Add support for NumPy 2.0 #232
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -3,10 +3,11 @@ | |
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| import copy | ||
| from functools import wraps | ||
| import warnings | ||
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| import numpy as np | ||
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| from . import markup, QuantitiesDeprecationWarning | ||
| from .dimensionality import Dimensionality, p_dict | ||
| from .registry import unit_registry | ||
| from .decorators import with_doc | ||
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@@ -114,15 +115,19 @@ class Quantity(np.ndarray): | |
| # TODO: what is an appropriate value? | ||
| __array_priority__ = 21 | ||
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| def __new__(cls, data, units='', dtype=None, copy=None): | ||
| if copy is not None: | ||
| warnings.warn(("The 'copy' argument in Quantity is deprecated and will be removed in the future. " | ||
| "The argument has no effect since quantities-0.16.0 (to aid numpy-2.0 support)."), | ||
| QuantitiesDeprecationWarning, stacklevel=2) | ||
| if isinstance(data, Quantity): | ||
| if units: | ||
| data = data.rescale(units) | ||
| if isinstance(data, unit_registry['UnitQuantity']): | ||
| return 1*data | ||
| return np.asanyarray(data, dtype=dtype).view(cls) | ||
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| ret = np.asarray(data, dtype=dtype).view(cls) | ||
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There was a problem hiding this comment. After these changes we are no longer using the |
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| ret._dimensionality.update(validate_dimensionality(units)) | ||
| return ret | ||
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@@ -210,15 +215,17 @@ def rescale(self, units=None, dtype=None): | |
| dtype = self.dtype | ||
| if self.dimensionality == to_dims: | ||
| return self.astype(dtype) | ||
| to_u = Quantity(1.0, to_dims, dtype=dtype) | ||
| from_u = Quantity(1.0, self.dimensionality, dtype=dtype) | ||
| try: | ||
| cf = get_conversion_factor(from_u, to_u) | ||
| except AssertionError: | ||
| raise ValueError( | ||
| 'Unable to convert between units of "%s" and "%s"' | ||
| %(from_u._dimensionality, to_u._dimensionality) | ||
| ) | ||
| if np.dtype(dtype).kind in 'fc': | ||
| cf = np.array(cf, dtype=dtype) | ||
| new_magnitude = cf*self.magnitude | ||
| dtype = np.result_type(dtype, new_magnitude) | ||
| return Quantity(new_magnitude, to_u, dtype=dtype) | ||
|
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@@ -272,7 +279,7 @@ def __array_prepare__(self, obj, context=None): | |
| uf, objs, huh = context | ||
| if uf.__name__.startswith('is'): | ||
| return obj | ||
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| try: | ||
| res._dimensionality = p_dict[uf](*objs) | ||
| except KeyError: | ||
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@@ -283,11 +290,12 @@ def __array_prepare__(self, obj, context=None): | |
| ) | ||
| return res | ||
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| def __array_wrap__(self, obj, context=None, return_scalar=False): | ||
| if not isinstance(obj, Quantity): | ||
| # backwards compatibility with numpy-1.3 | ||
| return self.__array_prepare__(obj, context) | ||
| else: | ||
| return super().__array_wrap__(obj, context, return_scalar) | ||
|
Comment on lines
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There was a problem hiding this comment. I'm confused by this. The previous array wrap would only do something if the object was not a Quantity if the object was already a quantity it would just return the object without any changes. Here you force the array_wrap even in the case of the object being a Quantity. It seems like for the case of some failing tests the objects are losing their dimensionality and this seems like it could be a place where a Quantity should be returned, no? I'm not super familiar with this deep level of array wrapping so just thought maybe a discussion of this could help point us to the actual test failure issue. There was a problem hiding this comment. @bjodah just wanted to bump my message. Does this seem like it could be the problem with the tests failing? |
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| @with_doc(np.ndarray.__add__) | ||
| @scale_other_units | ||
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@@ -476,7 +484,7 @@ def sum(self, axis=None, dtype=None, out=None): | |
| ret = self.magnitude.sum(axis, dtype, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -487,8 +495,7 @@ def nansum(self, axis=None, dtype=None, out=None): | |
| import numpy as np | ||
| return Quantity( | ||
| np.nansum(self.magnitude, axis, dtype, out), | ||
| self.dimensionality | ||
| ) | ||
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| @with_doc(np.ndarray.fill) | ||
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@@ -523,7 +530,7 @@ def argsort(self, axis=-1, kind='quick', order=None): | |
| @with_doc(np.ndarray.searchsorted) | ||
| def searchsorted(self,values, side='left'): | ||
| if not isinstance (values, Quantity): | ||
| values = Quantity(values) | ||
