Michy mick001

## solarSystem.py
import math
from bigfloat import *
import matplotlib.pyplot as plt
from visual import *

# A class to handle the time ranges
class timeHoursSeconds(object):
    def __init__(self,s,h,d,y):
        self.s = s
        self.h = h

## q-learning.py
import numpy as np

# R matrix
R = np.matrix([ [-1,-1,-1,-1,0,-1],
		[-1,-1,-1,0,-1,100],
		[-1,-1,-1,0,-1,-1],
		[-1,0,0,-1,0,-1],
		[-1,0,0,-1,-1,100],
		[-1,0,-1,-1,0,100] ])

## taylor_series_improved.py
import sympy as sy
import numpy as np
from sympy.functions import sin, cos, ln
import matplotlib.pyplot as plt
plt.style.use("ggplot")

# Factorial function
def factorial(n):
    if n <= 0:
        return 1

## Option_pricing_KDE_estimate.py
"""
MONTE CARLO PLAIN VANILLA OPTION PRICING

This script is used to estimate the price of a plain vanilla
option using the Monte Carlo method and assuming that returns
can be simulated using an estimated probability density (KDE estimate)

Call option quotations are available at:
http://www.google.com/finance/option_chain?q=NASDAQ%3AAAPL&ei=fNHBVaicDsbtsAHa7K-QDQ

## copulas_R_3.R
# Pseudo observations
p_obs <- pobs(mat)
plot(p_obs[,1],p_obs[,2],main="Pseudo/simulated observations: BLUE/RED",xlab="u",ylab="v",col="blue")

# Simulate data
set.seed(100)
u1 = rCopula(500,normalCopula(coef(fit.cop),dim=2))
points(u1[,1],u1[,2],col="red")

## magnetic_field.py
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-4,4,10)
y = np.linspace(-4,4,10)
z = np.linspace(-4,4,10)

x,y,z = np.meshgrid(x,y,z)

## poly_reg.R
# Example 1
p <- 0.5
q <- seq(0,100,1)
y <- p*q
plot(q,y,type='l',col='red',main='Linear relationship')

# Example 2
y <- 450 + p*(q-10)^3
plot(q,y,type='l',col='navy',main='Nonlinear relationship',lwd=3)

## RCRL.py
import numpy as np
import matplotlib.pyplot as plt

plt.style.use('ggplot')

l = 0.0229      #Inductance (H)
r = 3.34        #Resistance (Ohm)
v = 5           #Voltage (V) DC
i = v/r         #Peak current (A)
tau = l/r       #Tau time constant

## earthMoon.py
import math
from visual import *

# Data in units according to the International System of Units
G = 6.67 * math.pow(10,-11)

# Mass of the Earth
ME = 5.973 * math.pow(10,24)
# Mass of the Moon
MM = 7.347 * math.pow(10,22)

## slope_field_py.py
import numpy as np
from matplotlib import pyplot as plt

# Differential equation
# diff = y'= y/x (or say x+y)
def diff(x,y):
    return y/x # try also x+y

x = np.linspace(-10,10,50)
y = np.linspace(-10,10,50)
	import math
	from bigfloat import *
	import matplotlib.pyplot as plt
	from visual import *

	# A class to handle the time ranges
	class timeHoursSeconds(object):
	def __init__(self,s,h,d,y):
	self.s = s
	self.h = h
	import numpy as np

	# R matrix
	R = np.matrix([ [-1,-1,-1,-1,0,-1],
	[-1,-1,-1,0,-1,100],
	[-1,-1,-1,0,-1,-1],
	[-1,0,0,-1,0,-1],
	[-1,0,0,-1,-1,100],
	[-1,0,-1,-1,0,100] ])
	import sympy as sy
	import numpy as np
	from sympy.functions import sin, cos, ln
	import matplotlib.pyplot as plt
	plt.style.use("ggplot")

	# Factorial function
	def factorial(n):
	if n <= 0:
	return 1
	"""
	MONTE CARLO PLAIN VANILLA OPTION PRICING

	This script is used to estimate the price of a plain vanilla
	option using the Monte Carlo method and assuming that returns
	can be simulated using an estimated probability density (KDE estimate)

	Call option quotations are available at:
	http://www.google.com/finance/option_chain?q=NASDAQ%3AAAPL&ei=fNHBVaicDsbtsAHa7K-QDQ
	# Pseudo observations
	p_obs <- pobs(mat)
	plot(p_obs[,1],p_obs[,2],main="Pseudo/simulated observations: BLUE/RED",xlab="u",ylab="v",col="blue")

	# Simulate data
	set.seed(100)
	u1 = rCopula(500,normalCopula(coef(fit.cop),dim=2))
	points(u1[,1],u1[,2],col="red")
	from mpl_toolkits.mplot3d import axes3d
	import matplotlib.pyplot as plt
	import numpy as np

	x = np.linspace(-4,4,10)
	y = np.linspace(-4,4,10)
	z = np.linspace(-4,4,10)

	x,y,z = np.meshgrid(x,y,z)
	# Example 1
	p <- 0.5
	q <- seq(0,100,1)
	y <- p*q
	plot(q,y,type='l',col='red',main='Linear relationship')

	# Example 2
	y <- 450 + p*(q-10)^3
	plot(q,y,type='l',col='navy',main='Nonlinear relationship',lwd=3)
	import numpy as np
	import matplotlib.pyplot as plt

	plt.style.use('ggplot')

	l = 0.0229 #Inductance (H)
	r = 3.34 #Resistance (Ohm)
	v = 5 #Voltage (V) DC
	i = v/r #Peak current (A)
	tau = l/r #Tau time constant
	import math
	from visual import *

	# Data in units according to the International System of Units
	G = 6.67 * math.pow(10,-11)

	# Mass of the Earth
	ME = 5.973 * math.pow(10,24)
	# Mass of the Moon
	MM = 7.347 * math.pow(10,22)
	import numpy as np
	from matplotlib import pyplot as plt

	# Differential equation
	# diff = y'= y/x (or say x+y)
	def diff(x,y):
	return y/x # try also x+y

	x = np.linspace(-10,10,50)
	y = np.linspace(-10,10,50)