def test_FmtExpandMultiIndex_cell_css():
mi_df = make_multiindex_table()
fmt = pbtf.FmtExpandMultiIndex(bold=True,
hline_color=colors.LIGHT_GREY,
indent_px=123)
df = fmt._modify_dataframe(mi_df)
# Because this formatter is quite stateful, index column must be called first
data = FormatterData(0., 'a', pbtf.INDEX_COL_NAME, df)
res = fmt._create_cell_level_css(data)
# Test that level0 row gets index indented
assert 'padding-left:0px' in res
# Test that level0 row gets all highlighting
data = FormatterData(0., 'a', 'column1', df)
res = fmt._create_cell_level_css(data)
assert pbtf.CSS_BOLD in res
assert 'border-bottom' in res
assert 'border-top' in res
assert colors.css_color(colors.LIGHT_GREY) in res
# Run for index column again, so we proceed to next row
data = FormatterData(0., 'a', pbtf.INDEX_COL_NAME, df)
res = fmt._create_cell_level_css(data)
# Test that level1 row gets index indented more and no highlighting
data = FormatterData(0., 'aa', pbtf.INDEX_COL_NAME, df)
res = fmt._create_cell_level_css(data)
assert 'padding-left:123px' in res
assert pbtf.CSS_BOLD not in res
# Check that bold-highlighting is not used if not desired
fmt = pbtf.FmtExpandMultiIndex(bold=False)
df = fmt._modify_dataframe(mi_df)
data = FormatterData(0., 'a', pbtf.INDEX_COL_NAME, df)
res = fmt._create_cell_level_css(data)
assert pbtf.CSS_BOLD not in res
def test_multi_index_df_to_jinja_table():
# Create multi-index table
idx = np.array([['super1', 'super1', 'super1', 'super1', 'super2', 'super2', 'super2'],
['a', 'a', 'b', 'b', 'c', 'c', 'c'],
['aa', 'aa', 'ba', 'bb', 'ca', 'cb', 'cc']])
idx_tuples = list(zip(*idx))
multi_index = pd.MultiIndex.from_tuples(idx_tuples, names=['super-level', 'a-level', 'aa-level'])
columns = ['This is an incredibly long column name', 'column2', 'column3', 'column4', 'column5']
data = pd.DataFrame(np.random.rand(7, 5) * 2 - 1, index=multi_index, columns=columns)
fmt_expand_multi_index = tf.FmtExpandMultiIndex(operator=tf.OP_SUM, bold=True,
hline_color=colors.DARK_BLUE)
fmt_nDecimal = tf.FmtDecimals(n=2)
fmt_align_cells = tf.FmtAlignCellContents(alignment='right')
fmt_heatmap_1 = tf.FmtHeatmap(columns=['column2', 'column3'], rows=['aa', 'ac'], threshold=0., axis=0)
fmt_heatmap_2 = tf.FmtHeatmap(columns=['column4', 'column5'], rows=['ca', 'cc'], threshold=0.3,
min_color=colors.PURPLE, max_color=colors.ORANGE)
fmt_stripes_bg = tf.FmtStripeBackground(first_color=colors.LIGHT_GREY)
fmt_rotate_header = tf.FmtHeader(fixed_width='500px', top_padding='200px')
formatters = [fmt_expand_multi_index, fmt_align_cells, fmt_stripes_bg, fmt_heatmap_1, fmt_heatmap_2,
fmt_rotate_header, fmt_nDecimal]
# Create table
table = Block(data, formatters=formatters)
filename = 'Multi_index_table.pdf'
table.save(filename)
# And clean up file afterwards
os.remove(filename)
def test_FmtExpandMultiIndex_modify_dataframe():
mi_df = make_multiindex_table()
fmt = pbtf.FmtExpandMultiIndex(operator=pbtf.OP_SUM)
res = fmt._modify_dataframe(mi_df)
assert res.shape == (6, 4)
assert res.index.tolist() == ['a', 'aa', 'ab', 'b', 'ba', 'bb']
assert res.index.name == ''
assert res.ix['a'].tolist() == [3., 5., 7., ('a',)]
assert fmt.index_level == [0, 1, 1, 0, 1, 1]
fmt = pbtf.FmtExpandMultiIndex(operator=pbtf.OP_MEAN)
res = fmt._modify_dataframe(mi_df)
assert res.ix['a'].tolist() == [1.5, 2.5, 3.5, ('a',)]
fmt = pbtf.FmtExpandMultiIndex(operator=pbtf.OP_NONE)
res = fmt._modify_dataframe(mi_df)
assert res.ix['a'].tolist() == ['', '', '', ('a',)]
fmt = pbtf.FmtExpandMultiIndex(operator=pbtf.OP_SUM, total_columns=['column1'])
res = fmt._modify_dataframe(mi_df)
assert res.ix['a'].tolist() == ['', 5., '', ('a',)]
def test__jinja_hides_multiindex_flattening():
df = pd.DataFrame(columns=['a', 'b', 'c'],
index=pd.MultiIndex.from_tuples([('x', 'x'), ('y', 'y'),
