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45 fi
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116 ******************** file simple.dem ********************
117 QStandardPaths: XDG_RUNTIME_DIR not set, defaulting to '/tmp/runtime-andreas'
118 Hit return to continueHit return to continue"simple.dem" line 21: warning: Did you try to plot a complex-valued function?
119 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** file controls.dem ********************
120 Hit return to continue******************** file electron.dem ********************
121 Hit return to continueHit return to continueHit return to continue******************** file using.dem ********************
122 Hit return to continueHit return to continue******************** file fillstyle.dem ********************
123 Now draw the boxes with solid fillNow draw the boxes with a black borderNow make the boxes a little less wideAnd now let's try a different fill densityNow draw the boxes with no borderOr maybe a pattern fill, instead?Finished this demo******************** file fillcvrs.dem ********************
124 Press Return to continuePress Return to continuePress Return to continuePress Return to continuePress Return to continuePress Return to continuePress Return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** file candlesticks.dem ********************
125 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** file autoscale.dem ********************
126 Hit return to continueHit return to continueHit return to continue******************** file bins.dem *************************
127 Hit return to continue******************** smooth splines ********************
128 various splines for smoothing
129 Now apply a smoothing spline, weighted by 1/rel error (-> return)Make it smoother by changing the smoothing weights (-> return)Accentuate the relative changes with logscaling on yNow approximate the data with a bezier curve between the endpoints (-> return)You would rather use log-scales ? (-> return)Same thing in 3D - planar caseSame thing in 3D general caseHit return to continueHit <cr> to continueHit <cr> to continue<cr> to continue<cr> to continue****************** file convex_hull.dem ********************
130 <cr> to continue<cr> to continue<cr> to continue****************** file concave_hull.dem ********************
131 <cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue****************** file mask_pm3d.dem ********************
133 # Curve 0 of 1, 9 points
134 # Curve title: "Convex hull"
135 # x y type
136 -24.4571 1.79031 i
137 -15.0611 21.314 i
138 7.8984 20.8154 i
139 23.4373 14.0071 i
140 19.34 -12.7014 i
141 12.2994 -22.9166 i
142 2.14596 -24.9946 i
143 -5.11691 -18.2533 i
144 -24.4571 1.79031 i
147 <cr> to continue<cr> to continue<cr> to continue******************** file errorbars.dem ********************
148 various styles of errorbar
149 Would you like boxes? (-> return)Only X-Bars? (-> return)Only Y-Bars? (-> return)Logscaled? (-> return)X as well? (-> return)If you like bars without tics (-> return)X-Bars only (-> return)Y-Bars only (-> return)filledcurve shaded error regionHit return to continue******************** file zerror.dem ********************
150 Hit return to continueHit return to continue******************** file fit.dem ********************
151 Some examples how data fitting using nonlinear least squares fit can be done.
153 We fit a straight line to the data -- only as a demo without physical meaning.
154 fit function: l(x) = y0 + m*x
155 initial parameters: y0 = 1.1, m = -0.1
156 fit command: fit l(x) 'lcdemo.dat' via y0, m
158 Now start fitting... (-> return)Press enter to proceed with the next example.
159 Now use the real single-measurement weights from column 5. (Look at the file
160 lcdemo.dat and compare the columns to see the difference.)
161 Since these are weights we rescale the resulting parameter errors.
162 fit settings: set fit errorscaling
163 fit command : fit l(x) 'lcdemo.dat' using 1:2:5 yerr via y0, m
165 Press enter to start the fit.Press enter to proceed with the next example.
166 It's time now to try a more realistic model function:
168 density(x) = x < Tc ? curve(x)+lowlin(x) : high(x)
169 curve(x) = b*tanh(g*(Tc-x))
170 lowlin(x) = ml*(x-Tc) + dens_Tc
171 high(x) = mh*(x-Tc) + dens_Tc
173 density(x) is a function which shall fit the whole temperature range using
174 a ?: expression. It contains 6 model parameters which will all be varied. Now
175 take the start parameters out of the file 'start.par' and plot the function.
176 fit command: fit density(x) 'lcdemo.dat' using 1:2:5 yerror via 'start.par'
178 Press enter to start the fit.Press enter to proceed with the next example.
180 Now a brief demonstration of 3d fitting.
181 hemisphr.dat contains random points on a hemisphere of radius 1, but we let
182 fit figure this out for us. It takes many iterations, so we limit them to 50.
183 We also do not want intermediate results here.
184 fit settings: set fit results maxiter 50
185 "fit.dem" line 112: warning: Did you try to plot a complex-valued function?
186 fit function: h(x,y) = sqrt(r*r - (abs(x-x0))**2.2 - (abs(y-y0))**1.8) + z0
187 fit command : fit h(x,y) 'hemisphr.dat' using 1:2:3 via r, x0, y0, z0
188 Press enter to start the fit.
189 After 50 iterations the fit converged.
190 final sum of squares of residuals : 0.080165
191 rel. change during last iteration : 0
193 degrees of freedom (FIT_NDF) : 245
194 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0180888
195 variance of residuals (reduced chisquare) = WSSR/ndf : 0.000327204
197 Final set of parameters Asymptotic Standard Error
198 ======================= ==========================
199 r = 1.00225 +/- 0.0003641 (0.03632%)
200 x0 = -0.000450691 +/- 0.0003929 (87.17%)
201 y0 = 0.000201292 +/- 0.0004735 (235.3%)
202 z0 = -0.00104521 +/- 0.001526 (146%)
204 correlation matrix of the fit parameters:
205 r x0 y0 z0
206 r 1.000
207 x0 0.140 1.000
208 y0 0.085 -0.424 1.000
209 z0 -0.658 -0.045 -0.049 1.000
210 "fit.dem" line 120: warning: Did you try to plot a complex-valued function?
212 Notice, however, that this would converge much faster when fitted in a more
213 appropriate co-ordinate system:
214 fit r 'hemisphr.dat' using 0:($1*$1+$2*$2+$3*$3) via r
215 where we are fitting f(x)=r to the radii calculated as the data is read from
216 the file. No x value is required in this case.
217 (This is left as an exercise for the user).
219 Another possibility is to prescale the variables (set fit prescale),
220 which may improve convergence.
221 fit settings: set fit maxiter 50 prescale
222 fit command : fit h(x,y) 'hemisphr.dat' using 1:2:3 via r, x0,y0,z0
223 Press enter to proceed with the next example.
225 Now an example on how to fit multi-branch functions.
226 The model consists of two branches, the first describing longitudinal sound
227 velocity as function of propagation direction (upper data, from dataset 1),
228 the second describing transverse sound velocity (lower data, from dataset 0).
230 The model uses these data in order to fit elastic stiffnesses which occur
231 differently in both branches.
232 fit function: f(x,y) = y==1 ? vlong(x) : vtrans(x)
233 vlong(x) = sqrt(1.0/2.0/rho*1e9*(main(x) + mixed(x)))
234 vtrans(x) = sqrt(1.0/2.0/rho*1e9*(main(x) - mixed(x)))
235 y will be the index of the dataset.
236 fit command: fit f(x,y) 'soundvel.dat' using 1:-2:2 via 'sound.par'
238 Press enter to start the fit.iter chisq delta/lim lambda c33 c11 c44 c13 phi0
239 0 1.6651778833e+07 0.00e+00 1.06e+02 9.000000e+00 6.000000e+00 1.000000e+00 4.000000e+00 2.000000e+01
240 1 3.7115794520e+06 -3.49e+05 1.06e+01 1.107842e+01 5.715164e+00 1.112984e+00 5.269471e+00 5.489671e+00
241 2 3.0952217805e+05 -1.10e+06 1.06e+00 1.250349e+01 5.473118e+00 6.767568e-01 4.359096e+00 -2.308544e+00
242 3 7.9135498639e+04 -2.91e+05 1.06e-01 1.257557e+01 5.490760e+00 7.047546e-01 4.019414e+00 -3.385802e-01
243 4 7.8701397376e+04 -5.52e+02 1.06e-02 1.258878e+01 5.490036e+00 7.019290e-01 3.998785e+00 -3.997977e-01
244 5 7.8701391418e+04 -7.57e-03 1.06e-03 1.258874e+01 5.490047e+00 7.019482e-01 3.998746e+00 -3.995830e-01
245 iter chisq delta/lim lambda c33 c11 c44 c13 phi0
247 After 5 iterations the fit converged.
248 final sum of squares of residuals : 78701.4
249 rel. change during last iteration : -7.57102e-08
251 degrees of freedom (FIT_NDF) : 144
252 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 23.3781
253 variance of residuals (reduced chisquare) = WSSR/ndf : 546.537
255 Final set of parameters Asymptotic Standard Error
256 ======================= ==========================
257 c33 = 12.5887 +/- 0.02898 (0.2302%)
258 c11 = 5.49005 +/- 0.01846 (0.3363%)
259 c44 = 0.701948 +/- 0.009755 (1.39%)
260 c13 = 3.99875 +/- 0.03177 (0.7946%)
261 phi0 = -0.399583 +/- 0.13 (32.54%)
263 correlation matrix of the fit parameters:
264 c33 c11 c44 c13 phi0
265 c33 1.000
266 c11 -0.066 1.000
267 c44 -0.198 -0.278 1.000
268 c13 -0.141 0.028 -0.086 1.000
269 phi0 0.114 -0.022 0.034 0.181 1.000
271 Look at the file 'hexa.fnc' to see how the branches are realized using the
272 data index as input for a pseudo-3d fit.
274 Press enter to proceed with the next example.Next we only use every fifth data point for fitting by using the 'every'
275 keyword. Note the faster fit and its result.
276 fit command: fit f(x,y) 'soundvel.dat' every 5 using 1:-2:2 via 'sound.par'
278 Press enter to start the fit.iter chisq delta/lim lambda c33 c11 c44 c13 phi0
279 0 3.4156363488e+06 0.00e+00 1.06e+02 9.000000e+00 6.000000e+00 1.000000e+00 4.000000e+00 2.000000e+01
280 1 1.7633147044e+06 -9.37e+04 1.06e+01 1.068004e+01 5.714969e+00 1.220411e+00 5.416989e+00 1.563561e+01
281 2 3.6812403684e+05 -3.79e+05 1.06e+00 1.154575e+01 5.621298e+00 9.265286e-01 5.024562e+00 -3.686271e+00
282 3 2.6359224461e+04 -1.30e+06 1.06e-01 1.253003e+01 5.480326e+00 6.995740e-01 4.092691e+00 -5.722734e-02
283 4 1.9074727803e+04 -3.82e+04 1.06e-02 1.254656e+01 5.491040e+00 7.055514e-01 3.941309e+00 -9.110937e-01
284 5 1.9071441847e+04 -1.72e+01 1.06e-03 1.254887e+01 5.490704e+00 7.054929e-01 3.937661e+00 -8.989885e-01
285 6 1.9071441717e+04 -6.83e-04 1.06e-04 1.254886e+01 5.490728e+00 7.054880e-01 3.937655e+00 -8.989317e-01
286 iter chisq delta/lim lambda c33 c11 c44 c13 phi0
288 After 6 iterations the fit converged.
289 final sum of squares of residuals : 19071.4
290 rel. change during last iteration : -6.82518e-09
292 degrees of freedom (FIT_NDF) : 26
293 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 27.0835
294 variance of residuals (reduced chisquare) = WSSR/ndf : 733.517
296 Final set of parameters Asymptotic Standard Error
297 ======================= ==========================
298 c33 = 12.5489 +/- 0.07395 (0.5893%)
299 c11 = 5.49073 +/- 0.04794 (0.8732%)
300 c44 = 0.705488 +/- 0.02385 (3.381%)
301 c13 = 3.93766 +/- 0.08229 (2.09%)
302 phi0 = -0.898932 +/- 0.3067 (34.11%)
304 correlation matrix of the fit parameters:
305 c33 c11 c44 c13 phi0
306 c33 1.000
307 c11 -0.067 1.000
308 c44 -0.227 -0.251 1.000
309 c13 -0.196 0.051 -0.066 1.000
310 phi0 0.086 0.006 -0.005 0.147 1.000
312 When you compare the results (see 'fit.log') you will note that the error of
313 the fitted parameters have become larger, and the quality of the plot is only
314 slightly affected.
315 Press enter to proceed with the next example.
316 By marking some parameters as '# FIXED' in the parameter file, you fit only
317 the others (c44 and c13 are fixed here).
318 Press enter to start the fit.iter chisq delta/lim lambda c33 c11 phi0
319 0 9.7945909430e+06 0.00e+00 7.60e+01 9.000000e+00 6.000000e+00 1.000000e-04
320 1 5.6703596465e+05 -1.63e+06 7.60e+00 1.220149e+01 5.310817e+00 -7.224014e-01
321 2 5.3024065948e+05 -6.94e+03 7.60e-01 1.240113e+01 5.340579e+00 -1.080402e-01
322 3 5.3014685038e+05 -1.77e+01 7.60e-02 1.240095e+01 5.340080e+00 -1.667665e-01
323 4 5.3014624975e+05 -1.13e-01 7.60e-03 1.240106e+01 5.340147e+00 -1.620752e-01
324 iter chisq delta/lim lambda c33 c11 phi0
326 After 4 iterations the fit converged.
327 final sum of squares of residuals : 530146
328 rel. change during last iteration : -1.13295e-06
330 degrees of freedom (FIT_NDF) : 146
331 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 60.2589
332 variance of residuals (reduced chisquare) = WSSR/ndf : 3631.14
334 Final set of parameters Asymptotic Standard Error
335 ======================= ==========================
336 c33 = 12.4011 +/- 0.07251 (0.5847%)
337 c11 = 5.34015 +/- 0.0462 (0.8651%)
338 phi0 = -0.162075 +/- 0.3543 (218.6%)
340 correlation matrix of the fit parameters:
341 c33 c11 phi0
342 c33 1.000
343 c11 -0.135 1.000
344 phi0 0.153 0.001 1.000
346 This has the same effect as specifying only the real free parameters by
347 the 'via' syntax:
348 fit f(x) 'soundvel.dat' via c33, c11, phi0
350 Press enter to proceed with the next example.
352 Here comes an example of a rather complex function.
354 First a plot with all parameters set to initial values.
355 Now fit the model function to the data.
356 fit settings: set fit limit 1e-10
357 fit function: R(x) = sinh(A*a(x)) * exp(-1.*A*(1.+a(x)))
358 a(x) = W(x) * Q(tc) / mu
359 W(x) = 1./(sqrt(2.*pi)*eta) * exp( -1. * x**2 / (2.*eta**2) )
360 initial parameters:
361 eta = 0.00012
362 tc = 0.0018
363 fit command : fit R(x) 'moli3.dat' u 1:2:3 zerror via eta, tc
365 now start fitting... (-> return)iter chisq delta/lim lambda eta tc
366 0 1.1441984213e+04 0.00e+00 2.76e+05 1.200000e-04 1.800000e-03
367 1 5.3171854336e+03 -1.15e+10 2.76e+04 1.043852e-04 1.837367e-03
368 2 4.6879093617e+03 -1.34e+09 2.76e+03 1.018093e-04 2.009651e-03
369 3 4.6734120845e+03 -3.10e+07 2.76e+02 1.010031e-04 2.024420e-03
370 4 4.6729937953e+03 -8.95e+05 2.76e+01 1.008229e-04 2.021774e-03
371 5 4.6729736309e+03 -4.32e+04 2.76e+00 1.007894e-04 2.021375e-03
372 6 4.6729718879e+03 -3.73e+03 2.76e-01 1.007831e-04 2.021299e-03
373 7 4.6729716327e+03 -5.46e+02 2.76e-02 1.007819e-04 2.021285e-03
374 8 4.6729715874e+03 -9.70e+01 2.76e-03 1.007817e-04 2.021282e-03
375 9 4.6729715790e+03 -1.80e+01 2.76e-04 1.007817e-04 2.021282e-03
376 10 4.6729715774e+03 -3.37e+00 2.76e-05 1.007817e-04 2.021282e-03
377 11 4.6729715771e+03 -6.32e-01 2.76e-06 1.007817e-04 2.021282e-03
378 iter chisq delta/lim lambda eta tc
380 After 11 iterations the fit converged.
381 final sum of squares of residuals : 4672.97
382 rel. change during last iteration : -6.32403e-11
384 degrees of freedom (FIT_NDF) : 123
385 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.16374
386 variance of residuals (reduced chisquare) = WSSR/ndf : 37.9916
387 p-value of the Chisq distribution (FIT_P) : 0
389 Final set of parameters Standard Deviation
390 ======================= ==========================
391 eta = 0.000100782 +/- 3.184e-07 (0.3159%)
392 tc = 0.00202128 +/- 1.282e-05 (0.6342%)
394 correlation matrix of the fit parameters:
395 eta tc
396 eta 1.000
397 tc 0.426 1.000
399 Looking at the plot of the resulting fit curve, you can see that this function
400 doesn't really fit this set of data points. This would normally be a reason to
401 to check for measurement problems not yet accounted for, and maybe even re-think
402 the theoretic prediction in use.
404 Press enter to proceed with the next example.
405 Next we show a fit with three independent variables.
406 The file fit3.dat has four columns, with values of the three independent
407 variables x, y and t, and the'resulting value z. The data lines are in four
408 sections, with t being constant within each section. The sections are separated
409 by two blank lines, so we can select sections with "index" modifiers. Here are
410 the data in the first section, where t = -3.
412 We will fit the function a0/(1 + a1*x**2 + a2*y**2) to these data. Since at
413 this point we have two independent variables, our "using" spec has four entries,
414 representing x:y:z:s (where s is the estimated error in the z value).
415 fit function: f1(x,y)=a0/(1+a1*x**2+a2*y**2)
416 fit command : fit f1(x,y) 'fit3.dat' index 0 using 1:2:4 via a0,a1,a2
418 Press enter to start the fit.iter chisq delta/lim lambda a0 a1 a2
419 0 1.9200759829e+02 0.00e+00 1.08e+00 1.000000e+00 1.000000e-01 1.000000e-01
420 * 2.1341288746e+05 9.99e+09 1.08e+01 -1.996446e+00 -9.317214e-02 -6.894074e-02
421 1 1.2155812773e+02 -5.80e+09 1.08e+00 6.747760e-01 3.330668e-01 3.459786e-01
422 2 6.4591509465e+00 -1.78e+11 1.08e-01 -2.166519e+00 4.014935e-01 5.408270e-01
423 3 1.0813895568e+00 -4.97e+10 1.08e-02 -3.016252e+00 5.534097e-01 4.635940e-01
424 4 1.0604896443e+00 -1.97e+08 1.08e-03 -3.021526e+00 5.281087e-01 4.842650e-01
425 5 1.0604647203e+00 -2.35e+05 1.08e-04 -3.022777e+00 5.291208e-01 4.850168e-01
426 6 1.0604647180e+00 -2.20e+01 1.08e-05 -3.022759e+00 5.291036e-01 4.850186e-01
427 7 1.0604647180e+00 -2.62e-01 1.08e-06 -3.022760e+00 5.291039e-01 4.850187e-01
428 iter chisq delta/lim lambda a0 a1 a2
430 After 7 iterations the fit converged.
431 final sum of squares of residuals : 1.06046
432 rel. change during last iteration : -2.61567e-11
434 degrees of freedom (FIT_NDF) : 118
435 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0947997
436 variance of residuals (reduced chisquare) = WSSR/ndf : 0.00898699
438 Final set of parameters Asymptotic Standard Error
439 ======================= ==========================
440 a0 = -3.02276 +/- 0.05612 (1.857%)
441 a1 = 0.529104 +/- 0.02538 (4.798%)
442 a2 = 0.485019 +/- 0.02338 (4.821%)
444 correlation matrix of the fit parameters:
445 a0 a1 a2
446 a0 1.000
447 a1 -0.636 1.000
448 a2 -0.638 0.235 1.000
449 Press enter to proceed with the next example.
450 Here is the last set of data where t = 3.
451 We fit the same function to this set.
452 fit function: f1(x,y)=a0/(1+a1*x**2+a2*y**2)
453 fit command : fit f1(x,y) 'fit3.dat' index 3 using 1:2:4 via a0,a1,a2
455 Press enter to start the fit.iter chisq delta/lim lambda a0 a1 a2
456 0 2.7120346202e+02 0.00e+00 4.18e-01 -3.022760e+00 5.291039e-01 4.850187e-01
457 1 5.0488012023e+00 -5.27e+11 4.18e-02 2.596854e+00 6.962571e-01 5.636897e-01
458 2 1.4652164280e+00 -2.45e+10 4.18e-03 3.093112e+00 4.150048e-01 5.365566e-01
459 3 1.1757252546e+00 -2.46e+09 4.18e-04 3.115259e+00 4.994275e-01 5.471436e-01
460 4 1.1726111180e+00 -2.66e+07 4.18e-05 3.120030e+00 5.112617e-01 5.467837e-01
461 5 1.1726107387e+00 -3.23e+03 4.18e-06 3.119771e+00 5.112950e-01 5.466635e-01
462 6 1.1726107381e+00 -5.57e+00 4.18e-07 3.119777e+00 5.112965e-01 5.466665e-01
463 * 1.1726107381e+00 1.67e-01 4.18e-06 3.119777e+00 5.112964e-01 5.466664e-01
464 * 1.1726107381e+00 1.67e-01 4.18e-05 3.119777e+00 5.112964e-01 5.466664e-01
465 * 1.1726107381e+00 1.67e-01 4.18e-04 3.119777e+00 5.112964e-01 5.466664e-01
466 * 1.1726107381e+00 1.67e-01 4.18e-03 3.119777e+00 5.112964e-01 5.466664e-01
467 * 1.1726107381e+00 1.67e-01 4.18e-02 3.119777e+00 5.112964e-01 5.466664e-01
468 * 1.1726107381e+00 1.67e-01 4.18e-01 3.119777e+00 5.112964e-01 5.466664e-01
469 * 1.1726107381e+00 1.54e-01 4.18e+00 3.119777e+00 5.112964e-01 5.466664e-01
470 * 1.1726107381e+00 3.65e-02 4.18e+01 3.119777e+00 5.112964e-01 5.466665e-01
471 * 1.1726107381e+00 8.58e-04 4.18e+02 3.119777e+00 5.112965e-01 5.466665e-01
472 * 1.1726107381e+00 7.57e-06 4.18e+03 3.119777e+00 5.112965e-01 5.466665e-01
473 7 1.1726107381e+00 -1.89e-06 4.18e+02 3.119777e+00 5.112965e-01 5.466665e-01
474 iter chisq delta/lim lambda a0 a1 a2
476 After 7 iterations the fit converged.
477 final sum of squares of residuals : 1.17261
478 rel. change during last iteration : -1.89359e-16
480 degrees of freedom (FIT_NDF) : 117
481 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.100112
482 variance of residuals (reduced chisquare) = WSSR/ndf : 0.0100223
484 Final set of parameters Asymptotic Standard Error
485 ======================= ==========================
486 a0 = 3.11978 +/- 0.0752 (2.41%)
487 a1 = 0.511296 +/- 0.02835 (5.544%)
488 a2 = 0.546667 +/- 0.03019 (5.522%)
490 correlation matrix of the fit parameters:
491 a0 a1 a2
492 a0 1.000
493 a1 0.718 1.000
494 a2 0.716 0.376 1.000
495 Press enter to proceed with the next example.
496 We also have data for several intermediate values of t. We will fit the
497 function f(x,y,t)=a0*t/(1+a1*x**2+a2*y**2) to all the data.
498 fit function: f(x,y,t)=a0*t/(1+a1*x**2+a2*y**2)
499 fit command : fit f(x,y,t) 'fit3.dat' u 1:2:3:4 via a0,a1,a2
501 Press enter to start the fit.iter chisq delta/lim lambda a0 a1 a2
502 0 6.6327563650e+02 0.00e+00 9.11e-01 3.119777e+00 5.112965e-01 5.466665e-01
503 1 4.6639172676e+00 -1.41e+12 9.11e-02 1.058306e+00 5.232988e-01 5.443153e-01
504 2 4.5674642800e+00 -2.11e+08 9.11e-03 1.021357e+00 5.174850e-01 5.083111e-01
505 3 4.5674523432e+00 -2.61e+04 9.11e-04 1.021796e+00 5.177911e-01 5.088947e-01
506 4 4.5674523419e+00 -2.84e+00 9.11e-05 1.021790e+00 5.177820e-01 5.088906e-01
507 5 4.5674523419e+00 -4.67e-02 9.11e-06 1.021790e+00 5.177821e-01 5.088907e-01
508 iter chisq delta/lim lambda a0 a1 a2
510 After 5 iterations the fit converged.
511 final sum of squares of residuals : 4.56745
512 rel. change during last iteration : -4.67264e-12
514 degrees of freedom (FIT_NDF) : 480
515 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0975476
516 variance of residuals (reduced chisquare) = WSSR/ndf : 0.00951553
518 Final set of parameters Asymptotic Standard Error
519 ======================= ==========================
520 a0 = 1.02179 +/- 0.01414 (1.384%)
521 a1 = 0.517782 +/- 0.01766 (3.41%)
522 a2 = 0.508891 +/- 0.01737 (3.414%)
524 correlation matrix of the fit parameters:
525 a0 a1 a2
526 a0 1.000
527 a1 0.669 1.000
528 a2 0.669 0.289 1.000
530 Here are all the data together.
532 You can use ranges to rename variables and/or limit the data included in the
533 fit. The first range corresponds to the first "using" entry, etc. For example,
534 we could have gotten the same fit like this:
535 fit [lon=*:*][lat=*:*][time=*:*] \
536 a0*time/(1 + a1*lon**2 + a2*lat**2) \
537 "fit3.dat" u 1:2:3:4 via a0,a1,a2
538 Press enter to proceed with the next example.
540 The fit command can handle errors in the independent variable, too.
541 The problem shown here is Pearson's data with York's weights.
543 First draw the data with uncertainties and the initial function.
545 Press enter to fit the data using no error values
547 Press enter to fit the data using no error valueslambda start value set: 1
548 iter chisq delta/lim lambda a1 a2
549 0 4.6100000000e+00 0.00e+00 1.00e+00 5.000000e+00 -5.000000e-01
550 1 8.0189138044e-01 -4.75e+08 1.00e-01 5.741046e+00 -5.350813e-01
551 2 8.0066352303e-01 -1.53e+05 1.00e-02 5.761170e+00 -5.395735e-01
552 3 8.0066352224e-01 -9.89e-02 1.00e-03 5.761185e+00 -5.395773e-01
553 iter chisq delta/lim lambda a1 a2
555 After 3 iterations the fit converged.
556 final sum of squares of residuals : 0.800664
557 rel. change during last iteration : -9.88845e-10
559 degrees of freedom (FIT_NDF) : 8
560 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.316359
561 variance of residuals (reduced chisquare) = WSSR/ndf : 0.100083
563 Final set of parameters Asymptotic Standard Error
564 ======================= ==========================
565 a1 = 5.76119 +/- 0.1895 (3.289%)
566 a2 = -0.539577 +/- 0.04213 (7.807%)
568 correlation matrix of the fit parameters:
569 a1 a2
570 a1 1.000
571 a2 -0.849 1.000
573 Press enter to fit the data using only the uncertainties of the y-values.
575 Press enter to fit the data using only the uncertainties of the y-values.lambda start value set: 1
576 iter chisq delta/lim lambda a1y a2y
577 0 1.4962550000e+02 0.00e+00 1.00e+00 5.000000e+00 -5.000000e-01
578 1 3.4345697872e+01 -3.36e+08 1.00e-01 6.095602e+00 -6.101483e-01
579 2 3.4345207498e+01 -1.43e+03 1.00e-02 6.100109e+00 -6.108129e-01
580 3 3.4345207498e+01 -4.10e-06 1.00e-03 6.100109e+00 -6.108130e-01
581 iter chisq delta/lim lambda a1y a2y
583 After 3 iterations the fit converged.
584 final sum of squares of residuals : 34.3452
585 rel. change during last iteration : -4.09628e-14
587 degrees of freedom (FIT_NDF) : 8
588 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2.07199
589 variance of residuals (reduced chisquare) = WSSR/ndf : 4.29315
590 p-value of the Chisq distribution (FIT_P) : 3.51726e-05
592 Final set of parameters Standard Deviation
593 ======================= ==========================
594 a1y = 6.10011 +/- 0.2047 (3.355%)
595 a2y = -0.610813 +/- 0.03009 (4.926%)
597 correlation matrix of the fit parameters:
598 a1y a2y
599 a1y 1.000
600 a2y -0.985 1.000
602 Press enter to fit the data using the uncertainties of the x and y values.
604 Press enter to fit the data using the uncertainties of the x and y values.lambda start value set: 1
605 iter chisq delta/lim lambda a1xy a2xy
606 0 6.3259837889e+01 0.00e+00 1.00e+00 5.000000e+00 -5.000000e-01
607 1 1.1957122097e+01 -4.29e+08 1.00e-01 5.395533e+00 -4.633919e-01
608 2 1.1956475893e+01 -5.40e+03 1.00e-02 5.396066e+00 -4.634515e-01
609 * 1.1956484542e+01 7.23e+01 1.00e-01 5.396062e+00 -4.634507e-01
610 * 1.1956484540e+01 7.23e+01 1.00e+00 5.396062e+00 -4.634507e-01
611 * 1.1956484400e+01 7.12e+01 1.00e+01 5.396062e+00 -4.634507e-01
612 * 1.1956479134e+01 2.71e+01 1.00e+02 5.396064e+00 -4.634512e-01
613 * 1.1956475943e+01 4.19e-01 1.00e+03 5.396066e+00 -4.634515e-01
614 * 1.1956475893e+01 4.16e-03 1.00e+04 5.396066e+00 -4.634515e-01
615 * 1.1956475893e+01 4.03e-05 1.00e+05 5.396066e+00 -4.634515e-01
616 3 1.1956475893e+01 -1.63e-07 1.00e+04 5.396066e+00 -4.634515e-01
617 iter chisq delta/lim lambda a1xy a2xy
619 After 3 iterations the fit converged.
620 final sum of squares of residuals : 11.9565
621 rel. change during last iteration : -1.63425e-15
623 degrees of freedom (FIT_NDF) : 8
624 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 1.22252
625 variance of residuals (reduced chisquare) = WSSR/ndf : 1.49456
626 p-value of the Chisq distribution (FIT_P) : 0.153156
628 Final set of parameters Standard Deviation
629 ======================= ==========================
630 a1xy = 5.39607 +/- 0.2957 (5.479%)
631 a2xy = -0.463451 +/- 0.0578 (12.47%)
633 correlation matrix of the fit parameters:
634 a1xy a2xy
635 a1xy 1.000
636 a2xy -0.964 1.000
639 Summary of the fit results:
640 a1 a2 a1_err a2_err
641 ------------------------------------------------------------------------
642 initial values 5.000e+00 -5.00e-01
643 our result 5.396e+00 -4.63e-01 2.957e-01 5.78e-02
644 ROOT Minuit 5.480e+00 -4.81e-01 2.926e-01 5.76e-02
645 ------------------------------------------------------------------------
647 You can have a look at all previous fit results by looking into the file
648 'fit.log' (or whatever you defined the environment variable 'FIT_LOG' to).
649 Remember that this file will always be appended to, so remove it from time
650 to time.
652 Done with fitting demo (-> return)"fitmulti.dem" line 83: warning:
653 > Implied independent variable y not found in fit function.
654 > Assuming version 4 syntax with zerror in column 3 but no zerror keyword.
656 iter chisq delta/lim lambda c1
657 0 2.1892940362e+01 0.00e+00 6.81e-01 1.000000e+00
658 1 4.5233347856e-02 -4.83e+07 6.81e-02 2.431818e+00
659 2 1.0247233379e-08 -4.41e+11 6.81e-03 2.499968e+00
660 3 2.3236116830e-19 -4.41e+15 6.81e-04 2.500000e+00
661 4 1.9793013150e-28 -1.17e+14 6.81e-05 2.500000e+00
662 5 1.9793013150e-28 0.00e+00 6.81e-06 2.500000e+00
663 iter chisq delta/lim lambda c1
665 After 5 iterations the fit converged.
666 final sum of squares of residuals : 1.9793e-28
667 rel. change during last iteration : 0
669 degrees of freedom (FIT_NDF) : 20
670 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 3.14587e-15
671 variance of residuals (reduced chisquare) = WSSR/ndf : 9.89651e-30
672 p-value of the Chisq distribution (FIT_P) : 1
674 Final set of parameters Standard Deviation
675 ======================= ==========================
676 c1 = 2.5 +/- 0.3206 (12.82%)
677 ---------------------------------------------------------
678 1D fit: expected 2.5 c1 = 2.5
679 ---------------------------------------------------------
680 Hit return to try for a 2D fit"fitmulti.dem" line 99: warning:
681 > Implied independent variable t not found in fit function.
682 > Assuming version 4 syntax with zerror in column 4 but no zerror keyword.
684 iter chisq delta/lim lambda c1 c2
685 0 1.8402267568e+01 0.00e+00 5.08e-01 1.000000e+00 1.000000e+00
686 1 5.1495377021e-01 -3.47e+06 5.08e-02 2.363854e+00 -7.849976e-01
687 2 1.4278686826e-05 -3.61e+09 5.08e-03 2.499431e+00 -1.794590e+00
688 3 4.0743857030e-14 -3.50e+13 5.08e-04 2.500000e+00 -1.800000e+00
689 4 1.1747755312e-26 -3.47e+17 5.08e-05 2.500000e+00 -1.800000e+00
690 5 1.3330381888e-28 -8.71e+06 5.08e-06 2.500000e+00 -1.800000e+00
691 6 1.2866926106e-28 -3.60e+03 5.08e-07 2.500000e+00 -1.800000e+00
692 7 1.2866926106e-28 0.00e+00 5.08e-08 2.500000e+00 -1.800000e+00
693 iter chisq delta/lim lambda c1 c2
695 After 7 iterations the fit converged.
696 final sum of squares of residuals : 1.28669e-28
697 rel. change during last iteration : 0
699 degrees of freedom (FIT_NDF) : 18
700 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2.67363e-15
701 variance of residuals (reduced chisquare) = WSSR/ndf : 7.14829e-30
702 p-value of the Chisq distribution (FIT_P) : 1
704 Final set of parameters Standard Deviation
705 ======================= ==========================
706 c1 = 2.5 +/- 0.3503 (14.01%)
707 c2 = -1.8 +/- 1.432 (79.57%)
709 correlation matrix of the fit parameters:
710 c1 c2
711 c1 1.000
712 c2 -0.403 1.000
713 ---------------------------------------------------------
714 2D fit: expected 2.5 c1 = 2.5
715 2D fit: expected -1.8 c2 = -1.80000000000001
716 ---------------------------------------------------------
717 Hit return to try for a 3D fit"fitmulti.dem" line 115: warning:
718 > Implied independent variable x3 not found in fit function.
719 > Assuming version 4 syntax with zerror in column 5 but no zerror keyword.
721 iter chisq delta/lim lambda c1 c2 c3
722 0 5.0042107018e+05 0.00e+00 1.36e+00 1.000000e+00 1.000000e+00 1.000000e+00
723 1 1.5648231886e+02 -3.20e+08 1.36e-01 2.667794e+00 2.485896e-01 6.876787e+01
724 2 9.5026215217e-04 -1.65e+10 1.36e-02 2.498116e+00 -1.774715e+00 6.999949e+01
725 3 1.4986897994e-11 -6.34e+12 1.36e-03 2.500000e+00 -1.799997e+00 7.000000e+01
726 4 2.5594986155e-23 -5.86e+16 1.36e-04 2.500000e+00 -1.800000e+00 7.000000e+01
727 5 1.5865767741e-24 -1.51e+06 1.36e-05 2.500000e+00 -1.800000e+00 7.000000e+01
728 6 1.5657265887e-24 -1.33e+03 1.36e-06 2.500000e+00 -1.800000e+00 7.000000e+01
729 * 1.5852570098e-24 1.23e+03 1.36e-05 2.500000e+00 -1.800000e+00 7.000000e+01
730 * 1.5852570098e-24 1.23e+03 1.36e-04 2.500000e+00 -1.800000e+00 7.000000e+01
731 * 1.5852570098e-24 1.23e+03 1.36e-03 2.500000e+00 -1.800000e+00 7.000000e+01
732 * 1.5852570098e-24 1.23e+03 1.36e-02 2.500000e+00 -1.800000e+00 7.000000e+01
733 * 1.5852570098e-24 1.23e+03 1.36e-01 2.500000e+00 -1.800000e+00 7.000000e+01
734 * 1.5852570098e-24 1.23e+03 1.36e+00 2.500000e+00 -1.800000e+00 7.000000e+01
735 * 1.5854870121e-24 1.25e+03 1.36e+01 2.500000e+00 -1.800000e+00 7.000000e+01
736 7 1.5657265887e-24 0.00e+00 1.36e+00 2.500000e+00 -1.800000e+00 7.000000e+01
737 iter chisq delta/lim lambda c1 c2 c3
739 After 7 iterations the fit converged.
740 final sum of squares of residuals : 1.56573e-24
741 rel. change during last iteration : 0
743 degrees of freedom (FIT_NDF) : 18
744 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2.94932e-13
745 variance of residuals (reduced chisquare) = WSSR/ndf : 8.69848e-26
746 p-value of the Chisq distribution (FIT_P) : 1
748 Final set of parameters Standard Deviation
749 ======================= ==========================
750 c1 = 2.5 +/- 0.3341 (13.36%)
751 c2 = -1.8 +/- 0.8232 (45.73%)
752 c3 = 70 +/- 0.09942 (0.142%)
754 correlation matrix of the fit parameters:
755 c1 c2 c3
756 c1 1.000
757 c2 -0.228 1.000
758 c3 -0.144 -0.089 1.000
759 ---------------------------------------------------------
760 3D fit: expected 2.5 c1 = 2.50000000000001
761 3D fit: expected -1.8 c2 = -1.80000000000011
762 3D fit: expected 70.0 c3 = 70.0
763 ---------------------------------------------------------
764 Hit return to try for a 4D fit"fitmulti.dem" line 131: warning:
765 > Implied independent variable x4 not found in fit function.
766 > Assuming version 4 syntax with zerror in column 6 but no zerror keyword.
