Python numpy.c_() Examples

The following are 30 code examples of numpy.c_(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: 1logistic_regression.py    From Fundamentals-of-Machine-Learning-with-scikit-learn with MIT License 7 votes vote down vote up
def show_classification_areas(X, Y, lr):
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))
    Z = lr.predict(np.c_[xx.ravel(), yy.ravel()])

    Z = Z.reshape(xx.shape)
    plt.figure(1, figsize=(30, 25))
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel1)

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=np.abs(Y - 1), edgecolors='k', cmap=plt.cm.coolwarm)
    plt.xlabel('X')
    plt.ylabel('Y')

    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.xticks(())
    plt.yticks(())

    plt.show() 
Example #2
Source File: test_representation.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def setup_class(cls, dtype=float, nforecast=100, conserve_memory=0):
        super(Clark1989Forecast, cls).setup_class(
            dtype=dtype, conserve_memory=conserve_memory
        )
        cls.nforecast = nforecast

        # Add missing observations to the end (to forecast)
        cls.model.endog = np.array(
            np.c_[
                cls.model.endog,
                np.r_[[np.nan, np.nan]*nforecast].reshape(2, nforecast)
            ],
            ndmin=2, dtype=dtype, order="F"
        )
        cls.model.nobs = cls.model.endog.shape[1]

        cls.results = cls.run_filter() 
Example #3
Source File: twf.py    From sprocket with MIT License 6 votes vote down vote up
def align_data(org_data, tar_data, twf):
    """Get aligned joint feature vector

    Paramters
    ---------
    orgdata : array, shape (`T_org`, `dim_org`)
        Acoustic feature vector of original speaker
    tardata : array, shape (`T_tar`, `dim_tar`)
        Acoustic feature vector of target speaker
    twf : array, shape (`2`, `T`)
        Time warping function between original and target

    Returns
    -------
    jdata : array, shape (`T_new` `dim_org + dim_tar`)
        Joint feature vector between source and target

    """

    jdata = np.c_[org_data[twf[0]], tar_data[twf[1]]]
    return jdata 
Example #4
Source File: delta.py    From sprocket with MIT License 6 votes vote down vote up
def static_delta(data, win=[-1.0, 1.0, 0]):
    """Calculate static and delta component

    Parameters
    ----------
    data : array, shape (`T`, `dim`)
        Array of static matrix sequence.
    win: array, optional, shape (`3`)
        The shape of window matrix.
        Default set to [-1.0, 1.0, 0].

    Returns
    -------
    sddata: array, shape (`T`, `dim*2`)
        Array static and delta matrix sequence.

    """

    sddata = np.c_[data, delta(data, win)]
    return sddata 
Example #5
Source File: test_diagGMM.py    From sprocket with MIT License 6 votes vote down vote up
def test_blockdiag_GMM_train_and_convert(self):
        jnt = np.random.rand(100, 20)
        gmm_tr = GMMTrainer(n_mix=4, n_iter=100, covtype='block_diag')
        gmm_tr.train(jnt)

        gmm_cv = GMMConvertor(
            n_mix=4, covtype='block_diag', gmmmode=None)
        gmm_cv.open_from_param(gmm_tr.param)

        data = np.random.rand(200, 5)
        sddata = np.c_[data, delta(data)]
        odata = gmm_cv.convert(sddata, cvtype='mlpg')
        odata = gmm_cv.convert(sddata, cvtype='mmse')
        assert data.shape == odata.shape

        # test for singlepath
        Ajnt = np.random.rand(100, 120)
        Bjnt = np.random.rand(100, 140)
        gmm_tr.estimate_responsibility(jnt)
        Aparam = gmm_tr.train_singlepath(Ajnt)
        Bparam = gmm_tr.train_singlepath(Bjnt)
        assert np.allclose(Aparam.weights_, Bparam.weights_) 
Example #6
Source File: test_GMM.py    From sprocket with MIT License 6 votes vote down vote up
def test_GMM_train_and_convert(self):
        jnt = np.random.rand(100, 20)
        gmm_tr = GMMTrainer(n_mix=4, n_iter=100, covtype='full')
        gmm_tr.train(jnt)

        data = np.random.rand(200, 5)
        sddata = np.c_[data, delta(data)]
        gmm_cv = GMMConvertor(
            n_mix=4, covtype='full', gmmmode=None)
        gmm_cv.open_from_param(gmm_tr.param)

        odata = gmm_cv.convert(sddata, cvtype='mlpg')
        odata = gmm_cv.convert(sddata, cvtype='mmse')
        assert data.shape == odata.shape

