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| import solara | |
| import seaborn as sns | |
| import plotly.express as px | |
| from utils import selected_template | |
| 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 | |