def netChart(ymNetPath, qqNetPath, xyNetPath):
transLine = pyecharts.Line("游密、qq、小鱼性能对比", "transmit流量(KB)")
recLine = pyecharts.Line("游密、qq、小鱼性能对比", "receive流量(KB)")
ymTran, ymRec, ymbtran, ymbrec = netData(ymNetPath)
qqTran, qqRec, qqbtran, qqbrec = netData(qqNetPath)
xyTran, xyRec, xybtran, xybrec = netData(xyNetPath)
transLine.add("游密", [x for x in range(len(ymTran))],
ymTran,
is_more_utils=True) # 标题
transLine.add("qq", [x for x in range(len(qqTran))],
qqTran,
is_more_utils=True) # 标题
transLine.add("小鱼", [x for x in range(len(xyTran))],
xyTran,
is_more_utils=True) # 标题
# transLine.show_config() # 展示HTML源代码
transLine.render("Net_trans.html")
recLine.add("游密", [x for x in range(len(ymRec))],
ymRec,
is_more_utils=True) # 标题
recLine.add("qq", [x for x in range(len(qqRec))],
qqRec,
is_more_utils=True) # 标题
recLine.add("小鱼", [x for x in range(len(xyRec))],
xyRec,
is_more_utils=True) # 标题
# recLine.show_config() # 展示HTML源代码
recLine.render("Net_rec.html")
btranLine = pyecharts.Line("游密、qq、小鱼性能对比", "上行带宽码率(KB/s)")
brecLine = pyecharts.Line("游密、qq、小鱼性能对比", "下行带宽码率(KB/s)")
btranLine.add("游密", [x for x in range(len(ymbtran))],
ymbtran,
is_more_utils=True) # 标题
btranLine.add("qq", [x for x in range(len(qqbtran))],
qqbtran,
is_more_utils=True) # 标题
btranLine.add("小鱼", [x for x in range(len(xybtran))],
xybtran,
is_more_utils=True) # 标题
# btranLine.show_config() # 展示HTML源代码
btranLine.render("br_trans.html")
brecLine.add("游密", [x for x in range(len(ymbrec))],
ymbrec,
is_more_utils=True) # 标题
brecLine.add("qq", [x for x in range(len(qqbrec))],
qqbrec,
is_more_utils=True) # 标题
brecLine.add("小鱼", [x for x in range(len(xybrec))],
xybrec,
is_more_utils=True) # 标题
# brecLine.show_config() # 展示HTML源代码
brecLine.render("br_rec.html")
def year_line():
douban = pd.read_csv(os.getcwd() + "\douban_clean.csv")
douban2 = douban['star'].groupby(douban['name']).count().to_frame()
douban2 = douban2[douban2['star'] >= 5].reset_index()
douban3 = pd.merge(douban2, douban, on='name')
year_info = douban['name'].groupby(douban['year']).count()
year_info2 = douban['people'].groupby(douban['year']).mean()
year_info3 = douban['star'].groupby(douban['year']).mean()
year_info = pd.DataFrame(year_info)
year_info2 = pd.DataFrame(year_info2)
year_info3 = pd.DataFrame(year_info3)
year_all = pd.concat([year_info, year_info2, year_info3], axis=1)
year_all.index = year_all.index.astype('str')
year_all = year_all.round(2)
print(year_all)
line1 = py.Line(page_title="出版年份与出版数量折线图", width=1200)
line1.add(
"出版数量",
year_all.index,
year_all['name'],
is_label_show=True,
mark_point=["average", "max", "min"],
mark_point_symbol="diamond",
mark_point_textcolor="#40ff27",
is_more_utils=True,
)
line2 = py.Line(page_title="出版年份与评价平均人数折线图", width=1800)
line2.add(
"评价平均人数",
year_all.index,
year_all['people'],
is_label_show=True,
is_more_utils=True,
)
line3 = py.Line(page_title="出版年份与评价平均数折线图", width=1800)
line3.add(
"评分平均数",
year_all.index,
