def test_value_set_r1_rit(self):
"""Test of the pipe_control.value.set() function."""
# Set the current data pipe to 'mf'.
pipes.switch('relax_disp')
# Set variables.
exp_type = 'R1rho'
frq = 800.1 * 1E6
# Set an experiment type to the pipe.
set_exp_type(spectrum_id='test', exp_type=exp_type)
# Set a frequency to loop through.
spectrometer.set_frequency(id='test', frq=frq, units='Hz')
# Generate dic key.
r20_key = generate_r20_key(exp_type=exp_type, frq=frq)
# Set first similar to r2.
value.set(val=None, param='r2')
self.assertEqual(cdp.mol[0].res[0].spin[0].r2[r20_key], 10.0)
# Then set for r1.
value.set(val=None, param='r1')
print(cdp.mol[0].res[0].spin[0])
self.assertEqual(cdp.mol[0].res[0].spin[0].r1[r20_key], 2.0)
def test_value_set_r1_rit(self):
"""Test of the pipe_control.value.set() function."""
# Set the current data pipe to 'mf'.
pipes.switch('relax_disp')
# Set variables.
exp_type = 'R1rho'
frq = 800.1 * 1E6
# Set an experiment type to the pipe.
set_exp_type(spectrum_id='test', exp_type=exp_type)
# Set a frequency to loop through.
spectrometer.set_frequency(id='test', frq=frq, units='Hz')
# Generate dic key.
r20_key = generate_r20_key(exp_type=exp_type, frq=frq)
# Set first similar to r2.
value.set(val=None, param='r2')
self.assertEqual(cdp.mol[0].res[0].spin[0].r2[r20_key], 10.0)
# Then set for r1.
value.set(val=None, param='r1')
print(cdp.mol[0].res[0].spin[0])
self.assertEqual(cdp.mol[0].res[0].spin[0].r1[r20_key], 2.0)
def calc_point_par_chi2(self):
"""Function for chi2 value for the points."""
# Print out.
print("\nCalculate chi2 value for the point parameters.")
# Define nested listed, which holds parameter values and chi2 value.
par_chi2_vals = []
# Loop over the points.
for i in range(self.num_points):
i_point = self.point[i]
# Set the parameter values.
if self.spin_id:
value.set(val=i_point,
param=self.params,
spin_id=self.spin_id,
force=True)
else:
value.set(val=i_point, param=self.params, force=True)
# Calculate the function values.
if self.spin_id:
self.api.calculate(spin_id=self.spin_id, verbosity=0)
else:
self.api.calculate(verbosity=0)
# Get the minimisation statistics for the model.
if self.spin_id:
k, n, chi2 = self.api.model_statistics(spin_id=self.spin_id)
else:
k, n, chi2 = self.api.model_statistics(model_info=0)
# Assign value to nested list.
par_chi2_vals.append([i, i_point[0], i_point[1], i_point[2], chi2])
# Return list
return par_chi2_vals
def calc_point_par_chi2(self):
"""Function for chi2 value for the points."""
# Print out.
print("\nCalculate chi2 value for the point parameters.")
# Define nested listed, which holds parameter values and chi2 value.
par_chi2_vals = []
# Loop over the points.
for i in range(self.num_points):
i_point = self.point[i]
# Set the parameter values.
if self.spin_id:
value.set(val=i_point, param=self.params, spin_id=self.spin_id, force=True)
else:
value.set(val=i_point, param=self.params, force=True)
# Calculate the function values.
if self.spin_id:
self.api.calculate(spin_id=self.spin_id, verbosity=0)
else:
self.api.calculate(verbosity=0)
# Get the minimisation statistics for the model.
if self.spin_id:
k, n, chi2 = self.api.model_statistics(spin_id=self.spin_id)
else:
k, n, chi2 = self.api.model_statistics(model_info=0)
# Assign value to nested list.
par_chi2_vals.append([i, i_point[0], i_point[1], i_point[2], chi2])
# Return list
return par_chi2_vals
def map_3D_text(self, map_file):
"""Function for creating the text of a 3D map."""
