From 1d010bd59bc5cf100310b285c743ae27f921b681 Mon Sep 17 00:00:00 2001
From: Daniel Friesel <daniel.friesel@uos.de>
Date: Wed, 24 Jul 2019 14:46:46 +0200
Subject: pylint
---
lib/automata.py | 1 -
lib/dfatool.py | 57 ++++-----------------------------------------------------
lib/harness.py | 3 ---
3 files changed, 4 insertions(+), 57 deletions(-)
(limited to 'lib')
diff --git a/lib/automata.py b/lib/automata.py
index e8d9af5..d9a722b 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -396,7 +396,6 @@ class PTA:
arguments = list()
argument_values = list()
arg_to_param_map = dict()
- is_interrupt = False
if 'arguments' in transition:
for i, argument in enumerate(transition['arguments']):
arguments.append(argument['name'])
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
diff --git a/lib/harness.py b/lib/harness.py
index 1390e03..2002f8d 100644
--- a/lib/harness.py
+++ b/lib/harness.py
@@ -21,9 +21,6 @@ class TransitionHarness:
self.trace_id = 1
self.synced = False
- def start_benchmark(self):
- pass
-
def global_code(self):
ret = ''
if self.gpio_pin != None:
--
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