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, nsamples=1000, nthin=20):
"""
Quick model for both spectra and single wavelength experiments
Parameters
----------
inputs : dict
Dictionary of input information
nsamples : int, optional, default=1000
Number of MCMC samples to collect
nthin : int, optional, default=20
Thinning interval ; number of MCMC steps per sample collected
"""
[complex_fluorescence, ligand_fluorescence] = parser.get_data_using_inputs(inputs)
for name in complex_fluorescence.keys():
print(name)
metadata = {}
metadata = dict(inputs)
#these need to be changed so they are TAKEN FROM INPUTS!!!
# Uncertainties in protein and ligand concentrations.
try:
dPstated = inputs['P_error'] * inputs['Pstated'] # protein concentration uncertainty
except:
dPstated = 0.35 * inputs['Pstated'] # protein concentration uncertainty
try:
dLstated = inputs['L_error'] * inputs['Lstated']
except:
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=complex_fluorescence[name],
top_ligand_fluorescence=ligand_fluorescence[name],
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")
nburn = 0 # no longer need burn-in since we do automated equilibration detection
niter = nthin*nsamples # former total simulation time
# Note that nsamples and niter are not the same: Here nsamples is
# multiplied by nthin (default 20) so that your final mcmc samples will be the same
# as your nsamples, but the actual niter will be 20x that!
mcmc = pymcmodels.run_mcmc(pymc_model,
nthin=nthin, nburn=nburn, niter=niter,
db = 'pickle', dbname = '%s_mcmc-%s.pickle'%(name,my_datetime))
map = pymcmodels.map_fit(pymc_model)
# Save the trace for easy plotting later
DeltaG_map = map.DeltaG.value
DeltaG = mcmc.trace('DeltaG')[:].mean()
dDeltaG = mcmc.trace('DeltaG')[:].std()
## DEFINE EQUILIBRATION
#Calculate a mean and std from DeltaG trace after equil
[t,g,Neff_max] = pymbar.timeseries.detectEquilibration(mcmc.trace('DeltaG')[:])
DeltaG_equil = mcmc.trace('DeltaG')[t:].mean()
dDeltaG_equil = mcmc.trace('DeltaG')[t:].std()
#This is so plotting works on the cluster
plt.switch_backend('agg')
## 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.figure(figsize=(8,8))
plt.subplot(311)
property_name = 'top_complex_fluorescence'
complex = getattr(pymc_model, property_name)
property_name = 'top_ligand_fluorescence'
ligand = getattr(pymc_model, property_name)
for top_complex_fluorescence_model in mcmc.trace('top_complex_fluorescence_model')[::10]:
plt.semilogx(inputs['Lstated'], top_complex_fluorescence_model, marker='.',color='silver')
for top_ligand_fluorescence_model in mcmc.trace('top_ligand_fluorescence_model')[::10]:
plt.semilogx(inputs['Lstated'], top_ligand_fluorescence_model, marker='.',color='lightcoral', alpha=0.2)
plt.semilogx(inputs['Lstated'], complex.value, 'ko',label='complex')
plt.semilogx(inputs['Lstated'], ligand.value, marker='o',color='firebrick',linestyle='None',label='ligand')
plt.xlabel('$[L]_T$ (M)');
plt.ylabel('fluorescence units');
plt.legend(loc=0);
## PLOT HISTOGRAM
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
interval = np.percentile(a=mcmc.trace('DeltaG')[t:], q=[2.5, 50.0, 97.5])
[hist,bin_edges] = np.histogram(mcmc.trace('DeltaG')[t:],bins=40,normed=True)
binwidth = np.abs(bin_edges[0]-bin_edges[1])
#Print summary
print( 'Delta G (95% credibility interval after equilibration):')
print( ' %.3g [%.3g,%.3g] k_B T' %(interval[1],interval[0],interval[2]))
print( 'Delta G (mean and std after equil):')
print(' %.3g +- %.3g k_B T' %(DeltaG_equil,dDeltaG_equil) )
#set colors for 95% interval
clrs = [(0.7372549019607844, 0.5098039215686274, 0.7411764705882353) for xx in bin_edges]
idxs = bin_edges.argsort()
idxs = idxs[::-1]
gray_before = idxs[bin_edges[idxs] < interval[0]]
gray_after = idxs[bin_edges[idxs] > interval[2]]
for idx in gray_before:
clrs[idx] = (.5,.5,.5)
for idx in gray_after:
clrs[idx] = (.5,.5,.5)
plt.subplot(312)
plt.bar(bin_edges[:-1],hist,binwidth,color=clrs, edgecolor = "white");
