def html_handle(self, data):
"""
convert function to html by pyecharts
Args:
data(dict): the data info
Returns:
chart: chart generated by pyecharts: Bar, Pie, Line or Scatter
filename: html file name
"""
filename = self.table_name + str(data['order']) + '.html'
margin = str(data['title_top']) + '%'
# 设置图标基本属性
if data['chart'] == 'bar':
chart = (Bar().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'pie':
chart = (Pie().set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin)))
elif data['chart'] == 'line':
chart = (Line().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'scatter':
chart = (Scatter().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(
type_='value',
name=data['x_name'],
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_='value',
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
else:
print("not valid chart")
if not data["classify"]: # 在图片上只需展示一组数据
attr = data["x_data"][0] # 横坐标
val = data["y_data"][0] # 纵坐标
if data['chart'] == 'bar':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
if isinstance(attr[0], str):
attr = [x for x in attr if x != '']
attr = list(map(float, attr))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
else: # 在图片上需要展示多组数据
attr = data["x_data"][0] # 横坐标
for i in range(len(data["classify"])): # 循环输出每组数据
val = data["y_data"][i] # 每组纵坐标的值
name = (data["classify"][i][0] if type(data["classify"][i])
== type(('a', 'b')) else data["classify"][i])
if i == 0:
if data['chart'] != 'pie' and data['chart'] != 'scatter':
chart.add_xaxis(attr)
if data['chart'] == 'bar':
chart.add_yaxis(name,
val,
stack="stack1",
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_yaxis(name,
val,
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
attr_scatter = data["x_data"][i]
if isinstance(attr_scatter[0],
str): # 去除散点图的空点，并将字符类型转化为数字类型
attr_scatter = [x for x in attr_scatter if x != '']
attr_scatter = list(map(float, attr_scatter))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr_scatter).add_yaxis(
name, val, label_opts=opts.LabelOpts(is_show=False))
return chart, filename
[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],
]
c = (Kline().add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)
]).add_yaxis("kline", data).set_global_opts(
yaxis_opts=opts.AxisOpts(is_scale=True),
xaxis_opts=opts.AxisOpts(is_scale=True),
title_opts=opts.TitleOpts(title="Kline-基本示例"),
).render("5.kline_base.html"))
def show_czsc(name: str, contract, levels: [QuoteLevel]):
quotes, raw_df = get_all_quotes(contract, levels[0])
# macd = MACD()
traders = [ThirdTradeTrader(FIVE_MINUTE), ThirdTradeTrader(THIRTY_MINUTE)]
czsc = Czsc(quotes, levels[0], levels, traders, traders, traders)
# raw quotes candle
x_data = [quote.timestamp for quote in quotes]
y_data = [[quote.open, quote.close, quote.low, quote.high]
for quote in quotes]
candle = Candlestick(
init_opts=opts.InitOpts(width="1300px", height="600px"))
candle.add_xaxis(xaxis_data=x_data)
candle.add_yaxis(series_name="raw_quotes_" + levels[0].label,
y_axis=y_data)
candle.set_series_opts()
candle.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="Kline-ItemStyle"),
)
# czsc drawing lines
drawings = czsc.get_drawings()
line = Line()
x_drawing = [p.quote.timestamp for p in drawings]
y_drawing = [p.value() for p in drawings]
line.add_xaxis(xaxis_data=x_drawing)
line.add_yaxis(series_name=czsc.raw_level.label + "_draw",
y_axis=y_drawing)
candle.overlap(line)
# czsc segments
for level in levels:
segments = czsc.get_segments(level)
segment_line = Line()
x_segment = [p.quote.timestamp for p in segments]
y_segment = [p.value() for p in segments]
segment_line.add_xaxis(xaxis_data=x_segment)
segment_line.add_yaxis(series_name=level.label, y_axis=y_segment)
candle.overlap(segment_line)
for trader in traders:
trading_point_chart_bottom = EffectScatter()
trading_point_chart_bottom.add_xaxis([
point.quote.timestamp for point in trader.open_points
if point.point_type is PointType.BOTTOM
])
trading_point_chart_bottom.add_yaxis(trader.level.label + "_trade", [
point.value() for point in trader.open_points
if point.point_type is PointType.BOTTOM
],
symbol=SymbolType.TRIANGLE)
candle.overlap(trading_point_chart_bottom)
trading_point_chart_top = EffectScatter()
trading_point_chart_top.add_xaxis([
point.quote.timestamp for point in trader.open_points
if point.point_type is PointType.TOP
])
trading_point_chart_top.add_yaxis(trader.level.label + "_trade", [
point.value() for point in trader.open_points
if point.point_type is PointType.TOP
],
symbol=SymbolType.ARROW)
candle.overlap(trading_point_chart_top)
close_point_chart = EffectScatter()
close_point_chart.add_xaxis(
[point.quote.timestamp for point in trader.close_points])
close_point_chart.add_yaxis(
trader.level.label + "_trade",
[point.value() for point in trader.close_points])
candle.overlap(close_point_chart)
# maincenters
for level in levels:
maincenters = czsc.get_maincenters(level)
for maincenter in maincenters:
rectangle = Line()
x = [
maincenter.start, maincenter.end, maincenter.end,
maincenter.start, maincenter.start
]
y = [
maincenter.bottom, maincenter.bottom, maincenter.top,
maincenter.top, maincenter.bottom
]
rectangle.add_xaxis(xaxis_data=x)
rectangle.add_yaxis(y_axis=y,
series_name="maincenter_" + level.label)
candle.overlap(rectangle)
# show
candle.render(name)
def kaohsiung_page():
try:
db = MySQLdb.connect(host='127.0.0.1', user='******', passwd='20200428', db='fruveg', port=3307,
charset='utf8')
cursor = db.cursor()
db.autocommit(True)
Item_No = "A1" # 香蕉
market_list = [["830", "高雄", "鳳山區", "Kaohsiung_Banana.html"],
["800", "高雄", "高雄市", "Kaohsiung_Banana.html"]]
for market_index in range(0, len(market_list), 2):
yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y/%m/%d')
# 取得2012~昨天所有西元日期放入List trade_date, 並設定為Line Chart及Bar Chart的X軸
sql = """Select DATE_FORMAT(WDate, '%Y/%m/%d') as WDate
From date_map
Where wdate between '2012/01/01' and '""" + yesterday + """'
Order by WDate;"""
cursor.execute(sql)
datarows = cursor.fetchall()
trade_date_1 = [row[0] for row in datarows]
# 依果菜市場代碼取得2012~昨天[每天]的交易平均單價及交易量
# 並放入List avg_price trade_amount, 並加入Line Chart 及 Bar Chart Y軸系列
sql = """Select a.trade_date, b.market_no, b.avg_price, b.trade_amount
From
(Select wDate as trade_date
From date_map
Where wdate between '2012/01/01' and '%s') a
Left Join
(Select trade_date, market_no, avg_price, trade_amount
From trade_raws
Where item_no='%s'
and market_no='%s'
and trade_date between '2012/01/01' and '%s') b
on a.trade_date=b.trade_date
order by a.trade_date;""" % (yesterday, Item_No, market_list[market_index][0], yesterday)
cursor.execute(sql)
datarows = cursor.fetchall()
avg_price_1 = [row[2] for row in datarows]
trade_amount_1 = [row[3] for row in datarows]
# 設定Line Chart屬性
linechart_1 = (
Line(
init_opts=opts.InitOpts(width="100%", height="400px")
)
.add_xaxis(trade_date_1)
.add_yaxis(
"平均價格(NT$)",
avg_price_1,
is_connect_nones=True,
label_opts=opts.LabelOpts(is_show=False)
)
# 因後續Line Chart要合併Bar Chart, 在此新增Line Chart右邊X軸的刻度
# 為了讓平均價格趨勢線及成交量合併後畫面的好看, 因此定義右邊X軸的最小及最大刻度
.extend_axis(
yaxis=opts.AxisOpts(
name="交易量(公斤)",
type_="value",
min_=0,
max_=80000,
interval=10000,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.set_global_opts(
title_opts=opts.TitleOpts("%s果菜市場\n香蕉每日批發平均價格及交易量" % market_list[market_index][2], pos_left="center"),
