def take_snapshot(self, file_html=None, width="1400px", height="580px"):
"""获取快照
:param file_html: str
交易快照保存的 html 文件名
:param width: str
图表宽度
:param height: str
图表高度
:return:
"""
tab = Tab(page_title="{}@{}".format(self.symbol, self.end_dt.strftime("%Y-%m-%d %H:%M")))
for freq in self.freqs:
chart = self.kas[freq].to_echarts(width, height)
tab.add(chart, freq)
t1 = Table()
t1.add(["名称", "数据"], [[k, v] for k, v in self.s.items()
if "_" in k and isinstance(v, str)
and v not in ["Other~其他", "向下", 'Y~是', 'N~否', '向上']])
t1.set_global_opts(title_opts=ComponentTitleOpts(title="缠中说禅信号表", subtitle=""))
tab.add(t1, "信号表")
t2 = Table()
ths_ = [["同花顺F10", "http://basic.10jqka.com.cn/{}".format(self.symbol[:6])]]
t2.add(["名称", "数据"], [[k, v] for k, v in self.s.items() if "_" not in k and v != "Other~其他"] + ths_)
t2.set_global_opts(title_opts=ComponentTitleOpts(title="缠中说禅因子表", subtitle=""))
tab.add(t2, "因子表")
if file_html:
tab.render(file_html)
else:
return tab
def Create_Combine(my_data, ave_data, exam_name, class_data, grade_data, Exam_Number):
#历次排名对比
tab = Tab()
The_Line = Create_Line(class_data, grade_data)
tab.add(The_Line, "历次排名")
tab.add(table_base(), "参考上线排名")
#不同考试成绩概况
t1 = Timeline()
for i in range(len(ave_data)):
Now_Bar = Create_Bar(my_data[i], ave_data[i], exam_name[i], "你的成绩", "平均分", False)
t1.add(Now_Bar, exam_name[i])
tab.add(t1, "近段考试概况")
#历次单科成绩对比
for i in range(0, 6):
clone_score = []
for j in range(len(exam_name)):
clone_score.append(my_data[j][i])
clone_rank = []
for j in range(len(exam_name)):
clone_rank.append(All_Exam_RANK[j][i])
tab.add(Create_Line_RANK(clone_score, clone_rank), Subject[i]+"走势")
tab.render(str(Exam_Number)+"的综合分析.html")
def multiple():
tab = Tab()
tab.add(geo.set_map("confirmed",200000), "confirmed")
tab.add(geo.set_map("deaths",30000), "deaths")
tab.add(geo.set_map("recovered",90000), "recovered")
tab.render(output_path+"tab_base.html")
return True
def take_snapshot(self, file_html=None, width="1400px", height="580px"):
"""获取快照
:param file_html: str
交易快照保存的 html 文件名
:param width: str
图表宽度
:param height: str
图表高度
:return:
"""
tab = Tab(page_title="{}@{}".format(
self.symbol, self.end_dt.strftime("%Y-%m-%d %H:%M")))
for freq in self.freqs:
chart = ka_to_echarts(self.kas[freq], width, height)
tab.add(chart, freq)
t1 = Table()
t1.add(["名称", "数据"], [[k, v] for k, v in self.s.items() if "_" in k])
t1.set_global_opts(
title_opts=ComponentTitleOpts(title="缠中说禅信号表", subtitle=""))
tab.add(t1, "信号表")
t2 = Table()
t2.add(["名称", "数据"],
[[k, v] for k, v in self.s.items() if "_" not in k])
t2.set_global_opts(
title_opts=ComponentTitleOpts(title="缠中说禅因子表", subtitle=""))
tab.add(t2, "因子表")
if file_html:
tab.render(file_html)
else:
return tab
def test_tab_base(fake_writer):
bar = _create_bar()
line = _create_line()
tab = Tab().add(bar, "bar-example").add(line, "line-example")
tab.render()
_, content = fake_writer.call_args[0]
assert_in("bar-example", content)
assert_in("line-example", content)
def tab_base():
tab = Tab()
tab.add(bar_datazoom_slider(), "bar-example")
tab.add(line_markpoint(), "line-example")
tab.add(pie_rosetype(), "pie-example")
tab.add(grid_mutil_yaxis(), "grid-example")
tab.add(table_base(), "table-example")
tab.render()
def tab_layout(list, title, name):
tab = Tab()
tab.add(death_histogram(list, title, name), "每日死亡")
tab.add(CumulativeDeaths_histogram(list, title, name), '累计死亡')
tab.add(Confirmed_histogram(list, title, name), "每日确诊")
tab.add(CumulativeConfirmed_histogram(list, title, name), '累计确诊')
tab.add(timeline(list, title, name), '时间轴')
tab.render(name + '.html')
def main():
r = requests.get('http://127.0.0.1:5000/blocktree')
print(r)
data = r.json()
with open("new_all_blocks.json", 'w') as file:
json.dump(data, file, indent=2)
file_ = "new_all_blocks.json"
with open(file_, 'r') as file:
file_data = json.load(file)
blocks = file_data.values()
num = 1
