parameters={
'IFNAR1_IC': 0,
'IFNAR2_IC': 0,
'IFN_alpha2_IC': 0,
'IFN_beta_IC': 0
},
return_type='dataframe',
dataframe_labels='IFNgamma')
pSTAT1_df = IfnData(name='custom', df=model_res.xs(['pSTAT1']))
pSTAT3_df = IfnData(name='custom', df=model_res.xs(['pSTAT3']))
STAT1_palette = sns.color_palette("cubehelix", 6)
STAT3_palette = sns.color_palette("cubehelix", 6)
fig = DoseresponsePlot((1, 2))
fig.axes[0].set_title('pSTAT1')
fig.axes[1].set_title('pSTAT3')
for idx, t in enumerate(times):
fig.add_trajectory(pSTAT1_df,
t,
'plot',
STAT1_palette[idx], (0, 0),
'IFNgamma',
label=str(t) + ' min',
linewidth=2)
fig.add_trajectory(pSTAT3_df,
t,
'plot',
STAT3_palette[idx], (0, 1),
'IFNgamma',
parameters={'Ia': 0}, dataframe_labels='Beta', sf=scale_factor, **DR_KWARGS)
# ----------------------------------------
# Finally, plot both models in comparison
# ----------------------------------------
fig = plt.figure(figsize=(6.4 * 2.5, 4.8))
gs = gridspec.GridSpec(nrows=1, ncols=5)
panelA = fig.add_subplot(gs[0, 0:2])
panelB = fig.add_subplot(gs[0, 2:4])
for ax in [panelA, panelB]:
ax.set(xscale='log', yscale='linear')
ax.set_xlabel('Dose (pM)')
ax.set_ylabel('Response')
legend_panel = fig.add_subplot(gs[0, 4])
new_fit = DoseresponsePlot((1, 2))
new_fit.fig = fig
new_fit.axes = [panelA, panelB, legend_panel]
alpha_palette = sns.color_palette("rocket_r", 6)
beta_palette = sns.color_palette("rocket_r", 6)
t_mask = [2.5, 7.5, 20.]
# Add fits
for idx, t in enumerate(times):
if t not in t_mask:
new_fit.add_trajectory(dra_s, t, 'plot', alpha_palette[idx], (0, 0), 'Alpha', linewidth=2.0)
new_fit.add_trajectory(dra_d, t, 'plot', '--', (0, 0), 'Alpha', color=alpha_palette[idx], linewidth=2.0)
new_fit.add_trajectory(drb_s, t, 'plot', beta_palette[idx], (0, 1), 'Beta', linewidth=2.0)
new_fit.add_trajectory(drb_d, t, 'plot', '--', (0, 1), 'Beta', color=beta_palette[idx], linewidth=2.0)
new_fit.show_figure(show_flag=False, save_flag=False)
newdata_2 = IfnData("20190119_pSTAT1_IFN_Bcell")
newdata_3 = IfnData("20190121_pSTAT1_IFN_Bcell")
newdata_4 = IfnData("20190214_pSTAT1_IFN_Bcell")
times = [2.5, 5, 7.5, 10, 20, 60]
alpha_doses = [0, 10, 100, 300, 1000, 3000, 10000, 100000]
beta_doses = [0, 0.2, 6, 20, 60, 200, 600, 2000]
#beta_doses_20181113 = [0.1, 1, 5, 10, 100, 200, 1000]
#alpha_doses_20181113 = [5, 50, 250, 500, 5000, 25000, 50000]
alpha_palette_new = sns.color_palette("Reds", 6)
beta_palette_new = sns.color_palette("Greens", 6)
alpha_palette_old = sns.color_palette("Reds", 6)
beta_palette_old = sns.color_palette("Greens", 6)
data_plot_dr = DoseresponsePlot((4, 2))
alpha_mask = []
beta_mask = []
# Add data
for idx, t in enumerate(times):
if t not in alpha_mask:
data_plot_dr.add_trajectory(newdata_4,
t,
'scatter',
alpha_palette_new[idx], (3, 0),
'Alpha',
dn=1)
data_plot_dr.add_trajectory(newdata_4,
t,
'plot',
simulation_doses,
parameters={'Ia': 0},
return_type='dataframe',
dataframe_labels='Beta')
for i in range(len(times)):
dradf.loc['Alpha'].iloc[:, i] = dradf.loc['Alpha'].iloc[:, i].apply(
scale_data)
drbdf.loc['Beta'].iloc[:,
i] = drbdf.loc['Beta'].iloc[:,
i].apply(scale_data)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
new_fit = DoseresponsePlot((3, 1))
new_fit.axes[0].set_title("{} minutes".format(times[0]))
new_fit.axes[0].set_xlabel("")
new_fit.axes[1].set_title("{} minutes".format(times[1]))
new_fit.axes[1].set_xlabel("")
new_fit.axes[2].set_title("{} minutes".format(times[2]))
new_fit.axes[2].set_xlabel("Dose (pM)")
new_fit.fig.set_size_inches(8.5, 25.5)
alpha_mask = []
beta_mask = []
# Add fits
for idx, t in enumerate([el for el in times]):
if t not in alpha_mask:
new_fit.add_trajectory(dra60,
t,
'plot',
def check_plots():
smooth_plot = DoseresponsePlot((1, 2))
alpha_palette = sns.color_palette("Reds", 6)
beta_palette = sns.color_palette("Greens", 6)
smooth_plot.add_trajectory(a5smoothIfnData, 5, 'plot', alpha_palette[0],
(0, 0), 'Alpha')
smooth_plot.add_trajectory(a15smoothIfnData, 15, 'plot', alpha_palette[1],
(0, 0), 'Alpha')
smooth_plot.add_trajectory(a30smoothIfnData, 30, 'plot', alpha_palette[2],
(0, 0), 'Alpha')
smooth_plot.add_trajectory(a60smoothIfnData, 60, 'plot', alpha_palette[3],
(0, 0), 'Alpha')
smooth_plot.add_trajectory(b5smoothIfnData, 5, 'plot', beta_palette[0],
(0, 1), 'Beta')
smooth_plot.add_trajectory(b15smoothIfnData, 15, 'plot', beta_palette[1],
(0, 1), 'Beta')
smooth_plot.add_trajectory(b30smoothIfnData, 30, 'plot', beta_palette[2],
(0, 1), 'Beta')
smooth_plot.add_trajectory(b60smoothIfnData, 60, 'plot', beta_palette[3],
(0, 1), 'Beta')
