def test_timeline_map():
timeline = Timeline(timeline_bottom=0)
value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = ["福建", "山东", "北京", "上海", "甘肃", "新疆", "河南", "广西", "西藏"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test1")
value = [155, 10, 66, 78, 33]
attr = ["福建", "山东", "北京", "上海", "甘肃"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test2")
assert len(timeline.options.get("baseOption").get("series")) == 2
timeline.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True,
timeline_bottom=0,
width=1200,
height=600)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
timeline.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(
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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(
is_auto_play=True, timeline_bottom=0, width=1200, height=600
)
timeline.add(pie_1, "2012 年")
timeline.add(pie_2, "2013 年")
timeline.add(pie_3, "2014 年")
timeline.add(pie_4, "2015 年")
timeline.add(pie_5, "2016 年")
assert len(timeline.options.get("baseOption").get("series")) == 0
timeline.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(
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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True, timeline_bottom=0,
width=1200, height=600)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
timeline.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [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", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True,
timeline_bottom=0,
width=1200,
height=600)
timeline.add(pie_1, "2012 年")
timeline.add(pie_2, "2013 年")
timeline.add(pie_3, "2014 年")
timeline.add(pie_4, "2015 年")
timeline.add(pie_5, "2016 年")
assert len(timeline._option.get("baseOption").get("series")) == 0
timeline.render()
def test_timeline_map():
timeline = Timeline(timeline_bottom=0)
value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = ["福建", "山东", "北京", "上海", "甘肃", "新疆", "河南", "广西", "西藏"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test1")
value = [155, 10, 66, 78, 33]
attr = ["福建", "山东", "北京", "上海", "甘肃"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test2")
assert len(timeline._option.get("baseOption").get("series")) == 2
timeline.render()
def ShowNumWithYear(df):
# show bus num in different year at different cities
years = list(set(df['year']))
years.sort()
cities = []
values = []
total_num = 0
geos = [] # store the geo every year
timeline = Timeline(width=1500,height=800,is_auto_play=True, timeline_bottom=-10,timeline_symbol_size=20,\
timeline_play_interval = 800,timeline_left=20,timeline_right=100 , is_timeline_show = False )
for index in range(len(years)):
df_temp = df[df['year'] == years[index]]
cities = cities + list(df_temp['city'])
values = values + list(df_temp['num'])
total_num = sum(values)
geos.append(Geo( str(years[index]) + " , Fist level title" , title_top = "10%" , title_text_size=50 , subtitle = "second level title" , \
subtitle_text_size = 23 , subtitle_color="white", \
title_color="red", title_pos="center", width=1200, height=600, \
background_color='#404a59'))
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
geos[index].add("数量", cities, values, type="effectScatter", maptype='china' , is_random=True, effect_scale=3, is_selected = True,is_toolbox_show = True ,is_more_utils =True,\
visual_text_color="#fff", symbol_size=10, is_label_show = True , legend_orient = 'left' ,is_legend_show = False, legend_top = 'bottom' , label_formatter = '{b}' , \
is_visualmap=True, is_roam=True , label_text_color="#00FF00" , is_piecewise=True, label_text_size = 7,visual_range=[1, 300] , \
geo_cities_coords = {'柯桥': [120.443 , 30.0822] ,} , \
pieces=[
{"min":0.1, "max": 500 , "label": "0-500"},
{"min": 500, "max": 1000 , "label": "501-1000"},
{"min": 1001, "max": 2000 , "label": "1001-2000"},
{"min":2001, "max": 5000, "label": "2001-5000"},
{"min":5001, "max": 100000, "label": ">5000"}, ] )
geos[index].show_config()
geos[index].render("数量.html")
# 时间轴定义
timeline.add(geos[index], years[index])
timeline.render('redult.html')
def show_ruanke_best(begin_year,end_year):
'''
@author: Hong wentao
用于展示每一个学科的学校的排名可视化，使用的是软科最好大学排名的数据
'''
path=data_path+"ruanke_best_subject/"
ruanke_html_path=html_path+"ruanke/"
isExists=os.path.exists(ruanke_html_path)
if not isExists:
os.makedirs(ruanke_html_path)
first_names=os.listdir(path)
for first_name in first_names:
first_data_path=path+first_name+"/"
first_html_path=ruanke_html_path+first_name+"/"
isExists=os.path.exists(first_html_path)
if not isExists:
os.makedirs(first_html_path)
second_names=os.listdir(first_data_path)
for second_name in second_names:
second_data_path=first_data_path+second_name+"/"
second_html_path=first_html_path+second_name+"/"
isExists=os.path.exists(second_html_path)
if not isExists:
os.makedirs(second_html_path)
data=deal_ruanke.every_subject_ruanke(first_name,second_name)
timeline = Timeline(is_auto_play=False, timeline_bottom=0,width=stand_width)
for i in range(begin_year,end_year+1):
bar = Bar(second_name+str(i)+"年大学排名数据")
try:
want_data=data[i]
nan='空白区域说明该学校当年无该数据'
final_data=[]
have_name=[]
before_median=[]
for x in want_data:
have_name.append(x[2])
for name in university_name:
if name not in have_name:
final_data.append("nan")
else:
for x in want_data:
if x[2]==name:
final_data.append(x[0])
before_median.append(x[0])
break;
a=np.array(before_median).astype("int")
median=np.median(a).astype("int")
bar.add("排名 "+nan, university_name,final_data, is_label_show=True,is_stack=True,xaxis_interval=0,xaxis_name_size=15,xaxis_rotate=0,yaxis_name_rotate=0,xaxis_name_pos="end", xaxis_name="学校名",yaxis_name="排名",yaxis_name_gap=10,yaxis_name_pos="end",mark_point=["max","min"],mark_line_raw=[{"yAxis": median,"name":"中位数","lineStyle": {
"color": 'black'
}}])
timeline.add(bar, str(i)+'年')
timeline.render(second_html_path+second_name+".html")
except Exception:
print(first_name+" "+second_name+str(i)+"没有")
continue
timeline.render(second_html_path+second_name+".html")
def time_line():
timeline = Timeline(page_title="Time Line")
stack_bar, line = StackBar.stack_bar()
tree_chart = TreeChart.tree_chart()
stack_bar_chart = StackBarChart.stack_bar_chart2()
stack_line = StackChart.starck_line_chart()
timeline.add(stack_line, time_point=7)
timeline.add(stack_bar_chart, time_point=8)