# Initialise.
values = zeros(3, float64)
percent = 0.0
percent_inc = 100.0 / (self.inc + 1.0)**(self.n - 1.0)
print("%-10s%8.3f%-1s" % ("Progress:", percent, "%"))
# Collect all chi2, to help finding a reasobale chi level for the Innermost, Inner, Middle and Outer Isosurface.
all_chi = []
# Fix the diffusion tensor.
unfix = False
if hasattr(cdp, 'diff_tensor') and not cdp.diff_tensor.fixed:
cdp.diff_tensor.fixed = True
unfix = True
# Initial value of the first parameter.
values[0] = self.bounds[0, 0]
# Define counter
counter = 0
# Loop over the first parameter.
for i in range((self.inc + 1)):
# Initial value of the second parameter.
values[1] = self.bounds[1, 0]
# Loop over the second parameter.
for j in range((self.inc + 1)):
# Initial value of the third parameter.
values[2] = self.bounds[2, 0]
# Loop over the third parameter.
for k in range((self.inc + 1)):
# Set the parameter values.
if self.spin_id:
value.set(val=values,
param=self.params,
spin_id=self.spin_id,
verbosity=0,
force=True)
else:
value.set(val=values,
param=self.params,
verbosity=0,
force=True)
# Calculate the function values.
if self.spin_id:
self.api.calculate(spin_id=self.spin_id, verbosity=0)
else:
self.api.calculate(verbosity=0)
# Get the minimisation statistics for the model.
if self.spin_id:
k, n, chi2 = self.api.model_statistics(
spin_id=self.spin_id)
else:
k, n, chi2 = self.api.model_statistics(model_info=0)
# Set maximum value to 1e20 to stop the OpenDX server connection from breaking.
if chi2 > 1e20:
map_file.write("%30f\n" % 1e20)
else:
map_file.write("%30f\n" % chi2)
# Save all values of chi2. To help find reasonale level for the Innermost, Inner, Middle and Outer Isosurface.
all_chi.append(chi2)
# Assign value to nested list.
self.par_chi2_vals.append(
[counter, values[0], values[1], values[2], chi2])
# Add to counter.
counter += 1
# Increment the value of the third parameter.
values[2] = values[2] + self.step_size[2]
# Progress incrementation and printout.
percent = percent + percent_inc
print("%-10s%8.3f%-8s%-8g" %
("Progress:", percent,
"%, " + repr(values) + ", f(x): ", chi2))
# Increment the value of the second parameter.
values[1] = values[1] + self.step_size[1]
# Increment the value of the first parameter.
values[0] = values[0] + self.step_size[0]
# Unfix the diffusion tensor.
if unfix:
cdp.diff_tensor.fixed = False
# Save all chi2 values.
self.all_chi = all_chi
def map_3D_text(self, map_file):
"""Function for creating the text of a 3D map."""
# Initialise.
values = zeros(3, float64)
percent = 0.0
percent_inc = 100.0 / (self.inc + 1.0)**(self.n - 1.0)
print("%-10s%8.3f%-1s" % ("Progress:", percent, "%"))
# Collect all chi2, to help finding a reasobale chi level for the Innermost, Inner, Middle and Outer Isosurface.
all_chi = []
# Fix the diffusion tensor.
unfix = False
if hasattr(cdp, 'diff_tensor') and not cdp.diff_tensor.fixed:
cdp.diff_tensor.fixed = True
unfix = True
# Initial value of the first parameter.
values[0] = self.bounds[0, 0]
# Define counter
counter = 0
# Loop over the first parameter.
for i in range((self.inc + 1)):
# Initial value of the second parameter.
values[1] = self.bounds[1, 0]
# Loop over the second parameter.
for j in range((self.inc + 1)):
# Initial value of the third parameter.
values[2] = self.bounds[2, 0]
# Loop over the third parameter.
for k in range((self.inc + 1)):
# Set the parameter values.
if self.spin_id:
value.set(val=values, param=self.params, spin_id=self.spin_id, verbosity=0, force=True)
else:
value.set(val=values, param=self.params, verbosity=0, force=True)
# Calculate the function values.
if self.spin_id:
self.api.calculate(spin_id=self.spin_id, verbosity=0)
else:
self.api.calculate(verbosity=0)
# Get the minimisation statistics for the model.
if self.spin_id:
k, n, chi2 = self.api.model_statistics(spin_id=self.spin_id)
else:
k, n, chi2 = self.api.model_statistics(model_info=0)
# Set maximum value to 1e20 to stop the OpenDX server connection from breaking.
if chi2 > 1e20:
map_file.write("%30f\n" % 1e20)
else:
map_file.write("%30f\n" % chi2)
# Save all values of chi2. To help find reasonale level for the Innermost, Inner, Middle and Outer Isosurface.
all_chi.append(chi2)
