Add drop duplicates

ci/min_deps_check.py

@@ -7,7 +7,7 @@
 from datetime import datetime
 from typing import Dict, Iterator, Optional, Tuple
 
-import conda.api  # type: ignore[import]
+import conda.api
 import yaml
 from dateutil.relativedelta import relativedelta
 
@@ -76,9 +76,9 @@ def parse_requirements(fname) -> Iterator[Tuple[str, int, int, Optional[int]]]:
             raise ValueError("non-numerical version: " + row)
 
         if len(version_tup) == 2:
-            yield (pkg, *version_tup, None)  # type: ignore[misc]
+            yield (pkg, *version_tup, None)  # type: ignore
         elif len(version_tup) == 3:
-            yield (pkg, *version_tup)  # type: ignore[misc]
+            yield (pkg, *version_tup)  # type: ignore
         else:
             raise ValueError("expected major.minor or major.minor.patch: " + row)
 

doc/api.rst

@@ -103,6 +103,7 @@ Dataset contents
    Dataset.expand_dims
    Dataset.drop_vars
    Dataset.drop_dims
+   Dataset.drop_duplicate_coords
    Dataset.set_coords
    Dataset.reset_coords
 
@@ -179,7 +180,6 @@ Computation
    Dataset.integrate
    Dataset.map_blocks
    Dataset.polyfit
-   Dataset.curvefit
 
 **Aggregation**:
 :py:attr:`~Dataset.all`
@@ -292,6 +292,7 @@ DataArray contents
    DataArray.swap_dims
    DataArray.expand_dims
    DataArray.drop_vars
+   DataArray.drop_duplicate_coords
    DataArray.reset_coords
    DataArray.copy
 
@@ -376,7 +377,7 @@ Computation
    DataArray.integrate
    DataArray.polyfit
    DataArray.map_blocks
-   DataArray.curvefit
+
 
 **Aggregation**:
 :py:attr:`~DataArray.all`

doc/user-guide/computation.rst

@@ -444,89 +444,6 @@ The inverse operation is done with :py:meth:`~xarray.polyval`,
 .. note::
     These methods replicate the behaviour of :py:func:`numpy.polyfit` and :py:func:`numpy.polyval`.
 
-
-.. _compute.curvefit:
-
-Fitting arbitrary functions
-===========================
-
-Xarray objects also provide an interface for fitting more complex functions using
-:py:meth:`scipy.optimize.curve_fit`. :py:meth:`~xarray.DataArray.curvefit` accepts
-user-defined functions and can fit along multiple coordinates.
-
-For example, we can fit a relationship between two ``DataArray`` objects, maintaining
-a unique fit at each spatial coordinate but aggregating over the time dimension:
-
-.. ipython:: python
-
-    def exponential(x, a, xc):
-        return np.exp((x - xc) / a)
-
-
-    x = np.arange(-5, 5, 0.1)
-    t = np.arange(-5, 5, 0.1)
-    X, T = np.meshgrid(x, t)
-    Z1 = np.random.uniform(low=-5, high=5, size=X.shape)
-    Z2 = exponential(Z1, 3, X)
-    Z3 = exponential(Z1, 1, -X)
-
-    ds = xr.Dataset(
-        data_vars=dict(
-            var1=(["t", "x"], Z1), var2=(["t", "x"], Z2), var3=(["t", "x"], Z3)
-        ),
-        coords={"t": t, "x": x},
-    )
-    ds[["var2", "var3"]].curvefit(
-        coords=ds.var1,
-        func=exponential,
-        reduce_dims="t",
-        bounds={"a": (0.5, 5), "xc": (-5, 5)},
-    )
-
-We can also fit multi-dimensional functions, and even use a wrapper function to
-simultaneously fit a summation of several functions, such as this field containing
-two gaussian peaks:
-
-.. ipython:: python
-
-    def gaussian_2d(coords, a, xc, yc, xalpha, yalpha):
-        x, y = coords
-        z = a * np.exp(
-            -np.square(x - xc) / 2 / np.square(xalpha)
-            - np.square(y - yc) / 2 / np.square(yalpha)
-        )
-        return z
-
-
-    def multi_peak(coords, *args):
-        z = np.zeros(coords[0].shape)
-        for i in range(len(args) // 5):
-            z += gaussian_2d(coords, *args[i * 5 : i * 5 + 5])
-        return z
-
-
-    x = np.arange(-5, 5, 0.1)
-    y = np.arange(-5, 5, 0.1)
-    X, Y = np.meshgrid(x, y)
-
-    n_peaks = 2
-    names = ["a", "xc", "yc", "xalpha", "yalpha"]
-    names = [f"{name}{i}" for i in range(n_peaks) for name in names]
-    Z = gaussian_2d((X, Y), 3, 1, 1, 2, 1) + gaussian_2d((X, Y), 2, -1, -2, 1, 1)
-    Z += np.random.normal(scale=0.1, size=Z.shape)
-
-    da = xr.DataArray(Z, dims=["y", "x"], coords={"y": y, "x": x})
-    da.curvefit(
-        coords=["x", "y"],
-        func=multi_peak,
-        param_names=names,
-        kwargs={"maxfev": 10000},
-    )
-
-.. note::
-    This method replicates the behavior of :py:func:`scipy.optimize.curve_fit`.
-
-
 .. _compute.broadcasting:
 