datazoom_opts=opts.DataZoomOpts(type_="slider", range_start=97, range_end=100),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
name="新台幣(元)",
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
# 設定Bar Chart屬性
barchart_1 = (
Bar(init_opts=opts.InitOpts(width="100%", height="150px"))
.add_xaxis(trade_date_1)
.add_yaxis(
"成交量(公斤)",
trade_amount_1,
yaxis_index=1,
color="#00E3E3",
label_opts=opts.LabelOpts(is_show=False)
)
.set_global_opts(
datazoom_opts=opts.DataZoomOpts(type_="slider", range_start=97, range_end=100),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True),
yaxis_opts=opts.AxisOpts(
name="公斤",
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
# 合併Line Chart及Bar Chart
# linechart.overlap(barchart).render("Taipei_Banana.html")
myoverlap_1 = linechart_1.overlap(barchart_1)
today = datetime.now().strftime('%Y/%m/%d')
t30 = (datetime.now() + timedelta(days=30)).strftime('%Y/%m/%d')
# 產生今天起30天預測線Line Chart 並加入List pred_date
sql = """Select Date_Format(WDate, '%Y/%m/%d') as WDate
From date_map
Where WDate Between '""" + today + """' and '""" + t30 + """'
Order by WDate;"""
cursor.execute(sql)
datarows = cursor.fetchall()
pred_date_1 = [WDate[0] for WDate in datarows]
# 產生30筆20~50之間的亂數做為虛擬的預測平均價格, 放入List pred_price
pred_price_1 = [random.randrange(20, 50) for i in range(1, 31)]
pred_linechart_1 = (
Line(init_opts=opts.InitOpts(width="100%", height="250px"))
.add_xaxis(pred_date_1)
.add_yaxis(
"預測平均價格(NT$)",
pred_price_1,
is_connect_nones=True,
label_opts=opts.LabelOpts(is_show=False)
)
.set_global_opts(
title_opts=opts.TitleOpts("[預測]%s果菜市場\n香蕉批發平均價格及交易量" % market_list[market_index][2], pos_left="center"),
# datazoom_opts=opts.DataZoomOpts(type_="slider", range_start=97, range_end=100),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
name="新台幣(元)",
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
# 以上完成區域第一個市場
# -----------------------------------------------------------------------------------------------------------
# 以下開始區域第二個市場
trade_date_2 = trade_date_1
# 依果菜市場代碼取得2012~昨天[每天]的交易平均單價及交易量
# 並放入List avg_price trade_amount, 並加入Line Chart 及 Bar Chart Y軸系列
sql = """Select a.trade_date, b.market_no, b.avg_price, b.trade_amount
From
(Select wDate as trade_date
From date_map
Where wdate between '2012/01/01' and '%s') a
Left Join
(Select trade_date, market_no, avg_price, trade_amount
From trade_raws
Where item_no='%s'
and market_no='%s'
and trade_date between '2012/01/01' and '%s') b
on a.trade_date=b.trade_date
order by a.trade_date;""" % (yesterday, Item_No, market_list[market_index + 1][0], yesterday)
cursor.execute(sql)
datarows = cursor.fetchall()
avg_price_2 = [row[2] for row in datarows]
trade_amount_2 = [row[3] for row in datarows]
# 設定Line Chart屬性
linechart_2 = (
Line(
init_opts=opts.InitOpts(width="100%", height="400px")
)
.add_xaxis(trade_date_2)
.add_yaxis(
"平均價格(NT$)",
avg_price_2,
is_connect_nones=True,
label_opts=opts.LabelOpts(is_show=False)
)
# 因後續Line Chart要合併Bar Chart, 在此新增Line Chart右邊X軸的刻度
# 為了讓平均價格趨勢線及成交量合併後畫面的好看, 因此定義右邊X軸的最小及最大刻度
.extend_axis(
yaxis=opts.AxisOpts(
name="交易量(公斤)",
type_="value",
min_=0,
max_=80000,
interval=10000,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.set_global_opts(
title_opts=opts.TitleOpts("%s果菜市場\n香蕉每日批發平均價格及交易量" % market_list[market_index + 1][2], pos_left="center"),
datazoom_opts=opts.DataZoomOpts(type_="slider", range_start=97, range_end=100),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
name="新台幣(元)",
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
# 設定Bar Chart屬性
barchart_2 = (
Bar(init_opts=opts.InitOpts(width="100%", height="150px"))
.add_xaxis(trade_date_1)
.add_yaxis(
"成交量(公斤)",
trade_amount_2,
yaxis_index=1,
color="#00E3E3",
label_opts=opts.LabelOpts(is_show=False)
)
.set_global_opts(
datazoom_opts=opts.DataZoomOpts(type_="slider", range_start=97, range_end=100),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True),
yaxis_opts=opts.AxisOpts(
name="公斤",
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
# 合併Line Chart及Bar Chart
# linechart.overlap(barchart).render("Taipei_Banana.html")
myoverlap_2 = linechart_2.overlap(barchart_2)
# 產生預測線Line Chart
pred_date_2 = pred_date_1
# 產生30筆25~50之間的亂數做為虛擬的預測平均價格, 放入List pred_price
pred_price_2 = [random.randrange(25, 50) for i in range(1, 31)]
pred_linechart_2 = (
Line(init_opts=opts.InitOpts(width="100%", height="250px"))
.add_xaxis(pred_date_2)
.add_yaxis(
"預測平均價格(NT$)",
pred_price_2,
is_connect_nones=True,
label_opts=opts.LabelOpts(is_show=False)
)
.set_global_opts(
title_opts=opts.TitleOpts("[預測]%s果菜市場\n香蕉批發平均價格及交易量" % market_list[market_index + 1][2], pos_left="center"),
# datazoom_opts=opts.DataZoomOpts(type_="slider", range_start=97, range_end=100),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
name="新台幣(元)",
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
# 以上完成區域第二個市場
# ----------------------------------------------------------------------------------------------------------
# 以下將兩個市場的圖合併在一個網頁中
mypage = (
Page(page_title="%s" % market_list[market_index][1])
.add(myoverlap_1, pred_linechart_1)
.add(myoverlap_2, pred_linechart_2)
)
mypage.render(r"./static/%s" % market_list[market_index][3])
except Exception as Err:
print(Err)
db.close()
# 看评论量走势和时间的关系。
df['时间'] = df.评论时间.str.split('-').str[1] + '-' + df.评论时间.str.split('-').str[2]
# print(df.head())
# 下面用冒号：分割‘时间’这栏的数据就能得到我们需要的日期和小时的数据
df['时间'] = df.时间.str.split(':').str[0]
time_num = df.时间.value_counts().sort_index()
time_num[:5]
print(time_num[:5])
# 用下面两行代码设定横坐标为时间，纵坐标表示评论数量。
# 产生数据
x1_line1 = time_num.index.values.astype('str').tolist()
y1_line1 = time_num.values.tolist()
# 绘制面积图
line1 = Line(init_opts=opts.InitOpts(width='1350px', height='750px'))
line1.add_xaxis(x1_line1)
line1.add_yaxis('',
y1_line1,
areastyle_opts=opts.AreaStyleOpts(opacity=0.3),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_='max', name='最大值')]))
line1.set_global_opts(
title_opts=opts.TitleOpts('各个时段评论人数'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate='30')))
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts())
line1.render(
) # replace the file:///C:/Users/Maggie/Desktop/Udemy-desktop/xiaoxiangxueyuan/pythonCases/xiaoxiang_projects/python_in_16_days/render.html
# #从5月20日8:30发布MV后，评论数量逐渐上升，12点左右有一个快速增加，达到高峰。后续随着时间的推移，评论人数逐渐减少，趋于平缓。
[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],
]
c = (
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis("kline", data)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[opts.DataZoomOpts(pos_bottom="-2%")],
title_opts=opts.TitleOpts(title="Kline-DataZoom-slider-Position"),
)
)
c.width = "100%"
put_html(c.render_notebook())
93.58695857541488,
101.4126683297632,
108.48378461530217,
114.49355390682695,
119.16795429637915,
122.27931702317058,
123.65837448506679,
123.20413594805603,
120.89107255501017,
116.7731992576505,
110.98476877890735,
]
(Bar(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=category).add_yaxis(
series_name="bar",
yaxis_data=red_bar,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
series_name="bar2",
yaxis_data=blue_bar,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="柱状图动画延迟"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=False)),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).render("bar_chart_display_delay.html"))
def generate_a_view(data):
# 设置图标基本属性
margin = '5%'
if data['chart'] == 'bar':
chart = (Bar().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'pie':
