tda | TDA - Thread Dump Analyzer | Monitoring library

I am trying to pass one Python object as an argument to a function that I am evaluating in the background with parfeval. The Python object is an instance of a Python class, and I detail it below. However, to reproduce the error, I will exemplify with a Python dictionary... However, simply using struct(pydict) would not work because I would lose all the attributes and methods in the Python class.

Assume the Python dictionary is

I have the code below that is saving a file for each ticker in my list, but I am trying to workout how to append all tickers into one dataframe and then save to CSV.

I've managed to piece together the following code, and am wondering how to reference multiple tickers instead of only 'AAPL'. I'm new to python and have starting making some traction, but I'm scratching my head here. Thanks!

How to get coordinates of image pixels in the following form:

I am using the Python package ripser for persistence homology. I would like to leverage this to aide in segmenting 2D point clouds.

As an example, I am following Elizabeth Munch: Python Tutorial on Topological Data Analysis. Here, I take the DoubleAnnulus and increase the separation between the two:

I have 3 tables and at a moment I am selecting any 2 tables and I want to show the difference of each row element of selected tables in a popup in a different column along with the tables .I am stuck here how to compute the difference between the each row of two selected tables. How can i iterate both the tables and then get the result?

I have this piece of code which computes the difference between the rows of two selected tables and display the result on click of button. How can I display the result in a pop up? Basically I want the resultant table to come as popup, so that when we close the popup the tables checkbox are again reset.

Edit: I tried using modals but it seems to be not working. Can anyone please help where I am going wrong

I'm trying to reproduce this GitHub project on my machine, on Topological Data Analysis (TDA).

My steps:

• get best parameters from a cross-validation output
• load my dataset feature selection
• extract topological features from the dataset for prediction
• create a Random Forest Classifier model built on the best parameters
• calculate probabilities on test data

Background:

1. Feature selection

In order to decide which attributes belong to which group, we created a correlation matrix. From this, we saw that there were two big groups, where player attributes were strongly correlated with each other. Therefore, we decided to split the attributes into two groups, one to summarise the attacking characteristics of a player while the other one the defensiveness. Finally, since the goalkeeper has completely different statistics with respect to the other players, we decided to take into account only the overall rating. Below, is possible to see the 24 features used for each player:

Attack: "positioning", "crossing", "finishing", "heading_accuracy", "short_passing", "reactions", "volleys", "dribbling", "curve", "free_kick_accuracy", "acceleration", "sprint_speed", "agility", "penalties", "vision", "shot_power", "long_shots" Defense: "interceptions", "aggression", "marking", "standing_tackle", "sliding_tackle", "long_passing" Goalkeeper: "overall_rating"

From this set of features, the next step we did was to, for each non-goalkeeper player, compute the mean of the attack attributes and the defensive ones.

Finally, for each team in a given match, we compute the mean and the standard deviation for the attack and the defense from these stats of the team's players, as well as the best attack and best defense.

In this way a match is described by 14 features (GK overall value, best attack, std attack, mean attack, the best defense, std defense, mean defense), that mapped the match in the space, following the characterizes of the two teams.

1. Feature extraction

The aim of TDA is to catch the structure of the space underlying the data. In our project, we assume that the neighborhood of a data point hides meaningful information that is correlated with the outcome of the match. Thus, we explored the data space looking for this kind of correlation.

Methods:

I have a query that uses union to show totals from each request category. I'm trying to find a way to add the totals of all 6 categories and show them on the user page. I am using ajax to reload the totals as they come in or are completed. What I am getting for a response shows each category together in succession instead of a total.