# We want to draw the shadow for each pie but we will not use "shadow"
# option as it does'n save the references to the shadow patches.
pies = ax.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%')
for w in pies[0]:
# set the id with the label.
w.set_gid(w.get_label())
# we don't want to draw the edge of the pie
w.set_ec("none")
for w in pies[0]:
# create shadow patch
s = Shadow(w, -0.01, -0.01)
s.set_gid(w.get_gid()+"_shadow")
s.set_zorder(w.get_zorder() - 0.1)
ax.add_patch(s)
# save
from StringIO import StringIO
f = StringIO()
plt.savefig(f, format="svg")
import xml.etree.cElementTree as ET
# filter definition for shadow using a gaussian blur
# and lighteneing effect.
# The lightnening filter is copied from http://www.w3.org/TR/SVG/filters.html
def pie(self,
explode=None,
colors=None,
autopct=None,
pctdistance=0.6,
shadow=False):
start = time.time()
labels = self.pdata.getLabels()
if labels[0][0] == "NoLabels":
try:
self.pdata.initialize(key_type="string")
self.pdata.sortLabels()
labels = self.pdata.getLabels()
nLabels = self.pdata.getNumberOfLabels()
explode = [0.0] * nLabels
if nLabels > 0:
explode[0] = 0.1
except Exception as x:
print("PieGraph Error: can not interpret data for the plot")
# labels.reverse()
values = [l[1] for l in labels]
x = numpy.array(values, numpy.float64)
self.legendData = labels
sx = float(numpy.sum(x))
if sx > 0:
x = numpy.divide(x, sx)
labels = [l[0] for l in labels]
if explode is None:
explode = [0] * len(x)
assert len(x) == len(labels)
assert len(x) == len(explode)
plot_axis_labels = self.prefs.get("plot_axis_labels", True)
center = 0, 0
radius = 1.1
theta1 = 0
i = 0
texts = []
slices = []
autotexts = []
for frac, label, expl in zip(x, labels, explode):
x, y = center
theta2 = theta1 + frac
thetam = 2 * math.pi * 0.5 * (theta1 + theta2)
x += expl * math.cos(thetam)
y += expl * math.sin(thetam)
color = self.palette.getColor(label)
w = Wedge(
(x, y),
radius,
360.0 * theta1,
360.0 * theta2,
facecolor=color,
lw=pixelToPoint(0.5, self.dpi),
edgecolor="#999999",
)
slices.append(w)
self.ax.add_patch(w)
w.set_label(label)
if shadow:
# make sure to add a shadow after the call to
# add_patch so the figure and transform props will be
# set
shad = Shadow(w, -0.02, -0.02)
shad.set_zorder(0.9 * w.get_zorder())
self.ax.add_patch(shad)
if plot_axis_labels:
if frac > 0.03:
xt = x + 1.05 * radius * math.cos(thetam)
yt = y + 1.05 * radius * math.sin(thetam)
thetam %= 2 * math.pi
if 0 < thetam and thetam < math.pi:
valign = "bottom"
elif thetam == 0 or thetam == math.pi:
valign = "center"
else:
valign = "top"
if thetam > math.pi / 2.0 and thetam < 3.0 * math.pi / 2.0:
halign = "right"
elif thetam == math.pi / 2.0 or thetam == 3.0 * math.pi / 2.0:
halign = "center"
else:
halign = "left"
t = self.ax.text(
xt,
yt,
label,
size=pixelToPoint(self.prefs["subtitle_size"],
self.dpi),
horizontalalignment=halign,
verticalalignment=valign,
)
t.set_fontname(self.prefs["font"])
t.set_fontsize(
pixelToPoint(self.prefs["text_size"], self.dpi))
texts.append(t)
if autopct is not None:
xt = x + pctdistance * radius * math.cos(thetam)
yt = y + pctdistance * radius * math.sin(thetam)
if isinstance(autopct, str):
s = autopct % (100.0 * frac)
elif callable(autopct):
s = autopct(100.0 * frac)
else:
raise TypeError(
"autopct must be callable or a format string")
t = self.ax.text(xt,
yt,
s,
horizontalalignment="center",
verticalalignment="center")
t.set_fontname(self.prefs["font"])
t.set_fontsize(
pixelToPoint(self.prefs["text_size"], self.dpi))
autotexts.append(t)
theta1 = theta2
i += 1
self.legendData.reverse()
self.ax.set_xlim((-1.25, 1.25))
self.ax.set_ylim((-1.25, 1.25))
self.ax.set_axis_off()
if autopct is None:
return slices, texts
else:
return slices, texts, autotexts
def plot_mix_method():
'''
这种方法是混合方法，使用外部的SVG。
'''
import matplotlib
matplotlib.use("Svg")
import matplotlib.pyplot as plt
from matplotlib.patches import Shadow
fig1 = plt.figure(1, figsize=(6, 6))
ax = fig1.add_axes([0.1, 0.1, 0.8, 0.8])
labels = ['Frogs', 'Hogs', 'Dogs', 'Logs']
fracs = [15, 30, 45, 10] # 百分比
explode = (0, 0.05, 0, 0)
pies = ax.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%')
for w in pies[0]:
# set the id with the label.
w.set_gid(w.get_label())
# we don't want to draw the edge of the pie
w.set_ec("none")
for w in pies[0]:
# create shadow patch
s = Shadow(w, -0.01, -0.01)
s.set_gid(w.get_gid() + "_shadow")
s.set_zorder(w.get_zorder() - 0.1)
ax.add_patch(s)
# save
from io import BytesIO
f = BytesIO()
plt.savefig(f, format="svg")
