81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
import numpy as np
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from numpy.polynomial import polynomial
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import matplotlib.pyplot as plt
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import scipy
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colors = ['blue', 'green', 'orange', 'purple', 'red']
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wavelengths = {
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'blue': 465 * 10**(-9),
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'green': 520 * 10**(-9),
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'orange': 589 * 10**(-9),
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'purple': 390 * 10**(-9),
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'red': 622 * 10**(-9)
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}
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u_0 = np.empty((len(colors)))
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wavelength_inv = np.empty((len(colors)))
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fig, axs = plt.subplots(len(colors), sharex='all', sharey='all')
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fig.tight_layout()
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for color_index, color in enumerate(colors):
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with open(f'{color}.dat', 'r') as file:
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lines = file.readlines()
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lines.pop(0)
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voltage = np.empty((len(lines)))
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current = np.empty((len(lines)))
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for index, line in enumerate(lines):
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parts = line.split('\t')
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voltage[index] = float(parts[0])
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current[index] = float(parts[1]) / 1000
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## Line 1
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line_1_bound = len(lines) // 3
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line_2_bound = len(lines) - 12
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line_1 = polynomial.Polynomial.fit(voltage[1:line_1_bound], current[1:line_1_bound], 1)
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line_2 = polynomial.Polynomial.fit(voltage[line_2_bound:], current[line_2_bound:], 1)
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line_1 = line_1.convert(domain=(voltage[0], voltage[-1]))
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line_2 = line_2.convert(domain=line_1.domain)
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intersect_x = scipy.optimize.fsolve(line_1 - line_2, 0)
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intersect_y = line_1(intersect_x)
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ax = axs[color_index]
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ax.scatter(voltage, current, label="Raw Data", color=color)
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ax.set_ylim(ax.get_ylim())
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space = np.linspace(voltage[0], voltage[-1])
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ax.plot(space, line_1(space), color='red', label='Trendline 1')
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ax.plot(space, line_2(space), color='orange', label='Trendline 2')
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ax.scatter(intersect_x, intersect_y, color='pink', label=f'Intersect: {intersect_x[0]:0.3f}V')
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ax.legend()
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ax.set_title(f"Voltage v Current ({color})")
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ax.set_xlabel("Voltage (V)")
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ax.set_ylabel("Current (A)")
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u_0[color_index] = intersect_x[0]
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wavelength_inv[color_index] = 1 / wavelengths[color]
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plt.show()
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line = polynomial.Polynomial.fit(wavelength_inv, u_0, 1).convert()
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line = line.convert(domain = [min(wavelength_inv), max(wavelength_inv)]).convert()
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s_2 = (1 / (len(wavelength_inv) - 2)) * sum((u_0 - line.coef[1] * wavelength_inv - line.coef[0]) ** 2)
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delta_p = len(wavelength_inv) * sum(wavelength_inv ** 2) - (sum(wavelength_inv) ** 2)
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slope_error = np.sqrt(len(wavelength_inv) / delta_p) * np.sqrt(s_2)
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print(delta_p)
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print(slope_error)
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print(s_2)
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space = np.linspace(min(wavelength_inv), max(wavelength_inv))
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plt.plot(space, line(space), color='orange', label=f'{line.convert()}')
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plt.scatter(wavelength_inv,u_0)
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plt.ylabel("U_0")
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plt.xlabel("1/Wavelength")
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plt.legend()
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plt.show()
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