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Use array objects for samples and timestamps (as two items in a DotDict per trace); put +2 back into timeout calculation logic

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Jonathan Hogg
2017-07-29 15:04:38 +01:00
parent 36f85b8e8c
commit 33311848f0
2 changed files with 18 additions and 22 deletions
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35
scope.py
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@ -1,6 +1,7 @@
#!/usr/bin/env python3
import argparse
import array
import asyncio
from collections import namedtuple
import logging
@ -130,7 +131,7 @@ class Scope(vm.VirtualMachine):
if nsamples <= buffer_width:
break
else:
raise RuntimeError("Unable to find appropriate capture mode")
raise ValueError("Unable to find appropriate capture mode")
if raw:
lo, hi = low, high
@ -184,7 +185,7 @@ class Scope(vm.VirtualMachine):
if timeout is None:
trigger_timeout = 0
else:
trigger_timeout = max(1, int(math.ceil(((trigger_intro+trigger_outro+trace_outro)*ticks*clock_scale*self.capture_clock_period
trigger_timeout = max(1, int(math.ceil(((trigger_intro+trigger_outro+trace_outro+2)*ticks*clock_scale*self.capture_clock_period
+ timeout)/self.timeout_clock_period)))
async with self.transaction():
@ -215,6 +216,7 @@ class Scope(vm.VirtualMachine):
address -= address % 2
traces = DotDict()
timestamps = array.array('d', (t*self.capture_clock_period for t in range(start_timestamp, timestamp, ticks*clock_scale)))
for dump_channel, channel in enumerate(sorted(analog_channels)):
asamples = nsamples // len(analog_channels)
async with self.transaction():
@ -225,11 +227,8 @@ class Scope(vm.VirtualMachine):
await self.issue_analog_dump_binary()
value_multiplier, value_offset = (1, 0) if raw else ((high-low), low+self.analog_offsets[channel])
data = await self.read_analog_samples(asamples, capture_mode.sample_width)
data = (value*value_multiplier + value_offset for value in data)
ts = (t*self.capture_clock_period for t in range(start_timestamp+dump_channel*ticks*clock_scale, timestamp,
ticks*clock_scale*len(analog_channels)))
traces[channel] = dict(zip(ts, data))
traces[channel] = DotDict({'timestamps': timestamps[dump_channel::len(analog_channels)] if len(analog_channels) > 1 else timestamps,
'samples': array.array('d', (value*value_multiplier+value_offset for value in data))})
if logic_channels:
async with self.transaction():
await self.set_registers(SampleAddress=(address - nsamples) % buffer_width,
@ -237,11 +236,10 @@ class Scope(vm.VirtualMachine):
await self.issue_program_spock_registers()
await self.issue_analog_dump_binary()
data = await self.read_logic_samples(nsamples)
ts = [t*self.capture_clock_period for t in range(start_timestamp, timestamp, ticks*clock_scale)]
for i in logic_channels:
mask = 1<<i
traces[f'L{i}'] = {t: 1 if value & mask else 0 for (t, value) in zip(ts, data)}
traces[f'L{i}'] = DotDict({'timestamps': timestamps,
'samples': array.array('B', (1 if value & mask else 0 for value in data))})
return traces
async def start_generator(self, frequency, waveform='sine', wavetable=None, ratio=0.5,
@ -383,17 +381,13 @@ INFO:scope:Initialised scope, revision: BS000501
In [3]: generate(2000, 'triangle')
Out[3]: 2000.0
In [4]: t = pandas.DataFrame(capture(['A', 'B'], low=0, high=3.3))
In [4]: capturep(['A', 'B'], low=0, high=3.3).interpolate().plot()
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x10db77d30>
In [5]: t.interpolate().plot()
Out[5]: <matplotlib.axes._subplots.AxesSubplot at 0x10db77d30>
In [5]: capturep(['L'], low=0, high=3.3)).plot()
Out[5]: <matplotlib.axes._subplots.AxesSubplot at 0x10d05d5f8>
In [6]: t = pandas.DataFrame(capture(['L'], low=0, high=3.3))
In [7]: t.plot()
Out[7]: <matplotlib.axes._subplots.AxesSubplot at 0x10d05d5f8>
In [8]:
In [6]:
"""
async def main():
@ -418,7 +412,8 @@ def capture(*args, **kwargs):
def capturep(*args, **kwargs):
import pandas
return pandas.DataFrame(capture(*args, **kwargs))
traces = capture(*args, **kwargs)
return pandas.DataFrame({channel: pandas.Series(trace.samples, trace.timestamps) for (channel,trace) in traces.items()})
def calibrate(*args, **kwargs):
return await(s.calibrate(*args, **kwargs))