Python ma.make_mask方法代码示例

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import make_mask [as 别名]
def test_hardmask(self):
        # Test hardmask
        base = self.base.copy()
        mbase = base.view(mrecarray)
        mbase.harden_mask()
        assert_(mbase._hardmask)
        mbase.mask = nomask
        assert_equal_records(mbase._mask, base._mask)
        mbase.soften_mask()
        assert_(not mbase._hardmask)
        mbase.mask = nomask
        # So, the mask of a field is no longer set to nomask...
        assert_equal_records(mbase._mask,
                             ma.make_mask_none(base.shape, base.dtype))
        assert_(ma.make_mask(mbase['b']._mask) is nomask)
        assert_equal(mbase['a']._mask, mbase['b']._mask) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import make_mask [as 别名]
def test_hardmask(self):
        # Test hardmask
        base = self.base.copy()
        mbase = base.view(mrecarray)
        mbase.harden_mask()
        self.assertTrue(mbase._hardmask)
        mbase.mask = nomask
        assert_equal_records(mbase._mask, base._mask)
        mbase.soften_mask()
        self.assertTrue(not mbase._hardmask)
        mbase.mask = nomask
        # So, the mask of a field is no longer set to nomask...
        assert_equal_records(mbase._mask,
                             ma.make_mask_none(base.shape, base.dtype))
        self.assertTrue(ma.make_mask(mbase['b']._mask) is nomask)
        assert_equal(mbase['a']._mask, mbase['b']._mask) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import make_mask [as 别名]
def test_hardmask(self):
        "Test hardmask"
        base = self.base.copy()
        mbase = base.view(mrecarray)
        mbase.harden_mask()
        self.assertTrue(mbase._hardmask)
        mbase.mask = nomask
        assert_equal_records(mbase._mask, base._mask)
        mbase.soften_mask()
        self.assertTrue(not mbase._hardmask)
        mbase.mask = nomask
        # So, the mask of a field is no longer set to nomask...
        assert_equal_records(mbase._mask,
                             ma.make_mask_none(base.shape, base.dtype))
        self.assertTrue(ma.make_mask(mbase['b']._mask) is nomask)
        assert_equal(mbase['a']._mask, mbase['b']._mask)
    # 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import make_mask [as 别名]
def test_hdmedian():
    # 1-D array
    x = ma.arange(11)
    assert_allclose(ms.hdmedian(x), 5, rtol=1e-14)
    x.mask = ma.make_mask(x)
    x.mask[:7] = False
    assert_allclose(ms.hdmedian(x), 3, rtol=1e-14)

    # Check that `var` keyword returns a value.  TODO: check whether returned
    # value is actually correct.
    assert_(ms.hdmedian(x, var=True).size == 2)

    # 2-D array
    x2 = ma.arange(22).reshape((11, 2))
    assert_allclose(ms.hdmedian(x2, axis=0), [10, 11])
    x2.mask = ma.make_mask(x2)
    x2.mask[:7, :] = False
    assert_allclose(ms.hdmedian(x2, axis=0), [6, 7]) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import make_mask [as 别名]
def latin_sampler(locator, num_samples, variables):
    """
    This script creates a matrix of m x n samples using the latin hypercube sampler.
    for this, it uses the database of probability distribtutions stored in locator.get_uncertainty_db()
    it returns clean and normalized samples.

    :param locator: pointer to locator of files of CEA
    :param num_samples: number of samples to do
    :param variables: list of variables to sample
    :return:
        1. design: a matrix m x n with the samples where each feature is normalized from [0,1]
        2. design_norm: a matrix m x n with the samples where each feature is normalized from [0,1]
        3. pdf_list: a dataframe with properties of the probability density functions used in the exercise.

    """

    # get probability density function PDF of variables of interest
    variable_groups = ('ENVELOPE', 'INDOOR_COMFORT', 'INTERNAL_LOADS','SYSTEMS')
    database = pd.concat([pd.read_excel(locator.get_uncertainty_db(), group, axis=1)
                          for group in variable_groups])
    pdf_list = database[database['name'].isin(variables)].set_index('name')

    # get number of variables
    num_vars = pdf_list.shape[0]  # alternatively use len(variables)

    # get design of experiments
    samples = latin_hypercube.lhs(num_vars, samples=num_samples, criterion='maximin')
    for i, variable in enumerate(variables):

        distribution = pdf_list.loc[variable, 'distribution']
        #sampling into lhs
        min = pdf_list.loc[variable, 'min']
        max = pdf_list.loc[variable, 'max']
        mu = pdf_list.loc[variable, 'mu']
        stdv = pdf_list.loc[variable, 'stdv']
        if distribution == 'triangular':
            loc = min
            scale = max - min
            c = (mu - min) / (max - min)
            samples[:, i] = triang(loc=loc, c=c, scale=scale).ppf(samples[:, i])
        elif distribution == 'normal':
            samples[:, i] = norm(loc=mu, scale=stdv).ppf(samples[:, i])
        elif distribution == 'boolean': # converts a uniform (0-1) into True/False
            samples[:, i] = ma.make_mask(np.rint(uniform(loc=min, scale=max).ppf(samples[:, i])))
        else:  # assume it is uniform
            samples[:, i] = uniform(loc=min, scale=max).ppf(samples[:, i])

    min_max_scaler = preprocessing.MinMaxScaler(copy=True, feature_range=(0, 1))
    samples_norm = min_max_scaler.fit_transform(samples)

    return samples, samples_norm, pdf_list