def create_scatter_plot_rxn_c3(D_fba, L_r, title, xaxis_title, save_fig=False):
data = []
for r_id in L_r:
trace = go.Scatter(
y=[D_fba[exp][r_id] for exp in sorted(D_fba.keys())],
x=sorted(D_fba.keys()),
name=r_id,
mode='lines+markers',
)
data.append(trace)
layout = go.Layout(
title=title,
yaxis=dict(title='Flux [µmol/s/m2]'),
xaxis=dict(title=xaxis_title),
width=500,
)
fig = go.Figure(data=data, layout=layout)
if save_fig:
iplot(fig,
filename=title,
image='svg',
image_width=500,
image_height=500)
sleep(5)
else:
iplot(fig)
def create_bar_plot_met_c3(model,
D_fba,
L_r,
m_id,
title,
xaxis_title,
save_fig=False):
data = []
for r_id in L_r:
r_obj = model.reactions.get_by_id(r_id)
m_v = r_obj.get_coefficient(m_id)
trace = go.Bar(
y=[abs(m_v) * D_fba[exp][r_id] for exp in sorted(D_fba.keys())],
x=sorted(D_fba.keys()),
name=r_id,
)
data.append(trace)
layout = go.Layout(
title=title,
yaxis=dict(title='Flux [µmol/s/m2]'),
xaxis=dict(title=xaxis_title),
width=500,
barmode='stack',
)
fig = go.Figure(data=data, layout=layout)
if save_fig:
iplot(fig,
filename=title,
image='svg',
image_width=500,
image_height=500)
sleep(5)
else:
iplot(fig)
def plot_transport_BM(D_fba, D_exp, L_r, xaxis_title, save_fig, cut_off):
df = pd.DataFrame(index=[],
columns=[D_exp[exp] for exp in sorted(D_fba.keys())])
yTickNames_BM = []
D_rid_meanFlux_BM = {
r_id: abs(
np.mean([0] +
[D_fba[x][r_id] for x in D_exp if D_fba[x][r_id] < 0]))
for r_id in L_r if abs(
np.mean([0] +
[D_fba[x][r_id]
for x in D_exp if D_fba[x][r_id] < 0])) > cut_off
}
D_rid_meanFlux_BM = sorted(D_rid_meanFlux_BM.items(),
key=operator.itemgetter(1))
for entry in D_rid_meanFlux_BM:
r_id = entry[0]
for x in D_exp:
if D_fba[x][r_id] < 0:
df.set_value(
r_id, D_exp[x],
abs(D_fba[x][r_id])
if not abs(D_fba[x][r_id]) < cut_off else float('nan'))
if not r_id[5:-2] in yTickNames_BM:
yTickNames_BM.append(r_id[5:-2])
scl = cl.scales['9']['seq']['Blues']
colorscale = [[float(i) / float(len(scl) - 1), scl[i]]
for i in range(len(scl))]
trace_BM = go.Heatmap(z=df.values.tolist(),
x=df.columns,
y=yTickNames_BM,
colorbar={
'title': 'Flux [µmol/s/m2]',
'titleside': 'right'
},
colorscale=colorscale)
layout_BM = go.Layout(width=500,
margin={'b': 100},
yaxis={
'tickmode': 'array',
'tickvals': range(0, len(yTickNames_BM)),
'ticktext': yTickNames_BM,
'title': 'Transport Metabolites'
},
xaxis={
'title': xaxis_title,
'tickangle': 45 if len(D_exp) >= 8 else 0
},
title='Bundlesheat ==> Mesophyll Transport')
data_BM = [trace_BM]
fig_BM = go.Figure(
data=data_BM,
layout=layout_BM,
)
if save_fig:
iplot(fig_BM,
image='svg',
filename='transport_BM',
image_width=500,
image_height=500)
sleep(5)
else:
iplot(fig_BM)
def plot_transport_fva(c4_model,
D_exp,
D_pfva,
D_pfba,
L_r_org,
flux_max,
title,
save_fig=False,
L_r_index=None):
L_colors = ply.colors.DEFAULT_PLOTLY_COLORS
if len(D_exp) > len(L_colors):
print('Too many experiments.')
