solara plotly graphs init

.gitignore

@@ -137,6 +137,7 @@ venv.bak/
 
 # mkdocs documentation
 /site
+.history/
 
 # mypy
 .mypy_cache/

Dockerfile

@@ -1,6 +1,6 @@
 FROM jupyter/base-notebook:latest
 
-RUN mamba install -c conda-forge leafmap geopandas localtileserver -y && \
+RUN mamba install -c conda-forge geopandas localtileserver -y && \
     fix-permissions "${CONDA_DIR}" && \
     fix-permissions "/home/${NB_USER}"
 
@@ -18,4 +18,4 @@ USER ${NB_USER}
 
 EXPOSE 8765
 
-CMD ["solara", "run", "./pages", "--host=0.0.0.0"]
+CMD ["solara", "run", "./pages/main.py", "--host=0.0.0.0"]

pages/00_home.py

@@ -1,101 +0,0 @@
-from typing import Optional, cast
-import pandas as pd
-import os
-import solara
-from solara.components.file_drop import FileDrop
-from lat_lon_parser import parse,to_str_deg_min_sec
-
-
-class State:
-    options = ["Decimal", "Deg_Min_Sec"]
-    option = solara.reactive("")
-    df = solara.reactive(cast(Optional[pd.DataFrame], None))
-    dff = solara.reactive(cast(Optional[pd.DataFrame], None))
-    latitude = solara.reactive(cast(Optional[str],""))
-    longitude = solara.reactive(cast(Optional[str],""))
-    extension_check = solara.reactive(True)
-    error_message = solara.reactive("")
-
-    @staticmethod
-    def load_from_file(file):
-        file_name, file_extension = os.path.splitext(file["name"])
-        if file_extension == ".csv":
-            df = pd.read_csv(file["file_obj"], encoding='latin-1')
-            State.df.value = df
-            State.extension_check.value = True
-            State.reset_vars()
-        else:
-            State.extension_check.value = False
-            State.df.value = None
-            State.reset_vars()
-
-    def reset_vars():
-        State.latitude.value = str("")
-        State.longitude.value = str("")
-        State.option.value = str("")
-        State.error_message.value =str("")
-
-    @staticmethod
-    def convert ():
-        State.error_message.value =""
-        try :        
-            df = State.df.value
-            df['converted_latitude'] = df.loc[:, str(State.latitude.value)]
-            df['converted_longitude'] = df.loc[:, str(State.longitude.value)]
-            if State.option.value =="Decimal":
-                df['converted_longitude'] =df['converted_longitude'].str.replace('O','W')
-                df["converted_latitude"] =df["converted_latitude"].apply(parse)
-                df["converted_longitude"] =df["converted_longitude"].apply(parse)
-            elif State.option.value =="Deg_Min_Sec":
-                df["converted_latitude"] =df["converted_latitude"].apply(to_str_deg_min_sec)
-                df["converted_longitude"] =df["converted_longitude"].apply(to_str_deg_min_sec)
-                df["converted_latitude"] =df["converted_latitude"].apply(lambda x: x.replace("-", "") + "S" if "-" in x else x + "N")
-                df["converted_longitude"] =df["converted_longitude"].apply(lambda x: x.replace("-", "") + "W" if "-" in x else x + "E")
-        except Exception as es:
-            State.error_message.value = f"An error occurred. Please check your latitude and longitude format and ensure the correct conversion format ({es})"
-        else :
-            State.df.value = None
-            State.df.value = df
-            State.dff.value  = State.df.value
-            
-@solara.component
-def Page():
-    df = State.df.value
-    
-    solara.Title("GPS Coordinates Converter")
-    
-    with solara.Sidebar():
-        with solara.Column():
-            FileDrop(on_file=State.load_from_file, on_total_progress=lambda *args: None, label="Drag your file CSV here")
-            
-            if State.extension_check.value is not True :
-                solara.Warning("Only CSV file are allowed")
-            else :
-                if df is not None:
-                    columns = list(map(str, df.columns))
-                    solara.Select("Latitude", values=columns, value=State.latitude)
-                    solara.Select("Longitude", values=columns, value=State.longitude)
-                    solara.Select("Convert To", values=State.options, value=State.option)
-                    
-                    if State.longitude.value =="" or State.latitude.value == "" or State.option.value == "":
-                        solara.Info("Select Coordinates Columns and Ouput Format")
-                    else :
-                        solara.Button("Convert", color="primary", text=True, outlined=True, on_click=State.convert)
-                        if State.dff.value is not None:
-                            def get_data():
-                                return State.dff.value.to_csv(index=False)
-                            solara.FileDownload(get_data, label=f"Download {len(State.dff.value):,} converted coordinates", filename="converted.csv",)
-
-
-    if State.df.value is not None:
-        with solara.Column():
-            solara.DataFrame(State.df.value)
-            if State.error_message.value != "":
-                solara.Warning(State.error_message.value)
-    else:
-        solara.Info("Waiting for Data, Please upload a file.")
-
-@solara.component
-def Layout(children):
-    route, routes = solara.use_route()
-    return solara.AppLayout(children=children)

pages/animated.py

@@ -0,0 +1,45 @@
+import solara
+import plotly.express as px
+from utils import selected_template
+
+
+
+@solara.component
+def AnimatedPlots():
+        
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#Animated Plots")
+        # Create an animated plot that you can use to cycle through continent
+        # GDP & life expectancy changes
+        df_cnt = px.data.gapminder()
+        fig1= px.scatter(df_cnt, x="gdpPercap", y="lifeExp", animation_frame="year", 
+                animation_group="country",
+                template=selected_template.value,
+                size="pop", color="continent", hover_name="country",
+                log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])
+
+        # Watch as bars chart population changes
+        fig2 =px.bar(df_cnt, x="continent", y="pop", color="continent",template=selected_template.value,
+        animation_frame="year", animation_group="country", range_y=[0,4000000000])
+
+        solara.Markdown("""
+                        ```python
+                            # Create an animated plot that you can use to cycle through continent
+                            # GDP & life expectancy changes
+                            df_cnt = px.data.gapminder()
+                            px.scatter(df_cnt, x="gdpPercap", y="lifeExp", animation_frame="year", 
+                                    animation_group="country",
+                                    size="pop", color="continent", hover_name="country",
+                                    log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])
+
+                            # Watch as bars chart population changes
+                            px.bar(df_cnt, x="continent", y="pop", color="continent",
+                            animation_frame="year", animation_group="country", range_y=[0,4000000000])
+                        ```
+                        """
+                        )
+        solara.display(fig1)
+        solara.display(fig2)
+
+    return main

pages/bar.py

@@ -0,0 +1,73 @@
+import solara
+import plotly.express as px
+from utils import selected_template
+
+
+@solara.component
+def BarPlot():
+    with solara.Column(gap="10px",align="stretch" ) as main:
+        solara.Markdown(f"#BAR PLOTS")
+        # Get population change in US by querying for US data
+        df_us = px.data.gapminder().query("country == 'United States'")
+        fig1 = px.bar(df_us, x='year', y='pop',template=selected_template.value)
+        solara.FigurePlotly(fig1)
+
+        # Create a stacked bar with more customization
+        df_tips = px.data.tips()
+        fig2 = px.bar(df_tips, x='day', y='tip', color='sex', title='Tips by Sex on Each Day',template=selected_template.value,
+            labels={'tip': 'Tip Amount', 'day': 'Day of the Week'})
+        solara.Markdown(f"###Create a stacked bar with more customization")
+        solara.Markdown("""
+                        ```python
+                        fig2 = px.bar(df_tips, x='day', y='tip', color='sex', title='Tips by Sex on Each Day',
+                            labels={'tip': 'Tip Amount', 'day': 'Day of the Week'})
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+
+        # Place bars next to each other
+        fig3 = px.bar(df_tips, x="sex", y="total_bill",template=selected_template.value,
+                    color='smoker', barmode='group')
+        solara.Markdown(f"###Place bars next to each other")
+        solara.Markdown("""
+                        ```python
+                            fig3 = px.bar(df_tips, x="sex", y="total_bill",
+                                        color='smoker', barmode='group')
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig3)
+
+        # Display pop data for countries in Europe in 2007 greater than 2000000
+        df_europe = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
+        fig4 = px.bar(df_europe, y='pop', x='country', text='pop', color='country',template=selected_template.value)
+        # Put bar total value above bars with 2 values of precision
+        fig4.update_traces(texttemplate='%{text:.2s}', textposition='outside')
+        # Set fontsize and uniformtext_mode='hide' says to hide the text if it won't fit
+        fig4.update_layout(uniformtext_minsize=8)
+        # Rotate labels 45 degrees
+        fig4.update_layout(xaxis_tickangle=-45)
+
+        solara.Markdown(f"###Display pop data for countries in Europe in 2007 greater than 2000000")
+        solara.Markdown("""
+                        ```python
+                            df_europe = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
+                            fig4 = px.bar(df_europe, y='pop', x='country', text='pop', color='country')
+                            # Put bar total value above bars with 2 values of precision
+                            fig4.update_traces(texttemplate='%{text:.2s}', textposition='outside')
+                            # Set fontsize and uniformtext_mode='hide' says to hide the text if it won't fit
+                            fig4.update_layout(uniformtext_minsize=8)
+                            # Rotate labels 45 degrees
+                            fig4.update_layout(xaxis_tickangle=-45)
+                        ```
+                        """
+                        )
+
+
+
+        solara.FigurePlotly(fig4)
+
+    return main
+
+

pages/boxs.py

@@ -0,0 +1,97 @@
+import solara
+import plotly.express as px
+import plotly.graph_objects as go
+from utils import selected_template
+
+
+
+@solara.component
+def Boxs():
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#BOX PLOTS")
+
+        # A box plot allows you to compare different variables
+        # The box shows the quartiles of the data. The bar in the middle is the median 
+        # The whiskers extend to all the other data aside from the points that are considered
+        # to be outliers
+        df_tips = px.data.tips()
+        # We can see which sex tips the most, points displays all the data points
+        fig1= px.box(df_tips, x='sex', y='tip', points='all', template= selected_template.value)
+        
+        
+        # Display tip sex data by day
+        fig2=px.box(df_tips, x='day', y='tip', color='sex', template= selected_template.value)
+
+        # Adding standard deviation and mean
+        fig3 = go.Figure()
+        fig3.add_trace(go.Box(x=df_tips.sex, y=df_tips.tip, marker_color='blue',
+                            boxmean='sd'))
+        fig3.update_layout(template= selected_template.value)
+
+        solara.Markdown(f"#### Use Box plot to compare different variables ")
+        solara.Markdown("""
+                        ```python
+                            # A box plot allows you to compare different variables
+                            # The box shows the quartiles of the data. The bar in the middle is the median 
+                            # The whiskers extend to all the other data aside from the points that are considered
+                            # to be outliers
+                            df_tips = px.data.tips()
+                            # We can see which sex tips the most, points displays all the data points
+                            px.box(df_tips, x='sex', y='tip', points='all')
+
+                            # Display tip sex data by day
+                            px.box(df_tips, x='day', y='tip', color='sex')
+
+                            # Adding standard deviation and mean
+                            fig1 = go.Figure()
+                            fig1.add_trace(go.Box(x=df_tips.sex, y=df_tips.tip, marker_color='blue',
+                                                boxmean='sd'))
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+        solara.FigurePlotly(fig2)
+        solara.FigurePlotly(fig3)
+
+
+        # Complex Styling
+        df_stocks = px.data.stocks()
+        fig4 = go.Figure()
+        # Show all points, spread them so they don't overlap and change whisker width
+        fig4.add_trace(go.Box(y=df_stocks.GOOG, boxpoints='all', name='Google',
+                            fillcolor='blue', jitter=0.5, whiskerwidth=0.2))
+        fig4.add_trace(go.Box(y=df_stocks.AAPL, boxpoints='all', name='Apple',
+                            fillcolor='red', jitter=0.5, whiskerwidth=0.2))
+        # Change background / grid colors
+        fig4.update_layout(title='Google vs. Apple', 
+                        yaxis=dict(gridcolor='rgb(255, 255, 255)',
+                        gridwidth=3),template= selected_template.value,
+                        paper_bgcolor='rgb(243, 243, 243)',
+                        plot_bgcolor='rgb(243, 243, 243)'
+                        )
+
+        # fig4.update_layout(template= selected_template.value)
+        solara.Markdown(f"####Complex Styling")
+        solara.Markdown("""
+                        ```python
+                            # Complex Styling
+                            df_stocks = px.data.stocks()
+                            fig2 = go.Figure()
+                            # Show all points, spread them so they don't overlap and change whisker width
+                            fig2.add_trace(go.Box(y=df_stocks.GOOG, boxpoints='all', name='Google',
+                                                fillcolor='blue', jitter=0.5, whiskerwidth=0.2))
+                            fig2.add_trace(go.Box(y=df_stocks.AAPL, boxpoints='all', name='Apple',
+                                                fillcolor='red', jitter=0.5, whiskerwidth=0.2))
+                            # Change background / grid colors
+                            fig2.update_layout(title='Google vs. Apple', 
+                                            yaxis=dict(gridcolor='rgb(255, 255, 255)',
+                                            gridwidth=3),
+                                            paper_bgcolor='rgb(243, 243, 243)',
+                                            plot_bgcolor='rgb(243, 243, 243)')
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig4)
+
+    return main

