Python matplotlib.cbook.iterable() Examples

def set_clim(self, vmin=None, vmax=None):
        """
        set the norm limits for image scaling; if *vmin* is a length2
        sequence, interpret it as ``(vmin, vmax)`` which is used to
        support setp

        ACCEPTS: a length 2 sequence of floats
        """
        if (vmin is not None and vmax is None and
                cbook.iterable(vmin) and len(vmin) == 2):
            vmin, vmax = vmin

        if vmin is not None:
            self.norm.vmin = vmin
        if vmax is not None:
            self.norm.vmax = vmax
        self.changed() 

def _make_key(self, *args, **kwargs):
        'make a hashable key out of args and kwargs'

        def fixitems(items):
            #items may have arrays and lists in them, so convert them
            # to tuples for the key
            ret = []
            for k, v in items:
                if iterable(v):
                    v = tuple(v)
                ret.append((k, v))
            return tuple(ret)

        def fixlist(args):
            ret = []
            for a in args:
                if iterable(a):
                    a = tuple(a)
                ret.append(a)
            return tuple(ret)

        key = fixlist(args), fixitems(kwargs.iteritems())
        return key 

def __init__(self, o):
        """
        Initialize the artist inspector with an
        :class:`~matplotlib.artist.Artist` or sequence of :class:`Artists`.
        If a sequence is used, we assume it is a homogeneous sequence (all
        :class:`Artists` are of the same type) and it is your responsibility
        to make sure this is so.
        """
        if cbook.iterable(o) and len(o):
            o = o[0]

        self.oorig = o
        if not isinstance(o, type):
            o = type(o)
        self.o = o

        self.aliasd = self.get_aliases() 

def __init__(self, o):
        """
        Initialize the artist inspector with an
        :class:`~matplotlib.artist.Artist` or sequence of :class:`Artists`.
        If a sequence is used, we assume it is a homogeneous sequence (all
        :class:`Artists` are of the same type) and it is your responsibility
        to make sure this is so.
        """
        if cbook.iterable(o) and len(o):
            o = o[0]

        self.oorig = o
        if not isinstance(o, type):
            o = type(o)
        self.o = o

        self.aliasd = self.get_aliases() 

def _process_linestyles(self):
        linestyles = self.linestyles
        Nlev = len(self.levels)
        if linestyles is None:
            tlinestyles = ['solid'] * Nlev
            if self.monochrome:
                neg_ls = mpl.rcParams['contour.negative_linestyle']
                eps = - (self.zmax - self.zmin) * 1e-15
                for i, lev in enumerate(self.levels):
                    if lev < eps:
                        tlinestyles[i] = neg_ls
        else:
            if cbook.is_string_like(linestyles):
                tlinestyles = [linestyles] * Nlev
            elif cbook.iterable(linestyles):
                tlinestyles = list(linestyles)
                if len(tlinestyles) < Nlev:
                    nreps = int(np.ceil(Nlev / len(linestyles)))
                    tlinestyles = tlinestyles * nreps
                if len(tlinestyles) > Nlev:
                    tlinestyles = tlinestyles[:Nlev]
            else:
                raise ValueError("Unrecognized type for linestyles kwarg")
        return tlinestyles 

def _process_linewidths(self):
        linewidths = self.linewidths
        Nlev = len(self.levels)
        if linewidths is None:
            tlinewidths = [(mpl.rcParams['lines.linewidth'],)] * Nlev
        else:
            if not cbook.iterable(linewidths):
                linewidths = [linewidths] * Nlev
            else:
                linewidths = list(linewidths)
                if len(linewidths) < Nlev:
                    nreps = int(np.ceil(Nlev / len(linewidths)))
                    linewidths = linewidths * nreps
                if len(linewidths) > Nlev:
                    linewidths = linewidths[:Nlev]
            tlinewidths = [(w,) for w in linewidths]
        return tlinewidths 

