def render_js_py():
with st.echo("below"):
st.markdown("""Overwrite chart colors with JS.
Under 50 : red. Between 50 - 100 : blue. Over 100 : green""")
color_function = """
function (params) {
if (params.value > 0 && params.value < 50) {
return 'red';
} else if (params.value > 50 && params.value < 100) {
return 'blue';
}
return 'green';
}
"""
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
).add_yaxis(
"商家B",
Faker.values(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
).add_yaxis(
"商家C",
Faker.values(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
).set_global_opts(title_opts=opts.TitleOpts(title="Bar-自定义柱状颜色")))
st_pyecharts(c)
def calendar_plot(df):
"""[summary]
Parameters
----------
df : [type]
[description]
"""
#begin = datetime.date(2020, 2, 9)
#end = datetime.date(2020,11,11)
begin = df.Fecha.min()
end = df.Fecha.max()
c = (
Calendar(init_opts=opts.InitOpts(width="1000px", height="300px"))
.add(
series_name="",
yaxis_data=[[str(x),y] for x,y in zip(df.Fecha, df['Nuevos casos'])],
calendar_opts=opts.CalendarOpts(
pos_top="120",
pos_left="30",
pos_right="30",
range_="2020",
yearlabel_opts=opts.CalendarYearLabelOpts(is_show=False),
),
)
.set_global_opts(
title_opts=opts.TitleOpts(pos_top="20", pos_left="center", title="2020: Evolución del Covid19 en Andalucía"),
visualmap_opts=opts.VisualMapOpts(
max_=df['Nuevos casos'].max(), min_=0, orient="horizontal", is_piecewise=False
),
)
)
st_pyecharts(c)
def main():
st.sidebar.title("Kalkula")
action = st.sidebar.selectbox("Navigation", ["Productivity", "Planning", "Progress Report", "Chart"])
if action == "Productivity":
st.title("Productivity")
report()
elif action == "Planning":
st.title("Planning")
planning()
elif action == "Progress Report":
st.title("Progress Report")
progress()
elif action == "Chart":
st.title("Sample Chart")
b = (
Bar()
.add_xaxis(["Microsoft", "Amazon", "IBM", "Oracle", "Google", "Alibaba"])
.add_yaxis(
"2017-2018 Revenue in (billion $)", [21.2, 20.4, 10.3, 6.08, 4, 2.2]
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="Top cloud providers 2018", subtitle="2017-2018 Revenue"
)
)
)
st_pyecharts(b)
st_echarts({"xAxis": {
"type": "category",
"data": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
},
"yAxis": { "type": "value" },
"series": [
{"data": [820, 932, 901, 934, 1290, 1330, 1320], "type": "bar" }
],})
def render_liquid_py():
with st.echo("below"):
c = (
Liquid()
.add("lq", [0.6, 0.7])
.set_global_opts(title_opts=opts.TitleOpts(title="Liquid-基本示例"))
)
st_pyecharts(c)
def render_basic_line_chart():
c = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例"))
)
st_pyecharts(c)
def render_vertical_datazoom_py():
with st.echo("below"):
c = (Bar().add_xaxis(Faker.days_attrs).add_yaxis(
"商家A", Faker.days_values,
color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider-垂直）"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
))
st_pyecharts(c, height="400px")
def render_filter_legend_py():
with st.echo("below"):
c = (Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"))).add_xaxis(
Faker.choose()).add_yaxis("商家A", Faker.values()).add_yaxis(
"商家B",
Faker.values()).set_global_opts(title_opts=opts.TitleOpts(
title="Bar-动画配置基本示例", subtitle="我是副标题")))
st_pyecharts(c)
def render_map():
with open("./data/countries.geo.json", "r") as f:
map = st_Map("world", json.loads(f.read()),)
c = Map(init_opts=opts.InitOpts(bg_color="white"))
c.add("Demo", [list(z) for z in zip(Faker.country, Faker.values())], "world")
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
c.set_global_opts(
title_opts=opts.TitleOpts(title="Map world"),
visualmap_opts=opts.VisualMapOpts(max_=200),
)
st_pyecharts(c, map=map, height=500)
def render_map_py():
with st.echo("below"):
g = (Geo().add_schema(maptype="china").add(
"geo",
[list(z)
for z in zip(Faker.provinces, Faker.values())]).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="Geo-基本示例"),
))
st_pyecharts(g)
def render_timeline_py():
with st.echo("below"):
x = Faker.choose()
tl = Timeline()
for i in range(2015, 2020):
bar = (Bar().add_xaxis(x).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))
st_pyecharts(tl)
def render_bar_py():
with st.echo("below"):
b = (Bar().add_xaxis([
"Microsoft", "Amazon", "IBM", "Oracle", "Google", "Alibaba"
]).add_yaxis("2017-2018 Revenue in (billion $)",
[21.2, 20.4, 10.3, 6.08, 4, 2.2]).set_global_opts(
title_opts=opts.TitleOpts(
title="Top cloud providers 2018",
subtitle="2017-2018 Revenue"),
toolbox_opts=opts.ToolboxOpts(),
))
st_pyecharts(b)
def plot_timeline(df, data1 = 'Nuevos casos', data2 = 'Hospitalizados'):
"""[summary]
Parameters
----------
df : [type]
[description]
data1 : str, optional
[description], by default 'Nuevos casos'
data2 : str, optional
[description], by default 'Hospitalizados'
"""
tl = Timeline()
calendar_dict = {
1:'Ene',
2:'Feb',
3:'Mar',
4:'Abr',
5:'May',
6:'Jun',
7:'Jul',
8:'Ag',
9:'Sep',
10:'Oct',
11:'Nov',
12:'Dic'
}
for i in range(3, df.Mes.max()+1):
bar = (
Bar()
.add_xaxis(pd.unique(df.Territorio).tolist())
.add_yaxis(data1, df[df.Mes == i][data1].tolist())
.add_yaxis(data2, df[df.Mes == i][data2].tolist())
.set_global_opts(
title_opts=opts.TitleOpts("Covid19 mes a mes"),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=100,
bottom=110,
z=100,
),
)
],
)
)
tl.add(bar,calendar_dict[i])
st_pyecharts(tl)
def render_randomize_py():
with st.echo("below"):
b = (Bar().add_xaxis([
"Microsoft", "Amazon", "IBM", "Oracle", "Google", "Alibaba"
]).add_yaxis("2017-2018 Revenue in (billion $)",
random.sample(range(100), 10)).set_global_opts(
title_opts=opts.TitleOpts(
title="Top cloud providers 2018",
subtitle="2017-2018 Revenue"),
toolbox_opts=opts.ToolboxOpts(),
))
st_pyecharts(
b, key="echarts"
) # Add key argument to not remount component at every Streamlit run
st.button("Randomize data")
def render_custom_py():
with st.echo("below"):
b = (Bar().add_xaxis([
"Microsoft", "Amazon", "IBM", "Oracle", "Google", "Alibaba"
]).add_yaxis(
"2017-2018 Revenue in (billion $)",
[21.2, 20.4, 10.3, 6.08, 4, 2.2]).set_global_opts(
title_opts=opts.TitleOpts(title="Top cloud providers 2018",
subtitle="2017-2018 Revenue")))
st_pyecharts(b, theme=ThemeType.DARK)
st_pyecharts(
b,
theme={
"backgroundColor": "#f4cccc",
"textStyle": {
"color": "rgba(255, 0, 0, 0.8)"
},
},
)
def pyechart_comunidad_bar(df, data1, data2):
"""Plot comparing those two charasteristics.
Parameters
----------
df : DataFrame
[description]
data1 : Series
Column to plot between "nuevos casos", "hospitalizados", "UCI" y "fallecidos"
data2 : Series
Column to plot between "nuevos casos", "hospitalizados", "UCI" y "fallecidos"
"""
bar = (Bar(init_opts=opts.InitOpts(theme=ThemeType.ESSOS)).add_xaxis([
'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV'
]).add_yaxis(data1, df[data1].tolist()).add_yaxis(
data2, df[data2].tolist()).set_global_opts(
title_opts=opts.TitleOpts(
title="Covid Andalucía",
subtitle="Datos desde el inicio de la pandemia"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=0))),
toolbox_opts=opts.ToolboxOpts(is_show=True,
orient='vertical',
pos_left='95%'),
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=80,
is_zoom_lock=False)
],
).set_series_opts(
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="MAX"),
opts.MarkPointItem(type_="min", name="MIN"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="AVG")]),
label_opts=opts.LabelOpts(is_show=False)))
st_pyecharts(bar)
def render_liquid():
with st.echo("below"):
options = {
"series": [
{
"type": "liquidFill",
"data": [0.5, 0.4, 0.3],
"color": ["red", "#0f0", "rgb(0, 0, 255)"],
"itemStyle": {"opacity": 0.6},
"emphasis": {"itemStyle": {"opacity": 0.9}},
}
]
}
st_echarts(options)
c = (
Liquid()
.add("lq", [0.6, 0.7])
.set_global_opts(title_opts=opts.TitleOpts(title="Liquid-基本示例"))
)
st_pyecharts(c)
def main():
html_temp = """
<div style="background-color:#45637d;padding:10px;border-radius:10px">
<h1 style="color:white;text-align:center;">A-DAConsultancy</h1>
<h4 style="color:white;text-align:center;text-decoration: underline">Semi Auto Machine Learning App</h4>
<em style="color:white;text-align:center; margin-top:33px ">Using Streamlit = 0.78.0+</em>
</div>
"""
components.html(html_temp, height=300)
# st.subheader('Semi Auto ML App ')
# st.text('Using Streamlit == 0.78.0+')
activities =[ 'EDA', 'Plot', 'Model Building', 'Other Projects','About']
choice = st.sidebar.selectbox('Select Activity', activities)
if choice == 'EDA':
st.subheader('Exploratory data analysis')
data = getData()
if data is not None:
df = pd.read_csv(data)
st.dataframe(df.head())
if st.checkbox('show shape / Na(s) values'):
col1, col2, col3 = st.beta_columns(3)
col1.success('Shape of dataFrame')
col1.write(df.shape)
col2.success('Na(s) values')
col2.write(df.isnull().sum().sum())
if st.checkbox(' Show Columns / Data types '):
col1, col2, = st.beta_columns(2)
col1.success('Show Columns')
all_columns = df.columns.tolist()
col1.write(all_columns)
col2.success('Data type')
col2.write(df.dtypes)
if st.checkbox('Show Summary'):
st.write(df.describe())
if st.checkbox('Last 5 columns'):
st.write(df.tail())
if st.checkbox('Pick Columns'):
selected_columns = st.multiselect('Select Columns', df.columns.tolist())
new_df = df[selected_columns]
st.dataframe(new_df)
if st.checkbox('Highlight_max'):
st.dataframe(df.style.highlight_max(axis=0))
if st.checkbox('Transform to table'):
selected_columns = st.multiselect('Select Columns', df.columns.tolist())
new_df = df[selected_columns]
st.table(new_df)
elif choice == 'Plot':
st.subheader('Data visualization')
data = getData()
if data is not None:
df = pd.read_csv(data)
st.dataframe(df.head())
if st.checkbox('Correlation with seaborn'):
if data is not None:
fig, ax = plt.subplots(figsize=(20,15))
st.write(sns.heatmap(df.corr(), annot=True))
st.pyplot(fig)
else:
st.warning('NO DATA')
if st.checkbox('Pairplot'):
if data is not None:
fig, ax = plt.subplots(figsize=(20,15))
selected_columns = st.multiselect('Select Columns', df.columns)
st.write(sns.pairplot(df[selected_columns]))
st.pyplot(fig)
else:
st.warning('NO DATA')
if st.checkbox('Pie Chart'):
if data is not None:
pie = pieChart(df)
st.write(st_pyecharts(pie, height="1800px" ,theme={
"backgroundColor": "#eeeee4",
"textStyle": {"color": "#0080ff"},
},
))
else:
st.warning('NO DATA')
if st.checkbox('Bar Chart'):
if data is not None:
bar = barChart(df)
st.write(st_pyecharts(bar ,height="1000px", width="1000px",theme={
"backgroundColor": "#eeeee4",
"textStyle": {"color": "#0080ff"},
},))
else:
st.warning('NO DATA')
if st.checkbox('Polar Chart'):
if data is not None:
polar = polarChart(df)
st.write(st_pyecharts(polar ,height="900px" ,theme={
"backgroundColor": "#eeeee4",
"textStyle": {"color": "#0080ff"},
},))
else:
st.warning('NO DATA')
if st.checkbox('HexagonLayer/ScatterplotLayer/light map'):
if data is not None:
map = mapChart(df)
st.write((map ))
else:
st.warning('NO DATA')
if st.checkbox('TreeMap'):
if data is not None:
treemap = treeMapChart(df)
st.write(st_pyecharts(treemap,height='700px',width='1920px' ,theme={
"backgroundColor": "#eeeee4",
"font_size":20
},))
else:
st.warning('NO DATA')
if st.checkbox('Tree'):
if data is not None:
tree = treeChart(df)
st.write(st_pyecharts(tree,height='700px',width='1920px' ,theme={
"backgroundColor": "#eeeee4",
"font_size":20
},))
else:
st.warning('NO DATA')
if st.checkbox('Funnel'):
if data is not None:
funnel = funnelChart(df)
st.write(st_pyecharts(funnel,height='700px',width='1920px' ,theme={
"backgroundColor": "#eeeee4",
"font_size":20
},))
else:
st.warning('NO DATA')
if st.checkbox('Timeline_bar_with_graphic'):
if data is not None:
timeline = timelineChart(df)
st.write(st_pyecharts(timeline,height='700px',width='1920px' ,theme={
"backgroundColor": "#eeeee4",
"font_size":20
},))
else:
st.warning('NO DATA')
elif choice == 'Model Building':
st.subheader('Model Building')
data = st.file_uploader("Upload a Dataset", type=["csv", "txt"])
if data is not None:
df = pd.read_csv(data)
st.dataframe(df.head())
# Model Building
X = df.iloc[:,0:-1]
Y = df.iloc[:,-1]
seed = 7
# prepare models
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
model_names = []
model_mean = []
model_std = []
all_models = []
scoring = 'accuracy'
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
model_names.append(name)
model_mean.append(cv_results.mean())
model_std.append(cv_results.std())
accuracy_results = {"model name":name,"model_accuracy":cv_results.mean(),"standard deviation":cv_results.std()}
all_models.append(accuracy_results)
if st.checkbox("Metrics As Table"):
st.dataframe(pd.DataFrame(zip(model_names,model_mean,model_std),columns=["Algo","Mean of Accuracy","Std"]))
if st.checkbox("Metrics As JSON"):
st.json(all_models)
elif choice == 'Others':
st.subheader('some projects')
elif choice == 'About':
# st.subheader('About')
footer_temp = """
<!-- CSS -->
<!-- Font Awesome -->
<link
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/all.min.css"
rel="stylesheet"
/>
<!-- Google Fonts -->
<link
href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"
rel="stylesheet"
/>
<!-- MDB -->
<link
href="https://cdnjs.cloudflare.com/ajax/libs/mdb-ui-kit/3.3.0/mdb.min.css"
rel="stylesheet"
/>
<script
type="text/javascript"
src="https://cdnjs.cloudflare.com/ajax/libs/mdb-ui-kit/3.3.0/mdb.min.js"
></script>
<footer style="background-color:#f0f2f6;padding:50px; border-radius:10px" class="page-footer grey darken-4">
<div class="container" id="aboutapp">
<div>
<div class="col l6 s12">
<h5 class="white-text">Simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.</h5>
<p class="grey-text text-lighten-4">Packages used: Streamlit,Pandas,Pyecharts, Echarts.</p>
</div>
<div class="col l3 s12">
<h2>List of charts Used</h2>
<ul>
<li>BarChart</li>
<li>TreeMap</li>
<li>Rose Chart</li>
<li>Map Chart</li>
</ul>
</div>
<div style="text-align:center"; class="footer-copyright">
<div class="container">
<div class="container p-4 pb-0">
<!-- Section: Social media -->
<section class="mb-4">
<!-- Facebook -->
<a
class="btn btn-primary btn-floating m-1"
style="background-color: #3b5998;"
href="#!"
role="button"
><i class="fab fa-facebook-f"></i
></a>
<!-- Twitter -->
<a
class="btn btn-primary btn-floating m-1"
style="background-color: #55acee;"
href="#!"
role="button"
><i class="fab fa-twitter"></i
></a>
<!-- Google -->
<a
class="btn btn-primary btn-floating m-1"
style="background-color: #dd4b39;"
href="#!"
role="button"
><i class="fab fa-google"></i
></a>
<!-- Instagram -->
<a
class="btn btn-primary btn-floating m-1"
style="background-color: #ac2bac;"
href="#!"
role="button"
><i class="fab fa-instagram"></i
></a>
<!-- Linkedin -->
<a
class="btn btn-primary btn-floating m-1"
style="background-color: #0082ca;"
href="#!"
role="button"
><i class="fab fa-linkedin-in"></i
></a>
<!-- Github -->
<a
class="btn btn-primary btn-floating m-1"
style="background-color: #333333;"
href="#!"
role="button"
><i class="fab fa-github"></i
></a>
</section>
<!-- Section: Social media -->
</div>
<!-- Grid container -->
<!-- Copyright -->
<div class="text-center p-3"; style="background-color: rgba(0, 0, 0, 0.2); border-radius:10px; padding-bottom:50px;">
© 2021 Copyright
<a class="text-white" href="https://mdbootstrap.com/"></a>
</div>
</div>
</footer>
"""
components.html(footer_temp, height=800)
pos_left="30",
pos_right="30",
range_="2020",
yearlabel_opts=opts.CalendarYearLabelOpts(is_show=False),
),
).set_global_opts(
title_opts=opts.TitleOpts(
pos_top="30",
pos_left="center",
title=f"Número de tuits por día de {real_name}"),
visualmap_opts=opts.VisualMapOpts(max_=25,
min_=0,
orient="horizontal",
is_piecewise=False),
))
st_pyecharts(c)
# =============================================================================
# Comparador
# =============================================================================
if selection == 'Comparador':
perfil_1 = st.sidebar.selectbox('Elige un político', perfiles)
perfil_2 = st.sidebar.selectbox('Elige un político',
[p for p in perfiles if p != perfil_1])
st.markdown(
"<h1 style='text-align: center; color: #d84519;'>Política española en Twitter durante 2020</h1>",
unsafe_allow_html=True)
# =============================================================================
# Variables globales
# =============================================================================
def render_wordcloud():
with st.echo("below"):
options = {
"tooltip": {},
"series": [
{
"type": "wordCloud",
"gridSize": 2,
"sizeRange": [12, 50],
"rotationRange": [-90, 90],
"shape": "pentagon",
"width": 600,
"height": 400,
"drawOutOfBound": True,
"emphasis": {
"textStyle": {"shadowBlur": 10, "shadowColor": "#333"}
},
"data": [
{
"name": "Sam S Club",
"value": 10000,
"textStyle": {"color": "black"},
"emphasis": {"textStyle": {"color": "red"}},
},
{"name": "Macys", "value": 6181},
{"name": "Amy Schumer", "value": 4386},
{"name": "Jurassic World", "value": 4055},
{"name": "Charter Communications", "value": 2467},
{"name": "Chick Fil A", "value": 2244},
{"name": "Planet Fitness", "value": 1898},
{"name": "Pitch Perfect", "value": 1484},
{"name": "Express", "value": 1112},
{"name": "Home", "value": 965},
{"name": "Johnny Depp", "value": 847},
{"name": "Lena Dunham", "value": 582},
{"name": "Lewis Hamilton", "value": 555},
{"name": "KXAN", "value": 550},
{"name": "Mary Ellen Mark", "value": 462},
{"name": "Farrah Abraham", "value": 366},
{"name": "Rita Ora", "value": 360},
{"name": "Serena Williams", "value": 282},
{"name": "NCAA baseball tournament", "value": 273},
{"name": "Point Break", "value": 265},
],
}
],
}
st_echarts(options)
data = [
("生活资源", "999"),
("供热管理", "888"),
("供气质量", "777"),
("生活用水管理", "688"),
("一次供水问题", "588"),
("交通运输", "516"),
("城市交通", "515"),
("环境保护", "483"),
("房地产管理", "462"),
("城乡建设", "449"),
("社会保障与福利", "429"),
("社会保障", "407"),
("文体与教育管理", "406"),
("公共安全", "406"),
("公交运输管理", "386"),
("出租车运营管理", "385"),
("供热管理", "375"),
("市容环卫", "355"),
("自然资源管理", "355"),
("粉尘污染", "335"),
("噪声污染", "324"),
("土地资源管理", "304"),
("物业服务与管理", "304"),
("医疗卫生", "284"),
("粉煤灰污染", "284"),
("占道", "284"),
("供热发展", "254"),
("农村土地规划管理", "254"),
("生活噪音", "253"),
("供热单位影响", "253"),
("城市供电", "223"),
("房屋质量与安全", "223"),
("大气污染", "223"),
("房屋安全", "223"),
("文化活动", "223"),
("拆迁管理", "223"),
("公共设施", "223"),
("供气质量", "223"),
("供电管理", "223"),
("燃气管理", "152"),
("教育管理", "152"),
("医疗纠纷", "152"),
("执法监督", "152"),
("设备安全", "152"),
("政务建设", "152"),
("县区、开发区", "152"),
("宏观经济", "152"),
("教育管理", "112"),
("社会保障", "112"),
("生活用水管理", "112"),
("物业服务与管理", "112"),
("分类列表", "112"),
("农业生产", "112"),
("二次供水问题", "112"),
("城市公共设施", "92"),
("拆迁政策咨询", "92"),
("物业服务", "92"),
("物业管理", "92"),
("社会保障保险管理", "92"),
("低保管理", "92"),
("文娱市场管理", "72"),
("城市交通秩序管理", "72"),
("执法争议", "72"),
("商业烟尘污染", "72"),
("占道堆放", "71"),
("地上设施", "71"),
("水质", "71"),
("无水", "71"),
("供热单位影响", "71"),
("人行道管理", "71"),
("主网原因", "71"),
("集中供热", "71"),
("客运管理", "71"),
("国有公交（大巴）管理", "71"),
("工业粉尘污染", "71"),
("治安案件", "71"),
("压力容器安全", "71"),
("身份证管理", "71"),
("群众健身", "41"),
("工业排放污染", "41"),
("破坏森林资源", "41"),
("市场收费", "41"),
("生产资金", "41"),
("生产噪声", "41"),
("农村低保", "41"),
("劳动争议", "41"),
("劳动合同争议", "41"),
("劳动报酬与福利", "41"),
("医疗事故", "21"),
("停供", "21"),
("基础教育", "21"),
("职业教育", "21"),
("物业资质管理", "21"),
("拆迁补偿", "21"),
("设施维护", "21"),
("市场外溢", "11"),
("占道经营", "11"),
("树木管理", "11"),
("农村基础设施", "11"),
("无水", "11"),
("供气质量", "11"),
("停气", "11"),
("市政府工作部门（含部门管理机构、直属单位）", "11"),
("燃气管理", "11"),
("市容环卫", "11"),
("新闻传媒", "11"),
("人才招聘", "11"),
("市场环境", "11"),
("行政事业收费", "11"),
("食品安全与卫生", "11"),
("城市交通", "11"),
("房地产开发", "11"),
("房屋配套问题", "11"),
("物业服务", "11"),
("物业管理", "11"),
("占道", "11"),
("园林绿化", "11"),
("户籍管理及身份证", "11"),
("公交运输管理", "11"),
("公路（水路）交通", "11"),
("房屋与图纸不符", "11"),
("有线电视", "11"),
("社会治安", "11"),
("林业资源", "11"),
("其他行政事业收费", "11"),
("经营性收费", "11"),
("食品安全与卫生", "11"),
("体育活动", "11"),
("有线电视安装及调试维护", "11"),
("低保管理", "11"),
("劳动争议", "11"),
("社会福利及事务", "11"),
("一次供水问题", "11"),
]
c = (
WordCloud()
.add(series_name="热点分析", data_pair=data, word_size_range=[6, 66])
.set_global_opts(
title_opts=opts.TitleOpts(
title="热点分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
)
st_pyecharts(c)
from pyecharts.charts import Line
bTrends = (
Line()
#.add_xaxis(["Microsoft", "Amazon", "IBM", "Oracle", "Google", "Alibaba"])
.add_xaxis(TrendsDateList).add_yaxis(
"Revenue in B$",
TrendsTerm01List,
#category_gap = "10%"
#is_large = True,
#is_show_background = True,
).set_global_opts(title_opts=opts.TitleOpts(
title="Top cloud providers 2018", subtitle="2017-2018 Revenue"),
# toolbox_opts=opts.ToolboxOpts(),
))
st_pyecharts(bTrends)
############################
st.markdown('# LINE LINE WIP WIP - Google Trends test 02')
bTrends2 = (
Line()
#.add_xaxis([ "SEO" , "PPC" , "Social Media" , "Display" , "Direct" , "Affiliates" , "Else" ])
.add_xaxis(TrendsDateList)
#.add_yaxis( "Business A" , [ 114 , 55 , 27 , 101 , 125 , 27 , 105 ])
.add_yaxis("Search Term A", TrendsTerm01List).add_yaxis(
"Search Term B", TrendsTerm02List).add_yaxis("Search Term C",
TrendsTerm03List)
# .add_yaxis( "Business B" , [ 57 , 134 , 137 , 129 , 145 , 60 , 49 ])
.set_global_opts(title_opts=opts.TitleOpts(title="Google Trends")))
def render_wordcloud_py():
with st.echo("below"):
data = [
("生活资源", "999"),
("供热管理", "888"),
("供气质量", "777"),
("生活用水管理", "688"),
("一次供水问题", "588"),
("交通运输", "516"),
("城市交通", "515"),
("环境保护", "483"),
("房地产管理", "462"),
("城乡建设", "449"),
("社会保障与福利", "429"),
("社会保障", "407"),
("文体与教育管理", "406"),
("公共安全", "406"),
("公交运输管理", "386"),
("出租车运营管理", "385"),
("供热管理", "375"),
("市容环卫", "355"),
("自然资源管理", "355"),
("粉尘污染", "335"),
("噪声污染", "324"),
("土地资源管理", "304"),
("物业服务与管理", "304"),
("医疗卫生", "284"),
("粉煤灰污染", "284"),
("占道", "284"),
("供热发展", "254"),
("农村土地规划管理", "254"),
("生活噪音", "253"),
("供热单位影响", "253"),
("城市供电", "223"),
("房屋质量与安全", "223"),
("大气污染", "223"),
("房屋安全", "223"),
("文化活动", "223"),
("拆迁管理", "223"),
("公共设施", "223"),
("供气质量", "223"),
("供电管理", "223"),
("燃气管理", "152"),
("教育管理", "152"),
("医疗纠纷", "152"),
("执法监督", "152"),
("设备安全", "152"),
("政务建设", "152"),
("县区、开发区", "152"),
("宏观经济", "152"),
("教育管理", "112"),
("社会保障", "112"),
("生活用水管理", "112"),
("物业服务与管理", "112"),
("分类列表", "112"),
("农业生产", "112"),
("二次供水问题", "112"),
("城市公共设施", "92"),
("拆迁政策咨询", "92"),
("物业服务", "92"),
("物业管理", "92"),
("社会保障保险管理", "92"),
("低保管理", "92"),
("文娱市场管理", "72"),
("城市交通秩序管理", "72"),
("执法争议", "72"),
("商业烟尘污染", "72"),
("占道堆放", "71"),
("地上设施", "71"),
("水质", "71"),
("无水", "71"),
("供热单位影响", "71"),
("人行道管理", "71"),
("主网原因", "71"),
("集中供热", "71"),
("客运管理", "71"),
("国有公交（大巴）管理", "71"),
("工业粉尘污染", "71"),
("治安案件", "71"),
("压力容器安全", "71"),
("身份证管理", "71"),
("群众健身", "41"),
("工业排放污染", "41"),
("破坏森林资源", "41"),
("市场收费", "41"),
("生产资金", "41"),
("生产噪声", "41"),
("农村低保", "41"),
("劳动争议", "41"),
("劳动合同争议", "41"),
("劳动报酬与福利", "41"),
("医疗事故", "21"),
("停供", "21"),
("基础教育", "21"),
("职业教育", "21"),
("物业资质管理", "21"),
("拆迁补偿", "21"),
("设施维护", "21"),
("市场外溢", "11"),
("占道经营", "11"),
("树木管理", "11"),
("农村基础设施", "11"),
("无水", "11"),
("供气质量", "11"),
("停气", "11"),
("市政府工作部门（含部门管理机构、直属单位）", "11"),
("燃气管理", "11"),
("市容环卫", "11"),
("新闻传媒", "11"),
("人才招聘", "11"),
("市场环境", "11"),
("行政事业收费", "11"),
("食品安全与卫生", "11"),
("城市交通", "11"),
("房地产开发", "11"),
("房屋配套问题", "11"),
("物业服务", "11"),
("物业管理", "11"),
("占道", "11"),
("园林绿化", "11"),
("户籍管理及身份证", "11"),
("公交运输管理", "11"),
("公路（水路）交通", "11"),
("房屋与图纸不符", "11"),
("有线电视", "11"),
("社会治安", "11"),
("林业资源", "11"),
("其他行政事业收费", "11"),
("经营性收费", "11"),
("食品安全与卫生", "11"),
("体育活动", "11"),
("有线电视安装及调试维护", "11"),
("低保管理", "11"),
("劳动争议", "11"),
("社会福利及事务", "11"),
("一次供水问题", "11"),
]
c = (
WordCloud()
.add(series_name="热点分析", data_pair=data, word_size_range=[6, 66])
.set_global_opts(
title_opts=opts.TitleOpts(
title="热点分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
)
st_pyecharts(c)
def main():
global dps
new_input = {}
st.sidebar.markdown('## Set A DRFI Strategy:')
for k in show_name:
try:
dvalue = dps[user_input][0][k] * 100 if k in input_pct else dps[
user_input][0][k]
nv = st.sidebar.text_input(show_name[k], dvalue, key=k)
nv = float(nv) / 100.0 if k in input_pct else float(nv)
new_input[k] = nv
except:
pass
bg_params = st.sidebar.empty()
