def sanity_zerocross1dExample(self):
"""
Checking sanity of zerocross1d example
"""
import numpy as np
import matplotlib.pylab as plt
from PyAstronomy import pyaC
# Generate some 'data'
x = np.arange(100.)**2
y = np.sin(x)
# Set the last data point to zero.
# It will not be counted as a zero crossing!
y[-1] = 0
# Set point to zero. This will be counted as a
# zero crossing
y[10] = 0.0
# Get coordinates and indices of zero crossings
xc, xi = pyaC.zerocross1d(x, y, getIndices=True)
# Plot the data
plt.plot(x, y, 'b.-')
# Add black points where the zero line is crossed
plt.plot(xc, np.zeros(len(xc)), 'kp')
# Add green points at data points preceding an actual
# zero crossing.
plt.plot(x[xi], y[xi], 'gp')
def sanity_zerocross1dExample(self):
"""
Checking sanity of zerocross1d example
"""
import numpy as np
import matplotlib.pylab as plt
from PyAstronomy import pyaC
# Generate some 'data'
x = np.arange(100.)**2
y = np.sin(x)
# Set the last data point to zero.
# It will not be counted as a zero crossing!
y[-1] = 0
# Set point to zero. This will be counted as a
# zero crossing
y[10] = 0.0
# Get coordinates and indices of zero crossings
xc, xi = pyaC.zerocross1d(x, y, getIndices=True)
# Plot the data
plt.plot(x, y, 'b.-')
# Add black points where the zero line is crossed
plt.plot(xc, np.zeros(len(xc)), 'kp')
# Add green points at data points preceding an actual
# zero crossing.
plt.plot(x[xi], y[xi], 'gp')
def get_first_zc(enclosed_signal):
global_miny = min(enclosed_signal)
global_minx = np.argmin(enclosed_signal)
zero_finder = zero_crossing_finder(enclosed_signal)
list_of_new_zc = []
temp_zct = np.array(zero_finder)
temp_zct = np.append(global_minx, temp_zct)
temp_zct = sorted(temp_zct)
y = np.array(enclosed_signal)
x = [i for i, j in enumerate(enclosed_signal)]
x = np.array(x)
#lists of zero crossings of boundary
xc, xi = pyaC.zerocross1d(x, y, getIndices=True)
xc = np.append(global_minx, xc)
xc = sorted(xc)
ast_crossings = []
for x, y in enumerate(xc):
if y == global_minx:
ast_crossings.append(xc[x - 1])
break
ast_crossings = np.array(ast_crossings)
return ast_crossings
def sanity_zerocross1d(self):
"""
Checking sanity of zerocross1d
"""
import numpy as np
from PyAstronomy import pyaC
x = np.arange(3.)
y = np.array([0, 3, 0])
xz = pyaC.zerocross1d(x, y)
self.assertEqual(len(xz), 0, msg="Found zero crossing in 0,3,0 array (problem with first/last point).")
y = np.array([-1., 1., -2.])
xz, xi = pyaC.zerocross1d(x, y, getIndices=True)
self.assertEqual(len(xz), 2, msg="Found the following zero crossings in -1,1,-2 array: " + str(xz))
self.assertEqual(len(xz), len(xi), "Number of values and indicies is not identical.")
self.assertAlmostEqual(np.max(np.abs(np.array([0.5, 1.+1./3.])-xz)), 0.0, delta=1e-8, msg="Found unexpected zero crossings: " + str(xz))
self.assertEqual(np.max(np.abs(xi - np.array([0,1]))), 0, msg="Found unexpected indices: " + str(xi))
def SOH_model(adc_captures_readout=[]):
bounded_start, bounded_end = 2000, 40000
# interpolates signal array by 100
adc_captures_interp = interp(interp(adc_captures_readout, 10), 10)
# finds all zero crossings in signal
zc_loc = zero_crossing_finder(
adc_captures_interp[bounded_start:bounded_end])
# ensures that bounded area starts at the bounddary edges and not at 0
timed_shift = (bounded_start + 2000 / 7.2) / 100
shift = 2000
bounded_signal = adc_captures_interp[bounded_start:bounded_end]
global_miny = min(bounded_signal)
global_minx = np.argmin(bounded_signal)
zero_finder = zero_crossing_finder(bounded_signal)
list_of_new_zc = []
temp_zct = np.array(zero_finder)
temp_zct = np.append(global_minx, temp_zct)
temp_zct = sorted(temp_zct)
y = np.array(bounded_signal)
x = [i for i, j in enumerate(bounded_signal)]
x = np.array(x)
#lists of zero crossings of boundary
xc, xi = pyaC.zerocross1d(x, y, getIndices=True)
xc = np.append(global_minx, xc)
xc = sorted(xc)
ast_crossings = []
for x, y in enumerate(xc):
if y == global_minx:
ast_crossings.append(xc[x - 2])
ast_crossings.append(xc[x - 1])
ast_crossings.append(xc[x + 1])
ast_crossings.append(xc[x + 2])
ast_crossings.append(xc[x + 3])
break
# computes zero crossings
converted_ast = [(i + bounded_start) * 0.72 for i in ast_crossings]
tdiff = [converted_ast[x] - converted_ast[x - 1] for x in range(1, 5)]
value = tdiff[0] * tdiff[0] + tdiff[1] * tdiff[1] + tdiff[2] * tdiff[
2] + tdiff[3] * tdiff[3]
# y = mx+b of zero crossing model and returns SoH for the signal
SoH = value * 0.0000595 + 36.99
SoH = round(SoH, 2)
return SoH, bounded_signal, ast_crossings, global_minx, timed_shift, adc_captures_interp, shift
def sanity_zerocross1d(self):
"""
Checking sanity of zerocross1d
"""
import numpy as np
from PyAstronomy import pyaC
x = np.arange(3.)
y = np.array([0, 3, 0])
xz = pyaC.zerocross1d(x, y)
self.assertEqual(
len(xz),
0,
msg=
"Found zero crossing in 0,3,0 array (problem with first/last point)."
