def v_correlation(self, end=yesterdayobj(), **vkwds):
'''
各基金净值的相关程度热力图可视化
:param end: string or object of date, the end date of the line
:returns: pyecharts.Heatmap object
'''
ctable = self.correlation_table(end)
x_axis = list(ctable.columns)
data = [[i, j, ctable.iloc[i, j]] for i in range(len(ctable)) for j in range(len(ctable))]
heatmap = HeatMap()
heatmap.add("", x_axis, x_axis, data, is_visualmap=True, visual_pos='center',
visual_text_color="#000", visual_range=[-1, 1], visual_orient='horizontal', **vkwds)
return heatmap
def heat_corr():
# 将上面的相关系数矩阵处理一下
value = [[0, 0, 1.00], [0, 1, 0.04], [0, 2, -0.07], [0, 3, -0.05],
[0, 4, -0.28], [1, 0, 0.04], [1, 1, 1.00], [1, 2, 0.13],
[1, 3, 0.40], [1, 4, 0.33], [2, 0, -0.07], [2, 1, 0.13],
[2, 2, 1.00], [2, 3, 0.25], [2, 4, 0.52], [3, 0, -0.05],
[3, 1, 0.40], [3, 2, 0.25], [3, 3, 1.00], [3, 4, 0.68],
[4, 0, -0.28], [4, 1, 0.33], [4, 2, 0.52], [4, 3, 0.68],
[4, 4, 1.00]]
c = (HeatMap(init_opts=opts.InitOpts(
theme=ThemeType.PURPLE_PASSION)).add_xaxis([
'评论数', '人均价格', '口味评分', '环境评分', '服务评分'
]).add_yaxis(
"",
['评论数', '人均价格', '口味评分', '环境评分', '服务评分'],
value,
label_opts=opts.LabelOpts(is_show=True, position="inside"),
).set_global_opts(
title_opts=opts.TitleOpts(title="评论数，人均价格，口味评分，环境评分，服务评分相关性热力图",
pos_left='center'),
visualmap_opts=opts.VisualMapOpts(is_show=False, min_=-1, max_=1),
))
c.render('相关性热力图.html')
return c
def _plot_monthly_returns(self, stats):
"""
Plots the monthly returns heatmap
"""
returns = stats['returns']
monthly_ret = perf.aggregate_returns(returns, 'monthly')
monthly_ret = monthly_ret.unstack()
monthly_ret = np.round(monthly_ret, 3)
monthly_ret = monthly_ret.fillna(0) * 100
row_count = len(monthly_ret)
month = [
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep",
"Oct", "Nov", "Dec"
]
values = []
for i in range(12):
for j in range(row_count):
v = monthly_ret.iloc[j, i]
s = "%.3f" % v
values.append([i, j, round(v, 3), s])
c = (HeatMap().add_xaxis(month).add_yaxis("", list(
monthly_ret.index), values).set_global_opts(
title_opts=opts.TitleOpts(title="Monthly Returns (%)"),
visualmap_opts=opts.VisualMapOpts(
min_=min(values, key=lambda x: x[2])[2],
max_=max(values, key=lambda x: x[2])[2]),
))
return c
def heat_corr(x_data, y_data, val_list):
"""绘制【描述"评论数", "人均价格", "口味评分", "环境评分", "服务评分"这些指标之间相关程度】的热力图
6.1 设置主题"purple-passion"；
6.2 图例系列名为空，数值标签位居色块中央；
6.3 标题"价格口碑等相关性"
6.4 隐藏视觉映射配置项传入参数，但仍指定最小和最大映射值min_=-1, max_=1；
6.5 return返回c；
:param x_data: "评论数", "人均价格", "口味评分", "环境评分", "服务评分"指标
:param y_data: 也是"评论数", "人均价格", "口味评分", "环境评分", "服务评分"指标
:param val_list: 热力矩阵各个色块的取值
:return: 绘制好的热力图对象
"""
c = (HeatMap(init_opts=opts.InitOpts(
theme=ThemeType.PURPLE_PASSION)).set_global_opts(
title_opts=opts.TitleOpts(title="价格口碑等相关性"),
visualmap_opts=opts.VisualMapOpts(
is_show=False, min_=-1,
max_=1)).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="",
yaxis_data=y_data,
value=val_list,
label_opts=opts.LabelOpts(position="inside")))
return c
def effectscattere_symbol() -> 'html':