bg_expander = bg_params.beta_expander('Other model settings')
new_bg_input = {}
for k in dps[bg_input][0]:
new_bg_input[k] = float(
bg_expander.text_input(k, value=dps[bg_input][0][k], key=k))
st.sidebar.text('')
st.sidebar.text('')
if st.sidebar.button('Save this strategy'):
dps[bg_input].append(new_bg_input)
dps[user_input].append(new_input)
if st.sidebar.button('reset the strategies'):
reset()
pass
pses = [] # 计算过程的多组参数
cb_AMTs = []
for i in range(len(dps[user_input])):
dps_ = copy.deepcopy(dps)
uinp = copy.deepcopy(dps_[user_input][i])
bginp = copy.deepcopy(dps_[bg_input][i])
dps_.update(uinp)
dps_.update(bginp)
del dps_[user_input]
bginp = copy.deepcopy(dps_[bg_input][i])
dps_.update(bginp)
del dps_[bg_input]
dps_[cb_AMT] = dps_[cb_limit] - dps_[cb_att]
cb_AMTs.append(dps_[cb_AMT])
dps_[tier1_RL] = dps_[tier1_AMT] * dps_[tier1_BenRate]
dps_[tier1_Int] = dps_[tier1_AMT] * dps_[tier1_IntRate]
dps_[tier2_RL] = dps_[tier1_RL] * dps_[tier2_BenRate]
dps_[tier2_Int] = dps_[tier2_RL] * dps_[tier2_IntRate]
dps_[dataSR] = min(dps_[data_Invest] * dps_[dataSR_ImpRatio],
dps_[dataSR_Cap])
dps_[dataCBLoad] = dps_[dataCBLoad_Base] * max(
0, 1 - dps_[data_Invest] * dps_[dataCBLoad_ImpRatio])
dps_[ipSR] = min(dps_[ip_Invest] * dps_[ipSR_ImpRate], dps_[ipSR_Cap])
dps_[ipCBLoad] = dps_[ipCBLoad_Base] * max(
0, 1 - dps_[ip_Invest] * dps_[ipCBLoad_ImpRatio])
dps_[ipRLLoad] = dps_[ipRLLoad_Base] * max(
0, 1 - min(dps_[ip_Invest] * dps_[ipRLLoad_ImpRatio],
dps_[ipRLLoad_ImpRatio_Cap]))
dps_[inSize_Base] = dps_[GDP] * dps_[ip_Base] * dps_[cap_Converter]
# dps_[inSize] = dps_[inSize_Base] * (1 + min(dps_[ip_Invest] * dps_[inSize_ImpRatio], dps_[inSize_ImpRatio_Cap]))
dps_[inSize] = dps_[GDP] * dps_[ip_Revised] * dps_[cap_Converter]
dps_[unit] = round(3 * dps_[L200Y] / dps_[no_cells], 0)
dps_[SharpeRatio] = dps_[SharpeRatioBaseline] * (1 - dps_[dataSR]) * (
1 - dps_[ipSR])
pses.append(dps_)
pass
AnInt = np.array(range(0, int(dps[no_cells]) + 1, 1))
Unit = round(3 * dps[L200Y] / dps[no_cells], 0)
LossAmountMillion: np.ndarray = AnInt * Unit
pencentile = [0.75, 0.9, 0.95, 0.975, 0.99, 0.995]
pctile_year = [4, 10, 20, 40, 100, 200]
# x axis
EP_X = np.zeros(LossAmountMillion.size)
S2 = 1 / 2 * (0.01 + 0.05) * (dps[L100Y] - dps[L20Y])
S3 = 1 / 2 * (0.01 + 0.005) * (dps[L200Y] - dps[L100Y])
S3 = 1 / 2 * (0.01 + 0.005) * (dps[L200Y] - dps[L100Y])
S4 = dps[PctAELTail] * dps[AEL]
S1 = dps[AEL] - S2 - S3 - S4
f1 = S1 - dps[L20Y] * 0.05
f2 = f1 / (dps[L0Y] - 0.05)
b = 1 / f2
r = S4 / 0.005
t1 = LossAmountMillion < dps[L20Y]
t2 = LossAmountMillion >= dps[L20Y]
t3 = LossAmountMillion < dps[L100Y]
t4 = LossAmountMillion >= dps[L100Y]
t5 = LossAmountMillion < dps[L200Y]
t6 = LossAmountMillion >= dps[L200Y]
EP_X[t1] = func1(LossAmountMillion[t1], b, dps[L0Y])
EP_X[t2 & t3] = func2(LossAmountMillion[t2 & t3], dps[L20Y], dps[L100Y])
EP_X[t4 & t5] = func3(LossAmountMillion[t4 & t5], dps[L100Y], dps[L200Y])
EP_X[t6] = func4(LossAmountMillion[t6], r, dps[L200Y])
CDF = 1 - EP_X
st.markdown('*****')
st.markdown('### I. Exceedance Probablility Curve')
linedata = [e for e in zip(LossAmountMillion, EP_X)]
xdata = [e[0] for e in linedata]
ydata = [e[1] for e in linedata]
c2 = (Line(init_opts=opts.InitOpts()).add_xaxis(xdata).add_yaxis(
'Exceedance Probablility',
ydata,
is_smooth=True,
is_symbol_show=False,
symbol_size=0).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger='axis',
axis_pointer_type='line'),
xaxis_opts=opts.AxisOpts(type_='value', name='Loss Amount'),
yaxis_opts=opts.AxisOpts(type_='value',
name='Exceedance Probablility',
is_scale=True),
datazoom_opts=opts.DataZoomOpts(is_show=True,
type_='slider',
range_start=0,
range_end=100),
title_opts=opts.TitleOpts('')))
st_pyecharts(c2, height='400%', width='100%')
st.markdown('*****')
st.markdown('### II. Loss by return year period ($M)')
LossByReturnYearPeriod_empty = st.empty()
st.markdown('*****')
st.markdown('### III. Cost Comparison')
CostComparison_empty = st.empty()
st.markdown('****')
st.markdown('### IV. Disaster Risk Layering')
RetainedLossWithoutPolicy = LossAmountMillion / dps[natBdt]
series_num = 1
c = (Bar(init_opts=opts.InitOpts()).add_xaxis([
str(y) + '-Year' for y in pctile_year
]).add_yaxis(
'Without DRFI Strategy', [
round(
RetainedLossWithoutPolicy[np.argmin(np.abs(CDF - pctile))] *
100, 2) for pctile in pencentile
],
label_opts=opts.LabelOpts(
is_show=True, formatter='{c}%')).set_global_opts(
xaxis_opts=opts.AxisOpts(type_='category',
name='\n\nReturn\nYear\nPeriod'),
yaxis_opts=opts.AxisOpts(
type_='value',
name=' % of\nNational Budget',
is_scale=True,
axislabel_opts=opts.LabelOpts(
formatter=JsCode("function (x){return x + '%'}"))),
legend_opts=opts.LegendOpts(),
datazoom_opts=opts.DataZoomOpts(xaxis_index=[0, 1],
type_='inside',
range_start=0,
range_end=100,
pos_bottom='0%'),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger='axis',
axis_pointer_type='shadow',
is_show_content=True)))
c1 = (Bar(init_opts=opts.InitOpts()).add_xaxis(
[str(y) + '-Year' for y in pctile_year]).add_yaxis(
'', [0] * len(pctile_year),
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_='category',
name='',
axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(
type_='value',
name='Financing\nUtilization',
is_scale=True,
is_inverse=True,
axislabel_opts=opts.LabelOpts(
formatter=JsCode("function (x){return x + ' m'}"))),
legend_opts=opts.LegendOpts(pos_top='3%'),
tooltip_opts=opts.TooltipOpts(is_show=True,
is_show_content=True),
))
c1_label = opts.LabelOpts(
is_show=True,
position='inside',
color='white',
formatter=JsCode(
"function(x) {d=x.data; if(d!==0.0){return d.toFixed() + ' m'}else{return ''};}"
))
for i in range(len(pses)):
ps = pses[i]
if i == len(pses) - 1:
funnel_data = [
[
f'\n\n\nLayer 1:\n\nContingent budget or reserve\n\n0~{int(ps[tier1_UL])} m\n\n\nHigh{" " * 150}Low\nFrequency{" " * 140}Severity',
30
],
[
f'Layer 2:\n\nContingent credit\n\n{int(ps[tier1_UL])}~{int(ps[tier2_UL])} m',
20
],
[
f'Low Frequency{" " * 20}High Severity\n\n\n\nLayer 3:\n\nCapacity\nbuilding\nvehicles\n\n{int(ps[tier2_UL])}~{int(ps[tier3_UL])} m\n',
10
]
]
layerc = (Funnel(init_opts=opts.InitOpts(theme='dark')).add(
'layers',
funnel_data,
sort_='ascending',
label_opts=opts.LabelOpts(
position='inside', color='black',
font_weight=['bold'])).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=False)))
st_pyecharts(layerc, width='61.8%', height='600%')
Limit = ps[inSize]
RevisedLossRetainedRatio = ps[ipRLLoad]
PctLossRetained = RevisedLossRetainedRatio
LossTransfer = 1 - PctLossRetained
InsurancePayout = (LossAmountMillion - ps[attachment]).clip(
0, Limit) * LossTransfer
CatbondSize = ps[cb_limit] - ps[cb_att]
RetainedLossAfterInsurance = LossAmountMillion - InsurancePayout
tier1_utilization = np.minimum(RetainedLossAfterInsurance,
ps[tier1_AMT])
Layer1FinancingBenefit = tier1_utilization * ps[tier1_BenRate]
RetainedLossAfterLayer1Financing = np.maximum(
0, RetainedLossAfterInsurance - tier1_utilization -
Layer1FinancingBenefit)
tier2_utilization = np.minimum(RetainedLossAfterLayer1Financing,
ps[tier2_AMT])
Layer2FinancingBenefit = tier2_utilization * ps[tier2_BenRate]
RetainedLossAfterLayer2Financing = np.maximum(
0, RetainedLossAfterLayer1Financing - tier2_utilization -
Layer2FinancingBenefit)
CatbondRecovery = (RetainedLossAfterLayer2Financing - ps[cb_att]).clip(
0, CatbondSize)
rl_pct_NB = (RetainedLossAfterLayer2Financing -
CatbondRecovery) / ps[natBdt]
layer1_insurance_payout = np.minimum(LossAmountMillion, ps[tier1_UL])
layer2_insurance_payout = np.maximum(
np.minimum(LossAmountMillion, ps[tier2_UL]) - ps[tier1_UL], 0)
layer3_insurance_payout = np.maximum(
np.minimum(LossAmountMillion, ps[tier3_UL]) - ps[tier2_UL], 0)
WT_CDF = np.zeros(CDF.size)
WT_CDF[CDF != 0] = norm.cdf(norm.ppf(CDF[CDF != 0]) - ps[SharpeRatio])
TransformedProbability = np.diff(WT_CDF)
TransformedProbability = np.insert(TransformedProbability, 0,
WT_CDF[0])
InsurancePremium = (TransformedProbability * InsurancePayout).sum()
InsurancePremiumAsPctNationalBudget = InsurancePremium / ps[natBdt]
tier1_wtCost = (TransformedProbability * layer1_insurance_payout).sum()
tier1_wtCost /= float(ps[tier1_UL])
tier2_wtCost = (TransformedProbability * layer2_insurance_payout).sum()
tier2_wtCost /= (ps[tier2_UL] - ps[tier1_UL])
tier3_wtCost = (TransformedProbability * layer3_insurance_payout).sum()
tier3_wtCost /= (ps[tier3_UL] - ps[tier2_UL])
cc_df1 = pd.DataFrame([
str(round(ps[tier1_IntRate] * 100, 0)) + '%',
str(round(ps[tier2_IntRate] * 100, 0)) + '%'
],
index=['Debt', 'Credit Line'],
columns=[''])
cc_df2 = pd.DataFrame([
str(round(tier1_wtCost * 100, 1)) + '%',
str(round(tier2_wtCost * 100, 1)) + '%',
str(round(tier3_wtCost * 100, 1)) + '%'
],
index=[f'Layer {i}' for i in range(1, 4)],
columns=[''])
sb, ertcc = CostComparison_empty.beta_columns(2)
sb.markdown(
'               **Sovereign Borrowing**'
)
ertcc.markdown(
'          **Estimated Risk Transfer Capacity Cost**'
)
sb.table(cc_df1.T)
ertcc.table(cc_df2.T)
PDF = np.diff(CDF)
PDF = np.insert(PDF, 0, CDF[0])
ExpectedLoss = (CatbondRecovery * PDF).sum()
CatbondAnnualCost = ExpectedLoss * ps[hisMul] * (1 + ps[ipCBLoad]) * (
1 + ps[dataCBLoad])
CatbondCoupon = CatbondAnnualCost
CatbondCostAsPctNationalBudget = CatbondCoupon / ps[natBdt]
InsurancePenetrationCostAsPctNationalBudget = ps[ip_Invest]
DataInfrastractureCostAsPctNationalBudget = ps[data_Invest]
Layer1BorrowingCost = ps[tier1_Int]
Layer1CostAsPctNationalBudget = Layer1BorrowingCost / ps[natBdt]
Layer2BorrowingCost = ps[tier2_Int]
Layer2CostAsPctNationalBudget = Layer2BorrowingCost / ps[natBdt]
TotalDRFIStrategyCostAsPctNatlBudget = InsurancePremiumAsPctNationalBudget + CatbondCostAsPctNationalBudget \
+ DataInfrastractureCostAsPctNationalBudget + InsurancePenetrationCostAsPctNationalBudget \
+ Layer1CostAsPctNationalBudget + Layer2CostAsPctNationalBudget
VaR_pct_NationalBudget = rl_pct_NB + TotalDRFIStrategyCostAsPctNatlBudget
serie_name = f'DRFI Strategy {n2c[series_num]}'
serie_data = []
LossByReturnYearPeriod = []
l1FUs, l2Fus, cbrs = [], [], []
for pctile in pencentile:
position = np.argmin(np.abs(CDF - pctile))
var = VaR_pct_NationalBudget[position]
var = round(var * 100, 2)
serie_data.append(var)
loss = LossAmountMillion[position]
LossByReturnYearPeriod.append(loss)
l1FU = tier1_utilization[position]
l1FU = round(l1FU, 0)
l1FUs.append(l1FU)
l2FU = tier2_utilization[position]
l2FU = round(l2FU, 0)
l2Fus.append(l2FU)
cbr = CatbondRecovery[position]
cbr = round(cbr, 0)
cbrs.append(cbr)
c.add_yaxis(serie_name,
serie_data,
label_opts=opts.LabelOpts(is_show=True, formatter='{c}%'))
c1.add_yaxis('Layer 1 Financing Utilization', l1FUs, stack=serie_name, label_opts=c1_label) \
.add_yaxis('Layer 2 Financing Utilization', l2Fus, stack=serie_name, label_opts=c1_label) \
.add_yaxis('Capacity Building Vehicles Recovery', cbrs, stack=serie_name, label_opts=c1_label)
LossByReturnYearPeriod_df = pd.DataFrame(
{
'Loss ($m)': LossByReturnYearPeriod
},
index=[str(y) + '-year' for y in pctile_year]).T
LossByReturnYearPeriod_empty.table(LossByReturnYearPeriod_df)
series_num += 1
grid = (Grid().add(c,
grid_opts=opts.GridOpts(pos_top='15%',
pos_left='8%',
height='43%',
width='79%'),
grid_index=0).add(c1,
grid_opts=opts.GridOpts(
pos_bottom='8%',
pos_left='8%',
height='30%',
width='79%'),
grid_index=1))
df = pd.DataFrame(dps[user_input])
df[cb_AMT] = cb_AMTs
params_df = df.copy(deep=True)
rn = copy.deepcopy(show_name)
for pname in change_to_amt:
params_df[pname] = params_df[pname].map(
lambda x: str(round(x * dps[natBdt], 1))[:3])
for pname in pct_params:
params_df[pname] = params_df[pname].map(
lambda x: str(round(x * 100, 2)) + '%')
rn.update(bg_show_name)
params_df.rename(columns=rn, inplace=True)
params_df = params_df.loc[:, [show_name[k] for k in show_name]]
name_change = {}
for pname in change_to_amt:
if pname in show_name:
name_change[show_name[pname]] = show_name[pname].replace(
' as % National Budget', '')
params_df.rename(columns=name_change, inplace=True)
params_df = pd.DataFrame(
params_df.values.T,
index=params_df.columns,
columns=[f'DRFI Strategy {n2c[i + 1]}' for i in params_df.index])
st.markdown('*****')
st.markdown('### V. The DRFI Strategies')
st.dataframe(params_df)
st.markdown('*****')
st.markdown(
'### VI. Loss Impact as % of National Budget under Various Scenarios')
st_pyecharts(grid, width='100%', height='618%', renderer='canvas')
if press_button:
# any changes need to be performed in place
is_pressed.update({"pressed": True})
if is_pressed["pressed"]: # saved between sessions
c_positive, c_negative, data_positive, data_negative = dv.make_wordcloud_interactive(
rest_name_input2, checked_pos_words, [])
b_pos, b_neg = dv.make_barplot_interactive(rest_name_input2,
checked_pos_words, [])
with col4:
st.markdown("""## """)
with col5:
st_pyecharts(
c_positive,
theme={
"width": "1000",
"height": "800",
"subtitle_text_size": "20",
},
)
st_pyecharts(
b_pos,
theme={
"backgroundColor": "#afc3a1c7",
"textStyle": {
"color": "#F63366"
},
"yaxis_name_pos": "end",
"subtitle_textstyle_opts": {
"color": "#F63366"
},
},
st.markdown('正在分析 **' + option1 + '** 与 **' + option2 + '** 的比赛')
c = (Bar().add_xaxis([
"控球率",
"传球成功率",
"场均关键传球",
"绝佳机会",
"场均争顶成功",
"评分",
]).add_yaxis(option1, get_stats(team1, 0), color='#749f83').add_yaxis(
option2, get_stats(team2, 0), color='#d48265').set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=-10)),
title_opts=opts.TitleOpts(title="球队概况"),
))
st_pyecharts(c)
MoreStats = st.multiselect('Select', ['进攻', '防守', '组织'])
if '进攻' in MoreStats:
c = (Bar().add_xaxis([
"进球",
"场均射门",
"场均射正",
"把握机会能力",
"场均过人",
"场均被侵犯",
]).add_yaxis(option1, get_stats(team1, 1)).add_yaxis(
option2, get_stats(team2, 1)).set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=-10)),
title_opts=opts.TitleOpts(title="球队进攻对比"),
