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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,106 +1,106 @@ | ||
| import numpy as np | ||
| from scipy.interpolate import CubicHermiteSpline | ||
|
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| def lorentzian_smoothing(curve, vi): | ||
| """ | ||
| Draws a Lorentzian curve in the same grid as the given curve. | ||
| Performs the convolution of the two curves. | ||
| The given curve is represented by an array of (e,i) points, where e = energy and i = intensity. | ||
| Inputs: | ||
| - curve: numpy array of [e,i] arrays | ||
| - vi: predefined constant representing the width | ||
| Output: | ||
| The result of the convolution: a new array of [e,i] arrays | ||
| """ | ||
|
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||
| energies = curve[:, 0] | ||
| intensities = curve[:, 1] | ||
| result = [] | ||
|
|
||
| for j in range(energies.size): | ||
| c = 0 | ||
| l_value = 0 | ||
| for i in range(energies.size): | ||
| c += vi**2 / ((energies[i] - energies[j]) ** 2 + vi**2) | ||
| l_value += ( | ||
| intensities[i] * vi**2 / ((energies[i] - energies[j]) ** 2 + vi**2) | ||
| ) | ||
| result.append(l_value / c) | ||
|
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||
| return np.array(list(zip(energies, result))) | ||
|
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| def preprocessing_loop(theoretical_curves, experimental_curves, shift, r_factor, vi): | ||
| """ | ||
| Performs all the preprocessing steps and R-factor calculations, required before the Search. | ||
| A curve is represented by an array of (e,i) points, where e = energy and i = intensity. | ||
| Inputs: | ||
| - theoretical_curves: numpy array of arrays of [e,i] arrays | ||
| - experimental_curves: numpy array of arrays of [e,i] arrays | ||
| where: | ||
| - number of theoretical curves = number of experimental curves | ||
| - shift: provided by user, used to edit the energy value per step | ||
| - r_factor: provided by user, name of the preferred R-factor method | ||
| Design: | ||
| 1. Lorentzian smoothing of all curves | ||
| 2. For each energy point: | ||
| a. Shift theoretical curve | ||
| b. Find boundaries of overlap | ||
| c. Filter both curves to keep only the elements in the overlap | ||
| d. For each experimental curve: | ||
| i. Spline = interpolate it to the theoretical grid | ||
| ii. Calculate R-factor | ||
| e. Calculate average R-factor of all curves in this shift | ||
| 3. Find the min of the average values | ||
| Output: | ||
| The optimal R-factor value per curve. | ||
| """ | ||
|
|
||
| for i in range(len(theoretical_curves)): | ||
| theoretical_curves[i] = lorentzian_smoothing(theoretical_curves[i], vi) | ||
| experimental_curves[i] = lorentzian_smoothing(experimental_curves[i], vi) | ||
|
|
||
| energy_shift = [shift, 0] | ||
| shifted_theoretical_curves = theoretical_curves + energy_shift | ||
| print(shifted_theoretical_curves) | ||
|
|
||
| # Find the boundaries of the overlap after the shift | ||
| min_bound = np.maximum(shifted_theoretical_curves[0, 0], experimental_curves[0, 0])[ | ||
| 0 | ||
| ] | ||
| max_bound = np.minimum( | ||
| shifted_theoretical_curves[0, -1], experimental_curves[0, -1] | ||
| )[0] | ||
| print(min_bound, max_bound) | ||
|
|
||
| # Filter both curves based on the boundaries | ||
| filtered_theoretical_curves = shifted_theoretical_curves[ | ||
| shifted_theoretical_curves[:, 0] >= min_bound | ||
| ] | ||
| filtered_theoretical_curves = filtered_theoretical_curves[ | ||
| filtered_theoretical_curves[:, 0] <= max_bound | ||
| ] | ||
|
|
||
| filtered_experimental_curves = experimental_curves[ | ||
| experimental_curves[:, 0] >= min_bound | ||
| ] | ||
| filtered_experimental_curves = filtered_experimental_curves[ | ||
| filtered_experimental_curves[:, 0] <= max_bound | ||
| ] | ||
| print(filtered_theoretical_curves, filtered_experimental_curves) | ||
|
|
||
| # Spline | ||
| for i in range(len(filtered_experimental_curves)): | ||
| e_values = filtered_experimental_curves[i][:, 0] | ||
| i_values = filtered_experimental_curves[i][:, 1] | ||
| theoretical_e_grid = filtered_theoretical_curves[i][:, 0] | ||
