readme updated

Igor_procedure/wavenumber_calibration/README.md

@@ -0,0 +1,38 @@
+# Igor procedure for  wavenumber calibration
+
+ `wavenumber_calibration.ipf` for performing fit for obtaining wavenumber  axis. Data on the band positions in pixels with the uncertainties, as waves in the Igor experiment. Core program is just fitting the pixel with the reference wavelength data. It includes  the function `wavenumber_fit` which  performs the fit.
+
+
+
+`data_waves.itx`  in the present  folder includes  4 waves, `freq`, `freq_sigma`, `pixel` and `pixel_sigma` which  serve as example for  using  the procedure.
+
+Usage
+----------------
+```
+wavenumber_fit ( wavenum_reference, error_in_reference,  band_position_in_pixel, error_band_posn , num_pixel_on_CCD )
+
+```
+wavenum_reference       =   1D wave, list  of  reference Raman transition wavenumbers
+error_in_reference      =   1D wave, error in the above Raman transition wavenumbers
+band_position_in_pixel  =   1D wave, corresponding band  positions on  the CCD (in  pixel)
+error_band_posn         =   1D wave, error in the band positions
+num_pixel_on_CCD        =   variable,  number of pixels  on the CCD,  for example, 1024, 1340  or 1600. This is used for generating  the wavenumber (relative) axis.
+
+
+Example
+-------------------
+```
+•wavenumber_fit ( freq, freq_sigma,  pixel, pixel_sigma , 1600 )
+  Fit converged properly
+  fit_freq= poly(W_coef,x)
+  residual_y= freq[p] - (poly(W_coef,pixel[p]))
+  W_coef={-1045.7,2.0164,-0.00021219,1.8518e-008}
+  V_chisq= 47.7758;V_npnts= 50;V_numNaNs= 0;V_numINFs= 0;
+  V_startRow= 0;V_endRow= 50;
+  W_sigma={0.0871,0.000367,4.57e-07,1.68e-10}
+  Coefficient values ± one standard deviation
+  	K0	=-1045.7 ± 0.0871
+  	K1	=2.0164 ± 0.000367
+  	K2	=-0.00021219 ± 4.57e-007
+  	K3	=1.8518e-008 ± 1.68e-010
+```

Igor_procedure/wavenumber_calibration/data_waves.itx

@@ -0,0 +1,59 @@
+IGOR
+WAVES/D	freq	freq_sigma	pixel_sigma	pixel
+BEGIN
+	-1034.671	0.005	1.7789	5.56498
+	-814.4243	0.005	0.419719	116.162
+	-785.0025000000001	0.005	0.57138	131.091
+	-642.8066	0.005	0.322132	204.129
+	-616.0931	0.005	0.09430180000000001	217.982
+	-587.032	0.005	0.0217854	233.07
+	-529.9002	0.005	0.150557	262.874
+	-443.0814	0.005	0.0251449	308.616
+	-414.6485	0.005	0.0247068	323.659
+	-354.3732	0.005	0.0226542	355.726
+	-297.5338	0.005	0.0235126	386.184
+	-267.0692	0.005	0.0126169	402.58
+	-179.0671	0.005	0.0081102	450.277
+	179.0671	0.005	0.0102317	649.218
+	267.0692	0.005	0.0111202	699.361
+	297.5338	0.005	0.014617	716.829
+	354.3732	0.005	0.011901	749.592
+	414.6485	0.005	0.0101229	784.562
+	443.081	0.005	0.00885463	801.148
+	529.9002	0.005	0.0361401	852.096
+	587.032	0.005	0.00396808	885.934
+	616.093	0.005	0.0148241	903.217
+	642.8066	0.005	0.0463899	919.147
+	752.9199	0.005	0.318837	985.327
+	785.0025000000001	0.005	0.0407458	1004.84
+	814.4243	0.005	0.0259874	1022.73
+	859.8324	0.005	0.6153380000000001	1050.46
+	948.7991	0.005	0.175333	1105.27
+	1034.6707	0.005	0.0357817	1158.67
+	1106.582	0.005	1.15591	1203.82
+	1246.0995	0.005	0.850765	1292.45
+	1447.2809	0.005	0.78952	1421.9
+	1457.878	0.001	0.0813759	1429.8
+	1470.239	0.001	0.0617685	1437.95
+	1482.486	0.001	0.048855	1445.99
+	1494.619	0.001	0.0413536	1454.01
+	1506.636	0.004	0.0390356	1461.95
+	1518.535	0.002	0.0442853	1469.8
+	1530.316	0.001	0.0523088	1477.64
+	1570.566	0.001	0.09862170000000001	1504.34
+	1581.772	0.008999999999999999	0.0485645	1511.87
+	1592.865	0.0005	0.0365348	1519.22
+	1603.819	0.003	0.0330827	1526.57
+	1614.635	0.002	0.0344994	1533.81
+	1625.316	0.003	0.0390946	1540.99
+	1635.857	0.003	0.0511583	1548.03
+	1646.252	0.003	0.06662609999999999	1555.01
+	1656.511	0.003	0.0899358	1561.95
+	1666.7	0.2	0.138815	1568.75
+	1676.6	0.2	0.206553	1575.5
+	1686.4	0.2	0.395686	1582.15
+END
+X SetScale/P x 0,1,"", freq; SetScale y 0,0,"", freq
+X SetScale/P x 0,1,"", freq_sigma; SetScale y 0,0,"", freq_sigma
+X SetScale/P x 0,1,"", pixel_sigma; SetScale y 0,0,"", pixel_sigma
+X SetScale/P x 0,1,"", pixel; SetScale y 0,0,"", pixel

README.md

@@ -56,4 +56,6 @@ Clone the repository or download the zip file. As per your choice of the program
 ## Credits
 *Non-linear optimization in SciPy* :  Travis E. Oliphant. Python for Scientific Computing, Computing in Science & Engineering, 9, 10-20 (2007), DOI:10.1109/MCSE.2007.58
 
+*Matplotlib*  : J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.
+
 *Orthogonal Distance Regression as used in IgorPro and SciPy* : (***i***) P. T. Boggs, R. Byrd, R. Schnabel, SIAM J. Sci. Comput. 1987, 8, 1052. (***ii***) P. T. Boggs, J. R. Donaldson, R. h. Byrd, R. B. Schnabel, ACM Trans. Math. Softw. 1989, 15, 348. (***iii***) J. W. Zwolak, P. T. Boggs, L. T. Watson, ACM Trans. Math. Softw. 2007, 33, 27. (***iv***)  P. T. Boggs and J. E. Rogers, “Orthogonal Distance Regression,” in “Statistical analysis of measurement error models and applications: proceedings of the AMS-IMS-SIAM joint summer research conference held June 10-16, 1989,” Contemporary Mathematics, vol. 112, pg. 186, 1990.