Update backup-app.py

backup-app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import pandas as pd
 import altair as alt
 
-# Define list of largest hospitals with latitude and longitude
 largest_hospitals = [
     {
         'name': 'Florida Hospital Orlando',
@@ -10,8 +9,8 @@ largest_hospitals = [
         'state': 'FL',
         'zip_code': '32803',
         'bed_count': 2411,
-        'lat': 28.562229,
-        'lng': -81.362976
+        'lat': 28.555149,
+        'lng': -81.362244
     },
     {
         'name': 'Cleveland Clinic',
@@ -19,8 +18,8 @@ largest_hospitals = [
         'state': 'OH',
         'zip_code': '44195',
         'bed_count': 1730,
-        'lat': 41.501669,
-        'lng': -81.621275
+        'lat': 41.501642,
+        'lng': -81.621223
     },
     {
         'name': 'Mayo Clinic',
@@ -28,8 +27,8 @@ largest_hospitals = [
         'state': 'MN',
         'zip_code': '55905',
         'bed_count': 1372,
-        'lat': 44.020634,
-        'lng': -92.463476
+        'lat': 44.019126,
+        'lng': -92.463362
     },
     {
         'name': 'NewYork-Presbyterian Hospital-Columbia and Cornell',
@@ -37,8 +36,8 @@ largest_hospitals = [
         'state': 'NY',
         'zip_code': '10032',
         'bed_count': 2332,
-        'lat': 40.840886,
-        'lng': -73.942184
+        'lat': 40.841708,
+        'lng': -73.942635
     },
     {
         'name': 'UCHealth University of Colorado Hospital',
@@ -46,8 +45,8 @@ largest_hospitals = [
         'state': 'CO',
         'zip_code': '80045',
         'bed_count': 672,
-        'lat': 39.742401,
-        'lng': -104.834694
+        'lat': 39.743943,
+        'lng': -104.834322
     },
     {
         'name': 'Houston Methodist Hospital',
@@ -55,8 +54,8 @@ largest_hospitals = [
         'state': 'TX',
         'zip_code': '77030',
         'bed_count': 1063,
-        'lat': 29.710292,
-        'lng': -95.399262
+        'lat': 29.710773,
+        'lng': -95.399676
     },
     {
         'name': 'Johns Hopkins Hospital',
@@ -64,8 +63,8 @@ largest_hospitals = [
         'state': 'MD',
         'zip_code': '21287',
         'bed_count': 1293,
-        'lat': 39.297082,
-        'lng': -76.590726
+        'lat': 39.296154,
+        'lng': -76.591972
     },
     {
         'name': 'Massachusetts General Hospital',
@@ -73,8 +72,8 @@ largest_hospitals = [
         'state': 'MA',
         'zip_code': '02114',
         'bed_count': 1032,
-        'lat': 42.363371,
-        'lng': -71.068635
+        'lat': 42.362251,
+        'lng': -71.069405
     },
     {
         'name': 'University of Michigan Hospitals-Michigan Medicine',
@@ -82,8 +81,8 @@ largest_hospitals = [
         'state': 'MI',
         'zip_code': '48109',
         'bed_count': 1145,
-        'lat': 42.282531,
-        'lng': -83.728376
+        'lat': 42.285932,
+        'lng': -83.730833
     },
     {
         'name': 'Mount Sinai Hospital',
@@ -91,54 +90,59 @@ largest_hospitals = [
         'state': 'NY',
         'zip_code': '10029',
         'bed_count': 1168,
-        'lat': 40.789866,
-        'lng': -73.952348
+        'lat': 40.788127,
+        'lng': -73.952826
     }
 ]
 
 largest_hospitals_df = pd.DataFrame(largest_hospitals)
 
-# Define chart functions
-def stacked_bar_chart():
+def stacked_bar_chart_with_text_overlay():
     chart = alt.Chart(largest_hospitals_df).mark_bar().encode(
         y=alt.Y('state:N', sort='-x'),
         x=alt.X('bed_count:Q', stack='normalize'),
-        color=alt.Color('state:N'),
-        tooltip=['state', 'bed_count']
+        color=alt.Color('name:N'),
+        tooltip=['name', 'city', 'state', 'bed_count']
     ).properties(
         width=700,
         height=500,
-        title='Largest Hospitals by State (Stacked Bar Chart)'
+        title='Largest Hospitals by State (Stacked Bar Chart with Text Overlay)'
+    ).configure_axisX(
+        labelAngle=-45
     )
-    st.altair_chart(chart)
+    text = chart.mark_text(align='left', baseline='middle', dx=3).encode(
+        text=alt.Text('bed_count:Q', format='.1f')
+    )
+    st.altair_chart(chart + text)
 
 def bump_chart():
     chart = alt.Chart(largest_hospitals_df).transform_joinaggregate(
-        max_bed_count='max(bed_count)',
-    ).transform_window(
-        rank='rank(max_bed_count)',
-        sort=[alt.SortField('max_bed_count', order='descending')]
+        rank='rank(bed_count)',
+        groupby=['state']
     ).transform_filter(
-        alt.datum.rank <= 10
+        alt.datum.rank <= 3
+    ).transform_window(
+        y='row_number()',
+        sort=[alt.SortField('bed_count', order='descending')]
     ).mark_line().encode(
-        y=alt.Y('name:N', sort=alt.EncodingSortField('bed_count', order='descending')),
-        x=alt.X('bed_count:Q'),
-        color=alt.Color('state:N'),
-        tooltip=['name', 'bed_count', 'state']
+        x=alt.X('bed_count:Q', title='Bed Count'),
+        y=alt.Y('y:O', axis=None),
+        color=alt.Color('name:N'),
+        tooltip=['name', 'city', 'state', 'bed_count']
     ).properties(
         width=700,
         height=500,
-        title='Largest Hospitals by Bed Count (Bump Chart)'
+        title='Largest Hospitals by State (Bump Chart)'
     )
     st.altair_chart(chart)
 
 def radial_chart():
-    chart = alt.Chart(largest_hospitals_df).mark_circle().encode(
-        x='sum(bed_count)',
-        y='state',
-        size='sum(bed_count)',
-        color='state',
-        tooltip=['state', 'bed_count']
+    chart = alt.Chart(largest_hospitals_df).mark_bar().encode(
+        x=alt.X('count()', title='Count'),
+        y=alt.Y('state:N', sort='-x'),
+        color=alt.Color('bed_count:Q', legend=None),
+        column=alt.Column('bed_count:Q', bin=alt.Bin(maxbins=10)),
+        tooltip=['name', 'city', 'state', 'bed_count']
     ).properties(
         width=700,
         height=500,
@@ -147,82 +151,109 @@ def radial_chart():
     st.altair_chart(chart)
 
 def trellis_area_sort_chart():
-    chart = alt.Chart(largest_hospitals_df).mark_area().encode(
-        x=alt.X('year:O', title='Year'),
-        y=alt.Y('bed_count:Q', title='Bed Count'),
-        color=alt.Color('state:N', legend=alt.Legend(title='State')),
-        row=alt.Row('name:N', sort='-x', title='Hospital')
-    ).transform_calculate(
-        year='substring(zip_code, 0, 2) + "00"'
+    chart = alt.Chart(largest_hospitals_df).mark_area(opacity=0.8).encode(
+        x=alt.X('yearmonth(date):T', title='Date'),
+        y=alt.Y('sum(revenue):Q', stack='center', axis=None),
+        color=alt.Color('product:N', scale=alt.Scale(scheme='category10')),
+        row=alt.Row('market:N', header=alt.Header(title='Market')),
+        column=alt.Column('product:N', header=alt.Header(title='Product')),
+        tooltip=[alt.Tooltip('product:N'), alt.Tooltip('revenue:Q', format='$,.0f')]
     ).properties(
-        width=700,
-        height=500,
-        title='Largest Hospitals by Year (Trellis Area Sort Chart)'
+        width=300,
+        height=200,
+        title='Trellis Area Sort Chart'
+    ).configure_facet(
+        spacing=0
+    ).configure_view(
+        stroke=None
     )
     st.altair_chart(chart)
 
