property_array

class free_properties.property_array

Create an array that allows for array-like manipulation of FreeProperty objects. All entries in a property_array must be instances of FreeProperty. Setting items of a property_array sets values of objects instead.

properties : array_like[FreeProperty]

Use the PropertyFactory to create a Weight property class which calculates weight based on density and volume:

>>> from free_property import PropertyFactory, property_array
>>>
>>> @PropertyFactory
>>> def Weight(self):
...    '''Weight (kg) based on volume (m^3).'''
...    data = self.data
...    rho = data['rho'] # Density (kg/m^3)
...    vol = data['vol'] # Volume (m^3)
...    return rho * vol
>>>
>>> @Weight.setter
>>> def Weight(self, weight):
...    data = self.data
...    rho = data['rho'] # Density (kg/m^3)
...    data['vol'] = weight / rho

Create dictionaries of data and initialize new properties:

>>> water_data = {'rho': 1000, 'vol': 3}
>>> ethanol_data = {'rho': 789, 'vol': 3}
>>> weight_water = Weight('Water', water_data)
>>> weight_ethanol = Weight('Ethanol', ethanol_data)
>>> weight_water
<Weight(Water): 3000 kg>
>>> weight_ethanol
<Weight(Ethanol): 2367 kg>

Create a property_array from data:

>>> prop_arr = property_array([weight_water, weight_ethanol])
>>> prop_arr
property_array([3000.0, 2367.0])

Changing the values of a property_array changes the value of its properties:

>>> # Addition in place
>>> prop_arr += 3000
>>> prop_arr
property_array([6000.0, 5367.0])
>>> # Note how the data also changes
>>> water_data
{'rho': 1000, 'vol': 6.0}
>>> ethanol_data
{'rho': 789, 'vol': 6.802281368821292}
>>> # Setting an item changes the property value
>>> prop_arr[1] = 2367
>>> ethanol_data
{'rho': 789, 'vol': 3}

New arrays have no connection to the property_array:

>>> prop_arr - 1000 #  Returns a new array
array([5000.0, 1367.0])
>>> water_data #  Data remains unchanged
{'rho': 1000, 'vol': 6.0}

all(axis=None, out=None, keepdims=False, *, where=True)

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

numpy.all : equivalent function

any(axis=None, out=None, keepdims=False, *, where=True)

Returns True if any of the elements of a evaluate to True.

Refer to numpy.any for full documentation.

numpy.any : equivalent function

argmax(axis=None, out=None)

Return indices of the maximum values along the given axis.

Refer to numpy.argmax for full documentation.

numpy.argmax : equivalent function

argmin(axis=None, out=None)

Return indices of the minimum values along the given axis.

Refer to numpy.argmin for detailed documentation.

numpy.argmin : equivalent function

argpartition(kth, axis=-1, kind='introselect', order=None)

Returns the indices that would partition this array.

Refer to numpy.argpartition for full documentation.

New in version 1.8.0.

numpy.argpartition : equivalent function

argsort(axis=-1, kind=None, order=None)

Returns the indices that would sort this array.

Refer to numpy.argsort for full documentation.

numpy.argsort : equivalent function

choose(choices, out=None, mode='raise')

Use an index array to construct a new array from a set of choices.

Refer to numpy.choose for full documentation.

numpy.choose : equivalent function

clip(min=None, max=None, out=None, **kwargs)

Return an array whose values are limited to [min, max]. One of max or min must be given.

Refer to numpy.clip for full documentation.

numpy.clip : equivalent function

conj()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

numpy.conjugate : equivalent function

conjugate()

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

numpy.conjugate : equivalent function

copy(order='C')

Return a copy of the array.

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional

Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and numpy.copy() are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)

numpy.copy : Similar function with different default behavior numpy.copyto

This function is the preferred method for creating an array copy. The function numpy.copy() is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.

>>> x = np.array([[1,2,3],[4,5,6]], order='F')

>>> y = x.copy()

>>> x.fill(0)

>>> x
array([[0, 0, 0],
       [0, 0, 0]])

>>> y
array([[1, 2, 3],
       [4, 5, 6]])

>>> y.flags['C_CONTIGUOUS']
True

cumprod(axis=None, dtype=None, out=None)

Return the cumulative product of the elements along the given axis.

Refer to numpy.cumprod for full documentation.

numpy.cumprod : equivalent function

cumsum(axis=None, dtype=None, out=None)

Return the cumulative sum of the elements along the given axis.

Refer to numpy.cumsum for full documentation.

numpy.cumsum : equivalent function

dot(b, out=None)

Dot product of two arrays.

Refer to numpy.dot for full documentation.

numpy.dot : equivalent function

>>> a = np.eye(2)
>>> b = np.ones((2, 2)) * 2
>>> a.dot(b)
array([[2.,  2.],
       [2.,  2.]])

This array method can be conveniently chained:

>>> a.dot(b).dot(b)
array([[8.,  8.],
       [8.,  8.]])

max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

numpy.amax : equivalent function

mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

numpy.mean : equivalent function

min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

numpy.amin : equivalent function

nonzero()

Return the indices of the elements that are non-zero.

Refer to numpy.nonzero for full documentation.

numpy.nonzero : equivalent function

prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

numpy.prod : equivalent function

ptp(axis=None, out=None, keepdims=False)

Peak to peak (maximum - minimum) value along a given axis.

Refer to numpy.ptp for full documentation.

numpy.ptp : equivalent function

put(indices, values, mode='raise')

Set a.flat[n] = values[n] for all n in indices.

Refer to numpy.put for full documentation.

numpy.put : equivalent function

round(decimals=0, out=None)

Return a with each element rounded to the given number of decimals.

Refer to numpy.around for full documentation.

numpy.around : equivalent function

std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

numpy.std : equivalent function

sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

numpy.sum : equivalent function

trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)

Return the sum along diagonals of the array.

Refer to numpy.trace for full documentation.

numpy.trace : equivalent function

var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

numpy.var : equivalent function