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""" Test cases for time series specific (freq conversion, etc) """
from datetime import datetime, timedelta, date, time
import pickle
import pytest
from pandas.compat import lrange, zip
import numpy as np
from pandas import Index, Series, DataFrame, NaT
from pandas.compat import is_platform_mac, PY3
from pandas.core.indexes.datetimes import date_range, bdate_range
from pandas.core.indexes.timedeltas import timedelta_range
from pandas.tseries.offsets import DateOffset
from pandas.core.indexes.period import period_range, Period, PeriodIndex
from pandas.core.resample import DatetimeIndex
from pandas.util.testing import assert_series_equal, ensure_clean
import pandas.util.testing as tm
from pandas.tests.plotting.common import (TestPlotBase,
_skip_if_no_scipy_gaussian_kde)
tm._skip_if_no_mpl()
class TestTSPlot(TestPlotBase):
def setup_method(self, method):
TestPlotBase.setup_method(self, method)
freq = ['S', 'T', 'H', 'D', 'W', 'M', 'Q', 'A']
idx = [period_range('12/31/1999', freq=x, periods=100) for x in freq]
self.period_ser = [Series(np.random.randn(len(x)), x) for x in idx]
self.period_df = [DataFrame(np.random.randn(len(x), 3), index=x,
columns=['A', 'B', 'C'])
for x in idx]
freq = ['S', 'T', 'H', 'D', 'W', 'M', 'Q-DEC', 'A', '1B30Min']
idx = [date_range('12/31/1999', freq=x, periods=100) for x in freq]
self.datetime_ser = [Series(np.random.randn(len(x)), x) for x in idx]
self.datetime_df = [DataFrame(np.random.randn(len(x), 3), index=x,
columns=['A', 'B', 'C'])
for x in idx]
def teardown_method(self, method):
tm.close()
@pytest.mark.slow
def test_ts_plot_with_tz(self):
# GH2877
index = date_range('1/1/2011', periods=2, freq='H',
tz='Europe/Brussels')
ts = Series([188.5, 328.25], index=index)
_check_plot_works(ts.plot)
def test_fontsize_set_correctly(self):
# For issue #8765
df = DataFrame(np.random.randn(10, 9), index=range(10))
fig, ax = self.plt.subplots()
df.plot(fontsize=2, ax=ax)
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
assert label.get_fontsize() == 2
@pytest.mark.slow
def test_frame_inferred(self):
# inferred freq
idx = date_range('1/1/1987', freq='MS', periods=100)
idx = DatetimeIndex(idx.values, freq=None)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
_check_plot_works(df.plot)
# axes freq
idx = idx[0:40].union(idx[45:99])
df2 = DataFrame(np.random.randn(len(idx), 3), index=idx)
_check_plot_works(df2.plot)
# N > 1
idx = date_range('2008-1-1 00:15:00', freq='15T', periods=10)
idx = DatetimeIndex(idx.values, freq=None)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
_check_plot_works(df.plot)
def test_is_error_nozeroindex(self):
# GH11858
i = np.array([1, 2, 3])
a = DataFrame(i, index=i)
_check_plot_works(a.plot, xerr=a)
_check_plot_works(a.plot, yerr=a)
def test_nonnumeric_exclude(self):
idx = date_range('1/1/1987', freq='A', periods=3)
df = DataFrame({'A': ["x", "y", "z"], 'B': [1, 2, 3]}, idx)
fig, ax = self.plt.subplots()
df.plot(ax=ax) # it works
assert len(ax.get_lines()) == 1 # B was plotted
self.plt.close(fig)
pytest.raises(TypeError, df['A'].plot)
@pytest.mark.slow
def test_tsplot(self):
from pandas.tseries.plotting import tsplot
_, ax = self.plt.subplots()
ts = tm.makeTimeSeries()
f = lambda *args, **kwds: tsplot(s, self.plt.Axes.plot, *args, **kwds)
for s in self.period_ser:
_check_plot_works(f, s.index.freq, ax=ax, series=s)
for s in self.datetime_ser:
_check_plot_works(f, s.index.freq.rule_code, ax=ax, series=s)
for s in self.period_ser:
_check_plot_works(s.plot, ax=ax)
for s in self.datetime_ser:
_check_plot_works(s.plot, ax=ax)
_, ax = self.plt.subplots()
ts.plot(style='k', ax=ax)
color = (0., 0., 0., 1) if self.mpl_ge_2_0_0 else (0., 0., 0.)