768 iter chisq delta/lim lambda c1 c2 c3 c4
769 0 5.0434414591e+05 0.00e+00 1.23e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
770 1 1.0737393796e+02 -4.70e+08 1.23e-01 2.637883e+00 2.081428e-01 6.896240e+01 -3.465848e+00
771 2 7.6766393648e-04 -1.40e+10 1.23e-02 2.498380e+00 -1.775303e+00 6.999930e+01 -3.204055e+00
772 3 1.1976288234e-11 -6.41e+12 1.23e-03 2.500000e+00 -1.799997e+00 7.000000e+01 -3.200001e+00
773 4 1.9635651177e-23 -6.10e+16 1.23e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
774 5 6.5436327632e-25 -2.90e+06 1.23e-05 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
775 6 6.5213001110e-25 -3.42e+02 1.23e-06 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
776 7 6.5184207687e-25 -4.42e+01 1.23e-07 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
777 8 6.4608339226e-25 -8.91e+02 1.23e-08 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
778 * 6.5173794723e-25 8.68e+02 1.23e-07 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
779 * 6.5173794723e-25 8.68e+02 1.23e-06 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
780 * 6.5173794723e-25 8.68e+02 1.23e-05 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
781 * 6.5173794723e-25 8.68e+02 1.23e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
782 * 6.5173794723e-25 8.68e+02 1.23e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
783 * 6.5173794723e-25 8.68e+02 1.23e-02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
784 * 6.5173794723e-25 8.68e+02 1.23e-01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
785 * 6.5173794723e-25 8.68e+02 1.23e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
786 * 6.5178922319e-25 8.75e+02 1.23e+01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
787 9 6.4608339226e-25 0.00e+00 1.23e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00
788 iter chisq delta/lim lambda c1 c2 c3 c4
790 After 9 iterations the fit converged.
791 final sum of squares of residuals : 6.46083e-25
792 rel. change during last iteration : 0
794 degrees of freedom (FIT_NDF) : 17
795 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 1.94948e-13
796 variance of residuals (reduced chisquare) = WSSR/ndf : 3.80049e-26
797 p-value of the Chisq distribution (FIT_P) : 1
799 Final set of parameters Standard Deviation
800 ======================= ==========================
801 c1 = 2.5 +/- 0.3341 (13.36%)
802 c2 = -1.8 +/- 0.8947 (49.71%)
803 c3 = 70 +/- 0.1026 (0.1466%)
804 c4 = -3.2 +/- 0.3306 (10.33%)
806 correlation matrix of the fit parameters:
807 c1 c2 c3 c4
808 c1 1.000
809 c2 -0.214 1.000
810 c3 -0.137 -0.176 1.000
811 c4 0.010 -0.392 0.247 1.000
812 ---------------------------------------------------------
813 4D fit: expected 2.5 c1 = 2.4999999999999
814 4D fit: expected -1.8 c2 = -1.80000000000033
815 4D fit: expected 70.0 c3 = 70.0
816 4D fit: expected -3.2 c4 = -3.1999999999997
817 ---------------------------------------------------------
818 Hit return to try for a 5D fit"fitmulti.dem" line 149: warning:
819 > Implied independent variable x5 not found in fit function.
820 > Assuming version 4 syntax with zerror in column 7 but no zerror keyword.
822 iter chisq delta/lim lambda c1 c2 c3 c4 c5
823 0 3.5652827793e+05 0.00e+00 2.08e+01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
824 1 8.2628175865e+04 -3.31e+05 2.08e+00 1.131551e+00 1.200143e+00 4.015282e+00 1.106540e+00 3.274197e+00
825 2 2.8797393982e+03 -2.77e+06 2.08e-01 2.268438e+00 3.004089e+00 5.746308e+01 -1.174504e+00 9.548228e-01
826 3 5.7761101766e-02 -4.99e+09 2.08e-02 2.484844e+00 -1.615778e+00 6.995937e+01 -3.211773e+00 4.017438e-01
827 4 5.5575896136e-09 -1.04e+12 2.08e-03 2.499994e+00 -1.799932e+00 6.999999e+01 -3.200009e+00 4.000002e-01
828 5 7.5296124774e-20 -7.38e+15 2.08e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
829 6 6.8714202466e-25 -1.10e+10 2.08e-05 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
830 7 6.6497897753e-25 -3.33e+03 2.08e-06 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
831 * 6.6506654109e-25 1.32e+01 2.08e-05 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
832 * 6.6506654109e-25 1.32e+01 2.08e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
833 * 6.6506654109e-25 1.32e+01 2.08e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
834 * 6.6506654109e-25 1.32e+01 2.08e-02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
835 * 6.6506654109e-25 1.32e+01 2.08e-01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
836 * 6.6500422107e-25 3.80e+00 2.08e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
837 8 6.4747494290e-25 -2.70e+03 2.08e-01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
838 * 6.9279657963e-25 6.54e+03 2.08e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
839 * 6.6666004012e-25 2.88e+03 2.08e+01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
840 * 6.6524640137e-25 2.67e+03 2.08e+02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
841 * 6.5555287857e-25 1.23e+03 2.08e+03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
842 9 6.4747494290e-25 0.00e+00 2.08e+02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01
843 iter chisq delta/lim lambda c1 c2 c3 c4 c5
845 After 9 iterations the fit converged.
846 final sum of squares of residuals : 6.47475e-25
847 rel. change during last iteration : 0
849 degrees of freedom (FIT_NDF) : 16
850 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2.01165e-13
851 variance of residuals (reduced chisquare) = WSSR/ndf : 4.04672e-26
852 p-value of the Chisq distribution (FIT_P) : 1
854 Final set of parameters Standard Deviation
855 ======================= ==========================
856 c1 = 2.5 +/- 0.3351 (13.41%)
857 c2 = -1.8 +/- 0.9103 (50.57%)
858 c3 = 70 +/- 0.2366 (0.338%)
859 c4 = -3.2 +/- 0.3331 (10.41%)
860 c5 = 0.4 +/- 0.01144 (2.86%)
862 correlation matrix of the fit parameters:
863 c1 c2 c3 c4 c5
864 c1 1.000
865 c2 -0.224 1.000
866 c3 0.011 -0.241 1.000
867 c4 0.001 -0.360 -0.004 1.000
868 c5 -0.078 0.184 -0.901 0.123 1.000
869 ---------------------------------------------------------
870 5D fit: expected 2.5 c1 = 2.4999999999999
871 5D fit: expected -1.8 c2 = -1.80000000000034
872 5D fit: expected 70.0 c3 = 70.0
873 5D fit: expected -3.2 c4 = -3.1999999999997
874 5D fit: expected 0.4 c5 = 0.4
875 ---------------------------------------------------------
876 Hit return to try for a 6D fitThis 6D fit will fail in version 4 but version 5 can handle more parameters
877 "fitmulti.dem" line 171: warning:
878 > Implied independent variable not found in fit function.
879 > Assuming version 4 syntax with zerror in column 8 but no zerror keyword.
881 iter chisq delta/lim lambda c1 c2 c3 c4 c5 c6
882 0 3.4961271093e+05 0.00e+00 1.94e+01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
883 1 7.6637354181e+04 -3.56e+05 1.94e+00 9.608331e-01 1.184762e+00 4.200546e+00 1.106839e+00 3.265869e+00 9.641673e-01
884 2 2.4111848689e+03 -3.08e+06 1.94e-01 1.293024e+00 2.647546e+00 5.815961e+01 -1.307965e+00 9.256806e-01 1.373154e-01
885 3 4.0425600039e-02 -5.96e+09 1.94e-02 2.493419e+00 -1.646308e+00 6.996638e+01 -3.211246e+00 4.014427e-01 -1.585899e-01
886 4 7.1103832630e-08 -5.69e+10 1.94e-03 2.500262e+00 -1.799903e+00 7.000002e+01 -3.200052e+00 3.999987e-01 -2.480390e-01
887 5 3.2145956903e-15 -2.21e+12 1.94e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.499996e-01
888 6 1.2915932826e-24 -2.49e+14 1.94e-05 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
889 * 1.2994882026e-24 6.08e+02 1.94e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
890 * 1.2994882026e-24 6.08e+02 1.94e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
891 * 1.2994882026e-24 6.08e+02 1.94e-02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
892 7 1.2628874232e-24 -2.27e+03 1.94e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
893 8 1.1896282775e-24 -6.16e+03 1.94e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
894 * 1.2110348070e-24 1.77e+03 1.94e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
895 * 1.2110348070e-24 1.77e+03 1.94e-02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
896 * 1.2110348070e-24 1.77e+03 1.94e-01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
897 * 1.2219968181e-24 2.65e+03 1.94e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
898 * 1.2252721686e-24 2.91e+03 1.94e+01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
899 9 1.1888204839e-24 -6.79e+01 1.94e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
900 * 1.2998029581e-24 8.54e+03 1.94e+01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
901 10 1.1888204839e-24 0.00e+00 1.94e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
902 iter chisq delta/lim lambda c1 c2 c3 c4 c5 c6
904 After 10 iterations the fit converged.
905 final sum of squares of residuals : 1.18882e-24
906 rel. change during last iteration : 0
908 degrees of freedom (FIT_NDF) : 14
909 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2.91403e-13
910 variance of residuals (reduced chisquare) = WSSR/ndf : 8.49157e-26
911 p-value of the Chisq distribution (FIT_P) : 1
913 Final set of parameters Standard Deviation
914 ======================= ==========================
915 c1 = 2.5 +/- 1.079 (43.17%)
916 c2 = -1.8 +/- 0.9301 (51.67%)
917 c3 = 70 +/- 0.2616 (0.3737%)
918 c4 = -3.2 +/- 0.3759 (11.75%)
919 c5 = 0.4 +/- 0.01302 (3.256%)
920 c6 = -0.25 +/- 7.512 (3005%)
922 correlation matrix of the fit parameters:
923 c1 c2 c3 c4 c5 c6
924 c1 1.000
925 c2 0.124 1.000
926 c3 0.370 -0.129 1.000
927 c4 -0.440 -0.405 -0.182 1.000
928 c5 -0.447 0.062 -0.919 0.303 1.000
929 c6 0.945 0.197 0.366 -0.461 -0.428 1.000
930 ---------------------------------------------------------
931 6D fit: expected 2.5 c1 = 2.49999999999976
932 6D fit: expected -1.8 c2 = -1.80000000000026
933 6D fit: expected 70.0 c3 = 70.0
934 6D fit: expected -3.2 c4 = -3.19999999999975
935 6D fit: expected 0.4 c5 = 0.4
936 6D fit: expected -0.25 c6 = -0.250000000000491
937 FIT_NDF = 14 after range filters (expected 14)
938 ---------------------------------------------------------
939 Hit return to try fit with array variables"fitmulti.dem" line 183: warning:
940 > Implied independent variable not found in fit function.
941 > Assuming version 4 syntax with zerror in column 8 but no zerror keyword.
943 iter chisq delta/lim lambda A[1] A[2] A[3] A[4] A[5] A[6]
944 0 3.4961271093e+05 0.00e+00 1.94e+01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
945 1 7.6637354181e+04 -3.56e+05 1.94e+00 9.608331e-01 1.184762e+00 4.200546e+00 1.106839e+00 3.265869e+00 9.641673e-01
946 2 2.4111848689e+03 -3.08e+06 1.94e-01 1.293024e+00 2.647546e+00 5.815961e+01 -1.307965e+00 9.256806e-01 1.373154e-01
947 3 4.0425600039e-02 -5.96e+09 1.94e-02 2.493419e+00 -1.646308e+00 6.996638e+01 -3.211246e+00 4.014427e-01 -1.585899e-01
948 4 7.1103832630e-08 -5.69e+10 1.94e-03 2.500262e+00 -1.799903e+00 7.000002e+01 -3.200052e+00 3.999987e-01 -2.480390e-01
949 5 3.2145956903e-15 -2.21e+12 1.94e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.499996e-01
950 6 1.2915932826e-24 -2.49e+14 1.94e-05 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
951 * 1.2994882026e-24 6.08e+02 1.94e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
952 * 1.2994882026e-24 6.08e+02 1.94e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
953 * 1.2994882026e-24 6.08e+02 1.94e-02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
954 7 1.2628874232e-24 -2.27e+03 1.94e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
955 8 1.1896282775e-24 -6.16e+03 1.94e-04 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
956 * 1.2110348070e-24 1.77e+03 1.94e-03 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
957 * 1.2110348070e-24 1.77e+03 1.94e-02 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
958 * 1.2110348070e-24 1.77e+03 1.94e-01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
959 * 1.2219968181e-24 2.65e+03 1.94e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
960 * 1.2252721686e-24 2.91e+03 1.94e+01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
961 9 1.1888204839e-24 -6.79e+01 1.94e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
962 * 1.2998029581e-24 8.54e+03 1.94e+01 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
963 10 1.1888204839e-24 0.00e+00 1.94e+00 2.500000e+00 -1.800000e+00 7.000000e+01 -3.200000e+00 4.000000e-01 -2.500000e-01
964 iter chisq delta/lim lambda A[1] A[2] A[3] A[4] A[5] A[6]
966 After 10 iterations the fit converged.
967 final sum of squares of residuals : 1.18882e-24
968 rel. change during last iteration : 0
970 degrees of freedom (FIT_NDF) : 14
971 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2.91403e-13
972 variance of residuals (reduced chisquare) = WSSR/ndf : 8.49157e-26
973 p-value of the Chisq distribution (FIT_P) : 1
975 Final set of parameters Standard Deviation
976 ======================= ==========================
977 A[1] = 2.5 +/- 1.079 (43.17%)
978 A[2] = -1.8 +/- 0.9301 (51.67%)
979 A[3] = 70 +/- 0.2616 (0.3737%)
980 A[4] = -3.2 +/- 0.3759 (11.75%)
981 A[5] = 0.4 +/- 0.01302 (3.256%)
982 A[6] = -0.25 +/- 7.512 (3005%)
984 correlation matrix of the fit parameters:
985 A[1] A[2] A[3] A[4] A[5] A[6]
986 A[1] 1.000
987 A[2] 0.124 1.000
988 A[3] 0.370 -0.129 1.000
989 A[4] -0.440 -0.405 -0.182 1.000
990 A[5] -0.447 0.062 -0.919 0.303 1.000
991 A[6] 0.945 0.197 0.366 -0.461 -0.428 1.000
992 ---------------------------------------------------------
994 Variables beginning with A_:
995 A_1__err = 1.07920943632493
996 A_2__err = 0.930099132065864
997 A_3__err = 0.261566870454281
998 A_4__err = 0.375869300020404
999 A_5__err = 0.0130221281721936
1000 A_6__err = 7.51152824151569
1002 Array A after 6D fit: [2.49999999999976,-1.80000000000026,70.0,-3.19999999999975,0.4,-0.250000000000491]
1003 Hit return to end multidimension fit demo******************** file named_var.dem ********************
1004 Hit return to continue******************** file param.dem ********************
1005 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** file piecewise.dem ********************
1006 Hit <cr> to continueHit <cr> to continueHit <cr> to continue******************** file polar.dem ********************
1007 Hit return to continueHit return to continue"polar.dem" line 21: warning: Did you try to plot a complex-valued function?
