def main():
dutPl0 = []
dutpl1 = []
maxCntPl0 = []
maxCntPl1 = []
page = Page("Cluster Padding Figure")
txtFiles = [name for name in os.listdir('./') if name.endswith('.txt')]
for textLogName in txtFiles:
waferNum = re.split(r'_', textLogName)
with open(textLogName, 'rt') as clusterFile:
for line in clusterFile:
if re.match(r'IncomingBBKCount[A-Za-z0-9_.\+\-\*\/\s]*BBKP', line):
splitColon = re.split(r'\s*[:]\s*', line)
splitColon1 = splitColon[0]
splitColon2 = splitColon[1]
splitColon3 = splitColon[2]
listBlk = re.split(r'\s*[\s]\s*', splitColon3)
padBlk = paddingBlk(listBlk)
maxCnt = clusterBlk(padBlk)
splitSpace = re.split(r'\s*[\s]\s*', splitColon1)
intDut = int(splitSpace[1].replace('DUT', ''))
if splitSpace[2] == "BBKP0":
dutPl0.append(intDut)
maxCntPl0.append(maxCnt)
else:
dutpl1.append(intDut)
maxCntPl1.append(maxCnt)
scatter = Scatter(waferNum[2])
scatter.add("PL0", dutPl0, maxCntPl0, xaxis_name = "DUT", yaxis_name = "Max Padding BB", is_more_utils = True)
scatter.add("PL1", dutpl1, maxCntPl1, xaxis_name = "DUT", yaxis_name = "Max Padding BB", is_more_utils = True)
page.add(scatter)
page.render()
def test_page():
page = Page()
line = Line("折线图示例")
line.chart_id = "id_my_cell_line"
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center", width="600px")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie, create_a_bar(TITLE)])
# Start render and test
html = page._repr_html_()
# Test base html structure
assert html.count("<script>") == html.count("</script>") == 2
assert html.count("<div") == html.count("</div>") == 3
assert html.count("require.config") == html.count("function(echarts)") == 1
# Test some chart attributes
json_encoded_title = json.dumps(TITLE)
assert json_encoded_title in html
assert "nbextensions/echarts" in html # default jshost
assert html.count("height:400px") == 3
assert html.count("width:600px") == 1
assert html.count("width:800px") == 2
assert html.count("id_my_cell_line") == 6
def create_three():
page = Page(page_title=TEST_PAGE_TITLE)
# bar
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
page.add(bar)
# scatter3D
import random
data = [
[random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)] for _ in range(80)
]
scatter3d = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3d.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3d)
# guangdong
value = [20, 190, 253, 77, 65]
attr = ['汕头市', '汕尾市', '揭阳市', '阳江市', '肇庆市']
map = Map("广东地图示例", width=1200, height=600)
map.add("", attr, value, maptype='广东', is_visualmap=True,
visual_text_color='#000')
page.add(map)
return page
def show_backtesting_indicator(cls, event):
indicator_dict = {}
indicator_dict = collections.OrderedDict()
# （１）基准净值
benchmark_net_asset_value = cls().get_benchmark_net_asset_value(event)
# print(benchmark_net_asset_value, "benchmark_net_asset_value" * 3)
# （２）策略净值
strategy_net_asset_value = cls().get_strategy_net_asset_value()
# print(strategy_net_asset_value, "strategy_net_asset_value")
# （３）基准年化收益率
benchmark_year_yield = cls().get_year_yield(benchmark_net_asset_value)
# （４）策略年化收益率
strategy_year_yield = cls().get_year_yield(strategy_net_asset_value)
# print(benchmark_year_yield, strategy_year_yield)
# （５）beta
beta = cls().get_beta(benchmark_net_asset_value, strategy_net_asset_value)
# print(beta)
indicator_dict["beta"] = beta
# （６）alpha
alpha = cls().get_alpha(benchmark_year_yield, strategy_year_yield, beta)
# print(alpha)
indicator_dict["alpha"] = alpha
# （７）volatility
volatility = cls().get_volatility(strategy_net_asset_value)
# print(volatility)
indicator_dict["volatility"] = volatility
# （８）sharpe
sharpe = cls().get_sharp(strategy_year_yield, volatility)
# print(sharpe)
indicator_dict["sharpe"] = sharpe
# （９）downside_risk
downside_risk = cls().get_downside_risk(strategy_year_yield)
# print(downside_risk)
indicator_dict["downside_risk"] = downside_risk
# （１０）sortino_ratio
sortino_ratio = cls().get_sortino_ratio(strategy_year_yield, downside_risk)
# print(sortino_ratio)
indicator_dict["sortino_ratio"] = sortino_ratio
# （１１）tracking_error
tracking_error = cls().get_tracking_error(benchmark_net_asset_value, strategy_net_asset_value)
# print(tracking_error)
indicator_dict["tracking_error"] = tracking_error
# （１２）information_ratio
information_ratio = cls().get_information_ratio(benchmark_year_yield, strategy_year_yield, tracking_error)
# print(information_ratio)
indicator_dict["information_ratio"] = information_ratio
# （１３）max_drawdown
max_drawdown = cls().get_max_drawdown(strategy_net_asset_value)
# print(max_drawdown)
indicator_dict["max_drawdown"] = max_drawdown
# 展示到html
period = event.event_data_dict["strategy"].period
if period == Period.DAILY.value:
timetag_date = [millisecond_to_date(millisecond=i, format="%Y-%m-%d") for i in Environment.benchmark_index]
else:
timetag_date = [millisecond_to_date(millisecond=i, format="%Y-%m-%d %H:%M:%S") for i in
Environment.benchmark_index]
page = Page("strategy backtesting indicator")
line_net_asset_value = Line("net_asset_value", width=1300, height=400, title_pos="8%")
line_net_asset_value.add("benchmark_net_asset_value", timetag_date,
[round(i, 4) for i in benchmark_net_asset_value],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
line_net_asset_value.add("strategy_net_asset_value", timetag_date,
[round(i, 4) for i in strategy_net_asset_value],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
page.add(line_net_asset_value)
line_year_yield = Line("year_yield", width=1300, height=400, title_pos="8%")
line_year_yield.add("benchmark_year_yield", timetag_date,
[round(i, 4) for i in benchmark_year_yield],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
line_year_yield.add("strategy_year_yield", timetag_date,
[round(i, 4) for i in strategy_year_yield],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
page.add(line_year_yield)
for indicator_name, indicator in indicator_dict.items():
cls().add_to_page(page, indicator, indicator_name, timetag_date)
# print(indicator_name)
millisecond_timetag = str(int(time.time()) * 1000)
page.render(path=sys.argv[0][sys.argv[0].rfind(os.sep) + 1:][
:-3] + "_" + "strategy backtesting indicator" + millisecond_timetag + ".html") # 生成本地 HTML 文件
"""
1.从2010年到2017年之间最受欢迎的男女生英文名，画出男女生各前10名的年份-数量图
"""
#男生
top10_boy=data.loc[(data['Year'].isin(list(range(2010,2018))))&(data['Gender']=='M'),:].groupby('Name').Count.sum().nlargest(10)
boy_total=data.loc[(data['Year'].isin(list(range(2010,2018))))&(data['Gender']=='M'),:].groupby('Name').Count.sum().sum()
#print(boy_total)
bname=list(top10_boy.index)
bvalue=list(top10_boy.values)
wordcloud1=WordCloud("2010年以来最受欢迎的男生名Top10",width=1000,height=500,background_color='#f2eada')
wordcloud1.add("",bname,bvalue,word_size_range=[20,100],shape='dimand')
page.add(wordcloud1)
#wordcloud.render()
data_top10_boy=data.loc[(data['Year'].isin(list(range(2010,2018))))&(data['Gender']=='M')&(data['Name']).isin(list(top10_boy.index)),:]
#print(data_top15_boy)
#sns绘图
sns.set(font_scale=2) #标题大小
g1 = sns.FacetGrid(data_top10_boy, col="Name", col_wrap=4) #列名为Name，一行四个
t1=g1.map(plt.plot, "Year", "Count",color="b",marker="o") #绘图，横轴为Year,纵轴为Count
plt.show(t1)
#女生
top10_girl = data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'F'), :].groupby('Name').Count.sum().nlargest(10)
list(data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'F'), :].groupby('Name').Count.sum().nlargest(30).index)
gname = list(top10_girl.index)
is_smooth=True)
return line
week_report = open('week_report', 'rb')
week_report_str = week_report.read().decode()
week_report_list = week_report_str.split('FROM_UNIXTIME(clock) value_max')
tips_list = [
"支付核心 CPU", "支付核心 内存", "支付核心 磁盘", "门户/认证中心 CPU", "门户/认证中心 内存", "门户/认证中心网络",
"门户/认证中心磁盘", "核心交换机CPU", "核心交换机内存", "移动网络流量", "电信网络流量", "人保金服到集团核心网RT1",
"人保金服到集团核心网RT2", "人保金服到华夏银行RT1", "人保金服到华夏银行RT2"
]
unit_list = [
"%", "MB", "GB", "%", "MB", "Kbps", "GB", "%", "%", "Kbps", "Kbps", "Kbps",
"Kbps", "bps", "Kbps"
]
level_list = [
0.01, 0.000001, 0.000000001, 0.01, 0.000001, BPS_2_KBPS, 0.000000001, 1, 1,
BPS_2_KBPS, BPS_2_KBPS, BPS_2_KBPS, BPS_2_KBPS, 1, BPS_2_KBPS
]
page = Page()
count = 0
list_count = 1
for tips in tips_list:
line = make_line(week_report_list[list_count:list_count + 2], tips,
unit_list[count], level_list[count], count)
count += 1
list_count += 2
page.add(line)
page.render()
ma_30 = L_ine(date, close, 30)
#日K线
kline = K_line(date, v1, "比特币历史行情-MA以及交易量")
#交易量
bar = Volume_bar(date, volume)
overlap = Overlap(width=1500, height=600)
overlap.add(kline)
overlap.add(ma_5)
overlap.add(ma_10)
overlap.add(ma_20)
overlap.add(ma_30)
overlap.add(bar, is_add_yaxis=True, yaxis_index=1)
page = Page() # 第一个图表
page.add(overlap)
#MACD指标
#DIF=MA(12)-MA(26) a=12,b=16
#EDA=9日EMA c=9
#macd=DIF-EDA bar
def DIF(a, b):
dif = []
ma_a = MA(close, a)
ma_b = MA(close, b)
#让ma_a和ma_b具有相同维度
ma_a = ma_a[b - a:]
for i in range(len(ma_a)):
dif.append(ma_a[i] - ma_b[i])
def CreateCharts():
page = Page()
app = xw.App(visible=False, add_book=False)
app.display_alerts = False
app.screen_updating = False
book = app.books.open(filepath)
sheet = book.sheets['sheet1']
_list1 = []
_list2 = []
_list3 = []
_list4 = []
_list5 = []
# 从 excel 中获取数据
cells1 = sheet.range('F3:K3').value # 第 1 支球队
cells2 = sheet.range('F4:K4').value # 第 2 支球队
cells3 = sheet.range('F5:K5').value # 第 3 支球队
cells4 = sheet.range('F6:K6').value # 第 4 支球队
cells5 = sheet.range('F7:K7').value # 第 5 支球队
# print(data1)
for cell1,cell2,cell3,cell4,cell5 in zip(cells1,cells2,cells3,cells4,cells5):
_list1.append(cell1)
_list2.append(cell2)
_list3.append(cell3)
_list4.append(cell4)
_list5.append(cell5)
v1 = [_list1]
v2 = [_list2]
v3 = [_list3]
v4 = [_list4]
v5 = [_list5]
chart = Radar("进攻属性")
schema = [
("进球", 15),
("射门", 20),
("射正", 9),
("绝佳机会", 20),
("机会把握率", 70),
("过人", 16),
# ("被侵犯", 18),
# ("越位", 5),
]
chart.config(schema)
chart.add("利物浦", v1)
chart.add("诺维奇", v2)
chart.add("曼城", v3)
chart.add("西汉姆联", v4)
chart.add("伯恩利", v5)
page.add(chart)
# 玫瑰图
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v2 = [19, 21, 32, 20, 20, 33]
chart = Pie("饼图-玫瑰图示例", title_pos='center')
chart.add("商品A", attr, v2, is_random=True,
radius=[30, 75], rosetype='area',
is_legend_show=False, is_label_show=True)
page.add(chart)
# 雷达图
schema = [
("销售", 6500), ("管理", 16000), ("信息技术", 30000),
("客服", 38000), ("研发", 52000), ("市场", 25000)
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
v3 = [[3000, 9000, 27890, 33000, 43098, 18000]]
v4 = [[3890, 12098, 29022, 32000, 41000, 20000]]
chart = Radar("雷达图-默认指示器")
chart.config(schema)
chart.add("预算分配", v1)
chart.add("实际开销", v2)
chart.add("ddd", v3)
chart.add("fff", v4)
page.add(chart)
app.kill()
return page
def create_charts():
page = Page()
style = Style(width=1100, height=600)
df = pd.read_csv('./处置部门关联规则.csv')
x_list = list(df['lhs'].values)
y_list = list(df['rhs'].values)
sup = list(df['sup_lhs'])
conf = list(df['conf'])
nodes = []
links = []
all_list = []
all_list.extend(x_list)
all_list.extend(y_list)
all_list = list(set(all_list))
for i in range(len(all_list)):
nodes.append({
"name": all_list[i],
"symbolSize": 10,
"label": {
"normal": {
"show": "True"
}
}
})
for j in range(len(x_list)):
links.append({"source": x_list[j], "target": y_list[j]})
# print(nodes)
# print(links)
chart = Graph("关联规则", **style.init_style)
chart.add("",
nodes,
links,
label_pos="right",
graph_repulsion=1000,
is_legend_show=False,
line_curve=0.2,
label_text_color=None)
page.add(chart)
comm_name = [
'竹坑社区', '沙田社区', '龙田社区', '秀新社区', '金沙社区', '碧岭社区', '老坑社区', '和平社区', '六和社区',
'六联社区', '坪山社区'
]
nodes = []
links = []
categories = []
for name in comm_name:
# print(name)
df = pd.read_csv('./' + name + '.csv')
x_list = list(df['lhs'].values)
y_list = list(df['rhs'].values)
all_list = []
all_list.extend(x_list)
all_list.extend(y_list)
all_list = list(set(all_list))
for i in range(len(all_list)):
# print(all_list[i])
nodes.append({
"name": name + all_list[i],
"symbolSize": 10,
"draggable": "False",
"category": name,
"label": {
"normal": {
"show": "True"
}
}
})
categories.append({"name": name})
for j in range(len(x_list)):
links.append({
"source": name + x_list[j],
"target": name + y_list[j]
})
chart = Graph("", **style.init_style)
chart.add("",
nodes,
links,
categories,
label_pos="right",
graph_repulsion=1000,
is_legend_show=True,
line_curve=0.2,
label_text_color=None)
page.add(chart)
return page
def test_page_grid_timeline_overlap():
# Grid
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add("最高气温",
WEEK, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add("最低气温",
WEEK, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%")
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
# Timeline
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构", height=720, width=1200)
bar_5.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("冬季",
CLOTHES, [randint(10, 100) for _ in range(6)],
is_legend_show=True)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, '2012 年')
timeline.add(bar_2, '2013 年')
timeline.add(bar_3, '2014 年')
timeline.add(bar_4, '2015 年')
timeline.add(bar_5, '2016 年')
# Overlap
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(height=720, width=1200)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page = Page()
page.add(grid)
page.add(timeline)
page.add(overlap)
page.render()
def plotClassInfo():
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("上海")]
[list_address.append(i[1]) for i in foodClassinif("上海")]
print(list_address)
print(list_sum)
page = Page()
pie1 = Pie("上海商铺集中区域", title_pos='center')
pie1.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar1 = Bar("上海商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar1.add("上海",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("北京")]
[list_address.append(i[1]) for i in foodClassinif("北京")]
print(list_address)
print(list_sum)
pie2 = Pie("北京商铺集中区域", title_pos='center')
pie2.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar2 = Bar("北京商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar2.add("北京",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("广州")]
[list_address.append(i[1]) for i in foodClassinif("广州")]
print(list_address)
print(list_sum)
pie3 = Pie("广州商铺集中区域", title_pos='center')
pie3.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar3 = Bar("广州商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar3.add("广州",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("深圳")]
[list_address.append(i[1]) for i in foodClassinif("深圳")]
print(list_address)
print(list_sum)
pie4 = Pie("深圳商铺集中区域", title_pos='center')
pie4.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar4 = Bar("深圳商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar4.add("深圳",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("天津")]
[list_address.append(i[1]) for i in foodClassinif("天津")]
print(list_address)
print(list_sum)
pie5 = Pie("天津商铺集中区域", title_pos='center')
pie5.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar5 = Bar("天津商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar5.add("天津",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("杭州")]
[list_address.append(i[1]) for i in foodClassinif("杭州")]
print(list_address)
print(list_sum)
pie6 = Pie("杭州商铺集中区域", title_pos='center')
pie6.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar6 = Bar("杭州商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar6.add("杭州",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("南京")]
[list_address.append(i[1]) for i in foodClassinif("南京")]
print(list_address)
print(list_sum)
pie7 = Pie("南京商铺集中区域", title_pos='center')
pie7.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar7 = Bar("南京商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar7.add("南京",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("苏州")]
[list_address.append(i[1]) for i in foodClassinif("苏州")]
print(list_address)
print(list_sum)
pie8 = Pie("苏州商铺集中区域", title_pos='center')
pie8.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar8 = Bar("苏州商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar8.add("苏州",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("成都")]
[list_address.append(i[1]) for i in foodClassinif("成都")]
print(list_address)
print(list_sum)
pie9 = Pie("成都商铺集中区域", title_pos='center')
pie9.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar9 = Bar("成都商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar9.add("成都",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("武汉")]
[list_address.append(i[1]) for i in foodClassinif("武汉")]
print(list_address)
print(list_sum)
pie10 = Pie("武汉商铺集中区域", title_pos='center')
pie10.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar10 = Bar("武汉商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar10.add("武汉",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("重庆")]
[list_address.append(i[1]) for i in foodClassinif("重庆")]
print(list_address)
print(list_sum)
pie11 = Pie("重庆商铺集中区域", title_pos='center')
pie11.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar11 = Bar("重庆商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar11.add("重庆",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
list_address = []
list_sum = []
[list_sum.append(i[0]) for i in foodClassinif("西安")]
[list_address.append(i[1]) for i in foodClassinif("西安")]
print(list_address)
print(list_sum)
pie12 = Pie("西安商铺集中区域", title_pos='center')
pie12.add("",
list_address,
list_sum,
radius=[30, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='auto',
is_legend_show=False)
bar12 = Bar("西安商铺集中区域", "数据来源于大众点评TOP100", title_pos='center')
bar12.add("西安",
list_address,
list_sum,
mark_point=["max", "min"],
legend_pos="right")
page.add(pie1)
page.add(bar1)
page.add(pie2)
page.add(bar2)
page.add(pie3)
page.add(bar3)
page.add(pie4)
page.add(bar4)
page.add(pie5)
page.add(bar5)
page.add(pie6)
page.add(bar6)
page.add(pie7)
page.add(bar7)
page.add(pie8)
page.add(bar8)
page.add(pie9)
page.add(bar9)
page.add(pie10)
page.add(bar10)
page.add(pie11)
page.add(bar11)
page.add(pie12)
page.add(bar12)
page.render("商铺集中区域.html")
def test_more():
page = Page()
# line
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line = Line("折线图示例")
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"], mark_line=["average"])
page.add(line)
# pie
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos='center')
pie.add("", attr, v1, radius=[40, 75], label_text_color=None, is_label_show=True, legend_orient='vertical',
legend_pos='left')
page.add(pie)
# kline
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1)
page.add(kline)
# radar
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000), ("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False, legend_selectedmode='single')
page.add(radar)
page.render()
def create_charts():
page = Page()
data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14),
("盐城", 15), ("赤峰", 16), ("青岛", 18), ("乳山", 18), ("金昌", 19),
("泉州", 21), ("莱西", 21), ("日照", 21), ("胶南", 22), ("南通", 23),
("拉萨", 24), ("云浮", 24), ("梅州", 25), ("文登", 25), ("上海", 25),
("攀枝花", 25), ("威海", 25), ("承德", 25), ("厦门", 26), ("汕尾", 26),
("潮州", 26), ("丹东", 27), ("太仓", 27), ("曲靖", 27), ("烟台", 28),
("福州", 29), ("瓦房店", 30), ("即墨", 30), ("抚顺", 31), ("玉溪", 31),
("张家口", 31), ("阳泉", 31), ("莱州", 32), ("湖州", 32), ("汕头", 32),
("昆山", 33), ("宁波", 33), ("湛江", 33), ("揭阳", 34), ("荣成", 34),
("连云港", 35), ("葫芦岛", 35), ("常熟", 36), ("东莞", 36), ("河源", 36),
("淮安", 36), ("泰州", 36), ("南宁", 37), ("营口", 37), ("惠州", 37),
("江阴", 37), ("蓬莱", 37), ("韶关", 38), ("嘉峪关", 38), ("广州", 38),
("延安", 38), ("太原", 39), ("清远", 39), ("中山", 39), ("昆明", 39),
("寿光", 40), ("盘锦", 40), ("长治", 41), ("深圳", 41), ("珠海", 42),
("宿迁", 43), ("咸阳", 43), ("铜川", 44), ("平度", 44), ("佛山", 44),
("海口", 44), ("江门", 45), ("章丘", 45), ("肇庆", 46), ("大连", 47),
("临汾", 47), ("吴江", 47), ("石嘴山", 49), ("沈阳", 50), ("苏州", 50),
("茂名", 50), ("嘉兴", 51), ("长春", 51), ("胶州", 52), ("银川", 52),
("张家港", 52), ("三门峡", 53), ("锦州", 54), ("南昌", 54), ("柳州", 54),
("三亚", 54), ("自贡", 56), ("吉林", 56), ("阳江", 57), ("泸州", 57),
("西宁", 57), ("宜宾", 58), ("呼和浩特", 58), ("成都", 58), ("大同", 58),
("镇江", 59), ("桂林", 59), ("张家界", 59), ("宜兴", 59), ("北海", 60),
("西安", 61), ("金坛", 62), ("东营", 62), ("牡丹江", 63), ("遵义", 63),
("绍兴", 63), ("扬州", 64), ("常州", 64), ("潍坊", 65), ("重庆", 66),
("台州", 67), ("南京", 67), ("滨州", 70), ("贵阳", 71), ("无锡", 71),
("本溪", 71), ("克拉玛依", 72), ("渭南", 72), ("马鞍山", 72), ("宝鸡", 72),
("焦作", 75), ("句容", 75), ("北京", 79), ("徐州", 79), ("衡水", 80),
("包头", 80), ("绵阳", 80), ("乌鲁木齐", 84), ("枣庄", 84), ("杭州", 84),
("淄博", 85), ("鞍山", 86), ("溧阳", 86), ("库尔勒", 86), ("安阳", 90),
("开封", 90), ("济南", 92), ("德阳", 93), ("温州", 95), ("九江", 96),
("邯郸", 98), ("临安", 99), ("兰州", 99), ("沧州", 100), ("临沂", 103),
("南充", 104), ("天津", 105), ("富阳", 106), ("泰安", 112), ("诸暨", 112),
("郑州", 113), ("哈尔滨", 114), ("聊城", 116), ("芜湖", 117), ("唐山", 119),
("平顶山", 119), ("邢台", 119), ("德州", 120), ("济宁", 120), ("荆州", 127),
("宜昌", 130), ("义乌", 132), ("丽水", 133), ("洛阳", 134), ("秦皇岛", 136),
("株洲", 143), ("石家庄", 147), ("莱芜", 148), ("常德", 152), ("保定", 153),
("湘潭", 154), ("金华", 157), ("岳阳", 169), ("长沙", 175), ("衢州", 177),
("廊坊", 193), ("菏泽", 194), ("合肥", 229), ("武汉", 273), ("大庆", 279)]
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
chart = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
attr, value = chart.cast(data)
chart.add("",
attr,
value,
visual_range=[0, 200],
visual_text_color="#fff",
is_legend_show=False,
symbol_size=15,
is_visualmap=True,
tooltip_formatter='{b}',
label_emphasis_textsize=15,
label_emphasis_pos='right')
page.add(chart)
chart = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
attr, value = chart.cast(data)
chart.add("",
attr,
value,
type="heatmap",
is_visualmap=True,
visual_range=[0, 200],
visual_text_color='#fff',
is_legend_show=False)
page.add(chart)
data = [('汕头市', 50), ('汕尾市', 60), ('揭阳市', 35), ('阳江市', 44), ('肇庆市', 72)]
chart = Geo("广东地理坐标系", **style.init_style)
attr, value = chart.cast(data)
chart.add("",
attr,
value,
maptype='广东',
type="effectScatter",
is_random=True,
effect_scale=5,
is_legend_show=False,
tooltip_formatter='{b}',
label_emphasis_textsize=15,
label_emphasis_pos='right')
page.add(chart)
data = [('澄海区', 30), ('南澳县', 40), ('龙湖区', 50), ('金平区', 60)]
chart = Geo("汕头地理坐标系", **style.init_style)
attr, value = chart.cast(data)
chart.add("",
attr,
value,
maptype="汕头",
is_visualmap=True,
is_legend_show=False,
tooltip_formatter='{b}',
label_emphasis_textsize=15,
label_emphasis_pos='right')
page.add(chart)
return page
def Page_hello():
date, open, close, low, high, volume = Import()
v1 = V1(open, close, low, high)
#5日均线
ma_5 = L_ine(date, close, 5)
#10日均线
ma_10 = L_ine(date, close, 10)
#20日均线
ma_20 = L_ine(date, close, 20)
#5日均线
ma_30 = L_ine(date, close, 30)
#日K线
kline = K_line(date, v1, "比特币历史行情-MA以及交易量")
#交易量
bar = Volume_bar(date, volume)
overlap = Overlap(width=1500, height=600)
overlap.add(kline)
overlap.add(ma_5)
overlap.add(ma_10)
overlap.add(ma_20)
overlap.add(ma_30)
overlap.add(bar, is_add_yaxis=True, yaxis_index=1)
page = Page() # 第一个图表
page.add(overlap)
kline = K_line(date, v1, "比特币macd指标")
overlap_uper = Overlap()
overlap_uper.add(kline)
overlap_uper.add(L_ine(date, close, 12))
overlap_uper.add(L_ine(date, close, 26))
#dif曲线 grid 下面的图
dif_line = DIF_Line(date, 12, 26, close)
eda_line = EDA_Line(date, 12, 26, 9, close)
macd_bar = MACD_Bar(date, 12, 26, 9, close)
overlap_down = Overlap()
overlap_down.add(dif_line)
overlap_down.add(eda_line)
overlap_down.add(macd_bar)
grid = Grid(width=1500, height=600)
grid.add(overlap_down, grid_top="60%")
grid.add(overlap_uper, grid_bottom="60%")
#第二幅画
page.add(grid)
#增加RSI指标
#日k线图
kline = K_line(date, v1, "比特币RSI指标")
overlap_uper = Overlap()
overlap_uper.add(kline)
rsi_line = RSI_Line(close, date, 14)
overlap_down = Overlap()
overlap_down.add(rsi_line)
grid = Grid(width=1500, height=600)
grid.add(overlap_down, grid_top="60%")
grid.add(overlap_uper, grid_bottom="60%")
#第三幅画
page.add(grid)
#####mack stratege
# 寻找合适的slow_ma
kline = K_line(date, v1, "比特币macd策略回测平台")
overlap = Overlap(width=1500, height=600)
overlap.add(kline)
for i in range(20, 50):
macd_strategy_buyonly_line = MACD_strategy_buyonly_Line(
12, i, 9, close, date)
overlap.add(macd_strategy_buyonly_line)
# for i in range(1,30):
# macd_strategy_buyonly_line=MACD_strategy_buyonly_Line(i,36,9,close,date)
# overlap.add(macd_strategy_buyonly_line)
#第四幅画-回测平台
page.add(overlap)
kline = K_line(date, v1, "比特币macd策略回测平台")
overlap = Overlap(width=1500, height=600)
overlap.add(kline)
# for i in range(20,50):
#
# macd_strategy_buyonly_line=MACD_strategy_buyonly_Line(12,i,9,close,date)
# overlap.add(macd_strategy_buyonly_line)
for i in range(1, 30):
macd_strategy_buyonly_line = MACD_strategy_buyonly_Line(
i, 36, 9, close, date)
overlap.add(macd_strategy_buyonly_line)
#第四幅画-回测平台
page.add(overlap)
kline = K_line(date, v1, "比特币macd策略回测平台")
overlap = Overlap(width=1500, height=600)
overlap.add(kline)
# for i in range(20,50):
#
# macd_strategy_buyonly_line=MACD_strategy_buyonly_Line(12,i,9,close,date)
# overlap.add(macd_strategy_buyonly_line)
for i in range(1, 30):
macd_strategy_buyonly_line = MACD_strategy_buyonly_Line(
3, 36, i, close, date)
overlap.add(macd_strategy_buyonly_line)
#第四幅画-回测平台
page.add(overlap)
#####mack stratege
# 根据历史得到的 最佳macd 参数
kline = K_line(date, v1, "比特币macd策略最佳参数与默认参数对比效果")
overlap = Overlap(width=1500, height=600)
overlap.add(kline)
macd_strategy_buyonly_line = MACD_strategy_buyonly_Line(
3, 36, 9, close, date)
overlap.add(macd_strategy_buyonly_line)
macd_strategy_buyonly_line = MACD_strategy_buyonly_Line(
12, 26, 9, close, date)
overlap.add(macd_strategy_buyonly_line)
#第四幅画-回测平台
page.add(overlap)
#####基本面分析
# 梅特卡夫定律
#Value∝n^2 Value∝nln(n)
v2 = V1(Log(open), Log(close), Log(low), Log(high))
kline = K_line(date, v2, "比特币基本面分析之梅特卡夫定律")
upper_Bound = Upper_Bound(-16.5, 1)
lower_Bound = Lower_Bound(-11, 1)
overlap = Overlap(width=1500, height=600)
overlap.add(kline)
overlap.add(lower_Bound)
overlap.add(upper_Bound)
#
page.add(overlap)
#基本面分析之对数回归
#假设 每次投机产生的指数上涨有规律的 根据对数坐标系发掘这些规律
#预计 周期更加漫长 涨幅更加有限 量化找到其规律 趋势面前 一切都是徒劳的 顺势而为
#基本面分析之对数回归
#假设 每次投机产生的指数上涨有规律的 根据对数坐标系发掘这些规律
#预计 周期更加漫长 涨幅更加有限 量化找到其规律 趋势面前 一切都是徒劳的 顺势而为
#构造横坐标
x_axis = []
for i in range(len(date)):
x_axis.append(i)
#构造纵坐标
close_log = Log(close)
close_log_ma5, kline_log_ma5 = Kline_log_ma(5, x_axis, close)
close_log_ma100, kline_log_ma100 = Kline_log_ma(100, x_axis, close)
overlap = Overlap(width=1500, height=600)
#二次多项式拟合
a1, b1, c1, curve_line_QP1 = Curve_Line_num(1, 101, 324, close_log, 101,
100)
a2, b2, c2, curve_line_QP2 = Curve_Line_num(2, 324, 1232, close_log, 200,
200)
a3, b3, c3, curve_line_QP3 = Curve_Line_num(3, 1232, 2709, close_log, 500,
500)
a4, b4, c4, curve_line_QP4 = Curve_Line_num(4, 2709, 3073, close_log, 1000,
2000)
predicthigh_line = PredictLow_Line([325, 1229, 2709], [3.38, 7.00, 9.85],
5000)
predictlow_line = PredictHigh_Line([b1, b2, b3], [c1, c2, c3], 5000)
kline = K_line(x_axis, v2, "比特币基本面分析之二次多项式回归")
overlap.add(predictlow_line)
overlap.add(predicthigh_line)
#overlap.add(predicttrend_line)
overlap.add(kline)
# overlap.add(kline_log_ma5)
# overlap.add(kline_log_ma100)
overlap.add(curve_line_QP1)
overlap.add(curve_line_QP2)
overlap.add(curve_line_QP3)
overlap.add(curve_line_QP4)
#tomorror mission
#对数拟合
page.add(overlap)
overlap = Overlap(width=1500, height=600)
predict_date_line = Predict_date(date, close, 3106, 4545)
overlap.add(predict_date_line)
page.add(overlap)
return page
def draw_danmu_num(time_data, num_data, sum_data, avg_data, pos_data,
neg_data):
page = Page()
bar = Bar("弹幕数量分布图",
"2020-01-27",
title_color='black',
title_pos='center',
width=1000)
bar.add("弹幕数量",
time_data,
num_data,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("弹幕情感均值分布图",
"2020-01-27",
title_color='black',
title_pos='center',
width=1000)
bar.add("弹幕情感均值",
time_data,
avg_data,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("弹幕情感总和分布图",
"2020-01-27",
title_color='black',
title_pos='center',
width=1000)
bar.add("弹幕情感总和",
time_data,
sum_data,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("弹幕正面情感总和分布图",
"2020-01-27",
title_color='black',
title_pos='center',
width=1000)
bar.add("弹幕正面情感总和",
time_data,
pos_data,
mark_line=['average'],
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
bar = Bar("弹幕负面情感总和分布图",
"2020-01-27",
title_color='black',
title_pos='center',
width=1000)
bar.add("弹幕负面情感总和",
time_data,
neg_data,
mark_line=['average'],
yaxis_max=350,
mark_point=['max', 'min'],
legend_pos='right',
is_more_utils=True)
page.add(bar)
page.render("new_每30s弹幕数据.html")
def plotFoodRanging(list_y1, list_y2, list_y3, list_y4, list_y5, list_y6,
list_y7, list_y8, list_y9, list_y10, list_y11, list_y12):
page = Page()
line1 = Line("上海饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line1.add("口味", list_y1[0], list_y1[1], mark_point=["max", "min"])
line1.add("环境", list_y1[0], list_y1[2], mark_point=["max", "min"])
line1.add("服务", list_y1[0], list_y1[3], mark_point=["max", "min"])
line1.add("综合", list_y1[0], list_y1[4], mark_point=["max", "min"])
bar1 = Bar("上海饭店评分统计", "数据来源于大众点评TOP100")
bar1.add("上海", list_y1[0][:10], list_y1[4][:10], mark_point=["max", "min"])
wordcloud1 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud1.add("",
list_y1[0][:20],
list_y1[4][:20],
word_size_range=[20, 100])
line2 = Line("北京饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line2.add("口味", list_y2[0], list_y2[1], mark_point=["max", "min"])
line2.add("环境", list_y2[0], list_y2[2], mark_point=["max", "min"])
line2.add("服务", list_y2[0], list_y2[3], mark_point=["max", "min"])
line2.add("综合", list_y2[0], list_y2[4], mark_point=["max", "min"])
bar2 = Bar("北京饭店评分统计", "数据来源于大众点评TOP100")
bar2.add("北京", list_y2[0][:10], list_y2[4][:10], mark_point=["max", "min"])
wordcloud2 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud2.add("",
list_y2[0][:20],
list_y2[4][:20],
word_size_range=[20, 100])
line3 = Line("广州饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line3.add("口味", list_y3[0], list_y3[1], mark_point=["max", "min"])
line3.add("环境", list_y3[0], list_y3[2], mark_point=["max", "min"])
line3.add("服务", list_y3[0], list_y3[3], mark_point=["max", "min"])
line3.add("综合", list_y3[0], list_y3[4], mark_point=["max", "min"])
bar3 = Bar("广州饭店评分统计", "数据来源于大众点评TOP100")
bar3.add("广州", list_y3[0][:10], list_y3[4][:10], mark_point=["max", "min"])
wordcloud3 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud3.add("",
list_y3[0][:20],
list_y3[4][:20],
word_size_range=[20, 100])
line4 = Line("深圳饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line4.add("口味", list_y4[0], list_y4[1], mark_point=["max", "min"])
line4.add("环境", list_y4[0], list_y4[2], mark_point=["max", "min"])
line4.add("服务", list_y4[0], list_y4[3], mark_point=["max", "min"])
line4.add("综合", list_y4[0], list_y4[4], mark_point=["max", "min"])
bar4 = Bar("深圳饭店评分统计", "数据来源于大众点评TOP100")
bar4.add("深圳", list_y4[0][:10], list_y4[4][:10], mark_point=["max", "min"])
wordcloud4 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud4.add("",
list_y4[0][:20],
list_y4[4][:20],
word_size_range=[20, 100])
line5 = Line("天津饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line5.add("口味", list_y5[0], list_y5[1], mark_point=["max", "min"])
line5.add("环境", list_y5[0], list_y5[2], mark_point=["max", "min"])
line5.add("服务", list_y5[0], list_y5[3], mark_point=["max", "min"])
bar5 = Bar("天津饭店评分统计", "数据来源于大众点评TOP100")
bar5.add("天津", list_y5[0][:10], list_y5[4][:10], mark_point=["max", "min"])
wordcloud5 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud5.add("",
list_y5[0][:20],
list_y5[4][:20],
word_size_range=[20, 100])
line6 = Line("杭州饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line6.add("口味", list_y6[0], list_y6[1], mark_point=["max", "min"])
line6.add("环境", list_y6[0], list_y6[2], mark_point=["max", "min"])
line6.add("服务", list_y6[0], list_y6[3], mark_point=["max", "min"])
line6.add("综合", list_y6[0], list_y6[4], mark_point=["max", "min"])
bar6 = Bar("杭州饭店评分统计", "数据来源于大众点评TOP100")
bar6.add("杭州", list_y6[0][:10], list_y6[4][:10], mark_point=["max", "min"])
wordcloud6 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud6.add("",
list_y6[0][:20],
list_y6[4][:20],
word_size_range=[20, 100])
line7 = Line("南京饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line7.add("口味", list_y7[0], list_y7[1], mark_point=["max", "min"])
line7.add("环境", list_y7[0], list_y7[2], mark_point=["max", "min"])
line7.add("服务", list_y7[0], list_y7[3], mark_point=["max", "min"])
line7.add("综合", list_y7[0], list_y7[4], mark_point=["max", "min"])
bar7 = Bar("南京饭店评分统计", "数据来源于大众点评TOP100")
bar7.add("南京", list_y7[0][:10], list_y7[4][:10], mark_point=["max", "min"])
wordcloud7 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud7.add("",
list_y7[0][:20],
list_y7[4][:20],
word_size_range=[20, 100])
line8 = Line("苏州饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line8.add("口味", list_y8[0], list_y8[1], mark_point=["max", "min"])
line8.add("环境", list_y8[0], list_y8[2], mark_point=["max", "min"])
line8.add("服务", list_y8[0], list_y8[3], mark_point=["max", "min"])
line8.add("综合", list_y8[0], list_y8[4], mark_point=["max", "min"])
bar8 = Bar("苏州饭店评分统计", "数据来源于大众点评TOP100")
bar8.add("苏州", list_y8[0][:10], list_y8[4][:10], mark_point=["max", "min"])
wordcloud8 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud8.add("",
list_y8[0][:20],
list_y8[4][:20],
word_size_range=[20, 100])
line9 = Line("成都饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line9.add("口味", list_y9[0], list_y9[1], mark_point=["max", "min"])
line9.add("环境", list_y9[0], list_y9[2], mark_point=["max", "min"])
line9.add("服务", list_y9[0], list_y9[3], mark_point=["max", "min"])
line9.add("综合", list_y9[0], list_y9[4], mark_point=["max", "min"])
bar9 = Bar("成都饭店评分统计", "数据来源于大众点评TOP100")
bar9.add("成都", list_y9[0][:10], list_y9[4][:10], mark_point=["max", "min"])
wordcloud9 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud9.add("",
list_y9[0][:20],
list_y9[4][:20],
word_size_range=[20, 100])
line10 = Line("武汉饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line10.add("口味", list_y10[0], list_y10[1], mark_point=["max", "min"])
line10.add("环境", list_y10[0], list_y10[2], mark_point=["max", "min"])
line10.add("服务", list_y10[0], list_y10[3], mark_point=["max", "min"])
line10.add("综合", list_y10[0], list_y10[4], mark_point=["max", "min"])
bar10 = Bar("武汉饭店评分统计", "数据来源于大众点评TOP100")
bar10.add("武汉",
list_y10[0][:10],
list_y10[4][:10],
mark_point=["max", "min"])
wordcloud10 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud10.add("",
list_y10[0][:20],
list_y10[4][:20],
word_size_range=[20, 100])
line11 = Line("重庆饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line11.add("口味", list_y11[0], list_y11[1], mark_point=["max", "min"])
line11.add("环境", list_y11[0], list_y11[2], mark_point=["max", "min"])
line11.add("服务", list_y11[0], list_y11[3], mark_point=["max", "min"])
line11.add("综合", list_y11[0], list_y11[4], mark_point=["max", "min"])
bar11 = Bar("重庆饭店评分统计", "数据来源于大众点评TOP100")
bar11.add("重庆",
list_y11[0][:10],
list_y11[4][:10],
mark_point=["max", "min"])
wordcloud11 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud11.add("",
list_y11[0][:20],
list_y11[4][:20],
word_size_range=[20, 100])
line12 = Line("西安饭店评分统计", "数据来源于大众点评TOP100", width=1200)
line12.add("口味", list_y12[0], list_y12[1], mark_point=["max", "min"])
line12.add("环境", list_y12[0], list_y12[2], mark_point=["max", "min"])
line12.add("服务", list_y12[0], list_y12[3], mark_point=["max", "min"])
line12.add("综合", list_y12[0], list_y12[4], mark_point=["max", "min"])
bar12 = Bar("西安饭店评分统计", "数据来源于大众点评TOP100")
bar12.add("西安",
list_y12[0][:10],
list_y12[4][:10],
mark_point=["max", "min"])
wordcloud12 = WordCloud(width=1000, height=620, background_color="#EECFA1")
wordcloud12.add("",
list_y12[0][:20],
list_y12[4][:20],
word_size_range=[20, 100])
page.add(line1)
page.add(wordcloud1)
page.add(bar1)
page.add(line2)
page.add(wordcloud2)
page.add(bar2)
page.add(line3)
page.add(wordcloud3)
page.add(bar3)
page.add(line4)
page.add(wordcloud4)
page.add(bar4)
page.add(line5)
page.add(wordcloud5)
page.add(bar5)
page.add(line6)
page.add(wordcloud6)
page.add(bar6)
page.add(line7)
page.add(wordcloud7)
page.add(bar7)
page.add(line8)
page.add(wordcloud8)
page.add(bar8)
page.add(line9)
page.add(wordcloud9)
page.add(bar9)
page.add(line10)
page.add(wordcloud10)
page.add(bar10)
page.add(line11)
page.add(wordcloud11)
page.add(bar11)
page.add(line12)
page.add(wordcloud12)
page.add(bar12)
data1 = pd.DataFrame(list_y1[4][:10], list_y1[0][:10], ["综合值"])
print(data1)
data2 = pd.DataFrame(list_y2[4][:10], list_y2[0][:10], ["综合值"])
print(data2)
data3 = pd.DataFrame(list_y3[4][:10], list_y3[0][:10], ["综合值"])
print(data3)
data4 = pd.DataFrame(list_y4[4][:10], list_y4[0][:10], ["综合值"])
print(data4)
data5 = pd.DataFrame(list_y5[4][:10], list_y5[0][:10], ["综合值"])
print(data5)
data6 = pd.DataFrame(list_y6[4][:10], list_y6[0][:10], ["综合值"])
print(data6)
data7 = pd.DataFrame(list_y7[4][:10], list_y7[0][:10], ["综合值"])
print(data7)
data8 = pd.DataFrame(list_y8[4][:10], list_y8[0][:10], ["综合值"])
print(data8)
data9 = pd.DataFrame(list_y9[4][:10], list_y9[0][:10], ["综合值"])
print(data9)
data10 = pd.DataFrame(list_y10[4][:10], list_y10[0][:10], ["综合值"])
print(data10)
data11 = pd.DataFrame(list_y11[4][:10], list_y11[0][:10], ["综合值"])
print(data11)
data12 = pd.DataFrame(list_y12[4][:10], list_y12[0][:10], ["综合值"])
print(data12)
page.render("全部.html")
# encoding: utf-8
import time
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
from pyecharts import Bar, Scatter3D
from pyecharts import Page
page = Page() # step 1
# bar
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", attr, v1, is_stack=False)
bar.add("商家B", attr, v2, is_stack=False)
page.add(bar) # step 2
# scatter3D
# import random
# data = [[random.randint(0, 100), random.randint(0, 100), random.randint(0, 100)] for _ in range(80)]
# range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
# '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
# scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
# scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
# page.add(scatter3D) # step 2
page.render() # step 3
$ pip install echarts-china-counties-pypkg
$ pip install echarts-china-misc-pypkg
"""
from pyecharts import Page, Bar
page = Page()
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图数据堆叠示例")
#is_stack 数据叠堆
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True)
import random
page.add(bar)
attr = ["{}天".format(i) for i in range(30)]
v1 = [random.randint(1, 30) for _ in range(30)]
bar = Bar("Bar - datazoom - slider 示例")
#is_datazoom_show 显示下方可调整的日期
bar.add("",
attr,
v1,
is_label_show=True,
mark_line=['average'],
mark_point=['max', 'min'],
is_datazoom_show=True)
page.add(bar)
import datetime
url = "http://www.86pm25.com/paiming.htm"
head = {
'Referer': 'http://www.86pm25.com/city/Dazhou.html',
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36'
}
html = requests.get(url, headers=head)
html.encoding = 'utf-8'
soup = BeautifulSoup(html.text, "lxml")
data1 = soup.find(id="goodtable").find_all(name='a')
data2 = str(soup.find(id='goodtable').find_all(name='td'))
data = re.findall(r'<td>(\d{1,3}.)</td>', data2)
with open("城市.txt", 'r') as f:
CityList = f.read()
city = []
for i in range(0, 367):
if str(data1[i].string) in CityList:
citytuple = (data1[i].string, int(data[i]))
city.append(citytuple)
geo = Geo("全国主要城市空气质量实时监控", "实时：" + str(datetime.datetime.now()), title_color="#fff",
title_pos="center", width='100%',
height=790, background_color='#404a59')
attr, value = geo.cast(city)
geo.add("", attr, value, visual_range=[0, 150], maptype='china', visual_text_color="#fff",
symbol_size=13, is_visualmap=True)
page = Page()
page.add(geo)
page.render("全国主要城市空气质量实时监控.html")
webbrowser.open("全国主要城市空气质量实时监控.html", new=0, autoraise=True)
def create_charts():
page = Page()
style = Style(
width=WIDTH, height=HEIGHT
)
performance = Line("Performance Summary", **style.init_style)
us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())
dates = pd.date_range('2017-10-26', periods=100, freq=us_bd)
self_record = [0.135, 0.155, 0.108, 0.094, -0.052, 0.1, 0.16, -0.166, 0.198, 0.055, -0.04, 0.024, 0.077, 0.148,
0.035, -0.068, -0.108, -0.092, -0.016, 0.142, -
0.034, -0.077, -0.185, 0.077, -0.158, 0.021, -0.111,
-0.1, 0.079, 0.092, 0.119, -0.139, -0.104, 0.045, -
0.195, 0.053, -0.078, -0.046, -0.192, -0.05,
0.159, -0.058, 0.094, -0.072, 0.005, 0.057, 0.173, -
0.077, -0.164, 0.071, -0.088, 0.007, 0.12, -0.07,
-0.091, 0.059, 0.177, 0.107, -0.122, 0.005, 0.09, -
0.108, -0.102, -0.006, -0.07, 0.07, -0.111,
-0.164, -0.051, -0.129, -0.151, -0.192, -
0.083, 0.144, 0.132, 0.013, 0.09, 0.123, 0.187, -0.12,
0.102, -0.064, -0.014, -0.19, -0.093, -0.199, -
0.155, 0.078, -0.05, -0.123, 0.067, 0.186, -0.04,
-0.019, -0.146, -0.007, 0.167, -0.082, -0.17, 0.055]
mirror_record = [0.079, 0.024, 0.073, 0.105, 0.034, 0.085, 0.07, 0.091, 0.084, 0.053, 0.14, 0.195, 0.2, 0.195,
0.068, 0.212, 0.15, 0.071, 0.138, 0.1, 0.256, 0.288, 0.292, 0.28, 0.323, 0.308, 0.252, 0.324,
0.275, 0.197, 0.369, 0.222, 0.336, 0.315, 0.393, 0.36, 0.362, 0.416, 0.324, 0.435, 0.32, 0.36, 0.4,
0.361, 0.505, 0.407, 0.559, 0.503, 0.473, 0.402, 0.471, 0.472, 0.491, 0.484, 0.543, 0.584, 0.656,
0.628, 0.555, 0.635, 0.581, 0.525, 0.597, 0.603, 0.584, 0.679, 0.617, 0.65, 0.701, 0.61, 0.684,
0.709, 0.671, 0.677, 0.81, 0.73, 0.769, 0.698, 0.788, 0.745, 0.785, 0.732, 0.808, 0.922, 0.797,
0.905, 0.811, 0.969, 0.834, 0.928, 0.812, 0.947, 0.961, 0.873, 0.844, 1.009, 0.93, 1.039, 1.022,
0.968]
overall = [0.214, 0.179, 0.181, 0.199, -0.018, 0.185, 0.23, -0.075, 0.282, 0.108, 0.1, 0.219, 0.277, 0.343, 0.103,
0.144, 0.042, -0.021, 0.122, 0.242, 0.222, 0.211, 0.107, 0.357, 0.165, 0.329, 0.141, 0.224, 0.354, 0.289,
0.488, 0.083, 0.232, 0.36, 0.198, 0.413, 0.284, 0.37, 0.132, 0.385, 0.479, 0.302, 0.494, 0.289, 0.51,
0.464, 0.732, 0.426, 0.309, 0.473, 0.383, 0.479, 0.611, 0.414, 0.452, 0.643, 0.833, 0.735, 0.433, 0.64,
0.671, 0.417, 0.495, 0.597, 0.514, 0.749, 0.506, 0.486, 0.65, 0.481, 0.533, 0.517, 0.588, 0.821, 0.942,
0.743, 0.859, 0.821, 0.975, 0.625, 0.887, 0.668, 0.794, 0.732, 0.704, 0.706, 0.656, 1.047, 0.784, 0.805,
0.879, 1.133, 0.921, 0.854, 0.698, 1.002, 1.097, 0.957, 0.852, 1.023]
performance.add("Overall", dates, overall,
mark_point=["max", "min"], mark_line=["average"], is_datazoom_show=True,
datazoom_type='both',
datazoom_range=[0, 100])
performance.add("Self", dates, self_record,
mark_point=["max", "min"], mark_line=["average"], is_datazoom_show=True,
datazoom_type='both',
datazoom_range=[0, 100], legend_pos="40%")
performance.add("Mirror", dates, mirror_record,
mark_point=["max", "min"], mark_line=["average"], is_datazoom_show=True,
datazoom_type='both',
datazoom_range=[0, 100], legend_pos="40%")
page.add(performance)
portfolio = Pie("Portfolio Summary", **style.init_style)
sectors = ['Technology', 'Energy', 'Consumer Cyclical',
'Non-Consumer Cyclical', 'Unclassified']
self_placement = [10000, 20000, 20000, 5000, 2000]
mirror_placement = [4000, 2000, 1000, 1000, 2000]
over_placement = [self_placement[i] + mirror_placement[i]
for i in range(len(self_placement))]
portfolio.add("Self", sectors, self_placement,
radius=[10, 30], center=[75, 50], is_random=True, rosetype='radius', legend_pos="25%")
portfolio.add("Mirror", sectors, mirror_placement,
radius=[40, 60], center=[75, 50], is_random=True, rosetype='radius')
portfolio.add("Overall", sectors, over_placement,
radius=[10, 60], center=[25, 50], is_random=True, rosetype='radius', is_label_show=True)
page.add(portfolio)
return page
def home():
"""
主页信息
"""
# 将要显示的数据
print('test')
city_num_list = CITY_NUM_LIST[:100]
kw_num_city_list = KW_NUM_OF_ALL_CITY_LIST[:100]
lan_num_list = LAN_NUM_LIST
job_num_list = JOB_NUM_LIST
all_num = ALL_NUM
# 生成相应的图表
top_10_city_page = Page()
top_10_city_page.add(
bar_ssdd(
attr_v1_v2=kw_num_city_list[:10],
chart_name='前十城市中语言数与职位数对比-折线图',
v1_name='语言数量',
v2_name='职位数量',
))
top_10_city_map_page = Page()
top_10_city_map_page.add(
geo_qgtd(attr_v1=city_num_list[:10],
chart_name='前十城市地理位置',
v1_name='城市'))
top_100_city_page = Page()
top_100_city_page.add(
bar_sssf(attr_v1=city_num_list,
chart_name='前百城市职位数量-柱状图',
v1_name='职位数'))
top_100_city_map_page = Page()
top_100_city_map_page.add(
geo_qgtd(attr_v1=city_num_list[:100],
chart_name='前百城市地理位置',
v1_name='城市'))
lan_page = Page()
lan_page.add(
pie_yht(attr_v1=lan_num_list, chart_name='各种语言占总数比值-饼图', v1_name='语言'))
job_page = Page()
job_page.add(
pie_yht(attr_v1=job_num_list, chart_name='各种职位占总数比值-饼图', v1_name='职位'))
return render_template(
'index.html',
title='首页',
all_num=all_num,
top5_city=DATA.top5_city_name(),
top5_lan=DATA.top5_lan_name(),
top5_job=DATA.top5_job_name(),
top_10_city=top_10_city_page.render_embed(),
top_10_city_map_page=top_10_city_map_page.render_embed(),
top_100_city_page=top_100_city_page.render_embed(),
top_100_city_map_page=top_100_city_map_page.render_embed(),
lan_page=lan_page.render_embed(),
job_page=job_page.render_embed(),
script_list=top_10_city_page.get_js_dependencies() +
top_100_city_page.get_js_dependencies() +
lan_page.get_js_dependencies() + job_page.get_js_dependencies() +
top_10_city_map_page.get_js_dependencies() +
top_100_city_map_page.get_js_dependencies(),
)
print(data['score'].mean())
score_total = data['score'].value_counts().sort_index()
#sort_index(ascending=True) 方法可以对索引进行排序操作
print(score_total)
bar = Bar("《流浪地球》各评分数量", width=700)
line = Line("", width=700)
bar.add("", score_total.index, score_total.values, is_stack=True, is_label_show=True,
bar_category_gap='40%', label_color = ['#196845'],
legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)
line.add("", score_total.index, score_total.values+1000, is_smooth=True)
overlap = Overlap(width=700)
overlap.add(bar)
overlap.add(line)
#overlap.render()
page.add(overlap)
# 低分百分比
low_score=score_total[:4].sum()/score_total.sum()*100
# 高分百分比
high_score=score_total[8:].sum()/score_total.sum()*100
# 满分百分比
full_score=score_total[10:].sum()/score_total.sum()*100
print(u'低分占百分比为:{:.3f}%'.format(low_score))
print(u'高分占百分比为:{:.3f}%'.format(high_score))
print(u'满分占百分比为:{:.3f}%'.format(full_score))
'''
低分占百分比为:3.419%
高分占百分比为:90.625%
满分占百分比为:70.530%
def graphpage(labels, mode_combo, startdate, enddate, optInterval, width1,
height1):
#optInterval='D/W/M' labels
startdate = startdate.replace("/", "-") #convert to tushare readable date
enddate = enddate.replace("/", "-")
page = Page()
for label in labels: # generate numbers of graphs according to numbers of queries in treewidget
label1 = re.split("-", label)
overlap = Overlap()
if label1[2] != "分笔" and label1[2] != "季度饼图": #"K线" "复权" "历史分钟"
if mode_combo == "复权":
#if optInterval == "qfq" or optInterval == "hfq":#复权
array = ts.get_h_data(label1[1],
start=startdate,
end=enddate,
autype="qfq")
array = array.sort_index()
time = array.index.format() # 日期正是index
elif mode_combo == "K线":
#elif optInterval.isalnum() :#历史K线
array = ts.get_k_data(label1[1],
start=startdate,
end=enddate,
ktype=optInterval)
time = array['date'].tolist() # array.date
elif mode_combo == "历史分钟":
array_bfr = ts.get_tick_data(label1[1], date=startdate)
array_bfr.sort_values("time")
a = startdate + " " + array_bfr["time"]
array_bfr["time"] = a
array_bfr["time"] = pd.to_datetime(a)
array_bfr = array_bfr.set_index("time")
if label1[2] != "Volume" and label1[2] != "Amount":
price_df = array_bfr["price"].resample(optInterval).ohlc(
) # resample重新取样,选出ohlc=open/high/low，提取除价格之外另外4列.close,tushare是ohcl
price_df = price_df.dropna()
array = price_df
time = price_df.index.format()
elif label1[2] == "Volume":
vols = array_bfr["volume"].resample(optInterval).sum(
) #resample重新取样，累加间隔内数值 relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
array = vol_df
time = vol_df.index.format()
else:
amounts = array_bfr["amount"].resample(optInterval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
array = amount_df
time = amount_df.index.format()
#绘图方法
if label1[2] == 'Kline':
re_array = array[['open', 'close', 'high', 'low']]
data_li = list(row.tolist()
for index, row in re_array.iterrows()
) # data_list = list(re_array.as_matrix())
close = array['close'].tolist()
kline = Kline(label1[0] + "-" + optInterval,
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
kline.add(label1[0],
time,
data_li,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_interval=1)
overlap.add(kline)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(label1[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(label1[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(label1[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else: #When label1[2]==open/close/volume
if label1[2] == 'Open':
list_aft = array['open'].tolist()
elif label1[2] == 'Close':
list_aft = array['close'].tolist()
elif label1[2] == 'High':
list_aft = array['high'].tolist()
elif label1[2] == 'Low':
list_aft = array['low'].tolist()
elif label1[2] == 'Volume': #volume
list_aft = array['volume'].tolist()
else: #amount
list_aft = array['amount'].tolist()
line = Line(label1[0] + "-" + label1[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
line.add(label1[0] + "-" + label1[2],
time,
list_aft,
is_datazoom_show=True,
yaxis_max="dataMax",
yaxis_min="dataMin",
datazoom_type="slider")
overlap.add(line)
page.add(overlap)
elif label1[2] == "分笔":
array = ts.get_tick_data(label1[1], date=startdate)
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = list(zip(date, amount, flag, price))
form = [e[1] for e in returnarray]
time = [d[0] for d in returnarray]
bar = Bar(label1[0] + "-" + label1[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
bar.add(label1[0] + "-" + label1[2],
time,
form,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_min="dataMin",
yaxis_max="dataMax")
overlap.add(bar)
line = Line(label1[0] + "price",
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
price = [e[3] for e in returnarray]
line.add(label1[0] + "price",
time,
price,
yaxis_min="dataMin",
yaxis_max="dataMax",
is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
elif label1[2] == "季度饼图":
datestr = startdate.split("-")
thisyear = datestr[0]
df2 = ts.top10_holders(code=label1[1], gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in range(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in range(0, len(value), 10)]
quarterarray = [
quarter[k:k + 10] for k in range(0, len(quarter), 10)
]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = list(
zip(namearray, valuearray, quarterarray, flag, num))
timeline = Timeline(is_auto_play=False, timeline_bottom=0
) # zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in returnarray]
valuearray = [d[1] for d in returnarray]
quarter = [e[2] for e in returnarray]
num = returnarray[0][4]
for x in range(0, int(num / 10)):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(label1[0] + "-" + "前十股东",
width=width1 * 10 / 11,
height=(height1 * 10 / 11))
pie.add(label1[0] + "-" + "前十股东",
names,
list1,
radius=[30, 55],
is_legend_show=False,
is_label_show=True,
label_formatter="{b}: {c}\n{d}%")
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
page.render()
def create_charts():
page = Page()
style = Style(width=WIDTH, height=HEIGHT)
schema = ["data", "AQI", "PM2.5", "PM10", "CO", "NO2"]
data = [[1, 91, 45, 125, 0.82, 34], [
2,
65,
27,
78,
0.86,
45,
], [3, 83, 60, 84, 1.09, 73], [4, 109, 81, 121, 1.28, 68],
[5, 106, 77, 114, 1.07, 55], [6, 109, 81, 121, 1.28, 68],
[7, 106, 77, 114, 1.07, 55], [8, 89, 65, 78, 0.86, 51, 26],
[9, 53, 33, 47, 0.64, 50, 17], [10, 80, 55, 80, 1.01, 75, 24],
[11, 117, 81, 124, 1.03, 45]]
chart = Parallel("平行坐标系-默认指示器", **style.init_style)
chart.config(schema)
chart.add("parallel", data, is_random=True)
page.add(chart)
c_schema = [{
"dim": 0,
"name": "data"
}, {
"dim": 1,
"name": "AQI"
}, {
"dim": 2,
"name": "PM2.5"
}, {
"dim": 3,
"name": "PM10"
}, {
"dim": 4,
"name": "CO"
}, {
"dim": 5,
"name": "NO2"
}, {
"dim": 6,
"name": "CO2"
}, {
"dim": 7,
"name": "等级",
"type": "category",
"data": ['优', '良', '轻度污染', '中度污染', '重度污染', '严重污染']
}]
data = [[1, 91, 45, 125, 0.82, 34, 23, "良"],
[2, 65, 27, 78, 0.86, 45, 29, "良"],
[3, 83, 60, 84, 1.09, 73, 27, "良"],
[4, 109, 81, 121, 1.28, 68, 51, "轻度污染"],
[5, 106, 77, 114, 1.07, 55, 51, "轻度污染"],
[6, 109, 81, 121, 1.28, 68, 51, "轻度污染"],
[7, 106, 77, 114, 1.07, 55, 51, "轻度污染"],
[8, 89, 65, 78, 0.86, 51, 26, "良"],
[9, 53, 33, 47, 0.64, 50, 17, "良"],
[10, 80, 55, 80, 1.01, 75, 24, "良"],
[11, 117, 81, 124, 1.03, 45, 24, "轻度污染"],
[12, 99, 71, 142, 1.1, 62, 42, "良"],
[13, 95, 69, 130, 1.28, 74, 50, "良"],
[14, 116, 87, 131, 1.47, 84, 40, "轻度污染"]]
chart = Parallel("平行坐标系-用户自定义指示器", **style.init_style)
chart.config(c_schema=c_schema)
chart.add("parallel", data)
page.add(chart)
return page
def test_more():
page = Page()
# line
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line = Line("折线图示例")
