merge mean filtering from viz branch
This commit is contained in:
juliangaal
parent
2ed70201e5
commit
7cf5883eac
13 changed files with 0 additions and 11742 deletions
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@ -1,32 +0,0 @@
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Python script to visualize data, e.g. roll and pitch, used for testing and [demonstration purposes]()
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Accepted file format from data recording, e.g.
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```
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1,2
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2,3
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3,6
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4,9
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5,4
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6,7
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7,7
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8,4
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9,3
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10,7
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```
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"rate" defines the number of steps from one data point to the next. I.e. if the sensor pumps out 100 measurements a second, it takes way too long to visualize like this. Adjust rate to e.g. 25 to do 25% faster run:
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```
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$ python3 viz.py 25
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```
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For requirements, see [requirements.txt](requirements.txt)
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On linux, you need to install `tkinter` with
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```
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$ sudo apt-get install python3-tk
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```
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If you're having issues with matplotlib, install with apt
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```
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$ sudo apt-get install python3-matplotlib
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```
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59
viz/plot.py
59
viz/plot.py
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#!/usr/bin/env python3
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import sys
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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def plot(filename: str, filename_2: str, num_data_points: int):
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df_1 = pd.read_csv(filename, delimiter = ',', names=['roll', 'pitch'])
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df_2 = pd.read_csv(filename_2, delimiter = ',', names=['roll', 'pitch'])
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n = num_data_points
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y = df_1['roll'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='roll raw: var ' + str(np.var(y)), color='black')
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var_1 = np.var(y)
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y = df_2['roll'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='roll calibrated: var ' + str(np.var(y)), color='red')
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var_2 = np.var(y)
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plt.ylim([-0.1, 0.1])
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plt.legend()
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plt.show()
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if var_1 < var_2:
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print("Variance of {} is smaller: {}".format(filename, var_1))
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else:
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print("Variance of {} is smaller: {}".format(filename_2, var_2))
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y = df_1['pitch'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='pitch raw: var ' + str(np.var(y)), color='black')
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var_1 = np.var(y)
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y = df_2['pitch'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='pitch calibrated: var ' + str(np.var(y)), color='red')
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var_2 = np.var(y)
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plt.ylim([-0.1, 0.1])
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plt.legend()
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plt.show()
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if var_1 < var_2:
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print("Variance of {} is smaller: {}".format(filename, var_1))
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else:
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print("Variance of {} is smaller: {}".format(filename_2, var_2))
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file_1 = sys.argv[1]
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file_1 = sys.argv[1]
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file_2 = sys.argv[2]
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n = int(sys.argv[3])
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plot(file_1, file_2, n)
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#!/usr/bin/env python3
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import sys
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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def plot(filename: str, filename_2: str, num_data_points: int):
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df_1 = pd.read_csv(filename, delimiter = ',', names=['x', 'y', 'z'])
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df_2 = pd.read_csv(filename_2, delimiter = ',', names=['x', 'y', 'z'])
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n = num_data_points
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y = df_1['x'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='x raw: var ' + str(np.var(y)), color='black')
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var_1 = np.var(y)
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y = df_2['x'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='x calib: var ' + str(np.var(y)), color='red')
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var_2 = np.var(y)
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plt.ylim([-0.1, 0.1])
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plt.legend()
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plt.show()
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if var_1 < var_2:
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print("Variance of {} is smaller: {}".format(filename, var_1))
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else:
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print("Variance of {} is smaller: {}".format(filename_2, var_2))
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y = df_1['y'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='y raw: var ' + str(np.var(y)), color='black')
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var_1 = np.var(y)
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y = df_2['y'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='y calib: var ' + str(np.var(y)), color='red')
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var_2 = np.var(y)
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plt.ylim([-0.1, 0.1])
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plt.legend()
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plt.show()
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if var_1 < var_2:
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print("Variance of {} is smaller: {}".format(filename, var_1))
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else:
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print("Variance of {} is smaller: {}".format(filename_2, var_2))
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y = df_1['z'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='z raw: var ' + str(np.var(y)), color='black')
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var_1 = np.var(y)
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y = df_2['z'].to_list()
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x = np.linspace(0, n, n)
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plt.plot(x, y[:n], label='z calib: var ' + str(np.var(y)), color='red')
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var_2 = np.var(y)
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plt.ylim([-0.1, 0.1])
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plt.legend()
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plt.show()
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if var_1 < var_2:
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print("Variance of {} is smaller: {}".format(filename, var_1))
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else:
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print("Variance of {} is smaller: {}".format(filename_2, var_2))
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file_1 = sys.argv[1]
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file_1 = sys.argv[1]
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file_1 = sys.argv[1]
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file_2 = sys.argv[2]
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n = int(sys.argv[3])
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plot(file_1, file_2, n)
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numpy
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pandas
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matplotlib
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6295
viz/sample.txt
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viz/sample.txt
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viz/viz.py
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viz/viz.py
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#!/usr/bin/env python3
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import numpy as np
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import sys
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import math
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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import random
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import time
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fig = plt.figure()
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iteration = 0
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ax1 = plt.subplot(211)
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ax2 = plt.subplot(212)
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class RegrMagic(object):
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"""Mock for function Regr_magic()
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"""
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def __init__(self):
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raw_data = open("sample.txt","r").read()
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self.angles = raw_data.split('\n')
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self.num_data = len(self.angles)
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self.run_index = 0
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self.rate = 1
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if len(sys.argv) > 1 and len(sys.argv) < 3:
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self.rate = int(sys.argv[1])
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print("Rate set to", self.rate)
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def __call__(self) -> float:
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if self.run_index < self.num_data-1:
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x,y = self.angles[self.run_index].split(',')
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self.run_index += 100
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return float(x), float(y)
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else:
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sys.exit(0)
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regr_magic = RegrMagic()
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def frames():
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while True:
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yield regr_magic()
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def get_start_end(point: (float, float), length: float, angle: float):
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x, y = point
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# find the end point
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endx = x + length * math.cos(math.radians(angle))
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endy = y + length * math.sin(math.radians(angle))
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# find the start point
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startx = x - length * math.cos(math.radians(angle))
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starty = y - length * math.sin(math.radians(angle))
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return (endx, endy, startx, starty)
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def plot_line(args):
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'''
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angle1 - Angle you want your end point at in degrees.