1008 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** file poldat.dem ********************
1009 Hit return to continueHit return to continueHit return to continueHit return to continue******************** file polargrid.dem ********************
1011 polar mode is ON
1012 polar grid uses 36 theta wedges and 12 radial segments
1013 masked by theta range [-20:210] radial range [0:*]
1014 polar gridding scheme qnorm 1
1017 set rrange [ * : * ] noreverse writeback noextend # (currently [0.00000:285.833] )
1019 <cr> to continue<cr> to continue
1020 polar mode is ON
1021 polar grid uses 360 theta wedges and 50 radial segments
1022 masked by theta range [0:360] radial range [0:*]
1023 polar gridding scheme gauss kdensity scale 30
1025 Theta increases clockwise with origin at top of plot
1027 <cr> to try logscale R<cr> to continue******************** file polar_quadrants.dem ********************
1028 <cr> to continue******************** file sectors.dem ********************
1029 <cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue******************** file orbits.dem ********************
1030 ******************** file solar_path.dem ****************
1031 21-12-2016 sunrise 8:04 sunset 16:37 sunlight 8 h 32 m
1032 22-06-2017 sunrise 4:37 sunset 20:04 sunlight 15 h 27 m
1033 31-12-2023 sunrise 8:03 sunset 16:38 sunlight 8 h 35 m
1034 <cr> to continue******************** file ttics.dem ********************
1035 <cr> to continue<cr> to continue<cr> to continue******************** file boxplot.dem ********************
1036 *** Boxplot demo ***
1037 Hit <cr> to continue: Compare sub-datasetsHit <cr> to continue: Assign selected colors to each factorHit <cr> to continue: Sort factors alphabeticallyHit <cr> to continue: The same, with iteration and manual filteringHit <cr> to continue: boxplot demo finished******************** file jitter.dem ********************
1038 Hit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continue******************** file violinplot.dem ********************
1039 Hit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continue******************** file spiderplot.dem ********************
1040 <cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue******************** file sampling.dem ********************
1041 test 1: explicit trange distinct from xrange
1042 Hit <cr> to continuetest 2: range set by 'sample' keyword, linear x axis
1043 Hit <cr> to continuetest 3: range set by 'sample' keyword, logscale x axis
1044 Hit <cr> to continuetest 4: splot '++' with autoscaled y (linear xy)
1045 Hit <cr> to continuetest 5: splot '++' with autoscaled y (logscale xy)
1046 Hit <cr> to continuetest 6: plot '++' with image (linear xy)
1047 Hit <cr> to continuetest 7: plot '++' with image (logscale xy)
1048 Hit <cr> to continuetest 8: multiple sampling ranges in one 2D plot command
1049 Hit <cr> to continuetest 9: 3D sampling range distinct from plot x/y range
1050 Hit <cr> to continuetest 10: splot '++' with explicit sampling intervals
1051 Hit <cr> to continuetest 10: plot '++' with explicit sampling intervals
1052 Hit <cr> to continueHit return to continueHit return to continue******************** file multiplt.dem ********************
1053 Hit return to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue******************** file surface1.dem ********************
1054 Hit return to continueHit return to continue (1)Hit return to continue (2)Hit return to continue (3)Hit return to continue (4)Hit return to continue (5)Hit return to continue (6)Hit return to continue (7)Hit return to continue (8)Hit return to continue (9)Hit return to continue (10)Hit return to continue (11)Hit return to continue (12)Hit return to continue (13)Hit return to continue (14)Hit return to continue (15)Hit return to continue (16)Hit return to continue (17)Hit return to continue (18)Hit return to continue (19)Hit return to continue (20)Hit return to continue (21)Hit return to continue (22)Hit return to continue (23)Hit return to continue (24)Hit return to continue (25)******************** file surface_explicit.dem ********************
1055 <cr> to continue<cr> to continue******************** file discrete.dem ********************
1056 Hit return to continueHit return to continueHit return to continue******************** file hidden.dem ********************
1057 Hit return to continue (1)Hit return to continue (2)Hit return to continue (3)Hit return to continue (4)Hit return to continue (5)Hit return to continue (6)Hit return to continue (7)******************** file hidden_compare.dem ********************
1058 <return> to continue******************** dgrid3d ********************
1059 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continuePress Return to continue - the plot may take some time to appearPress Return to continue - the plot may take some time to appearHit return to continue******************** file world.dem ********************
1060 Hit return to continueHit return to continueHit return to continueHit return to continueSame plot with hidden line removalHit return to continue******************** file prob.dem ********************
1061 Statistical Library Demo, version 2.3
1063 Copyright (c) 1991, 1992, Jos van de Woude, jvdwoude@hut.nl
1066 Press Ctrl-C to exit right now
1067 Press Return to start demo ...Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** file prob2.dem ********************
1068 Hit return for inverse error function.Hit return for inverse normal distribution function.Press return to continue Statistical Approximations, version 1.1
1070 Copyright (c) 1991, 1992, Jos van de Woude, jvdwoude@hut.nl
1072 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** file random.dem ********************
1073 Hit return to continue3D plot ahead, one moment please ...
1074 Hit return to continue
1075 Multivariate normal distribution
1077 The surface plot shows a two variable multivariate probability
1078 density function. On the x-y plane are some samples of the random
1079 vector and a contour plot illustrating the correlation, which in
1080 this case is zero, i.e. a circle. (Easier to view in map mode.)
1082 "random.dem" line 73: warning: Cannot contour non grid data. Please use "set dgrid3d".
1083 Hit return to continue
1084 Simple Monte Carlo simulation
1086 The first curve is a histogram where the binned frequency of occurrence
1087 of a pseudo random variable distributed according to the normal
1088 (Gaussian) law is scaled such that the histogram converges to the
1089 normal probability density function with increasing number of samples
1090 used in the Monte Carlo simulation. The second curve is the normal
1091 probability density function with unit variance and zero mean.
1093 Hit return to continue
1094 Another Monte Carlo simulation
1096 This is similar to the previous simulation but uses multivariate
1097 zero mean, unit variance normal data by computing the distance
1098 each point is from the origin. That distribution is known to fit
1099 the Maxwell probability law, as shown.
1101 Hit return to continue******************** file rugplot.dem ********************
1102 Hit <cr> to continue******************** file smooth.dem ********************
1103 Hit enter to continueHit enter to continueHit enter to continueHit enter to continue******************** file spline.dem ********************
1104 Press return to continuePress return to continuePress return to continuePress return to continuePress return to continuePress return to continue******************** file sharpen.dem ********************
1105 <cr> to continue******************** file binary.dem ********************
1106 Hit return to continue (1)Hit return to continue (2)Hit return to continue (3)******************** file steps.dem ********************
1107 Hit return for demonstration of automatic histogram creationHit return to see the same plot with fillstepsPress return to continue******************** file scatter.dem ********************
1108 Hit return to continue (1)Hit return to continue (2)Hit return to continue (3)Hit return to continue (4)Hit return to continue (5)"scatter.dem" line 43: warning: Cannot contour non grid data. Please use "set dgrid3d".
1109 Hit return to continue (6)Hit return to continue (7)Hit return to continue (8)******************** file singulr.dem ********************
1110 Hit return to continue (1)Hit return to continue (2)Hit return to continue (3)Hit return to continue (4)Hit return to continue (5)Hit return to continue (6)Hit return to continue (7)Hit return to continue (8)Hit return to continue (9)Hit return to continue (10)Hit return to continue (11)Hit return to continue (12)Hit return to continue (13)Hit return to continue (14)Hit return to continue (15)Hit return to continue (16)Hit return to continue (17)Hit return to continue (18)Hit return to continue (19)Hit return to continue (20)******************** file airfoil.dem ********************
1111 NACA four series airfoils by bezier splines
1112 Will add pressure distribution later with Overplotting
1113 Press ReturnPress ReturnJoukowski Airfoil using Complex Variables
1114 Press ReturnPress Return******************** file surface2.dem ********************
1115 Hit return to continue (1)Hit return to continue (2)Hit return to continue (3)Hit return to continue (4)Hit return to continue (5)Hit return to continue (6)Hit return to continue (7)Hit return to continue (8)Hit return to continue (9)******************** file azimuth.dem ********************
1116 Hit return to continue******************** file projection.dem ******************
1117 Hit return to continue******************** contours ********************
1118 Hit return to continue (1)Hit return to continue (2)Hit return to continue (3)Hit return to continue (4)Hit return to continue (5)Hit return to continue (6)Hit return to continue (7)Hit return to continue (8)Hit return to continue (9)Hit return to continue (10)Hit return to continue (11)Hit return to continue (12)Hit return to continue (13)Hit return to continue (14)Hit return to continue (15)Hit return to continue (16)Hit return to continue (17)Hit return to continue (18)Hit return to continue (19)Hit Return to Continue (20)Hit Return to Continue (21)Hit Return to Continue (22)Hit Return to Continue (23)<cr> to continue******************** file contourfill.dem ********************
1119 <cr> to continue<cr> to set contourfill ztics<cr> for 2D projection<cr> to continue******************** file pixmap.dem ********************
1120 Hit <cr> to continue******************** file bivariat.dem ********************
1121 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue******************** Time/Date data ********************
1122 Hit return to continueHit return to continueHit return to continue
1123 Relative time output (strftime(), axis labels)
1124 t = -3672.5
1125 print strftime("%.2tM == %.2tS", t) -61.21 min == -3672.50 sec
1126 print strftime("%tM:%.2tS", t) -61:12.50
1127 print strftime("%tH:%tM:%.2tS", t) -1:01:12.50
1128 t = 3672.5
1129 print strftime("%.2tM == %.2tS", t) 61.21 min == 3672.50 sec
1130 print strftime("%tM:%.2tS", t) 61:12.50
1131 print strftime("%tH:%tM:%.2tS", t) 1:01:12.50
1133 Relative time input (strptime(), data files)
1134 print strptime("%tH:%tM:%tS", "-1:01:12.50") -3672.5
1135 print strptime(" %tM:%tS", "-61:12.50") -3672.5
1136 print strptime(" %tS", "-3672.50") -3672.5
1138 Timezones time output (strftime(), axis labels)
1139 t = 1550496278
1140 print strftime("%d/%m/%y\t%H:%M", t) 18/02/19 13:24
1141 print strftime("%d/%m/%y\t%H:%M%z", t) 18/02/19 13:24
1142 print strftime("%d/%m/%y\t%H:%M%Z", t) 18/02/19 13:24
1144 Timezones time input (strptime(), data files)
1145 print strptime("%d/%m/%y\t%H:%M", "18/02/19\t13:24") 1550496240.0
1146 print strptime("%d/%m/%y\t%H:%M%z", "18/02/19\t12:24+00:00") "timedat.dem" line 89: warning: Bad time format %z
1147 1550492640.0
1148 print strptime("%d/%m/%y\t%H:%M%z", "18/02/19\t13:24+01:00") "timedat.dem" line 90: warning: Bad time format %z
1149 1550496240.0
1150 print strptime("%d/%m/%y\t%H:%M %Z", "18/02/19\t13:24 CET") "timedat.dem" line 91: warning: Bad time format %Z
1151 1550496240.0
1152 print strptime("%d/%m/%y\t%H:%M %Z", "18/02/19\t14:24 CEST") "timedat.dem" line 92: warning: Bad time format %Z
1153 1550499840.0
1154 Hit return to check backwards compatibility with v4 syntaxHit return to continue********************** file rainbow.dem *********************
1156 # These are the input commands
1158 set style line 1 lt rgb "red" lw 3
1159 set style line 2 lt rgb "orange" lw 2
1160 set style line 3 lt rgb "yellow" lw 3
1161 set style line 4 lt rgb "green" lw 2
1162 set style line 5 lt rgb "cyan" lw 3
1163 set style line 6 lt rgb "blue" lw 2
1164 set style line 7 lt rgb "violet" lw 3
1166 # And this is the result
1167 linestyle 1, linecolor rgb "red" linewidth 3.000 dashtype solid pointtype 1 pointsize default
1168 linestyle 2, linecolor rgb "orange" linewidth 2.000 dashtype solid pointtype 2 pointsize default
1169 linestyle 3, linecolor rgb "yellow" linewidth 3.000 dashtype solid pointtype 3 pointsize default
1170 linestyle 4, linecolor rgb "green" linewidth 2.000 dashtype solid pointtype 4 pointsize default
1171 linestyle 5, linecolor rgb "cyan" linewidth 3.000 dashtype solid pointtype 5 pointsize default
1172 linestyle 6, linecolor rgb "blue" linewidth 2.000 dashtype solid pointtype 6 pointsize default
1173 linestyle 7, linecolor rgb "violet" linewidth 3.000 dashtype solid pointtype 7 pointsize default
1175 Hit return to continueHit return to continue********************** file rgb_variable.dem *********************
1176 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue********************** file rgba_lines.dem *********************
1177 Hit return to continue********************** file varcolor.dem *********************
1178 Hit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continue********************** file pt_variable.dem *********************
1179 <cr> to continue<cr> to continue<cr> to continue********************** file pm3d.dem *********************
1180 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continuePress Enter; I will continue by 'set autoscale cb' and much more...Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continuePlot by pm3d algorithm draws quadrangles filled with color calculated from
1181 the z- or color-value of the surrounding 4 corners. The following demo shows
1182 different color spots for a plot with very small number of quadrangles (here
1183 rectangular pixels). Note that the default option is 'mean'.
