def test_line_type_fil():
line = Line("折线图-面积图示例")
line.add("商家A", CLOTHES, clothes_v1, is_fill=True, line_opacity=0.2,
area_opacity=0.4, symbol=None)
line.add("商家B", CLOTHES, clothes_v2, is_fill=True, area_color='#000',
area_opacity=0.3, is_smooth=True)
assert '"step": true' not in line._repr_html_()
def test_grid_line_pie():
line = Line("折线图示例", width=1200)
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="55%")
pie.add(
"",
CLOTHES,
v1,
radius=[45, 65],
center=[65, 50],
legend_pos="80%",
legend_orient="vertical",
)
grid = Grid()
grid.add(line, grid_right="55%")
grid.add(pie, grid_left="60%")
grid.render()
def test_line_negative_value():
line = Line("折线图示例")
line.add("最高气温", WEEK, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", WEEK, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], mark_line=["average"])
line.render()
def test_grid_properties():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720)
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
line = Line("折线图示例", title_top="50%")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_top="50%",
)
grid = Grid(width=1024, height=768)
grid.add(bar, grid_bottom="60%")
grid.add(line, grid_top="60%")
eq_(grid.width, 1024)
eq_(grid.height, 768)
assert (
("echarts" in bar.js_dependencies)
or ("echarts.min" in bar.js_dependencies)
)
def test_grid_inverse_yaxis():
attr = ["{}天".format(i) for i in range(1, 31)]
line_top = Line("折线图示例", width=1200, height=700)
line_top.add(
"最高气温",
attr,
[random.randint(20, 100) for i in range(30)],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="38%",
)
line_bottom = Line()
line_bottom.add(
"最低气温",
attr,
[random.randint(20, 100) for i in range(30)],
mark_point=["max", "min"],
mark_line=["average"],
is_yaxis_inverse=True,
xaxis_pos="top",
)
grid = Grid()
grid.add(line_top, grid_bottom="60%")
grid.add(line_bottom, grid_top="50%")
grid.render()
def test_grid_top_bottom():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720)
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True)
line = Line("折线图示例", title_top="50%")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_top="50%",
)
grid = Grid()
grid.add(bar, grid_bottom="60%")
grid.add(line, grid_top="60%")
grid.render()
def test_timeline_label_color():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add(
"bar",
attr,
[randint(10, 50) for _ in range(6)],
label_color=["red", "#213", "black"],
)
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
content = timeline._repr_html_()
assert '"color": [' in content
assert "red" in content
assert "#213" in content
assert "black" in content
def test_grid_add_overlap():
from pyecharts import Overlap
grid = Grid()
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar("Overlap+Grid 示例", width=1200, height=600, title_pos="40%")
bar.add("蒸发量", attr, v1)
bar.add(
"降水量",
attr,
v2,
yaxis_formatter=" ml",
yaxis_max=250,
legend_pos="85%",
legend_orient="vertical",
legend_top="45%",
)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
grid.add(overlap, grid_right="20%")
grid.render()
def test_grid_four_direction():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add("最高气温", WEEK, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", WEEK, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], mark_line=["average"],
legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_top="50%", legend_pos="20%")
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
grid.render()
def test_line_user_define_markpoint():
line = Line("折线图示例")
line.add("商家A", CLOTHES, clothes_v1,
mark_point=["average", {
"coord": ["裤子", 10], "name": "这是我想要的第一个标记点"}])
line.add("商家B", CLOTHES, clothes_v2, is_smooth=True,
mark_point=[{
"coord": ["袜子", 80], "name": "这是我想要的第二个标记点"}])
line.render()
def draw_sentiment_pic(csv_file):
attr, val = [], []
info = count_sentiment(csv_file)
info = sorted(info.items(), key=lambda x: x[0], reverse=False) # dict的排序方法
for each in info[:-1]:
attr.append(each[0])
val.append(each[1])
line = Line(csv_file+":影评情感分析")
line.add("", attr, val, is_smooth=True, is_more_utils=True)
line.render(csv_file+"_情感分析曲线图.html")
def test_line_user_define_marks():
line = Line("折线图示例")
line.add("商家A", CLOTHES, clothes_v1,
mark_point=["average", "max", "min"],
mark_point_symbol='diamond', mark_point_textcolor='#40ff27')
line.add("商家B", CLOTHES, clothes_v2,
mark_point=["average", "max", "min"],
mark_point_symbol='arrow', mark_point_symbolsize=40)
line.show_config()
line.render()
def test_line_log_yaxis():
import math
import random
line = Line("折线图示例")
line.add("商家A", CLOTHES,
[math.log10(random.randint(1, 99999)) for _ in range(6)])
line.add("商家B", CLOTHES,
[math.log10(random.randint(1, 99999999)) for _ in range(6)],
yaxis_type="log")
line.render()
def test_line_es():
v1 = [5, 20, 36, 10, 10, 100]
line = Line("line-EffectScatter 示例")
line.add("", CLOTHES, v1, is_random=True)
es = EffectScatter()
es.add("", CLOTHES, v1, effect_scale=8)
overlap = Overlap()
overlap.add(line)
overlap.add(es)
overlap.render()
def test_line_marks():
line = Line("折线图示例")
line.add("商家A", CLOTHES, clothes_v1, mark_point=["average"])
line.add(
"商家B",
CLOTHES,
clothes_v2,
is_smooth=True,
mark_line=["max", "average"],
)
line.render()
def test_overlap_bar_line():
attr = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line-Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def test_overlap_bar_line():
attr = ["A", "B", "C", "D", "E", "F"]
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line-Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render()
def test_overlap_kline_line():
import random
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
attr = ["2017/7/{}".format(i + 1) for i in range(31)]
kline = Kline("Kline-Line 示例")
kline.add("日K", attr, v1)
line_1 = Line()
line_1.add("line-1", attr, [random.randint(2400, 2500) for _ in range(31)])
line_2 = Line()
line_2.add("line-2", attr, [random.randint(2400, 2500) for _ in range(31)])
overlap = Overlap()
overlap.add(kline)
overlap.add(line_1)
overlap.add(line_2)
overlap.render()
def test_timeline_bar_line():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add("bar", attr, [randint(10, 50) for _ in range(6)])
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
bar_2 = Bar("3 月份数据", "数据纯属虚构")
bar_2.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_2 = Line()
line_2.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_2 = Overlap()
overlap_2.add(bar_2)
overlap_2.add(line_2)
bar_3 = Bar("4 月份数据", "数据纯属虚构")
bar_3.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_3 = Line()
line_3.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_3 = Overlap()
overlap_3.add(bar_3)
overlap_3.add(line_3)
bar_4 = Bar("5 月份数据", "数据纯属虚构")
bar_4.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_4 = Line()
line_4.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_4 = Overlap()
