def sherekhan_input(spin_id=None, force=False, dir='ShereKhan'):
"""Create the ShereKhan input files.
@keyword spin_id: The spin ID string to restrict the file creation to.
@type spin_id: str
@keyword force: A flag which if True will cause all pre-existing files to be overwritten.
@type force: bool
@keyword dir: The optional directory to place the files into. If None, then the files will be placed into the current directory.
@type dir: str or None
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the experiment type has been set.
if not hasattr(cdp, 'exp_type'):
raise RelaxError("The relaxation dispersion experiment type has not been specified.")
# Test if the model has been set.
if not hasattr(cdp, 'model_type'):
raise RelaxError("The relaxation dispersion model has not been specified.")
# Directory creation.
if dir != None:
mkdir_nofail(dir, verbosity=0)
# Loop over the spin blocks.
cluster_index = 0
for spin_ids in loop_cluster():
# The spin containers.
spins = spin_ids_to_containers(spin_ids)
# Loop over the magnetic fields.
for exp_type, frq, ei, mi in loop_exp_frq(return_indices=True):
# Loop over the time, and count it.
time_i = 0
for time, ti in loop_time(exp_type=exp_type, frq=frq, return_indices=True):
time_i += 1
# Check that not more than one time point is returned.
if time_i > 1:
raise RelaxError("Number of returned time poins is %i. Only 1 time point is expected."%time_i)
# The ShereKhan input file for the spin cluster.
file_name = 'sherekhan_frq%s.in' % (mi+1)
if dir != None:
dir_name = dir + sep + 'cluster%s' % (cluster_index+1)
else:
dir_name = 'cluster%s' % (cluster_index+1)
file = open_write_file(file_name=file_name, dir=dir_name, force=force)
# The B0 field for the nuclei of interest in MHz (must be positive to be accepted by the server).
file.write("%.10f\n" % abs(frq / periodic_table.gyromagnetic_ratio('1H') * periodic_table.gyromagnetic_ratio('15N') / 1e6))
# The constant relaxation time for the CPMG experiment in seconds.
file.write("%s\n" % (time))
# The comment line.
file.write("# %-18s %-20s %-20s\n" % ("nu_cpmg (Hz)", "R2eff (rad/s)", "Error"))
# Loop over the spins of the cluster.
for i in range(len(spins)):
# Get the residue container.
res = return_residue(spin_ids[i])
# Name the residue if needed.
res_name = res.name
if res_name == None:
res_name = 'X'
# Initialise the lines to output (to be able to catch missing data).
lines = []
# The residue ID line.
lines.append("# %s%s\n" % (res_name, res.num))
# Loop over the dispersion points.
for offset, point in loop_offset_point(exp_type=exp_type, frq=frq, skip_ref=True):
# The parameter key.
param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
# No data.
if param_key not in spins[i].r2eff:
continue
# Store the data.
lines.append("%20.15g %20.13g %20.13g\n" % (point, spins[i].r2eff[param_key], spins[i].r2eff_err[param_key]))
# No data.
if len(lines) == 1:
continue
# Write out the data.
for line in lines:
file.write(line)
# Close the file.
file.close()
# Increment the cluster index.
cluster_index += 1
def sherekhan_input(spin_id=None, force=False, dir='ShereKhan'):
"""Create the ShereKhan input files.
@keyword spin_id: The spin ID string to restrict the file creation to.
@type spin_id: str
@keyword force: A flag which if True will cause all pre-existing files to be overwritten.
@type force: bool
@keyword dir: The optional directory to place the files into. If None, then the files will be placed into the current directory.
@type dir: str or None
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the experiment type has been set.
if not hasattr(cdp, 'exp_type'):
raise RelaxError("The relaxation dispersion experiment type has not been specified.")
# Test if the model has been set.
if not hasattr(cdp, 'model_type'):
raise RelaxError("The relaxation dispersion model has not been specified.")
