# -*- coding: utf-8 -*- # Licensed under a 3-clause BSD style license - see LICENSE.rst """ This module defines the `Quantity` object, which represents a number with some associated units. `Quantity` objects support operations like ordinary numbers, but will deal with unit conversions internally. """ from __future__ import (absolute_import, unicode_literals, division, print_function) # Standard library import numbers import numpy as np NUMPY_LT_1P9 = [int(x) for x in np.__version__.split('.')[:2]] < [1, 9] # AstroPy from ..extern import six from .core import (Unit, dimensionless_unscaled, UnitBase, UnitsError, get_current_unit_registry) from ..utils import lazyproperty from ..utils.compat.misc import override__dir__ from ..utils.misc import isiterable, InheritDocstrings from .utils import validate_power __all__ = ["Quantity"] # We don't want to run doctests in the docstrings we inherit from Numpy __doctest_skip__ = ['Quantity.*'] def _can_cast(arg, dtype): """ This is needed for compatibility with Numpy < 1.6, in which ``can_cast`` can only take a dtype or type as its first argument. """ return np.can_cast(getattr(arg, 'dtype', type(arg)), dtype) _UNIT_NOT_INITIALISED = "(Unit not initialised)" def _can_have_arbitrary_unit(value): """Test whether the items in value can have arbitrary units Numbers whose value does not change upon a unit change, i.e., zero, infinity, or not-a-number Parameters ---------- value : number or array Returns ------- `True` if each member is either zero or not finite, `False` otherwise """ return np.all(np.logical_or(np.equal(value, 0.), ~np.isfinite(value))) class QuantityIterator(object): """ Flat iterator object to iterate over Quantities A `QuantityIterator` iterator is returned by ``q.flat`` for any Quantity ``q``. It allows iterating over the array as if it were a 1-D array, either in a for-loop or by calling its `next` method. Iteration is done in C-contiguous style, with the last index varying the fastest. The iterator can also be indexed using basic slicing or advanced indexing. See Also -------- Quantity.flatten : Returns a flattened copy of an array. Notes ----- `QuantityIterator` is inspired by `~numpy.ma.core.MaskedIterator`. It is not exported by the `~astropy.units` module. Instead of instantiating a `QuantityIterator` directly, use `Quantity.flat`. """ def __init__(self, q): self._quantity = q self._dataiter = q.view(np.ndarray).flat def __iter__(self): return self def __getitem__(self, indx): out = self._dataiter.__getitem__(indx) return self._quantity._new_view(out) def __setitem__(self, index, value): self._dataiter[index] = self._quantity._to_own_unit(value) def __next__(self): """ Return the next value, or raise StopIteration. """ out = next(self._dataiter) return self._quantity._new_view(out) next = __next__ @six.add_metaclass(InheritDocstrings) class Quantity(np.ndarray): """ A `Quantity` represents a number with some associated unit. Parameters ---------- value : number, `~numpy.ndarray`, `Quantity` object, or sequence of `Quantity` objects. The numerical value of this quantity in the units given by unit. If a `Quantity` or sequence of them (or any other valid object with a ``unit`` attribute), creates a new `Quantity` object, converting to `unit` units as needed. unit : `~astropy.units.UnitBase` instance, str An object that represents the unit associated with the input value. Must be an `~astropy.units.UnitBase` object or a string parseable by the :mod:`~astropy.units` package. dtype : ~numpy.dtype, optional The dtype of the resulting Numpy array or scalar that will hold the value. If not provided, it is determined from the input, except that any input that cannot represent float (integer and bool) is converted to float. copy : bool, optional If `True` (default), then the value is copied. Otherwise, a copy will only be made if ``__array__`` returns a copy, if value is a nested sequence, or if a copy is needed to satisfy an explicitly given ``dtype``. (The `False` option is intended mostly for internal use, to speed up initialization where a copy is known to have been made. Use with care.) order : {'C', 'F', 'A'}, optional Specify the order of the array. As in `~numpy.array`. This parameter is ignored if the input is a `Quantity` and ``copy=False``. subok : bool, optional If `False` (default), the returned array will be forced to be a `Quantity`. Otherwise, `Quantity` subclasses will be passed through. ndmin : int, optional Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement. This parameter is ignored if the input is a `Quantity` and ``copy=False``. Raises ------ TypeError If the value provided is not a Python numeric type. TypeError