def figure_to_svg(fig): tempFile = tempfile.NamedTemporaryFile(suffix='.svg.tmp', delete=False) canvas = FigureCanvasSVG(fig) canvas.print_svg(tempFile.name) svgImg = tempFile.read() os.remove(tempFile.name) return svgImg
def get_graph_svg(guid):
"""Endpoint returning matplotlib svg graph
---
parameters:
- name: guid
in: path
required: true
type: string
- name: reference
in: query
type: string
required: false
- name: distance
in: query
type: string
required: false
- name: quality
in: query
type: string
required: false
responses:
200:
description:
"""
reference = request.args.get('reference')
if not reference: reference = cfg['default_reference']
quality = request.args.get('quality')
if not quality: quality = cfg['default_quality']
cutoff = request.args.get('cutoff')
if cutoff and int(cutoff) > 10:
cutoff = 10
if cutoff:
(xs, ys) = graph3(guid, reference, quality, cfg['elephantwalkurl'],
int(cutoff))
else:
(xs, ys) = graph2(guid, reference, quality, cfg['elephantwalkurl'])
if len(ys) == 0:
slopes = []
else:
slopes = [0]
print(xs)
for n in range(len(xs)):
if n == 0: continue
slopes.append((ys[n] - ys[n - 1]) / (xs[n] - xs[n - 1]))
fig = Figure(figsize=(12, 7), dpi=100)
fig.suptitle("Sample: {0}, reference: {1}, quality: {2}, ew: {3}".format(
guid, reference, quality, cfg['elephantwalkurl']))
ax = fig.add_subplot(111)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.plot(xs, ys, 'gx-', linewidth=1)
ax.plot(xs, slopes, 'r-', linewidth=1)
ax.set_xlabel("Distance")
ax.set_ylabel("Neighbours")
canvas = FigureCanvas(fig)
svg_output = StringIO()
canvas.print_svg(svg_output)
response = make_response(svg_output.getvalue())
response.headers['Content-Type'] = 'image/svg+xml'
return response
def malignancy_plot(age, tumour_size):
# Scale age and tumour_size (note: output is numpy array)
age_tumour_size = [[age] + [tumour_size]]
age_tumour_size_scaled = malign_scaler.transform(age_tumour_size)
# Create malignancy feature array
malign_features = [age_tumour_size_scaled[0].tolist()]
# Predict malignancy: 0 benign, 1 non-benign
yscore_cal = malign_clf.predict_proba(malign_features)
yscore_nonbenign = np.round(yscore_cal[0][1]*100, 1)
ypred_cal = np.array(yscore_cal[:, 1] > 0.05, dtype=int)[0] # threshold = 5%
# Select title and title colour
red = '#CC333F'
blue = '#1693A7'
if ypred_cal == 0:
title = 'Benign'
title_colour = blue
elif ypred_cal == 1:
title = 'Borderline malignancy / Malignant'
title_colour = red
##### Plot Figure #####
sns.set(font_scale=1.2)
sns.set_style('white',{'axes.linewidth': 2.5, 'axes.edgecolor': '#D3D3D3'})
# Create plot
fig = Figure()
ax = fig.add_subplot(1, 1, 1)
# Draw plot
sns.kdeplot(malign_valid_dist, color='orange', shade=True,
gridsize=500, bw='silverman', ax=ax)
ax.vlines(yscore_nonbenign, 0, 0.10, color='#D70E08', zorder=10) # prediction
txt = ax.text(yscore_nonbenign, 0.11, ' ' + str(yscore_nonbenign) + '%', color='#D70E08',
zorder=10, horizontalalignment='center') # prediction label
txt.set_path_effects([path_effects.withStroke(linewidth=3, foreground='w')]) # label outline
ax.vlines(5, 0, 0.25, color='#cccccc', zorder=10, linestyles='dashed') # threshold (5%)
ax.set_title('Prediction: ' + title, color=title_colour, fontsize=14)
ax.set_ylabel('Probability density', labelpad=10)
ax.set_xlabel('Probability (%)', labelpad=10)
ax.set_xlim(0, 30)
ax.set_ylim(0, 0.4)
