zint | Open Source barcode encoding and image generating library

could this be a release issue?

Yes, you are right. Publishing CDS entities as OData is supported since ABAP AS Netweaver 7.50 SP00.

This fix looks simple. The grouping in the while loop is wrong, change it to the following and it should work fine:

Are you missing a space in Program Files? Guessing that's just a typo, but you should be putting quotes around any path with a space.

The problem seems to be in this, and then trying to put $line_array into csv...

numpy arrays are a bit more powerful than you might think. If you want each cell of your 3D grid to have another 3D grid, what you are looking for is a 6D grid. Check the following example:

I do have severe problems understanding the purpose of most of the code. What I can tell you is that you may of course put the cube creation in a function and call that function several times, possibly with different parameters, to obtain several scatter plots.

For visualizing the goodness of fit of probabilistic regression models, "standard" residuals (e.g., Poisson or deviance) are often not so informative because they mostly capture the modeling of the mean but not of the entire distribution. One alternative that is somtimes used are (randomized) quantile residuals. Without randomization they are defined as qnorm(pdist(y)) where pdist() is the fitted distribution function (here a ZIP model), y are the observations, and qnorm() is the quantile function of the standard normal distribution. If the model fits, the distribution of the residuals should be standard normal and can be checked in a Q-Q plot. In case of discrete distributions (as here), randomization is needed to break up the discrete nature of the data. See Dunn & Smyth (1996, Journal of Computational and Graphical Statistics, 5, 236-244) for more details. In R you can use the countreg package from R-Forge (hopefully soon also on CRAN) for these.

Another alternative which checks the marginal distribution of the data is the so-called rootogram. It visually compares the observed and fitted frequencies for the counts, 0, 1, ... It is often better at displaying the problems of excess zeros and/or overdispersion than Q-Q plots of randomized quantile residuals. See our paper Kleiber & Zeileis (2016, The American Statistician, 70(3), 296–303, doi:10.1080/00031305.2016.1173590) for more details.

Applying these to your regression model quickly shows that a zero-inflated Poisson is not accounting for the overdispersion in the response. (With counts up to and beyond 100 a Poisson-based distribution almost never fits well.) Furthermore, a zero inflation model does not fit very well because for assnage = 1 and = 2 there are very few zeros and no zero inflation is needed. This leads to corresponding coefficients in the zero inflation part that go towards -Inf with very large standard errors (like in quasi-separation in binary regression). Therefore, a two-part hurdle model fits somewhat better and is likely easier to interpret. Finally, due to the two assnage groups being different I would code assnage as a factor (it is not clear to me whether you did that already).

Thus, for analyzing your data I use yc as provided in your post and ensure:

1. You need to run flatten if you want optimization across hierarchical boundaries.

2. You probably want to run opt -full shortly before running techmap, but after running high-level optimizations like fsm and share.

3. JFYI: If you do not provide all files necessary to run your test case, people will not be able to reproduce what you are talking about. I don't have your my_library.lib, so I did not even bother trying to run your code.

The following ABC script should be able to map the MUX4, or it least it is when using the ABC version that is bundled with Yosys 0.7: