pos = g.new_vertex_property('vector<float>')

for v in g.vertices():

r, c = int(int(v) / shape[1]), int(v) % shape[1]

pos[v] = np.array([r, c])

node_color_info={},

node_shape_info={},

node_size_info={},

edge_color_info={},

edge_pen_width_info={},

node_text_info={},

color_map=mpl.cm.Reds,

ax=None,

output=None):

def populate_property(dtype, info, on_edge=False):

if on_edge:

prop = g.new_edge_property(dtype)

else:

prop = g.new_vertex_property(dtype)

prop.set_value(info['default'])

del info['default']

for entries, v in info.items():

if on_edge:

for n in entries:

prop[g.edge(*n)] = v

else:

if dtype not in {'int', 'float'}:

for n in entries:

prop[n] = v

else:

prop.a[list(entries)] = v

return prop

# vertex color is a bit special

# can pass both ndarray and RGB

# for ndarray, it converted to cm.Reds

# vertex_fill_color.set_value(node_color_info['default'])

if isinstance(node_color_info, dict) and 'default' in node_color_info:

del node_color_info['default']

# colormap to convert to rgb

norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0)

m = cm.ScalarMappable(norm=norm, cmap=color_map)

if not isinstance(node_color_info, np.ndarray):

assert isinstance(node_color_info, dict)

vertex_fill_color = g.new_vertex_property('vector<float>')

for entries, v in node_color_info.items():

if isinstance(v, np.ndarray):

assert len(entries) == len(v)

for e, vv in zip(entries, v):

# convert to RGB

vertex_fill_color[e] = m.to_rgba(vv)

else:

for e in entries:

vertex_fill_color[e] = v

else:

vertex_fill_color = g.new_vertex_property('float')

vertex_fill_color.a = node_color_info

vertex_size = populate_property('int', node_size_info)

vertex_shape = populate_property('string', node_shape_info)

vertex_text = populate_property('string', node_text_info)

edge_color = populate_property('string', edge_color_info, True)

edge_pen_width = populate_property('float', edge_pen_width_info, True)

graph_draw(g, pos=pos,

vertex_fill_color=vertex_fill_color,

vertex_size=vertex_size,

vertex_shape=vertex_shape,

edge_color=edge_color,

edge_pen_width=edge_pen_width,

vertex_text=vertex_text,

mplfig=ax,

vcmap=color_map,

bg_color=[256, 256, 256, 256],

size_multiplier=1.0, edge_width_multiplier=1.0,

deemphasize_hidden_infs=False):

source = cascade_source(c)

inf_nodes = infected_nodes(c)

hidden_infs = set(inf_nodes) - set(X)

node_color_info = OrderedDict()

node_color_info[tuple(X)] = COLOR_BLUE

if not deemphasize_hidden_infs:

node_color_info[tuple(hidden_infs)] = COLOR_YELLOW

node_color_info[(source, )] = COLOR_GREEN

node_color_info['default'] = COLOR_WHITE

node_shape_info = OrderedDict()

node_shape_info[tuple(X)] = SHAPE_SQUARE

node_shape_info['default'] = SHAPE_CIRCLE

node_shape_info[(source, )] = SHAPE_PENTAGON

node_size_info = OrderedDict()

node_size_info[tuple(X)] = 15 * size_multiplier

node_size_info[(source, )] = 20 * size_multiplier

if not deemphasize_hidden_infs:

node_size_info[tuple(hidden_infs)] = 12.5 * size_multiplier

node_size_info['default'] = 5 * size_multiplier

node_text_info = {'default': ''}

edge_color_info = {

'default': 'white'

}

edge_pen_width_info = {

'default': 2.0 * edge_width_multiplier

}

return {

'node_color_info': node_color_info,

'node_shape_info': node_shape_info,

'node_size_info': node_size_info,

'edge_color_info': edge_color_info,

'edge_pen_width_info': edge_pen_width_info,

'node_text_info': node_text_info

node_size=20,

node_shape=SHAPE_TRIANGLE,

indicator_type='text',

color=1.0, **kwargs):

s = default_plot_setting(g, c, X, **kwargs)

s['node_shape_info'][tuple(qs)] = node_shape

s['node_size_info'][tuple(qs)] = node_size

# s['node_color_info'][tuple(qs)] = color

if isinstance(indicator_type, str):

indicator_types = {indicator_type}

elif isinstance(indicator_type, Iterable):

indicator_types = set(indicator_type)

else:

indicator_types = []

if 'text' in indicator_types:

for i, q in enumerate(qs):

s['node_text_info'][tuple([q])] = str(i)

if 'color' in indicator_types:

color_depth = np.zeros(len(qs), dtype=np.float)

for i, q in enumerate(qs):

color_depth[i] = i / len(qs)

s['node_color_info'][tuple(qs)] = color_depth

inf_nodes = infected_nodes(c)

hidden_infs = set(inf_nodes) - set(X)

multipler = kwargs.get('size_multiplier', 1.0)

s = default_plot_setting(g, c, X, **kwargs)

if False:

s['node_size_info'][tuple(X)] = 15

s['node_size_info'][tuple(hidden_infs)] = 15

s['node_size_info']['default'] = 7.5

else:

s['node_size_info'][tuple(X)] = 10 * multipler

s['node_size_info'][tuple(hidden_infs)] = 10 * multipler

s['node_size_info']['default'] = 10 * multipler

s['node_color_info'] = weight

"""illustrate the querying process"""

def __init__(self, g, obs, c,

pos,

output_size=(300, 300),

vertex_size=20,

vcmap=mpl.cm.Reds):

self.g_bak = g

self.g = remove_filters(g) # refresh

self.obs = obs

self.c = c

self.pos = pos

self.output_size = output_size

self.vertex_size = vertex_size

self.vcmap = vcmap

self.inf_nodes = set((self.c >= 0).nonzero()[0])

self.obs_inf = set(self.obs)

self.obs_uninf = set()

self.hidden_inf = self.inf_nodes - self.obs_inf

self.hidden_uninf = set(extract_nodes(g)) - self.inf_nodes

def add_query(self, query):

if self.c[query] >= 0: # infected

self.obs_inf |= {query}

self.hidden_inf -= {query}

else:

self.obs_uninf |= {query}

self.hidden_uninf -= {query}

observe_uninfected_node(self.g, query, self.obs_inf)

def plot_snapshot(self, query, n_samples, ax=None):

"""plot one snap shot using one query and update node infection/observailability

n_samples: num of samples used for inference

"""

self.add_query(query)

probas = infection_probability(self.g, self.obs_inf, n_samples=n_samples)

# print(probas.shape)

vcolor = self.node_colors(probas)

vcolor[query] = 1 # highlight query

vshape = self.node_shapes(query)

vshape[query] = SHAPE_PENTAGON # hack, override it

graph_draw(self.g_bak, # use the very earliest graph

pos=self.pos,

vcmap=self.vcmap,

output_size=self.output_size,

vertex_size=self.vertex_size,

vertex_fill_color=vcolor,

vertex_shape=vshape,

mplfig=ax)

def node_properties_by_group(self, g, value_groups, dtype, default_val):

"""

value_groups: dict of (dtype, list): (value, list of nodes)

"""

vprop = g.new_vertex_property(dtype)

vprop.set_value(default_val)

for val, grp in value_groups:

for i in grp:

vprop[i] = val

return vprop

def node_shapes(self, query):

groups = [(SHAPE_PENTAGON, [query]),

(SHAPE_SQUARE, self.obs_inf | self.obs_uninf),

(SHAPE_CIRCLE, self.hidden_inf),

(SHAPE_TRIANGLE, self.hidden_uninf)]

return self.node_properties_by_group(self.g_bak, groups, 'string', SHAPE_CIRCLE)

def node_colors(self, probas):

color = self.g_bak.new_vertex_property('float')

assert len(probas) == len(color.a)

color.a = probas

# might be fewer vertices in g than g_bak

# because of vertex removal

# for v, proba in zip(self.g.vertices(), probas):

# color[v] = proba

def __init__(self, g,

pos,

output_size=(300, 300),

vcmap=mpl.cm.Reds):

self.g = g

self.pos = pos

self.output_size = output_size

self.vcmap = vcmap

def plot(self, c, X, probas, interception_func=None, setting_kwargs={},

**kwargs):

setting = heatmap_plot_setting(self.g, c, X, probas,

**setting_kwargs)

if interception_func is not None:

interception_func(setting)

visualize(self.g, self.pos,

**setting,

ax.set_prop_cycle(cycler('color', [COLOR_ORANGE, COLOR_PINK, COLOR_BLUE, COLOR_GREEN, COLOR_YELLOW,

COLOR_BLACK, COLOR_GREY]) +

cycler('linestyle', ['-', ':', '--', '-.', '-', ':', '-']) +

cycler('marker', ['o', '*', '^', 'v', 's', 'd', 'p']) +

"""smooth the data using a window with requested size.

This method is based on the convolution of a scaled window with the signal.

The signal is prepared by introducing reflected copies of the signal

(with the window size) in both ends so that transient parts are minimized

in the begining and end part of the output signal.

input:

x: the input signal

window_len: the dimension of the smoothing window; should be an odd integer

window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'

flat window will produce a moving average smoothing.

output:

the smoothed signal

example:

t=linspace(-2,2,0.1)

x=sin(t)+randn(len(t))*0.1

y=smooth(x)

see also:

numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve

scipy.signal.lfilter

TODO: the window parameter could be the window itself if an array instead of a string

NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.

"""

if x.ndim != 1:

raise ValueError("smooth only accepts 1 dimension arrays.")

if x.size < window_len:

raise ValueError("Input vector needs to be bigger than window size.")

if window_len<3:

return x

if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:

raise ValueError("Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")

s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]

#print(len(s))

if window == 'flat': #moving average

w=np.ones(window_len,'d')

else:

w=eval('np.'+window+'(window_len)')

y=np.convolve(w/w.sum(),s,mode='valid')