 Broadcasting by dimension name

doc/whats-new.rst

@@ -64,11 +64,10 @@ New Features
   :py:class:`~core.groupby.DataArrayGroupBy`, inspired by pandas'
   :py:meth:`~pandas.core.groupby.GroupBy.get_group`.
   By `Deepak Cherian <https://github.com/dcherian>`_.
-- Add a ``combine_attrs`` parameter to :py:func:`open_mfdataset` (:pull:`4971`).
-  By `Justus Magin <https://github.com/keewis>`_.
+- Implement :py:meth:`Dataset.drop_duplicate_coords` and :py:meth:`DataArray.drop_duplicate_coords`
+  to remove duplicate coordinate values (:pull:`5089`).
+  By `Andrew Huang <https://github.com/ahuang11>`_.
 - Disable the `cfgrib` backend if the `eccodes` library is not installed (:pull:`5083`). By `Baudouin Raoult <https://github.com/b8raoult>`_.
-- Added :py:meth:`DataArray.curvefit` and :py:meth:`Dataset.curvefit` for general curve fitting applications. (:issue:`4300`, :pull:`4849`)
-  By `Sam Levang <https://github.com/slevang>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~
@@ -103,9 +102,6 @@ Documentation
 
 Internal Changes
 ~~~~~~~~~~~~~~~~
-- Enable displaying mypy error codes and ignore only specific error codes using
-  ``# type: ignore[error-code]`` (:pull:`5096`). By `Mathias Hauser <https://github.com/mathause>`_.
-
 
 .. _whats-new.0.17.0:
 
@@ -1367,7 +1363,9 @@ Enhancements
 
   - Added a repr (:pull:`3344`). Example::
 
-      >>> da.groupby("time.season")
+
+
+   da.groupby("time.season")
       DataArrayGroupBy, grouped over 'season'
       4 groups with labels 'DJF', 'JJA', 'MAM', 'SON'
 

setup.cfg

@@ -162,15 +162,12 @@ default_section = THIRDPARTY
 known_first_party = xarray
 
 [mypy]
-show_error_codes = True
 
 # Most of the numerical computing stack doesn't have type annotations yet.
 [mypy-affine.*]
 ignore_missing_imports = True
 [mypy-bottleneck.*]
 ignore_missing_imports = True
-[mypy-cartopy.*]
-ignore_missing_imports = True
 [mypy-cdms2.*]
 ignore_missing_imports = True
 [mypy-cf_units.*]

xarray/backends/api.py

@@ -717,7 +717,6 @@ def open_mfdataset(
     parallel=False,
     join="outer",
     attrs_file=None,
-    combine_attrs="override",
     **kwargs,
 ):
     """Open multiple files as a single dataset.
@@ -932,7 +931,7 @@ def open_mfdataset(
                 coords=coords,
                 ids=ids,
                 join=join,
-                combine_attrs=combine_attrs,
+                combine_attrs="drop",
             )
         elif combine == "by_coords":
             # Redo ordering from coordinates, ignoring how they were ordered
@@ -943,7 +942,7 @@ def open_mfdataset(
                 data_vars=data_vars,
                 coords=coords,
                 join=join,
-                combine_attrs=combine_attrs,
+                combine_attrs="drop",
             )
         else:
             raise ValueError(
@@ -966,6 +965,8 @@ def multi_file_closer():
         if isinstance(attrs_file, Path):
             attrs_file = str(attrs_file)
         combined.attrs = datasets[paths.index(attrs_file)].attrs
+    else:
+        combined.attrs = datasets[0].attrs
 
     return combined
 

xarray/core/combine.py

@@ -44,18 +44,6 @@ def _infer_tile_ids_from_nested_list(entry, current_pos):
         yield current_pos, entry
 
 
-def _ensure_same_types(series, dim):
-
-    if series.dtype == object:
-        types = set(series.map(type))
-        if len(types) > 1:
-            types = ", ".join(t.__name__ for t in types)
-            raise TypeError(
-                f"Cannot combine along dimension '{dim}' with mixed types."
-                f" Found: {types}."
-            )
-
-
 def _infer_concat_order_from_coords(datasets):
 
     concat_dims = []
@@ -100,15 +88,11 @@ def _infer_concat_order_from_coords(datasets):
                     raise ValueError("Cannot handle size zero dimensions")
                 first_items = pd.Index([index[0] for index in indexes])
 
-                series = first_items.to_series()
-
-                # ensure series does not contain mixed types, e.g. cftime calendars
-                _ensure_same_types(series, dim)
-
                 # Sort datasets along dim
                 # We want rank but with identical elements given identical
                 # position indices - they should be concatenated along another
                 # dimension, not along this one
+                series = first_items.to_series()
                 rank = series.rank(
                     method="dense", ascending=ascending, numeric_only=False
                 )

xarray/core/common.py

@@ -58,7 +58,7 @@ def wrapped_func(self, dim=None, axis=None, skipna=None, **kwargs):
 
         else:
 
-            def wrapped_func(self, dim=None, axis=None, **kwargs):  # type: ignore[misc]
+            def wrapped_func(self, dim=None, axis=None, **kwargs):  # type: ignore
                 return self.reduce(func, dim, axis, **kwargs)
 
         return wrapped_func
@@ -97,7 +97,7 @@ def wrapped_func(self, dim=None, skipna=None, **kwargs):
 
         else:
 
-            def wrapped_func(self, dim=None, **kwargs):  # type: ignore[misc]
+            def wrapped_func(self, dim=None, **kwargs):  # type: ignore
                 return self.reduce(func, dim, numeric_only=numeric_only, **kwargs)
 
         return wrapped_func

xarray/core/coordinates.py

@@ -50,7 +50,7 @@ def dims(self) -> Union[Mapping[Hashable, int], Tuple[Hashable, ...]]:
 
     @property
     def indexes(self) -> Indexes:
-        return self._data.indexes  # type: ignore[attr-defined]
+        return self._data.indexes  # type: ignore
 
     @property
     def variables(self):
@@ -105,11 +105,9 @@ def to_index(self, ordered_dims: Sequence[Hashable] = None) -> pd.Index:
             raise ValueError("no valid index for a 0-dimensional object")
         elif len(ordered_dims) == 1:
             (dim,) = ordered_dims
-            return self._data.get_index(dim)  # type: ignore[attr-defined]
+            return self._data.get_index(dim)  # type: ignore
         else:
-            indexes = [
-                self._data.get_index(k) for k in ordered_dims  # type: ignore[attr-defined]
-            ]
+            indexes = [self._data.get_index(k) for k in ordered_dims]  # type: ignore
 
             # compute the sizes of the repeat and tile for the cartesian product
             # (taken from pandas.core.reshape.util)

xarray/core/dask_array_compat.py

@@ -158,7 +158,7 @@ def ensure_minimum_chunksize(size, chunks):
 
     def sliding_window_view(x, window_shape, axis=None):
         from dask.array.overlap import map_overlap
-        from numpy.core.numeric import normalize_axis_tuple
+        from numpy.core.numeric import normalize_axis_tuple  # type: ignore
 
         from .npcompat import sliding_window_view as _np_sliding_window_view