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added 3d plot option
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Philipp Schneider committed May 13, 2022
1 parent ab3516c commit 44e7d5e
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2 changes: 1 addition & 1 deletion conda-recipe/meta.yaml
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
{% set version = '0.38' %}
{% set version = '0.33' %}

package:
name: straindesign
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277 changes: 143 additions & 134 deletions straindesign/lptools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,13 +8,15 @@
from straindesign.names import *
from typing import Dict, Tuple
from pandas import DataFrame
from numpy import floor, sign, mod, nan, isnan, unique, inf, isinf, zeros, full, linspace, prod
from numpy import floor, sign, mod, nan, isnan, unique, inf, isinf, full, linspace, \
prod, array, mean, flip, ceil, floor
from numpy.linalg import matrix_rank
from os import cpu_count
from contextlib import redirect_stdout, redirect_stderr
from io import StringIO
import matplotlib.pyplot as plt
from scipy.spatial import ConvexHull
import mpl_toolkits.mplot3d as a3
from scipy.spatial import ConvexHull, Delaunay
from matplotlib.cm import ScalarMappable, get_cmap

from straindesign.parse_constr import linexpr2mat, linexprdict2str

Expand Down Expand Up @@ -496,13 +498,13 @@ def yopt(model,**kwargs):
else:
status = INFEASIBLE

def phase_plane(model, axes, **kwargs):
def plot_flux_space(model, axes, **kwargs):
reaction_ids = model.reactions.list_attr("id")

if CONSTRAINTS in kwargs:
kwargs[CONSTRAINTS] = parse_constraints(kwargs[CONSTRAINTS],reaction_ids)
else:
kwargs[CONSTRAINTS] = None
kwargs[CONSTRAINTS] = []

if SOLVER not in kwargs:
kwargs[SOLVER] = None
Expand All @@ -513,7 +515,7 @@ def phase_plane(model, axes, **kwargs):
else:
show = True

axes = list(axes) # cast to list of lists
axes = [list(ax) if not isinstance(ax,str) else [ax] for ax in axes] # cast to list of lists
num_axes = len(axes)
if num_axes not in [2,3]:
raise Exception('Please define 2 or 3 axes as a list of tuples [ax1, ax2, (optional) ax3] with ax1 = (den,num).\n'+\
Expand All @@ -529,28 +531,54 @@ def phase_plane(model, axes, **kwargs):

ax_name = ["" for _ in range(num_axes)]
ax_limits = [(nan,nan) for _ in range(num_axes)]
ax_type = ["" for _ in range(num_axes)]
for i,ax in enumerate(axes):
ax = parse_linexpr(ax,reaction_ids)[0]
ax_name[i] = linexprdict2str(ax)
sol_min = fba(model,obj=ax,constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='minimize')
sol_max = fba(model,obj=ax,constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='maximize')
# abort if any of the fluxes are unbounded or undefined
unbnd = [i+1 for i,v in enumerate([sol_min,sol_max]) if v.status == UNBOUNDED]
if any(unbnd):
raise Exception('One of the specified reactions is unbounded or undefined. Phase plane cannot be generated.')
ax_limits[i] = [min((0,sol_min.objective_value)),max((0,sol_max.objective_value))]
axes[i] = ax
if len(ax) == 1:
ax_type[i] = 'rate'
elif len(ax) == 2:
ax_type[i] = 'yield'
if ax_type[i] == 'rate':
ax[0] = parse_linexpr(ax[0],reaction_ids)[0]
ax_name[i] = linexprdict2str(ax[0])
sol_min = fba(model,obj=ax[0],constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='minimize')
sol_max = fba(model,obj=ax[0],constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='maximize')
# abort if any of the fluxes are unbounded or undefined
inval = [i+1 for i,v in enumerate([sol_min,sol_max]) if v.status == UNBOUNDED or v.status == INFEASIBLE]
if any(inval):
raise Exception('One of the specified reactions is unbounded or problem is infeasible. Plot cannot be generated.')
ax_limits[i] = [min((0,sol_min.objective_value)),max((0,sol_max.objective_value))]
elif ax_type[i] == 'yield':
ax[0] = parse_linexpr(ax[0],reaction_ids)[0]
ax[1] = parse_linexpr(ax[1],reaction_ids)[0]
ax_name[i] = '('+linexprdict2str(ax[0])+') / ('+linexprdict2str(ax[1])+')'
sol_min = yopt(model,obj_num=ax[0],obj_den=ax[1],constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='minimize')
sol_max = yopt(model,obj_num=ax[0],obj_den=ax[1],constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='maximize')
# abort if any of the yields are unbounded or undefined
inval = [i+1 for i,v in enumerate([sol_min,sol_max]) if v.status == UNBOUNDED or v.status == INFEASIBLE]
if any(inval):
raise Exception('One of the specified yields is unbounded or undefined or problem is infeasible. Plot cannot be generated.')
ax_limits[i] = [min((0,ceil_dec(sol_min.objective_value,9))),max((0,floor_dec(sol_max.objective_value,9)))]

