tau | functional interpreted programming language | Functional Programming library

Trying to upgrade this awesome implementation of gumble-softmax-vae found here. However, I keep getting

I want to make the following network definition to a parametric one. The number of continuous and discrete columns varies for different data. I first pass the whole input data, which in this case is 110 dimensional , from a linear with a relu activation. The output of each categorical field of my data varies based on a previous one-hot encoding data transformation. I need to define a nn.Linear(110, number of encodings) for each of them.

I am running ubuntu 16.04 (it is on an OLD laptop). I am using 'pic | eqn -Tpdf -d@@ | groff -mm -Tpdf' software as per the ubuntu release. I need some symbols which are not in the standard groff TIMES font for the release. I have found the symbols I need in STIX and FreeMono that come with the ubuntu release. I have created the relevant font files for groff as per https://www.schaffter.ca/mom/momdoc/appendices.html.

It is almost working but there is extraneous spacing between characters / glyphs. See attached image. I have tried both STIX and FreeMono and they produce the same problem.

PDF output from groff eqn showing extraneous spacing

The groff, eqn mark up for the imaged example above is:

So I am writing a code for a Tkinter GUI, and in it, the code pulls data from FRED and uses it to present graphs. There is an option at the start to save the pulled data in a CSV file so you can run it without the internet. But when the code runs to use the CSV, something happens with the scale and it gives me a graph like this. I think it has something to do with the datetime data not being remembered. Current code situation follows:

Imports: from tkinter import *, from tkinter import ttk, pandas_datareader as pdr, pandas as pd, from datetime import datetime

Example of how data is called:

I have a function that I'd like to plot in python and shade the region of interest. I've tried using pyplot.fill_between() but can not quite get what I want. I've attached an image and shaded in orange the region I want to be filled:

I plot the function (in blue) and then the graph is bounded by y=0, y ≈ 0.05 and x = 0.And I wish to shade the relevant region (in orange).

Any tips as to how to go about this?

Thanks in advance.

Given N consecutive integers from 1 to N.

Count the number of ways to choose a number of numbers (at least 1 number) such that they form an arithmetic progression (AP) modulo 1e9+7

Define L[N] is the answer

The number of APs which begin with 2, end with N = The number of APs which begin with 1, end with N-1

The number of APs which begin with 1, end with N is the number of divisors of N-1 (=\tau(N-1))

So, L[N] = L[N-1] + \tau(N-1)

But N <= 10^10 . The algorithm must be O(n), how can I calculate \tau(n)?

This is a continuation of my questions:

Declaring a functional recursive sequence in Matlab

Is there a more efficient way of nesting logarithms?

Nesting a specific recursion in Pari-GP

But I'll keep this question self contained. I have made a coding project for myself; which is to program a working simple calculator for a tetration function I've constructed. This tetration function is holomorphic, and stated not to be Kneser's solution (as to all the jargon, ignore); long story short, I need to run the numbers; to win over the nay-sayers.

As to this, I have to use Pari-GP; as this is a fantastic language for handling large numbers and algebraic expressions. As we are dealing with tetration (think numbers of the order e^e^e^e^e^e); this language is, of the few that exist, the best for such affairs. It is the favourite when doing iterated exponential computations.

Now, the trouble I am facing is odd. It is not so much that my code doesn't work; it's that it's overflowing because it should over flow (think, we're getting inputs like e^e^e^e^e^e; and no computer can handle it properly). I'll post the first batch of code, before I dive deeper.

The following code works perfectly; and does everything I want. The trouble is with the next batch of code. This produces all the numbers I want.

For a project I have, I control a BLDC motor with it's own current controller.

To design a controller for the motor I fitted some time traces to the flowing function:

to capture the dynamics of the entire system, namely, motor, current controller/driver and load.

J is the rotor inertia, [kg*m^2]

r a dampening constant (linear friction)

tau is a torque constant [Nm/A]

u[t] a current input

der(der(phi)) is the angular acceleration

der(phi) the angular velocity

The fitted values represent the entire system more than good enough for a 'continuous' approximation of the motor, the load and the current controller/driver of the motor. With the control scheme, I give a signal u(t) in Amps, and I expect a torque and an angular velocity as an output. At the time I only needed the angular velocity, but I digress, this method worked fantastically, but was fitted and designed in mathematica.

I would like to build a much larger, and significantly more complex robotic system within modelica (Systemmodeler specifcally, however, modelica library 3.2.x), however I am having some issues.

My first attempt was:

However, when simulating this with another outside load (an external model), I have a lot of issues, from some those more experienced than I, I am told this form of modelling is 'one way' and not the 'modelica method' but more simulink in form.

Namely, it should be bi-directional, not just numerical outputs, in order to correctly react with the multibody external model, this will connect to.

My second attempt was:

Which when connecting to my larger multibody model, it does indeed work more like it's expected to, there is a reaction from this model, when external torques/loads are applied. However, when simulating this model to see how it fairs compared to the pure block model, they arn't the same by any means. I had to spend a lot of time trying to fit the inertia and friction data to get similar results.

SO my question is, what is the best method, to turn pure block models (mathematical ones) or atleast,my pure block model, into a more realistic model or atleast, the connecting outputs into more realistic, or I guess 'acasual' ones.

I would prefer not to use my second attempt, since I can't trust the values I had to adjust compared to the block to actually be correct, since they arn't fitted values to real world data, compared to my first model.

I want to analyse individual data frames within a list of data frames and then store the results of each individual analysis in a new data frame. Any advice would be great, i've tried lapply() and a for loop but can't get either working.

I have a list of data frames: