def _read_datafile(filename, dt, skip) :
"""
Read a data series and remove equilibration
"""
data = parsing.parse2ndarray(filename)
data[:,0] = data[:,0] * dt # Make the time column to ps
freq = data[1,0] - data[0,0]
nskip = int(skip/freq)
d = data[(nskip+1):,:]
return d
def _read_datafile(filename, dt, skip):
"""
Read a data series and remove equilibration
"""
data = parsing.parse2ndarray(filename)
data[:, 0] = data[:, 0] * dt # Make the time column to ps
freq = data[1, 0] - data[0, 0]
nskip = int(skip / freq)
d = data[(nskip + 1):, :]
return d
if args.ymax is not None and args.ymin is not None:
for i, x in enumerate(args.vertical):
if args.vstyle is not None:
stl = colors.style(args.vstyle[i])
else:
stl = colors.style(i + 1)
a.plot([x, x], [args.ymin, args.ymax], stl, color='k')
ylim = [10E10, -10E10]
xlim = [10E10, -10E10]
lines = []
for i, (filename, label,
axis) in enumerate(zip(args.file, args.label, plotax)):
data = parsing.parse2ndarray(filename)
if args.skip < 1:
n = int(np.floor(args.skip * data.shape[0]))
else:
n = args.skip
data = data[n:, :]
if args.multicol is not None:
args.ycol = args.multicol[i]
if data.shape[1] == 1: args.ycol = 0
y = data[n:, args.ycol] * args.yfactor
if data.shape[1] > 1:
x = data[n:, args.xcol] * args.xfactor
else:
x = np.arange(1, len(y) + 1) * args.xfactor
if args.stride is not None:
x = x[::args.stride]
# Author: Samuel Genheden [email protected] """ Script to calculate PMF properties, i.e. penetration and water/lipid barriers """ import sys import numpy as np from scipy.interpolate import interp1d from sgenlib import parsing from sgenlib import snum data = parsing.parse2ndarray(sys.argv[1]) x = data[:, 0] pmf = data[:, 1] err = data[:, 2] mini = np.argmin(pmf) dGdepth = pmf[mini] - pmf[-1] dGpen = pmf[0] - pmf[mini] errdepth = np.sqrt(err[mini]**2 + err[-1]**2) errpen = np.sqrt(err[0]**2 + err[mini]**2) kT = 300 * 0.00831446210 expav = np.exp(-pmf / kT) expstd = np.abs(expav * (-err / kT)) bndint, bndstd = snum.trapz(x, expav, expstd) freeint = np.trapz(np.ones(x.shape[0]), x)
a = figure.add_subplot(nrows, ncols, i)
a.plot(data[:, 1], data[:, 0], ".", color=colors.color(i - 1))
tau, tpval = stats.kendalltau(data[:, 1], data[:, 0])
r, rpval = stats.pearsonr(data[:, 1], data[:, 0])
print "%s\t%.5f\t%.5f\t%.5f\t%5f" % (label, r, rpval, tau, tpval)
a.plot([minv, maxv], [minv, maxv], "--k")
a.set_xlim(minv, maxv)
a.set_ylim(minv, maxv)
a.set_xticks(ticks)
a.set_yticks(ticks)
if ilabels or a.is_first_col():
a.set_ylabel(ylabel)
if ilabels or a.is_last_row():
a.set_xlabel(xlabel)
a.text(0.05, 0.92, label, transform=a.transAxes)
a.set_aspect('equal')
figure.tight_layout()
print ""
if __name__ == '__main__':
data = parsing.parse2ndarray(sys.argv[1])
figure = plt.figure(1, figsize=(7, 7))
_plot_data(figure, [data], [""],
r'$\mathrm{log} D_\mathrm{AA}$',
r'$\mathrm{log} D_\mathrm{exp.}$',
ncols=1)
figure.savefig(sys.argv[2], format="png", dpi=300)
# Author: Samuel Genheden [email protected] import sys import numpy as np from sgenlib import parsing if __name__ == '__main__' : data = parsing.parse2ndarray(sys.argv[1]) true_signs = (np.abs(data[:,0])>=1.96*data[:,1]).sum() print "Number of true signs is %d of %d, that is %d percent"%(true_signs, data.shape[0], float(true_signs)/float(data.shape[0])*100) print "Predictions with false signs %s"% \ ", ".join("%.1f+-%.2f"%tuple(d) for d in data[np.abs(data[:,0])<1.96*data[:,1],:])
def _dobar(filename):
"""
Read an oputput file from g_bar and return the dG in kcal/mol
"""
data = parsing.parse2ndarray(filename)
return 1.987*0.3*data[:,1].sum()
def _dobar(filename):
"""
Read an oputput file from g_bar and return the dG in kcal/mol
"""
data = parsing.parse2ndarray(filename)
return 1.987 * 0.3 * data[:, 1].sum()
x3 = x2*x
hfex = hfecoeff[0]*x3+hfecoeff[1]*x2+hfecoeff[2]*x+hfecoeff[3]
iodx = iodcoeff[0]*x3+iodcoeff[1]*x2+iodcoeff[2]*x+iodcoeff[3]
hfediff = np.abs(np.divide(hfex - hfefit,hfefit)).sum()
ioddiff = np.abs(np.divide(iodx - iodfit,iodfit)).sum()
return hfediff + ioddiff
if __name__ == '__main__' :