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], mark_line=["average"])
page.add(line)
# pie
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos='center')
pie.add("", attr, v1, radius=[40, 75], label_text_color=None,
is_label_show=True, legend_orient='vertical', legend_pos='left')
page.add(pie)
# kline
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)],
v1, is_datazoom_show=True)
page.add(kline)
# radar
schema = [
("销售", 6500), ("管理", 16000), ("信息技术", 30000),
("客服", 38000), ("研发", 52000), ("市场", 25000)
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False,
legend_selectedmode='single')
page.add(radar)
# scatter3d
import random
data = [
[random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)] for _ in range(80)
]
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3D)
# wordcloud
name = [
'Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World',
'Charter Communications', 'Chick Fil A', 'Planet Fitness',
'Pitch Perfect', 'Express', 'Home', 'Johnny Depp', 'Lena Dunham',
'Lewis Hamilton', 'KXAN', 'Mary Ellen Mark', 'Farrah Abraham',
'Rita Ora', 'Serena Williams', 'NCAA baseball tournament', 'Point Break'
]
value = [
10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112,
965, 847, 582, 555, 550, 462, 366, 360, 282, 273, 265
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[30, 100], rotate_step=66)
page.add(wordcloud)
# liquid
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6])
page.add(liquid)
page.render()
def pyecharts(request):
"""dashboard view"""
# 工单数量统计
chart_dao = ChartDao()
bar1 = Bar('SQL上线工单统计(数量)', width="100%")
data = chart_dao.workflow_by_date(30)
today = date.today()
one_month_before = today - relativedelta(days=+30)
attr = chart_dao.get_date_list(one_month_before, today)
_dict = {}
for row in data['rows']:
_dict[row[0]] = row[1]
value = [_dict.get(day) if _dict.get(day) else 0 for day in attr]
bar1.add("月统计", attr, value, is_stack=False, legend_selectedmode='single')
# 工单按组统计
pie1 = Pie('SQL上线工单统计(组)', width="100%")
data = chart_dao.workflow_by_group(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie1.add("月统计", attr, value, is_legend_show=False, is_label_show=True)
# 工单按人统计
bar2 = Bar('SQL上线工单统计(用户)', width="100%")
data = chart_dao.workflow_by_user(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar2.add("月统计", attr, value, is_label_show=True)
# SQL语句类型统计
pie2 = Pie("SQL上线工单统计(类型)", width="100%")
data = chart_dao.syntax_type()
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie2.add("", attr, value, is_label_show=True)
# SQL查询统计(每日检索行数)
line1 = Line("SQL查询统计", width="100%")
attr = chart_dao.get_date_list(one_month_before, today)
effect_data = chart_dao.querylog_effect_row_by_date(30)
effect_dict = {}
for row in effect_data['rows']:
effect_dict[row[0]] = int(row[1])
effect_value = [
effect_dict.get(day) if effect_dict.get(day) else 0 for day in attr
]
count_data = chart_dao.querylog_count_by_date(30)
count_dict = {}
for row in count_data['rows']:
count_dict[row[0]] = int(row[1])
count_value = [
count_dict.get(day) if count_dict.get(day) else 0 for day in attr
]
line1.add("检索行数",
attr,
effect_value,
is_stack=False,
legend_selectedmode='single',
mark_point=["average"])
line1.add("检索次数",
attr,
count_value,
is_stack=False,
legend_selectedmode='single',
is_smooth=True,
mark_line=["max", "average"])
# SQL查询统计(用户检索行数)
pie4 = Pie("SQL查询统计(用户检索行数)", width="100%")
data = chart_dao.querylog_effect_row_by_user(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie4.add("月统计",
attr,
value,
radius=[40, 75],
is_legend_show=False,
is_label_show=True)
# SQL查询统计(DB检索行数)
pie5 = Pie("SQL查询统计(DB检索行数)", width="100%")
data = chart_dao.querylog_effect_row_by_db(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie5.add("月统计", attr, value, is_legend_show=False, is_label_show=True)
# 可视化展示页面
page = Page()
page.add(bar1)
page.add(pie1)
page.add(bar2)
page.add(pie2)
page.add(line1)
page.add(pie4)
page.add(pie5)
myechart = page.render_embed() # 渲染配置
host = 'https://pyecharts.github.io/assets/js' # js文件源地址
script_list = page.get_js_dependencies() # 获取依赖的js文件名称（只获取当前视图需要的js）
return render(request, "dashboard.html", {
"myechart": myechart,
"host": host,
"script_list": script_list
})
def create_charts():
page = Page()
style = Style(
width=WIDTH, height=HEIGHT
)
schema = [
("销售", 6500), ("管理", 16000), ("信息技术", 30000),
("客服", 38000), ("研发", 52000), ("市场", 25000)
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
chart = Radar("雷达图-默认指示器", **style.init_style)
chart.config(schema)
chart.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
chart.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False,
legend_selectedmode='single')
page.add(chart)
value_bj = [
[55, 9, 56, 0.46, 18, 6, 1],
[25, 11, 21, 0.65, 34, 9, 2],
[56, 7, 63, 0.3, 14, 5, 3],
[33, 7, 29, 0.33, 16, 6, 4],
[42, 24, 44, 0.76, 40, 16, 5],
[82, 58, 90, 1.77, 68, 33, 6],
[74, 49, 77, 1.46, 48, 27, 7],
[78, 55, 80, 1.29, 59, 29, 8],
[267, 216, 280, 4.8, 108, 64, 9],
[185, 127, 216, 2.52, 61, 27, 10],
[39, 19, 38, 0.57, 31, 15, 11],
[41, 11, 40, 0.43, 21, 7, 12],
[64, 38, 74, 1.04, 46, 22, 13],
[108, 79, 120, 1.7, 75, 41, 14],
[108, 63, 116, 1.48, 44, 26, 15],
[33, 6, 29, 0.34, 13, 5, 16],
[94, 66, 110, 1.54, 62, 31, 17],
[186, 142, 192, 3.88, 93, 79, 18],
[57, 31, 54, 0.96, 32, 14, 19],
[22, 8, 17, 0.48, 23, 10, 20],
[39, 15, 36, 0.61, 29, 13, 21],
[94, 69, 114, 2.08, 73, 39, 22],
[99, 73, 110, 2.43, 76, 48, 23],
[31, 12, 30, 0.5, 32, 16, 24],
[42, 27, 43, 1, 53, 22, 25],
[154, 117, 157, 3.05, 92, 58, 26],
[234, 185, 230, 4.09, 123, 69, 27],
[160, 120, 186, 2.77, 91, 50, 28],
[134, 96, 165, 2.76, 83, 41, 29],
[52, 24, 60, 1.03, 50, 21, 30],
[46, 5, 49, 0.28, 10, 6, 31]
]
value_sh = [
[91, 45, 125, 0.82, 34, 23, 1],
[65, 27, 78, 0.86, 45, 29, 2],
[83, 60, 84, 1.09, 73, 27, 3],
[109, 81, 121, 1.28, 68, 51, 4],
[106, 77, 114, 1.07, 55, 51, 5],
[109, 81, 121, 1.28, 68, 51, 6],
[106, 77, 114, 1.07, 55, 51, 7],
[89, 65, 78, 0.86, 51, 26, 8],
[53, 33, 47, 0.64, 50, 17, 9],
[80, 55, 80, 1.01, 75, 24, 10],
[117, 81, 124, 1.03, 45, 24, 11],
[99, 71, 142, 1.1, 62, 42, 12],
[95, 69, 130, 1.28, 74, 50, 13],
[116, 87, 131, 1.47, 84, 40, 14],
[108, 80, 121, 1.3, 85, 37, 15],
[134, 83, 167, 1.16, 57, 43, 16],
[79, 43, 107, 1.05, 59, 37, 17],
[71, 46, 89, 0.86, 64, 25, 18],
[97, 71, 113, 1.17, 88, 31, 19],
[84, 57, 91, 0.85, 55, 31, 20],
[87, 63, 101, 0.9, 56, 41, 21],
[104, 77, 119, 1.09, 73, 48, 22],
[87, 62, 100, 1, 72, 28, 23],
[168, 128, 172, 1.49, 97, 56, 24],
[65, 45, 51, 0.74, 39, 17, 25],
[39, 24, 38, 0.61, 47, 17, 26],
[39, 24, 39, 0.59, 50, 19, 27],
[93, 68, 96, 1.05, 79, 29, 28],
[188, 143, 197, 1.66, 99, 51, 29],
[174, 131, 174, 1.55, 108, 50, 30],
[187, 143, 201, 1.39, 89, 53, 31]
]
c_schema = [
{"name": "AQI", "max": 300, "min": 5},
{"name": "PM2.5", "max": 250, "min": 20},
{"name": "PM10", "max": 300, "min": 5},
{"name": "CO", "max": 5},
{"name": "NO2", "max": 200},
{"name": "SO2", "max": 100}
]
chart = Radar("雷达图-用户自定义指示器", **style.init_style)
chart.config(c_schema=c_schema, shape='circle')
chart.add("北京", value_bj, item_color="#f9713c", symbol=None)
chart.add("上海", value_sh, item_color="#b3e4a1", symbol=None,
legend_selectedmode='single')
page.add(chart)
chart = Radar("雷达图-用户自定义指示器", **style.init_style)
chart.config(c_schema=c_schema, shape='circle')
chart.add("北京", value_bj, item_color="#f9713c", symbol=None)
chart.add("上海", value_sh, item_color="#b3e4a1", symbol=None)
page.add(chart)
return page
from pyecharts import Page, Style
x = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']
y1 = [5, 20, 36, 10, 75, 90]
y2 = [10, 25, 8, 60, 20, 80]
page = Page()
style = Style()
bar_style = style.add(mark_point=["max", "min"],
mark_line=["average"],
is_label_show=True,
is_more_utils=True)
bar = Bar()
bar.add('', x, y1, **bar_style)
bar.add('', x, y2, **bar_style)
page.add(bar)
line = Line()
line_style = style.add(mark_point=["max", "min"],
mark_line=["average"],
is_label_show=False,
is_more_utils=False,
is_stack=True,
is_fill=True,
area_opacity=0.5)
line.add('', x, y1, **line_style)
line.add('', x, y2, **line_style)
page.add(line)
pie = Pie("饼图-玫瑰图示例")
pie_style = style.add(is_random=True,
def analyst():
"""颜色，型号，内存"""
color_c = Counter()
model_c = Counter()
ram_c = Counter()
db = init_mongodb()
cur = db['comments']
for g in cur.find({}, {'color': 1, 'model': 1, 'ram': 1, '_id': 0}):
# 颜色清洗
color = g['color']
if ' ' in color:
color = color.split()[0]
color_c[color] += 1
# ram 清洗
ram = g['ram']
if ram:
ram_c[ram] += 1
# model 清洗
model = g['model']
excludes = ['手机', '白条免息', '电信老人机']
if model not in excludes:
model_c[model] += 1
# for (k, v) in c.most_common(100):
# print(k, v)
# 饼图
page = Page()
style = Style(width=800, height=800)
## color
attr = color_c.keys()
v1 = color_c.values()
chart = Pie("颜色", **style.init_style)
chart.add("", attr, v1, is_label_show=True)
page.add(chart)
## model
attr = model_c.keys()
v1 = model_c.values()
chart = Pie("型号", title_pos='center', **style.init_style)
chart.add("",
attr,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='left')
page.add(chart)
## ram
attr = ram_c.keys()
v1 = ram_c.values()
chart = Pie("内存", title_pos='center', **style.init_style)
chart.add("",
attr,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='right')
page.add(chart)
page.render(path="result.html")
def graphpage(labels,mode_combo,startdate,enddate,optInterval,width1, height1):
#optInterval='D/W/M' labels
startdate = startdate.replace("/","-")#convert to tushare readable date
enddate = enddate.replace("/","-")
page = Page()
for label in labels: # generate numbers of graphs according to numbers of queries in treewidget
label1 = re.split("-", label)
overlap = Overlap()
if label1[2]!="分笔" and label1[2]!="季度饼图": #"K线" "复权" "历史分钟"
if mode_combo == "复权":
#if optInterval == "qfq" or optInterval == "hfq":#复权
array = ts.get_h_data(label1[1], start=startdate, end=enddate, autype="qfq")
array = array.sort_index()
time = array.index.format() # 日期正是index
elif mode_combo == "K线":
#elif optInterval.isalnum() :#历史K线
array = ts.get_k_data(label1[1], start=startdate, end=enddate, ktype=optInterval)
time = array['date'].tolist() # array.date
elif mode_combo == "历史分钟":
array_bfr = ts.get_tick_data(label1[1], date=startdate)
array_bfr.sort_values("time")
a = startdate + " " + array_bfr["time"]
array_bfr["time"] = a
array_bfr["time"] = pd.to_datetime(a)
array_bfr = array_bfr.set_index("time")
if label1[2] != "Volume" and label1[2] != "Amount":