pages/density_heatmap.py

@@ -0,0 +1,56 @@
+import solara
+import seaborn as sns
+import plotly.express as px
+from utils import selected_template
+
+
+@solara.component
+def DensityHeatmap():
+
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#Density Heatmap")
+        # Create a heatmap using Seaborn data
+        flights = sns.load_dataset("flights")
+        flights
+
+        # You can set bins with nbinsx and nbinsy
+        fig1 = px.density_heatmap(flights, x='year', y='month', z='passengers', 
+                                color_continuous_scale="Viridis",
+                                template=selected_template.value)
+
+        solara.Markdown(f"####Create a heatmap using Seaborn data")
+        
+        solara.Markdown("""
+                        ```python
+                            # Create a heatmap using Seaborn data
+                            flights = sns.load_dataset("flights")
+                            flights
+
+                            # You can set bins with nbinsx and nbinsy
+                            fig1 = px.density_heatmap(flights, x='year', y='month', z='passengers', 
+                                                    color_continuous_scale="Viridis")
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+
+
+
+        # You can add histograms
+        fig2 = px.density_heatmap(flights, x='year', y='month', z='passengers', 
+                                marginal_x="histogram", marginal_y="histogram",
+                                template=selected_template.value)
+
+        solara.Markdown(f"####Add histograms")
+        solara.Markdown("""
+                        ```python
+                            # You can add histograms
+                            fig2 = px.density_heatmap(flights, x='year', y='month', z='passengers', 
+                                                    marginal_x="histogram", marginal_y="histogram")
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+
+    return main

pages/histogram.py

@@ -0,0 +1,72 @@
+import solara
+import numpy as np
+import plotly.express as px
+from utils import selected_template
+
+
+@solara.component
+def Histograms():
+
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#HISTOGRAMS")
+
+        # Plot histogram based on rolling 2 dice
+        dice_1 = np.random.randint(1,7,5000)
+        dice_2 = np.random.randint(1,7,5000)
+        dice_sum = dice_1 + dice_2
+        # bins represent the number of bars to make
+        # Can define x label, color, title
+        # marginal creates another plot (violin, box, rug)
+        fig1 = px.histogram(dice_sum, nbins=11, labels={'value':'Dice Roll'},
+                    title='5000 Dice Roll Histogram', marginal='violin',
+                    color_discrete_sequence=['green'])
+
+        fig1.update_layout(
+            xaxis_title_text='Dice Roll',
+            yaxis_title_text='Dice Sum',
+            bargap=0.2, showlegend=False,
+            template=selected_template.value
+        )
+
+        solara.Markdown(f"#### Plot histogram based on rolling 2 dice ")
+        solara.Markdown("""
+                        ```python
+                            dice_1 = np.random.randint(1,7,5000)
+                            dice_2 = np.random.randint(1,7,5000)
+                            dice_sum = dice_1 + dice_2
+                            # bins represent the number of bars to make
+                            # Can define x label, color, title
+                            # marginal creates another plot (violin, box, rug)
+                            fig1 = px.histogram(dice_sum, nbins=11, labels={'value':'Dice Roll'},
+                                        title='5000 Dice Roll Histogram', marginal='violin',
+                                        color_discrete_sequence=['green'])
+
+                            fig1.update_layout(
+                                xaxis_title_text='Dice Roll',
+                                yaxis_title_text='Dice Sum',
+                                bargap=0.2, showlegend=False
+                            )
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+
+
+
+        # Stack histograms based on different column data
+        df_tips = px.data.tips()
+        fig2= px.histogram(df_tips, x="total_bill", color="sex",template=selected_template.value)
+
+
+        solara.Markdown(f"####Stack histograms based on different column data")
+        solara.Markdown("""
+                        ```python
+                            df_tips = px.data.tips()
+                            px.histogram(df_tips, x="total_bill", color="sex")
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+        
+    return main

