nditer.debug_print() Print the current state of the nditer instance and debug info to stdout.

ndarray.__repr__() <==> repr(x)

numpy.random.lognormal(mean=0.0, sigma=1.0, size=None) Draw samples from a log-normal distribution. Draw samples from a log-normal distribution with specified mean, standard deviation, and array shape. Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from. Parameters: mean : float Mean value of the underlying normal distribution sigma : float, > 0. Standard deviation of the underlying nor

numpy.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.

classmethod Laguerre.fit(x, y, deg, domain=None, rcond=None, full=False, w=None, window=None) [source] Least squares fit to data. Return a series instance that is the least squares fit to the data y sampled at x. The domain of the returned instance can be specified and this will often result in a superior fit with less chance of ill conditioning. Parameters: x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates o

class numpy.memmap [source] Create a memory-map to an array stored in a binary file on disk. Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory. Numpy?s memmap?s are array-like objects. This differs from Python?s mmap module, which uses file-like objects. This subclass of ndarray has some unpleasant interactions with some operations, because it doesn?t quite fit properly as a subclass. An alternative to using this su

numpy.fromfile(file, dtype=float, count=-1, sep='') Construct an array from data in a text or binary file. A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function. Parameters: file : file or str Open file object or filename. dtype : data-type Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in th

generic.flags integer value of flags

numpy.array2string(a, max_line_width=None, precision=None, suppress_small=None, separator=' ', prefix='', style=, formatter=None) [source] Return a string representation of an array. Parameters: a : ndarray Input array. max_line_width : int, optional The maximum number of columns the string should span. Newline characters splits the string appropriately after array elements. precision : int, optional Floating point precision. Default is the current printing precision (usually 8), whic

numpy.convolve(a, v, mode='full') [source] Returns the discrete, linear convolution of two one-dimensional sequences. The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal [R17]. In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions. If v is longer than a, the arrays are swapped before computation. Parameters: a : (N,) ar