def ds_plot(G,
layout='circular',
method='direct',
bw=0.05,
name='',
kwargs=cvsopts):
'''
Returns graph plot using datashader layout
fir nodes positions from circular (default),
forceatlas2 or random position layout.
Accepted vars:
G => network graph obj
layout => circular; forceatlas2; random
method => connect; bundle
bw => initial bandwith for bundled
name => title or label
'''
n, e = nx_layout(G)
nodes = ds_layout(n, e, layout)
if method == 'bundle':
edges = hammer_bundle(nodes, e, initial_bandwidth=bw)
else: # lightweight
edges = connect_edges(nodes, e)
return graph_plot(nodes, edges, name, kwargs=kwargs)
def newPlotlyGeneration(relayoutData):
f = go.Figure(data=[{'x': [relayoutData['xaxis.range[0]'], relayoutData['xaxis.range[1]']],
'y': [relayoutData['yaxis.range[0]'], relayoutData['yaxis.range[1]']],
'mode': 'markers',
'marker': {'opacity': 0}}], # invisible trace to init axes and to support autoresize
layout={'width': 800,
'height': 600}
)
newImg = newGraphplot(fd, connect_edges(fd,edges), x_range=[relayoutData['xaxis.range[0]'], relayoutData['xaxis.range[1]']], y_range=[relayoutData['yaxis.range[0]'], relayoutData['yaxis.range[1]']])
newPil = tf.Image(newImg).to_pil()
#in_mem_file = io.BytesIO()
#newPil.save(in_mem_file, format = "PNG")
# reset file pointer to start
#in_mem_file.seek(0)
#img_bytes = in_mem_file.read()
#base64_encoded_result_bytes = base64.b64encode(img_bytes)
#base64_encoded_result_str = base64_encoded_result_bytes.decode('ascii')
f.layout.images = [go.layout.Image(
source = newPil, # plotly now performs auto conversion of PIL image to png data URI
xref = "x",
yref = "y",
x = relayoutData['xaxis.range[0]'],
y = relayoutData['yaxis.range[1]'],
sizex = relayoutData['xaxis.range[1]'] - relayoutData['xaxis.range[0]'],
sizey = relayoutData['yaxis.range[1]'] - relayoutData['yaxis.range[0]'],
#sizing = "stretch",
layer = "below")]
return f
def zoomButton():
jsonResp = request.get_json()
cvsopts['plot_height'] = int(jsonResp["height"])
cvsopts['plot_width'] = int(jsonResp["width"])
current_x_range = tuple(
(float(jsonResp["current_x_low"]), float(jsonResp["current_x_high"])))
current_y_range = tuple(
(float(jsonResp["current_y_low"]), float(jsonResp["current_y_high"])))
current_y_low, current_y_high = current_y_range[0], current_y_range[1]
current_x_low, current_x_high = current_x_range[0], current_x_range[1]
dsPlot = newGraphplot(randomloc,
connect_edges(randomloc, edges),
x_range=current_x_range,
y_range=current_y_range)
#convert datashder image to png
back_img = tf.Image(dsPlot).to_pil()
in_mem_file = io.BytesIO()
back_img.save(in_mem_file, format="PNG")
# reset file pointer to start
in_mem_file.seek(0)
img_bytes = in_mem_file.read()
base64_encoded_result_bytes = base64.b64encode(img_bytes)
base64_encoded_result_str = 'data:image/png;base64,' + base64_encoded_result_bytes.decode(
'ascii')
return jsonify(newImage=base64_encoded_result_str,
x_low=current_x_range[0],
y_low=current_y_range[0],
x_high=current_x_range[1],
y_high=current_y_range[1])
def nx_plot(graph, name=""):
print(graph.name, len(graph.edges))
nodes, edges = nx_layout(graph)
direct = connect_edges(nodes, edges)
bundled_bw005 = hammer_bundle(nodes, edges)
bundled_bw030 = hammer_bundle(nodes, edges, initial_bandwidth=0.30)
return [
graphplot(nodes, direct, graph.name),
graphplot(nodes, bundled_bw005, "Bundled bw=0.05"),
graphplot(nodes, bundled_bw030, "Bundled bw=0.30")
]
def zoomButton():
jsonResp = request.get_json()
cvsopts['plot_height'] = int(jsonResp["height"])
cvsopts['plot_width'] = int(jsonResp["width"])
current_x_range = tuple((float(jsonResp["current_x_low"]), float(jsonResp["current_x_high"])))
current_y_range = tuple((float(jsonResp["current_y_low"]), float(jsonResp["current_y_high"])))
current_y_low, current_y_high = current_y_range[0], current_y_range[1]
current_x_low, current_x_high = current_x_range[0], current_x_range[1]
dsPlot = newGraphplot(randomloc, connect_edges(randomloc,edges), x_range=current_x_range, y_range=current_y_range)
#convert datashder image to png
back_img = tf.Image(dsPlot).to_pil()
in_mem_file = io.BytesIO()
back_img.save(in_mem_file, format = "PNG")
# reset file pointer to start
in_mem_file.seek(0)
img_bytes = in_mem_file.read()