sns.kdeplot(mcmc.trace('DeltaG')[t:],bw=.4,color=(0.39215686274509803, 0.7098039215686275, 0.803921568627451),shade=False)
plt.axvline(x=interval[0],color=(0.5,0.5,0.5),linestyle='--')
plt.axvline(x=interval[1],color=(0.5,0.5,0.5),linestyle='--')
plt.axvline(x=interval[2],color=(0.5,0.5,0.5),linestyle='--')
plt.axvline(x=DeltaG_map,color='black')
plt.xlabel('$\Delta G$ ($k_B T$)',fontsize=16);
plt.ylabel('$P(\Delta G)$',fontsize=16);
plt.xlim(-20,-8)
hist_legend = mpatches.Patch(color=(0.7372549019607844, 0.5098039215686274, 0.7411764705882353),
label = '$\Delta G$ = %.3g [%.3g,%.3g] $k_B T$'
%(interval[1],interval[0],interval[2]) )
map_legend = mlines.Line2D([],[],color='black',label="MAP = %.1f $k_B T$"%DeltaG_map)
plt.legend(handles=[hist_legend,map_legend],fontsize=14,loc=0,frameon=True)
## PLOT TRACE
plt.subplot(313)
plt.plot(range(0,t),mcmc.trace('DeltaG')[:t], 'go',label='equil. at %s'%t);
plt.plot(range(t,len(mcmc.trace('DeltaG')[:])),mcmc.trace('DeltaG')[t:], 'o');
plt.xlabel('MCMC sample');
plt.ylabel('$\Delta G$ ($k_B T$)');
plt.legend(loc=2);
plt.suptitle("%s: %s" % (name, my_datetime))
plt.tight_layout()
fig1 = plt.gcf()
fig1.savefig('delG_%s-%s.png'%(name, my_datetime_filename))
plt.close('all') # close all figures
Kd = np.exp(DeltaG_equil)
dKd = np.exp(mcmc.trace('DeltaG')[t:]).std()
Kd_interval = np.exp(interval)
if (Kd < 1e-12):
Kd_summary_interval = '%.1f [%.1f,%.1f] fM' %(Kd_interval[1]/1e-15,Kd_interval[0]/1e-15,Kd_interval[2]/1e-15)
Kd_summary = "%.1f fM +- %.1f fM" % (Kd/1e-15, dKd/1e-15)
elif (Kd < 1e-9):
Kd_summary_interval = '%.1f [%.1f,%.1f] pM' %(Kd_interval[1]/1e-12,Kd_interval[0]/1e-12,Kd_interval[2]/1e-12)
Kd_summary = "%.1f pM +- %.1f pM" % (Kd/1e-12, dKd/1e-12)
elif (Kd < 1e-6):
Kd_summary_interval = '%.1f [%.1f,%.1f] nM' %(Kd_interval[1]/1e-9,Kd_interval[0]/1e-9,Kd_interval[2]/1e-9)
Kd_summary = "%.1f nM +- %.1f nM" % (Kd/1e-9, dKd/1e-9)
elif (Kd < 1e-3):
Kd_summary_interval = '%.1f [%.1f,%.1f] uM' %(Kd_interval[1]/1e-6,Kd_interval[0]/1e-6,Kd_interval[2]/1e-6)
Kd_summary = "%.1f uM +- %.1f uM" % (Kd/1e-6, dKd/1e-6)
elif (Kd < 1):
Kd_summary_interval = '%.1f [%.1f,%.1f] mM' %(Kd_interval[1]/1e-3,Kd_interval[0]/1e-3,Kd_interval[2]/1e-3)
Kd_summary = "%.1f mM +- %.1f mM" % (Kd/1e-3, dKd/1e-3)
else:
Kd_summary_interval = '%.3e [%.3e,%.3e] M' %(Kd_interval[1],Kd_interval[0],Kd_interval[2])
Kd_summary = "%.3e M +- %.3e M" % (Kd, dKd)
print('Kd (95% credibility interval after equilibration):')
print(' %s' %Kd_summary_interval)
print('Kd (mean and std after equil):')
print(' %s' %Kd_summary)
outputs = {
#'raw_data_file' : my_file,
'complex_fluorescence' : complex_fluorescence[name],
'ligand_fluorescence' : ligand_fluorescence[name],
't_equil' : t,
'name' : name,
'analysis' : 'pymcmodels', #right now this is hardcoded, BOOO
'outfiles' : '%s_mcmc-%s.pickle, delG_%s-%s.pdf,DeltaG_%s-%s.npy,DeltaG_trace_%s-%s.npy'%(name,my_datetime,name,my_datetime,name,my_datetime,name,my_datetime),
'DeltaG_cred_int' : '$\Delta G$ = %.3g [%.3g,%.3g] $k_B T$' %(interval[1],interval[0],interval[2]),
'DeltaG' : "DeltaG = %.1f +- %.1f kT, MAP estimate = %.1f" % (DeltaG, dDeltaG, DeltaG_map),
'Kd' : Kd_summary,
'bin_edges' : bin_edges,
'hist' : hist,
'binwidth' : binwidth,
'clrs' : clrs,
'datetime' : my_datetime
}
metadata.update(outputs)
metadata['ligand_fluorescence'] = metadata['ligand_fluorescence'].tolist()
metadata['complex_fluorescence'] = metadata['complex_fluorescence'].tolist()
metadata['t_equil'] = metadata['t_equil'].tolist()
metadata['bin_edges'] = metadata['bin_edges'].tolist()
metadata['hist'] = metadata['hist'].tolist()
metadata['Pstated'] = metadata['Pstated'].tolist()
metadata['Lstated'] = metadata['Lstated'].tolist()
import json
with open('%s-%s.json'%(name,my_datetime), 'w') as outfile:
json.dump(metadata, outfile, sort_keys = True, indent = 4, ensure_ascii=False)
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)