parser = argparse.ArgumentParser(description="Optimize LJ parameters")
parser.add_argument('-s','--scanfile',help="the data from the scans")
parser.add_argument('-f','--fitfile',help="the data points to fit")
args = parser.parse_args()
scans = parsing.parse2ndarray(args.scanfile)
fitpnt = parsing.parse2ndarray(args.fitfile)
hfecoeff = np.polyfit(scans[:,0],scans[:,1],3)
iodcoeff = np.polyfit(scans[:,0],scans[:,2],3)
xmin = scans[:,0].min()
xmax = scans[:,0].max()
x0 = np.ones(fitpnt.shape[0])*scans[:,0].mean()
arglst = (hfecoeff,iodcoeff,fitpnt[:,0],fitpnt[:,1])
bnds = np.ones((fitpnt.shape[0],2))
bnds[:,0] *= xmin
bnds[:,1] *= xmax
res = opt.minimize(objfunc,x0,args=arglst,bounds=bnds)
optx = res.x
hfex = hfecoeff[0] * x3 + hfecoeff[1] * x2 + hfecoeff[2] * x + hfecoeff[3]
iodx = iodcoeff[0] * x3 + iodcoeff[1] * x2 + iodcoeff[2] * x + iodcoeff[3]
hfediff = np.abs(np.divide(hfex - hfefit, hfefit)).sum()
ioddiff = np.abs(np.divide(iodx - iodfit, iodfit)).sum()
return hfediff + ioddiff
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Optimize LJ parameters")
parser.add_argument('-s', '--scanfile', help="the data from the scans")
parser.add_argument('-f', '--fitfile', help="the data points to fit")
args = parser.parse_args()
scans = parsing.parse2ndarray(args.scanfile)
fitpnt = parsing.parse2ndarray(args.fitfile)
hfecoeff = np.polyfit(scans[:, 0], scans[:, 1], 3)
iodcoeff = np.polyfit(scans[:, 0], scans[:, 2], 3)
xmin = scans[:, 0].min()
xmax = scans[:, 0].max()
x0 = np.ones(fitpnt.shape[0]) * scans[:, 0].mean()
arglst = (hfecoeff, iodcoeff, fitpnt[:, 0], fitpnt[:, 1])
bnds = np.ones((fitpnt.shape[0], 2))
bnds[:, 0] *= xmin
bnds[:, 1] *= xmax
res = opt.minimize(objfunc, x0, args=arglst, bounds=bnds)
optx = res.x
parser.add_argument(
'--extent',
type=float,
nargs=4,
help="the extent (left, right, bottom, top) of the image")
parser.add_argument('--nobar',
action="store_true",
help="don't draw a colorbar",
default=False)
parser.add_argument('--multiplot',
action="store_true",
help="draw more maps in subplots",
default=False)
parser.add_argument('-o',
'--out',
help="the output filename of the multiplot")
args = parser.parse_args()
all_mat = [parsing.parse2ndarray(filename) for filename in args.file]
if not args.multiplot:
for filename, mat in zip(args.file, all_mat):
outname = os.path.splitext(filename)[0] + ".png"
if args.out is not None and len(args.file) == 1:
outname = args.out
_draw_2dmap(mat, args.cutoff, args.max, args.ylabel, args.xlabel,
args.extent, args.cblabel, args.nobar, outname)
else:
_draw_multi_2dmap(all_mat, args.cutoff, args.max, args.ylabel,
args.xlabel, args.extent, args.cblabel, args.out)
# Author: Samuel Genheden [email protected] import sys import numpy as np from sgenlib import parsing if __name__ == '__main__': data = [] for filename in sys.argv[1:] : data.append(parsing.parse2ndarray(filename)) data = np.asarray(data) av = data.mean(axis=0) std = data.std(axis=0)/np.sqrt(data.shape[0]) for irow in range(data.shape[1]) : print data[0, irow, 0],"\t", print "\t".join("%.3f\t%.3f"%(a,s) for a, s in zip(av[irow, 1:], std[irow, 1:]))
default="w_rdf.txt")
parser.add_argument('-p',
'--phosphate',
help="the phosphate density",
default="p_rdf.txt")
parser.add_argument('-g',
'--glycerol',
help="the glycerol density",
default="g_rdf.txt")
parser.add_argument('-t',
'--tail',
help="the tail density",
default="t_rdf.txt")
args = parser.parse_args()
wdens = parsing.parse2ndarray(args.water)
pdens = parsing.parse2ndarray(args.phosphate)
gdens = parsing.parse2ndarray(args.glycerol)
tdens = parsing.parse2ndarray(args.tail)
n = int(np.round(pdens.shape[0] * 0.5))
leftmaxi = np.argmax(pdens[:n, 2])
rightmaxi = np.argmax(pdens[n:, 2]) + n
midz = pdens[leftmaxi,
0] + 0.5 * (pdens[rightmaxi, 0] - pdens[leftmaxi, 0])
try:
midi = np.where(pdens[:, 0] == midz)[0][0]
except:
midi = np.where(pdens[:, 0] == midz)[0]
if len(midi) == 0:
midi = int(leftmaxi + 0.5 * (rightmaxi - leftmaxi))
help="the temperature used in the simulation",
default=300.0)
args = parser.parse_args()
if len(args.files) == 0:
print "No files specified so nothing to do."
