Fix min_deps_check; revert to support numpy=1.14 and pandas=0.24

ci/min_deps_check.py

@@ -141,21 +141,20 @@ def process_pkg(
     policy_months = POLICY_MONTHS.get(pkg, POLICY_MONTHS_DEFAULT)
     policy_published = datetime.now() - timedelta(days=policy_months * 30)
 
-    policy_major = req_major
-    policy_minor = req_minor
-    policy_published_actual = req_published
-    for (major, minor), published in reversed(sorted(versions.items())):
-        if published < policy_published:
-            break
-        policy_major = major
-        policy_minor = minor
-        policy_published_actual = published
+    policy_major, policy_minor = max(
+        version for version, date in versions.items() if date < policy_published
+    )
+    policy_published_actual = versions[policy_major, policy_minor]
 
     if (req_major, req_minor) < (policy_major, policy_minor):
         status = "<"
     elif (req_major, req_minor) > (policy_major, policy_minor):
         status = "> (!)"
-        error("Package is too new: " + pkg)
+        error(
+            f"Package is too new: {pkg}={req_major}.{req_minor} was "
+            f"published on {versions[req_major, req_minor]:%Y-%m-%d} "
+            f"(within the past {policy_months} months)"
+        )
     else:
         status = "="
 

ci/requirements/py36-bare-minimum.yml

@@ -8,6 +8,6 @@ dependencies:
   - pytest
   - pytest-cov
   - pytest-env
-  - numpy=1.15
-  - pandas=0.25
+  - numpy=1.14
+  - pandas=0.24
   - setuptools=41.2

ci/requirements/py36-min-all-deps.yml

@@ -31,8 +31,8 @@ dependencies:
   - nc-time-axis=1.2
   - netcdf4=1.4
   - numba=0.44
-  - numpy=1.15
-  - pandas=0.25
+  - numpy=1.14
+  - pandas=0.24
   # - pint  # See py36-min-nep18.yml
   - pip
   - pseudonetcdf=3.0

doc/installing.rst

@@ -8,8 +8,8 @@ Required dependencies
 
 - Python (3.6 or later)
 - setuptools
-- `numpy <http://www.numpy.org/>`__ (1.15 or later)
-- `pandas <http://pandas.pydata.org/>`__ (0.25 or later)
+- `numpy <http://www.numpy.org/>`__ (1.14 or later)
+- `pandas <http://pandas.pydata.org/>`__ (0.24 or later)
 
 .. _optional-dependencies:
 

setup.cfg

@@ -74,8 +74,8 @@ zip_safe = False  # https://mypy.readthedocs.io/en/latest/installed_packages.htm
 include_package_data = True
 python_requires = >=3.6
 install_requires =
-    numpy >= 1.15
-    pandas >= 0.25
+    numpy >= 1.14
+    pandas >= 0.24
     setuptools >= 41.2  # For pkg_resources
 setup_requires =
     setuptools >= 41.2

xarray/core/nputils.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import pandas as pd
-from numpy.core.multiarray import normalize_axis_index
 
 try:
     import bottleneck as bn
@@ -14,6 +13,17 @@
     _USE_BOTTLENECK = False
 
 
+def normalize_axis_index(data, axis):
+    # matches numpy.core.multiarray.normalize_axis_index
+    # duplicated here because the NumPy function is not a public API
+    ndim = np.ndim(data)
+    if not -ndim <= axis < ndim:
+        raise np.AxisError(f"axis {axis!r} out of bounds [-{ndim}, {ndim})")
+    if axis < 0:
+        axis += ndim
+    return axis
+
+
 def _select_along_axis(values, idx, axis):
     other_ind = np.ix_(*[np.arange(s) for s in idx.shape])
     sl = other_ind[:axis] + (idx,) + other_ind[axis:]

xarray/core/variable.py

@@ -1774,14 +1774,20 @@ def quantile(
 
         from .computation import apply_ufunc
 
-        _quantile_func = np.nanquantile if skipna else np.quantile
+        # TODO: switch to nanquantile/quantile once numpy >= 1.15.0 is the
+        # minimum requirement
+        _percentile_func = np.nanpercentile if skipna else np.percentile
 
         if keep_attrs is None:
             keep_attrs = _get_keep_attrs(default=False)
 
         scalar = utils.is_scalar(q)
         q = np.atleast_1d(np.asarray(q, dtype=np.float64))
 
+        # TODO: remove once numpy >= 1.15.0 is the minimum requirement
+        if np.count_nonzero(q < 0.0) or np.count_nonzero(q > 1.0):
+            raise ValueError("Quantiles must be in the range [0, 1]")
+
         if dim is None:
             dim = self.dims
 
@@ -1790,7 +1796,7 @@ def quantile(
 
         def _wrapper(npa, **kwargs):
             # move quantile axis to end. required for apply_ufunc
-            return np.moveaxis(_quantile_func(npa, **kwargs), 0, -1)
+            return np.moveaxis(_percentile_func(npa, **kwargs), 0, -1)
 
         axis = np.arange(-1, -1 * len(dim) - 1, -1)
         result = apply_ufunc(
@@ -1802,7 +1808,7 @@ def _wrapper(npa, **kwargs):
             output_dtypes=[np.float64],
             output_sizes={"quantile": len(q)},
             dask="parallelized",
-            kwargs={"q": q, "axis": axis, "interpolation": interpolation},
+            kwargs={"q": q * 100, "axis": axis, "interpolation": interpolation},
         )
 
         # for backward compatibility