chart = (Pie().set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin)))
elif data['chart'] == 'line':
chart = (Line().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'scatter':
chart = (Scatter().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(
type_='value',
name=data['x_name'],
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_='value',
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
else:
print("not valid chart")
# 添加数据
attr = data["x_data"] # 横坐标
val = data["y_data"] # 纵坐标
if data['chart'] == 'bar':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
if isinstance(attr[0], str):
attr = [x for x in attr if x != '']
attr = list(map(float, attr))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
return chart
import datetime
import math
import pyecharts.options as opts
import random
from pyecharts.charts import *
from pyecharts.components import Table
from pyecharts.faker import POPULATION
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = "https://cdn.kesci.com/lib/pyecharts_assets/"
# %%
# 直角坐标系图表
# 直方图
x_data = ['apple', 'huawei', 'xiaomi', 'oppo', 'vivo', 'meizu']
y_data = [123, 312, 89, 107, 82, 23]
bar = (Bar().add_xaxis(x_data).add_yaxis('', y_data))
bar.render()
# %%
# 折线图
x_data = ['apple', 'huawei', 'xiaomi', 'oppo', 'vivo', 'meizu']
y_data = [123, 312, 89, 107, 82, 23]
bar = (Line().add_xaxis(x_data).add_yaxis('', y_data))
bar.render()
# %%
# 折线图
x_data = ['apple', 'huawei', 'xiaomi', 'oppo', 'vivo', 'meizu']
y_data = [[random.randint(100, 200) for i in range(10)] for item in x_data]
Box = Boxplot()
Box.add_xaxis(x_data)
Box.add_yaxis('', Box.prepare_data(y_data))
bar.render()
from pyecharts import options as opts
from pyecharts.charts import Bar, Line
from pyecharts.faker import Faker
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().add_xaxis(Faker.months).add_yaxis("蒸发量", v1).add_yaxis(
"降水量", v2).extend_axis(yaxis=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(
formatter="{value} °C"), interval=5)).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Overlap-bar+line"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value} ml")),
))
line = Line().add_xaxis(Faker.months).add_yaxis("平均温度", v3, yaxis_index=1)
bar.overlap(line)
bar.render("overlap_bar_line.html")
hubei_statis = statistics__data['湖北']
# 湖北以外的新增趋势
other_statis = [all_statis[i] - hubei_statis[i] for i in range(len(dateId))]
line = Line()
line.add_xaxis(dateId)
line.add_yaxis("全国新增确诊病例", #图例
all_statis, #数据
is_smooth=True, #是否平滑曲线
linestyle_opts=opts.LineStyleOpts(width=4, color='#B44038'),#线样式配置项
itemstyle_opts=opts.ItemStyleOpts(color='#B44038', #图元样式配置项
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis("湖北新增确诊病例", hubei_statis, is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis("其他省份新增病例", other_statis, is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(max_=16000, min_=1, type_="log", #坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True),#分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True)))#坐标轴刻度线配置项
line.render(path='新增确诊趋势图.html')
def get_year_chart(year: int):
map_data = [
[[x["name"], x["value"]] for x in d["data"]] for d in data if d["time"] == year
][0]
min_data, max_data = (
min([d[1][0] for d in map_data]),
max([d[1][0] for d in map_data]),
)
map_chart = (
Map()
.add(
series_name="",
data_pair=map_data,
label_opts=opts.LabelOpts(is_show=False),
is_map_symbol_show=False,
itemstyle_opts={
"normal": {"areaColor": "#323c48", "borderColor": "#404a59"},
"emphasis": {
"label": {"show": Timeline},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="1980年以来中国各省GDP排名变化情况",
subtitle="GDP单位:亿元",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"
),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"""function(params) {
if ('value' in params.data) {
return params.data.value[2] + ': ' + params.data.value[0];
}
}"""
),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
bar_x_data = [x[0] for x in map_data]
# 这里注释的部分会导致 label 和 value 与 饼图不一致
# 使用下面的 List[Dict] 就可以解决这个问题了。
# bar_y_data = [x[1][0] for x in map_data]
bar_y_data = [{"name": x[0], "value": x[1][0]} for x in map_data]
bar = (
Bar()
.add_xaxis(xaxis_data=bar_x_data)
.add_yaxis(
series_name="",
yaxis_index=1,
y_axis=bar_y_data,
label_opts=opts.LabelOpts(
is_show=True, position="right", formatter="{b}: {c}"
),
)
.reversal_axis()
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False)),
tooltip_opts=opts.TooltipOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=110,
bottom=110,
z=100,
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(left="center", top="center", z=100),
graphic_shape_opts=opts.GraphicShapeOpts(width=400, height=50),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(left="center", top="center", z=100),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=f"{str(year)} 年",
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(fill="#fff"),
),
),
],
)
],
)
)
pie_data = [[x[0], x[1][0]] for x in map_data]
percent_sum = sum([x[1][1] for x in map_data])
rest_value = 0
for d in map_data:
rest_percent = 100.0
rest_percent = rest_percent - percent_sum
rest_value = d[1][0] * (rest_percent / d[1][1])
pie_data.append(["其他省份", rest_value])
pie = (
Pie()
.add(
series_name="",
data_pair=pie_data,
radius=["12%", "20%"],
center=["75%", "85%"],
itemstyle_opts=opts.ItemStyleOpts(
border_width=1, border_color="rgba(0,0,0,0.3)"
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
legend_opts=opts.LegendOpts(is_show=False),
)
)
grid_chart = (
Grid()
.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10", pos_right="45%", pos_top="70%", pos_bottom="5"
),
)
.add(pie, grid_opts=opts.GridOpts())
.add(map_chart, grid_opts=opts.GridOpts())
)
return grid_chart
def get_bar_data(request):
x_field = request.GET.get('x_choice')
y_field = request.GET.get('y_choice')
print(request.GET.get('begin_date'))
start_date = request.GET.get('begin_date').split('-')
end_date = request.GET.get('end_date').split('-')
start = datetime.date(int(start_date[0]), int(start_date[1]),
int(start_date[2]))
end = datetime.date(int(end_date[0]), int(end_date[1]), int(end_date[2]))
y_lists = []
x_list = []
b = Bar()
if x_field == 'street' and y_field == 'property':
event_counts = Street.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
for street_node in event_counts:
new_len = 0
street = Street.objects.get(name=street_node['name'])
for community in Community.objects.filter(street=street):
new_len += len(
Event.objects.filter(community=community,
property=property,
create_time__range=(start, end)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for street_node in event_counts:
x_list.append(street_node['name'])
elif x_field == 'community' and y_field == 'property':
event_counts = Community.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
for street_node in event_counts:
community = Community.objects.get(name=street_node['name'])
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(community=community) & Q(property=property)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for community_node in event_counts:
x_list.append(community_node['name'])
elif x_field == 'dispose_unit' and y_field == 'property':
event_counts = DisposeUnit.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
limit = 0
for dispose_unit_node in event_counts:
dispose_unit = DisposeUnit.objects.get(
name=dispose_unit_node['name'])
if limit < 30:
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(property=property)))
new_list.append(new_len)
new_len = 0
else:
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(property=property)))
limit += 1
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for dispose_unit_node in event_counts:
if limit >= 30:
break
x_list.append(dispose_unit_node['name'])
limit += 1
x_list.append("其他")
elif x_field == 'main_type' and y_field == 'property':
event_counts = MainType.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
limit = 0
new_len = 0
for main_type_node in event_counts:
main_type = MainType.objects.get(name=main_type_node['name'])
for sub_type in SubType.objects.filter(main_type=main_type):
new_len += len(
Event.objects.filter(sub_type=sub_type,
property=property,
create_time__range=(start, end)))
if limit < 30:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for main_type_node in event_counts:
if limit >= 30:
break
x_list.append(main_type_node['name'])
limit += 1
x_list.append('其他')
elif x_field == 'sub_type' and y_field == 'property':
event_counts = SubType.objects.all().values(
'name', 'number').order_by('-number')
for property in properties:
new_list = [property.name]
limit = 0
new_len = 0
for sub_type_node in event_counts:
sub_type = SubType.objects.get(name=sub_type_node['name'])
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(property=property) & Q(sub_type=sub_type)))
if limit < 60:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for sub_type_node in event_counts:
if limit >= 60:
break
x_list.append(sub_type_node['name'])
limit += 1
x_list.append('其他')
elif x_field == 'street' and y_field == 'achieve':
event_counts = Street.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
for street_node in event_counts:
new_len = 0
street = Street.objects.get(name=street_node['name'])
for community in Community.objects.filter(street=street):
new_len += len(
Event.objects.filter(community=community,
achieve=achieve,
create_time__range=(start, end)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for street_node in event_counts:
x_list.append(street_node['name'])
elif x_field == 'community' and y_field == 'achieve':
event_counts = Community.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
for street_node in event_counts:
community = Community.objects.get(name=street_node['name'])
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(community=community) & Q(achieve=achieve)))
new_list.append(new_len)
# print(new_list)
y_lists.append(new_list)
for community_node in event_counts:
x_list.append(community_node['name'])
elif x_field == 'dispose_unit' and y_field == 'achieve':
event_counts = DisposeUnit.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
for dispose_unit_node in event_counts:
dispose_unit = DisposeUnit.objects.get(
name=dispose_unit_node['name'])
if limit < 30:
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
new_list.append(new_len)
new_len = 0
else:
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
limit += 1
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for dispose_unit_node in event_counts:
if limit >= 30:
break
x_list.append(dispose_unit_node['name'])
limit += 1
x_list.append("其他")
elif x_field == 'main_type' and y_field == 'achieve':
event_counts = MainType.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
new_len = 0
for main_type_node in event_counts:
main_type = MainType.objects.get(name=main_type_node['name'])
for sub_type in SubType.objects.filter(main_type=main_type):
new_len += len(
Event.objects.filter(sub_type=sub_type,
achieve=achieve,
create_time__range=(start, end)))
if limit < 30:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for main_type_node in event_counts:
if limit >= 30:
break
x_list.append(main_type_node['name'])
limit += 1
x_list.append('其他')
elif x_field == 'sub_type' and y_field == 'achieve':
event_counts = SubType.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
new_len = 0
for sub_type_node in event_counts:
sub_type = SubType.objects.get(name=sub_type_node['name'])
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end)) & Q(achieve=achieve)
& Q(sub_type=sub_type)))
if limit < 60:
limit += 1
new_list.append(new_len)
new_len = 0
# print(new_list)
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for sub_type_node in event_counts:
if limit >= 60:
break
x_list.append(sub_type_node['name'])
limit += 1
x_list.append('其他')
b.add_xaxis(x_list)
for y_list in y_lists:
b.add_yaxis(y_list[0], y_list[1:], stack="stack1", category_gap="60%")
b.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) \
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
# title_opts=opts.TitleOpts(title="处理事件最多的部门"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")], )
c = (b)
grid = Grid()
grid.add(c, grid_opts=opts.GridOpts(pos_bottom="20%"))
c = grid.dump_options_with_quotes()
return HttpResponse(c, content_type='application/json')
def show_visualizations(self, number=-1):
"""
show the charts in jupyter notebook.
Args:
number(int): the index of chart to be shown in jupyter notebook.
If number == -1, show all the charts in jupyter notebook.
Returns:
page(Page()): an object of class Page in pyecharts, containing the chart(s)
to be shown in jupyter notebook.
"""
instance = self.instance
if number > instance.view_num:
print(
"In function show_visualizations: Error, input number greater than the view numbers."
)
return Page()
if self.rank_method == methods_of_ranking[3]: # diversified_ranking
G = myGraph(instance.view_num)
for i in range(instance.view_num):
view = instance.tables[instance.views[i].table_pos].views[
instance.views[i].view_pos]
G.addNode(view)
G.getSim()
result = G.getTopK(instance.view_num)
if number != -1:
begin = number - 1
end = number
else:
begin = 0
end = instance.view_num
page = Page()
for order in range(begin, end):
if self.rank_method == methods_of_ranking[
3]: # diversified_ranking
view = G.nodes[result[order]]
else:
view = instance.tables[instance.views[order].table_pos].views[
instance.views[order].view_pos]
data = {}
data['order'] = order
data['chartname'] = instance.table_name
data['describe'] = view.table.describe
data['x_name'] = view.fx.name
data['y_name'] = view.fy.name
data['chart'] = Chart.chart[view.chart]
data['classify'] = [v[0] for v in view.table.classes]
data['x_data'] = view.X
data['y_data'] = view.Y
data['title_top'] = 5
# 以下代码与html_handle相似
margin = str(data['title_top']) + '%'
if data['chart'] == 'bar':
chart = (Bar().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'pie':
chart = (Pie().set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin)))
elif data['chart'] == 'line':
chart = (Line().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(name=data['x_name']),
yaxis_opts=opts.AxisOpts(
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
elif data['chart'] == 'scatter':
chart = (Scatter().set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=data['chartname'],
subtitle=data['describe'],
pos_left='center',
pos_top=margin),
xaxis_opts=opts.AxisOpts(
type_='value',
name=data['x_name'],
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_='value',
name=data['y_name'],
splitline_opts=opts.SplitLineOpts(is_show=True))))
else:
print("not valid chart")
if not data["classify"]:
attr = data["x_data"][0]
val = data["y_data"][0]
if data['chart'] == 'bar':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
if isinstance(attr[0], str):
attr = [x for x in attr if x != '']
attr = list(map(float, attr))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr).add_yaxis(
"", val, label_opts=opts.LabelOpts(is_show=False))
page.add(chart)
else:
attr = data["x_data"][0]
for i in range(len(data["classify"])):
val = data["y_data"][i]
name = (data["classify"][i][0] if type(data["classify"][i])
== type(('a', 'b')) else data["classify"][i])
if i == 0:
if data['chart'] != 'pie' and data[
'chart'] != 'scatter':
chart.add_xaxis(attr)
if data['chart'] == 'bar':
chart.add_yaxis(
name,
val,
stack="stack1",
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'line':
chart.add_yaxis(
name,
val,
label_opts=opts.LabelOpts(is_show=False))
elif data['chart'] == 'pie':
chart.add("", [list(z) for z in zip(attr, val)])
elif data['chart'] == 'scatter':
attr_scatter = data["x_data"][i]
if isinstance(attr_scatter[0], str):
attr_scatter = [x for x in attr_scatter if x != '']
attr_scatter = list(map(float, attr_scatter))
if isinstance(val[0], str):
val = [x for x in val if x != '']
val = list(map(float, val))
chart.add_xaxis(attr_scatter).add_yaxis(
name,
val,
label_opts=opts.LabelOpts(is_show=False))
page.add(chart)
return page
linestyle_opts=opts.LineStyleOpts(),
itemstyle_opts=opts.ItemStyleOpts(opacity=0),
color='#00FF00' # Ø10-2为绿色
).add_xaxis(data_listx3).add_yaxis(
'Ø10-3',
data_listy3,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(),
itemstyle_opts=opts.ItemStyleOpts(opacity=0),
color='#0000FF' # Ø10-3为红色
).set_global_opts( # 设置全局样式
title_opts=opts.TitleOpts(title='钢筋的应力应变图',
pos_bottom='0',
pos_right='35%'),
xaxis_opts=opts.AxisOpts(type_='value',
boundary_gap=False,
name="拉伸应变ε,mm"), # 设置x轴类型属性为value数值类型
yaxis_opts=opts.AxisOpts( # 单轴配置项
name="应力σ,MPa",
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True), # 显示坐标轴刻度
splitline_opts=opts.SplitLineOpts(is_show=True) # 显示分割线
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove|click",
axis_pointer_type="cross" # 启用十字准星指示器
),
toolbox_opts=opts.ToolboxOpts( # 工具箱，支持区域缩放和图片下载
is_show=True,
title = 'PYTHON'
subtitle = '年龄分布要求'
age_bar = (
Bar().add_xaxis(age_num[:-1]) # 剔除“年龄不限”类别
.add_yaxis('', age_counts[:-1]).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
]),
).set_global_opts(
title_opts=opts.TitleOpts(title=title,
subtitle=subtitle,
pos_left='center'),
xaxis_opts=opts.AxisOpts(
name='年龄段',
position='center',
axislabel_opts=opts.LabelOpts(formatter="{value}岁")),
yaxis_opts=opts.AxisOpts(name='职位数', ),
)
# .render(title + '_' + subtitle + '.html')
)
# In[4]:
# 获得各经验段职位数量
experience_dist = df3['experience_required'].value_counts()
exp_num = sorted(list(experience_dist.index))
last_num = exp_num.pop()
exp_num = sorted([int(i) for i in exp_num])
exp_num.append(last_num)
exp_counts = [int(experience_dist[str(i)]) for i in exp_num]
def visual(self, name='model_visual', path=None, gif=False):
"""
Arguments:
name : str, train end save last image or gif file with html format name.
path : str, train end save last image or gif file with html format to path;
save_gif : bool, default False, if save_gif=True, train end save all image to gif;
Return:
a html file path.
"""
if path is not None:
assert tf.io.gfile.exists(path), "`path` not exist."
file = path + '/' + '{}.html'.format(name)
else:
file = '{}.html'.format(name)
page = Page(interval=1, layout=Page.SimplePageLayout)
plot_list = []
width_len = '750px'
height_len = '450px'
for metric_id, metric in enumerate(self.metrics):
if not gif:
line = Line(opts.InitOpts(width=width_len, height=height_len))
line = line.add_xaxis(list(range(1, self.epoch + 1)))
line = line.add_yaxis(
'train',
Series(self.epoch_logs[metric]).round(4).tolist(),
is_smooth=True)
if self.valid_fmt.format(metric) in self.epoch_logs:
line = line.add_yaxis(
self.valid_fmt.split('_')[0],
Series(self.epoch_logs[self.valid_fmt.format(
metric)]).round(4).tolist(),
is_smooth=True)
line = line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='max_value'),
opts.MarkPointItem(type_='min', name='min_value')
]))
line = line.set_global_opts(
title_opts=opts.TitleOpts(title=metric),
xaxis_opts=opts.AxisOpts(name='Epoch',
name_location='center',
is_scale=True),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100)
],
toolbox_opts=opts.ToolboxOpts())
plot_list.append(line)
else:
timeline = Timeline(
opts.InitOpts(width=width_len,
height=height_len)).add_schema(
play_interval=100, is_auto_play=True)
for i in range(1, self.epoch + 1):
line = Line(
opts.InitOpts(width=width_len, height=height_len))
line = line.add_xaxis(list(range(1, i + 1)))
line = line.add_yaxis(
'train',
Series(self.epoch_logs[metric])[:i].round(4).tolist(),
is_smooth=True)
if self.valid_fmt.format(metric) in self.epoch_logs:
line = line.add_yaxis(
self.valid_fmt.split('_')[0],
Series(self.epoch_logs[self.valid_fmt.format(
metric)])[:i].round(4).tolist(),
is_smooth=True)
line = line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='max_value'),
opts.MarkPointItem(type_='min', name='min_value')
]))
line = line.set_global_opts(
title_opts=opts.TitleOpts(title=metric),
xaxis_opts=opts.AxisOpts(name='Epoch',
name_location='center',
is_scale=True))
timeline.add(line, str(i))
plot_list.append(timeline)
page.add(*plot_list).render(file)
return file
def bar_base() -> Bar:
# c = (
# Bar()
# .add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
# .add_yaxis("商家A", [randrange(0, 100) for _ in range(6)])
# .add_yaxis("商家B", [randrange(0, 100) for _ in range(6)])
# .set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副标题"))
# )
data = [
[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],
]
c = (Kline().add_xaxis(
["2017/7/{}".format(i + 1) for i in range(31)]).add_yaxis(
"kline",
data,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max", value_dim="close"),
opts.MarkLineItem(type_="min", value_dim="close"),
],
linestyle_opts=[]),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
title_opts=opts.TitleOpts(title="Kline-MarkLine"),
))
return c
def kline_pro(kline: List[dict],
fx: List[dict] = None,
bi: List[dict] = None,
xd: List[dict] = None,
bs: List[dict] = None,
title: str = "缠中说禅K线分析",
t_seq: List[int] = None,
width: str = "1400px",
height: str = '580px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param fx: 分型识别结果
:param bi: 笔识别结果
{'dt': Timestamp('2020-11-26 00:00:00'),
'fx_mark': 'd',
'start_dt': Timestamp('2020-11-25 00:00:00'),
'end_dt': Timestamp('2020-11-27 00:00:00'),
'fx_high': 144.87,
'fx_low': 138.0,
'bi': 138.0}
:param xd: 线段识别结果
:param bs: 买卖点
:param title: 图表标题
:param t_seq: 均线系统
:param width: 图表宽度
:param height: 图表高度
:return: 用Grid组合好的图表
"""
# 配置项设置
# ------------------------------------------------------------------------------------------------------------------
bg_color = "#1f212d" # 背景
up_color = "#F9293E"
down_color = "#00aa3b"
init_opts = opts.InitOpts(bg_color=bg_color, width=width, height=height, animation_opts=opts.AnimationOpts(False))
title_opts = opts.TitleOpts(title=title, pos_top="1%",
title_textstyle_opts=opts.TextStyleOpts(color=up_color, font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color=down_color, font_size=12))
label_not_show_opts = opts.LabelOpts(is_show=False)
legend_not_show_opts = opts.LegendOpts(is_show=False)
red_item_style = opts.ItemStyleOpts(color=up_color)
green_item_style = opts.ItemStyleOpts(color=down_color)