year_all['star'],
is_label_show=True,
mark_point=["average", "max", "min"],
mark_point_symbol="diamond",
mark_point_textcolor="#40ff27",
is_more_utils=True,
)
line1.render("numByYear1.html")
line2.render("numByYear2.html")
line3.render("numByYear3.html")
def netChart(ymNetPath,agNetPath):
transLine = pyecharts.Line("游密声网性能对比", "transmit流量(KB)")
recLine = pyecharts.Line("游密声网性能对比", "receive流量(KB)")
ymTran,ymRec= netData(ymNetPath)
agTran,agRec = netData(agNetPath)
transLine.add("游密", [x for x in range(len(ymTran))], ymTran, is_more_utils=True) # 标题
transLine.add("声网", [x for x in range(len(agTran))], agTran, is_more_utils=True) # 标题
transLine.show_config() # 展示HTML源代码
transLine.render("Net_trans.html")
recLine.add("游密", [x for x in range(len(ymRec))], ymRec, is_more_utils=True) # 标题
recLine.add("声网", [x for x in range(len(agRec))], agRec, is_more_utils=True) # 标题
recLine.show_config() # 展示HTML源代码
recLine.render("Net_rec.html")
def pr_curve(y_true,
y_pred,
pos_label=1,
jupyter=True,
path='Precision Recall Curve.html'):
t = pd.DataFrame({'prob': y_pred, 'label': y_true})
assert t.label.nunique() == 2, "`y_true` should be binary classification."
label_dict = {i: 1 if i == pos_label else 0 for i in t.label.unique()}
t['label'] = t.label.replace(label_dict)
t = t.sort_values(['prob', 'label'],
ascending=False).reset_index(drop=True)
t['tp'] = t.label.cumsum()
t['fp'] = t.index + 1 - t.tp
t['recall'] = t.tp / t.label.sum()
t['precision'] = t.tp / (t.tp + t.fp)
line = pe.Line("PR Curve")
line.add("PR", [0] + t.recall.round(4).tolist(),
[1] + t.precision.round(4).tolist(),
is_smooth=True,
is_fill=True,
area_opacity=0.4,
yaxis_type='value',
xaxis_type='value',
xaxis_name='recall',
yaxis_name='precision')
return line if jupyter else line.render(path)
def roc_curve(y_true,
y_pred,
pos_label=1,
jupyter=True,
path='Receiver Operating Characteristic Curve.html'):
t = pd.DataFrame({'prob': y_pred, 'label': y_true})
assert t.label.nunique() == 2, "`y_true` should be binary classification."
label_dict = {i: 1 if i == pos_label else 0 for i in t.label.unique()}
t['label'] = t.label.replace(label_dict)
t = t.sort_values(['prob', 'label'],
ascending=False).reset_index(drop=True)
t['tp'] = t.label.cumsum()
t['fp'] = t.index + 1 - t.tp
t['tpr'] = t.tp / t.label.sum()
t['fpr'] = t.fp / (t.label.count() - t.label.sum())
line = pe.Line("ROC Curve")
line.add("ROC", [0] + t.fpr.round(4).tolist(),
[0] + t.tpr.round(4).tolist(),
is_smooth=True,
is_fill=True,
area_opacity=0.4,
yaxis_type='value',
xaxis_type='value')
line.add("Random", [0] + t.fpr.round(4).tolist(),
[0] + t.fpr.round(4).tolist(),
yaxis_type='value',
xaxis_type='value',
xaxis_name='FPR',
yaxis_name='TPR')
return line if jupyter else line.render(path)
def gini_curve(y_true,
y_pred,
pos_label=1,
jupyter=True,
path='Gini Curve.html'):
t = pd.DataFrame({'prob': y_pred, 'label': y_true})
assert t.label.nunique() == 2, "`y_true` should be binary classification."
