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
Exemple #4
0
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'])
Exemple #7
0
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'})