reconnect moved files to git repo

venv/lib/python3.11/site-packages/pmdarima/datasets/woolyrnq.py

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+# -*- coding: utf-8 -*-
+#
+# Author: Taylor Smith <taylor.smith@alkaline-ml.com>
+#
+# This is the woolyrnq dataset found in the R forecast package.
+
+import numpy as np
+import pandas as pd
+
+from ..compat import DTYPE
+
+__all__ = [
+    'load_woolyrnq'
+]
+
+
+def load_woolyrnq(as_series=False, dtype=DTYPE):
+    """Quarterly production of woollen yarn in Australia.
+
+    This time-series records the quarterly production (in tonnes) of woollen
+    yarn in Australia between Mar 1965 and Sep 1994.
+
+    Parameters
+    ----------
+    as_series : bool, optional (default=False)
+        Whether to return a Pandas series. If True, the index will be set to
+        the observed years/quarters. If False, will return a 1d numpy array.
+
+    dtype : type, optional (default=np.float64)
+        The type to return for the array. Default is np.float64, which is used
+        throughout the package as the default type.
+
+    Examples
+    --------
+    >>> from pmdarima.datasets import load_woolyrnq
+    >>> load_woolyrnq()
+    array([6172, 6709, 6633, 6660, 6786, 6800, 6730, 6765, 6720, 7133, 6946,
+           7095, 7047, 6757, 6915, 6921, 7064, 7206, 7190, 7402, 7819, 7300,
+           7105, 7259, 7001, 7475, 6840, 7061, 5845, 7529, 7819, 6943, 5714,
+           6556, 7045, 5947, 5463, 6127, 5540, 4235, 3324, 4793, 5906, 5834,
+           5240, 5458, 5505, 5002, 3999, 4826, 5318, 4681, 4442, 5305, 5466,
+           4995, 4573, 5081, 5696, 5079, 4373, 4986, 5341, 4800, 4161, 5007,
+           5464, 5127, 4240, 5338, 5129, 4437, 3642, 4602, 5524, 4895, 4380,
+           5186, 6080, 5588, 5009, 5663, 6540, 6262, 5169, 5819, 6339, 5981,
+           4766, 5976, 6590, 5590, 5135, 5762, 6077, 5882, 4247, 5264, 5146,
+           4868, 4329, 4869, 5127, 4868, 3827, 4987, 5222, 4928, 3930, 4469,
+           4954, 4752, 3888, 4588, 5309, 4732, 4837, 6135, 6396])
+
+    >>> load_woolyrnq(True).head()
+    Q1 1965    6172
+    Q2 1965    6709
+    Q3 1965    6633
+    Q4 1965    6660
+    Q1 1966    6786
+    dtype: int64
+
+    Notes
+    -----
+    This is quarterly data, so *m* should be set to 4 when using in a seasonal
+    context.
+
+    References
+    ----------
+    .. [1] https://www.rdocumentation.org/packages/forecast/versions/8.1/topics/woolyrnq
+
+    Returns
+    -------
+    rslt : array-like, shape=(n_samples,)
+        The woolyrnq dataset. There are 119 observations.
+    """  # noqa: E501
+    rslt = np.array([
+        6172, 6709, 6633, 6660,
+        6786, 6800, 6730, 6765,
+        6720, 7133, 6946, 7095,
+        7047, 6757, 6915, 6921,
+        7064, 7206, 7190, 7402,
+        7819, 7300, 7105, 7259,
+        7001, 7475, 6840, 7061,
+        5845, 7529, 7819, 6943,
+        5714, 6556, 7045, 5947,
+        5463, 6127, 5540, 4235,
+        3324, 4793, 5906, 5834,
+        5240, 5458, 5505, 5002,
+        3999, 4826, 5318, 4681,
+        4442, 5305, 5466, 4995,
+        4573, 5081, 5696, 5079,
+        4373, 4986, 5341, 4800,
+        4161, 5007, 5464, 5127,
+        4240, 5338, 5129, 4437,
+        3642, 4602, 5524, 4895,
+        4380, 5186, 6080, 5588,
+        5009, 5663, 6540, 6262,
+        5169, 5819, 6339, 5981,
+        4766, 5976, 6590, 5590,
+        5135, 5762, 6077, 5882,
+        4247, 5264, 5146, 4868,
+        4329, 4869, 5127, 4868,
+        3827, 4987, 5222, 4928,
+        3930, 4469, 4954, 4752,
+        3888, 4588, 5309, 4732,
+        4837, 6135, 6396]).astype(dtype)
+
+    if not as_series:
+        return rslt
+
+    # Otherwise we want a series and have to cleverly create the index
+    # (with quarters, and we don't want Q4 in 1994)
+    index = [
+        "Q%i %i" % (i + 1, year)
+        for year in range(1965, 1995)
+        for i in range(4)
+    ][:-1]  # trim off the last one.
+
+    return pd.Series(rslt, index=index)