for block in blocks:
block_info = block.values()
for info in block_info:
data = {}
for i in info:
if info[i]["attribute"] == "head":
info[i]["attribute"] = 0
elif info[i]["attribute"] == "block":
info[i]["attribute"] = 1
elif info[i]["attribute"] == "NORMAL":
info[i]["attribute"] = 2
elif info[i]["attribute"] == "JUSTIFIED":
info[i]["attribute"] = 3
elif info[i]["attribute"] == "FINALIZED":
info[i]["attribute"] = 4
info[i].update({'x': 100, 'y': 800})
data['block'] = list(info.values())
with open("block%s.json" % num, 'w') as file1:
json.dump(data, file1, indent=2)
num += 1
tab = Tab()
for i in range(1, num):
same_height('block%s.json' % i)
f = open('block%s.json' % i, )
data = json.load(f)
nodes, edges, text = plot(data)
categories = [
{
"name": "head"
},
{
"name": "block"
},
{
"name": "normal checkpoint"
},
{
"name": "justified checkpoint"
},
{
"name": "finalized checkpoint"
}]
tab.add(datazoom_slider(nodes, edges, categories, text), "graph%s" % i)
tab.render('blockchain.html')
def Gather():
tab = Tab(page_title='碧桂园集中式商业消费者满意度研究')
CenterName, Data_Dict = CleanData()
TabNames = list(Data_Dict.keys())
for TabName in TabNames:
Scores_List = Data_Dict[TabName]
tab.add(Satisfaction(CenterName, TabName, Scores_List), TabName)
tab.add(NPS(CenterName), 'NPS')
tab.render('满意度得分展示Demo.html')
def all():
tab = Tab(page_title='2019年安全公众号统计分析')
tab.add(article_day_3d(), "公众号文章发布统计")
tab.add(account_article_num(), "公众号收录文章数")
tab.add(article_day_num(), "每天收录文章统计")
tab.add(article_hour_num(), "文章发布时间段")
tab.add(article_source_url(), "公众号文章来源")
tab.add(zhaopin(), "公众号文章主题统计")
tab.add(article_author(), "勤奋作者")
tab.render(path='./static/year/2019.html')
print('生成完毕')
def depLevels():
#获取处理后的依赖关系
depList = getDepList()
timestr = str(datetime.datetime.now())
g1 = levelRender('request Levels Dependencies' + timestr, 'request', depList)
g2 = levelRender('underscore Levels Dependencies' + timestr, 'underscore', depList)
tab = Tab()
tab.add(g1, "request")
tab.add(g2, "underscore")
tab.render("./templates/npmLevelDependencies.html")
def tab(filename="info"):
'''
分页组件
'''
tab = Tab()
tab.add(liquid_data_precision("111"), "前日留存")
tab.add(bar_datazoom_slider("222"), "分端留存")
tab.add(table_base("222"), "明细")
tab.add(line_markpoint("222"), "人群留存")
tab.add(pie_rosetype("222"), "城市")
tab.add(grid_mutil_yaxis("222"), "新增留存")
tab.render_notebook()
tab.render('{}.html'.format(filename))
def take_snapshot(self, file_html, width="950px", height="480px"):
tab = Tab(page_title="{}的交易快照@{}".format(
self.symbol, self.end_dt.strftime("%Y-%m-%d %H:%M")))
for freq in self.freqs:
chart = ka_to_echarts(self.kas[freq], width, height)
tab.add(chart, freq)
headers = ["名称", "数据"]
rows = [[k, v] for k, v in self.signals.items()]
table = Table()
table.add(headers, rows)
table.set_global_opts(
title_opts=ComponentTitleOpts(title="缠论信号", subtitle=""))
tab.add(table, "信号表")
tab.render(file_html)
def take_snapshot(self, file_html=None, width="1400px", height="580px"):
"""获取快照
:param file_html: str
交易快照保存的 html 文件名
:param width: str
图表宽度
:param height: str
图表高度
:return:
"""
tab = Tab(page_title="{}@{}".format(
self.symbol, self.end_dt.strftime("%Y-%m-%d %H:%M")))
for freq in self.freqs:
chart = self.kas[freq].to_echarts(width, height)
tab.add(chart, freq)
for freq in self.freqs:
t1 = Table()
t1.add(["名称", "数据"], [[k, v] for k, v in self.s.items()
if k.startswith("{}_".format(freq))])
t1.set_global_opts(
title_opts=ComponentTitleOpts(title="缠中说禅信号表", subtitle=""))
tab.add(t1, "{}信号表".format(freq))
t2 = Table()
ths_ = [[
"同花顺F10", "http://basic.10jqka.com.cn/{}".format(self.symbol[:6])
]]
t2.add(["名称", "数据"],
[[k, v] for k, v in self.s.items() if "_" not in k] + ths_)
t2.set_global_opts(
title_opts=ComponentTitleOpts(title="缠中说禅因子表", subtitle=""))
tab.add(t2, "因子表")
t3 = Table()
t3.add(["本级别", "次级别"],
[[k, v] for k, v in eval(self.s['级别映射']).items()])
