def test_cases(self): self.assertEqual(smath.factorize(1), []) self.assertEqual(smath.factorize(2), [2]) self.assertEqual(smath.factorize(3), [3]) self.assertEqual(smath.factorize(4), [2, 2]) self.assertEqual(smath.factorize(5), [5]) self.assertEqual(smath.factorize(6), [2, 3]) self.assertEqual(smath.factorize(7), [7]) self.assertEqual(smath.factorize(8), [2, 2, 2]) self.assertEqual(smath.factorize(9), [3, 3]) self.assertEqual(smath.factorize(48000), [2] * 7 + [3] + [5] * 3)
def test_cases(self):
self.assertEqual(smath.factorize(1), [])
self.assertEqual(smath.factorize(2), [2])
self.assertEqual(smath.factorize(3), [3])
self.assertEqual(smath.factorize(4), [2, 2])
self.assertEqual(smath.factorize(5), [5])
self.assertEqual(smath.factorize(6), [2, 3])
self.assertEqual(smath.factorize(7), [7])
self.assertEqual(smath.factorize(8), [2, 2, 2])
self.assertEqual(smath.factorize(9), [3, 3])
self.assertEqual(smath.factorize(48000), [2] * 7 + [3] + [5] * 3)
def test_error(self): self.assertRaises(ValueError, lambda: smath.factorize(0))
def _make_filter_plan_1(input_rate, output_rate):
assert input_rate > 0
assert output_rate > 0
total_decimation = max(1, int(input_rate // output_rate))
using_rational_resampler = _use_rational_resampler and input_rate % 1 == 0 and output_rate % 1 == 0
if using_rational_resampler:
# If using rational resampler, don't decimate to the point that we get a fractional rate, if possible.
input_rate = int(input_rate)
output_rate = int(output_rate)
if input_rate > output_rate:
total_decimation = input_rate // small_factor_at_least(input_rate, output_rate)
# print input_rate / total_decimation, total_decimation, input_rate, output_rate, input_rate // gcd(input_rate, output_rate)
# TODO: Don't re-factorize unnecessarily
stage_decimations = factorize(total_decimation)
stage_decimations.reverse()
# loop variables
stage_designs = []
stage_input_rate = input_rate
last_index = len(stage_decimations) - 1
if len(stage_decimations) == 0:
# interpolation or nothing -- don't put it in the stages
freq_xlate_stage = len(stage_designs)
stage_designs.append(_FilterPlanXlateStage(
rate=stage_input_rate))
else:
# decimation
for i, stage_decimation in enumerate(stage_decimations):
next_rate = stage_input_rate / stage_decimation
stage_type = _FilterPlanFinalDecimatingStage if i == last_index else _FilterPlanDecimatingStage
if i == 0:
freq_xlate_stage = len(stage_designs)
stage_designs.append(stage_type(
freq_xlating=True,
decimation=stage_decimation,
input_rate=stage_input_rate,
output_rate=next_rate))
else:
stage_designs.append(stage_type(
freq_xlating=False,
decimation=stage_decimation,
input_rate=stage_input_rate,
output_rate=next_rate))
stage_input_rate = next_rate
# final connection and resampling
if stage_input_rate == output_rate:
# exact multiple, no fractional resampling needed
stage_designs.append(_FilterPlanCommentStage(
comment='No final resampler stage.',
rate=output_rate))
else:
# TODO: systematically combine resampler with final filter stage
if using_rational_resampler:
if stage_input_rate % 1 != 0:
raise Exception("shouldn't happen", stage_input_rate)
stage_input_rate = int(stage_input_rate) # because of float division above
common = gcd(output_rate, stage_input_rate)
interpolation = output_rate // common
decimation = stage_input_rate // common
