Python Exam

#%% Loading txt

import numpy as np

# loading .txt or other file

data = np.loadtxt() # using delimiters

#%% Lineal Regression

from scipy import stats

linregress = stat.linregress(x_array, y_array) # It returns more than one variables: slope, intercept, rvalue
plt.plot(x_array, linregress.intercept + linregress.slope*dia[100:226], 'b', label='recta')

#%% Optimize Curve fit with a analytical function

Topt, Tcov = optimize.curve_fit(func, x, y)


#%% Finding max and min techniques

sr_res = np.abs(res[:, 2]-res[:,3])
sr_min = np.amin (sr_res)
idx_sr = np.where(sr_res == sr_min)

i_idxmax = np.argmax(res[:, 0])
imax = res[:, 0][i_idxmax]
frac = imax/1 * 100

#%% Sumatorio

N = len(txt)

Tref = 2.714

MSE = 0

for i in range(0, N):
    MSE += (datos[1][i] - rad(datos[0][i], Tref))*(1/N)
    


#%% Integration Techniques

from scipy import integrate

res = integrate.quad(func, lower limit, upper limit) # returns the value and the error

x = np.array([])
y = np.array([])

res = integrate.simpson(x, y)

#%% Differential Equation

from scipy import integrate

# Every index and eds should be in order
def func(x,t):
  return ...

ci = np.array([])
t = np.linspace(..., ..., ...)
res = integrate.odeint(func, ci, t)

#%% 2D array representation with function

# 1 variable

sigma = 0.15

n = 500
# coordenada radial
xx, yy = np.linspace (-1, 1, n), np.linspace(-1, 1, n)
X,Y = np.meshgrid(xx,yy[::-1])
r = np.sqrt(X**2 + Y**2)

s= lambda r: (1- (r**2)/(2* sigma**2)) * np.exp(-r**2/(2* sigma**2))

plt.imshow(s(r), cmap = 'rainbow', extent = [-1, 1, -1, 1])
plt.colorbar()

# 2 variables

xx, yy = np.linspace (-1, 1, 501), np.linspace (-1, 1, 501)
X, Y = np.meshgrid(xx, yy)

G2 = lambda X, Y: np.exp(-(X**2 + Y**2 * gamma**2)/(2 * sigma**2)) * np.cos(2*np.pi*X/l)

plt.subplot(1, 2, 2)
plt.grid(True)
plt.imshow(G2(X,Y), cmap = 'nipy_spectral')
plt.colorbar()