def norm_group(pos, _, **kargs):
"""Takes the drain current for each file in group and builds an analysis file and works out the mean drain"""
if "signal" in kargs:
signal = kargs["signal"]
else:
signal = "fluo"
lfit = kargs["lfit"]
rfit = kargs["rfit"]
posfile = Data()
posfile.metadata = pos[0].metadata
posfile = posfile & pos[0].column(0)
posfile.column_headers = ["Energy"]
for f in pos:
print(str(f["run"]) + str(f.find_col(signal)))
posfile = posfile & f.column(signal)
posfile.add_column(lambda r: np.mean(r[1:]), "mean drain")
ec = posfile.find_col("Energy")
md = posfile.find_col("mean drain")
linearfit = scipy.poly1d(
posfile.polyfit(ec, md, 1, lambda x, y: lfit[0] <= x <= lfit[1]))
posfile.add_column(lambda r: r[md] - linearfit(r[ec]), "minus linear")
highend = posfile.mean("minus", lambda r: rfit[0] <= r[ec] <= rfit[1])
ml = posfile.find_col("minus linear")
posfile.add_column(lambda r: r[ml] / highend, "normalised")
if "group_key" in kargs:
posfile[kargs["group_key"]] = pos.key
return posfile
def gen_func(*args, **kwargs):
r = workingfunc(self.clone, *args,
**kwargs) #send copy of self as the first arg
if isinstance(r, Data):
pass #Data return is ok
elif isinstance(r, np.ndarray) and np.prod(r.shape) == np.max(
r.shape): #1D Array
r = Data(r)
r.metadata = self.metadata.copy()
r.column_headers[0] = workingfunc.__name__
elif isinstance(r, np.ndarray) and not isinstance(
r, ImageArray): #make sure we return a ImageArray
r = r.view(type=ImageArray)
r.metadata = self.metadata.copy()
#NB we might not be returning an ndarray at all here !
return r
def norm_group(pos,_,**kargs):
"""Takes the drain current for each file in group and builds an analysis file and works out the mean drain"""
if "signal" in kargs:
signal=kargs["signal"]
else:
signal="fluo"
lfit=kargs["lfit"]
rfit=kargs["rfit"]
posfile=Data()
posfile.metadata=pos[0].metadata
posfile=posfile&pos[0].column(0)
posfile.column_headers=['Energy']
for f in pos:
print(str(f["run"])+str(f.find_col(signal)))
posfile=posfile&f.column(signal)
posfile.add_column(lambda r:np.mean(r[1:]),"mean drain")
ec=posfile.find_col('Energy')
md=posfile.find_col('mean drain')
linearfit=scipy.poly1d(posfile.polyfit(ec,md,1,lambda x,y:lfit[0]<=x<=lfit[1]))
posfile.add_column(lambda r:r[md]-linearfit(r[ec]),'minus linear')
highend=posfile.mean('minus',lambda r:rfit[0]<=r[ec]<=rfit[1])
ml=posfile.find_col('minus linear')
posfile.add_column(lambda r:r[ml]/highend,"normalised")
if "group_key" in kargs:
posfile[kargs["group_key"]]=pos.key
return posfile
def profile_line(img, src=None, dst=None, linewidth=1, order=1, mode="constant", cval=0.0, constrain=True, **kargs):
"""Wrapper for sckit-image method of the same name to get a line_profile.
Parameters:
img(ImageArray): Image data to take line section of
src, dst (2-tuple of int or float): start and end of line profile. If the co-ordinates
are given as intergers then they are assumed to be pxiel co-ordinates, floats are
assumed to be real-space co-ordinates using the embedded metadata.
linewidth (int): the wideth of the profile to be taken.
order (int 1-3): Order of interpolation used to find image data when not aligned to a point
mode (str): How to handle data outside of the image.
cval (float): The constant value to assume for data outside of the image is mode is "constant"
constrain (bool): Ensure the src and dst are within the image (default True).
Returns:
A :py:class:`Stoner.Data` object containing the line profile data and the metadata from the image.
"""
scale = img.get("MicronsPerPixel", 1.0)
r, c = img.shape
if src is None and dst is None:
if "x" in kargs:
src = (kargs["x"], 0)
dst = (kargs["x"], r)
if "y" in kargs:
src = (0, kargs["y"])
dst = (c, kargs["y"])
if isinstance(src, float):
src = (src, src)
if isinstance(dst, float):
dst = (dst, dst)
dst = _scale(dst, scale)
src = _scale(src, scale)
if not istuple(src, int, int):
raise ValueError("src co-ordinates are not a 2-tuple of ints.")
if not istuple(dst, int, int):
raise ValueError("dst co-ordinates are not a 2-tuple of ints.")
if constrain:
fix = lambda x, mx: int(round(sorted([0, x, mx])[1]))
r, c = img.shape
src = list(src)
src = (fix(src[0], r), fix(src[1], c))
dst = (fix(dst[0], r), fix(dst[1], c))
result = measure.profile_line(img, src, dst, linewidth, order, mode, cval)
points = measure.profile._line_profile_coordinates(src, dst, linewidth)[:, :, 0]
ret = Data()
ret.data = points.T
ret.setas = "xy"
ret &= np.sqrt(ret.x ** 2 + ret.y ** 2) * scale
ret &= result
ret.column_headers = ["X", "Y", "Distance", "Intensity"]
ret.setas = "..xy"
ret.metadata = img.metadata.copy()
return ret
def profile_line(img, src=None, dst=None, linewidth=1, order=1, mode="constant", cval=0.0, constrain=True, **kargs):
"""Wrapper for sckit-image method of the same name to get a line_profile.
