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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 |
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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) |
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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 |
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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) |
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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 |