stage_designs.append(_FilterPlanRationalResamplerStage(
interpolation=interpolation,
decimation=decimation,
input_rate=stage_input_rate,
output_rate=output_rate))
else:
# TODO: cache filter computation as optfir is used and takes a noticeable time
stage_designs.append(_FilterPlanPfbResamplerStage(
resample_rate=float(output_rate) / stage_input_rate,
input_rate=stage_input_rate,
output_rate=output_rate))
plan = _MultistageChannelFilterPlan(
stage_designs=stage_designs,
freq_xlate_stage=freq_xlate_stage,
cutoff_freq=-1,
transition_width=-1)
return plan
def __init__(self,
name='Multistage Channel Filter',
input_rate=0,
output_rate=0,
cutoff_freq=0,
transition_width=0,
center_freq=0):
assert input_rate > 0
assert output_rate > 0
assert cutoff_freq > 0
assert transition_width > 0
# cf. firdes.sanity_check_1f (which is private)
# TODO better errors for other cases
if cutoff_freq > output_rate / 2:
# early check for better errors since our cascaded filters might be cryptically nonsense
raise ValueError(
'cutoff_freq (%s) is too high for output_rate (%s)' %
(cutoff_freq, output_rate))
gr.hier_block2.__init__(
self,
name,
gr.io_signature(1, 1, gr.sizeof_gr_complex * 1),
gr.io_signature(1, 1, gr.sizeof_gr_complex * 1),
)
self.cutoff_freq = cutoff_freq
self.transition_width = transition_width
total_decimation = max(1, int(input_rate // output_rate))
using_rational_resampler = _use_rational_resampler and input_rate % 1 == 0 and output_rate % 1 == 0
if using_rational_resampler:
# If using rational resampler, don't decimate to the point that we get a fractional rate, if possible.
input_rate = int(input_rate)
output_rate = int(output_rate)
if input_rate > output_rate:
total_decimation = input_rate // small_factor_at_least(
input_rate, output_rate)
# print input_rate / total_decimation, total_decimation, input_rate, output_rate, input_rate // gcd(input_rate, output_rate)
# TODO: Don't re-factorize unnecessarily
stage_decimations = factorize(total_decimation)
stage_decimations.reverse()
self.stages = []
# loop variables
prev_block = self
stage_input_rate = input_rate
last_index = len(stage_decimations) - 1
if len(stage_decimations) == 0:
# interpolation or nothing -- don't put it in the stages
# TODO: consider using rotator block instead (has different API)
self.freq_filter_block = grfilter.freq_xlating_fir_filter_ccc(
1, [1], center_freq, stage_input_rate)
self.connect(prev_block, self.freq_filter_block)
prev_block = self.freq_filter_block
else:
# decimation
for i, stage_decimation in enumerate(stage_decimations):
next_rate = stage_input_rate / stage_decimation
if i == 0:
stage_filter = grfilter.freq_xlating_fir_filter_ccc(
stage_decimation,
[0], # placeholder
center_freq,
stage_input_rate)
self.freq_filter_block = stage_filter
else:
taps = self.__stage_taps(i == last_index, stage_input_rate,
next_rate)
if len(taps) > 10:
stage_filter = grfilter.fft_filter_ccc(
stage_decimation, taps, 1)
else:
stage_filter = grfilter.fir_filter_ccc(
stage_decimation, taps)
self.stages.append((stage_filter, stage_input_rate, next_rate))
self.connect(prev_block, stage_filter)
prev_block = stage_filter
stage_input_rate = next_rate
# final connection and resampling
if stage_input_rate == output_rate:
# exact multiple, no fractional resampling needed
self.connect(prev_block, self)
self.__resampler_explanation = 'No final resampler stage.'
