'this work\n(correction applied\nto all cells)', 'Garcia & Phillips\n2011',
'Brewster et al.\n 2014', 'Razo-Mejia et al.\n 2018'
])
ax[1].set_xlabel('repressors per cell')
ax[1].set_ylabel('fold-change')
# Plot the "gold standard" binding energy.
ax[0].fill_betweenx([-1, 10],
epRA_low,
epRA_high,
color=colors['black'],
alpha=0.25,
zorder=1)
# Plot the inferred DNA binding energies.
for g, d in stats.groupby('source'):
if g == 'no_correction':
alpha = 0.25
else:
alpha = 1
epRA_median, low, high = d[d['parameter'] == 'epRA'][[
'median', 'hpd_min', 'hpd_max'
]].values[0]
ax[0].hlines(position[g],
low,
high,
lw=1,
color=edge_colors[g],
alpha=alpha,
zorder=99)
ax[0].plot(epRA_median,
gamma = mwc.bayes.Jeffreys('gamma', lower=1E-9, upper=100)
# Compute the expected value.
time = data['elapsed_time_min'].values
mu = np.log(a0) + time * lam
# Define the likelihood and sample.
like = pm.Cauchy('like', mu, gamma, observed=np.log(data['OD_600'].values))
trace = pm.sample(tune=5000, draws=5000)
trace_df = mwc.stats.trace_to_dataframe(trace, model)
stats = mwc.stats.compute_statistics(trace_df)
# %% Compute the best fit and credible region
modes = {}
hpds = {}
grouped = stats.groupby('parameter')
for g, d in grouped:
modes[g] = d['mode'].values[0]
hpds[g] = [d[['hpd_min', 'hpd_max']].values]
time_range = np.linspace(data['elapsed_time_min'].min(),
data['elapsed_time_min'].max(), 500)
best_fit = modes['a0'] * np.exp(modes['lambda'] * time_range)
cred_region = np.zeros((2, len(time_range)))
for i, t in enumerate(time_range):
theo = trace_df['a0'] * np.exp(t * trace_df['lambda'])
cred_region[:, i] = mwc.stats.compute_hpd(theo, mass_frac=0.95)
# %% Compute the doubling time.
t_double = np.log(2) / modes['lambda']
# Load the plates and statistics
plates = pd.read_csv('../../data/compiled_growth_plates.csv', comment='#')
stats = pd.read_csv('../../data/compiled_growth_statistics.csv', comment="#")
plates['temp_C'] = np.round(plates['temp_C'])
plates['time_min'] = np.round(plates['time_min'])
plates = plates[plates['time_min'] <= 600]
#%%
# reform the stats
stats = stats[((stats['carbon'] == 'glucose') | (stats['carbon'] == 'acetate')
| (stats['carbon'] == 'glycerol'))
& ((stats['temp'] == 37) | (stats['temp'] == 32)
| (stats['temp'] == 42))]
tidy_stats = pd.DataFrame([])
for g, d in stats.groupby(['date', 'carbon', 'temp', 'run_number']):
growth_rate = d[d['parameter'] == 'max df']['value'].values[0]
growth_err = d[d['parameter'] == 'max df std']['value'].values[0]
dbl_time = d[d['parameter'] == 'inverse max df']['value'].values[0]
dbl_err = d[d['parameter'] == 'inverse max df std']['value'].values[0]
tidy_stats = tidy_stats.append(
{
'date': g[0],
'carbon': g[1],
'temp_C': g[2],
'run_number': g[3],
'growth_rate': growth_rate,
'dbl_time': dbl_time,
'growth_err': growth_err,
'dbl_err': dbl_err