superjax

# Copyright (c) 2016, James Jackson
# All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:

# 1. Redistributions of source code must retain the above copyright notice, this
#    list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
#    this list of conditions and the following disclaimer in the documentation

superjax

import numpy as np
import matplotlib.pyplot as plt
from math import sin, pi, asin, tan, atan

def f(x):
    return ((1.0 - pow(x / 101325.0, 0.190284)) * 145366.45) * 0.3048


def finv(x):
    return 101325.0 * (1 - 2.25577 * 10 ** -5 * x) ** 5.25588

superjax

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  <action id="CheckinProject" />
  <action id="CloseContent">
    <keyboard-shortcut first-keystroke="ctrl w" />
  </action>
  <action id="CollapseAll">

superjax

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superjax

import re

f = open('/home/ultron/Code/ROSflight/src/param.c')
text = f.read()


lines = re.split("\n+", text)

params = []
i = 0

superjax

def lqr(A,B,Q,R):
    #first, try to solve the ricatti equation
    X = np.matrix(solve_continuous_are(A, B, Q, R))

    #compute the LQR gain
    K = np.matrix(inv(R)*(B.T*X))

    eigVals, eigVecs = eig(A-B*K)

    return K, X, eigVals

superjax

# This is an implementation of Occupancy Grid Mapping as Presented
# in Chapter 9 of "Probabilistic Robotics" By Sebastian Thrun et al.
# In particular, this is an implementation of Table 9.1 and 9.2


import scipy.io
import scipy.stats
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm

superjax

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import numpy as np

def plot_point_cov(points, nstd=2, ax=None, **kwargs):
    """
    Plots an `nstd` sigma ellipse based on the mean and covariance of a point
    "cloud" (points, an Nx2 array).

    Parameters

superjax

import numpy as np
import scipy.linalg
import scipy.stats
import matplotlib.pyplot as plt
import scipy.io
import scipy.sparse
from plot_helper import plot_cov_ellipse
from tqdm import tqdm

def R(theta):

superjax

from tensorflow.python.ops.rnn_cell import RNNCell
from tensorflow.python.ops import math_ops
import tensorflow as tf

class myGRU(RNNCell):
    def __init__(self, num_units, forget_bias=1.0,
                 state_is_tuple=True, activation=None, reuse=None):
        super(RNNCell, self).__init__(_reuse=reuse)
        self._num_units = num_units
        self._forget_bias = forget_bias