If the unit provided is not either a :class:`~astropy.units.Unit` object or a parseable string unit. """ # Need to set a class-level default for _equivalencies, or # Constants can not initialize properly _equivalencies = [] __array_priority__ = 10000 def __new__(cls, value, unit=None, dtype=None, copy=True, order=None, subok=False, ndmin=0): if unit is not None: # convert unit first, to avoid multiple string->unit conversions unit = Unit(unit) # optimize speed for Quantity with no dtype given, copy=False if isinstance(value, Quantity): if unit is not None and unit is not value.unit: value = value.to(unit) # the above already makes a copy (with float dtype) copy = False if not subok and type(value) is not cls: value = value.view(cls) if dtype is None: if not copy: return value if not np.can_cast(np.float32, value.dtype): dtype = np.float return np.array(value, dtype=dtype, copy=copy, order=order, subok=True, ndmin=ndmin) rescale_value = None # Maybe list/tuple of Quantity? short-circuit array for speed if(not isinstance(value, np.ndarray) and isiterable(value) and all(isinstance(v, Quantity) for v in value)): if unit is None: unit = value[0].unit value = [q.to(unit).value for q in value] copy = False # copy already made else: # if the value has a `unit` attribute, treat it like a quantity by # rescaling the value appropriately if hasattr(value, 'unit'): try: value_unit = Unit(value.unit) except TypeError: if unit is None: unit = dimensionless_unscaled else: if unit is None: unit = value_unit else: rescale_value = value_unit.to(unit) #if it has no unit, default to dimensionless_unscaled elif unit is None: unit = dimensionless_unscaled value = np.array(value, dtype=dtype, copy=copy, order=order, subok=False, ndmin=ndmin) # check that array contains numbers or long int objects if (value.dtype.kind in 'OSU' and not (value.dtype.kind == 'O' and isinstance(value.item(() if value.ndim == 0 else 0), numbers.Number))): raise TypeError("The value must be a valid Python or " "Numpy numeric type.") # by default, cast any integer, boolean, etc., to float if dtype is None and (not np.can_cast(np.float32, value.dtype) or value.dtype.kind == 'O'): value = value.astype(np.float) if rescale_value is not None: value *= rescale_value value = value.view(cls) value._unit = unit return value def __array_finalize__(self, obj): self._unit = getattr(obj, '_unit', None) def __array_prepare__(self, obj, context=None): # This method gets called by Numpy whenever a ufunc is called on the # array. The object passed in ``obj`` is an empty version of the # output array which we can e.g. change to an array sub-class, add # attributes to, etc. After this is called, then the ufunc is called # and the values in this empty array are set. # If no context is set, just return the input if context is None: return obj # Find out which ufunc is being used function = context[0] from .quantity_helper import UNSUPPORTED_UFUNCS, UFUNC_HELPERS # Check whether we even support this ufunc if function in UNSUPPORTED_UFUNCS: raise TypeError("Cannot use function '{0}' with quantities" .format(function.__name__)) # Now find out what arguments were passed to the ufunc, usually, this # will include at least the present object, and another, which could # be a Quantity, or a Numpy array, etc. when using two-argument ufuncs. args = context[1][:function.nin] units = [getattr(arg, 'unit', None) for arg in args] # If the ufunc is supported, then we call a helper function (defined # in quantity_helper.py) which returns the scale by which the inputs # should be multiplied before being passed to the ufunc, as well as # the unit the output from the ufunc will have. if function in UFUNC_HELPERS: scales, result_unit = UFUNC_HELPERS[function](function, *units) else: raise TypeError("Unknown ufunc {0}. Please raise issue on " "https://github.com/astropy/astropy" .format(function.__name__)) if any(scale == 0. for scale in scales): # for two-argument ufuncs with a quantity and a non-quantity, # the quantity normally needs to be dimensionless, *except* # if the non-quantity can have arbitrary unit, i.e., when it # is all zero, infinity or NaN. In that case, the non-quantity # can just have the unit of the quantity # (this allows, e.g., `q > 0.` independent of unit) maybe_arbitrary_arg = args[scales.index(0.)] if _can_have_arbitrary_unit(maybe_arbitrary_arg): scales = [1., 1.] else: raise UnitsError("Can only apply '{0}' function to " "dimensionless quantities when other " "argument is not a quantity (unless the " "latter is all zero/infinity/nan)" .format(function.