How to use retrieve_config method in avocado

Best Python code snippet using avocado_python

service_manager.py

Source:service_manager.py

...43            and version == CtsVersion.ONE_DOT_ZERO44        )45    ):46        service = SiriusXmService(47            config=config_manager.retrieve_config(SiriusXmConfig),48            table=get_main_table(config_manager.retrieve_config(DynamoConfig)),49        )50    elif (51        programcode52        and programcode.lower() == ProgramCode.FCA.name.lower()53        and version == CtsVersion.ONE_DOT_ZERO54    ):55        service = FcaService(56            config=config_manager.retrieve_config(FcaConfig),57            table=get_main_table(config_manager.retrieve_config(DynamoConfig)),58            supplementtable=get_supplement_table(59                config_manager.retrieve_config(DynamoConfig)60            ),61        )62    elif (63        programcode64        and programcode.lower() == ProgramCode.VWCARNET.name.lower()65        and version == CtsVersion.ONE_DOT_ZERO66    ):67        service = VerizonService(68            config=config_manager.retrieve_config(VerizonConfig),69            table=get_main_table(config_manager.retrieve_config(DynamoConfig)),70            supplementtable=get_supplement_table(71                config_manager.retrieve_config(DynamoConfig)72            ),73        )74    elif (75        programcode76        and programcode.lower() == ProgramCode.VWCARNET.name.lower()77        and version == CtsVersion.TWO_DOT_ZERO78    ):79        service = AerisService(80            config=config_manager.retrieve_config(AerisConfig),81            table=get_main_table(config_manager.retrieve_config(DynamoConfig)),82        )83    elif (84        programcode85        and programcode.lower() == ProgramCode.PORSCHE.name.lower()86        and version == CtsVersion.ONE_DOT_ZERO87    ):88        service = VodafoneService(89            config=config_manager.retrieve_config(VodafoneConfig),90            table=get_main_table(config_manager.retrieve_config(DynamoConfig)),91            supplementtable=get_supplement_table(92                config_manager.retrieve_config(DynamoConfig)93            ),94        )95    elif (96        programcode97        and programcode.lower() == ProgramCode.TOYOTA.name.lower()98        and version == CtsVersion.TWO_DOT_ZERO99    ):100        service = TmnaService(101            config=config_manager.retrieve_config(TmnaConfig),102        )103    elif (104        programcode105        and programcode.lower() == ProgramCode.SUBARU.name.lower()106        and version == CtsVersion.TWO_DOT_ZERO107    ):108        service = WirelessCarService(109            config=config_manager.retrieve_config(WirelessCarConfig),110        )...

initialization.py

Source:initialization.py

...15    :param P: number of inducing inputs16    :param D: dimensionality of inducing input17    :return: inducing inputs (P, D)18    """19    method = retrieve_config(config_inducing_inputs, 'method',20                             "init_inducing_inputs: Keyword 'method' required. \21                             Can be 'kmeans', 'random', 'randomXpermutedU'")22    # Cluster GP input data, cluster centers become inducing input23    if method == 'kmeans':24        assert 'X' in kwargs, 'Keyword argument X required: GP training input'25        X = kwargs['X']26        from sklearn.cluster import KMeans27        m = KMeans(n_clusters=P, n_init=50, max_iter=500)28        m.fit(X.copy())29        Z = m.cluster_centers_.copy()30    # Randomly draw inducing inputs i.i.d. from N(0,1)31    if method == 'random':32        noise = retrieve_config(config_inducing_inputs, 'noise',33                                "init_inducing_inputs: Keyword 'noise' required.")34        Z = np.sqrt(noise) * np.random.randn(P, D)35    if method == 'uniform':36        low = retrieve_config(config_inducing_inputs, 'low',37                              "init_inducing_inputs: Keyword 'low' required.")38        high = retrieve_config(config_inducing_inputs, 'high',39                              "init_inducing_inputs: Keyword 'high' required.")40        Z = np.random.uniform(low=low, high=high, size=(P, D))41    # Random inducing inputs on state and random selection of input sequence42    if method == 'randomXpermutedU':43        assert 'X' in kwargs, 'Keyword argument X required: GP training input'44        assert 'dim_u' in kwargs, 'Keyword argument U required: input dim'45        X = kwargs['X']46        dim_u = kwargs['dim_u']47        U_ind = np.random.permutation(X[:, :dim_u])[:P]48        X_ind = 3. * np.random.randn(P, D-dim_u)49        Z = np.concatenate((U_ind, X_ind), axis=1)50    return Z51def init_inducing_outputs(config_inducing_outputs, P=20, D=1, **kwargs):52    """53    Initialize gaussian distribution on GP pseudo outputs54    :param config_inducing_outputs: dict, configuration55    :param P: number of pseudo points56    :param D: output dimensionality57    :return: q_mu, ndarray, NxD means and q_var, ndarray (N, D) variances of58    GP pseudo outputs.59    """60    method = retrieve_config(config_inducing_outputs, 'method',61                             "init_inducing_outputs: Keyword 'method' required. \62                             Can be 'random', 'lstm[1-3]', 'ssm', 'model'")63    # GP output: 0 + noise64    if method == 'zero':65        noise = retrieve_config(config_inducing_outputs, 'noise',66                                "init_inducing_inputs: Keyword 'noise' required.")67        q_mu = np.sqrt(noise) * np.random.rand(P, D)68    # GP output: 1 + noise69    if method == 'one':70        noise = retrieve_config(config_inducing_outputs, 'noise',71                                "init_inducing_inputs: Keyword 'noise' required.")72        q_mu = np.ones((P, D)) + np.sqrt(noise) * np.random.rand(P, D)73    # GP output: identity + noise74    if method == 'identity':75        noise = retrieve_config(config_inducing_outputs, 'noise',76                                "init_inducing_inputs: Keyword 'noise' required.")77        'Z' in kwargs, 'Keyword argument Z required: GP pseudo inputs'78        Z = kwargs['Z']79        assert Z.shape[1] >= D, 'Output dimension larger than input dimension'80        q_mu = Z[:, :D] + np.sqrt(noise) * np.random.rand(P, D)81    # Variance of inducing outputs82    var = retrieve_config(config_inducing_outputs, 'var',83                          "init_latent: Keyword 'var' required.")84    q_var = var * np.ones((P, D))...

FacebookService.py

Source:FacebookService.py

2import facebook3from decouple import config4from database.database import retrieve_config5async def getCurrentUserEmail(token: str) -> str:6    appSecret = await retrieve_config('facebook_appSecret')7    FB = facebook.GraphAPI(8        access_token=token, app_secret=appSecret, version="11.0")9    user = FB.get_object("me", fields='email')10    return user['email'] if user else False11async def fbPublish(msg: str, link: str) -> dict:12    appSecret = await retrieve_config('facebook_appSecret')13    pageUsername = await retrieve_config('facebook_pageUsername')14    accessToken = await retrieve_config('facebook_accessToken')15    FB = facebook.GraphAPI(access_token=accessToken,16                           app_secret=appSecret, version="11.0")17    post = FB.put_object(pageUsername, "feed", message=msg, link=link)18    return post19async def fbPublishImg(msg: str, imgLink: str) -> dict:20    appSecret = await retrieve_config('facebook_appSecret')21    pageUsername = await retrieve_config('facebook_pageUsername')22    accessToken = await retrieve_config('facebook_accessToken')23    FB = facebook.GraphAPI(access_token=accessToken,24                           app_secret=appSecret, version="11.0")25    post = FB.put_object(pageUsername, "photos", message=msg, url=imgLink)...

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