def plot_fluxes(
flux_dict,
escher_map,
output_file='map.html',
height=600,
width=800,
reaction_scale=None,
min_flux=-10,
max_flux=10,
):
if min_flux is None:
min_flux = min(flux_dict)
if max_flux is None:
max_flux = max(flux_dict)
if reaction_scale is None:
reaction_scale = [{
'type': 'value',
'value': min_flux,
'color': 'red',
'size': 32
}, {
'type': 'value',
'value': 0,
'color': '#c8c8c8',
'size': 12
}, {
'type': 'value',
'value': max_flux,
'color': 'green',
'size': 32
}]
builder = Builder(height=height,
width=width,
map_json=escher_map,
reaction_scale=reaction_scale)
builder.reaction_data = flux_dict
builder.save_html(output_file)
builder.close()
# Load a COBRA model
builder.model_name = 'e_coli_core'
# Find any reactions that are in the map and not in the model, and turn them red
builder.highlight_missing = True
get_ipython().getoutput("wget -nc http://bigg.ucsd.edu/static/models/iJO1366.json")
builder.model = cobra.io.load_json_model('iJO1366.json')
# Run FBA with the model and add the flux data to the map
solution = builder.model.optimize()
builder.reaction_data = solution.fluxes
# Add some data for metabolites
builder.metabolite_data = solution.shadow_prices
# Simplify the map by hiding some labels
builder.hide_secondary_metabolites = True
builder.hide_all_labels = True
builder.reaction_scale = [
{ 'type': 'min', 'color': '#000000', 'size': 12 },
{ 'type': 'median', 'color': '#ffffff', 'size': 20 },
{ 'type': 'max', 'color': '#ff0000', 'size': 25 }
def animate_fluxes(
flux_time_data,
escher_map,
outputfile='animation.mp4',
height=600,
width=800,
time_interval_ms=100,
chrome=DEFAULT_CHROME,
reaction_scale=None,
min_flux=None,
max_flux=None,
time_size=12,
time_unit='h',
x_time=0.95,
y_time=0.9,
):
if min_flux is None:
min_flux = flux_time_data.min().min()
if max_flux is None:
max_flux = flux_time_data.max().max()
if reaction_scale is None:
reaction_scale = [{
'type': 'value',
'value': min_flux,
'color': 'red',
'size': 32
}, {
'type': 'value',
'value': 0,
'color': '#c8c8c8',
'size': 12
}, {
'type': 'value',
'value': max_flux,
'color': 'green',
'size': 32
}]
builder = Builder(height=height,
width=width,
map_json=escher_map,
embedd_css=EMBEDD_CSS,
menu='none',
reaction_scale=reaction_scale)
myimages = []
fig = plt.figure()
XVFB_DOCKER = '/usr/bin/xvfb-run -a -s \"-screen 0 {}x{}x24\"'.format(
width, height)
SCREENSHOT = "--headless --disable-gpu --no-sandbox --virtual-time-budget=10000 --screenshot=\'{}\' {}"
for t, fluxes in flux_time_data.iterrows():
builder.reaction_data = fluxes
builder.save_html('tmp.html', )
# Hacky hack hack ...
# Use chrome to make a screenshot
cmd = "{} {} {}".format(XVFB_DOCKER, chrome,
SCREENSHOT.format('tmp.png', 'tmp.html'))
os.system(cmd)
# Add time text
ylim = plt.gca().get_ylim()
xlim = plt.gca().get_xlim()
y = (ylim[1] - ylim[0]) * y_time + ylim[0]
x = (xlim[1] - xlim[0]) * x_time + xlim[0]
text = plt.text(x,
y,
'{:.1f} {}'.format(t, time_unit),
horizontalalignment='right',
fontsize=time_size)
img = mgimg.imread('tmp.png')
imgplot = plt.imshow(img)
# append AxesImage object to the list
myimages.append([imgplot, text])
plt.axis('off')
fig.tight_layout()
anim = animation.ArtistAnimation(fig, myimages, interval=time_interval_ms)
anim.save(outputfile, dpi=300)
builder.close()
map_name='e_coli_core.Core metabolism',
model_name='e_coli_core',
)
Core
model = cobra.io.load_json_model('e_coli_core.json')
reverse = True
step = 0.1
timestep = 0.1
duration = 1500 # seconds
lim = [0, 0.5]
val = lim[-1]
for _ in range(int(duration / timestep)):
model.reactions.EX_o2_e.lower_bound = -val
solution = model.optimize()
Core.reaction_data = solution.fluxes
if val <= lim[0]:
reverse = True
if val >= lim[1]:
reverse = False
if reverse:
val += step
else:
val -= step
sleep(timestep)
Core.save_html("Core.html")
CarbonMeta = Builder(model_json="Recon3D.json",
map_name='RECON1.Carbohydrate metabolism')
CarbonMeta.save_html("CarbonMeta.html")