# Assign value to nested list.
self.par_chi2_vals.append([counter, values[0], values[1], values[2], chi2])
# Add to counter.
counter += 1
# Increment the value of the third parameter.
values[2] = values[2] + self.step_size[2]
# Progress incrementation and printout.
percent = percent + percent_inc
print("%-10s%8.3f%-8s%-8g" % ("Progress:", percent, "%, " + repr(values) + ", f(x): ", chi2))
# Increment the value of the second parameter.
values[1] = values[1] + self.step_size[1]
# Increment the value of the first parameter.
values[0] = values[0] + self.step_size[0]
# Unfix the diffusion tensor.
if unfix:
cdp.diff_tensor.fixed = False
# Save all chi2 values.
self.all_chi = all_chi
cur_spin_ids = cluster_spin_ids[0]
# Display spins
#display_spin()
# Loop over the first parameter.
# Start counter from 1003, so values correspond to line number.
counter = 1003
for i in range((dx_inc + 1)):
# Initial value of the second parameter.
values[1] = bounds[1, 0]
# Loop over the second parameter.
for j in range((dx_inc + 1)):
# Set the value.
value.set(val=values, param=params, spin_id=cur_spin_id, force=True)
# Calculate the function values.
api.calculate(spin_id=cur_spin_id, verbosity=0)
# Now copy the spin
new_res_name = "%s_%s=%1.1f_%s=%1.1f" % (cur_resn, params[0], values[0], params[1], values[1])
#spin.create(spin_name=cur_spin_name, spin_num=cur_spin_num, res_name=new_res_name, res_num=counter, mol_name=mol_name)
residue.create(res_num=counter, res_name=new_res_name, mol_name=mol_name)
new_spin_id = ":%i@%s"%(counter, 'N')
spin.copy(pipe_from=None, spin_from=cur_spin_id, pipe_to=None, spin_to=new_spin_id)
# Get the minimisation statistics for the model.
k_stat, n_stat, chi2 = api.model_statistics(spin_id=cur_spin_id)
#print(k_stat, n_stat, chi2, "point is %s=%3.3f, %s=%3.3f"% (params[0], values[0], params[1], values[1]))
def extract(dir):
"""Extract the data from the Dasha results files.
@param dir: The optional directory where the results file is located.
@type dir: str or None
"""
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# The directory.
if dir == None:
dir = pipes.cdp_name()
if not access(dir, F_OK):
raise RelaxDirError('Dasha', dir)
# Loop over the parameters.
for param in ['s2', 's2f', 's2s', 'te', 'tf', 'ts', 'rex']:
# The file name.
file_name = dir + sep + param + '.out'
# Test if the file exists.
if not access(file_name, F_OK):
raise RelaxFileError('Dasha', file_name)
# Scaling.
if param in ['te', 'tf', 'ts']:
scaling = 1e-9
elif param == 'rex':
scaling = 1.0 / (2.0 * pi * cdp.spectrometer_frq[cdp.ri_ids[0]]) ** 2
else:
scaling = 1.0
# Read the values.
data = read_results(file=file_name, scaling=scaling)
# Set the values.
for i in range(len(data)):
value.set(val=data[i][1], param=param, spin_id=data[i][0])
value.set(val=data[i][0], param=param, spin_id=data[i][0], error=True)
# Clean up of non-existent parameters (set the parameter to None!).
for spin in spin_loop():
# Skip the spin (don't set the parameter to None) if the parameter exists in the model.
if param in spin.params:
continue
# Set the parameter to None.
setattr(spin, param.lower(), None)
# Extract the chi-squared values.
file_name = dir + sep+'chi2.out'
# Test if the file exists.
if not access(file_name, F_OK):
raise RelaxFileError('Dasha', file_name)
# Read the values.
data = read_results(file=file_name)
# Set the values.
for i in range(len(data)):
spin = return_spin(data[i][0])
spin.chi2 = data[i][1]
all_chi = []
# Collect data.
data = []
# Append point as first data.
data.append(["%3.3f" % pcm[0], "%3.3f" % pcm[1], "%3.3f" % pre_chi2])
# Loop over the first parameter.
for i in range((dx_inc + 1)):
# Initial value of the second parameter.
values[1] = bounds[1, 0]
# Loop over the second parameter.
for j in range((dx_inc + 1)):
# Set the value.
value.set(val=values, param=params, spin_id=cur_spin_id, force=True)
# Calculate the function values.
api.calculate(spin_id=cur_spin_id, verbosity=0)
# Get the minimisation statistics for the model.
k_stat, n_stat, chi2 = api.model_statistics(spin_id=cur_spin_id)
#print(k_stat, n_stat, chi2, "point is %s=%3.3f, %s=%3.3f"% (params[0], values[0], params[1], values[1]))
# Progress incrementation and printout.
percent = percent + percent_inc
print(
"%-10s%8.3f%-8s%-8g" %
("Progress:", percent, "%, " + repr(values) + ", f(x): ", chi2))
# Append to data.
def map_3D_text(self, map_file):
"""Function for creating the text of a 3D map."""