# compute points
x_space = linspace(ax_limits[0][0], ax_limits[0][1], num=points)
lb = full(points, nan)
ub = full(points, nan)
for i,x in enumerate(x_space):
constr = [axes[0],'=',x]
sol_vmin = fba(model,constraints=constr,obj=axes[1],obj_sense='minimize')
lb[i] = sol_vmin.objective_value
sol_vmax = fba(model,constraints=constr,obj=axes[1],obj_sense='maximize')
ub[i] = sol_vmax.objective_value
constr = kwargs[CONSTRAINTS].copy()
if ax_type[0] == 'rate':
constr += [[axes[0][0],'=',x]]
elif ax_type[0] == 'yield':
constr += [[{**axes[0][0], **{k:-v*x for k,v in axes[0][1].items()}},'=',0]]
if ax_type[1] == 'rate':
sol_vmin = fba(model,constraints=constr,obj=axes[1][0],obj_sense='minimize')
lb[i] = ceil_dec(sol_vmin.objective_value,9)
sol_vmax = fba(model,constraints=constr,obj=axes[1][0],obj_sense='maximize')
ub[i] = floor_dec(sol_vmax.objective_value,9)
elif ax_type[1] == 'yield':
sol_vmin = yopt(model,constraints=constr,obj_num=axes[1][0],obj_den=axes[1][1],obj_sense='minimize')
lb[i] = ceil_dec(sol_vmin.objective_value,9)
sol_vmax = yopt(model,constraints=constr,obj_num=axes[1][0],obj_den=axes[1][1],obj_sense='maximize')
ub[i] = floor_dec(sol_vmax.objective_value,9)

