def Statistics(cellnums, resultfile):
"""
Get the statistics of the each cell type
and draw the pie chart
"""
cells = {"ram": 0, "rod": 0, "hyp": 0, "dys": 0, "amoe": 0}
for i in cells.keys():
for j in range(100):
cells[i] += cellnums[j][i]
colors = ['green', 'blue', 'yellow', 'cyan', 'red']
Allcells = cells['ram'] + cells['hyp'] + cells['dys'] + cells[
'amoe'] + cells['rod']
sizes = np.asarray([
cells['ram'], cells['hyp'], cells['dys'], cells['amoe'], cells['rod']
]) / Allcells
plt.figure(figsize=(3, 3))
plt.pie(sizes, autopct='%1.1f%%',colors=colors,\
shadow=True, startangle=90)
plt.savefig("Pie.png")
f = open(resultfile, 'w')
f.write('%4s\t%4s\t%4s\t%4s\t%4s\n' % ("ram", "rod", "hyp", "dys", "amoe"))
f.write('%4d\t%4d\t%4d\t%4d\t%4d\n' % (cells["ram"],cells["rod"],\
cells["hyp"],cells["dys"],cells["amoe"]))
f.close()
def data_visualization2():
#subprocess.check_output(['dos2unix', 'teste.txt'])
hamb = []
#subprocess.check_output(['dos2unix', 'write.fasta'], cwd='/home/livanski/Documents/danielsundfeld-hpc/seqs')
#with open("teste.txt") as file:
with open(
'/home/ubuntu/ferramenta_final/servidor_apresentacao/ferramenta/media/arquivo.fasta'
) as file:
for line in file:
hamb.append(line)
seq1 = Seq(hamb[1])
seq2 = Seq(hamb[3])
seq3 = seq1 + seq2
gc = GC(seq3)
au = 100 - gc
pylab.pie([gc, au]) # cria o grafico pizza
pylab.title('GC Content') # indica um titulo
pylab.xlabel('GC: %0.1f\nAT: %01.f' % (gc, au)) # porcentagens/info
# exibe
#pylab.savefig('/home/livanski/Pictures/graph.png', dpi=100)
#pylab.savefig('/home/livanski/Music/upload_final/django-upload-example/static/graph.png', dpi=100)
pylab.savefig(
'/home/ubuntu/ferramenta_final/servidor_apresentacao/ferramenta/mysite/static/css/graph_upload.png',
dpi=100)
return pylab.show
def save_plt(self):
x = [
len(self.plt_1),
len(self.plt_2),
len(self.plt_3),
len(self.plt_4)
]
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.pie(
x,
labels=['1-5K', '5-8K', '8-15K', '15K-20K'],
labeldistance=1.2, # 设置标签距离圆心的距离（0为 圆饼中心数 1为圆饼边缘）
autopct="%1.1f%%", # 1.1f% 保留一位小数的百分比
pctdistance=0.5, # 比例值文字距离圆心的距离
explode=[0, 0.2, 0, 0], # 每一块顶点距圆形的长度
colors=["lightskyblue", "green", "royalblue", "magenta"], # 最好一一对应
shadow=True, # 是否有阴影
startangle=60, # 第一块开始的角度
)
plt.axis('equal') # 该行代码使饼图长宽相等
# plt.legend(loc="upper right", fontsize=10, bbox_to_anchor=(1.1, 1.05), borderaxespad=0.3)
# plt.loc = 'upper right' # 位于右上角
# plt.bbox_to_anchor=[0.5, 0.5] # 外边距 上边 右边
# plt.ncol=2 # 分两列
# plt.borderaxespad = 0.3 # 图例的内边距
# plt.title("上海市{}岗位薪资占比".format(self.key))
# plt.title("Python Min")
# plt.savefig('Python_Min')
plt.title("Python Max")
plt.savefig('Python_Max')
plt.show()
def plot_tipo_tweet(self, archivo_imagen = ''):
"""
Esta función toma como entrada el archivo de datos preprocesados y
devuelve una imagen tipo diagrama de Venn con el tipo de tweets pescados
"""
#Levantar los datos preprocesados del archivo
originales = set(self.tweets.tw_id.values) #Conjunto de tweets originales
rt = set(self.tweets[self.tweets.relacion_nuevo_original == 'RT'].or_id.values) #Conjunto de retweets
citas = set(self.tweets[self.tweets.relacion_nuevo_original == 'QT'].or_id.values) #Conjunto de citas
total_tweets = len(originales.union(rt).union(citas)) #Cantidad total de tweets
# Realizar la figura
labels = ['RT', 'Originales', 'QT']
sizes = [100 * len(rt) / total_tweets, 100 * len(originales) / total_tweets, 100 * len(citas) / total_tweets]
sbn.set_context("paper", font_scale = 1.5)
plt.figure(figsize = (11,8), dpi = 300)
plt.title('Tipos de Tweets', fontsize = 20)
plt.pie(sizes, autopct='%1.1f%%')
plt.legend(labels)
plt.axis('equal')
plt.text(-.1,-1.2,'El total de tweets registrados durante el período fue de : {}'.format(total_tweets))
if archivo_imagen=='':
plt.show()
else:
plt.savefig(archivo_imagen,bbox_inches='tight')
plt.show()
plt.clf()
sbn.reset_orig()
def ZipByDemoCuisine(askNum ,Zipcodes, recorddict):
for zips in Zipcodes:
if askNum == zips.Zip:
print "This is located in " + str(zips.Hood) + "!"
labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue','pink', 'purple' ]
explode = (0, 0, 0, 0, 0, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
plt.axis('equal')
plt.show()
xlist = []
ylist = []
x = Counter(recorddict[askNum])
for i in x.most_common(10):
xlist.append(i[0])
ylist.append(int(i[1]))
#i[0] = category
#i[1] = number of category
labels = xlist
sizes = ylist
colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue', 'pink', 'white', 'silver', 'purple']
explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
plt.axis('equal')
plt.show()
def pie_plot_important_activities_2018(
): # pie_plot_important_activities_2018(kaggleSurveyDF_2018, schemaSurveyDF_2018 ):
responseCountsList = []
responseOptionsTextListAnnotated = [
'Analyze and understand data',
'Use ML services to improve product or wokflows',
'Build/run data infrastructure',
'Apply ML to new areas and build prototypes',
'Research/advance state of the art in ML',
'Other / None of these activities are important to my role'
]
responseCountsList = [9532, 5481, 5233, 7233, 4934, 4663]
plt.figure()
plt.pie(x=responseCountsList,
labels=responseOptionsTextListAnnotated,
startangle=90,
autopct='%1.1f%%',
wedgeprops={
'linewidth': 1,
'edgecolor': 'white'
},
colors=np.array(plt.get_cmap('tab20').colors))
plt.title(
'What activities make up an important part of your role at work?')
plt.show()
def score(self):
print('Caculating Your Activity Score.....')
colors = ['#ff3300', '#33cc33']
scoreValue = round(
math.log((len(urls) + len(searchClean * 2) + len(allLikes * 3) +
len(allLinks * 4)) / 9, 1.12), 1)
x_0 = [1, 0, 0, 0]
pl.pie([100 - scoreValue, scoreValue],
autopct='%1.1f%%',
startangle=90,
colors=colors,
pctdistance=10)
plt.pie(x_0, radius=0.7, colors='w')
plt.axis('equal')
plt.title("Your YouTube Activity Score",
fontsize=21,
color='steelblue',
fontweight="bold",
fontname="Comic Sans MS")
plt.annotate(scoreValue, (0, 0), (115, 154),
fontsize=54,
color='teal',
fontweight="bold",
fontname="Comic Sans MS",
xycoords='axes fraction',
textcoords='offset points',
va='top')
plt.savefig(image_dir + 'score.png', dpi=400)
plt.clf()
def piechart(data,labels,title=0):
fig = plt.figure(figsize=(8,8))
plt.pie(data,labels=labels, autopct='%1.1f%%', colors=colors(len(data)), shadow=True)
if(title):
plt.title(title)
return fig
def pie_plot(counts, label, colors, col_name):
plt.pie(counts,
labels=label,
colors=colors,
startangle=90,
autopct='%.1f%%')
plt.savefig(f'../output/graphs/{col_name}_pie_plt.png')
plt.show()
def get_region_chart():
count = df['区域'].value_counts()
print(count)
plt.pie(count, labels=count.keys(), labeldistance=1.4, autopct='%2.1f%%')
plt.axis('equal')
plt.title('岗位地区分布图')
plt.legend(loc='upper left', bbox_to_anchor=(-0.1, 1))
plt.savefig('pie_chart.jpg')
plt.show()
def rem_nrem(stages):
timespent_array=time_spent_in_stages(stages)
time_nrem=timespent_array[1]+timespent_array[2]+timespent_array[3]+timespent_array[4]
time_rem=timespent_array[5]
split=[(time_rem*100/(time_nrem+time_rem)),(time_nrem*100/(time_nrem+time_rem))]
plt.figure()
labels=['REM Sleep', 'NREM Sleep']
plt.pie(split,labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
plt.title('Subject 2- Bad Sleep - REM/NREM Splitup')
plt.show()
def default_value(self):
df = self.df
#统计数量，比率
count = df.isnull().sum().sort_values(
ascending=False) #统计出每个属性的缺省数量，再根据缺省数量倒排序
ratio = count / len(df) #每个属性的缺省占自己总数的比率
nulldata = pd.concat([count, ratio], axis=1, keys=['count', 'ratio'])
#饼状图
explode = [0]
explode = explode * len(nulldata.index)
explode[0] = 0.1
plt.pie(x=count,
labels=nulldata.index,
autopct='%1.1f%%',
shadow=True,
startangle=90,
explode=explode,
pctdistance=0.8,
textprops={
'fontsize': 16,
'color': 'w'
}) #饼状图画出每个属性的缺省在整体缺省数据的占比
plt.title('属性缺省占比图')
plt.legend(loc="upper right",
bbox_to_anchor=(1.4, 1.1),
borderaxespad=0.3)
# plt.savefig("picture/default_value_pie.jpg")
plt.show()
#水平柱状图
plt.figure()
plt.barh(nulldata.index, count, 0.5, color='#FFFF00',
label="缺省") #每个属性的缺省样本数
plt.barh(nulldata.index,
df.shape[0] - count,
0.5,
color='#97FFFF',
left=count,
label="不缺省") #每个属性的样本值不缺省的数量
plt.xlabel("属性")
plt.ylabel("样本数")
plt.legend()
plt.title("每个属性的缺省情况")
# plt.savefig("picture/default_value_bar.jpg")
plt.show()
# 填充缺省，字符串类型的属性用None填充,数值型用众数填充。简单填充，可能会产生更多的误差
for index in nulldata.index:
if type(index) is not object:
df[index].fillna("None", inplace=True)
else:
df[index].fillna(df[index].mode()[0], inplace=True)
self.df = df
return nulldata, df
def badge_bar(index):
data1 = pd.read_csv('data/split_class/large_IGNORE_404_badge_+1.txt', sep=' ', header=None)
data2 = pd.read_csv('data/split_class/large_IGNORE_404_badge_-1.txt', sep=' ', header=None)
col1 = data1[index]
col2 = data2[index]
print(col1.value_counts() / len(col1))
print(col2.value_counts() / len(col2))
plt.pie(col1.value_counts() / len(col1), colors=('b', 'r'), labels=['badge', 'non-badge'])
plt.show()
def DemosForZips(Zipcodes):
for zips in Zipcodes:
labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'blue', 'purple', 'silver']
explode = (0, 0, 0, 0, 0, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
plt.axis('equal')
plt.show()
print zips.Zip + " is located in " + str(zips.Hood) + "!"
def compute_initial_figure(self):
UsersPerCountry, UsersPerPlatform = Analytics.UsersPerCountryOrPlatform()
labels = []
sizes = []
print(UsersPerPlatform)
for p, c in sorted(UsersPerPlatform.iteritems()):
labels.append(p)
sizes.append(c)
colors = ['turquoise', 'yellowgreen', 'firebrick', 'lightsteelblue', 'royalblue']
pylab.pie(sizes, colors=colors, labels=labels, autopct='%1.1f%%', shadow=True)
pylab.title('Users Per Platform')
pylab.gca().set_aspect('1')
pylab.show()
def pie_chart(vintage_results, year=40):
'''
plot a pie chart show the release break down (by market) for the specific year
'''
labels = vintage_results.keys()
size = []
explode = (0,0.1,0,0,0,0,0) # only explode the second one
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
for each_key, each_val in vintage_results.iteritems():
this_tot_rel_year = each_val['In Use'][40] + each_val['End of Life'][40]
size.append(this_tot_rel_year)
plt.pie(size,explode=explode,labels=labels,autopct='%1.1f%%',colors=colors,shadow=True)
plt.show()
def score(self):
print("Caculating Your Activity Score.....")
colors = ["#ff3300", "#33cc33"]
score_value = round(
math.log(
(
len(urls)
+ len(search_clean * 2)
+ len(all_likes * 3)
+ len(all_links * 4)
)
/ 9,
1.12,
),
1,
)
x_0 = [1, 0, 0, 0]
pl.pie(
[100 - score_value, score_value],
autopct="%1.1f%%",
startangle=90,
colors=colors,
pctdistance=10,
)
plt.pie(x_0, radius=0.7, colors="w")
plt.axis("equal")
plt.title(
"Your YouTube Activity Score",
fontsize=21,
color="steelblue",
fontweight="bold",
fontname="Arial",
)
plt.annotate(
score_value,
(0, 0),
(123, 154),
fontsize=54,
color="teal",
fontweight="bold",
fontname="Arial",
xycoords="axes fraction",
textcoords="offset points",
va="top",
)
plt.savefig(image_dir + "score.png", dpi=400)
plt.clf()
def genPiePlot(x,y,fname):
colors = ['yellowgreen', 'red', 'gold', 'lightskyblue', 'white', 'lightcoral', 'blue', 'pink', 'darkgreen', 'yellow', 'grey', 'violet', 'magenta',
'cyan']
patches, texts = plt.pie(y, colors=colors, startangle=90, radius=1.2)
labels = []
for _x,_y in zip(x,y):
labels.append(str(_x) + ' - ' + str(_y))
sort_legend = False
if sort_legend:
patches, labels, dummy = zip(*sorted(zip(patches, labels, y),
key=lambda x: x[2],
reverse=True))
#plt.legend(patches, labels, loc='lower center', bbox_to_anchor=(0, .5),
# fontsize=12)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 18.5)
plt.savefig(fname, bbox_inches='tight', dpi=100)
plt.clf()
plt.legend(patches, x, fontsize=23, ncol=3, bbox_to_anchor=(1.,1.))
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5)
fig.savefig(fname + "legend.png", dpi=100)
plt.close()
def pie(self, key_word_sep=" ", title=None, **kwargs):
"""Generates a pylab pie chart from the result set.
``matplotlib`` must be installed, and in an
IPython Notebook, inlining must be on::
%%matplotlib inline
Values (pie slice sizes) are taken from the
rightmost column (numerical values required).
All other columns are used to label the pie slices.
Parameters
----------
key_word_sep: string used to separate column values
from each other in pie labels
title: Plot title, defaults to name of value column
Any additional keyword arguments will be passsed
through to ``matplotlib.pylab.pie``.
"""
self.guess_pie_columns(xlabel_sep=key_word_sep)
import matplotlib.pylab as plt
pie = plt.pie(self.ys[0], labels=self.xlabels, **kwargs)
plt.title(title or self.ys[0].name)
return pie
def getGenderImage(self):
for i in range(2):
tBD = self.getDurationBookData(i)
plotName = "Gender"
if i == 0:
plotName += " (Short "
else:
plotName += " (Long "
plotName += "BestSeller)"
gender = [0,0] # F,M
label = ['Female','Male']
for book in tBD:
if book[10] == 'F':
gender[0] += 1
elif book[10] == 'M':
gender[1] += 1
tempColors = self.colors[:2]
plt.title(plotName,fontdict={'fontsize':30})
a,b,c=plt.pie(gender, colors = tempColors, labels = label, autopct='%1.1f%%', startangle=90)
for text in b:
text.set_fontsize(20)
plt.axis('equal')
plt.show()
def pie(self, key_word_sep=" ", title=None, **kwargs):
"""Generates a pylab pie chart from the result set.
``matplotlib`` must be installed, and in an
IPython Notebook, inlining must be on::
%%matplotlib inline
Values (pie slice sizes) are taken from the
rightmost column (numerical values required).
All other columns are used to label the pie slices.
Parameters
----------
key_word_sep: string used to separate column values
from each other in pie labels
title: Plot title, defaults to name of value column
Any additional keyword arguments will be passsed
through to ``matplotlib.pylab.pie``.
"""
self.guess_pie_columns(xlabel_sep=key_word_sep)
import matplotlib.pylab as plt
pie = plt.pie(self.ys[0], labels=self.xlabels, **kwargs)
plt.title(title or self.ys[0].name)
return pie
def getFirstBSImage(self):
for i in range(2):
tBD = self.getDurationBookData(i)
plotName = "isFirstBestseller"
if i == 0:
plotName += " (Short "
else:
plotName += " (Long "
plotName += "BestSeller)"
isFBS = [0,0] # F,N
label = ['First','not First']
for book in tBD:
if book[11] == '1':
isFBS[0] += 1
elif book[11] == '0':
isFBS[1] += 1
tempColors = self.colors[:2]
plt.title(plotName,fontdict={'fontsize':30})
a,b,c=plt.pie(isFBS, colors = tempColors, labels = label, autopct='%1.1f%%', startangle=90)
for text in b:
text.set_fontsize(20)
plt.axis('equal')
plt.show()
def create_piecharts(grade, total_students, met_students, min_threshold, max_threshold):
for major, total in total_students.items():
# Make a square figure and axes
figure(1, figsize=(6,6))
ax = axes([0.1, 0.1, 0.8, 0.8])
# The slices will be ordered and plotted counter-clockwise.
labels = 'Met threshold', 'Did not meet threshold'
fracs = [met_students[major]/total, (total-met_students[major])/total]
# Pop the slice of the pie out for emphasis
explode=(0.1, 0)
pie(fracs, explode=explode, labels=labels,
autopct='%1.1f%%', shadow=True, startangle=90)
title('%s Students Between %s-%s for Assignment %s' % (major, min_threshold, max_threshold, grade),
bbox={'facecolor':'0.8', 'pad':5})
# Save the figure as a png
savefig("piechart_%s_%s_%s-%s.png" % (grade, major, min_threshold, max_threshold), bbox_inches="tight")
clf()
def dataDistribution(self):
print('Distribuição dos Dados')
objects = np.zeros(self._NUM_CLASSES)
for i in range(0,self.output_train.shape[0]):
out = np.argmax(self.output_train[i])
objects[out] += 1
fig=plt.figure(figsize=(10,5))
fig.add_subplot(1, 2, 1)
for i in range(0,self._NUM_CLASSES):
plt.bar(i, objects[i], 0.8)
plt.xlabel('Classes')
plt.ylabel('Elementos')
fig.add_subplot(1, 2, 2)
for i in range(0,self._NUM_CLASSES):
plt.pie(objects )
plt.xlabel('Classes x Elementos')
plt.show()
def pie_chart(vintage_results, year=40):
'''
plot a pie chart show the release break down (by market) for the specific year
'''
labels = vintage_results.keys()
size = []
explode = (0, 0.1, 0, 0, 0, 0, 0) # only explode the second one
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
for each_key, each_val in vintage_results.iteritems():
this_tot_rel_year = each_val['In Use'][40] + each_val['End of Life'][40]
size.append(this_tot_rel_year)
plt.pie(size,
explode=explode,
labels=labels,
autopct='%1.1f%%',
colors=colors,
shadow=True)
plt.show()
def default_value(self):
df = self.df
#统计数量，比率
count = df.isnull().sum().sort_values(
ascending=False) #统计出每个属性的缺省数量，再根据缺省数量倒排序
ratio = count / len(df) #每个属性的缺省占自己总数的比率
nulldata = pd.concat([count, ratio], axis=1, keys=['count', 'ratio'])
#饼状图
explode = [0]
explode = explode * len(nulldata.index)
explode[0] = 0.1
plt.figure()
plt.pie(x=count,
labels=nulldata.index,
autopct='%1.1f%%',
shadow=True,
startangle=90,
explode=explode,
pctdistance=0.8,
textprops={
'fontsize': 16,
'color': 'w'
}) #饼状图画出每个属性的缺省在整体缺省数据的占比
plt.title('属性缺省占比图')
plt.legend(loc="upper right",
bbox_to_anchor=(1.4, 1.1),
borderaxespad=0.3)
if self.port == 11111:
plt.savefig("default_value_pie1.jpg")
elif self.port == 22222:
plt.savefig("default_value_pie2.jpg")
elif self.port == 33333:
plt.savefig("default_value_pie3.jpg")
elif self.port == 44444:
plt.savefig("default_value_pie4.jpg")
# 填充缺省，字符串类型的属性用None填充,数值型用众数填充。简单填充，可能会产生更多的误差
for index in nulldata.index:
if type(index) is not object:
df[index].fillna("None", inplace=True)
else:
df[index].fillna(df[index].mode()[0], inplace=True)
return nulldata, df
def pie_chart(all_credit, graph_title):
"""Display the all_credit dictionary as a pie chart with matplotlib."""
if not ENABLED:
return
# magic numbers are magical. at least the user can resize afterwards
pylab.figure(1, figsize=(6, 6))
pylab.axes([0.1, 0.1, 0.8, 0.8])
pylab.pie(
all_credit.values(),
labels=all_credit.keys(),
autopct='%1.1f%%',
shadow=True,
startangle=90,
)
pylab.title(graph_title)
pylab.show()
def percentTimeIn():
""" Funcao que retorna o percentual de tempo em cada intervalo informado.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools',
'Please select a single pen before applying this function!',
'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
global nodes
runDialogInterval()
global nodes
t, y = rmNanAndOutliers2(epmData)
if len(nodes) < 2:
minVal = int(np.floor(np.nanmin(y)))
maxVal = int(np.ceil(np.nanmax(y)))
step = int((maxVal - minVal) / 3)
nodes = range(minVal, maxVal, step)
intervNum = np.size(nodes) + 1
totTime = np.empty(intervNum, dtype=object)
totTime.fill(dtt.timedelta(0, 0))
for i in range(1, np.size(y)):
dt = t[i] - t[i - 1]
ix = np.digitize([y[i]], nodes)
totTime[ix] += dt
nodesPercents = np.zeros([np.size(totTime), 2])
totalPeriod = totTime.sum().total_seconds()
for i in range(np.size(totTime)):
if i:
nodesPercents[i, 0] = nodes[i - 1]
else:
nodesPercents[i, 0] = -np.inf
nodesPercents[i, 1] = totTime[i].total_seconds() / totalPeriod
labels = []
for item in nodesPercents[:, 0]:
labels.append(str(item))
pl.pie(nodesPercents[:, 1], labels=labels, autopct='%1.1f%%', shadow=True)
pl.show()
return nodesPercents
def percentTimeIn():
""" Funcao que retorna o percentual de tempo em cada intervalo informado.
"""
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
global nodes
runDialogInterval()
global nodes
t,y = rmNanAndOutliers2(epmData)
if len(nodes) < 2:
minVal = int(np.floor(np.nanmin(y)))
maxVal = int(np.ceil(np.nanmax(y)))
step = int((maxVal-minVal)/3)
nodes = range(minVal,maxVal,step)
intervNum = np.size(nodes)+1
totTime = np.empty(intervNum, dtype=object)
totTime.fill(dtt.timedelta(0,0))
for i in range(1,np.size(y)):
dt = t[i] - t[i-1]
ix = np.digitize([y[i]], nodes)
totTime[ix] += dt
nodesPercents = np.zeros([np.size(totTime),2])
totalPeriod = totTime.sum().total_seconds()
for i in range(np.size(totTime)):
if i:
nodesPercents[i,0] = nodes[i-1]
else:
nodesPercents[i,0] = -np.inf
nodesPercents[i,1] = totTime[i].total_seconds()/totalPeriod
labels = []
for item in nodesPercents[:,0]:
labels.append(str(item))
pl.pie(nodesPercents[:,1], labels=labels, autopct='%1.1f%%', shadow=True)
pl.show()
return nodesPercents
def responses_pieplot():
response_data = pd.read_csv('response_percents.csv')
#response_data = pd.read_csv('response_percents_reordered.csv')
#print (response_data)
fig, ax = plt.subplots(figsize=(7, 7))
explode = [0, 0, 0, 0, 0, 0, 0.1]
labels = response_data['Hall']
#labels_gender = response_data['Gender']
colors = [
'#ffad30', '#c369ff', '#2ff59c', '#b5b5b5', '#ff4063', '#4dbeff',
'#ff69b6'
]
colors_gender = ['#2081C3', '#ff6969']
plt.pie(x=response_data['Percent'],
explode=explode,
labels=labels,
colors=colors,
labeldistance=1.05,
autopct='%.1f%%',
pctdistance=0.64)
#plt.pie(x=response_data['Percent'], labels=labels_gender, colors=colors_gender, labeldistance=0.5, autopct='%.1f%%', pctdistance=0.6)
plt.legend(frameon=False)
plt.show()
def getPublishYearImage(self,labelNum):
for durationNum in range(2):
plotName = "BSYear - PublishedYear"
if durationNum == 0:
plotName += " (Short "
else:
plotName += " (Long "
plotName += "BestSeller)"
tBD = self.getDurationBookData(durationNum)
yearDict = {}
for book in tBD:
bsYear = int(book[0])
pYear = int(book[4])
difYear = bsYear - pYear
if difYear in yearDict:
yearDict[difYear] += 1
else:
yearDict[difYear] = 1
yearNumList = []
for year in yearDict:
yearNumList.append((year,yearDict[year]))
yearNumList.sort(reverse = True, key = lambda year: year[0])
print(yearNumList)
if labelNum < len(yearNumList):
etcNum = 0
for i in range(labelNum,len(yearNumList)):
etcNum += yearNumList[i][1]
yearNumList = yearNumList[0:labelNum]
yearNumList.append(('etc',etcNum))
yearList = []
numList = []
for i in range(len(yearNumList)):
yearList.append(yearNumList[i][0])
numList.append(yearNumList[i][1])
tempColors = self.colors[:len(numList)]
plt.title(plotName,fontdict={'fontsize':30})
a,b,c=plt.pie(numList, colors = tempColors, labels = yearList, autopct='%1.1f%%', startangle=90)
for text in b:
text.set_fontsize(20)
plt.axis('equal')
plt.show()
def getSpecificGenreImage(self):
genreColorDict = {}
for i in range(20):
genreColorDict[i] = self.colors[i]
for genreNum in [2,3,6]:
tBD = self.getDurationBookData(2)
referenceDict = {}
for i in range(5):
referenceDict[i] = 0
for book in tBD:
if int(book[2]) == genreNum:
reference = int(book[9])+int(book[10])
if reference > 3:
referenceDict[4] += 1
else:
referenceDict[reference] += 1
genreDict = referenceDict
genreNumList = []
for genre in genreDict:
genreNumList.append((genre,genreDict[genre]))
genreNumList.sort(key = lambda genre: genre[0])
genreList = []
numList = []
tempColors = []
for i in range(len(genreNumList)):
print(genreNumList[i])
if genreNumList[i][0] == 4:
genreList.append(">3")
else:
genreList.append(genreNumList[i][0])
numList.append(genreNumList[i][1])
tempColors.append(genreColorDict[genreNumList[i][0]])
#plt.title(plotName,fontdict={'fontsize':20})
a,b,c = a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
for text in b:
text.set_fontsize(20)
plt.axis('equal')
plt.show()
def pie(self, sizes):
labels = ['Var_Credit(Closing)', 'Var_Interest(Closing)']
#print(sizes)
abc = [float(x) * -1 for x in sizes]
colors = ['lightsage', 'crimson']
#patches, texts = plt.pie(abc, colors=colors, startangle=90)
#plt.legend(patches, labels, loc="best")
plt.axis('equal')
plt.tight_layout()
plt.title("VaR Distribution for Closing Book")
explode = (0, 0.1)
pie = plt.pie(abc,
colors=colors,
labels=labels,
explode=explode,
shadow=True,
autopct='%1.3f%%')
plt.legend(pie[0], labels, loc="best")
plt.show()
def getPublisherImage(self,labelNum):
for durationNum in range(2):
plotName = "Genre"
if durationNum == 0:
plotName += " (Short "
else:
plotName += " (Long "
plotName += "BestSeller)"
tBD = self.getDurationBookData(durationNum)
genreDict = {}
for book in tBD:
genre = book[5]
if genre in genreDict:
genreDict[genre] += 1
else:
genreDict[genre] = 1
genreNumList = []
for genre in genreDict:
genreNumList.append((genre,genreDict[genre]))
genreNumList.sort(reverse = True, key = lambda genre: genre[1])
print(genreNumList)
if labelNum < len(genreNumList):
etcNum = 0
for i in range(labelNum,len(genreNumList)):
etcNum += genreNumList[i][1]
genreNumList = genreNumList[0:labelNum]
genreNumList.append(('etc',etcNum))
genreList = []
numList = []
for i in range(len(genreNumList)):
genreList.append(genreNumList[i][0])
numList.append(genreNumList[i][1])
tempColors = self.colors[:len(numList)]
plt.title(plotName,fontdict={'fontsize':30})
a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
for text in b:
text.set_fontsize(20)
plt.axis('equal')
plt.show()
def getGenreImage(self):
genreColorDict = {}
for i in range(10):
genreColorDict[i] = self.colors[i]
for durationNum in range(3):
plotName = "Genre"
if durationNum == 0:
plotName += " (Short "
else:
plotName += " (Long "
plotName += "BestSeller)"
tBD = self.getDurationBookData(durationNum)
genreDict = {}
for i in range(10):
genreDict[i] = 0
i = 0
for book in tBD:
genre = book[2]
genreDict[int(genre)] += 1
genreNumList = []
for genre in genreDict:
genreNumList.append((genre,genreDict[genre]))
genreNumList.sort(reverse = True, key = lambda genre: genre[1])
genreList = []
numList = []
tempColors = []
for i in range(8):
genreList.append(self.genreNumToName[genreNumList[i][0]])
numList.append(genreNumList[i][1])
tempColors.append(genreColorDict[genreNumList[i][0]])
# plt.title(plotName,fontdict={'fontsize':30})
a,b,c = a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
for text in b:
text.set_fontsize(30)
plt.axis('equal')
plt.show()
def getReferenceByImage(self):
tempRefList = []
for i in range(2):
bD = self.getDurationBookData(i)
for book in bD:
if book[1] == 'Literature&Fiction':
tempRefList.append((book[1],int(book[8])+int(book[9]),int(book[12])))
tempRefList.sort(reverse = True, key = lambda genre: genre[1])
refDict = {}
for book in tempRefList:
if book[1] in refDict:
refDict[book[1]] += 1
else:
refDict[book[1]] = 1
temp = []
for ref in refDict:
temp.append((ref,refDict[ref]))
temp.sort(key = lambda genre: genre[0])
genreList = []
numList = []
tempColors = []
for i in range(len(temp)):
genreList.append(temp[i][0])
numList.append(temp[i][1])
tempColors = self.colors[:len(numList)]
numList[6] = sum(numList[6:])
numList = numList[:7]
genreList = genreList[:6]
genreList.append('etc')
tempColors = self.colors[:len(genreList)]
a,b,c=plt.pie(numList, colors = tempColors, labels = genreList, autopct='%1.1f%%', startangle=90)
for text in b:
text.set_fontsize(20)
plt.axis('equal')
plt.show()
for s in page_grouped:
n = max(s)
n = n + (10 - n % 10)
dic[n] = len(s)
return dic
dic = getPageSorted(files)
print('le nombre de page pour chaque pdf')
print(dic)
dic = getGroupedPage(dic)
print("nombre de page regrouper")
print(dic)
dic = getRegroupedPage(dic)
print("regrouped pages ")
print(dic)
plt.subplots(figsize=(20, 10))
lists = sorted(dic.items()) # sorted by key, return a list of tuples
plt.pie([float(v) for v in dic.values()],
autopct='%2.2f%%',
shadow=True,
startangle=140)
plt.legend(labels=["page< " + str(k) + " " + str(dic[k]) for k in dic],
bbox_transform=plt.gcf().transFigure,
fontsize=30,
loc="best")
plt.axis('equal')
plt.show()
def checkmem(origvariable, descend=0, order='n', plot=False, verbose=0):
"""
Checks how much memory the variable in question uses by dumping it to file.
Example:
from utils import checkmem
checkmem(['spiffy',rand(2483,589)],descend=1)
"""
from os import getcwd, remove
from os.path import getsize
from cPickle import dump
from numpy import iterable, argsort
filename = getcwd()+'/checkmem.tmp'
def dumpfile(variable):
wfid = open(filename,'wb')
dump(variable, wfid)
return None
printnames = []
printbytes = []
printsizes = []
varnames = []
variables = []
if descend==False or not(iterable(origvariable)):
varnames = ['']
variables = [origvariable]
elif descend==1 and iterable(origvariable):
if hasattr(origvariable,'keys'):
for key in origvariable.keys():
varnames.append(key)
variables.append(origvariable[key])
else:
varnames = range(len(origvariable))
variables = origvariable
for v,variable in enumerate(variables):
if verbose: print('Processing variable %i of %i' % (v+1, len(variables)))
dumpfile(variable)
filesize = getsize(filename)
factor = 1
label = 'B'
labels = ['KB','MB','GB']
for i,f in enumerate([3,6,9]):
if filesize>10**f:
factor = 10**f
label = labels[i]
printnames.append(varnames[v])
printbytes.append(filesize)
printsizes.append('%0.3f %s' % (float(filesize/float(factor)), label))
remove(filename)
if order=='a' or order=='alpha' or order=='alphabetical':
inds = argsort(printnames)
else:
inds = argsort(printbytes)
for v in inds:
print('Variable %s is %s' % (printnames[v], printsizes[v]))
if plot==True:
from matplotlib.pylab import pie, array, axes
axes(aspect=1)
pie(array(printbytes)[inds], labels=array(printnames)[inds], autopct='%0.2f')
return None
# 各组销量的总计之和
sizeTotal = size1Count.sum()
# 吧series转换为dataFrame类型
size1 = size1Count.to_frame(name="销量")
# print(size)
# print(type(size))
# 插入比例
scal = 100.0 * size1Count / sizeTotal
# print(scal)
# 设置格式化输出
pandas.options.display.float_format = "{:,.2f}%".format
# 插入比例
size1.insert(0, "比例", scal)
# 修改索引名称
size1.index.names = ['罩杯']
# 打印结果
print(size1)
# 设置标签
lables1 = ["A", "B", "C"]
lables2 = ["A", "B", "C"]
sales_num = size1['销量']
# 绘制病状第一个参数是数据，第二个参数是数据的一个格式化，第三个参数是标签
plb.pie(sales_num, autopct="%.2f%%", labels=lables1)
# 设置圆形并且均匀
plb.axis('equal')
# 设置图的标题
plb.title("罩杯销售量", FontProperties=font)
# 设置示例
plb.legend(lables2)
plb.show()
import matplotlib.pylab as plt # graphic을 위한 모듈
from matplotlib import rc
rc('font', family="Malgun Gothic") # 한글 가능한 폰트 설정
labels = ['개구리', '돼지', '개', '통나무'] # 이름
sizes = [15, 30, 45, 10] # 데이터
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
explode = (0, 0.1, 0, 0) # 중심에서 간격 수치로 지정
plt.title("Pie Chart")
# wedgeprops : 부채꼴 설정
# width : 반지름 대비 비율
# linewidth : 구분선 굵기
wedgeprops = {'width': 0.7, 'edgecolor': 'w', 'linewidth': 2}
#그래프 작성
# autopct : 부채꼴 내부에 표시되는 내용의 형식.
# startangle : 시작되는 위치 각도 지정. 배치순서 반시계 방향.
plt.pie(sizes,
explode=explode,
labels=labels,
colors=colors,
autopct='%1.1f%%',
shadow=True,
startangle=90,
wedgeprops=wedgeprops)
plt.show()
[106.7882176183174, 51.407011395593209, 344.10490569403436, 322.25600000000003, 106.7882176183174, 410.40199893791083, 5.7000277630265392]]
#weight fraction pie chart
the_grid = GridSpec(2, 3)
for i in range(3):
M_fuse_empty = data[i]['fuse_weight.M_tot'] - data[i]['fuse_weight.M_pilots']
M_empty = data[i]['wing_weight.M_tot'] + M_fuse_empty
ax = plt.subplot(the_grid[0, i], aspect=1)
estimated_masses = np.array([data[i]['wing_weight.M_tot'], M_fuse_empty, M_fuse_empty-2.512*(i+1)-3.499*(i+1)])/M_empty
labels = ['wing','pod','control surf.']
#print M_empty, estimated_masses
#print data[i]['wing_weight.s']
plt.pie(estimated_masses, labels=labels, autopct='%.0f%%',)
ax.set_title('%d Pilot'%(i+1))
plt.tight_layout()
plt.savefig('pilot_study_pie.pdf')
#total power req
plt.figure()
fig, ax = plt.subplots()
raw_data = np.array(cantilever_data)
ax.plot([1,2,3,4], raw_data[:,5],label="cantilever")
raw_data = np.array(two_wire_data)
ax.plot([1,2,3,4], raw_data[:,5],label="two-wire")
ax.legend(loc="best")
grouped = df.groupby('Expenditure Account Code Description')
total = grouped.sum().sort_values('Amount',ascending=False)
# Pie chart
grouped = df[~df['Directorate'].isnull()].groupby('Directorate')
total = grouped.sum().sort_values('Amount',ascending=False)
plt.figure(figsize=(7,4))
labels = total.index.values
labels[5:] = ''
cmap = plt.cm.jet
colors = cmap(np.linspace(0., 2., len(total)))
explode = tuple(np.linspace(0,0.8,len(total)))
patches, texts = plt.pie(total['Amount'], radius=0.9, startangle=0,
colors=colors,explode=explode,labels=labels)
_ = [texts[i].set_fontsize(10) for i in range(0,len(total))]
plt.axis('equal')
plt.title('Total Spendings (2013-2016)')
plt.tight_layout()
#plt.legend(labels=labels, loc="best",fontsize=9,bbox_to_anchor=(0.43, 0.6))
plt.plot(data1,data3)
#Plot scatter
plt.cla()
plt.scatter(carDf.PRICE, carDf.WEIGHT, color='r')
#Plot bar charts
plt.cla()
plt.bar(carDf.ID, carDf.PRICE)
plt.cla()
plt.barh(carDf.ID, carDf.WEIGHT)
plt.yticks(carDf.ID, carDf.MODEL)
#Plot pie chart
plt.cla()
plt.pie(carDf.PRICE, labels=carDf.MODEL, shadow=True, autopct='%1.1f')
#Plot a histogram
plt.cla()
plt.hist(data3, color='g')
plt.title('Demo Histogram')
plt.xlabel('Sin Weights')
plt.ylabel('Frequency')
#Plot a boxplot
plt.cla() #pass a list of lists
plt.boxplot([[carDf.WEIGHT[carDf.MAKE=='Toyota']], [carDf.WEIGHT[carDf.MAKE=='Ford']] ], labels=('Toyota','Ford')) #show show weights change by make of car
canvas1 = FigureCanvasTkAgg(f, master=tab4)
canvas1.show()
#Create the FIRST plot
tartita=figure(1, figsize=(3,3),facecolor="white")
ax = axes([0.1, 0.1, 0.8, 0.8])
# The slices will be ordered and plotted counter-clockwise.
labels = 'Cash', 'Credit Card'
pieChartData = pos.generateCCReport()
fracs = [pieChartData[0], pieChartData[1]]
explode=(0, 0.1)
pie(fracs, explode=explode, labels=labels,
autopct=pos.make_autopct(pieChartData), shadow=True, startangle=90, colors= ["#E13F29", "#D69A80"])
canvas = FigureCanvasTkAgg(tartita, master=tab4)
canvas.show()
#Create the third plot
linechartita=figure(3, figsize=(3,3),facecolor="white")
# x axis
lineChartData = pos.getLastDaysSales()
lineChartdays = lineChartData[0]
# y axis
lineChartSales = lineChartData[1]
plot(lineChartdays, lineChartSales)
xlabel('Days')
def findSimilarZip(askZip, ziplist, recorddict, Zipcodes):
xlist = []
ylist = []
ZipList = []
x = Counter(recorddict[askZip])
for i in x.most_common(12):
xlist.append(i[0])
ylist.append(int(i[1]))
myZip = xlist[0:4]
nyZip = ylist[0:4]
labels = xlist
sizes = ylist
colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'blue', 'purple', 'white', 'silver', 'pink', 'lightsalmon', 'lightsage']
explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.2f%%', shadow=False, startangle=140)
plt.axis('equal')
plt.show()
for zips in Zipcodes:
if askZip == zips.Zip:
print "This is located in " + str(zips.Hood) + "!"
labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue','pink', 'purple' ]
explode = (0, 0, 0, 0, 0, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
plt.axis('equal')
plt.show()
for Zip in ziplist:
z = Counter(recorddict[Zip])
y1 = []
z1 = []
for i in z.most_common(12):
y1.append(i[0])
z1.append(i[1])
y2 = y1[0:4]
z2 = z1[0:4]
if myZip == y2 and askZip != Zip:
ZipList.append(Zip)
labels = y1
sizes = z1
colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'blue', 'purple', 'white', 'silver', 'pink', 'lightsalmon', 'lightsage']
explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.2f%%', shadow=False, startangle=140)
plt.axis('equal')
plt.show()
for zips in Zipcodes:
if Zip == zips.Zip:
print str(Zip) + " is similar to " + str(askZip) + "."
print "This is located in " + str(zips.Hood) + "!"
labels = ['White', 'Black', 'AI', 'Asian', 'NH/PI', 'Other', 'Multiple', 'Hispanic']
sizes = [zips.White, zips.Black, zips.AI_AN, zips.Asian, zips.NHOPI, zips.OthRace, zips.MultRaces, zips.Hispanic]
colors = ['red', 'orange', 'yellow', 'green', 'lightskyblue', 'darkblue','pink', 'purple' ]
explode = (0, 0, 0, 0, 0, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=140)
plt.axis('equal')
plt.show()
else:
continue
if len(ZipList) == 0:
print "This zip code is truly unique!"
