diff options
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/function_base.py | 10 | ||||
| -rw-r--r-- | numpy/lib/npyio.py | 69 | ||||
| -rw-r--r-- | numpy/lib/shape_base.py | 4 | ||||
| -rw-r--r-- | numpy/lib/twodim_base.py | 23 |
4 files changed, 60 insertions, 46 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 4ab1679e5..2de5c6193 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -2408,7 +2408,7 @@ def hamming(M): ----- The Hamming window is defined as - .. math:: w(n) = 0.54 + 0.46cos\\left(\\frac{2\\pi{n}}{M-1}\\right) + .. math:: w(n) = 0.54 - 0.46cos\\left(\\frac{2\\pi{n}}{M-1}\\right) \\qquad 0 \\leq n \\leq M-1 The Hamming was named for R. W. Hamming, an associate of J. W. Tukey and @@ -3000,8 +3000,8 @@ def percentile(a, q, axis=None, out=None, overwrite_input=False): Given a vector V of length N, the qth percentile of V is the qth ranked value in a sorted copy of V. A weighted average of the two nearest neighbors is used if the normalized ranking does not match q exactly. - The same as the median if ``q=0.5``, the same as the minimum if ``q=0`` - and the same as the maximum if ``q=1``. + The same as the median if ``q=50``, the same as the minimum if ``q=0`` + and the same as the maximum if ``q=100``. Examples -------- @@ -3108,7 +3108,7 @@ def trapz(y, x=None, dx=1.0, axis=-1): Returns ------- - out : float + trapz : float Definite integral as approximated by trapezoidal rule. See Also @@ -3547,7 +3547,7 @@ def append(arr, values, axis=None): Returns ------- - out : ndarray + append : ndarray A copy of `arr` with `values` appended to `axis`. Note that `append` does not occur in-place: a new array is allocated and filled. If `axis` is None, `out` is a flattened array. diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index c502e2cc5..82bac7d81 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -274,7 +274,7 @@ class NpzFile(object): def load(file, mmap_mode=None): """ - Load a pickled, ``.npy``, or ``.npz`` binary file. + Load an array(s) or pickled objects from .npy, .npz, or pickled files. Parameters ---------- @@ -283,13 +283,11 @@ def load(file, mmap_mode=None): If the filename extension is ``.gz``, the file is first decompressed. mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional If not None, then memory-map the file, using the given mode - (see `numpy.memmap`). The mode has no effect for pickled or - zipped files. - A memory-mapped array is stored on disk, and not directly loaded - into memory. However, it can be accessed and sliced like any - ndarray. Memory mapping is especially useful for accessing - small fragments of large files without reading the entire file - into memory. + (see `numpy.memmap` for a detailed description of the modes). + A memory-mapped array is kept on disk. However, it can be accessed + and sliced like any ndarray. Memory mapping is especially useful for + accessing small fragments of large files without reading the entire + file into memory. Returns ------- @@ -309,9 +307,9 @@ def load(file, mmap_mode=None): Notes ----- - - If the file contains pickle data, then whatever is stored in the - pickle is returned. - - If the file is a ``.npy`` file, then an array is returned. + - If the file contains pickle data, then whatever object is stored + in the pickle is returned. + - If the file is a ``.npy`` file, then a single array is returned. - If the file is a ``.npz`` file, then a dictionary-like object is returned, containing ``{filename: array}`` key-value pairs, one for each file in the archive. @@ -321,7 +319,7 @@ def load(file, mmap_mode=None): with load('foo.npz') as data: a = data['a'] - The underlyling file descriptor is always closed when exiting the with block. + The underlyling file descriptor is closed when exiting the 'with' block. Examples -------- @@ -334,8 +332,10 @@ def load(file, mmap_mode=None): Store compressed data to disk, and load it again: - >>> np.savez('/tmp/123.npz', a=np.array([[1, 2, 3], [4, 5, 6]]), b=np.array([1, 2])) - >>> data = np.load('/tmp/123.npy') + >>> a=np.array([[1, 2, 3], [4, 5, 6]]) + >>> b=np.array([1, 2]) + >>> np.savez('/tmp/123.npz', a=a, b=b) + >>> data = np.load('/tmp/123.npz') >>> data['a'] array([[1, 2, 3], [4, 5, 6]]) @@ -454,12 +454,12 @@ def savez(file, *args, **kwds): Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string, the ``.npz`` extension will be appended to the file name if it is not already there. - *args : Arguments, optional + args : Arguments, optional Arrays to save to the file. Since it is not possible for Python to know the names of the arrays outside `savez`, the arrays will be saved with names "arr_0", "arr_1", and so on. These arguments can be any expression. - **kwds : Keyword arguments, optional + kwds : Keyword arguments, optional Arrays to save to the file. Arrays will be saved in the file with the keyword names. @@ -471,6 +471,8 @@ def savez(file, *args, **kwds): -------- save : Save a single array to a binary file in NumPy format. savetxt : Save an array to a file as plain text. + numpy.savez_compressed : Save several arrays into a compressed .npz file + format Notes ----- @@ -491,7 +493,7 @@ def savez(file, *args, **kwds): >>> x = np.arange(10) >>> y = np.sin(x) - Using `savez` with *args, the arrays are saved with default names. + Using `savez` with \\*args, the arrays are saved with default names. >>> np.savez(outfile, x, y) >>> outfile.seek(0) # Only needed here to simulate closing & reopening file @@ -501,7 +503,7 @@ def savez(file, *args, **kwds): >>> npzfile['arr_0'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) - Using `savez` with **kwds, the arrays are saved with the keyword names. + Using `savez` with \\**kwds, the arrays are saved with the keyword names. >>> outfile = TemporaryFile() >>> np.savez(outfile, x=x, y=y) @@ -512,10 +514,6 @@ def savez(file, *args, **kwds): >>> npzfile['x'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) - See Also - -------- - numpy.savez_compressed : Save several arrays into a compressed .npz file format - """ _savez(file, args, kwds, False) @@ -1210,8 +1208,8 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, autostrip : bool, optional Whether to automatically strip white spaces from the variables. replace_space : char, optional - Character(s) used in replacement of white spaces in the variables names. - By default, use a '_'. + Character(s) used in replacement of white spaces in the variables + names. By default, use a '_'. case_sensitive : {True, False, 'upper', 'lower'}, optional If True, field names are case sensitive. If False or 'upper', field names are converted to upper case. @@ -1247,6 +1245,11 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, * Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces. + References + ---------- + .. [1] Numpy User Guide, section `I/O with Numpy + <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_. + Examples --------- >>> from StringIO import StringIO @@ -1770,8 +1773,9 @@ def ndfromtxt(fname, **kwargs): """ Load ASCII data stored in a file and return it as a single array. - Complete description of all the optional input parameters is available in - the docstring of the `genfromtxt` function. + Parameters + ---------- + fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- @@ -1786,7 +1790,9 @@ def mafromtxt(fname, **kwargs): """ Load ASCII data stored in a text file and return a masked array. - For a complete description of all the input parameters, see `genfromtxt`. + Parameters + ---------- + fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- @@ -1804,8 +1810,9 @@ def recfromtxt(fname, **kwargs): If ``usemask=False`` a standard `recarray` is returned, if ``usemask=True`` a MaskedRecords array is returned. - Complete description of all the optional input parameters is available in - the docstring of the `genfromtxt` function. + Parameters + ---------- + fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- @@ -1836,7 +1843,9 @@ def recfromcsv(fname, **kwargs): `recarray`) or a masked record array (if ``usemask=True``, see `ma.mrecords.MaskedRecords`). - For a complete description of all the input parameters, see `genfromtxt`. + Parameters + ---------- + fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py index 946cf172a..b88596ca4 100644 --- a/numpy/lib/shape_base.py +++ b/numpy/lib/shape_base.py @@ -29,7 +29,7 @@ def apply_along_axis(func1d,axis,arr,*args): Returns ------- - outarr : ndarray + apply_along_axis : ndarray The output array. The shape of `outarr` is identical to the shape of `arr`, except along the `axis` dimension, where the length of `outarr` is equal to the size of the return value of `func1d`. If `func1d` @@ -142,7 +142,7 @@ def apply_over_axes(func, a, axes): Returns ------- - val : ndarray + apply_over_axis : ndarray The output array. The number of dimensions is the same as `a`, but the shape can be different. This depends on whether `func` changes the shape of its output with respect to its input. diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index 6fb348275..58d8250a1 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -358,7 +358,7 @@ def tri(N, M=None, k=0, dtype=float): Returns ------- - T : ndarray of shape (N, M) + tri : ndarray of shape (N, M) Array with its lower triangle filled with ones and zero elsewhere; in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise. @@ -396,7 +396,7 @@ def tril(m, k=0): Returns ------- - L : ndarray, shape (M, N) + tril : ndarray, shape (M, N) Lower triangle of `m`, of same shape and data-type as `m`. See Also @@ -790,9 +790,10 @@ def triu_indices(n, k=0): Returns ------- - inds : tuple of arrays + inds : tuple, shape(2) of ndarrays, shape(`n`) The indices for the triangle. The returned tuple contains two arrays, - each with the indices along one dimension of the array. + each with the indices along one dimension of the array. Can be used + to slice a ndarray of shape(`n`, `n`). See also -------- @@ -852,17 +853,21 @@ def triu_indices(n, k=0): def triu_indices_from(arr, k=0): """ - Return the indices for the upper-triangle of an (n, n) array. + Return the indices for the upper-triangle of a (N, N) array. See `triu_indices` for full details. Parameters ---------- - arr : array_like - The indices will be valid for square arrays whose dimensions are - the same as arr. + arr : ndarray, shape(N, N) + The indices will be valid for square arrays. k : int, optional - Diagonal offset (see `triu` for details). + Diagonal offset (see `triu` for details). + + Returns + ------- + triu_indices_from : tuple, shape(2) of ndarray, shape(N) + Indices for the upper-triangle of `arr`. See Also -------- |