How to use calculate method in localstack

Best Python code snippet using localstack_python

_stop_criterion.py

Source:_stop_criterion.py

1"""2..3    Copyright (c) 2015-2017, Magni developers.4    All rights reserved.5    See LICENSE.rst for further information.6Module providing functions for calculating stop criteria used in Iterative7Threholding (IT) algorithms.8Routine listings9----------------10calculate_using_mse_convergence(var)11    Calculate stop criterion based on mse convergence.12calculate_using_normalised_mse_convergence(var)13    Calculate stop criterion based on normalised mse convergence.14calculate_using_residual(var)15    Calculate stop criterion based on residual.16calculate_using_residual_measurments_ratio(var)17    Calculate stop criterion based on the residual to measurements ratio.18get_function_handle(method)19    Return a function handle to a given calculation method.20"""21from __future__ import division22import numpy as np23def wrap_calculate_using_mse_convergence(var):24    """25    Arguments wrapper for `calculate_using_mse_convergence`.26    Calculate stop criterion based on mse convergence.27    """28    n = var['A'].shape[1]29    tolerance = var['tolerance']30    def calculate_using_mse_convergence(var):31        """32        Calculate stop criterion based on mse convergence.33        Parameters34        ----------35        var : dict36            Dictionary of variables used in calculating the stop criterion.37        Returns38        -------39        stop : bool40            The indicator of whether or not the stop criterion is satisfied.41        mse : float42            The current convergence mean squared error.43        Notes44        -----45        The IT algorithm should converge to a fixed point. This criterion46        is based on the mean squared error of the difference between the47        proposed solution in this iteration and the proposed solution in the48        previous solution.49        """50        mse = 1/n * np.linalg.norm(var['alpha_prev'] - var['alpha'])**251        stop = mse < tolerance52        return stop, mse53    return calculate_using_mse_convergence54def wrap_calculate_using_normalised_mse_convergence(var):55    """56    Arguments wrapper for `calculate_using_normalised_mse_convergence`.57    Calculate stop criterion based on normalised mse convergence.58    """59    tolerance = var['tolerance']60    def calculate_using_normalised_mse_convergence(var):61        """62        Calculate stop criterion based on normalised mse convergence.63        Parameters64        ----------65        var : dict66            Dictionary of variables used in calculating the stop criterion.67        Returns68        -------69        stop : bool70            The indicator of whether or not the stop criterion is satisfied.71        mse : float72            The current convergence normalised mean squared error.73        Notes74        -----75        The IT algorithm should converge to a fixed point. This criterion76        is based on the normalised mean squared error of the difference77        between the proposed solution in this iteration and the proposed78        solution in the previous solution normalised by the mean squared error79        of the proposed solution in the previous iteration80        """81        se = np.linalg.norm(var['alpha_prev'] - var['alpha'])**282        norm = np.linalg.norm(var['alpha_prev'])**283        if norm > 0:84            nmse = se / norm85            stop = se < tolerance * norm86        else:87            # Previous solution was zero-vector (most likely first iteration)88            nmse = se89            stop = False90        return stop, nmse91    return calculate_using_normalised_mse_convergence92def wrap_calculate_using_residual(var):93    """94    Arguments wrapper for `calculate_using_residual`.95    Calculate stop criterion based on residual.96    """97    tolerance = var['tolerance']98    m = var['A'].shape[0]99    def calculate_using_residual(var):100        """101        Calculate stop criterion based on residual.102        Parameters103        ----------104        var : dict105            Dictionary of variables used in calculating the stop criterion.106        Returns107        -------108        stop : bool109            The indicator of whether or not the stop criterion is satisfied.110        r_mse : float111            The current mean squared error of the residual.112        Notes113        -----114        This stopping criterion is based on the mean sqaured error of the115        residual.116        """117        r_mse = 1/m * np.linalg.norm(var['r'])**2118        stop = r_mse < tolerance119        return stop, r_mse120    return calculate_using_residual121def wrap_calculate_using_residual_measurements_ratio(var):122    """123    Arguments wrapper for `calculate_using_residual_measurements_ratio`.124    Calculate stop criterion based on the residual to measurements ratio.125    """126    tolerance = var['tolerance']127    y = var['y']128    def calculate_using_residual_measurements_ratio(var):129        """130        Calculate stop criterion based on the residual to measurements ratio.131        Parameters132        ----------133        var : dict134            Dictionary of variables used in calculating of the stop criterion.135        Returns136        -------137        stop : bool138            The indicator of whether or not the stop criterion is satisfied.139        r_norm : float140            The current 2-norm of the residual.141        Notes142        -----143        This stop criterion is based on the ratio of the 2-norm of the current144        residual to the 2-norm of the measurments.145        """146        r_norm = np.linalg.norm(var['r'])147        stop = r_norm < tolerance * np.linalg.norm(y)148        return stop, r_norm149    return calculate_using_residual_measurements_ratio150def get_function_handle(method, var):151    """152    Return a function handle to a given calculation method.153    Parameters154    ----------155    method : str156        Identifier of the calculation method to return a handle to.157    var : dict158        Local variables needed in the calculation method.159    Returns160    -------161    f_handle : function162        Handle to calculation `method` defined in this globals scope.163    """...

test_near_wake_length.py

Source:test_near_wake_length.py

1import pytest2from miniwake.near_wake_length import calculate_ambient_turbulence_wake_erosion_rate3from miniwake.near_wake_length import calculate_angular_velocity4from miniwake.near_wake_length import calculate_flow_field_ratio5from miniwake.near_wake_length import calculate_mechanical_turbulence_wake_erosion_rate6from miniwake.near_wake_length import calculate_n7from miniwake.near_wake_length import calculate_radius_of_inviscid_expanded_rotor_disk8from miniwake.near_wake_length import calculate_near_wake_length9def test_calculate_ambient_turbulence_wake_erosion_rate():10	ambientTurbuluence = 0.111	assert calculate_ambient_turbulence_wake_erosion_rate(ambientTurbuluence) == (2.5 * ambientTurbuluence + 0.05)12def test_calculate_angular_velocity():13	assert calculate_angular_velocity(0.0) == 0.014def test_calculate_flow_field_ratio():15	assert calculate_flow_field_ratio(1.0) == 3.016def test_calculate_mechanical_turbulence_wake_erosion_rate():17	assert calculate_mechanical_turbulence_wake_erosion_rate(3, 0.0) == 0.018def test_calculate_n():19	pass #todo20def test_calculate_radius_of_inviscid_expanded_rotor_disk():21	pass #todo22def test_calculate_shear_generated_turbulence_wake_erosion_rate():23	pass #todo24def test_calculate_tip_speed_ratio():25	pass #todo26def test_calculate_total_wake_erosion_rate():27	pass #todo28def testcalculate__near_wake_length():...

__init__.py

Source:__init__.py

1# ------------------------------------------------------------------------2# Copyright (c) 2022 megvii-model. All Rights Reserved.3# ------------------------------------------------------------------------4# Modified from BasicSR (https://github.com/xinntao/BasicSR)5# Copyright 2018-2020 BasicSR Authors6# ------------------------------------------------------------------------7from .niqe import calculate_niqe8from .psnr_ssim import calculate_psnr, calculate_ssim, calculate_ssim_left, calculate_psnr_left, calculate_skimage_ssim, calculate_skimage_ssim_left...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.