def quick_model(protein_data, buffer_data,name):
reorder_protein = reorder2list(protein_data,well)
reorder_buffer = reorder2list(buffer_data,well)
print name
from assaytools import pymcmodels
pymc_model = pymcmodels.make_model(Pstated, dPstated, Lstated, dLstated,
top_complex_fluorescence=reorder_protein,
top_ligand_fluorescence=reorder_buffer,
use_primary_inner_filter_correction=True,
use_secondary_inner_filter_correction=True,
assay_volume=assay_volume, DG_prior='uniform')
mcmc = pymcmodels.run_mcmc(pymc_model)
from assaytools import plots
figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc)
map = pymcmodels.map_fit(pymc_model)
pymcmodels.show_summary(pymc_model, map, mcmc)
DeltaG = map.DeltaG.value
np.save('DeltaG_%s.npy'%name,DeltaG)
np.save('DeltaG_trace_%s.npy'%name,mcmc.DeltaG.trace())
def quick_model(inputs):
xml_files = glob("%s/*.xml" % inputs['xml_file_path'])
print(xml_files)
for my_file in xml_files:
file_name = os.path.splitext(my_file)[0]
print(file_name)
data = platereader.read_icontrol_xml(my_file)
for i in range(0,15,2):
protein_row = ALPHABET[i]
buffer_row = ALPHABET[i+1]
name = "%s-%s%s"%(inputs['ligand_order'][i/2],protein_row,buffer_row)
print(name)
metadata = {}
metadata = dict(inputs)
try:
part1_data_protein = platereader.select_data(data, inputs['section'], protein_row)
part1_data_buffer = platereader.select_data(data, inputs['section'], buffer_row)
except:
continue
reorder_protein = reorder2list(part1_data_protein,well)
reorder_buffer = reorder2list(part1_data_buffer,well)
#these need to be changed so they are TAKEN FROM INPUTS!!!
# Uncertainties in protein and ligand concentrations.
dPstated = 0.35 * inputs['Pstated'] # protein concentration uncertainty
dLstated = 0.08 * inputs['Lstated'] # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)
from assaytools import pymcmodels
pymc_model = pymcmodels.make_model(inputs['Pstated'], dPstated, inputs['Lstated'], dLstated,
top_complex_fluorescence=reorder_protein,
top_ligand_fluorescence=reorder_buffer,
use_primary_inner_filter_correction=True,
use_secondary_inner_filter_correction=True,
assay_volume=inputs['assay_volume'], DG_prior='uniform')
import datetime
my_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
my_datetime_filename = datetime.datetime.now().strftime("%Y-%m-%d %H%M")
mcmc = pymcmodels.run_mcmc(pymc_model, db = 'pickle', dbname = '%s_mcmc-%s.pickle'%(name,my_datetime))
map = pymcmodels.map_fit(pymc_model)
pymcmodels.show_summary(pymc_model, map, mcmc)
DeltaG_map = map.DeltaG.value
DeltaG = mcmc.DeltaG.trace().mean()
dDeltaG = mcmc.DeltaG.trace().std()
## PLOT MODEL
#from assaytools import plots
#figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc)
#Code below inspired by import above, but didn't quite work to import it...
plt.clf()
plt.subplot(211)
property_name = 'top_complex_fluorescence'
complex = getattr(pymc_model, property_name)
plt.semilogx(inputs['Lstated'], complex.value, 'ko',label='complex')
property_name = 'top_ligand_fluorescence'
ligand = getattr(pymc_model, property_name)
plt.semilogx(inputs['Lstated'], ligand.value, 'ro',label='ligand')
for top_complex_fluorescence_model in mcmc.top_complex_fluorescence_model.trace()[::10]:
plt.semilogx(inputs['Lstated'], top_complex_fluorescence_model, 'k:')
for top_ligand_fluorescence_model in mcmc.top_ligand_fluorescence_model.trace()[::10]:
plt.semilogx(inputs['Lstated'], top_ligand_fluorescence_model, 'r:')
plt.xlabel('$[L]_T$ (M)');
plt.ylabel('fluorescence units');
plt.legend(loc=0);
## PLOT TRACE
plt.subplot(212)
plt.hist(mcmc.DeltaG.trace(), 40, alpha=0.75, label="DeltaG = %.1f +- %.1f kT"%(DeltaG, dDeltaG))
plt.axvline(x=DeltaG,color='blue')
plt.axvline(x=DeltaG_map,color='black',linestyle='dashed',label="MAP = %.1f"%DeltaG_map)
plt.legend(loc=0)
plt.xlabel('$\Delta G$ ($k_B T$)');
plt.ylabel('$P(\Delta G)$');
plt.xlim(-35,-10)
plt.suptitle("%s: %s" % (name, my_datetime))
plt.tight_layout()
fig1 = plt.gcf()
fig1.savefig('delG_%s-%s.png'%(name, my_datetime_filename))
np.save('DeltaG_%s-%s.npy'%(name, my_datetime_filename),DeltaG)
np.save('DeltaG_trace_%s-%s.npy'%(name, my_datetime_filename),mcmc.DeltaG.trace())
Kd = np.exp(mcmc.DeltaG.trace().mean())
dKd = np.exp(mcmc.DeltaG.trace()).std()
if (Kd < 1e-12):
Kd_summary = "%.1f nM +- %.1f fM" % (Kd/1e-15, dKd/1e-15)
elif (Kd < 1e-9):
Kd_summary = "%.1f pM +- %.1f pM" % (Kd/1e-12, dKd/1e-12)
elif (Kd < 1e-6):
Kd_summary = "%.1f nM +- %.1f nM" % (Kd/1e-9, dKd/1e-9)
elif (Kd < 1e-3):
Kd_summary = "%.1f uM +- %.1f uM" % (Kd/1e-6, dKd/1e-6)
elif (Kd < 1):
Kd_summary = "%.1f mM +- %.1f mM" % (Kd/1e-3, dKd/1e-3)
else:
Kd_summary = "%.3e M +- %.3e M" % (Kd, dKd)
outputs = {
'raw_data_file' : my_file,
'name' : name,
'analysis' : 'pymcmodels', #right now this is hardcoded, BOOO
'outfiles' : '%s_mcmc-%s.pickle, delG_%s-%s.png,DeltaG_%s-%s.npy,DeltaG_trace_%s-%s.npy'%(name,my_datetime,name,my_datetime,name,my_datetime,name,my_datetime),
'DeltaG' : "DeltaG = %.1f +- %.1f kT, MAP estimate = %.1f" % (DeltaG, dDeltaG, DeltaG_map),
'Kd' : Kd_summary,
'datetime' : my_datetime
}
metadata.update(outputs)
metadata['Pstated'] = metadata['Pstated'].tolist()
metadata['Lstated'] = metadata['Lstated'].tolist()
with open('%s-%s.json'%(name,my_datetime), 'w') as outfile:
json.dump(metadata, outfile, sort_keys = True, indent = 4, ensure_ascii=False)