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)
# 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 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")