for idx, t in enumerate([5, 15, 30, 60]):
smooth_plot.add_trajectory(newdata,
t,
'scatter',
alpha_palette[idx], (0, 0),
'Alpha',
dn=1)
smooth_plot.add_trajectory(newdata,
t,
'scatter',
beta_palette[idx], (0, 1),
'Beta',
dn=1)
fig, axes = smooth_plot.show_figure()
ec50_20190214 = newdata_4.get_ec50s()
# Aligned data, to get scale factors for each data set
alignment = DataAlignment()
alignment.add_data([newdata_4, newdata_3, newdata_2, newdata_1])
alignment.align()
alignment.get_scaled_data()
mean_data = alignment.summarize_data()
# -------------------------------
# Plot model dose response curves
# -------------------------------
alpha_palette = sns.color_palette("rocket_r")
beta_palette = sns.color_palette("rocket_r")
new_fit = DoseresponsePlot((1, 2))
new_fit.axes[0].set_ylabel('pSTAT1 (MFI)')
new_fit.axes[1].set_ylabel('pSTAT1 (MFI)')
alpha_mask = [7.5, 10.0]
beta_mask = [7.5, 10.0]
# Add fits
for idx, t in enumerate([2.5, 5.0, 7.5, 10.0, 20.0, 60.0]):
new_fit.add_trajectory(mean_data,
t,
'errorbar',
'o', (0, 0),
'Alpha',
color=alpha_palette[idx])
new_fit.add_trajectory(mean_data,
t,
dra25df = stepfit25.model.doseresponse([5, 10, 20, 60], 'TotalpSTAT', 'Ia', list(logspace(-3, 5)),
parameters={'Ib': 0}, return_type='dataframe', dataframe_labels='Alpha')
drb25df = stepfit25.model.doseresponse([5, 10, 20, 60], 'TotalpSTAT', 'Ib', list(logspace(-3, 4)),
parameters={'Ia': 0}, return_type='dataframe', dataframe_labels='Beta')
# Scale by best fit scale factor, so comparison to data is good
for i in range(4):
dra25df.loc['Alpha'].iloc[:, i] = dra25df.loc['Alpha'].iloc[:, i].apply(scale_data)
drb25df.loc['Beta'].iloc[:, i] = drb25df.loc['Beta'].iloc[:, i].apply(scale_data)
# Make the DR curves into IfnData objects, for easy plotting
dra25 = IfnData('custom', df=dra25df, conditions={'Alpha': {'Ib': 0}})
drb25 = IfnData('custom', df=drb25df, conditions={'Beta': {'Ia': 0}})
# Generate figure by adding simulations, then data
results_plot = DoseresponsePlot((1, 2))
for idx, t in enumerate(['5', '10', '20', '60']):
results_plot.add_trajectory(dra25, t, 'plot', alpha_palette[idx], (0, 0), 'Alpha')
results_plot.add_trajectory(drb25, t, 'plot', beta_palette[idx], (0,1), 'Beta')
for idx, t in enumerate([5, 10, 20, 60]):
results_plot.add_trajectory(newdata, t, 'scatter', alpha_palette[idx], (0, 0), 'Alpha', dn=1)
results_plot.add_trajectory(newdata, t, 'scatter', beta_palette[idx], (0, 1), 'Beta', dn=1)
results_plot.save_figure()
# Now try to fit the 60 minute data starting from the 2.5 minute results
# ----------------------------------------------------------------------
stepfit60 = StepwiseFit(stepfit25.model, smooth60IfnData,
{'kSOCSon': (1E-8, 1E-7), 'kd4': (0.03, 0.1), 'k_d4': (0.03, 0.1),
'krec_a2': (0.0005, 0.05), 'krec_b2': (0.01, 0.0001),
'krec_a1': (0.00003, 0.003), 'krec_b1': (0.001, 0.00001)}, n=2)
def testing_specific_values():
volEC1 = 1 / (25E9)
volEC2 = 1E-5
# --------------------
# Set up Model
# --------------------
# Parameters found by stepwise fitting GAB mean data
# Note: can remove multiplicative factors on all K1, K2, K4 and still get very good fit to data (worst is 5 min beta)
initial_parameters = {
'k_a1': 4.98E-14 * 2,
'k_a2': 8.30e-13 * 2,
'k_d4': 0.006 * 3.8,
'kpu': 0.00095,
'ka2': 4.98e-13 * 2.45,
'kd4': 0.3 * 2.867,
'kint_a': 0.000124,
'kint_b': 0.00086,
'krec_a1': 0.0028,
'krec_a2': 0.01,
'krec_b1': 0.005,
'krec_b2': 0.05
}
dual_parameters = {
'kint_a': 0.00052,
'kSOCSon': 6e-07,
'kint_b': 0.00052,
'krec_a1': 0.001,
'krec_a2': 0.1,
'krec_b1': 0.005,
'krec_b2': 0.05
}
scale_factor = 1.227
Mixed_Model = DualMixedPopulation('Mixed_IFN_ppCompatible', 0.8, 0.2)
Mixed_Model.model_1.set_parameters(initial_parameters)
Mixed_Model.model_1.set_parameters(dual_parameters)
Mixed_Model.model_1.set_parameters({'R1': 12000.0, 'R2': 1511.1})
Mixed_Model.model_2.set_parameters(initial_parameters)
Mixed_Model.model_2.set_parameters(dual_parameters)
Mixed_Model.model_2.set_parameters({'R1': 6755.56, 'R2': 1511.1})
# ---------------------------------
# Make theory dose response curves
# ---------------------------------
# Make predictions
times = np.arange(0, 120, 0.5) # [2.5, 5.0, 7.5, 10.0, 20.0, 60.0]
alpha_doses_20190108 = [0, 10, 100, 300, 1000, 3000, 10000, 100000]
beta_doses_20190108 = [0, 0.2, 6, 20, 60, 200, 600, 2000]
# 1 uL
ka1 = Mixed_Model.model_1.parameters['ka1']
ka2 = Mixed_Model.model_1.parameters['ka2']
k_a1 = Mixed_Model.model_1.parameters['k_a1']
k_a2 = Mixed_Model.model_1.parameters['k_a2']
Mixed_Model.set_global_parameters({
'volEC': volEC1,