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| if values._dimensionality != self._dimensionality: | ||
| raise ValueError("values does not have the same units as self") | ||
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@@ -539,7 +546,7 @@ def max(self, axis=None, out=None): | |
| ret = self.magnitude.max(axis, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
|
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@@ -553,16 +560,15 @@ def argmax(self, axis=None, out=None): | |
| def nanmax(self, axis=None, out=None): | ||
| return Quantity( | ||
| np.nanmax(self.magnitude), | ||
| self.dimensionality | ||
| ) | ||
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| @with_doc(np.ndarray.min) | ||
| def min(self, axis=None, out=None): | ||
| ret = self.magnitude.min(axis, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -572,8 +578,7 @@ def min(self, axis=None, out=None): | |
| def nanmin(self, axis=None, out=None): | ||
| return Quantity( | ||
| np.nanmin(self.magnitude), | ||
| self.dimensionality | ||
| ) | ||
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| @with_doc(np.ndarray.argmin) | ||
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@@ -590,10 +595,10 @@ def nanargmax(self,axis=None, out=None): | |
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| @with_doc(np.ndarray.ptp) | ||
| def ptp(self, axis=None, out=None): | ||
| ret = np.ptp(self.magnitude, axis, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -620,7 +625,7 @@ def clip(self, min=None, max=None, out=None): | |
| ) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(clipped, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -631,7 +636,7 @@ def round(self, decimals=0, out=None): | |
| ret = self.magnitude.round(decimals, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -642,7 +647,7 @@ def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None): | |
| ret = self.magnitude.trace(offset, axis1, axis2, dtype, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -652,16 +657,15 @@ def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None): | |
| def squeeze(self, axis=None): | ||
| return Quantity( | ||
| self.magnitude.squeeze(axis), | ||
| self.dimensionality | ||
| ) | ||
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| @with_doc(np.ndarray.mean) | ||
| def mean(self, axis=None, dtype=None, out=None): | ||
| ret = self.magnitude.mean(axis, dtype, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -672,15 +676,14 @@ def nanmean(self, axis=None, dtype=None, out=None): | |
| import numpy as np | ||
| return Quantity( | ||
| np.nanmean(self.magnitude, axis, dtype, out), | ||
| self.dimensionality) | ||
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| @with_doc(np.ndarray.var) | ||
| def var(self, axis=None, dtype=None, out=None, ddof=0): | ||
| ret = self.magnitude.var(axis, dtype, out, ddof) | ||
| dim = self._dimensionality**2 | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -691,7 +694,7 @@ def std(self, axis=None, dtype=None, out=None, ddof=0): | |
| ret = self.magnitude.std(axis, dtype, out, ddof) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -701,8 +704,7 @@ def std(self, axis=None, dtype=None, out=None, ddof=0): | |
| def nanstd(self, axis=None, dtype=None, out=None, ddof=0): | ||
| return Quantity( | ||
| np.nanstd(self.magnitude, axis, dtype, out, ddof), | ||
| self._dimensionality | ||
| ) | ||
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| @with_doc(np.ndarray.prod) | ||
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@@ -715,7 +717,7 @@ def prod(self, axis=None, dtype=None, out=None): | |
| ret = self.magnitude.prod(axis, dtype, None if out is None else out.magnitude) | ||
| dim = self._dimensionality**power | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -726,7 +728,7 @@ def cumsum(self, axis=None, dtype=None, out=None): | |
| ret = self.magnitude.cumsum(axis, dtype, None if out is None else out.magnitude) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if not isinstance(out, Quantity): | ||
| raise TypeError("out parameter must be a Quantity") | ||
| out._dimensionality = dim | ||
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@@ -743,7 +745,7 @@ def cumprod(self, axis=None, dtype=None, out=None): | |
| ret = self.magnitude.cumprod(axis, dtype, out) | ||
| dim = self.dimensionality | ||
| if out is None: | ||
| return Quantity(ret, dim) | ||
| if isinstance(out, Quantity): | ||
| out._dimensionality = dim | ||
| return out | ||
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This is now breaking the tests in MNE:
x-ref: mne-tools/mne-python#12815
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Are you all testing against a development version of NumPy? I don't think @bjodah or I thought about that in these PRs for parsing the numpy version. I guess a version parse instead would be better if you want to defend against dev version testing.
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MNE is testing against stable and the development version of
numpy, all my projects are also tested against both, and I'm probably not the only one ;)Quick fix in #238 but maybe you want something a bit more elaborate 😃