('z', 'z')]))
table = abt.HTMLJinjaTableBlock(df,
formatters=[abtf.FmtExpandMultiIndex()],
use_default_formatters=False)
container = MagicMock()
actual_cfg = MagicMock()
table._write_contents(container, actual_cfg)
assert "('x', 'x')" not in str(container.append.call_args_list[0][0][0])
def test_multi_index_df_to_jinja_table():
# Create multi-index table
idx = np.array([['super1', 'super1', 'super1', 'super1', 'super2', 'super2', 'super2'],
['a', 'a', 'b', 'b', 'c', 'c', 'c'],
['aa', 'ab', 'ba', 'bb', 'ca', 'cb', 'cc']])
idx_tuples = list(zip(*idx))
multi_index = pd.MultiIndex.from_tuples(idx_tuples, names=['super-level', 'a-level', 'aa-level'])
columns = ['This is an incredibly long column name', 'column2', 'column3', 'column4', 'column5']
data = pd.DataFrame(TABLE_DATA, index=multi_index, columns=columns)
fmt_expand_multi_index = blformat.FmtExpandMultiIndex(operator=blformat.OP_SUM, bold=True,
hline_color=colors.DARK_BLUE)
fmt_ndecimal = blformat.FmtDecimals(n=2)
fmt_align_cells = blformat.FmtAlignCellContents(alignment='right')
fmt_heatmap_1 = blformat.FmtHeatmap(columns=['column2', 'column3'], rows=['aa', 'ab', 'ac'], threshold=0., axis=0)
fmt_heatmap_2 = blformat.FmtHeatmap(columns=['column4', 'column5'], rows=['ca', 'cb', 'cc'], threshold=0.3,
min_color=colors.PURPLE, max_color=colors.ORANGE)
fmt_rotate_header = blformat.FmtHeader(fixed_width='500px', top_padding='200px')
formatters = [fmt_expand_multi_index, fmt_align_cells, fmt_heatmap_1, fmt_heatmap_2,
fmt_rotate_header, fmt_ndecimal]
return Block(data, formatters=formatters, use_default_formatters=False)
def get_performance_stats_html(self):
pct_rows = [('P&L', 'Total return'), ('P&L', 'Annual return'),
('P&L', 'Annual return (asset mode)'),
('Risk-adjusted return based on Drawdown',
'Max percentage drawdown')]
dec_rows = [
('P&L', 'Starting cash'),
('P&L', 'End value'),
('Risk-adjusted return based on Drawdown', 'Max money drawdown'),
# Distribution
('Distribution moments', 'Returns volatility'),
('Distribution moments', 'Returns skewness'),
('Distribution moments', 'Returns kurtosis'),
# Risk-adjusted return based on Volatility
('Risk-adjusted return based on Volatility', 'Treynor ratio'),
('Risk-adjusted return based on Volatility', 'Sharpe ratio'),
('Risk-adjusted return based on Volatility', 'Information ratio'),
# Risk-adjusted return based on Value at Risk
('Risk-adjusted return based on Value at Risk', 'VaR'),
('Risk-adjusted return based on Value at Risk',
'Expected Shortfall'),
('Risk-adjusted return based on Value at Risk', 'Excess var'),
('Risk-adjusted return based on Value at Risk',
'Conditional sharpe ratio'),
# Risk-adjusted return based on Lower Partial Moments
('Risk-adjusted return based on Lower Partial Moments',
'Omega ratio'),
('Risk-adjusted return based on Lower Partial Moments',
'Sortino ratio'),
('Risk-adjusted return based on Lower Partial Moments',
'Kappa three ratio'),
('Risk-adjusted return based on Lower Partial Moments',
'Gain loss ratio'),
('Risk-adjusted return based on Lower Partial Moments',
'Upside potential ratio'),
# Risk-adjusted return based on Drawdown
('Risk-adjusted return based on Drawdown', 'Calmar ratio')
]
fmt_pct = tf.FmtPercent(1,
rows=pct_rows,
apply_to_header_and_index=False)
fmt_dec = tf.FmtDecimals(2,
rows=dec_rows,
apply_to_header_and_index=False)
fmt_align = tf.FmtAlignTable("left")
fmt_background = tf.FmtStripeBackground(
first_color=tf.colors.LIGHT_GREY,
second_color=tf.colors.WHITE,
header_color=tf.colors.BLACK)
fmt_multiindex = tf.FmtExpandMultiIndex(operator=tf.OP_NONE)
perf_data = Block(self.InputList[2],
formatters=[
fmt_multiindex, fmt_pct, fmt_dec, fmt_align,
fmt_background
],
use_default_formatters=False)._repr_html_()
perf_data = {'performance_table': perf_data}
return perf_data
