diff options
Diffstat (limited to 'lib/dfatool.py')
| -rwxr-xr-x | lib/dfatool.py | 126 |
1 files changed, 120 insertions, 6 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 657a7b1..3ff38e0 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -79,7 +79,22 @@ def vprint(verbose, string): return 1. def gplearn_to_function(function_str): - + """ + Convert gplearn-style function string to Python function. + + Takes a function string like "mul(add(X0, X1), X2)" and returns + a Python function implementing the specified behaviour, + e.g. "lambda x, y, z: (x + y) * z". + + Supported functions: + add -- x + y + sub -- x - y + mul -- x * y + div -- x / y if |y| > 0.001, otherwise 1 + sqrt -- sqrt(|x|) + log -- log(|x|) if |x| > 0.001, otherwise 0 + inv -- 1 / x if |x| > 0.001, otherwise 0 + """ eval_globals = { 'add' : lambda x, y : x + y, 'sub' : lambda x, y : x - y, @@ -120,11 +135,7 @@ def append_if_set(aggregate, data, key): aggregate.append(data[key]) def mean_or_none(arr): - """ - Compute mean of NumPy array arr. - - Return -1 if arr is empty. - """ + """Compute mean of NumPy array arr, return -1 if empty.""" if len(arr): return np.mean(arr) return -1 @@ -197,11 +208,18 @@ def regression_measures(predicted, actual): return measures class KeysightCSV: + """Simple loader for Keysight CSV data, as exported by the windows software.""" def __init__(self): + """Create a new KeysightCSV object.""" pass def load_data(self, filename): + """ + Load log data from filename, return timestamps and currents. + + Returns two one-dimensional NumPy arrays: timestamps and corresponding currents. + """ with open(filename) as f: for i, l in enumerate(f): pass @@ -1203,8 +1221,24 @@ class EnergyModel: class MIMOSA: + """ + MIMOSA log loader for DFA traces with auto-calibration. + + Expects a MIMOSA log file generated via dfatool and a dfatool-generated + benchmark: There is an automatic calibration step at the start and the + trigger pin is high iff a transition is active. The resulting data + is a list of state/transition/state/transition/... measurements. + """ def __init__(self, voltage, shunt, verbose = True): + """ + Initialize MIMOSA loader for a specific voltage and shunt setting. + + arguments: + voltage -- MIMOSA voltage used for measurements + shunt -- Shunt value in Ohms + verbose -- notify about invalid data and the likes + """ self.voltage = voltage self.shunt = shunt self.verbose = verbose @@ -1212,6 +1246,7 @@ class MIMOSA: self.r2 = 99013 # "100k" def charge_to_current_nocal(self, charge): + u"""Convert charge per 10µs to mean currents without accounting for calibration.""" ua_max = 1.836 / self.shunt * 1000000 ua_step = ua_max / 65535 return charge * ua_step @@ -1232,20 +1267,32 @@ class MIMOSA: def load_data(self, raw_data): + """Load a MIMOSA log archive from a raw bytestring.""" with io.BytesIO(raw_data) as data_object: with tarfile.open(fileobj = data_object) as tf: return self._load_tf(tf) def load_file(self, filename): + """Load a MIMOSA log archive from a filename.""" with tarfile.open(filename) as tf: return self._load_tf(tf) def currents_nocal(self, charges): + u"""Convert charge per 10µs to mean currents without accounting for calibration.""" ua_max = 1.836 / self.shunt * 1000000 ua_step = ua_max / 65535 return charges.astype(np.double) * ua_step def trigger_edges(self, triggers): + """ + Return indexes of trigger edges (both 0->1 and 1->0) in log data. + + arguments: + triggers -- trigger array as returned by load_data + + Ignores the first 10 seconds, which are used for calibration and may + contain bogus triggers due to DUT resets. Returns a list of int. + """ trigidx = [] prevtrig = triggers[0] # the device is reset for MIMOSA calibration in the first 10s and may @@ -1260,6 +1307,22 @@ class MIMOSA: return trigidx def calibration_edges(self, currents): + """ + Return start/stop indexes of calibration measurements. + + arguments: + currents -- uncalibrated currents as reported by MIMOSA. For best results, + it may help to use a running mean, like so: + currents = running_mean(currents_nocal(..., 10)) + + Returns six indexes: + - Disconnected start + - Disconnected stop + - R1 (1 kOhm) start + - R1 (1 kOhm) stop + - R2 (100 kOhm) start + - R2 (100 kOhm) stop + """ r1idx = 0 r2idx = 0 ua_r1 = self.voltage / self.r1 * 1000000 @@ -1274,6 +1337,32 @@ class MIMOSA: return r1idx - 180500, r1idx - 500, r1idx + 500, r2idx - 500, r2idx + 500, r2idx + 180500 def calibration_function(self, charges, cal_edges): + u""" + Calculate calibration function from previously determined calibration phase. + + arguments: + charges -- raw charges from MIMOSA + cal_edges -- calibration edges as returned by calibration_edges + + returns (calibration_function, calibration_data): + calibration_function -- charge in pJ (float) -> current in uA (float). + Converts the amount of charge in a 10 µs interval to the + mean current during the same interval. + calibration_data -- dict containing the following keys: + edges -- calibration points in the log file, in µs + offset -- ... + offset2 -- ... + slope_low -- ... + slope_high -- ... + add_low -- ... + add_high -- .. + r0_err_uW -- mean error of uncalibrated data at "∞ Ohm" in µW + r0_std_uW -- standard deviation of uncalibrated data at "∞ Ohm" in µW + r1_err_uW -- mean error of uncalibrated data at 1 kOhm + r1_std_uW -- stddev at 1 kOhm + r2_err_uW -- mean error at 100 kOhm + r2_std_uW -- stddev at 100 kOhm + """ dis_start, dis_end, r1_start, r1_end, r2_start, r2_end = cal_edges if dis_start < 0: dis_start = 0 @@ -1341,6 +1430,7 @@ class MIMOSA: return calfunc, caldata + """ def calcgrad(self, currents, threshold): grad = np.gradient(running_mean(currents * self.voltage, 10)) # len(grad) == len(currents) - 9 @@ -1382,8 +1472,32 @@ class MIMOSA: threshold = np.mean([gradmin, gradmax]) gradidx = self.calcgrad(currents, threshold) return threshold, gradidx + """ def analyze_states(self, charges, trigidx, ua_func): + u""" + Split log data into states and transitions and return mean power and duration for each element. + + arguments: + charges -- raw charges (each element describes the charge transferred during 10 µs) + trigidx -- "charges" indexes corresponding to a trigger edge + ua_func -- charge -> current function as returned by calibration_function + + returns a list of (alternating) states and transitions. + Each element is a dict containing: + - isa: 'state' oder 'transition' + - clip_rate: range(0..1) Anteil an Clipping im Energieverbrauch + - raw_mean: Mittelwert der Rohwerte + - raw_std: Standardabweichung der Rohwerte + - uW_mean: Mittelwert der (kalibrierten) Leistungsaufnahme + - uW_std: Standardabweichung der (kalibrierten) Leistungsaufnahme + - us: Dauer + + Nur falls isa == 'transition': + - timeout: Dauer des vorherigen Zustands + - uW_mean_delta_prev: Differenz zwischen uW_mean und uW_mean des vorherigen Zustands + - uW_mean_delta_next: Differenz zwischen uW_mean und uW_mean des Folgezustands + """ previdx = 0 is_state = True iterdata = [] |