t3.set_global_opts(
title_opts=ComponentTitleOpts(title="缠中说禅级别映射表", subtitle=""))
tab.add(t3, "级别映射表")
if file_html:
tab.render(file_html)
else:
return tab
def user_tab(data_set,user_id,title="用户数据分析"):
"""
:param data_set:
:param user_id:
:param title:
:return:
"""
file_name = "user_data/" + str(user_id) + "_data.html"
url = "cloud_" + str(user_id) + ".jpg"
tab = Tab()
tab.add(grid_recoder(data_set[0],user_id), "用户行为统计")
tab.add(grid_like(data_set[1],user_id), "用户偏爱分析")
# tab.add(page_plays(data_set[2],data_set[3],user_id), "用户习惯")
tab.add(draw_bar_plays(data_set[2], user_id), "用户播放方式")
tab.add(draw_line_clip(data_set[3], user_id), "片段播放频率统计")
tab.add(draw_cloud(url, user_id), "歌词词云")
tab.render(file_name)
def visualize_layer(model, image, layer_name, layer_max_image=32, jupyter=True, path='visualize_layer.html'):
"""network layer visualize.
Args:
model: a tf.keras model or keras model.
image: a image array with shape (1, height, width, channel), height and width are necessary.
layer_name: a list of model layers name.
layer_max_image: every layer max plot images.
jupyter: if plot in jupyter, default True.
path: if jupyter is False, result save a html file.
Returns:
A pyecharts polt object.
"""
if tf.io.gfile.exists('feature_map'):
tf.io.gfile.rmtree('feature_map')
tf.io.gfile.makedirs('feature_map')
if np.ndim(image)==2:
image = tf.expand_dims(tf.expand_dims(image, 0), -1)
elif np.ndim(image)==3:
image = tf.expand_dims(image, 0)
elif np.ndim(image)==4:
pass
else:
raise ValueError('image shape is error.')
temp_model = tf.keras.backend.function(model.inputs, [i.output for i in model.layers if i.name in layer_name])
temp_name = [i.name for i in model.layers if i.name in layer_name]
result = temp_model(image)
images_per_row = 16
count = 0
name_dict = {}
tab = Tab()
for feature, name in zip(result, temp_name):
if feature.ndim==4:
if feature.shape[-1]==3:
display_grid = feature[0,:,:,:].astype('uint8')
else:
n_features = feature.shape[-1] if feature.shape[-1]<layer_max_image else layer_max_image
size = feature.shape[1]
n_cols = int(np.ceil(n_features/images_per_row))
display_grid = np.ones((size * n_cols, images_per_row * size),dtype=np.uint8)*255
for col in range(n_cols):
for row in range(images_per_row):
if (col+1)*(row+1)>n_features:
break
channel_image = feature[0, :, :, col * images_per_row + row]
channel_image -= channel_image.mean()
channel_image /= channel_image.std()
channel_image *= 64
channel_image += 128
channel_image = np.clip(channel_image, 0, 255).astype('uint8')
display_grid[col * size : (col + 1) * size, row * size : (row + 1) * size] = channel_image
display_grid = np.expand_dims(display_grid, axis=-1)
name_dict[name] = f'./feature_map/{count}.png'
ImageAug(display_grid).save_image(name_dict[name])
tab.add(image_base(name_dict[name], name, 'shape='+str(feature.shape)), name)
count += 1
return tab.render_notebook() if jupyter else tab.render(path)
def get_data():
file = open('数据.txt', "r", encoding="utf-8", errors="ignore")
list = []
while True:
mystr = file.readline() #表示一次读取一行
if not mystr:
#读到数据最后跳出，结束循环。数据的最后也就是读不到数据了，mystr为空的时候
break
data = json.loads(mystr)
# print(data)#打印每次读到的内容
list.append(data)
data = generate_data(list)
tab = Tab()
tab.add(bar_datazoom_slider(data), "北成上广深直方图")
tab.add(line_markpoint(data), "北成上广深折线图")
tab.add(pie_rosetype(data), "北成上广深饼状图")
tab.add(table_base(data), "北成上广深成表格")
tab.render("北上广深成15 in 1图.html")
def makechart(data, debug=False):
day, shsum, shpending, shcured, shdeath, shsum_date, new_date, newconfirmed, newpending = data_process(
data)
title = "上海新型冠状病毒统计"
grid_chart = Grid()
grid_chart.add(total_trend(day, shdeath, shcured, shsum, shpending),
grid_opts=GridOpts(height="57%"))
grid_chart.add(new_trend(day, newconfirmed, newpending),
grid_opts=GridOpts(pos_top="75%", ))
bk = bkey()
if debug:
ak = bk.ak_dev
else:
ak = bk.ak_web
