def test_violinplot_beanplot():
# Test violinplot and beanplot with the same dataset.
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican",
"Weak Republican", "Strong Republican"]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
plt.close(fig)
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
plt.close(fig)
def test_violinplot_beanplot(close_figures):
# Test violinplot and beanplot with the same dataset.
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican",
"Weak Republican", "Strong Republican"]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30,
'bw_factor':.2})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels,
plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
'label_fontsize':'small',
'label_rotation':30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, jitter=True,
plot_opts={'cutoff_val': 5, 'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, jitter=True, side='right',
plot_opts={'cutoff_val': 5, 'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, jitter=True, side='left',
plot_opts={'cutoff_val': 5, 'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels,
plot_opts={'bean_legend_text': 'text'})
def age_and_labels():
# Test violinplot and beanplot with the same dataset.
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = [
"Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican", "Weak Republican",
"Strong Republican"
]
age = [data.exog['age'][data.endog == id] for id in party_ID]
return age, labels
def test_violinplot_beanplot():
"""Test violinplot and beanplot with the same dataset."""
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = [
"Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican", "Weak Republican",
"Strong Republican"
]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
plt.close(fig)
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
plt.close(fig)
def test_init_api():
# make a multi-index panel
grun_data = grunfeld.load_pandas().data
multi_index_panel = grun_data.set_index(['firm', 'year']).index
grouping = Grouping(multi_index_panel)
# check group_names
np.testing.assert_array_equal(grouping.group_names, ['firm', 'year'])
# check shape
np.testing.assert_array_equal(grouping.index_shape, (11, 20))
# check index_int
np.testing.assert_array_equal(grouping.labels, [[
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19
]])
grouping = Grouping(multi_index_panel, names=['firms', 'year'])
np.testing.assert_array_equal(grouping.group_names, ['firms', 'year'])
# make a multi-index grouping
anes_data = anes96.load_pandas().data
multi_index_groups = anes_data.set_index(['educ', 'income',
'TVnews']).index
grouping = Grouping(multi_index_groups)
np.testing.assert_array_equal(grouping.group_names,
['educ', 'income', 'TVnews'])
np.testing.assert_array_equal(grouping.index_shape, (7, 24, 8))
# make a list multi-index panel
list_panel = multi_index_panel.tolist()
grouping = Grouping(list_panel, names=['firms', 'year'])
np.testing.assert_array_equal(grouping.group_names, ['firms', 'year'])
np.testing.assert_array_equal(grouping.index_shape, (11, 20))
# make a list multi-index grouping
list_groups = multi_index_groups.tolist()
grouping = Grouping(list_groups, names=['educ', 'income', 'TVnews'])
np.testing.assert_array_equal(grouping.group_names,
['educ', 'income', 'TVnews'])
np.testing.assert_array_equal(grouping.index_shape, (7, 24, 8))
# single-variable index grouping
index_group = multi_index_panel.get_level_values(0)
grouping = Grouping(index_group)
# the original multi_index_panel had it's name changed inplace above
np.testing.assert_array_equal(grouping.group_names, ['firms'])
np.testing.assert_array_equal(grouping.index_shape, (220, ))
# single variable list grouping
list_group = multi_index_panel.get_level_values(0).tolist()
grouping = Grouping(list_group)
np.testing.assert_array_equal(grouping.group_names, ["group0"])
np.testing.assert_array_equal(grouping.index_shape, 11 * 20)
# test generic group names
grouping = Grouping(list_groups)
np.testing.assert_array_equal(grouping.group_names,
['group0', 'group1', 'group2'])
def test_init_api():
# make a multi-index panel
grun_data = grunfeld.load_pandas().data
multi_index_panel = grun_data.set_index(['firm', 'year']).index
grouping = Grouping(multi_index_panel)
# check group_names
np.testing.assert_array_equal(grouping.group_names, ['firm', 'year'])
# check shape
np.testing.assert_array_equal(grouping.index_shape, (11, 20))
# check index_int
np.testing.assert_array_equal(grouping.labels,
[[ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])
grouping = Grouping(multi_index_panel, names=['firms', 'year'])
np.testing.assert_array_equal(grouping.group_names, ['firms', 'year'])
# make a multi-index grouping
anes_data = anes96.load_pandas().data
multi_index_groups = anes_data.set_index(['educ', 'income',
'TVnews']).index
grouping = Grouping(multi_index_groups)
np.testing.assert_array_equal(grouping.group_names,
['educ', 'income', 'TVnews'])
np.testing.assert_array_equal(grouping.index_shape, (7, 24, 8))
# make a list multi-index panel
list_panel = multi_index_panel.tolist()
grouping = Grouping(list_panel, names=['firms', 'year'])
np.testing.assert_array_equal(grouping.group_names, ['firms', 'year'])
np.testing.assert_array_equal(grouping.index_shape, (11, 20))
# make a list multi-index grouping
list_groups = multi_index_groups.tolist()
grouping = Grouping(list_groups, names=['educ', 'income', 'TVnews'])
np.testing.assert_array_equal(grouping.group_names,
['educ', 'income', 'TVnews'])
np.testing.assert_array_equal(grouping.index_shape, (7, 24, 8))
# single-variable index grouping
index_group = multi_index_panel.get_level_values(0)
grouping = Grouping(index_group)
# the original multi_index_panel had it's name changed inplace above
np.testing.assert_array_equal(grouping.group_names, ['firms'])
np.testing.assert_array_equal(grouping.index_shape, (220,))
# single variable list grouping
list_group = multi_index_panel.get_level_values(0).tolist()
grouping = Grouping(list_group)
np.testing.assert_array_equal(grouping.group_names, ["group0"])
np.testing.assert_array_equal(grouping.index_shape, 11*20)
# test generic group names
grouping = Grouping(list_groups)
np.testing.assert_array_equal(grouping.group_names,
['group0', 'group1', 'group2'])
def test_violinplot_beanplot(close_figures):
# Test violinplot and beanplot with the same dataset.
data = anes96.load_pandas()
party_ID = np.arange(7)
labels = [
"Strong Democrat", "Weak Democrat", "Independent-Democrat",
"Independent-Independent", "Independent-Republican", "Weak Republican",
"Strong Republican"
]
age = [data.exog['age'][data.endog == id] for id in party_ID]
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
violinplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30,
'bw_factor': .2
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
jitter=True,
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
jitter=True,
side='right',
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age,
ax=ax,
labels=labels,
jitter=True,
side='left',
plot_opts={
'cutoff_val': 5,
'cutoff_type': 'abs',
'label_fontsize': 'small',
'label_rotation': 30
})
fig = plt.figure()
ax = fig.add_subplot(111)
beanplot(age, ax=ax, labels=labels, plot_opts={'bean_legend_text': 'text'})