timeline.add(tree_chart, time_point=6)
timeline.add(stack_bar,time_point=2)
timeline.add(line, time_point=3)
timeline.render("timeline.html")
def geographical_location_GDP_ratio_distribution():
data = pd.read_excel("./data/car_sales_data.xlsx", sheet_name=u"人均GDP", encoding='utf-8')
data.columns = [str(col).split('-')[0] for col in data.columns]
float_cols = [str(year) for year in range(1998, 2018)]
not_replace = [
u'池州市', u'宣城市', u'眉山市', u'达州市', u'庆阳市',
u'广安市', u'贺州市', u'来宾市', u'崇左市', u'临沧市',
u'固原市', u'中卫市', u'丽江市'
]
data['city_name'] = data['city_name'].apply(lambda xx: xx.replace(u'市', '') if xx not in not_replace else xx)
city_none = [u'黔西南布依族苗族自治州', u'乌兰察布', u'巴音郭楞蒙古自治州（库尔勒）']
data = data[~data['city_name'].isin(city_none)]
data[float_cols] = data[float_cols].fillna(0).applymap(lambda xx: round(xx, 2) if xx else 0)
cols_show = ['city_name'] + float_cols
data = data[cols_show]
data = calc_ratio_percent(data)
data[range(1999, 2018)] = data[range(1999, 2018)].applymap(lambda xx: round(xx * 100, 2))
data.to_excel('./car_sales_visualization/GDP_ratio_distribution.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1999, 2018):
dataset = [(city, sales) for city, sales in data[['city_name'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100.0, 100.0],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/GDP_ratio_distribution.html')
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')
assert len(timeline._option.get("baseOption").get("series")) == 10
timeline.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_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 geographical_location_ratio_distribution():
data = pd.read_csv("./data/car_sales_volume_data.csv", encoding='utf-8')
data = data[data['city'].str.len() > 1]
data = calc_ratio_percent(data)
data[range(1998, 2019)] = data[range(1998, 2019)].applymap(lambda xx: round(xx * 100, 4))
cols_show = ['city'] + range(1998, 2019)
data = data[cols_show]
city_none = [
u'三沙', u'中卫', u'临沧', u'丽江',
u'克孜勒苏柯尔克孜自治州', u'其它',
u'固原', u'新疆自治区直辖', u'普洱',
u'河南省省直辖', u'海南省省直辖', u'湖北省省直辖'
]
data = data[~data['city'].isin(city_none)]
data.to_excel('./car_sales_visualization/car_sales_num_ratio.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1998, 2019):
dataset = [(city, sales) for city, sales in data[['city'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100, 100],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/car_sales_num_ratio.html')
def get_html_project_report(project_name, type=0):
mylogger = common.LogOutput.Log().get_logger()
mylogger.info(
"------------------------------------------------------------------")
mylogger.info("当前运行文件：{}".format(__file__))
try:
project_run_num = get_project_run_num(project_name, type)
# num = [successNum, failedNum, noRunNum]
num = [project_run_num[0], project_run_num[1], project_run_num[2]]
attr = ["成功用例", "失败用例", "不执行用例"]
bar = Bar(project_name, "用例执行结果")
bar.add("", attr, num, is_more_utils=True)
bar.show_config()
pie_1 = Pie(project_name, "用例执行结果")
pie_1.add(project_name,
attr,
num,
is_label_show=True,
radius=[30, 55],
rosetype='radius')
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar, '树形图')
timeline.add(pie_1, '饼形图')
PATH = lambda P: os.path.abspath(
os.path.join(os.path.dirname(__file__), P))
html_dirname = PATH(
"../report") + "\\" + project_name + "\\" + "htmlReport\\"
# 判断是否存在case目录，没有，则创建
if not os.path.exists(html_dirname):
os.mkdir(html_dirname)
mylogger.info("生成用例目录路径：{}".format(html_dirname))
if type == 0:
html_report_name = time.strftime('%Y-%m-%d %H-%M-%S') + '.html'
elif type == 1:
html_report_name = 'settingReport' + time.strftime(
'%Y-%m-%d %H-%M-%S') + '.html'
htmlReportPath = html_dirname + html_report_name
timeline.render(htmlReportPath)
return 'success'
except TypeError as e:
return 'caseEmpty', e
def test_timeline_bar():
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")
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")
assert len(timeline._option.get("baseOption").get("series")) == 0
timeline.render()
def test_timeline_bar():
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")
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")
assert len(timeline.options.get("baseOption").get("series")) == 0
timeline.render()
def geographical_location_amount_distribution():
data = pd.read_csv("./data/car_sales_volume_data.csv", encoding='utf-8')
data = data[data['city'].str.len() > 1]
city_none = [
u'三沙', u'中卫', u'临沧', u'丽江',
u'克孜勒苏柯尔克孜自治州', u'其它',
u'固原', u'新疆自治区直辖', u'普洱',
u'河南省省直辖', u'海南省省直辖', u'湖北省省直辖'
]
data = data[~data['city'].isin(city_none)]
int_cols = [str(col) for col in range(1998, 2019)]
data[int_cols] = data[int_cols].applymap(lambda xx: float(xx))
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1998, 2019):
dataset = [(city, sales) for city, sales in data[('city %s' % year).split()].values]
print dataset
geo = Geo(
"%s - Car Sales Num Amount Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2700,
height=1340,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
maptype='china',
visual_range=[0, 300000],
visual_text_color="black",
type="heatmap",
is_visualmap=True,
effect_scale=5,
symbol_size=5,
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/car_sales_num_amount.html')
def bar_map():
kwargs = dict(
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='right',
legend_top='center',
legend_selectedmode='single',
legend_text_size=18,
label_text_size=18,
label_emphasis_textsize=18,
xaxis_name_size=18,
xaxis_label_textsize=18,
xaxis_margin=5,
xaxis_line_width=2,
yaxis_name_size=18,
yaxis_label_textsize=18,
bar_category_gap=25,
yaxis_line_width=2,
is_splitline_show=False,
is_xaxis_boundarygap=True,
is_toolbox_show=False,
)
timeline = Timeline(timeline_bottom=0,
width=1400,
height=700,
timeline_symbol_size=10)
for year in value_base.current_years:
bar = Bar('村镇对比图',
title_pos='center',
title_text_size=30,
title_top=10)
for index in value_base.current_indexs:
data = []
for i in value_base.current_areas:
for veti_data in value_base.veti_data_dict[year]:
if veti_data[0] == i:
data.append(
veti_data[value_base.index_num_dict[index]])
bar.add(index, value_base.current_areas, data, **kwargs)
timeline.add(bar, year)
result = str(value_base.output_dir + u"村镇对比.html")
timeline.render(result)
def plot_bar_graph():
