def update_grid(self, *args):
"""
Update the plotter grid.
This update method is dependent on the variables below.
Determine the x and y axis grid parameters.
The x axis depends on sample rate, baseband freq, and x divs.
The y axis depends on y per div, y divs, and ref level.
"""
# grid parameters
# sample_rate = self[SAMPLE_RATE_KEY]
frame_rate = 1.0 # self[FRAME_RATE_KEY]
# if frame_rate < 1.0:
# frame_rate = 1.0
x_offset = self[BASEBAND_FREQ_KEY]
num_lines = self[NUM_LINES_KEY]
y_divs = self[Y_DIVS_KEY]
x_divs = self[X_DIVS_KEY]
# determine best fitting x_per_div
x_width = self.data_len # sample_rate/1.0
x_per_div = common.get_clean_num(x_width / x_divs)
# update the x grid
self.plotter.set_x_grid(x_offset, x_offset + self.data_len, x_per_div, True) # sample_rate/2.0,
# update x units
self.plotter.set_x_label("Data", "points")
# update y grid
duration = float(num_lines) / frame_rate
y_per_div = common.get_clean_num(duration / y_divs)
self.plotter.set_y_grid(0, duration, y_per_div, True)
# update y units
self.plotter.set_y_label("Time", "s")
# update plotter
self.plotter.update()
def update_grid(self): #update the x axis x_max = 1.75 self.plotter.set_x_grid(-x_max, x_max, common.get_clean_num(2.0*x_max/self[X_DIVS_KEY])) #update the y axis y_max = 1.75 self.plotter.set_y_grid(-y_max, y_max, common.get_clean_num(2.0*y_max/self[Y_DIVS_KEY])) #update plotter self.plotter.update()
def update_grid(self):
#update the x axis
x_max = 1.75
self.plotter.set_x_grid(
-x_max, x_max,
common.get_clean_num(2.0 * x_max / self[X_DIVS_KEY]))
#update the y axis
y_max = 1.75
self.plotter.set_y_grid(
-y_max, y_max,
common.get_clean_num(2.0 * y_max / self[Y_DIVS_KEY]))
#update plotter
self.plotter.update()
def autoscale(self, *args):
"""
Autoscale the plot to the last frame.
Set the dynamic range and reference level.
"""
if not len(self.samples):
return
min_level, max_level = common.get_min_max_fft(self.samples)
#set the range to a clean number of the dynamic range
self[constants.Y_PER_DIV_KEY] = common.get_clean_num(1+(max_level - min_level)/self[constants.Y_DIVS_KEY])
#set the reference level to a multiple of y per div
self[constants.REF_LEVEL_KEY] = self[constants.Y_PER_DIV_KEY]*round(.5+max_level/self[constants.Y_PER_DIV_KEY])
def update_grid(self, *args):
"""
Update the plotter grid.
This update method is dependent on the variables below.
Determine the x and y axis grid parameters.
The x axis depends on sample rate, baseband freq, and x divs.
The y axis depends on y per div, y divs, and ref level.
"""
for trace in TRACES:
channel = '%s' % trace.upper()
if self[TRACE_SHOW_KEY + trace]:
self.plotter.set_waveform(
channel=channel,
samples=self._traces[trace],
color_spec=TRACES_COLOR_SPEC[trace],
)
else:
self.plotter.clear_waveform(channel=channel)
#grid parameters
sample_rate = self[SAMPLE_RATE_KEY]
baseband_freq = self[BASEBAND_FREQ_KEY]
y_per_div = self[Y_PER_DIV_KEY]
y_divs = self[Y_DIVS_KEY]
x_divs = self[X_DIVS_KEY]
ref_level = self[REF_LEVEL_KEY]
#determine best fitting x_per_div
if self.real: x_width = sample_rate / 2.0
else: x_width = sample_rate / 1.0
x_per_div = common.get_clean_num(x_width / x_divs)
#update the x grid
if self.real:
self.plotter.set_x_grid(
baseband_freq,
baseband_freq + sample_rate / 2.0,
x_per_div,
True,
)
else:
self.plotter.set_x_grid(
baseband_freq - sample_rate / 2.0,
baseband_freq + sample_rate / 2.0,
x_per_div,
True,
)
#update x units
self.plotter.set_x_label('Frequency', 'Hz')
#update y grid
self.plotter.set_y_grid(ref_level - y_per_div * y_divs, ref_level,
y_per_div)
#update y units
self.plotter.set_y_label('Amplitude', 'dB')
#update plotter
self.plotter.update()
def update_grid(self, *args):
"""
Update the plotter grid.