else:
# TODO: systematically combine resampler with final filter stage
if using_rational_resampler:
if stage_input_rate % 1 != 0:
raise Exception("shouldn't happen", stage_input_rate)
stage_input_rate = int(
stage_input_rate) # because of float division above
common = gcd(output_rate, stage_input_rate)
interpolation = output_rate // common
decimation = stage_input_rate // common
self.__resampler_explanation = 'rational_resampler by %s/%s (stage rates %s/%s)' % (
interpolation, decimation, output_rate, stage_input_rate)
resampler = rational_resampler.rational_resampler_ccf(
interpolation=interpolation, decimation=decimation)
else:
# TODO: cache filter computation as optfir is used and takes a noticeable time
self.__resampler_explanation = 'arb_resampler %s/%s = %s' % (
output_rate, stage_input_rate,
float(output_rate) / stage_input_rate)
resampler = pfb.arb_resampler_ccf(
float(output_rate) / stage_input_rate)
self.connect(prev_block, resampler, self)
# TODO: Shouldn't be necessary since we compute the taps in the loop above...
self.__do_taps()
def test_error(self):
self.assertRaises(ValueError, lambda: smath.factorize(0))
def __init__(self,
name='Multistage Channel Filter',
input_rate=0,
output_rate=0,
cutoff_freq=0,
transition_width=0,
center_freq=0):
assert input_rate > 0
assert output_rate > 0
assert cutoff_freq > 0
assert transition_width > 0
# cf. firdes.sanity_check_1f (which is private)
# TODO better errors for other cases
if cutoff_freq > output_rate / 2:
# early check for better errors since our cascaded filters might be cryptically nonsense
raise ValueError('cutoff_freq (%s) is too high for output_rate (%s)' % (cutoff_freq, output_rate))
gr.hier_block2.__init__(
self, name,
gr.io_signature(1, 1, gr.sizeof_gr_complex * 1),
gr.io_signature(1, 1, gr.sizeof_gr_complex * 1),
)
self.cutoff_freq = cutoff_freq
self.transition_width = transition_width
total_decimation = max(1, int(input_rate // output_rate))
using_rational_resampler = _use_rational_resampler and input_rate % 1 == 0 and output_rate % 1 == 0
if using_rational_resampler:
# If using rational resampler, don't decimate to the point that we get a fractional rate, if possible.
input_rate = int(input_rate)
output_rate = int(output_rate)
if input_rate > output_rate:
total_decimation = input_rate // small_factor_at_least(input_rate, output_rate)
# print input_rate / total_decimation, total_decimation, input_rate, output_rate, input_rate // gcd(input_rate, output_rate)
# TODO: Don't re-factorize unnecessarily
stage_decimations = factorize(total_decimation)
stage_decimations.reverse()
self.stages = []
# loop variables
prev_block = self
stage_input_rate = input_rate
last_index = len(stage_decimations) - 1
if len(stage_decimations) == 0:
# interpolation or nothing -- don't put it in the stages
# TODO: consider using rotator block instead (has different API)
self.freq_filter_block = grfilter.freq_xlating_fir_filter_ccc(
1,
[1],
center_freq,
stage_input_rate)
self.connect(prev_block, self.freq_filter_block)
prev_block = self.freq_filter_block
else:
# decimation
for i, stage_decimation in enumerate(stage_decimations):
next_rate = stage_input_rate / stage_decimation
if i == 0:
stage_filter = grfilter.freq_xlating_fir_filter_ccc(
stage_decimation,
[0], # placeholder
center_freq,
stage_input_rate)
self.freq_filter_block = stage_filter
else:
taps = self.__stage_taps(i == last_index, stage_input_rate, next_rate)
if len(taps) > 10:
stage_filter = grfilter.fft_filter_ccc(stage_decimation, taps, 1)
else:
stage_filter = grfilter.fir_filter_ccc(stage_decimation, taps)
self.stages.append((stage_filter, stage_input_rate, next_rate))
self.connect(prev_block, stage_filter)
prev_block = stage_filter
stage_input_rate = next_rate
# final connection and resampling
if stage_input_rate == output_rate:
# exact multiple, no fractional resampling needed
self.connect(prev_block, self)
self.__resampler_explanation = 'No final resampler stage.'
