def plot_count_ARPU(df, window_type):
if window_type == "Days":
df1 = df.resample("D", on="install_time").ARPU.sum()
df2 = df.resample("D", on="install_time").user_id.count()
else:
df1 = df.resample("W", on="install_time").ARPU.sum()
df2 = df.resample("W", on="install_time").user_id.count()
fig_count = px.area(df2,
x=df2.index,
y=df2.values,
title="Installations by:" + window_type,
color_discrete_sequence=colors)
fig_count.update_xaxes(title_text=window_type)
fig_count.update_yaxes(title_text='Installation counts')
fig_count.update_layout(plot_bgcolor="#fbfbfb")
fig_count.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#d4d4d4')
fig_ARPU = px.area(df1,
x=df1.index,
y=df1.values,
title="Income by:" + window_type,
color_discrete_sequence=colors)
fig_ARPU.update_xaxes(title_text=window_type)
fig_ARPU.update_yaxes(title_text='ARPU Sum')
fig_ARPU.update_layout(plot_bgcolor="#fbfbfb")
fig_ARPU.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#d4d4d4')
return fig_count, fig_ARPU
def update_charts(draw_type, contents, name, value):
ctx = dash.callback_context
global DATA
global TRIGER
if ctx.inputs['my-slider.value'] is not None:
TRIGER = value
DATA = données(DATA)
if ctx.inputs['upload-data.contents'] is not None:
children = parse_contents(contents, name)
data = données(children)
DATA = data
if ctx.inputs['type-filter.value'] is not None:
mask = ((DATA.taglist == draw_type))
filtered_data = DATA.loc[mask, :]
drawup_figure = px.area(filtered_data,
x=filtered_data['date'],
y=filtered_data[draw_type])
else:
drawup_figure = {}
elif ctx.inputs['type-filter.value'] is not None:
mask = ((DATA.taglist == draw_type))
filtered_data = DATA.loc[mask, :]
drawup_figure = px.area(filtered_data,
x=filtered_data['date'],
y=filtered_data[draw_type])
else:
drawup_figure = {}
return drawup_figure
def update_figure(comboDigestores, interval_component):
datos = readDB()
df = pd.DataFrame.from_records(
datos, columns=["FOS", "TAC", "FOS/TAC", "Fecha", "dige_id"])
filDig = 1
if comboDigestores == "Principal":
filDig = 1
elif comboDigestores == "Secundario":
filDig = 2
filtered_df = df[df.dige_id == filDig]
fig1 = px.area(filtered_df,
x="Fecha",
y="FOS",
color_discrete_sequence=["yellow"])
fig2 = px.area(filtered_df,
x="Fecha",
y="TAC",
color_discrete_sequence=["blue"])
fig3 = px.area(filtered_df,
x="Fecha",
y="FOS/TAC",
color_discrete_sequence=["green"])
fig1.update_layout(transition_duration=500)
fig2.update_layout(transition_duration=500)
fig3.update_layout(transition_duration=500)
return fig1, fig2, fig3
def area_graph():
st.subheader(
"Trades (Import or Export) strength could be analyzed using AREA GRAPH"
)
st.subheader("Area Plot of Malaysia's Trade Strength from 2013 to 2019")
sitc = st.sidebar.selectbox('SITC:', ['SITC 1 DIGIT', 'SITC 2 DIGIT'])
if sitc == 'SITC 1 DIGIT':
df_sitc_1 = df.groupby(['SITC 1 DIGIT',
'YEAR'])[show].agg(['sum']).reset_index()
df_sitc_1 = df_sitc_1.merge(sitc_1, on='SITC 1 DIGIT')
fig = px.area(df_sitc_1, x="YEAR", y="sum", color="1D DESC")
fig.update_layout(height=600, width=900)
else:
df_sitc_1 = df.groupby(['SITC 2 DIGIT',
'YEAR'])[show].agg(['sum']).reset_index()
df_sitc_1 = df_sitc_1.merge(sitc_2, on='SITC 2 DIGIT')
fig = px.area(df_sitc_1, x="YEAR", y="sum", color="2D DESC")
fig.update_layout(showlegend=False, height=600, width=900)