__name__)) # In the case of np.power, the unit itself needs to be modified by an # amount that depends on one of the input values, so we need to treat # this as a special case. # TODO: find a better way to deal with this case if function is np.power and result_unit is not dimensionless_unscaled: if units[1] is None: p = args[1] else: p = args[1].to(dimensionless_unscaled).value result_unit = result_unit ** validate_power(p) # We now prepare the output object if self is obj: # happens if the output object is self, which happens # for in-place operations such as q1 += q2 # In some cases, the result of a ufunc should be a plain Numpy # array, which we can't do if we are doing an in-place operation. if result_unit is None: raise TypeError("Cannot store non-quantity output from {0} " "function in {1} instance" .format(function.__name__, type(self))) if self.__quantity_subclass__(result_unit)[0] is not type(self): raise TypeError( "Cannot store output with unit '{0}' from {1} function " "in {2} instance. Use {3} instance instead." .format(result_unit, function.__name__, type(self), self.__quantity_subclass__(result_unit)[0])) # If the Quantity has an integer dtype, in-place operations are # dangerous because in some cases the quantity will be e.g. # decomposed, which involves being scaled by a float, but since # the array is an integer the output then gets converted to an int # and truncated. if(any(not _can_cast(arg, obj.dtype) for arg in args) or np.any(np.array(scales, dtype=obj.dtype) != np.array(scales))): raise TypeError("Arguments cannot be cast safely to inplace " "output with dtype={0}".format(self.dtype)) result = self # no view needed since we return the object itself # in principle, if self is also an argument, it could be rescaled # here, since it won't be needed anymore. But maybe not change # inputs before the calculation even if they will get destroyed else: # normal case: set up output as a Quantity result = self._new_view(obj, result_unit) # We now need to treat the case where the inputs have to be scaled - # the issue is that we can't actually scale the inputs since that # would be changing the objects passed to the ufunc, which would not # be expected by the user. if any(scale != 1. for scale in scales): # If self is both output and input (which happens for in-place # operations), input will get overwritten with junk. To avoid # that, hide it in a new object if self is obj and any(self is arg for arg in args): # but with two outputs it would become unhidden too soon # [ie., np.modf(q1, q1, other)]. Bail. if context[2] < function.nout - 1: raise TypeError("Cannot apply multi-output {0} function " "to quantities with in-place replacement " "of an input by any but the last output." .format(function.__name__)) # If self is already contiguous, we don't need to do # an additional copy back into the original array, so # we store it in `result._result`. Otherwise, we # store it in `result._contiguous`. `__array_wrap__` # knows how to handle putting either form back into # the original array. if self.flags['C_CONTIGUOUS']: result = self.copy() result._result = self else: result._contiguous = self.copy() # ensure we remember the scales we need result._scales = scales # unit output will get (setting _unit could prematurely change input # if obj is self, which happens for in-place operations; see above) result._result_unit = result_unit return result def __array_wrap__(self, obj, context=None): if context is not None: if hasattr(obj, '_result_unit'): result_unit = obj._result_unit del obj._result_unit else: result_unit = None # We now need to re-calculate quantities for which the input # needed to be scaled. if hasattr(obj, '_scales'): scales = obj._scales del obj._scales # For in-place operations, input will get overwritten with # junk. To avoid that, we hid it in a new object in # __array_prepare__ and retrieve it here. if hasattr(obj, '_result'): obj = obj._result elif hasattr(obj, '_contiguous'): obj[()] = obj._contiguous del obj._contiguous # take array view to which output can be written without # getting back here obj_array = obj.view(np.ndarray) # Find out which ufunc was called and with which inputs function = context[0] args = context[1][:function.nin] # Set the inputs, rescaling as necessary inputs = [] for arg, scale in zip(args, scales): if scale != 1.: inputs.append(arg.value * scale) else: # for scale==1, input is not necessarily a Quantity inputs.append(getattr(arg, 'value', arg)) # For output arrays that require scaling, we can reuse the # output array to perform the scaling in place, as long as the # array is not integral. Here, we set the obj_array to `None` # when it can not be used to store the scaled result. if(result_unit is not None and any(not _can_cast(scaled_arg, obj_array.dtype) for scaled_arg in inputs)): obj_array = None # Re-compute the output using the ufunc if function.nin == 1: if function.nout == 1: out = function(inputs[0], obj_array) else: # 2-output function (np.modf, np.frexp); 1 input if context[2] == 0: out, _ = function(inputs[0], obj_array, None) else: _, out = function(inputs[0], None, obj_array) else: out = function(inputs[0], inputs[1], obj_array) if obj_array is None: obj = self._new_view(out, result_unit) if result_unit is None: # return a plain array obj = obj.view(np.ndarray) else: obj._unit = result_unit return obj def __deepcopy__(self, memo): # If we don't define this, ``copy.deepcopy(quantity)`` will # return a bare Numpy array. return self.copy() def __quantity_subclass__(self, unit): """ Overridden by subclasses to change what kind of view is created based on the output unit of an operation. Parameters ---------- unit : UnitBase The unit for which the appropriate class should be returned Returns ------- tuple : - `Quantity` subclass - bool: True is subclasses of the given class are ok """ return Quantity, True def _new_view(self, obj, unit=None): """ Create a Quantity view of obj, and set the unit By default, return a view of ``obj`` of the same class as ``self`` and with the unit passed on, or that of ``self``. Subclasses can override the type of class used with ``__quantity_subclass__``, and can ensure other properties of ``self`` are copied using `__array_finalize__`. Parameters ---------- obj : ndarray The array to create a view of. If obj is a numpy or python scalar, it will be converted to an array scalar. unit : `UnitBase`, or anything convertible to a :class:`~astropy.units.Unit`, or `None` The unit of the resulting object. It is used to select a subclass, and explicitly assigned to the view if not `None`. If `None` (default), the unit is set by `__array_finalize__` to self._unit. Returns ------- view : Quantity subclass """ # python and numpy scalars cannot be viewed as arrays and thus not as # Quantity either; turn them into zero-dimensional arrays # (These are turned back into scalar in `.value`) if not isinstance(obj, np.ndarray): obj = np.array(obj) if unit is None: subclass = self.__class__ else: unit = Unit(unit) subclass, subok = self.__quantity_subclass__(unit) if subok: subclass = self.__class__ view = obj.view(subclass) view.__array_finalize__(self) if unit is not None: view._unit = unit return view def __reduce__(self): # patch to pickle Quantity objects (ndarray subclasses), see # http://www.mail-archive.com/numpy-discussion@scipy.org/msg02446.html object_state = list(super(Quantity, self).__reduce__()) object_state[2] = (object_state[2], self.__dict__) return tuple(object_state) def __setstate__(self, state): # patch to unpickle Quantity objects (ndarray subclasses), see # http://www.mail-archive.com/numpy-discussion@scipy.org/msg02446.html nd_state, own_state = state super(Quantity, self).__setstate__(nd_state) self.__dict__.update(own_state) def to(self, unit, equivalencies=[]): """ Returns a new `~astropy.units.Quantity` object with the specified units. Parameters ---------- unit : `~astropy.units.UnitBase` instance, str An object that represents the unit to convert to. Must be an `~astropy.units.UnitBase` object or a string parseable by the `~astropy.units` package. equivalencies : list of equivalence pairs, optional A list of equivalence pairs to try if the units are not directly convertible. See :ref:`unit_equivalencies`. If not provided or ``[]``, class default equivalencies will be used (none for `~astropy.units.Quantity`, but may be set for subclasses) If `None`, no equivalencies will be applied at all, not even any set globally or within a context. """ if equivalencies == []: equivalencies = self._equivalencies unit = Unit(unit) new_val = np.asarray( self.unit.to(unit, self.value, equivalencies=equivalencies)) return self._new_view(new_val, unit) @property def value(self): """ The numerical value of this quantity. """ value = self.view(np.ndarray) if self.shape: return value else: return value.item() @property def unit(self): """ A `~astropy.units.UnitBase` object representing the unit of this quantity. """ return self._unit # this ensures that if we do a view, __repr__ and __str__ do not balk _unit = None @property def equivalencies(self): """ A list of equivalencies that will be applied by default during unit conversions. """ return self._equivalencies @property def si(self): """ Returns a copy of the current `Quantity` instance with SI units. The value of the resulting object will be scaled. """ si_unit = self.unit.si return self._new_view(self.value * si_unit.scale, si_unit / si_unit.scale) @property def cgs(self): """ Returns a copy of the current `Quantity` instance with CGS units. The value of the resulting object will be scaled. """ cgs_unit = self.unit.cgs return self._new_view(self.value * cgs_unit.scale, cgs_unit / cgs_unit.scale) @lazyproperty def isscalar(self): """ True if the `value` of this quantity is a scalar, or False if it is an array-like object. .. note:: This is subtly different from `numpy.isscalar` in that `numpy.isscalar` returns False for a zero-dimensional array (e.g. ``np.array(1)``), while this is True for quantities, since quantities cannot represent true numpy scalars. """ return not isiterable(self.value) # This flag controls whether convenience conversion members, such # as `q.m` equivalent to `q.to(u.m).value` are available. This is # not turned on on Quantity itself, but is on some subclasses of # Quantity, such as `astropy.coordinates.Angle`. _include_easy_conversion_members = False @override__dir__ def __dir__(self): """ Quantities are able to directly convert to other units that have the same physical type. This function is implemented in order to make autocompletion still work correctly in IPython. """ if not self._include_easy_conversion_members: return [] extra_members = set() equivalencies = Unit._normalize_equivalencies(self.equivalencies) for equivalent in self.unit._get_units_with_same_physical_type( equivalencies): extra_members.update(equivalent.names) return extra_members def __getattr__(self, attr): """ Quantities are able to directly convert to other units that have the same physical type. """ if not self._include_easy_conversion_members: raise AttributeError( "'{0}' object has no '{1}' member".format( self.__class__.__name__, attr)) def get_virtual_unit_attribute(): registry = get_current_unit_registry().registry to_unit = registry.get(attr, None) if to_unit is None: return None try: return self.unit.to( to_unit, self.value, equivalencies=self.equivalencies) except UnitsError: return None value = get_virtual_unit_attribute() if value is None: raise AttributeError( "{0} instance has no attribute '{1}'".format( self.__class__.__name__, attr)) else: return value if not NUMPY_LT_1P9: # Equality (return False if units do not match) needs to be handled # explicitly for numpy >=1.9, since it no longer traps errors. def __eq__(self, other): try: try: return super(Quantity, self).__eq__(other) except DeprecationWarning: # We treat the DeprecationWarning separately, since it may # mask another Exception. But we do not want to just use # np.equal, since super's __eq__ treats recarrays correctly. return np.equal(self, other) except UnitsError: return False except TypeError: return NotImplemented def __ne__(self, other): try: try: return super(Quantity, self).__ne__(other) except DeprecationWarning: return np.not_equal(self, other) except UnitsError: return True except TypeError: return NotImplemented # Arithmetic operations def __mul__(self, other): """ Multiplication between `Quantity` objects and other objects.""" if isinstance(other, (UnitBase, six.string_types)): return self._new_view(self.copy(), other * self.unit) return np.multiply(self, other) def __imul__(self, other): """In-place multiplication between `Quantity` objects and others.""" if isinstance(other, (UnitBase, six.string_types)): self._unit = other * self.unit return self return np.multiply(self, other, self) def __rmul__(self, other): """ Right Multiplication between `Quantity` objects and other objects. """ return self.__mul__(other) def __div__(self, other): """ Division between `Quantity` objects and other objects.""" if isinstance(other, (UnitBase, six.string_types)): return self._new_view(self.copy(), self.unit / other) return np.true_divide(self, other) def __idiv__(self, other): """Inplace division between `Quantity` objects and other objects.""" if isinstance(other, (UnitBase, six.string_types)): self._unit = self.unit / other return self return np.true_divide(self, other, self) def __rdiv__(self, other): """ Right Division between `Quantity` objects and other objects.""" if isinstance(other, (UnitBase, six.string_types)): return self._new_view(1. / self.value, other / self.unit) return np.divide(other, self) def __truediv__(self, other): """ Division between `Quantity` objects. """ return self.__div__(other) def __itruediv__(self, other): """ Division between `Quantity` objects. """ return self.__idiv__(other) def __rtruediv__(self, other): """ Division between `Quantity` objects. """ return self.__rdiv__(other) def __divmod__(self, other): if isinstance(other, (six.string_types, UnitBase)): return (self / other, self._new_view(np.array(0.), dimensionless_unscaled)) other_value = self._to_own_unit(other) result_tuple = super(Quantity, self.