use f-strings everywhere
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@ -114,11 +114,11 @@ def cg(
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_, err2 = rhoerr_step(r, [])
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_, err2 = rhoerr_step(r, [])
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b_norm = numpy.sqrt(err2)
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b_norm = numpy.sqrt(err2)
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logging.debug('b_norm check: ', b_norm)
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logging.debug(f'b_norm check: {b_norm}')
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success = False
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success = False
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for k in range(max_iters):
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for k in range(max_iters):
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logging.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
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logging.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
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rho_prev = rho
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rho_prev = rho
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e = xr_step(x, p, r, v, alpha, [])
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e = xr_step(x, p, r, v, alpha, [])
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@ -126,7 +126,7 @@ def cg(
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errs += [numpy.sqrt(err2) / b_norm]
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errs += [numpy.sqrt(err2) / b_norm]
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logging.debug('err {}'.format(errs[-1]))
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logging.debug(f'err {errs[-1]}')
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if errs[-1] < err_threshold:
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if errs[-1] < err_threshold:
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success = True
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success = True
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@ -136,8 +136,8 @@ def cg(
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e = a_step(v, m, p, e)
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e = a_step(v, m, p, e)
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alpha = rho / dot(p, v, e)
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alpha = rho / dot(p, v, e)
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if verbose and k % 1000 == 0:
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if k % 1000 == 0:
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logging.info('iteration {}'.format(k))
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logger.info(f'iteration {k}')
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'''
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'''
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Done solving
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Done solving
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@ -150,12 +150,12 @@ def cg(
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logging.info('Solve success')
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logging.info('Solve success')
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else:
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else:
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logging.warning('Solve failure')
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logging.warning('Solve failure')
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logging.info('{} iterations in {} sec: {} iterations/sec \
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logging.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
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'.format(k, time_elapsed, k / time_elapsed))
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logging.debug(f'final error {errs[-1]}')
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logging.debug('final error {}'.format(errs[-1]))
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logging.debug(f'overhead {start_time2 - start_time} sec')
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logging.debug('overhead {} sec'.format(start_time2 - start_time))
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logging.info('Final residual: {}'.format(norm(A @ x - b) / norm(b)))
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residual = norm(A @ x - b) / norm(b)
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logging.info(f'Final residual: {residual}')
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return x
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return x
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@ -179,13 +179,13 @@ def cg_solver(
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_, err2 = rhoerr_step(r, [])
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_, err2 = rhoerr_step(r, [])
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b_norm = numpy.sqrt(err2)
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b_norm = numpy.sqrt(err2)
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logging.debug('b_norm check: {}'.format(b_norm))
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logging.debug(f'b_norm check: {b_norm}')
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success = False
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success = False
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for k in range(max_iters):
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for k in range(max_iters):
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do_print = (k % 100 == 0)
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do_print = (k % 100 == 0)
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if do_print:
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if do_print:
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logger.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
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logger.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
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rho_prev = rho
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rho_prev = rho
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e = xr_step(x, p, r, v, alpha, [])
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e = xr_step(x, p, r, v, alpha, [])
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@ -194,7 +194,7 @@ def cg_solver(
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errs += [numpy.sqrt(err2) / b_norm]
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errs += [numpy.sqrt(err2) / b_norm]
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if do_print:
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if do_print:
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logger.debug('err {}'.format(errs[-1]))
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logger.debug(f'err {errs[-1]}')
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if errs[-1] < err_threshold:
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if errs[-1] < err_threshold:
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success = True
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success = True
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@ -205,7 +205,7 @@ def cg_solver(
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alpha = rho / dot(p, v, e)
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alpha = rho / dot(p, v, e)
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if k % 1000 == 0:
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if k % 1000 == 0:
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logger.info('iteration {}'.format(k))
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logger.info(f'iteration {k}')
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'''
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'''
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Done solving
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Done solving
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@ -222,15 +222,16 @@ def cg_solver(
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logger.info('Solve success')
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logger.info('Solve success')
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else:
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else:
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logger.warning('Solve failure')
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logger.warning('Solve failure')
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logger.info('{} iterations in {} sec: {} iterations/sec \
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logger.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
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'.format(k, time_elapsed, k / time_elapsed))
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logger.debug(f'final error {errs[-1]}')
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logger.debug('final error {}'.format(errs[-1]))
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logger.debug(f'overhead {start_time2 - start_time} sec')
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logger.debug('overhead {} sec'.format(start_time2 - start_time))
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A0 = meanas.fdfd.operators.e_full(omega, dxes, epsilon, mu).tocsr()
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A0 = meanas.fdfd.operators.e_full(omega, dxes, epsilon, mu).tocsr()
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if adjoint:
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if adjoint:
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# Remember we conjugated all the contents of A earlier
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# Remember we conjugated all the contents of A earlier
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A0 = A0.T
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A0 = A0.T
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logger.info('Post-everything residual: {}'.format(norm(A0 @ x - b) / norm(b)))
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residual = norm(A0 @ x - b) / norm(b)
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logger.info(f'Post-everything residual: {residual}')
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return x
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return x
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