quit()
boltzmann = 0.001982923700
RT = args.temp * wham.KB[wham.KJMOL]
const = args.apl * np.power(10,
-24.0) / (786.14 * 1.66 * np.power(10, -27.0))
densities = []
if args.densities is not None:
densities = [parsing.parse2ndarray(d) for d in args.densities]
f = plt.figure(figsize=(3.33, 2.5))
#a = fig1.add_axes((0.15,0.2,0.8,0.75))
a = f.add_axes((0.18, 0.2, 0.75, 0.75))
max_pmf = -1000
min_pmf = 1000
pmfs = []
for i, (filename, label) in enumerate(zip(args.files, args.labels)):
pmfs.append(wham.UmbrellaPmf())
pmfs[-1].read(filename)
pmfs[-1].z *= 0.1
transfer_dg = pmfs[-1].transfer_dg()
wat_barr = pmfs[-1].waterlipid_barrier()
pen_barr = pmfs[-1].penetration_barrier()
# Author: Samuel Genheden [email protected] import sys import numpy as np from sgenlib import parsing if __name__ == '__main__': data = [] for filename in sys.argv[1:]: data.append(parsing.parse2ndarray(filename)) data = np.asarray(data) av = data.mean(axis=0) std = data.std(axis=0) / np.sqrt(data.shape[0]) for irow in range(data.shape[1]): print data[0, irow, 0], "\t", print "\t".join("%.3f\t%.3f" % (a, s) for a, s in zip(av[irow, 1:], std[irow, 1:]))
# Author: Samuel Genheden [email protected] """ Script to calculate PMF properties, i.e. penetration and water/lipid barriers """ import sys import numpy as np from scipy.interpolate import interp1d from sgenlib import parsing from sgenlib import snum data = parsing.parse2ndarray(sys.argv[1]) x = data[:,0] pmf = data[:,1] err = data[:,2] mini = np.argmin(pmf) dGdepth = pmf[mini] - pmf[-1] dGpen = pmf[0] - pmf[mini] errdepth = np.sqrt(err[mini]**2 + err[-1]**2) errpen = np.sqrt(err[0]**2 + err[mini]**2) kT = 300*0.00831446210 expav = np.exp(-pmf/kT) expstd = np.abs(expav*(-err/kT)) bndint, bndstd = snum.trapz(x, expav, expstd)
if args.twin : plotax[1] = a.twinx()
if args.ymax is not None and args.ymin is not None :
for i, x in enumerate(args.vertical) :
if args.vstyle is not None :
stl = colors.style(args.vstyle[i])
else :
stl = colors.style(i+1)
a.plot([x,x],[args.ymin,args.ymax], stl, color='k')
ylim = [10E10, -10E10]
xlim = [10E10, -10E10]
lines = []
for i, (filename,label, axis) in enumerate(zip(args.file,args.label, plotax)) :
data = parsing.parse2ndarray(filename)
if args.skip < 1 :
n = int(np.floor(args.skip * data.shape[0]))
else :
n = args.skip
data = data[n:,:]
if args.multicol is not None :
args.ycol = args.multicol[i]
if data.shape[1] == 1 : args.ycol = 0
y = data[n:,args.ycol]*args.yfactor
if data.shape[1] > 1 :
x = data[n:,args.xcol]*args.xfactor
else:
x = np.arange(1,len(y)+1)*args.xfactor
if args.stride is not None :
x = x[::args.stride]
if np.sum(data[idx,1]) > 0 and np.sum(data[idx,1]) != data.shape[0]: break
scoreddata = croc.ScoredData(data[idx,:])
bedrocs[i] = croc.BEDROC(scoreddata,1.0)['BEDROC']
return bedrocs
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description="Script to compute BEDROC analysis for a groups of solutes")
argparser.add_argument('-f', '--file', help="the results")
argparser.add_argument('-l', '--labels', help="the labels")
argparser.add_argument('-g', '--groupfolder', help="the folder for groups")
argparser.add_argument('--debug', action="store_true", help="turn on debugging", default=False)
argparser.add_argument('--plot', action="store_true", help="turn on plotting", default=False)
args = argparser.parse_args()
data = parsing.parse2ndarray(args.file)
with open(args.labels, 'r') as f:
labels = [line.strip() for line in f.readlines()]
solutegroups = []
for label in labels:
with open(os.path.join(args.groupfolder, label+".groups"), 'r') as f:
groups = [line.strip() for line in f.readlines()]
solutegroups.append(groups)
if args.debug:
print "Unique basic groups and their counts"
uniquelist = {}
for g in solutegroups:
for l in g :
if l not in uniquelist : uniquelist[l] = 0