overlap_4.add(bar_4)
overlap_4.add(line_4)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
timeline.add(overlap_2, "3 月")
timeline.add(overlap_3, "4 月")
timeline.add(overlap_4, "5 月")
timeline.render()
def test_page():
page = Page()
line = Line("折线图示例")
line.chart_id = "id_my_cell_line"
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center", width="600px")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie, create_a_bar(TITLE)])
# Start render and test
html = page._repr_html_()
# Test base html structure
assert html.count("<script>") == html.count("</script>") == 2
assert html.count("<div") == html.count("</div>") == 3
assert html.count("require.config") == html.count("function(echarts)") == 1
# Test some chart attributes
json_encoded_title = json.dumps(TITLE)
assert json_encoded_title in html
assert "nbextensions/echarts" in html # default jshost
assert html.count("height:400px") == 3
assert html.count("width:600px") == 1
assert html.count("width:800px") == 2
assert html.count("id_my_cell_line") == 6
def test_grid_multiple_datazoom_index():
line = Line("折线图示例", width=1200, height=700)
line.add("最高气温", WEEK, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", WEEK, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], legend_top="50%",
mark_line=["average"], is_datazoom_show=True,
datazoom_xaxis_index=[0, 1])
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_top="50%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1,
is_datazoom_show=True)
grid = Grid()
grid.add(line, grid_top="60%")
grid.add(kline, grid_bottom="60%")
grid.render()
def test_overlap_two_yaxis():
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(width=1200, height=600)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render()
def iplot(indicator, new=True, axes=None,
legend_on=False, text_on=False, text_color='k',
zero_on=False, label=None, *args, **kwargs):
"""绘制indicator曲线
:param Indicator indicator: indicator实例
:param axes: 指定的坐标轴
:param new: pyecharts中无效
:param legend_on: 是否打开图例
:param text_on: 是否在左上角显示指标名称及其参数
:param text_color: 指标名称解释文字的颜色，默认为黑色
:param zero_on: 是否需要在y=0轴上绘制一条直线
:param str label: label显示文字信息，text_on 及 legend_on 为 True 时生效
:param args: pylab plot参数
:param kwargs: pylab plot参数，如：marker（标记类型）、
markerfacecolor（标记颜色）、
markeredgecolor（标记的边缘颜色）
"""
if not indicator:
print("indicator is None")
return
if axes is None:
axes = create_figure() if new else gca()
if not label:
label = "%s %.2f" % (indicator.long_name, indicator[-1])
x_list = gcf().get_xaxis()
if x_list is None:
x_list = [i for i in range(len(indicator))]
y_list = [ '-' if x == constant.null_price else round(x,3) for x in indicator]
line = Line()
style = gcf().get_style(axes, **kwargs)
line.add(label, x_list, y_list,
yaxis_min=min(indicator),
is_legend_show=legend_on,
*args, **style)
axes.add(line)
gcf().add_axis(axes)
return gcf()
def test_line_user_define_markpoint():
line = Line("折线图示例")
line.add(
"商家A",
CLOTHES,
clothes_v1,
mark_point=["average", {"coord": ["裤子", 10], "name": "这是我想要的第一个标记点"}],
)
line.add(
"商家B",
CLOTHES,
clothes_v2,
is_smooth=True,
mark_point=[{"coord": ["袜子", 80], "name": "这是我想要的第二个标记点"}],
)
html_content = line._repr_html_()
assert '"value": 80' in html_content
assert '"value": 10' in html_content
def test_line_user_define_marks():
line = Line("折线图示例")
line.add(
"商家A",
CLOTHES,
clothes_v1,
mark_point=["average", "max", "min"],
symbol_size=50,
mark_point_symbol="diamond",
mark_point_textcolor="#40ff27",
)
line.add(
"商家B",
CLOTHES,
clothes_v2,
mark_point=["average", "max", "min"],
mark_point_symbol="arrow",
mark_point_symbolsize=40,
)
assert '"symbolSize":50' not in line._repr_html_()
def TimeFilter():
#优化后选择13：45-09：45作为交易时间
#首次交易时间点为14：00，最后一次交易时间点为09：45（15分钟窗口等待新的数据）
from pyecharts import Line
json_path = "E:/Program Files/other/TimeFilter/"
netprofit_data,maxdd_data,sharpratio_data = read_indicator_TimeFilter(json_path)
line = Line("TimeFilter")
line.add("netprofit", [item[0] for item in netprofit_data], [item[1] for item in netprofit_data])
line.add("maxdd", [item[0] for item in maxdd_data], [item[1] for item in maxdd_data])
line.add("sharpratio", [item[0] for item in sharpratio_data], [item[1] for item in sharpratio_data]
,xaxis_interval=0,xaxis_name="Time",xaxis_rotate=30,xaxis_name_pos="end")
line.render("TimeFilter.html")
#选择13：45-09：45作为交易时间
table_result = read_json.read_backtest_json("E:/Program Files/other/交易系统作业/代码/作业二/回测-TimeFilter.json")
table_result.to_excel("project2.3_table3.1.xlsx")
def CPUintoChart(cpufile, name):
row = []
row_cpu = 1
for line in cpufile:
if row_cpu%2 == 1:
line = line.split(' ')
cpudata = [line[i] for i in (2, 4, 7)]
row.append(cpudata)
row_cpu += 1
data = []
for i in row:
if i[1][0].isdigit() is False:
pass
else:
data.append(i)
package = []
user = []
kernel = []
for j, k, l in data:
package.append(j[0:-1])
user.append(k[0:-1])
kernel.append(l[0:-1])
package.insert(0, 'package')
user.insert(0, 'user')
kernel.insert(0, 'kernel')
print(package)
print(user)
print(kernel)
id = []
for i in range(len(package) - 1):
id.append(i)
bar = Line(f"{name}__CPU数据图形报表", "图表纵轴为数据大小，横轴为时间节点，直线为平均值")
bar.add("package", id, package[1:], label_color=['#800080'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.add("user", id, user[1:], label_color=['#0000FF'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.add("kernel", id, kernel[1:], label_color=['#2E8B57'], mark_line=["average"],
mark_point=["max", "min"], xaxis_interval=0, xaxis_rotate=90)
bar.use_theme("vintage")
t = time.time()
bar.render('%s\\cpu%s.html' % (setting.data, int(t)))
def nomoral_line(stock_num):
v2, v3, v4 = predictation(stock_num)
attr = []
for i in range(1, 11):
attr.append(i)
predict_line = Line("Prediction")
predict_line.add("Close price(linear regression)",
attr,
v2,
mark_point=["max", "min"])
predict_line.add("Close price(Neural Network)",
attr,
v4,
mark_point=["max", "min"])
predict_line.add("Close price(SVM-poly_kernal)",
attr,
v3,
yaxis_min=round(min(min(v3), min(v2), min(v4))) -
0.01 * round(min(min(v3), min(v2), min(v4))),
mark_point=["max", "min"])
return predict_line
def sameUserDataframe(df, numlist):
df_new = df[df['用户标识'] == numlist]
list3 = coltype_filter(df, '账期')
list3.sort()
n = len(list3)
yearIncome = month_Chuzhang(df_new)
# monthIncome = yearIncome/n
list5 = All_DOU(df)
list6 = all_MOU(df)
list7 = all_APRU(df)
page = Page()
charts1 = Line("DOU")
charts1.add(str(numlist) + "的DOU", list3, list5)
# charts2 = Line("MOU")
charts1.add(str(numlist) + "的MOU", list3, list6)
# charts3 =Line('APRU')
charts1.add(str(numlist) + "的APRU", list3, list7)
page.add(charts1)
# page.add(charts2)
# page.add(charts3)
return page
def line_three_html(self, title, key, val_min, val_max, val_avg):
"""折线面积图"""
line = Line(
title,
title_pos='left',