label_dict = {i: 1 if i == pos_label else 0 for i in t.label.unique()}
t['label'] = t.label.replace(label_dict)
t = t.sort_values(['prob', 'label']).reset_index(drop=True)
t['cum'] = t.label.cumsum() / t.label.sum()
t.index = np.round(((t.index + 1) / len(t)), 2)
line = pe.Line("Gini Curve")
line.add("Random", [0] + t.index.tolist(), [0] + t.index.tolist(),
is_fill=True,
area_opacity=0.4,
is_smooth=True,
yaxis_type='value',
xaxis_type='value')
line.add("Gini", [0] + t.index.tolist(), [0] + t.cum.round(4).tolist(),
is_fill=True,
area_opacity=0.4,
is_smooth=True,
yaxis_type='value',
xaxis_type='value')
return line if jupyter else line.render(path)
def ks_curve(y_true,
y_pred,
pos_label=1,
jupyter=True,
path='Kolmogorov-Smirnov Curve.html'):
t = pd.DataFrame({'prob': y_pred, 'label': y_true})
assert t.label.nunique() == 2, "`y_true` should be binary classification."
label_dict = {i: 1 if i == pos_label else 0 for i in t.label.unique()}
t['label'] = t.label.replace(label_dict)
t = t.sort_values(['prob', 'label'],
ascending=False).reset_index(drop=True)
t['tp'] = t.label.cumsum()
t['fp'] = t.index + 1 - t.tp
t['tpr'] = t.tp / t.label.sum()
t['fpr'] = t.fp / (t.label.count() - t.label.sum())
t['ks'] = (t.tpr - t.fpr).abs().round(4)
t.index = np.round(((t.index + 1) / len(t)), 2)
line = pe.Line("Kolmogorov-Smirnov Curve")
line.add("TPR", [0] + t.index.tolist(), [0] + t.tpr.round(4).tolist(),
is_smooth=True,
yaxis_type='value',
xaxis_type='value')
line.add("FPR", [0] + t.index.tolist(), [0] + t.fpr.round(4).tolist(),
is_smooth=True,
yaxis_type='value',
xaxis_type='value')
line.add("KS", [0] + t.index.tolist(), [0] + t.ks.tolist(),
is_smooth=True,
mark_point=["max"],
yaxis_type='value',
xaxis_type='value')
return line if jupyter else line.render(path)
def plot_oil_price_(re_dic, to_path):
dw = my_(config.MYSQL_BI_RW_ENV)
sql1 = "select * from bi_papapa.city_oil_price where origin='%(origin)s' and province='%(region)s' group by offer_date,oil_number order by offer_date desc limit 400; " % re_dic
data2 = dw.to_dataframe(sql1)
data2 = data2[::-1]
# data2['offer_date'] = pd.DatetimeIndex(data2['offer_date']).strftime('%Y-%m-%d')
# offer_date=pd.DatetimeIndex(data2['offer_date']).strftime('%Y-%m-%d')
# func = lambda x, y: x if y in x else x + [y]
price_min = data2['price'].min()
offer_date = data2.loc[data2['offer_date'].duplicated() == False, ['offer_date', 'price']]
oil_number_0 = data2[data2['oil_number'] == '0'][['offer_date', 'price']] # data2.query('oil_number=0')[['offer_date', 'price']]
oil_number_89 = data2[data2['oil_number'] == '89'][['offer_date', 'price']]
oil_number_92 = data2[data2['oil_number'] == '92'][['offer_date', 'price']]
oil_number_95 = data2[data2['oil_number'] == '95'][['offer_date', 'price']]
oil_number_98 = data2[data2['oil_number'] == '98'][['offer_date', 'price']]
dfr = offer_date.merge(oil_number_0, how='left', on='offer_date', suffixes=['','_0'])\
.merge(oil_number_89, how='left', on='offer_date', suffixes=['','_89'])\
.merge(oil_number_92, how='left', on='offer_date', suffixes=['','_92'])\
.merge(oil_number_95, how='left', on='offer_date', suffixes=['','_95'])\
.merge(oil_number_98, how='left', on='offer_date', suffixes=['', '_98'])
line = pyecharts.Line(re_dic['region'], "油价趋势："+re_dic['origin'], width=1400, height=700)
tem_dic = dict(label_text_size=12, is_label_show=True,
is_fill=True, area_opacity=0.12, line_width=2, is_smooth=True, mark_point=["average"],
yaxis_min=price_min)
line.add("0号柴油", dfr['offer_date'], dfr['price_0'], **tem_dic)
line.add("89号汽油", dfr['offer_date'], dfr['price_89'], **tem_dic)
line.add("92号汽油", dfr['offer_date'], dfr['price_92'], **tem_dic)
line.add("95号汽油", dfr['offer_date'], dfr['price_95'], **tem_dic)
line.add("98号汽油", dfr['offer_date'], dfr['price_98'], **tem_dic)
line.render(to_path)
def plot_more_line(data,name,title,x_roate = 0,y_roate = 0):
"""
绘制多条折现
:param data:
:param name:
:param title:
:param x_roate:
:param y_roate:
:return:
"""
stackBar = pyecharts.Line(title)
label = []
legend = ['max','min','ave','most']
maxSizes = []
minSizes = []
aveSizes = []
mostSizes = []
for row in data:
label.append(row[0])
maxSizes.append(row[1]['max'])
minSizes.append(row[1]['min'])
aveSizes.append(row[1]['ave'])
mostSizes.append(row[1]['most'])
stackBar.add(legend[1],label,minSizes,)
stackBar.add(legend[2],label,aveSizes,)
stackBar.add(legend[3],label,mostSizes,mark_line=["average"])
stackBar.add(legend[0],label,maxSizes,is_datazoom_show=True,is_smooth=True,xaxis_interval=0,xaxis_rotate=x_roate, yaxis_rotate=y_roate)
stackBar.render('templates/{}.html'.format(name))
def read_csv():
pddata = pd.read_csv('/Users/ludanqing/Desktop/test3.csv')
# 返回全部列名
# cols = pddata.columns
# print(pddata['日期'][1])
bar = pyecharts.Line("一周访问量", "千里眼")
time = pddata['日期']
today = pddata['数据1']
yesterday = pddata['数据2']
# time = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
# today = [5, 20, 36, 10, 75, 90, 28]
# yesterday = [25, 10, 56, 70, 25, 40, 88]
bar.add("这周",
time,
today,
mark_point=["max", "min"],
mark_line=["average"],
is_more_utils=True)
bar.add("上周",
time,
yesterday,
mark_point=["max", "min"],
mark_line=["average"],
is_more_utils=True)
return bar
def create_chart():
g_r, result = get_data()
result[0] = 0
line = pyecharts.Line(str1, strTime)
line.add(str1, g_r, result, is_label_show=True)
#line.add("最低气温", cities, lows, mark_line=['average'], is_smooth=True)
line.render('hezhi.png')
def visual_histo_pye(self, savePath):
names = self.data[0][:self.TOP]
boxfiles = self.data[1][:self.TOP]
bar = pye.Bar('TOP{} Films in {}'.format(self.TOP, self.YEAR),
width=1080,
height=640)
bar.add("bar",
names,
boxfiles,
yaxis_formatter=" M$",
is_toolbox_show=False,
xaxis_rotate=15)
line = pye.Line(width=1080, height=640)
line.add('line',
names,
boxfiles,
is_toolbox_show=False,
xaxis_rotate=15)
overlap = pye.Overlap('TOP{} Films in {}'.format(self.TOP, self.YEAR),
width=1080,
height=640)
overlap.add(bar)
overlap.add(line)
overlap.render(savePath)
def click2(self):
conn = pymysql.connect(host='localhost',
user='******',
password='******',
port=3306,
db='maoyan',
charset='utf8mb4')
sql = "select * from films"
db = pd.read_sql(sql, conn)
y = [([0] * 13) for i in range(4)]
t1 = threading.Thread(target=self.myshow)
t2 = threading.Thread(target=self.mytr)
for i in range(2015, 2019, 1):
for j in range(1, 13, 1):
y[i - 2015][j] = db[(db.year == i)
& (db.month == j)].sum()["box_office"]
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
y1 = y[0]
y2 = y[1]
y3 = y[2]
y4 = y[3]
line = pyecharts.Line("2015 to 2018", width=1200, height=500)
line.add("2015", x, y1)
line.add("2016", x, y2)
line.add("2017", x, y3)
line.add("2018", x, y4)
line.render("2015_to_2018.html")
self.widget.load(QUrl('file:///2015_to_2018.html'))
t1.start()
t2.start()
def lift_curve(y_true,
y_pred,
pos_label=1,
jupyter=True,
path='Lift Curve.html'):
t = pd.DataFrame({'prob': y_pred, 'label': y_true})
assert t.label.nunique() == 2, "`y_true` should be binary classification."
label_dict = {i: 1 if i == pos_label else 0 for i in t.label.unique()}
t['label'] = t.label.replace(label_dict)
t = t.sort_values(['prob', 'label'],
ascending=False).reset_index(drop=True)
t['tp'] = t.label.cumsum()
t['fp'] = t.index + 1 - t.tp
t['precision'] = t.tp / (t.tp + t.fp)
t['lift'] = t.precision / t.label.sum() * t.label.count()
t.index = np.round(((t.index + 1) / len(t)), 2)
line = pe.Line("Lift Curve")
line.add("Lift",
t.index.tolist(),
t.lift.round(3).tolist(),
is_smooth=True,
yaxis_type='value',
xaxis_type='value')
line.add("Random",
t.index.tolist(), [1] * len(t),
is_smooth=True,
yaxis_type='value',
xaxis_type='value')
return line if jupyter else line.render(path)
def plot_fill_line(data,name,title):
label = [i[0] for i in data]
lengend = ['0-10','10-20','20-30','30-40','40-50',
'50-60','60-70','70-80','80-90','90-100','>100']
size_0 = [i[1]['0-10'] for i in data]
size_1 = [i[1]['10-20'] for i in data]
size_2 = [i[1]['20-30'] for i in data]
size_3 = [i[1]['30-40'] for i in data]
size_4 = [i[1]['40-50'] for i in data]
size_5 = [i[1]['50-60'] for i in data]
size_6 = [i[1]['60-70'] for i in data]
size_7 = [i[1]['70-80'] for i in data]
size_8 = [i[1]['80-90'] for i in data]
size_9 = [i[1]['90-100'] for i in data]
size_10 = [i[1]['>100'] for i in data]
fillLine = pyecharts.Line(title)
fillLine.add(lengend[0],label,size_0,is_smooth=True,is_fill=True)
fillLine.add(lengend[1],label,size_1,is_smooth=True,is_fill=True)
fillLine.add(lengend[2],label,size_2,is_smooth=True,is_fill=True)
fillLine.add(lengend[3],label,size_3,is_smooth=True,is_fill=True)
fillLine.add(lengend[4],label,size_4,is_smooth=True,is_fill=True)
fillLine.add(lengend[5],label,size_5,is_smooth=True,is_fill=True)
fillLine.add(lengend[6],label,size_6,is_smooth=True,is_fill=True)
fillLine.add(lengend[7],label,size_7,is_smooth=True,is_fill=True)
fillLine.add(lengend[8],label,size_8,is_smooth=True,is_fill=True)
fillLine.add(lengend[9],label,size_9,is_smooth=True,is_fill=True)
fillLine.add(lengend[10],label,size_10,is_smooth=True,is_fill=True)
fillLine.render('templates/{}.html'.format(name))
def plot_station_lostavg(re_dic, to_path):
re_dic = add_whereadd_redic(re_dic)
dic_station, dic_smarttime = get_station_detail_(re_dic)
re_dic['stname'] = '%s-%s' % (dic_station['city'], dic_station['stname'])
dw = my_(config.MYSQL_BI_RW_ENV)
sql_mer = """SELECT
sum(if(spr_id<3,value_c,0)) users_7,sum(if(spr_id<4,value_c,0)) users_15,sum(value_c*value_e) lostday_a,sum(value_c) users_a,batch_date
FROM profile.pr_stations_user_dis_ where mer_id = %(mer_id)s and tar_id = 30 %(whereadd)s group by batch_date""" % re_dic
sql_all = """SELECT
sum(if(spr_id<3,value_c,0)) users_7,sum(if(spr_id<4,value_c,0)) users_15,sum(value_c*value_e) lostday_a,sum(value_c) users_a,batch_date