assert color == ax.get_lines()[0].get_color()
def test_both_style_and_color(self):
ts = tm.makeTimeSeries()
pytest.raises(ValueError, ts.plot, style='b-', color='#000099')
s = ts.reset_index(drop=True)
pytest.raises(ValueError, s.plot, style='b-', color='#000099')
@pytest.mark.slow
def test_high_freq(self):
freaks = ['ms', 'us']
for freq in freaks:
_, ax = self.plt.subplots()
rng = date_range('1/1/2012', periods=100000, freq=freq)
ser = Series(np.random.randn(len(rng)), rng)
_check_plot_works(ser.plot, ax=ax)
def test_get_datevalue(self):
from pandas.plotting._converter import get_datevalue
assert get_datevalue(None, 'D') is None
assert get_datevalue(1987, 'A') == 1987
assert (get_datevalue(Period(1987, 'A'), 'M') ==
Period('1987-12', 'M').ordinal)
assert (get_datevalue('1/1/1987', 'D') ==
Period('1987-1-1', 'D').ordinal)
@pytest.mark.slow
def test_ts_plot_format_coord(self):
def check_format_of_first_point(ax, expected_string):
first_line = ax.get_lines()[0]
first_x = first_line.get_xdata()[0].ordinal
first_y = first_line.get_ydata()[0]
try:
assert expected_string == ax.format_coord(first_x, first_y)
except (ValueError):
pytest.skip("skipping test because issue forming "
"test comparison GH7664")
annual = Series(1, index=date_range('2014-01-01', periods=3,
freq='A-DEC'))
_, ax = self.plt.subplots()
annual.plot(ax=ax)
check_format_of_first_point(ax, 't = 2014 y = 1.000000')
# note this is added to the annual plot already in existence, and
# changes its freq field
daily = Series(1, index=date_range('2014-01-01', periods=3, freq='D'))
daily.plot(ax=ax)
check_format_of_first_point(ax,
't = 2014-01-01 y = 1.000000')
tm.close()
# tsplot
_, ax = self.plt.subplots()
from pandas.tseries.plotting import tsplot
tsplot(annual, self.plt.Axes.plot, ax=ax)
check_format_of_first_point(ax, 't = 2014 y = 1.000000')
tsplot(daily, self.plt.Axes.plot, ax=ax)
check_format_of_first_point(ax, 't = 2014-01-01 y = 1.000000')
@pytest.mark.slow
def test_line_plot_period_series(self):
for s in self.period_ser:
_check_plot_works(s.plot, s.index.freq)
@pytest.mark.slow
def test_line_plot_datetime_series(self):
for s in self.datetime_ser:
_check_plot_works(s.plot, s.index.freq.rule_code)
@pytest.mark.slow
def test_line_plot_period_frame(self):
for df in self.period_df:
_check_plot_works(df.plot, df.index.freq)
@pytest.mark.slow
def test_line_plot_datetime_frame(self):
for df in self.datetime_df:
freq = df.index.to_period(df.index.freq.rule_code).freq
_check_plot_works(df.plot, freq)
@pytest.mark.slow
def test_line_plot_inferred_freq(self):
for ser in self.datetime_ser:
ser = Series(ser.values, Index(np.asarray(ser.index)))
_check_plot_works(ser.plot, ser.index.inferred_freq)
ser = ser[[0, 3, 5, 6]]
_check_plot_works(ser.plot)
def test_fake_inferred_business(self):
_, ax = self.plt.subplots()
rng = date_range('2001-1-1', '2001-1-10')
ts = Series(lrange(len(rng)), rng)
ts = ts[:3].append(ts[5:])
ts.plot(ax=ax)
assert not hasattr(ax, 'freq')
@pytest.mark.slow
def test_plot_offset_freq(self):
ser = tm.makeTimeSeries()
_check_plot_works(ser.plot)
dr = date_range(ser.index[0], freq='BQS', periods=10)
ser = Series(np.random.randn(len(dr)), dr)
_check_plot_works(ser.plot)
@pytest.mark.slow
def test_plot_multiple_inferred_freq(self):
dr = Index([datetime(2000, 1, 1), datetime(2000, 1, 6), datetime(
2000, 1, 11)])
ser = Series(np.random.randn(len(dr)), dr)
_check_plot_works(ser.plot)
@pytest.mark.slow
def test_uhf(self):
import pandas.plotting._converter as conv
idx = date_range('2012-6-22 21:59:51.960928', freq='L', periods=500)
df = DataFrame(np.random.randn(len(idx), 2), idx)
_, ax = self.plt.subplots()
df.plot(ax=ax)
axis = ax.get_xaxis()
tlocs = axis.get_ticklocs()
tlabels = axis.get_ticklabels()
for loc, label in zip(tlocs, tlabels):
xp = conv._from_ordinal(loc).strftime('%H:%M:%S.%f')
rs = str(label.get_text())
if len(rs):
assert xp == rs
@pytest.mark.slow
def test_irreg_hf(self):
idx = date_range('2012-6-22 21:59:51', freq='S', periods=100)
df = DataFrame(np.random.randn(len(idx), 2), idx)
irreg = df.iloc[[0, 1, 3, 4]]
_, ax = self.plt.subplots()
irreg.plot(ax=ax)
diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()
sec = 1. / 24 / 60 / 60
assert (np.fabs(diffs[1:] - [sec, sec * 2, sec]) < 1e-8).all()
_, ax = self.plt.subplots()
df2 = df.copy()
df2.index = df.index.asobject
df2.plot(ax=ax)
diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()
assert (np.fabs(diffs[1:] - sec) < 1e-8).all()
def test_irregular_datetime64_repr_bug(self):
ser = tm.makeTimeSeries()
ser = ser[[0, 1, 2, 7]]
_, ax = self.plt.subplots()
ret = ser.plot(ax=ax)
assert ret is not None
for rs, xp in zip(ax.get_lines()[0].get_xdata(), ser.index):
assert rs == xp
def test_business_freq(self):
bts = tm.makePeriodSeries()
_, ax = self.plt.subplots()
bts.plot(ax=ax)
assert ax.get_lines()[0].get_xydata()[0, 0] == bts.index[0].ordinal
idx = ax.get_lines()[0].get_xdata()
assert PeriodIndex(data=idx).freqstr == 'B'
@pytest.mark.slow
def test_business_freq_convert(self):
n = tm.N
tm.N = 300
bts = tm.makeTimeSeries().asfreq('BM')
tm.N = n
ts = bts.to_period('M')
_, ax = self.plt.subplots()