def _process_linestyles(self):
        linestyles = self.linestyles
        Nlev = len(self.levels)
        if linestyles is None:
            tlinestyles = ['solid'] * Nlev
            if self.monochrome:
                neg_ls = mpl.rcParams['contour.negative_linestyle']
                eps = - (self.zmax - self.zmin) * 1e-15
                for i, lev in enumerate(self.levels):
                    if lev < eps:
                        tlinestyles[i] = neg_ls
        else:
            if cbook.is_string_like(linestyles):
                tlinestyles = [linestyles] * Nlev
            elif cbook.iterable(linestyles):
                tlinestyles = list(linestyles)
                if len(tlinestyles) < Nlev:
                    nreps = int(np.ceil(Nlev / len(linestyles)))
                    tlinestyles = tlinestyles * nreps
                if len(tlinestyles) > Nlev:
                    tlinestyles = tlinestyles[:Nlev]
            else:
                raise ValueError("Unrecognized type for linestyles kwarg")
        return tlinestyles 

def _process_linewidths(self):
        linewidths = self.linewidths
        Nlev = len(self.levels)
        if linewidths is None:
            tlinewidths = [(mpl.rcParams['lines.linewidth'],)] * Nlev
        else:
            if not cbook.iterable(linewidths):
                linewidths = [linewidths] * Nlev
            else:
                linewidths = list(linewidths)
                if len(linewidths) < Nlev:
                    nreps = int(np.ceil(Nlev / len(linewidths)))
                    linewidths = linewidths * nreps
                if len(linewidths) > Nlev:
                    linewidths = linewidths[:Nlev]
            tlinewidths = [(w,) for w in linewidths]
        return tlinewidths 

def num2date(x, tz=None):
    """
    *x* is a float value which gives the number of days
    (fraction part represents hours, minutes, seconds) since
    0001-01-01 00:00:00 UTC *plus* *one*.
    The addition of one here is a historical artifact.  Also, note
    that the Gregorian calendar is assumed; this is not universal
    practice.  For details, see the module docstring.

    Return value is a :class:`datetime` instance in timezone *tz* (default to
    rcparams TZ value).

    If *x* is a sequence, a sequence of :class:`datetime` objects will
    be returned.
    """
    if tz is None:
        tz = _get_rc_timezone()
    if not cbook.iterable(x):
        return _from_ordinalf(x, tz)
    else:
        return [_from_ordinalf(val, tz) for val in x] 

def poly_between(x, ylower, yupper):
    """
    Given a sequence of *x*, *ylower* and *yupper*, return the polygon
    that fills the regions between them.  *ylower* or *yupper* can be
    scalar or iterable.  If they are iterable, they must be equal in
    length to *x*.

    Return value is *x*, *y* arrays for use with
    :meth:`matplotlib.axes.Axes.fill`.
    """
    if ma.isMaskedArray(ylower) or ma.isMaskedArray(yupper) or ma.isMaskedArray(x):
        numpy = ma
    else:
        numpy = np

    Nx = len(x)
    if not cbook.iterable(ylower):
        ylower = ylower*numpy.ones(Nx)

    if not cbook.iterable(yupper):
        yupper = yupper*numpy.ones(Nx)

    x = numpy.concatenate( (x, x[::-1]) )
    y = numpy.concatenate( (yupper, ylower[::-1]) )
    return x,y 

def default_units( value, axis ):
      """: Return the default unit for value, or None.

      = INPUT VARIABLES
      - value   The value or list of values that need units.

      = RETURN VALUE
      - Returns the default units to use for value.
      Return the default unit for value, or None.
      """

      # Determine the default units based on the user preferences set for
      # default units when printing a UnitDbl.
      if ( iterable(value) and not isinstance(value, str) ):
         return UnitDblConverter.default_units( value[0], axis )
      else:
         return UnitDblConverter.defaults[ value.type() ] 

def default_units( value, axis ):
      """: Return the default unit for value, or None.

      = INPUT VARIABLES
      - value   The value or list of values that need units.

      = RETURN VALUE
      - Returns the default units to use for value.
      """
      frame = None
      if ( iterable(value) and not isinstance(value, str) ):
         return EpochConverter.default_units( value[0], axis )
      else:
         frame = value.frame()

      return frame 

def poly_between(x, ylower, yupper):
    """
    Given a sequence of *x*, *ylower* and *yupper*, return the polygon
    that fills the regions between them.  *ylower* or *yupper* can be
    scalar or iterable.  If they are iterable, they must be equal in
    length to *x*.