This update method is dependent on the variables below.
Determine the x and y axis grid parameters.
The x axis depends on sample rate, baseband freq, and x divs.
The y axis depends on y per div, y divs, and ref level.
"""
#grid parameters
#sample_rate = self[SAMPLE_RATE_KEY]
frame_rate = 1.0 #self[FRAME_RATE_KEY]
#if frame_rate < 1.0:
# frame_rate = 1.0
x_offset = self[BASEBAND_FREQ_KEY]
num_lines = self[NUM_LINES_KEY]
y_divs = self[Y_DIVS_KEY]
x_divs = self[X_DIVS_KEY]
#determine best fitting x_per_div
x_width = self.data_len #sample_rate/1.0
x_per_div = common.get_clean_num(x_width / x_divs)
#update the x grid
self.plotter.set_x_grid(
x_offset,
x_offset + self.data_len, #sample_rate/2.0,
x_per_div,
True,
)
#update x units
self.plotter.set_x_label('Data', 'points')
#update y grid
duration = float(num_lines) / frame_rate
y_per_div = common.get_clean_num(duration / y_divs)
self.plotter.set_y_grid(0, duration, y_per_div, True)
#update y units
self.plotter.set_y_label('Time', 's')
#update plotter
self.plotter.update()
def update_grid(self, *args):
"""
Update the plotter grid.
This update method is dependent on the variables below.
Determine the x and y axis grid parameters.
The x axis depends on sample rate, baseband freq, and x divs.
The y axis depends on y per div, y divs, and ref level.
"""
for trace in TRACES:
channel = '%s' % trace.upper()
if self[constants.TRACE_SHOW_KEY+trace]:
self.plotter.set_waveform(
channel=channel,
samples=self._traces[trace],
color_spec=TRACES_COLOR_SPEC[trace],
)
else: self.plotter.clear_waveform(channel=channel)
#grid parameters
sample_rate = self[constants.SAMPLE_RATE_KEY]
baseband_freq = self[constants.BASEBAND_FREQ_KEY]
y_per_div = self[constants.Y_PER_DIV_KEY]
y_divs = self[constants.Y_DIVS_KEY]
x_divs = self[constants.X_DIVS_KEY]
ref_level = self[constants.REF_LEVEL_KEY]
#determine best fitting x_per_div
if self.real: x_width = sample_rate/2.0
else: x_width = sample_rate/1.0
x_per_div = common.get_clean_num(x_width/x_divs)
#update the x grid
if self.real:
self.plotter.set_x_grid(
baseband_freq,
baseband_freq + sample_rate/2.0,
x_per_div, True,
)
else:
self.plotter.set_x_grid(
baseband_freq - sample_rate/2.0,
baseband_freq + sample_rate/2.0,
x_per_div, True,
)
#update x units
self.plotter.set_x_label('Frequency', 'Hz')
#update y grid
self.plotter.set_y_grid(ref_level-y_per_div*y_divs, ref_level, y_per_div)
#update y units
self.plotter.set_y_label('Amplitude', 'dB')
#update plotter
self.plotter.update()
def autoscale(self, *args):
"""
Autoscale the waterfall plot to the last frame.
Set the dynamic range and reference level.
Does not affect the current data in the waterfall.
"""
if not len(self.samples):
return
# min_level, max_level = common.get_min_max_fft(self.samples)
min_level, max_level = numpy.min(self.samples), numpy.max(self.samples)
print "Min/max:", min_level, max_level
# set the range and level
self[DYNAMIC_RANGE_KEY] = common.get_clean_num(max_level - min_level)
print "DYNAMIC_RANGE_KEY", self[DYNAMIC_RANGE_KEY]
self[REF_LEVEL_KEY] = DYNAMIC_RANGE_STEP * round(0.5 + max_level / DYNAMIC_RANGE_STEP)
print "REF_LEVEL_KEY", self[REF_LEVEL_KEY]
def autoscale(self, *args):
"""
Autoscale the waterfall plot to the last frame.
Set the dynamic range and reference level.
Does not affect the current data in the waterfall.
