I'm writing a code and I have a function that calculates the values that are not fulfilling a condition with the values that are fulfilling the condition, but I'm having a lot of trouble with managing the shape of the arrays. I have a similar function, but with other logical structure that does this (MWE for the function that works) import numpy as np def f_est(f, fd, mask): """ This function returns the estimated value given a function f and its derivative fd for the points selected by the mask, estimating forward. Parameters: - f: Array of function values. - fd: Array of derivative values of the function. - mask: Boolean mask to select points in f and fd. """ h = 0.000001 # Find the last index that satisfies the mask last_index = np.max(np.where(mask)[0]) # Create shifted masks (inspired by f_cal), but centered on the last index mask_current = mask[:last_index + 1] # Mask for current positions up to the last index mask_prev = mask[:last_index] # Mask for previous positions (for fd_prev_slice) mask_prev2 = mask[:last_index - 1] # Mask for previous positions (for fd_prev2_slice) # Apply masks to f and fd (with shifts), centered on the last index f_slice = f[:last_index + 1][mask_current] # Note: adjusted to align with mask_current fd_slice = fd[:last_index + 1][mask_current] fd_prev_slice = fd[:last_index][mask_prev] fd_prev2_slice = fd[:last_index - 1][mask_prev2] # Perform the calculations with consistent slices, estimating forward # Use the last value of f_slice, fd_slice, fd_prev_slice, and fd_prev2_slice for estimation last_f = f_slice[-1] last_fd = fd_slice[-1] last_fd_prev = fd_prev_slice[-1] if len(fd_prev_slice) > 0 else 0 last_fd_prev2 = fd_prev2_slice[-1] if len(fd_prev2_slice) > 0 else 0 estimated_next_value = ( last_f + h * last_fd + 1 / 2 * (h * last_fd - h * last_fd_prev) + 5 / 12 * ((h * last_fd - h * last_fd_prev) - (h * last_fd_prev - h * last_fd_prev2)) ) return estimated_next_value f = np.array([1, 2, 3, 4, 5, 6, 7]) fd = f mask = np.array([True, True, True, False, False, False, False]) print("Original Array:", f) print("Length of Original Array:", len(f)) print("Masked Array:", f[~mask]) print("Length of Masked Array:", len(f[~mask])) f[~mask] = f_est(f, fd, mask) print("Final Array:", f) print("Length of Final Array:", len(f)) But with this function (MWE that doesn't work): import numpy as np def f_cal(var, dvar, mask): """ Calculates next value using trapezoidal method with masks, estimating forward. Parameters: var: array of current values dvar: array of derivatives mask: boolean array indicating which positions to calculate """ h = 0.0000001 # Encontrar el último índice que verifica la máscara last_index = np.max(np.where(mask)[0]) # Crear máscaras para posiciones actuales y la siguiente mask_current = mask[:last_index] mask_next = mask[1:last_index+1] # Marcar como True el índice siguiente # Ajustar los arreglos para alinear con las máscaras var_current = var[:last_index+1][mask_current] dvar_current = dvar[:last_index+1][mask_current] dvar_next = dvar[:last_index+2][mask_next][:1] # Solo el valor siguiente # Calculate using trapezoidal method with masks, estimating forward result = var_current + h * dvar_next - 1/2*(h*dvar_next-h*dvar_current[-1]) return result f = np.array([1, 2, 3, 4, 5,6,7]) fd = f mask = np.array([True, True, True, False, False, False, False]) print("Original Array:", f) print("Length of Original Array:", len(f)) print("Masked Array:", f[~mask]) print("Length of Masked Array:", len(f[~mask])) f[~mask] = f_cal(f, fd, mask) print("Final Array:", f) print("Length of Final Array:", len(f)) I'm having a lot of trouble keeping the length of the array to match the number of elements that are not satisfying the condition Continue reading...
