cdf | automatically test the correctness and security

My Issue: I have a dataset of continuous values, but need to generate more "artificial" data points so that I have enough power to do some analyses.

My proposed solution: Sample from the density distribution of the dataset by dividing the dataset into bins of equal width and then sampling a random number from that bin's range based on its height. Something like this:

My Attempt:

I am having difficulties customising the aesthetics of an interactive line plot in Plotly Python, and would appreciate some assistance from members of the community here.

Here is a picture of the plot I would like to amend, a working example of which is supplied further down the page:

In particular, I want to know how or what can be modified so that I can

1. Remove 'step-#' in 'Sample size n = step-#', and replace with 'Sample size n = #', where # is a number. Currently the value of # is also out of sync with the title. So in this figure # should be displayed as 300.

2. Remove the 'step-#' annotating the slider, and replace it with something else.

3. Adjust number of 'ticks' on the slider line.

Ideally, I would appreciate if someone could tell me definitively what keywords or parameters I need to be amending in Plotly.

Having read the documentation, it is not clear to me what parameters/keyword arguments I need to be tweaking in order to customise the aesthetics to be closer to what I desire. And I don't know if the customisation I'm looking for can be achieved with these parameter/keyword argument tweaks, or if it would require a rewriting of the code I have reproduced below.

Essentially, I want the slider looking something more like the one in this example:

Minimal working code example.

I adapted this code from the example from the Plotly documentation on sliders here

I got a sample data and i'm trying to obtain the parameters for two-parameter exponential function calculed based on maximum likelihood.

My sample:

Following is a kind of data set I am working on it:

I want to draw CDF and PDF plots with different axes. Here is my data;

Tl;dr: I would like a function that randomly returns a float (or optionally an ndarray of floats) in an interval following a probability distribution that resembles the sum of a "Gaussian" and a uniform distributions.

The function (or class) - let's say custom_distr() - should have as inputs (with default values already given):

• the lower and upper bounds of the interval: low=0.0, high=1.0
• the mean and standard deviation parameters of the "Gaussian": loc=0.5, scale=0.02
• the size of the output: size=None
• size can be an integer or a tuple of integers. If so, then loc and scale can either both simultaneously be scalars, or ndarrays whose shape corresponds to size.

The output is a scalar or an ndarray, depending on size.

The output has to be scaled to certify that the cumulative distribution is equal to 1 (I'm uncertain how to do this).

Note that I'm following numpy.random.Generator's naming convention from uniform and normal distributions as reference, but the nomenclature and the utilized packages does not really matter to me.

Since I couldn't find a way to "add" numpy.random.Generator's uniform and Gaussian distributions directly, I've tried using scipy.stats.rv_continuous subclassing, but I'm stuck at how to define the _rvs method, or the _ppf method to make it fast.

From what I've understood of rv_continuous class definition in Github, _rvs uses numpy's random.RandomState (which is out of date in comparison to random.Generator) to make the distributions. This seems to defeat the purpose of using scipy.stats.rv_continuous subclassing.

Another option would be to define _ppf, the percent-point function of my custom distribution, since according to rv_generic class definition in Github, the default function _rvs uses _ppf. But I'm having trouble defining this function by hand.

Following, there is a MWE, tested using low=0.0, high=1.0, loc=0.3 and scale=0.02. The names are different than the "The issue" section, because terminologies of terms are different between numpy and scipy.

i get the GMM models of generation of electricity for my SPS (solar power station) through scikit-learn and search Probability Density Function (PDF, black line):

But i want get a probability function (CDF or Cumulative distribution function). In other words, i want to get a function like an example:

that can receive values on axes y in range [0, 1] and grows on all x-axis. Does scikit-learn allow it or not?

I was trying to calculate the p-value for the following coin toss example:

I'm using the np package in R, and I would like to use the nonparametric distribution function npudist as for instance we normally use the ecdf function.