 def wind_vector_map():
-    airports_df = pd.read_csv('https://raw.githubusercontent.com/hvo/datasets/master/nyc_airports.csv')
-    airports = alt.Chart(airports_df).mark_circle(size=100).encode(
-        longitude='lon:Q',
-        latitude='lat:Q',
-        tooltip=['name', 'city', 'state']
-    )
-
-    wind_df = pd.read_csv('https://raw.githubusercontent.com/vega/vega/master/docs/data/wind.csv')
-    wind = alt.Chart(wind_df).mark_line().encode(
-        x='u:Q',
-        y='v:Q',
-        color=alt.Color('speed:Q', scale=alt.Scale(scheme='inferno')),
-        size='speed:Q',
-        tooltip=['u', 'v', 'speed']
-    )
+    source = pd.DataFrame({
+        'lat': largest_hospitals_df['lat'],
+        'lon': largest_hospitals_df['lng'],
+        'u': [10, 20, 30, 40, 50, -10, -20, -30, -40, -50],
+        'v': [-10, -20, -30, -40, -50, 10, 20, 30, 40, 50],
+        'names': largest_hospitals_df['name']
+    })
+    max_speed = 60
 
-    chart = alt.layer(
-        alt.themes.dark(),
-        alt.repeat(row=range(4), column=range(4), layer=0, data=largest_hospitals_df),
-        airports,
-        wind.transform_filter(
-            alt.datum.zip_code == str(largest_hospitals_df.iloc[0]['zip_code'])
-        ).transform_calculate(
-            azimuth='atan2(v, u)',
-            speed='sqrt(u * u + v * v)',
-            dx='cos(azimuth * PI/180) * speed',
-            dy='sin(azimuth * PI/180) * speed'
-        ).mark_arrow().encode(
-            longitude='lng:Q',
-            latitude='lat:Q',
-            angle='azimuth:Q',
-            size=alt.Size('speed:Q', scale=alt.Scale(range=[0, 50])),
-            tooltip=['u', 'v', 'speed', 'azimuth']
-        )
+    # Create a layer of the world map
+    background = alt.Chart(
+        data=topo_feature('world-110m')
+    ).mark_geoshape(
+        fill='white',
+        stroke='lightgray'
     ).properties(
         width=700,
-        height=500,
-        title='Wind Vectors and Airports in New York City'
+        height=400
+    ).project('naturalEarth1')
+
+    # Add the wind vectors as arrows
+    vectors = background.mark_arrow(
+        length=300,
+        stroke='black',
+        strokeWidth=0.5
+    ).encode(
+        longitude='lon:Q',
+        latitude='lat:Q',
+        angle=alt.Angle('atan2(v, u):Q'),
+        size=alt.Size(alt.Color('length:Q', legend=None), scale=alt.Scale(range=[0, 0.08]), title='Wind speed'),
+        opacity=alt.Opacity(alt.Color('length:Q', legend=None), scale=alt.Scale(range=[0, 1]), title='Wind speed'),
+        tooltip=['names:N', alt.Tooltip('length:Q', format='.1f')]
+    ).transform_calculate(
+        # Cartographic rotation for arrows
+        angle=calc_wind_angle('u', 'v'),
+        # Vector length
+        length=calc_wind_speed('u', 'v'),
+        # Limit vector length
+        length=alt.datum.length > max_speed ? max_speed : alt.datum.length
     )
-    st.altair_chart(chart)
+
+    st.altair_chart(background + vectors)
 
 def table_bubble_plot():
     chart = alt.Chart(largest_hospitals_df).mark_circle().encode(
         x=alt.X('bed_count:Q', title='Bed Count'),
-        y=alt.Y('state:N', sort='-x', title='State'),
+        y=alt.Y('state:N', sort='-x'),
         size=alt.Size('bed_count:Q', title='Bed Count'),
-        color=alt.Color('state:N'),
-        tooltip=['name', 'bed_count', 'city', 'state']
+        color=alt.Color('bed_count:Q', legend=None),
+        tooltip=['name', 'city', 'state', 'bed_count']
     ).properties(
         width=700,
         height=500,
-        title='Largest Hospitals in the US (Table Bubble Plot)'
+        title='Largest Hospitals by State (Table Bubble Plot)'
     )
     st.altair_chart(chart)
 
 def locations_of_us_airports():
-    airports_df = pd.read_csv('https://raw.githubusercontent.com/hvo/datasets/master/nyc_airports.csv')
-    chart = alt.Chart(airports_df).mark_circle(size=100).encode(
-        longitude='lon:Q',
-        latitude='lat:Q',
-        tooltip=['name', 'city', 'state']
+    airports = data.airports.url
+
+    states = alt.topo_feature(data.us_10m.url, 'states')
+    lookup = {'New York City': 'New York', 'Chicago': 'Illinois', 'Los Angeles': 'California', 'San Francisco': 'California', 'Houston': 'Texas'}
+
+    chart = alt.Chart(states).mark_geoshape(
+        fill='lightgray',
+        stroke='white'
+    ).encode(
+        color=alt.Color('count()', scale=alt.Scale(scheme='yelloworangered')),
+        tooltip=[alt.Tooltip('state:N'), alt.Tooltip('count():Q')]
+    ).transform_lookup(
+        lookup='state',
+        from_=alt.LookupData(airports, 'state', ['latitude', 'longitude'])
+    ).transform_fold(
+        ['latitude', 'longitude'],
+        as_=['key', 'value']
+    ).transform_filter(
+        (alt.datum.value[0] != 'NaN') & (alt.datum.value[1] != 'NaN')
+    ).mark_circle(
+        size=10
+    ).encode(
+        longitude='value:Q',
+        latitude='key:Q',
+        color=alt.Color('count()', scale=alt.Scale(scheme='yelloworangered')),
+        tooltip=[alt.Tooltip('state:N'), alt.Tooltip('count():Q')]
     ).properties(
         width=700,
         height=500,
@@ -231,135 +262,195 @@ def locations_of_us_airports():
     st.altair_chart(chart)
 
 def connections_among_us_airports_interactive():
-    airports_df = pd.read_csv('https://raw.githubusercontent.com/hvo/datasets/master/nyc_airports.csv')
-    routes_df = pd.read_csv('https://raw.githubusercontent.com/vega/vega/master/docs/data/flights-2k.csv')
-    source = alt.selection_multi(fields=['origin'])
-    chart = alt.Chart(routes_df).mark_geoshape(stroke='white', strokeWidth=0.5).encode(
-        color=alt.condition(source, alt.value('red'), alt.value('lightgray')),
-        tooltip=['origin', 'destination', 'count']
-    ).transform_lookup(
-        lookup='id',
-        from_=alt.LookupData(airports_df, 'id', ['name', 'city', 'state'])
-    ).project(
-        type='albersUsa'
+    airports = data.airports.url
+    routes = data.routes.url
+
+    states = alt.topo_feature(data.us_10m.url, 'states')
+
+    source = pd.read_json(airports)
+    lookup = {'New York City': 'New York', 'Chicago': 'Illinois', 'Los Angeles': 'California', 'San Francisco': 'California', 'Houston': 'Texas'}
+    source['state'] = source['state'].apply(lambda x: lookup[x] if x in lookup.keys() else x)
+
+    base = alt.Chart(states).mark_geoshape(
+        fill='lightgray',
+        stroke='white'
     ).properties(
         width=700,
-        height=500,
-        title='Connections Among US Airports (Interactive)'
-    )
-    points = alt.Chart(airports_df).mark_circle(size=100).encode(
-        longitude='lon:Q',
-        latitude='lat:Q',
-        tooltip=['name', 'city', 'state', 'id']
+        height=400
+    ).project('albersUsa')
+
+    airports = base.mark_circle(size=10).encode(
+        longitude='longitude:Q',
+        latitude='latitude:Q',
+        tooltip=['name:N', 'city:N', 'state:N', 'country:N']
+    ).transform_lookup(
+        lookup='iata',
+        from_=alt.LookupData(airports, 'iata', ['name', 'city', 'state', 'country', 'latitude', 'longitude'])
+    ).properties(title='US Airports')
+
+    routes = base.mark_geoshape(
+        stroke='black',
+        strokeWidth=0.1
+    ).encode(
+        longitude='start_lon:Q',
+        latitude='start_lat:Q',
+        longitude2='end_lon:Q',
+        latitude2='end_lat:Q'
+    ).transform_lookup(
+        lookup='start',
+        from_=alt.LookupData(source, 'iata', ['state', 'latitude', 'longitude']),
+        as_=['start_state', 'start_lat', 'start_lon']
+    ).transform_lookup(
+        lookup='end',
+        from_=alt.LookupData(source, 'iata', ['state', 'latitude', 'longitude']),
+        as_=['end_state', 'end_lat', 'end_lon']
+    ).transform_filter(
+        (alt.datum.start_lat != None) & (alt.datum.start_lon != None) & (alt.datum.end_lat != None) & (alt.datum.end_lon != None)
+    ).transform_aggregate(
+        count='count()',
+        groupby=['start', 'start_state', 'end', 'end_state']
     ).transform_filter(
-        source
+        (alt.datum['count'] > 10)
+    ).transform_calculate(
+        start_lon=-alt.datum.start_lon,
+        end_lon=-alt.datum.end_lon
     )
-    st.altair_chart(chart + points)
+
+    chart = (base + routes + airports).configure_view(
+        width=800,
+        height=500,
+        stroke=None
+    )
+    st.altair_chart(chart)
 
 def one_dot_per_zipcode():
-    chart = alt.Chart(largest_hospitals_df).mark_circle(size=50).encode(
+    chart = alt.Chart(largest_hospitals_df).mark_circle().encode(
         longitude='lng:Q',
         latitude='lat:Q',
-        color=alt.Color('state:N', scale=alt.Scale(scheme='category10')),
-        tooltip=['name', 'city', 'state', 'bed_count']
+        size=alt.Size('bed_count:Q', title='Bed Count'),
+        color=alt.Color('bed_count:Q', legend=None),
+        tooltip=['name', 'city', 'state', 'zip_code', 'bed_count']
     ).properties(
         width=700,
         height=500,
-        title='Largest Hospitals in the US (One Dot per Zip Code)'
+        title='One Dot Per Zipcode'
     )
     st.altair_chart(chart)
 
 def isotype_visualization_with_emoji():
-    chart = alt.Chart(largest_hospitals_df).mark_image(width=50, height=50).encode(
-        x=alt.X('bed_count:Q', axis=None),
-        y=alt.Y('state:N', sort='-x', axis=None),
-        url='https://raw.githubusercontent.com/twitter/twemoji/v13.0.1/assets/svg/1f628.svg',
-        tooltip=['name', 'bed_count']
+    chart = alt.Chart(largest_hospitals_df).mark_point().encode(
+        x=alt.X('bed_count:Q', title='Bed Count'),
+        y=alt.Y('state:N', sort='-x'),
+        color=alt.Color('state:N'),
+        shape=alt.Shape('state:N'),
+        tooltip=['name', 'city', 'state', 'zip_code', 'bed_count']
+    )
+
+    shape_lookup = {'CO': '🏥', 'FL': '🏥', 'MA': '🏥', 'MD': '🏥', 'MI': '🏥', 'MN': '🏥', 'NY': '🏥', 'OH': '🏥', 'TX': '🏥'}
+    chart = chart.transform_calculate(
+        shape=f'"{shape_lookup}"[datum.state]'
+    )
+
+    chart = chart.mark_text(
+        align='center',
+        baseline='middle',
+        size=30,
+        font='Segoe UI Emoji',
+        dx=0,
+        dy=0,
+    ).encode(
+        text='shape:N'
     ).properties(
         width=700,
         height=500,
-        title='Largest Hospitals in the US (Isotype Visualization with Emoji)'
+        title='Isotype Visualization with Emoji'
     )
     st.altair_chart(chart)
 
 def binned_heatmap():
     chart = alt.Chart(largest_hospitals_df).mark_rect().encode(
         x=alt.X('bed_count:Q', bin=True),
-        y=alt.Y('state:N', sort='-x'),
-        color=alt.Color('count()'),
-        tooltip=['state', 'bed_count']
+        y=alt.Y('state:N'),
+        color=alt.Color('count()', scale=alt.Scale(scheme='yelloworangered')),
+        tooltip=[alt.Tooltip('state:N'), alt.Tooltip('count():Q')]
     ).properties(
         width=700,
         height=500,
-        title='Largest Hospitals by State (Binned Heatmap)'
+        title='Binned Heatmap'
     )
     st.altair_chart(chart)
 
 def facetted_scatterplot_with_marginal_histograms():
-    chart = alt.Chart(largest_hospitals_df).mark_circle(size=50).encode(
+    brush = alt.selection(type='interval', encodings=['x'])
+
+    base = alt.Chart(largest_hospitals_df).transform_filter(
+        brush
+    ).properties(
+        width=500,
+        height=500
+    )
+
+    points = base.mark_point().encode(
         x=alt.X('bed_count:Q', title='Bed Count'),
-        y=alt.Y('state:N', sort='-x', title='State'),
+        y=alt.Y('state:N', sort='-x', title=None),
         color=alt.Color('state:N'),
-        tooltip=['name', 'bed_count', 'city', 'state']
-    ).properties(
-        width=700,
-        height=500,
-        title='Largest Hospitals in the US (Facetted Scatterplot with Marginal Histograms)'
-    ).facet(
-        column=alt.Column('state:N', sort='-y', title='State'),
-        spacing={'column': 30}
+        tooltip=['name', 'city', 'state', 'zip_code', 'bed_count']
     )
-    histogram_x = largest_hospitals_df[['bed_count']].reset_index().rename(columns={'bed_count': 'value'})
-    histogram_x['variable'] = 'bed_count'
-    histogram_y = largest_hospitals_df[['state']].reset_index().rename(columns={'state': 'value'})
-    histogram_y['variable'] = 'state'
-    chart_x = alt.Chart(histogram_x).mark_bar().encode(
-        x=alt.X('value:Q', bin=True),
-        y=alt.Y('count()'),
-        color=alt.Color('variable:N', scale=alt.Scale(range=['#675193', '#ca8861'])),
-        tooltip=['value']
+
+    top_hist = base.mark_bar().encode(
+        x=alt.X('bed_count:Q', title='Bed Count'),
+        y=alt.Y('count()', title='Number of Hospitals'),
+        color=alt.condition(brush, alt.ColorValue('gray'), alt.ColorValue('lightgray')),
     ).properties(
-        width=700,
+        title='Bed Count Distribution',
+        width=500,
         height=100
     )
-    chart_y = alt.Chart(histogram_y).mark_bar().encode(
-        y=alt.Y('value:N', sort='-x'),
-        x=alt.X('count()'),
-        color=alt.Color('variable:N', scale=alt.Scale(range=['#675193', '#ca8861'])),
-        tooltip=['value']
+
+    right_hist = base.mark_bar().encode(
+        y=alt.X('state:N', title='State'),
+        x=alt.X('count()', title='Number of Hospitals'),
+        color=alt.condition(brush, alt.ColorValue('gray'), alt.ColorValue('lightgray')),
     ).properties(
+        title='Hospital Count by State',
         width=100,
         height=500
     )
-    st.altair_chart(chart | chart_x, use_container_width=True)
-    st.altair_chart(chart_y, use_container_width=True)
 
+    chart = ((points | top_hist) & right_hist).add_selection(
+        brush
+    ).configure_view(
+        stroke=None
+    ).properties(
+        title='Facetted Scatterplot with Marginal Histograms',
+        width=700,
+        height=500
+    )
+    st.altair_chart(chart)
+    
 def ridgeline_plot():
-    chart = alt.Chart(largest_hospitals_df).transform_joinaggregate(
-        count='count()',
-        groupby=['state']
-    ).transform_window(
-        rank='rank(count)',
-        sort=[alt.SortField('count', order='descending')]
-    ).transform_filter(
-        alt.datum.rank <= 5
-    ).transform_density(
-        density='bed_count',
+    base = alt.Chart(largest_hospitals_df).transform_density(
+        'bed_count',
         as_=['bed_count', 'density'],
         extent=[0, 3000],
+        bandwidth=50,
         groupby=['state']
-    ).mark_area().encode(
-        y=alt.Y('state:N', sort='-x'),
+    )
+
+    chart = base.mark_area().encode(
         x=alt.X('bed_count:Q', title='Bed Count'),
+        y=alt.Y('state:N', sort='-x', title=None),
         color=alt.Color('state:N'),
-        row=alt.Row('rank:O', sort='descending', title=None)
+        opacity=alt.Opacity('density:Q', legend=None, scale=alt.Scale(range=[0.3, 1]))
     ).properties(
+        title='Ridgeline Plot',
         width=700,
-        height=500,
-        title='Largest Hospitals by State (Ridgeline Plot)'
+        height=500
     )
+
     st.altair_chart(chart)
 
+
 def create_sidebar():
     chart_functions = {
         'Stacked Bar Chart with Text Overlay': stacked_bar_chart,
@@ -403,3 +494,4 @@ def emoji(chart_name):
     return emojis.get(chart_name, '')
 
 create_sidebar()
+