def _normalize_data(data, index):
"""normalize the data to a dict with tuples of strings as keys
right now it works with:
0 - dictionary (or equivalent mappable)
1 - pandas.Series with simple or hierarchical indexes
2 - numpy.ndarrays
3 - everything that can be converted to a numpy array
4 - pandas.DataFrame (via the _normalize_dataframe function)
"""
# if data is a dataframe we need to take a completely new road
# before coming back here. Use the hasattr to avoid importing
# pandas explicitly
if hasattr(data, 'pivot') and hasattr(data, 'groupby'):
data = _normalize_dataframe(data, index)
index = None
# can it be used as a dictionary?
try:
items = list(data.items())
except AttributeError:
# ok, I cannot use the data as a dictionary
# Try to convert it to a numpy array, or die trying
data = np.asarray(data)
temp = OrderedDict()
for idx in np.ndindex(data.shape):
name = tuple(i for i in idx)
temp[name] = data[idx]
data = temp
items = list(data.items())
# make all the keys a tuple, even if simple numbers
data = OrderedDict([_tuplify(k), v] for k, v in items)
categories_levels = _categories_level(list(data.keys()))
# fill the void in the counting dictionary
indexes = product(*categories_levels)
contingency = OrderedDict([(k, data.get(k, 0)) for k in indexes])
data = contingency
# reorder the keys order according to the one specified by the user
# or if the index is None convert it into a simple list
# right now it doesn't do any check, but can be modified in the future
index = list(range(len(categories_levels))) if index is None else index
contingency = OrderedDict()
for key, value in list(data.items()):
new_key = tuple(key[i] for i in index)
contingency[new_key] = value
data = contingency
return data
def test_mosaic_very_complex():
# make a scattermatrix of mosaic plots to show the correlations between
# each pair of variable in a dataset. Could be easily converted into a
# new function that does this automatically based on the type of data
key_name = ['gender', 'age', 'health', 'work']
key_base = (['male', 'female'], ['old',
'young'], ['healty',
'ill'], ['work', 'unemployed'])
keys = list(product(*key_base))
data = OrderedDict(zip(keys, range(1, 1 + len(keys))))
props = {}
props[('male', 'old')] = {'color': 'r'}
props[('female', )] = {'color': 'pink'}
L = len(key_base)
fig, axes = pylab.subplots(L, L)
for i in range(L):
for j in range(L):
m = set(range(L)).difference(set((i, j)))
if i == j:
axes[i, i].text(0.5,
0.5,
key_name[i],
ha='center',
va='center')
axes[i, i].set_xticks([])
axes[i, i].set_xticklabels([])
axes[i, i].set_yticks([])
axes[i, i].set_yticklabels([])
else:
ji = max(i, j)
ij = min(i, j)
temp_data = OrderedDict([((k[ij], k[ji]) + tuple(k[r]
for r in m),
v) for k, v in data.items()])
keys = temp_data.keys()
for k in keys:
value = _reduce_dict(temp_data, k[:2])
temp_data[k[:2]] = value
del temp_data[k]
mosaic(temp_data,
ax=axes[i, j],
axes_label=False,
properties=props,
gap=0.05,
horizontal=i > j)
pylab.suptitle('old males should look bright red, (plot 4 of 4)')
def _normalize_data(data, index):
"""normalize the data to a dict with tuples of strings as keys
right now it works with:
0 - dictionary (or equivalent mappable)
1 - pandas.Series with simple or hierarchical indexes
2 - numpy.ndarrays
3 - everything that can be converted to a numpy array
4 - pandas.DataFrame (via the _normalize_dataframe function)
"""
# if data is a dataframe we need to take a completely new road
# before coming back here. Use the hasattr to avoid importing
# pandas explicitly
if hasattr(data, 'pivot') and hasattr(data, 'groupby'):
data = _normalize_dataframe(data, index)
index = None
# can it be used as a dictionary?
try:
items = list(data.iteritems())
except AttributeError:
# ok, I cannot use the data as a dictionary
# Try to convert it to a numpy array, or die trying
data = np.asarray(data)
temp = OrderedDict()
for idx in np.ndindex(data.shape):
name = tuple(i for i in idx)
temp[name] = data[idx]
data = temp
items = data.items()
# make all the keys a tuple, even if simple numbers
data = OrderedDict([_tuplify(k), v] for k, v in items)
categories_levels = _categories_level(list(data.keys()))
# fill the void in the counting dictionary
indexes = product(*categories_levels)
contingency = OrderedDict([(k, data.get(k, 0)) for k in indexes])
data = contingency
# reorder the keys order according to the one specified by the user
# or if the index is None convert it into a simple list
# right now it doesn't do any check, but can be modified in the future
index = list(range(len(categories_levels))) if index is None else index
contingency = OrderedDict()
for key, value in data.items():
new_key = tuple(key[i] for i in index)
contingency[new_key] = value
data = contingency
return data
def test_mosaic_very_complex():
# make a scattermatrix of mosaic plots to show the correlations between
# each pair of variable in a dataset. Could be easily converted into a
# new function that does this automatically based on the type of data
key_name = ['gender', 'age', 'health', 'work']
key_base = (['male', 'female'], ['old', 'young'],
['healty', 'ill'], ['work', 'unemployed'])
keys = list(product(*key_base))
data = OrderedDict(zip(keys, range(1, 1 + len(keys))))
props = {}
props[('male', 'old')] = {'color': 'r'}
props[('female',)] = {'color': 'pink'}
L = len(key_base)
fig, axes = pylab.subplots(L, L)
for i in range(L):
for j in range(L):
m = set(range(L)).difference(set((i, j)))
if i == j:
axes[i, i].text(0.5, 0.5, key_name[i],
ha='center', va='center')
axes[i, i].set_xticks([])
axes[i, i].set_xticklabels([])
axes[i, i].set_yticks([])
axes[i, i].set_yticklabels([])
else:
ji = max(i, j)
ij = min(i, j)
temp_data = OrderedDict([((k[ij], k[ji]) + tuple(k[r] for r in m), v)
for k, v in data.items()])
keys = temp_data.keys()
for k in keys:
value = _reduce_dict(temp_data, k[:2])
temp_data[k[:2]] = value
del temp_data[k]
mosaic(temp_data, ax=axes[i, j], axes_label=False,
properties=props, gap=0.05, horizontal=i > j)
pylab.suptitle('old males should look bright red, (plot 4 of 4)')
def test_mosaic_very_complex():
# make a scattermatrix of mosaic plots to show the correlations between
# each pair of variable in a dataset. Could be easily converted into a
# new function that does this automatically based on the type of data
key_name = ["gender", "age", "health", "work"]
key_base = (["male", "female"], ["old", "young"], ["healty", "ill"], ["work", "unemployed"])
keys = list(product(*key_base))
data = OrderedDict(list(zip(keys, list(range(1, 1 + len(keys))))))
props = {}
props[("male", "old")] = {"color": "r"}
props[("female",)] = {"color": "pink"}
L = len(key_base)
fig, axes = pylab.subplots(L, L)
for i in range(L):
for j in range(L):
m = set(range(L)).difference(set((i, j)))
if i == j:
axes[i, i].text(0.5, 0.5, key_name[i], ha="center", va="center")
axes[i, i].set_xticks([])
axes[i, i].set_xticklabels([])
axes[i, i].set_yticks([])
axes[i, i].set_yticklabels([])
else:
ji = max(i, j)
ij = min(i, j)
temp_data = OrderedDict([((k[ij], k[ji]) + tuple(k[r] for r in m), v) for k, v in list(data.items())])
keys = list(temp_data.keys())
for k in keys:
value = _reduce_dict(temp_data, k[:2])
temp_data[k[:2]] = value
del temp_data[k]
mosaic(temp_data, ax=axes[i, j], axes_label=False, properties=props, gap=0.05, horizontal=i > j)
pylab.suptitle("old males should look bright red, (plot 4 of 4)")