base64_encoded_result_bytes = base64.b64encode(img_bytes)
base64_encoded_result_str = 'data:image/png;base64,' + base64_encoded_result_bytes.decode('ascii')
return jsonify(newImage=base64_encoded_result_str, x_low=current_x_range[0], y_low=current_y_range[0], x_high=current_x_range[1], y_high=current_y_range[1])
def plot_graph(self, method='networkx', options={}):
digr = self.make_graph()
if method == 'networkx':
dflt_options = {
'node_color': 'black',
'node_size': 20,
'width': 1,
}
opts = {**dflt_options, **options}
nx.draw_kamada_kawai(digr, **opts)
elif method == 'datashader':
digr = digr.to_undirected()
nodes = pd.DataFrame(digr.nodes(), columns=['name'])
edges = pd.DataFrame(digr.edges(), columns=['source', 'target'])
iterations = {
'iterations': int(np.ceil(np.sqrt(len(nodes)))),
**options
}['iterations']
fd = forceatlas2_layout(nodes, edges, iterations=iterations)
bundle = {'bundle': False, **options}['bundle']
if bundle:
return graphplot(fd, hammer_bundle(fd, edges))
else:
return graphplot(fd, connect_edges(fd, edges))
#combines nodes and edges in datashader image
def graphplot(nodes, edges, name="", canvas=None, cat=None):
if canvas is None:
xr = nodes.x.min(), nodes.x.max()
yr = nodes.y.min(), nodes.y.max()
canvas = ds.Canvas(x_range=xr, y_range=yr, **cvsopts)
np = nodesplot(nodes, name + " nodes", canvas, cat)
#print(np)
ep = edgesplot(edges, name + " edges", canvas)
#print(ep)
return tf.stack(ep, np, how="over", name=name)
fd = forcedirected
fd_d = graphplot(fd, connect_edges(fd,edges))
#convert datashder image to png
back_img = tf.Image(fd_d).to_pil()
x_range=fd.x.min(), fd.x.max()
y_range=fd.y.min(), fd.y.max()
#xr = nodes.x.min(), nodes.x.max()
#yr = nodes.y.min(), nodes.y.max()
plot_height=600
plot_width=800
#Create initial plotly graph with datashader image as background
name="",
canvas=None,
cat=None,
margin=0.05):
if canvas is None:
xr = nodes.x.min() - margin, nodes.x.max() + margin
yr = nodes.y.min() - margin, nodes.y.max() + margin
canvas = ds.Canvas(x_range=xr, y_range=yr, **cvsopts)
np = my_nodesplot(nodes, name + " nodes", canvas, cat)
ep = edgesplot(edges, name + " edges", canvas)
return tf.stack(ep, np, how="over", name=name)
forcedirected = forceatlas2_layout(nodes, edges)
fd = forcedirected
fd.iat[0, 2] = (fd.x.min() + fd.x.max()) / 2 # center focus node on x
fd.iat[0, 3] = 1 # center focus node on y
## save image?!
image = tf.Image(
my_graphplot(fd,
connect_edges(fd, edges),
"Force-directed",
cat="type",
margin=0.02))
export_image(image, filename=file)
os.rename(file_name, saved_file_name)
#Tedges.head()
Tedges.source = pd.to_numeric(Tedges.source)
Tedges.target = pd.to_numeric(Tedges.target)
# In[9]:
#Tedges.isnull().values.any()
Tnodes = adrs[['GEO_ID', 'LATITUDE', 'LONGITUDE']]
Tnodes.columns = ['id', 'x', 'y']
Tnodes.set_index('id', inplace=True)
#Tnodes.head()
#location2.loc["GEOID","LATITUDE", "LONGITUDE"]]
# In[10]:
direct = connect_edges(Tnodes, Tedges[['source', 'target']])
# In[28]:
location2.COORDS2
# In[32]:
# =============================================================================
# Plotting with Holoview and Datashade
# =============================================================================
get_ipython().magic(u'output size=150')
points = hv.Points((location2.LATITUDE, location2.LONGITUDE),
label="Buildings")
paths = hv.Path([direct])
def my_graphplot(nodes, edges, name="", canvas=None, cat=None, margin=0.05):
if canvas is None:
xr = nodes.x.min() - margin, nodes.x.max() + margin
yr = nodes.y.min() - margin, nodes.y.max() + margin
canvas = ds.Canvas(x_range=xr, y_range=yr, **cvsopts)
np = my_nodesplot(nodes, name + " nodes", canvas, cat)
ep = edgesplot(edges, name + " edges", canvas)
return tf.stack(ep, np, how="over", name=name)
forcedirected = forceatlas2_layout(nodes, edges)
fd = forcedirected
fd.iat[0,2] = (fd.x.min() + fd.x.max())/2 # center focus node on x
fd.iat[0,3] = 1 # center focus node on y
## save image?!
from datashader.utils import export_image
image = tf.Image(my_graphplot(fd, connect_edges(fd,edges), "Force-directed", cat="type", margin=0.02))
file = company_name.replace(" ","_")
export_image(image, filename=file)
import os
file_name = file + ".png"
os.rename(file_name, '../../ent-out/' + file_name)