else:
# TODO: systematically combine resampler with final filter stage
if using_rational_resampler:
if stage_input_rate % 1 != 0:
raise Exception("shouldn't happen", stage_input_rate)
stage_input_rate = int(stage_input_rate) # because of float division above
common = gcd(output_rate, stage_input_rate)
interpolation = output_rate // common
decimation = stage_input_rate // common
self.__resampler_explanation = 'rational_resampler by %s/%s (stage rates %s/%s)' % (interpolation, decimation, output_rate, stage_input_rate)
resampler = rational_resampler.rational_resampler_ccf(
interpolation=interpolation,
decimation=decimation)
else:
# TODO: cache filter computation as optfir is used and takes a noticeable time
self.__resampler_explanation = 'arb_resampler %s/%s = %s' % (output_rate, stage_input_rate, float(output_rate) / stage_input_rate)
resampler = pfb.arb_resampler_ccf(float(output_rate) / stage_input_rate)
self.connect(
prev_block,
resampler,
self)
# TODO: Shouldn't be necessary since we compute the taps in the loop above...
self.__do_taps()
def _make_filter_plan_1(input_rate, output_rate):
assert input_rate > 0
assert output_rate > 0
total_decimation = max(1, int(input_rate // output_rate))
using_rational_resampler = _use_rational_resampler and input_rate % 1 == 0 and output_rate % 1 == 0
if using_rational_resampler:
# If using rational resampler, don't decimate to the point that we get a fractional rate, if possible.
input_rate = int(input_rate)
output_rate = int(output_rate)
if input_rate > output_rate:
total_decimation = input_rate // small_factor_at_least(
input_rate, output_rate)
# print input_rate / total_decimation, total_decimation, input_rate, output_rate, input_rate // gcd(input_rate, output_rate)
# TODO: Don't re-factorize unnecessarily
stage_decimations = factorize(total_decimation)
stage_decimations.reverse()
# loop variables
stage_designs = []
stage_input_rate = input_rate
last_index = len(stage_decimations) - 1
if len(stage_decimations) == 0:
# interpolation or nothing -- don't put it in the stages
freq_xlate_stage = len(stage_designs)
stage_designs.append(_FilterPlanXlateStage(rate=stage_input_rate))
else:
# decimation
for i, stage_decimation in enumerate(stage_decimations):
next_rate = stage_input_rate / stage_decimation
stage_type = _FilterPlanFinalDecimatingStage if i == last_index else _FilterPlanDecimatingStage
if i == 0:
freq_xlate_stage = len(stage_designs)
stage_designs.append(
stage_type(freq_xlating=True,
decimation=stage_decimation,
input_rate=stage_input_rate,
output_rate=next_rate))
else:
stage_designs.append(
stage_type(freq_xlating=False,
decimation=stage_decimation,
input_rate=stage_input_rate,
output_rate=next_rate))
stage_input_rate = next_rate
# final connection and resampling
if stage_input_rate == output_rate:
# exact multiple, no fractional resampling needed
stage_designs.append(
_FilterPlanCommentStage(comment='No final resampler stage.',
rate=output_rate))
else:
# TODO: systematically combine resampler with final filter stage
if using_rational_resampler:
if stage_input_rate % 1 != 0:
raise Exception("shouldn't happen", stage_input_rate)
stage_input_rate = int(
stage_input_rate) # because of float division above
common = gcd(output_rate, stage_input_rate)
interpolation = output_rate // common
decimation = stage_input_rate // common
stage_designs.append(
_FilterPlanRationalResamplerStage(interpolation=interpolation,
decimation=decimation,
input_rate=stage_input_rate,
output_rate=output_rate))
else:
# TODO: cache filter computation as optfir is used and takes a noticeable time
stage_designs.append(
_FilterPlanPfbResamplerStage(resample_rate=float(output_rate) /
stage_input_rate,
input_rate=stage_input_rate,
output_rate=output_rate))
plan = _MultistageChannelFilterPlan(stage_designs=stage_designs,
freq_xlate_stage=freq_xlate_stage,
cutoff_freq=-1,
transition_width=-1)
return plan
