uncertainties | Transparent calculations with uncertainties

Trying to (re)install Jupyter's nbextension via the following steps in terminal

1. pip install jupyter_contrib_nbextensions
2. jupyter contrib nbextension install --user
3. install --user jupyter nbextension enable varInspector/main

Step 1 = runs and i am able to launch notebooks via "jupyter notebook" in terminal just fine.

Step 2 = fails with

TL;DR: I tried adding a new Object defined exclusively in Torque and use it as a new member for the Name class. This resulted in compilation failures due to function used but never defined errors, pertaining to a function that IS defined in torque-generated files, however for some reason not properly included.

I am unsure how to proceed and trying to just include different combinations of the torque-generated files in the appropriate locations simply led to redefinition errors.

The question: Is it possible to define a new object and use it in the way I intend to, without adding C++ class definitions (that inherit from the torque-generated classes and so forth) and if yes, where do I seem to be going wrong. In the following I will describe the steps I took so far:

• Add a myClass.tq/ file to src/objects that includes a definition of my class along the lines of:

My problem is the following. I measured a bunch of different physical properties and propagated the methodic and measurement uncertainties all the way to some kind of efficiency ratio. For all my physical properties, a normal distribution seemed to be a good choice, and for the first few calculations and corresponding propagations I had fairly low uncertainties. I forwarded all uncertainties as an extended uncertainty of kp=2 meaning coverage of 95.45% of all possible values.

However, for the calculations I am performing right now in order to get the efficiency I end up with results like (16+/-31)%, which is not possible since my efficiency can only spread from 0 to 1. My assumption would be, that I found the "correct" expectation value for the efficiency, but my probability distribution should be a positive skewed gamma-distribution rather than a normal-distribution. With the assumption that the intervals [0; 0.16+0.31], [0; 0.16+0.31*3/2] and [0;1] cover 95.45%, 99.73% and 100% of all possible values I should be able to calculate the gamma-distribution parameters alpha and beta analytically. Unfortunately, the following code using sympy doesn't work because sympy can't handle it.

Does someone have an idea how to solve my problem?

I understand basics of abstraction but still I have some uncertainties.

1. We cannot create obj of abstract class.
2. Abstract methods are declared in abstract class but defined in child class.
3. And we call abstract methods using obj of child class.

I don't get why we using abstract classes if we can do all the things through objects of the child class. We defined abstract methods in child class so it also means we declared it in child class too and we calling that method so it working like normal classes and child.

What is practical purpose of abstract classes?

i have written a fairly large class for the calculation of measurement uncertainties, but it is painfully slow. Profiling the code shows that the slowest operation, by far, is to insert the computation results into a large sparse matrix. About 97% of all time is spent on that operation. The matrix keeps the uncertainties of all measurement data, and I cannot change the data structures without breaking a lot of other code. So my only option is to optimize the data insertion step. This is done about 5700 times in my benchmark, and every time the amout of data increases.

First solution, extremely slow:

I would like to know if there exists a python interval arithmetic library with the following features:

If x and y are both intervals, eg: [1.2,2.1], [3.8,9.9], then their sum/product/ratio is an interval, and in particular, any real number in the first interval plus/times/over the a real number in the second interval, will be contained in the resulting interval.

In particular, I would like to know of such a library that does this and accounts for floating point error so that the results can be used in mathematical proofs. I have found some libraries such as this one: https://pythonhosted.org/uncertainties/ that account for error on real numbers, however they don't do so in this stricter sense that I require.

Thanks to TimPeters for suggesting this library:
https://mpmath.org/doc/current/contexts.html#arbitrary-precision-interval-arithmetic-iv

I think this should be the correct tool to use.

I am working to combine multiple years of national BRFSS data into one set and incorporate the appropriate complex survey design with the survey package in order to be able to calculate uncertainties. I have seen several examples on how to do this with one year, and know that when doing it for multiple years that I need to nest for year, but I'm not quite sure how to include this. Here is the data I am working from reweighted with the variable finalwt:

Let's try to phrase this properly, here is a summary of my relevant tables:

1. Table "students" containing student_id, first_name, last_name.
2. Table "courses" containing course_id, title.
3. Table "majors" containing major_id, major_name.

Students are assigned to majors, and courses are assigned to majors (i.e. courses required to obtain the specific major), as a result, I have the two following tables defining which students and courses are assigned to which majors.

1. Table "students_assigned_majors" containing ref_id (PK), student_id (FK), major_id (FK).
2. Table "courses_assigned_majors" containing ref_id (PK), course_id (FK), major_id (FK).

Finally, I have this table called 6. "completed_courses" containing information on which students completed which courses, with columns ref_id (PK), student_id (FK), course_id (FK), completion_date.

I want to create a query that checks which students are not eligible to graduate from the major because they are missing a course required by the major. Any course is not valid if it was completed more than 5 years ago.

I want to display these students and their majors on an existing Form.

I am unsure if this can be done entirely in MS Access SQL... I drafted a VBA procedure on the On_current event of the relevant form, and for now it looks like this: (still Pseudo-code not tested, but here is the idea)

I have a variable "n" that contains a number of digits that need to be displayed for the value. How to pass the "n" to '{:10.nf}'.format()?