# Directory creation.
if dir != None:
mkdir_nofail(dir, verbosity=0)
# Loop over the spin blocks.
cluster_index = 0
for spin_ids in loop_cluster():
# The spin containers.
spins = spin_ids_to_containers(spin_ids)
# Loop over the magnetic fields.
for exp_type, frq, ei, mi in loop_exp_frq(return_indices=True):
# Loop over the time, and count it.
time_i = 0
for time, ti in loop_time(exp_type=exp_type, frq=frq, return_indices=True):
time_i += 1
# Check that not more than one time point is returned.
if time_i > 1:
raise RelaxError("Number of returned time poins is %i. Only 1 time point is expected."%time_i)
# The ShereKhan input file for the spin cluster.
file_name = 'sherekhan_frq%s.in' % (mi+1)
if dir != None:
dir_name = dir + sep + 'cluster%s' % (cluster_index+1)
else:
dir_name = 'cluster%s' % (cluster_index+1)
file = open_write_file(file_name=file_name, dir=dir_name, force=force)
# The B0 field for the nuclei of interest in MHz (must be positive to be accepted by the server).
file.write("%.10f\n" % abs(frq / periodic_table.gyromagnetic_ratio('1H') * periodic_table.gyromagnetic_ratio('15N') / 1e6))
# The constant relaxation time for the CPMG experiment in seconds.
file.write("%s\n" % (time))
# The comment line.
file.write("# %-18s %-20s %-20s\n" % ("nu_cpmg (Hz)", "R2eff (rad/s)", "Error"))
# Loop over the spins of the cluster.
for i in range(len(spins)):
# Get the residue container.
res = return_residue(spin_ids[i])
# Name the residue if needed.
res_name = res.name
if res_name == None:
res_name = 'X'
# Initialise the lines to output (to be able to catch missing data).
lines = []
# The residue ID line.
lines.append("# %s%s\n" % (res_name, res.num))
# Loop over the dispersion points.
for offset, point in loop_offset_point(exp_type=exp_type, frq=frq, skip_ref=True):
# The parameter key.
param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
# No data.
if param_key not in spins[i].r2eff:
continue
# Store the data.
lines.append("%20.15g %20.13g %20.13g\n" % (point, spins[i].r2eff[param_key], spins[i].r2eff_err[param_key]))
# No data.
if len(lines) == 1:
continue
# Write out the data.
for line in lines:
file.write(line)
# Close the file.
file.close()
# Increment the cluster index.
cluster_index += 1
def copy(pipe_from=None, pipe_to=None):
"""Copy dispersion parameters from one data pipe to another, taking the median of previous values to a start value for clusters.
Taking the median prevent averaging extreme outliers.
@param pipe_from: The data pipe to copy the value from. This defaults to the current data pipe.
@type pipe_from: str
@param pipe_to: The data pipe to copy the value to. This defaults to the current data pipe.
@type pipe_to: str
"""
# The current data pipe.
pipe_orig = pipes.cdp_name()
if pipe_from == None:
pipe_from = pipe_orig
if pipe_to == None:
pipe_to = pipe_orig
# Test that the pipes exist.
check_pipe(pipe_from)
check_pipe(pipe_to)
# Test that the pipes are not the same.
if pipe_from == pipe_to:
raise RelaxError("The source and destination pipes cannot be the same.")
# Test if the sequence data for pipe_from is loaded.
if not exists_mol_res_spin_data(pipe_from):
raise RelaxNoSequenceError(pipe_from)
# Test if the sequence data for pipe_to is loaded.
if not exists_mol_res_spin_data(pipe_to):
raise RelaxNoSequenceError(pipe_to)
# Switch to the destination data pipe.
pipes.switch(pipe_to)
# Loop over the clusters.
for spin_ids in loop_cluster():
# Initialise some variables.
model = None
pA = []
pB = []
pC = []
kex = []
kex_AB = []
kex_AC = []
kex_BC = []
k_AB = []
kB = []
kC = []
tex = []
count = 0
spins_from = []
spins_to = []
