def eu_filter(quantity, kind, domain, f=None):
"""
EuFilter computes filter parameters for the Weighted Peak Method
:param year: reference guidelines. '1998' or '2010'
:param receptor: 'occupational' or 'public'
:param quantity: 'B', 'J', 'Ecns' or 'Epns' (to be defined according to the guidelines)
:param domain: 'freq' or 'time' to build the frequency of time filter, respectively
:param domain_var: variable related to the domain chosen. Sampling time (Ts) for time-domain
or frequency array (f) in freqency-domain
:param kwargs:
* 'RCseries': option to build the filters related to the 1998 guidelines according to the 2010
guidelines approach (series of RC filters)
:return: parameters of the filter. (WF, phase) in frequency domain. (num, den, num, den) in time-domain
AUTHOR: Luca Giaccone ([email protected])
DATE: 29.02.2016
HISTORY:
EXAMPLE:
>>> quantity = 'B'
>>> kind = 'low'
>>> Ts = 2e-4
>>> num, den, num, den = EuFilter(quantity, kind, 'time', Ts)
"""
# Create filter according to input
if quantity.upper() == 'B':
if kind.lower() == 'low':
from exposure.icnirp import icnirp_filter
if domain.lower() == 'freq':
weight_fun, phase = icnirp_filter('2010', 'occupational',
quantity, domain, f)
elif domain.lower() == 'time':
num, den = icnirp_filter('2010', 'occupational', quantity,
domain)
elif kind.lower() == 'high':
if domain.lower() == 'freq':
# get sign
isgn = np.sign(f)
f = np.abs(f)
# Initialize output
weight_fun = np.zeros(f.shape)
phase = np.zeros(f.shape)
weight_fun[(f >= 1)
& (f < 3000)] = f[(f >= 1)
& (f < 3000)] / (np.sqrt(2) * 0.3)
weight_fun[(f >= 3000) & (f <= 10e6)] = 1 / (np.sqrt(2) * 1e-4)
phase[f < 3000] = 90 * isgn[f < 3000]
elif domain.lower() == 'time':
a = 2 * np.pi * 3000
fref = np.array([1e5])
s = 2j * np.pi * fref
Href = s / (s + a)
lim_ref = eu_limit(fref, quantity, kind) * np.sqrt(2)
k = np.asscalar(1.0 / (lim_ref * np.abs(Href)))
num = np.array([k, 0])
den = np.array([1, a])
elif kind.lower() == 'loc':
if domain.lower() == 'freq':
# get sign
isgn = np.sign(f)
f = np.abs(f)
# Initialize output
weight_fun = np.zeros(f.shape)
phase = np.zeros(f.shape)
weight_fun[(f >= 1)
& (f < 3000)] = f[(f >= 1)
& (f < 3000)] / (np.sqrt(2) * 0.9)
weight_fun[(f >= 3000) & (f <= 10e6)] = 1 / (np.sqrt(2) * 3e-4)
phase[f < 3000] = 90 * isgn[f < 3000]
elif domain.lower() == 'time':
a = 2 * np.pi * 3000
fref = np.array([1e5])
s = 2j * np.pi * fref
Href = s / (s + a)
lim_ref = eu_limit(fref, quantity, kind) * np.sqrt(2)
k = np.asscalar(1.0 / (lim_ref * np.abs(Href)))
num = np.array([k, 0])
den = np.array([1, a])
elif quantity.upper() == 'E':
if kind.lower() == 'sensory':
if domain.lower() == 'freq':
# get sign
isgn = np.sign(f)
f = np.abs(f)
# Initialize output
weight_fun = np.zeros(f.shape)
phase = np.zeros(f.shape)
weight_fun[(f >= 1) & (f < 10)] = f[(f >= 1) & (f < 10)] / 0.7
weight_fun[(f >= 10) & (f < 25)] = 1 / 0.07
weight_fun[(f >= 25)
& (f < 400)] = 1 / (0.0028 *
f[(f >= 25) & (f < 400)])
weight_fun[(f >= 400) & (f < 3e3)] = 1 / 1.1
weight_fun[(f >= 3e3)
& (f <= 10e6)] = 1 / (3.8e-4 *
f[(f >= 3e3) & (f < 10e6)])
phase[(f >= 1) & (f < 10)] = 90 * isgn[(f >= 1) & (f < 10)]
phase[(f >= 10) & (f < 25)] = 0
phase[(f >= 25)
& (f < 400)] = -90 * isgn[(f >= 25) & (f < 400)]
phase[(f >= 400) & (f < 3000)] = 0
phase[(f >= 3000)
& (f < 10e6)] = -90 * isgn[(f >= 3000) & (f < 10e6)]
if domain.lower() == 'time':
a = 2 * np.pi * 10
b = 2 * np.pi * 25
c = 2 * np.pi * 400
d = 2 * np.pi * 3000
fref = np.array([30e3])
s = 2j * np.pi * fref
Href = (s * (s + c)) / ((s + a) * (s + b) * (s + d))
lim_ref = eu_limit(fref, quantity, kind)
k = np.asscalar(1.0 / (lim_ref * np.abs(Href)))
num = [k, k * c, 0]
den = [1, (a + b + d), (a * b + a * d + b * d), a * b * d]
elif kind.lower() == 'health':
if domain.lower() == 'freq':
# get sign
isgn = np.sign(f)
f = np.abs(f)
# Initialize output
weight_fun = np.zeros(f.shape)
phase = np.zeros(f.shape)
weight_fun[(f >= 1) & (f < 3e3)] = 1 / 1.1
weight_fun[(f >= 3e3)
& (f <= 10e6)] = 1 / (3.8e-4 *
f[(f >= 3e3) & (f < 10e6)])
phase[(f >= 1) & (f < 3000)] = 0
phase[(f >= 3000)
& (f < 10e6)] = -90 * isgn[(f >= 3000) & (f < 10e6)]
elif domain.lower() == 'time':
a = 2 * np.pi * 3000
fref = np.array([1])
s = 2j * np.pi * fref
Href = 1 / (s + a)
lim_ref = eu_limit(fref, quantity, kind)
k = np.asscalar(1.0 / (lim_ref * np.abs(Href)))
num = [k]
den = [1, a]
# Assign outputs
if domain == 'time':
return num, den
elif domain == 'freq':
return weight_fun, phase
# load modules
from exposure.icnirp import icnirp_filter
from scipy.signal import bode
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
# all possible parameters
year = '2010'
receptor = 'occupational'
quantity = 'B'
# frequency
domain = 'freq'
f = np.logspace(0, 6, 1000)
H_f, theta_f = icnirp_filter(year, receptor, quantity, domain, f)
# time
domain = 'time'
num, den = icnirp_filter(year, receptor, quantity, domain)
omega, Hdb, theta_t = bode((num, den), 2 * np.pi * f)
H_t = 10**(Hdb / 20)
# plot
plt.figure()
plt.subplot(2, 1, 1)
plt.title("{} - {} (ICNIRP {})".format(receptor, quantity, year), fontsize=12)
plt.loglog(f, H_f)
plt.loglog(f, H_t)
plt.ylabel('magnitude', fontsize=14)
plt.subplot(2, 1, 2)
year = '2010'
receptor = 'occupational'
quantity = 'B'
Glim = icnirp_limit(fr, year, receptor, quantity)
B = np.zeros((t.size, 1))
B[id1:id2, 0] = np.sqrt(2) * Glim * np.sin(2 * np.pi * fr * t[id1:id2])
# Evaluate spectrum
f, BFT = fft_analysis(t, B)
# Frequency summation rule
Blim = icnirp_limit(np.abs(f), year, receptor, quantity) * np.sqrt(2)
IS, ISv = sum_rule(np.abs(BFT), Blim)
# WP in frequency domain
WF, phi = icnirp_filter(year, receptor, quantity, 'freq', f)
IWf, WPf = wpm_freq(WF, phi, f, BFT, makeplot='n')
# WP in time domain
num, den = icnirp_filter(year, receptor, quantity, 'time')
IW, WP = wpm_time(num, den, t, B, makeplot='n')
print("Frequency summation rule: {:.2f}".format(IS))
print("Weighted peak method (freq): {:.2f}".format(IWf))
print("Weighted peak method (time): {:.2f}".format(IW))
# final plot
hsig = plt.figure(facecolor='w')
plt.plot(t, B, 'b', linewidth=2, label='signal')
plt.xlabel('time (s)', fontsize=14)
plt.ylabel('signal', fontsize=14)