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(2)
sns.despine(ax=ax)
fig.set_tight_layout(True)
# Write plot to SVG
canvas = FigureCanvas(fig)
output = BytesIO()
# canvas.print_svg('test.svg') # For testing fig output locally
canvas.print_svg(output)
response = make_response(output.getvalue())
response.mimetype = 'image/svg+xml'
return response
def canvas(self):
type = self.get("imageType", "png")
fig = Figure()
if type == "png":
canvas = FigureCanvasAgg(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
elif type == "svg":
canvas = FigureCanvasSVG(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
elif type == "pdf":
canvas = FigureCanvasPdf(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
elif type == "ps" or type == "eps":
canvas = FigureCanvasPS(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
else:
raise "Invalid render target requested"
# Set basic figure parameters
dpi = float(self.get('dpi'))
(w, h) = (float(self.get('width')), float(self.get('height')))
(win, hin) = (w/dpi, h/dpi)
fig.set_size_inches(win, hin)
fig.set_dpi(dpi)
fig.set_facecolor('white')
return (fig, canvas, w, h)
def equations_plot_svg(f, g):
print(f, g)
x = np.linspace(-10, 10, 1000000)
y1 = ne.evaluate(f)
y2 = ne.evaluate(g)
chart, curve = plt.subplots()
# Disegno la curva
curve.plot(x, y1, 'blue', linewidth=2)
curve.plot(x, y2, 'green', linewidth=2)
intersetions_points = []
idx = np.argwhere(np.diff(np.sign(y1 - y2))).flatten()
for i in idx:
print(y2[i])
if not np.isnan(y1[i]) and not np.isnan(y2[i]):
intersetions_points.append(np.around(x[i], decimals=2))
plt.plot(x[i], y1[i], 'ro')
intesetion_point = '; '.join([str(elem) for elem in intersetions_points])
chart.text(.1, .9, f'Risultato: {intesetion_point}')
output = io.BytesIO()
FigureCanvasSVG(chart).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def _test_determinism_save(filename, usetex):
# This function is mostly copy&paste from "def test_visibility"
# To require no GUI, we use Figure and FigureCanvasSVG
# instead of plt.figure and fig.savefig
from matplotlib.figure import Figure
from matplotlib.backends.backend_svg import FigureCanvasSVG
from matplotlib import rc
rc('svg', hashsalt='asdf')
rc('text', usetex=usetex)
fig = Figure()
ax = fig.add_subplot(111)
x = np.linspace(0, 4 * np.pi, 50)
y = np.sin(x)
yerr = np.ones_like(y)
a, b, c = ax.errorbar(x, y, yerr=yerr, fmt='ko')
for artist in b:
artist.set_visible(False)
ax.set_title('A string $1+2+\\sigma$')
ax.set_xlabel('A string $1+2+\\sigma$')
ax.set_ylabel('A string $1+2+\\sigma$')
FigureCanvasSVG(fig).print_svg(filename)
def fugure_to_image(self, fig: FigureCanvasSVG, svg: bool) -> io.BytesIO:
output = io.BytesIO()
if svg:
FigureCanvasSVG(fig).print_svg(output)
else:
FigureCanvasAgg(fig).print_png(output)
return output
def plot_pie(parts=5):
import matplotlib.pyplot as plt
from utils.rndm import randomword
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = [randomword(6) for i in range(parts)]
sizes = [random.randint(1, 101) for i in range(parts)] #15, 30, 45, 10]
explode = [
0
] * parts #(0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
explode[random.randint(0, parts)] = 0.1
fig1, ax1 = plt.subplots()
ax1.pie(sizes,
explode=explode,
labels=labels,
autopct='%1.1f%%',
shadow=True,
startangle=90)
ax1.axis(
'equal') # Equal aspect ratio ensures that pie is drawn as a circle.
