Numeric Results — Fast Numeric Sampling

NumericResult channels perform high-speed acquisition on physical channels, computing summary statistics (mean, min, max, standard deviation) server-side. This avoids transferring large sample arrays over the network when only statistics are needed.

Methods

Method	Returns	Description
get_channels()	list[NumericResultChannelDto]	All NumericResult channels with sampling capabilities.
get_targets(channel_net_name)	list[str]	Physical channels that a NumericResult channel can sample.
measure(channel, target, samples, reduced_set)	NumericMeasureResultDto	Trigger an acquisition (result cached server-side).
get_mean(channel_net_name)	float	Mean of the last measurement.
get_min(channel_net_name)	float	Minimum of the last measurement.
get_max(channel_net_name)	float	Maximum of the last measurement.
get_stdev(channel_net_name)	float	Standard deviation of the last measurement.
get_samples(channel_net_name)	list[float]	Raw sample array (only if reduced_set=False).

Typical Workflow

# 1. Discover available NumericResult channels
channels = client.numeric_results.get_channels()
for ch in channels:
    print(f"{ch.net_name} (rate={ch.sample_rate} Hz, "
          f"default_samples={ch.default_samples})")

# 2. Check what a channel can sample
targets = client.numeric_results.get_targets(channels[0].net_name)
print("Available targets:", targets)

# 3. Trigger acquisition (result cached server-side)
meta = client.numeric_results.measure(
    channels[0].net_name, targets[0],
    samples=1000, reduced_set=True)
print(f"Acquired {meta.sample_count} samples in {meta.duration}")

# 4. Fetch summary statistics
mean  = client.numeric_results.get_mean(channels[0].net_name)
stdev = client.numeric_results.get_stdev(channels[0].net_name)
min_v = client.numeric_results.get_min(channels[0].net_name)
max_v = client.numeric_results.get_max(channels[0].net_name)
print(f"Mean={mean:.6f}, StdDev={stdev:.6f}, Min={min_v:.6f}, Max={max_v:.6f}")

Getting Raw Samples

When you need the full sample array, set reduced_set=False:

meta = client.numeric_results.measure(
    channels[0].net_name, targets[0],
    samples=100, reduced_set=False)

samples = client.numeric_results.get_samples(channels[0].net_name)
print(f"First 5 samples: {samples[:5]}")

Warning

Calling get_samples() after a measurement with reduced_set=True will raise an AccordionQ2ApiError (HTTP 400) because raw samples are discarded when only statistics are computed.

Response Models

NumericResultChannelDto

Field	Type	Description
net_name	str	Net name of the NumericResult channel
alias	str	Alias of the channel
possible_target_names	list[str]	Physical channels this channel can sample
sample_rate	int	Sampling rate in Hz
default_samples	int	Default number of samples

NumericMeasureResultDto

Field	Type	Description
channel_net_name	str	NumericResult channel used
target_net_name	str	Physical channel sampled
sample_count	int	Number of samples acquired
sample_rate	int	Actual sampling rate in Hz
reduced_set	bool	Whether raw samples were discarded
started	str	Acquisition start timestamp
stopped	str	Acquisition stop timestamp
duration	str	Acquisition duration