1184 Hit return to continueEnd of pm3d demo.
1185 ********************** file pm3d_clip.dem *********************
1186 <return> to continue<return> to continue********************** file complex_trig.dem *********************
1187 ********************** libcerf routines *************************
1188 This copy of gnuplot was not linked against libcerf
1189 This copy of gnuplot was not linked against libcerf
1190 This copy of gnuplot was not linked against libcerf
1191 ********************** libamos routines *************************
1192 This copy of gnuplot does not support Ai, Bi
1193 This copy of gnuplot does not support BesselK
1194 <cr> to continueThis copy of gnuplot does not support complex expint
1195 ********************** special functions *************************
1196 <cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continueHit return to continueHit return to continueHit return to continueHit return to continue<cr> to continue********************** file heatmaps *********************
1197 Hit return to continueHit return to continueHit return to continueLoaded 14 points into 5 x 5 sparse matrix
1198 Hit return to continueLoaded 14 points into 5 x 5 sparse matrix
1199 Hit return to continueHit return to continueHit return to continue<cr> to use a finer grid<cr> to continue<cr> to continue********************** file matrix_index.dem *********************
1200 Hit return to continue********************** file matrix_every.dem *********************
1201 <cr> to continue********************** file pm3dgamma.dem *********************
1202 Hit return to continue********************** file hidden2.dem ***********************
1203 Hit return to continueHit return to continueHit return to continue********************** file textcolor.dem *********************
1204 Hit return to continueHit return to continue********************** file textrotate.dem *********************
1205 Hit return to continue********************** enhanced text *********************
1206 Hit return to continueHit return to continue********************** unicode text *********************
1207 <cr> to continue********************** file dashtypes.dem *********************
1208 Hit return to continueHit return to continue********************** file arrowstyle.dem *********************
1209 We have defined the following arrowstyles:
1210 arrowstyle 1, head back linecolor rgb "dark-violet" linewidth 2.000 dashtype solid
1211 arrow heads: filled, length (screen units) 0.025, angle 30 deg, backangle 45 deg
1212 arrowstyle 2, head back linecolor rgb "#56b4e9" linewidth 2.000 dashtype solid
1213 arrow heads: nofilled, length (screen units) 0.03, angle 15 deg
1214 arrowstyle 3, head back linecolor rgb "dark-violet" linewidth 2.000 dashtype solid
1215 arrow heads: filled, length (screen units) 0.03, angle 15 deg, backangle 45 deg
1216 arrowstyle 4, head back linecolor rgb "#56b4e9" linewidth 2.000 dashtype solid
1217 arrow heads: filled, length (screen units) 0.03, angle 15 deg, backangle 90 deg
1218 arrowstyle 5, heads back linecolor rgb "dark-violet" linewidth 2.000 dashtype solid
1219 arrow heads: noborder, length (screen units) 0.03, angle 15 deg, backangle 135 deg
1220 arrowstyle 6, head back linecolor rgb "#56b4e9" linewidth 2.000 dashtype solid
1221 arrow heads: empty, length (screen units) 0.03, angle 15 deg, backangle 135 deg
1222 arrowstyle 7, nohead back linecolor rgb "dark-violet" linewidth 2.000 dashtype solid
1223 arrowstyle 8, heads back linecolor rgb "#56b4e9" linewidth 2.000 dashtype solid
1224 arrow heads: nofilled, length (screen units) 0.008, angle 90 deg
1226 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHi return to continue********************** file vector.dem *********************
1228 This file demonstrates
1229 -1- saving contour lines as a gnuplottable datablock
1230 -2- plotting a vector field on the same graph
1231 -3- manipulating columns using the '$1,$2' syntax.
1232 the example is taken here from Physics is the display of equipotential
1233 lines and electrostatic field for a dipole (+q,-q)
1234 Now create a in-memory datablock with equipotential lines
1235 Hit return to continueNow create a x/y datablock for plotting with vectors
1236 and display vectors parallel to the electrostatic field
1237 Hit return to continue"vector.dem" line 85: warning: Warning - difficulty fitting plot titles into key
1238 Hit return to continue********************** file arrows.dem *********************
1239 Hit <cr> to continueHit <cr> to continue********************** file short_vector.dem *********************
1240 <cr> to continue********************** file tics.dem *********************
1241 Hit return to continueHit return to continueHit return to continueHit return to continueEnd of tics demo.
1242 ********************** file break_continue.dem *********************
1243 start 1
1244 continue if i == 3, break if i > 4
1245 still in load file
1246 end 1
1247 start 2
1248 continue if i == 3, break if i > 4
1249 still in load file
1250 end 2
1251 start 3
1252 continue if i == 3, break if i > 4
1253 start 4
1254 continue if i == 3, break if i > 4
1255 still in load file
1256 end 4
1257 start 5
1258 continue if i == 3, break if i > 4
1259 done with for loop
1260 ********************** file callargs.dem *********************
1262 Entering callargs.dem with 0 parameters
1263 Now exercise the call mechanism at line 47
1265 Entering callargs.dem with 8 parameters
1267 Test whether this copy of gnuplot also supports deprecated
1268 call parameter syntax $0 $1 $2 etc:
1269 no
1271 Variables beginning with ARG:
1272 ARGC = 8
1273 ARGV = <8 element array>
1274 ARG0 = "callargs.dem"
1275 ARG1 = "1.23e4"
1276 ARG2 = "string constant"
1277 ARG3 = "FOO"
1278 ARG4 = "5.67"
1279 ARG5 = "3 + log(BAZ)"
1280 ARG6 = "a string"
1281 ARG7 = "8"
1282 ARG8 = "3.14159"
1283 ARG9 = ""
1285 ARG1 (numerical constant) came through as 1.23e4
1286 @ARG1 = 12300.0
1287 (ARG1 == @ARG1) is TRUE
1288 ARG2 (string constant) came through as string constant
1289 words(ARG2) = 2
1290 ARG3 (undefined variable FOO) came through as FOO
1291 ARG4 (numerical variable BAZ=5.67) came through as 5.67
1292 @ARG4 = 5.67
1293 ARG5 (quoted expression) came through as 3 + log(BAZ)
1294 @ARG5 = 4.73518911773966
1295 ARG6 (string variable) came through as a string
1296 words(ARG6) = 2
1297 ARG7 (expression) came through as 8
1298 ARG8 (pi) came through as 3.14159
1299 ARGV = [12300.0,"string constant","FOO",5.67,"3 + log(BAZ)","a string",8,3.14159265358979]
1300 ********************** file volatile.dem *********************
1301 ********************** file datastrings.dem *********************
1302 <cr> to plot again using x2ticlabels<cr> to plot again using x2ticlabels<cr> to plot same data from table format<cr> to show double use of y values<cr> to show use of boxed labels<cr> to end demo********************** file textbox.dem *********************
1303 <cr> to continue********************** file hypertext.dem *********************
1304 hit return to continue********************** file rotate_labels.dem *****************
1305 <cr> to continue********************** file stats.dem *********************
1307 * FILE:
1308 Records: 20
1309 Out of range: 0
1310 Invalid: 0
1311 Header records: 0
1312 Blank: 1
1313 Data Blocks: 1
1315 * COLUMN:
1316 Mean: 2.5408
1317 Std Dev: 0.2227
1318 Sample StdDev: 0.2285
1319 Skewness: 0.9783
1320 Kurtosis: 3.6056
1321 Avg Dev: 0.1785
1322 Sum: 50.8156
1323 Sum Sq.: 130.1035
1325 Mean Err.: 0.0498
1326 Std Dev Err.: 0.0352
1327 Skewness Err.: 0.5477
1328 Kurtosis Err.: 1.0954
1330 Minimum: 2.2009 [ 7]
1331 Maximum: 3.1397 [ 9]
1332 Quartile: 2.3866
1333 Median: 2.4610
1334 Quartile: 2.6562
1336 Hit return to continue
1337 * FILE:
1338 Records: 20
1339 Out of range: 0
1340 Invalid: 0
1341 Header records: 0
1342 Blank: 1
1343 Data Blocks: 1
1345 * COLUMNS:
1346 Mean: 2.1168 2.5408
1347 Std Dev: 0.7921 0.2227
1348 Sample StdDev: 0.8127 0.2285
1349 Skewness: -1.1174 0.9783
1350 Kurtosis: 3.5250 3.6056
1351 Avg Dev: 0.6330 0.1785
1352 Sum: 42.3356 50.8156
1353 Sum Sq.: 102.1648 130.1035
1355 Mean Err.: 0.1771 0.0498
1356 Std Dev Err.: 0.1252 0.0352
1357 Skewness Err.: 0.5477 0.5477
1358 Kurtosis Err.: 1.0954 1.0954
1360 Minimum: 0.0000 [ 0] 2.2009 [ 7]
1361 Maximum: 2.9957 [19] 3.1397 [ 9]
1362 Quartile: 1.7006 2.3866
1363 Median: 2.3502 2.4610
1364 Quartile: 2.7403 2.6562
1366 Linear Model: y = -0.06694 x + 2.682
1367 Slope: -0.06694 +- 0.06437
1368 Intercept: 2.682 +- 0.1455
1369 Correlation: r = -0.2381
1370 Sum xy: 106.7
1372 Hit return to continue********************** file iterate.dem *********************
1373 Hit return to continueHit return to continueHit return to continue
1374 dynamic reevaluation of numeric iteration limits
1376 J = [1,4,3]
1377 do for [i=1:3] for [j=J[i]:3] { save(i,j) }
1378 1-1 1-2 1-3 3-3
1380 J = [4,1,3]
1381 do for [i=1:3] for [j=J[i]:3] { save(i,j) }
1382 2-1 2-2 2-3 3-3
1384 do for [i=1:4] for [k=i:i] for [j=1:k] { save(i,j) }
1385 1-1 2-1 2-2 3-1 3-2 3-3 4-1 4-2 4-3 4-4
1387 dynamic reevaluation of iteration string
1388 A = ["a b","","d"]
1389 do for [ i = 1:|A| ] { do for [ j in A[i]] { print "".i.": ".j }}
1390 1: a 1: b 3: d
1391 do for [ i = 1:|A| ] for [ j in A[i]] { print "".i.": ".j }
1392 1: a 1: b 3: d
1393 ********************** histograms *********************
1394 <cr> to plot the same data as a histogram<cr> to change the gap between clusters<cr> to plot the same dataset as stacked histogram<cr> to rescale each stack to % of totalNow try histograms stacked by columnsNext we do several sets of parallel histogramsSame plot using rowstacked histogram<cr> to finish histogram demoSame plot using explicit histogram start colorsSame plot using explicit histogram start patternSame plot with both explicit color and patternHit return to continue<cr> to continue<cr> to continue<cr> to continue********************** file boxclusters.dem *********************
1395 <cr> to continue********************** Array functions *********************
1396 Sum[ 5] = 132551
1397 Sum[ 6] = 218066
1398 Sum[ 7] = 363446
1399 Sum[ 8] = 2441994
1400 Sum[ 9] = 570452
1401 Sum[10] = 1191986
1402 Sum[11] = 4787900
1403 Sum[12] = 250579
1404 Sum[13] = 473705
1405 <cr> to continue<cr> to fit function to array valuesiter chisq delta/lim lambda a b c
1406 0 1.0028328575e+02 0.00e+00 2.34e-01 1.000000e-02 1.000000e-02 1.000000e-02
1407 1 1.5801742112e+01 -5.35e+05 2.34e-02 1.606160e-02 3.087431e-02 -1.313622e-02
1408 2 1.4408428171e+01 -9.67e+03 2.34e-03 -9.632124e-02 3.471172e-02 9.547984e-02
1409 3 8.1377220374e+00 -7.71e+04 2.34e-04 -6.860755e-01 4.691257e-02 8.742445e-02
1410 4 1.2433205970e+00 -5.55e+05 2.34e-05 -1.820671e+00 6.718158e-02 -2.845982e-03
1411 5 6.1954576313e-02 -1.91e+06 2.34e-06 -1.591434e+00 6.308333e-02 4.504819e-02
1412 6 6.0984370182e-02 -1.59e+03 2.34e-07 -1.584251e+00 6.295384e-02 4.625961e-02
1413 7 6.0984340867e-02 -4.81e-02 2.34e-08 -1.584224e+00 6.295329e-02 4.625481e-02