'ka1': ka1 * 1E-5 / volEC1,
'ka2': ka2 * 1E-5 / volEC1,
'k_a1': k_a1 * 1E-5 / volEC1,
'k_a2': k_a2 * 1E-5 / volEC1
})
dradf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra15uL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb15uL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# 1 mL
Mixed_Model.set_global_parameters({
'volEC': volEC2,
'ka1': ka1 * 1E-5 / volEC2,
'ka2': ka2 * 1E-5 / volEC2,
'k_a1': k_a1 * 1E-5 / volEC2,
'k_a2': k_a2 * 1E-5 / volEC2
})
dradf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra5mL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb5mL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# -------------------------------
# Plot model dose response curves
# -------------------------------
alpha_palette = sns.color_palette("deep", 6)
beta_palette = sns.color_palette("deep", 6)
new_fit = DoseresponsePlot((2, 2))
alpha_mask = [2.5, 5.0, 10.0, 20.0, 60.0]
beta_mask = [2.5, 5.0, 10.0, 20.0, 60.0]
# Add fits
color_counter = -1
for idx, t in enumerate(times):
if t in alpha_mask:
color_counter += 1
new_fit.add_trajectory(dra15uL,
t,
'plot',
alpha_palette[color_counter], (0, 0),
'Alpha',
label=str(t) + ' min',
linewidth=2)
new_fit.add_trajectory(dra5mL,
t,
'plot',
alpha_palette[color_counter], (1, 0),
'Alpha',
label=str(t) + ' min',
linewidth=2)
if t in beta_mask:
new_fit.add_trajectory(drb15uL,
t,
'plot',
beta_palette[color_counter], (0, 1),
'Beta',
label=str(t) + ' min',
linewidth=2)
new_fit.add_trajectory(drb5mL,
t,
'plot',
beta_palette[color_counter], (1, 1),
'Beta',
label=str(t) + ' min',
linewidth=2)
dr_fig, dr_axes = new_fit.show_figure()
dr_axes[0][0].set_title(r'IFN$\alpha$ at {} L'.format(volEC1))
dr_axes[0][1].set_title(r'IFN$\beta$ at {} L'.format(volEC1))
dr_axes[1][0].set_title(r'IFN$\alpha$ at {} L'.format(volEC2))
dr_axes[1][1].set_title(r'IFN$\beta$ at {} L'.format(volEC2))
dr_fig.tight_layout()
dr_fig.savefig('varying_reaction_volume_dr.pdf')
# -------------------------------
# Plot model dose response curves
# -------------------------------
tc_figure = TimecoursePlot((1, 2))
alpha_testdose = list(logspace(1, 5.2))[24]
beta_testdose = list(logspace(-1, 4))[17]
print("Using {} for IFNalpha".format(alpha_testdose))
print("Using {} for IFNbeta".format(beta_testdose))
tc_figure.add_trajectory(dra15uL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC1),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[1])
tc_figure.add_trajectory(dra5mL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC2),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[4])
tc_figure.add_trajectory(drb15uL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC1),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[0])
tc_figure.add_trajectory(drb5mL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC2),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[2])
tc_figure.show_figure(save_flag=True,
save_dir='varying_reaction_volume_tc.pdf')
# ------------------------------------
# Now let's quickly look at IFN levels
# ------------------------------------
# 1 uL
Mixed_Model.set_global_parameters({
'volEC': volEC1,
'ka1': ka1 * 1E-5 / volEC1,
'ka2': ka2 * 1E-5 / volEC1,
'k_a1': k_a1 * 1E-5 / volEC1,
'k_a2': k_a2 * 1E-5 / volEC1
})
dradf = Mixed_Model.mixed_dose_response(times,
'Free_Ia',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'Free_Ib',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra15uL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb15uL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# 1 mL
Mixed_Model.set_global_parameters({
'volEC': volEC2,
'ka1': ka1 * 1E-5 / volEC2,
'ka2': ka2 * 1E-5 / volEC2,
'k_a1': k_a1 * 1E-5 / volEC2,
'k_a2': k_a2 * 1E-5 / volEC2
})
dradf = Mixed_Model.mixed_dose_response(times,
'Free_Ia',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'Free_Ib',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra5mL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb5mL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
tc_figure = TimecoursePlot((1, 2))
tc_figure.add_trajectory(dra15uL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC1),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[1])
tc_figure.add_trajectory(dra5mL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC2),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[4])
tc_figure.add_trajectory(drb15uL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC1),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[0])
tc_figure.add_trajectory(drb5mL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC2),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[2])
for ax in tc_figure.axes.flatten():
ax.set_yscale('log')
ifn_fig, ifn_axes = tc_figure.show_figure()
ifn_axes[0].set_ylabel('IFN-alpha2')
ifn_axes[1].set_ylabel('IFN-beta')
ifn_fig.savefig('varying_reaction_volume_tc_IFN.pdf')
# Show SOCS effects
Mixed_Model.reset_parameters()
Mixed_Model.set_parameters({
'kIntBasal_r1': 0,
'kIntBasal_r2': 0,
'kint_a': 0,
'kint_b': 0
})
dra60_SOCS, drb60_SOCS = aggregate_response(Mixed_Model, DR_method,
MODEL_TYPE, R_samples)
# --------------------
# Make Plot
# --------------------
dr_plot = DoseresponsePlot((1, 1))
alpha_mask = []
beta_mask = []
for idx, t in enumerate([el for el in times]):
if t not in alpha_mask:
dr_plot.add_trajectory(dra60,
t,
'plot',
alpha_palette[5], (0, 0),
'Alpha',
linewidth=2.0)
dr_plot.add_trajectory(dra60_int,
t,
'plot',
'--', (0, 0),
'Alpha',
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# Plot primary predictions
alpha_palette = sns.color_palette("deep", 6)
beta_palette = sns.color_palette("deep", 6)
new_fit = DoseresponsePlot((1, 2))
for idx, t in enumerate(times):
new_fit.add_trajectory(dra60,
t,
'plot',
alpha_palette[idx], (0, 0),
'Alpha',
label=str(t) + ' min',
linewidth=2)
new_fit.add_trajectory(drb60,
t,
'plot',
beta_palette[idx], (0, 1),
'Beta',
label=str(t) + ' min',
linewidth=2)
'R1': 1200,
'R2': 4920},
return_type='dataframe', dataframe_labels='Alpha',
scale_factor=scale_factor * 0.2)
large_cells_beta = Mixed_Model.doseresponse(times, 'TotalpSTAT', 'Ib',
list(np.logspace(np.log10(doses_beta[0]), np.log10(doses_beta[-1]))),
parameters={'Ia': 0,
'R1': 1200,
'R2': 4920},
return_type='dataframe', dataframe_labels='Beta',
scale_factor=scale_factor * 0.2)
large_cells_alpha_IFNdata = IfnData('custom', df=large_cells_alpha, conditions={'Alpha': {'Ib': 0}})
large_cells_beta_IFNdata = IfnData('custom', df=large_cells_beta, conditions={'Beta': {'Ia': 0}})
# Plot
alpha_palette = sns.color_palette("Reds", 6)
beta_palette = sns.color_palette("Greens", 6)
dr_plot = DoseresponsePlot((1, 2))
# Add fits
dr_plot.add_trajectory(large_cells_alpha_IFNdata, 60.0, 'plot', alpha_palette[5], (0, 0), 'Alpha',
label='Large Cells at 60 minutes')
dr_plot.add_trajectory(small_cells_alpha_IFNdata, 60.0, 'plot', alpha_palette[2], (0, 0), 'Alpha',
label='Small Cells at 60 minutes')
dr_plot.add_trajectory(large_cells_beta_IFNdata, 60.0, 'plot', beta_palette[5], (0, 1), 'Beta',
label='Large Cells at 60 minutes')
dr_plot.add_trajectory(small_cells_beta_IFNdata, 60.0, 'plot', beta_palette[2], (0, 1), 'Beta',
label='Small Cells at 60 minutes')
dr_fig, dr_axes = dr_plot.show_figure(save_flag=False)
best_fit_model.model_1.set_parameters(best_mixed_params[0])
best_fit_model.model_2.set_parameters(best_mixed_params[1])
times = [5, 15, 30, 60]
alpha_response = best_fit_model.mixed_dose_response(times, 'TotalpSTAT', 'Ia',
np.logspace(1, 3), parameters={'Ib': 0}, sf=best_sf)
beta_response = best_fit_model.mixed_dose_response(times, 'TotalpSTAT', 'Ib',
np.logspace(np.log10(0.2), 4),
parameters={'Ia': 0}, sf=best_sf)"""
#total_response_df = pd.concat([alpha_response, beta_response])
total_response_df = pd.concat([dradf, drbdf])
total_response = IfnData('custom', df=total_response_df)
# Plot
new_fit = DoseresponsePlot((1, 2))
alpha_palette = sns.color_palette("deep", 6)
beta_palette = sns.color_palette("deep", 6)
alpha_mask = []
beta_mask = []
# Add fits
for idx, t in enumerate([el for el in times]):
if t not in alpha_mask:
new_fit.add_trajectory(total_response,
t,
'plot',
alpha_palette[idx], (0, 0),
'Alpha',
label='Alpha ' + str(t))
if t not in beta_mask:
new_fit.add_trajectory(total_response,
parameters={'Ia': 0},
scale_factor=scale_factor,
return_type='dataframe',
dataframe_labels='Beta')
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# ------------------------
# Plot
# ------------------------
alpha_mask = [2.5, 5.0, 7.5, 10.0]
beta_mask = [2.5, 5.0, 7.5, 10.0]
plot_original_fit = False
if plot_original_fit == True:
DRplot = DoseresponsePlot((1, 2))
for idx, t in enumerate(times):
if t not in alpha_mask:
DRplot.add_trajectory(dra60,
t,
'plot',
alpha_palette[idx], (0, 0),
'Alpha',
label=str(t) + ' min',
linewidth=2)
DRplot.add_trajectory(mean_data,
t,
'errorbar',
'o', (0, 0),
'Alpha',
color=alpha_palette[idx])
from ifnclass.ifnmodel import IfnModel
from ifnclass.ifnplot import DoseresponsePlot
from ifnclass.ifndata import IfnData
import numpy as np
from numpy import logspace
import matplotlib.pyplot as plt
import seaborn as sns
ImmuneCell_Model = IfnModel('Immune_Cell_model')
