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plot0180.py
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plot0180.py
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import pandas as pd
import numpy as np
import streamlit as st
import time
from plotly import graph_objects as go
import os
import inspect
from google.protobuf.json_format import MessageToJson
import argparse
from gym_robotable.envs import logging
import plotly.express as px
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
os.sys.path.insert(0, parentdir)
def normalize_0_180(img):
b = (img - np.min(img))/np.ptp(img)
#normalized_input = (img - np.amin(img)) / (np.amax(img) - np.amin(img))
normalized_0_180 = (180*(img - np.min(img))/np.ptp(img)).astype(int)
return normalized_0_180
def normalize_negative_one(img):
normalized_input = (img - np.amin(img)) / (np.amax(img) - np.amin(img))
return 2*normalized_input - 1
if __name__ == "__main__":
st.title('Analyticz')
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--log_file', help='path to protobuf file', default='/media/chrx/0FEC49A4317DA4DA/walkinglogs/robotable_log_2021-01-17-231240')
args = parser.parse_args()
logging = logging.RobotableLogging()
episode_proto = logging.restore_episode(args.log_file)
times = []
velocities = [[] for i in range(4)]
for step in range(len(episode_proto.state_action)):
step_log = episode_proto.state_action[step]
times.append(str(step_log.time.seconds) + '.' + str(step_log.time.nanos))
for i in range(4):
velocities[i].append(step_log.motor_states[i].velocity)
#truncate because a bunch of trailing zeros
velocities[0] = velocities[0][0:3000]
velocities[1] = velocities[1][0:3000]
velocities[2] = velocities[2][0:3000]
velocities[3] = velocities[3][0:3000]
times = times[0:3000]
#get moving averages
window_size_0=40
numbers_series_0 = pd.Series(velocities[0])
windows_0 = numbers_series_0.rolling(window_size_0)
moving_averages_0 = windows_0.mean()
moving_averages_list_0 = moving_averages_0.tolist()
without_nans_0 = moving_averages_list_0[window_size_0 - 1:]
window_size_1=40
numbers_series_1 = pd.Series(velocities[1])
windows_1 = numbers_series_1.rolling(window_size_1)
moving_averages_1 = windows_1.mean()
moving_averages_list_1 = moving_averages_1.tolist()
without_nans_1 = moving_averages_list_1[window_size_1 - 1:]
window_size_2=40
numbers_series_2 = pd.Series(velocities[2])
windows_2 = numbers_series_2.rolling(window_size_2)
moving_averages_2 = windows_2.mean()
moving_averages_list_2 = moving_averages_2.tolist()
without_nans_2 = moving_averages_list_2[window_size_2 - 1:]
window_size_3=40
numbers_series_3 = pd.Series(velocities[3])
windows_3 = numbers_series_3.rolling(window_size_3)
moving_averages_3 = windows_3.mean()
moving_averages_list_3 = moving_averages_3.tolist()
without_nans_3 = moving_averages_list_3[window_size_3 - 1:]
#normalize between -1 and 1
avg_0 = np.asarray(without_nans_0)
avg_1 = np.asarray(without_nans_1)
avg_2 = np.asarray(without_nans_2)
avg_3 = np.asarray(without_nans_3)
avg_0 = normalize_negative_one(avg_0)
avg_1 = normalize_negative_one(avg_1)
avg_2 = normalize_negative_one(avg_2)
avg_3 = normalize_negative_one(avg_3)
np.save('velocity_front_right', avg_0)
np.save('velocity_front_left', avg_1)
np.save('velocity_back_right', avg_2)
np.save('velocity_back_left', avg_3)
np.save('times', times)
#normalize between 0 and 180
avg_0 = np.asarray(without_nans_0)
avg_1 = np.asarray(without_nans_1)
avg_2 = np.asarray(without_nans_2)
avg_3 = np.asarray(without_nans_3)
avg_0 = normalize_0_180(avg_0)
avg_1 = normalize_0_180(avg_1)
avg_2 = normalize_0_180(avg_2)
avg_3 = normalize_0_180(avg_3)
np.save('velocity_front_right_180', avg_0)
np.save('velocity_front_left_180', avg_1)
np.save('velocity_back_right_180', avg_2)
np.save('velocity_back_left_180', avg_3)
# Create traces
fig0 = go.Figure()
fig0.add_trace(go.Scatter(x=times, y=velocities[0],
mode='lines',
name='Velocities 0'))
fig0.add_trace(go.Scatter(x=times, y=avg_0.tolist(),
mode='lines',
name='Norm Moving Average 0'))
st.plotly_chart(fig0)
fig1 = go.Figure()
fig1.add_trace(go.Scatter(x=times, y=velocities[1],
mode='lines',
name='Velocities 1'))
fig1.add_trace(go.Scatter(x=times, y=avg_1.tolist(),
mode='lines',
name='Norm Moving Average 1'))
st.plotly_chart(fig1)
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=times, y=velocities[2],
mode='lines',
name='Velocities 2'))
fig2.add_trace(go.Scatter(x=times, y=avg_2.tolist(),
mode='lines',
name='Norm Moving Average 2'))
st.plotly_chart(fig2)
fig3 = go.Figure()
fig3.add_trace(go.Scatter(x=times, y=velocities[3],
mode='lines',
name='Velocities 3'))
fig3.add_trace(go.Scatter(x=times, y=avg_3.tolist(),
mode='lines',
name='Norm Moving Average 3'))
st.plotly_chart(fig3)