get_detail()
with open(shsumary, 'r', encoding='utf-8') as f:
j = json.loads(f.read())
sumary = [(k, j[k]) for k in j.keys()]
tab = Tab(page_title=title)
tab.add(grid_chart, "趋势")
tab.add(map_visualmap(sumary), "区县分布")
tab.add(bmap_base(BAIDU_AK=ak), '详细地图')
tab.render(outfile)
def plot_geo_summary(self,
_geo_data,
series_name=None,
threshold_plot=None):
'''
plot geo 经纬度聚类信息
:param geo_data: geo对应的城市分布(Series)
:return:
'''
self.series_name = series_name if series_name else self.series_name
geo_data = np.array(list(dict(_geo_data).items()))
self.threshold_plot = np.percentile(
_geo_data, 80) if not threshold_plot else threshold_plot
_geo = self.get_geo_base(geo_data)
_map = self.get_map_base(geo_data)
table = self.get_table_base(geo_data)
tab = Tab(page_title=self.country, ) # 选项卡多图
tab.add(_map, "VisualMap")
tab.add(_geo, "HeatMap")
tab.add(table, "Data")
tab.render(self.html_name)
def plot_summary(self,
page_name='',
page_type='page',
web_open=False,
html_name=None):
assert page_type in ('page', 'tab'), '仅支持单页多图(page)和选项卡多图(tab)'
if html_name:
self.html_name = os.path.abspath(str(html_name) + '.html')
if 'page' == page_type:
page = Page(page_title=page_name) # 单页多图
for idata in self.datas:
page.add(idata)
page.render(self.html_name)
else:
tab = Tab(page_title=page_name, ) # 选项卡多图
for idata in self.datas:
tab_name = idata.options.get('title').opts[0].get('text')
tab.add(idata, tab_name)
tab.render(self.html_name)
if web_open:
webbrowser.open(url='file://' + self.html_name,
new=0,
autoraise=True)
def visualize_heatmaps(model, image, layer_name, jupyter=True, path='visualize_heatmaps.html'):
"""network layer visualize.
Args:
model: a tf.keras model or keras model.
image: a image array with shape (1, height, width, channel), height and width are necessary.
layer_name: a list of model layers name.
jupyter: if plot in jupyter, default True.
path: if jupyter is False, result save a html file.
Returns:
A pyecharts polt object.
"""
if tf.io.gfile.exists('feature_map'):
tf.io.gfile.rmtree('feature_map')
tf.io.gfile.makedirs('feature_map')
if np.ndim(image)==2:
image = tf.expand_dims(tf.expand_dims(image, 0), -1)
elif np.ndim(image)==3:
image = tf.expand_dims(image, 0)
elif np.ndim(image)==4:
pass
else:
raise ValueError('image shape is error.')
temp_model = tf.keras.backend.function(model.inputs, [i.output for i in model.layers if i.name in layer_name])
temp_name = [i.name for i in model.layers if i.name in layer_name]
result = temp_model(image)
images_per_row = 16
count = 0
name_dict = {}
tab = Tab()
for feature, name in zip(result, temp_name):
if feature.ndim==4:
if model.get_layer(name).__class__.__name__=='InputLayer':
out = np.squeeze(image, 0).astype('uint8')
else:
out = tf.image.resize(tf.expand_dims(tf.squeeze(tf.reduce_sum(tf.abs(feature), axis=-1)), axis=-1), (image.shape[1], image.shape[2]))
out = 255-tf.cast(out/tf.reduce_max(out)*255., tf.uint8)
# print(np.squeeze(image, 0).shape)
out = addWeighted(applyColorMap(out.numpy(), COLORMAP_JET), 0.7, np.squeeze(image, 0).astype('uint8'), 0.3, 0)
# out = np.expand_dims(out, axis=-1)
name_dict[name] = f'./feature_map/{count}.png'
ImageAug(out).save_image(name_dict[name])
tab.add(image_base(name_dict[name], name, 'shape='+str(feature.shape)), name)
count += 1
return tab.render_notebook() if jupyter else tab.render(path)
title_opts=opts.TitleOpts(title=name),
))
return c
def tab_add_html(tab, keys, datas, type):
index = 1
for key in keys:
tab.add(get_Line(key, datas[0], datas[index]), type + key)
index = index + 1
if __name__ == "__main__":
view = False
while True:
tab = Tab(js_host="./")
keys, datas = read_log("_test_value.log", "Test net output ")
tab_add_html(tab, keys, datas, "test-")
keys, datas = read_log("_test_value.log", "Train net output ")
tab_add_html(tab, keys, datas, "train-")
tab.render("_test_value.html")
print("update..")