1414 iter chisq delta/lim lambda a b c
1416 After 7 iterations the fit converged.
1417 final sum of squares of residuals : 0.0609843
1418 rel. change during last iteration : -4.80707e-07
1420 degrees of freedom (FIT_NDF) : 97
1421 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.025074
1422 variance of residuals (reduced chisquare) = WSSR/ndf : 0.000628705
1424 Final set of parameters Asymptotic Standard Error
1425 ======================= ==========================
1426 a = -1.58422 +/- 0.006802 (0.4294%)
1427 b = 0.0629533 +/- 0.0001141 (0.1812%)
1428 c = 0.0462548 +/- 0.002508 (5.422%)
1430 correlation matrix of the fit parameters:
1431 a b c
1432 a 1.000
1433 b -0.854 1.000
1434 c 0.015 -0.020 1.000
1435 <cr> to continue
1436 * FILE:
1437 Records: 20
1438 Out of range: 0
1439 Invalid: 0
1440 Header records: 0
1441 Blank: 1
1442 Data Blocks: 1
1444 * COLUMN:
1445 Mean: 5.9866
1446 Std Dev: 5.5330
1447 Sample StdDev: 5.6768
1448 Skewness: 1.4119
1449 Kurtosis: 3.1850
1450 Avg Dev: 4.3558
1451 Sum: 119.7324
1452 Sum Sq.: 1329.0836
1454 Mean Err.: 1.2372
1455 Std Dev Err.: 0.8749
1456 Skewness Err.: 0.5477
1457 Kurtosis Err.: 1.0954
1459 Minimum: 1.0000 [ 8]
1460 Maximum: 17.8341 [ 0]
1461 Quartile: 3.2693
1462 Median: 3.5672
1463 Quartile: 4.8706
1466 * FILE:
1467 Records: 20
1468 Out of range: 0
1469 Invalid: 0
1470 Header records: 0
1471 Blank: 1
1472 Data Blocks: 1
1474 * COLUMN:
1475 Mean: 5.9866
1476 Std Dev: 5.5330
1477 Sample StdDev: 5.6768
1478 Skewness: 1.4119
1479 Kurtosis: 3.1850
1480 Avg Dev: 4.3558
1481 Sum: 119.7324
1482 Sum Sq.: 1329.0836
1484 Mean Err.: 1.2372
1485 Std Dev Err.: 0.8749
1486 Skewness Err.: 0.5477
1487 Kurtosis Err.: 1.0954
1489 Minimum: 1.0000 [ 8]
1490 Maximum: 17.8341 [ 0]
1491 Quartile: 3.2693
1492 Median: 3.5672
1493 Quartile: 4.8706
1495 <cr> to continueA[ 1 ] = 1 A[ 1 :6] = [1,2,3.0,4.0,"five","six"]
1496 A[ 2 ] = 2 A[ 2 :6] = [2,3.0,4.0,"five","six"]
1497 A[ 3 ] = 3.0 A[ 3 :6] = [3.0,4.0,"five","six"]
1498 A[ 4 ] = 4.0 A[ 4 :6] = [4.0,"five","six"]
1499 A[ 5 ] = five A[ 5 :6] = ["five","six"]
1500 A[ 6 ] = six A[ 6 :6] = ["six"]
1501 A[ 7 ] = {0.0, 7.0} A[ 7 :6] = []
1502 A[ 8 ] = {8.0, 8.0} A[ 8 :6] = []
1503 no member of A matches NaN
1504 array B = [2,3.0,4.0]
1506 Key/value pairs
1508 water is blue
1509 dirt is brown
1510 sky is blue
1511 split: OK
1512 join: OK
1513 ********************** Image formats *********************
1515 The plotting styles `image` and `rgbimage` are intended for plotting
1516 images described in a data file either in the conventional ASCII format or
1517 in a binary format described by the qualifiers `binary` and `using`.
1518 All pixels have an (x,y) or (x,y,z) coordinate. These values can be
1519 included in the data file or implicitly determined with the sampling
1520 'array' key word and sampling periods 'dx' and 'dy'. The key words
1521 'rotate' and, for 3d plots, 'perpendicular' control orientation.
1523 The data for this image was stored as RGB triples, one byte per channel,
1524 without (x,y) coordinate information. This yields a most compact file.
1525 The plotting command is displayed on the graph.
1527 Hit return to continue
1528 Images are typically stored in a file with the first datum being the
1529 top, left pixel. Without the ability to translate coordinates, the
1530 the result would be an upside down image.
1532 The key word 'array' means an implied sample array is applied
1533 to generate the locations of file data using the sampling periods
1534 dx, dy and dz. The x-dimension is always the contiguous points in
1535 a binary file. The y-dimension is the line number which is incremented
1536 upon the x-dimension reaching the line length. The z-dimension is
1537 the plane number which is incremented upon the y-dimension reaching
1538 the number of lines per plane.
1540 To alter the location of the binary data when displayed via the 'plot'
1541 command, use the key word 'rotate' along with changing the sign of dx, dy
1542 and dz.
1544 Hit return to continue
1545 There is the ability to plot both color images and palette based
1546 images. This is controlled by the styles `image`, which derives
1547 color information from the current palette, and `rgbimage`,
1548 which requires three components representing the red, blue and
1549 green primary color scheme.
1551 By the way, if you have a mouse active, click the right button
1552 inside the image to isolate a portion of the image and see what
1553 happens.
1555 Hit return to continue
1556 Naturally, as with 3d color surfaces, the palette may be changed.
1557 This is an example of gray scale.
1559 Also, notice in the plot command the key word 'flipy'. This
1560 means to change the direction of the sample along the y dimension
1561 and is useful for the situation where images or other data are
1562 stored in some direction other than that of the Cartesion system.
1563 Alone, 'flipD' means do flipping in the D (x y or z) direction
1564 for all records. Individual records can be controlled using the
1565 syntax 'flipD=#,...,#', where # is '0' or '1'.
1567 Hit return to continue
1568 Also, similar to 3d color surface plots, a color box showing the
1569 palette mapping scheme can be added to the plot. The default
1570 location is the right edge of the plot. The location can be set
1571 manually using `set colorbox` and `set margin`.
1573 As a prelude to the next graph, resize the plot window to judge
1574 the refresh speed of the image drawing routine. Notice that when
1575 the window is smaller, the image refresh is faster. There is more
1576 decimation in the data of the original image and less data to plot.
1577 Furthermore, the window continues to refresh at a reasonable rate
1578 even when the input image size becomes large (e.g., 1024 x 1024)
1579 because the number of pixels on the screen remains about the same
1580 while much of the hi resolution data is decimated.
1582 Hit return to continue
1583 The 'rotation' key word works not only with angles of integer
1584 multiples of 90 degrees but also arbitrary rotations. When
1585 constructing an image, Gnuplot verifies that pixel locations
1586 form a valid grid. Pixel widths are based upon the grid
1587 spacing. If the image orientation is aligned with the view
1588 axes, Gnuplot uses an efficient image driver routine. Otherwise,
1589 individual pixels are drawn using polygon shapes.
1591 Resize this window and compare the plot's refresh rate to that of
1592 the previous and next plot. Notice how in this example if the
1593 window is small the image refresh does not speed up. Unlike the
1594 image routine where image data is decimated, all color rectangles
1595 must be redrawn no matter the size of the output image.
1597 Also notice how the center of the image matches the tuple specified
1598 with the key word 'center' in the plot command. Before doing the
1599 rotation, the image's natural center is subtracted, and after doing
1600 the rotation, the specified center is added.
1602 Hit return to continue
1603 The image of this plot is rotated 90 degrees and can utilize the
1604 efficient image functions of terminal drivers. The plot refresh
1605 is faster than the previous plot.
1607 Furthermore, the image location in this case is specified via the
1608 'origin' key word. As such, the rotation is done about the origin
1609 as opposed to the center of the image. Notice the difference in
1610 axis ranges.
1612 Hit return to continue
1613 Algebraic manipulation of the input variables can select various
1614 components of the image. Here are three examples where two
1615 channels--or analogous to the ASCII file, data "columns"--are ignored
1616 This is done by using `*` in the format to indicate that a variable
1617 of a certain size should be discarded. For example, to select the
1618 green channel, `%*uchar%uchar%*uchar` is one alternative.
1620 Hit return to continue
1621 The range of valid RGB color component values is [0:255]
1622 This is a CHANGE in gnuplot version 5.2.
1623 To adjust the color balance you can filter the individual values
1624 through a scaling function. Here we multiply by a constant c,
1625 c > 1 to brighten, c < 1 to dim.
1627 Hit return to continue
1628 Not only can the 2d binary data mode be used for image data.
1629 Here is an example that repeats the `using.dem` demo with the
1630 same data, but stored in binary format of differing sizes. It
1631 uses different format specifiers within the 'format' string.
1632 There are machine dependent and machine independent specifiers,
1633 display by the command 'show datafile binary datasizes':
1636 The following binary data sizes are machine dependent:
1638 name (size in bytes)
1640 "char" "schar" "c" (1)
1641 "uchar" (1)
1642 "short" (2)
1643 "ushort" (2)
1644 "int" "sint" "i" "d" (4)
1645 "uint" "u" (4)
1646 "long" "ld" (8)
1647 "ulong" "lu" (8)
1648 "float" "f" (4)
1649 "double" "lf" (8)
1650 (8)
1651 (8)
1653 The following binary data sizes attempt to be machine independent:
1655 name (size in bytes)
1657 "int8" "byte" (1)
1658 "uint8" "ubyte" (1)
1659 "int16" "word" (2)
1660 "uint16" "uword" (2)
1661 "int32" (4)
1662 "uint32" (4)
1663 "int64" (8)
1664 "uint64" (8)
1665 "float32" (4)
1666 "float64" (8)
1669 Hit return to continue
1670 Again, a different format specification for `using` can be
1671 used to select different "columns" within the file.
1673 Hit return to continue
1674 Here is another example, one repeating the `scatter.dem`
1675 demo. With binary data we cannot have blank lines to
1676 indicate a break in data, as is done with ASCII files.
1677 Instead, we can specify the record lengths in the command.
1678 In this case, the data file contains the (x,y,z) coordinate
1679 information, hence implicit derivation of that information
1680 is not desired. Instead, the record lengths can be specified
1681 using the keyword 'record', which behaves the same as
1682 'array' but does not generate coordinates. The command is
1683 displayed on the graph.
1685 Hit return to continue
1686 For binary data, the byte endian format of the file and of the
1687 compiler often require attention. Therefore, the key word
1688 'endian' is provided for setting or interchanging byte
1689 order. The allowable types are 'little', 'big', and
1690 depending upon how your version of Gnuplot was compiled,
1691 'middle' (or 'pdp') for those still living in the medieval
1692 age of computers. These types refer to the file's endian.
1693 Gnuplot arranges bytes according to this endian and what it
1694 determines to be the compiler's endian.
1696 There are also the special types 'default' and 'swap' (or
1697 'swab') for those who don't know the file type but realize
1698 their data looks incorrect and want to change the byte read
1699 order.
1701 Here is an example showing the `scatter.dem` data plotted
1702 with correct and incorrect byte order. The file is known
1703 to be little endian, so the upper left plot is correct
1704 appearance and the upper right plot is incorrect appearance.
1705 The lower two plots are default and swapped bytes. If the
1706 plots within the columns match, your compiler uses little
1707 endian. If diagonal plots match then your compiler uses
1708 big endian. If neither of the bottom plots matches the
1709 upper plots, Tux says you're living in the past.
1711 Hit return to continue
1712 This close up of a 2x2 image illustrates how pixels surround the
1713 sampling grid points. This behavior is slightly different than
1714 that for pm3d where the four grid points would be used to create
1715 a single polygon element using an average, or similar mathematical
1716 combination, of the four values at those points.
1718 Hit return to continue
1719 Lower dimensional data may be extended to higher dimensional plots
1720 via a number of simple, logical rules. The first step Gnuplot does
1721 is sets the components for higher than the natural dimension of the
1722 input data to zero. For example, a two dimensional image can be
1723 displayed in the three dimensional plot as shown. Without
1724 translation, the image lies in the x/y-plane.
1726 Warning: empty z range [0:0], adjusting to [-1:1]
1727 Hit return to continue
1728 The key words 'rotate' and 'center' still apply in 'splot' with
1729 rules similar to their use in 'plot'. However, the center must be
1730 specified as a three element tuple.
1732 Warning: empty z range [50:50], adjusting to [49.5:50.5]
1733 Hit return to continue
1734 To have full degrees of freedom in orienting the image, an additional
1735 key word, 'perpendicular', can translate the x/y-plane of the 2d
1736 data so that it lies orthogonal to a vector given as a three element
1737 tuple. The default vector is, of course, (0,0,1). The vector need
1738 not be of unit length, as this example illustrates. Viewing this
1739 plot with the mouse active can help visualize the image's orientation
1740 by panning the axes.
1742 Hit return to continue
1743 These concepts of extending lower dimensional data also apply
1744 to temporal-like signals. For example, a uniformly sampled
1745 sinusoid, sin(1.75*pi*x), in a binary file having no data for
1746 the independent variable can be displayed along any direction
1747 for both 'plot'...
