def test_ninteraction():
simple_vectors = [
list(string.ascii_lowercase),
random.sample(string.ascii_lowercase, 26),
list(range(1, 27))
]
# vector of unique values is equivalent to rank
for case in simple_vectors:
rank = pd.DataFrame(case).rank(method='min')
rank = rank[0].astype(int).tolist()
rank_df = ninteraction(pd.DataFrame(case))
assert rank == rank_df
# duplicates are numbered sequentially
# df ids
# [6, 6, 4, 4, 5, 5] -> [3, 3, 1, 1, 2, 2]
for case in simple_vectors:
rank = pd.DataFrame(case).rank(method='min')
rank = rank[0].astype(int).repeat(2).tolist()
rank_df = ninteraction(pd.DataFrame(np.array(case).repeat(2)))
assert rank == rank_df
# grids are correctly ranked
df = pd.DataFrame(list(itertools.product([1, 2], range(1, 11))))
assert ninteraction(df) == list(range(1, len(df) + 1))
assert ninteraction(df, drop=True) == list(range(1, len(df) + 1))
# zero length dataframe
df = pd.DataFrame()
assert ninteraction(df) == []
def compute_layout(self, data):
if not self.rows and not self.cols:
return layout_null()
base_rows = combine_vars(data,
self.plot.environment,
self.rows,
drop=self.drop)
if not self.as_table:
# Reverse the order of the rows
base_rows = base_rows[::-1]
base_cols = combine_vars(data,
self.plot.environment,
self.cols,
drop=self.drop)
base = cross_join(base_rows, base_cols)
if self.margins:
base = add_margins(base, [self.rows, self.cols], self.margins)
base = base.drop_duplicates().reset_index(drop=True)
n = len(base)
panel = ninteraction(base, drop=True)
panel = pd.Categorical(panel, categories=range(1, n + 1))
if self.rows:
rows = ninteraction(base[self.rows], drop=True)
else:
rows = 1
if self.cols:
cols = ninteraction(base[self.cols], drop=True)
else:
cols = 1
layout = pd.DataFrame({'PANEL': panel, 'ROW': rows, 'COL': cols})
layout = pd.concat([layout, base], axis=1)
layout = layout.sort_values('PANEL')
layout.reset_index(drop=True, inplace=True)
# Relax constraints, if necessary
layout['SCALE_X'] = layout['COL'] if self.free['x'] else 1
layout['SCALE_Y'] = layout['ROW'] if self.free['y'] else 1
layout['AXIS_X'] = layout['ROW'] == layout['ROW'].max()
layout['AXIS_Y'] = layout['COL'] == layout['COL'].min()
self.nrow = layout['ROW'].max()
self.ncol = layout['COL'].max()
return layout
def test_ninteraction():
simple_vectors = [
list(string.ascii_lowercase),
random.sample(string.ascii_lowercase, 26),
list(range(1, 27))]
# vector of unique values is equivalent to rank
for case in simple_vectors:
df = pd.DataFrame(case)
rank = df.rank(method='min')
rank = rank[0].astype(int).tolist()
rank_df = ninteraction(df)
assert rank == rank_df
# duplicates are numbered sequentially
# df ids
# [6, 6, 4, 4, 5, 5] -> [3, 3, 1, 1, 2, 2]
for case in simple_vectors:
rank = pd.DataFrame(case).rank(method='min')
rank = rank[0].astype(int).repeat(2).tolist()
rank_df = ninteraction(
pd.DataFrame(np.array(case).repeat(2)))
assert rank == rank_df
# grids are correctly ranked
df = pd.DataFrame(list(itertools.product([1, 2], range(1, 11))))
assert ninteraction(df) == list(range(1, len(df)+1))
assert ninteraction(df, drop=True) == list(range(1, len(df)+1))
# zero length dataframe
df = pd.DataFrame()
assert ninteraction(df) == []
# dataframe with single variable
df = pd.DataFrame({'a': ['a']})
assert ninteraction(df) == [1]
df = pd.DataFrame({'a': ['b']})
assert ninteraction(df) == [1]