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funciones_MPU6050.py
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#!/usr/bin/python
# -*- coding: iso-8859-15 -*-
import numpy as np
import time
from scipy import signal
PACKET_LEN = 14
DATA_LEN = 7
A_X = 0
A_Y = 1
A_Z = 2
TMP = 3
G_X = 4
G_Y = 5
G_Z = 6
SSF_A_def = 16384 #[LSB/g]
SSF_G_def = 131 # [LSB / (º/s)]
PROM_SIZE_ACEL_def = 25
PROM_SIZE_GYRO_def = 3
A_err_def=3
A_err_temp_def=0.02
A_err_offset_def = 50e-3
G_err_def=3
G_err_offset_def = 20
def get_datos_estatico(ser, Ndatos = 10):
lista_in = list()
medicion = np.zeros((DATA_LEN))
time_list = list()
DELTA_t = 0.05
# Pido un par de paquetes mas del reposo
for i in range(Ndatos):
ser.write(b'.')
data = ser.read(PACKET_LEN)
np_data = np.array(list(data),dtype=np.uint8)
act_read = np.zeros((DATA_LEN), dtype=np.int16)
act_feed = np.zeros((DATA_LEN), dtype=np.float32)
for i in range(DATA_LEN):
act_read[i] = np_data[(i*2)+0]*(2**8) + np_data[(i*2)+1]
if i == TMP:
act_feed[i] = (act_read[i]/340.00)+36.53
else:
act_feed[i] = act_read[i]
medicion[i] = act_feed[i]
lista_in.append(np.copy(medicion))
time.sleep(DELTA_t)
medicion = np.array(lista_in)
mean_time_sample = np.array(time_list).mean()
return (medicion, mean_time_sample)
def get_datos(ser, orden, orden2 = 'a', time2orden = -1):
lista_in = list()
medicion = np.zeros((DATA_LEN))
time_list = list()
DELTA_t = 0.05
count_to_speedup = 15/DELTA_t
ser.write(str.encode(orden))
while(1):
t_ini = time.time()
if time2orden > 0 :
count_to_speedup = count_to_speedup - 1
if count_to_speedup == 0:
ser.write(str.encode(orden2))
else:
ser.write(b'.')
data = ser.read(PACKET_LEN)
np_data = np.array(list(data),dtype=np.uint8)
act_read = np.zeros((DATA_LEN), dtype=np.int16)
act_feed = np.zeros((DATA_LEN), dtype=np.float32)
suma = 0
for i in range(DATA_LEN):
act_read[i] = np_data[(i*2)+0]*(2**8) + np_data[(i*2)+1]
suma = suma+act_read[i]
if i == TMP:
act_feed[i] = (act_read[i]/340.00)+36.53
else:
act_feed[i] = act_read[i]
medicion[i] = act_feed[i]
if suma == 0:
break
lista_in.append(np.copy(medicion))
time.sleep(DELTA_t)
time_list.append(time.time()-t_ini)
# Pido un par de paquetes mas del reposo
for i in range(5):
ser.write(b'.')
data = ser.read(PACKET_LEN)
np_data = np.array(list(data),dtype=np.uint8)
act_read = np.zeros((DATA_LEN), dtype=np.int16)
act_feed = np.zeros((DATA_LEN), dtype=np.float32)
for i in range(DATA_LEN):
act_read[i] = np_data[(i*2)+0]*(2**8) + np_data[(i*2)+1]
suma = suma+act_read[i]
if i == TMP:
act_feed[i] = (act_read[i]/340.00)+36.53
else:
act_feed[i] = act_read[i]
medicion[i] = act_feed[i]
lista_in.append(np.copy(medicion))
time.sleep(DELTA_t)
medicion = np.array(lista_in)
mean_time_sample = np.array(time_list).mean()
return (medicion, mean_time_sample)
def signal0to360_HARDCODED(signal_in, eje):
if eje == 0:
# Es el eje X
idx_max = np.argmax(signal_in)
signal_in[idx_max:] = (signal_in.max() - signal_in[idx_max:])+signal_in.max()
else:
# Es el eje Y
# Busco el primer pico
idx_max = np.argmax(np.abs(signal_in[:int(len(signal_in)/2)]))
# me fijo para donde va
direccion = np.sign( signal_in[idx_max] )
if direccion > 0:
# Corrijo el primer pico
signal_in[idx_max:] = (signal_in[idx_max] - signal_in[idx_max:]) + signal_in[idx_max]
# corrijo el segundo
idx_max = np.argmax(signal_in)
signal_in[idx_max:] = (signal_in.max() - signal_in[idx_max:])+signal_in.max()
else:
signal_in[:idx_max] = -signal_in[:idx_max]
signal_in[idx_max:] = (signal_in[idx_max:] - signal_in[idx_max]) - signal_in[idx_max]
# corrijo el segundo
idx_max = np.argmax(signal_in)
signal_in[idx_max:] = (signal_in.max() - signal_in[idx_max:])+signal_in.max()
return signal_in
def signal360to90_HARDCODED(signal_in, base_ang = 90):
idx_sup = np.argwhere(signal_in > base_ang)
signal_in[idx_sup] = (2*base_ang)-signal_in[idx_sup]
idx_sup = np.argwhere(signal_in < -base_ang)
