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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def plot_markov_model( P, pos=None, state_sizes=None, state_scale=1.0, state_colors='#ff5500', state_labels='auto', minflux=1e-6, arrow_scale=1.0, arrow_curvature=1.0, arrow_labels='weights', arrow_label_format='%2.e', max_width=12, max_height=12, figpadding=0.2, show_frame=False, ax=None, **textkwargs):
 r"""Network representation of MSM transition matrix This visualization is not optimized for large matrices. It is meant to be used for the visualization of small models with up to 10-20 states, e.g. obtained by a HMM coarse-graining. If used with large network, the automatic node positioning will be very slow and may still look ugly. Parameters P : ndarray(n,n) or MSM object with attribute 'transition matrix' Transition matrix or MSM object pos : ndarray(n,2), optional, default=None User-defined positions to draw the states on. If not given, will try to place them automatically. state_sizes : ndarray(n), optional, default=None User-defined areas of the discs drawn for each state. If not given, the stationary probability of P will be used. state_colors : string, ndarray(n), or list, optional, default='#ff5500' (orange) string : a Hex code for a single color used for all states array : n values in [0,1] which will result in a grayscale plot list : of len = nstates, with a color for each state. The list can mix strings, RGB values and hex codes, e.g. :py:obj:`state_colors` = ['g', 'red', [.23, .34, .35], '#ff5500'] is possible. state_labels : list of strings, optional, default is 'auto' A list with a label for each state, to be displayed at the center of each node/state. If left to 'auto', the labels are automatically set to the state indices. minflux : float, optional, default=1e-6 The minimal flux (p_i * p_ij) for a transition to be drawn arrow_scale : float, optional, default=1.0 Relative arrow scale. Set to a value different from 1 to increase or decrease the arrow width. arrow_curvature : float, optional, default=1.0 Relative arrow curvature. Set to a value different from 1 to make arrows more or less curved. arrow_labels : 'weights', None or a ndarray(n,n) with label strings. Optional, default='weights' Strings to be placed upon arrows. If None, no labels will be used. If 'weights', the elements of P will be used. If a matrix of strings is given by the user these will be used. arrow_label_format : str, optional, default='%10.2f' The numeric format to print the arrow labels max_width = 12 The maximum figure width max_height = 12 The maximum figure height figpadding = 0.2 The relative figure size used for the padding show_frame: boolean (default=False) Draw a frame around the network. ax : matplotlib Axes object, optional, default=None The axes to plot to. When set to None a new Axes (and Figure) object will be used. textkwargs : optional argument for the text of the state and arrow labels. See http://matplotlib.org/api/text_api.html#matplotlib.text.Text for more info. The parameter 'size' refers to the size of the state and arrow labels and overwrites the matplotlib default. The parameter 'arrow_label_size' is only used for the arrow labels; please note that 'arrow_label_size' is not part of matplotlib.text.Text's set of parameters and will raise an exception when passed to matplotlib.text.Text directly. Returns ------- fig, pos : matplotlib.Figure, ndarray(n,2) a Figure object containing the plot and the positions of states. Can be used later to plot a different network representation (e.g. the flux) Examples -------- """

    from msmtools import analysis as msmana
    if isinstance(P, _np.ndarray):
        P = P.copy()
    else:
        # MSM object? then get transition matrix first
        P = P.transition_matrix.copy()
    if state_sizes is None:
        state_sizes = msmana.stationary_distribution(P)
    if minflux > 0:
        F = _np.dot(_np.diag(msmana.stationary_distribution(P)), P)
        I, J = _np.where(F < minflux)
        P[I, J] = 0.0
    plot = NetworkPlot(P, pos=pos, ax=ax)
    fig = plot.plot_network(
        state_sizes=state_sizes, state_scale=state_scale, state_colors=state_colors,
        state_labels=state_labels, arrow_scale=arrow_scale, arrow_curvature=arrow_curvature,
        arrow_labels=arrow_labels, arrow_label_format=arrow_label_format, max_width=max_width,
        max_height=max_height, figpadding=figpadding, xticks=False, yticks=False,
        show_frame=show_frame, **textkwargs)
    return fig, plot.pos