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length - Length of the line you want to plot.
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Will plot the line on a 5 x 5 plot.
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'''
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global iteration
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# unpack the first point
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x, y = (2., 0.)
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length = 5.0
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roll_angle = args[0] * 180.0 / math.pi
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pitch_angle = args[1] * 180.0 / math.pi
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print(roll_angle, pitch_angle)
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ax1.clear()
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ax2.clear()
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ax1.set_ylim([-2, 2]) # set the bounds to be -20, 20
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ax2.set_ylim([-2, 2]) # set the bounds to be -20, 20
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ax1.set_xlim([0, 4])
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ax2.set_xlim([0, 4])
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# plot the points
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endx, endy, startx, starty = get_start_end((x,y), length, roll_angle)
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ax1.plot([startx, endx], [starty, endy], c='black', linewidth='8.0')
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endx, endy, startx, starty = get_start_end((x,y), length, pitch_angle)
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ax2.plot([startx, endx], [starty, endy], c='black', linewidth='8.0')
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ani = animation.FuncAnimation(fig, plot_line, frames=frames, interval=10)
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# save the animation as an mp4. This requires ffmpeg or mencoder to be
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# installed. The extra_args ensure that the x264 codec is used, so that
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# the video can be embedded in html5. You may need to adjust this for
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# your system: for more information, see
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# http://matplotlib.sourceforge.net/api/animation_api.html
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#ani.save('double_pendulum.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
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plt.legend()
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plt.show()
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[target.armv7-unknown-linux-gnueabihf]
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linker = "arm-linux-gnueabihf-gcc"
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[package]
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name = "viz"
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version = "0.1.0"
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authors = ["Julian Gaal <juliangaal@protonmail.com>"]
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edition = "2018"
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[dependencies]
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mpu6050 = { path = "../../" }
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i2cdev = "0.4.1"
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linux-embedded-hal = "0.2.2"
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use mpu6050::*;
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use linux_embedded_hal::{I2cdev, Delay};
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use i2cdev::linux::LinuxI2CError;
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use std::io::prelude::*;
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use std::fs::File;
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use std::path::Path;
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use std::error::Error;
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fn new_file(name: &str) -> File {
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let path = Path::new(name);
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let display = path.display();
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let _file = match File::create(&path) {
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Err(why) => panic!("couldn't create {}: {}",
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display,
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why.description()),
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Ok(file) => return file,
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};
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}
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fn write_x_to(file: &mut File, content: String) {
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match file.write_all(content.as_bytes()) {
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Err(why) => {
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println!("couldn't write to file: {}", why.description());
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},
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Ok(_) => {},
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}
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}
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fn main() -> Result<(), Mpu6050Error<LinuxI2CError>> {
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let i2c = I2cdev::new("/dev/i2c-1")
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.map_err(Mpu6050Error::I2c)?;
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let delay = Delay;
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let mut mpu = Mpu6050::new(i2c, delay);
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mpu.init()?;
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mpu.soft_calib(200)?;
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mpu.calc_variance(200)?;
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println!("Calculated variance: {:?}", mpu.get_variance().unwrap());
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let mut acc_file = new_file("acc_data.txt");
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let mut gyro_file = new_file("gyro_data.txt");
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let mut temp_file = new_file("temp_data.txt");
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let mut angles_file = new_file("angles_data.txt");
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loop {
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// get roll and pitch estimate
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let acc = mpu.get_acc_angles()?;
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write_x_to(&mut angles_file, format!("{},{}\n", acc.0, acc.1));
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// get temp
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let temp = mpu.get_temp()?;
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write_x_to(&mut temp_file, format!("{}\n", temp));
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// get gyro data, scaled with sensitivity
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let gyro = mpu.get_gyro()?;
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write_x_to(&mut gyro_file, format!("{},{},{}\n", gyro.0, gyro.1, gyro.2));
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// get accelerometer data, scaled with sensitivity
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let acc = mpu.get_acc()?;
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write_x_to(&mut acc_file, format!("{},{},{}\n", acc.0, acc.1, acc.2));
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}
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}
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