| CubicHermiteSpline(e_values, i_values, theoretical_e_grid) | ||
|
|
||
| print(globals().get(r_factor)) | ||
| return | ||
| import numpy as np | ||
| from scipy.interpolate import CubicHermiteSpline | ||
|
|
||
|
|
||
| def lorentzian_smoothing(curve, vi): | ||
| """ | ||
| Draws a Lorentzian curve in the same grid as the given curve. | ||
| Performs the convolution of the two curves. | ||
| The given curve is represented by an array of (e,i) points, where e = energy and i = intensity. | ||
| Inputs: | ||
| - curve: numpy array of [e,i] arrays | ||
| - vi: predefined constant representing the width | ||
| Output: | ||
| The result of the convolution: a new array of [e,i] arrays | ||
| """ | ||
|
|
||
| energies = curve[:, 0] | ||
| intensities = curve[:, 1] | ||
| result = [] | ||
|
|
||
| for j in range(energies.size): | ||
| c = 0 | ||
| l_value = 0 | ||
| for i in range(energies.size): | ||
| c += vi**2 / ((energies[i] - energies[j]) ** 2 + vi**2) | ||
| l_value += ( | ||
| intensities[i] * vi**2 / ((energies[i] - energies[j]) ** 2 + vi**2) | ||
| ) | ||
| result.append(l_value / c) | ||
|
|
||
| return np.array(list(zip(energies, result))) | ||
|
|
||
|
|
||
| def preprocessing_loop(theoretical_curves, experimental_curves, shift, r_factor, vi): | ||
| """ | ||
| Performs all the preprocessing steps and R-factor calculations, required before the Search. | ||
| A curve is represented by an array of (e,i) points, where e = energy and i = intensity. | ||
| Inputs: | ||
| - theoretical_curves: numpy array of arrays of [e,i] arrays | ||
| - experimental_curves: numpy array of arrays of [e,i] arrays | ||
| where: | ||
| - number of theoretical curves = number of experimental curves | ||
| - shift: provided by user, used to edit the energy value per step | ||
| - r_factor: provided by user, name of the preferred R-factor method | ||
| Design: | ||
| 1. Lorentzian smoothing of all curves | ||
| 2. For each energy point: | ||
| a. Shift theoretical curve | ||
| b. Find boundaries of overlap | ||
| c. Filter both curves to keep only the elements in the overlap | ||
| d. For each experimental curve: | ||
| i. Spline = interpolate it to the theoretical grid | ||
| ii. Calculate R-factor | ||
| e. Calculate average R-factor of all curves in this shift | ||
| 3. Find the min of the average values | ||
| Output: | ||
| The optimal R-factor value per curve. | ||
| """ | ||
|
|
||
| for i in range(len(theoretical_curves)): | ||
| theoretical_curves[i] = lorentzian_smoothing(theoretical_curves[i], vi) | ||
| experimental_curves[i] = lorentzian_smoothing(experimental_curves[i], vi) | ||
|
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||
| energy_shift = [shift, 0] | ||
| shifted_theoretical_curves = theoretical_curves + energy_shift | ||
| print(shifted_theoretical_curves) | ||
|
|
||
| # Find the boundaries of the overlap after the shift | ||
| min_bound = np.maximum(shifted_theoretical_curves[0, 0], experimental_curves[0, 0])[ | ||
| 0 | ||
| ] | ||
| max_bound = np.minimum( | ||
| shifted_theoretical_curves[0, -1], experimental_curves[0, -1] | ||
| )[0] | ||
| print(min_bound, max_bound) | ||
|
|
||
| # Filter both curves based on the boundaries | ||
| filtered_theoretical_curves = shifted_theoretical_curves[ | ||
| shifted_theoretical_curves[:, 0] >= min_bound | ||
| ] | ||
| filtered_theoretical_curves = filtered_theoretical_curves[ | ||
| filtered_theoretical_curves[:, 0] <= max_bound | ||
| ] | ||
|
|
||
| filtered_experimental_curves = experimental_curves[ | ||
| experimental_curves[:, 0] >= min_bound | ||
| ] | ||
| filtered_experimental_curves = filtered_experimental_curves[ | ||
| filtered_experimental_curves[:, 0] <= max_bound | ||
| ] | ||
| print(filtered_theoretical_curves, filtered_experimental_curves) | ||
|
|
||
| # Spline | ||
| for i in range(len(filtered_experimental_curves)): | ||
| e_values = filtered_experimental_curves[i][:, 0] | ||
| i_values = filtered_experimental_curves[i][:, 1] | ||
| theoretical_e_grid = filtered_theoretical_curves[i][:, 0] | ||
| CubicHermiteSpline(e_values, i_values, theoretical_e_grid) | ||
|
|
||
| print(globals().get(r_factor)) | ||
| return |
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