"""
if not len(self.samples): return
#min_level, max_level = common.get_min_max_fft(self.samples)
min_level, max_level = numpy.min(self.samples), numpy.max(self.samples)
print "Min/max:", min_level, max_level
#set the range and level
self[DYNAMIC_RANGE_KEY] = common.get_clean_num(max_level - min_level)
print "DYNAMIC_RANGE_KEY", self[DYNAMIC_RANGE_KEY]
self[REF_LEVEL_KEY] = DYNAMIC_RANGE_STEP * round(.5 + max_level /
DYNAMIC_RANGE_STEP)
print "REF_LEVEL_KEY", self[REF_LEVEL_KEY]
def update_grid(self, *args):
"""
Update the plotter grid.
This update method is dependent on the variables below.
Determine the x and y axis grid parameters.
The x axis depends on sample rate, baseband freq, and x divs.
The y axis depends on y per div, y divs, and ref level.
"""
for trace in TRACES:
channel = "%s" % trace.upper()
if self[TRACE_SHOW_KEY + trace]:
self.plotter.set_waveform(
channel=channel, samples=self._traces[trace], color_spec=TRACES_COLOR_SPEC[trace]
)
else:
self.plotter.clear_waveform(channel=channel)
# grid parameters
sample_rate = self[SAMPLE_RATE_KEY]
baseband_freq = self[BASEBAND_FREQ_KEY]
y_per_div = self[Y_PER_DIV_KEY]
y_divs = self[Y_DIVS_KEY]
x_divs = self[X_DIVS_KEY]
ref_level = self[REF_LEVEL_KEY]
# determine best fitting x_per_div
x_width = self.data_len / 1.0
x_per_div = common.get_clean_num(x_width / x_divs)
# update the x grid
self.plotter.set_x_grid(
0, # baseband_freq - sample_rate/2.0,
self.data_len / 1.0, # baseband_freq + sample_rate/2.0,
x_per_div,
True,
)
# update x units
self.plotter.set_x_label("Data", "") # 'Hz'
# update y grid
self.plotter.set_y_grid(ref_level - y_per_div * y_divs, ref_level, y_per_div)
# update y units
self.plotter.set_y_label("Amplitude", "dB")
# update plotter
self.plotter.update()
def handle_samples(self):
"""
Handle the cached samples from the scope input.
Perform ac coupling, triggering, and auto ranging.
"""
if not self.sampleses: return
sampleses = self.sampleses
#if self[XY_MODE_KEY]:
if True:
self[DECIMATION_KEY] = 1
x_samples = sampleses[self[X_CHANNEL_KEY]]
y_samples = sampleses[self[Y_CHANNEL_KEY]]
z_samples = sampleses[2]
#autorange
if self[AUTORANGE_KEY] and time.time(
) - self.autorange_ts > AUTORANGE_UPDATE_RATE:
x_min, x_max = common.get_min_max(x_samples)
y_min, y_max = common.get_min_max(y_samples)
#adjust the x per div
x_per_div = common.get_clean_num(
(x_max - x_min) / self[X_DIVS_KEY])
if x_per_div != self[X_PER_DIV_KEY]:
self[X_PER_DIV_KEY] = x_per_div
return
#adjust the x offset
x_off = x_per_div * round((x_max + x_min) / 2 / x_per_div)
if x_off != self[X_OFF_KEY]:
self[X_OFF_KEY] = x_off
return
#adjust the y per div
y_per_div = common.get_clean_num(
(y_max - y_min) / self[Y_DIVS_KEY])
if y_per_div != self[Y_PER_DIV_KEY]:
self[Y_PER_DIV_KEY] = y_per_div
return
#adjust the y offset
y_off = y_per_div * round((y_max + y_min) / 2 / y_per_div)
if y_off != self[Y_OFF_KEY]:
self[Y_OFF_KEY] = y_off
return
self.autorange_ts = time.time()
#plot xy channel