k_style_opts = opts.ItemStyleOpts(color=up_color, color0=down_color, border_color=up_color,
border_color0=down_color, opacity=0.8)
legend_opts = opts.LegendOpts(is_show=True, pos_top="1%", pos_left="30%", item_width=14, item_height=8,
textstyle_opts=opts.TextStyleOpts(font_size=12, color="#0e99e2"))
brush_opts = opts.BrushOpts(tool_box=["rect", "polygon", "keep", "clear"],
x_axis_index="all", brush_link="all",
out_of_brush={"colorAlpha": 0.1}, brush_type="lineX")
axis_pointer_opts = opts.AxisPointerOpts(is_show=True, link=[{"xAxisIndex": "all"}])
dz_inside = opts.DataZoomOpts(False, "inside", xaxis_index=[0, 1, 2], range_start=80, range_end=100)
dz_slider = opts.DataZoomOpts(True, "slider", xaxis_index=[0, 1, 2], pos_top="96%",
pos_bottom="0%", range_start=80, range_end=100)
yaxis_opts = opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(color="#c7c7c7", font_size=8, position="inside"))
grid0_xaxis_opts = opts.AxisOpts(type_="category", grid_index=0, axislabel_opts=label_not_show_opts,
split_number=20, min_="dataMin", max_="dataMax",
is_scale=True, boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False))
tool_tip_opts = opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
position=JsCode("""
function (pos, params, el, elRect, size) {
var obj = {top: 10};
obj[['left', 'right'][+(pos[0] < size.viewSize[0] / 2)]] = 30;
return obj;
}
"""),
textstyle_opts=opts.TextStyleOpts(color="#000"),
)
# 数据预处理
# ------------------------------------------------------------------------------------------------------------------
dts = [x['dt'] for x in kline]
# k_data = [[x['open'], x['close'], x['low'], x['high']] for x in kline]
k_data = [opts.CandleStickItem(name=i, value=[x['open'], x['close'], x['low'], x['high']])
for i, x in enumerate(kline)]
vol = []
for i, row in enumerate(kline):
item_style = red_item_style if row['close'] > row['open'] else green_item_style
bar = opts.BarItem(name=i, value=row['vol'], itemstyle_opts=item_style, label_opts=label_not_show_opts)
vol.append(bar)
close = np.array([x['close'] for x in kline], dtype=np.double)
diff, dea, macd = MACD(close)
macd_bar = []
for i, v in enumerate(macd.tolist()):
item_style = red_item_style if v > 0 else green_item_style
bar = opts.BarItem(name=i, value=round(v, 4), itemstyle_opts=item_style,
label_opts=label_not_show_opts)
macd_bar.append(bar)
diff = diff.round(4)
dea = dea.round(4)
# K 线主图
# ------------------------------------------------------------------------------------------------------------------
chart_k = Kline()
chart_k.add_xaxis(xaxis_data=dts)
chart_k.add_yaxis(series_name="Kline", y_axis=k_data, itemstyle_opts=k_style_opts)
chart_k.set_global_opts(
legend_opts=legend_opts,
datazoom_opts=[dz_inside, dz_slider],
yaxis_opts=yaxis_opts,
tooltip_opts=tool_tip_opts,
axispointer_opts=axis_pointer_opts,
brush_opts=brush_opts,
title_opts=title_opts,
xaxis_opts=grid0_xaxis_opts
)
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
if not t_seq:
t_seq = [10, 20, 60, 120, 250]
ma_keys = dict()
for t in t_seq:
ma_keys[f"MA{t}"] = SMA(close, timeperiod=t)
for i, (name, ma) in enumerate(ma_keys.items()):
chart_ma.add_yaxis(series_name=name, y_axis=ma, is_smooth=True,
is_selected=True, symbol_size=0, label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1))
chart_ma.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_ma)
# 缠论结果
# ------------------------------------------------------------------------------------------------------------------
if fx:
fx_dts = [x['dt'] for x in fx]
fx_val = [x['fx'] for x in fx]
chart_fx = Scatter()
chart_fx.add_xaxis(fx_dts)
chart_fx.add_yaxis(series_name="FX", y_axis=fx_val, is_selected=False,
symbol="circle", symbol_size=6, label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(152, 147, 193, 1.0)", ))
chart_fx.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_fx)
if bi:
bi_dts = [x['dt'] for x in bi]
bi_val = [x['bi'] for x in bi]
chart_bi = Line()
chart_bi.add_xaxis(bi_dts)
chart_bi.add_yaxis(series_name="BI", y_axis=bi_val, is_selected=True,
symbol="diamond", symbol_size=10, label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(184, 117, 225, 1.0)", ),
linestyle_opts=opts.LineStyleOpts(width=1.5))
chart_bi.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_bi)
if xd:
xd_dts = [x['dt'] for x in xd]
xd_val = [x['xd'] for x in xd]
chart_xd = Line()
chart_xd.add_xaxis(xd_dts)
chart_xd.add_yaxis(series_name="XD", y_axis=xd_val, is_selected=True, symbol="triangle", symbol_size=10,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(37, 141, 54, 1.0)", ))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
if bs:
b_dts = [x['dt'] for x in bs if x['mark'] == 'buy']
if len(b_dts) > 0:
b_val = [x['price'] for x in bs if x['mark'] == 'buy']
chart_b = Scatter()
chart_b.add_xaxis(b_dts)
chart_b.add_yaxis(series_name="BUY", y_axis=b_val, is_selected=False, symbol="arrow", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="#f31e1e", ))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_b)
s_dts = [x['dt'] for x in bs if x['mark'] == 'sell']
if len(s_dts) > 0:
s_val = [x['price'] for x in bs if x['mark'] == 'sell']
chart_s = Scatter()
chart_s.add_xaxis(s_dts)
chart_s.add_yaxis(series_name="SELL", y_axis=s_val, is_selected=False, symbol="pin", symbol_size=12,
itemstyle_opts=opts.ItemStyleOpts(color="#45b97d", ))
chart_s.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_s)
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
chart_vol.add_yaxis(series_name="Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True, font_size=8, color="#9b9da9"),
),
yaxis_opts=yaxis_opts, legend_opts=legend_not_show_opts,
)
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
chart_macd.add_yaxis(series_name="MACD", y_axis=macd_bar, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
line.add_yaxis(series_name="DIFF", y_axis=diff, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#da6ee8"))
line.add_yaxis(series_name="DEA", y_axis=dea, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#39afe6"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="12%", height="58%")
grid1_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="74%", height="8%")
grid2_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="86%", height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
def find_value_render(query_filter, x_label, x, result, Data, query,
table_path, answer):
colorList = ['#f36c6c', '#e6cf4e', '#20d180', '#0093ff']
# x = ['GDP', 'Industry', 'Architecture', 'Service']
# y = [990865, 317109, 70904, 534233]
# x1 = ["周一"]
# y1 = [11]
Y = []
# for i in range(len(y)):
# if i==0:
# Y.append(
# opts.BarItem(
# name=x[i],
# value=round(y[i], 2),
# label_opts=opts.LabelOpts(position="insideTop"),
# itemstyle_opts={
# "normal": {
# "color": colorList[0],
# "barBorderRadius": [30, 30, 30, 30],
# }
# }
# ))
# else:
# Y.append(
# opts.BarItem(
# name=x[i],
# value=round(y[i], 2),
# label_opts=opts.LabelOpts(position="insideTop"),
# itemstyle_opts={
# "normal": {
# "color": "blue",
# "barBorderRadius": [30, 30, 30, 30],
# }
# }
# ))
bar1 = Bar()
bar1.add_xaxis(x)
bar1.add_yaxis(result[0][0],
y_axis=result[0][1],
label_opts=opts.LabelOpts(position="insideTop"))
bar1.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axisline_opts=opts.AxisLineOpts(is_show=False),
axispointer_opts=opts.AxisPointerOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)),