        # test for singlepath
        Ajnt = np.random.rand(100, 120)
        Bjnt = np.random.rand(100, 140)
        gmm_tr.estimate_responsibility(jnt)
        Aparam = gmm_tr.train_singlepath(Ajnt)
        Bparam = gmm_tr.train_singlepath(Bjnt)
        assert np.allclose(Aparam.weights_, Bparam.weights_) 
Example #7
Source File: astra2ocelot.py    From ocelot with GNU General Public License v3.0 6 votes vote down vote up
def exact_xxstg_2_xp_de(xxstg, gamref):
    # dE/E0
    N = len(xxstg) / 6
    xp = np.zeros((N, 6))
    pref = m_e_eV * np.sqrt(gamref ** 2 - 1)
    gamma = gamref * (1 + xxstg[5::6])
    beta = np.sqrt(1 - gamma ** -2)
    u = np.c_[xxstg[1::6], xxstg[3::6], np.ones(N)]
    if np.__version__ > "1.8":
        norm = np.linalg.norm(u, 2, 1).reshape((N, 1))
    else:
        norm = np.sqrt(u[:, 0] ** 2 + u[:, 1] ** 2 + u[:, 2] ** 2).reshape((N, 1))
    u = u / norm
    xp[:, 0] = xxstg[0::6] - u[:, 0] * beta * xxstg[4::6]
    xp[:, 1] = xxstg[2::6] - u[:, 1] * beta * xxstg[4::6]
    xp[:, 2] = -u[:, 2] * beta * xxstg[4::6]
    xp[:, 3] = u[:, 0] * gamma * beta * m_e_eV
    xp[:, 4] = u[:, 1] * gamma * beta * m_e_eV
    xp[:, 5] = u[:, 2] * gamma * beta * m_e_eV - pref
    return xp 
Example #8
Source File: test_index_tricks.py    From mars with Apache License 2.0 6 votes vote down vote up
def testC_(self):
        r = mt.c_[mt.array([1, 2, 3]), mt.array([4, 5, 6])]

        result = self.executor.execute_tensor(r, concat=True)[0]
        expected = np.c_[np.array([1, 2, 3]), np.array([4, 5, 6])]
        np.testing.assert_array_equal(result, expected)

        r = mt.c_[mt.array([[1, 2, 3]]), 0, 0, mt.array([[4, 5, 6]])]

        result = self.executor.execute_tensor(r, concat=True)[0]
        expected = np.c_[np.array([[1, 2, 3]]), 0, 0, np.array([[4, 5, 6]])]
        np.testing.assert_array_equal(result, expected)

        r = mt.c_[:3, 1:4]
        result = self.executor.execute_tensor(r, concat=True)[0]
        expected = np.c_[:3, 1:4]
        np.testing.assert_array_equal(result, expected) 
Example #9
Source File: test_transformations.py    From pybids with MIT License 6 votes vote down vote up
def test_orthogonalize_dense(collection):
    transform.Factor(collection, 'trial_type', sep=sep)

    sampling_rate = collection.sampling_rate
    # Store pre-orth variables needed for tests
    pg_pre = collection['trial_type/parametric gain'].to_dense(sampling_rate)
    rt = collection['RT'].to_dense(sampling_rate)

    # Orthogonalize and store result
    transform.Orthogonalize(collection, variables='trial_type/parametric gain',
                            other='RT', dense=True, groupby=['run', 'subject'])
    pg_post = collection['trial_type/parametric gain']