else:
p_value = 0.1 #p value
#prepare data for plotting
D_r_flux_range = {}
D_exp_r_FVA = {}
data = []
for exp in D_exp:
D_exp_r_FVA[exp] = {}
for r_id in L_r_org:
D_FVA = {}
r_obj = c4_model.reactions.get_by_id(r_id)
D_FVA['max'] = D_pfva[exp].get_value(r_id, 'maximum')
D_FVA['min'] = D_pfva[exp].get_value(r_id, 'minimum')
D_FVA['flux'] = D_pfba[exp][r_id]
#filter flux ranges of particular size (p_value % of maximum C transport rate)
if abs(D_FVA['max']) > p_value * flux_max or abs(
D_FVA['min']) > p_value * flux_max:
#determine maximum flux range over all exp for each reaction
if not r_id in D_r_flux_range:
D_r_flux_range[r_id] = D_FVA['max'] - D_FVA['min']
else:
if D_r_flux_range[r_id] < (D_FVA['max'] -
D_FVA['min']):
D_r_flux_range[r_id] = D_FVA['max'] - D_FVA['min']
D_exp_r_FVA[exp][r_id] = D_FVA
#sort reactions by size of flux range
L_r = [
r_flux_range[0]
for r_flux_range in sorted(D_r_flux_range.items(),
key=operator.itemgetter(1),
reverse=True)
]
create_plot = True
if L_r_index:
L_r_ex1 = list(set(L_r_index) - set(L_r))
L_r_ex2 = list(set(L_r) - set(L_r_index))
L_r_ex = L_r_ex1 + L_r_ex2
if not L_r_ex:
L_r = L_r_index
else:
print('Warning: L_r and L_r_index are not matching: %s' %
L_r_ex)
create_plot = False
if create_plot:
#set up x-values for each experiment
L_x_axis = np.arange(1, len(L_r) * 2 + 1, 2)
D_exp_x_axis = {exp: [] for exp in D_exp}
for x in L_x_axis:
L_x_exp = np.linspace(x - 0.3, x + 0.3, len(D_exp))
for i_exp, exp in enumerate(D_exp.keys()):
D_exp_x_axis[exp].append(L_x_exp[i_exp])
#prepare trace for each experiment
for i_exp, (exp, exp_name) in enumerate(D_exp.items()):
L_r_flux = [D_exp_r_FVA[exp][r_id]['flux'] for r_id in L_r]
L_r_min = [
D_exp_r_FVA[exp][r_id]['flux'] -
D_exp_r_FVA[exp][r_id]['min'] for r_id in L_r
]
L_r_max = [
D_exp_r_FVA[exp][r_id]['max'] -
D_exp_r_FVA[exp][r_id]['flux'] for r_id in L_r
]
trace = go.Scatter(x=D_exp_x_axis[exp],
y=L_r_flux,
error_y={
'type': 'data',
'symmetric': False,
'array': L_r_max,
'arrayminus': L_r_min
},
marker={'color': L_colors[i_exp]},
name=exp_name,
mode='markers')
data.append(trace)
#prepare layout
layout = go.Layout(
xaxis={
'tickvals': L_x_axis,
'ticktext': [r_id[5:-2] for r_id in L_r],
'title': 'Exchange Metabolites'
},
#margin = {'b': 300},
title=title,
yaxis={'title': 'Flux [µmol/s/m2]'},
legend={
'orientation': 'h',
'x': 0,
'y': -0.15
},
shapes=[
{
'type': 'rect',
'x0': -0.1,
'y0': -0.5,
'x1': -1.5,
'y1': -50,
'line': {
'color': 'rgba(255, 255, 255, 1)',
'width': 1,
},
'fillcolor': 'rgba(255, 255, 255, 1)',
},
{
'type': 'rect',
'x0': -0.1,
'y0': 0.5,
'x1': -1.5,
'y1': 50,
'line': {
'color': 'rgba(255, 255, 255, 1)',
'width': 1,
},
'fillcolor': 'rgba(255, 255, 255, 1)',
},
],
annotations=[
dict(
x=-0.5,
y=25,
xref='x',
yref='y',
text='Mesophyll -> Bundle Sheath',
textangle=-90,
showarrow=False,
font=dict(size=10, color='#000000'),
align='center',
),
dict(
x=-0.5,
y=-25,
xref='x',
yref='y',
text='Bundle Sheath -> Mesophyll',
textangle=-90,
showarrow=False,
font=dict(size=10, color='#000000'),
align='center',
)
])
#create figure
fig = go.Figure(data=data, layout=layout)
if save_fig:
iplot(fig, image='svg', filename='FVA')
sleep(5)
else:
iplot(fig)
def create_bar_plot_met(D_fba,
D_exp,
D_rxn,
m_id,
title,
xaxis_title,
c3_model,
stacked=False,
y_max=None,
c=False,
save_fig=False):
#Coloring of bars
L_colM = cl.scales['5']['seq']['Oranges']
L_colM = L_colM[::-1]
L_colB = cl.scales['5']['seq']['Blues']
L_colB = L_colB[::-1]
if len(D_rxn) >= 5 or c:
L_colM = cl.scales['11']['qual']['Paired']
L_colB = cl.scales['11']['qual']['Paired']
if isinstance(D_rxn, list):
D_rxn = {r_id: r_id for r_id in D_rxn}
D_rid_colM = {
r_id2: L_colM[i]
for i, r_id2 in enumerate(
[r_id1 for r_id1 in D_rxn if r_id1[1] == 'M'])
}
D_rid_colB = {
r_id2: L_colB[i]
for i, r_id2 in enumerate(
[r_id1 for r_id1 in D_rxn if r_id1[1] == 'B'])
}
D_rid_col = D_rid_colM.copy()
D_rid_col.update(D_rid_colB)
#Create bar plot
data = []
for r_id, r_name in sorted(D_rxn.items()):
r_obj = c3_model.reactions.get_by_id(r_id[4:])
m_v = r_obj.get_coefficient(m_id)
trace = go.Bar(
name=r_name,
y=[abs(m_v * D_fba[x][r_id]) for x in sorted(D_fba.keys())],
x=[D_exp[exp] for exp in sorted(D_fba.keys())],
marker={'color': D_rid_col[r_id] if D_rid_col else '#1f77b4'})
data.append(trace)
layout = go.Layout(height=400,
width=600,
margin=dict(b=100),
barmode='stack' if stacked else 'group',
xaxis={
'title': xaxis_title,
'tickangle': 45 if len(D_exp) >= 8 else 0
},
title=title,
yaxis={
'title': 'Flux [µmol/s/m2]',
'range': [0, y_max] if y_max else None,
'autorange': False if y_max else True
})
fig = go.Figure(data=data, layout=layout)
if save_fig:
iplot(fig,
image='svg',
filename=title,
image_width=500,
image_height=500)
sleep(5)
else:
iplot(fig)