import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as ss
import scipy.optimize as scp
import puk as puk
# fig, ax = plt.subplots(figsize = (16,8))
puk1 = puk.Puk(['Uelastisk/KastetCenter', 'Uelastisk/KastetSide'], 0.0569,
0.0435)
puk2 = puk.Puk(['Uelastisk/StilleCenter', 'Uelastisk/StilleSide'], 0.0285,
0.0432)
def findColUelastisk(puk1, puk2):
ts = puk1.get_center(0)
xs1 = puk1.get_center(1)
ys1 = puk1.get_center(2)
xs2 = puk2.get_center(1)
ys2 = puk2.get_center(2)
t_point = 0
i = 0
#Tjek om de to radier er tæt nok på hinanden til at de har kollideret, hvor efter t-værdiens
#index retuneres
for t in ts:
r1 = np.array(xs1[i], ys1[i])
r2 = np.array(xs2[i], ys2[i])
if (np.linalg.norm(r2 - r1) <= puk1.get_r() + puk2.get_r()):
t_point = i
else:
i += 1
return t_point
import puk as puk
import matplotlib.pyplot as plt
from matplotlib import animation, rc
fig, ax = plt.subplots(figsize=(8, 8))
Rota_Kastet = puk.Puk(['Rota/KastetCenter', 'Rota/KastetSide'], 1, 1)
Rota_Stille = puk.Puk(['Rota/StilleCenter', 'Rota/StilleSide'], 1, 1)
Rota_Kastet = puk.Puk(['Elastisk/KastetCenter', 'Elastisk/KastetSide'], 1, 1)
Rota_Stille = puk.Puk(['Elastisk/StilleCenter', 'Elastisk/StilleSide'], 1, 1)
puks = [Rota_Kastet, Rota_Stille]
def pukanim(puks, ax):
N = len(puks[0].get_center(0))
color = ['bo', 'ro', 'ko', 'ko']
puk11 = ax.plot(puks[0].get_center(1)[0],
puks[0].get_center(2)[0],
color[0],
label='Puk distance',
ms=18)[0]
puk21 = ax.plot(puks[1].get_center(1)[0],
puks[1].get_center(2)[0],
color[1],
label='Puk distance',
ms=18)[0]
puk12 = ax.plot(puks[0].get_edge(1)[0],
puks[0].get_edge(2)[0],
import numpy as np
import matplotlib.pyplot as plt
import puk as puk
import fejlpropagering as fejl
import chi_sq as chi
fig, ax = plt.subplots(figsize = (30,15))
strings = ['Data0', 'Data2', 'Data4']
trials = []
trials.append([puk.Puk(['Rota/KastetCenter', 'Rota/KastetSide'], 0.0278, 0.0807),
puk.Puk(['Rota/StilleCenter', 'Rota/StilleSide'], 0.0278, 0.0807)])
colors = ['r', 'b', 'g', 'k--']
a = []
a.append(puk.plot_Puks_angular_momentum(trials[0], ax, colors, alpha = 0.6))
ax.set_xlabel('t/s', fontsize = 20)
ax.set_ylabel('L', fontsize = 20)
ax.set_title('Impulsmoment over tid', fontsize = 24)
ax.legend()
plt.show()
fig.savefig('rota_impuls')
import numpy as np
import matplotlib.pyplot as plt
import puk as puk
import fejlpropagering as fejl
import chi_sq as chi
fig, ax = plt.subplots(figsize=(30, 15))
strings = ['Data0', 'Data2', 'Data4']
trials = []
trials.append([
puk.Puk(['Rota/KastetCenter', 'Rota/KastetSide'], 0.0278, 0.0807),
puk.Puk(['Rota/StilleCenter', 'Rota/StilleSide'], 0.0278, 0.0807)
])
colors = ['r', 'b', 'g', 'k--']
a = []
a.append(puk.plot_Puks_energy(trials[0], ax, colors, alpha=0.6))
ax.set_xlabel('t/s', fontsize=20)
ax.set_ylabel('E', fontsize=20)
ax.set_title('Energi_over tid', fontsize=24)
ax.legend()
plt.show()
fig.savefig('rota_energi')
import numpy as np
import puk as puk
import matplotlib.pyplot as plt