fig.update_layout(
title_text="Trade Performance of Malaysia from 2013 to 2019 based on {}"
.format(show.split()[0]))
st.plotly_chart(fig)
st.subheader(
"Revealed Comparative Advantage measures (RCA) of Malaysia's Goods from 2013 to 2019"
)
st.text(
'This is a measure of how a country’s exports compare to those of a bigger group, such as a region or the rest of the world. For example, if a country’s RCA in wheat is high (typically greater than one), that means wheat makes up a higher share of that country’s total exports than it does of the world’s exports'
)
rca_df = rca.transpose().rename(columns=rca.transpose().iloc[0])
rca_df.columns = rca_df.columns.rename('Product')
rca_df = rca_df[1:]
fig2 = px.area(rca_df, facet_col="Product", facet_col_wrap=2)
fig2.layout.update(showlegend=False)
st.plotly_chart(fig2)
def area_graph():
st.subheader("Area Plot of Malaysia's Trade Performance from 2013 to 2019")
sitc = st.sidebar.selectbox('SITC:', ['SITC 1 DIGIT', 'SITC 2 DIGIT'])
if sitc == 'SITC 1 DIGIT':
df_sitc_1 = df.groupby(['SITC 1 DIGIT', 'YEAR'])[show].agg(['sum']).reset_index()
df_sitc_1 = df_sitc_1.merge(sitc_1, on='SITC 1 DIGIT')
fig = px.area(df_sitc_1, x="YEAR", y="sum", color="1D DESC")
fig.update_layout(height=600, width=900)
else:
df_sitc_1 = df.groupby(['SITC 2 DIGIT', 'YEAR'])[show].agg(['sum']).reset_index()
df_sitc_1 = df_sitc_1.merge(sitc_2, on='SITC 2 DIGIT')
fig = px.area(df_sitc_1, x="YEAR", y="sum", color="2D DESC")
fig.update_layout(showlegend=False, height=600, width=900)
fig.update_layout(title_text="Trade Performance of Malaysia from 2013 to 2019 based on {}".format(show.split()[0]))
st.plotly_chart(fig)
def plot(data_points: list,
height: int = 38,
width: int = 95,
top_margin: int = 0,
left_margin: int = 0,
bottom_margin: int = 0,
right_margin: int = 0,
title: str = ""):
# image width = width - left_margin - right_margin
# image height = height - top_margin - bottom_margin
fig = px.area(
pd.DataFrame(data_points),
height=height,
width=width,
title=title,
range_y=[0, max(data_points)],
)
fig.update_traces(line=dict(width=2), line_color='#92acc8')
fig.update_xaxes(visible=False, fixedrange=True)
fig.update_yaxes(visible=False, fixedrange=True)
fig.update_layout(annotations=[], overwrite=True)
fig.update_layout(showlegend=False,
plot_bgcolor="white",
margin=dict(t=top_margin,
l=left_margin,
b=bottom_margin,
r=right_margin))
fig.write_image("images/repository_size.png")
def prices(product_name):
df = pd.read_csv('pricing/assets/historical_pricing.csv')
df_copy = df.copy()
product_df = df_copy.loc[df['NAME'] == product_name]
x = product_df['DATE'].to_list()
y = product_df['PRICE'].to_list()
# fig = px.line(df, x, y)
fig = px.area(df, x="DATE", y="PRICE")
# https://plotly.com/python/reference/layout/#layout-paper_bgcolor
colors = {
'background': 'rgba(0,0,0,0)',
'paper': 'rgba(0,0,0,0)',
'text': 'black',
'colorway': 'red'
}
fig.update_layout(
plot_bgcolor=colors['background'], # background
paper_bgcolor=colors['paper'], # graph area total div
font_color=colors['text'], # text
colorway=(colors['colorway'], colors['colorway'])
)
fig.update_traces(line_color='red')
fig.update_xaxes(rangeslider_visible=True)
return fig
def show_evolution_norm(self,
figsize=(15, 4),
plotly=True,
show=True,
return_plot=False):
