import altair as alt
import pandas as pd
from typing import Optional
from variationist.visualization.altair_chart import AltairChart
[docs]class TemporalLineChart(AltairChart):
"""A class for building a TemporalLineChart 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 TemporalLineChart 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
if ("size" in chart_dims) or ("shape" in chart_dims):
self.base_chart = self.base_chart.mark_trail(
point=alt.OverlayMarkDef(size=75, strokeWidth=0.5))
else:
self.base_chart = self.base_chart.mark_line(point=True, strokeDash=[1, 0])
# Get relevant dimensions
x_name, x_type = self.get_dim("x", chart_dims)
y_name, y_type = self.get_dim("y", chart_dims)
if "size" in chart_dims:
size_name, size_type = self.get_dim("size", chart_dims)
color_name, color_type = self.get_dim("color", chart_dims)
if "shape" in chart_dims:
shape_name, shape_type = self.get_dim("shape", chart_dims)
color_name, color_type = self.get_dim("color", chart_dims)
# Set dimensions
y_domain = list(df_data[y_name].astype(float).unique())
x_dim = alt.X(x_name, type=x_type)
if "size" in chart_dims:
y_dim = alt.Y(y_name, type=y_type)
size = alt.Size(size_name, type=size_type)
color = alt.Color(color_name, type=color_type)
elif "shape" in chart_dims:
y_dim = alt.Y(y_name, type=y_type)
shape = alt.Shape(shape_name, type=shape_type)
color = alt.Color(color_name, type=color_type)
else:
y_dim = alt.Y(y_name, type=y_type, title=chart_metric)
color = alt.Color("ngram", type="nominal", title="", legend=None)
# Set tooltip
tooltip = [
alt.Tooltip("ngram", type="nominal", title=self.text_label),
alt.Tooltip(x_name, type=x_type),
]
if "size" in chart_dims:
tooltip.append(alt.Tooltip(y_name, type=y_type))
tooltip.append(alt.Tooltip(size_name, type=size_type, title=self.metric_label))
elif "shape" in chart_dims:
tooltip.append(alt.Tooltip(shape_name, type=shape_type))
tooltip.append(alt.Tooltip(y_name, type=y_type))
else:
tooltip.append(alt.Tooltip(y_name, type=y_type, title=self.metric_label))
# Encoding the data
if "shape" in chart_dims:
self.base_chart = self.base_chart.encode(
x_dim,
y_dim,
color,
shape,
# Note: opacity will be conditionally added by the "add_dropdown_component", if needed
tooltip
)
else:
self.base_chart = self.base_chart.encode(
x_dim,
y_dim,
color,
# Note: opacity will be conditionally added by the "add_dropdown_component", if needed
tooltip
)
# Set extra properties
chart_width = 800
self.base_chart = self.base_chart.properties(width=chart_width, center=True)
# The chart has to be filterable, therefore create and add a search component to it
if ("size" in chart_dims) or ("shape" in chart_dims):
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])))
if "size" in chart_dims:
self.base_chart = self.add_dropdown_components(
self.base_chart, tooltip, dropdown_keys, dropdown_values, color, "size")
else:
self.base_chart = self.add_dropdown_components(
self.base_chart, tooltip, dropdown_keys, dropdown_values, color, "shape")
else:
self.base_chart = self.add_search_component(self.base_chart, tooltip, color)
# 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