from pyecharts import charts
from pyecharts import options as opts
value = 200
gu = charts.Gauge(init_opts=opts.InitOpts(width="640px", height="480px"))
gu.add(
"指标",
[("昨日单位面积能耗", value)],
split_number=10,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[(
0.2, "#00B050"), (0.4, "#67e0e3"), (0.7, "#FF6600"), (1,
"#fd666d")],
width=25)),
min_=0, # 最小刻度
max_=1400, # 最大刻度
)
gu.set_global_opts(
title_opts=opts.TitleOpts(
title="楼宇能耗指标表盘\n昨日单位面积能耗{0}Wh/m^2".format(value)),
legend_opts=opts.LegendOpts(is_show=False),
)
gu.render(r"C:\Users\CQ375\Desktop\ex\Guage-eg.html")
f_y_data_6 = get_v(y_data_6)
f_y_data_7 = get_v(y_data_7)
f_y_data_8 = get_v(y_data_8)
f_y_data_9 = get_v(y_data_9)
f_y_data_10 = get_v(y_data_10)
f_y_data_11 = get_v(y_data_11)
f_y_data_12 = get_v(y_data_12)
f_y_data_13 = get_v(y_data_13)
f_y_data_14 = get_v(y_data_14)
print(x_data)
c = (
Line().add_xaxis(xaxis_data=x_data).add_yaxis(
"J1925",
y_axis=f_y_data_1,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
symbol_size=1).add_yaxis(
"J2801",
y_axis=f_y_data_2,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
symbol_size=1).add_yaxis(
"J4568",
y_axis=f_y_data_3,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
symbol_size=1).add_yaxis(
"J4578",
y_axis=f_y_data_4,
linestyle_opts=opts.LineStyleOpts(width=1),
"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=JsCode(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
],
label_opts=opts.LabelOpts(is_show=False),
def kline(self):
data = self.calculate().to_dict('list')
kline = (
Kline().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="portfolio index",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
range_end=100),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
kline_line = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MA5",
y_axis=self.calculate_ma(day_count=5, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=self.calculate_ma(day_count=10, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=self.calculate_ma(day_count=20, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA30",
y_axis=self.calculate_ma(day_count=30, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
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),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="Volumn",
y_axis=data["volume"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MACD",
y_axis=data["MACD"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).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),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="DIFF",
y_axis=data["DIFF"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=data["DEA"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# 最下面的柱状图和折线图
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width="1500px", height="750px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
height="60%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"),
)
makedir(self.path, 'kline')
grid_chart.render(
f"{self.path}\\kline\\{str(self.names)}_{time.strftime('%Y-%m-%d', time.localtime())}.html"
)
def line_time_genera():
'''
根据三部电影影评的时间分布，
绘制折线区域图；
:return:
'''
csv_path = 'C:/Users/FREEDOM/Desktop/{}.csv'.format('pandeng')
csv_path1 = 'C:/Users/FREEDOM/Desktop/{}.csv'.format('zuguo')
csv_path2 = 'C:/Users/FREEDOM/Desktop/{}.csv'.format('jizhang')
data_csv = pd.read_csv(csv_path, encoding='gbk')
data_csv1 = pd.read_csv(csv_path1, encoding='gbk')
data_csv2 = pd.read_csv(csv_path2, encoding='gbk')
time_list = []
time_list1 = []
time_list2 = []
'''
数据处理，时间处理为0：00-01：00的格式；
'''
for i, j, k in zip(data_csv['time'], data_csv1['time'], data_csv2['time']):
new_i = str(i).split(' ')[1].split(':')[0]
new_i1 = '{}:00-{}.00'.format(new_i, int(new_i) + 1)
new_j = str(j).split(' ')[1].split(':')[0]
new_j1 = '{}:00-{}.00'.format(new_j, int(new_j) + 1)
new_k1 = str(k).split(' ')[1].split(':')[0]
time_list.append(new_i1)
time_list1.append(new_j1)
time_list2.append(new_k1)
dict = {}
dict1 = {}
dict2 = {}
a_list = []
a_list.extend(sorted(set(time_list2))[:1])
a_list.extend(sorted(set(time_list2))[-11:-10])
a_list.extend(sorted(set(time_list2))[-6:])
a_list.extend(sorted(set(time_list2))[1:-11])
for j in a_list:
'''
若在列表中j存在，则就把j的数量匹配上去，
否则直接设为0；
'''
if j in time_list:
count = time_list.count(j)
dict[j] = count
else:
dict[j] = 0
if j in time_list1:
count1 = time_list1.count(j)
dict1[j] = count1
else:
dict1[j] = 0
if j in time_list2:
count2 = time_list2.count(j)
dict2[j] = count2
else:
dict2[j] = 0
