Roberto Zegers R. rfzeg

## arm_config.py
import matplotlib.pyplot as plt
import numpy as np
from math import sin, cos

# Define a function to compute the arm configuration
def compute_arm_config(link1_length, link2_length, joint0_angle, joint1_angle):
    # compute the (x, y) position of the p1 joint and the end effector at p2.
    # input angle in radians
    # X0 = distance1 * cos(angle0)
    # Y0 = distance1 * sin(angle0)

## angle_conversion_factors.py
# Conversion Factors
rtd = 180./np.pi # radians to degrees
dtr = np.pi/180. # degrees to radians

## intrinsic_rotation_template.py
from sympy import symbols, cos, sin, pi, sqrt
from sympy.matrices import Matrix

### Create symbols for joint variables
q1, q2 = symbols('q1:3')

# Create a symbolic matrix representing an intrinsic sequence of rotations
# about the Y and then Z axes. Let the rotation about the Y axis be described
# by q1 and the rotation about Z by q2.

## euler_from_rotation_matrix.py
#!/usr/bin/env python
import numpy as np
from sympy.matrices import Matrix
from sympy import symbols, atan2, sqrt

# Conversion Factors
rtd = 180./np.pi # radians to degrees

# Fixed Axis X-Y-Z Rotation Matrix
R_XYZ = Matrix([[ 0.353553390593274, -0.306186217847897, 0.883883476483184],

## homogeneous_transform_matrix .py
T0_1 = Matrix([[            cos(q1),            -sin(q1),            0,              a0],
               [sin(q1)*cos(alpha0), cos(q1)*cos(alpha0), -sin(alpha0), -sin(alpha0)*d1],
               [sin(q1)*sin(alpha0), cos(q1)*sin(alpha0),  cos(alpha0),  cos(alpha0)*d1],
               [                  0,                   0,            0,               1]])

## search_list_of_dictionaries.py
# Function to search list of dictionaries and return a selected value in selected dictionary
def search_dictionaries(key1, value1, key2, list_of_dictionaries):
    selected_dic = [element for element in list_of_dictionaries if element[key1] == value1][0]
    selected_val = selected_dic.get(key2)
    return selected_val

## normalize_grayscale.py
def normalize_grayscale(image_data):
    """
    Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
    :param image_data: The image data to be normalized
    :return: Normalized image data
    """
    # Implement Min-Max scaling for grayscale image data
    a = 0.1
    b = 0.9
    grayscale_min = 0

## one_hot_encode.py
def one_hot_encode(labels):
    """
    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
    : x: List of sample Labels
    : return: Numpy array of one-hot encoded labels
    """
    # TODO: Implement Function
    depth = 10
    one_hot = np.eye(depth)[labels]


## respawn.py
#!/usr/bin/python
#################################################################
##\file
#
# \note
# Copyright (c) 2010 \n
# Fraunhofer Institute for Manufacturing Engineering
# and Automation (IPA) \n\n
#
#################################################################

## cham_bot.urdf
<?xml version="1.0"?>
<robot name="cham_bot">

 <link name="z_axis">
    <visual>
        <geometry>
            <box size="0.2 0.2 6.5"/>

        </geometry>
        <origin rpy="0 0 0" xyz="0.25 0 7"/>
	import matplotlib.pyplot as plt
	import numpy as np
	from math import sin, cos

	# Define a function to compute the arm configuration
	def compute_arm_config(link1_length, link2_length, joint0_angle, joint1_angle):
	# compute the (x, y) position of the p1 joint and the end effector at p2.
	# input angle in radians
	# X0 = distance1 * cos(angle0)
	# Y0 = distance1 * sin(angle0)
	# Conversion Factors
	rtd = 180./np.pi # radians to degrees
	dtr = np.pi/180. # degrees to radians
	from sympy import symbols, cos, sin, pi, sqrt
	from sympy.matrices import Matrix

	### Create symbols for joint variables
	q1, q2 = symbols('q1:3')

	# Create a symbolic matrix representing an intrinsic sequence of rotations
	# about the Y and then Z axes. Let the rotation about the Y axis be described
	# by q1 and the rotation about Z by q2.
	#!/usr/bin/env python
	import numpy as np
	from sympy.matrices import Matrix
	from sympy import symbols, atan2, sqrt

	# Conversion Factors
	rtd = 180./np.pi # radians to degrees

	# Fixed Axis X-Y-Z Rotation Matrix
	R_XYZ = Matrix([[ 0.353553390593274, -0.306186217847897, 0.883883476483184],
	T0_1 = Matrix([[ cos(q1), -sin(q1), 0, a0],
	[sin(q1)cos(alpha0), cos(q1)cos(alpha0), -sin(alpha0), -sin(alpha0)*d1],
	[sin(q1)sin(alpha0), cos(q1)sin(alpha0), cos(alpha0), cos(alpha0)*d1],
	[ 0, 0, 0, 1]])
	# Function to search list of dictionaries and return a selected value in selected dictionary
	def search_dictionaries(key1, value1, key2, list_of_dictionaries):
	selected_dic = [element for element in list_of_dictionaries if element[key1] == value1][0]
	selected_val = selected_dic.get(key2)
	return selected_val
	def normalize_grayscale(image_data):
	"""
	Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
	:param image_data: The image data to be normalized
	:return: Normalized image data
	"""
	# Implement Min-Max scaling for grayscale image data
	a = 0.1
	b = 0.9
	grayscale_min = 0
	def one_hot_encode(labels):
	"""
	One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
	: x: List of sample Labels
	: return: Numpy array of one-hot encoded labels
	"""
	# TODO: Implement Function
	depth = 10
	one_hot = np.eye(depth)[labels]
	#!/usr/bin/python
	#################################################################
	##\file
	#
	# \note
	# Copyright (c) 2010 \n
	# Fraunhofer Institute for Manufacturing Engineering
	# and Automation (IPA) \n\n
	#
	#################################################################
	<?xml version="1.0"?>
	<robot name="cham_bot">

	<link name="z_axis">
	<visual>
	<geometry>
	<box size="0.2 0.2 6.5"/>

	</geometry>
	<origin rpy="0 0 0" xyz="0.25 0 7"/>