price_df = array_bfr["price"].resample(optInterval).ohlc() # resample重新取样,选出ohlc=open/high/low，提取除价格之外另外4列.close,tushare是ohcl
price_df = price_df.dropna()
array = price_df
time = price_df.index.format()
elif label1[2] == "Volume":
vols = array_bfr["volume"].resample(optInterval).sum() #resample重新取样，累加间隔内数值 relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
array = vol_df
time = vol_df.index.format()
else:
amounts = array_bfr["amount"].resample(optInterval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
array = amount_df
time = amount_df.index.format()
#绘图方法
if label1[2] == 'Kline':
re_array = array[['open', 'close', 'high', 'low']]
data_li = list(row.tolist() for index, row in re_array.iterrows()) # data_list = list(re_array.as_matrix())
close = array['close'].tolist()
kline = Kline(label1[0] + "-" + optInterval, width=width1*10/11, height = (height1*10/11) / len(labels))
kline.add(label1[0], time, data_li, is_datazoom_show=True, datazoom_type="slider", yaxis_interval=1)
overlap.add(kline)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(label1[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(label1[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(label1[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:#When label1[2]==open/close/volume
if label1[2] == 'Open':
list_aft = array['open'].tolist()
elif label1[2] == 'Close':
list_aft = close
elif label1[2] == 'High':
list_aft = array['high'].tolist()
elif label1[2] == 'Low':
list_aft = array['low'].tolist()
elif label1[2] == 'Volume':#volume
list_aft = array['volume'].tolist()
else:#amount
list_aft = array['amount'].tolist()
line = Line(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
line.add(label1[0] + "-" + label1[2], time, list_aft, is_datazoom_show=True, yaxis_max="dataMax",
yaxis_min="dataMin", datazoom_type="slider")
overlap.add(line)
page.add(overlap)
elif label1[2]=="分笔":
array = ts.get_tick_data(label1[1], date=startdate)
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = list(zip(date, amount, flag, price))
form = [e[1] for e in returnarray]
time = [d[0] for d in returnarray]
bar = Bar(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
bar.add(label1[0] + "-" + label1[2], time, form, is_datazoom_show=True, datazoom_type="slider", yaxis_min="dataMin", yaxis_max="dataMax")
overlap.add(bar)
line = Line(label1[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
price = [e[3] for e in returnarray]
line.add(label1[0] + "price", time, price, yaxis_min="dataMin", yaxis_max="dataMax", is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
elif label1[2]=="季度饼图":
datestr = startdate.split("-")
thisyear = datestr[0]
df2 = ts.top10_holders(code=label1[1], gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in range(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in range(0, len(value), 10)]
quarterarray = [quarter[k:k + 10] for k in range(0, len(quarter), 10)]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = list(zip(namearray, valuearray, quarterarray, flag, num))
timeline = Timeline(is_auto_play=False, timeline_bottom=0) # zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in returnarray]
valuearray = [d[1] for d in returnarray]
quarter = [e[2] for e in returnarray]
num = returnarray[0][4]
for x in range(0, int(num / 10)):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(label1[0] + "-" + "前十股东", width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(label1[0] + "-" + "前十股东", names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter="{b}: {c}\n{d}%")
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
page.render()
def PL_draw_echarts(PL_df, PL_slices):
# 初始化一个画图的页面.每一种信号画一张图片
PL_page = Page()
PL_columns = PL_df.columns.values.tolist()
# 所有统计的信号种类
PL_signal_items = list(set(PL_df['signal_name'].tolist()))
PL_signal_items.sort()
# 所有的N[第n个bar]
PL_bar_nos = []
for PL_col in PL_columns:
if re.match('chg_pct_', PL_col):
# 获取当前统计的是第几根bar的收益
PL_bar_nos.append(PL_col.replace('chg_pct_', ''))
for PL_sig in PL_signal_items:
# 获取一种信号在M个bar内的收益统计
PL_bar = Bar(PL_sig + "收益分布", title_text_size=14, width='100%')
PL_sdf = PL_df[(PL_df['signal_name'] == PL_sig)]
if PL_sdf.empty:
continue
# 划分收益区间,从0开始往正负两端划分收益区间
# 计算每个收益区间的大小
PL_max_profit = 0
PL_min_profit = 0
for PL_no in PL_bar_nos:
PL_pcol = 'chg_pct_' + PL_no
PL_profits = PL_sdf[PL_pcol].tolist()
PL_max_profit = max(max(PL_profits), PL_max_profit)
PL_min_profit = min(min(PL_profits), PL_min_profit)
PL_intervals = []
if PL_min_profit < 0:
PL_unit = round(
max(PL_max_profit, abs(PL_min_profit)) / PL_slices, 4)
# 先划分小于0的区间
for i in range(0, int(math.ceil(abs(PL_min_profit) / PL_unit))):
PL_intervals.append(
[round(-(i + 1) * PL_unit, 4),
round(-i * PL_unit, 4)])
PL_intervals.reverse()
else:
PL_unit = PL_max_profit / PL_slices
for i in range(0, int(math.ceil(abs(PL_max_profit) / PL_unit))):
PL_intervals.append([i * PL_unit, (i + 1) * PL_unit])
# 显示收益区间之前先格式化成百分比。
PL_format_intervals = [
'%.2f%%~%.2f%%' % (i[0] * 100, i[1] * 100) for i in PL_intervals
]
for PL_no in PL_bar_nos:
# 计算第M个bar收益，落在某一个收益区间的概率
PL_win_ratios = []
PL_pcol = 'chg_pct_' + PL_no
for PL_interval in PL_intervals:
# 统计在收益落在某个收益区间的概率
PL_conf = (PL_sdf[PL_pcol] > PL_interval[0]) & (
PL_sdf[PL_pcol] <= PL_interval[1])
# 避免int类型直接除之后返回的还是int,这里*1.0
PL_win_ratios.append(
round(len(PL_sdf[PL_conf]) / (len(PL_sdf) * 1.0), 2))
PL_bar.add(
"第%s个bar" % PL_no,
PL_format_intervals,
PL_win_ratios,
xaxis_name='收益区间',
xaxis_name_pos='end',
xaxis_name_size=12,
xaxis_label_textsize=12,
xaxis_interval=1,
xaxis_rotate=45,
yaxis_name='概率',
yaxis_name_pos='end',
yaxis_name_size=12,
yaxis_label_textsize=12,
is_splitline_show=False,
# 默认为 X 轴，横向
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[40, 60],
# 额外的 dataZoom 控制条，纵向
# is_datazoom_extra_show=True,
# datazoom_extra_type="slider",
# datazoom_extra_range=[10, 25],
# is_toolbox_show=False,
)
PL_grid = Grid(width='100%')
PL_grid.add(PL_bar, grid_bottom=120)
PL_page.add(PL_grid)
PL_page.render()
def test_page_grid_timeline_overlap():
# Grid
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add(
"es",
[11, 11, 15, 13, 12, 13, 10],
[1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%",
)
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
# Timeline
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构", height=720, width=1200)
bar_5.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add(
"冬季",
CLOTHES,
[randint(10, 100) for _ in range(6)],
is_legend_show=True,
)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
timeline.add(bar_2, "2013 年")
timeline.add(bar_3, "2014 年")
timeline.add(bar_4, "2015 年")
timeline.add(bar_5, "2016 年")
# Overlap
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(height=720, width=1200)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page = Page()
page.add(grid)
page.add(timeline)
page.add(overlap)
page.render()
Title = 数据可视化
"""
from pyecharts import Bar, Scatter3D, Line, Overlap
from pyecharts import Page
import random
page = Page()
# 竖行条形图
bar = Bar('阳阳日常分析表','女神阳阳分析表')
bar.use_theme('dark')
# 添加条数据
bar.add('时间',['睡觉','吃饭','化妆','做饭','发呆','看电视'],[10,0.5,1,1,3,4])
# 添加条数据
bar.add('钱',['睡觉','吃饭','化妆','做饭','发呆','看电视'],[0,1,30,1,0,0.5])
page.add(bar)
# 带折线的竖行条形图
bar = Bar('阳阳日常分析表2','女神阳阳分析表')
bar.use_theme('dark')
data1 = [10,0.5,1,1,3,4]
data2 = [0,1,30,1,0,0.5]
data_line1 = [11,1.5,2,3,4,6]
data_line2 = [1,1.5,31,3,4,6]
# 添加条数据
bar.add('时间',['睡觉','吃饭','化妆','做饭','发呆','看电视'],data1)
# 添加条数据
bar.add('钱',['睡觉','吃饭','化妆','做饭','发呆','看电视'],data2)
line = Line()
def create_charts():
page = Page()
data = [['2015/11/08', 10, 'DQ'], ['2015/11/09', 15, 'DQ'],
['2015/11/10', 35, 'DQ'], ['2015/11/14', 7, 'DQ'],
['2015/11/15', 2, 'DQ'], ['2015/11/16', 17, 'DQ'],
['2015/11/17', 33, 'DQ'], ['2015/11/18', 40, 'DQ'],
['2015/11/19', 32, 'DQ'], ['2015/11/20', 26, 'DQ'],
['2015/11/21', 35, 'DQ'], ['2015/11/22', 40, 'DQ'],
['2015/11/23', 32, 'DQ'], ['2015/11/24', 26, 'DQ'],
['2015/11/25', 22, 'DQ'], ['2015/11/08', 35, 'TY'],
['2015/11/09', 36, 'TY'], ['2015/11/10', 37, 'TY'],
['2015/11/11', 22, 'TY'], ['2015/11/12', 24, 'TY'],
['2015/11/13', 26, 'TY'], ['2015/11/14', 34, 'TY'],
['2015/11/15', 21, 'TY'], ['2015/11/16', 18, 'TY'],
['2015/11/17', 45, 'TY'], ['2015/11/18', 32, 'TY'],
['2015/11/19', 35, 'TY'], ['2015/11/20', 30, 'TY'],
['2015/11/21', 28, 'TY'], ['2015/11/22', 27, 'TY'],
['2015/11/23', 26, 'TY'], ['2015/11/24', 15, 'TY'],
['2015/11/25', 30, 'TY'], ['2015/11/26', 35, 'TY'],
['2015/11/27', 42, 'TY'], ['2015/11/28', 42, 'TY'],
['2015/11/08', 21, 'SS'], ['2015/11/09', 25, 'SS'],
['2015/11/10', 27, 'SS'], ['2015/11/11', 23, 'SS'],
['2015/11/12', 24, 'SS'], ['2015/11/13', 21, 'SS'],
['2015/11/14', 35, 'SS'], ['2015/11/15', 39, 'SS'],
['2015/11/16', 40, 'SS'], ['2015/11/17', 36, 'SS'],
['2015/11/18', 33, 'SS'], ['2015/11/19', 43, 'SS'],
['2015/11/20', 40, 'SS'], ['2015/11/21', 34, 'SS'],
['2015/11/22', 28, 'SS'], ['2015/11/14', 7, 'QG'],
['2015/11/15', 2, 'QG'], ['2015/11/16', 17, 'QG'],
['2015/11/17', 33, 'QG'], ['2015/11/18', 40, 'QG'],
['2015/11/19', 32, 'QG'], ['2015/11/20', 26, 'QG'],
['2015/11/21', 35, 'QG'], ['2015/11/22', 40, 'QG'],
['2015/11/23', 32, 'QG'], ['2015/11/24', 26, 'QG'],
['2015/11/25', 22, 'QG'], ['2015/11/26', 16, 'QG'],
['2015/11/27', 22, 'QG'], ['2015/11/28', 10, 'QG'],
['2015/11/08', 10, 'SY'], ['2015/11/09', 15, 'SY'],
['2015/11/10', 35, 'SY'], ['2015/11/11', 38, 'SY'],
['2015/11/12', 22, 'SY'], ['2015/11/13', 16, 'SY'],
['2015/11/14', 7, 'SY'], ['2015/11/15', 2, 'SY'],
['2015/11/16', 17, 'SY'], ['2015/11/17', 33, 'SY'],
['2015/11/18', 40, 'SY'], ['2015/11/19', 32, 'SY'],
['2015/11/20', 26, 'SY'], ['2015/11/21', 35, 'SY'],
['2015/11/22', 4, 'SY'], ['2015/11/23', 32, 'SY'],
['2015/11/24', 26, 'SY'], ['2015/11/25', 22, 'SY'],
['2015/11/26', 16, 'SY'], ['2015/11/27', 22, 'SY'],
['2015/11/28', 10, 'SY'], ['2015/11/08', 10, 'DD'],
['2015/11/09', 15, 'DD'], ['2015/11/10', 35, 'DD'],
['2015/11/11', 38, 'DD'], ['2015/11/12', 22, 'DD'],
['2015/11/13', 16, 'DD'], ['2015/11/14', 7, 'DD'],
['2015/11/15', 2, 'DD'], ['2015/11/16', 17, 'DD'],
['2015/11/17', 33, 'DD'], ['2015/11/18', 4, 'DD'],
['2015/11/19', 32, 'DD'], ['2015/11/20', 26, 'DD'],
['2015/11/21', 35, 'DD'], ['2015/11/22', 40, 'DD'],
['2015/11/23', 32, 'DD'], ['2015/11/24', 26, 'DD'],
['2015/11/25', 22, 'DD']]
chart = ThemeRiver("主题河流图")
chart.add(['DQ', 'TY', 'SS', 'QG', 'SY', 'DD'], data, is_label_show=True)
page.add(chart)
return page
class JDPage:
def __init__(self, product):
self.page = Page()
self.product = product
self.attr_title_dict = {'product_size': "不同配置购买量", 'product_color': "不同颜色购买量",