pages/line.py

@@ -0,0 +1,184 @@
+import solara
+import plotly.express as px
+import plotly.graph_objects as go
+from utils import selected_template
+
+@solara.component
+def LinePlot():
+
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#LINE PLOTS")
+        
+        df_stocks = px.data.stocks()
+        fig1 = px.line(df_stocks, x='date', y='GOOG', labels={'x':'Date', 'y':'Price'},
+                    template=selected_template.value
+                    )
+        solara.Markdown(f"###Use included Google price data to make one plot")
+        
+        solara.Markdown("""
+                        ```python
+                        fig1 = px.line(df_stocks, x='date', y='GOOG', labels={'x':'Date', 'y':'Price'},
+                        template=selection_data.value)
+                        ```
+                        """
+                        )
+                        
+                        
+
+        solara.FigurePlotly(fig1)
+        
+        all_stocks =df_stocks.columns.values.tolist()
+        del all_stocks[0]
+        stocks,set_stocks = solara.use_state ([all_stocks[0],all_stocks[1]])
+
+        fig2 = px.line(df_stocks, x='date', y=stocks, labels={'x':'Date', 'y':'Price'},
+            title=" VS ".join(stocks),
+            template=selected_template.value)
+
+        with solara.Card():
+            solara.Markdown(f"###Make multiple line plots")
+            
+            solara.Markdown("""
+                            ```python
+                            fig2 = px.line(df_stocks, x='date', y=['GOOG','AAPL'], labels={'x':'Date', 'y':'Price'},
+                                title='Apple Vs. Google',template=selection_data.value)
+                            ```
+                            """
+                            )
+            
+            solara.FigurePlotly(fig2)
+            solara.SelectMultiple("Stocks", values= stocks, all_values= all_stocks, on_value =set_stocks)
+
+
+        # Create a figure to which I'll add plots
+        fig3 = go.Figure()
+        # You can pull individual columns of data from the dataset and use markers or not
+        fig3.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AAPL, 
+                                mode='lines', name='Apple'))
+        fig3.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AMZN, 
+                                mode='lines+markers', name='Amazon'))
+        # You can create custom lines (Dashes : dash, dot, dashdot)
+        fig3.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.GOOG, 
+                                mode='lines+markers', name='Google',
+                                line=dict(color='firebrick', width=2, dash='dashdot')))
+        fig3.update_layout(template= selected_template.value)
+
+        solara.Markdown(f"###Create a figure to which I'll add plots")
+        
+        solara.Markdown("""
+                        ```python
+                            fig3 = go.Figure()
+                            # You can pull individual columns of data from the dataset and use markers or not
+                            fig3.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AAPL, 
+                                                    mode='lines', name='Apple'))
+                            fig3.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AMZN, 
+                                                    mode='lines+markers', name='Amazon'))
+                            # You can create custom lines (Dashes : dash, dot, dashdot)
+                            fig3.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.GOOG, 
+                                                    mode='lines+markers', name='Google',
+                                                    line=dict(color='firebrick', width=2, dash='dashdot')))
+                        ```
+                        """
+                        )
+        
+        solara.FigurePlotly(fig3)
+
+
+        solara.Markdown(f"###Go crazy styling the figure")
+
+        fig4 =  go.Figure()
+        # You can pull individual columns of data from the dataset and use markers or not
+        fig4.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AAPL, 
+                                mode='lines', name='Apple'))
+        fig4.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AMZN, 
+                                mode='lines+markers', name='Amazon'))
+        # You can create custom lines (Dashes : dash, dot, dashdot)
+        fig4.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.GOOG, 
+                                mode='lines+markers', name='Google',
+                                line=dict(color='firebrick', width=2, dash='dashdot')))
+        fig4.update_layout(template= selected_template.value)
+        fig4.update_layout(
+            # Shows gray line without grid, styling fonts, linewidths and more
+                xaxis=dict(
+                    showline=True,
+                    showgrid=False,
+                    showticklabels=True,
+                    linecolor='rgb(204, 204, 204)',
+                    linewidth=2,
+                    ticks='outside',
+                    tickfont=dict(
+                        family='Arial',
+                        size=12,
+                        color='rgb(82, 82, 82)',
+                    ),
+                ),
+                # Turn off everything on y axis
+                yaxis=dict(
+                    showgrid=False,
+                    zeroline=False,
+                    showline=False,
+                    showticklabels=False,
+                ),
+                # autosize=False,
+                margin=dict(
+                    autoexpand=False,
+                    l=100,
+                    r=20,
+                    t=110,
+                ),
+                showlegend=False,
+                plot_bgcolor='white'
+            )
+        
+        solara.Markdown("""
+                ```python
+                    fig4 =  go.Figure()
+                            # You can pull individual columns of data from the dataset and use markers or not
+                            fig4.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AAPL, 
+                                                    mode='lines', name='Apple'))
+                            fig4.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AMZN, 
+                                                    mode='lines+markers', name='Amazon'))
+                            # You can create custom lines (Dashes : dash, dot, dashdot)
+                            fig4.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.GOOG, 
+                                                    mode='lines+markers', name='Google',
+                                                    line=dict(color='firebrick', width=2, dash='dashdot')))
+                            fig4.update_layout(template= selection_data.value)
+                            fig4.update_layout(
+                                # Shows gray line without grid, styling fonts, linewidths and more
+                                    xaxis=dict(
+                                        showline=True,
+                                        showgrid=False,
+                                        showticklabels=True,
+                                        linecolor='rgb(204, 204, 204)',
+                                        linewidth=2,
+                                        ticks='outside',
+                                        tickfont=dict(
+                                            family='Arial',
+                                            size=12,
+                                            color='rgb(82, 82, 82)',
+                                        ),
+                                    ),
+                                    # Turn off everything on y axis
+                                    yaxis=dict(
+                                        showgrid=False,
+                                        zeroline=False,
+                                        showline=False,
+                                        showticklabels=False,
+                                    ),
+                                    # autosize=False,
+                                    margin=dict(
+                                        autoexpand=False,
+                                        l=100,
+                                        r=20,
+                                        t=110,
+                                    ),
+                                    showlegend=False,
+                                    plot_bgcolor='white'
+                                )
+                ```
+                """
+                )
+        solara.FigurePlotly(fig4)
+    
+    return main