self.plotter.set_waveform(
channel='XY',
samples=(x_samples, y_samples, z_samples),
color_spec=CHANNEL_COLOR_SPECS[0],
marker=self[XY_MARKER_KEY],
)
#turn off each waveform
for i, samples in enumerate(sampleses):
self.plotter.clear_waveform(channel='Ch%d' % (i + 1))
else:
#autorange
if self[AUTORANGE_KEY] and time.time(
) - self.autorange_ts > AUTORANGE_UPDATE_RATE:
bounds = [common.get_min_max(samples) for samples in sampleses]
y_min = numpy.min([bound[0] for bound in bounds])
y_max = numpy.max([bound[1] for bound in bounds])
#adjust the y per div
y_per_div = common.get_clean_num(
(y_max - y_min) / self[Y_DIVS_KEY])
if y_per_div != self[Y_PER_DIV_KEY]:
self[Y_PER_DIV_KEY] = y_per_div
return
#adjust the y offset
y_off = y_per_div * round((y_max + y_min) / 2 / y_per_div)
if y_off != self[Y_OFF_KEY]:
self[Y_OFF_KEY] = y_off
return
self.autorange_ts = time.time()
#number of samples to scale to the screen
actual_rate = self.get_actual_rate()
time_span = self[T_PER_DIV_KEY] * self[T_DIVS_KEY]
num_samps = int(round(time_span * actual_rate))
#handle the time offset
t_off = self[T_FRAC_OFF_KEY] * (len(sampleses[0]) / actual_rate -
time_span)
if t_off != self[T_OFF_KEY]:
self[T_OFF_KEY] = t_off
return
samps_off = int(round(actual_rate * self[T_OFF_KEY]))
#adjust the decim so that we use about half the samps
self[DECIMATION_KEY] = int(
round(time_span * self[SAMPLE_RATE_KEY] /
(0.5 * len(sampleses[0]))))
#num samps too small, auto increment the time
if num_samps < 2:
self[T_PER_DIV_KEY] = common.get_clean_incr(
self[T_PER_DIV_KEY])
#num samps in bounds, plot each waveform
elif num_samps <= len(sampleses[0]):
for i, samples in enumerate(sampleses):
#plot samples
self.plotter.set_waveform(
channel='Ch%d' % (i + 1),
samples=samples[samps_off:num_samps + samps_off],
color_spec=CHANNEL_COLOR_SPECS[i],
marker=self[common.index_key(MARKER_KEY, i)],
trig_off=self.trigger_offset,
)
#turn XY channel off
self.plotter.clear_waveform(channel='XY')
#keep trigger level within range
if self[TRIGGER_LEVEL_KEY] > self.get_y_max():
self[TRIGGER_LEVEL_KEY] = self.get_y_max()
return
if self[TRIGGER_LEVEL_KEY] < self.get_y_min():
self[TRIGGER_LEVEL_KEY] = self.get_y_min()
return
#disable the trigger channel
if not self[TRIGGER_SHOW_KEY] or self[XY_MODE_KEY] or self[
TRIGGER_MODE_KEY] == wxgui.TRIG_MODE_FREE:
self.plotter.clear_waveform(channel='Trig')
else: #show trigger channel
trigger_level = self[TRIGGER_LEVEL_KEY]
trigger_point = (len(self.sampleses[0]) -
1) / self.get_actual_rate() / 2.0
self.plotter.set_waveform(
channel='Trig',
samples=([
self.get_t_min(), trigger_point, trigger_point,
trigger_point, trigger_point,
self.get_t_max()
], [
trigger_level, trigger_level,
self.get_y_max(),
self.get_y_min(), trigger_level, trigger_level
]),
color_spec=TRIGGER_COLOR_SPEC,
)
#update the plotter
self.plotter.update()
def handle_samples(self):
"""
Handle the cached samples from the scope input.
Perform ac coupling, triggering, and auto ranging.
