Skip to content
GitLab
Commit 17d79d78 authored by Valentin Valls's avatar Valentin Valls
Browse files

Normalize numpy import

parent 7f73b1c2
Hide whitespace changes
Inline Side-by-side
bliss/controllers/simulation_counter.py
View file @ 17d79d78
......@@ -5,7 +5,7 @@
# Copyright (c) 2015-2020 Beamline Control Unit, ESRF
# Distributed under the GNU LGPLv3. See LICENSE for more info.
import numpy as np
import numpy
from scipy import signal
from scipy.special import erf
......@@ -221,14 +221,14 @@ class SimulationCounterAcquisitionSlave(AcquisitionSlave):
#### Generation of the distribution
if self.distribution == "FLAT":
self.data = np.ones(nbpoints) * self.shape_param["height_factor"]
self.data = numpy.ones(nbpoints) * self.shape_param["height_factor"]
elif self.distribution == "LINEAR":
xdata = np.linspace(scan_start, scan_stop, nbpoints)
xdata = numpy.linspace(scan_start, scan_stop, nbpoints)
self.data = (
xdata * self.shape_param["sigma_factor"] + self.shape_param["mu_offset"]
)
else:
xdata = np.linspace(scan_start, scan_stop, nbpoints)
xdata = numpy.linspace(scan_start, scan_stop, nbpoints)
self.data = self.gauss(
xdata, self.shape_param["mu_offset"], self.shape_param["sigma_factor"]
)
......@@ -239,9 +239,9 @@ class SimulationCounterAcquisitionSlave(AcquisitionSlave):
# applying noise
if self.timescan_or_ct():
noise = (np.random.rand(1)[0] * self.noise_factor) + 1
noise = (numpy.random.rand(1)[0] * self.noise_factor) + 1
else:
noise = (np.random.rand(nbpoints) * self.noise_factor) + 1
noise = (numpy.random.rand(nbpoints) * self.noise_factor) + 1
self.data = self.data * noise
log_debug_data(
self, "SIMULATION_COUNTER_ACQ_DEV -- prepare() -- data+noise=", self.data
......@@ -252,8 +252,8 @@ class SimulationCounterAcquisitionSlave(AcquisitionSlave):
log_debug(self, "SIMULATION_COUNTER_ACQ_DEV -- prepare() END")
def calc_gaussian(self, x, mu, sigma):
one_over_sqtr = 1.0 / np.sqrt(2.0 * np.pi * np.square(sigma))
exp = np.exp(-np.square(x - mu) / (2.0 * np.square(sigma)))
one_over_sqtr = 1.0 / numpy.sqrt(2.0 * numpy.pi * numpy.square(sigma))
exp = numpy.exp(-numpy.square(x - mu) / (2.0 * numpy.square(sigma)))
_val = one_over_sqtr * exp
......@@ -283,7 +283,7 @@ class SimulationCounterAcquisitionSlave(AcquisitionSlave):
sigma = sigma_factor * (xmax - xmin) / 6.0
self.sigma = sigma
self.fwhm = 2 * np.sqrt(2 * np.log(2)) * sigma # ~ 2.35 * sigma
self.fwhm = 2 * numpy.sqrt(2 * numpy.log(2)) * sigma # ~ 2.35 * sigma
log_debug(
self,
......@@ -408,19 +408,23 @@ class _Signal:
def _missing_edge_of_gaussian_left(npoints, frac_missing):
p = npoints // 2
p2 = int(p * frac_missing)
return np.concatenate(
(signal.gaussian(p, .1 * npoints)[p2:], np.zeros(p2), np.zeros(npoints - p))
return numpy.concatenate(
(
signal.gaussian(p, .1 * npoints)[p2:],
numpy.zeros(p2),
numpy.zeros(npoints - p),
)
)
SIGNALS = {
"sawtooth": lambda npoints: signal.sawtooth(
np.arange(0, 2 * np.pi * 1.1, 2 * np.pi * 1.1 / npoints), width=.9
numpy.arange(0, 2 * numpy.pi * 1.1, 2 * numpy.pi * 1.1 / npoints), width=.9
),
"gaussian": lambda npoints: signal.gaussian(npoints, .2 * npoints),
"flat": lambda npoints: np.ones(npoints),
"off_center_gaussian": lambda npoints: np.concatenate(
"flat": lambda npoints: numpy.ones(npoints),
"off_center_gaussian": lambda npoints: numpy.concatenate(
(
np.zeros(npoints - npoints // 2),
numpy.zeros(npoints - npoints // 2),
signal.gaussian(npoints // 2, .1 * npoints),
)
),
......@@ -438,35 +442,35 @@ class _Signal:
"half_gaussian_left": lambda npoints: _Signal._missing_edge_of_gaussian_left(
npoints, 0.4
)[::-1],
"triangle": lambda npoints: np.concatenate(
"triangle": lambda npoints: numpy.concatenate(
(
np.arange(0, 1, 1 / (npoints // 2)),
np.flip(np.arange(0, 1, 1 / (npoints - npoints // 2))),
numpy.arange(0, 1, 1 / (npoints // 2)),
numpy.flip(numpy.arange(0, 1, 1 / (npoints - npoints // 2))),
)
),
"square": lambda npoints: np.concatenate(
"square": lambda npoints: numpy.concatenate(
(
np.zeros(npoints // 3),
np.ones(npoints // 3),
np.zeros(npoints - 2 * (npoints // 3)),
numpy.zeros(npoints // 3),
numpy.ones(npoints // 3),
numpy.zeros(npoints - 2 * (npoints // 3)),
)
),
"bimodal": lambda npoints: np.concatenate(
"bimodal": lambda npoints: numpy.concatenate(
(
signal.gaussian(npoints - npoints // 2, .15 * npoints) * 1.5,
signal.gaussian(npoints // 2, .15 * npoints),
)
),
"step_down": lambda npoints: np.concatenate(
(np.ones(npoints // 2), np.zeros(npoints - npoints // 2))
"step_down": lambda npoints: numpy.concatenate(
(numpy.ones(npoints // 2), numpy.zeros(npoints - npoints // 2))
),
"step_up": lambda npoints: np.concatenate(
(np.zeros(npoints // 2), np.ones(npoints - npoints // 2))
"step_up": lambda npoints: numpy.concatenate(
(numpy.zeros(npoints // 2), numpy.ones(npoints - npoints // 2))
),
"erf_down": lambda npoints: 1 - erf(np.arange(-3, 3, 6 / (npoints))),
"erf_up": lambda npoints: erf(np.arange(-3, 3, 6 / (npoints))),
"erf_down": lambda npoints: 1 - erf(numpy.arange(-3, 3, 6 / (npoints))),
"erf_up": lambda npoints: erf(numpy.arange(-3, 3, 6 / (npoints))),
"inverted_gaussian": lambda npoints: 1 - signal.gaussian(npoints, .2 * npoints),
"expo_gaussian": lambda npoints: np.exp(
"expo_gaussian": lambda npoints: numpy.exp(
signal.gaussian(npoints, .1 * npoints) * 30
),
}
......@@ -477,7 +481,7 @@ class _Signal:
self.name = name
self.npoints = npoints
def compute(self) -> np.ndarray:
def compute(self) -> numpy.ndarray:
return self.SIGNALS[self.name](self.npoints)
......@@ -610,7 +614,7 @@ class AutoFilterDetMon:
def init_signal(self):
n = self._npoints + 1
stdev = .1 * self._npoints + 1
self._data = np.exp(signal.gaussian(n, stdev) * 10)
self._data = numpy.exp(signal.gaussian(n, stdev) * 10)
@property
def npoints(self):
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment