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How to use _normalize method in Slash

Best Python code snippet using slash

test_filtering.py

Source:test_filtering.py

...50cumprod_expected = data.cumprod().ffill()51cumprod_filtered_expected = data[filter].cumprod().reindex(index).ffill()52cumprod_df_expected = df.cumprod().ffill()53cumprod_df_filtered_expected = df[filter].cumprod().reindex(index).ffill()54def _normalize(x):55    """try and get a vector/dataframe in a format unittest can compare them"""56    if isinstance(x, float):57        if np.isnan(x):58            return -np.inf59        return x60    if isinstance(x, (pa.DataFrame, pa.Series)):61        x = x.values62    if x.ndim == 1:63        return tuple([_normalize(y) for y in x])64    r = x.copy()65    r[np.isnan(r)] = -np.inf66    return tuple(r.tolist())67class NodeFilterTest(unittest.TestCase):68    def setUp(self):69        self.ctx = MDFContext(index[0])70    def test_queue(self):71        actual = self._run(datanode.queuenode(as_list=True))72        self.assertEqual(actual.tolist(), queue_expected)73        actual = self._run(datanode.queuenode(filter=filternode, as_list=True))74        self.assertEqual(actual.tolist(), queue_filtered_expected)75    def test_delay(self):76        actual = self._run(datanode.delaynode(periods=1, initial_value=np.nan))77        self.assertEqual(_normalize(actual), _normalize(delay_expected))78        actual = self._run(datanode.delaynode(periods=1, initial_value=np.nan, filter=filternode))79        self.assertEqual(_normalize(actual), _normalize(delay_filtered_expected))80    def test_delay_df(self):81        actual = self._run(dfnode.delaynode(periods=1, initial_value=np.nan))82        self.assertEqual(_normalize(actual), _normalize(delay_df_expected))83        actual = self._run(dfnode.delaynode(periods=1, initial_value=np.nan, filter=filternode))84        self.assertEqual(_normalize(actual), _normalize(delay_df_filtered_expected))85    def test_nansum(self):86        actual = self._run(datanode.nansumnode())87        self.assertEqual(_normalize(actual), _normalize(nansum_expected))88        actual = self._run(datanode.nansumnode(filter=filternode))89        self.assertEqual(_normalize(actual), _normalize(nansum_filtered_expected))90    def test_nansum_df(self):91        actual = self._run(dfnode.nansumnode())92        self.assertEqual(_normalize(actual), _normalize(nansum_df_expected))93        actual = self._run(dfnode.nansumnode(filter=filternode))94        self.assertEqual(_normalize(actual), _normalize(nansum_df_filtered_expected))95    def test_cumprod(self):96        actual = self._run(datanode.cumprodnode())97        self.assertEqual(_normalize(actual), _normalize(cumprod_expected))98        actual = self._run(datanode.cumprodnode(filter=filternode))99        self.assertEqual(_normalize(actual), _normalize(cumprod_filtered_expected))100    def test_cumprod_df(self):101        actual = self._run(dfnode.cumprodnode())102        self.assertEqual(_normalize(actual), _normalize(cumprod_df_expected))103        actual = self._run(dfnode.cumprodnode(filter=filternode))104        self.assertEqual(_normalize(actual), _normalize(cumprod_df_filtered_expected))105    def _run(self, node):106        result = []107        for t in index:108            self.ctx.set_date(t)109            result.append(self.ctx[node])110        if isinstance(result[0], pa.Series):111            return pa.DataFrame(result, index=index)...

normalize.py

Source:normalize.py

1# -*- coding: utf-8 -*-2import six3from geodata.text import _normalize4from geodata.text.token_types import token_types5from geodata.encoding import safe_decode6# String options7NORMALIZE_STRING_LATIN_ASCII = _normalize.NORMALIZE_STRING_LATIN_ASCII8NORMALIZE_STRING_TRANSLITERATE = _normalize.NORMALIZE_STRING_TRANSLITERATE9NORMALIZE_STRING_STRIP_ACCENTS = _normalize.NORMALIZE_STRING_STRIP_ACCENTS10NORMALIZE_STRING_DECOMPOSE = _normalize.NORMALIZE_STRING_DECOMPOSE11NORMALIZE_STRING_LOWERCASE = _normalize.NORMALIZE_STRING_LOWERCASE12NORMALIZE_STRING_TRIM = _normalize.NORMALIZE_STRING_TRIM13NORMALIZE_STRING_REPLACE_HYPHENS = _normalize.NORMALIZE_STRING_REPLACE_HYPHENS14NORMALIZE_STRING_SIMPLE_LATIN_ASCII = _normalize.NORMALIZE_STRING_SIMPLE_LATIN_ASCII15DEFAULT_STRING_OPTIONS = _normalize.NORMALIZE_DEFAULT_STRING_OPTIONS16# Token options17NORMALIZE_TOKEN_REPLACE_HYPHENS = _normalize.NORMALIZE_TOKEN_REPLACE_HYPHENS18NORMALIZE_TOKEN_DELETE_HYPHENS = _normalize.NORMALIZE_TOKEN_DELETE_HYPHENS19NORMALIZE_TOKEN_DELETE_FINAL_PERIOD = _normalize.NORMALIZE_TOKEN_DELETE_FINAL_PERIOD20NORMALIZE_TOKEN_DELETE_ACRONYM_PERIODS = _normalize.NORMALIZE_TOKEN_DELETE_ACRONYM_PERIODS21NORMALIZE_TOKEN_DROP_ENGLISH_POSSESSIVES = _normalize.NORMALIZE_TOKEN_DROP_ENGLISH_POSSESSIVES22NORMALIZE_TOKEN_DELETE_OTHER_APOSTROPHE = _normalize.NORMALIZE_TOKEN_DELETE_OTHER_APOSTROPHE23NORMALIZE_TOKEN_SPLIT_ALPHA_FROM_NUMERIC = _normalize.NORMALIZE_TOKEN_SPLIT_ALPHA_FROM_NUMERIC24NORMALIZE_TOKEN_REPLACE_DIGITS = _normalize.NORMALIZE_TOKEN_REPLACE_DIGITS25DEFAULT_TOKEN_OPTIONS = _normalize.NORMALIZE_DEFAULT_TOKEN_OPTIONS26TOKEN_OPTIONS_DROP_PERIODS = _normalize.NORMALIZE_TOKEN_OPTIONS_DROP_PERIODS27DEFAULT_TOKEN_OPTIONS_NUMERIC = _normalize.NORMALIZE_DEFAULT_TOKEN_OPTIONS_NUMERIC28def remove_parens(tokens):29    new_tokens = []30    open_parens = 031    for t, c in tokens:32        if c == token_types.PUNCT_OPEN:33            open_parens += 134        elif c == token_types.PUNCT_CLOSE:35            if open_parens > 0:36                open_parens -= 137        elif open_parens <= 0:38            new_tokens.append((t, c))39    return new_tokens40def normalize_string(s, string_options=DEFAULT_STRING_OPTIONS):41    s = safe_decode(s)42    return _normalize.normalize_string(s, string_options)43def normalized_tokens(s, string_options=DEFAULT_STRING_OPTIONS,44                      token_options=DEFAULT_TOKEN_OPTIONS,45                      strip_parentheticals=True, whitespace=False):46    '''47    Normalizes a string, tokenizes, and normalizes each token48    with string and token-level options.49    This version only uses libpostal's deterministic normalizations50    i.e. methods with a single output. The string tree version will51    return multiple normalized strings, each with tokens.52    Usage:53        normalized_tokens(u'St.-BarthÃ©lemy')54    '''55    s = safe_decode(s)56    normalized_tokens = _normalize.normalized_tokens(s, string_options, token_options, whitespace)57    if strip_parentheticals:58        normalized_tokens = remove_parens(normalized_tokens)...

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