UnboundLocalError: local variable 'dotAB' referenced before assignment about luxpy HOT 5 CLOSED

Forgive me if I'm doing something stupid ... The following code produces the following error in basics.py dot23 import luxpy as lx import numpy as np data = np.array([185, 206, 163]) Rw = np.array([256, 264, 202]) camout = lx.cam.zcam(data, Rw) UnboundLocalError: local variable 'dotAB' referenced before assignment --------------------------------------------------------------------------- UnboundLocalError Traceback (most recent call last) <ipython-input-4-599aff8f528b> in <module> 2 Rw = np.array([256, 264, 202]) 3 ----> 4 camout = lx.cam.zcam(data, Rw) /usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/luxpy/color/cam/zcam.py in run(data, xyzw, outin, cieobs, conditions, forward, mcat, **kwargs) 377 #-------------------------------------------- 378 # Apply CAT to white point: --> 379 xyzwc = cat.apply_vonkries1(xyzw, xyzw1 = xyzw, xyzw2 = xyzw_d65, 380 D = D, mcat = mcat, invmcat = invmcat, 381 use_Yw = True) /usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/luxpy/color/cat/chromaticadaptation.py in apply_vonkries1(xyz, xyzw1, xyzw2, D, mcat, invmcat, in_, out_, use_Yw) 671 # transform from xyz to cat sensor space: 672 if in_ == 'xyz': --> 673 rgb = math.dot23(mcat, xyz.T) 674 rgbw1 = math.dot23(mcat, xyzw1.T) 675 rgbw2 = math.dot23(mcat, xyzw2.T) /usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/luxpy/math/basics.py in dot23(A, B, keepdims) 235 dotAB = np.expand_dims(dotAB,axis=1) 236 --> 237 return dotAB 238 239 #------------------------------------------------------------------------------ UnboundLocalError: local variable 'dotAB' referenced before assignment — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#20>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACS4LX2T4FLOS5FGVDGKTLLUGTFPBANCNFSM5F35A2BA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

Thanks for the quick reply. Sorry for the confusion. Not really a Python guy. Just a follow up ... When running zcam.py I seem to be unable to match the reference values found in the supplemental document <https://doi.org/10.6084/m9.figshare.13640927> For Sample 1 I'm doing : import numpy as np import luxpy as lx np.set_printoptions(formatter={'float_kind':"{:.2f}".format}) data = np.array([[185, 206, 163]]) Rw = np.array([[256, 264, 202]]) cond = {'La': 264, 'Yb': 100, 'D': None, 'surround': 'avg', 'Dtype': None} outin = "h,Q,J,aM,bM,M,C,Sz,Vz,Kz,Wz" camout = lx.cam.zcam(data, Rw, cieobs='1931_2', conditions=cond, outin=outin) print(camout) I get [[196.34 321.35 91.46 -10.04 -2.94 10.46 2.98 19.07 33.91 26.52 90.95]] Where I would expect [[196.3524 321.3464 92.25 -0.0165 -0.0048 10.53 3.0216 19.1314 34.7022 25.2994 91.6837]] Some of the values match (within rounding), some are sort of close, and some seem significantly off. I suspect maybe I'm misusing the function. Again, apologies if there's something basic here I'm missing. — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub <#20 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACS4LXZ3C2DDA32LMTXIFYDUGXO5FANCNFSM5F35A2BA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

The aM,bM are not the same as the az, bz reported in the supplemental material. Apart from that I had noted earlier that there are some differences between what is calculated and the values in the supplementary material, which I tracked down due to the CAT. I had coded an earlier version of Z-CAM, but the authors changed it a lot. Later, they got back to me with matlab code for a newer version, but I found some errors in that. All this was pre-publication. Since publication, I've contacted the authors for an update of their matlab code for an older version of ZCAM (prepublication), but haven't heard back yet. I therefore adjusted their old matlab code (for a previous version, but which includes the specifics on how they deal with chromatic adaptation) to follow the publication and now the luxpy and matlab code agree. They also agree better with the published values (only check first column) than before. However, to get this better agreement, no CAT must be applied to the white point (which is used in later calculations), but which doesn't really make sense, as one would also be adapted for this stimulus and relative perceptual attributes should really be calculated with respect to the adapted white point, in my opinion, and not to the unadapted input white point. In addition, the authors used as XYZ for the D65 white point the following values: 95.0429, 100, 108.89, which are slightly different from the 'true' values: 95.047, 100.000, 108.883. Not sure where they got those values from (probably from calculating XYZ from a 380-780 nm D65 spectrum with 5 nm spacing, instead of the CIE recommended 360-830 nm range with 1 nm spacing). In addition, they changed the way Iz is calculated from their previous publication on the Iz, az,bz color space. Op wo 13 okt. 2021 om 21:47 schreef Alexander Forsythe < ***@***.***>: > Thanks for the quick reply. Sorry for the confusion. Not really a Python > guy. > > Just a follow up ... > > When running zcam.py I seem to be unable to match the reference values > found in the supplemental document > <https://doi.org/10.6084/m9.figshare.13640927> > > For Sample 1 I'm doing : > > import numpy as np > import luxpy as lx > > np.set_printoptions(formatter={'float_kind':"{:.2f}".format}) > > data = np.array([[185, 206, 163]]) > Rw = np.array([[256, 264, 202]]) > cond = {'La': 264, 'Yb': 100, 'D': None, 'surround': 'avg', 'Dtype': None} > outin = "h,Q,J,aM,bM,M,C,Sz,Vz,Kz,Wz" > > camout = lx.cam.zcam(data, Rw, cieobs='1931_2', conditions=cond, outin=outin) > print(camout) > > I get > > [[196.34 321.35 91.46 -10.04 -2.94 10.46 2.98 19.07 33.91 26.52 90.95]] > > Where I would expect > > [[196.3524 321.3464 92.25 -0.0165 -0.0048 10.53 3.0216 19.1314 34.7022 25.2994 91.6837]] > > Some of the values match (within rounding), some are sort of close, and > some seem significantly off. I suspect maybe I'm misusing the function. > Again, apologies if there's something basic here I'm missing. > > — > You are receiving this because you modified the open/close state. > Reply to this email directly, view it on GitHub > <#20 (comment)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACS4LXZ3C2DDA32LMTXIFYDUGXO5FANCNFSM5F35A2BA> > . > Triage notifications on the go with GitHub Mobile for iOS > <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> > or Android > <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. > >