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")
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)
timeline = Timeline(page_title=u'销量分析',
is_auto_play=False,
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')
timeline.render(FILE_DIR + 'bar_graph.html')
def radar_map():
kwargs = dict(label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='right',
legend_top='center',
legend_text_size=18,
label_text_size=18,
label_emphasis_textsize=18,
xaxis_name_size=18,
xaxis_label_textsize=18,
xaxis_margin=5,
xaxis_line_width=2,
yaxis_name_size=18,
yaxis_label_textsize=18,
yaxis_line_width=2,
is_splitline_show=False,
is_xaxis_boundarygap=True,
is_toolbox_show=False,
line_width=3)
timeline = Timeline(timeline_bottom=0,
width=1400,
height=700,
timeline_symbol_size=10)
for year in value_base.current_years:
radar_map = Radar('雷达图',
title_pos='center',
title_text_size=30,
title_top=10)
for area in value_base.current_areas:
data = [[]]
for index in value_base.current_indexs:
for veti_data in value_base.veti_data_dict[year]:
if veti_data[0] == area:
data[0].append(
round(veti_data[value_base.index_num_dict[index]],
2))
radar_map.set_radar_component(schema=value_base.radar_schema)
radar_map.add(area, data, **kwargs)
timeline.add(radar_map, year)
result = str(value_base.output_dir + u"雷达图.html")
timeline.render(result)
def plot_map(df):
# maptype='china' 只显示全国直辖市和省级
# 数据只能是省名和直辖市的名称
# province_distribution = {'青岛': 22, '龙口': 37.56, '河北': 21, '辽宁': 12, '江西': 6, '上海': 20, '安徽': 10, '江苏': 16, '湖南': 9, '浙江': 13, '海南': 2, '广东': 22, '湖北': 8, '黑龙江': 11, '澳门': 1, '陕西': 11, '四川': 7, '内蒙古': 3, '重庆': 3, '云南': 6, '贵州': 2, '吉林': 3, '山西': 12, '山东': 11, '福建': 4, '青海': 1, '舵主科技，质量保证': 1, '天津': 1, '其他': 1}
# provice=list(province_distribution.keys())
# values=list(province_distribution.values())
years = list(df.index)
geos = []
timeline = Timeline(width=1700,height=900,is_auto_play=True, timeline_bottom=-10,timeline_symbol_size=20,timeline_play_interval=400,timeline_left=20,timeline_right=100 , \
is_timeline_show = False )
for index in range(len(years)):
cities = list(df.columns)
cities.remove('total')
values = list(df.loc[years[index], :])
total_num = values[-1]
del (values[-1])
# print(cities)
# print(values)
geos.append(Geo( str(int(total_num)), title_top="10%" , title_text_size=50 , subtitle = years[index] +" , subtitle", \
subtitle_text_size = 23 , subtitle_color="white", \
title_color="red", title_pos="center", width=1200, height=600, \
background_color='#404a59'))
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
geos[index].add("title level1", cities, values, type="effectScatter", maptype='china' , is_random=True, effect_scale=3, is_selected = True,is_toolbox_show = True ,is_more_utils =True,\
visual_text_color="#fff", symbol_size=10, is_label_show = True , legend_orient = 'left' ,is_legend_show = False, legend_top = 'bottom' , label_formatter = '{b}' , \
is_visualmap=True, is_roam=True , label_text_color="#00FF00" , is_piecewise=True, label_text_size = 7,visual_range=[1, 300] , \
geo_cities_coords = {'赣江': [115.934192 , 28.826235] , '红河州' : [103.381549,23.369996] , '蒙自' : [103.371546,23.40208] , '海安' : [120.469259,32.544553] , \
'济阳' : [117.023094,36.965519] , '库车' : [82.970183,41.733785] , '文山-砚山' : [104.334442,23.621612] , '文安':[116.455985,38.891083] , '罗平':[104.309188,24.890519] , \
'宣城' : [118.762662,30.957007] , '古田' : [118.747401,26.596702] , '泗阳':[118.699691,33.723524] , } , \
pieces=[
{"min":0.1, "max": 50 , "label": "0-50"},
{"min": 51, "max": 100 , "label": "51-100"},
{"min": 101, "max": 200 , "label": "101-200"},
{"min":201, "max": 500, "label": "201-500"},
{"min":500, "max": 2900, "label": ">500"}, ] )
geos[index].show_config()
geos[index].render("xxxx售出数量.html")
# 时间轴定义
timeline.add(geos[index], years[index])
timeline.render('final_graph.html')
def test_timeline_bar():
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")
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')
timeline.render()
def test_timeline_bar():
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")
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')
timeline.render()
rosetype='radius')
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季",
attr, [random.randint(10, 100) for _ in range(6)],
is_label_show=True,
radius=[30, 55],
rosetype='radius')
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季",
attr, [random.randint(10, 100) for _ in range(6)],
is_label_show=True,
radius=[30, 55],
rosetype='radius')
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季",
attr, [random.randint(10, 100) for _ in range(6)],
is_label_show=True,
radius=[30, 55],
rosetype='radius')
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
timeline.render('./picture13.html')
timeline = Timeline(is_auto_play=False, timeline_bottom=0)
df["time_ymd"] = df["time"].apply(lambda x: x[:10])
grouped = copy.deepcopy(df.groupby("time_ymd")) #根据日期进行分组
for single in grouped:
date = single[0]
dff = pd.DataFrame()
# dff=single[1]
# print(dff.loc[:,"area"])
dff.loc[:, "area"] = single[1].loc[:, "area"].apply(handleArea)
dff.dropna(axis=0)
arr = dff.groupby("area")["area"].count(
) #该时间段的每个地区的评论数,返回Series,index是每组的"area"，values是评论数
# attr=dff.groupby("area")
# print(type(arr))
tempGeo = Geo("%s日评论数" % date,
"“中国年轻人正带领国家走向危机”，这锅背是不背？",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59")
# print(arr.values)
tempGeo.add("", arr.index, arr.values, is_visualmap=True)
timeline.add(tempGeo, "%s" % date)
# print(single[1])
timeline.render("时间线图.html")
def queren(self):
date_field = self.comboBox.currentText()
startdate = self.comboBox_3.currentText()
enddate = self.comboBox_2.currentText()
spacefield = self.comboBox_4.currentText()
space_val = self.comboBox_6.currentText()
maptype_val = self.comboBox_7.currentText()
aoi_val = self.comboBox_8.currentText()
db = pymysql.connect("localhost",
"root",
"960609",
"biye",
charset='utf8')
# 使用cursor()方法获取操作游标
cursor = db.cursor()
#找对应的时间
if space_val == 'china' or space_val == 'world':
datetime_sql='select distinct '+ date_field+' from AOI where '+date_field+\
' between \''+startdate+'\' and \''+enddate+'\''
else:
#这里字段写死了，表中一定要有province字段
datetime_sql='select distinct '+ date_field+' from AOI where province'+'=\''+space_val+'\' and '+date_field+\
' between \''+startdate+'\' and \''+enddate+'\''