bts.plot(ax=ax)
assert ax.get_lines()[0].get_xydata()[0, 0] == ts.index[0].ordinal
idx = ax.get_lines()[0].get_xdata()
assert PeriodIndex(data=idx).freqstr == 'M'
def test_nonzero_base(self):
# GH2571
idx = (date_range('2012-12-20', periods=24, freq='H') + timedelta(
minutes=30))
df = DataFrame(np.arange(24), index=idx)
_, ax = self.plt.subplots()
df.plot(ax=ax)
rs = ax.get_lines()[0].get_xdata()
assert not Index(rs).is_normalized
def test_dataframe(self):
bts = DataFrame({'a': tm.makeTimeSeries()})
_, ax = self.plt.subplots()
bts.plot(ax=ax)
idx = ax.get_lines()[0].get_xdata()
tm.assert_index_equal(bts.index.to_period(), PeriodIndex(idx))
@pytest.mark.slow
def test_axis_limits(self):
def _test(ax):
xlim = ax.get_xlim()
ax.set_xlim(xlim[0] - 5, xlim[1] + 10)
ax.get_figure().canvas.draw()
result = ax.get_xlim()
assert result[0] == xlim[0] - 5
assert result[1] == xlim[1] + 10
# string
expected = (Period('1/1/2000', ax.freq),
Period('4/1/2000', ax.freq))
ax.set_xlim('1/1/2000', '4/1/2000')
ax.get_figure().canvas.draw()
result = ax.get_xlim()
assert int(result[0]) == expected[0].ordinal
assert int(result[1]) == expected[1].ordinal
# datetime
expected = (Period('1/1/2000', ax.freq),
Period('4/1/2000', ax.freq))
ax.set_xlim(datetime(2000, 1, 1), datetime(2000, 4, 1))
ax.get_figure().canvas.draw()
result = ax.get_xlim()
assert int(result[0]) == expected[0].ordinal
assert int(result[1]) == expected[1].ordinal
fig = ax.get_figure()
self.plt.close(fig)
ser = tm.makeTimeSeries()
_, ax = self.plt.subplots()
ser.plot(ax=ax)
_test(ax)
_, ax = self.plt.subplots()
df = DataFrame({'a': ser, 'b': ser + 1})
df.plot(ax=ax)
_test(ax)
df = DataFrame({'a': ser, 'b': ser + 1})
axes = df.plot(subplots=True)
for ax in axes:
_test(ax)
def test_get_finder(self):
import pandas.plotting._converter as conv
assert conv.get_finder('B') == conv._daily_finder
assert conv.get_finder('D') == conv._daily_finder
assert conv.get_finder('M') == conv._monthly_finder
assert conv.get_finder('Q') == conv._quarterly_finder
assert conv.get_finder('A') == conv._annual_finder
assert conv.get_finder('W') == conv._daily_finder
@pytest.mark.slow
def test_finder_daily(self):
day_lst = [10, 40, 252, 400, 950, 2750, 10000]
if self.mpl_ge_2_0_0:
xpl1 = [7565, 7564, 7553, 7546, 7518, 7428, 7066]
xpl2 = [7566, 7564, 7554, 7546, 7519, 7429, 7066]
else:
xpl1 = xpl2 = [Period('1999-1-1', freq='B').ordinal] * len(day_lst)
for i, n in enumerate(day_lst):
xp = xpl1[i]
rng = bdate_range('1999-1-1', periods=n)
ser = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
ser.plot(ax=ax)
xaxis = ax.get_xaxis()
rs = xaxis.get_majorticklocs()[0]
assert xp == rs
xp = xpl2[i]
vmin, vmax = ax.get_xlim()
ax.set_xlim(vmin + 0.9, vmax)
rs = xaxis.get_majorticklocs()[0]
assert xp == rs
self.plt.close(ax.get_figure())
@pytest.mark.slow
def test_finder_quarterly(self):
yrs = [3.5, 11]
if self.mpl_ge_2_0_0:
xpl1 = [68, 68]
xpl2 = [72, 68]
else:
xpl1 = xpl2 = [Period('1988Q1').ordinal] * len(yrs)
for i, n in enumerate(yrs):
xp = xpl1[i]
rng = period_range('1987Q2', periods=int(n * 4), freq='Q')
ser = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
ser.plot(ax=ax)
xaxis = ax.get_xaxis()
rs = xaxis.get_majorticklocs()[0]
assert rs == xp
xp = xpl2[i]
(vmin, vmax) = ax.get_xlim()
ax.set_xlim(vmin + 0.9, vmax)
rs = xaxis.get_majorticklocs()[0]
assert xp == rs
self.plt.close(ax.get_figure())
@pytest.mark.slow
def test_finder_monthly(self):
yrs = [1.15, 2.5, 4, 11]
if self.mpl_ge_2_0_0:
xpl1 = [216, 216, 204, 204]
xpl2 = [216, 216, 216, 204]
else:
xpl1 = xpl2 = [Period('Jan 1988').ordinal] * len(yrs)
for i, n in enumerate(yrs):
xp = xpl1[i]
rng = period_range('1987Q2', periods=int(n * 12), freq='M')
ser = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
ser.plot(ax=ax)
xaxis = ax.get_xaxis()
rs = xaxis.get_majorticklocs()[0]
assert rs == xp
xp = xpl2[i]
vmin, vmax = ax.get_xlim()
ax.set_xlim(vmin + 0.9, vmax)
rs = xaxis.get_majorticklocs()[0]
assert xp == rs
self.plt.close(ax.get_figure())
def test_finder_monthly_long(self):
rng = period_range('1988Q1', periods=24 * 12, freq='M')
ser = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
ser.plot(ax=ax)
xaxis = ax.get_xaxis()
rs = xaxis.get_majorticklocs()[0]
xp = Period('1989Q1', 'M').ordinal
assert rs == xp
@pytest.mark.slow
def test_finder_annual(self):
if self.mpl_ge_2_0_0:
xp = [1986, 1986, 1990, 1990, 1995, 2020, 1970, 1970]
else:
xp = [1987, 1988, 1990, 1990, 1995, 2020, 2070, 2170]
for i, nyears in enumerate([5, 10, 19, 49, 99, 199, 599, 1001]):
rng = period_range('1987', periods=nyears, freq='A')
ser = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
ser.plot(ax=ax)
xaxis = ax.get_xaxis()
rs = xaxis.get_majorticklocs()[0]
assert rs == Period(xp[i], freq='A').ordinal
self.plt.close(ax.get_figure())
@pytest.mark.slow
def test_finder_minutely(self):
nminutes = 50 * 24 * 60
rng = date_range('1/1/1999', freq='Min', periods=nminutes)
ser = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
ser.plot(ax=ax)
xaxis = ax.get_xaxis()
rs = xaxis.get_majorticklocs()[0]
if self.mpl_ge_2_0_0:
xp = Period('1998-12-29 12:00', freq='Min').ordinal
else:
xp = Period('1/1/1999', freq='Min').ordinal
assert rs == xp
def test_finder_hourly(self):
nhours = 23
rng = date_range('1/1/1999', freq='H', periods=nhours)
ser = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
ser.plot(ax=ax)
xaxis = ax.get_xaxis()
rs = xaxis.get_majorticklocs()[0]
if self.mpl_ge_2_0_0:
xp = Period('1998-12-31 22:00', freq='H').ordinal
else:
xp = Period('1/1/1999', freq='H').ordinal
assert rs == xp
@pytest.mark.slow
def test_gaps(self):
ts = tm.makeTimeSeries()
ts[5:25] = np.nan
_, ax = self.plt.subplots()
ts.plot(ax=ax)
lines = ax.get_lines()
tm._skip_if_mpl_1_5()
assert len(lines) == 1
l = lines[0]
data = l.get_xydata()
assert isinstance(data, np.ma.core.MaskedArray)
mask = data.mask
assert mask[5:25, 1].all()
self.plt.close(ax.get_figure())
# irregular
ts = tm.makeTimeSeries()
ts = ts[[0, 1, 2, 5, 7, 9, 12, 15, 20]]
ts[2:5] = np.nan
_, ax = self.plt.subplots()
ax = ts.plot(ax=ax)
lines = ax.get_lines()
assert len(lines) == 1
l = lines[0]
data = l.get_xydata()
assert isinstance(data, np.ma.core.MaskedArray)
mask = data.mask
assert mask[2:5, 1].all()
self.plt.close(ax.get_figure())
# non-ts
idx = [0, 1, 2, 5, 7, 9, 12, 15, 20]
ser = Series(np.random.randn(len(idx)), idx)
ser[2:5] = np.nan
_, ax = self.plt.subplots()
ser.plot(ax=ax)
lines = ax.get_lines()
assert len(lines) == 1
l = lines[0]
data = l.get_xydata()
assert isinstance(data, np.ma.core.MaskedArray)
mask = data.mask
assert mask[2:5, 1].all()
@pytest.mark.slow
def test_gap_upsample(self):
low = tm.makeTimeSeries()
low[5:25] = np.nan
_, ax = self.plt.subplots()
low.plot(ax=ax)
idxh = date_range(low.index[0], low.index[-1], freq='12h')
s = Series(np.random.randn(len(idxh)), idxh)
s.plot(secondary_y=True)
lines = ax.get_lines()
assert len(lines) == 1
assert len(ax.right_ax.get_lines()) == 1
l = lines[0]
data = l.get_xydata()
tm._skip_if_mpl_1_5()
assert isinstance(data, np.ma.core.MaskedArray)
mask = data.mask
assert mask[5:25, 1].all()
@pytest.mark.slow
def test_secondary_y(self):
ser = Series(np.random.randn(10))
ser2 = Series(np.random.randn(10))
fig, _ = self.plt.subplots()
ax = ser.plot(secondary_y=True)
assert hasattr(ax, 'left_ax')
assert not hasattr(ax, 'right_ax')
axes = fig.get_axes()
l = ax.get_lines()[0]
xp = Series(l.get_ydata(), l.get_xdata())
assert_series_equal(ser, xp)
assert ax.get_yaxis().get_ticks_position() == 'right'
assert not axes[0].get_yaxis().get_visible()
self.plt.close(fig)
_, ax2 = self.plt.subplots()
ser2.plot(ax=ax2)
assert (ax2.get_yaxis().get_ticks_position() ==
self.default_tick_position)
self.plt.close(ax2.get_figure())
ax = ser2.plot()
ax2 = ser.plot(secondary_y=True)
assert ax.get_yaxis().get_visible()
assert not hasattr(ax, 'left_ax')
assert hasattr(ax, 'right_ax')
assert hasattr(ax2, 'left_ax')
assert not hasattr(ax2, 'right_ax')
@pytest.mark.slow
def test_secondary_y_ts(self):
idx = date_range('1/1/2000', periods=10)
ser = Series(np.random.randn(10), idx)
ser2 = Series(np.random.randn(10), idx)
fig, _ = self.plt.subplots()
ax = ser.plot(secondary_y=True)
assert hasattr(ax, 'left_ax')
assert not hasattr(ax, 'right_ax')
axes = fig.get_axes()
l = ax.get_lines()[0]
xp = Series(l.get_ydata(), l.get_xdata()).to_timestamp()
assert_series_equal(ser, xp)
assert ax.get_yaxis().get_ticks_position() == 'right'
assert not axes[0].get_yaxis().get_visible()
self.plt.close(fig)
_, ax2 = self.plt.subplots()
ser2.plot(ax=ax2)
assert (ax2.get_yaxis().get_ticks_position() ==
self.default_tick_position)
self.plt.close(ax2.get_figure())
ax = ser2.plot()
ax2 = ser.plot(secondary_y=True)
assert ax.get_yaxis().get_visible()
@pytest.mark.slow
def test_secondary_kde(self):
if not self.mpl_ge_1_5_0:
pytest.skip("mpl is not supported")
tm._skip_if_no_scipy()
_skip_if_no_scipy_gaussian_kde()
ser = Series(np.random.randn(10))
fig, ax = self.plt.subplots()
ax = ser.plot(secondary_y=True, kind='density', ax=ax)
assert hasattr(ax, 'left_ax')
assert not hasattr(ax, 'right_ax')
axes = fig.get_axes()
assert axes[1].get_yaxis().get_ticks_position() == 'right'
@pytest.mark.slow
def test_secondary_bar(self):
ser = Series(np.random.randn(10))
fig, ax = self.plt.subplots()
ser.plot(secondary_y=True, kind='bar', ax=ax)
axes = fig.get_axes()
assert axes[1].get_yaxis().get_ticks_position() == 'right'
@pytest.mark.slow
def test_secondary_frame(self):
df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c'])
axes = df.plot(secondary_y=['a', 'c'], subplots=True)
assert axes[0].get_yaxis().get_ticks_position() == 'right'
assert (axes[1].get_yaxis().get_ticks_position() ==
self.default_tick_position)
assert axes[2].get_yaxis().get_ticks_position() == 'right'
@pytest.mark.slow
def test_secondary_bar_frame(self):
df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c'])
axes = df.plot(kind='bar', secondary_y=['a', 'c'], subplots=True)
assert axes[0].get_yaxis().get_ticks_position() == 'right'
assert (axes[1].get_yaxis().get_ticks_position() ==
self.default_tick_position)
assert axes[2].get_yaxis().get_ticks_position() == 'right'
def test_mixed_freq_regular_first(self):
# TODO
s1 = tm.makeTimeSeries()
s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]