title_opts=opts.TitleOpts(subtitle="When the search condition is " +
query_filter,
pos_left='center'),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=[
'\n' + "Q:" + ' ' + query + '\n' + "\n" + 'A:' + ' ' +
answer
],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
bar2 = Bar()
bar2.add_xaxis(x)
for i in range(len(result)):
bar2.add_yaxis(result[i][0], y_axis=result[i][1])
bar2.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axisline_opts=opts.AxisLineOpts(is_show=False),
axispointer_opts=opts.AxisPointerOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=[
'\n' + "\n" + "Q:" + ' ' + query + '\n' + "\n" + 'A:' +
' ' + answer
],
font="16px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
Label = []
data = []
for i in range(len(Data)):
Label.append(str(Data[i][0]))
data.append(Data[i][1])
data = list(map(list, zip(*data)))
parallel = Parallel()
parallel_axis = []
for i in range(len(Label)):
parallel_axis.append({"dim": i, "name": Label[i]})
parallel.add_schema(schema=parallel_axis)
parallel.add("", data, is_smooth=True)
parallel.set_global_opts(graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' + 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
grid0 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid0.add(bar1, grid_opts={'left': '20%', 'bottom': '34%'})
option0 = grid0.dump_options_with_quotes()
option0 = json.loads(option0)
grid = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid.add(bar2, grid_opts={'left': '15%', 'bottom': '50%'})
option1 = grid.dump_options_with_quotes()
option1 = json.loads(option1)
grid1 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid1.add(parallel, grid_opts={'left': '30%', 'bottom': '34%'})
option2 = grid1.dump_options_with_quotes()
option2 = json.loads(option2)
option = {"option": [option1, option2], "query": query}
return option
def get_year_chart(year: str):
map_data = [
[[x["name"], x["value"]] for x in d["data"]] for d in MapData if d["time"] == year
][0]
min_data, max_data = (minNum, maxNum)
data_mark: List = []
i = 0
for x in time_list:
if x == year:
data_mark.append(total_num[i])
else:
data_mark.append("")
i = i + 1
map_chart = (
Map()
.add(
series_name="",
data_pair=map_data,
zoom=1,
center=[119.5, 34.5],
is_map_symbol_show=False,
itemstyle_opts={
"normal": {"areaColor": "#323c48", "borderColor": "#404a59"},
"emphasis": {
"label": {"show": Timeline},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="" +
str(year) + "全国各省份NCP实时动态(数据来源:丁香园; 数据仓库:BlankerL/DXY-2019-nCoV-Data)",
subtitle="",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"
),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"""function(params) {
if ('value' in params.data) {
return params.data.value[2] + ': ' + params.data.value[0];
}
}"""
),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="30",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
line_chart = (
Line()
.add_xaxis(time_list)
.add_yaxis("", total_num)
.add_yaxis(
"",
data_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(
title="全国各省份NCP实时动态(单位: 百人)", pos_left="72%", pos_top="5%"
)
)
)
bar_x_data = [x[0] for x in map_data]
bar_y_data = [{"name": x[0], "value": x[1][0]} for x in map_data]
bar = (
Bar()
.add_xaxis(xaxis_data=bar_x_data)
.add_yaxis(
series_name="",
yaxis_data=bar_y_data,
label_opts=opts.LabelOpts(
is_show=True, position="right", formatter="{b} : {c}"
),
)
.reversal_axis()
.set_global_opts(
xaxis_opts=opts.AxisOpts(
max_=40000, axislabel_opts=opts.LabelOpts(is_show=False)
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False)),
tooltip_opts=opts.TooltipOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="top",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
pie_data = [[x[0], x[1][0]] for x in map_data]
pie = (
Pie()
.add(
series_name="",
data_pair=pie_data,
radius=["15%", "35%"],
center=["80%", "82%"],
itemstyle_opts=opts.ItemStyleOpts(
border_width=1, border_color="rgba(0,0,0,0.5)"
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
legend_opts=opts.LegendOpts(is_show=False),
)
)
grid_chart = (
Grid()
.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10", pos_right="45%", pos_top="50%", pos_bottom="5"
),
)
.add(
line_chart,
grid_opts=opts.GridOpts(
pos_left="65%", pos_right="80", pos_top="10%", pos_bottom="50%"
),
)
.add(pie, grid_opts=opts.GridOpts(pos_left="45%", pos_top="60%"))
.add(map_chart, grid_opts=opts.GridOpts())
)
return grid_chart
def generate_price_bar_html(rows, title):
xx1 = []
xx2 = []
yy1 = []
yy2 = []
count = 0
for row in rows:
bond_code = row[0]
bond_code = trade_utils.rebuild_bond_code(bond_code)
count += 1
if count <= 20:
xx1.append(row[1].replace('转债', ''))
yy1.append({
'value': row[2],
'bond_code': bond_code,
'rise': row[3],
'premium': row[4]
})
else:
xx2.insert(0, row[1].replace('转债', ''))
yy2.insert(
0, {
'value': round(-float(row[2]) + 0, 2),
'bond_code': bond_code,
'rise': row[3],
'premium': row[4]
})
max_value = 0
size = len(yy1)
for i in range(size):
if yy1[i]['value'] + abs(yy2[i]['value']) > max_value:
max_value = yy1[i]['value'] + abs(yy2[i]['value'])
max_value = round(max_value * 0.7, 2) + 1
chart_id = str(abs(hash(title)))
bar = Bar(init_opts=opts.InitOpts(height='700px',
width='1424px',
theme=ThemeType.SHINE,
chart_id=chart_id))
add_popwin_js_code(bar, chart_id)
# 底部x轴
bar.add_xaxis(xx1)
# 顶部x轴
bar.extend_axis(
xaxis_data=xx2,
xaxis=opts.AxisOpts(type_="category",
position='top',
axislabel_opts=opts.LabelOpts(rotate=-60, )),
)
# 右侧y轴
bar.extend_axis(yaxis=opts.AxisOpts(
type_="value",
position="right",
name='可转债价格(元)',
name_gap=45,
name_location='middle',
min_=-max_value,
is_scale=True,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(is_show=True, )),
# axislabel_opts=opts.LabelOpts(formatter="{value}"),
axislabel_opts=opts.LabelOpts(
formatter=JsCode("function(x){return (x + '元').substring(1);}")),
))
# 添加高价格的柱状图
bar.add_yaxis(
"高价格",
yy1,
bar_width=25,
category_gap='1%',
gap='1%',
label_opts=opts.LabelOpts(
is_show=True,
position="top",
formatter=JsCode("function(x){return x.data['value']+'元';}")),
)
# 添加低价格的柱状图
bar.add_yaxis(
"低价格",
yy2,
bar_width=25,
yaxis_index=1,
label_opts=opts.LabelOpts(
is_show=True,
position="bottom",
formatter=JsCode(
"function(x){return (x.data['value'] + '元').substring(1);}")),
)
# bar.reversal_axis()
bar.set_series_opts(
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode(
# "function(x){return x.data['value']>0?'#c23531':'#1d953f'}"
# )
# )
)
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="=========" + title + "=========",
pos_left='center',
pos_top='-1px',
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"function (params){return '名 称: ' + params.name + '<br/>' + '涨 跌: ' + params.data['rise'] + '%<br/>' + '溢价率: ' + params.data['premium']}"
)),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
# data=None,
# type_='category',
# name_gap=0,
# name_rotate=90,
axislabel_opts=opts.LabelOpts(rotate=-60, ),
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=True,
# symbol=['none', 'arrow']
)),
yaxis_opts=opts.AxisOpts(
type_='value',
name='可转债价格(元)',
# name_rotate=90,
name_gap=35,
name_location='middle',
# min_=0,
max_=max_value,
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}元'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
# symbol=['none', 'arrow']
)),
)
bar_html = bar.render_embed('template.html', html_utils.env)
return bar_html
week_name_list = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