cursor.execute(datetime_sql)
rows = cursor.fetchall()
time_val = []
for (row, ) in rows:
timeval = str(row)
time_val.append(timeval)
# 加入时间轴
timeline = Timeline()
#单张地图
for i in range(len(time_val)):
# 查找地名
if space_val == 'china' or space_val == 'world':
location_sql = 'select ' + spacefield + ' from AOI where ' + date_field + '=\'' + time_val[
i] + '\''
else:
location_sql = 'select ' + spacefield + ' from AOI where province =\'' + space_val + '\' and ' + date_field + \
'=\'' + time_val[i]+'\''
cursor.execute(location_sql)
rows = cursor.fetchall()
location_val = []
for (row, ) in rows:
locationval = str(row)
location_val.append(locationval)
# 查找值
if space_val == 'china' or space_val == 'world':
value_sql = 'select ' + aoi_val + ' from AOI where ' + date_field + '=\'' + time_val[
i] + '\''
else:
value_sql = 'select ' + aoi_val + ' from AOI where province'+ '=\'' + space_val + '\' and ' + date_field + \
'=\'' + time_val[i]+'\''
cursor.execute(value_sql)
rows = cursor.fetchall()
val = []
for (row, ) in rows:
aoival = str(row)
val.append(aoival)
# 设置一张地图
geo = Geo("", width=1000, height=600, background_color='#404a59')
self.drawtimeline(location_val, val, geo, space_val, maptype_val)
timeline.add(geo, str(time_val[i]))
timeline.render("test.html")
self.dywebmap.show_table()
def stock_render_page(stock_info, start_date, end_date, interval, width,
height):
page = Page()
stock_name = stock_info.split('-', 2)[0]
stock_id = stock_info.split('-', 2)[1]
action = stock_info.split('-', 2)[2]
a = generate_stock_line(
stock_id, action, start_date, end_date,
interval) # stock number, Type, startdate, enddate, 30 or 15 or days
if a is None:
return
if action == "Kline":
time, open, close, low, high = zip(
*a) # get time from returned dictionary
else:
time, target = zip(*a)
a = [time, target]
if action != "Kline":
if len(time[0]) == 4 and time[0][2] == "bar": # for 分笔data
overlap = Overlap()
form = [e[1] for e in a]
bar = Bar(stock_name + "-" + stock_id + "-" + action,
width=width * 10 / 11,
height=(height * 10 / 11))
bar.add(stock_name + "-" + stock_id + "-" + action,
time,
form,
yaxis_min="dataMin",
yaxis_max="dataMax",
is_datazoom_show=True,
datazoom_type="slider")
overlap.add(bar)
line = Line(stock_name + "price" + "-" + action,
width=width * 10 / 11,
height=(height * 10 / 11))
price = [e[3] for e in a]
line.add(stock_name + "price" + "-" + action,
time,
price,
yaxis_min="dataMin",
yaxis_max="dataMax",
is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
if len(a[0]) == 5 and a[0][3] == "pie":
overlap = Overlap()
time_line = Timeline(
is_auto_play=False, timeline_bottom=0
) # zip(namearray,valuearray,quarter,flag,num)
name_array = [c[0] for c in a]
value_array = [d[1] for d in a]
quarter = [e[2] for e in a]
num = a[0][4]
for x in range(0, num / 10):
list1 = value_array[x]
names = name_array[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie("前十股东",
width=width * 10 / 11,
height=(height * 10 / 11))
pie.add("前十股东",
names,
list1,
radius=[30, 55],
is_legend_show=False,
is_label_show=True,
label_formatter="{b}: {c}\n{d}%")
# print list
# print names
# print quarterarray
time_line.add(pie, quarters)
# namearray = [y for y in namearray[x]]
time_line.render()
return
# need more statement
else:
line = Line(stock_name + "-" + stock_id + "-" + action,
width=width * 10 / 11,
height=(height * 10 / 11))
line.add(stock_name + "-" + stock_id + "-" + action,
time,
target,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_min="dataMin",
yaxis_max="dataMax")
page.add(line)
page.add(line)
else:
overlap = Overlap() #for k线
candle = [open, close, low, high]
candlestick = Kline(stock_name + "-" + stock_id + "-" + action,
width=width * 10 / 11,
height=(height * 10 / 11))
candlestick.add(stock_name + "-" + stock_id + "-" + action,
time,
candle,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_interval=1)
overlap.add(candlestick)
if len(close) > 10:
ma10 = calculate_month(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(stock_name + "-" + "MA10" + "-" + action, time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculate_month(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(stock_name + "-" + "MA20" + "-" + action, time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculate_month(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(stock_name + "-" + "MA30" + "-" + action, time, ma30)
overlap.add(line3)
page.add(overlap)
page.render()
def draw_month_timeline(df,time_col,tgt,time_type=None,time_format='%Y-%m-%d %H:%M:%S',file_name='month_timeline.html',draw_type='timeline'):
month_30=[4,6,9,11]
month_31=[1,3,5,7,8,10,12]
if time_type == 'timestamp':
temp_time = df[time_col].map(lambda x: datetime.datetime.date(datetime.datetime.fromtimestamp(int(x))))
elif time_type == 'str':
temp_time = df[time_col].map(lambda x: datetime.datetime.date(datetime.datetime.strptime(x,time_format)))
else:
temp_time = df[time_col].map(lambda x: datetime.datetime.date(x))
temp = pd.concat([temp_time,df[tgt]],axis=1)
temp['day'] = temp[time_col].map(lambda x: x.day)
min_time = temp[time_col].min()
max_time = temp[time_col].max()
month_interval = (max_time.year-min_time.year)*12+(max_time.month-min_time.month)+1
color_list = draw.random_color_list(month_interval)
start_time = datetime.datetime.date(datetime.datetime.\
strptime(str(temp[time_col].min().year)+'-'+str(temp[time_col].min().month)+'-01',"%Y-%m-%d"))
if draw_type == 'timeline':
timeline = Timeline(timeline_bottom=0)
while start_time<max_time:
if (start_time.month in month_30):
month_day = 30
elif (start_time.month in month_31):
month_day = 31
elif (start_time.month==2&(((start_time.year%100 == 0)&((start_time.year/100%4)==0))|((start_time.year%100!=0)&((start_time.year%4)==0)))):
month_day = 29
else:
month_day = 28
temp_df = temp[((temp[time_col]>=start_time)&(temp[time_col]<start_time+relativedelta(months=1)))]
temp_df_size = temp_df.groupby(['day']).size()
temp_df_mean = temp_df.groupby(['day']).mean()
time_df = pd.concat([temp_df_size,temp_df_mean],axis=1)
time_df.rename(columns={0:'size'},inplace=True)
idx = time_df.index.values[(np.abs(time_df[tgt].values-time_df[tgt].mean())).argmin()]
mean_val = time_df[tgt].loc[idx]
missing_day = list(set(range(1,month_day+1))-set(time_df.index.values))
if len(missing_day)>0:
missing_len = len(missing_day)
