Welcome to tinynumpy’s documentation!¶
A lightweight, pure Python, numpy compliant ndarray class.
The documenation in this module is rather compact. For details on each function, see the corresponding documentation at: http://docs.scipy.org/doc/numpy/reference/index.html Be aware that the behavior of tinynumpy may deviate in some ways from numpy, or that certain features may not be supported.
- tinynumpy.arange([start, ]stop, [step, ]dtype=None)¶
Return evenly spaced values within a given interval.
Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns an ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases.
- tinynumpy.array(obj, dtype=None, copy=True, order=None)¶
Create a new array. If obj is an ndarray, and copy=False, a view of that array is returned. For details see: http://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html
- tinynumpy.empty(shape, dtype=None, order=None)¶
Return a new array of given shape and type, without initializing entries
- tinynumpy.empty_like(a, dtype=None, order=None)¶
Return a new array with the same shape and type as a given array.
- tinynumpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)¶
Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. The endpoint of the interval can optionally be excluded.
- tinynumpy.ones(shape, dtype=None, order=None)¶
Return a new array of given shape and type, filled with ones
- tinynumpy.ones_like(a, dtype=None, order=None)¶
Return an array of ones with the same shape and type as a given array.
- tinynumpy.squeeze_strides(s)¶
Pop strides for singular dimensions.
- tinynumpy.zeros(shape, dtype=None, order=None)¶
Return a new array of given shape and type, filled with zeros
- tinynumpy.zeros_like(a, dtype=None, order=None)¶
Return an array of zeros with the same shape and type as a given array.
- class tinynumpy.ndarray(shape, dtype='float64', buffer=None, offset=0, strides=None, order=None)¶
Array class similar to numpy’s ndarray, implemented in pure Python. This class can be distinguished from a real numpy array in that the repr always shows the dtype as a string, and for larger arrays (more than 100 elements) it shows a short one-line repr.
An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type property describes the format of each element in the array.
Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(...)) for instantiating an array.
Parameters: shape : tuple of ints
Shape of created array.
dtype : data-type, optional
Any object that can be interpreted as a numpy data type.
buffer : object contaning data, optional
Used to fill the array with data. If another ndarray is given, the underlying data is used. Can also be a ctypes.Array or any object that exposes the buffer interface.
offset : int, optional
Offset of array data in buffer.
strides : tuple of ints, optional
Strides of data in memory.
order : {‘C’, ‘F’}, optional NOT SUPPORTED
Row-major or column-major order.
See also
Notes
There are two modes of creating an array:
- If buffer is None, then only shape, dtype, and order are used.
- If buffer is an object exposing the buffer interface, then all keywords are interpreted.
Attributes
Methods
- T¶
- all(axis=None)¶
- any(axis=None)¶
- argmax(axis=None)¶
- argmin(axis=None)¶
- astype(dtype)¶
- base¶
- clip(a_min, a_max, out=None)¶
- copy()¶
- cumprod(axis=None, out=None)¶
- cumsum(axis=None, out=None)¶
- data¶
- dtype¶
- fill(value)¶
- flags¶
- flat¶
- flatten()¶
- itemsize¶
- max(axis=None)¶
- mean(axis=None)¶
- min(axis=None)¶
- nbytes¶
- ndim¶
- prod(axis=None)¶
- ravel()¶
- repeat(repeats, axis=None)¶
- reshape(newshape)¶
- shape¶
- size¶
- strides¶
- sum(axis=None)¶
- transpose()¶
- view(dtype=None, type=None)¶