import xml.etree.cElementTree as ET
# filter definition for shadow using a gaussian blur
# and lightening effect.
# The lightening filter is copied from http://www.w3.org/TR/SVG/filters.html
# I tested it with Inkscape and Firefox3. "Gaussian blur" is supported
# in both, but the lightening effect only in the Inkscape. Also note
# that, Inkscape's exporting also may not support it.
filter_def = """
<defs xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'>
<filter id='dropshadow' height='1.2' width='1.2'>
<feGaussianBlur result='blur' stdDeviation='2'/>
</filter>
<filter id='MyFilter' filterUnits='objectBoundingBox' x='0' y='0' width='1' height='1'>
<feGaussianBlur in='SourceAlpha' stdDeviation='4%' result='blur'/>
<feOffset in='blur' dx='4%' dy='4%' result='offsetBlur'/>
<feSpecularLighting in='blur' surfaceScale='5' specularConstant='.75'
specularExponent='20' lighting-color='#bbbbbb' result='specOut'>
<fePointLight x='-5000%' y='-10000%' z='20000%'/>
</feSpecularLighting>
<feComposite in='specOut' in2='SourceAlpha' operator='in' result='specOut'/>
<feComposite in='SourceGraphic' in2='specOut' operator='arithmetic'
k1='0' k2='1' k3='1' k4='0'/>
</filter>
</defs>
"""
tree, xmlid = ET.XMLID(f.getvalue())
# insert the filter definition in the svg dom tree.
tree.insert(0, ET.XML(filter_def))
for i, pie_name in enumerate(labels):
pie = xmlid[pie_name]
pie.set("filter", 'url(#MyFilter)')
shadow = xmlid[pie_name + "_shadow"]
shadow.set("filter", 'url(#dropshadow)')
fn = "svg_filter_pie.svg"
print("Saving '%s'" % fn)
ET.ElementTree(tree).write(fn)
def make_graphs(individual_means, individual_means_2):
# bar graph compared to average in each category (2 phase bar graph)
labels = ['Electricity consumption (kwh * 1000)', '# of flights per year', '# commute miles per year (thousands)', '# of ride sharing trips per year', 'food choice (tons of CO2 emissions/year)']
population_means = [11.698, 2.1, 15, 7.86, 2.5]
population_means=list(map(int,population_means))
print(labels)
print(individual_means)
print(population_means)
x = np.arange(len(labels)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, individual_means, width, label='Your score', color='#5dcf60')
rects2 = ax.bar(x + width/2, population_means, width, label='Average score', color='#595959')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Scores by label')
ax.set_xticks(x)
ax.set_xticklabels(labels, rotation='vertical') #rotation='vertical', fontsize='x-small',
ax.legend()
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
fig.tight_layout()
# plt.show()
plt.savefig('bar.png', format="png")
# bar 2
labels = ['electricity', 'flights', 'transportation', 'food', 'retail']
population_means = [7252.76, 602.45, 4515.27, 2267.96, 22.41]
population_means=list(map(int,population_means))
individual_means_2=list(map(int, individual_means_2))
print(labels)
print(individual_means_2)
print(population_means)
x = np.arange(len(labels)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, individual_means_2, width, label='Your score', color='#5dcf60')
rects2 = ax.bar(x + width/2, population_means, width, label='Average score', color='#595959')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('kg Co2/year')
ax.set_title('Scores by label')
ax.set_xticks(x)
ax.set_xticklabels(labels, rotation='vertical') #rotation='vertical', fontsize='x-small',
ax.legend()
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
fig.tight_layout()
# plt.show()
plt.savefig('bar_2.png', format="png")
# % of contributions to your carbon footpint
fig = plt.figure(figsize=(6, 6))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
labels = ['electricitiy', 'flights', 'transport', 'food', 'retail']
fracs = [individual_means_2[0], individual_means_2[1], individual_means_2[2], individual_means_2[3], individual_means_2[4]]
colors = ['#5dcf60', '#999999', '#A4efa4', '#595959', '#c3dbc3', '#70b170']
explode = (0, 0, 0, 0, 0)
# We want to draw the shadow for each pie but we will not use "shadow"
# option as it does'n save the references to the shadow patches.
pies = ax.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%', colors=colors)
for w in pies[0]:
# set the id with the label.
w.set_gid(w.get_label())
# we don't want to draw the edge of the pie
w.set_edgecolor("none")
for w in pies[0]:
# create shadow patch
s = Shadow(w, -0.01, -0.01)
s.set_gid(w.get_gid() + "_shadow")
s.set_zorder(w.get_zorder() - 0.1)
ax.add_patch(s)
# save
plt.savefig('pi.png', format="png")
