p_arr = np.append(p_arr, [[po2, ptest]], axis=0)
#When T- is severely oxygen limited
parm_name = 'p_o2-p_test'
parm_name_array = ['p_o2', 'p_test']
post_path = 'Tneg-o2limited_'
cf.timeseries(pre_path=pre_path,
parm_name=parm_name,
parm_array=p_arr,
parm_format=parm_format,
plot_Tpos=False,
post_path=post_path)
df = cf.eq_values(pre_path=pre_path,
parm_name=parm_name,
parm_array=p_arr,
parm_format=parm_format,
parm_name_array=parm_name_array,
post_path=post_path)
df = cf.cell_eq_ratio(df, 'Tneg', 'Tpro')
cf.plot_2parm(df=df,
pre_path=pre_path,
parm_name=parm_name,
post_path=post_path,
pri_parm=parm_name_array[0],
sec_parm=parm_name_array[1],
plot_y='Tneg_ratio')
# Looking closely between the phase transition, p_test = 1e-6
p_arr = np.linspace(0.0675, 0.085, 10)
parm_name = 'p_o2'
cf.timeseries(pre_path=pre_path,
parm_name_array = np.array(['l_lim_o2Tneg', 'u_lim_o2Tneg'])
## OG Equation
pre_path = 'EnvEq/singlecelltype/Tneg/'
cf.mkdirs(pre_path=pre_path, parm_name=parm_name)
### celleq=1E4: rho s.t T- at equilibrium is 10^4
cf.timeseries(pre_path=pre_path,
parm_name=parm_name,
parm_array=o2_lim_arr,
parm_format=parm_format,
plot_Tpos=False,
plot_Tpro=False,
plot_test=False)
df = cf.eq_values(pre_path=pre_path,
parm_name=parm_name,
parm_array=o2_lim_arr,
parm_format=parm_format,
parm_name_array=parm_name_array)
df['l_lim_o2Tneg'] = df['l_lim_o2Tneg'].round(1)
df['u_lim_o2Tneg'] = df['u_lim_o2Tneg'].round(1)
cf.heatmap_eqvparm(df,
pre_path=pre_path,
parm_name=parm_name,
parm_name_array=parm_name_array,
plot_Tpos=False,
plot_Tpro=False,
plot_test=False)
# Tp
## OG Equation
parm_name = 'l_lim_o2Tpro-u_lim_o2Tpro'
])
###
for scenario in scenarios:
for p_min in p_min_arr:
post_path = scenario + 'p={:.1e}-'.format(p_min)
cf.timeseries(pre_path=pre_path,
parm_name=parm_name,
parm_array=parms_array,
parm_format=parm_format,
post_path=post_path,
plot_tot=True)
df = cf.eq_values(pre_path=pre_path,
parm_name=parm_name,
parm_array=parms_array,
parm_format=parm_format,
post_path=post_path,
ttp=True,
limit=9000)
## High o2 efficiency, High test efficiency
parm_name = 'o2-HE_test-HE'
cf.mkdirs(pre_path=pre_path, parm_name=parm_name)
scenarios = np.array([
'', ### Tp:T+:T- 1:1:1 x 666 (total ~2000)
'0.8Tp-', ### Tp:T+:T- 8:1:1 x 200 (total 2000)
])
for scenario in scenarios:
for p_min in p_min_arr:
post_path = scenario + 'p={:.1e}-'.format(p_min)
#Input parms
p_o2_arr = np.linspace(0.1, 0.2, 20)
parm_name = 'p_o2'
parm_format = '{:.2E}'
parm_unit = '(prop/min)'
## OG Eq
pre_path = 'EnvEq/singlecelltype/Tneg/'
cf.mkdirs(pre_path=pre_path, parm_name=parm_name)
### celleq=1E4: rho s.t T- at equilibrium is 10^4
cf.timeseries(pre_path=pre_path,
parm_name=parm_name,
parm_array=p_o2_arr,
parm_format=parm_format,
plot_Tpos=False,
plot_Tpro=False,
plot_test=False)
df = cf.eq_values(pre_path=pre_path,
parm_name=parm_name,
parm_array=p_o2_arr,
parm_format=parm_format)
cf.eqvparm(df,
pre_path=pre_path,
parm_name=parm_name,
parm_unit=parm_unit,
plot_Tpos=False,
plot_Tpro=False,
plot_test=False)