'level': "用户等级", 'client': "用户客户端", 'after_days': '购买多少天后评论'}
def generate_stacked_bar_charts(self):
"""
生成不同配置购买量、不同颜色购买量、用户等级和用户客户端的层叠条形图
"""
titles = ['好评', '中评', '差评']
for attr in self.attr_title_dict.keys():
d = {}
for c in self.product.get_all_comments():
temp = getattr(c, attr)
if c.score > 3:
index = 0
elif c.score > 1:
index = 1
else:
index = 2
if d.get(temp):
d[temp][index] += 1
else:
d[temp] = [0, 0, 0]
d[temp][index] = 1
v = [[], [], []]
if attr == 'after_days':
# 使得x轴名称能按照从少到多的顺序
a = ['0-10天', '11-20天', '21-30天', '31-60天', '60天后']
for k in a:
for i in range(3):
v[i].append(d.get(k)[i])
else:
a = []
for k in d.keys():
if k == '':
a.append('网页')
else:
a.append(k)
for i in range(3):
v[i].append(d.get(k)[i])
chart = Bar(self.attr_title_dict.get(attr))
for i in range(3):
chart.add(titles[i], a, v[i], xaxis_interval=0, is_stack=True)
self.page.add(chart)
def generate_pie_charts(self):
"""
生成饼图
"""
# 生成各种评价占比的饼图
a = ["默认好评", "好评", "中评", "差评"]
v = [self.product.default_good_count, self.product.good_count - self.product.default_good_count,
self.product.general_count, self.product.poor_count]
pie = Pie("好评，中评，差评")
pie.add("", a, v, is_label_show=True)
self.page.add(pie)
comments = self.product.get_all_comments()
# 匿名评论情况饼图
v = [0, 0]
for c in comments:
if c.isAnonymous:
v[0] += 1
else:
v[1] += 1
chart = Pie('匿名评论情况')
chart.add('', ['匿名', '非匿名'], v, xaxis_interval=0)
self.page.add(chart)
# 生成不同配置购买量、不同颜色购买量、用户等级和用户客户端的饼图
for attr in self.attr_title_dict.keys():
d = {}
for c in comments:
temp = getattr(c, attr)
if d.get(temp):
d[temp] += 1
else:
d[temp] = 1
a = []
v = []
for k in d.keys():
if k == '':
a.append('网页')
else:
a.append(k)
v.append(d.get(k))
if attr == 'level':
# 解决图例过长遮挡标题的bug
chart = Pie('')
else:
chart = Pie(self.attr_title_dict.get(attr))
chart.add("", a, v, is_label_show=True)
self.page.add(chart)
def generate_word_cloud_charts(self):
"""
生成词云图
"""
# 热评词云
attr = []
val = []
for t in self.product.hot_comment_tags:
attr.append(t.get('name'))
val.append(int(t.get('count')))
hot_tags_wc = make_word_cloud("热评词云", attr, val)
self.page.add(hot_tags_wc)
# 好评，中评，差评词云
d = [('好评词云', 'good_comments'), ('中评词云', 'general_comments'), ('差评词云', 'poor_comments')]
for i in range(3):
a, v = get_summary_and_weight(getattr(self.product, d[i][1]))
wc = make_word_cloud(d[i][0], a, v)
self.page.add(wc)
def line_charts():
page = Page()
chart_init = {
"width": WIDTH,
"height": HEIGHT,
}
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
v2 = [55, 60, 16, 20, 15, 80]
chart = Line("折线图-默认标记", **chart_init)
chart.add("商家A", attr, v1, mark_point=["average"])
chart.add("商家B",
attr,
v2,
is_smooth=True,
mark_line=["max", "average"],
is_more_utils=True)
page.add(chart)
chart = Line("折线图-自定义标记", **chart_init)
chart.add(
"商家A",
attr,
v1,
mark_point=["average", {
"coord": ["裤子", 10],
"name": "这是我想要的第一个标记点"
}])
chart.add("商家B",
attr,
v2,
is_smooth=True,
is_more_utils=True,
mark_point=[{
"coord": ["袜子", 80],
"name": "这是我想要的第二个标记点"
}])
page.add(chart)
chart = Line("折线图-标记图标", **chart_init)
chart.add("商家A",
attr,
v1,
mark_point=["average", "max", "min"],
mark_point_symbol='diamond',
mark_point_textcolor='#40ff27')
chart.add("商家B",
attr,
v2,
mark_point=["average", "max", "min"],
mark_point_symbol='arrow',
mark_point_symbolsize=40)
page.add(chart)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
chart = Line("折线图-某地气温", **chart_init)
chart.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
chart.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"])
page.add(chart)
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
v2 = [55, 60, 16, 20, 15, 80]
chart = Line("折线图-数据堆叠", **chart_init)
chart.add("商家A", attr, v1, is_stack=True, is_label_show=True)
chart.add("商家B", attr, v2, is_stack=True, is_label_show=True)
page.add(chart)
chart = Line("折线图-阶梯图", **chart_init)
chart.add("商家A", attr, v1, is_step=True, is_label_show=True)
page.add(chart)
chart = Line("折线图-面积图", **chart_init)
chart.add("商家A",
attr,
v1,
is_fill=True,
line_opacity=0.2,
area_opacity=0.4,
symbol=None)
chart.add("商家B",
attr,
v2,
is_fill=True,
area_color='#000',
area_opacity=0.3,
is_smooth=True)
page.add(chart)
chart = Line("折线图-对数坐标轴", width=WIDTH, height=HEIGHT)
chart.add("商家A", attr,
[math.log10(random.randint(1, 99999)) for _ in range(6)])
chart.add("商家B",
attr,
[math.log10(random.randint(1, 99999999)) for _ in range(6)],
yaxis_type="log")
page.add(chart)
return page
def test_more():
page = Page()
# line
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie])
# kline
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
kline = Kline("K 线图示例")
kline.add(
"日K",
["2017/7/{}".format(i + 1) for i in range(31)],
v1,
is_datazoom_show=True,
)
page.add(kline)
# radar
schema = [
("销售", 6500),
("管理", 16000),
("信息技术", 30000),
("客服", 38000),
("研发", 52000),
("市场", 25000),
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add(
"实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode="single",
)
page.add(radar)
# scatter3d
import random
data = [
[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100),
]
for _ in range(80)
]
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3D)
# wordcloud
name = [
"Sam S Club",
"Macys",
"Amy Schumer",
"Jurassic World",
"Charter Communications",
"Chick Fil A",
"Planet Fitness",
"Pitch Perfect",
"Express",
"Home",
"Johnny Depp",
"Lena Dunham",
"Lewis Hamilton",
"KXAN",
"Mary Ellen Mark",
"Farrah Abraham",
"Rita Ora",
"Serena Williams",
"NCAA baseball tournament",
"Point Break",
]
value = [
10000,
6181,
4386,
4055,
2467,
2244,
1898,
1484,
1112,
965,
847,
582,
555,
550,
462,
366,
360,
282,
273,
265,
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[30, 100], rotate_step=66)
page.add(wordcloud)
# liquid
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6])
page.add(liquid)
assert len(page) == 7
assert isinstance(page[0], Line)
assert (
("echarts" in page.js_dependencies)
or ("echarts.min" in page.js_dependencies)
)
page.render()
def graphpage(items,startdate,enddate,option,width1, height1): #labels:复权ork线or分笔 option:hfq, qfq or 15, 30, D, etc
page = Page()
for i in items:#generate numbers of graphs according to numbers of queries in treewidget
j = re.split("-",i)
if len(j)==3:
a = generateline(j[1],j[2],startdate,enddate,option)#stock number, Type, startdate, enddate, 30 or 15 or days
if a is None:
continue
time = [d[0] for d in a]#get time from returned dictionary
if j[2]!="Kline":
if len(a[0])==4 and a[0][2]=="bar": #for 分笔data
overlap = Overlap()
form = [e[1] for e in a]
bar = Bar(j[0] + "-" + j[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(j[0] + "-" + j[2], time, form, yaxis_min = "dataMin",yaxis_max = "dataMax",is_datazoom_show = True, datazoom_type = "slider")
overlap.add(bar)
line = Line(j[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in a]
line.add(j[0] + "price", time, price, yaxis_min = "dataMin",yaxis_max = "dataMax", is_datazoom_show = True, datazoom_type = "slider",
yaxis_type="value")
overlap.add(line,yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
if len(a[0])==5 and a[0][3]=="pie":
overlap = Overlap()
timeline = Timeline(is_auto_play=False, timeline_bottom=0) #zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in a]
valuearray = [d[1] for d in a]
quarter = [e[2] for e in a]
num = a[0][4]
for x in range(0, num / 10):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(j[0]+"-"+"前十股东".decode("utf-8"),width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(j[0]+"-"+"前十股东".decode("utf-8"), names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter = "{b}: {c}\n{d}%")
# print list
# print names
# print quarterarray
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
return
#need more statement
else:
form = [e[1] for e in a]#for not分笔 data
line = Line(j[0] + "-" + j[2], width=width1*10/11, height=(height1*10/11)/len(items))
line.add(j[0] + "-" + j[2], time, form, is_datazoom_show=True, datazoom_type="slider",yaxis_min="dataMin",yaxis_max="dataMax")
page.add(line)
else:
overlap = Overlap()#for k线
close = zip(*a)[2]
candle = [[x[1], x[2], x[3], x[4]] for x in a]
candlestick = Kline(j[0] + "-" + j[2], width=width1*10/11, height = (height1*10/11) / len(items))
candlestick.add(j[0], time, candle, is_datazoom_show=True, datazoom_type="slider",yaxis_interval = 1)
overlap.add(candlestick)
if len(close)>10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(j[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close)>20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(j[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close)>30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(j[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:
for k in range(1, len(j)/3):#if graphs are combined
j[3*k-1] = re.sub("\n&","",j[3*k-1])
sizearray=[]
#if j[1] != "Candlestick"
layout = Overlap()
for i in xrange(0, len(j),3):
array = j[i:i +3]
b = generateline(array[1],array[2],startdate,enddate,option)
if b is None:
continue
btime = [d[0] for d in b]
if array[2] != "Kline":
if len(b[0])==4 and b[0][2]=="bar":
form = [e[1] for e in b]
bar = Bar(array[0] + "-" + array[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(array[0] + "-" + array[2], btime, form, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_max="dataMax")
layout.add(bar)
line = Line(array[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in b]
line.add(array[0] + "price", btime, price, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_type="value")
layout.add(line, yaxis_index=1, is_add_yaxis=True)
else:
line = Line(array[0] + "-" + array[2],width=width1*10/11, height=(height1*10/11) / len(items))
line.add(array[0]+"-"+array[2], btime, b, is_datazoom_show=True, yaxis_max = "dataMax", yaxis_min = "dataMin",datazoom_type="slider")
layout.add(line)
else:
candle = [[x[1], x[2], x[3], x[4]] for x in b]
candlestick = Kline(array[0] + "-" + array[1], width=width1*10/11,
height=(height1*10/11) / len(items))
candlestick.add(array[0], btime, candle, is_datazoom_show=True, datazoom_type=["slider"])
#if i == 0:
close = zip(*b)[2]
if len(close)>10:
ma10 = calculateMa(close, 10)
line4 = Line(title_color="#C0C0C0")
line4.add(array[0] + "-" + "MA10", btime, ma10)
layout.add(line4)
if len(close)>20:
ma20 = calculateMa(close, 20)
line5 = Line(title_color="#C0C0C0")
line5.add(array[0] + "-" + "MA20", btime, ma20)
layout.add(line5)
if len(close)>30:
ma30 = calculateMa(close, 30)
line6 = Line(title_color="#C0C0C0")
line6.add(array[0] + "-" + "MA30", btime, ma30)
layout.add(line6)
layout.add(candlestick)
page.add(layout)
page.render()