def test_repeat():
# wrapped repeat
chart1 = (alt.Chart("data.csv").mark_point().encode(
x=alt.X(alt.repeat(), type="quantitative"),
y="y:Q",
).repeat(["A", "B", "C", "D"], columns=2))
dct1 = chart1.to_dict()
assert dct1["repeat"] == ["A", "B", "C", "D"]
assert dct1["columns"] == 2
assert dct1["spec"]["encoding"]["x"]["field"] == {"repeat": "repeat"}
# explicit row/col repeat
chart2 = (alt.Chart("data.csv").mark_point().encode(
x=alt.X(alt.repeat("row"), type="quantitative"),
y=alt.Y(alt.repeat("column"), type="quantitative"),
).repeat(row=["A", "B", "C"], column=["C", "B", "A"]))
dct2 = chart2.to_dict()
assert dct2["repeat"] == {
"row": ["A", "B", "C"],
"column": ["C", "B", "A"]
}
assert "columns" not in dct2
assert dct2["spec"]["encoding"]["x"]["field"] == {"repeat": "row"}
assert dct2["spec"]["encoding"]["y"]["field"] == {"repeat": "column"}
def test_repeat():
# wrapped repeat
chart1 = alt.Chart('data.csv').mark_point().encode(
x=alt.X(alt.repeat(), type='quantitative'),
y='y:Q',
).repeat(['A', 'B', 'C', 'D'], columns=2)
dct1 = chart1.to_dict()
assert dct1['repeat'] == ['A', 'B', 'C', 'D']
assert dct1['columns'] == 2
assert dct1['spec']['encoding']['x']['field'] == {'repeat': 'repeat'}
# explicit row/col repeat
chart2 = alt.Chart('data.csv').mark_point().encode(
x=alt.X(alt.repeat('row'), type='quantitative'),
y=alt.Y(alt.repeat('column'),
type='quantitative')).repeat(row=['A', 'B', 'C'],
column=['C', 'B', 'A'])
dct2 = chart2.to_dict()
assert dct2['repeat'] == {
'row': ['A', 'B', 'C'],
'column': ['C', 'B', 'A']
}
assert 'columns' not in dct2
assert dct2['spec']['encoding']['x']['field'] == {'repeat': 'row'}
assert dct2['spec']['encoding']['y']['field'] == {'repeat': 'column'}
def test_chart_infer_types():
data = pd.DataFrame({'x': pd.date_range('2012', periods=10, freq='Y'),
'y': range(10),
'c': list('abcabcabca')})
def _check_encodings(chart):
dct = chart.to_dict()
assert dct['encoding']['x']['type'] == 'temporal'
assert dct['encoding']['x']['field'] == 'x'
assert dct['encoding']['y']['type'] == 'quantitative'
assert dct['encoding']['y']['field'] == 'y'
assert dct['encoding']['color']['type'] == 'nominal'
assert dct['encoding']['color']['field'] == 'c'
# Pass field names by keyword
chart = alt.Chart(data).mark_point().encode(x='x', y='y', color='c')
_check_encodings(chart)
# pass Channel objects by keyword
chart = alt.Chart(data).mark_point().encode(x=alt.X('x'), y=alt.Y('y'),
color=alt.Color('c'))
_check_encodings(chart)
# pass Channel objects by value
chart = alt.Chart(data).mark_point().encode(alt.X('x'), alt.Y('y'),
alt.Color('c'))
_check_encodings(chart)
# override default types
chart = alt.Chart(data).mark_point().encode(alt.X('x', type='nominal'),
alt.Y('y', type='ordinal'))
dct = chart.to_dict()
assert dct['encoding']['x']['type'] == 'nominal'
assert dct['encoding']['y']['type'] == 'ordinal'
def _make_chart_type(chart_type):
data = pd.DataFrame({
"x": [28, 55, 43, 91, 81, 53, 19, 87],
"y": [43, 91, 81, 53, 19, 87, 52, 28],
"color": list("AAAABBBB"),
})
base = alt.Chart(data).mark_point().encode(
x="x",
y="y",
color="color",
)
if chart_type in ["layer", "hconcat", "vconcat", "concat"]:
func = getattr(alt, chart_type)
return func(base.mark_square(), base.mark_circle())
elif chart_type == "facet":
return base.facet("color")
elif chart_type == "facet_encoding":
return base.encode(facet="color")
elif chart_type == "repeat":
return base.encode(alt.X(alt.repeat(),
type="quantitative")).repeat(["x", "y"])
elif chart_type == "chart":
return base
else:
raise ValueError(
"chart_type='{}' is not recognized".format(chart_type))
def _make_chart_type(chart_type):
data = pd.DataFrame({
'x': [28, 55, 43, 91, 81, 53, 19, 87],
'y': [43, 91, 81, 53, 19, 87, 52, 28],
'color': list('AAAABBBB'),
})
base = alt.Chart(data).mark_point().encode(
x='x',
y='y',
color='color',
)
if chart_type in ['layer', 'hconcat', 'vconcat', 'concat']:
func = getattr(alt, chart_type)
return func(base.mark_square(), base.mark_circle())
elif chart_type == 'facet':
return base.facet('color')
elif chart_type == 'facet_encoding':
return base.encode(facet='color')
elif chart_type == 'repeat':
return base.encode(alt.X(alt.repeat(),
type='quantitative')).repeat(['x', 'y'])
elif chart_type == 'chart':
return base
else:
raise ValueError(
"chart_type='{}' is not recognized".format(chart_type))
def deco(ch):
h = ch.mark_line().encode(
x=A.X(x, title=x),
y=A.Y(y, title=y, **yargs),
color=color,
tooltip=[color, x, y],
)
return h
def make_ridge_chart(self):
self.plot_df['year'] = pd.to_datetime(
self.plot_df['year'].astype(str) + '-01-01')
brush = alt.selection_interval(encodings=['x'])
step = 18
overlap = 7
# Topic selection
selection = alt.selection_multi(fields=['Topic'])
# смена цвета на выборе темы -- всё что не выделили делается серым
color = alt.condition(
selection,
alt.Color('Topic:N',
legend=None,
scale=alt.Scale(scheme="rainbow")),
alt.value('lightgray'))
a = alt.hconcat(
alt.Chart(self.plot_df).mark_area(
fillOpacity=0.6, interpolate='monotone').encode(
x=alt.X('year:T'),
y=alt.Y('rate:Q',
scale=alt.Scale(range=[0, -overlap * (step + 1)]),
axis=None),
row=alt.Row('Topic:N',
header=alt.Header(title=None,
labelPadding=0,
labelFontSize=0)),
color=color).add_selection(selection).properties(
width=800,
height=step,
bounds='flush',
).transform_filter(brush),
alt.Chart(self.plot_df,
height=500).mark_point().encode(
y=alt.Y('Topic:N', axis=alt.Axis(orient='right')),
color=color).add_selection(selection))
b = alt.Chart(self.plot_df).mark_area(interpolate='monotone').encode(
y='sum(rate):Q',
x='year:T',
).properties(width=800, height=100).add_selection(brush)
if self.add_data_names != ['All']:
add_charts = [
self.make_add_data_chart(i, brush) for i in self.add_data_names
]
chart = alt.vconcat(a, *add_charts, b, padding=0,
spacing=0).configure_view(
stroke=None).configure_axis(grid=False)
return chart
else:
chart = alt.vconcat(a, b, padding=0, spacing=0).configure_view(
stroke=None).configure_axis(grid=False)
return chart
def test_chart_infer_types():
data = pd.DataFrame({
"x": pd.date_range("2012", periods=10, freq="Y"),
"y": range(10),
"c": list("abcabcabca"),
})
def _check_encodings(chart):
dct = chart.to_dict()
assert dct["encoding"]["x"]["type"] == "temporal"
assert dct["encoding"]["x"]["field"] == "x"
assert dct["encoding"]["y"]["type"] == "quantitative"
assert dct["encoding"]["y"]["field"] == "y"
assert dct["encoding"]["color"]["type"] == "nominal"
assert dct["encoding"]["color"]["field"] == "c"
# Pass field names by keyword
chart = alt.Chart(data).mark_point().encode(x="x", y="y", color="c")
_check_encodings(chart)
# pass Channel objects by keyword
chart = (alt.Chart(data).mark_point().encode(x=alt.X("x"),
y=alt.Y("y"),
color=alt.Color("c")))
_check_encodings(chart)
# pass Channel objects by value
chart = alt.Chart(data).mark_point().encode(alt.X("x"), alt.Y("y"),
alt.Color("c"))
_check_encodings(chart)
# override default types
chart = (alt.Chart(data).mark_point().encode(alt.X("x", type="nominal"),
alt.Y("y", type="ordinal")))
dct = chart.to_dict()
assert dct["encoding"]["x"]["type"] == "nominal"
assert dct["encoding"]["y"]["type"] == "ordinal"
def make_ridge_chart(self):
self.plot_df['year'] = pd.to_datetime(self.plot_df['year'].astype(str) + '-01-01')
brush = alt.selection(type='interval', encodings=['x'])
step = 18
overlap = 4
a = alt.Chart(self.plot_df).mark_area(stroke='black', strokeWidth=0, fillOpacity=0.6).encode(
x=alt.X('year:T'),
y=alt.Y('rate:Q', scale=alt.Scale(range=[0, -overlap * (step + 1)]), axis=None),
row=alt.Row('Topic:N', header=alt.Header(title=None, labelPadding=0, labelFontSize=0)),
color='Topic:N'
).properties(
width=800,
height=step,
bounds='flush',
).transform_filter(
brush
)
b = alt.Chart(self.plot_df).mark_area().encode(
y='sum(rate):Q',
x='year:T',
).properties(
width=800,
height=100
).add_selection(
brush
)
if self.add_data_names != ['All']:
add_charts = [self.make_add_data_chart(i, brush) for i in self.add_data_names]
chart = alt.vconcat(
a, *add_charts, b, padding=0, spacing=0
).configure_view(
stroke=None
).configure_axis(
grid=False
)
return chart
else:
chart = alt.vconcat(
a, b, padding=0, spacing=0
).configure_view(
stroke=None
).configure_axis(
grid=False
)
return chart
def pl(
pdf,
color="state",
x="date",
y="positive",
ii=True,
logy=True,
logx=False,
tt=[],
):
tt = list(tt)
ykw = dict(scale=lgs) if logy else {}
xkw = dict(scale=lgs) if logx else {}
h = (A.Chart(pdf).mark_line().encode(
x=A.X(x, title=x, **xkw),
y=A.Y(y, title=y, **ykw),
color=color,
tooltip=[color, x, y] + tt,
))
hh = h + h.mark_point()
if ii:
return hh.interactive()
return hh
def test_layer_encodings():
chart = alt.LayerChart().encode(x="column:Q")
assert chart.encoding.x == alt.X(shorthand="column:Q")
.rename(columns=lambda x: "p{:02}".format(int(x * 100)))
.reset_index(drop=0)
.sort_values("p05", ascending=True)
.reset_index(drop=1)
)
# %% [markdown]
# Doubling rate: if the value is around 2, then expect the number of deaths to double every 2 days.
# %%
color = 'dvers'
x = 'state'
y = 'p50'
h = Chart(growth_dbl).mark_point().encode(
x=A.X(x, title=x, sort=None),
y=A.Y(y, title='Exponent'),
tooltip=[x, y]
)
herr = h.mark_errorbar().encode(y=A.Y('p05', title='Exponent'), y2='p95')
# (h + h.mark_point()).interactive()
(herr + h).properties(title='Doubling rates')
# %%
# xs = np.arange(100)
# yx = np.exp(xs * .1)
# expdf = DataFrame(dict(x=xs, y=yx))
# ixs = np.searchsorted(expdf.y, [1, 2, 4, 8, 16])