# Compute proportion
states_norm = self._normalize()
if plotly:
states_plotly = self._melt(states_norm)
fig = px.area(states_plotly,
x="days",
y="value",
line_group="compartment",
color="compartment")
if return_plot:
return fig
else:
fig.show()
else:
# Plot
if show:
states_norm.plot(kind="area", figsize=figsize)
plt.show()
def plot_cumsum(pca):
exp_var_cumul = np.cumsum(pca.explained_variance_ratio_)
fig = px.area(
x=range(1, exp_var_cumul.shape[0] + 1),
y=exp_var_cumul,
labels={"x": "# Components", "y": "Explained Variance"})
fig.show()
def viz(dt,stock,sec):
fig = px.area(dt)
fig.update_xaxes(
title_text = 'Date',
rangeslider_visible = True,
rangeselector = dict(
buttons = list([
dict(count = 1, label = '1M', step = 'month', stepmode = 'backward'),
dict(count = 6, label = '6M', step = 'month', stepmode = 'backward'),
dict(count = 1, label = 'YTD', step = 'year', stepmode = 'todate'),
dict(count = 1, label = '1Y', step = 'year', stepmode = 'backward'),
dict(step = 'all')])))
fig.update_yaxes(title_text = 'FB Close Price', tickprefix = '$')
fig.update_layout(showlegend = False,
title = {
'text': f'{stock} Share Price',
'y':0.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
sec.plotly_chart(fig)
def headcount_plot(data_frame):
"""Make a line plot of headcount.
The data frame should have start_date and headcount columns
"""
if len(data_frame) == 0:
fig = go.Figure()
else:
fig = px.area(data_frame,
x="start_date",
y="headcount",
color="organization",
line_shape="hv")
fig.update_layout(
xaxis_title="Reporting Period Start Date",
yaxis_title="",
title_text="Number of Tockers vs. Time",
hovermode="x",
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1,
),
)
fig.update_traces(hovertemplate="%{y}")
plot_div = plot(fig, output_type="div", include_plotlyjs=False)
return plot_div
def orders_over_time(self):
st.markdown('''
# Advanced Order Filter
This page shows advanced filters, fine-tunable using the left panel.
It's useful when wanting to find specific statistics about orders
by time, location or order type.
''')
st.markdown('''
## Orders by Country
''')
df = self.df.groupby(
[pd.Grouper('COUNTRY'),
self.df['ORDERDATE'].dt.strftime('%Y-%m')])['SALES'].sum()
df = df.sort_values(ascending=False)
df = df.reset_index()
df = df.rename({'level_0': 'COUNTRY', 'level_1': 'ORDERDATE'})
fig = px.area(df,
x="ORDERDATE",
y="SALES",
color="COUNTRY",
line_group="COUNTRY")
st.plotly_chart(fig, use_container_width=True)
def update_index_df(n, sel_col):
df_index = query_mongo("index", "index_level_1000")
dff = df_index.copy()
dff = dff.loc[dff["Date"] > "2016-09-30"]
dff_last = dff.tail(2)
dff_y = dff_last[dff_last['Date'] ==
dff_last['Date'].min()]['Index Value'].values[0]
dff_t = dff_last[dff_last['Date'] ==
dff_last['Date'].max()]['Index Value'].values[0]
variation = (dff_t >= dff_y)
dff["Var"] = variation
index_area = area(data_frame=dff,
x="Date",
y="Index Value",
labels={
"value": "",
"Index Value": "",
"Date": ""
},
template=sel_col,
title='Crypto Index Level (USD)',
color="Var",
color_discrete_map={
False: '#FD3216',
True: '#1CA71C',
})
index_area.update_layout(showlegend=False)
index_area.update_yaxes(tickprefix="$")
return index_area
def area_chart(dataframe, x, y):
'''
Función para generar gráficas lineales con area_chart
'''
fig = px.area(dataframe, x=x, y=y, title='Suscriptores De Internet Venezuela')
fig.update_xaxes(rangeslider_visible=True)
fig.update_yaxes(title=None)
fig.update_layout(
hovermode='x',
paper_bgcolor='#212529',
plot_bgcolor='#212529',
font=dict(color='white', size=12),
width=700,
height=500,
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
)
)
graph_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return graph_json
def update_graph(country, stat):
filtered_df = df.loc[df['Country']==country]
filtered_df = filtered_df[::-1] # without this the graph is backwards
date_list = filtered_df['Date'].to_list()
date_list = date_list[::30]
print(date_list)# Only get every 4th value - we're going to use this for showing fewer tickers
filtered_df['colour']='orange'
colormap = {'orange':'orange', 'lightblue':'lightblue','darkskyblue':'darkskyblue'}
fig = px.area(filtered_df, x='Date', y=stat, template='plotly_dark',color='colour',color_discrete_map=colormap,
title= 'Worldwide COVID-19 Tracker')
fig.update_layout(title_font_family='Arial')
fig.update_xaxes(tickangle=45, tickfont=dict(family='Arial', size=10))
fig.update_xaxes(showgrid=False) # hide x-axis gridlines
fig.update_yaxes(showgrid=True)
fig.update_layout(showlegend=False) # hide legend
fig.update_layout(width=600, height=450)
fig.update_layout(
xaxis=dict(
tickmode='array',
tickvals=date_list,
ticktext=date_list
),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)'
)
return fig
def create_generation_plot(self, market_result, show_plot=True, filepath=None):
"""Create Generation plot.
"""
gen = market_result.generation()
if "tech" in market_result.data.plants.columns:
gen = pd.merge(gen, market_result.data.plants.tech, left_on="p", right_index=True)
else:
gen["tech"] = gen.plant_type
gen = gen[["fuel", "tech", "t", "G"]].groupby(["fuel", "tech", "t"]).sum().reset_index()
gen_colors = self.color_map(gen)
gen = pd.merge(gen, gen_colors, on=["fuel", "tech"])
gen.loc[:, "G"] *= 1/1000
fig = px.area(gen, x="t", y="G", color="name",
color_discrete_map=gen_colors[["color", "name"]].set_index("name").color.to_dict())
fig.layout.xaxis.title="Time"
fig.layout.yaxis.title="Generation/Load [GW]"
inf = market_result.infeasibility(drop_zero=False)
inf["infeasibility"] = inf.pos - inf.neg
inf = inf[["t", "infeasibility"]].groupby("t").sum()/1000
d = pd.merge(market_result.demand(), market_result.data.net_export, left_on=["t", "n"], right_on=["timestep", "node"])
d = d[["t", "demand_el", "D_ph", "D_es", "net_export"]].groupby("t").sum()/1000
d.loc[:, "demand_el"] -= (d.net_export)
d = d.loc[market_result.model_horizon, :]
fig.add_trace(go.Scatter(x=d.index, y=d.demand_el - inf.infeasibility, line=dict(color="#000000"), name="demand"))
fig.add_trace(go.Scatter(x=d.index, y=d.demand_el - inf.infeasibility + d.D_es + d.D_ph, fill='tonexty', mode= 'none', fillcolor="#BFBDE5", name="storage charging"))
if show_plot:
plot(fig)
if filepath:
fig.write_html(str(filepath))
def update_figure_valadds_total(services_valadd_trend, mining_valadd_trend, manufacturing_valadd_trend, gas_water_waste_valadd_trend,
construction_valadd_trend, com_transp_valadd_trend, agrifor_valadd_trend,
electricity_growth_trend, tab):
df_select = df_full[(df_full['geo']==tab) & (df_full['year']>=2009) & (df_full['sector']!="Overall")]
### Value added calculation:
### For value added trends, we need picker 1 plus picker value
df_select.loc[(df_select['sector']=='Services') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(services_valadd_trend/100)),df_select['yrs_since_final_obs'])
df_select.loc[(df_select['sector']=='Mining') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(mining_valadd_trend/100)),df_select['yrs_since_final_obs'])
df_select.loc[(df_select['sector']=='Manufacturing') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(manufacturing_valadd_trend/100)),df_select['yrs_since_final_obs'])
df_select.loc[(df_select['sector']=='Gas, Water & Waste Services') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(gas_water_waste_valadd_trend/100)),df_select['yrs_since_final_obs'])
df_select.loc[(df_select['sector']=='Construction') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(construction_valadd_trend/100)),df_select['yrs_since_final_obs'])
df_select.loc[(df_select['sector']=='Commercial Transport') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(com_transp_valadd_trend/100)),df_select['yrs_since_final_obs'])
df_select.loc[(df_select['sector']=='Agriculture & Forestry') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(agrifor_valadd_trend/100)),df_select['yrs_since_final_obs'])
### Note electricity generation here has added value grow together with value added
df_select.loc[(df_select['sector']=='Electricity generation') & (df_select['yrs_since_final_obs']>0),'ind_val_add_output'] = df_select['ind_val_add_2019_bln_finaly']*np.power((1+(electricity_growth_trend/100)),df_select['yrs_since_final_obs'])
### Redefine value added figure again, but with dynamic input
df_select_val_add = df_select[df_select.sector != 'LULUCF']
df_select_val_add = df_select_val_add[df_select_val_add .sector != 'Residential']
fig_added_value_total = px.area(df_select_val_add, x="year", y="ind_val_add_output", color="sector",
color_discrete_sequence=['#D62728', '#2CA02C', '#9467BD', '#8C564B', '#E377C2', '#BCBD22', '#7F7F7F', '#17BECF'],
labels={"year": "", "ind_val_add_output": "Value added (billion 2019 AUD)<sub> </sub>"},
title="Value added by industry")
fig_added_value_total.update_layout(transition_duration=500,
template="plotly_white",
legend_traceorder="reversed",
title_font_color="#1F77B4",
title_font_size=18,
title_font_family="Rockwell",
title_x = 0.02,
margin=dict(t=40, r=0, b=0, l=60, pad=0))
fig_added_value_total.update_xaxes(showline=True, linewidth=1, linecolor='black', gridcolor='rgba(149, 165, 166, 0.6)', mirror=True)
fig_added_value_total.update_yaxes(showline=True, linewidth=1, linecolor='black', gridcolor='rgba(149, 165, 166, 0.6)', mirror=True)
return fig_added_value_total
def update_figure(agri_emis_trend, elec_emis_trend, lulucf_emis_trend):
### Emissions output agriculutre: emissions levels at last observation, minus number of years since final observation *annual emission reductions. Second line si so they cannot go negative
df_nat.loc[(df_nat['sector'] == 'Agriculture') &
(df_nat['yrs_since_final_obs'] > 0),
'emissions_MtCo2_output'] = df_nat[
'emissions_MtCo2_finaly'] + agri_emis_trend * df_nat[
'yrs_since_final_obs']
df_nat.loc[(df_nat['sector'] == 'Agriculture') &
(df_nat['emissions_MtCo2_output'] < 0),
'emissions_MtCo2_output'] = 0
### Emissions output electricity: emissions levels at last observation, minus number of years since final observation *annual emission reductions. Second line si so they cannot go negative
df_nat.loc[(df_nat['sector'] == 'Electricity generation') &
(df_nat['yrs_since_final_obs'] > 0),
'emissions_MtCo2_output'] = df_nat[
'emissions_MtCo2_finaly'] + elec_emis_trend * df_nat[
'yrs_since_final_obs']
df_nat.loc[(df_nat['sector'] == 'Electricity generation') &
(df_nat['emissions_MtCo2_output'] < 0),
'emissions_MtCo2_output'] = 0
### Emissions output LULUCF: emissions levels at last observation, minus number of years since final observation *annual emission reductions
df_nat.loc[(df_nat['sector'] == 'LULUCF') &
(df_nat['yrs_since_final_obs'] > 0),
'emissions_MtCo2_output'] = df_nat[
'emissions_MtCo2_finaly'] + lulucf_emis_trend * df_nat[
'yrs_since_final_obs']
### Redefine figure again, but with dynamic input
fig_emissions_total = px.area(df_nat,
x="year",
y="emissions_MtCo2_output",
color="sector")
fig_emissions_total.update_layout(transition_duration=500)
return fig_emissions_total
def plot_capacity_line():
data = make_cumulative_by_district()
fig = px.area(
data,
x="Commissioned",
y="Cumulative",
color="Region",
title='Cumulative installed capacity (kWp) over time',
line_group="PLN_AREA_N",
labels={
"Commissioned": "Date",
"Cumulative": "Cumulative installed capacity (kWp)"
},
)
fig.update_xaxes(
rangeslider_visible=True,
rangeselector=dict(buttons=list([
dict(count=5, label="5y", step="year", stepmode="backward"),
dict(count=2, label="2y", step="year", stepmode="backward"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(step="all")
])))
fig.show()
pio.write_html(fig, file='cumulative_installations.html', auto_open=True)
def get_figure2(explained_var):
print("COMP")
# set which points are selected with the `selectedpoints` property
# and style those points with the `selected` and `unselected`
# attribute. see
# https://medium.com/@plotlygraphs/notes-from-the-latest-plotly-js-release-b035a5b43e21
exp_var_cumul = np.cumsum(explained_var)
fig = px.area(x=range(1, exp_var_cumul.shape[0] + 1),
y=exp_var_cumul,
labels={
"x": "# Components",
"y": "Explained Variance"
})
#fig = px.histogram(selectedpoints)
#fig.update_layout(autosize=True,margin=dict(l=30, r=30, b=20, t=40),hovermode="closest",plot_bgcolor="#F9F9F9",paper_bgcolor="#E9E9E9",legend=dict(font=dict(size=10), orientation="h"))
#fig = px.scatter(df, x=df[x_col], y=df[y_col], text=df.index)
#print("HISTO POINTS")
#print(selectedpoints)
#fig.update_layout(margin={'l': 20, 'r': 0, 'b': 15, 't': 5}, dragmode='select', hovermode=False)
#fig.add_shape(dict({'type': 'rect',
#'line': { 'width': 1, 'dash': 'dot', 'color': 'darkgrey' }},
#**selection_bounds))
return fig
def time_series(data_init,data_end,is_table,is_plot):
df = load_data(data_init,data_end)
title = f'Evolução energética de {data_init} a {data_end}'
if (df.size > 0):
if is_plot:
fig = px.area(df)
fig.update_yaxes(
title_text = "Energia/kWh"
)
fig.update_layout(
title=dict(
text=title,
xanchor="center",
yanchor="top",
x=0.5,
y=0.93
),
legend_title = '',
legend=dict(
orientation="h",
yanchor="top",
xanchor="center",
x = 0.5,
y=-0.2,
itemwidth=40
)
)
st.plotly_chart(fig)
if is_table:
st.dataframe(df.style.format("{:.2f}"))
else:
st.write("Periodo sem dados.")
def plot_stock_price_area_chart(stock_prices: pd.DataFrame) -> None:
c_area = px.area(stock_prices['Close'], title='Stock Price')
c_area.update_xaxes(
title_text='Date',
rangeslider_visible=True,
rangeselector=dict(buttons=list([
dict(count=1, label='1M', step='month', stepmode='backward'),
dict(count=6, label='6M', step='month', stepmode='backward'),
dict(count=1, label='YTD', step='year', stepmode='todate'),
dict(count=1, label='1Y', step='year', stepmode='backward'),
dict(count=3, label='3Y', step='year', stepmode='backward'),
dict(count=5, label='5Y', step='year', stepmode='backward'),
dict(step='all')
])))
c_area.update_yaxes(title_text='Close Price', tickprefix='$')
c_area.update_layout(showlegend=False,
title={
'text': 'Closing Share Price',
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
})
return c_area
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--csv_file_path')
args = parser.parse_args()
st = TokyoCovid19Stat(args.csv_file_path)
cases_by_age = melt(st.cases_by_age,
value_columns=TokyoCovid19Stat.AGECOLNAMES,
var_name='Age')
sma_by_age = melt(sma(st.cases_by_age),
value_columns=TokyoCovid19Stat.AGECOLNAMES,
var_name='Age')
title = 'Tokyo Covid-19 New Cases By Age'
fig = px.area(cases_by_age, x='Date', y='Cases', color='Age', title=title)
py.plot(fig, filename=title, auto_open=False)
title = 'Tokyo Covid-19 New Cases 7-day Moving Average By Age'
fig = px.line(sma_by_age, x='Date', y='Cases', color='Age', title=title)
fig.add_bar(x=st.cases.index,
y=st.cases['Cases'],
name='Raw Total',
marker=dict(color='#dddddd'))
py.plot(fig, filename=title, auto_open=False)
def update_app_ui3(chart_dp_value, search, year_selector):
chart_df = pd.DataFrame() #same as the above
year_range = [x for x in range(year_selector[0], year_selector[1] + 1)]
for i in year_range:
tmpdf = pd.DataFrame(df[df['iyear'] == i])
chart_df = chart_df.append(tmpdf)
if chart_dp_value is not None:
if search is not None:
chart_df = chart_df.groupby(
'iyear')[chart_dp_value].value_counts().reset_index(
name='count'
) #to merge the data using count of the region occured
chart_df = chart_df[chart_df[chart_dp_value].str.contains(
search, case=False)] #to filter the data using search bar
else:
chart_df = chart_df.groupby('iyear')[chart_dp_value].value_counts(
).reset_index(name='count')
print('chart_df is', chart_df)
else:
raise PreventUpdate
chartFigure = px.area(chart_df,
x='iyear',
y='count',
color=chart_dp_value,
height=500)
fig = chartFigure
return fig
def unemploymentviz(city, state, metric='unemployment_rate'):
"""
Visualize historical population metrics from 2010 to 2018 for one city
### Query Parameters:
- `metric`: 'unemployment_rank', 'rank'; default='unemployment_rate',case sensitive,
unemployment_rate in percentage (ex: Spokane - 5.5% of city population unemployed),
rank in how the city compares to other metropolitan areas (ex: lower the better, higher means
more unemployment)
- `city`: [city name], case sensitive(ex: Birmingham)
- `state `: [state abbreviation], 2-letters; case sensitive (ex: AL)
### Response
JSON string to render with react-plotly.js
"""
df = pd.read_csv(historical_unemp_filepath, encoding='utf-8')
subset = df[(df.city == city) & (df.state == state)]
fig = px.area(subset,
x='year',
y=metric,
title=f'{metric} in {city},{state}')
return fig.to_json()
def fig_c002(regions=["Lombardia", "Veneto"], norm="fraction"):
value_name = "Valore % sul Tot." if norm == "fraction" else "Valore"
yformat = ".1%" if norm == "fraction" else ".0"
data = get_pop_covid_regions()
covid_lomb_ven = data[data.region.isin(regions)]
covid_lomb_ven = covid_lomb_ven.melt(
id_vars=["time", "region"],
value_vars=["n_home_quarantine", "n_hospitalized", "n_intensive_care"],
var_name="Variabile",
value_name=value_name,
)
covid_lomb_ven["Variabile"] = covid_lomb_ven["Variabile"].replace(labels)
fig = px.area(
covid_lomb_ven.sort_values(by=["time", "Variabile"], ascending=False),
x="time",
y=value_name,
