diff options
| -rwxr-xr-x | lib/dfatool.py | 27 | ||||
| -rw-r--r-- | lib/functions.py | 10 |
2 files changed, 35 insertions, 2 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 3ff38e0..f7be534 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -704,10 +704,35 @@ def _corr_by_param(by_name, state_or_trans, key, param_index): return 0. class EnergyModel: - """ + u""" parameter-aware PTA-based energy model. Supports both static and parameter-based model attributes, and automatic detection of parameter-dependence. + + The model heavily relies on two internal data structures: + EnergyModel.by_name and EnergyModel.by_param. + + These provide measurements aggregated by state/transition name + and (in case of by_para) parameter values. Layout: + dictionary with one key per state/transition ('send', 'TX', ...) or + one key per state/transition and parameter combination + (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...). + For by_param, parameter values are ordered corresponding to the lexically sorted parameter names. + + Each element is in turn a dict with the following elements: + - isa: 'state' or 'transition' + - power: list of mean power measurements in µW + - duration: list of durations in µs + - power_std: list of stddev of power per state/transition + - energy: consumed energy (power*duration) in pJ + - paramkeys: list of parameter names in each measurement (-> list of lists) + - param: list of parameter values in each measurement (-> list of lists) + - attributes: list of keys that should be analyzed, + e.g. ['power', 'duration'] + additionally, only if isa == 'transition': + - timeout: list of duration of previous state in µs + - rel_energy_prev: transition energy relative to previous state mean power in pJ + - rel_energy_next: transition energy relative to next state mean power in pJ """ def __init__(self, preprocessed_data, ignore_trace_indexes = [], discard_outliers = None, function_override = {}, verbose = True, use_corrcoef = False, hwmodel = None): diff --git a/lib/functions.py b/lib/functions.py index cc97f9a..22d7e46 100644 --- a/lib/functions.py +++ b/lib/functions.py @@ -157,7 +157,12 @@ class AnalyticFunction: Return training data suitable for scipy.optimize.least_squares. arguments: - by_param -- measurement data, partitioned by state/transition name and parameter/arg values + by_param -- measurement data, partitioned by state/transition name and parameter/arg values. + This function only uses by_param[(state_or_tran, *)][model_attribute], + which must be a list or 1-D NumPy array containing the ground truth. + The parameter values in (state_or_tran, *) must be numeric for + all parameters this function depends on -- otherwise, the + corresponding data will be left out. state_or_tran -- state or transition name, e.g. "TX" or "send" model_attribute -- model attribute name, e.g. "power" or "duration" @@ -207,6 +212,9 @@ class AnalyticFunction: by_param -- measurement data, partitioned by state/transition name and parameter/arg values state_or_tran -- state or transition name, e.g. "TX" or "send" model_attribute -- model attribute name, e.g. "power" or "duration" + + The ground truth is read from by_param[(state_or_tran, *)][model_attribute], + which must be a list or 1-D NumPy array. """ X, Y, num_valid, num_total = self.get_fit_data(by_param, state_or_tran, model_attribute) if num_valid > 2: |