__class__).__divmod__( self.view(np.ndarray), other_value) return (self._new_view(result_tuple[0], dimensionless_unscaled), self._new_view(result_tuple[1])) def __pos__(self): """ Plus the quantity. This is implemented in case users use +q where q is a quantity. (Required for scalar case.) """ return self.copy() # other overrides of special functions def __hash__(self): return hash(self.value) ^ hash(self.unit) def __iter__(self): if self.isscalar: raise TypeError( "'{cls}' object with a scalar value is not iterable" .format(cls=self.__class__.__name__)) # Otherwise return a generator def quantity_iter(): for val in self.value: yield self._new_view(val) return quantity_iter() def __getitem__(self, key): if self.isscalar: raise TypeError( "'{cls}' object with a scalar value does not support " "indexing".format(cls=self.__class__.__name__)) out = super(Quantity, self).__getitem__(key) return self._new_view(out) def __setitem__(self, i, value): self.view(np.ndarray).__setitem__(i, self._to_own_unit(value)) def __setslice__(self, i, j, value): self.view(np.ndarray).__setslice__(i, j, self._to_own_unit(value)) # __contains__ is OK def __nonzero__(self): """Quantities should always be treated as non-False; there is too much potential for ambiguity otherwise. """ return True if six.PY3: __bool__ = __nonzero__ def __len__(self): if self.isscalar: raise TypeError("'{cls}' object with a scalar value has no " "len()".format(cls=self.__class__.__name__)) else: return len(self.value) # Numerical types def __float__(self): try: return float(self.to(dimensionless_unscaled).value) except (UnitsError, TypeError): raise TypeError('Only dimensionless scalar quantities can be ' 'converted to Python scalars') def __int__(self): try: return int(self.to(dimensionless_unscaled).value) except (UnitsError, TypeError): raise TypeError('Only dimensionless scalar quantities can be ' 'converted to Python scalars') def __index__(self): # for indices, we do not want to mess around with scaling at all, # so unlike for float, int, we insist here on unscaled dimensionless try: assert self.unit.is_unity() return self.value.__index__() except: raise TypeError('Only integer dimensionless scalar quantities ' 'can be converted to a Python index') if six.PY2: def __long__(self): try: return long(self.to(dimensionless_unscaled).value) except (UnitsError, TypeError): raise TypeError('Only dimensionless scalar quantities can be ' 'converted to Python scalars') # Display # TODO: we may want to add a hook for dimensionless quantities? def __str__(self): if self.unit is None: unitstr = _UNIT_NOT_INITIALISED else: unitstr = self.unit.to_string() if unitstr: unitstr = ' ' + unitstr return '{0}{1:s}'.format(self.value, unitstr) def __repr__(self): prefixstr = '<' + self.__class__.__name__ + ' ' arrstr = np.array2string(self.view(np.ndarray), separator=',', prefix=prefixstr) if self.unit is None: unitstr = _UNIT_NOT_INITIALISED else: unitstr = self.unit.to_string() if unitstr: unitstr = ' ' + unitstr return '{0}{1}{2:s}>'.format(prefixstr, arrstr, unitstr) def _repr_latex_(self): """ Generate latex representation of the quantity and its unit. This is used by the IPython notebook to show it all latexified. It only works for scalar quantities; for arrays, the standard reprensation is returned. Returns ------- lstr LaTeX string """ if not self.isscalar: raise NotImplementedError('Cannot represent Quantity arrays ' 'in LaTex format') # Format value latex_value = "{0:g}".format(self.value) if "e" in latex_value: latex_value = latex_value.replace('e', '\\times 10^{') + '}' # Format unit # [1:-1] strips the '$' on either side needed for math mode latex_unit = (self.unit._repr_latex_()[1:-1] # note this is unicode if self.unit is not None else _UNIT_NOT_INITIALISED) return '${0} \; {1}$'.format(latex_value, latex_unit) def __format__(self, format_spec): """ Format quantities using the new-style python formatting codes as specifiers for the number. If the format specifier correctly applies itself to the value, then it is used to format only the value. If it cannot be applied to the value, then it is applied to the whole string. """ try: value = format(self.value, format_spec) full_format_spec = "s" except ValueError: value = self.value full_format_spec = format_spec return format("{0} {1:s}".format(value, self.unit.to_string() if self.unit is not None else _UNIT_NOT_INITIALISED), full_format_spec) def decompose(self, bases=[]): """ Generates a new `Quantity` with the units decomposed. Decomposed units have only irreducible units in them (see `astropy.units.UnitBase.decompose`). Parameters ---------- bases : sequence of UnitBase, optional The bases to decompose into. When not provided, decomposes down to any irreducible units. When provided, the decomposed result will only contain the given units. This will raises a `~astropy.units.UnitsError` if it's not possible to do so. Returns ------- newq : `~astropy.units.Quantity` A new object equal to this quantity with units decomposed. """ return self._decompose(False, bases=bases) def _decompose(self, allowscaledunits=False, bases=[]): """ Generates a new `Quantity` with the units decomposed. Decomposed units have only irreducible units in them (see `astropy.units.UnitBase.decompose`). Parameters ---------- allowscaledunits : bool If True, the resulting `Quantity` may have a scale factor associated with it. If False, any scaling in the unit will be subsumed into the value of the resulting `Quantity` bases : sequence of UnitBase, optional The bases to decompose into. When not provided, decomposes down to any irreducible units. When provided, the decomposed result will only contain the given units. This will raises a `~astropy.units.UnitsError` if it's not possible to do so. Returns ------- newq : `~astropy.units.Quantity` A new object equal to this quantity with units decomposed. """ new_unit = self.unit.decompose(bases=bases) # Be careful here because self.value usually is a view of self; # be sure that the original value is not being modified. if not allowscaledunits and hasattr(new_unit, 'scale'): new_value = self.value * new_unit.scale new_unit = new_unit / new_unit.scale return self._new_view(new_value, new_unit) else: return self._new_view(self.copy(), new_unit) # These functions need to be overridden to take into account the units # Array conversion # http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html#array-conversion def item(self, *args): # item returns python built-ins, so use initializer, not _new_view return self.__class__(super(Quantity, self).item(*args), self.unit) def list(self): raise NotImplementedError("cannot make a list of Quantities. Get " "list of values with q.value.list()") def _to_own_unit(self, value, check_precision=True): try: _value = value.to(self.unit).value except AttributeError: try: _value = dimensionless_unscaled.to(self.unit, value) except UnitsError as exc: if _can_have_arbitrary_unit(value): _value = value else: raise exc if check_precision: value_dtype = getattr(value, 'dtype', None) if self.dtype != value_dtype: self_dtype_array = np.array(_value, self.dtype) value_dtype_array = np.array(_value, dtype=value_dtype, copy=False) if not np.all(np.logical_or(self_dtype_array == value_dtype_array, np.isnan(value_dtype_array))): raise TypeError("cannot convert value type to array type " "without precision loss") return _value def itemset(self, *args): if len(args) == 0: raise ValueError("itemset must have at least one argument") self.view(np.ndarray).itemset(*(args[:-1] + (self._to_own_unit(args[-1]),))) def tostring(self, order='C'): raise NotImplementedError("cannot write Quantities to string. Write " "array with q.value.tostring(...).") def tofile(self, fid, sep="", format="%s"): raise NotImplementedError("cannot write Quantities to file. Write " "array with q.value.tofile(...)") def dump(self, file): raise NotImplementedError("cannot dump Quantities to file. Write " "array with q.value.dump()") def dumps(self): raise NotImplementedError("cannot dump Quantities to string. Write " "array with q.value.dumps()") # astype, byteswap, copy, view, getfield, setflags OK as is def fill(self, value): self.view(np.ndarray).fill(self._to_own_unit(value)) # Shape manipulation: resize cannot be done (does not own data), but # shape, transpose, swapaxes, flatten, ravel, squeeze all OK. Only # the flat iterator needs to be overwritten, otherwise single items are # returned as numbers. @property def flat(self): """A 1-D iterator over the Quantity array. This returns a ``QuantityIterator`` instance, which behaves the same as the `~numpy.flatiter` instance returned by `~numpy.ndarray.flat`, and is similar to, but not a subclass of, Python's built-in iterator object. """ return QuantityIterator(self) @flat.setter def flat(self, value): y = self.ravel() y[:] = value # Item selection and manipulation # take, repeat, sort, compress, diagonal OK def put(self, indices, values, mode='raise'): self.view(np.ndarray).put(indices, self._to_own_unit(values), mode) def choose(self, choices, out=None, mode='raise'): raise NotImplementedError("cannot choose based on quantity. Choose " "using array with q.value.choose(...)") # ensure we do not return indices as quantities def argsort(self, axis=-1, kind='quicksort', order=None): return self.view(np.ndarray).argsort(axis=axis, kind=kind, order=order) def searchsorted(self, v, *args, **kwargs): return np.searchsorted(np.array(self), self._to_own_unit(v, check_precision=False), *args, **kwargs) # avoid numpy 1.6 problem # Calculation # ensure we do not return indices as quantities # conj OK def argmax(self, axis=None, out=None): return self.view(np.ndarray).argmax(axis=axis, out=out) def argmin(self, axis=None, out=None): return self.view(np.ndarray).argmin(axis=axis, out=out) def _prepare_out(self, out=None, unit=None): if out is None: return if not isinstance(out, Quantity): raise TypeError("out has to be assigned to a Quantity instance") if unit is None: out._unit = self._unit else: if out.__quantity_subclass__(unit)[0] is not out.__class__: raise TypeError("out cannot be assigned to a {0} instance; " "use a {1} instance instead.".format( out.__class__, out.__quantity_subclass__(out, unit)[0])) out._unit = unit def clip(self, a_min, a_max, out=None): self._prepare_out(out=out) value = np.clip(self.value, self._to_own_unit(a_min), self._to_own_unit(a_max), out=out) return self._new_view(value) def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None): self._prepare_out(out=out) value = np.trace(self.value, offset=offset, axis1=axis1, axis2=axis2, dtype=None, out=out) return self._new_view(value) def var(self, axis=None, dtype=None, out=None, ddof=0): result_unit = self.unit ** 2 self._prepare_out(out=out, unit=result_unit) value = np.var(self.value, axis=axis, dtype=dtype, out=out, ddof=ddof), return self._new_view(value, result_unit) def std(self, axis=None, dtype=None, out=None, ddof=0): self._prepare_out(out=out) value = np.std(self.value, axis=axis, dtype=dtype, out=out, ddof=ddof) return self._new_view(value) def mean(self, axis=None, dtype=None, out=None): self._prepare_out(out=out) value = np.mean(self.value, axis=axis, dtype=dtype, out=out) return self._new_view(value) def ptp(self, axis=None, out=None): self._prepare_out(out=out) value = np.ptp(self.value, axis=axis, out=out) return self._new_view(value) def max(self, axis=None, out=None, keepdims=False): self._prepare_out(out=out) try: value = np.max(self.value, axis=axis, out=out, keepdims=keepdims) except: # numpy < 1.7 value = np.max(self.value, axis=axis, out=out) return self._new_view(value) def min(self, axis=None, out=None, keepdims=False): self._prepare_out(out=out) try: value = np.min(self.value, axis=axis, out=out, keepdims=keepdims) except: # numpy < 1.7 value = np.min(self.value, axis=axis, out=out) return self._new_view(value) def dot(self, b, out=None): result_unit = self.unit * getattr(b, 'unit', 1.) self._prepare_out(out=out, unit=result_unit) try: value = np.dot(self, b, out=out) except TypeError: # numpy < 1.7 value = np.dot(self, b) return self._new_view(value, result_unit) def diff(self, n=1, axis=-1): value = np.diff(self.value, n=n, axis=axis) return self._new_view(value) def ediff1d(self, to_end=None, to_begin=None): value = np.ediff1d(self.value, to_end=to_end, to_begin=to_begin) return self._new_view(value) def nansum(self, axis=None): value = np.nansum(self.value, axis=axis) return self._new_view(value) def sum(self, axis=None, dtype=None, out=None, keepdims=False): self._prepare_out(out=out) try: value = np.sum(self.value, axis=axis, dtype=dtype, out=out, keepdims=keepdims) except: # numpy < 1.7 value = np.sum(self.value, axis=axis, dtype=dtype, out=out) return self._new_view(value) def cumsum(self, axis=None, dtype=None, out=None): self._prepare_out(out=out) value = np.cumsum(self.value, axis=axis, dtype=dtype, out=out) return self._new_view(value) def prod(self, axis=None, dtype=None, out=None, keepdims=False): if self.unit.is_unity(): self._prepare_out(out=out) try: value = np.prod(self.value, axis=axis, dtype=dtype, out=out, keepdims=keepdims) except: # numpy < 1.7 value = np.prod(self.value, axis=axis, dtype=dtype, out=out) return self._new_view(value) else: raise ValueError("cannot use prod on scaled or " "non-dimensionless Quantity arrays") def cumprod(self, axis=None, dtype=None, out=None): if self.unit.is_unity(): self._prepare_out(out=out) value = np.cumprod(self.value, axis=axis, dtype=dtype, out=out) return self._new_view(value) else: raise ValueError("cannot use cumprod on scaled or " "non-dimensionless Quantity arrays") def all(self, axis=None, out=None): raise NotImplementedError("cannot evaluate truth value of quantities. " "Evaluate array with q.value.all(...)") def any(self, axis=None, out=None): raise NotImplementedError("cannot evaluate truth value of quantities. " "Evaluate array with q.value.any(...)")