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# Copyright (C) 2010 Antoine Drouin |
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# |
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# This file is part of Paparazzi. |
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# |
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# Paparazzi is free software; you can redistribute it and/or modify |
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# it under the terms of the GNU General Public License as published by |
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# the Free Software Foundation; either version 2, or (at your option) |
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# any later version. |
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# |
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# Paparazzi is distributed in the hope that it will be useful, |
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# but WITHOUT ANY WARRANTY; without even the implied warranty of |
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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# GNU General Public License for more details. |
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# |
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# You should have received a copy of the GNU General Public License |
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# along with Paparazzi; see the file COPYING. If not, write to |
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# the Free Software Foundation, 59 Temple Place - Suite 330, |
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# Boston, MA 02111-1307, USA. |
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# |
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from __future__ import print_function, division |
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import re |
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import numpy as np |
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from numpy import sin, cos |
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from scipy import linalg, stats |
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import matplotlib |
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import matplotlib.pyplot as plt |
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from mpl_toolkits.mplot3d import Axes3D |
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def get_ids_in_log(filename): |
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"""Returns available ac_id from a log.""" |
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f = open(filename, 'r') |
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ids = [] |
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pattern = re.compile("\S+ (\S+)") |
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while True: |
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line = f.readline().strip() |
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if line == '': |
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break |
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m = re.match(pattern, line) |
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if m: |
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ac_id = m.group(1) |
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if not ac_id in ids: |
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ids.append(ac_id) |
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return ids |
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def read_log(ac_id, filename, sensor): |
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"""Extracts raw sensor measurements from a log.""" |
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f = open(filename, 'r') |
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pattern = re.compile("(\S+) "+ac_id+" IMU_"+sensor+"_RAW (\S+) (\S+) (\S+)") |
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list_meas = [] |
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while True: |
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line = f.readline().strip() |
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if line == '': |
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break |
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m = re.match(pattern, line) |
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if m: |
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list_meas.append([float(m.group(2)), float(m.group(3)), float(m.group(4))]) |
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return np.array(list_meas) |
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def read_log_mag_current(ac_id, filename): |
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"""Extracts raw magnetometer and current measurements from a log.""" |
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f = open(filename, 'r') |
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pattern = re.compile("(\S+) "+ac_id+" IMU_MAG_CURRENT_CALIBRATION (\S+) (\S+) (\S+) (\S+)") |
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list_meas = [] |
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while True: |
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line = f.readline().strip() |
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if line == '': |
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break |
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m = re.match(pattern, line) |
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if m: |
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list_meas.append([float(m.group(2)), float(m.group(3)), float(m.group(4)), float(m.group(5))]) |
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return np.array(list_meas) |
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def filter_meas(meas, window_size, noise_threshold): |
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"""Select only non-noisy data.""" |
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filtered_meas = [] |
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filtered_idx = [] |
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for i in range(window_size, len(meas)-window_size): |
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noise = meas[i-window_size:i+window_size, :].std(axis=0) |
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if linalg.norm(noise) < noise_threshold: |
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filtered_meas.append(meas[i, :]) |
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filtered_idx.append(i) |
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return np.array(filtered_meas), filtered_idx |
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def get_min_max_guess(meas, scale): |
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"""Initial boundary based calibration.""" |
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max_meas = meas[:, :].max(axis=0) |
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min_meas = meas[:, :].min(axis=0) |
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n = (max_meas + min_meas) / 2 |
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sf = 2*scale/(max_meas - min_meas) |
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return np.array([n[0], n[1], n[2], sf[0], sf[1], sf[2]]) |
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def scale_measurements(meas, p): |
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"""Scale the set of measurements.""" |
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l_comp = [] |
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l_norm = [] |
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for m in meas[:, ]: |
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sm = (m - p[0:3])*p[3:6] |
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l_comp.append(sm) |
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l_norm.append(linalg.norm(sm)) |
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return np.array(l_comp), np.array(l_norm) |
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def estimate_mag_current_relation(meas): |
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"""Calculate linear coefficient of magnetometer-current relation.""" |
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coefficient = [] |
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for i in range(0, 3): |
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gradient, intercept, r_value, p_value, std_err = stats.linregress(meas[:, 3], meas[:, i]) |
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coefficient.append(gradient) |
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return coefficient |
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def print_xml(p, sensor, res): |
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"""Print xml for airframe file.""" |
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print("") |
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print("<define name=\""+sensor+"_X_NEUTRAL\" value=\""+str(int(round(p[0])))+"\"/>") |
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print("<define name=\""+sensor+"_Y_NEUTRAL\" value=\""+str(int(round(p[1])))+"\"/>") |
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print("<define name=\""+sensor+"_Z_NEUTRAL\" value=\""+str(int(round(p[2])))+"\"/>") |
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print("<define name=\""+sensor+"_X_SENS\" value=\""+str(p[3]*2**res)+"\" integer=\"16\"/>") |
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print("<define name=\""+sensor+"_Y_SENS\" value=\""+str(p[4]*2**res)+"\" integer=\"16\"/>") |
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print("<define name=\""+sensor+"_Z_SENS\" value=\""+str(p[5]*2**res)+"\" integer=\"16\"/>") |
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def plot_results(block, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref): |
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"""Plot calibration results.""" |
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plt.subplot(3, 1, 1) |
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plt.plot(measurements[:, 0]) |
