Add timeseries filtering example to illustrate subsetting of columns from selector values

tests/examples_arguments_syntax/interactive_column_selection.py

@@ -0,0 +1,82 @@
+"""
+Interactive Selection of Columns
+================================
+This example shows how columns can be selected interactively
+by accessing the values from selector widgets,
+and then compute the difference of the selected columns.
+It also illustrates how to use `indexof` to filter
+columns based on active selection values.
+"""
+# category: interactive charts
+
+import pandas as pd
+import numpy as np
+import altair as alt
+
+
+# Create timeseries data
+rng = np.random.default_rng(905)
+ex_ts = pd.DataFrame(
+    rng.random((10, 4)),
+    columns=['a', 'b', 'c', 'd'],
+).assign(
+    date=pd.date_range(
+        start=pd.to_datetime('2022-02-22')-pd.Timedelta(9, unit='D'),
+        end=pd.to_datetime('2022-02-22')).strftime('%Y-%m-%d'),
+)
+
+# Create heatmap with selection
+select_x = alt.selection_point(fields=['level_0'], name='select_x', value='b')
+select_y = alt.selection_point(fields=['level_1'], name='select_y', value='d')
+heatmap = alt.Chart(
+    ex_ts.drop(columns='date').corr().stack().reset_index().rename(columns={0: 'correlation'}),
+    title='Click a tile to compare timeseries',
+    height=250,
+    width=250,
+).mark_rect().encode(
+    alt.X('level_0', title=None),
+    alt.Y('level_1', title=None),
+    alt.Color('correlation', scale=alt.Scale(domain=[-1, 1], scheme='blueorange')),
+    opacity=alt.condition(select_x & select_y, alt.value(1), alt.value(0.4))
+).add_params(
+    select_x, select_y
+)
+
+# Create chart with individual lines/timeseries
+base = alt.Chart(
+    ex_ts.melt(
+        id_vars='date',
+        var_name='category',
+        value_name='value',
+    ),
+    height=100,
+    width=300,
+    title='Individual timeseries',
+)
+lines = base.transform_filter(
+    # If the category is not in the selected values, the returned index is -1
+    'indexof(datum.category, select_x.level_0) !== -1'
+   '| indexof(datum.category, select_y.level_1) !== -1'
+).mark_line().encode(
+    alt.X('date:T', axis=alt.Axis(labels=False), title=None),
+    alt.Y('value', scale=alt.Scale(domain=(0, 1))),
+    alt.Color('category', legend=alt.Legend(orient='top', offset=-20), title=None)
+)
+
+# Create chart with difference between lines/timeseries
+dynamic_title = alt.Title(alt.expr(f'"Difference " + {select_x.name}.level_0 + " - " + {select_y.name}.level_1'))
+# We pivot transform to get each category as a column
+lines_diff = base.transform_pivot(
+    'category', 'value', groupby=['date']
+# In the calculate transform we use the values from the selection to subset the columns to substract
+).transform_calculate(
+    difference = f'datum[{select_x.name}.level_0] - datum[{select_y.name}.level_1]'
+).mark_line(color='grey').encode(
+    alt.X('date:T', axis=alt.Axis(format='%Y-%m-%d'), title=None),
+    alt.Y('difference:Q', scale=alt.Scale(domain=(-1, 1))),
+).properties(
+    title=dynamic_title
+)
+
+# Layout the charts
+(lines & lines_diff) | heatmap

tests/examples_methods_syntax/interactive_column_selection.py

@@ -0,0 +1,80 @@
+"""
+Interactive Selection of Columns
+================================
+This example shows how columns can be selected interactively
+by accessing the values from selector widgets,
+and then compute the difference of the selected columns.
+It also illustrates how to use `indexof` to filter
+columns based on active selection values.
+"""
+import pandas as pd
+import numpy as np
+import altair as alt
+
+
+# Create timeseries data
+rng = np.random.default_rng(905)
+ex_ts = pd.DataFrame(
+    rng.random((10, 4)),
+    columns=['a', 'b', 'c', 'd'],
+).assign(
+    date=pd.date_range(
+        start=pd.to_datetime('2022-02-22')-pd.Timedelta(9, unit='D'),
+        end=pd.to_datetime('2022-02-22')).strftime('%Y-%m-%d'),
+)
+
+# Create heatmap with selection
+select_x = alt.selection_point(fields=['level_0'], name='select_x', value='b')
+select_y = alt.selection_point(fields=['level_1'], name='select_y', value='d')
+heatmap = alt.Chart(
+    ex_ts.drop(columns='date').corr().stack().reset_index().rename(columns={0: 'correlation'}),
+    title='Click a tile to compare timeseries',
+    height=250,
+    width=250,
+).mark_rect().encode(
+    alt.X('level_0').title(None),
+    alt.Y('level_1').title(None),
+    alt.Color('correlation').scale(domain=[-1, 1], scheme='blueorange'),
+    opacity=alt.condition(select_x & select_y, alt.value(1), alt.value(0.4))
+).add_params(
+    select_x, select_y
+)
+
+# Create chart with individual lines/timeseries
+base = alt.Chart(
+    ex_ts.melt(
+        id_vars='date',
+        var_name='category',
+        value_name='value',
+    ),
+    height=100,
+    width=300,
+    title='Individual timeseries',
+)
+lines = base.transform_filter(
+    # If the category is not in the selected values, the returned index is -1
+    'indexof(datum.category, select_x.level_0) !== -1'
+   '| indexof(datum.category, select_y.level_1) !== -1'
+).mark_line().encode(
+    alt.X('date:T').axis(labels=False).title(None),
+    alt.Y('value').scale(domain=(0, 1)),
+    alt.Color('category').legend(orient='top', offset=-20).title(None)
+)
+
+# Create chart with difference between lines/timeseries
+dynamic_title = alt.Title(alt.expr(f'"Difference " + {select_x.name}.level_0 + " - " + {select_y.name}.level_1'))
+# We pivot transform to get each category as a column
+lines_diff = base.transform_pivot(
+    'category', 'value', groupby=['date']
+# In the calculate transform we use the values from the selection to subset the columns to substract
+).transform_calculate(
+    difference = f'datum[{select_x.name}.level_0] - datum[{select_y.name}.level_1]'
+).mark_line(color='grey').encode(
+    alt.X('date:T').axis(format='%Y-%m-%d').title(None),
+    alt.Y('difference:Q').scale(domain=(-1, 1)),
+).properties(
+    title=dynamic_title
+)
+
+# Layout the charts
+(lines & lines_diff) | heatmap