rpicatoste

import pandas as pd
class MyDF(pd.DataFrame):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.definition = 'my_definition'

my_df = MyDF()
my_df.__dict__ = df.__dict__.copy()
print(len(my_df))
print(my_df.definition)

rpicatoste

import json
from keras.models import model_from_json

def convert_to_inference_model(original_model):
    original_model_json = original_model.to_json()
    inference_model_dict = json.loads(original_model_json)

    layers = inference_model_dict['config']
    for layer in layers:
        if 'stateful' in layer['config']:

rpicatoste

tch_size += direction

            if training_config.batch_size < 1:
                direction *= -1

            previous_epoch_time = current_epoch_time
            file_name = 'model_epoch_{:04}.h5'.format(epoch)
            history_file_name = 'model_history.pickle'
            try:
                if 'linux' in sys

rpicatoste

# %%
import csv
import numpy as np
from task import Task
from agents.agent import DDPG
from plot_functions import plot_results
from collections import defaultdict
import copy

import matplotlib.pyplot as plt

rpicatoste

from pid import PID
from lowpass import LowPassFilter
from yaw_controller import YawController
import rospy
import pickle
import copy
import os

controller_log_time_seconds = 5
controller_log_file_base = '/home/student/shared_vm/controller_id_log_'

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#%%
import pickle  
import numpy as np
import matplotlib.pyplot as plt  

server_log_filename = 'C:\workspace\shared_vm\server_pid_log_0.pkl'


with open(server_log_filename, 'rb') as f: 
    log = pickle.load(f)