return ZipList
def first_round_user_query_visualization(df, plot_flag, fig_dir_path):
if plot_flag == 'min':
title_prefix = "Minimum"
else:
title_prefix = "Preferred"
df3, category, perc = get_degree(df, plot_flag)
df4_sorted, color = get_sorted_degree_area(df, plot_flag)
df5_sorted = get_sorted_skills(df, plot_flag)
#print df5_sorted
#df5_sorted = df5_sorted.head(10)
# Create 2x2 sub plots
pie_colors = [[183, 183, 183], [230, 252, 209], [252, 230, 174],
[168, 247, 239], [255, 188, 122], [214, 198, 255],
[255, 226, 242]]
pie_colors = [[
pie_colors[i][0] / 255.0, pie_colors[i][1] / 255.0,
pie_colors[i][2] / 255.0
] for i in range(len(pie_colors))]
gs = gridspec.GridSpec(2, 2)
#plot pie chart for degree
fig = pl.figure(figsize=(18, 12))
ax = pl.subplot(gs[0, 0]) # row 0, col 0
n = pl.pie(perc,
labels=category,
colors=pie_colors[:len(category)],
startangle=90,
autopct='%.1f%%',
textprops={'fontsize': small_fontsize})
ax.set_aspect('equal')
pl.title(title_prefix + " Qualifications - Degree",
fontsize=20,
fontweight='bold')
# plot bar chart for degree areas
ax = pl.subplot(gs[0, 1]) # row 0, col 1
objects = df4_sorted["Degree_Area"]
y_pos = np.arange(len(objects))
performance = df4_sorted["Percent"]
pl.barh(y_pos, performance, align='center', alpha=0.5, color=tuple(color))
pl.yticks(y_pos, objects, rotation=0, fontsize=small_fontsize)
pl.xlabel('Percent %', fontsize=14)
pl.xlim([0, 100])
pl.title('Degree Majors', fontsize=20, fontweight='bold')
#pl.rcParams.update({'font.size': 8})
ax = pl.subplot(gs[1, :]) # row 1, span all columns
df5_sorted = df5_sorted.iloc[:15]
df5_sorted = df5_sorted.sort_values(['Count'], ascending=[1])
objects = df5_sorted["Exp/Skills"]
y_pos = np.arange(len(objects))
performance = df5_sorted["Percent"]
pl.barh(y_pos, performance, align='center', alpha=0.5)
pl.yticks(y_pos, objects, rotation=0, fontsize=small_fontsize)
pl.xlabel('Percent %', fontsize=14)
pl.xlim([0, 30])
pl.title('Top 15 Exp/Skills', fontsize=20, fontweight='bold')
pl.show()
def tradeanalyze4(rpt,fname,outputDir,data):
""" エグジット要因ごとの勝率
"""
print "\n--------------------------------------------"
print "- Exit \n"
print 'Entry -> Exit : profit rate [win/total]'
cc = []
cd = []
count = 1
for t in ['close','t/p','s/l']:
f = filter(lambda n:n[6]==t ,rpt.buyorder)
cc.append(len(f))
pc,nc = winlossCount(f)
if pc+nc>0:
t0 = "%6.2f %% [ %3d / %3d ]" % (100*float(pc)/float(pc+nc),pc,\
(pc+nc))
cd.append(100*float(pc)/float(pc+nc))
t1 = "Long - %6s : " % (t)
print t1 + t0
t3 = "$$T0040%02d$$" % (count)
count = count+1
data.update({t3:t0})
else:
t3 = "$$T0040%02d$$" % (count)
count = count+1
data.update({t3:'NA'})
cd.append(0.)
for t in ['close','t/p','s/l']:
f = filter(lambda n:n[6]==t ,rpt.sellorder)
cc.append(len(f))
pc,nc = winlossCount(f)
if pc+nc>0:
t0 = "%6.2f %% [ %3d / %3d ]" % (100*float(pc)/float(pc+nc),pc,\
(pc+nc))
cd.append(100*float(pc)/float(pc+nc))
t1 = "Short- %6s : " % (t)
print t1 + t0
t3 = "$$T0040%02d$$" % (count)
count = count+1
data.update({t3:t0})
else:
t3 = "$$T0040%02d$$" % (count)
count = count+1
data.update({t3:'NA'})
cd.append(0.)
s = np.array([cd[0],cd[1],cd[2]])
b = np.array([cd[3],cd[4],cd[5]])
x = np.array(range(len(s)))
xt = ['close','t/p','s/l']
gw = 0.4
plt.figure(figsize=(5,2))
plt.title('Profit rate by exit ')
plt.xlabel("Profit rate")
plt.xlim(0,100)
#plt.ylim(0+0.5,5-0.5)
plt.yticks(x,xt)
plt.grid(True)
plt.barh(x -gw/2, s, height = gw, align='center',color='b',alpha=0.5,\
label='Short')
plt.barh(x +gw/2, b, height = gw, align='center',color='g',alpha=0.5,\
label='Long' )
plt.legend()
plt.axvline(50,color='r')
plt.savefig(outputDir+fname+"test41.png",dpi=72)
data.update({'$$I004001$$':fname+"test41.png"})
#plt.show()
plt.draw()
ac = cc[0]+cc[3]
tc = cc[1]+cc[4]
sc = cc[2]+cc[5]
tt = ac+tc+sc
print " "
t0 = "%6.2f %% [ %4d / %4d ]" % (100*float(ac)/float(tt),ac,tt)
t1 = "%6.2f %% [ %4d / %4d ]" % (100*float(tc)/float(tt),tc,tt)
t2 = "%6.2f %% [ %4d / %4d ]" % (100*float(sc)/float(tt),sc,tt)
print "Exit : rate [count/total]"
print "Close : " + t0
print "t/p : " + t1
print "s/l : " + t2
data.update({'$$T005001$$':t0})
data.update({'$$T005002$$':t1})
data.update({'$$T005003$$':t2})
from matplotlib import cm
plt.figure(figsize=(2,2))
names =[ 'close','t/p', 's/l']
# それぞれの割合を用意
ratios = [ac, tc,sc]
# どれだけ飛び出すか指定
moves=(0, 0, 0)
# 適当なカラーをマッピング
col = cm.Set2(np.arange(3)/3.,0.7)
# 円グラフを描画（影付き）
plt.pie(ratios, explode=moves, labels=names, autopct='%1d%%',\
shadow=True,colors=col)
# 円グラフ他ので縦横比は等しく
plt.gca().set_aspect('equal')
plt.title('Exit count')
plt.savefig(outputDir+fname+"test42.png",dpi=72)
data.update({'$$I004002$$':fname+"test42.png"})
#plt.show()
plt.draw()
plt.close('all')
