How to enage 3D rotation in Tellurium Notebook

[hsauro]

In Tellurium IDE it is possible to display 3D graphs and rotate them using the mouse. The 3D graphs in Tellurium Notebook appear to be static.

Can Tellurium Notebook support interactive 3d graphs?

Code below will draw two 3D graphs.

[0u812]

It looks like you may have forgotten to attach the code snippet you mentioned. If the graphs are drawn via Plotly, they will be interactive & rotatable. If they're drawn via matplotlib, they will be static images. I need to add a utility function for 3d plotting.

[hsauro]

How do I plot via plotly? Does this mean I have to use different code in the notebook and ide to plot?

Here is the script:

import tellurium as te import roadrunner

from mpl_toolkits.mplot3d import Axes3D import numpy as np import matplotlib.pyplot as plt

def plotConcentrationControlIn3D (r, upperLimit=1, lowerLimit=-1): import matplotlib.colors as colors import matplotlib.pyplot as plt import matplotlib.cm as cm

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

hist = r.getScaledConcentrationControlCoefficientMatrix()

xedges = np.arange (float (hist.shape[0]) + 1)
yedges = np.arange (float (hist.shape[1]) + 1)

# Construct arrays for the anchor positions
# Note: np.meshgrid gives arrays in (ny, nx) so we use 'F' to flatten

xpos,

ypos in column-major order. For numpy >= 1.7, we could instead call

meshgrid

with indexing='ij'.

xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25)
xpos = xpos.flatten('F')
ypos = ypos.flatten('F')
zpos = np.zeros_like(xpos)

# Construct arrays with the dimensions for the 16 bars.
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = hist.flatten()

offset = dz + np.abs(dz.min())
fracs = offset.astype(float)/offset.max()
norm = colors.Normalize(fracs.min(), fracs.max())
colors = cm.YlOrRd (norm(fracs))

ax.set_zlim3d(lowerLimit, upperLimit)
ax.set_zlabel('Control Coefficient')
ax.set_xlabel('Species')
ax.set_ylabel('Enzymes')
ax.w_xaxis.set_ticks(np.arange (float (hist.shape[0]) + 1))
ax.w_xaxis.set_ticklabels(r.getFloatingSpeciesIds())
ax.w_yaxis.set_ticks(np.arange (float (hist.shape[1]) + 1))
ax.w_yaxis.set_ticks(ypos + dy/2.)
ax.w_yaxis.set_ticklabels(r.getReactionIds())

ax.bar3d (xpos, ypos, zpos, dx, dy, dz, color=colors, zsort='average')

def plotFluxControlIn3D (r, upperLimit=1, lowerLimit=-1): import matplotlib.cm as cm import matplotlib.colors as colors

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

hist = r.getScaledFluxControlCoefficientMatrix()

xedges = np.arange (float (hist.shape[0]) + 1)
yedges = np.arange (float (hist.shape[1]) + 1)

# Construct arrays for the anchor positions
# Note: np.meshgrid gives arrays in (ny, nx) so we use 'F' to flatten

xpos,

ypos in column-major order. For numpy >= 1.7, we could instead call

meshgrid

with indexing='ij'.

xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25)
xpos = xpos.flatten('F')
ypos = ypos.flatten('F')
zpos = np.zeros_like(xpos)

# Construct arrays with the dimensions for the 16 bars.
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = hist.flatten()

offset = dz + np.abs(dz.min())
fracs = offset.astype(float)/offset.max()
norm = colors.Normalize(fracs.min(), fracs.max())
colors = cm.YlOrRd (norm(fracs))

ax.set_zlim3d(lowerLimit, upperLimit)
ax.set_zlabel('Control Coefficient')
ax.set_xlabel('Fluxes')
ax.set_ylabel('Enzymes')
ax.w_xaxis.set_ticks(np.arange (float (hist.shape[0]) + 1))
ax.w_xaxis.set_ticklabels(r.getReactionIds())
ax.w_yaxis.set_ticks(np.arange (float (hist.shape[1]) + 1))
ax.w_yaxis.set_ticks(ypos + dy/2.)
ax.w_yaxis.set_ticklabels(r.getReactionIds())

ax.bar3d (xpos, ypos, zpos, dx, dy, dz, color=colors, zsort='average')

def plotFloatingSpecies (r, width=12, height=6): import matplotlib.pyplot as plt

xlabels = r.getFloatingSpeciesIds()
concs = r.getFloatingSpeciesConcentrations()
plt.figure(figsize=(width,height))
plt.bar(xlabels, concs, label=xlabels)
plt.xticks(range (len (xlabels)), xlabels,  ha='right', rotation=45)

r = te.loada(""" $Xo -> S1; k1Xo - k11S1; S1 -> S2; k2S1 - k22S2; S2 -> S3; k3S2 - k33S3; S3 -> S4; k3S3 - k44S4; S4 -> S5; k4S4 - k44S5; S5 -> S6; k5S5 - k55S6; S6 -> S7; k4S6 - k44S7; S7 -> S8; k3S7 - k33S8; S8 -> ; k4*S8;

  k1 = 0.3; k11 = 0.26;
  k2 = 0.5; k22 = 0.41;
  k3 = 0.27; k33 = 0.12;
  k4 = 0.9; k44 = 0.56
  k5 = 0.14; k55 = 0.02
  Xo = 10;

""")

r.steadyState()

plotFloatingSpecies (r, width=6,height=3)

plotConcentrationControlIn3D (r)

plotFluxControlIn3D (r, lowerLimit=0)

Herbert

On Sun, Oct 22, 2017 at 9:37 PM, Kyle Medley notifications@github.com wrote:

It looks like you may have forgotten to attach the code snippet you mentioned. If the graphs are drawn via Plotly, they will be interactive & rotatable. If they're drawn via matplotlib, they will be static images. I need to add a utility function for 3d plotting.

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