pyemma.plots.NetworkPlot¶
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class
pyemma.plots.
NetworkPlot
(A, pos=None, xpos=None, ypos=None, ax=None)¶ Plot of network with nodes and arcs
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__init__
(A, pos=None, xpos=None, ypos=None, ax=None)¶ Parameters: - A (ndarray(n,n)) – weight matrix or adjacency matrix of the network to visualize
- pos (ndarray(n,2)) – user-defined positions
- xpos (ndarray(n,)) – user-defined x-positions
- ypos (ndarray(n,)) – user-defined y-positions
- 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.
Examples
We define first define a reactive flux by taking the following transition matrix and computing TPT from state 2 to 3.
>>> import numpy as np >>> P = np.array([[0.8, 0.15, 0.05, 0.0, 0.0], ... [0.1, 0.75, 0.05, 0.05, 0.05], ... [0.05, 0.1, 0.8, 0.0, 0.05], ... [0.0, 0.2, 0.0, 0.8, 0.0], ... [0.0, 0.02, 0.02, 0.0, 0.96]]) >>> from pyemma import msm >>> F = msm.tpt(msm.markov_model(P), [2], [3])
now plot the gross flux >>> NetworkPlot(F.gross_flux).plot_network() # doctest:+ELLIPSIS <matplotlib.figure.Figure...
Methods
__init__
(A[, pos, xpos, ypos, ax])param A: weight matrix or adjacency matrix of the network to visualize layout_automatic
()plot_network
([state_sizes, state_scale, ...])Draws a network using discs and curved arrows. -
plot_network
(state_sizes=None, state_scale=1.0, state_colors='#ff5500', state_labels='auto', arrow_scale=1.0, arrow_curvature=1.0, arrow_labels='weights', arrow_label_format='%10.2f', max_width=12, max_height=12, figpadding=0.2, xticks=False, yticks=False, show_frame=False, **textkwargs)¶ Draws a network using discs and curved arrows.
The thicknesses and labels of the arrows are taken from the off-diagonal matrix elements in A.
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