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#-------------------------------------------------------------------------------
#  Another linear model using more information y = a*x1 + b*x2 + c*x3 + d*x4
#-------------------------------------------------------------------------------

# Plot data
plot(disp,wt,type='p',xlab='Disp',ylab='Wt',main='Linear regression')

# Add a legend
legend("topleft",c("Observ.","Predicted"), col=c("black","red"), lwd=3)

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#-------------------------------------------------------------------------------
#                           Loading data
#-------------------------------------------------------------------------------

# Observed data
data <- read.csv('data.txt',header=T)

# Take a look of the data
View(data)

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#-------------------------------------------------------------------------------
#                           Simulation
#-------------------------------------------------------------------------------

# Estimated parameters of the exponential distribution
x.rate <- length(data$num)

# Remember that mean = 1/x.rate
# meaning that, on average, we expect a new arrival every 1/74 of an hour.
# (1/74 =~ 0.01355)

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#-------------------------------------------------------------------------------
#                           Compare simulated to obseved data
#-------------------------------------------------------------------------------

# Let's compare observed data with simulated data

# Comparative boxplot
boxplot(interarrivals,simulated.min,xlab='Minutes',col=c('cyan','chartreuse'),
        border=c('blue','seagreen'),names=c('Observed','Simulated'),
        main='Interarrival times',notch=TRUE,horizontal=TRUE)

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#-------------------------------------------------------------------------------
#                           Probability distributions
#-------------------------------------------------------------------------------

# Density plot
    #Since the rate is given as person/hour we need to set time in hours

            #Initial time  Final time       Step
            #0 hour        0.15 of an hour  0.001 of an hour
            #0 minutes     9 minutes        0.06 minutes = 3.6 seconds

mick001

import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.animation as animation

plt.style.use('dark_background')

fig = plt.figure()
fig.set_dpi(100)
ax1 = fig.add_subplot(1,1,1)

mick001

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

mick001

import numpy as np
import matplotlib.pyplot as plt

plt.style.use('ggplot')

c = 100 * 10**(-6)
v = 5
r = 2000

t = np.linspace(0,1,1000)

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#Transport equation with decay implementation

import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure()
fig.set_dpi(100)
ax1 = fig.add_subplot(1,1,1)

mick001

import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure()
fig.set_dpi(100)
ax1 = fig.add_subplot(1,1,1)

#Diffusion constant