if num_axes == 2:
x = [v for v in x_space] + [v for v in reversed(x_space)]
Expand All @@ -559,6 +587,7 @@ def phase_plane(model, axes, **kwargs):
x.extend([x_space[0], x_space[0]])
y.extend([lb[0], ub[0]])
plot1 = plt.fill(x, y)
# plot1 = plt.plot(x, y)
plot1[0].axes.set_xlabel(ax_name[0])
plot1[0].axes.set_ylabel(ax_name[1])
plot1[0].axes.set_xlim(ax_limits[0][0]*1.05,ax_limits[0][1]*1.05)
Expand All @@ -570,22 +599,87 @@ def phase_plane(model, axes, **kwargs):
elif num_axes == 3:
max_diff_y = max([abs(l-u) for l,u in zip(lb,ub)])
datapoints = []
for x,l,u in zip(x_space,lb,ub):
datapoints_top = []
datapoints_bottom = []
for i,(x,l,u) in enumerate(zip(x_space,lb,ub)):
if l-u != 0:
y_space = linspace(l,u,int(-(-points // (max_diff_y/abs(l-u)))))
else:
y_space = [0.0]
for y in y_space:
constr = [[axes[0],'=',x], [axes[1],'=',y]]
sol_vmin = fba(model,constraints=constr,obj=axes[2],obj_sense='minimize')
datapoints += [[x,y,sol_vmin.objective_value]]
sol_vmax = fba(model,constraints=constr,obj=axes[2],obj_sense='maximize')
datapoints += [[x,y,sol_vmax.objective_value]]
hull = ConvexHull(datapoints)
datapoints_top += [[]]
datapoints_bottom += [[]]
for j,y in enumerate(y_space):
constr = kwargs[CONSTRAINTS].copy()
if ax_type[0] == 'rate':
constr += [[axes[0][0],'=',x]]
elif ax_type[0] == 'yield':
constr += [[{**axes[0][0], **{k:-v*x for k,v in axes[0][1].items()}},'=',0]]
if ax_type[1] == 'rate':
constr += [[axes[1][0],'=',y]]
elif ax_type[1] == 'yield':
constr += [[{**axes[1][0], **{k:-v*y for k,v in axes[1][1].items()}},'=',0]]
if ax_type[2] == 'rate':
sol_vmin = fba(model,constraints=constr,obj=axes[2][0],obj_sense='minimize')
sol_vmax = fba(model,constraints=constr,obj=axes[2][0],obj_sense='maximize')
elif ax_type[2] == 'yield':
sol_vmin = yopt(model,constraints=constr,obj_num=axes[2][0],obj_den=axes[2][1],obj_sense='minimize')
sol_vmax = yopt(model,constraints=constr,obj_num=axes[2][0],obj_den=axes[2][1],obj_sense='maximize')
datapoints_top[i] += [len(datapoints)]
datapoints += [[x,y,floor_dec(sol_vmax.objective_value,9)]]
datapoints_bottom[i] += [len(datapoints)]
datapoints += [[x,y,ceil_dec(sol_vmin.objective_value,9)]]

# Construct Denaunay triangles for plotting from all 6 perspectives
triang = []
# triangles top
for i in range(len(datapoints_top)-1):
temp_points = datapoints_top[i]+datapoints_top[i+1]
pts = [[datapoints[idx_p][0],datapoints[idx_p][1]] for idx_p in temp_points]
if matrix_rank(pts) > 1:
triang_temp = Delaunay(pts).simplices
triang += [[temp_points[idx] for idx in p] for p in triang_temp]
# triangles bottom
for i in range(len(datapoints_bottom)-1):
temp_points = datapoints_bottom[i]+datapoints_bottom[i+1]
pts = [[datapoints[idx_p][0],datapoints[idx_p][1]] for idx_p in temp_points]
if matrix_rank(pts) > 1:
triang_temp = Delaunay(pts).simplices
triang += [[temp_points[idx] for idx in flip(p)] for p in triang_temp]
# triangles front
for i in range(len(x_space)-1):
temp_points = [datapoints_top[i][0],datapoints_top[i+1][0],datapoints_bottom[i][0],datapoints_bottom[i+1][0]]
pts = [[datapoints[idx_p][0],datapoints[idx_p][2]] for idx_p in temp_points]
if matrix_rank(pts) > 1:
triang_temp = Delaunay(pts).simplices
triang += [[temp_points[idx] for idx in flip(p)] for p in triang_temp]
# triangles back
for i in range(len(x_space)-1):
temp_points = [datapoints_top[i][-1],datapoints_top[i+1][-1],datapoints_bottom[i][-1],datapoints_bottom[i+1][-1]]
pts = [[datapoints[idx_p][0],datapoints[idx_p][2]] for idx_p in temp_points]
if matrix_rank(pts) > 1:
triang_temp = Delaunay(pts).simplices
triang += [[temp_points[idx] for idx in flip(p)] for p in triang_temp]
# triangles left
for i in range(len(datapoints_top[0])-1):
temp_points = [datapoints_top[0][i],datapoints_top[0][i+1],datapoints_bottom[0][i],datapoints_bottom[0][i+1]]
pts = [[datapoints[idx_p][1],datapoints[idx_p][2]] for idx_p in temp_points]
if matrix_rank(pts) > 1:
triang_temp = Delaunay(pts).simplices
triang += [[temp_points[idx] for idx in p] for p in triang_temp]
# triangles right
for i in range(len(datapoints_top[-1])-1):
temp_points = [datapoints_top[-1][i],datapoints_top[-1][i+1],datapoints_bottom[-1][i],datapoints_bottom[-1][i+1]]
pts = [[datapoints[idx_p][1],datapoints[idx_p][2]] for idx_p in temp_points]
if matrix_rank(pts) > 1:
triang_temp = Delaunay(pts).simplices
triang += [[temp_points[idx] for idx in p] for p in triang_temp]