# Initialise.
values = zeros(3, float64)
percent = 0.0
percent_inc = 100.0 / (self.inc + 1.0)**(self.n - 1.0)
print("%-10s%8.3f%-1s" % ("Progress:", percent, "%"))
# Fix the diffusion tensor.
unfix = False
if hasattr(cdp, 'diff_tensor') and not cdp.diff_tensor.fixed:
cdp.diff_tensor.fixed = True
unfix = True
# Initial value of the first parameter.
values[0] = self.bounds[0, 0]
# The model identifier.
# Loop over the first parameter.
for i in range((self.inc + 1)):
# Initial value of the second parameter.
values[1] = self.bounds[1, 0]
# Loop over the second parameter.
for j in range((self.inc + 1)):
# Initial value of the third parameter.
values[2] = self.bounds[2, 0]
# Loop over the third parameter.
for k in range((self.inc + 1)):
# Set the parameter values.
if self.spin_id:
value.set(val=values, param=self.params, spin_id=self.spin_id, force=True)
else:
value.set(val=values, param=self.params, force=True)
# Calculate the function values.
if self.spin_id:
self.calculate(spin_id=self.spin_id, verbosity=0)
else:
self.calculate(verbosity=0)
# Get the minimisation statistics for the model.
if self.spin_id:
k, n, chi2 = self.model_stats(spin_id=self.spin_id)
else:
k, n, chi2 = self.model_stats(model_info=0)
# Set maximum value to 1e20 to stop the OpenDX server connection from breaking.
if chi2 > 1e20:
map_file.write("%30f\n" % 1e20)
else:
map_file.write("%30f\n" % chi2)
# Increment the value of the third parameter.
values[2] = values[2] + self.step_size[2]
# Progress incrementation and printout.
percent = percent + percent_inc
print("%-10s%8.3f%-8s%-8g" % ("Progress:", percent, "%, " + repr(values) + ", f(x): ", chi2))
# Increment the value of the second parameter.
values[1] = values[1] + self.step_size[1]
# Increment the value of the first parameter.
values[0] = values[0] + self.step_size[0]
# Unfix the diffusion tensor.
if unfix:
cdp.diff_tensor.fixed = False
def extract(dir):
"""Extract the data from the Dasha results files.
@param dir: The optional directory where the results file is located.
@type dir: str or None
"""
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# The directory.
if dir == None:
dir = pipes.cdp_name()
if not access(dir, F_OK):
raise RelaxDirError('Dasha', dir)
# Loop over the parameters.
for param in ['s2', 's2f', 's2s', 'te', 'tf', 'ts', 'rex']:
# The file name.
file_name = dir + sep + param + '.out'
# Test if the file exists.
if not access(file_name, F_OK):
raise RelaxFileError('Dasha', file_name)
# Scaling.
if param in ['te', 'tf', 'ts']:
scaling = 1e-9
elif param == 'rex':
scaling = 1.0 / (2.0 * pi * cdp.spectrometer_frq[cdp.ri_ids[0]])**2
else:
scaling = 1.0
# Read the values.
data = read_results(file=file_name, scaling=scaling)
# Set the values.
for i in range(len(data)):
value.set(val=data[i][1], param=param, spin_id=data[i][0])
value.set(val=data[i][0],
param=param,
spin_id=data[i][0],
error=True)
# Clean up of non-existent parameters (set the parameter to None!).
for spin in spin_loop():
# Skip the spin (don't set the parameter to None) if the parameter exists in the model.
if param in spin.params:
continue
# Set the parameter to None.
setattr(spin, param.lower(), None)
# Extract the chi-squared values.
file_name = dir + sep + 'chi2.out'
# Test if the file exists.
if not access(file_name, F_OK):
raise RelaxFileError('Dasha', file_name)
# Read the values.
data = read_results(file=file_name)
# Set the values.
for i in range(len(data)):
spin = return_spin(spin_id=data[i][0])
spin.chi2 = data[i][1]