    # Verify that the to_dense() calls result in identical indexing
    ent_cols = ['subject', 'run']
    assert pg_pre.to_df()[ent_cols].equals(rt.to_df()[ent_cols])
    assert pg_post.to_df()[ent_cols].equals(rt.to_df()[ent_cols])

    vals = np.c_[rt.values, pg_pre.values, pg_post.values]
    df = pd.DataFrame(vals, columns=['rt', 'pre', 'post'])
    groupby = rt.get_grouper(['run', 'subject'])
    pre_r = df.groupby(groupby).apply(lambda x: x.corr().iloc[0, 1])
    post_r = df.groupby(groupby).apply(lambda x: x.corr().iloc[0, 2])
    assert (pre_r > 0.2).any()
    assert (post_r < 0.0001).all() 
Example #10
Source File: astra2ocelot.py    From ocelot with GNU General Public License v3.0 6 votes vote down vote up
def exact_xp_2_xxstg_de(xp, gamref):
    # dE/E0
    N = xp.shape[0]
    xxstg = np.zeros((N, 6))
    pref = m_e_eV * np.sqrt(gamref ** 2 - 1)
    u = np.c_[xp[:, 3], xp[:, 4], xp[:, 5] + pref]
    gamma = np.sqrt(1 + np.sum(u * u, 1) / m_e_eV ** 2)
    beta = np.sqrt(1 - gamma ** -2)
    if np.__version__ > "1.8":
        p0 = np.linalg.norm(u, 2, 1).reshape((N, 1))
    else:
        p0 = np.sqrt(u[:, 0] ** 2 + u[:, 1] ** 2 + u[:, 2] ** 2).reshape((N, 1))
    u = u / p0
    cdt = -xp[:, 2] / (beta * u[:, 2])
    xxstg[:, 0] = xp[:, 0] + beta * u[:, 0] * cdt
    xxstg[:, 2] = xp[:, 1] + beta * u[:, 1] * cdt
    xxstg[:, 4] = cdt
    xxstg[:, 1] = u[:, 0] / u[:, 2]
    xxstg[:, 3] = u[:, 1] / u[:, 2]
    xxstg[:, 5] = gamma / gamref - 1
    return xxstg 
Example #11
Source File: DataModule.py    From sgd-influence with MIT License 6 votes vote down vote up
def fetch(self, n_tr, n_val, n_test, seed=0):
        x, y = self.load()
        
        # split data
        x_tr, x_val, y_tr, y_val = train_test_split(
            x, y, train_size=n_tr, test_size=n_val+n_test, random_state=seed)
        x_val, x_test, y_val, y_test = train_test_split(
            x_val, y_val, train_size=n_val, test_size=n_test, random_state=seed+1)
        
        # process x
        if self.normalize:
            scaler = StandardScaler()
            scaler.fit(x_tr)
            x_tr = scaler.transform(x_tr)
            x_val = scaler.transform(x_val)
            x_test = scaler.transform(x_test)
        if self.append_one:
            x_tr = np.c_[x_tr, np.ones(n_tr)]
            x_val = np.c_[x_val, np.ones(n_val)]
            x_test = np.c_[x_test, np.ones(n_test)]
        
        return (x_tr, y_tr), (x_val, y_val), (x_test, y_test) 
Example #12
Source File: astra2ocelot.py    From ocelot with GNU General Public License v3.0 6 votes vote down vote up
def exact_xp_2_xxstg_dp(xp, gamref):
    # dp/p0
    N = xp.shape[0]
    xxstg = np.zeros((N, 6))
    pref = m_e_eV * np.sqrt(gamref ** 2 - 1)
    u = np.c_[xp[:, 3], xp[:, 4], xp[:, 5] + pref]
    gamma = np.sqrt(1 + np.sum(u * u, 1) / m_e_eV ** 2)
    beta = np.sqrt(1 - gamma ** -2)
    if np.__version__ > "1.8":
        p0 = np.linalg.norm(u, 2, 1).reshape((N, 1))
    else:
        p0 = np.sqrt(u[:, 0] ** 2 + u[:, 1] ** 2 + u[:, 2] ** 2).reshape((N, 1))
    u = u / p0
    cdt = -xp[:, 2] / (beta * u[:, 2])
    xxstg[:, 0] = xp[:, 0] + beta * u[:, 0] * cdt
    xxstg[:, 2] = xp[:, 1] + beta * u[:, 1] * cdt
    xxstg[:, 4] = cdt
    xxstg[:, 1] = u[:, 0] / u[:, 2]
    xxstg[:, 3] = u[:, 1] / u[:, 2]
    xxstg[:, 5] = p0.reshape(N) / pref - 1
    return xxstg 
Example #13
Source File: test_tools.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def test_cases(self):
        # Basic cases
        for series, diff, seasonal_diff, seasonal_periods, result in self.cases:
            
            # Test numpy array
            x = tools.diff(series, diff, seasonal_diff, seasonal_periods)
            assert_almost_equal(x, result)

            # Test as Pandas Series
            series = pd.Series(series)

            # Rewrite to test as n-dimensional array
            series = np.c_[series, series]
            result = np.c_[result, result]

            # Test Numpy array
            x = tools.diff(series, diff, seasonal_diff, seasonal_periods)
            assert_almost_equal(x, result)

            # Test as Pandas Dataframe
            series = pd.DataFrame(series)
            x = tools.diff(series, diff, seasonal_diff, seasonal_periods)
            assert_almost_equal(x, result) 
Example #14
Source File: astra2ocelot.py    From ocelot with GNU General Public License v3.0 6 votes vote down vote up
def exact_xxstg_2_xp_mad(xxstg, gamref):
    # from mad format
    N = int(xxstg.size / 6)
    xp = np.zeros((N, 6))
    pref = m_e_eV * np.sqrt(gamref ** 2 - 1)
    betaref = np.sqrt(1 - gamref ** -2)
    gamma = (betaref * xxstg[5] + 1) * gamref
    beta = np.sqrt(1 - gamma ** -2)

    pz2pref = np.sqrt(((gamma * beta) / (gamref * betaref)) ** 2 - xxstg[1] ** 2 - xxstg[3] ** 2)

    u = np.c_[xxstg[1] / pz2pref, xxstg[3] / pz2pref, np.ones(N)]
    if np.__version__ > "1.8":
        norm = np.linalg.norm(u, 2, 1).reshape((N, 1))
    else:
        norm = np.sqrt(u[:, 0] ** 2 + u[:, 1] ** 2 + u[:, 2] ** 2).reshape((N, 1))
    u = u / norm
    xp[:, 0] = xxstg[0] - u[:, 0] * beta * xxstg[4]
    xp[:, 1] = xxstg[2] - u[:, 1] * beta * xxstg[4]
    xp[:, 2] = -u[:, 2] * beta * xxstg[4]
    xp[:, 3] = u[:, 0] * gamma * beta * m_e_eV
    xp[:, 4] = u[:, 1] * gamma * beta * m_e_eV
    xp[:, 5] = u[:, 2] * gamma * beta * m_e_eV - pref
    return xp 
Example #15
Source File: test_kalman.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def setup_class(cls, dtype=float, nforecast=100, conserve_memory=0):
        super(Clark1989Forecast, cls).setup_class(dtype, conserve_memory)
        cls.nforecast = nforecast

        # Add missing observations to the end (to forecast)
        cls._obs = cls.obs
        cls.obs = np.array(
            np.c_[
                cls._obs,
                np.r_[[np.nan, np.nan]*nforecast].reshape(2, nforecast)
            ],
            ndmin=2, dtype=dtype, order="F"
        )

        cls.model, cls.filter = cls.init_filter()
        cls.result = cls.run_filter() 
Example #16
Source File: astra2ocelot.py    From ocelot with GNU General Public License v3.0 6 votes vote down vote up
def exact_xp_2_xxstg_mad(xp, gamref):
    # to mad format
    N = xp.shape[0]
    xxstg = np.zeros((N, 6))
    pref = m_e_eV * np.sqrt(gamref ** 2 - 1)
    u = np.c_[xp[:, 3], xp[:, 4], xp[:, 5] + pref]
    gamma = np.sqrt(1 + np.sum(u * u, 1) / m_e_eV ** 2)
    beta = np.sqrt(1 - gamma ** -2)
    betaref = np.sqrt(1 - gamref ** -2)
    if np.__version__ > "1.8":
        p0 = np.linalg.norm(u, 2, 1).reshape((N, 1))
    else:
        p0 = np.sqrt(u[:, 0] ** 2 + u[:, 1] ** 2 + u[:, 2] ** 2).reshape((N, 1))
    u = u / p0
    cdt = -xp[:, 2] / (beta * u[:, 2])
    xxstg[:, 0] = xp[:, 0] + beta * u[:, 0] * cdt
    xxstg[:, 2] = xp[:, 1] + beta * u[:, 1] * cdt
    xxstg[:, 4] = cdt
    xxstg[:, 1] = xp[:, 3] / pref
    xxstg[:, 3] = xp[:, 4] / pref
    xxstg[:, 5] = (gamma / gamref - 1) / betaref
    return xxstg 
Example #17
Source File: elm.py    From Python-ELM with MIT License 6 votes vote down vote up
def _add_bias(self, X):
        """add bias to list

        Args:
        x_vs [[float]] Array: vec to add bias

        Returns:
        [float]: added vec

        Examples:
        >>> e = ELM(10, 3)
        >>> e._add_bias(np.array([[1,2,3], [1,2,3]]))
        array([[1., 2., 3., 1.],
               [1., 2., 3., 1.]])
        """

        return np.c_[X, np.ones(X.shape[0])] 
Example #18
Source File: estimate_pose.py    From centerpose with MIT License 6 votes vote down vote up
def compute_similarity_transform(points_static, points_to_transform):
    # http://nghiaho.com/?page_id=671
    p0 = np.copy(points_static).T
    p1 = np.copy(points_to_transform).T

    t0 = -np.mean(p0, axis=1).reshape(3, 1)
    t1 = -np.mean(p1, axis=1).reshape(3, 1)
    t_final = t1 - t0

    p0c = p0 + t0
    p1c = p1 + t1

    covariance_matrix = p0c.dot(p1c.T)
    U, S, V = np.linalg.svd(covariance_matrix)
    R = U.dot(V)
    if np.linalg.det(R) < 0:
        R[:, 2] *= -1

    rms_d0 = np.sqrt(np.mean(np.linalg.norm(p0c, axis=0) ** 2))
    rms_d1 = np.sqrt(np.mean(np.linalg.norm(p1c, axis=0) ** 2))

    s = (rms_d0 / rms_d1)
    P = np.c_[s * np.eye(3).dot(R), t_final]
    return P 
Example #19
Source File: estimate_pose.py    From LipReading with MIT License 6 votes vote down vote up
def compute_similarity_transform(points_static, points_to_transform):
    #http://nghiaho.com/?page_id=671
    p0 = np.copy(points_static).T
    p1 = np.copy(points_to_transform).T

    t0 = -np.mean(p0, axis=1).reshape(3,1)
    t1 = -np.mean(p1, axis=1).reshape(3,1)
    t_final = t1 -t0

    p0c = p0+t0
    p1c = p1+t1

    covariance_matrix = p0c.dot(p1c.T)
    U,S,V = np.linalg.svd(covariance_matrix)
    R = U.dot(V)
    if np.linalg.det(R) < 0:
        R[:,2] *= -1

    rms_d0 = np.sqrt(np.mean(np.linalg.norm(p0c, axis=0)**2))
    rms_d1 = np.sqrt(np.mean(np.linalg.norm(p1c, axis=0)**2))

    s = (rms_d0/rms_d1)
    P = np.c_[s*np.eye(3).dot(R), t_final]
    return P 
Example #20
Source File: test_dynamic_factor.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_predict(self):
        exog = np.c_[np.ones((16, 1)), (np.arange(75, 75+16) + 2)[:, np.newaxis]]
        super(TestSUR, self).test_predict(exog=exog) 
Example #21
Source File: test_dynamic_factor.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_predict(self):
        exog = np.c_[np.ones((16, 1)), (np.arange(75, 75+16) + 2)[:, np.newaxis]]
        super(TestDynamicFactor_exog2, self).test_predict(exog=exog) 
Example #22
Source File: test_dynamic_factor.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def setup_class(cls):
        true = results_dynamic_factor.lutkepohl_sur.copy()
        true['predict'] = output_results.iloc[1:][['predict_sur_1', 'predict_sur_2', 'predict_sur_3']]
        true['dynamic_predict'] = output_results.iloc[1:][['dyn_predict_sur_1', 'dyn_predict_sur_2', 'dyn_predict_sur_3']]
        exog = np.c_[np.ones((75,1)), (np.arange(75) + 2)[:, np.newaxis]]
        super(TestSUR, cls).setup_class(true, k_factors=0, factor_order=0, exog=exog, error_cov_type='unstructured') 
Example #23
Source File: test_dynamic_factor.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_dynamic_predict(self):
        exog = np.c_[np.ones((16, 1)), (np.arange(75, 75+16) + 2)[:, np.newaxis]]
        super(TestDynamicFactor_exog2, self).test_dynamic_predict(exog=exog) 
Example #24
Source File: test_data.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def setup_class(cls):
        super(TestArrays2dEndog, cls).setup_class()
        cls.endog = np.random.random((10,1))
        cls.exog = np.c_[np.ones(10), np.random.random((10,2))]
        cls.data = sm_data.handle_data(cls.endog, cls.exog)
        #cls.endog = endog.squeeze() 
Example #25
Source File: plot_robust_classification_toy.py    From scikit-learn-extra with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def plot_classif(clf, X, y, ax):
    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
    h = 0.02  # step size in the mesh
    xx, yy = np.meshgrid(
        np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)
    )
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    ax.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
    ax.scatter(X[:, 0], X[:, 1], c=y) 
Example #26
Source File: twod_mjc_env.py    From imitation with MIT License 5 votes vote down vote up
def plot_maps(combined_list=None, *heatmaps):
    import matplotlib.pyplot as plt

    combined = np.c_[heatmaps]
    if combined_list is not None:
        combined_list.append(combined)
        combined = np.concatenate(combined_list)
    else:
        combined_list = []
    plt.figure()
    plt.imshow(combined, cmap="afmhot", interpolation="none")
    plt.show()
    return combined_list 
Example #27
Source File: wave.py    From ocelot with GNU General Public License v3.0 5 votes vote down vote up
def save_trf(trf, attr, flePath):
    if hasattr(trf, attr):
        filt = getattr(trf, attr)
    else:
        raise ValueError('no attribute', attr, 'in fransfer function')

    f = open(flePath, 'wb')
    header = 'Energy[eV] Filter_Abs Filter_Ang'
    np.savetxt(f, np.c_[trf.ev(), np.abs(trf.tr), np.angle(trf.tr)], header=header, fmt="%.8e", newline='\n',
               comments='')
    f.close() 
Example #28
Source File: xfel_utils.py    From ocelot with GNU General Public License v3.0 5 votes vote down vote up
def save_xhrss_dump_proj(dump_proj, filePath):
    # saves the dfl_hxrss_filt radiation projections dump to text files

    (t_l_scale, _, t_l_int_a, t_l_pha_a), (f_l_scale, f_l_filt, _, f_l_int_a) = dump_proj

    f = open(filePath + '.t.txt', 'wb')
    header = 'Distance Power Phase'
    np.savetxt(f, np.c_[t_l_scale, t_l_int_a, t_l_pha_a], header=header, fmt="%e", newline='\n', comments='')
    f.close()

    f = open(filePath + '.f.txt', 'wb')
    header = 'Wavelength Spectrum Filter_Abs Filter_Ang'
    np.savetxt(f, np.c_[f_l_scale, f_l_int_a, np.abs(f_l_filt), np.unwrap(np.angle(f_l_filt))], header=header, fmt="%.8e", newline='\n', comments='')
    f.close() 
Example #29
Source File: iou_matching.py    From centerpose with MIT License 5 votes vote down vote up
def iou(bbox, candidates):
    """Computer intersection over union.

    Parameters
    ----------
    bbox : ndarray
        A bounding box in format `(top left x, top left y, width, height)`.
    candidates : ndarray
        A matrix of candidate bounding boxes (one per row) in the same format
        as `bbox`.

    Returns
    -------
    ndarray
        The intersection over union in [0, 1] between the `bbox` and each
        candidate. A higher score means a larger fraction of the `bbox` is
        occluded by the candidate.

    """
    bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
    candidates_tl = candidates[:, :2]
    candidates_br = candidates[:, :2] + candidates[:, 2:]

    tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
               np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
    br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
               np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
    wh = np.maximum(0., br - tl)

    area_intersection = wh.prod(axis=1)
    area_bbox = bbox[2:].prod()
    area_candidates = candidates[:, 2:].prod(axis=1)
    return area_intersection / (area_bbox + area_candidates - area_intersection) 
Example #30
Source File: test_index_tricks.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up
def test_c_():
    a = np.c_[np.array([[1, 2, 3]]), 0, 0, np.array([[4, 5, 6]])]
    assert_equal(a, [[1, 2, 3, 0, 0, 4, 5, 6]])