syrte

from __future__ import division, print_function, absolute_import
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Arc
from matplotlib.collections import PatchCollection

__all__ = ['arcs']

def arcs(x, y, w, h=None, rot=0.0, theta1=0.0, theta2=360.0,
         c='b', vmin=None, vmax=None, **kwargs):

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def siground(x, n):
    from math import log10, floor
    x, n = float(x), int(n)
    assert n > 0
    if x == 0:
        return ("%%.%if" % (n - 1)) % x
    m = 10 ** floor(log10(abs(x)))
    x = round(x / m, n - 1) * m
    p = floor(log10(abs(x)))

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def mid(x, base=None):
    '''Return mean value of adjacent member of an array.
    Useful for plotting bin counts.
    '''
    if base is None:
        x = np.asarray(x)
        return  (x[1:] + x[:-1])/2.
    elif base == 'log':
        return np.exp(mid(np.log(x)))
    elif base == 'exp':

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def quantile(a, q=None, nsig=None, weights=None, sorted=False, nmin=0):
    '''
    nmin: int
        Set `nmin` if you want a more reliable result. 
        Return `nan` when the tail probability is less than `nmin/a.size`.
    '''
    import numpy as np
    from scipy.stats import norm
    
    a = np.asarray(a)

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import numpy as np
import scipy.ndimage as ndimage

__all__ = ["EqualGridInterpolator"]


class EqualGridInterpolator(object):
    """
    Interpolation on a equal spaced regular grid in arbitrary dimensions.
    Fock from https://github.com/JohannesBuchner/regulargrid

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def findroot(y0, x, y):
    """
    y0: scalar
    x: 1D array
    y: function or 1D array
    """
    import numpy as np
    x = np.asarray(x)
    assert x.ndim == 1
    

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from matplotlib import pyplot as plt


def twin(show="xy", ax=None):
    """
    Call signature::
      ax = twin()
    create a twin of Axes for generating a plot with a shared
    x-axis and y-axis.  
    The x-axis (y-axis) of ax will have ticks on bottom (left)

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from __future__ import division, print_function, absolute_import

import warnings
import numpy as np
from collections import namedtuple

BinStats = namedtuple('BinStats',
                      ('stats', 'bin_edges', 'bin_count'))

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import numpy as np
from matplotlib import pyplot as plt

def hist_stats(x, y, bins=10, func=np.mean, nmin=None, **kwds):
    """
    x = np.random.rand(1000)
    hist_stats(x, x, func=lambda x:np.percentile(x, [15,50,85]), 
               ls=['--', '-', '--'], lw=[1, 2, 1])
    """
    stats, edges, count = binstats(x, y, bins=bins, func=func, nmin=nmin)

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from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D

__all__ = ["abline", "ABLine2D"]


def abline(a, b, *args, **kwargs):
    """