color="Variabile",
color_discrete_sequence=["#EF553B", "#FF7F0E", "#54A24B"],
facet_col="region",
labels=labels,
groupnorm=norm,
template="plotly_white",
)
fig.update_xaxes(title=None)
fig.add_annotation(
font=dict(size=9, color="grey"),
showarrow=False,
yref="paper",
xref="paper",
text="Fonte: ISTAT, Dipartimento di Protezione Civile Italiana",
y=-0.15,
yanchor="top",
)
fig.update_layout(
title=dict(
text=f"<br><b>Composizione dei Casi COVID-19 Attivi:"
" Lombardia e Veneto</b></br>"
f'<span style="font-size: 12px">{value_name} - Clicca sulle Voci '
"nella Legenda per Selezionare o Deselezionare</span>",
x=0.5,
y=0.98,
),
legend=dict(orientation="h", y=-0.2, yanchor="top", title=None),
yaxis=dict(tickformat=yformat),
margin={
"l": 60,
"r": 30,
"t": 130,
"b": 100,
"autoexpand": False
},
dragmode=False,
width=650,
height=450,
)
watermark(fig)
return fig
def update_graph_prediction(age, scan, visual, loading, model_name):
model = models[model_name]
future_ages = [age + offset for offset in np.arange(0, 10, 1)]
samples = np.hstack([
oh_enc.transform([[visual]] * 10),
np.array([[age, scan, loading] for age in future_ages]),
])
preds = pd.DataFrame(sp.special.softmax(model.decision_function(samples),
axis=1),
columns=model.classes_)
fig = px.area(preds,
labels={
'value': 'Softmax Score',
'index': 'Number of years from present',
'variable': 'Oil Leak'
},
title='Predicted Future Risk of Oil Leak')
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)', font_color='white')
return fig
def update_charts(selected_stock, selected_metric):
"""Update the price chart and fundamental analysis
for a selected stock. Inputs are the selected stock and chosen
fundamentals metric."""
ratios = ratio_analysis(selected_stock, api_key)
prices, ticker = get_prices(ratios, selected_stock)
company = get_companyname(selected_stock, api_key)
# fig = make_subplots(rows=2, cols=1, shared_xaxes=True,\
# subplot_titles=(f'{ticker}',\
# '{} {}'.format(ticker,selected_metric)))
# fig.append_trace(go.Line(x=prices['date'],y=prices['close'],),row=1,col=1)
# fig.append_trace(go.Line(x=ratios['date'],y=ratios[selected_metric],),row=2,col=1)
#plot prices
price_fig = px.area(prices, x='date', y='close')
if prices['close'].iloc[0] < prices['close'].iloc[-1]:
fill = '#98FB98'
else:
fill = '#CD5C5C'
price_fig.update_traces(line_color=fill)
price_fig.update_layout(margin=dict(l=20, r=20, t=20, b=20),
yaxis_title='Closing Price [$]',
plot_bgcolor=colors['background'],
paper_bgcolor=colors['background'],
font_family='monospace',
font_color=colors['text'],
title={
'text': f'{ticker}: {company}',
'y': 1,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
})
#plot metrics
metric_fig = px.line(ratios, x='date', y=selected_metric)
# fig.update_xaxes(title_text="Date", row=1, col=1)
# fig.update_xaxes(title_text="Date", row=2, col=1)
# fig.update_yaxes(title_text="Closing Price",row=1, col=1)
# fig.update_yaxes(title_text="{}".format(selected_metric), row=2, col=1)
metric_fig.update_xaxes(
range=[prices['date'].iloc[0], prices['date'].iloc[-1]])
metric_fig.update_traces(line_color='#FFFF00')
metric_fig.update_layout(margin=dict(l=20, r=20, t=20, b=20),
plot_bgcolor=colors['background'],
paper_bgcolor=colors['background'],
font_family='monospace',
font_color=colors['text'],
title={
'text':
f'{company} ({ticker}) : {selected_metric}',
'y': 1,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
})
return price_fig, metric_fig
def plot_cases_per_date(df, ylabel):
temp = df['Date reported'].value_counts().sort_index().fillna(0).rolling(7).mean()
fig = px.area(temp,
labels = {'value':ylabel, 'index': 'Date reported'},
color_discrete_sequence=px.colors.qualitative.D3)
fig.add_hline(y=500)
fig.update_layout(showlegend=False)
return fig
def total_hospi_new_out_provinces():
fig = px.area(df,
x="DATE",
y="NEW_OUT",
color="PROVINCE",
title="Number of hospital discharges that day")
fig.update_layout(template="plotly_white")
return fig