    Return value is *x*, *y* arrays for use with
    :meth:`matplotlib.axes.Axes.fill`.
    """
    if ma.isMaskedArray(ylower) or ma.isMaskedArray(yupper) or ma.isMaskedArray(x):
        numpy = ma
    else:
        numpy = np

    Nx = len(x)
    if not cbook.iterable(ylower):
        ylower = ylower*numpy.ones(Nx)

    if not cbook.iterable(yupper):
        yupper = yupper*numpy.ones(Nx)

    x = numpy.concatenate( (x, x[::-1]) )
    y = numpy.concatenate( (yupper, ylower[::-1]) )
    return x,y 

def _make_key(self, *args, **kwargs):
        'make a hashable key out of args and kwargs'

        def fixitems(items):
            #items may have arrays and lists in them, so convert them
            # to tuples for the key
            ret = []
            for k, v in items:
                if iterable(v):
                    v = tuple(v)
                ret.append((k, v))
            return tuple(ret)

        def fixlist(args):
            ret = []
            for a in args:
                if iterable(a):
                    a = tuple(a)
                ret.append(a)
            return tuple(ret)

        key = fixlist(args), fixitems(kwargs.iteritems())
        return key 

def num2date(x, tz=None):
    """
    *x* is a float value which gives the number of days
    (fraction part represents hours, minutes, seconds) since
    0001-01-01 00:00:00 UTC *plus* *one*.
    The addition of one here is a historical artifact.  Also, note
    that the Gregorian calendar is assumed; this is not universal
    practice.  For details, see the module docstring.

    Return value is a :class:`datetime` instance in timezone *tz* (default to
    rcparams TZ value).

    If *x* is a sequence, a sequence of :class:`datetime` objects will
    be returned.
    """
    if tz is None:
        tz = _get_rc_timezone()
    if not cbook.iterable(x):
        return _from_ordinalf(x, tz)
    else:
        return [_from_ordinalf(val, tz) for val in x] 

def set_clim(self, vmin=None, vmax=None):
        """
        set the norm limits for image scaling; if *vmin* is a length2
        sequence, interpret it as ``(vmin, vmax)`` which is used to
        support setp

        ACCEPTS: a length 2 sequence of floats
        """
        if (vmin is not None and vmax is None and
                cbook.iterable(vmin) and len(vmin) == 2):
            vmin, vmax = vmin

        if vmin is not None:
            self.norm.vmin = vmin
        if vmax is not None:
            self.norm.vmax = vmax
        self.changed() 

def get_dir_vector(zdir):
    if zdir == 'x':
        return np.array((1, 0, 0))
    elif zdir == 'y':
        return np.array((0, 1, 0))
    elif zdir == 'z':
        return np.array((0, 0, 1))
    elif zdir is None:
        return np.array((0, 0, 0))
    elif iterable(zdir) and len(zdir) == 3:
        return zdir
    else:
        raise ValueError("'x', 'y', 'z', None or vector of length 3 expected") 

def _determine_lims(self, xmin=None, xmax=None, *args, **kwargs):
        if xmax is None and cbook.iterable(xmin):
            xmin, xmax = xmin
        if xmin == xmax:
            xmin -= 0.05
            xmax += 0.05
        return (xmin, xmax) 

def set_zbound(self, lower=None, upper=None):
        """
        Set the lower and upper numerical bounds of the z-axis.
        This method will honor axes inversion regardless of parameter order.
        It will not change the :attr:`_autoscaleZon` attribute.

        .. versionadded :: 1.1.0
            This function was added, but not tested. Please report any bugs.
        """
        if upper is None and cbook.iterable(lower):
            lower,upper = lower

        old_lower,old_upper = self.get_zbound()

        if lower is None: lower = old_lower
        if upper is None: upper = old_upper

        if self.zaxis_inverted():
            if lower < upper:
                self.set_zlim(upper, lower, auto=None)
            else:
                self.set_zlim(lower, upper, auto=None)
        else :
            if lower < upper:
                self.set_zlim(lower, upper, auto=None)
            else :
                self.set_zlim(upper, lower, auto=None) 

def from_list(name, colors, N=256, gamma=1.0):
        """
        Make a linear segmented colormap with *name* from a sequence
        of *colors* which evenly transitions from colors[0] at val=0
        to colors[-1] at val=1.  *N* is the number of rgb quantization
        levels.
        Alternatively, a list of (value, color) tuples can be given
        to divide the range unevenly.
        """

        if not cbook.iterable(colors):
            raise ValueError('colors must be iterable')