pages/main.py

@@ -0,0 +1,105 @@
+import solara
+from utils import SharedSidebar
+from bar import BarPlot
+from line import LinePlot
+from scatter import ScatterPlot
+from pie import PieChart
+from histogram import Histograms
+from boxs import Boxs
+from violin import Violins
+from density_heatmap import DensityHeatmap
+from treed_plot import TreeD
+from maps import Maps
+from other_plot import OtherPlots
+from animated import AnimatedPlots
+
+
+@solara.component
+def line():
+    with solara.Sidebar():
+        SharedSidebar()
+    LinePlot()
+
+@solara.component
+def bar():
+    with solara.Sidebar():
+        SharedSidebar()
+    BarPlot()
+    
+
+@solara.component
+def scatter():
+    with solara.Sidebar():
+        SharedSidebar()
+    ScatterPlot()
+
+@solara.component
+def pie_chart():
+    with solara.Sidebar():
+        SharedSidebar()
+    PieChart()
+
+@solara.component
+def histogram():
+    with solara.Sidebar():
+        SharedSidebar()
+    Histograms()
+
+@solara.component
+def Box():
+    with solara.Sidebar():
+        SharedSidebar()
+    Boxs()
+
+@solara.component
+def violin():
+    with solara.Sidebar():
+        SharedSidebar()
+    Violins()
+
+@solara.component
+def density():
+    with solara.Sidebar():
+        SharedSidebar()
+    DensityHeatmap()
+
+@solara.component
+def treeD_plot():
+    with solara.Sidebar():
+        SharedSidebar()
+    TreeD()
+
+@solara.component
+def map():
+    with solara.Sidebar():
+        SharedSidebar()
+    Maps()
+
+@solara.component
+def other():
+    with solara.Sidebar():
+        SharedSidebar()
+    OtherPlots()
+    
+
+@solara.component
+def animate():
+    with solara.Sidebar():
+        SharedSidebar()
+    AnimatedPlots()
+
+routes = [
+    solara.Route(path="/", component=line, label="Line"),
+    solara.Route(path="bar", component=bar, label="Bar"),
+    solara.Route(path="scatter", component=scatter, label="Scatter"),
+    solara.Route(path="pie", component=pie_chart, label="Pie"),
+    solara.Route(path="histogram", component=histogram, label="Histogram"),
+    solara.Route(path="box", component=Box, label="Box"),
+    solara.Route(path="violin", component=violin, label="Violin"),
+    solara.Route(path="density-heatmap", component=density, label="Density-Heatmap"),
+    solara.Route(path="Plot_3D", component=treeD_plot, label="3D-Plot"),
+    solara.Route(path="map", component=map, label="Maps"),
+    solara.Route(path="other", component=other, label="Other_Plot"),
+    solara.Route(path="animated", component=animate, label="Animated_Plot"),
+
+]

pages/maps.py

@@ -0,0 +1,96 @@
+import solara
+import pandas as pd
+import plotly.express as px
+from utils import selected_template
+
+# You can color complex maps like we do here representing unemployment data
+
+# Allows us to grab data from a supplied URL
+from urllib.request import urlopen
+# Used to decode JSON data
+import json
+# Grab US county geometry data
+with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
+    counties = json.load(response)
+
+# Grab unemployment data based on each counties Federal Information Processing number
+df2 = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
+                dtype={"fips": str})
+
+
+
+@solara.component
+def Maps():
+
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#Map Scatter Plots")
+
+        # There are many interesting ways of working with maps
+        # plotly.com/python-api-reference/generated/plotly.express.scatter_geo.html
+        df = px.data.gapminder().query("year == 2007")
+        fig1 = px.scatter_geo(df, locations="iso_alpha",
+                            color="continent", # which column to use to set the color of markers
+                            hover_name="country", # column added to hover information
+                            size="pop", # size of markers
+                            projection="orthographic",
+                            template=selected_template.value
+                            )
+
+
+        # solara.Markdown(f"#### Use Box plot to compare different variables ")
+        solara.Markdown("""
+                        ```python
+                            # There are many interesting ways of working with maps
+                            # plotly.com/python-api-reference/generated/plotly.express.scatter_geo.html
+                            df = px.data.gapminder().query("year == 2007")
+                            fig = px.scatter_geo(df, locations="iso_alpha",
+                                                color="continent", # which column to use to set the color of markers
+                                                hover_name="country", # column added to hover information
+                                                size="pop", # size of markers
+                                                projection="orthographic")
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+
+        # Draw map using the county JSON data, color using unemployment values on a range of 12
+        fig2 = px.choropleth(df2, geojson=counties, locations='fips', color='unemp',
+                                color_continuous_scale="Viridis",
+                                range_color=(0, 12),
+                                scope="usa",
+                                labels={'unemp':'unemployment rate'},
+                                template=selected_template.value
+                                )
+
+        solara.Markdown(f"####Choropleth Maps")
+        solara.Markdown("""
+                        ```python
+                            # You can color complex maps like we do here representing unemployment data
+
+                            # Allows us to grab data from a supplied URL
+                            from urllib.request import urlopen
+                            # Used to decode JSON data
+                            import json
+                            # Grab US county geometry data
+                            with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
+                                counties = json.load(response)
+
+                            # Grab unemployment data based on each counties Federal Information Processing number
+                            df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
+                                            dtype={"fips": str})
+
+                            # Draw map using the county JSON data, color using unemployment values on a range of 12
+                            fig2 = px.choropleth(df, geojson=counties, locations='fips', color='unemp',
+                                                    color_continuous_scale="Viridis",
+                                                    range_color=(0, 12),
+                                                    scope="usa",
+                                                    labels={'unemp':'unemployment rate'}
+                                                    )
+
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+
+    return main

pages/other_plot.py

@@ -0,0 +1,105 @@
+import solara
+import seaborn as sns
+import plotly.express as px
+from utils import selected_template
+
+
+@solara.component
+def OtherPlots():
+
+        
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#Polar Chart")
+        
+        # Polar charts display data radially 
+        # Let's plot wind data based on direction and frequency
+        # You can change size and auto-generate different symbols as well
+        df_wind = px.data.wind()
+        px.scatter_polar(df_wind, r="frequency", theta="direction", color="strength",
+                        size="frequency", symbol="strength",
+                        template=selected_template.value)
+
+        # Data can also be plotted using lines radially
+        # A template makes the data easier to see
+        fig1 = px.line_polar(df_wind, r="frequency", theta="direction", color="strength",
+                line_close=True, template="plotly_dark", width=800, height=400)
+        
+        solara.Markdown("""
+                        ```python
+                            # Polar charts display data radially 
+                            # Let's plot wind data based on direction and frequency
+                            # You can change size and auto-generate different symbols as well
+                            df_wind = px.data.wind()
+                            px.scatter_polar(df_wind, r="frequency", theta="direction", color="strength",
+                                            size="frequency", symbol="strength")
+
+                            # Data can also be plotted using lines radially
+                            # A template makes the data easier to see
+                            fig1 = px.line_polar(df_wind, r="frequency", theta="direction", color="strength",
+                                    line_close=True, template="plotly_dark", width=800, height=400)
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+
+
+        # Used to represent ratios of 3 variables
+        df_exp = px.data.experiment()
+        fig2 = px.scatter_ternary(df_exp, a="experiment_1", b="experiment_2", 
+                        c='experiment_3', hover_name="group", color="gender",template=selected_template.value)
+
+        solara.Markdown(f"#Ternary Plot")
+        solara.Markdown("""
+                        ```python
+                        # Used to represent ratios of 3 variables
+                        df_exp = px.data.experiment()
+                        px.scatter_ternary(df_exp, a="experiment_1", b="experiment_2", 
+                                        c='experiment_3', hover_name="group", color="gender")
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+
+
+
+        # You can create numerous subplots
+        df_tips = px.data.tips()
+        fig3= px.scatter(df_tips, x="total_bill", y="tip", color="smoker", facet_col="sex",
+                         template=selected_template.value)
+
+        # We can line up data in rows and columns
+        fig4 = px.histogram(df_tips, x="total_bill", y="tip", color="sex", facet_row="time", facet_col="day",template=selected_template.value,
+            category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})
+
+        # This dataframe provides scores for different students based on the level
+        # of attention they could provide during testing
+        att_df = sns.load_dataset("attention")
+        fig5 = px.line(att_df, x='solutions', y='score', facet_col='subject',
+                    facet_col_wrap=5, title='Scores Based on Attention',
+                    template=selected_template.value)
+
+        solara.Markdown(f"#Facets")
+        solara.Markdown("""
+                        ```python
+                            # You can create numerous subplots
+                            df_tips = px.data.tips()
+                            px.scatter(df_tips, x="total_bill", y="tip", color="smoker", facet_col="sex")
+
+                            # We can line up data in rows and columns
+                            fig3 = px.histogram(df_tips, x="total_bill", y="tip", color="sex", facet_row="time", facet_col="day",
+                                category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})
+
+                            # This dataframe provides scores for different students based on the level
+                            # of attention they could provide during testing
+                            att_df = sns.load_dataset("attention")
+                            fig4 = fig = px.line(att_df, x='solutions', y='score', facet_col='subject',
+                                        facet_col_wrap=5, title='Scores Based on Attention')
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig3)
+        solara.FigurePlotly(fig4)
+        solara.FigurePlotly(fig5)
+
+    return main

pages/pie.py

@@ -0,0 +1,61 @@
+import solara
+import plotly.express as px
+import plotly.graph_objects as go
+from utils import selected_template
+
+@solara.component
+def PieChart():
+    
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#PIE CHARTS")
+        # Use included Iris data set
+        df_samer = px.data.gapminder().query("year == 2007").query("continent == 'Asia'")
+        
+        fig1= px.pie(df_samer, values='pop', names='country', height= 700,
+            title='Population of Asian continent',template= selected_template.value,
+            color_discrete_sequence=px.colors.sequential.RdBu)
+
+        solara.Markdown(f"#### Create Pie chart of the largest nations in Asia ")
+        solara.Markdown("[Color maps here](https://plotly.com/python/builtin-colorscales)")
+        solara.Markdown("""
+                        ```python
+                            # Use included Iris data set
+                            df_samer = px.data.gapminder().query("year == 2007").query("continent == 'Asia'")
+                            fig1= px.pie(df_samer, values='pop', names='country', 
+                                title='Population of Asian continent', 
+                                color_discrete_sequence=px.colors.sequential.RdBu)
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+
+
+
+        # Customize pie chart
+        colors = ['blue', 'green', 'black', 'purple', 'red', 'brown']
+        fig2 = go.Figure(data=[go.Pie(labels=['Water','Grass','Normal','Psychic', 'Fire', 'Ground'], 
+                            values=[110,90,80,80,70,60])])
+        # Define hover info, text size, pull amount for each pie slice, and stroke
+        fig2.update_traces(hoverinfo='label+percent', textfont_size=15,
+                        textinfo='label+percent', pull=[0.1, 0, 0.2, 0, 0, 0],
+                        marker=dict(colors=colors, line=dict(color='yellow', width=2)))
+        fig2.update_layout(template= selected_template.value)
+
+
+        solara.Markdown(f"#### Customize pie chart")
+        solara.Markdown("""
+                        ```python
+                            # Customize pie chart
+                            colors = ['blue', 'green', 'black', 'purple', 'red', 'brown']
+                            fig2 = go.Figure(data=[go.Pie(labels=['Water','Grass','Normal','Psychic', 'Fire', 'Ground'], 
+                                                values=[110,90,80,80,70,60])])
+                            # Define hover info, text size, pull amount for each pie slice, and stroke
+                            fig2.update_traces(hoverinfo='label+percent', textfont_size=15,
+                                            textinfo='label+percent', pull=[0.1, 0, 0.2, 0, 0, 0],
+                                            marker=dict(colors=colors, line=dict(color='yellow', width=2)))
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+    return main

pages/scatter.py

@@ -0,0 +1,155 @@
+import solara
+import numpy as np
+import seaborn as sns
+import plotly.express as px
+import plotly.graph_objects as go
+from utils import selected_template
+
+
+@solara.component
+def ScatterPlot():
+    with solara.Row(justify='space-around') as main:
+        with solara.Column(gap="10px",align="stretch"):
+            with solara.Card():
+                with solara.Row():
+                    with solara.Column():
+                        selection_data, set_selection_data = solara.use_state(None)
+                        click_data, set_click_data = solara.use_state(None)
+                        hover_data, set_hover_data = solara.use_state(None)
+                        unhover_data, set_unhover_data = solara.use_state(None)
+                        deselect_data, set_deselect_data = solara.use_state(None)
+                        
+                        
+                        solara.Markdown(f"#SCATTER PLOTS")
+                        # Use included Iris data set
+                        df_iris = px.data.iris()
+                        # Create a scatter plot by defining x, y, different color for count of provided
+                        # column, size based on supplied column and additional data to display on hover
+                        fig1= px.scatter(df_iris, x="sepal_width", y="sepal_length", color="species", template=selected_template.value,
+                                        size='petal_length', hover_data=['petal_width'])
+
+                        solara.Markdown(f"###Create a scatter plot by defining x, y, different color for count of provided")
+                        solara.Markdown("""
+                                        ```python
+                                            fig1= px.scatter(df_iris, x="sepal_width", y="sepal_length", color="species",
+                                            size='petal_length', hover_data=['petal_width'])
+                                        ```
+                                        """
+                                        )
+                        solara.FigurePlotly(fig1, on_selection=set_selection_data, 
+                                            on_click=set_click_data, on_hover=set_hover_data, 
+                                            on_unhover=set_unhover_data, on_deselect=set_deselect_data)
+
+                    with solara.Columns():
+                            solara.Markdown(
+                                                        f"""
+                                            # Events data
+                                            ## selection
+                                            ```
+                                            {selection_data}
+                                            ```
+
+                                            ## click
+                                            ```
+                                            {click_data}
+                                            ```
+
+                                            ## hover
+                                            ```
+                                            {hover_data}
+                                            ```
+
+                                            ## unhover
+                                            ```
+                                            {unhover_data}
+                                            ```
+
+                                            ## deselect
+                                            ```
+                                            {deselect_data}
+                                            ```
+                                            """
+                                                    )
+
+            with solara.Column(gap="10px",align="stretch"):
+                # Create a customized scatter with black marker edges with line width 2, opaque
+                # and colored based on width. Also show a scale on the right
+                fig2 = go.Figure()
+                fig2.add_trace(go.Scatter(
+                    x=df_iris.sepal_width, y=df_iris.sepal_length,
+                    mode='markers',
+                    marker_color=df_iris.sepal_width,
+                    text=df_iris.species,
+                    marker=dict(showscale=True)
+                ))
+                fig2.update_traces(marker_line_width=2, marker_size=10)
+                fig2.update_layout(template= selected_template.value)
+                solara.Markdown(f"##Create a customized scatter with black marker edges with line width 2, opaque and colored based on width. Also show a scale on the right")
+                solara.Markdown("""
+                                ```python
+                                    fig2 = go.Figure()
+                                    fig2.add_trace(go.Scatter(
+                                        x=df_iris.sepal_width, y=df_iris.sepal_length,
+                                        mode='markers',
+                                        marker_color=df_iris.sepal_width,
+                                        text=df_iris.species,
+                                        marker=dict(showscale=True)
+                                    ))
+                                    fig2.update_traces(marker_line_width=2, marker_size=10)
+                                ```
+                                """
+                                )
+                solara.FigurePlotly(fig2)
+
+                    # Working with a lot of data use Scattergl
+                fig3 = go.Figure(data=go.Scattergl(
+                    x = np.random.randn(10000),
+                    y = np.random.randn(10000),
+                    mode='markers',
+                    marker=dict(
+                        color=np.random.randn(10000),
+                        colorscale='Viridis',
+                        line_width=1
+                    )
+                ))
+                fig3.update_layout(template= selected_template.value)
+
+                solara.Markdown(f"#Working with a lot of data use *Scattergl*")
+                solara.Markdown("""
+                                ```python
+                                    fig3 = go.Figure(data=go.Scattergl(
+                                        x = np.random.randn(10000),
+                                        y = np.random.randn(10000),
+                                        mode='markers',
+                                        marker=dict(
+                                            color=np.random.randn(10000),
+                                            colorscale='Viridis',
+                                            line_width=1
+                                        )
+                                    ))
+                                ```
+                                """
+                                )
+                solara.FigurePlotly(fig3)
+
+
+
+
+                # With a scatter matrix we can compare changes when comparing column data
+                flights = sns.load_dataset("flights")
+                
