arr
def main():
"""Collect input filename, create output file, and filter each slice"""
input_path = sys.argv[1]
if not os.path.exists(input_path):
print "Cannot find file: " + input_path
sys.exit()
else:
# Fourier Filter Stripes: copy to new file (data will be overwritten)
output_path = addTag(input_path, "FFS")
shutil.copy2(input_path, output_path)
# NB. Mrc is a special numpy ndarray with extra metadata attached
fMrc = Mrc.bindFile(output_path, writable=1)
# make a view of the data ndarray that is a flat list of XY slices
nplanes = reduce(lambda x, y: x * y, fMrc.shape[:-2])
ny, nx = fMrc.shape[-2:]
xy_slices = fMrc.reshape((nplanes, ny, nx))
# filter out stripes from each slice of the whole stack (in-place)
for p in range(nplanes):
xy_slices[p, :, :] = filter_stripes(xy_slices[p, :, :])
def __init__(self, MRC_path):
## gb, Oct2012 - load an Mrc file here in DataDoc - previously this
# Class was initialized with an existing Mrc object.
# Note an Mrc object is not just a numpy ndarray of pixels.
image = Mrc.bindFile(MRC_path)
self.image = image
## Header for the image data, which tells us e.g. what the ordering
# of X/Y/Z/time/wavelength is in the MRC file.
self.imageHeader = Mrc.implement_hdr(image.Mrc.hdr._array.copy())
## Location the file is saved on disk.
self.filePath = image.Mrc.path
## Number of wavelengths in the array.
self.numWavelengths = self.imageHeader.NumWaves
numTimepoints = self.imageHeader.NumTimes
numX = self.imageHeader.Num[0]
numY = self.imageHeader.Num[1]
numZ = self.imageHeader.Num[2] // (self.numWavelengths * numTimepoints)
## Size in pixels of the data, since having it as a Numpy array
# instead of a tuple (from self.imageArray.shape) is occasionally
# handy.
self.size = numpy.array([self.numWavelengths, numTimepoints, numZ, numY, numX], dtype = numpy.int)
## 5D array of pixel data, indexed as
# self.imageArray[wavelength][time][z][y][x]
# In other words, in WTZYX order. In general we try to treat
# Z and time as "just another axis", but wavelength is dealt with
# specially.
self.imageArray = self.getImageArray()
## Datatype of our array.
self.dtype = self.imageArray.dtype.type
## Averages for each wavelength, used to provide fill values when
# taking slices.
self.averages = []
for wavelength in xrange(self.numWavelengths):
self.averages.append(self.imageArray[wavelength].mean())
## Lower boundary of the cropped data.
self.cropMin = numpy.array([0, 0, 0, 0, 0], numpy.int32)
## Upper boundary of the cropped data.
self.cropMax = numpy.array(self.size, numpy.int32)
## Index of the single pixel that is visible in all different data
# views.
self.curViewIndex = numpy.array(self.size / 2, numpy.int)
# Initial time view is at 0
self.curViewIndex[1] = 0
## Parameters for transforming different wavelengths so they align
# with each other. Order is dx, dy, dz, angle, zoom
self.alignParams = numpy.zeros((self.size[0], 5), numpy.float32)
# Default zoom to 1.0
self.alignParams[:,4] = 1.0
## List of functions to call whenever the alignment parameters change.
# Each will be passed self.alignParams so it can take whatever
# action is necessary.
self.alignCallbacks = []
## gb, Oct2012
# get a list of the true wavelengths so we can tell the user
self.channelWaves = self.getChannelWaves()