selected_cluster = False
# Loop over the spins, adding parameters to a list, which in the end will be used to find the median.
for id in spin_ids:
# Get the spins, then store them.
spin_from = return_spin(spin_id=id, pipe=pipe_from)
spin_to = return_spin(spin_id=id, pipe=pipe_to)
spins_from.append(spin_from)
spins_to.append(spin_to)
# Skip deselected spins.
if not spin_from.select or not spin_to.select:
continue
# The first printout.
if not selected_cluster:
subsection(file=sys.stdout, text="Copying parameters for the spin block %s"%spin_ids, prespace=2)
# Change the cluster selection flag.
selected_cluster = True
# The model.
if not model:
model = spin_from.model
# Check that the models match for all spins of the cluster.
if spin_from.model != model:
raise RelaxError("The model '%s' of spin '%s' from the source data pipe does not match the '%s' model of previous spins of the cluster." % (spin_from.model, id, model))
if spin_to.model != model:
raise RelaxError("The model '%s' of spin '%s' from the destination data pipe does not match the '%s' model of previous spins of the cluster." % (spin_from.model, id, model))
# Sum the source parameters.
if 'pA' in spin_from.params:
pA.append(spin_from.pA)
if 'pB' in spin_from.params:
pB.append(spin_from.pB)
if 'pC' in spin_from.params:
pC.append(spin_from.pC)
if 'kex' in spin_from.params:
kex.append(spin_from.kex)
if 'kex_AB' in spin_from.params:
kex_AB.append(spin_from.kex_AB)
if 'kex_AC' in spin_from.params:
kex_AC.append(spin_from.kex_AC)
if 'kex_BC' in spin_from.params:
kex_BC.append(spin_from.kex_BC)
if 'k_AB' in spin_from.params:
k_AB.append(spin_from.k_AB)
if 'kB' in spin_from.params:
kB.append(spin_from.kB)
if 'kC' in spin_from.params:
kC.append(spin_from.kC)
if 'tex' in spin_from.params:
tex.append(spin_from.tex)
# Increment the spin count.
count += 1
# The cluster is not selected, so move to the next.
if not selected_cluster:
continue
# Take median of parameters.
if len(pA) > 0:
pA = median(pA)
print("Median pA value: %.15f" % pA)
if len(pB) > 0:
pB = median(pB)
print("Median pB value: %.15f" % pB)
if len(pC) > 0:
pC = median(pC)
print("Median pC value: %.15f" % pC)
if len(kex) > 0:
kex = median(kex)
print("Median kex value: %.15f" % kex)
if len(kex_AB) > 0:
kex_AB = median(kex_AB)
print("Median k_AB value: %.15f" % kex_AB)
if len(kex_AC) > 0:
kex_AC = median(kex_AC)
print("Median k_AC value: %.15f" % kex_AC)
if len(kex_BC) > 0:
kex_BC = median(kex_BC)
print("Median k_BC value: %.15f" % kex_BC)
if len(k_AB) > 0:
k_AB = median(k_AB)
print("Median k_AB value: %.15f" % k_AB)
if len(kB) > 0:
kB = median(kB)
print("Median kB value: %.15f" % kB)
if len(kC) > 0:
kC = median(kC)
print("Median kC value: %.15f" % kC)
if len(tex) > 0:
tex = median(tex)
print("Median tex value: %.15f" % tex)
# Loop over the spins, this time copying the parameters.
for i in range(len(spin_ids)):
# Alias the containers.
spin_from = spins_from[i]
spin_to = spins_to[i]
# Skip deselected spins.
if not spin_from.select or not spin_to.select:
continue
# The R20 parameters.
if 'r2' in spin_from.params:
spin_to.r2 = deepcopy(spin_from.r2)
if 'r2a' in spin_from.params:
spin_to.r2a = deepcopy(spin_from.r2a)
if 'r2b' in spin_from.params:
spin_to.r2b = deepcopy(spin_from.r2b)
# The median parameters.
if 'pB' in spin_from.params and 'pC' not in spin_from.params:
spin_to.pA = pA
spin_to.pB = pB
spin_to.pC = 1.0 - pA - pB
elif 'pA' in spin_from.params:
spin_to.pA = pA
spin_to.pB = 1.0 - pA
if 'kex' in spin_from.params:
spin_to.kex = kex
if 'kex_AB' in spin_from.params:
spin_to.kex_AB = kex_AB
if 'kex_AC' in spin_from.params:
spin_to.kex_AC = kex_AC
if 'kex_BC' in spin_from.params:
spin_to.kex_BC = kex_BC
if 'k_AB' in spin_from.params:
spin_to.k_AB = k_AB
if 'kB' in spin_from.params:
spin_to.kB = kB
if 'kC' in spin_from.params:
spin_to.kC = kC
if 'tex' in spin_from.params:
spin_to.tex = tex
# All other spin specific parameters.
for param in spin_from.params:
if param in ['r2', 'r2a', 'r2b', 'pA', 'pB', 'pC', 'kex', 'kex_AB', 'kex_AC', 'kex_BC', 'k_AB', 'kB', 'kC', 'tex']:
continue
# Copy the value.
setattr(spin_to, param, deepcopy(getattr(spin_from, param)))
# Switch back to the original data pipe.
pipes.switch(pipe_orig)
def copy(pipe_from=None, pipe_to=None):
"""Copy dispersion parameters from one data pipe to another, taking the median of previous values to a start value for clusters.
Taking the median prevent averaging extreme outliers.
@param pipe_from: The data pipe to copy the value from. This defaults to the current data pipe.
@type pipe_from: str
@param pipe_to: The data pipe to copy the value to. This defaults to the current data pipe.
@type pipe_to: str
"""
# The current data pipe.
pipe_orig = pipes.cdp_name()
if pipe_from == None:
pipe_from = pipe_orig
if pipe_to == None:
pipe_to = pipe_orig
# Test that the pipes exist.
check_pipe(pipe_from)
check_pipe(pipe_to)
# Test that the pipes are not the same.
if pipe_from == pipe_to:
raise RelaxError("The source and destination pipes cannot be the same.")
# Test if the sequence data for pipe_from is loaded.
if not exists_mol_res_spin_data(pipe_from):
raise RelaxNoSequenceError(pipe_from)
# Test if the sequence data for pipe_to is loaded.
if not exists_mol_res_spin_data(pipe_to):
raise RelaxNoSequenceError(pipe_to)
# Switch to the destination data pipe.
pipes.switch(pipe_to)
# Loop over the clusters.
for spin_ids in loop_cluster():
# Initialise some variables.
model = None
pA = []
pB = []
pC = []
kex = []
kex_AB = []
kex_AC = []
kex_BC = []
k_AB = []
kB = []
kC = []
tex = []
count = 0
spins_from = []
spins_to = []
selected_cluster = False
# Loop over the spins, adding parameters to a list, which in the end will be used to find the median.
for id in spin_ids:
# Get the spins, then store them.
spin_from = return_spin(id, pipe=pipe_from)
spin_to = return_spin(id, pipe=pipe_to)
spins_from.append(spin_from)
spins_to.append(spin_to)
# Skip deselected spins.
if not spin_from.select or not spin_to.select:
continue
# The first printout.
if not selected_cluster:
subsection(file=sys.stdout, text="Copying parameters for the spin block %s"%spin_ids, prespace=2)
# Change the cluster selection flag.
selected_cluster = True
# The model.
if not model:
model = spin_from.model
# Check that the models match for all spins of the cluster.
if spin_from.model != model:
raise RelaxError("The model '%s' of spin '%s' from the source data pipe does not match the '%s' model of previous spins of the cluster." % (spin_from.model, id, model))
if spin_to.model != model:
raise RelaxError("The model '%s' of spin '%s' from the destination data pipe does not match the '%s' model of previous spins of the cluster." % (spin_from.model, id, model))