if view == False:
view = True
os.system("see _test_value.html &")
time.sleep(10)
data1x.append(datarow[0]) # pyrcharts中横坐标要求为字符串，故不进行转换
bar1.add_xaxis(data1x)
bar1.add_yaxis('A', data1y, label_opts=opt.LabelOpts(is_show=False))
bar1.set_global_opts(
title_opts=opt.TitleOpts(title='柱状图示例'),
)
bar2 = Bar()
filename = 'hot-dog-places.csv'
data2x = []
data2y = []
with open(filename) as f:
reader = csv.reader(f)
for datarow in reader:
data2x.append(datarow)
x2 = data2x[0]
y21 = [float(x) for x in data2x[1]]
y22 = [float(x) for x in data2x[2]]
y23 = [float(x) for x in data2x[3]]
bar2.add_xaxis(x2)
bar2.add_yaxis('A', y21, stack=1)
bar2.add_yaxis('B', y21, stack=1)
bar2.add_yaxis('C', y21, stack=1)
bar2.set_series_opts(label_opts=opt.LabelOpts(is_show=False))
tab.add(bar1, 'Bar')
tab.add(bar2, 'Stack')
tab.render('tab.html')
tl = Timeline(init_opts=opts.InitOpts(width="900px", height="500px"))
for i in range(2015, 2020):
bar = (Bar().add_xaxis(formatTimeList).add_yaxis(
"商家A",
Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts("某商店{}年营业额".format(i))))
(tl.add(bar, "{}年".format(i)).add_schema(orient="horizontal",
play_interval=500,
symbol="circle",
symbol_size=2,
pos_bottom="-5px",
height="37px"))
return tl
if __name__ == '__main__':
title = "viewTimes"
timestampList = [
'1578059280100', '1578059460100', '1578059640100', '1578059820100',
'1578060000100', '1578060180100', '1578060360100'
]
filePath = title + ".html"
data1 = [465, 1488, 2474, 3951, 4903, 5345, 7304]
data2 = [265, 1388, 2374, 3541, 4123, 5045, 7004]
tab = Tab()
tab.add(viewTime(timestampList, data1, data2), "viewTimes")
tab.add(timeline_bar(timestampList, data1, data2), "Temp")
tab.render(filePath)
def generate_city(city_list):
input()
china_province = {}
for _province in province:
china_province.update({_province: {}})
for x in city_list:
if x[1] in province:
china_province[x[1]].update({x[2]: [0, 0, 0]})
for item in city_list:
name = item[1]
if name in province:
day = china_province[name][item[2]]
# if name == '北京' or name == '上海' or name == '天津' or name == '重庆':
# item[2] = item[2] + "区"
# elif "自治" in name:
# continue
# else:
# item[2] = item[2] + '市'
china_province[name].update({
item[2]:
[day[0] + item[3], day[1] + item[4], day[2] + item[5]]
})
tab = Tab()
for x in province:
dict_province = china_province[x]
confirm = [(k, v[0]) for k, v in dict_province.items()]
if x == "湖北":
_map = (
Map().add('确诊人数', confirm, x).set_series_opts(
label_opts=opts.LabelOpts(is_show=True)).
