xr.infer_freq

doc/api.rst

@@ -26,6 +26,7 @@ Top-level functions
    combine_nested
    where
    set_options
+   infer_freq
    full_like
    zeros_like
    ones_like

doc/weather-climate.rst

@@ -74,6 +74,15 @@ instance, we can create the same dates and DataArray we created above using:
     dates = xr.cftime_range(start="0001", periods=24, freq="MS", calendar="noleap")
     da = xr.DataArray(np.arange(24), coords=[dates], dims=["time"], name="foo")
 
+Mirroring pandas' method with the same name, :py:meth:`~xarray.infer_freq` allows one to
+infer the sampling frequency of a :py:class:`~xarray.CFTimeIndex` or a 1-D
+:py:class:`~xarray.DataArray` containing cftime objects. It also works transparently with
+``np.datetime64[ns]`` and ``np.timedelta64[ns]`` data.
+
+.. ipython:: python
+
+    xr.infer_freq(dates)
+
 With :py:meth:`~xarray.CFTimeIndex.strftime` we can also easily generate formatted strings from
 the datetime values of a :py:class:`~xarray.CFTimeIndex` directly or through the
 :py:meth:`~xarray.DataArray.dt` accessor for a :py:class:`~xarray.DataArray`

doc/whats-new.rst

@@ -43,7 +43,8 @@ Enhancements
 
 New Features
 ~~~~~~~~~~~~
-
+- Added :py:meth:`xarray.infer_freq` for extending frequency inferring to CFTime indexes and data (:pull:`4033`).
+  By `Pascal Bourgault <https://github.com/aulemahal>`_.
 - ``chunks='auto'`` is now supported in the ``chunks`` argument of
   :py:meth:`Dataset.chunk`. (:issue:`4055`)
   By `Andrew Williams <https://github.com/AndrewWilliams3142>`_ 

xarray/__init__.py

@@ -13,6 +13,7 @@
 from .backends.zarr import open_zarr
 from .coding.cftime_offsets import cftime_range
 from .coding.cftimeindex import CFTimeIndex
+from .coding.frequencies import infer_freq
 from .conventions import SerializationWarning, decode_cf
 from .core.alignment import align, broadcast
 from .core.combine import auto_combine, combine_by_coords, combine_nested
@@ -57,6 +58,7 @@
     "cov",
     "corr",
     "full_like",
+    "infer_freq",
     "load_dataarray",
     "load_dataset",
     "map_blocks",

xarray/coding/cftimeindex.py

@@ -578,7 +578,8 @@ def asi8(self):
             [
                 _total_microseconds(exact_cftime_datetime_difference(epoch, date))
                 for date in self.values
-            ]
+            ],
+            dtype=np.int64,
         )
 
     def _round_via_method(self, freq, method):

xarray/coding/frequencies.py

@@ -0,0 +1,272 @@
+"""FrequencyInferer analog for cftime.datetime objects"""
+# The infer_freq method and the _CFTimeFrequencyInferer
+# subclass defined here were copied and adapted for
+# use with cftime.datetime objects based on the source code in
+# pandas.tseries.Frequencies._FrequencyInferer
+
+# For reference, here is a copy of the pandas copyright notice:
+
+# (c) 2011-2012, Lambda Foundry, Inc. and PyData Development Team
+# All rights reserved.
+
+# Copyright (c) 2008-2011 AQR Capital Management, LLC
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are
+# met:
+
+#     * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+
+#     * Redistributions in binary form must reproduce the above
+#        copyright notice, this list of conditions and the following
+#        disclaimer in the documentation and/or other materials provided
+#        with the distribution.
+
+#     * Neither the name of the copyright holder nor the names of any
+#        contributors may be used to endorse or promote products derived
+#        from this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+import pandas as pd
+
+from ..core.common import _contains_datetime_like_objects
+from .cftime_offsets import _MONTH_ABBREVIATIONS
+from .cftimeindex import CFTimeIndex
+
+_ONE_MICRO = 1
+_ONE_MILLI = _ONE_MICRO * 1000
+_ONE_SECOND = _ONE_MILLI * 1000
+_ONE_MINUTE = 60 * _ONE_SECOND
+_ONE_HOUR = 60 * _ONE_MINUTE
+_ONE_DAY = 24 * _ONE_HOUR
+
+
+def infer_freq(index):
+    """
+    Infer the most likely frequency given the input index.
+
+    Parameters
+    ----------
+    index : CFTimeIndex, DataArray, pd.DatetimeIndex, pd.TimedeltaIndex, pd.Series
+      If not passed a CFTimeIndex, this simply calls `pandas.infer_freq`.
+      If passed a Series or a DataArray will use the values of the series (NOT THE INDEX).
+
+    Returns
+    -------
+    str or None
+        None if no discernible frequency.
+
+    Raises
+    ------
+    TypeError
+        If the index is not datetime-like.
+    ValueError
+        If there are fewer than three values or the index is not 1D.
+    """
+    from xarray.core.dataarray import DataArray
+
+    if isinstance(index, (DataArray, pd.Series)):
+        if index.ndim != 1:
+            raise ValueError("'index' must be 1D")
+        elif not _contains_datetime_like_objects(DataArray(index)):
+            raise ValueError("'index' must contain datetime-like objects")
+        dtype = np.asarray(index).dtype
+        if dtype == "datetime64[ns]":
+            index = pd.DatetimeIndex(index.values)
+        elif dtype == "timedelta64[ns]":
+            index = pd.TimedeltaIndex(index.values)
+        else:
+            index = CFTimeIndex(index.values)
+
+    if isinstance(index, CFTimeIndex):
+        inferer = _CFTimeFrequencyInferer(index)
+        return inferer.get_freq()
+
+    return pd.infer_freq(index)
+
+
+class _CFTimeFrequencyInferer:  # (pd.tseries.frequencies._FrequencyInferer):
+    def __init__(self, index):
+        self.index = index