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plt.plot(measurements[:, 1]) |
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plt.plot(measurements[:, 2]) |
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plt.plot(flt_idx, flt_meas[:, 0], 'ro') |
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plt.plot(flt_idx, flt_meas[:, 1], 'ro') |
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plt.plot(flt_idx, flt_meas[:, 2], 'ro') |
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plt.xlabel('time (s)') |
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plt.ylabel('ADC') |
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plt.title('Raw sensors') |
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plt.subplot(3, 2, 3) |
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plt.plot(cp0[:, 0]) |
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plt.plot(cp0[:, 1]) |
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plt.plot(cp0[:, 2]) |
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plt.plot(-sensor_ref*np.ones(len(flt_meas))) |
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plt.plot(sensor_ref*np.ones(len(flt_meas))) |
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plt.subplot(3, 2, 4) |
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plt.plot(np0) |
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plt.plot(sensor_ref*np.ones(len(flt_meas))) |
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plt.subplot(3, 2, 5) |
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plt.plot(cp1[:, 0]) |
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plt.plot(cp1[:, 1]) |
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plt.plot(cp1[:, 2]) |
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plt.plot(-sensor_ref*np.ones(len(flt_meas))) |
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plt.plot(sensor_ref*np.ones(len(flt_meas))) |
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plt.subplot(3, 2, 6) |
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plt.plot(np1) |
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plt.plot(sensor_ref*np.ones(len(flt_meas))) |
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# if we want to have another plot we only draw the figure (non-blocking) |
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# also in matplotlib before 1.0.0 there is only one call to show possible |
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if block: |
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plt.show() |
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else: |
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plt.draw() |
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def plot_mag_3d(measured, calibrated, p): |
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"""Plot magnetometer measurements on 3D sphere.""" |
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# set up points for sphere and ellipsoid wireframes |
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u = np.r_[0:2 * np.pi:20j] |
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v = np.r_[0:np.pi:20j] |
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wx = np.outer(cos(u), sin(v)) |
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wy = np.outer(sin(u), sin(v)) |
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wz = np.outer(np.ones(np.size(u)), cos(v)) |
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ex = p[0] * np.ones(np.size(u)) + np.outer(cos(u), sin(v)) / p[3] |
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ey = p[1] * np.ones(np.size(u)) + np.outer(sin(u), sin(v)) / p[4] |
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ez = p[2] * np.ones(np.size(u)) + np.outer(np.ones(np.size(u)), cos(v)) / p[5] |
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# measurements |
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mx = measured[:, 0] |
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my = measured[:, 1] |
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mz = measured[:, 2] |
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# calibrated values |
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cx = calibrated[:, 0] |
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cy = calibrated[:, 1] |
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cz = calibrated[:, 2] |
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# axes size |
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left = 0.02 |
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bottom = 0.05 |
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width = 0.46 |
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height = 0.9 |
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rect_l = [left, bottom, width, height] |
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rect_r = [left/2+0.5, bottom, width, height] |
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fig = plt.figure(figsize=plt.figaspect(0.5)) |
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if matplotlib.__version__.startswith('0'): |
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ax = Axes3D(fig, rect=rect_l) |
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else: |
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ax = fig.add_subplot(1, 2, 1, position=rect_l, projection='3d') |
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# plot measurements |
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ax.scatter(mx, my, mz) |
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plt.hold(True) |
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# plot line from center to ellipsoid center |
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ax.plot([0.0, p[0]], [0.0, p[1]], [0.0, p[2]], color='black', marker='+', markersize=10) |
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# plot ellipsoid |
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ax.plot_wireframe(ex, ey, ez, color='grey', alpha=0.5) |
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# Create cubic bounding box to simulate equal aspect ratio |
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max_range = np.array([mx.max() - mx.min(), my.max() - my.min(), mz.max() - mz.min()]).max() |
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Xb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][0].flatten() + 0.5 * (mx.max() + mx.min()) |
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Yb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][1].flatten() + 0.5 * (my.max() + my.min()) |
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Zb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][2].flatten() + 0.5 * (mz.max() + mz.min()) |
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# add the fake bounding box: |
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for xb, yb, zb in zip(Xb, Yb, Zb): |
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ax.plot([xb], [yb], [zb], 'w') |
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ax.set_title('MAG raw with fitted ellipsoid and center offset') |
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ax.set_xlabel('x') |
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ax.set_ylabel('y') |
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ax.set_zlabel('z') |
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if matplotlib.__version__.startswith('0'): |
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ax = Axes3D(fig, rect=rect_r) |
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else: |
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ax = fig.add_subplot(1, 2, 2, position=rect_r, projection='3d') |
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ax.plot_wireframe(wx, wy, wz, color='grey', alpha=0.5) |
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plt.hold(True) |
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ax.scatter(cx, cy, cz) |
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ax.set_title('MAG calibrated on unit sphere') |
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ax.set_xlabel('x') |
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ax.set_ylabel('y') |
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ax.set_zlabel('z') |
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ax.set_xlim3d(-1, 1) |
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ax.set_ylim3d(-1, 1) |
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ax.set_zlim3d(-1, 1) |
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plt.show() |
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def read_turntable_log(ac_id, tt_id, filename, _min, _max): |
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""" Read a turntable log. |
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return an array which first column is turnatble and next 3 are gyro |
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""" |
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f = open(filename, 'r') |
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pattern_g = re.compile("(\S+) "+str(ac_id)+" IMU_GYRO_RAW (\S+) (\S+) (\S+)") |
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pattern_t = re.compile("(\S+) "+str(tt_id)+" IMU_TURNTABLE (\S+)") |
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last_tt = None |
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list_tt = [] |
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while True: |
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line = f.readline().strip() |
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if line == '': |
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break |
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m = re.match(pattern_t, line) |
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if m: |
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last_tt = float(m.group(2)) |
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m = re.match(pattern_g, line) |
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if m and last_tt and _min < last_tt < _max: |
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list_tt.append([last_tt, float(m.group(2)), float(m.group(3)), float(m.group(4))]) |
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return np.array(list_tt) |