IFNg_doses = list(logspace(-1.5, 2, num=15))
times = [15, 30, 60]
fig = DoseresponsePlot((2, 2))
fig.axes[0][0].set_title('pSTAT1')
fig.axes[0][1].set_title('pSTAT3')
print("Dose-response 1")
IFNg_naive_res = ImmuneCell_Model.doseresponse(times, ['pSTAT1', 'pSTAT3'],
'IFN_gamma_IC',
IFNg_doses,
parameters={
'IFNAR1_IC': 0,
'IFNAR2_IC': 0,
'IFN_alpha2_IC': 0,
'IFN_beta_IC': 0,
'SOCS2_IC': 0
},
return_type='dataframe',
dataframe_labels='IFNgamma')
naive_IFNg_pSTAT1_df = IfnData(name='custom', df=IFNg_naive_res.xs(['pSTAT1']))
naive_IFNg_pSTAT3_df = IfnData(name='custom', df=IFNg_naive_res.xs(['pSTAT3']))
'R2': (volPM / volPM_typical) * 4920,
'S': (volCP / volCP_typical) * 1E4
},
return_type='list',
scale_factor=scale_factor)['TotalpSTAT'][-1]
normalized_response = response / ((volCP / volCP_typical) * 1E4)
beta_cell_size_curve.append(normalized_response)
# -------------------------------
# Plot cell size panel
# -------------------------------
alpha_palette = sns.color_palette("Reds", 8)
beta_palette = sns.color_palette("Greens", 8)
matplotlib.rcParams['xtick.labelsize'] = 12
matplotlib.rcParams['ytick.labelsize'] = 12
dr_fig = DoseresponsePlot((1, 2))
ax = dr_fig.axes[0]
xlabels = np.divide(radii, 1E-6)
ax.plot(xlabels,
alpha_cell_size_curve,
color=alpha_palette[-1],
label='Alpha',
linewidth=2)
ax.plot(xlabels,
beta_cell_size_curve,
color=beta_palette[-1],
label='Beta',
linewidth=2)
ax.legend()
ax.set_xlabel(r'Cell radius ($\mu$m)', fontsize=14)
ax.set_ylabel('pSTAT/STAT', fontsize=14)
small_alignment.get_scaled_data()
mean_small_data = small_alignment.summarize_data()
large_alignment = DataAlignment()
large_alignment.add_data([large_4, large_3, large_2, large_1])
large_alignment.align()
large_alignment.get_scaled_data()
mean_large_data = large_alignment.summarize_data()
# ------------
# Plot data
# ------------
alpha_palette = sns.color_palette("deep", 6)
beta_palette = sns.color_palette("deep", 6)
dr_plot = DoseresponsePlot((2, 2))
for r_idx, cell_size in enumerate([mean_small_data, mean_large_data]):
for c_idx, species in enumerate(['Alpha', 'Beta']):
for t_idx, time in enumerate(cell_size.get_times()[species]):
if species == 'Alpha':
c = alpha_palette[t_idx]
else:
c = beta_palette[t_idx]
dr_plot.add_trajectory(cell_size, time, 'errorbar', 'o--', (r_idx, c_idx), species,
label=str(time)+" min", color=c)
dr_plot.axes[0][0].set_title(r'IFN$\alpha$')
dr_plot.axes[0][0].set_ylabel('Small cell response')
dr_plot.axes[0][1].set_title(r'IFN$\beta$')
dr_plot.axes[1][0].set_ylabel('Large cell response')
for ax in [item for sublist in dr_plot.axes for item in sublist]:
list(logspace(-2, 4)),
parameters={'Ia': 0},
return_type='dataframe',
dataframe_labels='Beta')
for i in range(len(times)):
dradf.loc['Alpha'].iloc[:, i] = dradf.loc['Alpha'].iloc[:, i].apply(
scale_data)
drbdf.loc['Beta'].iloc[:,
i] = drbdf.loc['Beta'].iloc[:,
i].apply(scale_data)
dra = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
dose_response_plot = DoseresponsePlot((3, 2))
alpha_mask = []
beta_mask = []
# Add fits
for idx, t in enumerate([str(el) for el in times]):
if t not in [str(el) for el in alpha_mask]:
dose_response_plot.add_trajectory(dra,
t,
'plot',
alpha_palette[idx], (0, 0),
'Alpha',
label='Alpha ' + t,
linewidth=2.0)
if t not in [str(el) for el in beta_mask]:
dose_response_plot.add_trajectory(drb,
t,
from ifnclass.ifnfit import DualMixedPopulation
from numpy import linspace, logspace, transpose
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os
from ifnclass.ifnplot import Trajectory, DoseresponsePlot
import pandas as pd
from sys import platform
if __name__ == '__main__':
# ----------------------
# Set up figure layout
# ----------------------
spec_fig = DoseresponsePlot((3, 2))
spec_fig.fig.set_size_inches(12, 8)
# ---------------------------
# Set up data for comparison
# ---------------------------
alpha_palette = sns.color_palette("Reds", 6)
beta_palette = sns.color_palette("Greens", 6)
data_palette = sns.color_palette("muted", 6)
marker_shape = ["o", "v", "s", "P", "d", "1", "x", "*"]
dataset_names = ["20190108", "20190119", "20190121", "20190214"]
newdata_1 = IfnData("20190108_pSTAT1_IFN_Bcell")
newdata_2 = IfnData("20190119_pSTAT1_IFN_Bcell")
newdata_3 = IfnData("20190121_pSTAT1_IFN_Bcell")
newdata_4 = IfnData("20190214_pSTAT1_IFN_Bcell")
