def plot():
fd = open('Biso_v_resolution.gnuplotdata', 'r')
lines = fd.readlines()
fd.close()
d_splot = {}
l_resolution = []
l_Biso = []
for line in lines:
l = line.split()
resolution = float(l[1])
Biso = float(l[0])
resolution_rounded = round(resolution, 1)
## resolution_rounded = 0.1*round(resolution/0.1,0)
Biso_rounded = round(Biso, 0)
if not resolution_rounded in d_splot.keys():
d_splot[resolution_rounded] = {}
if not Biso_rounded in d_splot[resolution_rounded].keys():
d_splot[resolution_rounded][Biso_rounded] = 0
d_splot[resolution_rounded][Biso_rounded] += 1
l_resolution += [resolution]
l_Biso += [Biso]
a, b, r, p = statistics.do_regression(
l_resolution,
l_Biso,
verbose=False,
)
print 'correlation = r =', r
tcrit = 1.960 ## 95% confidence 2 tail (student table...)
## tcrit = 76.
## f,g,h = statistics.do_confidence_bands(l_resolution,l_Biso,tcrit,)
lines = []
## for resolution in range(5,35+1):
for resolution in range(5, 35 + 1):
resolution /= 10.
for Biso in range(0, 100 + 1):
Biso /= 1.
if not resolution in d_splot.keys():
count = 0
elif not Biso in d_splot[resolution].keys():
count = 0
else:
count = d_splot[resolution][Biso]
lines += ['%s %s %s\n' % (
resolution,
Biso,
count,
)]
lines += ['\n']
gnuplot.contour_plot(
'validation_Biso_v_resolution',
lines,
xlabel='resolution (Angstrom)',
ylabel='<Biso>',
bool_remove=False,
)
return
def plot():
fd = open('Biso_v_resolution.gnuplotdata','r')
lines = fd.readlines()
fd.close()
d_splot = {}
l_resolution = []
l_Biso = []
for line in lines:
l = line.split()
resolution = float(l[1])
Biso = float(l[0])
resolution_rounded = round(resolution,1)
## resolution_rounded = 0.1*round(resolution/0.1,0)
Biso_rounded = round(Biso,0)
if not resolution_rounded in d_splot.keys():
d_splot[resolution_rounded] = {}
if not Biso_rounded in d_splot[resolution_rounded].keys():
d_splot[resolution_rounded][Biso_rounded] = 0
d_splot[resolution_rounded][Biso_rounded] += 1
l_resolution += [resolution]
l_Biso += [Biso]
a,b,r,p = statistics.do_regression(l_resolution,l_Biso,verbose=False,)
print 'correlation = r =', r
tcrit = 1.960 ## 95% confidence 2 tail (student table...)
## tcrit = 76.
## f,g,h = statistics.do_confidence_bands(l_resolution,l_Biso,tcrit,)
lines = []
## for resolution in range(5,35+1):
for resolution in range(5,35+1):
resolution /= 10.
for Biso in range(0,100+1):
Biso /= 1.
if not resolution in d_splot.keys():
count = 0
elif not Biso in d_splot[resolution].keys():
count = 0
else:
count = d_splot[resolution][Biso]
lines += ['%s %s %s\n' %(resolution,Biso,count,)]
lines += ['\n']
gnuplot.contour_plot(
'validation_Biso_v_resolution',lines,
xlabel='resolution (Angstrom)',ylabel='<Biso>',
bool_remove = False,
)
return
lines = fd.readlines()
fd.close()
d_resolution = {}
for line in lines:
l = line.strip().split()
pdb = l[0]
v = l[1]
if v == "['.']":
continue
if float(v[2:-2]) > 5.0:
## print pdb, 'resolution', v
continue
d_resolution[pdb] = v
l_MV = []
l_resolution = []
lines_gnuplot = []
set_pdbs = set(d_MV.keys())&set(d_resolution.keys())
for pdb in set_pdbs:
MV = float(d_MV[pdb])
resolution = float(d_resolution[pdb][2:-2])
l_MV += [MV]
l_resolution += [resolution]
lines_gnuplot += ['%s %s\n' %(resolution,MV,)]
t = statistics.do_regression(l_resolution,l_MV)
fd = open('tmp.txt','w')
fd.writelines(lines_gnuplot)
fd.close()
for line in lines:
l = line.strip().split()
pdb = l[0]
v = l[1]
if v == "['.']":
continue
if float(v[2:-2]) > 5.0:
## print pdb, 'resolution', v
continue
d_resolution[pdb] = v
l_MV = []
l_resolution = []
lines_gnuplot = []
set_pdbs = set(d_MV.keys()) & set(d_resolution.keys())
for pdb in set_pdbs:
MV = float(d_MV[pdb])
resolution = float(d_resolution[pdb][2:-2])
l_MV += [MV]
l_resolution += [resolution]
lines_gnuplot += ['%s %s\n' % (
resolution,
MV,
)]
t = statistics.do_regression(l_resolution, l_MV)
fd = open('tmp.txt', 'w')
fd.writelines(lines_gnuplot)
fd.close()