diff options
Diffstat (limited to 'lib/dfatool.py')
| -rwxr-xr-x | lib/dfatool.py | 57 |
1 files changed, 4 insertions, 53 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index c44f4de..8f08211 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -15,7 +15,7 @@ from multiprocessing import Pool from automata import PTA from functions import analytic from functions import AnalyticFunction -from utils import * +from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, compute_param_statistics arg_support_enabled = True @@ -30,44 +30,6 @@ def running_mean(x: np.ndarray, N: int) -> np.ndarray: cumsum = np.cumsum(np.insert(x, 0, 0)) return (cumsum[N:] - cumsum[:-N]) / N -def soft_cast_int(n): - """ - Convert to int, if possible. - - If it is empty, returns None. - If it is not numeric, it is left unchanged. - """ - if n == None or n == '': - return None - try: - return int(n) - except ValueError: - return n - -def vprint(verbose: bool, string: str): - """ - Print string if verbose. - - Prints string if verbose is a True value - """ - if verbose: - print(string) - - # I don't recall what these are for. - # --df, 2019-01-29 - def _gplearn_add_(x, y): - return x + y - - def _gplearn_sub_(x, y): - return x - y - - def _gplearn_mul_(x, y): - return x * y - - def _gplearn_div_(x, y): - if np.abs(y) > 0.001: - return x / y - return 1. def gplearn_to_function(function_str: str): """ @@ -162,7 +124,7 @@ def regression_measures(predicted: np.ndarray, actual: np.ndarray): if type(actual) != np.ndarray: raise ValueError('second arg must be ndarray, is {}'.format(type(actual))) deviations = predicted - actual - mean = np.mean(actual) + #mean = np.mean(actual) if len(deviations) == 0: return {} measures = { @@ -203,7 +165,7 @@ class KeysightCSV: Returns two one-dimensional NumPy arrays: timestamps and corresponding currents. """ with open(filename) as f: - for i, l in enumerate(f): + for i, _ in enumerate(f): pass timestamps = np.ndarray((i-3), dtype=float) currents = np.ndarray((i-3), dtype=float) @@ -330,7 +292,7 @@ class CrossValidator: 'smape_list': list() } - for i in range(count): + for _ in range(count): res = self._single_montecarlo(model_getter) for name in self.names: for attribute in self.by_name[name]['attributes']: @@ -1113,7 +1075,6 @@ class AnalyticModel: for name in self.by_name.keys(): for attribute in self.by_name[name]['attributes']: for param_index, param in enumerate(self.parameters): - ratio = self.stats.param_dependence_ratio(name, attribute, param) if self.stats.depends_on_param(name, attribute, param): paramfit.enqueue(name, attribute, param_index, param, False) if arg_support_enabled and name in self._num_args: @@ -1455,9 +1416,6 @@ class PTAModel: return lut_median_getter - def get_param_analytic(self): - static_model = self._get_model_from_dict(self.by_name, np.median) - def param_index(self, param_name): if param_name in self._parameter_names: return self._parameter_names.index(param_name) @@ -1483,8 +1441,6 @@ class PTAModel: param_model = dict([[state_or_tran, {}] for state_or_tran in self.by_name.keys()]) paramfit = ParallelParamFit(self.by_param) for state_or_tran in self.by_name.keys(): - param_keys = filter(lambda k: k[0] == state_or_tran, self.by_param.keys()) - param_subdict = dict(map(lambda k: [k, self.by_param[k]], param_keys)) for model_attribute in self.by_name[state_or_tran]['attributes']: fit_results = {} for parameter_index, parameter_name in enumerate(self._parameter_names): @@ -1823,11 +1779,8 @@ class MIMOSA: chg_r1 = charges[r1_start:r1_end] chg_r2 = charges[r2_start:r2_end] cal_0_mean = np.mean(chg_r0) - cal_0_std = np.std(chg_r0) cal_r1_mean = np.mean(chg_r1) - cal_r1_std = np.std(chg_r1) cal_r2_mean = np.mean(chg_r2) - cal_r2_std = np.std(chg_r2) ua_r1 = self.voltage / self.r1 * 1000000 ua_r2 = self.voltage / self.r2 * 1000000 @@ -1839,11 +1792,9 @@ class MIMOSA: b_lower = 0 b_upper = (ua_r1 - ua_r2) / (cal_r1_mean - cal_r2_mean) - b_total = (ua_r1 - 0) / (cal_r1_mean - cal_0_mean) a_lower = -b_lower * cal_0_mean a_upper = -b_upper * cal_r2_mean - a_total = -b_total * cal_0_mean if self.shunt == 680: # R1 current is higher than shunt range -> only use R2 for calibration |