df = pd.read_csv('data2.csv',encoding = 'utf8', index_col="名称")
省 = list(df.loc["省"].values)[-24:]
数量 = list(df.loc["数量"].values)[-24:]
value = [[i, j, random.randint(0, 80)] for i in range(24) for j in range(24)]
c = (
HeatMap()
.add_xaxis(省)
.add_yaxis("数量", 数量, value)
.set_global_opts(
title_opts=opts.TitleOpts(title="各省Python相关岗位数量"),
visualmap_opts=opts.VisualMapOpts(),
)
)
c.render("./templates/effectscatter_symbol.html")
with open("./templates/effectscatter_symbol.html", encoding="utf8", mode="r") as f:
sym = "".join(f.readlines())
return render_template('python_effectscatter_symbol.html',
the_sym=sym,
)
1
data_pie1 = data_pie.T.to_html()
pie1_list = [num for num in data_pie['分类']]
labels = [index for index in data_pie.index]
def pyecharts_heatmap(to_arr, chip_file_name, category, max_value, min_value,
lower_q, sequence_flag):
values = [[i, j, round(to_arr[i][j], 3)] for i in range(40)
for j in range(40)]
print(values)
c = (HeatMap(
init_opts=opts.InitOpts(width="1200px", height="800px")).add_xaxis(
[i for i in range(1, 41)]).add_yaxis(
category,
[i for i in range(1, 41)],
values,
label_opts=opts.LabelOpts(is_show=False, position="inside"),
).set_global_opts(
title_opts=opts.TitleOpts(title=sequence_flag),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
min_=min_value,
max_=max_value,
item_height=30,
item_width=15,
),
).render("heatmap_with_label_show.html"))
import webbrowser
webbrowser.open(
"file:///D:/GUI/MGI Engineering Tools/utils/heatmap_with_label_show.html"
)
def heatmap_base() -> HeatMap:
value = [[i, j, random.randint(0, 50)] for i in range(24)
for j in range(7)]
c = (HeatMap().add_xaxis(Faker.clock).add_yaxis(
"series0", Faker.week, value).set_global_opts(
title_opts=opts.TitleOpts(title="HeatMap-基本示例"),
visualmap_opts=opts.VisualMapOpts(),
))
return c
def plot(self):
self.chart = HeatMap(init_opts=opts.InitOpts(page_title=self.title))
# load data
self.chart.add_xaxis(self.data_x)
self.chart.add_yaxis(self.zaxis_name, self.data_y, self.heat_map)
self.chart.set_global_opts(
title_opts=opts.TitleOpts(title=self.title),
xaxis_opts=opts.AxisOpts(name=self.xaxis_name,
name_location='end',
name_gap=15),
yaxis_opts=opts.AxisOpts(name=self.yaxis_name,
name_location='end',
name_gap=15),
visualmap_opts=opts.VisualMapOpts(max_=self.max_value,
min_=self.min_value))
return self
def test_heatmap_base():
value = [[i, j, random.randint(0, 50)] for i in range(24)
for j in range(7)]
c = (HeatMap().add_xaxis(Faker.clock).add_yaxis(
"series0", Faker.week,
value).set_global_opts(visualmap_opts=opts.VisualMapOpts()))
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
c.render()
def test_heatmap_base(fake_writer):
value = [[i, j, random.randint(0, 50)] for i in range(24)
for j in range(7)]
c = (HeatMap().add_xaxis(Faker.clock).add_yaxis(
"series0", Faker.week,
value).set_global_opts(visualmap_opts=opts.VisualMapOpts()))
c.render()
_, content = fake_writer.call_args[0]
assert_equal(c.theme, "white")
assert_equal(c.renderer, "canvas")
def draw():
value = [[i, j, Data[i][j]] for i in range(50) for j in range(50)]
c = (
HeatMap()
.add_xaxis(["ID:"+str(i) for i in range(50)])
.add_yaxis("Regularized Similarity",["ID:"+str(i) for i in range(50)],value)
.set_global_opts(
title_opts=opts.TitleOpts(title="样本相关度可视化"),
visualmap_opts=opts.VisualMapOpts(),
)
)
return c
class HeatMapChart(base_chart.BaseChart):
chart = None
zaxis_name = None
data_x = []
data_y = []
heat_map = []
max_value = 0
min_value = 0
def __init__(self, title, xaxis_name, yaxis_name, zaxis_name):
base_chart.BaseChart.__init__(self, title, xaxis_name, yaxis_name)
self.zaxis_name = zaxis_name
def clear(self):
self.data_x.clear()
self.data_y.clear()
self.heat_map.clear()
def set_data(self, **data):
if ('x' in data) & ('y' in data) & ('map' in data):
self.data_x = data['x']
self.data_y = data['y']
self.to_echarts_map(data['map'])
return self
def show(self):
return self.chart
def to_echarts_map(self, map) -> []:
self.max_value = map[0][0]
self.min_value = map[0][0]
for i in range(len(map)):
for j in range(len(map[i])):
if self.max_value < map[i][j]:
self.max_value = map[i][j]
if self.min_value > map[i][j]:
self.min_value = map[i][j]
self.heat_map.append([j, i, map[i][j]])
return self.heat_map
def plot(self):
self.chart = HeatMap(init_opts=opts.InitOpts(page_title=self.title))
# load data
self.chart.add_xaxis(self.data_x)
self.chart.add_yaxis(self.zaxis_name, self.data_y, self.heat_map)
self.chart.set_global_opts(
title_opts=opts.TitleOpts(title=self.title),
xaxis_opts=opts.AxisOpts(name=self.xaxis_name,
name_location='end',
name_gap=15),
yaxis_opts=opts.AxisOpts(name=self.yaxis_name,
name_location='end',
name_gap=15),
visualmap_opts=opts.VisualMapOpts(max_=self.max_value,
min_=self.min_value))
return self
def heatmap_with_label_show() -> HeatMap:
value = [[i, j, random.randint(0, 50)] for i in range(24)
for j in range(7)]
c = (HeatMap().add_xaxis(Faker.clock).add_yaxis(
"series0",
Faker.week,
value,
label_opts=opts.LabelOpts(is_show=True, position="inside"),
).set_global_opts(
title_opts=opts.TitleOpts(title="HeatMap-Label 显示"),
visualmap_opts=opts.VisualMapOpts(),
))
return c
def corr_plot(data: list):
from pyecharts import options as opts
from pyecharts.charts import HeatMap
from pyecharts.faker import Faker
corr = (HeatMap().add_xaxis('series0').add_yaxis(
"series1",
data,
label_opts=opts.LabelOpts(is_show=True, position="inside"),
).set_global_opts(
title_opts=opts.TitleOpts(title="HeatMap-Label 显示"),
visualmap_opts=opts.VisualMapOpts(),
).render("相关性图.html"))
def v_correlation(self, end=yesterdayobj(), vopts=None):
"""
各基金净值的相关程度热力图可视化
:param end: string or object of date, the end date of the line
:returns: pyecharts.charts.Heatmap.render_notebook object
"""
ctable = self.correlation_table(end)
x_axis = list(ctable.columns)
data = [[i, j, ctable.iloc[i, j]] for i in range(len(ctable))
for j in range(len(ctable))]
heatmap = HeatMap()
heatmap.add_xaxis(x_axis)
heatmap.add_yaxis(series_name="相关性", yaxis_data=x_axis, value=data)
if vopts is None:
vopts = heatmap_opts