+                fig4 = px.scatter_matrix(flights, color='month',template=selected_template.value,)
+
+
+                solara.Markdown(f"##Scatter Matrix")
+                solara.Markdown("""
+                                ```python
+                                    # With a scatter matrix we can compare changes when comparing column data
+                                    flights = sns.load_dataset("flights")                            
+                                    fig3 = px.scatter_matrix(flights, color='month')
+                                ```
+                                """
+                                )
+                solara.FigurePlotly(fig4)
+
+        return main

pages/treed_plot.py

@@ -0,0 +1,48 @@
+import solara
+import seaborn as sns
+import plotly.express as px
+from utils import selected_template
+
+@solara.component
+def TreeD():
+
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#3D Scatter Plots")
+
+        flights = sns.load_dataset("flights")
+        # Create a 3D scatter plot using flight data
+        fig1 = px.scatter_3d(flights, x='year', y='month', z='passengers', color='year',
+                        opacity=0.7, 
+                        width=800, height=400,
+                        template=selected_template.value)
+
+
+        # solara.Markdown(f"#### Use Box plot to compare different variables ")
+        solara.Markdown("""
+                        ```python
+                            flights = sns.load_dataset("flights")
+                            # Create a 3D scatter plot using flight data
+                            fig1 = px.scatter_3d(flights, x='year', y='month', z='passengers', color='year',
+                                            opacity=0.7, width=800, height=400)
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+
+
+        # With a scatter matrix we can compare changes when comparing column data
+        fig2 = px.line_3d(flights, x='year', y='month', z='passengers', color='year',template=selected_template.value)
+
+        solara.Markdown(f"#3D Line Plots")
+        solara.Markdown("""
+                        ```python
+                            # With a scatter matrix we can compare changes when comparing column data
+                            fig2 = px.line_3d(flights, x='year', y='month', z='passengers', color='year')
+
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+
+    return main

pages/utils.py

@@ -0,0 +1,20 @@
+import solara
+
+selected_template = solara.reactive("plotly")
+def change_theme(e):
+    print (selected_template.value)
+templates =["plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white", "none"]
+
+
+@solara.component
+def SharedSidebar():
+    with solara.Card("Solara + Plotly Graphs", style={"max-width": "500px"}):
+        solara.Markdown(
+            f"""
+            ###This project utilizes the Solara Framework to create interactive graphs using Plotly.
+            *The code and examples in this project are based on the Plotly tutorial by Derek Banas.* 
+            *Please refer to his repository for the original tutorial [here](https://github.com/derekbanas/plotly-tutorial/blob/master/Plotly%20Tut.ipynb)*
+            """
+        )
+    with solara.Card(style={"max-width": "500px"}):
+        solara.Select(label="Themes", value=selected_template, values=templates, on_value=change_theme)

pages/violin.py

@@ -0,0 +1,84 @@
+import solara
+import plotly.express as px
+import plotly.graph_objects as go
+from utils import selected_template
+
+@solara.component
+def Violins():
+
+    with solara.Column(gap="20px", align = "stretch") as main:
+        
+        solara.Markdown(f"#VIOLIN PLOTS")
+
+        # Violin Plot is a combination of the boxplot and KDE
+        # While a box plot corresponds to data points, the violin plot uses the KDE estimation
+        # of the data points
+        df_tips = px.data.tips()
+        fig1= px.violin(df_tips, y="total_bill", box=True, points='all',template=selected_template.value)
+
+
+        # solara.Markdown(f"#### Use Box plot to compare different variables ")
+        solara.Markdown("""
+                        ```python
+                            # Violin Plot is a combination of the boxplot and KDE
+                            # While a box plot corresponds to data points, the violin plot uses the KDE estimation
+                            # of the data points
+                            df_tips = px.data.tips()
+                            px.violin(df_tips, y="total_bill", box=True, points='all')
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig1)
+
+
+        # Multiple plots
+        fig2 = px.violin(df_tips, y="tip", x="smoker", color="sex", box=True, points="all",
+                hover_data=df_tips.columns,template=selected_template.value)
+
+        solara.Markdown(f"####Multiple plots")
+        solara.Markdown("""
+                        ```python
+                            # Multiple plots
+                            px.violin(df_tips, y="tip", x="smoker", color="sex", box=True, points="all",
+                                    hover_data=df_tips.columns)
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig2)
+
+
+
+        # Morph left and right sides based on if the customer smokes
+        fig3 = go.Figure()
+        fig3.add_trace(go.Violin(x=df_tips['day'][ df_tips['smoker'] == 'Yes' ],
+                                y=df_tips['total_bill'][ df_tips['smoker'] == 'Yes' ],
+                                legendgroup='Yes', scalegroup='Yes', name='Yes',
+                                side='negative',
+                                line_color='blue'))
+        fig3.add_trace(go.Violin(x=df_tips['day'][ df_tips['smoker'] == 'No' ],
+                                y=df_tips['total_bill'][ df_tips['smoker'] == 'No' ],
+                                legendgroup='Yes', scalegroup='Yes', name='No',
+                                side='positive',
+                                line_color='red'))
+        fig3.update_layout(template= selected_template.value)
+        
+        solara.Markdown(f"####MMorph left and right sides based on if the customer smokes")
+        solara.Markdown("""
+                        ```python
+                            # Morph left and right sides based on if the customer smokes
+                            fig3 = go.Figure()
+                            fig3.add_trace(go.Violin(x=df_tips['day'][ df_tips['smoker'] == 'Yes' ],
+                                                    y=df_tips['total_bill'][ df_tips['smoker'] == 'Yes' ],
+                                                    legendgroup='Yes', scalegroup='Yes', name='Yes',
+                                                    side='negative',
+                                                    line_color='blue'))
+                            fig3.add_trace(go.Violin(x=df_tips['day'][ df_tips['smoker'] == 'No' ],
+                                                    y=df_tips['total_bill'][ df_tips['smoker'] == 'No' ],
+                                                    legendgroup='Yes', scalegroup='Yes', name='No',
+                                                    side='positive',
+                                                    line_color='red'))
+                        ```
+                        """
+                        )
+        solara.FigurePlotly(fig3)
+    return main

requirements.txt

@@ -1,3 +1,4 @@
 solara
 pandas
-lat_lon_parser
+plotly
+seaborn