# it works!
s1.plot()
ax2 = s2.plot(style='g')
lines = ax2.get_lines()
idx1 = PeriodIndex(lines[0].get_xdata())
idx2 = PeriodIndex(lines[1].get_xdata())
assert idx1.equals(s1.index.to_period('B'))
assert idx2.equals(s2.index.to_period('B'))
left, right = ax2.get_xlim()
pidx = s1.index.to_period()
assert left <= pidx[0].ordinal
assert right >= pidx[-1].ordinal
@pytest.mark.slow
def test_mixed_freq_irregular_first(self):
s1 = tm.makeTimeSeries()
s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]
_, ax = self.plt.subplots()
s2.plot(style='g', ax=ax)
s1.plot(ax=ax)
assert not hasattr(ax, 'freq')
lines = ax.get_lines()
x1 = lines[0].get_xdata()
tm.assert_numpy_array_equal(x1, s2.index.asobject.values)
x2 = lines[1].get_xdata()
tm.assert_numpy_array_equal(x2, s1.index.asobject.values)
def test_mixed_freq_regular_first_df(self):
# GH 9852
s1 = tm.makeTimeSeries().to_frame()
s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :]
_, ax = self.plt.subplots()
s1.plot(ax=ax)
ax2 = s2.plot(style='g', ax=ax)
lines = ax2.get_lines()
idx1 = PeriodIndex(lines[0].get_xdata())
idx2 = PeriodIndex(lines[1].get_xdata())
assert idx1.equals(s1.index.to_period('B'))
assert idx2.equals(s2.index.to_period('B'))
left, right = ax2.get_xlim()
pidx = s1.index.to_period()
assert left <= pidx[0].ordinal
assert right >= pidx[-1].ordinal
@pytest.mark.slow
def test_mixed_freq_irregular_first_df(self):
# GH 9852
s1 = tm.makeTimeSeries().to_frame()
s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :]
_, ax = self.plt.subplots()
s2.plot(style='g', ax=ax)
s1.plot(ax=ax)
assert not hasattr(ax, 'freq')
lines = ax.get_lines()
x1 = lines[0].get_xdata()
tm.assert_numpy_array_equal(x1, s2.index.asobject.values)
x2 = lines[1].get_xdata()
tm.assert_numpy_array_equal(x2, s1.index.asobject.values)
def test_mixed_freq_hf_first(self):
idxh = date_range('1/1/1999', periods=365, freq='D')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
_, ax = self.plt.subplots()
high.plot(ax=ax)
low.plot(ax=ax)
for l in ax.get_lines():
assert PeriodIndex(data=l.get_xdata()).freq == 'D'
@pytest.mark.slow
def test_mixed_freq_alignment(self):
ts_ind = date_range('2012-01-01 13:00', '2012-01-02', freq='H')
ts_data = np.random.randn(12)
ts = Series(ts_data, index=ts_ind)
ts2 = ts.asfreq('T').interpolate()
_, ax = self.plt.subplots()
ax = ts.plot(ax=ax)
ts2.plot(style='r', ax=ax)
assert ax.lines[0].get_xdata()[0] == ax.lines[1].get_xdata()[0]
@pytest.mark.slow
def test_mixed_freq_lf_first(self):
idxh = date_range('1/1/1999', periods=365, freq='D')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
_, ax = self.plt.subplots()
low.plot(legend=True, ax=ax)
high.plot(legend=True, ax=ax)
for l in ax.get_lines():
assert PeriodIndex(data=l.get_xdata()).freq == 'D'
leg = ax.get_legend()
assert len(leg.texts) == 2
self.plt.close(ax.get_figure())
idxh = date_range('1/1/1999', periods=240, freq='T')
idxl = date_range('1/1/1999', periods=4, freq='H')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
_, ax = self.plt.subplots()
low.plot(ax=ax)
high.plot(ax=ax)
for l in ax.get_lines():
assert PeriodIndex(data=l.get_xdata()).freq == 'T'
def test_mixed_freq_irreg_period(self):
ts = tm.makeTimeSeries()
irreg = ts[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 29]]
rng = period_range('1/3/2000', periods=30, freq='B')
ps = Series(np.random.randn(len(rng)), rng)
_, ax = self.plt.subplots()
irreg.plot(ax=ax)
ps.plot(ax=ax)
def test_mixed_freq_shared_ax(self):
# GH13341, using sharex=True
idx1 = date_range('2015-01-01', periods=3, freq='M')
idx2 = idx1[:1].union(idx1[2:])
s1 = Series(range(len(idx1)), idx1)
s2 = Series(range(len(idx2)), idx2)
fig, (ax1, ax2) = self.plt.subplots(nrows=2, sharex=True)
s1.plot(ax=ax1)
s2.plot(ax=ax2)
assert ax1.freq == 'M'
assert ax2.freq == 'M'
assert (ax1.lines[0].get_xydata()[0, 0] ==
ax2.lines[0].get_xydata()[0, 0])
# using twinx
fig, ax1 = self.plt.subplots()
ax2 = ax1.twinx()
s1.plot(ax=ax1)
s2.plot(ax=ax2)
assert (ax1.lines[0].get_xydata()[0, 0] ==
ax2.lines[0].get_xydata()[0, 0])
# TODO (GH14330, GH14322)
# plotting the irregular first does not yet work
# fig, ax1 = plt.subplots()
# ax2 = ax1.twinx()
# s2.plot(ax=ax1)
# s1.plot(ax=ax2)
# assert (ax1.lines[0].get_xydata()[0, 0] ==
# ax2.lines[0].get_xydata()[0, 0])
def test_nat_handling(self):
_, ax = self.plt.subplots()
dti = DatetimeIndex(['2015-01-01', NaT, '2015-01-03'])
s = Series(range(len(dti)), dti)
s.plot(ax=ax)
xdata = ax.get_lines()[0].get_xdata()
# plot x data is bounded by index values
assert s.index.min() <= Series(xdata).min()
assert Series(xdata).max() <= s.index.max()
@pytest.mark.slow
def test_to_weekly_resampling(self):
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
_, ax = self.plt.subplots()
high.plot(ax=ax)
low.plot(ax=ax)
for l in ax.get_lines():
assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
# tsplot
from pandas.tseries.plotting import tsplot
_, ax = self.plt.subplots()
tsplot(high, self.plt.Axes.plot, ax=ax)
lines = tsplot(low, self.plt.Axes.plot, ax=ax)
for l in lines:
assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
@pytest.mark.slow
def test_from_weekly_resampling(self):
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
_, ax = self.plt.subplots()
low.plot(ax=ax)
high.plot(ax=ax)
expected_h = idxh.to_period().asi8.astype(np.float64)
expected_l = np.array([1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544,
1549, 1553, 1558, 1562], dtype=np.float64)
for l in ax.get_lines():
assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
xdata = l.get_xdata(orig=False)
if len(xdata) == 12: # idxl lines
tm.assert_numpy_array_equal(xdata, expected_l)
else:
tm.assert_numpy_array_equal(xdata, expected_h)
tm.close()
# tsplot
from pandas.tseries.plotting import tsplot
_, ax = self.plt.subplots()
tsplot(low, self.plt.Axes.plot, ax=ax)
lines = tsplot(high, self.plt.Axes.plot, ax=ax)
for l in lines:
assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
xdata = l.get_xdata(orig=False)
if len(xdata) == 12: # idxl lines
tm.assert_numpy_array_equal(xdata, expected_l)
else:
tm.assert_numpy_array_equal(xdata, expected_h)
@pytest.mark.slow
def test_from_resampling_area_line_mixed(self):
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = DataFrame(np.random.rand(len(idxh), 3),
index=idxh, columns=[0, 1, 2])
low = DataFrame(np.random.rand(len(idxl), 3),
index=idxl, columns=[0, 1, 2])
# low to high
for kind1, kind2 in [('line', 'area'), ('area', 'line')]:
_, ax = self.plt.subplots()
low.plot(kind=kind1, stacked=True, ax=ax)
high.plot(kind=kind2, stacked=True, ax=ax)
# check low dataframe result
expected_x = np.array([1514, 1519, 1523, 1527, 1531, 1536, 1540,
1544, 1549, 1553, 1558, 1562],
dtype=np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[i]
assert PeriodIndex(l.get_xdata()).freq == idxh.freq
tm.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
# check stacked values are correct
expected_y += low[i].values
tm.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)
# check high dataframe result
expected_x = idxh.to_period().asi8.astype(np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[3 + i]
assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
tm.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
expected_y += high[i].values
tm.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)
# high to low
for kind1, kind2 in [('line', 'area'), ('area', 'line')]:
_, ax = self.plt.subplots()
high.plot(kind=kind1, stacked=True, ax=ax)
low.plot(kind=kind2, stacked=True, ax=ax)
# check high dataframe result
expected_x = idxh.to_period().asi8.astype(np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[i]
assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
tm.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
expected_y += high[i].values
tm.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)
# check low dataframe result
expected_x = np.array([1514, 1519, 1523, 1527, 1531, 1536, 1540,
1544, 1549, 1553, 1558, 1562],
dtype=np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[3 + i]
assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
tm.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
expected_y += low[i].values
tm.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)
@pytest.mark.slow
def test_mixed_freq_second_millisecond(self):
# GH 7772, GH 7760
idxh = date_range('2014-07-01 09:00', freq='S', periods=50)
idxl = date_range('2014-07-01 09:00', freq='100L', periods=500)
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
# high to low
_, ax = self.plt.subplots()
high.plot(ax=ax)
low.plot(ax=ax)
assert len(ax.get_lines()) == 2
for l in ax.get_lines():
assert PeriodIndex(data=l.get_xdata()).freq == 'L'
tm.close()
# low to high
_, ax = self.plt.subplots()
low.plot(ax=ax)
high.plot(ax=ax)
assert len(ax.get_lines()) == 2
for l in ax.get_lines():
assert PeriodIndex(data=l.get_xdata()).freq == 'L'
@pytest.mark.slow
def test_irreg_dtypes(self):
# date
idx = [date(2000, 1, 1), date(2000, 1, 5), date(2000, 1, 20)]
df = DataFrame(np.random.randn(len(idx), 3), Index(idx, dtype=object))
_check_plot_works(df.plot)
# np.datetime64
idx = date_range('1/1/2000', periods=10)
idx = idx[[0, 2, 5, 9]].asobject
df = DataFrame(np.random.randn(len(idx), 3), idx)
_, ax = self.plt.subplots()
_check_plot_works(df.plot, ax=ax)
@pytest.mark.slow
def test_time(self):
t = datetime(1, 1, 1, 3, 30, 0)
deltas = np.random.randint(1, 20, 3).cumsum()
ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas])
df = DataFrame({'a': np.random.randn(len(ts)),
'b': np.random.randn(len(ts))},
index=ts)
_, ax = self.plt.subplots()
df.plot(ax=ax)
# verify tick labels
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime('%H:%M:%S')
assert xp == rs
# change xlim
ax.set_xlim('1:30', '5:00')
# check tick labels again
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime('%H:%M:%S')
assert xp == rs
@pytest.mark.slow
def test_time_musec(self):
t = datetime(1, 1, 1, 3, 30, 0)
deltas = np.random.randint(1, 20, 3).cumsum()
ts = np.array([(t + timedelta(microseconds=int(x))).time()
for x in deltas])
df = DataFrame({'a': np.random.randn(len(ts)),
'b': np.random.randn(len(ts))},
index=ts)
_, ax = self.plt.subplots()
ax = df.plot(ax=ax)