set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒 {} 疫情地图".format(x),
subtitle="更新时间：{}".format(
city_list[-1][0])),
legend_opts=opts.LegendOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
max_=5000,
is_piecewise=True,
pieces=[
# 数据范围
{
"min": 5000,
"label": ">=5000",
"color": "#807070"
},
{
"max": 4999,
"min": 2000,
"label": "2000 - 4999 人",
"color": "#8B0000"
},
{
"max": 1999,
"min": 1500,
"label": "1500 - 1999 人",
"color": "#CB0000"
},
{
"max": 1499,
"min": 500,
"label": "500 - 1499 人",
"color": "#DD5C5C"
},
{
"max": 499,
"min": 200,
"label": "200 - 499 人",
"color": "#FFA07A"
},
{
"max": 199,
"min": 1,
"label": "1 - 199 人",
"color": "#FFFF00"
},
]
# range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'])
))
# .render("全国确诊.html")
)
else:
_map = (
Map().add('确诊人数', confirm, x).set_series_opts(
label_opts=opts.LabelOpts(is_show=True)).set_global_opts(
title_opts=opts.TitleOpts(
title="新型冠状病毒 {} 疫情地图".format(x),
subtitle="更新时间：{}".format(city_list[-1][0])),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
max_=int(max([x[1] for x in confirm])),
is_piecewise=True,
range_color=[
'#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'
]))
# .render("全国确诊.html")
)
tab.add(_map, x)
# page.add(tab)
tab.render(path="新型冠状病毒全国疫情地图.html")
def china_total(city_list):
china_dict = {}
# print(city_list)
for name in province:
china_dict.update({name: [0, 0, 0]})
for row in city_list:
name = row[1]
if name in province:
day = china_dict[name]
china_dict[name] = [
day[0] + row[3], day[1] + row[4], day[2] + row[5]
]
print(china_dict)
confirm = [(k, v[0]) for k, v in china_dict.items()]
heal = [(k, v[1]) for k, v in china_dict.items()]
dead = [(k, v[2]) for k, v in china_dict.items()]
current_confirm = [(k, v[0] - v[1] - v[2]) for k, v in china_dict.items()]
tab = Tab()
_map = (
Map().add('确诊人数', confirm, "china").set_series_opts(
label_opts=opts.LabelOpts(is_show=True)).set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(
city_list[-1][0])),
legend_opts=opts.LegendOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
max_=1000,
is_piecewise=True,
pieces=[
# 数据范围
{
"min": 10000,
"label": ">=10000",
"color": "#80707"
},
{
"max": 9999,
"min": 1000,
"label": "1000 - 9999 人",
"color": "#8B0000"
},
{
"max": 999,
"min": 500,
"label": "500 - 999 人",
"color": "#CB0000"
},
{
"max": 499,
"min": 100,
"label": "100 - 499 人",
"color": "#DD5C5C"
},
{
"max": 99,
"min": 10,
"label": "10 - 99 人",
"color": "#FFA07A"
},
{
"max": 9,
"min": 1,
"label": "1 - 9 人",
"color": "#FFFF00"
},
]
#range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'])
))
# .render("全国确诊.html")
)
tab.add(_map, "累计确诊")
_map = (
Map().add('当前确诊人数', current_confirm, "china").set_series_opts(
label_opts=opts.LabelOpts(is_show=True)).set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(
city_list[-1][0])),
legend_opts=opts.LegendOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
max_=30,
is_piecewise=True,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000']))
# .render("全国确诊.html")
)
tab.add(_map, "当前确诊")
_map = (
Map().add('当前治愈人数', heal, "china").set_series_opts(
label_opts=opts.LabelOpts(is_show=True)).set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(
city_list[-1][0])),
legend_opts=opts.LegendOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
max_=1500,
is_piecewise=True,
pieces=[
# 数据范围
{
"min": 10000,
"label": ">=10000",
"color": "#143601"
},
{
"max": 9999,
"min": 1000,
"label": "1000 - 9999 人",
"color": "#1A4301"
},
{
"max": 999,
"min": 500,
"label": "500 - 999 人",
"color": "#245501"
},
{
"max": 499,
"min": 300,
"label": "300 - 499 人",
"color": "#538D22"
},
{
"max": 299,
"min": 100,
"label": "100 - 299 人",
"color": "#73A942"
},
{
"max": 99,
"min": 0,
"label": "1 - 99 人",
"color": "#AAD576"
},
]))
# .render("全国确诊.html")
)
tab.add(_map, "累计治愈")
_map = (
Map().add('当前死亡人数', dead, "china").set_series_opts(
label_opts=opts.LabelOpts(is_show=True)).set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(
city_list[-1][0])),
legend_opts=opts.LegendOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
max_=50,
is_piecewise=True,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000']))
# .render("全国确诊.html")
)
tab.add(_map, "累计死亡")
tab.render(path="各省确诊人数.html")
if case["final_cheat"]:
yes += 1
else:
no += 1
case_oriented.append(yes)
no_case_oriented.append(no)
c = (
Bar()
.add_xaxis(xaxis)
.add_yaxis("面向用例题数", case_oriented, stack="stack1")
.add_yaxis("正常完成题数", no_case_oriented, stack="stack1")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="面向用例比例图"),
yaxis_opts=opts.AxisOpts(name="题目数量"),
xaxis_opts=opts.AxisOpts(name="用户ID"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
)
)
return c
tab = Tab()
tab.add(case_oriented(), "面向用例分布图")
tab.add(bar_difficult(), "难度分布图")