c = (Line().add_xaxis(xaxis_data=[i for i in dict.keys()]).add_yaxis(
"《我和我的祖国》",
y_axis=[j for j in dict1.values()],
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
"《攀登者》",
y_axis=[j for j in dict.values()],
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
"《中国机长》",
y_axis=[j for j in dict2.values()],
linestyle_opts=opts.LineStyleOpts(width=2),
).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="豆瓣暑假档影评时间分布",
subtitle='数据来源：www.douban.com'),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
))
#生成html网页；
c.render('line.html')
def chart():
kline=(
Kline()
.add_xaxis(stock_date)
.add_yaxis("Kline", stock_price)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
# 顯示分割
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
# 圖例位置
legend_opts=opts.LegendOpts(is_show=True, pos_top=10, pos_left="center"),
# DataZoom slider
datazoom_opts=[opts.DataZoomOpts(is_show=False, type_="inside",
xaxis_index=[0, 1], range_start=0, range_end=100, ),
opts.DataZoomOpts(is_show=True, xaxis_index=[0, 1], type_="slider",
pos_top="90%", range_start=0, range_end=100, )],
tooltip_opts=opts.TooltipOpts(
trigger="axis", axis_pointer_type="cross", background_color="rgba(245,245,245,0.8)",
border_width=1, border_color="#ccc", textstyle_opts=opts.TextStyleOpts(color="#000")
),
# axispointer_opts.AxisPointerOpts(is_show=True,link=[{"xAxisIndex":"all"}],
# label=opts.LabelOpts(background_color=''#777'),),
# 區域選擇組件
# brush_opts=opts.BrushOpts(#指定所有數列對應的座標系x_axis_index="all",#指定哪些 series
# 可以被聯動brush_link="all",#定義顏色透明度out_of_brush={"colorAlpha":0.1},brush_type="lineX",),
)
)
line=(
Line()
.add_xaxis(stock_date)
.add_yaxis("10日移動平均線", sma_10, is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5))
.add_yaxis("120日移動平均線", sma_120, is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5))
.add_yaxis("240日移動平均線", sma_240, is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5))
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category"),
)
)
bar = (
Bar()
.add_xaxis(stock_date)
.add_yaxis("交易量", stock_volume_red, gap="0%",
xaxis_index=1, yaxis_index=1, label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#A73835"),
)
.add_yaxis("交易量", stock_volume_green,gap="0%",
xaxis_index=1, yaxis_index=1, label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#424A56"),
)
.add_yaxis("交易量", stock_volume_gray,gap="0%",
xaxis_index=1, yaxis_index=1, label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#404143")
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category", is_scale=True, grid_index=1, boundary_gap=False,
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=False),
split_number=20, ),
yaxis_opts=opts.AxisOpts(grid_index=1, is_scale=True, split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
overlap_kline_line = kline.overlap(line)
global grid_chart
grid_chart = (
Grid()
.add(overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
.add(bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="70%", height="16%"), )
)
grid_chart.render()
def Pycharts():
with open('reviews/pos.txt', "r") as f: # 训练集可持续化，读取上次写文件名，这次接着来
total_reviews_ups = f.read()
if total_reviews_ups == '':
print('请爬取训练集')
else:
total_reviews_up = int(total_reviews_ups)
with open('reviews/neg.txt', "r") as f: # 训练集可持续化
total_reviews_downs = f.read()
if total_reviews_downs == '':
print('请爬取训练集')
else:
total_reviews_down = int(total_reviews_downs)
file = open('reviews_over.txt', 'r', encoding='UTF-8') # 路径自定义
text = file.read()
file.close()
cut_text = " ".join(jieba.cut(text))
words = cut_text.split()
words1 = [re.sub("[\，\。\！\；\？\、\”\“]", '', word) for word in words] # 去掉标点符号
#停用词 自定义词典
jieba.load_userdict("dict.txt")
stopwords_file = "stopwords.txt"
stop_f = open(stopwords_file, "r", encoding='utf-8')
stop_words = list()
for line in stop_f.readlines():
line = line.strip()
if not len(line):
continue
stop_words.append(line)
stop_f.close
DIY_stopwords = ['一个','没有','说','玩','做','里','中','-','⣿','','感','spoiler','2','点','完','·','\\','4','走','一款',"'",'会','没',',','游戏','帧','/']
for line in range(0,len(DIY_stopwords)):
stop_words.append(DIY_stopwords[line])
words2 = [word for word in words1 if word not in stop_words] #停用词
wordcounts = collections.Counter(words2).most_common(200)
#print(wordcounts)
#词云
cloud = (
WordCloud(opts.InitOpts(width="100%", height="950px"))
.add("词频统计", wordcounts, word_size_range=[20, 300], )
.set_global_opts(
title_opts=opts.TitleOpts(
title="词频统计", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
.render("html/wordcloud_diamond.html")
)
UP = (
Gauge(init_opts=opts.InitOpts(width="500px", height="500px"))
.add(series_name="推荐指数", data_pair=[["", sa.pos/(sa.pos+sa.neg)*100]],
title_label_opts=opts.LabelOpts(font_size=40, color="blue", font_family="Microsoft YaHei"),
)
.set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{a} <br/>{b} : {c}%"),
)
.set_series_opts(axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[[0.3, "#67e0e3"], [0.7, "#37a2da"], [1, "#fd666d"]], width=30)
)
)
.render("html/gauge_up.html")
)
DOWN = (
Gauge(init_opts=opts.InitOpts(width="500px", height="500px"))
.add(series_name="推荐指数", data_pair=[["", sa.neg/(sa.pos+sa.neg)*100]],
title_label_opts=opts.LabelOpts(font_size=40, color="blue", font_family="Microsoft YaHei"),
)
.set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{a} <br/>{b} : {c}%"),
)
.set_series_opts(axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[[0.3, "#67e0e3"], [0.7, "#37a2da"], [1, "#fd666d"]], width=30)
)
)
.render("html/gauge_down.html")
)
pie = (
Pie()
.add(
"1",
[['正面训练集数量',total_reviews_up ], ['负面训练集数量', total_reviews_down]],
radius=["40%", "75%"],
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Pie-Radius"),
legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
.render("html/pie.html")
)
bar = (
Bar()
.add_xaxis(['推荐', '不推荐'])
.add_yaxis("识别成功", [pospic,negpic], stack="stack1")
.add_yaxis("识别失败", [totpos-pospic,totneg-negpic], stack="stack1")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="准确度为%s" %str(RecommendCent)))
.render("html/bar.html")
)
webbrowser.open('file:///F:/Project/WorkSpace/2077CommentAnalyze/Python/html/index.html')
def draw_chart(ts_code, start_date, end_date):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data = get_process_datas(ts_code, start_date, end_date)
stock_data.index = pd.to_datetime(stock_data['TIME'], format="%Y/%m/%d")
x = stock_data["TIME"].values.tolist()
stock_basic_data = stock_data[[
"TIME", "OPEN", "CLOSE", "LOW", "HIGH", "VOLUME"
]]
# stock_data = stock_data.sort_index(ascending=True) # 倒序，看时间顺序是否正常决定是不是要用
# k线图
kline = (
Kline().add_xaxis(x).add_yaxis(
"K线图",
stock_basic_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='20%'),
title_opts=opts.TitleOpts(title="股票价格",
subtitle=ts_code,
pos_left='22%',
pos_top="20%"),
#把所有的x轴连接在一起
# axispointer_opts=opts.AxisPointerOpts(is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1, 2, 3, 4], # 设置第0轴和第1轴同时缩放
range_start=0,
range_end=100,
),
],
))
# 成交量柱形图
bar_volumn = (Bar().add_xaxis(x).add_yaxis(
"成交量",
stock_basic_data["VOLUME"].values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="成交量", pos_left='22%', pos_top="48%"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='48%'),
))
#绘制均线图
line_ma = (
Line().add_xaxis(x).add_yaxis(
series_name="MA5",
y_axis=stock_data["MA5"].values.tolist(),
is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA10",
y_axis=stock_data["MA10"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA20",
y_axis=stock_data["MA20"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="MA",
pos_left='22%',
pos_top="88%"),
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
is_scale=True),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='85%'),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制jdk线
line_kdj = (Line().add_xaxis(x).add_yaxis(
series_name="K",
y_axis=stock_data["K"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="D",
y_axis=stock_data["D"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="J",
y_axis=stock_data["J"].values.tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="KDJ", pos_left='22%', pos_top="62%"),
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="7%",
pos_top='60%'),
xaxis_opts=opts.AxisOpts(
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制macd柱状图
bar_macd = (Bar().add_xaxis(x).add_yaxis(
series_name='MACD',
yaxis_data=stock_data['MACD'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="MACD", pos_left='22%', pos_top="75%"),
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),
),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='70%'),
))
#绘制DIF和DEA
line_macd = (Line().add_xaxis(x).add_yaxis(
series_name="DIF",
y_axis=stock_data['DIF'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="DEA",
y_axis=stock_data['DEA'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
#绘制布林线
line_boll = (Line().add_xaxis(x).add_yaxis(
series_name="MID",
y_axis=stock_data["MID"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="UPPER",
y_axis=stock_data["UPPER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="LOWER",
y_axis=stock_data["LOWER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
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)),
),
))
overlap_kline_linema = kline.overlap(line_boll)
overlap_macd = bar_macd.overlap(line_macd)
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 为了把 data.datas 的数据写入到html中作为全局变量,目前无法跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(
stock_basic_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
overlap_kline_linema,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="3%",
height="40%"),
)
grid_chart.add(
bar_volumn,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="45%",
height="10%"),
)
grid_chart.add(line_kdj,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="60%",
height="10%"))
grid_chart.add(overlap_macd,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="72%",
height="10%"))
grid_chart.add(line_ma,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="85%",
height="10%"))
grid_chart.render('stock_{}/stock_{}.html'.format(
ts_code, ts_code)) # 保存成用股票代码命名的文档
return 0
itemstyle_opts=opts.ItemStyleOpts(border_color="#111",
color='#454545'))
# 添加系列
geo.add(
'',
[('成都', 10), ('武汉', 20), ('西安', 30), ('南京', 40), ('深圳', 50)],
type_=ChartType.EFFECT_SCATTER, # 散点图一种形式
label_opts=opts.LabelOpts(is_show=True), # 不显示设置False
)
geo.add(
'',
[('北京', 100), ('上海', 100)],
type_=ChartType.HEATMAP, # 散点图的一种形式
label_opts=opts.LabelOpts(is_show=False),
)
# 设置流向
geo.add(
'流向图',
[('上海', '成都'), ('上海', '南京'), ('上海', '深圳'), ('上海', '武汉'), ('北京', '西安'),
('北京', '成都'), ('北京', '南京'), ('北京', '武汉')],
type_=ChartType.LINES,
linestyle_opts=opts.LineStyleOpts(curve=0.3, color="#63B8FF"),
effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW,
symbol_size=6,
color='#FF7F00'),
)
# 生成图片
geo.render('./data/geo_liuxiang.html')
def grid_mutil_yaxis() -> Grid:
x_data = ["{}月".format(i) for i in range(1, 13)]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"蒸发量",
[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
yaxis_index=0,
color="#d14a61",
)
.add_yaxis(
"降水量",
[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
)
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
min_=0,
max_=250,
position="right",
offset=80,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#5793f3")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"平均温度",
[3.3, 10.2, 20.3, 23.0, 16.5],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line)
return Grid().add(bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True)
def draw_peak_charts(ts_code, start_date, end_date, name):
stock_data = get_process_datas(ts_code, start_date, end_date)
x = stock_data['TIME'].values.tolist()
y = stock_data[name].values.tolist()
indexes_peaks, line_peaks = get_line_peaks(stock_data[name])
indexes_valleys, line_valleys = get_line_valleys(stock_data[name])
line = (
Line().add_xaxis(x).add_yaxis(
'收盘价',
y,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).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="股票价格",
subtitle=ts_code,
pos_left='15%',
pos_top="40%"),
# 把所有的x轴连接在一起
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
# 极大值的散点图
scatter_peak = (Scatter().add_xaxis(indexes_peaks).add_yaxis(
'Peaks',
line_peaks,
label_opts=opts.LabelOpts(is_show=False),
symbol='triangle',
symbol_rotate=180,
itemstyle_opts=opts.ItemStyleOpts(color='#ef232a')).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")],
))
scatter_valley = (Scatter().add_xaxis(indexes_valleys).add_yaxis(
'Valleys',
line_valleys,
label_opts=opts.LabelOpts(is_show=False),
symbol='triangle',
itemstyle_opts=opts.ItemStyleOpts(color='#14b143')).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")],
))
#绘制布林带
line_boll = (Line().add_xaxis(x).add_yaxis(
series_name="MID",
y_axis=stock_data["MID"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="UPPER",
y_axis=stock_data["UPPER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="LOWER",
y_axis=stock_data["LOWER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
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)),
),
))
stock_basic_data = stock_data[["TIME", "OPEN", "CLOSE", "LOW", "HIGH"]]
# 绘制K线图
kline = (
Kline().add_xaxis(x).add_yaxis(
"K线图",
stock_basic_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='20%'),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
),
],
))
line_close = (Line().add_xaxis(x).add_yaxis(
'五日均线',
stock_data["MA5"].values.tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).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")],
))
overlap_findpeak = line.overlap(scatter_peak)
overlap_findpeak = overlap_findpeak.overlap(scatter_valley)
overlap_findpeak = overlap_findpeak.overlap(line_boll)
overlap_findpeak = overlap_findpeak.overlap(kline)
overlap_findpeak = overlap_findpeak.overlap(line_close)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
overlap_findpeak,
grid_opts=opts.GridOpts(pos_left="25%", pos_right="15%",
pos_top="10%"),
)
grid_chart.render('stock_{}/find_peak.html'.format(ts_code))
1、暂无
"""
x_data = ["一", "二", "三", "四", "五", "六", "七", "八", "九"]
y_data_3 = [1, 3, 9, 27, 81, 247, 741, 2223, 6669]
y_data_2 = [1, 2, 4, 8, 16, 32, 64, 128, 256]
y_data_05 = [1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32, 1 / 64, 1 / 128, 1 / 256, 1 / 512]
(
Line(init_opts=opts.InitOpts(width="1600px", height="800px"))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="1/2的指数",
y_axis=y_data_05,
linestyle_opts=opts.LineStyleOpts(width=2),
)
.add_yaxis(
series_name="2的指数", y_axis=y_data_2, linestyle_opts=opts.LineStyleOpts(width=2)
)
.add_yaxis(
series_name="3的指数", y_axis=y_data_3, linestyle_opts=opts.LineStyleOpts(width=2)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="对数轴示例", pos_left="center"),
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b} : {c}"),
legend_opts=opts.LegendOpts(pos_left="left"),
xaxis_opts=opts.AxisOpts(type_="category", name="x"),
yaxis_opts=opts.AxisOpts(
type_="log",
name="y",
from pyecharts.faker import Faker
tl = Timeline()
names = ("商家A", "商家B", "商家C")
nodes = [{"name": name} for name in names]
for i in range(2015, 2020):
links = [
{
"source": names[0],
"target": names[1],
"value": Faker.values()[0]
},
{
"source": names[1],
"target": names[2],
"value": Faker.values()[0]
},
]
sankey = (Sankey().add(
"sankey",
nodes,
links,
linestyle_opt=opts.LineStyleOpts(opacity=0.2,
curve=0.5,
color="source"),
label_opts=opts.LabelOpts(position="right"),
).set_global_opts(title_opts=opts.TitleOpts(
title="{}年商店（A, B, C）营业额差".format(i))))
tl.add(sankey, "{}年".format(i))
put_html(tl.render_notebook())
from pyecharts import options as opts
from pyecharts.charts import PictorialBar
from pyecharts.globals import SymbolType
location = ["山西", "四川", "西藏", "北京", "上海", "内蒙古", "云南", "黑龙江", "广东", "福建"]
values = [13, 42, 67, 81, 86, 94, 166, 220, 249, 262]
c = (PictorialBar().add_xaxis(location).add_yaxis(
"",
values,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=18,
symbol_repeat="fixed",
symbol_offset=[0, 0],
is_symbol_clip=True,
symbol=SymbolType.ROUND_RECT,
).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(title="PictorialBar-各省份人口数量（虚假数据）"),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
opacity=0)),
),
).render("Pictorialbar_base.html"))
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="注册总量",
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="OCTOBER 2015",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
),
xaxis_opts=opts.AxisOpts(
schema=[
opts.RadarIndicatorItem(name="灵活性", max_=100,color='#98F5FF'),
opts.RadarIndicatorItem(name="功能性", max_=100,color='#98F5FF'),
opts.RadarIndicatorItem(name="平稳性", max_=100,color='#98F5FF'),
opts.RadarIndicatorItem(name="安全性", max_=100,color='#98F5FF'),
opts.RadarIndicatorItem(name="耐用性", max_=100,color='#98F5FF'),
],
center=['50%', '50%'],
textstyle_opts=opts.TextStyleOpts(
color='red',
),
splitline_opt=opts.SplitLineOpts(
is_show= True,
linestyle_opts=opts.LineStyleOpts(
color='#98F5FF',
width=1,
type_='dotted'
)
),
axisline_opt=opts.AxisLineOpts(
linestyle_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(
color='#98F5FF',
)
)
),
splitarea_opt=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(
color=['#141c42', '#141c42']
)
def draw_chart(df):
# temp = df[df['signal'].notnull()][['signal']]
# temp = temp[temp['signal'] != temp['signal'].shift(1)]
signal = df[df['signal'] == 1].values.tolist()
high = df['high'].values.tolist()
date = df['candle_begin_time'].values.tolist()
new_data = df[['open', 'close', 'high', 'low']].values.tolist()
line_upper_data = list(df['line_upper'].values)
line_median_data = list(df['line_median'].values)
line_lower_data = list(df['line_lower'].values)
es = (
EffectScatter()
.add_xaxis(date)
.add_yaxis("", signal)
)
kline = (
Kline()
.add_xaxis(xaxis_data=date)
.add_yaxis("k_线图",
y_axis=new_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",color0="#00da3c",border_color="#8A0000",border_color0="#008F28",),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="signal", name="signal")
]
),)
.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")],
)
)
overlap_kline = kline.overlap(es)
kline_line = (
Line()
.add_xaxis(xaxis_data=date)
.add_yaxis(
series_name="upper",
y_axis=line_upper_data,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
itemstyle_opts=opts.ItemStyleOpts(color="#0004a1"),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="median",
y_axis=line_median_data,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
itemstyle_opts=opts.ItemStyleOpts(color="#fff401"),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="lower",
y_axis=line_lower_data,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
itemstyle_opts=opts.ItemStyleOpts(color="#71f401"),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
overlap_kline_line = kline_line.overlap(overlap_kline)
# Kline And Line
# overlap_kline_line = kline.overlap(line)
# overlap_kline_line.render_notebook()
# grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="800px"))
# grid_chart.add(
# overlap_kline_line,
# # 设置位置
# grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="2%", height="80%"),
# )
overlap_kline.render()
value=2))
# %%
c = (
Graph(init_opts=opts.InitOpts(
width="100%", # 图宽
height="700px", # 图高
renderer=
"canvas", # 渲染模式 svg 或 canvas，即 RenderType.CANVAS 或 RenderType.SVG
)).add(
"",
nodes_data,
links_data,
repulsion=50,
linestyle_opts=opts.LineStyleOpts(curve=0.2),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
title_opts=opts.TitleOpts(
title="Graph-GraphNode-GraphLink-WithEdgeLabel"),
).render("graph_with_edge_options.html"))
# %%
# influence_man_data
influence_man.columns = ["influencer_name", "influence_value"]
influence_man_full_data = pd.merge(influence_man,
influence_man_data,
on=['influencer_name'])
influence_man_full_data = influence_man_full_data.drop_duplicates(
"influencer_name")
nodes = [{
"x": node["x"],
"y": node["y"],
"id": node["id"],
"name": node["label"],
"symbolSize": node["size"],
"itemStyle": {
"normal": {
"color": node["color"]
}
},
} for node in data["nodes"]]
edges = [{
"source": edge["sourceID"],
"target": edge["targetID"]
} for edge in data["edges"]]
(Graph().add(
series_name="",
nodes=nodes,
links=edges,
layout="none",
is_roam=True,
is_focusnode=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=0.5, curve=0.3, opacity=0.7),
).set_global_opts(title_opts=opts.TitleOpts(
title="NPM Dependencies")).render("npm_dependencies.html"))
textstyle_opts=opts.TextStyleOpts(
color='#fff'
)
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="shadow",
textstyle_opts=opts.TextStyleOpts(
color="#fff"
)
),
xaxis_opts=opts.AxisOpts(
type_="category",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(
color="white"
)
),
axislabel_opts=opts.LabelOpts(
)
),
yaxis_opts=opts.AxisOpts(
type_="value",
max_=90,
axistick_opts=opts.AxisTickOpts(
is_show=False
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(
color='rgba(255,255,255,0.3)'
def levelRender(title, source, depList):
#构建依赖关系图参数
dataLevels = dependLevels(depList,source)
#设置echart节点参数
nodes = [
{
"x": node["x"],
"y": node["y"],
"id": node["name"],
"name": node["name"],
"value": "123",
"symbol":"roundRect",
"symbolSize": [5 + dataLevels.maxDeep - node["deep"], 5 + dataLevels.maxDeep - node["deep"]],
"itemStyle": {"normal": {"color": 'green'}}
#"label":{"formatter": "{c}"}
}
for node in dataLevels.nodes
]
#设置边
edges = [
{
"source": edge["parent"],
"target": edge["child"],
"lineStyle":opts.LineStyleOpts
(
width=2 if edge["is_circle"] is True else 0.5,
curve=0.1,
opacity=0.7,
color= 'blue' if edge["is_circle"] is True else 'grey'
)
}
for edge in dataLevels.deps
]
#配置Graph关系图
g = (
Graph(init_opts=opts.InitOpts(width="1800px", height="1200px"))
.add(
series_name="",
nodes=nodes,
links=edges,
layout="none",
is_roam=True,
is_focusnode=True,
label_opts=opts.LabelOpts(is_show=True, color='red',position='top'),
#linestyle_opts=opts.LineStyleOpts(width=0.5, curve=0.1, opacity=0.7),
edge_label=opts.LabelOpts(
is_show=False,
position="middle",
formatter="{b}"
),
edge_symbol = ['none', 'arrow']
)
.set_global_opts(title_opts=opts.TitleOpts(title=title))
.set_series_opts(
emphasis = {
"edgeLabel": opts.LabelOpts(
is_show=True,
position="middle",
formatter="{b}"
)
}
)
#.render(file)
)
return g
def print_line():
x_data = range(0, len(Grid))
timeline2 = Timeline(init_opts=opts.InitOpts(bg_color="black"))
for i in range(len(Grid)):
line = (Line(init_opts=opts.InitOpts(bg_color="black")).add_xaxis(
xaxis_data=time[0:i + 1]).add_yaxis(
series_name="grid",
y_axis=Grid[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(
width=3,
color="rgb(255,0,255)",
),
color="rgb(255,255,0)",
symbol="diamond",
symbol_size=2,
).add_yaxis(
series_name="pv",
y_axis=PV[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3,
color="rgb(0,255,255)"),
color="rgb(255,255,255)",
symbol="circle",
symbol_size=2,
).add_yaxis(
series_name="battery",
y_axis=Battery[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3,
color="rgb(255,255,255)"),
color="rgb(0,255,255)",
symbol="triangle",
symbol_size=2,
).add_yaxis(
series_name="demand",
y_axis=demand[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3,
color="rgb(255,255,0)"),
color="rgb(255,0,255)",
symbol="rect",
symbol_size=2,
).set_global_opts(
title_opts=opts.TitleOpts(
title="PV, battery, demand, grid(kW) till time {}".format(
time[i]),
title_textstyle_opts=opts.TextStyleOpts(color="white")),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="none"),
xaxis_opts=opts.AxisOpts(
boundary_gap=False,
min_='dataMin',
max_='dataMax',
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="white"), ),
axislabel_opts=opts.LabelOpts(),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value}"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="white")),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
is_piecewise=False,
dimension=0,
textstyle_opts=opts.TextStyleOpts(color="white")),
legend_opts=opts.LegendOpts(
textstyle_opts=opts.TextStyleOpts(color="white"),
pos_left="right",
pos_top="5%"),
))
timeline2.add(line, "{}年".format(i)).add_schema(play_interval=2000,
is_auto_play=True,
is_timeline_show=False)
timeline2.render("C:/Users/suqi/Desktop/allcode/linenew.html")
def bar_line(request):
"""各种类型房屋的平均价格和数量 柱状图——折线图"""
districts = [] # 区县名称
infos = {} # 各类新房的总数和均价
# {'商业': [821, 21854707.0], '住宅': [567, 6424626.0], '别墅': [79, 1200119.0], '写字楼': [77, 1120913.0]}
cursor = Mongo().connect_mongo('runoob', 'test').find()
for data in cursor:
if data["district"] not in districts: # 获取区县
districts.append(data["district"])
if data["type"] not in infos:
infos[data["type"]] = [1, float(data["price"])]
else:
infos[data["type"]][0] += 1
infos[data["type"]][-1] += float(data["price"])
x_data = [x for x in infos.keys()]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"平均价格",
["{:.2f}".format(x[-1]/x[0]) for x in infos.values()],
yaxis_index=0,
color="#675bba",
category_gap="65%",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="",
type_="value",
# min_=0,
# max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="",
# min_=0,
# max_=25,
position="none",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(
is_show=False, linestyle_opts=opts.LineStyleOpts(opacity=1)
)
)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="成都各种类型新房价格与数量")
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"新房数量",
["{}".format(x[0]) for x in infos.values()],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=True),
)
)
bar.overlap(line)
bar.overlap(line).render("templates/barline.html")
return HttpResponse(bar.overlap(line).render_embed())
def tradeDataShowKLine(product_code, ma=None, autoType=None) -> Grid:
'''
展示该产品的K线图，默认前复权，等值等比例的展示
:param product_code: 产品代码
:param ma=[30,60,99,120,250] 均线
:param autoType: autoType=qfq-前复权 hfq-后复权 None-不复权
:param *avg_line: 均线 暂时不做
:return:
'''
ma = [30, 60, 99, 120, 250] if ma is None else ma
(productname,
tradeDataDf) = sttradepb.getTradeDataFromDataBase(product_code,
ma,
autotype=autoType)
tradeDataDf = tradeDataDf.where(tradeDataDf.notnull(), None)
workDateList = tradeDataDf['trade_date'].tolist()
workDateList = list(map(str, workDateList))
# open,close,low,high数据获取
new_data = tradeDataDf.loc[:, gc.PRICE_COLS].values
tradeDataList = new_data.tolist()
kline = (Kline().add_xaxis(xaxis_data=workDateList).add_yaxis(
series_name="kline",
y_axis=tradeDataList,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title=productname + "(%s)" % product_code,
subtitle="MA%s" % str(ma),
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
line = (Line().add_xaxis(xaxis_data=workDateList).add_yaxis(
series_name="MA%s" % ma[0],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[0]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[1],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[1]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[2],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[2]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[3],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[3]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[4],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[4]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
bar = (Bar().add_xaxis(xaxis_data=workDateList).add_yaxis(
series_name="Volume",
yaxis_data=tradeDataDf.loc[:, "volume"].values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
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=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
},
{
"featureType": "manmade",
"elementType": "all",
"stylers": {
"color": "#022338"
},
},
{
"featureType": "label",
"elementType": "all",
"stylers": {
"visibility": "off"
},
},
]
},
).add(
"",
type_="lines",
is_polyline=True,
data_pair=bus_lines,
linestyle_opts=opts.LineStyleOpts(opacity=0.2, width=0.5),
# 如果不是最新版本的话可以注释下面的参数（效果差距不大）
progressive=200,
progressive_threshold=500,
))
c.width = "100%"
put_html(c.render_notebook())
"""
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
(Radar(init_opts=opts.InitOpts(
width="1600px", height="1000px", bg_color="#CCCCCC")).add_schema(
schema=[
opts.RadarIndicatorItem(name="销售（sales）", max_=6500),
opts.RadarIndicatorItem(name="管理（Administration）", max_=16000),
opts.RadarIndicatorItem(name="信息技术（Information Technology）",
max_=30000),
opts.RadarIndicatorItem(name="客服（Customer Support）", max_=38000),
opts.RadarIndicatorItem(name="研发（Development）", max_=52000),
opts.RadarIndicatorItem(name="市场（Marketing）", max_=25000),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
).add(
series_name="预算分配（Allocated Budget）",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
).add(
series_name="实际开销（Actual Spending）",
data=v2,
linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="基础雷达图"),
legend_opts=opts.LegendOpts()).render("basic_radar_chart.html"))
400,
500,
600,
750,
800,
700,
600,
400,
]
(Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="用电量",
y_axis=y_data,
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).set_global_opts(
title_opts=opts.TitleOpts(title="一天用电量分布", subtitle="纯属虚构"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} W"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
dimension=0,
pieces=[
{
"lte": 6,
"color": "green"
from pyecharts import options as opts
from pyecharts.charts import Gauge
c = (Gauge().add(
"业务指标",
[("完成率", 55.5)],
split_number=5,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color=[(0.3, "#67e0e3"), (0.7, "#37a2da"), (1, "#fd666d")], width=30)),
detail_label_opts=opts.LabelOpts(formatter="{value}"),
).set_global_opts(
title_opts=opts.TitleOpts(title="Gauge-分割段数-Label"),
legend_opts=opts.LegendOpts(is_show=False),
).render("gauge_splitnum_label.html"))
"stylers": {
"color": "#d1d1d1"
},
},
{
"featureType": "label",
"elementType": "labels.text.fill",
"stylers": {
"color": "#999999"
},
},
]
},
).add(
series_name="",
type_=ChartType.LINES,
data_pair=map_data,
is_polyline=True,
is_large=True,
linestyle_opts=opts.LineStyleOpts(color="purple", opacity=0.6, width=1),
effect_opts=opts.EffectOpts(trail_length=0.5),
).add_control_panel(
copyright_control_opts=opts.BMapCopyrightTypeOpts(position=3),
maptype_control_opts=opts.BMapTypeControlOpts(
type_=BMapType.MAPTYPE_CONTROL_DROPDOWN),
scale_control_opts=opts.BMapScaleControlOpts(),
overview_map_opts=opts.BMapOverviewMapControlOpts(is_open=True),
navigation_control_opts=opts.BMapNavigationControlOpts(),
geo_location_control_opts=opts.BMapGeoLocationControlOpts(),
).render("hiking_trail_in_hangzhou.html"))
def confirm_line(request):
'''
确诊/疑似折线图，以json返回
'''
line2 = (
Line()
.add_xaxis(cure_data['updateDate'])
.add_yaxis('确诊', cure_data['list'][0]['data'],color='#f9b97c',linestyle_opts = opts.LineStyleOpts(width=2),is_smooth=True,label_opts=opts.LabelOpts(is_show=False))
.add_yaxis('疑似', cure_data['list'][1]['data'],color='#ae212c',linestyle_opts = opts.LineStyleOpts(width=2),is_smooth=True,label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title='确诊/疑似累计趋势图',pos_top='top'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
yaxis_opts=opts.AxisOpts(
type_="value",
splitline_opts=opts.SplitLineOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(is_show=False),
),
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross",
),
)
.dump_options_with_quotes()
)
return JsonResponse(json.loads(line2))