signal_in[idx_sup] = -(2*base_ang)-signal_in[idx_sup]
idx_sup = np.argwhere(signal_in > base_ang)
signal_in[idx_sup] = (2*base_ang)-signal_in[idx_sup]
return signal_in
def acel2deg(signal_in):
out_signal = np.copy(signal_in)
out_signal[out_signal > 1]= 1
out_signal[out_signal < -1]= -1
out_signal = np.arcsin(out_signal)
out_signal = np.rad2deg(out_signal)
return out_signal
def get_signals(medicion,
PROM_SIZE_ACEL = PROM_SIZE_ACEL_def,
PROM_SIZE_GYRO = PROM_SIZE_GYRO_def,
sample_time = -1,
ang_ini = 0,
SSF_A = SSF_A_def,
SSF_G = SSF_G_def,
A_err = A_err_def,
A_err_temp = A_err_temp_def,
A_err_offset = A_err_offset_def,
G_err = G_err_def,
G_err_offset = G_err_offset_def):
num_samp , _ = medicion.shape
out_signals = np.zeros((3,num_samp))
out_std = np.zeros((3,num_samp))
out_rel_err = np.zeros((3,num_samp))
#sample_time = sample_time*1.05
# Acelerometro X
out_signals[0,:] = np.copy(medicion[:,A_X])
out_signals[0,:] = signal.medfilt(out_signals[0,:], kernel_size=PROM_SIZE_ACEL)
out_std[0,:] = np.sqrt( ((out_signals[0,:]/(SSF_A**2) )*SSF_A*(A_err + (A_err_temp*medicion[:,TMP]))/(100*np.sqrt(3)) )**2 + (A_err_offset/np.sqrt(3))**2 )
out_signals[0,:] = out_signals[0,:]/SSF_A
out_std[0,:] = acel2deg(out_std[0,:])
out_rel_err[0,:] = np.divide(acel2deg(out_std[0,:]),acel2deg(out_signals[0,:]))
out_signals[0,:] = acel2deg(out_signals[0,:])
# Acelerometro Y
out_signals[1,:] = np.copy(medicion[:,A_Y])
out_signals[1,:] = signal.medfilt(out_signals[1,:], kernel_size=PROM_SIZE_ACEL)
out_std[1,:] = np.sqrt( ((out_signals[1,:]/(SSF_A**2) )*SSF_A*(A_err + (A_err_temp*medicion[:,TMP]))/(100*np.sqrt(3)) )**2 + (A_err_offset/np.sqrt(3))**2 )
out_signals[1,:] = out_signals[1,:]/SSF_A
out_std[1,:] = acel2deg(out_std[1,:])
out_rel_err[1,:] = np.divide(acel2deg(out_std[1,:]),acel2deg(out_signals[1,:]))
out_signals[1,:] = acel2deg(out_signals[1,:])
# Gyroscopo
out_signals[2,:] = np.copy(medicion[:,G_Z])
out_signals[2,:] = signal.medfilt(out_signals[2,:], kernel_size=PROM_SIZE_GYRO)
dirreccion = np.sign(out_signals[2,:].mean())
if sample_time > 0:
out_std[2,:] = ( ( (sample_time*out_signals[2,:]/(SSF_G**2) )*SSF_G*(G_err)/(100*np.sqrt(3)) ) ** 2 + (sample_time*G_err_offset/np.sqrt(3))**2 )
out_std[2,:] = out_std[2,:].cumsum()
out_std[2,:] = np.sqrt(out_std[2,:])
out_signals[2,:] = (sample_time*(out_signals[2,:]/SSF_G))
out_signals[2,0] = out_signals[2,0] + ang_ini
out_signals[2,:] = out_signals[2,:].cumsum()
out_rel_err[2,:] = np.divide(out_std[2,:],out_signals[2,:])
#if (out_signals[2,-1] < 0):
# out_signals[2,:] = -out_signals[2,:]
#out_signals[2,:] = signal360to90_HARDCODED(out_signals[2,:])
else:
out_std[2,:] = ( ( (out_signals[2,:]/(SSF_G**2) )*SSF_G*(G_err)/(100*np.sqrt(3)) ) ** 2 + (G_err_offset/np.sqrt(3))**2 )
out_std[2,:] = np.sqrt(out_std[2,:])
out_signals[2,:] = (out_signals[2,:]/SSF_G)
out_rel_err[2,:] = np.divide(out_std[2,:],out_signals[2,:])
# PAra poder saber la direccion usamos la info del otro eje
pos0_pos1 = np.logical_and(out_signals[0,:] > 0, out_signals[1,:] > 0)
neg0_pos1 = np.logical_and(out_signals[0,:] < 0, out_signals[1,:] > 0)
neg0_neg1 = np.logical_and(out_signals[0,:] > 0, out_signals[1,:] < 0)
pos0_neg1 = np.logical_and(out_signals[0,:] < 0, out_signals[1,:] < 0)
out_signals[0,pos0_pos1] = out_signals[0,pos0_pos1] - 180 - 90
out_signals[0,neg0_pos1] = out_signals[0,neg0_pos1] - 180 - 90
out_signals[0,neg0_neg1] = -out_signals[0,neg0_neg1] - 90
out_signals[0,pos0_neg1] = -out_signals[0,pos0_neg1] - 90
out_signals[1,pos0_pos1] = -out_signals[1,pos0_pos1] - 180
out_signals[1,neg0_pos1] = out_signals[1,neg0_pos1] - 180 - 180
out_signals[1,neg0_neg1] = -out_signals[1,neg0_neg1] - 180
out_signals[1,pos0_neg1] = out_signals[1,pos0_neg1]
if dirreccion > 0:
out_signals[0,:] = 360+out_signals[0,:]
out_signals[1,:] = 360+out_signals[1,:]
return out_signals, out_std, out_rel_err