        if cbook.iterable(colors[0]) and len(colors[0]) == 2 and \
                not cbook.is_string_like(colors[0]):
            # List of value, color pairs
            vals, colors = zip(*colors)
        else:
            vals = np.linspace(0., 1., len(colors))

        cdict = dict(red=[], green=[], blue=[], alpha=[])
        for val, color in zip(vals, colors):
            r, g, b, a = colorConverter.to_rgba(color)
            cdict['red'].append((val, r, r))
            cdict['green'].append((val, g, g))
            cdict['blue'].append((val, b, b))
            cdict['alpha'].append((val, a, a))

        return LinearSegmentedColormap(name, cdict, N, gamma) 

def _get_bool(val):
        if not cbook.iterable(val):
            val = (val,)
        try:
            bool(val[0])
        except (TypeError, IndexError):
            raise TypeError('val must be a bool or nonzero sequence of them')
        return val 

def _get_value(val):
        try:
            return (float(val), )
        except TypeError:
            if cbook.iterable(val) and len(val):
                try:
                    float(val[0])
                except (TypeError, ValueError):
                    pass  # raise below
                else:
                    return val

        raise TypeError('val must be a float or nonzero sequence of floats') 

def _findoffset_loc(self, width, height, xdescent, ydescent, renderer):
        "Helper function to locate the legend using the location code"

        if iterable(self._loc) and len(self._loc) == 2:
            # when loc is a tuple of axes(or figure) coordinates.
            fx, fy = self._loc
            bbox = self.get_bbox_to_anchor()
            x, y = bbox.x0 + bbox.width * fx, bbox.y0 + bbox.height * fy
        else:
            bbox = Bbox.from_bounds(0, 0, width, height)
            x, y = self._get_anchored_bbox(self._loc, bbox,
                                           self.get_bbox_to_anchor(),
                                           renderer)

        return x + xdescent, y + ydescent 

def __call__(self, path, mutation_size, linewidth,
                     aspect_ratio=1.):
            """
            The __call__ method is a thin wrapper around the transmute method
            and take care of the aspect ratio.
            """

            path = make_path_regular(path)

            if aspect_ratio is not None:
                # Squeeze the given height by the aspect_ratio

                vertices, codes = path.vertices[:], path.codes[:]
                # Squeeze the height
                vertices[:, 1] = vertices[:, 1] / aspect_ratio
                path_shrinked = Path(vertices, codes)
                # call transmute method with squeezed height.
                path_mutated, fillable = self.transmute(path_shrinked,
                                                        linewidth,
                                                        mutation_size)
                if cbook.iterable(fillable):
                    path_list = []
                    for p in zip(path_mutated):
                        v, c = p.vertices, p.codes
                        # Restore the height
                        v[:, 1] = v[:, 1] * aspect_ratio
                        path_list.append(Path(v, c))
                    return path_list, fillable
                else:
                    return path_mutated, fillable
            else:
                return self.transmute(path, mutation_size, linewidth) 

def offset_line(y, yerr):
    """
    Offsets an array *y* by +/- an error and returns a tuple (y - err, y + err).

    The error term can be:

    * A scalar. In this case, the returned tuple is obvious.
    * A vector of the same length as *y*. The quantities y +/- err are computed
      component-wise.
    * A tuple of length 2. In this case, yerr[0] is the error below *y* and
      yerr[1] is error above *y*. For example::

        from pylab import *
        x = linspace(0, 2*pi, num=100, endpoint=True)
        y = sin(x)
        y_minus, y_plus = mlab.offset_line(y, 0.1)
        plot(x, y)
        fill_between(x, ym, y2=yp)
        show()

