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How to use in_robot method in robotframework-pageobjects

Best Python code snippet using robotframework-pageobjects_python

test.py

Source:test.py

1from datetime import datetime2import matplotlib.pyplot as plt3from matplotlib import style4import pandas5# My classes for simulating trading6import SmoothAlgo as SAlgo7from StockSim import HistoricalStock8from BrokerSim import Broker9from RobotTrader import TrendRobot10############################11###### BEGIN : CONFIG ######12############################13TICKER = 'aapl'14START = datetime(2015,1,1)15START_WALLET = 5000016BUY_RATE = 1317BUY_MARGIN = 1.0818SELL_RATE = 2119SELL_MARGIN = 0.920SMOOTH_ALGO = SAlgo.MovingAverage(6)21# SMOOTH_ALGO = SAlgo.WeightedMovingAverage(32)22# SMOOTH_ALGO = SAlgo.ExponentialMovingAverage(16)23############################24####### END : CONFIG #######25############################26def simulate_robot_and_graph(in_robot):27    ### Keep track of data in these classes ###28    stocks_held = {}29    total_wallet = {}30    raw_wallet = {}31    raw_stock_price = {}32    smooth_stock_price = {}33    ### While there is still stock data,   ###34    ### Get the robot to go through it and ###35    ### record the data                    ###36    while not in_robot.done():37        # Get robot to buy and sell for the day38        in_robot.simulate_day()39        # Get the current date from the stock class40        date = in_robot.get_broker().get_stock().get_date()41        # Get the amount of stocks held42        stocks_held[date] = in_robot.get_stocks_held() / STOCKS_HELD_SCALE43        # Get the wallet amount with and without stocks in them44        total_wallet[date] = in_robot.get_total_wallet() / WALLET_SCALE45        raw_wallet[date] = in_robot.get_raw_wallet() / WALLET_SCALE46        # get the raw and the stock prices47        raw_stock_price[date] = in_robot.get_stock_price()48        # Check if you should record two smooth values or just one49        smooth_stock_price[date] = in_robot.get_smooth_stock_price()50    # Plot all of the data51    pandas.Series(raw_stock_price).plot(label='Stock Price')52    pandas.Series(smooth_stock_price).plot(label='Smooth Stock Price')53    pandas.Series(total_wallet).plot(label='Total Wallet / ' + str(WALLET_SCALE))54    pandas.Series(raw_wallet).plot(label='Wallet / ' + str(WALLET_SCALE))55    #pandas.Series(stocks_held).plot(label='Stocks Held / ' + str(STOCKS_HELD_SCALE))56    # Show trading information57    plt.legend()58    plt.show()59    return in_robot60# Simulate the robot going through the stocks61def simulate_robot(in_robot):62    while not in_robot.done():63        in_robot.simulate_day()64    return in_robot65### The amount that the data will be scaled on the graph66STOCKS_HELD_SCALE = 1067WALLET_SCALE = 100068### Get the stock data into a class called my_stock ###69my_stock = HistoricalStock(ticker=TICKER, start=START)70### Simulate broker that holds all the money and my stocks ###71my_broker = Broker(stock=my_stock, wallet=START_WALLET)72### Create robot class and give it all the parameters ###73my_robot = TrendRobot(74    broker=my_broker, smoother=SMOOTH_ALGO, 75    buy_rate=BUY_RATE, sell_rate=SELL_RATE,76    buy_margin=BUY_MARGIN, sell_margin=SELL_MARGIN77)78# print(simulate_robot(my_robot).get_total_wallet())...

feedbackcontroller.py

Source:feedbackcontroller.py

1# @author Simon Stepputtis <sstepput@asu.edu>, Interactive Robotics Lab, Arizona State University2import tensorflow as tf3from model_src.basismodel import BasisModel4class FeedbackController(tf.keras.layers.Layer):5    def __init__(self, robot_state_size, rnn_state_size, dimensions, basis_functions, special, **kwargs):6        super(FeedbackController, self).__init__(**kwargs)7        self.robot_state_size = robot_state_size8        self.state_size       = rnn_state_size9        self.dims             = dimensions10        self.n_bfuncs         = basis_functions11    def build(self, input_shape):12        self.robot_gru      = tf.keras.layers.GRUCell(units=self.robot_state_size)13        self.weight_dense_1 = tf.keras.layers.Dense(units=self.dims * self.n_bfuncs, activation=tf.keras.activations.relu)14        self.weight_dense_2 = tf.keras.layers.Dense(units=self.dims * self.n_bfuncs, activation=tf.keras.activations.relu)15        self.weight_dense_3 = tf.keras.layers.Dense(units=self.dims * self.n_bfuncs, activation=tf.keras.activations.linear)16        self.phase_dense_1 = tf.keras.layers.Dense(units=int(self.robot_state_size / 2.0), activation=tf.keras.activations.relu)17        self.phase_dense_2 = tf.keras.layers.Dense(units=1, activation=tf.keras.activations.hard_sigmoid)18        self.basismodel = BasisModel(dimensions=self.dims, nfunctions=self.n_bfuncs, scale=0.012)19    # @tf.function20    def call(self, inputs, states, constants=None, training=False, mask=None, **kwargs):21        # Get data ready22        in_robot       = inputs23        st_robot_last  = states[0]24        st_gru_last    = states[1]25        cn_features    = constants[0]26        cn_delta_t     = constants[1]27        28        # Robot GRU:29        in_robot           = tf.cond(tf.convert_to_tensor(training), lambda: st_robot_last, lambda: in_robot)30        e_robot, gru_state = self.robot_gru(inputs=in_robot, states=[st_gru_last])31        # Internal state:32        x = tf.keras.backend.concatenate((cn_features, e_robot), axis=1)33        # Use x to calcate the weights:34        weights = self.weight_dense_3(self.weight_dense_2(self.weight_dense_1(x)))35        weights = tf.keras.backend.reshape(weights, shape=[-1, self.dims, self.n_bfuncs])36        # Phase estimation, based on x:37        dt    = 1.0 / (500.0 * cn_delta_t) # Calculates the actual dt38        phase = self.phase_dense_2(self.phase_dense_1(x))39        phase = phase + dt40        # Apply basis model:41        action, _ = self.basismodel((weights, tf.zeros_like(weights), phase))42        action = tf.squeeze(action)43        # Rebuild the state:44        new_states = (action, gru_state[0])45        # Return results (and state)...

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