# Add equilibrium expressions
# max_response_axes[0].plot(time_list,
# [avg_pSTAT_Max_Alpha for _ in time_list],
# '--', color=alpha_palette[5], linewidth=2)
# max_response_axes[1].plot(time_list,
# [avg_pSTAT_Max_Beta for _ in time_list],
# '--', color=alpha_palette[5], linewidth=2)
# -------------------------------
# Plot model dose response curves
# -------------------------------
alpha_palette = sns.color_palette("rocket_r", 6)
beta_palette = sns.color_palette("rocket_r", 6)
new_fit = DoseresponsePlot((1, 2))
new_fit.axes = [
Figure_2.add_subplot(gs[0, 0:2]),
Figure_2.add_subplot(gs[0, 2:4])
]
new_fit.axes[0].set_xscale('log')
new_fit.axes[0].set_xlabel('Dose (pM)')
new_fit.axes[0].set_ylabel('pSTAT (MFI)')
new_fit.axes[1].set_xscale('log')
new_fit.axes[1].set_xlabel('Dose (pM)')
new_fit.axes[1].set_ylabel('pSTAT (MFI)')
new_fit.fig = Figure_2
alpha_mask = [7.5, 10.0]
beta_mask = [7.5, 10.0]
# Add fits
newdata_2 = IfnData("20190119_pSTAT1_IFN_Bcell")
newdata_3 = IfnData("20190121_pSTAT1_IFN_Bcell")
newdata_4 = IfnData("20190214_pSTAT1_IFN_Bcell")
# Aligned data, to get scale factors for each data set
alignment = DataAlignment()
alignment.add_data([newdata_4, newdata_3, newdata_2, newdata_1])
alignment.align()
alignment.get_scaled_data()
mean_data = alignment.summarize_data()
# Plot posterior samples
alpha_palette = sns.color_palette("deep", 6)
beta_palette = sns.color_palette("deep", 6)
new_fit = DoseresponsePlot((1, 2))
alpha_mask = [2.5, 7.5, 10.0] #[2.5, 5.0, 7.5, 10.0, 20.0, 60.0]
beta_mask = [2.5, 7.5, 10.0]
plot_data = True
# Add fits
for idx, t in enumerate(times):
if t not in alpha_mask:
new_fit.add_trajectory(mean_model,
t,
'envelope',
alpha_palette[idx], (0, 0),
'Alpha',
label='{} min'.format(t),
linewidth=2,
alpha=0.2)
'small_alignment', os.path.join(os.getcwd(), 'small_alignment'))
large_alignment = DataAlignment()
large_alignment.load_from_save_file(
'large_alignment', os.path.join(os.getcwd(), 'large_alignment'))
small_alignment.align()
small_alignment.get_scaled_data()
mean_small_data = small_alignment.summarize_data()
large_alignment.align()
large_alignment.get_scaled_data()
mean_large_data = large_alignment.summarize_data()
# ----------------------
# Set up Figure layout
# ----------------------
# Set up dose response figures
new_fit = DoseresponsePlot((1, 2), figsize=(9, 1.5 * 2.5))
new_fit.axes[0].set_ylabel('pSTAT1 (MFI)')
# new_fit.axes[1].set_ylabel('pSTAT1 (MFI)')
# Plot Dose respsonse data
times = [2.5, 5.0, 7.5, 10.0, 20.0, 60.0]
color_palette = sns.color_palette("rocket_r",
6) # sns.color_palette("Paired", 10)
alpha_mask = [2.5, 5.0, 7.5, 20.0]
beta_mask = [2.5, 5.0, 7.5, 20.0]
for idx, t in enumerate([10.0, 60.0]):
if t == 10.0:
new_fit.add_trajectory(mean_large_data,
t,
'errorbar',
'v', (0, 0),
response_species,
dose_species,
Epo_doses,
parameters={n: 0
for n in IC_names if n != dose_species},
return_type='dataframe',
dataframe_labels=dose_species,
scale_factor=100.0 / model.parameters['EpoR_IC']),
conditions={})
# -------------------------------
# Plot model dose response curves
# -------------------------------
palette = sns.color_palette("muted")
Epo_plot = DoseresponsePlot((1, 1))
# Add data
Epo_plot.add_trajectory(Moraga_data,
60,
'errorbar',
'o', (0, 0),
'T_Epo',
label='EPO',
color=palette[1])
Epo_plot.add_trajectory(Moraga_data,
60,
'errorbar',
'o', (0, 0),
'EMP_1',
label='EMP-1',
color=palette[2])
def check_plots():
smooth_plot = DoseresponsePlot((2, 2))
alpha_palette = sns.color_palette("Reds", 6)
beta_palette = sns.color_palette("Greens", 6)
smooth_plot.add_trajectory(a25smoothIfnData, 2.5, 'plot', alpha_palette[4],
(0, 0), 'Alpha')
smooth_plot.add_trajectory(a60smoothIfnData, 60, 'plot', alpha_palette[5],
(0, 0), 'Alpha')
smooth_plot.add_trajectory(a25smoothIfnData, 2.5, 'plot', alpha_palette[0],
(1, 0), 'Alpha')
smooth_plot.add_trajectory(a5smoothIfnData, 5, 'plot', alpha_palette[1],
(1, 0), 'Alpha')
smooth_plot.add_trajectory(a75smoothIfnData, 7.5, 'plot', alpha_palette[2],
(1, 0), 'Alpha')
smooth_plot.add_trajectory(a10smoothIfnData, 10, 'plot', alpha_palette[3],
(1, 0), 'Alpha')
smooth_plot.add_trajectory(a20smoothIfnData, 20, 'plot', alpha_palette[4],
(1, 0), 'Alpha')
smooth_plot.add_trajectory(a60smoothIfnData, 60, 'plot', alpha_palette[5],
(1, 0), 'Alpha')
smooth_plot.add_trajectory(b25smoothIfnData, 2.5, 'plot', beta_palette[4],
(0, 1), 'Beta')
smooth_plot.add_trajectory(b60smoothIfnData, 60, 'plot', beta_palette[5],
(0, 1), 'Beta')
smooth_plot.add_trajectory(b25smoothIfnData, 2.5, 'plot', beta_palette[0],
(1, 1), 'Beta')
smooth_plot.add_trajectory(b5smoothIfnData, 5, 'plot', beta_palette[1],
(1, 1), 'Beta')
smooth_plot.add_trajectory(b75smoothIfnData, 7.5, 'plot', beta_palette[2],
(1, 1), 'Beta')