# hull = ConvexHull(datapoints)
x = [d[0] for d in datapoints]
y = [d[1] for d in datapoints]
z = [d[2] for d in datapoints]
ax = a3.Axes3D(plt.figure())
# ax = a3.Axes3D(plt.figure())
ax = plt.figure().add_subplot(projection='3d')
ax.dist=10
ax.azim=30
ax.elev=10
Expand All @@ -595,112 +689,27 @@ def phase_plane(model, axes, **kwargs):
ax.set_xlabel(ax_name[0])
ax.set_ylabel(ax_name[1])
ax.set_zlabel(ax_name[2])
plot1 = ax.plot_trisurf(x,y,z,triangles=hull.simplices,linewidth=0.2, antialiased=True, alpha=0.67)
# set higher color value for 'larger' values in all dimensions
# use middle value of triangles instead of means and then norm
colors = [nan for _ in triang]
for i,t in enumerate(triang):
p = [datapoints[i] for i in t]
interior_point = [0.0, 0.0, 0.0]
for d in range(3):
v = [q[d] for q in p]
interior_point[d] = mean([max(v),min(v)])/max(abs(array(ax_limits[d])))
colors[i] = sum(interior_point) # norm(interior_point)
lw = min([1,6.0/len(triang)])
plot1 = ax.plot_trisurf(x,y,z,triangles=triang,linewidth=lw,edgecolors='black', antialiased=True, alpha=0.90) # array=colors, cmap=plt.cm.winter
colors = colors/max(colors)
colors = get_cmap("Spectral")(colors)
plot1.set_fc(colors)
if show:
plt.show()
return plot1

def yield_space(model, axes, **kwargs):
reaction_ids = model.reactions.list_attr("id")

if CONSTRAINTS in kwargs:
kwargs[CONSTRAINTS] = parse_constraints(kwargs[CONSTRAINTS],reaction_ids)
else:
kwargs[CONSTRAINTS] = None

if SOLVER not in kwargs:
kwargs[SOLVER] = None
solver = select_solver(kwargs[SOLVER],model)

if 'show' in kwargs:
show = kwargs['show']
else:
show = True

axes = [list(ax) for ax in axes] # cast to list of lists
num_axes = len(axes)
if num_axes not in [2,3]:
raise Exception('Please define 2 or 3 axes as a list of tuples [ax1, ax2, (optional) ax3] with ax1 = (den,num).\n'+\
'"den" and "num" being linear expressions.')