Parameters:
img(ImageArray): Image data to take line section of
src, dst (2-tuple of int or float): start and end of line profile. If the co-ordinates
are given as intergers then they are assumed to be pxiel co-ordinates, floats are
assumed to be real-space co-ordinates using the embedded metadata.
linewidth (int): the wideth of the profile to be taken.
order (int 1-3): Order of interpolation used to find image data when not aligned to a point
mode (str): How to handle data outside of the image.
cval (float): The constant value to assume for data outside of the image is mode is "constant"
constrain (bool): Ensure the src and dst are within the image (default True).
Returns:
A :py:class:`Stoner.Data` object containing the line profile data and the metadata from the image.
"""
scale = img.get("MicronsPerPixel", 1.0)
r, c = img.shape
if src is None and dst is None:
if "x" in kargs:
src = (kargs["x"], 0)
dst = (kargs["x"], r)
if "y" in kargs:
src = (0, kargs["y"])
dst = (c, kargs["y"])
if isinstance(src, float):
src = (src, src)
if isinstance(dst, float):
dst = (dst, dst)
dst = _scale(dst, scale)
src = _scale(src, scale)
if not istuple(src, int, int):
raise ValueError("src co-ordinates are not a 2-tuple of ints.")
if not istuple(dst, int, int):
raise ValueError("dst co-ordinates are not a 2-tuple of ints.")
if constrain:
fix = lambda x, mx: int(round(sorted([0, x, mx])[1]))
r, c = img.shape
src = list(src)
src = (fix(src[0], r), fix(src[1], c))
dst = (fix(dst[0], r), fix(dst[1], c))
result = measure.profile_line(img, src, dst, linewidth, order, mode, cval)
points = measure.profile._line_profile_coordinates(src, dst, linewidth)[:, :, 0]
ret = Data()
ret.data = points.T
ret.setas = "xy"
ret &= np.sqrt(ret.x ** 2 + ret.y ** 2) * scale
ret &= result
ret.column_headers = ["X", "Y", "Distance", "Intensity"]
ret.setas = "..xy"
ret.metadata = img.metadata.copy()
return ret
def profile_line(img,
src,
dst,
linewidth=1,
order=1,
mode='constant',
cval=0.0):
"""Wrapper for sckit-image method of the same name to get a line_profile.
Parameters:
img(ImageArray): Image data to take line section of
src, dst (2-tuple of int or float): start and end of line profile. If the co-ordinates
are given as intergers then they are assumed to be pxiel co-ordinates, floats are
assumed to be real-space co-ordinates using the embedded metadata.
linewidth (int): the wideth of the profile to be taken.
order (int 1-3): Order of interpolation used to find image data when not aligned to a point
mode (str): How to handle data outside of the image.
cval (float): The constant value to assume for data outside of the image is mode is "constant"
Returns:
A :py:class:`Stoner.Data` object containing the line profile data and the metadata from the image.
"""
scale = img.get("MicronsPerPixel", 1.0)
if isinstance(src[0], float):
src = (int(src[0] / scale), int(src[1] / scale))
if isinstance(dst[0], float):
dst = (int(dst[0] / scale), int(dst[1] / scale))
result = measure.profile_line(img, src, dst, linewidth, order, mode, cval)
points = measure.profile._line_profile_coordinates(src, dst,
linewidth)[:, :, 0]
ret = Data()
ret.data = points.T
ret.setas = "xy"
ret &= np.sqrt(ret.x**2 + ret.y**2) * scale
ret &= result
ret.column_headers = ["X", "Y", "Distance", "Intensity"]
ret.setas = "..xy"
ret.metadata = img.metadata.copy()
return ret
def test_constructor(self):
"""Constructor Tests"""
d=Data()
self.assertTrue(d.shape==(1,0),"Bare constructor failed")
d=Data(self.d)
self.assertTrue(np.all(d.data==self.d.data),"Constructor from DataFile failed")
d=Data([np.ones(100),np.zeros(100)])
self.assertTrue(d.shape==(100,2),"Constructor from iterable list of nd array failed")
d=Data([np.ones(100),np.zeros(100)],["X","Y"])
self.assertTrue(d.column_headers==["X","Y"],"Failed to set column headers in constructor: {}".format(d.column_headers))
c=np.zeros(100)
d=Data({"X-Data":c,"Y-Data":c,"Z-Data":c})
self.assertEqual(d.shape,(100,3),"Construction from dictionary of columns failed.")
d=Data(False)
self.assertEqual(self.d,d,"Faked file-dialog test")
d=self.d.clone
df=d.to_pandas()
e=Data(df)
self.assertEqual(d,e,"Roundtripping through Pandas DataFrame failed.")
d=self.d.clone
e=Data(d.dict_records)
e.metadata=d.metadata
self.assertEqual(d,e,"Copy from dict records failed.")