smooth_plot.add_trajectory(b10smoothIfnData, 10, 'plot', beta_palette[3],
(1, 1), 'Beta')
smooth_plot.add_trajectory(b20smoothIfnData, 20, 'plot', beta_palette[4],
(1, 1), 'Beta')
smooth_plot.add_trajectory(b60smoothIfnData, 60, 'plot', beta_palette[5],
(1, 1), 'Beta')
for idx, t in enumerate([2.5, 60]):
smooth_plot.add_trajectory(newdata,
t,
'scatter',
alpha_palette[idx * 2], (0, 0),
'Alpha',
dn=1)
smooth_plot.add_trajectory(newdata,
t,
'scatter',
beta_palette[idx * 2], (0, 1),
'Beta',
dn=1)
fig, axes = smooth_plot.show_figure()
list(logspace(-2, 4)),
parameters={'Ia': 0},
return_type='dataframe',
dataframe_labels='Beta')
for i in range(len(times)):
dradf.loc['Alpha'].iloc[:, i] = dradf.loc['Alpha'].iloc[:, i].apply(
scale_data)
drbdf.loc['Beta'].iloc[:,
i] = drbdf.loc['Beta'].iloc[:,
i].apply(scale_data)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
new_fit = DoseresponsePlot((1, 2))
alpha_mask = [2.5, 7.5]
beta_mask = [2.5, 7.5]
# Add fits
for idx, t in enumerate([str(el) for el in times]):
if t not in [str(el) for el in alpha_mask]:
new_fit.add_trajectory(dra60,
t,
'plot',
alpha_palette[idx], (0, 0),
'Alpha',
label='Alpha ' + t)
if t not in [str(el) for el in beta_mask]:
new_fit.add_trajectory(drb60,
t,
'plot',
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
dra60_int = IfnData('custom',
df=dradf_int,
conditions={'Alpha': {
'Ib': 0
}})
drb60_int = IfnData('custom', df=drbdf_int, conditions={'Beta': {'Ia': 0}})
dra60_rec = IfnData('custom',
df=dradf_rec,
conditions={'Alpha': {
'Ib': 0
}})
drb60_rec = IfnData('custom', df=drbdf_rec, conditions={'Beta': {'Ia': 0}})
dr_plot = DoseresponsePlot((3, 2))
alpha_mask = []
beta_mask = []
# Add fits
for idx, t in enumerate([el for el in times]):
if t not in alpha_mask:
dr_plot.add_trajectory(dra60,
t,
'plot',
alpha_palette[idx], (0, 0),
'Alpha',
label='Alpha ' + str(t),
linewidth=2.0)
dr_plot.add_trajectory(dra60_int,
t,
'plot',
list(logspace(1, log10(11000))),
parameters={'Ia': 0},
return_type='dataframe',
dataframe_labels='Beta')
for i in range(len(times)):
dradf.loc['Alpha'].iloc[:, i] = dradf.loc['Alpha'].iloc[:, i].apply(
scale_data)
drbdf.loc['Beta'].iloc[:,
i] = drbdf.loc['Beta'].iloc[:,
i].apply(scale_data)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
new_fit = DoseresponsePlot((1, 2))
alpha_mask = []
beta_mask = []
# Add fits
for idx, t in enumerate([el for el in times]):
if t not in alpha_mask:
new_fit.add_trajectory(dra60,
t,
'plot',
alpha_palette[idx], (0, 0),
'Alpha',
label='Alpha ' + str(t))
if t not in beta_mask:
new_fit.add_trajectory(drb60,
t,
'plot',
#Mixed_Model.set_parameters({'kSOCS': Mixed_Model.parameters['kSOCS'] * 2.5})
dradf_rec = Mixed_Model.doseresponse(times, 'TotalpSTAT', 'Ia', list(logspace(-2, 8)),
parameters={'Ib': 0}, return_type='dataframe', dataframe_labels='Alpha',
scale_factor = scale_factor)
drbdf_rec = Mixed_Model.doseresponse(times, 'TotalpSTAT', 'Ib', list(logspace(-2, 8)),
parameters={'Ia': 0}, return_type='dataframe', dataframe_labels='Beta',
scale_factor=scale_factor)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
dra60_int = IfnData('custom', df=dradf_int, conditions={'Alpha': {'Ib': 0}})
drb60_int = IfnData('custom', df=drbdf_int, conditions={'Beta': {'Ia': 0}})
dra60_rec = IfnData('custom', df=dradf_rec, conditions={'Alpha': {'Ib': 0}})
drb60_rec = IfnData('custom', df=drbdf_rec, conditions={'Beta': {'Ia': 0}})
dr_plot = DoseresponsePlot((1, 1))
alpha_mask = []
beta_mask = []
# Add fits
for idx, t in enumerate([el for el in times]):
if t not in alpha_mask:
dr_plot.add_trajectory(dra60, t, 'plot', alpha_palette[5], (0, 0), 'Alpha', label='Alpha - No Feedback', linewidth=2.0)
dr_plot.add_trajectory(dra60_int, t, 'plot', '--', (0, 0), 'Alpha', color=alpha_palette[3],
label='Internalization only', linewidth=2.0, )
dr_plot.add_trajectory(dra60_rec, t, 'plot', ':', (0, 0), 'Alpha', color=alpha_palette[1], label='SOCS only', linewidth=2.0)
if t not in beta_mask:
dr_plot.add_trajectory(drb60, t, 'plot', beta_palette[5], (0, 0), 'Beta', label='Beta - No Feedback', linewidth=2.0)
dr_plot.add_trajectory(drb60_int, t, 'plot', '--', (0, 0), 'Beta', color=beta_palette[3],
label='Internalization only', linewidth=2.0)
dr_plot.add_trajectory(drb60_rec, t, 'plot', ':', (0, 0), 'Beta', color=beta_palette[1], label='SOCS only', linewidth=2.0)
dr_plot.fig.suptitle('Internalization vs SOCS')
def plot_posterior(param_file, parameter_names, num_checks, model, posterior,
sf, time_mask, save_dir, plot_data=True):
""" Plot the predictions from ensemble sampling of model parameters.
