def test_color_legend():
# from pudb import set_trace; set_trace(paused=True)
np.random.seed(10)
gobli = (np.random.random((30, 4)) - 0.5) * 2
pda = pd.DataFrame(gobli)
pda.columns = ["V{0}".format(i+1) for i in range(pda.shape[1])]
pda.index = ["O{0}".format(i+1) for i in range(pda.shape[0])]
irds = mx.DataSet(mat=pda)
# Subset for index
ss1 = mx.SubSet(id='1', name='set1', row_selector=["O{0}".format(i+1) for i in range(10)])
ss1.gr_style = mx.VisualStyle(fg_color=from_palette())
ss2 = mx.SubSet(id='2', name='set2', row_selector=["O{0}".format(i+10) for i in range(10)])
ss2.gr_style = mx.VisualStyle(fg_color=from_palette())
ss3 = mx.SubSet(id='3', name='set3', row_selector=["O{0}".format(i+20) for i in range(11)])
ss3.gr_style = mx.VisualStyle(fg_color=from_palette())
sl = [ss1, ss2, ss3]
subs = {'GrEn': sl}
# factor = mx.Factor('TestColorFactor', irds.mat.shape[0])
irds.subs = subs
plot = pps.PCScatterPlot(irds, title='Test color plot')
plot.color_subsets_group('GrEn')
plot_control = pps.PCPlotControl(plot)
pw = SinglePlotWindow(plot=plot_control)
with np.errstate(invalid='ignore'):
pw.configure_traits()
def expvar1ds():
"""Simulated explained variance"""
ds = mx.DataSet(
mat=pd.DataFrame(
[[50.8,20.7,5.1,1.9],
[30.2,12.4,5.4,2.3]],
index=['calibrated', 'validated'],
columns=['V1', 'V2', 'V3', 'V4']),
display_name='Some values', kind='Descriptive analysis / sensory profiling',
style=mx.VisualStyle(fg_color='olive')
)
return ds
def clust3ds():
"""Manual random pick from the Iris datast: virginica"""
ds = mx.DataSet(
mat=pd.DataFrame(
[[5.8,2.7,5.1,1.9],
[6.5,3.0,5.8,2.2],
[7.2,3.6,6.1,2.5],
[6.8,3.0,5.5,2.1],
[6.2,2.8,4.8,1.8],
[6.4,3.1,5.5,1.8],
[6.2,3.4,5.4,2.3]],
index=['O1', 'O2', 'O3', 'O4', 'O5', 'O6', 'O7'],
columns=['V1', 'V2', 'V3', 'V4']),
display_name='Some values', kind='Descriptive analysis / sensory profiling',
style=mx.VisualStyle(fg_color='olive')
)
return ds
def clust2ds():
"""Manual random pick from the Iris datast: versicolor"""
ds = mx.DataSet(
mat=pd.DataFrame(
[[6.9,3.1,4.9,1.5],
[4.9,2.4,3.3,1.0],
[5.7,3.0,4.2,1.2],
[5.1,2.5,3.0,1.1],
[5.7,2.6,3.5,1.0],
[5.1,2.5,3.0,1.1],
[6.1,2.9,4.7,1.4]],
index=['O1', 'O2', 'O3', 'O4', 'O5', 'O6', 'O7'],
columns=['V1', 'V2', 'V3', 'V4']),
display_name='Some values', kind='Descriptive analysis / sensory profiling',
style=mx.VisualStyle(fg_color='saddlebrown')
)
return ds
def clust1ds():
"""Manual random pick from the Iris datast: setosa"""
ds = mx.DataSet(
mat=pd.DataFrame(
[[5.1,3.5,1.4,0.2],
[4.6,3.4,1.4,0.3],
[5.4,3.7,1.5,0.2],
[5.7,3.8,1.7,0.3],
[5.4,3.4,1.7,0.2],
[4.8,3.1,1.6,0.2],
[4.6,3.6,1.0,0.2]],
index=['O1', 'O2', 'O3', 'O4', 'O5', 'O6', 'O7'],
columns=['V1', 'V2', 'V3', 'V4']),
display_name='Some values', kind='Descriptive analysis / sensory profiling',
# style=VisualStyle(fg_color=(0.8, 0.2, 0.1, 1.0)),
style=mx.VisualStyle(fg_color='indigo')
)
return ds