def test_colormap_construction(self):
colors = [ (0.0, (1.0,1.0,1.0)), (1.0,(0.,0.,0.)) ]
base_cmap = cmap.Colormap(colors, 1.0, 10.0)
if bMPL:
mpl_norm, mpl_cmap = base_cmap.get_matplotlib_norm_and_cmap()
for llcolor in ("red","blue","green","cyan","yellow","purple"):
llcmap = cmap.LinlogColormap(1.0, 10.0, 16, 0.95, 1, llcolor)
if bMPL:
mpl_norm, mpl_cmap = llcmap.get_matplotlib_norm_and_cmap()
with self.assertRaises(ValueError):
cmap.LinlogColormap(1.0, 10.0, 16, 0.95, 1, color="foobar")
divcmap = cmap.DivergingColormap(1.0, 10.0, color="RdBu")
with self.assertRaises(ValueError):
cmap.DivergingColormap(1.0, 10.0, color="foobar")
for seqcolor in ("whiteToBlack","blackToWhite","whiteToBlue","whiteToRed"):
seqcmap = cmap.SequentialColormap(1.0, 10.0, seqcolor)
with self.assertRaises(ValueError):
cmap.SequentialColormap(1.0, 10.0, color="foobar")
cmap.PiecewiseLinearColormap( colors )
def _construct(self, **kwargs):
return cmap.LinlogColormap(1.0, 10.0, 16, 0.95, 1, **kwargs)
def test_mpl_conversion(self):
try:
import matplotlib
from pygsti.report import mpl_colormaps
from pygsti.report.mpl_colormaps import plotly_to_matplotlib
except ImportError:
return # no matplotlib => stop here
#Quick test of linlog inverse function (never used)
llcmap = cmap.LinlogColormap(1.0, 10.0, 16, 0.95, 1, "red")
mpl = pygsti.report.mpl_colormaps.mpl_LinLogNorm(llcmap)
x = mpl.inverse(1.0) # test out inverse() function
xar = mpl.inverse(np.array([1.0,2.0],'d'))
# Test plotly -> matplotlib conversion
data = []
data.append( go.Scatter(
x = [1,2,3],
y = [3,4,5],
mode = 'lines+markers',
line = dict(width=2, color="blue",dash='dash'),
name = 'Test1',
showlegend=True
))
data.append( go.Scattergl(x=[2,4,6], y=[1,3,9],
mode="markers",
showlegend=True) )
layout = go.Layout(
width=800,
height=400,
title="my title",
titlefont=dict(size=16),
xaxis=dict(
title="my xlabel",
titlefont=dict(size=14),
side="top",
type="log",
),
yaxis=dict(
title='Mean survival probability',
titlefont=dict(size=14),
side="right",
type="log",
),
legend=dict(
font=dict(
size=13,
),
)
)
plotly_fig = go.Figure(data=list(data), layout=layout)
pygsti_fig = ReportFigure(plotly_fig, colormap=None, pythonValue=None)
mpl_fig = plotly_to_matplotlib(pygsti_fig)
plotly_to_matplotlib(pygsti_fig,temp_files + "/testMPL.pdf")
with self.assertRaises(ValueError):
fig = ReportFigure(plotly_fig, colormap=None, pythonValue=None, special="foobar")
plotly_to_matplotlib(fig)
#Heatmap
nX = 5
nY = 3
heatmap_data = [ go.Heatmap(z=np.ones((nY,nX),'d'),
colorscale=[ [0, 'white'], [1, 'black'] ],
showscale=False, zmin=0,zmax=1,hoverinfo='none') ]
seqcmap = cmap.SequentialColormap(0, 10.0, "whiteToBlack")
del layout['xaxis']['type'] # heatmaps don't play well with
del layout['yaxis']['type'] # log scales
heatmap_fig = ReportFigure(go.Figure(data=heatmap_data, layout=layout), seqcmap, plt_data=np.ones((nY,nX),'d'))
plotly_to_matplotlib(heatmap_fig, temp_files + "/testMPLHeatmap.pdf")
#bad mode
data = [ go.Scatter(x = [0,1,2], y = [3,4,5],
mode = 'foobar') ]
fig = ReportFigure(go.Figure(data=data, layout=layout))
with self.assertRaises(ValueError):
plotly_to_matplotlib(fig) #invalid mode
