def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 30:
return qfilter_warning(self, self.N, "Zero-phase Elliptic")
else:
return True
def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 30:
return qfilter_warning(None, self.N, "Chebychev 1")
else:
return True
def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 25:
return qfilter_warning(None, self.N, "Butterworth")
else:
return True
def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 1000:
return qfilter_warning(self, self.N, "FirWin")
else:
return True
def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 25:
return qfilter_warning(None, self.N, "Butterworth")
else:
return True
def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 30:
return qfilter_warning(None, self.N, "Chebychev 1")
else:
return True
def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 2000:
return qfilter_warning(self, self.N, "Equiripple")
else:
return True
def _test_N(self):
"""
Warn the user if the calculated order is too high for a reasonable filter
design.
"""
if self.N > 30:
return qfilter_warning(self, self.N, "Zero-phase Elliptic")
else:
return True
def calc_ma(self, fil_dict, rt='LP'):
"""
Calculate coefficients and P/Z for moving average filter based on
filter length L = N + 1 and number of cascaded stages and save the
result in the filter dictionary.
"""
b = 1.
k = 1.
L = self.delays + 1
if rt == 'LP':
b0 = np.ones(L) # h[n] = {1; 1; 1; ...}
i = np.arange(1, L)
norm = L
elif rt == 'HP':
b0 = np.ones(L)
b0[::2] = -1. # h[n] = {1; -1; 1; -1; ...}
i = np.arange(L)
if (L % 2 == 0): # even order, remove middle element
i = np.delete(i ,round(L/2.))
else: # odd order, shift by 0.5 and remove middle element
i = np.delete(i, int(L/2.)) + 0.5
norm = L
elif rt == 'BP':
# N is even, L is odd
b0 = np.ones(L)
b0[1::2] = 0
b0[::4] = -1 # h[n] = {1; 0; -1; 0; 1; ... }
L = L + 1
i = np.arange(L) # create N + 2 zeros around the unit circle, ...
# ... remove first and middle element and rotate by L / 4
i = np.delete(i, [0, L // 2]) + L / 4
norm = np.sum(abs(b0))
elif rt == 'BS':
# N is even, L is odd
b0 = np.ones(L)
b0[1::2] = 0
L = L + 1
i = np.arange(L) # create N + 2 zeros around the unit circle and ...
i = np.delete(i, [0, L // 2]) # ... remove first and middle element
norm = np.sum(b0)
if self.delays > 1000:
if not qfilter_warning(None, self.delays*self.stages, "Moving Average"):
return -1
z0 = np.exp(-2j*np.pi*i/L)
# calculate filter for multiple cascaded stages
for i in range(self.stages):
b = np.convolve(b0, b)
z = np.repeat(z0, self.stages)
# normalize filter to |H_max| = 1 if checked:
if self.chk_norm.isChecked():
b = b / (norm ** self.stages)
k = 1./norm ** self.stages
p = np.zeros(len(z))
# store in class attributes for the _save method
self.zpk = [z,p,k]
self.b = b
self._save(fil_dict)
def calc_ma(self, fil_dict, rt='LP'):
"""
Calculate coefficients and P/Z for moving average filter based on
filter length L = N + 1 and number of cascaded stages and save the
result in the filter dictionary.
"""
b = 1.
k = 1.
L = self.delays + 1
if rt == 'LP':
b0 = np.ones(L) # h[n] = {1; 1; 1; ...}
i = np.arange(1, L)
norm = L
elif rt == 'HP':
b0 = np.ones(L)
b0[::2] = -1. # h[n] = {1; -1; 1; -1; ...}
i = np.arange(L)
if (L % 2 == 0): # even order, remove middle element
i = np.delete(i ,round(L/2.))
else: # odd order, shift by 0.5 and remove middle element
i = np.delete(i, int(L/2.)) + 0.5
norm = L
elif rt == 'BP':
# N is even, L is odd
b0 = np.ones(L)
b0[1::2] = 0
b0[::4] = -1 # h[n] = {1; 0; -1; 0; 1; ... }
L = L + 1
i = np.arange(L) # create N + 2 zeros around the unit circle, ...
# ... remove first and middle element and rotate by L / 4
i = np.delete(i, [0, L // 2]) + L / 4
norm = np.sum(abs(b0))
elif rt == 'BS':
# N is even, L is odd
b0 = np.ones(L)
b0[1::2] = 0
L = L + 1
i = np.arange(L) # create N + 2 zeros around the unit circle and ...
i = np.delete(i, [0, L // 2]) # ... remove first and middle element
norm = np.sum(b0)
if self.delays > 1000:
if not qfilter_warning(None, self.delays*self.stages, "Moving Average"):
return -1
z0 = np.exp(-2j*np.pi*i/L)
# calculate filter for multiple cascaded stages
for i in range(self.stages):
b = np.convolve(b0, b)
z = np.repeat(z0, self.stages)
# normalize filter to |H_max| = 1 if checked:
if self.chk_norm.isChecked():
b = b / (norm ** self.stages)
k = 1./norm ** self.stages
p = np.zeros(len(z))
# store in class attributes for the _save method
self.zpk = [z,p,k]
self.b = b
self._save(fil_dict)