dsc: only use low temperature to baseline correct calibration without enthalpy
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@ -5,6 +5,8 @@ import re
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from collections import namedtuple
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import numpy as np
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from matplotlib import pyplot as plt
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from scipy.stats import linregress
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try:
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from scipy.integrate import simpson
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@ -200,35 +202,24 @@ class DSCCalibrator:
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high_border = np.argmin(np.abs(measurement[0] - t_high_lim))
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ref_zoom = measurement[:, low_border:high_border]
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x_val = np.array([[ref_zoom[0, 0], 1], [ref_zoom[0, -1], 1]])
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y_val = np.array([ref_zoom[1, 0], ref_zoom[1, -1]])
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sol = np.linalg.solve(x_val, y_val)
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ref_zoom[1] -= (ref_zoom[0] * sol[0] + sol[1])
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ref_grad = np.gradient(ref_zoom[1])
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crossing = np.where(np.diff(np.sign(np.abs(ref_grad)-0.001)))[0]
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almost_flat = np.sort(crossing-np.argmax(ref_zoom[1]))
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integ_limit = (almost_flat[np.where((almost_flat < -20))[0][-1]]+np.argmax(ref_zoom[1]),
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almost_flat[np.where((almost_flat > 20))[0][0]]+np.argmax(ref_zoom[1]))
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# subtract baseline around reference peak
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sol = self.solve_linear_eq(integ_limit, ref_zoom)
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ref_zoom[1] -= (ref_zoom[0] * sol[0] + sol[1])
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# calculate onset slope (use points at position of maximum gradient - 100/rate (+50/rate))
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ref_grad = np.gradient(ref_zoom[1])
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max_grad = np.argmax(ref_grad)
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grad_pos = max_grad-max(1, int(160 / rate)), max_grad
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sol = self.solve_linear_eq(grad_pos, ref_zoom)
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onset = sol[0] * ref_zoom[0] + sol[1]
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melts.append(-sol[1] / sol[0])
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if enthalpy is not None:
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x_val = np.array([[ref_zoom[0, 0], 1], [ref_zoom[0, -1], 1]])
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y_val = np.array([ref_zoom[1, 0], ref_zoom[1, -1]])
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sol = np.linalg.solve(x_val, y_val)
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ref_zoom[1] -= (ref_zoom[0] * sol[0] + sol[1])
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ref_grad = np.gradient(ref_zoom[1])
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crossing = np.where(np.diff(np.sign(np.abs(ref_grad)-0.001)))[0]
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almost_flat = np.sort(crossing-np.argmax(ref_zoom[1]))
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integ_limit = (almost_flat[np.where((almost_flat < -40))[0][-1]]+np.argmax(ref_zoom[1]),
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almost_flat[np.where((almost_flat > 40))[0][0]]+np.argmax(ref_zoom[1]))
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# subtract baseline around reference peak
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sol = self.solve_linear_eq(integ_limit, ref_zoom)
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ref_zoom[1] -= (ref_zoom[0] * sol[0] + sol[1])
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# integrate over peak to calibrate y axis
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# delta H in J/g: Integrate Peak over time and divide by weight
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area = simpson(ref_zoom[1, integ_limit[0]:integ_limit[1]],
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@ -236,6 +227,23 @@ class DSCCalibrator:
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even='avg') * 1e-3
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calib_y.append(enthalpy / (area / data.weight))
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else:
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ref_grad = np.gradient(ref_zoom[1])
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res = linregress(ref_zoom[0, :len(ref_grad)//5], ref_zoom[1, :len(ref_grad)//5])
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ref_zoom[1] -= (res.slope*ref_zoom[0] + res.intercept)
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# calculate onset slope (use points at position of maximum gradient - 100/rate (+50/rate))
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ref_grad = np.gradient(ref_zoom[1])
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max_grad = np.argmax(ref_grad)
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grad_pos = max_grad - max(1, int(160 / rate)), max_grad
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sol = self.solve_linear_eq(grad_pos, ref_zoom)
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onset = sol[0] * ref_zoom[0] + sol[1]
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melts.append(-sol[1] / sol[0])
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results.append([ref_zoom, onset, ref_zoom[:, grad_pos]])
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if len(melts) > 1:
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