missing_size = list(np.zeros(missing_len))
missing_mean = list(np.zeros(missing_len))
temp_missing_df = pd.DataFrame([missing_day,missing_size,missing_mean]).T
temp_missing_df.rename(columns={0:'day',1:'size',2:tgt},inplace=True)
temp_missing_df.set_index('day',inplace=True)
time_df = pd.concat([time_df,temp_missing_df])
time_df.sort_index(inplace=True)
time_df.index = time_df.index.values.astype(int)
x_axis_label = ['{}'.format(i) for i in time_df.index.values.tolist()]
bar = Bar("{}年{}月份数据".format(start_time.year,start_time.month))
bar.add("数量", x_axis_label, time_df['size'].values.tolist(),label_color=color_list,\
label_emphasis_textcolor='#000000',is_more_utils=True)
line = Line()
line.add("{} rate".format(tgt), x_axis_label, round(time_df[tgt],2).values.tolist(),label_color=color_list,\
mark_point=[{"coord":[str(idx),round(mean_val,2)],"name":"mean value"}],\
label_emphasis_textcolor='#000000')
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
timeline.add(overlap,"{}-{}".format(start_time.year,start_time.month))
start_time += relativedelta(months=1)
print('File rendered at {}'.format(os.path.join(ROOT_DIR,file_name)))
return timeline.render(os.path.join(ROOT_DIR,file_name))
elif draw_type == 'all_in_one':
month_day = 31
overlap = Overlap()
while start_time<max_time:
temp_df = temp[((temp[time_col]>=start_time)&(temp[time_col]<start_time+relativedelta(months=1)))]
time_df = temp_df.groupby(['day']).mean()
idx = time_df.index.values[(np.abs(time_df[tgt].values-time_df[tgt].mean())).argmin()]
mean_val = time_df[tgt].loc[idx]
missing_day = list(set(range(1,month_day+1))-set(time_df.index.values))
time_df.sort_index(inplace=True)
time_df.index = time_df.index.values.astype(int)
x_axis_label = ['{}'.format(i) for i in time_df.index.values.tolist()]
line = Line("{} rate数据".format(tgt))
line.add("{}-{}".format(start_time.year,start_time.month), \
x_axis_label, round(time_df[tgt],2).values.tolist(),label_color=color_list,\
mark_point=[{"coord":[str(idx),round(mean_val,2)],"name":"mean value"}],\
label_emphasis_textcolor='#000000',is_more_utils=True)
overlap.add(line)
start_time += relativedelta(months=1)
return overlap
def graphpage(items,startdate,enddate,option,width1, height1): #labels:复权ork线or分笔 option:hfq, qfq or 15, 30, D, etc
page = Page()
for i in items:#generate numbers of graphs according to numbers of queries in treewidget
j = re.split("-",i)
if len(j)==3:
a = generateline(j[1],j[2],startdate,enddate,option)#stock number, Type, startdate, enddate, 30 or 15 or days
if a is None:
continue
time = [d[0] for d in a]#get time from returned dictionary
if j[2]!="Kline":
if len(a[0])==4 and a[0][2]=="bar": #for 分笔data
overlap = Overlap()
form = [e[1] for e in a]
bar = Bar(j[0] + "-" + j[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(j[0] + "-" + j[2], time, form, yaxis_min = "dataMin",yaxis_max = "dataMax",is_datazoom_show = True, datazoom_type = "slider")
overlap.add(bar)
line = Line(j[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in a]
line.add(j[0] + "price", time, price, yaxis_min = "dataMin",yaxis_max = "dataMax", is_datazoom_show = True, datazoom_type = "slider",
yaxis_type="value")
overlap.add(line,yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
if len(a[0])==5 and a[0][3]=="pie":
overlap = Overlap()
timeline = Timeline(is_auto_play=False, timeline_bottom=0) #zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in a]
valuearray = [d[1] for d in a]
quarter = [e[2] for e in a]
num = a[0][4]
for x in range(0, num / 10):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(j[0]+"-"+"前十股东".decode("utf-8"),width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(j[0]+"-"+"前十股东".decode("utf-8"), names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter = "{b}: {c}\n{d}%")
# print list
# print names
# print quarterarray
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
return
#need more statement
else:
form = [e[1] for e in a]#for not分笔 data
line = Line(j[0] + "-" + j[2], width=width1*10/11, height=(height1*10/11)/len(items))
line.add(j[0] + "-" + j[2], time, form, is_datazoom_show=True, datazoom_type="slider",yaxis_min="dataMin",yaxis_max="dataMax")
page.add(line)
else:
overlap = Overlap()#for k线
close = zip(*a)[2]
candle = [[x[1], x[2], x[3], x[4]] for x in a]
candlestick = Kline(j[0] + "-" + j[2], width=width1*10/11, height = (height1*10/11) / len(items))
candlestick.add(j[0], time, candle, is_datazoom_show=True, datazoom_type="slider",yaxis_interval = 1)
overlap.add(candlestick)
if len(close)>10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(j[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close)>20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(j[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close)>30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(j[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:
for k in range(1, len(j)/3):#if graphs are combined
j[3*k-1] = re.sub("\n&","",j[3*k-1])
sizearray=[]
#if j[1] != "Candlestick"
layout = Overlap()
for i in xrange(0, len(j),3):
array = j[i:i +3]
b = generateline(array[1],array[2],startdate,enddate,option)
if b is None:
continue
btime = [d[0] for d in b]
if array[2] != "Kline":
if len(b[0])==4 and b[0][2]=="bar":
form = [e[1] for e in b]
bar = Bar(array[0] + "-" + array[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(array[0] + "-" + array[2], btime, form, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_max="dataMax")
layout.add(bar)
line = Line(array[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in b]
line.add(array[0] + "price", btime, price, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_type="value")
layout.add(line, yaxis_index=1, is_add_yaxis=True)
else:
line = Line(array[0] + "-" + array[2],width=width1*10/11, height=(height1*10/11) / len(items))
line.add(array[0]+"-"+array[2], btime, b, is_datazoom_show=True, yaxis_max = "dataMax", yaxis_min = "dataMin",datazoom_type="slider")
layout.add(line)
else:
candle = [[x[1], x[2], x[3], x[4]] for x in b]
candlestick = Kline(array[0] + "-" + array[1], width=width1*10/11,
height=(height1*10/11) / len(items))
candlestick.add(array[0], btime, candle, is_datazoom_show=True, datazoom_type=["slider"])
#if i == 0:
close = zip(*b)[2]
if len(close)>10:
ma10 = calculateMa(close, 10)
line4 = Line(title_color="#C0C0C0")
line4.add(array[0] + "-" + "MA10", btime, ma10)
layout.add(line4)
if len(close)>20:
ma20 = calculateMa(close, 20)
line5 = Line(title_color="#C0C0C0")
line5.add(array[0] + "-" + "MA20", btime, ma20)
layout.add(line5)
if len(close)>30:
ma30 = calculateMa(close, 30)
line6 = Line(title_color="#C0C0C0")
line6.add(array[0] + "-" + "MA30", btime, ma30)
layout.add(line6)
layout.add(candlestick)
page.add(layout)
page.render()
rosetype="radius",
)
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)],
is_label_show=True,
radius=[30, 55],
rosetype="radius",
)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add(
"秋季",
attr,
[randint(10, 100) for _ in range(6)],
is_label_show=True,
radius=[30, 55],
rosetype="radius",
)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
timeline.render()
def graphpage(labels,mode_combo,startdate,enddate,optInterval,width1, height1):
#optInterval='D/W/M' labels
startdate = startdate.replace("/","-")#convert to tushare readable date
enddate = enddate.replace("/","-")
page = Page()
for label in labels: # generate numbers of graphs according to numbers of queries in treewidget
label1 = re.split("-", label)
overlap = Overlap()
if label1[2]!="分笔" and label1[2]!="季度饼图": #"K线" "复权" "历史分钟"
if mode_combo == "复权":
#if optInterval == "qfq" or optInterval == "hfq":#复权
array = ts.get_h_data(label1[1], start=startdate, end=enddate, autype="qfq")
array = array.sort_index()
time = array.index.format() # 日期正是index
elif mode_combo == "K线":
#elif optInterval.isalnum() :#历史K线
array = ts.get_k_data(label1[1], start=startdate, end=enddate, ktype=optInterval)
time = array['date'].tolist() # array.date
elif mode_combo == "历史分钟":
array_bfr = ts.get_tick_data(label1[1], date=startdate)
array_bfr.sort_values("time")
a = startdate + " " + array_bfr["time"]
array_bfr["time"] = a
array_bfr["time"] = pd.to_datetime(a)
array_bfr = array_bfr.set_index("time")
if label1[2] != "Volume" and label1[2] != "Amount":
price_df = array_bfr["price"].resample(optInterval).ohlc() # resample重新取样,选出ohlc=open/high/low，提取除价格之外另外4列.close,tushare是ohcl
price_df = price_df.dropna()
array = price_df
time = price_df.index.format()
elif label1[2] == "Volume":
vols = array_bfr["volume"].resample(optInterval).sum() #resample重新取样，累加间隔内数值 relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
array = vol_df
time = vol_df.index.format()
else:
amounts = array_bfr["amount"].resample(optInterval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
array = amount_df
time = amount_df.index.format()
#绘图方法
if label1[2] == 'Kline':
re_array = array[['open', 'close', 'high', 'low']]
data_li = list(row.tolist() for index, row in re_array.iterrows()) # data_list = list(re_array.as_matrix())
close = array['close'].tolist()
kline = Kline(label1[0] + "-" + optInterval, width=width1*10/11, height = (height1*10/11) / len(labels))
kline.add(label1[0], time, data_li, is_datazoom_show=True, datazoom_type="slider", yaxis_interval=1)
overlap.add(kline)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(label1[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(label1[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(label1[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:#When label1[2]==open/close/volume
if label1[2] == 'Open':
list_aft = array['open'].tolist()
elif label1[2] == 'Close':
list_aft = close
elif label1[2] == 'High':
list_aft = array['high'].tolist()
elif label1[2] == 'Low':
list_aft = array['low'].tolist()
elif label1[2] == 'Volume':#volume
list_aft = array['volume'].tolist()
else:#amount
list_aft = array['amount'].tolist()
line = Line(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
line.add(label1[0] + "-" + label1[2], time, list_aft, is_datazoom_show=True, yaxis_max="dataMax",
yaxis_min="dataMin", datazoom_type="slider")
overlap.add(line)
page.add(overlap)
elif label1[2]=="分笔":
array = ts.get_tick_data(label1[1], date=startdate)
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = list(zip(date, amount, flag, price))
form = [e[1] for e in returnarray]
time = [d[0] for d in returnarray]
bar = Bar(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
bar.add(label1[0] + "-" + label1[2], time, form, is_datazoom_show=True, datazoom_type="slider", yaxis_min="dataMin", yaxis_max="dataMax")
overlap.add(bar)
line = Line(label1[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
price = [e[3] for e in returnarray]
line.add(label1[0] + "price", time, price, yaxis_min="dataMin", yaxis_max="dataMax", is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
elif label1[2]=="季度饼图":
datestr = startdate.split("-")
thisyear = datestr[0]
df2 = ts.top10_holders(code=label1[1], gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in range(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in range(0, len(value), 10)]
quarterarray = [quarter[k:k + 10] for k in range(0, len(quarter), 10)]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = list(zip(namearray, valuearray, quarterarray, flag, num))
timeline = Timeline(is_auto_play=False, timeline_bottom=0) # zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in returnarray]
valuearray = [d[1] for d in returnarray]
quarter = [e[2] for e in returnarray]
num = returnarray[0][4]
for x in range(0, int(num / 10)):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(label1[0] + "-" + "前十股东", width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(label1[0] + "-" + "前十股东", names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter="{b}: {c}\n{d}%")
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
page.render()
def graphpage(items, startdate, enddate, option, width1,
height1): #labels:复权ork线or分笔 option:hfq, qfq or 15, 30, D, etc
page = Page()
for i in items: #generate numbers of graphs according to numbers of queries in treewidget
j = re.split("-", i)
if len(j) == 3:
a = generateline(
j[1], j[2], startdate, enddate, option
) #stock number, Type, startdate, enddate, 30 or 15 or days
if a is None:
continue
time = [d[0] for d in a] #get time from returned dictionary
if j[2] != "Kline":
if len(a[0]) == 4 and a[0][2] == "bar": #for 分笔data
overlap = Overlap()
form = [e[1] for e in a]
bar = Bar(j[0] + "-" + j[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(items))
bar.add(j[0] + "-" + j[2],
time,
form,
