import altair as alt
import pandas as pd
from typing import Optional
from variationist.visualization.altair_chart import AltairChart
[docs]class StatsBarChart(AltairChart):
"""A class for building a BarChart object for basic stats."""
def __init__(
self,
df_data: pd.core.frame.DataFrame,
chart_metric: str,
metadata: dict,
extra_args: dict = {},
chart_dims: dict = {},
zoomable: Optional[bool] = True,
top_per_class_ngrams: Optional[int] = None,
) -> None:
"""
Initialization function for a building a BarChart object for basic stats.
Parameters
----------
df_data: pd.core.frame.DataFrame
A long-form dataframe storing the results of a prior analysis for a
given metric that will be used for visualization purposes.
chart_metric: str
The metric associated to the "df_data" dataframe and thus to the chart.
metadata: dict
A dictionary storing the metadata about the prior analysis.
extra_args: dict = {}
A dictionary storing the extra arguments for this chart type. Default = {}.
chart_dims: dict
The mapping dictionary for the variables for the given chart.
zoomable: Optional[bool] = True
Whether the (HTML) chart should be zoomable using the mouse or not (if this
is allowed for the resulting chart type by the underlying visualization
library).
top_per_class_ngrams: int = 20
The maximum number of highest scoring per-class n-grams to show (for bar
charts only). If set to None, it will show all the n-grams in the corpus
(it may easily be overwhelming). By default is 20 to keep the visualization
compact. This parameter is ignored when creating other chart types.
"""
super().__init__(df_data, chart_metric, metadata, extra_args, zoomable)
# Get relevant dimensions
variables = list(self.df_data.keys())
for main_col in ["statistics", "val_1", "val_2"]:
variables.remove(main_col)
y_name, y_type = variables[0], "nominal"
# Set attributes
self.top_per_class_ngrams = top_per_class_ngrams
self.metric_label = chart_metric + " value"
self.text_label = y_name
# Set base chart style
self.base_chart = self.base_chart.mark_bar(height=15, binSpacing=0.5, cornerRadiusEnd=5)
# Set dimensions
x_dim = alt.X("val_1", type="quantitative", title="")
y_dim = alt.Y(y_name, type=y_type, title="").sort("-x")
column_dim = alt.Column("statistics", type="nominal",
header=alt.Header(labelFontWeight="bold"))
color = alt.Color("statistics", "nominal", legend=None) # for aestethics only
# Set tooltip
tooltip = [
alt.Tooltip(y_name, type=y_type, title=self.text_label),
alt.Tooltip("val_1", type="quantitative", title="mean"),
alt.Tooltip("val_2", type="quantitative", title="stdev")
]
# Encoding the data
self.base_chart = self.base_chart.encode(
x_dim,
y_dim,
column_dim,
color,
tooltip
)
# Set the independent dimensions
self.base_chart = self.base_chart.resolve_scale(
x="independent",
y="independent"
)
# Set extra properties
chart_width = max(100, 800 / len(list(df_data["statistics"].unique())))
self.base_chart = self.base_chart.properties(width=chart_width, center=True)
# The chart has not to be filterable
self.base_chart = self.base_chart.encode(color=y_dim)
# If the chart has to be zoomable, set the property (disallowed for bar chart)
# if self.zoomable == True:
# print(f"INFO: Zoom is disallowed for bar charts.")
# self.base_chart = self.base_chart.interactive()
# Create the final chart
self.chart = self.base_chart