width='100%',
height=300,
)
line.add(
"最小值",
key,
val_min,
mark_point=['average', 'max', 'min'], # 标记点
is_datazoom_show=True, # 缩放
datazoom_range=[0, 100],
is_smooth=True,
)
line.add(
"最大值",
key,
val_max,
mark_point=['average', 'max', 'min'], # 标记点
is_datazoom_show=True, # 缩放
datazoom_range=[0, 100],
is_smooth=True,
)
line.add(
"平均值",
key,
val_avg,
mark_point=['average', 'max', 'min'], # 标记点
is_datazoom_show=True, # 缩放
datazoom_range=[0, 100],
is_smooth=True,
)
return line.render_embed()
from pyecharts import Bar, Line, Overlap
import pandas as pd
sheet0 = pd.read_excel("./data_resources/4.1各月出差天数.xls")
month0 = sheet0["2018年"].values.tolist()
days0 = sheet0["出差天数"].values.tolist()
line0 = Line('', title_pos="10%")
line0.add('',
month0,
days0,
yaxis_name='出差数(天)',
yaxis_name_pos='middle',
yaxis_name_gap=40,
legend_pos="35%",
is_label_show=True,
label_text_size=10)
group = []
sheet1 = pd.read_excel("./data_resources/4.2各月每个组出差天数统计表格.xls")
dates1 = sheet1["2018年月份"].values.tolist()
for i in ["综合管理", "领导组", "硬件组", "销售组", "售前组", "市场商务", "软件组", "大数据云计算组"]:
group.append((i, sheet1[i].values.tolist()))
bar1 = Bar('各月各组出差人天数情况', title_pos="0%")
for i in group:
bar1.add(i[0],
dates1,
i[1],
label_text_size=6,
yaxis_name='出差数(天)',
yaxis_name_pos='middle',
def test_line_type_stack():
line = Line("折线图-数据堆叠示例")
line.add("商家A", CLOTHES, clothes_v1, is_stack=True, is_label_show=True)
line.add("商家B", CLOTHES, clothes_v2, is_stack=True, is_label_show=True)
line.render()
for item in site_dist.keys():
site_name = str(item)
attr = []
v = []
for a in data_sort.keys():
key_value = str(a) + "," + str(data_sort[a])
string = key_value.replace('\'', '').replace('[', '').replace(
']', '').replace(' ', '')
last_data = string.split(',')
site = last_data[1]
for value in Webmaster_value.keys():
if str(site) == str(value):
site = Webmaster_value[value]
if site == site_name:
attr.append(last_data[0])
v.append(last_data[2])
try:
line.add(
site_name,
attr,
v,
is_label_show=True,
yaxis_formatter=" IP",
is_datazoom_show=True,
)
except BaseException:
a = 1
line.render("/opt/jumpserver/apps/templates/daystat.html")
def echart2(request):
template = loader.get_template('ewpyecharts.html')
line = Line("压力分析", width=1200, height=700)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高压力",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add(
"最低压力",
attr,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
legend_top="50%",
mark_line=["average"],
# 设置 dataZoom 控制索引为 0,1 的 x 轴，即第一个和第二个
is_datazoom_show=True,
datazoom_xaxis_index=[0, 1])
v1 = [[2320.26, 2320.26, 2287.3,
2362.94], [2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58,
2440.38], [2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("考核表分析", title_top="50%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)],
v1,
is_datazoom_show=True)
grid = Grid()
grid.add(line, grid_top="60%")
grid.add(kline, grid_bottom="60%")
# grid.render()
context = dict(myechart=grid.render_embed(),
host=REMOTE_HOST,
script_list=grid.get_js_dependencies())
return HttpResponse(template.render(context, request))
#提取中国从疫情开始每一疫情具体数据
tempBaseData = data["data"]["chinaDayList"]
#遍历每天具体数据编程pandas
for item in tempBaseData:
line = [
item["date"], item["confirm"], item["suspect"], item["dead"],
item["heal"], item["nowConfirm"], item["nowSevere"]
]
#创建pandas中的行
series = pd.Series(line, chinaDayColumnList)
chinaDaySeriesList.append(series)
#创建表格
chainDayDateFrame = pd.DataFrame(chinaDaySeriesList)
print(chainDayDateFrame)
#生成全国实时疫情趋势图
#创建折线图
line = Line("全国实时疫情趋势图", "人数", width=1200, height=600)
#从表格中拿到所以日期
dateAttr = list(chainDayDateFrame.loc[chainDayDateFrame.index, "date"])
confirm = list(chainDayDateFrame.loc[chainDayDateFrame.index, "confirm"])
suspect = list(chainDayDateFrame.loc[chainDayDateFrame.index, "suspect"])
nowConfirm = list(chainDayDateFrame.loc[chainDayDateFrame.index, "nowConfirm"])
nowSevere = list(chainDayDateFrame.loc[chainDayDateFrame.index, "nowSevere"])
line.add("累计确诊", dateAttr, confirm, mark_point=['max'])
line.add("现有疑似", dateAttr, suspect, mark_point=['max'])
line.add("现有确诊", dateAttr, nowConfirm, mark_point=['max'])
line.add("现有重症", dateAttr, nowSevere, mark_point=['max'])
line.show_config()
line.render(path="./output/全国实时疫情趋势图.html")
from pyecharts import Pie
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例")
pie.add("", attr, v1, is_label_show=True)
pie.render("pie.html")
from pyecharts import Gauge
gauge = Gauge("仪表盘示例")
gauge.add("业务指标", "完成率", 66.66)
gauge.show_config()
gauge.render("gauge.html")
from pyecharts import Line, Overlap, Bar
attr = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar('Line-Bar')
bar.add('bar', attr, v1)
line = Line()
line.add('line', attr, v2)
overlap = Overlap()
overlap.add(bar)
overlap.add(line)
overlap.render('bar-line.html')
def test_line_type_step():
line = Line("折线图-阶梯图示例")
line.add("商家A", CLOTHES, clothes_v1, is_step=True, is_label_show=True)
assert '"step": true' in line._repr_html_()
def graphing(self):
df = self.df
df2 = self.df2
# df3 = self.df3
frequency = self.freqency
if frequency == 'daily':
dateSearch = pd.to_datetime(df_raw.FailureTaskGeneratedTime.max())
#datetime.now()
elif frequency == 'weekly':
dateSearch = pd.to_datetime(df_raw.FailureTaskGeneratedTime.max()) - timedelta(weeks=1)
#datetime.now() - timedelta(weeks=1)
elif frequency == 'monthly':
dateSearch = pd.to_datetime(df_raw.FailureTaskGeneratedTime.max()) - relativedelta(months=1)
#datetime.now() - relativedelta(months=1)
elif frequency == 'quarterly':
dateSearch = pd.to_datetime(df_raw.FailureTaskGeneratedTime.max()) - relativedelta(months=3)
#datetime.now() - relativedelta(months=3)
df.loc[:, 'FailureTaskGeneratedTime'] = pd.to_datetime(df.loc[:, 'FailureTaskGeneratedTime']).copy()
df2.loc[:, 'FailureTaskGeneratedTime'] = pd.to_datetime(df2.loc[:, 'FailureTaskGeneratedTime']).copy()
df.loc[:, 'FailureTaskGeneratedDate'] = df.loc[:, 'FailureTaskGeneratedTime'].map(lambda x: x.date()).copy()
df2.loc[:, 'FailureTaskGeneratedDate'] = df2.loc[:, 'FailureTaskGeneratedTime'].map(lambda x: x.date()).copy()
if frequency == 'daily':
df = df[df.loc[:, 'FailureTaskGeneratedDate'] == dateSearch.date()].copy()
df2 = df2[df2.loc[:, 'FailureTaskGeneratedDate'] == dateSearch.date()].copy()
else:
df = df[(dateSearch.date() <= df.loc[:, 'FailureTaskGeneratedDate']) & (df.loc[:, 'FailureTaskGeneratedDate'] <= datetime.now().date())].copy()
df2 = df2[(dateSearch.date() <= df2.loc[:, 'FailureTaskGeneratedDate']) & (df2.loc[:, 'FailureTaskGeneratedDate'] <= datetime.now().date())].copy()
# -----------------------------count weekend to SZ
df_copy = df.copy()
df2_copy = df2.copy()
weekend_inscope = df_copy['InvestmentId'][df_copy.loc[:, 'workday'] == 'weekend'].count()
weekend_outofscope = df2_copy['InvestmentId'][df2_copy.loc[:, 'workday'] == 'weekend'].count()
df = df[df.loc[:, 'workday'] == 'weekday']
df2 = df2[df2.loc[:, 'workday'] == 'weekday']
# ---------------------------------
df_time_list = []
df_time_list2 = []
time_list_name = []
# You can set your time here:
if tm.localtime().tm_isdst == 1:
time_set = datetime.now().strftime("%Y/%m/%d 19/30/00")
print("Now is Summer time")
else:
time_set = datetime.now().strftime("%Y/%m/%d 18/30/00")
print("Now is Winter time")
# --------------------------
time_set = dt.datetime.strptime(time_set, "%Y/%m/%d %H/%M/%S")
for i in range(24):
df_time_list.append(time_set.strftime("%I:%M%p"))
df_time_list2.append(time_set.strftime("df2_%I_%M%p"))
time_list_name.append(time_set.strftime("%H:%M"))
time_set = time_set + timedelta(hours=1)
df.loc[:,'FailureTaskGeneratedTime'] = pd.to_datetime(df.loc[:,'FailureTaskGeneratedTime']).copy()
df.loc[:,'FailureTaskGeneratedTime'].dropna(inplace=True)
df.loc[:,'FailureTaskGeneratedTime_time'] = df.loc[:,'FailureTaskGeneratedTime'].apply(lambda x: x.strftime("%d:%H:%M:%S")).copy()
date_series = df.groupby('FailureTaskGeneratedTime_time')['InvestmentId'].count()
df2.loc[:,'FailureTaskGeneratedTime'] = pd.to_datetime(df2.loc[:,'FailureTaskGeneratedTime']).copy()
df2.loc[:,'FailureTaskGeneratedTime'].dropna(inplace=True)
df2.loc[:,'FailureTaskGeneratedTime_time'] = df2.loc[:,'FailureTaskGeneratedTime'].apply(lambda x: x.strftime("%d:%H:%M:%S")).copy()
date_series2 = df2.groupby('FailureTaskGeneratedTime_time')['InvestmentId'].count()
timezone_list = list(date_series.index)
timezone_list = [dt.datetime.strptime(x, "%d:%H:%M:%S") for x in timezone_list]
timez_list = [x.strftime("%H:%M") for x in timezone_list]
day_list = [x.strftime("%d") for x in timezone_list]
day_num = len(set(day_list))
timezone_list2 = list(date_series2.index)
timezone_list2 = [dt.datetime.strptime(x, "%d:%H:%M:%S") for x in timezone_list2]
timez_list2 = [x.strftime("%H:%M") for x in timezone_list2]
day_list2 = [x.strftime("%d") for x in timezone_list2]
day_num2 = len(set(day_list2))
time_list = []
for i in range(len(timez_list)):
time_list.append(dt.time(int(timez_list[i].split(':')[0]), int(timez_list[i].split(':')[1])))
time_list2 = []
for i in range(len(timez_list2)):
time_list2.append(dt.time(int(timez_list2[i].split(':')[0]), int(timez_list2[i].split(':')[1])))
time_list = np.array(time_list)
date_series.index = time_list
date_frame = date_series.to_frame()
date_frame = date_frame.reset_index()
time_list2 = np.array(time_list2)
date_series2.index = time_list2
date_frame2 = date_series2.to_frame()
date_frame2 = date_frame2.reset_index()
average_list = []
average_list2 = []
# ---------------------------------------------making & count dataframe by date
if len(df_time_list) == len(time_list_name):
for i in range(0, len(df_time_list)):
if i + 1 > len(df_time_list) - 1:
globals()[df_time_list[i]] = date_frame[
(dt.time(int(time_list_name[i].split(':')[0]), 30) <= date_frame['index']) & (
date_frame['index'] < dt.time(int(time_list_name[0].split(':')[0]), 30))]
globals()[df_time_list2[i]] = date_frame2[
(dt.time(int(time_list_name[i].split(':')[0]), 30) <= date_frame2['index']) & (
date_frame2['index'] < dt.time(int(time_list_name[0].split(':')[0]), 30))]
elif i == 5:
globals()[df_time_list[i]] = date_frame[
(dt.time(int(time_list_name[i].split(':')[0]), 30) <= date_frame['index']) | (
date_frame['index'] < dt.time(int(time_list_name[i + 1].split(':')[0]), 30))]
globals()[df_time_list2[i]] = date_frame2[
(dt.time(int(time_list_name[i].split(':')[0]), 30) <= date_frame2['index']) | (
date_frame2['index'] < dt.time(int(time_list_name[i + 1].split(':')[0]), 30))]
else:
globals()[df_time_list[i]] = date_frame[
(dt.time(int(time_list_name[i].split(':')[0]), 30) <= date_frame['index']) & (
date_frame['index'] < dt.time(int(time_list_name[i + 1].split(':')[0]), 30))]
globals()[df_time_list2[i]] = date_frame2[
(dt.time(int(time_list_name[i].split(':')[0]), 30) <= date_frame2['index']) & (
date_frame2['index'] < dt.time(int(time_list_name[i + 1].split(':')[0]), 30))]
# ---------------------------------------------make the cake to "average_list"
globals()[df_time_list[i]] = globals()[df_time_list[i]]['InvestmentId'].sum()
globals()[df_time_list2[i]] = globals()[df_time_list2[i]]['InvestmentId'].sum()
average_list.append(globals()[df_time_list[i]])
average_list2.append(globals()[df_time_list2[i]])
average_array = np.array(average_list)
average_array[np.isnan(average_array)] = 0
average_list_inscope = [int(x) for x in list(average_array)]
average_array2 = np.array(average_list2)
average_array2[np.isnan(average_array2)] = 0
average_list_outofscope = [int(x) for x in list(average_array2)]
average_list_inscope[0] = average_list_inscope[0] + weekend_inscope
average_list_outofscope[0] = average_list_outofscope[0] + weekend_outofscope
try:
average_inscope = list(np.array(average_list_inscope) // day_num)
average_outofscope = list(np.array(average_list_outofscope) // day_num)
except:
pass
#------------------------------------------cut the cake for sz, mardrid and mumbai
sz_average_list_inscope = average_list_inscope.copy()
sz_average_list_inscope[int(len(df_time_list) / 3):] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
mardrid_average_list_inscope = average_list_inscope.copy()
mardrid_average_list_inscope[:int(len(df_time_list) / 3)] = [0] * (int(len(df_time_list) / 3))
mardrid_average_list_inscope[int(len(df_time_list) * 2 / 3):] = [0] * (int(len(df_time_list) / 3))
mumbai_average_list_inscope = average_list_inscope.copy()
mumbai_average_list_inscope[:int(len(df_time_list) * 2 / 3)] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
sz_average_list_outofscope = average_list_outofscope.copy()
sz_average_list_outofscope[int(len(df_time_list) / 3):] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
mardrid_average_list_outofscope = average_list_outofscope.copy()
mardrid_average_list_outofscope[:int(len(df_time_list) / 3)] = [0] * (int(len(df_time_list) / 3))
mardrid_average_list_outofscope[int(len(df_time_list) * 2 / 3):] = [0] * (int(len(df_time_list) / 3))
mumbai_average_list_outofscope = average_list_outofscope.copy()
mumbai_average_list_outofscope[:int(len(df_time_list) * 2 / 3)] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
if len(np.array(average_list_inscope)) == len(np.array(average_list_outofscope)):
total_list = list(np.array(average_list_inscope) + np.array(average_list_outofscope))
if len(Pre_value)>len(np.array(average_list_inscope)):
pre_total_list = Pre_value[len(Pre_value)-len(np.array(average_list_inscope)):].tolist()
if len(Pre_value)==len(np.array(average_list_inscope)):
pre_total_list = Pre_value.tolist()
# ------------------------------------------cut the cake for sz, mardrid and mumbai second graph
bar1 = Bar(title = 'Failure Total (%s) from %s to %s'%(frequency,dateSearch.strftime('%Y-%m-%d'),datetime.now().strftime('%Y-%m-%d')))
bar1.add("%s"%'SZ', df_time_list,sz_average_list_inscope, is_stack=True,is_label_show=False,is_label_emphasis = True,legend_pos = "88%",legend_orient = 'vertical')
bar1.add("%s"%'outofscope', df_time_list,sz_average_list_outofscope, is_stack=True,is_label_show=False,is_label_emphasis = True,legend_pos = "88%",legend_orient = 'vertical')
bar2 = Bar()
bar2.add("%s"%'Madrid', df_time_list,mardrid_average_list_inscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
bar2.add("%s"%'outofscope', df_time_list,mardrid_average_list_outofscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
bar3 = Bar()
bar3.add("%s"%'Mumbai', df_time_list,mumbai_average_list_inscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
bar3.add("%s"%'outofscope', df_time_list,mumbai_average_list_outofscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
line =Line('failure_line',background_color = 'white',title_text_size = 20,width = '100%')
line.add("Actual",df_time_list,total_list)
line.add("Prediction",df_time_list,pre_total_list,is_more_utils = True,is_splitline_show = False,
is_label_show = False,is_smooth=False,line_color = [' red'],
legend_pos = 'left',legend_orient = 'vertical',line_type = 'dotted',line_width = '2')
overlap = Overlap(width='100%',height=360)
overlap.add(bar1)
overlap.add(bar2)
overlap.add(bar3)
overlap.add(line)
# ------------------------------------------cut the cake for sz, mardrid and mumbai second graph
sz_avg_list_inscope = average_inscope.copy()
sz_avg_list_inscope[int(len(df_time_list) / 3):] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
mardrid_avg_list_inscope = average_inscope.copy()
mardrid_avg_list_inscope[:int(len(df_time_list) / 3)] = [0] * (int(len(df_time_list) / 3))
mardrid_avg_list_inscope[int(len(df_time_list) * 2 / 3):] = [0] * (int(len(df_time_list) / 3))
mumbai_avg_list_inscope = average_inscope.copy()
mumbai_avg_list_inscope[:int(len(df_time_list) * 2 / 3)] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
sz_avg_list_outofscope = average_outofscope.copy()
sz_avg_list_outofscope[int(len(df_time_list) / 3):] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
mardrid_avg_list_outofscope = average_outofscope.copy()
mardrid_avg_list_outofscope[:int(len(df_time_list) / 3)] = [0] * (int(len(df_time_list) / 3))
mardrid_avg_list_outofscope[int(len(df_time_list) * 2 / 3):] = [0] * (int(len(df_time_list) / 3))
mumbai_avg_list_outofscope = average_outofscope.copy()
mumbai_avg_list_outofscope[:int(len(df_time_list) * 2 / 3)] = [0] * (len(df_time_list) - int(len(df_time_list) / 3))
if len(np.array(average_inscope)) == len(np.array(average_outofscope)):
avg_num = list(np.array(average_inscope) + np.array(average_outofscope))
# ------------------------------------------cut the cake for sz, mardrid and mumbai second graph
bar1 = Bar(title = 'Failure Average (%s) from %s to %s'%(frequency,dateSearch.strftime('%Y-%m-%d'),datetime.now().strftime('%Y-%m-%d')))
bar1.add("%s"%'SZ', df_time_list,sz_avg_list_inscope, is_stack=True,is_label_show=False,is_label_emphasis = True,legend_pos = "88%",legend_orient = 'vertical')
bar1.add("%s"%'outofscope', df_time_list,sz_avg_list_outofscope, is_stack=True,is_label_show=False,is_label_emphasis = True,legend_pos = "88%",legend_orient = 'vertical')
bar2 = Bar()
bar2.add("%s"%'Madrid', df_time_list,mardrid_avg_list_inscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
bar2.add("%s"%'outofscope', df_time_list,mardrid_avg_list_outofscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
bar3 = Bar()
bar3.add("%s"%'Mumbai', df_time_list,mumbai_avg_list_inscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
bar3.add("%s"%'outofscope', df_time_list,mumbai_avg_list_outofscope, is_stack=True,is_label_show=False,is_label_emphasis = True)
line =Line('failure_line',background_color = 'white',title_text_size = 20,width = '100%') # total
line.add("Actual",df_time_list,avg_num)
overlap2 = Overlap(width='100%',height=360)
overlap2.add(bar1)
overlap2.add(bar2)
overlap2.add(bar3)
overlap2.add(line)
return overlap,overlap2,frequency
def draw_new_weeks(x, y):
line = Line("最近七日访问量")
line.add("新增用户数", x, y, is_smooth=True, mark_line=["max", "average"])
line.render('../my_result/trend_weeks.html')
def index(request):
over_list = []
style_coding_list = []
err = ''
batchs = ''
style = ''
shops_list = ''
shops = ''
batchs_msg_dic = ''
if request.method == 'POST':
batchs = request.POST.get('batch')
batchs_msg = BatchComparison.objects.filter(batch=batchs).all()
batchs_msg_dic = {}
for batch in batchs_msg:
batchs_msg_dic['model'] = batch.model
batchs_msg_dic['batch'] = batch.batch
batchs_msg_dic['remark'] = batch.remark
batchs_msg_dic['type'] = batch.type
batchs_msg_dic['stylist'] = batch.stylist
batchs_msg_dic['shooting_date'] = str(batch.shooting_date)
batchs_msg_dic['location'] = batch.location
# 判断款式号是否有误
if len(batchs) > 0:
# 查询款式
batch_data = BatchComparison.objects.filter(batch=batchs).all()
style_coding_data = SpuidComparison.objects.filter(
batch=batchs).values('style_coding').all()
# 判断是否存在对应款式
if len(style_coding_data) > 0:
# 该款式的所有查询spuid的
if request.method == 'POST':
style = request.POST.get('style_coding')
spuid_data = SpuidComparison.objects.filter(
style_coding=style).all()
else:
spuid_data = SpuidComparison.objects.filter(
style_coding=style_coding_data.first()).all()
spuid_list = []
for spuids in spuid_data:
spuid_dic = {}
spuid_dic['spuid'] = spuids.spuid
spuid_dic['brand'] = spuids.brand
spuid_list.append(spuid_dic)
# 款式列表
style_coding_data_2 = SpuidComparison.objects.filter(
batch=batchs).values('style_coding').distinct().all()
style_coding_list = [
i['style_coding'] for i in style_coding_data_2
]
# 店铺列表
shops_data = SpuidComparison.objects.filter(
batch=batchs, style_coding=style).all()
shops_list = []
for i in shops_data:
tmp = {}
tmp['brand'] = i.brand
tmp['spuid'] = i.spuid
shops_list.append(tmp)
shops = request.POST.get('shopname')
# 判断该款式是否存在spuid号
if len(spuid_list) > 0:
# 查询data并生成图表
over_list = []
if shops == '全部':
for shop in shops_list:
data = StoreDailyData.objects.filter(
spuid=shop['spuid'], brand=shop['brand']).values(
'uv', 'title', 'status', 'spuid',
'conversion_rate_of_order_payment',
'conversion_rate_of_payment',
'number_of_additional_purchases',
'collection_number', 'number_of_order_items',
'date').all()
x_list = []
y_list_1 = []
y_list_1_2 = []
y_list_1_3 = []
for i in data:
x_list.append(i['date'])
y_list_1.append(
'%.2f' %
(i['conversion_rate_of_payment'] * 100))
if i['uv'] == 0:
tmp = '0.00'
else:
tmp = '%.2f' % (i['collection_number'] /
i['uv'] * 100)
tmp_2 = '%.2f' % (
i['number_of_additional_purchases'] /
i['uv'] * 100)
y_list_1_2.append(tmp)
y_list_1_3.append(tmp_2)
overlap = Overlap()
line = Line()
line.add('转化率',
x_list,
y_list_1,
yaxis_formatter='%',
is_smooth=True)
line.add('收藏率',
x_list,
y_list_1_2,
yaxis_formatter='%',
is_smooth=True)
line.add('加购率',
x_list,
y_list_1_3,
yaxis_formatter='%',
is_smooth=True)
line_2 = Line(shop['brand'] + '-' + shop['spuid'])
y_list_2 = [i['uv'] for i in data]
y_list_2_2 = [i['number_of_order_items'] for i in data]
line_2.add('UV', x_list, y_list_2, is_smooth=True)
line_2.add('成交量', x_list, y_list_2_2, is_smooth=True)
overlap.add(line_2)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
ds = overlap.render_embed()
over_list.append(ds)
else:
spuid_all = SpuidComparison.objects.filter(
batch=batchs, style_coding=style, brand=shops).all()
spuid_list = [i.spuid for i in spuid_all]
for spuid_ in spuid_list:
data = StoreDailyData.objects.filter(
spuid=spuid_, ).values(
'uv', 'conversion_rate_of_order_payment',
'conversion_rate_of_payment',
'number_of_additional_purchases',
'collection_number', 'number_of_order_items',
'date').all()
x_list = []
y_list_1 = []
y_list_1_2 = []
y_list_1_3 = []
for i in data:
x_list.append(i['date'])
y_list_1.append(
'%.2f' %
(i['conversion_rate_of_payment'] * 100))
if i['uv'] == 0:
tmp = '0.00'
else:
tmp = '%.2f' % (i['collection_number'] /
i['uv'] * 100)
tmp_2 = '%.2f' % (
i['number_of_additional_purchases'] /
i['uv'] * 100)
y_list_1_2.append(tmp)
y_list_1_3.append(tmp_2)
overlap = Overlap()
line = Line()
line.add('转化率',
x_list,
y_list_1,
yaxis_formatter='%',
is_smooth=True)
line.add('收藏率',
x_list,
y_list_1_2,
yaxis_formatter='%',
is_smooth=True)
line.add('加购率',
x_list,
y_list_1_3,
yaxis_formatter='%',
is_smooth=True)
line_2 = Line(shops + '-' + spuid_)
y_list_2 = [i['uv'] for i in data]
y_list_2_2 = [i['number_of_order_items'] for i in data]
line_2.add('UV', x_list, y_list_2, is_smooth=True)
line_2.add('成交量', x_list, y_list_2_2, is_smooth=True)
overlap.add(line_2)
overlap.add(line, is_add_yaxis=True, yaxis_index=1)
ds = overlap.render_embed()
over_list.append(ds)
shops_list.append({'brand': '全部'})
else:
err = '该款式没有对应的spuid'
else:
err = '该批次没有对应的款式'
# else:
# err = '批次号有误'
batch_form = Batch()
return render(
request, 'index.html', {
'style_coding': style_coding_list,
"line_list": over_list,
'err': err,
'batch_form': batch_form,
'batch': batchs,
'style': style,
'style_coding_list': style_coding_list,
'shops_list': shops_list,
'shops': shops,
'batchs_msg_dic': batchs_msg_dic
})
# )
# pie1.render('scores.html')
###########################################################################
# 评论时间分布
data['dates'] = data.date.apply(lambda x: pd.Timestamp(x).date())
data['time'] = data.date.apply(lambda x: pd.Timestamp(x).time().hour)
# print(data.author)
num_date = data.author.groupby(data['dates']).count()
chart = Line("评论数时间分布")
chart.use_theme('dark')
chart.add('评论时间',
num_date.index,
num_date.values,
is_fill=True,
line_opacity=0.2,
area_opacity=0.4,
symbol=None)
chart.render('comment_time_stamp.html')
# 好评字数分析
datalikes = ['num', 'likes']
datalikes = data.loc[data.likes > 5]
datalikes['num'] = datalikes.content.apply(lambda x: len(x))
chart = Scatter("likes")
chart.use_theme('dark')
chart.add('likes',
np.log(datalikes.likes),
datalikes.num,
import json
import sys
import glob
import os
from pyecharts import Line
def load_js(jsfile):
x_axis = []
points = []
with open(jsfile, 'r') as fp:
while True:
l = fp.readline()
if not l:
break
deal_dict = json.loads(l)
x_axis.append(deal_dict['dealDate'][0])
points.append(deal_dict['unitPrice'][0])
return x_axis, points
if __name__ == '__main__':
dir_path = sys.argv[1]
curve_name = ''
line = Line("Deal Curve")
cwd = os.getcwd()
os.chdir(dir_path)
for js in glob.glob("*.json"):
x_axis, points = load_js(js)
curve_name = js[:js.find(".json")]
line.add(curve_name, x_axis, points, is_stack=True, is_label_show=True)
os.chdir(cwd)
line.render()
def test_page_grid_timeline_overlap():
# Grid
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add(
"es",
[11, 11, 15, 13, 12, 13, 10],
[1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%",
)
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
# Timeline
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构", height=720, width=1200)
bar_5.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add(
"冬季",
CLOTHES,
[randint(10, 100) for _ in range(6)],
is_legend_show=True,
)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
timeline.add(bar_2, "2013 年")
timeline.add(bar_3, "2014 年")
timeline.add(bar_4, "2015 年")
timeline.add(bar_5, "2016 年")
# Overlap
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(height=720, width=1200)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page = Page()
page.add(grid)
page.add(timeline)
page.add(overlap)
page.render()
def test_more():
page = Page()
# line
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie])
# kline
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
kline = Kline("K 线图示例")
kline.add(
"日K",
["2017/7/{}".format(i + 1) for i in range(31)],
v1,
is_datazoom_show=True,
)
page.add(kline)
# radar
schema = [
("销售", 6500),
("管理", 16000),
("信息技术", 30000),
("客服", 38000),
("研发", 52000),
("市场", 25000),
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add(
"实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode="single",
)
page.add(radar)
# scatter3d
import random
data = [
[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100),
]
for _ in range(80)
]
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3D)
# wordcloud
name = [
"Sam S Club",
"Macys",
"Amy Schumer",
"Jurassic World",
"Charter Communications",
"Chick Fil A",
"Planet Fitness",
"Pitch Perfect",
"Express",
"Home",
"Johnny Depp",
"Lena Dunham",
"Lewis Hamilton",
"KXAN",
"Mary Ellen Mark",
"Farrah Abraham",
"Rita Ora",
"Serena Williams",
"NCAA baseball tournament",
"Point Break",
]
value = [
10000,
6181,
4386,
4055,
2467,
2244,
1898,
1484,
1112,
965,
847,
582,
555,
550,
462,
366,
360,
282,
273,
265,
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[30, 100], rotate_step=66)
page.add(wordcloud)
# liquid
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6])
page.add(liquid)
assert len(page) == 7
assert isinstance(page[0], Line)
assert (
("echarts" in page.js_dependencies)
or ("echarts.min" in page.js_dependencies)
)
page.render()
for i in range(4):
filename = 'D:/data/' + citys[i] + '_AQI' + '_2018.csv'
aqi_data = pd.read_csv(filename)
get_data = aqi_data[['Date', 'AQI']]
month_for_data = []
for j in get_data['Date']:
time = j.split('/')[1]
month_for_data.append(time)
aqi_data['Month'] = month_for_data
#AQI平均值
month_data = aqi_data.groupby(['Month'])
month_AQI = month_data['AQI'].agg(['mean'])
month_AQI.reset_index(inplace=True)
month_AQI_average = month_AQI.sort_index()
month_AQI_data = np.array(month_AQI_average['mean'])
month_AQI_data_int = ["{}".format(int(i)) for i in month_AQI_data]
cityes_AQI.append(month_AQI_data_int)
months = ["{}".format(str(i) + '月') for i in range(1, 13)]
line = Line("2018年北上广深AQI全年走势图",
title_pos='center',
title_top='0',
width=800,
height=400)
line.add("北京", months, cityes_AQI[0], line_color='red', legend_top='8%')
line.add("上海", months, cityes_AQI[1], line_color='purple', legend_top='8%')
line.add("广州", months, cityes_AQI[2], line_color='blue', legend_top='8%')
line.add("深圳", months, cityes_AQI[3], line_color='orange', legend_top='8%')
line.render("2018年北上广深AQI全年走势图.html")
def test_grid_multiple_datazoom_index():
line = Line("折线图示例", width=1200, height=700)
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
legend_top="50%",
mark_line=["average"],
is_datazoom_show=True,
datazoom_xaxis_index=[0, 1],
)
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
kline = Kline("K 线图示例", title_top="50%")
kline.add(
"日K",
["2017/7/{}".format(i + 1) for i in range(31)],
v1,
is_datazoom_show=True,
)
grid = Grid()
grid.add(line, grid_top="60%")
grid.add(kline, grid_bottom="60%")
grid.render()
)
pie1.render('评分.html')
#评论数
datas['dates'] = datas['date'].apply(
lambda x: pd.Timestamp(x).date()) #注意，此处为dates，区别原date
datas['hour'] = datas['date'].apply(lambda x: pd.Timestamp(x).time().hour)
num_of_date = datas.author.groupby(datas.dates).count() #每天有多少作者评论
#评论数时间分布
chart = Line("评论数时间分布")
chart.use_theme('dark')
chart.add(
'评论时间分布',
num_of_date.index,
num_of_date.values,
is_fill=True, #填充曲线所绘制面积
line_opacity=0.2, #折线透明度
area_opacity=0.4, #区域透明度
symbol=None,
)
chart.render('评论时间分布.html')
#时间分布
num_of_hour = datas.author.groupby(datas.hour).count() #各个小时段有多少作者评论
chart = Line("评论日内时间分布")
chart.use_theme('dark')
chart.add('评论日内时间分布',
num_of_hour.index,
num_of_hour.values,
mark_point_symbol='diamond',
mark_point_textcolor='#40ff27',
line_width=2)
ungoods_center_area_sum = row[13]
ungoods_center_house_num = row[14]
ungoods_center_house_area = row[15]
ungoods_center_unhouse_area = row[16]
ungoods_suburb_area_sum = row[17]
ungoods_suburb_house_num = row[18]
ungoods_suburb_house_area = row[19]
ungoods_suburb_unhouse_area = row[20]
ungoods_city_area_sum = row[21]
ungoods_city_house_num = row[22]
ungoods_city_house_area = row[23]
ungoods_city_unhouse_area = row[24]
list_date.append(date)
list_goods_center_house_num.append(goods_center_house_num)
list_goods_suburb_house_num.append(goods_suburb_house_num)
list_goods_city_house_num.append(goods_city_house_num)
list_ungoods_center_house_num.append(ungoods_center_house_num)
list_ungoods_suburb_house_num.append(ungoods_suburb_house_num)
list_ungoods_city_house_num.append(ungoods_city_house_num)
line = Line("房产走势图")
line.add("城区商品房",
list_date,
list_goods_center_house_num,
mark_point=["max", "min"])
line.add("郊区商品房", list_date, list_goods_suburb_house_num)
line.add("全市商品房", list_date, list_goods_city_house_num)
line.add("城区二手房", list_date, list_ungoods_center_house_num)
line.add("郊区二手房", list_date, list_ungoods_suburb_house_num)
line.add("全市二手房", list_date, list_ungoods_city_house_num)
line.show_config()
line.render()
list_pre2=[]
list_pre3=[]
list_pre4=[]
for j in range(0,df_plot.shape[1]):
list_pre1.append(df_plot.iloc[j,j])
list_pre2.append(df_plot.iloc[j+1,j])
list_pre3.append(df_plot.iloc[j+2,j])
list_pre4.append(df_plot.iloc[j+3,j])
date_all=df.timestamp[df.shape[0]-months+3:]
sales_real= df.sales[df.shape[0]-months+3:]
date_pre=df_plot.columns
sales1=list_pre1
sales2=list_pre2
sales3=list_pre3
sales4=list_pre4
line = Line("空调销售预测")
line.add("实际销量结果", date_all, sales_real,line_color='black',line_width=2)
#line.add("第一次预测结果", date_pre, sales1,line_type='dashed',line_color='green')
#line.add("第二次预测结果", date_pre, sales2,line_type='dashed',line_color='green')
#line.add("第三次预测结果", date_pre, sales3,line_type='dashed',line_color='green')
line.add("第四次预测结果", date_pre, sales4,line_type='dashed',line_color='green')
#line.add("python版arima预测结果",date_pre,sales_arima_python,line_type='dashed',line_color='red',line_width=2)
#line.add("商家B", attr, v2, is_smooth=True,mark_line=["max", "average"])
line.render('../data/python_ets2.html')
from pyecharts import Bar, Line, Overlap
xline = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 35, 58, 65, 70]
bar = Bar('Line - Bar示例')
bar.add('bar', xline, v1)
line = Line()
line.add('line', xline, v2)
overlop = Overlap()
overlop.add(bar)
overlop.add(line)
overlop.render('./picture11.html')
def timeline_charts():
page = Page()
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构", width=WIDTH, height=HEIGHT)
bar_5.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("冬季", attr, [randint(10, 100) for _ in range(6)],
is_legend_show=True)
chart = Timeline(is_auto_play=True, timeline_bottom=0)
chart.add(bar_1, '2012 年')
chart.add(bar_2, '2013 年')
chart.add(bar_3, '2014 年')
chart.add(bar_4, '2015 年')
chart.add(bar_5, '2016 年')
page.add(chart)
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
pie_style = {
"is_label_show": True,
"radius": [30, 55],
"rosetype":'radius'
}
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", attr, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", attr, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", attr, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", attr, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构", width=WIDTH, height=HEIGHT)
pie_5.add("秋季", attr, [randint(10, 100) for _ in range(6)], **pie_style)
chart = Timeline(is_auto_play=True, timeline_bottom=0)
chart.add(pie_1, '2012 年')
chart.add(pie_2, '2013 年')
chart.add(pie_3, '2014 年')
chart.add(pie_4, '2015 年')
chart.add(pie_5, '2016 年')
page.add(chart)
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add("bar", attr, [randint(10, 50) for _ in range(6)])
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
bar_2 = Bar("3 月份数据", "数据纯属虚构")
bar_2.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_2 = Line()
line_2.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_2 = Overlap()
overlap_2.add(bar_2)
overlap_2.add(line_2)
bar_3 = Bar("4 月份数据", "数据纯属虚构")
bar_3.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_3 = Line()
line_3.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_3 = Overlap()
overlap_3.add(bar_3)
overlap_3.add(line_3)
bar_4 = Bar("5 月份数据", "数据纯属虚构", width=WIDTH, height=HEIGHT)
bar_4.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_4 = Line()
line_4.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_4 = Overlap()
overlap_4.add(bar_4)
overlap_4.add(line_4)
chart = Timeline(timeline_bottom=0)
chart.add(overlap_0.chart, '1 月')
chart.add(overlap_1.chart, '2 月')
chart.add(overlap_2.chart, '3 月')
chart.add(overlap_3.chart, '4 月')
chart.add(overlap_4.chart, '5 月')
page.add(chart)
return page
y_temp_10 = [random.randint(20, 35) for j in range(30)]
line = Line("折线图")
#
# line.add("", x_times, y_temp_3, mark_line=['max'], mark_point=['min'])
# line.add("", x_times, y_temp_10, mark_line=['max'], mark_point=['min'])
# # 折线图---阶梯图
# line.add("", x_times, y_temp_3, mark_line=['max'], mark_point=['min'], is_step=True)
# line.add("", x_times, y_temp_10, mark_line=['max'], mark_point=['min'], is_step=True)
# pip install echarts-countries-pypkg
# pip install echarts-china-provinces-pypkg
# pip install echarts-china-cities-pypkg
# pip install echarts-china-counties-pypkg
# # # 折线图---面积图
# 设置透明度
line.add("",
x_times,
y_temp_3,
is_fill=True,
area_color='red',
area_opacity=0.3)
line.add("",
x_times,
y_temp_10,
is_fill=True,
area_color='green',
area_opacity=0.2)
line.render()
def test_line_type_stack():
line = Line("折线图-数据堆叠示例")
line.add("商家A", CLOTHES, clothes_v1, is_stack=True, is_label_show=True)
line.add("商家B", CLOTHES, clothes_v2, is_stack=True, is_label_show=True)
line.render()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from pyecharts import Line
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
line = Line("折线图-阶梯图示例")
line.add("商家A", attr, v1, is_step=True, is_label_show=True)
line.show_config()
line.render()
def test_more():
page = Page()
# line
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line = Line("折线图示例")
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"])
page.add(line)
# pie
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos='center')
pie.add("",
attr,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='left')
page.add(pie)
# kline
v1 = [[2320.26, 2320.26, 2287.3,
2362.94], [2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58,
2440.38], [2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1)
page.add(kline)
# radar
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000),
("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode='single')
page.add(radar)
# scatter3d
import random
data = [[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)
] for _ in range(80)]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
page.add(scatter3D)
# wordcloud
name = [
'Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World',
'Charter Communications', 'Chick Fil A', 'Planet Fitness',
'Pitch Perfect', 'Express', 'Home', 'Johnny Depp', 'Lena Dunham',
'Lewis Hamilton', 'KXAN', 'Mary Ellen Mark', 'Farrah Abraham',
'Rita Ora', 'Serena Williams', 'NCAA baseball tournament',
'Point Break'
]
value = [
10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112, 965, 847, 582,
555, 550, 462, 366, 360, 282, 273, 265
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[30, 100], rotate_step=66)
page.add(wordcloud)
# liquid
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6])
page.add(liquid)
page.render()
from pyecharts import Line, Bar, Pie, EffectScatter
# 数据
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
v2 = [55, 60, 16, 20, 15, 80]
# 普通折线图
line = Line('折线图')
line.add('商家A', attr, v1, mark_point=['max'])
line.add('商家B', attr, v2, mark_point=['min'], is_smooth=True)
line.show_config()
line.render(path='./data/01-04折线图.html')
# 阶梯折线图
line2 = Line('阶梯折线图')
line2.add('商家A', attr, v1, is_step=True, is_label_show=True)
line2.show_config()
line2.render(path='./data/01-05阶梯折线图.html')
# 面积折线图
line3 = Line("面积折线图")
line3.add("商家A",
attr,
v1,
is_fill=True,
line_opacity=0.2,
area_opacity=0.4,
symbol=None,
mark_point=['max'])
line3.add("商家B",
attr,
def Line_network(d, title, title1, date, network_in, network_put):
bar = Line(d, width=1600, height=500)
bar.add(title, date, network_in)
bar.add(title1, date, network_put)
return bar
import pandas as pd
from pyecharts import Line
df = pd.read_csv('air_tianjin_2017.csv', header=None, names=["Date", "Quality_grade", "AQI", "AQI_rank", "PM"])
attr = df['Date']
v1 = df['PM']
line = Line("2017年天津PM2.5全年走势图", title_pos='center', title_top='18', width=800, height=400)
line.add("", attr, v1, mark_line=['average'], is_fill=True, area_color="#000", area_opacity=0.3, mark_point=["max", "min"], mark_point_symbol="circle", mark_point_symbolsize=25)
line.render("2017年天津PM2.5全年走势图.html")
def test_grid():
# grid_0
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%")
line = Line("折线图示例", title_top="50%")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_top="50%")
bar.grid(line.get_series(), grid_top="60%")
bar.show_config()
bar.render()
# grid_1
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200, is_grid=True)
scatter.add("散点图示例", v1, v2, grid_left="60%", legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_pos="20%")
scatter.grid(es.get_series(), grid_right="60%")
scatter.show_config()
scatter.render()
# grid_2
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%", is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%", grid_left="60%")
bar.add("商家B",
attr,
v2,
is_stack=True,
grid_bottom="60%",
grid_left="60%",
legend_pos="80%")
line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%")
bar.grid(line.get_series(), grid_bottom="60%", grid_right="60%")
bar.grid(scatter.get_series(), grid_top="60%", grid_left="60%")
bar.grid(es.get_series(), grid_top="60%", grid_right="60%")
bar.show_config()
bar.render()
# grid_3
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
grid_right="65%")
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="45%")
pie.add("",
attr,
v1,
radius=[30, 55],
legend_pos="65%",
legend_orient='vertical')
line.grid(pie.get_series(), grid_left="60%")
line.show_config()
line.render()
# grid_4
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
grid_right="60%")
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
grid_right="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
v1 = [[2320.26, 2320.26, 2287.3,
2362.94], [2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58,
2440.38], [2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_pos="60%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)],
v1,
legend_pos="80%")
line.grid(kline.get_series(), grid_left="55%")
line.show_config()
line.render()
# grid_5
import random
x_axis = [
"12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a",
"11a", "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p",
"10p", "11p"
]
y_aixs = [
"Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday",
"Sunday"
]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例", height=700, is_grid=True)
heatmap.add("热力图直角坐标系",
x_axis,
y_aixs,
data,
is_visualmap=True,
visual_top="45%",
visual_text_color="#000",
visual_orient='horizontal',
grid_bottom="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", title_top="52%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_top="50%")
heatmap.grid(bar.get_series(), grid_top="60%")
heatmap.show_config()
heatmap.render()
def test_line_type_step():
line = Line("折线图-阶梯图示例")
line.add("商家A", CLOTHES, clothes_v1, is_step=True, is_label_show=True)
assert '"step": true' in line._repr_html_()