"""
if not self.sampleses: return
sampleses = self.sampleses
#if self[XY_MODE_KEY]:
if True:
self[DECIMATION_KEY] = 1
x_samples = sampleses[self[X_CHANNEL_KEY]]
y_samples = sampleses[self[Y_CHANNEL_KEY]]
z_samples = sampleses[2]
#autorange
if self[AUTORANGE_KEY] and time.time() - self.autorange_ts > AUTORANGE_UPDATE_RATE:
x_min, x_max = common.get_min_max(x_samples)
y_min, y_max = common.get_min_max(y_samples)
#adjust the x per div
x_per_div = common.get_clean_num((x_max-x_min)/self[X_DIVS_KEY])
if x_per_div != self[X_PER_DIV_KEY]: self[X_PER_DIV_KEY] = x_per_div; return
#adjust the x offset
x_off = x_per_div*round((x_max+x_min)/2/x_per_div)
if x_off != self[X_OFF_KEY]: self[X_OFF_KEY] = x_off; return
#adjust the y per div
y_per_div = common.get_clean_num((y_max-y_min)/self[Y_DIVS_KEY])
if y_per_div != self[Y_PER_DIV_KEY]: self[Y_PER_DIV_KEY] = y_per_div; return
#adjust the y offset
y_off = y_per_div*round((y_max+y_min)/2/y_per_div)
if y_off != self[Y_OFF_KEY]: self[Y_OFF_KEY] = y_off; return
self.autorange_ts = time.time()
#plot xy channel
self.plotter.set_waveform(
channel='XY',
samples=(x_samples, y_samples, z_samples),
color_spec=CHANNEL_COLOR_SPECS[0],
marker=self[XY_MARKER_KEY],
)
#turn off each waveform
for i, samples in enumerate(sampleses):
self.plotter.clear_waveform(channel='Ch%d'%(i+1))
else:
#autorange
if self[AUTORANGE_KEY] and time.time() - self.autorange_ts > AUTORANGE_UPDATE_RATE:
bounds = [common.get_min_max(samples) for samples in sampleses]
y_min = numpy.min([bound[0] for bound in bounds])
y_max = numpy.max([bound[1] for bound in bounds])
#adjust the y per div
y_per_div = common.get_clean_num((y_max-y_min)/self[Y_DIVS_KEY])
if y_per_div != self[Y_PER_DIV_KEY]: self[Y_PER_DIV_KEY] = y_per_div; return
#adjust the y offset
y_off = y_per_div*round((y_max+y_min)/2/y_per_div)
if y_off != self[Y_OFF_KEY]: self[Y_OFF_KEY] = y_off; return
self.autorange_ts = time.time()
#number of samples to scale to the screen
actual_rate = self.get_actual_rate()
time_span = self[T_PER_DIV_KEY]*self[T_DIVS_KEY]
num_samps = int(round(time_span*actual_rate))
#handle the time offset
t_off = self[T_FRAC_OFF_KEY]*(len(sampleses[0])/actual_rate - time_span)
if t_off != self[T_OFF_KEY]: self[T_OFF_KEY] = t_off; return
samps_off = int(round(actual_rate*self[T_OFF_KEY]))
#adjust the decim so that we use about half the samps
self[DECIMATION_KEY] = int(round(
time_span*self[SAMPLE_RATE_KEY]/(0.5*len(sampleses[0]))
)
)
#num samps too small, auto increment the time
if num_samps < 2: self[T_PER_DIV_KEY] = common.get_clean_incr(self[T_PER_DIV_KEY])
#num samps in bounds, plot each waveform
elif num_samps <= len(sampleses[0]):
for i, samples in enumerate(sampleses):
#plot samples
self.plotter.set_waveform(
channel='Ch%d'%(i+1),
samples=samples[samps_off:num_samps+samps_off],
color_spec=CHANNEL_COLOR_SPECS[i],
marker=self[common.index_key(MARKER_KEY, i)],
trig_off=self.trigger_offset,
)
#turn XY channel off
self.plotter.clear_waveform(channel='XY')
#keep trigger level within range
if self[TRIGGER_LEVEL_KEY] > self.get_y_max():
self[TRIGGER_LEVEL_KEY] = self.get_y_max(); return
if self[TRIGGER_LEVEL_KEY] < self.get_y_min():
self[TRIGGER_LEVEL_KEY] = self.get_y_min(); return
#disable the trigger channel
if not self[TRIGGER_SHOW_KEY] or self[XY_MODE_KEY] or self[TRIGGER_MODE_KEY] == wxgui.TRIG_MODE_FREE:
self.plotter.clear_waveform(channel='Trig')
else: #show trigger channel
trigger_level = self[TRIGGER_LEVEL_KEY]
trigger_point = (len(self.sampleses[0])-1)/self.get_actual_rate()/2.0
self.plotter.set_waveform(
channel='Trig',
samples=(
[self.get_t_min(), trigger_point, trigger_point, trigger_point, trigger_point, self.get_t_max()],
[trigger_level, trigger_level, self.get_y_max(), self.get_y_min(), trigger_level, trigger_level]
),
color_spec=TRIGGER_COLOR_SPEC,
)
#update the plotter
self.plotter.update()
