def test_tick_locator(self, t):
locator = mpl.dates.YearLocator(month=3, day=15)
s = Temporal().tick(locator)
a = PseudoAxis(s._setup(t, Coordinate())._matplotlib_scale)
a.set_view_interval(0, 365)
assert 73 in a.major.locator()
def test_label_formatter(self, t):
formatter = mpl.dates.DateFormatter("%Y")
s = Temporal().label(formatter)
a = PseudoAxis(s._setup(t, Coordinate())._matplotlib_scale)
a.set_view_interval(10, 1000)
label, = a.major.formatter.format_ticks([100])
assert label == "1970"
def test_tick_upto(self, t, x):
n = 8
ax = mpl.figure.Figure().subplots()
Temporal().tick(upto=n)._setup(t, Coordinate(), ax.xaxis)
locator = ax.xaxis.get_major_locator()
assert set(locator.maxticks.values()) == {n}
def test_color_named_values(self, t, x):
name = "viridis"
cmap = color_palette(name, as_cmap=True)
s = Temporal(name)._setup(t, Color())
normed = (x - x.min()) / (x.max() - x.min())
assert_array_equal(s(t), cmap(normed)[:, :3]) # FIXME RGBA
def test_interval_with_norm(self, t, x):
norm = t[1], t[2]
s = Temporal(norm=norm)._setup(t, IntervalProperty())
n = mpl.dates.date2num(norm)
normed = (x - n[0]) / (n[1] - n[0])
assert_array_equal(s(t), normed)
def test_interval_with_range(self, t, x):
values = (1, 3)
s = Temporal((1, 3))._setup(t, IntervalProperty())
normed = (x - x.min()) / (x.max() - x.min())
expected = normed * (values[1] - values[0]) + values[0]
assert_array_equal(s(t), expected)
def test_coordinate_axis(self, t, x):
ax = mpl.figure.Figure().subplots()
s = Temporal()._setup(t, Coordinate(), ax.xaxis)
assert_array_equal(s(t), x)
locator = ax.xaxis.get_major_locator()
formatter = ax.xaxis.get_major_formatter()
assert isinstance(locator, mpl.dates.AutoDateLocator)
assert isinstance(formatter, mpl.dates.AutoDateFormatter)
def infer_scale(self, arg: Any, data: Series) -> Scale:
"""Given data and a scaling argument, initialize appropriate scale class."""
# TODO infer continuous based on log/sqrt etc?
if isinstance(arg, (list, dict)):
return Nominal(arg)
elif variable_type(data) == "categorical":
return Nominal(arg)
elif variable_type(data) == "datetime":
return Temporal(arg)
# TODO other variable types
else:
return Continuous(arg)
def default_scale(self, data: Series) -> Scale:
"""Given data, initialize appropriate scale class."""
# TODO allow variable_type to be "boolean" if that's a scale?
# TODO how will this handle data with units that can be treated as numeric
# if passed through a registered matplotlib converter?
var_type = variable_type(data, boolean_type="numeric")
if var_type == "numeric":
return Continuous()
elif var_type == "datetime":
return Temporal()
# TODO others
# time-based (TimeStamp, TimeDelta, Period)
# boolean scale?
else:
return Nominal()
def test_label_concise(self, t, x):
ax = mpl.figure.Figure().subplots()
Temporal().label(concise=True)._setup(t, Coordinate(), ax.xaxis)
formatter = ax.xaxis.get_major_formatter()
assert isinstance(formatter, mpl.dates.ConciseDateFormatter)
def test_color_defaults(self, t, x):
cmap = color_palette("ch:", as_cmap=True)
s = Temporal()._setup(t, Color())
normed = (x - x.min()) / (x.max() - x.min())
assert_array_equal(s(t), cmap(normed)[:, :3]) # FIXME RGBA
def test_interval_defaults(self, t, x):
s = Temporal()._setup(t, IntervalProperty())
normed = (x - x.min()) / (x.max() - x.min())
assert_array_equal(s(t), normed)
def test_coordinate_defaults(self, t, x):
s = Temporal()._setup(t, Coordinate())
assert_array_equal(s(t), x)