### Andregrads 1
###
x = np.linspace(-1, 3, 200)
y = x**2 - 2*x
figure_begin()
plt.axhline(y=0, color = 'black') # Horisontal linje ved y = 0
plt.axvline(x=0, color = 'black') # Vertikal linje ved x = 0
plt.plot(x, y, color=(1,0.3,0.3))
plt.xlabel("x")
plt.ylabel("f(x)")
plt.grid()
figure_end()
figure_save("andregrads1")
###
### Graf 1
###
x = np.linspace(-1, 2, 200)
y = x**3 + x -1
figure_begin()
plt.axhline(y=0, color = 'black') # Horisontal linje ved y = 0
plt.axvline(x=0, color = 'black') # Vertikal linje ved x = 0
plt.plot(x, y, color=(0.4,0.4,1))
plt.xlabel("x")
###
### Motstand eksperiment
###
figure_begin()
data = np.loadtxt("datasett/motstand_eksperiment.txt")
spenning = data[:, 0]
strøm = data[:, 1]
plt.plot(spenning, strøm, "o")
plt.xlabel("Spenning (V)")
plt.ylabel("Strøm (A)")
figure_end()
figure_save("motstand_eksperiment")
###
### SAS-fly
###
figure_begin()
data = np.loadtxt("datasett/sas_fly.txt",
skiprows=1,
usecols=(1, 2, 3, 4, 5, 6))
navn = np.loadtxt("datasett/sas_fly.txt", skiprows=1, usecols=(0), dtype=str)
plt.barh(navn, data[:, 5])
plt.xlabel(
"Drivstofforbruk $(\mathrm{liter} \cdot \mathrm{sete}^{-1} \cdot \mathrm{km}^{-1})$"
)
import numpy as np
from generate_figure_common import figure_begin, figure_end, figure_save
tid = [0, 15, 30, 45, 60, 90] # Tid i minutter
kontroll = [4.9, 5.1, 4.8, 4.3, 4.5, 4.7]
sukkerbrus = [4.5, 8.2, 9.5, 7.5, 5.4, 4.9]
eple = [4.6, 7.5, 9.2, 8.4, 6.4, 5.8]
###
### Blodsukker 1
###
figure_begin()
plt.plot(tid, kontroll) # Plotter kontrollverdiene mot tida
figure_end()
figure_save("blodsukker1")
###
### Blodsukker 2
###
figure_begin()
plt.plot(tid, kontroll)
plt.title('Blodsukkermåling') # Tittel på plottet
plt.xlabel('Tid (s)') # Aksetittel på x-aksen
plt.ylabel('Blodsukkerkonsentrasjon (mmol/L)') # Aksetittel på y-aksen
plt.xlim(-10,100) # Definisjonsmengde
plt.ylim(-1,12) # Verdimengde
plt.axhline(y=0, color = 'black') # Horisontal linje ved y = 0
plt.axvline(x=0, color = 'black') # Vertikal linje ved x = 0
plt.grid()
figure_end()
b = 0.3 # Ns/m
g = 9.81
for i in range(N-1):
a = -m*g - b*v[i]
v[i+1] = v[i] + a*dt
s[i+1] = s[i] + v[i]*dt + 0.5*a*dt**2
t[i+1] = t[i] + dt
plt.plot(t, s, "-o", label=r"$\Delta t = %.2f$" % dt)
plt.xlabel("Tid (s)")
plt.ylabel("Posisjon (m)")
plt.legend()
figure_end()
figure_save("rettlinjet_bevegelse")
###
### Ball med luftmotstand
###
def akselerasjon_skrått_kast(r, v, t):
m = 0.15
b = 0.2
g = np.array([0, 9.81])
return -m*g - b*np.sqrt(np.dot(v, v)) * v
dt = 0.01
T = 4
N = int(T/dt)
fder = (f(x + delta_x) - f(x)) / delta_x
return fder
# Plotting
x = np.linspace(-2, 5, 1000)
y = f(x)
yder = numerisk_derivert(f, x, 1E-8)
plt.plot(x, y, color='green', label='f(x)')
plt.plot(x, yder, color='red', label='f\'(x)')
plt.xlabel('x')
plt.legend()
plt.grid()
figure_end()
figure_save("der_plott")
###
### Heistur
###
figure_begin()
# Leser av fila
data = np.loadtxt('datasett/heistur.csv', delimiter=',', skiprows=1)
t = data[:, 0]
h = data[:, 2]
# Derivasjonsvariabler
n = len(t)
v = np.zeros(n)
a = np.zeros(n)
###
### Linear spread
###
def f(x):
return 4*x + 3
N = 100
x = np.linspace(0, 1, N)
y = f(x) + np.random.uniform(-1,1, N)
figure_begin()
plt.plot(x, y, "o")
plt.xlabel("x")
plt.ylabel("y")
figure_end()
figure_save("data_ml_linear")
###
### ML linear
###
import tensorflow as tf
from tensorflow.keras import layers
model = tf.keras.Sequential()
model.add(layers.Dense(1, activation="linear", input_shape=(1,)))
def loss(y, y_pred):
return tf.math.reduce_mean((y-y_pred)**2)
model.compile(optimizer=tf.keras.optimizers.Adam(0.1),
loss=loss)