Plots the dose-response as an envelope containing 1 sigma of predictions.
"""
# Preparation of parameter ensemble
if type(param_file) == str:
log10_parameters = np.load(param_file)
elif type(param_file) == list:
log10_parameters = np.load(param_file[0])
for f in param_file[1:]:
batch_params = np.load(f)
log10_parameters = np.append(log10_parameters, batch_params,
axis=0)
parameters = np.power(10, log10_parameters)
parameters_to_check = [parameters[i] for i in
list(np.random.randint(0, high=len(parameters),
size=num_checks))]
# Compute posterior sample trajectories
posterior_trajectories = []
for p in parameters_to_check:
param_dict = {key: value for key, value in zip(parameter_names, p)}
pp = posterior_prediction(model, param_dict, parameter_names, sf)
posterior_trajectories.append(pp)
# Make aggregate predicitions
mean_alpha, std_alpha, mean_beta, std_beta = \
posterior_IFN_summary_statistics(posterior_trajectories)
std_predictions = {'Alpha': std_alpha,
'Beta': std_beta}
mean_predictions = {'Alpha': mean_alpha,
'Beta': mean_beta}
mean_model = copy.deepcopy(posterior_trajectories[0])
for s in ['Alpha', 'Beta']:
for didx, d in enumerate(mean_model.get_doses()[s]):
for tidx, t in enumerate(mean_model.get_times()[s]):
mean_model.data_set.loc[s][str(t)].loc[d] =\
(mean_predictions[s][didx][tidx],
std_predictions[s][didx][tidx])
# Get aligned data
mean_data = posterior.experiment
# Plot posterior samples
alpha_palette = sns.color_palette("rocket_r", 6)
beta_palette = sns.color_palette("rocket_r", 6)
new_fit = DoseresponsePlot((1, 2))
# Add fits
for idx, t in enumerate([2.5, 5.0, 7.5, 10.0, 20.0, 60.0]):
if t not in time_mask:
new_fit.add_trajectory(mean_model, t, 'envelope',
alpha_palette[idx], (0, 0), 'Alpha',
label='{} min'.format(t),
linewidth=2, alpha=0.2)
if plot_data:
new_fit.add_trajectory(mean_data, t, 'errorbar', 'o',
(0, 0), 'Alpha',
color=alpha_palette[idx])
if t not in time_mask:
new_fit.add_trajectory(mean_model, t, 'envelope',
beta_palette[idx], (0, 1), 'Beta',
label='{} min'.format(t),
linewidth=2, alpha=0.2)
if plot_data:
new_fit.add_trajectory(mean_data, t, 'errorbar', 'o',
(0, 1), 'Beta',
color=beta_palette[idx])
# Change legend transparency
leg = new_fit.fig.legend()
for lh in leg.legendHandles:
lh._legmarker.set_alpha(1)
dr_fig, dr_axes = new_fit.show_figure()
dr_fig.set_size_inches(14, 6)
dr_axes[0].set_title(r'IFN$\alpha$')
dr_axes[1].set_title(r'IFN$\beta$')
if plot_data:
dr_fig.savefig(os.path.join(save_dir,
'posterior_predictions_with_data.pdf'))
else:
dr_fig.savefig(os.path.join(save_dir,
'posterior_predictions.pdf'))
drbdf.loc['Beta'].iloc[:,
i] = drbdf.loc['Beta'].iloc[:,
i].apply(scale_data)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# -------------------------
# Plot mean trajectory
# -------------------------
alpha_palette = sns.color_palette("Reds", 1)
alpha_model_palette = sns.color_palette("Greys", 1)
beta_palette = sns.color_palette("Greens", 1)
beta_model_palette = sns.color_palette("Greys", 1)
dr_plot = DoseresponsePlot((6, 2))
for idx, t in enumerate(newdata_4.get_times('Alpha')):
dr_plot.add_trajectory(mean_data,
t,
'errorbar',
'o--', (idx, 0),
'Alpha',
label='',
color=alpha_palette[0])
for idx, t in enumerate(newdata_4.get_times('Beta')):
dr_plot.add_trajectory(mean_data,
t,
'errorbar',
'o--', (idx, 1),
'Beta',
label='',
# -------------------------------
# Plot alignment of Dose Response
# -------------------------------
species = alignment.scaled_data[0].get_dose_species()
# Make subtitles
recorded_times = alignment.scaled_data[0].get_times()[species[0]]
titles = alignment.scaled_data[0].get_dose_species()
titles[0] += "\r\n\r\n{} min".format(recorded_times[0])
titles[1] += "\r\n\r\n{} min".format(recorded_times[0])
for t in recorded_times[1:]:
for s in species:
titles.append(str(t) + ' min')
num_times = len(alignment.scaled_data[0].get_times()[species[0]])
clist = ["Reds", "Greens", "Blues", "Purples", "Oranges", "Greys"]
dr_plot = DoseresponsePlot((num_times, len(species)))
for i in range(num_times):
for j in range(len(species)):
dr_plot.axes[i][j].set_title(titles[i * len(species) + j])
# Add fits
for d_idx, dataset in enumerate(alignment.scaled_data):
colour_palette = sns.color_palette(clist[d_idx], 1)
for ax_idx, spec in enumerate(dataset.get_dose_species()):
for idx, t in enumerate(
alignment.scaled_data[0].get_times()[spec]):
spec_data = pd.concat([dataset.data_set.loc[spec]],
keys=[spec],
names=['Dose_Species'])
spec_IfnData = IfnData('custom',
df=spec_data,
