def callback(self, *args, **kwargs) -> List[Any]:
"""Renders the parameter dependent plots and tables
"""
pars = kwargs.get("pars")
projection_admits, census_df = self._build_frames(**kwargs)
# Create admissions figure
admissions_data = plot_dataframe(
projection_admits.head(pars.n_days - 10), max_y_axis=pars.max_y_axis,
)
# Create admissions table data
if kwargs["as_date"]:
projection_admits.index = projection_admits.index.strftime("%b, %d")
admissions_table_data = (
df_to_html_table(projection_admits, data_only=True, n_mod=7)
if kwargs["show_tables"]
else None
)
# Create census figure
census_data = plot_dataframe(
census_df.head(pars.n_days - 10), max_y_axis=pars.max_y_axis
)
# Create admissions table data
if kwargs["as_date"]:
census_df.index = census_df.index.strftime("%b, %d")
census_table_data = (
df_to_html_table(census_df, data_only=True, n_mod=7)
if kwargs["show_tables"]
else None
)
return [admissions_data, admissions_table_data, census_data, census_table_data]
def prepare_visualization_group(df: DataFrame = None, **kwargs) -> List[Any]:
"""Creates plot, table and download link for data frame.
"""
result = [{}, None, None]
if df is not None and isinstance(df, DataFrame):
plot_data = plot_dataframe(
df.dropna().set_index("date").drop(columns=["day"]),
max_y_axis=kwargs.get("max_y_axis", None),
)
table = (
df_to_html_table(
build_table(
df=df,
labels=kwargs.get("labels", df.columns),
modulo=kwargs.get("table_mod", 7),
),
format={
float: int,
(date, datetime): lambda d: d.strftime(DATE_FORMAT),
},
)
# if kwargs.get("show_tables", None)
# else None
)
csv = build_csv_download(df)
result = [plot_data, table, csv]
return result
def prepare_visualization_group(df: DataFrame = None, **kwargs) -> List[Any]:
"""Creates plot, table and download link for data frame.
Arguments:
df: The Dataframe to plot
content: Dict[str, str]
Mapping for translating columns and index.
labels: List[str]
Columns to display
table_mod: int
Displays only each `table_mod` row in table
"""
result = [{}, None, None]
if df is not None and isinstance(df, DataFrame):
date_column = "date"
day_column = "day"
# Translate column and index if specified
content = kwargs.get("content", None)
if content:
columns = {col: content[col] for col in df.columns if col in content}
index = (
{df.index.name: content[df.index.name]}
if df.index.name and df.index.name in content
else None
)
df = df.rename(columns=columns, index=index)
date_column = content.get(date_column, date_column)
day_column = content.get(day_column, day_column)
plot_data = plot_dataframe(
df.dropna().set_index(date_column).drop(columns=[day_column])
)
# translate back for backwards compability of build_table
column_map = {day_column: "day", date_column: "date"}
table = (
df_to_html_table(
build_table(
df=df.rename(columns=column_map),
labels=kwargs.get("labels", df.columns),
modulo=kwargs.get("table_mod", 7),
),
formats={
float: int,
(date, datetime): lambda d: d.strftime(DATE_FORMAT),
},
)
# if kwargs.get("show_tables", None)
# else None
)
# Convert columnnames to lowercase
column_map = {col: col.lower() for col in df.columns}
csv = build_csv_download(df.rename(columns=column_map))
result = [plot_data, table, csv]
return result
def callback(self, *args, **kwargs) -> List[Any]:
"""Renders the parameter dependent plots and tables
"""
pars = kwargs["pars"]
if kwargs["show_additional_projections"]:
title = self.content["infected-v-revovered-title"]
time_evolution = self._build_frame(**kwargs)
time_evolution_data = plot_dataframe(
time_evolution.drop(columns=self.content["susceptible"]),
max_y_axis=pars.max_y_axis,
)
children = [
H4(title, id="infected-v-revovered-title"),
Graph(figure=time_evolution_data,
id="infected-v-revovered-graph"),
]
if kwargs["show_tables"]:
if kwargs["as_date"]:
time_evolution.index = time_evolution.index.strftime(
"%b, %d")
time_evolution_table_data = (df_to_html_table(
time_evolution, data_only=True, n_mod=7)
if kwargs["show_tables"] else {})
children.append(
Table(time_evolution_table_data,
id="infected-v-revovered-table"))
else:
children = []
return [children]