if 'points' in kwargs:
points = kwargs['points']
else:
if num_axes == 2:
points = 40
else:
points = 25

ax_name = ["" for _ in range(num_axes)]
ax_limits = [(nan,nan) for _ in range(num_axes)]
for i,ax in enumerate(axes):
ax[0] = parse_linexpr(ax[0],reaction_ids)[0]
ax[1] = parse_linexpr(ax[1],reaction_ids)[0]
ax_name[i] = '('+linexprdict2str(ax[0])+') / ('+linexprdict2str(ax[1])+')'
sol_min = yopt(model,obj_num=ax[0],obj_den=ax[1],constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='minimize')
sol_max = yopt(model,obj_num=ax[0],obj_den=ax[1],constraints=kwargs[CONSTRAINTS],solver=solver,obj_sense='maximize')
# abort if any of the yields are unbounded or undefined
unbnd = [i+1 for i,v in enumerate([sol_min,sol_max]) if v.status == UNBOUNDED]
if any(unbnd):
raise Exception('One of the specified yields is unbounded or undefined. Yield space cannot be generated.')
ax_limits[i] = [min((0,sol_min.objective_value)),max((0,sol_max.objective_value))]

# compute points
x_space = linspace(ax_limits[0][0], ax_limits[0][1], num=points)
lb = full(points, nan)
ub = full(points, nan)
for i,x in enumerate(x_space):
constr = [{**axes[0][0], **{k:-v*x for k,v in axes[0][1].items()}},'=',0]
sol_vmin = yopt(model,constraints=constr,obj_num=axes[1][0],obj_den=axes[1][1],obj_sense='minimize')
lb[i] = sol_vmin.objective_value
sol_vmax = yopt(model,constraints=constr,obj_num=axes[1][0],obj_den=axes[1][1],obj_sense='maximize')
ub[i] = sol_vmax.objective_value

if num_axes == 2:
x = [v for v in x_space] + [v for v in reversed(x_space)]
y = [v for v in lb] + [v for v in reversed(ub)]
if lb[0] != ub[0]:
x.extend([x_space[0], x_space[0]])
y.extend([lb[0], ub[0]])
plot1 = plt.fill(x, y)
plot1[0].axes.set_xlabel(ax_name[0])
plot1[0].axes.set_ylabel(ax_name[1])
plot1[0].axes.set_xlim(ax_limits[0][0]*1.05,ax_limits[0][1]*1.05)
plot1[0].axes.set_ylim(ax_limits[1][0]*1.05,ax_limits[1][1]*1.05)
if show:
plt.show()
return plot1
def ceil_dec(v,n):
return ceil(v*(10**n))/(10**n)

elif num_axes == 3:
max_diff_y = max([abs(l-u) for l,u in zip(lb,ub)])
datapoints = []
for x,l,u in zip(x_space,lb,ub):
if l-u != 0:
y_space = linspace(l,u,int(-(-points // (max_diff_y/abs(l-u)))))
else:
y_space = [0.0]
for y in y_space:
constr = [[{**axes[0][0], **{k:-v*x for k,v in axes[0][1].items()}},'=',0],\
[{**axes[1][0], **{k:-v*y for k,v in axes[1][1].items()}},'=',0]]
sol_vmin = yopt(model,constraints=constr,obj_num=axes[2][0],obj_den=axes[2][1],obj_sense='minimize')
datapoints += [[x,y,sol_vmin.objective_value]]
sol_vmax = yopt(model,constraints=constr,obj_num=axes[2][0],obj_den=axes[2][1],obj_sense='maximize')
datapoints += [[x,y,sol_vmax.objective_value]]
hull = ConvexHull(datapoints)
x = [d[0] for d in datapoints]
y = [d[1] for d in datapoints]
z = [d[2] for d in datapoints]
ax = a3.Axes3D(plt.figure())
ax.dist=10
ax.azim=30
ax.elev=10
ax.set_xlim(ax_limits[0])
ax.set_ylim(ax_limits[1])
ax.set_zlim(ax_limits[2])
ax.set_xlabel(ax_name[0])
ax.set_ylabel(ax_name[1])
ax.set_zlabel(ax_name[2])
plot1 = ax.plot_trisurf(x,y,z,triangles=hull.simplices,linewidth=0.2, antialiased=True, alpha=0.67)
if show:
plt.show()
return plot1
def floor_dec(v,n):
return floor(v*(10**n))/(10**n)
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