tab.add(scatter_debug_diff(), "字符串debug时间和难度点图")
tab.add(wordcloud(), "tag词云图")
tab.render("tab_summary.html")
offset=80,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#5793f3")),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
))
line = (Line().add_xaxis(x_data).add_yaxis(
"平均温度",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
))
bar.overlap(line)
return Grid().add(bar,
opts.GridOpts(pos_left="5%", pos_right="20%"),
is_control_axis_index=True)
tab = Tab()
tab.add(bar_datazoom_slider(), "bar-example")
tab.add(line_markpoint(), "line-example")
tab.add(pie_rosetype(), "pie-example")
tab.add(grid_mutil_yaxis(), "grid-example")
tab.render("Tab_base.html")
# 计算未提交人数
df['已提交'] = df.apply(lambda x: x.value_counts().get(0, 0), axis=1)
print(df)
df['提交率'] = 1 - df.已提交 / (df.columns.size - 1)
df['提交率'] = df['提交率'].apply(lambda x: format(x, '.2'))
print(df.提交率)
xaxis = [
'1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',
'1'
]
for i in range(16):
xaxis[i] = '第' + str(i + 1) + '次'
print(xaxis)
def line_xaxis_type() -> Line:
c = (Line().add_xaxis(xaxis).add_yaxis(
'1班_赵丽华', submission_rate(dfz)).add_yaxis(
"4班_李烨", submission_rate(dfz)).set_global_opts(
title_opts=opts.TitleOpts(title="正确率"), ))
return c
tab = Tab()
tab.add(timeline_bar(), "作业平均分对比")
tab.add(line_xaxis_type(), '作业提交率')
tab.render()
def draw_charts(kline_data ,canshu ={} ,canshu2={},vol_bar=False,markline_show1 =False ,markline_show2 =False, path = '0501'):
'''
df['candle_begin_time','open','high','low','close','volume']
[['candle_begin_time','open','high','low','close','volume']]
kdata = df[['open', 'high', 'low', 'close']].values.tolist()
df['candle_begin_time'].values.tolist()
:return:
'''
df = kline_data.copy()
df['candle_begin_time'] = pd.to_datetime(df['candle_begin_time'])
time_list = df['candle_begin_time'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S')).values.tolist()
vol = df['volume'].values.tolist()
kline = Kline()
kline.add_xaxis(xaxis_data=time_list)
signal_pos_list = []
if 'signal' in df.columns:
print('signal,存在！')
df['pos'] = df['signal'].shift(1)
for i in df[df['pos'] > 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做多",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low -2],
value='买',
symbol ='circle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'red'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] < 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做空",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.high +3],
value='卖',
symbol='circle',
symbol_size=[20, 40],
itemstyle_opts={'color': 'blue'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] == 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="平仓",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low - 2],
value='平',
symbol ='triangle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'green'})
signal_pos_list.append(sig_pos0)
else :
df['pos'] =None
markline =[]
if markline_show1 and ('signal' in df.columns) :
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
if ix+1 > len(area_index_list)-1:
break
i_now = df.iloc[area_index_list[ix]]
i_next = df.iloc[area_index_list[ix+1]]
if (i_now['pos'] >0) or (i_now['pos'] <0) :
log_info = f"价差：={i_next['open']-i_now['open']}--({i_next['open']}-{i_now['open']})"
else :
log_info =f"平仓：{i_next['open']}---开仓：{i_now['open']}"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
sig_area_v = [{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_next['open'],
"value":None}]
markline.append(sig_area)
markline.append(sig_area_v)
elif markline_show2 and ('signal' in df.columns):
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
i_now = df.iloc[area_index_list[ix]]
i_1_now = df.iloc[area_index_list[ix-1]] if ix != 0 else 0
if i_now['pos'] == 0:
log_info = f"交易价: {round(i_now['open'], 1)} +- 2__盈亏:{round(i_now['open'], 1) - round(i_1_now['open'], 1)}"
else:
log_info = f"交易价: {round(i_now['open'],1)} +- 2"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": (i_now['candle_begin_time']+timedelta(minutes=15)).strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
markline.append(sig_area)
colors = {'red': 'rgb((220,20,60)','red2': 'rgb((250,20,40)',
'yangzi': 'rgb(255,0,255)', 'zi': 'rgb(128,0,128)',
'sehnzi': 'rgb(148,0,211)', 'blue': 'rgb(0,0,255)',
'qing': 'rgb(0,255,255)', 'black': 'rgb(0,0,0)',
'shengreen': 'rgb(157,255,212)', 'green': 'rgb(0,255,0)',
'yellow': 'rgb(255,255,0)', 'glod': 'rgb(218,165,32)',
'orange': 'rgb(255,165,0)', 'brown': 'rgb(165,42,42)'}
kline.add_xaxis(xaxis_data=time_list)
kline.add_yaxis(series_name="oclh",
xaxis_index=1,
yaxis_index=1,
y_axis =df.loc[:, ['open', 'close', 'low', 'high']].values.tolist(),
markline_opts=opts.MarkLineOpts(data=markline,