1749 Hit return to continue
1750 ...and 'splot'. Here is the 'scatter.dem' example again,
1751 but this simulates the case of the redundant x coordinates not
1752 being in the binary file. The first "column" of the binary
1753 file is ignored and reconstructed by orienting the various
1754 data records.
1756 Hit return to continue
1757 Some binary data files have headers, which may be skipped via
1758 the 'skip' key word. Here is the 'scatter.dem' example
1759 again, but this time the first and third traces are skipped.
1760 The first trace is 30 samples of three floats so takes up 360
1761 bytes of space. Similarly, the third trace takes up 348 bytes.
1763 Hit return to continue
1764 Generating uniformly spaced coordinates is valid for polar
1765 plots as well. This is useful for data acquired by machines
1766 sampling in a circular fashion. Here the sinusoidal data
1767 of the previous 2D plot put on a polar plot. Note the
1768 pseudonyms 'dt' meaning sample period along the angular,
1769 or theta, direction. In Gnuplot, cylindrical coordinate
1770 notation is (t,r,z). [Different from common math convention
1771 (r,t,z).]
1773 Hit return to continue
1774 Binary data stored in matrix format (i.e., gnuplot binary)
1775 may also be translated with similar syntax. However, the
1776 binary keywords `format`, `array` and `record` do not apply
1777 because gnuplot binary has the requirements of float data
1778 and grid information as part of the file. Here is an
1779 example of a single matrix binary file input four times,
1780 each translated to a different location with different
1781 orientation.
1783 Hit return to continue
1784 As with ASCII data, decimation in various directions can
1785 be achieved via the `every` keyword. (Note that no down-
1786 sampling filter is applied such that you risk aliasing data
1787 with the `every` keyword.
1789 Here is a series of plots with increasing decimation.
1791 Hit return to continueHit return to continueHit return to continueHit return to continue
1792 Decimation works on general binary data files as well. Here is the
1793 image file with increasing decimation.
1795 Hit return to continueHit return to continue
1796 Gnuplot understands a few common binary formats. Internally
1797 a function is linked with various extensions. When the
1798 extension is specified at the command line or recognized via
1799 a special file type called 'auto', Gnuplot will call the
1800 function that sets up the necessary binary information. The
1801 known extensions are displayed using the 'show filetype'
1802 command. E.g.,
1805 This version of gnuplot understands the following binary file types:
1806 avs bin edf ehf gif gpbin jpeg jpg png raw rgb auto
1808 Here's an example where an EDF file is recognized when Gnuplot
1809 is in 'auto' mode. Details are pulled from the header of
1810 file itself and not specified at the command line. The command
1811 line can still be used to over-ride in-file attributes.
1813 Hit return to continue
1814 The 'flip', 'rotate' and 'perpendicular' qualifiers
1815 should provide adequate freedom to orient data as desired.
1816 However, there is an additional key words 'scan' which may
1817 offer a more direct and intuitive manner of orienting data
1818 depending upon the user's application and perspective.
1820 'scan' is a 2 or 3 letter string representing how Gnuplot
1821 should derive (x,y), (x,y,z), (t,r) or (t,r,z) from the
1822 the datafile's scan order. The first letter pertains to the
1823 fastest rate or point-by-point increment. The second letter
1824 pertains to the medium rate or line-by-line increment. If
1825 there is a third letter, it pertains to the slowest rate or
1826 plane-by-plane increment. The default or inherent scan order
1827 is 'scan=xyz'.
1829 The pseudonym 'transpose' is equivalent to 'scan=yx' when
1830 generating 2D coordinates and 'scan=yxz' when generating
1831 3D coordinates.
1833 There is a subtle difference between the behavior of 'scan'
1834 when dimension info is taken from the file itself as opposed
1835 to entered at the command line. When information is gathered
1836 from the file, internal scanning is unaltered so that issuing
1837 the 'scan' command may cause the number of samples along
1838 the various dimensions to change. However, when the qualifier
1839 'array' is entered at the command line, the array dimensions
1840 adjust so that 'array=XxYxZ' is always the number of samples
1841 along the Cartesian x, y and z directions, respectively.
1843 Hit return to continue
1844 It is possible to enter binary data at the command line. Of
1845 course, the limitation to this approach is that keyboards will
1846 allow entering only a limited subset of the possible character
1847 values necessary to represent general binary data. For this
1848 reason, the primary application for binary data at the command
1849 line is using Gnuplot through a pipe. For example, if a pipe
1850 is established with a C program, the function 'fputs()' can
1851 send ASCII strings containing the Gnuplot commands while the
1852 function 'fwrite()' can send binary data.
1854 Furthermore, there can be no special ending character such as
1855 in the case of ASCII data entry where 'e' represents the end
1856 of data for the special file '-'. It is important to note
1857 that when 'binary' is specified, Gnuplot will continue
1858 reading until reaching the number of elements specified via
1859 the 'array' or 'record' command.
1861 Here is an example of binary data in the range [0:1] inserted
1862 into the command stream by copying 48 bytes from a pre-existing
1863 binary file into this demo file.
1865 Hit return to continue
1866 ASCII data files have a matrix variant. Unlike matrix binary,
1867 ASCII binary may have multiple matrices per file, separated
1868 by a blank line. The keyword `index` can select the desired
1869 matrix to plot.
1871 Hit return to continue
1872 Images maintain orientation with respect to axis direction.
1873 All plots show the same exact plot, but with various states
1874 of reversed axes. The upper left plot has reversed x axis,
1875 the upper right plot has conventional axes, the lower left
1876 plot has both reversed x and y axes, and the lower right
1877 plot has reversed y axis.
1879 Hit return to continue
1880 Tux says "bye-bye".
1882 Hit return to continue
1883 End of image demo...
1884 "imageNaN.dem" line 38: warning: matrix contains missing or undefined values
1885 Hit return to continue"imageNaN.dem" line 45: warning: matrix contains missing or undefined values
1886 Hit return to continue"imageNaN.dem" line 51: warning: matrix contains missing or undefined values
1887 Hit return to continue"imageNaN.dem" line 57: warning: matrix contains missing or undefined values
1888 Hit return to continue"imageNaN.dem" line 63: warning: matrix contains missing or undefined values
1889 Hit return to continue"imageNaN.dem" line 69: warning: matrix contains missing or undefined values
1890 Hit return to continueHit return to continueHit return to continueHit return to continue********************** file stringvar.dem *********************
1891 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue
1892 time_str = " 2005-05-09 19:44:12 "
1893 -> seconds = 1115667852.0
1894 seconds + 10. = 1115667862.0
1895 -> time_str2 = " 2005-05-09 19:44:22 "
1897 read_time(fmt, c) = strptime(fmt, stringcolumn(c).' '.stringcolumn(c+1))
1899 Hit return to continue********************** file running_avg.dem *********************
1900 Hit return to continue********************** file pointsize.dem *********************
1901 Hit return to continueHit return to continueHit return to continueHit return to continue********************** file circles.dem *********************
1902 Hit return to continueHit return to continue********************** file armillary.dem *********************
1903 Hit <cr> to continue********************** file ellipses_style.dem *********************
1904 Hit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continueHit <cr> to continue********************** file key.dem *********************
1905 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue********************** custom key layout *********************
1906 Hit return to continueHit return to continueHit return to continue<cr> to continue<cr> to continue********************** file walls.dem *********************
1907 <cr> to continue********************** file boxes3d.dem *********************
1908 hit return to continuehit return to continuehit return to continuehit return to continuehit return to continuehit return to continue********************** file borders.dem *********************
1909 Hit return to continue********************** file columnhead.dem *********************
1910 ********************** file margins.dem *********************
1911 Hit return to continue********************** file rectangle.dem *********************
1912 Hit return to continueHit return to continue********************** clipping *********************
1913 Polygon is not closed - adding extra vertex
1914 Polygon is not closed - adding extra vertex
1915 Polygon is not closed - adding extra vertex
1916 Hit return to continueHit return to continueHit return to continue<cr> to continue********************** file approximate.dem *********************
1917 Hit return to continue********************** file parallel.dem *********************
1918 Hit return to continueHit return to continueHit return to continue********************** nonlinear axis demos *********************
1919 <cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<cr> to continue<return> to continue********************** linked axes **************************
1920 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continue********************** file map_projection.dem *********************
1921 <cr> to continue<cr> to continue<cr> to continue********************** file transparent.dem *********************
1922 Hit return to continueHit return to continueHit return to continueHit return to continue********************** file transparent_solids.dem *********************
1923 Hit return to continueHit return to continue********************** file pm3d_lighting.dem *********************
1924 Hit <return> to continueHit return to continue********************** file polygons.dem *********************
1925 Polygon is not closed - adding extra vertex
1926 Polygon is not closed - adding extra vertex
1927 Polygon is not closed - adding extra vertex
1928 Polygon is not closed - adding extra vertex
1929 Polygon is not closed - adding extra vertex
1930 Polygon is not closed - adding extra vertex
1931 Polygon is not closed - adding extra vertex
1932 Polygon is not closed - adding extra vertex
1933 Polygon is not closed - adding extra vertex
1934 Polygon is not closed - adding extra vertex
1935 Polygon is not closed - adding extra vertex
1936 Polygon is not closed - adding extra vertex
1937 Polygon is not closed - adding extra vertex
1938 Polygon is not closed - adding extra vertex
1939 Polygon is not closed - adding extra vertex
1940 Polygon is not closed - adding extra vertex
1941 Polygon is not closed - adding extra vertex
1942 Polygon is not closed - adding extra vertex
1943 Polygon is not closed - adding extra vertex
1944 Polygon is not closed - adding extra vertex
1945 Hit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continueHit return to continue<cr> to continue********************** file named_palettes.dem *********************
1946 <cr> to continue<cr> to continue<cr> to continue<cr> to continue********************** file palette+alpha.dem *********************
1947 Hit return to continue********************** file argb_hexdata.dem *********************
1948 <cr> to continueWarning: empty z range [1:1], adjusting to [0.99:1.01]
1949 <cr> to continue********************** file vplot.dem *********************
1950 vfill from - :
1951 radius 1 gives a brick of 25 voxels on x, 25 voxels on y, 25 voxels on z
1952 number of points input: 1
1953 number of voxels modified: 7901
1954 vfill from - :
1955 radius 1 gives a brick of 25 voxels on x, 25 voxels on y, 25 voxels on z
1956 number of points input: 1
1957 number of voxels modified: 7930
1958 vfill from - :
1959 radius 2 gives a brick of 19 voxels on x, 19 voxels on y, 19 voxels on z
1960 number of points input: 1
1961 number of voxels modified: 3690
1962 $v122:
1963 size 100 X 100 X 100
1964 vxrange [-4:4] vyrange[-4:4] vzrange[-4:4]
1965 non-zero voxel values: min 1.1e-14 max 97 mean 25 stddev 19
1966 number of zero voxels: 992104 (99.21%)
1967 $v032:
1968 size 100 X 100 X 100
1969 vxrange [-4:4] vyrange[-4:4] vzrange[-4:4]
1970 non-zero voxel values: min 0.061 max 95 mean 25 stddev 19
1971 number of zero voxels: 992070 (99.21%)
1972 $v201: (active)
1973 size 25 X 25 X 25
1974 vxrange [0:5] vyrange[-2.5:2.5] vzrange[-1:4]
1975 non-zero voxel values: min 15 max 97 mean 26 stddev 12
1976 number of zero voxels: 11935 (76.38%)
1978 <cr> to continue<cr> to continue********************** file isosurface.dem *********************
1979 <cr> to continuevfill from + :
1980 radius 0.9 gives a brick of 15 voxels on x, 15 voxels on y, 5 voxels on z
1981 Warning:
1982 voxel grid spacing on x, y, and z is very anisotropic.
1983 Consider using vgfill rather than vfill
1984 number of points input: 55
1985 number of voxels modified: 29540
1986 <cr> to continue<cr> to continue<cr> to continue*********************** file watchpoints.dem *********************
1987 Plot title: plot FOO smooth cnormal
1988 watch y=.25 watch y=.50 watch y=.75
1989 Watch 1 target y = 0.25 (1 hits)
1990 hit 1 x 50.6 y 0.25
1991 Watch 2 target y = 0.5 (1 hits)
1992 hit 1 x 63.6 y 0.5
1993 Watch 3 target y = 0.75 (1 hits)
1994 hit 1 x 68.3 y 0.75
1996 <cr> to continue<cr> to continue
1997 Variables beginning with INTERSECT:
1998 INTERSECT_X = 167.511137525825
1999 INTERSECT_Y = 20.2444312370877
2001 <cr> to continue"watchpoints.dem" line 81: undefined function: FresnelC
2003 make[4]: *** [Makefile:742: check-noninteractive] Error 1
2004 make[4]: Leaving directory '$(@D)/demo'
2005 make[3]: *** [Makefile:596: check-am] Error 2
2006 make[3]: Leaving directory '$(@D)/demo'
2007 make[2]: *** [Makefile:446: check-recursive] Error 1
2008 make[2]: Leaving directory '$(@D)/demo'
2009 make[1]: *** [Makefile:425: check-recursive] Error 1
2010 make[1]: Leaving directory '$(@D)'