FROM profile.pr_platforms_user_dis_ where mer_id = 0 and tar_id = 30 %(whereadd)s group by batch_date""" % re_dic
data_mer = dw.to_dataframe(sql_mer)
data_all = dw.to_dataframe(sql_all)
page = pyecharts.Page(page_title='流失分析',)
X = data_all['batch_date']
tem_dic = dict(label_text_size=12, is_label_show=False,
is_fill=True, area_opacity=0.08, line_width=2, is_smooth=True, mark_line=["average"]
, xaxis_interval=1, xaxis_rotate=45)
line1 = pyecharts.Line("平均流失天数趋势：", "%(stname)s(%(mer_id)s)" % re_dic, width=1500, height=600)
lostavg_mer = (data_mer['lostday_a'] / data_mer['users_a']).round(2)
lostavg_all = (data_all['lostday_a'] / data_all['users_a']).round(2)
line1.add("油站", X, lostavg_mer, **tem_dic)
line1.add("全平台", X, lostavg_all, **tem_dic)
page.add(line1)
rrate_mer = (data_mer['users_15'] / data_mer['users_a']).round(4)
rrate_all = (data_all['users_15'] / data_all['users_a']).round(4)
line2 = pyecharts.Line("流失15日内的用户占比：", "%(stname)s(%(mer_id)s)" % re_dic, width=1500, height=600)
conb_ = np.append(rrate_mer, rrate_all)
tem_dic.update({'yaxis_min': conb_.min() * 0.6})
lineadd_sted(line2, dic_smarttime, X, conb_.max() * 1.2)
line2.add("油站", X, rrate_mer, **tem_dic)
line2.add("全平台", X, rrate_all, **tem_dic)
page.add(line2)
rrate_mer = (data_mer['users_7'] / data_mer['users_a']).round(4)
rrate_all = (data_all['users_7'] / data_all['users_a']).round(4)
line2 = pyecharts.Line("流失7日内的用户占比：", "%(stname)s(%(mer_id)s)" % re_dic, width=1500, height=600)
conb_ = np.append(rrate_mer, rrate_all)
tem_dic.update({'yaxis_min': conb_.min() * 0.6})
lineadd_sted(line2, dic_smarttime, X, conb_.max() * 1.2)
line2.add("油站", X, rrate_mer, **tem_dic)
line2.add("全平台", X, rrate_all, **tem_dic)
page.add(line2)
page.render(to_path)
def cpuChart(ymCpuPath,agCpuPath):
cpuLine = pyecharts.Line("游密声网性能对比", "cpu(%)")
ymCpu = cpuData(ymCpuPath)
agCpu = cpuData(agCpuPath)
cpuLine.add("游密", [x for x in range(len(ymCpu))], ymCpu, is_more_utils=True) # 标题
cpuLine.add("声网", [x for x in range(len(agCpu))], agCpu, is_more_utils=True) # 标题
cpuLine.show_config() # 展示HTML源代码
cpuLine.render("cpu.html")
def memChart(ymMemPath,agMemPath):
memLine = pyecharts.Line("游密声网性能对比", "内存(Mb)")
ymMem = memData(ymMemPath)
agMem = memData(agMemPath)
memLine.add("游密", [x for x in range(len(ymMem))], ymMem, is_more_utils=True) # 标题
memLine.add("声网", [x for x in range(len(agMem))], agMem, is_more_utils=True) # 标题
memLine.show_config() # 展示HTML源代码
memLine.render("mem.html")
def create_Line():
line = pyecharts.Line("气温变化折线图", '2018-4-16', width=1200, height=600)
line.add("最高气温",
cities,
highs,
mark_point=['average'],
is_datazoom_show=True)
line.add("最低气温", cities, lows, mark_line=['average'], is_smooth=True)
# line.render('Line-High-Low.html')
page.add(line)
def test_add(*args, **kwargs):
line = pe.Line('Test Line add ' + kwargs.get('data_type', ''))
line.add(*args)
line.show_config()
line.render('./test_line_add_{}.html'.format(kwargs.get('data_type', '')))
bar = pe.Bar('Test Bar add ' + kwargs.get('data_type', ''))
bar.add(*args)
bar.show_config()
bar.render('./test_bar_add_{}.html'.format(kwargs.get('data_type', '')))
def test_add_df(df):
line = pe.Line('Test Line add_df')
line.add_df(df)
line.show_config()
line.render('./test_line_add_df.html')
bar = pe.Bar('Test Bar add_df')
bar.add_df(df)
bar.show_config()
bar.render('./test_bar_add_df.html')
def volChart(ymVolPath,agVolPath):
volLine = pyecharts.Line("游密声网性能对比", "电压(V)")
ymVol = volData(ymVolPath)
agVol = volData(agVolPath)
print(ymVol)
print(agVol)
volLine.add("游密", [x for x in range(len(ymVol))], ymVol, is_more_utils=True) # 标题
volLine.add("声网", [x for x in range(len(agVol))], agVol, is_more_utils=True) # 标题
volLine.show_config() # 展示HTML源代码
volLine.render("vol.html")
def curChart(ymCurPath,agCurPath):
curLine = pyecharts.Line("游密声网性能对比", "电流(mA)")
ymCur = curData(ymCurPath)
agCur = curData(agCurPath)
print(ymCur)
print(agCur)
curLine.add("游密", [x for x in range(len(ymCur))], ymCur, is_more_utils=True) # 标题
curLine.add("声网", [x for x in range(len(agCur))], agCur, is_more_utils=True) # 标题
curLine.show_config() # 展示HTML源代码
curLine.render("cur.html")
def visual_histo_pye(self, savePath):
names = list(self.data.keys())[-self.TOP:]
joinFilms = list(self.data.values())[-self.TOP:]
bar = pye.Bar(width=1080, height=640)
bar.add("bar", names, joinFilms, is_toolbox_show=False)
line = pye.Line(width=1080, height=640)
line.add('line', names, joinFilms, is_toolbox_show=False)
overlap = pye.Overlap('TOP{} Actors in {}'.format(self.TOP, self.YEAR),
'sorted by JoinFilms')
overlap.add(bar)
overlap.add(line)
overlap.render(savePath)
def visual_line_pye(self, data, savePath):
_xticks_labels = ['spring', 'summer', 'fall', 'winter']
line = pye.Line("Boxfile Change Trend by seasons",
'in Millions',
width=1080,
height=640)
for YEAR in self.years:
y = data[YEAR]
line.add('Year' + str(YEAR),
_xticks_labels,
y,
is_toolbox_show=False)
line.render(savePath)
def construct_graph(self, x_kv, y_kv, x_label_name):
"""
construct graph (x axis is the thread count)
:param x_kv:
:param y_kv:
:param x_label_name:
:return:
"""
lines = {}
for x_value in x_kv.values():
db_type = x_value.get(self.db_type_key)
if lines.get(db_type) is None: lines.setdefault(db_type, [])
x_tmp_list = []
for k, x_value in x_kv.items():
y_value = y_kv.get(k)
db_type = x_value.get(self.db_type_key)
x, y = x_value.get(x_label_name), y_value.get(self.ops_key)
x_tmp_list.append(x)
li = lines.get(db_type)
li += [(x, y)]
lines.update({db_type: li})
# sort by x value
for k in lines.keys():