linestyle_opts=opts.LineStyleOpts(type_='dotted',width=3,color='red',opacity=0.5),
label_opts=opts.LabelOpts(position="right", color="blue", font_size=13),),
#官网给出的样本
markpoint_opts=opts.MarkPointOpts(data=signal_pos_list),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0090", color0="#00aa3c"), )
kline.set_global_opts(
legend_opts=opts.LegendOpts(is_show=True,pos_top=30, pos_left="left",orient='vertical'),
datazoom_opts=[ opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=90,
range_end=100,
orient='vertical'),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="20%",
range_start=90,
range_end=100,orient='vertical'),],
yaxis_opts =opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),),
title_opts=opts.TitleOpts(
title = 'K_line',
pos_top='middle',
title_textstyle_opts=opts.TextStyleOpts(
color='black',font_weight='bold' ,font_size=20)),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
trigger_on='"mousemove"',#click|mousemove
axis_pointer_type="cross",
is_show_content=True,
is_always_show_content=True,
background_color="rgba(20, 105, 245, 0.1)",
border_width=1,
border_color= "#ccc",
position = ['70%','30%'],
textstyle_opts=opts.TextStyleOpts(font_size=10,color="#000"),),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[{"value": 1, "color": "#00da3c"},{"value": -1, "color": "#ec0000"},],),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",),
)
if len(canshu.keys())>0:
cos = list(colors.values())
line = Line()
for k,v in canshu.items():
line.add_xaxis(xaxis_data=time_list)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
co = cos.pop()
line.add_yaxis(
series_name=k,
y_axis = [y for y in v.values.tolist() if y != np.nan],
xaxis_index=1,
yaxis_index=1,
is_smooth=False,
is_connect_nones=False,# 是否连接空数据
is_symbol_show=False,#是否显示值的位置，默认显示。
color = co,
is_hover_animation = False, # 是否开启 hover 在拐点标志上的提示动画效果。
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9,color=co),
label_opts=opts.LabelOpts(is_show =True,position='middle',distance=2,rotate=5,color = 'rgb(165,42,42)'),
itemstyle_opts=opts.ItemStyleOpts(color=co),)
kline = kline.overlap(line)
grid_chart = Grid(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK))
grid_chart.add(kline,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%",pos_top='4%' ,height="70%"))
if( vol_bar == True) or (len(canshu2.keys())==0):
bar = Bar()
bar.add_xaxis(xaxis_data=time_list)
bar.add_yaxis(
series_name="volume",
y_axis=vol,
xaxis_index=1,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False), )
bar.set_global_opts(xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax", ),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False), ),
legend_opts=opts.LegendOpts(is_show=True), )
grid_chart.add(bar,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="80%", height="15%"),)
if len(canshu2.keys())>0 :
line2 = Line()
line2.add_xaxis(xaxis_data=time_list)
for i, k in enumerate(canshu2.keys()):
co = random.choice(list(colors.values()))
line2.add_yaxis(
series_name=k,
y_axis=[y for y in canshu2[k].values.tolist()],
xaxis_index=1,
yaxis_index=i + 1,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=True,
color=co,
z_level=0,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5, color=co),
label_opts=opts.LabelOpts(is_show=False), )
if k == list(canshu2.keys())[-1]: continue
line2.extend_axis(yaxis=opts.AxisOpts(name=k, type_="value", position="right", min_=None, max_=None,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color=co)),
axislabel_opts=opts.LabelOpts(formatter="{value}"), ))
line2.set_global_opts(xaxis_opts=opts.AxisOpts(
type_="category", is_scale=True, grid_index=1, split_number=20, min_="dataMin", max_="dataMax",
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False), ),
yaxis_opts=opts.AxisOpts(name=k, grid_index=i + 1, position='right',
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
min_=None, max_=None, is_scale=True, offset=50, ), )
grid_chart.add(line2,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%", height="23%"),is_control_axis_index=True)
tab = Tab()
zijin_data = (kline_data['per_lr'].cumsum())
zijin_data.fillna(method='ffill', inplace=True)
zijin_line = Line(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK)).add_xaxis(time_list)
zijin_line.add_yaxis(series_name="zijin_line:", y_axis=zijin_data.values, color="#FF0000")
zijin_line.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="资金曲线变化"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts = opts.AxisOpts(
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False), ),
legend_opts = opts.LegendOpts(is_show=True)
)
tab.add(grid_chart,'kline' )
tab.add(zijin_line,'资金曲线变化' )
tab.render(f"{path}.html")
html0 = tab.render(f"{path}.html")
if os.path.exists(html0):
print("ok!保存在：")
print(html0)
else:
print('保存失败！')