+        self.values = index.asi8
+
+        if len(index) < 3:
+            raise ValueError("Need at least 3 dates to infer frequency")
+
+        self.is_monotonic = (
+            self.index.is_monotonic_decreasing or self.index.is_monotonic_increasing
+        )
+
+        self._deltas = None
+        self._year_deltas = None
+        self._month_deltas = None
+
+    def get_freq(self):
+        """Find the appropriate frequency string to describe the inferred frequency of self.index
+
+        Adapted from `pandas.tsseries.frequencies._FrequencyInferer.get_freq` for CFTimeIndexes.
+
+        Returns
+        -------
+        str or None
+        """
+        if not self.is_monotonic or not self.index.is_unique:
+            return None
+
+        delta = self.deltas[0]  # Smallest delta
+        if _is_multiple(delta, _ONE_DAY):
+            return self._infer_daily_rule()
+        # There is no possible intraday frequency with a non-unique delta
+        # Different from pandas: we don't need to manage DST and business offsets in cftime
+        elif not len(self.deltas) == 1:
+            return None
+
+        if _is_multiple(delta, _ONE_HOUR):
+            return _maybe_add_count("H", delta / _ONE_HOUR)
+        elif _is_multiple(delta, _ONE_MINUTE):
+            return _maybe_add_count("T", delta / _ONE_MINUTE)
+        elif _is_multiple(delta, _ONE_SECOND):
+            return _maybe_add_count("S", delta / _ONE_SECOND)
+        elif _is_multiple(delta, _ONE_MILLI):
+            return _maybe_add_count("L", delta / _ONE_MILLI)
+        else:
+            return _maybe_add_count("U", delta / _ONE_MICRO)
+
+    def _infer_daily_rule(self):
+        annual_rule = self._get_annual_rule()
+        if annual_rule:
+            nyears = self.year_deltas[0]
+            month = _MONTH_ABBREVIATIONS[self.index[0].month]
+            alias = f"{annual_rule}-{month}"
+            return _maybe_add_count(alias, nyears)
+
+        quartely_rule = self._get_quartely_rule()
+        if quartely_rule:
+            nquarters = self.month_deltas[0] / 3
+            mod_dict = {0: 12, 2: 11, 1: 10}
+            month = _MONTH_ABBREVIATIONS[mod_dict[self.index[0].month % 3]]
+            alias = f"{quartely_rule}-{month}"
+            return _maybe_add_count(alias, nquarters)
+
+        monthly_rule = self._get_monthly_rule()
+        if monthly_rule:
+            return _maybe_add_count(monthly_rule, self.month_deltas[0])
+
+        if len(self.deltas) == 1:
+            # Daily as there is no "Weekly" offsets with CFTime
+            days = self.deltas[0] / _ONE_DAY
+            return _maybe_add_count("D", days)
+
+        # CFTime has no business freq and no "week of month" (WOM)
+        return None
+
+    def _get_annual_rule(self):
+        if len(self.year_deltas) > 1:
+            return None
+
+        if len(np.unique(self.index.month)) > 1:
+            return None
+
+        return {"cs": "AS", "ce": "A"}.get(month_anchor_check(self.index))
+
+    def _get_quartely_rule(self):
+        if len(self.month_deltas) > 1:
+            return None
+
+        if not self.month_deltas[0] % 3 == 0:
+            return None
+
+        return {"cs": "QS", "ce": "Q"}.get(month_anchor_check(self.index))
+
+    def _get_monthly_rule(self):
+        if len(self.month_deltas) > 1:
+            return None
+
+        return {"cs": "MS", "ce": "M"}.get(month_anchor_check(self.index))
+
+    @property
+    def deltas(self):
+        """Sorted unique timedeltas as microseconds."""
+        if self._deltas is None:
+            self._deltas = _unique_deltas(self.values)
+        return self._deltas
+
+    @property
+    def year_deltas(self):
+        """Sorted unique year deltas."""
+        if self._year_deltas is None:
+            self._year_deltas = _unique_deltas(self.index.year)
+        return self._year_deltas
+
+    @property
+    def month_deltas(self):
+        """Sorted unique month deltas."""
+        if self._month_deltas is None:
+            self._month_deltas = _unique_deltas(self.index.year * 12 + self.index.month)
+        return self._month_deltas
+
+
+def _unique_deltas(arr):
+    """Sorted unique deltas of numpy array"""
+    return np.sort(np.unique(np.diff(arr)))
+
+
+def _is_multiple(us, mult: int):
+    """Whether us is a multiple of mult"""
+    return us % mult == 0
+
+
+def _maybe_add_count(base: str, count: float):
+    """If count is greater than 1, add it to the base offset string"""
+    if count != 1:
+        assert count == int(count)
+        count = int(count)
+        return f"{count}{base}"
+    else:
+        return base
+
+
+def month_anchor_check(dates):
+    """Return the monthly offset string.
+
+    Return "cs" if all dates are the first days of the month,
+    "ce" if all dates are the last day of the month,
+    None otherwise.
+
+    Replicated pandas._libs.tslibs.resolution.month_position_check
+    but without business offset handling.
+    """
+    calendar_end = True
+    calendar_start = True
+
+    for date in dates:
+        if calendar_start:
+            calendar_start &= date.day == 1
+
+        if calendar_end:
+            cal = date.day == date.daysinmonth
+            if calendar_end:
+                calendar_end &= cal
+        elif not calendar_start:
+            break
+
+    if calendar_end:
+        return "ce"
+    elif calendar_start:
+        return "cs"
+    else:
+        return None