Author: tlinnet
Date: Mon Jun 9 00:09:59 2014
New Revision: 23749
URL: http://svn.gna.org/viewcvs/relax?rev=23749&view=rev
Log:
Changed all calls to numpy np.X functions to just the numpy function.
Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion
models for Clustered analysis.
Timing is now showing, 17% loss per single spin, but but 277 % gain on 100
spin.
3 fields, 1000 iterations.
1 spin
1 0.000 0.000 0.677 0.677 <string>:1(<module>)
1 0.001 0.001 0.677 0.677 pf:419(single)
1000 0.002 0.000 0.671 0.001 pf:405(calc)
1000 0.009 0.000 0.669 0.001
relax_disp.py:979(func_CR72_full)
1000 0.102 0.000 0.655 0.001
relax_disp.py:507(calc_CR72_chi2)
1003 0.160 0.000 0.365 0.000 cr72.py:101(r2eff_CR72)
23029 0.188 0.000 0.188 0.000 {numpy.core.multiarray.array}
4003 0.119 0.000 0.182 0.000 numeric.py:1862(allclose)
100 spin
1 0.000 0.000 19.783 19.783 <string>:1(<module>)
1 0.002 0.002 19.783 19.783 pf:441(cluster)
1000 0.004 0.000 19.665 0.020 pf:405(calc)
1000 0.013 0.000 19.661 0.020
relax_disp.py:979(func_CR72_full)
1000 6.541 0.007 19.634 0.020
relax_disp.py:507(calc_CR72_chi2)
916108 11.127 0.000 11.127 0.000 {numpy.core.multiarray.array}
1300 1.325 0.001 2.026 0.002 cr72.py:101(r2eff_CR72)
4300 0.495 0.000 0.634 0.000 numeric.py:1862(allclose)
Modified:
branches/disp_spin_speed/target_functions/relax_disp.py
Modified: branches/disp_spin_speed/target_functions/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/target_functions/relax_disp.py?rev=23749&r1=23748&r2=23749&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Mon Jun 9
00:09:59 2014
@@ -28,7 +28,7 @@
from copy import deepcopy
from math import pi
import numpy as np
-from numpy import complex64, dot, float64, int16, sqrt, zeros
+from numpy import array, asarray, complex64, dot, float64, int16, max, ones,
sqrt, sum, zeros
# relax module imports.
from lib.dispersion.b14 import r2eff_B14
@@ -399,26 +399,26 @@
# Get the shape of back_calc structure.
# If using just one field, or having the same number of
dispersion points, the shape would extend to that number.
# Shape has to be: [ei][si][mi][oi].
- back_calc_shape = list( np.asarray(self.back_calc).shape )[:4]
+ back_calc_shape = list( asarray(self.back_calc).shape )[:4]
# Find which frequency has the maximum number of disp points.
# To let the numpy array operate well together, the broadcast
size has to be equal for all shapes.
- self.max_num_disp_points = np.max(self.num_disp_points)
+ self.max_num_disp_points = max(self.num_disp_points)
# Create numpy arrays to pass to the lib function.
# All numpy arrays have to have same shape to allow to multiply
together.
# The dimensions should be [ei][si][mi][oi][di].
[Experiment][spins][spec. frq][offset][disp points].
# The number of disp point can change per spectrometer, so we
make the maximum size.
- self.R20A_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.R20B_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.pA_a = np.zeros(back_calc_shape +
[self.max_num_disp_points], float64)
- self.dw_frq_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.kex_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.cpmg_frqs_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.num_disp_points_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.back_calc_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.errors_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
- self.values_a = np.ones(back_calc_shape +
[self.max_num_disp_points], float64)
+ self.R20A_a = ones(back_calc_shape + [self.max_num_disp_points],
float64)
+ self.R20B_a = ones(back_calc_shape + [self.max_num_disp_points],
float64)
+ self.pA_a = zeros(back_calc_shape + [self.max_num_disp_points],
float64)
+ self.dw_frq_a = ones(back_calc_shape +
[self.max_num_disp_points], float64)
+ self.kex_a = ones(back_calc_shape + [self.max_num_disp_points],
float64)
+ self.cpmg_frqs_a = ones(back_calc_shape +
[self.max_num_disp_points], float64)
+ self.num_disp_points_a = ones(back_calc_shape +
[self.max_num_disp_points], float64)
+ self.back_calc_a = ones(back_calc_shape +
[self.max_num_disp_points], float64)
+ self.errors_a = ones(back_calc_shape +
[self.max_num_disp_points], float64)
+ self.values_a = ones(back_calc_shape +
[self.max_num_disp_points], float64)
self.has_missing = False
# Loop over the experiment types.
@@ -528,8 +528,8 @@
num_disp_points = self.num_disp_points[0][0][mi][0]
# Calculate pA and kex per frequency.
- pA_arr = np.array( [pA] * num_disp_points, float64)
- kex_arr = np.array( [kex] * num_disp_points, float64)
+ pA_arr = array( [pA] * num_disp_points, float64)
+ kex_arr = array( [kex] * num_disp_points, float64)
# Loop over the spins.
for si in range(self.num_spins):
@@ -537,14 +537,14 @@
r20_index = mi + si*self.num_frq
# Store r20a and r20b values per disp point.
- self.R20A_a[0][si][mi][0][:num_disp_points] = np.array(
[R20A[r20_index]] * num_disp_points, float64)
- self.R20B_a[0][si][mi][0][:num_disp_points] = np.array(
[R20B[r20_index]] * num_disp_points, float64)
+ self.R20A_a[0][si][mi][0][:num_disp_points] = array(
[R20A[r20_index]] * num_disp_points, float64)
+ self.R20B_a[0][si][mi][0][:num_disp_points] = array(
[R20B[r20_index]] * num_disp_points, float64)
# Convert dw from ppm to rad/s.
dw_frq = dw[si] * self.frqs[0][si][mi]
# Store dw_frq per disp point.
- self.dw_frq_a[0][si][mi][0][:num_disp_points] = np.array(
[dw_frq] * num_disp_points, float64)
+ self.dw_frq_a[0][si][mi][0][:num_disp_points] = array(
[dw_frq] * num_disp_points, float64)
# Store pA and kex per disp point.
self.pA_a[0][si][mi][0][:num_disp_points] = pA_arr
@@ -566,7 +566,7 @@
self.back_calc_a[0][si][mi][0][di] =
self.values[0][si][mi][0][di]
## Calculate the chi-squared statistic.
- return chi2_sum = np.sum((1.0 / self.errors_a * (self.values_a -
self.back_calc_a))**2)
+ return sum((1.0 / self.errors_a * (self.values_a -
self.back_calc_a))**2)
def calc_ns_cpmg_2site_3D_chi2(self, R20A=None, R20B=None, dw=None,
pA=None, kex=None):
r23749 - /branches/disp_spin_speed/target_functions/relax_disp.py