yaxis_min="dataMin",
yaxis_max="dataMax",
is_datazoom_show=True,
datazoom_type="slider")
overlap.add(bar)
line = Line(j[0] + "price",
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in a]
line.add(j[0] + "price",
time,
price,
yaxis_min="dataMin",
yaxis_max="dataMax",
is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
if len(a[0]) == 5 and a[0][3] == "pie":
overlap = Overlap()
timeline = Timeline(
is_auto_play=False, timeline_bottom=0
) #zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in a]
valuearray = [d[1] for d in a]
quarter = [e[2] for e in a]
num = a[0][4]
for x in range(0, num / 10):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(j[0] + "-" + "前十股东".decode("utf-8"),
width=width1 * 10 / 11,
height=(height1 * 10 / 11))
pie.add(j[0] + "-" + "前十股东".decode("utf-8"),
names,
list1,
radius=[30, 55],
is_legend_show=False,
is_label_show=True,
label_formatter="{b}: {c}\n{d}%")
# print list
# print names
# print quarterarray
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
return
#need more statement
else:
form = [e[1] for e in a] #for not分笔 data
line = Line(j[0] + "-" + j[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(items))
line.add(j[0] + "-" + j[2],
time,
form,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_min="dataMin",
yaxis_max="dataMax")
page.add(line)
else:
overlap = Overlap() #for k线
close = zip(*a)[2]
candle = [[x[1], x[2], x[3], x[4]] for x in a]
candlestick = Kline(j[0] + "-" + j[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(items))
candlestick.add(j[0],
time,
candle,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_interval=1)
overlap.add(candlestick)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(j[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(j[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(j[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:
for k in range(1, len(j) / 3): #if graphs are combined
j[3 * k - 1] = re.sub("\n&", "", j[3 * k - 1])
sizearray = []
#if j[1] != "Candlestick"
layout = Overlap()
for i in xrange(0, len(j), 3):
array = j[i:i + 3]
b = generateline(array[1], array[2], startdate, enddate,
option)
if b is None:
continue
btime = [d[0] for d in b]
if array[2] != "Kline":
if len(b[0]) == 4 and b[0][2] == "bar":
form = [e[1] for e in b]
bar = Bar(array[0] + "-" + array[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(items))
bar.add(array[0] + "-" + array[2],
btime,
form,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_min="dataMin",
yaxis_max="dataMax")
layout.add(bar)
line = Line(array[0] + "price",
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in b]
line.add(array[0] + "price",
btime,
price,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_min="dataMin",
yaxis_type="value")
layout.add(line, yaxis_index=1, is_add_yaxis=True)
else:
line = Line(array[0] + "-" + array[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(items))
line.add(array[0] + "-" + array[2],
btime,
b,
is_datazoom_show=True,
yaxis_max="dataMax",
yaxis_min="dataMin",
datazoom_type="slider")
layout.add(line)
else:
candle = [[x[1], x[2], x[3], x[4]] for x in b]
candlestick = Kline(array[0] + "-" + array[1],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) /
len(items))
candlestick.add(array[0],
btime,
candle,
is_datazoom_show=True,
datazoom_type=["slider"])
#if i == 0:
close = zip(*b)[2]
if len(close) > 10:
ma10 = calculateMa(close, 10)
line4 = Line(title_color="#C0C0C0")
line4.add(array[0] + "-" + "MA10", btime, ma10)
layout.add(line4)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line5 = Line(title_color="#C0C0C0")
line5.add(array[0] + "-" + "MA20", btime, ma20)
layout.add(line5)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line6 = Line(title_color="#C0C0C0")
line6.add(array[0] + "-" + "MA30", btime, ma30)
layout.add(line6)
layout.add(candlestick)
page.add(layout)
page.render()
height=700)
for hour in range(24):
for minuteOfHour in range(12):
mask = (pred_df['时'] == hour) & (pred_df['第x个五分钟'] == minuteOfHour)
temp_df = temp_df.sort_values('站点')
temp_df = pred_df[mask]
attrs = temp_df['站点']
values = temp_df['客流量']
bar = Bar("24小时上海地铁客流量", "以每5分钟为统计单位", width=1400, height=600)
bar.add("", attrs, values)
timeline.add(
bar, '{}时 ：{}～{}分钟'.format(hour, 5 * minuteOfHour,
5 * minuteOfHour + 5))
timeline.render('LSTM_24小时上海地铁客流量.html')
## 对预测结果用pyplot可视化
DFs = [] # 读取预测数据
for fileName in os.listdir('./LSTM_output/'):
temp_df = pd.read_csv('./LSTM_output/' + fileName, encoding='gbk')
station = fileName.split('.')[0]
temp_df['站点'] = station
DFs.append(temp_df)
pred_df = pd.concat(DFs) # 把所有预测数据合并到一个数据表中
del DFs
def drop_num(station):
'''
把完整到站点名称删去"x号线"
bar_3.add("冬季", attr, [random.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, [random.randint(10, 100) for _ in range(6)])
bar_4.add("夏季", attr, [random.randint(10, 100) for _ in range(6)])
bar_4.add("秋季", attr, [random.randint(10, 100) for _ in range(6)])
bar_4.add("冬季", attr, [random.randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016年销量", "数据纯属虚构")
bar_5.add("春季", attr, [random.randint(10, 100) for _ in range(6)])
bar_5.add("夏季", attr, [random.randint(10, 100) for _ in range(6)])
bar_5.add("秋季", attr, [random.randint(10, 100) for _ in range(6)])
bar_5.add("冬季", attr, [random.randint(10, 100) for _ in range(6)])
bar_6 = Bar("2017年销量", "数据纯属虚构")
bar_6.add("春季", attr, [random.randint(10, 100) for _ in range(6)])
bar_6.add("夏季", attr, [random.randint(10, 100) for _ in range(6)])
bar_6.add("秋季", attr, [random.randint(10, 100) for _ in range(6)])
bar_6.add("冬季",
attr, [random.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")
timeline.add(bar_6, "2017年")
timeline.render(path="Timeline.html")
height=700)
for hour in range(24):
for minuteOfHour in range(12):
mask = (pred_df['时'] == hour) & (pred_df['第x个五分钟'] == minuteOfHour)
temp_df = temp_df.sort_values('站点')
temp_df = pred_df[mask]
attrs = temp_df['站点']
values = temp_df['客流量']
bar = Bar("24小时上海地铁客流量", "以每5分钟为统计单位", width=1400, height=600)
bar.add("", attrs, values)
timeline.add(
bar, '{}时 ：{}～{}分钟'.format(hour, 5 * minuteOfHour,
5 * minuteOfHour + 5))
timeline.render('KNN_24小时上海地铁客流量.html')
## 对预测结果用pyplot可视化
DFs = [] # 读取预测数据
for fileName in os.listdir('./knn_output/'):
temp_df = pd.read_csv('./knn_output/' + fileName, encoding='gbk')
station = fileName.split('.')[0]
