Author: bugman
Date: Tue Aug 26 12:29:49 2014
New Revision: 25281
URL: http://svn.gna.org/viewcvs/relax?rev=25281&view=rev
Log:
Increased the printouts for the script for calculating the numerical gradient
for an exponential curve.
Modified:
trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log
trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py
Modified:
trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log
URL:
http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log?rev=25281&r1=25280&r2=25281&view=diff
==============================================================================
--- trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log
(original)
+++ trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log
Tue Aug 26 12:29:49 2014
@@ -1,4 +1,58 @@
+
+
+On-minimum:
+
+dR: R=0.999950, I=[1000.0, 367.89783560335786, 135.34881744163533,
49.79453698825166, 18.319302382849127], chi2=0.000006
+dR: R=0.999960, I=[1000.0, 367.8941566433967, 135.3461104923561,
49.79304317454932, 18.31856962540906], chi2=0.000004
+dR: R=0.999970, I=[1000.0, 367.89047772022485, 135.34340359721526,
49.79154940566073, 18.317836897278703], chi2=0.000002
+dR: R=0.999980, I=[1000.0, 367.88679883384214, 135.34069675621183,
49.790055681584526, 18.31710419845689], chi2=0.000001
+dR: R=0.999990, I=[1000.0, 367.883119984248, 135.33798996934465,
49.78856200231937, 18.316371528942433], chi2=0.000000
+dR: R=1.000000, I=[1000.0, 367.87944117144235, 135.3352832366127,
49.787068367863945, 18.315638888734178], chi2=0.000000
+dR: R=1.000010, I=[1000.0, 367.87576239542454, 135.3325765580148,
49.78557477821685, 18.314906277830943], chi2=0.000000
+dR: R=1.000020, I=[1000.0, 367.8720836561943, 135.32986993355004,
49.784081233376824, 18.314173696231567], chi2=0.000001
+dR: R=1.000030, I=[1000.0, 367.8684049537513, 135.32716336321715,
49.7825877333424, 18.31344114393486], chi2=0.000002
+dR: R=1.000040, I=[1000.0, 367.8647262880951, 135.3244568470151,
49.781094278112356, 18.312708620939656], chi2=0.000004
+dR: R=1.000050, I=[1000.0, 367.86104765922533, 135.32175038494287,
49.779600867685254, 18.311976127244783], chi2=0.000006
+dI0: I0=999.999950, I=[999.99995, 367.87942277747027, 135.33527646984854,
49.787065878510525, 18.315637972952235], chi2=0.000000
+dI0: I0=999.999960, I=[999.99996, 367.8794264562647, 135.33527782320138,
49.78706637638121, 18.315638156108623], chi2=0.000000
+dI0: I0=999.999970, I=[999.99997, 367.87943013505907, 135.3352791765542,
49.78706687425189, 18.31563833926501], chi2=0.000000
+dI0: I0=999.999980, I=[999.99998, 367.87943381385355, 135.33528052990704,
49.78706737212258, 18.315638522421402], chi2=0.000000
+dI0: I0=999.999990, I=[999.99999, 367.8794374926479, 135.33528188325988,
49.787067869993265, 18.31563870557779], chi2=0.000000
+dI0: I0=1000.000000, I=[1000.0, 367.87944117144235, 135.3352832366127,
49.787068367863945, 18.315638888734178], chi2=0.000000
+dI0: I0=1000.000010, I=[1000.00001, 367.8794448502367, 135.33528458996554,
49.787068865734625, 18.315639071890566], chi2=0.000000
+dI0: I0=1000.000020, I=[1000.00002, 367.87944852903115, 135.33528594331835,
49.78706936360531, 18.315639255046957], chi2=0.000000
+dI0: I0=1000.000030, I=[1000.00003, 367.8794522078256, 135.3352872966712,
49.787069861476, 18.315639438203345], chi2=0.000000
+dI0: I0=1000.000040, I=[1000.00004, 367.87945588662, 135.33528865002404,
49.78707035934668, 18.315639621359736], chi2=0.000000
+dI0: I0=1000.000050, I=[1000.00005, 367.8794595654144, 135.33529000337685,
49.787070857217365, 18.315639804516124], chi2=0.000000
+
The gradient at [1.0, 1000.0] is:
[-1.0995282792650802e-09, 2.1826111665238544e-12]
+
+
+Off-minimum:
+
+dR: R=1.999950, I=[500.0, 67.67102508497324, 9.158735272102152,
1.239562008690108, 0.16776486356889617], chi2=3587.318201
+dR: R=1.999960, I=[500.0, 67.67034837810591, 9.158552099228444,
1.2395248223876438, 0.16775815310856348], chi2=3587.322764
+dR: R=1.999970, I=[500.0, 67.66967167800564, 9.158368930018156,
1.239487637200753, 0.16775144291664382], chi2=3587.327328
