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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
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