def graph(d, I, n):
plt.clf()
#Representación
x = d**2
y = I
plt.scatter(x, y)
#Regresión Lineal
a, b = rl(x, y)[0:2]
xr = np.linspace(min(x), max(x), 20)
yr = a + b * xr
plt.plot(xr, yr)
mpl.guardar("MI3_Steiner_{}".format(n), "$d^2 (m^2)$",
"$I (kg \\cdot m^2)$", False)
x = T[exp,r][~np.isnan(T[exp,r])]
y = LnI[exp,r][~np.isnan(LnI[exp,r])]
sy = sLnI[exp,r][~np.isnan(sLnI[exp,r])]
a, b = rl(x,y,sy)[0:2]
xr = np.linspace(min(x), max(x), 20)
yr = a + b*xr
yi = 1 if r%2 == 1 else 0
xi = 0 if r < 2 else 1
axs[xi, yi].scatter(x, y, color=c[r], edgecolors="black", linewidth=0.5)
axs[xi, yi].plot(xr, yr, color=c[r])
axs[xi, yi].set_title('$C_{}$'.format(r+1))
for ax in axs.flat:
ax.set(xlabel='T(s)', ylabel='$\ln{I}(A)$')
ax.label_outer()"""
mpl.guardar("CC-3LR", "T(s)", "$\ln{I}(A)$")#, False, False)
import uncertainties as unc
from uncertainties.umath import *
v = unc.ufloat(10.2, 0.1)
c = unc.ufloat(10**(-5), 0.05*10**(-5))
r = unc.ufloat(4.4*10**6, 0.05*4.4*10**6)
a = log(v/r)
b = (-1)/(r*c)
print("a = {:.2u}, b = {:.2u}".format(a, b))
z2 = d2["z"]
Be2 = d2["Bexp"]
d3 = pd.read_csv("BH-3.csv", sep=';', decimal=',')
z3 = d3["z"]
Be3 = d3["Bexp"]
#Curvas teóricas
z = np.linspace(-0.450, 0.450, 450)
Bt1 = B(pm, i, n, r, r, z)
Bt2 = B(pm, i, n, r, r/2, z)
Bt3 = B(pm, i, n, r, 2*r, z)
#Gráficas
plt.plot(z,Bt1,label="a=R")
plt.plot(z,Bt2,label="a=R/2")
plt.plot(z,Bt3,label="a=2R")
plt.scatter(z1,Be1,linewidth=0.5)
plt.scatter(z2,Be2,linewidth=0.5)
plt.scatter(z3,Be3,linewidth=0.5)
#Desviación típica
s = lambda Be, Bt: (1/len(Be)) * (np.sum(((Be - Bt)**2).to_numpy()))**0.5
s1 = s(Be1, B(pm, i, n, r, r, z1))
s2 = s(Be2, B(pm, i, n, r, r, z2))
s3 = s(Be3, B(pm, i, n, r, r, z3))
print("Desviaciones - s1: {}, s2: {}, s3: {}".format(s1, s2, s3))
mpl.guardar("BH", "z(m)", "B(T)")
xr3 = np.linspace(0, max(I3), 10)
yr3 = b3 * xr3
#Gráficas
plt.plot(xr1, yr1, label="a=R")
plt.plot(xr2, yr2, label="a=R/2")
plt.plot(xr3, yr3, label="a=2R")
plt.scatter(I1, Be1, linewidth=0.5)
plt.scatter(I2, Be2, linewidth=0.5)
plt.scatter(I3, Be3, linewidth=0.5)
R = 0.2
N = 154
a, r, n, m = sy.symbols("a r n m")
frac = 2 / (1 + ((a**2) / (4 * r**2)))**(3/2)
mu = ((2 * m * r) / n) * (1 / frac)
fmu = sy.lambdify([a, r, n, m], mu, "numpy")
frac1 = frac.subs(a, r)
frac2 = frac.subs(a, r/2)
frac3 = frac.subs(a, 2r)
mu1 = fmu(R, R, N, b1)
mu2 = fmu(R/2, R, N, b2)
mu3 = fmu(2*R, R, N, b3)
mpl.guardar("BH2R", "I(A)", "B(T)")
print("Permeabilidad orginal: {}, P1: {}, P2: {} P3:{}".format(4 * np.pi * 10**(-7), mu1, mu2, mu3))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.insert(1, '../Base')
from reg_lin import reg_lin_b as rl
import mpl_config as mpl
mpl.inicio(1)
#Datos
d = pd.read_csv("MI1_PhiF.csv", sep=';', decimal=',')
phi = d["PhiRad"]
M = d["M"]
#Regresión lineal ponderada sin término independiente
b = rl(phi, M)[0]
xr = np.linspace(min(phi), max(phi), 10)
yr = b * xr
#Gráficas
plt.scatter(phi, M, linewidth=0.5)
plt.plot(xr, yr)
mpl.guardar("MI1_PhiFL", "$\\varphi (rad)$", "M(Nm)", False)
for i in range(len(V2u)):
for j in range(len(V2u[i])):
print("V-{}-{} = {:.2u}".format(i, j, V2u[i,j]))
for i in range(3):
plt.clf()
#Gráficas
plt.scatter(T[i], V2[i], color=c[i], edgecolors="black", linewidth=0.5)
#Ajuste
b = rl(T[i], V2[i])[0]
xr = np.linspace(min(T[i]), max(T[i]), 10)
yr = b*xr
plt.plot(xr, yr, color=c[i])
mpl.guardar("LN_MRUA_A{}".format(i+1), "$T(s)$", "V(m/s)", False)
#TEORICO
g = unc.ufloat(9.8, 0.1)
a1 = g * (m/m1); a2 = g * (m/m2); a3 = g * (m/m3)
sa = lambda M: sm * ((g/M)**2 + ((g*m) / M**2)**2)**0.5
sa1 = sa(m1); sa2 = sa(m2); sa3 = sa(m3)
Mu = unp.uarray([m1, m2, m3], sm); mu = unc.ufloat(m, sm)
au = g*(mu / Mu)
for i in range(3):
print("A = {:.2u}".format(au[i]))
np.savetxt("LN_MRUA_VA.csv", au, "%r", delimiter=";")