import altair as alt
import pandas as pd
from typing import Optional
from variationist.visualization.altair_chart import AltairChart
[docs]class HeatmapChart(AltairChart):
"""A class for building a HeatmapChart object."""
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 HeatmapChart object.
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)
# Set attributes
self.top_per_class_ngrams = top_per_class_ngrams
self.metric_label = chart_metric + " value"
if self.n_cooc == 1:
self.text_label = (str(self.n_tokens) + "-gram") if self.n_tokens > 1 else "token"
else:
self.text_label = "tokens"
# Set base chart style
self.base_chart = self.base_chart.mark_rect()
# Get relevant dimensions
x_name, x_type = self.get_dim("x", chart_dims)
y_name, y_type = self.get_dim("y", chart_dims)
color_name, color_type = self.get_dim("color", chart_dims)
# Handle label ordering for bins
no_bins = all(var_bin == 0 for var_bin in metadata["var_bins"])
if no_bins:
x_domain = sorted(list(df_data[x_name].unique()))
y_domain = sorted(list(df_data[y_name].unique()), reverse=True)
else:
# Heuristics: if there are no bins based on the first element, avoid reversing
to_reverse = False if df_data[x_name][0].startswith("(") else True
x_domain = sorted(list(df_data[x_name].unique()),
key=lambda x: float(x.split(", ")[0][1:]) if x.startswith("(") else x, reverse=False)
y_domain = sorted(list(df_data[y_name].unique()),
key=lambda y: float(y.split(", ")[0][1:]) if y.startswith("(") else y, reverse=to_reverse)
# Set dimensions
x_dim = alt.X(x_name, type=x_type, scale=alt.Scale(domain=x_domain))
y_dim = alt.Y(y_name, type=y_type, scale=alt.Scale(domain=y_domain))
color = alt.Color(color_name, type=color_type, title=chart_metric)
# Set tooltip
tooltip = [
alt.Tooltip("ngram", type="nominal", title=self.text_label),
alt.Tooltip(x_name, type=x_type),
alt.Tooltip(y_name, type=y_type),
alt.Tooltip(color_name, type=color_type, title=self.metric_label)
]
# Encoding the data
self.base_chart = self.base_chart.encode(
x_dim,
y_dim,
# Note: color will be conditionally added by the "add_search_component"
tooltip
)
# Set extra properties
num_labels_x = len(list(df_data[x_name].unique()))
num_labels_y = len(list(df_data[y_name].unique()))
chart_width = min(num_labels_x * 50, 800)
chart_height = min(num_labels_y * 50, 600)
self.base_chart = self.base_chart.properties(width=chart_width, height=chart_height, center=True)
# The chart has to be filterable, therefore create and add search/dropdown components to it
dropdown_keys = []
dropdown_values = []
for i in range(len(chart_dims["dropdown"])):
dropdown_keys.append(self.get_dim("dropdown", {"dropdown": chart_dims["dropdown"][i]})[0])
for dropdown_key in dropdown_keys:
dropdown_values.append(list(set(df_data[dropdown_key])))
self.base_chart = self.add_dropdown_components(
self.base_chart, tooltip, dropdown_keys, dropdown_values, color, "fill")
# If the chart has to be zoomable, set the property
if self.zoomable == True:
self.base_chart = self.base_chart.interactive()
# Create the final chart
self.chart = self.base_chart