def v_positions_history(self, end=yesterdaydash(), rendered=True):
"""
river chart visulization of positions ratio history
use text size to avoid legend overlap in some sense, eg. legend_text_size=8
"""
start = self.totcftable.iloc[0].date
times = pd.date_range(start, end)
tdata = []
for date in times:
sdata = sorted(
[(
date,
fob.briefdailyreport(date).get("currentvalue", 0),
fob.name,
) for fob in self.fundtradeobj],
key=lambda x: x[1],
reverse=True,
)
tdata.extend(sdata)
tr = ThemeRiver()
tr.add(
series_name=[foj.name for foj in self.fundtradeobj],
data=tdata,
label_opts=opts.LabelOpts(is_show=False),
singleaxis_opts=opts.SingleAxisOpts(type_="time",
pos_bottom="10%"),
)
if rendered:
return tr.render_notebook()
else:
return tr
def v_positions_history(self, end=yesterdaydash(), **vkwds):
'''
river chart visulization of positions ratio history
use text size to avoid legend overlap in some sense, eg. legend_text_size=8
'''
start = self.totcftable.iloc[0].date
times = pd.date_range(start, end)
tdata = []
for date in times:
sdata = sorted([(date, fob.briefdailyreport(date).get('currentvalue', 0), fob.aim.name)
for fob in self.fundtradeobj], key=lambda x: x[1], reverse=True)
tdata.extend(sdata)
tr = ThemeRiver()
tr.add([foj.aim.name for foj in self.fundtradeobj], tdata, is_datazoom_show=True,
is_label_show=False, legend_top="0%", legend_orient='horizontal', **vkwds)
return tr
with open("news_top_title.json", "r") as f:
top_titles = json.load(f)[topic]
with open("news_emotion_count.json", "r") as f:
news_ec = json.load(f)
emotions = ["负面", "中性", "正面"]
with open("cov_cnt.json", "r") as f:
cc_data = json.load(f)
theme_river = ThemeRiver(
init_opts=opts.InitOpts(width="1200px", height="600px"))
theme_river.add(
series_name=tr_series[topic],
data=tr_data[topic],
label_opts=opts.LabelOpts(is_show=False),
singleaxis_opts=opts.SingleAxisOpts(pos_top="50",
pos_bottom="50",
type_="time"),
)
theme_river.set_global_opts(tooltip_opts=opts.TooltipOpts(
trigger="axis", axis_pointer_type="line"),
legend_opts=opts.LegendOpts(pos_top="5%",
is_show=True))
wc_tl = Timeline(init_opts=opts.InitOpts(width="1200px", height="600px"))
idx = 0
for date, tmp_data in wc_data:
wc = WordCloud()
wc.add(series_name="", data_pair=tmp_data)
wc.set_global_opts(
title_opts=opts.TitleOpts(title="词频统计",
dim=7,
name="等级",
type_="category",
data=["优", "良", "轻度污染", "中度污染", "重度污染", "严重污染"],
),
]
parallel = Parallel()
# 添加坐标轴和数据
parallel.add_schema(schema=schema).add("", data)
parallel.render_notebook()
# %% [markdown]
# ### Radar -- 雷达图
radar = Radar()
radar.add_schema(schema=[
opts.RadarIndicatorItem(name=_k, max_=200) for _k in list("ABCDFG")
])
radar.add("Expectation", [Faker.values()]).add("Reality", [Faker.values()])
radar.render_notebook()
# %% [markdown]
# ### ThemeRiver -- 流量图
themeriver = ThemeRiver()
with open("data/themeriver.json") as j:
data = json.load(j)
cats = list(set([i[-1] for i in data]))
themeriver.add(cats, data, singleaxis_opts=opts.SingleAxisOpts(type_="time"))
themeriver.render_notebook()
columns=['year'],
index=df_year['year'].unique())
df_river['year'] = df_river['year'].apply(str)
df_river['count'] = df_year[df_year[g] == 1].groupby(by=['year'])[g].sum()
df_river['genre'] = np.full(len(df_river), g)
df_river['count'] = df_river['count'].fillna(0)
data_river.extend(df_river.values.tolist())
river = ThemeRiver(init_opts=opts.InitOpts(
width="2000px", height="600px", theme=ThemeType.LIGHT))
river.add(
series_name=genres,
data=data_river,
label_opts=opts.LabelOpts(font_size=10),
singleaxis_opts=opts.SingleAxisOpts(
pos_top="50",
pos_bottom="50",
type_="time",
),
)
river.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
title_opts=opts.TitleOpts(title="1901-2020 曲風流變",
subtitle="1901-2020",
pos_bottom="85%",
pos_right="80%"),
)
river.set_series_opts(label_opts=opts.LabelOpts(is_show=0))
# themeriver().load_javascript()
river.render_notebook()