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_add_margins():
df = mtcars.loc[:, ['mpg', 'disp', 'vs', 'am', 'gear']]
n = len(df)
all_lst = ['(all)'] * n
vars = [('vs', 'am'), ('gear',)]
dfx = add_margins(df, vars, True)
assert(dfx['vs'].dtype == 'category')
assert(dfx['am'].dtype == 'category')
assert(dfx['gear'].dtype == 'category')
# What we expect, where each row is of
# column length n
#
# mpg disp vs am gear
# --- ---- -- -- ----
# * * * * *
# * * (all) (all) *
# * * * (all) *
# * * * * (all)
# * * (all) (all) (all)
# * * * (all) (all)
assert(all(dfx.loc[0:n-1, 'am'] != all_lst))
assert(all(dfx.loc[0:n-1, 'vs'] != all_lst))
assert(all(dfx.loc[0:n-1, 'gear'] != all_lst))
assert(all(dfx.loc[n:2*n-1, 'vs'] == all_lst))
assert(all(dfx.loc[n:2*n-1, 'am'] == all_lst))
assert(all(dfx.loc[2*n:3*n-1, 'am'] == all_lst))
assert(all(dfx.loc[3*n:4*n-1, 'gear'] == all_lst))
assert(all(dfx.loc[4*n:5*n-1, 'am'] == all_lst))
assert(all(dfx.loc[4*n:5*n-1, 'vs'] == all_lst))
assert(all(dfx.loc[4*n:5*n-1, 'gear'] == all_lst))
assert(all(dfx.loc[5*n:6*n-1, 'am'] == all_lst))
assert(all(dfx.loc[5*n:6*n-1, 'gear'] == all_lst))
def test_add_margins():
df = mtcars.loc[:, ['mpg', 'disp', 'vs', 'am', 'gear']]
n = len(df)
all_lst = ['(all)'] * n
vars = [('vs', 'am'), ('gear', )]
dfx = add_margins(df, vars, True)
assert (dfx['vs'].dtype == 'category')
assert (dfx['am'].dtype == 'category')
assert (dfx['gear'].dtype == 'category')
# What we expect, where each row is of
# column length n
#
# mpg disp vs am gear
# --- ---- -- -- ----
# * * * * *
# * * (all) (all) *
# * * * (all) *
# * * * * (all)
# * * (all) (all) (all)
# * * * (all) (all)
assert (all(dfx.loc[0:n - 1, 'am'] != all_lst))
assert (all(dfx.loc[0:n - 1, 'vs'] != all_lst))
assert (all(dfx.loc[0:n - 1, 'gear'] != all_lst))
assert (all(dfx.loc[n:2 * n - 1, 'vs'] == all_lst))
assert (all(dfx.loc[n:2 * n - 1, 'am'] == all_lst))
assert (all(dfx.loc[2 * n:3 * n - 1, 'am'] == all_lst))
assert (all(dfx.loc[3 * n:4 * n - 1, 'gear'] == all_lst))
assert (all(dfx.loc[4 * n:5 * n - 1, 'am'] == all_lst))
assert (all(dfx.loc[4 * n:5 * n - 1, 'vs'] == all_lst))
assert (all(dfx.loc[4 * n:5 * n - 1, 'gear'] == all_lst))
assert (all(dfx.loc[5 * n:6 * n - 1, 'am'] == all_lst))
assert (all(dfx.loc[5 * n:6 * n - 1, 'gear'] == all_lst))
def map(self, data, layout):
if not len(data):
data['PANEL'] = pd.Categorical(
[], categories=layout['PANEL'].cat.categories, ordered=True)
return data
vars = [x for x in self.rows + self.cols]
margin_vars = [
list(data.columns & self.rows),
list(data.columns & self.cols)
]
data = add_margins(data, margin_vars, self.margins)
facet_vals = eval_facet_vars(data, vars, self.plot.environment)
data, facet_vals = add_missing_facets(data, layout, vars, facet_vals)
# assign each point to a panel
if len(facet_vals) == 0:
# Special case of no facetting
data['PANEL'] = 1
else:
keys = join_keys(facet_vals, layout, vars)
data['PANEL'] = match(keys['x'], keys['y'], start=1)
data = data.sort_values('PANEL', kind='mergesort')
# matching dtype and
# the categories(panel numbers) for the data should be in the
# same order as the panels. i.e the panels are the reference,
# they "know" the right order
data['PANEL'] = pd.Categorical(
data['PANEL'],
categories=layout['PANEL'].cat.categories,
ordered=True)
data.reset_index(drop=True, inplace=True)
return data
