from __future__ import print_function, division
import numpy as np
import sys


scalarTypes = (float, int, np.float_, np.float64, np.int64, np.int32, np.number)
if sys.version_info < (3,):
    scalarTypes += (long, )

def isScalar(f):
    if isinstance(f, scalarTypes):
        return True
    elif isinstance(f, np.ndarray) and f.size == 1 and type(f[0]) in scalarTypes:
        return True
    return False


def asArray_N_x_Dim(pts, dim):
        if type(pts) == list:
            pts = np.array(pts)
        assert isinstance(pts, np.ndarray), "pts must be a numpy array"

        if dim > 1:
            pts = np.atleast_2d(pts)
        elif len(pts.shape) == 1:
            pts = pts[:, np.newaxis]

        assert pts.shape[1] == dim, "pts must be a column vector of shape (nPts, {0:d}) not ({1:d}, {2:d})".format(*((dim,)+pts.shape))

        return pts


def requires(modules):
    """Decorator to wrap functions with soft dependencies.

    This function was inspired by the `requires` function of pysal,
    which is released under the 'BSD 3-Clause "New" or "Revised" License'.

    https://github.com/pysal/pysal/blob/master/pysal/lib/common.py

    Parameters
    ----------
    modules : dict
        Dictionary containing soft dependencies, e.g.,
        {'matplotlib': matplotlib}.

    Returns
    -------
    decorated_function : function
        Original function if all soft dependencies are met, otherwise
        it returns an empty function which prints why it is not running.

    """

    # Check the required modules, add missing ones in the list `missing`.
    missing = []
    for key, item in modules.items():
        if item is False:
            missing.append(key)

    def decorated_function(function):
        """Wrap function."""
        if not missing:
            return function
        else:
            def passer(*args, **kwargs):
                print(('Missing dependencies: {d}.'.format(d=missing)))
                print(('Not running `{}`.'.format(function.__name__)))
            return passer

    return decorated_function

from __future__ import print_function, division
import numpy as np
import sys


scalarTypes = (float, int, np.number)
if sys.version_info < (3,):
    scalarTypes += (long, )

def isScalar(f):
    if isinstance(f, scalarTypes):
        return True
    elif isinstance(f, np.ndarray) and f.size == 1 and type(f[0]) in scalarTypes:
        return True
    return False


def asArray_N_x_Dim(pts, dim):
        if type(pts) == list:
            pts = np.array(pts)
        assert isinstance(pts, np.ndarray), "pts must be a numpy array"

        if dim > 1:
            pts = np.atleast_2d(pts)
        elif len(pts.shape) == 1:
            pts = pts[:, np.newaxis]

        assert pts.shape[1] == dim, "pts must be a column vector of shape (nPts, {0:d}) not ({1:d}, {2:d})".format(*((dim,)+pts.shape))

        return pts


def requires(modules):
    """Decorator to wrap functions with soft dependencies.

    This function was inspired by the `requires` function of pysal,
    which is released under the 'BSD 3-Clause "New" or "Revised" License'.

    https://github.com/pysal/pysal/blob/master/pysal/lib/common.py

    Parameters
    ----------
    modules : dict
        Dictionary containing soft dependencies, e.g.,
        {'matplotlib': matplotlib}.

    Returns
    -------
    decorated_function : function
        Original function if all soft dependencies are met, otherwise
        it returns an empty function which prints why it is not running.

    """

    # Check the required modules, add missing ones in the list `missing`.
    missing = []
    for key, item in modules.items():
        if item is False:
            missing.append(key)

    def decorated_function(function):
        """Wrap function."""
        if not missing:
            return function
        else:
            def passer(*args, **kwargs):
                print(('Missing dependencies: {d}.'.format(d=missing)))
                print(('Not running `{}`.'.format(function.__name__)))
            return passer

    return decorated_function

use numpy.number to check isScalar