high_temperature = [11, 11, 15, 13, 12, 13, 10]
low_temperature = [1, -2, 2, 5, 3, 2, 0]
(Line(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=week_name_list).add_yaxis(
series_name="最高气温",
y_axis=high_temperature,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="最低气温",
y_axis=low_temperature,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
opts.MarkLineItem(symbol="none", x="90%", y="max"),
opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
]),
).set_global_opts(
title_opts=opts.TitleOpts(title="城市气温变化图表", subtitle="数据来源中国天气网"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
).render("temperature_change_line_chart.html"))
from pyecharts.charts import Bar # 柱状图对象
from pyecharts import options # 配置对象
from pyecharts.globals import ThemeType
# 创建柱状图对象
(Bar({'theme': ThemeType.DARK})
# 添加横坐标
.add_xaxis(["冰箱", "洗衣机", "彩电", "空调", "电脑", "电饭煲"])
# 添加纵坐标（数据）可以添加多个
.add_yaxis("格力", [100, 200, 250, 150, 50, 300],
stack="strack").add_yaxis("小米", [200, 100, 50, 150, 240, 180],
stack="strack")
# 创建标题对象
.set_global_opts(
title_opts=options.TitleOpts(title="家电销售情况"),
# visualmap_opts=options.VisualMapOpts(max_=200), # 设置阈值
datazoom_opts=[options.DataZoomOpts()], # 拉的进度条
brush_opts=options.BrushOpts(), # 工具栏
yaxis_opts=options.AxisOpts(name="y轴名字"),
xaxis_opts=options.AxisOpts(name="x轴名字"),
).render("家电柱状图.html"))
opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="OCTOBER 2015",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
"2015-4",
"2015-5",
"2015-6",
"2015-7",
"2015-8",
"2015-9",
"2015-10",
"2015-11",
"2015-12",
],
xaxis=opts.AxisOpts(
type_="category",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False,
linestyle_opts=opts.LineStyleOpts(color="#6e9ef1")),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
label=opts.LabelOpts(formatter=produce_js_func(js_formatter)),
),
),
).add_yaxis(
series_name="2015 降水量",
is_smooth=True,
symbol="emptyCircle",
is_symbol_show=False,
# xaxis_index=1,
color="#d14a61",
y_axis=[
2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3
],
[57.1, 78254, "Milk Tea"],
[74.4, 41032, "Cheese Cocoa"],
[50.1, 12755, "Cheese Brownie"],
[89.7, 20145, "Matcha Cocoa"],
[68.1, 79146, "Tea"],
[19.6, 91852, "Orange Juice"],
[10.6, 101852, "Lemon Juice"],
[32.7, 20112, "Walnut Brownie"],
]).add_yaxis(
series_name="",
yaxis_data=[],
encode={
"x": "amount",
"y": "product"
},
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="Dataset normal bar example"),
xaxis_opts=opts.AxisOpts(name="amount"),
yaxis_opts=opts.AxisOpts(type_="category"),
visualmap_opts=opts.VisualMapOpts(
orient="horizontal",
pos_left="center",
min_=10,
max_=100,
range_text=["High Score", "Low Score"],
dimension=0,
range_color=["#D7DA8B", "#E15457"],
),
).render("dataset_bar_1.html"))
def citedLine(x_data, y_data):
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
cl = (Line(init_opts=opts.InitOpts(
bg_color=JsCode(background_color_js), width="1350px",
height="750px")).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="cites",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white"),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="Cites per Years",
pos_bottom="92%",
pos_left="8%",
title_textstyle_opts=opts.TextStyleOpts(color="#fff",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
legend_opts=opts.LegendOpts(is_show=False),
))
return cl
#if count == 10:
# break
book.close()
from pyecharts import options as opts
from pyecharts.charts import Bar
#按从大到小的顺序显示红包发放榜
slist = list(sdict.items())
slist.sort(key=lambda x: x[1], reverse=True)
sbar = Bar()
sbar.add_xaxis([x[0] for x in slist])
sbar.add_yaxis(
"交大校友交流学习群" + _date[:4] + "年" + _date[4:6] + "月" + _date[6:] +
"日红包爱心榜", [x[1] for x in slist])
sbar.set_global_opts(xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(interval=0, rotate=30)))
sbar.render(_send)
#按从小到大的顺序显示红包收益榜
plist = list(pdict.items())
plist.sort(key=lambda x: x[1], reverse=False)
pbar = Bar()
pbar.add_xaxis([x[0] for x in plist])
pbar.add_yaxis(
"交大校友交流学习群" + _date[:4] + "年" + _date[4:6] + "月" + _date[6:] +
"日红包福利榜", [x[1] for x in plist])
pbar.set_global_opts(xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(interval=0, rotate=45)))
pbar.render(_play)
def analyse_data():
client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client['testcomment']
col = db[collection_name]
result = col.find({}, {'_id': 0, '评论日期': 1, "评论": 1, "追加评论": 1})
comment_list = []
date_list = []
date_dic = {}
for date in result: # 把日期和评论字数放入列表中
date_list.append(date['评论日期'])
comment_list.append(len(date['评论']))
if len(date['追加评论']) != 0:
comment_list.append(len(date['追加评论']))
for date in date_list: # 统计日销量
if date in date_dic:
date_dic[date] += 1
else:
date_dic[date] = 1
# date_list = list(date_dic)
data_dic = {}
arr = np.array(list(date_dic)) # 给日期排序
date_list = arr[np.argsort(
[datetime.strptime(i, '%m.%d') for i in list(date_dic)])].tolist()
for i in date_list:
data_dic[i] = date_dic[i]
# s = pd.Series(data_dic) # series表示一维数组
# comment_list.sort()
word_num_dic = {
'0-10': 0,
'11-20': 0,
'21-30': 0,
'31-40': 0,
'41-50': 0,
'51-60': 0,
'61-70': 0,
'71-80': 0,
'91-100': 0,
'101-110': 0,
'111-120': 0,
'121-130': 0,
'131-140': 0,
'141-150': 0,
'151-160': 0,
'161-170': 0,
'171-180': 0,
'181-190': 0,
'191-200': 0,
'201-300': 0
}
for i in word_num_dic: # 统计字数
for j in comment_list:
if j >= int(i.split('-')[0]) and j <= int(i.split('-')[-1]):
word_num_dic[i] += 1
# print(ndic)
bar1 = ( # 生成柱状图
Bar().add_xaxis(list(data_dic.keys())).add_yaxis(
'商品', list(data_dic.values())).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='商品日销量'),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)), # 添加纵坐标标线
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=80,
is_zoom_lock=False)
] # 添加滑动条
))
bar1.chart_id = '6f180e81787a48539de004c1eb847c1e'
bar2 = ( # 生成柱状图
Bar({
'theme': ThemeType.MACARONS
}).add_xaxis(list(word_num_dic.keys())).add_yaxis(
'商品', list(word_num_dic.values())).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='字数统计'),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=80,
is_zoom_lock=False)
]))
bar2.chart_id = '9a412341322b4d92bba49e3fd932a7f0'
pie1 = ( # 生成柱状图
Pie().add(series_name='用户身份',
data_pair=[('超级会员', vip_num), ('普通用户', comment_num - vip_num)
]).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='超级会员比例'), ))
pie1.chart_id = '7ed5d413532646909f127fd36f584081'
pie2 = ( # 生成柱状图
Pie(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS)).add(
series_name='有无图片',
data_pair=[('有图评价', img_num), ('无图评价', comment_num - img_num)
]).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='有图评价比例'), ))
pie2.chart_id = 'aa3f038854e144dbaa6e9892d4ca31fc'
pie3 = ( # 生成柱状图
Pie(init_opts=opts.InitOpts(theme=ThemeType.INFOGRAPHIC)).add(
series_name='是否追加',
data_pair=[('追加评价', append_num), ('普通评价', comment_num - append_num)
]).set_global_opts( # 设置全局参数
title_opts=opts.TitleOpts(title='追加评价比例'), ))
pie3.chart_id = '235863de6fe74a2aa9273e30ed4a7f66'
page = Page(page_title='评论分析报告')
page.add(bar1, bar2, pie1, pie2, pie3)
page.render()
Page.save_resize_html(cfg_file=r'chart_config.json')
with open('resize_render.html', 'r+', encoding='utf-8') as f:
f.seek(573)
html = f.read()
f.seek(573)
f.write(' <h1>评价分析结果：</h1>\n <p>' + content + '</p>\n ')
f.write(html)
print(
r'已完成分析 请打开E:\Python\learnPython\testspider\FinalProject\resize_render.html查看分析结果'
)
