def __sub__(self, other):
"""Subtract two maps. Currently does not take into account the
alignment between the two maps.
numpy dtype nums:
1 int8
2 uint8
3 int16
4 uint16
"""
# if data is stored as unsigned, cast up (e.g. uint8 => int16)
if self.dtype.kind == "u":
self = self.astype(to_signed(self.dtype))
if other.dtype.kind == "u":
other = other.astype(to_signed(other.dtype))
result = np.ndarray.__sub__(self, other)
def norm():
mean = result.mean()
std = result.std()
vmin = max(result.min(), mean - 6 * std)
vmax = min(result.max(), mean + 6 * std)
return colors.Normalize(vmin, vmax)
result.norm = norm
result.cmap = cm.gray # @UndefinedVariable
return result
def __sub__(self, other):
"""Subtract two maps. Currently does not take into account the
alignment between the two maps.
numpy dtype nums:
1 int8
2 uint8
3 int16
4 uint16
"""
# if data is stored as unsigned, cast up (e.g. uint8 => int16)
if self.dtype.kind == "u":
self = self.astype(to_signed(self.dtype))
if other.dtype.kind == "u":
other = other.astype(to_signed(other.dtype))
result = np.ndarray.__sub__(self, other)
def norm():
mean = result.mean()
std = result.std()
vmin = max(result.min(), mean - 6 * std)
vmax = min(result.max(), mean + 6 * std)
return colors.Normalize(vmin, vmax)
result.norm = norm
result.cmap = cm.gray # @UndefinedVariable
return result
def randomized_auto_const_bg(self, amount):
"""Automatically determine background. Only consider a randomly
chosen subset of the image.
Parameters
----------
amount : int
Size of random sample that is considered for calculation of
the background.
"""
cols = [randint(0, self.shape[1] - 1) for _ in range(amount)]
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
tmp = tmp[:, cols]
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(range(amount), key=lambda y: sdevs[y])
# Only consider the best 5 %.
realcand = cand[:max(1, int(0.05 * len(cand)))]
# Average the best 5 %
bg = np.average(self[:, [cols[r] for r in realcand]], 1)
return bg.reshape(self.shape[0], 1)
def randomized_auto_const_bg(self, amount):
"""Automatically determine background. Only consider a randomly
chosen subset of the image.
Parameters
----------
amount : int
Size of random sample that is considered for calculation of
the background.
"""
cols = [randint(0, self.shape[1] - 1) for _ in range(amount)]
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
tmp = tmp[:, cols]
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(range(amount), key=lambda y: sdevs[y])
# Only consider the best 5 %.
realcand = cand[:max(1, int(0.05 * len(cand)))]
# Average the best 5 %
bg = np.average(self[:, [cols[r] for r in realcand]], 1)
return bg.reshape(self.shape[0], 1)
def auto_find_background(self, amount=0.05):
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(xrange(self.shape[1]), key=lambda y: sdevs[y])
# Only consider the best 5 %.
return cand[:max(1, int(amount * len(cand)))]
def auto_find_background(self, amount=0.05):
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(xrange(self.shape[1]), key=lambda y: sdevs[y])
# Only consider the best 5 %.
return cand[:max(1, int(amount * len(cand)))]
def auto_find_background(self, amount=0.05):
"""Automatically find the background. This
is done by first subtracting the average value in each channel and then
finding those times which have the lowest standard deviation.
Parameters
----------
amount : float
The percent amount (out of 1) of lowest standard deviation to consider.
"""
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(xrange(self.shape[1]), key=lambda y: sdevs[y])
# Only consider the best 5 %.
return cand[:max(1, int(amount * len(cand)))]
def auto_find_background(self, amount=0.05):
"""Automatically find the background. This
is done by first subtracting the average value in each channel and then
finding those times which have the lowest standard deviation.
Parameters
----------
amount : float
The percent amount (out of 1) of lowest standard deviation to consider.
"""
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(xrange(self.shape[1]), key=lambda y: sdevs[y])
# Only consider the best 5 %.
return cand[:max(1, int(amount * len(cand)))]