)
y = np.array([-1., 1., -2.])
xz, xi = pyaC.zerocross1d(x, y, getIndices=True)
self.assertEqual(
len(xz),
2,
msg="Found the following zero crossings in -1,1,-2 array: " +
str(xz))
self.assertEqual(len(xz), len(xi),
"Number of values and indicies is not identical.")
self.assertAlmostEqual(
np.max(np.abs(np.array([0.5, 1. + 1. / 3.]) - xz)),
0.0,
delta=1e-8,
msg="Found unexpected zero crossings: " + str(xz))
self.assertEqual(np.max(np.abs(xi - np.array([0, 1]))),
0,
msg="Found unexpected indices: " + str(xi))
def get_Vlims(pr0):
"""Cruce por cero de pr0 - pr
Devuelve solo los extremos
"""
Vextreme = pyaC.zerocross1d(Vr, pr0 - pr)
# Excepciones
assert len(
Vextreme
) == 3, '{} intersecciones entre PV y pr0. Posiblemente sea necesario modificar los límites de Vr'.format(
len(Vextreme))
return Vextreme[0], Vextreme[2]
# %% import numpy as np import matplotlib.pylab as plt from PyAstronomy import pyaC # Generate some 'data' x = np.arange(100.)**2 y = np.sin(x) # Set the last data point to zero. # It will not be counted as a zero crossing! y[-1] = 0 # Set point to zero. This will be counted as a # zero crossing y[10] = 0.0 # Get coordinates and indices of zero crossings xc, xi = pyaC.zerocross1d(x, y, getIndices=True) # Plot the data plt.plot(x, y, 'b.-') # Add black points where the zero line is crossed plt.plot(xc, np.zeros(len(xc)), 'kp') # Add green points at data points preceding an actual # zero crossing. plt.plot(x[xi], y[xi], 'gp') plt.show() # %%
i] == "Sleep stage 3" or v_Hyp_Petit[
i] == "Sleep stage 4" or v_Hyp_Petit[
i] == "Sleep stage R":
etiquetas.append(v_Hyp_Petit[i])
datos.append(
np.array(v_Sig_Petit[s_FirstInd_Petit:s_LastInd_Petit]))
dato_df = pd.DataFrame(
v_Sig_Petit[s_FirstInd_Petit:s_LastInd_Petit])
descriptores[9 * s_SigRef_Petit].append(dato_df.mean()[0])
descriptores[9 * s_SigRef_Petit + 1].append(dato_df.var()[0])
descriptores[9 * s_SigRef_Petit + 2].append(dato_df.kurt()[0])
descriptores[9 * s_SigRef_Petit + 3].append(dato_df.skew()[0])
descriptores[9 * s_SigRef_Petit + 4].append(
len(
pyaC.zerocross1d(
x=arreglo_x,
y=np.array(
v_Sig_Petit[s_FirstInd_Petit:s_LastInd_Petit]))
))
# Frecuencia
dato_fft = sp.fftpack.fft(dato_df)
fftfreq = sp.fftpack.fftfreq(len(dato_fft), 1 / s_FsHz_Petit)
potencia_02 = 0
potencia_28 = 0
potencia_814 = 0
potencia_14inf = 0
for i in range(len(fftfreq)):
if abs(fftfreq[i]) <= 2:
potencia_02 = potencia_02 + abs(dato_fft[i][0])**2
elif abs(fftfreq[i]) < 8:
potencia_28 = potencia_28 + abs(dato_fft[i][0])**2
elif abs(fftfreq[i]) < 14:
#ptl.figure()
while 1:
s_LastInd = s_FirstInd + s_WinSizeSam
if s_LastInd > 501:
break
for i in range(len(v_Hyp)-1):
if ((v_HypTime[i] <= s_FirstInd/s_FsHz) and (s_FirstInd/s_FsHz < v_HypTime[i+1])):
if v_Hyp[i] != "Sleep stage ?":
etiquetas.append(v_Hyp[i])
datos.append(v_Sig[s_FirstInd:s_LastInd])
dato_df = pd.DataFrame(v_Sig[s_FirstInd:s_LastInd])
descriptores[0].append(dato_df.mean()[0])
descriptores[1].append(dato_df.var()[0])
descriptores[2].append(dato_df.kurt()[0])
descriptores[3].append(dato_df.skew()[0])
descriptores[4].append(len(pyaC.zerocross1d(x=arreglo_x, y=np.array(v_Sig[s_FirstInd:s_LastInd]))))
sw = yasa.sw_detect(dato_df.transpose(), s_FsHz, remove_outliers=True)
descriptores[5].append(sw.summary().shape[0])
suma = 0
for i in range(sw.summary().shape[0]):
suma = suma + (sw.summary()['Duration'].iloc[i])
descriptores[6].append(suma)
# Frecuencia
dato_fft = sp.fftpack.fft(dato_df)
fftfreq = sp.fftpack.fftfreq(len(dato_fft), 1/s_FsHz)
potencia_02 = 0
potencia_28 = 0
potencia_814 = 0
potencia_14inf = 0
for i in range(len(fftfreq)):
if abs(fftfreq[i]) <= 2:
