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_FmtHeatmap__get_selected_cell_values():
df_pn = df - 5.
fmt = pbtf.FmtHeatmap()
res = fmt._get_selected_cell_values(None, None, df_pn)
assert res.equals(df_pn)
res = fmt._get_selected_cell_values(['a'], ['aa', 'bb'], df)
assert res.equals(df.loc[['a'], ['aa', 'bb']])
def test_df_to_jinja_table_add_extra_formatters():
fmt_heatmap = blformat.FmtHeatmap(threshold=0., axis=0)
fmt_add_totals_mean = blformat.FmtAppendTotalsRow(row_name='Sum', operator=blformat.OP_SUM, bold=True)
fmt_fontsize_20 = blformat.FmtFontsize(fontsize=20)
formatters = [fmt_fontsize_20, fmt_heatmap, fmt_add_totals_mean]
return Block(FLAT_DATA, formatters=formatters, use_default_formatters=False)
def test_FmtHeatmap_cell_css():
df_pn = df - 5.
fmt = pbtf.FmtHeatmap(threshold=0.5)
data = FormatterData(df_pn.min().min(), None, None, df_pn)
res = fmt._create_cell_level_css(data)
assert res == pbtf.CSS_BACKGROUND_COLOR + colors.css_color(colors.HEATMAP_RED)
data = FormatterData(df_pn.max().max(), None, None, df_pn)
res = fmt._create_cell_level_css(data)
assert res == pbtf.CSS_BACKGROUND_COLOR + colors.css_color(colors.HEATMAP_GREEN)
data = FormatterData(0.1, None, None, df_pn)
res = fmt._create_cell_level_css(data)
assert res == pbtf.CSS_BACKGROUND_COLOR + colors.css_color(colors.WHITE)
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 test_FmtHeatmap__get_min_max_from_selected_cell_values_without_cache():
df_pn = df - 5.
cache = None
fmt = pbtf.FmtHeatmap(cache=cache)
res = fmt._get_min_max_from_selected_cell_values(None, None, df_pn)
assert cache is None
assert res == (np.nanmin(df_pn), np.nanmax(df_pn))
min_value, max_value = np.nanmin(df.loc[['a'], ['aa', 'bb']]), np.nanmax(df.loc[['a'], ['aa', 'bb']])
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert cache is None
assert res == (min_value, max_value)
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert cache is None
assert res == (min_value, max_value)
def test_FmtHeatmap__get_min_max_from_selected_cell_values_with_cache():
df_pn = df - 5.
cache = {}
fmt = pbtf.FmtHeatmap(cache=cache)
res = fmt._get_min_max_from_selected_cell_values(None, None, df_pn)
assert len(cache) == 1 and (None, None) in cache.keys()
assert res == (np.nanmin(df_pn), np.nanmax(df_pn))
min_value, max_value = np.nanmin(df.loc[['a'], ['aa', 'bb']]), np.nanmax(df.loc[['a'], ['aa', 'bb']])
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert len(cache) == 2 and (frozenset(['a']), frozenset(['aa', 'bb'])) in cache.keys()
assert res == (min_value, max_value)
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert len(cache) == 2 and (frozenset(['a']), frozenset(['aa', 'bb'])) in cache.keys()
assert res == (min_value, max_value)
def test_df_to_data_tables_jinja_table_extra_formatters_only():
return DataTablesHTMLJinjaTableBlock(
FLAT_DATA,
formatters=[blformat.FmtHeatmap(threshold=0., axis=0)],
use_default_formatters=False)