temp_df['站点'] = station
DFs.append(temp_df)
pred_df = pd.concat(DFs) # 把所有预测数据合并到一个数据表中
del DFs
def drop_num(station):
'''
把完整到站点名称删去"x号线"
pie_time.render(r'Amout_in_Each_Timezone_Pie.html')
if not os.path.exists(r'Amount_of_Country_in_Timezone_Pie.html'):
# 时区内小时区的数量统计
df_gp = group(starbucks_df_time, ['Time', 'Timezone'])
df_gp.columns = ['count']
df_index = group(starbucks_df_time, ['Time'])
df_index = df_index.reset_index(drop=False)
timeline = Timeline(is_auto_play=False, timeline_bottom=0)
for i in range(df_index.shape[0]):
string = df_index['Time'][i]
df_a_time = df_gp['count'][string].to_frame()
df_a_time = df_a_time.reset_index(drop=False)
pie = pie_pic(df_a_time, 'Amount of Starbucks in ' + string, 'Timezone', 'count')
timeline.add(pie, 'Amount of Starbucks in ' + string)
timeline.render(r'Amount_of_Country_in_Timezone_Pie.html')
if not os.path.exists(r'map_time.html'):
df_time = group(starbucks_df_time, 'Time')
df_time_index = count_by_group(df_time, True)
# 按时区的所有星巴克分布地图
starbucks_category_color_time = []
timezone_amount = []
for index in range(starbucks_df_time.shape[0]):
i = df_time_index['count'][df_time_index.Time == starbucks_df_time['Time'][index]]
timezone_amount.append(i)
starbucks_df_time.insert(0, 'timezone_amount', timezone_amount)
draw_timezone_map(starbucks_df_time, r'map_time.html')
if not os.path.exists(r'distribution_map_country.html'):
def graphpage(labels, mode_combo, startdate, enddate, optInterval, width1,
height1):
#optInterval='D/W/M' labels
startdate = startdate.replace("/", "-") #convert to tushare readable date
enddate = enddate.replace("/", "-")
page = Page()
for label in labels: # generate numbers of graphs according to numbers of queries in treewidget
label1 = re.split("-", label)
overlap = Overlap()
if label1[2] != "分笔" and label1[2] != "季度饼图": #"K线" "复权" "历史分钟"
if mode_combo == "复权":
#if optInterval == "qfq" or optInterval == "hfq":#复权
array = ts.get_h_data(label1[1],
start=startdate,
end=enddate,
autype="qfq")
array = array.sort_index()
time = array.index.format() # 日期正是index
elif mode_combo == "K线":
#elif optInterval.isalnum() :#历史K线
array = ts.get_k_data(label1[1],
start=startdate,
end=enddate,
ktype=optInterval)
time = array['date'].tolist() # array.date
elif mode_combo == "历史分钟":
array_bfr = ts.get_tick_data(label1[1], date=startdate)
array_bfr.sort_values("time")
a = startdate + " " + array_bfr["time"]
array_bfr["time"] = a
array_bfr["time"] = pd.to_datetime(a)
array_bfr = array_bfr.set_index("time")
if label1[2] != "Volume" and label1[2] != "Amount":
price_df = array_bfr["price"].resample(optInterval).ohlc(
) # resample重新取样,选出ohlc=open/high/low，提取除价格之外另外4列.close,tushare是ohcl
price_df = price_df.dropna()
array = price_df
time = price_df.index.format()
elif label1[2] == "Volume":
vols = array_bfr["volume"].resample(optInterval).sum(
) #resample重新取样，累加间隔内数值 relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
array = vol_df
time = vol_df.index.format()
else:
amounts = array_bfr["amount"].resample(optInterval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
array = amount_df
time = amount_df.index.format()
#绘图方法
if label1[2] == 'Kline':
re_array = array[['open', 'close', 'high', 'low']]
data_li = list(row.tolist()
for index, row in re_array.iterrows()
) # data_list = list(re_array.as_matrix())
close = array['close'].tolist()
kline = Kline(label1[0] + "-" + optInterval,
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
kline.add(label1[0],
time,
data_li,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_interval=1)
overlap.add(kline)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(label1[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(label1[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(label1[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else: #When label1[2]==open/close/volume
if label1[2] == 'Open':
list_aft = array['open'].tolist()
elif label1[2] == 'Close':
list_aft = array['close'].tolist()
elif label1[2] == 'High':
list_aft = array['high'].tolist()
elif label1[2] == 'Low':
list_aft = array['low'].tolist()
elif label1[2] == 'Volume': #volume
list_aft = array['volume'].tolist()
else: #amount
list_aft = array['amount'].tolist()
line = Line(label1[0] + "-" + label1[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
line.add(label1[0] + "-" + label1[2],
time,
list_aft,
is_datazoom_show=True,
yaxis_max="dataMax",
yaxis_min="dataMin",
datazoom_type="slider")
overlap.add(line)
page.add(overlap)
elif label1[2] == "分笔":
array = ts.get_tick_data(label1[1], date=startdate)
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = list(zip(date, amount, flag, price))
form = [e[1] for e in returnarray]
time = [d[0] for d in returnarray]
bar = Bar(label1[0] + "-" + label1[2],
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
bar.add(label1[0] + "-" + label1[2],
time,
form,
is_datazoom_show=True,
datazoom_type="slider",
yaxis_min="dataMin",
yaxis_max="dataMax")
overlap.add(bar)
line = Line(label1[0] + "price",
width=width1 * 10 / 11,
height=(height1 * 10 / 11) / len(labels))
price = [e[3] for e in returnarray]
line.add(label1[0] + "price",
time,
price,
yaxis_min="dataMin",
yaxis_max="dataMax",
is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
elif label1[2] == "季度饼图":
datestr = startdate.split("-")
thisyear = datestr[0]
df2 = ts.top10_holders(code=label1[1], gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in range(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in range(0, len(value), 10)]
quarterarray = [
quarter[k:k + 10] for k in range(0, len(quarter), 10)
]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = list(
zip(namearray, valuearray, quarterarray, flag, num))
timeline = Timeline(is_auto_play=False, timeline_bottom=0
) # zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in returnarray]
valuearray = [d[1] for d in returnarray]
quarter = [e[2] for e in returnarray]
num = returnarray[0][4]
for x in range(0, int(num / 10)):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(label1[0] + "-" + "前十股东",
width=width1 * 10 / 11,
height=(height1 * 10 / 11))
pie.add(label1[0] + "-" + "前十股东",
names,
list1,
radius=[30, 55],
is_legend_show=False,
is_label_show=True,
label_formatter="{b}: {c}\n{d}%")
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
page.render()
