def test_sparse_index(self):
"""
Make sure that we match the correct dates even if the
freq gives us days not in index.
"""
freq = 'MS'
index = pd.date_range(start="2000-1-1", freq="B", periods=1000)
tl = formatter.TimestampLocator(plot_index, freq=freq)
test = tl._process(0, 900)
new_ind = index[test]
assert np.all(new_ind.day < 5)
freq = 'M'
index = pd.date_range(start="2000-1-1", freq="B", periods=1000)
tl = formatter.TimestampLocator(plot_index, freq=freq)
test = tl._process(0, 900)
new_ind = index[test]
assert np.all(new_ind.day > 25)
def test_fixed_freq(self):
"""
Test passing in a fixed freq. This will allow len(xticks)
less than min_ticks
"""
plot_index = pd.date_range(start="2000-1-1", freq="D", periods=10000)
tl = formatter.TimestampLocator(plot_index, 'MS')
xticks = tl._process(0, 30 * 3)
assert len(xticks) == 3
tl = formatter.TimestampLocator(plot_index, 'MS')
xticks = tl._process(0, 30 * 6)
assert len(xticks) == 6
tl = formatter.TimestampLocator(plot_index, 'W')
xticks = tl._process(0, 10 * 7)
assert len(xticks) == 10
tl = formatter.TimestampLocator(plot_index, 'AS')
xticks = tl._process(0, 10 * 365)
assert len(xticks) == 10
def test_list_of_datetimes(self):
"""
The other xticks option is sending in a DatetimeIndex of the dates you want
"""
plot_index = pd.date_range(start="2000-1-1", freq="D", periods=10000)
freq = 'M'
dates = pd.Series(1, index=plot_index).resample(freq).index
tl = formatter.TimestampLocator(plot_index, xticks=dates)
test = tl._process(3, 900)
tl = formatter.TimestampLocator(plot_index, freq=freq)
correct = tl._process(3, 900)
tm.assert_almost_equal(test, correct)
freq = 'MS'
dates = pd.Series(1, index=plot_index).resample(freq).index
tl = formatter.TimestampLocator(plot_index, xticks=dates)
test = tl._process(3, 900)
tl = formatter.TimestampLocator(plot_index, freq=freq)
correct = tl._process(3, 900)
tm.assert_almost_equal(test, correct)
# straight list of dates
freq = 'MS'
dates = pd.Series(1, index=plot_index).resample(freq).index
dates = list(dates)
tl = formatter.TimestampLocator(plot_index, xticks=dates)
test = tl._process(3, 900)
tl = formatter.TimestampLocator(plot_index, freq=freq)
correct = tl._process(3, 900)
tm.assert_almost_equal(test, correct)
def test_inferred_freq(self):
"""
inferred freqs are based off of min_ticks
"""
plot_index = pd.date_range(start="2000-1-1", freq="B", periods=10000)
tl = formatter.TimestampLocator(plot_index)
# showing only the first 10 should give us days
xticks = tl._process(1, 10)
assert tl.gen_freq == 'D'
# showing only the first 70 should give us weeks
xticks = tl._process(1, 6 * 7 + 1)
assert tl.gen_freq == 'W'
# months should trigger at around 6 * 31
xticks = tl._process(1, 6 * 31)
assert tl.gen_freq == 'MS'
# year should trigger at around 6 *366
xticks = tl._process(1, 6 * 366 + 1)
assert tl.gen_freq == 'AS'
def test_bool_xticks(self):
"""
ability to set ticks with a bool series where True == tick
"""
plot_index = pd.date_range(start="2000-1-1", freq="D", periods=10000)
freq = 'M'
ds = pd.Series(1, index=plot_index)
# True when freq market is hit
bool_ticks = ds.resample(freq).reindex(plot_index).fillna(0).astype(
bool)
tl = formatter.TimestampLocator(plot_index, xticks=bool_ticks)
xticks = tl._process(0, 90)
tl = formatter.TimestampLocator(plot_index, freq=freq)
correct = tl._process(0, 90)
tm.assert_almost_equal(xticks, correct)
freq = 'MS'
ds = pd.Series(1, index=plot_index)
# True when freq market is hit
bool_ticks = ds.resample(freq).reindex(plot_index).fillna(0).astype(
bool)
tl = formatter.TimestampLocator(plot_index, xticks=bool_ticks)
xticks = tl._process(3, 94)
tl = formatter.TimestampLocator(plot_index, freq=freq)
correct = tl._process(3, 94)
tm.assert_almost_equal(xticks, correct)
freq = 'W'
ds = pd.Series(1, index=plot_index)
# True when freq market is hit
bool_ticks = ds.resample(freq).reindex(plot_index).fillna(0).astype(
bool)
tl = formatter.TimestampLocator(plot_index, xticks=bool_ticks)
xticks = tl._process(3, 94)
tl = formatter.TimestampLocator(plot_index, freq=freq)
correct = tl._process(3, 94)
tm.assert_almost_equal(xticks, correct)