output = io.BytesIO()
FigureCanvasSVG(fig1).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def cli():
args = get_args()
fig = matplotlib.figure.Figure(figsize=(args.dimensions[0] / 2.54,
args.dimensions[1] / 2.54))
ax = fig.add_subplot(1, 1, 1, frameon=False)
if args.svg:
FigureCanvasSVG(fig)
else:
FigureCanvasAgg(fig)
table = pandas.read_table(args.table, sep=" ")
args.table.close()
spider(args.algorithm,
args.problem,
args.statistic,
args.metric,
table,
ax,
draw_ci=args.draw_ci)
fn = filename(args.algorithm, args.problem, args.statistic, args.metric)
if args.svg:
fn = fn + ".svg"
else:
fn = fn + ".png"
fig.savefig(os.path.join(args.output_dir, fn))
def main(restaurant, order, show_count, token):
restaurant_id = restaurant
fig = get_statistics_per_restaurant(restaurant_id, Orders.all(token),
order, int(show_count))
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return output.getvalue()
def plot_png(id):
response = requests.get(APIurl + 'getDataLatest?id={}&nVals={}'.format(id,10))
data = pd.read_json(response.text)
fig, output = createPlot(data)
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def polar_pie(parts):
"""
=======================
Pie chart on polar axis
=======================
Demo of bar plot on a polar axis.
"""
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
# Compute pie slices
N = 20
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = 10 * np.random.rand(N)
width = np.pi / 4 * np.random.rand(N)
ax = plt.subplot(111, projection='polar')
bars = ax.bar(theta, radii, width=width, bottom=0.0)
# Use custom colors and opacity
for r, bar in zip(radii, bars):
bar.set_facecolor(plt.cm.viridis(r / 10.))
bar.set_alpha(0.5)
#
# plt.savefig('foo.png')
# plt.close()
fig.savefig('temp.png', dpi=fig.dpi)
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def build_graph_multiproc(chunk,lstOfwavelengths,pltcodeWithSuffix):
global lstOfPlots
with cx_Oracle.connect(user,passwrd,dsn_tns) as conn:
cr = conn.cursor()
for i, wid in chunk:
img = io.BytesIO()
fig = Figure(figsize=(0.6,0.6))
axis = fig.add_subplot(1,1,1)
absVals = getAllWellVals(cr,tableName_abs,pltcodeWithSuffix,wid)
if int(pltcodeWithSuffix[2:4]) < 18:
lstOfwavelengths = getLstOfwavelengths('WELL_ABSORBANCE',pltcodeWithSuffix)
axis.plot(lstOfwavelengths,absVals)
axis.set_title(f'{wid}',fontsize=9)
axis.title.set_position([.5, .6])
axis.tick_params(
which='both',
bottom=False,
left=False,
labelbottom=False,
labelleft=False)
FigureCanvasSVG(fig).print_svg(img)
result = img.getvalue()
try:
lstOfPlots[i] = (i,result)
except IndexError:
lstOfPlots.append((i,result))
def plot_svg(num_x_points=50):
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
x_points = range(num_x_points)
axis.plot(x_points, [random.randint(1, 30) for x in x_points])
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype='image/svg+xml')
def plot_heatmap(df):
#df = generate_df()
fig = Figure()
axes = fig.add_subplot(1, 1, 1)
axes.imshow(df)
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def draw_custom_graph(user_agents):
plot_x_y = []
# requests = log_parser.get_log_dicts(user_agent = r'SiteSucker.*')
for user_agent in user_agents:
requests = logger.get_log_dicts(user_agent=user_agent)
first_date = None
x = []
y = []
for index, request in enumerate(requests):
if index is not 0:
x.append(time_delta(request['datetime'], first_date))
else:
first_date = request['datetime']
x.append(0)
y.append(index)
plot_x_y.append({
'x': x,
'y': y
})
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
current_index = 0
for xy in plot_x_y:
# todo: Use different color (not only random) and add the ability to choose multiple user_agent.
ax.plot(
xy['x'], xy['y'],
color=graph_colors[current_index % len(graph_colors)],
label=user_agents[current_index]
)
ax.legend(framealpha=0.5, loc=4, prop={'size': 8})
current_index += 1
plt.xlabel('Delta Time (seconds)')
plt.ylabel('Number of requests')
ax.xaxis.set_major_formatter(formatter)
output = StringIO.StringIO()
canvas = FigureCanvas(fig)
canvas.print_svg(output)
return output
def plotdists(dists, labelmap=None, bins=20, alpha=0.5):
svg = StringIO()
fig = figure.Figure(figsize=PLOTSIZE)
ax = fig.add_subplot(1, 1, 1)
if labelmap is None:
labelmap = {key: key for key in dists.keys()}
for key, dist in dists.items():
ax.hist(dist, bins=bins, alpha=alpha, label=labelmap[key])
ax.legend()
FigureCanvasSVG(fig).print_svg(svg)
svg.seek(0)
return svg.read()
def plot_emes01g(V, f):
fig = Figure()
fig.set_size_inches(15, 12)
Torq, slip, Nr = torque_vs_slip(V, f)
axis = fig.add_subplot(2, 1, 1)
axis.xlabel = 'Nr'
axis.ylabel = 'Torque'
axis.plot(Nr, Torq, c='red', linewidth=4)
axis.grid()
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def plot_hvg(R, C):
C *= 1e-6
fig = Figure()
f, Gain, phase = high_pass_filter(R, C)
axis = fig.add_subplot(2, 1, 1)
axis.semilogx(f, Gain)
axis = fig.add_subplot(2, 1, 2)
axis.semilogx(f, phase)
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def total_dist_svg():
# https://gist.github.com/illume/1f19a2cf9f26425b1761b63d9506331f
fig = Figure(tight_layout=True)
ax = fig.add_subplot(1, 1, 1)
x = []
y = []
a_file = session["a_file"]
with open(a_file, 'rb') as af:
acts = pickle.load(af)
for i in range(len(acts)-1, -1, -1):
x.append(mdates.date2num([acts[i]['start_date']])[0])
dist = meters_to_miles(acts[i]['distance'])
if len(y) > 0:
y.append(y[-1] + dist)
else:
y.append(dist)
strava_orange = '#fc4c02'
ax.plot(x, y, color=strava_orange, marker='.')
ax.fill_between(x, y, color=strava_orange, alpha=0.5)
ax.set_xlabel('Date')
date_formatter = mdates.DateFormatter('%Y-%m-%d')
lower_lim_x = x[0]
upper_lim_x = x[-1]
x_buffer = 0.05
width = upper_lim_x - lower_lim_x
lower_lim_x -= x_buffer * width
upper_lim_x += x_buffer * width
ax.set_xlim(lower_lim_x, upper_lim_x)
x_ticks = ax.get_xticks().tolist()
ax.xaxis.set_major_locator(mticker.FixedLocator(x_ticks))
ax.set_xticklabels(x_ticks, rotation=45)
ax.xaxis.set_major_formatter(date_formatter)
ax.set_ylabel('Distance (Miles)')
y_buffer = 0.1
top_lim_y = (1 + y_buffer) * y[-1]
ax.set_ylim(0, top_lim_y)
gray = "#808080"
ax.plot([lower_lim_x, upper_lim_x], [y[-1], y[-1]], color=gray,
linestyle='--', alpha=0.5)
text_gap_x = 1
text_gap_y = 1
pt = (lower_lim_x, y[-1])
figure_pt = (pt[0] + text_gap_x, pt[1] + text_gap_y)
ax.annotate(f"{round(y[-1], 1)} Miles", pt, xytext=figure_pt)
ax.set_title('Total Distance')
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def drawPlot2(self):
self.axt.clear()
X = linspace(self.X_Mmin, self.X_Mmax, self.N)
Y = X
self.B = (self.k * self.b * X) / (2.0 * self.f_2)
self.A = (self.k * self.h * Y) / (2.0 * self.f_2)
BB, HH = meshgrid(self.B, self.A)
self.I = ((sin(BB) / BB)**2) * (sin(HH) / (HH))**2
self.axt.plot_surface(BB, HH, self.I, cmap='viridis', edgecolor='none')
self.axt.view_init(10, 45)
output = io.BytesIO()
FigureCanvasSVG(self.fig3).print_svg(output)
return output.getvalue()
def plot_svg(subject):
signal = np.loadtxt('uploads/'+subject+'.csv')
signal = signal[:10000]
_, _, r_peaks, _, _, _, _ = ecg.ecg(
signal=signal, sampling_rate=500, show=False)
fig = Figure(figsize=(12, 4))
axis = fig.add_subplot(1, 1, 1)
axis.plot(signal)
axis.plot(r_peaks, signal[r_peaks], 'r*')
axis.grid()
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def plotOhm():
a = []
b = []
reco = {}
for reco in ohm.allrecord:
b.append(reco['voltage'])
a.append(reco['current'])
figure = plt.figure(figsize=(10, 5))
plt.xlabel("Current in ampere")
plt.ylabel("Voltage (volt)")
plt.plot(a, b, linestyle='--', marker='o', color='b')
plt.grid()
output = io.BytesIO()
FigureCanvasSVG(figure).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def drawPlot(self):
self.axs.clear()
self.i = (1 / self.n**2) * ((sin(self.Bb) / self.Bb)**2) * (
sin(self.n * self.Aa) / sin(self.Aa))**2
self.sinc = (sin(self.Bb) / self.Bb)**2
self.axs.plot(self.x,
self.i,
'-k',
self.x,
self.sinc,
':b',
linewidth=2)
self.axs.set_xlim(self.X_Mmin, self.X_Mmax)
self.axs.set_xlabel('X (m)', fontsize=12, fontweight='bold')
self.axs.set_ylabel('I(X,Y)/I_0', fontsize=12, fontweight='bold')
output = io.BytesIO()
FigureCanvasSVG(self.fig2).print_svg(output)
return output.getvalue()
def plot_svg():
""" renders the plot on the fly.
"""
datapoints_dict = session["datapoints_dict"]
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
lists = sorted(datapoints_dict.items())
x, y = zip(*lists)
ts = []
for t in x:
print("#######", t)
ts.append(utils.get_time_str_from_epoch(float(t)))
print(x)
print(ts)
axis.plot(ts, y)
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def plot(self, gridspec=None, figsize=(11, 8.5), filetype="pdf"):
if gridspec is None:
gridspec = dict(left=0.05, right=0.94, wspace=0.05)
nplots = len(self.plotspecs)
fig = mpl.figure.Figure(figsize=figsize)
if filetype.lower() == "pdf":
FigureCanvasPdf(fig)
elif filetype.lower() == "svg":
FigureCanvasSVG(fig)
else:
raise ValueError("Unknown filetype: %s" % filetype)
if self.title:
fig.suptitle(self.title)
gs = GridSpec(nplots, 1)
gs.update(**gridspec)
for i in xrange(0, nplots):
self.plotspecs[i].plot(fig, gs, i)
fig.set_size_inches(figsize)
fig.savefig(self.filename + "." + filetype,
format=filetype.lower(), facecolor="none")
def plot_graph():
# load f1 scores from session into local list
score_f1 = score = session["f1_scores"]
# create matplotlib graph showing f1 scores
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
x_points = range(len(score_f1))
axis.plot(x_points, [score_f1[x] for x in x_points])
axis.set_title('Federated Model - F1 Score History')
axis.set_ylabel('F1 Score')
axis.set_xlabel('Epoch')
# initialise raw output for rendering graph as svg data
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
# returns svg formatted graph
return Response(output.getvalue(), mimetype="image/svg+xml")
def definite_intergrals_plot_svg(fx, a, b):
a, b = int(a), int(b)
PRECISION = (b - a) * 20
x = np.linspace(a, b, PRECISION)
y = ne.evaluate(fx)
chart, curve = plt.subplots()
# Disegno la curva
curve.plot(x, y, 'red', linewidth=2)
underlying_area = 0
for index in range(0, len(x) - 1):
deltax = x[index + 1] - x[index]
# Calcolo l'area del rettangolo piccolo
if not np.isnan(y[index]) and not np.isnan(y[index + 1]):
rectangle_min = plt.Rectangle((x[index], 0),
deltax,
y[index],
alpha=.2,
fc='green',
ec="black")
area_min = rectangle_min.get_height() * rectangle_min.get_width()
# Calcolo l'area del rettangolo grande
rectangle_max = plt.Rectangle((x[index], 0),
deltax,
y[index + 1],
alpha=.1,
fc='blue',
ec="black")
area_max = rectangle_max.get_height() * rectangle_max.get_width()
# Disegno i due rettangoli sul grafico
curve.add_patch(rectangle_min)
curve.add_patch(rectangle_max)
# Aggiungo la media dei due rettangoli all'area totale
underlying_area += np.mean([area_min, area_max])
# Mostro il grafico e stampo il risultato
chart.text(.1, .9, f'Risultato: {np.around(underlying_area, decimals=2)}')
output = io.BytesIO()
FigureCanvasSVG(chart).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def plot_gating_signal(alpha):
t = 0
t_array = np.arange(0, 8, 0.01)
V = 0
Vt = []
for t in np.arange(0, 8, 0.01):
if (t - int(t)) <= (alpha / 100):
Vt.append(600)
else:
Vt.append(0)
figure = plt.figure(figsize=(10, 5))
plt.axis([0, 8, 0, 800])
plt.xlabel("time (micro-seconds)", fontsize=14)
plt.ylabel("Terminal voltage of Motor (Vt)", fontsize=14)
plt.plot(t_array, Vt, color='b')
plt.text(3.4,
750,
'Chopping Frequency (f) = 1 Mega-Hertz (MHz)',
fontsize=12,
fontweight='bold',
bbox={
'facecolor': 'yellow',
'alpha': 0.5,
'pad': 4
})
plt.text(3.4,
680,
'T = (1/f) = Toff + Ton = 1 micro-second',
fontsize=12,
fontweight='bold',
bbox={
'facecolor': 'yellow',
'alpha': 0.5,
'pad': 4
})
plt.grid()
output = io.BytesIO()
FigureCanvasSVG(figure).print_svg(output)
return Response(output.getvalue(), mimetype="image/svg+xml")
def draw_graph_rev2(y, G, edge_labs, pos, labs, ax=None):
""" Function to draw the graph.
INPUTS
======
y : ADnum
OUTPUTS
=======
A plot of the graph
"""
fig = Figure()
fig.set_size_inches(9, 6)
#G = gen_graph(y)
G = G.reverse()
#edge_labs = nx.get_edge_attributes(G, 'label')
#pos = nx.spring_layout(G)
#labs = get_labels(y)
#labs = get_labels_rev(y)
ax = fig.add_subplot(1, 1, 1)
nx.draw_networkx(G,
pos,
labels=labs,
node_color=get_colors(G, y),
node_size=get_sizes(G, y, labs),
font_color='white',
ax=ax)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labs, ax=ax)
ax.axis('off')
mag_patch = mpatches.Patch(color='magenta', label='input')
red_patch = mpatches.Patch(color='red', label='intermediate')
blue_patch = mpatches.Patch(color='blue', label='constant')
green_patch = mpatches.Patch(color='green', label='output')
ax.legend(handles=[mag_patch, red_patch, blue_patch, green_patch])
#plt.show()
#return fig
output = io.BytesIO()
FigureCanvasSVG(fig).print_svg(output)
return output
from matplotlib.backends.backend_svg import FigureCanvasSVG as FigureCanvas
# from matplotlib.backends.backend_cairo import FigureCanvasCairo as FigureCanvas
from matplotlib.figure import Figure
xlim = [0, 10.0]
YMAX = 240.00
ylim = [0, YMAX]
fig = Figure()
canvas = FigureCanvas(fig)
# xticks = [ 1,2,3,4,5,6,7,8 ]
ax = fig.add_subplot(111, xlim=xlim, ylim=ylim, xticks=range(10))
data = [[False, False], [1, 200], [4, 100], [6, 120]]
# ax.plot(data)
D = dict(data)
ax.stem(D.keys(), D.values(), "-.")
ax.set_title("hi mom")
ax.grid(True)
ax.set_xlabel("time")
ax.set_ylabel("glucose")
canvas.print_figure("test")
#####
# EOF
# using the matplotlib API - look, maw, no matlab!
from matplotlib.backends.backend_svg import FigureCanvasSVG
from matplotlib.figure import Figure
fig = Figure()
ax = fig.add_subplot(211)
ax.plot([1,2,3])
canvas = FigureCanvasSVG(fig)
canvas.print_figure('myfile.svg')