lines.update({k: sorted(lines.get(k), key=lambda item: item[0])})
# Now every line could construct the single line in the graph
# Every line denotes the target DB type
workload_map = read_workload()
title = x_value[self.workload_key].title()
sub_title = workload_map[x_value[self.workload_key]] if x_value[self.workload_key] in workload_map else ''
line = pyecharts.Line(title,
sub_title,
height=300,
title_text_size=14,
subtitle_text_size=10,
title_pos='center')
# https://pyecharts.readthedocs.io/en/latest/zh-cn/%E5%9B%BE%E5%BD%A2%E7%AF%87/
for label, v in lines.items():
X, y = [str(item[0]) for item in v], [item[1] for item in v]
line.add(label, X, y,
xaxis_name=' '.join(x_label_name.split('_')),
yaxis_name=self.ops_key,
xaxis_name_pos='middle',
yaxis_name_pos='end',
legend_pos='right',
legend_orient='vertical'
)
return line
def curChart(ymCurPath, qqCurPath, xyCurPath):
curLine = pyecharts.Line("游密、qq、小鱼性能对比", "电流(mA)")
ymCur = curData(ymCurPath)
qqCur = curData(qqCurPath)
xyCur = curData(xyCurPath)
curLine.add("游密", [x for x in range(len(ymCur))],
ymCur,
is_more_utils=True) # 标题
curLine.add("qq", [x for x in range(len(qqCur))],
qqCur,
is_more_utils=True) # 标题
curLine.add("xy", [x for x in range(len(xyCur))],
xyCur,
is_more_utils=True) # 标题
# curLine.show_config() # 展示HTML源代码
curLine.render("cur.html")
def memChart(ymMemPath, qqMemPath, xyMemPath):
memLine = pyecharts.Line("游密、qq、小鱼性能对比", "内存(MB)")
ymMem = memData(ymMemPath)
qqMem = memData(qqMemPath)
xyMem = memData(xyMemPath)
memLine.add("游密", [x for x in range(len(ymMem))],
ymMem,
is_more_utils=True) # 标题
memLine.add("qq", [x for x in range(len(qqMem))],
qqMem,
is_more_utils=True) # 标题
memLine.add("小鱼", [x for x in range(len(xyMem))],
xyMem,
is_more_utils=True) # 标题
# memLine.show_config() # 展示HTML源代码
memLine.render("mem.html")
def cpuChart(ymCpuPath, qqCpuPath, xyCpuPath):
cpuLine = pyecharts.Line("游密、qq、小鱼性能对比", "cpu(%)")
ymCpu = cpuData(ymCpuPath)
qqCpu = cpuData(qqCpuPath)
xyCpu = cpuData(xyCpuPath)
cpuLine.add("游密", [x for x in range(len(ymCpu))],
ymCpu,
is_more_utils=True) # 标题
cpuLine.add("qq", [x for x in range(len(qqCpu))],
qqCpu,
is_more_utils=True) # 标题
cpuLine.add("小鱼", [x for x in range(len(xyCpu))],
xyCpu,
is_more_utils=True) # 标题
# cpuLine.show_config() # 展示HTML源代码
cpuLine.render("cpu.html")
def volChart(ymVolPath, qqVolPath, xyVolPath):
volLine = pyecharts.Line("游密、qq、小鱼性能对比", "电压(V)")
ymVol = volData(ymVolPath)
qqVol = volData(qqVolPath)
xyVol = volData(xyVolPath)
volLine.add("游密", [x for x in range(len(ymVol))],
ymVol,
is_more_utils=True) # 标题
volLine.add("qq", [x for x in range(len(qqVol))],
qqVol,
is_more_utils=True) # 标题
volLine.add("小鱼", [x for x in range(len(xyVol))],
xyVol,
is_more_utils=True) # 标题
# volLine.show_config() # 展示HTML源代码
volLine.render("vol.html")