# verify tick labels
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
# TODO: unused?
# us = int((t - int(t)) * 1e6)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime('%H:%M:%S.%f')
assert xp == rs
@pytest.mark.slow
def test_secondary_upsample(self):
idxh = date_range('1/1/1999', periods=365, freq='D')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
_, ax = self.plt.subplots()
low.plot(ax=ax)
ax = high.plot(secondary_y=True, ax=ax)
for l in ax.get_lines():
assert PeriodIndex(l.get_xdata()).freq == 'D'
assert hasattr(ax, 'left_ax')
assert not hasattr(ax, 'right_ax')
for l in ax.left_ax.get_lines():
assert PeriodIndex(l.get_xdata()).freq == 'D'
@pytest.mark.slow
def test_secondary_legend(self):
fig = self.plt.figure()
ax = fig.add_subplot(211)
# ts
df = tm.makeTimeDataFrame()
df.plot(secondary_y=['A', 'B'], ax=ax)
leg = ax.get_legend()
assert len(leg.get_lines()) == 4
assert leg.get_texts()[0].get_text() == 'A (right)'
assert leg.get_texts()[1].get_text() == 'B (right)'
assert leg.get_texts()[2].get_text() == 'C'
assert leg.get_texts()[3].get_text() == 'D'
assert ax.right_ax.get_legend() is None
colors = set()
for line in leg.get_lines():
colors.add(line.get_color())
# TODO: color cycle problems
assert len(colors) == 4
self.plt.close(fig)
fig = self.plt.figure()
ax = fig.add_subplot(211)
df.plot(secondary_y=['A', 'C'], mark_right=False, ax=ax)
leg = ax.get_legend()
assert len(leg.get_lines()) == 4
assert leg.get_texts()[0].get_text() == 'A'
assert leg.get_texts()[1].get_text() == 'B'
assert leg.get_texts()[2].get_text() == 'C'
assert leg.get_texts()[3].get_text() == 'D'
self.plt.close(fig)
fig, ax = self.plt.subplots()
df.plot(kind='bar', secondary_y=['A'], ax=ax)
leg = ax.get_legend()
assert leg.get_texts()[0].get_text() == 'A (right)'
assert leg.get_texts()[1].get_text() == 'B'
self.plt.close(fig)
fig, ax = self.plt.subplots()
df.plot(kind='bar', secondary_y=['A'], mark_right=False, ax=ax)
leg = ax.get_legend()
assert leg.get_texts()[0].get_text() == 'A'
assert leg.get_texts()[1].get_text() == 'B'
self.plt.close(fig)
fig = self.plt.figure()
ax = fig.add_subplot(211)
df = tm.makeTimeDataFrame()
ax = df.plot(secondary_y=['C', 'D'], ax=ax)
leg = ax.get_legend()
assert len(leg.get_lines()) == 4
assert ax.right_ax.get_legend() is None
colors = set()
for line in leg.get_lines():
colors.add(line.get_color())
# TODO: color cycle problems
assert len(colors) == 4
self.plt.close(fig)
# non-ts
df = tm.makeDataFrame()
fig = self.plt.figure()
ax = fig.add_subplot(211)
ax = df.plot(secondary_y=['A', 'B'], ax=ax)
leg = ax.get_legend()
assert len(leg.get_lines()) == 4
assert ax.right_ax.get_legend() is None
colors = set()
for line in leg.get_lines():
colors.add(line.get_color())
# TODO: color cycle problems
assert len(colors) == 4
self.plt.close()
fig = self.plt.figure()
ax = fig.add_subplot(211)
ax = df.plot(secondary_y=['C', 'D'], ax=ax)
leg = ax.get_legend()
assert len(leg.get_lines()) == 4
assert ax.right_ax.get_legend() is None
colors = set()
for line in leg.get_lines():
colors.add(line.get_color())
# TODO: color cycle problems
assert len(colors) == 4
def test_format_date_axis(self):
rng = date_range('1/1/2012', periods=12, freq='M')
df = DataFrame(np.random.randn(len(rng), 3), rng)
_, ax = self.plt.subplots()
ax = df.plot(ax=ax)
xaxis = ax.get_xaxis()
for l in xaxis.get_ticklabels():
if len(l.get_text()) > 0:
assert l.get_rotation() == 30
@pytest.mark.slow
def test_ax_plot(self):
x = DatetimeIndex(start='2012-01-02', periods=10, freq='D')
y = lrange(len(x))
_, ax = self.plt.subplots()
lines = ax.plot(x, y, label='Y')
tm.assert_index_equal(DatetimeIndex(lines[0].get_xdata()), x)
@pytest.mark.slow
def test_mpl_nopandas(self):
dates = [date(2008, 12, 31), date(2009, 1, 31)]
values1 = np.arange(10.0, 11.0, 0.5)
values2 = np.arange(11.0, 12.0, 0.5)
kw = dict(fmt='-', lw=4)
_, ax = self.plt.subplots()
ax.plot_date([x.toordinal() for x in dates], values1, **kw)
ax.plot_date([x.toordinal() for x in dates], values2, **kw)
line1, line2 = ax.get_lines()
exp = np.array([x.toordinal() for x in dates], dtype=np.float64)
tm.assert_numpy_array_equal(line1.get_xydata()[:, 0], exp)
exp = np.array([x.toordinal() for x in dates], dtype=np.float64)
tm.assert_numpy_array_equal(line2.get_xydata()[:, 0], exp)
@pytest.mark.slow
def test_irregular_ts_shared_ax_xlim(self):
# GH 2960
ts = tm.makeTimeSeries()[:20]
ts_irregular = ts[[1, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 17, 18]]
# plot the left section of the irregular series, then the right section
_, ax = self.plt.subplots()
ts_irregular[:5].plot(ax=ax)
ts_irregular[5:].plot(ax=ax)
# check that axis limits are correct
left, right = ax.get_xlim()
assert left <= ts_irregular.index.min().toordinal()
assert right >= ts_irregular.index.max().toordinal()
@pytest.mark.slow
def test_secondary_y_non_ts_xlim(self):
# GH 3490 - non-timeseries with secondary y
index_1 = [1, 2, 3, 4]
index_2 = [5, 6, 7, 8]
s1 = Series(1, index=index_1)
s2 = Series(2, index=index_2)
_, ax = self.plt.subplots()
s1.plot(ax=ax)
left_before, right_before = ax.get_xlim()
s2.plot(secondary_y=True, ax=ax)
left_after, right_after = ax.get_xlim()
assert left_before >= left_after
assert right_before < right_after
@pytest.mark.slow
def test_secondary_y_regular_ts_xlim(self):
# GH 3490 - regular-timeseries with secondary y
index_1 = date_range(start='2000-01-01', periods=4, freq='D')
index_2 = date_range(start='2000-01-05', periods=4, freq='D')
s1 = Series(1, index=index_1)
s2 = Series(2, index=index_2)
_, ax = self.plt.subplots()
s1.plot(ax=ax)
left_before, right_before = ax.get_xlim()
s2.plot(secondary_y=True, ax=ax)
left_after, right_after = ax.get_xlim()
assert left_before >= left_after
assert right_before < right_after
@pytest.mark.slow
def test_secondary_y_mixed_freq_ts_xlim(self):
# GH 3490 - mixed frequency timeseries with secondary y
rng = date_range('2000-01-01', periods=10000, freq='min')
ts = Series(1, index=rng)
_, ax = self.plt.subplots()
ts.plot(ax=ax)
left_before, right_before = ax.get_xlim()
ts.resample('D').mean().plot(secondary_y=True, ax=ax)
left_after, right_after = ax.get_xlim()
# a downsample should not have changed either limit
assert left_before == left_after
assert right_before == right_after
@pytest.mark.slow
def test_secondary_y_irregular_ts_xlim(self):
# GH 3490 - irregular-timeseries with secondary y
ts = tm.makeTimeSeries()[:20]
ts_irregular = ts[[1, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 17, 18]]
_, ax = self.plt.subplots()
ts_irregular[:5].plot(ax=ax)
# plot higher-x values on secondary axis
ts_irregular[5:].plot(secondary_y=True, ax=ax)
# ensure secondary limits aren't overwritten by plot on primary
ts_irregular[:5].plot(ax=ax)
left, right = ax.get_xlim()
assert left <= ts_irregular.index.min().toordinal()
assert right >= ts_irregular.index.max().toordinal()
def test_plot_outofbounds_datetime(self):
# 2579 - checking this does not raise
values = [date(1677, 1, 1), date(1677, 1, 2)]
_, ax = self.plt.subplots()
ax.plot(values)
values = [datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)]
ax.plot(values)
def test_format_timedelta_ticks_narrow(self):
if is_platform_mac():
pytest.skip("skip on mac for precision display issue on older mpl")
if self.mpl_ge_2_0_0:
expected_labels = [''] + [
'00:00:00.00000000{:d}'.format(2 * i)
for i in range(5)] + ['']
else:
expected_labels = [
'00:00:00.00000000{:d}'.format(i)
for i in range(10)]
rng = timedelta_range('0', periods=10, freq='ns')
df = DataFrame(np.random.randn(len(rng), 3), rng)
fig, ax = self.plt.subplots()
df.plot(fontsize=2, ax=ax)
fig.canvas.draw()
labels = ax.get_xticklabels()
assert len(labels) == len(expected_labels)
for l, l_expected in zip(labels, expected_labels):
assert l.get_text() == l_expected
def test_format_timedelta_ticks_wide(self):
if is_platform_mac():
pytest.skip("skip on mac for precision display issue on older mpl")
if self.mpl_ge_2_0_0:
expected_labels = [
'',
'00:00:00',
'1 days 03:46:40',
'2 days 07:33:20',
'3 days 11:20:00',
'4 days 15:06:40',
'5 days 18:53:20',
'6 days 22:40:00',
'8 days 02:26:40',
'9 days 06:13:20',
''
]
else:
expected_labels = [
'00:00:00',
'1 days 03:46:40',
'2 days 07:33:20',
'3 days 11:20:00',
'4 days 15:06:40',
'5 days 18:53:20',
'6 days 22:40:00',
'8 days 02:26:40',
''
]
rng = timedelta_range('0', periods=10, freq='1 d')
df = DataFrame(np.random.randn(len(rng), 3), rng)
fig, ax = self.plt.subplots()
ax = df.plot(fontsize=2, ax=ax)
fig.canvas.draw()
labels = ax.get_xticklabels()
assert len(labels) == len(expected_labels)
for l, l_expected in zip(labels, expected_labels):
assert l.get_text() == l_expected
def test_timedelta_plot(self):
# test issue #8711
s = Series(range(5), timedelta_range('1day', periods=5))
_, ax = self.plt.subplots()
_check_plot_works(s.plot, ax=ax)
# test long period
index = timedelta_range('1 day 2 hr 30 min 10 s',
periods=10, freq='1 d')
s = Series(np.random.randn(len(index)), index)
_, ax = self.plt.subplots()
_check_plot_works(s.plot, ax=ax)
# test short period
index = timedelta_range('1 day 2 hr 30 min 10 s',
periods=10, freq='1 ns')
s = Series(np.random.randn(len(index)), index)
_, ax = self.plt.subplots()
_check_plot_works(s.plot, ax=ax)
def test_hist(self):
# https://github.com/matplotlib/matplotlib/issues/8459
rng = date_range('1/1/2011', periods=10, freq='H')
x = rng
w1 = np.arange(0, 1, .1)
w2 = np.arange(0, 1, .1)[::-1]
_, ax = self.plt.subplots()
ax.hist([x, x], weights=[w1, w2])
@pytest.mark.slow
def test_overlapping_datetime(self):
# GB 6608
s1 = Series([1, 2, 3], index=[datetime(1995, 12, 31),
datetime(2000, 12, 31),
datetime(2005, 12, 31)])
s2 = Series([1, 2, 3], index=[datetime(1997, 12, 31),
datetime(2003, 12, 31),
datetime(2008, 12, 31)])
# plot first series, then add the second series to those axes,
# then try adding the first series again
_, ax = self.plt.subplots()
s1.plot(ax=ax)
s2.plot(ax=ax)
s1.plot(ax=ax)
def _check_plot_works(f, freq=None, series=None, *args, **kwargs):
import matplotlib.pyplot as plt
fig = plt.gcf()
try:
plt.clf()
ax = fig.add_subplot(211)
orig_ax = kwargs.pop('ax', plt.gca())
orig_axfreq = getattr(orig_ax, 'freq', None)
ret = f(*args, **kwargs)
assert ret is not None # do something more intelligent
ax = kwargs.pop('ax', plt.gca())
if series is not None:
dfreq = series.index.freq
if isinstance(dfreq, DateOffset):
dfreq = dfreq.rule_code
if orig_axfreq is None:
assert ax.freq == dfreq
if freq is not None and orig_axfreq is None:
assert ax.freq == freq
ax = fig.add_subplot(212)
try:
kwargs['ax'] = ax
ret = f(*args, **kwargs)
assert ret is not None # do something more intelligent
except Exception:
pass
with ensure_clean(return_filelike=True) as path:
plt.savefig(path)
# GH18439
# this is supported only in Python 3 pickle since
# pickle in Python2 doesn't support instancemethod pickling
if PY3:
with ensure_clean(return_filelike=True) as path:
pickle.dump(fig, path)
finally:
plt.close(fig)