import scipy.optimize as scp
fig, ax = plt.subplots()
puk1 = puk.Puk(['Elastisk/StilleCenter', 'Elastisk/StilleSide'], 0.0278,
0.0807)
puk2 = puk.Puk(['Elastisk/KastetCenter', 'Elastisk/KastetSide'], 0.0278,
0.0807)
puk3 = puk.Puk(['Rota/KastetCenter', 'Rota/KastetSide'], 0.0278, 0.0807)
puk4 = puk.Puk(['Rota/StilleCenter', 'Rota/StilleSide'], 0.0278, 0.0807)
def linear(t, a, b):
return a * t + b
def findcol(Puk):
xs = Puk.center[:, 1]
ts = Puk.center[:, 0]
ax.scatter(ts, xs)
curve1 = [ts[:8], xs[:8]]
inte = len(xs) - 8
curve2 = [ts[inte:], xs[inte:]]
popt1, cov1 = scp.curve_fit(linear,
curve1[0],
curve1[1],
absolute_sigma=True)
popt2, cov2 = scp.curve_fit(linear,
curve2[0],
curve2[1],
import numpy as np
import puk as puk
Rota_Kastet = puk.Puk(['Data/Data0/KastetCenter', 'Data/Data0/KastetSide'], 1,
1)
Rota_Stille = puk.Puk(['Data/Data0/StilleCenter', 'Data/Data0/StilleSide'], 1,
1)
puks = [Rota_Kastet, Rota_Stille]
def b_vardi(puks):
angle = np.arctan2(puks[0].y_velocity()[0], puks[0].x_velocity()[0])
puk1 = np.array(puks[0].get_center(2) * np.cos(angle) -
puks[0].get_center(1) * np.sin(angle))
puk2 = np.array(puks[1].get_center(2) * np.cos(angle) -
puks[1].get_center(1) * np.sin(angle))
t = puks[0].col_t()
b = sum(puk1[:t]) / t - sum(puk2[:t]) / t
print(len(puk1[:t]), t)
print(puk1[:t])
return b
print(b_vardi(puks))
import numpy as np
import matplotlib.pyplot as plt
import puk as puk
import fejlpropagering as fejl
import chi_sq as chi
fig, ax = plt.subplots(2, 2, figsize=(30, 15))
ax = ax.ravel()
strings = ['Data0', 'Data2', 'Data4']
trials = []
for s in strings:
trials.append([
puk.Puk(['Data/' + s + '/KastetCenter', 'Data/' + s + '/KastetSide'],
0.0278, 0.0807),
puk.Puk(['Data/' + s + '/StilleCenter', 'Data/' + s + '/StilleSide'],
0.0278, 0.0807)
])
colors = ['r', 'b', 'g', 'k--']
a = []
for t, ax in zip(trials, ax):
a.append(puk.plot_Puks_energy(t, ax, colors, alpha=0.6))
def tot_angu_err(Puks):
return np.sqrt(Puks[0].angu_err()**2 + Puks[1].angu_err()**2)
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as ss
import scipy.optimize as scp
from matplotlib import animation, rc
import puk as puk
from scipy.integrate import solve_ivp
rc('animation', html='jshtml')
fig, ax = plt.subplots()
puk2 = puk.Puk(['Rota/KastetCenter', 'Rota/KastetSide'], 0.0278, 0.04035)
puk1 = puk.Puk(['Rota/StilleCenter', 'Rota/StilleSide'], 0.0278, 0.04035)
r02 = [puk2.get_center(1)[0], puk2.get_center(2)[0]]
r01 = [puk1.get_center(1)[0], puk1.get_center(2)[0]]
I = 1 / 2 * 0.0278 * 0.04035**2
R = 0.04035
m = 0.0278
def findv0(puk):
xs = puk.get_center(1)[:5]
ys = puk.get_center(2)[:5]
ts = puk.get_center(0)[:5]
angs = puk.angle()[:5]
f = lambda x, a, b: a * x + b
popt1, cov1 = scp.curve_fit(f, ts, xs, absolute_sigma=True)
popt2, cov2 = scp.curve_fit(f, ts, ys, absolute_sigma=True)
popt3, cov3 = scp.curve_fit(f, ts, angs, absolute_sigma=True)
return [popt1[0], popt2[0], popt3[0]]
v01 = findv0(puk1)