heatmap.set_global_opts(**vopts)
return heatmap.render_notebook()
def heat_plot(data):
n, m = data.shape
x, y = np.arange(0, n, 1), np.arange(0, m, 1)
x, y = np.meshgrid(x, y, indexing='ij')
z = data.values.flatten()
x = m - 1 - x
_data = list(zip(y.flatten(), x.flatten(), z))
_data = [[int(d[0]), int(d[1]), np.round(d[2], 4)] for d in _data]
h = (
HeatMap(
init_opts=opts.InitOpts(width="1000px", height="300px")).add_xaxis(
xaxis_data=data.columns.astype(str).tolist()).add_yaxis(
series_name="",
yaxis_data=data.index.astype(str).tolist()[::-1],
value=_data,
label_opts=opts.LabelOpts(position='inside',
is_show=True,
font_size=15),
).set_series_opts().set_global_opts(
# toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, ),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
visualmap_opts=opts.VisualMapOpts(min_=z.min(),
max_=z.max(),
is_calculable=True,
orient="vertical",
pos_left="left"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=2,
name="pic",
), )),
))
return h
def heatOutPortTime(self, data) -> HeatMap:
# 每周出港航班按小时热力图
out_port_time_heat = (HeatMap(
init_opts=opts.InitOpts(width='1000px', height='500px')).add_xaxis(
data[0]).add_yaxis("", data[1], data[2]).set_global_opts(
title_opts=opts.TitleOpts(
title='本周出港航班时间热力图',
title_textstyle_opts=opts.TextStyleOpts(font_size=20),
subtitle='{} [数据来源：ctrip.com]'.format(self.today_ymd)),
visualmap_opts=opts.VisualMapOpts(type_='color',
min_=0,
max_=1000),
))
return out_port_time_heat
def heatmap_car() -> HeatMap:
x = ['宝马', '法拉利', '奔驰', '奥迪', '大众', '丰田', '特斯拉']
y = ['中国','日本','南非','澳大利亚','阿根廷','阿尔及利亚','法国','意大利','加拿大']
value = [[i, j, random.randint(0, 100)]
for i in range(len(x)) for j in range(len(y))]
c = (
HeatMap()
.add_xaxis(x)
.add_yaxis("销量", y, value)
.set_global_opts(
title_opts=opts.TitleOpts(title="HeatMap"),
visualmap_opts=opts.VisualMapOpts(),
)
)
return c
def heatmap_base() -> HeatMap:
value = inputData
# value = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
c = (
HeatMap()
.add_xaxis(SidesList)
.add_yaxis("HeatMap", USRegionOrder, value,
label_opts=opts.LabelOpts(is_show=True, position="inside",interval= 0) )
.set_global_opts(
#title_opts=opts.TitleOpts(title="HeatMap-基本示例"),
visualmap_opts=opts.VisualMapOpts(pos_left = "right"),
# axis =opts.AxisOpts(offset=100,is_show= False,name_gap = 0, interval = 200)
)
)
return c
def heat_map(data: List[dict],
x_label: List[str] = None,
y_label: List[str] = None,
title: str = "热力图",
width: str = "900px",
height: str = "680px") -> HeatMap:
"""绘制热力图
:param data: 用于绘制热力图的数据，示例如下
[{'x': '0hour', 'y': '0day', 'heat': 11},
{'x': '0hour', 'y': '1day', 'heat': 40},
{'x': '0hour', 'y': '2day', 'heat': 38},
{'x': '0hour', 'y': '3day', 'heat': 36},
{'x': '0hour', 'y': '4day', 'heat': 11}]
:param x_label: x轴标签
:param y_label: y轴标签
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 图表
"""
value = [[s['x'], s['y'], s['heat']] for s in data]
heat = [s['heat'] for s in data]
if not x_label:
x_label = sorted(list(set([s['x'] for s in data])))
if not y_label:
y_label = sorted(list(set([s['y'] for s in data])))
vis_map_opts = opts.VisualMapOpts(pos_left="90%", pos_top="20%", min_=min(heat), max_=max(heat))
title_opts = opts.TitleOpts(title=title)
init_opts = opts.InitOpts(page_title=title, width=width, height=height)
dz_inside = opts.DataZoomOpts(False, "inside", xaxis_index=[0], range_start=80, range_end=100)
dz_slider = opts.DataZoomOpts(True, "slider", xaxis_index=[0], pos_top="96%", pos_bottom="0%",
range_start=80, range_end=100)
legend_opts = opts.LegendOpts(is_show=False)
hm = HeatMap(init_opts=init_opts)
hm.add_xaxis(x_label)
hm.add_yaxis("heat", y_label, value, label_opts=opts.LabelOpts(is_show=True, position="inside"))
hm.set_global_opts(title_opts=title_opts, visualmap_opts=vis_map_opts, legend_opts=legend_opts,
xaxis_opts=opts.AxisOpts(grid_index=0), datazoom_opts=[dz_inside, dz_slider])
return hm
def _timeline(perf, name=""):
tl = Timeline(init_opts=opts.InitOpts(width="1000px", height="400px"))
series = perf.index.get_level_values(0).unique() if isinstance(
perf.index, pd.MultiIndex) else [name]
for i, _w in enumerate(series):
_data = perf.loc[_w] if isinstance(perf.index, pd.MultiIndex) else perf
n, m = _data.shape
x, y = np.arange(0, n, 1), np.arange(0, m, 1)
x, y = np.meshgrid(x, y, indexing='ij')
z = _data.values.flatten()
data = list(zip(y.flatten(), x.flatten(), z))
data = [[int(d[0]), int(d[1]), np.round(d[2], 4)] for d in data]
h = (HeatMap(
init_opts=opts.InitOpts(width="1000px", height="300px")).add_xaxis(
xaxis_data=_data.columns.astype(str).tolist()).add_yaxis(
series_name="",
yaxis_data=_data.index.astype(str).tolist(),
value=data,
label_opts=opts.LabelOpts(position='inside', is_show=True),
).set_series_opts().set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, ),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
visualmap_opts=opts.VisualMapOpts(min_=z.min(),
max_=z.max(),
is_calculable=True,
orient="vertical",
pos_left="left"),
))
tl.add(h, "{}".format(_w))
if not isinstance(perf.index, pd.MultiIndex):
tl.add_schema(is_timeline_show=False)
return tl
def plot_confusion_by_heat_map(matrix, xaxis, yaxis, fig_name):
"""
drawing confusion matrix with heatmap
:param matrix: confusion matrix
:param xaxis: x axis label
:param yaxis: y axis label
:return:
"""
from pyecharts import options as opts
from pyecharts.charts import HeatMap
(row, col) = matrix.shape
data = []
for i in range(row):
for j in range(col):
data.append([i, j, np.float32(matrix[i][j]).item()])
heat_map = (HeatMap().add_xaxis(xaxis).add_yaxis(
'f1_score', yaxis, data).set_global_opts(
title_opts=opts.TitleOpts(title=fig_name),
visualmap_opts=opts.VisualMapOpts(
range_color=['#121122', 'rgba(3,4,5,0.4)', 'red']),
))
heat_map.render(path='../chart/' + fig_name + '.html')
splitline_opts=opts.SplitLineOpts(is_show=True,
linestyle_opts=opts.LineStyleOpts(
is_show=True,
color='black',
width=10,
)),
)
c = (
HeatMap(init_opts=opts.InitOpts(bg_color='black', )).add_xaxis(
xdata).add_yaxis(
"月份",
ydata,
data,
).set_global_opts(
title_opts=opts.TitleOpts(),
legend_opts=opts.LegendOpts(is_show=False, ),
visualmap_opts=opts.VisualMapOpts(
min_=1,
max_=158,
is_calculable=True,
orient='horizontal',
pos_left='center',
pos_bottom='-1%',
range_color=['#C0F1F4', '#60BDCB', '#316870', '#EA7552'],
textstyle_opts=opts.TextStyleOpts(color='#A3C7E7')),
tooltip_opts=opts.TooltipOpts(position='top'),
xaxis_opts=axisOpts,
yaxis_opts=axisOpts,
).set_series_opts(zlevel=-1 # 这是一个重要属性，设置图层
).render("sale-heatmap.html"))
data.append([x, y, cn[x][y]])
(HeatMap(init_opts=opts.InitOpts(width="1440px", height="720px")).add_xaxis(
xaxis_data=country).add_yaxis(
series_name="前百在榜数",
yaxis_data=years,
value=data,
label_opts=opts.LabelOpts(is_show=True,
color="#fff",
position="inside",
horizontal_align="50%"),
).set_series_opts().set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
visualmap_opts=opts.VisualMapOpts(min_=0,
max_=10,
is_calculable=True,
orient="horizontal",
pos_left="center"),
).render("不同国家前百在榜大学数.html"))
# 平行坐标图代码
for i in range(4, 10):
kw_counts_by_score[i] = pd.read_csv(r'.\data_set\{0}_movie_keywords.csv'.format(i))
kw_percentage_df = pd.DataFrame([], columns=list(range(4, 10)), index=kw_counts_by_score[9]['kw'][:10])
# 将数据写入空表
for i in range(4, 10):
kw_df = kw_counts_by_score[i]
kw_df = kw_df[kw_df['kw'].isin(kw_percentage_df.index)]
kw_percentage_df[i] = pd.Series(list(kw_df['percentage']), index=kw_df['kw'])
# 用0替换NAN，否则无法计算
kw_percentage_df.fillna(0, inplace=True)
# 建立有三个元素的列表，作为绘制的数据
data = []
i = 0
for index in kw_percentage_df.index:
j = 0
for column in kw_percentage_df.columns:
data.append([j, i, kw_percentage_df[column][index] * 100])
j += 1
i += 1
# 绘制热力图
heatmap = HeatMap()
heatmap.add_xaxis(list(kw_percentage_df.columns))
heatmap.add_yaxis('电影评论关键词热力图', list(kw_percentage_df.index), data)
heatmap.set_global_opts(visualmap_opts=opts.VisualMapOpts(min_=0, max_=10, orient='horizontal'))
heatmap.render('comment_heatmap.html')
def bar_base() -> Bar:
path = "Video_Monitor/video_list.json"
ztstatus = Readjson(path)
now = datetime.datetime.now()
zsf = now.minute % 10
time = []
if zsf > 0:
dqzsj = now - datetime.timedelta(minutes=zsf)
else:
dqzsj = now
for j in range(720):
tssj1 = dqzsj - datetime.timedelta(minutes=1 * (j - 1))
tssj = tssj1.strftime('%Y-%m-%d %H:%M')
time.insert(0, tssj)
# app.logger.info(time)
data = select(time[0])
# app.logger.info(data)
name = ztstatus[1][0]
newData = []
for value in range(len(data)):
x = time.index(data[value].get('testtime'))
y = name.index(data[value].get('ztname'))
z = data[value].get('ztstatus')
newData.append([x, y, z])
data = [[d[0], d[1], d[2] or "-"] for d in newData]
c = (
HeatMap().add_xaxis(xaxis_data=time).add_yaxis(
series_name="详情",
yaxis_data=name,
value=data,
label_opts=opts.LabelOpts(is_show=True,
color="#fff",
position="bottom",
horizontal_align="100%"),
).set_series_opts(
label_opts=opts.LabelOpts(is_show=True),
minortick_opts=opts.MinorTickOpts(is_show=True, split_number=8),
minorsplitkine_opts=opts.MinorSplitLineOpts(is_show=True)).
set_global_opts(
# tooltip_opts=opts.TooltipOpts(formatter='{a},{@time}:\n,{@[0]}'),
# axistick_opts=opts.AxisTickOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True,
is_piecewise=True,
pos_left='center',
orient='horizontal',
pieces=[{
"value": 200,
"label": '正常',
"color": 'green'
}, {
"value": 404,
"label": '异常',
"color": 'Orange'
}, {
"value": 408,
"label": '超时',
"color": 'grey'
}]),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=True,
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(opacity=1)),
minor_split_line_opts=opts.MinorSplitLineOpts(is_show=True),
),
yaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=True,
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(opacity=1)),
),
))
return c
(
HeatMap()
.add_xaxis(xaxis_data=hours)
.add_yaxis(
series_name="Punch Card",
yaxis_data=days,
value=data,
label_opts=opts.LabelOpts(
is_show=True, color="#fff", position="bottom", horizontal_align="50%"
),
)
.set_series_opts()
.set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
visualmap_opts=opts.VisualMapOpts(
min_=0, max_=10, is_calculable=True, orient="horizontal", pos_left="center"
),
)
.render("heatmap_on_cartesian.html")
)
]
parallel_data = [[12.99, 100, 82, "Good"], [9.99, 80, 77, "OK"],
[20, 120, 60, "Excellent"]]
# init_opts=opts.InitOpts(width="800px",height="300px")
parallel = Parallel(init_opts=opts.InitOpts(width="800px", height="300px"))
parallel.add_schema(parallel_axis)
parallel.add(series_name="Related Indexes",
data=parallel_data,
linestyle_opts=opts.LineStyleOpts(width=4, opacity=0.5))
liquid = Liquid(init_opts=opts.InitOpts(width="300px", height="300px"))
liquid.add("liquid", [0.6, 0.7, 0.3])
liquid.set_global_opts(title_opts=opts.TitleOpts(title="Basic Example"))
heatmap_data = [[i, j, random.randint(0, 50)] for i in range(7)
for j in range(7)]
heatmap = HeatMap()
heatmap.add_xaxis(Faker.week)
heatmap.add_yaxis(
"相关表",
Faker.week,
heatmap_data,
label_opts=opts.LabelOpts(is_show=True, position="inside"),
)
heatmap.set_global_opts(
title_opts=opts.TitleOpts(title="HeatMap-Label 显示"),
visualmap_opts=opts.VisualMapOpts(),
)
import random
from pyecharts import options as opts
from pyecharts.charts import HeatMap
from pyecharts.faker import Faker
value = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
c = (HeatMap().add_xaxis(Faker.clock).add_yaxis(
"series0",
Faker.week,
value,
label_opts=opts.LabelOpts(is_show=True, position="inside"),
).set_global_opts(
title_opts=opts.TitleOpts(title="HeatMap-Label 显示"),
visualmap_opts=opts.VisualMapOpts(),
).render("heatmap_with_label_show.html"))
from pyecharts.charts import HeatMap
import datetime
import random
import os, sys
os.chdir(sys.path[0]) # 改变目录
begin = datetime.date(2018, 1, 1) #设置起始日期
end = datetime.date(2019, 12, 31) #设置终止日期
data = [
[str(begin + datetime.timedelta(days=i)),
random.randint(1000, 25000)] #设置日期间隔，步数范围
for i in range((end - begin).days + 1)
]
heatmap_k = HeatMap('2018年')
heatmap_k.add('',
data,
is_calendar_heatmap=True,
visual_range=[1000, 25000],
is_visualmap=True,
calendar_data_range=[data[0][0], data[-1][0]],
visual_orient='horizontal',
visual_pos='center',
visual_top='90%',
calendar_cell_size=[25, 35])
heatmap_k.render('heatmap.html')