    """
    if cbook.is_numlike(yerr) or (cbook.iterable(yerr) and len(yerr) == len(y)):
        ymin = y - yerr
        ymax = y + yerr
    elif len(yerr) == 2:
        ymin, ymax = y - yerr[0], y + yerr[1]
    else:
        raise ValueError("yerr must be scalar, 1xN or 2xN")
    return ymin, ymax 

def rec_append_fields(rec, names, arrs, dtypes=None):
    """
    Return a new record array with field names populated with data
    from arrays in *arrs*.  If appending a single field, then *names*,
    *arrs* and *dtypes* do not have to be lists. They can just be the
    values themselves.
    """
    if (not cbook.is_string_like(names) and cbook.iterable(names) \
            and len(names) and cbook.is_string_like(names[0])):
        if len(names) != len(arrs):
            raise ValueError("number of arrays do not match number of names")
    else: # we have only 1 name and 1 array
        names = [names]
        arrs = [arrs]
    arrs = map(np.asarray, arrs)
    if dtypes is None:
        dtypes = [a.dtype for a in arrs]
    elif not cbook.iterable(dtypes):
        dtypes = [dtypes]
    if len(arrs) != len(dtypes):
        if len(dtypes) == 1:
            dtypes = dtypes * len(arrs)
        else:
            raise ValueError("dtypes must be None, a single dtype or a list")

    newdtype = np.dtype(rec.dtype.descr + zip(names, dtypes))
    newrec = np.recarray(rec.shape, dtype=newdtype)
    for field in rec.dtype.fields:
        newrec[field] = rec[field]
    for name, arr in zip(names, arrs):
        newrec[name] = arr
    return newrec 

def fftsurr(x, detrend=detrend_none, window=window_none):
    """
    Compute an FFT phase randomized surrogate of *x*.
    """
    if cbook.iterable(window):
        x=window*detrend(x)
    else:
        x = window(detrend(x))
    z = np.fft.fft(x)
    a = 2.*np.pi*1j
    phase = a * np.random.rand(len(x))
    z = z*np.exp(phase)
    return np.fft.ifft(z).real 

def prctile(x, p = (0.0, 25.0, 50.0, 75.0, 100.0)):
    """
    Return the percentiles of *x*.  *p* can either be a sequence of
    percentile values or a scalar.  If *p* is a sequence, the ith
    element of the return sequence is the *p*(i)-th percentile of *x*.
    If *p* is a scalar, the largest value of *x* less than or equal to
    the *p* percentage point in the sequence is returned.
    """

    # This implementation derived from scipy.stats.scoreatpercentile
    def _interpolate(a, b, fraction):
        """Returns the point at the given fraction between a and b, where
        'fraction' must be between 0 and 1.
        """
        return a + (b - a)*fraction

    scalar = True
    if cbook.iterable(p):
        scalar = False
    per = np.array(p)
    values = np.array(x).ravel()  # copy
    values.sort()

    idxs = per /100. * (values.shape[0] - 1)
    ai = idxs.astype(np.int)
    bi = ai + 1
    frac = idxs % 1

    # handle cases where attempting to interpolate past last index
    cond = bi >= len(values)
    if scalar:
        if cond:
            ai -= 1
            bi -= 1
            frac += 1
    else:
        ai[cond] -= 1
        bi[cond] -= 1
        frac[cond] += 1

    return _interpolate(values[ai],values[bi],frac) 

def add_lines(self, levels, colors, linewidths, erase=True):
        '''
        Draw lines on the colorbar.

        *colors* and *linewidths* must be scalars or
        sequences the same length as *levels*.

        Set *erase* to False to add lines without first
        removing any previously added lines.
        '''
        y = self._locate(levels)
        igood = (y < 1.001) & (y > -0.001)
        y = y[igood]
        if cbook.iterable(colors):
            colors = np.asarray(colors)[igood]
        if cbook.iterable(linewidths):
            linewidths = np.asarray(linewidths)[igood]
        N = len(y)
        x = np.array([0.0, 1.0])
        X, Y = np.meshgrid(x, y)
        if self.orientation == 'vertical':
            xy = [zip(X[i], Y[i]) for i in xrange(N)]
        else:
            xy = [zip(Y[i], X[i]) for i in xrange(N)]
        col = collections.LineCollection(xy, linewidths=linewidths)

        if erase and self.lines:
            for lc in self.lines:
                lc.remove()
            self.lines = []
        self.lines.append(col)
        col.set_color(colors)
        self.ax.add_collection(col) 

def set_ticks(self, ticks, update_ticks=True):
        """
        set tick locations. Tick locations are updated immediately unless
        update_ticks is *False*. To manually update the ticks, call
        *update_ticks* method explicitly.
        """
        if cbook.iterable(ticks):
            self.locator = ticker.FixedLocator(ticks, nbins=len(ticks))
        else:
            self.locator = ticks

        if update_ticks:
            self.update_ticks()