In DuckDB gespeicherte Markdown-Notation, angezeigt in der „Shiny“ WebApp als mathematischer Ausdruck ⇐ Python

[Anonymous]

Wie kann mit DataGrid (Data Grid – Shiny für Python) die LaTex/Markdown-Notation, also welche Zeichenfolgen in den Zellen einer DuckDB-Datenbankdatei gespeichert sind, in der Shiny-Webanwendung als für Menschen lesbare mathematische Ausdrücke angezeigt werden?
Experimenteller Test (siehe Screenshot-Beispiel):webapp
Beispiel-Webbapp (Dummy): https://019aa26c-aae3-5c92-392d-c22d6c7 ... osit.cloud
Erwartet wird die Anzeige der in der mathematischen Notation enthaltenen Datenbank im Browser (siehe Beispiel-App oben für die Anzeige des Summensymbols $\sum$. Siehe beigefügten Screenshot. Geben Sie hier eine Bildbeschreibung ein. Geben Sie hier eine Bildbeschreibung ein
z. B. „app.py“ (Zeigt im Browser die in DuckDB gespeicherten Datenbankeinträge, die Markdown-Strings enthalten):

Code: Select all

from shiny.express import input, ui, render, session
from shiny import reactive
from shinywidgets import render_plotly
import plotly.express as px
# import pandas as pd
import ibis
from plotly.callbacks import Points
from faicons import icon_svg
import helpers

import pandas as pd
#import seaborn as sns
from shared import mathjax, prose, restrict_width

# Import MathJax for LaTeX rendering
mathjax

# Explanation and Explore prose
#with restrict_width(sm=10, md=10, lg=6):
#    prose

# Initialize the Ibis connection
con = ibis.duckdb.connect(database=':memory:')

# Read initial data and initialize the table
helpers.initialize_database(con, "data/ozone.duckdb", "ozone")

ozone = con.table("ozone").rename(
{
"ID":"id",
"State": "state_name",
"Date": "date_local",
"PPM": "arithmetic_mean",
"AQI": "aqi",
"Flag": "flag",
}
).select(["ID", "State", "Date", "PPM", "AQI", "Flag"])

ozone = ozone.mutate(Flag=ozone.Flag.cast('string'))
outliers, ozone = helpers.create_outliers_table(ozone, "PPM")
# Need the entire outliers table
outliers = outliers.to_pandas()

ui.page_opts(fillable=True, title="Identify suspicious values in air quality data \( \sqrt{3x-1}+(1+x)^2 \) ")

with ui.layout_columns():

with ui.card():
ui.card_header(
ui.markdown(
f"{icon_svg('circle-info')} Click on a suspicious value (in blue) to highlight the point in the table."
),
class_="bg-light"
)

with ui.layout_columns():
ui.input_select("x", "X-axis variable:", choices=["Date", "PPM", "AQI"])
ui.input_select("y", "Y-axis variable:", choices=["PPM", "AQI"])

@render_plotly
def plot():
fig = helpers.plot_ozone(input.x(), input.y(), ozone, outliers_editable.data_view())
fig.data[0].on_click(on_point_click) # Color 1
fig.data[1].on_click(on_point_click) # Color 2
return fig

pt_selected = reactive.value()

def on_point_click(trace, points, state):
if len(points.point_inds) > 0:
pt_selected.set(points)

with ui.layout_column_wrap(width=1, heights_equal="row"):
with ui.card():
ui.card_header(
ui.markdown(
f"{icon_svg('circle-info')} \\\(\sum\\\) Change `Flag` to `1` to flag a value as an error.  Flagged points will appear red in the plot."
),
class_="bg-light"
)

@render.data_frame
def outliers_editable():
outliers["Date"] = outliers.Date.astype("string")
return helpers.create_editable_table(outliers)

ui.input_action_button("write_data", "Write to database", width="40%")

with ui.card():
ui.card_header("About this app \( \sqrt{3x-1}+(1+x)^2 \) ", class_="bg-light")

ui.markdown(
"\\\( \\frac{\sqrt{3x-1}+(1+x)^2}{n} \\\)"
""" This sentence uses "\\ \(" delimiters to show math inline: \\\( \sqrt{3x-1}+(1+x)^2 \\\) --- This app uses ozone data from the [EPA](https://www.epa.gov/outdoor-air-quality-data).
The values shown in blue represent rows where `PPM` (ozone level in parts-per-million) was an outlier,
identified using the [IQR method](https://en.wikipedia.org/wiki/Interquartile_range#Outliers).
Some of these values are real, but some are errors, created for the purposes of this app.
\nThe app reads from and writes to an in-memory DuckDB database.
When you refresh the page, the database will be regenerated from scratch, so you will not see your changes."""
)

# Highlight corresponding row on point click
@reactive.effect
async def _():
points: Points | None = pt_selected.get()
if points is None:
await outliers_editable.update_cell_selection({"type": "row", "rows": []})

else:
original_index = helpers.find_row_number(points, outliers_editable)
await outliers_editable.update_cell_selection({"type": "row", "rows": original_index})

# Validate edit
@outliers_editable.set_patch_fn
def upgrade_patch(*, patch):
pt_selected.set(None)
return helpers.validate_patch(
patch,
outliers_editable.data().iloc[patch["row_index"], patch["column_index"]]
)

# Write data to the database
@reactive.Effect
@reactive.event(input.write_data)
def write_data():

# Capture the current state of the edited data from DataGrid
edited_data = outliers_editable.data_view()

# Find the rows where the flag value has changed
changed_values = pd.merge(
outliers,
edited_data,
on=["ID", "State", "Date", "PPM", "AQI"],
suffixes=('_old', '_new')
)

rows_to_update = changed_values[changed_values["Flag_old"] != changed_values["Flag_new"]]

# Update only the changed rows
for _, row in rows_to_update.iterrows():
outliers.loc[outliers['ID'] == row['ID'], 'Flag'] = row['Flag_new']
sql_query = f"UPDATE ozone SET flag = '{row['Flag_new']}' WHERE id = '{row['ID']}'"
con.raw_sql(sql_query)

n_rows = len(rows_to_update)
if n_rows > 0:
if n_rows == 1:
values = "value"
else:
values = "values"
ui.notification_show(
ui.markdown(f"`{n_rows}` {values} successfully updated in database."),
type="message",
duration=5
)
else:
ui.notification_show(ui.markdown("No changes to write to database."), type="warning")