+dR: R=1.999980, I=[500.0, 67.66899498467232, 9.158185764471217,
1.2394504531294002, 0.16774473299312648], chi2=3587.331892
+dR: R=1.999990, I=[500.0, 67.66831829810592, 9.158002602587555,
1.2394132701735547, 0.16773802333800084], chi2=3587.336456
+dR: R=2.000000, I=[500.0, 67.66764161830635, 9.157819444367089,
1.2393760883331792, 0.16773131395125593], chi2=3587.341019
+dR: R=2.000010, I=[500.0, 67.66696494527353, 9.157636289809753,
1.239338907608242, 0.16772460483288107], chi2=3587.345583
+dR: R=2.000020, I=[500.0, 67.6662882790074, 9.157453138915471,
1.239301727998711, 0.16771789598286563], chi2=3587.350147
+dR: R=2.000030, I=[500.0, 67.66561161950791, 9.157269991684169,
1.2392645495045516, 0.1677111874011988], chi2=3587.354710
+dR: R=2.000040, I=[500.0, 67.66493496677502, 9.157086848115783,
1.2392273721257314, 0.16770447908787017], chi2=3587.359274
+dR: R=2.000050, I=[500.0, 67.66425832080859, 9.156903708210226,
1.2391901958622136, 0.1676977710428684], chi2=3587.363837
+dI0: I0=499.999950, I=[499.99995, 67.66763485154219, 9.157818528585144,
1.2393759643955704, 0.16773129717812454], chi2=3587.341562
+dI0: I0=499.999960, I=[499.99996, 67.66763620489502, 9.157818711741534,
1.2393759891830922, 0.16773130053275082], chi2=3587.341454
+dI0: I0=499.999970, I=[499.99997, 67.66763755824786, 9.157818894897924,
1.239376013970614, 0.1677313038873771], chi2=3587.341345
+dI0: I0=499.999980, I=[499.99998, 67.66763891160069, 9.157819078054311,
1.2393760387581356, 0.16773130724200336], chi2=3587.341237
+dI0: I0=499.999990, I=[499.99999, 67.66764026495352, 9.157819261210701,
1.2393760635456574, 0.16773131059662966], chi2=3587.341128
+dI0: I0=500.000000, I=[500.0, 67.66764161830635, 9.157819444367089,
1.2393760883331792, 0.16773131395125593], chi2=3587.341019
+dI0: I0=500.000010, I=[500.00001, 67.66764297165918, 9.157819627523478,
1.2393761131207008, 0.1677313173058822], chi2=3587.340911
+dI0: I0=500.000020, I=[500.00002, 67.66764432501202, 9.157819810679868,
1.2393761379082229, 0.1677313206605085], chi2=3587.340802
+dI0: I0=500.000030, I=[500.00003, 67.66764567836485, 9.157819993836256,
1.2393761626957445, 0.16773132401513477], chi2=3587.340694
+dI0: I0=500.000040, I=[500.00004, 67.66764703171768, 9.157820176992646,
1.2393761874832663, 0.16773132736976104], chi2=3587.340585
+dI0: I0=500.000050, I=[500.00005, 67.6676483850705, 9.157820360149033,
1.239376212270788, 0.1677313307243873], chi2=3587.340476
+
The gradient at [2.0, 500.0] is:
[456.36655522098829, -10.8613338920982]
Modified:
trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py?rev=25281&r1=25280&r2=25281&view=diff
==============================================================================
--- trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py
(original)
+++ trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py
Tue Aug 26 12:29:49 2014
@@ -21,6 +21,9 @@
for i in range(len(times)):
chi2 += (I[i] - back_calc[i])**2 / errors[i]**2
+ # Printout.
+ print("dR: R=%f, I=%s, chi2=%f" % (R, back_calc, chi2))
+
# Return the value.
return chi2
@@ -40,6 +43,9 @@
for i in range(len(times)):
chi2 += (I[i] - back_calc[i])**2 / errors[i]**2
+ # Printout.
+ print("dI0: I0=%f, I=%s, chi2=%f" % (I0, back_calc, chi2))
+
# Return the value.
return chi2
@@ -58,13 +64,15 @@
errors = [10.0, 10.0, 10.0, 10.0, 10.0]
# The numeric gradient at the minimum.
+print("\n\nOn-minimum:\n")
grad_R = derivative(func_R, R, dx=1e-5, order=11)
grad_I = derivative(func_I, I0, dx=1e-5, order=11)
-print("The gradient at %s is:\n %s" % ([R, I0], [grad_R, grad_I]))
+print("\nThe gradient at %s is:\n %s" % ([R, I0], [grad_R, grad_I]))
# The numeric gradient off the minimum.
+print("\n\nOff-minimum:\n")
R = 2.0
I0 = 500.0
grad_R = derivative(func_R, R, dx=1e-5, order=11)
grad_I = derivative(func_I, I0, dx=1e-5, order=11)
-print("The gradient at %s is:\n %s" % ([R, I0], [grad_R, grad_I]))
+print("\nThe gradient at %s is:\n %s" % ([R, I0], [grad_R, grad_I]))
r25281 - in /trunk/test_suite/shared_data/curve_fitting/numeric_gradient: integrate.log integrate.py