# Sum the source parameters.
if 'pA' in spin_from.params:
pA.append(spin_from.pA)
if 'pB' in spin_from.params:
pB.append(spin_from.pB)
if 'pC' in spin_from.params:
pC.append(spin_from.pC)
if 'kex' in spin_from.params:
kex.append(spin_from.kex)
if 'kex_AB' in spin_from.params:
kex_AB.append(spin_from.kex_AB)
if 'kex_AC' in spin_from.params:
kex_AC.append(spin_from.kex_AC)
if 'kex_BC' in spin_from.params:
kex_BC.append(spin_from.kex_BC)
if 'k_AB' in spin_from.params:
k_AB.append(spin_from.k_AB)
if 'kB' in spin_from.params:
kB.append(spin_from.kB)
if 'kC' in spin_from.params:
kC.append(spin_from.kC)
if 'tex' in spin_from.params:
tex.append(spin_from.tex)
# Increment the spin count.
count += 1
# The cluster is not selected, so move to the next.
if not selected_cluster:
continue
# Take median of parameters.
if len(pA) > 0:
pA = median(pA)
print("Median pA value: %.15f" % pA)
if len(pB) > 0:
pB = median(pB)
print("Median pB value: %.15f" % pB)
if len(pC) > 0:
pC = median(pC)
print("Median pC value: %.15f" % pC)
if len(kex) > 0:
kex = median(kex)
print("Median kex value: %.15f" % kex)
if len(kex_AB) > 0:
kex_AB = median(kex_AB)
print("Median k_AB value: %.15f" % kex_AB)
if len(kex_AC) > 0:
kex_AC = median(kex_AC)
print("Median k_AC value: %.15f" % kex_AC)
if len(kex_BC) > 0:
kex_BC = median(kex_BC)
print("Median k_BC value: %.15f" % kex_BC)
if len(k_AB) > 0:
k_AB = median(k_AB)
print("Median k_AB value: %.15f" % k_AB)
if len(kB) > 0:
kB = median(kB)
print("Median kB value: %.15f" % kB)
if len(kC) > 0:
kC = median(kC)
print("Median kC value: %.15f" % kC)
if len(tex) > 0:
tex = median(tex)
print("Median tex value: %.15f" % tex)
# Loop over the spins, this time copying the parameters.
for i in range(len(spin_ids)):
# Alias the containers.
spin_from = spins_from[i]
spin_to = spins_to[i]
# Skip deselected spins.
if not spin_from.select or not spin_to.select:
continue
# The R20 parameters.
if 'r2' in spin_from.params:
spin_to.r2 = deepcopy(spin_from.r2)
if 'r2a' in spin_from.params:
spin_to.r2a = deepcopy(spin_from.r2a)
if 'r2b' in spin_from.params:
spin_to.r2b = deepcopy(spin_from.r2b)
# The median parameters.
if 'pB' in spin_from.params and 'pC' not in spin_from.params:
spin_to.pA = pA
spin_to.pB = pB
spin_to.pC = 1.0 - pA - pB
elif 'pA' in spin_from.params:
spin_to.pA = pA
spin_to.pB = 1.0 - pA
if 'kex' in spin_from.params:
spin_to.kex = kex
if 'kex_AB' in spin_from.params:
spin_to.kex_AB = kex_AB
if 'kex_AC' in spin_from.params:
spin_to.kex_AC = kex_AC
if 'kex_BC' in spin_from.params:
spin_to.kex_BC = kex_BC
if 'k_AB' in spin_from.params:
spin_to.k_AB = k_AB
if 'kB' in spin_from.params:
spin_to.kB = kB
if 'kC' in spin_from.params:
spin_to.kC = kC
if 'tex' in spin_from.params:
spin_to.tex = tex
# All other spin specific parameters.
for param in spin_from.params:
if param in ['r2', 'r2a', 'r2b', 'pA', 'pB', 'pC', 'kex', 'kex_AB', 'kex_AC', 'kex_BC', 'k_AB', 'kB', 'kC', 'tex']:
continue
# Copy the value.
setattr(spin_to, param, deepcopy(getattr(spin_from, param)))
# Switch back to the original data pipe.
pipes.switch(pipe_orig)