Author: semor
Date: Mon Jan 7 20:03:39 2008
New Revision: 4466
URL: http://svn.gna.org/viewcvs/relax?rev=4466&view=rev
Log:
Modified the consistency tests math_fns code.
This is a copy of the 1.2 line code without the 'cos' import and with updated
copyrights.
Modified:
branches/consistency_tests_1.3/maths_fns/consistency_tests.py
Modified: branches/consistency_tests_1.3/maths_fns/consistency_tests.py
URL:
http://svn.gna.org/viewcvs/relax/branches/consistency_tests_1.3/maths_fns/consistency_tests.py?rev=4466&r1=4465&r2=4466&view=diff
==============================================================================
--- branches/consistency_tests_1.3/maths_fns/consistency_tests.py (original)
+++ branches/consistency_tests_1.3/maths_fns/consistency_tests.py Mon Jan 7
20:03:39 2008
@@ -27,14 +27,30 @@
from ri_comps import calc_fixed_csa, calc_fixed_dip, comp_csa_const_func,
comp_dip_const_func
-class Mapping:
+class Consistency:
def __init__(self, frq=None, gx=None, gh=None, mu0=None, h_bar=None):
- """Reduced spectral density mapping."""
+ """Consistency tests for data acquired at different magnetic fields.
+
+ These three tests are used to assess the consistency of datasets
aquired at different
+ magnetic fields. Inconsistency can affect extracted information from
experimental data and
+ can be caused by variations in temperature, concentration, pH, water
suppression, etc.
+
+ This code calculates three functions for each residue. When
comparing datasets from
+ different magnetic field, the value should be the same for each
function as these are field
+ independent. The J(0) function is the spectral density at the zero
frequency and is obtained
+ using a reduced spectral density approach. The F_eta and F_R2
functions are the
+ consistency functions proposed by Fushman D. et al. (1998) JACS,
120: 10947-10952.
+
+ To assess the consistency of its datasets, one should first
calculate those values (J(0),
+ F_eta and F_R2) for each field. Then, the user should compare values
obtained for different
+ magnetic fields. Comparisons could proceed using correlation plots
and histograms, and the
+ user could also calculate correlation, skewness and kurtosis
coefficients.
+ """
# Initialise the data container.
self.data = Data()
- # Add the initial data to self.data
+ # Add the initial data to self.data.
self.data.gx = gx
self.data.gh = gh
self.data.mu0 = mu0
@@ -68,10 +84,10 @@
return (noe - 1.0) * r1 * self.data.gx / self.data.gh
- def func(self, r=None, csa=None, r1=None, r2=None, noe=None):
- """Function for calculating the three spectal density values.
+ def func(self, orientation=None, tc=None, r=None, csa=None, r1=None,
r2=None, noe=None):
+ """Function for calculating the three consistency testing values.
- Three values are returned, J(0), J(wX), and J(wH) (or J(0.87wH)).
+ Three values are returned, J(0), F_eta and F_R2.
"""
# Calculate the fixed component of the dipolar and CSA constants.
@@ -95,11 +111,35 @@
# Calculate J(wX).
jwx = 1.0 / (3.0*d + c) * (r1 - 1.4*sigma_noe)
- # Calculate J(wH).
- jwh = sigma_noe / (5.0*d)
+ # Calculate P_2.
+ # p_2 is a second rank Legendre polynomial as p_2(x) = 0.5 * (3 * (x
** 2) -1)
+ # where x is the cosine of the angle Theta when expressed in radians.
+ #
+ # Note that the angle Theta (called 'orientation' in relax) is the
angle between the 15N-1H
+ # vector and the principal axis of the 15N chemical shift tensor.
+ p_2 = 0.5 * ((3.0 * (cos(orientation * pi / 180)) ** 2) -1)
+
+ # Calculate eta.
+ # eta is the cross-correlation rate between 15N CSA and 15N-1H
dipolar interaction. It is
+ # expressed here as proposed in Fushman D. & Cowburn D. (1998) JACS,
120: 7109-7110.
+ eta = ((d * c) ** 0.5) * (4.0 * j0 + 3.0 * jwx) * p_2
+
+ # Calculate F_eta.
+ # F_eta is independent of the magnetic field for residues with local
mobility
+ f_eta = eta * self.data.gh / (self.data.frq_list[0, 3] * (4.0 + 3.0
/ (1 + (self.data.frq_list[0, 1] * tc) ** 2)))
+
+ # Calculate P_HF.
+ # P_HF is the contribution to R2 from high frequency motions.
+ # P_HF = 0.5 * d * [J(wH-wN) + 6 * J(wH) + 6 * J(wH+wN)].
+ # Here, P_HF is described using a reduced spectral density approach.
+ p_hf = 1.3 * (self.data.gx / self.data.gh) * (1.0 - noe) * r1
+
+ # Calculate F_R2.
+ # F_R2 tests the consistency of the transverse relaxation data.
+ f_r2 = (r2 - p_hf) / ((4.0 + 3.0 / (1 + (self.data.frq_list[0, 1] *
tc) ** 2)) * (d + c/3.0))
# Return the three values.
- return j0, jwx, jwh
+ return j0, f_eta, f_r2
class Data:
r4466 - /branches/consistency_tests_1.3/maths_fns/consistency_tests.py